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<h1>Overview</h1>

</div> <div class=”row variablerow”> <div class=”col-md-6 namecol”>

<p class=”h4”>Dataset info</p> <table class=”stats” style=”margin-left: 1em;”>

<tbody> <tr>

<th>Number of variables</th> <td>328 </td>

</tr> <tr>

<th>Number of observations</th> <td>3210 </td>

</tr> <tr>

<th>Total Missing (%)</th> <td>6.9% </td>

</tr> <tr>

<th>Total size in memory</th> <td>7.9 MiB </td>

</tr> <tr>

<th>Average record size in memory</th> <td>2.5 KiB </td>

</tr> </tbody>

</table>

</div> <div class=”col-md-6 namecol”>

<p class=”h4”>Variables types</p> <table class=”stats” style=”margin-left: 1em;”>

<tbody> <tr>

<th>Numeric</th> <td>193 </td>

</tr> <tr>

<th>Categorical</th> <td>0 </td>

</tr> <tr>

<th>Boolean</th> <td>3 </td>

</tr> <tr>

<th>Date</th> <td>0 </td>

</tr> <tr>

<th>Text (Unique)</th> <td>0 </td>

</tr> <tr>

<th>Rejected</th> <td>132 </td>

</tr> <tr>

<th>Unsupported</th> <td>0 </td>

</tr> </tbody>

</table>

</div> <div class=”col-md-12” style=”padding-left: 1em;”>

<p class=”h4”>Warnings</p> <ul class=”list-unstyled”><li><a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS07”><code>AGRITRSM_OPS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS07”><code>AGRITRSM_OPS07</code></a> has 362 / 11.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_OPS12”><code>AGRITRSM_OPS12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_OPS12”><code>AGRITRSM_OPS12</code></a> has 263 / 8.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_RCT07”><code>AGRITRSM_RCT07</code></a> has 1239 / 38.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_RCT07”><code>AGRITRSM_RCT07</code></a> has 362 / 11.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_AGRITRSM_RCT12”><code>AGRITRSM_RCT12</code></a> has 1095 / 34.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_AGRITRSM_RCT12”><code>AGRITRSM_RCT12</code></a> has 263 / 8.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> has 909 / 28.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRES12”><code>BERRY_ACRES12</code></a> is highly correlated with <a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> (ρ = 0.98128) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> has 909 / 28.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> is highly skewed (γ1 = 32.243) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_BERRY_ACRESPTH12”><code>BERRY_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> (ρ = 0.98479) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> has 843 / 26.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_BERRY_FARMS12”><code>BERRY_FARMS12</code></a> is highly correlated with <a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> (ρ = 0.93937) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CHILDPOVRATE15”><code>CHILDPOVRATE15</code></a> is highly correlated with <a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> (ρ = 0.93822) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> has 2138 / 66.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CHIPSTAX_VENDM14”><code>CHIPSTAX_VENDM14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CHIPSTAX_VENDM14”><code>CHIPSTAX_VENDM14</code></a> has 713 / 22.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CH_FOODINSEC_12_15”><code>CH_FOODINSEC_12_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CH_FOODINSEC_12_15”><code>CH_FOODINSEC_12_15</code></a> has 163 / 5.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CH_VLFOODSEC_12_15”><code>CH_VLFOODSEC_12_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CH_VLFOODSEC_12_15”><code>CH_VLFOODSEC_12_15</code></a> has 327 / 10.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CONVS09”><code>CONVS09</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> (ρ = 0.92485) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CONVS14”><code>CONVS14</code></a> is highly correlated with <a href=”#pp_var_CONVS09”><code>CONVS09</code></a> (ρ = 0.99646) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_CONVSPTH09”><code>CONVSPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CONVSPTH09”><code>CONVSPTH09</code></a> has 46 / 1.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CONVSPTH14”><code>CONVSPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CONVSPTH14”><code>CONVSPTH14</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CSA07”><code>CSA07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CSA07”><code>CSA07</code></a> has 732 / 22.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_CSA12”><code>CSA12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_CSA12”><code>CSA12</code></a> has 944 / 29.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> (ρ = 0.99474) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> (ρ = 0.99338) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> (ρ = 0.99269) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> (ρ = 0.9954) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> (ρ = 0.95709) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> (ρ = 0.99714) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> has 359 / 11.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_DIRSALES12”><code>DIRSALES12</code></a> is highly correlated with <a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> (ρ = 0.90428) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_DIRSALES_FARMS12”><code>DIRSALES_FARMS12</code></a> is highly correlated with <a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> (ρ = 0.96228) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL09”><code>FARM_TO_SCHOOL09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL09”><code>FARM_TO_SCHOOL09</code></a> has 2939 / 91.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL13”><code>FARM_TO_SCHOOL13</code></a> has 286 / 8.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FARM_TO_SCHOOL13”><code>FARM_TO_SCHOOL13</code></a> has 1117 / 34.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FDPIR12”><code>FDPIR12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FDPIR12”><code>FDPIR12</code></a> has 2911 / 90.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FFR09”><code>FFR09</code></a> is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> (ρ = 0.95189) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FFR14”><code>FFR14</code></a> is highly correlated with <a href=”#pp_var_FFR09”><code>FFR09</code></a> (ρ = 0.99882) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FFRPTH09”><code>FFRPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FFRPTH09”><code>FFRPTH09</code></a> has 141 / 4.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FFRPTH14”><code>FFRPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FFRPTH14”><code>FFRPTH14</code></a> has 153 / 4.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> has 1360 / 42.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT16”><code>FMRKT16</code></a> is highly correlated with <a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> (ρ = 0.91012) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKTPTH09”><code>FMRKTPTH09</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKTPTH09”><code>FMRKTPTH09</code></a> has 1360 / 42.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKTPTH16”><code>FMRKTPTH16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKTPTH16”><code>FMRKTPTH16</code></a> has 889 / 27.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> (ρ = 0.96896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> (ρ = 0.98152) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> (ρ = 0.92193) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> (ρ = 0.97114) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_OTHERFOOD16”><code>FMRKT_OTHERFOOD16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> (ρ = 0.98659) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_SFMNP16”><code>FMRKT_SFMNP16</code></a> is highly correlated with <a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> (ρ = 0.93896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> has 1289 / 40.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> has 1416 / 44.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FMRKT_WICCASH16”><code>FMRKT_WICCASH16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FMRKT_WICCASH16”><code>FMRKT_WICCASH16</code></a> has 1730 / 53.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FOODHUB16”><code>FOODHUB16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODHUB16”><code>FOODHUB16</code></a> has 2991 / 93.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FOODINSEC_10_12”><code>FOODINSEC_10_12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FOODINSEC_CHILD_03_11”><code>FOODINSEC_CHILD_03_11</code></a> is highly correlated with <a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> (ρ = 0.94773) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FOOD_TAX14”><code>FOOD_TAX14</code></a> is highly correlated with <a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> (ρ = 1) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> has 1370 / 42.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> has 300 / 9.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> is highly skewed (γ1 = 22.249) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_FRESHVEG_ACRES12”><code>FRESHVEG_ACRES12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> (ρ = 0.99083) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> has 1370 / 42.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> has 300 / 9.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FRESHVEG_ACRESPTH12”><code>FRESHVEG_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> (ρ = 0.94863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> (ρ = 0.97465) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FRESHVEG_FARMS12”><code>FRESHVEG_FARMS12</code></a> is highly correlated with <a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> (ρ = 0.90251) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSR09”><code>FSR09</code></a> is highly correlated with <a href=”#pp_var_FFR14”><code>FFR14</code></a> (ρ = 0.97433) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSR14”><code>FSR14</code></a> is highly correlated with <a href=”#pp_var_FSR09”><code>FSR09</code></a> (ρ = 0.99878) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> has 57 / 1.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_FSRPTH14”><code>FSRPTH14</code></a> is highly correlated with <a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> (ρ = 0.90099) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_GHVEG_FARMS07”><code>GHVEG_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_FARMS07”><code>GHVEG_FARMS07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_FARMS12”><code>GHVEG_FARMS12</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_FARMS12”><code>GHVEG_FARMS12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT07”><code>GHVEG_SQFT07</code></a> has 913 / 28.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFT07”><code>GHVEG_SQFT07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> has 966 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFT12”><code>GHVEG_SQFT12</code></a> is highly skewed (γ1 = 21.474) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH07”><code>GHVEG_SQFTPTH07</code></a> has 913 / 28.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH07”><code>GHVEG_SQFTPTH07</code></a> has 1878 / 58.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH12”><code>GHVEG_SQFTPTH12</code></a> has 966 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GHVEG_SQFTPTH12”><code>GHVEG_SQFTPTH12</code></a> has 1412 / 44.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROC09”><code>GROC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROC09”><code>GROC09</code></a> has 56 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROC14”><code>GROC14</code></a> is highly correlated with <a href=”#pp_var_GROC09”><code>GROC09</code></a> (ρ = 0.9926) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_GROCPTH09”><code>GROCPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROCPTH09”><code>GROCPTH09</code></a> has 56 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_GROCPTH14”><code>GROCPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_GROCPTH14”><code>GROCPTH14</code></a> has 67 / 2.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_BLACK15”><code>LACCESS_BLACK15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_BLACK15”><code>LACCESS_BLACK15</code></a> has 189 / 5.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> (ρ = 0.93398) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> (ρ = 0.994) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> has 278 / 8.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_CHILD_10_15”><code>LACCESS_CHILD_10_15</code></a> is highly skewed (γ1 = 50.355) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> (ρ = 0.97302) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_HISP15”><code>LACCESS_HISP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_HISP15”><code>LACCESS_HISP15</code></a> has 53 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> (ρ = 0.907) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> (ρ = 0.98469) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_MULTIR15”><code>LACCESS_MULTIR15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_MULTIR15”><code>LACCESS_MULTIR15</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHASIAN15”><code>LACCESS_NHASIAN15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHASIAN15”><code>LACCESS_NHASIAN15</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHNA15”><code>LACCESS_NHNA15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHNA15”><code>LACCESS_NHNA15</code></a> has 170 / 5.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> has 1175 / 36.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_LACCESS_NHPI15”><code>LACCESS_NHPI15</code></a> is highly skewed (γ1 = 40.47) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> (ρ = 0.99406) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> is highly correlated with <a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> (ρ = 0.91878) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> (ρ = 0.9937) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_SNAP15”><code>LACCESS_SNAP15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> (ρ = 0.90806) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_LACCESS_WHITE15”><code>LACCESS_WHITE15</code></a> is highly correlated with <a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> (ρ = 0.95929) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_MEDHHINC15”><code>MEDHHINC15</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_METRO13”><code>METRO13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_METRO13”><code>METRO13</code></a> has 1968 / 61.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_MILK_SODA_PRICE10”><code>MILK_SODA_PRICE10</code></a> is highly correlated with <a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> (ρ = 0.91655) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> (ρ = 0.96736) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> (ρ = 0.99972) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> (ρ = 0.99994) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> (ρ = 0.99978) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> (ρ = 0.9999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> (ρ = 0.99976) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> has 619 / 19.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_ACRES12”><code>ORCHARD_ACRES12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> (ρ = 0.99592) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> has 619 / 19.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_ACRESPTH12”><code>ORCHARD_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> (ρ = 0.96957) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> has 359 / 11.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_ORCHARD_FARMS12”><code>ORCHARD_FARMS12</code></a> is highly correlated with <a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> (ρ = 0.99031) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_AGRITRSM_OPS_07_12”><code>PCH_AGRITRSM_OPS_07_12</code></a> has 496 / 15.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_AGRITRSM_OPS_07_12”><code>PCH_AGRITRSM_OPS_07_12</code></a> has 243 / 7.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_AGRITRSM_RCT_07_12”><code>PCH_AGRITRSM_RCT_07_12</code></a> has 1945 / 60.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_ACRESPTH_07_12”><code>PCH_BERRY_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> (ρ = 0.99863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> has 1939 / 60.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> has 69 / 2.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_BERRY_FARMS_07_12”><code>PCH_BERRY_FARMS_07_12</code></a> has 977 / 30.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_BERRY_FARMS_07_12”><code>PCH_BERRY_FARMS_07_12</code></a> has 218 / 6.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_CACFP_09_15”><code>PCH_CACFP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CONVSPTH_09_14”><code>PCH_CONVSPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> (ρ = 0.98861) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> has 542 / 16.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_CSA_07_12”><code>PCH_CSA_07_12</code></a> has 866 / 27.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_CSA_07_12”><code>PCH_CSA_07_12</code></a> has 219 / 6.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> has 529 / 16.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> is highly skewed (γ1 = 22.614) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_DIRSALES_FARMS_07_12”><code>PCH_DIRSALES_FARMS_07_12</code></a> has 194 / 6.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_DIRSALES_FARMS_07_12”><code>PCH_DIRSALES_FARMS_07_12</code></a> has 148 / 4.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FFRPTH_09_14”><code>PCH_FFRPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> (ρ = 0.9901) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> has 124 / 3.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> has 547 / 17.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FMRKTPTH_09_16”><code>PCH_FMRKTPTH_09_16</code></a> is highly correlated with <a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> (ρ = 0.99444) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> has 628 / 19.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> has 1505 / 46.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_FRESHVEG_ACRESPTH_07_12”><code>PCH_FRESHVEG_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> (ρ = 0.99841) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> has 2160 / 67.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FRESHVEG_FARMS_07_12”><code>PCH_FRESHVEG_FARMS_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> (ρ = 0.96454) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FSRPTH_09_14”><code>PCH_FSRPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> (ρ = 0.98863) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> has 109 / 3.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> has 401 / 12.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_GHVEG_FARMS_07_12”><code>PCH_GHVEG_FARMS_07_12</code></a> has 2012 / 62.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GHVEG_FARMS_07_12”><code>PCH_GHVEG_FARMS_07_12</code></a> has 187 / 5.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_GHVEG_SQFTPTH_07_12”><code>PCH_GHVEG_SQFTPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> (ρ = 0.99987) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> has 2870 / 89.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GROCPTH_09_14”><code>PCH_GROCPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> (ρ = 0.99149) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> has 93 / 2.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> has 961 / 29.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_LACCESS_HHNV_10_15”><code>PCH_LACCESS_HHNV_10_15</code></a> has 91 / 2.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_LACCESS_HHNV_10_15”><code>PCH_LACCESS_HHNV_10_15</code></a> is highly skewed (γ1 = 52.642) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> is highly correlated with <a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> (ρ = 0.99999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> has 104 / 3.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> has 253 / 7.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> is highly skewed (γ1 = 55.725) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_LACCESS_SENIORS_10_15”><code>PCH_LACCESS_SENIORS_10_15</code></a> is highly correlated with <a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> (ρ = 0.99981) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_NSLP_09_15”><code>PCH_NSLP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_ACRESPTH_07_12”><code>PCH_ORCHARD_ACRESPTH_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> (ρ = 0.99826) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> has 1192 / 37.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_FARMS_07_12”><code>PCH_ORCHARD_FARMS_07_12</code></a> has 493 / 15.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_ORCHARD_FARMS_07_12”><code>PCH_ORCHARD_FARMS_07_12</code></a> has 223 / 6.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_PC_DIRSALES_07_12”><code>PCH_PC_DIRSALES_07_12</code></a> is highly correlated with <a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> (ρ = 0.99896) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_PC_SNAPBEN_10_15”><code>PCH_PC_SNAPBEN_10_15</code></a> has 158 / 4.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_PC_WIC_REDEMP_08_12”><code>PCH_PC_WIC_REDEMP_08_12</code></a> has 1273 / 39.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_RECFACPTH_09_14”><code>PCH_RECFACPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> (ρ = 0.9968) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> has 201 / 6.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> has 1285 / 40.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_REDEMP_SNAPS_12_16”><code>PCH_REDEMP_SNAPS_12_16</code></a> has 351 / 10.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_REDEMP_WICS_08_12”><code>PCH_REDEMP_WICS_08_12</code></a> has 1273 / 39.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SBP_09_15”><code>PCH_SBP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SFSP_09_15”><code>PCH_SFSP_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SLHOUSE_07_12”><code>PCH_SLHOUSE_07_12</code></a> has 243 / 7.6% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SLHOUSE_07_12”><code>PCH_SLHOUSE_07_12</code></a> has 2464 / 76.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SNAPSPTH_12_16”><code>PCH_SNAPSPTH_12_16</code></a> is highly correlated with <a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> (ρ = 0.98953) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> has 107 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> has 120 / 3.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SNAP_12_16”><code>PCH_SNAP_12_16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SPECSPTH_09_14”><code>PCH_SPECSPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> (ρ = 0.99719) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> has 266 / 8.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> has 1435 / 44.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_SUPERCPTH_09_14”><code>PCH_SUPERCPTH_09_14</code></a> is highly correlated with <a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> (ρ = 0.99027) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> has 216 / 6.7% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> has 2596 / 80.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_VEG_ACRESPTH_07_12”><code>PCH_VEG_ACRESPTH_07_12</code></a> has 968 / 30.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_ACRES_07_12”><code>PCH_VEG_ACRES_07_12</code></a> has 1153 / 35.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_ACRES_07_12”><code>PCH_VEG_ACRES_07_12</code></a> is highly skewed (γ1 = 37.151) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> has 420 / 13.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_WICSPTH_08_12”><code>PCH_WICSPTH_08_12</code></a> is highly correlated with <a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> (ρ = 0.98279) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> has 1325 / 41.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCH_WIC_09_15”><code>PCH_WIC_09_15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_18YOUNGER10”><code>PCT_18YOUNGER10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_65OLDER10”><code>PCT_65OLDER10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_CACFP09”><code>PCT_CACFP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_CACFP15”><code>PCT_CACFP15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_DIABETES_ADULTS08”><code>PCT_DIABETES_ADULTS08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_DIABETES_ADULTS13”><code>PCT_DIABETES_ADULTS13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_ANMLPROD16”><code>PCT_FMRKT_ANMLPROD16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_ANMLPROD16”><code>PCT_FMRKT_ANMLPROD16</code></a> has 636 / 19.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_BAKED16”><code>PCT_FMRKT_BAKED16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_BAKED16”><code>PCT_FMRKT_BAKED16</code></a> has 576 / 17.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_CREDIT16”><code>PCT_FMRKT_CREDIT16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_CREDIT16”><code>PCT_FMRKT_CREDIT16</code></a> has 726 / 22.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> has 488 / 15.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_OTHERFOOD16”><code>PCT_FMRKT_OTHERFOOD16</code></a> is highly correlated with <a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> (ρ = 0.90806) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_FMRKT_SFMNP16”><code>PCT_FMRKT_SFMNP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_SFMNP16”><code>PCT_FMRKT_SFMNP16</code></a> has 1329 / 41.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_SNAP16”><code>PCT_FMRKT_SNAP16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_SNAP16”><code>PCT_FMRKT_SNAP16</code></a> has 1289 / 40.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_WIC16”><code>PCT_FMRKT_WIC16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_WIC16”><code>PCT_FMRKT_WIC16</code></a> has 1416 / 44.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FMRKT_WICCASH16”><code>PCT_FMRKT_WICCASH16</code></a> has 967 / 30.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FMRKT_WICCASH16”><code>PCT_FMRKT_WICCASH16</code></a> has 1730 / 53.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_FREE_LUNCH09”><code>PCT_FREE_LUNCH09</code></a> has 153 / 4.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_FREE_LUNCH14”><code>PCT_FREE_LUNCH14</code></a> has 314 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_HISP10”><code>PCT_HISP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_HSPA15”><code>PCT_HSPA15</code></a> has 1196 / 37.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_BLACK15”><code>PCT_LACCESS_BLACK15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_BLACK15”><code>PCT_LACCESS_BLACK15</code></a> has 189 / 5.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_CHILD10”><code>PCT_LACCESS_CHILD10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.96149) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_CHILD15”><code>PCT_LACCESS_CHILD15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.95773) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_HHNV10”><code>PCT_LACCESS_HHNV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HHNV15”><code>PCT_LACCESS_HHNV15</code></a> has 80 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HISP15”><code>PCT_LACCESS_HISP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_HISP15”><code>PCT_LACCESS_HISP15</code></a> has 53 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_LOWI10”><code>PCT_LACCESS_LOWI10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.90295) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_LOWI15”><code>PCT_LACCESS_LOWI15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.90394) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_MULTIR15”><code>PCT_LACCESS_MULTIR15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_MULTIR15”><code>PCT_LACCESS_MULTIR15</code></a> has 38 / 1.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHASIAN15”><code>PCT_LACCESS_NHASIAN15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHASIAN15”><code>PCT_LACCESS_NHASIAN15</code></a> has 230 / 7.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHNA15”><code>PCT_LACCESS_NHNA15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHNA15”><code>PCT_LACCESS_NHNA15</code></a> has 170 / 5.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> has 1175 / 36.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LACCESS_NHPI15”><code>PCT_LACCESS_NHPI15</code></a> is highly skewed (γ1 = 25.235) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_SENIORS10”><code>PCT_LACCESS_SENIORS10</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> (ρ = 0.92085) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> (ρ = 0.91044) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LACCESS_SNAP15”><code>PCT_LACCESS_SNAP15</code></a> has 97 / 3.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LACCESS_WHITE15”><code>PCT_LACCESS_WHITE15</code></a> is highly correlated with <a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> (ρ = 0.93066) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_LOCLFARM07”><code>PCT_LOCLFARM07</code></a> has 139 / 4.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLFARM07”><code>PCT_LOCLFARM07</code></a> has 55 / 1.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLFARM12”><code>PCT_LOCLFARM12</code></a> has 138 / 4.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLFARM12”><code>PCT_LOCLFARM12</code></a> has 72 / 2.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLSALE07”><code>PCT_LOCLSALE07</code></a> has 416 / 13.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLSALE07”><code>PCT_LOCLSALE07</code></a> has 48 / 1.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_LOCLSALE12”><code>PCT_LOCLSALE12</code></a> has 357 / 11.1% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_LOCLSALE12”><code>PCT_LOCLSALE12</code></a> has 65 / 2.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHASIAN10”><code>PCT_NHASIAN10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHBLACK10”><code>PCT_NHBLACK10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHBLACK10”><code>PCT_NHBLACK10</code></a> has 33 / 1.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHNA10”><code>PCT_NHNA10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> has 484 / 15.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_NHPI10”><code>PCT_NHPI10</code></a> is highly skewed (γ1 = 45.127) <span class=”label label-info”>Skewed</span></li><li><a href=”#pp_var_PCT_NHWHITE10”><code>PCT_NHWHITE10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_NSLP15”><code>PCT_NSLP15</code></a> is highly correlated with <a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> (ρ = 0.98444) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_OBESE_ADULTS08”><code>PCT_OBESE_ADULTS08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_OBESE_ADULTS13”><code>PCT_OBESE_ADULTS13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH09”><code>PCT_REDUCED_LUNCH09</code></a> has 153 / 4.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH14”><code>PCT_REDUCED_LUNCH14</code></a> has 314 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_REDUCED_LUNCH14”><code>PCT_REDUCED_LUNCH14</code></a> has 151 / 4.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SBP15”><code>PCT_SBP15</code></a> is highly correlated with <a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> (ρ = 0.92649) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_SFSP09”><code>PCT_SFSP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SFSP15”><code>PCT_SFSP15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_SNAP16”><code>PCT_SNAP16</code></a> is highly correlated with <a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> (ρ = 0.91642) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PCT_WIC09”><code>PCT_WIC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PCT_WIC15”><code>PCT_WIC15</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES07”><code>PC_DIRSALES07</code></a> has 359 / 11.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES07”><code>PC_DIRSALES07</code></a> has 60 / 1.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PC_DIRSALES12”><code>PC_DIRSALES12</code></a> has 313 / 9.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_DIRSALES12”><code>PC_DIRSALES12</code></a> has 76 / 2.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PC_FFRSALES07”><code>PC_FFRSALES07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FFRSALES12”><code>PC_FFRSALES12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_FSRSALES12”><code>PC_FSRSALES12</code></a> is highly correlated with <a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> (ρ = 0.91163) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> has 158 / 4.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_SNAPBEN15”><code>PC_SNAPBEN15</code></a> is highly correlated with <a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> (ρ = 0.94136) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> has 1136 / 35.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PC_WIC_REDEMP12”><code>PC_WIC_REDEMP12</code></a> is highly correlated with <a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> (ρ = 0.931) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_PERCHLDPOV10”><code>PERCHLDPOV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PERCHLDPOV10”><code>PERCHLDPOV10</code></a> has 2426 / 75.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_PERPOV10”><code>PERPOV10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_PERPOV10”><code>PERPOV10</code></a> has 2781 / 86.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_POPLOSS10”><code>POPLOSS10</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_POPLOSS10”><code>POPLOSS10</code></a> has 2606 / 81.2% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> has 79 / 2.5% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> is highly correlated with <a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> (ρ = 0.99996) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> is highly correlated with <a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> is highly correlated with <a href=”#pp_var_FSR14”><code>FSR14</code></a> (ρ = 0.95735) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFAC14”><code>RECFAC14</code></a> is highly correlated with <a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> (ρ = 0.99353) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_RECFACPTH09”><code>RECFACPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_RECFACPTH09”><code>RECFACPTH09</code></a> has 885 / 27.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_RECFACPTH14”><code>RECFACPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_RECFACPTH14”><code>RECFACPTH14</code></a> has 1007 / 31.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_REDEMP_SNAPS12”><code>REDEMP_SNAPS12</code></a> has 317 / 9.9% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_SNAPS16”><code>REDEMP_SNAPS16</code></a> has 250 / 7.8% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> has 1136 / 35.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_REDEMP_WICS12”><code>REDEMP_WICS12</code></a> is highly correlated with <a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> (ρ = 0.91221) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SLHOUSE07”><code>SLHOUSE07</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SLHOUSE07”><code>SLHOUSE07</code></a> has 1979 / 61.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SLHOUSE12”><code>SLHOUSE12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SLHOUSE12”><code>SLHOUSE12</code></a> has 2023 / 63.0% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> is highly correlated with <a href=”#pp_var_SPECS14”><code>SPECS14</code></a> (ρ = 0.92214) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> is highly correlated with <a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> (ρ = 0.99663) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAPSPTH12”><code>SNAPSPTH12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAPSPTH16”><code>SNAPSPTH16</code></a> has 104 / 3.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE09”><code>SNAP_BBCE09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE09”><code>SNAP_BBCE09</code></a> has 1673 / 52.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_BBCE16”><code>SNAP_BBCE16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_BBCE16”><code>SNAP_BBCE16</code></a> has 819 / 25.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> has 1968 / 61.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_CAP16”><code>SNAP_CAP16</code></a> is highly correlated with <a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> (ρ = 0.94179) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SNAP_OAPP09”><code>SNAP_OAPP09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_OAPP09”><code>SNAP_OAPP09</code></a> has 1343 / 41.8% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_OAPP16”><code>SNAP_OAPP16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_OAPP16”><code>SNAP_OAPP16</code></a> has 342 / 10.7% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_PART_RATE08”><code>SNAP_PART_RATE08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_PART_RATE13”><code>SNAP_PART_RATE13</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE09”><code>SNAP_REPORTSIMPLE09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE09”><code>SNAP_REPORTSIMPLE09</code></a> has 81 / 2.5% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SNAP_REPORTSIMPLE16”><code>SNAP_REPORTSIMPLE16</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_STORES14”><code>SODATAX_STORES14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_STORES14”><code>SODATAX_STORES14</code></a> has 686 / 21.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SODATAX_VENDM14”><code>SODATAX_VENDM14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SODATAX_VENDM14”><code>SODATAX_VENDM14</code></a> has 332 / 10.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SODA_PRICE10”><code>SODA_PRICE10</code></a> has 106 / 3.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECS09”><code>SPECS09</code></a> is highly correlated with <a href=”#pp_var_GROC14”><code>GROC14</code></a> (ρ = 0.9563) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SPECS14”><code>SPECS14</code></a> is highly correlated with <a href=”#pp_var_SPECS09”><code>SPECS09</code></a> (ρ = 0.99301) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SPECSPTH09”><code>SPECSPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECSPTH09”><code>SPECSPTH09</code></a> has 1132 / 35.3% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SPECSPTH14”><code>SPECSPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SPECSPTH14”><code>SPECSPTH14</code></a> has 1208 / 37.6% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> has 1480 / 46.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERC14”><code>SUPERC14</code></a> is highly correlated with <a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> (ρ = 0.98347) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_SUPERCPTH09”><code>SUPERCPTH09</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERCPTH09”><code>SUPERCPTH09</code></a> has 1480 / 46.1% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_SUPERCPTH14”><code>SUPERCPTH14</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_SUPERCPTH14”><code>SUPERCPTH14</code></a> has 1346 / 41.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> (ρ = 0.92183) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2011”><code>School Breakfast Program participants FY 2011</code></a> has 3210 / 100.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_School Breakfast Program participants FY 2011”><code>School Breakfast Program participants FY 2011</code></a> has constant value <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2012”><code>School Breakfast Program participants, FY 2012</code></a> has 3210 / 100.0% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2012”><code>School Breakfast Program participants, FY 2012</code></a> has constant value <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> (ρ = 0.98406) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> (ρ = 0.99862) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_School Breakfast Program participants, FY 2015”><code>School Breakfast Program participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> (ρ = 0.99676) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> is highly correlated with <a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> is highly correlated with <a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> (ρ = 0.99999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> is highly correlated with <a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> is highly correlated with <a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> (ρ = 0.99997) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2016”><code>State Population, 2016</code></a> is highly correlated with <a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> (ρ = 0.99996) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> (ρ = 0.92968) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> is highly correlated with <a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> (ρ = 0.9999) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> is highly correlated with <a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> (ρ = 0.99998) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_StateFIPS “><code>StateFIPS </code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> (ρ = 0.98245) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> (ρ = 0.99308) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> (ρ = 0.98386) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> (ρ = 0.98127) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food participants, FY 2015”><code>Summer Food participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> (ρ = 0.99324) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_USDA Model And”><code>USDA Model And</code></a> is highly correlated with <a href=”#pp_var_USDA Model Percent”><code>USDA Model Percent</code></a> (ρ = 0.99358) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_USDA Model Or”><code>USDA Model Or</code></a> is highly correlated with <a href=”#pp_var_USDA Model Count”><code>USDA Model Count</code></a> (ρ = 0.99064) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> has 682 / 21.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_ACRES12”><code>VEG_ACRES12</code></a> is highly correlated with <a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> (ρ = 0.9886) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> has 682 / 21.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_ACRESPTH12”><code>VEG_ACRESPTH12</code></a> is highly correlated with <a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> (ρ = 0.99906) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> has 134 / 4.2% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> has 286 / 8.9% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_VEG_FARMS12”><code>VEG_FARMS12</code></a> is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> (ρ = 0.91017) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_VLFOODSEC_10_12”><code>VLFOODSEC_10_12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_VLFOODSEC_13_15”><code>VLFOODSEC_13_15</code></a> is highly correlated with <a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> (ρ = 0.93787) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> has 555 / 17.3% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> is highly correlated with <a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> (ρ = 0.99857) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> is highly correlated with <a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> (ρ = 0.9754) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> (ρ = 0.99975) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> (ρ = 0.99951) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> is highly correlated with <a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> (ρ = 0.9992) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICS08”><code>WICS08</code></a> is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> (ρ = 0.91514) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICS12”><code>WICS12</code></a> is highly correlated with <a href=”#pp_var_WICS08”><code>WICS08</code></a> (ρ = 0.98574) <span class=”label label-primary”>Rejected</span></li><li><a href=”#pp_var_WICSPTH08”><code>WICSPTH08</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WICSPTH08”><code>WICSPTH08</code></a> has 141 / 4.4% zeros <span class=”label label-info”>Zeros</span></li><li><a href=”#pp_var_WICSPTH12”><code>WICSPTH12</code></a> has 78 / 2.4% missing values <span class=”label label-default”>Missing</span></li><li><a href=”#pp_var_WICSPTH12”><code>WICSPTH12</code></a> has 149 / 4.6% zeros <span class=”label label-info”>Zeros</span></li> </ul>

</div>

</div>
<div class=”row headerrow highlight”>

<h1>Variables</h1>

</div> <div class=”row variablerow”> <div class=”col-md-3 namecol”>

<p class=”h4 pp-anchor” id=”pp_var_2010 Census Population”>2010 Census Population<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3081</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>98449</td>

</tr> <tr>

<th>Minimum</th> <td>82</td>

</tr> <tr>

<th>Maximum</th> <td>9818600</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram553040985187473125”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAvNJREFUeJzt2jFIamEchvH3RjS0BEUEGYhTmxAIQdDSEBEZVJs0V2s4JLQFd4tqKGhxCGoI2oToDNGcODQEEUbUXFtDZIZ3k%2But%2B94C9Uj3%2BcEBPX5H/oOPfhz8UalUKgLwobawBwBaWXvYA/wpsXry5WsKPycaMAnALwhgEQhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgtIc9QD0kVk%2B%2BfE3h50QDJsF386NSqVTCHgJoVWyxAINAAINAAINA0BClUklTU1M6Pz//1PqxsTENDg6%2BO7a3txs8qfct7mKhtby8vCidTqtYLH76mqOjI729vVWfB0Ggra0tzczMNGLETyMQ1NXNzY3S6bS%2BenO0u7u7%2Bvjp6Uk7OztaWVlRJBKp94hfwhYLdZXP5zU8PKzDw8N3rxUKBc3OzioejyuZTCoIgg/fI5vNqre3V3Nzc40e95/4BUFdpVKpD88/PDxocXFRy8vLGh0d1cXFhTKZjHp6epRIJKrrnp%2Bftb%2B/r7W1NbW1hf/9TSBoioODA42MjGh%2Bfl6SFI1GdXV1pb29vZpAjo%2BP1dnZqfHx8bBGrUEgaIrb21udnZ1paGioeu719VWxWKxmXRAEmpycVHt7a3w0W2MKfHvlclnJZFJLS0s1538PoVQqKZ/Pa2Fhodnj/VX4mzz8F2KxmO7v7xWNRqvH6empcrlcdc319bXK5bLi8XiIk9YiEDRFKpXS5eWlNjc3dXd3p1wup42NDfX391fXFItFDQwMqKOjI8RJa7HFQlNEIhHt7u5qfX1d2WxWfX19ymQymp6erq55fHxUV1dXiFO%2Bx9/dAYMtFmAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGD8AlN9qL5tox75AAAAAElFTkSuQmCC”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives553040985187473125,#minihistogram553040985187473125”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives553040985187473125”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles553040985187473125”

aria-controls=”quantiles553040985187473125” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram553040985187473125” aria-controls=”histogram553040985187473125”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common553040985187473125” aria-controls=”common553040985187473125”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme553040985187473125” aria-controls=”extreme553040985187473125”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles553040985187473125”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>82</td>

</tr> <tr>

<th>5-th percentile</th> <td>2963.8</td>

</tr> <tr>

<th>Q1</th> <td>11155</td>

</tr> <tr>

<th>Median</th> <td>25902</td>

</tr> <tr>

<th>Q3</th> <td>66618</td>

</tr> <tr>

<th>95-th percentile</th> <td>423250</td>

</tr> <tr>

<th>Maximum</th> <td>9818600</td>

</tr> <tr>

<th>Range</th> <td>9818500</td>

</tr> <tr>

<th>Interquartile range</th> <td>55463</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>313410</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.1835</td>

</tr> <tr>

<th>Kurtosis</th> <td>344.32</td>

</tr> <tr>

<th>Mean</th> <td>98449</td>

</tr> <tr>

<th>MAD</th> <td>117020</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.335</td>

</tr> <tr>

<th>Sum</th> <td>308340000</td>

</tr> <tr>

<th>Variance</th> <td>98227000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram553040985187473125”>

<img 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PF57vvvv68hQ4YoPj5e48aNU1FRkc/8M888o759%2ByoxMVEzZ85URUWFbesCAADO58iAZVmW0tLSVFFRoeeee06PPPKI3nrrLS1ZskSWZSk1NVWtWrVSTk6Ohg4dqsmTJ2v//v2SpP379ys1NVUpKSlav369IiMjdeedd8qyLEnSxo0blZWVpfT0dK1Zs0b5%2BfnKzMwM5HIBAIDDODJg7du3T3l5ecrIyFCnTp2UnJystLQ0bdiwQVu3blVRUZHS09PVsWNHTZo0SQkJCcrJyZEkvfjii7rwwgs1YcIEderUSRkZGfruu%2B%2B0bds2SdLatWs1fvx49evXT927d9fcuXOVk5PDUSwAAFBvjgxY0dHRWrVqlVq1auUz/uOPPyo/P18XXHCBmjZt6h1PSkpSXl6eJCk/P1/JycneufDwcHXt2lV5eXmqqanRJ5984jOfkJCgY8eOaffu3X5eFQAAaCwcGbAiIiLUt29f7%2BPa2lqtW7dOvXv3ltvtVkxMjM/2UVFROnjwoCSdcL68vFxVVVU%2B8y6XSy1atPA%2BHwAA4Le4Al2ACZmZmdq1a5fWr1%2BvZ555RqGhoT7zoaGh8ng8kqSKiopfna%2BsrPQ%2B/rXn10dxcbHcbrfPmMvVtE6wO1UhIc7Kxy6Xs%2BqV/t1jp/XaSeixf9Ff/6PH/ufEHjs%2BYGVmZmrNmjV65JFH1LlzZ4WFhamsrMxnG4/HoyZNmkiSwsLC6oQlj8ejiIgIhYWFeR//fD48PLzeNWVnZysrK8tnLDU1VWlpafXeR2PUsmWzQJdw0iIi6v//j5NDj/2L/vofPfY/J/XY0QFr3rx5ev7555WZmamrrrpKkhQbG6s9e/b4bFdSUuI9ehQbG6uSkpI68126dFGLFi0UFhamkpISdezYUZJUXV2tsrIyRUdH17uuUaNGqX///j5jLldTlZYeafAaT8RJSV6S8fXbISQkWBER4Sovr1BNTW2gy2mU6LF/0V//o8f%2Bdyo9DtQP944NWFlZWfrrX/%2Bqhx9%2BWIMGDfKOx8fHa%2BXKlaqsrPQetcrNzVVSUpJ3Pjc317t9RUWFdu3apcmTJys4OFjdunVTbm6uevXqJUnKy8uTy%2BVSXFxcvWuLiYmpczrQ7T6s6uoz%2B43n5PXX1NQ6un4noMf%2BRX/9jx77n5N67KxDIP9n7969WrFihW677TYlJSXJ7XZ7v3r27KnWrVtrxowZKiws1MqVK1VQUKCRI0dKkkaMGKEdO3Zo5cqVKiws1IwZM9SuXTtvoBozZoxWr16tTZs2qaCgQHPmzNENN9zQoFOEAADgzGb7Eazrr79eI0aM0DXXXKOzzjrrpPaxefNm1dTU6LHHHtNjjz3mM/f5559rxYoVmjVrllJSUnTuuedq%2BfLlatOmjSSpXbt2evTRR/Xggw9q%2BfLlSkxM1PLlyxUUFCRJuuaaa/Tdd99p9uzZ8ng8uvLKKzVt2rRTWzQAADijBFnHb2Fuk8WLF%2Bv1119XaWmprrjiCqWkpKhPnz7egNNYud2Hje/T5QrWwEXvGd%2Bvv7x5d59Al9BgLlewWrZsptLSI445LO009Ni/6K//0WP/O5UeR0ef3MGcU2X7KcIpU6borbfe0ooVKxQSEqK77rpLl19%2BuR555BF9%2BeWXdpcDAABgXEAucg8KClKfPn3Up08fVVRU6Nlnn9WKFSu0cuVK9ejRQ%2BPHj9eVV14ZiNIAAABOWcB%2Bi7C4uFivvfaaXnvtNX3xxRfq0aOHhg8froMHD%2Br%2B%2B%2B/X9u3bNWvWrECVBwAAcNJsD1ivvvqqXn31VX344YeKjIzUsGHDtGzZMrVv3967TevWrbVgwQICFgAAcCTbA9asWbPUr18/LV%2B%2BXJdddpmCg%2BteBtahQweNHTvW7tIAAACMsD1gvfvuu2rZsqXKysq84aqgoEBdu3ZVSEiIJKlHjx7q0aOH3aUBAAAYYftvEf74448aNGiQnnzySe/Y7bffrqFDh%2BrAgQN2lwMAAGCc7QHrwQcf1LnnnqtbbrnFO/bGG2%2BodevWysjIsLscAAAA42wPWB999JHuu%2B8%2Bnz%2BeHBkZqXvvvVdbt261uxwAAADjbA9YLpdL5eXldcYrKipk803lAQAA/ML2gHXZZZdp/vz5%2Buabb7xjRUVFysjIUN%2B%2Bfe0uBwAAwDjbf4tw%2BvTpuuWWW3TVVVcpIiJCklReXq6uXbtqxowZdpcDAABgnO0BKyoqSi%2B//LLef/99FRYWyuVy6bzzztPFF1/c6P/gMwAAODME5E/lhISEqG/fvpwSBAAAjZLtAcvtdmvJkiXasWOHjh07VufC9s2bN9tdEgAAgFG2B6wHHnhAO3fu1DXXXKOzzjrL7pcHAADwO9sD1tatW7Vq1SolJyfb/dIAAAC2sP02DU2bNlVUVJTdLwsAAGAb2wPW0KFDtWrVKtXU1Nj90gAAALaw/RRhWVmZNmzYoLffflvnnHOOQkNDfebXrl1rd0kAAABGBeQ2DUOGDAnEywIAANjC9oCVkZFh90sCAADYyvZrsCSpuLhYWVlZmjJlir7//nv97W9/0759%2BwJRCgAAgHG2B6yvv/5a1157rV5%2B%2BWVt3LhRR48e1RtvvKERI0YoPz/f7nIAAACMsz1gPfTQQxowYIA2bdqk3/3ud5Kkhx9%2BWP3799eiRYvsLgcAAMA42wPWjh07dMstt/j8YWeXy6U777xTu3btsrscAAAA42wPWLW1taqtra0zfuTIEYWEhNhdDgAAgHG2B6xLL71UTzzxhE/IKisrU2Zmpnr37m13OQAAAMbZHrDuu%2B8%2B7dy5U5deeqmqqqr03//93%2BrXr5%2B%2B/fZbTZ8%2B3e5yAAAAjLP9PlixsbF65ZVXtGHDBn322Weqra3V6NGjNXToUDVv3tzucgAAAIwLyJ3cw8PDdf311wfipQEAAPzO9oA1bty4E87ztwgBAIDT2R6w2rZt6/O4urpaX3/9tb744guNHz/e7nIAAACMO23%2BFuHy5ct18OBBm6sBAAAwLyB/i/CXDB06VG%2B%2B%2BWagywAAADhlp03A%2Bvjjj7nRKAAAaBROi4vcf/zxR33%2B%2BecaM2aM3eUAAAAYZ3vAatOmjc/fIZSk3/3udxo7dqyuu%2B46u8sBAAAwzvaA9dBDDxndn8fjUUpKih544AH16tVLkjR//nw9%2B%2ByzPts98MADGjt2rCRpw4YNWrJkidxuty699FLNmzdPkZGRkiTLsrR48WKtX79etbW1GjlypKZOnarg4NPmbCoAADjN2R6wtm/fXu9tL7roohPOV1VVacqUKSosLPQZ37t3r6ZMmaLhw4d7x47fJb6goECzZs3S3LlzFRcXpwULFmjGjBl64oknJElPP/20NmzYoKysLFVXV2vatGmKiorSxIkT6103AAA4s9kesG666SbvKULLsrzjPx8LCgrSZ5999qv72bNnj6ZMmeKzj%2BP27t2riRMnKjo6us7cunXrNHjwYA0bNkyStHDhQvXr109FRUU655xztHbtWqWlpSk5OVmSNHXqVC1dupSABQAA6s32816PP/642rZtqyVLluiDDz5Qbm6unnnmGf3hD3/Qn//8Z23evFmbN2/Wpk2bTrifbdu2qVevXsrOzvYZ//HHH3Xo0CG1b9/%2BF5%2BXn5/vDU%2BS1Lp1a7Vp00b5%2Bfk6dOiQDhw44HPkLCkpSd99952Ki4tPftEAAOCMEpAbjc6ePVuXXXaZd6x3795KT0/Xvffeq9tuu61e%2B/m13zjcu3evgoKC9Pjjj%2Bvdd99VixYtdMstt3hPFxYXFysmJsbnOVFRUTp48KDcbrck%2Bcy3atVKknTw4ME6z/s1xcXF3n0d53I1rffz6yskxFnXhblczqpX%2BnePndZrJ6HH/kV//Y8e%2B58Te2x7wCouLq7z53Kkn66RKi0tPeX979u3T0FBQerQoYPGjh2r7du364EHHlDz5s01cOBAVVZWKjQ01Oc5oaGh8ng8qqys9D7%2Bzznpp4vp6ys7O1tZWVk%2BY6mpqUpLSzvZZTUKLVs2C3QJJy0iIjzQJTR69Ni/6K//0WP/c1KPbQ9YCQkJevjhh/WXv/zFe%2BF5WVmZMjMzdfHFF5/y/ocNG6Z%2B/fqpRYsWkqS4uDh99dVXev755zVw4ECFhYXVCUsej0fh4eE%2BYSosLMz7b0kKD6//f%2BqoUaPUv39/nzGXq6lKS4%2Bc9Lp%2BiZOSvCTj67dDSEiwIiLCVV5eoZqa2kCX0yjRY/%2Biv/5Hj/3vVHocqB/ubQ9Y999/v8aNG6fLLrtM7du3l2VZ%2BuqrrxQdHa21a9ee8v6DgoK84eq4Dh06aOvWrZKk2NhYlZSU%2BMyXlJQoOjpasbGxkiS326127dp5/y3pFy%2BY/zUxMTF1Tge63YdVXX1mv/GcvP6amlpH1%2B8E9Ni/6K//0WP/c1KPbT8E0rFjR73xxhuaMmWKEhISlJiYqFmzZunVV1/V73//%2B1Pe/9KlS3XzzTf7jO3evVsdOnSQJMXHxys3N9c7d%2BDAAR04cEDx8fGKjY1VmzZtfOZzc3PVpk0b49dPAQCAxsv2I1iSdPbZZ%2Bv666/Xt99%2Bq3POOUfST3dzN6Ffv35auXKlVq9erYEDB2rLli165ZVXvEfHRo8erZtuukkJCQnq1q2bFixYoMsvv9xbx%2BjRo7Vo0SJv2Fu8eLEmTJhgpDYAAHBmsD1gHb9T%2BrPPPqtjx45p48aNeuSRRxQeHq45c%2BacctDq3r27li5dqmXLlmnp0qVq27atFi9erMTERElSYmKi0tPTtWzZMv3rX/9Snz59NG/ePO/zJ06cqO%2B//16TJ09WSEiIRo4cWeeIGAAAwIkEWb90p04/Wrt2rZ588kndc889Sk9P1%2Buvv65PPvlEc%2BfO1Y033qh77rnHznJs43YfNr5PlytYAxe9Z3y//vLm3X0CXUKDuVzBatmymUpLjzjmvL/T0GP/or/%2BR4/971R6HB19lp%2BqOjHbr8HKzs7W7NmzlZKS4r17%2B9VXX6358%2Bfr9ddft7scAAAA42wPWN9%2B%2B626dOlSZzwuLq7OzTkBAACcyPaA1bZtW33yySd1xt99913vheYAAABOZvtF7hMnTtTcuXPldrtlWZY%2B%2BOADZWdn69lnn9V9991ndzkAAADG2R6wRowYoerqaj322GOqrKzU7NmzFRkZqbvvvlujR4%2B2uxwAAADjbA9YGzZs0KBBgzRq1Cj98MMPsixLUVFRdpcBAADgN7Zfg5Wenu69mD0yMpJwBQAAGh3bA1b79u31xRdf2P2yAAAAtrH9FGFcXJymTp2qVatWqX379goLC/OZz8jIsLskAAAAo2wPWF9%2B%2BaWSkpIkifteAQCARsmWgLVw4UJNnjxZTZs21bPPPmvHSwIAAASMLddgPf3006qoqPAZu/3221VcXGzHywMAANjKloD1S39Pevv27aqqqrLj5QEAAGxl%2B28RAgAANHYELAAAAMNsC1hBQUF2vRQAAEBA2Xabhvnz5/vc8%2BrYsWPKzMxUs2bNfLbjPlgAAMDpbAlYF110UZ17XiUmJqq0tFSlpaV2lAAAAGAbWwIW974CAABnEi5yBwAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmOMDlsfj0ZAhQ/Thhx96x4qKinTzzTcrISFBV199tbZs2eLznPfff19DhgxRfHy8xo0bp6KiIp/5Z555Rn379lViYqJmzpypiooKW9YCAAAaB0cHrKqqKv35z39WYWGhd8yyLKWmpqpVq1bKycnR0KFDNXnyZO3fv1%2BStH//fqWmpiolJUXr169XZGSk7rzzTlmWJUnauHGjsrKylJ6erjVr1ig/P1%2BZmZkBWR8AAHAmxwasPXv26IYbbtA333zjM75161YVFRUpPT1dHTt21KRJk5SQkKCcnBxJ0osvvqgLL7xQEyZMUKdOnZSRkaHvvvtO27ZtkyStXbtW48ePV79%2B/dS9e3fNnTtXOTk5HMUCAAD15tiAtW3bNvXq1UvZ2dk%2B4/n5%2BbrgggvUtGlT71hSUpLy8vK888nJyd658PBwde3aVXl5eaqpqdEnn3ziM5%2BQkKBjx45p9%2B7dfl4RAABoLFyBLuBkjRkz5hfH3W63YmJifMaioqJ08ODB35wvLy9XVVWVz7zL5VKLFi28z6%2BP4uJiud1unzGXq2md1z1VISHOysfume7QAAASwUlEQVQul7Pqlf7dY6f12knosX/RX/%2Bjx/7nxB47NmD9moqKCoWGhvqMhYaGyuPx/OZ8ZWWl9/GvPb8%2BsrOzlZWV5TOWmpqqtLS0eu%2BjMWrZslmgSzhpERHhgS6h0aPH/kV//Y8e%2B5%2BTetzoAlZYWJjKysp8xjwej5o0aeKd/3lY8ng8ioiIUFhYmPfxz%2BfDw%2Bv/nzpq1Cj179/fZ8zlaqrS0iP13kd9OCnJSzK%2BfjuEhAQrIiJc5eUVqqmpDXQ5jRI99i/663/02P9OpceB%2BuG%2B0QWs2NhY7dmzx2espKTEe3ouNjZWJSUldea7dOmiFi1aKCwsTCUlJerYsaMkqbq6WmVlZYqOjq53DTExMXVOB7rdh1VdfWa/8Zy8/pqaWkfX7wT02L/or//RY/9zUo%2BddQikHuLj4/Xpp596T/dJUm5uruLj473zubm53rmKigrt2rVL8fHxCg4OVrdu3Xzm8/Ly5HK5FBcXZ98iAACAozW6gNWzZ0%2B1bt1aM2bMUGFhoVauXKmCggKNHDlSkjRixAjt2LFDK1euVGFhoWbMmKF27dqpV69ekn66eH716tXatGmTCgoKNGfOHN1www0NOkUIAADObI0uYIWEhGjFihVyu91KSUnRa6%2B9puXLl6tNmzaSpHbt2unRRx9VTk6ORo4cqbKyMi1fvlxBQUGSpGuuuUaTJk3S7NmzNWHCBHXv3l3Tpk0L5JIAAIDDBFnHb2EOv3K7Dxvfp8sVrIGL3jO%2BX3958%2B4%2BgS6hwVyuYLVs2UylpUccc97faeixf9Ff/6PH/ncqPY6OPstPVZ1YozuCBQAAEGgELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY1mgD1j/%2B8Q%2Bdf/75Pl9paWmSpF27dun6669XfHy8RowYoZ07d/o8d8OGDRowYIDi4%2BOVmpqqH374IRBLAAAADtVoA9aePXvUr18/bdmyxfs1f/58HT16VLfffruSk5P10ksvKTExUZMmTdLRo0clSQUFBZo1a5YmT56s7OxslZeXa8aMGQFeDQAAcJJGG7D27t2rzp07Kzo62vsVERGhN954Q2FhYbr33nvVsWNHzZo1S82aNdPf/vY3SdK6des0ePBgDRs2THFxcVq4cKHeeecdFRUVBXhFAADAKRp1wGrfvn2d8fz8fCUlJSkoKEiSFBQUpB49eigvL887n5yc7N2%2BdevWatOmjfLz822pGwAAOF%2BjDFiWZenLL7/Uli1bdNVVV2nAgAFatGiRPB6P3G63YmJifLaPiorSwYMHJUnFxcUnnAcAAPgtrkAX4A/79%2B9XRUWFQkNDtWTJEn377beaP3%2B%2BKisrveP/KTQ0VB6PR5JUWVl5wvn6KC4ultvt9hlzuZrWCW6nKiTEWfnY5XJWvdK/e%2By0XjsJPfYv%2But/9Nj/nNjjRhmw2rZtqw8//FBnn322goKC1KVLF9XW1mratGnq2bNnnbDk8XjUpEkTSVJYWNgvzoeHh9f79bOzs5WVleUzlpqa6v0txjNVy5bNAl3CSYuIqP//P04OPfYv%2But/9Nj/nNTjRhmwJKlFixY%2Bjzt27KiqqipFR0erpKTEZ66kpMR7dCk2NvYX56Ojo%2Bv92qNGjVL//v19xlyupiotPdKQJfwmJyV5ScbXb4eQkGBFRISrvLxCNTW1gS6nUaLH/kV//Y8e%2B9%2Bp9DhQP9w3yoD13nvvaerUqXr77be9R54%2B%2B%2BwztWjRQklJSXryySdlWZaCgoJkWZZ27NihO%2B64Q5IUHx%2Bv3NxcpaSkSJIOHDigAwcOKD4%2Bvt6vHxMTU%2Bd0oNt9WNXVZ/Ybz8nrr6mpdXT9TkCP/Yv%2B%2Bh899j8n9dhZh0DqKTExUWFhYbr//vu1b98%2BvfPOO1q4cKFuvfVWDRo0SOXl5VqwYIH27NmjBQsWqKKiQoMHD5YkjR49Wq%2B%2B%2BqpefPFF7d69W/fee68uv/xynXPOOQFeFQAAcIpGGbCaN2%2Bu1atX64cfftCIESM0a9YsjRo1SrfeequaN2%2BuJ554wnuUKj8/XytXrlTTpk0l/RTO0tPTtXz5co0ePVpnn322MjIyArwiAADgJEGWZVmBLuJM4HYfNr5PlytYAxe9Z3y//vLm3X0CXUKDuVzBatmymUpLjzjmsLTT0GP/or/%2BR4/971R6HB19lp%2BqOrFGeQQLAAAgkAhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAesXVFVVaebMmUpOTtall16qp556KtAlAQAAB3EFuoDT0cKFC7Vz506tWbNG%2B/fv1/Tp09WmTRsNGjQo0KUBAAAHIGD9zNGjR/Xiiy/qySefVNeuXdW1a1cVFhbqueeeI2CdosFL/l%2BgS2iQN%2B/uE%2BgSAAAOxSnCn9m9e7eqq6uVmJjoHUtKSlJ%2Bfr5qa2sDWBkAAHAKjmD9jNvtVsuWLRUaGuoda9WqlaqqqlRWVqbIyMjf3EdxcbHcbrfPmMvVVDExMUZrDQkhH/uT0464/WNq30CXcFKOfx/z/ewf9Nf/6LH/ObHHBKyfqaio8AlXkryPPR5PvfaRnZ2trKwsn7HJkyfrrrvuMlPk/ykuLtb43xdq1KhRxsMbflJcXKzs7Gx67EfFxcVas2YVPfYT%2But/9Nj/nNhj50RBm4SFhdUJUscfN2nSpF77GDVqlF566SWfr1GjRhmv1e12Kysrq87RMphDj/2PHvsX/fU/eux/TuwxR7B%2BJjY2VqWlpaqurpbL9VN73G63mjRpooiIiHrtIyYmxjEJGwAAmMcRrJ/p0qWLXC6X8vLyvGO5ubnq1q2bgoNpFwAA%2BG0khp8JDw/XsGHDNGfOHBUUFGjTpk166qmnNG7cuECXBgAAHCJkzpw5cwJdxOmmd%2B/e2rVrlxYvXqwPPvhAd9xxh0aMGBHosn5Rs2bN1LNnTzVr1izQpTRa9Nj/6LF/0V//o8f%2B57QeB1mWZQW6CAAAgMaEU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYp7mqqirNnDlTycnJuvTSS/XUU0/96ra7du3S9ddfr/j4eI0YMUI7d%2B60sVLnakiP3377bQ0dOlSJiYm69tprtXnzZhsrda6G9Pi4b7/9VomJifrwww9tqNDZGtLfzz//XKNHj1b37t117bXXauvWrTZW6lwN6fE//vEPDR48WImJiRo9erQ%2B/fRTGyt1Po/HoyFDhpzwve%2BIzzsLp7X09HTr2muvtXbu3Gn9/e9/txITE60333yzznZHjhyx%2BvTpYz300EPWnj17rHnz5lmXXHKJdeTIkQBU7Sz17fFnn31mde3a1VqzZo311VdfWevWrbO6du1qffbZZwGo2lnq2%2BP/NHHiRKtz587W1q1bbarSuerb3/LycuuSSy6x7r//fuurr76yli5daiUlJVklJSUBqNpZ6tvjL774wurWrZv18ssvW19//bU1d%2B5cq0%2BfPtbRo0cDULXzVFZWWqmpqSd87zvl846AdRo7cuSI1a1bN59vsuXLl1tjx46ts%2B2LL75o9e/f36qtrbUsy7Jqa2utgQMHWjk5ObbV60QN6XFmZqY1ceJEn7EJEyZYDz/8sN/rdLKG9Pi4V1991brxxhsJWPXQkP6uWbPGGjBggFVdXe0dS0lJsd5%2B%2B21banWqhvT46aeftoYPH%2B59fPjwYatz585WQUGBLbU6WWFhoXXddddZ11577Qnf%2B075vOMU4Wls9%2B7dqq6uVmJioncsKSlJ%2Bfn5qq2t9dk2Pz9fSUlJCgoKkiQFBQWpR48eysvLs7Vmp2lIj4cPH66pU6fW2cfhw4f9XqeTNaTHklRaWqrMzEylp6fbWaZjNaS/27Zt0xVXXKGQkBDvWE5Ojv74xz/aVq8TNaTHLVq00J49e5Sbm6va2lq99NJLat68uf7rv/7L7rIdZ9u2berVq5eys7NPuJ1TPu9cgS4Av87tdqtly5YKDQ31jrVq1UpVVVUqKytTZGSkz7bnnXeez/OjoqJUWFhoW71O1JAed%2BzY0ee5hYWF%2BuCDD3TjjTfaVq8TNaTHkvTQQw9p%2BPDh6tSpk92lOlJD%2BltUVKTu3bvrgQce0D//%2BU%2B1bdtW06dPV1JSUiBKd4yG9Pjqq6/WP//5T40ZM0YhISEKDg7WE088obPPPjsQpTvKmDFj6rWdUz7vOIJ1GquoqPB5Q0vyPvZ4PPXa9ufbwVdDevyffvjhB911113q0aOHrrjiCr/W6HQN6fH777%2Bv3Nxc3XnnnbbV53QN6e/Ro0e1cuVKRUdH68knn9RFF12kiRMn6sCBA7bV60QN6XFpaancbrdmz56tF154QUOHDtWMGTP0/fff21ZvY%2BeUzzsC1mksLCyszjfM8cdNmjSp17Y/3w6%2BGtLj40pKSjR%2B/HhZlqVly5YpOJi30YnUt8eVlZWaPXu2/vd//5fv2wZoyPdwSEiIunTporS0NF1wwQWaNm2a2rdvr1dffdW2ep2oIT1etGiROnfurD/96U%2B68MILNW/ePIWHhysnJ8e2ehs7p3ze8clwGouNjVVpaamqq6u9Y263W02aNFFERESdbUtKSnzGSkpKFBMTY0utTtWQHkvSoUOH9Kc//Ukej0dr166tc3oLddW3xwUFBSoqKlJaWpoSExO917vcdtttmj17tu11O0VDvoejo6PVoUMHn7H27dtzBOs3NKTHn376qeLi4ryPg4ODFRcXp/3799tWb2PnlM87AtZprEuXLnK5XD4X7uXm5qpbt251jprEx8fr448/lmVZkiTLsrRjxw7Fx8fbWrPTNKTHR48e1a233qrg4GCtW7dOsbGxdpfrSPXtcffu3fX3v/9dr7zyivdLkubPn6//%2BZ//sb1up2jI93BCQoI%2B//xzn7F9%2B/apbdu2ttTqVA3pcUxMjPbu3esz9uWXX6pdu3a21HomcMrnHQHrNBYeHq5hw4Zpzpw5Kigo0KZNm/TUU09p3Lhxkn76CaqyslKSNGjQIJWXl2vBggXas2ePFixYoIqKCg0ePDiQSzjtNaTHTzzxhL755hv95S9/8c653W5%2Bi/A31LfHTZo00bnnnuvzJf3002pUVFQgl3Baa8j38I033qjPP/9cjz76qL7%2B%2BmstXbpURUVFGjp0aCCXcNprSI9vuOEGvfDCC3rllVf09ddfa9GiRdq/f7%2BGDx8eyCU4niM/7wJ6kwj8pqNHj1r33nuvlZCQYF166aXW008/7Z3r3Lmzz30/8vPzrWHDhlndunWzRo4caX366acBqNh56tvjq666yurcuXOdr%2BnTpweocudoyPfxf%2BI%2BWPXTkP5%2B9NFH1vDhw60LL7zQGjp0qLVt27YAVOw8DenxCy%2B8YA0aNMhKSEiwRo8ebe3cuTMAFTvbz9/7Tvy8C7Ks/zvGBgAAACM4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAIDThsfj0ZAhQ/Thhx/Wa/v%2B/fvr/PPPr/OVlZXl50pPzBXQVwcAAPg/VVVVmjJligoLC%2Bv9nPXr16umpsb7eOPGjVqyZEnA755PwAIAAAG3Z88eTZkyRQ29/3lkZKT334cPH9by5cs1ffr0gP%2BNTU4RAgCAgNu2bZt69eql7OzsOnMfffSRUlJS1L17d1177bXauHHjL%2B5j9erVio6O1ogRI/xd7m/iCBYAAAi4MWPG/OK42%2B3WpEmTdM8996hv377Ky8vTfffdp6ioKCUnJ3u3q6io0Lp165Senq7g4MAfPyJgAQCA09Zzzz2nSy65RGPHjpUknXvuufrss8%2B0Zs0an4D1xhtvqGnTprryyisDVaoPAhYAADht7du3T2%2B99ZYSExO9Y8eOHdMf/vAHn%2B02btyoq6%2B%2BWi7X6RFt/j9WQeMjKAINOQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common553040985187473125”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21720.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11539.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23370.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32727.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45058.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6970.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7042.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9980.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2756.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>783.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3070)</td> <td class=”number”>3111</td> <td class=”number”>96.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme553040985187473125”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>82.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>286.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>416.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3095313.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3817117.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4092459.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5194675.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9818605.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_OPS07”>AGRITRSM_OPS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>77</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.578</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>161</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5032295527492203371”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5032295527492203371,#minihistogram5032295527492203371”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5032295527492203371”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5032295527492203371”

aria-controls=”quantiles5032295527492203371” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5032295527492203371” aria-controls=”histogram5032295527492203371”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5032295527492203371” aria-controls=”common5032295527492203371”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5032295527492203371” aria-controls=”extreme5032295527492203371”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5032295527492203371”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>2</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>9</td>

</tr> <tr>

<th>95-th percentile</th> <td>24</td>

</tr> <tr>

<th>Maximum</th> <td>161</td>

</tr> <tr>

<th>Range</th> <td>161</td>

</tr> <tr>

<th>Interquartile range</th> <td>7</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.412</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3739</td>

</tr> <tr>

<th>Kurtosis</th> <td>42.06</td>

</tr> <tr>

<th>Mean</th> <td>7.578</td>

</tr> <tr>

<th>MAD</th> <td>6.0738</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.9777</td>

</tr> <tr>

<th>Sum</th> <td>23310</td>

</tr> <tr>

<th>Variance</th> <td>108.4</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5032295527492203371”>

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rBatTp05asWKFunTpoieeeEJ79%2B6VZVneHgMAAOCG8XrBevLJJ/Xuu%2B9q2bJlCgwM1GOPPaZu3bpp4cKF%2Bvrrr709DgAAgHEOXzxpQECAOnfurM6dO6ugoEAbNmzQsmXLtGrVKrVv314jR47UXXfd5YvRAAAArptPCpYkZWdn69VXX9Wrr76qr776Su3bt9c999yjkydP6plnntGBAwc0depUX40HAABwzbxesF555RW98sor2r9/v2rXrq2BAwdqyZIlatKkifs%2B9erV05w5cyhYAADAlrxesKZOnaru3btr6dKluuOOO1SlSvmXgTVt2lT333%2B/t0cDAAAwwusF6/3331etWrWUl5fnLleHDx9WmzZtFBgYKElq37692rdv7%2B3RAAAAjPD6TxGePXtWffr00QsvvOBeGzt2rAYMGKCsrCxvjwMAAGCc1wvWX//6VzVu3FgPPvige%2B2NN95QvXr1lJCQ4O1xAAAAjPN6wfrkk0/09NNPKywszL1Wu3ZtTZw4Ufv27fP2OAAAAMZ5vWA5HA7l5%2BeXWy8oKOAd3QEAgF/wesG64447NHv2bH333XfutYyMDCUkJKhr167eHgcAAMA4r/8U4aRJk/Tggw%2Bqd%2B/eCg0NlSTl5%2BerTZs2mjx5srfHAQAAMM7rBatOnTp6%2BeWX9eGHHyotLU0Oh0N/%2BMMfdPvttysgIMDb4wAAABjnk1%2BVExgYqK5du3JJEAAA%2BCWvFyyXy6VFixbp4MGDunDhQrkXtr/zzjveHgkAAMAorxesZ599VkeOHFG/fv3029/%2B1ttPDwAAcMN5vWDt27dPq1evVkxMjLefGgAAwCu8/jYN1apVU506dbz9tAAAAF7j9YI1YMAArV69WqWlpd5%2BagAAAK/w%2BiXCvLw87dq1S%2B%2B9954aNWqkoKAgj/3169d7eyQAAACjvH4GS5L69%2B%2BvO%2B64Q7///e/VoEEDj4/KKi4uVv/%2B/bV//3732uzZs3Xrrbd6fGzcuNG9v2vXLvXs2VNOp1NxcXE6ffq0e8%2ByLM2bN0%2BdOnVSbGys5s6dq7KysusLDAAAflW8fgYrISHB2LGKior05JNPKi0tzWM9PT1dTz75pO655x73Wo0aNSRJhw8f1tSpUzVjxgy1bNlSc%2BbM0eTJk7Vy5UpJ0tq1a7Vr1y4lJSWppKREEyZMUJ06dTRmzBhjcwMAAP/mkzNY2dnZSkpK0pNPPqkff/xRb731lk6cOFGpYxw/flz33nuvx%2B80vCg9PV2tW7dWWFiY%2ByMkJESStHHjRt19990aOHCgWrZsqblz52rPnj3KyMiQ9NMlyvj4eMXExKhTp0566qmntGnTpusPDQAAfjW8XrC%2B/fZb/fGPf9TLL7%2Bst99%2BW%2BfPn9cbb7yhwYMHKzU1tcLH%2Bfjjj9WxY0clJyd7rJ89e1anTp1SkyZNLvu41NRUj7eIqFevnurXr6/U1FSdOnVKWVlZ6tChg3s/OjpaP/zwg7KzsysXFAAA/Gp5/RLh3/72N/Xs2VOzZ89W%2B/btJUkLFizQpEmTNG/ePG3YsKFCx7nvvvsuu56enq6AgACtWLFC77//vmrWrKkHH3zQfbkwOztb4eHhHo%2BpU6eOTp48KZfLJUke%2B3Xr1pUknTx5stzjriQ7O9t9rIscjmoVfnxFBQb65ATkNXM4KjbvxVx2y3c15LIff81GLnshlz15vWAdPHhQmzZt8vjFzg6HQ48%2B%2Bqjuvffe6z7%2BiRMnFBAQoKZNm%2Br%2B%2B%2B/XgQMH9Oyzz6pGjRrq1auXCgsLy/3kYlBQkIqLi1VYWOi%2B/fM96acX01dUcnKykpKSPNbi4uIUHx9/rbH8Qq1a1St1/9DQkBs0iW%2BRy378NRu57IVc9uL1glVWVnbZn8o7d%2B6cAgMDr/v4AwcOVPfu3VWzZk1JUsuWLfXNN99o8%2BbN6tWrl4KDg8uVpeLiYoWEhHiUqeDgYPefJblfw1URw4YNU48ePTzWHI5qys09d825Lsdurb%2Bi%2BQMDqyg0NET5%2BQUqLfWfn%2BAkl/34azZy2Qu5rk9lv7k3xesFq0uXLlq5cqUSExPda3l5eUpMTFSnTp2u%2B/gBAQHucnVR06ZNtW/fPklSRESEcnJyPPZzcnIUFhamiIgIST/9QuqGDRu6/yxJYWFhFZ4hPDy83OVAl%2BuMSkr85xPjWlQ2f2lpmV/%2BnZHLfvw1G7nshVz24vVTIE8//bSOHDmiLl26qKioSI888oi6d%2B%2Bu77//XpMmTbru4y9evFijRo3yWDt27JiaNm0qSXI6nUpJSXHvZWVlKSsrS06nUxEREapfv77HfkpKiurXr2/89VMAAMB/ef0MVkREhHbu3Kldu3bpiy%2B%2BUFlZmYYPH64BAwa436vqenTv3l2rVq3SmjVr1KtXL%2B3du1c7d%2B50v0P88OHD9cADDygyMlJt27bVnDlz1K1bNzVq1Mi9P2/ePP3ud7%2BTJM2fP1%2BjR4%2B%2B7rkAAMCvh9cLlvTT65mGDh16Q47drl07LV68WEuWLNHixYvVoEEDzZ8/X1FRUZKkqKgozZw5U0uWLNF//vMfde7cWbNmzXI/fsyYMfrxxx81fvx4BQYGasiQIeXOiAEAAPySAMuyLG8%2B4YgRI35x319/F6HLdcb4MR2OKuo17wPjx71R3ny8c4Xu53BUUa1a1ZWbe86vrsuTy378NRu57IVc1ycs7Lc37Ni/xOtnsC79fYMlJSX69ttv9dVXX2nkyJHeHgcAAMC4m%2BZ3ES5dulQnT5708jQAAADm3TRvpDRgwAC9%2Beabvh4DAADgut00BevTTz818kajAAAAvub1S4SXe5H72bNn9eWXX17x9wsCAADYidcLVv369T1%2BD6Ek/eY3v9H999%2BvP/3pT94eBwAAwDivF6y//e1v3n5KAAAAr/J6wTpw4ECF79uhQ4cbOAkAAMCN4fWC9cADD7gvEf78PU4vXQsICNAXX3zh7fEAAACum9cL1ooVKzR79mxNmDBBsbGxCgoK0meffaaZM2fqnnvuUd%2B%2Bfb09EgAAgFFef5uGhIQETZs2Tb1791atWrVUvXp1derUSTNnztTmzZvVoEED9wcAAIAdeb1gZWdnX7Y81ahRQ7m5ud4eBwAAwDivF6zIyEgtWLBAZ8%2Beda/l5eUpMTFRt99%2Bu7fHAQAAMM7rr8F65plnNGLECN1xxx1q0qSJLMvSN998o7CwMK1fv97b4wAAABjn9YLVrFkzvfHGG9q1a5fS09MlSX/%2B85/Vr18/hYSEeHscAAAA47xesCTplltu0dChQ/X999%2BrUaNGkn56N3cAAAB/4PXXYFmWpXnz5qlDhw7q37%2B/Tp48qUmTJmnq1Km6cOGCt8cBAAAwzusFa8OGDXrllVf03HPPKSgoSJLUs2dP7d69W0lJSd4eBwAAwDivF6zk5GRNmzZNgwYNcr97e9%2B%2BfTV79my99tpr3h4HAADAOK8XrO%2B//16tWrUqt96yZUu5XC5vjwMAAGCc1wtWgwYN9Nlnn5Vbf//9990veAcAALAzr/8U4ZgxYzRjxgy5XC5ZlqWPPvpIycnJ2rBhg55%2B%2BmlvjwMAAGCc1wvW4MGDVVJSouXLl6uwsFDTpk1T7dq19fjjj2v48OHeHgcAAMA4rxesXbt2qU%2BfPho2bJhOnz4ty7JUp04db48BAABww3j9NVgzZ850v5i9du3alCsAAOB3vF6wmjRpoq%2B%2B%2BsrbTwsAAOA1Xr9E2LJlSz311FNavXq1mjRpouDgYI/9hIQEb48EAABglNcL1tdff63o6GhJ4n2vAACAX/JKwZo7d67Gjx%2BvatWqacOGDd54SgAAAJ/xymuw1q5dq4KCAo%2B1sWPHKjs72xtPDwAA4FVeKViWZZVbO3DggIqKirzx9AAAAF7l9Z8iBAAA8HcULAAAAMO8VrACAgK89VQAAAA%2B5bW3aZg9e7bHe15duHBBiYmJql69usf9eB8sAABgd14pWB06dCj3nldRUVHKzc1Vbm6uN0YAAADwGq8ULN77CgAA/JrwIncAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgmO0LVnFxsfr376/9%2B/e71zIyMjRq1ChFRkaqb9%2B%2B2rt3r8djPvzwQ/Xv319Op1MjRoxQRkaGx/5LL72krl27KioqSlOmTFFBQYFXsgAAAP9g64JVVFSkJ554Qmlpae41y7IUFxenunXravv27RowYIDGjx%2BvzMxMSVJmZqbi4uI0aNAgbdu2TbVr19ajjz4qy7IkSW%2B//baSkpI0c%2BZMrVu3TqmpqUpMTPRJPgAAYE%2B2LVjHjx/Xvffeq%2B%2B%2B%2B85jfd%2B%2BfcrIyNDMmTPVrFkzjRs3TpGRkdq%2BfbskaevWrbrttts0evRoNW/eXAkJCfrhhx/08ccfS5LWr1%2BvkSNHqnv37mrXrp1mzJih7du3cxYLAABUmG0L1scff6yOHTsqOTnZYz01NVWtW7dWtWrV3GvR0dE6dOiQez8mJsa9FxISojZt2ujQoUMqLS3VZ5995rEfGRmpCxcu6NixYzc4EQAA8Bde%2B2XPpt13332XXXe5XAoPD/dYq1Onjk6ePHnV/fz8fBUVFXnsOxwO1axZ0/14AACAq7FtwbqSgoICBQUFeawFBQWpuLj4qvuFhYXu21d6fEVkZ2eX%2B%2BXWDke1csXuegUG2usEpMNRsXkv5rJbvqshl/34azZy2Qu57MnvClZwcLDy8vI81oqLi1W1alX3/qVlqbi4WKGhoQoODnbfvnQ/JCSkwjMkJycrKSnJYy0uLk7x8fEVPoY/qlWreqXuHxpa8b9zOyGX/fhrNnLZC7nsxe8KVkREhI4fP%2B6xlpOT4z57FBERoZycnHL7rVq1Us2aNRUcHKycnBw1a9ZMklRSUqK8vDyFhYVVeIZhw4apR48eHmsORzXl5p67lkhXZLfWX9H8gYFVFBoaovz8ApWWlt3gqbyHXPbjr9nIZS/kuj6V/ebeFL8rWE6nU6tWrVJhYaH7rFVKSoqio6Pd%2BykpKe77FxQU6OjRoxo/fryqVKmitm3bKiUlRR07dpQkHTp0SA6HQy1btqzwDOHh4eUuB7pcZ1RS4j%2BfGNeisvlLS8v88u%2BMXPbjr9nIZS/kshd7nQKpgNjYWNWrV0%2BTJ09WWlqaVq1apcOHD2vIkCGSpMGDB%2BvgwYNatWqV0tLSNHnyZDVs2NBdqO677z6tWbNGu3fv1uHDhzV9%2BnTde%2B%2B9lbpECAAAft38rmAFBgZq2bJlcrlcGjRokF599VUtXbpU9evXlyQ1bNhQzz//vLZv364hQ4YoLy9PS5cuVUBAgCSpX79%2BGjdunKZNm6bRo0erXbt2mjBhgi8jAQAAm/GLS4Rffvmlx%2B3GjRtr48aNV7z/nXfeqTvvvPOK%2B2PHjtXYsWONzQcAAH5d/O4MFgAAgK9RsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOa3Betf//qXbr31Vo%2BP%2BPh4SdLRo0c1dOhQOZ1ODR48WEeOHPF47K5du9SzZ085nU7FxcXp9OnTvogAAABsym8L1vHjx9W9e3ft3bvX/TF79mydP39eY8eOVUxMjHbs2KGoqCiNGzdO58%2BflyQdPnxYU6dO1fjx45WcnKz8/HxNnjzZx2kAAICd%2BG3BSk9PV4sWLRQWFub%2BCA0N1RtvvKHg4GBNnDhRzZo109SpU1W9enW99dZbkqSNGzfq7rvv1sCBA9WyZUvNnTtXe/bsUUZGho8TAQAAu/DrgtWkSZNy66mpqYqOjlZAQIAkKSAgQO3bt9ehQ4fc%2BzExMe7716tXT/Xr11dqaqpX5gYAAPbnlwXLsix9/fXX2rt3r3r37q2ePXtq3rx5Ki4ulsvlUnh4uMf969Spo5MnT0qSsrOzf3EfAADgahy%2BHuBGyMzMVEFBgYKCgrRo0SJ9//33mj17tgoLC93rPxcUFKTi4mJJUmFh4S/uV0R2drZcLpfHmsNRrVxxu16BgfZ9eB8iAAARSElEQVTqxw5Hxea9mMtu%2Ba6GXPbjr9nIZS/ksie/LFgNGjTQ/v37dcsttyggIECtWrVSWVmZJkyYoNjY2HJlqbi4WFWrVpUkBQcHX3Y/JCSkws%2BfnJyspKQkj7W4uDj3TzH%2BWtWqVb1S9w8NrfjfuZ2Qy378NRu57IVc9uKXBUuSatas6XG7WbNmKioqUlhYmHJycjz2cnJy3GeXIiIiLrsfFhZW4eceNmyYevTo4bHmcFRTbu65ykS4Kru1/ormDwysotDQEOXnF6i0tOwGT%2BU95LIff81GLnsh1/Wp7Df3pvhlwfrggw/01FNP6b333nOfefriiy9Us2ZNRUdH64UXXpBlWQoICJBlWTp48KD%2B8pe/SJKcTqdSUlI0aNAgSVJWVpaysrLkdDor/Pzh4eHlLge6XGdUUuI/nxjXorL5S0vL/PLvjFz246/ZyGUv5LIXe50CqaCoqCgFBwfrmWee0YkTJ7Rnzx7NnTtXDz30kPr06aP8/HzNmTNHx48f15w5c1RQUKC7775bkjR8%2BHC98sor2rp1q44dO6aJEyeqW7duatSokY9TAQAAu/DLglWjRg2tWbNGp0%2Bf1uDBgzV16lQNGzZMDz30kGrUqKGVK1e6z1KlpqZq1apVqlatmqSfytnMmTO1dOlSDR8%2BXLfccosSEhJ8nAgAANiJX14ilKTmzZtr7dq1l91r166dXn755Ss%2BdtCgQe5LhAAAAJXll2ewAAAAfImCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADHP4egD8ety96N%2B%2BHqFS3ny8s69HAADYFGewAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXrMoqKijRlyhTFxMSoS5cuevHFF309EgAAsBGHrwe4Gc2dO1dHjhzRunXrlJmZqUmTJql%2B/frq06ePr0eDF9296N%2B%2BHqFS3ny8s69HAAD8fxSsS5w/f15bt27VCy%2B8oDZt2qhNmzZKS0vTpk2bKFgAAKBCKFiXOHbsmEpKShQVFeVei46O1ooVK1RWVqYqVbiqipuTnc64cbYNgL%2BjYF3C5XKpVq1aCgoKcq/VrVtXRUVFysvLU%2B3ata96jOzsbLlcLo81h6OawsPDjc4aGEjZgz3ZqQxK0v9OutPXIxh18WuHv30NIZe9%2BGuuiyhYlygoKPAoV5Lct4uLiyt0jOTkZCUlJXmsjR8/Xo899piZIf%2B/7OxsjfxdmoYNG2a8vPlSdna2kpOTyWUT/ppL%2Br9shYXt/Spbdna21q1b7Xf/zchlL/6a6yL/rI3XITg4uFyRuni7atWqFTrGsGHDtGPHDo%2BPYcOGGZ/V5XIpKSmp3NkyuyOXvfhrLsl/s5HLXshlT5zBukRERIRyc3NVUlIih%2BOnvx6Xy6WqVasqNDS0QscIDw/3yzYOAAAqhjNYl2jVqpUcDocOHTrkXktJSVHbtm15gTsAAKgQGsMlQkJCNHDgQE2fPl2HDx/W7t279eKLL2rEiBG%2BHg0AANhE4PTp06f7eoibTadOnXT06FHNnz9fH330kf7yl79o8ODBvh7rsqpXr67Y2FhVr17d16MYRS578ddckv9mI5e9kMt%2BAizLsnw9BAAAgD/hEiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAqWTRUVFWnKlCmKiYlRly5d9OKLL/p6pGty6tQpxcfHKzY2Vl27dlVCQoKKiookSRkZGRo1apQiIyPVt29f7d2718fTXpuxY8fq6aefdt8%2BevSohg4dKqfTqcGDB%2BvIkSM%2BnK5yiouLNWPGDHXo0EH/9V//pQULFujiexXbOVdWVpbGjRun9u3bq0ePHnrppZfce3bNVVxcrP79%2B2v//v3utat9Tn344Yfq37%2B/nE6nRowYoYyMDG%2BPfVWXy3Xo0CH993//t6KiotS7d29t3brV4zF2zXXRmTNn1LVrV%2B3YscNjfdeuXerZs6ecTqfi4uJ0%2BvRpb41bYZfLlZmZqYcfflhOp1O9evXSG2%2B84fEYO%2BSqCAqWTc2dO1dHjhzRunXr9NxzzykpKUlvvfWWr8eqFMuyFB8fr4KCAm3atEkLFy7Uu%2B%2B%2Bq0WLFsmyLMXFxalu3bravn27BgwYoPHjxyszM9PXY1fK66%2B/rj179rhvnz9/XmPHjlVMTIx27NihqKgojRs3TufPn/fhlBU3e/Zsffjhh1qzZo3mz5%2Bvf/7zn0pOTrZ9rscff1zVqlXTjh07NGXKFC1atEj/%2Bte/bJurqKhITzzxhNLS0txrV/ucyszMVFxcnAYNGqRt27apdu3aevTRR3Uz/bKPy%2BVyuVx6%2BOGHFRsbq5dfflnx8fGaNWuW3nvvPUn2zfVziYmJys7O9lg7fPiwpk6dqvHjxys5OVn5%2BfmaPHmyN8atsMvlKikp0bhx4%2BRwOPTyyy9rzJgxmjhxor766itJ9shVYRZs59y5c1bbtm2tffv2udeWLl1q3X///T6cqvKOHz9utWjRwnK5XO611157zerSpYv14YcfWpGRkda5c%2BfceyNHjrSWLFnii1GvSW5urnXHHXdYgwcPtiZNmmRZlmVt3brV6tGjh1VWVmZZlmWVlZVZvXr1srZv3%2B7LUSskNzfXat26tbV//3732sqVK62nn37a1rny8vKsFi1aWF9%2B%2BaV7bfz48daMGTNsmSstLc3605/%2BZP3xj3%2B0WrRo4f46cbXPqUWLFnl8DTl//rwVFRXl8XXGl66U6x//%2BIfVp08fj/s%2B%2B%2Byz1hNPPGFZln1zXXTgwAGrV69eVufOnT3%2B3U2YMMH9dcWyLCszM9O69dZbre%2B%2B%2B85rs/%2BSK%2BXavXu3FR0dbZ05c8Z930ceecTasmWLZVk3f67K4AyWDR07dkwlJSWKiopyr0VHRys1NVVlZWU%2BnKxywsLCtHr1atWtW9dj/ezZs0pNTVXr1q1VrVo193p0dLQOHTrk7TGv2d///ncNGDBAf/jDH9xrqampio6OVkBAgCQpICBA7du3t0WulJQU1ahRQ7Gxse61sWPHKiEhwda5qlatqpCQEO3YsUMXLlzQiRMndPDgQbVq1cqWuT7%2B%2BGN17NhRycnJHutX%2B5xKTU1VTEyMey8kJERt2rS5abJeKdfFlxZc6uzZs5Lsm0v66fLas88%2Bq2nTpikoKMhj79Jc9erVU/369ZWamnrDZ66IK%2BX6%2BOOPdfvtt6tGjRrutWXLlmnYsGGSbv5cleHw9QCoPJfLpVq1anl8wtWtW1dFRUXKy8tT7dq1fThdxYWGhqpr167u22VlZdq4caM6deokl8ul8PBwj/vXqVNHJ0%2Be9PaY1%2BSjjz7SJ598otdee03Tp093r7tcLo/CJf2U60qXBm4mGRkZatCggXbu3KkVK1bowoULGjRokB555BFb5woODta0adM0a9YsrV%2B/XqWlpRo0aJCGDh2qd955x3a57rvvvsuuX%2B1z6mb/nLtSroYNG6phw4bu2z/%2B%2BKNef/11PfbYY5Lsm0uSVqxYodatW6tLly7l9rKzs22Z6%2BLXkXnz5umVV15RrVq1FB8fr549e0q6%2BXNVBgXLhgoKCsp9N3PxdnFxsS9GMiIxMVFHjx7Vtm3b9NJLL102ox3yFRUV6bnnntO0adNUtWpVj70r/bezQ67z58/r22%2B/1ZYtW5SQkCCXy6Vp06YpJCTE1rkkKT09Xd27d9eDDz6otLQ0zZo1S7fffrvtc/3c1bL4Q9bCwkI99thjqlu3rvuMiF1zHT9%2BXFu2bNGrr7562f3CwkJb5jp//rxefvll9e3bVytWrND%2B/fsVHx%2Bv5ORktW3b1ra5LoeCZUPBwcHl/rFdvH3p/9DtIjExUevWrdPChQvVokULBQcHKy8vz%2BM%2BxcXFtsiXlJSk2267zePs3EVX%2Bm9nh1wOh0Nnz57V/Pnz1aBBA0k/vYB48%2BbNaty4sW1zffTRR9q2bZv27NmjqlWrqm3btjp16pSWL1%2BuRo0a2TbXpa72OXWlf5uhoaFem/F6nDt3To8%2B%2Bqi%2B%2BeYb/eMf/1BISIgke%2BayLEvPPPOM4uPjy72E4qIr5bqY%2B2YVGBiomjVravr06apSpYratGmjTz75RP/85z/Vtm1b2%2Ba6HF6DZUMRERHKzc1VSUmJe83lcqlq1ao39ReNK5k1a5bWrl2rxMRE9e7dW9JPGXNycjzul5OTU%2B7U8c3o9ddf1%2B7duxUVFaWoqCi99tpreu211xQVFWXrXGFhYQoODnaXK0n6/e9/r6ysLFvnOnLkiBo3buxRmlq3bq3MzExb57rU1bJcaT8sLMxrM16rs2fPasyYMUpLS9O6devUpEkT954dc2VmZurTTz/V3//%2Bd/fXkczMTD333HN66KGHJNkzlySFh4erSZMmqlLl/%2BrHxa8jkn1zXQ4Fy4ZatWolh8Ph8SLNlJQUtW3b1uMfrR0kJSVpy5YtWrBggfr16%2Bdedzqd%2Bvzzz1VYWOheS0lJkdPp9MWYlbJhwwa99tpr2rlzp3bu3KkePXqoR48e2rlzp5xOpz799FP3j4hblqWDBw/aIpfT6VRRUZG%2B/vpr99qJEyfUoEEDW%2BcKDw/Xt99%2B6/Fd84kTJ9SwYUNb57rU1T6nnE6nUlJS3HsFBQU6evToTZ%2B1rKxM48eP1/fff68NGzaoefPmHvt2zBUREaH/%2BZ//cX8N2blzp8LDwxUfH685c%2BZIKp8rKytLWVlZN3Uu6ae509LSVFpa6l5LT093f%2BNm11yXY6//G0PSTz8FM3DgQE2fPl2HDx/W7t279eKLL2rEiBG%2BHq1S0tPTtWzZMj388MOKjo6Wy%2BVyf8TGxqpevXqaPHmy0tLStGrVKh0%2BfFhDhgzx9dhX1aBBAzVu3Nj9Ub16dVWvXl2NGzdWnz59lJ%2Bfrzlz5uj48eOaM2eOCgoKdPfdd/t67Ktq2rSpunXrpsmTJ%2BvYsWP64IMPtGrVKg0fPtzWuXr06KHf/OY3euaZZ/T111/rf//3f7VixQo98MADts51qat9Tg0ePFgHDx7UqlWrlJaWpsmTJ6thw4bq2LGjjyf/Zdu2bdP%2B/fs1e/ZshYaGur%2BGXLwcasdcDofD42tI48aN5XA4VKdOHUVEREiShg8frldeeUVbt27VsWPHNHHiRHXr1k2NGjXy8fS/rH///iorK9OMGTP07bffatOmTfrggw907733SrJvrsvy4VtE4DqcP3/emjhxohUZGWl16dLFWrt2ra9HqrSVK1daLVq0uOyHZVnWN998Y/35z3%2B2brvtNqtfv37Wv//9bx9PfG0mTZrk8b4uqamp1sCBA622bdtaQ4YMsT7//HMfTlc5%2Bfn51oQJE6zIyEjr9ttvt55//nn3e0TZOVdaWpo1atQoq3379lbPnj2ttWvX%2BkWuS99X6WqfU%2B%2B995511113We3atbNGjhx507730M9zjR49%2BrJfQ37%2B3ld2zHWp7t27l3v/te3bt1t33nmnFRkZacXFxVmnT5/2xpiVdmmutLQ097/Du%2B66y3r77bc97m%2BXXFcTYFk30dvZAgAA%2BAEuEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYf8Pm0U8AJZkDzUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5032295527492203371”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>232</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (66)</td> <td class=”number”>754</td> <td class=”number”>23.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5032295527492203371”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>108.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>116.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>130.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>161.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_OPS12”>AGRITRSM_OPS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>104</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>10.754</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>236</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.2%</td>

</tr>

</table>

</div>

</div>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5856077567733371051”

aria-controls=”quantiles-5856077567733371051” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5856077567733371051” aria-controls=”histogram-5856077567733371051”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5856077567733371051” aria-controls=”common-5856077567733371051”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5856077567733371051” aria-controls=”extreme-5856077567733371051”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>6</td>

</tr> <tr>

<th>Q3</th> <td>13</td>

</tr> <tr>

<th>95-th percentile</th> <td>36</td>

</tr> <tr>

<th>Maximum</th> <td>236</td>

</tr> <tr>

<th>Range</th> <td>236</td>

</tr> <tr>

<th>Interquartile range</th> <td>10</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>15.44</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4358</td>

</tr> <tr>

<th>Kurtosis</th> <td>35.943</td>

</tr> <tr>

<th>Mean</th> <td>10.754</td>

</tr> <tr>

<th>MAD</th> <td>8.9273</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7087</td>

</tr> <tr>

<th>Sum</th> <td>33078</td>

</tr> <tr>

<th>Variance</th> <td>238.38</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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9YI0aNUobNmzQmDFjdP/992vfvn2yLMvuMgAAALqM7QFr/vz5evPNN7V%2B/XoFBwfrN7/5ja655hqtWrVKn3/%2Bud3lAAAAGOf0xU4dDodGjx6t0aNHq7GxUc8%2B%2B6zWr1%2BvgoICjRgxQrNnz9Z1113ni9IAAAAumE8CliTV1NTo5Zdf1ssvv6zPPvtMI0aM0M0336zq6mo9%2BuijOnDggBYuXOir8gAAAH402wPWSy%2B9pJdeeknvvfeeoqKiNHnyZK1du1YDBw70vKZv375atmwZAQsAAPgl2wPWwoULlZ6ernXr1unqq69WUFDHy8AGDRqkmTNn2l0aAACAEbYHrLfffluRkZGqr6/3hKuysjINGzZMwcHBkqQRI0ZoxIgRdpcGAABghO3/ivDUqVMaN26c/vCHP3jG7r77bk2aNElVVVV2lwMAAGCc7QHrd7/7nX7xi1/ojjvu8Iy98sor6tu3r3Jzc%2B0uBwAAwDjbA9b//u//6uGHH1ZMTIxnLCoqSg8%2B%2BKD2799vdzkAAADG2R6wnE6nGhoaOow3NjZyR3cAABAQbA9YV199tXJycvTll196xiorK5Wbm6u0tDS7ywEAADDO9n9F%2BNBDD%2BmOO%2B7Q9ddfr/DwcElSQ0ODhg0bpuzsbLvLAQAAMM72gBUdHa0XXnhB7777rsrLy%2BV0OnXJJZfol7/8pRwOh93lAAAAGOeTr8oJDg5WWloapwQBAEBAsj1gud1urV69WgcPHtS3337b4cL2N954w%2B6SAAAAjLI9YC1atEgfffSRJkyYoIsuusju3QMAAHQ52wPW/v37tWnTJqWkpNi9awAAAFvYfpuGnj17Kjo62u7dAgAA2Mb2gDVp0iRt2rRJbW1tdu8aAADAFrafIqyvr1dRUZH%2B/Oc/a8CAAQoJCfGa37Ztm90lAQAAGOWT2zRMnDjRF7sFAACwhe0BKzc31%2Bj2WlpaNGXKFC1atEgjR46UJOXk5OjZZ5/1et2iRYs0c%2BZMSVJRUZFWr14tt9utMWPGaOnSpYqKipIkWZallStXavfu3Wpvb9e0adP0wAMPKCjI9rOpAADAT/nkCFZNTY3%2B%2B7//W59//rkeeeQRHThwQEOGDNGgQYN%2B0Haam5s1f/58lZeXe41XVFRo/vz5uvnmmz1jvXv3liSVlZVp4cKFWrx4sRISErRs2TJlZ2dr48aNkqRnnnlGRUVFys/PV2trqxYsWKDo6GjdeeedF7hqAADQXdh%2BWOZvf/ubbrzxRr3wwgt67bXXdObMGb3yyiuaOnWqSktLO72dI0eO6NZbb/X60uizKioqdPnllysmJsbzCAsLkyRt375d48eP1%2BTJk5WQkKDly5frrbfeUmVlpaR/XAOWlZWllJQUjRo1Sg888IB27NhhZvEAAKBbsD1g/f73v9e1116rvXv36mc/%2B5kk6YknntDYsWO1YsWKTm/n/fff18iRI1VYWOg1furUKZ04cUIDBw487/tKS0u97sHVt29fxcfHq7S0VCdOnFBVVZWuuuoqz3xycrKOHTummpqaH7BKAADQndl%2BivDgwYPasWOH1xc7O51O3Xvvvbr11ls7vZ3bbrvtvOMVFRVyOBzasGGD3n77bUVEROiOO%2B7wnC6sqalRbGys13uio6NVXV0tt9stSV7zffr0kSRVV1d3eB8AAMD52B6w2tvb1d7e3mH89OnTCg4OvuDtHz16VA6HQ4MGDdLMmTN14MABLVq0SL1791ZGRoaampo63BoiJCRELS0tampq8jz/5znpHxfTd1ZNTY0nrJ3ldPY0HtCCg/3rwnun07/qPdfZfvtb3/0ZPbcX/bYfPQ9ctgesMWPGaOPGjcrLy/OM1dfXKy8vT6NGjbrg7U%2BePFnp6emKiIiQJCUkJOiLL77Qzp07lZGRodDQ0A5hqaWlRWFhYV5hKjQ01PNnSZ5ruDqjsLBQ%2Bfn5XmOZmZnKysr60esKBJGRvXxdghHh4Z3/LMAMem4v%2Bm0/eh54bA9YDz/8sGbNmqUxY8aoublZ//Ef/6Fjx44pIiJCv//97y94%2Bw6HwxOuzho0aJD2798vSYqLi1Ntba3XfG1trWJiYhQXFydJcrvd6t%2B/v%2BfPkhQTE9PpGqZPn66xY8d6jTmdPVVXd/qHLeZ7%2BNtvPKbXb7fg4CCFh4epoaFRbW0dj8LCPHpuL/ptP3re9Xz1y73tASsuLk4vvviiioqK9Mknn6i9vV0zZszQpEmTPLdSuBBr1qzRBx98oC1btnjGDh8%2B7LkFhMvlUnFxsaZMmSJJqqqqUlVVlVwul%2BLi4hQfH6/i4mJPwCouLlZ8fPwPOr0XGxvb4fVu9zdqbe3ePzyBsv62tvaAWYu/oOf2ot/2o%2BeBxyf3wQoLC9Mtt9zSJdtOT09XQUGBNm/erIyMDO3bt08vvvii5yt4ZsyYodtvv12JiYkaPny4li1bpmuuuUYDBgzwzK9YsUI///nPJUkrV67U3Llzu6RWAAAQmGwPWLNmzfqX8xf6XYRXXnml1qxZo7Vr12rNmjXq16%2BfVq5cqaSkJElSUlKSlixZorVr1%2Brrr7/W6NGjtXTpUs/777zzTv3973/XvHnzFBwcrGnTpmnOnDkXVBMAAOheHJZlWXbuMDs72%2Bt5a2ur/va3v%2Bmzzz7T7Nmz9Z//%2BZ92lmMbt/sb49t0OoOUseId49vtKq/eN9rXJVwQpzNIkZG9VFd3mkP5NqHn9qLf9qPnXS8m5iKf7Pcn812E69atU3V1tc3VAAAAmPeT%2BWdokyZN0quvvurrMgAAAC7YTyZgffDBB0ZuNAoAAOBrP4mL3E%2BdOqVPP/30O7/%2BBgAAwJ/YHrDi4%2BO9vodQkn72s59p5syZuummm%2BwuBwAAwDjbA5aJu7UDAAD8lNkesA4cONDp11511VVdWAkAAEDXsD1g3X777Z5ThP98C65zxxwOhz755BO7ywMAALhgtgesDRs2KCcnRwsWLFBqaqpCQkL04YcfasmSJbr55pt1ww032F0SAACAUbbfpiE3N1ePPfaYrr/%2BekVGRqpXr14aNWqUlixZop07d6pfv36eBwAAgD%2ByPWDV1NScNzz17t1bdXV1dpcDAABgnO0BKzExUU888YROnTrlGauvr1deXp5%2B%2Bctf2l0OAACAcbZfg/Xoo49q1qxZuvrqqzVw4EBZlqUvvvhCMTEx2rZtm93lAAAAGGd7wBo8eLBeeeUVFRUVqaKiQpL0q1/9ShMmTFBYWJjd5QAAABhne8CSpIsvvli33HKLvvrqKw0YMEDSP%2B7mDgAAEAhsvwbLsiytWLFCV111lSZOnKjq6mo99NBDWrhwob799lu7ywEAADDO9oD17LPP6qWXXtJvf/tbhYSESJKuvfZa7d27V/n5%2BXaXAwAAYJztAauwsFCPPfaYpkyZ4rl7%2Bw033KCcnBz98Y9/tLscAAAA42wPWF999ZWGDh3aYTwhIUFut9vucgAAAIyzPWD169dPH374YYfxt99%2B23PBOwAAgD%2Bz/V8R3nnnnVq8eLHcbrcsy9Jf//pXFRYW6tlnn9XDDz9sdzkAAADG2R6wpk6dqtbWVj311FNqamrSY489pqioKN13332aMWOG3eUAAAAYZ3vAKioq0rhx4zR9%2BnSdPHlSlmUpOjra7jIAAAC6jO3XYC1ZssRzMXtUVBThCgAABBzbA9bAgQP12Wef2b1bAAAA29h%2BijAhIUEPPPCANm3apIEDByo0NNRrPjc31%2B6SAAAAjLI9YH3%2B%2BedKTk6WJO57BQAAApItAWv58uWaN2%2BeevbsqWeffdaOXQIAAPiMLddgPfPMM2psbPQau/vuu1VTU2PH7gEAAGxlS8CyLKvD2IEDB9Tc3GzH7gEAAGxl%2B78iBAAACHQELAAAAMNsC1gOh8OuXQEAAPiUbbdpyMnJ8brn1bfffqu8vDz16tXL63XcBwsAAPg7WwLWVVdd1eGeV0lJSaqrq1NdXZ0dJQAAANjGloDFva8AAEB3wkXuAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGF%2BH7BaWlo0ceJEvffee56xyspKzZkzR4mJibrhhhu0b98%2Br/e8%2B%2B67mjhxolwul2bNmqXKykqv%2BS1btigtLU1JSUl65JFH1NjYaMtaAABAYPDrgNXc3Kz7779f5eXlnjHLspSZmak%2Bffpoz549mjRpkubNm6fjx49Lko4fP67MzExNmTJFu3fvVlRUlO69915ZliVJeu2115Sfn68lS5Zo69atKi0tVV5enk/WBwAA/JPfBqwjR47o1ltv1Zdffuk1vn//flVWVmrJkiUaPHiw7rnnHiUmJmrPnj2SpF27dumKK67Q3Llzdemllyo3N1fHjh3T%2B%2B%2B/L0natm2bZs%2BerfT0dF155ZVavHix9uzZw1EsAADQaX4bsN5//32NHDlShYWFXuOlpaW6/PLL1bNnT89YcnKySkpKPPMpKSmeubCwMA0bNkwlJSVqa2vThx9%2B6DWfmJiob7/9VocPH%2B7iFQEAgEBhy3cRdoXbbrvtvONut1uxsbFeY9HR0aqurv7e%2BYaGBjU3N3vNO51ORUREeN7fGTU1NR2%2B3Nrp7NlhvxcqONi/8rHT6V/1nutsv/2t7/6MntuLftuPngcuvw1Y36WxsVEhISFeYyEhIWppafne%2BaamJs/z73p/ZxQWFio/P99rLDMzU1lZWZ3eRiCKjOzl6xKMCA8P83UJ3Q49txf9th89DzwBF7BCQ0NVX1/vNdbS0qIePXp45s8NSy0tLQoPD1doaKjn%2BbnzYWGd//BPnz5dY8eO9RpzOnuqru50p7fRGf72G4/p9dstODhI4eFhamhoVFtbu6/L6Rboub3ot/3oedfz1S/3ARew4uLidOTIEa%2Bx2tpaz%2Bm5uLg41dbWdpgfOnSoIiIiFBoaqtraWg0ePFiS1Nraqvr6esXExHS6htjY2A6nA93ub9Ta2r1/eAJl/W1t7QGzFn9Bz%2B1Fv%2B1HzwOPfx0C6QSXy6WPP/7Yc7pPkoqLi%2BVyuTzzxcXFnrnGxkYdOnRILpdLQUFBGj58uNd8SUmJnE6nEhIS7FsEAADwawEXsFJTU9W3b19lZ2ervLxcBQUFKisr07Rp0yRJU6dO1cGDB1VQUKDy8nJlZ2erf//%2BGjlypKR/XDy/efNm7d27V2VlZXr88cd16623/qBThAAAoHsLuIAVHBys9evXy%2B12a8qUKXr55Ze1bt06xcfHS5L69%2B%2BvJ598Unv27NG0adNUX1%2BvdevWyeFwSJImTJige%2B65R4899pjmzp2rK6%2B8UgsWLPDlkgAAgJ9xWGdvYY4u5XZ/Y3ybTmeQMla8Y3y7XeXV%2B0b7uoQL4nQGKTKyl%2BrqTnOthE3oub3ot/3oedeLibnIJ/sNuCNYAAAAvkbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWMAGrNdff12XXXaZ1yMrK0uSdOjQId1yyy1yuVyaOnWqPvroI6/3FhUV6dprr5XL5VJmZqZOnjzpiyUAAAA/FbAB68iRI0pPT9e%2Bffs8j5ycHJ05c0Z33323UlJS9PzzzyspKUn33HOPzpw5I0kqKyvTwoULNW/ePBUWFqqhoUHZ2dk%2BXg0AAPAnARuwKioqNGTIEMXExHge4eHheuWVVxQaGqoHH3xQgwcP1sKFC9WrVy/96U9/kiRt375d48eP1%2BTJk5WQkKDly5frrbfeUmVlpY9XBAAA/EVAB6yBAwd2GC8tLVVycrIcDockyeFwaMSIESopKfHMp6SkeF7ft29fxcfHq7S01Ja6AQCA/3P6uoCuYFmWPv/8c%2B3bt08bN25UW1ubxo0bp6ysLLndbl1yySVer4%2BOjlZ5ebkkqaamRrGxsR3mq6urO73/mpoaud1urzGns2eH7V6o4GD/ysdOp3/Ve66z/fa3vvszem4v%2Bm0/eh64AjJgHT9%2BXI2NjQoJCdHq1av11VdfKScnR01NTZ7xfxaaDgSDAAANLElEQVQSEqKWlhZJUlNT07%2Bc74zCwkLl5%2Bd7jWVmZnousu%2BuIiN7%2BboEI8LDw3xdQrdDz%2B1Fv%2B1HzwNPQAasfv366b333tPFF18sh8OhoUOHqr29XQsWLFBqamqHsNTS0qIePXpIkkJDQ887HxbW%2BQ//9OnTNXbsWK8xp7On6upO/8gVnZ%2B//cZjev12Cw4OUnh4mBoaGtXW1u7rcroFem4v%2Bm0/et71fPXLfUAGLEmKiIjwej548GA1NzcrJiZGtbW1XnO1tbWe03dxcXHnnY%2BJien0vmNjYzucDnS7v1Fra/f%2B4QmU9be1tQfMWvwFPbcX/bYfPQ88/nUIpJPeeecdjRw5Uo2NjZ6xTz75RBEREUpOTtYHH3wgy7Ik/eN6rYMHD8rlckmSXC6XiouLPe%2BrqqpSVVWVZx4AAOD7BGTASkpKUmhoqB599FEdPXpUb731lpYvX6677rpL48aNU0NDg5YtW6YjR45o2bJlamxs1Pjx4yVJM2bM0EsvvaRdu3bp8OHDevDBB3XNNddowIABPl4VAADwFwEZsHr37q3Nmzfr5MmTmjp1qhYuXKjp06frrrvuUu/evbVx40YVFxdrypQpKi0tVUFBgXr27CnpH%2BFsyZIlWrdunWbMmKGLL75Yubm5Pl4RAADwJw7r7LkydCm3%2Bxvj23Q6g5Sx4h3j2%2B0qr9432tclXBCnM0iRkb1UV3eaayVsQs/tRb/tR8%2B7XkzMRT7Zb0AewQIAAPAlAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwp68LQPcxfvVffF3CD/LqfaN9XQIAwE9xBAsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCnrwv4KWpubtbixYv1P//zP%2BrRo4fmzp2ruXPn%2Bros2Gz86r/4uoQf5NX7Rvu6BADA/yNgncfy5cv10UcfaevWrTp%2B/LgeeughxcfHa9y4cb4uDQAA%2BAEC1jnOnDmjXbt26Q9/%2BIOGDRumYcOGqby8XDt27CBgAQCATiFgnePw4cNqbW1VUlKSZyw5OVkbNmxQe3u7goK4bA0/Tf52StOfcPoVwA9FwDqH2%2B1WZGSkQkJCPGN9%2BvRRc3Oz6uvrFRUV9b3bqKmpkdvt9hpzOnsqNjbWaK3BwYQ9wA7%2BFl5ffyDtR73v7H9T%2BG%2BLfeh54CJgnaOxsdErXEnyPG9paenUNgoLC5Wfn%2B81Nm/ePP3mN78xU%2BT/q6mp0eyfl2v69OnGwxs6qqmpUWFhIf22ET23V01NjbZu3US/bUTPAxeR%2BRyhoaEdgtTZ5z169OjUNqZPn67nn3/e6zF9%2BnTjtbrdbuXn53c4WoauQb/tR8/tRb/tR88DF0ewzhEXF6e6ujq1trbK6fxHe9xut3r06KHw8PBObSM2NpbfRAAA6MY4gnWOoUOHyul0qqSkxDNWXFys4cOHc4E7AADoFBLDOcLCwjR58mQ9/vjjKisr0969e/X0009r1qxZvi4NAAD4ieDHH3/8cV8X8VMzatQoHTp0SCtXrtRf//pX/fu//7umTp3q67LOq1evXkpNTVWvXr18XUq3QL/tR8/tRb/tR88Dk8OyLMvXRQAAAAQSThECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNg%2Banm5mY98sgjSklJ0ZgxY/T000/7uqSA8vrrr%2Buyyy7zemRlZUmSDh06pFtuuUUul0tTp07VRx995ONq/VtLS4smTpyo9957zzNWWVmpOXPmKDExUTfccIP27dvn9Z53331XEydOlMvl0qxZs1RZWWl32X7rfP3Oycnp8Hnfvn27Z76oqEjXXnutXC6XMjMzdfLkSV%2BU7ldOnDihrKwspaamKi0tTbm5uWpubpbE57u7IGD5qeXLl%2Bujjz7S1q1b9dvf/lb5%2Bfn605/%2B5OuyAsaRI0eUnp6uffv2eR45OTk6c%2BaM7r77bqWkpOj5559XUlKS7rnnHp05c8bXJful5uZm3X///SovL/eMWZalzMxM9enTR3v27NGkSZM0b948HT9%2BXJJ0/PhxZWZmasqUKdq9e7eioqJ07733ii%2Bl%2BH7n67ckVVRUaP78%2BV6f97NfD1ZWVqaFCxdq3rx5KiwsVENDg7Kzs31Rvt%2BwLEtZWVlqbGzUjh07tGrVKr355ptavXo1n%2B/uxILfOX36tDV8%2BHBr//79nrF169ZZM2fO9GFVgWX%2B/PnWypUrO4zv2rXLGjt2rNXe3m5ZlmW1t7dbGRkZ1p49e%2Bwu0e%2BVl5dbN910k3XjjTdaQ4YM8Xye3333XSsxMdE6ffq057WzZ8%2B21q5da1mWZa1evdrrs37mzBkrKSnJ6%2BcBHX1Xvy3LstLS0qx33nnnvO9bsGCB9dBDD3meHz9%2B3LrsssusL7/8sstr9ldHjhyxhgwZYrndbs/YH//4R2vMmDF8vrsRjmD5ocOHD6u1tVVJSUmeseTkZJWWlqq9vd2HlQWOiooKDRw4sMN4aWmpkpOT5XA4JEkOh0MjRoxQSUmJzRX6v/fff18jR45UYWGh13hpaakuv/xy9ezZ0zOWnJzs6XFpaalSUlI8c2FhYRo2bBh/B9/ju/p96tQpnThx4ryfd6ljv/v27av4%2BHiVlpZ2Zbl%2BLSYmRps2bVKfPn28xk%2BdOsXnuxtx%2BroA/HBut1uRkZEKCQnxjPXp00fNzc2qr69XVFSUD6vzf5Zl6fPPP9e%2Bffu0ceNGtbW1ady4ccrKypLb7dYll1zi9fro6OgOp1zw/W677bbzjrvdbsXGxnqNRUdHq7q6ulPzOL/v6ndFRYUcDoc2bNigt99%2BWxEREbrjjjt08803S5Jqamro9w8UHh6utLQ0z/P29nZt375do0aN4vPdjRCw/FBjY6NXuJLked7S0uKLkgLK8ePHPT1evXq1vvrqK%2BXk5Kipqek7e0/fzfm%2BHvN3YNbRo0flcDg0aNAgzZw5UwcOHNCiRYvUu3dvZWRkqKmpiX5foLy8PB06dEi7d%2B/Wli1b%2BHx3EwQsPxQaGtrhh%2B3s8x49eviipIDSr18/vffee7r44ovlcDg0dOhQtbe3a8GCBUpNTT1v7%2Bm7OaGhoaqvr/ca%2B%2Bcef9fnPzw83LYaA8nkyZOVnp6uiIgISVJCQoK%2B%2BOIL7dy5UxkZGd/Z77CwMF%2BU63fy8vK0detWrVq1SkOGDOHz3Y1wDZYfiouLU11dnVpbWz1jbrdbPXr04IfQkIiICM91VpI0ePBgNTc3KyYmRrW1tV6vra2t7XBIHz9eXFzcv%2Bzxd83HxMTYVmMgcTgcnnB11qBBg3TixAlJ9PtCLF26VM8884zy8vJ0/fXXS%2BLz3Z0QsPzQ0KFD5XQ6vS56LC4u1vDhwxUUxF/phXrnnXc0cuRINTY2esY%2B%2BeQTRUREKDk5WR988IHnn0xblqWDBw/K5XL5qtyA43K59PHHH6upqckzVlxc7Omxy%2BVScXGxZ66xsVGHDh3i7%2BBHWrNmjebMmeM1dvjwYQ0aNEhSx35XVVWpqqqKfn%2BP/Px8Pffcc3riiSc0YcIEzzif7%2B6D/xv7obCwME2ePFmPP/64ysrKtHfvXj399NOaNWuWr0sLCElJSQoNDdWjjz6qo0eP6q233tLy5ct11113ady4cWpoaNCyZct05MgRLVu2TI2NjRo/fryvyw4Yqamp6tu3r7Kzs1VeXq6CggKVlZVp2rRpkqSpU6fq4MGDKigoUHl5ubKzs9W/f3%2BNHDnSx5X7p/T0dB04cECbN2/Wl19%2Bqf/6r//Siy%2B%2BqLlz50qSZsyYoZdeekm7du3S4cOH9eCDD%2Bqaa67RgAEDfFz5T1dFRYXWr1%2BvX//610pOTpbb7fY8%2BHx3Iz6%2BTQR%2BpDNnzlgPPviglZiYaI0ZM8Z65plnfF1SQPnss8%2BsOXPmWImJidbo0aOtJ5980nPvq9LSUmvy5MnW8OHDrWnTplkff/yxj6v1f%2Bfel%2BmLL76wfvWrX1lXXHGFNWHCBOsvf/mL1%2Bv//Oc/W9ddd5115ZVXWrNnz%2BaeTD/Quf1%2B/fXXrRtvvNEaPny4NW7cOOu1117zev2ePXusf/u3f7MSExOtzMxM6%2BTJk3aX7Fc2btxoDRky5LwPy%2BLz3V04LIvbwwIAAJjEKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AETIS01ndHajAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5856077567733371051”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>238</td> <td class=”number”>7.4%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>196</td> <td class=”number”>6.1%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>157</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>144</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>123</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (93)</td> <td class=”number”>1096</td> <td class=”number”>34.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5856077567733371051”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>241</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>238</td> <td class=”number”>7.4%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>139.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>141.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>147.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>181.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>236.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_RCT07”>AGRITRSM_RCT07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>540</td>

</tr> <tr>

<th>Unique (%)</th> <td>16.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>38.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1239</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>211000</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8464000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8797408421319895038”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAv5JREFUeJzt2jFIonEcxvHnIhpagiKCDMSpTQiEIGhpiCMyqDZprtZwSGgLbotqKGhxCGoI2oS4d4jmxKEhiDCi5toaIjO8Tc6rey4P9e267wde0Nf/K7/Br%2B/ri18qlUpFAN7UFvYAwEfWHvYAv0qsfK/7mMK3r02YBOAMAlgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhjtYQ/QCImV73UfU/j2tQmT4LP5FIH8jb%2BJql5E%2BO/7UqlUKmEPAXxU/AYBDAIBDAIBDAJBU5RKJU1OTur09PRd68fGxjQ4OPhq29raavKk3n97FwvN8/T0pHQ6rWKx%2BO5jDg8P9fLyUn0eBIE2Nzc1PT3djBHfjUDQUFdXV0qn06r35mh3d3f18cPDg7a3t7W8vKxIJNLoEevCJRYaKp/Pa3h4WAcHB69eKxQKmpmZUTweVzKZVBAEb75HNptVb2%2BvZmdnmz3uH3EGQUOlUqk399/d3WlhYUFLS0saHR3V2dmZMpmMenp6lEgkquseHx%2B1t7en1dVVtbWF//1NIGiJ/f19jYyMaG5uTpIUjUZ1cXGh3d3dmkCOjo7U2dmp8fHxsEatQSBoievra52cnGhoaKi67/n5WbFYrGZdEASamJhQe/vH%2BGh%2BjCnw6ZXLZSWTSS0uLtbs/zmEUqmkfD6v%2Bfn5Vo/3W%2BFf5OG/EIvFdHt7q2g0Wt2Oj4%2BVy%2BWqay4vL1UulxWPx0OctBaBoCVSqZTOz8%2B1sbGhm5sb5XI5ra%2Bvq7%2B/v7qmWCxqYGBAHR0dIU5ai0sstEQkEtHOzo7W1taUzWbV19enTCajqamp6pr7%2B3t1dXWFOOVr/N0dMLjEAgwCAQwCAQwCAQwCAQwCAQwCAQwCAQwCAQwCAYwfWC2rwJBCcoMAAAAASUVORK5CYII%3D”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8797408421319895038,#minihistogram-8797408421319895038”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8797408421319895038”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8797408421319895038”

aria-controls=”quantiles-8797408421319895038” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8797408421319895038” aria-controls=”histogram-8797408421319895038”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8797408421319895038” aria-controls=”common-8797408421319895038”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8797408421319895038” aria-controls=”extreme-8797408421319895038”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8797408421319895038”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>7000</td>

</tr> <tr>

<th>Median</th> <td>42000</td>

</tr> <tr>

<th>Q3</th> <td>168500</td>

</tr> <tr>

<th>95-th percentile</th> <td>1048500</td>

</tr> <tr>

<th>Maximum</th> <td>8464000</td>

</tr> <tr>

<th>Range</th> <td>8464000</td>

</tr> <tr>

<th>Interquartile range</th> <td>161500</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>542750</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5722</td>

</tr> <tr>

<th>Kurtosis</th> <td>71.001</td>

</tr> <tr>

<th>Mean</th> <td>211000</td>

</tr> <tr>

<th>MAD</th> <td>261930</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.9714</td>

</tr> <tr>

<th>Sum</th> <td>415880000</td>

</tr> <tr>

<th>Variance</th> <td>294570000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8797408421319895038”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl0FGXa/vGrkzYhEEJC6EQCjggKQYQQEhYFFBAQEQVEzYAgKi6vLHkdI7siIIiHoCMYXBBUEMeJiIoyOIx4XH/KYpQACgoBFBBIR4JsWUhSvz986WMTHDvyUN2dfD/n9Dn0U9VV9%2BPdmbm6qrraYVmWJQAAABgT4u8CAAAAqhsCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzOnvAmoKt/uo8W2GhDhUv34dHTp0XBUVlvHt48%2BjN4GL3gQuehO4grk3Llddv%2ByXI1hBLCTEIYfDoZAQh79LwWnoTeCiN4GL3gQuelN1BCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMzp7wJwdlIn/9vfJfjsvfs7%2B7sEAABswREsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYFfcAqLS1Vv379tG7dOknShAkT1KJFi0qP2267zfOa1NTUSsuPHz8uSSopKdGkSZOUmpqqLl266MUXX/TLvAAAQPAK6t8iLCkpUUZGhrZv3%2B4Zmzx5sjIyMjzP9%2B3bp2HDhnkC1sGDB3X06FGtWbNGtWrV8qxXu3ZtSdLs2bO1ZcsWLV68WD/99JPGjx%2BvhIQE9enTx6ZZAQCAYBe0AWvHjh3KyMiQZVle43Xr1lXdunU9zydMmKA%2BffqoZ8%2BekqS8vDy5XC5dcMEFlbZ54sQJLVu2TC%2B88IJatWqlVq1aafv27Xr11VcJWAAAwGdBe4pw/fr16tixo7Kzs393nS%2B%2B%2BEIbNmzQAw884BnbsWOHLrroojOuv23bNpWVlSk5OdkzlpKSotzcXFVUVJgrHgAAVGtBewRryJAhf7jOggULNHDgQDVs2NAzlpeXp6KiIg0bNky7du1Sy5YtNWnSJF100UVyu92KiYlRWFiYZ/0GDRqopKREhw8fVv369X2qLT8/X26322vM6aytuLg4H2fnm9DQ4MrHTmdw1Xs2TvUm2HpUE9CbwEVvAhe9qbqgDVh/ZM%2BePVq7dq0mT57sNb5z50798ssveuCBBxQZGakXXnhBt99%2Bu/71r3%2BpqKjIK1xJ8jwvLS31ed/Z2dnKysryGhs1apTS09P/5Gyqh5iYOv4uwXZRURH%2BLgG/g94ELnoTuOiN76ptwFq9erVatmypiy%2B%2B2Gt80aJFOnnypOrU%2BfX/7OfMmaOrrrpKH374ocLDwysFqVPPf3tB/B9JS0tTjx49vMacztoqLDz%2BZ6byu4Ltk4Tp%2BQey0NAQRUVF6MiRIpWXc3o5kNCbwEVvAlcw98ZfH%2B6rbcD69NNPdfXVV1caDwsL8zpKFR4ersaNG%2BvgwYNq166dCgsLVVZWJqfz1/80brdbtWrVUlRUlM/7jouLq3Q60O0%2BqrKy4HpTmlYT519eXlEj5x0M6E3gojeBi974LrgOgfjIsixt3rxZ7dq1qzTes2dPvfnmm56xEydO6IcfflDTpk3VsmVLOZ1Obdy40bM8JydHrVu3VkhItfxPBQAAzoFqeQRr3759On78eKXTgw6HQ926ddPTTz%2BtRo0aqX79%2Bpo7d67OP/98XXXVVQoNDdWAAQM0depUPfbYY8rPz9eLL76oWbNm%2BWkmAAAgGFXLgPXzzz9LkurVq1dp2dixY%2BV0OpWRkaFjx46pU6dOWrBggUJDQyVJEydO1NSpUzV8%2BHBFRkZqzJgx6t27t631AwCA4OawTr9TJ84Jt/uo8W06nSHqNedT49s9V967v7O/S7CN0xmimJg6Kiw8zvUKAYbeBC56E7iCuTcuV90/Xukc4MIiAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCzoA1Zpaan69eundevWecZmzJihFi1aeD2WLl3qWb5y5Ur17NlTSUlJGjVqlA4dOuRZZlmW5syZo06dOqlDhw6aPXu2KioqbJ0TAAAIbk5/F3A2SkpKlJGRoe3bt3uN5%2BXlKSMjQwMHDvSMRUZGSpI2bdqkyZMna9q0aUpMTNTMmTM1ceJEPf/885Kkl156SStXrlRWVpbKyso0duxYxcbGasSIEfZNDAAABLWgPYK1Y8cO3XLLLfrxxx8rLcvLy9Oll14ql8vleUREREiSli5dqmuvvVYDBgxQYmKiZs%2BerY8//lh79uyRJC1ZskTp6elKTU1Vp06d9OCDD%2BrVV1%2B1dW4AACC4BW3AWr9%2BvTp27Kjs7Gyv8WPHjungwYNq0qTJGV%2BXm5ur1NRUz/OGDRsqISFBubm5OnjwoPbv36/27dt7lqekpGjfvn3Kz88/J/MAAADVT9CeIhwyZMgZx/Py8uRwOPTcc8/pk08%2BUXR0tO644w7P6cL8/HzFxcV5vSY2NlYHDhyQ2%2B2WJK/lDRo0kCQdOHCg0ut%2BT35%2BvmdbpzidtX1%2Bva9CQ4MrHzudwVXv2TjVm2DrUU1AbwIXvQlc9KbqgjZg/Z6dO3fK4XCoadOmGjp0qDZs2KCHH35YkZGR6tWrl4qLixUWFub1mrCwMJWWlqq4uNjz/LfLpF8vpvdVdna2srKyvMZGjRql9PT0PzutaiEmpo6/S7BdVFSEv0vA76A3gYveBC5647tqF7AGDBig7t27Kzo6WpKUmJio3bt367XXXlOvXr0UHh5eKSyVlpYqIiLCK0yFh4d7/i3Jcw2XL9LS0tSjRw%2BvMaeztgoLj//peZ1JsH2SMD3/QBYaGqKoqAgdOVKk8nK%2BhRpI6E3gojeBK5h7468P99UuYDkcDk%2B4OqVp06Zau3atJCk%2BPl4FBQVeywsKCuRyuRQfHy9Jcrvdaty4seffkuRyuXyuIS4urtLpQLf7qMrKgutNaVpNnH95eUWNnHcwoDeBi94ELnrju%2BA6BOKDuXPn6vbbb/ca27Ztm5o2bSpJSkpKUk5OjmfZ/v37tX//fiUlJSk%2BPl4JCQley3NycpSQkGD8%2BikAAFB9VbsjWN27d9eCBQu0aNEi9erVS5999pnefvttLVmyRJI0ePBgDRs2TG3btlXr1q01c%2BZMdevWTRdccIFn%2BZw5c3T%2B%2BedLkp544gndeeedfpsPAAAIPtUuYLVp00Zz587VvHnzNHfuXDVq1EhPPPGEkpOTJUnJycmaPn265s2bp19%2B%2BUWdO3fWo48%2B6nn9iBEj9PPPP2v06NEKDQ3VTTfdVOmIGAAAwH/jsCzL8ncRNYHbfdT4Np3OEPWa86nx7Z4r793f2d8l2MbpDFFMTB0VFh7neoUAQ28CF70JXMHcG5errl/2W%2B2uwQIAAPA3AhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC/qAVVpaqn79%2BmndunWesY0bN%2Bqvf/2rkpOTdc0112jZsmVer7nhhhvUokULr8f3338vSbIsS3PmzFGnTp3UoUMHzZ49WxUVFbbOCQAABDenvws4GyUlJcrIyND27ds9Y263W3fffbcGDx6sxx9/XN98840mTpwol8ulbt26qby8XLt379bSpUvVpEkTz%2BtiYmIkSS%2B99JJWrlyprKwslZWVaezYsYqNjdWIESPsnh4AAAhSQRuwduzYoYyMDFmW5TW%2BZs0aNWjQQA888IAkqUmTJlq3bp3effdddevWTXv37tXJkyfVpk0bhYeHV9rukiVLlJ6ertTUVEnSgw8%2BqLlz5xKwAACAz4L2FOH69evVsWNHZWdne4137dpVs2bNqrT%2BsWPHJP0azBo2bHjGcHXw4EHt379f7du394ylpKRo3759ys/PNzwDAABQXQXtEawhQ4accbxx48Zq3Lix5/nPP/%2Bsf/3rXxozZowkKS8vT%2Bedd57uvfdebdmyRRdddJHGjRunNm3ayO12S5Li4uI8r2/QoIEk6cCBA17jAAAAvydoA5YviouLNWbMGDVo0EBpaWmSpF27dumXX37RzTffrPT0dL3%2B%2BusaPny4Vq1apeLiYklSWFiYZxun/l1aWurzfvPz8z1h7RSns7bxgBYaGlwHIJ3O4Kr3bJzqTbD1qCagN4GL3gQuelN11TZgHT9%2BXCNHjtTu3bv1j3/8QxEREZKkRx99VMXFxYqMjJQkTZ06VV999ZVWrFihK664QtKvYerUKcRTwerU632RnZ2trKwsr7FRo0YpPT39rOcVzGJi6vi7BNtFRfn%2BvoG96E3gojeBi974rloGrGPHjumuu%2B7Sjz/%2BqMWLF3t9W9DpdHrClSQ5HA41bdpUBw8eVHx8vKRfv4l46jTjqSNRLpfL5/2npaWpR48eXmNOZ20VFh7/s1M6o2D7JGF6/oEsNDREUVEROnKkSOXl3OYjkNCbwEVvAlcw98ZfH%2B6rXcCqqKjQ6NGjtXfvXr3yyitq1qyZ1/Jhw4apY8eOGj16tGf97777Trfeeqvi4%2BOVkJCgnJwcT8DKyclRQkJClU7vxcXFVVrf7T6qsrLgelOaVhPnX15eUSPnHQzoTeCiN4GL3viu2gWsN954Q%2BvWrdOzzz6rqKgozxGo8847T9HR0erRo4fmz5%2Bvli1b6qKLLtKSJUt09OhRDRw4UJI0ePBgzZkzR%2Beff74k6YknntCdd97pt/kAAIDgU%2B0C1urVq1VRUaF7773Xa7xDhw565ZVXdPvtt6ukpEQzZsxQQUGBkpKS9NJLL3lOG44YMUI///yzRo8erdDQUN100026/fbb/TATAAAQrBzW6XfqxDnhdh81vk2nM0S95nxqfLvnynv3d/Z3CbZxOkMUE1NHhYXHOZweYOhN4KI3gSuYe%2BNy1fXLfoPrKmkAAIAgQMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhtkesG6%2B%2BWb985//1NGj5u8LBQAAEAhsD1idOnXSc889py5duuiBBx7QZ599Ju51CgAAqhPbA1ZGRoY%2B/PBDPfPMMwoNDdWYMWPUrVs3/f3vf9euXbvsLgcAAMA4v/wWocPhUOfOndW5c2cVFRXplVde0TPPPKMFCxaoXbt2Gj58uHr37u2P0gAAAM6a337sOT8/X%2B%2B8847eeecdff/992rXrp0GDhyoAwcO6KGHHtKGDRs0efJkf5UHAADwp9kesFasWKEVK1Zo3bp1ql%2B/vgYMGKB58%2BapSZMmnnUaNmyomTNnErAAAEBQsj1gTZ48Wd27d9f8%2BfN15ZVXKiSk8mVgTZs21dChQ%2B0uDQAAwAjbA9Ynn3yimJgYHT582BOuNm3apFatWik0NFSS1K5dO7Vr187u0gAAAIyw/VuEx44dU58%2BffTCCy94xu655x71799f%2B/fvt7scAAAA42wPWI899pguvPBC3XHHHZ6xVatWqWHDhpo1a5bd5QAAABhne8D68ssvNWHCBLlcLs9Y/fr1NW7cOK1du9bucgAAAIyzPWA5nU4dOXKk0nhRURF3dAcAANWC7QHryiuv1IwZM/Tjjz96xvbs2aNZs2apa9eudpcDAABgnO3fIhw/frzuuOMOXXPNNYqKipIkHTlyRK1atdLEiRPtLgcAAMA42wNWbGys3nrrLX3%2B%2Befavn27nE6nLr74Yl1%2B%2BeVyOBx2lwMAAGCcX34qJzQ0VF27duWUIAAAqJZsD1hut1tPPfWUvvrqK508ebLShe0ffPCB3SUBAAAYZXvAevjhh7VlyxZdd911qlu3rt27BwAAOOdsD1hr167VwoULlZqaaveuAQAAbGH7bRpq166t2NhYu3cLAABgG9sDVv/%2B/bVw4UKVl5fbvWsAAABb2H6K8PDhw1q5cqU%2B%2BugjXXDBBQoLC/NavmTJErtLAgAAMMr2I1iS1K9fP1155ZW66KKL1KhRI69HVZWWlqpfv35at26dZ2zPnj26/fbb1bZtW/Xt21efffaZ12s%2B//xz9evXT0lJSbrtttu0Z88er%2BUvv/yyunbtquTkZE2aNElFRUV/bqIAAKBGsv0I1qxZs4xtq6SkRBkZGdq%2BfbtnzLIsjRo1Ss2bN9fy5cu1Zs0ajR49WqtWrVJCQoJ%2B%2BuknjRo1SmPGjFHXrl01f/58jRw5Uu%2B8844cDodWr16trKwsZWZmKjY2VhMnTlRmZqamTJlirG4AAFC9%2BeUIVn5%2BvrKyspSRkaGff/5Z//73v7Vz584qbWPHjh265ZZbvH7TUPr1W4p79uzR9OnT1axZM917771q27atli9fLklatmyZLrvsMt1555265JJLNGvWLO3bt0/r16%2BX9OspyuHDh6t79%2B5q06aNpk2bpuXLl3MUCwAA%2BMz2gPXDDz/o%2Buuv11tvvaXVq1frxIkTWrVqlQYNGqTc3Fyft7N%2B/Xp17NhR2dnZXuO5ubm69NJLVbt2bc9YSkqKNm7c6Fn%2B21tEREREqFWrVtq4caPKy8u1efNmr%2BVt27bVyZMntW3btj87ZQAAUMPYHrAef/xx9ezZU2vWrNF5550nSXryySfVo0cPzZkzx%2BftDBkyRJMmTVJERITXuNvtVlxcnNdYbGysDhw48IfLjxw5opKSEq/lTqdT0dHRntcDAAD8Eduvwfrqq6/06quvev2ws9Pp1MiRI3XLLbec9faLiooqfTMxLCxMpaWlf7i8uLjY8/z3Xu%2BL/Px8ud1urzGns3alYHe2QkP9cob3T3M6g6ves3GqN8HWo5qA3gQuehO46E3V2R6wKioqVFFRUWn8%2BPHjCg0NPevth4eH6/Dhw15jpaWlqlWrlmf56WGptLRUUVFRCg8P9zw/ffnpR8r%2Bm%2BzsbGVlZXmNjRo1Sunp6T5vozqKianj7xJsFxXl%2B/sG9qI3gYveBC564zvbA1aXLl30/PPPKzMz0zN2%2BPBhZWZmqlOnTme9/fj4eO3YscNrrKCgwHP0KD4%2BXgUFBZWWt2zZUtHR0QoPD1dBQYGaNWsmSSorK9Phw4flcrl8riEtLU09evTwGnM6a6uw8PifmdLvCrZPEqbnH8hCQ0MUFRWhI0eKVF5e%2BQMF/IfeBC56E7iCuTf%2B%2BnBve8CaMGGCbrvtNnXp0kUlJSW67777tG/fPkVHR%2Bvxxx8/6%2B0nJSVpwYIFKi4u9hy1ysnJUUpKimd5Tk6OZ/2ioiJ9%2B%2B23Gj16tEJCQtS6dWvl5OSoY8eOkqSNGzfK6XQqMTHR5xri4uIqnQ50u4%2BqrCy43pSm1cT5l5dX1Mh5BwN6E7joTeCiN76zPWDFx8fr7bff1sqVK7V161ZVVFRo8ODB6t%2B/vyIjI896%2Bx06dFDDhg01ceJEjRw5Uh9%2B%2BKE2bdrkuf/WoEGDtGjRIi1YsEDdu3fX/Pnz1bhxY0%2BgGjJkiKZMmaLmzZsrLi5OU6dO1S233FKlU4QAAKBmsz1gSb/eGuHmm28%2BJ9sODQ3VM888o8mTJ%2BvGG2/UhRdeqPnz5yshIUGS1LhxYz399NN67LHHNH/%2BfCUnJ2v%2B/Pmei%2B6vu%2B467du3T1OmTFFpaal69%2B6tsWPHnpNaAQBA9eSwLMuyc4e33Xbbf11eXX%2BL0O0%2BanybTmeIes351Ph2z5X37u/s7xJs43SGKCamjgoLj3M4PcDQm8BFbwJXMPfG5arrl/3afgTr9N8bLCsr0w8//KDvv/9ew4cPt7scAAAA4wLmtwjnz5/PzTwBAEC1EDDf8%2B/fv7/ee%2B89f5cBAABw1gImYH399ddGbjQKAADgb7afIjzTRe7Hjh3Td999pyFDhthdDgAAgHG2B6yEhASv3yGUpPPOO09Dhw7VDTfcYHc5AAAAxtkesEzcrR0AACCQ2R6wNmzY4PO67du3P4eVAAAAnBu2B6xhw4Z5ThH%2B9h6np485HA5t3brV7vIAAADOmu0B67nnntOMGTM0duxYdejQQWFhYdq8ebOmT5%2BugQMHqm/fvnaXBAAAYJTtt2mYNWuWpkyZomuuuUYxMTGqU6eOOnXqpOnTp%2Bu1115To0aNPA8AAIBgZHvAys/PP2N4ioyMVGFhod3lAAAAGGd7wGrbtq2efPJJHTt2zDN2%2BPBhZWZm6vLLL7e7HAAAAONsvwbroYce0m233aYrr7xSTZo0kWVZ2r17t1wul5YsWWJ3OQAAAMbZHrCaNWumVatWaeXKlcrLy5Mk3XrrrbruuusUERFhdzkAAADG2R6wJKlevXq6%2BeabtXfvXl1wwQWSfr2bOwAAQHVg%2BzVYlmVpzpw5at%2B%2Bvfr166cDBw5o/Pjxmjx5sk6ePGl3OQAAAMbZHrBeeeUVrVixQo888ojCwsIkST179tSaNWuUlZVldzkAAADG2R6wsrOzNWXKFN14442eu7f37dtXM2bM0Lvvvmt3OQAAAMbZHrD27t2rli1bVhpPTEyU2%2B22uxwAAADjbA9YjRo10ubNmyuNf/LJJ54L3gEAAIKZ7d8iHDFihKZNmya32y3LsvTFF18oOztbr7zyiiZMmGB3OQAAAMbZHrAGDRqksrIyPfvssyouLtaUKVNUv3593X///Ro8eLDd5QAAABhne8BauXKl%2BvTpo7S0NB06dEiWZSk2NtbuMgAAAM4Z26/Bmj59uudi9vr16xOuAABAtWN7wGrSpIm%2B//57u3cLAABgG9tPESYmJurBBx/UwoUL1aRJE4WHh3stnzVrlt0lAQAAGGV7wNq1a5dSUlIkifteAQCAasmWgDV79myNHj1atWvX1iuvvGLHLgEAAPzGlmuwXnrpJRUVFXmN3XPPPcrPz7dj9wAAALayJWBZllVpbMOGDSopKTkn%2B3vzzTfVokWLSo/ExERJ0n333Vdp2Ycffuh5/csvv6yuXbsqOTlZkyZNqhQOAQAA/hvbr8GyQ9%2B%2BfdW1a1fP87KyMg0fPlzdunWTJOXl5SkzM1OXX365Z5169epJklavXq2srCxlZmYqNjZWEydOVGZmpqZMmWLrHAAAQPCy/TYNdqhVq5ZcLpfn8c4778iyLD344IMqLS3V3r171bp1a691wsLCJElLlizR8OHD1b17d7Vp00bTpk3T8uXLOYoFAAB8ZlvAcjgcdu3Ky%2BHDh/XCCy8oIyNDYWFh2rlzpxwOxxl/WLq8vFybN29WamqqZ6xt27Y6efKktm3bZmfZAAAgiNl2inDGjBle97w6efKkMjMzVadOHa/1TN8H67XXXlNcXJz69OkjSdq5c6ciIyM1btw4rV%2B/Xueff77GjBmjq666SkeOHFFJSYni4uI8r3c6nYqOjtaBAwd83md%2Bfn6lW1A4nbW9tmtCaGhwHYB0OoOr3rNxqjfB1qOagN4ELnoTuOhN1dkSsNq3b18pcCQnJ6uwsFCFhYXnbL%2BWZWnZsmW66667PGM7d%2B5UcXGxunTponvuuUfvv/%2B%2B7rvvPmVnZ6tBgwaS5DldeEpYWJhKS0t93m92draysrK8xkaNGqX09PSzmE3wi4mp88crVTNRURH%2BLgG/g94ELnoTuOiN72wJWP6699XmzZt18OBBXXfddZ6xkSNHatiwYZ6L2hMTE/XNN9/o9ddf19/%2B9jdJqhSmSktLFRHh%2B5sqLS1NPXr08BpzOmursPD4n53KGQXbJwnT8w9koaEhioqK0JEjRSovr/B3OfgNehO46E3gCube%2BOvDfbX8FuEpn376qVJTUz1hSpJCQkK8nktS06ZNtWPHDkVHRys8PFwFBQVq1qyZpF%2B/gXj48GG5XC6f9xsXF1fpdKDbfVRlZcH1pjStJs6/vLyiRs47GNCbwEVvAhe98V1wHQKpok2bNqldu3ZeYxMmTNDEiRO9xrZt26amTZsqJCRErVu3Vk5OjmfZxo0b5XQ6PffQAgAA%2BCPVOmBt375dF198sddYjx499O677%2Brtt9/WDz/8oKysLOXk5Gjo0KGSpCFDhmjRokVas2aNNm3apKlTp%2BqWW26p0ilCAABQs1XrU4QFBQWKioryGuvdu7ceeeQRPfvss/rpp590ySWXaOHChWrcuLEk6brrrtO%2Bffs0ZcoUlZaWqnfv3ho7dqw/ygcAAEHKYZ3pd2xgnNt91Pg2nc4Q9ZrzqfHtnivv3d/Z3yXYxukMUUxMHRUWHud6hQBDbwIXvQlcwdwbl6uuX/ZbrU8RAgAA%2BAMBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbn5LfdAAATpklEQVRV24D1/vvvq0WLFl6P9PR0SdK3336rm2%2B%2BWUlJSRo0aJC2bNni9dqVK1eqZ8%2BeSkpK0qhRo3To0CF/TAEAAASpahuwduzYoe7du%2Buzzz7zPGbMmKETJ07onnvuUWpqqt58800lJyfr3nvv1YkTJyRJmzZt0uTJkzV69GhlZ2fryJEjmjhxop9nAwAAgkm1DVh5eXlq3ry5XC6X5xEVFaVVq1YpPDxc48aNU7NmzTR58mTVqVNH//73vyVJS5cu1bXXXqsBAwYoMTFRs2fP1scff6w9e/b4eUYAACBYVOuA1aRJk0rjubm5SklJkcPhkCQ5HA61a9dOGzdu9CxPTU31rN%2BwYUMlJCQoNzfXlroBAEDwc/q7gHPBsizt2rVLn332mZ5//nmVl5erT58%2BSk9Pl9vt1sUXX%2By1fmxsrLZv3y5Jys/PV1xcXKXlBw4c8Hn/%2Bfn5crvdXmNOZ%2B1K2z1boaHBlY%2BdzuCq92yc6k2w9agmoDeBi94ELnpTddUyYP30008qKipSWFiYnnrqKe3du1czZsxQcXGxZ/y3wsLCVFpaKkkqLi7%2Br8t9kZ2draysLK%2BxUaNGeS6yr6liYur4uwTbRUVF%2BLsE/A56E7joTeCiN76rlgGrUaNGWrdunerVqyeHw6GWLVuqoqJCY8eOVYcOHSqFpdLSUtWqVUuSFB4efsblERG%2Bv6nS0tLUo0cPrzGns7YKC4//yRmdWbB9kjA9/0AWGhqiqKgIHTlSpPLyCn%2BXg9%2BgN4GL3gSuYO6Nvz7cV8uAJUnR0dFez5s1a6aSkhK5XC4VFBR4LSsoKPCcvouPjz/jcpfL5fO%2B4%2BLiKp0OdLuPqqwsuN6UptXE%2BZeXV9TIeQcDehO46E3goje%2BC65DID769NNP1bFjRxUVFXnGtm7dqujoaKWkpOjrr7%2BWZVmSfr1e66uvvlJSUpIkKSkpSTk5OZ7X7d%2B/X/v37/csBwAA%2BCPVMmAlJycrPDxcDz30kHbu3KmPP/5Ys2fP1l133aU%2BffroyJEjmjlzpnbs2KGZM2eqqKhI1157rSRp8ODBWrFihZYtW6Zt27Zp3Lhx6tatmy644AI/zwoAAASLahmwIiMjtWjRIh06dEiDBg3S5MmTlZaWprvuukuRkZF6/vnnlZOToxtvvFG5ublasGCBateuLenXcDZ9%2BnTNnz9fgwcPVr169TRr1iw/zwgAAAQTh3XqXBnOKbf7qPFtOp0h6jXnU%2BPbPVfeu7%2Bzv0uwjdMZopiYOiosPM71CgGG3gQuehO4grk3Llddv%2By3Wh7BAgAA8CcCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyrtgHr4MGDSk9PV4cOHdS1a1fNmjVLJSUlkqQZM2aoRYsWXo%2BlS5d6Xrty5Ur17NlTSUlJGjVqlA4dOuSvaQAAgCDk9HcB54JlWUpPT1dUVJReffVV/fLLL5o0aZJCQkI0fvx45eXlKSMjQwMHDvS8JjIyUpK0adMmTZ48WdOmTVNiYqJmzpypiRMn6vnnn/fXdAAAQJCplkewdu7cqY0bN2rWrFm65JJLlJqaqvT0dK1cuVKSlJeXp0svvVQul8vziIiIkCQtXbpU1157rQYMGKDExETNnj1bH3/8sfbs2ePPKQEAgCBSLQOWy%2BXSwoUL1aBBA6/xY8eO6dixYzp48KCaNGlyxtfm5uYqNTXV87xhw4ZKSEhQbm7uuSwZAABUI9UyYEVFRalr166e5xUVFVq6dKk6deqkvLw8ORwOPffcc7ryyit1ww036K233vKsm5%2Bfr7i4OK/txcbG6sCBA7bVDwAAglu1vAbrdJmZmfr222/1xhtv6JtvvpHD4VDTpk01dOhQbdiwQQ8//LAiIyPVq1cvFRcXKywszOv1YWFhKi0t9Xl/%2Bfn5crvdXmNOZ%2B1Kwe1shYYGVz52OoOr3rNxqjfB1qOagN4ELnoTuOhN1VX7gJWZmanFixfr73//u5o3b65LLrlE3bt3V3R0tCQpMTFRu3fv1muvvaZevXopPDy8UpgqLS31XKPli%2BzsbGVlZXmNjRo1Sunp6Wc/oSAWE1PH3yXYLirK9/cN7EVvAhe9CVz0xnfVOmA9%2Buijeu2115SZmalrrrlGkuRwODzh6pSmTZtq7dq1kqT4%2BHgVFBR4LS8oKJDL5fJ5v2lpaerRo4fXmNNZW4WFx//MNH5XsH2SMD3/QBYaGqKoqAgdOVKk8vIKf5eD36A3gYveBK5g7o2/PtxX24CVlZWlf/7zn3ryySfVp08fz/jcuXP19ddf6%2BWXX/aMbdu2TU2bNpUkJSUlKScnRzfeeKMkaf/%2B/dq/f7%2BSkpJ83ndcXFyl04Fu91GVlQXXm9K0mjj/8vKKGjnvYEBvAhe9CVz0xnfBdQjER3l5eXrmmWd09913KyUlRW632/Po3r27NmzYoEWLFunHH3/UP/7xD7399tu68847JUmDBw/WihUrtGzZMm3btk3jxo1Tt27ddMEFF/h5VgAAIFhUyyNYH3zwgcrLy/Xss8/q2Wef9Vr23Xffae7cuZo3b57mzp2rRo0a6YknnlBycrIkKTk5WdOnT9e8efP0yy%2B/qHPnznr00Uf9MQ0AABCkHJZlWf4uoiZwu48a36bTGaJecz41vt1z5b37O/u7BNs4nSGKiamjwsLjHE4PMPQmcNGbwBXMvXG56vplv9XyFCEAAIA/EbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY098FoOa49qn/5%2B8SquS9%2Bzv7uwQAQJDiCBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMALWGZSUlGjSpElKTU1Vly5d9OKLL/q7JAAAEES4k/sZzJ49W1u2bNHixYv1008/afz48UpISFCfPn38XRpsxJ3nAQB/FgHrNCdOnNCyZcv0wgsvqFWrVmrVqpW2b9%2BuV199lYAFAAB8QsA6zbZt21RWVqbk5GTPWEpKip577jlVVFQoJISzqghMwXTEjaNtAKo7AtZp3G63YmJiFBYW5hlr0KCBSkpKdPjwYdWvX/8Pt5Gfny%2B32%2B015nTWVlxcnNFaQ0MJewhOTqf/3run/m74%2Bwk89CZw0ZuqI2CdpqioyCtcSfI8Ly0t9Wkb2dnZysrK8hobPXq0xowZY6bI/5Ofn6/h529XWlqa8fCGs5Ofn6/s7Gx6E4Dy8/O1ePFCehOA6E3gojdVRxQ9TXh4eKUgdep5rVq1fNpGWlqa3nzzTa9HWlqa8VrdbreysrIqHS2D/9GbwEVvAhe9CVz0puo4gnWa%2BPh4FRYWqqysTE7nr/953G63atWqpaioKJ%2B2ERcXR8IHAKAG4wjWaVq2bCmn06mNGzd6xnJyctS6dWsucAcAAD4hMZwmIiJCAwYM0NSpU7Vp0yatWbNGL774om677TZ/lwYAAIJE6NSpU6f6u4hA06lTJ3377bd64okn9MUXX%2Bh//ud/NGjQIH%2BXdUZ16tRRhw4dVKdOHX%2BXgtPQm8BFbwIXvQlc9KZqHJZlWf4uAgAAoDrhFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAJWgCspKdGkSZOUmpqqLl266MUXX/zddb/99lvdfPPNSkpK0qBBg7RlyxYbK615qtKbjz76SP3791dycrKuv/56ffDBBzZWWvNUpTen7N27V8nJyVq3bp0NFdZcVenNd999p8GDB6tNmza6/vrrtXbtWhsrrXmq0pv3339f1157rZKTkzV48GB98803NlYaJCwEtOnTp1vXX3%2B9tWXLFus///mPlZycbL333nuV1jt%2B/LjVuXNn6/HHH7d27NhhPfroo9YVV1xhHT9%2B3A9V1wy%2B9mbr1q1Wq1atrMWLF1u7d%2B%2B2li5darVq1craunWrH6quGXztzW%2BNGDHCat68ubV27VqbqqyZfO3NkSNHrCuuuMJ66KGHrN27d1tz5861UlJSrIKCAj9UXTP42pvvv//eat26tfXWW29ZP/zwgzVt2jSrc%2BfO1okTJ/xQdeAiYAWw48ePW61bt/b6H/z58%2BdbQ4cOrbTusmXLrB49elgVFRWWZVlWRUWF1atXL2v58uW21VuTVKU3mZmZ1ogRI7zG7rzzTuvJJ58853XWRFXpzSkrVqyw/vrXvxKwzrGq9Gbx4sVWz549rbKyMs/YjTfeaH300Ue21FrTVKU3L730kjVw4EDP86NHj1rNmze3Nm3aZEutwYJThAFs27ZtKisrU3JysmcsJSVFubm5qqio8Fo3NzdXKSkpcjgckiSHw6F27dpp48aNttZcU1SlNwMHDtSDDz5YaRtHjx4953XWRFXpjSQVFhYqMzNT06dPt7PMGqkqvVm/fr2uvvpqhYaGesaWL1%2Buq666yrZ6a5Kq9CY6Olo7duxQTk6OKioq9OabbyoyMlJ/%2Bctf7C47oBGwApjb7VZMTIzCwsI8Yw0aNFBJSYkOHz5cad24uDivsdjYWB04cMCWWmuaqvSmWbNmSkxM9Dzfvn27vvjiC11%2B%2BeW21VuTVKU3kvT4449r4MCBuuSSS%2Bwss0aqSm/27Nmj%2BvXr6%2BGHH1bnzp11yy23KCcnx%2B6Sa4yq9KZv377q1q2bhgwZossuu0yzZ8/WvHnzVK9ePbvLDmgErABWVFTk9WaX5HleWlrq07qnrwczqtKb3zp06JDGjBmjdu3a6eqrrz6nNdZUVenN559/rpycHI0cOdK2%2BmqyqvTmxIkTWrBggVwul1544QW1b99eI0aM0P79%2B22rtyapSm8KCwvldrs1ZcoUvf766%2Brfv78mTpyon3/%2B2bZ6gwEBK4CFh4dXemOfel6rVi2f1j19PZhRld6cUlBQoOHDh8uyLM2bN08hIfz5nQu%2B9qa4uFhTpkzRI488wt%2BJTarydxMaGqqWLVsqPT1dl156qcaOHasmTZpoxYoVttVbk1SlN3PmzFHz5s1166236rLLLtOjjz6qiIgILV%2B%2B3LZ6gwH/Cx/A4uPjVVhYqLKyMs%2BY2%2B1WrVq1FBUVVWndgoICr7GCgoJKpw1hRlV6I0kHDx7UrbfeqtLSUi1ZskT169e3s9waxdfebNq0SXv27FF6erqSk5M9157cfffdmjJliu111wRV%2BbtxuVxq2rSp11iTJk04gnWOVKU333zzjddlDyEhIUpMTNRPP/1kW73BgIAVwFq2bCmn0%2Bl1oXpOTo5at25d6ehHUlKSvv76a1mWJUmyLEtfffWVkpKSbK25pqhKb06cOKG77rpLISEhWrp0qeLj4%2B0ut0bxtTdt2rTRf/7zH7399tuehyTNmDFD//u//2t73TVBVf5u2rZtq%2B%2B%2B%2B85rbOfOnWrUqJEttdY0VelNXFyc8vLyvMZ27dqlxo0b21JrsAidOnXqVH8XgTM777zztH//fr322mu67LLLtHnzZmVmZiojI0PNmjWT2%2B1WaGionE6n/vKXv2jhwoU6cOCAEhIS9Oyzz2rbtm2aPn26zjvvPH9PpdqpSm%2BysrL08ccf65lnnlHdunV14sQJnThxQhUVFQoPD/f3VKodX3tTq1YtRUdHez2ysrI0bNgwLng/R6ryd3PhhRfq6aef1smTJ3X%2B%2Bedr8eLF%2BuSTTzRjxgxFRkb6eyrVTlV6ExYWprlz56phw4aKiIjQwoUL9cUXX2jmzJmqXbu2v6cSOPx8mwj8gRMnTljjxo2z2rZta3Xp0sV66aWXPMuaN2/udZ%2Br3Nxca8CAAVbr1q2tm266yfrmm2/8UHHN4WtvrrnmGqt58%2BaVHuPHj/dT5dVfVf5ufov7YJ17VenNl19%2BaQ0cONC67LLLrP79%2B1vr16/3Q8U1R1V68/rrr1t9%2BvSx2rZtaw0ePNjasmWLHyoObA7L%2Br9zSgAAADCCa7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAQMEpLS9WvXz%2BtW7fOp/V79OihFi1aVHpkZWWd40r/O6df9w4AAPB/SkpKlJGRoe3bt/v8mjfeeEPl5eWe56tXr9ZTTz2lgQMHnosSfUbAAgAAfrdjxw5lZGSoqvc/r1%2B/vuffR48e1fz58zV%2B/Hi//24lpwgBAIDfrV%2B/Xh07dlR2dnalZV9%2B%2BaVuvPFGtWnTRtdff71Wr159xm0sWrRILpdLgwYNOtfl/iGOYAEAAL8bMmTIGcfdbrfuvfde/e1vf1PXrl21ceNGTZgwQbGxsUpNTfWsV1RUpKVLl2r69OkKCfH/8SMCFgAACFivvvqqrrjiCg0dOlSSdOGFF2rr1q1avHixV8BatWqVateurd69e/urVC8ELAAAELB27typDz/8UMnJyZ6xkydP6qKLLvJab/Xq1erbt6%2BczsCINv8ffKlv2hhJwJ8AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8797408421319895038”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10000.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5000.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7000.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15000.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26000.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (529)</td> <td class=”number”>1393</td> <td class=”number”>43.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1239</td> <td class=”number”>38.6%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8797408421319895038”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5727000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5896000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6647000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6929000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8464000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_AGRITRSM_RCT12”>AGRITRSM_RCT12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>643</td>

</tr> <tr>

<th>Unique (%)</th> <td>20.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>34.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1095</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>276830</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>23723000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-971730208540950451”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAvNJREFUeJzt2jFIamEchvH3RjS0BEUEGYhTmxAIQdDSEBEZVJs0V2s4JLQFd4tqKGhxCGoI2oToDNGcODQEEUbUXFtDZIZ3k%2But%2B94C9Uj3%2BcEBPX5H/oOPfhz8UalUKgLwobawBwBaWXvYA/wpsXry5WsKPycaMAnALwhgEQhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgEAhgtIc9QD0kVk%2B%2BfE3h50QDJsF386NSqVTCHgJoVWyxAINAAINAAINA0BClUklTU1M6Pz//1PqxsTENDg6%2BO7a3txs8qfct7mKhtby8vCidTqtYLH76mqOjI729vVWfB0Ggra0tzczMNGLETyMQ1NXNzY3S6bS%2BenO0u7u7%2Bvjp6Uk7OztaWVlRJBKp94hfwhYLdZXP5zU8PKzDw8N3rxUKBc3OzioejyuZTCoIgg/fI5vNqre3V3Nzc40e95/4BUFdpVKpD88/PDxocXFRy8vLGh0d1cXFhTKZjHp6epRIJKrrnp%2Bftb%2B/r7W1NbW1hf/9TSBoioODA42MjGh%2Bfl6SFI1GdXV1pb29vZpAjo%2BP1dnZqfHx8bBGrUEgaIrb21udnZ1paGioeu719VWxWKxmXRAEmpycVHt7a3w0W2MKfHvlclnJZFJLS0s1538PoVQqKZ/Pa2Fhodnj/VX4mzz8F2KxmO7v7xWNRqvH6empcrlcdc319bXK5bLi8XiIk9YiEDRFKpXS5eWlNjc3dXd3p1wup42NDfX391fXFItFDQwMqKOjI8RJa7HFQlNEIhHt7u5qfX1d2WxWfX19ymQymp6erq55fHxUV1dXiFO%2Bx9/dAYMtFmAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGAQCGD8AlN9qL5tox75AAAAAElFTkSuQmCC”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-971730208540950451”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-971730208540950451”

aria-controls=”quantiles-971730208540950451” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-971730208540950451” aria-controls=”histogram-971730208540950451”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-971730208540950451” aria-controls=”common-971730208540950451”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-971730208540950451” aria-controls=”extreme-971730208540950451”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-971730208540950451”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>13000</td>

</tr> <tr>

<th>Median</th> <td>65000</td>

</tr> <tr>

<th>Q3</th> <td>233000</td>

</tr> <tr>

<th>95-th percentile</th> <td>1284200</td>

</tr> <tr>

<th>Maximum</th> <td>23723000</td>

</tr> <tr>

<th>Range</th> <td>23723000</td>

</tr> <tr>

<th>Interquartile range</th> <td>220000</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>775710</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.8021</td>

</tr> <tr>

<th>Kurtosis</th> <td>404.2</td>

</tr> <tr>

<th>Mean</th> <td>276830</td>

</tr> <tr>

<th>MAD</th> <td>332680</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.141</td>

</tr> <tr>

<th>Sum</th> <td>585500000</td>

</tr> <tr>

<th>Variance</th> <td>601730000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-971730208540950451”>

<img 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eqlUrSdLRo0cbvA4AAOB8/DZg/ZDS0lLZbDbFx8dr/Pjx2rFjh2bPnq3w8HANGjRI1dXVCgkJ8XpNSEiIXC6XqqurPc%2B/v0z67sP0jVVWVuYJa2fZ7c2NB7TgYP86AWm3%2B1e95zrbb3/ru7%2Bi39ai39ai34Ev4ALW8OHDNWDAAEVGRkqSEhIS9MUXX%2Bill17SoEGDFBoa2iAsuVwuhYWFeYWp0NBQz78leT7D1Rj5%2BfnKy8vzGsvIyFBmZuZ/PK9AEBXVwtclGBER0fhjAU1Hv61Fv61FvwNXwAUsm83mCVdnxcfHa%2BvWrZKk2NhYlZeXey0vLy%2BXw%2BFQbGysJMnpdKpt27aef0uSw%2BFodA3p6elKS0vzGrPbm6ui4tSFTeYn%2BNs7H9Pzt1pwcJAiIsJ0/HiV6uq4dcfFRr%2BtRb%2BtRb%2Bt46s39wEXsBYvXqx//etfeuGFFzxj%2B/btU3x8vCQpMTFRBQUFGjlypCTpyJEjOnLkiBITExUbG6u4uDgVFBR4AlZBQYHi4uIu6PJeTExMg/WdzhOqrb28/4gCZf51dfUBMxd/QL%2BtRb%2BtRb8DV8AFrAEDBmj58uVauXKlBg0apI8%2B%2Bkivv/66Vq9eLUkaO3asJkyYoB49eqhbt25asGCB%2Bvfvr3bt2nmWL1q0SFdffbUk6bHHHtMdd9zhs/kAAAD/E3ABq3v37lq8eLGeeuopLV68WG3atNFjjz2mpKQkSVJSUpLmzZunp556St9%2B%2B6369u2rhx9%2B2PP6O%2B%2B8U19//bUmT56s4OBgjR49WrfddpuPZgMAAPyRze12u31dxOXA6TxhfJt2e5AGLfrQ%2BHYvlrfv7%2BvrEprEbg9SVFQLVVSc4pS%2BBei3tei3tei3dRyOK32yX//6lDQAAIAfIGABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY5QFrzJgxevnll3XixAmrdw0AAGAJywNWnz599PTTT6tfv3564IEH9NFHH8ntdltdBgAAwEVjecDKysrSli1btHTpUgUHB2vKlCnq37%2B/nnjiCX3%2B%2BedWlwMAAGCc3Rc7tdls6tu3r/r27auqqiq9%2BOKLWrp0qZYvX66ePXtq4sSJGjx4sC9KAwAAaDKfBCxJKisr05tvvqk333xT//73v9WzZ0%2BNGDFCR48e1R//%2BEft2LFDs2bN8lV5AAAA/zHLA9Ybb7yhN954Q9u2bVPLli01fPhwPfXUU2rfvr1nndatW2vBggWNClgul0sjR47U7Nmz1bt3b0lSYWGhHn30UX322WeKiYnRXXfdpTFjxnhe89vf/lafffaZ13beeustderUSW63W4899pheffVV1dfXa/To0XrwwQcVFMQXLgEAQONYHrBmzZqlAQMGaMmSJfrVr3513uASHx%2Bv8ePH/%2BS2ampqlJWVpeLiYs%2BY0%2BnU3XffrbFjx%2BrRRx/VJ598ouzsbDkcDvXv3191dXX64osvtGbNGq9QFxUVJUl6/vnntWHDBuXl5am2tlZTp05VdHS07rzzzqZPHgAAXBYsD1gffPCBoqKiVFlZ6QlXu3btUteuXRUcHCxJ6tmzp3r27Pmj29m/f7%2BysrIafANx8%2BbNatWqlR544AFJUvv27bVt2za99dZb6t%2B/vw4ePKgzZ86oe/fuCg0NbbDd1atXKzMzUykpKZKkBx98UIsXLyZgAQCARrP8utfJkyc1ZMgQPfvss56xe%2B65RzfffLOOHDnS6O1s375dvXv3Vn5%2Bvtd4amqqcnJyzrtf6btg1rp16/OGq2PHjunIkSO6/vrrPWPJyck6dOiQysrKGl0bAAC4vFl%2BBuuRRx7Rz372M91%2B%2B%2B2esY0bN2r69OnKycnRU0891ajtjBs37rzjbdu2Vdu2bT3Pv/76a/3tb3/TlClTJEklJSW64oorNGnSJO3Zs0fXXHONpk2bpu7du8vpdEqSYmJiPK9v1aqVJOno0aNe4z%2BmrKzMs62z7PbmjX59YwUH%2B9fnwux2/6r3XGf77W9991f021r021r0O/BZHrD%2B%2Bc9/6pVXXpHD4fCMtWzZUtOmTdPvfvc7o/uqrq7WlClT1KpVK6Wnp0uSPv/8c3377bcaM2aMMjMz9corr2jixInauHGjqqurJUkhISGebZz9t8vlavR%2B8/PzlZeX5zWWkZGhzMzMpk7Jr0VFtfB1CUZERIT5uoTLCv22Fv22Fv0OXJYHLLvdruPHjzcYr6qqMnpH91OnTum%2B%2B%2B7TF198ob/85S8KC/vuIH744YdVXV2t8PBwSdLcuXO1c%2BdOvfHGG/rlL38p6bswdfYS4tlgdfb1jZGenq60tDSvMbu9uSoqTjV5Xt/nb%2B98TM/fasHBQYqICNPx41Wqq6v3dTkBj35bi35bi35bx1dv7i0PWL/61a80f/58Pf744/qv//ovSdKBAweUk5Oj1NRUI/s4efKk7rrrLn311VdatWqV17cF7Xa7J1xJ3930ND4%2BXseOHVNsbKyk776JePYy49lLfd8/4/ZTYmJiGlwOdDpPqLb28v4jCpT519XVB8xc/AH9thb9thb9DlyWnwKZPn26XC6XbrzxRvXu3Vu9e/fW4MGDdebMGWVnZzd5%2B/X19Zo8ebIOHjyoF198UR07dvRaPmHCBK/Ld/X19frss88UHx%2Bv2NhYxcXFqaCgwLO8oKBAcXFxxj8/BQAAApflZ7Cio6P117/%2BVf/4xz9UXFwsu92ua6%2B9Vr/4xS9ks9mavP1XX31V27Zt07JlyxQREeE5A3XFFVcoMjJSaWlpWrJkibp06aJrrrlGq1ev1okTJzRixAhJ0tixY7Vo0SJdffXVkqTHHntMd9xxR5PrAgAAlw%2Bf/FROcHCwUlNTjV0S/L5Nmzapvr5ekyZN8hrv1auXXnzxRd12222qqanR/PnzVV5ersTERD3//POey4Z33nmnvv76a02ePFnBwcEaPXq0brvtNuN1AgCAwGVzm/xkeSM4nU49%2BeST2rlzp86cOdPgg%2B3vvfeeleVYxuk8YXybdnuQBi360Ph2L5a37%2B/r6xKaxG4PUlRUC1VUnOIzExag39ai39ai39ZxOK70yX4tP4M1e/Zs7dmzR0OHDtWVV/pm0gAAABeT5QFr69atWrFiheenaAAAAAKN5d8ibN68uaKjo63eLQAAgGUsD1g333yzVqxYobq6Oqt3DQAAYAnLLxFWVlZqw4YN%2Bvvf/6527dp5/SyNJK1evdrqkgAAAIzyyW0ahg0b5ovdAgAAWMLygJWTk2P1LgEAACzlk18LLisrU15enrKysvT111/rnXfeUWlpqS9KAQAAMM7ygPXll1/qN7/5jf76179q06ZNOn36tDZu3KhRo0apqKjI6nIAAACMszxgPfrooxo4cKA2b96sK664QpL0%2BOOPKy0tTYsWLbK6HAAAAOMsD1g7d%2B7U7bff7vXDzna7Xffdd5/27t1rdTkAAADGWR6w6uvrVV/f8HeXTp06peDgYKvLAQAAMM7ygNWvXz8988wzXiGrsrJSubm56tOnj9XlAAAAGGd5wJoxY4b27Nmjfv36qaamRvfee68GDBiggwcPavr06VaXAwAAYJzl98GKjY3V66%2B/rg0bNujTTz9VfX29xo4dq5tvvlnh4eFWlwMAAGCcT%2B7kHhYWpjFjxvhi1wAAABed5QHr1ltv/dHl/BYhAADwd5YHrDZt2ng9r62t1Zdffql///vfmjhxotXlAAAAGHfJ/BbhkiVLdPToUYurAQAAMM8nv0V4PjfffLPefvttX5cBAADQZJdMwPrXv/7FjUYBAEBAuCQ%2B5H7y5El99tlnGjdunNXlAAAAGGd5wIqLi/P6HUJJuuKKKzR%2B/Hj99re/tbocAAAA4ywPWI8%2B%2BqjVuwQAALCU5QFrx44djV73%2Buuv/8l1XC6XRo4cqdmzZ6t3796SpAMHDmj27NkqLCxUXFycZs6cqX79%2Bnle849//EOPPPKIDhw4oMTERC1YsEDt2rXzLH/hhRe0cuVKnTx5UjfddJNmz56tsLCwC5glAAC4nFkesCZMmOC5ROh2uz3j547ZbDZ9%2BumnP7qtmpoaZWVlqbi42DPmdruVkZGhTp06af369dq8ebMmT56sjRs3Ki4uTocPH1ZGRoamTJmi1NRULVmyRPfdd5/efPNN2Ww2bdq0SXl5ecrNzVV0dLSys7OVm5urOXPmmG4FAAAIUJZ/i/Dpp59WmzZt9OSTT%2Brjjz9WQUGBXnjhBV1zzTV64IEH9N577%2Bm9997T5s2bf3Q7%2B/fv1y233KKvvvrKa3zr1q06cOCA5s2bpw4dOmjSpEnq0aOH1q9fL0lat26dfv7zn%2BuOO%2B5Qx44dlZOTo0OHDmn79u2SvruT/MSJEzVgwAB1795dDz30kNavX6%2BqqqqL0xAAABBwLA9YOTk5mjNnjm688UZFRUWpRYsW6tOnj%2BbNm6eXXnpJbdq08Tx%2BzPbt29W7d2/l5%2Bd7jRcVFem6665T8%2BbNPWPJyckqLCz0LE9JSfEsCwsLU9euXVVYWKi6ujrt3r3ba3mPHj105swZ7du3z8T0AQDAZcDyS4RlZWXnDU/h4eGqqKho9HZ%2B6JYOTqdTMTExXmPR0dGeu8T/2PLjx4%2BrpqbGa7ndbldkZCR3mQcAAI1mecDq0aOHHn/8cf3P//yPwsPDJUmVlZXKzc3VL37xiyZvv6qqSiEhIV5jISEhcrlcP7m8urra8/yHXt8YZWVlcjqdXmN2e/MGwa6pgoMvmfvENord7l/1nutsv/2t7/6KfluLfluLfgc%2BywPWH//4R91666361a9%2Bpfbt28vtduuLL76Qw%2BHQ6tWrm7z90NBQVVZWeo25XC41a9bMs/zcsORyuRQREaHQ0FDP83OXX8i3CPPz85WXl%2Bc1lpGRoczMzEZvIxBFRbXwdQlGRETwjVIr0W9r0W9r0e/AZXnA6tChgzZu3KgNGzaopKREkvS73/1OQ4cONXIrhNjYWO3fv99rrLy83HP2KDY2VuXl5Q2Wd%2BnSRZGRkQoNDVV5ebk6dOggSaqtrVVlZaUcDkeja0hPT1daWprXmN3eXBUVp/6TKf0gf3vnY3r%2BVgsODlJERJiOH69SXV29r8sJePTbWvTbWvTbOr56c295wJKkq666SmPGjNHBgwc995%2B64oorjGw7MTFRy5cvV3V1teesVUFBgZKTkz3LCwoKPOtXVVVp7969mjx5soKCgtStWzcVFBR47qlVWFgou92uhISERtcQExPT4HKg03lCtbWX9x9RoMy/rq4%2BYObiD%2Bi3tei3teh34LL8FIjb7daiRYt0/fXXa9iwYTp69KimT5%2BuWbNm6cyZM03efq9evdS6dWtlZ2eruLhYy5cv165duzR69GhJ0qhRo7Rz504tX75cxcXFys7OVtu2bT2Baty4cVq5cqU2b96sXbt2ae7cubrlllu40SgAAGg0ywPWiy%2B%2BqDfeeEN/%2BtOfPB8mHzhwoDZv3tzgc0v/ieDgYC1dulROp1MjR47Um2%2B%2BqSVLliguLk6S1LZtW/35z3/W%2BvXrNXr0aFVWVmrJkiWeG50OHTpUkyZN0pw5c3THHXeoe/fumjp1apPrAgAAlw%2Bb%2B/u3U7fA0KFDdf/992vQoEFKSkrSm2%2B%2BqXbt2undd99VTk6O/vd//9fKcizjdJ4wvk27PUiDFn1ofLsXy9v39/V1CU1itwcpKqqFKipOcUrfAvTbWvTbWvTbOg7HlT7Zr%2BVnsA4ePKguXbo0GE9ISGhwawMAAAB/ZHnAatOmjXbv3t1g/IMPPvD6wWUAAAB/Zfm3CO%2B880499NBDcjqdcrvd%2Bvjjj5Wfn68XX3xRM2bMsLocAAAA4ywPWKNGjVJtba2WLVum6upqzZkzRy1bttT999%2BvsWPHWl0OAACAcZYHrA0bNmjIkCFKT0/XN998I7fbrejoaKvLAAAAuGgs/wzWvHnzPB9mb9myJeEKAAAEHMsDVvv27fXvf//b6t0CAABYxvJLhAkJCXrwwQe1YsUKtW/f3vMDy2fl5ORYXRIAAIBRlgeszz//3PO7gNz3CgAABCJLAtbChQs1efJkNW/eXC%2B%2B%2BKIVuwQAAPAZSz6D9fzzz6uqqspr7J577lFZWZkVuwcAALCUJQHrfD93uGPHDtXU1FixewAAAEtZ/i1CAACAQEfAAgAAMMyygGWz2azaFQAAgE9ZdpuG%2BfPne93z6sxrq3qDAAAWEklEQVSZM8rNzVWLFi281uM%2BWAAAwN9ZErCuv/76Bve8SkpKUkVFhSoqKqwoAQAAwDKWBCzufQUAAC4nfMgdAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCwgA9Zrr72mzp07N3gkJCRIku69994Gy7Zs2eJ5/QsvvKDU1FQlJSVp5syZqqqq8tVUAACAH7Lsp3Ks9Otf/1qpqame57W1tZo4caL69%2B8vSSopKVFubq5%2B8YtfeNa56qqrJEmbNm1SXl6ecnNzFR0drezsbOXm5mrOnDmWzgEAAPivgDyD1axZMzkcDs/jzTfflNvt1oMPPiiXy6WDBw%2BqW7duXuuEhIRIklavXq2JEydqwIAB6t69ux566CGtX7%2Bes1gAAKDRAjJgfV9lZaWeffZZZWVlKSQkRKWlpbLZbGrXrl2Ddevq6rR7926lpKR4xnr06KEzZ85o3759VpYNAAD8WEBeIvy%2Bl156STExMRoyZIgkqbS0VOHh4Zo2bZq2b9%2Buq6%2B%2BWlOmTNENN9yg48ePq6amRjExMZ7X2%2B12RUZG6ujRo43eZ1lZWYMft7bbm3tt14TgYP/Kx3a7f9V7rrP99re%2B%2Byv6bS36bS36HfgCOmC53W6tW7dOd911l2estLRU1dXV6tevn%2B655x69%2B%2B67uvfee5Wfn69WrVpJkudy4VkhISFyuVyN3m9%2Bfr7y8vK8xjIyMpSZmdmE2fi/qKgWvi7BiIiIMF%2BXcFmh39ai39ai34EroAPW7t27dezYMQ0dOtQzdt9992nChAmeD7UnJCTok08%2B0SuvvKI//OEPktQgTLlcLoWFNf6PID09XWlpaV5jdntzVVSc%2Bk%2Bncl7%2B9s7H9PytFhwcpIiIMB0/XqW6unpflxPw6Le16Le16Ld1fPXmPqAD1ocffqiUlBRPmJKkoKAgr%2BeSFB8fr/379ysyMlKhoaEqLy9Xhw4dJH33DcTKyko5HI5G7zcmJqbB5UCn84Rqay/vP6JAmX9dXX3AzMUf0G9r0W9r0e/A5V%2BnQC7Qrl271LNnT6%2BxGTNmKDs722ts3759io%2BPV1BQkLp166aCggLPssLCQtntds89tAAAAH5KQAes4uJiXXvttV5jaWlpeuutt/T666/ryy%2B/VF5engoKCjR%2B/HhJ0rhx47Ry5Upt3rxZu3bt0ty5c3XLLbdc0CVCAABweQvoS4Tl5eWKiIjwGhs8eLD%2B9Kc/admyZTp8%2BLA6duyoFStWqG3btpKkoUOH6tChQ5ozZ45cLpcGDx6sqVOn%2BqJ8AADgp2xut9vt6yIuB07nCePbtNuDNGjRh8a3e7G8fX9fX5fQJHZ7kKKiWqii4hSfmbAA/bYW/bYW/baOw3GlT/Yb0JcIAQAAfIGABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBawAevdd99V586dvR6ZmZmSpL1792rMmDFKTEzUqFGjtGfPHq/XbtiwQQMHDlRiYqIyMjL0zTff%2BGIKAADATwVswNq/f78GDBigjz76yPOYP3%2B%2BTp8%2BrXvuuUcpKSl67bXXlJSUpEmTJun06dOSpF27dmnWrFmaPHmy8vPzdfz4cWVnZ/t4NgAAwJ8EbMAqKSlRp06d5HA4PI%2BIiAht3LhRoaGhmjZtmjp06KBZs2apRYsWeueddyRJa9as0U033aThw4crISFBCxcu1Pvvv68DBw74eEYAAMBfBHTAat%2B%2BfYPxoqIiJScny2azSZJsNpt69uypwsJCz/KUlBTP%2Bq1bt1ZcXJyKioosqRsAAPi/gAxYbrdbn3/%2BuT766CPdeOONGjhwoBYtWiSXyyWn06mYmBiv9aOjo3X06FFJUllZ2Y8uBwAA%2BCl2XxdwMRw%2BfFhVVVUKCQnRk08%2BqYMHD2r%2B/Pmqrq72jH9fSEiIXC6XJKm6uvpHlzdGWVmZnE6n15jd3rxBcGuq4GD/ysd2u3/Ve66z/fa3vvsr%2Bm0t%2Bm0t%2Bh34AjJgtWnTRtu2bdNVV10lm82mLl26qL6%2BXlOnTlWvXr0ahCWXy6VmzZpJkkJDQ8%2B7PCwsrNH7z8/PV15entdYRkaG51uMl6uoqBa%2BLsGIiIjGHwtoOvptLfptLfoduAIyYElSZGSk1/MOHTqopqZGDodD5eXlXsvKy8s9Z5diY2PPu9zhcDR63%2Bnp6UpLS/Mas9ubq6Li1IVM4Sf52zsf0/O3WnBwkCIiwnT8eJXq6up9XU7Ao9/Wot/Wot/W8dWb%2B4AMWB9%2B%2BKEefPBB/f3vf/ecefr0008VGRmp5ORkPfvss3K73bLZbHK73dq5c6d%2B//vfS5ISExNVUFCgkSNHSpKOHDmiI0eOKDExsdH7j4mJaXA50Ok8odray/uPKFDmX1dXHzBz8Qf021r021r0O3D51ymQRkpKSlJoaKj%2B%2BMc/qrS0VO%2B//74WLlyou%2B66S0OGDNHx48e1YMEC7d%2B/XwsWLFBVVZVuuukmSdLYsWP1xhtvaN26ddq3b5%2BmTZum/v37q127dj6eFQAA8BcBGbDCw8O1cuVKffPNNxo1apRmzZql9PR03XXXXQoPD9czzzzjOUtVVFSk5cuXq3nz5pK%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%2BvAwcO%2BHJKAADAjwRkwHI4HFqxYoVatWrlNX7y5EmdPHlSx44dU/v27c/72qKiIqWkpHiet27dWnFxcSoqKrqYJQMAgAASkJcIIyIilJqa6nleX1%2BvNWvWqE%2BfPiopKZHNZtPTTz%2BtDz74QJGRkbr99ts9lwvLysoUExPjtb3o6GgdPXq00fsvKyuT0%2Bn0GrPbmzfYblMFB/tXPrbb/avec53tt7/13V/Rb2vRb2vR78AXkAHrXLm5udq7d69effVVffLJJ7LZbIqPj9f48eO1Y8cOzZ49W%2BHh4Ro0aJCqq6sVEhLi9fqQkBC5XK5G7y8/P195eXleYxkZGcrMzDQyH38VFdXC1yUYERER5usSLiv021r021r0O3AFfMDKzc3VqlWr9MQTT6hTp07q2LGjBgwYoMjISElSQkKCvvjiC7300ksaNGiQQkNDG4Qpl8vl%2BYxWY6SnpystLc1rzG5vroqKU02f0Pf42zsf0/O3WnBwkCIiwnT8eJXq6up9XU7Ao9/Wot/Wot/W8dWb%2B4AOWA8//LBeeukl5ebm6sYbb5Qk2Ww2T7g6Kz4%2BXlu3bpUkxcbGqry83Gt5eXm5HA5Ho/cbExPT4HKg03lCtbWX9x9RoMy/rq4%2BYObiD%2Bi3tei3teh34PKvUyAXIC8vTy%2B//LIef/xxDR061DO%2BePFi3XbbbV7r7tu3T/Hx8ZKkxMREFRQUeJYdOXJER44cUWJioiV1AwAA/xeQAaukpERLly7V3XffreTkZDmdTs9jwIAB2rFjh1auXKmvvvpKf/nLX/T666/rjjvukCSNHTtWb7zxhtatW6d9%2B/Zp2rRp6t%2B/v9q1a%2BfjWQEAAH8RkJcI33vvPdXV1WnZsmVatmyZ17LPPvtMixcv1lNPPaXFixerTZs2euyxx5SUlCRJSkpK0rx58/TUU0/p22%2B/Vd%2B%2BffXwww/7YhoAAMBP2dxut9vXRVwOnM4Txrdptwdp0KIPjW/3Ynn7/r6%2BLqFJ7PYgRUW1UEXFKT4zYQH6bS36bS36bR2H40qf7DcgLxECAAD4EgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHrPGpqajRz5kylpKSoX79%2Beu6553xdEgAA8CN2XxdwKVq4cKH27NmjVatW6fDhw5o%2Bfbri4uI0ZMgQX5cGAAD8AAHrHKdPn9a6dev07LPPqmvXruratauKi4u1du1aAlYT3fTk/%2BfrEi7I2/f39XUJAAA/xSXCc%2Bzbt0%2B1tbVKSkryjCUnJ6uoqEj19fU%2BrAwAAPgLzmCdw%2Bl0KioqSiEhIZ6xVq1aqaamRpWVlWrZsuVPbqOsrExOp9NrzG5vrpiYGKO1BgeTjy8mfzvj9u6Dqb4uwaizxzfHuTXot7Xod%2BAjYJ2jqqrKK1xJ8jx3uVyN2kZ%2Bfr7y8vK8xiZPnqwpU6aYKfL/lJWVaeLVxUpPTzce3tBQWVmZ8vPz6bdFysrKtGrVCvptEfptLfod%2BIjO5wgNDW0QpM4%2Bb9asWaO2kZ6ertdee83rkZ6ebrxWp9OpvLy8BmfLcHHQb2vRb2vRb2vR78DHGaxzxMbGqqKiQrW1tbLbv2uP0%2BlUs2bNFBER0ahtxMTE8I4EAIDLGGewztGlSxfZ7XYVFhZ6xgoKCtStWzcFBdEuAADw00gM5wgLC9Pw4cM1d%2B5c7dq1S5s3b9Zzzz2nW2%2B91delAQAAPxE8d%2B7cub4u4lLTp08f7d27V4899pg%2B/vhj/f73v9eoUaN8XdZ5tWjRQr169VKLFi18XcplgX5bi35bi35bi34HNpvb7Xb7uggAAIBAwiVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErEtcTU2NZs6cqZSUFPXr10/PPffcD667d%2B9ejRkzRomJiRo1apT27NljYaWB4UL6fe%2B996pz585ejy1btlhYbeBwuVwaNmyYtm3b9oPrcHyb05h%2Bc3w3zbFjx5SZmalevXopNTVVOTk5qqmpOe%2B6HNuBiYB1iVu4cKH27NmjVatW6U9/%2BpPy8vL0zjvvNFjv9OnTuueee5SSkqLXXntNSUlJmjRpkk6fPu2Dqv1XY/stSSUlJcrNzdVHH33kefTt29fiiv1fTU2NHnjgARUXF//gOhzf5jSm3xLHd1O43W5lZmaqqqpKa9eu1RNPPKEtW7boySefbLAux3YAc%2BOSderUKXe3bt3cW7du9YwtWbLEPX78%2BAbrrlu3zp2Wluaur693u91ud319vXvQoEHu9evXW1avv7uQftfU1Li7dOniLi0ttbLEgFNcXOz%2B7W9/6/7Nb37j7tSpk1fvv4/j24zG9pvju2n279/v7tSpk9vpdHrG3nrrLXe/fv0arMuxHbg4g3UJ27dvn2pra5WUlOQZS05OVlFRkerr673WLSoqUnJysmw2myTJZrOpZ8%2BeKiwstLRmf3Yh/S4tLZXNZlO7du2sLjOgbN%2B%2BXb1791Z%2Bfv6PrsfxbUZj%2B83x3TQOh0MrVqxQq1atvMZPnjzZYF2O7cBl93UB%2BGFOp1NRUVEKCQnxjLVq1Uo1NTWqrKxUy5Ytvda99tprvV4fHR39k5cB8P9cSL9LS0sVHh6uadOmafv27br66qs1ZcoU3XDDDb4o3W%2BNGzeuUetxfJvR2H5zfDdNRESEUlNTPc/r6%2Bu1Zs0a9enTp8G6HNuBizNYl7Cqqiqv/9lL8jx3uVyNWvfc9fDDLqTfpaWlqq6uVr9%2B/bRixQrdcMMNuvfee7V7927L6r2ccHxbi%2BPbrNzcXO3du1d/%2BMMfGizj2A5cnMG6hIWGhjb4Izv7vFmzZo1a99z18MMupN/33XefJkyYoKuuukqSlJCQoE8%2B%2BUSvvPKKunXrZk3BlxGOb2txfJuTm5urVatW6YknnlCnTp0aLOfYDlycwbqExcbGqqKiQrW1tZ4xp9OpZs2aKSIiosG65eXlXmPl5eWKiYmxpNZAcCH9DgoK8vzP56z4%2BHgdO3bMklovNxzf1uL4NuPhhx/W888/r9zcXN14443nXYdjO3ARsC5hXbp0kd1u9/qwY0FBgbp166agIO//dImJifrXv/4lt9st6buvCe/cuVOJiYmW1uzPLqTfM2bMUHZ2ttfYvn37FB8fb0mtlxuOb2txfDddXl6eXn75ZT3%2B%2BOMaOnToD67HsR24CFiXsLCwMA0fPlxz587Vrl27tHnzZj333HO69dZbJX13dqW6ulqSNGTIEB0/flwLFizQ/v37tWDBAlVVVemmm27y5RT8yoX0Oy0tTW%2B99ZZef/11ffnll8rLy1NBQYHGjx/vyykEFI5va3F8m1NSUqKlS5fq7rvvVnJyspxOp%2BchcWxfNnx5jwj8tNOnT7unTZvm7tGjh7tfv37u559/3rOsU6dOXvdKKSoqcg8fPtzdrVs39%2BjRo92ffPKJDyr2bxfS71deecU9ePBg989//nP3iBEj3Nu3b/dBxYHj3PsycXxfXD/Vb47v/9wzzzzj7tSp03kfbjfH9uXC5nb/33lJAAAAGMElQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAFwyXC6Xhg0bpm3btjVq/bS0NHXu3LnBIy8v7yJX%2BuP4sWcAAHBJqKmpUVZWloqLixv9mldffVV1dXWe55s2bdKTTz6pESNGXIwSG42ABQAAfG7//v3KysrShd7/vGXLlp5/nzhxQkuWLNH06dPVpk0b0yVeEC4RAgAAn9u%2Bfbt69%2B6t/Pz8Bsv%2B%2Bc9/auTIkerevbt%2B85vfaNOmTefdxsqVK%2BVwODRq1KiLXe5P4gwWAADwuXHjxp133Ol0atKkSfrDH/6g1NRUFRYWasaMGYqOjlZKSopnvaqqKq1Zs0bz5s1TUJDvzx8RsAAAwCVr7dq1%2BuUvf6nx48dLkn72s5/p008/1apVq7wC1saNG9W8eXMNHjzYV6V6IWABAIBLVmlpqbZs2aKkpCTP2JkzZ3TNNdd4rbdp0yb9%2Bte/lt1%2BaUSb/x/l/XJzCzirhwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-971730208540950451”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6000.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10000.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16000.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9000.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (632)</td> <td class=”number”>1634</td> <td class=”number”>50.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1095</td> <td class=”number”>34.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-971730208540950451”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>263</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2000.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4000.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5393000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5433000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6416000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6449000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23723000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRES07”>BERRY_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>235</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>909</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>108.83</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>28904</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5663808880518422577”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5663808880518422577,#minihistogram-5663808880518422577”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5663808880518422577”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5663808880518422577”

aria-controls=”quantiles-5663808880518422577” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5663808880518422577” aria-controls=”histogram-5663808880518422577”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5663808880518422577” aria-controls=”common-5663808880518422577”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5663808880518422577” aria-controls=”extreme-5663808880518422577”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5663808880518422577”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>22</td>

</tr> <tr>

<th>95-th percentile</th> <td>137</td>

</tr> <tr>

<th>Maximum</th> <td>28904</td>

</tr> <tr>

<th>Range</th> <td>28904</td>

</tr> <tr>

<th>Interquartile range</th> <td>22</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>925.52</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.504</td>

</tr> <tr>

<th>Kurtosis</th> <td>465.01</td>

</tr> <tr>

<th>Mean</th> <td>108.83</td>

</tr> <tr>

<th>MAD</th> <td>179.01</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>18.587</td>

</tr> <tr>

<th>Sum</th> <td>250430</td>

</tr> <tr>

<th>Variance</th> <td>856600</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5663808880518422577”>

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GhYWpX79%2B6tevn6qqqvTSSy9p0aJFWrx4sXr37q3x48fruuuuC0ZpAAAA5yxoj2koLy/XO%2B%2B8o3feeUc7d%2B5U7969deONN6qsrEwPPPCANm3apFmzZgWrPAAAgH%2Bb4wHr7bff1ttvv62NGzeqbdu2GjFihJ555hl17tzZ%2B54OHTro0UcfJWABAICQ5HjAmjVrlvr376%2BFCxfqqquuUnh449vAunTporFjxzpdGgAAgBWOB6xPPvlEbdq0UWVlpTdcbdmyRT179lRERIQkqXfv3urdu7fTpQEAAFjh%2BE8RHj16VIMGDdKf//xn79jkyZM1fPhw7d%2B/3%2BlyAAAArHM8YD322GO66KKLNHHiRO/Ye%2B%2B9pw4dOig3N9fpcgAAAKxzPGD97//%2Br/74xz8qLi7OO9a2bVvdd9992rBhg9PlAAAAWOd4wHK5XDp8%2BHCj8aqqKp7oDgAAmgXHA9ZVV12luXPn6ocffvCOlZaWKjc3V1lZWU6XAwAAYJ3jP0V4//33a%2BLEibr%2B%2BusVExMjSTp8%2BLB69uypGTNmOF0OAACAdY4HrHbt2unNN9/U%2BvXrVVJSIpfLpYsvvlhXXnmlwsLCnC4HAADAuqD8qpyIiAhlZWVxSRAAADRLjgcsj8ejBQsWaPPmzTpx4kSjG9v/9re/OV0SAACAVY4HrAcffFBbt27VkCFD9Otf/9rpjwcAADjvHA9YGzZs0JIlS5Senu70RwMAADjC8cc0tGzZUu3atXP6YwEAABzjeMAaPny4lixZovr6eqc/GgAAwBGOXyKsrKzU6tWrtXbtWl144YWKjIz0mV%2BxYoXTJQEAAFgVlMc0DB06NBgfCwAA4AjHA1Zubq7THwkAAOAox%2B/BkqTy8nLl5%2Bdr2rRp%2Buc//6kPPvhA33zzTTBKAQAAsM7xgPX9999r2LBhevPNN/Xhhx/q%2BPHjeu%2B99zRq1Ci53W6nywEAALDO8YD1%2BOOP69prr9WaNWv0q1/9SpL05JNPasCAAZo/f77T5QAAAFjneMDavHmzJk6c6POLnV0ul%2B68805t27bN6XIAAACsczxgNTQ0qKGhodH4sWPHFBER4XQ5AAAA1jkesDIzM1VQUOATsiorK5WXl6e%2Bffs6XQ4AAIB1jgesP/7xj9q6dasyMzNVU1Oj//qv/1L//v21Z88e3X///U6XAwAAYJ3jz8FKSEjQW2%2B9pdWrV2v79u1qaGjQrbfequHDh6t169ZOlwMAAGBdUJ7kHh0drZtvvjkYHw0AAHDeOR6wxo0bd9Z5fhchAAAIdY4HrI4dO/q8rqur0/fff6%2BdO3dq/PjxTpcDAABgXZP5XYQLFy5UWVmZw9UAAADYF5TfRXg6w4cP1/vvvx/sMgAAAM5ZkwlYX3zxBQ8aBQAAzUKTuMn96NGj%2BvrrrzVmzBinywEAALDO8YCVmJjo83sIJelXv/qVxo4dq9/97ndOlwMAAGCd4wHr8ccfd/ojAQAAHOV4wNq0aZPf773iiivOYyUAAADnh%2BMB67bbbvNeIjTGeMdPHQsLC9P27dudLg8AAOCcOR6wnn/%2Bec2dO1fTp09XRkaGIiMj9eWXX2r27Nm68cYbNXjwYKdLAgAAsMrxxzTk5ubqoYce0vXXX682bdqoVatW6tu3r2bPnq1XXnlFHTt29H4BAACEIscDVnl5%2BWnDU%2BvWrXXo0CGnywEAALDO8YCVkpKiJ598UkePHvWOVVZWKi8vT1deeaXT5QAAAFjn%2BD1YDzzwgMaNG6errrpKnTt3ljFG3333neLi4rRixQqnywEAALDO8YDVtWtXvffee1q9erV2794tSfr973%2BvIUOGKDo62ulyAAAArHM8YEnSBRdcoJtvvll79uzRhRdeKOnHp7kDAAA0B47fg2WM0fz583XFFVdo6NChKisr0/33369Zs2bpxIkTTpcDAABgneMB66WXXtLbb7%2BtP/3pT4qMjJQkXXvttVqzZo3y8/OdLgcAAMA6xwNWYWGhHnroIY0cOdL79PbBgwdr7ty5evfdd50uBwAAwDrHA9aePXvUo0ePRuPdu3eXx%2BNxuhwAAADrHA9YHTt21Jdfftlo/JNPPvHe8B6I2tpaDR06VBs3bvSOlZaWasKECUpJSdHgwYO1bt06n23Wr1%2BvoUOHKjk5WePGjVNpaanP/LJly5SVlaXU1FTNnDlTVVVVAdcFAAB%2BuRwPWLfffrseeeQRrVixQsYYffbZZ5o/f77mzZun2267LaB91dTU6J577lFJSYl3zBij7OxstW/fXqtWrdLw4cM1depU7du3T5K0b98%2BZWdna%2BTIkXrjjTfUtm1b3Xnnnd5fMv3hhx8qPz9fs2fP1vLly%2BV2u5WXl2evAQAAoNlz/DENo0aNUl1dnZ577jlVV1froYceUtu2bXX33Xfr1ltv9Xs/u3bt0rRp07zB6KQNGzaotLRUr776qlq2bKmuXbvqs88%2B06pVq3TXXXfp9ddf1%2BWXX65JkyZJ%2BvF3I/br10%2Bff/65%2BvTpoxUrVmj8%2BPHq37%2B/JOmRRx7R7bffrunTp/OcLgAA4BfHz2CtXr1agwYN0tq1a7V%2B/Xr94x//0Pr16zVx4sSA9nMyEBUWFvqMu91uXXbZZWrZsqV3LC0tTcXFxd759PR071x0dLR69uyp4uJi1dfX68svv/SZT0lJ0YkTJ7Rjx45/53ABAMAvkONnsGbPnq2//OUvuuCCC9S2bdt/ez9jxow57bjH41F8fLzPWLt27VRWVvaz84cPH1ZNTY3PvMvlUmxsrHd7f5SXlze6Yd/latnoc89VRITj%2BficuFzBq/dkr0KtZ8FCv/xHrwJDv/xHrwLT1PrleMDq3Lmzdu7cqYsvvvi87L%2Bqqsr7fK2TIiMjVVtb%2B7Pz1dXV3tdn2t4fhYWFjZ7plZ2drZycHL/30Ry1adMq2CUoJobLvIGgX/6jV4GhX/6jV4FpKv1yPGB1795d9957r5YsWaLOnTsrKirKZz43N/ec9h8VFaXKykqfsdraWrVo0cI7f2pYqq2tVUxMjLeW080Hcv/V6NGjNWDAAJ8xl6ulDh065vc%2B/NFUUrq/bB9/ICIiwhUTE63Dh6tUX98QtDpCBf3yH70KDP3yH70KzJn6Fax/3DsesL799lulpaVJ0nl57lVCQoJ27drlM1ZRUeG9PJeQkKCKiopG8z169FBsbKyioqJUUVGhrl27SpLq6upUWVmpuLg4v2uIj49vdDnQ4zmiurpf9jdIUzj%2B%2BvqGJlFHqKBf/qNXgaFf/qNXgWkq/XIkYM2bN09Tp05Vy5Yt9dJLL53Xz0pOTtbixYtVXV3tPWtVVFTkDXXJyckqKiryvr%2Bqqkrbtm3T1KlTFR4erqSkJBUVFalPnz6SpOLiYrlcLnXv3v281g0AAJoPR64xvfjii40e1jl58mSVl5db/6yMjAx16NBBM2bMUElJiRYvXqwtW7bopptukvTjYyI2b96sxYsXq6SkRDNmzFCnTp28gWrMmDFaunSp1qxZoy1btujhhx/WLbfcwiMaAACA3xwJWKc%2Bq0qSNm3apJqaGuufFRERoUWLFsnj8WjkyJF65513tHDhQiUmJkqSOnXqpGeffVarVq3STTfdpMrKSi1cuND7exGHDBmiKVOm6KGHHtKkSZPUq1cvTZ8%2B3XqdAACg%2BXL8Hqzz4euvv/Z5fdFFF2nlypVnfP/VV1%2Btq6%2B%2B%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%2BXzk5OZKkbdu26eabb1ZycrJGjRqlrVu3%2Bmy7evVqXXvttUpOTlZ2drYOHjwYjEMAAAAhqtkGrF27dql///5at26d92vu3Lk6fvy4Jk%2BerPT0dP31r39VamqqpkyZouPHj0uStmzZolmzZmnq1KkqLCzU4cOHNWPGjCAfDQAACCXNNmDt3r1b3bp1U1xcnPcrJiZG7733nqKionTfffepa9eumjVrllq1aqUPPvhAkrRy5UrdcMMNGjFihLp376558%2Bbp448/VmlpaZCPCAAAhIpmHbA6d%2B7caNztdistLU1hYWGSpLCwMPXu3VvFxcXe%2BfT0dO/7O3TooMTERLndbkfqBgAAoc8V7ALOB2OMvv32W61bt04FBQWqr6/XoEGDlJOTI4/Ho4svvtjn/e3atVNJSYkkqby8XPHx8Y3my8rK/P788vJyeTwenzGXq2Wj/Z6riIjQyscuV/DqPdmrUOtZsNAv/9GrwNAv/9GrwDS1fjXLgLVv3z5VVVUpMjJSCxYs0J49ezR37lxVV1d7x38qMjJStbW1kqTq6uqzzvujsLBQ%2Bfn5PmPZ2dnem%2Bx/qdq0aRXsEhQTEx3sEkIK/fIfvQoM/fIfvQpMU%2BlXswxYHTt21MaNG3XBBRcoLCxMPXr0UENDg6ZPn66MjIxGYam2tlYtWrSQJEVFRZ12Pjra/z%2Bw0aNHa8CAAT5jLldLHTp07N88otNrKindX7aPPxAREeGKiYnW4cNVqq9vCFodoYJ%2B%2BY9eBYZ%2B%2BY9eBeZM/QrWP%2B6bZcCSpNjYWJ/XXbt2VU1NjeLi4lRRUeEzV1FR4b18l5CQcNr5uLg4vz87Pj6%2B0eVAj%2BeI6up%2B2d8gTeH46%2BsbmkQdoYJ%2B%2BY9eBYZ%2B%2BY9eBaap9Cu0ToH46dNPP1WfPn1UVVXlHdu%2BfbtiY2OVlpamL774QsYYST/er7V582YlJydLkpKTk1VUVOTdbv/%2B/dq/f793HgAA4Oc0y4CVmpqqqKgoPfDAA/rmm2/08ccfa968ebrjjjs0aNAgHT58WI8%2B%2Bqh27dqlRx99VFVVVbrhhhskSbfeeqvefvttvf7669qxY4fuu%2B8%2BXXPNNbrwwguDfFQAACBUNMuA1bp1ay1dulQHDx7UqFGjNGvWLI0ePVp33HGHWrdurYKCAhUVFWnkyJFyu91avHixWrZsKenHcDZ79mwtXLhQt956qy644ALl5uYG%2BYgAAEAoCTMnr5XhvPJ4jljfp8sVroHzP7W%2B3/Pl/bv7Be2zXa5wtWnTSocOHWsS1%2BabOvrlP3oVGPrlP3oVmDP1Ky7u10Gpp1mewQIAAAgmAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIB1GjU1NZo5c6bS09OVmZmpF154IdglAQCAEOIKdgFN0bx587R161YtX75c%2B/bt0/3336/ExEQNGjQo2KUBAIAQQMA6xfHjx/X666/rz3/%2Bs3r27KmePXuqpKREL7/8MgELAAD4hUuEp9ixY4fq6uqUmprqHUtLS5Pb7VZDQ0MQKwMAAKGCM1in8Hg8atOmjSIjI71j7du3V01NjSorK9W2bduf3Ud5ebk8Ho/PmMvVUvHx8VZrjYgIrXx8w4J/BLvrZFxJAAAM4UlEQVQENBEf3ZsV7BKsOfl9GGrfj8FCv/xHrwLT1PpFwDpFVVWVT7iS5H1dW1vr1z4KCwuVn5/vMzZ16lTddddddor8f%2BXl5Rr/mxKNHj3aenhrbsrLy1VYWEiv/ES//FdeXq7ly5fQKz/RL//Rq8A0tX41jZjXhERFRTUKUidft2jRwq99jB49Wn/96199vkaPHm29Vo/Ho/z8/EZny9AYvQoM/fIfvQoM/fIfvQpMU%2BsXZ7BOkZCQoEOHDqmurk4u14/t8Xg8atGihWJiYvzaR3x8fJNIzwAAIDg4g3WKHj16yOVyqbi42DtWVFSkpKQkhYfTLgAA8PNIDKeIjo7WiBEj9PDDD2vLli1as2aNXnjhBY0bNy7YpQEAgBAR8fDDDz8c7CKamr59%2B2rbtm164okn9Nlnn%2Bk///M/NWrUqGCXdVqtWrVSRkaGWrVqFexSmjx6FRj65T96FRj65T96FZim1K8wY4wJdhEAAADNCZcIAQAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsEJUTU2NZs6cqfT0dGVmZuqFF14IdkmO%2Beijj3TppZf6fOXk5EiStm3bpptvvlnJyckaNWqUtm7d6rPt6tWrde211yo5OVnZ2dk6ePCgd84Yo/nz56tv377KyMjQvHnz1NDQ4Oix2VRbW6uhQ4dq48aN3rHS0lJNmDBBKSkpGjx4sNatW%2Bezzfr16zV06FAlJydr3LhxKi0t9ZlftmyZsrKylJqaqpkzZ6qqqso7F%2Bpr8nT9mjt3bqO1tnLlSu/8uaynQ4cO6a677lJqaqoGDBigt99%2B25kDPQcHDhxQTk6OMjIylJWVpdzcXNXU1EhibZ3qbL1iXTX2/fff6/bbb1dqaqquueYaLVmyxDsXsmvLICTNnj3bDBs2zGzdutX893//t0lNTTXvv/9%2BsMtyxKJFi8yUKVNMeXm59%2Btf//qXOXbsmOnXr595/PHHza5du8ycOXPMb3/7W3Ps2DFjjDFut9v06tXLvPnmm2b79u1m7NixZvLkyd79Ll261Fx99dVm06ZN5rPPPjOZmZlmyZIlwTrMc1JdXW2ys7NNt27dzIYNG4wxxjQ0NJhhw4aZadOmmV27dpnnn3/eJCcnm7179xpjjNm7d69JSUkxS5cuNTt37jR/%2BMMfzNChQ01DQ4MxxpgPPvjApKWlmf/5n/8xbrfbDB482DzyyCPezwzlNXm6fhljzIQJE0xBQYHPWjt%2B/Lgx5tzX05QpU8z48ePN119/bV577TVz%2BeWXG7fb7dxBB6ihocHccsst5o477jA7d%2B40mzZtMgMHDjSPP/44a%2BsUZ%2BuVMayrU9XX15vrrrvOTJs2zXz77bdm7dq1pnfv3uadd94J6bVFwApBx44dM0lJST7/I1i4cKEZO3ZsEKtyzrRp08wTTzzRaPz11183AwYM8H5jNTQ0mIEDB5pVq1YZY4yZPn26uf/%2B%2B73v37dvn7n00kvNDz/8YIwx5uqrr/a%2B1xhj3nrrLdO/f//zeSjnRUlJifnd735nhg0b5hMY1q9fb1JSUryB0xhjxo8fb5555hljjDELFizwWUPHjx83qamp3u3HjBnjfa8xxmzatMn06tXLHD9%2BPKTX5Jn6ZYwxWVlZ5tNPPz3tdueynr7//nvTrVs3U1pa6p2fOXOmz/6aml27dplu3boZj8fjHXv33XdNZmYma%2BsUZ%2BuVMayrUx04cMD84Q9/MEeOHPGOZWdnmz/96U8hvba4RBiCduzYobq6OqWmpnrH0tLS5Ha7Q/qSlr92796tzp07Nxp3u91KS0tTWFiYJCksLEy9e/dWcXGxdz49Pd37/g4dOigxMVFut1sHDhzQ/v37dcUVV3jn09LStHfvXpWXl5/fA7Ls888/V58%2BfVRYWOgz7na7ddlll6lly5besbS0tDP2Jzo6Wj179lRxcbHq6%2Bv15Zdf%2BsynpKToxIkT2rFjR0ivyTP16%2BjRozpw4MBp15p0buvJ7XarQ4cO6tSpk8/8F198YffgLIqLi9OSJUvUvn17n/GjR4%2Bytk5xtl6xrhqLj4/XggUL1Lp1axljVFRUpE2bNikjIyOk15brnPcAx3k8HrVp00aRkZHesfbt26umpkaVlZVq27ZtEKs7v4wx%2Bvbbb7Vu3ToVFBSovr5egwYNUk5Ojjwejy6%2B%2BGKf97dr104lJSWSpPLycsXHxzeaLysrk8fjkSSf%2BZN/OZaVlTXarikbM2bMacc9Hs8Zj//n5g8fPqyamhqfeZfLpdjYWJWVlSk8PDxk1%2BSZ%2BrV7926FhYXp%2Beef1yeffKLY2FhNnDhRN954o6RzW09n6vWBAwesHZdtMTExysrK8r5uaGjQypUr1bdvX9bWKc7WK9bV2Q0YMED79u1T//79df311%2Buxxx4L2bVFwApBVVVVPgtCkvd1bW1tMEpyzL59%2B7zHv2DBAu3Zs0dz585VdXX1GftysifV1dVnnK%2Burva%2B/umc1Hx6%2BnP9Odv86frz03ljTLNbk998843CwsLUpUsXjR07Vps2bdKDDz6o1q1ba%2BDAgee0nn7uzyIU5OXladu2bXrjjTe0bNky1tZZ/LRXX331FevqLJ555hlVVFTo4YcfVm5ubkj/vUXACkFRUVGN/vBPvm7RokUwSnJMx44dtXHjRl1wwQUKCwtTjx491NDQoOnTpysjI%2BO0fTnZkzP1LTo62uebKioqyvvf0o%2BnnJuDqKgoVVZW%2Boz505%2BYmJhGPfnpfHR0tOrr65vdmhwxYoT69%2B%2Bv2NhYSVL37t313Xff6ZVXXtHAgQPPaT2dadtQ6VVeXp6WL1%2Bup556St26dWNtncWpvbrkkktYV2eRlJQk6cef7rv33ns1atQon5/6k0JnbXEPVghKSEjQoUOHVFdX5x3zeDxq0aKFYmJigliZM2JjY733WUlS165dVVNTo7i4OFVUVPi8t6Kiwnt6OCEh4bTzcXFxSkhIkCTvKfif/ndcXNx5OQ6nnen4/elPbGysoqKifObr6upUWVnp7V9zW5NhYWHe/wme1KVLF%2B/llnNZT2fbtqmbM2eOXnzxReXl5en666%2BXxNo6k9P1inXVWEVFhdasWeMzdvHFF%2BvEiRPn9Pd6sNcWASsE9ejRQy6Xy3uTnyQVFRUpKSlJ4eHN%2B4/0008/VZ8%2BfXz%2BRbN9%2B3bFxsZ6b%2BY0xkj68X6tzZs3Kzk5WZKUnJysoqIi73b79%2B/X/v37lZycrISEBCUmJvrMFxUVKTExMaTuvzqb5ORkffXVV97T5tKPx3im/lRVVWnbtm1KTk5WeHi4kpKSfOaLi4vlcrnUvXv3Zrkmn376aU2YMMFnbMeOHerSpYukc1tPKSkp2rt3r/c%2BkpPzKSkp5/egzlF%2Bfr5effVVPfnkkxoyZIh3nLXV2Jl6xbpqbM%2BePZo6darPvWJbt25V27ZtlZaWFrpry8rPIsJxDz74oBkyZIhxu93mo48%2BMr179zYffvhhsMs6744cOWKysrLMPffcY3bv3m3Wrl1rMjMzzeLFi82RI0dM3759zZw5c0xJSYmZM2eO6devn/fHezdv3mx69uxpXnvtNe/zZaZMmeLdd0FBgcnMzDQbNmwwGzZsMJmZmeaFF14I1qFa8dPHDtTV1ZnBgwebu%2B%2B%2B2%2BzcudMUFBSYlJQU7/NkSktLTVJSkikoKPA%2BT2bYsGHex16sXr3a9O7d23z00UfG7XabIUOGmDlz5ng/qzmsyZ/2y%2B12m8suu8wsWbLEfP/99%2Bbll182l19%2Budm8ebMx5tzX06RJk8zYsWPN9u3bzWuvvWaSkpKa9POKdu3aZXr06GGeeuopn%2Bc3lZeXs7ZOcbZesa4aq6urMyNHjjSTJk0yJSUlZu3atea3v/2tWbZsWUivLQJWiDp%2B/Li57777TEpKisnMzDQvvvhisEtyzM6dO82ECRNMSkqK6devn3n22We930xut9uMGDHCJCUlmZtuusl89dVXPtuuWrXKXH311SYlJcVkZ2ebgwcPeufq6urMY489ZtLT002fPn1MXl6ed7%2Bh6tTnOn333Xfm97//vbn88svNkCFDzD/%2B8Q%2Bf969du9Zcd911plevXmb8%2BPHeZ%2B%2BcVFBQYK688kqTlpZmZsyYYaqrq71zzWFNntqvjz76yAwbNswkJSWZQYMGNfqL91zWU0VFhZkyZYpJSkoyAwYMMO%2B%2B%2B%2B75P8BzUFBQYLp163baL2NYWz/1c71iXTVWVlZmsrOzTe/evU2/fv3Mc8895z2uUF1bYcb8//UUAAAAWNF0L2ADAACEKAIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACz7Py9NmPY/zN7dAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5663808880518422577”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:92%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (224)</td> <td class=”number”>899</td> <td class=”number”>28.0%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>909</td> <td class=”number”>28.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5663808880518422577”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11190.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11241.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11379.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12335.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28904.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRES12”><s>BERRY_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_ACRES07”><code>BERRY_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98128</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRESPTH07”>BERRY_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1459</td>

</tr> <tr>

<th>Unique (%)</th> <td>45.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>909</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.7288</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>864.77</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7303012412924014518”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7303012412924014518,#minihistogram7303012412924014518”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7303012412924014518”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7303012412924014518”

aria-controls=”quantiles7303012412924014518” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7303012412924014518” aria-controls=”histogram7303012412924014518”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7303012412924014518” aria-controls=”common7303012412924014518”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7303012412924014518” aria-controls=”extreme7303012412924014518”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7303012412924014518”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.10081</td>

</tr> <tr>

<th>Q3</th> <td>0.40021</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.5569</td>

</tr> <tr>

<th>Maximum</th> <td>864.77</td>

</tr> <tr>

<th>Range</th> <td>864.77</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.40021</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>21.171</td>

</tr> <tr>

<th>Coef of variation</th> <td>12.246</td>

</tr> <tr>

<th>Kurtosis</th> <td>1233.6</td>

</tr> <tr>

<th>Mean</th> <td>1.7288</td>

</tr> <tr>

<th>MAD</th> <td>2.815</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>32.243</td>

</tr> <tr>

<th>Sum</th> <td>3978</td>

</tr> <tr>

<th>Variance</th> <td>448.21</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7303012412924014518”>

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TX3zxhXr27Kl7771XRUVFevrpp/Xxxx9r3rx53ioPAADgmtkesN544w298cYbOnTokJo3b65Ro0bphRdeUPv27d2vadWqlZYsWULAAgAAfsn2gDVv3jwNGDBA6enpuuOOO9So0ZWXgV36omYAAAB/ZHvA%2BvDDDxUZGamysjJ3uDpy5Ii6deum4OBgSVLPnj3Vs2dPu0sDAAAwwva/Ijx37pyGDh2qV155xT02depUjRw5UoWFhXaXAwAAYJztAesPf/iD2rVrpylTprjH3n77bbVq1UopKSl2lwMAAGCc7QHrb3/7m5566ilFRUW5x5o3b64nn3xSBw8etLscAAAA42wPWA6HQ%2BXl5VeMV1ZWckd3AAAQEGwPWHfccYcWL16sb775xj2Wn5%2BvlJQU9e/f3%2B5yAAAAjLP9rwhnz56tKVOmaMiQIYqIiJAklZeXq1u3bpozZ47d5QAAABhne8Bq0aKF/vSnP2n//v3Ky8uTw%2BFQx44ddfvttysoKMjucgAAAIzzylflBAcHq3///pwSBAAAAcn2gOVyubRq1SodPnxYFy9evOLC9r/85S92lwQAAGCU7QHr97//vY4dO6bhw4frxhtvtHv3AAAADc72gHXw4EGtW7dOiYmJdu8aAADAFrbfpiE8PFwtWrSwe7cAAAC2sT1gjRw5UuvWrVNtba3duwYAALCF7acIy8rKlJWVpQ8%2B%2BEBt27ZVSEiIx/zmzZvtLgkAAMAor9ymYcSIEd7YLQAAgC1sD1gpKSl27xIAAMBWtl%2BDJUnFxcVKS0vTzJkz9fe//13vvvuuTp8%2B7Y1SAAAAjLM9YH399df6zW9%2Boz/96U967733VFFRobfffltjxoxRbm6u3eUAAAAYZ3vAeu655zRo0CDt2bNHN9xwgyRpxYoVGjhwoJYtW2Z3OQAAAMbZHrAOHz6sKVOmeHyxs8Ph0PTp03X8%2BHG7ywEAADDO9oBVV1enurq6K8bPnz%2Bv4OBgu8sBAAAwzvaA1a9fP61Zs8YjZJWVlSk1NVV9%2BvSxuxwAAADjbA9YTz31lI4dO6Z%2B/fqpqqpK//Ef/6EBAwbo22%2B/1ezZs%2B0uBwAAwDjb74MVExOjXbt2KSsrS5999pnq6uo0YcIEjRw5Uk2bNrW7HAAAAOO8cif3sLAwjRs3zhu7BgAAaHC2B6yJEyf%2Bw3m%2BixAAAPg72wNW69atPZ7X1NTo66%2B/1hdffKFJkybZXQ4AAIBxPvNdhOnp6SoqKrK5GgAAAPO88l2EP2bkyJF65513vF0GAADAL%2BYzAeuTTz7hRqMAACAg%2BMRF7ufOndPnn3%2Bu%2B%2B%2B/3%2B5yAAAAjLM9YMXGxnp8D6Ek3XDDDXrwwQf1b//2b3aXAwAAYJztAeu5556ze5cAAAC2sj1gffzxx/V%2Bba9evRqwEgAAgIZhe8B66KGH3KcILctyj18%2BFhQUpM8%2B%2B8zu8gAAAH4x2wPWyy%2B/rMWLF2vWrFnq3bu3QkJCdPToUS1atEj33nuvhg0bZndJAAAARtl%2Bm4aUlBTNnz9fQ4YMUWRkpJo0aaI%2Bffpo0aJF2rp1q1q3bu1%2BAAAA%2BCPbA1ZxcfGPhqemTZuqtLTU7nIAAACMsz1gxcXFacWKFTp37px7rKysTKmpqbr99tvtLgcAAMA426/BevrppzVx4kTdcccdat%2B%2BvSzL0ldffaWoqCht3rzZ7nIAAACMsz1gdejQQW%2B//baysrJ06tQpSdIDDzyg4cOHKywszO5yAAAAjLM9YEnSTTfdpHHjxunbb79V27ZtJf1wN3cAAIBAYPs1WJZladmyZerVq5dGjBihoqIizZ49W/PmzdPFixftLgcAAMA42wPWa6%2B9pjfeeEPPPPOMQkJCJEmDBg3Snj17lJaWZnc5AAAAxtkesDIyMjR//nyNHj3afff2YcOGafHixdq9e7fd5QAAABhne8D69ttv1aVLlyvGO3fuLJfLZXc5AAAAxtkesFq3bq2jR49eMf7hhx%2B6L3gHAADwZ7YHrEceeUQLFy7U5s2bZVmWDhw4oGXLlmnp0qV66KGHrnp71dXVGjFihA4dOuQey8/P1%2BTJkxUXF6dhw4Zp3759Hu/Zv3%2B/RowYIafTqYkTJyo/P99jfuPGjerfv7/i4%2BM1d%2B5cVVZWXttiAQDAdcn2gDVmzBj953/%2Bp1599VVduHBB8%2BfP186dO/XYY49pwoQJV7WtqqoqPf7448rLy3OPWZalpKQktWzZUpmZmRo5cqRmzJihgoICSVJBQYGSkpI0evRo7dixQ82bN9f06dNlWZYk6b333lNaWpoWLVqkTZs2KTc3V6mpqeZ%2BAAAAIODZfh%2BsrKwsDR06VOPHj9fZs2dlWZZatGhx1ds5efKkZs6c6Q5Glxw8eFD5%2Bfnatm2bwsPD1aFDBx04cECZmZl69NFHtX37dnXv3l0PP/ywpB%2B%2BfLpv37766KOPdNttt2nz5s2aNGmSBgwYIElauHChHnnkEc2aNYsboQIAgHqx/QjWokWL3BezN2/e/JrClSR3IMrIyPAYz83NVdeuXRUeHu4eS0hIUE5Ojns%2BMTHRPRcWFqZu3bopJydHtbW1Onr0qMd8XFycLl68qBMnTlxTnQAA4Ppj%2BxGs9u3b64svvlDHjh1/0Xbuv//%2BHx13uVyKjo72GGvRooWKiop%2Bdr68vFxVVVUe8w6HQ82aNXO/vz6Ki4uv%2BItIhyP8iv3%2BUsHBtufjX8Th8K96r8WlnvhbbwIZPfE99MS30I%2BGYXvA6ty5s5544gmtW7dO7du3V2hoqMd8SkrKL9p%2BZWWl%2Bwaml4SEhKi6uvpn5y9cuOB%2B/lPvr4%2BMjIwrbpqalJSk5OTkem8jEEVGNvF2CbaJiOB0sq%2BhJ76HnvgW%2BmGW7QHryy%2B/VEJCgiQ1yH2vQkNDVVZW5jFWXV2txo0bu%2BcvD0vV1dWKiIhwh70fm7%2Ba66/Gjx%2BvgQMHeow5HOEqLT1f723Uh7992jC9fl8UHNxIERFhKi%2BvVG1tnbfLgeiJL6InviXQ%2B%2BGtD/e2BKylS5dqxowZCg8P12uvvdag%2B4qJidHJkyc9xkpKStyn52JiYlRSUnLFfJcuXdSsWTOFhoaqpKREHTp0kCTV1NSorKxMUVFR9a4hOjr6itOBLtf3qqkJvF/cq3E9rb%2B2tu66Wq8/oCe%2Bh574Fvphli2HQDZs2HDFvaSmTp2q4uJi4/tyOp369NNP3af7JCk7O1tOp9M9n52d7Z6rrKzU8ePH5XQ61ahRI/Xo0cNjPicnRw6HQ507dzZeKwAACEy2BKzLb6UgSR9//LGqqqqM76t3795q1aqV5syZo7y8PK1du1ZHjhzR2LFjJf1wH67Dhw9r7dq1ysvL05w5c9SmTRvddtttkn64eH79%2BvXas2ePjhw5ogULFui%2B%2B%2B7jFg0AAKDe/OsinnoIDg7W6tWr5XK5NHr0aL355ptKT09XbGysJKlNmzZ68cUXlZmZqbFjx6qsrEzp6enuL54ePny4pk2bpvnz5%2Bvhhx/WrbfeqlmzZnlzSQAAwM/YfpF7Q/j88889nrdr105btmz5ydffeeeduvPOO39yfurUqZo6daqx%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%2B92Px4sWqqKjQ1KlTlZiYqJ07dyo%2BPl7Tpk1TRUWFJOnIkSOaN2%2BeZsyYoYyMDJWXl2vOnDleXg0AAPAnARuwTp06pU6dOikqKsr9iIiI0Ntvv63Q0FA9%2BeST6tChg%2BbNm6cmTZro3XfflSRt2bJF99xzj0aNGqXOnTtr6dKl2rt3r/Lz8728IgAA4C8COmC1b9/%2BivHc3FwlJCQoKChIkhQUFKSePXsqJyfHPZ%2BYmOh%2BfatWrRQbG6vc3Fxb6gYAAP4vIAOWZVn68ssvtW/fPg0ZMkSDBg3SsmXLVF1dLZfLpejoaI/Xt2jRQkVFRZKk4uLifzgPAADwcxzeLqAhFBQUqLKyUiEhIVq1apW%2B/fZbLV68WBcuXHCP/38hISGqrq6WJF24cOEfztdHcXGxXC6Xx5jDEX5FcPulgoP9Kx87HP5V77W41BN/600goye%2Bh574FvrRMAIyYLVu3VqHDh3STTfdpKCgIHXp0kV1dXWaNWuWevfufUVYqq6uVuPGjSVJoaGhPzofFhZW7/1nZGQoLS3NYywpKcn9V4zXq8jIJt4uwTYREfX/fYE96InvoSe%2BhX6YFZABS5KaNWvm8bxDhw6qqqpSVFSUSkpKPOZKSkrcR5diYmJ%2BdD4qKqre%2Bx4/frwGDhzoMeZwhKu09PzVLOFn%2BdunDdPr90XBwY0UERGm8vJK1dbWebsciJ74InriWwK9H976cB%2BQAeuvf/2rnnjiCX3wwQfuI0%2BfffaZmjVrpoSEBL3yyiuyLEtBQUGyLEuHDx/W7373O0mS0%2BlUdna2Ro8eLUkqLCxUYWGhnE5nvfcfHR19xelAl%2Bt71dQE3i/u1bie1l9bW3ddrdcf0BPfQ098C/0wy78OgdRTfHy8QkND9fTTT%2Bv06dPau3evli5dqt/%2B9rcaOnSoysvLtWTJEp08eVJLlixRZWWl7rnnHknShAkT9MYbb2j79u06ceKEnnzySd11111q27atl1cFAAD8RUAGrKZNm2r9%2BvU6e/asxowZo3nz5mn8%2BPH67W9/q6ZNm2rNmjXuo1S5ublau3atwsPDJf0QzhYtWqT09HRNmDBBN910k1JSUry8IgAA4E%2BCLMuyvF3E9cDl%2Bt74Nh2ORhq87K/Gt9tQ3nmsr7dLaHAORyNFRjZRael5DrX7CHrie%2BiJbwn0fkRF3eiV/QbkESwAAABvImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYP6Kqqkpz585VYmKi%2BvXrp1dffdXbJQEAAD/i8HYBvmjp0qU6duyYNm3apIKCAs2ePVuxsbEaOnSot0sDAAB%2BgIB1mYqKCm3fvl2vvPKKunXrpm7duikvL0%2Bvv/46AQsAANQLpwgvc%2BLECdXU1Cg%2BPt49lpCQoNzcXNXV1XmxMgAA4C84gnUZl8ulyMhIhYSEuMdatmypqqoqlZWVqXnz5j%2B7jeLiYrlcLo8xhyNc0dHRRmsNDvavfOxw%2BFe91%2BJST/ytN4GMnvgeeuJb6EfDIGBdprKy0iNcSXI/r66urtc2MjIylJaW5jE2Y8YMPfroo2aK/D/FxcWa9Ks8jR8/3nh4w7UpLi7Wpk3r6IkPoSe%2Bh574FvrRMIirlwkNDb0iSF163rhx43ptY/z48dq5c6fHY/z48cZrdblcSktLu%2BJoGbyHnvgeeuIteETSAAAJHElEQVR76IlvoR8NgyNYl4mJiVFpaalqamrkcPzw43G5XGrcuLEiIiLqtY3o6Gg%2BBQAAcB3jCNZlunTpIofDoZycHPdYdna2evTooUaN%2BHEBAICfR2K4TFhYmEaNGqUFCxboyJEj2rNnj1599VVNnDjR26UBAAA/EbxgwYIF3i7C1/Tp00fHjx/X8uXLdeDAAf3ud7/TmDFjvF3Wj2rSpIl69%2B6tJk2aeLsU/B964nvoie%2BhJ76FfpgXZFmW5e0iAAAAAgmnCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbD8VFVVlebOnavExET169dPr776qrdLCnhnzpxRcnKyevfurf79%2ByslJUVVVVWSpPz8fE2ePFlxcXEaNmyY9u3b5/He/fv3a8SIEXI6nZo4caLy8/O9sYSANXXqVD311FPu58ePH9e4cePkdDo1ZswYHTt2zOP1WVlZGjRokJxOp5KSknT27Fm7Sw5Y1dXVWrhwoXr16qV/%2BZd/0YoVK3Tpftb0xX6FhYWaNm2aevbsqYEDB2rjxo3uOfrRsAhYfmrp0qU6duyYNm3apGeeeUZpaWl69913vV1WwLIsS8nJyaqsrNTrr7%2BulStX6v3339eqVatkWZaSkpLUsmVLZWZmauTIkZoxY4YKCgokSQUFBUpKStLo0aO1Y8cONW/eXNOnTxdfomDGW2%2B9pb1797qfV1RUaOrUqUpMTNTOnTsVHx%2BvadOmqaKiQpJ05MgRzZs3TzNmzFBGRobKy8s1Z84cb5UfcBYvXqz9%2B/dr/fr1Wr58uf7rv/5LGRkZ9MVLHnvsMYWHh2vnzp2aO3euVq1apT//%2Bc/0ww4W/M758%2BetHj16WAcPHnSPpaenWw8%2B%2BKAXqwpsJ0%2BetDp16mS5XC732O7du61%2B/fpZ%2B/fvt%2BLi4qzz58%2B75yZNmmS98MILlmVZ1qpVqzx6U1FRYcXHx3v0D9emtLTUuuOOO6wxY8ZYs2fPtizLsrZv324NHDjQqqursyzLsurq6qzBgwdbmZmZlmVZ1qxZs9yvtSzLKigosG655Rbrm2%2B%2BsX8BAaa0tNTq2rWrdejQIffYmjVrrKeeeoq%2BeEFZWZnVqVMn6/PPP3ePzZgxw1q4cCH9sAFHsPzQiRMnVFNTo/j4ePdYQkKCcnNzVVdX58XKAldUVJTWrVunli1beoyfO3dOubm56tq1q8LDw93jCQkJysnJkSTl5uYqMTHRPRcWFqZu3bq553Ht/vjHP2rkyJHq2LGjeyw3N1cJCQkKCgqSJAUFBalnz54/2Y9WrVopNjZWubm59hYfgLKzs9W0aVP17t3bPTZ16lSlpKTQFy9o3LixwsLCtHPnTl28eFGnT5/W4cOH1aVLF/phAwKWH3K5XIqMjFRISIh7rGXLlqqqqlJZWZkXKwtcERER6t%2B/v/t5XV2dtmzZoj59%2Bsjlcik6Otrj9S1atFBRUZEk/ew8rs2BAwf0t7/9TdOnT/cY/7mfd3FxMf1oIPn5%2BWrdurV27dqloUOH6l//9V%2BVnp6uuro6%2BuIFoaGhmj9/vjIyMuR0OnXPPffojjvu0Lhx4%2BiHDRzeLgBXr7Ky0iNcSXI/r66u9kZJ153U1FQdP35cO3bs0MaNG3%2B0H5d68VP9olfXrqqqSs8884zmz5%2Bvxo0be8z93M/7woUL9KOBVFRU6Ouvv9a2bduUkpIil8ul%2BfPnKywsjL54yalTpzRgwABNmTJFeXl5evbZZ3X77bfTDxsQsPxQaGjoFb/kl55f/p8NzEtNTdWmTZu0cuVKderUSaGhoVccOayurnb34qf6FRERYVvNgSYtLU3du3f3OKp4yU/9vH%2BuH2FhYQ1X8HXC4XDo3LlzWr58uVq3bi3phz/y2Lp1q9q1a0dfbHbgwAHt2LFDe/fuVePGjdWjRw%2BdOXNGL730ktq2bUs/GhinCP1QTEyMSktLVVNT4x5zuVxq3Lgx/2k3sGeffVYbNmxQamqqhgwZIumHfpSUlHi8rqSkxH14/afmo6Ki7Ck6AL311lvas2eP4uPjFR8fr927d2v37t2Kj4%2BnH14UFRWl0NBQd7iSpH/%2B539WYWEhffGCY8eOqV27dh4fvLt27aqCggL6YQMClh/q0qWLHA6Hx0XS2dnZ6tGjhxo1oqUNJS0tTdu2bdOKFSs0fPhw97jT6dSnn36qCxcuuMeys7PldDrd89nZ2e65yspKHT9%2B3D2Pq/faa69p9%2B7d2rVrl3bt2qWBAwdq4MCB2rVrl5xOpz755BP3bTAsy9Lhw4d/sh%2BFhYUqLCykHwY4nU5VVVXpyy%2B/dI%2BdPn1arVu3pi9eEB0dra%2B//trjSNTp06fVpk0b%2BmGD4AULFizwdhG4OjfccIMKCwu1detWde/eXUePHlVqaqpmzpypDh06eLu8gHTq1Ck9/vjjmjp1qoYMGaKKigr3o2PHjsrKylJOTo46dOigzMxMvfXWW1qyZIluvPFGtWnTRsuWLVNwcLAiIiKUkpKi2tpaPfHEE%2B6/4MHViYiIULNmzdyPDz/8UCEhIRo7dqz%2B6Z/%2BSevWrVNRUZFiY2P10ksv6cSJE1q0aJFuuOEGtWzZUikpKYqKilJwcLDmz5%2BvTp066YEHHvD2svxeZGSkjh07prfeekvdu3fXZ599pueee05TpkzRb37zG/pis9atW2vr1q3Ky8tTx44d9cknn%2BiPf/yjpk2bpmHDhtGPhubNe0Tg2lVUVFhPPvmkFRcXZ/Xr18/asGGDt0sKaGvWrLE6der0ow/LsqyvvvrKeuCBB6zu3btbw4cPt/77v//b4/0ffPCBdffdd1u33nqrNWnSJO4lY9js2bM97tmTm5trjRo1yurRo4c1duxY69NPP/V4fWZmpnXnnXdacXFxVlJSknX27Fm7Sw5Y5eXl1qxZs6y4uDjr9ttvt1588UX3vZboi/3y8vKsyZMnWz179rQGDRpkbdiwgX7YJMiyuJ00AACASVywAwAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/S/sAO2wWtNSLwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7303012412924014518”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2540327702273593</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.022822713164140952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03142875102143441</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09522513943681131</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23300492827404898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5083884087442806</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8164598301763554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.44671864847303444</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13294615680649338</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1448)</td> <td class=”number”>1448</td> <td class=”number”>45.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>909</td> <td class=”number”>28.3%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7303012412924014518”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0001940152127328304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005387997803852095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000777250801734202</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010713359773905256</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>151.13462203984773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>206.07734806629836</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>276.2582056892779</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>289.7045919859608</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>864.7678314983245</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_ACRESPTH12”><s>BERRY_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_ACRESPTH07”><code>BERRY_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98479</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_FARMS07”>BERRY_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>99</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.0832</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>395</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>26.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3484269036173249908”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3484269036173249908,#minihistogram-3484269036173249908”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3484269036173249908”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3484269036173249908”

aria-controls=”quantiles-3484269036173249908” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3484269036173249908” aria-controls=”histogram-3484269036173249908”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3484269036173249908” aria-controls=”common-3484269036173249908”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3484269036173249908” aria-controls=”extreme-3484269036173249908”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3484269036173249908”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>3</td>

</tr> <tr>

<th>Q3</th> <td>8</td>

</tr> <tr>

<th>95-th percentile</th> <td>31</td>

</tr> <tr>

<th>Maximum</th> <td>395</td>

</tr> <tr>

<th>Range</th> <td>395</td>

</tr> <tr>

<th>Interquartile range</th> <td>8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.528</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4159</td>

</tr> <tr>

<th>Kurtosis</th> <td>117.26</td>

</tr> <tr>

<th>Mean</th> <td>8.0832</td>

</tr> <tr>

<th>MAD</th> <td>8.7311</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.9245</td>

</tr> <tr>

<th>Sum</th> <td>24864</td>

</tr> <tr>

<th>Variance</th> <td>381.35</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3484269036173249908”>

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mZqqyslLPPfecHn/8cf3973/XsmXLZIxRRkaG2rVrp7y8PI0YMULTpk3TgQMHJEkHDhxQRkaGRo0apZdeeklt2rTRfffdJ2OMJOmtt95STk6OFixYoI0bN8rr9So7O7sp4wIAAIdxZMHau3evCgsLlZWVpa5duyo1NVWZmZnKz8/X1q1bVVJSogULFqhLly6aOnWqkpKSlJeXJ0l68cUXdd1112nSpEnq2rWrsrKytH//fn300UeSpGeeeUb33HOPBgwYoF69emn%2B/PnKy8vjLBYAAGg0RxasmJgYrVu3Tu3atQsYP378uLxer3r06KEWLVr4x1NSUlRYWChJ8nq9Sk1N9c9FRkaqZ8%2BeKiwsVF1dnT7%2B%2BOOA%2BaSkJJ06dUq7d%2B%2B%2BzKkAAECocDf1Ai5FVFSU0tPT/Y/r6%2Bu1adMm9e3bVz6fT7GxsQHPb9u2rUpLSyXpvPNHjx5VdXV1wLzb7VZ0dLR/%2B8YoKyuTz%2BcLGHO7WzR43e8rLMxZ/djtbvx6T2dzWsbGCOVsUmjnI5szkc25nJzPkQXrTNnZ2dq1a5deeuklbdiwQeHh4QHz4eHhqqmpkSRVVlaec76qqsr/%2BFzbN0Zubq5ycnICxjIyMpSZmdnofYSi1q1bXvQ2UVGRl2ElV4ZQziaFdj6yORPZnMuJ%2BRxfsLKzs7Vx40Y9/vjj6tatmyIiIlRRURHwnJqaGjVv3lySFBER0aAs1dTUKCoqShEREf7HZ85HRjb%2B4I4dO1YDBw4MGHO7W%2BjIkRON3kdjOK3RX0z%2BsLBmioqK1NGjlaqrq7%2BMqwq%2BUM4mhXY%2BsjkT2ZzLRr5L%2Bce9DY4uWAsXLtTzzz%2Bv7OxsDRkyRJIUFxenPXv2BDyvvLzcf3kuLi5O5eXlDea7d%2B%2Bu6OhoRUREqLy8XF26dJEk1dbWqqKiQjExMY1eV2xsbIPLgT7fMdXWht4X/8W4lPx1dfUh%2B3kL5WxSaOcjmzORzbmcmM9Zp0C%2BIycnRy%2B88IIee%2BwxDRs2zD/u8Xj0ySef%2BC/3SVJBQYE8Ho9/vqCgwD9XWVmpXbt2yePxqFmzZkpMTAyYLywslNvtVkJCQhBSAQCAUODIglVcXKxVq1bp17/%2BtVJSUuTz%2BfwfaWlpat%2B%2BvWbNmqWioiKtXbtWO3bs0JgxYyRJo0eP1vbt27V27VoVFRVp1qxZ6tixo/r06SNJGjdunNavX6/Nmzdrx44dmjdvnu68886LukQIAAB%2B2Bx5ifBvf/ub6urqtHr1aq1evTpg7rPPPtOqVas0Z84cjRo1Sj/96U%2B1cuVKxcfHS5I6duyoJ554Qn/605%2B0cuVKJScna%2BXKlXK5XJKkYcOGaf/%2B/Zo7d65qamp08803a8aMGUHPCAAAnMtlTt/CHJeVz3fM%2Bj7d7mYavPR96/u9XN68v1%2Bjn%2Bt2N1Pr1i115MgJx113v5BQziaFdj6yORPZnMtGvpiYH1teVeM48hIhAADAlYyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMuCXrDuuOMOvfDCCzp2zP5P1QEAAFwJgl6w%2BvbtqyeffFL9%2B/fX73//e23ZskXcKQIAAISSoBes6dOn6%2B9//7tWrVqlsLAw/fa3v9VNN92kxx9/XF988UWwlwMAAGBdk9zJ3eVyqV%2B/furXr58qKyv17LPPatWqVVq7dq169%2B6te%2B65RzfffHNTLA0AAOB7a7JflVNWVqbXXntNr732mj7//HP17t1bt99%2Bu0pLS/WHP/xB27Zt05w5c5pqeQAAAJcs6AXr1Vdf1auvvqoPP/xQbdq00ciRI7VixQp16tTJ/5z27dtr8eLFFCwAAOBIQS9Yc%2BbM0YABA7Ry5UrdcMMNatas4dvAOnfurPHjxwd7aQAAAFYEvWC99957at26tSoqKvzlaseOHerZs6fCwsIkSb1791bv3r2DvTQAAAArgv5ThMePH9ctt9yiP//5z/6xKVOmaMSIETp48GCwlwMAAGBd0AvWn/70J/30pz/Vvffe6x9744031L59e2VlZQV7OQAAANYFvWD985//1EMPPaSYmBj/WJs2bfTggw9q69atwV4OAACAdUEvWG63W0ePHm0wXllZyR3dAQBASAh6wbrhhhu0aNEiff311/6xkpISZWVlKT09PdjLAQAAsC7oP0U4c%2BZM3XvvvRoyZIiioqIkSUePHlXPnj01a9asYC8HAADAuqAXrLZt2%2Brll1/WBx98oKKiIrndbl1zzTX62c9%2BJpfLFezlAAAAWNckvyonLCxM6enpXBIEAAAhKegFy%2BfzadmyZdq%2BfbtOnTrV4I3tf/vb34K9JAAAAKuCXrAefvhh7dy5U8OGDdOPf/zjYL88AADAZRf0grV161atW7dOqampwX5pAACAoAj6bRpatGihtm3bBvtlAQAAgiboBWvEiBFat26d6urqgv3SAAAAQRH0S4QVFRXKz8/XO%2B%2B8o6uvvlrh4eEB888880ywlwQAAGBVk9ymYfjw4U3xsgAAAEER9IKVlZUV7JcEAAAIqqC/B0uSysrKlJOTo%2BnTp%2Bvf//63/vrXv2rv3r1NsRQAAADrgl6wvvrqK9122216%2BeWX9dZbb%2BnkyZN64403NHr0aHm93mAvBwAAwLqgF6xHHnlEgwYN0ubNm/WjH/1IkvTYY49p4MCBWrp0abCXAwAAYF3QC9b27dt17733BvxiZ7fbrfvuu0%2B7du0K9nIAAACsC3rBqq%2BvV319fYPxEydOKCwsLNjLAQAAsC7oBat///5as2ZNQMmqqKhQdna2%2BvbtG%2BzlAAAAWBf0gvXQQw9p586d6t%2B/v6qrq/Uf//EfGjBggPbt26eZM2cGezkAAADWBf0%2BWHFxcXrllVeUn5%2BvTz/9VPX19brrrrs0YsQItWrVKtjLAQAAsK5J7uQeGRmpO%2B64oyleGgAA4LILesGaMGHCeef5XYQAAMDpgv4erA4dOgR8xMXFqaqqSjt27FBycvJF76%2BmpkbDhw/Xhx9%2B6B9btGiRrr322oCPTZs2%2Befz8/M1aNAgeTweZWRk6PDhw/45Y4yWLl2qvn37Ki0tTUuWLDnrTz0CAACcyxXzuwhXrlyp0tLSi9pXdXW1pk%2BfrqKiooDx4uJiTZ8%2BXbfffrt/7PT7u3bs2KE5c%2BZo/vz5SkhI0OLFizVr1iytWbNGkvT0008rPz9fOTk5qq2t1YwZM9S2bVtNnjz5otYGAAB%2BuJrkdxGezYgRI/Tmm282%2Bvl79uzRnXfeqa%2B//rrBXHFxsXr06KGYmBj/R2RkpCRp06ZNuvXWWzVy5EglJCRoyZIlevfdd1VSUiLp20uUmZmZSk1NVd%2B%2BffXAAw/oueeesxMSAAD8IFwxBetf//rXRd1o9KOPPlKfPn2Um5sbMH78%2BHEdOnRInTp1Out2Xq9Xqamp/sft27dXfHy8vF6vDh06pIMHD%2Br666/3z6ekpGj//v0qKyu7uEAAAOAH64p4k/vx48f12Wefady4cY3ez7meW1xcLJfLpSeffFLvvfeeoqOjde%2B99/ovF5aVlSk2NjZgm7Zt26q0tFQ%2Bn0%2BSAubbtWsnSSotLW2w3bmUlZX593Wa292i0ds3VljYFdOPG8Xtbvx6T2dzWsbGCOVsUmjnI5szkc25nJwv6AUrPj4%2B4PcQStKPfvQjjR8/Xr/4xS%2B%2B9/737t0rl8ulzp07a/z48dq2bZsefvhhtWrVSoMHD1ZVVZXCw8MDtgkPD1dNTY2qqqr8j787J337ZvrGys3NVU5OTsBYRkaGMjMzLzVWSGjduuVFbxMVFXkZVnJlCOVsUmjnI5szkc25nJgv6AXrkUceuaz7HzlypAYMGKDo6GhJUkJCgr788ks9//zzGjx4sCIiIhqUpZqaGkVGRgaUqYiICP%2BfJfnfw9UYY8eO1cCBAwPG3O4WOnLkxCXnOhunNfqLyR8W1kxRUZE6erRSdXWh9VOcoZxNCu18ZHMmsjmXjXyX8o97G4JesLZt29bo5373vVCN5XK5/OXqtM6dO2vr1q2Svr2TfHl5ecB8eXm5YmJiFBcXJ0ny%2BXzq2LGj/8%2BSFBMT0%2Bg1xMbGNrgc6PMdU21t6H3xX4xLyV9XVx%2Byn7dQziaFdj6yORPZnMuJ%2BYJesO6%2B%2B27/JUJjjH/8zDGXy6VPP/30ove/fPly/etf/9KGDRv8Y7t371bnzp0lSR6PRwUFBRo1apQk6eDBgzp48KA8Ho/i4uIUHx%2BvgoICf8EqKChQfHy89fdPAQCA0BX0gvXkk09q0aJFmjFjhtLS0hQeHq6PP/5YCxYs0O23366hQ4d%2Br/0PGDBAa9eu1fr16zV48GBt2bJFr7zyiv8O8XfddZfuvvtuJSUlKTExUYsXL9ZNN92kq6%2B%2B2j%2B/dOlS/eQnP5EkPfroo5o0adL3Cw0AAH5QmuRGo3PnztUNN9zgH%2Bvbt68WLFigBx98UL/%2B9a%2B/1/579eql5cuXa8WKFVq%2BfLk6dOigRx991H%2BX%2BOTkZC1YsEArVqzQN998o379%2BmnhwoX%2B7SdPnqx///vfmjZtmsLCwjRmzBhNnDjxe60JAAD8sAS9YJWVlalDhw4Nxlu1aqUjR45c0j4/%2B%2ByzgMeDBg3SoEGDzvn8UaNG%2BS8RniksLEyzZs3SrFmzLmktAAAAQf8xtKSkJD322GM6fvy4f6yiokLZ2dn62c9%2BFuzlAAAAWBf0M1h/%2BMMfNGHCBN1www3q1KmTjDH68ssvFRMT43%2BfFAAAgJMFvWB16dJFb7zxhvLz81VcXCxJ%2BuUvf6lhw4Zd1L2mAAAArlRBL1iSdNVVV%2BmOO%2B7Qvn37/D%2B996Mf/agplgIAAGBd0N%2BDZYzR0qVLdf3112v48OEqLS3VzJkzNWfOHJ06dSrYywEAALAu6AXr2Wef1auvvqo//vGP/l9NM2jQIG3evLnB7%2B8DAABwoqAXrNzcXM2dO1ejRo3y37196NChWrRokV5//fVgLwcAAMC6oBesffv2qXv37g3GExIS/L/3DwAAwMmCXrA6dOigjz/%2BuMH4e%2B%2B953/DOwAAgJMF/acIJ0%2BerPnz58vn88kYo3/84x/Kzc3Vs88%2Bq4ceeijYywEAALAu6AVr9OjRqq2t1erVq1VVVaW5c%2BeqTZs2uv/%2B%2B3XXXXcFezkAAADWBb1g5efn65ZbbtHYsWN1%2BPBhGWPUtm3bYC8DAADgsgn6e7AWLFjgfzN7mzZtKFcAACDkBL1gderUSZ9//nmwXxYAACBogn6JMCEhQQ888IDWrVunTp06KSIiImA%2BKysr2EsCAACwKugF64svvlBKSookcd8rAAAQkoJSsJYsWaJp06apRYsWevbZZ4PxkgAAAE0mKO/Bevrpp1VZWRkwNmXKFJWVlQXj5QEAAIIqKAXLGNNgbNu2baqurg7GywMAAARV0H%2BKEAAAINRRsAAAACwLWsFyuVzBeikAAIAmFbTbNCxatCjgnlenTp1Sdna2WrZsGfA87oMFAACcLigF6/rrr29wz6vk5GQdOXJER44cCcYSAAAAgiYoBYt7XwEAgB8S3uQOAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACxzfMGqqanR8OHD9eGHH/rHSkpKNHHiRCUlJWno0KHasmVLwDYffPCBhg8fLo/HowkTJqikpCRgfsOGDUpPT1dycrJmz56tysrKoGQBAAChwdEFq7q6Wr///e9VVFTkHzPGKCMjQ%2B3atVNeXp5GjBihadOm6cCBA5KkAwcOKCMjQ6NGjdJLL72kNm3a6L777pMxRpL01ltvKScnRwsWLNDGjRvl9XqVnZ3dJPkAAIAzObZg7dmzR3feeae%2B/vrrgPGtW7eqpKRECxYsUJcuXTR16lQlJSUpLy9PkvTiiy/quuuu06RJk9S1a1dlZWVp//79%2BuijjyRJzzzzjO655x4NGDBAvXr10vz585WXl8dZLAAA0GiOLVgfffSR%2BvTpo9zc3IBxr9erHj16qEWLFv6xlJQUFRYW%2BudTU1P9c5GRkerZs6cKCwtVV1enjz/%2BOGA%2BKSlJp06d0u7duy9zIgAAECrcTb2ASzVu3Lizjvt8PsXGxgaMtW3bVqWlpRecP3r0qKqrqwPm3W63oqOj/dsDAABciGML1rlUVlYqPDw8YCw8PFw1NTUXnK%2BqqvI/Ptf2jVFWViafzxcw5na3aFDsvq%2BwMGedgHS7G7/e09mclrExQjmbFNr5yOZMZHMuJ%2BcLuYIVERGhioqKgLFEwKv2AAAUG0lEQVSamho1b97cP39mWaqpqVFUVJQiIiL8j8%2Bcj4yMbPQacnNzlZOTEzCWkZGhzMzMRu8jFLVu3fKit4mKavzn3WlCOZsU2vnI5kxkcy4n5gu5ghUXF6c9e/YEjJWXl/vPHsXFxam8vLzBfPfu3RUdHa2IiAiVl5erS5cukqTa2lpVVFQoJiam0WsYO3asBg4cGDDmdrfQkSMnLiXSOTmt0V9M/rCwZoqKitTRo5Wqq6u/jKsKvlDOJoV2PrI5E9mcy0a%2BS/nHvQ0hV7A8Ho/Wrl2rqqoq/1mrgoICpaSk%2BOcLCgr8z6%2BsrNSuXbs0bdo0NWvWTImJiSooKFCfPn0kSYWFhXK73UpISGj0GmJjYxtcDvT5jqm2NvS%2B%2BC/GpeSvq6sP2c9bKGeTQjsf2ZyJbM7lxHzOOgXSCGlpaWrfvr1mzZqloqIirV27Vjt27NCYMWMkSaNHj9b27du1du1aFRUVadasWerYsaO/UI0bN07r16/X5s2btWPHDs2bN0933nnnRV0iBAAAP2whV7DCwsK0atUq%2BXw%2BjRo1Sq%2B99ppWrlyp%2BPh4SVLHjh31xBNPKC8vT2PGjFFFRYVWrlwpl8slSRo2bJimTp2quXPnatKkSerVq5dmzJjRlJEAAIDDhMQlws8%2B%2Byzg8U9/%2BlNt2rTpnM%2B/8cYbdeONN55zfsqUKZoyZYq19QEAgB%2BWkDuDBQAA0NQoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAspAtWG%2B//bauvfbagI/MzExJ0q5du3THHXfI4/Fo9OjR2rlzZ8C2%2Bfn5GjRokDwejzIyMnT48OGmiAAAABwqZAvWnj17NGDAAG3ZssX/sWjRIp08eVJTpkxRamqq/vKXvyg5OVlTp07VyZMnJUk7duzQnDlzNG3aNOXm5uro0aOaNWtWE6cBAABOErIFq7i4WN26dVNMTIz/IyoqSm%2B88YYiIiL04IMPqkuXLpozZ45atmypv/71r5KkTZs26dZbb9XIkSOVkJCgJUuW6N1331VJSUkTJwIAAE4R0gWrU6dODca9Xq9SUlLkcrkkSS6XS71791ZhYaF/PjU11f/89u3bKz4%2BXl6vNyjrBgAAzheSBcsYoy%2B%2B%2BEJbtmzRkCFDNGjQIC1dulQ1NTXy%2BXyKjY0NeH7btm1VWloqSSorKzvvPAAAwIW4m3oBl8OBAwdUWVmp8PBwLVu2TPv27dOiRYtUVVXlH/%2Bu8PBw1dTUSJKqqqrOO98YZWVl8vl8AWNud4sGxe37CgtzVj92uxu/3tPZnJaxMUI5mxTa%2BcjmTGRzLifnC8mC1aFDB3344Ye66qqr5HK51L17d9XX12vGjBlKS0trUJZqamrUvHlzSVJERMRZ5yMjIxv9%2Brm5ucrJyQkYy8jI8P8U4w9V69YtL3qbqKjGf96dJpSzSaGdj2zORDbncmK%2BkCxYkhQdHR3wuEuXLqqurlZMTIzKy8sD5srLy/1nl%2BLi4s46HxMT0%2BjXHjt2rAYOHBgw5na30JEjJy4mwgU5rdFfTP6wsGaKiorU0aOVqqurv4yrCr5QziaFdj6yORPZnMtGvkv5x70NIVmw3n//fT3wwAN65513/GeePv30U0VHRyslJUV//vOfZYyRy%2BWSMUbbt2/Xb37zG0mSx%2BNRQUGBRo0aJUk6ePCgDh48KI/H0%2BjXj42NbXA50Oc7ptra0PvivxiXkr%2Burj5kP2%2BhnE0K7XxkcyayOZcT8znrFEgjJScnKyIiQn/4wx%2B0d%2B9evfvuu1qyZIl%2B9atf6ZZbbtHRo0e1ePFi7dmzR4sXL1ZlZaVuvfVWSdJdd92lV199VS%2B%2B%2BKJ2796tBx98UDfddJOuvvrqJk4FAACcIiQLVqtWrbR%2B/XodPnxYo0eP1pw5czR27Fj96le/UqtWrbRmzRr/WSqv16u1a9eqRYsWkr4tZwsWLNDKlSt111136aqrrlJWVlYTJwIAAE4SkpcIJalr1656%2BumnzzrXq1cvvfzyy%2BfcdtSoUf5LhAAAABcrJM9gAQAANCUKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABY5m7qBeCH49Zl/9vUS7gob97fr6mXAABwKM5gAQAAWEbBOovq6mrNnj1bqamp6t%2B/v5566qmmXhIAAHAQLhGexZIlS7Rz505t3LhRBw4c0MyZMxUfH69bbrmlqZcGAAAcgIJ1hpMnT%2BrFF1/Un//8Z/Xs2VM9e/ZUUVGRnnvuOQoWAABoFC4RnmH37t2qra1VcnKyfywlJUVer1f19fVNuDIAAOAUnME6g8/nU%2BvWrRUeHu4fa9eunaqrq1VRUaE2bdpccB9lZWXy%2BXwBY253C8XGxlpda1gY/fhyctpPPeLyefuBdEn//%2B9cKP7dI5szhXI2ydn5KFhnqKysDChXkvyPa2pqGrWP3Nxc5eTkBIxNmzZNv/3tb%2B0s8v%2BUlZXpnp8UaezYsdbLW1MrKytTbm4u2RwolPOVlZVp48Z1ZHMYsjmXk/M5rxJeZhEREQ2K1OnHzZs3b9Q%2Bxo4dq7/85S8BH2PHjrW%2BVp/Pp5ycnAZny0IB2ZwrlPORzZnI5lxOzscZrDPExcXpyJEjqq2tldv97afH5/OpefPmioqKatQ%2BYmNjHde0AQCAPZzBOkP37t3ldrtVWFjoHysoKFBiYqKaNePTBQAALozGcIbIyEiNHDlS8%2BbN044dO7R582Y99dRTmjBhQlMvDQAAOETYvHnz5jX1Iq40ffv21a5du/Too4/qH//4h37zm99o9OjRTb2ss2rZsqXS0tLUsmXLpl6KdWRzrlDORzZnIptzOTWfyxhjmnoRAAAAoYRLhAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgOVV1drdmzZys1NVX9%2B/fXU0891dRLumRvv/22rr322oCPzMxMSdKuXbt0xx13yOPxaPTo0dq5c2cTr7ZxampqNHz4cH344Yf%2BsZKSEk2cOFFJSUkaOnSotmzZErDNBx98oOHDh8vj8WjChAkqKSkJ9rIb7Wz5Fi1a1OA4btq0yT%2Bfn5%2BvQYMGyePxKCMjQ4cPH26KpZ/ToUOHlJmZqbS0NKWnpysrK0vV1dWSnH/szpfN6cftq6%2B%2B0uTJk5WcnKybbrpJ69at8885/bhJ58/n9GN32pQpU/TQQw/5H1/o%2B75TcsnAkRYsWGBuu%2B02s3PnTvM///M/Jjk52bz55ptNvaxLsmrVKjN16lRTVlbm//jmm2/MiRMnTL9%2B/cwjjzxi9uzZYxYuXGh%2B/vOfmxMnTjT1ks%2BrqqrKZGRkmG7dupmtW7caY4ypr683t912m5k%2BfbrZs2ePefLJJ43H4zH79%2B83xhizf/9%2Bk5SUZNavX28%2B//xz87vf/c4MHz7c1NfXN2WUszpbPmOMmThxolmzZk3AcTx58qQxxhiv12t69eplXn75ZfPpp5%2Ba8ePHmylTpjRVhAbq6%2BvNnXfeaX71q1%2BZzz//3Gzbts0MHjzYPPLII44/dufLZoyzj1tdXZ25%2BeabzfTp080XX3xh3nnnHdO7d2/z2muvOf64GXP%2BfMY4%2B9idlp%2Bfb7p162ZmzpxpjDEX/L7vlFzGGEPBcqATJ06YxMTEgP%2B5rVy50owfP74JV3Xppk%2Bfbh599NEG4y%2B%2B%2BKIZOHCg/xtefX29GTx4sMnLywv2EhutqKjI/OIXvzC33XZbQAH54IMPTFJSUkA5vOeee8yKFSuMMcYsW7Ys4PidPHnSJCcnBxzjK8G58hljTHp6unn//ffPut2MGTP830CNMebAgQPm2muvNV9//fVlX3Nj7Nmzx3Tr1s34fD7/2Ouvv2769%2B/v%2BGN3vmzGOPu4HTp0yPzud78zx44d849lZGSYP/7xj44/bsacP58xzj52xhhz5MgRc8MNN5jRo0f713qh7/tOyHUalwgdaPfu3aqtrVVycrJ/LCUlRV6vV/X19U24sktTXFysTp06NRj3er1KSUmRy%2BWSJLlcLvXu3VuFhYVBXmHjffTRR%2BrTp49yc3MDxr1er3r06KEWLVr4x1JSUvxZvF6vUlNT/XORkZHq2bPnFZf1XPmOHz%2BuQ4cOnfU4Sg3ztW/fXvHx8fJ6vZdzuY0WExOjdevWqV27dgHjx48fd/yxO182px%2B32NhYLVu2TK1atZIxRgUFBdq2bZvS0tIcf9yk8%2Bdz%2BrGTpP/8z//UiBEjdM011/jHLvR93wm5TqNgOZDP51Pr1q0VHh7uH2vXrp2qq6tVUVHRhCu7eMYYffHFF9qyZYuGDBmiQYMGaenSpaqpqZHP51NsbGzA89u2bavS0tImWu2FjRs3TrNnz1ZkZGTA%2BIWyOCXrufIVFxfL5XLpySef1A033KBf/OIXevnll/3zZWVlV3S%2BqKgopaen%2Bx/X19dr06ZN6tu3r%2BOP3fmyOf24fdfAgQM1btw4JScna8iQIY4/bmc6M5/Tj90//vEP/fOf/9R9990XMH6h43Kl5/oud1MvABevsrIyoFxJ8j%2BuqalpiiVdsgMHDvjzLFu2TPv27dOiRYtUVVV1zpxOyyid%2B5idzuL0rHv37pXL5VLnzp01fvx4bdu2TQ8//LBatWqlwYMHq6qqylH5srOztWvXLr300kvasGFDSB2772b75JNPQua4rVixQuXl5Zo3b56ysrJC7u/cmfl69uzp2GNXXV2tP/7xj5o7d66aN28eMHeh43Il5zoTBcuBIiIiGnwxnX585hfrla5Dhw768MMPddVVV8nlcql79%2B6qr6/XjBkzlJaWdtacTssofXvMzjy7%2BN0s5zqmUVFRQVvj9zFy5EgNGDBA0dHRkqSEhAR9%2BeWXev755zV48OBz5jvzTNiVIDs7Wxs3btTjjz%2Bubt26hdSxOzNb165dQ%2Ba4JSYmSvr2f94PPPCARo8ercrKyoDnOPW4SQ3zbd%2B%2B3bHHLicnR9ddd13AmdXTzrXuCx23KyHXmbhE6EBxcXE6cuSIamtr/WM%2Bn0/Nmze/Yr85nE90dLT/erskdenSRdXV1YqJiVF5eXnAc8vLyxucHnaCuLi482Y513xMTEzQ1vh9uFwu/zf60zp37qxDhw5Jck6%2BhQsX6umnn1Z2draGDBkiKXSO3dmyOf24lZeXa/PmzQFj11xzjU6dOnXB7x9Xejbp/PmOHz/u2GP33//939q8ebOSk5OVnJys119/Xa%2B//rqSk5ND5u%2BbRMFypO7du8vtdge8GbOgoECJiYlq1sxZh/T9999Xnz59Av6l%2Bemnnyo6OlopKSn617/%2BJWOMpG/fr7V9%2B3Z5PJ6mWu4l83g8%2BuSTT1RVVeUfKygo8GfxeDwqKCjwz1VWVmrXrl2Oybp8%2BXJNnDgxYGz37t3q3LmzpIb5Dh48qIMHD15R%2BXJycvTCCy/oscce07Bhw/zjoXDszpXN6cdt3759mjZtmr9USNLOnTvVpk0bpaSkOP64nS/fs88%2B69hj9%2Byzz%2Br111/XK6%2B8oldeeUUDBw7UwIED9corr8jj8Zz3%2B/6VnKuBJvwJRnwPDz/8sBk2bJjxer3m7bffNr179zZvvfVWUy/roh07dsykp6eb3//%2B96a4uNi88847pn///mbt2rXm2LFjpm/fvmbhwoWmqKjILFy40PTr1%2B%2BKvw/Wad%2B9jUFtba0ZOnSouf/%2B%2B83nn39u1qxZY5KSkvz35CkpKTGJiYlmzZo1/nvy3HbbbVfUPXnO9N18Xq/X9OjRw6xbt8589dVX5rnnnjPXXXed2b59uzHGmO3bt5uePXua//qv//Lfu2bq1KlNufwAe/bsMd27dzePP/54wD2FysrKHH/szpfN6cettrbWjBo1ykyaNMkUFRWZd955x/z85z83GzZscPxxM%2Bb8%2BZx%2B7L5r5syZ/lsvXOj7vpNyUbAc6uTJk%2BbBBx80SUlJpn///ubpp59u6iVdss8//9xMnDjRJCUlmX79%2BpknnnjC/03O6/WakSNHmsTERDNmzBjzySefNPFqG%2B/M%2B0R9%2BeWX5pe//KW57rrrzLBhw8z//u//Bjz/nXfeMTfffLPp1auXueeee67I%2B7p815n53n77bXPbbbeZxMREc8sttzQo/Hl5eebGG280SUlJJiMjwxw%2BfDjYSz6nNWvWmG7dup31wxhnH7sLZXPycTPGmNLSUpORkWF69%2B5t%2BvXrZ1avXu3//uHk43ba%2BfI5/did9t2CZcyFv%2B87JZfLmP87DwcAAAArnPWGHQAAAAegYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAsv8HvIZ4JM6Y7jYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3484269036173249908”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>136</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>107</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (88)</td> <td class=”number”>685</td> <td class=”number”>21.3%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3484269036173249908”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>843</td> <td class=”number”>26.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>247.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>256.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>295.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>301.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>395.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_BERRY_FARMS12”><s>BERRY_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_BERRY_FARMS07”><code>BERRY_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93937</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHILDPOVRATE15”><s>CHILDPOVRATE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_POVRATE15”><code>POVRATE15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93822</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHIPSTAX_STORES14”>CHIPSTAX_STORES14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>12</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1044</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>66.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6166214209919658761”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6166214209919658761,#minihistogram6166214209919658761”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6166214209919658761”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6166214209919658761”

aria-controls=”quantiles6166214209919658761” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6166214209919658761” aria-controls=”histogram6166214209919658761”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6166214209919658761” aria-controls=”common6166214209919658761”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6166214209919658761” aria-controls=”extreme6166214209919658761”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6166214209919658761”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1.225</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.15</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.225</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.0149</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.8245</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.6051</td>

</tr> <tr>

<th>Mean</th> <td>1.1044</td>

</tr> <tr>

<th>MAD</th> <td>1.5145</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7407</td>

</tr> <tr>

<th>Sum</th> <td>3458.9</td>

</tr> <tr>

<th>Variance</th> <td>4.0598</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6166214209919658761”>

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mTZuqWbNmKigo0OnTp3Xy5Endeeedru1JSUk6fvy4SkpK6iUHAADwPz57inDkyJFXXS8uLlZAQIBeeuklffDBB4qMjNSDDz7oOl1YUlKi2NhYt%2Bc0adJEp06dkt1ulyS37dHR0ZKkU6dO1XredykpKXHt6wqbrWGdn19XQUG%2B1Y9tNjPzXsnta/mvl1VzS9bNbtXcknWzk9t/cvtswfouhw4dUkBAgFq3bq0HHnhAe/bs0e9//3uFh4erb9%2B%2BunTpkoKDg92eExwcLIfDoUuXLrnu//c26Zs309dVbm6ucnJy3NbS0tKUnp7%2BQ2P5haioMKP7i4gINbo/X2HV3JJ1s1s1t2Td7OT2fX5XsAYPHqzevXsrMjJSkhQfH68vvvhC69evV9%2B%2BfRUSElKrLDkcDoWGhrqVqZCQENd/S3K9h6suUlNTlZKS4rZmszVUWdmFH5zranyt6ZvKHxQUqIiIUJ09W6Hqauv8hqdVc0vWzW7V3JJ1s5PbfG7TP9zXld8VrICAAFe5uqJ169b66KOPJElxcXEqLS11215aWqqYmBjFxcVJkux2u1q0aOH6b0mKiYmp8wyxsbG1Tgfa7edUVWWdvyxXYzp/dXWNJb%2BmVs0tWTe7VXNL1s1Obt/nW4dA6mDRokUaO3as29qBAwfUunVrSVJCQoLy8vJc206ePKmTJ08qISFBcXFxatasmdv2vLw8NWvWzPj7pwAAgP/yuyNYvXv31rJly7Ry5Ur17dtXO3fu1KZNm7R69WpJ0ogRIzRq1Ch16tRJHTp00NNPP61evXqpZcuWru0LFizQTTfdJElauHChxo0b57U8AADA9/hdwerYsaMWLVqkF154QYsWLVLz5s21cOFCJSYmSpISExM1Z84cvfDCCzpz5oy6d%2B%2BuuXPnup4/fvx4ffXVV5o0aZKCgoJ033331ToiBgAA8L8EOJ1Op7eHsAK7/Zzxfdpsgeq7YIfx/daXv/2uu5H92GyBiooKU1nZBb85V18XVs0tWTe7VXNL1s1ObvO5Y2JuNLq/uvK792ABAAB4GwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAzzeMEaPny4Xn/9dZ07d87TLw0AAOARHi9Y3bp100svvaQePXro8ccf186dO%2BV0Oj09BgAAQL3xeMHKyMjQ%2B%2B%2B/ryVLligoKEiTJ09Wr1699Nxzz%2Bnw4cOeHgcAAMA4mzdeNCAgQN27d1f37t1VUVGhNWvWaMmSJVq2bJk6d%2B6sMWPGqF%2B/ft4YDQAA4Lp5pWBJUklJif7617/qr3/9qz7//HN17txZQ4YM0alTp/Tkk09qz549mjlzprfGAwAA%2BME8XrA2b96szZs3a9euXWrcuLEGDx6sF154Qa1atXI9pmnTpnr66acpWAAAwCd5vGDNnDlTvXv31uLFi/Wzn/1MgYG13wbWunVrPfDAA54eDQAAwAiPF6wPPvhAUVFRKi8vd5WrwsJCtW/fXkFBQZKkzp07q3Pnzp4eDQAAwAiP/xbh%2BfPnNWDAAC1fvty1NmHCBN177706efKkp8cBAAAwzuMF65lnntHNN9%2BsBx980LX27rvvqmnTpsrKyrrm/TkcDg0aNEi7du1yreXn5%2BvXv/61EhMT1b9/f23YsMHtOb/61a/Utm1bt9vnn38uSXI6nVqwYIG6deumLl26aP78%2BaqpqfmBaQEAgBV5/BThv//9b/3lL39RTEyMa61x48aaNm2afvOb31zTviorK5WRkaGioiLXmt1u18MPP6wRI0boj3/8oz755BNlZmYqJiZGvXr1UnV1tb744gutXbvW7Y31UVFRkqRXX31VW7ZsUU5OjqqqqjR16lQ1adJE48ePv77gAADAMjxesGw2m86ePVtrvaKi4pqu6H7w4EFlZGTUes62bdsUHR2txx9/XJLUqlUr7dq1S2%2B//bZ69eqlY8eO6fLly%2BrYsaNCQkJq7Xf16tVKT09XcnKyJGnKlClatGgRBQsAANSZx08R/uxnP9O8efP05ZdfutaOHj2qrKws9ezZs8772b17t7p27arc3Fy39Z49e171VOP58%2BclfVPMmjZtetVydfr0aZ08eVJ33nmnay0pKUnHjx9XSUlJnWcDAADW5vEjWNOnT9eDDz6o/v37KyIiQpJ09uxZtW/fXpmZmXXez8iRI6%2B63qJFC7Vo0cJ1/6uvvtI777yjyZMnS5KKi4t1ww036JFHHtG%2Bfft0yy23aNq0aerYsaPsdrskKTY21vX86OhoSdKpU6fc1gEAAL6LxwtWkyZN9NZbb%2Blf//qXioqKZLPZdOutt%2Bquu%2B5SQECA0de6dOmSJk%2BerOjoaKWmpkqSDh8%2BrDNnzmj48OFKT0/XX/7yF40ZM0bvvvuuLl26JEkKDg527ePKfzscjjq/bklJiausXWGzNTRe0IKCPH4A8rrYbGbmvZLb1/JfL6vmlqyb3aq5JetmJ7f/5PbKR%2BUEBQWpZ8%2Be13RK8FpduHBBEydO1BdffKE///nPCg0NlSTNnTtXly5dUnh4uCRp1qxZ%2Bvjjj7V582b99Kc/lfRNmbpyCvFKsbry/LrIzc1VTk6O21paWprS09OvO5cvi4oKM7q/iIi6f0/8iVVzS9bNbtXcknWzk9v3ebxg2e12Pf/88/r44491%2BfLlWm9Sf%2B%2B99677Nc6fP6%2BHHnpIX375pVatWuX224I2m81VrqRvPni6devWOn36tOLi4lwzXjnNeOVI1H//1uP3SU1NVUpKituazdZQZWUXfmikq/K1pm8qf1BQoCIiQnX2bIWqq61zCQ2r5pasm92quSXrZie3%2Bdymf7ivK48XrN///vfat2%2BfBg4cqBtvvNH4/mtqajRp0iQdO3ZMa9asUZs2bdy2jxo1Sl27dtWkSZNcj//ss8/0m9/8RnFxcWrWrJny8vJcBSsvL0/NmjW7ptN7sbGxtR5vt59TVZV1/rJcjen81dU1lvyaWjW3ZN3sVs0tWTc7uX2fxwvWRx99pBUrVrgug2DaG2%2B8oV27dmnp0qWKiIhwHYG64YYbFBkZqZSUFC1evFjt2rXTLbfcotWrV%2BvcuXMaMmSIJGnEiBFasGCBbrrpJknSwoULNW7cuHqZFQAA%2BCePF6yGDRuqSZMm9bb/rVu3qqamRo888ojbepcuXbRmzRqNHTtWlZWVmjdvnkpLS5WQkKBXX33Vddpw/Pjx%2BuqrrzRp0iQFBQXpvvvu09ixY%2BttXgAA4H8CnNdydU8D/vSnP%2Bns2bOaM2eO68OdrcBuP2d8nzZboPou2GF8v/Xlb7/rbmQ/NlugoqLCVFZ2wW8OJdeFVXNL1s1u1dySdbOT23zumBjzb0eqC48fwSovL9eWLVv0z3/%2BUy1btnS7JIL0zZXUAQAAfJlXLtMwaNAgb7wsAACAR3i8YF3tY2wAAAD8iVcupFRSUqKcnBxlZGToq6%2B%2B0t///ncdOnTIG6MAAAAY5/GCdeTIEf3yl7/UW2%2B9pa1bt%2BrixYt69913NWzYMBUUFHh6HAAAAOM8XrD%2B%2BMc/qk%2BfPtq2bZtuuOEGSdKzzz6rlJQULViwwNPjAAAAGOfxgvXxxx/rwQcfdPtgZ5vNpokTJ2r//v2eHgcAAMA4jxesmpoa1dTUvsbFhQsXLHVdLAAA4L88XrB69Oihl19%2B2a1klZeXKzs7W926dfP0OAAAAMZ5vGA98cQT2rdvn3r06KHKykr99re/Ve/evXXs2DFNnz7d0%2BMAAAAY5/HrYMXFxWnTpk3asmWLPv30U9XU1GjEiBG69957XZ8HCAAA4Mu8ciX30NBQDR8%2B3BsvDQAAUO88XrBGjx79P7fzWYQAAMDXebxgNW/e3O1%2BVVWVjhw5os8//1xjxozx9DgAAADG/Wg%2Bi3Dx4sU6deqUh6cBAAAwzyufRXg19957r/72t795ewwAAIDr9qMpWP/5z3%2B40CgAAPALP4o3uZ8/f16fffaZRo4c6elxAAAAjPN4wWrWrJnb5xBK0g033KAHHnhAv/rVrzw9DgAAgHEeL1h//OMfPf2SAAAAHuXxgrVnz546P/bOO%2B%2Bsx0kAAADqh8cL1qhRo1ynCJ1Op2v922sBAQH69NNPPT0eAADAdfN4wXrppZc0b948TZ06VV26dFFwcLD27t2rOXPmaMiQIbrnnns8PRIAAIBRHr9MQ1ZWlp566in1799fUVFRCgsLU7du3TRnzhytX79ezZs3d90AAAB8kccLVklJyVXLU3h4uMrKyq55fw6HQ4MGDdKuXbtca0ePHtXYsWPVqVMn3XPPPdq5c6fbc/71r39p0KBBSkhI0OjRo3X06FG37a%2B99pp69uypxMREzZgxQxUVFdc8FwAAsC6PF6xOnTrp2Wef1fnz511r5eXlys7O1l133XVN%2B6qsrNTjjz%2BuoqIi15rT6VRaWpqio6O1ceNG3XvvvZo0aZJOnDghSTpx4oTS0tI0dOhQvfHGG2rcuLEmTpzoeu/X1q1blZOTozlz5mjVqlUqKChQdna2geQAAMAqPP4erCeffFKjR4/Wz372M7Vq1UpOp1NffPGFYmJitHr16jrv5%2BDBg8rIyHB7o7wkffTRRzp69Khef/11NWzYUG3atNGHH36ojRs3avLkydqwYYN%2B8pOfaNy4cZK%2BOWXZvXt37d69W127dtXq1as1ZswY9e7dW5I0e/ZsjR8/XlOnTlVoaKi5LwQAAPBbHj%2BC1aZNG7377rvKyMhQp06dlJiYqJkzZ2rz5s266aab6ryfK4UoNzfXbb2goEB33HGHGjZs6FpLSkpSfn6%2Ba3tycrJrW2hoqNq3b6/8/HxVV1dr7969bts7deqky5cv68CBAz80MgAAsBiPH8GSpEaNGmn48OE6duyYWrZsKembq7lfi%2B/6WB273a7Y2Fi3tSZNmujUqVPfu/3s2bOqrKx0226z2RQZGel6fl2UlJTIbre7rdlsDWu97vUKCvrRfJRkndhsZua9ktvX8l8vq%2BaWrJvdqrkl62Ynt//k9njBcjqdWrhwodasWaPLly9r69ateu655xQaGqpZs2Zdc9H6toqKCgUHB7utBQcHy%2BFwfO/2S5cuue5/1/PrIjc3Vzk5OW5raWlpSk9Pr/M%2B/FFUVJjR/UVEWPOUrVVzS9bNbtXcknWzk9v3ebxgrVmzRps3b9Yf/vAHzZkzR5LUp08fzZ49W9HR0Xrssceua/8hISEqLy93W3M4HGrQoIFr%2B7fLksPhUEREhEJCQlz3v739Wt5/lZqaqpSUFLc1m62hysou1HkfdeFrTd9U/qCgQEVEhOrs2QpVV9cY2acvsGpuybrZrZpbsm52cpvPbfqH%2B7ryeMHKzc3VU089pb59%2B2ru3LmSpHvuuUc33HCDsrKyrrtgxcXF6eDBg25rpaWlrtNzcXFxKi0trbW9Xbt2ioyMVEhIiEpLS9WmTRtJUlVVlcrLyxUTE1PnGWJjY2udDrTbz6mqyjp/Wa7GdP7q6hpLfk2tmluybnar5pasm53cvs/jh0COHTumdu3a1VqPj4%2Bv9b6lHyIhIUGffPKJ63SfJOXl5SkhIcG1PS8vz7WtoqJC%2B/fvV0JCggIDA9WhQwe37fn5%2BbLZbIqPj7/u2QAAgDV4vGA1b95ce/furbX%2BwQcfuN7wfj26dOmipk2bKjMzU0VFRVq2bJkKCwt13333SZKGDRumjz/%2BWMuWLVNRUZEyMzPVokULde3aVdI3b55fuXKltm3bpsLCQs2aNUv3338/l2gAAAB15vFThOPHj9fs2bNlt9vldDr14YcfKjc3V2vWrNETTzxx3fsPCgrSkiVLNHPmTA0dOlQ333yzFi9erGbNmkmSWrRooRdffFHPPPOMFi9erMTERC1evNj1YdMDBw7U8ePH9dRTT8nhcKhfv36aOnXqdc8FAACsI8D57St1ekBubq6WLl3quvRB48aN9fDDD%2BvBBx/09CgeY7efM75Pmy1QfRfsML7f%2BvK333U3sh%2BbLVBRUWEqK7vgN%2Bfq68KquSXrZrdqbsm62cltPndMzI1G91dXHj%2BCtWXLFg0YMECpqan6%2Buuv5XQ61aRJE0%2BPAQAAUG88/h6sOXPmuN7M3rhxY8oVAADwOx4vWK1atdLnn3/u6ZcFAADwGI%2BfIoyPj9eUKVO0YsUKtWrVynVxzyuysrI8PRIAAIBRHi9Yhw8fVlJSkiQZue4VAADAj41HCtb8%2BfM1adIkNWzYUGvWrPHESwIAAHiNR96D9eqrr6qiosJtbcK65gnbAAAXmUlEQVSECSopKfHEywMAAHiURwrW1S61tWfPHlVWVnri5QEAADzK479FCAAA4O8oWAAAAIZ5rGBd%2Baw/AAAAf%2BexyzTMmzfP7ZpXly9fVnZ2tsLCwtwex3WwAACAr/NIwbrzzjtrXfMqMTFRZWVlKisr88QIAAAAHuORgsW1rwAAgJXwJncAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhvllwXrzzTfVtm3bWrf4%2BHhJ0m9/%2B9ta295//33X81977TX17NlTiYmJmjFjhioqKrwVBQAA%2BCCPfBahp91zzz3q2bOn635VVZXGjBmjXr16SZKKi4uVnZ2tu%2B66y/WYRo0aSZK2bt2qnJwcZWdnq0mTJsrMzFR2draeeuopj2YAAAC%2Byy%2BPYDVo0EAxMTGu21//%2Blc5nU5NmTJFDodDx44dU4cOHdweExwcLElavXq1xowZo969e6tjx46aPXu2Nm7cyFEsAABQZ35ZsP5beXm5li9froyMDAUHB%2BvQoUMKCAhQy5Ytaz22urpae/fuVXJysmutU6dOunz5sg4cOODJsQEAgA/zy1OE/239%2BvWKjY3VgAEDJEmHDh1SeHi4pk2bpt27d%2Bumm27S5MmT9fOf/1xnz55VZWWlYmNjXc%2B32WyKjIzUqVOn6vyaJSUlstvtbms2W0O3/ZoQFORb/dhmMzPvldy%2Blv96WTW3ZN3sVs0tWTc7uf0nt18XLKfTqQ0bNuihhx5yrR06dEiXLl1Sjx49NGHCBP3jH//Qb3/7W%2BXm5io6OlqSXKcLrwgODpbD4ajz6%2Bbm5ionJ8dtLS0tTenp6deRxvdFRYUZ3V9ERKjR/fkKq%2BaWrJvdqrkl62Ynt%2B/z64K1d%2B9enT59WgMHDnStTZw4UaNGjXK9qT0%2BPl6ffPKJ/vKXv%2Bixxx6TpFplyuFwKDS07t/01NRUpaSkuK3ZbA1VVnbhh0a5Kl9r%2BqbyBwUFKiIiVGfPVqi6usbIPn2BVXNL1s1u1dySdbOT23xu0z/c15VfF6wdO3YoOTnZVaYkKTAw0O2%2BJLVu3VoHDx5UZGSkQkJCVFpaqjZt2kj65jcQy8vLFRMTU%2BfXjY2NrXU60G4/p6oq6/xluRrT%2Baurayz5NbVqbsm62a2aW7JudnL7Pt86BHKNCgsL1blzZ7e1J554QpmZmW5rBw4cUOvWrRUYGKgOHTooLy/PtS0/P182m811DS0AAIDv49cFq6ioSLfeeqvbWkpKit5%2B%2B21t2rRJR44cUU5OjvLy8vTAAw9IkkaOHKmVK1dq27ZtKiws1KxZs3T//fdf0ylCAABgbX59irC0tFQRERFua/369dMf/vAHLV26VCdOnNBtt92mFStWqEWLFpKkgQMH6vjx43rqqafkcDjUr18/TZ061RvjAwAAH%2BXXBauwsPCq68OHD9fw4cO/83kTJkzQhAkT6mssAADg5/z6FCEAAIA3ULAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhfluw/vGPf6ht27Zut/T0dEnS/v37NXz4cCUkJGjYsGHat2%2Bf23O3bNmiPn36KCEhQWlpafr666%2B9EQEAAPgovy1YBw8eVO/evbVz507Xbd68ebp48aImTJig5ORkvfnmm0pMTNQjjzyiixcvSpIKCws1c%2BZMTZo0Sbm5uTp79qwyMzO9nAYAAPgSvy1YxcXFuv322xUTE%2BO6RURE6N1331VISIimTZumNm3aaObMmQoLC9Pf//53SdLatWt19913a/DgwYqPj9f8%2BfO1fft2HT161MuJAACAr/DrgtWqVata6wUFBUpKSlJAQIAkKSAgQJ07d1Z%2Bfr5re3JysuvxTZs2VbNmzVRQUOCRuQEAgO/zy4LldDp1%2BPBh7dy5U/3791efPn20YMECORwO2e12xcbGuj2%2BSZMmOnXqlCSppKTkf24HAAD4PjZvD1AfTpw4oYqKCgUHB%2Bv555/XsWPHNG/ePF26dMm1/t%2BCg4PlcDgkSZcuXfqf2%2BuipKREdrvdbc1ma1iruF2voCDf6sc2m5l5r%2BT2tfzXy6q5Jetmt2puybrZye0/uf2yYDVv3ly7du1So0aNFBAQoHbt2qmmpkZTp05Vly5dapUlh8OhBg0aSJJCQkKuuj00NLTOr5%2Bbm6ucnBy3tbS0NNdvMVpVVFSY0f1FRNT9e%2BJPrJpbsm52q%2BaWrJud3L7PLwuWJEVGRrrdb9OmjSorKxUTE6PS0lK3baWlpa6jS3FxcVfdHhMTU%2BfXTk1NVUpKituazdZQZWUXriXC9/K1pm8qf1BQoCIiQnX2bIWqq2uM7NMXWDW3ZN3sVs0tWTc7uc3nNv3DfV35ZcHasWOHpkyZon/%2B85%2BuI0%2BffvqpIiMjlZSUpOXLl8vpdCogIEBOp1Mff/yxHn30UUlSQkKC8vLyNHToUEnSyZMndfLkSSUkJNT59WNjY2udDrTbz6mqyjp/Wa7GdP7q6hpLfk2tmluybnar5pasm53cvs%2B3DoHUUWJiokJCQvTkk0/q0KFD2r59u%2BbPn6%2BHHnpIAwYM0NmzZ/X000/r4MGDevrpp1VRUaG7775bkjRixAht3rxZGzZs0IEDBzRt2jT16tVLLVu29HIqAADgK/yyYIWHh2vlypX6%2BuuvNWzYMM2cOVOpqal66KGHFB4erpdfftl1lKqgoEDLli1Tw4YNJX1TzubMmaPFixdrxIgRatSokbKysrycCAAA%2BBK/PEUoSbfddpteffXVq27r2LGj3nrrre987tChQ12nCAEAAK6VXx7BAgAA8CYKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjfFqzTp08rPT1dXbp0Uc%2BePZWVlaXKykpJ0rx589S2bVu329q1a13P3bJli/r06aOEhASlpaXp66%2B/9lYMAADgg2zeHqA%2BOJ1OpaenKyIiQuvWrdOZM2c0Y8YMBQYGavr06SouLlZGRoaGDBniek54eLgkqbCwUDNnztTs2bMVHx%2Bvp59%2BWpmZmXr55Ze9FQcAAPgYvzyCdejQIeXn5ysrK0u33XabkpOTlZ6eri1btkiSiouLdccddygmJsZ1Cw0NlSStXbtWd999twYPHqz4%2BHjNnz9f27dv19GjR70ZCQAA%2BBC/LFgxMTFasWKFoqOj3dbPnz%2Bv8%2BfP6/Tp02rVqtVVn1tQUKDk5GTX/aZNm6pZs2YqKCioz5EBAIAf8ctThBEREerZs6frfk1NjdauXatu3bqpuLhYAQEBeumll/TBBx8oMjJSDz74oOt0YUlJiWJjY93216RJE506darOr19SUiK73e62ZrM1rLXf6xUU5Fv92GYzM%2B%2BV3L6W/3pZNbdk3exWzS1ZNzu5/Se3Xxasb8vOztb%2B/fv1xhtv6JNPPlFAQIBat26tBx54QHv27NHvf/97hYeHq2/fvrp06ZKCg4Pdnh8cHCyHw1Hn18vNzVVOTo7bWlpamtLT043k8VVRUWFG9xcREWp0f77Cqrkl62a3am7JutnJ7fv8vmBlZ2dr1apVeu6553T77bfrtttuU%2B/evRUZGSlJio%2BP1xdffKH169erb9%2B%2BCgkJqVWmHA6H6z1adZGamqqUlBS3NZutocrKLlx/oP/ia03fVP6goEBFRITq7NkKVVfXGNmnL7Bqbsm62a2aW7JudnKbz236h/u68uuCNXfuXK1fv17Z2dnq37%2B/JCkgIMBVrq5o3bq1PvroI0lSXFycSktL3baXlpYqJiamzq8bGxtb63Sg3X5OVVXW%2BctyNabzV1fX1OvX9O7n/6/e9l0f/va77t4eod7V9/f8x8qquSXrZie37/OtQyDXICcnR6%2B//rqeffZZDRw40LW%2BaNEijR071u2xBw4cUOvWrSVJCQkJysvLc207efKkTp48qYSEBI/MDQAAfJ9fFqzi4mItWbJEDz/8sJKSkmS321233r17a8%2BePVq5cqW%2B/PJL/fnPf9amTZs0btw4SdKIESO0efNmbdiwQQcOHNC0adPUq1cvtWzZ0supAACAr/DLU4TvvfeeqqurtXTpUi1dutRt22effaZFixbphRde0KJFi9S8eXMtXLhQiYmJkqTExETNmTNHL7zwgs6cOaPu3btr7ty53ojhd3ztlBsAAD9UgNPpdHp7CCuw288Z36fNFqi%2BC3YY3y98kz%2B/B8tmC1RUVJjKyi74zfsz6sKquSXrZie3%2BdwxMTca3V9d%2BeUpQgAAAG%2BiYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG%2BeWV3AEAqA%2B%2B9okU/nwB4h87ChYAfA/%2BUQVwrThFCAAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIbxW4QAAK/xtd/QBOqKI1gAAACGcQQLAAA/5UtHCP/99ABvj2AUBQsA/Iwv/aMK%2BCtOEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFKyrqKys1IwZM5ScnKwePXrolVde8fZIAADAh3CZhquYP3%2B%2B9u3bp1WrVunEiROaPn26mjVrpgED/OsaHQAAoH5QsL7l4sWL2rBhg5YvX6727durffv2Kioq0rp16yhYAACgTjhF%2BC0HDhxQVVWVEhMTXWtJSUkqKChQTU2NFycDAAC%2BgiNY32K32xUVFaXg4GDXWnR0tCorK1VeXq7GjRt/7z5KSkpkt9vd1my2hoqNjTU6a1AQ/Rj/P5vNf/88XPmzzp95wL/5099xCta3VFRUuJUrSa77DoejTvvIzc1VTk6O29qkSZM0efJkM0P%2BPyUlJRpzU5FSU1ONl7cfs5KSEuXm5pLbQkpKSrRq1QqvZffWZ6RZ/XtuxexWzv3iiy/6VW7/qYqGhISE1CpSV%2B43aNCgTvtITU3Vm2%2B%2B6XZLTU01PqvdbldOTk6to2X%2BjtzWyi1ZN7tVc0vWzU5u/8nNEaxviYuLU1lZmaqqqmSzffPlsdvtatCggSIiIuq0j9jYWL9p4AAA4NpxBOtb2rVrJ5vNpvz8fNdaXl6eOnTooMBAvlwAAOD70Ri%2BJTQ0VIMHD9asWbNUWFiobdu26ZVXXtHo0aO9PRoAAPARQbNmzZrl7SF%2BbLp166b9%2B/dr4cKF%2BvDDD/Xoo49q2LBh3h7rqsLCwtSlSxeFhYV5exSPIre1ckvWzW7V3JJ1s5PbP3IHOJ1Op7eHAAAA8CecIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsHyUZWVlZoxY4aSk5PVo0cPvfLKK94eyaMcDocGDRqkXbt2eXsUjzh9%2BrTS09PVpUsX9ezZU1lZWaqsrPT2WB5x5MgRjR8/XomJierVq5dWrFjh7ZE8asKECXriiSe8PYbH/OMf/1Dbtm3dbunp6d4eyyMcDodmz56tO%2B%2B8Uz/96U/17LPPyt%2BvBf7mm2/W%2Bn63bdtW8fHx3h7tutm8PQB%2BmPnz52vfvn1atWqVTpw4oenTp6tZs2YaMGCAt0erd5WVlcrIyFBRUZG3R/EIp9Op9PR0RUREaN26dTpz5oxmzJihwMBATZ8%2B3dvj1auamhpNmDBBHTp00FtvvaUjR47o8ccfV1xcnH75y196e7x6984772j79u0aMmSIt0fxmIMHD6p3796aO3euay0kJMSLE3nOvHnztGvXLq1cuVIXLlzQY489pmbNmunXv/61t0erN/fcc4969uzpul9VVaUxY8aoV69e3hvKEAqWD7p48aI2bNig5cuXq3379mrfvr2Kioq0bt06vy9YBw8eVEZGht//VPffDh06pPz8fP3f//2foqOjJUnp6en605/%2B5PcFq7S0VO3atdOsWbMUHh6uVq1a6a677lJeXp7fF6zy8nLNnz9fHTp08PYoHlVcXKzbb79dMTEx3h7Fo8rLy7Vx40a9%2Buqr6tixoyRp3LhxKigo8OuC1aBBAzVo0MB1/%2BWXX5bT6dSUKVO8OJUZnCL0QQcOHFBVVZUSExNda0lJSSooKFBNTY0XJ6t/u3fvVteuXZWbm%2BvtUTwmJiZGK1ascJWrK86fP%2B%2BliTwnNjZWzz//vMLDw%2BV0OpWXl6c9e/aoS5cu3h6t3v3pT3/Svffeq1tvvdXbo3hUcXGxWrVq5e0xPC4vL0/h4eFuf7YnTJigrKwsL07lWeXl5Vq%2BfLkyMjIUHBzs7XGuGwXLB9ntdkVFRbn9AYyOjlZlZaXKy8u9OFn9GzlypGbMmKHQ0FBvj%2BIxERERbofQa2pqtHbtWnXr1s2LU3leSkqKRo4cqcTERPXv39/b49SrDz/8UP/%2B9781ceJEb4/iUU6nU4cPH9bOnTvVv39/9enTRwsWLJDD4fD2aPXu6NGjat68uTZt2qQBAwboF7/4hRYvXuz3PzT/t/Xr1ys2NtZvzsRQsHxQRUVFrXZ/5b4V/kdkddnZ2dq/f78ee%2Bwxb4/iUS%2B88IJeeuklffrpp379U31lZaX%2B8Ic/6KmnnnI7dWIFJ06ccP3/7fnnn9f06dP19ttva/78%2Bd4erd5dvHhRR44c0euvv66srCxNnz5da9as0Wuvvebt0TzC6XRqw4YNeuCBB7w9ijG8B8sHhYSE1CpSV%2B5b7X/IVpOdna1Vq1bpueee0%2B233%2B7tcTzqynuRKisrNWXKFE2bNs0vTiN8W05Ojn7yk5%2B4HbW0iubNm2vXrl1q1KiRAgIC1K5dO9XU1Gjq1KnKzMxUUFCQt0esNzabTefPn9fChQvVvHlzSd8UzvXr12vcuHFenq7%2B7d27V6dPn9bAgQO9PYoxFCwfFBcXp7KyMlVVVclm%2B%2BZbaLfb1aBBA0VERHh5OtSXuXPnav369crOzvb7U2RXlJaWKj8/X3369HGt3Xrrrbp8%2BbLOnz%2Bvxo0be3G6%2BvHOO%2B%2BotLTU9R7LKz88bd26Vf/5z3%2B8OZpHREZGut1v06aNKisrdebMGb/8fl8RExOjkJAQV7mSpFtuuUUnT5704lSes2PHDiUnJ6tRo0beHsUYThH6oHbt2slmsyk/P9%2B1lpeXpw4dOigwkG%2BpP8rJydHrr7%2BuZ5991q9%2Bwvs%2Bx44d06RJk3T69GnX2r59%2B9S4cWO//cd2zZo1evvtt7Vp0yZt2rRJKSkpSklJ0aZNm7w9Wr3bsWOHunbtqoqKCtfap59%2BqsjISL/9fl%2BRkJCgyspKHT582LV26NAht8LlzwoLC9W5c2dvj2EU/xr7oNDQUA0ePFizZs1SYWGhtm3bpldeeUWjR4/29mioB8XFxVqyZIkefvhhJSUlyW63u27%2BrkOHDmrfvr1mzJihgwcPavv27crOztajjz7q7dHqTfPmzXXzzTe7bmFhYQoLC9PNN9/s7dHqXWJiokJCQvTkk0/q0KFD2r59u%2BbPn6%2BHHnrI26PVu9atW6tXr17KzMzUgQMHtGPHDi1btkwjRozw9mgeUVRU5He/McspQh%2BVmZmpWbNmacyYMQoPD9fkyZPVr18/b4%2BFevDee%2B%2BpurpaS5cu1dKlS922ffbZZ16ayjOCgoK0ZMkSzZ07V6mpqQoNDdWoUaP4YcJPhYeHa%2BXKlXrmmWc0bNgwhYWF6de//rUlCpYkLViwQHPnztWIESMUGhqq3/zmNxo1apS3x/KI0tJSv3uLS4DTSldsBAAA8ABOEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYf8f10A8nKuW7r4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6166214209919658761”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2138</td> <td class=”number”>66.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6166214209919658761”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2138</td> <td class=”number”>66.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:78%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CHIPSTAX_VENDM14”>CHIPSTAX_VENDM14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>22</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.8955</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7498806200537346787”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7498806200537346787,#minihistogram-7498806200537346787”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7498806200537346787”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7498806200537346787”

aria-controls=”quantiles-7498806200537346787” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7498806200537346787” aria-controls=”histogram-7498806200537346787”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7498806200537346787” aria-controls=”common-7498806200537346787”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7498806200537346787” aria-controls=”extreme-7498806200537346787”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7498806200537346787”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>4.75</td>

</tr> <tr>

<th>Q3</th> <td>6</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr>

<th>Range</th> <td>8</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.6282</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.67468</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.3539</td>

</tr> <tr>

<th>Mean</th> <td>3.8955</td>

</tr> <tr>

<th>MAD</th> <td>2.3128</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.44778</td>

</tr> <tr>

<th>Sum</th> <td>12201</td>

</tr> <tr>

<th>Variance</th> <td>6.9077</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7498806200537346787”>

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2vLysqUmpoqSaqtrVVtba0cDofP7x8TE9PucqDT%2BY1On7bOznAxOtp/a2ubZT8zq/Zm1b4kesMZ/vI5WXWbWamvwDoF4qMf/ehHmjBhgnJycrR//3598MEHKikpUXp6upKTk9XY2Ki8vDxVV1crLy9PLpdLKSkpkqT09HS98cYb2rBhg/bv36%2B5c%2BdqwoQJPKIBAAD4zJIBS5Kefvpp/cu//IvS09M1b9483X777brzzjvVtWtXrVq1ynOWqqKiQiUlJQoPD5ckxcfHa/HixSouLlZ6erq6deum/Px8k7sBAACBxJKXCCXpyiuvVEFBwTnnhg0bps2bN5/3tampqZ5LhAAAAB1l2TNYAAAAZiFgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYzLJ/KgcAjJKYu93sEjrkjw%2BPMbsE4LLHGSwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYJYNWO%2B%2B%2B66uvfZar6/s7GxJ0r59%2BzRz5kw5HA5Nnz5de/fu9Xrt1q1bNWnSJDkcDmVmZuqrr74yowUAABCgLBuwqqurNXHiRO3cudPztXTpUjU1NSkjI0OJiYnatGmT4uPjNXv2bDU1NUmSKisrlZubq6ysLJWWlqqxsVE5OTkmdwMAAAKJZQPWgQMHdM011yg6OtrzFRkZqW3btikkJERz587VgAEDlJubq4iICG3fvl2StG7dOqWkpGjatGm67rrrVFBQoB07dqimpsbkjgAAQKCwdMDq379/u/GKigolJCTIZrNJkmw2m0aMGKHy8nLPfGJiomf5Xr16KS4uThUVFT9I3QAAIPDZzS6gM7jdbh06dEg7d%2B7UqlWr1NraquTkZGVnZ8vpdGrgwIFey/fo0UNVVVWSpLq6OsXExLSbP3r0qM/vX1dXJ6fT6TVmt4e3W%2B%2BlCgoKrHxst/tW79m%2BAq0/X1i1N6v2JQVmT%2BxrncfXz7azWHWbWbEvvwtYM2fO1PTp03XLLbfoyiuvvKh1HDlyRC6XS8HBwVqxYoW%2B/PJLLV26VCdPnvSM/7Pg4GC1tLRIkk6ePPm9874oLS1VUVGR11hmZqbnJvvLVffuER1aPjIyrJMqMZ9Ve7NqX4GGfa3zdPSz7SxW3WZW6svvAtbo0aO1cuVK5efn66c//alSU1M1ZswYzyU9X/Tu3VsfffSRunXrJpvNpkGDBqmtrU2//e1vNXLkyHZhqaWlRaGhoZKkkJCQc86Hhfm%2B0dPS0pSUlOQ1ZreH6/jxb31ehy8CLen72n9QUBdFRoapsdGl1ta2Tq7qh2XV3qzalxR4%2B5nEvtaZjD6Od5RVt1ln9mVWKPa7gPXII4/oN7/5jXbt2qUtW7booYceUmRkpKZNm6Zp06bp6quv9mk9V111ldf3AwYMUHNzs6Kjo1VfX%2B81V19f77l8Fxsbe8756Ohon3uIiYlpdznQ6fxGp09bZ2e4GB3tv7W1zbKfmVV7s2pfgYZ9rfP4y%2Bdk1W1mpb788lczm82mMWPGqLCwULt27dLtt9%2BuV155RZMnT9btt9%2BuP/3pT9/7%2Bg8%2B%2BECjRo2Sy%2BXyjH3xxRe66qqrlJCQoM8%2B%2B0xut1vSmfu19uzZI4fDIUlyOBwqKyvzvK62tla1tbWeeQAAgAvxy4AlnblRfM2aNUpLS9Py5ct1/fXXa/HixRo9erQee%2Bwx5eXlnfe18fHxCgkJ0WOPPaaDBw9qx44dKigo0H333afk5GQ1NjYqLy9P1dXVysvLk8vlUkpKiiQpPT1db7zxhjZs2KD9%2B/dr7ty5mjBhgvr27ftDtQ4AAAKc310ifOONN/TGG2/oo48%2BUlRUlKZNm6Znn33W65ELvXr1Ul5ennJzc8%2B5jq5du%2Bp3v/udnnjiCU2fPl0RERG69dZbdd9998lms2nVqlVauHCh/vCHP%2Bjaa69VSUmJwsPDJZ0JZ4sXL9azzz6rr7/%2BWmPGjNGSJUt%2BiNYBAIBF%2BF3Ays3N1cSJE1VcXKzx48erS5f2J9l%2B9KMf6Y477vje9fz4xz/WSy%2B9dM65YcOGafPmzed9bWpqqlJTUztWOAAAwP/xu4D1/vvvq3v37mpoaPCEq8rKSg0ePFhBQUGSpBEjRmjEiBFmlgkAAHBefncP1okTJ5ScnKzVq1d7xjIyMjR16lTV1taaWBkAAIBv/C5gPfHEE%2BrXr5/uvvtuz9i2bdvUq1cv5efnm1gZAACAb/wuYH366ad69NFHvZ47FRUVpblz5%2Bqvf/2riZUBAAD4xu8Clt1uV2NjY7txl8vleXYVAACAP/O7gDV%2B/HgtXbpU//jHPzxjNTU1ys/P17hx40ysDAAAwDd%2B968I582bp7vvvls333yzIiMjJUmNjY0aPHiwcnJyTK4OAADgwvwuYPXo0UObN2/Wrl27VFVVJbvdroEDB%2BrGG2/s0B98BgAAMIvfBSxJCgoK0rhx47gkCAAAApLfBSyn06kVK1Zoz549OnXqVLsb2//85z%2BbVBkAAIBv/C5g/fu//7v27t2rW265RVdeeaXZ5QAAAHSY3wWsv/71r1qzZo0SExPNLgUAAOCi%2BN1jGsLDw9WjRw%2BzywAAALhofhewpk6dqjVr1qi1tdXsUgAAAC6K310ibGho0NatW/WXv/xFffv2VXBwsNf873//e5MqAwAA8I3fBSxJmjJlitklAAAAXDS/C1j5%2BflmlwAAAHBJ/O4eLEmqq6tTUVGRHnnkEf3v//6vtm/froMHD5pdFgAAgE/8LmAdPnxYP/vZz7R582a98847ampq0rZt2zR9%2BnRVVFSYXR4AAMAF%2BV3AevLJJzVp0iS99957uuKKKyRJy5cvV1JSkp5%2B%2BmmTqwMAALgwvwtYe/bs0d133%2B31h53tdrsefPBB7du3z8TKAAAAfON3AautrU1tbW3txr/99lsFBQWZUBEAAEDH%2BF3AGjt2rFatWuUVshoaGlRYWKjRo0ebWBkAAIBv/C5gPfroo9q7d6/Gjh2r5uZmPfDAA5o4caK%2B/PJLzZs3z%2BzyAAAALsjvnoMVGxurLVu2aOvWrfriiy/U1tam9PR0TZ06VV27djW7PAAAgAvyu4AlSWFhYZo5c6bZZQAAAFwUvwtYs2bN%2Bt75jv4twoyMDEVFRenJJ5%2BUJO3bt08LFy7U3//%2Bdw0cOFCPP/64hgwZ4ll%2B69atWrFihZxOp8aOHaslS5YoKiqq440AAIDLlt/dg9W7d2%2Bvr9jYWJ08eVKVlZWKj4/v0Lrefvtt7dixw/N9U1OTMjIylJiYqE2bNik%2BPl6zZ89WU1OTJKmyslK5ubnKyspSaWmpGhsblZOTY2h/AADA%2BvzuDNb5/hZhcXGxjh496vN6GhoaVFBQoKFDh3rGtm3bppCQEM2dO1c2m025ubl6//33tX37dqWmpmrdunVKSUnRtGnTJEkFBQWaOHGiampq1Ldv30trDAAAXDb87gzW%2BUydOlV//OMffV7%2Bqaee0tSpUzVw4EDPWEVFhRISEjwPMbXZbBoxYoTKy8s984mJiZ7le/Xqpbi4OP5EDwAA6JCACVifffaZzw8a3b17tz799FM9%2BOCDXuNOp1MxMTFeYz169PCcGaurq/veeQAAAF/43SXCc93kfuLECf3tb3/TbbfddsHXNzc3a%2BHChVqwYIFCQ0O95lwul4KDg73GgoOD1dLSIkk6efLk9877qq6uTk6n02vMbg9vF94uVVBQwORjSZLd7lu9Z/sKtP58YdXerNqXFJg9sa91Hl8/285i1W1mxb78LmDFxcV5/R1CSbriiit0xx136Oc///kFX19UVKQhQ4Zo3Lhx7eZCQkLahaWWlhZPEDvffFhYWId6KC0tVVFRkddYZmamsrOzO7Qeq%2BnePaJDy0dGduxzDyRW7c2qfQUa9rXO09HPtrNYdZtZqS%2B/C1hnH6dwsd5%2B%2B23V19d7/sXh2cD0zjvvaMqUKaqvr/davr6%2B3nNmKTY29pzz0dHRHaohLS1NSUlJXmN2e7iOH/%2B2Q%2Bu5kEBL%2Br72HxTURZGRYWpsdKm1tf3fpQxkVu3Nqn1JgbefSexrncno43hHWXWbdWZfZoVivwtYn3zyic/L3nDDDe3G1q5dq9OnT3u%2Bf/rppyVJc%2BbM0SeffKLVq1fL7XbLZrPJ7XZrz549uv/%2B%2ByVJDodDZWVlSk1NlSTV1taqtrZWDoejQz3ExMS0uxzodH6j06etszNcjI7239raZtnPzKq9WbWvQMO%2B1nn85XOy6jazUl9%2BF7DuvPNOzyVCt9vtGf/umM1m0xdffNHu9b179/b6PiLiTHLt16%2BfevTooWXLlikvL0%2B33nqr1q9fL5fLpZSUFElSenq67rzzTg0fPlxDhw5VXl6eJkyYwCMaAABAh/hdwFq5cqWWLl2q3/72txo5cqSCg4P1%2Beefa/HixfrFL36hyZMnX/S6u3btqlWrVmnhwoX6wx/%2BoGuvvVYlJSUKDw%2BXJMXHx2vx4sV69tln9fXXX2vMmDFasmSJUa0BAIDLhN8FrPz8fC1YsEDjx4/3jI0ePVqLFy/W3Llz9atf/apD6/vuPV3Dhg3T5s2bz7t8amqq5xIhAADAxfC7uzfr6uraXeaTzpx9On78uAkVAQAAdIzfBazhw4dr%2BfLlOnHihGesoaFBhYWFuvHGG02sDAAAwDd%2Bd4nwscce06xZszR%2B/Hj1799fbrdb//3f/63o6Gj9/ve/N7s8AACAC/K7gDVgwABt27ZNW7du1YEDByRJt99%2Bu2655ZYOP/ATAADADH4XsCSpW7dumjlzpr788kvPIxKuuOIKk6sCAADwjd8FLLfbrWXLlmnt2rU6deqU3nnnHT3zzDMKCwvTokWLCFoAcAEpKz40uwTgsud3N7mvXbtWb7zxhhYuXOj5w8uTJk3Se%2B%2B91%2B7v%2BwEAAPgjvwtYpaWlWrBggVJTUz1Pb588ebKWLl2qt956y%2BTqAAAALszvAtaXX36pQYMGtRu/7rrr5HQ6TagIAACgY/wuYPXu3Vuff/55u/H333%2BfvwkIAAACgt/d5H7vvffq8ccfl9PplNvt1u7du1VaWqq1a9fq0UcfNbs8AACAC/K7gDV9%2BnSdPn1aL7zwgk6ePKkFCxYoKipKDz/8sNLT080uDwAA4IL8LmBt3bpVycnJSktL01dffSW3260ePXqYXRYAAIDP/O4erMWLF3tuZo%2BKiiJcAQCAgON3Aat///76%2B9//bnYZAAAAF83vLhFed911mjNnjtasWaP%2B/fsrJCTEaz4/P9%2BkygAAAHzjdwHr0KFDSkhIkCSeewUAAAKSXwSsgoICZWVlKTw8XGvXrjW7HAAAgEviF/dgvfTSS3K5XF5jGRkZqqurM6kiAACAi%2BcXAcvtdrcb%2B%2BSTT9Tc3GxCNQAAAJfGLwIWAACAlRCwAAAADOY3Actms5ldAgAAgCH84l8RStLSpUu9nnl16tQpFRYWKiIiwms5noMFAAD8nV8ErBtuuKHdM6/i4%2BN1/PhxHT9%2B3KSqAAAALo5fBCyefQUAAKzEb%2B7BAgAAsArLBqzDhw/r3nvvVXx8vCZMmKA1a9Z45mpqanTXXXdp%2BPDhmjx5snbu3On12l27dmnKlClyOByaNWuWampqfujyAQBAALNkwGpra1NGRoa6d%2B%2BuzZs36/HHH9cLL7ygt956S263W5mZmerZs6c2btyoqVOnKisrS0eOHJEkHTlyRJmZmUpNTdXrr7%2BuqKgoPfjgg%2Bd8GCoAAMC5%2BMU9WEarr6/XoEGDtGjRInXt2lX9%2B/fXjTfeqLKyMvXs2VM1NTVav369wsPDNWDAAO3evVsbN27UQw89pA0bNmjIkCG65557JJ35V4tjxozRxx9/rFGjRpncGQAACASWPIMVExOjFStWqGvXrnK73SorK9Mnn3yikSNHqqKiQtdff73Cw8M9yyckJKi8vFySVFFRocTERM9cWFiYBg8e7JkHAAC4EEuewfpnSUlJOnLkiCZOnKibb75ZTzzxhGJiYryW6dGjh44ePSpJcjqd3zvvi7q6unaPnbDbw9ut91IFBQVWPrbbfav3bF%2BB1p8vrNqbVfuSrNkTLp6vx7HOYtV9zYp9WT5gPfvss6qvr9eiRYuUn58vl8ul4OBgr2WCg4PV0tIiSRec90VpaamKioq8xjIzM5WdnX2RXVhD9%2B4RF17on0RGhnVSJeazam9W7Qs4q6MRScosAAAVu0lEQVTHsc5i1X3NSn1ZPmANHTpUktTc3Kw5c%2BZo%2BvTpcrlcXsu0tLQoNDRUkhQSEtIuTLW0tCgyMtLn90xLS1NSUpLXmN0eruPHv72YFs4r0JK%2Br/0HBXVRZGSYGhtdam1t6%2BSqflhW7c2qfUmBt5%2Bhcxl9HO8oq%2B5rndmXWaHYkgGrvr5e5eXlmjRpkmds4MCBOnXqlKKjo3Xw4MF2y5%2B9fBcbG6v6%2Bvp284MGDfL5/WNiYtpdDnQ6v9Hp09bZGS5GR/tvbW2z7Gdm1d6s2hdw1r8%2B/YHZJXTIHx8eY3YJHWKlY4glA9aXX36prKws7dixQ7GxsZKkvXv3KioqSgkJCXrxxRd18uRJz1mrsrIyJSQkSJIcDofKyso863K5XNq3b5%2BysrJ%2B%2BEYsJmXFh2aX0CGBdmACAPgPS577Hjp0qAYPHqz58%2BerurpaO3bsUGFhoe6//36NHDlSvXr1Uk5OjqqqqlRSUqLKykrNmDFDkjR9%2BnTt2bNHJSUlqqqqUk5Ojvr06cMjGgAAgM8sGbCCgoL0/PPPKywsTGlpacrNzdWdd96pWbNmeeacTqdSU1P15ptvqri4WHFxcZKkPn366LnnntPGjRs1Y8YMNTQ0qLi4WDabzeSuAABAoLDkJULpzL1U3/2XfGf169dP69atO%2B9rb7rpJt10002dVRoAALA4S57BAgAAMBMBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGCWDVjHjh1Tdna2Ro4cqXHjxik/P1/Nzc2SpJqaGt11110aPny4Jk%2BerJ07d3q9dteuXZoyZYocDodmzZqlmpoaM1oAAAABypIBy%2B12Kzs7Wy6XS6%2B%2B%2BqqeeeYZ/ed//qdWrFght9utzMxM9ezZUxs3btTUqVOVlZWlI0eOSJKOHDmizMxMpaam6vXXX1dUVJQefPBBud1uk7sCAACBwm52AZ3h4MGDKi8v14cffqiePXtKkrKzs/XUU09p/Pjxqqmp0fr16xUeHq4BAwZo9%2B7d2rhxox566CFt2LBBQ4YM0T333CNJys/P15gxY/Txxx9r1KhRZrYFAAAChCXPYEVHR2vNmjWecHXWiRMnVFFRoeuvv17h4eGe8YSEBJWXl0uSKioqlJiY6JkLCwvT4MGDPfMAAAAXYsmAFRkZqXHjxnm%2Bb2tr07p16zR69Gg5nU7FxMR4Ld%2BjRw8dPXpUki44DwAAcCGWvET4XYWFhdq3b59ef/11vfzyywoODvaaDw4OVktLiyTJ5XJ977wv6urq5HQ6vcbs9vB2we1SBQVZMh/7Dbvd%2BM/37Daz2razal%2BSNXvC5aMzjmOdwYrHEMsHrMLCQr3yyit65plndM011ygkJEQNDQ1ey7S0tCg0NFSSFBIS0i5MtbS0KDIy0uf3LC0tVVFRkddYZmamsrOzL7ILmKF794hOW3dkZFinrdtMVu0LCFSdeRzrDFY6hlg6YC1ZskSvvfaaCgsLdfPNN0uSYmNjVV1d7bVcfX295%2BxSbGys6uvr280PGjTI5/dNS0tTUlKS15jdHq7jx7%2B9mDbOy0pJ3x8Zvb2kM9ssMjJMjY0utba2Gb5%2Bs1i1L4n9DIGtM45jnaEzjyFmhUzLBqyioiKtX79ey5cvV3Jysmfc4XCopKREJ0%2Be9Jy1KisrU0JCgme%2BrKzMs7zL5dK%2BffuUlZXl83vHxMS0uxzodH6j06et9T8eq%2BvM7dXa2mbJnwer9gUEqkDbH610DLHkr2YHDhzQ888/r1/96ldKSEiQ0%2Bn0fI0cOVK9evVSTk6OqqqqVFJSosrKSs2YMUOSNH36dO3Zs0clJSWqqqpSTk6O%2BvTpwyMaAACAzywZsP785z%2BrtbVVL7zwgsaOHev1FRQUpOeff15Op1Opqal68803VVxcrLi4OElSnz599Nxzz2njxo2aMWOGGhoaVFxcLJvNZnJXAAAgUFjyEmFGRoYyMjLOO9%2BvXz%2BtW7fuvPM33XSTbrrpps4oDQAAXAYseQYLAADATAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADGb5gNXS0qIpU6boo48%2B8ozV1NTorrvu0vDhwzV58mTt3LnT6zW7du3SlClT5HA4NGvWLNXU1PzQZQMAgABm6YDV3Nys3/zmN6qqqvKMud1uZWZmqmfPntq4caOmTp2qrKwsHTlyRJJ05MgRZWZmKjU1Va%2B//rqioqL04IMPyu12m9UGAAAIMJYNWNXV1fq3f/s3/eMf//Aa/%2Btf/6qamhotXrxYAwYM0OzZszV8%2BHBt3LhRkrRhwwYNGTJE99xzj3784x8rPz9f//M//6OPP/7YjDYAAEAAsmzA%2BvjjjzVq1CiVlpZ6jVdUVOj6669XeHi4ZywhIUHl5eWe%2BcTERM9cWFiYBg8e7JkHAAC4ELvZBXSW22677ZzjTqdTMTExXmM9evTQ0aNHfZr3RV1dnZxOp9eY3R7ebr2XKijIsvnYL9jtxn%2B%2BZ7eZ1badVfuSrNkTLh%2BdcRzrDFY8hlg2YJ2Py%2BVScHCw11hwcLBaWlp8mvdFaWmpioqKvMYyMzOVnZ19kVXDDN27R3TauiMjwzpt3Wayal9AoOrM41hnsNIx5LILWCEhIWpoaPAaa2lpUWhoqGf%2Bu2GqpaVFkZGRPr9HWlqakpKSvMbs9nAdP/7tRVZ9blZK%2Bv7I6O0lndlmkZFhamx0qbW1zfD1m8WqfUnsZwhsnXEc6wydeQwxK2RedgErNjZW1dXVXmP19fWey3exsbGqr69vNz9o0CCf3yMmJqbd5UCn8xudPm2t//FYXWdur9bWNkv%2BPFi1LyBQBdr%2BaKVjyGX3q5nD4dB//dd/6eTJk56xsrIyORwOz3xZWZlnzuVyad%2B%2BfZ55AACAC7nsAtbIkSPVq1cv5eTkqKqqSiUlJaqsrNSMGTMkSdOnT9eePXtUUlKiqqoq5eTkqE%2BfPho1apTJlQMAgEBx2QWsoKAgPf/883I6nUpNTdWbb76p4uJixcXFSZL69Omj5557Ths3btSMGTPU0NCg4uJi2Ww2kysHAACB4rK4B%2Btvf/ub1/f9%2BvXTunXrzrv8TTfdpJtuuqmzywIAABZ1WQQsAAAuRykrPjS7BJ99mpdsdgmGuuwuEQIAAHQ2AhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGMxudgGAv0pZ8aHZJXTIHx8eY3YJAID/wxksAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsM6hublZ8%2BfPV2JiosaOHasXX3zR7JIAAEAA4TEN51BQUKC9e/fqlVde0ZEjRzRv3jzFxcUpOTnZ7NIAAEAAIGB9R1NTkzZs2KDVq1dr8ODBGjx4sKqqqvTqq68SsAAAgE%2B4RPgd%2B/fv1%2BnTpxUfH%2B8ZS0hIUEVFhdra2kysDAAABArOYH2H0%2BlU9%2B7dFRwc7Bnr2bOnmpub1dDQoKioqAuuo66uTk6n02vMbg9XTEyMobUGBZGP8f/Y7eb9PJz9WbTiz6QVewL8lZX2NwLWd7hcLq9wJcnzfUtLi0/rKC0tVVFRkddYVlaWHnroIWOK/D91dXX65f9XpbS0NMPDm5nq6upUWlpqub4k6/ZWV1enV15ZY7m%2BJOvuZ5K1fx6t2Jdk3d7q6ur03HPPWaov60RFg4SEhLQLUme/Dw0N9WkdaWlp2rRpk9dXWlqa4bU6nU4VFRW1O1sW6Kzal2Td3qzal0RvgciqfUnW7c2KfXEG6ztiY2N1/PhxnT59Wnb7mY/H6XQqNDRUkZGRPq0jJibGMgkcAAB0HGewvmPQoEGy2%2B0qLy/3jJWVlWno0KHq0oWPCwAAXBiJ4TvCwsI0bdo0LVq0SJWVlXrvvff04osvatasWWaXBgAAAkTQokWLFpldhL8ZPXq09u3bp2XLlmn37t26//77NX36dLPLOqeIiAiNHDlSERERZpdiKKv2JVm3N6v2JdFbILJqX5J1e7NaXza32%2B02uwgAAAAr4RIhAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAlaAam5u1vz585WYmKixY8fqxRdfNLskQ7W0tGjKlCn66KOPzC7FEMeOHVN2drZGjhypcePGKT8/X83NzWaXZYjDhw/r3nvvVXx8vCZMmKA1a9aYXZLhMjIy9Oijj5pdhmHeffddXXvttV5f2dnZZpdliJaWFj3%2B%2BOO64YYb9JOf/ETLly9XoD9Pe9OmTe2217XXXqvrrrvO7NIMUVtbq9mzZ2vEiBFKSkrSyy%2B/bHZJhrCbXQAuTkFBgfbu3atXXnlFR44c0bx58xQXF6fk5GSzS7tkzc3NeuSRR1RVVWV2KYZwu93Kzs5WZGSkXn31VX399deaP3%2B%2BunTponnz5pld3iVpa2tTRkaGhg4dqs2bN%2Bvw4cP6zW9%2Bo9jYWP3sZz8zuzxDvP3229qxY4d%2B8YtfmF2KYaqrqzVx4kQtWbLEMxYSEmJiRcZZunSpPvroI/3ud7/Tt99%2Bq1//%2BteKi4vTrbfeanZpF23y5MkaN26c5/vTp0/rl7/8pSZMmGBeUQZ6%2BOGHFRcXp02bNqm6ulpz5sxR79699a//%2Bq9ml3ZJCFgBqKmpSRs2bNDq1as1ePBgDR48WFVVVXr11VcDPmBVV1frkUceCfjfOP/ZwYMHVV5erg8//FA9e/aUJGVnZ%2Bupp54K%2BIBVX1%2BvQYMGadGiReratav69%2B%2BvG2%2B8UWVlZZYIWA0NDSooKNDQoUPNLsVQBw4c0DXXXKPo6GizSzFUQ0ODNm7cqJdeeknDhg2TJN1zzz2qqKgI6IAVGhqq0NBQz/erVq2S2%2B3WnDlzTKzKGF9//bXKy8u1ZMkS9e/fX/3799e4ceO0e/fugA9YXCIMQPv379fp06cVHx/vGUtISFBFRYXa2tpMrOzSffzxxxo1apRKS0vNLsUw0dHRWrNmjSdcnXXixAmTKjJOTEyMVqxYoa5du8rtdqusrEyffPKJRo4caXZphnjqqac0depUDRw40OxSDHXgwAH179/f7DIMV1ZWpq5du3r9/GVkZCg/P9/EqozV0NCg1atX65FHHlFwcLDZ5Vyy0NBQhYWFadOmTTp16pQOHjyoPXv2aNCgQWaXdskIWAHI6XSqe/fuXjtXz5491dzcrIaGBhMru3S33Xab5s%2Bfr7CwMLNLMUxkZKTX6f22tjatW7dOo0ePNrEq4yUlJem2225TfHy8br75ZrPLuWS7d%2B/Wp59%2BqgcffNDsUgzldrt16NAh7dy5UzfffLMmTZqkp59%2BWi0tLWaXdslqamrUu3dvbdmyRcnJyfrpT3%2Bq4uLigP/F85%2B99tpriomJCfirFWeFhIRowYIFKi0tlcPhUEpKisaPH6%2BZM2eaXdolI2AFIJfL1e43l7PfW%2BEgaXWFhYXat2%2Bffv3rX5tdiqGeffZZrVy5Ul988UXAnzFobm7WwoULtWDBAq9LM1Zw5MgRzzFkxYoVmjdvnt566y0VFBSYXdola2pq0uHDh7V%2B/Xrl5%2Bdr3rx5Wrt2rWVumna73dqwYYPuuOMOs0sx1IEDBzRx4kSVlpYqPz9f27dv15tvvml2WZeMe7ACUEhISLsgdfZ7q/3PwGoKCwv1yiuv6JlnntE111xjdjmGOnufUnNzs%2BbMmaO5c%2BcG7CWMoqIiDRkyxOvMo1X07t1bH330kbp16yabzaZBgwapra1Nv/3tb5WTk6OgoCCzS7xodrtdJ06c0LJly9S7d29JZwLla6%2B9pnvuucfk6i7d559/rmPHjumWW24xuxTD7N69W6%2B//rp27Nih0NBQDR06VMeOHdMLL7ygn//852aXd0kIWAEoNjZWx48f1%2BnTp2W3n9mETqdToaGhioyMNLk6nM%2BSJUv02muvqbCw0BKX0KQzN7mXl5dr0qRJnrGBAwfq1KlTOnHihKKiokys7uK9/fbbqq%2Bv99znePYXmHfeeUefffaZmaUZ4qqrrvL6fsCAAWpubtbXX38dsNtMOnO/Y0hIiCdcSdLVV1%2Bt2tpaE6syzgcffKDExER169bN7FIMs3fvXvXr18/r5MD111%2BvlStXmliVMbhEGIAGDRoku92u8vJyz1hZWZmGDh2qLl3YpP6oqKhI69ev1/Llyy312%2BeXX36prKwsHTt2zDO2d%2B9eRUVFBfT/qNeuXau33npLW7Zs0ZYtW5SUlKSkpCRt2bLF7NIu2QcffKBRo0bJ5XJ5xr744gtdddVVAb3NJMnhcKi5uVmHDh3yjB08eNArcAWyyspKjRgxwuwyDBUTE6PDhw97XZU5ePCg%2BvTpY2JVxuD/xgEoLCxM06ZN06JFi1RZWan33ntPL774ombNmmV2aTiHAwcO6Pnnn9evfvUrJSQkyOl0er4C3dChQzV48GDNnz9f1dXV2rFjhwoLC3X//febXdol6d27t/r16%2Bf5ioiIUEREhPr162d2aZcsPj5eISEheuyxx3Tw4EHt2LFDBQUFuu%2B%2B%2B8wu7ZL96Ec/0oQJE5STk6P9%2B/frgw8%2BUElJidLT080uzRBVVVWW%2BxetSUlJuuKKK/TYY4/p0KFD%2Bo//%2BA%2BtXLlSd955p9mlXTKb20oPHLqMuFwuLVq0SH/605/UtWtX3XvvvbrrrrvMLstQ1157rX7/%2B99r1KhRZpdySUpKSrRs2bJzzv3tb3/7gasx3rFjx7RkyRLt3r1bYWFhuuOOOzR79mzZbDazSzPM2ae4P/nkkyZXYoyqqio98cQTKi8vV0REhG699VZlZmZaYpt98803WrJkid59912FhYXptttus0xvw4YNU3FxseXuDayurlZeXp4qKysVFRWl22%2B/Xb/85S8DfpsRsAAAAAzGJUIAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACD/f9MGZP4J47n6AAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7498806200537346787”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>713</td> <td class=”number”>22.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>431</td> <td class=”number”>13.4%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>381</td> <td class=”number”>11.9%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (11)</td> <td class=”number”>689</td> <td class=”number”>21.5%</td> <td>

<div class=”bar” style=”width:96%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7498806200537346787”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>713</td> <td class=”number”>22.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:84%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CH_FOODINSEC_12_15”>CH_FOODINSEC_12_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>38</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.75811</td>

</tr> <tr>

<th>Minimum</th> <td>-4.3</td>

</tr> <tr>

<th>Maximum</th> <td>2.7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2993217927209522984”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2993217927209522984,#minihistogram2993217927209522984”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2993217927209522984”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2993217927209522984”

aria-controls=”quantiles2993217927209522984” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2993217927209522984” aria-controls=”histogram2993217927209522984”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2993217927209522984” aria-controls=”common2993217927209522984”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2993217927209522984” aria-controls=”extreme2993217927209522984”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2993217927209522984”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-4.3</td>

</tr> <tr>

<th>5-th percentile</th> <td>-3</td>

</tr> <tr>

<th>Q1</th> <td>-2</td>

</tr> <tr>

<th>Median</th> <td>-0.8</td>

</tr> <tr>

<th>Q3</th> <td>0.2</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>2.7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.4965</td>

</tr> <tr>

<th>Coef of variation</th> <td>-1.974</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.58956</td>

</tr> <tr>

<th>Mean</th> <td>-0.75811</td>

</tr> <tr>

<th>MAD</th> <td>1.247</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.35002</td>

</tr> <tr>

<th>Sum</th> <td>-2374.4</td>

</tr> <tr>

<th>Variance</th> <td>2.2395</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2993217927209522984”>

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8/dnq6779tgfo4Ha%2B%2Bn923VG6SAC1hvvvmmmpqalJKSIknuwPT2229r%2BvTpampq8li%2BqanJvesuMTHxjPPx8fHnVUNCQkKX3YF2%2B7dqb/f/P/COjs4eUadpwdo3vMPfRvfx1X0bzM/xYO3d6r4DLmBt2LBB7e3t7surV6%2BWJC1cuFC7d%2B/Wc889J5fLpZCQELlcLu3Zs0d33323JCk5OVlVVVXKzMyUJB05ckRHjhxRcnKy7xsBAAA9VsAFrIEDB3pc7t37%2B02DgwcPVlxcnB5//HGtXLlS//zP/6zNmzfL4XBo2rRpkqQ5c%2BZo7ty5GjlypEaMGKGVK1dq4sSJuuSSS3zeBwAA6LmC6qMF0dHRevbZZ91bqWpqalReXq6oqChJUkpKipYvX66ysjLNmTNHffr0UVFRkcVVAwCAnibgtmCd7tFHH/W4fNVVV2nr1q1nXT4zM9O9ixAAAOBCBNUWLAAAAF8gYAEAABjmdwFr9uzZ2rx5s7799lurSwEAALggfhewxowZo2eeeUbjxo3Tvffeqw8%2B%2BEAul8vqsgAAALzmdwHrvvvu03vvvae1a9cqNDRUCxYs0MSJE/Xkk0/qq6%2B%2Bsro8AACAc/LLTxGGhIRo7NixGjt2rBwOhzZs2KC1a9eqvLxco0aN0q233qrrrrvO6jIBAADOyC8DliQ1Njbq9ddf1%2Buvv64vv/xSo0aN0o033qijR4/qwQcf1O7du1VYWGh1mQAAAF34XcDatm2btm3bpk8%2B%2BUSxsbGaOXOmnnrqKQ0ZMsS9TP/%2B/bVy5UoCFgAA8Et%2BF7AKCws1adIklZWVacKECerVq%2BthYj/72c90yy23WFAdAADAufldwHr//ffVt29fNTc3u8PV3r17lZSUpNDQUEnSqFGjNGrUKCvLBAAAOCu/%2BxThiRMnNHXqVD333HPusZycHM2YMUNHjhyxsDIAAADv%2BF3AWrVqlQYPHqzbb7/dPbZjxw7179%2BfL14GAAA9gt8FrM8%2B%2B0z333%2B/4uPj3WOxsbFatGiRPv74YwsrAwAA8I7fBSybzaaWlpYu4w6HgzO6AwCAHsHvAtaECRO0YsUK/fWvf3WP1dfXq6ioSOPHj7ewMgAAAO/43acIFy9erNtvv11TpkxRTEyMJKmlpUVJSUkqKCiwuDoAAIBz87uAFRcXp61bt%2BrDDz9UXV2dbDabhg0bpmuuuUYhISFWlwcAAHBOfhewJCk0NFTjx49nlyAAAOiR/C5g2e12rVmzRnv27NGpU6e6HNj%2B7rvvWlQZAACAd/wuYD300EPat2%2BfbrjhBl188cVWlwMAAHDe/C5gffzxx1q/fr3S0tKsLgUAAOCC%2BN1pGqKiohQXF2d1GQAAABfM7wLWjBkztH79enV0dFhdCgAAwAXxu12Ezc3N2r59u/7whz/okksuUVhYmMf8yy%2B/bFFlAAAA3vG7gCVJ06dPt7oEAACAC%2BZ3AauoqMjqEgAAAP5f/O4YLElqbGxUaWmp7rvvPv3tb3/TW2%2B9pUOHDlldFgAAgFf8LmB9/fXX%2BuUvf6mtW7fq7bffVmtrq3bs2KGsrCzV1NRYXR4AAMA5%2BV3AevTRRzV58mS98847uuiiiyRJTzzxhDIyMrR69WqLqwMAADg3vwtYe/bs0e233%2B7xxc42m0333HOPamtrLawMAADAO34XsDo7O9XZ2dll/LvvvlNoaKgFFQEAAJwfvwtY48aN07PPPusRspqbm1VSUqIxY8ZYWBkAAIB3/C5g3X///dq3b5/GjRuntrY2/frXv9akSZN0%2BPBhLV682OryAAAAzsnvzoOVmJio3/3ud9q%2Bfbu%2B%2BOILdXZ2as6cOZoxY4aio6OtLg8AAOCc/C5gSVJkZKRmz55tdRkAIEmatmaX1SUA6GH8LmDNmzfvR%2Bf5LkIAAODv/C5gDRw40ONye3u7vv76a3355Ze69dZbLaoKAADAe34XsM72XYRlZWU6evSoj6sBAAA4f34XsM5mxowZmjlzph555BGvlv/666%2B1fPly7dmzR3369NEtt9yiO%2B64Q5JUX1%2Bvhx56SNXV1RowYIAeeOABjRs3zn3dDz/8UKtWrVJ9fb2Sk5O1cuVKXXLJJd3SVzDpacex/P43Y60uAQDQQ/ndaRrO5k9/%2BpPXJxrt7OxUTk6O%2Bvbtq61bt2rZsmVat26d3njjDblcLuXm5qpfv36qrKzUjBkzlJeXp4aGBklSQ0ODcnNzlZmZqVdffVWxsbG655575HK5urM9AAAQQPxuC9aZDnI/ceKE/vKXv%2Bimm27yah1NTU0aPny4li5dqujoaA0ZMkTXXHONqqqq1K9fP9XX12vz5s2KiorS0KFD9dFHH6myslILFizQli1bdOWVV2r%2B/PmSvt9lOXbsWH366acaPXq00V4BAEBg8ruANWDAAI/vIZSkiy66SLfccov%2B8R//0at1JCQkaM2aNZIkl8ulPXv2aPfu3Xr44YdVU1OjK664QlFRUe7lU1NTVV1dLUmqqalRWlqaey4yMlJJSUmqrq4mYAEAAK/4XcB69NFHja4vIyNDDQ0NmjRpkqZMmaJVq1YpISHBY5m4uDj3AfR2u/1H5wEAAM7F7wLW7t27vV726quvPucyTz31lJqamrR06VIVFRXJ4XAoLCzMY5mwsDA5nU5JOue8NxobG2W32z3GbLaoLsHNn4SG9vL4Dclm474Ault3P8%2BC%2BbUtWHv3l779LmDNnTvXvYvw7w8sP30sJCREX3zxxTnXN2LECElSW1ubFi5cqKysLDkcDo9lnE6nIiIiJEnh4eFdwpTT6VRMTIzXPVRUVKi0tNRjLDc3V/n5%2BV6vwyoxMZFWl%2BA3%2BvbtbXUJQMDz1fMsmF/bgrV3q/v2u4D1zDPPaMWKFfrtb3%2Br9PR0hYWF6c9//rOWL1%2BuG2%2B8Uddff/0519HU1KTq6mpNnjzZPTZs2DCdOnVK8fHxOnToUJflf9i6lJiYqKampi7zw4cP97qH7OxsZWRkeIzZbFE6fvw7r9fha6GhvRQTE6mWFoc6OjqtLscv%2BPPjBQSK7n6eBfNrW7D2fnrfVr1Z9ruAVVRUpCVLlmjChAnusTFjxmj58uVatGiR7rzzznOu4/Dhw8rLy9POnTuVmJgoSdq3b59iY2OVmpqqF154QSdPnnRvtaqqqlJqaqokKTk5WVVVVe51ORwO1dbWKi8vz%2BseEhISuuwOtNu/VXu7//%2BBd3R09og6fYH7Aeh%2BvnqeBfNrW7D2bnXffrdjtrGxscvX5UhSdHS0jh8/7tU6RowYoaSkJD3wwAM6cOCAdu7cqZKSEt19991KT09X//79VVBQoLq6OpWXl2vv3r2aNWuWJCkrK0t79uxReXm56urqVFBQoEGDBvEJQgAA4DW/C1gjR47UE088oRMnTrjHmpubVVJSomuuucardYSGhmrt2rWKjIxUdna2CgsLNXfuXM2bN889Z7fblZmZqddff11lZWUaMGCAJGnQoEF6%2BumnVVlZqVmzZqm5uVllZWVdTh0BAABwNn63i/DBBx/UvHnzNGHCBA0ZMkQul0v//d//rfj4eL388sterycxMbHLgeY/GDx4sDZu3HjW61577bW69tprz7t2AAAAyQ8D1tChQ7Vjxw5t375dBw8elCTdfPPNuuGGGxQZGZyfhAAAAD2L3wUsSerTp49mz56tw4cPu79k%2BaKLLrK4KgAAAO/43TFYLpdLq1ev1tVXX63p06fr6NGjWrx4sQoLC3Xq1CmrywMAADgnvwtYGzZs0LZt2/Twww%2B7z6g%2BefJkvfPOO2c9pgoAAMCf%2BF3Aqqio0JIlS5SZmen%2B5N7111%2BvFStW6I033rC4OgAAgHPzu4B1%2BPDhM541/fLLL%2B/y/X4AAAD%2ByO8C1sCBA/XnP/%2B5y/j777/vPuAdAADAn/ndpwh/9atfadmyZbLb7XK5XProo49UUVGhDRs26P7777e6PAAAgHPyu4CVlZWl9vZ2rVu3TidPntSSJUsUGxur3/zmN5ozZ47V5QEAAJyT3wWs7du3a%2BrUqcrOztaxY8fkcrkUFxdndVkAAABe87tjsJYvX%2B4%2BmD02NpZwBQAAehy/C1hDhgzRl19%2BaXUZAAAAF8zvdhFefvnlWrhwodavX68hQ4YoPDzcY76oqMiiygAAALzjdwHrq6%2B%2BUmpqqiRx3isAANAj%2BUXAKi4uVl5enqKiorRhwwarywEAAPh/8YtjsF588UU5HA6PsZycHDU2NlpUEQAAwIXzi4Dlcrm6jO3evVttbW0WVAMAAPD/4xcBCwAAIJAQsAAAAAzzm4AVEhJidQkAAABG%2BMWnCCVpxYoVHue8OnXqlEpKStS7d2%2BP5TgPFgAA8Hd%2BEbCuvvrqLue8SklJ0fHjx3X8%2BHGLqgIAALgwfhGwOPcVAAAIJH5zDBYAAECgIGABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMwvvosQAACYl1b4ltUleO33vxlrdQlGsQULAADAMAIWAACAYQQsAAAAwwhYAAAAhgVswPrmm2%2BUn5%2Bv9PR0jR8/XkVFRWpra5Mk1dfX67bbbtPIkSN1/fXX64MPPvC47ocffqjp06crOTlZ8%2BbNU319vRUtAACAHiogA5bL5VJ%2Bfr4cDodeeeUVPfnkk3rvvfe0Zs0auVwu5ebmql%2B/fqqsrNSMGTOUl5enhoYGSVJDQ4Nyc3OVmZmpV199VbGxsbrnnnvkcrks7goAAPQUAXmahkOHDqm6ulq7du1Sv379JEn5%2Bfl67LHHNGHCBNXX12vz5s2KiorS0KFD9dFHH6myslILFizQli1bdOWVV2r%2B/PmSpKKiIo0dO1affvqpRo8ebWVbAACghwjILVjx8fFav369O1z94MSJE6qpqdEVV1yhqKgo93hqaqqqq6slSTU1NUpLS3PPRUZGKikpyT0PAABwLgG5BSsmJkbjx493X%2B7s7NTGjRs1ZswY2e12JSQkeCwfFxeno0ePStI5573R2Ngou93uMWazRXVZrz8JDe3l8RuSzcZ9AXS37n6eBfNrW0/r2dTfgr885gEZsE5XUlKi2tpavfrqq/q3f/s3hYWFecyHhYXJ6XRKkhwOx4/Oe6OiokKlpaUeY7m5ucrPz7/ADnwnJibS6hL8Rt%2B%2Bva0uAQh4vnqe8drm/0z/LVj9mAd8wCopKdFLL72kJ598UpdeeqnCw8PV3NzssYzT6VRERIQkKTw8vEuYcjqdiomJ8fo2s7OzlZGR4TFms0Xp%2BPHvLrCL7hca2ksxMZFqaXGoo6PT6nL8gj8/XkCg6O7nWTC/tlm9Bed8mfpbOP0xt%2BrNckAHrEceeUSbNm1SSUmJpkyZIklKTEzUgQMHPJZrampy775LTExUU1NTl/nhw4d7fbsJCQlddgfa7d%2Bqvd3/n9wdHZ09ok5f4H4Aup%2Bvnme8tvk/04%2BP1Y95z4q356G0tFSbN2/WE088oRtuuME9npycrM8//1wnT550j1VVVSk5Odk9X1VV5Z5zOByqra11zwMAAJxLQG7BOnjwoNauXaucnBylpqZ6HHCenp6u/v37q6CgQPfcc4/ee%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%2BbNU319va/LBgAAPVhAB6y2tjbde%2B%2B9qqurc4%2B5XC7l5uaqX79%2Bqqys1IwZM5RmIRwXAAANC0lEQVSXl6eGhgZJUkNDg3Jzc5WZmalXX31VsbGxuueee%2BRyuaxqAwAA9DABG7AOHDigf/qnf9Jf//pXj/GPP/5Y9fX1Wr58uYYOHaq77rpLI0eOVGVlpSRpy5YtuvLKKzV//nz9/Oc/V1FRkf7nf/5Hn376qRVtAACAHihgA9ann36q0aNHq6KiwmO8pqZGV1xxhaKiotxjqampqq6uds%2BnpaW55yIjI5WUlOSeBwAAOBeb1QV0l5tuuumM43a7XQkJCR5jcXFxOnr0qFfzAAAA5xKwAetsHA6HwsLCPMbCwsLkdDq9mvdGY2Oj7Ha7x5jNFtUluPmT0NBeHr8BAPAlm83M/x9/%2BX8WdAErPDxczc3NHmNOp1MRERHu%2BdPDlNPpVExMjNe3UVFRodLSUo%2Bx3Nxc5efnX2DVvhMTE2l1CQCAINS3b2%2Bj67P6/1nQBazExEQdOHDAY6ypqcm9dSkxMVFNTU1d5ocPH%2B71bWRnZysjI8NjzGaL0vHj311g1d0vNLSXYmIi1dLiUEdHp9XlAACCjKn/kaf/PzMd3LwVdAErOTlZ5eXlOnnypHurVVVVlVJTU93zVVVV7uUdDodqa2uVl5fn9W0kJCR02R1ot3%2Br9nb/Dy4dHZ09ok4AQGAx/b/H6v9nQXfATXp6uvr376%2BCggLV1dWpvLxce/fu1axZsyRJWVlZ2rNnj8rLy1VXV6eCggINGjRIo0ePtrhyAADQUwRdwAoNDdXatWtlt9uVmZmp119/XWVlZRowYIAkadCgQXr66adVWVmpWbNmqbm5WWVlZQoJCbG4cgAA0FOEuDhFuU/Y7d9aXcKPstl6qW/f3jp%2B/Ltu26Q6bc2ublkvAKDn%2B/1vxhpZz%2Bn/z%2BLjLzay3vMVdFuwAAAAuhsBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZrO6AH/U1tamZcuW6T/%2B4z8UERGh%2BfPna/78%2BVaXdUbT1uyyugQAAHAaAtYZFBcXa9%2B%2BfXrppZfU0NCgxYsXa8CAAZo6darVpQEAgB6AgHWa1tZWbdmyRc8995ySkpKUlJSkuro6vfLKKwQsAADgFY7BOs3%2B/fvV3t6ulJQU91hqaqpqamrU2dlpYWUAAKCnYAvWaex2u/r27auwsDD3WL9%2B/dTW1qbm5mbFxsaecx2NjY2y2%2B0eYzZblBISEozXCwBAILDZzGzzCQ3t5fHbKgSs0zgcDo9wJcl92el0erWOiooKlZaWeozl5eVpwYIFZor8O5%2BtNLPbsrGxURUVFcrOzg6qIEjfwdW3FLy903dw9S0Fb%2B%2BNjY166aX1lvfNLsLThIeHdwlSP1yOiIjwah3Z2dl67bXXPH6ys7ON12qS3W5XaWlply1vgY6%2Bg6tvKXh7p%2B/g6lsK3t79pW%2B2YJ0mMTFRx48fV3t7u2y27%2B8eu92uiIgIxcTEeLWOhISEoHq3AAAAPLEF6zTDhw%2BXzWZTdXW1e6yqqkojRoxQr17cXQAA4NxIDKeJjIzUzJkztXTpUu3du1fvvPOOXnjhBc2bN8/q0gAAQA8RunTp0qVWF%2BFvxowZo9raWj3%2B%2BOP66KOPdPfddysrK8vqsrpd7969lZ6ert69e1tdik/Rd3D1LQVv7/QdXH1Lwdu7P/Qd4nK5XJbdOgAAQABiFyEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWzmjZsmWaO3eu1WX4zN/%2B9jfl5%2BcrNTVVY8eOVUlJidrb260uq9u1tLSosLBQv/jFLzRmzBjdf//9amlpsbosn3G5XJo/f75ee%2B01q0vpVm1tbXrggQeUlpamcePG6YUXXrC6JJ9yOp2aPn26PvnkE6tL8YlvvvlG%2Bfn5Sk9P1/jx41VUVKS2tjary/KJr7/%2BWr/61a%2BUkpKiiRMnav369ZbVQsBCF3v27NGmTZusLsOnFi5cqBMnTqiiokL/%2Bq//qjfffNPSJ6avPPzww9q/f7/Ky8v1/PPP6%2BDBg3rwwQetLssnOjs7tWLFCu3atcvqUrpdcXGx9u3bp5deekkPP/ywSktL9dZbb1ldlk%2B0tbXp3nvvVV1dndWl%2BITL5VJ%2Bfr4cDodeeeUVPfnkk3rvvfe0Zs0aq0vrdp2dncrJyVHfvn21detWLVu2TOvWrdMbb7xhST02S24VfsvpdGrJkiUaOXKk1aX4jNPpVFxcnBYsWKDBgwdLkqZMmaKqqiqLK%2Btera2tevvtt7Vp0yZdeeWVkqQHHnhAN998s9ra2hQeHm5xhd3nm2%2B%2B0cKFC3X48GHFxMRYXU63am1t1ZYtW/Tcc88pKSlJSUlJqqur0yuvvKKpU6daXV63OnDggO677z4F0zfCHTp0SNXV1dq1a5f69esnScrPz9djjz2mxYsXW1xd92pqatLw4cO1dOlSRUdHa8iQIbrmmmtUVVWlX/7ylz6vhy1Y8FBeXq7LLrtMY8eOtboUnwkLC9Pq1avd4aqurk7/9V//pfT0dIsr6169evXSM888o%2BHDh3uMd3R06LvvvrOoKt/4/PPP1b9/f1VWVuriiy%2B2upxutX//frW3tyslJcU9lpqaqpqaGnV2dlpYWff79NNPNXr0aFVUVFhdis/Ex8dr/fr17nD1gxMnTlhUke8kJCRozZo1io6OlsvlUlVVlXbv3m3ZazlbsOB28OBBbdq0Sdu2bQu6XYQ/uOWWW7R7924lJSXp5ptvtrqcbhUREaEJEyZ4jL388su67LLLFBsba1FVvpGRkaGMjAyry/AJu92uvn37KiwszD3Wr18/tbW1qbm5OaAf65tuusnqEnwuJiZG48ePd1/u7OzUxo0bNWbMGAur8r2MjAw1NDRo0qRJmjJliiU1ELCCyMmTJ/XNN9%2BccS4%2BPl5LlizRggULurzzCQTn6j0qKkqS9OCDD%2Bp///d/tWLFCt1777165plnfFmmcd72LUkbN27U73//%2B4A49ux8%2Bg50DofDI1xJcl92Op1WlAQfKikpUW1trV599VWrS/Gpp556Sk1NTVq6dKmKioosObaUgBVEampqNG/evDPO3Xfffero6FB2draPq/KNH%2Bu9rKxMkydPliRdfvnlkqRVq1Zp1qxZOnz4sAYNGuSzOk3ztu9XXnlFK1asUEFBgcaNG%2BfLEruFt30Hg/Dw8C5B6ofLERERVpQEHykpKdFLL72kJ598UpdeeqnV5fjUiBEjJH3/IYeFCxdq0aJFXd5odDcCVhAZPXq0/vKXv5xxbu7cudq3b59GjRolSTp16pQ6OjqUkpKiN998UwMGDPBlqcb9WO8nTpzQjh07NHXqVPXq9f1hicOGDZMkHT9%2BvEcHrB/r%2BwfPP/%2B8iouLtWjRIt16660%2Bqqx7edN3sEhMTNTx48fV3t4um%2B37l3y73a6IiIiAP8A/mD3yyCPatGmTSkpKLNtF5mtNTU2qrq72eAM1bNgwnTp1SidOnPD57nACFiRJq1ev1smTJ92XN2zYoJqaGq1evVoJCQkWVtb9HA6H/uVf/kX9%2B/d3Hwj8%2BeefKzQ0VD/96U8trq57bd26VcXFxSooKNBtt91mdTnoBsOHD5fNZlN1dbXS0tIkSVVVVRoxYoT7DQUCS2lpqTZv3qwnnngi4D8p%2BvcOHz6svLw87dy5U4mJiZKkffv2KTY21pJjDXl2QdL373IHDx7s/unTp48iIiI0ePBg97veQBUfH6/rrrtOjzzyiGpra/XZZ5%2BpsLBQt9xyi6Kjo60ur9s0Nzdr%2BfLluvHGG3XDDTfIbre7fzo6OqwuD4ZERkZq5syZWrp0qfbu3at33nlHL7zwwll3oaJnO3jwoNauXas777xTqampHs/rQDdixAglJSXpgQce0IEDB7Rz506VlJTo7rvvtqSewP7PCXhp1apVWrVqlW6//XZJ0syZM3XfffdZXFX32rVrl1pbW7V161Zt3brVY%2B7dd9/t0btG4amgoEBLly7VrbfequjoaC1YsEDXXXed1WWhG7z77rvq6OjQunXrtG7dOo%2B5QN9tHhoaqrVr1%2BqRRx5Rdna2IiMjNXfuXMveTIS4gukMbAAAAD7ALkIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AC80FIp40y2mAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2993217927209522984”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-3.0</td> <td class=”number”>312</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.0</td> <td class=”number”>267</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2</td> <td class=”number”>203</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>188</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.9</td> <td class=”number”>148</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5</td> <td class=”number”>117</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (27)</td> <td class=”number”>1359</td> <td class=”number”>42.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2993217927209522984”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-4.3</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.6</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.0</td> <td class=”number”>312</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.9</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.4</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.4</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CH_VLFOODSEC_12_15”>CH_VLFOODSEC_12_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>28</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.10093</td>

</tr> <tr>

<th>Minimum</th> <td>-2.6</td>

</tr> <tr>

<th>Maximum</th> <td>2.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-424863190207511903”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-424863190207511903,#minihistogram-424863190207511903”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-424863190207511903”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-424863190207511903”

aria-controls=”quantiles-424863190207511903” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-424863190207511903” aria-controls=”histogram-424863190207511903”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-424863190207511903” aria-controls=”common-424863190207511903”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-424863190207511903” aria-controls=”extreme-424863190207511903”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-424863190207511903”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-2.6</td>

</tr> <tr>

<th>5-th percentile</th> <td>-1.1</td>

</tr> <tr>

<th>Q1</th> <td>-0.5</td>

</tr> <tr>

<th>Median</th> <td>-0.2</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1</td>

</tr> <tr>

<th>Maximum</th> <td>2.9</td>

</tr> <tr>

<th>Range</th> <td>5.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.79923</td>

</tr> <tr>

<th>Coef of variation</th> <td>-7.919</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.4345</td>

</tr> <tr>

<th>Mean</th> <td>-0.10093</td>

</tr> <tr>

<th>MAD</th> <td>0.57788</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.2103</td>

</tr> <tr>

<th>Sum</th> <td>-316.1</td>

</tr> <tr>

<th>Variance</th> <td>0.63878</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-424863190207511903”>

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x0fzJkyebvP6Kigq53e6AMYcjptHrXs0iIsIDfuLy0D9rORz0WWK/ayn613zB2LuQDFiS9MUXX2j48OG69957VVZWpsWLF%2BuWW26Rx%2BNRZGRkwLKRkZHyer2SpLNnz150vimKi4tVUFAQMJaVlaXs7Oxmbk3oio2NtruEoEb/rBEX18buEloV9ruWoX/NF0y9C8mAtWfPHm3evFkffPCBoqKi1LdvX3377bdauXKlunbt2igseb1eRUVFSfrh6NeF5qOjm/4fNTMzU%2Bnp6QFjDkeMqqpON3OLQk9ERLhiY6NVU%2BNRfX2D3eUEHfpnLd67P2C/axn613wt6Z1dvyCFZMA6ePCgrr/%2Ben9okqQbb7xRq1atUlpamiorKwOWr6ys9J%2B%2BS0pKuuB8QkJCk9efmJjY6HSg2/296up4Q52vvr6BvrQA/bMGPQ7Eftcy9K/5gql3wXMy8zIkJibqq6%2B%2BCjgSdfToUXXp0kXJycn69NNP5fP5JEk%2Bn0/79%2B9XcnKyJCk5OVklJSX%2B5504cUInTpzwzwMAAFyK5QFr4sSJ2rhxo77//vsrto709HRdc801euKJJ/Tll1/qv//7v7Vq1SpNnjxZI0eOVE1NjZYuXaojR45o6dKl8ng8GjVqlCRp0qRJevPNN7Vp0yYdPnxYc%2BbM0a233qquXbtesXoBAEBosTxgDRo0SKtWrdKQIUM0e/Zs/elPf/IfTTKlXbt2WrNmjdxutyZMmKC8vDw9%2BOCDyszMVNu2bfXSSy%2BppKREGRkZcrlcKioqUkxMjCQpJSVFixYtUmFhoSZNmqRrr71WeXl5RusDAAChLcxnOt00gc/n00cffaQ33nhDO3fuVGxsrMaNG6dx48bphhtusLocS7jdV%2B6IXTByOMIVF9dGVVWng%2BZ8emtyof6NWr7b5qpC13sPD7a7hFaB923L0L/ma0nvEhLaXaGqLs6Wi9zDwsI0ePBgDR48WB6PR%2BvXr9eKFStUVFSk/v37a%2BrUqbrtttvsKA0AAKDFbPsrwoqKCr311lt666239Pnnn6t///76xS9%2BoZMnT%2BqJJ57QJ598otzcXLvKAwAAaDbLA9abb76pN998U3v37lV8fLzGjRun3/72t%2BrWrZt/mY4dO2rp0qUELAAAEJQsD1i5ubkaPny4CgsLNWzYMIWHN77O/ic/%2BYl%2B9atfWV0aAACAEZYHrA8//FBxcXGqrq72h6sDBw6od%2B/eioiIkCT1799f/fv3t7o0AAAAIyy/TcOpU6c0cuRIvfzyy/6x6dOna%2BzYsTpx4oTV5QAAABhnecB66qmndP311%2Bvee%2B/1j7377rvq2LEj95sCAAAhwfKA9ec//1mPP/54wHf7xcfHa86cOfr444%2BtLgcAAMA4ywOWw%2BFQTU1No3GPx2P8ju4AAAB2sDxgDRs2TEuWLNHXX3/tHysvL1deXp6GDh1qdTkAAADGWf5XhHPnztW9996r22%2B/XbGxsZKkmpoa9e7dWzk5OVaXAwAAYJzlAat9%2B/batm2bPvroI5WVlcnhcKhHjx665ZZbFBYWZnU5AAAAxtnyVTkREREaOnQopwQBAEBIsjxgud1uLV%2B%2BXPv379e5c%2BcaXdi%2Ba9cuq0sCAAAwyvKA9Zvf/EYHDx7U6NGj1a5dO6tXDwAAcMVZHrA%2B/vhjrV69WmlpaVavGgAAwBKW36YhJiZG7du3t3q1AAAAlrE8YI0dO1arV69WfX291asGAACwhOWnCKurq7V9%2B3b98Y9/VNeuXRUZGRkwv27dOqtLAgAAMMqW2zSMGTPGjtUCAABYwvKAlZeXZ/UqAQAALGX5NViSVFFRoYKCAj366KP6xz/%2Boffff19Hjx61oxQAAADjLA9YX331le68805t27ZNO3bs0JkzZ/Tuu%2B9q/PjxcrlcVpcDAABgnOUB6%2Bmnn9aIESO0c%2BdOXXPNNZKk559/Xunp6Xr22WetLgcAAMA4ywPW/v37de%2B99wZ8sbPD4dDMmTN16NAhq8sBAAAwzvKA1dDQoIaGhkbjp0%2BfVkREhNXlAAAAGGd5wBoyZIheeumlgJBVXV2t/Px8DRo0yOpyAAAAjLM8YD3%2B%2BOM6ePCghgwZotraWj344IMaPny4jh07prlz51pdDgAAgHGW3wcrKSlJb7zxhrZv366//vWvamho0KRJkzR27Fi1bdvW6nIAAACMs%2BVO7tHR0Zo4caIdqwYAALjiLA9YU6ZMueg830UIAACCneUBq3PnzgGP6%2Brq9NVXX%2Bnzzz/X1KlTrS4HAADAuFbzXYSFhYU6efKkxdUAAACYZ8t3EV7I2LFj9d5779ldBgAAQIu1moD16aefcqNRAAAQElrFRe6nTp3S3/72N91zzz1WlwMAAGCc5QGrU6dOAd9DKEnXXHONfvWrX%2BnnP/%2B51eUAAAAYZ3nAevrppy1Zj9frVV5enrZv365rrrlGEyZM0COPPKKwsDAdOnRITz75pD7//HP16NFDCxcuVJ8%2BffzP3b59u5YvXy63260hQ4Zo8eLFio%2BPt6RuAAAQ/CwPWJ988kmTl7355pubvZ4lS5Zo7969euWVV3T69Gk98sgj6tSpk37%2B859r%2BvTpuvPOO/X000/r9ddf14wZM/T73/9eMTExOnDggHJzc7Vw4UL17NlTS5cuVU5Ojl566aVm1wIAAK4ulgesyZMn%2B08R%2Bnw%2B//j5Y2FhYfrrX//arHVUV1dry5Yt%2Bt3vfqebbrpJkjRt2jS5XC45HA45nU7NmTNHYWFhys3N1Ycffqj3339fGRkZ2rBhg0aNGqVx48ZJkpYtW6bhw4ervLxcXbt2bfZ2AwCAq4flf0W4atUqde7cWcuXL9eePXtUUlKiNWvW6IYbbtDs2bO1a9cu7dq1Szt37mz2OkpKStS2bVsNGDDAPzZ9%2BnTl5eXJ5XIpNTXVH%2BjCwsLUv39/lZaWSpJcLpfS0tL8z%2BvYsaM6deokl8vV7HoAAMDVxZYbjc6fP1/Dhg3zjw0aNEiLFi3SnDlzdP/997d4HeXl5ercubPeeOMNrVq1SufOnVNGRoYefPBBud1u9ejRI2D59u3bq6ysTJJUUVGhxMTERvOXcxPUiooKud3ugDGHI6bR617NIiLCA37i8tA/azkc9Fliv2sp%2Btd8wdg7ywNWRUVFo6/LkaS2bduqqqrKyDrOnDmjr776Shs3blReXp7cbrfmz5%2Bv6OhoeTweRUZGBiwfGRkpr9crSTp79uxF55uiuLhYBQUFAWNZWVnKzs5u5haFrtjYaLtLCGr0zxpxcW3sLqFVYb9rGfrXfMHUO8sDVr9%2B/fT888/rmWeeUdu2bSX9cM1Ufn6%2BbrnlFiPrcDgcOnXqlJ577jl/mDt%2B/Lhef/11XX/99Y3CktfrVVRUlCTJ6XRecD46uun/UTMzM5Wenn5eTTGqqjrdnM0JSRER4YqNjVZNjUf19Q12lxN06J%2B1eO/%2BgP2uZehf87Wkd3b9gmR5wHriiSc0ZcoUDRs2TN26dZPP59Pf//53JSQkaN26dUbWkZCQIKfTGXCk7IYbbtCJEyc0YMAAVVZWBixfWVnpP32XlJR0wfmEhIQmrz8xMbHR6UC3%2B3vV1fGGOl99fQN9aQH6Zw16HIj9rmXoX/MFU%2B8sP5nZvXt3vfvuu3r00UfVr18/paSkKDc3V2%2B%2B%2Bab%2B5V/%2Bxcg6kpOTVVtbqy%2B//NI/dvToUXXu3FnJycn69NNP/X%2Bt6PP5tH//fiUnJ/ufW1JS4n/eiRMndOLECf88AADApVh%2BBEuSrr32Wk2cOFHHjh3z3/rgmmuuMfb6P/nJT3TrrbcqJydHCxYskNvtVlFRkR588EGNHDlSzz33nJYuXaq7775bGzdulMfj0ahRoyRJkyZN0uTJk9WvXz/17dtXS5cu1a233sotGgAAQJNZfgTL5/Pp2Wef1c0336wxY8bo5MmTmjt3rnJzc3Xu3Dlj63n22Wf1r//6r5o0aZLmzp2rX/7yl5o8ebLatm2rl156SSUlJcrIyJDL5VJRUZFiYmIkSSkpKVq0aJEKCws1adIkXXvttcrLyzNWFwAACH2WH8Fav3693nzzTT355JNatGiRJGnEiBFauHChOnTooEceecTIetq1a6dly5ZdcO6mm27Stm3b/ulzMzIylJGRYaQOAABw9bH8CFZxcbHmz5%2BvjIwM/80%2B77jjDi1ZskRvv/221eUAAAAYZ3nAOnbsmHr16tVovGfPno1uzgkAABCMLA9YnTt31meffdZo/MMPP%2BRCcgAAEBIsvwbrvvvu08KFC%2BV2u%2BXz%2BbRnzx4VFxdr/fr1evzxx60uBwAAwDjLA9b48eNVV1enlStX6uzZs5o/f77i4%2BP18MMPa9KkSVaXAwAAYJzlAWv79u0aOXKkMjMz9d1338nn86l9%2B/ZWlwEAAHDFWH4N1qJFi/wXs8fHxxOuAABAyLE8YHXr1k2ff/651asFAACwjOWnCHv27KnHHntMq1evVrdu3eR0OgPmuWs6AAAIdpYHrC%2B//FKpqamSxH2vAABASLIkYC1btkyzZs1STEyM1q9fb8UqAQAAbGPJNVi/%2B93v5PF4AsamT5%2BuiooKK1YPAABgKUsCls/nazT2ySefqLa21orVAwAAWMryvyIEAAAIdQQsAAAAwywLWGFhYVatCgAAwFaW3aZhyZIlAfe8OnfunPLz89WmTZuA5bgPFgAACHaWBKybb7650T2vUlJSVFVVpaqqKitKAAAAsIwlAYt7XwEAgKsJF7kDAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAs5APW9OnT9fjjj/sfHzp0SBMnTlRycrLGjx%2BvgwcPBiy/fft2jRgxQsnJycrKytJ3331ndckAACDIhXTAeuedd/TBBx/4H585c0bTp09XWlqatm7dqpSUFM2YMUNnzpyRJB04cEC5ubmaNWuWiouLVVNTo5ycHLvKBwAAQSpkA1Z1dbWWLVumvn37%2BsfeffddOZ1OzZkzR927d1dubq7atGmj999/X5K0YcMGjRo1SuPGjVPPnj21bNkyffDBByovL7drMwAAQBAK2YD1zDPPaOzYserRo4d/zOVyKTU1VWFhYZKksLAw9e/fX6Wlpf75tLQ0//IdO3ZUp06d5HK5rC0eAAAEtZAMWHv27NGf//xnzZw5M2Dc7XYrMTExYKx9%2B/Y6efKkJKmiouKi8wAAAE3hsLsA02pra/Xkk09q/vz5ioqKCpjzeDyKjIwMGIuMjJTX65UknT179qLzTVVRUSG32x0w5nDENApvV7OIiPCAn7g89M9aDgd9ltjvWor%2BNV8w9i7kAlZBQYH69OmjoUOHNppzOp2NwpLX6/UHsX82Hx0dfVk1FBcXq6CgIGAsKytL2dnZl/U6V4PY2MvrLQLRP2vExbWxu4RWhf2uZehf8wVT70IuYL3zzjuqrKxUSkqKJPkD044dOzRmzBhVVlYGLF9ZWek/spSUlHTB%2BYSEhMuqITMzU%2Bnp6QFjDkeMqqpOX9brhLKIiHDFxkarpsaj%2BvoGu8sJOvTPWrx3f8B%2B1zL0r/la0ju7fkEKuYC1fv161dXV%2BR8/%2B%2ByzkqTHHntMn3zyiV5%2B%2BWX5fD6FhYXJ5/Np//79euCBByRJycnJKikpUUZGhiTpxIkTOnHihJKTky%2BrhsTExEanA93u71VXxxvqfPX1DfSlBeifNehxIPa7lqF/zRdMvQu5gNW5c%2BeAx23a/JBcr7/%2BerVv317PPfecli5dqrvvvlsbN26Ux%2BPRqFGjJEmTJk3S5MmT1a9fP/Xt21dLly7Vrbfeqq5du1q%2BHQAAIHgFz9ViBrRt21YvvfSS/yiVy%2BVSUVGRYmJiJEkpKSlatGiRCgsLNWnSJF177bXKy8uzuWoAABBswnw%2Bn8/uIq4Gbvf3dpfQqjgc4YqLa6OqqtNBc7i3NblQ/0Yt321zVaHrvYcH211Cq8D7tmXoX/O1pHcJCe2uUFUXd1UdwQIAALBCyF2DBQBXu2A6msnRQYQqjmABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMcdhcAtFajlu%2B2uwQAQJDiCBYAAIBhBCwAAADDCFgAAACGEbCoZxN6AAAM4UlEQVQAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAxz2F3AlfLtt99q6dKl%2Bvjjj%2BV0OnXHHXdo9uzZcjqdKi8v129%2B8xuVlpaqU6dOmjdvnoYMGeJ/7kcffaSnnnpK5eXlSk5O1tKlS9W1a1cbtwaAnUYt3213CQCCTEgewfL5fMrOzpbH49Frr72mF154QX/4wx%2B0fPly%2BXw%2BZWVlqUOHDtqyZYvGjh2rWbNm6fjx45Kk48ePKysrSxkZGdq8ebPi4%2BM1c%2BZM%2BXw%2Bm7cKAAAEi5A8gnX06FGVlpZq9%2B7d6tChgyQpOztbzzzzjIYNG6by8nJt3LhRMTEx6t69u/bs2aMtW7booYce0qZNm9SnTx9NmzZNkpSXl6fBgwdr3759GjhwoJ2bBQAAgkRIHsFKSEjQ6tWr/eHqR6dOnZLL5dKNN96omJgY/3hqaqpKS0slSS6XS2lpaf656Oho9e7d2z8PAABwKSEZsGJjYzV06FD/44aGBm3YsEGDBg2S2%2B1WYmJiwPLt27fXyZMnJemS8wAAAJcSkqcIz5efn69Dhw5p8%2BbNWrNmjSIjIwPmIyMj5fV6JUkej%2Bei801RUVEht9sdMOZwxDQKbleziIjwgJ8Ark4Ox9XzGcDnXvMFY%2B9CPmDl5%2Bdr7dq1euGFF/Szn/1MTqdT1dXVAct4vV5FRUVJkpxOZ6Mw5fV6FRsb2%2BR1FhcXq6CgIGAsKytL2dnZzdyK0BUbG213CQBsFBfXxu4SLMfnXvMFU%2B9COmAtXrxYr7/%2BuvLz83X77bdLkpKSknTkyJGA5SorK/1Hl5KSklRZWdlovlevXk1eb2ZmptLT0wPGHI4YVVWdbs5mhKSIiHDFxkarpsaj%2BvoGu8sBYJOr6XORz73ma0nv7ArxIRuwCgoKtHHjRj3//PMaOXKkfzw5OVlFRUU6e/as/6hVSUmJUlNT/fMlJSX%2B5T0ejw4dOqRZs2Y1ed2JiYmNTge63d%2Brro431Pnq6xvoC3AVuxrf/3zuNV8w9S54TmZehi%2B%2B%2BEIrVqzQ/fffr9TUVLndbv%2B/AQMGqGPHjsrJyVFZWZmKiop04MABTZgwQZI0fvx47d%2B/X0VFRSorK1NOTo66dOnCLRoAAECThWTA2rVrl%2Brr67Vy5UoNGTIk4F9ERIRWrFght9utjIwMvfXWWyosLFSnTp0kSV26dNGLL76oLVu2aMKECaqurlZhYaHCwsJs3ioAABAswnzcotwSbvf3dpfQqjgc4YqLa6OqqtOt9nAvX48CXHnvPTzY7hIsEwyfe61VS3qXkNDuClV1cSF5BAsAAMBOBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYw%2B4CAABXr1HLd9tdwmV57%2BHBdpeAIMERLAAAAMMIWAAAAIYRsAAAAAzjGiwAAEJUMF3jFmrXt3EECwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKwLqK2t1bx585SWlqYhQ4bo1VdftbskAAAQRPguwgtYtmyZDh48qLVr1%2Br48eOaO3euOnXqpJEjR9pdGgAACAIErPOcOXNGmzZt0ssvv6zevXurd%2B/eKisr02uvvUbAAgAATcIpwvMcPnxYdXV1SklJ8Y%2BlpqbK5XKpoaHBxsoAAECw4AjWedxut%2BLi4hQZGekf69Chg2pra1VdXa34%2BPhLvkZFRYXcbnfAmMMRo8TEROP1/p9n/6/x17xSfv/YUP//jogID/gJAMHA4Wj%2BZxafexd3sd4GY%2B8IWOfxeDwB4UqS/7HX623SaxQXF6ugoCBgbNasWXrooYfMFPm//HlpcJ62rKio0Nq1q5WZmXlFgqcJrbm3FRUVKi4ubtX9a63oXfPRu5ax43OvNX%2BOXY5g%2BP%2BM8wVPFLSI0%2BlsFKR%2BfBwVFdWk18jMzNTWrVsD/mVmZhqvNZi53W4VFBQ0OtKHpqF/zUfvmo/etQz9a75g7B1HsM6TlJSkqqoq1dXVyeH4oT1ut1tRUVGKjY1t0mskJiYGTcIGAADmcQTrPL169ZLD4VBpaal/rKSkRH379lV4OO0CAACXRmI4T3R0tMaNG6cFCxbowIED2rlzp1599VVNmTLF7tIAAECQiFiwYMECu4tobQYNGqRDhw7pueee0549e/TAAw9o/PjxdpcVctq0aaMBAwaoTZs2dpcSlOhf89G75qN3LUP/mi/Yehfm8/l8dhcBAAAQSjhFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAVb1dTUKDc3V//%2B7/%2BuQYMG6fHHH1dNTY3dZQUdn8%2BnadOmaevWrXaX0qrV1tZq3rx5SktL05AhQ/Tqq6/aXVLQ8Xq9GjNmjPbu3Wt3KUHj22%2B/VXZ2tgYMGKChQ4cqLy9PtbW1dpcVNL766ivdd999SklJ0a233qrVq1fbXVKTELBgqyeffFKHDx9WUVGRXnnlFX3xxRd64okn7C4rqDQ0NGjJkiXavXu33aW0esuWLdPBgwe1du1aPfnkkyooKND7779vd1lBo7a2VrNnz1ZZWZndpQQNn8%2Bn7OxseTwevfbaa3rhhRf0hz/8QcuXL7e7tKDQ0NCg6dOnKy4uTtu2bdPChQu1cuVKvf3223aXdkkOuwvA1evMmTPasWOHXn/9dfXp00eSNG/ePP3yl79UbW2tnE6nzRW2ft9%2B%2B60ee%2BwxHTt2TLGxsXaX06qdOXNGmzZt0ssvv6zevXurd%2B/eKisr02uvvaaRI0faXV6rd%2BTIET366KPi29Uuz9GjR1VaWqrdu3erQ4cOkqTs7Gw988wzmjt3rs3VtX6VlZXq1auXFixYoLZt26pbt2665ZZbVFJSojvvvNPu8i6KI1iwTXh4uFatWqVevXoFjNfX1%2Bv06dM2VRVc/vKXv6hjx47asmWL2rVrZ3c5rdrhw4dVV1enlJQU/1hqaqpcLpcaGhpsrCw47Nu3TwMHDlRxcbHdpQSVhIQErV692h%2BufnTq1CmbKgouiYmJWr58udq2bSufz6eSkhJ98sknGjBggN2lXRJHsGCbqKgoDRs2LGBs3bp1%2Brd/%2BzfFx8fbVFVwSU9PV3p6ut1lBAW32624uDhFRkb6xzp06KDa2lpVV1ezz13CPffcY3cJQSk2NlZDhw71P25oaNCGDRs0aNAgG6sKTunp6Tp%2B/LiGDx%2Bu22%2B/3e5yLomAhSvq7Nmz%2Bvbbby84l5CQoJiYGP/jDRs26L333guaCxitcDn9w8V5PJ6AcCXJ/9jr9dpREq5C%2Bfn5OnTokDZv3mx3KUHnt7/9rSorK7VgwQLl5eW1%2But1CVi4olwul6ZMmXLBucLCQo0YMUKS9Nprr2nJkiXKycnRkCFDrCyxVWtq/3BpTqezUZD68XFUVJQdJeEqk5%2Bfr7Vr1%2BqFF17Qz372M7vLCTp9%2B/aV9MMfWzz22GOaM2dOo1%2BaWhMCFq6ogQMH6m9/%2B9tFl3nllVe0bNkyzZkzR1OnTrWosuDQlP6haZKSklRVVaW6ujo5HD989LndbkVFRfEHArjiFi9erNdff135%2BflBcXqrtaisrFRpaWnAL5M9evTQuXPndOrUqVZ9ap%2BL3GGrbdu2admyZcrJydF9991ndzkIYb169ZLD4VBpaal/rKSkRH379lV4OB%2BFuHIKCgq0ceNGPf/88xo9erTd5QSVY8eOadasWQGXShw8eFDx8fGtOlxJBCzYqLq6WosWLdIvfvELjR49Wm632/%2Bvvr7e7vIQYqKjozVu3DgtWLBABw4c0M6dO/Xqq6/%2B01OwgAlffPGFVqxYofvvv1%2BpqakBn3O4tL59%2B6p3796aN2%2Bejhw5og8%2B%2BED5%2Bfl64IEH7C7tkjhFCNvs3r1bZ86c0bZt27Rt27aAuV27dqlLly42VYZQlZOTowULFmjq1Klq27atHnroId122212l4UQtmvXLtXX12vlypVauXJlwByn/y8tIiJCK1as0OLFi5WZmano6GhNnjw5KH4xCvNx1zgAAACjOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9PxZoUHvSeuYjAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-424863190207511903”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.9</td> <td class=”number”>336</td> <td class=”number”>10.5%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>327</td> <td class=”number”>10.2%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1</td> <td class=”number”>217</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3</td> <td class=”number”>140</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.4</td> <td class=”number”>140</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (17)</td> <td class=”number”>735</td> <td class=”number”>22.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-424863190207511903”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-2.6</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:53%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1</td> <td class=”number”>217</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVS09”><s>CONVS09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2016”><code>Population Estimate, 2016</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92485</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVS14”><s>CONVS14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_CONVS09”><code>CONVS09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99646</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVSPTH09”>CONVSPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3053</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.60158</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.1217</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2745857438423942852”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2745857438423942852,#minihistogram-2745857438423942852”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2745857438423942852”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2745857438423942852”

aria-controls=”quantiles-2745857438423942852” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2745857438423942852” aria-controls=”histogram-2745857438423942852”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2745857438423942852” aria-controls=”common-2745857438423942852”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2745857438423942852” aria-controls=”extreme-2745857438423942852”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2745857438423942852”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.24987</td>

</tr> <tr>

<th>Q1</th> <td>0.40638</td>

</tr> <tr>

<th>Median</th> <td>0.55159</td>

</tr> <tr>

<th>Q3</th> <td>0.72818</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.0915</td>

</tr> <tr>

<th>Maximum</th> <td>3.1217</td>

</tr> <tr>

<th>Range</th> <td>3.1217</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.3218</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30918</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51396</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.812</td>

</tr> <tr>

<th>Mean</th> <td>0.60158</td>

</tr> <tr>

<th>MAD</th> <td>0.21523</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2134</td>

</tr> <tr>

<th>Sum</th> <td>1884.1</td>

</tr> <tr>

<th>Variance</th> <td>0.095595</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2745857438423942852”>

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dwAas48ePa/bs2fr73/%2Bu//7v/1Zk5Le/8RERER5hyel0Kjo6WhEREa7Xp85/9/62yMrKUnp6utuYzRal2trj53IqpxUWFqro6EjV1zeopaXV0n1Dlv9%2B%2BRrrxRM9MUdfzNEXc529L776y3JABqxjx47pF7/4hb788ks9//zz6tu3r2suISFBNTU1btvX1NRo4MCBuvDCCxUREaGamhr169dPktTc3Ky6ujrFxcW1%2Bfjx8fEetwMdjqNqbu6YhdfS0tph%2Bw5mgdpT1osnemKOvpijL%2Bboi7uAu2Ha2tqqnJwcffXVV1q3bp2%2B//3vu83b7XYVFxe7Xjc0NGj//v2y2%2B0KDQ1VUlKS23xJSYlsNpsGDBjgtXMAAAD%2BLeAC1iuvvKJdu3bp4YcfVnR0tBwOhxwOh%2Brq6iRJkyZN0p49e7Rq1SqVlZVp/vz56t27t4YNGybp2w/P/%2B53v9P27du1d%2B9eLVq0SFOmTGnXLUIAABDcAu4W4VtvvaXW1lbNmjXLbXzo0KFat26devfurd/%2B9rf69a9/rcLCQiUnJ6uwsFAhISGSpPHjx%2Bvrr7/WwoUL5XQ6de211%2BqXv/ylL04FAAD4qRDju0eYo0M5HEct36fNFqqYmK6qrT3uF/e9x614z9cltMsbd4/wdQmW8rf14g30xBx9MUdfzHX2vsTFne%2BT4wbcLUIAAABfI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYzO8DltPpVEZGhnbt2uUaq6io0M0336zBgwfr%2Buuv15///Ge397z//vvKyMiQ3W7XjBkzVFFR4Tb/3HPPKS0tTcnJybr//vvV0NDglXMBAACBwa8DVlNTk%2BbMmaOysjLXmGEYys7OVo8ePbRx40ZNmDBBOTk5OnTokCTp0KFDys7OVmZmpl555RV1795ds2fPlmEYkqS33npLBQUFWrx4sZ5//nmVlpYqPz/fJ%2BcHAAD8k98GrPLyck2ZMkVffvml2/gHH3ygiooKLV68WP369dOsWbM0ePBgbdy4UZK0YcMGXX755br11lv1/e9/X3l5efr666%2B1e/duSdLatWt10003afTo0briiiv00EMPaePGjVzFAgAAbea3AWv37t0aNmyYioqK3MZLS0t12WWXKSoqyjWWkpKikpIS13xqaqprLjIyUoMGDVJJSYlaWlr0ySefuM0PHjxYJ0%2Be1IEDBzr4jAAAQKCw%2BbqAczVt2jTTcYfDofj4eLex2NhYVVVVnXW%2Bvr5eTU1NbvM2m00XXnih6/0AAABn4/WANXnyZE2aNEnjx4/X%2Beefb/n%2BGxoaFB4e7jYWHh4up9N51vnGxkbX69O9vy2qq6vlcDjcxmy2KI9g968KCwt1%2BxXWstkCq6%2BsF0/0xBx9MUdfzNEXc14PWMOHD9fKlSuVl5enH/3oR8rMzNSIESMUEhJiyf4jIiJUV1fnNuZ0OtWlSxfX/Klhyel0Kjo6WhEREa7Xp85HRka2uYaioiIVFBS4jWVnZys3N7fN%2B2iP6Oi214a2i4np6usSOgTrxRM9MUdfzNEXc/TFndcD1j333KM5c%2Bbo/fff12uvvaa77rpL0dHRmjhxoiZOnKiLL774X9p/QkKCysvL3cZqampcV48SEhJUU1PjMT9w4EBdeOGFioiIUE1Njfr16ydJam5uVl1dneLi4tpcQ1ZWltLT093GbLYo1dYeP5dTOq2wsFBFR0eqvr5BLS2tlu4bsvz3y9dYL57oiTn6Yo6%2BmOvsffHVX5Z98hmskJAQjRgxQiNGjFBDQ4PWrVunJ598UqtWrdKQIUN000036dprrz2nfdvtdq1atUqNjY2uq1bFxcVKSUlxzRcXF7u2b2ho0P79%2B5WTk6PQ0FAlJSWpuLhYw4YNkySVlJTIZrNpwIABba4hPj7e43agw3FUzc0ds/BaWlo7bN/BLFB7ynrxRE/M0Rdz9MUcfXHnsw%2B5V1dX6/XXX9frr7%2BuTz/9VEOGDNFPfvITVVVV6YEHHtCHH36oBQsWtHu/Q4cOVc%2BePTV//nzNnj1b77zzjvbu3au8vDxJ0qRJk/S73/1Oq1at0ujRo1VYWKjevXu7AtW0adO0cOFC9e/fX/Hx8Vq0aJGmTJnSrluEAAAguHk9YG3evFmbN2/Wrl271L17d02cOFFPPPGE%2Bvbt69qmZ8%2BeWrp06TkFrLCwMD355JNasGCBMjMzddFFF6mwsFCJiYmSpN69e%2Bu3v/2tfv3rX6uwsFDJyckqLCx0fQZs/Pjx%2Bvrrr7Vw4UI5nU5de%2B21%2BuUvf2nJuQMAgOAQYnz3CHMvufzyyzV69GhNmjRJV199tUJDPf/VwZ49e/TOO%2B/onnvu8WZpHcrhOGr5Pm22UMXEdFVt7XG/uCw7bsV7vi6hXd64e4SvS7CUv60Xb6An5uiLOfpirrP3JS7O%2BicWtIXXr2C9%2B%2B67iomJUV1dnStc7d27V4MGDVJYWJgkaciQIRoyZIi3SwMAALCE1x9acezYMY0dO1bPPPOMa2zmzJmaMGGCKisrvV0OAACA5bwesH7961/roosu0i233OIa27Ztm3r27On6IDoAAIA/83rA%2Buijj3Tfffe5PVeqe/fumjdvnj744ANvlwMAAGA5rwcsm82m%2Bvp6j/GGhgZ5%2BfP2AAAAHcLrAevqq6/Www8/rC%2B//NI1VlFRoby8PKWlpXm7HAAAAMt5/V8R3nvvvbrlllt03XXXKTo6WpJUX1%2BvQYMGaf78%2Bd4uBwAAwHJeD1ixsbHatGmT3n//fZWVlclms%2BnSSy/VVVddZdkXPgMAAPiST74qJywsTGlpadwSBAAAAcnrAcvhcGjFihXas2ePTp486fHB9rffftvbJQEAAFjK6wHrV7/6lfbt26fx48fr/PN98/h6AACAjuT1gPXBBx9o9erVSk1N9fahAQAAvMLrj2mIiopSbGystw8LAADgNV6/gjVhwgStXr1aixcvdn25M9AZjVvxnq9LaJc37h7h6xIAAP/H6wGrrq5OW7du1R//%2BEf16dNH4eHhbvNr1671dkkAAACW8sljGjIyMnxxWAAAAK/wesDKy8vz9iEBAAC8yusfcpek6upqFRQU6J577tE333yjN998UwcPHvRFKQAAAJbzesD64osvdMMNN2jTpk166623dOLECW3btk2TJk1SaWmpt8sBAACwnNcD1iOPPKIxY8Zo%2B/btOu%2B88yRJjz32mNLT07V8%2BXJvlwMAAGA5rwesPXv26JZbbnH7YmebzabZs2dr//793i4HAADAcl4PWK2trWptbfUYP378OM/FAgAAAcHrAWvkyJF6%2Bumn3UJWXV2d8vPzNXz4cG%2BXAwAAYDmvB6z77rtP%2B/bt08iRI9XU1KQ777xTo0eP1ldffaV7773X2%2BUAAABYzuvPwUpISNBrr72mrVu36q9//ataW1s1depUTZgwQd26dfN2OQAAAJbzyZPcIyMjNXnyZF8cGgAAoMN5PWDNmDHjjPN8FyEAAPB3Xv8MVq9evdx%2BEhIS1NjYqL179yo5Odmy41RWVmrWrFkaMmSI0tPT9dxzz7nm9u/fr8mTJ8tut2vSpEnat2%2Bf23u3bt2qMWPGyG63Kzs7W0eOHLGsLgAAEPg6zXcRFhYWqqqqyrLj3H333UpMTNSrr76q8vJyzZ07V7169dKIESM0c%2BZM3XDDDXrkkUf04osvatasWfqf//kfRUVFae/evVqwYIEeeughDRgwQEuXLtX8%2BfP19NNPW1YbAAAIbD75LkIzEyZM0BtvvGHJvv7xj3%2BopKREd955p/r27asxY8YoLS1NO3fu1LZt2xQREaF58%2BapX79%2BWrBggbp27ao333xTkrR%2B/XqNGzdOEydO1IABA7Rs2TLt2LFDFRUVltQGAAACX6cJWB9//LFlDxrt0qWLIiMj9eqrr%2BrkyZM6ePCg9uzZo4EDB6q0tFQpKSmuJ8mHhIRoyJAhKikpkSSVlpYqNTXVta%2BePXsqMTGR70kEAABt1ik%2B5H7s2DH97W9/07Rp0yw5RkREhBYuXKglS5Zo7dq1amlpUWZmpiZPnqy3335bl156qdv2sbGxKisrkyRVV1crPj7eY749ty%2Brq6vlcDjcxmy2KI/9/qvCwkLdfkVws9nOvA5YL57oiTn6Yo6%2BmKMv5rwesBITE92%2Bh1CSzjvvPP385z/Xj3/8Y8uO89lnn2n06NG65ZZbVFZWpiVLluiqq65SQ0ODwsPD3bYNDw%2BX0%2BmUJDU2Np5xvi2KiopUUFDgNpadna3c3NxzPJszi46O7JD9wr/ExHRt03asF0/0xBx9MUdfzNEXd14PWI888kiHH2Pnzp165ZVXtGPHDnXp0kVJSUk6fPiwnnrqKfXp08cjLDmdTnXp0kXSt1e/zOYjI9u%2BcLKyspSenu42ZrNFqbb2%2BDmekbmwsFBFR0eqvr5BLS2e3%2B%2BI4HK29cV68URPzNEXc/TFXGfvS1v/8mk1rwesDz/8sM3bXnnlled0jH379umiiy5yhSZJuuyyy7Ry5UqlpqaqpqbGbfuamhrX7buEhATT%2Bbi4uDYfPz4%2B3uN2oMNxVM3NHbPwWlpaO2zf8B9tXQOsF0/0xBx9MUdfzNEXd14PWNOnT3fdIjQMwzV%2B6lhISIj%2B%2Bte/ntMx4uPj9cUXX8jpdLpu9x08eFC9e/eW3W7XM888I8MwFBISIsMwtGfPHt1xxx2SJLvdruLiYmVmZkr69nlalZWVstvt53bCAAAg6Hj9E2krV65Ur169tGLFCu3cuVPFxcV67rnndPHFF2vOnDl6%2B%2B239fbbb2v79u3nfIz09HSdd955euCBB/T555/rf//3f7Vy5UpNnz5dY8eOVX19vZYuXary8nItXbpUDQ0NGjdunCRp6tSp2rx5szZs2KADBw5o3rx5GjVqlPr06WNVCwAAQIDzesDKy8vTwoULdd111ykmJkZdu3bV8OHDtXjxYr344otuT3k/V%2Beff76ee%2B45ORwO3XjjjcrLy9Odd96prKwsdevWTU8//bTrKlVpaalWrVqlqKgoSVJycrIWL16swsJCTZ06VRdccMFpH44KAABgxuu3CKurq03DU7du3VRbW2vZcS699FKtWbPGdO6KK67Qpk2bTvvezMxM1y1CAACA9vL6FazBgwfrscce07Fjx1xjdXV1ys/P11VXXeXtcgAAACzn9StYDzzwgGbMmKGrr75affv2lWEY%2Bvvf/664uDitXbvW2%2BUAAABYzusBq1%2B/ftq2bZu2bt2qzz77TJL0s5/9TOPHj2/Xs6YAAAA6K68HLEm64IILNHnyZH311Veuf5133nnn%2BaIUAAAAy3n9M1iGYWj58uW68sorlZGRoaqqKt17771asGCBTp486e1yAAAALOf1gLVu3Tpt3rxZDz74oOshoGPGjNH27ds9vr8PAADAH3k9YBUVFWnhwoXKzMx0Pb39%2Buuv18MPP6wtW7Z4uxwAAADLeT1gffXVVxo4cKDH%2BIABA%2BRwOLxdDgAAgOW8HrB69eqlTz75xGP83Xff5etoAABAQPD6vyK87bbb9NBDD8nhcMgwDO3cuVNFRUVat26d7rvvPm%2BXAwAAYDmvB6xJkyapublZTz31lBobG7Vw4UJ1795dd999t6ZOnertcgAAACzn9YC1detWjR07VllZWTpy5IgMw1BsbKy3ywAAAOgwXv8M1uLFi10fZu/evTvhCgAABByvB6y%2Bffvq008/9fZhAQAAvMbrtwgHDBiguXPnavXq1erbt68iIiLc5vPy8rxdEgAAgKW8HrA%2B//xzpaSkSBLPvQIAAAHJKwFr2bJlysnJUVRUlNatW%2BeNQwIAAPiMVz6DtWbNGjU0NLiNzZw5U9XV1d44PAAAgFd5JWAZhuEx9uGHH6qpqckbhwcAAPAqr/8rQgAAgEBHwAIAALCY1wJWSEiItw4FAADgU157TMPDDz/s9syrkydPKj8/X127dnXbjudgAQAAf%2BeVgHXllVd6PPMqOTlZtbW1qq2t9UYJAAAAXuOVgMWzrwAAQDDhQ%2B4AAAAWC9iA5XQ69dBDD%2BnKK6/UD37wAz322GOu53Ht379fkydPlt1u16RJk7Rv3z63927dulVjxoyR3W5Xdna2jhw54otTAAAAfipgA9bDDz%2Bs999/X7/73e/06KOP6uWjM/Y1AAAVwUlEQVSXX1ZRUZFOnDihmTNnKjU1Va%2B%2B%2BqqSk5M1a9YsnThxQpK0d%2B9eLViwQDk5OSoqKlJ9fb3mz5/v47MBAAD%2BxOtf9uwNdXV12rhxo9asWaMrrrhCknTrrbeqtLRUNptNERERmjdvnkJCQrRgwQK9%2B%2B67evPNN5WZman169dr3LhxmjhxoqRvv0dx9OjRqqioUJ8%2BfXx5WgAAwE8E5BWs4uJidevWTUOHDnWNzZw5U3l5eSotLVVKSorruVwhISEaMmSISkpKJEmlpaVKTU11va9nz55KTExUaWmpd08CAAD4rYAMWBUVFerVq5dee%2B01jR07Vj/60Y9UWFio1tZWORwOxcfHu20fGxurqqoqSVJ1dfUZ5wEAAM4mIG8RnjhxQl988YVeeukl5eXlyeFwaOHChYqMjFRDQ4PCw8Pdtg8PD5fT6ZQkNTY2nnG%2BLaqrqz2e%2B2WzRXkEt39VWFio268IbjbbmdcB68UTPTFHX8zRF3P0xVxABiybzaZjx47p0UcfVa9evSRJhw4d0osvvqiLLrrIIyw5nU516dJFkhQREWE6HxkZ2ebjFxUVqaCgwG0sOztbubm553I6ZxUd3fbaELhiYrqefSOxXszQE3P0xRx9MUdf3AVkwIqLi1NERIQrXEnSxRdfrMrKSg0dOlQ1NTVu29fU1LiuLiUkJJjOx8XFtfn4WVlZSk9Pdxuz2aJUW3u8vadyRmFhoYqOjlR9fYNaWlot3Tf8z9nWF%2BvFEz0xR1/M0Rdznb0vbf3Lp9UCMmDZ7XY1NTXp888/18UXXyxJOnjwoHr16iW73a5nnnlGhmEoJCREhmFoz549uuOOO1zvLS4uVmZmpiSpsrJSlZWVstvtbT5%2BfHy8x%2B1Ah%2BOomps7ZuG1tLR22L7hP9q6BlgvnuiJOfpijr6Yoy/uAvKG6SWXXKJRo0Zp/vz5OnDggP70pz9p1apVmjp1qsaOHav6%2BnotXbpU5eXlWrp0qRoaGjRu3DhJ0tSpU7V582Zt2LBBBw4c0Lx58zRq1Cge0QAAANosIAOWJC1fvlz/9m//pqlTp%2Bree%2B/Vz372M02fPl3dunXT008/7bpKVVpaqlWrVikqKkrSt19CvXjxYhUWFmrq1Km64IILlJeX5%2BOzAQAA/iTE%2BO77Y9ChHI6jlu/TZgtVTExX1dYe94vLsuNWvOfrEgLaG3ePOOO8v60Xb6An5uiLOfpirrP3JS7ufJ8cN2CvYAEAAPgKAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLBXzAmjlzpu677z7X6/3792vy5Mmy2%2B2aNGmS9u3b57b91q1bNWbMGNntdmVnZ%2BvIkSPeLhkAAPi5gA5Yv//977Vjxw7X6xMnTmjmzJlKTU3Vq6%2B%2BquTkZM2aNUsnTpyQJO3du1cLFixQTk6OioqKVF9fr/nz5/uqfAAA4KcCNmDV1dVp2bJlSkpKco1t27ZNERERmjdvnvr166cFCxaoa9euevPNNyVJ69ev17hx4zRx4kQNGDBAy5Yt044dO1RRUeGr0wAAAH4oYAPWb37zG02YMEGXXnqpa6y0tFQpKSkKCQmRJIWEhGjIkCEqKSlxzaemprq279mzpxITE1VaWurd4gEAgF%2Bz%2BbqAjrBz50599NFH2rJlixYtWuQadzgcboFLkmJjY1VWViZJqq6uVnx8vMd8VVVVu45fXV0th8PhNmazRXns%2B18VFhbq9iuCm8125nXAevFET8zRF3P0xRx9MRdwAaupqUkPPvigFi5cqC5durjNNTQ0KDw83G0sPDxcTqdTktTY2HjG%2BbYqKipSQUGB21h2drZyc3PbtZ%2B2io6O7JD9wr/ExHRt03asF0/0xBx9MUdfzNEXdwEXsAoKCnT55ZcrLS3NYy4iIsIjLDmdTlcQO918ZGT7Fk1WVpbS09Pdxmy2KNXWHm/Xfs4mLCxU0dGRqq9vUEtLq6X7hv852/pivXiiJ%2Bboizn6Yq6z96Wtf/m0WsAFrN///veqqalRcnKyJLkC01tvvaWMjAzV1NS4bV9TU%2BO6dZeQkGA6HxcX164a4uPjPW4HOhxH1dzcMQuvpaW1w/YN/9HWNcB68URPzNEXc/TFHH1xF3ABa926dWpubna9Xr58uSRp7ty5%2BvDDD/XMM8/IMAyFhITIMAzt2bNHd9xxhyTJbreruLhYmZmZkqTKykpVVlbKbrd7/0QAAIDfCriA1atXL7fXXbt%2Be2nwoosuUmxsrB599FEtXbpUP/3pT/XSSy%2BpoaFB48aNkyRNnTpV06dP1%2BDBg5WUlKSlS5dq1KhR6tOnj9fPAwAA%2BK%2Bg%2Bsh/t27d9PTTT7uuUpWWlmrVqlWKioqSJCUnJ2vx4sUqLCzU1KlTdcEFFygvL8/HVQMAAH8TcFewTvXII4%2B4vb7iiiu0adOm026fmZnpukUIAABwLoLqChYAAIA3ELAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsFjAPwcr0KUueNPXJQAAgFNwBQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYjZfFwDAGuNWvOfrEtrsjbtH%2BLoEAOhQAXsF6/Dhw8rNzdXQoUOVlpamvLw8NTU1SZIqKip08803a/Dgwbr%2B%2Buv15z//2e2977//vjIyMmS32zVjxgxVVFT44hQAAICfCsiAZRiGcnNz1dDQoBdeeEGPP/643nnnHa1YsUKGYSg7O1s9evTQxo0bNWHCBOXk5OjQoUOSpEOHDik7O1uZmZl65ZVX1L17d82ePVuGYfj4rAAAgL8IyFuEBw8eVElJid577z316NFDkpSbm6vf/OY3uvrqq1VRUaGXXnpJUVFR6tevn3bu3KmNGzfqrrvu0oYNG3T55Zfr1ltvlSTl5eVpxIgR2r17t4YNG%2BbL0wIAAH4iIK9gxcXFafXq1a5w9Z1jx46ptLRUl112maKiolzjKSkpKikpkSSVlpYqNTXVNRcZGalBgwa55gEAAM4mIANWdHS00tLSXK9bW1u1fv16DR8%2BXA6HQ/Hx8W7bx8bGqqqqSpLOOg8AAHA2AXmL8FT5%2Bfnav3%2B/XnnlFT333HMKDw93mw8PD5fT6ZQkNTQ0nHG%2BLaqrq%2BVwONzGbLYoj%2BD2rwoLC8h8jCBgs3WOtfvdnyH%2BLLmjL%2Bboizn6Yi7gA1Z%2Bfr6ef/55Pf744%2Brfv78iIiJUV1fnto3T6VSXLl0kSRERER5hyul0Kjo6us3HLCoqUkFBgdtYdna2cnNzz/EsgMASE9PV1yW4iY6O9HUJnRJ9MUdfzNEXdwEdsJYsWaIXX3xR%2Bfn5uu666yRJCQkJKi8vd9uupqbGdXUpISFBNTU1HvMDBw5s83GzsrKUnp7uNmazRam29vi5nMZp8bcF%2BCur/yycq7CwUEVHR6q%2BvkEtLa2%2BLqfToC/m6Iu5zt4XX/2FLmADVkFBgV566SU99thjGjt2rGvcbrdr1apVamxsdF21Ki4uVkpKimu%2BuLjYtX1DQ4P279%2BvnJycNh87Pj7e43agw3FUzc2db%2BEBvtDZ/iy0tLR2upo6A/pijr6Yoy/uAvISyGeffaYnn3xSt99%2Bu1JSUuRwOFw/Q4cOVc%2BePTV//nyVlZVp1apV2rt3r2688UZJ0qRJk7Rnzx6tWrVKZWVlmj9/vnr37s0jGgAAQJsFZMB6%2B%2B231dLSoqeeekojR450%2BwkLC9OTTz4ph8OhzMxMvf766yosLFRiYqIkqXfv3vrtb3%2BrjRs36sYbb1RdXZ0KCwsVEhLi47MCAAD%2BIsTgEeVe4XActXyfNluorln%2BJ8v3C3S0zvJdhDZbqGJiuqq29ji3Nv4JfTFHX8x19r7ExZ3vk%2BMG5BUsAAAAXyJgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxWy%2BLgBA8Bm34j1fl9Aub9w9wtclAPAzXMECAACwGAELAADAYgQsE01NTbr//vuVmpqqkSNH6tlnn/V1SQAAwI/wGSwTy5Yt0759%2B/T888/r0KFDuvfee5WYmKixY8f6ujQAPsBnxgC0FwHrFCdOnNCGDRv0zDPPaNCgQRo0aJDKysr0wgsvELAAAECbELBOceDAATU3Nys5Odk1lpKSopUrV6q1tVWhodxVBdC5%2BdMVN662IVARsE7hcDgUExOj8PBw11iPHj3U1NSkuro6de/e/az7qK6ulsPhcBuz2aIUHx9vaa1hYYQ9AP7Nn8IgOtb/zE3zdQmWImCdoqGhwS1cSXK9djqdbdpHUVGRCgoK3MZycnJ01113WVPk/6murtZN3ytTVlaW5eHNn1VXV6uoqIi%2BnIK%2BeKIn5uiLOfpijr6Y4xLIKSIiIjyC1Hevu3Tp0qZ9ZGVl6dVXX3X7ycrKsrxWh8OhgoICj6tlwY6%2BmKMvnuiJOfpijr6Yoy/muIJ1ioSEBNXW1qq5uVk227ftcTgc6tKli6Kjo9u0j/j4eFI8AABBjCtYpxg4cKBsNptKSkpcY8XFxUpKSuID7gAAoE1IDKeIjIzUxIkTtWjRIu3du1fbt2/Xs88%2BqxkzZvi6NAAA4CfCFi1atMjXRXQ2w4cP1/79%2B/Xoo49q586duuOOOzRp0iRfl2Wqa9euGjp0qLp27errUjoV%2BmKOvniiJ%2Bboizn6Yo6%2BeAoxDMPwdREAAACBhFuEAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFidXFNTk%2B6//36lpqZq5MiRevbZZ0%2B77f79%2BzV58mTZ7XZNmjRJ%2B/bt82Kl3tWevtx5553693//d7efd955x4vVepfT6VRGRoZ27dp12m2Caa18py19Caa1cvjwYeXm5mro0KFKS0tTXl6empqaTLcNpvXSnr4E03r54osvdNtttyk5OVmjRo3S6tWrT7ttMK2XMzLQqS1evNi44YYbjH379hl/%2BMMfjOTkZOONN97w2O748ePGiBEjjEceecQoLy83lixZYvzgBz8wjh8/7oOqO15b%2B2IYhnHNNdcYmzdvNqqrq10/TU1NXq7YOxobG43s7Gyjf//%2BxgcffGC6TbCtFcNoW18MI3jWSmtrqzFlyhTjF7/4hfHpp58aH374oXHNNdcYjzzyiMe2wbRe2tMXwwie9dLS0mJce%2B21xj333GN8/vnnxh//%2BEdjyJAhxuuvv%2B6xbTCtl7MhYHVix48fN5KSktz%2BD6GwsND4%2Bc9/7rHthg0bjPT0dKO1tdUwjG//h%2BKaa64xNm7c6LV6vaU9fWlqajIGDhxoHDx40Jsl%2BkRZWZnx4x//2LjhhhvOGCSCaa0YRtv7Ekxrpby83Ojfv7/hcDhcY1u2bDFGjhzpsW0wrZf29CWY1svhw4eN//iP/zCOHj3qGsvOzjYefPBBj22Dab2cDbcIO7EDBw6oublZycnJrrGUlBSVlpaqtbXVbdvS0lKlpKQoJCREkhQSEqIhQ4aopKTEqzV7Q3v6cvDgQYWEhKhPnz7eLtPrdu/erWHDhqmoqOiM2wXTWpHa3pdgWitxcXFavXq1evTo4TZ%2B7Ngxj22Dab20py/BtF7i4%2BO1YsUKdevWTYZhqLi4WB9%2B%2BKGGDh3qsW0wrZezsfm6AJyew%2BFQTEyMwsPDXWM9evRQU1OT6urq1L17d7dtL730Urf3x8bGqqyszGv1ekt7%2BnLw4EF169ZN8%2BbN0%2B7du/W9731Pd911l374wx/6ovQONW3atDZtF0xrRWp7X4JprURHRystLc31urW1VevXr9fw4cM9tg2m9dKevgTTevln6enpOnTokEaPHq3rrrvOYz6Y1svZcAWrE2toaHALEZJcr51OZ5u2PXW7QNCevhw8eFCNjY0aOXKkVq9erR/%2B8Ie688479cknn3it3s4mmNZKewTzWsnPz9f%2B/fv1n//5nx5zwbxeztSXYF0vTzzxhFauXKm//vWvysvL85gP5vVyKq5gdWIREREei/K71126dGnTtqduFwja05fZs2dr%2BvTpuuCCCyRJAwYM0F/%2B8he9/PLLSkpK8k7BnUwwrZX2CNa1kp%2Bfr%2Beff16PP/64%2Bvfv7zEfrOvlbH0J1vXy3bk1NTVp7ty5mjdvnlugCtb1YoYrWJ1YQkKCamtr1dzc7BpzOBzq0qWLoqOjPbatqalxG6upqVF8fLxXavWm9vQlNDTU9T%2BA37nkkkt0%2BPBhr9TaGQXTWmmPYFwrS5Ys0Zo1a5Sfn296u0cKzvXSlr4E03qpqanR9u3b3cYuvfRSnTx50uPzacG4Xk6HgNWJDRw4UDabze3DgcXFxUpKSlJoqPtvnd1u18cffyzDMCRJhmFoz549stvtXq3ZG9rTl/vuu0/z5893Gztw4IAuueQSr9TaGQXTWmmPYFsrBQUFeumll/TYY49p/Pjxp90u2NZLW/sSTOvlq6%2B%2BUk5Ojlt43Ldvn7p37%2B72mVcp%2BNbLmRCwOrHIyEhNnDhRixYt0t69e7V9%2B3Y9%2B%2ByzmjFjhqRvr9o0NjZKksaOHav6%2BnotXbpU5eXlWrp0qRoaGjRu3DhfnkKHaE9f0tPTtWXLFr322mv64osvVFBQoOLiYv385z/35Sl4XbCulbMJ1rXy2Wef6cknn9Ttt9%2BulJQUORwO148UvOulPX0JpvWSlJSkQYMG6f7771d5ebl27Nih/Px83XHHHZKCd72clc8eEIE2OXHihDFv3jxj8ODBxsiRI401a9a45vr37%2B/2bJHS0lJj4sSJRlJSknHjjTcaf/nLX3xQsXe0py8vv/yyce211xqXX3658ZOf/MTYvXu3Dyr2rlOf9xTMa%2BWfna0vwbJWnn76aaN///6mP4YRvOulvX0JlvViGIZRVVVlZGdnG0OGDDFGjBhhPPXUU65nXQXrejmbEMP4v%2Bt4AAAAsAS3CAEAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACz2/wDTEw2Zngbg3wAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2745857438423942852”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.529661017</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.599655198</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.542299349</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.677659815</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.47692858</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.418760469</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.571183778</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.432900433</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3042)</td> <td class=”number”>3067</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2745857438423942852”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.074046649</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.082731739</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08364701</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.095620578</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.861230329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.962336014</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.03030303</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.04414003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.121748179</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CONVSPTH14”>CONVSPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3050</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.5968</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.6012</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3994779847427794597”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3994779847427794597,#minihistogram3994779847427794597”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3994779847427794597”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3994779847427794597”

aria-controls=”quantiles3994779847427794597” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3994779847427794597” aria-controls=”histogram3994779847427794597”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3994779847427794597” aria-controls=”common3994779847427794597”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3994779847427794597” aria-controls=”extreme3994779847427794597”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3994779847427794597”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.25984</td>

</tr> <tr>

<th>Q1</th> <td>0.40079</td>

</tr> <tr>

<th>Median</th> <td>0.54456</td>

</tr> <tr>

<th>Q3</th> <td>0.72369</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.0759</td>

</tr> <tr>

<th>Maximum</th> <td>4.6012</td>

</tr> <tr>

<th>Range</th> <td>4.6012</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.3229</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.31053</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52032</td>

</tr> <tr>

<th>Kurtosis</th> <td>22.687</td>

</tr> <tr>

<th>Mean</th> <td>0.5968</td>

</tr> <tr>

<th>MAD</th> <td>0.21151</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.0191</td>

</tr> <tr>

<th>Sum</th> <td>1869.2</td>

</tr> <tr>

<th>Variance</th> <td>0.096429</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3994779847427794597”>

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j9c033%2Bivf/2rQkNDJUlz5szR%2BfPnFRERIUmaOXOmPv30U23cuFG/%2Bc1vJH0fpi6eQrwYrC6%2Bvjqys7OVlZXlNZaSkqLU1NSr3q9LiYysfm2ovqio8MsuQ%2B/tQd/tQ%2B/tQ%2B99i18GrJKSEj3wwAP69ttvtWrVKq9vCzocDk%2B4kqSAgAA1a9ZMJ06cUGxsrKTvv4l48TTjxSNR0dHR1d5%2BcnKyunXr5jXmcISpqOjsz92lSwoKClRkZKhOny5VRQW3kjDtp94vem8P%2Bm4fem8fen91qvNhuSb4XcCqrKzUuHHjdPjwYa1Zs0bNmzf3mh82bJg6dOigcePGeZb/8ssv9Yc//EGxsbGKi4tTTk6OJ2Dl5OQoLi7uik7vxcTEVFne6Tyj8vKa%2BcWoqKissXXXZtXpKb23B323D723D733LX4XsF555RVt27ZNS5cuVWRkpOcI1DXXXKO6deuqW7duWrx4sVq1aqUbb7xRq1ev1pkzZzRgwABJ0pAhQ7RgwQJdf/31kqSnnnpKo0aNsm1/AACA7/G7gLV582ZVVlbqwQcf9Bpv37691qxZo5EjR6qsrExz585VYWGh4uPj9eKLL3pOG44ePVrfffedxo0bp6CgIP3%2B97/XyJEjbdgTAADgqwLcP7xTJ2qE03nG%2BDodjkBFRYWrqOisTxw27v3sP%2B0u4Yq8%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%2BvXTsWPHrC4HAADAOMsD1hNPPKEbbrhB999/v2fsnXfeUcOGDZWRkWF1OQAAAMZZHrD%2B/e9/a%2BrUqYqOjvaM1atXT5MnT9bWrVutLgcAAMA4ywOWw%2BHQ6dOnq4yXlpZyR3cAAOAXLA9Yd955p%2BbOnatvv/3WM3bo0CFlZGSoS5cuV7w%2Bl8ulvn37atu2bV7rGzlypG677Tbdc889%2Bvjjj71e869//Ut9%2B/ZVfHy8hg8frkOHDnnNv/TSS%2BrSpYsSEhI0ffp0lZaWXnFdAACg9rI8YE2ZMkUul0t33323OnTooA4dOqhnz566cOGCpk2bdkXrKisr04QJE7Rv3z7PmNvtVkpKiho0aKBXX31V/fr107hx43T06FFJ0tGjR5WSkqKBAwfqlVdeUb169TR27FjP0bPNmzcrKytLs2fP1qpVq5SXl6fMzExzDQAAAH7P8ts01K9fX6%2B//rr%2B9a9/ad%2B%2BfXI4HGrRooXuuOMOBQQEVHs9%2B/fvV1paWpXTilu3btWhQ4f08ssvKywsTM2bN9cnn3yiV199VePHj9eGDRv061//WqNGjZIkZWRkqFOnTtq%2Bfbs6dOig1atXa8SIEUpKSpIkzZo1S6NHj9akSZMUGhpqrhEAAMBvWX4ES5KCgoLUpUsXjRo1SsOHD9dvfvObKwpXkjyBKDs722s8Ly9Pv/rVrxQWFuYZa9eunXJzcz3ziYmJnrnQ0FC1bt1aubm5qqio0K5du7zmb7vtNl24cEF79%2B79ObsKAABqIcuPYDmdTj377LP69NNPdeHChSpHoN57771qrWfo0KE/uv6YmBivsfr16%2Bv48eOXnT99%2BrTKysq85h0Oh%2BrWret5fXUUFBTI6XR6jTkcYVW2e7WCgmzJx7WGw/Hj/b3Ye94Da9F3%2B9B7%2B9B732R5wHrssce0e/du9enTR9dee63x9ZeWlio4ONhrLDg4WC6X67Lz58%2Bf9zz/sddXR3Z2trKysrzGUlJSlJqaWu11wH5RUeGXXSYyktPGdqDv9qH39qH3vsXygLV161atWLHC6zScSSEhISouLvYac7lcqlOnjmf%2Bh2HJ5XIpMjJSISEhnuc/nL%2BS66%2BSk5PVrVs3rzGHI0xFRWervY7q4NNMzfqp9ysoKFCRkaE6fbpUFRWVFlZVu9F3%2B9B7%2B9D7q1OdD8s1wfKAFRYWpvr169fY%2BmNjY7V//36vscLCQs/pudjYWBUWFlaZb9WqlerWrauQkBAVFhaqefPmkqTy8nIVFxd73Rj1cmJiYqqcDnQ6z6i8nF8MX1Kd96uiopL31Qb03T703j703rdYfgikX79%2BWrFihSoqKmpk/fHx8fr88889p/skKScnR/Hx8Z75nJwcz1xpaan27Nmj%2BPh4BQYGqk2bNl7zubm5cjgcatmyZY3UCwAA/I/lR7CKi4u1adMm/d///Z%2BaNGlS5Xqn1atXX9X627dvr4YNG2ratGkaO3as3n//fe3cudPzdw4HDRqklStXavny5UpKStLixYvVuHFjdejQQdL3F8/PmDFDN998s2JiYjRz5kzde%2B%2B93KIBAABUm%2BUBS5L69u1bY%2BsOCgrSkiVLlJ6eroEDB%2BqGG27Q4sWLFRcXJ0lq3Lix/vKXv%2BiJJ57Q4sWLlZCQoMWLF3tuE9GnTx8dOXJEM2bMkMvlUs%2BePTVp0qQaqxcAAPifADd/ANASTucZ4%2Bt0OALVY8FHxteL7737SKcfnXM4AhUVFa6iorNcE2Eh%2Bm4fem8fen91oqPN37GgOmz5GlpBQYGysrKUlpam7777Tn//%2B9914MABO0oBAAAwzvKAdfDgQf32t7/V66%2B/rs2bN%2BvcuXN65513NGjQIOXl5VldDgAAgHGWB6wnn3xS3bt315YtW3TNNddIkp5%2B%2Bml169ZNCxYssLocAAAA4ywPWJ9%2B%2Bqnuv/9%2Br7896HA4NHbsWO3Zs8fqcgAAAIyzPGBVVlaqsrLqRXpnz55VUFCQ1eUAAAAYZ3nA6ty5s5577jmvkFVcXKzMzEx17NjR6nIAAACMszxgTZ06Vbt371bnzp1VVlamhx9%2BWElJSTp8%2BLCmTJlidTkAAADGWX6j0djYWL3xxhvatGmTvvjiC1VWVmrIkCHq16%2BfIiIirC4HAADAOFvu5B4aGqrBgwfbsWkAAIAaZ3nAGj58%2BE/OX%2B3fIgQAALCb5QGrUaNGXs/Ly8t18OBBffXVVxoxYoTV5QAAABhnecDKyMi45PjixYt1/Phxi6sBAAAwz5a/RXgp/fr107vvvmt3GQAAAFftFxOwPvvsM240CgAA/MIv4iL3kpISffnllxo6dKjV5QAAABhnecCKi4vz%2BjuEknTNNdfovvvu0%2B9%2B9zurywEAADDO8oD15JNPWr1JAAAAS1kesHbs2FHtZW%2B//fYarAQAAKBmWB6whg0b5jlF6Ha7PeM/HAsICNAXX3xhdXkAAABXzfKAtWzZMs2dO1eTJk1S%2B/btFRwcrF27dmn27NkaMGCA7rnnHqtLAgAAMMry2zRkZGRoxowZuvvuuxUVFaXw8HB17NhRs2fP1vr169WoUSPPAwAAwBdZHrAKCgouGZ4iIiJUVFRkdTkAAADGWR6wbrvtNj399NMqKSnxjBUXFyszM1N33HGH1eUAAAAYZ/k1WI8%2B%2BqiGDx%2BuO%2B%2B8U02bNpXb7dY333yj6OhorV692upyAAAAjLM8YDVv3lzvvPOONm3apPz8fEnSH/7wB/Xp00ehoaFWlwMAAGCc5QFLkq677joNHjxYhw8fVpMmTSR9fzd3AAAAf2D5NVhut1sLFizQ7bffrr59%2B%2Br48eOaMmWK0tPTdeHCBavLAQAAMM7ygLVmzRpt3LhRjz/%2BuIKDgyVJ3bt315YtW5SVlWV1OQAAAMZZHrCys7M1Y8YMDRw40HP39nvuuUdz587VW2%2B9ZXU5AAAAxlkesA4fPqxWrVpVGW/ZsqWcTqfV5QAAABhnecBq1KiRdu3aVWX8ww8/9FzwDgAA4Mss/xbh6NGjNWvWLDmdTrndbn3yySfKzs7WmjVrNHXqVKvLAQAAMM7ygDVo0CCVl5dr6dKlOn/%2BvGbMmKF69erpkUce0ZAhQ4xs47XXXtO0adOqjAcEBGjv3r16%2BOGH9b//%2B79ec8uWLVNSUpIk6aWXXtLKlStVUlKi3r1767HHHuMeXQAAoNosD1ibNm1Sr169lJycrJMnT8rtdqt%2B/fpGt3HPPfeoS5cunufl5eUaMWKEunbtKknKz8%2Bv8qd5rrvuOknS5s2blZWVpczMTNWvX1/Tpk1TZmamZsyYYbRGAADgvyy/Bmv27Nmei9nr1atnPFxJUp06dRQdHe15vPnmm3K73Zo4caJcLpcOHz6sNm3aeC1z8ZYRq1ev1ogRI5SUlKRbb71Vs2bN0quvvqrS0lLjdQIAAP9kecBq2rSpvvrqK8u2V1xcrOeff15paWkKDg7WgQMHFBAQcMkL6isqKrRr1y4lJiZ6xm677TZduHBBe/futaxmAADg2yw/RdiyZUtNnDhRK1asUNOmTRUSEuI1n5GRYXR769evV0xMjHr16iVJOnDggCIiIjR58mRt375d119/vcaPH6%2B77rpLp0%2BfVllZmWJiYjyvdzgcqlu3ro4fP17tbRYUFFS55YTDEea1XhOCgizPx7WKw/Hj/b3Ye94Da9F3%2B9B7%2B9B732R5wPr666/Vrl07Sarx%2B1653W5t2LBBDzzwgGfswIEDOn/%2BvDp37qwxY8boH//4hx5%2B%2BGFlZ2erQYMGkuQ5XXhRcHCwXC5XtbebnZ1d5a70KSkpSk1NvYq9gdWiosIvu0xkJF9%2BsAN9tw%2B9tw%2B99y2WBKz58%2Bdr3LhxCgsL05o1a6zYpCRp165dOnHihPr06eMZGzt2rIYNG%2Ba5qL1ly5b6/PPP9be//U1/%2BtOfJKlKmHK5XFf0LcLk5GR169bNa8zhCFNR0dmfuyuXxKeZmvVT71dQUKAiI0N1%2BnSpKioqLayqdqPv9qH39qH3V6c6H5ZrgiUB68UXX9To0aMVFhbmGRszZozmzp1r/LTZf/roo4%2BUmJjoCVOSFBgY6PVckpo1a6b9%2B/erbt26CgkJUWFhoZo3by7p%2B28gFhcXKzo6utrbjYmJqbJfTucZlZfzi%2BFLqvN%2BVVRU8r7agL7bh97bh977FksOgbjd7ipjO3bsUFlZWY1ud%2BfOnWrbtq3X2NSpU6vcI2vv3r1q1qyZAgMD1aZNG%2BXk5HjmcnNz5XA41LJlyxqtFQAA%2BA%2B/Pse0b98%2BtWjRwmusW7dueuutt/TGG2/o4MGDysrKUk5Oju677z5J0tChQ7Vy5Upt2bJFO3fu1MyZM3Xvvfdyo1EAAFBtll/kbqXCwkJFRkZ6jfXs2VOPP/64li5dqqNHj%2Bqmm27SihUr1LhxY0lSnz59dOTIEc2YMUMul0s9e/bUpEmT7CgfAAD4KMsCVkBAgFWb8ti5c%2BclxwcPHqzBgwf/6OvGjBmjMWPG1FRZAADAz1kWsObOnet1z6sLFy4oMzNT4eHeV/ebvg8WAACA1SwJWLfffnuVe14lJCSoqKhIRUVFVpQAAABgGUsClpX3vgIAALCbX3%2BLEAAAwA4ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjmtwHrH//4h2655RavR2pqqiRpz549Gjx4sOLj4zVo0CDt3r3b67WbNm1S9%2B7dFR8fr5SUFJ08edKOXQAAAD7KbwPW/v37lZSUpI8//tjzmDt3rs6dO6cxY8YoMTFRr732mhISEvTggw/q3LlzkqSdO3cqPT1d48YAoK8oAAAPqUlEQVSNU3Z2tk6fPq1p06bZvDcAAMCX%2BG3Ays/P180336zo6GjPIzIyUu%2B8845CQkI0efJkNW/eXOnp6QoPD9ff//53SdLatWvVu3dv9e/fXy1bttT8%2BfP1wQcf6NChQzbvEQAA8BV%2BHbCaNm1aZTwvL0/t2rVTQECAJCkgIEBt27ZVbm6uZz4xMdGzfMOGDRUXF6e8vDxL6gYAAL7PYXcBNcHtduvrr7/Wxx9/rOeee04VFRXq1auXUlNT5XQ61aJFC6/l69evr3379kmSCgoKFBMTU2X%2B%2BPHj1d5%2BQUGBnE6n15jDEVZlvVcrKMhv8/EvgsPx4/292HveA2vRd/vQe/vQe9/klwHr6NGjKi0tVXBwsJ599lkdPnxYc%2BfO1fnz5z3j/yk4OFgul0uSdP78%2BZ%2Bcr47s7GxlZWV5jaWkpHgusodviIoKv%2BwykZGhFlSCH6Lv9qH39qH3vsUvA1ajRo20bds2XXfddQoICFCrVq1UWVmpSZMmqX379lXCksvlUp06dSRJISEhl5wPDa3%2BD3ZycrK6devmNeZwhKmo6OzP3KNL49NMzfqp9ysoKFCRkaE6fbpUFRWVFlZVu9F3%2B9B7%2B9D7q1OdD8s1wS8DliTVrVvX63nz5s1VVlam6OhoFRYWes0VFhZ6Tt/FxsZecj46Orra246JialyOtDpPKPycn4xfEl13q%2BKikreVxvQd/vQe/vQe9/il4dAPvroI3Xo0EGlpaWesS%2B%2B%2BEJ169ZVu3bt9Nlnn8ntdkv6/nqtTz/9VPHx8ZKk%2BPh45eTkeF537NgxHTt2zDMPAABwOX4ZsBISEhQSEqJHH31UBw4c0AcffKD58%2BfrgQceUK9evXT69GnNmzdP%2B/fv17x581RaWqrevXtLkoYMGaKNGzdqw4YN2rt3ryZPnqyuXbuqSZMmNu8VAADwFX4ZsCIiIrRy5UqdPHlSgwYNUnp6upKTk/XAAw8oIiJCzz33nHJycjRw4EDl5eVp%2BfLlCgsLk/R9OJs9e7YWL16sIUOG6LrrrlNGRobNewQAAHxJgPviuTLUKKfzjPF1OhyB6rHgI%2BPrxffefaTTj845HIGKigpXUdFZromwEH23D723D72/OtHR19qyXb88ggUAAGAnAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmN8GrBMnTig1NVXt27dXly5dlJGRobKyMknS3Llzdcstt3g91q5d63ntpk2b1L17d8XHxyslJUUnT560azcAAIAPcthdQE1wu91KTU1VZGSk1q1bp1OnTmn69OkKDAzUlClTlJ%2Bfr7S0NA0YMMDzmoiICEnSzp07lZ6erlmzZqlly5aaN2%2Bepk2bpueee86u3QEAAD7GLwPWgQMHlJubq3/%2B859q0KCBJCk1NVV//vOfPQFr9OjRio6OrvLatWvXqnfv3urfv78kaf78%2BUpKStKhQ4fUpEkTS/cD9ur97D/tLuGKvPtIJ7tLAAD8P355ijA6OlorVqzwhKuLSkpKVFJSohMnTqhp06aXfG1eXp4SExM9zxs2bKi4uDjl5eXVZMkAAMCP%2BOURrMjISHXp0sXzvLKyUmvXrlXHjh2Vn5%2BvgIAALVu2TB9%2B%2BKHq1q2r%2B%2B%2B/33O6sKCgQDExMV7rq1%2B/vo4fP17t7RcUFMjpdHqNORxhVdZ7tYKC/DIf42dyOPz/5%2BHizzw/%2B9aj9/ah977JLwPWD2VmZmrPnj165ZVX9PnnnysgIEDNmjXTfffdpx07duixxx5TRESEevToofPnzys4ONjr9cHBwXK5XNXeXnZ2trKysrzGUlJSlJqaamR/gEuJigq3uwTLREaG2l1CrUXv7UPvfYvfB6zMzEytWrVKzzzzjG6%2B%2BWbddNNNSkpKUt26dSVJLVu21DfffKP169erR48eCgkJqRKmXC6XQkOr/4OdnJysbt26eY05HGEqKjp79Tv0H/g0g/9k%2BufrlygoKFCRkaE6fbpUFRWVdpdTq9B7%2B9D7q2PXh0%2B/Dlhz5szR%2BvXrlZmZqbvvvluSFBAQ4AlXFzVr1kxbt26VJMXGxqqwsNBrvrCw8JIXxP%2BYmJiYKqcDnc4zKi/nFwM1pzb9fFVUVNaq/f0loff2ofe%2BxW8PgWRlZenll1/W008/rT59%2BnjGFy5cqJEjR3otu3fvXjVr1kySFB8fr5ycHM/csWPHdOzYMcXHx1tSNwAA8H1%2BGbDy8/O1ZMkS/fGPf1S7du3kdDo9j6SkJO3YsUMrV67Ut99%2Bq7/%2B9a964403NGrUKEnSkCFDtHHjRm3YsEF79%2B7V5MmT1bVrV27RAAAAqs0vTxG%2B9957qqio0NKlS7V06VKvuS%2B//FILFy7UokWLtHDhQjVq1EhPPfWUEhISJEkJCQmaPXu2Fi1apFOnTqlTp06aM2eOHbsBAAB8VIDb7XbbXURt4HSeMb5OhyNQPRZ8ZHy98E214UajDkegoqLCVVR0lmtRLEbv7UPvr0509LW2bNcvTxECAADYiYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGOawu4BforKyMs2aNUv/8z//ozp16mjUqFEaNWqU3WUBP6n3s/%2B0u4Rqe/eRTnaXAAA1ioB1CfPnz9fu3bu1atUqHT16VFOmTFFcXJx69epld2kAAMAHELB%2B4Ny5c9qwYYOef/55tW7dWq1bt9a%2Bffu0bt06AhYAAKgWAtYP7N27V%2BXl5UpISPCMtWvXTsuWLVNlZaUCA7lsDbhavnQ6U%2BKUJoArR8D6AafTqaioKAUHB3vGGjRooLKyMhUXF6tevXqXXUdBQYGcTqfXmMMRppiYGKO1BgUR9gAr%2BFog9CX/mNjF7hJ%2B8S7%2Bv57/5/sWAtYPlJaWeoUrSZ7nLperWuvIzs5WVlaW19i4ceM0fvx4M0X%2BPwUFBRpx/T4lJycbD2/4aQUFBcrOzqb3FqPv9qH39ikoKNCqVSvovY8hDv9ASEhIlSB18XmdOnWqtY7k5GS99tprXo/k5GTjtTqdTmVlZVU5WoaaR%2B/tQd/tQ%2B/tQ%2B99E0ewfiA2NlZFRUUqLy%2BXw/F9e5xOp%2BrUqaPIyMhqrSMmJoZPGQAA1GIcwfqBVq1ayeFwKDc31zOWk5OjNm3acIE7AACoFhLDD4SGhqp///6aOXOmdu7cqS1btuiFF17Q8OHD7S4NAAD4iKCZM2fOtLuIX5qOHTtqz549euqpp/TJJ5/ooYce0qBBg%2Bwu65LCw8PVvn17hYeH211KrUPv7UHf7UPv7UPvfU%2BA2%2B12210EAACAP%2BEUIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwApaPKisr0/Tp05WYmKjOnTvrhRdesLukWsXlcqlv377atm2b3aXUGidOnFBqaqrat2%2BvLl26KCMjQ2VlZXaXVSscPHhQo0ePVkJCgrp27aoVK1bYXVKtM2bMGE2dOtXuMnAFHHYXgJ9n/vz52r17t1atWqWjR49qypQpiouLU69evewuze%2BVlZUpLS1N%2B/bts7uUWsPtdis1NVWRkZFat26dTp06penTpyswMFBTpkyxuzy/VllZqTFjxqhNmzZ6/fXXdfDgQU2YMEGxsbH67W9/a3d5tcLbb7%2BtDz74QAMGDLC7FFwBjmD5oHPnzmnDhg1KT09X69at1aNHDz3wwANat26d3aX5vf379%2Bvee%2B/Vt99%2Ba3cptcqBAweUm5urjIwM3XTTTUpMTFRqaqo2bdpkd2l%2Br7CwUK1atdLMmTPVtGlT3XXXXbrjjjuUk5Njd2m1QnFxsebPn682bdrYXQquEAHLB%2B3du1fl5eVKSEjwjLVr1055eXmqrKy0sTL/t337dnXo0EHZ2dl2l1KrREdHa8WKFWrQoIHXeElJiU0V1R4xMTF69tlnFRERIbfbrZycHO3YsUPt27e3u7Ra4c9//rP69eunFi1a2F0KrhCnCH2Q0%2BlUVFSUgoODPWMNGjRQWVmZiouLVa9ePRur829Dhw61u4RaKTIyUl26dPE8r6ys1Nq1a9WxY0cbq6p9unXrpqNHjyopKUl333233eX4vU8%2B%2BUT//ve/9dZbb2nmzJl2l4MrxBEsH1RaWuoVriR5nrtcLjtKAiyVmZmpPXv26E9/%2BpPdpdQqixYt0rJly/TFF18oIyPD7nL8WllZmR5//HHNmDFDderUsbsc/AwcwfJBISEhVYLUxef8IsLfZWZmatWqVXrmmWd08803211OrXLxOqCysjJNnDhRkydPrvJhD2ZkZWXp17/%2BtdeRW/gWApYPio2NVVFRkcrLy%2BVwfP8WOp1O1alTR5GRkTZXB9ScOXPmaP369crMzOQUlUUKCwuVm5ur7t27e8ZatGihCxcuqKSkhEsSasjbb7%2BtwsJCz7W2Fz9Eb968WZ999pmdpaGaCFg%2BqFWrVnI4HMrNzVViYqIkKScnR23atFFgIGd94Z%2BysrL08ssv6%2Bmnn%2BZ2JBY6fPiwxo0bpw8%2B%2BECxsbGSpN27d6tevXqEqxq0Zs0alZeXe54vWLBAkjRx4kS7SsIVImD5oNDQUPXv318zZ87UE088oYKCAr3wwgtcEwG/lZ%2BfryVLlmjMmDFq166dnE6nZy46OtrGyvxfmzZt1Lp1a02fPl3Tpk3TkSNHlJmZqYceesju0vxao0aNvJ6Hh4dLkm644QY7ysHPQMDyUdOmTdPMmTM1YsQIRUREaPz48erZs6fdZQE14r333lNFRYWWLl2qpUuXes19%2BeWXNlVVOwQFBWnJkiWaM2eOkpOTFRoaqmHDhmn48OF2lwb8ogW43W633UUAAAD4Ey7YAQAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/j/BTuE49xT8owAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3994779847427794597”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.696864111</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.465441005</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.81300813</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.869565217</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.645399226</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.768639508</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4004004</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.432664756</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.654212401</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3039)</td> <td class=”number”>3075</td> <td class=”number”>95.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3994779847427794597”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.071500072</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07536931</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.088082445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.101864113</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.923976608</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.023758099</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.726708075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.816793893</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.601226994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CSA07”>CSA07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>44</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.0543</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>79</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-325924926784105186”>

</div> <div class=”col-md-12 text-right”>

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aria-controls=”quantiles-325924926784105186” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-325924926784105186” aria-controls=”histogram-325924926784105186”

role=”tab” data-toggle=”tab”>Histogram</a></li>

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<li role=”presentation”><a href=”#extreme-325924926784105186” aria-controls=”extreme-325924926784105186”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-325924926784105186”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>2</td>

</tr> <tr>

<th>Q3</th> <td>6</td>

</tr> <tr>

<th>95-th percentile</th> <td>13</td>

</tr> <tr>

<th>Maximum</th> <td>79</td>

</tr> <tr>

<th>Range</th> <td>79</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.4609</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3469</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.694</td>

</tr> <tr>

<th>Mean</th> <td>4.0543</td>

</tr> <tr>

<th>MAD</th> <td>3.5302</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.0409</td>

</tr> <tr>

<th>Sum</th> <td>12471</td>

</tr> <tr>

<th>Variance</th> <td>29.821</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-325924926784105186”>

<img 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AYRQsAAAAwyhYAAAAhlGwAAAADPN5wRo5cqSef/55ff31175%2BagAAAJ/wecHq3r27Vq5cqZ49e%2BqPf/yj9uzZI8uyfD0GAADAdePzgjVp0iS99dZbWr58uYKCgvTII4%2Bod%2B/eWrRokU6cOOHrcQAAAIxz%2BuNJHQ6HevTooR49eqi8vFzPPfecli9frpycHHXp0kX33Xef7rjjDn%2BMBgAA8JP5pWBJUklJiV5%2B%2BWW9/PLL%2BvTTT9WlSxfdddddKi4u1pNPPqn9%2B/dr2rRp/hoPAADgmvm8YL300kt66aWXtG/fPjVp0kRDhw7V0qVL1aZNG89tmjdvrrlz51KwAACALfm8YE2bNk19%2BvTRsmXLdPvtt6tevZpvA2vbtq3uueceX48GAABghM8L1ttvv63GjRurrKzMU64OHTqkzp07KygoSJLUpUsXdenSxdejAQAAGOHzTxFeuHBBAwYM0DPPPONZmzBhgoYMGaLTp0/7ehwAAADjfF6w/vznP6t169a6//77PWuvvPKKmjdvrszMTF%2BPAwAAYJzPC9Y//vEPPfHEE4qIiPCsNWnSRI8//rj27t1b58dzu90aPHiw9u3b51mbM2eObr75Zq/Lxo0bPfs7d%2B5U3759FRcXp9TUVJ09e9azZ1mW5s%2Bfr%2B7duys5OVnz5s1TdXX1NaYFAAC/RD5/D5bT6dT58%2BdrrJeXl9f5jO6VlZWaNGmSCgoKvNYLCws1adIk3XXXXZ61Ro0aSfrm/V7Tpk3TzJkzFRMTo7lz5yojI0OrVq2SJK1du1Y7d%2B5Udna2qqqqNHnyZDVt2lTjx4%2Bva1QAAPAL5fMjWLfffrvmzJmjf/7zn561oqIiZWZmqlevXrV%2BnGPHjunuu%2B/2epyrCgsL1alTJ0VERHguoaGhkqSNGzdq4MCBGjp0qGJiYjRv3jzt3r1bRUVFkqQNGzYoPT1dSUlJ6t69ux577DFt2rTpJ6YGAAC/JD4vWFOmTJHb7Vb//v3VrVs3devWTXfccYcuX76sjIyMWj/O%2B%2B%2B/r27duik3N9dr/cKFCzpz5ozXebW%2BLT8/X0lJSZ7rzZs3V3R0tPLz83XmzBmdPn1aXbt29ewnJibqiy%2B%2BUElJSd2CAgCAXyyfv0TYtGlTvfjii3r33XdVUFAgp9Opm266SbfddpscDketH2fMmDHfu15YWCiHw6GVK1fq7bffVnh4uO6//37Py4UlJSWKjIysMVNxcbFcLpckee03a9ZMklRcXFzjfgAAAN/HL38qJygoSL169arTS4K1dfz4cTkcDs/JSvfv368//elPatSokfr166eKigoFBwd73Sc4OFhut1sVFRWe69/ek755M31tlZSUeMraVU5nA%2BMFLSjI5wcgfxKns/bzXs1mt4y1EcjZpMDORzZ7Ipt92TmfzwuWy%2BXS4sWLdeDAAV2%2BfLnGG9vffPPNn/T4Q4cOVZ8%2BfRQeHi5JiomJ0WeffabNmzerX79%2BCgkJqVGW3G63QkNDvcpUSEiI59%2BSPO/hqo3c3FxlZ2d7raWmpio9Pf2acwWCxo0b1vk%2BYWG1/7rbTSBnkwI7H9nsiWz2Zcd8Pi9Yf/rTn3T48GENGjRIv/rVr4w/vsPh8JSrq9q2bes5BURUVJRKS0u99ktLSxUREaGoqChJ35TAli1bev4tyeu0Ej9m1KhRSklJ8VpzOhvo3LmLdQvzI%2BzW6OuSPyionsLCQnX%2BfLmuXAms02QEcjYpsPORzZ7IZl8m8l3LL/cm%2BLxg7d27V6tXr/Z6o7lJS5Ys0QcffKB169Z51o4ePaq2bdtKkuLi4pSXl6dhw4ZJkk6fPq3Tp08rLi5OUVFRio6OVl5enqdg5eXlKTo6uk4v70VGRta4vcv1taqqAu%2BHvy6uJf%2BVK9UB%2B3UL5GxSYOcjmz2Rzb7smM/nBatBgwZq2rTpdXv8Pn36KCcnR2vWrFG/fv20Z88ebd%2B%2BXRs2bJAkjR49Wvfee6/i4%2BMVGxuruXPnqnfv3mrVqpVnf/78%2Bfr1r38tSVqwYIEeeOCB6zYvAAAIPD4vWEOGDNHq1as1a9Yszx93NunWW2/VkiVLtHTpUi1ZskQtWrTQggULlJCQIElKSEjQrFmztHTpUn311Vfq0aOHZs%2Be7bn/%2BPHj9eWXXyotLU1BQUEaMWKExo0bZ3xOAAAQuBxWXU%2Bf/hNlZGRo586dCgsLU6tWrWp8ou/qkaZA43J9bfwxnc566jf/HeOPe728%2BmiPWt/W6aynxo0b6ty5i7Y7LPxjAjmbFNj5yGZPZLMvE/kiIsy/37s2/HKahsGDB/vjaQEAAHzC5wUrMzPT108JAADgU375nH9JSYmys7M1adIkffnll3rttdd0/Phxf4wCAABgnM8L1ueff67f/va3evHFF/X666/r0qVLeuWVVzR8%2BHDl5%2Bf7ehwAAADjfF6w/vKXv6hv377atWuXbrjhBknSwoULlZKSovnz5/t6HAAAAON8XrAOHDig%2B%2B%2B/3%2BsPOzudTj388MM6cuSIr8cBAAAwzucFq7q6WtXVNT9qefHixetyXiwAAABf83nB6tmzp1atWuVVssrKypSVlaXu3bv7ehwAAADjfF6wnnjiCR0%2BfFg9e/ZUZWWl/vM//1N9%2BvTRyZMnNWXKFF%2BPAwAAYJzPz4MVFRWl7du3a%2BfOnfr4449VXV2t0aNHa8iQIWrUqJGvxwEAADDOL2dyDw0N1ciRI/3x1AAAANedzwvW2LFj/%2BV%2BoP4tQgAA8Mvh84LVokULr%2BtVVVX6/PPP9emnn%2Bq%2B%2B%2B7z9TgAAADG/Wz%2BFuGyZctUXFzs42kAAADM88vfIvw%2BQ4YM0auvvurvMQAAAH6yn03B%2BuCDDzjRKAAACAg/ize5X7hwQZ988onGjBnj63EAAACM83nBio6O9vo7hJJ0ww036J577tHvfvc7X48DAABgnM8L1l/%2B8hdfPyUAAIBP%2Bbxg7d%2B/v9a37dq163WcBAAA4PrwecG69957PS8RWpblWf/umsPh0Mcff%2Bzr8QAAAH4ynxeslStXas6cOZo8ebKSk5MVHBysDz/8ULNmzdJdd92lO%2B%2B809cjAQAAGOXz0zRkZmZq%2BvTp6t%2B/vxo3bqyGDRuqe/fumjVrljZv3qwWLVp4LgAAAHbk84JVUlLyveWpUaNGOnfunK/HAQAAMM7nBSs%2BPl4LFy7UhQsXPGtlZWXKysrSbbfd5utxAAAAjPP5e7CefPJJjR07VrfffrvatGkjy7L02WefKSIiQhs2bPD1OAAAAMb5vGC1a9dOr7zyinbu3KnCwkJJ0u9//3sNGjRIoaGhvh4HAADAOJ8XLEm68cYbNXLkSJ08eVKtWrWS9M3Z3AEAAAKBz9%2BDZVmW5s%2Bfr65du2rw4MEqLi7WlClTNG3aNF2%2BfNnX4wAAABjn84L13HPP6aWXXtJTTz2l4OBgSVLfvn21a9cuZWdn%2B3ocAAAA43xesHJzczV9%2BnQNGzbMc/b2O%2B%2B8U3PmzNGOHTt8PQ4AAIBxPi9YJ0%2BeVMeOHWusx8TEyOVy%2BXocAAAA43xesFq0aKEPP/ywxvrbb7/tecM7AACAnfn8U4Tjx4/XzJkz5XK5ZFmW3nvvPeXm5uq5557TE0884etxAAAAjPN5wRo%2BfLiqqqq0YsUKVVRUaPr06WrSpIkeffRRjR492tfjAAAAGOfzgrVz504NGDBAo0aN0tmzZ2VZlpo2berrMQAAAK4bn78Ha9asWZ43szdp0oRyBQAAAo7PC1abNm306aef%2BvppAQAAfMbnLxHGxMToscce0%2BrVq9WmTRuFhIR47WdmZvp6JAAAAKN8XrBOnDihxMRESeK8VwAAICD5pGDNmzdPaWlpatCggZ577jlfPCUAAIDf%2BOQ9WGvXrlV5ebnX2oQJE1RSUuKLpwcAAPApnxQsy7JqrO3fv1%2BVlZW%2BeHoAAACf8vmnCAEAAAIdBQsAAMAwnxUsh8Phq6cCAADwK5%2BdpmHOnDle57y6fPmysrKy1LBhQ6/bcR4sAABgdz45gtW1a1e5XC6dPHnSc0lISNC5c%2Be81k6ePFnnx3a73Ro8eLD27dvnWSsqKtK4ceMUHx%2BvO%2B%2B8U3v27PG6z7vvvqvBgwcrLi5OY8eOVVFRkdf%2BunXr1KtXLyUkJGjq1Kk1PgEJAADwr/jkCNb1OvdVZWWlJk2apIKCAs%2BaZVlKTU1Vhw4dtHXrVu3atUtpaWl65ZVXFB0drVOnTik1NVWPPPKIevXqpWXLlunhhx/Wyy%2B/LIfDoddff13Z2dnKyspS06ZNlZGRoaysLE2fPv26ZAAAAIHHtm9yP3bsmO6%2B%2B27985//9Frfu3evioqKNGvWLLVr104TJ05UfHy8tm7dKknasmWLbrnlFj3wwANq3769MjMz9cUXX%2Bj999%2BXJG3YsEH33Xef%2BvTpo1tvvVUzZ87U1q1bOYoFAABqzbYF6/3331e3bt2Um5vrtZ6fn69OnTqpQYMGnrXExEQdPHjQs5%2BUlOTZCw0NVefOnXXw4EFduXJFH374odd%2BfHy8Ll%2B%2BrKNHj17nRAAAIFD4/G8RmjJmzJjvXXe5XIqMjPRaa9q0qYqLi390//z586qsrPTadzqdCg8P99wfAADgx9i2YP2Q8vJyBQcHe60FBwfL7Xb/6H5FRYXn%2Bg/dvzZKSkpq/CFrp7NBjWL3UwUF2esApNNZ%2B3mvZrNbxtoI5GxSYOcjmz2Rzb7snC/gClZISIjKysq81txut%2BrXr%2B/Z/25ZcrvdCgsL85xG4vv2Q0NDaz1Dbm6usrOzvdZSU1OVnp5e68cIRI0bN/zxG31HWFjtv%2B52E8jZpMDORzZ7Ipt92TFfwBWsqKgoHTt2zGuttLTUc/QoKipKpaWlNfY7duyo8PBwhYSEqLS0VO3atZMkVVVVqaysTBEREbWeYdSoUUpJSfFaczob6Ny5i9cS6QfZrdHXJX9QUD2FhYXq/PlyXblSfR2n8r1AziYFdj6y2RPZ7MtEvmv55d6EgCtYcXFxysnJUUVFheeoVV5enhITEz37eXl5ntuXl5fryJEjSktLU7169RQbG6u8vDx169ZNknTw4EE5nU7FxMTUeobIyMgaLwe6XF%2Brqirwfvjr4lryX7lSHbBft0DOJgV2PrLZE9nsy4757HUIpBaSk5PVvHlzZWRkqKCgQDk5OTp06JBGjBghSRo%2BfLgOHDignJwcFRQUKCMjQy1btvQUqjFjxmjNmjXatWuXDh06pBkzZujuu%2B%2Bu00uEAADgly3gClZQUJCWL18ul8ulYcOG6eWXX9ayZcsUHR0tSWrZsqWefvppbd26VSNGjFBZWZmWLVvm%2BVuJgwYN0sSJEzV9%2BnQ98MADuvXWWzV58mR/RgIAADYTEC8RfvLJJ17XW7durY0bN/7g7X/zm9/oN7/5zQ/uT5gwQRMmTDA2HwAA%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%2B7d%2B9WUVGRnxMBAAC7COiC1aZNmxrr%2Bfn5SkxMlMPhkCQ5HA516dJFBw8e9OwnJSV5bt%2B8eXNFR0crPz/fJ3MDAAD7C8iCZVmWTpw4oT179qh///7q27ev5s%2BfL7fbLZfLpcjISK/bN23aVMXFxZKkkpKSf7kPAADwY5z%2BHuB6OHXqlMrLyxUcHKzFixfr5MmTmjNnjioqKjzr3xYcHCy32y1Jqqio%2BJf3n8NDAAAPtElEQVT7tVFSUiKXy%2BW15nQ2qFHcfqqgIHv1Y6ez9vNezWa3jLURyNmkwM5HNnsim33ZOV9AFqwWLVpo3759uvHGG%2BVwONSxY0dVV1dr8uTJSk5OrlGW3G636tevL0kKCQn53v3Q0NBaP39ubq6ys7O91lJTUz2fYvylaty4YZ3vExZW%2B6%2B73QRyNimw85HNnshmX3bMF5AFS5LCw8O9rrdr106VlZWKiIhQaWmp115paann6FJUVNT37kdERNT6uUeNGqWUlBSvNaezgc6du1iXCD/Kbo2%2BLvmDguopLCxU58%2BX68qV6us4le8FcjYpsPORzZ7IZl8m8l3LL/cmBGTBeuedd/TYY4/pb3/7m%2BfI08cff6zw8HAlJibqmWeekWVZcjgcsixLBw4c0EMPPSRJiouLU15enoYNGyZJOn36tE6fPq24uLhaP39kZGSNlwNdrq9VVRV4P/x1cS35r1ypDtivWyBnkwI7H9nsiWz2Zcd89joEUksJCQkKCQnRk08%2BqePHj2v37t2aN2%2BeHnzwQQ0YMEDnz5/X3LlzdezYMc2dO1fl5eUaOHCgJGn06NF66aWXtGXLFh09elSPP/64evfurVatWvk5FQAAsIuALFiNGjXSmjVrdPbsWQ0fPlzTpk3TqFGj9OCDD6pRo0ZatWqV5yhVfn6%2BcnJy1KBBA0nflLNZs2Zp2bJlGj16tG688UZlZmb6OREAALCTgHyJUJLat2%2BvtWvXfu/erbfeqhdffPEH7zts2DDPS4QAAAB1FZBHsAAAAPyJggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC9jzYOHnZ%2BDiv/t7hDp59dEe/h4BAGBTHMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDCnvwcAfq4GLv67v0eok1cf7eHvEQAA/4cjWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMgvU9KisrNXXqVCUlJalnz5569tln/T0SAACwET5F%2BD3mzZunw4cPa/369Tp16pSmTJmi6OhoDRgwwN%2BjAQAAG6BgfcelS5e0ZcsWPfPMM%2BrcubM6d%2B6sgoICbdq0iYIFAABqhYL1HUePHlVVVZUSEhI8a4mJiVq5cqWqq6tVrx6vquLnyU7n7eKcXQACHQXrO1wulxo3bqzg4GDPWrNmzVRZWamysjI1adLkRx%2BjpKRELpfLa83pbKDIyEijswYFUfZgT3Yqg5L0xmO9JP3//%2BYC8b89stlTIGeT7J2PgvUd5eXlXuVKkue62%2B2u1WPk5uYqOzvbay0tLU2PPPKImSH/T0lJie77dYFGjRplvLz5W0lJiXJzc8lmQ4Gcr6SkROvXryabzZDNvuycz36V8DoLCQmpUaSuXq9fv36tHmPUqFHatm2b12XUqFHGZ3W5XMrOzq5xtCwQkM2%2BAjkf2eyJbPZl53wcwfqOqKgonTt3TlVVVXI6v/nyuFwu1a9fX2FhYbV6jMjISNs1bQAAYA5HsL6jY8eOcjqdOnjwoGctLy9PsbGxvMEdAADUCo3hO0JDQzV06FDNmDFDhw4d0q5du/Tss89q7Nix/h4NAADYRNCMGTNm%2BHuIn5vu3bvryJEjWrBggd577z099NBDGj58uL/H%2Bl4NGzZUcnKyGjZs6O9RjCObfQVyPrLZE9nsy675HJZlWf4eAgAAIJDwEiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAqWTVVWVmrq1KlKSkpSz5499eyzz/p7pJ/M7XZr8ODB2rdvn2etqKhI48aNU3x8vO68807t2bPHjxPW3ZkzZ5Senq7k5GT16tVLmZmZqqyslGT/bJ9//rnGjx%2BvhIQE9e7dW6tXr/bs2T3bt02YMEFPPPGE5/qRI0c0cuRIxcXFafjw4Tp8%2BLAfp7s2b7zxhm6%2B%2BWavS3p6uiT753O73Zo5c6a6du2qf//3f9fChQt19Xzads62bdu2Gt%2Bzm2%2B%2BWTExMZLsne2q06dPa%2BLEierSpYtSUlK0bt06z54d81GwbGrevHk6fPiw1q9fr6eeekrZ2dl67bXX/D3WNausrNQf//hHFRQUeNYsy1JqaqqaNWumrVu3asiQIUpLS9OpU6f8OGntWZal9PR0lZeXa9OmTVq0aJHeeustLV682PbZqqurNWHCBDVu3FgvvviiZs6cqRUrVmjHjh22z/Ztf/3rX7V7927P9UuXLmnChAlKSkrStm3blJCQoIkTJ%2BrSpUt%2BnLLujh07pj59%2BmjPnj2ey5w5cwIi35w5c/Tuu%2B9qzZo1WrBggf7nf/5Hubm5ts929ReVq5e//e1vat26tcaOHWv7bFc9%2BuijatCggbZt26apU6dq8eLFeuONN%2Bybz4LtXLx40YqNjbX27t3rWVu2bJl1zz33%2BHGqa1dQUGD97ne/s377299aHTp08OR69913rfj4eOvixYue2953333W0qVL/TVqnRw7dszq0KGD5XK5PGs7duywevbsaftsZ86csf7rv/7L%2Bvrrrz1rqamp1lNPPWX7bFedO3fOuv32263hw4dbU6ZMsSzLsrZs2WKlpKRY1dXVlmVZVnV1tdWvXz9r69at/hy1ziZNmmQtWLCgxrrd8507d87q1KmTtW/fPs/aqlWrrCeeeML22b5r5cqVVt%2B%2Bfa3KysqAyFZWVmZ16NDB%2BuSTTzxraWlp1syZM22bjyNYNnT06FFVVVUpISHBs5aYmKj8/HxVV1f7cbJr8/7776tbt27Kzc31Ws/Pz1enTp3UoEEDz1piYqIOHjzo6xGvSUREhFavXq1mzZp5rV%2B4cMH22SIjI7V48WI1atRIlmUpLy9P%2B/fvV3Jysu2zXfXf//3fGjJkiG666SbPWn5%2BvhITE%2BVwOCRJDodDXbp0sV22wsJCtWnTpsa63fPl5eWpUaNGSk5O9qxNmDBBmZmZts/2bWVlZXrmmWc0adIkBQcHB0S2%2BvXrKzQ0VNu2bdPly5d1/PhxHThwQB07drRtPgqWDblcLjVu3FjBwcGetWbNmqmyslJlZWV%2BnOzajBkzRlOnTlVoaKjXusvlUmRkpNda06ZNVVxc7MvxrllYWJh69erluV5dXa2NGzeqe/futs/2bSkpKRozZowSEhLUv3//gMj23nvv6R//%2BIcefvhhr/VAyGZZlk6cOKE9e/aof//%2B6tu3r%2BbPny%2B32237fEVFRWrRooW2b9%2BuAQMG6D/%2B4z%2B0bNkyVVdX2z7bt23evFmRkZEaMGCApMD4uQwJCdH06dOVm5uruLg4DRw4ULfffrtGjhxp23xOfw%2BAuisvL/cqV5I8191utz9Gui5%2BKKddM2ZlZenIkSN64YUXtG7duoDJtnTpUpWWlmrGjBnKzMy0/fetsrJSTz31lKZPn6769et77dk9mySdOnXKk2Px4sU6efKk5syZo4qKCtvnu3Tpkj7//HM9//zzyszMlMvl0vTp0xUaGmr7bFdZlqUtW7bowQcf9KwFSrbCwkL16dNH999/vwoKCjR79mzddtttts1HwbKhkJCQGj9YV69/938IdhYSElLjiJzb7bZlxqysLK1fv16LFi1Shw4dAipbbGyspG%2BKyWOPPabhw4ervLzc6zZ2ypadna1bbrnF6%2BjjVT/0355dsklSixYttG/fPt14441yOBzq2LGjqqurNXnyZCUnJ9s6n9Pp1IULF7RgwQK1aNFC0jeFcvPmzWrdurWts1314Ycf6syZMxo0aJBnLRB%2BLt977z298MIL2r17t%2BrXr6/Y2FidOXNGK1asUKtWrWyZj5cIbSgqKkrnzp1TVVWVZ83lcql%2B/foKCwvz42RmRUVFqbS01GuttLS0xqHin7vZs2dr7dq1ysrKUv/%2B/SXZP1tpaal27drltXbTTTfp8uXLioiIsHW2v/71r9q1a5cSEhKUkJCgHTt2aMeOHUpISLD99%2B2q8PBwz/tZJKldu3aqrKy0/fcuIiJCISEhnnIlSf/2b/%2Bm06dPB8z37p133lFSUpJuvPFGz1ogZDt8%2BLBat27tVZo6deqkU6dO2TYfBcuGOnbsKKfT6fUGv7y8PMXGxqpevcD5lsbFxemjjz5SRUWFZy0vL09xcXF%2BnKpusrOz9fzzz2vhwoVev3HaPdvJkyeVlpamM2fOeNYOHz6sJk2aKDEx0dbZnnvuOe3YsUPbt2/X9u3blZKSopSUFG3fvl1xcXH64IMPPOdVsixLBw4csE026Zv/QXfr1s3rKOPHH3%2Bs8PBwJSYm2jpfXFycKisrdeLECc/a8ePH1aJFi4D43knSoUOH1KVLF6%2B1QMgWGRmpzz//3OtI1fHjx9WyZUvb5guc/xv/goSGhmro0KGaMWOGDh06pF27dunZZ5/V2LFj/T2aUcnJyWrevLkyMjJUUFCgnJwcHTp0SCNGjPD3aLVSWFio5cuX6w9/%2BIMSExPlcrk8F7tni42NVefOnTV16lQdO3ZMu3fvVlZWlh566CHbZ2vRooVat27tuTRs2FANGzZU69atNWDAAJ0/f15z587VsWPHNHfuXJWXl2vgwIH%2BHrvWEhISFBISoieffFLHjx/X7t27NW/ePD344IO2z9e2bVv17t1bGRkZOnr0qN555x3l5ORo9OjRts92VUFBgdcnWyUFRLaUlBTdcMMNevLJJ3XixAn97//%2Br1auXKl7773Xvvn8cnII/GSXLl2yHn/8cSs%2BPt7q2bOntXbtWn%2BPZMS3z4NlWZb12WefWb///e%2BtW265xRo0aJD197//3Y/T1c2qVausDh06fO/FsuydzbIsq7i42EpNTbW6dOli9ejRw1qxYoXnPDV2z/ZtU6ZM8ZwHy7IsKz8/3xo6dKgVGxtrjRgxwvroo4/8ON21%2BfTTT61x48ZZ8fHxVo8ePaynn37a872ze77z589bkydPtuLj463bbrstoLJZlmXFxsZab7/9do31QMhWUFBgjRs3zurSpYvVt29fa%2B3atbb%2B3jks6/%2BOuQEAAMAIXiIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMP%2BH3fBAicaqFBqAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-325924926784105186”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>449</td> <td class=”number”>14.0%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>365</td> <td class=”number”>11.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>311</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>205</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>162</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>121</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>89</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (33)</td> <td class=”number”>325</td> <td class=”number”>10.1%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-325924926784105186”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>449</td> <td class=”number”>14.0%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>365</td> <td class=”number”>11.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>311</td> <td class=”number”>9.7%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>59.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>79.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_CSA12”>CSA12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>54</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.0514</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>88</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>29.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7584625089778804349”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-7584625089778804349”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7584625089778804349”

aria-controls=”quantiles-7584625089778804349” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7584625089778804349” aria-controls=”histogram-7584625089778804349”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7584625089778804349” aria-controls=”common-7584625089778804349”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7584625089778804349” aria-controls=”extreme-7584625089778804349”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7584625089778804349”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>2</td>

</tr> <tr>

<th>Q3</th> <td>5</td>

</tr> <tr>

<th>95-th percentile</th> <td>16</td>

</tr> <tr>

<th>Maximum</th> <td>88</td>

</tr> <tr>

<th>Range</th> <td>88</td>

</tr> <tr>

<th>Interquartile range</th> <td>5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.9362</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.7121</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.277</td>

</tr> <tr>

<th>Mean</th> <td>4.0514</td>

</tr> <tr>

<th>MAD</th> <td>4.0204</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6227</td>

</tr> <tr>

<th>Sum</th> <td>12462</td>

</tr> <tr>

<th>Variance</th> <td>48.111</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7584625089778804349”>

<img 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/zzOnfunNW7BgAAsITlAWvgwIHavHmzbrnlFj3wwAPav3%2B/3G631WUAAAA0G8sD1oMPPqh33nlHGzduVHBwsH73u9/ptttu05o1a3Ty5EmrywEAADDO7oud2mw2DRo0SIMGDVJ1dbV27typjRs3Kjs7W/3799e0adP0q1/9yhelAQAAXDWfBCxJKisr08svv6yXX35ZX3zxhfr376877rhDpaWleuyxx3Tw4EEtWLDAV%2BUBAAD8yywPWC%2B99JJeeuklffTRR2rXrp3GjRun9evXq1u3bp7XdOzYUcuXLydgAQCAgGR5wFqwYIGGDBmiDRs26NZbb1VQUOPLwLp37667777b6tIAAACMsDxgvffee4qOjlZlZaUnXBUWFqpv374KDg6WJPXv31/9%2B/e3ujQAAAAjLP8twvPnz2vkyJF6%2BumnPWOzZs3S2LFjVVJSYnU5AAAAxlkesH7/%2B9%2Bra9eumjFjhmfs1VdfVceOHZWRkWF1OQAAAMZZHrD%2B8pe/6NFHH1VMTIxnrF27dnrkkUd04MABq8sBAAAwzvKAZbfbVVVV1Wi8urqaO7oDAIAWwfKAdeutt2rZsmX629/%2B5hkrLi5WRkaGBg8ebHU5AAAAxln%2BW4Rz587VjBkzNGLECEVGRkqSqqqq1LdvX82bN8/qcgAAAIyzPGC1b99e//3f/60PPvhAx44dk91u1y9%2B8QvdfPPNstlsVpcDAABgnE%2B%2BKic4OFiDBw/mlCAAAGiRLA9YTqdTa9eu1aFDh3Tp0qVGF7a//fbbVpcEAABglOUB6/HHH9fhw4c1evRoXXPNNVbvHgAAoNlZHrAOHDigLVu2KDk52epdAwAAWMLy2zSEh4erffv2xrbncrk0ZswYffTRR56xZcuW6frrr/d67Nq1yzOfm5urYcOGyeFwKDU1VWfPnvXMud1urVy5UgMHDlRKSopWrFihhoYGY/UCAICWz/KANXbsWG3ZskX19fVXva3a2lo98MADOnbsmNd4UVGRHnzwQe3fv9/zmDBhgqRvv1h6wYIFSktLU05OjqqqqrxuD/Hss88qNzdXWVlZWr9%2BvV555RU9%2B%2ByzV10rAABoPSw/RVhZWanc3Fz9%2Bc9/VpcuXRQSEuI1v2PHjiZt5/jx43rwwQevePf3oqIizZw50%2BvreC7btWuXRo0apXHjxkmSVqxYoSFDhqi4uFhdunTRjh07lJ6e7jmF%2BdBDD2ndunWaOXPmT10qAABopXxym4YxY8Zc9TY%2B/vhj3XTTTfrP//xPJSQkeMbPnz%2BvM2fOqFu3bld8X0FBge677z7P844dOyo%2BPl4FBQUKCQlRSUmJBgwY4JlPSkrSqVOnVFZWptjY2KuuGwAAtHyWB6yMjAwj27nrrruuOF5UVCSbzabNmzfrvffeU1RUlGbMmKE77rhDkq4YlNq3b6/S0lI5nU5J8prv0KGDJKm0tLTJAausrMyzrcvs9nDjAS042PIzvFfFbg%2Bsen%2BKy70ItJ60RPTCf9AL/0I/rOWTI1hlZWX64x//qJMnT2r%2B/Pk6ePCgevbsqe7du1/1tk%2BcOCGbzabu3bvr7rvv1sGDB/X444%2Brbdu2Gj58uGpqahqdlgwJCZHL5VJNTY3n%2BT/PSd9eTN9UOTk5ysrK8hpLTU1Venr6v7qsFiE6OsLXJTS7yMgwX5eAf6AX/oNe%2BBf6YQ3LA9ZXX32lO%2B%2B8U23bttWZM2c0Z84cvfrqq5o3b562bdsmh8NxVdsfN26chgwZoqioKElSr1699OWXX2r37t0aPny4QkNDG4Ull8ulsLAwrzAVGhrq%2BbMkhYU1/Qdy8uTJGjp0qNeY3R6uiooL//K6riTQPoWYXr8/CQ4OUmRkmKqqqlVfz2%2Bd%2BhK98B/0wr%2B01n746sO95QHrySef1LBhw7Rs2TL1799fkrR69WrNnTtXK1eu1M6dO69q%2BzabzROuLuvevbsOHDggSYqLi1N5ebnXfHl5uWJiYhQXFyfp27vNd%2B7c2fNnSVe8YP77xMbGNjod6HSeU11d6/mBvpLWsP76%2BoZWsc5AQC/8B73wL/TDGpYfAjl06JBmzJjh9cXOdrtd999/v44cOXLV21%2B3bp2mT5/uNXb06FHP6UeHw6G8vDzPXElJiUpKSuRwOBQXF6f4%2BHiv%2Bby8PMXHx3OBOwAAaDLLj2A1NDRc8cadFy5cUHBw8FVvf8iQIcrOztbWrVs1fPhw7d%2B/X/v27fPc/mHKlCm65557lJCQoH79%2Bmn58uW67bbb1KVLF8/8ypUr9fOf/1yStGrVKt17771XXRcAAGg9LA9Yt9xyi5566illZmZ6xiorK5WZmamBAwde9fZvvPFGrVu3TuvXr9e6devUqVMnrVq1SomJiZKkxMRELVmyROvXr9c333yjQYMGaenSpZ73z5w5U3//%2B9%2BVlpam4OBgTZw4sdERMQAAgB9ic1/pTp3N6MyZM5o6darOnTunyspKde/eXadOnVJUVJR27dqlTp06WVmOZZzOc8a3abcHafjK941vt7m8NmeQr0toNnZ7kKKjI1RRcYFrG3yMXvgPeuFfWms/YmKu8cl%2BLT%2BCFRcXp3379ik3N1efffaZGhoaNGXKFI0dO1Zt27a1uhwAAADjfHIfrLCwME2aNMkXuwYAAGh2lgesqVOn/uB8U7%2BLEAAAwF9ZHrC%2Be41VXV2dvvrqK33xxReaNm2a1eUAAAAY5zffRbhhwwaVlpZaXA0AAIB5fvNdK2PHjtVrr73m6zIAAACumt8ErL/%2B9a9GbjQKAADga35xkfv58%2Bf1%2Beef66677rK6HAAAAOMsD1jx8fFe30MoST/72c9099136z/%2B4z%2BsLgcAAMA4ywPWk08%2BafUuAQAALGV5wDp48GCTXztgwIBmrAQAAKB5WB6w7rnnHs8pwn/%2BGsTvjtlsNn322WdWlwcAAHDVLA9Ymzdv1rJly/Twww8rJSVFISEh%2BuSTT7RkyRLdcccduv32260uCQAAwCjLb9OQkZGhhQsXasSIEYqOjlZERIQGDhyoJUuWaPfu3erUqZPnAQAAEIgsD1hlZWVXDE9t27ZVRUWF1eUAAAAYZ3nASkhI0OrVq3X%2B/HnPWGVlpTIzM3XzzTdbXQ4AAIBxll%2BD9dhjj2nq1Km69dZb1a1bN7ndbn355ZeKiYnRjh07rC4HAADAOMsDVo8ePfTqq68qNzdXRUVFkqRf//rXGj16tMLCwqwuBwAAwDjLA5YkXXvttZo0aZK%2B/vprdenSRdK3d3MHAABoCSy/BsvtdmvlypUaMGCAxowZo9LSUs2dO1cLFizQpUuXrC4HAADAOMsD1s6dO/XSSy/piSeeUEhIiCRp2LBheuutt5SVlWV1OQAAAMZZHrBycnK0cOFCjR8/3nP39ttvv13Lli3TK6%2B8YnU5AAAAxlkesL7%2B%2Bmv17t270XivXr3kdDqtLgcAAMA4ywNWp06d9MknnzQaf%2B%2B99zwXvAMAAAQyy3%2BLcObMmVq8eLGcTqfcbrc%2B/PBD5eTkaOfOnXr00UetLgcAAMA4ywPWhAkTVFdXp02bNqmmpkYLFy5Uu3btNGfOHE2ZMsXqcgAAAIyzPGDl5uZq5MiRmjx5ss6ePSu326327dtbXQYAAECzsfwarCVLlnguZm/Xrh3hCgAAtDiWB6xu3brpiy%2B%2BsHq3AAAAlrH8FGGvXr300EMPacuWLerWrZtCQ0O95jMyMqwuCQAAwCjLA9bJkyeVlJQkSdz3CgAAtEiWBKwVK1YoLS1N4eHh2rlzpxW7BAAA8BlLrsF69tlnVV1d7TU2a9YslZWVWbF7AAAAS1kSsNxud6OxgwcPqra21ordAwAAWMry3yIEAABo6QhYAAAAhlkWsGw2m1W7AgAA8CnLbtOwbNkyr3teXbp0SZmZmYqIiPB6HffBAgAAgc6SgDVgwIBG97xKTExURUWFKioqrCgBAADAMpYELO59BQAAWhMucgcAADCMgAUAAGAYAQsAAMAwAhYAAIBhAR%2BwXC6XxowZo48%2B%2BsgzVlxcrOnTpyshIUG333679u/f7/WeDz74QGPGjJHD4dDUqVNVXFzsNb9t2zYNHjxYiYmJmj9/fqPvUQQAAPghAR2wamtr9cADD%2BjYsWOeMbfbrdTUVHXo0EF79%2B7V2LFjlZaWptOnT0uSTp8%2BrdTUVI0fP1579uxRu3btdP/993u%2BL/GNN95QVlaWlixZou3bt6ugoECZmZk%2BWR8AAAhMARuwjh8/rjvvvFN/%2B9vfvMYPHDig4uJiLVmyRD169NDs2bOVkJCgvXv3SpJeeOEF3XDDDbr33nt13XXXKSMjQ6dOndLHH38sSdqxY4emTZumIUOG6MYbb9TixYu1d%2B9ejmIBAIAmC9iA9fHHH%2Bumm25STk6O13hBQYH69Omj8PBwz1hSUpLy8/M988nJyZ65sLAw9e3bV/n5%2Baqvr9cnn3ziNZ%2BQkKBLly7p6NGjzbwiAADQUlj2VTmm3XXXXVccdzqdio2N9Rpr3769SktLf3S%2BqqpKtbW1XvN2u11RUVGe9zdFWVlZozvX2%2B3hjfZ7tYKDAysf2%2B2BVe9PcbkXgdaTlohe%2BA964V/oh7UCNmB9n%2BrqaoWEhHiNhYSEyOVy/eh8TU2N5/n3vb8pcnJylJWV5TWWmpqq9PT0Jm%2BjJYqOjvjxFwW4yMgwX5eAf6AX/oNe%2BBf6YY0WF7BCQ0NVWVnpNeZyudSmTRvP/HfDksvlUmRkpOfLqK80HxbW9B/IyZMna%2BjQoV5jdnu4KiouNHkbTRFon0JMr9%2BfBAcHKTIyTFVV1aqvb/B1Oa0avfAf9MK/tNZ%2B%2BOrDfYsLWHFxcTp%2B/LjXWHl5uef0XFxcnMrLyxvN9%2B7dW1FRUQoNDVV5ebl69OghSaqrq1NlZaViYmKaXENsbGyj04FO5znV1bWeH%2BgraQ3rr69vaBXrDAT0wn/QC/9CP6wRWIdAmsDhcOjTTz/1nO6TpLy8PDkcDs98Xl6eZ666ulpHjhyRw%2BFQUFCQ%2BvXr5zWfn58vu92uXr16WbcIAAAQ0FpcwEpJSVHHjh01b948HTt2TNnZ2SosLNTEiRMlSRMmTNChQ4eUnZ2tY8eOad68eercubNuuukmSd9ePL9161a99dZbKiws1KJFi3TnnXf%2BpFOEAACgdWtxASs4OFgbN26U0%2BnU%2BPHj9fLLL2vDhg2Kj4%2BXJHXu3Fl/%2BMMftHfvXk2cOFGVlZXasGGDbDabJGn06NGaPXu2Fi5cqHvvvVc33nijHn74YV8uCQAABBib%2B/ItzNGsnM5zxrdptwdp%2BMr3jW%2B3ubw2Z5CvS2g2dnuQoqMjVFFxgWsbfIxe%2BA964V9aaz9iYq7xyX5b3BEsAAAAXyNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWIsNWG%2B%2B%2Baauv/56r0d6erok6ciRI5o0aZIcDocmTJigw4cPe703NzdXw4YNk8PhUGpqqs6ePeuLJQAAgADVYgPW8ePHNWTIEO3fv9/zWLZsmS5evKhZs2YpOTlZL774ohITEzV79mxdvHhRklRYWKgFCxYoLS1NOTk5qqqq0rx583y8GgAAEEhabMAqKipSz549FRMT43lERkbq1VdfVWhoqB555BH16NFDCxYsUEREhF5//XVJ0q5duzRq1CiNGzdOvXr10ooVK/Tuu%2B%2BquLjYxysCAACBokUHrG7dujUaLygoUFJSkmw2myTJZrOpf//%2Bys/P98wnJyd7Xt%2BxY0fFx8eroKDAkroBAEDga5EBy%2B126%2BTJk9q/f79GjBihYcOGaeXKlXK5XHI6nYqNjfV6ffv27VVaWipJKisr%2B8F5AACAH2P3dQHN4fTp06qurlZISIjWrl2rr7/%2BWsuWLVNNTY1n/J%2BFhITI5XJJkmpqan5wvinKysrkdDq9xuz28EbB7WoFBwdWPrbbA6ven%2BJyLwKtJy0RvfAf9MK/0A9rtciA1alTJ3300Ue69tprZbPZ1Lt3bzU0NOjhhx9F2O3xAAAMg0lEQVRWSkpKo7DkcrnUpk0bSVJoaOgV58PCwpq8/5ycHGVlZXmNpaamen6LsbWKjo7wdQnNLjKy6T8naF70wn/QC/9CP6zRIgOWJEVFRXk979Gjh2praxUTE6Py8nKvufLycs/Rpbi4uCvOx8TENHnfkydP1tChQ73G7PZwVVRc%2BClL%2BFGB9inE9Pr9SXBwkCIjw1RVVa36%2BgZfl9Oq0Qv/QS/8S2vth68%2B3LfIgPX%2B%2B%2B/roYce0p///GfPkafPPvtMUVFRSkpK0tNPPy232y2bzSa3261Dhw7pt7/9rSTJ4XAoLy9P48ePlySVlJSopKREDoejyfuPjY1tdDrQ6TynurrW8wN9Ja1h/fX1Da1inYGAXvgPeuFf6Ic1AusQSBMlJiYqNDRUjz32mE6cOKF3331XK1as0G9%2B8xuNHDlSVVVVWr58uY4fP67ly5erurpao0aNkiRNmTJFL730kl544QUdPXpUjzzyiG677TZ16dLFx6sCAACBokUGrLZt22rr1q06e/asJkyYoAULFmjy5Mn6zW9%2Bo7Zt2%2Bqpp57yHKUqKChQdna2wsPDJX0bzpYsWaINGzZoypQpuvbaa5WRkeHjFQEAgEBic7vdbl8X0Ro4neeMb9NuD9Lwle8b325zeW3OIF%2BX0Gzs9iBFR0eoouICh959jF74D3rhX1prP2JirvHJflvkESwAAABfImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD7L4uAK3HqLX/5%2BsSfpLX5gzydQkAgADFESwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZfV0A4K9Grf0/X5fwk7w2Z5CvSwAA/ANHsAAAAAwjYAEAABhGwLqC2tpazZ8/X8nJybrlllv0zDPP%2BLokAAAQQLgG6wpWrFihw4cPa/v27Tp9%2BrTmzp2r%2BPh4jRw50telAd8rkK4Z43oxAC0dAes7Ll68qBdeeEFPP/20%2Bvbtq759%2B%2BrYsWN67rnnCFgAAKBJOEX4HUePHlVdXZ0SExM9Y0lJSSooKFBDQ4MPKwMAAIGCI1jf4XQ6FR0drZCQEM9Yhw4dVFtbq8rKSrVr1%2B5Ht1FWVian0%2Bk1ZreHKzY21mitwcHkYwSmQDqdGYjefGiwr0uQ9P//H8X/q/wD/bAWAes7qqurvcKVJM9zl8vVpG3k5OQoKyvLaywtLU2/%2B93vzBT5D2VlZZr282OaPHmy8fCGn6asrEw5OTn0wg/QC/9RVlam7du30As/QT%2BsRYz9jtDQ0EZB6vLzNm3aNGkbkydP1osvvuj1mDx5svFanU6nsrKyGh0tg/Xohf%2BgF/6DXvgX%2BmEtjmB9R1xcnCoqKlRXVye7/dv/PE6nU23atFFkZGSTthEbG8unAwAAWjGOYH1H7969ZbfblZ%2Bf7xnLy8tTv379FBTEfy4AAPDjSAzfERYWpnHjxmnRokUqLCzUW2%2B9pWeeeUZTp071dWkAACBABC9atGiRr4vwNwMHDtSRI0e0atUqffjhh/rtb3%2BrCRMm%2BLqsK4qIiFBKSooiIiJ8XUqrRy/8B73wH/TCv9AP69jcbrfb10UAAAC0JJwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwApQtbW1mj9/vpKTk3XLLbfomWee8XVJrcaZM2eUnp6ulJQUDR48WBkZGaqtrZUkFRcXa/r06UpISNDtt9%2Bu/fv3%2B7ja1mPWrFl69NFHPc%2BPHDmiSZMmyeFwaMKECTp8%2BLAPq2v5XC6XFi9erAEDBuiXv/ylVq9ercv3saYX1iopKdHs2bPVv39/DR06VNu2bfPM0QvrELAC1IoVK3T48GFt375dTzzxhLKysvT666/7uqwWz%2B12Kz09XdXV1Xruuee0Zs0avfPOO1q7dq3cbrdSU1PVoUMH7d27V2PHjlVaWppOnz7t67JbvD/96U969913Pc8vXryoWbNmKTk5WS%2B%2B%2BKISExM1e/ZsXbx40YdVtmzLli3TBx98oK1bt2rVqlX64x//qJycHHrhA3PmzFF4eLhefPFFzZ8/X2vXrtWbb75JL6zmRsC5cOGCu1%2B/fu4DBw54xjZs2OC%2B%2B%2B67fVhV63D8%2BHF3z5493U6n0zP2yiuvuG%2B55Rb3Bx984E5ISHBfuHDBMzdt2jT3%2BvXrfVFqq1FRUeG%2B9dZb3RMmTHDPnTvX7Xa73S%2B88IJ76NCh7oaGBrfb7XY3NDS4hw8f7t67d68vS22xKioq3H369HF/9NFHnrGnnnrK/eijj9ILi1VWVrp79uzp/vzzzz1jaWlp7sWLF9MLi3EEKwAdPXpUdXV1SkxM9IwlJSWpoKBADQ0NPqys5YuJidGWLVvUoUMHr/Hz58%2BroKBAffr0UXh4uGc8KSlJ%2Bfn5VpfZqvzXf/2Xxo4dq1/84heesYKCAiUlJclms0mSbDab%2BvfvTy%2BaSV5entq2bauUlBTP2KxZs5SRkUEvLNamTRuFhYXpxRdf1KVLl3TixAkdOnRIvXv3phcWI2AFIKfTqejoaIWEhHjGOnTooNraWlVWVvqwspYvMjJSgwcP9jxvaGjQrl27NHDgQDmdTsXGxnq9vn379iotLbW6zFbjww8/1F/%2B8hfdf//9XuP0wlrFxcXq1KmT9u3bp5EjR%2Brf//3ftWHDBjU0NNALi4WGhmrhwoXKycmRw%2BHQqFGjdOutt2rSpEn0wmJ2XxeAn666utorXEnyPHe5XL4oqdXKzMzUkSNHtGfPHm3btu2KfaEnzaO2tlZPPPGEFi5cqDZt2njNfd/fEXrRPC5evKivvvpKzz//vDIyMuR0OrVw4UKFhYXRCx8oKirSkCFDNGPGDB07dkxLly7VzTffTC8sRsAKQKGhoY3%2BQlx%2B/t1/aNB8MjMztX37dq1Zs0Y9e/ZUaGhooyOILpeLnjSTrKws3XDDDV5HFC/7vr8j9KJ52O12nT9/XqtWrVKnTp0kSadPn9bu3bvVtWtXemGhDz/8UHv27NG7776rNm3aqF%2B/fjpz5ow2bdqkLl260AsLEbACUFxcnCoqKlRXVye7/dsWOp1OtWnTRpGRkT6urnVYunSpdu/erczMTI0YMULSt305fvy41%2BvKy8sbHZKHGX/6059UXl7uuRbx8j8cb7zxhsaMGaPy8nKv19OL5hMTE6PQ0FBPuJKkf/u3f1NJSYlSUlLohYUOHz6srl27eoWmPn36aPPmzUpOTqYXFuIarADUu3dv2e12rwsT8/Ly1K9fPwUF0dLmlpWVpeeff16rV6/W6NGjPeMOh0OffvqpampqPGN5eXlyOBy%2BKLPF27lzp1555RXt27dP%2B/bt09ChQzV06FDt27dPDodDf/3rXz33YXK73Tp06BC9aCYOh0O1tbU6efKkZ%2BzEiRPq1KkTvbBYbGysvvrqK68jVSdOnFDnzp3phcX41zgAhYWFady4cVq0aJEKCwv11ltv6ZlnntHUqVN9XVqLV1RUpI0bN%2Bq%2B%2B%2B5TUlKSnE6n55GSkqKOHTtq3rx5OnbsmLKzs1VYWKiJEyf6uuwWqVOnTuratavnERERoYiICHXt2lUjR45UVVWVli9fruPHj2v58uWqrq7WqFGjfF12i9S9e3fddtttmjdvno4ePar3339f2dnZmjJlCr2w2NChQ/Wzn/1Mjz32mE6ePKn//d//1ebNm3XPPffQC4vZ3JejLAJKdXW1Fi1apP/5n/9R27ZtNXPmTE2fPt3XZbV42dnZWrVq1RXnPv/8c3311VdasGCBCgoK1LVrV82fP1%2B//OUvLa6ydbp8F/cnn3xSklRYWKgnnnhCRUVFuv7667V48WL16dPHlyW2aOfOndPSpUv15ptvKiwsTHfddZdSU1Nls9nohcUuh6fCwkK1a9dOv/71rzVt2jR6YTECFgAAgGGcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4f4/FE0BStaysAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7584625089778804349”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>944</td> <td class=”number”>29.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>412</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>110</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>94</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (43)</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7584625089778804349”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>944</td> <td class=”number”>29.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>412</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2012”><s>Child and Adult Care participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2011”><code>Child and Adult Care particpants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99474</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2013”><s>Child and Adult Care participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2012”><code>Child and Adult Care participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99338</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2014”><s>Child and Adult Care participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2013”><code>Child and Adult Care participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99269</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care participants, FY 2015”><s>Child and Adult Care participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2014”><code>Child and Adult Care participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9954</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care particpants FY 2009”><s>Child and Adult Care particpants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95709</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Child and Adult Care particpants FY 2011”><s>Child and Adult Care particpants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care particpants FY 2009”><code>Child and Adult Care particpants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99714</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES07”>DIRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>887</td>

</tr> <tr>

<th>Unique (%)</th> <td>27.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>359</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>400.34</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>17170</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2777681042516120416”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2777681042516120416,#minihistogram2777681042516120416”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2777681042516120416”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2777681042516120416”

aria-controls=”quantiles2777681042516120416” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2777681042516120416” aria-controls=”histogram2777681042516120416”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2777681042516120416” aria-controls=”common2777681042516120416”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2777681042516120416” aria-controls=”extreme2777681042516120416”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2777681042516120416”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>7</td>

</tr> <tr>

<th>Q1</th> <td>44</td>

</tr> <tr>

<th>Median</th> <td>123</td>

</tr> <tr>

<th>Q3</th> <td>338.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>1640.5</td>

</tr> <tr>

<th>Maximum</th> <td>17170</td>

</tr> <tr>

<th>Range</th> <td>17170</td>

</tr> <tr>

<th>Interquartile range</th> <td>294.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>993.08</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4806</td>

</tr> <tr>

<th>Kurtosis</th> <td>87.534</td>

</tr> <tr>

<th>Mean</th> <td>400.34</td>

</tr> <tr>

<th>MAD</th> <td>450.46</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.8306</td>

</tr> <tr>

<th>Sum</th> <td>1141400</td>

</tr> <tr>

<th>Variance</th> <td>986200</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2777681042516120416”>

<img 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IyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWebxgjRo1Si%2B//LJOnz7t6W8NAADgER4vWL169dLy5cvVp08fPfjgg9q6dauMMZ5eBgAAQJ3xeMGaNm2a3nvvPS1btkwBAQG6//77dfPNN%2Bvpp5/WoUOHPL0cAAAA6xz18U39/PzUu3dv9e7dW8XFxXrxxRe1bNkypaenq3v37powYYJuueWW%2BlgaAADAFauXgiVJBQUF%2Butf/6q//vWv2rdvn7p3767hw4crPz9ff/zjH7Vjxw7Nnj27vpYHAABw2TxesDZv3qzNmzfrk08%2BUfPmzTVs2DAtWbJE7dq1c12nVatWmj9/PgULAAB4JY8XrNmzZ6tv375aunSpbrzxRvn7V/8YWPv27TV27FhPLw0AAMAKjxesDz74QM2aNVNRUZGrXO3atUtdunRRQECAJKl79%2B7q3r27p5cGAABghcd/i/DMmTMaNGiQnn/%2BedfYvffeqzvuuEPHjh3z9HIAAACs83jB%2BvOf/6yf/exnuueee1xjb731llq1aqWUlBRPLwcAAMA6jxes//mf/9EjjzyisLAw11jz5s318MMPa9u2bZ5eDgAAgHUeL1gOh0OnTp2qNl5cXMwZ3QEAQIPg8YJ14403at68efr6669dY3l5eUpJSVFiYqKnlwMAAGCdx3%2BLcMaMGbrnnns0cOBAhYSESJJOnTqlLl26aObMmZ5eDgAAgHUeL1gtWrTQ66%2B/ro8%2B%2Bkj79%2B%2BXw%2BHQL37xC/3yl7%2BUn5%2Bfp5cDAABgXb38qZyAgAAlJibyliAAAGiQPF6wnE6nnnnmGe3cuVPnz5%2Bv9sH2v//9755eEgAAgFUeL1iPPvqoPvvsMw0ZMkQ//elPPf3tAQAA6pzHC9a2bdu0cuVKJSQkePpbAwAAeITHT9PQuHFjtWjRwtr2ysrKNHToUH3yySeusXnz5um6665z%2B1q/fr1rPjMzU/3791dMTIySkpJ04sQJ15wxRgsXLlSvXr3Uo0cPLViwQJWVldbWCwAAGj6PF6w77rhDK1euVEVFxRVvq7S0VA8%2B%2BKD279/vNp6bm6tp06Zp69atrq%2BRI0dK%2Bu4PS8%2BePVtTp05VRkaGTp065XZ6iNWrVyszM1NpaWlasmSJ3nzzTa1evfqK1woAAHyHx98iLCoqUmZmpt5//321bdtWgYGBbvPr1q2r1XYOHDigadOmXfTs77m5uZo8ebLbn%2BOpsn79et16660aNmyYJGnBggXq27ev8vLy1LZtW61bt07JycmutzAfeughLV68WJMnT/6xUQEAgI%2Bql9M0DB069Iq3sX37dvXs2VN/%2BMMfFBsb6xo/c%2BaMjh8/rnbt2l30djk5Ofrtb3/rutyqVStFRkYqJydHgYGBOnbsmG644QbXfHx8vI4cOaKCggKFh4df8boBAEDD5/GClZKSYmU7Y8aMueh4bm6u/Pz8tHz5cn3wwQcKDQ3VPffco%2BHDh0vSRYtSixYtlJ%2BfL6fTKUlu8y1btpQk5efnU7AAAECt1MsRrIKCAr3yyis6dOiQZs2apR07dqhTp05q3779FW/74MGD8vPzU/v27TV27Fjt2LFDjz76qJo2baoBAwaopKSk2tuSgYGBKisrU0lJievyhXPSdx%2Bm/zH5qspaFYejsfWCFhDg8Y/QXRGHw956q7J7231giy/n9%2BXskm/n9%2BXsEvm9Lb/HC9ZXX32lu%2B66S02bNtXx48f1wAMP6K233tLMmTO1Zs0axcTEXNH2hw0bpr59%2Byo0NFSSFBUVpcOHD2vDhg0aMGCAgoKCqpWlsrIyBQcHu5WpoKAg178lKTg4uNZryMjIUFpamttYUlKSkpOTLztXQ9CsWRPr2wwJqf3j0hD5cn5fzi75dn5fzi6R31vye7xgPfnkk%2Brfv7/mzZun7t27S5KeeuopzZgxQwsXLtSLL754Rdv38/Nzlasq7du317Zt2yRJERERKiwsdJsvLCxUWFiYIiIiJH13tvk2bdq4/i3poh%2BYv5TRo0erX79%2BbmMOR2OdPHn2x4Wpgbe0%2BCo28wcE%2BCskJFinThWrosL3TqPhy/l9Obvk2/l9ObtE/svNXxc/3NeGxwvWzp079dJLL7n9YWeHw6H77rtPd9111xVvf/Hixfr000%2B1Zs0a19jevXtdbz/GxMQoKytLI0aMkCQdO3ZMx44dU0xMjCIiIhQZGamsrCxXwcrKylJkZOSPensvPDy82vWdztMqL/e9HeJCdZG/oqLSp%2B9XX87vy9kl387vy9kl8ntLfo8XrMrKyoueuPPs2bMKCAi44u337dtX6enpWrVqlQYMGKCtW7fqjTfecJ3%2B4e6779a4ceMUGxur6OhozZ8/XzfffLPatm3rml%2B4cKGuvfZaSdKiRYs0adKkK14XAADwHR4vWH369NGKFSuUmprqGisqKlJqaqp69ep1xdvv1q2bFi9erCVLlmjx4sVq3bq1Fi1apLi4OElSXFyc5syZoyVLlujbb79V7969NXfuXNftJ0%2BerH/961%2BaOnWqAgICdOedd2rixIlXvC4AAOA7/MzFztRZh44fP67x48fr9OnTKioqUvv27XXkyBGFhoZq/fr1at26tSeX4zFO52nr23Q4/DVg4YfWt1tX3n6gt7VtORz%2BatasiU6ePOsVh4pt8%2BX8vpxd8u38vpxdIv/l5g8L%2B2kdrurSPH4EKyIiQm%2B88YYyMzP1xRdfqLKyUnfffbfuuOMONW3a1NPLAQAAsK5ezoMVHBysUaNG1ce3BgAAqHMeL1jjx4//wfna/i1CAACAq5XHC9b3P2NVXl6ur776Svv27dOECRM8vRwAAADrrpq/Rbh06VLl5%2Bd7eDUAAAD2XTWnAr/jjjv09ttv1/cyAAAArthVU7A%2B/fRTKycaBQAAqG9XxYfcz5w5oy%2B//FJjxozx9HIAAACs83jBioyMdPs7hJL0k5/8RGPHjtXtt9/u6eUAAABY5/GC9eSTT3r6WwIAAHiUxwvWjh07an3dG264oQ5XAgAAUDc8XrDGjRvneovwwj%2BD%2BP0xPz8/ffHFF55eHgAAwBXzeMFavny55s2bp%2BnTp6tHjx4KDAzU7t27NWfOHA0fPlyDBw/29JIAAACs8vhpGlJSUvTYY49p4MCBatasmZo0aaJevXppzpw52rBhg1q3bu36AgAA8EYeL1gFBQUXLU9NmzbVyZMnPb0cAAAA6zxesGJjY/XUU0/pzJkzrrGioiKlpqbql7/8paeXAwAAYJ3HP4P1xz/%2BUePHj9eNN96odu3ayRijw4cPKywsTOvWrfP0cgAAAKzzeMHq0KGD3nrrLWVmZio3N1eS9Otf/1pDhgxRcHCwp5cDAABgnccLliRdc801GjVqlL755hu1bdtW0ndncwcAAGgIPP4ZLGOMFi5cqBtuuEFDhw5Vfn6%2BZsyYodmzZ%2Bv8%2BfOeXg4AAIB1Hi9YL774ojZv3qw//elPCgwMlCT1799f7777rtLS0jy9HAAAAOs8XrAyMjL02GOPacSIEa6ztw8ePFjz5s3Tm2%2B%2B6enlAAAAWOfxgvXNN9%2Boc%2BfO1cajoqLkdDo9vRwAAADrPF6wWrdurd27d1cb/%2BCDD1wfeAcAAPBmHv8twsmTJ%2BuJJ56Q0%2BmUMUYff/yxMjIy9OKLL%2BqRRx7x9HIAAACs83jBGjlypMrLy/Xcc8%2BppKREjz32mJo3b64HHnhAd999t6eXAwAAYJ3HC1ZmZqYGDRqk0aNH68SJEzLGqEWLFp5eBgAAQJ3x%2BGew5syZ4/owe/PmzSlXAACgwfF4wWrXrp327dvn6W8LAADgMR5/izAqKkoPPfSQVq5cqXbt2ikoKMhtPiUlxdNLAgAAsMrjBevQoUOKj4%2BXJM57BQAAGiSPFKwFCxZo6tSpaty4sV588UVPfEsAAIB645HPYK1evVrFxcVuY/fee68KCgo88e0BAAA8yiMFyxhTbWzHjh0qLS31xLcHAADwKI//FiEAAEBDR8ECAACwzGMFy8/Pz1PfCgAAoF557DQN8%2BbNczvn1fnz55WamqomTZq4XY/zYAEAAG/nkYJ1ww03VDvnVVxcnE6ePKmTJ096YgkAAAAe45GCxbmvAACAL%2BFD7gAAAJZRsAAAACyjYAEAAFhGwQIAALDM6wtWWVmZhg4dqk8%2B%2BcQ1lpeXp4kTJyo2NlaDBw/W1q1b3W7z0UcfaejQoYqJidH48eOVl5fnNr9mzRolJiYqLi5Os2bNqvZ3FAEAAH6IVxes0tJSPfjgg9q/f79rzBijpKQktWzZUps2bdIdd9yhqVOn6ujRo5Kko0ePKikpSSNGjNCrr76q5s2b67777nP9vcR33nlHaWlpmjNnjtauXaucnBylpqbWSz4AAOCdvLZgHThwQHfddZe%2B/vprt/Ft27YpLy9Pc%2BbMUYcOHTRlyhTFxsZq06ZNkqSNGzeqa9eumjRpkjp27KiUlBQdOXJE27dvlyStW7dOEyZMUN%2B%2BfdWtWzc98cQT2rRpE0exAABArXltwdq%2Bfbt69uypjIwMt/GcnBxdf/31aty4sWssPj5e2dnZrvmEhATXXHBwsLp06aLs7GxVVFRo9%2B7dbvOxsbE6f/689u7dW8eJAABAQ%2BGxP5Vj25gxYy467nQ6FR4e7jbWokUL5efn1zh/6tQplZaWus07HA6Fhoa6bg8AAFATry1Yl1JcXKzAwEC3scDAQJWVldU4X1JS4rp8qdvXRkFBQbU/DeRwNK5W7K5UQIB3HYB0OOyttyq7t90Htvhyfl/OLvl2fl/OLpHf2/I3uIIVFBSkoqIit7GysjI1atTINf/9slRWVqaQkBDXH6O%2B2HxwcHCt15CRkaG0tDS3saSkJCUnJ9d6Gw1Rs2ZNar7SjxQSUvvHpSHy5fy%2BnF3y7fy%2BnF0iv7fkb3AFKyIiQgcOHHAbKywsdB09ioiIUGFhYbX5zp07KzQ0VEFBQSosLFSHDh0kSeXl5SoqKlJYWFit1zB69Gj169fPbczhaKyTJ89eTqRL8pYWX8Vm/oAAf4WEBOvUqWJVVFRa26638OX8vpxd8u38vpxdIv/l5q%2BLH%2B5ro8EVrJiYGKWnp6ukpMR11CorK0vx8fGu%2BaysLNf1i4uLtWfPHk2dOlX%2B/v6Kjo5WVlaWevbsKUnKzs6Ww%2BFQVFRUrdcQHh5e7e1Ap/O0yst9b4e4UF3kr6io9On71Zfz%2B3J2ybfz%2B3J2ifzekt%2B7DoHUQo8ePdSqVSvNnDlT%2B/fvV3p6unbt2qU777xTkjRy5Ejt3LlT6enp2r9/v2bOnKk2bdq4CtWYMWO0atUqvfvuu9q1a5cef/xx3XXXXT/qLUIAAODbGlzBCggI0LJly%2BR0OjVixAj99a9/1dKlSxUZGSlJatOmjZ599llt2rRJd955p4qKirR06VL5%2BflJkoYMGaIpU6boscce06RJk9StWzdNnz69PiMBAAAv42eqTmGOOuV0nra%2BTYfDXwMWfmh9u3Xl7Qd6W9uWw%2BGvZs2a6OTJs15xqNg2X87vy9kl387vy9kl8l9u/rCwn9bhqi6twR3BAgAAqG8ULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALGuwBWvLli267rrr3L6Sk5MlSXv27NGoUaMUExOjkSNH6rPPPnO7bWZmpvr376%2BYmBglJSXpxIkT9REBAAB4qQZbsA4cOKC%2Bfftq69atrq958%2Bbp3Llzuvfee5WQkKDXXntNcXFxmjJlis6dOydJ2rVrl2bPnq2pU6cqIyNDp06d0syZM%2Bs5DQAA8CYNtmDl5uaqU6dOCgsLc32FhITorbfeUlBQkB5%2B%2BGF16NBBs2fPVpMmTfS3v/1NkrR%2B/XrdeuutGjZsmKKiorRgwQL94x//UF5eXj0nAgAA3qJBF6x27dpVG8/JyVF8fLz8/PwkSX5%2Bfurevbuys7Nd8wkkHcPKAAAVqklEQVQJCa7rt2rVSpGRkcrJyfHIugEAgPdrkAXLGKNDhw5p69atGjhwoPr376%2BFCxeqrKxMTqdT4eHhbtdv0aKF8vPzJUkFBQU/OA8AAFATR30voC4cPXpUxcXFCgwM1DPPPKNvvvlG8%2BbNU0lJiWv8QoGBgSorK5MklZSU/OB8bRQUFMjpdLqNORyNqxW3KxUQ4F392OGwt96q7N52H9jiy/l9Obvk2/l9ObtEfm/L3yALVuvWrfXJJ5/ommuukZ%2Bfnzp37qzKykpNnz5dPXr0qFaWysrK1KhRI0lSUFDQReeDg4Nr/f0zMjKUlpbmNpaUlOT6LUZf1axZE%2BvbDAmp/ePSEPlyfl/OLvl2fl/OLpHfW/I3yIIlSaGhoW6XO3TooNLSUoWFhamwsNBtrrCw0HV0KSIi4qLzYWFhtf7eo0ePVr9%2B/dzGHI7GOnny7I%2BJUCNvafFVbOYPCPBXSEiwTp0qVkVFpbXtegtfzu/L2SXfzu/L2SXyX27%2BuvjhvjYaZMH68MMP9dBDD%2Bn99993HXn64osvFBoaqvj4eD3//PMyxsjPz0/GGO3cuVO/%2B93vJEkxMTHKysrSiBEjJEnHjh3TsWPHFBMTU%2BvvHx4eXu3tQKfztMrLfW%2BHuFBd5K%2BoqPTp%2B9WX8/tydsm38/tydon83pLfuw6B1FJcXJyCgoL0xz/%2BUQcPHtQ//vEPLViwQL/5zW80aNAgnTp1SvPnz9eBAwc0f/58FRcX69Zbb5Uk3X333dq8ebM2btyovXv36uGHH9bNN9%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%2BoHd9LwEA4KU4ggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgXUVpaqlmzZikhIUF9%2BvTRCy%2B8UN9LAgAAXoTfIryIBQsW6LPPPtPatWt19OhRzZgxQ5GRkRo0aFB9Lw0AAHgBCtb3nDt3Ths3btTzzz%2BvLl26qEuXLtq/f79eeuklCpaP4bQSAIDLxVuE37N3716Vl5crLi7ONRYfH6%2BcnBxVVlbW48oAAIC34AjW9zidTjVr1kyBgYGusZYtW6q0tFRFRUVq3rx5jdsoKCiQ0%2Bl0G3M4Gis8PNzqWgMC6Mf4f950xG3LQ4mXfduq572vPv99Ob8vZ5fI7235KVjfU1xc7FauJLkul5WV1WobGRkZSktLcxubOnWq7r//fjuL/D8FBQWacO1%2BjR492np5u9oVFBQoIyPDJ7NLvp2/oKBAa9eu9Mnskm/n9%2BXsEvm9Lb931EAPCgoKqlakqi43atSoVtsYPXq0XnvtNbev0aNHW1%2Br0%2BlUWlpataNlvsCXs0u%2Bnd%2BXs0u%2Bnd%2BXs0vk97b8HMH6noiICJ08eVLl5eVyOL67e5xOpxo1aqSQkJBabSM8PNwr2jUAAKgbHMH6ns6dO8vhcCg7O9s1lpWVpejoaPn7c3cBAICa0Ri%2BJzg4WMOGDdPjjz%2BuXbt26d1339ULL7yg8ePH1/fSAACAlwh4/PHHH6/vRVxtevXqpT179mjRokX6%2BOOP9bvf/U4jR46s72VdVJMmTdSjRw81adKkvpficb6cXfLt/L6cXfLt/L6cXSK/N%2BX3M8aY%2Bl4EAABAQ8JbhAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFheqrS0VLNmzVJCQoL69OmjF154ob6XdNmOHz%2Bu5ORk9ejRQ4mJiUpJSVFpaakkad68ebruuuvcvtavX%2B%2B6bWZmpvr376%2BYmBglJSXpxIkTrjljjBYuXKhevXqpR48eWrBggSorKz2eryZbtmypljE5OVmStGfPHo0aNUoxMTEaOXKkPvvsM7fbenv%2B1157rVr26667TlFRUZKk3//%2B99Xm3nvvPdft16xZo8TERMXFxWnWrFkqLi52zV3N%2B0hZWZmGDh2qTz75xDWWl5eniRMnKjY2VoMHD9bWrVvdbvPRRx9p6NChiomJ0fjx45WXl%2Bc27033xcXyZ2dn69///d8VFxengQMHauPGjW63uf3226s9F/bt2yep5uf6yZMndf/99ysuLk79%2BvXT5s2bPRP0Ii6WvS5f566m7FL1/I888shFXwMu/PN0CQkJ1ebPnj0rqebndk37VZ0y8Epz5swxt912m/nss8/Mf/3Xf5m4uDjz9ttv1/eyfrTKykpz1113md/85jdm3759ZseOHWbAgAHmySefNMYYM3HiRLNixQpTUFDg%2Bjp37pwxxpicnBzTrVs38/rrr5svvvjCjB071tx7772uba9atcrcdNNNZseOHebjjz82ffr0MStXrqyXnD9k2bJlZsqUKW4Zv/32W3P27FnTu3dv8%2BSTT5oDBw6YuXPnml/96lfm7NmzxpiGkb%2B4uNgt99GjR82AAQPM/PnzjTHGDBgwwGzevNntOqWlpcYYY/72t7%2BZ%2BPh489///d8mJyfHDB482DzxxBOubV%2Bt%2B0hJSYlJSkoynTp1Mtu2bTPGfLcf3HbbbWbatGnmwIEDZvny5SYmJsYcOXLEGGPMkSNHTGxsrFm1apXZt2%2Bf%2BY//%2BA8zdOhQU1lZaYzxrvviYvkLCgpMQkKCWbRokTl06JDJzMw00dHR5r333jPGGFNeXm6io6PN9u3b3Z4L58%2BfN8bU/FyfMmWKmTBhgvnyyy/NK6%2B8Yrp27WpycnKuiuzG1O3r3NWS3ZiL5z916pRb7k8//dR07drVbNmyxRhjTH5%2BvunUqZP5%2Buuv3a5X9dz/oed2TftVXaNgeaGzZ8%2Ba6Ohotx106dKlZuzYsfW4qstz4MAB06lTJ%2BN0Ol1jb775punTp48xxpjExETz4YcfXvS206dPNzNmzHBdPnr0qLnuuuvM119/bYwx5qabbjKbNm1yzb/xxhumb9%2B%2BdRHjikybNs0sWrSo2vjGjRtNv379XC8klZWVZsCAAa5MDSX/hZYvX2769%2B9vSktLTWlpqencubM5ePDgRa87ZswYs2TJEtflHTt2mG7duplz585dtfvI/v37ze23325uu%2B02t/9kPvroIxMbG%2Bsqz8YYM2HCBFe%2BZ555xm3t586dM3Fxca7be8t9can8f/nLX8ygQYPcrvvoo4%2BaBx980BhjzOHDh01UVJQpKSm56HZ/6Ln%2B1VdfmU6dOpm8vDzX/KxZs9z2HU%2B4VHZj6u517mrJbswP57/QpEmTzEMPPeS6/M9//tP07t37otet6bld035V13iL0Avt3btX5eXliouLc43Fx8crJyfnqnsLqCZhYWFauXKlWrZs6TZ%2B5swZnTlzRsePH1e7du0uetucnBwlJCS4Lrdq1UqRkZHKycnR8ePHdezYMd1www2u%2Bfj4eB05ckQFBQV1kuVy5ebmXjRjTk6O4uPj5efnJ0ny8/NT9%2B7dlZ2d7ZpvCPmrFBUV6fnnn9e0adMUGBiogwcPys/PT23btq123YqKCu3evdstf2xsrM6fP6%2B9e/detfvI9u3b1bNnT2VkZLiN5%2BTk6Prrr1fjxo1dY/Hx8Zd8rIODg9WlSxdlZ2d71X1xqfxVHw34vjNnzkiSDhw4oFatWikoKKjadWp6rufk5KhVq1Zq06aN2/ynn35qK1atXCp7Xb7OXS3ZpUvnv9DHH3%2BsHTt26MEHH3SNHThwQD//%2Bc8vev2ants17Vd1zeGR7wKrnE6nmjVrpsDAQNdYy5YtVVpaqqKiIjVv3rweV/fjhISEKDEx0XW5srJS69evV69evZSbmys/Pz8tX75cH3zwgUJDQ3XPPfdo%2BPDhkqSCggKFh4e7ba9FixbKz8%2BX0%2BmUJLf5qhKXn59f7Xb1xRijQ4cOaevWrVqxYoUqKio0aNAgJScny%2Bl06he/%2BIXb9Vu0aKH9%2B/dLahj5L7RhwwaFh4dr0KBBkqSDBw%2BqadOmevjhh7V9%2B3Zde%2B21uv/%2B%2B3XTTTfp1KlTKi0tdcvhcDgUGhqq/Px8%2Bfv7X5X7yJgxYy467nQ6L/lY1jTvTffFpfK3adPGrQT861//0n/%2B53/q/vvvl/TdDyE/%2BclPNGXKFH322Wf6%2Bc9/rocffljdunWr8bl%2Bqfvu%2BPHjVrPV5FLZ6/J17mrJLl06/4XS09M1fPhwtWrVyjWWm5ur4uJijRs3TocOHVLnzp01a9Ys/fznP6/x/8Ka9qu6xhEsL1RcXOz2hJLkulxWVlYfS7ImNTVVe/bs0R/%2B8AfXEYz27dsrPT1do0aN0qOPPqotW7ZIkkpKSi56P5SVlamkpMR1%2BcI56eq6j44ePep6PJ955hnNmDFDb775phYsWHDJx7lq/Q0hfxVjjDZu3KixY8e6xg4ePKiSkhL16dNHK1eu1E033aTf//732r1790XzVV0uKyvzun2kpsf6h%2BYb2n1RUlKi%2B%2B%2B/Xy1bttTo0aMlSYcOHdK3336rUaNGKT09XR06dNCECRN07NixGp/rNd239a0uX%2Beu9uwXysvL07Zt2zRu3Di38YMHD%2Brbb7/V73//ey1btkyNGjXSxIkTdebMmRqf2/WdnyNYXigoKKjaE6TqcqNGjepjSVakpqZq7dq1evrpp9WpUyd17NhRffv2VWhoqCQpKipKhw8f1oYNGzRgwIBL3g/BwcFuO1nV2wpV1w0ODvZgqh/WunVrffLJJ7rmmmvk5%2Benzp07q7KyUtOnT1ePHj0umq/qMW4I%2Bavs3r1bx48f15AhQ1xj9913n8aNG6drrrlG0neP/%2Beff65XXnlFf/jDHyRVLwhV%2BSsqKrxqHwkKClJRUZHbWG0e65CQkGqP74Xz3nZfnD17Vvfdd58OHz6sv/zlL67n6ty5c1VSUqKmTZtKkh5//HHt3LlTmzdv1q9%2B9StJl36uX%2Bq%2Bu1qyDxs2rM5e56727Bd655131Llz52pH7VetWqXz58%2BrSZMmkqSFCxfqpptu0nvvvVfj/4U17Vd1jSNYXigiIkInT55UeXm5a8zpdKpRo0YKCQmpx5Vdvrlz52r16tVKTU3VwIEDJX33maOqF50q7du3dx3ejoiIUGFhodt8YWGhwsLCFBERIUmuQ%2BgX/jssLKzOclyO0NBQ1%2BesJKlDhw4qLS1VWFjYRfNVHfJuKPkl6cMPP1RCQoKrTEmSv7%2B/22Xp/x//0NBQBQUFueUvLy9XUVGRK7837SOXeixr81g3lPvizJkzmjx5svbv36%2B1a9e6fSbJ4XC4ypUk1xGf48eP1/hc/6H77mpQl69zV3v2C3344Yf6t3/7t2rjgYGBrnIlfffDRps2bVyP/Q89t2var%2BoaBcsLde7cWQ6Hw%2B2DellZWYqOjpa/v/c9pGlpaXr55Zf11FNPuR3BWLx4sSZOnOh23b1796p9%2B/aSpJiYGGVlZbnmjh07pmPHjikmJkYRERGKjIx0m8/KylJkZORV9fmjDz/8UD179nQ7Z9EXX3yh0NBQ14dRjTGSvnsbbefOnYqJiZHUMPJX2bVrl7p37%2B429sgjj2jmzJluY1WPv7%2B/v6Kjo93yZWdny%2BFwKCoqyuv2kZiYGH3%2B%2Beeut3yk79Z7qce6uLhYe/bsUUxMTIO4LyorKzV16lR98803evHFF9WxY0e3%2BXHjxiktLc3t%2Bl9%2B%2BaXat29f43M9NjZWR44ccfvcTVZWlmJjY%2Bs%2BWC3U5evc1Z69ijFGu3fvrvYaYIxR//799dprr7nGzp07p6%2B%2B%2Bkrt27ev8bld035V5zzyu4qw7tFHHzVDhgwxOTk5ZsuWLaZ79%2B7mnXfeqe9l/WgHDhwwnTt3Nk8//bTbOU4KCgpMTk6Ouf76683KlSvNV199ZV566SXTtWtXs3PnTmOMMTt37jRdunQxr7zyiuv8MFOmTHFte8WKFaZPnz5m27ZtZtu2baZPnz7mhRdeqK%2BoF3X69GmTmJhoHnzwQZObm2vef/9906dPH5Oenm5Onz5tevXqZebOnWv2799v5s6da3r37u36leOGkL9K3759TWZmptvYO%2B%2B8Y7p06WJef/11c/jwYfPss8%2Babt26uX7lPDMz03Tv3t1s2bLF5OTkmCFDhpi5c%2Be6bn%2B17yMX/qp6eXm5GTx4sHnggQfMvn37zIoVK0xsbKzrfD15eXkmOjrarFixwnUerNtuu811Cg9vvC8uzJ%2BRkWGioqLMe%2B%2B95/YacPLkSWOMMS%2B88IKJj4837777rsnNzTV/%2BtOfzK9%2B9Stz%2BvRpY0zNz/VJkyaZsWPHmi%2B%2B%2BMK88sorJjo6ut7OBWWMe/a6fp272rIbY6qdpiEvL8906tTJFBQUVLvu3Llzzc0332y2bdtm9u3bZ5KSkszQoUNNeXm5MeaHn9s17Vd1jYLlpc6dO2cefvhhExsba/r06WNWr15d30u6LCtWrDCdOnW66JcxxmzZssXcdtttJjo62gwaNKjafwqbNm0yN910k4mNjTVJSUnmxIkTrrny8nLz5z//2SQkJJiePXua1NRU139IV5N9%2B/aZiRMnmtjYWNO7d2/z7LPPutaZk5Njhg0bZqKjo82dd95pPv/8c7fbNoT8xhgTHR1tPvjgg2rjr7zyirnllltM165dzfDhw8327dvd5lesWGF%2B%2Bctfmvj4eDNz5ky38yRd7fvI9/%2BTOXz4sPn1r39tunbtaoYMGWL%2B%2Bc9/ul3//fffN7fccovp1q2bmTBhgus8SFW87b64MP%2BkSZMu%2BhpQdT6jyspK89xzz5mbb77ZdO3a1fz61782X375pWtbNT3XCwsLzZQpU0x0dLTp16%2BfefPNNz0b9nu%2B/9jX5evc1ZbdmOr5s7OzTadOnVwnEb5QSUmJSUlJMb179zYxMTFmypQp5ujRo675mp7bNe1XdcnPmP97/wEAAABWXB1vwAMAADQgFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWPa/O6EVfVqyuEsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2777681042516120416”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (876)</td> <td class=”number”>2598</td> <td class=”number”>80.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2777681042516120416”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11677.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11837.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13920.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14245.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17170.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES12”><s>DIRSALES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_DIRSALES07”><code>DIRSALES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90428</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES_FARMS07”>DIRSALES_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>237</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>44.322</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>787</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-169590017887420232”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-169590017887420232,#minihistogram-169590017887420232”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-169590017887420232”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-169590017887420232”

aria-controls=”quantiles-169590017887420232” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-169590017887420232” aria-controls=”histogram-169590017887420232”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-169590017887420232” aria-controls=”common-169590017887420232”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-169590017887420232” aria-controls=”extreme-169590017887420232”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-169590017887420232”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>3</td>

</tr> <tr>

<th>Q1</th> <td>11</td>

</tr> <tr>

<th>Median</th> <td>26</td>

</tr> <tr>

<th>Q3</th> <td>56</td>

</tr> <tr>

<th>95-th percentile</th> <td>142</td>

</tr> <tr>

<th>Maximum</th> <td>787</td>

</tr> <tr>

<th>Range</th> <td>787</td>

</tr> <tr>

<th>Interquartile range</th> <td>45</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>57.006</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.2862</td>

</tr> <tr>

<th>Kurtosis</th> <td>34.636</td>

</tr> <tr>

<th>Mean</th> <td>44.322</td>

</tr> <tr>

<th>MAD</th> <td>35.678</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.3875</td>

</tr> <tr>

<th>Sum</th> <td>136340</td>

</tr> <tr>

<th>Variance</th> <td>3249.6</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-169590017887420232”>

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IBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALAt4wbr11lv1wgsv6Pjx44H%2B0AAAAAER8IKVmpqqpUuXqkePHvrzn/%2BsTZs2yRgT6DEAAAAumIAXrAkTJujvf/%2B7Fi9erODgYN1333267rrrNH/%2BfO3bty/Q4wAAAFjnqY8PGhQUpO7du6t79%2B4qKyvTc889p8WLF2v58uVKTEzU6NGj1a9fv/oYDQAA4LzVS8GSpOLiYr322mt67bXXtHv3biUmJurmm2/WoUOH9Mgjj2jLli2aMmVKfY0HAADwiwW8YL366qt69dVXtXnzZjVv3lxDhgzRwoUL1bZtW99jWrVqpVmzZlGwAACAIwW8YE2ZMkW9evXSokWLdO2116pBg5qngbVr10633357oEcDAACwIuAF6/3331ezZs1UWlrqK1fbt29Xly5dFBwcLElKTExUYmJioEcDAACwIuA/RXjixAkNGDBATz/9tG9t7NixGjx4sIqKigI9DgAAgHUBL1h/%2BctfdNlll%2BnOO%2B/0rb3xxhtq1aqVMjIyAj0OAACAdQEvWP/85z/10EMPKSIiwrfWvHlzPfjgg/r444/r/HqVlZUaNGiQNm/e7FubOXOmOnbs6Hdbu3atbz8nJ0d9%2BvRRXFyc0tLSdOTIEd%2BeMUZz5sxRamqqUlJSNHv2bFVXV//CtAAA4Nco4OdgeTweHTt2rMZ6WVlZna/oXlFRoQkTJig/P99vvaCgQBMmTNDNN9/sW2vSpImk78/3mjJliqZNm6aYmBjNmjVLkydP1rJlyyRJK1euVE5OjrKyslRVVaWJEyeqRYsWuvvuu%2BsaFQAA/EoF/AjWtddeq5kzZ%2Bqbb77xrRUWFiojI0M9e/as9evs2bNHt912m9/rnFVQUKDOnTsrIiLCdwsLC5MkrV27Vtdff72GDBmimJgYzZ49W%2B%2B9954KCwslSWvWrFF6erqSk5OVmpqqBx54QM8///x5pgYAAL8mAS9YkyZNUmVlpfr3769u3bqpW7du6tevn06fPq3JkyfX%2BnU%2B%2BeQTdevWTdnZ2X7rJ06c0OHDh/2uq/VDeXl5Sk5O9t1v1aqVoqOjlZeXp8OHD6uoqEhXXXWVbz8pKUkHDhxQcXFx3YICAIBfrYC/RdiiRQu98sor%2BvDDD5Wfny%2BPx6PLL79cV199tYKCgmr9OiNHjvzJ9YKCAgUFBWnp0qV6//331bRpU915552%2BtwuLi4sVGRlZY6ZDhw7J6/VKkt9%2By5YtJUmHDh2q8TwAAICfUi%2B/Kic4OFg9e/as01uCtbV3714FBQX5Lla6ZcsWPfroo2rSpIn69u2r8vJyhYSE%2BD0nJCRElZWVKi8v993/4Z70/cn0tVVcXOwra2d5PI2sF7Tg4IAfgDwvHk/t5z2bzWkZa8vN%2BdycTXJ3PrI5l5vzOTVbwAuW1%2BvVggULtHXrVp0%2BfbrGie3vvPPOeb3%2BkCFD1KtXLzVt2lSSFBMTo6%2B%2B%2Bkrr1q1T3759FRoaWqMsVVZWKiwszK9MhYaG%2Bv4syXcOV21kZ2crKyvLby0tLU3p6em/OJcbNGvWuM7PCQ%2Bv/efdidycz83ZJHfnI5tzuTmf07IFvGA9%2Buij2rFjhwYOHKjf/va31l8/KCjIV67Oateune8SEFFRUSopKfHbLykpUUREhKKioiR9XwLbtGnj%2B7Mkv8tKnMvw4cPVu3dvvzWPp5GOHj1ZtzDn4LQ2X5f8wcENFB4epmPHynTmjPsuk%2BHmfG7OJrk7H9mcy835zjfbL/nPvQ0BL1gff/yxVqxY4XeiuU1PPvmkPv30U61atcq3tmvXLrVr106SFBcXp9zcXA0dOlSSVFRUpKKiIsXFxSkqKkrR0dHKzc31Fazc3FxFR0fX6e29yMjIGo/3eo%2Brqspd3/R19UvynzlT7erPm5vzuTmb5O58ZHMuN%2BdzWraAF6xGjRqpRYsWF%2Bz1e/XqpeXLl%2BuZZ55R3759tWnTJm3YsEFr1qyRJI0YMUJ33HGH4uPjFRsbq1mzZum6667TpZde6tufM2eOfve730mS5s6dq7vuuuuCzQsAANwn4AVr8ODBWrFihaZPn%2B775c42de3aVU8%2B%2BaQWLlyoJ598Uq1bt9bcuXOVkJAgSUpISND06dO1cOFCfffdd%2BrevbtmzJjhe/7dd9%2Btb7/9VuPHj1dwcLBuueUWjRkzxvqcAADAvYJMXS%2Bffp4mT56snJwchYeH69JLL63xE31njzS5jdd73PprejwN1HfOB9Zf90J58/7utX6sx9NAzZo11tGjJx11SLi23JzPzdkkd%2Bcjm3O5Od/5ZouIsH%2B%2Bd23Uy2UaBg0aVB8fFgAAICACXrAyMjIC/SEBAAACql5%2Bzr%2B4uFhZWVmaMGGCvv32W/31r3/V3r1762MUAAAA6wJesL7%2B%2BmvdeOONeuWVV/TWW2/p1KlTeuONNzRs2DDl5eUFehwAAADrAl6wnnjiCfXp00cbN27Ub37zG0nSvHnz1Lt3b82ZMyfQ4wAAAFgX8IK1detW3XnnnX6/2Nnj8ejee%2B/Vzp07Az0OAACAdQEvWNXV1aqurvljlidPnrwg18UCAAAItIAXrB49emjZsmV%2BJau0tFSZmZlKTU0N9DgAAADWBbxgPfTQQ9qxY4d69OihiooK/fd//7d69eql/fv3a9KkSYEeBwAAwLqAXwcrKipKGzZsUE5Ojr744gtVV1drxIgRGjx4sJo0aRLocQAAAKyrlyu5h4WF6dZbb62PDw0AAHDBBbxgjRo16t/uu/V3EQIAgF%2BPgBes1q1b%2B92vqqrS119/rd27d2v06NGBHgcAAMC6i%2BZ3ES5atEiHDh0K8DQAAAD21cvvIvwpgwcP1ptvvlnfYwAAAJy3i6Zgffrpp1xoFAAAuMJFcZL7iRMn9OWXX2rkyJGBHgcAAMC6gBes6Ohov99DKEm/%2Bc1vdPvtt%2Bumm24K9DgAAADWBbxgPfHEE4H%2BkAAAAAEV8IK1ZcuWWj/2qquuuoCTAAAAXBgBL1h33HGH7y1CY4xv/cdrQUFB%2BuKLLwI9HgAAwHkLeMFaunSpZs6cqYkTJyolJUUhISH67LPPNH36dN1888264YYbAj0SAACAVQG/TENGRoamTp2q/v37q1mzZmrcuLFSU1M1ffp0rVu3Tq1bt/bdAAAAnCjgBau4uPgny1OTJk109OjRQI8DAABgXcALVnx8vObNm6cTJ0741kpLS5WZmamrr7460OMAAABYF/BzsB555BGNGjVK1157rdq2bStjjL766itFRERozZo1gR4HAADAuoAXrPbt2%2BuNN95QTk6OCgoKJEl/%2BMMfNHDgQIWFhQV6HAAAAOsCXrAk6ZJLLtGtt96q/fv369JLL5X0/dXcAQAA3CDg52AZYzRnzhxdddVVGjRokA4dOqRJkyZpypQpOn36dKDHAQAAsC7gBeu5557Tq6%2B%2Bqscee0whISGSpD59%2Bmjjxo3KysoK9DgAAADWBbxgZWdna%2BrUqRo6dKjv6u033HCDZs6cqddffz3Q4wAAAFgX8IK1f/9%2BderUqcZ6TEyMvF5voMcBAACwLuAFq3Xr1vrss89qrL///vu%2BE94BAACcLOA/RXj33Xdr2rRp8nq9Msboo48%2BUnZ2tp577jk99NBDgR4HAADAuoAXrGHDhqmqqkpLlixReXm5pk6dqubNm%2Bv%2B%2B%2B/XiBEjAj0OAACAdQEvWDk5ORowYICGDx%2BuI0eOyBijFi1aBHoMAACACybg52BNnz7ddzJ78%2BbNKVcAAMB1Al6w2rZtq927dwf6wwIAAARMwN8ijImJ0QMPPKAVK1aobdu2Cg0N9dvPyMgI9EgAAABWBbxg7du3T0lJSZLEda8AAIArBaRgzZ49W%2BPHj1ejRo303HPPBeJDAgAA1JuAnIO1cuVKlZWV%2Ba2NHTtWxcXFgfjwAAAAARWQgmWMqbG2ZcsWVVRUBOLDAwAABFTAf4oQAADA7ShYAAAAlgWsYAUFBQXqQwEAANSrgF2mYebMmX7XvDp9%2BrQyMzPVuHFjv8dxHSwAAOB0ATmCddVVV8nr9Wr//v2%2BW0JCgo4ePeq3tn///jq/dmVlpQYNGqTNmzf71goLCzVmzBjFx8frhhtu0KZNm/ye8%2BGHH2rQoEGKi4vTqFGjVFhY6Le/atUq9ezZUwkJCXr44Ydr/AQkAADAvxOQI1gX6tpXFRUVmjBhgvLz831rxhilpaWpQ4cOWr9%2BvTZu3Kjx48frjTfeUHR0tA4ePKi0tDTdd9996tmzpxYtWqR7771Xr732moKCgvTWW28pKytLmZmZatGihSZPnqzMzExNnTr1gmQAAADu49iT3Pfs2aPbbrtN33zzjd/6xx9/rMLCQk2fPl3t27fXuHHjFB8fr/Xr10uSXnzxRV155ZW66667dMUVVygjI0MHDhzQJ598Iklas2aNRo8erV69eqlr166aNm2a1q9fz1EsAABQa44tWJ988om6deum7Oxsv/W8vDx17txZjRo18q0lJSVp27Ztvv3k5GTfXlhYmLp06aJt27bpzJkz%2Buyzz/z24%2BPjdfr0ae3atesCJwIAAG4R8N9FaMvIkSN/ct3r9SoyMtJvrUWLFjp06NA5948dO6aKigq/fY/Ho6ZNm/qeDwAAcC6OLVg/p6ysTCEhIX5rISEhqqysPOd%2BeXm57/7PPb82iouLa/wia4%2BnUY1id76Cg511ANLjqf28Z7M5LWNtuTmfm7NJ7s5HNudycz6nZnNdwQoNDVVpaanfWmVlpRo2bOjb/3FZqqysVHh4uO8yEj%2B1HxYWVusZsrOzlZWV5beWlpam9PT0Wr%2BGGzVr1vjcD/qR8PDaf96dyM353JxNcnc%2BsjmXm/M5LZvrClZUVJT27Nnjt1ZSUuI7ehQVFaWSkpIa%2B506dVLTpk0VGhqqkpIStW/fXpJUVVWl0tJSRURE1HqG4cOHq3fv3n5rHk8jHT168pdE%2BllOa/N1yR8c3EDh4WE6dqxMZ85UX8Cp6oeb87k5m%2BTufGRzLjfnO99sv%2BQ/9za4rmDFxcVp%2BfLlKi8v9x21ys3NVVJSkm8/NzfX9/iysjLt3LlT48ePV4MGDRQbG6vc3Fx169ZNkrRt2zZ5PB7FxMTUeobIyMgabwd6vcdVVeWub/q6%2BiX5z5ypdvXnzc353JxNcnc%2BsjmXm/M5LZuzDoHUQkpKilq1aqXJkycrPz9fy5cv1/bt23XLLbdIkoYNG6atW7dq%2BfLlys/P1%2BTJk9WmTRtfoRo5cqSeeeYZbdy4Udu3b9fjjz%2Bu2267rU5vEQIAgF831xWs4OBgLV68WF6vV0OHDtVrr72mRYsWKTo6WpLUpk0bPfXUU1q/fr1uueUWlZaWatGiRb7flThw4ECNGzdOU6dO1V133aWuXbtq4sSJ9RkJAAA4jCveIvzyyy/97l922WVau3btzz7%2B97//vX7/%2B9//7P7YsWM1duxYa/MBAIBfF9cdwQIAAKhvFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGCZawvW22%2B/rY4dO/rd0tPTJUk7d%2B7Urbfeqri4OA0bNkw7duzwe25OTo769OmjuLg4paWl6ciRI/URAQAAOJRrC9aePXvUq1cvbdq0yXebOXOmTp06pbFjxyo5OVkvv/yyEhISNG7cOJ06dUqStH37dk2ZMkXjx49Xdna2jh07psmTJ9dzGgAA4CSuLVgFBQXq0KGDIiIifLfw8HC98cYbCg0N1YMPPqj27dtrypQpaty4sf76179KktauXavrr79eQ4YMUUxMjGbPnq333ntPhYWF9ZwIAAA4hasLVtu2bWus5%2BXlKSkpSUFBQZKkoKAgJSYmatu2bb795ORk3%2BNbtWql6Oho5eXlBWRuAADgfK4sWMYY7du3T5s2bVL//v3Vp0/Ok2ITAAARSUlEQVQfzZkzR5WVlfJ6vYqMjPR7fIsWLXTo0CFJUnFx8b/dBwAAOBdPfQ9wIRw8eFBlZWUKCQnRggULtH//fs2cOVPl5eW%2B9R8KCQlRZWWlJKm8vPzf7tdGcXGxvF6v35rH06hGcTtfwcHO6sceT%2B3nPZvNaRlry8353JxNcnc%2BsjmXm/M5NZsrC1br1q21efNmXXLJJQoKClKnTp1UXV2tiRMnKiUlpUZZqqysVMOGDSVJoaGhP7kfFhZW64%2BfnZ2trKwsv7W0tDTfTzH%2BWjVr1rjOzwkPr/3n3YncnM/N2SR35yObc7k5n9OyubJgSVLTpk397rdv314VFRWKiIhQSUmJ315JSYnv6FJUVNRP7kdERNT6Yw8fPly9e/f2W/N4Guno0ZN1iXBOTmvzdckfHNxA4eFhOnasTGfOVF/AqeqHm/O5OZvk7nxkcy435zvfbL/kP/c2uLJgffDBB3rggQf07rvv%2Bo48ffHFF2ratKmSkpL09NNPyxijoKAgGWO0detW/fGPf5QkxcXFKTc3V0OHDpUkFRUVqaioSHFxcbX%2B%2BJGRkTXeDvR6j6uqyl3f9HX1S/KfOVPt6s%2Bbm/O5OZvk7nxkcy4353NaNmcdAqmlhIQEhYaG6pFHHtHevXv13nvvafbs2brnnns0YMAAHTt2TLNmzdKePXs0a9YslZWV6frrr5ckjRgxQq%2B%2B%2BqpefPFF7dq1Sw8%2B%2BKCuu%2B46XXrppfWcCgAAOIUrC1aTJk30zDPP6MiRIxo2bJimTJmi4cOH65577lGTJk20bNky31GqvLw8LV%2B%2BXI0aNZL0fTmbPn26Fi1apBEjRuiSSy5RRkZGPScCAABO4sq3CCXpiiuu0MqVK39yr2vXrnrllVd%2B9rlDhw71vUUIAABQV648ggUAAFCfKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZa690CguPtcv%2BEd9j1Anb97fvb5HAAA4FEewAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALDMU98DABer6xf8o75HqJM37%2B9e3yMAAP6FI1gAAACWUbAAAAAso2ABAABYRsH6CRUVFXr44YeVnJysHj166Nlnn63vkQAAgINwkvtPmD17tnbs2KHVq1fr4MGDmjRpkqKjozVgwID6Hg0AADgABetHTp06pRdffFFPP/20unTpoi5duig/P1/PP/88BQsAANQKBetHdu3apaqqKiUkJPjWkpKStHTpUlVXV6tBA95VxcXJaZeVcBIugQGgrihYP%2BL1etWsWTOFhIT41lq2bKmKigqVlpaqefPm53yN4uJieb1evzWPp5EiIyOtzhocTNkDAsHj%2Bf9/187%2BvXPj3z%2ByOZeb8zk1GwXrR8rKyvzKlSTf/crKylq9RnZ2trKysvzWxo8fr/vuu8/OkP9SXFys0b/L1/Dhw62Xt/pWXFys7OxsV2aT3J3Pzdmk7/OtXr3ClfnI5lxuzufUbM6qgwEQGhpao0idvd%2BwYcNavcbw4cP18ssv%2B92GDx9ufVav16usrKwaR8vcwM3ZJHfnc3M2yd35yOZcbs7n1GwcwfqRqKgoHT16VFVVVfJ4vv/0eL1eNWzYUOHh4bV6jcjISEe1bAAAYBdHsH6kU6dO8ng82rZtm28tNzdXsbGxnOAOAABqhcbwI2FhYRoyZIgef/xxbd%2B%2BXRs3btSzzz6rUaNG1fdoAADAIYIff/zxx%2Bt7iItNamqqdu7cqblz5%2Bqjjz7SH//4Rw0bNqy%2Bx/pJjRs3VkpKiho3blzfo1jn5mySu/O5OZvk7nxkcy4353NitiBjjKnvIQAAANyEtwgBAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwHKqiokIPP/ywkpOT1aNHDz377LP1PVKdVVZWatCgQdq8ebNvrbCwUGPGjFF8fLxuuOEGbdq0ye85H374oQYNGqS4uDiNGjVKhYWFgR77nA4fPqz09HSlpKSoZ8%2BeysjIUEVFhSTn5/v666919913KyEhQdddd51WrFjh23N6th8aO3asHnroId/9nTt36tZbb1VcXJyGDRumHTt2%2BD0%2BJydHffr0UVxcnNLS0nTkyJFAj3xOb7/9tjp27Oh3S09Pl%2BT8fJWVlZo2bZquuuoqXXPNNZo3b57OXkPb6dlefvnlGl%2B3jh07KiYmRpLz8xUVFWncuHFKTExU7969tWrVKt%2Be07PJwJGmT59ubrzxRrNjxw7zt7/9zSQkJJg333yzvseqtfLycpOWlmY6dOhgPv74Y2OMMdXV1ebGG280EyZMMHv27DFLly41cXFx5sCBA8YYYw4cOGDi4%2BPNM888Y3bv3m3%2B9Kc/mUGDBpnq6ur6jOKnurra3Hbbbeaee%2B4xu3fvNlu2bDF9%2B/Y1TzzxhOPznTlzxvTr189MmDDB7Nu3z7z77rsmMTHRvPbaa47P9kM5OTmmQ4cOZtKkScYYY06ePGm6d%2B9unnjiCbNnzx4zY8YMc80115iTJ08aY4zJy8szXbt2Na%2B88or54osvzO23327Gjh1bnxF%2B0uLFi824ceNMcXGx7/bdd9%2B5It%2Bjjz5q%2BvXrZ/Ly8syHH35ounXrZtatW%2BeKbGVlZX5fs4MHD5q%2BffuaWbNmuSLfbbfdZu6//36zb98%2B8/bbb5u4uDjzt7/9zRXZKFgOdPLkSRMbG%2BsrJsYYs2jRInP77bfX41S1l5%2Bfb2666SZz4403%2BhWsDz/80MTHx/v%2BAhljzOjRo83ChQuNMcYsWLDAL%2BOpU6dMQkKC3%2Behvu3Zs8d06NDBeL1e39rrr79uevTo4fh8hw8fNn/605/M8ePHfWtpaWnmsccec3y2s44ePWquvfZaM2zYMF/BevHFF03v3r19ZbC6utr07dvXrF%2B/3hhjzMSJE32PNcaYgwcPmo4dO5pvvvkm8AH%2BjQkTJpi5c%2BfWWHd6vqNHj5rOnTubzZs3%2B9aWLVtmHnroIcdn%2BylLly41ffr0MRUVFY7PV1paajp06GC%2B/PJL39r48ePNtGnTHJ/NGGN4i9CBdu3apaqqKiUkJPjWkpKSlJeXp%2Brq6nqcrHY%2B%2BeQTdevWTdnZ2X7reXl56ty5sxo1auRbS0pK0rZt23z7ycnJvr2wsDB16dLFt38xiIiI0IoVK9SyZUu/9RMnTjg%2BX2RkpBYsWKAmTZrIGKPc3Fxt2bJFKSkpjs921v/8z/9o8ODBuvzyy31reXl5SkpKUlBQkCQpKChIiYmJP5utVatWio6OVl5eXmCHP4eCggK1bdu2xrrT8%2BXm5qpJkyZKSUnxrY0dO1YZGRmOz/ZjpaWlevrppzVhwgSFhIQ4Pl/Dhg0VFhaml19%2BWadPn9bevXu1detWderUyfHZJM7BciSv16tmzZopJCTEt9ayZUtVVFSotLS0HiernZEjR%2Brhhx9WWFiY37rX61VkZKTfWosWLXTo0KFa7V8MwsPD1bNnT9/96upqrV27Vqmpqa7Id1bv3r01cuRIJSQkqH///q7I9tFHH%2Bmf//yn7r33Xr/1c81eXFx80Wczxmjfvn3atGmT%2Bvfvrz59%2BmjOnDmqrKx0fL7CwkK1bt1aGzZs0IABA/Sf//mfWrRokaqrqx2f7cfWrVunyMhIDRgwQJLzvzdDQ0M1depUZWdnKy4uTtdff72uvfZa3XrrrY7PJkme%2Bh4AdVdWVuZXriT57ldWVtbHSFb8XK6zmc61fzHKzMzUzp079dJLL2nVqlWuybdw4UKVlJTo8ccfV0ZGhuO/dhUVFXrsscc0depUNWzY0G/vXLOXl5df1Nkk6eDBg74cCxYs0P79%2BzVz5kyVl5c7Pt%2BpU6f09ddf64UXXlBGRoa8Xq%2BmTp2qsLAwx2f7IWOMXnzxRd1zzz2%2BNTfkKygoUK9evXTnnXcqPz9fM2bM0NVXX%2B2KbBQsBwoNDa3xTXT2/o//cXCS0NDQGkfgKisrfZl%2BLnd4eHjAZqyLzMxMrV69WvPnz1eHDh1clS82NlbS98XkgQce0LBhw1RWVub3GCdly8rK0pVXXul39PGsn5v9XNl%2BfIS2PrVu3VqbN2/WJZdcoqCgIHXq1EnV1dWaOHGiUlJSHJ3P4/HoxIkTmjt3rlq3bi3p%2B0K5bt06XXbZZY7O9kOfffaZDh8%2BrIEDB/rWnP69%2BdFHH%2Bmll17Se%2B%2B9p4YNGyo2NlaHDx/WkiVLdOmllzo6m8RbhI4UFRWlo0ePqqqqyrfm9XrVsGHDi%2BYfrF8iKipKJSUlfmslJSW%2Bw8A/tx8RERGwGWtrxowZWrlypTIzM9W/f39Jzs9XUlKijRs3%2Bq1dfvnlOn36tCIiIhyd7f/%2B7/%2B0ceNGJSQkKCEhQa%2B//rpef/11JSQkOP7rdlbTpk1957NIUvv27VVRUeH4r11ERIRCQ0N95UqS/uM//kNFRUWu%2BdpJ0gcffKDk5GRdcsklvjWn59uxY4cuu%2BwyvwMDnTt31sGDBx2fTaJgOVKnTp3k8Xj8ThDOzc1VbGysGjRw7pc0Li5On3/%2BucrLy31rubm5iouL8%2B3n5ub69srKyrRz507f/sUiKytLL7zwgubNm%2Bf3v02n59u/f7/Gjx%2Bvw4cP%2B9Z27Nih5s2bKykpydHZnnvuOb3%2B%2BuvasGGDNmzYoN69e6t3797asGGD4uLi9Omnn/quq2SM0datW382W1FRkYqKii6abNL3/zh369bN7yjjF198oaZNmyopKcnR%2BeLi4lRRUaF9%2B/b51vbu3avWrVu74mt31vbt25WYmOi35vR8kZGR%2Bvrrr/2ORO3du1dt2rRxfDZJXAfLqR599FEzcOBAk5eXZ95%2B%2B22TmJho3nrrrfoeq85%2BeJmGqqoqc8MNN5j777/f7N692yxbtszEx8f7rqVUWFhoYmNjzbJly3zXUrrxxhsvqmsp7dmzx3Tq1MnMnz/f79o1xcXFjs9XVVVlhg4dau666y6Tn59v3n33XXPNNdeYVatWOT7bj02aNMn3I%2BDHjx83qampZsaMGSY/P9/MmDHDdO/e3XdJiq1bt5ouXbqY//3f//Vdj2fcuHH1OX4Nx48fNz179jR//vOfTUFBgXn33XdNjx49zPLly12Rb%2BzYsWb48OHmiy%2B%2BMO%2B//75JTU01q1evdkW2s3r16mVycnL81pye79ixY6Z79%2B5m4sSJZu/eveadd94xKSkpZt26dY7PZgzXwXKsU6dOmQcffNDEx8ebHj16mJUrV9b3SL/IDwuWMcZ89dVX5g9/%2BIO58sorzcCBA80//vEPv8e/%2B%2B67pl%2B/fqZr165m9OjRF9U1T4z5/vo7HTp0%2BMmbMc7Pd%2BjQIZOWlmYSExNN9%2B7dzZIlS3wlyenZfuiHBcuY7y9qOGTIEBMbG2tuueUW8/nnn/s9fv369eb3v/%2B9iY%2BPN2lpaebIkSOBHvmcdu/ebcaMGWPi4%2BNN9%2B7dzVNPPeX72jk937Fjx8zEiRNNfHy8ufrqq12V7azY2Fjz/vvv11h3er78/HwzZswYk5iYaPr06WNWrlzpmq9dkDH/Ov4GAAAAK5x7wg4AAMBFioIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMv%2BH2um3mxZywTfAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-169590017887420232”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>89</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>70</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (226)</td> <td class=”number”>2365</td> <td class=”number”>73.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-169590017887420232”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:70%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>553.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>620.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>695.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>753.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>787.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_DIRSALES_FARMS12”><s>DIRSALES_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_DIRSALES_FARMS07”><code>DIRSALES_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96228</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FARM_TO_SCHOOL09”>FARM_TO_SCHOOL09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.061622</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>91.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram861980731868187465”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives861980731868187465,#minihistogram861980731868187465”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives861980731868187465”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles861980731868187465”

aria-controls=”quantiles861980731868187465” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram861980731868187465” aria-controls=”histogram861980731868187465”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common861980731868187465” aria-controls=”common861980731868187465”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme861980731868187465” aria-controls=”extreme861980731868187465”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles861980731868187465”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.24051</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9029</td>

</tr> <tr>

<th>Kurtosis</th> <td>11.314</td>

</tr> <tr>

<th>Mean</th> <td>0.061622</td>

</tr> <tr>

<th>MAD</th> <td>0.11565</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.6478</td>

</tr> <tr>

<th>Sum</th> <td>193</td>

</tr> <tr>

<th>Variance</th> <td>0.057843</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram861980731868187465”>

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A93NlwLIsS5mZmaqqqtIzzzyjhx9%2BWP/4xz/0yCOPyLIsZWRkqE2bNsrPz9fw4cM1ZcoU7d%2B/X5K0f/9%2BZWRkaNSoUXrxxRfVunVr3XHHHbIsS5L05ptvKjc3V1lZWVqzZo2KioqUk5MTzOUCAACXcWXA2rt3rwoLC5Wdna0uXbooPT1dmZmZ2rBhg7Zs2aLS0lJlZWWpc%2BfOuv3225WSkqL8/HxJ0gsvvKDzzz9fkyZNUpcuXZSdna2vvvpKW7dulSStXbtWN954owYOHKiePXtq3rx5ys/P5ygWAAAImCsDVnx8vFatWqU2bdr4jR89elRFRUU677zz1Lx5c994WlqaCgsLJUlFRUVKT0/3zUVHR6t79%2B4qLCxUfX29PvroI7/5lJQUnThxQjt37rR5VQAAIFS4MmDFxMRowIABvucNDQ1at26d%2BvbtK6/Xq4SEBL/t4%2BLidPDgQUn62fnKykrV1NT4zXs8HrVq1cr3egAAgF/iCXYBJuTk5GjHjh168cUXtXr1akVGRvrNR0ZGqra2VpJUVVX1k/PV1dW%2B5z/1%2BkCUlZXJ6/X6jXk8zRsFu39XRIS78rHH4556T/bWbT12A3prL/prH3prn1DsresDVk5OjtasWaOHH35YXbt2VVRUlA4fPuy3TW1trZo1ayZJioqKahSWamtrFRMTo6ioKN/zH85HR0cHXNP69euVm5vrN5aRkaHMzMyA9xGKYmNbBLuEJouJCfz/O5qG3tqL/tqH3tonlHrr6oA1f/58Pfvss8rJydHll18uSUpMTNTu3bv9tisvL/cdPUpMTFR5eXmj%2BW7duqlVq1aKiopSeXm5OnfuLEmqq6vT4cOHFR8fH3BdY8eO1aBBg/zGPJ7mqqg41uQ1/hy3JX3T67dTRES4YmKiVVlZpfr6hmCXE1Lorb3or33orX3s7G2wfrh3bcDKzc3Vc889p4ceekhDhgzxjScnJysvL0/V1dW%2Bo1YFBQVKS0vzzRcUFPi2r6qq0o4dOzRlyhSFh4erR48eKigoUJ8%2BfSRJhYWF8ng8SkpKCri2hISERqcDvd4jqqs7vT%2BQblx/fX2DK%2Bt2A3prL/prH3prn1DqrbsOgfyvPXv2aPny5br11luVlpYmr9fre/Tu3Vtt27bVjBkzVFJSory8PBUXF2vMmDGSpNGjR2vbtm3Ky8tTSUmJZsyYoQ4dOvgC1fjx4/XEE09o48aNKi4u1ty5c3Xttdc26RQhAAA4vbnyCNZbb72l%2Bvp6rVixQitWrPCb27Vrl5YvX65Zs2Zp1KhROuecc7Rs2TK1a9dOktShQwc9%2Buijuv/%2B%2B7Vs2TKlpqZq2bJlCgsLkyRdeeWV%2BuqrrzRnzhzV1tbqsssu07Rp0xxfIwAAcK8w6%2BQtzGErr/eI8X16POEavPifxvdrlzfu6hfsEgLm8YQrNraFKiqOhczh6lMFvbUX/bUPvbWPnb2Njz/T6P4C5cpThAAAAKcyAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmOMB65prrtFzzz2nI0fM33gTAADgVOB4wOrbt68ee%2Bwx9e/fX3/84x%2B1efNmcTN5AAAQShwPWHfffbf%2B8Y9/aPny5YqIiNCdd96pSy65RA8//LA%2B%2B%2Bwzp8sBAAAwLih/7DksLEz9%2BvVTv379VFVVpaefflrLly9XXl6eevXqpRtvvFGXXXZZMEoDAAD4twUlYElSWVmZXnvtNb322mv69NNP1atXL40cOVIHDx7Ufffdpw8%2B%2BECzZs0KVnkAAAC/muMB69VXX9Wrr76q999/X61bt9aIESO0dOlSdezY0bdN27ZttXDhQgIWAABwJccD1qxZszRw4EAtW7ZMF198scLDG18G1qlTJ02YMMHp0gAAAIxwPGC98847io2N1eHDh33hqri4WN27d1dERIQkqVevXurVq5fTpQEAABjh%2BG8RHj16VEOGDNHjjz/uG7vttts0fPhwHThwwOlyAAAAjHM8YN1///0655xzdPPNN/vGXn/9dbVt21bZ2dlOlwMAAGCc4wHrX//6l%2B69917Fx8f7xlq3bq177rlHW7ZscbocAAAA4xwPWB6PR5WVlY3Gq6qquKM7AAAICY4HrIsvvlgLFizQF1984RsrLS1Vdna2BgwY4HQ5AAAAxjn%2BW4TTp0/XzTffrMsvv1wxMTGSpMrKSnXv3l0zZsxwuhwAAADjHA9YcXFxevnll/Xuu%2B%2BqpKREHo9H5557ri688EKFhYU5XQ4AAIBxQflTORERERowYACnBAEAQEhyPGB5vV498sgj2rZtm06cONHowva33nrL6ZIAAACMcjxgzZ49W9u3b9eVV16pM8880%2Bm3BwAAsJ3jAWvLli1atWqV0tPTnX5rAAAARzh%2Bm4bmzZsrLi7O6bcFAABwjOMBa/jw4Vq1apXq6%2BudfmsAAABHOH6K8PDhw9qwYYPefvttnX322YqMjPSbX7t2rdMlAQAAGBWU2zQMGzYsGG8LAADgCMcDVnZ2ttNvCQAA4CjHr8GSpLKyMuXm5uruu%2B/W119/rb/85S/au3dvMEoBAAAwzvGAtW/fPl111VV6%2BeWX9eabb%2Br48eN6/fXXNXr0aBUVFTldDgAAgHGOB6wHHnhAl156qTZu3KgzzjhDkvTQQw9p0KBBWrx4sdPlAAAAGOd4wNq2bZtuvvlmvz/s7PF4dMcdd2jHjh1OlwMAAGCc4wGroaFBDQ0NjcaPHTumiIgIp8sBAAAwzvGA1b9/f61cudIvZB0%2BfFg5OTnq27ev0%2BUAAAAY53jAuvfee7V9%2B3b1799fNTU1%2Bs///E8NHDhQX375paZPn%2B50OQAAAMY5fh%2BsxMREvfLKK9qwYYM%2B%2BeQTNTQ0aNy4cRo%2BfLhatmzpdDkAAADGBeVO7tHR0brmmmuM7Ku2tlajRo3S7Nmz1adPH0nSggUL9PTTT/ttN3v2bE2YMEGStGHDBj3yyCPyer3q37%2B/5s%2Bfr9atW0uSLMvSgw8%2BqBdffFENDQ0aM2aMpk6dqvDwoNwyDAAAuJDjAWvixIk/O9%2BUv0VYU1Oju%2B%2B%2BWyUlJX7je/bs0d13362RI0f6xk4eHSsuLtasWbM0b948JSUlaeHChZoxY4ZWrlwpSXrqqae0YcMG5ebmqq6uTtOmTVNcXJwmT54ccF0AAOD05vhhmfbt2/s9EhMTVV1dreLiYqWmpga8n927d%2Bvaa6/VF1980Whuz549Ou%2B88xQfH%2B97REdHS5LWrVunoUOHasSIEUpKStKiRYu0adMmlZaWSvou4GVmZio9PV19%2B/bV1KlT9cwzz5hZPAAAOC2cMn%2BLcNmyZTp48GDA%2B9m6dav69Omj//7v/1ZKSopv/OjRozp06JA6duz4o68rKirSrbfe6nvetm1btWvXTkVFRYqMjNSBAwd0wQUX%2BObT0tL01VdfqaysTAkJCQHXBwAATl9BuQbrxwwfPlwjRozQ/PnzA9p%2B/PjxPzq%2BZ88ehYWF6bHHHtM777yjVq1a6eabb/adLvyxoBQXF6eDBw/K6/VKkt98mzZtJEkHDx4MOGCVlZX59nWSx9PceECLiHDXdWEej3vqPdlbt/XYDeitveivfeitfUKxt6dMwPrwww%2BN3Gh07969CgsLU6dOnTRhwgR98MEHmj17tlq2bKnBgwerurpakZGRfq%2BJjIxUbW2tqqurfc%2B/Pyd9dzF9oNavX6/c3Fy/sYyMDGVmZv7aZYWE2NgWwS6hyWJiooNdQsiit/aiv/aht/YJpd6eEhe5Hz16VLt27frJo1JNMWLECA0cOFCtWrWSJCUlJenzzz/Xs88%2Bq8GDBysqKqpRWKqtrVV0dLRfmIqKivL9W5LvGq5AjB07VoMGDfIb83iaq6Li2K9e149xW9I3vX47RUSEKyYmWpWVVaqvb/yXB/Dr0Vt70V/70Fv72NnbYP1w73jAateund/fIZSkM844QxMmTNDVV1/9b%2B8/LCzMF65O6tSpk7Zs2SLpu/twlZeX%2B82Xl5crPj5eiYmJkiSv16sOHTr4/i1J8fHxAdeQkJDQ6HSg13tEdXWn9wfSjeuvr29wZd1uQG/tRX/tQ2/tE0q9dTxgPfDAA7buf8mSJfrwww%2B1evVq39jOnTvVqVMnSVJycrIKCgo0atQoSdKBAwd04MABJScnKzExUe3atVNBQYEvYBUUFKhdu3Zc4A4AAALmeMD64IMPAt72%2B7/NF6iBAwcqLy9PTzzxhAYPHqzNmzfrlVde8d1fa9y4cbrhhhuUkpKiHj16aOHChbrkkkt09tln%2B%2BYXL16s3/zmN5KkBx98UJMmTWpyHQAA4PTleMC64YYbfKcILcvyjf9wLCwsTJ988kmT99%2BzZ08tWbJES5cu1ZIlS9S%2BfXs9%2BOCDvntspaamKisrS0uXLtW3336rfv36%2Bf3m4uTJk/X1119rypQpioiI0JgxY3TTTTf92uUCAIDTUJj1/ZTjgLffflsLFizQtGnT1Lt3b0VGRuqjjz5SVlaWRo4cqSuuuMK3bfv27Z0szVZe7xHj%2B/R4wjV48T%2BN79cub9zVL9glBMzjCVdsbAtVVBwLmesBThX01l701z701j529jY%2B/kyj%2BwuU47%2BGlp2drTlz5ujyyy9XbGysWrRoob59%2ByorK0vPPvus313eAQAA3MjxgFVWVvaj4ally5aqqKhwuhwAAADjHA9YKSkpeuihh3T06FHf2OHDh5WTk6MLL7zQ6XIAAACMc/wi9/vuu08TJ07UxRdfrI4dO8qyLH3%2B%2BeeKj4/3/aYfAACAmzkesDp37qzXX39dGzZs0J49eyRJ119/va688som3S0dAADgVBWUv0V41lln6ZprrtGXX37pu//UGWecEYxSAAAAjHP8GizLsrR48WJdcMEFGjZsmA4ePKjp06dr1qxZOnHihNPlAAAAGOd4wHr66af16quv6k9/%2BpPvjytfeuml2rhxo3Jzc50uBwAAwDjHA9b69es1Z84cjRo1ynf39iuuuEILFizQn//8Z6fLAQAAMM7xgPXll1%2BqW7dujcaTkpLk9XqdLgcAAMA4xwNW%2B/bt9dFHHzUaf%2Bedd3wXvAMAALiZ479FOHnyZM2bN09er1eWZem9997T%2BvXr9fTTT%2Bvee%2B91uhwAAADjHA9Yo0ePVl1dnVasWKHq6mrNmTNHrVu31l133aVx48Y5XQ4AAIBxjgesDRs2aMiQIRo7dqy%2B%2BeYbWZaluLg4p8sAAACwjePXYGVlZfkuZm/dujXhCgAAhBzHA1bHjh316aefOv22AAAAjnH8FGFSUpKmTp2qVatWqWPHjoqKivKbz87OdrokAAAAoxwPWJ999pnS0tIkifteAQCAkORIwFq0aJGmTJmi5s2b6%2Bmnn3biLQEAAILGkWuwnnrqKVVVVfmN3XbbbSorK3Pi7QEAABzlSMCyLKvR2AcffKCamhon3h4AAMBRjv8WIQAAQKgjYAEAABjmWMAKCwtz6q0AAACCyrHbNCxYsMDvnlcnTpxQTk6OWrRo4bcd98ECAABu50jAuuCCCxrd8yo1NVUVFRWqqKhwogQAAADHOBKwuPcVAAA4nXCROwAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5vqAVVtbq2HDhun999/3jZWWluqmm25SSkqKrrjiCm3evNnvNe%2B%2B%2B66GDRum5ORkTZw4UaWlpX7zq1ev1oABA5SamqqZM2eqqqrKkbUAAIDQ4OqAVVNToz/%2B8Y8qKSnxjVmWpYyMDLVp00b5%2BfkaPny4pkyZov3790uS9u/fr4yMDI0aNUovvviiWrdurTvuuEOWZUmS3nzzTeXm5iorK0tr1qxRUVGRcnJygrI%2BAADgTq4NWLt379a1116rL774wm98y5YtKi0tVVZWljp37qzbb79dKSkpys/PlyS98MILOv/88zVp0iR16dJF2dnZ%2Buqrr7R161ZJ0tq1a3XjjTdq4MCB6tmzp%2BbNm6f8/HyOYgEAgIC5NmBt3bpVffr00fr16/3Gi4qKdN5556l58%2Ba%2BsbS0NBUWFvrm09PTfXPR0dHq3r27CgsLVV9fr48%2B%2BshvPiUlRSdOnNDOnTttXhEAAAgVnmAX8GuNHz/%2BR8e9Xq8SEhL8xuLi4nTw4MFfnK%2BsrFRNTY3fvMfjUatWrXyvD0RZWZm8Xq/fmMfTvNH7/rsiItyVjz0e99R7srdu67Eb0Ft70V/70Fv7hGJvXRuwfkpVVZUiIyNCItJ4AAARp0lEQVT9xiIjI1VbW/uL89XV1b7nP/X6QKxfv165ubl%2BYxkZGcrMzAx4H6EoNrZFsEtospiY6GCXELLorb3or33orX1CqbchF7CioqJ0%2BPBhv7Ha2lo1a9bMN//DsFRbW6uYmBhFRUX5nv9wPjo68P/pY8eO1aBBg/zGPJ7mqqg4FvA%2BAuG2pG96/XaKiAhXTEy0KiurVF/fEOxyQgq9tRf9tQ%2B9tY%2BdvQ3WD/chF7ASExO1e/duv7Hy8nLf6bnExESVl5c3mu/WrZtatWqlqKgolZeXq3PnzpKkuro6HT58WPHx8QHXkJCQ0Oh0oNd7RHV1p/cH0o3rr69vcGXdbkBv7UV/7UNv7RNKvXXXIZAAJCcn6%2BOPP/ad7pOkgoICJScn%2B%2BYLCgp8c1VVVdqxY4eSk5MVHh6uHj16%2BM0XFhbK4/EoKSnJuUUAAABXC7mA1bt3b7Vt21YzZsxQSUmJ8vLyVFxcrDFjxkiSRo8erW3btikvL08lJSWaMWOGOnTooD59%2Bkj67uL5J554Qhs3blRxcbHmzp2ra6%2B9tkmnCAEAwOkt5AJWRESEli9fLq/Xq1GjRum1117TsmXL1K5dO0lShw4d9Oijjyo/P19jxozR4cOHtWzZMoWFhUmSrrzySt1%2B%2B%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%2BPHjuu2225Senq6XXnpJqampuv3223X8%2BHFJUnFxsWbNmqUpU6Zo/fr1qqys1IwZM4K8GgAA4CYhG7D27Nmjrl27Kj4%2B3veIiYnR66%2B/rqioKN1zzz3q3LmzZs2apRYtWugvf/mLJGndunUaOnSoRowYoaSkJC1atEibNm1SaWlpkFcEAADcIqQDVseOHRuNFxUVKS0tTWFhYZKksLAw9erVS4WFhb759PR03/Zt27ZVu3btVFRU5EjdAADA/TzBLsAOlmXps88%2B0%2BbNm7Vy5UrV19dryJAhyszMlNfr1bnnnuu3fVxcnEpKSiRJZWVlSkhIaDR/8ODBgN%2B/rKxMXq/Xb8zjad5ov/%2BuiAh35WOPxz31nuyt23rsBvTWXvTXPvTWPqHY25AMWPv371dVVZUiIyP1yCOP6Msvv9SCBQtUXV3tG/%2B%2ByMhI1dbWSpKqq6t/dj4Q69evV25urt9YRkaG7yL701VsbItgl9BkMTHRwS4hZNFbe9Ff%2B9Bb%2B4RSb0MyYLVv317vv/%2B%2BzjrrLIWFhalbt25qaGjQtGnT1Lt370Zhqba2Vs2aNZMkRUVF/eh8dHTg/9PHjh2rQYMG%2BY15PM1VUXHsV67ox7kt6Ztev50iIsIVExOtysoq1dc3BLuckEJv7UV/7UNv7WNnb4P1w31IBixJatWqld/zzp07q6amRvHx8SovL/ebKy8v952%2BS0xM/NH5%2BPj4gN87ISGh0elAr/eI6upO7w%2BkG9dfX9/gyrrdgN7ai/7ah97aJ5R6665DIAH65z//qT59%2Bqiqqso39sknn6hVq1ZKS0vThx9%2BKMuyJH13vda2bduUnJwsSUpOTlZBQYHvdQcOHNCBAwd88wAAAL8kJANWamqqoqKidN9992nv3r3atGmTFi1apFtuuUVDhgxRZWWlFi5cqN27d2vhwoWqqqrS0KFDJUnjxo3Tq6%2B%2BqhdeeEE7d%2B7UPffco0suuURnn312kFcFAADcIiQDVsuWLfXEE0/om2%2B%2B0ejRozVr1iyNHTtWt9xyi1q2bKmVK1eqoKBAo0aNUlFRkfLy8tS8eXNJ34WzrKwsLVu2TOPGjdNZZ52l7OzsIK8IAAC4SZh18lwZbOX1HjG%2BT48nXIMX/9P4fu3yxl39gl1CwDyecMXGtlBFxbGQuR7gVEFv7UV/7UNv7WNnb%2BPjzzS6v0CF5BEsAACAYCJgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPMEuwAAAGCPoY/8v2CXELB/LRwS7BKM4ggWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgPUjampqNHPmTKWnp6t///568skng10SAABwEU%2BwCzgVLVq0SNu3b9eaNWu0f/9%2BTZ8%2BXe3atdOQIUOCXRoAAHABAtYPHD9%2BXC%2B88IIef/xxde/eXd27d1dJSYmeeeYZAhYAAAgIpwh/YOfOnaqrq1NqaqpvLC0tTUVFRWpoaAhiZQAAwC04gvUDXq9XsbGxioyM9I21adNGNTU1Onz4sFq3bv2L%2BygrK5PX6/Ub83iaKyEhwWitERHuyscej3vqPdlbt/XYDeitveivfeit/UKptwSsH6iqqvILV5J8z2trawPax/r165Wbm%2Bs3NmXKFN15551mivxfZWVluvE3JRo7dqzx8Ha6Kysr05o1q%2BitDeitveivfdzY238tdMelLWVlZXr00Udd1dtfEjpR0ZCoqKhGQerk82bNmgW0j7Fjx%2Bqll17ye4wdO9Z4rV6vV7m5uY2OluHfR2/tQ2/tRX/tQ2/tE4q95QjWDyQmJqqiokJ1dXXyeL5rj9frVbNmzRQTExPQPhISEkImgQMAgKbjCNYPdOvWTR6PR4WFhb6xgoIC9ejRQ%2BHhtAsAAPwyEsMPREdHa8SIEZo7d66Ki4u1ceNGPfnkk5o4cWKwSwMAAC4RMXfu3LnBLuJU07dvX%2B3YsUMPPvig3nvvPf3hD3/Q6NGjg13Wj2rRooV69%2B6tFi1aBLuUkENv7UNv7UV/7UNv7RNqvQ2zLMsKdhEAAAChhFOEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWKe4mpoazZw5U%2Bnp6erfv7%2BefPLJn9x2x44duuaaa5ScnKzRo0dr%2B/btDlbqPk3p7dtvv63hw4crNTVVV111ld566y0HK3WfpvT2pC%2B//FKpqal6//33HajQ3ZrS3127dmncuHHq2bOnrrrqKm3ZssXBSt2nKb3929/%2BpqFDhyo1NVXjxo3Txx9/7GCl7lVbW6thw4b97Gc9JL6fWTilZWVlWVdddZW1fft2669//auVmppqvfHGG422O3bsmNWvXz/rgQcesHbv3m3Nnz/fuuiii6xjx44FoWp3CLS3n3zyidW9e3drzZo11ueff26tW7fO6t69u/XJJ58EoWp3CLS33zd58mSra9eu1pYtWxyq0r0C7W9lZaV10UUXWffdd5/1%2BeefW0uWLLHS0tKs8vLyIFTtDoH29tNPP7V69Ohhvfzyy9a%2BffusefPmWf369bOOHz8ehKrdo7q62srIyPjZz3qofD8jYJ3Cjh07ZvXo0cPvi3DZsmXWhAkTGm37wgsvWIMGDbIaGhosy7KshoYGa/DgwVZ%2Bfr5j9bpJU3qbk5NjTZ482W9s0qRJ1kMPPWR7nW7UlN6e9Oqrr1rXXXcdASsATenvmjVrrEsvvdSqq6vzjY0aNcp6%2B%2B23HanVbZrS26eeesoaOXKk7/mRI0esrl27WsXFxY7U6kYlJSXW1VdfbV111VU/%2B1kPle9nnCI8he3cuVN1dXVKTU31jaWlpamoqEgNDQ1%2B2xYVFSktLU1hYWGSpLCwMPXq1UuFhYWO1uwWTentyJEjNXXq1Eb7OHLkiO11ulFTeitJFRUVysnJUVZWlpNlulZT%2Brt161b9/ve/V0REhG8sPz9fv/vd7xyr102a0ttWrVpp9%2B7dKigoUENDg1566SW1bNlS//Ef/%2BF02a6xdetW9enTR%2BvXr//Z7ULl%2B5kn2AXgp3m9XsXGxioyMtI31qZNG9XU1Ojw4cNq3bq137bnnnuu3%2Bvj4uJUUlLiWL1u0pTedu7c2e%2B1JSUleu%2B993Tdddc5Vq%2BbNKW3kvTAAw9o5MiR6tKli9OlulJT%2BltaWqqePXtq9uzZ%2Bvvf/6727dtr%2BvTpSktLC0bpp7ym9PaKK67Q3//%2Bd40fP14REREKDw/XypUrddZZZwWjdFcYP358QNuFyvczjmCdwqqqqvw%2B6JJ8z2trawPa9ofb4TtN6e33ffPNN7rzzjvVq1cv/f73v7e1RrdqSm/fffddFRQU6I477nCsPrdrSn%2BPHz%2BuvLw8xcfH6/HHH9cFF1ygyZMn68CBA47V6yZN6W1FRYW8Xq/mzJmj559/XsOHD9eMGTP09ddfO1ZvqAqV72cErFNYVFRUoy%2Bok8%2BbNWsW0LY/3A7faUpvTyovL9eNN94oy7K0dOlShYfz8fkxgfa2urpac%2BbM0Z/%2B9Ce%2BTpugKV%2B7ERER6tatmzIzM3Xeeedp2rRp6tixo1599VXH6nWTpvR28eLF6tq1q66//nqdf/75mj9/vqKjo5Wfn%2B9YvaEqVL6f8R3iFJaYmKiKigrV1dX5xrxer5o1a6aYmJhG25aXl/uNlZeXKyEhwZFa3aYpvZWkQ4cO6frrr1dtba3Wrl3b6DQX/k%2BgvS0uLlZpaakyMzOVmprqu%2B7l1ltv1Zw5cxyv2y2a8rUbHx%2BvTp06%2BY117NiRI1g/oSm9/fjjj5WUlOR7Hh4erqSkJO3fv9%2BxekNVqHw/I2Cdwrp16yaPx%2BN3YV9BQYF69OjR6OhJcnKyPvzwQ1mWJUmyLEvbtm1TcnKyozW7RVN6e/z4cd1yyy0KDw/XunXrlJiY6HS5rhJob3v27Km//vWveuWVV3wPSVqwYIH%2B67/%2By/G63aIpX7spKSnatWuX39jevXvVvn17R2p1m6b0NiEhQXv27PEb%2B%2Byzz9ShQwdHag1lofL9jIB1CouOjtaIESM0d%2B5cFRcXa%2BPGjXryySc1ceJESd/9ZFVdXS1JGjJkiCorK7Vw4ULt3r1bCxcuVFVVlYYOHRrMJZyymtLblStX6osvvtD//M//%2BOa8Xi%2B/RfgTAu1ts2bNdM455/g9pO9%2Beo2LiwvmEk5pTfnave6667Rr1y49%2Buij2rdvn5YsWaLS0lINHz48mEs4ZTWlt9dee62ef/55vfLKK9q3b58WL16s/fv3a%2BTIkcFcgmuF5PezoN4kAr/o%2BPHj1j333GOlpKRY/fv3t5566infXNeuXf3uC1JUVGSNGDHC6tGjhzVmzBjr448/DkLF7hFoby%2B//HKra9eujR7Tp08PUuWnvqZ83X4f98EKTFP6%2B69//csaOXKkdf7551vDhw%2B3tm7dGoSK3aMpvX3%2B%2BeetIUOGWCkpKda4ceOs7du3B6Fid/rhZz0Uv5%2BFWdb/HoMDAACAEZwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/j/kxrIuLYBdwQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common861980731868187465”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme861980731868187465”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2939</td> <td class=”number”>91.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>193</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FARM_TO_SCHOOL13”>FARM_TO_SCHOOL13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>8.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>286</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.61799</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>34.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8996266013503537447”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8996266013503537447,#minihistogram8996266013503537447”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8996266013503537447”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8996266013503537447”

aria-controls=”quantiles8996266013503537447” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8996266013503537447” aria-controls=”histogram8996266013503537447”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8996266013503537447” aria-controls=”common8996266013503537447”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8996266013503537447” aria-controls=”extreme8996266013503537447”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8996266013503537447”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48596</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.78636</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7651</td>

</tr> <tr>

<th>Mean</th> <td>0.61799</td>

</tr> <tr>

<th>MAD</th> <td>0.47216</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.48592</td>

</tr> <tr>

<th>Sum</th> <td>1807</td>

</tr> <tr>

<th>Variance</th> <td>0.23616</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8996266013503537447”>

<img 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gwYOVnp6uVatWafjw4XrrrbdUUlIiSQoLC3Pv48yfy8rKvH7dzMxMZWRkeKylpaUpPT39XA/prKKiImzZL8jWTmRrL/K1D9naJ5iyDdqCdfLkSd13333at2%2Bf/va3vyki4of/aTNnzlRJSYkiIyMlSdOnT9fmzZu1du1a/fGPf5T0Q5k6cwrxTLE683xvpKamqnv37h5rTmdNFRScPOfj%2BrHQ0BBFRUWoqKhYFRX8pKNJZGsfsrUX%2BdqHbO1nR7bR0bWM7s9bQVmwTpw4obvuukvffvutli1b5vHTgk6n012uJMnhcCg%2BPl5Hjx5VXFycpB9%2BEvHMacYzp/piYmK8fv3Y2NgqpwNdruMqL7fnC7KiotK2ff/eka19yNZe5GsfsrVPMGUbPCc7/6OyslKjR4/WgQMHtGLFCjVv3txj%2B%2B233%2B5x%2Bq6yslJfffWV4uPjFRcXp4YNGyorK8u9PSsrSw0bNjR%2B/RQAAAheQfcO1iuvvKINGzboqaeeUlRUlPsdqAsuuEB16tRR9%2B7dtWjRIrVs2VIXX3yxli9fruPHj2vAgAGSpCFDhmj%2B/Pm66KKLJEmPPvqoRowY4bfjAQAAgSfoCtbbb7%2BtyspK3XPPPR7r7du314oVK3THHXeotLRUs2bNUn5%2BvhITE7V06VL3acORI0fqu%2B%2B%2B0%2BjRoxUaGqpBgwbpjjvu8MORAACAQOWwfvpJnbCFy3Xc%2BD6dzhBFR9dSQcHJoDlnfb4gW/uQrb3I1z6BmG2fx//t7xG89vns3rZkGxNzodH9eSvorsECAADwNwoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMN8XrAGDx6sl19%2BWcePm//dfAAAAOcDnxesjh076umnn1bnzp01duxYffLJJ%2BL3TQMAgGDi84I1btw4/etf/9KTTz6p0NBQjRkzRl27dtVf//pX7d2719fjAAAAGOf0x4s6HA516tRJnTp1UnFxsVasWKEnn3xSixcvVrt27TR8%2BHBde%2B21/hgNAADgnPmlYElSXl6e3njjDb3xxhv6%2Buuv1a5dOw0YMEBHjhzRAw88oE2bNmnq1Kn%2BGg8AAOA383nBWrt2rdauXasNGzaobt266t%2B/vxYuXKimTZu6H9OgQQPNnj2bguWFlKn/5%2B8RvPaPP3fy9wgAAPiEzwvW1KlT1a1bNy1atEhXXXWVQkKqXgYWHx%2Bv2267zdejAQAAGOHzgvXRRx8pOjpahYWF7nK1detWtWrVSqGhoZKkdu3aqV27dr4eDQAAwAif/xThiRMn1Lt3by1ZssS9NmrUKPXr10%2BHDx/29TgAAADG%2Bbxg/eUvf9Ef/vAH3Xnnne61t956Sw0aNNCcOXN8PQ4AAIBxPi9Yn3/%2Bue6//37FxMS41%2BrWrauJEydq/fr1vh4HAADAOJ8XLKfTqaKioirrxcXFfKI7AAAICj4vWFdddZVmzZqlb7/91r22f/9%2BzZkzR126dPH1OAAAAMb5/KcIJ02apDvvvFO9evVSVFSUJKmoqEitWrXS5MmTfT0OAACAcT4vWPXq1dNrr72mTz/9VDk5OXI6nbrkkkt05ZVXyuFw%2BHocAAAA4/zyq3JCQ0PVpUsXTgkCAICg5POC5XK59Pjjj2vz5s06ffp0lQvb33vvPV%2BPBAAAYJTPC9aDDz6o7du36/rrr9eFF17o65cHAACwnc8L1vr16/Xss88qJSXF1y8NAADgEz7/mIaaNWuqXr16xvZXVlamvn37asOGDe61/fv364477lDbtm113XXX6ZNPPvF4zqeffqq%2BffsqMTFRw4YN0/79%2Bz22v/DCC%2BrSpYuSkpI0ZcoUFRcXG5sXAAAEP58XrH79%2BunZZ59VRUXFOe%2BrtLRUY8eOVU5OjnvNsiylpaWpfv36WrNmjfr166fRo0fr0KFDkqRDhw4pLS1NAwcO1CuvvKK6devqvvvuc18L9vbbbysjI0MzZszQsmXLlJ2drXnz5p3zrAAA4PfD56cICwsLtW7dOn3wwQdq0qSJwsLCPLYvX77cq/3s3r1b48aNq3KR/Pr167V//369/PLLqlmzppo1a6bPPvtMa9as0ZgxY7R69WpdfvnlGjFihCRpzpw56tSpkzZu3KgOHTpo%2BfLlGj58uLp16yZJevjhhzVy5EhNmDBBERERBhIAAADBzufvYElS3759ddVVV%2Bniiy9Wo0aNPG7eOlOIMjMzPdazs7N12WWXqWbNmu615ORkbdmyxb39x9d/RUREqFWrVtqyZYsqKiq0bds2j%2B1t27bV6dOntWvXrt96uAAA4HfG5%2B9gzZkzx8h%2Bhg4detZ1l8ul2NhYj7V69erpyJEjv7q9qKhIpaWlHtudTqfq1Knjfr438vLy5HK5PNaczppVXvdchYb6pR//Zk5n4Mx7JttAyzgQkK29yNc%2BZGu/YMrWLx80mpeXp1WrVmnv3r2aMmWKNm3apBYtWig%2BPv6c911cXFzltGNYWJjKysp%2BdXtJSYn7/s893xuZmZnKyMjwWEtLS1N6errX%2BwhG0dG1/D1CtUVFcVrYLmRrL/K1D9naJ5iy9XnB%2Buabb3TzzTcrMjJSR48e1Z///Ge99dZbmjx5sl544QUlJiae0/7Dw8NVWFjosVZWVqYaNWq4t/%2B0LJWVlSkqKkrh4eHu%2Bz/dXp3rr1JTU9W9e3ePNaezpgoKTnq9D28EWtM3ffx2Cg0NUVRUhIqKilVRUenvcYIK2dqLfO1DtvazI1t//ePe5wXrkUceUY8ePTRr1iy1a9dOkvTYY49p0qRJmj9/vlasWHFO%2B4%2BLi9Pu3bs91vLz892n5%2BLi4pSfn19le8uWLVWnTh2Fh4crPz9fzZo1kySVl5ersLBQMTExXs8QGxtb5XSgy3Vc5eW/7y/IQDz%2BiorKgJw7EJCtvcjXPmRrn2DK1udvgWzevFl33nmnxy92djqduu%2B%2B%2B7Rz585z3n9iYqJ27NjhPt0nSVlZWe53xhITE5WVleXeVlxcrJ07dyoxMVEhISFq3bq1x/YtW7bI6XQqISHhnGcDAAC/Dz4vWJWVlaqsrNpOT548qdDQ0HPef/v27dWgQQNNnjxZOTk5Wrx4sbZu3apBgwZJkm666SZt3rxZixcvVk5OjiZPnqzGjRurQ4cOkn64eP65557Tu%2B%2B%2Bq61bt2r69Om6%2Beab%2BYgGAADgNZ8XrM6dO%2BuZZ57xKFmFhYWaN2%2BeOnbseM77Dw0N1ZNPPimXy6WBAwfqjTfe0KJFi9SwYUNJUuPGjfXEE09ozZo1GjRokAoLC7Vo0SL3O2rXX3%2B97rnnHk2bNk0jRoxQmzZtNGHChHOeCwAA/H44rJ9%2BUqfNjh49qmHDhun48eMqLCxUfHy8Dh48qDp16mjlypXV%2BiysQOJyHTe%2BT6czRD3nf2x8v3b5x587%2BXsErzmdIYqOrqWCgpNBcz3A%2BYJs7UW%2B9gnEbPs8/m9/j%2BC1z2f3tiXbmJgLje7PWz6/yD0uLk6vv/661q1bpy%2B//FKVlZUaMmSI%2BvXrp8jISF%2BPAwAAYJxfPgcrIiJCgwcP9sdLAwAA2M7nBWvYsGG/uN3b30UIAABwvvJ5wfrpNVbl5eX65ptv9PXXX2v48OG%2BHgcAAMC48%2BZ3ES5atKhav%2B8PAADgfHXe/K6Vfv366R//%2BIe/xwAAADhn503B%2BuKLL4x80CgAAIC/nRcXuZ84cUJfffWVhg4d6utxAAAAjPN5wWrYsKHH7yGUpAsuuEC33XabbrzxRl%2BPAwAAYJzPC9Yjjzzi65cEAADwKZ8XrE2bNnn92CuuuMLGSQAAAOzh84J1%2B%2B23u08R/vjXIP50zeFw6Msvv/T1eAAAAOfM5wXr6aef1qxZszRhwgS1b99eYWFh2rZtm2bMmKEBAwbouuuu8/VIAAAARvn8YxrmzJmjadOmqVevXoqOjlatWrXUsWNHzZgxQy%2B99JIaNWrkvgEAAAQinxesvLy8s5anyMhIFRQU%2BHocAAAA43xesNq2bavHHntMJ06ccK8VFhZq3rx5uvLKK309DgAAgHE%2BvwbrgQce0LBhw3TVVVepadOmsixL%2B/btU0xMjJYvX%2B7rcQAAAIzzecFq1qyZ3nrrLa1bt065ubmSpFtvvVXXX3%2B9IiIifD0OAACAcT4vWJJUu3ZtDR48WAcOHFCTJk0k/fBp7gAAAMHA59dgWZal%2BfPn64orrlDfvn115MgRTZo0SVOnTtXp06d9PQ4AAIBxPi9YK1as0Nq1a/XQQw8pLCxMktSjRw%2B9%2B%2B67ysjI8PU4AAAAxvm8YGVmZmratGkaOHCg%2B9Pbr7vuOs2aNUtvvvmmr8cBAAAwzucF68CBA2rZsmWV9YSEBLlcLl%2BPAwAAYJzPC1ajRo20bdu2KusfffSR%2B4J3AACAQObznyIcOXKkHn74YblcLlmWpc8%2B%2B0yZmZlasWKF7r//fl%2BPAwAAYJzPC9ZNN92k8vJyPfXUUyopKdG0adNUt25d/fnPf9aQIUN8PQ4AAIBxPi9Y69atU%2B/evZWamqpjx47JsizVq1fP12MAAADYxufXYM2YMcN9MXvdunUpVwAAIOj4vGA1bdpUX3/9ta9fFgAAwGd8foowISFB48eP17PPPqumTZsqPDzcY/ucOXN8PRIAAIBRPi9Ye/fuVXJysiTZ9rlXr776qiZPnlxl3eFwaNeuXfrTn/6k999/32Pb008/rW7dukmSXnjhBT333HM6ceKE%2BvTpowcffJBfRA0AALzmk4I1d%2B5cjR49WjVr1tSKFStsf73rrrtOXbp0cd8vLy/X8OHD1bVrV0lSbm6u5s2bpyuvvNL9mNq1a0uS3n77bWVkZGjevHmqV6%2BeJk%2BerHnz5mnatGm2zw0AAIKDT67BWrp0qYqLiz3WRo0apby8PFter0aNGoqJiXHf3njjDVmWpfHjx6usrEwHDhxQ69atPR5z5vciLl%2B%2BXMOHD1e3bt3Upk0bPfzww1qzZk2V%2BQEAAH6OTwqWZVlV1jZt2qTS0lLbX7uwsFBLlizRuHHjFBYWpj179sjhcJz1U%2BMrKiq0bds2paSkuNfatm2r06dPa9euXbbPCgAAgoPPf4rQ11566SXFxsaqd%2B/ekqQ9e/YoMjJSEydOVOfOnTVo0CB9%2BOGHkqSioiKVlpYqNjbW/Xyn06k6deroyJEjfpkfAAAEHp9f5O5LlmVp9erVuuuuu9xre/bsUUlJiTp37qxRo0bpnXfe0Z/%2B9CdlZmaqfv36kuQ%2BXXhGWFiYysrKvH7dvLy8KhfwO501PYqbCaGhgdWPnc7AmfdMtoGWcSAgW3uRr33I1n7BlK3PCpbD4fDVS7lt27ZNR48e1fXXX%2B9eu%2B%2B%2B%2B3T77be7L2pPSEjQjh07tGrVKv3P//yPJFUpU2VlZdX6KcLMzExlZGR4rKWlpSk9Pf23HkpQiI6u5e8Rqi0qip8etQvZ2ot87UO29gmmbH1WsGbNmuXxmVenT5/WvHnzVKuW5zddk5%2BD9fHHHyslJcVdpiQpJCTE474kxcfHa/fu3apTp47Cw8OVn5%2BvZs2aSfrhJxALCwsVExPj9eumpqaqe/fuHmtOZ00VFJw8h6OpKtCavunjt1NoaIiioiJUVFSsiopKf48TVMjWXuRrH7K1nx3Z%2Busf9z4pWFdccUWVU2ZJSUkqKChQQUGBba%2B7detWtWvXzmPt/vvvl8Ph8Chyu3btUosWLRQSEqLWrVsrKytLHTp0kCRt2bJFTqdTCQkJXr9ubGxsldOBLtdxlZf/vr8gA/H4KyoqA3LuQEC29iJf%2B5CtfYIpW58ULF989tXZ5OTk6MYbb/RY6969u8aOHasOHTooKSlJb775prKysjRjxgxJ0tChQzVt2jS1aNFCsbGxmj59um6%2B%2BWY%2BaBQAAHgtqC9yz8/PV1RUlMfatddeq4ceekhPPfWUDh06pObNm%2BvZZ59V48aNJUnXX3%2B9Dh48qGnTpqmsrEzXXnutJkyY4I/xAQBAgArqgrV169azrg8ePFiDBw/%2B2eeNGjVKo0aNsmssAAAQ5ALrKmkAAIAAQMECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw4KsasPiAAASqUlEQVS2YL3zzju69NJLPW7p6emSpJ07d2rw4MFKTEzUTTfdpO3bt3s8d926derRo4cSExOVlpamY8eO%2BeMQAABAgAragrV7925169ZNn3zyifs2a9YsnTp1SqNGjVJKSopeffVVJSUl6Z577tGpU6ckSVu3btXUqVM1evRoZWZmqqioSJMnT/bz0QAAgEAStAUrNzdXLVq0UExMjPsWFRWlt956S%2BHh4Zo4caKaNWumqVOnqlatWvq///s/SdLKlSvVp08f9e/fXwkJCZo7d64%2B/PBD7d%2B/389HBAAAAkVQF6ymTZtWWc/OzlZycrIcDockyeFwqF27dtqyZYt7e0pKivvxDRo0UMOGDZWdne2TuQEAQOALyoJlWZb27t2rTz75RL169VKPHj00f/58lZWVyeVyKTY21uPx9erV05EjRyRJeXl5v7gdAADg1zj9PYAdDh06pOLiYoWFhenxxx/XgQMHNGvWLJWUlLjXfywsLExlZWWSpJKSkl/c7o28vDy5XC6PNaezZpXidq5CQwOrHzudgTPvmWwDLeNAQLb2Il/7kK39ginboCxYjRo10oYNG1S7dm05HA61bNlSlZWVmjBhgtq3b1%2BlLJWVlalGjRqSpPDw8LNuj4iI8Pr1MzMzlZGR4bGWlpbm/inG36vo6Fr%2BHqHaoqK8//%2BO6iFbe5GvfcjWPsGUbVAWLEmqU6eOx/1mzZqptLRUMTExys/P99iWn5/vfncpLi7urNtjYmK8fu3U1FR1797dY83prKmCgpPVOYRfFWhN3/Tx2yk0NERRUREqKipWRUWlv8cJKmRrL/K1D9naz45s/fWP%2B6AsWB9//LHGjx%2BvDz74wP3O05dffqk6deooOTlZS5YskWVZcjgcsixLmzdv1r333itJSkxMVFZWlgYOHChJOnz4sA4fPqzExESvXz82NrbK6UCX67jKy3/fX5CBePwVFZUBOXcgIFt7ka99yNY%2BwZRtYL0F4qWkpCSFh4frgQce0J49e/Thhx9q7ty5uuuuu9S7d28VFRVp9uzZ2r17t2bPnq3i4mL16dNHkjRkyBCtXbtWq1ev1q5duzRx4kR17dpVTZo08fNRAQCAQBGUBSsyMlLPPfecjh07pptuuklTp05Vamqq7rrrLkVGRuqZZ55xv0uVnZ2txYsXq2bNmpJ%2BKGczZszQokWLNGTIENWuXVtz5szx8xEBAIBAEpSnCCWpefPmWrp06Vm3tWnTRq%2B99trPPnfgwIHuU4QAAADVFZTvYAEAAPgTBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC9qCdfToUaWnp6t9%2B/bq0qWL5syZo9LSUknSrFmzdOmll3rcVq5c6X7uunXr1KNHDyUmJiotLU3Hjh3z12EAAIAA5PT3AHawLEvp6emKiorSiy%2B%2BqO%2B//15TpkxRSEiIJk2apNzcXI0bN04DBgxwPycyMlKStHXrVk2dOlUPP/ywEhISNHv2bE2ePFnPPPOMvw4HAAAEmKB8B2vPnj3asmWL5syZo%2BbNmyslJUXp6elat26dJCk3N1eXXXaZYmJi3LeIiAhJ0sqVK9WnTx/1799fCQkJmjt3rj788EPt37/fn4cEAAACSFAWrJiYGD377LOqX7%2B%2Bx/qJEyd04sQJHT16VE2bNj3rc7Ozs5WSkuK%2B36BBAzVs2FDZ2dl2jgwAAIJIUBasqKgodenSxX2/srJSK1euVMeOHZWbmyuHw6Gnn35aV111lW688Ua99tpr7sfm5eUpNjbWY3/16tXTkSNHfDY/AAAIbEF5DdZPzZs3Tzt37tQrr7yiHTt2yOFwKD4%2BXrfddps2bdqkBx98UJGRkerZs6dKSkoUFhbm8fywsDCVlZV5/Xp5eXlyuVwea05nzSrF7VyFhgZWP3Y6A2feM9kGWsaBgGztRb72IVv7BVO2QV%2Bw5s2bp2XLlumvf/2rWrRooebNm6tbt26qU6eOJCkhIUH79u3TSy%2B9pJ49eyo8PLxKmSorK3Nfo%2BWNzMxMZWRkeKylpaUpPT393A8ogEVH1/L3CNUWFeX9/3dUD9nai3ztQ7b2CaZsg7pgzZw5Uy%2B99JLmzZunXr16SZIcDoe7XJ0RHx%2Bv9evXS5Li4uKUn5/vsT0/P18xMTFev25qaqq6d%2B/useZ01lRBwcnfchg/K9Cavunjt1NoaIiioiJUVFSsiopKf48TVMjWXuRrH7K1nx3Z%2Busf90FbsDIyMvTyyy/rscceU%2B/evd3rCxYs0BdffKEXXnjBvbZr1y7Fx8dLkhITE5WVlaWBAwdKkg4fPqzDhw8rMTHR69eOjY2tcjrQ5Tqu8vLf9xdkIB5/RUVlQM4dCMjWXuRrH7K1TzBlG1hvgXgpNzdXTz75pO6%2B%2B24lJyfL5XK5b926ddOmTZv03HPP6dtvv9Xf/vY3vf766xoxYoQkaciQIVq7dq1Wr16tXbt2aeLEieratauaNGni56MCAACBIijfwXrvvfdUUVGhp556Sk899ZTHtq%2B%2B%2BkoLFizQwoULtWDBAjVq1EiPPvqokpKSJElJSUmaMWOGFi5cqO%2B//16dOnXSzJkz/XEYAAAgQAVlwRo1apRGjRr1s9t79OihHj16/Oz2gQMHuk8RAgAAVFdQniIEAADwJwoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCdRalpaWaMmWKUlJS1LlzZz3//PP%2BHgkAAAQQp78HOB/NnTtX27dv17Jly3To0CFNmjRJDRs2VO/evf09GgAACAAUrJ84deqUVq9erSVLlqhVq1Zq1aqVcnJy9OKLL1KwAACAVzhF%2BBO7du1SeXm5kpKS3GvJycnKzs5WZWWlHycDAACBgnewfsLlcik6OlphYWHutfr166u0tFSFhYWqW7fur%2B4jLy9PLpfLY83prKnY2Fijs4aGBlY/djoDZ94z2QZaxoGAbO1FvvYhW/sFU7YUrJ8oLi72KFeS3PfLysq82kdmZqYyMjI81kaPHq0xY8aYGfI/8vLyNPyiHKWmphovb793eXl5WrbsWbK1Adnai3ztE4jZfj47MC5tycvL0xNPPBFQ2f6a4KmKhoSHh1cpUmfu16hRw6t9pKam6tVXX/W4paamGp/V5XIpIyOjyrtlOHdkax%2BytRf52ods7ROM2fIO1k/ExcWpoKBA5eXlcjp/iMflcqlGjRqKioryah%2BxsbFB08ABAED18Q7WT7Rs2VJOp1Nbtmxxr2VlZal169YKCSEuAADw62gMPxEREaH%2B/ftr%2BvTp2rp1q9599109//zzGjZsmL9HAwAAASJ0%2BvTp0/09xPmmY8eO2rlzpx599FF99tlnuvfee3XTTTf5e6yzqlWrltq3b69atWr5e5SgQ7b2IVt7ka99yNY%2BwZatw7Isy99DAAAABBNOEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2Cd50pLSzVlyhSlpKSoc%2BfOev7553/2sTt37tTgwYOVmJiom266Sdu3b/fhpIGnOtl%2B8MEH6tevn5KSknTDDTfovffe8%2BGkgac62Z5x4MABJSUlacOGDT6YMLBVJ9%2BvvvpKQ4YMUZs2bXTDDTdo/fr1Ppw08FQn23feeUd9%2BvRRUlKShgwZoh07dvhw0sBVVlamvn37/uLXelB8P7NwXpsxY4Z1ww03WNu3b7f%2B%2Bc9/WklJSdY//vGPKo87efKk1alTJ%2BuRRx6xdu/ebc2cOdP64x//aJ08edIPUwcGb7P98ssvrVatWlnLli2z9u3bZ61cudJq1aqV9eWXX/ph6sDgbbY/NnLkSKtFixbW%2BvXrfTRl4PI236KiIuuPf/yj9cADD1j79u2zFixYYCUnJ1v5%2Bfl%2BmDoweJvt119/bbVu3dp67bXXrG%2B%2B%2BcZ6%2BOGHrU6dOlmnTp3yw9SBo6SkxEpLS/vFr/Vg%2BX5GwTqPnTx50mrdurXHX8JFixZZt912W5XHrl692urevbtVWVlpWZZlVVZWWj179rTWrFnjs3kDSXWynTdvnjVy5EiPtREjRliPPfaY7XMGoupke8batWutW265hYLlherku2zZMqtHjx5WeXm5e23gwIHWBx984JNZA011sl26dKk1YMAA9/3jx49bLVq0sLZu3eqTWQNRTk6OdeONN1o33HDDL36tB8v3M04Rnsd27dql8vJyJSUludeSk5OVnZ2tyspKj8dmZ2crOTlZDodDkuRwONSuXTtt2bLFpzMHiupkO2DAAI0fP77KPo4fP277nIGoOtlKUkFBgebNm6cZM2b4csyAVZ18N27cqGuuuUahoaHutTVr1ujqq6/22byBpDrZ1qlTR7t371ZWVpYqKyv16quvKjIyUv/1X//l67EDxsaNG9WhQwdlZmb%2B4uOC5fuZ098D4Oe5XC5FR0crLCzMvVa/fn2VlpaqsLBQdevW9XjsJZdc4vH8evXqKScnx2fzBpLqZNusWTOP5%2Bbk5Oizzz7TLbfc4rN5A0l1spWkRx55RAMGDFDz5s19PWpAqk6%2B%2B/fvV5s2bfTggw/q/fffV6NGjTRp0iQlJyf7Y/TzXnWyve666/T%2B%2B%2B9r6NChCg0NVUhIiJ555hnVrl3bH6MHhKFDh3r1uGD5fsY7WOex4uJijy90Se77ZWVlXj32p4/DD6qT7Y8dO3ZMY8aMUbt27XTNNdfYOmOgqk62n376qbKysnTffff5bL5AV518T506pcWLFysmJkZLlizRFVdcoZEjR%2Brw4cM%2BmzeQVCfbgoICuVwuTZs2TatWrVK/fv00efJkfffddz6bN1gFy/czCtZ5LDw8vMpfqDP3a9So4dVjf/o4/KA62Z6Rn5%2Bv4cOHy7IsLVy4UCEhfPmcjbfZlpSUaNq0aXrooYf4e1oN1fm7GxoaqpYtWyo9PV2XXXaZJkyYoKZNm2rt2rU%2BmzeQVCfb%2BfPnq0WLFrr11lt1%2BeWXa%2BbMmYqIiNCaNWt8Nm%2BwCpbvZ3yHOI/FxcWpoKBA5eXl7jWXy6UaNWooKiqqymPz8/M91vLz8xUbG%2BuTWQNNdbKVpKNHj%2BrWW29VWVmZli9fXuU0F/4/b7PdunWr9u/fr/T0dCUlJbmve7n77rs1bdo0n88dKKrzdzcmJkbx8fEea02bNuUdrJ9RnWx37NihhIQE9/2QkBAlJCTo0KFDPps3WAXL9zMK1nmsZcuWcjqdHhf2ZWVlqXXr1lXePUlMTNQXX3why7IkSZZlafPmzUpMTPTpzIGiOtmeOnVKd911l0JCQrRy5UrFxcX5etyA4m22bdq00T//%2BU%2B9/vrr7pskzZo1S//93//t87kDRXX%2B7rZt21ZfffWVx9qePXvUqFEjn8waaKqTbWxsrHJzcz3W9u7dq8aNG/tk1mAWLN/PKFjnsYiICPXv31/Tp0/X1q1b9e677%2Br555/XsGHDJP3wL6uSkhJJUu/evVVUVKTZs2dr9%2B7dmj17toqLi9WnTx9/HsJ5qzrZPvPMM/r222/1v//7v%2B5tLpeLnyL8Gd5mW6NGDf3hD3/wuEk//Ou1Xr16/jyE81p1/u7ecsst%2Buqrr/TEE0/om2%2B%2B0YIFC7R//37169fPn4dw3qpOtjfffLNWrVql119/Xd98843mz5%2BvQ4cOacCAAf48hIAVlN/P/PohEfhVp06dsiZOnGi1bdvW6ty5s7V06VL3thYtWnh8Lkh2drbVv39/q3Xr1tagQYOsHTt2%2BGHiwOFttr169bJatGhR5TZp0iQ/TX7%2Bq87f2x/jc7C8U518P//8c2vAgAHW5ZdfbvXr18/auHGjHyYOHNXJdtWqVVbv3r2ttm3bWkOGDLG2b9/uh4kD00%2B/1oPx%2B5nDsv7zHhwAAACM4BQhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2/wCT65pQNpqvxQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8996266013503537447”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8996266013503537447”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1117</td> <td class=”number”>34.8%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1807</td> <td class=”number”>56.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FDPIR12”>FDPIR12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>18</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.14974</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>22</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>90.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2505051343536988700”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2505051343536988700,#minihistogram2505051343536988700”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2505051343536988700”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2505051343536988700”

aria-controls=”quantiles2505051343536988700” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2505051343536988700” aria-controls=”histogram2505051343536988700”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2505051343536988700” aria-controls=”common2505051343536988700”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2505051343536988700” aria-controls=”extreme2505051343536988700”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2505051343536988700”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>22</td>

</tr> <tr>

<th>Range</th> <td>22</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.95921</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.4056</td>

</tr> <tr>

<th>Kurtosis</th> <td>239.91</td>

</tr> <tr>

<th>Mean</th> <td>0.14974</td>

</tr> <tr>

<th>MAD</th> <td>0.27836</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>13.751</td>

</tr> <tr>

<th>Sum</th> <td>469</td>

</tr> <tr>

<th>Variance</th> <td>0.92008</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2505051343536988700”>

<img 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kpOjGG2/Unj17vF77zjvvaOzYsUpOTtbUqVNVVVXlNb9582YNGzZMqampmjdvnurr6y1bFwAACHwBGbDcbreys7NVX1%2Bv7du3a9WqVfrrX/%2Bq1atXy%2B12KysrS127dlVxcbHGjRunWbNm6cSJE5KkEydOKCsrSxMmTNDOnTsVExOje%2B%2B9V263W5L0%2Buuvq6CgQLm5udqyZYvKysqUn5/vy%2BUCAIAAE5AB6%2BjRoyotLVVeXp769Omj9PR0ZWdna9euXdq7d6%2BqqqqUm5ur3r17a%2BbMmUpJSVFxcbEkaceOHbriiis0ffp09enTR3l5eTp%2B/Lj27dsnSdq6datuv/12ZWRkaODAgVq8eLGKi4s5igUAANosIANWXFycNm7cqK5du3qNnz17VmVlZbr88ssVERHhGU9LS1NpaakkqaysTOnp6Z658PBw9e/fX6WlpWpubtb777/vNZ%2BSkqJz587p8OHD7bwqAADQUQRkwIqMjNSwYcM8j1taWrRt2zYNGTJETqdT8fHxXs%2BPjY3VqVOnJOlH58%2BcOaPGxkavebvdrqioKM/rAQAA/hm7rwswIT8/X4cOHdLOnTu1efNmhYWFec2HhYXJ5XJJkurr639wvqGhwfP4h17fFtXV1XI6nV5jdntEq2D3S4WGBlY%2BttsDq96f4nwvAq0nHRG98B/0wr/QD2sFfMDKz8/Xli1btGrVKvXt21cOh0N1dXVez3G5XOrUqZMkyeFwtApLLpdLkZGRcjgcnsffnw8PD29zTUVFRSooKPAay8rKUnZ2dpu30RFFR3f2dQntLjKy7e8TtC964T/ohX%2BhH9YI6IC1ZMkSPffcc8rPz9eoUaMkSQkJCTpy5IjX82pqajxHjxISElRTU9Nqvl%2B/foqKipLD4VBNTY169%2B4tSWpqalJdXZ3i4uLaXFdmZqZGjBjhNWa3R6i29pufvMYfE2jfQkyv35%2BEhoYoMjJcZ87Uq7m5xdflBDV64T/ohX8J1n746st9wAasgoICPf/883rsscc0evRoz3hycrIKCwvV0NDgOWpVUlKitLQ0z3xJSYnn%2BfX19Tp06JBmzZqlkJAQDRgwQCUlJRo8eLAkqbS0VHa7XUlJSW2uLT4%2BvtXpQKfzazU1Bc8b%2BkKCYf3NzS1Bsc5AQC/8B73wL/TDGoF1COQfKisrtX79et11111KS0uT0%2Bn0/AwaNEjdunVTTk6OKioqVFhYqPLyck2aNEmSNHHiRB04cECFhYWqqKhQTk6OevTo4QlUt956qzZt2qTdu3ervLxcixYt0i233PKTThECAIDgZvkRrJtvvlkTJ07UmDFjdNFFF/2sbbzxxhtqbm7Whg0btGHDBq%2B5jz76SOvXr9f8%2BfM1YcIEXXrppVq3bp0SExMlST169NDjjz%2BuRx99VOvWrVNqaqrWrVsnm80mSRozZoyOHz%2BuhQsXyuVy6frrr9fcuXN/2aIBAEBQsbnP38LcIitXrtQrr7yi2tpa/fu//7smTJigoUOHegJOR%2BV0fm18m3Z7iEaueNv4dtvLa/cP9XUJ7cZuD1F0dGfV1n7DoXcfoxf%2Bg174l2DtR1zczzuY80tZfopw9uzZ%2Butf/6r169crNDRU9913n6699lqtWrVKn3zyidXlAAAAGOeTi9xtNpuGDh2qoUOHqr6%2BXs8%2B%2B6zWr1%2BvwsJCXXnllbr99tt1/fXX%2B6I0AACAX8xnv0VYXV2tl19%2BWS%2B//LI%2B/vhjXXnllbrpppt06tQpPfLII9q/f7/mz5/vq/IAAAB%2BNssD1ksvvaSXXnpJ7733nmJiYjR%2B/HitXbtWPXv29DynW7duWrZsGQELAAAEJMsD1vz585WRkaF169Zp%2BPDhCglpfRlYr169NGXKFKtLAwAAMMLygPXWW28pOjpadXV1nnBVXl6u/v37KzQ0VJJ05ZVX6sorr7S6NAAAACMs/y3Cs2fPavTo0Xrqqac8Y3fffbfGjRunkydPWl0OAACAcZYHrEcffVSXXnqp7rjjDs/Yq6%2B%2Bqm7duikvL8/qcgAAAIyzPGD97//%2Brx5%2B%2BGGvP54cExOjBx98UHv37rW6HAAAAOMsD1h2u11nzpxpNV5fXy%2BLbyoPAADQLiwPWMOHD9fSpUv1%2Beefe8aqqqqUl5enYcOGWV0OAACAcZb/FuFDDz2kO%2B64Q6NGjVJkZKQk6cyZM%2Brfv79ycnKsLgcAAMA4ywNWbGysXnjhBb3zzjuqqKiQ3W7Xb37zG1199dUd/g8%2BAwCA4OCTP5UTGhqqYcOGcUoQAAB0SJYHLKfTqdWrV%2BvAgQM6d%2B5cqwvb33jjDatLAgAAMMrygLVgwQIdPHhQY8aM0UUXXWT17gEAANqd5QFr79692rhxo9LT063eNQAAgCUsv01DRESEYmNjrd4tAACAZSwPWOPGjdPGjRvV3Nxs9a4BAAAsYfkpwrq6Ou3atUt/%2B9vfdMkllygsLMxrfuvWrVaXBAAAYJRPbtMwduxYX%2BwWAADAEpYHrLy8PKt3CQAAYCnLr8GSpOrqahUUFGj27Nn64osv9Oc//1lHjx71RSkAAADGWR6wPvvsM/3Hf/yHXnjhBb3%2B%2Buv69ttv9eqrr2rixIkqKyuzuhwAAADjLA9Yf/jDH3Tddddp9%2B7d%2BtWvfiVJeuyxxzRixAitWLHC6nIAAACMszxgHThwQHfccYfXH3a22%2B269957dejQIavLAQAAMM7ygNXS0qKWlpZW4998841CQ0OtLgcAAMA4ywPWNddcoyeffNIrZNXV1Sk/P19DhgyxuhwAAADjLA9YDz/8sA4ePKhrrrlGjY2N%2Bs///E9lZGTo2LFjeuihh6wuBwAAwDjL74OVkJCgF198Ubt27dKHH36olpYWTZ48WePGjVOXLl2sLgcAAMA4n9zJPTw8XDfffLMvdg0AANDuLA9YU6dO/dF5/hYhAAAIdJYHrO7du3s9bmpq0meffaaPP/5Yt99%2Bu9XlAAAAGOc3f4tw3bp1OnXqlMXVAAAAmOeTv0V4IePGjdNrr73m6zIAAAB%2BMb8JWH//%2B9%2B50SgAAOgQ/OIi97Nnz%2Bqjjz7SrbfeanU5AAAAxlkesBITE73%2BDqEk/epXv9KUKVP029/%2B1upyAAAAjLM8YP3hD38wuj2Xy6UJEyZowYIFGjx4sCRp6dKlevbZZ72et2DBAk2ZMkWStGvXLq1evVpOp1PXXHONlixZopiYGEmS2%2B3WypUrtXPnTrW0tGjSpEmaM2eOQkL85mwqAADwc5YHrP3797f5uVddddWPzjc2Nmr27NmqqKjwGq%2BsrNTs2bN10003ecbO3yW%2BvLxc8%2BfP1%2BLFi5WUlKRly5YpJydHTz75pCTpmWee0a5du1RQUKCmpibNnTtXsbGxmjFjRpvrBgAAwc3ygHXbbbd5ThG63W7P%2BPfHbDabPvzwwx/czpEjRzR79myvbZxXWVmpGTNmKC4urtXctm3bdMMNN2j8%2BPGSpOXLlysjI0NVVVW65JJLtHXrVmVnZys9PV2SNGfOHK1Zs4aABQAA2szy815PPPGEunfvrtWrV%2Bvdd99VSUmJNm/erH/5l3/R73//e73xxht64403tHv37h/dzr59%2BzR48GAVFRV5jZ89e1anT59Wz549L/i6srIyT3iSpG7duikxMVFlZWU6ffq0Tp486XXkLC0tTcePH1d1dfXPXzQAAAgqPrnR6MKFCzV8%2BHDP2JAhQ5Sbm6sHH3xQd911V5u280O/cVhZWSmbzaYnnnhCb731lqKionTHHXd4ThdWV1crPj7e6zWxsbE6deqUnE6nJHnNd%2B3aVZJ06tSpVq/7IdXV1Z5tnWe3R7T59W0VGhpY14XZ7YFV709xvheB1pOOiF74D3rhX%2BiHtSwPWNXV1a3%2BXI703TVStbW1v3j7R48elc1mU69evTRlyhTt379fCxYsUJcuXTRy5Eg1NDQoLCzM6zVhYWFyuVxqaGjwPP7/c9J3F9O3VVFRkQoKCrzGsrKylJ2d/XOX1SFER3f2dQntLjIy3Ncl4B/ohf%2BgF/6FfljD8oCVkpKixx57TH/84x89F57X1dUpPz9fV1999S/e/vjx45WRkaGoqChJUlJSkj799FM999xzGjlypBwOR6uw5HK5FB4e7hWmHA6H59%2BSFB7e9jdkZmamRowY4TVmt0eotvabn72uCwm0byGm1%2B9PQkNDFBkZrjNn6tXc3OLrcoIavfAf9MK/BGs/fPXl3vKA9cgjj2jq1KkaPny4evbsKbfbrU8//VRxcXHaunXrL96%2BzWbzhKvzevXqpb1790qSEhISVFNT4zVfU1OjuLg4JSQkSJKcTqd69Ojh%2BbekC14w/0Pi4%2BNbnQ50Or9WU1PwvKEvJBjW39zcEhTrDAT0wn/QC/9CP6xh%2BSGQ3r1769VXX9Xs2bOVkpKi1NRUzZ8/Xy%2B99JJ%2B/etf/%2BLtr1mzRtOmTfMaO3z4sHr16iVJSk5OVklJiWfu5MmTOnnypJKTk5WQkKDExESv%2BZKSEiUmJhq/fgoAAHRclh/BkqSLL75YN998s44dO6ZLLrlE0nd3czchIyNDhYWF2rRpk0aOHKk9e/boxRdf9Bwdmzx5sm677TalpKRowIABWrZsma699lpPHZMnT9aKFSs8YW/lypWaPn26kdoAAEBwsDxgnb9T%2BrPPPqtz587p9ddf16pVqxQeHq5Fixb94qA1cOBArVmzRmvXrtWaNWvUvXt3rVy5UqmpqZKk1NRU5ebmau3atfrqq680dOhQLVmyxPP6GTNm6IsvvtCsWbMUGhqqSZMmtToiBgAA8GNs7gvdqbMdbd26VU899ZQeeOAB5ebm6pVXXtH777%2BvxYsX63e/%2B50eeOABK8uxjNP5tfFt2u0hGrnibePbbS%2Bv3T/U1yW0G7s9RNHRnVVb%2Bw3XNvgYvfAf9MK/BGs/4uIu8sl%2BLb8Gq6ioSAsXLtSECRM8d2%2B/8cYbtXTpUr3yyitWlwMAAGCc5QHr2LFj6tevX6vxpKSkVjfnBAAACESWB6zu3bvr/fffbzX%2B1ltveS40BwAACGSWX%2BQ%2BY8YMLV68WE6nU263W%2B%2B%2B%2B66Kior07LPP6uGHH7a6HAAAAOMsD1gTJ05UU1OTNmzYoIaGBi1cuFAxMTG6//77NXnyZKvLAQAAMM7ygLVr1y6NHj1amZmZ%2BvLLL%2BV2uxUbG2t1GQAAAO3G8muwcnNzPRezx8TEEK4AAECHY3nA6tmzpz7%2B%2BGOrdwsAAGAZy08RJiUlac6cOdq4caN69uwph8PhNZ%2BXl2d1SQAAAEZZHrA%2B%2BeQTpaWlSRL3vQIAAB2SJQFr%2BfLlmjVrliIiIvTss89asUsAAACfseQarGeeeUb19fVeY3fffbeqq6ut2D0AAIClLAlYF/p70vv371djY6MVuwcAALCU5b9FCAAA0NERsAAAAAyzLGDZbDardgUAAOBTlt2mYenSpV73vDp37pzy8/PVuXNnr%2BdxHywAABDoLAlYV111Vat7XqWmpqq2tla1tbVWlAAAAGAZSwIW974CAADBhIvcAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFvABy%2BVyaezYsXrvvfc8Y1VVVZo2bZpSUlJ04403as%2BePV6veeeddzR27FglJydr6tSpqqqq8prfvHmzhg0bptTUVM2bN0/19fWWrAUAAHQMAR2wGhsb9fvf/14VFRWeMbfbraysLHXt2lXFxcUaN26cZs2apRMnTkiSTpw4oaysLE2YMEE7d%2B5UTEyM7r33XrndbknS66%2B/roKCAuXm5mrLli0qKytTfn6%2BT9YHAAACU8AGrCNHjuiWW27R559/7jW%2Bd%2B9eVVVVKTc3V71799bMmTOVkpKi4uJiSdKOHTt0xRVXaPr06erTp4/y8vJ0/Phx7du3T5K0detW3X777crIyNDAgQO1ePFiFRcXcxQLAAC0WcAGrH379mnw4MEqKiryGi8rK9Pll1%2BuiIgIz1haWppKS0s98%2Bnp6Z658PBw9e/fX6WlpWpubtb777/vNZ%2BSkqJz587p8OHD7bwiAADQUdh9XcDPdeutt15w3Ol0Kj4%2B3mssNjZWp06d%2BqfzZ86cUWNjo9e83W5XVFSU5/VtUV1dLafT6TVmt0e02u8vFRoaWPnYbg%2Bsen%2BK870ItJ50RPTCf9AL/0I/rBWwAeuH1NfXKywszGssLCxMLpfrn843NDR4Hv/Q69uiqKhIBQUFXmNZWVnKzs6h2oSFAAAPvklEQVRu8zY6oujozr4uod1FRob7ugT8A73wH/TCv9APa3S4gOVwOFRXV%2Bc15nK51KlTJ8/898OSy%2BVSZGSkHA6H5/H358PD2/6GzMzM1IgRI7zG7PYI1dZ%2B0%2BZttEWgfQsxvX5/EhoaosjIcJ05U6/m5hZflxPU6IX/oBf%2BJVj74asv9x0uYCUkJOjIkSNeYzU1NZ7TcwkJCaqpqWk1369fP0VFRcnhcKimpka9e/eWJDU1Namurk5xcXFtriE%2BPr7V6UCn82s1NQXPG/pCgmH9zc0tQbHOQEAv/Ae98C/0wxqBdQikDZKTk/XBBx94TvdJUklJiZKTkz3zJSUlnrn6%2BnodOnRIycnJCgkJ0YABA7zmS0tLZbfblZSUZN0iAABAQOtwAWvQoEHq1q2bcnJyVFFRocLCQpWXl2vSpEmSpIkTJ%2BrAgQMqLCxURUWFcnJy1KNHDw0ePFjSdxfPb9q0Sbt371Z5ebkWLVqkW2655SedIgQAAMGtwwWs0NBQrV%2B/Xk6nUxMmTNDLL7%2BsdevWKTExUZLUo0cPPf744youLtakSZNUV1endevWyWazSZLGjBmjmTNnauHChZo%2BfboGDhyouXPn%2BnJJAAAgwNjc529hjnbldH5tfJt2e4hGrnjb%2BHbby2v3D/V1Ce3Gbg9RdHRn1dZ%2Bw7UNPkYv/Ae98C/B2o%2B4uIt8st8OdwQLAADA1whYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCswwasv/zlL7rsssu8frKzsyVJhw4d0s0336zk5GRNnDhRBw8e9Hrtrl27dN111yk5OVlZWVn68ssvfbEEAAAQoDpswDpy5IgyMjK0Z88ez8/SpUv17bff6u6771Z6err%2B9Kc/KTU1VTNnztS3334rSSovL9f8%2BfM1a9YsFRUV6cyZM8rJyfHxagAAQCDpsAGrsrJSffv2VVxcnOcnMjJSr776qhwOhx588EH17t1b8%2BfPV%2BfOnfXnP/9ZkrRt2zbdcMMNGj9%2BvJKSkrR8%2BXK9%2Beabqqqq8vGKAABAoOjQAatnz56txsvKypSWliabzSZJstlsuvLKK1VaWuqZT09P9zy/W7duSkxMVFlZmSV1AwCAwNchA5bb7dYnn3yiPXv2aNSoUbruuuu0YsUKuVwuOZ1OxcfHez0/NjZWp06dkiRVV1f/6DwAAMA/Y/d1Ae3hxIkTqq%2BvV1hYmFavXq1jx45p6dKlamho8Iz/f2FhYXK5XJKkhoaGH51vi%2BrqajmdTq8xuz2iVXD7pUJDAysf2%2B2BVe9Pcb4XgdaTjohe%2BA964V/oh7U6ZMDq3r273nvvPV188cWy2Wzq16%2BfWlpaNHfuXA0aNKhVWHK5XOrUqZMkyeFwXHA%2BPDy8zfsvKipSQUGB11hWVpbntxiDVXR0Z1%2BX0O4iI9v%2BPkH7ohf%2Bg174F/phjQ4ZsCQpKirK63Hv3r3V2NiouLg41dTUeM3V1NR4ji4lJCRccD4uLq7N%2B87MzNSIESO8xuz2CNXWfvNTlvBPBdq3ENPr9yehoSGKjAzXmTP1am5u8XU5QY1e%2BA964V%2BCtR%2B%2B%2BnLfIQPW22%2B/rTlz5uhvf/ub58jThx9%2BqKioKKWlpempp56S2%2B2WzWaT2%2B3WgQMHdM8990iSkpOTVVJSogkTJkiSTp48qZMnTyo5ObnN%2B4%2BPj291OtDp/FpNTcHzhr6QYFh/c3NLUKwzENAL/0Ev/Av9sEZgHQJpo9TUVDkcDj3yyCM6evSo3nzzTS1fvlx33nmnRo8erTNnzmjZsmU6cuSIli1bpvr6et1www2SpMmTJ%2Bull17Sjh07dPjwYT344IO69tprdckll/h4VQAAIFB0yIDVpUsXbdq0SV9%2B%2BaUmTpyo%2BfPnKzMzU3feeae6dOmiJ5980nOUqqysTIWFhYqIiJD0XTjLzc3VunXrNHnyZF188cXKy8vz8YoAAEAgsbndbreviwgGTufXxrdpt4do5Iq3jW%2B3vbx2/1Bfl9Bu7PYQRUd3Vm3tNxx69zF64T/ohX8J1n7ExV3kk/12yCNYAAAAvkTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgX0NjYqHnz5ik9PV3XXHONnn76aV%2BXBAAAAojd1wX4o%2BXLl%2BvgwYPasmWLTpw4oYceekiJiYkaPXq0r0sDAAABgID1Pd9%2B%2B6127Nihp556Sv3791f//v1VUVGh7du3E7AAAECbELC%2B5/Dhw2pqalJqaqpnLC0tTU888YRaWloUEsJZ1Z/rhtX/4%2BsSfpLX7h/q6xIAAAGKgPU9TqdT0dHRCgsL84x17dpVjY2NqqurU0xMzD/dRnV1tZxOp9eY3R6h%2BPh4o7WGhhL22lOgBcK/zBnm6xL8wvnPBZ8P36MX/oV%2BWIuA9T319fVe4UqS57HL5WrTNoqKilRQUOA1NmvWLN13331mivyH6upq3f7rCmVmZhoPb/hpqqurVVRURC/8QHV1tbZs2Ugv/AC98C/0w1rE2O9xOBytgtT5x506dWrTNjIzM/WnP/3J6yczM9N4rU6nUwUFBa2OlsF69MJ/0Av/QS/8C/2wFkewvichIUG1tbVqamqS3f7dfx6n06lOnTopMjKyTduIj4/n2wEAAEGMI1jf069fP9ntdpWWlnrGSkpKNGDAAC5wBwAAbUJi%2BJ7w8HCNHz9eixYtUnl5uXbv3q2nn35aU6dO9XVpAAAgQIQuWrRoka%2BL8DdDhgzRoUOHtHLlSr377ru65557NHHiRF%2BXdUGdO3fWoEGD1LlzZ1%2BXEvTohf%2BgF/6DXvgX%2BmEdm9vtdvu6CAAAgI6EU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAaqxsVHz5s1Tenq6rrnmGj399NO%2BLilo/eUvf9Fll13m9ZOdne3rsoKKy%2BXS2LFj9d5773nGqqqqNG3aNKWkpOjGG2/Unj17fFhh8LhQL5YuXdrqM7Jt2zYfVtmxnT59WtnZ2Ro0aJCGDRumvLw8NTY2SuJzYSW7rwvAz7N8%2BXIdPHhQW7Zs0YkTJ/TQQw8pMTFRo0eP9nVpQefIkSPKyMjQkiVLPGMOh8OHFQWXxsZGzZ49WxUVFZ4xt9utrKws9e3bV8XFxdq9e7dmzZqlV199VYmJiT6stmO7UC8kqbKyUrNnz9ZNN93kGevSpYvV5QUFt9ut7OxsRUZGavv27frqq680b948hYSE6MEHH%2BRzYSECVgD69ttvtWPHDj311FPq37%2B/%2Bvfvr4qKCm3fvp2A5QOVlZXq27ev4uLifF1K0Dly5Ihmz56t7//Fr71796qqqkrPP/%2B8IiIi1Lt3b7377rsqLi7Wfffd56NqO7Yf6oX03WdkxowZfEYscPToUZWWlup//ud/1LVrV0lSdna2/vjHP2r48OF8LizEKcIAdPjwYTU1NSk1NdUzlpaWprKyMrW0tPiwsuBUWVmpnj17%2BrqMoLRv3z4NHjxYRUVFXuNlZWW6/PLLFRER4RlLS0tTaWmp1SUGjR/qxdmzZ3X69Gk%2BIxaJi4vTxo0bPeHqvLNnz/K5sBhHsAKQ0%2BlUdHS0wsLCPGNdu3ZVY2Oj6urqFBMT48Pqgovb7dYnn3yiPXv26Mknn1Rzc7NGjx6t7Oxsr/6gfdx6660XHHc6nYqPj/cai42N1alTp6woKyj9UC8qKytls9n0xBNP6K233lJUVJTuuOMOr9OFMCcyMlLDhg3zPG5padG2bds0ZMgQPhcWI2AFoPr6%2Blb/8z7/2OVy%2BaKkoHXixAlPP1avXq1jx45p6dKlamho0COPPOLr8oLWD31G%2BHxY7%2BjRo7LZbOrVq5emTJmi/fv3a8GCBerSpYtGjhzp6/I6vPz8fB06dEg7d%2B7U5s2b%2BVxYiIAVgBwOR6sPxPnHnTp18kVJQat79%2B567733dPHFF8tms6lfv35qaWnR3LlzlZOTo9DQUF%2BXGJQcDofq6uq8xlwuF58PHxg/frwyMjIUFRUlSUpKStKnn36q5557joDVzvLz87VlyxatWrVKffv25XNhMa7BCkAJCQmqra1VU1OTZ8zpdKpTp06KjIz0YWXBKSoqSjabzfO4d%2B/eamxs1FdffeXDqoJbQkKCampqvMZqampanR5B%2B7PZbJ5wdV6vXr10%2BvRpH1UUHJYsWaJnnnlG%2Bfn5GjVqlCQ%2BF1YjYAWgfv36yW63e12YWFJSogEDBigkhJZa6e2339bgwYNVX1/vGfvwww8VFRXFtXA%2BlJycrA8%2B%2BEANDQ2esZKSEiUnJ/uwquC0Zs0aTZs2zWvs8OHD6tWrl28KCgIFBQV6/vnn9dhjj2nMmDGecT4X1uL/xgEoPDxc48eP16JFi1ReXq7du3fr6aef1tSpU31dWtBJTU2Vw%2BHQI488oqNHj%2BrNN9/U8uXLdeedd/q6tKA2aNAgdevWTTk5OaqoqFBhYaHKy8s1adIkX5cWdDIyMrR//35t2rRJn3/%2Buf7rv/5LL774oqZPn%2B7r0jqkyspKrV%2B/XnfddZfS0tLkdDo9P3wurGVzX%2BimJfB79fX1WrRokf77v/9bXbp00YwZM1p9S4Q1Kioq9Oijj6q0tFSdO3fW7373O2VlZXmdNkT7u%2Byyy7R161YNHjxYkvTZZ59p/vz5Kisr06WXXqp58%2BbpX//1X31cZXD4fi92796ttWvX6tNPP1X37t31wAMP6Prrr/dxlR1TYWGhVq5cecG5jz76iM%2BFhQhYAAAAhnGKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2Bz9gJHn8zuZcMwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2505051343536988700”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2911</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>152</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (7)</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2505051343536988700”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2911</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>152</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFR09”><s>FFR09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95189</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFR14”><s>FFR14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FFR09”><code>FFR09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99882</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFRPTH09”>FFRPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2955</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.56274</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6.0883</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5407362693225477791”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5407362693225477791”

aria-controls=”quantiles5407362693225477791” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5407362693225477791” aria-controls=”histogram5407362693225477791”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5407362693225477791” aria-controls=”common5407362693225477791”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5407362693225477791” aria-controls=”extreme5407362693225477791”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
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<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.10513</td>

</tr> <tr>

<th>Q1</th> <td>0.40478</td>

</tr> <tr>

<th>Median</th> <td>0.56605</td>

</tr> <tr>

<th>Q3</th> <td>0.70435</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.94875</td>

</tr> <tr>

<th>Maximum</th> <td>6.0883</td>

</tr> <tr>

<th>Range</th> <td>6.0883</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.29957</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30023</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.53351</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.309</td>

</tr> <tr>

<th>Mean</th> <td>0.56274</td>

</tr> <tr>

<th>MAD</th> <td>0.20323</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.1513</td>

</tr> <tr>

<th>Sum</th> <td>1762.5</td>

</tr> <tr>

<th>Variance</th> <td>0.090135</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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n0ylJiomJcT%2B/RYsWkqQjR454jHtTWFjoXtdZdnvTOj%2B/roKD/Ssf2%2B2%2Bq/dsb/ytR75Cf7yjP97Rn9rRG%2B8aU3/8NmDVxalTp/Twww%2BrRYsWSkpKkiTt27dPR48e1V133aWUlBS98cYbGjt2rD744AOdOnVKkhQSEuJex9n/rqioqPN2s7KylJmZ6TGWnJyslJSUi52SXxu0YIvVJfxb/jF/sNUl1LvIyDCrS2jQ6I939Kd29Ma7xtCfgA1YJ06c0EMPPaTvv/9ef/7znxUWdubFnDt3rk6dOqWIiAhJ0qxZs/T555/rnXfe0a9//WtJZ8LU2UOIZ4PV2efXRVJSkgYMGOAxZrc3VXHxiYue179qDN8ArGT69WpIgoODFBkZpmPHylRVxa9kz0V/vKM/taM33lnRn%2BjocJ9s51wBGbBKS0t133336YcfftDKlSs9fi1ot9vd4UqSbDab4uLiVFBQoJYtW0o680vEs4cZzx7qczgcdd5%2BTExMjcOBTudxVVbyYfMnjeH1qqqqbhTzvFD0xzv6Uzt6411j6E/A7QKprq7WpEmTdPDgQa1evVrXXHONx/LRo0d7HL6rrq7Wt99%2Bq7i4OLVs2VKxsbHKzs52L8/OzlZsbKzx86cAAEDgCrg9WG%2B%2B%2Baa2b9%2Bul156SZGRke49UJdccomioqI0YMAALV68WJ06ddJVV12lVatW6fjx4xo%2BfLgkacSIEVqwYIEuv/xySdLChQs1fvx4y%2BYDAAD8T8AFrI8%2B%2BkjV1dV64IEHPMZ79Oih1atXa9y4cSovL9e8efNUVFSk%2BPh4vfrqq%2B7DhhMmTNBPP/2kSZMmKTg4WHfeeafGjRtnwUwAAIC/srnOvVIn6oXTedz4Ou32IL/7ZZ4/%2BfCR3laXUG/s9iBFR4eruPhEwJ8HcSHoj3f0p3b0xjsr%2BuNwXOqT7Zwr4M7BAgAAsBoBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYzwPWXXfdpddff13Hj5u/sjkAAEBD4POA1atXLy1ZskR9%2BvTRY489pq1bt4q/1gMAAAKJzwNWamqq/va3v%2BnFF19UcHCwHn74YfXr10/PPfec9u3b5%2BtyAAAAjLNbsVGbzabevXurd%2B/eKisr0%2BrVq/Xiiy9q6dKl6tatm8aOHaubbrrJitIAAAAumiUBS5IKCwv17rvv6t1339V3332nbt26afjw4Tpy5IieeOIJ7dixQzNnzrSqPAAAgAvm84D1zjvv6J133tH27dvVrFkzDRs2TC%2B88ILatm3rfkyrVq00f/58AhYAAPBLPg9YM2fOVP/%2B/bV48WLdeOONCgqqeRpYXFycRo0a5evSAAAAjPB5wPrkk08UHR2tkpISd7jauXOnOnfurODgYElSt27d1K1bN1%2BXBgAAYITPf0VYWlqqwYMHa9myZe6xiRMn6rbbbtPhw4d9XQ4AAIBxPg9YTz/9tK688krde%2B%2B97rEPPvhArVq1Ulpamq/LAQAAMM7nAesf//iHpk2bJofD4R5r1qyZpkyZok8//dTX5QAAABjn84Blt9t17NixGuNlZWVc0R0AAAQEnwesG2%2B8UfPmzdMPP/zgHjtw4IDS0tLUt29fX5cDAABgnM9/RTh16lTde%2B%2B9uvnmmxUZGSlJOnbsmDp37qzp06f7uhwAAADjfB6wmjdvrr/85S/6%2B9//rry8PNntdl199dW64YYbZLPZfF0OAACAcZb8qZzg4GD17duXQ4IAACAg%2BTxgOZ1OPf/88/r88891%2BvTpGie2//Wvf/V1SQAAAEb5PGA9%2BeST2rVrl4YMGaJLL73U15sHAACodz4PWJ9%2B%2BqmWL1%2BuxMREX28aAADAJ3x%2BmYamTZuqefPmvt4sAACAz/g8YN12221avny5qqqqjKyvoqJCQ4cO1fbt291jBw4c0Lhx49S1a1fdeuut2rp1q8dz/v73v2vo0KGKj4/XmDFjdODAAY/lr732mvr27auEhATNmDFDZWVlRmoFAACNg88DVklJid59913deOONuueeezRmzBiP27%2BjvLxcjz32mPLy8txjLpdLycnJatGihd566y3ddtttmjRpkg4dOiRJOnTokJKTk3X77bfrzTffVLNmzfTQQw%2B5T7b/6KOPlJmZqTlz5mjlypXKzc1VRkaGuQYAAICAZ8llGoYOHXrR69izZ49SU1Nr/Arx008/1YEDB/T666%2BradOmateunbZt26a33npLDz/8sNavX69f/epXGj9%2BvCQpLS1NvXv31meffaaePXtq1apVGjt2rPr37y9Jmj17tiZMmKDJkycrLCzsousGAACBz%2BcBKy0tzch6zgaiRx99VF27dnWP5%2Bbm6tprr1XTpk3dY927d1dOTo57%2Bb%2BeYB8WFqbOnTsrJydHiYmJ%2BvLLLzVp0iT38q5du%2Br06dPavXu3EhISjNQOAAACmyV7sAoLC/XGG29o3759mjFjhnbs2KH27dsrLi6uzusYOXLkecedTqdiYmI8xpo3b64jR4784vJjx46pvLzcY7ndbldUVJT7%2BQAAAL/E5wFr//79uvvuuxUREaGCggI98sgj%2BuCDDzR9%2BnS99tprio%2BPv6j1l5WVKSQkxGMsJCREFRUVv7j81KlT7vu1Pb8uCgsL5XQ6Pcbs9qY1gt3FCg72%2BSl0jYrdHrj9Pfve4T10fvTHO/pTO3rjXWPqj88D1jPPPKOBAwdq3rx56tatmyTp2Wef1dSpU7VgwQKtXr36otYfGhqqkpISj7GKigo1adLEvfzcsFRRUaHIyEiFhoa675%2B7/N85/yorK0uZmZkeY8nJyUpJSanzOmC96Ohwq0uod5GRnFfoDf3xjv7Ujt541xj64/OA9fnnn2vt2rUef9jZbrfroYce0t13333R62/ZsqX27NnjMVZUVOTee9SyZUsVFRXVWN6pUydFRUUpNDRURUVFateunSSpsrJSJSUlcjgcda4hKSlJAwYM8Biz25uquPjEhUypVo3hG4CVTL9eDUlwcJAiI8N07FiZqqqqrS6nwaE/3tGf2tEb76zoj1Vfln0esKqrq1VdXbOpJ06cUHBw8EWvPz4%2BXkuXLtWpU6fce62ys7PVvXt39/Ls7Gz348vKyvT1119r0qRJCgoKUpcuXZSdna2ePXtKknJycmS329WxY8c61xATE1PjcKDTeVyVlXzY/EljeL2qqqobxTwvFP3xjv7Ujt541xj64/NdIH369NHLL7/sEbJKSkqUkZGhXr16XfT6e/TooVatWmn69OnKy8vT0qVLtXPnTt15552SpDvuuEOff/65li5dqry8PE2fPl1t2rRxB6qRI0dqxYoV2rRpk3bu3KlZs2bp7rvv5hINAACgznwesKZNm6Zdu3apT58%2BKi8v14MPPqj%2B/fvr4MGDmjp16kWvPzg4WC%2B%2B%2BKKcTqduv/12vfvuu1q8eLFiY2MlSW3atNGf/vQnvfXWW7rzzjtVUlKixYsXuw9ZDhkyRA888ICeeuopjR8/Xtddd50mT5580XUBAIDGw%2BY690qdPlBWVqaNGzfqm2%2B%2BUXV1ta655hrddtttioiI8HUpPuN0Hje%2BTrs9SIMWbDG%2BXpzx4SO9rS6h3tjtQYqODldx8YmA301/IeiPd/SndvTGOyv643Bc6pPtnMuS62CFhYXprrvusmLTAAAA9c7nAeuX/t7gqlWrfFQJAABA/fB5wGrdurXH/crKSu3fv1/fffedxo4d6%2BtyAAAAjGswf4tw8eLF/DkaAAAQEBrMlSpvu%2B02ffjhh1aXAQAAcNEaTMD64osvjFxoFAAAwGoN4iT30tJSffvttxo5cqSvywEAADDO5wErNjbW4%2B8QStIll1yiUaNG6Xe/%2B52vywEAADDO5wHrmWee8fUmAQAAfMrnAWvHjh11fuz1119fj5UAAADUD58HrNGjR7sPEf7rX%2Bk5d8xms%2Bmbb77xdXkAAAAXzecBa8mSJZo3b54mT56sHj16KCQkRF9%2B%2BaXmzJmj4cOH69Zbb/V1SQAAAEb5/DINaWlpeuqpp3TzzTcrOjpa4eHh6tWrl%2BbMmaN169apdevW7hsAAIA/8nnAKiwsPG94ioiIUHFxsa/LAQAAMM7nAatr16569tlnVVpa6h4rKSlRRkaGbrjhBl%2BXAwAAYJzPz8F64oknNGbMGN14441q27atXC6Xvv/%2BezkcDq1atcrX5QAAABjn84DVrl07ffDBB9q4caPy8/MlSf/5n/%2BpIUOGKCwszNflAAAAGOfzgCVJl112me666y4dPHhQV1xxhaQzV3MHAAAIBD4/B8vlcmnBggW6/vrrNXToUB05ckRTp07VzJkzdfr0aV%2BXAwAAYJzPA9bq1av1zjvv6A9/%2BINCQkIkSQMHDtSmTZuUmZnp63IAAACM83nAysrK0lNPPaXbb7/dffX2W2%2B9VfPmzdN7773n63IAAACM83nAOnjwoDp16lRjvGPHjnI6nb4uBwAAwDifB6zWrVvryy%2B/rDH%2BySefuE94BwAA8Gc%2B/xXhhAkTNHv2bDmdTrlcLm3btk1ZWVlavXq1pk2b5utyAAAAjPN5wLrjjjtUWVmpl156SadOndJTTz2lZs2a6ZFHHtGIESN8XQ4AAIBxPg9YGzdu1ODBg5WUlKSff/5ZLpdLzZs393UZAAAA9cbn52DNmTPHfTJ7s2bNCFcAACDg%2BDxgtW3bVt99952vNwsAAOAzPj9E2LFjRz3%2B%2BONavny52rZtq9DQUI/laWlpvi4JAADAKJ8HrH379ql79%2B6SxHWvAABAQPJJwEpPT9ekSZPUtGlTrV69ut639/bbb2v69Ok1xm02m3bv3q0HH3xQ//M//%2BOxbMmSJerfv78k6bXXXtOKFStUWlqqW265RU8%2B%2BaTCwsLqvW4AABAYfHIO1quvvqqysjKPsYkTJ6qwsLBetnfrrbdq69at7tv//u//6sorr9SYMWMkSfn5%2BcrIyPB4TO/evSVJH330kTIzMzVnzhytXLlSubm5ysjIqJc6AQBAYPJJwHK5XDXGduzYofLy8nrZXpMmTeRwONy3d999Vy6XS48//rgqKip08OBBdenSxeMxZ//w9KpVqzR27Fj1799f1113nWbPnq233nqrRkAEAACojc9/RehrJSUlWrZsmVJTUxUSEqK9e/fKZrOd98/yVFVV6csvv1RiYqJ7rGvXrjp9%2BrR2797ty7IBAIAfC/iAtW7dOsXExGjw4MGSpL179yoiIkJTpkxRnz59dOedd2rz5s2SpGPHjqm8vFwxMTHu59vtdkVFRenIkSOW1A8AAPyPz35FaLPZfLUpN5fLpfXr1%2Bu%2B%2B%2B5zj%2B3du1enTp1Snz59NHHiRH388cd68MEHlZWVpRYtWkiS%2B3DhWSEhIaqoqKjzdgsLC2v8QtJub%2BoR3EwIDg74fGwpuz1w%2B3v2vcN76Pzoj3f0p3b0xrvG1B%2BfBax58%2BZ5XPPq9OnTysjIUHh4uMfjTF4H68svv1RBQYGGDBniHnvooYc0evRoXXbZZZLOXJfrq6%2B%2B0htvvKFHH31UkmqEqYqKin/rV4RZWVnKzMz0GEtOTlZKSsqFTgUWiI4O/%2BUH%2BbnISH4d6w398Y7%2B1I7eeNcY%2BuOTgHX99dfX2KOTkJCg4uJiFRcX19t2t2zZosTERHeYkqSgoCCP%2B5IUFxenPXv2KCoqSqGhoSoqKlK7du0kSZWVlSopKZHD4ajzdpOSkjRgwACPMbu9qYqLT1zEbGpqDN8ArGT69WpIgoODFBkZpmPHylRVVW11OQ0O/fGO/tSO3nhnRX%2Bs%2BrLsk4Dli2tfnc/OnTvVrVs3j7Fp06bJZrN57CnbvXu32rdvr6CgIHXp0kXZ2dnq2bOnJCknJ0d2u10dO3as83ZjYmJqHA50Oo%2BrspIPmz9pDK9XVVV1o5jnhaI/3tGf2tEb7xpDfwJ6F0heXp6uvvpqj7EBAwbovffe04YNG7R//35lZmYqOztbo0aNkiSNHDlSK1as0KZNm7Rz507NmjVLd999NxcaBQAAdebzP5XjS0VFRYqMjPQYu%2Bmmm/SHP/xBL730kg4dOqRrrrlGy5cvV5s2bSRJQ4YM0Y8//qinnnpKFRUVuummmzR58mQrygcAAH7K5jrfVUBhnNN53Pg67fYgDVqwxfh6ccaHj/S2uoR6Y7cHKTo6XMXFJwJ%2BN/2FoD/e0Z/a0RvvrOiPw3GpT7ZzroA%2BRAgAAGAFAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhFHyOkvAAASz0lEQVSwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFrAB6%2BOPP1aHDh08bikpKZKkr7/%2BWnfddZfi4%2BN1xx13aNeuXR7P3bhxowYOHKj4%2BHglJyfr559/tmIKAADATwVswNqzZ4/69%2B%2BvrVu3um/z5s3TyZMnNXHiRCUmJurtt99WQkKCHnjgAZ08eVKStHPnTs2cOVOTJk1SVlaWjh07punTp1s8GwAA4E8CNmDl5%2Berffv2cjgc7ltkZKQ%2B%2BOADhYaGasqUKWrXrp1mzpyp8PBw/dd//Zckac2aNbrllls0bNgwdezYUenp6dq8ebMOHDhg8YwAAIC/COiA1bZt2xrjubm56t69u2w2myTJZrOpW7duysnJcS9PTEx0P75Vq1aKjY1Vbm6uT%2BoGAAD%2Bz251AfXB5XJp37592rp1q15%2B%2BWVVVVVp8ODBSklJkdPp1NVXX%2B3x%2BObNmysvL0%2BSVFhYqJiYmBrLjxw5UuftFxYWyul0eozZ7U1rrPdiBQcHbD5uEOz2wO3v2fcO76Hzoz/e0Z/a0RvvGlN/AjJgHTp0SGVlZQoJCdHzzz%2BvgwcPat68eTp16pR7/F%2BFhISooqJCknTq1Cmvy%2BsiKytLmZmZHmPJycnuk%2BzhH6Kjw60uod5FRoZZXUKDRn%2B8oz%2B1ozfeNYb%2BBGTAat26tbZv367LLrtMNptNnTp1UnV1tSZPnqwePXrUCEsVFRVq0qSJJCk0NPS8y8PC6v5mSEpK0oABAzzG7PamKi4%2BcYEzOr/G8A3ASqZfr4YkODhIkZFhOnasTFVV1VaX0%2BDQH%2B/oT%2B3ojXdW9MeqL8sBGbAkKSoqyuN%2Bu3btVF5eLofDoaKiIo9lRUVF7sN3LVu2PO9yh8NR523HxMTUOBzodB5XZSUfNn/SGF6vqqrqRjHPC0V/vKM/taM33jWG/gTkLpAtW7aoZ8%2BeKisrc4998803ioqKUvfu3fXFF1/I5XJJOnO%2B1ueff674%2BHhJUnx8vLKzs93PO3z4sA4fPuxeDgAA8EsCMmAlJCQoNDRUTzzxhPbu3avNmzcrPT1d9913nwYPHqxjx45p/vz52rNnj%2BbPn6%2BysjLdcsstkqQRI0bonXfe0fr167V7925NmTJF/fr10xVXXGHxrAAAgL8IyIAVERGhFStW6Oeff9Ydd9yhmTNnKikpSffdd58iIiL08ssvKzs7W7fffrtyc3O1dOlSNW3aVNKZcDZnzhwtXrxYI0aM0GWXXaa0tDSLZwQAAPyJzXX2WBnqldN53Pg67fYgDVqwxfh6ccaHj/S2uoR6Y7cHKTo6XMXFJwL%2BPIgLQX%2B8oz%2B1ozfeWdEfh%2BNSn2znXAG5BwsAAMBKBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwLGADVkFBgVJSUtSjRw/17dtXaWlpKi8vlyTNmzdPHTp08LitWbPG/dyNGzdq4MCBio%2BPV3Jysn7%2B%2BWerpgEAAPyQ3eoC6oPL5VJKSooiIyO1du1aHT16VDNmzFBQUJCmTp2q/Px8paamavjw4e7nRERESJJ27typmTNnavbs2erYsaPmz5%2Bv6dOn6%2BWXX7ZqOgAAwM8E5B6svXv3KicnR2lpabrmmmuUmJiolJQUbdy4UZKUn5%2Bva6%2B9Vg6Hw30LCwuTJK1Zs0a33HKLhg0bpo4dOyo9PV2bN2/WgQMHrJwSAADwIwEZsBwOh5YvX64WLVp4jJeWlqq0tFQFBQVq27bteZ%2Bbm5urxMRE9/1WrVopNjZWubm59VkyAAAIIAF5iDAyMlJ9%2B/Z136%2BurtaaNWvUq1cv5efny2azacmSJfrkk08UFRWle%2B%2B91324sLCwUDExMR7ra968uY4cOVLn7RcWFsrpdHqM2e1Na6z3YgUHB2Q%2BbjDs9sDt79n3Du%2Bh86M/3tGf2tEb7xpTfwIyYJ0rIyNDX3/9td5880199dVXstlsiouL06hRo7Rjxw49%2BeSTioiI0KBBg3Tq1CmFhIR4PD8kJEQVFRV13l5WVpYyMzM9xpKTk5WSkmJkPvCN6Ohwq0uod5GRYVaX0KDRH%2B/oT%2B3ojXeNoT8BH7AyMjK0cuVKPffcc2rfvr2uueYa9e/fX1FRUZKkjh076vvvv9e6des0aNAghYaG1ghTFRUV7nO06iIpKUkDBgzwGLPbm6q4%2BMTFT%2BhfNIZvAFYy/Xo1JMHBQYqMDNOxY2Wqqqq2upwGh/54R39qR2%2B8s6I/Vn1ZDuiANXfuXK1bt04ZGRm6%2BeabJUk2m80drs6Ki4vTp59%2BKklq2bKlioqKPJYXFRXJ4XDUebsxMTE1Dgc6ncdVWcmHzZ80hterqqq6UczzQtEf7%2BhP7eiNd42hPwG7CyQzM1Ovv/66nn32WQ0ZMsQ9vmjRIo0bN87jsbt371ZcXJwkKT4%2BXtnZ2e5lhw8f1uHDhxUfH%2B%2BTugEAgP8LyICVn5%2BvF198Uffff7%2B6d%2B8up9PpvvXv3187duzQihUr9MMPP%2BjPf/6zNmzYoPHjx0uSRowYoXfeeUfr16/X7t27NWXKFPXr109XXHGFxbMCAAD%2BIiAPEf71r39VVVWVXnrpJb300ksey7799lstWrRIL7zwghYtWqTWrVtr4cKFSkhIkCQlJCRozpw5euGFF3T06FH17t1bc%2BfOtWIaAADAT9lcLpfL6iIaA6fzuPF12u1BGrRgi/H14owPH%2BltdQn1xm4PUnR0uIqLTwT8eRAXgv54R39qR2%2B8s6I/DselPtnOuQLyECEAAICVCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZre6AKChuuX5/7O6hH/Lh4/0troEAMA/sQcLAADAMALWeZSXl2vGjBlKTExUnz599Morr1hdEgAA8CMcIjyP9PR07dq1SytXrtShQ4c0depUxcbGavDgwVaXBgAA/AAB6xwnT57U%2BvXrtWzZMnXu3FmdO3dWXl6e1q5dS8ACAAB1QsA6x%2B7du1VZWamEhAT3WPfu3bVkyRJVV1crKIijqmiY/OmkfE7IBxDoCFjncDqdio6OVkhIiHusRYsWKi8vV0lJiZo1a/aL6ygsLJTT6fQYs9ubKiYmxmitwcGEPfgnu71hv3fPfrb4jJ0f/akdvfGuMfWHgHWOsrIyj3AlyX2/oqKiTuvIyspSZmamx9ikSZP08MMPmynynwoLCzX28jwlJSUZD2/%2BrrCwUFlZWfSmFvTHu8LCQq1cuZz%2B1IL%2B1I7eeNeY%2BhP4EfLfFBoaWiNInb3fpEmTOq0jKSlJb7/9tsctKSnJeK1Op1OZmZk19paB3vwS%2BuMd/fGO/tSO3njXmPrDHqxztGzZUsXFxaqsrJTdfqY9TqdTTZo0UWRkZJ3WERMTE/DJHAAA1I49WOfo1KmT7Ha7cnJy3GPZ2dnq0qULJ7gDAIA6ITGcIywsTMOGDdOsWbO0c%2BdObdq0Sa%2B88orGjBljdWkAAMBPBM%2BaNWuW1UU0NL169dLXX3%2BthQsXatu2bfr973%2BvO%2B64w%2Bqyzis8PFw9evRQeHi41aU0OPTGO/rjHf3xjv7Ujt5411j6Y3O5XC6riwAAAAgkHCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbA8lPl5eWaMWOGEhMT1adPH73yyitWl9TgVFRUaOjQodq%2BfbvVpTQoBQUFSklJUY8ePdS3b1%2BlpaWpvLzc6rIajP3792vChAlKSEhQv379tHz5cqtLapAmTpyoadOmWV1Gg/Lxxx%2BrQ4cOHreUlBSry2owKioqNHv2bF1//fX69a9/rWeffVaBfK1zu9UF4MKkp6dr165dWrlypQ4dOqSpU6cqNjZWgwcPtrq0BqG8vFypqanKy8uzupQGxeVyKSUlRZGRkVq7dq2OHj2qGTNmKCgoSFOnTrW6PMtVV1dr4sSJ6tKli/7yl79o//79euyxx9SyZUv9x3/8h9XlNRjvv/%2B%2BNm/erOHDh1tdSoOyZ88e9e/fX3PnznWPhYaGWlhRwzJv3jxt375dK1as0IkTJ/Too48qNjZW99xzj9Wl1QsClh86efKk1q9fr2XLlqlz587q3Lmz8vLytHbtWgKWzvwjl5qaGtDfjC7U3r17lZOTo//7v/9TixYtJEkpKSn64x//SMCSVFRUpE6dOmnWrFmKiIhQ27ZtdcMNNyg7O5uA9U8lJSVKT09Xly5drC6lwcnPz1f79u3lcDisLqXBKSkp0VtvvaVXX31V1113nSRp/Pjxys3NDdiAxSFCP7R7925VVlYqISHBPda9e3fl5uaqurrawsoahs8%2B%2B0w9e/ZUVlaW1aU0OA6HQ8uXL3eHq7NKS0stqqhhiYmJ0fPPP6%2BIiAi5XC5lZ2drx44d6tGjh9WlNRh//OMfddttt%2Bnqq6%2B2upQGJz8/X23btrW6jAYpOztbERERHp%2BliRMnKi0tzcKq6hcByw85nU5FR0crJCTEPdaiRQuVl5erpKTEwsoahpEjR2rGjBkKCwuzupQGJzIyUn379nXfr66u1po1a9SrVy8Lq2qYBgwYoJEjRyohIUE333yz1eU0CNu2bdM//vEPPfTQQ1aX0uC4XC7t27dPW7du1c0336yBAwdqwYIFqqiosLq0BuHAgQNq3bq1NmzYoMGDB%2Bu3v/2tFi9eHNA7BQhYfqisrMwjXEly3%2BfDjH9HRkaGvv76az366KNWl9LgvPDCC1qyZIm%2B%2BeabgP6WXVfl5eX6wx/%2BoKeeekpNmjSxupwG59ChQ%2B5/m59//nlNnTpV7733ntLT060urUE4efKk9u/fr9dff11paWmaOnWqVq9erddee83q0uoN52D5odDQ0BpB6ux9/uFDXWVkZGjlypV67rnn1L59e6vLaXDOnmNUXl6uxx9/XFOmTKnxxaYxyczM1K9%2B9SuPPaD4/1q3bq3t27frsssuk81mU6dOnVRdXa3Jkydr%2BvTpCg4OtrpES9ntdpWWlmrhwoVq3bq1pDOhdN26dRo/frzF1dUPApYfatmypYqLi1VZWSm7/cxL6HQ61aRJE0VGRlpcHfzB3LlztW7dOmVkZHD4618UFRUpJydHAwcOdI9dffXVOn36tEpLS9WsWTMLq7PW%2B%2B%2B/r6KiIve5n2e/1H300Uf64osvrCytwYiKivK4365dO5WXl%2Bvo0aON%2Br0jnTn/MzQ01B2uJOmqq67S4cOHLayqfnGI0A916tRJdrtdOTk57rHs7Gx16dJFQUG8pPAuMzNTr7/%2Bup599lkNGTLE6nIalIMHD2rSpEkqKChwj%2B3atUvNmjVr9P%2BDXL16td577z1t2LBBGzZs0IABAzRgwABt2LDB6tIahC1btqhnz54qKytzj33zzTeKiopq9O8dSYqPj1d5ebn27dvnHtu7d69H4Ao0/N/YD4WFhWnYsGGaNWuWdu7cqU2bNumVV17RmDFjrC4NDVx%2Bfr5efPFF3X///erevbucTqf7hjOHBTt37qwZM2Zoz5492rx5szIyMvT73//e6tIs17p1a1155ZXuW3h4uMLDw3XllVdaXVqDkJCQoNDQUD3xxBPau3evNm/erPT0dN13331Wl9YgxMXFqV%2B/fpo%2Bfbp2796tLVu2aOnSpRoxYoTVpdUbm4uLBfmlsrIyzZo1S//93/%2BtiIgITZgwQePGjbO6rAanQ4cOWrVqlXr27Gl1KQ3C0qVLtXDhwvMu%2B/bbb31cTcNUUFCguXPnatu2bQoLC9OoUaP0wAMPyGazWV1ag3L2Ku7PPPOMxZU0HHl5eXr66aeVk5Oj8PBw3XPPPUpOTua980/Hjx/X3Llz9fHHHyssLEwjR44M6P4QsAAAAAzjECEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AIXIh/u83HX%2BAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5407362693225477791”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.447227191</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.438020149</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.942211055</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.437062937</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.826446281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.453514739</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.580088463</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.419375131</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2944)</td> <td class=”number”>2972</td> <td class=”number”>92.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5407362693225477791”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0583158</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.068031839</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.069208942</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.077375426</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.647837599</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.988669819</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.074558032</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.139051497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.088280061</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FFRPTH14”>FFRPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2952</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.57739</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>5.5556</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2378133966848595566”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2378133966848595566”

aria-controls=”quantiles-2378133966848595566” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2378133966848595566” aria-controls=”histogram-2378133966848595566”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2378133966848595566” aria-controls=”common-2378133966848595566”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2378133966848595566” aria-controls=”extreme-2378133966848595566”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2378133966848595566”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.089371</td>

</tr> <tr>

<th>Q1</th> <td>0.41406</td>

</tr> <tr>

<th>Median</th> <td>0.5783</td>

</tr> <tr>

<th>Q3</th> <td>0.73295</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.97678</td>

</tr> <tr>

<th>Maximum</th> <td>5.5556</td>

</tr> <tr>

<th>Range</th> <td>5.5556</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.31889</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.30513</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52846</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.248</td>

</tr> <tr>

<th>Mean</th> <td>0.57739</td>

</tr> <tr>

<th>MAD</th> <td>0.21058</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.5717</td>

</tr> <tr>

<th>Sum</th> <td>1808.4</td>

</tr> <tr>

<th>Variance</th> <td>0.093102</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2378133966848595566”>

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UlZWppKTEayw3N1f5%2Bfm%2B7ApMEhsbZXYJ3VZMTOd/H/BP9M139M539M4cQRmwjh07pt/97nf6/PPPtXr1ag0cONAzl5iYqLq6Oq/l6%2BrqNGTIEPXs2VNWq1V1dXUaNGiQJKmlpUUNDQ2Kj4/v9PZzcnKUmZnpNWaxRKq%2B/rjvO3UavCs5u4x%2BvYJBWFioYmIi1NjoVGtrm9nlBAz65jt65zt6186sN8tBF7Da2tqUl5enL774QmvXrvUEpZNsNpvKy8s9j51Op/bv36%2B8vDyFhoYqOTlZ5eXlGjFihKT2e2pZLBYNHjy40zUkJCR0uBzocBxVS8u5e4AHIl6vM2ttbaM/PqBvvqN3vqN35gi6UyAvvviidu3apYceekgxMTFyOBxyOBxqaGiQJE2ePFl79uzRihUrVFlZqYKCAvXr188TqKZOnapnnnlG27dv1969ezV//nzdcMMNXbpECAAAzm1BdwbrjTfeUFtbm26//Xav8fT0dK1du1b9%2BvXTn/70Jz388MMqLS1VSkqKSktLFRISIkm69tpr9eWXX2revHlyuVy66qqrdO%2B995qxKwAAIECFuE/ewhxnlcNx1PB1WiyhunLJ24avF%2B1eu3uU2SV0OxZLqGJjo1Rff5xLDl1A33xH73xH79rFx59vynaD7hIhAACA2QhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABgv4gOVyuZSVlaVdu3Z5xqqqqnTTTTdp2LBhuuaaa/S3v/3N6znvvPOOsrKyZLPZNH36dFVVVXnNP/fcc8rIyFBKSormzp0rp9Ppl30BAADBIaADVnNzs2bOnKnKykrPmNvtVm5urnr37q2NGzdq4sSJysvL05EjRyRJR44cUW5urrKzs/Xiiy%2BqV69euvPOO%2BV2uyVJb7zxhkpKSrRgwQKtXr1adrtdixcvNmX/AABAYArYgHXw4EHdcMMN%2Bvzzz73G3333XVVVVWnBggUaNGiQbr/9dg0bNkwbN26UJG3YsEGXXnqpbrnlFv30pz9VUVGRvvzyS%2B3evVuStGbNGt14440aO3asfvazn%2BnBBx/Uxo0bOYsFAAA6ze8B6/rrr9f69et19OjRH7We3bt3a8SIESorK/Mat9vtuuSSSxQZGekZS01NVUVFhWc%2BLS3NMxcREaGhQ4eqoqJCra2t%2BvDDD73mhw0bpu%2B%2B%2B04HDhz4UfUCAIBzh8XfGxw5cqSWL1%2BuoqIiXXHFFcrOztaoUaMUEhLSpfVMnTr1tOMOh0MJCQleY3FxcaqpqfnB%2BcbGRjU3N3vNWywW9ezZ0/N8AACAH%2BL3gDVr1izNnDlT77zzjl5%2B%2BWXdddddiomJ0aRJkzRp0iRdeOGFP2r9TqdT4eHhXmPh4eFyuVw/ON/U1OR5fKbnd0Ztba0cDofXmMUS2SHY/VhhYQF7hTcgWCz091QnjzmOva6hb76jd76jd%2Bbye8CSpJCQEI0aNUqjRo2S0%2BnU2rVrtWzZMq1YsULDhw/XjTfeqKuuusqndVutVjU0NHiNuVwu9ejRwzN/alhyuVyKiYmR1Wr1PD51PiIiotM1lJWVqaSkxGssNzdX%2Bfn5nV4HzBcbG2V2Cd1WTEznfx/wT/TNd/TOd/TOHKYELKn9LM8rr7yiV155RZ988omGDx%2BuX/3qV6qpqdH999%2Bv9957T4WFhV1eb2Jiog4ePOg1VldX5zl7lJiYqLq6ug7zQ4YMUc%2BePWW1WlVXV6dBgwZJklpaWtTQ0KD4%2BPhO15CTk6PMzEyvMYslUvX1x7u8P9%2BHdyVnl9GvVzAICwtVTEyEGhudam1tM7ucgEHffEfvfEfv2pn1ZtnvAWvz5s3avHmzdu3apV69emnSpEl64oknNHDgQM8yffr00aJFi3wKWDabTStWrFBTU5PnrFV5eblSU1M98%2BXl5Z7lnU6n9u/fr7y8PIWGhio5OVnl5eUaMWKEJKmiokIWi0WDBw/udA0JCQkdLgc6HEfV0nLuHuCBiNfrzFpb2%2BiPD%2Bib7%2Bid7%2BidOfx%2BCqSwsFBRUVEqLS3Vjh07NGvWLK9wJUk/%2BclP9Nvf/tan9aenp6tPnz4qKChQZWWlVqxYob179%2Bq6666TJE2ePFl79uzRihUrVFlZqYKCAvXr188TqKZOnapnnnlG27dv1969ezV//nzdcMMNXbpECAAAzm1%2BP4P11ltvKTY2Vg0NDQoNbc93e/fu1dChQxUWFiZJGj58uIYPH%2B7T%2BsPCwrRs2TIVFhYqOztbAwYMUGlpqZKSkiRJ/fr105/%2B9Cc9/PDDKi0tVUpKikpLSz3/ivHaa6/Vl19%2BqXnz5snlcumqq67Svffea8CeAwCAc0WI%2B%2BQtzP3k888/12233aYrrrhCs2fPltR%2B64bevXvr6aefVp8%2BffxZjt84HD/uvl%2BnY7GE6solbxu%2BXrR77e5RZpfQ7VgsoYqNjVJ9/XEuOXQBffMdvfMdvWsXH3%2B%2BKdv1%2ByXChx9%2BWAMGDNDNN9/sGdu2bZv69OmjoqIif5cDAABgOL8HrPfff1/33Xef17/K69Wrl2bPnq13333X3%2BUAAAAYzu8By2KxqLGxscO40%2BmUn69WAgAAnBV%2BD1g///nP9dBDD3l9SXNVVZWKioqUkZHh73IAAAAM5/d/RThnzhzdfPPNuvrqqxUTEyNJamxs1NChQ1VQUODvcgAAAAzn94AVFxenl156Se%2B8844qKytlsVh00UUX6fLLL%2B/yFz4DAAB0R6Z8VU5YWJgyMjK4JAgAAIKS3wOWw%2BHQ0qVLtWfPHn333XcdPtj%2B5ptv%2BrskAAAAQ/k9YP3hD3/Qvn37dO211%2Br88825%2BRcAAMDZ5PeA9e6772rlypVKS0vz96YBAAD8wu%2B3aYiMjFRcXJy/NwsAAOA3fg9YEydO1MqVK9Xa2urvTQMAAPiF3y8RNjQ0aOvWrfrrX/%2Bq/v37Kzw83Gt%2BzZo1/i4JAADAUKbcpiErK8uMzQIAAPiF3wNWUVGRvzcJAADgV37/DJYk1dbWqqSkRLNmzdLXX3%2Bt119/XZ9%2B%2BqkZpQAAABjO7wHr8OHD%2BsUvfqGXXnpJb7zxhk6cOKFt27Zp8uTJstvt/i4HAADAcH4PWH/84x81btw4bd%2B%2BXeedd54k6bHHHlNmZqaWLFni73IAAAAM5/eAtWfPHt18881eX%2BxssVh05513av/%2B/f4uBwAAwHB%2BD1htbW1qa2vrMH78%2BHGFhYX5uxwAAADD%2BT1gjR49Wk899ZRXyGpoaNDixYs1cuRIf5cDAABgOL8HrPvuu0/79u3T6NGj1dzcrN///vcaO3asvvjiC82ZM8ff5QAAABjO7/fBSkxM1Msvv6ytW7fqo48%2BUltbm6ZMmaKJEycqOjra3%2BUAAAAYzpQ7uUdEROj66683Y9MAAABnnd8D1vTp0793nu8iBAAAgc7vAatv375ej1taWnT48GF98sknuvHGG/1dDgAAgOG6zXcRlpaWqqamxrDtVFdXa/78%2BXrvvffUs2dPTZ8%2BXTfddJMkaf/%2B/XrggQf0ySef6KKLLtKDDz6oSy%2B91PPcrVu3aunSpXI4HBo9erQWLlyoXr16GVYbAAAIbqZ8F%2BHpTJw4Ua%2B99pph67v77rsVGRmpTZs2ae7cuVq6dKn%2B8pe/6MSJE5oxY4bS0tK0adMmpaSk6Pbbb9eJEyckSXv37lVhYaHy8vJUVlamxsZGFRQUGFYXAAAIft0mYH3wwQeG3Wj022%2B/VUVFhX7/%2B99r4MCBGjdunDIyMrRz505t27ZNVqtVs2fP1qBBg1RYWKioqCi9/vrrkqR169ZpwoQJmjRpkgYPHqzi4mLt2LFDVVVVhtQGAACCX7f4kPuxY8f08ccfa%2BrUqYZso0ePHoqIiNCmTZs0a9YsVVVVac%2BePbr77rtlt9uVmprq%2BaqekJAQDR8%2BXBUVFcrOzpbdbtdtt93mWVefPn2UlJQku92u/v37G1IfAAAIbn4PWElJSV7fQyhJ5513nn7729/ql7/8pSHbsFqtmjdvnhYuXKg1a9aotbVV2dnZuv766/Xmm2/qoosu8lo%2BLi5OlZWVkqTa2lolJCR0mDfy82EAACC4%2BT1g/fGPf/TLdg4dOqSxY8fq5ptvVmVlpRYuXKjLL79cTqdT4eHhXsuGh4fL5XJJkpqamr53vjNqa2vlcDi8xiyWyA7B7ccKC%2Bs2V3iDksVCf0918pjj2Osa%2BuY7euc7emcuvwes9957r9PLXnbZZT5tY%2BfOnXrxxRe1Y8cO9ejRQ8nJyfrqq6/05JNPqn///h3CksvlUo8ePSS1n/063XxERESnt19WVqaSkhKvsdzcXOXn5/u0PzBHbGyU2SV0WzExnf99wD/RN9/RO9/RO3P4PWBNmzbNc4nQ7XZ7xk8dCwkJ0UcffeTTNvbt26cBAwZ4QpMkXXLJJVq%2BfLnS0tJUV1fntXxdXZ3n7FJiYuJp5%2BPj4zu9/ZycHGVmZnqNWSyRqq8/3tVd%2BV68Kzm7jH69gkFYWKhiYiLU2OhUa2vbDz8Bkujbj0HvfEfv2pn1ZtnvAWv58uV66KGHdO%2B99yo9PV3h4eH68MMPtWDBAv3qV7/SNddc86O3kZCQoMOHD8vlcnku93366afq16%2BfbDabnn76abndboWEhMjtdmvPnj264447JEk2m03l5eXKzs6W1H4/rerqatlsti5t/9TLgQ7HUbW0nLsHeCDi9Tqz1tY2%2BuMD%2BuY7euc7emcOv58CKSoq0rx583T11VcrNjZWUVFRGjlypBYsWKAXXnhBffv29fz4KjMzU%2Bedd57uv/9%2BffbZZ/qf//kfLV%2B%2BXNOmTdP48ePV2NioRYsW6eDBg1q0aJGcTqcmTJggSZoyZYo2b96sDRs26MCBA5o9e7bGjBnDvyAEAACd5veAVVtbe9rwFB0drfr6ekO2cf755%2Bu5556Tw%2BHQddddp6KiIv3%2B979XTk6OoqOj9dRTT3nOUtntdq1YsUKRkZGSpJSUFC1YsEClpaWaMmWKLrjggjPefR4AAOB0Qtz/94NQfnDzzTcrMjJSjzzyiKKjoyVJDQ0NmjVrlqxWq5YtW%2BbPcvzG4Thq%2BDotllBdueRtw9eLdq/dPcrsErodiyVUsbFRqq8/ziWHLqBvvqN3vqN37eLjzzdlu37/DNb999%2Bv6dOn6%2Bc//7kGDhwot9utf/zjH4qPj9eaNWv8XQ4AAIDh/B6wBg0apG3btmnr1q06dOiQJOk3v/mNrr322i7dCgEAAKC78nvAkqQLLrhA119/vb744gvPh8fPO%2B88M0oBAAAwnN8/5O52u7VkyRJddtllysrKUk1NjebMmaPCwkJ99913/i4HAADAcH4PWGvXrtXmzZv1wAMPeO5RNW7cOG3fvr3D3c8BAAACkd8DVllZmebNm6fs7GzP3duvueYaPfTQQ9qyZYu/ywEAADCc3wPWF198oSFDhnQYHzx4cIcvSAYAAAhEfg9Yffv21Ycffthh/K233uJu6QAAICj4/V8R3nrrrXrwwQflcDjkdru1c%2BdOlZWVae3atbrvvvv8XQ4AAIDh/B6wJk%2BerJaWFj355JNqamrSvHnz1KtXL919992aMmWKv8sBAAAwnN8D1tatWzV%2B/Hjl5OTom2%2B%2BkdvtVlxcnL/LAAAAOGv8/hmsBQsWeD7M3qtXL8IVAAAIOn4PWAMHDtQnn3zi780CAAD4jd8vEQ4ePFj33HOPVq5cqYEDB8pqtXrNFxUV%2BbskAAAAQ/k9YH322WdKTU2VJO57BQAAgpJfAlZxcbHy8vIUGRmptWvX%2BmOTAAAApvHLZ7BWrVolp9PpNTZjxgzV1tb6Y/MAAAB%2B5ZeA5Xa7O4y99957am5u9sfmAQAA/Mrv/4oQAAAg2BGwAAAADOa3gBUSEuKvTQEAAJjKb7dpeOihh7zuefXdd99p8eLFioqK8lqO%2B2ABAIBA55eAddlll3W451VKSorq6%2BtVX1/vjxIAAAD8xi8Bi3tfAQCAcwkfcgcAADAYAQsAAMBgQRuwXC6XHnzwQV122WX693//dz322GOeG57u379f119/vWw2myZPnqx9%2B/Z5PXfr1q0aN26cbDabcnNz9c0335ixCwAAIEAFbcB66KGH9M477%2BiZZ57Ro48%2Bqj//%2Bc92kFpPAAAUj0lEQVQqKyvTiRMnNGPGDKWlpWnTpk1KSUnR7bffrhMnTkiS9u7dq8LCQuXl5amsrEyNjY0qKCgweW8AAEAg8dttGvypoaFBGzdu1KpVq/Szn/1MknTLLbfIbrfLYrHIarVq9uzZCgkJUWFhod566y29/vrrys7O1rp16zRhwgRNmjRJUvsXVY8dO1ZVVVXq37%2B/mbsFAAACRFCewSovL1d0dLTS09M9YzNmzFBRUZHsdrtSU1M9Nz4NCQnR8OHDVVFRIUmy2%2B1KS0vzPK9Pnz5KSkqS3W73704AAICAFZQBq6qqSn379tXLL7%2Bs8ePH64orrlBpaana2trkcDiUkJDgtXxcXJxqamokSbW1td87DwAA8EOC8hLhiRMndPjwYa1fv15FRUVyOByaN2%2BeIiIi5HQ6FR4e7rV8eHi4XC6XJKmpqel75zujtra2w41VLZbIDsHtxwoLC8p83G1YLPT3VCePOY69rqFvvqN3vqN35grKgGWxWHTs2DE9%2Buij6tu3ryTpyJEjeuGFFzRgwIAOYcnlcqlHjx6SJKvVetr5iIiITm%2B/rKxMJSUlXmO5ubnKz8/3ZXdgktjYqB9e6BwVE9P53wf8E33zHb3zHb0zR1AGrPj4eFmtVk%2B4kqQLL7xQ1dXVSk9PV11dndfydXV1nrNLiYmJp52Pj4/v9PZzcnKUmZnpNWaxRKq%2B/nhXd%2BV78a7k7DL69QoGYWGhiomJUGOjU62tbWaXEzDom%2B/one/oXTuz3iwHZcCy2Wxqbm7WZ599pgsvvFCS9Omnn6pv376y2Wx6%2Bumn5Xa7FRISIrfbrT179uiOO%2B7wPLe8vFzZ2dmSpOrqalVXV8tms3V6%2BwkJCR0uBzocR9XScu4e4IGI1%2BvMWlvb6I8P6Jvv6J3v6J05gvIUyE9%2B8hONGTNGBQUFOnDggN5%2B%2B22tWLFCU6ZM0fjx49XY2KhFixbp4MGDWrRokZxOpyZMmCBJmjJlijZv3qwNGzbowIEDmj17tsaMGcMtGgAAQKcFZcCSpCVLluhf//VfNWXKFM2ZM0e/%2Bc1vNG3aNEVHR%2Bupp57ynKWy2%2B1asWKFIiMjJUkpKSlasGCBSktLNWXKFF1wwQUqKioyeW8AAEAgCXGf/P4YnFUOx1HD12mxhOrKJW8bvl60e%2B3uUWaX0O1YLKGKjY1Sff1xLjl0AX3zHb3zHb1rFx9/vinbDdozWAAAAGYhYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABgs6APWjBkzdN9993ke79%2B/X9dff71sNpsmT56sffv2eS2/detWjRs3TjabTbm5ufrmm2/8XTIAAAhwQR2wXn31Ve3YscPz%2BMSJE5oxY4bS0tK0adMmpaSk6Pbbb9eJEyckSXv37lVhYaHy8vJUVlamxsZGFRQUmFU%2BAAAIUEEbsBoaGlRcXKzk5GTP2LZt22S1WjV79mwNGjRIhYWFioqK0uuvvy5JWrdunSZMmKBJkyZp8ODBKi4u1o4dO1RVVWXWbgAAgAAUtAHrkUce0cSJE3XRRRd5xux2u1JTUxUSEiJJCgkJ0fDhw1VRUeGZT0tL8yzfp08fJSUlyW63%2B7d4AAAQ0IIyYO3cuVPvv/%2B%2B7rzzTq9xh8OhhIQEr7G4uDjV1NRIkmpra793HgAAoDMsZhdgtObmZj3wwAOaN2%2BeevTo4TXndDoVHh7uNRYeHi6XyyVJampq%2Bt75zqqtrZXD4fAas1giO4S3HyssLCjzcbdhsdDfU5085jj2uoa%2B%2BY7e%2BY7emSvoAlZJSYkuvfRSZWRkdJizWq0dwpLL5fIEsTPNR0REdKmGsrIylZSUeI3l5uYqPz%2B/S%2BuBuWJjo8wuoduKiena7wTa0Tff0Tvf0TtzBF3AevXVV1VXV6eUlBRJ8gSmN954Q1lZWaqrq/Navq6uznNmKTEx8bTz8fHxXaohJydHmZmZXmMWS6Tq6493aT0/hHclZ5fRr1cwCAsLVUxMhBobnWptbTO7nIBB33xH73xH79qZ9WY56ALW2rVr1dLS4nm8ZMkSSdI999yj9957T08//bTcbrdCQkLkdru1Z88e3XHHHZIkm82m8vJyZWdnS5Kqq6tVXV0tm83WpRoSEhI6XA50OI6qpeXcPcADEa/XmbW2ttEfH9A339E739E7cwRdwOrbt6/X46io9uQ6YMAAxcXF6dFHH9WiRYv061//WuvXr5fT6dSECRMkSVOmTNG0adM0bNgwJScna9GiRRozZoz69%2B/v9/0AAACB65y6xhQdHa2nnnrKc5bKbrdrxYoVioyMlCSlpKRowYIFKi0t1ZQpU3TBBReoqKjI5KoBAECgCXG73W6zizgXOBxHDV%2BnxRKqK5e8bfh60e61u0eZXUK3Y7GEKjY2SvX1x7nk0AX0zXf0znf0rl18/PmmbPecOoMFAADgDwQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgwVtwPrqq6%2BUn5%2Bv9PR0ZWRkqKioSM3NzZKkqqoq3XTTTRo2bJiuueYa/e1vf/N67jvvvKOsrCzZbDZNnz5dVVVVZuwCAAAIUEEZsNxut/Lz8%2BV0OvX888/r8ccf1//%2B7/9q6dKlcrvdys3NVe/evbVx40ZNnDhReXl5OnLkiCTpyJEjys3NVXZ2tl588UX16tVLd955p9xut8l7BQAAAoXF7ALOhk8//VQVFRX6%2B9//rt69e0uS8vPz9cgjj%2BjnP/%2B5qqqqtH79ekVGRmrQoEHauXOnNm7cqLvuuksbNmzQpZdeqltuuUWSVFRUpFGjRmn37t0aMWKEmbsFAAACRFCewYqPj9fKlSs94eqkY8eOyW6365JLLlFkZKRnPDU1VRUVFZIku92utLQ0z1xERISGDh3qmQcAAPghQRmwYmJilJGR4Xnc1tamdevWaeTIkXI4HEpISPBaPi4uTjU1NZL0g/MAAAA/JCgvEZ5q8eLF2r9/v1588UU999xzCg8P95oPDw%2BXy%2BWSJDmdzu%2Bd74za2lo5HA6vMYslskNw%2B7HCwoIyH3cbFgv9PdXJY45jr2vom%2B/one/onbmCPmAtXrxYq1ev1uOPP66LL75YVqtVDQ0NXsu4XC716NFDkmS1WjuEKZfLpZiYmE5vs6ysTCUlJV5jubm5ys/P93EvYIbY2CizS%2Bi2YmIizC4hINE339E739E7cwR1wFq4cKFeeOEFLV68WFdffbUkKTExUQcPHvRarq6uznN2KTExUXV1dR3mhwwZ0unt5uTkKDMz02vMYolUff1xX3bjjHhXcnYZ/XoFg7CwUMXERKix0anW1jazywkY9M139M539K6dWW%2BWgzZglZSUaP369Xrsscc0fvx4z7jNZtOKFSvU1NTkOWtVXl6u1NRUz3x5eblneafTqf379ysvL6/T205ISOhwOdDhOKqWlnP3AA9EvF5n1traRn98QN98R%2B98R%2B/MEZSnQA4dOqRly5bptttuU2pqqhwOh%2BcnPT1dffr0UUFBgSorK7VixQrt3btX1113nSRp8uTJ2rNnj1asWKHKykoVFBSoX79%2B3KIBAAB0WlAGrDfffFOtra168sknNXr0aK%2BfsLAwLVu2TA6HQ9nZ2XrllVdUWlqqpKQkSVK/fv30pz/9SRs3btR1112nhoYGlZaWKiQkxOS9AgAAgSLEzS3K/cLhOGr4Oi2WUF255G3D14t2r909yuwSuh2LJVSxsVGqrz/OJYcuoG%2B%2Bo3e%2Bo3ft4uPPN2W7QXkGCwAAwEwELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAg1nMLgDoriYs/bvZJXTJa3ePMrsEAMD/xxksAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAErNNobm7W3LlzlZaWptGjR%2BvZZ581uyQAABBA%2BFeEp1FcXKx9%2B/Zp9erVOnLkiObMmaOkpCSNHz/e7NIAAEAAIGCd4sSJE9qwYYOefvppDR06VEOHDlVlZaWef/55Aha6tUC6rQS3lAAQ7LhEeIoDBw6opaVFKSkpnrHU1FTZ7Xa1tbWZWBkAAAgUnME6hcPhUGxsrMLDwz1jvXv3VnNzsxoaGtSrV68fXEdtba0cDofXmMUSqYSEBENrDQsjHyMwBdLZtkD0l3syzC7BMCf/P8f/77qO3pmLgHUKp9PpFa4keR67XK5OraOsrEwlJSVeY3l5ebrrrruMKfL/q62t1Y3/UqmcnBzDw1swq62tVVlZGX3zAb3zDX3zXW1trVavXknvfEDvzEWsPYXVau0QpE4%2B7tGjR6fWkZOTo02bNnn95OTkGF6rw%2BFQSUlJh7Nl%2BH70zXf0zjf0zXf0znf0zlycwTpFYmKi6uvr1dLSIoulvT0Oh0M9evRQTExMp9aRkJDAuwUAAM5hnME6xZAhQ2SxWFRRUeEZKy8vV3JyskJDaRcAAPhhJIZTREREaNKkSZo/f7727t2r7du369lnn9X06dPNLg0AAASIsPnz5883u4juZuTIkdq/f78effRR7dy5U3fccYcmT55sdlmnFRUVpfT0dEVFRZldSkChb76jd76hb76jd76jd%2BYJcbvdbrOLAAAACCZcIgQAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAClDNzc2aO3eu0tLSNHr0aD377LNmlxRQXC6XsrKytGvXLrNLCQhfffWV8vPzlZ6eroyMDBUVFam5udnssgLC4cOHdeuttyolJUVjxozRypUrzS4p4MyYMUP33Xef2WUEjL/85S/6t3/7N6%2Bf/Px8s8s651jMLgC%2BKS4u1r59%2B7R69WodOXJEc%2BbMUVJSksaPH292ad1ec3OzZs2apcrKSrNLCQhut1v5%2BfmKiYnR888/r2%2B//VZz585VaGio5syZY3Z53VpbW5tmzJih5ORkvfTSSzp8%2BLBmzpypxMRE/eIXvzC7vIDw6quvaseOHfrVr35ldikB4%2BDBgxo7dqwWLlzoGbNarSZWdG4iYAWgEydOaMOGDXr66ac1dOhQDR06VJWVlXr%2B%2BecJWD/g4MGDmjVrlviGqM779NNPVVFRob///e/q3bu3JCk/P1%2BPPPIIAesH1NXVaciQIZo/f76io6M1cOBAXX755SovLydgdUJDQ4OKi4uVnJxsdikB5dChQ7r44osVHx9vdinnNC4RBqADBw6opaVFKSkpnrHU1FTZ7Xa1tbWZWFn3t3v3bo0YMUJlZWVmlxIw4uPjtXLlSk%2B4OunYsWMmVRQ4EhIStHTpUkVHR8vtdqu8vFzvvfee0tPTzS4tIDzyyCOaOHGiLrroIrNLCSiHDh3SwIEDzS7jnMcZrADkcDgUGxur8PBwz1jv3r3V3NyshoYG9erVy8TqurepU6eaXULAiYmJUUZGhudxW1ub1q1bp5EjR5pYVeDJzMzUkSNHNHbsWF199dVml9Pt7dy5U%2B%2B//762bNmi%2BfPnm11OwHC73frss8/0t7/9TU899ZRaW1s1fvx45efne/2dgbOPM1gByOl0dvhFOfnY5XKZURLOIYsXL9b%2B/fv1H//xH2aXElCeeOIJLV%2B%2BXB999JGKiorMLqdba25u1gMPPKB58%2BapR48eZpcTUI4cOeL5O2Lp0qWaM2eOtmzZouLiYrNLO%2BdwBisAWa3WDkHq5GP%2BZ4SzafHixVq9erUef/xxXXzxxWaXE1BOfo6oublZ99xzj2bPns0ZhTMoKSnRpZde6nXmFJ3Tt29f7dq1SxdccIFCQkI0ZMgQtbW16d5771VBQYHCwsLMLvGcQcAKQImJiaqvr1dLS4sslvaX0OFwqEePHoqJiTG5OgSrhQsX6oUXXtDixYu5xNVJdXV1qqio0Lhx4zxjF110kb777jsdO3aMy/ln8Oqrr6qurs7zOdOTbyDfeOMNffDBB2aWFhB69uzp9XjQoEFqbm7Wt99%2ByzHnR1wiDEBDhgyRxWJRRUWFZ6y8vFzJyckKDeUlhfFKSkq0fv16PfbYY7r22mvNLidgfPHFF8rLy9NXX33lGdu3b5969erFX3TfY%2B3atdqyZYtefvllvfzyy8rMzFRmZqZefvlls0vr9t5%2B%2B22NGDFCTqfTM/bRRx%2BpZ8%2BeHHN%2Bxt/GASgiIkKTJk3S/PnztXfvXm3fvl3PPvuspk%2BfbnZpCEKHDh3SsmXLdNtttyk1NVUOh8Pzg%2B%2BXnJysoUOHau7cuTp48KB27NihxYsX64477jC7tG6tb9%2B%2BGjBggOcnKipKUVFRGjBggNmldXspKSmyWq26//779emnn2rHjh0qLi7W7373O7NLO%2BdwiTBAFRQUaP78%2BbrxxhsVHR2tu%2B66S1dddZXZZSEIvfnmm2ptbdWTTz6pJ5980mvu448/NqmqwBAWFqZly5Zp4cKFysnJUUREhKZNm8abIZw10dHReuaZZ/Twww9r8uTJioqK0q9//WsClglC3NxxEQAAwFBcIgQAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADDY/wPlsjF63pQ6SQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2378133966848595566”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.385728062</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.377073906</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.477099237</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.388349515</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.555447552</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.491364273</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.550660793</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.74906367</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.424268137</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2941)</td> <td class=”number”>2960</td> <td class=”number”>92.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2378133966848595566”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.066640011</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.070731362</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.080729797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.089349535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.786624204</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.976928802</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.052769298</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.512641425</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT09”>FMRKT09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>39</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.6643</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>94</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>42.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6343540888012280682”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6343540888012280682,#minihistogram-6343540888012280682”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6343540888012280682”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6343540888012280682”

aria-controls=”quantiles-6343540888012280682” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6343540888012280682” aria-controls=”histogram-6343540888012280682”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6343540888012280682” aria-controls=”common-6343540888012280682”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6343540888012280682” aria-controls=”extreme-6343540888012280682”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6343540888012280682”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>2</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>94</td>

</tr> <tr>

<th>Range</th> <td>94</td>

</tr> <tr>

<th>Interquartile range</th> <td>2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.9986</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4025</td>

</tr> <tr>

<th>Kurtosis</th> <td>169</td>

</tr> <tr>

<th>Mean</th> <td>1.6643</td>

</tr> <tr>

<th>MAD</th> <td>1.821</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.02</td>

</tr> <tr>

<th>Sum</th> <td>5211</td>

</tr> <tr>

<th>Variance</th> <td>15.989</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6343540888012280682”>

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7b98%2BjRgxQlFRUZo0aZKKi4vd5jdt2qSEhATFxMRozpw5qqiosGxdAADA%2B3llwHI6nUpJSVFFRYVeeuklrVy5Uu%2B//75WrVolp9OppKQktWrVSjk5ORo5cqSSk5N16tQpSdKpU6eUlJSk0aNHa8eOHWrZsqUefPBBOZ1OSdI777yjjIwMLVy4UJs3b1Z%2Bfr7S09M9uVwAAOBlvDJgHT9%2BXHl5eUpLS1OnTp0UFxenlJQU7dq1S/v371dxcbEWLlyojh07asaMGYqOjlZOTo4kafv27brttts0depUderUSWlpaTp58qQ%2B/vhjSdKWLVs0efJkDRgwQD179tSCBQuUk5PDUSwAANBgXhmwwsLCtGHDBrVq1cpt/MKFC8rPz1e3bt0UHBzsGo%2BNjVVeXp4kKT8/X3Fxca65oKAgde/eXXl5eaqtrdWnn37qNh8dHa3Lly/r6NGjjbwqAADgK7wyYIWEhCghIcH1vK6uTlu3blXv3r3lcDgUHh7u9vrQ0FCdOXNGkv7u/Llz51RVVeU2b7fb1bx5c9f7AQAAfo7d0wWYkJ6eriNHjmjHjh3atGmTAgIC3OYDAgJUXV0tSaqoqPjJ%2BcrKStfzn3p/Q5SUlMjhcLiN2e3B9YLdtfL39658bLd7V71Xc6Xn3tZ7b0bPrUW/rUfPfZPXB6z09HRt3rxZK1euVOfOnRUYGKjy8nK311RXV6tJkyaSpMDAwHphqbq6WiEhIQoMDHQ9//F8UFBQg2vKzs5WRkaG21hSUpJSUlIavA1f1KJFU0%2BXYExISMN/HmAGPbcW/bYePfctXh2wFi1apG3btik9PV2DBw%2BWJEVEROjYsWNurystLXUdPYqIiFBpaWm9%2Ba5du6p58%2BYKDAxUaWmpOnbsKEmqqalReXm5wsLCGlzX%2BPHjlZiY6DZmtwerrOziL17j3%2BNtn3ZMr98T/P39FBISpHPnKlRbW%2Bfpcq4L9Nxa9Nt69LxxeerDvdcGrIyMDL388stasWKFhgwZ4hqPiopSVlaWKisrXUetcnNzFRsb65rPzc11vb6iokJHjhxRcnKy/Pz81KNHD%2BXm5uqOO%2B6QJOXl5clut6tLly4Nri08PLze6UCH47xqaq7vXxxfWn9tbZ1Prccb0HNr0W/r0XPf4l2HQP5PUVGR1q5dq/vvv1%2BxsbFyOByuR3x8vFq3bq3U1FQVFhYqKytLBQUFGjt2rCRpzJgxOnTokLKyslRYWKjU1FS1bdvWFagmTpyojRs3avfu3SooKND8%2BfN19913/6JThAAA4PrmlUew3nvvPdXW1mrdunVat26d29znn3%2ButWvXau7cuRo9erTatWunzMxMRUZGSpLatm2rZ555Rn/84x%2BVmZmpmJgYZWZmymazSZKGDx%2BukydPat68eaqurtbvfvc7zZo1y/I1AgAA72VzXrmFORqVw3He%2BDbtdj8NWvah8e02lrce7uPpEq6Z3e6nFi2aqqzsIofyLULPrUW/rUfPG1dY2I0e2a9XniIEAAD4R2Z5wBo3bpxefvllnT9v/ogOAADAPwLLA1bv3r21fv169e3bV4888oj27t0rzlICAABfYnnAmjlzpt5//32tXbtW/v7%2Beuihh9S/f3%2BtXLlSX375pdXlAAAAGOeRvyK02Wzq06eP%2BvTpo4qKCr344otau3atsrKy1KtXL02ePFm/%2B93vPFEaAADANfPYbRpKSkr0%2Buuv6/XXX9cXX3yhXr166a677tKZM2f0xBNP6ODBg5o7d66nygMAAPjVLA9Yr732ml577TUdOHBALVu21KhRo7RmzRq1b9/e9ZrWrVtryZIlBCwAAOCVLA9Yc%2BfO1YABA5SZmal%2B/frJz6/%2BZWAdOnTQPffcY3VpAAAARlgesD744AO1aNFC5eXlrnBVUFCg7t27y9/fX5LUq1cv9erVy%2BrSAAAAjLD8rwgvXLigIUOG6LnnnnONTZ8%2BXSNHjtTp06etLgcAAMA4ywPWH//4R7Vr10733Xefa%2BzNN99U69atlZaWZnU5AAAAxlkesP7yl7/o8ccfV1hYmGusZcuWeuyxx7R//36rywEAADDO8oBlt9t17ty5euMVFRXc0R0AAPgEywNWv379tHjxYn399deuseLiYqWlpSkhIcHqcgAAAIyz/K8IZ8%2Berfvuu0%2BDBw9WSEiIJOncuXPq3r27UlNTrS4HAADAOMsDVmhoqF599VXt27dPhYWFstvtuuWWW3TnnXfKZrNZXQ4AAIBxHvmqHH9/fyUkJHBKEAAA%2BCTLA5bD4dCqVat06NAhXb58ud6F7e%2B9957VJQEAABhlecB68skndfjwYQ0fPlw33nij1bsHAABodJYHrP3792vDhg2Ki4uzetcAAACWsPw2DcHBwQoNDbV6twAAAJaxPGCNHDlSGzZsUG1trdW7BgAAsITlpwjLy8u1a9cu/fnPf9bNN9%2BsgIAAt/ktW7ZYXRIAAIBRHrlNw4gRIzyxWwAAAEtYHrDS0tKs3iUAAIClLL8GS5JKSkqUkZGhmTNn6ttvv9Xbb7%2Bt48ePe6IUAAAA4ywPWCdOnNC//uu/6tVXX9U777yjS5cu6c0339SYMWOUn59vdTkAAADGWR6wnn76aQ0cOFC7d%2B/WDTfcIElasWKFEhMTtWzZMqvLAQAAMM7ygHXo0CHdd999bl/sbLfb9eCDD%2BrIkSNWlwMAAGCc5QGrrq5OdXV19cYvXrwof39/q8sBAAAwzvKA1bdvXz377LNuIau8vFzp6enq3bu31eUAAAAYZ3nAevzxx3X48GH17dtXVVVV%2Bvd//3cNGDBA33zzjWbPnm11OQAAAMZZfh%2BsiIgI7dy5U7t27dJnn32muro6TZgwQSNHjlSzZs2sLgcAAMA4j9zJPSgoSOPGjfPErgEAABqd5QFr0qRJf3ee7yIEAADezvKA1aZNG7fnNTU1OnHihL744gtNnjzZ6nIAAACM%2B4f5LsLMzEydOXPG4moAAADM88h3EV7NyJEj9dZbb3m6DAAAgGv2DxOwPvnkk191o9Hq6mqNGDFCBw4ccI0tXrxYt956q9tj69atrvldu3Zp4MCBioqKUlJSkr777jvXnNPp1LJly9S7d2/Fx8dr6dKlV70xKgAAwE/5h7jI/cKFC/r88881ceLEX7StqqoqzZw5U4WFhW7jRUVFmjlzpu666y7X2JVbQBQUFGju3LlasGCBunTpoiVLlig1NVXPPvusJOmFF17Qrl27lJGRoZqaGs2aNUuhoaGaNm3aL10qAAC4TlkesCIjI92%2Bh1CSbrjhBt1zzz36/e9/3%2BDtHDt2TDNnzpTT6aw3V1RUpGnTpiksLKze3NatWzV06FCNGjVKkrR06VINGDBAxcXFuvnmm7VlyxalpKQoLi5OkvToo49q9erVBCwAANBglgesp59%2B2sh2Pv74Y91xxx36j//4D0VHR7vGL1y4oLNnz6p9%2B/ZXfV9%2Bfr7uv/9%2B1/PWrVsrMjJS%2Bfn5CggI0OnTp3X77be75mNjY3Xy5EmVlJQoPDzcSO0AAMC3WR6wDh482ODX/m3Q%2BbGfOp1YVFQkm82m9evX64MPPlDz5s113333uU4XXi0ohYaG6syZM3I4HJLkNt%2BqVStJ0pkzZwhYAACgQSwPWPfee6/rFOHfnt778ZjNZtNnn332i7d//Phx2Ww2dejQQffcc48OHjyoJ598Us2aNdOgQYNUWVmpgIAAt/cEBASourpalZWVrud/Oyf9cDF9Q5WUlLjC2hV2e7DxgObv/w/zNwoNYrd7V71Xc6Xn3tZ7b0bPrUW/rUfPfZPlAWv9%2BvVavHixZs2apfj4eAUEBOjTTz/VwoULddddd2nYsGHXtP1Ro0ZpwIABat68uSSpS5cu%2Buqrr7Rt2zYNGjRIgYGB9cJSdXW1goKC3MJUYGCg69/SD1/v01DZ2dnKyMhwG0tKSlJKSsqvXpcvaNGiqadLMCYkpOE/DzCDnluLfluPnvsWj9xodN68eerXr59rrHfv3lq4cKEee%2Bwxt%2Bujfg2bzeYKV1d06NBB%2B/fvl/TDl02Xlpa6zZeWliosLEwRERGSJIfDobZt27r%2BLemqF8z/lPHjxysxMdFtzG4PVlnZxV%2B2mJ/hbZ92TK/fE/z9/RQSEqRz5ypUW8vtO6xAz61Fv61HzxuXpz7cWx6wSkpK6n1djvTDbRTKysquefurV6/WJ598ok2bNrnGjh49qg4dOkiSoqKilJubq9GjR0uSTp8%2BrdOnTysqKkoRERGKjIxUbm6uK2Dl5uYqMjLyF53eCw8Pr/d6h%2BO8amqu718cX1p/bW2dT63HG9Bza9Fv69Fz32L5IZDo6GitWLFCFy5ccI2Vl5crPT1dd9555zVvf8CAATp48KA2btyor7/%2BWv/1X/%2BlnTt3aurUqZKkCRMm6LXXXtP27dt19OhRPfbYY%2Brfv79uvvlm1/yyZct04MABHThwQMuXL//ZL6gGAAD4W5YfwXriiSc0adIk9evXT%2B3bt5fT6dRXX32lsLAwbdmy5Zq337NnT61evVpr1qzR6tWr1aZNGy1fvlwxMTGSpJiYGC1cuFBr1qzR999/rz59%2BmjRokWu90%2BbNk3ffvutkpOT5e/vr7Fjx2rKlCnXXBcAALh%2B2JxXu1NnI/v%2B%2B%2B%2B1a9cuFRUVSZK6deum4cOH/6ILyb2Nw3He%2BDbtdj8NWvah8e02lrce7uPpEq6Z3e6nFi2aqqzsIofyLULPrUW/rUfPG1dY2I0e2a/lR7Ak6aabbtK4ceP0zTffuE7N3XDDDZ4oBQAAwDjLr8G68mXKt99%2Bu0aMGKEzZ85o9uzZmjt3ri5fvmx1OQAAAMZZHrBefPFFvfbaa3rqqadc950aOHCgdu/eXe/eUQAAAN7I8oCVnZ2tefPmafTo0a67tw8bNkyLFy/WG2%2B8YXU5AAAAxlkesL755ht17dq13niXLl3qfb0MAACAN7I8YLVp00affvppvfEPPvjAdcE7AACAN7P8rwinTZumBQsWyOFwyOl06qOPPlJ2drZefPFFPf7441aXAwAAYJzlAWvMmDGqqanRunXrVFlZqXnz5qlly5Z6%2BOGHNWHCBKvLAQAAMM7ygLVr1y4NGTJE48eP13fffSen06nQ0FCrywAAAGg0ll%2BDtXDhQtfF7C1btiRcAQAAn2N5wGrfvr2%2B%2BOILq3cLAABgGctPEXbp0kWPPvqoNmzYoPbt2yswMNBtPi0tzeqSAAAAjLI8YH355ZeKjY2VJO57BQAAfJIlAWvp0qVKTk5WcHCwXnzxRSt2CQAA4DGWXIP1wgsvqKKiwm1s%2BvTpKikpsWL3AAAAlrIkYDmdznpjBw8eVFVVlRW7BwAAsJTlf0UIAADg6whYAAAAhlkWsGw2m1W7AgAA8CjLbtOwePFit3teXb58Wenp6WratKnb67gPFgAA8HaWBKzbb7%2B93j2vYmJiVFZWprKyMitKAAAAsIwlAYt7XwEAgOsJF7kDAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzz%2BoBVXV2tESNG6MCBA66x4uJiTZkyRdHR0Ro2bJj27t3r9p59%2B/ZpxIgRioqK0qRJk1RcXOw2v2nTJiUkJCgmJkZz5sxRRUWFJWsBAAC%2BwasDVlVVlR555BEVFha6xpxOp5KSktSqVSvl5ORo5MiRSk5O1qlTpyRJp06dUlJSkkaPHq0dO3aoZcuWevDBB%2BV0OiVJ77zzjjIyMrRw4UJt3rxZ%2Bfn5Sk9P98j6AACAd/LagHXs2DHdfffd%2Bvrrr93G9%2B/fr%2BLiYi1cuFAdO3bUjBkzFB0drZycHEnS9u3bddttt2nq1Knq1KmT0tLSdPLkSX388ceSpC1btmjy5MkaMGCAevbsqQULFignJ4ejWAAAoMG8NmB9/PHHuuOOO5Sdne02np%2Bfr27duik4ONg1Fhsbq7y8PNd8XFycay4oKEjdu3dXXl6eamtr9emnn7rNR0dH6/Llyzp69GgjrwgAAPgKu6cL%2BLUmTpx41XGHw6Hw8HC3sdDQUJ05c%2BZn58%2BdO6eqqiq3ebvdrubNm7veDwAA8HO8NmD9lIqKCgUEBLiNBQQEqLq6%2BmfnKysrXc9/6v0NUVJSIofD4TZmtwfXC3bXyt/fuw5A2u3eVe/VXOm5t/Xem9Fza9Fv69Fz3%2BRzASswMFDl5eVuY9XV1WrSpIlr/sdhqbq6WiEhIQoMDHQ9//F8UFBQg2vIzs5WRkaG21hSUpJSUlIavA09B/qfAAAQx0lEQVRf1KJFU0%2BXYExISMN/HmAGPbcW/bYePfctPhewIiIidOzYMbex0tJS19GjiIgIlZaW1pvv2rWrmjdvrsDAQJWWlqpjx46SpJqaGpWXlyssLKzBNYwfP16JiYluY3Z7sMrKLv6aJf0kb/u0Y3r9nuDv76eQkCCdO1eh2to6T5dzXaDn1qLf1qPnjctTH%2B59LmBFRUUpKytLlZWVrqNWubm5io2Ndc3n5ua6Xl9RUaEjR44oOTlZfn5%2B6tGjh3Jzc3XHHXdIkvLy8mS329WlS5cG1xAeHl7vdKDDcV41Ndf3L44vrb%2B2ts6n1uMN6Lm16Lf16Llv8a5DIA0QHx%2Bv1q1bKzU1VYWFhcrKylJBQYHGjh0rSRozZowOHTqkrKwsFRYWKjU1VW3btnUFqokTJ2rjxo3avXu3CgoKNH/%2BfN19992/6BQhAAC4vvlcwPL399fatWvlcDg0evRovf7668rMzFRkZKQkqW3btnrmmWeUk5OjsWPHqry8XJmZmbLZbJKk4cOHa8aMGZo3b56mTp2qnj17atasWZ5cEgAA8DI255VbmKNRORznjW/TbvfToGUfGt9uY3nr4T6eLuGa2e1%2BatGiqcrKLnIo3yL03Fr023r0vHGFhd3okf363BEsAAAATyNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhPhuw3n33Xd16661uj5SUFEnSkSNHNG7cOEVFRWnMmDE6fPiw23t37dqlgQMHKioqSklJSfruu%2B88sQQAAOClfDZgHTt2TAMGDNDevXtdj8WLF%2BvSpUuaPn264uLi9MorrygmJkYzZszQpUuXJEkFBQWaO3eukpOTlZ2drXPnzik1NdXDqwEAAN7EZwNWUVGROnfurLCwMNcjJCREb775pgIDA/XYY4%2BpY8eOmjt3rpo2baq3335bkrR161YNHTpUo0aNUpcuXbR06VLt2bNHxcXFHl4RAADwFj4dsNq3b19vPD8/X7GxsbLZbJIkm82mXr16KS8vzzUfFxfnen3r1q0VGRmp/Px8S%2BoGAADezycDltPp1Jdffqm9e/dq8ODBGjhwoJYtW6bq6mo5HA6Fh4e7vT40NFRnzpyRJJWUlPzdeQAAgJ9j93QBjeHUqVOqqKhQQECAVq1apW%2B%2B%2BUaLFy9WZWWla/xvBQQEqLq6WpJUWVn5d%2BcboqSkRA6Hw23Mbg%2BuF9yulb%2B/d%2BVju9276r2aKz33tt57M3puLfptPXrum3wyYLVp00YHDhzQTTfdJJvNpq5du6qurk6zZs1SfHx8vbBUXV2tJk2aSJICAwOvOh8UFNTg/WdnZysjI8NtLCkpyfVXjNerFi2aeroEY0JCGv7zADPoubXot/XouW/xyYAlSc2bN3d73rFjR1VVVSksLEylpaVuc6Wlpa6jSxEREVedDwsLa/C%2Bx48fr8TERLcxuz1YZWUXf8kSfpa3fdoxvX5P8Pf3U0hIkM6dq1BtbZ2ny7ku0HNr0W/r0fPG5akP9z4ZsD788EM9%2Buij%2BvOf/%2Bw68vTZZ5%2BpefPmio2N1XPPPSen0ymbzSan06lDhw7pgQcekCRFRUUpNzdXo0ePliSdPn1ap0%2BfVlRUVIP3Hx4eXu90oMNxXjU11/cvji%2Btv7a2zqfW4w3oubXot/XouW/xrkMgDRQTE6PAwEA98cQTOn78uPbs2aOlS5fqD3/4g4YMGaJz585pyZIlOnbsmJYsWaKKigoNHTpUkjRhwgS99tpr2r59u44eParHHntM/fv318033%2BzhVQEAAG/hkwGrWbNm2rhxo7777juNGTNGc%2BfO1fjx4/WHP/xBzZo107PPPus6SpWfn6%2BsrCwFBwdL%2BiGcLVy4UJmZmZowYYJuuukmpaWleXhFAADAm9icTqfT00VcDxyO88a3abf7adCyD41vt7G89XAfT5dwzex2P7Vo0VRlZRc5lG8Rem4t%2Bm09et64wsJu9Mh%2BffIIFgAAgCcRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMLunC8D1Y%2Biq/8/TJfwibz3cx9MlAAC8FEewrqKqqkpz5sxRXFyc%2Bvbtq%2Beff97TJQEAAC/CEayrWLp0qQ4fPqzNmzfr1KlTmj17tiIjIzVkyBBPlwYAALwAAetHLl26pO3bt%2Bu5555T9%2B7d1b17dxUWFuqll14iYAEAgAYhYP3I0aNHVVNTo5iYGNdYbGys1q9fr7q6Ovn5cVb1esE1YwCAX4uA9SMOh0MtWrRQQECAa6xVq1aqqqpSeXm5WrZs%2BbPbKCkpkcPhcBuz24MVHh5utFZ/f8Ie/h9vCoTvPprQ4Nde%2BTnn590a9Nt69Nw3EbB%2BpKKiwi1cSXI9r66ubtA2srOzlZGR4TaWnJyshx56yEyR/6ekpESTf1Oo8ePHGw9vuLqSkhJlZ2fTcwuVlJRo8%2BYN9Nwi9Nt69Nw3EZd/JDAwsF6QuvK8SZMmDdrG%2BPHj9corr7g9xo8fb7xWh8OhjIyMekfL0HjoufXoubXot/XouW/iCNaPREREqKysTDU1NbLbf2iPw%2BFQkyZNFBIS0qBthIeH8ykEAIDrGEewfqRr166y2%2B3Ky8tzjeXm5qpHjx5c4A4AABqExPAjQUFBGjVqlObPn6%2BCggLt3r1bzz//vCZNmuTp0gAAgJfwnz9//nxPF/GPpnfv3jpy5IiWL1%2Bujz76SA888IDGjBnj6bKuqmnTpoqPj1fTpk09Xcp1g55bj55bi35bj577HpvT6XR6uggAAABfwilCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELC9VVVWlOXPmKC4uTn379tXzzz/v6ZJ8ytmzZ5WSkqL4%2BHglJCQoLS1NVVVVkqTi4mJNmTJF0dHRGjZsmPbu3evhan3P9OnT9fjjj7ueHzlyROPGjVNUVJTGjBmjw4cPe7A631FdXa0FCxbo9ttv129/%2B1utWLFCV%2B49Tc8bx%2BnTpzVjxgz16tVLiYmJ2rRpk2uOnvsWApaXWrp0qQ4fPqzNmzfrqaeeUkZGht5%2B%2B21Pl%2BUTnE6nUlJSVFFRoZdeekkrV67U%2B%2B%2B/r1WrVsnpdCopKUmtWrVSTk6ORo4cqeTkZJ06dcrTZfuMP/3pT9qzZ4/r%2BaVLlzR9%2BnTFxcXplVdeUUxMjGbMmKFLly55sErfsHjxYu3bt08bN27U8uXL9d///d/Kzs6m543o4YcfVnBwsF555RXNmTNHq1at0rvvvkvPfZETXufixYvOHj16OPfv3%2B8ay8zMdN5zzz0erMp3HDt2zNm5c2enw%2BFwjb3xxhvOvn37Ovft2%2BeMjo52Xrx40TU3efJk55o1azxRqs8pKytz9uvXzzlmzBjn7NmznU6n07l9%2B3ZnYmKis66uzul0Op11dXXOQYMGOXNycjxZqtcrKytzduvWzXngwAHX2LPPPut8/PHH6XkjKS8vd3bu3Nn5%2Beefu8aSk5OdCxYsoOc%2BiCNYXujo0aOqqalRTEyMayw2Nlb5%2Bfmqq6vzYGW%2BISwsTBs2bFCrVq3cxi9cuKD8/Hx169ZNwcHBrvHY2Fjl5eVZXaZP%2Bs///E%2BNHDlSt9xyi2ssPz9fsbGxstlskiSbzaZevXrR82uUm5urZs2aKT4%2B3jU2ffp0paWl0fNG0qRJEwUFBemVV17R5cuXdfz4cR06dEhdu3al5z6IgOWFHA6HWrRooYCAANdYq1atVFVVpfLycg9W5htCQkKUkJDgel5XV6etW7eqd%2B/ecjgcCg8Pd3t9aGiozpw5Y3WZPuejjz7SX/7yFz344INu4/S8cRQXF6tNmzbauXOnhgwZon/5l39RZmam6urq6HkjCQwM1Lx585Sdna2oqCgNHTpU/fr107hx4%2Bi5D7J7ugD8chUVFW7hSpLreXV1tSdK8mnp6ek6cuSIduzYoU2bNl219/T92lRVVempp57SvHnz1KRJE7e5n/p5p%2BfX5tKlSzpx4oRefvllpaWlyeFwaN68eQoKCqLnjaioqEgDBgzQfffdp8LCQi1atEh33nknPfdBBCwvFBgYWO%2BX7srzH//PCdcmPT1dmzdv1sqVK9W5c2cFBgbWO0pYXV1N369RRkaGbrvtNrcjh1f81M87Pb82drtdFy5c0PLly9WmTRtJ0qlTp7Rt2za1a9eOnjeCjz76SDt27NCePXvUpEkT9ejRQ2fPntW6det0880303MfQ8DyQhERESorK1NNTY3s9h/%2BEzocDjVp0kQhISEers53LFq0SNu2bVN6eroGDx4s6YfeHzt2zO11paWl9Q7t45f505/%2BpNLSUtd1hVf%2BR/POO%2B9oxIgRKi0tdXs9Pb92YWFhCgwMdIUrSfqnf/onnT59WvHx8fS8ERw%2BfFjt2rVzC03dunXT%2BvXrFRcXR899DNdgeaGuXbvKbre7XfyYm5urHj16yM%2BP/6QmZGRk6OWXX9aKFSs0fPhw13hUVJT%2B%2Bte/qrKy0jWWm5urqKgoT5TpM1588UW98cYb2rlzp3bu3KnExEQlJiZq586dioqK0ieffOK6P5PT6dShQ4fo%2BTWKiopSVVWVvvzyS9fY8ePH1aZNG3reSMLDw3XixAm3I1XHjx9X27Zt6bkP4v/GXigoKEijRo3S/PnzVVBQoN27d%2Bv555/XpEmTPF2aTygqKtLatWt1//33KzY2Vg6Hw/WIj49X69atlZqaqsLCQmVlZamgoEBjx471dNlerU2bNmrXrp3r0bRpUzVt2lTt2rXTkCFDdO7cOS1ZskTHjh3TkiVLVFFRoaFDh3q6bK/WoUMH9e/fX6mpqTp69Kg%2B/PBDZWVlacKECfS8kSQmJuqGG27QE088oS%2B//FL/%2B7//q/Xr1%2Bvee%2B%2Bl5z7I5rwSl%2BFVKioqNH/%2BfP3P//yPmjVrpmnTpmnKlCmeLssnZGVlafny5Ved%2B/zzz3XixAnNnTtX%2Bfn5ateunebMmaPf/va3Flfp267cxf3pp5%2BWJBUUFOipp55SUVGRbr31Vi1YsEDdunXzZIk%2B4fz581q0aJHeffddBQUFaeLEiUpKSpLNZqPnjeRKeCooKFDLli31b//2b5o8eTI990EELAAAAMM4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv3/7XQjw1%2BNGJgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6343540888012280682”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>884</td> <td class=”number”>27.5%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>371</td> <td class=”number”>11.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (28)</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6343540888012280682”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>884</td> <td class=”number”>27.5%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>371</td> <td class=”number”>11.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>173</td> <td class=”number”>5.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT16”><s>FMRKT16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT09”><code>FMRKT09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91012</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKTPTH09”>FMRKTPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1765</td>

</tr> <tr>

<th>Unique (%)</th> <td>55.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.035911</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.0199</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>42.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-954898814274691125”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-954898814274691125”

aria-controls=”quantiles-954898814274691125” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-954898814274691125” aria-controls=”histogram-954898814274691125”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-954898814274691125” aria-controls=”common-954898814274691125”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-954898814274691125” aria-controls=”extreme-954898814274691125”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.010186</td>

</tr> <tr>

<th>Q3</th> <td>0.04389</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.14706</td>

</tr> <tr>

<th>Maximum</th> <td>1.0199</td>

</tr> <tr>

<th>Range</th> <td>1.0199</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.04389</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.070202</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9549</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.863</td>

</tr> <tr>

<th>Mean</th> <td>0.035911</td>

</tr> <tr>

<th>MAD</th> <td>0.040894</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7004</td>

</tr> <tr>

<th>Sum</th> <td>112.44</td>

</tr> <tr>

<th>Variance</th> <td>0.0049283</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

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bAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYbYHrEmTJum5557T6dOn7d40AACALWwPWP369dPjjz%2Buq6%2B%2BWnfddZd27twpy7LsLgMAAMBnbA9Yd999t/75z39q7dq1Cg4O1h133KFrr71WDz/8sL744gu7ywEAADDO6Y%2BNOhwO9e/fX/3791dFRYWefvpprV27VuvXr1fv3r01Y8YM/fGPf/RHaQAAAL%2BaXwKWJBUXF%2BvVV1/Vq6%2B%2Bqs8//1y9e/fWuHHjdOLECT3wwAP66KOPNH/%2BfH%2BVBwAA8IvZHrC2b9%2Bu7du368MPP1Tbtm01duxYrV69Wp06dXIv0759ey1dupSABQAAApLtAWv%2B/PkaOHCg1qxZo2uuuUZBQfUvA%2BvcubNuuOEGu0sDAAAwwvaA9d5776lNmzYqKytzh6v8/Hz16NFDwcHBkqTevXurd%2B/edpcGAABghO2/RXjmzBkNGzZMTzzxhHvs1ltv1ZgxY3T8%2BHG7ywEAADDO9oD117/%2BVZdeeqluvvlm99hrr72m9u3bKz093e5yAAAAjLM9YP373//Wfffdp6ioKPdY27Ztde%2B992rXrl12lwMAAGCc7QHL6XSqvLy83nhFRQV3dAcAAE2C7QHrmmuu0ZIlS/Tll1%2B6x4qKipSenq4BAwbYXQ4AAIBxtv8W4dy5c3XzzTdr6NChCg8PlySVl5erR48emjdvnt3lAAAAGGd7wIqMjNTLL7%2Bs999/XwUFBXI6nbrsssv0u9/9Tg6Hw%2B5yAAAAjPPLn8oJDg7WgAEDOCUIAACaJNsDlsvl0iOPPKI9e/bo22%2B/rXdh%2B9tvv213SQAAAEbZHrAefPBB7d27VyNHjtRFF11k9%2BYBAAB8zvaAtWvXLm3YsEHJycl2bxoAAMAWtt%2BmoWXLloqMjLR7swAAALaxPWCNGTNGGzZsUG1trd2bBgAAsIXtpwjLysqUnZ2td955R5dccolCQkI85rds2WJ3SQAAAEb55TYNo0aN8sdmAQAAbGF7wEpPT7d7kwAAALay/RosSSouLlZmZqbuvvtuff3113rjjTd06NAhf5QCAABgnO0B68iRIxo9erRefvllvfnmmzp37pxee%2B01TZgwQXl5eXaXAwAAYJztAeuhhx7S4MGDtWPHDv3mN7%2BRJK1cuVKDBg3S8uXLG7y%2B6upqjRo1Sh9%2B%2BKF7bMmSJbr88ss9Hlu3bnXPZ2dna/DgwYqPj1dKSopOnTrlnrMsS8uXL1e/fv3Up08fLVu2THV1db9ijwEAQHNje8Das2ePbr75Zo8/7Ox0OnX77bdr3759DVpXVVWV7rrrLhUUFHiMFxYW6u6779bOnTvdjwkTJkiS8vPzNX/%2BfM2ePVtZWVkqLy/XvHnz3K/dtGmTsrOzlZmZqdWrV%2Btvf/ubNm3a9Cv2GAAANDe2B6y6urofPCJ09uxZBQcHe72egwcPavLkyfryyy/rzRUWFuqKK65QVFSU%2BxEWFiZJ2rp1q4YPH66xY8cqLi5Oy5Yt07vvvquioiJJ390mIjU1VcnJyerXr5/uuecePfPMM79wbwEAQHNke8C6%2BuqrtW7dOo%2BQVVZWpoyMDPXr18/r9ezevVt9%2B/ZVVlaWx/iZM2d08uRJderU6Qdfl5eX5/Fnetq3b6/Y2Fjl5eXp5MmTOn78uK666ir3fFJSko4ePari4mKvawMAAM2b7bdpuO%2B%2B%2BzR9%2BnRdffXVqqqq0n//93/r6NGjioiI0EMPPeT1eqZNm/aD44WFhXI4HHr88cf13nvvKSIiQjfffLPGjRsn6bvfYIyOjvZ4TWRkpE6cOCGXyyVJHvPt2rWTJJ04caLe6wAAAH6I7QErJiZGr7zyirKzs/XZZ5%2Bprq5OU6dO1ZgxY9S6detfvf5Dhw7J4XCoc%2BfOuuGGG/TRRx/pwQcfVOvWrTVkyBBVVlbWu3t8SEiIqqurVVlZ6X7%2B/Tnpu4vpvVVcXOwOa%2Bc5nS2NB7TgYL/cZeMXczobf73nexpovW3s6Ktv0Ffz6KlvNMe%2B%2BuVO7mFhYZo0aZJP1j127FgNHDhQERERkqS4uDgdPnxYzz77rIYMGaLQ0NB6Yam6ulphYWEeYSo0NNT97/M1eysrK0uZmZkeYykpKUpNTf3F%2B9UUtGnTyt8leC083Pv/3vAeffUN%2BmoePfWN5tRX2wPW9OnTf3L%2B1/4tQofD4Q5X53Xu3Fm7du2S9N0RtJKSEo/5kpISRUVFKSYmRpLkcrnUsWNH978lKSoqyusapkyZokGDBnmMOZ0tVVp6tmE78zMC7ZuA6f33heDgIIWHh6m8vEK1tdyewxT66hv01Tx66hv%2B7Ku/vtzbHrA6dOjg8bympkZHjhzR559/rhkzZvzq9a9atUoff/yxnnrqKffY/v371blzZ0lSfHy8cnJyNH78eEnS8ePHdfz4ccXHxysmJkaxsbHKyclxB6ycnBzFxsY26PRedHR0veVdrtOqqWneb9ZA2v/a2rqAqjdQ0FffoK/m0VPfaE59bTR/i3DNmjU6ceLEr17/wIEDtX79em3cuFFDhgzRzp079corr7iPjE2dOlU33nijEhIS1LNnTy1dulTXXnutLrnkEvf88uXL9dvf/laStGLFCs2cOfNX1wUAAJoPv1yD9UPGjBmjsWPHavHixb9qPb169dKqVau0evVqrVq1Sh06dNCKFSuUmJgoSUpMTFRaWppWr16tb775Rv379/fY5qxZs/T1119r9uzZCg4O1sSJE3XTTTf9qpoAAEDz0mgC1scff9ygG41%2B34EDBzyeDx48WIMHD/7R5cePH%2B8%2BRXih4OBgzZs3z%2BPu7gAAAA3RKC5yP3PmjA4cOPCj97YCAAAIJLYHrNjYWI%2B/QyhJv/nNb3TDDTfoT3/6k93lAAAAGGd7wGrI3doBAAACke0B66OPPvJ62e//TUAAAIBAYXvAuvHGG92nCC3Lco9fOOZwOPTZZ5/ZXR4AAMCvZnvAevzxx7VkyRLNmTNHffr0UUhIiD755BOlpaVp3LhxGjFihN0lAQAAGGX731pJT0/XggULNHToULVp00atWrVSv379lJaWpmeffVYdOnRwPwAAAAKR7QGruLj4B8NT69atVVpaanc5AAAAxtkesBISErRy5UqdOXPGPVZWVqaMjAz97ne/s7scAAAA42y/BuuBBx7Q9OnTdc0116hTp06yLEuHDx9WVFSU%2B%2B8FAgAABDLbA1aXLl302muvKTs7W4WFhZKk66%2B/XiNHjlRYWJjd5QAAABjnl79FePHFF2vSpEn66quvdMkll0j67m7uAAAATYHt12BZlqXly5frqquu0qhRo3TixAnNnTtX8%2BfP17fffmt3OQAAAMbZHrCefvppbd%2B%2BXX/5y18UEhIiSRo8eLB27NihzMxMu8sBAAAwzvaAlZWVpQULFmj8%2BPHuu7ePGDFCS5Ys0d/%2B9je7ywEAADDO9oD11VdfqXv37vXG4%2BLi5HK57C4HAADAONsDVocOHfTJJ5/UG3/vvffcF7wDAAAEMtt/i3DWrFlatGiRXC6XLMvSBx98oKysLD399NO677777C4HAADAONsD1oQJE1RTU6PHHntMlZWVWrBggdq2bas777xTU6dOtbscAAAA42wPWNnZ2Ro2bJimTJmiU6dOybIsRUZG2l0GAACAz9h%2BDVZaWpr7Yva2bdsSrgAAQJNje8Dq1KmTPv/8c7s3CwAAYBvbTxHGxcXpnnvu0YYNG9SpUyeFhoZ6zKenp9tdEgAAgFG2B6wvvvhCSUlJksR9rwAAQJNkS8BatmyZZs%2BerZYtW%2Brpp5%2B2Y5MAAAB%2BY8s1WJs2bVJFRYXH2K233qri4mI7Ng8AAGArWwKWZVn1xj766CNVVVXZsXkAAABb2f5bhAAAAE0dAQsAAMAw2wKWw%2BGwa1MAAAB%2BZdttGpYsWeJxz6tvv/1WGRkZatWqlcdy3AcLAAAEOlsC1lVXXVXvnleJiYkqLS1VaWmpHSUAAADYxpaAxb2vAABAc8JF7gAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGBXzAqq6u1qhRo/Thhx%2B6x4qKinTTTTcpISFBI0aM0M6dOz1e8/7772vUqFGKj4/X9OnTVVRU5DH/1FNPacCAAUpMTNT999%2BviooKW/YFAAA0DQEdsKqqqnTXXXepoKDAPWZZllJSUtSuXTtt27ZNY8aM0ezZs3Xs2DFJ0rFjx5SSkqLx48frxRdfVNu2bXX77bfLsixJ0ptvvqnMzEylpaVp8%2BbNysvLU0ZGhl/2DwAABKaADVgHDx7U5MmT9eWXX3qM79q1S0VFRUpLS1OXLl102223KSEhQdu2bZMkvfDCC7ryyis1c%2BZMde3aVenp6Tp69Kh2794tSdqyZYtmzJihgQMHqlevXlq0aJG2bdvGUSwAAOC1gA1Yu3fvVt%2B%2BfZWVleUxnpeXpyuuuEItW7Z0jyUlJSk3N9c9n5yc7J4LCwtTjx49lJubq9raWn3yySce8wkJCfr222%2B1f/9%2BH%2B8RAABoKpz%2BLuCXmjZt2g%2BOu1wuRUdHe4xFRkbqxIkTPztfXl6uqqoqj3mn06mIiAj36wEAAH5OwAasH1NRUaGQkBCPsZCQEFVXV//sfGVlpfv5j73eG8XFxXK5XB5jTmfLesHu1woODqwDkE5n46/3fE8DrbeNHX31DfpqHj31jebY1yYXsEJDQ1VWVuYxVl1drRYtWrjnLwxL1dXVCg8PV2hoqPv5hfNhYWFe15CVlaXMzEyPsZSUFKWmpnq9jqaoTZtW/i7Ba%2BHh3v/3hvfoq2/QV/PoqW80p742uYAVExOjgwcPeoyVlJS4jx7FxMSopKSk3nz37t0VERGh0NBQlZSUqEuXLpKkmpoalZWVKSoqyusapkyZokGDBnmMOZ0tVVp69pfs0o8KtG8CpvffF4KDgxQeHqby8grV1tb5u5wmg776Bn01j576hj/76q8v900uYMXHx2v9%2BvWqrKx0H7XKyclRUlKSez4nJ8e9fEVFhfbt26fZs2crKChIPXv2VE5Ojvr27StJys3NldPpVFxcnNc1REdH1zsd6HKdVk1N836zBtL%2B19bWBVS9gYK%2B%2BgZ9NY%2Be%2BkZz6mtgHQLxQp8%2BfdS%2BfXvNmzdPBQUFWr9%2BvfLz8zVx4kRJ0oQJE7Rnzx6tX79eBQUFmjdvnjp27OgOVNOmTdPGjRu1Y8cO5efna%2BHChZo8eXKDThECAIDmrckFrODgYK1du1Yul0vjx4/Xq6%2B%2BqjVr1ig2NlaS1LFjRz366KPatm2bJk6cqLKyMq1Zs0YOh0OSNHLkSN12221asGCBZs6cqV69emnOnDn%2B3CUAABBgHNb5W5jDp1yu08bX6XQGacjyfxlfr6%2B8fmd/f5fws5zOILVp00qlpWebzWFsO9BX36Cv5tFT3/BnX6OiLrJ1e%2Bc1uSNYAAAA/kbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwrMkGrL///e%2B6/PLLPR6pqamSpH379mnSpEmKj4/XhAkTtHfvXo/XZmdna/DgwYqPj1dKSopOnTrlj10AAAABqskGrIMHD2rgwIHauXOn%2B7FkyRKdO3dOt956q5KTk/XSSy8pMTFRt912m86dOydJys/P1/z58zV79mxlZWWpvLxc8%2BbN8/PeAACAQNJkA1ZhYaG6deumqKgo9yM8PFyvvfaaQkNDde%2B996pLly6aP3%2B%2BWrVqpTfeeEOStHXrVg0fPlxjx45VXFycli1bpnfffVdFRUV%2B3iMAABAomnTA6tSpU73xvLw8JSUlyeFwSJIcDod69%2B6t3Nxc93xycrJ7%2Bfbt2ys2NlZ5eXm21A0AAAKf098F%2BIJlWfriiy%2B0c%2BdOrVu3TrW1tRo2bJhSU1Plcrl02WWXeSwfGRmpgoICSVJxcbGio6PrzZ84ccLr7RcXF8vlcnmMOZ0t66331woODqx87HQ2/nrP9zTQetvY0VffoK/m0VPfaI7ZK4Q5AAANIklEQVR9bZIB69ixY6qoqFBISIgeeeQRffXVV1qyZIkqKyvd498XEhKi6upqSVJlZeVPznsjKytLmZmZHmMpKSnui%2BybqzZtWvm7BK%2BFh4f5u4Qmib76Bn01j576RnPqa5MMWB06dNCHH36oiy%2B%2BWA6HQ927d1ddXZ3mzJmjPn361AtL1dXVatGihSQpNDT0B%2BfDwrz/oZgyZYoGDRrkMeZ0tlRp6dlfuEc/LNC%2BCZjef18IDg5SeHiYyssrVFtb5%2B9ymgz66hv01Tx66hv%2B7Ku/vtw3yYAlSRERER7Pu3TpoqqqKkVFRamkpMRjrqSkxH36LiYm5gfno6KivN52dHR0vdOBLtdp1dQ07zdrIO1/bW1dQNUbKOirb9BX8%2BipbzSnvgbWIRAv/etf/1Lfvn1VUVHhHvvss88UERGhpKQkffzxx7IsS9J312vt2bNH8fHxkqT4%2BHjl5OS4X3f8%2BHEdP37cPQ8AAPBzmmTASkxMVGhoqB544AEdOnRI7777rpYtW6ZbbrlFw4YNU3l5uZYuXaqDBw9q6dKlqqio0PDhwyVJU6dO1fbt2/XCCy9o//79uvfee3Xttdfqkksu8fNeAQCAQNEkA1br1q21ceNGnTp1ShMmTND8%2BfM1ZcoU3XLLLWrdurXWrVunnJwcjR8/Xnl5eVq/fr1atmwp6btwlpaWpjVr1mjq1Km6%2BOKLlZ6e7uc9AgAAgcRhnT9XBp9yuU4bX6fTGaQhy/9lfL2%2B8vqd/f1dws9yOoPUpk0rlZaebTbXCdiBvvoGfTWPnvqGP/saFXWRrds7r0kewQIAAPAnAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwp78LQPMx/JH/5%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%2B9p8oqSXQkND6wWp889btGjh1TqmTJmil156yeMxZcoU47W6XC5lZmbWO1qGX46e%2BgZ99Q36ah499Y3m2FeOYF0gJiZGpaWlqqmpkdP5XXtcLpdatGih8PBwr9YRHR3dbBI6AACojyNYF%2BjevbucTqdyc3PdYzk5OerZsycXuAMAAK%2BQGC4QFhamsWPHauHChcrPz9eOHTv05JNPavr06f4uDQAABIjghQsXLvR3EY1Nv379tG/fPq1YsUIffPCB/uu//ksTJkzwd1k/qFWrVurTp49atWrl71KaDHrqG/TVN%2BirefTUN5pbXx2WZVn%2BLgIAAKAp4RQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACViNXVVWl%2B%2B%2B/X8nJybr66qv15JNP/uiy%2B/bt06RJkxQfH68JEyZo7969NlYaOBrS03feeUdjxoxRYmKiRo8erbffftvGSgNLQ/p63ldffaXExER9%2BOGHNlQYmBrS1wMHDmjq1Knq1auXRo8erV27dtlYaeBoSE///ve/a/jw4UpMTNTUqVP16aef2lhpYKqurtaoUaN%2B8n3dLD6vLDRqaWlp1ujRo629e/dab731lpWYmGi9/vrr9ZY7e/as1b9/f%2Buhhx6yDh48aC1evNj6/e9/b509e9YPVTdu3vb0s88%2Bs3r06GFt3rzZOnz4sLV161arR48e1meffeaHqhs/b/v6fbNmzbK6detm7dq1y6YqA4%2B3fS0vL7d%2B//vfWw888IB1%2BPBha9WqVVZSUpJVUlLih6obN297%2Bvnnn1s9e/a0Xn75ZevIkSPWokWLrP79%2B1vnzp3zQ9WBobKy0kpJSfnJ93Vz%2BbwiYDViZ8%2BetXr27OnxQ7pmzRrrhhtuqLfsCy%2B8YA0aNMiqq6uzLMuy6urqrCFDhljbtm2zrd5A0JCeZmRkWLNmzfIYmzlzprVy5Uqf1xloGtLX87Zv325dd911BKyf0JC%2Bbt682Ro8eLBVU1PjHhs/frz1zjvv2FJroGhITzdt2mSNGzfO/fz06dNWt27drPz8fFtqDTQFBQXWn/70J2v06NE/%2Bb5uLp9XnCJsxPbv36%2BamholJia6x5KSkpSXl6e6ujqPZfPy8pSUlCSHwyFJcjgc6t27t3Jzc22tubFrSE/HjRune%2B65p946Tp8%2B7fM6A01D%2BipJpaWlysjIUFpamp1lBpyG9HX37t36wx/%2BoODgYPfYtm3b9J//%2BZ%2B21RsIGtLTiIgIHTx4UDk5Oaqrq9NLL72k1q1b6z/%2B4z/sLjsg7N69W3379lVWVtZPLtdcPq%2Bc/i4AP87lcqlNmzYKCQlxj7Vr105VVVUqKytT27ZtPZa97LLLPF4fGRmpgoIC2%2BoNBA3paZcuXTxeW1BQoA8%2B%2BEDXXXedbfUGiob0VZIeeughjRs3Tl27drW71IDSkL4WFRWpV69eevDBB/WPf/xDHTp00Ny5c5WUlOSP0huthvR0xIgR%2Bsc//qFp06YpODhYQUFBWrdunS6%2B%2BGJ/lN7oTZs2zavlmsvnFUewGrGKigqP/wlIcj%2Bvrq72atkLl2vuGtLT7zt16pTuuOMO9e7dW3/4wx98WmMgakhf33//feXk5Oj222%2B3rb5A1ZC%2Bnjt3TuvXr1dUVJSeeOIJXXXVVZo1a5aOHz9uW72BoCE9LS0tlcvl0oIFC/T8889rzJgxmjdvnr7%2B%2Bmvb6m2KmsvnFQGrEQsNDa33A3f%2BeYsWLbxa9sLlmruG9PS8kpISzZgxQ5ZlafXq1QoK4m1zIW/7WllZqQULFugvf/kLP5teaMjPa3BwsLp3767U1FRdccUVmjNnjjp16qTt27fbVm8gaEhPly9frm7duun666/XlVdeqcWLFyssLEzbtm2zrd6mqLl8XvFJ0YjFxMSotLRUNTU17jGXy6UWLVooPDy83rIlJSUeYyUlJYqOjral1kDRkJ5K0smTJ3X99derurpaW7ZsqXeqC9/xtq/5%2BfkqKipSamqqEhMT3dfB/PnPf9aCBQtsr7uxa8jPa1RUlDp37uwx1qlTJ45gXaAhPf30008VFxfnfh4UFKS4uDgdO3bMtnqboubyeUXAasS6d%2B8up9PpceFfTk6OevbsWe8oSnx8vD7%2B%2BGNZliVJsixLe/bsUXx8vK01N3YN6em5c%2Bd0yy23KCgoSFu3blVMTIzd5QYMb/vaq1cvvfXWW3rllVfcD0lasmSJ/ud//sf2uhu7hvy8JiQk6MCBAx5jhw4dUocOHWypNVA0pKfR0dEqLCz0GPviiy/UsWNHW2ptqprL5xUBqxELCwvT2LFjtXDhQuXn52vHjh168sknNX36dEnffeuqrKyUJA0bNkzl5eVaunSpDh48qKVLl6qiokLDhw/35y40Og3p6bp16/Tll1/qf//3f91zLpeL3yL8Ad72tUWLFrr00ks9HtJ332gjIyP9uQuNUkN%2BXq%2B77jodOHBAjz76qI4cOaJVq1apqKhIY8aM8ecuNDoN6enkyZP1/PPP65VXXtGRI0e0fPlyHTt2TOPGjfPnLgSkZvl55debROBnnTt3zrr33nuthIQE6%2Bqrr7Y2bdrknuvWrZvHfUPy8vKssWPHWj179rQmTpxoffrpp36ouPHztqdDhw61unXrVu8xd%2B5cP1XeuDXkZ/X7uA/WT2tIX//9739b48aNs6688kprzJgx1u7du/1QcePXkJ4%2B//zz1rBhw6yEhARr6tSp1t69e/1QceC58H3dHD%2BvHJb1f8foAAAAYASnCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsP8PNTh5bWWXGzwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-954898814274691125”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.083633018315631</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0851136266916333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0501027105566411</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0418130122093996</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.111308993766696</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.118793062485151</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.141063619692481</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.146756677428823</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.105887335874629</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1754)</td> <td class=”number”>1754</td> <td class=”number”>54.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-954898814274691125”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1360</td> <td class=”number”>42.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000736921666946287</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000815750510863757</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000920403504896547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000967346259658952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.616903146206046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.618429189857761</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.662690523525514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.744047619047619</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.01988781234064</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKTPTH16”>FMRKTPTH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2226</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.05841</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.4451</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>27.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8213180129407926978”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8213180129407926978,#minihistogram-8213180129407926978”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8213180129407926978”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8213180129407926978”

aria-controls=”quantiles-8213180129407926978” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8213180129407926978” aria-controls=”histogram-8213180129407926978”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8213180129407926978” aria-controls=”common-8213180129407926978”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8213180129407926978” aria-controls=”extreme-8213180129407926978”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8213180129407926978”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.029681</td>

</tr> <tr>

<th>Q3</th> <td>0.071945</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.21455</td>

</tr> <tr>

<th>Maximum</th> <td>1.4451</td>

</tr> <tr>

<th>Range</th> <td>1.4451</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.071945</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.098157</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6805</td>

</tr> <tr>

<th>Kurtosis</th> <td>45.472</td>

</tr> <tr>

<th>Mean</th> <td>0.05841</td>

</tr> <tr>

<th>MAD</th> <td>0.056582</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1903</td>

</tr> <tr>

<th>Sum</th> <td>182.94</td>

</tr> <tr>

<th>Variance</th> <td>0.0096348</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8213180129407926978”>

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AyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDbC9YV111lV544QUdPnzY7pcGAACwhe0Fa9CgQXriiSd0ySWX6I477tDmzZtlWZbdYwAAAJwxthesO%2B%2B8U//85z%2B1YsUKhYWF6bbbbtNll12mRx55RF9%2B%2BaXd4wAAABjn8seLhoSEaPDgwRo8eLBqamr07LPPasWKFVq1apX69%2B%2BvadOm6Q9/%2BIM/RgMAAGg1vxQsSSovL9frr7%2Bu119/XV988YX69%2B%2Bv8ePH68CBA7rvvvv08ccfa%2B7cuf4aDwAA4LTZXrA2btyojRs36qOPPlLHjh01btw4LVu2TN26dfM8pnPnzlq0aBEFCwAABCTbC9bcuXM1dOhQLV%2B%2BXJdeeqlCQ5t/DKx79%2B667rrr7B4NAADACNsL1vvvv68OHTqoqqrKU66Ki4vVp08fhYWFSZL69%2B%2Bv/v372z0aAACAEbb/FuGRI0c0cuRIPfnkk561m2%2B%2BWWPHjtX%2B/fvtHgcAAMA42wvWX/7yF5133nm68cYbPWtvvPGGOnfurOzsbLvHAQAAMM72gvXvf/9b99xzj2JjYz1rHTt21N13360tW7bYPQ4AAIBxthcsl8ul6urqZus1NTVc0R0AAAQF2wvWpZdeqoULF%2Bqbb77xrJWVlSk7O1tDhgyxexwAAADjbP8twtmzZ%2BvGG2/U5ZdfrqioKElSdXW1%2BvTpozlz5tg9DgAAgHG2F6yYmBi9%2Buqr%2BuCDD1RSUiKXy6Xzzz9fv/3tbxUSEmL3OAAAAMb55U/lhIWFaciQIbwlCAAAgpLtBcvtduvRRx/Vtm3b9MMPPzT7YPs777xj90gAAABG2V6w7r//fm3fvl2jR4/Wr3/9a7tfHgAA4IyzvWBt2bJFq1evVlpamt0vDQAAYAvbL9PQtm1bxcTE2P2yAAAAtrG9YI0dO1arV69WY2Oj3S8NAABgC9vfIqyqqlJ%2Bfr7effddnXvuuQoPD/favm7dOrtHAgAAMMovl2kYM2aMP14WAADAFrYXrOzsbLtfEgAAwFa2fwZLksrLy5Wbm6s777xT3333nf72t79pz549/hgFAADAONsL1tdff60rr7xSr776qt566y0dO3ZMb7zxhiZMmKCioiK7xwEAADDO9oL14IMPavjw4dq0aZN%2B9atfSZIefvhhDRs2TEuWLGnx/urr6zVmzBh99NFHnrWFCxfqggsu8LqtX7/esz0/P1/Dhw9XUlKSMjIydOjQIc82y7K0ZMkSDRo0SAMGDNDixYvV1NTUisQAAMBpbC9Y27Zt04033uj1h51dLpduvfVW7dixo0X7qqur0x133KGSkhKv9dLSUt15553avHmz5zZhwgRJUnFxsebOnauZM2cqLy9P1dXVmjNnjue5a9asUX5%2BvnJzc7Vs2TL99a9/1Zo1a1qRGAAAOI3tBaupqemUZ4SOHj2qsLAwn/eze/duXX311frmm2%2BabSstLdWFF16o2NhYzy0yMlKStH79eo0aNUrjxo1TYmKiFi9erPfee09lZWWSfrxMRGZmptLS0jRo0CDdddddeu65504zLQAAcCLbC9Yll1yilStXepWsqqoq5eTkaNCgQT7vZ%2BvWrRo4cKDy8vK81o8cOaKDBw%2BqW7dup3xeUVGR15/p6dy5sxISElRUVKSDBw9q//79uvjiiz3bU1NTtXfvXpWXl/s8GwAAcDbbL9Nwzz33aOrUqbrkkktUV1en//7v/9bevXsVHR2tBx980Of9TJky5ZTrpaWlCgkJ0RNPPKH3339f0dHRuvHGGzV%2B/HhJP/4GY1xcnNdzYmJidODAAbndbkny2t6pUydJ0oEDB5o9DwAA4FRsL1jx8fF67bXXlJ%2Bfr88//1xNTU2aPHmyxo4dq/bt27d6/3v27FFISIi6d%2B%2Bu6667Th9//LHuv/9%2BtW/fXiNGjFBtbW2zq8eHh4ervr5etbW1nvsnbpN%2B/DC9r8rLyz1l7TiXq63xghYW5perbJw2l6t18x7PG2i5W8Npmckb/JyW2Wl5JWdmPhW/XMk9MjJSV1111RnZ97hx4zR06FBFR0dLkhITE/XVV1/p%2Beef14gRIxQREdGsLNXX1ysyMtKrTEVERHj%2BfXxmX%2BXl5Sk3N9drLSMjQ5mZmaedKxh06NDOyH6ionz/WgQLp2Umb/BzWman5ZWcmflEthesqVOn/uz21v4twpCQEE%2B5Oq579%2B7asmWLpB/PoFVUVHhtr6ioUGxsrOLj4yVJbrdbXbt29fxbkmJjY32eYdKkSRo2bJjXmsvVVpWVR1sW5hcE2k8Hrc0fFhaqqKhIVVfXqLHRGZfOcFpm8gY/p2V2Wl7p7Mts6of7lrK9YHXp0sXrfkNDg77%2B%2Bmt98cUXmjZtWqv3v3TpUn3yySd65plnPGs7d%2B5U9%2B7dJUlJSUkqKChQenq6JGn//v3av3%2B/kpKSFB8fr4SEBBUUFHgKVkFBgRISElr09l5cXFyzx7vdh9XQ4P9vNH8ylb%2Bxsclx/5dOy0ze4Oe0zE7LKzkz84nOmr9FuHz5ch04cKDV%2Bx86dKhWrVqlp556SiNGjNDmzZv12muvec6MTZ48Wddff72Sk5PVt29fLVq0SJdddpnOPfdcz/YlS5boN7/5jSTpoYce0vTp01s9FwAAcA6/fAbrVMaOHatx48ZpwYIFrdpPv379tHTpUi1btkxLly5Vly5d9NBDDyklJUWSlJKSoqysLC1btkzff/%2B9Bg8e7PWaM2bM0HfffaeZM2cqLCxMEydO1A033NCqmQAAgLOcNQXrk08%2BadGFRk%2B0a9cur/vDhw/X8OHDf/Lx6enpnrcITxYWFqY5c%2BZ4Xd0dAACgJc6KD7kfOXJEu3bt%2BslrWwEAAAQS2wtWQkKC198hlKRf/epXuu666/THP/7R7nEAAACMs71gteRq7QAAAIHI9oL18ccf%2B/zYE/8mIAAAQKCwvWBdf/31nrcILcvyrJ%2B8FhISos8//9zu8QAAAFrN9oL1xBNPaOHChZo1a5YGDBig8PBwffrpp8rKytL48eN1xRVX2D0SAACAUbb/rZXs7GzNmzdPl19%2BuTp06KB27dpp0KBBysrK0vPPP68uXbp4bgAAAIHI9oJVXl5%2ByvLUvn17VVZW2j0OAACAcbYXrOTkZD388MM6cuSIZ62qqko5OTn67W9/a/c4AAAAxtn%2BGaz77rtPU6dO1aWXXqpu3brJsix99dVXio2N9fy9QAAAgEBme8Hq0aOH3njjDeXn56u0tFSSdO2112r06NGKjIy0exwAAADj/PK3CM855xxdddVV%2Bvbbb3XuuedK%2BvFq7gAAAMHA9s9gWZalJUuW6OKLL9aYMWN04MABzZ49W3PnztUPP/xg9zgAAADG2V6wnn32WW3cuFF//vOfFR4eLkkaPny4Nm3apNzcXLvHAQAAMM72gpWXl6d58%2BYpPT3dc/X2K664QgsXLtRf//pXu8cBAAAwzvaC9e2336p3797N1hMTE%2BV2u%2B0eBwAAwDjbC1aXLl306aefNlt///33PR94BwAACGS2/xbhjBkzNH/%2BfLndblmWpQ8//FB5eXl69tlndc8999g9DgAAgHG2F6wJEyaooaFBjz/%2BuGprazVv3jx17NhRt99%2BuyZPnmz3OAAAAMbZXrDy8/M1cuRITZo0SYcOHZJlWYqJibF7DAAAgDPG9s9gZWVleT7M3rFjR8oVAAAIOrYXrG7duumLL76w%2B2UBAABsY/tbhImJibrrrru0evVqdevWTREREV7bs7Oz7R4JAADAKNsL1pdffqnU1FRJ4rpXAAAgKNlSsBYvXqyZM2eqbdu2evbZZ%2B14SQAAAL%2Bx5TNYa9asUU1NjdfazTffrPLycjteHgAAwFa2FCzLspqtffzxx6qrq7Pj5QEAAGxl%2B28RAgAABDsKFgAAgGG2FayQkBC7XgoAAMCvbLtMw8KFC72uefXDDz8oJydH7dq183oc18ECAACBzpaCdfHFFze75lVKSooqKytVWVlpxwgAAAC2saVgce0rAADgJHzIHQAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCwgC9Y9fX1GjNmjD766CPPWllZmW644QYlJyfriiuu0ObNm72e88EHH2jMmDFKSkrS1KlTVVZW5rX9mWee0ZAhQ5SSkqJ7771XNTU1tmQBAADBIaALVl1dne644w6VlJR41izLUkZGhjp16qQNGzZo7Nixmjlzpvbt2ydJ2rdvnzIyMpSenq6XX35ZHTt21K233irLsiRJb731lnJzc5WVlaW1a9eqqKhIOTk5fskHAAACU8AWrN27d%2Bvqq6/WN99847W%2BZcsWlZWVKSsrSz169NAtt9yi5ORkbdiwQZL00ksv6aKLLtL06dPVs2dPZWdna%2B/evdq6daskad26dZo2bZqGDh2qfv36af78%2BdqwYQNnsQAAgM8CtmBt3bpVAwcOVF5entd6UVGRLrzwQrVt29azlpqaqsLCQs/2tLQ0z7bIyEj16dNHhYWFamxs1Keffuq1PTk5WT/88IN27tx5hhMBAIBg4fL3AKdrypQpp1x3u92Ki4vzWouJidGBAwd%2BcXt1dbXq6uq8trtcLkVHR3ueDwAA8EsCtmD9lJqaGoWHh3uthYeHq76%2B/he319bWeu7/1PN9UV5eLrfb7bXmcrVtVuxaKywssE5Aulytm/d43kDL3RpOy0ze4Oe0zE7LKzkz86kEXcGKiIhQVVWV11p9fb3atGnj2X5yWaqvr1dUVJQiIiI890/eHhkZ6fMMeXl5ys3N9VrLyMhQZmamz/sIRh06tDOyn6go378WwcJpmckb/JyW2Wl5JWdmPlHQFaz4%2BHjt3r3ba62iosJz9ig%2BPl4VFRXNtvfu3VvR0dGKiIhQRUWFevToIUlqaGhQVVWVYmNjfZ5h0qRJGjZsmNeay9VWlZVHTyfSTwq0nw5amz8sLFRRUZGqrq5RY2OToanObk7LTN7g57TMTssrnX2ZTf1w31JBV7CSkpK0atUq1dbWes5aFRQUKDU11bO9oKDA8/iamhrt2LFDM2fOVGhoqPr27auCggINHDhQklRYWCiXy6XExESfZ4iLi2v2dqDbfVgNDf7/RvMnU/kbG5sc93/ptMzkDX5Oy%2By0vJIzM58osE6B%2BGDAgAHq3Lmz5syZo5KSEq1atUrFxcWaOHGiJGnChAnatm2bVq1apZKSEs2ZM0ddu3b1FKopU6boqaee0qZNm1RcXKwHHnhAV199dYveIgQAAM4WdAUrLCxMK1askNvtVnp6ul5//XUtX75cCQkJkqSuXbvqscce04YNGzRx4kRVVVVp%2BfLlCgkJkSSNHj1at9xyi%2BbNm6fp06erX79%2BmjVrlj8jAQCAABNiHb%2BEOc4ot/uw8X26XKEaseRfxvd7prx5%2B%2BBWPd/lClWHDu1UWXnUMaednZaZvMHPaZmdllc6%2BzLHxv7aL68bdGewAAAA/I2CBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgWNAWrL///e%2B64IILvG6ZmZmSpB07duiqq65SUlKSJkyYoO3bt3s9Nz8/X8OHD1dSUpIyMjJ06NAhf0QAAAABKmgL1u7duzV06FBt3rzZc1u4cKGOHTumm2%2B%2BWWlpaXrllVeUkpKiW265RceOHZMkFRcXa%2B7cuZo5c6by8vJUXV2tOXPm%2BDkNAAAIJEFbsEpLS9WrVy/FxsZ6blFRUXrjjTcUERGhu%2B%2B%2BWz169NDcuXPVrl07/e1vf5MkrV%2B/XqNGjdK4ceOUmJioxYsX67333lNZWZmfEwEAgEAR1AWrW7duzdaLioqUmpqqkJAQSVJISIj69%2B%2BvwsJCz/a0tDTP4zt37qyEhAQVFRXZMjcAAAh8Ln8PcCZYlqUvv/xSmzdv1sqVK9XY2KiRI0cqMzNTbrdb559/vtfjY2JiVFJSIkkqLy9XXFxcs%2B0HDhxFt2SJAAAN1klEQVTw%2BfXLy8vldru91lyuts3221phYYHVj12u1s17PG%2Bg5W4Np2Umb/BzWman5ZWcmflUgrJg7du3TzU1NQoPD9ejjz6qb7/9VgsXLlRtba1n/UTh4eGqr6%2BXJNXW1v7sdl/k5eUpNzfXay0jI8PzIXun6tChnZH9REVFGtlPIHFaZvIGP6dldlpeyZmZTxSUBatLly766KOPdM455ygkJES9e/dWU1OTZs2apQEDBjQrS/X19WrTpo0kKSIi4pTbIyN9/0aZNGmShg0b5rXmcrVVZeXR00x0aoH200Fr84eFhSoqKlLV1TVqbGwyNNXZzWmZyRv8nJbZaXmlsy%2BzqR/uWyooC5YkRUdHe93v0aOH6urqFBsbq4qKCq9tFRUVnrfv4uPjT7k9NjbW59eOi4tr9nag231YDQ3%2B/0bzJ1P5GxubHPd/6bTM5A1%2BTsvstLySMzOfKLBOgfjoX//6lwYOHKiamhrP2ueff67o6Gilpqbqk08%2BkWVZkn78vNa2bduUlJQkSUpKSlJBQYHnefv379f%2B/fs92wEAAH5JUBaslJQURURE6L777tOePXv03nvvafHixbrppps0cuRIVVdXa9GiRdq9e7cWLVqkmpoajRo1SpI0efJkbdy4US%2B99JJ27typu%2B%2B%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%2BvTp/h4LNhv16P/z9wgt8ubtg/09AgDg/1CwTmHx4sXavn271q5dq3379mn27NlKSEjQyJEj/T0aAAAIABSskxw7dkwvvfSSnnzySfXp00d9%2BvRRSUmJnnvuOQoWzmqBdMaNs20Agh2fwTrJzp071dDQoJSUFM9aamqqioqK1NTU5MfJAABAoOAM1kncbrc6dOig8PBwz1qnTp1UV1enqqoqdezY8Rf3UV5eLrfb7bXmcrVVXFyc0VnDwujHCEyBdLYtEP39riH%2BHuEnHT9uOeX45bS8kjMznwoF6yQ1NTVe5UqS5359fb1P%2B8jLy1Nubq7X2syZM3XbbbeZGfL/lJeXa9pvSjRp0iTj5e1sVF5erry8PMfklZyXmbzBr7y8XGvXrnZMZqfllZyZ%2BVScXS9PISIiolmROn6/TZs2Pu1j0qRJeuWVV7xukyZNMj6r2%2B1Wbm5us7NlwcppeSXnZSZv8HNaZqfllZyZ%2BVQ4g3WS%2BPh4VVZWqqGhQS7Xj/89brdbbdq0UVRUlE/7iIuLc3RrBwDA6TiDdZLevXvL5XKpsLDQs1ZQUKC%2BffsqNJT/LgAA8MtoDCeJjIzUuHHj9MADD6i4uFibNm3S008/ralTp/p7NAAAECDCHnjggQf8PcTZZtCgQdqxY4ceeughffjhh/qv//ovTZgwwd9jnVK7du00YMAAtWvXzt%2Bj2MJpeSXnZSZv8HNaZqfllZyZ%2BWQhlmVZ/h4CAAAgmPAWIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwCtZZrq6uTvfee6/S0tJ0ySWX6Omnn/7Jx%2B7YsUNXXXWVkpKSNGHCBG3fvt3GSc1oSd53331XY8eOVUpKiq688kq98847Nk5qTksyH/ftt98qJSVFH330kQ0TmtWSvLt27dLkyZPVr18/XXnlldqyZYuNk5rRkrx///vfNWrUKKWkpGjy5Mn67LPPbJzUvPr6eo0ZM%2BZnv0%2BD4bh1nC95g%2BW4dZwvmY8L5OPWabFwVsvKyrKuvPJKa/v27dbbb79tpaSkWG%2B%2B%2BWazxx09etQaPHiw9eCDD1q7d%2B%2B2FixYYP3ud7%2Bzjh496oepT5%2BveT///HOrT58%2B1tq1a62vvvrKWr9%2BvdWnTx/r888/98PUreNr5hPNmDHD6tWrl7VlyxabpjTH17zV1dXW7373O%2Bu%2B%2B%2B6zvvrqK2vp0qVWamqqVVFR4YepT5%2Bveb/44gurb9%2B%2B1quvvmp9/fXX1vz5863Bgwdbx44d88PUrVdbW2tlZGT87PdpsBy3LMu3vMF03LIs3zKfKJCPW6eDgnUWO3r0qNW3b1%2Bvb8bly5db1113XbPHvvTSS9awYcOspqYmy7Isq6mpyRoxYoS1YcMG2%2BZtrZbkzcnJsWbMmOG1Nn36dOvhhx8%2B43Oa1JLMx23cuNG65pprAvJA1ZK8a9eutYYPH241NDR41tLT0613333XlllNaEneNWvWWOPHj/fcP3z4sNWrVy%2BruLjYlllNKikpsf74xz9aV1555c9%2BnwbDccuyfM8bLMcty/I983GBfNw6XbxFeBbbuXOnGhoalJKS4llLTU1VUVGRmpqavB5bVFSk1NRUhYSESJJCQkLUv39/FRYW2jpza7Qk7/jx43XXXXc128fhw4fP%2BJwmtSSzJFVWVionJ0dZWVl2jmlMS/Ju3bpVv//97xUWFuZZ27Bhg/7zP//TtnlbqyV5o6OjtXv3bhUUFKipqUmvvPKK2rdvr//4j/%2Bwe%2BxW27p1qwYOHKi8vLyffVwwHLck3/MGy3FL8j2zFPjHrdPl8vcA%2BGlut1sdOnRQeHi4Z61Tp06qq6tTVVWVOnbs6PXY888/3%2Bv5MTExKikpsW3e1mpJ3h49eng9t6SkRB9%2B%2BKGuueYa2%2BY1oSWZJenBBx/U%2BPHj1bNnT7tHNaIlecvKytSvXz/df//9%2Bsc//qEuXbpo9uzZSk1N9cfop6Ulea%2B44gr94x//0JQpUxQWFqbQ0FCtXLlS55xzjj9Gb5UpU6b49LhgOG5JvucNluOW5HtmKfCPW6eLM1hnsZqaGq8DsyTP/fr6ep8ee/LjzmYtyXuiQ4cO6bbbblP//v31%2B9///ozOaFpLMn/wwQcqKCjQrbfeatt8prUk77Fjx7Rq1SrFxsbqySef1MUXX6wZM2Zo//79ts3bWi3JW1lZKbfbrXnz5unFF1/U2LFjNWfOHH333Xe2zWu3YDhuna5APm61RDAct04XBessFhER0exAc/x%2BmzZtfHrsyY87m7Uk73EVFRWaNm2aLMvSsmXLFBoaWN/Svmaura3VvHnz9Oc//zmgvqYna8nXOCwsTL1791ZmZqYuvPBCzZo1S926ddPGjRttm7e1WpJ3yZIl6tWrl6699lpddNFFWrBggSIjI7Vhwwbb5rVbMBy3TkegH7d8FSzHrdMVnF/VIBEfH6/Kyko1NDR41txut9q0aaOoqKhmj62oqPBaq6ioUFxcnC2zmtCSvJJ08OBBXXvttaqvr9e6deuavZ0WCHzNXFxcrLKyMmVmZiolJcXzmZ4//elPmjdvnu1zn66WfI1jY2PVvXt3r7Vu3boF1BmsluT97LPPlJiY6LkfGhqqxMRE7du3z7Z57RYMx62WCobjlq%2BC5bh1uihYZ7HevXvL5XJ5feCzoKBAffv2bfYTT1JSkj755BNZliVJsixL27ZtU1JSkq0zt0ZL8h47dkw33XSTQkNDtX79esXHx9s9rhG%2BZu7Xr5/efvttvfbaa56bJC1cuFD/8z//Y/vcp6slX%2BPk5GTt2rXLa23Pnj3q0qWLLbOa0JK8cXFxKi0t9Vr78ssv1bVrV1tm9YdgOG61RLAct3wVLMet00XBOotFRkZq3LhxeuCBB1RcXKxNmzbp6aef1tSpUyX9%2BJNwbW2tJGnkyJGqrq7WokWLtHv3bi1atEg1NTUaNWqUPyO0SEvyrly5Ut98843%2B93//17PN7XYH3G/j%2BJq5TZs2Ou%2B887xu0o9nAGJiYvwZoUVa8jW%2B5pprtGvXLj322GP6%2BuuvtXTpUpWVlWns2LH%2BjNAiLcl79dVX68UXX9Rrr72mr7/%2BWkuWLNG%2Bffs0fvx4f0YwLtiOW78kGI9bvyTYjlunzZ/XiMAvO3bsmHX33XdbycnJ1iWXXGKtWbPGs61Xr15e14spKiqyxo0bZ/Xt29eaOHGi9dlnn/lh4tbxNe/ll19u9erVq9lt9uzZfpr89LXka3yiQL2eTEvy/vvf/7bGjx9vXXTRRdbYsWOtrVu3%2BmHi1mlJ3hdffNEaOXKklZycbE2ePNnavn27HyY26%2BTv02A8bp3o5/IG03HrRL/0Nf65xwazEMv6v3OzAAAAMIK3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8PQtVeWA6kFPsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8213180129407926978”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>889</td> <td class=”number”>27.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1396843134515994</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11904761904761904</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1644195988161789</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11566042100393245</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.10354110581901015</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1268284433922381</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3700962250185048</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0279814203368963</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0465614378171998</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2215)</td> <td class=”number”>2223</td> <td class=”number”>69.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8213180129407926978”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>889</td> <td class=”number”>27.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002389257896497348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002614420095478622</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002698192346037772</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0028233035474809075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9099181073703367</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1695906432748537</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2531328320802004</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.366120218579235</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.445086705202312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_ANMLPROD16”><s>FMRKT_ANMLPROD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_FRVEG16”><code>FMRKT_FRVEG16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96896</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_BAKED16”><s>FMRKT_BAKED16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_ANMLPROD16”><code>FMRKT_ANMLPROD16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98152</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_CREDIT16”><s>FMRKT_CREDIT16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_SNAP16”><code>FMRKT_SNAP16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92193</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_FRVEG16”><s>FMRKT_FRVEG16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_CREDIT16”><code>FMRKT_CREDIT16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97114</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_OTHERFOOD16”><s>FMRKT_OTHERFOOD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_BAKED16”><code>FMRKT_BAKED16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98659</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_SFMNP16”><s>FMRKT_SFMNP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FMRKT_WIC16”><code>FMRKT_WIC16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93896</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_SNAP16”>FMRKT_SNAP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>33</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.2073</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>52</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-46822099144299067”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-46822099144299067,#minihistogram-46822099144299067”
aria-expanded=”false” aria-controls=”collapseExample”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-46822099144299067”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-46822099144299067”

aria-controls=”quantiles-46822099144299067” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-46822099144299067” aria-controls=”histogram-46822099144299067”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-46822099144299067” aria-controls=”common-46822099144299067”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-46822099144299067” aria-controls=”extreme-46822099144299067”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-46822099144299067”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>5</td>

</tr> <tr>

<th>Maximum</th> <td>52</td>

</tr> <tr>

<th>Range</th> <td>52</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.309</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.7408</td>

</tr> <tr>

<th>Kurtosis</th> <td>76.445</td>

</tr> <tr>

<th>Mean</th> <td>1.2073</td>

</tr> <tr>

<th>MAD</th> <td>1.4847</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.4843</td>

</tr> <tr>

<th>Sum</th> <td>2708</td>

</tr> <tr>

<th>Variance</th> <td>10.949</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-46822099144299067”>

<img 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AFfNLwZKk8vJy/elPf9Kf/vQnffPNN%2Brdu7fuuecelZWV6emnn9bevXuVkZHhr/EAAAAum%2B0Fa%2BvWrdq6dav27NmjNm3aaMyYMVq5cqW6dOnifUz79u21ePFiChYAAAhIthesjIwMDRw4UDk5ObrzzjsVFNT4Y2A333yz7r//frtHAwAAMML2gvXJJ58oOjpaVVVV3nJVVFSknj17Kjg4WJLUu3dv9e7d2%2B7RAAAAjLD9bxGeOnVKw4YN0yuvvOJdmzFjhkaPHq1jx47ZPQ4AAIBxthes5557Tp07d9a0adO8a%2B%2B//77at2%2BvzMxMu8cBAAAwzvaC9fe//11PPfWUYmJivGtt2rTRk08%2Bqd27d9s9DgAAgHG2Fyyn06nq6upG6263myu6AwCAFsH2gnXnnXdq0aJF%2Bte//uVdKy0tVWZmpgYMGGD3OAAAAMbZ/rcIZ8%2BerWnTpmno0KGKjIyUJFVXV6tnz56aM2eO3eMAAAAYZ3vBatu2rd5991199tlnKi4ultPp1C233KJf/epXcjgcdo8DAABgnF%2B%2BKic4OFgDBgzgLUEAANAi2V6wXC6XVqxYoX379uns2bONPtj%2Bl7/8xe6RAAAAjLK9YP3%2B97/X/v37NXLkSF1//fV2vzwAAMBVZ3vB2r17t9asWaPk5GS7XxoAAMAWtl%2BmITw8XG3btrX7ZQEAAGxje8EaPXq01qxZo/r6ertfGgAAwBa2v0VYVVWlbdu26a9//atuuukmhYSE%2BGxfv3693SMBAAAY5ZfLNIwaNcofLwsAAGAL2wtWZmam3S8JAABgK9s/gyVJ5eXlys7O1syZM/Xvf/9bH374oQ4fPuyPUQAAAIyzvWB9//33%2BvWvf613331X27dv15kzZ/T%2B%2B%2B9r3LhxKiwstHscAAAA42wvWH/4wx80ePBg7dixQ9ddd50k6fnnn9egQYO0dOlSu8cBAAAwzvaCtW/fPk2bNs3ni52dTqceffRRHThwwO5xAAAAjLO9YDU0NKihoaHR%2BunTpxUcHGz3OAAAAMbZXrD69%2B%2Bvl19%2B2adkVVVVKSsrS3379rV7HAAAAONsL1hPPfWU9u/fr/79%2B6u2tlb/%2BZ//qYEDB%2BrIkSOaPXu23eMAAAAYZ/t1sOLi4rRlyxZt27ZNX3/9tRoaGnTfffdp9OjRat26td3jAAAAGOeXK7mHhYVpwoQJ/nhpAACAq872gjV58uSf3c53EQIAgEBne8Hq2LGjz/26ujp9//33%2BuabbzRlyhS7xwEAADCu2XwXYU5OjsrKymyeBgAAwDy/fBfhhYwePVoffPCBv8cAAAC4Ys2mYH355ZdcaBQAALQIzeJD7qdOndI///lPTZo0ye5xAAAAjLO9YHXo0MHnewgl6brrrtP999%2Bv3/zmN3aPAwAAYJztBesPf/iD3S8JAABgK9sL1t69e5v82DvuuOMqTgIAAHB12F6wfvvb33rfIrQsy7t%2B/prD4dDXX39t93gAAABXzPaC9dJLL2nRokWaNWuWUlJSFBISoq%2B%2B%2BkoLFy7UPffcoxEjRtg9EgAAgFG2X6YhMzNT8%2BbN09ChQxUdHa2IiAj17dtXCxcu1JtvvqmOHTt6bwAAAIHI9oJVXl5%2BwfLUunVrVVZWXvL%2BPB6PRo0apT179njXSktLNXXqVCUkJGjEiBHatWuXz3M%2B%2B%2BwzjRo1SvHx8Zo8ebJKS0t9tq9du1YDBgxQYmKi5s6dK7fbfclzAQCAa5ftBSshIUHPP/%2B8Tp065V2rqqpSVlaWfvWrX13Svmpra/X444%2BruLjYu2ZZllJTU9WuXTtt3rxZo0ePVlpamo4ePSpJOnr0qFJTUzV27Fi9/fbbatOmjR599FHvZ7%2B2b9%2Bu7OxsLVy4UOvWrVNhYaGysrIMHDkAALhW2P4ZrKefflqTJ0/WnXfeqS5dusiyLH333XeKiYnR%2BvXrm7yfkpISzZw50%2BeD8pK0e/dulZaW6q233lJ4eLi6du2qzz//XJs3b9Zjjz2mTZs26fbbb9cDDzwg6ce3LPv166cvvvhCffr00fr16zVlyhQNHDhQkrRgwQJNnz5ds2bNUlhYmLkgAABAi2X7GayuXbvq/fff18yZM5WQkKDExERlZGRo69atuvHGG5u8n3OFKC8vz2e9sLBQt912m8LDw71rSUlJKigo8G5PTk72bgsLC1PPnj1VUFCg%2Bvp6ffXVVz7bExISdPbsWR08ePByDxkAAFxjbD%2BDJUk33HCDJkyYoCNHjuimm26S9OPV3C/FT32tjsvlUmxsrM9a27ZtVVZWdtHt1dXVqq2t9dnudDoVFRXlfT4AAMDF2F6wLMvSsmXLtGHDBp09e1bbt2/X8uXLFRYWpvnz519y0Tqf2%2B1WSEiIz1pISIg8Hs9Ft9fU1Hjv/9Tzm6K8vFwul8tnzekMb1TsrlRwcLP5ru4mcTqb97zn8gy0XJsr8jSLPM0hS7PI88JsL1gbNmzQ1q1b9cwzz2jhwoWSpMGDB2vBggVq166d/vu///uK9h8aGqqqqiqfNY/Ho1atWnm3n1%2BWPB6PIiMjFRoa6r1//vZL%2BfxVXl6esrOzfdZSU1OVnp7e5H20RNHREf4eoUkiI/msnUnkaRZ5mkOWZpGnL9sLVl5enubNm6chQ4bo2WeflSSNGDFC1113nTIzM6%2B4YMXFxamkpMRnraKiwnv2KC4uThUVFY229%2BjRQ1FRUQoNDVVFRYW6du0qSaqrq1NVVZViYmKaPMPEiRM1aNAgnzWnM1yVlacv55B%2BUqD9tmD6%2BE0LDg5SZGSYqqvdqq9v8Pc4AY88zSJPc8jSrOaep79%2Bube9YB05ckQ9evRotN69e/dGb6tdjvj4eOXm5qqmpsZ71io/P19JSUne7fn5%2Bd7Hu91uHThwQGlpaQoKClKvXr2Un5%2BvPn36SJIKCgrkdDrVvXv3Js8QGxvb6O1Al%2Buk6uqa3w%2BenQLl%2BOvrGwJm1kBAnmaRpzlkaRZ5%2BrL9FEjHjh311VdfNVr/5JNPvB94vxIpKSlq37695syZo%2BLiYuXm5qqoqEjjx4%2BXJI0bN0779u1Tbm6uiouLNWfOHHXq1MlbqCZNmqRXX31VO3bsUFFRkebPn697772XSzQAAIAms/0M1vTp07VgwQK5XC5ZlqXPP/9ceXl52rBhg5566qkr3n9wcLBWrVqljIwMjR07Vp07d1ZOTo46dOggSerUqZNefPFFPffcc8rJyVFiYqJycnK8XzY9cuRI/fDDD5o3b548Ho/uvvtuzZo164rnAgAA1w6Hdf6VOm2Ql5en1atXey990KZNGz300EOaNm2a3aPYxuU6aXyfTmeQhiz91Ph%2Br5YPftfP3yP8LKczSNHREaqsPM1pbgPI0yzyNIcszWruecbEXO%2BX17X9DNa2bds0bNgwTZw4USdOnJBlWWrbtq3dYwAAAFw1tn8Ga%2BHChd4Ps7dp04ZyBQAAWhzbC1aXLl30zTff2P2yAAAAtrH9LcLu3bvriSee0Jo1a9SlSxfvxT3PyczMtHskAAAAo2wvWN9%2B%2B633mlQmrnsFAADQ3NhSsJYsWaK0tDSFh4drw4YNdrwkAACA39jyGazXX39dbrfbZ23GjBkqLy%2B34%2BUBAABsZUvButCltvbu3ava2lo7Xh4AAMBWgfVtwQAAAAGAggUAAGCYbQXr3Hf9AQAAtHS2XaZh0aJFPte8Onv2rLKyshQREeHzOK6DBQAAAp0tBeuOO%2B5odM2rxMREVVZWqrKy0o4RAAAAbGNLweLaVwAA4FrCh9wBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCwFluwPvroI916660%2Bt/T0dEnSgQMHNGHCBMXHx2vcuHHav3%2B/z3O3bdumwYMHKz4%2BXqmpqTpx4oQ/DgEAAASoFluwSkpKNHDgQO3atct7W7Rokc6cOaMZM2YoOTlZ77zzjhITE/Xwww/rzJkzkqSioiJlZGQoLS1NeXl5qq6u1pw5c/x8NAAAIJC02IJ16NAhdevWTTExMd5bZGSk3n//fYWGhurJJ59U165dlZGRoYiICH344YeSpI0bN2r48OEaM2aMunfvriVLlmjnzp0qLS318xEBAIBA0aILVpcuXRqtFxYWKikpSQ6HQ5LkcDjUu3dvFRQUeLcnJyd7H9%2B%2BfXt16NBBhYWFtswNAAACX4ssWJZl6dtvv9WuXbs0dOhQDR48WEuXLpXH45HL5VJsbKzP49u2bauysjJJUnl5%2Bc9uBwAAuBinvwe4Go4ePSq3262QkBCtWLFCR44c0aJFi1RTU%2BNd/79CQkLk8XgkSTU1NT%2B7vSnKy8vlcrl81pzO8EbF7UoFBwdWP3Y6m/e85/IMtFybK/I0izzNIUuzyPPCWmTB6tixo/bs2aMbbrhBDodDPXr0UENDg2bNmqWUlJRGZcnj8ahVq1aSpNDQ0AtuDwsLa/Lr5%2BXlKTs722ctNTXV%2B7cYr1XR0RH%2BHqFJIiOb/meNiyNPs8jTHLI0izx9tciCJUlRUVE%2B97t27ara2lrFxMSooqLCZ1tFRYX37FJcXNwFt8fExDT5tSdOnKhBgwb5rDmd4aqsPH0ph3BRgfbbgunjNy04OEiRkWGqrnarvr7B3%2BMEPPI0izzNIUuzmnue/vrlvkUWrE8//VRPPPGE/vrXv3rPPH399deKiopSUlKSXnnlFVmWJYfDIcuytG/fPj3yyCOSpPj4eOXn52vs2LGSpGPHjunYsWOKj49v8uvHxsY2ejvQ5Tqpurrm94Nnp0A5/vr6hoCZNRCQp1nkaQ5ZmkWevgLrFEgTJSYmKjQ0VE8//bQOHz6snTt3asmSJXrwwQc1bNgwVVdXa/HixSopKdHixYvldrs1fPhwSdJ9992nrVu3atOmTTp48KCefPJJ3XXXXbrpppv8fFQAACBQtMiC1bp1a7366qs6ceKExo0bp4yMDE2cOFEPPvigWrdurZdfftl7lqqwsFC5ubkKDw%2BX9GM5W7hwoXJycnTffffphhtuUGZmpp%2BPCAAABBKHZVmWv4e4FrhcJ43v0%2BkM0pClnxrf79Xywe/6%2BXuEn%2BV0Bik6OkKVlac5zW0AeZpFnuaQpVnNPc%2BYmOv98rot8gwWAACAP1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMOc/h4A147hK/7m7xEuyQe/6%2BfvEQAAAYozWAAAAIZRsAAAAAyjYAEAABhGwbqA2tpazZ07V8nJyerfv79ee%2B01f48EAAACCB9yv4AlS5Zo//79WrdunY4eParZs2erQ4cOGjZsmL9HAwAAAYCCdZ4zZ85o06ZNeuWVV9SzZ0/17NlTxcXFeuONNyhY1xj%2B1iMA4HLxFuF5Dh48qLq6OiUmJnrXkpKSVFhYqIaGBj9OBgAAAgVnsM7jcrkUHR2tkJAQ71q7du1UW1urqqoqtWnT5qL7KC8vl8vl8llzOsMVGxtrdNbgYPox/r9AO%2BMWSD56YoC/R/D%2B%2B86/91eOLM0izwujYJ3H7Xb7lCtJ3vsej6dJ%2B8jLy1N2drbPWlpamh577DEzQ/5Pzl0tAAAIa0lEQVSv8vJyTbmxWBMnTjRe3q5F5eXlysvLI09DyNOs8vJyrVu3hjwNIEuzyPPCqJvnCQ0NbVSkzt1v1apVk/YxceJEvfPOOz63iRMnGp/V5XIpOzu70dkyXB7yNIs8zSJPc8jSLPK8MM5gnScuLk6VlZWqq6uT0/ljPC6XS61atVJkZGST9hEbG0uLBwDgGsYZrPP06NFDTqdTBQUF3rX8/Hz16tVLQUHEBQAALo7GcJ6wsDCNGTNG8%2BfPV1FRkXbs2KHXXntNkydP9vdoAAAgQATPnz9/vr%2BHaG769u2rAwcOaNmyZfr888/1yCOPaNy4cf4e64IiIiKUkpKiiIgIf4/SIpCnWeRpFnmaQ5ZmkWdjDsuyLH8PAQAA0JLwFiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMApWgKqtrdXcuXOVnJys/v3767XXXvP3SAHJ4/Fo1KhR2rNnj3ettLRUU6dOVUJCgkaMGKFdu3b5ccLm7/jx40pPT1dKSooGDBigzMxM1dbWSiLLy/H9999r%2BvTpSkxM1F133aU1a9Z4t5HnlZkxY4aeeuop7/0DBw5owoQJio%2BP17hx47R//34/ThcYPvroI916660%2Bt/T0dEnkeT4KVoBasmSJ9u/fr3Xr1umZZ55Rdna2PvzwQ3%2BPFVBqa2v1%2BOOPq7i42LtmWZZSU1PVrl07bd68WaNHj1ZaWpqOHj3qx0mbL8uylJ6eLrfbrTfeeEPLly/Xxx9/rBUrVpDlZWhoaNCMGTMUHR2td999VwsWLNDq1av13nvvkecV%2BvOf/6ydO3d67585c0YzZsxQcnKy3nnnHSUmJurhhx/WmTNn/Dhl81dSUqKBAwdq165d3tuiRYvI80IsBJzTp09bvXr1snbv3u1dy8nJse6//34/ThVYiouLrd/85jfWr3/9a6tbt27eLD/77DMrISHBOn36tPexU6ZMsVauXOmvUZu1kpISq1u3bpbL5fKuvffee1b//v3J8jIcP37c%2Bq//%2Bi/r5MmT3rXU1FTrmWeeIc8rUFlZad15553WuHHjrNmzZ1uWZVmbNm2yBg0aZDU0NFiWZVkNDQ3WkCFDrM2bN/tz1GZv5syZ1rJlyxqtk2djnMEKQAcPHlRdXZ0SExO9a0lJSSosLFRDQ4MfJwscX3zxhfr06aO8vDyf9cLCQt12220KDw/3riUlJamgoMDuEQNCTEyM1qxZo3bt2vmsnzp1iiwvQ2xsrFasWKHWrVvLsizl5%2Bdr7969SklJIc8r8Mc//lGjR4/WLbfc4l0rLCxUUlKSHA6HJMnhcKh3797keRGHDh1Sly5dGq2TZ2MUrADkcrkUHR2tkJAQ71q7du1UW1urqqoqP04WOCZNmqS5c%2BcqLCzMZ93lcik2NtZnrW3btiorK7NzvIARGRmpAQMGeO83NDRo48aN6tu3L1leoUGDBmnSpElKTEzU0KFDyfMyff755/r73/%2BuRx991GedPC%2BdZVn69ttvtWvXLg0dOlSDBw/W0qVL5fF4yPMCnP4eAJfO7Xb7lCtJ3vsej8cfI7UYP5UtuTZNVlaWDhw4oLfffltr164lyyuwcuVKVVRUaP78%2BcrMzORn8zLU1tbqmWee0bx589SqVSufbeR56Y4ePerNbcWKFTpy5IgWLVqkmpoa8rwAClYACg0NbfRDe%2B7%2B%2Bf8RwaUJDQ1tdBbQ4/GQaxNkZWVp3bp1Wr58ubp160aWV6hXr16SfiwJTzzxhMaNGye32%2B3zGPL8ednZ2br99tt9zrKe81P/HSXPn9axY0ft2bNHN9xwgxwOh3r06KGGhgbNmjVLKSkp5HkeClYAiouLU2Vlperq6uR0/vhH6HK51KpVK0VGRvp5usAWFxenkpISn7WKiopGp77h69lnn9Wbb76prKwsDR06VBJZXo6KigoVFBRo8ODB3rVbbrlFZ8%2BeVUxMjA4fPtzo8eT50/785z%2BroqLC%2B3nVcwVg%2B/btGjVqlCoqKnweT54XFxUV5XO/a9euqq2tVUxMDHmeh89gBaAePXrI6XT6fHgwPz9fvXr1UlAQf6RXIj4%2BXv/4xz9UU1PjXcvPz1d8fLwfp2resrOz9dZbb%2Bn555/XyJEjvetkeemOHDmitLQ0HT9%2B3Lu2f/9%2BtWnTRklJSeR5iTZs2KD33ntPW7Zs0ZYtWzRo0CANGjRIW7ZsUXx8vL788ktZliXpx88X7du3jzx/xqeffqo%2Bffr4nEn9%2BuuvFRUVpaSkJPI8D/83DkBhYWEaM2aM5s%2Bfr6KiIu3YsUOvvfaaJk%2Be7O/RAl5KSorat2%2BvOXPmqLi4WLm5uSoqKtL48eP9PVqzdOjQIa1atUoPPfSQkpKS5HK5vDeyvHS9evVSz549NXfuXJWUlGjnzp3KysrSI488Qp6XoWPHjurcubP3FhERoYiICHXu3FnDhg1TdXW1Fi9erJKSEi1evFhut1vDhw/399jNVmJiokJDQ/X000/r8OHD2rlzp5YsWaIHH3yQPC/En9eIwOU7c%2BaM9eSTT1oJCQlW//79rddff93fIwWs/3sdLMuyrO%2B%2B%2B876j//4D%2Bv222%2B3Ro4caf3tb3/z43TN28svv2x169btgjfLIsvLUVZWZqWmplq9e/e2%2BvXrZ61evdp7bSHyvDKzZ8/2XgfLsiyrsLDQGjNmjNWrVy9r/Pjx1j/%2B8Q8/ThcYvvnmG2vq1KlWQkKC1a9fP%2BvFF1/0/nySpy%2BHZf3v%2BTwAAAAYwVuEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wMr3NTFjZ/csAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-46822099144299067”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>525</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:41%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>171</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (22)</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-46822099144299067”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>525</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:41%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>171</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>36.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>38.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_WIC16”>FMRKT_WIC16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>34</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1694</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>48</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7361554752996460197”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7361554752996460197,#minihistogram-7361554752996460197”
aria-expanded=”false” aria-controls=”collapseExample”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-7361554752996460197”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7361554752996460197”

aria-controls=”quantiles-7361554752996460197” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7361554752996460197” aria-controls=”histogram-7361554752996460197”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7361554752996460197” aria-controls=”common-7361554752996460197”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7361554752996460197” aria-controls=”extreme-7361554752996460197”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7361554752996460197”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>5</td>

</tr> <tr>

<th>Maximum</th> <td>48</td>

</tr> <tr>

<th>Range</th> <td>48</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.4149</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.9202</td>

</tr> <tr>

<th>Kurtosis</th> <td>68.142</td>

</tr> <tr>

<th>Mean</th> <td>1.1694</td>

</tr> <tr>

<th>MAD</th> <td>1.5392</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.1692</td>

</tr> <tr>

<th>Sum</th> <td>2623</td>

</tr> <tr>

<th>Variance</th> <td>11.662</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7361554752996460197”>

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HjRp10fXCwkLZbDY9//zz%2Bsc//qGwsDA9%2BOCD7suFRUVFioyM9HhO8%2BbNdfLkSTmdTkny2B4RESFJOnnyZK3n/ZyioiL3vi6w2xvX%2Bfl1FRjoW/3YbveteX/sQta%2BlrkvImvrkLU1yNk6DSlrny1YP%2Bfw4cOy2Wxq166d7r//fu3bt09//OMf1bRpU/Xv318VFRUKCgryeE5QUJBcLpcqKirc93%2B8TfrhzfR1lZWVpczMTI%2B1lJQUpaamXu5h%2BYXw8CbeHqHeQkNDvD3CVYOsrUPW1iBn6zSErP2uYA0dOlR9%2B/ZVWFiYJKlTp0766quvtGnTJvXv31/BwcG1ypLL5VJISIhHmQoODnb/syT3e7jqIjk5WUlJSR5rdntjlZScvezjupiG0NAvhdnHb6XAwACFhoaorKxc1dX8VumVRNbWIWtrkLN1Lpa1t36497uCZbPZ3OXqgnbt2mn37t2SpKioKBUXF3tsLy4ulsPhUFRUlCTJ6XSqdevW7n%2BWJIfDUecZIiMja10OdDpPq6rq6v7G8ofjr66u8Yvj8AVkbR2ytgY5W6chZO1bp0DqYMmSJRo7dqzH2qFDh9SuXTtJUnR0tLKzs93bTpw4oRMnTig6OlpRUVFq2bKlx/bs7Gy1bNnS9PdPAQAA/%2BV3Z7D69u2rlStXavXq1erfv7927dqlrVu3at26dZKkkSNH6oEHHlBMTIy6du2qefPmqU%2BfPmrTpo17%2B4IFC/SrX/1KkrRw4UI99NBDXjseAADge/yuYHXr1k1LlizR0qVLtWTJErVq1UoLFy5UbGysJCk2NlZz5szR0qVL9f3336tXr1565pln3M9/%2BOGH9e2332rChAkKDAzUiBEjap0RAwAA%2BHdshmEY3h7iauB0njZ9n3Z7gPov%2BND0/V4p7z7Ry9sjXDa7PUDh4U1UUnLW69f1/R1ZW4esrUHO1rlY1g7HtV6Zxe/egwUAAOBtFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZJYXrHvvvVevvPKKTp8%2BbfVLAwAAWMLygtWzZ089//zz6t27tyZOnKhdu3bJMAyrxwAAALhiLC9YaWlp%2BuCDD7R8%2BXIFBgbq8ccfV58%2BfbRo0SJ9%2BeWXVo8DAABgOrs3XtRms6lXr17q1auXysvLtX79ei1fvlwrV65U9%2B7dNWbMGN1xxx3eGA0AAKDevFKwJKmoqEhvvvmm3nzzTX3xxRfq3r277rnnHp08eVJPPfWU9u3bpxkzZnhrPAAAgMtmecF644039MYbb2jPnj1q1qyZhg4dqqVLl6pt27bux7Ro0ULz5s2jYAEAAJ9kecGaMWOG%2Bvbtq2XLlum2225TQEDtt4G1a9dO999/v9WjAQAAmMLyN7n/4x//0NKlSxUdHe0uV3l5eaqurnY/pnv37kpLS6vT/lwulwYPHqw9e/a413JycvSf//mfio2N1YABA7R582aP59x9993q2LGjx%2B2LL76QJBmGoQULFqhnz55KSEjQ/PnzVVNTU9/DBgAAVxHLz2CdOXNGI0eO1G9%2B8xtNnjxZkjRu3DhFREToxRdfVIsWLeq8r8rKSqWlpSk/P9%2B95nQ69eijj2rkyJH685//rE8//VTTpk2Tw%2BFQnz59VF1dra%2B%2B%2BkobNmzwuCwZHh4uSXr55Ze1bds2ZWZmqqqqSpMmTVLz5s318MMPmxMAAADwe5afwfrTn/6k66%2B/Xg8%2B%2BKB77Z133lGLFi2Unp5e5/0UFBTovvvu0zfffOOxvmPHDkVERGjixIlq27atBg0apKFDh%2Bqtt96SJB09elTnz59Xt27d5HA43De7/YeuuW7dOqWmpio%2BPl49e/bUk08%2BqY0bN5pw5AAA4GphecH65z//qalTp8rhcLjXmjVrpsmTJ2v37t113s/evXvVo0cPZWVleawnJiZetKidOXNG0g/FrEWLFgoODq71mFOnTunEiRO69dZb3WtxcXE6duyYioqK6jwbAAC4ull%2BidBut6usrKzWenl5%2BSV9ovuoUaMuut66dWu1bt3aff/bb7/V22%2B/rccff1ySVFhYqGuuuUbjx4/XgQMHdMMNN2jy5Mnq1q2bnE6nJCkyMtL9/IiICEnSyZMnPdb/naKiIve%2BLrDbG9f5%2BXUVGOhbf0rSbveteX/sQta%2BlrkvImvrkLU1yNk6DSlrywvWbbfdprlz5%2BrZZ5/V//t//0%2BSdOTIEaWnpysxMdHU16qoqNDjjz%2BuiIgIJScnS5K%2B/PJLff/997r33nuVmpqqv/71rxozZozeeecdVVRUSJKCgoLc%2B7jwzy6Xq86vm5WVpczMTI%2B1lJQUpaam1veQfFp4eBNvj1BvoaEh3h7hqkHW1iFra5CzdRpC1pYXrClTpujBBx/UgAEDFBoaKkkqKytTly5dNG3aNNNe5%2BzZs3rsscf01Vdf6S9/%2BYtCQn4I%2B5lnnlFFRYWaNm0qSZo1a5b279%2BvN954Q//xH/8h6YcydeES4oVideH5dZGcnKykpCSPNbu9sUpKztb7uH6sITT0S2H28VspMDBAoaEhKisrV3U1v1V6JZG1dcjaGuRsnYtl7a0f7i0vWM2bN9frr7%2Bujz76SPn5%2BbLb7brxxhv161//WjabzZTXOHPmjB555BF98803Wrt2rcdvC9rtdne5kn74sz3t2rXTqVOnFBUVJemH30S8cJnxwqW%2BH79n7JdERkbWuhzodJ5WVdXV/Y3lD8dfXV3jF8fhC8jaOmRtDXK2TkPI2it/KicwMFCJiYmmXxKUpJqaGk2YMEFHjx7V%2BvXr1b59e4/tDzzwgHr06KEJEya4H//555/rd7/7naKiotSyZUtlZ2e7C1Z2drZatmxp%2BvunAACA/7K8YDmdTi1evFj79%2B/X%2BfPna72x/f3336/X/l999VXt2bNHK1asUGhoqPsM1DXXXKOwsDAlJSVp2bJl6ty5s2644QatW7dOp0%2Bf1j333CNJGjlypBYsWKBf/epXkqSFCxfqoYceqtdMAADg6mJ5wfrjH/%2BoAwcOaNCgQbr22mtN3//27dtVU1Oj8ePHe6wnJCRo/fr1Gjt2rCorKzV37lwVFxcrOjpaL7/8svuy4cMPP6xvv/1WEyZMUGBgoEaMGKGxY8eaPicAAPBfNuNSPhvBBDExMVq1apXi4%2BOtfFmvczpPm75Puz1A/Rd8aPp%2Br5R3n%2Bjl7REum90eoPDwJiopOev16/r%2BjqytQ9bWIGfrXCxrh8P8kzl1YfmvoTVu3FjNmze3%2BmUBAAAsY3nBGjJkiFatWuXxx50BAAD8ieXvwSotLdW2bdv097//XW3atPH4UE/ph78FCAAA4Mu88jENgwcP9sbLAgAAWMLygnWxP8QMAADgT7zyt1aKioqUmZmptLQ0ffvtt3rvvfd0%2BPBhb4wCAABgOssL1tdff63f/va3ev3117V9%2B3adO3dO77zzjoYPH67c3FyrxwEAADCd5QXrz3/%2Bs/r166cdO3bommuukSQ9%2B%2ByzSkpK0oIFC6weBwAAwHSWF6z9%2B/frwQcf9PjDzna7XY899pgOHjxo9TgAAACms7xg1dTUqKam9ifZnj17VoGBgVaPAwAAYDrLC1bv3r31wgsveJSs0tJSZWRkqGfPnlaPAwAAYDrLC9bUqVN14MAB9e7dW5WVlfrDH/6gvn376ujRo5oyZYrV4wAAAJjO8s/BioqK0tatW7Vt2zZ99tlnqqmp0ciRIzVkyBA1bdrU6nEAAABM55VPcg8JCdG9997rjZcGAAC44iwvWKNHj/632/lbhAAAwNdZXrBatWrlcb%2Bqqkpff/21vvjiC40ZM8bqcQAAAEzXYP4W4bJly3Ty5EmLpwEAADCfV/4W4cUMGTJE7777rrfHAAAAqLcGU7D%2B9a9/8UGjAADALzSIN7mfOXNGn3/%2BuUaNGmX1OAAAAKazvGC1bNnS4%2B8QStI111yj%2B%2B%2B/X3fffbfV4wAAAJjO8oL15z//2eqXBAAAsJTlBWvfvn11fuytt956BScBAAC4MiwvWA888ID7EqFhGO71n67ZbDZ99tlnVo8HAABQb5b/FuHzzz%2BvVq1aafHixfr444%2BVnZ2tNWvW6IYbbtDEiRP1/vvv6/3339eOHTvqtD%2BXy6XBgwdrz5497rUjR45o7NixiomJ0V133aVdu3Z5POejjz7S4MGDFR0drdGjR%2BvIkSMe29esWaPExETFxsZq%2BvTpKi8vr/%2BBAwCAq4blBSs9PV0zZ87UgAEDFB4eriZNmqhnz56aM2eONm3apFatWrlvv6SyslITJ05Ufn6%2Be80wDKWkpCgiIkJbtmzRkCFDNGHCBB0/flySdPz4caWkpGjYsGF69dVX1axZMz322GPuM2fbt29XZmam5syZo7Vr1yo3N1cZGRlXJgwAAOCXLC9YRUVFFy1PTZs2VUlJSZ33U1BQoPvuu0/ffPONx/ru3bt15MgRzZkzR%2B3bt9f48eMVExOjLVu2SJI2b96sW265RQ899JBuuukmpaen69ixY9q7d6%2BkH/4W4pgxY9S3b19169ZNs2fP1pYtWziLBQAA6szyghUTE6Nnn31WZ86cca%2BVlpYqIyNDv/71r%2Bu8n71796pHjx7KysryWM/NzdXNN9%2Bsxo0bu9fi4uKUk5Pj3h4fH%2B/eFhISoi5duignJ0fV1dX65JNPPLbHxMTo/PnzOnTo0CUfKwAAuDpZ/ib3p556SqNHj9Ztt92mtm3byjAMffXVV3I4HFq3bl2d9/NzH0rqdDoVGRnpsda8eXP33zn8d9vLyspUWVnpsd1utyssLOyS/k5iUVGRnE6nx5rd3rjW69ZXYGCD%2BSD%2BOrHbfWveH7uQta9l7ovI2jpkbQ1ytk5DytrygtW%2BfXu988472rZtmwoLCyVJv/vd7zRo0CCFhITUe//l5eUKCgryWAsKCpLL5frF7RUVFe77P/f8usjKylJmZqbHWkpKilJTU%2Bu8D38UHt7E2yPUW2ho/b9GUTdkbR2ytgY5W6chZG15wZKk6667Tvfee6%2BOHj2qNm3aSPrh09zNEBwcrNLSUo81l8ulRo0aubf/tCy5XC6FhoYqODjYff%2Bn2y%2Bl/CUnJyspKcljzW5vrJKSs3XeR100hIZ%2BKcw%2BfisFBgYoNDREZWXlqq6u8fY4fo2srUPW1iBn61wsa2/9cG95wTIMQwsXLtT69et1/vx5bd%2B%2BXYsWLVJISIhmzZpV76IVFRWlgoICj7Xi4mL35bmoqCgVFxfX2t65c2eFhYUpODhYxcXFat%2B%2BvSSpqqpKpaWlcjgcdZ4hMjKy1uVAp/O0qqqu7m8sfzj%2B6uoavzgOX0DW1iFra5CzdRpC1pafAlm/fr3eeOMNPf300%2B5Lcf369dOOHTtqXVa7HNHR0fr000/dl/skKTs7W9HR0e7t2dnZ7m3l5eU6ePCgoqOjFRAQoK5du3psz8nJkd1uV6dOneo9GwAAuDpYXrCysrI0c%2BZMDRs2zP3p7XfddZfmzp2rt956q977T0hIUIsWLTRt2jTl5%2Bdr5cqVysvL04gRIyRJw4cP1/79%2B7Vy5Url5%2Bdr2rRpat26tXr06CHphzfPr169Wjt27FBeXp5mzZql%2B%2B67z5T3hwEAgKuD5QXr6NGj6ty5c631Tp061frNu8sRGBio5cuXy%2Bl0atiwYXrzzTe1bNkytWzZUpLUunVrPffcc9qyZYtGjBih0tJSLVu2zF32Bg0apPHjx2vmzJl66KGH1K1bN02aNKnecwEAgKuH5e/BatWqlT755BO1bt3aY/0f//iH%2Bw3vl%2Brzzz/3uH/99ddrw4YNP/v422%2B/XbfffvvPbh83bpzGjRt3WbMAAABYXrAefvhhzZ49W06nU4Zh6OOPP1ZWVpbWr1%2BvqVOnWj0OAACA6SwvWMOHD1dVVZVWrFihiooKzZw5U82aNdMTTzyhkSNHWj0OAACA6SwvWNu2bdPAgQOVnJys7777ToZhqHnz5laPAQAAcMVY/ib3OXPmuN/M3qxZM8oVAADwO5YXrLZt2%2BqLL76w%2BmUBAAAsY/klwk6dOunJJ5/UqlWr1LZtW/efp7kgPT3d6pEAAABMZXnB%2BvLLLxUXFydJpnzuFQAAQENjScGaP3%2B%2BJkyYoMaNG2v9%2BvVWvCQAAIDXWPIerJdfflnl5eUea%2BPGjVNRUZEVLw8AAGApSwqWYRi11vbt26fKykorXh4AAMBSlv8WIQAAgL/zEoe0AAAXVklEQVSjYAEAAJjMsoJls9mseikAAACvsuxjGubOnevxmVfnz59XRkaGmjRp4vE4PgcLAAD4OksK1q233lrrM69iY2NVUlKikpISK0YAAACwjCUFi8%2B%2BAgAAVxPe5A4AAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmMwvC9Zrr72mjh071rp16tRJkvSHP/yh1rYPPvjA/fw1a9YoMTFRsbGxmj59usrLy711KAAAwAdZ9rcIrXTXXXcpMTHRfb%2BqqkpjxoxRnz59JEmFhYXKyMjQr3/9a/djrrvuOknS9u3blZmZqYyMDDVv3lzTpk1TRkaGZs6caekxAAAA3%2BWXZ7AaNWokh8Phvr355psyDENPPvmkXC6Xjh49qq5du3o8JigoSJK0bt06jRkzRn379lW3bt00e/ZsbdmyhbNYAACgzvyyYP1YaWmpXnzxRaWlpSkoKEiHDx%2BWzWZTmzZtaj22urpan3zyieLj491rMTExOn/%2BvA4dOmTl2AAAwIf5fcHatGmTIiMjNXDgQEnS4cOH1bRpU02ePFm9e/fWiBEjtHPnTklSWVmZKisrFRkZ6X6%2B3W5XWFiYTp486ZX5AQCA7/HL92BdYBiGNm/erEceecS9dvjwYVVUVKh3794aN26c/va3v%2BkPf/iDsrKyFBERIUnuy4UXBAUFyeVy1fl1i4qK5HQ6Pdbs9sYexc0MgYG%2B1Y/tdt%2Ba98cuZO1rmfsisrYOWVuDnK3TkLL264L1ySef6NSpUxo0aJB77bHHHtMDDzzgflN7p06d9Omnn%2Bqvf/2r/vu//1uSapUpl8ulkJCQOr9uVlaWMjMzPdZSUlKUmpp6uYfiF8LDm3h7hHoLDa371wHqh6ytQ9bWIGfrNISs/bpgffjhh4qPj3eXKUkKCAjwuC9J7dq1U0FBgcLCwhQcHKzi4mK1b99e0g%2B/gVhaWiqHw1Hn101OTlZSUpLHmt3eWCUlZ%2BtxNLU1hIZ%2BKcw%2BfisFBgYoNDREZWXlqq6u8fY4fo2srUPW1iBn61wsa2/9cO/XBSsvL0/du3f3WJs6dapsNpvS09Pda4cOHVKHDh0UEBCgrl27Kjs7Wz169JAk5eTkyG63uz9Dqy4iIyNrXQ50Ok%2Brqurq/sbyh%2BOvrq7xi%2BPwBWRtHbK2BjlbpyFk7VunQC5Rfn6%2BbrzxRo%2B1pKQkvfXWW9q6dau%2B/vprZWZmKjs7W/fff78kadSoUVq9erV27NihvLw8zZo1S/fdd98lXSIEAABXN78%2Bg1VcXKzQ0FCPtTvuuENPP/20VqxYoePHj%2Bumm27SqlWr1Lp1a0nSoEGDdOzYMc2cOVMul0t33HGHJk2a5I3xAQCAj7IZhmF4e4irgdN52vR92u0B6r/gQ9P3e6W8%2B0Qvb49w2ez2AIWHN1FJyVmvn3b2d2RtHbK2Bjlb52JZOxzXemUWv75ECAAA4A0ULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMJnfFqy//e1v6tixo8ctNTVVknTw4EHde%2B%2B9io6O1vDhw3XgwAGP527btk39%2BvVTdHS0UlJS9N1333njEAAAgI/y24JVUFCgvn37ateuXe7b3Llzde7cOY0bN07x8fF67bXXFBsbq/Hjx%2BvcuXOSpLy8PM2YMUMTJkxQVlaWysrKNG3aNC8fDQAA8CV%2BW7AKCwvVoUMHORwO9y00NFTvvPOOgoODNXnyZLVv314zZsxQkyZN9N5770mSNmzYoDvvvFNDhw5Vp06dNH/%2BfO3cuVNHjhzx8hEBAABf4dcFq23btrXWc3NzFRcXJ5vNJkmy2Wzq3r27cnJy3Nvj4%2BPdj2/RooVatmyp3NxcS%2BYGAAC%2Bzy8LlmEY%2BvLLL7Vr1y4NGDBA/fr104IFC%2BRyueR0OhUZGenx%2BObNm%2BvkyZOSpKKion%2B7HQAA4JfYvT3AlXD8%2BHGVl5crKChIixcv1tGjRzV37lxVVFS4138sKChILpdLklRRUfFvt9dFUVGRnE6nx5rd3rhWcauvwEDf6sd2u2/N%2B2MXsva1zH0RWVuHrK1BztZpSFn7ZcFq1aqV9uzZo%2Buuu042m02dO3dWTU2NJk2apISEhFplyeVyqVGjRpKk4ODgi24PCQmp8%2BtnZWUpMzPTYy0lJcX9W4xXq/DwJt4eod5CQ%2Bv%2BdYD6IWvrkLU1yNk6DSFrvyxYkhQWFuZxv3379qqsrJTD4VBxcbHHtuLiYvfZpaioqItudzgcdX7t5ORkJSUleazZ7Y1VUnL2Ug7hFzWEhn4pzD5%2BKwUGBig0NERlZeWqrq7x9jh%2BjaytQ9bWIGfrXCxrb/1w75cF68MPP9STTz6pv//97%2B4zT5999pnCwsIUFxenF198UYZhyGazyTAM7d%2B/X7///e8lSdHR0crOztawYcMkSSdOnNCJEycUHR1d59ePjIysdTnQ6Tytqqqr%2BxvLH46/urrGL47DF5C1dcjaGuRsnYaQtW%2BdAqmj2NhYBQcH66mnntLhw4e1c%2BdOzZ8/X4888ogGDhyosrIyzZs3TwUFBZo3b57Ky8t15513SpJGjhypN954Q5s3b9ahQ4c0efJk9enTR23atPHyUQEAAF/hlwWradOmWr16tb777jsNHz5cM2bMUHJysh555BE1bdpUL7zwgvssVW5urlauXKnGjRtL%2BqGczZkzR8uWLdPIkSN13XXXKT093ctHBAAAfInNMAzD20NcDZzO06bv024PUP8FH5q%2B3yvl3Sd6eXuEy2a3Byg8vIlKSs56/bSzvyNr65C1NcjZOhfL2uG41iuz%2BOUZLAAAAG%2BiYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMn8tmCdOnVKqampSkhIUGJiotLT01VZWSlJmjt3rjp27Ohx27Bhg/u527ZtU79%2B/RQdHa2UlBR999133joMAADgg%2BzeHuBKMAxDqampCg0N1caNG/X9999r%2BvTpCggI0JQpU1RYWKi0tDTdc8897uc0bdpUkpSXl6cZM2Zo9uzZ6tSpk%2BbNm6dp06bphRde8NbhAAAAH%2BOXZ7AOHz6snJwcpaen66abblJ8fLxSU1O1bds2SVJhYaFuvvlmORwO9y0kJESStGHDBt15550aOnSoOnXqpPnz52vnzp06cuSINw8JAAD4EL8sWA6HQ6tWrVJERITH%2BpkzZ3TmzBmdOnVKbdu2vehzc3NzFR8f777fokULtWzZUrm5uVdyZAAA4Ef8smCFhoYqMTHRfb%2BmpkYbNmxQz549VVhYKJvNpueff1633Xab7r77br3%2B%2BuvuxxYVFSkyMtJjf82bN9fJkyctmx8AAPg2v3wP1k9lZGTo4MGDevXVV/Xpp5/KZrOpXbt2uv/%2B%2B7Vv3z798Y9/VNOmTdW/f39VVFQoKCjI4/lBQUFyuVx1fr2ioiI5nU6PNbu9ca3iVl%2BBgb7Vj%2B1235r3xy5k7WuZ%2ByKytg5ZW4OcrdOQsvb7gpWRkaG1a9dq0aJF6tChg2666Sb17dtXYWFhkqROnTrpq6%2B%2B0qZNm9S/f38FBwfXKlMul8v9Hq26yMrKUmZmpsdaSkqKUlNT639APiw8vIm3R6i30NC6fx2gfsjaOmRtDXK2TkPI2q8L1jPPPKNNmzYpIyNDAwYMkCTZbDZ3ubqgXbt22r17tyQpKipKxcXFHtuLi4vlcDjq/LrJyclKSkryWLPbG6uk5OzlHMbPaggN/VKYffxWCgwMUGhoiMrKylVdXePtcfwaWVuHrK1Bzta5WNbe%2BuHebwtWZmamXnnlFT377LMaOHCge33JkiX617/%2BpTVr1rjXDh06pHbt2kmSoqOjlZ2drWHDhkmSTpw4oRMnTig6OrrOrx0ZGVnrcqDTeVpVVVf3N5Y/HH91dY1fHIcvIGvrkLU1yNk6DSFr3zoFUkeFhYVavny5Hn30UcXFxcnpdLpvffv21b59%2B7R69Wp98803%2Bstf/qKtW7fqoYcekiSNHDlSb7zxhjZv3qxDhw5p8uTJ6tOnj9q0aePlowIAAL7CL89gvf/%2B%2B6qurtaKFSu0YsUKj22ff/65lixZoqVLl2rJkiVq1aqVFi5cqNjYWElSbGys5syZo6VLl%2Br7779Xr1699Mwzz3jjMAAAgI%2ByGYZheHuIq4HTedr0fdrtAeq/4EPT93ulvPtEL2%2BPcNns9gCFhzdRSclZr5929ndkbR2ytgY5W%2BdiWTsc13plFr%2B8RAgAAOBNFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZHZvD4Crx52L/9fbI1ySd5/o5e0RAAA%2BijNYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyCtZFVFZWavr06YqPj1fv3r310ksveXskAADgQ/ig0YuYP3%2B%2BDhw4oLVr1%2Br48eOaMmWKWrZsqYEDB3p7NAAA4AMoWD9x7tw5bd68WS%2B%2B%2BKK6dOmiLl26KD8/Xxs3bqRgXWX45HkAwOWiYP3EoUOHVFVVpdjYWPdaXFycnn/%2BedXU1CgggKuqaJh8qRBSBgH4OwrWTzidToWHhysoKMi9FhERocrKSpWWlqpZs2a/uI%2BioiI5nU6PNbu9sSIjI02dNTCQsgffZLf//Nfuha9rvr6vPLK2BjlbpyFlTcH6ifLyco9yJcl93%2BVy1WkfWVlZyszM9FibMGGCHn/8cXOG/D9FRUUa86t8JScnm17e4KmoqEhZWVlkbYGioiKtXbuKrC1A1tYgZ%2Bs0pKy9X/EamODg4FpF6sL9Ro0a1WkfycnJeu211zxuycnJps/qdDqVmZlZ62wZzEfW1iFr65C1NcjZOg0pa85g/URUVJRKSkpUVVUlu/2HeJxOpxo1aqTQ0NA67SMyMtLrzRkAAHgPZ7B%2BonPnzrLb7crJyXGvZWdnq2vXrrzBHQAA1AmN4SdCQkI0dOhQzZo1S3l5edqxY4deeukljR492tujAQAAHxE4a9asWd4eoqHp2bOnDh48qIULF%2Brjjz/W73//ew0fPtzbY11UkyZNlJCQoCZNmnh7FL9H1tYha%2BuQtTXI2ToNJWubYRiGVycAAADwM1wiBAAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsHyUZWVlZo%2Bfbri4%2BPVu3dvvfTSS94eye%2B4XC4NHjxYe/bsca8dOXJEY8eOVUxMjO666y7t2rXLixP6tlOnTik1NVUJCQlKTExUenq6KisrJZGz2b7%2B%2Bms9/PDDio2NVZ8%2BfbRq1Sr3NrK%2BcsaNG6epU6e67x88eFD33nuvoqOjNXz4cB04cMCL0/m%2Bv/3tb%2BrYsaPHLTU1VVLDyJqC5aPmz5%2BvAwcOaO3atXr66aeVmZmp9957z9tj%2BY3KykpNnDhR%2Bfn57jXDMJSSkqKIiAht2bJFQ4YM0YQJE3T8%2BHEvTuqbDMNQamqqysvLtXHjRi1atEgffPCBFi9eTM4mq6mp0bhx4xQeHq7XX39ds2fP1ooVK/TWW2%2BR9RX09ttva%2BfOne77586d07hx4xQfH6/XXntNsbGxGj9%2BvM6dO%2BfFKX1bQUGB%2Bvbtq127drlvc%2BfObThZG/A5Z8%2BeNbp27Wrs3r3bvbZs2TLj/vvv9%2BJU/iM/P9%2B4%2B%2B67jd/%2B9rdGhw4d3Dl/9NFHRkxMjHH27Fn3Y8eMGWMsXbrUW6P6rIKCAqNDhw6G0%2Bl0r7311ltG7969ydlkp06dMv7rv/7LOH36tHstJSXFePrpp8n6CikpKTFuu%2B02Y/jw4caUKVMMwzCMzZs3G0lJSUZNTY1hGIZRU1Nj9O/f39iyZYs3R/VpaWlpxsKFC2utN5SsOYPlgw4dOqSqqirFxsa61%2BLi4pSbm6uamhovTuYf9u7dqx49eigrK8tjPTc3VzfffLMaN27sXouLi1NOTo7VI/o8h8OhVatWKSIiwmP9zJkz5GyyyMhILV68WE2bNpVhGMrOzta%2BffuUkJBA1lfI//zP/2jIkCG68cYb3Wu5ubmKi4uTzWaTJNlsNnXv3p2s66GwsFBt27attd5QsqZg%2BSCn06nw8HAFBQW51yIiIlRZWanS0lIvTuYfRo0apenTpyskJMRj3el0KjIy0mOtefPmOnnypJXj%2BYXQ0FAlJia679fU1GjDhg3q2bMnOV9BSUlJGjVqlGJjYzVgwACyvgI%2B/vhj/fOf/9Rjjz3msU7W5jIMQ19%2B%2BaV27dqlAQMGqF%2B/flqwYIFcLleDydpu6avBFOXl5R7lSpL7vsvl8sZIV4Wfy53M6y8jI0MHDx7Uq6%2B%2BqjVr1pDzFbJ06VIVFxdr1qxZSk9P52vaZJWVlXr66ac1c%2BZMNWrUyGMbWZvr%2BPHj7kwXL16so0ePau7cuaqoqGgwWVOwfFBwcHCtL5QL93/6TQ3zBAcH1zpD6HK5yLyeMjIytHbtWi1atEgdOnQg5yuoa9eukn4oAk8%2B%2BaSGDx%2Bu8vJyj8eQ9eXLzMzULbfc4nF29oKf%2B%2B82WV%2BeVq1aac%2BePbruuutks9nUuXNn1dTUaNKkSUpISGgQWVOwfFBUVJRKSkpUVVUlu/2Hf4VOp1ONGjVSaGiol6fzX1FRUSooKPBYKy4urnUqGnX3zDPPaNOmTcrIyNCAAQMkkbPZiouLlZOTo379%2BrnXbrzxRp0/f14Oh0OHDx%2Bu9Xiyvjxvv/22iouL3e%2BPvfA/%2Be3bt2vw4MEqLi72eDxZ109YWJjH/fbt26uyslIOh6NBZM17sHxQ586dZbfbPd6wl52dra5duyoggH%2BlV0p0dLQ%2B/fRTVVRUuNeys7MVHR3txal8V2Zmpl555RU9%2B%2ByzGjRokHudnM119OhRTZgwQadOnXKvHThwQM2aNVNcXBxZm2j9%2BvV66623tHXrVm3dulVJSUlKSkrS1q1bFR0drX/9618yDEPSD%2B8h2r9/P1lfpg8//FA9evTwOAP72WefKSwsTHFxcQ0ia/5v7INCQkI0dOhQzZo1S3l5edqxY4deeukljR492tuj%2BbWEhAS1aNFC06ZNU35%2BvlauXKm8vDyNGDHC26P5nMLCQi1fvlyPPvqo4uLi5HQ63TdyNlfXrl3VpUsXTZ8%2BXQUFBdq5c6cyMjL0%2B9//nqxN1qpVK11//fXuW5MmTdSkSRNdf/31GjhwoMrKyjRv3jwVFBRo3rx5Ki8v15133untsX1SbGysgoOD9dRTT%2Bnw4cPauXOn5s%2Bfr0ceeaThZG3ph0LANOfOnTMmT55sxMTEGL179zZefvllb4/kl378OViGYRhfffWV8bvf/c645ZZbjEGDBhn/%2B7//68XpfNcLL7xgdOjQ4aI3wyBns508edJISUkxunfvbvTq1ctYsWKF%2BzOCyPrKmTJlivtzsAzDMHJzc42hQ4caXbt2NUaMGGF8%2BumnXpzO933xxRfG2LFjjZiYGKNXr17Gc8895/66bghZ2wzj/86hAQAAwBRcIgQAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADDZ/wf9ds1FJ0H1sgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7361554752996460197”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>415</td> <td class=”number”>12.9%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (23)</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7361554752996460197”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>415</td> <td class=”number”>12.9%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>35.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FMRKT_WICCASH16”>FMRKT_WICCASH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>22</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.55729</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>39</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>53.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7964445839843230236”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7964445839843230236,#minihistogram-7964445839843230236”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7964445839843230236”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7964445839843230236”

aria-controls=”quantiles-7964445839843230236” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7964445839843230236” aria-controls=”histogram-7964445839843230236”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7964445839843230236” aria-controls=”common-7964445839843230236”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7964445839843230236” aria-controls=”extreme-7964445839843230236”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7964445839843230236”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>3</td>

</tr> <tr>

<th>Maximum</th> <td>39</td>

</tr> <tr>

<th>Range</th> <td>39</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.9752</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.5444</td>

</tr> <tr>

<th>Kurtosis</th> <td>136.62</td>

</tr> <tr>

<th>Mean</th> <td>0.55729</td>

</tr> <tr>

<th>MAD</th> <td>0.85966</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.6551</td>

</tr> <tr>

<th>Sum</th> <td>1250</td>

</tr> <tr>

<th>Variance</th> <td>3.9016</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7964445839843230236”>

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nzp1VUlKiVatWaeHChVqyZInat2%2BvkSNH6oEHHvBHaQAAADfNLwFLkgoLC/WnP/1Jf/rTn3TgwAG1b99eAwcO1MmTJ/X8889r165dSktL81d5AAAAN8z2gLVp0yZt2rRJO3fuVP369TVgwAAtWLBAzZo1876mUaNGmj17NgELAAA4ku0BKy0tTcnJycrKylLXrl0VFFT1MrDmzZtrxIgRdpcGAABghO0B65NPPlG9evV0%2BvRpb7jas2eP2rZtq%2BDgYElS%2B/bt1b59e7tLAwAAMML2vyI8d%2B6cevXqpTfffNM7NmbMGPXv318nTpywuxwAAADjbA9YL730ku688049/vjj3rEtW7aoUaNGSk9Pt7scAAAA42wPWH//%2B9/13HPPKTIy0jtWv359TZw4UTt27LC7HAAAAONsD1hut1tnzpypMl5SUsId3QEAQECwPWB17dpVs2bN0j//%2BU/vWEFBgdLT05WUlGR3OQAAAMbZ/leEkyZN0uOPP66ePXsqPDxcknTmzBm1bdtWkydPtrscAAAA42wPWA0aNNCGDRv02WefKT8/X263W3fddZd%2B97vfyeVy2V0OAACAcX75qJzg4GAlJSVxShAAAAQk2wOWx%2BPR/PnztXv3bl26dKnKhe1//etf7S4JAADAKNsD1gsvvKC9e/eqT58%2B%2Bs1vfmP37gEAAG452wPWjh07tHTpUiUmJtq9awAAAFvYfpuG2rVrq0GDBnbvFgAAwDa2B6z%2B/ftr6dKlunz5st27BgAAsIXtpwhPnz6tzZs366OPPtIdd9yhkJAQn/mVK1faXRIAAIBRfrlNQ9%2B%2Bff2xWwAAAFvYHrDS09Pt3iUAAICtbL8GS5IKCwuVmZmp8ePH61//%2Bpf%2B8pe/6PDhw/4oBQAAwDjbA9aRI0fUr18/bdiwQR988IEuXLigLVu2aPDgwcrLy7O7HAAAAONsD1gvv/yyunfvrm3btum2226TJL366qvq1q2b5s6da3c5AAAAxtkesHbv3q3HH3/c54Od3W63xo4dq3379tldDgAAgHG2B6yKigpVVFRUGT9//ryCg4PtLgcAAMA42wNWly5dtHjxYp%2BQdfr0aWVkZKhTp052lwMAAGCc7QHrueee0969e9WlSxeVlpbqP//zP5WcnKyjR49q0qRJdpcDAABgnO33wYqOjtbGjRu1efNmffPNN6qoqNCwYcPUv39/1a1b1%2B5yAAAAjPPLndzDwsI0ZMgQf%2BwaAADglrM9YD322GPXnOezCAEAgNPZHrCaNGni87y8vFxHjhzRgQMHNHLkSLvLAQAAMK7GfBZhVlaWTp48aXM1AAAA5vnlswivpn///nr//ff9XQYAAMBNqzEB68svv%2BRGowAAICDUiIvcz507p3/84x8aPny43eUAAAAYZ3vAaty4sc/nEErSbbfdphEjRuihhx6yuxwAAADjbA9YL7/8st27BAAAsJXtAWvXrl3Vfu199913CysBAAC4NWwPWI8%2B%2Bqj3FKFlWd7xymMul0vffPON3eUBAADcNNsD1qJFizRr1ixNmDBBHTp0UEhIiL766ivNmDFDAwcOVO/eve0uCQAAwCjbb9OQnp6uqVOnqmfPnqpXr57q1KmjTp06acaMGVq7dq2aNGnifQAAADiR7QGrsLDwquGpbt26Ki4utrscAAAA42wPWHFxcXr11Vd17tw579jp06eVkZGh3/3ud3aXAwAAYJztAev5559Xbm6uunbtqkGDBmngwIFKTk5WQUGBpk6det3bKysrU9%2B%2BfbVz507vWEFBgUaNGqW4uDj17t1b27dv93nPZ599pr59%2Byo2NlaPPfaYCgoKfOaXL1%2BupKQkxcfHa8qUKSopKbmxZgEAwK%2BS7QGrRYsW2rJli8aPH6%2B4uDjFx8crLS1NmzZt0m9/%2B9vr2lZpaameeeYZ5efne8csy1JKSooaNmyo9evXq3///ho3bpyOHz8uSTp%2B/LhSUlI0aNAgvfvuu6pfv77Gjh3r/evFDz74QJmZmZoxY4ZWrFihvLw8ZWRkmPsCAACAgGf7XxFK0u23364hQ4bo6NGjuuOOOyT9eDf363Hw4EGNHz/e51YPkrRjxw4VFBTonXfeUe3atdWiRQt9/vnnWr9%2BvZ566imtW7dO7dq10xNPPCHpx4vuO3furC%2B%2B%2BEIdO3bUypUrNXLkSCUnJ0uSpk%2BfrtGjR2vChAkKCwsz0D0AAAh0th/BsixLc%2BfO1X333ae%2Bffvq5MmTmjRpktLS0nTp0qVqb%2BdKIMrOzvYZz8vLU5s2bVS7dm3vWEJCgnJzc73ziYmJ3rmwsDC1bdtWubm5unz5sr766iuf%2Bbi4OF26dEn79%2B%2B/0ZYBAMCvjO1HsFatWqVNmzbpxRdf1IwZMyRJ3bt31/Tp09WwYUP993//d7W283MfDO3xeBQVFeUz1qBBA508efIX58%2BcOaPS0lKfebfbrYiICO/7q6OwsFAej8dnzO2uXWW/Nys42PZ8fFPc7uur90p/TuuzuujP2ejPuQK5N4n%2BagrbA1Z2dramTp2qHj16aObMmZKk3r1767bbblN6enq1A9bPKSkpUUhIiM9YSEiIysrKfnH%2B4sWL3uc/9/7qyM7OVmZmps9YSkqKUlNTq72NQFSvXp0bel94eGCfmqU/Z6M/5wrk3iT68zfbA9bRo0fVunXrKuOtWrWqctTnRoSGhur06dM%2BY2VlZapVq5Z3vnJYKisrU3h4uEJDQ73PK89fz/VXQ4cOVbdu3XzG3O7aKi4%2BX%2B1tVEdNT%2B%2BVXW//wcFBCg8P05kzJbp8ueIWVeU/9Ods9OdcgdybRH%2BV3egv9zfL9oDVpEkTffXVV2ratKnP%2BCeffOK94P1mREdH6%2BDBgz5jRUVF3tNz0dHRKioqqjLfunVrRUREKDQ0VEVFRWrRooUkqby8XKdPn1ZkZGS1a4iKiqpyOtDjOavy8sD7Rr8eN9r/5csVAf21oz9noz/nCuTeJPrzN9sPgYwePVrTp0/XypUrZVmWPv/8c82dO1dz5szRo48%2BetPbj42N1ddff%2B093SdJOTk5io2N9c7n5OR450pKSrRv3z7FxsYqKChIMTExPvO5ublyu91q1arVTdcGAAB%2BHWw/gjV48GCVl5frjTfe0MWLFzV16lTVr19fTz/9tIYNG3bT2%2B/QoYMaNWqkyZMna%2BzYsfrwww%2B1Z88epaene/f/1ltvacmSJUpOTlZWVpaaNm2qjh07Svrx4vmpU6eqZcuWioqK0rRp0/TII49wiwYAAFBttgeszZs3q1evXho6dKi%2B//57WZalBg0aGNt%2BcHCwFi5cqLS0NA0aNEh33nmnsrKy1LhxY0lS06ZN9frrr%2Bull15SVlaW4uPjlZWVJZfLJUnq06ePjh07pqlTp6qsrEwPPPCAJkyYYKw%2BAAAQ%2BFxW5Tt13mIdOnTQ//7v/%2Bquu%2B6yc7d%2B5/GcNb5NtztIPeZ%2Bany7t8r7T3e%2Brte73UGqV6%2BOiovP1%2Bjz7DeK/pyN/pwrkHuT6K%2ByyMjf2FBVVbZfg9WsWTMdOHDA7t0CAADYxvZThK1atdKzzz6rpUuXqlmzZt5bI1xx5VopAAAAp7I9YH377bdKSEiQJCP3vQIAAKhpbAlYc%2BbM0bhx41S7dm2tWrXKjl0CAAD4jS3XYC1btkwlJSU%2BY2PGjFFhYaEduwcAALCVLQHran%2BouGvXLpWWltqxewAAAFs568PsAAAAHICABQAAYJhtAevKndIBAAACnW23aZg1a5bPPa8uXbqkjIwM1alTx%2Bd13AcLAAA4nS0B67777qtyz6v4%2BHgVFxeruLjYjhIAAABsY0vA4t5XAADg14SL3AEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEBG7C2bt2qe%2B65x%2BeRmpoqSdq3b5%2BGDBmi2NhYDR48WHv37vV57%2BbNm9W9e3fFxsYqJSVF33//vT9aAAAADhWwAevgwYNKTk7W9u3bvY9Zs2bpwoULGjNmjBITE/XHP/5R8fHxevLJJ3XhwgVJ0p49e5SWlqZx48YpOztbZ86c0eTJk/3cDQAAcJKADViHDh1Sy5YtFRkZ6X2Eh4dry5YtCg0N1cSJE9WiRQulpaWpTp06%2Bstf/iJJWr16tR588EENGDBArVq10pw5c/Txxx%2BroKDAzx0BAACnCOiA1axZsyrjeXl5SkhIkMvlkiS5XC61b99eubm53vnExETv6xs1aqTGjRsrLy/PlroBAIDzBWTAsixL3377rbZv366ePXuqe/fumjt3rsrKyuTxeBQVFeXz%2BgYNGujkyZOSpMLCwmvOAwAA/BK3vwu4FY4fP66SkhKFhIRo/vz5Onr0qGbNmqWLFy96x38qJCREZWVlkqSLFy9ec746CgsL5fF4fMbc7tpVgtvNCg52Vj52u6%2Bv3iv9Oa3P6qI/Z6M/5wrk3iT6qykCMmA1adJEO3fu1O233y6Xy6XWrVuroqJCEyZMUIcOHaqEpbKyMtWqVUuSFBoaetX5sLCwau8/OztbmZmZPmMpKSnev2L8tapXr84NvS88vPpfeyeiP2ejP%2BcK5N4k%2BvO3gAxYkhQREeHzvEWLFiotLVVkZKSKiop85oqKirxHl6Kjo686HxkZWe19Dx06VN26dfMZc7trq7j4/PW08Itqenqv7Hr7Dw4OUnh4mM6cKdHlyxW3qCr/oT9noz/nCuTeJPqr7EZ/ub9ZARmwPv30Uz377LP66KOPvEeevvnmG0VERCghIUFvvvmmLMuSy%2BWSZVnavXu3/vCHP0iSYmNjlZOTo0GDBkmSTpw4oRMnTig2Nrba%2B4%2BKiqpyOtDjOavy8sD7Rr8eN9r/5csVAf21oz9noz/nCuTeJPrzN2cdAqmm%2BPh4hYaG6vnnn9fhw4f18ccfa86cOfr973%2BvXr166cyZM5o9e7YOHjyo2bNnq6SkRA8%2B%2BKAkadiwYdq0aZPWrVun/fv3a%2BLEibr//vt1xx13%2BLkrAADgFAEZsOrWrau33npL33//vQYPHqy0tDQNHTpUv//971W3bl0tXrzYe5QqLy9PS5YsUe3atSX9GM5mzJihrKwsDRs2TLfffrvS09P93BEAAHCSgDxFKEl33323li1bdtW5e%2B%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%2BXBAAAHIRThFcxZ84c7d27VytWrNDx48c1adIkNW7cWL169fJ3aQAAwAEIWJVcuHBB69at05tvvqm2bduqbdu2ys/P15o1awhYAACgWghYlezfv1/l5eWKj4/3jiUkJGjRokWqqKhQUBBnVX8tuCgfAHCjCFiVeDwe1atXTyEhId6xhg0bqrS0VKdPn1b9%2BvV/cRuFhYXyeDw%2BY253bUVFRRmtNTiYsIf/z0mBcOuzSf4u4aZd%2BfkL1J/DQO4vkHuT6K%2BmIGBVUlJS4hOuJHmfl5WVVWsb2dnZyszM9Bl3YO6xAAAKJElEQVQbN26cnnrqKTNF/p/CwkKN/G2%2Bhg4dajy81QSFhYXKzs6mP4f6NfS3YsVS%2BnOgQO5Nor%2BaombHPz8IDQ2tEqSuPK9Vq1a1tjF06FD98Y9/9HkMHTrUeK0ej0eZmZlVjpYFCvpzNvpztkDuL5B7k%2BivpuAIViXR0dEqLi5WeXm53O4fvzwej0e1atVSeHh4tbYRFRVVo1M1AAC4tTiCVUnr1q3ldruVm5vrHcvJyVFMTAwXuAMAgGohMVQSFhamAQMGaNq0adqzZ4%2B2bdumt99%2BW4899pi/SwMAAA4RPG3atGn%2BLqKm6dSpk/bt26dXXnlFn3/%2Buf7whz9o8ODB/i7rqurUqaMOHTqoTp06/i7llqA/Z6M/Zwvk/gK5N4n%2BagKXZVmWv4sAAAAIJJwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwHKo0tJSTZkyRYmJierSpYvefvttf5dk1NatW3XPPff4PFJTU/1d1k0rKytT3759tXPnTu9YQUGBRo0apbi4OPXu3Vvbt2/3Y4U352r9zZo1q8parl692o9VXp9Tp04pNTVVHTp0UFJSktLT01VaWiopMNbuWv05fe0k6ciRIxo9erTi4%2BN1//33a%2BnSpd65QFi/a/UXCOv3U2PGjNFzzz3nfb5v3z4NGTJEsbGxGjx4sPbu3evH6qpy%2B7sA3Jg5c%2BZo7969WrFihY4fP65JkyapcePG6tWrl79LM%2BLgwYNKTk7WzJkzvWOhoaF%2BrOjmlZaWavz48crPz/eOWZallJQUtWzZUuvXr9e2bds0btw4bdmyRY0bN/Zjtdfvav1J0qFDhzR%2B/HgNHDjQO1a3bl27y7shlmUpNTVV4eHhWrNmjX744QdNmTJFQUFBmjhxouPX7lr9TZo0ydFrJ0kVFRUaM2aMYmJitGHDBh05ckTPPPOMoqOj1bdvX8ev37X669evn%2BPX76f%2B/Oc/6%2BOPP/b2cuHCBY0ZM0b9%2BvXTyy%2B/rLVr1%2BrJJ5/U1q1bVbt2bT9X%2B38sOM758%2BetmJgYa8eOHd6xrKwsa8SIEX6syqzx48dbr7zyir/LMCY/P9966KGHrH79%2BlktW7b0rt1nn31mxcXFWefPn/e%2BduTIkdaCBQv8VeoN%2Bbn%2BLMuykpKSrE8//dSP1d24gwcPWi1btrQ8Ho937L333rO6dOkSEGt3rf4sy9lrZ1mWderUKeu//uu/rLNnz3rHUlJSrBdffDEg1u9a/VmW89fviuLiYqtr167W4MGDrUmTJlmWZVnr1q2zunXrZlVUVFiWZVkVFRVWjx49rPXr1/uzVB%2BcInSg/fv3q7y8XPHx8d6xhIQE5eXlqaKiwo%2BVmXPo0CE1a9bM32UY88UXX6hjx47Kzs72Gc/Ly1ObNm18fuNKSEhQbm6u3SXelJ/r79y5czp16pRj1zIyMlJLly5Vw4YNfcbPnTsXEGt3rf6cvnaSFBUVpfnz56tu3bqyLEs5OTnatWuXOnToEBDrd63%2BAmH9rvif//kf9e/fX3fddZd3LC8vTwkJCXK5XJIkl8ul9u3b16j1I2A5kMfjUb169RQSEuIda9iwoUpLS3X69Gk/VmaGZVn69ttvtX37dvXs2VPdu3fX3LlzVVZW5u/Sbtjw4cM1ZcoUhYWF%2BYx7PB5FRUX5jDVo0EAnT560s7yb9nP9HTp0SC6XS4sWLVLXrl310EMPacOGDX6q8vqFh4crKSnJ%2B7yiokKrV69Wp06dAmLtrtWf09eusm7dumn48OGKj49Xz549A2L9fqpyf4Gyfp9//rn%2B/ve/a%2BzYsT7jTlg/rsFyoJKSEp9wJcn73Mkh5Irjx497e5w/f76OHj2qWbNm6eLFi3r%2B%2Bef9XZ5RP7eWgbCOknT48GG5XC41b95cI0aM0K5du/TCCy%2Bobt266tGjh7/Lu24ZGRnat2%2Bf3n33XS1fvjzg1u6n/X399dcBtXYLFixQUVGRpk2bpvT09ID72avcX9u2bR2/fqWlpXrxxRc1depU1apVy2fOCetHwHKg0NDQKt9EV55X/iZ0oiZNmmjnzp26/fbb5XK51Lp1a1VUVGjChAmaPHmygoOD/V2iMaGhoVWOOpaVlQXEOkrSgAEDlJycrIiICElSq1at9N1332nt2rWO%2BUf%2BioyMDK1YsULz5s1Ty5YtA27tKvd39913B8zaSVJMTIykH/%2Bn/eyzz2rw4MEqKSnxeY2T169yf7t373b8%2BmVmZqpdu3Y%2BR1mv%2BLn/D9ak9eMUoQNFR0eruLhY5eXl3jGPx6NatWopPDzcj5WZExER4T23LkktWrRQaWmpfvjhBz9WZV50dLSKiop8xoqKiqoc%2BnYql8vl/Qf%2BiubNm%2BvUqVN%2BqujGzJw5U8uWLVNGRoZ69uwpKbDW7mr9BcLaFRUVadu2bT5jd911ly5duqTIyEjHr9%2B1%2Bjt37pzj1%2B/Pf/6ztm3bpvj4eMXHx%2Bu9997Te%2B%2B9p/j4eEf8/BGwHKh169Zyu90%2BF/Pl5OQoJiZGQUHOX9JPP/1UHTt29Pnt8ptvvlFERITq16/vx8rMi42N1ddff62LFy96x3JychQbG%2BvHqsx57bXXNGrUKJ%2Bx/fv3q3nz5v4p6AZkZmbqnXfe0auvvqo%2Bffp4xwNl7X6uv0BYu6NHj2rcuHE%2BoWLv3r2qX7%2B%2BEhISHL9%2B1%2Bpv1apVjl%2B/VatW6b333tPGjRu1ceNGdevWTd26ddPGjRsVGxurL7/8UpZlSfrx2t3du3fXrPXz698w4oa98MILVp8%2Bfay8vDxr69atVvv27a0PPvjA32UZcfbsWSspKcl65plnrEOHDlkfffSR1aVLF2vJkiX%2BLs2In97GoLy83Ordu7f19NNPWwcOHLAWL15sxcXFWceOHfNzlTfup/3l5eVZbdq0sZYuXWodOXLEWrNmjdWuXTtr9%2B7dfq6yeg4ePGi1bt3amjdvnlVYWOjzCIS1u1Z/Tl87y/rx52vQoEHWE088YeXn51sfffSR9W//9m/W8uXLA2L9rtVfIKxfZZMmTfLepuHs2bNWp06drJkzZ1r5%2BfnWzJkzrc6dO/vcdsPfCFgOdeHCBWvixIlWXFyc1aVLF2vZsmX%2BLsmoAwcOWKNGjbLi4uKszp07W6%2B//rr3fidOV/k%2BUd9995317//%2B71a7du2sPn36WH/729/8WN3Nq9zf1q1brX79%2BlkxMTFWr169HPWLwOLFi62WLVte9WFZzl%2B7X%2BrPyWt3xcmTJ62UlBSrffv2VufOna033njD%2B2%2BJ09fPsq7dXyCs30/9NGBZ1o%2B/wA0YMMCKiYmxHn74Yevrr7/2Y3VVuSzr/46vAQAAwAjnX7ADAABQwxCwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wMj2M15/lXZjQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7964445839843230236”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>302</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (11)</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7964445839843230236”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>302</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODHUB16”>FOODHUB16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>6</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.054598</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>93.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7440678085305071876”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7440678085305071876,#minihistogram-7440678085305071876”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7440678085305071876”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7440678085305071876”

aria-controls=”quantiles-7440678085305071876” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7440678085305071876” aria-controls=”histogram-7440678085305071876”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7440678085305071876” aria-controls=”common-7440678085305071876”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7440678085305071876” aria-controls=”extreme-7440678085305071876”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7440678085305071876”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6</td>

</tr> <tr>

<th>Range</th> <td>6</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.29019</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.3151</td>

</tr> <tr>

<th>Kurtosis</th> <td>129.12</td>

</tr> <tr>

<th>Mean</th> <td>0.054598</td>

</tr> <tr>

<th>MAD</th> <td>0.10428</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.884</td>

</tr> <tr>

<th>Sum</th> <td>171</td>

</tr> <tr>

<th>Variance</th> <td>0.084211</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7440678085305071876”>

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RfHy8Zs6cqcrKSrftFwAA8H5eGbDsdrtSU1NVWVmpzZs36/nnn9c//vEPLV26VHa7XSkpKWrTpo1ycnI0dOhQTZkyRSdPnpQknTx5UikpKRo%2BfLi2bdum8PBwPfzww7Lb7ZKkd955R5mZmZo3b57Wr1%2Bv/Px8ZWRkeHJ3AQCAl/HKgHX06FHl5eUpPT1dnTt3VmJiolJTU7Vjxw7t2bNHxcXFmjdvnjp16qTJkycrLi5OOTk5kqStW7fq5ptv1oQJE9S5c2elp6frxIkT2rdvnyRpw4YNuv/%2B%2B5WUlKTu3btr7ty5ysnJ4SgWAABoNK8MWBEREVqzZo3atGnjNH7u3Dnl5%2BfrpptuUsuWLR3jCQkJysvLkyTl5%2BcrMTHRMRcUFKRu3bopLy9PtbW1%2BuSTT5zm4%2BLidOnSJR0%2BfLiJ9woAAPgKi6cL%2BDVCQkLUp08fx/O6ujpt2rRJvXr1ks1mU2RkpNPrW7durdOnT0tSg/MVFRWqqqpymrdYLAoNDXW8vzFKSkpks9mcxiyWlvW2%2B1sFBHhXPrZY3F/v5R55W6/chf64Ro9co0cNoz%2Bu%2BWKPvDJg/VhGRoYOHTqkbdu2ad26dQoMDHSaDwwMVHV1tSSpsrLyZ%2BcvXrzoeP5z72%2BM7OxsZWZmOo2lpKQoNTW10Wv4orCwVh7bdkhIkMe27Q3oj2v0yDV61DD645ov9cjrA1ZGRobWr1%2Bv559/XjfccIOsVqvKy8udXlNdXa0WLVpIkqxWa72wVF1drZCQEFmtVsfzH88HBTX%2Bi56cnKx%2B/fo5jVksLVVWdr7RazSGtyV90/vfGAEB/goJCVJFRaVqa%2Bvcvv3mjv64Ro9co0cNoz%2BuNWWPPPXDvVcHrPnz52vLli3KyMjQwIEDJUlRUVE6cuSI0%2BtKS0sdp%2BeioqJUWlpab75r164KDQ2V1WpVaWmpOnXqJEmqqalReXm5IiIiGl1XZGRkvdOBNttZ1dRc2d9Yntz/2tq6K77/DaE/rtEj1%2BhRw%2BiPa77UI%2B86BPIDmZmZevXVV/Xcc89p8ODBjvHY2Fh9%2BumnjtN9kpSbm6vY2FjHfG5urmOusrJShw4dUmxsrPz9/RUTE%2BM0n5eXJ4vFoi5durhhrwAAgC/wyoBVVFSklStX6sEHH1RCQoJsNpvj0aNHD7Vt21ZpaWkqLCxUVlaWCgoKNHLkSEnSiBEjdODAAWVlZamwsFBpaWlq3769evbsKUkaPXq01q5dq507d6qgoEBz5szRvffe%2B4tOEQIAgCubV54ifO%2B991RbW6tVq1Zp1apVTnOff/65Vq5cqVmzZmn48OG67rrrtGLFCkVHR0uS2rdvrxdeeEFPP/20VqxYofj4eK1YsUJ%2Bfn6SpMGDB%2BvEiROaPXu2qqur9Yc//EHTp093%2Bz4CAADv5We/fAtzNCmb7azxNS0Wfw1Y/IHxdZvK24/0dvs2LRZ/hYW1UlnZeZ85r28S/XGNHrlGjxpGf1xryh5FRFxtdL3G8spThAAAAM0ZAQsAAMAwAhYAAIBhBCwAAADD3B6w7rnnHr366qs6e9b8Rd8AAADNgdsDVq9evbR69Wrdfvvteuyxx7R7927xi4wAAMCXuD1gTZ06Vf/4xz%2B0cuVKBQQE6M9//rP69u2r559/Xl9%2B%2BaW7ywEAADDOIzca9fPzU%2B/evdW7d29VVlZq48aNWrlypbKysnTLLbfo/vvv1x/%2B8AdPlAYAAPCbeexO7iUlJXrjjTf0xhtv6IsvvtAtt9yiu%2B%2B%2BW6dPn9aTTz6p/fv3a9asWZ4qDwAA4Fdze8B6/fXX9frrr2vv3r0KDw/XsGHDtHz5cnXo0MHxmrZt22rhwoUELAAA4JXcHrBmzZqlpKQkrVixQnfccYf8/etfBtaxY0eNGTPG3aUBAAAY4faA9f777yssLEzl5eWOcFVQUKBu3bopICBAknTLLbfolltucXdpAAAARrj9twjPnTunQYMG6aWXXnKMTZo0SUOHDtWpU6fcXQ4AAIBxbg9YTz/9tK677jqNHz/eMfbWW2%2Bpbdu2Sk9Pd3c5AAAAxrk9YP3rX//SE088oYiICMdYeHi4Hn/8ce3Zs8fd5QAAABjn9oBlsVhUUVFRb7yyspI7ugMAAJ/g9oB1xx13aMGCBfr6668dY8XFxUpPT1efPn3cXQ4AAIBxbv8twhkzZmj8%2BPEaOHCgQkJCJEkVFRXq1q2b0tLS3F0OAACAcW4PWK1bt9Zrr72mDz/8UIWFhbJYLLr%2B%2But12223yc/Pz93lAAAAGOeRj8oJCAhQnz59OCUIAAB8ktsDls1m09KlS3XgwAFdunSp3oXt7733nrtLAgAAMMrtAeupp57SwYMHNXjwYF199dXu3jwAAECTc3vA2rNnj9asWaPExER3bxoAAMAt3H6bhpYtW6p169bu3iwAAIDbuD1gDR06VGvWrFFtba27Nw0AAOAWbj9FWF5erh07duh///d/de211yowMNBpfsOGDe4uCQAAwCiP3KZhyJAhntgsAACAW7g9YKWnp7t7kwAAAG7l9muwJKmkpESZmZmaOnWqvvnmG/3973/X0aNHPVEKAACAcW4PWMeOHdMf//hHvfbaa3rnnXd04cIFvfXWWxoxYoTy8/PdXQ4AAIBxbg9YzzzzjPr376%2BdO3fqqquukiQ999xz6tevnxYvXuzucgAAAIxze8A6cOCAxo8f7/TBzhaLRQ8//LAOHTrk7nIAAACMc3vAqqurU11dXb3x8%2BfPKyAgwN3lAAAAGOf2gHX77bfrxRdfdApZ5eXlysjIUK9evdxdDgAAgHFuD1hPPPGEDh48qNtvv11VVVX67//%2BbyUlJen48eOaMWOGu8sBAAAwzu33wYqKitL27du1Y8cOffbZZ6qrq9OoUaM0dOhQBQcHu7scAAAA4zxyJ/egoCDdc889ntg0AABAk3N7wBo7dmyD83wWIQAA8HZuD1jt2rVzel5TU6Njx47piy%2B%2B0P333%2B/ucgAAAIxrNp9FuGLFCp0%2BffoXr1ddXa3hw4frqaeeUs%2BePSVJCxYs0MaNG51e99RTT2nMmDGSpB07dmjp0qWy2Wy6/fbbNX/%2BfIWHh0uS7Ha7lixZom3btqmurk4jR47UtGnT5O/vkU8VAgAAXqjZpIahQ4fq7bff/kXvqaqq0mOPPabCwkKn8aKiIk2dOlW7d%2B92PEaMGCFJKigo0KxZszRlyhRlZ2eroqJCaWlpjve%2B8sor2rFjhzIzM7V8%2BXK9%2BeabeuWVV377DgIAgCtGswlYH3/88S%2B60eiRI0d077336uuvv643V1RUpJtuukkRERGOR1BQkCRp06ZNuvPOOzVs2DB16dJFixYt0q5du1RcXCzp%2B2vAUlNTlZiYqF69emnatGnavHmzmZ0EAABXhGZxkfu5c%2Bf0%2Beefa/To0Y1eZ9%2B%2BferZs6ceffRRxcXFOa115swZdejQ4Sffl5%2BfrwcffNDxvG3btoqOjlZ%2Bfr4CAwN16tQp3XrrrY75hIQEnThxQiUlJYqMjGx0fQAA4Mrl9oAVHR3t9DmEknTVVVdpzJgx%2Bs///M9Gr/NzYayoqEh%2Bfn5avXq13n//fYWGhmr8%2BPG6%2B%2B67Jekng1Lr1q11%2BvRp2Ww2SXKab9OmjSTp9OnTjQ5YJSUljrUus1haGg9oAQHN5gBko1gs7q/3co%2B8rVfuQn9co0eu0aOG0R/XfLFHbg9YzzzzTJOuf/ToUfn5%2Baljx44aM2aM9u/fr6eeekrBwcEaMGCALl68qMDAQKf3BAYGqrq6WhcvXnQ8/%2BGc9P3F9I2VnZ2tzMxMp7GUlBSlpqb%2B2t3yCWFhrTy27ZCQII9t2xvQH9fokWv0qGH0xzVf6pHbA9b%2B/fsb/dofnqprrGHDhikpKUmhoaGSpC5duuirr77Sli1bNGDAAFmt1nphqbq6WkFBQU5hymq1Ov4syXENV2MkJyerX79%2BTmMWS0uVlZ3/xfvTEG9L%2Bqb3vzECAvwVEhKkiopK1dbW/5DxKx39cY0euUaPGkZ/XGvKHnnqh3u3B6z77rvPcYrQbrc7xn885ufnp88%2B%2B%2BwXr%2B/n5%2BcIV5d17NhRe/bskfT9R/WUlpY6zZeWlioiIkJRUVGSJJvNpvbt2zv%2BLEkRERGNriEyMrLe6UCb7axqaq7sbyxP7n9tbd0V3/%2BG0B/X6JFr9Khh9Mc1X%2BqR2w%2BBrF69Wu3atdPSpUv10UcfKTc3V%2BvWrdPvf/97PfbYY3rvvff03nvvaefOnb9q/WXLlmncuHFOY4cPH1bHjh0lSbGxscrNzXXMnTp1SqdOnVJsbKyioqIUHR3tNJ%2Bbm6vo6GgucAcAAI3mkRuNzp49W3fccYdjrFevXpo3b54ef/xxp9/w%2BzWSkpKUlZWltWvXasCAAdq9e7e2b9/u%2BAieUaNG6b777lNcXJxiYmK0cOFC9e3bV9dee61jfvHixfrd734nSVqyZIkmTJjwm2oCAABXFrcHrJKSknoflyNJwcHBKisr%2B83rd%2B/eXcuWLdPy5cu1bNkytWvXTkuWLFF8fLwkKT4%2BXvPmzdPy5cv13XffqXfv3po/f77j/RMnTtQ333yjKVOmKCAgQCNHjqx3RAwAAKAhfvYfXgjlBuPHj1fLli317LPPKjg4WJJUXl6uqVOnymq1auXKle4sx21strPG17RY/DVg8QfG120qbz/S2%2B3btFj8FRbWSmVl533mvL5J9Mc1euQaPWoY/XGtKXsUEXG10fUay%2B1HsJ588kmNHTtWd9xxhzp06CC73a6vvvpKERERjtN4AAAA3sztAatTp0566623tGPHDhUVFUmS/uu//kuDBw/%2BRbdCAAAAaK7cHrAk6ZprrtE999yj48ePOy4uv%2BqqqzxRCgAAgHFuv02D3W7X4sWLdeutt2rIkCE6ffq0ZsyYoVmzZunSpUvuLgcAAMA4twesjRs36vXXX9df/vIXx53T%2B/fvr507d9b7eBkAAABv5PaAlZ2drdmzZ2v48OGOu7ffddddWrBggd588013lwMAAGCc2wPW8ePH1bVr13rjXbp0cXwsDQAAgDdze8Bq166dPvnkk3rj77//vuOCdwAAAG/m9t8inDhxoubOnSubzSa73a6PPvpI2dnZ2rhxo5544gl3lwMAAGCc2wPWiBEjVFNTo1WrVunixYuaPXu2wsPD9cgjj2jUqFHuLgcAAMA4twesHTt2aNCgQUpOTta3334ru92u1q1bu7sMAACAJuP2a7DmzZvnuJg9PDyccAUAAHyO2wNWhw4d9MUXX7h7swAAAG7j9lOEXbp00bRp07RmzRp16NBBVqvVaT49Pd3dJQEAABjl9oD15ZdfKiEhQZK47xUAAPBJbglYixYt0pQpU9SyZUtt3LjRHZsEAADwGLdcg/XKK6%2BosrLSaWzSpEkqKSlxx%2BYBAADcyi0By2631xvbv3%2B/qqqq3LF5AAAAt3L7bxECAAD4OgIWAACAYW4LWH5%2Bfu7aFAAAgEe57TYNCxYscLrn1aVLl5SRkaFWrVo5vY77YAEAAG/nloB166231rvnVXx8vMrKylRWVuaOEgAAANzGLQGLe18BAIArCRe5AwAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGeX3Aqq6u1pAhQ7R3717HWHFxscaNG6e4uDjddddd2r17t9N7PvzwQw0ZMkSxsbEaO3asiouLnebXrVunPn36KD4%2BXjNnzlRlZaVb9gUAAPgGrw5YVVVVeuyxx1RYWOgYs9vtSklJUZs2bZSTk6OhQ4dqypQpOnnypCTp5MmTSklJ0fDhw7Vt2zaFh4fr4Ycflt1ulyS98847yszM1Lx587R%2B/Xrl5%2BcrIyPDI/sHAAC8k9cGrCNHjujee%2B/V119/7TS%2BZ88eFRcXa968eerUqZMmT56suLg45eTkSJK2bt2qm2%2B%2BWRMmTFDnzp2Vnp6uEydOaN%2B%2BfZKkDRs26P7771dSUpK6d%2B%2BuuXPnKicnh6NYAACg0bw2YO3bt089e/ZUdna203h%2Bfr5uuukmtWzZ0jGWkJCgvLw8x3xiYqJjLigoSN26dVNeXp5qa2v1ySefOM3HxcXp0qVLOnz4cBPvEQAA8BUWTxfwa40ePfonx202myIjI53GWrdurdOnT7ucr6ioUFVVldO8xWJRaGio4/2NUVJSIpvN5jRmsbSst93fKiDAu/KxxeL%2Bei/3yNt65S70xzV65Brq6gdLAAARTElEQVQ9ahj9cc0Xe%2BS1AevnVFZWKjAw0GksMDBQ1dXVLucvXrzoeP5z72%2BM7OxsZWZmOo2lpKQoNTW10Wv4orCwVh7bdkhIkMe27Q3oj2v0yDV61DD645ov9cjnApbValV5ebnTWHV1tVq0aOGY/3FYqq6uVkhIiKxWq%2BP5j%2BeDghr/RU9OTla/fv2cxiyWliorO9/oNRrD25K%2B6f1vjIAAf4WEBKmiolK1tXVu335zR39co0eu0aOG0R/XmrJHnvrh3ucCVlRUlI4cOeI0Vlpa6jg9FxUVpdLS0nrzXbt2VWhoqKxWq0pLS9WpUydJUk1NjcrLyxUREdHoGiIjI%2BudDrTZzqqm5sr%2BxvLk/tfW1l3x/W8I/XGNHrlGjxpGf1zzpR551yGQRoiNjdWnn37qON0nSbm5uYqNjXXM5%2BbmOuYqKyt16NAhxcbGyt/fXzExMU7zeXl5slgs6tKli/t2AgAAeDWfC1g9evRQ27ZtlZaWpsLCQmVlZamgoEAjR46UJI0YMUIHDhxQVlaWCgsLlZaWpvbt26tnz56Svr94fu3atdq5c6cKCgo0Z84c3Xvvvb/oFCEAALiy%2BVzACggI0MqVK2Wz2TR8%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%2BM1efJkXbhwQZJUUFCgWbNmacqUKcrOzlZFRYXS0tI8vDcAAMCb%2BGzAKioq0g033KCIiAjHIyQkRG%2B99ZasVqsef/xxderUSbNmzVKrVq3097//XZK0adMm3XnnnRo2bJi6dOmiRYsWadeuXSouLvbwHgEAAG/h0wGrQ4cO9cbz8/OVkJAgPz8/SZKfn59uueUW5eXlOeYTExMdr2/btq2io6OVn5/vlroBAID3s3i6gKZgt9v15Zdfavfu3XrxxRdVW1urQYMGKTU1VTabTddff73T61u3bq3CwkJJUklJiSIjI%2BvNnz59utHbLykpkc1mcxqzWFrWW/e3Cgjwrnxssbi/3ss98rZeuQv9cY0euUaPGkZ/XPPFHvlkwDp58qQqKysVGBiopUuX6vjx41qwYIEuXrzoGP%2BhwMBAVVdXS5IuXrzY4HxjZGdnKzMz02ksJSXFcZH9lSosrJXHth0SEuSxbXsD%2BuMaPXKNHjWM/rjmSz3yyYDVrl077d27V9dcc438/PzUtWtX1dXVafr06erRo0e9sFRdXa0WLVpIkqxW60/OBwU1/ouenJysfv36OY1ZLC1VVnb%2BV%2B7RT/O2pG96/xsjIMBfISFBqqioVG1tndu339zRH9fokWv0qGH0x7Wm7JGnfrj3yYAlSaGhoU7PO3XqpKqqKkVERKi0tNRprrS01HH6Lioq6ifnIyIiGr3tyMjIeqcDbbazqqm5sr%2BxPLn/tbV1V3z/G0J/XKNHrtGjhtEf13ypR951CKSRPvjgA/Xs2VOVlZWOsc8%2B%2B0yhoaFKSEjQxx9/LLvdLun767UOHDig2NhYSVJsbKxyc3Md7zt16pROnTrlmAcAAHDFJwNWfHy8rFarnnzySR09elS7du3SokWL9MADD2jQoEGqqKjQwoULdeTIES1cuFCVlZW68847JUmjRo3S66%2B/rq1bt%2Brw4cN6/PHH1bdvX1177bUe3isAAOAtfDJgBQcHa%2B3atfr22281YsQIzZo1S8nJyXrggQcUHBysF198Ubm5uRo%2BfLjy8/OVlZWlli1bSvo%2BnM2bN08rVqzQqFGjdM011yg9Pd3DewQAALyJn/3yuTI0KZvtrPE1LRZ/DVj8gfF1m8rbj/R2%2BzYtFn%2BFhbVSWdl5nzmvbxL9cY0euUaPGkZ/XGvKHkVEXG10vcbyySNYAAAAnkTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyzeLoAXDnuXPpPT5fwi7z9SG9PlwAA8FIcwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbB%2BQlVVlWbOnKnExETdfvvtevnllz1dEgAA8CLcB%2BsnLFq0SAcPHtT69et18uRJzZgxQ9HR0Ro0aJCnSwMAAF6AgPUjFy5c0NatW/XSSy%2BpW7du6tatmwoLC7V582YCFgAAaBQC1o8cPnxYNTU1io%2BPd4wlJCRo9erVqqurk78/Z1XRPHnTnfK5Sz4AX0fA%2BhGbzaawsDAFBgY6xtq0aaOqqiqVl5crPDzc5RolJSWy2WxOYxZLS0VGRhqtNSCAsNeULBb621S8qbeXv8/4fvt59Khh9Mc1X%2BwRAetHKisrncKVJMfz6urqRq2RnZ2tzMxMp7EpU6boz3/%2Bs5ki/09JSYnu/12hkpOTjYc3X1FSUqLs7Owrokf/WvjLT2FfSf35tUpKSrR%2B/Rp61AB61DD645ov9sh3oqIhVqu1XpC6/LxFixaNWiM5OVl//etfnR7JycnGa7XZbMrMzKx3tAz/Hz1qGP1xjR65Ro8aRn9c88UecQTrR6KiolRWVqaamhpZLN%2B3x2azqUWLFgoJCWnUGpGRkT6TwAEAwC/HEawf6dq1qywWi/Ly8hxjubm5iomJ4QJ3AADQKCSGHwkKCtKwYcM0Z84cFRQUaOfOnXr55Zc1duxYT5cGAAC8RMCcOXPmeLqI5qZXr146dOiQlixZoo8%2B%2BkgPPfSQRowY4emyflKrVq3Uo0cPtWrVytOlNFv0qGH0xzV65Bo9ahj9cc3XeuRnt9vtni4CAADAl3CKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYActLVVVVaebMmUpMTNTtt9%2Bul19%2B2dMlNUvV1dUaMmSI9u7d6%2BlSmp0zZ84oNTVVPXr0UJ8%2BfZSenq6qqipPl9WsHDt2TBMnTlR8fLz69u2rNWvWeLqkZmvSpEl64oknPF1Gs/Puu%2B/qxhtvdHqkpqZ6uqxmpbq6WnPnztWtt96qf/u3f9Nzzz0nX7gHusXTBeDXWbRokQ4ePKj169fr5MmTmjFjhqKjozVo0CBPl9ZsVFVVaerUqSosLPR0Kc2O3W5XamqqQkJCtHnzZn333XeaOXOm/P39NWPGDE%2BX1yzU1dVp0qRJiomJ0WuvvaZjx47pscceU1RUlP74xz96urxm5W9/%2B5t27dqlu%2B%2B%2B29OlNDtHjhxRUlKS5s%2Bf7xizWq0erKj5WbBggfbu3au1a9fq/PnzevTRRxUdHa0//elPni7tNyFgeaELFy5o69ateumll9StWzd169ZNhYWF2rx5MwHr/xw5ckRTp071iZ%2BCmsLRo0eVl5enf/7zn2rTpo0kKTU1Vc8%2B%2BywB6/%2BUlpaqa9eumjNnjoKDg9WhQwfddtttys3NJWD9QHl5uRYtWqSYmBhPl9IsFRUV6YYbblBERISnS2mWysvLlZOTo1deeUXdu3eXJE2YMEH5%2BfleH7A4ReiFDh8%2BrJqaGsXHxzvGEhISlJ%2Bfr7q6Og9W1nzs27dPPXv2VHZ2tqdLaZYiIiK0Zs0aR7i67Ny5cx6qqPmJjIzU0qVLFRwcLLvdrtzcXO3fv189evTwdGnNyrPPPquhQ4fq%2Buuv93QpzVJRUZE6dOjg6TKardzcXAUHBzt9X02aNEnp6ekerMoMApYXstlsCgsLU2BgoGOsTZs2qqqqUnl5uQcraz5Gjx6tmTNnKigoyNOlNEshISHq06eP43ldXZ02bdqkXr16ebCq5qtfv34aPXq04uPjNXDgQE%2BX02x89NFH%2Bte//qWHH37Y06U0S3a7XV9%2B%2BaV2796tgQMHqn///lq8eLGqq6s9XVqzUVxcrHbt2mn79u0aNGiQ/uM//kMrVqzwiYMFBCwvVFlZ6RSuJDme842LXyMjI0OHDh3So48%2B6ulSmqXly5dr9erV%2Buyzz3ziJ2sTqqqq9Je//EWzZ89WixYtPF1Os3Ty5EnHv9dLly7VjBkz9Oabb2rRokWeLq3ZuHDhgo4dO6ZXX31V6enpmjFjhjZu3Kh169Z5urTfjGuwvJDVaq0XpC4/5x86/FIZGRlav369nn/%2Bed1www2eLqdZunx9UVVVlaZNm6bHH3%2B83g85V5rMzEzdfPPNTkdC4axdu3bau3evrrnmGvn5%2Balr166qq6vT9OnTlZaWpoCAAE%2BX6HEWi0Xnzp3TkiVL1K5dO0nfB9MtW7ZowoQJHq7utyFgeaGoqCiVlZWppqZGFsv3X0KbzaYWLVooJCTEw9XBm8yfP19btmxRRkYGp75%2BpLS0VHl5eerfv79j7Prrr9elS5d07tw5hYeHe7A6z/vb3/6m0tJSx7Wgl3/Ie%2Bedd/Txxx97srRmJTQ01Ol5p06dVFVVpe%2B%2B%2B%2B6K/zskfX89qNVqdYQrSfr973%2BvU6dOebAqMzhF6IW6du0qi8WivLw8x1hubq5iYmLk78%2BXFI2TmZmpV199Vc8995wGDx7s6XKanePHj2vKlCk6c%2BaMY%2BzgwYMKDw/nf4ySNm7cqDfffFPbt2/X9u3b1a9fP/Xr10/bt2/3dGnNxgcffKCePXuqsrLSMfbZZ58pNDSUv0P/JzY2VlVVVfryyy8dY0ePHnUKXN6K/xt7oaCgIA0bNkxz5sxRQUGBdu7cqZdfflljx471dGnwEkVFRVq5cqUefPBBJSQkyGazOR74XkxMjLp166aZM2fqyJEj2rVrlzIyMvTQQw95urRmoV27drruuuscj1atWqlVq1a67rrrPF1asxEfHy%2Br1aonn3xSR48e1a5du7Ro0SI98MADni6t2ejYsaP69u2rtLQ0HT58WB988IGysrI0atQoT5f2m/nZuVGQV6qsrNScOXP0P//zPwoODtbEiRM1btw4T5fVLN14443asGGDevbs6elSmo2srCwtWbLkJ%2Bc%2B//xzN1fTfJ05c0bz58/XRx99pKCgII0ZM0aTJ0%2BWn5%2Bfp0trdi7fxf2ZZ57xcCXNS2FhoZ5%2B%2Bmnl5eWpVatW%2BtOf/qSUlBT%2BDv3A2bNnNX/%2BfL377rsKCgrS6NGjfaJHBCwAAADDOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9P7LKZ%2Bx7GYKMAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7440678085305071876”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7440678085305071876”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2991</td> <td class=”number”>93.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_10_12”>FOODINSEC_10_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>43</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.032</td>

</tr> <tr>

<th>Minimum</th> <td>8.7</td>

</tr> <tr>

<th>Maximum</th> <td>20.9</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8955113361359147267”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-8955113361359147267”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8955113361359147267”

aria-controls=”quantiles-8955113361359147267” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8955113361359147267” aria-controls=”histogram-8955113361359147267”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8955113361359147267” aria-controls=”common-8955113361359147267”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8955113361359147267” aria-controls=”extreme-8955113361359147267”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8955113361359147267”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>8.7</td>

</tr> <tr>

<th>5-th percentile</th> <td>11.2</td>

</tr> <tr>

<th>Q1</th> <td>13.4</td>

</tr> <tr>

<th>Median</th> <td>14.9</td>

</tr> <tr>

<th>Q3</th> <td>16.7</td>

</tr> <tr>

<th>95-th percentile</th> <td>18.4</td>

</tr> <tr>

<th>Maximum</th> <td>20.9</td>

</tr> <tr>

<th>Range</th> <td>12.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.3785</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15823</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.10833</td>

</tr> <tr>

<th>Mean</th> <td>15.032</td>

</tr> <tr>

<th>MAD</th> <td>1.8931</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.21839</td>

</tr> <tr>

<th>Sum</th> <td>47080</td>

</tr> <tr>

<th>Variance</th> <td>5.6574</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8955113361359147267”>

<img 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7PVy1tacu6HECVXBwkCIjw1Rf71JjY5PV5fgd%2BoPOpL2Oi7yvWhfI/WnvEP%2BdgAtYb7zxhmpqapSQkCBJ3sD09ttva9KkSaqpqfHZvqamxnvqLjY29pzz0dHRF1RDTExMs9OBTucJnT0bWC/aS9XY2ERPWkF/0Bm092uc91Xr6I85ARew1q5dq7Nnz3pvP/HEE5Kk%2B%2B%2B/Xx988IGee%2B45eTwe2Ww2eTweHThwQLNnz5YkORwOFRcXKyUlRZJUVVWlqqoqORyO9t8RAADQYQVcwOrdu7fP7a5dv10a7Nu3r6KiorRs2TItWbJEv/jFL7R%2B/Xq5XC5NmDBBkpSWlqbp06crPj5eQ4YM0ZIlSzR69Ghdc8017b4fAACg4/K7rwvccsstWr9%2BvU6cOGH8sSMiIrRq1SrvKlVpaakKCgoUHh4uSUpISNDChQuVn5%2BvtLQ0XXXVVcrJyTFeBwAACGw2j8fjsbqIf7Rs2TJt3rxZtbW1%2BslPfqKUlBQNHz7ce%2B2qjsrpNB8YOyq7PUjdunVVbe0pzvWfw8X2Z8KKXZexKuDyePOe4e3yPBx3WhfI/YmOvtKS5/W7Faz77rtP7777rlauXKng4GDNnTtXo0eP1vLly/XFF19YXR4AAMB5%2BeVnsGw2m4YPH67hw4fL5XJp7dq1WrlypQoKCjR06FD9%2Bte/1r//%2B79bXSYAAMA5%2BWXAkr69Gvrrr7%2Bu119/XZ999pmGDh2qn/3sZzp%2B/LgefvhhffDBB8rOzra6TAAAgGb8LmBt2rRJmzZt0t69e9W9e3dNmTJFTz31lPr16%2BfdpmfPnlqyZAkBCwAA%2BCW/C1jZ2dkaM2aM8vPzNWrUKAUFNf%2BY2HXXXadf/epXFlQHAABwfn4XsHbs2KFu3bqprq7OG64OHjyouLg4BQcHS5KGDh2qoUOHWlkmAABAi/zuW4QnT57U%2BPHj9dxzz3nHZs6cqcmTJ6uqqsrCygAAANrG7wLWY489pr59%2B%2Br222/3jm3dulU9e/bkop8AAKBD8LuAtX//fj344IM%2Bf2C5e/fumjdvnt5//30LKwMAAGgbvwtYdrtd9fX1zcZdLpf87KLzAAAA5%2BR3AWvUqFFavHix/v73v3vHKioqlJOTo5EjR1pYGQAAQNv43bcIH3jgAd1%2B%2B%2B26%2BeabFRkZKUmqr69XXFycsrKyLK4OAADg/PwuYEVFRWnjxo3avXu3ysvLZbfbdf311%2Bumm27q8H/wGQAAdA5%2BF7AkKTg4WCNHjuSUIAAA6JD8LmA5nU6tWLFCBw4c0DfffNPsg%2B3vvPOORZUBAAC0jd8FrN/97nc6dOiQfvrTn%2BrKK6%2B0uhwAAIAL5ncB6/3339fq1auVlJRkdSkAAAAXxe8u0xAeHq6oqCirywAAALhofhewJk%2BerNWrV6uxsdHqUgAAAC6K350irKur05YtW/SXv/xF11xzjUJCQnzmX3rpJYsqAwAAaBu/C1iSNGnSJKtLAAAAuGh%2BF7BycnKsLgEAAOCS%2BN1nsCSpurpaeXl5uu%2B%2B%2B/TVV1/prbfe0pEjR6wuCwAAoE38LmAdPXpU//Ef/6GNGzfq7bff1unTp7V161ZNnTpVpaWlVpcHAABwXn4XsH7/%2B99r3Lhx2rZtm6644gpJ0pNPPqmxY8fqiSeesLg6AACA8/O7gHXgwAHdfvvtPn/Y2W63a86cOSorK7OwMgAAgLbxu4DV1NSkpqamZuOnTp1ScHCwBRUBAABcGL8LWCNGjNCqVat8QlZdXZ1yc3N14403WlgZAABA2/hdwHrwwQd16NAhjRgxQg0NDfqv//ovjRkzRseOHdMDDzxgdXkAAADn5XfXwYqNjdWf/vQnbdmyRR9//LGampqUlpamyZMnKyIiwuryAAAAzsvvApYkhYWF6ZZbbrG6DAAAgIvidwFrxowZrc7ztwgBAIC/87uA1bt3b5/bZ8%2Be1dGjR/XZZ5/p17/%2BtUVVAQAuhwkrdlldwgV5857hVpeADsLvAlZLf4swPz9fx48fb%2BdqAAAALpzffYuwJZMnT9abb75pdRkAAADn1WEC1ocffsiFRgEAQIfgd6cIz/Uh95MnT%2BrTTz/VrbfeakFFAAAAF8bvVrB69eql3r17%2B/wMHjxYixYtuqALjR49elR33nmnEhISNHr0aK1evdo7V1FRodtuu03x8fGaOHGidu7c6XPf3bt3a9KkSXI4HJoxY4YqKiqM7R8AAAh8freC9fvf//6SH6OpqUkzZ87UkCFDtHHjRh09elT33nuvYmNjNWnSJKWnp2vAgAEqKirStm3blJGRoa1bt6pXr16qrKxUenq65s6dq5EjRyo/P19z5szR66%2B/7vMHqAEAAFridwHrgw8%2BaPO2P/rRj845XlNTo0GDBmnBggWKiIhQv379dNNNN6m4uFg9evRQRUWF1q9fr/DwcPXv31979uxRUVGR5s6dqw0bNmjw4MG64447JH37rcbhw4dr3759GjZsmJF9BAAAgc3vAtb06dO9K0Uej8c7/v0xm82mjz/%2B%2BJyPERMToxUrVni3P3DggD744AM98sgjKi0t1Q033KDw8HDv9omJiSopKZEklZaWKikpyTsXFhamuLg4lZSUELAAAECb%2BF3AevbZZ7V48WL99re/VXJyskJCQvTRRx9p4cKF%2BtnPfqaJEyde0OONHTtWlZWVGjNmjG6%2B%2BWY99thjiomJ8dkmKirKe40tp9PZ6jwAAMD5%2BF3AysnJ0fz58zVq1Cjv2I033qiFCxdq3rx5uvvuuy/o8Z566inV1NRowYIFysnJkcvlUkhIiM82ISEhcrvdknTe%2Bbaorq6W0%2Bn0GbPbw5sFt84qODjI5zd80R/Af9ntgfm%2B5Lhjnt8FrOrq6mZ/LkeSIiIiVFtbe8GPN2TIEElSQ0OD7r//fk2dOlUul8tnG7fbrS5dukiSQkNDm4Upt9utyMjINj9nYWGh8vLyfMbS09OVmZl5wfUHssjIMKtL8Gv0B/A/3bp1tbqEy4rjjjl%2BF7Di4%2BP15JNP6vHHH1dERIQkqa6uTrm5ubrpppva9Bg1NTUqKSnRuHHjvGPXX3%2B9vvnmG0VHR%2BvIkSPNtv9udSk2NlY1NTXN5gcNGtTmfUhNTdXYsWN9xuz2cNXWnmrzYwSy4OAgRUaGqb7epcbGJqvL8Tv0B/BfgXocD%2BTjjlWh2O8C1sMPP6wZM2Zo1KhR6tevnzwej/72t78pOjpaL730Upse49ixY8rIyND27dsVGxsrSTp06JC6d%2B%2BuxMREPf/88zpz5ox31aq4uFiJiYmSJIfDoeLiYu9juVwulZWVKSMjo837EBMT0%2Bx0oNN5QmfPBtaL9lI1NjbRk1bQH8D/BPp7kuOOOX53srV///7aunWr7rvvPsXHxyshIUHZ2dnatGmT/umf/qlNjzFkyBDFxcXpoYce0uHDh7V9%2B3bl5uZq9uzZSk5OVs%2BePZWVlaXy8nIVFBTo4MGDmjZtmiRp6tSpOnDggAoKClReXq6srCz16dOHbxACAIA2s3n%2B8VoIfsTtduvYsWO65pprJElXXHHFBd3/yy%2B/1KJFi7Rnzx6FhYXpV7/6lWbNmiWbzaajR48qOztbpaWl6tu3rx566CH967/%2Bq/e%2B27dv12OPPabjx48rISFBixYt8tZxsZzOE5d0/0BitwepW7euqq09xb%2BUzuFi%2BzNhxa7LWBUASXrznuFWl3BZBPJxOTr6Skue1%2B8Clsfj0bJly7R27Vp98803evvtt7V8%2BXKFhYVpwYIFFxy0/AUB6/8L5DeyCQQswH8RsDoeqwKW350iXLt2rTZt2qRHHnnEe7mEcePGadu2bc2%2BmQcAAOCP/C5gFRYWav78%2BUpJSfFevX3ixIlavHixNm/ebHF1AAAA5%2Bd3AevYsWPnvCTCwIEDm128EwAAwB/5XcDq3bu3Pvroo2bjO3bsuOQPmgMAALQHv7sO1p133qlHH31UTqdTHo9He/bsUWFhodauXasHH3zQ6vIAAADOy%2B8C1tSpU3X27Fk988wzOnPmjObPn6/u3bvrnnvuUVpamtXlAQAAnJffBawtW7Zo/PjxSk1N1ddffy2Px6OoqCirywIAAGgzv/sM1sKFC70fZu/evTvhCgAAdDh%2BF7D69eunzz77zOoyAAAALprfnSIcOHCg7r//fq1evVr9%2BvVTaGioz3xOTo5FlQEAALSN3wWsL774QomJiZLEda8AAECH5BcBa%2BnSpcrIyFB4eLjWrl1rdTkAAACXxC8%2Bg7VmzRq5XC6fsZkzZ6q6utqiigAAAC6eXwQsj8fTbOyDDz5QQ0ODBdUAAABcGr8IWAAAAIGEgAUAAGCY3wQsm81mdQkAAABG%2BMW3CCVp8eLFPte8%2Buabb5Sbm6uuXbv6bMd1sAAAgL/zi4D1ox/9qNk1rxISElRbW6va2lqLqgIAALg4fhGwuPYVAAAIJH7zGSwAAIBAQcACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAsYAPWl19%2BqczMTCUnJ2vkyJHKyclRQ0ODJKmiokK33Xab4uPjNXHiRO3cudPnvrt379akSZPkcDg0Y8YMVVRUWLELAACggwrIgOXxeJSZmSmXy6WXX35Zy5cv17vvvqsVK1bI4/EoPT1dPXr0UFFRkSZPnqyMjAxVVlZKkiorK5Wenq6UlBS9%2Buqr6t69u%2BbMmSOPx2PxXgEAgI7CbnUBl8ORI0dUUlKiXbt2qUePHpKkzMxMPf744xo1apQqKiq0fv16hYeHq3///tqzZ4%2BKioo0d%2B5cbdiwQYMHD9Ydd9whScrJydHw4cO1b98%2BDRs2zMrdAgAAHURArmBFR0dr9erV3nD1nZMnT6q0tFQ33HCDwsPDveOJiYkqKSmRJJWWliopKck7FxYWpri4OO88AADA%2BQTkClZkZKRGjhzpvd3U1KR169bpxhtvlNPpVExMjM/2UVFROn78uCSdd74tqqur5XQ6fcbs9vBmj9tZBQcH%2BfyGL/oD%2BC%2B7PTDflxx3zAvIgPV9ubm5Kisr06uvvqoXXnhBISEhPvMhISFyu92SJJfL1ep8WxQWFiovL89nLD09XZmZmRe5B4EpMjLM6hL8Gv0B/E%2B3bl2tLuGy4rhjTsAHrNzcXL344otavny5BgwYoNDQUNXV1fls43a71aVLF0lSaGhoszDldrsVGRnZ5udMTU3V2LFjfcbs9nDV1p66yL0ILMHBQYqMDFN9vUuNjU1Wl9Oif3viPatLAOBnAvU43lGOyxfDqlAc0AFr0aJFeuWVV5Sbm6ubb75ZkhQbG6vDhw/7bFdTU%2BM9fRcbG6uamppm84MGDWrz88bExDQ7Heh0ntDZs4H1or1UjY1N9ARAhxLoxyyOy%2BYE7MnWvLw8rV%2B/Xk8%2B%2BaR%2B%2BtOfescdDof%2B%2Bte/6syZM96x4uJiORwO73xxcbF3zuVyqayszDsPAABwPgEZsD7//HOtXLlSd999txITE%2BV0Or0/ycnJ6tmzp7KyslReXq6CggIdPHhQ06ZNkyRNnTpVBw4cUEFBgcrLy5WVlaU%2BffpwiQYAANBmARmw3nnnHTU2NuqZZ57RiBEjfH6Cg4O1cuVKOZ1OpaSk6PXXX1d%2Bfr569eolSerTp4%2BefvppFRUVadq0aaqrq1N%2Bfr5sNpvFewUAADoKm4dLlLcLp/OE1SX4Dbs9SN26dVVt7Sm/Ptc/YcUuq0sA4GfevGe41SVcFh3luHwxoqOvtOR5A3IFCwAAwEoELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyzW10AAAC4PCas2GV1CW325j3DrS7BKFawAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYFfMByu92aNGmS9u7d6x2rqKjQbbfdpvj4eE2cOFE7d%2B70uc/u3bs1adIkORwOzZgxQxUVFe1dNgAA6MACOmA1NDTo3nvvVXl5uXfM4/EoPT1dPXr0UFFRkSZPnqyMjAxVVlZKkiorK5Wenq7JFIr%2BAAAMpklEQVSUlBS9%2Buqr6t69u%2BbMmSOPx2PVbgAAgA4mYAPW4cOH9fOf/1x///vffcbff/99VVRUaOHCherfv79mzZql%2BPh4FRUVSZI2bNigwYMH64477tAPfvAD5eTk6P/%2B7/%2B0b98%2BK3YDAAB0QAEbsPbt26dhw4apsLDQZ7y0tFQ33HCDwsPDvWOJiYkqKSnxziclJXnnwsLCFBcX550HAAA4n4D9Y8%2B33nrrOcedTqdiYmJ8xqKionT8%2BPE2zQMAAJxPwAaslrhcLoWEhPiMhYSEyO12t2m%2BLaqrq%2BV0On3G7PbwZsGtswoODvL5DQAdhd3OcetyCbTedrqAFRoaqrq6Op8xt9utLl26eOe/H6bcbrciIyPb/ByFhYXKy8vzGUtPT1dmZuZFVh2YIiPDrC4BAC5It25drS4hYAVabztdwIqNjdXhw4d9xmpqaryrS7GxsaqpqWk2P2jQoDY/R2pqqsaOHeszZreHq7b21EVWHViCg4MUGRmm%2BnqXGhubrC4HANqM4/jlc7l6a1Vw63QBy%2BFwqKCgQGfOnPGuWhUXFysxMdE7X1xc7N3e5XKprKxMGRkZbX6OmJiYZqcDnc4TOnuWMPGPGhub6AmADoVj1uUTaL0NrBOebZCcnKyePXsqKytL5eXlKigo0MGDBzVt2jRJ0tSpU3XgwAEVFBSovLxcWVlZ6tOnj4YNG2Zx5QAAoKPodAErODhYK1eulNPpVEpKil5//XXl5%2BerV69ekqQ%2Bffro6aefVlFRkaZNm6a6ujrl5%2BfLZrNZXDkAAOgoOsUpwk8//dTndt%2B%2BfbVu3boWt//xj3%2BsH//4x5e7LAAAEKA6RcCCf5iwYpfVJQAA0C463SlCAACAy42ABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZre6AFyaCSt2WV0CAAD4HlawAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHrHBoaGvTQQw8pKSlJI0aM0PPPP291SQAAoAOxW12AP1q6dKkOHTqkF198UZWVlXrggQfUq1cvjR8/3urSAABAB0DA%2Bp7Tp09rw4YNeu655xQXF6e4uDiVl5fr5ZdfJmABAIA24RTh93zyySc6e/asEhISvGOJiYkqLS1VU1OThZUBAICOghWs73E6nerWrZtCQkK8Yz169FBDQ4Pq6urUvXv38z5GdXW1nE6nz5jdHq6YmBjj9QIA2o/dzrrE5RJovSVgfY/L5fIJV5K8t91ud5seo7CwUHl5eT5jGRkZmjt3rpki/8H%2BJR3vtGV1dbUKCwuVmppK6DwH%2BtMyetM6%2BtOyztqbtv4/orP253IKrLhoQGhoaLMg9d3tLl26tOkxUlNT9dprr/n8pKamGq%2B1o3I6ncrLy2u2yodv0Z%2BW0ZvW0Z%2BW0ZvW0R/zWMH6ntjYWNXW1urs2bOy279tj9PpVJcuXRQZGdmmx4iJieFfAAAAdGKsYH3PoEGDZLfbVVJS4h0rLi7WkCFDFBREuwAAwPmRGL4nLCxMU6ZM0YIFC3Tw4EFt27ZNzz//vGbMmGF1aQAAoIMIXrBgwQKri/A3N954o8rKyrRs2TLt2bNHs2fP1tSpU60uK6B07dpVycnJ6tq1q9Wl%2BCX60zJ60zr60zJ60zr6Y5bN4/F4rC4CAAAgkHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQvtwu12a9KkSdq7d693rKKiQrfddpvi4%2BM1ceJE7dy508IKrXWu/pSUlOgXv/iFEhISdPPNN2vDhg0WVmidc/XmOydOnNDIkSP12muvWVCZfzhXfyorK3X33XfL4XDo3/7t37R161YLK7TOuXqzf/9%2BpaSkKD4%2BXpMnT9bu3bstrNAaX375pTIzM5WcnKyRI0cqJydHDQ0Nkjgum0TAwmXX0NCge%2B%2B9V%2BXl5d4xj8ej9PR09ejRQ0VFRZo8ebIyMjJUWVlpYaXWOFd/nE6n7r77biUnJ2vjxo3KzMzUokWL9Je//MW6Qi1wrt78o9zcXFVXV7dzVf7jXP05e/asZs2aJbvdro0bN%2BrOO%2B/UvHnz9Nlnn1lYafs7V2%2B%2B%2BuorzZ49WxMnTtTmzZs1YcIEzZkzR8ePH7ew0vbl8XiUmZkpl8ull19%2BWcuXL9e7776rFStWcFw2zG51AQhshw8f1n333afv/0Wm999/XxUVFVq/fr3Cw8PVv39/7dmzR0VFRZo7d65F1ba/lvqzbds29ejRQ/fee68kqV%2B/ftq7d682b96s0aNHW1Bp%2B2upN9/Zv3%2B/3n//fUVHR7dzZf6hpf5s375dVVVVeuWVVxQREaHrrrtOO3bs0IcffqgBAwZYVG37aqk3Bw4cUHBwsO666y5J0uzZs7VmzRqVlJRo/PjxVpTa7o4cOaKSkhLt2rVLPXr0kCRlZmbq8ccf16hRozguG8QKFi6rffv2adiwYSosLPQZLy0t1Q033KDw8HDvWGJiokpKStq7REu11J/vlu2/7%2BTJk%2B1VmuVa6o307amf3/3ud5o/f75CQkIsqM56LfVn3759uummmxQREeEdW7lypVJTU9u7RMu01Jurr75adXV1%2BvOf/yyPx6Nt27bp1KlTnSZ4SlJ0dLRWr17tDVffOXnyJMdlw1jBwmV16623nnPc6XQqJibGZywqKqpTLdVLLfenT58%2B6tOnj/f2V199pTfeeKNT/Suypd5I0rPPPqsbbrhBI0aMaMeK/EtL/amoqFDv3r31xBNPaNOmTerWrZsyMzM1bty4dq7QOi31JikpSb/85S%2BVmZmpoKAgNTY2KicnR9ddd107V2idyMhIjRw50nu7qalJ69at04033shx2TBWsGAJl8vVbOUhJCREbrfboor815kzZzR37lz16NGjU61CtOTw4cNav369srKyrC7FL50%2BfVobN25UfX29nn32WU2ZMkWZmZn66KOPrC7NcqdOnVJFRYUyMjK0YcMGzZ49W4sXL9bnn39udWmWyc3NVVlZmX7zm99wXDaMFSxYIjQ0VHV1dT5jbrdbXbp0sagi/3Tq1CnNmTNHf/vb3/Q///M/CgsLs7okS3k8Hj388MPKzMxsdooD3woODtbVV1%2BtBQsWKCgoSHFxcdq/f7/%2B%2BMc/asiQIVaXZ6nVq1fL4/EoIyNDkhQXF6eDBw/qpZde0qOPPmpxde0vNzdXL774opYvX64BAwZwXDaMFSxYIjY2VjU1NT5jNTU1zZanO7OTJ0/qzjvvVHl5uV588UX169fP6pIsV1lZqQ8//FCPP/64EhISlJCQoMrKSj3yyCPeDy53djExMerXr5%2BCgv7/4f3aa69VVVWVhVX5h7/%2B9a8aOHCgz9igQYM65bfkFi1apDVr1ig3N1c333yzJI7LprGCBUs4HA4VFBTozJkz3n8dFRcXKzEx0eLK/ENTU5MyMjJ07NgxrV27Vv3797e6JL8QGxurP//5zz5j06dP1/Tp0/Wf//mfFlXlXxwOh5555hk1NjYqODhYkvT555%2Brd%2B/eFldmvZiYGB0%2BfNhn7MiRIz6fd%2BwM8vLytH79ej355JM%2B357kuGwWK1iwRHJysnr27KmsrCyVl5eroKBABw8e1LRp06wuzS%2B8%2Buqr2rt3rxYvXqzIyEg5nU45nc5my/edjd1uV9%2B%2BfX1%2B7Ha7oqKiFBsba3V5fmHSpElqamrSo48%2BqqNHj%2Brll1/We%2B%2B9p5///OdWl2a5W265RTt27NALL7ygiooKvfDCC9q5c2erX6gINJ9//rlWrlypu%2B%2B%2BW4mJid5ji9Pp5LhsGCtYsERwcLBWrlyp7OxspaSkqG/fvsrPz1evXr2sLs0vvP3222pqatKsWbN8xpOTk7V27VqLqkJHEBERoTVr1mjBggWaNGmSevXqpeXLlysuLs7q0iwXHx%2Bvp59%2BWk899ZT%2B%2B7//W9dee60KCgr0gx/8wOrS2s0777yjxsZGPfPMM3rmmWd85j799FOOywbZPC1dxQ8AAAAXhVOEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wOyx1%2Bo7soeIgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8955113361359147267”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.6</td> <td class=”number”>178</td> <td class=”number”>5.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.9</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.1</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>116</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.9</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.4</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.3</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.4</td> <td class=”number”>101</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (32)</td> <td class=”number”>1749</td> <td class=”number”>54.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8955113361359147267”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.7</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.2</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.6</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.9</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.7</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.9</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_13_15”>FOODINSEC_13_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>42</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>14.274</td>

</tr> <tr>

<th>Minimum</th> <td>8.5</td>

</tr> <tr>

<th>Maximum</th> <td>20.8</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4564154773625592162”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4564154773625592162,#minihistogram4564154773625592162”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4564154773625592162”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4564154773625592162”

aria-controls=”quantiles4564154773625592162” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4564154773625592162” aria-controls=”histogram4564154773625592162”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4564154773625592162” aria-controls=”common4564154773625592162”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4564154773625592162” aria-controls=”extreme4564154773625592162”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4564154773625592162”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>8.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>10.1</td>

</tr> <tr>

<th>Q1</th> <td>12.4</td>

</tr> <tr>

<th>Median</th> <td>14.8</td>

</tr> <tr>

<th>Q3</th> <td>15.4</td>

</tr> <tr>

<th>95-th percentile</th> <td>18.4</td>

</tr> <tr>

<th>Maximum</th> <td>20.8</td>

</tr> <tr>

<th>Range</th> <td>12.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4612</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.17242</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.32044</td>

</tr> <tr>

<th>Mean</th> <td>14.274</td>

</tr> <tr>

<th>MAD</th> <td>1.9092</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33494</td>

</tr> <tr>

<th>Sum</th> <td>44706</td>

</tr> <tr>

<th>Variance</th> <td>6.0573</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4564154773625592162”>

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Be6Lwdx6NAhDRkyRLGxsRo0aJAOHjzocd/NmzerV69eio2NVXJysr777jtjdQEAAN9necB65ZVX1LRpUz377LPusS1btqhhw4ZKT083tp1Zs2Zp165deueddzRnzhy9//77ys7O1oULFzR8%2BHAlJCRo/fr1iouL04gRI3ThwgVJV1bTJk6cqJSUFGVnZ6u0tFTjx483VhcAAPB9lp8i3Ldvn95//31FRka6x8LDwzVu3Dg99dRTRrZRUlKidevWaenSpWrXrp0k6bnnnlNeXp4cDoeCgoI0btw4%2Bfn5aeLEidq5c6e2bt2qxMRErVq1Sn379tXAgQMlSbNnz1aPHj1UUFCgJk2aGKkPAAD4NstXsBwOh0pLS6uNl5WVGbuie05OjurWrauOHTu6x4YPH6709HTl5eUpPj5efn5%2Bkq5ck6tDhw7Kzc2VJOXl5SkhIcF9v4YNGyomJkZ5eXlGagMAAL7P8hWsbt26adasWZo7d67%2B6Z/%2BSZJUUFCg9PR0de3a1cg2CgoK1KhRI/3%2B97/X4sWLdfnyZSUmJmrUqFFyOp1q0aKFx%2B0jIiKUn58v6cr1uaKioqrNnz59%2Boa3X1RUJKfT6THmcIS6HzcgwN/jK35Ab7yjN97RG08Oxw99oDfe0Rvv6M3tszxgvfzyy3r22WfVu3dvhYWFSZJKS0vVpk0bY%2B91unDhgk6cOKHVq1crPT1dTqdTU6ZMUUhIiMrKyhQYGOhx%2B8DAQJWXl0uSLl68%2BKPzNyI7O1uZmZkeY8nJyUpNTfUYCwsLuZndqlXojXf0xjt6c0X9%2BnWqjdEb7%2BiNd/Tm1lkesCIiIrRhwwbt2rVL%2Bfn5cjgcatGihR599FH3abvb5XA4dO7cOc2ZM0eNGjWSJJ06dUrvvfeemjZtWi0slZeXKzg4WJIUFBR03fmQkBt/kiUlJalnz57X1BSqM2fOS7ryiiAsLESlpWWqrKy66f3zZfTGO3rjHb3xdPVYI9GbH0NvvPOl3lzvBYcVbPmonICAAHXt2tXYKcFrRUZGKigoyB2uJOn%2B%2B%2B9XYWGhOnbsqOLiYo/bFxcXu0/fRUdHX3f%2BH9%2BU/1OioqKqnWZ0Os%2BqosLzSVpZWVVtDFfQG%2B/ojXf05orr9YDeeEdvvKM3t87ygOV0OjV//nzt379fly9frvbG9o8//vi2txEbG6tLly7p%2BPHjuv/%2B%2ByVJx44dU6NGjRQbG6u3335bLpdLfn5%2Bcrlc2r9/v0aOHOm%2Bb05OjhITEyVJhYWFKiwsVGxs7G3XBQAAagfLA9bkyZN18OBBPfbYY6pXr94d2cYDDzyg7t27a/z48Zo2bZqcTqeysrI0atQo9enTR3PmzFFaWpqeeOIJrV69WmVlZerbt68kaejQoRo2bJjat2%2Bvtm3bKi0tTd27d%2BcSDQAA4IZZHrD27NmjJUuWeFwK4U54/fXXNXPmTA0dOlQhISF66qmnNGzYMPn5%2Bemtt97S1KlT9f777%2Buf//mflZWVpdDQUElSXFycZsyYoTfeeEPff/%2B9OnfurJkzZ97RWgEAgG%2BxPGCFhoYqIiLijm%2BnXr16mj179nXn2rVrpw0bNni9b2JiovsUIQAAwM2y/AIXAwYM0JIlS1RZWWn1pgEAACxh%2BQpWSUmJNm/erD//%2Bc9q0qRJtWtOrVixwuqSAAAAjLLlMg39%2B/e3Y7MAAACWsDxgpaenW71JAAAAS9nyIUNFRUXKzMzUSy%2B9pG%2B//VZbt27VsWPH7CgFAADAOMsD1okTJ/Rv//Zv2rBhg7Zt26YLFy5oy5YtGjRokPLy8qwuBwAAwDjLA9arr76qXr16afv27brnnnskSXPnzlXPnj31%2BuuvW10OAACAcZYHrP379%2BvZZ5/1%2BGBnh8Oh0aNH69ChQ1aXAwAAYJzlAauqqkpVVdU/OPL8%2BfMKCAiwuhwAAADjLA9YXbp00VtvveURskpKSpSRkaFOnTpZXQ4AAIBxlges3/72tzp48KC6dOmiS5cuadSoUerRo4dOnjypl19%2B2epyAAAAjLP8OljR0dH6/e9/r82bN%2Buvf/2rqqqqNHToUA0YMEB169a1uhwAAADjbLmSe0hIiIYMGWLHpgEAAO44ywPW008//aPzfBYhAACo6SwPWI0aNfL4uaKiQidOnNDhw4f1zDPPWF0OAACAcXfNZxEuWLBAp0%2BftrgaAAAA82z5LMLrGTBggD766CO7ywAAALhtd03A%2BvLLL7nQKAAA8Al3xZvcz507p7/97W968sknrS4HAADAOMsDVkxMjMfnEErSPffco1/96lf6xS9%2BYXU5AAAAxlkesF599VWrNwkAAGApywPWF198ccO3ffjhh%2B9gJQAAAHeG5QFr2LBh7lOELpfLPX7tmJ%2Bfn/76179aXR4AAMBtszxgLV68WLNmzdJvfvMbdezYUYGBgfrqq680Y8YM/fKXv1S/fv2sLgkAAMAoyy/TkJ6erilTpqh3796qX7%2B%2B6tSpo06dOmnGjBl677331KhRI/c/AACAmsjygFVUVHTd8FS3bl2dOXPG6nIAAACMszxgtW/fXnPnztW5c%2BfcYyUlJcrIyNCjjz5qdTkAAADGWf4erEmTJunpp59Wt27d1KxZM7lcLv39739XZGSkVqxYYXU5AAAAxlkesJo3b64tW7Zo8%2BbNOnr0qCTpqaee0mOPPaaQkBCrywEAADDO8oAlSffee6%2BGDBmikydPqkmTJpKuXM0dAADAF1gesFwul%2BbMmaOVK1fq8uXL2rZtm%2BbNm6eQkBBNmzaNoIW7Rt/5n9ldwk356MXOdpcAAPj/LH%2BT%2B8qVK7Vx40ZNnTpVgYGBkqRevXpp%2B/btyszMtLocAAAA4ywPWNnZ2ZoyZYoSExPdV2/v16%2BfZs2apQ8%2B%2BMDqcgAAAIyzPGCdPHlSrVq1qjbesmVLOZ1Oq8sBAAAwzvKA1ahRI3311VfVxnfu3Ol%2BwzsAAEBNZvmb3J9//nlNnz5dTqdTLpdLu3fvVnZ2tlauXKnf/va3VpcDAABgnOUBa9CgQaqoqNCiRYt08eJFTZkyReHh4XrxxRc1dOhQq8sBAAAwzvKAtXnzZvXp00dJSUn67rvv5HK5FBERYXUZAAAAd4zl78GaMWOG%2B83s4eHhhCsAAOBzLA9YzZo10%2BHDh63eLAAAgGUsP0XYsmVL/frXv9aSJUvUrFkzBQUFecynp6dbXRIAAIBRlges48ePKz4%2BXpK47hUAAPBJlgSs2bNnKyUlRaGhoVq5cqUVmwQAALCNJe/BWrp0qcrKyjzGhg8frqKiIis2DwAAYClLApbL5ao29sUXX%2BjSpUtWbB4AAMBSlv8VIQAAgK8jYAEAABhm2V8R%2Bvn5WbUpAHe5vvM/s7sEALijLAtYs2bN8rjm1eXLl5WRkaE6dep43M70dbCGDx%2Bu8PBwvfrqq5KkQ4cOaerUqTp8%2BLBatGih6dOn66GHHnLffvPmzZo/f76cTqe6dOmimTNnKjw83GhNAADAt1lyivDhhx%2BW0%2BnUyZMn3f/i4uJ05swZj7GTJ08a3e6HH36oHTt2uH%2B%2BcOGChg8froSEBK1fv15xcXEaMWKELly4IEk6cOCAJk6cqJSUFGVnZ6u0tFTjx483WhMAAPB9lqxg2XHtq5KSEs2ePVtt27Z1j23ZskVBQUEaN26c/Pz8NHHiRO3cuVNbt25VYmKiVq1apb59%2B2rgwIGSrly/q0ePHiooKFCTJk0s3wcAAFAz%2Beyb3F977TUNGDBALVq0cI/l5eUpPj7e/X4wPz8/dejQQbm5ue75hIQE9%2B0bNmyomJgY5eXlWVs8AACo0Sz/qBwr7N69W/v27dMHH3ygadOmucedTqdH4JKkiIgI5efnS5KKiooUFRVVbf706dM3tf2ioqJqHwPkcIS6HzsgwN/jK35Ab26dw0HPcMU/Phf4nfKO3nhHb26fzwWsS5cuaerUqZoyZYqCg4M95srKyhQYGOgxFhgYqPLycknSxYsXf3T%2BRmVnZyszM9NjLDk5WampqR5jYWEhN/W4tQm9uXn169f56RuhVrjec4HfKe/ojXf05tb5XMDKzMzUQw89pK5du1abCwoKqhaWysvL3UHM23xIyM09wZKSktSzZ0%2BPMYcjVGfOnJd05RVBWFiISkvLVFlZdVOP7evoza27%2BvwC/vG5wO%2BUd/TGO1/qjV0vPn0uYH344YcqLi5WXFycJLkD07Zt29S/f38VFxd73L64uNh96i46Ovq685GRkTdVQ1RUVLVTjU7nWVVUeD5JKyurqo3hCnpz8%2BgXrrrec4HfKe/ojXf05tb5XMBauXKlKioq3D%2B//vrrkqRf//rX%2BuKLL/T222/L5XLJz89PLpdL%2B/fv18iRIyVJsbGxysnJUWJioiSpsLBQhYWFio2NtX5HgJvExTsB4O7hcwGrUaNGHj9fvZBp06ZNFRERoTlz5igtLU1PPPGEVq9erbKyMvXt21eSNHToUA0bNkzt27dX27ZtlZaWpu7du3OJBgAAcFNq1Z8H1K1bV2%2B99ZZ7lSovL09ZWVkKDQ2VJMXFxWnGjBlasGCBhg4dqnvvvdf4leUBAIDv83O5XC67i6gNnM6z7u8dDn/Vr19HZ86c59z2Ne6m3nDKDTXVRy92dn9/N/1O3W3ojXe%2B1JvIyHq2bLdWrWABAABYgYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5rC7gDvlm2%2B%2BUVpamvbs2aOgoCD169dPY8eOVVBQkAoKCjR58mTl5uYqJiZGEyZMUJcuXdz33bVrl1555RUVFBQoNjZWaWlpatKkiY17AwA3ru/8z%2Bwu4YZ99GJnu0sA7gifXMFyuVxKTU1VWVmZ3n33Xc2bN0%2BffPKJ5s%2BfL5fLpeTkZDVo0EDr1q3TgAEDlJKSolOnTkmSTp06peTkZCUmJmrt2rUKDw/X6NGj5XK5bN4rAABQU/jkCtaxY8eUm5urzz77TA0aNJAkpaam6rXXXlO3bt1UUFCg1atXKzQ0VM2bN9fu3bu1bt06jRkzRmvWrNFDDz2k5557TpKUnp6uzp07a%2B/evXrkkUfs3C0AAFBD%2BOQKVmRkpJYsWeIOV1edO3dOeXl5at26tUJDQ93j8fHxys3NlSTl5eUpISHBPRcSEqI2bdq45wEAAH6KT65ghYWFqWvXru6fq6qqtGrVKnXq1ElOp1NRUVEet4%2BIiNDp06cl6Sfnb0RRUZGcTqfHmMMR6n7cgAB/j6/4Ab0BaheHw77fdY433tGb2%2BeTAetaGRkZOnTokNauXatly5YpMDDQYz4wMFDl5eWSpLKysh%2BdvxHZ2dnKzMz0GEtOTlZqaqrHWFhYyM3sRq1Cb4DaoX79OnaXwPHmR9CbW%2BfzASsjI0PLly/XvHnz9OCDDyooKEglJSUetykvL1dwcLAkKSgoqFqYKi8vV1hY2A1vMykpST179vQYczhCdebMeUlXXhGEhYWotLRMlZVVt7JbPoveALXL1eOiHTjeeOdLvbErxPt0wJo5c6bee%2B89ZWRkqHfv3pKk6OhoHTlyxON2xcXF7tN30dHRKi4urjbfqlWrG95uVFRUtdOMTudZVVR4PkkrK6uqjeEKegPUDnfD7znHG%2B/oza3z2YCVmZmp1atXa%2B7cuerTp497PDY2VllZWbp48aJ71SonJ0fx8fHu%2BZycHPfty8rKdOjQIaWkpFi7Az6oJl2bBwCA2%2BGTAevo0aNauHChhg8frvj4eI83nHfs2FENGzbU%2BPHjNXr0aH3yySc6cOCA0tPTJUmDBg3SO%2B%2B8o6ysLPXo0UMLFixQ48aNuUQDANwBNe2FFxdGxY3yyT8P%2BPjjj1VZWalFixapS5cuHv8CAgK0cOFCOZ1OJSYmatOmTVqwYIFiYmIkSY0bN9abb76pdevWafDgwSopKdGCBQvk5%2Bdn814BAICaws/FJcot4XSedX9wJ8VAAAALuElEQVTvcPirfv06OnPmfK06t13TXqkCwLVqywqWL/0/FRlZz5bt%2BuQKFgAAgJ0IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHPYXQAAALgz%2Bs7/zO4SbthHL3a2uwSjWMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIwruQMAcINq0pXRYS9WsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGB/2XMPxwaMAANx9WMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhY13Hp0iVNmDBBCQkJ6tKli373u9/ZXRIAAKhBuEzDdcyePVsHDx7U8uXLderUKb388suKiYlRnz597C4NAADUAASsa1y4cEFr1qzR22%2B/rTZt2qhNmzbKz8/Xu%2B%2B%2BS8ACAAA3hFOE1/j6669VUVGhuLg491h8fLzy8vJUVVVlY2UAAKCmYAXrGk6nU/Xr11dgYKB7rEGDBrp06ZJKSkoUHh7%2Bk49RVFQkp9PpMeZwhCoqKkqSFBDg7/EVAIDazuHwrf8TCVjXKCsr8whXktw/l5eX39BjZGdnKzMz02MsJSVFY8aMkXQlgC1fvkRJSUnu0HWr9qX51mnLoqIiZWdnG%2BmNr6E33tEb7%2BiNd/TGO3pz%2B3wrLhoQFBRULUhd/Tk4OPiGHiMpKUnr16/3%2BJeUlOSedzqdyszMrLbKBXrzY%2BiNd/TGO3rjHb3xjt7cPlawrhEdHa0zZ86ooqJCDseV9jidTgUHByssLOyGHiMqKorEDwBALcYK1jVatWolh8Oh3Nxc91hOTo7atm0rf3/aBQAAfhqJ4RohISEaOHCgpk2bpgMHDmj79u363e9%2Bp6efftru0gAAQA0RMG3atGl2F3G36dSpkw4dOqQ5c%2BZo9%2B7dGjlypAYNGmR0G3Xq1FHHjh1Vp04do4/rC%2BiNd/TGO3rjHb3xjt54R29uj5/L5XLZXQQAAIAv4RQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAClsUKCws1YsQIdejQQT179tSyZcvsLsl25eXl6t%2B/vz7//HP3WEFBgf793/9d7du3V79%2B/fTpp5/aWKF9rteb3NxcPfHEE4qLi1Pv3r21Zs0aGyu0z/V6c9XZs2fVtWtXrV%2B/3obK7He93pw6dUovvPCCYmNj9fOf/1xbtmyxsUL7XK83%2B/btU2Jiotq3b68BAwZo165dNlZorW%2B%2B%2BUapqanq2LGjunbtqvT0dF26dEkSx%2BHbRcCy2IsvvqjQ0FCtX79eEyZM0Pz58/XHP/7R7rJsc%2BnSJY0dO1b5%2BfnuMZfLpeTkZDVo0EDr1q3TgAEDlJKSolOnTtlYqfWu1xun06kXXnhBHTt21IYNG5SamqqZM2fqz3/%2Bs32F2uB6vflHGRkZKioqsriqu8P1elNRUaERI0bI4XBow4YNev755zVu3DgdPnzYxkqtd73efPvttxo5cqT69eunDz74QH379tXo0aN1%2BvRpGyu1hsvlUmpqqsrKyvTuu%2B9q3rx5%2BuSTTzR//nyOwwY47C6gNvn%2B%2B%2B%2BVm5urmTNnqlmzZmrWrJm6du2q3bt36%2Bc//7nd5VnuyJEjeumll3TtpzXt2bNHBQUFWr16tUJDQ9W8eXPt3r1b69at05gxY2yq1lreerN9%2B3Y1aNBAY8eOlSQ1a9ZMn3/%2BuT744AN1797dhkqt5603V%2B3bt0979uxRZGSkxZXZz1tvduzYocLCQr333nuqW7euHnjgAe3cuVNffvmlHnzwQZuqtZa33uzfv18BAQH6j//4D0nSyJEjtXTpUuXm5qpPnz52lGqZY8eOKTc3V5999pkaNGggSUpNTdVrr72mbt261frj8O1iBctCwcHBCgkJ0fr163X58mUdO3ZM%2B/fvV6tWrewuzRZ79%2B7VI488ouzsbI/xvLw8tW7dWqGhoe6x%2BPh45ebmWl2ibbz15uoS/rXOnTtnVWm289Yb6crpn8mTJ2vKlCkKDAy0oTp7eevN3r179eijj6pu3brusYULFyopKcnqEm3jrTf33XefSkpK9Ic//EEul0vbt2/X%2BfPna0XwjIyM1JIlS9zh6qpz585xHDaAFSwLBQUFacqUKZo5c6ZWrFihyspKJSYmasiQIXaXZosnn3zyuuNOp1NRUVEeYxEREbViyf4qb71p3LixGjdu7P7522%2B/1YcfflirXlF6640kLV68WK1bt1aXLl0srOju4a03BQUFatSokV5//XVt3LhR9evXV2pqqnr16mVxhfbx1puEhAQ99dRTSk1Nlb%2B/vyorK5Wenq4HHnjA4gqtFxYWpq5du7p/rqqq0qpVq9SpUyeOwwawgmWxo0ePqkePHsrOzlZ6erq2bt2qTZs22V3WXaWsrKza6kNgYKDKy8ttqujudPHiRY0ZM0YNGjSoVSsR3hw5ckSrV6/W%2BPHj7S7lrnPhwgVt2LBBpaWlWrx4sQYOHKjU1FR99dVXdpdmu/Pnz6ugoEApKSlas2aNRo4cqVmzZuno0aN2l2a5jIwMHTp0SP/5n//JcdgAVrAstHv3bq1du1Y7duxQcHCw2rZtq2%2B%2B%2BUaLFi3SL37xC7vLu2sEBQWppKTEY6y8vFzBwcE2VXT3OX/%2BvEaPHq2///3v%2Bu///m%2BFhITYXZKtXC6XJk2apNTU1GqnOyAFBATovvvu07Rp0%2BTv7682bdpo3759ev/999W2bVu7y7PVkiVL5HK5lJKSIklq06aNDhw4oBUrVmj69Ok2V2edjIwMLV%2B%2BXPPmzdODDz7IcdgAVrAsdPDgQTVt2tTjCdq6dWv%2BKuMa0dHRKi4u9hgrLi6utlxdW507d07PP/%2B88vPztXz5cjVr1szukmx36tQpffnll3rttdcUFxenuLg4nTp1SlOnTnW/ebk2i4qKUrNmzeTv/8Mh//7771dhYaGNVd0d/vKXv6hly5YeY61atapVx%2BWZM2dq6dKlysjIUO/evSVxHDaBFSwLRUVF6cSJEyovL3cvvR47dszjPTWQYmNjlZWVpYsXL7rDaE5OjuLj422uzH5VVVVKSUnRyZMntXLlSjVv3tzuku4K0dHR%2BsMf/uAxNmzYMA0bNozVYV35nVq0aJEqKysVEBAg6crbFRo1amRzZfaLiorSkSNHPMZq03E5MzNTq1ev1ty5cz3%2BapLj8O1jBctCPXv21D333KNJkybp%2BPHj%2BtOf/qTFixdr2LBhdpd2V%2BnYsaMaNmyo8ePHKz8/X1lZWTpw4IAGDx5sd2m2W7t2rT7//HPNmjVLYWFhcjqdcjqd1ZbyaxuHw6GmTZt6/HM4HIqIiFB0dLTd5dmuf//%2Bqqqq0vTp03XixAm9%2B%2B67%2Bp//%2BR89/vjjdpdmuyFDhmjnzp1atmyZCgoKtGzZMn366ac/%2BscUvuLo0aNauHChXnjhBcXHx7uPJ06nk%2BOwAaxgWahevXpatmyZ0tLSNHjwYIWHh2vUqFG8QfkaAQEBWrhwoSZOnKjExEQ1bdpUCxYsUExMjN2l2W7btm2qqqrSiBEjPMY7duyolStX2lQV7nZ169bV0qVLNW3aNPXv318xMTGaN2%2Be2rRpY3dptmvfvr3efPNNvfHGG/qv//ov3X///crKytLPfvYzu0u74z7%2B%2BGNVVlZq0aJFWrRokcfc3/72N47Dt8nP5e1qfQAAALglnCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BH7DJhcOzqSenAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4564154773625592162”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>14.9</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.4</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.6</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.4</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>125</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.1</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.8</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.8</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.6</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.8</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (31)</td> <td class=”number”>1561</td> <td class=”number”>48.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4564154773625592162”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.5</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.7</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.8</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.1</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>16.1</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.6</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.4</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.2</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.8</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_CHILD_01_07”>FOODINSEC_CHILD_01_07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.5867</td>

</tr> <tr>

<th>Minimum</th> <td>4.8</td>

</tr> <tr>

<th>Maximum</th> <td>12.6</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram572560681944074733”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives572560681944074733,#minihistogram572560681944074733”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives572560681944074733”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles572560681944074733”

aria-controls=”quantiles572560681944074733” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram572560681944074733” aria-controls=”histogram572560681944074733”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common572560681944074733” aria-controls=”common572560681944074733”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme572560681944074733” aria-controls=”extreme572560681944074733”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles572560681944074733”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>4.8</td>

</tr> <tr>

<th>5-th percentile</th> <td>6.2</td>

</tr> <tr>

<th>Q1</th> <td>7.2</td>

</tr> <tr>

<th>Median</th> <td>8.3</td>

</tr> <tr>

<th>Q3</th> <td>9.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>12.6</td>

</tr> <tr>

<th>Maximum</th> <td>12.6</td>

</tr> <tr>

<th>Range</th> <td>7.8</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8944</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.22063</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.27613</td>

</tr> <tr>

<th>Mean</th> <td>8.5867</td>

</tr> <tr>

<th>MAD</th> <td>1.5052</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.56804</td>

</tr> <tr>

<th>Sum</th> <td>26893</td>

</tr> <tr>

<th>Variance</th> <td>3.5889</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram572560681944074733”>

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d58hSQkLCSefj4uI6VUNWVpZGjx7tNWazRai%2B/lin1mNScHAPRUeHq7HRqdbWNsvq6Cr0B/xTd7/WBPr%2BSX/%2Bzao/OHwuYF111VV65plnVFhYqB//%2BMfKyMjQ8OHDPRemn86KFSvU0tLiefzYY49Jku655x598sknevbZZ%2BV2uxUUFCS3263t27frjjvukCTZ7XaVl5crIyNDknTo0CEdOnRIdru9Uz3Ex8e3Ox3ocHytlhbrd9zW1jafqKOr0B8gy/aRQN8/6Q%2Bd4XMnXO%2B%2B%2B2798Y9/1LJlyxQcHKxZs2Zp5MiRevLJJ7V///7TPj8pKUn9%2B/f3fERGRioyMlL9%2B/dXenq6GhsbtWjRIu3Zs0eLFi2S0%2BnU2LFjJUnZ2dlau3atVq1apd27d2vOnDkaOXKk%2BvXr19VtAwCAAOJzAUv6x%2B0Thg8friVLlmjLli2aPHmyXnrpJY0bN06TJ0/W//7v/57ReqOiorR8%2BXLPUarKykqVlJQoIiJCkpScnKz58%2BeruLhY2dnZ6tmzpwoLC022BgAAzgI%2Bd4rwG7W1tVq3bp3WrVunL774QkOHDtV//Md/6PDhw7r//vv1ySefqKCg4LTreeSRR7weX3755XrttddOuXxGRobnFCEAAMCZ8LmAtXbtWq1du1bbtm1TTEyMbrjhBv3617/WgAEDPMv06dNHixYt6lDAAgAA6G4%2BF7AKCgo0atQoFRcX69prr1WPHu3PYl5wwQW6%2BeabLagO8F1jl35odQkAgP/P5wLW%2B%2B%2B/r169eqmhocETrnbs2KEhQ4YoODhYkjR06FANHTrUyjIBAABOyecucj969KjS09P17LPPesamT5%2BuCRMm6NChQxZWBgAA0DE%2BF7Aefvhh9e/fX7fddptnbMOGDerTpw//0QcAAPyCz50i/NOf/qTf/e53XndPj4mJ0Zw5czR58mQLK8PZhmuaAABnyueOYNlsNjU2NrYbdzqdcrvdFlQEAADQOT4XsK699lotXLhQf/3rXz1j1dXVKiwsPOkbOAMAAPganztFOHfuXN1222267rrrFB0dLUlqbGzUkCFDlJ%2Bfb3F1AAAAp%2BdzASs2NlavvfaatmzZoqqqKtlsNl144YW6%2BuqrO/yGzwAAAFbyuYAlScHBwRoxYgSnBAEAgF/yuYDlcDi0dOlSbd%2B%2BXSdOnGh3Yfu7775rUWUAAAAd43MB61e/%2BpV27typ66%2B/Xueee67V5QAAAHSazwWsjz76SM8995xSU1OtLgUAAOCM%2BNxtGiIiIhQbG2t1GQAAAGfM5wLWhAkT9Nxzz6m1tdXqUgAAAM6Iz50ibGho0Pr16/Xee%2B%2BpX79%2BCgkJ8Zp/%2BeWXLaoMAACgY3wuYEnS%2BPHjrS4BAADgjPlcwCosLLS6BAAAgO/F567BkqTa2loVFRXp7rvv1ldffaW3335b%2B/bts7osAACADvG5gHXgwAH99Kc/1WuvvaZ33nlHTU1N2rBhgzIzM1VZWWl1eQAAAKflcwHrkUce0ZgxY7Rx40adc845kqQnnnhCo0eP1mOPPWZxdQAAAKfncwFr%2B/btuu2227ze2Nlms2nmzJnatWuXhZUBAAB0jM8FrLa2NrW1tbUbP3bsmIKDgy2oCAAAoHN8LmBdc801Wr58uVfIamho0JIlS3TVVVdZWBkAAEDH%2BFzAuvfee7Vz505dc801am5u1p133qlRo0bp4MGDmjt3rtXlAQAAnJbP3QcrISFBr7/%2ButavX6/PP/9cbW1tys7O1oQJExQVFWV1eQAAAKflcwFLksLDwzVp0iSrywAAADgjPhewpkyZ8p3zvBchAADwdT4XsJKSkrwet7S06MCBA/riiy/085//3KKqAAAAOs7nAtap3ouwuLhYhw8f7uZqAAAAOs/n/ovwVCZMmKC33nrL6jIAAABOy28C1qeffsqNRgEAgF/wuVOEJ7vI/ejRo/q///s/3XTTTRZUBAAA0Dk%2BF7ASExO93odQks455xzdfPPN%2BtnPfmZRVQAAAB3ncwHrkUcesboEdJGxSz%2B0ugQAALqFzwWsTz75pMPLXnnllV1YCQAAwJnxuYB1yy23eE4Rut1uz/i3x4KCgvT555%2Bfcj0HDhzQ/PnztX37dvXs2VM333yzpk2bJkmqrq7Wr371K1VUVCgxMVH33XefrrnmGs9zt2zZoocffljV1dWy2%2B1atGiR%2BvXrZ7xXAAAQmHzuvwifeeYZJSUlaenSpdq6davKy8v14osv6vzzz9ddd92ld999V%2B%2B%2B%2B642btx4ynW0tbVp%2BvTp6tWrl1577TU99NBDevrpp/XGG2/I7XYrJydHvXv3VllZmSZMmKDc3FzV1NRIkmpqapSTk6OMjAytXr1aMTExmjlzplfYAwAA%2BC4%2BdwSrsLBQ8%2BbN07XXXusZu%2BqqqzR//nzNmTNHt99%2B%2B2nXUVdXp0GDBunBBx9UVFSUBgwYoKuvvlrl5eXq3bu3qqurtXLlSkVERGjgwIHaunWrysrKNGvWLK1atUqXXnqppk6d6qln%2BPDh%2BvjjjzVs2LAu6xsAAAQOnzuCVVtb2%2B7tciQpKipK9fX1HVpHfHy8li5dqqioKLndbpWXl%2BuTTz5RWlqaKisrNXjwYEVERHiWT0lJUUVFhSSpsrJSqampnrnw8HANGTLEMw8AAHA6PncE64orrtATTzyhRx99VFFRUZKkhoYGLVmyRFdffXWn1zd69GjV1NRo1KhRuu666/Twww8rPj7ea5nY2FjP2/A4HI7vnO%2BI2tpaORwOrzGbLaLdertTcHAPr88AApfN1r0/54H%2B%2BkJ/OBM%2BF7Duv/9%2BTZkyRddee60GDBggt9utv/zlL4qLi9PLL7/c6fX9%2Bte/Vl1dnR588EEVFhbK6XQqJCTEa5mQkBC5XC5JOu18R5SWlqqoqMhrLCcnR3l5eZ2u37To6HCrSwDQxXr1irRku4H%2B%2BkJ/6AyfC1gDBw7Uhg0btH79eu3du1eSNHnyZF1//fUKD%2B/8N/%2Byyy6TJDU3N%2Buee%2B5RZmamnE6n1zIul0thYWGSpNDQ0HZhyuVyKTo6usPbzMrK0ujRo73GbLYI1dcf63T9pgQH91B0dLgaG51qbW2zrA4AXa%2B7X2sC/fWF/vybVX9w%2BFzAkqSePXtq0qRJOnjwoOf2COecc06Hn19XV6eKigqNGTPGM3bhhRfqxIkTiouL0759%2B9ot/83pu4SEBNXV1bWbHzRoUIe3Hx8f3%2B50oMPxtVparN9xW1vbfKIOAF3Hqp/xQH99oT90hs%2BdcHW73Xrsscd05ZVXavz48Tp8%2BLDmzp2rgoICnThxokPrOHjwoHJzc/Xll196xnbu3KmYmBilpKTos88%2B0/Hjxz1z5eXlstvtkiS73a7y8nLPnNPp1K5duzzzAAAAp%2BNzAWvFihVau3atHnjgAc%2B1UGPGjNHGjRvbXdd0KpdddpmGDBmi%2B%2B67T3v27NGmTZu0ZMkS3XHHHUpLS1OfPn2Un5%2BvqqoqlZSUaMeOHZo4caIkKTMzU9u3b1dJSYmqqqqUn5%2Bvvn37cosGAADQYT4XsEpLSzVv3jxlZGR47t4%2Bbtw4LVy4UG%2B88UaH1hEcHKxly5YpPDxcWVlZKigo0C233KIpU6Z45hwOhzIyMrRu3ToVFxcrMTFRktS3b1899dRTKisr08SJE9XQ0KDi4uJ2b0ANAABwKj53DdbBgwdPer3TJZdc0u7WB98lISHhlEe8%2Bvfvr1deeeWUz/3Rj36kH/3oRx3eFgAAwL/yuYCVlJSkP//5z%2Brbt6/X%2BPvvv8/7AQIA0Aljl35odQkd9tbs4VaXYJTPBaxf/OIXeuihh%2BRwOOR2u7V161aVlpZqxYoVuvfee60uDwAA4LR8LmBlZmaqpaVFTz/9tI4fP6558%2BYpJiZGs2fPVnZ2ttXlAQAAnJbPBaz169crPT1dWVlZOnLkiNxut2JjY60uCwAAoMN87r8I58%2Bf77mYPSYmhnAFAAD8js8FrAEDBuiLL76wugwAAIAz5nOnCC%2B55BLdc889eu655zRgwACFhoZ6zRcWFlpUGQAAQMf4XMDav3%2B/UlJSJKlT970CAADwFT4RsBYvXqzc3FxFRERoxYoVVpcDAADwvfjENVgvvPCCnE6n19j06dNVW1trUUUAAABnzicCltvtbjf2ySefqLm52YJqAAAAvh%2BfCFgAAACBhIAFAABgmM8ErKCgIKtLAAAAMMIn/otQkhYuXOh1z6sTJ05oyZIlioyM9FqO%2B2ABAABf5xMB68orr2x3z6vk5GTV19ervr7eoqoAAADOjE8ELO59BQAAAonPXIMFAAAQKAhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMZnUBAACzxi790OoSOuyt2cOtLgHoEhzBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMCNmB9%2BeWXysvLU1pamkaMGKHCwkI1NzdLkqqrq3Xrrbfqiiuu0Lhx47R582av527ZskXjx4%2BX3W7XlClTVF1dbUULAADATwVkwHK73crLy5PT6dRvf/tbPfnkk/rjH/%2BopUuXyu12KycnR71791ZZWZkmTJig3Nxc1dTUSJJqamqUk5OjjIwMrV69WjExMZo5c6bcbrfFXQEAAH8RkPfB2rdvnyoqKvThhx%2Bqd%2B/ekqS8vDw9%2Buijuvbaa1VdXa2VK1cqIiJCAwcO1NatW1VWVqZZs2Zp1apVuvTSSzV16lRJUmFhoYYPH66PP/5Yw4YNs7ItAADgJwLyCFZcXJyee%2B45T7j6xtGjR1VZWanBgwcrIiLCM56SkqKKigpJUmVlpVJTUz1z4eHhGjJkiGceAADgdAIyYEVHR2vEiBGex21tbXrllVd01VVXyeFwKD4%2B3mv52NhYHT58WJJOOw8AAHA6AXmK8NuWLFmiXbt2afXq1XrxxRcVEhLiNR8SEiKXyyVJcjqd3znfEbW1tXI4HF5jNltEu%2BDWnYKDe3h9BgBfYLP5/msSr5/dwx/2hc4I%2BIC1ZMkSvfTSS3ryySd10UUXKTQ0VA0NDV7LuFwuhYWFSZJCQ0PbhSmXy6Xo6OgOb7O0tFRFRUVeYzk5OcrLyzvDLsyJjg63ugQA8OjVK9LqEjqM18%2Bu5U/7QkcEdMBasGCBXn31VS1ZskTXXXedJCkhIUF79uzxWq6urs5zdCkhIUF1dXXt5gcNGtTh7WZlZWn06NFeYzZbhOrrj51JG0YEB/dQdHS4Ghudam1ts6wOAPhXVr4udhSvn92jq/YFq4JbwAasoqIirVy5Uk888YTS09M943a7XSUlJTp%2B/LjnqFV5eblSUlI88%2BXl5Z7lnU6ndu3apdzc3A5vOz4%2Bvt3pQIfja7W0WP%2BD2dra5hN1AIAkv3o94vWzawXa1zawTnj%2Bf3v37tWyZct0%2B%2B23KyUlRQ6Hw/ORlpamPn36KD8/X1VVVSopKdGOHTs0ceJESVJmZqa2b9%2BukpISVVVVKT8/X3379uUWDQAAoMMCMmC9%2B%2B67am1t1dNPP61rrrnG6yM4OFjLli2Tw%2BFQRkaG1q1bp%2BLiYiUmJkqS%2Bvbtq6eeekplZWWaOHGiGhoaVFxcrKCgIIu7AgAA/iLIzS3Ku4XD8bWl27fZeqhXr0jV1x%2Bz7DDs2KUfWrJdAL7rrdnDrS7htHzh9fNM%2BdPrblftC3Fx53bJek8nII9gAQAAWImABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMs1ldAAAA/mLs0g%2BtLgF%2BgiNYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIZxJ3cAgGW4MzoCFUewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAs4AOWy%2BXS%2BPHjtW3bNs9YdXW1br31Vl1xxRUaN26cNm/e7PWcLVu2aPz48bLb7ZoyZYqqq6u7u2wAAODHAjpgNTc366677lJVVZVnzO12KycnR71791ZZWZkmTJig3Nxc1dTUSJJqamqUk5OjjIwMrV69WjExMZo5c6bcbrdVbQAAAD8TsAFrz549%2Bs///E/99a9/9Rr/6KOPVF1drfnz52vgwIGaMWOGrrjiCpWVlUmSVq1apUtWcaAAAAANhklEQVQvvVRTp07VD37wAxUWFupvf/ubPv74YyvaAAAAfihgA9bHH3%2BsYcOGqbS01Gu8srJSgwcPVkREhGcsJSVFFRUVnvnU1FTPXHh4uIYMGeKZBwAAOB2b1QV0lZtuuumk4w6HQ/Hx8V5jsbGxOnz4cIfmO6K2tlYOh8NrzGaLaLfe7hQc3MPrMwAAvsRmC6zfTwEbsE7F6XQqJCTEaywkJEQul6tD8x1RWlqqoqIir7GcnBzl5eWdYdXmREeHW10CAADt9OoVaXUJRp11ASs0NFQNDQ1eYy6XS2FhYZ75b4cpl8ul6OjoDm8jKytLo0eP9hqz2SJUX3/sDKv%2B/oKDeyg6OlyNjU61trZZVgcAACfTVb8jrQpuZ13ASkhI0J49e7zG6urqPKfvEhISVFdX125%2B0KBBHd5GfHx8u9OBDsfXammxPti0trb5RB0AAPyrQPvdFFgnPDvAbrfrs88%2B0/Hjxz1j5eXlstvtnvny8nLPnNPp1K5duzzzAAAAp3PWBay0tDT16dNH%2Bfn5qqqqUklJiXbs2KGJEydKkjIzM7V9%2B3aVlJSoqqpK%2Bfn56tu3r4YNG2Zx5QAAwF%2BcdQErODhYy5Ytk8PhUEZGhtatW6fi4mIlJiZKkvr27aunnnpKZWVlmjhxohoaGlRcXKygoCCLKwcAAP4iyM0tyruFw/G1pdu32XqoV69I1dcfs%2Bw899ilH1qyXQCA73tr9vAuWW9c3Lldst7TOeuOYAEAAHQ1AhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw2xWF4Dvh/f3AwDA93AECwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAC1kk0NzfrvvvuU2pqqq655hr95je/sbokAADgR2xWF%2BCLFi9erJ07d%2Bqll15STU2N5s6dq8TERKWnp1tdGgAA8AMErG9pamrSqlWr9Oyzz2rIkCEaMmSIqqqq9Nvf/paABQAAOoRThN%2Bye/dutbS0KDk52TOWkpKiyspKtbW1WVgZAADwFxzB%2BhaHw6FevXopJCTEM9a7d281NzeroaFBMTExp11HbW2tHA6H15jNFqH4%2BHjj9QIAEAhstsA65kPA%2Bhan0%2BkVriR5Hrtcrg6to7S0VEVFRV5jubm5mjVrlpki/8WfFnXstGVtba1KS0uVlZUVkEGP/vwb/fk3%2BvNvgd6fVQIrLhoQGhraLkh98zgsLKxD68jKytKaNWu8PrKysozX2hkOh0NFRUXtjqwFCvrzb/Tn3%2BjPvwV6f1bhCNa3JCQkqL6%2BXi0tLbLZ/vHlcTgcCgsLU3R0dIfWER8fz18BAACcxTiC9S2DBg2SzWZTRUWFZ6y8vFyXXXaZevTgywUAAE6PxPAt4eHhuuGGG/Tggw9qx44d2rhxo37zm99oypQpVpcGAAD8RPCDDz74oNVF%2BJqrrrpKu3bt0uOPP66tW7fqjjvuUGZmptVlfW%2BRkZFKS0tTZGSk1aV0Cfrzb/Tn3%2BjPvwV6f1YIcrvdbquLAAAACCScIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsA6C/z%2B97/XxRdf7PWRl5dndVnGuFwuPfTQQ7ryyiv1wx/%2BUE888YQC5f65a9asafe9u/jii3XJJZdYXZoxhw4d0owZMzR06FCNHj1aL774otUlGfXVV18pLy9Pqamp%2BslPfqI1a9ZYXZIRLpdL48eP17Zt2zxj1dXVuvXWW3XFFVdo3Lhx2rx5s4UVnrmT9faNffv2KTk52YKqzDlZfxUVFbrxxhuVnJys6667TqtWrbKwwsBgs7oAdL09e/Zo1KhRWrBggWcsNDTUworMWrhwobZt26bnn39ex44d03/9138pMTFRN954o9WlfW/jxo3TiBEjPI9bWlr085//XCNHjrSuKMNmz56txMRErVmzRnv27NE999yjpKQk/eQnP7G6tO/N7XYrJydHbW1tevnll/Xll19q7ty5ioqK0r//%2B79bXd4Za25u1t13362qqirP2De9XnTRRSorK9PGjRuVm5urDRs2KDEx0cJqO%2BdkvX3jb3/7m%2B688041NzdbUJkZJ%2BvP4XDo9ttvV3Z2th555BF99tlnys/PV1xcXEC91nQ3AtZZYO/evbrooosUFxdndSnGNTQ0qKysTC%2B88IIuv/xySdLUqVNVWVkZEAErLCxMYWFhnsfLly%2BX2%2B3WPffcY2FV5vz9739XRUWFFixYoAEDBmjAgAEaMWKEtm7dGhABa%2BfOnfr000%2B1ceNG9evXT4MHD9a0adP0/PPP%2B23A2rNnj%2B6%2B%2B%2B52R4k/%2BugjVVdXa%2BXKlYqIiNDAgQO1detWlZWVadasWRZV2zmn6k2S3nnnHT3wwAOKj4%2B3oDIzTtXfxo0b1bt3b911112SpAEDBmjbtm164403CFjfA6cIzwJ79%2B7VgAEDrC6jS5SXlysqKkppaWmesenTp6uwsNDCqrpGQ0ODnn32Wd19990KCQmxuhwjwsLCFB4erjVr1ujEiRPat2%2Bftm/frkGDBlldmhHV1dWKiYlRv379PGMXX3yxdu7cqRMnTlhY2Zn7%2BOOPNWzYMJWWlnqNV1ZWavDgwYqIiPCMpaSkqKKiortLPGOn6k2S3nvvPd1111269957LajMjFP1N2LEiJO%2BZh49erS7SgtIHMEKcG63W/v379fmzZu1fPlytba2Kj09XXl5eQHxS7q6ulpJSUl6/fXX9cwzz%2BjEiRPKyMjQnXfeqR49Auvvh1dffVXx8fFKT0%2B3uhRjQkNDNW/ePC1YsEAvv/yyWltblZGRoUmTJlldmhG9e/fW119/LafTqfDwcEnS4cOH1dLSoq%2B//loxMTEWV9h5N91000nHHQ5Hu6M7sbGxOnz4cHeUZcSpepPkCSBbtmzprnKMO1V/ffv2Vd%2B%2BfT2Pv/rqK7355pt%2Bc%2BTRVwXWbyC0U1NTI6fTqZCQEC1dulRz587VG2%2B8ocWLF1tdmhFNTU06cOCAVq5cqcLCQs2dO1crVqwIuAul3W63Vq1apZtvvtnqUozbu3evRo0apdLSUhUWFurtt9/WunXrrC7LCLvdrvj4eC1YsMCzr77wwguS5LdHsE7lm9eZfxUSEiKXy2VRRTgTx48f16xZs9S7d29lZWVZXY5f4whWgEtKStK2bdvUs2dPBQUFadCgQWpra9Mvf/lL5efnKzg42OoSvxebzaajR4/q8ccfV1JSkqR/hMpXX31VU6dOtbg6c/785z/ryy%2B/1PXXX291KUZt3bpVq1ev1qZNmxQWFqbLLrtMX375pZ5%2B%2Bmn97Gc/s7q87y00NFRLly7V7NmzlZKSotjYWE2bNk2FhYWKioqyujyjQkND1dDQ4DXmcrm8riGEbzt27Jhmzpypv/zlL/qf//kfz1FXnBmOYJ0FzjvvPAUFBXkeDxw4UM3Nzfr73/9uYVVmxMXFKTQ01BOuJOn888/XoUOHLKzKvA8%2B%2BECpqanq2bOn1aUYtXPnTvXv39/rl/DgwYNVU1NjYVVmXX755frDH/6g999/X%2B%2B9957OP/989erVS5GRkVaXZlRCQoLq6uq8xurq6vz6ovCzydGjR/WLX/xCVVVVeumllwL2ut3uRMAKcB988IGGDRsmp9PpGfv888913nnn%2BeX1H99mt9vV3Nys/fv3e8b27dvnFbgCwY4dOzR06FCryzAuPj5eBw4c8DqNtG/fPq/rQfxZQ0ODsrOzVV9fr7i4ONlsNr333nte/5QRKOx2uz777DMdP37cM1ZeXi673W5hVeiItrY25ebm6uDBg1qxYoV%2B8IMfWF1SQCBgBbjk5GSFhobq/vvv1759%2B7Rp0yYtXrxY06ZNs7o0Iy644AKNHDlS%2Bfn52r17tz744AOVlJQoOzvb6tKMqqqq0oUXXmh1GcaNHj1a55xzju6//37t379ff/jDH/TMM8/olltusbo0I8477zw1NTVpyZIlqq6u1qpVq1RWVhYwP3//Ki0tTX369FF%2Bfr6qqqpUUlKiHTt2aOLEiVaXhtNYvXq1tm3bpoULFyo6OloOh0MOh6PdKV90DtdgBbioqCg9//zzevjhh5WZmanIyEjdeOONAfUC/9hjj2nBggXKzs5WeHi4Jk%2BeHDC/oL9RV1en6Ohoq8sw7txzz9WLL76oRYsWaeLEiYqJidGdd94ZUBfXPvnkk3rggQf005/%2BVH379tV///d/e%2B7ZFkiCg4O1bNkyFRQUKCMjQ/3791dxcbFf3WT0bPXOO%2B%2Bora1NM2bM8BpPS0vTihUrLKrK/wW5A%2BU9RQAAAHwEpwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/BybEltw4dEpWAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common572560681944074733”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>12.6</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>246</td> <td class=”number”>7.7%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.2</td> <td class=”number”>205</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.3</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.5</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.8</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.5</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1575</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme572560681944074733”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.8</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.7</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.3</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10.5</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.3</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.4</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.6</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOODINSEC_CHILD_03_11”><s>FOODINSEC_CHILD_03_11</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FOODINSEC_CHILD_01_07”><code>FOODINSEC_CHILD_01_07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94773</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FOOD_TAX14”><s>FOOD_TAX14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_CHIPSTAX_STORES14”><code>CHIPSTAX_STORES14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>1</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRES07”>FRESHVEG_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>631</td>

</tr> <tr>

<th>Unique (%)</th> <td>19.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>42.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1370</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1084.1</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>246870</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>9.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5392072284891479856”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5392072284891479856,#minihistogram5392072284891479856”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5392072284891479856”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5392072284891479856”

aria-controls=”quantiles5392072284891479856” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5392072284891479856” aria-controls=”histogram5392072284891479856”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5392072284891479856” aria-controls=”common5392072284891479856”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5392072284891479856” aria-controls=”extreme5392072284891479856”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5392072284891479856”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>10</td>

</tr> <tr>

<th>Median</th> <td>49.5</td>

</tr> <tr>

<th>Q3</th> <td>256.25</td>

</tr> <tr>

<th>95-th percentile</th> <td>3716.5</td>

</tr> <tr>

<th>Maximum</th> <td>246870</td>

</tr> <tr>

<th>Range</th> <td>246870</td>

</tr> <tr>

<th>Interquartile range</th> <td>246.25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>7487.8</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.907</td>

</tr> <tr>

<th>Kurtosis</th> <td>657.87</td>

</tr> <tr>

<th>Mean</th> <td>1084.1</td>

</tr> <tr>

<th>MAD</th> <td>1697.2</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>22.249</td>

</tr> <tr>

<th>Sum</th> <td>1994700</td>

</tr> <tr>

<th>Variance</th> <td>56068000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5392072284891479856”>

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BSVJCQoL//c2bN5ckHTx4MGD8%2B5SUlPi3dYrX27je76%2Bv8HB35WOv1131nuqv2/rsJvTYWfTXWfTXeW7usWsDVn1UVFRo7Nixat68uYYMGSJJ%2Buyzz/TVV1/pxhtvVE5Ojl566SWNHDlSb7zxhioqKiRJERER/m2c%2Bu%2Bqqqp67zc/P195eXkBY9nZ2crJyTnbJblaXFyTUJfwo8TERIW6hAaPHjuL/jqL/jrPjT1usAHr%2BPHjuvPOO7V792795S9/UVTU138406ZNU0VFhaKjoyVJU6ZM0fr167VixQr96le/kvR1mDp1CfFUsDr1/voYMmSIevfuHTDm9TZWWdnxs17XN7kt0dtev9PCw8MUExOlI0fKVVPDN0mdQI%2BdRX%2BdRX%2BdZ6PHofrHfYMMWMeOHdPtt9%2BuL774QosXLw74tqDX6/WHK0nyeDxKTk5WcXGxEhMTJX39TcRTlxlPXeqLj4%2Bv9/4TEhLqXA70%2BY6quvrc/gF06/prampdW7tb0GNn0V9n0V/nubHH7joFUg%2B1tbUaM2aM9u7dq6VLl%2Briiy8OmL/55psDLt/V1tbq008/VXJyshITE5WUlKSCggL/fEFBgZKSkqx/fgoAADRcDe4M1ssvv6y1a9fqqaeeUkxMjP8M1M9%2B9jPFxsaqd%2B/emjt3rjp06KALL7xQS5Ys0dGjRzV48GBJ0tChQzV79mz9/Oc/lyQ98sgjuvXWW0O2HgAA4D4NLmC9/fbbqq2t1ejRowPGu3TpoqVLl2rUqFGqrKzU9OnTVVpaqtTUVD377LP%2By4a33XabvvzyS40ZM0bh4eG64YYbNGrUqBCsBAAAuJXHfPtOnXCEz3fU%2Bja93jD1nb3K%2Bnad8uZd3UNdwg/i9YYpLq6JysqOu%2B7av1vQY2fRX2fRX%2BfZ6HF8/HmWq6qfBvcZLAAAgFAjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsC3rAuvHGG/Xiiy/q6FH7dzYHAAD4KQh6wOrWrZvmzZunrKws3X333Vq9erX4bT0AAKAhCXrAys3N1b/%2B9S89%2BeSTCg8P19ixY9WzZ0899thj%2Buyzz4JdDgAAgHXeUOzU4/Goe/fu6t69u8rLy7V06VI9%2BeSTWrBggTp37qyRI0fqyiuvDEVpAAAAZy0kAUuSSkpK9Nprr%2Bm1117T9u3b1blzZw0ePFgHDx7UH//4R61bt06TJ08OVXkAAAA/WtAD1ooVK7RixQqtXbtWTZs21aBBg/TEE0%2BodevW/te0aNFCDz74IAELAAC4UtAD1uTJk9WrVy/NnTtXl19%2BucLC6n4MLDk5WcOHDw92aQAAAFYEPWB98MEHiouL0%2BHDh/3hauPGjerYsaPCw8MlSZ07d1bnzp2DXRoAAIAVQf8W4bFjx9SvXz89/fTT/rE77rhD1157rQ4cOBDscgAAAKwLesB66KGH9Itf/EK33HKLf%2ByNN95QixYtNGPGjGCXAwAAYF3QA9b//d//6d5771V8fLx/rGnTpho/frzWrFkT7HIAAACsC3rA8nq9OnLkSJ3x8vJy7ugOAAAahKAHrMsvv1zTp0/XF1984R/bs2ePZsyYoR49egS7HAAAAOuC/i3CCRMm6JZbbtFVV12lmJgYSdKRI0fUsWNHTZw4MdjlAAAAWBf0gNWsWTP97W9/04cffqgdO3bI6/Xqoosu0i9/%2BUt5PJ5glwMAAGBdSH5VTnh4uHr06MElQQAA0CAFPWD5fD49/vjjWr9%2BvU6ePFnng%2B3/%2BMc/gl0SAACAVUEPWPfdd582b96sAQMG6Lzzzgv27gEAABwX9IC1Zs0aLVy4UJmZmcHeNQAAQFAE/TYNjRs3VrNmzYK9WwAAgKAJesC69tprtXDhQtXU1AR71wAAAEER9IB1%2BPBhvfbaa7r88sv1m9/8RiNGjAh4/FBVVVUaOHCg1q5d6x/bs2ePRo0apbS0NPXv31%2BrV68OeM%2BHH36ogQMHKjU1VSNGjNCePXsC5p977jn16NFD6enpmjRpksrLy3/cYgEAwDkp6AFLkgYOHKjLL79cF154oVq2bBnw%2BCEqKyt19913a8eOHf4xY4yys7PVvHlzvfLKK7r22ms1ZswY7d%2B/X5K0f/9%2BZWdn67rrrtPLL7%2Bspk2b6s477/R/m/Htt99WXl6epk6dqsWLF6uwsFCzZs2yt3gAANDgBf1D7jNmzLCynZ07dyo3N7fObR7WrFmjPXv26MUXX1Tjxo3Vpk0bffTRR3rllVc0duxYLV%2B%2BXJdeeqluvfVWfz3du3fXxx9/rK5du2rJkiUaOXKkevXqJUl64IEHdNttt2ncuHGKioqyUjsAAGjYQnIGq6SkRHl5ecrNzdWXX36pt956S7t27fpB2zgViPLz8wPGCwsLdckll6hx48b%2BsYyMDG3YsME//81vMEZFRaljx47asGGDampqtGnTpoD5tLQ0nTx5Utu2bfsxSwUAAOegoJ/B%2Bvzzz3XTTTcpOjpaxcXFuuuuu/TGG29o4sSJeu6555Samlqv7QwbNuy04z6fTwkJCQFjzZo108GDB884f%2BTIEVVWVgbMe71excbG%2Bt9fHyUlJfL5fAFjXm/jOvs9W%2BHhIcnHP5rX6656T/XXbX12E3rsLPrrLPrrPDf3OOgB6%2BGHH1afPn00ffp0de7cWZL06KOPasKECZo9e7aWLl16VtsvLy9XREREwFhERISqqqrOOF9RUeF//l3vr4/8/Hzl5eUFjGVnZysnJ6fe22iI4uKahLqEHyUmhkvDTqPHzqK/zqK/znNjj4MesNavX6/nn38%2B4Bc7e71e3XnnnbrpppvOevuRkZE6fPhwwFhVVZUaNWrkn/92WKqqqlJMTIwiIyP9z789/0M%2BfzVkyBD17t07YMzrbayysuP13kZ9uC3R216/08LDwxQTE6UjR8pVU1Mb6nIaJHrsLPrrLPrrPBs9DtU/7oMesGpra1VbW7dJx48fV3h4%2BFlvPzExUTt37gwYKy0t9V%2BeS0xMVGlpaZ35Dh06KDY2VpGRkSotLVWbNm0kSdXV1Tp8%2BLDi4%2BPrXUNCQkKdy4E%2B31FVV5/bP4BuXX9NTa1ra3cLeuws%2Buss%2Bus8N/Y46KdAsrKyNH/%2B/ICQdfjwYc2aNUvdunU76%2B2npqZqy5Yt/st9klRQUOD/bFdqaqoKCgr8c%2BXl5dq6datSU1MVFhamlJSUgPkNGzbI6/Wqffv2Z10bAAA4NwQ9YN17773avHmzsrKyVFlZqT/84Q/q1auX9u7dqwkTJpz19rt06aIWLVpo4sSJ2rFjhxYsWKCNGzfqhhtukCRdf/31Wr9%2BvRYsWKAdO3Zo4sSJatWqlbp27Srp6w/PL1q0SO%2B%2B%2B642btyoKVOm6KabbuIWDQAAoN6CfokwMTFRr776qlauXKlPPvlEtbW1Gjp0qK699lpFR0ef9fbDw8P15JNPavLkybruuuv0i1/8QnPnzlVSUpIkqVWrVvrzn/%2Bshx56SHPnzlV6errmzp3r/0zYgAEDtG/fPt1///2qqqrSlVdeqXHjxp11XQAA4NzhMd%2B%2BUycc4fMdtb5NrzdMfWevsr5dp7x5V/dQl/CDeL1hiotrorKy46679u8W9NhZ9NdZ9Nd5NnocH3%2Be5arqJ%2BhnsM70%2BwaXLFkSpEoAAACcEfSA9e3fN1hdXa3PP/9c27dv18iRI4NdDgAAgHU/md9FOHfu3B90t3QAAICfqp/MnSqvvfZavfnmm6EuAwAA4Kz9ZALWf/7zHys3GgUAAAi1n8SH3I8dO6ZPP/30O3%2BBMwAAgJsEPWAlJSUF/B5CSfrZz36m4cOH69e//nWwywEAALAu6AHr4YcfDvYuAQAAgiroAWvdunX1fu1ll13mYCUAAADOCHrAuvnmm/2XCL95E/lvj3k8Hn3yySfBLg8AAOCsBT1gzZs3T9OnT9e4cePUpUsXRUREaNOmTZo6daoGDx6s/v37B7skAAAAq4J%2Bm4YZM2bo/vvv11VXXaW4uDg1adJE3bp109SpU/XCCy%2BoZcuW/gcAAIAbBT1glZSUnDY8RUdHq6ysLNjlAAAAWBf0gJWWlqZHH31Ux44d848dPnxYs2bN0i9/%2BctglwMAAGBd0D%2BD9cc//lEjRozQ5ZdfrtatW8sYo927dys%2BPl5LliwJdjkAAADWBT1gtWnTRm%2B88YZWrlypoqIiSdJvf/tbDRgwQFFRUcEuBwAAwLqgByxJOv/883XjjTdq7969uuCCCyR9fTd3AACAhiDon8Eyxmj27Nm67LLLNHDgQB08eFATJkzQ5MmTdfLkyWCXAwAAYF3QA9bSpUu1YsUK/elPf1JERIQkqU%2BfPnr33XeVl5cX7HIAAACsC3rAys/P1/3336/rrrvOf/f2/v37a/r06Xr99deDXQ4AAIB1QQ9Ye/fuVYcOHeqMt2/fXj6fL9jlAAAAWBf0gNWyZUtt2rSpzvgHH3zg/8A7AACAmwX9W4S33XabHnjgAfl8Phlj9NFHHyk/P19Lly7VvffeG%2BxyAAAArAt6wLr%2B%2ButVXV2tp556ShUVFbr//vvVtGlT3XXXXRo6dGiwywEAALAu6AFr5cqV6tevn4YMGaJDhw7JGKNmzZoFuwwAAADHBP0zWFOnTvV/mL1p06aEKwAA0OAEPWC1bt1a27dvD/ZuAQAAgibolwjbt2%2Bve%2B65RwsXLlTr1q0VGRkZMD9jxoxglwQAAGBV0APWZ599poyMDEnivlcAAKBBCkrAmjlzpsaMGaPGjRtr6dKlju/vr3/9qyZOnFhn3OPxaNu2bfrDH/6gf/7znwFz8%2BbNU69evSRJzz33nBYtWqRjx47p6quv1n333aeoqCjH6wYAAA1DUD6D9eyzz6q8vDxg7I477lBJSYkj%2B%2Bvfv79Wr17tf7z33nv6xS9%2BoREjRkiSioqKNGvWrIDXdO/eXZL09ttvKy8vT1OnTtXixYtVWFioWbNmOVInAABomIISsIwxdcbWrVunyspKR/bXqFEjxcfH%2Bx%2BvvfaajDG65557VFVVpb179yolJSXgNad%2B8fSSJUs0cuRI9erVS506ddIDDzygV155pU5ABAAA%2BC5B/xZhsB0%2BfFhPP/20cnNzFRERoV27dsnj8Zz21/LU1NRo06ZNyszM9I%2BlpaXp5MmT2rZtWzDLBgAALtbgA9YLL7yghIQE9evXT5K0a9cuRUdHa/z48crKytINN9yg999/X5J05MgRVVZWKiEhwf9%2Br9er2NhYHTx4MCT1AwAA9wnatwg9Hk%2BwduVnjNHy5ct1%2B%2B23%2B8d27dqliooKZWVl6Y477tA777yjP/zhD8rPz1fz5s0lyX%2B58JSIiAhVVVXVe78lJSV1viHp9TYOCG42hIe7Kx97ve6q91R/3dZnN6HHzqK/zqK/znNzj4MWsKZPnx5wz6uTJ09q1qxZatKkScDrbN4Ha9OmTSouLtaAAQP8Y3feeaduvvlmnX/%2B%2BZK%2Bvi/Xli1b9NJLL%2Bl//ud/JKlOmKqqqvpB3yLMz89XXl5ewFh2drZycnJ%2B7FIahLi4Jmd%2B0U9QTAzfIHUaPXYW/XUW/XWeG3sclIB12WWX1Tmjk56errKyMpWVlTm231WrVikzM9MfpiQpLCws4LkkJScna%2BfOnYqNjVVkZKRKS0vVpk0bSVJ1dbUOHz6s%2BPj4eu93yJAh6t27d8CY19tYZWXHz2I1dbkt0dtev9PCw8MUExOlI0fKVVNTG%2BpyGiR67Cz66yz66zwbPQ7VP%2B6DErCCce%2Br09m4caM6d%2B4cMHbvvffK4/EEnCnbtm2b2rZtq7CwMKWkpKigoEBdu3aVJG3YsEFer1ft27ev934TEhLqXA70%2BY6quvrc/gF06/prampdW7tb0GNn0V9n0V/nubHH7joF8gPt2LFDF110UcBY79699frrr%2BvVV1/V559/rry8PBUUFGj48OGSpGHDhmnRokV69913tXHjRk2ZMkU33XQTNxoFAAD1FvRflRNMpaWliomJCRi78sor9ac//UlPPfWU9u/fr4svvlgLFy5Uq1atJEkDBgzQvn37dP/996uqqkpXXnmlxo0bF4ryAQCAS3nM6e4CCut8vqPWt%2Bn1hqnv7FXWt%2BuUN%2B/qHuoSfhCvN0xxcU1UVnbcdaem3YIeO4v%2BOov%2BOs9Gj%2BPjz7NcVf006EuEAAAAoUDAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAADvRj/7AAAWXklEQVSwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsabMB655131K5du4BHTk6OJGnr1q268cYblZqaquuvv16bN28OeO/KlSvVp08fpaamKjs7W4cOHQrFEgAAgEs12IC1c%2BdO9erVS6tXr/Y/pk%2BfrhMnTuiOO%2B5QZmam/vrXvyo9PV2jR4/WiRMnJEkbN27U5MmTNWbMGOXn5%2BvIkSOaOHFiiFcDAADcpMEGrKKiIrVt21bx8fH%2BR0xMjN544w1FRkZq/PjxatOmjSZPnqwmTZrorbfekiQtW7ZMV199tQYNGqT27dtr5syZev/997Vnz54QrwgAALhFgw5YrVu3rjNeWFiojIwMeTweSZLH41Hnzp21YcMG/3xmZqb/9S1atFBSUpIKCwuDUjcAAHC/BhmwjDH67LPPtHr1al111VXq06ePZs%2BeraqqKvl8PiUkJAS8vlmzZjp48KAkqaSk5HvnAQAAzsQb6gKcsH//fpWXlysiIkKPP/649u7dq%2BnTp6uiosI//k0RERGqqqqSJFVUVHzvfH2UlJTI5/MFjHm9jesEt7MVHu6ufOz1uqveU/11W5/dhB47i/46i/46z809bpABq2XLllq7dq3OP/98eTwedejQQbW1tRo3bpy6dOlSJyxVVVWpUaNGkqTIyMjTzkdFRdV7//n5%2BcrLywsYy87O9n%2BL8VwVF9ck1CX8KDEx9f%2Bzx49Dj51Ff51Ff53nxh43yIAlSbGxsQHP27Rpo8rKSsXHx6u0tDRgrrS01H92KTEx8bTz8fHx9d73kCFD1Lt374Axr7exysqO/5AlnJHbEr3t9TstPDxMMTFROnKkXDU1taEup0Gix86iv86iv86z0eNQ/eO%2BQQasVatW6Z577tF7773nP/P0ySefKDY2VhkZGXr66adljJHH45ExRuvXr9fvf/97SVJqaqoKCgp03XXXSZIOHDigAwcOKDU1td77T0hIqHM50Oc7qurqc/sH0K3rr6mpdW3tbkGPnUV/nUV/nefGHrvrFEg9paenKzIyUn/84x%2B1a9cuvf/%2B%2B5o5c6Zuv/129evXT0eOHNGDDz6onTt36sEHH1R5ebmuvvpqSdLQoUO1YsUKLV%2B%2BXNu2bdP48ePVs2dPXXDBBSFeFQAAcIsGGbCio6O1aNEiHTp0SNdff70mT56sIUOG6Pbbb1d0dLTmz5/vP0tVWFioBQsWqHHjxpK%2BDmdTp07V3LlzNXToUJ1//vmaMWNGiFcEAADcxGOMMaEu4lzg8x21vk2vN0x9Z6%2Byvl2nvHlX91CX8IN4vWGKi2uisrLjrjs17Rb02Fn011n013k2ehwff57lquqnQZ7BAgAACCUCFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjWYANWcXGxcnJy1KVLF/Xo0UMzZsxQZWWlJGn69Olq165dwGPZsmX%2B965cuVJ9%2BvRRamqqsrOzdejQoVAtAwAAuJA31AU4wRijnJwcxcTE6Pnnn9dXX32lSZMmKSwsTBMmTFBRUZFyc3M1ePBg/3uio6MlSRs3btTkyZP1wAMPqH379nrwwQc1ceJEzZ8/P1TLAQAALtMgz2Dt2rVLGzZs0IwZM3TxxRcrMzNTOTk5WrlypSSpqKhIl1xyieLj4/2PqKgoSdKyZct09dVXa9CgQWrfvr1mzpyp999/X3v27AnlkgAAgIs0yIAVHx%2BvhQsXqnnz5gHjx44d07Fjx1RcXKzWrVuf9r2FhYXKzMz0P2/RooWSkpJUWFjoZMkAAKABaZCXCGNiYtSjRw//89raWi1btkzdunVTUVGRPB6P5s2bpw8%2B%2BECxsbG65ZZb/JcLS0pKlJCQELC9Zs2a6eDBg/Xef0lJiXw%2BX8CY19u4znbPVni4u/Kx1%2Buuek/11219dhN67Cz66yz66zw397hBBqxvmzVrlrZu3aqXX35ZW7ZskcfjUXJysoYPH65169bpvvvuU3R0tPr27auKigpFREQEvD8iIkJVVVX13l9%2Bfr7y8vICxrKzs5WTk2NlPW4VF9ck1CX8KDExUaEuocGjx86iv86iv85zY48bfMCaNWuWFi9erMcee0xt27bVxRdfrF69eik2NlaS1L59e%2B3evVsvvPCC%2Bvbtq8jIyDphqqqqyv8ZrfoYMmSIevfuHTDm9TZWWdnxs1/QN7gt0dtev9PCw8MUExOlI0fKVVNTG%2BpyGiR67Cz66yz66zwbPQ7VP%2B4bdMCaNm2aXnjhBc2aNUtXXXWVJMnj8fjD1SnJyclas2aNJCkxMVGlpaUB86WlpYqPj6/3fhMSEupcDvT5jqq6%2Btz%2BAXTr%2Bmtqal1bu1vQY2fRX2fRX%2Be5scfuOgXyA%2BTl5enFF1/Uo48%2BqgEDBvjH58yZo1GjRgW8dtu2bUpOTpYkpaamqqCgwD934MABHThwQKmpqUGpGwAAuF%2BDDFhFRUV68skn9bvf/U4ZGRny%2BXz%2BR69evbRu3TotWrRIX3zxhf7yl7/o1Vdf1a233ipJGjp0qFasWKHly5dr27ZtGj9%2BvHr27KkLLrggxKsCAABu0SAvEf7jH/9QTU2NnnrqKT311FMBc59%2B%2BqnmzJmjJ554QnPmzFHLli31yCOPKD09XZKUnp6uqVOn6oknntBXX32l7t27a9q0aaFYBgAAcCmPMcaEuohzgc931Po2vd4w9Z29yvp2nfLmXd1DXcIP4vWGKS6uicrKjrvu2r9b0GNn0V9n0V/n2ehxfPx5lquqnwZ5iRAAACCUCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQHrNCorKzVp0iRlZmYqKytLzzzzTKhLAgAALuINdQE/RTNnztTmzZu1ePFi7d%2B/XxMmTFBSUpL69esX6tIAAIALELC%2B5cSJE1q%2BfLmefvppdezYUR07dtSOHTv0/PPPE7AAAEC9cInwW7Zt26bq6mqlp6f7xzIyMlRYWKja2toQVgYAANyCM1jf4vP5FBcXp4iICP9Y8%2BbNVVlZqcOHD6tp06Zn3EZJSYl8Pl/AmNfbWAkJCVZrDQ93Vz6%2B%2BvF/h7qEBu2de3qEuoQf7NQx7LZj2S3or7Por/Pc3GMC1reUl5cHhCtJ/udVVVX12kZ%2Bfr7y8vICxsaMGaOxY8faKfL/Kykp0cif79CQIUOshzd83d/8/Hz666CSkhItXryQHjuE/jqL/jrPzT12XyR0WGRkZJ0gdep5o0aN6rWNIUOG6K9//WvAY8iQIdZr9fl8ysvLq3O2DHbQX%2BfRY2fRX2fRX%2Be5ucecwfqWxMRElZWVqbq6Wl7v1%2B3x%2BXxq1KiRYmJi6rWNhIQE1yVtAABgD2ewvqVDhw7yer3asGGDf6ygoEApKSkKC6NdAADgzEgM3xIVFaVBgwZpypQp2rhxo959910988wzGjFiRKhLAwAALhE%2BZcqUKaEu4qemW7du2rp1qx555BF99NFH%2Bv3vf6/rr78%2B1GWdVpMmTdSlSxc1adIk1KU0SPTXefTYWfTXWfTXeW7tsccYY0JdBAAAQEPCJUIAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsl6qsrNSkSZOUmZmprKwsPfPMM6Eu6SflnXfeUbt27QIeOTk5kqStW7fqxhtvVGpqqq6//npt3rw54L0rV65Unz59lJqaquzsbB06dMg/Z4zR7Nmz1a1bN3Xp0kUzZ85UbW2tf76srExjx45Venq6evfurRUrVgRnwUFUVVWlgQMHau3atf6xPXv2aNSoUUpLS1P//v21evXqgPd8%2BOGHGjhwoFJTUzVixAjt2bMnYP65555Tjx49lJ6erkmTJqm8vNw/d6Zj/Uz7dpvT9Xf69Ol1judly5b55508Zs/08%2BImxcXFysnJUZcuXdSjRw/NmDFDlZWVkjiGbfi%2B/p6Tx7CBK02dOtVcc801ZvPmzeZ///d/TXp6unnzzTdDXdZPxpNPPmlGjx5tSkpK/I%2BvvvrKHD9%2B3HTv3t08/PDDZufOnWbatGnmV7/6lTl%2B/LgxxpjCwkLTqVMn87e//c188sknZvjw4eaOO%2B7wb3fRokXmiiuuMOvWrTMfffSRycrKMgsXLvTPjx492owcOdJ8%2Bumn5qWXXjKXXnqpKSwsDPr6nVJRUWGys7NN27ZtzZo1a4wxxtTW1pprrrnG5Obmmp07d5p58%2BaZ1NRUs2/fPmOMMfv27TNpaWlm0aJFZvv27ea///u/zcCBA01tba0xxpi33nrLZGRkmH/%2B85%2BmsLDQ9O/f3zzwwAP%2BfX7fsX6mfbvN6fprjDGjRo0y8%2BfPDzieT5w4YYxx9pg908%2BLm9TW1pqbbrrJ3H777Wb79u1m3bp1pm/fvubhhx/mGLbg%2B/przLl5DBOwXOj48eMmJSUl4C/guXPnmuHDh4ewqp%2BW3Nxc88gjj9QZX758uendu7f/L8ba2lrTt29f88orrxhjjBk3bpyZMGGC//X79%2B837dq1M1988YUxxpgrrrjC/1pjjHn11VdNr169jDHGfP7556Zt27Zmz549/vlJkyYFbM/NduzYYX7961%2Bba665JiAAfPjhhyYtLS3gL6yRI0eaJ554whhjzOOPPx5wbJ44ccKkp6f73z9s2DD/a40xZt26daZTp07mxIkTZzzWz7RvN/mu/hpjTI8ePcyqVatO%2Bz4nj9kz/by4yc6dO03btm2Nz%2Bfzj73%2B%2BusmKyuLY9iC7%2BuvMefmMcwlQhfatm2bqqurlZ6e7h/LyMhQYWFhwGnTc1lRUZFat25dZ7ywsFAZGRnyeDySJI/Ho86dO2vDhg3%2B%2BczMTP/rW7RooaSkJBUWFqq4uFgHDhzQZZdd5p/PyMjQvn37VFJSosLCQrVo0UKtWrUKmP/Pf/7j0CqD6%2BOPP1bXrl2Vn58fMF5YWKhLLrlEjRs39o9lZGR8Z0%2BjoqLUsWNHbdiwQTU1Ndq0aVPAfFpamk6ePKlt27ad8Vg/077d5Lv6e%2BzYMRUXF5/2eJacPWbP9PPiJvHx8Vq4cKGaN28eMH7s2DGOYQu%2Br7/n6jHsdXwPsM7n8ykuLk4RERH%2BsebNm6uyslKHDx9W06ZNQ1hd6Blj9Nlnn2n16tWaP3%2B%2Bampq1K9fP%2BXk5Mjn8%2Bmiiy4KeH2zZs20Y8cOSVJJSYkSEhLqzB88eFA%2Bn0%2BSAuZP/WVyav507y0uLra%2BxlAYNmzYace/a90HDx484/yRI0dUWVkZMO/1ehUbG6uDBw8qLCzse4/1M%2B3bTb6rv0VFRfJ4PJo3b54%2B%2BOADxcbG6pZbbtHgwYMlOXvMnunnxU1iYmLUo0cP//Pa2lotW7ZM3bp14xi24Pv6e64ewwQsFyovLw/4YZXkf15VVRWKkn5S9u/f7%2B/R448/rr1792r69OmqqKj4zt6d6ltFRcV3zldUVPiff3NO%2BrrvZ9p2Q3WmdX/f/Ol6%2Bs15Y8z3HuvnQs937dolj8ej5ORkDR8%2BXOvWrdN9992n6Oho9e3b19FjtiH3d9asWdq6datefvllPffccxzDln2zv1u2bDknj2EClgtFRkbWOThOPW/UqFEoSvpJadmypdauXavzzz9fHo9HHTp0UG1trcaNG6cuXbqctnen%2BvZdvY2Kigr4oY6MjPT/t/T1JYPvem9D/zOJjIzU4cOHA8bq09OYmJg6ffzmfFRUlGpqar73WD/TvhuCQYMGqVevXoqNjZUktW/fXrt379YLL7ygvn37OnrMNtRjetasWVq8eLEee%2BwxtW3blmPYsm/39%2BKLLz4nj2E%2Bg%2BVCiYmJKisrU3V1tX/M5/OpUaNGiomJCWFlPx2xsbH%2Ba%2B6S1KZNG1VWVio%2BPl6lpaUBry0tLfWfYk5MTDztfHx8vBITEyXJf8r6m/99av673tuQfde669PT2NhYRUZGBsxXV1fr8OHD/p5%2B37F%2Bpn03BB6Px/8/plOSk5P9l0CcPGYbYn%2BnTZumZ599VrNmzdJVV10liWPYptP191w9hglYLtShQwd5vd6AD%2BkVFBQoJSVFYWH8ka5atUpdu3YNuA/NJ598otjYWP%2BHH40xkr7%2BvNb69euVmpoqSUpNTVVBQYH/fQcOHNCBAweUmpqqxMREJSUlBcwXFBQoKSlJCQkJSktL0759%2BwI%2BO1FQUKC0tDSnlxxSqamp2rJli/9UvvT1ur%2Brp%2BXl5dq6datSU1MVFhamlJSUgPkNGzbI6/Wqffv2ZzzWz7TvhmDOnDkaNWpUwNi2bduUnJwsydljNjU19Xt/XtwmLy9PL774oh599FENGDDAP84xbMd39fecPYYd/54iHHHfffeZAQMGmMLCQvPOO%2B%2BYzp07m7fffjvUZf0kHD161PTo0cPcfffdpqioyLz33nsmKyvLLFiwwBw9etR069bNTJs2zezYscNMmzbNdO/e3f8V6fXr15uOHTual156yX8/ltGjR/u3PX/%2BfJOVlWXWrFlj1qxZY7Kysswzzzzjn7/11lvN8OHDzSeffGJeeuklk5KS0qDug3XKN28jUF1dbfr372/uuusus337djN//nyTlpbmv4/Pnj17TEpKipk/f77/HkLXXHON/2vTK1euNJ07dzbvvPOOKSwsNAMGDDDTpk3z7%2Bv7jvUz7dutvtnfwsJCc8kll5iFCxeazz//3Dz//PPm0ksvNevXrzfGOHvMnunnxU127txpOnToYB577LGAezGVlJRwDFvwff09V49hApZLnThxwowfP96kpaWZrKws8%2Byzz4a6pJ%2BU7du3m1GjRpm0tDTTvXt38%2Bc//9n/l2FhYaEZNGiQSUlJMTfccIPZsmVLwHtfeeUVc8UVV5i0tDSTnZ1tDh065J%2Brrq42Dz30kMnMzDRdu3Y1s2bN8m/XGGNKS0vN6NGjTUpKiundu7d5/fXXg7PgIPv2fZp2795tfvvb35pLL73UDBgwwPz73/8OeP17771nrrzyStOpUyczcuRI//1tTpk/f7755S9/aTIyMszEiRNNRUWFf%2B5Mx/qZ9u1G3%2B7vO%2B%2B8Y6655hqTkpJi%2BvXrV%2BcfU04es2f6eXGL%2BfPnm7Zt2572YQzH8Nk6U3/PxWPYY8z/P28GAAAAK/jADgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABY9v8AROOAzcLf2BMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5392072284891479856”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (620)</td> <td class=”number”>1351</td> <td class=”number”>42.1%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1370</td> <td class=”number”>42.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5392072284891479856”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>63543.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>73924.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>74791.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89856.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>246867.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRES12”><s>FRESHVEG_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_ACRES07”><code>FRESHVEG_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99083</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRESPTH07”>FRESHVEG_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1542</td>

</tr> <tr>

<th>Unique (%)</th> <td>48.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>42.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1370</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>19.919</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1831.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>9.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8648415248600580545”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8648415248600580545,#minihistogram8648415248600580545”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8648415248600580545”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8648415248600580545”

aria-controls=”quantiles8648415248600580545” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8648415248600580545” aria-controls=”histogram8648415248600580545”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8648415248600580545” aria-controls=”common8648415248600580545”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8648415248600580545” aria-controls=”extreme8648415248600580545”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8648415248600580545”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.31513</td>

</tr> <tr>

<th>Median</th> <td>1.4401</td>

</tr> <tr>

<th>Q3</th> <td>4.833</td>

</tr> <tr>

<th>95-th percentile</th> <td>66.799</td>

</tr> <tr>

<th>Maximum</th> <td>1831.9</td>

</tr> <tr>

<th>Range</th> <td>1831.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.5179</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>102.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1457</td>

</tr> <tr>

<th>Kurtosis</th> <td>132.64</td>

</tr> <tr>

<th>Mean</th> <td>19.919</td>

</tr> <tr>

<th>MAD</th> <td>31.121</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.401</td>

</tr> <tr>

<th>Sum</th> <td>36651</td>

</tr> <tr>

<th>Variance</th> <td>10506</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8648415248600580545”>

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113ncaOHatdu3bpxhtv1OTJk9W%2BfXvZ7XZJUlhYmGP9xo0bS5KOHz/uNP6f5ObmOrZ1kc1Wt9LrV5afn3v1Y5ut6vO9mNXdMleVN%2BX1pqwSeT2ZN2WVvC/v1XLbglUZRUVFGj9%2BvBo3bqzExERJ0oEDB3Tq1CkNHjxYycnJev/99zVixAh9%2BumnKioqkiT5%2B/s7tnHx3yUlJZV%2B3NTUVKWkpDiNJSUlKTk5ubqR3FqDBvWqvY3g4EALZuI%2BvCmvN2WVyOvJvCmr5H15K8tjC9bZs2f1%2BOOP6%2BDBg3r33XcVGHjhG2Du3LkqKipSUFCQJGn27Nnavn271q1bp9tvv13ShTJ18RDixWJ1cf3KSExMVK9evZzGbLa6yss7W%2B1cl3K3Tw3Vye/n56vg4ECdPl2osjLP/6tOb8rrTVkl8noyb8oquU9eKz7cV4VHFqyCggI9%2Buij%2Bumnn7Rq1Sqnvxa02WyOciVJPj4%2BioiIUE5Ojpo0aSLpwl8iXjzMePFQX2hoaKUfPywsrMLhQLv9jEpLa%2B83oCtYkb%2BsrNyrnkdvyutNWSXyejJvyip5X97Kcq9dIJVQXl6ucePG6fDhw3r77bfVunVrp%2BXDhw93OnxXXl6uH374QREREWrSpInCw8OVlpbmWJ6Wlqbw8HDLz58CAACey%2BP2YP3lL3/Rli1b9Nprryk4ONixB%2Bq6665TSEiIevXqpVdeeUVRUVG68cYbtXr1ap05c0YDBw6UJA0ZMkSLFy/WDTfcIEl67rnnNGrUqBrLAwAA3I/HFazPP/9c5eXlGjt2rNN4p06d9Pbbb2vkyJEqLi7WvHnzdOLECcXExOjNN990HDYcPXq0fv75Z40bN05%2Bfn568MEHNXLkyBpIAgAA3JWP%2BeWVOnFN2O1nLN%2BmzearPos3Wb7da%2BWzJ7pWeV2bzVcNGtRTXt5ZrzjW7015vSmrRF5P5k1ZJffJGxp6fY08rsedgwUAAFDTKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxlxeswYMH689//rPOnLH%2BPz8GAACoDVxesLp06aKlS5cqISFBEyZM0Ndffy1jjKunAQAAcM24vGBNnDhRX3zxhV599VX5%2Bflp/Pjx6tGjh1544QUdOHDA1dMBAACwnK0mHtTHx0ddu3ZV165dVVhYqLfffluvvvqqli9fro4dO2rEiBG68847a2JqAAAA1VYjBUuScnNz9de//lV//etftW/fPnXs2FEDBw7U8ePH9cc//lHbtm3TjBkzamp6AAAAVebygrVu3TqtW7dOW7ZsUcOGDTVgwAC99NJLatmypeM%2BTZs21fz58ylYAADALbm8YM2YMUM9e/bUK6%2B8ojvuuEO%2BvhVPA4uIiNCwYcNcPTUAAABLuLxgffXVV2rQoIHy8/Md5WrHjh1q166d/Pz8JEkdO3ZUx44dXT01AAAAS7j8rwgLCgrUt29fvf76646xMWPG6P7779exY8dcPR0AAADLubxgPfPMM/rNb36jRx55xDH26aefqmnTplqwYIGrpwMAAGA5lxes//3f/9XUqVMVGhrqGGvYsKEmT56szZs3u3o6AAAAlnN5wbLZbDp9%2BnSF8cLCQq7oDgAAPILLC9Ydd9yhefPm6aeffnKMHTp0SAsWLFC3bt1cPR0AAADLufyvCKdMmaJHHnlEd911l4KDgyVJp0%2BfVrt27TRt2jRXTwcAAMByLi9YjRo10kcffaRvvvlGmZmZstlsuummm3TbbbfJx8fH1dMBAACwXI38Vzl%2Bfn7q1q0bhwQBAIBHcnnBstvtevHFF7V9%2B3adP3%2B%2Bwont//jHP1w9JQAAAEu5vGDNnDlTu3btUr9%2B/XT99de7%2BuEBAACuOZcXrM2bN2vFihWKj4%2B3ZHslJSUaNGiQZs6cqc6dO0u68FeJM2fOVHp6usLDwzV9%2BnQlJCQ41vnmm2/0zDPP6NChQ4qJidH8%2BfPVokULx/K33npLK1euVEFBge6%2B%2B27NnDlTgYGBlswXAAB4PpdfpqFu3bpq1KiRJdsqLi7WhAkTlJmZ6RgzxigpKUmNGzfWBx98oPvvv1/jxo3T0aNHJUlHjx5VUlKSBg0apL/85S9q2LChHn/8ccehys8//1wpKSmaM2eOVq1apYyMDC1atMiS%2BQIAAO/g8oJ1//33a8WKFSorK6vWdrKysvTQQw85XU9LurCH7NChQ5ozZ45atWqlsWPHqkOHDvrggw8kSWvXrtUtt9yiUaNGqXXr1lqwYIGOHDmirVu3SpJWr16tESNGqGfPnmrfvr2efvppffDBByosLKzWfAEAgPdw%2BSHC/Px8rV%2B/Xv/617/UokUL%2Bfv7Oy1fvXp1pbazdetWde7cWU8%2B%2BaQ6dOjgGM/IyNDNN9%2BsunXrOsbi4uKUnp7uWH7p4cnAwEC1a9dO6enpio%2BP186dOzVu3DjH8g4dOuj8%2BfPau3evYmNjq5QZAAB4lxq5TEP//v2rvY2hQ4dedtxutyssLMxprFGjRjp%2B/PivLj99%2BrSKi4udlttsNoWEhDjWr4zc3FzZ7XanMZutboXHrS4/P5fvgKwWm63q872Y1d0yV5U35fWmrBJ5PZk3ZZW8L%2B/VcnnBWrBgwTXdfmFhYYW9Yv7%2B/iopKfnV5UVFRY7bV1q/MlJTU5WSkuI0lpSUpOTk5EpvwxM1aFCv2tsIDvauPzbwprzelFUiryfzpqyS9%2BWtrBrZg5Wbm6v3339fBw4c0PTp07Vt2za1adNGERER1d52QECA8vPzncZKSkpUp04dx/JflqWSkhIFBwcrICDAcfuXy6/mrwgTExPVq1cvpzGbra7y8s5WehuV4W6fGqqT38/PV8HBgTp9ulBlZeUWzqp28qa83pRVIq8n86askvvkteLDfVW4vGD9%2BOOPeuihhxQUFKScnBw98cQT%2BvTTTzVt2jS99dZbiomJqdb2mzRpoqysLKexEydOOA7PNWnSRCdOnKiwPCoqSiEhIQoICNCJEyfUqlUrSVJpaany8/MVGhpa6TmEhYVVOBxot59RaWnt/QZ0BSvyl5WVe9Xz6E15vSmrRF5P5k1ZJe/LW1ku3wXy7LPPqnfv3tq4caOuu%2B46SdLzzz%2BvXr16afHixdXefkxMjHbv3u043CdJaWlpjuIWExOjtLQ0x7LCwkLt2bNHMTEx8vX1VXR0tNPy9PR02Ww2RUZGVntuAADAO7i8YG3fvl2PPPKI03/sbLPZ9Pjjj2vPnj3V3n6nTp3UtGlTTZs2TZmZmVq%2BfLl27NihBx98UJL0wAMPaPv27Vq%2BfLkyMzM1bdo0NW/e3HGR0qFDh2rlypXauHGjduzYodmzZ%2Buhhx7iQqMAAKDSXF6wysvLVV5ecVfi2bNn5efnV%2B3t%2B/n56dVXX5XdbtegQYP017/%2BVa%2B88orCw8MlSc2bN9fLL7%2BsDz74QA8%2B%2BKDy8/P1yiuvOApfv379NHbsWM2aNUujRo1S%2B/btNWnSpGrPCwAAeA%2BXn4OVkJCgZcuWOV0dPT8/X4sWLVKXLl2qtM0ffvjB6fZvfvMbrVmz5or37969u7p3737F5WPGjNGYMWOqNBcAAACX78GaOnWqdu3apYSEBBUXF%2Buxxx5Tz549dfjwYU2ZMsXV0wEAALCcy/dgNWnSRB9//LHWr1%2Bv77//XuXl5RoyZIjuv/9%2BBQUFuXo6AAAAlquR62AFBgZq8ODBNfHQAAAA15zLC9bDDz/8H5dX9v8iBAAAqK1cXrCaNWvmdLu0tFQ//vij9u3bpxEjRrh6OgAAAJarNf8X4SuvvHJV/6EyAABAbVVr/jO7%2B%2B%2B/X5999llNTwMAAKDaak3B%2Bu677yy50CgAAEBNqxUnuRcUFOiHH37Q0KFDXT0dAAAAy7m8YIWHhzv9P4SSdN1112nYsGG67777XD0dAAAAy7m8YD377LOufkgAAACXcnnB2rZtW6Xve%2Butt17DmQAAAFwbLi9Yw4cPdxwiNMY4xn855uPjo%2B%2B//97V0wMAAKg2lxespUuXat68eZo0aZI6deokf39/7dy5U3PmzNHAgQN1zz33uHpKAAAAlnL5ZRoWLFigWbNm6a677lKDBg1Ur149denSRXPmzNF7772nZs2aOb4AAADckcsLVm5u7mXLU1BQkPLy8lw9HQAAAMu5vGB16NBBzz//vAoKChxj%2Bfn5WrRokW677TZXTwcAAMByLj8H649//KMefvhh3XHHHWrZsqWMMTp48KBCQ0O1evVqV08HAADAci4vWK1atdKnn36q9evXKzs7W5L0%2B9//Xv369VNgYKCrpwMAAGA5lxcsSapfv74GDx6sw4cPq0WLFpIuXM0dAADAE7j8HCxjjBYvXqxbb71V/fv31/HjxzVlyhTNmDFD58%2Bfd/V0AAAALOfygvX2229r3bp1%2BtOf/iR/f39JUu/evbVx40alpKS4ejoAAACWc3nBSk1N1axZszRo0CDH1dvvuecezZs3T5988omrpwMAAGA5lxesw4cPKyoqqsJ4ZGSk7Ha7q6cDAABgOZcXrGbNmmnnzp0Vxr/66ivHCe8AAADuzOV/RTh69Gg9/fTTstvtMsbo22%2B/VWpqqt5%2B%2B21NnTrV1dMBAACwnMsL1gMPPKDS0lK99tprKioq0qxZs9SwYUM98cQTGjJkiKunAwAAYDmXF6z169erb9%2B%2BSkxM1MmTJ2WMUaNGjVw9DQAAgGvG5edgzZkzx3Eye8OGDSlXAADA47i8YLVs2VL79u1z9cMCAAC4jMsPEUZGRuqpp57SihUr1LJlSwUEBDgtX7BgQbUf48MPP9S0adMqjPv4%2BGjv3r167LHH9M9//tNp2dKlS9WzZ09J0ltvvaWVK1eqoKBAd999t2bOnMn/kwgAACrN5QXrwIEDiouLk6Rrdt2re%2B65R926dXPcLi0t1YgRI9SjRw9JUnZ2thYtWqTbbrvNcZ/69etLkj7//HOlpKRo0aJFatSokaZNm6ZFixZp1qxZ12SuAADA87ikYC1cuFDjxo1T3bp19fbbb1/zx6tTp47q1KnjuL1s2TIZY/TUU0%2BppKREhw8fVnR0tEJDQyusu3r1ao0YMcKxN%2Bvpp5/W6NGjNWnSJPZiAQCASnHJOVhvvvmmCgsLncbGjBmj3Nzca/7Y%2Bfn5ev311zVx4kT5%2B/tr//798vHxuexFTcvKyrRz507Fx8c7xjp06KDz589r796913yuAADAM7ikYBljKoxt27ZNxcXF1/yx33vvPYWFhalv376SpP379ysoKEiTJ09WQkKCHnzwQX355ZeSpNOnT6u4uFhhYWGO9W02m0JCQnT8%2BPFrPlcAAOAZXH4OlisZY7R27Vo9%2BuijjrH9%2B/erqKhICQkJGjNmjDZs2KDHHntMqampaty4sSTJ39/faTv%2B/v4qKSmp9OPm5uZWOL/MZqvrVNys4Ofn8j8CrRabrerzvZjV3TJXlTfl9aasEnk9mTdllbwv79Xy6IK1c%2BdO5eTkqF%2B/fo6xxx9/XMOHD3ec1B4ZGandu3fr/fff15NPPilJFcpUSUnJVZ1/lZqaqpSUFKexpKQkJScnVzWKR2jQoF61txEc7F3nwXlTXm/KKpHXk3lTVsn78laWywqWj4%2BPqx7KYdOmTYqPj3eUKUny9fV1ui1JERERysrKUkhIiAICAnTixAm1atVK0oW/QMzPz7/sCfFXkpiYqF69ejmN2Wx1lZd3thppKnK3Tw3Vye/n56vg4ECdPl2osrJyC2dVO3lTXm/KKpHXk3lTVsl98lrx4b4qXFaw5s2b53TNq/Pnz2vRokWqV885uBXXwbpox44d6tixo9PY1KlT5ePj4/Q4e/fuVZs2beTr66vo6GilpaWpc%2BfOkqT09HTZbDZFRkZW%2BnHDwsIqHA6028%2BotLT2fgO6ghX5y8rKvep59Ka83pRVIq8n86askvflrSyXFKxbb721wjlJsbGxysvLU15e3jV73MzMTN13331OY7169dKECRPUuXNnxcbG6pNPPlFaWprmzJkjSRo6dKhmzZqlNm3aKCwsTLNnz9ZDDz3EJRoAAECluaRgueLaV5dz4sQJBQcHO43deeed%2BtOf/qTXXntNR48eVevWrbVixQo1b95cktSvXz8dOXJEs2bNUklJie68805NmjSpJqYPAADclEef5L5jx47Ljg8ePFiDBw%2B%2B4npjxozRmDFjrtW0AACAh3Ovs6QBAADcAAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAJXwIQAAAam0lEQVQAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIt5bMHasGGD2rZt6/SVnJwsSdqzZ48GDx6smJgYPfDAA9q1a5fTuuvXr1fv3r0VExOjpKQknTx5siYiAAAAN%2BWxBSsrK0s9e/bU119/7fiaN2%2Bezp07pzFjxig%2BPl4ffvihYmNjNXbsWJ07d06StGPHDs2YMUPjxo1TamqqTp8%2BrWnTptVwGgAA4E48tmBlZ2erTZs2Cg0NdXwFBwfr008/VUBAgCZPnqxWrVppxowZqlevnv7%2B979LktasWaO7775bAwYMUGRkpBYuXKgvv/xShw4dquFEAADAXXh0wWrZsmWF8YyMDMXFxcnHx0eS5OPjo44dOyo9Pd2xPD4%2B3nH/pk2bKjw8XBkZGS6ZNwAAcH%2B2mp7AtWCM0YEDB/T1119r2bJlKisrU9%2B%2BfZWcnCy73a6bbrrJ6f6NGjVSZmamJCk3N1dhYWEVlh8/frzSj5%2Bbmyu73e40ZrPVrbDd6vLzc69%2BbLNVfb4Xs7pb5qryprzelFUiryfzpqyS9%2BW9Wh5ZsI4eParCwkL5%2B/vrxRdf1OHDhzVv3jwVFRU5xi/l7%2B%2BvkpISSVJRUdF/XF4ZqampSklJcRpLSkpynGTvrRo0qFftbQQHB1owE/fhTXm9KatEXk/mTVkl78tbWR5ZsJo1a6YtW7aofv368vHxUVRUlMrLyzVp0iR16tSpQlkqKSlRnTp1JEkBAQGXXR4YWPlvoMTERPXq1ctpzGarq7y8s1VMdHnu9qmhOvn9/HwVHByo06cLVVZWbuGsaidvyutNWSXyejJvyiq5T14rPtxXhUcWLEkKCQlxut2qVSsVFxcrNDRUJ06ccFp24sQJx%2BG7Jk2aXHZ5aGhopR87LCyswuFAu/2MSktr7zegK1iRv6ys3KueR2/K601ZJfJ6Mm/KKnlf3spyr10glbRp0yZ17txZhYWFjrHvv/9eISEhiouL03fffSdjjKQL52tt375dMTExkqSYmBilpaU51jt27JiOHTvmWA4AAPBrPLJgxcbGKiAgQH/84x%2B1f/9%2Bffnll1q4cKEeffRR9e3bV6dPn9b8%2BfOVlZWl%2BfPnq7CwUHfffbckaciQIVq3bp3Wrl2rvXv3avLkyerRo4datGhRw6kAAIC78MiCFRQUpJUrV%2BrkyZN64IEHNGPGDCUmJurRRx9VUFCQli1bprS0NA0aNEgZGRlavny56tatK%2BlCOZszZ45eeeUVDRkyRPXr19eCBQtqOBEAAHAnHnsOVuvWrfXmm29edln79u310UcfXXHdQYMGadCgQddqagAAwMN55B4sAACAmkTBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALCYxxasnJwcJScnq1OnTurWrZsWLFig4uJiSdK8efPUtm1bp681a9Y41l2/fr169%2B6tmJgYJSUl6eTJkzUVAwAAuCFbTU/gWjDGKDk5WcHBwXrnnXd06tQpTZ8%2BXb6%2BvpoyZYqys7M1ceJEDRw40LFOUFCQJGnHjh2aMWOGnn76aUVGRmr%2B/PmaNm2ali1bVlNxAACAm/HIPVj79%2B9Xenq6FixYoNatWys%2BPl7Jyclav369JCk7O1s333yzQkNDHV%2BBgYGSpDVr1ujuu%2B/WgAEDFBkZqYULF%2BrLL7/UoUOHajISAABwIx5ZsEJDQ7VixQo1btzYabygoEAFBQXKyclRy5YtL7tuRkaG4uPjHbebNm2q8PBwZWRkXMspAwAAD%2BKRBSs4OFjdunVz3C4vL9eaNWvUpUsXZWdny8fHR0uXLtUdd9yh%2B%2B67Tx999JHjvrm5uQoLC3PaXqNGjXT8%2BHGXzR8AALg3jzwH65cWLVqkPXv26C9/%2BYt2794tHx8fRUREaNiwYdq2bZtmzpypoKAg9enTR0VFRfL393da39/fXyUlJZV%2BvNzcXNntdqcxm61uheJWXX5%2B7tWPbbaqz/diVnfLXFXelNebskrk9WTelFXyvrxXy%2BML1qJFi7Rq1Sq98MILatOmjVq3bq2ePXsqJCREkhQZGamDBw/qvffeU58%2BfRQQEFChTJWUlDjO0aqM1NRUpaSkOI0lJSUpOTm5%2BoHcWIMG9aq9jeDgyr8OnsCb8npTVom8nsybskrel7eyPLpgzZ07V%2B%2B9954WLVqku%2B66S5Lk4%2BPjKFcXRUREaPPmzZKkJk2a6MSJE07LT5w4odDQ0Eo/bmJionr16uU0ZrPVVV7e2arEuCJ3%2B9RQnfx%2Bfr4KDg7U6dOFKisrt3BWtZM35fWmrBJ5PZk3ZZXcJ68VH%2B6rwmMLVkpKiv785z/r%2BeefV9%2B%2BfR3jS5Ys0Xfffae33nrLMbZ3715FRERIkmJiYpSWlqZBgwZJko4dO6Zjx44pJiam0o8dFhZW4XCg3X5GpaW19xvQFazIX1ZW7lXPozfl9aasEnk9mTdllbwvb2W51y6QSsrOztarr76qP/zhD4qLi5Pdbnd89ezZU9u2bdPKlSv1008/6d1339XHH3%2BsUaNGSZKGDBmidevWae3atdq7d68mT56sHj16qEWLFjWcCgAAuAuP3IP1j3/8Q2VlZXrttdf02muvOS374YcftGTJEr300ktasmSJmjVrpueee06xsbGSpNjYWM2ZM0cvvfSSTp06pa5du2ru3Lk1EQMAALgpH2OMqelJeAO7/Yzl27TZfNVn8SbLt3utfPZE1yqva7P5qkGDesrLO%2BsVu6K9Ka83ZZXI68m8KavkPnlDQ6%2Bvkcf1yEOEAAAANYmCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFiXUVxcrOnTpys%2BPl4JCQl64403anpKAADAjdhqegK10cKFC7Vr1y6tWrVKR48e1ZQpUxQeHq6%2BffvW9NTc2t0v/rump3BVPnuia01PAQDgpihYv3Du3DmtXbtWr7/%2Butq1a6d27dopMzNT77zzDgULAABUCocIf2Hv3r0qLS1VbGysYywuLk4ZGRkqLy%2BvwZkBAAB3wR6sX7Db7WrQoIH8/f0dY40bN1ZxcbHy8/PVsGHDX91Gbm6u7Ha705jNVldhYWGWztXPj358LbnbIc0NT3Wr6SlUWp/Fm2p6ClfFyuf24s%2Btt/z8elNeb8oqeV/eq0XB%2BoXCwkKnciXJcbukpKRS20hNTVVKSorT2Lhx4zR%2B/HhrJvl/cnNzNeKGTCUmJlpe3mqb3NxcpaamekVWyfPz/u/8/3%2B43dOz/lJubq5WrVpBXg/kTVkl78t7taidvxAQEFChSF28XadOnUptIzExUR9%2B%2BKHTV2JiouVztdvtSklJqbC3zBN5U1bJu/J6U1aJvJ7Mm7JK3pf3arEH6xeaNGmivLw8lZaWyma78PTY7XbVqVNHwcHBldpGWFgYbR4AAC/GHqxfiIqKks1mU3p6umMsLS1N0dHR8vXl6QIAAL%2BOxvALgYGBGjBggGbPnq0dO3Zo48aNeuONN/Twww/X9NQAAICb8Js9e/bsmp5EbdOlSxft2bNHzz33nL799lv913/9lx544IGantZl1atXT506dVK9evVqeirXnDdllbwrrzdllcjrybwpq%2BR9ea%2BGjzHG1PQkAAAAPAmHCAEAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1Gw3FRxcbGmT5%2Bu%2BPh4JSQk6I033qjpKVVZTk6OkpOT1alTJ3Xr1k0LFixQcXGxJGnevHlq27at09eaNWsc665fv169e/dWTEyMkpKSdPLkyZqKUWkbNmyokCk5OVmStGfPHg0ePFgxMTF64IEHtGvXLqd13S3vhx9%2BWCFr27ZtFRkZKUl67LHHKiz74osvHOu/9dZb6tatm2JjYzV9%2BnQVFhbWVJT/qKSkRP3799eWLVscY4cOHdLIkSPVoUMH3XPPPfr666%2Bd1vnmm2/Uv39/xcTE6OGHH9ahQ4ecltfm7JfLm56ert/97neKjY3VXXfdpbVr1zqtc99991V4rfft2ydJMsZo8eLF6tKlizp16qSFCxeqvLzcpZmu5HJZq/N7qTZnlSrmnTp16mV/hi/97%2BPi4%2BMrLD979qwkz3qvumoGbmnOnDnm3nvvNbt27TL/8z//Y2JjY81nn31W09O6auXl5eahhx4yjz76qNm3b5/Ztm2b6dOnj3n22WeNMcaMHDnSLFu2zOTm5jq%2Bzp07Z4wxJiMjw7Rv39589NFH5vvvvzfDhg0zY8aMqck4lfLqq6%2BasWPHOmU6deqUOXv2rOnatat59tlnTVZWlpk7d665/fbbzdmzZ40x7pm3sLDQKefRo0dNnz59zPz5840xxvTp08esW7fO6T7FxcXGGGP%2B/ve/m7i4OPPPf/7TZGRkmHvuucc8/fTTNRnnsoqKikxSUpJp06aN2bx5szHmwvf1vffeayZOnGiysrLM0qVLTUxMjDly5IgxxpgjR46YDh06mJUrV5p9%2B/aZ//7v/zb9%2B/c35eXlxpjanf1yeXNzc018fLx57rnnzIEDB8z69etNdHS0%2BeKLL4wxxpSWlpro6GizdetWp9f6/PnzxhhjVq5cabp37262bdtmvv32W5OQkGBWrFhRUxEdLpfVmOr9XqqtWY25fN7Tp0875fzuu%2B/MLbfcYjZs2GCMMeb48eOmTZs25qeffnK638XvZU95r6oKCpYbOnv2rImOjnb6gX/llVfMsGHDanBWVZOVlWXatGlj7Ha7Y%2ByTTz4xCQkJxhhjunXrZjZt2nTZdSdNmmSmTJniuH306FHTtm1b89NPP13bSVfTxIkTzXPPPVdhfO3ataZXr16OX0zl5eWmT58%2B5oMPPjDGuG/eSy1dutT07t3bFBcXm%2BLiYhMVFWX2799/2fsOHTrUvPTSS47b27ZtM%2B3bt3e8kdUGmZmZ5r777jP33nuv05vSN998Yzp06OAox8YYM2LECEeeF1980enn9dy5cyY2Ntaxfm3NfqW87777runbt6/TfWfOnGkmTJhgjDHm4MGDJjIy0hQVFV12u927d3d8nxtjzMcff2x69ux5jVJUzpWyGlO930u1Masx/znvpUaNGmWeeuopx%2B1///vfpmvXrpe9rye9V1UFhwjd0N69e1VaWqrY2FjHWFxcnDIyMmrVrubKCA0N1YoVK9S4cWOn8YKCAhUUFCgnJ0ctW7a87LoZGRmKj4933G7atKnCw8OVkZFxLadcbdnZ2ZfNlJGRobi4OPn4%2BEiSfHx81LFjR6WnpzuWu2Pei/Lz8/X6669r4sSJ8vf31/79%2B%2BXj46MWLVpUuG9ZWZl27tzplLdDhw46f/689u7d68pp/0dbt25V586dlZqa6jSekZGhm2%2B%2BWXXr1nWMxcXFXfG1DAwMVLt27ZSenl6rs18p78VD%2B79UUFAgScrKylLTpk0VEBBQ4T45OTk6duyYbr31VsdYXFycjhw5otzcXIsTVN6Vslbn91JtzSpdOe%2Blvv32W23btk0TJkxwjGVlZenGG2%2B87P096b2qKmw1PQFcPbvdrgYNGsjf398x1rhxYxUXFys/P18NGzaswdldneDgYHXr1s1xu7y8XGvWrFGXLl2UnZ0tHx8fLV26VF999ZVCQkL0yCOPaODAgZKk3NxchYWFOW2vUaNGOn78uEszXA1jjA4cOKCvv/5ay5YtU1lZmfr27avk5GTZ7XbddNNNTvdv1KiRMjMzJbln3ku99957CgsLU9%2B%2BfSVJ%2B/fvV1BQkCZPnqytW7fqhhtu0Pjx49W9e3edPn1axcXFTnltNptCQkJqVd6hQ4dedtxut//H1%2Bo/La/N2a%2BUt3nz5mrevLnj9s8//6y//e1vGj9%2BvKQLHyquu%2B46jR07Vrt27dKNN96oyZMnq3379rLb7ZLklPfiB67jx49XeJ5c5UpZq/N7qbZmla6c91LLly/XwIED1bRpU8dYdna2CgsLNXz4cB04cEBRUVGaPn26brzxRo96r6oK9mC5ocLCQqdvWEmO2yUlJTUxJcssWrRIe/bs0ZNPPunYwxEREaHly5dr8ODBmjlzpjZs2CBJKioquuzzUJufg6NHjzpevxdffFFTpkzRJ598ooULF17xdb2Yxx3zXmSM0dq1azVs2DDH2P79%2B1VUVKSEhAStWLFC3bt312OPPaadO3eqqKhIktw276%2B9lv9pubtnLyoq0vjx49W4cWMlJiZKkg4cOKBTp05p8ODBWr58uVq1aqURI0bo2LFjl81bm3%2BfVef3krtlvdShQ4e0efNmDR8%2B3Gl8//79OnXqlB577DG9%2BuqrqlOnjkaOHKmCggKPfq%2BqDPZguaGAgIAK35wXb9epU6cmpmSJRYsWadWqVXrhhRfUpk0btW7dWj179lRISIgkKTIyUgcPHtR7772nPn36XPF5CAwMrInpV0qzZs20ZcsW1a9fXz4%2BPoqKilJ5ebkmTZqkTp06XTbPxdfUHfNetHPnTuXk5Khfv36Osccff1zDhw9X/fr1JV14fXfv3q33339fTz75pKSKv4TdJW9AQIDy8/OdxirzWgYHBzsOo7lj9rNnz%2Brxxx/XwYMH9e677zrmO3fuXBUVFSkoKEiSNHv2bG3fvl3r1q3T7bffLulCvl9mr415BwwYUOXfS5eWC3fIeqnPP/9cUVFRFfayr1y5UufPn1e9evUkSYsXL1b37t31xRdfeOx7VWWxB8sNNWnSRHl5eSotLXWM2e121alTR8HBwTU4s6qbO3eu3nzzTS1atEh33XWXpAvnIF38JXZRRESEcnJyJF14Hk6cOOG0/MSJEwoNDXXNpKsoJCTEcZ6VJLVq1UrFxcUKDQ29bJ6Lhw3cNa8kbdq0SfHx8Y4yJUm%2Bvr5Ot6X///qGhIQoICDAKW9paany8/PdIu%2BVXqvKvJbumr2goECjR49WZmamVq1a5XSOks1mc5QrSY49QDk5OWrSpIkkOQ6fXfrv2pi3Or%2BX3C3rpTZt2qTf/va3Fcb9/f0d5Uq68OGhefPmjtfW096rrgYFyw1FRUXJZrM5TpiVpLS0NEVHR8vX1/1e0pSUFP35z3/W888/77SHY8mSJRo5cqTTfffu3auIiAhJUkxMjNLS0hzLjh07pmPHjikmJsYl866KTZs2qXPnzk7XNPr%2B%2B%2B8VEhKiuLg4fffddzLGSLpwWG379u2OPO6Y96IdO3aoY8eOTmNTp07VtGnTnMYuvr6%2Bvr6Kjo52ypueni6bzea4hlZtFhMTo927dzsOCUkXfkav9FoWFhZqz549iomJccvs5eXlGjdunA4fPqy3335brVu3dlo%2BfPhwpaSkON3/hx9%2BUEREhJo0aaLw8HCnvGlpaQoPD6/Rc5KupDq/l9wt60XGGO3cubPCz7AxRr1799aHH37oGDt37px%2B/PFHRUREeNx71VWryT9hRNXNnDnT9OvXz2RkZJgNGzaYjh07ms8//7ymp3XVsrKyTFRUlHnhhRecrqGSm5trMjIyzM0332xWrFhhfvzxR/POO%2B%2BYW265xWzfvt0YY8z27dtNu3btzPvvv%2B%2B43szYsWNrONF/dubMGdOtWzczYcIEk52dbf71r3%2BZhIQEs3z5cnPmzBnTpUsXM3fuXJOZmWnmzp1runbt6vhTf3fMe1HPnj3N%2BvXrncY%2B//xz065dO/PRRx%2BZgwcPmpdfftm0b9/eHDp0yBhjzPr1603Hjh3Nhg0bTEZGhunXr5%2BZO3duTUy/Ui790/bS0lJzzz33mCeeeMLs27fPLFu2zHTo0MFxHaxDhw6Z6Ohos2zZMsd1sO69917HJTrcIfuleVNTU01kZKT54osvnH6G8/LyjDHGvPHGGyYuLs5s3LjRZGdnmz/96U/m9ttvN2fOnDHGGLNs2TKTkJBgNm/ebDZv3mwSEhLMG2%2B8UWPZfunSrNX9vVTbsxpjKlym4dChQ6ZNmzYmNze3wn3nzp1revToYTZv3mz27dtnkpKSTP/%2B/U1paakxxnPeq6qCguWmzp07ZyZPnmw6dOhgEhISzJtvvlnTU6qSZcuWmTZt2lz2yxhjNmzYYO69914THR1t%2BvbtW%2BEH84MPPjDdu3c3HTp0MElJSebkyZM1EeOq7Nu3z4wcOdJ06NDBdO3a1bz88suON9aMjAwzYMAAEx0dbR588EGze/dup3XdMa8xxkRHR5uvvvqqwvj7779v7rzzTnPLLbeYgQMHmq1btzotX7ZsmbnttttMXFycmTZt2hWvo1Qb/PJN6eDBg%2Bb3v/%2B9ueWWW0y/fv3Mv//9b6f7/%2Btf/zJ33nmnad%2B%2BvRkxYkSF65nV9uyX5h01atRlf4YvXu%2BovLzcvPbaa6ZHjx7mlltuMb///e/NDz/84NhWaWmpeeaZZ0x8fLzp3LmzWbRokeNnojb45Wtbnd9LtT2rMRXzpqenmzZt2jguAnypoqIis2DBAtO1a1cTExNjxo4da44ePepY7invVVXhY8z/HY8AAACAJbzgICgAAIBrUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACL/T/cWrkU3tH8mAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8648415248600580545”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.958456177968373</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.62267701717243</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0220990996663097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.56256036234056</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1319471434123591</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.931842385516507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6005645306588193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>118.08359707519371</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1622378361244652</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1531)</td> <td class=”number”>1531</td> <td class=”number”>47.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1370</td> <td class=”number”>42.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8648415248600580545”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>300</td> <td class=”number”>9.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005838126272833154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006026883920206736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011715384115227833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01868605416655886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1042.4707135250267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1207.211782630777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1290.5797101449275</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1625.9900038446751</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1831.9271009112385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_ACRESPTH12”><s>FRESHVEG_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_ACRESPTH07”><code>FRESHVEG_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94863</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_FARMS07”><s>FRESHVEG_FARMS07</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97465</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FRESHVEG_FARMS12”><s>FRESHVEG_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FRESHVEG_FARMS07”><code>FRESHVEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90251</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSR09”><s>FSR09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FFR14”><code>FFR14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97433</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSR14”><s>FSR14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSR09”><code>FSR09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99878</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSRPTH09”>FSRPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3057</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.776</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>13.699</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2647939433451509480”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2647939433451509480,#minihistogram-2647939433451509480”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2647939433451509480”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2647939433451509480”

aria-controls=”quantiles-2647939433451509480” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2647939433451509480” aria-controls=”histogram-2647939433451509480”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2647939433451509480” aria-controls=”common-2647939433451509480”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2647939433451509480” aria-controls=”extreme-2647939433451509480”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2647939433451509480”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.22976</td>

</tr> <tr>

<th>Q1</th> <td>0.49485</td>

</tr> <tr>

<th>Median</th> <td>0.66888</td>

</tr> <tr>

<th>Q3</th> <td>0.89804</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.6054</td>

</tr> <tr>

<th>Maximum</th> <td>13.699</td>

</tr> <tr>

<th>Range</th> <td>13.699</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.40319</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.58415</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.75277</td>

</tr> <tr>

<th>Kurtosis</th> <td>100.32</td>

</tr> <tr>

<th>Mean</th> <td>0.776</td>

</tr> <tr>

<th>MAD</th> <td>0.33317</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.5936</td>

</tr> <tr>

<th>Sum</th> <td>2430.4</td>

</tr> <tr>

<th>Variance</th> <td>0.34123</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2647939433451509480”>

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2BaYWL14st9ut9PR0RUZGasuWLRo%2BfLgyMjJ06NAhSdKhQ4eUnp6u1NRUbd68WW3bttVdd90lt9stSXr99deVm5urOXPmaM2aNSoqKlJOTo4v4wIAAD/jlwXr4MGDKiwsVHZ2trp27aqkpCRlZmZq27Zt2rVrl0pLSzVnzhx16dJFkydPVnx8vLZs2SJJ2rRpky699FJNmDBBXbt2VXZ2tr755hu99957kqS1a9fq1ltv1YABA9S7d2/Nnj1bW7Zs4SwWAABoNL8sWFFRUVq5cqUiIyO91o8fP66ioiL16NFDoaGhnvXExEQVFhZKkoqKipSUlOTZCwkJUc%2BePVVYWKi6ujp99NFHXvvx8fE6ceKE9u3b18SpAABAc%2BHw9QBnIiwsTCkpKZ7b9fX1Wr9%2Bvfr27Sun06no6Giv%2B0dEROjIkSOS9C/3jx07ppqaGq99h8OhNm3aeB7fGGVlZXI6nV5rDkdog%2Bc9W4GB/tWPHQ6z857M72%2BfBxPsnF0iv53z2zm7RH5/yu%2BXBeuXcnJytHfvXm3evFmrV69WUFCQ135QUJBcLpckqaqq6lf3q6urPbd/7fGNkZ%2Bfr9zcXK%2B19PR0ZWZmNvoYzVF4eKsmOW5YWEiTHNcf2Dm7RH4757dzdon8/pDf7wtWTk6O1qxZo8cff1wXX3yxgoODVVlZ6XUfl8ulli1bSpKCg4MblCWXy6WwsDAFBwd7bv9yPySk8X%2BZaWlpGjhwoNeawxGqioofG32MxvCHBn%2BqpsgfFhaiY8eqVFdXb/TY5zo7Z5fIb%2Bf8ds4ukf9M8jfVP%2B5/i18XrLlz52rDhg3KycnR4MGDJUkxMTHav3%2B/1/3Ky8s9L8/FxMSovLy8wX737t3Vpk0bBQcHq7y8XF26dJEk1dbWqrKyUlFRUY2eKzo6usHLgU7nD6qttd8Xw6maKn9dXb1tP7d2zi6R38757ZxdIr8/5PevUyCnyM3N1caNG/XYY49p6NChnvW4uDj985//9LzcJ0kFBQWKi4vz7BcUFHj2qqqqtHfvXsXFxalFixbq1auX135hYaEcDoe6detmQSoAANAc%2BGXBOnDggJYtW6Y77rhDiYmJcjqdno/k5GS1a9dOWVlZKikpUV5enoqLizVq1ChJ0siRI7Vnzx7l5eWppKREWVlZ6tChg/r06SNJGjNmjFatWqXt27eruLhYs2bN0k033fS7XiIEAAD25pcvEb7xxhuqq6vT8uXLtXz5cq%2B9Tz/9VMuWLdOMGTOUmpqqjh07aunSpYqNjZUkdejQQU8%2B%2BaQeeeQRLV26VAkJCVq6dKkCAgIkSUOHDtU333yjmTNnyuVy6ZprrtG0adMszwgAAPxXgPvkW5ijSTmdPxg/psPRQoMWvm38uE3lr3f3M3o8h6OFwsNbqaLix3P%2BtXjT7JxdIr%2Bd89s5u0T%2BM8kfFXV%2BE091en75EiEAAMC5jIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDDLC9aNN96ojRs36ocfzP9uPgAAgHOB5QWrb9%2B%2BWrFiha688krdc8892rlzp/h90wAAoDmxvGBNnTpVb775ppYtW6bAwEBNmTJF/fv31%2BOPP67PP//c6nEAAACMc/jiSQMCAtSvXz/169dPVVVVWrdunZYtW6a8vDxddtlluvXWW3XNNdf4YjQAAICz5pOCJUllZWV66aWX9NJLL%2Bmzzz7TZZddphtuuEFHjhzRgw8%2BqPfff18zZszw1XgAAABnzPKC9eKLL%2BrFF1/U7t271bZtW40YMUJPPPGEOnXq5LlPu3btNH/%2BfAoWAADwS5YXrBkzZmjAgAFaunSprrrqKrVo0fAysM6dO%2BuWW26xejQAAAAjLC9Yb731lsLDw1VZWekpV8XFxerZs6cCAwMlSZdddpkuu%2Bwyq0cDAAAwwvKfIjx%2B/LiGDBmip59%2B2rM2adIkDR8%2BXIcPH7Z6HAAAAOMsL1iPPPKIOnbsqNtuu82z9uqrr6pdu3bKzs62ehwAAADjLC9YH3zwgR544AFFRUV51tq2bav77rtPu3btsnocAAAA4ywvWA6HQ8eOHWuwXlVVxTu6AwCAZsHygnXVVVdp3rx5%2BuqrrzxrpaWlys7OVkpKitXjAAAAGGf5TxHef//9uu222zR48GCFhYVJko4dO6aePXsqKyvL6nEAAACMs7xgRUREaOvWrXrnnXdUUlIih8Ohiy66SFdccYUCAgKsHgcAAMA4n/yqnMDAQKWkpPCSIAAAaJYsL1hOp1OLFy/Wnj17dOLEiQYXtr/xxhtWjwQAAGCU5QXroYce0scff6yhQ4fq/PPPt/rpAQAAmpzlBWvXrl1auXKlkpKSrH5qAAAAS1j%2BNg2hoaGKiIiw%2BmkBAAAsY3nBGj58uFauXKm6ujqrnxoAAMASlr9EWFlZqW3btunvf/%2B7LrzwQgUFBXntr1271uqRAAAAjPLJ2zQMGzbMF08LAABgCcsLVnZ2ttVPCQAAYCnLr8GSpLKyMuXm5mrq1Kn69ttv9dprr%2BngwYO%2BGAUAAMA4ywvWl19%2BqX//93/X1q1b9frrr%2Bunn37Sq6%2B%2BqpEjR6qoqMjqcQAAAIyzvGD95S9/0dVXX63t27frvPPOkyQ99thjGjhwoBYuXGj1OAAAAMZZXrD27Nmj2267zesXOzscDt11113au3ev1eMAAAAYZ3nBqq%2BvV319fYP1H3/8UYGBgVaPAwAAYJzlBevKK6/UU0895VWyKisrlZOTo759%2B/7u47lcLg0bNky7d%2B/2rM2bN0%2BXXHKJ18f69es9%2B9u2bdPVV1%2BtuLg4paen67vvvvPsud1uLVy4UH379lVycrIWLFhw2kIIAADwayx/m4YHHnhA48aN05VXXqmamhrdeeed%2Buabb9SmTRv95S9/%2BV3Hqqmp0dSpU1VSUuK1fuDAAU2dOlU33HCDZ61169aSpOLiYs2YMUOzZ89Wt27dNH/%2BfGVlZempp56SJD377LPatm2bcnNzVVtbq2nTpikiIkITJ048y%2BQAAMAuLC9YMTExeuGFF7Rt2zZ98sknqq%2Bv1%2BjRozV8%2BHBPCWqM/fv3a%2BrUqXK73Q32Dhw4oIkTJyoqKqrB3vr163XttddqxIgRkqQFCxZowIABKi0t1YUXXqi1a9cqMzPT88uo7733Xi1ZsoSCBQAAGs0n7%2BQeEhKiG2%2B88ayO8d5776lPnz76j//4D8XHx3vWjx8/rqNHj6pTp06nfVxRUZHuuOMOz%2B127dopNjZWRUVFCgoK0uHDh3X55Zd79hMTE/XNN9%2BorKxM0dHRZzUzAACwB8sL1rhx4/7lfmN/F%2BGYMWNOu37gwAEFBARoxYoVeuutt9SmTRvddtttnpcLT1eUIiIidOTIETmdTkny2o%2BMjJQkHTlyhIIFAAAaxfKC1b59e6/btbW1%2BvLLL/XZZ5/p1ltvPevjHzx4UAEBAercubNuueUWvf/%2B%2B3rooYfUunVrDRo0SNXV1Q1%2BwXRQUJBcLpeqq6s9t0/dk36%2BmL6xysrKPGXtJIcj1HhBCwz0yRvxnzGHw%2By8J/P72%2BfBBDtnl8hv5/x2zi6R35/ynzO/i3Dp0qU6cuTIWR9/xIgRGjBggNq0aSNJ6tatm7744gtt2LBBgwYNUnBwcIOy5HK5FBIS4lWmgoODPX%2BWfn5Zs7Hy8/OVm5vrtZaenq7MzMwzztUchIe3apLjhoU1/u%2BmubFzdon8ds5v5%2BwS%2Bf0hv0%2BuwTqd4cOHa8SIEZo7d%2B5ZHScgIMBTrk7q3Lmzdu3aJenni%2BzLy8u99svLyxUVFaWYmBhJktPpVIcOHTx/lnTaC%2BZ/TVpamgYOHOi15nCEqqLix98X5jf4Q4M/VVPkDwsL0bFjVaqrs9dbadg5u0R%2BO%2Be3c3aJ/GeSv6n%2Bcf9bzpmC9eGHHxp5o9ElS5boww8/1OrVqz1r%2B/btU%2BfOnSVJcXFxKigoUGpqqiTp8OHDOnz4sOLi4hQTE6PY2FgVFBR4ClZBQYFiY2N/18t70dHRDe7vdP6g2lr7fTGcqqny19XV2/Zza%2BfsEvntnN/O2SXy%2B0P%2Bc%2BIi9%2BPHj%2BvTTz/91QvXf48BAwYoLy9Pq1at0qBBg7Rz50698MILnovnR48erbFjxyo%2BPl69evXS/Pnz1b9/f1144YWe/YULF%2BoPf/iDJGnRokWaMGHCWc8FAADsw/KCFRsb6/V7CCXpvPPO0y233KLrr7/%2BrI/fu3dvLVmyRE888YSWLFmi9u3ba9GiRUpISJAkJSQkaM6cOXriiSf0/fffq1%2B/fl4vS06cOFHffvutMjIyFBgYqFGjRmn8%2BPFnPRcAALCPAPfp3qkTxjmdPxg/psPRQoMWvm38uE3lr3f3M3o8h6OFwsNbqaLix3P%2BVLFpds4ukd/O%2Be2cXSL/meSPijq/iac6PcvPYL3//vuNvu%2Bpb/gJAADgLywvWGPHjvW8RHjqybNfrgUEBOiTTz6xejwAAICzZnnBWrFihebNm6dp06YpOTlZQUFB%2BuijjzRnzhzdcMMNuu6666weCQAAwCjL30gpOztbM2fO1ODBgxUeHq5WrVqpb9%2B%2BmjNnjjZs2KD27dt7PgAAAPyR5QWrrKzstOWpdevWqqiosHocAAAA4ywvWPHx8Xrsscd0/Phxz1plZaVycnJ0xRVXWD0OAACAcZZfg/Xggw9q3Lhxuuqqq9SpUye53W598cUXioqK8rwZKAAAgD%2BzvGB16dJFr776qrZt26YDBw5Ikm6%2B%2BWYNHTr0d/1CZQAAgHOVT34X4QUXXKAbb7xRX3/9tedX1Jx33nm%2BGAUAAMA4y6/BcrvdWrhwoS6//HINGzZMR44c0f33368ZM2boxIkTVo8DAABgnOUFa926dXrxxRf18MMPKygoSJJ09dVXa/v27crNzbV6HAAAAOMsL1j5%2BfmaOXOmUlNTPe/eft1112nevHl6%2BeWXrR4HAADAOMsL1tdff63u3bs3WO/WrZucTqfV4wAAABhnecFq3769Pvroowbrb731lueCdwAAAH9m%2BU8RTpw4UbNnz5bT6ZTb7da7776r/Px8rVu3Tg888IDV4wAAABhnecEaOXKkamtrtXz5clVXV2vmzJlq27at7r77bo0ePdrqcQAAAIyzvGBt27ZNQ4YMUVpamr777ju53W5FRERYPQYAAECTsfwarDlz5nguZm/bti3lCgAANDuWF6xOnTrps88%2Bs/ppAQAALGP5S4TdunXTvffeq5UrV6pTp04KDg722s/OzrZ6JAAAAKMsL1iff/65EhMTJYn3vQIAAM2SJQVrwYIFysjIUGhoqNatW2fFUwIAAPiMJddgPfvss6qqqvJamzRpksrKyqx4egAAAEtZUrDcbneDtffff181NTVWPD0AAIClLP8pQgAAgOaOggUAAGCYZQUrICDAqqcCAADwKcvepmHevHle73l14sQJ5eTkqFWrVl73432wAACAv7OkYF1%2B%2BeUN3vMqISFBFRUVqqiosGIEAAAAy1hSsHjvKwAAYCdc5A4AAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYX5fsFwul4YNG6bdu3d71kpLSzV%2B/HjFx8fruuuu086dO70e884772jYsGGKi4vTuHHjVFpa6rW/evVqpaSkKCEhQdOnT1dVVZUlWQAAQPPg1wWrpqZG99xzj0pKSjxrbrdb6enpioyM1JYtWzR8%2BHBlZGTo0KFDkqRDhw4pPT1dqamp2rx5s9q2bau77rpLbrdbkvT6668rNzdXc%2BbM0Zo1a1RUVKScnByf5AMAAP7JbwvW/v37ddNNN%2Bmrr77yWt%2B1a5dKS0s1Z84cdenSRZMnT1Z8fLy2bNkiSdq0aZMuvfRSTZgwQV27dlV2dra%2B%2BeYbvffee5KktWvX6tZbb9WAAQPUu3dvzZ49W1u2bOEsFgAAaDS/LVjvvfee%2BvTpo/z8fK/1oqIi9ejRQ6GhoZ61xMREFRYWevaTkpI8eyEhIerZs6cKCwtVV1enjz76yGs/Pj5eJ06c0L59%2B5o4EQAAaC4cvh7gTI0ZM%2Ba0606nU9HR0V5rEREROnLkyG/uHzt2TDU1NV77DodDbdq08TweAADgt/htwfo1VVVVCgoK8loLCgqSy%2BX6zf3q6mrP7V97fGOUlZXJ6XR6rTkcoQ2K3dkKDPSvE5AOh9l5T%2Bb3t8%2BDCXbOLpHfzvntnF0ivz/lb3YFKzg4WJWVlV68P0aIAAAS2ElEQVRrLpdLLVu29Oz/siy5XC6FhYUpODjYc/uX%2ByEhIY2eIT8/X7m5uV5r6enpyszMbPQxmqPw8FZNctywsMb/3TQ3ds4ukd/O%2Be2cXSK/P%2BRvdgUrJiZG%2B/fv91orLy/3nD2KiYlReXl5g/3u3burTZs2Cg4OVnl5ubp06SJJqq2tVWVlpaKioho9Q1pamgYOHOi15nCEqqLixzOJ9Kv8ocGfqinyh4WF6NixKtXV1Rs99rnOztkl8ts5v52zS%2BQ/k/xN9Y/739LsClZcXJzy8vJUXV3tOWtVUFCgxMREz35BQYHn/lVVVdq7d68yMjLUokUL9erVSwUFBerTp48kqbCwUA6HQ926dWv0DNHR0Q1eDnQ6f1Btrf2%2BGE7VVPnr6upt%2B7m1c3aJ/HbOb%2BfsEvn9Ib9/nQJphOTkZLVr105ZWVkqKSlRXl6eiouLNWrUKEnSyJEjtWfPHuXl5amkpERZWVnq0KGDp1CNGTNGq1at0vbt21VcXKxZs2bppptu%2Bl0vEQIAAHtrdgUrMDBQy5Ytk9PpVGpqql566SUtXbpUsbGxkqQOHTroySef1JYtWzRq1ChVVlZq6dKlCggIkCQNHTpUkydP1syZMzVhwgT17t1b06ZN82UkAADgZwLcJ9/CHE3K6fzB%2BDEdjhYatPBt48dtKn%2B9u5/R4zkcLRQe3koVFT%2Be86eKTbNzdon8ds5v5%2BwS%2Bc8kf1TU%2BU081ek1uzNYAAAAvkbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY1mwL1t/%2B9jddcsklXh%2BZmZmSpL179%2BrGG29UXFycRo4cqY8//tjrsdu2bdPVV1%2BtuLg4paen67vvvvNFBAAA4KeabcHav3%2B/BgwYoJ07d3o%2B5s2bp59%2B%2BkmTJk1SUlKSnn/%2BeSUkJGjy5Mn66aefJEnFxcWaMWOGMjIylJ%2Bfr2PHjikrK8vHaQAAgD9ptgXrwIEDuvjiixUVFeX5CAsL06uvvqrg4GDdd9996tKli2bMmKFWrVrptddekyStX79e1157rUaMGKFu3bppwYIF2rFjh0pLS32cCAAA%2BItmXbA6derUYL2oqEiJiYkKCAiQJAUEBOiyyy5TYWGhZz8pKclz/3bt2ik2NlZFRUWWzA0AAPyfw9cDNAW3263PP/9cO3fu1FNPPaW6ujoNGTJEmZmZcjqduuiii7zuHxERoZKSEklSWVmZoqOjG%2BwfOXKk0c9fVlYmp9PpteZwhDY47tkKDPSvfuxwmJ33ZH5/%2BzyYYOfsEvntnN/O2SXy%2B1P%2BZlmwDh06pKqqKgUFBWnx4sX6%2BuuvNW/ePFVXV3vWTxUUFCSXyyVJqq6u/pf7jZGfn6/c3FyvtfT0dM9F9nYVHt6qSY4bFhbSJMf1B3bOLpHfzvntnF0ivz/kb5YFq3379tq9e7cuuOACBQQEqHv37qqvr9e0adOUnJzcoCy5XC61bNlSkhQcHHza/ZCQxv9lpqWlaeDAgV5rDkeoKip%2BPMNEp%2BcPDf5UTZE/LCxEx45Vqa6u3uixz3V2zi6R38757ZxdIv%2BZ5G%2Bqf9z/lmZZsCSpTZs2Xre7dOmimpoaRUVFqby83GuvvLzc8/JdTEzMafejoqIa/dzR0dENXg50On9Qba39vhhO1VT56%2Brqbfu5tXN2ifx2zm/n7BL5/SG/f50CaaS3335bffr0UVVVlWftk08%2BUZs2bZSYmKgPP/xQbrdb0s/Xa%2B3Zs0dxcXGSpLi4OBUUFHged/jwYR0%2BfNizDwAA8FuaZcFKSEhQcHCwHnzwQR08eFA7duzQggULdPvtt2vIkCE6duyY5s%2Bfr/3792v%2B/PmqqqrStddeK0kaPXq0XnzxRW3atEn79u3Tfffdp/79%2B%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%2BHg0WunbxP3w9wu/y17v7%2BXoEAMD/oWD9wk8//aRNmzbp6aefVs%2BePdWzZ0%2BVlJToueeeo2ABAIBG4SXCX9i3b59qa2uVkJDgWUtMTFRRUZHq6%2Bt9OBkAAPAXnMH6BafTqfDwcAUFBXnWIiMjVVNTo8rKSrVt2/Y3j1FWVian0%2Bm15nCEKjo62uisgYH0Y/w/h8M%2B/z2c/G/frl8Dds5v5%2BwS%2Bf0pPwXrF6qqqrzKlSTPbZfL1ahj5OfnKzc312stIyNDU6ZMMTPk/ykrK9OtfyhRWlqa8fLmD8rKypSfn2/L/HbOLv2cf82aleS3YX47Z5fI70/5z/0KaLHg4OAGRerk7ZYtWzbqGGlpaXr%2B%2Bee9PtLS0ozP6nQ6lZub2%2BBsmV3YOb%2Bds0vkt3N%2BO2eXyO9P%2BTmD9QsxMTGqqKhQbW2tHI6fPz1Op1MtW7ZUWFhYo44RHR19zjdrAADQdDiD9Qvdu3eXw%2BFQYWGhZ62goEC9evVSixZ8ugAAwG%2BjMfxCSEiIRowYoVmzZqm4uFjbt2/XM888o3Hjxvl6NAAA4CcCZ82aNcvXQ5xr%2Bvbtq71792rRokV699139ec//1kjR4709Vin1apVKyUnJ6tVq1a%2BHsUn7Jzfztkl8ts5v52zS%2BT3l/wBbrfb7eshAAAAmhNeIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsHyUzU1NZo%2BfbqSkpJ05ZVX6plnnvH1SJY5evSoMjMzlZycrJSUFGVnZ6umpsbXY/nEpEmT9MADD/h6DEu5XC7Nnj1bl19%2Buf7t3/5Njz32mOzyfsmHDx/W5MmTddlll2ngwIFavXq1r0eyhMvl0rBhw7R7927PWmlpqcaPH6/4%2BHhdd9112rlzpw8nbFqny19YWKg//elPSkhI0ODBg7Vp0yYfTti0Tpf/pB9%2B%2BEEpKSl6/vnnfTDZv0bB8lMLFizQxx9/rDVr1ujhhx9Wbm6uXnvtNV%2BP1eTcbrcyMzNVVVWl5557To8//rjefPNNLV682NejWe6VV17Rjh07fD2G5ebNm6d33nlHq1at0qJFi/Rf//Vfys/P9/VYlrj77rsVGhqq559/XtOnT9fixYv1t7/9zddjNamamhrdc889Kikp8ay53W6lp6crMjJSW7Zs0fDhw5WRkaFDhw75cNKmcbr8TqdTd9xxh5KTk7V161ZlZmZq7ty5%2Bvvf/%2B67QZvI6fKfKicnR2VlZRZP1TgOXw%2BA3%2B%2Bnn37Spk2b9PTTT6tnz57q2bOnSkpK9Nxzz2nIkCG%2BHq9JHTx4UIWFhfrHP/6hyMhISVJmZqYeffRR3X///T6ezjqVlZVasGCBevXq5etRLFVZWaktW7bo2WefVe/evSVJEyZMUFFRkf70pz/5eLqm9f3336uwsFBz585Vp06d1KlTJ6WkpOjdd9/VoEGDfD1ek9i/f7%2BmTp3a4Azlrl27VFpaqo0bNyo0NFRdunTRu%2B%2B%2Bqy1btmjKlCk%2Bmta8X8u/fft2RUZG6p577pEkderUSbt379bLL7%2Bs/v37%2B2DSpvFr%2BU/64IMPtGvXLkVFRVk8WeNwBssP7du3T7W1tUpISPCsJSYmqqioSPX19T6crOlFRUVp5cqVnnJ10vHjx300kW88%2BuijGj58uC666CJfj2KpgoICtW7dWsnJyZ61SZMmKTs724dTWaNly5YKCQnR888/rxMnTujgwYPas2ePunfv7uvRmsx7772nPn36NDhDWVRUpB49eig0NNSzlpiYqMLCQqtHbFK/lv/kpRG/1Ny%2BD/5afunnlw0feughzZw5U0FBQT6Y7rdxBssPOZ1OhYeHe/1HFRkZqZqaGlVWVqpt27Y%2BnK5phYWFKSUlxXO7vr5e69evV9%2B%2BfX04lbXeffddffDBB3r55Zc1a9YsX49jqdLSUrVv314vvPCCVqxYoRMnTig1NVV33nmnWrRo3v9eDA4O1syZMzV37lytXbtWdXV1Sk1N1Y033ujr0ZrMmDFjTrvudDoVHR3ttRYREaEjR45YMZZlfi1/hw4d1KFDB8/tb7/9Vq%2B88kqzOnsn/Xp%2BSVqxYoV69OihK6%2B80sKJfh8Klh%2Bqqqpq0NhP3na5XL4YyWdycnK0d%2B9ebd682dejWKKmpkYPP/ywZs6cqZYtW/p6HMv99NNP%2BvLLL7Vx40ZlZ2fL6XRq5syZCgkJ0YQJE3w9XpM7cOCABgwYoNtuu00lJSWaO3eurrjiCl1//fW%2BHs1Sv/Y90G7f/ySpurpaU6ZMUWRkpNLS0nw9jiX279%2BvjRs36qWXXvL1KP8SBcsPBQcHN/hGcvK2nf5PNycnR2vWrNHjjz%2Buiy%2B%2B2NfjWCI3N1eXXnqp11k8O3E4HDp%2B/LgWLVqk9u3bS5IOHTqkDRs2NPuC9e6772rz5s3asWOHWrZsqV69euno0aNavny57QpWcHCwKisrvdZcLpetvv9J0o8//qi77rpLX3zxhf7zP/9TISEhvh6pybndbj344IPKzMxscKnIuYaC5YdiYmJUUVGh2tpaORw//xU6nU61bNlSYWFhPp7OGnPnztWGDRuUk5OjwYMH%2B3ocy7zyyisqLy/3XH93sli//vrr%2BvDDD305miWioqIUHBzsKVeS9Mc//lGHDx/24VTW%2BPjjj9WxY0evEtGjRw%2BtWLHCh1P5RkxMjPbv3%2B%2B1Vl5e3uBlw%2Bbs%2BPHjuv322/XVV19pzZo16tSpk69HssShQ4f04Ycf6tNPP9Wjjz4q6eczmg8//LBeffVVrVy50scT/j8Klh/q3r27HA6HCgsLlZSUJOnni3979erV7K9DkX4%2Bi7Nx40Y99thjzf6nJn9p3bp1qq2t9dxeuHChJOnee%2B/11UiWiouLU01NjT7//HP98Y9/lPTzT5aeWriaq%2BjoaH355ZdyuVyel8cOHjzodS2OXcTFxSkvL0/V1dWewllQUKDExEQfT2aN%2Bvp6ZWRk6Ouvv9a6devUpUsXX49kmZiYGP33f/%2B319rYsWM1duzYc%2B5MbvP/f%2BNmKCQkRCNGjNCsWbNUXFys7du365lnntG4ceN8PVqTO3DggJYtW6Y77rhDiYmJcjqdng87aN%2B%2BvTp27Oj5aNWqlVq1aqWOHTv6ejRLdO7cWf3791dWVpb27dunt99%2BW3l5eRo9erSvR2tyAwcO1HnnnacHH3xQn3/%2Buf7nf/5HK1as0NixY309muWSk5PVrl07ZWVlqaSkRHl5eSouLtaoUaN8PZolNm/erN27d2vevHkKCwvzfA/85cumzZHD4fD6HtixY0c5HA5FREQoJibG1%2BN54QyWn8rKytKsWbN06623qnXr1poyZYquueYaX4/V5N544w3V1dVp%2BfLlWr58udfep59%2B6qOpYKWFCxdq7ty5Gj16tEJCQnTzzTfbomScf/75Wr16tebPn69Ro0apbdu2uvPOO21zYfOpAgMDtWzZMs2YMUOpqanq2LGjli5dqtjYWF%2BPZonXX39d9fX1mjx5std6cnKy1q1b56Op8EsBbrv8jgkAAACL8BIhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2vz5oiFXi/2Z6AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2647939433451509480”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.049180328</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.509683996</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.744463056</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.776699029</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.415282392</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.876040298</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.883392226</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.694444444</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.130710086</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3046)</td> <td class=”number”>3057</td> <td class=”number”>95.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2647939433451509480”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0425387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.058924</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0604485</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0609645</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.025125628</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.688282139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.196721311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.393285372</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.698630137</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_FSRPTH14”><s>FSRPTH14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSRPTH09”><code>FSRPTH09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90099</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_FARMS07”>GHVEG_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>24</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.3153</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8453545972155396141”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8453545972155396141,#minihistogram-8453545972155396141”
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</div> <div class=”row collapse col-md-12” id=”descriptives-8453545972155396141”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8453545972155396141”

aria-controls=”quantiles-8453545972155396141” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8453545972155396141” aria-controls=”histogram-8453545972155396141”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8453545972155396141” aria-controls=”common-8453545972155396141”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8453545972155396141” aria-controls=”extreme-8453545972155396141”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8453545972155396141”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>2</td>

</tr> <tr>

<th>95-th percentile</th> <td>6</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr>

<th>Range</th> <td>80</td>

</tr> <tr>

<th>Interquartile range</th> <td>2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.1534</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3974</td>

</tr> <tr>

<th>Kurtosis</th> <td>207.81</td>

</tr> <tr>

<th>Mean</th> <td>1.3153</td>

</tr> <tr>

<th>MAD</th> <td>1.6885</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>10.212</td>

</tr> <tr>

<th>Sum</th> <td>4046</td>

</tr> <tr>

<th>Variance</th> <td>9.9441</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8453545972155396141”>

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vvaWxY8cqLi5OU6dOVVlZmc/85s2bNXToUCUkJGjevHmqqanxWy4AAGB/tixYlmUpMzNTNTU1eu655/Tkk0/qz3/%2Bs1atWiXLspSenq6OHTtq586dGjdunDIyMnTq1ClJ0qlTp5Senq60tDTt2LFD7du31/333y/LsiRJr732mnJycrR48WJt2bJFRUVFys7ODmRcAABgM7YsWMePH1dhYaGysrLUq1cvJSUlKTMzU3v27NGBAwdUVlamxYsXq2fPnpo5c6bi4%2BO1c%2BdOSdL27dt1/fXX695771WvXr2UlZWlkydP6t1335Ukbd26VXfffbdSUlJ0ww03aNGiRdq5cydHsQAAQLPZsmBFRUVpw4YN6tixo8/4uXPnVFRUpH79%2Bql169be8cTERBUWFkqSioqKlJSU5J0LDw9X//79VVhYqIaGBr3//vs%2B8/Hx8bp48aKOHj16hVMBAACnsGXBioiI0NChQ73PGxsbtW3bNg0aNEhut1vR0dE%2B23fo0EHl5eWS9E/nz549q7q6Op95l8ulyMhI7%2BsBAAD%2BFVegF2BCdna2jhw5oh07dmjz5s0KDQ31mQ8NDZXH45Ek1dTUfON8bW2t9/k3vb45Kioq5Ha7fcZcrtZNit13FRJir37scjVvvZdy2S1fczg1m1NzSWSzI6fmkpybzYm5bF%2BwsrOztWXLFj355JPq3bu3wsLCVF1d7bONx%2BNRq1atJElhYWFNypLH41FERITCwsK8z78%2BHx4e3uw15efnKycnx2csPT1dmZmZzd6HE7Vr16ZF20dENP9rbjdOzebUXBLZ7MipuSTnZnNSLlsXrCVLluj5559Xdna2Ro0aJUmKiYnRsWPHfLarrKz0Hj2KiYlRZWVlk/m%2BffsqMjJSYWFhqqysVM%2BePSVJ9fX1qq6uVlRUVLPXNWnSJKWmpvqMuVytVVV1vsUZ/xm7Nf3m5g8JCVZERLjOnq1RQ0PjFV6Vfzk1m1NzSWSzI6fmkpyb7Urmaukv96bYtmDl5OTohRde0BNPPKHRo0d7x%2BPi4pSXl6fa2lrvUauCggIlJiZ65wsKCrzb19TU6MiRI8rIyFBwcLBiY2NVUFCggQMHSpIKCwvlcrnUp0%2BfZq8tOjq6yelAt/sL1dc754fh22hp/oaGRsd%2BzZyazam5JLLZkVNzSc7N5qRc9joE8v9KS0u1du1a/eIXv1BiYqLcbrf3kZycrE6dOmnu3LkqKSlRXl6eiouLNXHiREnShAkTdOjQIeXl5amkpERz585V165dvYVqypQp2rhxo/bu3avi4mItXLhQd9xxR4tOEQIAgB82Wx7B%2BuMf/6iGhgatW7dO69at85n76KOPtHbtWs2fP19paWnq1q2bcnNz1blzZ0lS165d9dRTT%2Bnxxx9Xbm6uEhISlJubq6CgIEnSmDFjdPLkSS1YsEAej0c333yzZs%2Be7feMAADAvoKsS7cwxxXldn9hfJ8uV7BGrnjT%2BH6vlD88MLhZ27lcwWrXro2qqs475lDxJU7N5tRcEtnsyKm5JOdmu5K5oqJ%2BZHR/zWXLU4QAAADfZxQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjfC9btt9%2BuF154QV98Yf6%2BUAAAAN8Hfi9YgwYN0tNPP60hQ4bo17/%2Btfbv3y/udQoAAJzE7wVr1qxZ%2BvOf/6y1a9cqJCREv/rVrzR8%2BHA9%2BeST%2BuSTT/y9HAAAAOMC8lmEQUFBGjx4sAYPHqyamho9%2B%2ByzWrt2rfLy8jRgwADdfffduvnmmwOxNAAAgO8sYB/2XFFRod/97nf63e9%2Bp48//lgDBgzQbbfdpvLycj3yyCM6ePCg5s%2BfH6jlAQAAfGt%2BL1gvv/yyXn75Zb3zzjtq3769xo8frzVr1qh79%2B7ebTp16qRly5ZRsAAAgC35vWDNnz9fKSkpys3N1bBhwxQc3PQysB49eujOO%2B/099IAAACM8HvBeuONN9SuXTtVV1d7y1VxcbH69%2B%2BvkJAQSdKAAQM0YMAAfy8NAADACL//K8Jz585p9OjR%2Bu1vf%2BsdmzFjhsaNG6fTp0/7ezkAAADG%2Bb1gPf744%2BrWrZvuuece79grr7yiTp06KSsry9/LAQAAMM7vBeuvf/2rHn74YUVFRXnH2rdvr4ceekgHDhzw93IAAACM83vBcrlcOnv2bJPxmpoa7ugOAAAcwe8Fa9iwYVq6dKn%2B9re/ecfKysqUlZWloUOH%2Bns5AAAAxvn9XxHOmTNH99xzj0aNGqWIiAhJ0tmzZ9W/f3/NnTvX38sBAAAwzu8Fq0OHDnrppZf01ltvqaSkRC6XS9dee61uuukmBQUF%2BXs5AAAAxgXko3JCQkI0dOhQTgkCAABH8nvBcrvdWrVqlQ4dOqSLFy82ubD9j3/8o7%2BXBAAAYJTfC9ajjz6qw4cPa8yYMfrRj37k77cHAAC44vxesA4cOKANGzYoKSnJ328NAADgF36/TUPr1q3VoUMHf78tAACA3/i9YI0bN04bNmxQQ0ODv98aAADAL/x%2BirC6ulp79uzRX/7yF11zzTUKDQ31md%2B6dau/lwQAAGBUQG7TMHbs2EC8LQAAgF/4vWBlZWX5%2By0BAAD8yu/XYElSRUWFcnJyNGvWLP3973/Xq6%2B%2BquPHjwdiKQAAAMb5vWB99tln%2BulPf6qXXnpJr732mi5cuKBXXnlFEyZMUFFRkb%2BXAwAAYJzfC9ZvfvMbjRgxQnv37tVVV10lSXriiSeUmpqqFStW%2BHs5AAAAxvm9YB06dEj33HOPzwc7u1wu3X///Tpy5Ii/lwMAAGCc3wtWY2OjGhsbm4yfP39eISEh/l4OAACAcX4vWEOGDNH69et9SlZ1dbWys7M1aNAgfy8HAADAOL8XrIcffliHDx/WkCFDVFdXp//8z/9USkqKTpw4oTlz5vh7OQAAAMb5/T5YMTEx2rVrl/bs2aMPP/xQjY2Nmjx5ssaNG6e2bdv6ezkAAADGBeRO7uHh4br99tsD8dYAAABXnN8L1tSpU//pfEs/i9Dj8SgtLU2PPvqoBg4cKElaunSpnn32WZ/tHn30Ud15552SpD179mjVqlVyu90aMmSIlixZovbt20uSLMvSypUrtWPHDjU2NmrixIl68MEHFRwckHuyAgAAG/J7werSpYvP8/r6en322Wf6%2BOOPdffdd7doX3V1dZo1a5ZKSkp8xktLSzVr1izddttt3rFLpx%2BLi4s1f/58LVq0SH369NGyZcs0d%2B5crV%2B/XpK0adMm7dmzRzk5Oaqvr9fs2bPVoUMHTZ8%2B/dvEBQAAP0Dfm88izM3NVXl5ebP3c%2BzYMc2aNUuWZTWZKy0t1fTp0xUVFdVkbtu2bbrllls0fvx4SdLy5cuVkpKisrIyXXPNNdq6dasyMzOVlJQkSXrwwQe1evVqChYAAGi27815r3HjxukPf/hDs7d/9913NXDgQOXn5/uMnzt3TmfOnFH37t0v%2B7qioiJveZKkTp06qXPnzioqKtKZM2d0%2BvRp3Xjjjd75xMREnTx5UhUVFS0LBAAAfrACcpH75bz33nstutHolClTLjteWlqqoKAgPf3003rjjTcUGRmpe%2B65x3u6sKKiQtHR0T6v6dChg8rLy%2BV2uyXJZ75jx46SpPLy8iav%2ByYVFRXefV3icrVu9uubKyTke9OPm8Xlat56L%2BWyW77mcGo2p%2BaSyGZHTs0lOTebE3N9Ly5yP3funD766KNvLE0tcfz4cQUFBalHjx668847dfDgQT366KNq27atRo4cqdraWoWGhvq8JjQ0VB6PR7W1td7nX52TvryYvrny8/OVk5PjM5aenq7MzMxvG8sR2rVr06LtIyLCr9BKAs%2Bp2ZyaSyKbHTk1l%2BTcbE7K5feC1blzZ5/PIZSkq666Snfeead%2B9rOffef9jx8/XikpKYqMjJQk9enTR59%2B%2Bqmef/55jRw5UmFhYU3KksfjUXh4uE%2BZCgsL8/5Z%2BvLWEs01adIkpaam%2Boy5XK1VVXX%2BW%2Be6HLs1/ebmDwkJVkREuM6erVFDQ9OPVbIzp2Zzai6JbHbk1FySc7NdyVwt/eXeFL8XrN/85jdXdP9BQUHecnVJjx49dODAAUlf3ui0srLSZ76yslJRUVGKiYmRJLndbnXt2tX7Z0mXvWD%2Bm0RHRzc5Heh2f6H6euf8MHwbLc3f0NDo2K%2BZU7M5NZdENjtyai7JudmclMvvBevgwYPN3varF5s31%2BrVq/Xee%2B9p8%2BbN3rGjR4%2BqR48ekqS4uDgVFBQoLS1NknT69GmdPn1acXFxiomJUefOnVVQUOAtWAUFBercubPx66cAAIBz%2Bb1g3XXXXd5ThF%2B9xcLXx4KCgvThhx%2B2eP8pKSnKy8vTxo0bNXLkSO3fv1%2B7du3y3sB08uTJuuuuuxQfH6/Y2FgtW7ZMw4cP1zXXXOOdX7FihX784x9LklauXKl777332wcGAAA/OH4vWE8//bSWLl2q2bNnKzk5WaGhoXr//fe1ePFi3Xbbbbr11lu/0/5vuOEGrV69WmvWrNHq1avVpUsXrVy5UgkJCZKkhIQELV68WGvWrNE//vEPDR48WEuWLPG%2Bfvr06fr73/%2BujIwMhYSEaOLEiZo2bdp3WhMAAPhhCbIud6fOK2jUqFGaP3%2B%2Bhg0b5jP%2B17/%2BVQ899JD%2B9Kc/%2BXM5fuN2f2F8ny5XsEaueNP4fq%2BUPzwwuFnbuVzBateujaqqzjvmXPwlTs3m1FwS2ezIqbkk52a7krmion5kdH/N5fd/hlZRUdHk43KkLz/Kpqqqyt/LAQAAMM7vBSs%2BPl5PPPGEzp075x2rrq5Wdna2brrpJn8vBwAAwDi/X4P1yCOPaOrUqRo2bJi6d%2B8uy7L06aefKioqynshOgAAgJ35vWD17NlTr7zyivbs2aPS0lJJ0s9//nONGTOmRTfzBAAA%2BL4KyGcRXn311br99tt14sQJ7%2B0RrrrqqkAsBQAAwDi/X4NlWZZWrFihG2%2B8UWPHjlV5ebnmzJmj%2BfPn6%2BLFi/5eDgAAgHF%2BL1jPPvusXn75ZT322GPez/4bMWKE9u7d2%2BQDkgEAAOzI7wUrPz9fCxYsUFpamvfu7bfeequWLl2q3bt3%2B3s5AAAAxvm9YJ04cUJ9%2B/ZtMt6nTx/vBysDAADYmd8LVpcuXfT%2B%2B%2B83GX/jjTe8F7wDAADYmd//FeH06dO1aNEiud1uWZalt99%2BW/n5%2BXr22Wf18MMP%2B3s5AAAAxvm9YE2YMEH19fVat26damtrtWDBArVv314PPPCAJk%2Be7O/lAAAAGOf3grVnzx6NHj1akyZN0ueffy7LstShQwd/LwMAAOCK8fs1WIsXL/ZezN6%2BfXvKFQAAcBy/F6zu3bvr448/9vfbAgAA%2BI3fTxH26dNHDz74oDZs2KDu3bsrLCzMZz4rK8vfSwIAADDK7wXrk08%2BUWJioiRx3ysAAOBIfilYy5cvV0ZGhlq3bq1nn33WH28JAAAQMH65BmvTpk2qqanxGZsxY4YqKir88fYAAAB%2B5ZeCZVlWk7GDBw%2Bqrq7OH28PAADgV37/V4QAAABOR8ECAAAwzG8FKygoyF9vBQAAEFB%2Bu03D0qVLfe55dfHiRWVnZ6tNmzY%2B23EfLAAAYHd%2BKVg33nhjk3teJSQkqKqqSlVVVf5YAgAAgN/4pWBx7ysAAPBDwkXuAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCY7QuWx%2BPR2LFj9c4773jHysrKNG3aNMXHx%2BvWW2/V/v37fV7z1ltvaezYsYqLi9PUqVNVVlbmM79582YNHTpUCQkJmjdvnmpqavySBQAAOIOtC1ZdXZ1%2B/etfq6SkxDtmWZbS09PVsWNH7dy5U%2BPGjVNGRoZOnTolSTp16pTS09OVlpamHTt2qH379rr//vtlWZYk6bXXXlNOTo4WL16sLVu2qKioSNnZ2QHJBwAA7Mm2BevYsWO644479Le//c1n/MCBAyorK9PixYvVs2dPzZw5U/Hx8dq5c6ckafv27br%2B%2But17733qlevXsrKytLJkyf17rvvSpK2bt2qu%2B%2B%2BWykpKbrhhhu0aNEi7dy5k6NYAACg2WxbsN59910NHDhQ%2Bfn5PuNFRUXq16%2BfWrdu7R1LTExUYWGhdz4pKck7Fx4erv79%2B6uwsFANDQ16//33febj4%2BN18eJFHT169AonAgAATuEK9AK%2BrSlTplx23O12Kzo62mesQ4cHd9TEAAAUk0lEQVQOKi8v/5fzZ8%2BeVV1dnc%2B8y%2BVSZGSk9/UAAAD/im0L1jepqalRaGioz1hoaKg8Hs%2B/nK%2BtrfU%2B/6bXN0dFRYXcbrfPmMvVukmx%2B65CQux1ANLlat56L%2BWyW77mcGo2p%2BaSyGZHTs0lOTebE3M5rmCFhYWpurraZ8zj8ahVq1be%2Ba%2BXJY/Ho4iICIWFhXmff30%2BPDy82WvIz89XTk6Oz1h6eroyMzObvQ8nateuTYu2j4ho/tfcbpyazam5JLLZkVNzSc7N5qRcjitYMTExOnbsmM9YZWWl9%2BhRTEyMKisrm8z37dtXkZGRCgsLU2VlpXr27ClJqq%2BvV3V1taKiopq9hkmTJik1NdVnzOVqraqq898m0jeyW9Nvbv6QkGBFRITr7NkaNTQ0XuFV%2BZdTszk1l0Q2O3JqLsm52a5krpb%2Bcm%2BK4wpWXFyc8vLyVFtb6z1qVVBQoMTERO98QUGBd/uamhodOXJEGRkZCg4OVmxsrAoKCjRw4EBJUmFhoVwul/r06dPsNURHRzc5Heh2f6H6euf8MHwbLc3f0NDo2K%2BZU7M5NZdENjtyai7JudmclMteh0CaITk5WZ06ddLcuXNVUlKivLw8FRcXa%2BLEiZKkCRMm6NChQ8rLy1NJSYnmzp2rrl27egvVlClTtHHjRu3du1fFxcVauHCh7rjjjhadIgQAAD9sjitYISEhWrt2rdxut9LS0vS73/1Oubm56ty5sySpa9eueuqpp7Rz505NnDhR1dXVys3NVVBQkCRpzJgxmjlzphYsWKB7771XN9xwg2bPnh3ISAAAwGYccYrwo48%2B8nnerVs3bdu27Ru3/8lPfqKf/OQn3zg/Y8YMzZgxw9j6AADAD4vjjmABAAAEGgULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDMsQXr9ddf13XXXefzyMzMlCQdOXJEt99%2Bu%2BLi4jRhwgQdPnzY57V79uzRiBEjFBcXp/T0dH3%2B%2BeeBiAAAAGzKsQXr2LFjSklJ0f79%2B72PpUuX6sKFC5oxY4aSkpL04osvKiEhQTNnztSFCxckScXFxZo/f74yMjKUn5%2Bvs2fPau7cuQFOAwAA7MSxBau0tFS9e/dWVFSU9xEREaFXXnlFYWFheuihh9SzZ0/Nnz9fbdq00auvvipJ2rZtm2655RaNHz9effr00fLly7Vv3z6VlZUFOBEAALALRxes7t27NxkvKipSYmKigoKCJElBQUEaMGCACgsLvfNJSUne7Tt16qTOnTurqKjIL%2BsGAAD25wr0Aq4Ey7L0ySefaP/%2B/Vq/fr0aGho0evRoZWZmyu1269prr/XZvkOHDiopKZEkVVRUKDo6usl8eXl5s9%2B/oqJCbrfbZ8zlat1kv99VSIi9%2BrHL1bz1Xsplt3zN4dRsTs0lkc2OnJpLcm42J%2BZyZME6deqUampqFBoaqlWrVunEiRNaunSpamtrveNfFRoaKo/HI0mqra39p/PNkZ%2Bfr5ycHJ%2Bx9PR070X2P1Tt2rVp0fYREeFXaCWB59RsTs0lkc2OnJpLcm42J%2BVyZMHq0qWL3nnnHV199dUKCgpS37591djYqNmzZys5OblJWfJ4PGrVqpUkKSws7LLz4eHN/6ZPmjRJqampPmMuV2tVVZ3/lokuz25Nv7n5Q0KCFRERrrNna9TQ0HiFV%2BVfTs3m1FwS2ezIqbkk52a7krla%2Bsu9KY4sWJIUGRnp87xnz56qq6tTVFSUKisrfeYqKyu9p%2B9iYmIuOx8VFdXs946Ojm5yOtDt/kL19c75Yfg2Wpq/oaHRsV8zp2Zzai6JbHbk1FySc7M5KZe9DoE005tvvqmBAweqpqbGO/bhhx8qMjJSiYmJeu%2B992RZlqQvr9c6dOiQ4uLiJElxcXEqKCjwvu706dM6ffq0dx4AAOBfcWTBSkhIUFhYmB555BEdP35c%2B/bt0/Lly3Xfffdp9OjROnv2rJYtW6Zjx45p2bJlqqmp0S233CJJmjx5sl5%2B%2BWVt375dR48e1UMPPaThw4frmmuuCXAqAABgF44sWG3bttXGjRv1%2Beefa8KECZo/f74mTZqk%2B%2B67T23bttX69etVUFCgtLQ0FRUVKS8vT61bt5b0ZTlbvHixcnNzNXnyZF199dXKysoKcCIAAGAnjr0Gq1evXtq0adNl52644Qa99NJL3/jatLQ0paWlXamlAQAAh3PkESwAAIBAomABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADHMFegH44bhl1f8Gegkt8ocHBgd6CQAAm%2BIIFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMgnUZdXV1mjdvnpKSkjRkyBA988wzgV4SAACwEe6DdRnLly/X4cOHtWXLFp06dUpz5sxR586dNXr06EAvDQAA2AAF62suXLig7du367e//a369%2B%2Bv/v37q6SkRM899xwFCwAANAunCL/m6NGjqq%2BvV0JCgncsMTFRRUVFamxsDODKAACAXXAE62vcbrfatWun0NBQ71jHjh1VV1en6upqtW/f/l/uo6KiQm6322fM5Wqt6Ohoo2sNCaEfX0l2%2B2if1x8cGrD3vvR30Yl/J8lmP07NJTk3mxNzUbC%2BpqamxqdcSfI%2B93g8zdpHfn6%2BcnJyfMYyMjL0q1/9yswi/19FRYXu/nGJJk2aZLy8BVJFRYXy8/Mdl0tybraKigpt2bLBcbkkstmRU3NJzs3mxFzOqYqGhIWFNSlSl563atWqWfuYNGmSXnzxRZ/HpEmTjK/V7XYrJyenydEyu3NqLsm52ZyaSyKbHTk1l%2BTcbE7MxRGsr4mJiVFVVZXq6%2Bvlcn355XG73WrVqpUiIiKatY/o6GjHNHAAANByHMH6mr59%2B8rlcqmwsNA7VlBQoNjYWAUH8%2BUCAAD/Go3ha8LDwzV%2B/HgtXLhQxcXF2rt3r5555hlNnTo10EsDAAA2EbJw4cKFgV7E982gQYN05MgRrVy5Um%2B//bZ%2B%2BctfasKECYFe1mW1adNGycnJatOmTaCXYpRTc0nOzebUXBLZ7MipuSTnZnNariDLsqxALwIAAMBJOEUIAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCZVN1dXWaN2%2BekpKSNGTIED3zzDOBXtJ34vF4NHbsWL3zzjvesbKyMk2bNk3x8fG69dZbtX///gCusOXOnDmjzMxMJScna%2BjQocrKylJdXZ0ke2f77LPPNH36dCUkJGj48OHasGGDd87Oub5qxowZevjhh73Pjxw5ottvv11xcXGaMGGCDh8%2BHMDVtdzrr7%2Bu6667zueRmZkpyf7ZPB6PFi1apBtvvFH//u//rieeeEKX7p9t52wvvvhik%2B/Zddddpz59%2Bkiyd7bTp09r5syZGjBggFJTU7V582bvnJ1zfR0Fy6aWL1%2Buw4cPa8uWLXrssceUk5OjV199NdDL%2Blbq6ur061//WiUlJd4xy7KUnp6ujh07aufOnRo3bpwyMjJ06tSpAK60%2BSzLUmZmpmpqavTcc8/pySef1J///GetWrXK1tkaGxs1Y8YMtWvXTi%2B99JIWLVqkdevWaffu3bbO9VW///3vtW/fPu/zCxcuaMaMGUpKStKLL76ohIQEzZw5UxcuXAjgKlvm2LFjSklJ0f79%2B72PpUuXOiLb0qVL9dZbb2njxo1auXKl/ud//kf5%2Bfm2z3bpF5RLj7/85S/q1q2bpk6davtsDzzwgFq3bq0XX3xR8%2BbN06pVq/T666/bPlcTFmzn/PnzVmxsrHXgwAHvWG5urnXnnXcGcFXfTklJifWzn/3M%2BulPf2r17t3bm%2Bmtt96y4uPjrfPnz3u3vfvuu601a9YEaqktcuzYMat3796W2%2B32ju3evdsaMmSIrbOdOXPG%2Bq//%2Bi/riy%2B%2B8I6lp6dbjz32mK1zXVJVVWUNGzbMmjBhgjVnzhzLsixr%2B/btVmpqqtXY2GhZlmU1NjZaI0eOtHbu3BnIpbbIrFmzrJUrVzYZt3u2qqoqq1%2B/ftY777zjHVu/fr318MMP2z7b1z399NPWiBEjrLq6Oltnq66utnr37m199NFH3rGMjAxr0aJFts51ORzBsqGjR4%2Bqvr5eCQkJ3rHExEQVFRWpsbExgCtruXfffVcDBw5Ufn6%2Bz3hRUZH69eun1q1be8cSExNVWFjo7yV%2BK1FRUdqwYYM6duzoM37u3DlbZ4uOjtaqVavUtm1bWZalgoICHTx4UMnJybbOdcl///d/a9y4cbr22mu9Y0VFRUpMTFRQUJAkKSgoSAMGDLBVrtLSUnXv3r3JuN2zFRQUqG3btkpOTvaOzZgxQ1lZWbbP9lXV1dX67W9/q1mzZik0NNTW2Vq1aqXw8HC9%2BOKLunjxoo4fP65Dhw6pb9%2B%2Bts51ORQsG3K73WrXrp1CQ0O9Yx07dlRdXZ2qq6sDuLKWmzJliubNm6fw8HCfcbfbrejoaJ%2BxDh06qLy83J/L%2B9YiIiI0dOhQ7/PGxkZt27ZNgwYNsn22S1JTUzVlyhQlJCRo1KhRts/19ttv669//avuv/9%2Bn3G757IsS5988on279%2BvUaNGacSIEVqxYoU8Ho/ts5WVlalLly7atWuXRo8erf/4j/9Qbm6uGhsbbZ/tq55//nlFR0dr9OjRkuz9dzIsLEwLFixQfn6%2B4uLidMstt2jYsGG6/fbbbZ3rclyBXgBarqamxqdcSfI%2B93g8gViScd%2BU0a75srOzdeTIEe3YsUObN292RLY1a9aosrJSCxcuVFZWlq2/Z3V1dXrssce0YMECtWrVymfOzrkk6dSpU94Mq1at0okTJ7R06VLV1tbaPtuFCxf02Wef6YUXXlBWVpbcbrcWLFig8PBw22e7xLIsbd%2B%2BXffdd593zO7ZSktLlZKSonvuuUclJSVasmSJbrrpJtvn%2BjoKlg2FhYU1%2BQt36fnX/%2BdgV2FhYU2Oxnk8Hlvmy87O1pYtW/Tkk0%2Bqd%2B/ejskWGxsr6cty8uCDD2rChAmqqanx2cYuuXJycnT99df7HHW85Jt%2B3uyQS5K6dOmid955R1dffbWCgoLUt29fNTY2avbs2UpOTrZ1NpfLpXPnzmnlypXq0qWLpC8L5fPPP69u3brZOtsl77//vs6cOaMxY8Z4x%2Bz8d/Ltt9/Wjh07tG/fPrVq1UqxsbE6c%2BaM1q1bp2uuuca2uS6HU4Q2FBMTo6qqKtXX13vH3G63WrVqpYiIiACuzJyYmBhVVlb6jFVWVjY5fPx9t2TJEm3atEnZ2dkaNWqUJHtnq6ys1N69e33Grr32Wl28eFFRUVG2zfX73/9ee/fuVUJCghISErR7927t3r1bCQkJtv5%2BXRIZGem9rkWSevbsqbq6Olt/z6Qvr3UMCwvzlitJ%2Brd/%2BzedPn3aEd83SXrzzTeVlJSkq6%2B%2B2jtm52yHDx9Wt27dfEpTv379dOrUKVvnuhwKlg317dtXLpfL58K/goICxcbGKjjYGd/SuLg4ffDBB6qtrfWOFRQUKC4uLoCrapmcnBy98MILeuKJJ3x%2B%2B7RzthMnTigjI0Nnzpzxjh0%2BfFjt27dXYmKibXM9%2B%2Byz2r17t3bt2qVdu3YpNTVVqamp2rVrl%2BLi4vTee%2B95761kWZYOHTpki1zSl/%2BDHjhwoM/RxQ8//FCRkZFKTEy0dba4uDjV1dXpk08%2B8Y4dP35cXbp0sf337ZLi4mINGDDAZ8zO2aKjo/XZZ5/5HKk6fvy4unbtautcl%2BOM/xv/wISHh2v8%2BPFauHChiouLtXfvXj3zzDOaOnVqoJdmTHJysjp16qS5c%2BeqpKREeXl5Ki4u1sSJEwO9tGYpLS3V2rVr9Ytf/EKJiYlyu93eh52zxcbGqn///po3b56OHTumffv2KTs7W7/85S9tnatLly7q1q2b99GmTRu1adNG3bp10%2BjRo3X27FktW7ZMx44d07Jly1RTU6Nbbrkl0MtuloSEBIWFhemRRx7R8ePHtW/fPi1fvlz33Xef7bP16NFDw4cP19y5c3X06FG9%2BeabysvL0%2BTJk22f7ZKSkhKff9UqydbZUlNTddVVV%2BmRRx7RJ598oj/96U96%2Bumnddddd9k612UF5OYQ%2BM4uXLhgPfTQQ1Z8fLw1ZMgQa9OmTYFe0nf21ftgWZZlffrpp9bPf/5z6/rrr7fGjBlj/e///m8AV9cy69evt3r37n3Zh2XZO1t5ebmVnp5uDRgwwBo8eLC1bt06731r7Jzrq%2BbMmeO9D5ZlWVZRUZE1fvx4KzY21po4caL1wQcfBHB1Lffxxx9b06ZNs%2BLj463BgwdbTz31lPd7ZvdsZ8%2BetWbPnm3Fx8dbN910k6OyWZZlxcbGWm%2B88UaTcTtnKykpsaZNm2YNGDDAGjFihLVp0yZHfc8uCbKs/z8WBwAAACM4RQgAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhv0fvfoby2S2gCQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8453545972155396141”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>402</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>296</td> <td class=”number”>9.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (13)</td> <td class=”number”>70</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8453545972155396141”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>402</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>296</td> <td class=”number”>9.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>71.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_FARMS12”>GHVEG_FARMS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>47</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.8079</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>150</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4004756344577184503”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4004756344577184503,#minihistogram-4004756344577184503”
aria-expanded=”false” aria-controls=”collapseExample”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-4004756344577184503”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4004756344577184503”

aria-controls=”quantiles-4004756344577184503” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4004756344577184503” aria-controls=”histogram-4004756344577184503”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4004756344577184503” aria-controls=”common-4004756344577184503”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4004756344577184503” aria-controls=”extreme-4004756344577184503”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4004756344577184503”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>3</td>

</tr> <tr>

<th>95-th percentile</th> <td>12</td>

</tr> <tr>

<th>Maximum</th> <td>150</td>

</tr> <tr>

<th>Range</th> <td>150</td>

</tr> <tr>

<th>Interquartile range</th> <td>3</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.991</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1336</td>

</tr> <tr>

<th>Kurtosis</th> <td>133.23</td>

</tr> <tr>

<th>Mean</th> <td>2.8079</td>

</tr> <tr>

<th>MAD</th> <td>3.215</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.8305</td>

</tr> <tr>

<th>Sum</th> <td>8637</td>

</tr> <tr>

<th>Variance</th> <td>35.892</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4004756344577184503”>

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gAA3s8rC5ZhGEpLS1NVVZVeeOEFPfPMM3r77be1atUqGYahlJQUdejQQTk5ORo3bpxSU1NVXFwsSSouLlZKSoqSkpK0e/duhYaGatasWTIMQ5L05ptvKisrS4sXL9a2bduUn5%2BvzMxMT8YFAABexisL1qlTp5SXl6eMjAz17NlTcXFxSktL0759%2B3To0CEVFRVp8eLF6tGjh2bOnKno6Gjl5ORIknbt2qVbb71V06ZNU8%2BePZWRkaHTp0/rww8/lCRt375dU6ZMUUJCggYMGKBFixYpJyeHo1gAAKDJvLJghYeHa9OmTerQoYPL%2BPnz55Wfn6%2B%2BffuqdevWzvHY2Fjl5eVJkvLz8xUXF%2BecCw4OVr9%2B/ZSXl6f6%2Bnp9/PHHLvPR0dG6dOmSjh8/3sKpAACAVdg8vYAbERISomHDhjkfNzQ0aOfOnRo8eLAcDociIiJcnh8WFqYzZ85I0n%2Bcr6ysVE1Njcu8zWZTu3btnNs3RWlpqRwOh8uYzda60es2V0CAd/Vjm%2B3G1ns5p7flvV7ktB5fyUpOa/GVnC3NKwvWlTIzM3Xs2DHt3r1bW7duVWBgoMt8YGCgamtrJUlVVVXXnK%2BurnY%2Bvtb2TZGdna2srCyXsZSUFKWlpTV5H1bUvn2bZm0fEhJs0kpubuS0Hl/JSk5r8ZWcLcXrC1ZmZqa2bdumZ555Rr169VJQUJAqKipcnlNbW6tWrVpJkoKCghqVpdraWoWEhCgoKMj5%2BMr54OCmv9GSk5OVmJjoMmaztVZ5%2BYUm76MpvO23ixvNHxDgr5CQYFVWVqm%2BvsHkVd08yGk9vpKVnNZitZzN/eX%2BRnl1wVqyZIlefPFFZWZmatSoUZKkyMhInThxwuV5ZWVlztNzkZGRKisrazTfp08ftWvXTkFBQSorK1OPHj0kSXV1daqoqFB4eHiT1xUREdHodKDD8bXq6rz/jdoczc1fX9/gE3%2BG5LQeX8lKTmvxlZwtxbsOgXxLVlaWXnrpJT399NMaM2aMc9xut%2BuTTz5xnu6TpNzcXNntdud8bm6uc66qqkrHjh2T3W6Xv7%2B/%2Bvfv7zKfl5cnm82m3r17uyEVAACwAq8sWCdPntS6dev0y1/%2BUrGxsXI4HM6v%2BPh4dezYUenp6SosLNTGjRtVUFCgiRMnSpImTJigI0eOaOPGjSosLFR6ero6d%2B6sQYMGSZLuu%2B8%2Bbd68Wfv371dBQYEWLlyoe%2B%2B997pOEQIAAN/mlacI33rrLdXX12v9%2BvVav369y9ynn36qdevWad68eUpKSlLXrl21du1aRUVFSZI6d%2B6sZ599Vr/5zW%2B0du1axcTEaO3atfLz85MkjRkzRqdPn9aCBQtUW1urn/zkJ5o9e7bbMwIAAO/lZ1y%2BhTlalMPxten7tNn8NXLFe6bvt6X86eEhN7Sdzeav9u3bqLz8gqWvByCn9fhKVnJai9Vyhod/zyOv65WnCAEAAG5mFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk7m9YN1zzz166aWX9PXX5t%2B2AAAA4Gbg9oI1ePBgbdiwQUOHDtUjjzyiAwcOiFtxAQAAK3F7wXr00Uf19ttva926dQoICNBDDz2k4cOH65lnntFnn33m7uUAAACYziMflePn56chQ4ZoyJAhqqqq0o4dO7Ru3Tpt3LhRAwcO1JQpU/STn/zEE0sDAABoNo99FmFpaalee%2B01vfbaa/rnP/%2BpgQMH6u6779aZM2f05JNP6vDhw5o3b56nlgcAAHDD3F6wXn31Vb366qv64IMPFBoaqvHjx2vNmjXq1q2b8zkdO3bUsmXLKFgAAMArub1gzZs3TwkJCVq7dq3uuOMO%2Bfs3vgyse/fuuv/%2B%2B929NAAAAFO4vWD99a9/Vfv27VVRUeEsVwUFBerXr58CAgIkSQMHDtTAgQPdvTQAAABTuP1fEZ4/f16jR4/W73//e%2BfYjBkzNG7cOJWUlLh7OQAAAKZze8H6zW9%2Bo65du%2BrBBx90jr3%2B%2Buvq2LGjMjIy3L0cAAAA07m9YP3973/XE088ofDwcOdYaGioHn/8cR06dMjdywEAADCd2wuWzWZTZWVlo/Gqqiru6A4AACzB7QXrjjvu0NKlS/Wvf/3LOVZUVKSMjAwNGzbM3csBAAAwndv/FeGcOXP04IMPatSoUQoJCZEkVVZWql%2B/fkpPT3f3cgAAAEzn9oIVFhamV155RQcPHlRhYaFsNpt%2B%2BMMf6vbbb5efn5%2B7lwMAAGA6j3xUTkBAgIYNG8YpQQAAYEluL1gOh0OrVq3SkSNHdOnSpUYXtr/11lvuXhIAAICp3F6w5s%2Bfr6NHj2rMmDH63ve%2B5%2B6XBwAAaHFuL1iHDh3Spk2bFBcX5%2B6XBgAAcAu336ahdevWCgsLc/fLAgAAuI3bC9a4ceO0adMm1dfXu/ulAQAA3MLtpwgrKiq0b98%2BvfPOO%2BrSpYsCAwNd5rdv3%2B7uJQEAAJjKI7dpGDt2rCdeFgAAwC3cXrAyMjLc/ZIAAABu5fZrsCSptLRUWVlZevTRR/XVV1/pjTfe0KlTpzyxFAAAANO5vWB98cUX%2BulPf6pXXnlFb775pi5evKjXX39dEyZMUH5%2BvruXAwAAYDq3F6zf/va3GjFihPbv369bbrlFkvT0008rMTFRK1ascPdyAAAATOf2gnXkyBE9%2BOCDLh/sbLPZNGvWLB07dszdywEAADCd2wtWQ0ODGhoaGo1fuHBBAQEB7l4OAACA6dxesIYOHarnnnvOpWRVVFQoMzNTgwcPdvdyAAAATOf2gvXEE0/o6NGjGjp0qGpqavTrX/9aCQkJ%2BvLLLzVnzhx3LwcAAMB0br8PVmRkpPbs2aN9%2B/bpH//4hxoaGjRp0iSNGzdObdu2dfdyAAAATOeRO7kHBwfrnnvu8cRLAwAAtDi3F6zJkyf/x/nr/SzC2tpaJSUlaf78%2BRo0aJAkaenSpdqxY4fL8%2BbPn6/7779fkrRv3z6tWrVKDodDQ4cO1ZIlSxQaGipJMgxDK1eu1O7du9XQ0KCJEyfqsccek7%2B/R%2B7JCgAAvJDbC1anTp1cHtfV1emLL77QP//5T02ZMuW69lVTU6NHH31UhYWFLuMnT57Uo48%2Bqrvvvts5dvn0Y0FBgebNm6dFixapd%2B/eWrZsmdLT0/Xcc89JkrZs2aJ9%2B/YpKytLdXV1mj17tsLCwjR9%2BvQbiQsAAHzQTfNZhGvXrtWZM2eavJ8TJ07o0UcflWEYjeZOnjyp6dOnKzw8vNHczp07deedd2r8%2BPGSpOXLlyshIUFFRUXq0qWLtm/frrS0NMXFxUmSHnvsMa1evZqCBQAAmuymOe81btw4/elPf2ry8z/88EMNGjRI2dnZLuPnz5/X2bNn1a1bt6tul5%2Bf7yxPktSxY0dFRUUpPz9fZ8%2BeVUlJiW677TbnfGxsrE6fPq3S0tLrCwQAAHyWRy5yv5qPPvroum40et999111/OTJk/Lz89OGDRv017/%2BVe3atdODDz7oPF1YWlqqiIgIl23CwsJ05swZORwOSXKZ79ChgyTpzJkzjbYDAAC4mpviIvfz58/r008/vWZpuh6nTp2Sn5%2Bfunfvrvvvv1%2BHDx/W/Pnz1bZtW40cOVLV1dUKDAx02SYwMFC1tbWqrq52Pv72nPTNxfRNVVpa6ixrl9lsrU0vaAEBN80ByCax2W5svZdzelve60VO6/GVrOS0Fl/J2dLcXrCioqJcPodQkm655Rbdf//9%2BtnPftbs/Y8fP14JCQlq166dJKl37976/PPP9eKLL2rkyJEKCgpqVJZqa2sVHBzsUqaCgoKc/y19c2uJpsrOzlZWVpbLWEpKitLS0m44lxW0b9%2BmWduHhDT978CbkdN6fCUrOa3FV3K2FLcXrN/%2B9rctun8/Pz9nubqse/fuOnTokKRvbnRaVlbmMl9WVqbw8HBFRkZKkhwOhzp37uz8b0lXvWD%2BWpKTk5WYmOgyZrO1Vnn5hesL8x287beLG80fEOCvkJBgVVZWqb6%2B8edYWgU5rcdXspLTWqyWs7m/3N8otxesw4cPN/m5377YvKlWr16tjz76SFu3bnWOHT9%2BXN27d5ck2e125ebmKikpSZJUUlKikpIS2e12RUZGKioqSrm5uc6ClZubq6ioqOs6vRcREdHo%2BQ7H16qr8/43anM0N399fYNP/BmS03p8JSs5rcVXcrYUtxesBx54wHmK8Nu3WLhyzM/PT//4xz%2Bue/8JCQnauHGjNm/erJEjR%2BrAgQPas2eP8wamkyZN0gMPPKDo6Gj1799fy5Yt0/Dhw9WlSxfn/IoVK/SDH/xAkrRy5UpNmzbtxgMDAACf4/aCtWHDBi1dulSzZ89WfHy8AgMD9fHHH2vx4sW6%2B%2B67dddddzVr/wMGDNDq1au1Zs0arV69Wp06ddLKlSsVExMjSYqJidHixYu1Zs0a/fvf/9aQIUO0ZMkS5/bTp0/XV199pdTUVAUEBGjixImaOnVqs9YEAAB8i59xtTt1tqBRo0Zp3rx5uuOOO1zG//73v%2Bvxxx/XX/7yF3cux20cjq9N36fN5q%2BRK94zfb8t5U8PD7mh7Ww2f7Vv30bl5RcsfbianNbjK1nJaS1Wyxke/j2PvK7br5IuLS1t9HE50jcfZVNeXu7u5QAAAJjO7QUrOjpaTz/9tM6fP%2B8cq6ioUGZmpm6//XZ3LwcAAMB0br8G68knn9TkyZN1xx13qFu3bjIMQ59//rnCw8OdF6IDAAB4M7cXrB49euj111/Xvn37dPLkSUnSz3/%2Bc40ZM%2Ba6buYJAABws/LIZxF%2B//vf1z333KMvv/zSeXuEW265xRNLAQAAMJ3br8EyDEMrVqzQbbfdprFjx%2BrMmTOaM2eO5s2bp0uXLrl7OQAAAKZze8HasWOHXn31VT311FPOz/4bMWKE9u/f3%2Bjz%2BwAAALyR2wtWdna2FixYoKSkJOfd2%2B%2B66y4tXbpUe/fudfdyAAAATOf2gvXll1%2BqT58%2BjcZ79%2B7t/GBlAAAAb%2Bb2gtWpUyd9/PHHjcb/%2Bte/Oi94BwAA8GZu/1eE06dP16JFi%2BRwOGQYht5//31lZ2drx44deuKJJ9y9HAAAANO5vWBNmDBBdXV1Wr9%2Bvaqrq7VgwQKFhobq4Ycf1qRJk9y9HAAAANO5vWDt27dPo0ePVnJyss6dOyfDMBQWFubuZQAAALQYt1%2BDtXjxYufF7KGhoZQrAABgOW4vWN26ddM///lPd78sAACA27j9FGHv3r312GOPadOmTerWrZuCgoJc5jMyMty9JAAAAFO5vWB99tlnio2NlSTuewUAACzJLQVr%2BfLlSk1NVevWrbVjxw53vCQAAIDHuOUarC1btqiqqsplbMaMGSotLXXHywMAALiVWwqWYRiNxg4fPqyamhp3vDwAAIBbuf1fEQIAAFgdBQsAAMBkbitYfn5%2B7nopAAAAj3LbbRqWLl3qcs%2BrS5cuKTMzU23atHF5HvfBAgAA3s4tBeu2225rdM%2BrmJgYlZeXq7y83B1LAAAAcBu3FCzufQUAAHwJF7kDAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMq8vWLW1tRo7dqw%2B%2BOAD51hRUZGmTp2q6Oho3XXXXTpw4IDLNgcPHtTYsWNlt9s1efJkFRUVucxv3bpVw4YNU0xMjObOnauqqiq3ZAEAANbg1QWrpqZGjzzyiAoLC51jhmEoJSVFHTp0UE5OjsaNG6fU1FQVFxdLkoqLi5WSkqKkpCTt3r1boaGhmjVrlgzDkCS9%2BeabysrK0uLFi7Vt2zbl5%2BcrMzPTI/kAAIB38tqCdeLECd17773617/%2B5TJ%2B6NAhFRUVafHixerRo4dmzpyp6Oho5eTkSJJ27dqlW2%2B9VdOmTVPPnj2VkZGh06dP68MPP5Qkbd%2B%2BXVOmTFFCQoIGDBigRYsWKScnh6NYAACgyby2YH344YcaNGiQsrOzXcbz8/PVt29ftW7d2jkWGxurvLw853xcXJxzLjg4WP369VNeXp7q6%2Bv18ccfu8xHR0fr0qVLOn78eAsnAgAAVmHz9AJu1H333XfVcYfDoYiICJexsLAwnTlz5jvnKysrVVNT4zJvs9nUrl075/ZNUVpaKofD4TJms7Vu9LrNFRDgXf3YZrux9V7O6W15rxc5rcdXspLTWnwlZ0vz2oJ1LVVVVQoMDHQZCwwMVG1t7XfOV1dXOx9fa/umyM7OVlZWlstYSkqK0tKCXJgGAAAUfElEQVTSmrwPK2rfvk2ztg8JCTZpJTc3clqPr2Qlp7X4Ss6WYrmCFRQUpIqKCpex2tpatWrVyjl/ZVmqra1VSEiIgoKCnI%2BvnA8ObvobLTk5WYmJiS5jNltrlZdfaPI%2BmsLbfru40fwBAf4KCQlWZWWV6usbTF7VzYOc1uMrWclpLVbL2dxf7m%2BU5QpWZGSkTpw44TJWVlbmPD0XGRmpsrKyRvN9%2BvRRu3btFBQUpLKyMvXo0UOSVFdXp4qKCoWHhzd5DREREY1OBzocX6uuzvvfqM3R3Pz19Q0%2B8WdITuvxlazktBZfydlSvOsQSBPY7XZ98sknztN9kpSbmyu73e6cz83Ndc5VVVXp2LFjstvt8vf3V//%2B/V3m8/LyZLPZ1Lt3b/eFAAAAXs1yBSs%2BPl4dO3ZUenq6CgsLtXHjRhUUFGjixImSpAkTJujIkSPauHGjCgsLlZ6ers6dO2vQoEGSvrl4fvPmzdq/f78KCgq0cOFC3Xvvvdd1ihAAAPg2yxWsgIAArVu3Tg6HQ0lJSXrttde0du1aRUVFSZI6d%2B6sZ599Vjk5OZo4caIqKiq0du1a%2Bfn5SZLGjBmjmTNnasGCBZo2bZoGDBig2bNnezISAADwMn7G5VuYo0U5HF%2Bbvk%2BbzV8jV7xn%2Bn5byp8eHnJD29ls/mrfvo3Kyy9Y%2BnoAclqPr2Qlp7VYLWd4%2BPc88rqWO4IFAADgaRQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATGbZgvXnP/9ZP/rRj1y%2B0tLSJEnHjh3TPffcI7vdrgkTJujo0aMu2%2B7bt08jRoyQ3W5XSkqKzp0754kIAADAS1m2YJ04cUIJCQk6cOCA82vp0qW6ePGiZsyYobi4OL388suKiYnRzJkzdfHiRUlSQUGB5s2bp9TUVGVnZ6uyslLp6ekeTgMAALyJZQvWyZMn1atXL4WHhzu/QkJC9PrrrysoKEiPP/64evTooXnz5qlNmzZ64403JEk7d%2B7UnXfeqfHjx6t3795avny53n33XRUVFXk4EQAA8BaWLljdunVrNJ6fn6/Y2Fj5%2BflJkvz8/DRw4EDl5eU55%2BPi4pzP79ixo6KiopSfn%2B%2BWdQMAAO9nyYJlGIY%2B%2B%2BwzHThwQKNGjdKIESO0YsUK1dbWyuFwKCIiwuX5YWFhOnPmjCSptLT0P84DAAB8F5unF9ASiouLVVVVpcDAQK1atUpffvmlli5dqurqauf4twUGBqq2tlaSVF1d/R/nm6K0tFQOh8NlzGZr3ai4NVdAgHf1Y5vtxtZ7Oae35b1e5LQeX8lKTmvxlZwtzZIFq1OnTvrggw/0/e9/X35%2BfurTp48aGho0e/ZsxcfHNypLtbW1atWqlSQpKCjoqvPBwcFNfv3s7GxlZWW5jKWkpDj/FaOvat%2B%2BTbO2Dwlp%2Bt%2BBNyOn9fhKVnJai6/kbCmWLFiS1K5dO5fHPXr0UE1NjcLDw1VWVuYyV1ZW5jy6FBkZedX58PDwJr92cnKyEhMTXcZsttYqL79wPRG%2Bk7f9dnGj%2BQMC/BUSEqzKyirV1zeYvKqbBzmtx1eyktNarJazub/c3yhLFqz33ntPjz32mN555x3nkad//OMfateunWJjY/X73/9ehmHIz89PhmHoyJEj%2BtWvfiVJstvtys3NVVJSkiSppKREJSUlstvtTX79iIiIRqcDHY6vVVfn/W/U5mhu/vr6Bp/4MySn9fhKVnJai6/kbCnedQikiWJiYhQUFKQnn3xSp06d0rvvvqvly5frF7/4hUaPHq3KykotW7ZMJ06c0LJly1RVVaU777xTkjRp0iS9%2Buqr2rVrl44fP67HH39cw4cPV5cuXTycCgAAeAtLFqy2bdtq8%2BbNOnfunCZMmKB58%2BYpOTlZv/jFL9S2bVs999xzzqNU%2Bfn52rhxo1q3bi3pm3K2ePFirV27VpMmTdL3v/99ZWRkeDgRAADwJpY8RShJPXv21JYtW646N2DAAL3yyivX3DYpKcl5ihAAAOB6WfIIFgAAgCdRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQ2Ty8AvuPOVX/z9BKuy58eHuLpJQAAvBRHsAAAAExGwQIAADAZBQsAAMBkFKyrqKmp0dy5cxUXF6ehQ4fq%2Beef9/SSAACAF%2BEi96tYvny5jh49qm3btqm4uFhz5sxRVFSURo8e7emlAQAAL0DBusLFixe1a9cu/f73v1e/fv3Ur18/FRYW6oUXXqBgAQCAJqFgXeH48eOqq6tTTEyMcyw2NlYbNmxQQ0OD/P05q%2BoruK0EAOBGUbCu4HA41L59ewUGBjrHOnTooJqaGlVUVCg0NPQ791FaWiqHw%2BEyZrO1VkREhKlrDQig7OH/sdm84/1w%2BX3rC%2B9fX8lKTmvxlZwtjYJ1haqqKpdyJcn5uLa2tkn7yM7OVlZWlstYamqqHnroIXMW%2Bf8rLS3VlB8UKjk52fTydjMpLS1VdnY2OS2itLRU27ZtsnxOyXeyktNafCVnS6OeXiEoKKhRkbr8uFWrVk3aR3Jysl5%2B%2BWWXr%2BTkZNPX6nA4lJWV1ehomdWQ01p8JafkO1nJaS2%2BkrOlcQTrCpGRkSovL1ddXZ1stm/%2BeBwOh1q1aqWQkJAm7SMiIoLWDwCAD%2BMI1hX69Okjm82mvLw851hubq769%2B/PBe4AAKBJaAxXCA4O1vjx47Vw4UIVFBRo//79ev755zV58mRPLw0AAHiJgIULFy709CJuNoMHD9axY8e0cuVKvf/%2B%2B/rVr36lCRMmeHpZV9WmTRvFx8erTZs2nl5KiyKntfhKTsl3spLTWnwlZ0vyMwzD8PQiAAAArIRThAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYXqqmpkZz585VXFychg4dqueff97TSzLF2bNnlZaWpvj4eA0bNkwZGRmqqamRJBUVFWnq1KmKjo7WXXfdpQMHDnh4teaYMWOGnnjiCefjY8eO6Z577pHdbteECRN09OhRD66ueWpra7Vo0SLddttt%2Bu///m89/fTTunxvYyvlLCkp0cyZMzVw4EAlJiZq69atzjmr5KytrdXYsWP1wQcfOMe%2B63vy4MGDGjt2rOx2uyZPnqyioiJ3L/u6XS1nXl6e/ud//kcxMTEaNWqUdu3a5bKNVXJe9vXXX2vYsGF6%2BeWXXcb37dunESNGyG63KyUlRefOnXPXcr0SBctLLV%2B%2BXEePHtW2bdv01FNPKSsrS2%2B88Yanl9UshmEoLS1NVVVVeuGFF/TMM8/o7bff1qpVq2QYhlJSUtShQwfl5ORo3LhxSk1NVXFxsaeX3Sx//OMf9e677zofX7x4UTNmzFBcXJxefvllxcTEaObMmbp48aIHV3njli5dqoMHD2rz5s1auXKl/vd//1fZ2dmWy/nwww%2BrdevWevnllzV37lytWrVKf/7zny2Ts6amRo888ogKCwudY9/1PVlcXKyUlBQlJSVp9%2B7dCg0N1axZs3Qzf3jI1XI6HA798pe/VHx8vF555RWlpaVpyZIleueddyRZJ%2Be3ZWZmqrS01GWsoKBA8%2BbNU2pqqrKzs1VZWan09HR3LNd7GfA6Fy5cMPr3728cOnTIObZ27Vrj/vvv9%2BCqmu/EiRNGr169DIfD4Rzbu3evMXToUOPgwYNGdHS0ceHCBefclClTjDVr1nhiqaYoLy837rjjDmPChAnGnDlzDMMwjF27dhmJiYlGQ0ODYRiG0dDQYIwcOdLIycnx5FJvSHl5udG3b1/jgw8%2BcI4999xzxhNPPGGpnBUVFUavXr2MTz/91DmWmppqLFq0yBI5CwsLjZ/97GfGT3/6U6NXr17Onzvf9T25atUql59JFy9eNGJiYlx%2Bbt1MrpXzD3/4gzF69GiX586fP9945JFHDMOwTs7LDh8%2BbIwcOdIYMmSIy/t09uzZzp9ThmEYxcXFxo9%2B9CPjX//6l9vW7m04guWFjh8/rrq6OsXExDjHYmNjlZ%2Bfr4aGBg%2BurHnCw8O1adMmdejQwWX8/Pnzys/PV9%2B%2BfdW6dWvneGxsrPLy8ty9TNP87ne/07hx4/TDH/7QOZafn6/Y2Fj5%2BflJkvz8/DRw4ECvzJmbm6u2bdsqPj7eOTZjxgxlZGRYKmerVq0UHBysl19%2BWZcuXdKpU6d05MgR9enTxxI5P/zwQw0aNEjZ2dku49/1PZmfn6%2B4uDjnXHBwsPr163fTZr9WzsuXKlzp/PnzkqyTU/rmtOH8%2BfO1YMECBQYGusxdmbNjx46KiopSfn5%2Bi6/ZW9k8vQBcP4fDofbt27t8A3To0EE1NTWqqKhQaGioB1d340JCQjRs2DDn44aGBu3cuVODBw%2BWw%2BFQRESEy/PDwsJ05swZdy/TFO%2B//77%2B/ve/a%2B/evVq4cKFz3OFwuBQu6Zuc1zqUfzMrKipSp06dtGfPHm3YsEGXLl1SUlKSfv3rX1sqZ1BQkBYsWKAlS5Zo%2B/btqq%2BvV1JSku655x699dZbXp/zvvvuu%2Br4d31Petv37LVydu7cWZ07d3Y%2B/uqrr/THP/5RDz30kCTr5JSkDRs2qG/fvho6dGijudLSUq/KeTOgYHmhqqqqRr9dXH5cW1vriSW1iMzMTB07dky7d%2B/W1q1br5rZG/PW1NToqaee0oIFC9SqVSuXuWv93XpjzosXL%2BqLL77QSy%2B9pIyMDDkcDi1YsEDBwcGWyilJJ0%2BeVEJCgh588EEVFhZqyZIluv322y2X89u%2BK5sVs1dXV%2Buhhx5Shw4dlJycLMk6OU%2BcOKGXXnpJr7322lXnq6urLZHTnShYXigoKKjRm/ry4yv/D9tbZWZmatu2bXrmmWfUq1cvBQUFqaKiwuU5tbW1Xpk3KytLt956q8vRusuu9XfrjTltNpvOnz%2BvlStXqlOnTpK%2BuSD4xRdfVNeuXS2T8/3339fu3bv17rvvqlWrVurfv7/Onj2r9evXq0uXLpbJeaXv%2Bp681ns5JCTEbWs004ULFzRr1ix9/vnn%2BsMf/qDg4GBJ1shpGIaefPJJpaWlNbpE47Jr5bz854DGuAbLC0VGRqq8vFx1dXXOMYfDoVatWnnVN/W1LFmyRFu2bFFmZqZGjRol6ZvMZWVlLs8rKytrdMjaG/zxj3/U/v37FRMTo5iYGO3du1d79%2B5VTEyMpXKGh4crKCjIWa4k6b/%2B679UUlJiqZxHjx5V165dXUpT3759VVxcbKmcV/qubNeaDw8Pd9sazXL%2B/HlNnz5dhYWF2rZtm7p16%2Bacs0LO4uJiffTRR/rd737n/LlUXFysp556Sr/4xS8kWSOnu1GwvFCfPn1ks9lcLqLMzc1V//795e/v3X%2BlWVlZeumll/T0009rzJgxznG73a5PPvlE1dXVzrHc3FzZ7XZPLLNZduzYob1792rPnj3as2ePEhMTlZiYqD179shut%2Bujjz5y/hNvwzB05MgRr8xpt9tVU1Ojzz77zDl26tQpderUyVI5IyIi9MUXX7j8dn/q1Cl17tzZUjmv9F3fk3a7Xbm5uc65qqoqHTt2zOuyNzQ0KDU1VV9%2B%2BaV27Nihnj17usxbIWdkZKT%2B7//%2Bz/kzac%2BePYqIiFBaWpqWLVsmqXHOkpISlZSUeFVOd/Pu/zf2UcHBwRo/frwWLlyogoIC7d%2B/X88//7wmT57s6aU1y8mTJ7Vu3Tr98pe/VGxsrBwOh/MrPj5eHTt2VHp6ugoLC7Vx40YVFBRo4sSJnl72devUqZO6du3q/GrTpo3atGmjrl27avTo0aqsrNSyZct04sQJLVu2TFVVVbrzzjs9vezr1r17dw0fPlzp6ek6fvy43nvvPW3cuFGTJk2yVM7ExETdcsstevLJJ/XZZ5/pL3/5izZs2KAHHnjAUjmv9F3fkxMmTNCRI0e0ceNGFRYWKj09XZ07d9agQYM8vPLrs3v3bn3wwQdaunSpQkJCnD%2BTLp8etUJOm83m8jOpa9eustlsCgsLU2RkpCRp0qRJevXVV7Vr1y4dP35cjz/%2BuIYPH64uXbp4ePU3MQ/eIgLNcPHiRePxxx83oqOjjaFDhxpbtmzx9JKa7bnnnjN69ep11S/DMIzPP//c%2BPnPf27ceuutxpgxY4y//e1vHl6xOebMmeNyf5n8/Hxj/PjxRv/%2B/Y2JEycan3zyiQdX1zyVlZXG7NmzjejoaOP22283nn32Wec9oayUs7Cw0Jg6daoxcOBAY8SIEcaWLVssmfPK%2ByZ91/fkO%2B%2B8Y/zkJz8xBgwYYEyZMsVr7pn07ZzTpk276s%2Bkb9/7ygo5r5SQkNDofm05OTnGj3/8YyM6OtpISUkxzp07545lei0/w7iJbzcLAADghThFCAAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYLL/D6lSksDo4r3SAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4004756344577184503”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>404</td> <td class=”number”>12.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>309</td> <td class=”number”>9.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (36)</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4004756344577184503”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>404</td> <td class=”number”>12.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>309</td> <td class=”number”>9.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>233</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>46.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFT07”>GHVEG_SQFT07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>384</td>

</tr> <tr>

<th>Unique (%)</th> <td>12.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>913</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6341.8</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>785600</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5460572042520248924”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5460572042520248924,#minihistogram5460572042520248924”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5460572042520248924”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5460572042520248924”

aria-controls=”quantiles5460572042520248924” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5460572042520248924” aria-controls=”histogram5460572042520248924”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5460572042520248924” aria-controls=”common5460572042520248924”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5460572042520248924” aria-controls=”extreme5460572042520248924”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5460572042520248924”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>29075</td>

</tr> <tr>

<th>Maximum</th> <td>785600</td>

</tr> <tr>

<th>Range</th> <td>785600</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>36705</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.7879</td>

</tr> <tr>

<th>Kurtosis</th> <td>247.25</td>

</tr> <tr>

<th>Mean</th> <td>6341.8</td>

</tr> <tr>

<th>MAD</th> <td>10644</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.192</td>

</tr> <tr>

<th>Sum</th> <td>14567000</td>

</tr> <tr>

<th>Variance</th> <td>1347300000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5460572042520248924”>

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1b9%2B%2BYC8HAADAugq5TUNISIjatGmjNm3aKD8/X2%2B88YZmzZqluXPnKiEhQYMGDdIdd9xREUsDAAC4YhV2H6ycnBx98MEH%2BuCDD7Rz504lJCTo7rvv1pEjR/Tkk09q48aNGjduXEUtDwAA4LIFvWC9//77ev/997VhwwbVqlVLPXv21Msvv6yGDRv6n1O3bl1NmTKFggUAABwp6AVr3Lhxat%2B%2BvWbOnKlbb71VlSpdeBnYddddpwEDBgR7aQAAAFYEvWB9%2BeWXqlmzpvLy8vzlavPmzWrevLlCQ0MlSQkJCUpISAj20gAAAKwI%2Bm8Rnjp1Sl26dNFrr73mHxs6dKh69Oihw4cPB3s5AAAA1gW9YD377LO69tprdf/99/vHPvzwQ9WtW1epqanBXg4AAIB1QS9Yf//73/XEE08oKirKP1arVi09/vjjWr9%2BfbCXAwAAYF3QC5bH49GJEycuGM/Pz%2BeO7gAAwBWCXrBuvfVWTZ48WT/%2B%2BKN/LDs7W6mpqWrXrl2wlwMAAGBd0H%2BLcPTo0br//vvVuXNnRUZGSpJOnDih5s2ba8yYMcFeDgAAgHVBL1i1a9fWe%2B%2B9p3Xr1mnXrl3yeDy6/vrrdfPNNyskJCTYywEAALCuQv5UTmhoqNq1a8cpQQAA4EpBL1g%2Bn08zZszQpk2bdPbs2QsubP/ss8%2BCvSQAAACrgl6wnnrqKW3dulXdunXTH//4x2DvHgAA4KoLesFav3695s2bp6SkpGDvGgAAICiCfpuGKlWqqHbt2sHeLQAAQNAEvWD16NFD8%2BbNU0lJSbB3DQAAEBRBP0WYl5enVatWac2aNbrmmmsUFhYWML948eJgLwkAAMCqCrlNQ/fu3StitwAAAEER9IKVmpoa7F0CAAAEVdCvwZKknJwcpaWlaeTIkfrHP/6hjz/%2BWHv37q2IpQAAAFgX9IK1f/9%2B3XXXXXrvvff0ySef6MyZM/rwww/Vu3dvZWVlBXs5AAAA1gW9YD333HPq2LGjVq9erT/84Q%2BSpBdeeEEdOnTQ9OnTg70cAAAA64JesDZt2qT7778/4A87ezwePfTQQ9q2bVuwlwMAAGBd0AtWaWmpSktLLxg/ffq0QkNDg70cAAAA64JesNq2bas5c%2BYElKy8vDxNmzZNrVu3DvZyAAAArAt6wXriiSe0detWtW3bVoWFhfqP//gPtW/fXgcOHNDo0aODvRwAAADrgn4frJiYGK1YsUKrVq3S9u3bVVpaqnvvvVc9evRQtWrVgr0cAAAA6yrkTu4RERHq06dPRewaAADgqgt6wRo4cOCvzvO3CAEAgNMFvWDVr18/4HFxcbH279%2BvnTt3atCgQcFeDgAAgHW/mb9FOHPmTB05ciTIqwEAALCvQv4W4S/p0aOHPvroo4peBgAAwBX7zRSsb7/9lhuNAgAAV/hNXOR%2B6tQpff/99%2Brfv3%2BwlwMAAGBd0AtWvXr1Av4OoST94Q9/0IABA/SnP/0p2MsBAACwLugF67nnngv2LgEAAIIq6AVr48aNZX5uy5Ytr%2BJKAAAAro6gF6z77rvPf4rQGOMfP38sJCRE27dvD/byAAAArljQC9bs2bM1efJkjRo1SsnJyQoLC9OWLVs0ceJE3X333eratWuwlwQAAGBV0G/TkJqaqvHjx6tz586qWbOmqlatqtatW2vixIlaunSp6tev7/8CAABwoqAXrJycnF8sT9WqVdPx48eDvRwAAADrgl6w4uLi9MILL%2BjUqVP%2Bsby8PE2bNk0333xzsJcDAABgXdCvwXryySc1cOBA3XrrrWrYsKGMMfrhhx8UFRWlxYsXB3s5AAAA1gW9YDVq1EgffvihVq1apT179kiS/vznP6tbt26KiIgI9nIAAACsC3rBkqTq1aurT58%2BOnDggK655hpJP93NHQAAwA2Cfg2WMUbTp09Xy5Yt1b17dx05ckSjR4/WuHHjdPbs2WAvBwAAwLqgF6w33nhD77//vp5%2B%2BmmFhYVJkjp27KjVq1crLS0t2MsBAACwLugFKz09XePHj1evXr38d2/v2rWrJk%2BerJUrVwZ7OQAAANYFvWAdOHBATZs2vWC8SZMm8vl85d5eUVGRunfvrg0bNvjHsrOzNXjwYMXFxalr165au3ZtwGvWrVun7t27y%2Bv1auDAgcrOzg6YX7hwodq1a6f4%2BHiNHTtW%2Bfn55V4XAAD4/Qp6wapfv762bNlywfiXX37pv%2BC9rAoLC/Xoo49q165d/jFjjFJSUlSnTh0tX75cPXr00PDhw3Xo0CFJ0qFDh5SSkqJevXrpnXfeUa1atfTQQw/5/wbiJ598orS0NE2cOFGLFi1SVlaWpk2bdgWJAQDA703QC9YDDzygCRMmaPHixTLG6Ouvv9b06dM1depU3XfffWXezu7du9W3b1/9%2BOOPAePr169Xdna2Jk6cqEaNGmnYsGGKi4vT8uXLJUnLli3TTTfdpCFDhuiGG25QamqqDh48qG%2B%2B%2BUaStHjxYg0aNEjt27dXixYtNGHCBC1fvpyjWAAAoMyCXrB69%2B6t//zP/9Trr7%2BugoICjR8/Xu%2B%2B%2B64eeeQR3XvvvWXezjfffKNWrVopPT09YDwrK0vNmjVTlSpV/GOJiYnKzMz0zyclJfnnIiIi1Lx5c2VmZqqkpERbtmwJmI%2BLi9PZs2e1Y8eOy40MAAB%2BZ4J%2BH6xVq1apS5cu6tevn44dOyZjjGrXrl3u7fTv3/8Xx30%2Bn6KjowPGateurSNHjlxy/sSJEyosLAyY93g8qlGjhv/1AAAAlxL0gjVx4kT993//t6pXr65atWpZ335%2Bfr7/9g/nhIWFqaio6JLzBQUF/scXe31Z5OTkXHDBvsdT5YJid6VCQ4N%2BAPKKeDxlX%2B%2B5bE7LWFZuzufmbBL5nMzN2SR353NitqAXrIYNG2rnzp26/vrrr8r2w8PDlZeXFzBWVFSkypUr%2B%2BfPL0tFRUWKjIxUeHi4//H58%2BX5Mz7p6ekX3NMrJSVFI0aMKPM23Khmzarlfk1kpLv/fJKb87k5m0Q%2BJ3NzNsnd%2BZyULegFq0mTJnrsscc0b948NWzY0F9qzklNTb2i7cfExGj37t0BY7m5uf6jRzExMcrNzb1gvmnTpqpRo4bCw8OVm5urRo0aSZKKi4uVl5enqKioMq%2BhX79%2B6tChQ8CYx1NFx4%2BfvpxIF%2BWkJi%2BpXPlDQyspMjJCJ07kq6Sk9CquqmK4OZ%2Bbs0nkczI3Z5Pcne9Ksl3OD/c2BL1g7du3T4mJiZJ0Wfe9uhSv16u5c%2BeqoKDAf9QqIyPDv0%2Bv16uMjAz/8/Pz87Vt2zYNHz5clSpVUmxsrDIyMtSqVStJUmZmpjwej5o0aVLmNURHR19wOtDnO6niYnd9w5fX5eQvKSl19fvm5nxuziaRz8ncnE1ydz4nZQtKwZo6daqGDx%2BuKlWq6I033riq%2B0pOTlbdunU1ZswYPfTQQ/r888%2B1efNm/5Gx3r17a/78%2BZo7d67at2%2BvmTNnqkGDBv5C1b9/f40fP16NGzdWdHS0nnnmGfXt27dcpwgBAMDvW1DOMS1YsOCC%2B0gNHTpUOTk51vcVGhqqWbNmyefzqVevXvrggw80c%2BZM1atXT5LUoEEDvfLKK1q%2BfLnuuece5eXlaebMmf4/29OtWzcNGzZM48eP15AhQ9SiRQuNGjXK%2BjoBAIB7BeUI1rm7pP/cxo0bVVhYaGX733//fcDja6%2B9VkuWLLno82%2B77TbddtttF50fOnSohg4damVtAADg98dZV0kDAAA4AAULAADAsqAVrHPXOAEAALhd0G7TMHny5IB7Xp09e1bTpk1T1aqB96e40vtgAQAAVLSgFKyWLVtecM%2Br%2BPh4HT9%2BXMePHw/GEgAAAIImKAXrat/7CgAA4LeEi9wBAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAsc23B%2BvTTT3XjjTcGfI0YMUKStG3bNvXp00der1e9e/fW1q1bA167atUqdezYUV6vVykpKTp27FhFRAAAAA7l2oK1e/dutW/fXmvXrvV/TZ48WWfOnNHQoUOVlJSkd999V/Hx8Ro2bJjOnDkjSdq8ebPGjRun4cOHKz09XSdOnNCYMWMqOA0AAHAS1xasPXv2qHHjxoqKivJ/RUZG6sMPP1R4eLgef/xxNWrUSOPGjVPVqlX18ccfS5KWLFmiO%2B%2B8Uz179lSTJk00depUffHFF8rOzq7gRAAAwClcXbAaNmx4wXhWVpYSExMVEhIiSQoJCVFCQoIyMzP980lJSf7n161bV/Xq1VNWVlZQ1g0AAJzPlQXLGKN9%2B/Zp7dq16ty5szp27Kjp06erqKhIPp9P0dHRAc%2BvXbu2jhw5IknKycn51XkAAIBL8VT0Aq6GQ4cOKT8/X2FhYZoxY4YOHDigyZMnq6CgwD/%2Bc2FhYSoqKpIkFRQU/Op8WeTk5Mjn8wWMeTxVLihuVyo01Fn92OMp%2B3rPZXNaxrJycz43Z5PI52Ruzia5O58Ts7myYNWvX18bNmxQ9erVFRISoqZNm6q0tFSjRo1ScnLyBWWpqKhIlStXliSFh4f/4nxERESZ95%2Benq60tLSAsZSUFP9vMf5e1axZtdyviYws%2B/vuRG7O5%2BZsEvmczM3ZJHfnc1I2VxYsSapRo0bA40aNGqmwsFBRUVHKzc0NmMvNzfUfXYqJifnF%2BaioqDLvu1%2B/furQoUPAmMdTRcePny5PhEtyUpOXVK78oaGVFBkZoRMn8lVSUnoVV1Ux3JzPzdkk8jmZm7NJ7s53Jdku54d7G1xZsL766is99thjWrNmjf/I0/bt21WjRg0lJibqtddekzFGISEhMsZo06ZN%2Bstf/iJJ8nq9ysjIUK9evSRJhw8f1uHDh%2BX1esu8/%2Bjo6AtOB/p8J1Vc7K5v%2BPK6nPwlJaWuft/cnM/N2STyOZmbs0nuzuekbM46BFJG8fHxCg8P15NPPqm9e/fqiy%2B%2B0NSpU/Xggw%2BqS5cuOnHihKZMmaLdu3drypQpys/P15133ilJuvfee/X%2B%2B%2B9r2bJl2rFjhx5//HHdfvvtuuaaayo4FQAAcApXFqxq1app/vz5OnbsmHr37q1x48apX79%2BevDBB1WtWjXNmTPHf5QqKytLc%2BfOVZUqVST9VM4mTpyomTNn6t5771X16tWVmppawYkAAICTuPIUoSTdcMMNWrBgwS/OtWjRQu%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%2BeunTpUtFLAwAADkDBOs%2BZM2e0bNkyvfbaa2revLmaN2%2BuXbt26c0336RgAQCAMqFgnWfHjh0qLi5WfHy8fywxMVGzZ89WaWmpKlXirOrlunPG/1X0Esrlo0faVPQSAAAORcE6j8/nU82aNRUWFuYfq1OnjgoLC5WXl6datWpdchs5OTlUhHIPAAAPQklEQVTy%2BXwBYx5PFUVHR1tda2goZe9qcloh/PSxdhW9BEn///elW78/yedcbs4muTufE7NRsM6Tn58fUK4k%2BR8XFRWVaRvp6elKS0sLGBs%2BfLgefvhhO4v8f3JycjToX3apX79%2B1stbRcvJyVF6erors0nuzpeTk6NFi%2Ba5MptEPidzczbJ3fmcmM05VTBIwsPDLyhS5x5Xrly5TNvo16%2Bf3n333YCvfv36WV%2Brz%2BdTWlraBUfL3MDN2SR353NzNol8TubmbJK78zkxG0ewzhMTE6Pjx4%2BruLhYHs9Pb4/P51PlypUVGRlZpm1ER0c7pmEDAAD7OIJ1nqZNm8rj8SgzM9M/lpGRodjYWC5wBwAAZUJjOE9ERIR69uypZ555Rps3b9bq1av1%2Buuva%2BDAgRW9NAAA4BChzzzzzDMVvYjfmtatW2vbtm16/vnn9fXXX%2Bsvf/mLevfuXdHL%2BkVVq1ZVcnKyqlatWtFLsc7N2SR353NzNol8TubmbJK78zktW4gxxlT0IgAAANyEU4QAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYDlVYWKixY8cqKSlJbdu21euvv17RS5IkFRUVqXv37tqwYYN/LDs7W4MHD1ZcXJy6du2qtWvXBrxm3bp16t69u7xerwYOHKjs7OyA%2BYULF6pdu3aKj4/X2LFjlZ%2Bf75%2B71PtwqX2XxdGjRzVixAglJyerXbt2Sk1NVWFhoSuySdL%2B/fv1wAMPKD4%2BXrfffrvmzZtX5n04Id85Q4cO1RNPPOF/vG3bNvXp00der1e9e/fW1q1bA56/atUqdezYUV6vVykpKTp27Jh/zhij6dOnq3Xr1kpOTtbUqVNVWlrqnz9%2B/LgefvhhxcfHq0OHDnr//fcDtn2pfZfVp59%2BqhtvvDHga8SIEa7IV1RUpAkTJqhly5a65ZZb9MILL%2BjcfbGdnu3dd9%2B94HO78cYb1aRJE1fkO3z4sIYNG6aEhAR16NBBCxcuLPP2f%2BvZysXAkSZOnGjuuusus3XrVvO3v/3NxMfHm48%2B%2BqhC11RQUGBSUlJM48aNzfr1640xxpSWlpq77rrLjBw50uzevdvMnj3beL1ec/DgQWOMMQcPHjRxcXFm/vz5ZufOneavf/2r6d69uyktLTXGGPPxxx%2BbxMRE87//%2B78mKyvLdO3a1UyYMMG/z197Hy6177IoLS01ffv2NQ8%2B%2BKDZuXOn2bhxo%2BnUqZN57rnnHJ/NGGNKSkrMHXfcYUaOHGn27dtn1qxZYxISEswHH3zginznrFq1yjRu3NiMHj3aGGPM6dOnTZs2bcxzzz1ndu/ebSZNmmRuueUWc/r0aWOMMVlZWaZFixbmvffeM9u3bzcDBgwwQ4cO9W9v/vz55rbbbjMbN240X3/9tWnbtq2ZN2%2Bef37YsGFm0KBB5vvvvzdvv/22uemmm0xWVlaZ9l0es2bNMsOGDTM5OTn%2Br3/%2B85%2BuyPfUU0%2BZO%2B64w2RlZZl169aZVq1amaVLl7oiW35%2BfsBndujQIdOpUyczZcoUV%2BTr27eveeSRR8y%2BffvMp59%2Barxer/nb3/7mimzlQcFyoNOnT5vY2Fh/iTHGmJkzZ5oBAwZU2Jp27dpl/vSnP5m77roroGCtW7fOxMXFBXwTDxo0yLz88svGGGNmzJgRsO4zZ86Y%2BPh4/%2Bv79%2B/vf64xxmzcuNG0aNHCnDlz5pLvw6X2XRa7d%2B82jRs3Nj6fzz%2B2cuVK07ZtW8dnM8aYo0ePmr/%2B9a/m5MmT/rGUlBTz9NNPuyKfMcYcP37c3HrrraZ3797%2BgrVs2TLToUMHfxksLS01nTp1MsuXLzfGGDNq1Cj/c40x5tChQ%2BbGG280P/74ozHGmNtuu83/XGOMWbFihWnfvr0xxpj9%2B/ebxo0bm%2BzsbP/82LFjy7zv8hg5cqR5/vnnLxh3er7jx4%2BbZs2amQ0bNvjH5syZY5544gnHZ/sls2fPNh07djSFhYWOz5eXl2caN25svv/%2Be//Y8OHDzYQJExyfrbw4RehAO3bsUHFxseLj4/1jiYmJysrKCjhcGkzffPONWrVqpfT09IDxrKwsNWvWTFWqVPGPJSYmKjMz0z%2BflJTkn4uIiFDz5s2VmZmpkpISbdmyJWA%2BLi5OZ8%2Be1Y4dOy75Plxq32URFRWlefPmqU6dOgHjp06dcnw2SYqOjtaMGTNUrVo1GWOUkZGhjRs3Kjk52RX5JOm//uu/1KNHD11//fX%2BsaysLCUmJiokJESSFBISooSEhItmq1u3rurVq6esrCwdPXpUhw8fVsuWLQPWdvDgQeXk5CgrK0t169ZVgwYNAua//fbbMu27PPbs2aOGDRteMO70fBkZGapWrZqSk5P9Y0OHDlVqaqrjs50vLy9Pr732mkaOHKmwsDDH56tcubIiIiL07rvv6uzZs9q7d682bdqkpk2bOj5beVGwHMjn86lmzZoKCwvzj9WpU0eFhYXKy8urkDX1799fY8eOVURERMC4z%2BdTdHR0wFjt2rV15MiRS86fOHFChYWFAfMej0c1atTQkSNHLvk%2BXGrfZREZGal27dr5H5eWlmrJkiVq3bq147Odr0OHDurfv7/i4%2BPVuXNnV%2BT7%2Buuv9fe//10PPfRQwPiltp%2BTk3PReZ/PJ0kB8%2BcK%2BLn5X3rt0aNHy7TvsjLGaN%2B%2BfVq7dq06d%2B6sjh07avr06SoqKnJ8vuzsbNWvX18rVqxQly5d9G//9m%2BaOXOmSktLHZ/tfEuXLlV0dLS6dOlSpn381vOFh4dr/PjxSk9Pl9fr1Z133qlbb71Vffr0cXy28vJctS3jqsnPzw/4H5Mk/%2BOioqKKWNJFXWyt59b5a/MFBQX%2Bx780b4z51ffhUvu%2BHNOmTdO2bdv0zjvvaOHCha7K9vLLLys3N1fPPPOMUlNTHf/ZFRYW6umnn9b48eNVuXLlgLlLbb%2BgoKBc2cqzdluf3aFDh/zbmjFjhg4cOKDJkyeroKDA8fnOnDmj/fv366233lJqaqp8Pp/Gjx%2BviIgIx2f7OWOMli1bpgcffNA/5oZ8e/bsUfv27XX//fdr165dmjRpkm6%2B%2BWZXZCsPCpYDhYeHX/BNce7x%2Bf8jqWjh4eEXHFUrKiryr/NiWSIjIxUeHu5/fP58RESESkpKfvV9uNS%2By2vatGlatGiRXnzxRTVu3NhV2SQpNjZW0k/F5LHHHlPv3r0DfuvPafnS0tJ00003BRyBPOdia79UtoiIiIB/1M/PGRERcdnbLu9nV79%2BfW3YsEHVq1dXSEiImjZtqtLSUo0aNUrJycmOzufxeHTq1Ck9//zzql%2B/vqSfCuXSpUt17bXXOjrbz23ZskVHjx5Vt27d/GNO/978%2Buuv9c477%2BiLL75Q5cqVFRsbq6NHj%2BrVV1/VNddc4%2Bhs5cUpQgeKiYnR8ePHVVxc7B/z%2BXyqXLmyIiMjK3BlF4qJiVFubm7AWG5urv9Q7cXmo6KiVKNGDYWHhwfMFxcXKy8vT1FRUZd8Hy617/KYNGmSFixYoGnTpqlz586uyZabm6vVq1cHjF1//fU6e/asoqKiHJ3vf/7nf7R69WrFx8crPj5eK1eu1MqVKxUfH39Fn11MTIx/vT9fuyT//MVe%2B2vbvpzvyxo1avivKZGkRo0aqbCw8Io%2Bu99CvqioKIWHh/vLlST967/%2Bqw4fPuyaz06SvvrqKyUlJal69er%2BMafn27p1q6699tqA4tKsWTMdOnTI8dnKi4LlQE2bNpXH4wm4OC8jI0OxsbGqVOm39ZF6vV599913/sO70k9r9Xq9/vmMjAz/XH5%2BvrZt2yav16tKlSopNjY2YD4zM1Mej0dNmjS55PtwqX2XVVpamt566y298MILAT9puiHbgQMHNHz4cP91CtJP/0DWqlVLiYmJjs73xhtvaOXKlVqxYoVWrFihDh06qEOHDlqxYoW8Xq%2B%2B/fZb/32VjDHatGnTRbMdPnxYhw8fltfrVUxMjOrVqxcwn5GRoXr16ik6OlpxcXE6ePBgwLUdGRkZiouL82/71/ZdVl999ZVatWoVcJRx%2B/btqlGjhv/iXqfm83q9Kiws1L59%2B/xje/fuVf369V3x2Z2zefNmJSQkXJDdyfmio6O1f//%2BgKNFe/fuVYMGDRyfrdyu2u8n4qp66qmnTLdu3UxWVpb59NNPTUJCgvnkk08qelnGGBNwm4bi4mLTtWtX88gjj5idO3eaOXPmmLi4OP/9jLKzs01sbKyZM2eO/15Kd911l/9XaVetWmUSEhLMp59%2BarKysky3bt3MpEmT/Pv6tffhUvsui927d5umTZuaF198MeC%2BNTk5OY7Pdm47vXr1MkOGDDG7du0ya9asMbfccotZuHChK/L93OjRo/2/sn3y5EnTunVrM2nSJLNr1y4zadIk06ZNG/9tITZt2mSaN29u3n77bf/9eIYNG%2Bbf1pw5c0zbtm3N%2BvXrzfr1603btm3N66%2B/7p8fMmSIGTBggNm%2Bfbt5%2B%2B23TWxsrP9%2BPJfad1mdPHnStGvXzjz66KNmz549Zs2aNaZt27Zm7ty5rsg3dOhQ069fP7N9%2B3bz5ZdfmtatW5tFixa5Its57du3N6tWrQoYc3q%2BEydOmDZt2phRo0aZvXv3ms8%2B%2B8wkJyebpUuXOj5beVGwHOrMmTPm8ccfN3FxcaZt27ZmwYIFFb0kv58XLGOM%2BeGHH8yf//xnc9NNN5lu3bqZ//u//wt4/po1a8wdd9xhWrRoYQYNGuS/58k5c%2BbMMTfffLNJTEw0Y8aMMQUFBf65S70Pl9r3pcyZM8c0btz4F7%2Bcnu2cI0eOmJSUFJOQkGDatGljXn31VX9JckO%2Bc35esIz56aaGPXv2NLGxseaee%2B4x3333XcDzly9fbm677TYTFxdnUlJSzLFjx/xzxcXF5tlnnzVJSUmmVatWZtq0af73zBhjcnNzzbBhw0xsbKzp0KGDWblyZcC2L7Xvstq5c6cZPHiwiYuLM23atDGvvPKKfx1Oz3fixAkzatQoExcXZ26%2B%2BWZXZTsnNjbWfPnllxeMOz3frl27zODBg01CQoLp2LGjWbBgges%2Bu7IIMeb/HS8DAACAFb%2BtC3YAAABcgIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMv%2BP2lBrQghRv2ZAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5460572042520248924”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8400.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2160.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7950.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8640.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22030.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (373)</td> <td class=”number”>396</td> <td class=”number”>12.3%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>913</td> <td class=”number”>28.4%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5460572042520248924”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>439552.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>541749.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>613540.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>773748.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>785600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFT12”>GHVEG_SQFT12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>783</td>

</tr> <tr>

<th>Unique (%)</th> <td>24.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>966</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21460</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4650000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3450505162714088577”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-3450505162714088577”>

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<li role=”presentation” class=”active”><a href=”#quantiles-3450505162714088577”

aria-controls=”quantiles-3450505162714088577” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3450505162714088577” aria-controls=”histogram-3450505162714088577”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3450505162714088577” aria-controls=”common-3450505162714088577”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3450505162714088577” aria-controls=”extreme-3450505162714088577”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3450505162714088577”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>10662</td>

</tr> <tr>

<th>95-th percentile</th> <td>69646</td>

</tr> <tr>

<th>Maximum</th> <td>4650000</td>

</tr> <tr>

<th>Range</th> <td>4650000</td>

</tr> <tr>

<th>Interquartile range</th> <td>10662</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>177490</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.2711</td>

</tr> <tr>

<th>Kurtosis</th> <td>505.06</td>

</tr> <tr>

<th>Mean</th> <td>21460</td>

</tr> <tr>

<th>MAD</th> <td>31948</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>21.474</td>

</tr> <tr>

<th>Sum</th> <td>48155000</td>

</tr> <tr>

<th>Variance</th> <td>31504000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3450505162714088577”>

<img 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iK99dZbeuutt7R792517txZt912mwoLC/Xoo49q8%2BbNmjZtWrDKAwAA%2BKe5HrDefPNNvfnmm/rss8/UrFkzDRkyRAsWLFCbNm2cx7Rs2VJz5swhYAEAgJDkesCaNm2aevXqpezsbN14440KDz/9a2BXXnmlRo0a5XZpAAAAVrgesD7%2B%2BGM1bdpUpaWlTrjaunWrOnbsqIiICElS586d1blzZ7dLAwAAsML13yI8duyYbrnlFr3wwgvO2P3336/Bgwfr4MGDbpcDAABgnesB68knn9QVV1yhu%2B%2B%2B2xl7%2B%2B231bJlS2VkZLhdDgAAgHWuB6y//e1veuSRRxQbG%2BuMNWvWTA8//LA2bdrkdjkAAADWuR6wPB6Pjhw5ctp4WVkZV3QHAAD1gusB68Ybb9Ts2bP1/fffO2MFBQXKyMhQz5493S4HAADAOtd/i3Dy5Mm6%2B%2B671a9fP8XExEiSjhw5oo4dO2rKlClulwMAAGCd6wGrefPmeuONN/TJJ59oz5498ng8uuqqq3TDDTcoLCzM7XIAAACsC8qfyomIiFDPnj35SBAAANRLrgcsv9%2Bv%2BfPna8uWLTp58uRpX2z/y1/%2B4nZJAAAAVrkesB577DFt375dAwYM0KWXXur28gAAAHXO9YC1adMmLV26VKmpqW4vDQAA4ArXL9PQsGFDNW/e3O1lAQAAXON6wBo8eLCWLl2q6upqt5cGAABwhesfEZaWlio3N1cffvihLr/8ckVGRgbMr1y50u2SAAAArArKZRoGDhwYjGUBAABc4XrAysjIcHtJAAAAV7n%2BHSxJKioqUlZWliZOnKi///3vevfdd/X1118HoxQAAADrXA9Y3333nQYNGqQ33nhD7733nk6cOKG3335bw4YNk8/nc7scAAAA61wPWE899ZR69%2B6tDRs26JJLLpEkPfPMM0pLS9O8efPcLgcAAMA61wPWli1bdPfddwf8YWePx6Nx48Zpx44dbpcDAABgnesBq6amRjU1NaeNHz9%2BXBEREW6XAwAAYJ3rAatHjx5avHhxQMgqLS1VZmamunXr5nY5AAAA1rkesB555BFt375dPXr0UEVFhf71X/9VvXr10g8//KDJkye7XQ4AAIB1rl8HKz4%2BXuvWrVNubq527typmpoa3XnnnRo8eLAaN27sdjkAAADWBeVK7tHR0Ro%2BfHgwlgYAAKhzrges0aNH/%2Bw8f4sQAACEOtcDVqtWrQLuV1VV6bvvvtPu3bs1ZswYt8sBAACw7oL5W4TZ2dkqLCx0uRoAAAD7gvK3CM9k8ODBeuedd4JdBgAAwHm7YALWF198wYVGAQBAvXBBfMn92LFj%2BuqrrzRy5Ei3ywEAALDO9YCVkJAQ8HcIJemSSy7RqFGj9Jvf/MbtcgAAAKxzPWA99dRTbi8JAADgKtcD1ubNm2v92Ouvv74OKwEAAKgbrges3/3ud85HhMYYZ/ynY2FhYdq5c6fb5QEAAJw31wPWokWLNHv2bE2aNEldunRRZGSktm3bppkzZ%2Bq2225T//793S4JAADAKtcv05CRkaHp06erX79%2Batq0qRo1aqRu3bpp5syZeuWVV9SqVSvnBgAAEIpcD1hFRUVnDE%2BNGzdWSUmJ2%2BUAAABY53rASkpK0jPPPKNjx445Y6WlpcrMzNQNN9zgdjkAAADWuf4drEcffVSjR4/WjTfeqDZt2sgYo2%2B//VaxsbFauXKl2%2BUAAABY53rAatu2rd5%2B%2B23l5uZq3759kqTf/va3GjBggKKjo90uBwAAwDrXA5YkXXbZZRo%2BfLh%2B%2BOEHXX755ZJ%2BvJo7AABAfeD6d7CMMZo3b56uv/56DRw4UIWFhZo8ebKmTZumkydPul0OAACAda4HrFWrVunNN9/U448/rsjISElS7969tWHDBmVlZf3i41VWVmrgwIH67LPPnLGCggLdddddSkpKUv/%2B/bVx48aA53zyyScaOHCgvF6vRo8erYKCgoD55cuXq2fPnkpOTtbUqVNVVlb2T%2BwUAABcrFwPWDk5OZo%2BfbqGDh3qXL29f//%2Bmj17ttavX/%2BLjlVRUaE//vGP2rNnjzNmjFF6erpatGihtWvXavDgwRo/frwOHDggSTpw4IDS09M1dOhQvf7662rWrJnGjRvnXEH%2BvffeU1ZWlmbOnKkVK1bI5/MpMzPT0u4BAMDFwPWA9cMPP6hDhw6njbdv315%2Bv7/Wx9m7d6/uuOMOff/99wHjmzZtUkFBgWbOnKm2bdtq7NixSkpK0tq1ayVJa9as0XXXXad77rlHV199tTIyMrR//359/vnnkqSVK1dqzJgx6tWrlzp16qQZM2Zo7dq1nMUCAAC15nrAatWqlbZt23ba%2BMcff%2Bx84b02Pv/8c3Xt2lU5OTkB4z6fT9dee60aNmzojKWkpCg/P9%2BZT01Ndeaio6PVsWNH5efnq7q6Wtu2bQuYT0pK0smTJ7Vr165a1wYAAC5urv8W4b333qsZM2bI7/fLGKNPP/1UOTk5WrVqlR555JFaH2fkyJFnHPf7/YqLiwsYa968uQoLC885f%2BTIEVVUVATMezweNWnSxHk%2BAADAubgesIYNG6aqqio9//zzKi8v1/Tp09WsWTM9%2BOCDuvPOO8/7%2BGVlZc6X50%2BJjIxUZWXlOefLy8ud%2B2d7fm0UFRWd9nGnx9PwtGB3viIiXD8BeV48ntCq92xO9T3U%2Bh/q6Lv76Hlw0Pf6wfWAlZubq1tuuUUjRozQ4cOHZYxR8%2BbNrR0/KipKpaWlAWOVlZVq0KCBM//TsFRZWamYmBhFRUU59386/0sugpqTk3Pab0Smp6drwoQJtT5GfdS0aaNgl2BVTAwXxg0G%2Bu4%2Beh4c9D20uR6wZs6cqf/4j//QZZddpmbNmlk/fnx8vPbu3RswVlxc7Jw9io%2BPV3Fx8WnzHTp0UJMmTRQVFaXi4mK1bdtWklRVVaXS0lLFxsbWuoYRI0YoLS0tYMzjaaiSkuP/zJbOKtTe3djef7BERIQrJiZaR46Uqbq6JtjlXDTou/voeXDQd7uC9ebe9YDVpk0b7d69W1dddVWdHN/r9WrJkiUqLy93zlrl5eUpJSXFmc/Ly3MeX1ZWph07dmj8%2BPEKDw9XYmKi8vLy1LVrV0lSfn6%2BPB6P2rdvX%2Bsa4uLiTvs40O8/qqqqi/sHpb7tv7q6pt7tKRTQd/fR8%2BCg76HN9YDVvn17PfTQQ1q6dKnatGnjfCx3SkZGxnkdv0uXLmrZsqWmTJmicePG6YMPPtDWrVud4w4bNkzLli3TkiVL1KtXL2VnZ6t169ZOoBo5cqSmT5%2Budu3aKS4uTk888YTuuOMO/k4iAACoNdcD1jfffOOcTfol172qrYiICC1cuFDTpk3T0KFDdcUVVyg7O1sJCQmSpNatW%2BtPf/qTnnzySWVnZys5OVnZ2dnORU8HDBig/fv3a/r06aqsrFTfvn01adIk63UCAID6K8ycuoR5HZo7d67Gjx8fcG2qi43ff9T6MT2ecPWZ91frx60r7zzYPdglWOHxhKtp00YqKTnO6XsX0Xf30fPgoO92xcZeGpR1XfmW9EsvvXTaldDvv/9%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%2B%2BfWrXrp1iY2OdW0xMjN5%2B%2B21FRUXp4YcfVtu2bTVt2jQ1atRI7777riRp9erVuvXWWzVkyBC1b99ec%2BfO1UcffaSCgoIg7wgAAISKeh2w2rRpc9q4z%2BdTSkqKwsLCJElhYWHq3Lmz8vPznfnU1FTn8S1btlRCQoJ8Pp8rdQMAgNBXLwOWMUbffPONNm7cqH79%2Bql3796aN2%2BeKisr5ff7FRcXF/D45s2bq7CwUJJUVFT0s/MAAADn4gl2AXXhwIEDKisrU2RkpObPn68ffvhBs2fPVnl5uTP%2BjyIjI1VZWSlJKi8v/9n52igqKpLf7w8Y83ganhbczldERGjlY48ntOo9m1N9D7X%2Bhzr67j56Hhz0vX6olwGrVatW%2Buyzz3TZZZcpLCxMHTp0UE1NjSZNmqQuXbqcFpYqKyvVoEEDSVJUVNQZ56Ojo2u9fk5OjrKysgLG0tPTnd9ivFg1bdoo2CVYFRNT%2B/8mYA99dx89Dw76HtrqZcCSpCZNmgTcb9u2rSoqKhQbG6vi4uKAueLiYufsUnx8/BnnY2Nja732iBEjlJaWFjDm8TRUScnxX7KFcwq1dze29x8sERHhiomJ1pEjZaqurgl2ORcN%2Bu4%2Beh4c9N2uYL25r5cB669//aseeughffjhh86Zp507d6pJkyZKSUnRCy%2B8IGOMwsLCZIzRli1b9Pvf/16S5PV6lZeXp6FDh0qSDh48qIMHD8rr9dZ6/bi4uNM%2BDvT7j6qq6uL%2BQalv%2B6%2Burql3ewoF9N199Dw46HtoC61TILWUnJysqKgoPfroo/r666/10Ucfae7cubrvvvt0yy236MiRI5ozZ4727t2rOXPmqKysTLfeeqsk6c4779Sbb76pNWvWaNeuXXr44Yd188036/LLLw/yrgAAQKiolwGrcePGWrZsmQ4fPqxhw4Zp2rRpGjFihO677z41btxYixcvds5S%2BXw%2BLVmyRA0bNpT0YzibOXOmsrOzdeedd%2Bqyyy5TRkZGkHcEAABCSZgxxgS7iIuB33/U%2BjE9nnD1mfdX68etK%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%2B%2B%2BGKwSwIAACHEE%2BwCLkRz587V9u3btWLFCh04cECTJ09WQkKCbrnllmCXBgAAQgAB6ydOnDihNWvW6IUXXlDHjh3VsWNH7dmzRy%2B//DIBCwAA1AofEf7Erl27VFVVpeTkZGcsJSVFPp9PNTU1QawMAACECs5g/YTf71fTpk0VGRnpjLVo0UIVFRUqLS1Vs2bNznmMoqIi%2Bf3%2BgDGPp6Hi4uKs1hoREVr52OMJrXrP5lTfQ63/oY6%2Bu4%2BeBwd9rx8IWD9RVlYWEK4kOfcrKytrdYycnBxlZWUFjI0fP14PPPCAnSL/X1FRkcb8ao9GjBhhPbzh7IqKirRixVL67jL67j56Hhz0vX4gHv9EVFTUaUHq1P0GDRrU6hgjRozQn//854D5fdJKAAAKqklEQVTbiBEjrNfq9/uVlZV12tky1C36Hhz03X30PDjoe/3AGayfiI%2BPV0lJiaqqquTx/Ngev9%2BvBg0aKCYmplbHiIuL410HAAAXMc5g/USHDh3k8XiUn5/vjOXl5SkxMVHh4bQLAACcG4nhJ6KjozVkyBA98cQT2rp1qzZs2KAXX3xRo0ePDnZpAAAgREQ88cQTTwS7iAtNt27dtGPHDj399NP69NNP9fvf/17Dhg0Ldlln1KhRI3Xp0kWNGjUKdikXFfoeHPTdffQ8OOh76AszxphgFwEAAFCf8BEhAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICVoiqqKjQ1KlTlZqaqh49eujFF18MdkkXlMrKSg0cOFCfffaZM1ZQUKC77rpLSUlJ6t%2B/vzZu3BjwnE8%2B%2BUQDBw6U1%2BvV6NGjVVBQEDC/fPly9ezZU8nJyZo6darKysqcuXO9Hue79oXu0KFDmjBhgrp06aKePXsqIyNDFRUVkuh7Xfruu%2B907733Kjk5WTfffLOWLl3qzNH3unf//ffrkUcece7v2LFDw4cPl9fr1bBhw7R9%2B/aAx%2Bfm5qp3797yer1KT0/X4cOHnTljjObNm6du3bqpS5cumjt3rmpqapz5kpISPfDAA0pOTlZaWprefPPNgGOfz9qoIwYhaebMmWbQoEFm%2B/bt5r/%2B679McnKyeeedd4Jd1gWhvLzcpKenm3bt2plNmzYZY4ypqakxgwYNMhMnTjR79%2B41ixYtMl6v1%2Bzfv98YY8z%2B/ftNUlKSWbZsmdm9e7f5wx/%2BYAYOHGhqamqMMca8%2B%2B67JiUlxfz3f/%2B38fl8pn///mbGjBnOmj/3epzv2he6mpoac8cdd5j77rvP7N6922zevNn06dPHPPXUU/S9DlVXV5u%2BffuaiRMnmm%2B%2B%2BcZ8%2BOGHpnPnzuatt96i7y7Izc017dq1M5MnTzbGGHP8%2BHHTvXt389RTT5m9e/eaWbNmmV//%2Btfm%2BPHjxhhjfD6f6dSpk3njjTfMzp07zahRo8z999/vHG/ZsmXmpptuMps3bzaffvqp6dGjh1m6dKkzP3bsWDNmzBjz1Vdfmddee81cd911xufzWVkbdYOAFYKOHz9uEhMTnfBgjDHZ2dlm1KhRQazqwrBnzx7zm9/8xgwaNCggYH3yyScmKSnJ%2BR%2BOMcaMGTPGLFiwwBhjzPz58wP6d%2BLECZOcnOw8f%2BTIkc5jjTFm8%2BbNplOnTubEiRPnfD3Od%2B0L3d69e027du2M3%2B93xtavX2969OhB3%2BvQoUOHzB/%2B8Adz9OhRZyw9Pd08/vjj9L2OlZSUmBtvvNEMGzbMCVhr1qwxaWlpTlCsqakxffr0MWvXrjXGGDNp0iTnscYYc%2BDAAXPNNdeY77//3hhjzE033eQ81hhj1q1bZ3r16mWMMea7774z7dq1MwUFBc781KlTra2NusFHhCFo165dqqqqUnJysjOWkpIin88XcEr5YvT555%2Bra9euysnJCRj3%2BXy69tpr1bBhQ2csJSVF%2Bfn5znxqaqozFx0drY4dOyo/P1/V1dXatm1bwHxSUpJOnjypXbt2nfP1OJ%2B1Q0FsbKyWLl2qFi1aBIwfO3aMvtehuLg4zZ8/X40bN5YxRnl5edq8ebO6dOlC3%2BvYv//7v2vw4MG66qqrnDGfz6eUlBSFhYVJksLCwtS5c%2Bez7rtly5ZKSEiQz%2BfToUOHdPDgQV1//fXOfEpKivbv36%2BioiL5fD61bNlSrVu3Dpj/4osvzntt1B0CVgjy%2B/1q2rSpIiMjnbEWLVqooqJCpaWlQaws%2BEaOHKmpU6cqOjo6YNzv9ysuLi5grHnz5iosLDzn/JEjR1RRUREw7/F41KRJExUWFp7z9TiftUNBTEyMevbs6dyvqanR6tWr1a1bN/rukrS0NI0cOVLJycnq168ffa9Dn376qf72t79p3LhxAePn2ldRUdFZ5/1%2BvyQFzJ96w3Jq/kzPPXTo0HmvjbpDwApBZWVlAf9zk%2BTcr6ysDEZJF7yz9exUv35uvry83Ll/pvlzvR7ns3YoyszM1I4dO/Rv//Zv9N0lCxYs0KJFi7Rz505lZGTQ9zpSUVGhxx9/XNOnT1eDBg0C5s61r/Ly8l/Uc5s9/bm1UXcIWCEoKirqtB%2BMU/d/%2BkOPH52tZ6f6dbb56OhoRUVFOffPNv9zr8f5rB1qMjMztWLFCmVmZqpdu3b03SWJiYnq1auXpkyZoldffVWXXHIJfa8DWVlZuu666wLO2J5yPvs%2B0xvkU//%2BuZ6f69j1oeehjIAVguLj41VSUqKqqipnzO/3q0GDBoqJiQliZReu%2BPh4FRcXB4wVFxc7p83PNh8bG6smTZooKioqYL6qqkqlpaWKjY095%2BtxPmuHklmzZumll15SZmam%2BvXrJ4m%2B16Xi4mJt2LAhYOyqq67SyZMnFRsbS9/rwH/%2B539qw4YNSk5OVnJystavX6/169crOTn5vPYdHx8vSc5Hhf/471PzP9ez%2BtzzUEbACkEdOnSQx%2BMJ%2BFJoXl6eEhMTFR7OS3omXq9XX375pXMqXvqxZ16v15nPy8tz5srKyrRjxw55vV6Fh4crMTExYD4/P18ej0ft27c/5%2BtxPmuHiqysLL366qt65plnNGDAAGecvtedH374QePHj3e%2BhyNJ27dvV7NmzZSSkkLf68CqVau0fv16rVu3TuvWrVNaWprS0tK0bt06eb1effHFFzLGSPrxulZbtmw5674PHjyogwcPyuv1Kj4%2BXgkJCQHzeXl5SkhIUFxcnJKSkrR///6A70zl5eUpKSnJOfY/uzbqUFB/hxH/tMcee8wMGDDA%2BHw%2B8/7775vOnTub9957L9hlXVD%2B8TINVVVVpn///ubBBx80u3fvNosXLzZJSUnOtXkKCgpMYmKiWbx4sXNtnkGDBjm/9pybm2s6d%2B5s3n//fePz%2BcyAAQPMrFmznLV%2B7vU437UvdHv37jUdOnQwzz77rCkqKgq40fe6U1VVZYYOHWruueces2fPHvPhhx%2BaX//612b58uX03SWTJ092Ln9w9OhR061bNzNr1iyzZ88eM2vWLNO9e3fnchVbtmwxHTt2NK%2B99ppzLaqxY8c6x1q8eLHp0aOH2bRpk9m0aZPp0aOHefHFF535e%2B65x4waNcrs3LnTvPbaayYxMdG5Dtb5ro26QcAKUSdOnDAPP/ywSUpKMj169DAvvfRSsEu64PxjwDLGmG%2B//db89re/Ndddd50ZMGCA%2BZ//%2BZ%2BAx3/44Yemb9%2B%2BplOnTmbMmDGnXSNm8eLF5oYbbjApKSlmypQppry83Jk71%2BtxvmtfyBYvXmzatWt3xpsx9L0uFRYWmvT0dNO5c2fTvXt38/zzzztBhb7XvX8MWMb8eEHPIUOGmMTERHP77bebL7/8MuDxa9euNTfddJNJSkoy6enp5vDhw85cVVWVefLJJ01qaqrp2rWryczMDAidxcXFZuzYsSYxMdGkpaWZ9evXBxz7fNZG3Qgz5v/PKQIAAMAKvrADAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJb9HyqL64e3vf7sAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3450505162714088577”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14400.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6000.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8760.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10740.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2920.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17500.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (772)</td> <td class=”number”>808</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>966</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3450505162714088577”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>134.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1582888.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2599822.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3826760.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4508936.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4649990.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFTPTH07”>GHVEG_SQFTPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>421</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>28.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>913</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>92.898</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>19024</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>58.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7209621045099109419”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAABAhJREFUeJzt279LHGsUh/HvigYimCLg7iIW2vkHiKhNBotkcVdJEDUp1BRpRLESgsh2xipRQUlrmhQpbCySgLBCIKIhjZWYxhRB3FkVUWOx/ji3uLDc3JgD0ZtVLs%2BnfGfnndM8zDDMRszMBOBcJVc9AHCdlV71AP9WP/r%2Bt8/5/CzxByYBuIMALgIBHAQCOAgEcBAI4CAQwEEggINAAAeBAA4CARwEAjgIBHAQCOAgEMBBIICDQAAHgQAOAgEcBAI4CARwEAjgIBDAQSCAg0AAB4EADgIBHAQCOAgEcBAI4CAQwEEggINAAAeBAA4CARwEAjgIBHAQCOAgEMBBIICDQAAHgQAOAgEcBAI4CARwEAjgKL3qAf4L9aPvf/ucz88Sf2AS/N9EzMyuegjAE4ah3rx5o%2B7ubkWj0aJem0csXHu5XE4zMzPK5XJFvzaBAA4CARwEAjgIBNdeZWWlBgcHVVlZWfRr8xYLcHAHARwEAjgIBHAQCOAgEMBBIICDQAAHgaCoZmdnlUqllEqlNDIyonw%2Bry9fvqirq0uJREJDQ0M6OjqSJB0eHqq/v1%2Btra3q6OjQ169fC/tMTEwokUjo3r17ymQyhfV3794pmUzq7t27mpmZufzABhTJ6uqqpVIp%2B/79u52dndnw8LDNzs5ae3u7raysmJnZ1NSUPX/%2B3MzMxsbGbHp62szMlpaWrLu728zMFhYW7PHjx3Z8fGzZbNZaWlpsb2/PwjC0IAhsZ2fH8vm89fX12YcPHy41M3cQFM2tW7eUTqdVXl6uSCSiuro6ra%2Bv6%2BDgQA0NDZKkzs5OvX37VpK0uLioBw8eSJKampqUy%2BW0ubmpTCaj9vZ2lZaWKhqNqqGhQYuLi/r48aMaGxt1%2B/ZtlZWV6f79%2B4W9LopAUDQ1NTWFEHZ2dvT69WvV1tYqFosVfhONRpXNZiVJ2Wz2p2NbW1u/XA/D8Ic/VP1zr4siEBTdt2/f1Nvbq87OTtXX1/90PBKJSJLsnM8ES0pKfrl%2Bdnb2y70uikBQVGtra3r06JEePnyo/v5%2BxePxH/4pmMvlFI/HJUmxWOzcY7FYTGEY/rAei8V%2B2isMw8JeF0UgKJrd3V09efJE6XRaPT09kqSqqirdvHlTnz59kiTNzc3pzp07kqQgCDQ3NydJWllZUXl5ueLxuIIg0Pz8vE5OTrS9va3l5WU1NzerqalJy8vL2t7e1vHxsebn5xUEwaVm5nN3FM3k5KRevXqlmpqawloQBEomk0qn0zo4OFB1dbVevHihiooK7e/va3R0VBsbG7px44bGx8dVV1cnM9PExIQymYxOT081MDCgtrY2SX%2B/5n358qXy%2BbxaWlr09OnTS81MIICDRyzAQSCAg0AAx1/v1Au%2BH1v9oAAAAABJRU5ErkJggg%3D%3D”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7209621045099109419,#minihistogram7209621045099109419”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7209621045099109419”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7209621045099109419”

aria-controls=”quantiles7209621045099109419” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7209621045099109419” aria-controls=”histogram7209621045099109419”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7209621045099109419” aria-controls=”common7209621045099109419”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7209621045099109419” aria-controls=”extreme7209621045099109419”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7209621045099109419”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>431</td>

</tr> <tr>

<th>Maximum</th> <td>19024</td>

</tr> <tr>

<th>Range</th> <td>19024</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>582.4</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.2692</td>

</tr> <tr>

<th>Kurtosis</th> <td>532.19</td>

</tr> <tr>

<th>Mean</th> <td>92.898</td>

</tr> <tr>

<th>MAD</th> <td>158.55</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.318</td>

</tr> <tr>

<th>Sum</th> <td>213390</td>

</tr> <tr>

<th>Variance</th> <td>339190</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7209621045099109419”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XtU1OWi//EPMoGoG/GCbC%2BtbVqGKQJCaqmZHi3zkrfMk3mvo6dQf%2B1MTa12ecmW2B1NSSvNLmRWlqfy6D52MfNyMFDzBl6KVGQoyVQuAs/vj46zndBAfRiY4f1ai7Wc55nvzPNhvjN%2BmO/wxc8YYwQAAABrqlX0AgAAAHwNBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWOao6AVUFU7nb9Zvs1o1P9WtW1O//HJaxcXG%2Bu1XBlUho1Q1cpLRd1SFnGT0HaGhf6mQ%2B%2BUdLC9WrZqf/Pz8VK2aX0UvpdxUhYxS1chJRt9RFXKSEVeKggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAljkqegG4MrEzPq/oJZTZZw91rOglAADgEbyDBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFjmtQXr%2BPHjmjhxotq1a6fOnTtr7ty5ys/PlyRlZGRo1KhRioqKUq9evbRx40a3bTdt2qQ%2BffooMjJSI0aMUEZGhtv8G2%2B8oc6dOys6OlrTp09Xbm6ux3IBAADv55UFyxijiRMnKjc3V2%2B99Zaef/55bdiwQS%2B88IKMMYqLi1P9%2BvW1atUq9evXT%2BPHj9fRo0clSUePHlVcXJwGDhyo999/X3Xr1tWDDz4oY4wkae3atUpISNDMmTO1bNkypaamKj4%2BviLjAgAAL%2BOVBevgwYNKSUnR3Llzdd111yk2NlYTJ07UmjVrtHnzZmVkZGjmzJlq3ry5xo0bp6ioKK1atUqStHLlSrVu3VpjxozRddddp7lz5%2BrIkSPaunWrJGn58uUaOXKkunbtqjZt2uipp57SqlWreBcLAACUmVcWrNDQUC1ZskT169d3Gz916pRSU1N1ww03qEaNGq7xmJgYpaSkSJJSU1MVGxvrmgsKClKrVq2UkpKioqIi7dy5020%2BKipKZ8%2Be1d69e8s5FQAA8BWOil7A5QgODlbnzp1dl4uLi7VixQp16NBBTqdTDRo0cLt%2BvXr1lJmZKUl/On/y5Enl5%2Be7zTscDoWEhLi2L4usrCw5nU63MYejRon7vVL%2B/t7Vjx2OS1/vuYzelvVSVYWcZPQdVSEnGXGlvLJg/VF8fLx2796t999/X2%2B88YYCAgLc5gMCAlRQUCBJys3Nveh8Xl6e6/LFti%2BLpKQkJSQkuI3FxcVp4sSJZb4NX1SnTs3L3jY4OMjiSiqvqpCTjL6jKuQkIy6X1xes%2BPh4LVu2TM8//7xatGihwMBA5eTkuF2noKBA1atXlyQFBgaWKEsFBQUKDg5WYGCg6/If54OCyr4DDhkyRN26dXMbczhq6MSJ02W%2BjbLwtp86Lie/v381BQcH6eTJXBUVFZfDqiqHqpCTjL6jKuQko%2B%2B4kh/ur4RXF6xZs2bpnXfeUXx8vG6//XZJUlhYmNLT092ul52d7To8FxYWpuzs7BLzLVu2VEhIiAIDA5Wdna3mzZtLkgoLC5WTk6PQ0NAyr6tBgwYlDgc6nb%2BpsNB3d%2BCyuJL8RUXFVeL7VxVyktF3VIWcZMTl8q63QM6TkJCgd999V88995x69%2B7tGo%2BMjNT333/vOtwnScnJyYqMjHTNJycnu%2BZyc3O1e/duRUZGqlq1aoqIiHCbT0lJkcPhUHh4uAdSAQAAX%2BCVBevAgQNauHCh/uM//kMxMTFyOp2ur3bt2qlhw4aaNm2a0tLSlJiYqB07duiuu%2B6SJA0aNEjbt29XYmKi0tLSNG3aNDVp0kTt27eXJA0dOlRLly7V%2BvXrtWPHDj355JO6%2B%2B67L%2BkQIQAAqNq88hDhP//5TxUVFemVV17RK6%2B84ja3b98%2BLVy4UDNmzNDAgQP1t7/9TQsWLFCjRo0kSU2aNNHLL7%2Bsp59%2BWgsWLFB0dLQWLFggPz8/SVLv3r115MgRPfHEEyooKNBtt92myZMnezwjAADwXn7m3CnMUa6czt%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%2BmjLli2usdmzZ%2Bv66693%2B1qxYoVrfs2aNerevbsiIyMVFxenX375xTVnjNH8%2BfPVoUMHtWvXTvPmzVNxcbFHMwEAAO/mqOgFXIn8/HxNmjRJaWlpbuMHDhzQpEmTNGDAANdYrVq1JEk7duzQjBkz9NRTTyk8PFxz5szRtGnTtHjxYknS66%2B/rjVr1ighIUGFhYWaPHmy6tWrp/vuu89zwQAAgFfz2new0tPTdffdd%2BvHH38sMXfgwAHdcMMNCg0NdX0FBQVJklasWKE77rhD/fv3V3h4uObNm6cvv/xSGRkZkqTly5dr4sSJio2NVYcOHfTII4/orbfe8mg2AADg3by2YG3dulXt27dXUlKS2/ipU6d0/PhxNW3a9ILbpaamKjY21nW5YcOGatSokVJTU3X8%2BHEdO3ZMN954o2s%2BJiZGR44cUVZWVrnkAAAAvsdrDxEOHTr0guMHDhyQn5%2BfFi1apK%2B%2B%2BkohISEaPXq063BhVlaWGjRo4LZNvXr1lJmZKafTKUlu8/Xr15ckZWZmltjuYrKysly3dY7DUaPM25eVv7939WOH49LXey6jt2W9VFUhJxl9R1XISUZcKa8tWBdz8OBB%2Bfn5qVmzZho2bJi2bdumxx9/XLVq1VKPHj2Ul5engIAAt20CAgJUUFCgvLw81%2BXz56TfP0xfVklJSUpISHAbi4uL08SJEy83lk%2BoU6fmZW8bHBxkcSWVV1XISUbfURVykhGXy%2BcKVv/%2B/dW1a1eFhIRIksLDw3X48GG988476tGjhwIDA0uUpYKCAgUFBbmVqcDAQNe/Jbk%2Bw1UWQ4YMUbdu3dzGHI4aOnHi9GXnuhBv%2B6njcvL7%2B1dTcHCQTp7MVVGR7/42Z1XISUbfURVyktF3XMkP91fC5wqWn5%2Bfq1yd06xZM23evFmSFBYWpuzsbLf57OxshYaGKiwsTJLkdDrVpEkT178lKTQ0tMxraNCgQYnDgU7nbyos9N0duCyuJH9RUXGV%2BP5VhZxk9B1VIScZcbm86y2QMnjxxRc1atQot7G9e/eqWbNmkqTIyEglJye75o4dO6Zjx44pMjJSYWFhatSokdt8cnKyGjVqZP3zUwAAwHf53DtYXbt2VWJiopYuXaoePXpo48aN%2Buijj7R8%2BXJJ0j333KPhw4crKipKERERmjNnjm699VZdffXVrvn58%2Bfrr3/9qyTp2Wef1ZgxYyosDwAA8D4%2BV7DatGmjF198US%2B99JJefPFFNW7cWM8%2B%2B6yio6MlSdHR0Zo5c6Zeeukl/frrr%2BrYsaNmzZrl2v6%2B%2B%2B7Tzz//rPHjx8vf31933XVXiXfEAAAA/oyfMcZU9CKqAqfzN%2Bu36XBUU4/5X1u/3fLy2UMdL3kbh6Oa6tSpqRMnTvv0ZwSqQk4y%2Bo6qkJOMviM09C8Vcr8%2B9xksAACAiubxgjV48GC9%2B%2B67%2Bu03%2B%2B/oAAAAVAYeL1gdOnTQokWL1KlTJz388MPauHGjOEoJAAB8iccL1qRJk7RhwwYtXLhQ/v7%2BmjBhgm699VY9//zzOnTokKeXAwAAYF2F/Bahn5%2BfOnbsqI4dOyo3N1dvvvmmFi5cqMTERLVt21YjR47UbbfdVhFLAwAAuGIVdpqGrKwsffzxx/r444%2B1f/9%2BtW3bVgMGDFBmZqYee%2Bwxbdu2TTNmzKio5QEAAFw2jxes1atXa/Xq1dqyZYvq1q2r/v3766WXXlLTpk1d12nYsKHmzJlDwQIAAF7J4wVrxowZ6tq1qxYsWKBbbrlF1aqV/BhYs2bNNGzYME8vDQAAwAqPF6yvvvpKderUUU5Ojqtc7dixQ61atZK/v78kqW3btmrbtq2nlwYAAGCFx3%2BL8NSpU%2BrZs6deffVV19jYsWPVr18/HTt2zNPLAQAAsM7jBevpp5/W3/72N40ePdo19umnn6phw4aaO3eup5cDAABgnccL1v/%2B7//q0UcfVWhoqGusbt26mjJlijZv3uzp5QAAAFjn8YLlcDh08uTJEuO5ubmc0R0AAPgEjxesW265RbNnz9aPP/7oGsvIyNDcuXPVuXNnTy8HAADAOo//FuHUqVM1evRo3X777QoODpYknTx5Uq1atdK0adM8vRwAAADrPF6w6tWrpw8//FCbNm1SWlqaHA6Hrr32Wt10003y8/Pz9HIAAACsq5A/lePv76/OnTtzSBAAAPgkjxcsp9OpF154Qdu3b9fZs2dLfLD9n//8p6eXBAAAYJXHC9bjjz%2BuXbt2qXfv3vrLX/7i6bsHAAAodx4vWJs3b9aSJUsUGxvr6bsGAADwCI%2BfpqFGjRqqV6%2Bep%2B8WAADAYzxesPr166clS5aoqKjI03cNAADgER4/RJiTk6M1a9boiy%2B%2B0NVXX62AgAC3%2BeXLl3t6SQAAAFZVyGka%2BvTpUxF3CwAA4BEeL1hz58719F0CAAB4lMc/gyVJWVlZSkhI0KRJk/Tzzz/r888/18GDBytiKQAAANZ5vGD98MMP6tu3rz788EOtXbtWZ86c0aeffqpBgwYpNTXV08sBAACwzuMF65lnnlH37t21fv16XXXVVZKk5557Tt26ddP8%2BfM9vRwAAADrPF6wtm/frtGjR7v9YWeHw6EHH3xQu3fv9vRyAAAArPN4wSouLlZxcXGJ8dOnT8vf39/TywEAALDO4wWrU6dOWrx4sVvJysnJUXx8vDp06ODp5QAAAFjn8YL16KOPateuXerUqZPy8/P1wAMPqGvXrvrpp580depUTy8HAADAOo%2BfByssLEwfffSR1qxZoz179qi4uFj33HOP%2BvXrp1q1anl6OQAAANZVyJncg4KCNHjw4Iq4awAAgHLn8YI1YsSIP53nbxECAABv5/GC1bhxY7fLhYWF%2BuGHH7R//36NHDnS08sBAACwrtL8LcIFCxYoMzPTw6sBAACwr0L%2BFuGF9OvXT5999llFLwMAAOCKVZqC9d1333GiUQAA4BMqxYfcT506pX379mno0KGeXg4AAIB1Hi9YjRo1cvs7hJJ01VVXadiwYbrzzjs9vRwAAADrPF6wnnnmGU/fJQAAgEd5vGBt27atzNe98cYby3ElAAAA5cPjBWv48OGuQ4TGGNf4H8f8/Py0Z88eTy8PAADginm8YC1atEizZ8/W5MmT1a5dOwUEBGjnzp2aOXOmBgwYoF69enl6SQAAAFZ5/DQNc%2BfO1RNPPKHbb79dderUUc2aNdWhQwfNnDlT77zzjho3buz6AgAA8EYeL1hZWVkXLE%2B1atXSiRMnPL0cAAAA6zxesKKiovTcc8/p1KlTrrGcnBzFx8frpptu8vRyAAAArPP4Z7Aee%2BwxjRgxQrfccouaNm0qY4wOHz6s0NBQLV%2B%2B3NPLAQAAsM7jBat58%2Bb69NNPtWbNGh04cECSdO%2B996p3794KCgry9HIAAACs83jBkqTatWtr8ODB%2Bumnn3T11VdL%2Bv1s7gAAAL7A45/BMsZo/vz5uvHGG9WnTx9lZmZq6tSpmjFjhs6ePevp5QAAAFjn8YL15ptvavXq1frHP/6hgIAASVL37t21fv16JSQkeHo5AAAA1nm8YCUlJemJJ57QwIEDXWdv79Wrl2bPnq1PPvnE08sBAACwzuMF66efflLLli1LjIeHh8vpdHp6OQAAANZ5vGA1btxYO3fuLDH%2B1VdfuT7wfikKCgrUp08fbdmyxTWWkZGhUaNGKSoqSr169dLGjRvdttm0aZP69OmjyMhIjRgxQhkZGW7zb7zxhjp37qzo6GhNnz5dubm5l7wuAABQdXm8YN1333166qmntHz5chlj9O2332r%2B/PmaN2%2Behg8ffkm3lZ%2Bfr4cfflhpaWmuMWOM4uLiVL9%2Bfa1atUr9%2BvXT%2BPHjdfToUUnS0aNHFRcXp4EDB%2Br9999X3bp19eCDD7r%2ByPTatWuVkJCgmTNnatmyZUpNTVV8fLy9bwAAAPB5Hj9Nw6BBg1RYWKhXXnlFeXl5euKJJ1S3bl099NBDuueee8p8O%2Bnp6Zo0aZKrGJ2zefNmZWRk6N1331WNGjXUvHlzffvtt1q1apUmTJiglStXqnXr1hozZoyk3/82YseOHbV161a1b99ey5cv18iRI9W1a1dJ0lNPPaX77rtPkydP5jxdAACgTDz%2BDtaaNWvUs2dPffHFF9q0aZO%2B%2BeYbbdq0SaNHj76k2zlXiJKSktzGU1NTdcMNN6hGjRqusZiYGKWkpLjmY2NjXXNBQUFq1aqVUlJSVFRUpJ07d7rNR0VF6ezZs9q7d%2B/lxAUAAFWQx9/Bmjlzpt5%2B%2B23Vrl1bdevWvezbGTp06AXHnU6nGjRo4DZWr149ZWZmljp/8uRJ5efnu807HA6FhIS4ti%2BLrKysEh/YdzhqlLjfK%2BXv7/F%2BfEUcjktf77mM3pb1UlWFnGT0HVUhJxlxpTxesJo2bar9%2B/fr2muvLZfbz83NdZ1f65yAgAAVFBSUOp%2BXl%2Be6fLHtyyIpKanEOb3i4uI0ceLEMt%2BGL6pTp%2BZlbxscXDUOz1aFnGT0HVUhJxlxuTxesMLDw/XII49oyZIlatq0qQIDA93m586de0W3HxgYqJycHLexgoICVa9e3TX/x7JUUFCg4OBg11ouNH8pn78aMmSIunXr5jbmcNTQiROny3wbZeFtP3VcTn5//2oKDg7SyZO5KioqLodVVQ5VIScZfUdVyElG33ElP9xfCY8XrEOHDikmJkaSyuW8V2FhYUpPT3cby87Odh2eCwsLU3Z2don5li1bKiQkRIGBgcrOzlbz5s0lSYWFhcrJyVFoaGiZ19CgQYMShwOdzt9UWOi7O3BZXEn%2BoqLiKvH9qwo5yeg7qkJOMuJyeaRgzZs3T%2BPHj1eNGjX05ptvlut9RUZGKjExUXl5ea53rZKTk12lLjIyUsnJya7r5%2Bbmavfu3Ro/fryqVaumiIgIJScnq3379pKklJQUORwOhYeHl%2Bu6AQCA7/DIMabXX3%2B9xMk6x44dq6ysLOv31a5dOzVs2FDTpk1TWlqaEhMTtWPHDt11112Sfj9NxPbt25WYmKi0tDRNmzZNTZo0cRWqoUOHaunSpVq/fr127NihJ598UnfffTenaAAAAGXmkYL1x3NVSdK2bduUn59v/b78/f21cOFCOZ1ODRw4UB9//LEWLFigRo0aSZKaNGmil19%2BWatWrdJdd92lnJwcLViwwPV3EXv37q1x48bpiSee0JgxY9SmTRtNnjzZ%2BjoBAIDv8vhnsMrDvn373C7/7W9/04oVKy56/S5duqhLly4XnR87dqzGjh1rbX0AAKBq8a5fQwMAAPACHitY5w7BAQAA%2BDqPHSKcPXu22zmvzp49q/j4eNWs6X5%2Biis9DxYAAEBF80jBuvHGG0uc8yo6OlonTpzQiRMnPLEEAAAAj/FIwSrvc18BAABUJnzIHQAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGU%2BW7DWrVun66%2B/3u1r4sSJkqTdu3dr8ODBioyM1KBBg7Rr1y63bdesWaPu3bsrMjJScXFx%2BuWXXyoiAgAA8FI%2BW7DS09PVtWtXbdy40fU1e/ZsnTlzRmPHjlVsbKw%2B%2BOADRUdHa9y4cTpz5owkaceOHZoxY4bGjx%2BvpKQknTx5UtOmTavgNAAAwJv4bME6cOCAWrRoodDQUNdXcHCwPv30UwUGBmrKlClq3ry5ZsyYoZo1a%2Brzzz%2BXJK1YsUJ33HGH%2Bvfvr/DwcM2bN09ffvmlMjIyKjgRAADwFj5dsJo2bVpiPDU1VTExMfLz85Mk%2Bfn5qW3btkpJSXHNx8bGuq7fsGFDNWrUSKmpqR5ZNwAA8H6Oil5AeTDG6NChQ9q4caMWL16soqIi9ezZUxMnTpTT6dS1117rdv169eopLS1NkpSVlaUGDRqUmM/MzCzz/WdlZcnpdLqNORw1StzulfL3965%2B7HBc%2BnrPZfS2rJeqKuQko%2B%2BoCjnJiCvlkwXr6NGjys3NVUBAgF544QX99NNPmj17tvLy8lzj5wsICFBBQYEkKS8v70/nyyIpKUkJCQluY3Fxca4P2VdVderUvOxtg4ODLK6k8qoKOcnoO6pCTjLicvlkwWrcuLG2bNmi2rVry8/PTy1btlRxcbEmT56sdu3alShLBQUFql69uiQpMDDwgvNBQWXfAYcMGaJu3bq5jTkcNXTixOnLTHRh3vZTx%2BXk9/evpuDgIJ08mauiouJyWFXlUBVyktF3VIWcZPQdV/LD/ZXwyYIlSSEhIW6Xmzdvrvz8fIWGhio7O9ttLjs723X4Liws7ILzoaGhZb7vBg0alDgc6HT%2BpsJC392By%2BJK8hcVFVeJ719VyElG31EVcpIRl8u73gIpo6%2B//lrt27dXbm6ua2zPnj0KCQlRTEyMvvvuOxljJP3%2Bea3t27crMjJSkhQZGank5GTXdseOHdOxY8dc8wAAAKXxyYIVHR2twMBAPfbYYzp48KC%2B/PJLzZs3T/fff7969uypkydPas6cOUpPT9ecOXOUm5urO%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%2BoMzZ85o5cqVevXVV9WqVSu1atVKaWlpeuuttyhYAACgTChYf7B3714VFhYqOjraNRYTE6NFixapuLhY1apxVPVy3fHCNxW9hEvy2UMdK3oJAAAvRcH6A6fTqTp16iggIMA1Vr9%2BfeXn5ysnJ0d169Yt9TaysrLkdDrdxhyOGmrQoIHVtfr7U/bKk7cVwnWPdK7oJfypc/urL%2B%2B3VSGjVDVykhFXioL1B7m5uW7lSpLrckFBQZluIykpSQkJCW5j48eP14QJE%2Bws8v9kZWVp5F/TNGTIEOvlrbJHziFbAAAQd0lEQVTIyspSUlKST2eUqkbOrKwsLVu2hIw%2BoCrkJCOuFLX1DwIDA0sUqXOXq1evXqbbGDJkiD744AO3ryFDhlhfq9PpVEJCQol3y3xJVcgoVY2cZPQdVSEnGXGleAfrD8LCwnTixAkVFhbK4fj92%2BN0OlW9enUFBweX6TYaNGjATwMAAFRhvIP1By1btpTD4VBKSoprLDk5WREREXzAHQAAlAmN4Q%2BCgoLUv39/Pfnkk9qxY4fWr1%2Bv1157TSNGjKjopQEAAC/h/%2BSTTz5Z0YuobDp06KDdu3fr2Wef1bfffqv//M//1KBBgyp6WRdUs2ZNtWvXTjVr1qzopZSbqpBRqho5yeg7qkJOMuJK%2BBljTEUvAgAAwJdwiBAAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQXLS%2BXn52v69OmKjY1Vp06d9Nprr1X0kkp1/PhxTZw4Ue3atVPnzp01d%2B5c5efnS5Jmz56t66%2B/3u1rxYoVrm3XrFmj7t27KzIyUnFxcfrll19cc8YYzZ8/Xx06dFC7du00b948FRcXezzfOevWrSuRZeLEiZKk3bt3a/DgwYqMjNSgQYO0a9cut229IecHH3xQIt/111%2Bv8PBwSdIDDzxQYm7Dhg2u7d944w117txZ0dHRmj59unJzc11zlWG/LigoUJ8%2BfbRlyxbXWEZGhkaNGqWoqCj16tVLGzdudNtm06ZN6tOnjyIjIzVixAhlZGS4zVfGzBfKmZKSon//939XdHS0br/9dq1cudJtmzvvvLPEY7t//35Jpe%2BfJ06c0IQJExQdHa1u3bpp9erVFZKxPF9rKiLjhXI%2B%2BuijF3yOnv8n32JjY0vMnz59WlLp%2B2Rpzwf8HwOvNHPmTNO3b1%2Bza9cu89///d8mOjrafPbZZxW9rIsqLi42d999t7n//vvN/v37zbZt20yPHj3MM888Y4wxZtSoUWbx4sUmKyvL9XXmzBljjDGpqammTZs25sMPPzR79uwxw4YNM2PHjnXd9tKlS02XLl3Mtm3bzLfffms6depklixZUiE5jTFm4cKFZty4cW5Zfv31V3P69GnTsWNH88wzz5j09HQza9Ysc/PNN5vTp08bY7wnZ25urlu2o0ePmh49epg5c%2BYYY4zp0aOHWb16tdt18vPzjTHGfP755yYmJsb8z//8j0lNTTW9evUyTz31lOu2K3q/zsvLM3FxcaZFixZm8%2BbNxpjf992%2BffuaSZMmmfT0dLNo0SITGRlpjhw5Yowx5siRIyYqKsosXbrU7N%2B/3/y///f/TJ8%2BfUxxcXGlzXyhnFlZWSY2NtY8%2B%2Byz5tChQ2bNmjUmIiLCbNiwwRhjTGFhoYmIiDBbt251e2zPnj1rjCl9/xw3bpwZOXKk2bdvn3nvvfdM69atTWpqqkczGlO%2BrzWeznixnCdPnnTL991335nWrVubdevWGWOMyczMNC1atDA//vij2/XO7bN/tk%2BW9nzAv1CwvNDp06dNRESE24vGggULzLBhwypwVX8uPT3dtGjRwjidTtfYJ598Yjp16mSMMaZz587m66%2B/vuC2kydPNlOnTnVdPnr0qLn%2B%2BuvNjz/%2BaIwxpkuXLmbVqlWu%2BY8%2B%2Bsh07dq1PGKUyaRJk8yzzz5bYnzlypWmW7durhex4uJi06NHD9favS3nOYsWLTLdu3c3%2Bfn5Jj8/37Rs2dIcPHjwgtcdOnSoeemll1yXt23bZtq0aWPOnDlT4ft1WlqaufPOO03fvn3d/rPatGmTiYqKchVhY4wZOXKkK8cLL7zgtsYzZ86Y6Oho1/aVLfPFcr799tumZ8%2Bebtd9/PHHzcMPP2yMMebw4cMmPDzc5OXlXfB2/2z//OGHH0yLFi1MRkaGa3769Olu%2B7tNF8toTPm91ng6ozF/nvN8Y8aMMY888ojr8jfffGM6dux4weuWtk%2BW9nzAv3CI0Avt3btXhYWFio6Odo3FxMQoNTW1Qg%2BN/ZnQ0FAtWbJE9evXdxs/deqUTp06pePHj6tp06YX3DY1NVWxsbGuyw0bNlSjRo2Umpqq48eP69ixY7rxxhtd8zExMTpy5IiysrLKJUtpDhw4cMEsqampiomJkZ%2BfnyTJz89Pbdu2VUpKimvem3JKUk5Ojl599VVNmjRJAQEBOnjwoPz8/HT11VeXuG5RUZF27tzpljEqKkpnz57V3r17K3y/3rp1q9q3b6%2BkpCS38dTUVN1www2qUaOG27ou9rgFBQWpVatWSklJqZSZL5bz3GH7Pzp16pQkKT09XQ0bNlRgYGCJ65S2f6ampqphw4Zq0qSJ2/x3331nK5abi2Usz9caT2eULp7zfN9%2B%2B622bdumhx9%2B2DWWnp6ua6655oLXL22fLO35gH9xVPQCcOmcTqfq1KmjgIAA11j9%2BvWVn5%2BvnJwc1a1btwJXd2HBwcHq3Lmz63JxcbFWrFihDh066MCBA/Lz89OiRYv01VdfKSQkRKNHj9aAAQMkSVlZWWrQoIHb7dWrV0%2BZmZlyOp2S5DZ/rsRlZmaW2K68GWN06NAhbdy4UYsXL1ZRUZF69uypiRMnyul06tprr3W7fr169ZSWlibJu3Ke884776hBgwbq2bOnJOngwYOqVauWpkyZoq1bt%2Bqvf/2rJkyYoC5duujkyZPKz893W6vD4VBISIgyMzNVrVq1Ct2vhw4desFxp9N50celtPnKmPliOZs0aeJWDn7%2B%2BWf913/9lyZMmCDp9x8crrrqKo0bN067du3SNddcoylTpqhNmzal7p8X%2Bx4dP37carZzLpaxPF9rPJ1RunjO8yUmJmrAgAFq2LCha%2BzAgQPKzc3V8OHDdejQIbVs2VLTp0/XNddcU%2Br/L6U9H/AvvIPlhXJzc912fkmuywUFBRWxpEsWHx%2Bv3bt36%2B9//7vrXY9mzZopMTFRgwcP1uOPP65169ZJkvLy8i6Yt6CgQHl5ea7L589JFfO9OHr0qOvxeeGFFzR16lR98sknmjdv3kUft3Pr9Kac0u9lcuXKlRo2bJhr7ODBg8rLy1OnTp20ZMkSdenSRQ888IB27tx5wQznLhcUFFTa/bq0x%2B3P5r01c15eniZMmKD69etryJAhkqRDhw7p119/1eDBg5WYmKjmzZtr5MiROnbsWKn7Z2nfQ08pz9eaypLxfBkZGdq8ebOGDx/uNn7w4EH9%2BuuveuCBB7Rw4UJVr15do0aN0qlTp0rdJytjzsqKd7C8UGBgYImd%2Bdzl6tWrV8SSLkl8fLyWLVum559/Xi1atNB1112nrl27KiQkRJIUHh6uw4cP65133lGPHj0umjcoKMjtiX/u0MW56wYFBXkw1e8aN26sLVu2qHbt2vLz81PLli1VXFysyZMnq127dhfMce4x86ackrRz504dP35cvXv3do09%2BOCDGj58uGrXri3p98fy%2B%2B%2B/13vvvae///3vkkoWh3MZi4qKKuV%2BHRgYqJycHLexsjxuwcHBJR6r8%2Bcra%2BbTp0/rwQcf1OHDh/X222%2B79q9Zs2YpLy9PtWrVkiQ9%2BeST2r59u1avXq2bb77ZtfYL7Z8X%2Bx55OmP//v3L7bWmsmQ839q1a9WyZcsS75wvXbpUZ8%2BeVc2aNSVJ8%2BfPV5cuXbRhw4ZS/38p7fmAf%2BEdLC8UFhamEydOqLCw0DXmdDpVvXp1BQcHV%2BDKSjdr1iy9/vrrio%2BP1%2B233y7p988inXvBO6dZs2aut9bDwsKUnZ3tNp%2Bdna3Q0FCFhYVJkuvt%2B/P/HRoaWm45/kxISIjrc1aS1Lx5c%2BXn5ys0NPSCOc693e5tOb/%2B%2BmvFxsa6ypQkVatWze2y9K/HMiQkRIGBgW4ZCwsLlZOT48pYGffriz0uZXncvC3zqVOndN999yktLU3Lli1z%2B6ySw%2BFwlStJrneCjh8/Xur%2B%2BWffI08qz9eaypLxfF9//bX%2B7d/%2BrcR4QECAq1xJv/%2BQ0KRJE9dj%2BWf7ZGnPB/wLBcsLtWzZUg6Hw%2B1DhcnJyYqIiFC1apX3IU1ISNC7776r5557zu1djxdffFGjRo1yu%2B7evXvVrFkzSVJkZKSSk5Ndc8eOHdOxY8cUGRmpsLAwNWrUyG0%2BOTlZjRo1qpAn/Ndff6327du7nedoz549CgkJcX3g1Rgj6fdDbNu3b1dkZKQk78opSTt27FDbtm3dxh599FFNmzbNbezcY1mtWjVFRES4ZUhJSZHD4VB4eHil3a8jIyP1/fffuw4RnVvXxR633Nxc7d69W5GRkV6Vubi4WOPHj9dPP/2kN998U9ddd53b/PDhw5WQkOB2/X379qlZs2al7p9RUVE6cuSI2%2Bd0kpOTFRUVVf7BzlOerzWVJeM5xhjt3LmzxHPUGKPu3bvrgw8%2BcI2dOXNGP/zwg5o1a1bqPlna8wHnqchfYcTle/zxx03v3r1NamqqWbdunWnbtq1Zu3ZtRS/rotLT003Lli3N888/73belaysLJOammpuuOEGs2TJEvPDDz%2BYt956y7Ru3dps377dGGPM9u3bTatWrcx7773nOjfNuHHjXLe9ePFi06lTJ7N582azefNm06lTJ/Paa69VSM7ffvvNdO7c2Tz88MPmwIED5osvvjCdOnUyiYmJ5rfffjMdOnQws2bNMmlpaWbWrFmmY8eOrl939qacxhjTtWtXs2bNGrextWvXmlatWpkPP/zQHD582Lz88sumTZs2rl9dX7NmjWnbtq1Zt26dSU1NNb179zazZs1ybV9Z9uvzf%2BW9sLDQ9OrVyzz00ENm//79ZvHixSYqKsp13p%2BMjAwTERFhFi9e7DoPVt%2B%2BfV2n46jMmc/PmZSUZMLDw82GDRvcnp8nTpwwxhjz2muvmZiYGLN%2B/Xpz4MAB849//MPcfPPN5rfffjPGlL5/jhkzxgwbNszs2bPHvPfeeyYiIqLczxH1x4zl/VpTURn/mNOY3/fLFi1amKysrBLXnTVrlrn11lvN5s2bzf79%2B01cXJzp06ePKSwsNMb8%2BT5Z2vMB/0LB8lJnzpwxU6ZMMVFRUaZTp07m9ddfr%2Bgl/anFixebFi1aXPDLGGPWrVtn%2BvbtayIiIkzPnj1L/AezatUq06VLFxMVFWXi4uLML7/84porLCw0Tz/9tImNjTXt27c38fHxrv/cKsL%2B/fvNqFGjTFRUlOnYsaN5%2BeWXXetJTU01/fv3NxEREeauu%2B4y33//vdu23pQzIiLCfPXVVyXG33vvPXPbbbeZ1q1bmwEDBpitW7e6zS9evNjcdNNNJiYmxkybNs3tvEqVZb/%2B439Whw8fNvfee69p3bq16d27t/nmm2/crv/FF1%2BY2267zbRp08aMHDnSdd6kcypr5vNzjhkz5oLPz3PnPyouLjavvPKKufXWW03r1q3Nvffea/bt2%2Be6rdL2z%2BzsbDNu3DgTERFhunXrZj755BOPZzSmfF9rKiqjMSVzpqSkmBYtWrhO8nu%2BvLw8M3fuXNOxY0cTGRlpxo0bZ44ePeqaL22fLO35gN/5GfN/xysAAABgReX9wA4AAICXomABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwLL/D0cA6csWW4RcAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7209621045099109419”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>212.27943992452617</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.90221642764016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>667.9375790805567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>243.36214158684595</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>386.8969499711271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>315.66128115612565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>181.96856906534327</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>58.20302292637978</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.13172426194852</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (410)</td> <td class=”number”>410</td> <td class=”number”>12.8%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>913</td> <td class=”number”>28.4%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7209621045099109419”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1878</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3049651374020426</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3879620905614366</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4121032294348368</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9667778461189703</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4725.162265187678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6867.913352513116</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7289.204097714736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8717.656106576696</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19024.429602402615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GHVEG_SQFTPTH12”>GHVEG_SQFTPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>834</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>966</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>302.14</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>42244</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2810389062741519466”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives2810389062741519466”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2810389062741519466”

aria-controls=”quantiles2810389062741519466” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2810389062741519466” aria-controls=”histogram2810389062741519466”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2810389062741519466” aria-controls=”common2810389062741519466”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2810389062741519466” aria-controls=”extreme2810389062741519466”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2810389062741519466”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>139.82</td>

</tr> <tr>

<th>95-th percentile</th> <td>1246</td>

</tr> <tr>

<th>Maximum</th> <td>42244</td>

</tr> <tr>

<th>Range</th> <td>42244</td>

</tr> <tr>

<th>Interquartile range</th> <td>139.82</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1404.2</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.6476</td>

</tr> <tr>

<th>Kurtosis</th> <td>387.95</td>

</tr> <tr>

<th>Mean</th> <td>302.14</td>

</tr> <tr>

<th>MAD</th> <td>452.82</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.885</td>

</tr> <tr>

<th>Sum</th> <td>678000</td>

</tr> <tr>

<th>Variance</th> <td>1971800</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2810389062741519466”>

<img 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uzYoW7duummm25ScXGxHn74YW3cuFHTpk0LVnkAAAC/musB6%2B2339bbb7%2Bt9evXq0WLFho6dKiee%2B45tWvXznlN69atNXv2bAIWAAAIS64HrGnTpqlfv36aP3%2B%2BrrrqKkVGnnwZ2CWXXKLRo0e7XRoAAIAVrgesTz75RM2bN1d5ebkTrjZv3qzOnTsrKipKktStWzd169bN7dIAAACscP23CA8fPqwbbrhBf/nLX5yxe%2B65R0OGDNG%2BffvcLgcAAMA61wPWE088oYsvvlh33HGHM/buu%2B%2BqdevWysrKcrscAAAA61wPWP/85z/10EMPKT4%2B3hlr0aKFHnzwQa1bt87tcgAAAKxzPWB5PB4dPHjwpPGKigru6A4AABoE1wPWVVddpVmzZumHH35wxoqKipSVlaW%2Bffu6XQ4AAIB1rv8W4eTJk3XHHXfo%2BuuvV1xcnCTp4MGD6ty5s6ZMmeJ2OQAAANa5HrBatmypt956S5999pkKCwvl8Xh06aWX6sorr1RERITb5QAAAFgXlI/KiYqKUt%2B%2BfTklCAAAGiTXA5bf79e8efO0adMmHTt27KQL2//2t7%2B5XRIAAIBVrgesRx55RFu2bNHAgQN1wQUXuL17AACAc871gLVu3TotWrRI6enpbu8aAADAFa7fpqFx48Zq2bKl27sFAABwjesBa8iQIVq0aJFqa2vd3jUAAIArXD9FWF5erry8PH300Ue66KKLFB0dHTC/bNkyt0sCAACwKii3aRg0aFAwdgsAAOAK1wNWVlaW27sEAABwlevXYElSSUmJcnJyNHHiRP3rX//S%2B%2B%2B/r2%2B%2B%2BSYYpQAAAFjnesD6/vvvNXjwYL311lv64IMPdPToUb377rsaPny4fD6f2%2BUAAABY53rAevLJJ3XttddqzZo1%2Bs1vfiNJevrpp5WRkaG5c%2Be6XQ4AAIB1rgesTZs26Y477gj4YGePx6N7771XW7dudbscAAAA61wPWHV1daqrqztp/MiRI4qKinK7HAAAAOtcD1h9%2BvTRggULAkJWeXm5srOz1atXL7fLAQAAsM71gPXQQw9py5Yt6tOnj6qqqvQf//Ef6tevn3bv3q3Jkye7XQ4AAIB1rt8HKzExUatWrVJeXp62bdumuro6jRw5UkOGDFHTpk3dLgcAAMC6oNzJPTY2Vrfcckswdg0AAHDOuR6wxowZ87PzfBYhAAAId64HrDZt2gQ8r6mp0ffff68dO3Zo7NixbpcDAABgXch8FuH8%2BfNVXFzscjUAAAD2BeWzCE9lyJAheu%2B994JdBgAAwFkLmYD1xRdfcKNRAADQIITERe6HDx/W119/rVGjRrldDgAAgHWuB6ykpKSAzyGUpN/85jcaPXq0fv/737tdDgAAgHWuB6wnn3zS7V0CAAC4yvWAtXHjxnq/tnv37uewEgAAgHPD9YB12223OacIjTHO%2BIljERER2rZtm9vlAQAAnDXXA9aLL76oWbNmadKkSerRo4eio6P15ZdfasaMGbrppps0YMAAt0sCAACwyvXbNGRlZWn69Om6/vrr1bx5czVp0kS9evXSjBkz9Oqrr6pNmzbOAwAAIBy5HrBKSkpOGZ6aNm2qsrIyt8sBAACwzvWAlZKSoqefflqHDx92xsrLy5Wdna0rr7zS7XIAAACsc/0arIcfflhjxozRVVddpXbt2skYo%2B%2B%2B%2B07x8fFatmyZ2%2BUAAABY53rAat%2B%2Bvd59913l5eVp165dkqQ//OEPGjhwoGJjY90uBwAAwDrXA5YkXXjhhbrlllu0e/duXXTRRZJ%2BvJs7AABAQ%2BD6NVjGGM2dO1fdu3fXoEGDVFxcrMmTJ2vatGk6duzYGW%2BvurpagwYN0vr1652xoqIi3X777UpJSdGAAQO0du3agPd89tlnGjRokLxer8aMGaOioqKA%2BSVLlqhv375KTU3V1KlTVVFR8esWCwAAzkuuB6zly5fr7bff1qOPPqro6GhJ0rXXXqs1a9YoJyfnjLZVVVWlP/3pTyosLHTGjDHKzMxUq1attHLlSg0ZMkTjx4/X3r17JUl79%2B5VZmamhg0bpjfffFMtWrTQvffe69zg9IMPPlBOTo5mzJihpUuXyufzKTs729LqAQDA%2BcD1gJWbm6vp06dr2LBhzt3bBwwYoFmzZmn16tX13s7OnTt166236ocffggYX7dunYqKijRjxgy1b99e48aNU0pKilauXClJeuONN9SlSxfdeeeduuyyy5SVlaU9e/Zow4YNkqRly5Zp7Nix6tevn7p27arHH39cK1eu5CgWAACoN9cD1u7du9WpU6eTxjt27Ci/31/v7WzYsEE9e/ZUbm5uwLjP59MVV1yhxo0bO2NpaWkqKChw5tPT05252NhYde7cWQUFBaqtrdWXX34ZMJ%2BSkqJjx45p%2B/bt9a4NAACc31y/yL1Nmzb68ssv1bZt24DxTz75xLngvT5GjRp1ynG/36%2BEhISAsZYtW6q4uPgX5w8ePKiqqqqAeY/Ho2bNmjnvr4%2BSkpKTwqLH0/ik/Z6tqCjX8/FZ8XjCq96zcbw34daj8wG9CW30J3TRmzPjesC666679Pjjj8vv98sYo88//1y5ublavny5HnroobPefkVFhXNt13HR0dGqrq7%2BxfnKykrn%2BeneXx%2B5ubknXU%2BWmZmpCRMm1HsbDVHz5k2CXYLr4uK49Uioojehjf6ELnpTP64HrOHDh6umpkYvvPCCKisrNX36dLVo0UL333%2B/Ro4cedbbj4mJUXl5ecBYdXW1GjVq5MyfGJaqq6sVFxenmJgY5/mJ82dyj64RI0YoIyMjYMzjaayysiP13kZ9hNtPEbbXH8qioiIVFxergwcrVFtbF%2Bxy8BP0JrTRn9AVrr0J1g/3rgesvLw83XDDDRoxYoQOHDggY4xatmxpbfuJiYnauXNnwFhpaalzei4xMVGlpaUnzXfq1EnNmjVTTEyMSktL1b59e0lSTU2NysvLFR8fX%2B8aEhISTjod6PcfUk1N%2BHxDngvn4/pra%2BvOy3WHA3oT2uhP6KI39eP6IZAZM2Y41ye1aNHCariSJK/Xq6%2B%2B%2Bso53SdJ%2Bfn58nq9znx%2Bfr4zV1FRoa1bt8rr9SoyMlLJyckB8wUFBfJ4POrYsaPVOgEAQMPlesBq166dduzYcc6236NHD7Vu3VpTpkxRYWGhFi5cqM2bN%2Bvmm2%2BW9OMpyk2bNmnhwoUqLCzUlClT1LZtW/Xs2VPSjxfPL168WGvWrNHmzZv12GOP6dZbb%2BVjfAAAQL25foqwY8eOeuCBB7Ro0SK1a9fOue7puKysrLPaflRUlJ5//nlNmzZNw4YN08UXX6z58%2BcrKSlJktS2bVv9%2Bc9/1hNPPKH58%2BcrNTVV8%2BfPd%2B7JNXDgQO3Zs0fTp09XdXW1rrvuOk2aNOmsagIAAOeXCHP8FuYuue222352fvny5S5V4i6//5D1bXo8keo/91Pr2z1X3ru/d7BLcI3HE6nmzZuorOwI1yqEGHoT2uhP6ArX3sTHXxCU/bpyBGvOnDkaP368Gjdu3GADFAAAwHGuXIP18ssvn/RRM/fcc49KSkrc2D0AAICrXAlYpzoLuXHjRlVVVbmxewAAAFeF150qAQAAwgABCwAAwDLXAtbx2yAAAAA0dK7dB2vWrFkB97w6duyYsrOz1aRJ4GcEne19sAAAAILNlYDVvXt35%2BNxjktNTVVZWZnKysrcKAEAAMA1rgQs7n0FAADOJ1zkDgAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsKzBBqwPP/xQl19%2BecBjwoQJkqStW7fqlltukdfr1fDhw7Vly5aA9%2Bbl5enaa6%2BV1%2BtVZmamDhw4EIwlAACAMNVgA9bOnTvVr18/rV271nnMmjVLR48e1T333KP09HT99a9/VWpqqsaNG6ejR49KkjZv3qxp06Zp/Pjxys3N1cGDBzVlypQgrwYAAISTBhuwdu3apQ4dOig%2BPt55xMXF6d1331VMTIwefPBBtW/fXtOmTVOTJk30/vvvS5JWrFihG2%2B8UUOHDlXHjh01Z84cffzxxyoqKgryigAAQLho0AGrXbt2J437fD6lpaUpIiJCkhQREaFu3bqpoKDAmU9PT3de37p1ayUlJcnn87lSNwAACH%2BeYBdwLhhj9O2332rt2rVasGCBamtrdcMNN2jChAny%2B/269NJLA17fsmVLFRYWSpJKSkqUkJBw0nxxcXG9919SUiK/3x8w5vE0Pmm7ZysqKrzysccTXvWejeO9CbcenQ/oTWijP6GL3pyZBhmw9u7dq4qKCkVHR2vevHnavXu3Zs2apcrKSmf8p6Kjo1VdXS1Jqqys/Nn5%2BsjNzVVOTk7AWGZmpnOR/fmqefMmwS7BdXFxscEuAadBb0Ib/Qld9KZ%2BGmTAatOmjdavX68LL7xQERER6tSpk%2Brq6jRp0iT16NHjpLBUXV2tRo0aSZJiYmJOOR8bW/9vqBEjRigjIyNgzONprLKyI79yRacWbj9F2F5/KIuKilRcXKwOHqxQbW1dsMvBT9Cb0EZ/Qle49iZYP9w3yIAlSc2aNQt43r59e1VVVSk%2BPl6lpaUBc6Wlpc7pu8TExFPOx8fH13vfCQkJJ50O9PsPqaYmfL4hz4Xzcf21tXXn5brDAb0JbfQndNGb%2BgmvQyD19Omnn6pnz56qqKhwxrZt26ZmzZopLS1NX3zxhYwxkn68XmvTpk3yer2SJK/Xq/z8fOd9%2B/bt0759%2B5x5AACAX9IgA1ZqaqpiYmL08MMP65tvvtHHH3%2BsOXPm6O6779YNN9yggwcPavbs2dq5c6dmz56tiooK3XjjjZKkkSNH6u2339Ybb7yh7du368EHH9Q111yjiy66KMirAgAA4aJBBqymTZtq8eLFOnDggIYPH65p06ZpxIgRuvvuu9W0aVMtWLBA%2Bfn5GjZsmHw%2BnxYuXKjGjRtL%2BjGczZgxQ/Pnz9fIkSN14YUXKisrK8grAgAA4STCHD9XhnPK7z9kfZseT6T6z/3U%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%2Bf8CYx9NYCQkJVmuNiiIfn0vhdsQtnHz4QN%2Bg7fv43xv%2B/oQm%2BhO66M2ZIWCdoKKiIiBcSXKeV1dX12sbubm5ysnJCRgbP3687rvvPjtF/n8lJSUa%2B9tCjRgxwnp4w9kpKSlRbm4uvQlBJSUlWrp0Eb0JUfROXiLFAAAKoUlEQVQndNGbM0MMPUFMTMxJQer480aNGtVrGyNGjNBf//rXgMeIESOs1%2Br3%2B5WTk3PS0TIEH70JXfQmtNGf0EVvzgxHsE6QmJiosrIy1dTUyOP58cvj9/vVqFEjxcXF1WsbCQkJpHsAAM5jHME6QadOneTxeFRQUOCM5efnKzk5WZGRfLkAAMAvIzGcIDY2VkOHDtVjjz2mzZs3a82aNXrppZc0ZsyYYJcGAADCRNRjjz32WLCLCDW9evXS1q1b9dRTT%2Bnzzz/Xv//7v2v48OHBLuuUmjRpoh49eqhJkybBLgUnoDehi96ENvoTuuhN/UUYY0ywiwAAAGhIOEUIAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyAFaaqqqo0depUpaenq0%2BfPnrppZeCXVKDU11drUGDBmn9%2BvXOWFFRkW6//XalpKRowIABWrt2bcB7PvvsMw0aNEher1djxoxRUVFRwPySJUvUt29fpaamaurUqaqoqHDm6Okv279/vyZMmKAePXqob9%2B%2BysrKUlVVlSR6Ewq%2B//573XXXXUpNTdU111yjRYsWOXP0J3Tcc889euihh5znW7du1S233CKv16vhw4dry5YtAa/Py8vTtddeK6/Xq8zMTB04cMCZM8Zo7ty56tWrl3r06KE5c%2Baorq7OmS8rK9N9992n1NRUZWRk6O233z73CwwVBmFpxowZZvDgwWbLli3mf/7nf0xqaqp57733gl1Wg1FZWWkyMzNNhw4dzLp164wxxtTV1ZnBgwebiRMnmp07d5oXX3zReL1es2fPHmOMMXv27DEpKSlm8eLFZseOHeaPf/yjGTRokKmrqzPGGPP%2B%2B%2B%2BbtLQ087//%2B7/G5/OZAQMGmMcff9zZJz39eXV1debWW281d999t9mxY4fZuHGj6d%2B/v3nyySfpTQiora011113nZk4caL59ttvzUcffWS6detm3nnnHfoTQvLy8kyHDh3M5MmTjTHGHDlyxPTu3ds8%2BeSTZufOnWbmzJnmd7/7nTly5Igxxhifz2e6du1q3nrrLbNt2zYzevRoc8899zjbW7x4sbn66qvNxo0bzeeff2769OljFi1a5MyPGzfOjB071nz99dfm9ddfN126dDE%2Bn8/dRQcJASsMHTlyxCQnJzv/8RtjzPz5883o0aODWFXDUVhYaH7/%2B9%2BbwYMHBwSszz77zKSkpDj/8BhjzNixY81zzz1njDFm3rx5AT04evSoSU1Ndd4/atQo57XGGLNx40bTtWtXc/ToUXpaDzt37jQdOnQwfr/fGVu9erXp06cPvQkB%2B/fvN3/84x/NoUOHnLHMzEzz6KOP0p8QUVZWZq666iozfPhwJ2C98cYbJiMjwwmzdXV1pn///mblypXGGGMmTZrkvNYYY/bu3Wsuv/xy88MPPxhjjLn66qud1xpjzKpVq0y/fv2MMcZ8//33pkOHDqaoqMiZnzp1asD2GjJOEYah7du3q6amRqmpqc5YWlqafD5fwKFZ/DobNmxQz549lZubGzDu8/l0xRVXqHHjxs5YWlqaCgoKnPn09HRnLjY2Vp07d1ZBQYFqa2v15ZdfBsynpKTo2LFj2r59Oz2th/j4eC1atEitWrUKGD98%2BDC9CQEJCQmaN2%2BemjZtKmOM8vPztXHjRvXo0YP%2BhIj/%2Bq//0pAhQ3TppZc6Yz6fT2lpaYqIiJAkRUREqFu3bqftTevWrZWUlCSfz6f9%2B/dr37596t69uzOflpamPXv2qKSkRD6fT61bt1bbtm0D5r/44otzvdSQQMAKQ36/X82bN1d0dLQz1qpVK1VVVam8vDyIlTUMo0aN0tSpUxUbGxsw7vf7lZCQEDDWsmVLFRcX/%2BL8wYMHVVVVFTDv8XjUrFkzFRcX09N6iIuLU9%2B%2BfZ3ndXV1WrFihXr16kVvQkxGRoZGjRql1NRUXX/99fQnBHz%2B%2Bef65z//qXvvvTdg/Jd6U1JSctp5v98vSQHzx38AOj5/qvfu37/fzqJCHAErDFVUVAT8YyLJeV5dXR2Mks4Lp/u6H/%2Ba/9x8ZWWl8/xU8/T0zGVnZ2vr1q36z//8T3oTYp577jm9%2BOKL2rZtm7KysuhPkFVVVenRRx/V9OnT1ahRo4C5X%2BpNZWXlGfXmp1/7X9p2Q%2BcJdgE4czExMSd9gx5/fuJfHtgTExNz0k/E1dXVztf8dH2Ji4tTTEyM8/zE%2BdjYWNXW1tLTM5Cdna2lS5fqmWeeUYcOHehNiElOTpb043/sDzzwgIYPHx7wW38S/XFTTk6OunTpEnAE%2BLjTfe1/qTexsbEBYerEPsXGxv7iths6jmCFocTERJWVlammpsYZ8/v9atSokeLi4oJYWcOWmJio0tLSgLHS0lLnEPjp5uPj49WsWTPFxMQEzNfU1Ki8vFzx8fH09AzMnDlTL7/8srKzs3X99ddLojehoLS0VGvWrAkYu/TSS3Xs2DHFx8fTnyD67//%2Bb61Zs0apqalKTU3V6tWrtXr1aqWmpp7V353ExERJck4V/vTPx%2BdP997zAQErDHXq1Ekej8e5CFGS8vPzlZycrMhIWnqueL1effXVV85hcenHr7vX63Xm8/PznbmKigpt3bpVXq9XkZGRSk5ODpgvKCiQx%2BNRx44d6Wk95eTk6LXXXtPTTz%2BtgQMHOuP0Jvh2796t8ePHB1xfs2XLFrVo0UJpaWn0J4iWL1%2Bu1atXa9WqVVq1apUyMjKUkZGhVatWyev16osvvpAxRtKP97XatGnTaXuzb98%2B7du3T16vV4mJiUpKSgqYz8/PV1JSkhISEpSSkqI9e/Y413Mdn09JSXFp5UEW5N9ixK/0yCOPmIEDBxqfz2c%2B/PBD061bN/PBBx8Eu6wG56e3aaipqTEDBgww999/v9mxY4dZsGCBSUlJce7lU1RUZJKTk82CBQuce/kMHjzY%2BfXnvLw8061bN/Phhx8an89nBg4caGbOnOnsi57%2BvJ07d5pOnTqZZ555xpSUlAQ86E3w1dTUmGHDhpk777zTFBYWmo8%2B%2Bsj87ne/M0uWLKE/IWby5MnOrRIOHTpkevXqZWbOnGkKCwvNzJkzTe/evZ1bamzatMl07tzZvP766859sMaNG%2Bdsa8GCBaZPnz5m3bp1Zt26daZPnz7mpZdecubvvPNOM3r0aLNt2zbz%2Buuvm%2BTkZO6DhdB29OhR8%2BCDD5qUlBTTp08f8/LLLwe7pAbppwHLGGO%2B%2B%2B4784c//MF06dLFDBw40PzjH/8IeP1HH31krrvuOtO1a1czduxY514xxy1YsMBceeWVJi0tzUyZMsVUVlY6c/T05y1YsMB06NDhlA9j6E0oKC4uNpmZmaZbt26md%2B/e5oUXXnBCEv0JHT8NWMb8eDPRoUOHmuTkZHPzzTebr776KuD1K1euNFdffbVJSUkxmZmZ5sCBA85cTU2NeeKJJ0x6errp2bOnyc7OdnpujDGlpaVm3LhxJjk52WRkZJjVq1ef%2BwWGiAhj/v9xQQAAAFjBCWoAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsOz/Ad2Gdnc7vy3rAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2810389062741519466”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.5663740281769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.085931844012006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>81.71538078185769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1044.2988204456094</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>455.85733000717767</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55.22515423162826</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1672.7987872331707</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1706.9956875898417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>210.62450337989566</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (823)</td> <td class=”number”>823</td> <td class=”number”>25.6%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>966</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2810389062741519466”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1412</td> <td class=”number”>44.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04855401294061553</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0733370816730633</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2569122233697635</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8523069189145818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13692.117524008749</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14644.931609518457</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16274.54303353105</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16577.621931781956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42244.15676224981</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROC09”>GROC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>188</td>

</tr> <tr>

<th>Unique (%)</th> <td>5.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>20.276</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2027</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram195359095317213847”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives195359095317213847,#minihistogram195359095317213847”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives195359095317213847”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles195359095317213847”

aria-controls=”quantiles195359095317213847” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram195359095317213847” aria-controls=”histogram195359095317213847”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common195359095317213847” aria-controls=”common195359095317213847”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme195359095317213847” aria-controls=”extreme195359095317213847”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles195359095317213847”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>6</td>

</tr> <tr>

<th>Q3</th> <td>13</td>

</tr> <tr>

<th>95-th percentile</th> <td>70.45</td>

</tr> <tr>

<th>Maximum</th> <td>2027</td>

</tr> <tr>

<th>Range</th> <td>2027</td>

</tr> <tr>

<th>Interquartile range</th> <td>10</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>79.809</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9361</td>

</tr> <tr>

<th>Kurtosis</th> <td>283.77</td>

</tr> <tr>

<th>Mean</th> <td>20.276</td>

</tr> <tr>

<th>MAD</th> <td>24.251</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.596</td>

</tr> <tr>

<th>Sum</th> <td>63505</td>

</tr> <tr>

<th>Variance</th> <td>6369.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram195359095317213847”>

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GbbgnX8%2BHGlpqYqMTFRSUlJSktLU3l5uSSpsLBQ48ePV2xsrAYMGKAtW7a4Xfejjz7SoEGDFBMTo7Fjx6qwsNBt/vnnn1dSUpLi4uI0Y8YMlZaWeiwXAACwP1sWLGOMUlNTVVpaqhdffFGLFy/Wu%2B%2B%2BqyeffFLGGKWkpKh58%2BbasGGDBg8erClTpujIkSOSpCNHjiglJUXDhg3Tyy%2B/rKZNm%2Bree%2B%2BVMUaStHHjRmVkZGjOnDlas2aN8vLylJ6e7s24AADAZmxZsA4cOKDc3FylpaWpXbt2SkhIUGpqqrKzs7V161YVFhZqzpw5atu2rSZPnqzY2Fht2LBBkrR%2B/Xp16tRJEyZMULt27ZSWlqbDhw9r27ZtkqS1a9dq3LhxSk5OVufOnTV79mxt2LCBo1gAAKDObFmwwsLCtGrVKjVv3txt/PTp08rLy9ONN96ohg0busbj4%2BOVm5srScrLy1NCQoJrLjg4WB07dlRubq6qqqq0a9cut/nY2FidO3dOe/fuvcapAACAr7BlwQoJCVFSUpLrcnV1tdatW6du3brJ6XQqPDzcbftmzZrp2LFjknTZ%2BVOnTqm8vNxt3uFwKDQ01HV9AACAH%2BLw9gKskJ6erj179ujll1/W888/r8DAQLf5wMBAVVRUSJJKS0trnS8rK3Ndru36dVFUVCSn0%2Bk25nA0rFHsrlZAgL36scNRt/Wez2W3fJfji5kkctkNuezFF3P5Yqba2L5gpaena82aNVq8eLHat2%2BvoKAglZSUuG1TUVGhBg0aSJKCgoJqlKWKigqFhIQoKCjIdfni%2BeDg4DqvKSsrSxkZGW5jKSkpSk1NrfM%2BfFGTJo2uaPuQkLrf53bhi5kkctkNuezFF3P5YqaL2bpgzZ07Vy%2B99JLS09PVr18/SVJERIT279/vtl1xcbHr6FFERISKi4trzHfo0EGhoaEKCgpScXGx2rZtK0mqrKxUSUmJwsLC6ryuUaNGqXfv3m5jDkdDnTx55oozXo7dfgKoa/6AAH%2BFhATr1KlSVVVVX%2BNVeYYvZpLIZTfkshdfzOWNTFf6w71VbFuwMjIy9I9//ENPPPGE%2Bvfv7xqPiYlRZmamysrKXEetcnJyFB8f75rPyclxbV9aWqo9e/ZoypQp8vf3V3R0tHJyctS1a1dJUm5urhwOh6Kiouq8tvDw8BpvBzqd36my0jceID/Wleavqqr2ufvMFzNJ5LIbctmLL%2BbyxUwXs9chkP9TUFCg5cuX649//KPi4%2BPldDpdX4mJiWrRooWmT5%2Bu/Px8ZWZmaufOnRoxYoQkafjw4dqxY4cyMzOVn5%2Bv6dOnq1WrVq5CNXr0aK1evVqbN2/Wzp07NWvWLN1xxx1X9BYhAAD4afP4EayRI0dq%2BPDhGjhwoH7%2B85//qH3861//UlVVlVasWKEVK1a4zX3xxRdavny5Zs6cqWHDhumXv/ylli1bpsjISElSq1at9NRTT%2Blvf/ubli1bpri4OC1btkx%2Bfn6SpIEDB%2Brw4cN67LHHVFFRoVtvvVXTpk27utAAAOAnxc%2BcP4W5hyxatEhvvvmmTp48qd/%2B9rcaNmyYunfv7io4vsrp/M7yfToc/uq78EPL93utvH1/9zpt53D4q0mTRjp58ozPHEL2xUwSueyGXPbii7m8kSks7McdzLlaHn%2BLcOrUqXr33Xe1fPlyBQQE6L777lOvXr20ePFiHTx40NPLAQAAsJxXPuTu5%2Ben7t27q3v37iotLdULL7yg5cuXKzMzU126dNG4ceN06623emNpAAAAV81rv0VYVFSkN954Q2%2B88Yb27dunLl26aOjQoTp27Jj%2B8pe/aPv27Zo5c6a3lgcAAPCjebxgvf7663r99df1ySefqGnTphoyZIiWLl2q1q1bu7Zp0aKF5s%2BfT8ECAAC25PGCNXPmTCUnJ2vZsmW65ZZb5O9f82Ngbdq00V133eXppQEAAFjC4wXrgw8%2BUJMmTVRSUuIqVzt37lTHjh0VEBAgSerSpYu6dOni6aUBAABYwuO/RXj69Gn1799fzzzzjGts0qRJGjx4sI4ePerp5QAAAFjO4wXrb3/7m375y1/q7rvvdo299dZbatGihdLS0jy9HAAAAMt5vGD97//%2Brx555BG3P57ctGlTPfTQQ9q6daunlwMAAGA5jxcsh8OhU6dO1RgvLS2Vh08qDwAAcE14vGDdcsstmjdvnr766ivXWGFhodLS0pSUlOTp5QAAAFjO479F%2BPDDD%2Bvuu%2B9Wv379FBISIkk6deqUOnbsqOnTp3t6OQAAAJbzeMFq1qyZXn31VX300UfKz8%2BXw%2BHQr3/9a918880%2B/wefAQDAT4NX/lROQECAkpKSeEsQAAD4JI8XLKfTqSeffFI7duzQuXPnanyw/V//%2BpenlwQAAGApjxesRx99VLt379bAgQP185//3NM3DwAAcM15vGBt3bpVq1atUkJCgqdvGgAAwCM8fpqGhg0bqlmzZp6%2BWQAAAI/xeMEaPHiwVq1apaqqKk/fNAAAgEd4/C3CkpISZWdn67333tP111%2BvwMBAt/m1a9d6ekkAAACW8sppGgYNGuSNmwUAAPAIjxestLQ0T98kAACAR3n8M1iSVFRUpIyMDE2dOlXffPON3nnnHR04cMAbSwEAALCcxwvWl19%2Bqdtvv12vvvqqNm7cqLNnz%2Bqtt97S8OHDlZeX5%2BnlAAAAWM7jBevxxx9Xnz59tHnzZv3sZz%2BTJD3xxBPq3bu3Fi5c6OnlAAAAWM7jBWvHjh26%2B%2B673f6ws8Ph0L333qs9e/Z4ejkAAACW83jBqq6uVnV1dY3xM2fOKCAgwNPLAQAAsJzHC1aPHj20cuVKt5JVUlKi9PR0devWzdPLAQAAsJzHC9Yjjzyi3bt3q0ePHiovL9c999yj5ORkff3113r44Yc9vRwAAADLefw8WBEREXrttdeUnZ2tzz//XNXV1brzzjs1ePBgNW7c2NPLAQAAsJxXzuQeHByskSNHeuOmAQAArjmPF6yxY8dedp6/RQgAAOzO4wWrZcuWbpcrKyv15Zdfat%2B%2BfRo3bpynlwMAAGC5evO3CJctW6Zjx455eDUAAADW88rfIryUwYMH6%2B233/b2MgAAAK5avSlYn376KScaBQAAPqFefMj99OnT%2BuKLLzR69GhPLwcAAMByHj%2BCFRkZqZYtW7p9derUSXPnzv1RJxqtqKjQoEGD9Mknn7jG5s2bpxtuuMHta926da757Oxs9enTRzExMUpJSdGJEydcc8YYLVy4UN26dVNiYqIWLFhwyT/tAwAAUBuPH8F6/PHHLdtXeXm5pk6dqvz8fLfxgoICTZ06VUOHDnWNnT%2BJ6c6dOzVz5kzNnj1bUVFRmj9/vqZPn66VK1dKkp577jllZ2crIyNDlZWVmjZtmpo1a6aJEydatm4AAODbPF6wtm/fXudtb7rpplrn9u/fr6lTp8oYU2OuoKBAEydOVFhYWI25devW6bbbbtOQIUMkSQsWLFBycrIKCwt1/fXXa%2B3atUpNTVVCQoIk6cEHH9SSJUsoWAAAoM48XrDGjBkjPz8/SXIrRxeP%2Bfn56fPPP691P9u2bVPXrl315z//WbGxsa7x06dP6/jx42rduvUlr5eXl6c//vGPrsstWrRQZGSk8vLyFBgYqKNHj7oVu/j4eB0%2BfFhFRUUKDw%2B/8sAAAOAnx%2BMF6%2Bmnn9a8efM0bdo0JSYmKjAwULt27dKcOXM0dOhQDRgwoE77qe0D8QUFBfLz89PTTz%2BtDz74QKGhobr77rtdbxdeqig1a9ZMx44dk9PplCS3%2BebNm0uSjh07RsECAAB14pUTjT722GO65ZZbXGPdunXTnDlz9NBDD7kdXfoxDhw4ID8/P7Vp00Z33XWXtm/frkcffVSNGzdW3759VVZWpsDAQLfrBAYGqqKiQmVlZa7LF85J33%2BYvq6KiopcZe08h6Oh5QUtIKDenGWjThyOuq33fC675bscX8wkkctuyGUvvpjLFzPVxuMFq6ioqMafy5G%2B/xD6yZMnr3r/Q4YMUXJyskJDQyVJUVFROnTokF566SX17dtXQUFBNcpSRUWFgoOD3cpUUFCQ69/S93%2Bguq6ysrKUkZHhNpaSkqLU1NQfncsXNGnS6Iq2Dwmp%2B31uF76YSSKX3ZDLXnwxly9mupjHC1ZsbKyeeOIJ/ed//qfrN/tKSkqUnp6um2%2B%2B%2Bar37%2Bfn5ypX57Vp00Zbt26VJEVERKi4uNhtvri4WGFhYYqIiJAkOZ1OtWrVyvVvSZf8wHxtRo0apd69e7uNORwNdfLkmSsL8wPs9hNAXfMHBPgrJCRYp06VqqrKN06R4YuZJHLZDbnsxRdzeSPTlf5wbxWPF6y//OUvGjt2rG655Ra1bt1axhgdOnRIYWFhWrt27VXvf8mSJfr000/1/PPPu8b27t2rNm3aSJJiYmKUk5OjYcOGSZKOHj2qo0ePKiYmRhEREYqMjFROTo6rYOXk5CgyMvKK3t4LDw%2Bvsb3T%2BZ0qK33jAfJjXWn%2Bqqpqn7vPfDGTRC67IZe9%2BGIuX8x0MY8XrLZt2%2Bqtt95Sdna2CgoKJEm///3vNXDgwCt6G642ycnJyszM1OrVq9W3b19t2bJFr732mqu83XnnnRozZoxiY2MVHR2t%2BfPnq1evXrr%2B%2Butd8wsXLtQvfvELSdKiRYs0YcKEq14XAAD46fB4wZKk6667TiNHjtTXX3/tKjY/%2B9nPLNl3586dtWTJEi1dulRLlixRy5YttWjRIsXFxUmS4uLiNGfOHC1dulTffvutunfvrrlz57quP3HiRH3zzTeaMmWKAgICNGLECI0fP96StQEAgJ8GP3OpM3VeQ8YYLVq0SC%2B88ILOnTunjRs3avHixQoODtasWbMsK1r1jdP5neX7dDj81Xfhh5bv91p5%2B/7uddrO4fBXkyaNdPLkGZ85hOyLmSRy2Q257MUXc3kjU1jYzz1yOxfz%2BKekX3jhBb3%2B%2Buv661//6vqtvT59%2Bmjz5s01fvMOAADAjjxesLKysvTYY49p2LBhrrO3DxgwQPPmzdObb77p6eUAAABYzuMF6%2Buvv1aHDh1qjEdFRdU4OScAAIAdebxgtWzZUrt27aox/sEHH7g%2B8A4AAGBnHv8twokTJ2r27NlyOp0yxujjjz9WVlaWXnjhBT3yyCOeXg4AAIDlPF6whg8frsrKSq1YsUJlZWV67LHH1LRpU91///268847Pb0cAAAAy3m8YGVnZ6t///4aNWqUTpw4IWOMmjVr5ullAAAAXDMe/wzWnDlzXB9mb9q0KeUKAAD4HI8XrNatW2vfvn2evlkAAACP8fhbhFFRUXrwwQe1atUqtW7dWkFBQW7zaWlpnl4SAACApTxesA4ePKj4%2BHhJ4rxXAADAJ3mkYC1YsEBTpkxRw4YN9cILL3jiJgEAALzGI5/Beu6551RaWuo2NmnSJBUVFXni5gEAADzKIwXLGFNjbPv27SovL/fEzQMAAHiUx3%2BLEAAAwNdRsAAAACzmsYLl5%2BfnqZsCAADwKo%2BdpmHevHlu57w6d%2B6c0tPT1ahRI7ftOA8WAACwO48UrJtuuqnGOa/i4uJ08uRJnTx50hNLAAAA8BiPFCzOfQUAAH5K%2BJA7AACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABazfcGqqKjQoEGD9Mknn7jGCgsLNX78eMXGxmrAgAHasmWL23U%2B%2BugjDRo0SDExMRo7dqwKCwvd5p9//nklJSUpLi5OM2bMUGlpqUeyAAAA32DrglVeXq4HHnhA%2Bfn5rjFjjFJSUtS8eXNt2LBBgwcP1pQpU3TkyBFJ0pEjR5SSkqJhw4bp5ZdfVtOmTXXvvffKGCNJ2rhxozIyMjRnzhytWbNGeXl5Sk9P90o%2BAABgT7YtWPv379cdd9yhr776ym1869atKiws1Jw5c9S2bVtNnjxZsbGx2rBhgyRp/fr16tSpkyZMmKB27dopLS1Nhw8f1rZt2yRJa9eu1bhx45ScnKzOnTtr9uzZ2rBhA0exAABAndm2YG3btk1du3ZVVlaW23heXp5uvPFGNWzY0DUWHx%2Bv3Nxc13xCQoJrLjg4WB07dlRubq6qqqq0a9cut/nY2FidO3dOe/fuvcaJAACAr3B4ewE/1ujRoy857nQ6FR4e7jbWrFkzHTt27AfnT53eH1JXAAAY8klEQVQ6pfLycrd5h8Oh0NBQ1/XroqioSE6n023M4WhY43avVkCAvfqxw1G39Z7PZbd8l%2BOLmSRy2Q257MUXc/liptrYtmDVprS0VIGBgW5jgYGBqqio%2BMH5srIy1%2BXarl8XWVlZysjIcBtLSUlRampqnffhi5o0aXRF24eEBF%2BjlXiPL2aSyGU35LIXX8zli5ku5nMFKygoSCUlJW5jFRUVatCggWv%2B4rJUUVGhkJAQBQUFuS5fPB8cXPf/DKNGjVLv3r3dxhyOhjp58kyd91EXdvsJoK75AwL8FRISrFOnSlVVVX2NV%2BUZvphJIpfdkMtefDGXNzJd6Q/3VvG5ghUREaH9%2B/e7jRUXF7venouIiFBxcXGN%2BQ4dOig0NFRBQUEqLi5W27ZtJUmVlZUqKSlRWFhYndcQHh5e4%2B1Ap/M7VVb6xgPkx7rS/FVV1T53n/liJolcdkMue/HFXL6Y6WL2OgRSBzExMfrss89cb/dJUk5OjmJiYlzzOTk5rrnS0lLt2bNHMTEx8vf3V3R0tNt8bm6uHA6HoqKiPBcCAADYms8VrMTERLVo0ULTp09Xfn6%2BMjMztXPnTo0YMUKSNHz4cO3YsUOZmZnKz8/X9OnT1apVK3Xt2lXS9x%2BeX716tTZv3qydO3dq1qxZuuOOO67oLUIAAPDT5nMFKyAgQMuXL5fT6dSwYcP0xhtvaNmyZYqMjJQktWrVSk899ZQ2bNigESNGqKSkRMuWLZOfn58kaeDAgZo8ebIee%2BwxTZgwQZ07d9a0adO8GQkAANiMT3wG64svvnC7/Mtf/lLr1q2rdfuePXuqZ8%2Betc5PmjRJkyZNsmx9AADgp8XnjmABAAB4GwULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwmM8WrE2bNumGG25w%2B0pNTZUk7dmzRyNHjlRMTIyGDx%2Bu3bt3u103Oztbffr0UUxMjFJSUnTixAlvRAAAADblswVr//79Sk5O1pYtW1xf8%2BbN09mzZzVp0iQlJCTolVdeUVxcnCZPnqyzZ89Kknbu3KmZM2dqypQpysrK0qlTpzR9%2BnQvpwEAAHbiswWroKBA7du3V1hYmOsrJCREb731loKCgvTQQw%2Bpbdu2mjlzpho1aqR33nlHkrRu3TrddtttGjJkiKKiorRgwQK9//77Kiws9HIiAABgFz5dsFq3bl1jPC8vT/Hx8fLz85Mk%2Bfn5qUuXLsrNzXXNJyQkuLZv0aKFIiMjlZeX55F1AwAA%2B/PJgmWM0cGDB7Vlyxb169dPffr00cKFC1VRUSGn06nw8HC37Zs1a6Zjx45JkoqKii47DwAA8EMc3l7AtXDkyBGVlpYqMDBQTz75pL7%2B%2BmvNmzdPZWVlrvELBQYGqqKiQpJUVlZ22fm6KCoqktPpdBtzOBrWKG5XKyDAXv3Y4ajbes/nslu%2By/HFTBK57IZc9uKLuXwxU218smC1bNlSn3zyia677jr5%2BfmpQ4cOqq6u1rRp05SYmFijLFVUVKhBgwaSpKCgoEvOBwcH1/n2s7KylJGR4TaWkpLi%2Bi3Gn6omTRpd0fYhIXW/z%2B3CFzNJ5LIbctmLL%2BbyxUwX88mCJUmhoaFul9u2bavy8nKFhYWpuLjYba64uNh1dCkiIuKS82FhYXW%2B7VGjRql3795uYw5HQ508eeZKIvwgu/0EUNf8AQH%2BCgkJ1qlTpaqqqr7Gq/IMX8wkkctuyGUvvpjLG5mu9Id7q/hkwfrwww/14IMP6r333nMdefr8888VGhqq%2BPh4PfPMMzLGyM/PT8YY7dixQ3/6058kSTExMcrJydGwYcMkSUePHtXRo0cVExNT59sPDw%2Bv8Xag0/mdKit94wHyY11p/qqqap%2B7z3wxk0QuuyGXvfhiLl/MdDF7HQKpo7i4OAUFBekvf/mLDhw4oPfff18LFizQH/7wB/Xv31%2BnTp3S/PnztX//fs2fP1%2BlpaW67bbbJEl33nmnXn/9da1fv1579%2B7VQw89pF69eun666/3cioAAGAXPlmwGjdurNWrV%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%2Bv7u3lwAAsCneIrzI3r17VVlZqbi4ONdYfHy88vLyVF1d7cWVAQAAu%2BAI1kWcTqeaNGmiwMBA11jz5s1VXl6ukpISNW3a9Af3UVRUJKfT6TbmcDRUeHi4pWsNCKAfX0t2O%2BK26cEkj9/m%2Bf%2BDvvZ/kVz2Qi778MVMtaFgXaS0tNStXElyXa6oqKjTPrKyspSRkeE2NmXKFN13333WLPL/FBUVadwv8jVq1CjLy5s3FRUVKSsry6dy%2BWIm6ftca9asIpdNkMtefDGXL2aqje9XyCsUFBRUo0idv9ygQYM67WPUqFF65ZVX3L5GjRpl%2BVqdTqcyMjJqHC2zO1/M5YuZJHLZDbnsxRdz%2BWKm2nAE6yIRERE6efKkKisr5XB8f/c4nU41aNBAISEhddpHeHi4zzdzAABQO45gXaRDhw5yOBzKzc11jeXk5Cg6Olr%2B/txdAADgh9EYLhIcHKwhQ4Zo1qxZ2rlzpzZv3qxnn31WY8eO9fbSAACATQTMmjVrlrcXUd9069ZNe/bs0aJFi/Txxx/rT3/6k4YPH%2B7tZV1So0aNlJiYqEaNGnl7KZbyxVy%2BmEkil92Qy158MZcvZroUP2OM8fYiAAAAfAlvEQIAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNg2VR5eblmzJihhIQE9ejRQ88%2B%2B6y3l1Qnx48fV2pqqhITE5WUlKS0tDSVl5dLkubNm6cbbrjB7WvdunWu62ZnZ6tPnz6KiYlRSkqKTpw44a0YbjZt2lRj3ampqZKkPXv2aOTIkYqJidHw4cO1e/dut%2BvW10yvvPJKjUw33HCDoqKiJEn33HNPjbl3333Xdf3nn39eSUlJiouL04wZM1RaWuqtKC4VFRUaNGiQPvnkE9dYYWGhxo8fr9jYWA0YMEBbtmxxu85HH32kQYMGKSYmRmPHjlVhYaHbfH3Iealcubm5%2Bo//%2BA/FxcWpX79%2BWr9%2Bvdt1fve739X4/u3bt0%2BSZIzRwoUL1a1bNyUmJmrBggWqrq72aCbp0rmu5jmivuZ65JFHLvlYu/DPsyUkJNSYP3PmjCTvvhZc7vncFx5bV83AlubMmWNuv/12s3v3bvPf//3fJi4uzrz99tveXtZlVVdXmzvuuMP84Q9/MPv27TPbt283ffv2NY8//rgxxpjx48eblStXmqKiItfX2bNnjTHG5OXlmc6dO5tXX33VfP755%2Bauu%2B4ykyZN8mYcl%2BXLl5vJkye7rfvbb781Z86cMd27dzePP/642b9/v5k7d675zW9%2BY86cOWOMqd%2BZSktL3fIcOXLE9O3b18yfP98YY0zfvn3N66%2B/7rZNeXm5McaYd955x8THx5v/%2BZ//MXl5eWbAgAFm9uzZ3oxjysrKTEpKimnfvr3ZunWrMeb7/4%2B33367mTp1qtm/f795%2BumnTUxMjDl8%2BLAxxpjDhw%2Bb2NhYs3r1arNv3z7z//7f/zODBg0y1dXVxpj6kfNSuYqKikxCQoJZtGiROXjwoMnOzjbR0dHm3XffNcYYU1lZaaKjo822bdvcvn/nzp0zxhizevVq07NnT7N9%2B3bz8ccfmx49ephVq1Z5PZcxV/ccUV9znTp1yi3Pp59%2Bajp16mQ2bdpkjDHm2LFjpn379uarr75y2%2B78/0NvvRZc7vncFx5bVqBg2dCZM2dMdHS02xPPsmXLzF133eXFVf2w/fv3m/bt2xun0%2Bkae/PNN02PHj2MMcYkJSWZDz/88JLXnTZtmnn44Yddl48cOWJuuOEG89VXX13bRdfB1KlTzaJFi2qMr1%2B/3vTu3dv1pFFdXW369u1rNmzYYIyp35ku9vTTT5s%2BffqY8vJyU15ebjp06GAOHDhwyW1Hjx5tli5d6rq8fft207lzZ9cLoafl5%2Beb3/3ud%2Bb22293e2H76KOPTGxsrKvwGmPMuHHjXGt/8skn3R5TZ8%2BeNXFxca7reztnbbn%2B/ve/m/79%2B7tt%2B%2Bijj5oHHnjAGGPMoUOHTFRUlCkrK7vkfnv27On6P2qMMa%2B99ppJTk6%2BRilqqi2XMVf3HFGfc11owoQJ5sEHH3Rd/ve//226d%2B9%2ByW29%2BVpwuedzuz%2B2rMJbhDa0d%2B9eVVZWKi4uzjUWHx%2BvvLw8rxzyrquwsDCtWrVKzZs3dxs/ffq0Tp8%2BrePHj6t169aXvG5eXp4SEhJcl1u0aKHIyEjl5eVdyyXXSUFBwSXXnZeXp/j4ePn5%2BUmS/Pz81KVLF%2BXm5rrm62umC5WUlOiZZ57R1KlTFRgYqAMHDsjPz0/XX399jW2rqqq0a9cut1yxsbE6d%2B6c9u7d68llu2zbtk1du3ZVVlaW23heXp5uvPFGNWzY0DUWHx9f6/cnODhYHTt2VG5ubr3IWVuu82/VXOz06dOSpP3796tFixYKCgqqsc3x48d19OhR3XTTTa6x%2BPh4HT58WEVFRRYnuLTacl3Nc0R9znWhjz/%2BWNu3b9cDDzzgGtu/f79%2B9atfXXJ7b74WXO753O6PLas4vL0AXDmn06kmTZooMDDQNda8eXOVl5erpKRETZs29eLqahcSEqKkpCTX5erqaq1bt07dunVTQUGB/Pz89PTTT%2BuDDz5QaGio7r77bg0dOlSSVFRUpPDwcLf9NWvWTMeOHfNohosZY3Tw4EFt2bJFK1euVFVVlfr376/U1FQ5nU79%2Bte/dtu%2BWbNmys/Pl1R/M13spZdeUnh4uPr37y9JOnDggBo3bqyHHnpI27Zt0y9%2B8Qvdd9996tmzp06dOqXy8nK3XA6HQ6GhoV7LNXr06EuOO53Oy97/l5uvDzlry9WqVSu1atXKdfmbb77Rf/3Xf%2Bm%2B%2B%2B6T9P0PBD/72c80efJk7d69W7/61a/00EMPqXPnznI6nZLkluv8C%2BixY8dq3B/XQm25ruY5oj7nulBmZqaGDh2qFi1auMYKCgpUWlqqMWPG6ODBg%2BrQoYNmzJihX/3qV159Lbjc87ndH1tW4QiWDZWWlro9oCS5LldUVHhjST9Kenq69uzZoz//%2Bc%2BuoyJt2rRRZmamRo4cqUcffVSbNm2SJJWVlV0ys7fzHjlyxPX9ePLJJ/Xwww/rzTff1IIFC2r9Pp1fc33NdCFjjNavX6%2B77rrLNXbgwAGVlZWpR48eWrVqlXr27Kl77rlHu3btUllZmSTV%2B1xS7Y%2Bj8%2Bu83LxdcpaVlem%2B%2B%2B5T8%2BbNNWrUKEnSwYMH9e2332rkyJHKzMxU27ZtNW7cOB09evSSuerLc8vVPEfU51znFRYWauvWrRozZozb%2BIEDB/Ttt9/qnnvu0fLly9WgQQONHz9ep0%2BfrlevBRc%2Bn/8UHlt1wREsGwoKCqrxH%2B385QYNGnhjSVcsPT1da9as0eLFi9W%2BfXu1a9dOycnJCg0NlSRFRUXp0KFDeumll9S3b99aMwcHB3tj%2BS4tW7bUJ598ouuuu05%2Bfn7q0KGDqqurNW3aNCUmJl5yzee/R/U104V27dql48ePa%2BDAga6xe%2B%2B9V2PGjNF1110n6fvv1WeffaZ//vOf%2BvOf/yyp5pN7fcslfX//l5SUuI3V5fsTEhLienutPuc8c%2BaM7r33Xh06dEh///vfXeuaO3euysrK1LhxY0nSrFmztGPHDr3%2B%2Buv6zW9%2BI%2Bn7HBdn9HauIUOG/OjniAtLR33Ldd7GjRvVoUOHGke9V69erXPnzqlRo0aSpIULF6pnz5569913681rwcXP577%2B2KorjmDZUEREhE6ePKnKykrXmNPpVIMGDRQSEuLFldXN3Llz9dxzzyk9PV39%2BvWT9P3nk84/cZ7Xpk0bHT9%2BXNL3mYuLi93mi4uLFRYW5plFX0ZoaKjrc1aS1LZtW5WXlyssLOySaz5/6Ls%2BZzrvww8/VEJCgqtMSZK/v7/bZen//16FhoYqKCjILVdlZaVKSkrqVS6p9vu/Lt%2Bf%2Bp7z9OnTmjhxovLz87VmzRq3zy05HA5XuZLkOip0/PhxRURESJLrLbUL/%2B3tXFfzHFGfc5334Ycf6re//W2N8cDAQFe5kr4vJ61atXJ9v7z9WnCp53NffmxdCQqWDXXo0EEOh8P1gUFJysnJUXR0tPz96/e3NCMjQ//4xz/0xBNPuB0VWbJkicaPH%2B%2B27d69e9WmTRtJUkxMjHJyclxzR48e1dGjRxUTE%2BORddfmww8/VNeuXd3O0fL5558rNDRU8fHx%2BvTTT2WMkfT92207duxwrbm%2BZrrQzp071aVLF7exRx55RNOnT3cbO/%2B98vf3V3R0tFuu3NxcORwO1zm06ouYmBh99tlnrrckpO8fR7V9f0pLS7Vnzx7FxMTU65zV1dWaMmWKvv76a73wwgtq166d2/yYMWOUkZHhtv0XX3yhNm3aKCIiQpGRkW65cnJyFBkZ6ZHPKV3O1TxH1Odc0vfPDbt27arxWDPGqE%2BfPnrllVdcY2fPntWXX36pNm3aeP21oLbnc199bF0xb/4KI368Rx991AwcONDk5eWZTZs2mS5dupiNGzd6e1mXtX//ftOhQwezePFit/O5FBUVmby8PHPjjTeaVatWmS%2B//NK8%2BOKLplOnTmbHjh3GGGN27NhhOnbsaP75z3%2B6znEzefJkLycy5rvvvjNJSUnmgQceMAUFBea9994zPXr0MJmZmea7774z3bp1M3PnzjX5%2Bflm7ty5pnv37q5fXa6vmS6UnJxssrOz3cY2btxoOnbsaF599VVz6NAh89RTT5nOnTubwsJCY4wx2dnZpkuXLmbTpk0mLy/PDBw40MydO9cby6/hwl%2BPr6ysNAMGDDD333%2B/2bdvn1m5cqWJjY11naunsLDQREdHm5UrV7rO1XP77be7TrtRn3JemCsrK8tERUWZd9991%2B0xdvLkSWOMMc8%2B%2B6yJj483mzdvNgUFBeavf/2r%2Bc1vfmO%2B%2B%2B47Y4wxK1euND169DBbt241W7duNT169DDPPvus13Nd7XNEfc1lzPf/19q3b2%2BKiopqbDt37lzTq1cvs3XrVrNv3z6TkpJiBg0aZCorK40x3nstuNzzuS89tq4GBcumzp49ax566CETGxtrevToYZ577jlvL%2BkHrVy50rRv3/6SX8YYs2nTJnP77beb6Oho079//xpPEhs2bDA9e/Y0sbGxJiUlxZw4ccIbMWrYt2%2BfGT9%2BvImNjTXdu3c3Tz31lOuJIi8vzwwZMsRER0ebESNGmM8%2B%2B8ztuvU103nR0dHmgw8%2BqDH%2Bz3/%2B09x6662mU6dOZujQoWbbtm1u8ytXrjQ333yziY%2BPN9OnT6/1nEuedvEL26FDh8zvf/9706lTJzNw4EDz73//22379957z9x6662mc%2BfOZty4cTXOUVZfcl6Ya8KECZd8jJ0/71B1dbVZsWKF6dWrl%2BnUqZP5/e9/b7744gvXviorK83f/vY3k5CQYLp27WrS09Nd/5%2B9mcuYq3uOqM%2B5cnNzTfv27V0n671QWVmZSUtLM927dzcxMTFm8uTJ5siRI655b70W/NDzua88tq6GnzH/9/4FAAAALFG/P7ADAABgQxQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYv8fyFISXGCzwdkAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common195359095317213847”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>344</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>340</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>301</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>265</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>214</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>142</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>103</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (177)</td> <td class=”number”>975</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme195359095317213847”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>265</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>344</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>340</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:98%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>301</td> <td class=”number”>9.4%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1230.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1261.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1804.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2027.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROC14”><s>GROC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_GROC09”><code>GROC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9926</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROCPTH09”>GROCPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3033</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.26804</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.0738</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3244519064893463793”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3244519064893463793,#minihistogram-3244519064893463793”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3244519064893463793”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3244519064893463793”

aria-controls=”quantiles-3244519064893463793” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3244519064893463793” aria-controls=”histogram-3244519064893463793”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3244519064893463793” aria-controls=”common-3244519064893463793”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3244519064893463793” aria-controls=”extreme-3244519064893463793”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3244519064893463793”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.084503</td>

</tr> <tr>

<th>Q1</th> <td>0.14991</td>

</tr> <tr>

<th>Median</th> <td>0.20692</td>

</tr> <tr>

<th>Q3</th> <td>0.30563</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.68157</td>

</tr> <tr>

<th>Maximum</th> <td>3.0738</td>

</tr> <tr>

<th>Range</th> <td>3.0738</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.15572</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.2245</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.83754</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.154</td>

</tr> <tr>

<th>Mean</th> <td>0.26804</td>

</tr> <tr>

<th>MAD</th> <td>0.13847</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.8496</td>

</tr> <tr>

<th>Sum</th> <td>839.51</td>

</tr> <tr>

<th>Variance</th> <td>0.050398</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3244519064893463793”>

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huuWWW1pdEQMAADgfl2VZlpMH3Lx5szZv3qwdO3YoOjpa06ZNU1pams9zq9544w0tX75cn332mZOlXVQezzfG9%2Bl2h2j8yo%2BN7/diefeBVMeO5XaHKCqqm44fPxl0n/tfCL2xR2/s0Rt79MZeR%2BhNbKz5H6hrC8evYGVlZWns2LHKz8/Xtddeq5CQ1reBnXnEAgAAQCByPGB99NFHioqKUk1NjTdclZaWasiQIQoNDZUkDR8%2BXMOHD3e6NAAAACMc/ynCEydOaOLEifrtb3/rHbvnnns0depUHTlyxOlyAAAAjHM8YD3%2B%2BOO6/PLLddddd3nH3nnnHfXq1Us5OTlOlwMAAGCc4wHrf//3f/Xwww/7PLgzOjpaCxcu9D4MFAAAIJA5HrDcbrdqa2tbjdfV1cnhH2gEAAC4KBwPWNdee62WLVumv//9796xiooK5eTkaMyYMU6XAwAAYJzjP0X40EMP6a677tINN9ygyMhISVJtba2GDBmiRYsWOV0OAACAcY4HrJiYGP3hD3/QJ598orKyMrndbl1xxRX68Y9/bOwXPgMAAPiTX35VTmhoqMaMGcNHggAAoFNyPGB5PB6tWrVKu3fv1unTp1vd2P6nP/3J6ZIAAACMcjxg/epXv9LevXs1efJkXXKJf34/EAAAwMXkeMDavn271q5dq5SUFKcPDQAA4AjHH9PQtWtXxcTEOH1YAAAAxzgesKZOnaq1a9equbnZ6UMDAAA4wvGPCGtqarRlyxb9%2Bc9/Vr9%2B/RQWFuYzv379eqdLAgAAMMovj2mYMmWKPw4LAADgCMcDVk5OjtOHBAAAcJTj92BJUlVVlfLy8jR//nz94x//0HvvvaeDBw/6oxQAAADjHA9YX331lW666Sb94Q9/0Pvvv69Tp07pnXfeUVpamkpKSpwuBwAAwDjHA9YTTzyh66%2B/Xlu3btUPfvADSdJTTz2lcePGaeXKlU6XAwAAYJzjAWv37t266667fH6xs9vt1n333ad9%2B/Y5XQ4AAIBxjgeslpYWtbS0tBo/efKkQkNDnS4HAADAOMcD1ujRo7VmzRqfkFVTU6Pc3FyNGjXK6XIAAACMczxgPfzww9q7d69Gjx6thoYG3XvvvRo7dqwOHTqkhx56yOlyAAAAjHP8OVjx8fF68803tWXLFn3%2B%2BedqaWnRrFmzNHXqVHXv3t3pcgAAAIzzy5PcIyIiNGPGDH8cGgAA4KJzPGDNmTPnvPP8LkIAABDoHA9Yffr08fm6qalJX331lb744gvdcccdTpcDAABgXIf5XYT5%2BfmqrKx0uBoAAADz/PK7CM9l6tSpevfdd/1dBgAAwPfWYQLWZ599xoNGAQBAp9AhbnI/ceKE/vrXv2r27NlOlwMAAGCc4wGrd%2B/ePr%2BHUJJ%2B8IMf6LbbbtPPfvYzp8sBAAAwzvGA9cQTTzh9SAAAAEc5HrB27drV5m2vueaai1gJAADAxeF4wLr99tu9HxFaluUdP3vM5XLp888/d7o8AACA783xgPX8889r2bJlWrBggUaMGKGwsDDt2bNH2dnZuvnmm3XjjTc6XRIAAIBRjj%2BmIScnR4sXL9YNN9ygqKgodevWTaNGjVJ2drZee%2B019enTx/sCAAAIRI4HrKqqqnOGp%2B7du%2Bv48eNOlwMAAGCc4wFr2LBheuqpp3TixAnvWE1NjXJzc/XjH//Y6XIAAACMc/werEcffVRz5szRtddeq/79%2B8uyLP3tb39TbGys1q9f73Q5AAAAxjkesAYOHKh33nlHW7Zs0YEDByRJ//7v/67JkycrIiLC6XIAAACMczxgSdKll16qGTNm6NChQ%2BrXr5%2Bkb5/mDgAA0Bk4fg%2BWZVlauXKlrrnmGk2ZMkWVlZV66KGHlJWVpdOnTztdDgAAgHGOB6xXXnlFmzdv1q9//WuFhYVJkq6//npt3bpVeXl5TpcDAABgnOMBq7CwUIsXL9b06dO9T2%2B/8cYbtWzZMr399ttOlwMAAGCc4wHr0KFDGjx4cKvxhIQEeTwep8sBAAAwzvGA1adPH%2B3Zs6fV%2BEcffeS94R0AACCQOf5ThPPmzdPSpUvl8XhkWZY%2B/fRTFRYW6pVXXtHDDz/sdDkAAADGOR6w0tLS1NTUpOeee0719fVavHixoqOj9cADD2jWrFlOlwMAAGCc4wFry5YtmjhxombOnKljx47JsizFxMQ4XQYAAMBF4/g9WNnZ2d6b2aOjo793uGpsbNSUKVO0Y8cO71hFRYXuvPNODRs2TDfeeKO2bdvm855PPvlEU6ZMUWJioubMmaOKigqf%2BZdfflljxoxRUlKSHnnkEdXV1X2vGgEAQHBxPGD1799fX3zxhZF9NTQ06L/%2B679UVlbmHbMsS%2Bnp6erZs6c2bdqkqVOnKiMjQ4cPH5YkHT58WOnp6Zo%2BfbreeOMNRUdH67777pNlWZKk999/X3l5ecrOzta6detUUlKi3NxcI/UCAIDg4PhHhAkJCXrwwQe1du1a9e/fX%2BHh4T7zOTk5bdpPeXm55s%2Bf7w1GZ2zfvl0VFRV6/fXX1bVrVw0cOFCffvqpNm3apPvvv18bN27U1Vdfrblz53qPl5qaqp07d2rkyJFav3697rjjDo0dO1aStHTpUs2bN08LFizgdyUCAIA2cfwK1pdffqnk5GR169ZNHo9Hhw4d8nm11ZlAVFhY6DNeUlKiq666Sl27dvWOJScnq7i42DufkpLinYuIiNCQIUNUXFys5uZm7dmzx2d%2B2LBhOn36tPbv3/9dTxkAAAQZR65grVixQhkZGeratateeeUVI/ucPXv2Occ9Ho/i4uJ8xmJiYlRZWXnB%2BdraWjU0NPjMu91u9ejRw/t%2BAACAC3EkYL300kuaN2%2Bez1Wle%2B65R8uWLWsVdr6vuro67%2B84PCMsLEyNjY0XnK%2Bvr/d%2Bbff%2Btqiqqmr1VHq3u6vxcw0NdfwC5PfidjtX75neBFqPnEBv7NEbe/TGHr2xF8y9cSRgnX2flCTt2rVLDQ0Nxo8VHh6umpoan7HGxkZ16dLFO392WGpsbFRkZKT3frBzzbfn/qvCwsJWv7g6PT1dmZmZbd5HZxQV1c3xY0ZGct%2BcHXpjj97Yozf26I29YOyN4ze5X2zx8fEqLy/3GauurvZePYqPj1d1dXWr%2BcGDB6tHjx4KDw9XdXW1Bg4cKElqampSTU2NYmNj21zDzJkzNW7cOJ8xt7urjh8/%2BV1OyVagfUdg%2BvzPJzQ0RJGREaqtrVNzc4tjxw0E9MYevbFHb%2BzRG3sdoTf%2B%2BOZe6oQBKzExUQUFBaqvr/detSoqKlJycrJ3vqioyLt9XV2d9u3bp4yMDIWEhGjo0KEqKirSyJEjJUnFxcVyu91KSEhocw1xcXGtPg70eL5RU1Nw/8Xzx/k3N7cEfd/t0Bt79MYevbFHb%2BwFY28cuwTicrkcOc6IESPUq1cvLVq0SGVlZSooKFBpaaluueUWSd/%2Bqp7du3eroKBAZWVlWrRokfr27esNVLNnz9YLL7ygrVu3qrS0VEuWLNGtt97KIxoAAECbOXYFa9myZT7PvDp9%2BrRyc3PVrZvvpbu2PgfLTmhoqFavXq2srCxNnz5dl19%2BufLz89W7d29JUt%2B%2BffWb3/xGjz/%2BuPLz85WUlKT8/HxvAJw8ebK%2B/vprLV68WI2NjZowYYIWLFjwvWoCAADBxWWd6w50w26//fY2b2vqMQ4djcfzjfF9ut0hGr/yY%2BP7vVjefSDVsWO53SGKiuqm48dPBt1l6QuhN/bojT16Y4/e2OsIvYmNvcQvx3XkClZnDU0AAADnElg/hgYAABAACFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY298FIHhMWvU//i6hXd59INXfJQAAAhRXsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwThuw/vu//1tXXnmlzyszM1OStG/fPs2YMUOJiYlKS0vT3r17fd67ZcsWXX/99UpMTFR6erqOHTvmj1MAAAABqtMGrPLyco0dO1bbtm3zvpYtW6ZTp07pnnvuUUpKin7/%2B98rKSlJv/jFL3Tq1ClJUmlpqbKyspSRkaHCwkLV1tZq0aJFfj4bAAAQSDptwDpw4IAGDRqk2NhY7ysyMlLvvPOOwsPDtXDhQg0cOFBZWVnq1q2b3nvvPUnShg0bNGnSJE2bNk0JCQlasWKFPvzwQ1VUVPj5jAAAQKDo1AGrf//%2BrcZLSkqUnJwsl8slSXK5XBo%2BfLiKi4u98ykpKd7te/Xqpd69e6ukpMSRugEAQODrlE9ytyxLX375pbZt26Y1a9aoublZEydOVGZmpjwej6644gqf7WNiYlRWViZJqqqqUlxcXKv5ysrKNh%2B/qqpKHo/HZ8zt7tpqv99XaGinzccdgtvdOft7Zt2wflqjN/bojT16Yy%2BYe9MpA9bhw4dVV1ensLAwrVq1SocOHdKyZctUX1/vHf9XYWFhamxslCTV19efd74tCgsLlZeX5zOWnp7uvckegSEqqpu/S7ioIiMj/F1Ch0Vv7NEbe/TGXjD2plMGrD59%2BmjHjh269NJL5XK5NHjwYLW0tGjBggUaMWJEq7DU2NioLl26SJLCw8PPOR8R0fbFMXPmTI0bN85nzO3uquPHT37HMzq3YPyOwEmm/7w6itDQEEVGRqi2tk7NzS3%2BLqdDoTf26I09emOvI/TGX98sd8qAJUk9evTw%2BXrgwIFqaGhQbGysqqurfeaqq6u9H9/Fx8efcz42NrbNx46Li2v1caDH842amviLF0g6%2B59Xc3NLpz/H74re2KM39uiNvWDsTae8BPLxxx9r5MiRqqur8459/vnn6tGjh5KTk/XZZ5/JsixJ396vtXv3biUmJkqSEhMTVVRU5H3fkSNHdOTIEe88AADAhXTKgJWUlKTw8HA9%2BuijOnjwoD788EOtWLFCd999tyZOnKja2lotX75c5eXlWr58uerq6jRp0iRJ0qxZs7R582Zt3LhR%2B/fv18KFC3XdddepX79%2Bfj4rAAAQKDplwOrevbteeOEFHTt2TGlpacrKytLMmTN19913q3v37lqzZo2Kioo0ffp0lZSUqKCgQF27dpX0bTjLzs5Wfn6%2BZs2apUsvvVQ5OTl%2BPiMAABBIXNaZz8pwUXk83xjfp9sdovErPza%2BX3zr3QdS/V3CReF2hygqqpuOHz8ZdPdEXAi9sUdv7NEbex2hN7Gxl/jluJ3yChYAAIA/EbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjm9ncBQEc1adX/%2BLuEdnn3gVR/lwAA%2BCeuYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYxpPcz6GhoUFLly7VH//4R3Xp0kVz587V3Llz/V0WcF6B9OR5njoPoLMjYJ3DihUrtHfvXq1bt06HDx/WQw89pN69e2vixIn%2BLg0AAAQAAtZZTp06pY0bN%2Bq3v/2thgwZoiFDhqisrEyvvvoqAQswJJCutklccQPQftyDdZb9%2B/erqalJSUlJ3rHk5GSVlJSopaXFj5UBAIBAwRWss3g8HkVFRSksLMw71rNnTzU0NKimpkbR0dEX3EdVVZU8Ho/PmNvdVXFxcUZrDQ0lHwNQAN9sAAAIKklEQVROcLv5u3bm3xv%2B3WmN3tgL5t4QsM5SV1fnE64keb9ubGxs0z4KCwuVl5fnM5aRkaH777/fTJH/VFVVpTsuK9PMmTONh7dAV1VVpcLCQnpzDvTGHr2xV1VVpXXr1tKbc6A39oK5N8EXKS8gPDy8VZA683WXLl3atI%2BZM2fq97//vc9r5syZxmv1eDzKy8trdbUM9OZ86I09emOP3tijN/aCuTdcwTpLfHy8jh8/rqamJrnd37bH4/GoS5cuioyMbNM%2B4uLigi6pAwCA/48rWGcZPHiw3G63iouLvWNFRUUaOnSoQkJoFwAAuDASw1kiIiI0bdo0LVmyRKWlpdq6datefPFFzZkzx9%2BlAQCAABG6ZMmSJf4uoqMZNWqU9u3bpyeffFKffvqpfvnLXyotLc3fZZ1Tt27dNGLECHXr1s3fpXQ49MYevbFHb%2BzRG3v0xl6w9sZlWZbl7yIAAAA6Ez4iBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwOrgGhoa9MgjjyglJUWjR4/Wiy%2B%2BaLvtvn37NGPGDCUmJiotLU179%2B51sFLntac39957r6688kqf1wcffOBgtf7R2NioKVOmaMeOHbbbBNu6OaMtvQmmdXP06FFlZmZqxIgRGjNmjHJyctTQ0HDObYNxzbSnP8G0biTpq6%2B%2B0rx585SUlKTrrrtOa9eutd02qNaOhQ4tOzvbuummm6y9e/daf/zjH62kpCTr3XffbbXdyZMnrdTUVOuJJ56wysvLrccee8z6t3/7N%2BvkyZN%2BqNoZbe2NZVnW%2BPHjrc2bN1tVVVXeV0NDg8MVO6u%2Bvt5KT0%2B3Bg0aZG3fvv2c2wTjurGstvXGsoJn3bS0tFi33nqrdffdd1tffPGFtWvXLmv8%2BPHWE0880WrbYFwz7emPZQXPurEsy2pubrYmTJhgzZ8/3/ryyy%2BtP//5z9bw4cOtt956q9W2wbZ2CFgd2MmTJ62hQ4f6/A8gPz/fuu2221ptu3HjRmvcuHFWS0uLZVnf/oMwfvx4a9OmTY7V66T29KahocEaPHiwdfDgQSdL9KuysjLrZz/7mXXTTTedN0QE27qxrLb3JpjWTXl5uTVo0CDL4/F4x95%2B%2B21r9OjRrbYNxjXTnv4E07qxLMs6evSo9R//8R/WN9984x1LT0%2B3fv3rX7faNtjWDh8RdmD79%2B9XU1OTkpKSvGPJyckqKSlRS0uLz7YlJSVKTk6Wy%2BWSJLlcLg0fPlzFxcWO1uyU9vTm4MGDcrlc6tevn9Nl%2Bs3OnTs1cuRIFRYWnne7YFs3Utt7E0zrJjY2VmvXrlXPnj19xk%2BcONFq22BcM%2B3pTzCtG0mKi4vTqlWr1L17d1mWpaKiIu3atUsjRoxotW2wrR23vwuAPY/Ho6ioKIWFhXnHevbsqYaGBtXU1Cg6Otpn2yuuuMLn/TExMSorK3OsXie1pzcHDx5U9%2B7dtXDhQu3cuVOXXXaZ7r//fv3kJz/xR%2BmOmD17dpu2C7Z1I7W9N8G0biIjIzVmzBjv1y0tLdqwYYNGjRrVattgXDPt6U8wrZuzjRs3TocPH9bYsWN1ww03tJoPtrXDFawOrK6uzidASPJ%2B3djY2KZtz96us2hPbw4ePKj6%2BnqNHj1aa9eu1U9%2B8hPde%2B%2B92rNnj2P1dlTBtm7aI5jXTW5urvbt26f//M//bDXHmjl/f4J53Tz77LN6/vnn9fnnnysnJ6fVfLCtHa5gdWDh4eGtFt6Zr7t06dKmbc/errNoT2/uu%2B8%2B3X777br00kslSQkJCfrLX/6i3/3udxo6dKgzBXdQwbZu2iNY101ubq7WrVunp59%2BWoMGDWo1H%2Bxr5kL9CdZ1I8l7fg0NDXrwwQe1cOFCn0AVbGuHK1gdWHx8vI4fP66mpibvmMfjUZcuXRQZGdlq2%2Brqap%2Bx6upqxcXFOVKr09rTm5CQEO8/dmcMGDBAR48edaTWjizY1k17BOO6eeyxx/TSSy8pNzf3nB/xSMG9ZtrSn2BbN9XV1dq6davP2BVXXKHTp0%2B3ukct2NYOAasDGzx4sNxut88NgEVFRRo6dKhCQnz/6BITE/XZZ5/JsixJkmVZ2r17txITEx2t2Snt6c3DDz%2BsRYsW%2BYzt379fAwYMcKTWjizY1k17BNu6ycvL0%2Buvv66nnnpKkydPtt0uWNdMW/sTbOvm0KFDysjI8AmQe/fuVXR0tM%2B9sFLwrR0CVgcWERGhadOmacmSJSotLdXWrVv14osvas6cOZK%2BvWJTX18vSZo4caJqa2u1fPlylZeXa/ny5aqrq9OkSZP8eQoXTXt6M27cOL399tt688039dVXXykvL09FRUW67bbb/HkKfhPM6%2BZCgnXdHDhwQKtXr9bPf/5zJScny%2BPxeF8Sa6Y9/QmmdSN9%2B7HgkCFD9Mgjj6i8vFwffvihcnNz9ctf/lJSkK8dvz0gAm1y6tQpa%2BHChdawYcOs0aNHWy%2B99JJ3btCgQT7PDykpKbGmTZtmDR061Lrlllusv/zlL36o2Dnt6c3vfvc7a8KECdbVV19t3XzzzdbOnTv9ULF/nP2sp2BfN//qQr0JlnWzZs0aa9CgQed8WRZrpr39CZZ1c0ZlZaWVnp5uDR8%2B3EpNTbWee%2B4577OugnntuCzrn9fqAAAAYAQfEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8PZKAjVAwJnUUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3244519064893463793”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4952947</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.452284034</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.288850376</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.126823082</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.166112957</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.288683603</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.264830508</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.394632991</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.181521147</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3022)</td> <td class=”number”>3054</td> <td class=”number”>95.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3244519064893463793”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0138614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0313834</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0315169</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0348311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.049180328</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.183803458</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.262443439</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.007518797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.073770492</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_GROCPTH14”>GROCPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3024</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.25192</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>3.1496</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2896886954587520405”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2896886954587520405,#minihistogram-2896886954587520405”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2896886954587520405”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2896886954587520405”

aria-controls=”quantiles-2896886954587520405” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2896886954587520405” aria-controls=”histogram-2896886954587520405”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2896886954587520405” aria-controls=”common-2896886954587520405”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2896886954587520405” aria-controls=”extreme-2896886954587520405”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2896886954587520405”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.071782</td>

</tr> <tr>

<th>Q1</th> <td>0.13884</td>

</tr> <tr>

<th>Median</th> <td>0.19453</td>

</tr> <tr>

<th>Q3</th> <td>0.28548</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.64199</td>

</tr> <tr>

<th>Maximum</th> <td>3.1496</td>

</tr> <tr>

<th>Range</th> <td>3.1496</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.14664</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.21937</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.87081</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.76</td>

</tr> <tr>

<th>Mean</th> <td>0.25192</td>

</tr> <tr>

<th>MAD</th> <td>0.1323</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.9671</td>

</tr> <tr>

<th>Sum</th> <td>789.02</td>

</tr> <tr>

<th>Variance</th> <td>0.048125</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2896886954587520405”>

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AYBgFCwAAwDDHC9ZVV12lF154QUeOHHH61AAAAI5wvGANHDhQK1eu1ODBg/WnP/1JmzdvlmVZTk8DAACgxThesKZPn6633npLy5cvV2RkpO666y5dfPHFevTRR/XZZ585PR0AAADjgvIZLJfLpUGDBiknJ0fvv/%2B%2Brr32Wq1Zs0aXXnqprr32Wv3zn/8M%2BFh1dXUaO3astm7d6htbsGCBzjnnHL%2BvdevW%2Bbbn5%2Bdr%2BPDh8ng8ysjI0OHDh33bLMvS4sWLNXDgQKWnp2vRokVqbGw0c%2BEAACAsuIN14rKyMr3yyit65ZVX9Omnn2rAgAG68sorVVpaqvvuu0/bt2/X3LlzT3mM2tpaTZ8%2BXcXFxX7jJSUlmj59uq688krfWPv27SVJO3fu1Ny5c/Xggw8qOTlZCxcu1OzZs/X4449Lkp5%2B%2Bmnl5%2BcrNzdX9fX1mjFjhjp16qSpU6caTgAAALRWjhesjRs3auPGjdq6das6duyocePGaenSperRo4dvny5dumjhwoWnLFh79%2B7V9OnTm/z8VklJiaZOnar4%2BHjbtnXr1mn06NEaN26cJGnRokUaOnSo9u/fr%2B7du2vt2rXKzMxUWlqaJOmee%2B7RkiVLKFgAACBgjr9FOHfuXLVr107Lli3TO%2B%2B8o%2BnTp/uVK0nq2bOnrrvuulMeZ9u2bbrwwguVl5fnN3706FEdOnTIdswTioqKfOVJ%2Br7MJSUlqaioSIcOHdLBgwd1wQUX%2BLanpqbq66%2B/VllZWfMuFAAAhC3H72C9%2B%2B67iouLU2VlpSIivu93O3fuVN%2B%2BfRUZGSlJGjBggAYMGHDK41xzzTVNjpeUlMjlcmnlypV699131aFDB910002%2BtwvLysqUkJDg95pOnTqptLRUXq9Xkvy2d%2B7cWZJUWlpqex0AAEBTHC9YR48e1eTJk/X73/9eM2fOlCTdeuut6ty5s5544gl16dLlFx1/3759crlcvrtg27dv1/3336/27dtrxIgRqqmpUVRUlN9roqKiVFdXp5qaGt/3P9wmff9h%2BkCVlZX5ytoJbndb4wUtMjK0nhPrdrf8fE9kEmrZtCQyaRq52JGJHZnYkUlgHC9Yf/nLX/Sb3/xGN910k2/stdde06xZs5Sdna2lS5f%2BouOPGzdOQ4cOVYcOHSRJycnJ%2Bvzzz/X8889rxIgRio6OtpWluro6xcTE%2BJWp6Oho368lKSYmJuA55OXlKTc3128sIyNDmZmZP/u6WoO4uHaOnSs2NvDfr3BBJk0jFzsysSMTOzI5NccL1r///W%2B9%2BOKLfh9A79ixo2bOnKlrr732Fx/f5XL5ytUJPXv21JYtWyRJiYmJKi8v99teXl6u%2BPh4JSYmSpK8Xq%2B6devm%2B7WkJj8w/2MmTZqkYcOG%2BY253W1VUXGseRfzE0Ltbw%2Bmr78pkZERio2NUVVVtRoaeLyGRCY/hlzsyMSOTOxCLRMn/3L/Q44XLLfbraqqKtt4dXW1kSe6L1myRB988IGeeeYZ39iePXvUs2dPSZLH41FBQYHGjx8vSTp48KAOHjwoj8ejxMREJSUlqaCgwFewCgoKlJSU1Ky39xISEmz7e71HVF9/%2Bi/EluTk9Tc0NIZ93icjk6aRix2Z2JGJHZmcmuO3QC666CItWLBAX375pW9s//79ys7O1pAhQ37x8YcOHart27frySef1Jdffqn/%2BZ//0csvv6ybb75ZkjR58mRt3LhR69ev1549ezRz5kxdfPHF6t69u2/74sWLtXXrVm3dulUPP/ywpkyZ8ovnBQAAwofjd7BmzZqlm266SSNHjlRsbKwkqaqqSn379tXs2bN/8fHPP/98LVmyREuXLtWSJUvUtWtXPfzww0pJSZEkpaSkKCsrS0uXLtW3336rQYMGaf78%2Bb7XT506Vd98842mTZumyMhITZw4UTfeeOMvnhcAAAgfLisIP2m5oaFB77//voqLi%2BV2u3XWWWfpt7/9rVwul9NTcYzXe8T4Md3uCI1Y/J7x47aU1%2B8e1OLncLsjFBfXThUVx7h1/R9k0jRysSMTOzKxC7VM4uPPCMp5g/KjciIjIzVkyBAjbwkCAACcbhwvWF6vV4899ph27Nih48eP2z7Y/uabbzo9JQAAAKMcL1j333%2B/du3apTFjxuiMM4Jz2w4AAKAlOV6wtmzZotWrV/v9PEAAAIDWxPHHNLRt21adOnVy%2BrQAAACOcbxgXXHFFVq9erUaGhqcPjUAAIAjHH%2BLsLKyUvn5%2BXr77bfVvXt32w9eXrt2rdNTAgAAMCooj2kYO3ZsME4LAADgCMcLVnZ2ttOnBAAAcJTjn8GSpLKyMuXm5mr69On65ptv9I9//EP79u0LxlQAAACMc7xgffHFF7rsssv097//XW%2B88Ya%2B%2B%2B47vfbaa5owYYKKioqcng4AAIBxjheshx56SMOHD9emTZv0q1/9SpL0yCOPaNiwYVq8eLHT0wEAADDO8YK1Y8cO3XTTTX4/2NntduvOO%2B/U7t27nZ4OAACAcY4XrMbGRjU22n/69rFjxxQZGen0dAAAAIxzvGANHjxYjz/%2BuF/JqqysVE5OjgYOHOj0dAAAAIxzvGDde%2B%2B92rVrlwYPHqza2lrdcccdGjp0qL766ivNmjXL6ekAAAAY5/hzsBITE/Xyyy8rPz9fH3/8sRobGzV58mRdccUVat%2B%2BvdPTAQAAMC4oT3KPiYnRVVddFYxTAwAAtDjHC9aUKVNOuZ2fRQgAAEKd4wWra9euft/X19friy%2B%2B0KeffqobbrjB6ekAAAAYd9r8LMJly5aptLTU4dkAAACYF5SfRdiUK664Qq%2B//nqwpwEAAPCLnTYF64MPPuBBowAAoFU4LT7kfvToUX3yySe65pprnJ4OAACAcY4XrKSkJL%2BfQyhJv/rVr3Tdddfp8ssvd3o6AAAAxjlesB566CGnTwkAAOAoxwvW9u3bA973ggsuaMGZAAAAtAzHC9b111/ve4vQsizf%2BMljLpdLH3/8sdPTAwAA%2BMUcL1grV67UggULNGPGDKWnpysqKkoffvihsrKydOWVV%2BrSSy91ekoAAABGOf6YhuzsbD3wwAMaOXKk4uLi1K5dOw0cOFBZWVl6/vnn1bVrV98XAABAKHK8YJWVlTVZntq3b6%2BKigqnpwMAAGCc4wWrf//%2BeuSRR3T06FHfWGVlpXJycvTb3/7W6ekAAAAY5/hnsO677z5NmTJFF110kXr06CHLsvT5558rPj5ea9eudXo6AAAAxjlesHr16qXXXntN%2Bfn5KikpkSRde%2B21GjNmjGJiYpyeDgAAgHGOFyxJOvPMM3XVVVfpq6%2B%2BUvfu3SV9/zR3AACA1sDxz2BZlqXFixfrggsu0NixY1VaWqpZs2Zp7ty5On78uNPTAQAAMM7xgvXss89q48aN%2BvOf/6yoqChJ0vDhw7Vp0ybl5uY6PR0AAADjHC9YeXl5euCBBzR%2B/Hjf09svvfRSLViwQK%2B%2B%2BqrT0wEAADDO8YL11VdfqU%2BfPrbx5ORkeb1ep6cDAABgnOMFq2vXrvrwww9t4%2B%2B%2B%2B67vA%2B8AAAChzPF/RTh16lQ9%2BOCD8nq9sixL//rXv5SXl6dnn31W9957r9PTAQAAMM7xgjVhwgTV19drxYoVqqmp0QMPPKCOHTvq7rvv1uTJk52eDgAAgHGOF6z8/HyNGjVKkyZN0uHDh2VZljp16uT0NAAAAFqM45/BysrK8n2YvWPHjpQrAADQ6jhesHr06KFPP/3U6dMCAAA4xvG3CJOTk3XPPfdo9erV6tGjh6Kjo/22Z2dnOz0lAAAAoxwvWJ999plSU1MliedeAQCAVsmRgrVo0SJNmzZNbdu21bPPPuvEKQEAAILGkc9gPf3006qurvYbu/XWW1VWVubE6QEAABzlSMGyLMs2tn37dtXW1jpxegAAAEc5/q8ITaurq9PYsWO1detW39j%2B/ft14403qn///rr00ku1efNmv9e8//77Gjt2rDwej6ZMmaL9%2B/f7bX/mmWc0ZMgQpaSkaM6cOba7bwAAAKcS0gWrtrZWf/rTn1RcXOwbsyxLGRkZ6ty5szZs2KArrrhC06ZN04EDByRJBw4cUEZGhsaPH6%2BXXnpJHTt21J133um7y/bGG28oNzdXWVlZWrNmjYqKipSTkxOU6wMAAKHJsYLlcrmMHm/v3r26%2Buqr9eWXX/qNb9myRfv371dWVpZ69eql2267Tf3799eGDRskSevXr9d5552nm2%2B%2BWWeffbays7P19ddfa9u2bZKktWvX6oYbbtDQoUN1/vnn68EHH9SGDRu4iwUAAALm2GMaFixY4PfMq%2BPHjysnJ0ft2rXz2y/Q52Bt27ZNF154of74xz%2Bqf//%2BvvGioiKde%2B65atu2rW8sNTVVhYWFvu1paWm%2BbTExMerbt68KCwuVlpamDz/8UNOmTfNt79%2B/v44fP649e/YoJSWleRcNAADCkiMF64ILLrA98yolJUUVFRWqqKj4Wce85pprmhz3er1KSEjwG%2BvUqZNKS0t/cntVVZVqa2v9trvdbnXo0MH3%2BkCUlZXZrtftbms77y8VGRla7/C63S0/3xOZhFo2LYlMmkYudmRiRyZ2ZBIYRwqWk8%2B%2Bqq6uVlRUlN9YVFSU6urqfnJ7TU2N7/sfe30g8vLylJub6zeWkZGhzMzMgI/RGsXFtfvpnQyJjY1x7FyhgkyaRi52ZGJHJnZkcmqOP8m9pUVHR6uystJvrK6uTm3atPFtP7ks1dXVKTY21vcWZlPbY2ICX0iTJk3SsGHD/Mbc7raqqDgW8DECEWp/ezB9/U2JjIxQbGyMqqqq1dDQ2OLnCwVk0jRysSMTOzKxC7VMnPzL/Q%2B1uoKVmJiovXv3%2Bo2Vl5f73p5LTExUeXm5bXufPn3UoUMHRUdHq7y8XL169ZIk1dfXq7KyUvHx8QHPISEhwfZ2oNd7RPX1p/9CbElOXn9DQ2PY530yMmkaudiRiR2Z2JHJqYXWLZAAeDweffTRR763%2BySpoKBAHo/Ht72goMC3rbq6Wrt375bH41FERIT69evnt72wsFBut1vJycnOXQQAAAhpra5gpaenq0uXLpo9e7aKi4u1atUq7dy5UxMnTpQkTZgwQTt27NCqVatUXFys2bNnq1u3brrwwgslff/h%2BSeffFKbNm3Szp07NW/ePF199dXNeosQAACEt1ZXsCIjI7V8%2BXJ5vV6NHz9er7zyipYtW6akpCRJUrdu3fTXv/5VGzZs0MSJE1VZWally5b5ntM1ZswY3XbbbXrggQd088036/zzz9eMGTOCeUkAACDEuKymflAgjPN6jxg/ptsdoRGL3zN%2B3Jby%2Bt2DWvwcbneE4uLaqaLiGJ8N%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%2B9jelpKTotttu03fffSdJ2rlzp%2BbOnatp06YpLy9PVVVVmj17dpCvBgAAhJJWW7BKSkrUu3dvxcfH%2B75iY2P12muvKTo6WjNnzlSvXr00d%2B5ctWvXTv/4xz8kSevWrdPo0aM1btw4JScna9GiRXrnnXe0f//%2BIF8RAAAIFa26YPXo0cM2XlRUpNTUVLlcLkmSy%2BXSgAEDVFhY6Nuelpbm279Lly5KSkpSUVGRI/MGAAChr1UWLMuy9Nlnn2nz5s0aOXKkhg8frsWLF6uurk5er1cJCQl%2B%2B3fq1EmlpaWSpLKyslNuBwAA%2BCnuYE%2BgJRw4cEDV1dWKiorSY489pq%2B%2B%2BkoLFixQTU2Nb/yHoqKiVFdXJ0mqqak55fZAlJVu1wvCAAAMmElEQVSVyev1%2Bo253W1txe2XiowMrX7sdrf8fE9kEmrZtCQyaRq52JGJHZnYkUlgWmXB6tq1q7Zu3aozzzxTLpdLffr0UWNjo2bMmKH09HRbWaqrq1ObNm0kSdHR0U1uj4mJCfj8eXl5ys3N9RvLyMjw/SvGcDVi8XvBnkKz/HvhqGBPwajY2MDXcDghFzsysSMTOzI5tVZZsCSpQ4cOft/36tVLtbW1io%2BPV3l5ud%2B28vJy392lxMTEJrfHx8cHfO5JkyZp2LBhfmNud1tVVBxrziX8JP720LJM/34FS2RkhGJjY1RVVa2GhsZgT%2Be0QS52ZGJHJnahlklcXLugnLdVFqz33ntP99xzj95%2B%2B23fnaePP/5YHTp0UGpqqp544glZliWXyyXLsrRjxw7dfvvtkiSPx6OCggKNHz9eknTw4EEdPHhQHo8n4PMnJCTY3g70eo%2Bovv70X4j4P63t96uhobHVXZMJ5GJHJnZkYkcmp9Yqb4GkpKQoOjpa9913n/bt26d33nlHixYt0i233KJRo0apqqpKCxcu1N69e7Vw4UJVV1dr9OjRkqTJkydr48aNWr9%2Bvfbs2aOZM2fq4osvVvfu3YN8VQAAIFS0yoLVvn17Pfnkkzp8%2BLAmTJiguXPnatKkSbrlllvUvn17Pf744767VEVFRVq1apXatm0r6ftylpWVpWXLlmny5Mk688wzlZ2dHeQrAgAAocRlWZYV7EmEA6/3iPFjut0RIffB8VDy%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%2BzZs0f19fVKSUnxjaWmpmrlypVqbGxURATvqgK/VCjdbZO44wag%2BShYJ/F6vYqLi1NUVJRvrHPnzqqtrVVlZaU6duz4k8coKyuT1%2Bv1G3O72yohIcHoXCMjKXuAE9zu8Phv7cSfKfzZ8n/IxI5MAkPBOkl1dbVfuZLk%2B76uri6gY%2BTl5Sk3N9dvbNq0abrrrrvMTPI/ysrKdMOvizVp0iTj5S1UlZWVKS8vj0x%2BgEyaRi52ZWVlWrNmNZn8AJnYkUlgqJ8niY6OthWpE9%2B3adMmoGNMmjRJf/vb3/y%2BJk2aZHyuXq9Xubm5trtl4YxM7MikaeRiRyZ2ZGJHJoHhDtZJEhMTVVFRofr6ernd38fj9XrVpk0bxcbGBnSMhIQEWj0AAGGMO1gn6dOnj9xutwoLC31jBQUF6tevHx9wBwAAAaExnCQmJkbjxo3TvHnztHPnTm3atElPPfWUpkyZEuypAQCAEBE5b968ecGexOlm4MCB2r17tx5%2B%2BGH961//0u23364JEyYEe1pNateundLT09WuXbtgT%2BW0QSZ2ZNI0crEjEzsysSOTn%2BayLMsK9iQAAABaE94iBAAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwTrN1dbWas6cOUpLS9PgwYP11FNP/ei%2Bu3fv1lVXXSWPx6MJEyZo165dDs7UOc3J5I477tA555zj9/XWW285OFtn1dXVaezYsdq6deuP7hMu6%2BSHAsklXNbKoUOHlJmZqfT0dA0ZMkTZ2dmqra1tct9wWSvNySRc1skXX3yhqVOnKiUlRRdffLFWr179o/uGyzppNguntaysLOuyyy6zdu3aZf3zn/%2B0UlJSrNdff92237Fjx6xBgwZZDz30kLV3715r/vz51n/9139Zx44dC8KsW1agmViWZY0YMcLauHGjVVZW5vuqra11eMbOqKmpsTIyMqzevXtbW7ZsaXKfcFonJwSSi2WFx1ppbGy0rr76auuWW26xPv30U2v79u3WiBEjrIceesi2b7isleZkYlnhsU4aGhqsSy65xJo%2Bfbr12WefWW%2B//bY1YMAA65VXXrHtGy7r5OegYJ3Gjh07ZvXr18/v/xSWLVtmXXfddbZ9169fbw0bNsxqbGy0LOv7PzRGjBhhbdiwwbH5OqE5mdTW1lp9%2BvSx9u3b5%2BQUg6K4uNi6/PLLrcsuu%2ByURSJc1skJgeYSLmtl7969Vu/evS2v1%2Bsbe/XVV63Bgwfb9g2XtdKcTMJlnRw6dMj67//%2Bb%2BvIkSO%2BsYyMDOvPf/6zbd9wWSc/B28Rnsb27Nmj%2Bvp6paSk%2BMZSU1NVVFSkxsZGv32LioqUmpoql8slSXK5XBowYIAKCwsdnXNLa04m%2B/btk8vlUvfu3Z2epuO2bdumCy%2B8UHl5eafcL1zWyQmB5hIuayU%2BPl6rV69W586d/caPHj1q2zdc1kpzMgmXdZKQkKDHHntM7du3l2VZKigo0Pbt25Wenm7bN1zWyc/hDvYE8OO8Xq/i4uIUFRXlG%2BvcubNqa2tVWVmpjh07%2Bu171lln%2Bb2%2BU6dOKi4udmy%2BTmhOJvv27VP79u01c%2BZMbdu2Tb/%2B9a9111136Xe/%2B10wpt6irrnmmoD2C5d1ckKguYTLWomNjdWQIUN83zc2NmrdunUaOHCgbd9wWSvNySRc1skPDRs2TAcOHNDQoUM1cuRI2/ZwWSc/B3ewTmPV1dV%2BRUKS7/u6urqA9j15v1DXnEz27dunmpoaDR48WKtXr9bvfvc73XHHHfrwww8dm%2B/pJlzWSXOF61rJycnR7t279cc//tG2LVzXyqkyCcd1snTpUq1cuVIff/yxsrOzbdvDdZ0EgjtYp7Ho6GjbIj3xfZs2bQLa9%2BT9Ql1zMrnzzjt1/fXX68wzz5QkJScn66OPPtKLL76ofv36OTPh00y4rJPmCse1kpOTozVr1ujRRx9V7969bdvDca38VCbhuE5OXFdtba3uuecezZw5069QheM6CRR3sE5jiYmJqqioUH19vW/M6/WqTZs2io2Nte1bXl7uN1ZeXq6EhARH5uqU5mQSERHh%2B4PwhJ49e%2BrQoUOOzPV0FC7rpLnCba3Mnz9fTz/9tHJycpp820cKv7USSCbhsk7Ky8u1adMmv7GzzjpLx48ft302LdzWSXNQsE5jffr0kdvt9vuwYEFBgfr166eICP/fOo/How8%2B%2BECWZUmSLMvSjh075PF4HJ1zS2tOJvfee69mz57tN7Znzx717NnTkbmejsJlnTRXOK2V3NxcvfDCC3rkkUc0ZsyYH90vnNZKoJmEyzr56quvNG3aNL/iuGvXLnXs2NHvc65SeK2T5qJgncZiYmI0btw4zZs3Tzt37tSmTZv01FNPacqUKZK%2Bv3NTU1MjSRo1apSqqqq0cOFC7d27VwsXLlR1dbVGjx4dzEswrjmZDBs2TK%2B%2B%2BqpefvllffHFF8rNzVVBQYGuu%2B66YF6C48JxnQQiHNdKSUmJli9frj/84Q9KTU2V1%2Bv1fUnhuVaak0m4rJN%2B/fqpb9%2B%2BmjNnjvbu3at33nlHOTk5uv322yWF5zr5WYL2gAgE5LvvvrNmzpxp9e/f3xo8eLD19NNP%2B7b17t3b71kjRUVF1rhx46x%2B/fpZEydOtD766KMgzLjlNSeTF1980brkkkus8847z7ryyiutbdu2BWHGzjr5eU/huk5O9lO5hMNaefzxx63evXs3%2BWVZ4blWmptJOKwTy7Ks0tJSKyMjwxowYIA1aNAga8WKFb5nXYXjOvk5XJb1n/t6AAAAMIK3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8PyYwXv52TftIAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2896886954587520405”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.166002656</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.274951883</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.240124865</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.176460208</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.096432015</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.138861888</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16307893</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.645577792</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.187038249</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3013)</td> <td class=”number”>3046</td> <td class=”number”>94.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2896886954587520405”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0264194</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0304294</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0316797</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0368772</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.06185567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.089864159</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.207505519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.544529262</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.149606299</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_BLACK15”>LACCESS_BLACK15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2885</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2276.5</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>167910</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5880046620144668249”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5880046620144668249,#minihistogram-5880046620144668249”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5880046620144668249”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5880046620144668249”

aria-controls=”quantiles-5880046620144668249” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5880046620144668249” aria-controls=”histogram-5880046620144668249”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5880046620144668249” aria-controls=”common-5880046620144668249”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5880046620144668249” aria-controls=”extreme-5880046620144668249”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5880046620144668249”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>7</td>

</tr> <tr>

<th>Median</th> <td>87.873</td>

</tr> <tr>

<th>Q3</th> <td>1013.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>10754</td>

</tr> <tr>

<th>Maximum</th> <td>167910</td>

</tr> <tr>

<th>Range</th> <td>167910</td>

</tr> <tr>

<th>Interquartile range</th> <td>1006.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>8808.4</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.8692</td>

</tr> <tr>

<th>Kurtosis</th> <td>122.71</td>

</tr> <tr>

<th>Mean</th> <td>2276.5</td>

</tr> <tr>

<th>MAD</th> <td>3335.9</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.4904</td>

</tr> <tr>

<th>Sum</th> <td>7086800</td>

</tr> <tr>

<th>Variance</th> <td>77588000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5880046620144668249”>

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zVw4EBFRkZq7Nixys3NdZl/9dVXFR8fr%2BjoaM2YMUPFxcVu2y8AAOD9vLJgGWOUlJSk4uJivf7663rhhRf00Ucf6cUXX5QxRomJiWrWrJk2btyoQYMGafLkyTp%2B/Lgk6fjx40pMTNTQoUP11ltvqUmTJnrkkUdkjJEkbd68WampqZozZ47WrFkju92ulJQUT%2B4uAADwMl5ZsA4fPqysrCwlJyerbdu2io2NVVJSkjIyMrRjxw7l5uZqzpw5atOmjSZNmqSoqCht3LhRkrRhwwZ17NhREyZMUNu2bZWcnKxjx45p586dkqS1a9dq3LhxSkhIUKdOnTR79mxt3LiRs1gAAKDGvLJgBQcHa9WqVWrWrJnL%2BNmzZ2W323XnnXeqQYMGzvGYmBhlZWVJkux2u2JjY51zAQEB6tChg7KyslRRUaG9e/e6zEdFRenChQs6cOBALe8VAACoK7yyYAUGBio%2BPt55u7KyUuvWrVPXrl3lcDgUEhLicv%2BmTZsqLy9Pkq44f/r0aZWWlrrM22w2BQUFOR8PAABwNTZPL8AKKSkp2r9/v9566y29%2Buqr8vPzc5n38/NTWVmZJKm4uPiy8yUlJc7bl3t8TeTn58vhcLiM2WwNqhW761Wvnnf1Y5vN%2BvVWZeBtWViJDMhAIgOJDCQykG6cDLy%2BYKWkpGjNmjV64YUX1K5dO/n7%2B6uoqMjlPmVlZapfv74kyd/fv1pZKisrU2BgoPz9/Z23fzofEBBQ4zWlp6crNTXVZSwxMVFJSUk13kZd1Lhxw1rbdmBgzb8%2BdRUZkIFEBhIZSGQgeT4Dry5Yc%2BfO1fr165WSkqK%2BfftKkkJDQ3Xo0CGX%2BxUUFDjPHoWGhqqgoKDafPv27RUUFCR/f38VFBSoTZs2kqTy8nIVFRUpODi4xusaOXKkevXq5TJmszVQYeG5a97HK/F0O79WVu%2B/dDGDwMAAnT5drIqKSsu37w3IgAwkMpDIQCIDqXoGtfnD/ZV4bcFKTU3VG2%2B8oeeff179%2BvVzjkdGRiotLU0lJSXOs1aZmZmKiYlxzmdmZjrvX1xcrP3792vy5Mny9fVVRESEMjMz1aVLF0lSVlaWbDabwsPDa7y2kJCQai8HOhxnVF5%2Bcz7Zq9Tm/ldUVN70%2BZIBGUhkIJGBRAaS5zPwrlMg/5STk6Nly5bpd7/7nWJiYuRwOJwfcXFxat68uaZPn67s7GylpaVpz549Gj58uCRp2LBh2r17t9LS0pSdna3p06erZcuWzkI1atQorV69Wlu3btWePXs0a9Ys3X///df0EiEAALi5eeUZrL/97W%2BqqKjQ8uXLtXz5cpe5r7/%2BWsuWLdPMmTM1dOhQ/du//ZuWLl2qsLAwSVLLli310ksv6dlnn9XSpUsVHR2tpUuXysfHR5I0YMAAHTt2TM8884zKysp0zz33aOrUqW7fRwAA4L18TNVbmKNWORxnLN%2BmzearPgu3Wb7d2vLhY90s36bN5qvGjRuqsPDcTXs6nAzIQCIDiQwkMpCqZxAc/DOPrMMrXyIEAAC4kVGwAAAALEbBAgAAsJjbC9aIESP0xhtv6MwZ669JAgAAuBG4vWB17dpVK1asUPfu3fX4449r%2B/bt4jp7AABQl7i9YE2ZMkUfffSRli1bpnr16unRRx9Vz5499cILL%2BjIkSPuXg4AAIDlPPI%2BWD4%2BPurWrZu6deum4uJivfbaa1q2bJnS0tLUuXNnjRs3Tvfcc48nlgYAAHDdPPZGo/n5%2BXrvvff03nvv6eDBg%2BrcubOGDBmivLw8/fGPf9SuXbs0c%2BZMTy0PAADgX%2Bb2grVp0yZt2rRJn3/%2BuZo0aaLBgwdryZIlatWqlfM%2BzZs31/z58ylYAADAK7m9YM2cOVMJCQlaunSpfvWrX8nXt/plYK1bt9bo0aPdvTQAAABLuL1gffLJJ2rcuLGKioqc5WrPnj3q0KGD6tWrJ0nq3LmzOnfu7O6lAQAAWMLtv0V49uxZ9evXT3/%2B85%2BdYw899JAGDRqkEydOuHs5AAAAlnN7wXr22Wf1b//2b3rwwQedYx988IGaN2%2Bu5ORkdy8HAADAcm4vWP/3f/%2Bnp556SsHBwc6xJk2a6Mknn9SOHTvcvRwAAADLub1g2Ww2nT59utp4cXEx7%2BgOAADqBLcXrF/96leaN2%2Bevv32W%2BdYbm6ukpOTFR8f7%2B7lAAAAWM7tv0U4bdo0Pfjgg%2Brbt68CAwMlSadPn1aHDh00ffp0dy8HAADAcm4vWE2bNtU777yjTz/9VNnZ2bLZbLr99tt19913y8fHx93LAQAAsJxH/lROvXr1FB8fz0uCAACgTnJ7wXI4HHrxxRe1e/duXbhwodqF7X/729/cvSQAAABLub1gPf3009q3b58GDBign/3sZ%2B7%2B9AAAALXO7QVrx44dWrVqlWJjY939qQEAANzC7W/T0KBBAzVt2tTdnxYAAMBt3F6wBg0apFWrVqmiosLdnxoAAMAt3P4SYVFRkTIyMvT3v/9dt912m/z8/Fzm165d6%2B4lAQAAWMojb9MwcOBAT3xaAAAAt3B7wUpOTnb3pwQAAHArt1%2BDJUn5%2BflKTU3VlClT9P333%2Buvf/2rDh8%2B7ImlAAAAWM7tBeubb77Rfffdp3feeUebN2/W%2BfPn9cEHH2jYsGGy2%2B3uXg4AAIDl3F6wnnvuOfXu3Vtbt27VLbfcIkl6/vnn1atXLy1cuNDdywEAALCc2wvW7t279eCDD7r8YWebzaZHHnlE%2B/fvd/dyAAAALOf2glVZWanKyspq4%2BfOnVO9evXcvRwAAADLub1gde/eXStXrnQpWUVFRUpJSVHXrl3dvRwAAADLub1gPfXUU9q3b5%2B6d%2B%2Bu0tJSPfzww0pISNB3332nadOmuXs5AAAAlnP7%2B2CFhobq3XffVUZGhr766itVVlbqgQce0KBBg9SoUSN3LwcAAMByHnkn94CAAI0YMcITnxoAAKDWub1gjR079orz/C1CAADg7dxesFq0aOFyu7y8XN98840OHjyocePGuXs5AAAAlrth/hbh0qVLlZeX5%2BbVAAAAWM8jf4vwUgYNGqQPP/zwmh9XVlamgQMH6vPPP3eOzZs3T3fccYfLx7p165zzGRkZ6t27tyIjI5WYmKhTp04554wxWrhwobp27aq4uDgtWLDgku/bBQAAcDkeucj9Ur744otrfqPR0tJSTZkyRdnZ2S7jOTk5mjJlioYMGeIcq/oNxT179mjmzJmaPXu2wsPDNX/%2BfE2fPl0rV66UJL3yyivKyMhQamqqysvLNXXqVDVt2lQTJ068zj0EAAA3ixviIvezZ8/q66%2B/1qhRo2q8nUOHDmnKlCkyxlSby8nJ0cSJExUcHFxtbt26dbr33ns1ePBgSdKCBQuUkJCg3Nxc3XbbbVq7dq2SkpIUGxsrSXriiSe0ePFiChYAAKgxtxessLAwl79DKEm33HKLRo8erV//%2Btc13s7OnTvVpUsX/eEPf1BUVJRz/OzZszp58qRatWp1ycfZ7Xb97ne/c95u3ry5wsLCZLfb5efnpxMnTuiuu%2B5yzsfExOjYsWPKz89XSEhIjdcHAABuXm4vWM8995wl27nc2a6cnBz5%2BPhoxYoV%2BuSTTxQUFKQHH3zQ%2BXLhpYpS06ZNlZeXJ4fDIUku882aNZMk5eXl1bhg5efnO7dVxWZrYHlBq1fvhrmErkZsNuvXW5WBt2VhJTIgA4kMJDKQyEC6cTJwe8HatWtXje/74zNJNXX48GH5%2BPiodevWGj16tHbt2qWnn35ajRo1Up8%2BfVRSUiI/Pz%2BXx/j5%2BamsrEwlJSXO2z%2Beky5eTF9T6enpSk1NdRlLTExUUlLSNe9PXdK4ccNa23ZgYECtbdtbkAEZSGQgkYFEBpLnM3B7wRozZozzJcIfXz/10zEfHx999dVX17z9wYMHKyEhQUFBQZKk8PBwHT16VOvXr1efPn3k7%2B9frSyVlZUpICDApUz5%2B/s7/y1dfPf5mho5cqR69erlMmazNVBh4blr3p8r8XQ7v1ZW7790MYPAwACdPl2sioqb87c9yYAMJDKQyEAiA6l6BrX5w/2VuL1grVixQvPmzdPUqVMVFxcnPz8/7d27V3PmzNGQIUPUv3//69q%2Bj4%2BPs1xVad26tXbs2CHp4t9CLCgocJkvKChQcHCwQkNDJUkOh0MtW7Z0/lvSJS%2BYv5yQkJBqLwc6HGdUXn5zPtmr1Ob%2BV1RU3vT5kgEZSGQgkYFEBpLnM3D7KZDk5GQ988wz6tu3rxo3bqyGDRuqa9eumjNnjtavX68WLVo4P/4Vixcv1vjx413GDhw4oNatW0uSIiMjlZmZ6Zw7ceKETpw4ocjISIWGhiosLMxlPjMzU2FhYVzgDgAAasztZ7Dy8/MvWZ4aNWqkwsLC695%2BQkKC0tLStHr1avXp00fbt2/Xu%2B%2B%2B6/wbhw888IDGjBmjqKgoRUREaP78%2BerZs6duu%2B025/zChQv185//XJK0aNEiTZgw4brXBQAAbh5uL1hRUVF6/vnn9V//9V/ON/8sKipSSkqK7r777uvefqdOnbR48WItWbJEixcvVosWLbRo0SJFR0dLkqKjozVnzhwtWbJEP/zwg7p166a5c%2Bc6Hz9x4kR9//33mjx5surVq6fhw4dXOyMGAABwJT7mUu/UWYtycnI0duxYFRcXq1WrVjLG6OjRowoODtbatWudZ47qGofjjOXbtNl81WfhNsu3W1s%2BfKyb5du02XzVuHFDFRaeu2mvNyADMpDIQCIDiQyk6hkEB//MM%2Btw9yds06aNPvjgA2VkZCgnJ0eS9Jvf/EYDBgy4pt/UAwAAuFF55G8R3nrrrRoxYoS%2B%2B%2B4757VPt9xyiyeWAgAAYDm3/xahMUYLFy7UXXfdpYEDByovL0/Tpk3TzJkzdeHCBXcvBwAAwHJuL1ivvfaaNm3apD/96U/ON/bs3bu3tm7dWu3dzwEAALyR2wtWenq6nnnmGQ0dOtT57u39%2B/fXvHnz9P7777t7OQAAAJZze8H67rvv1L59%2B2rj4eHh1f5AMgAAgDdye8Fq0aKF9u7dW238k08%2BcV7wDgAA4M3c/luEEydO1OzZs%2BVwOGSM0Weffab09HS99tpreuqpp9y9HAAAAMu5vWANGzZM5eXlWr58uUpKSvTMM8%2BoSZMmeuyxx/TAAw%2B4ezkAAACWc3vBysjIUL9%2B/TRy5EidOnVKxhg1bdrU3csAAACoNW6/BmvOnDnOi9mbNGlCuQIAAHWO2wtWq1atdPDgQXd/WgAAALdx%2B0uE4eHheuKJJ7Rq1Sq1atVK/v7%2BLvPJycnuXhIAAICl3F6wjhw5opiYGEnifa8AAECd5JaCtWDBAk2ePFkNGjTQa6%2B95o5PCQAA4DFuuQbrlVdeUXFxscvYQw89pPz8fHd8egAAALdyS8EyxlQb27Vrl0pLS93x6QEAANzK7b9FCAAAUNdRsAAAACzmtoLl4%2BPjrk8FAADgUW57m4Z58%2Ba5vOfVhQsXlJKSooYNG7rcj/fBAgAA3s4tBeuuu%2B6q9p5X0dHRKiwsVGFhoTuWAAAA4DZuKVi89xUAALiZcJE7AACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWMzrC1ZZWZkGDhyozz//3DmWm5ur8ePHKyoqSv3799f27dtdHvPpp59q4MCBioyM1NixY5Wbm%2Bsy/%2Bqrryo%2BPl7R0dGaMWOGiouL3bIvAACgbvDqglVaWqrHH39c2dnZzjFjjBITE9WsWTNt3LhRgwYN0uTJk3X8%2BHFJ0vHjx5WYmKihQ4fqrbfeUpMmTfTII4/IGCNJ2rx5s1JTUzVnzhytWbNGdrtdKSkpHtk/AADgnby2YB06dEj333%2B/vv32W5fxHTt2KDc3V3PmzFGbNm00adIkRUVFaePGjZKkDRs2qGPHjpowYYLatm2r5ORkHTt2TDt37pQkrV27VuPGjVNCQoI6deqk2bNna%2BPGjZzFAgAANea1BWvnzp3q0qWL0tPTXcbtdrvuvPNONWjQwDkWExOjrKws53xsbKxzLiAgQB06dFBWVpYqKiq0d%2B9el/moqChduHBBBw4cqOU9AgAAdYXN0wv4V40aNeqS4w6HQyEhIS5jTZs2VV5e3lXnT58%2BrdLSUpd5m82moKAg5%2BMBAACuxmsL1uUUFxfLz8/PZczPz09lZWVXnS8pKXHevtzjayI/P18Oh8NlzGauhx9gAAAZxUlEQVRrUK3YXa969bzrBKTNZv16qzLwtiysRAZkIJGBRAYSGUg3TgZ1rmD5%2B/urqKjIZaysrEz169d3zv%2B0LJWVlSkwMFD%2B/v7O2z%2BdDwgIqPEa0tPTlZqa6jKWmJiopKSkGm%2BjLmrcuGGtbTswsOZfn7qKDMhAIgOJDCQykDyfQZ0rWKGhoTp06JDLWEFBgfPsUWhoqAoKCqrNt2/fXkFBQfL391dBQYHatGkjSSovL1dRUZGCg4NrvIaRI0eqV69eLmM2WwMVFp77V3bpsjzdzq%2BV1fsvXcwgMDBAp08Xq6Ki0vLtewMyIAOJDCQykMhAqp5Bbf5wfyV1rmBFRkYqLS1NJSUlzrNWmZmZiomJcc5nZmY6719cXKz9%2B/dr8uTJ8vX1VUREhDIzM9WlSxdJUlZWlmw2m8LDw2u8hpCQkGovBzocZ1RefnM%2B2avU5v5XVFTe9PmSARlIZCCRgUQGkucz8K5TIDUQFxen5s2ba/r06crOzlZaWpr27Nmj4cOHS5KGDRum3bt3Ky0tTdnZ2Zo%2BfbpatmzpLFSjRo3S6tWrtXXrVu3Zs0ezZs3S/ffff00vEQIAgJtbnStY9erV07Jly%2BRwODR06FC99957Wrp0qcLCwiRJLVu21EsvvaSNGzdq%2BPDhKioq0tKlS%2BXj4yNJGjBggCZNmqRnnnlGEyZMUKdOnTR16lRP7hIAAPAyPqbqLcxRqxyOM5Zv02bzVZ%2BF2yzfbm358LFulm/TZvNV48YNVVh47qY9HU4GZCCRgUQGEhlI1TMIDv6ZR9ZR585gAQAAeBoFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIvV2YK1ZcsW3XHHHS4fSUlJkqT9%2B/drxIgRioyM1LBhw7Rv3z6Xx2ZkZKh3796KjIxUYmKiTp065YldAAAAXqrOFqxDhw4pISFB27dvd37MmzdP58%2Bf10MPPaTY2Fi9/fbbio6O1qRJk3T%2B/HlJ0p49ezRz5kxNnjxZ6enpOn36tKZPn%2B7hvQEAAN6kzhasnJwctWvXTsHBwc6PwMBAffDBB/L399eTTz6pNm3aaObMmWrYsKH%2B%2Bte/SpLWrVune%2B%2B9V4MHD1Z4eLgWLFigjz/%2BWLm5uR7eIwAA4C3qdMFq1apVtXG73a6YmBj5%2BPhIknx8fNS5c2dlZWU552NjY533b968ucLCwmS3292ybgAA4P1snl5AbTDG6MiRI9q%2BfbtWrlypiooK9evXT0lJSXI4HLr99ttd7t%2B0aVNlZ2dLkvLz8xUSElJtPi8vr8afPz8/Xw6Hw2XMZmtQbbvXq1497%2BrHNpv1663KwNuysBIZkIFEBhIZSGQg3TgZ1MmCdfz4cRUXF8vPz08vvviivvvuO82bN08lJSXO8R/z8/NTWVmZJKmkpOSK8zWRnp6u1NRUl7HExETnRfY3q8aNG9batgMDA2pt296CDMhAIgOJDCQykDyfQZ0sWC1atNDnn3%2BuW2%2B9VT4%2BPmrfvr0qKys1depUxcXFVStLZWVlql%2B/viTJ39//kvMBATX/Qo0cOVK9evVyGbPZGqiw8Ny/uEeX5ul2fq2s3n/pYgaBgQE6fbpYFRWVlm/fG5ABGUhkIJGBRAZS9Qxq84f7K6mTBUuSgoKCXG63adNGpaWlCg4OVkFBgctcQUGB8%2BW70NDQS84HBwfX%2BHOHhIRUeznQ4Tij8vKb88lepTb3v6Ki8qbPlwzIQCIDiQwkMpA8n4F3nQKpoW3btqlLly4qLi52jn311VcKCgpSTEyMvvjiCxljJF28Xmv37t2KjIyUJEVGRiozM9P5uBMnTujEiRPOeQAAgKupkwUrOjpa/v7%2B%2BuMf/6jDhw/r448/1oIFC/Tb3/5W/fr10%2BnTpzV//nwdOnRI8%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%2B%2BA9PL%2BGafPhYN08vAQDgpTiDdQmlpaWaMWOGYmNj1b17d7388sueXhIAAPAinMG6hAULFmjfvn1as2aNjh8/rmnTpiksLEz9%2BvXz9NIAAIAXoGD9xPnz57Vhwwb9%2Bc9/VocOHdShQwdlZ2fr9ddfp2ABAIAaoWD9xIEDB1ReXq7o6GjnWExMjFasWKHKykr5%2BvKq6s3C264Z2/JEvKeXAAD4JwrWTzgcDjVu3Fh%2Bfn7OsWbNmqm0tFRFRUVq0qTJVbeRn58vh8PhMmazNVBISIila61Xj7KH/6/Pwm2eXkKNWV0Gq46Fm/mYIAMykMhAunEyoGD9RHFxsUu5kuS8XVZWVqNtpKenKzU11WVs8uTJevTRR61Z5D/l5%2Bdr3M%2BzNXLkSMvLm7fIz89Xeno6GZCB1qxZRQZkQAZkcMNkcPNW3Mvw9/evVqSqbtevX79G2xg5cqTefvttl4%2BRI0davlaHw6HU1NRqZ8tuJmRABhIZSGQgkYFEBtKNkwFnsH4iNDRUhYWFKi8vl812MR6Hw6H69esrMDCwRtsICQm5aX9yAAAAnMGqpn379rLZbMrKynKOZWZmKiIiggvcAQBAjdAYfiIgIECDBw/WrFmztGfPHm3dulUvv/yyxo4d6%2BmlAQAAL1Fv1qxZszy9iBtN165dtX//fi1atEifffaZfv/732vYsGGeXtYlNWzYUHFxcWrYsKGnl%2BIxZEAGEhlIZCCRgUQG0o2RgY8xxnjsswMAANRBvEQIAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMguWlSktLNWPGDMXGxqp79%2B56%2BeWXPb2ka3by5EklJSUpLi5O8fHxSk5OVmlpqSRp3rx5uuOOO1w%2B1q1b53xsRkaGevfurcjISCUmJurUqVPOOWOMFi5cqK5duyouLk4LFixQZWWlc76wsFCPPvqooqOj1atXL23atMl9O/0TW7ZsqbafSUlJkqT9%2B/drxIgRioyM1LBhw7Rv3z6Xx9aFDN5%2B%2B%2B1q%2B3/HHXcoPDxckvTwww9Xm/voo4%2Bcj3/11VcVHx%2Bv6OhozZgxQ8XFxc65qx0jubm5Gj9%2BvKKiotS/f39t377dPTv9I2VlZRo4cKA%2B//zzGq/r008/1cCBAxUZGamxY8cqNzfXZd7bMrlUBllZWfqP//gPRUdHq2/fvtqwYYPLY379619Xe14cPHhQ0vU/96923NWGS2Xgye%2BBN0IGTz311CW/N/z4z9bFxsZWmz937pyk63%2BuX%2B04qxEDrzRnzhxz3333mX379pn/%2BZ//MdHR0ebDDz/09LJqrLKy0tx///3mt7/9rTl48KDZtWuX6dOnj3nuueeMMcaMHz/erFy50uTn5zs/zp8/b4wxxm63m06dOpl33nnHfPXVV2b06NHmoYcecm579erVpkePHmbXrl3ms88%2BM927dzerVq1yzk%2BaNMmMGzfOfP311%2BbNN980HTt2NHa73b0B/NOyZcvMpEmTXPbzhx9%2BMOfOnTPdunUzzz33nDl06JCZO3eu%2BeUvf2nOnTtnjKk7GRQXF7vs%2B/Hjx02fPn3M/PnzjTHG9OnTx2zatMnlPqWlpcYYY/7617%2BamJgY87//%2B7/Gbreb/v37m9mzZzu3faVjpLKy0tx3331mypQp5tChQ2bFihUmMjLSHDt2zG37XlJSYhITE027du3Mjh07arSuY8eOmaioKLN69Wpz8OBB85//%2BZ9m4MCBprKy0iszuVQG%2Bfn5JjY21ixatMgcOXLEZGRkmIiICPPRRx8ZY4wpLy83ERERZufOnS7PiwsXLhhjru%2B5f7Xjzl0ZGOO574E3SganT5922fcvvvjCdOzY0WzZssUYY0xeXp5p166d%2Bfbbb13uV3UsXM9z/WrHWU1RsLzQuXPnTEREhMvBuHTpUjN69GgPruraHDp0yLRr1844HA7n2Pvvv2%2B6d%2B9ujDEmPj7ebNu27ZKPnTp1qpk2bZrz9vHjx80dd9xhvv32W2OMMT169DAbN250zr/77rsmISHBGGPMN998Y9q1a2dyc3Od8zNmzHDZnjtNmTLFLFq0qNr4hg0bTK9evZwHdGVlpenTp49zv%2BpSBj%2B2YsUK07t3b1NaWmpKS0tN%2B/btzeHDhy9531GjRpklS5Y4b%2B/atct06tTJnD9//qrHyKeffmqioqJc/tMYN26cy/ZqU3Z2tvn1r39t7rvvPpf/VK62rhdffNHlOD9//ryJjo52Pt6bMrlcBn/5y19Mv379XO779NNPm8cff9wYY8zRo0dNeHi4KSkpueR2r%2Be5f7XjzmqXy8AYz30PvJEy%2BLEJEyaYJ554wnn7H//4h%2BnWrdsl73u9z/WrHWc1xUuEXujAgQMqLy9XdHS0cywmJkZ2u93lNPCNLDg4WKtWrVKzZs1cxs%2BePauzZ8/q5MmTatWq1SUfa7fbFRsb67zdvHlzhYWFyW636%2BTJkzpx4oTuuusu53xMTIyOHTum/Px82e12NW/eXC1btnSZ/%2BKLL6zdwRrKycm55H7a7XbFxMTIx8dHkuTj46POnTsrKyvLOV9XMqhSVFSkP//5z5oyZYr8/Px0%2BPBh%2Bfj46Lbbbqt234qKCu3du9clg6ioKF24cEEHDhy46jFit9t15513qkGDBi7zVfnWtp07d6pLly5KT093Gb/aun76dQ8ICFCHDh2UlZXldZlcLoOqywV%2B6uzZs5KkQ4cOqXnz5vL39692n%2Bt97l/tuLPa5TLw5PfAGyWDH/vss8%2B0a9cuPf74486xQ4cO6Re/%2BMUl73%2B9z/UrHWfXwnZN98YNweFwqHHjxvLz83OONWvWTKWlpSoqKlKTJk08uLqaCQwMVHx8vPN2ZWWl1q1bp65duyonJ0c%2BPj5asWKFPvnkEwUFBenBBx/UkCFDJEn5%2BfkKCQlx2V7Tpk2Vl5cnh8MhSS7zVSWuav5Sjz158mSt7OeVGGN05MgRbd%2B%2BXStXrlRFRYX69eunpKQkORwO3X777dXWmZ2dLanuZPBj69evV0hIiPr16ydJOnz4sBo1aqQnn3xSO3fu1M9//nM9%2Buij6tGjh06fPq3S0lKX/bDZbAoKClJeXp58fX2veIxcLoO8vDy37OuoUaMuOX61dV1p3tsyuVwGLVu2dPnP//vvv9d///d/69FHH5V08YeSW265RZMmTdK%2Bffv0i1/8Qk8%2B%2BaQ6dep03c/9qx13VrtcBp78HnijZPBjaWlpGjJkiJo3b%2B4cy8nJUXFxscaMGaMjR46offv2mjFjhn7xi19c9f/I6znOrgVnsLxQcXGxyxNHkvN2WVmZJ5Z03VJSUrR//3794Q9/cJ65aN26tdLS0jRixAg9/fTT2rJliySppKTkkvtfVlamkpIS5%2B0fz0kXs7lcdp7I7fjx4871vPjii5o2bZref/99LViw4KrrrCsZVDHGaMOGDRo9erRz7PDhwyopKVH37t21atUq9ejRQw8//LD27t17yX2sun2lfZRu3Aykyx/bVeu60nxdzKSkpESPPvqomjVrppEjR0qSjhw5oh9%2B%2BEEjRoxQWlqa2rRpo3HjxunEiRPX/dy/UTLw5PfAGyWDKrm5udqxY4fGjBnjMn748GH98MMPevjhh7Vs2TLVr19f48eP19mzZ6/7uW5VBpzB8kL%2B/v7VvtBVt%2BvXr%2B%2BJJV2XlJQUrVmzRi%2B88ILatWuntm3bKiEhQUFBQZKk8PBwHT16VOvXr1efPn0uu/8BAQEuB1HVSwhV9w0ICLjsYz2RW4sWLfT555/r1ltvlY%2BPj9q3b6/KykpNnTpVcXFxV1xnXcmgyt69e3Xy5EkNGDDAOfbII49ozJgxuvXWWyVdfB58%2BeWXevPNN/WHP/xBUvUfKKoyqKiouOIx4u/vr6Kiomrznj5%2Brrauy33tAgMDq32tfzzvjZmcO3dOjzzyiI4ePaq//OUvCggIkCTNnTtXJSUlatSokSRp1qxZ2r17tzZt2qRf/vKXznX/K8/9G%2BXYGDx4sMe%2BB94oGVTZvHmz2rdvX%2B2s2urVq3XhwgU1bNhQkrRw4UL16NFDH3300VX/j7ye4%2BxacAbLC4WGhqqwsFDl5eXOMYfDofr161/zE8DT5s6dq1deeUUpKSnq27evpIuv%2BVd9Y6nSunVr5yns0NBQFRQUuMwXFBQoODhYoaGhkuQ8Tf7jf1fNX%2B6xnhAUFOS81kGS2rRpo9LSUgUHB19ynVWnretSBpK0bds2xcbGOsuUJPn6%2Brrclv7/8yAoKEj%2B/v4u%2B1FeXq6ioiLnPl7pGLlcBj99WcDdrrauK33t6lImZ8%2Be1cSJE5Wdna01a9a4XItks9mc5UqS80zPyZMnr/u5f6Nk4MnvgTdKBlW2bdumf//3f6827ufn5yxX0sVS1LJlS%2Bfz4Hqe61Z9j6RgeaH27dvLZrO5XHCXmZmpiIgI%2Bfp6z5c0NTVVb7zxhp5//nmXMxeLFy/W%2BPHjXe574MABtW7dWpIUGRmpzMxM59yJEyd04sQJRUZGKjQ0VGFhYS7zmZmZCgsLU0hIiKKionTs2DGX19IzMzMVFRVVS3t5edu2bVOXLl1c3qfoq6%2B%2BUlBQkPOiU2OMpIsvoe3evVuRkZGS6k4GVfbs2aPOnTu7jD311FOaPn26y1jV88DX11cREREu%2B5iVlSWbzabw8PCrHiORkZH68ssvnS%2BnVM1X5espV1vXT7/uxcXF2r9/vyIjI%2BtMJpWVlZo8ebK%2B%2B%2B47vfbaa2rbtq3L/JgxY5Samupy/6%2B//lqtW7e%2B7ud%2BZGTkFY87d/Hk98AbJYOqz713795q3xuMMerdu7fefvtt59j58%2Bf1zTffqHXr1tf9XL/ScXatOwAv9PTTT5sBAwYYu91utmzZYjp37mw2b97s6WXV2KFDh0z79u3NCy%2B84PIeJvn5%2BcZut5s777zTrFq1ynzzzTfm9ddfNx07djS7d%2B82xhize/du06FDB/Pmm2863wNm0qRJzm2vXLnSdO/e3ezYscPs2LHDdO/e3bz88svO%2BQkTJpjRo0ebr776yrz55psmIiLCI%2B8BdebMGRMfH28ef/xxk5OTY/7%2B97%2Bb7t27m7S0NHPmzBnTtWtXM3fuXJOdnW3mzp1runXr5vy14rqSQZWEhASTkZHhMrZ582bToUMH884775ijR4%2Bal156yXTq1Mn56%2BUZGRmmc%2BfOZsuWLcZut5sBAwaYuXPnOh9/pWOkvLzc9O/f3zz22GPm4MGDZuXKlSYqKsqt74NV5ce/mn61deXm5pqIiAizcuVK5/vz3Hfffc5fqffWTH6cQXp6ugkPDzcfffSRy/eFwsJCY4wxL7/8somJiTFbt241OTk55k9/%2BpP55S9/ac6cOWOMub7n/tWOO3dl4MnvgTdKBsZcfL63a9fO5OfnV7vv3LlzTc%2BePc2OHTvMwYMHTWJiohk4cKApLy83xlzfc/1qx1lNUbC81Pnz582TTz5poqKiTPfu3c0rr7zi6SVdk5UrV5p27dpd8sMYY7Zs2WLuu%2B8%2BExERYfr161etPG7cuNH06NHDREVFmcTERHPq1CnnXHl5uXn22WdNbGys6dKli0lJSXE5MAoKCsykSZNMRESE6dWrl3n//ffds9OXcPDgQTN%2B/HgTFRVlunXrZl566SXnWu12uxk8eLCJiIgww4cPN19%2B%2BaXLY%2BtKBsYYExERYT755JNq42%2B%2B%2Baa55557TMeOHc2QIUPMzp07XeZXrlxp7r77bhMTE2OmT5/u8t5IVztGjh49an7zm9%2BYjh07mgEDBph//OMftbJvV/PT/1Sutq6///3v5p577jGdOnUy48aNc773URVvzOTHGUyYMOGS3xeq3peosrLSLF%2B%2B3PTs2dN07NjR/OY3vzFff/21c1vX%2B9y/2nFXW376PPDk98AbJYOsrCzTrl0755sL/1hJSYlJTk423bp1M5GRkWbSpEnm%2BPHjzvnrfa5f7TirCR9j/nkeEAAAAJbwngt2AAAAvAQFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsNj/A%2BWekhjiDhmAAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5880046620144668249”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000001490116</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.99999997019768</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002933666</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99999999627471</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2874)</td> <td class=”number”>2879</td> <td class=”number”>89.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5880046620144668249”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.26221166852e-08</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0002072595839309</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010663634166122</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013897542376071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>123766.895145556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125888.219965812</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>136917.020467991</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>137560.129882083</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>167913.190404415</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD10”><s>LACCESS_CHILD10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_LOWI15”><code>LACCESS_LOWI15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93398</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD15”><s>LACCESS_CHILD15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_CHILD10”><code>LACCESS_CHILD10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.994</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_CHILD_10_15”>LACCESS_CHILD_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2826</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>278.32</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>592130</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-603067566021566046”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-603067566021566046,#minihistogram-603067566021566046”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-603067566021566046”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-603067566021566046”

aria-controls=”quantiles-603067566021566046” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-603067566021566046” aria-controls=”histogram-603067566021566046”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-603067566021566046” aria-controls=”common-603067566021566046”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-603067566021566046” aria-controls=”extreme-603067566021566046”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-603067566021566046”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-51.396</td>

</tr> <tr>

<th>Q1</th> <td>-11.103</td>

</tr> <tr>

<th>Median</th> <td>-0.052298</td>

</tr> <tr>

<th>Q3</th> <td>7.2269</td>

</tr> <tr>

<th>95-th percentile</th> <td>84.676</td>

</tr> <tr>

<th>Maximum</th> <td>592130</td>

</tr> <tr>

<th>Range</th> <td>592230</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.33</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>11080</td>

</tr> <tr>

<th>Coef of variation</th> <td>39.81</td>

</tr> <tr>

<th>Kurtosis</th> <td>2644.5</td>

</tr> <tr>

<th>Mean</th> <td>278.32</td>

</tr> <tr>

<th>MAD</th> <td>544.32</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>50.355</td>

</tr> <tr>

<th>Sum</th> <td>863900</td>

</tr> <tr>

<th>Variance</th> <td>122760000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-603067566021566046”>

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Ut29fbdmyxee1W7duVf/%2B/ZWYmKiRI0eqsLDQZ3716tXq0aOHkpOTNXXqVJWVlfntuAAAQPALyoJljFFmZqbKysr03HPP6bHHHtP//u//atGiRTLGKCMjQ82bN1dubq4GDBigCRMm6OjRo5Kko0ePKiMjQ4MGDdKGDRvUtGlT3XfffTLGSJLeeOMN5eTkaPbs2VqzZo3cbreys7MDebgAACDIBGXBOnTokAoKCpSVlaVrr71WqampyszMVF5enrZt26bCwkLNnj1bbdu21fjx45WUlKTc3FxJ0osvvqgbbrhBY8aM0bXXXqusrCwdOXJEH374oSRp7dq1GjVqlHr27KlOnTpp1qxZys3N5SwWAABwLCgLVkxMjFauXKnmzZv7jJ8%2BfVput1vXX3%2B9GjZs6B1PSUlRQUGBJMntdis1NdU7FxkZqY4dO6qgoEDV1dX6%2BOOPfeaTkpJ07tw57d279xIfFQAAqC%2BCsmBFRUWpR48e3sc1NTVat26dunbtKo/Ho9jYWJ/nN2vWTMePH5ekb50/efKkKioqfOZdLpeio6O9rwcAAKiLK9ALsCE7O1u7d%2B/Whg0btHr1aoWHh/vMh4eHq7KyUpJUVlZ20fny8nLv44u93omioiJ5PB6fMZerYa1i90OFhQVXP3a5ArPe8zkFW17%2BREbOkJMz5FQ3MnImmHMK%2BoKVnZ2tNWvW6LHHHlO7du0UERGh0tJSn%2BdUVlaqQYMGkqSIiIhaZamyslJRUVGKiIjwPv7mfGRkpOM1rV%2B/Xjk5OT5jGRkZyszMdLyN%2BqhJk0YB3X9UlPPP4ZWKjJwhJ2fIqW5k5Eww5hTUBWvOnDl6/vnnlZ2drT59%2BkiS4uLidODAAZ/nFRcXe88excXFqbi4uNZ8hw4dFB0drYiICBUXF6tt27aSpKqqKpWWliomJsbxuoYOHar09HSfMZeroUpKznznY/w2wdbobR%2B/U2FhoYqKitTJk2Wqrq4JyBoud2TkDDk5Q051IyNnbOQUqB/ug7Zg5eTk6IUXXtCjjz6q22%2B/3TuemJioFStWqLy83HvWKj8/XykpKd75/Px87/PLysq0e/duTZgwQaGhoUpISFB%2Bfr66dOkiSSooKJDL5VL79u0dry02NrbW24EezylVVV3Z30SBPv7q6pqAr%2BFyR0bOkJMz5FQ3MnImGHPy%2BymQu%2B66Sy%2B88IJOnTr1vbdx8OBBLVu2TL/5zW%2BUkpIij8fj/UhLS1OLFi00ZcoU7d%2B/XytWrNDOnTs1ZMgQSdLgwYO1Y8cOrVixQvv379eUKVPUqlUrb6EaPny4Vq1apc2bN2vnzp2aOXOm7r777u/0FiEAALiy%2Bb1gde3aVU8%2B%2BaS6d%2B%2BuBx54QFu2bPHe5NOpt956S9XV1XriiSfUvXt3n4%2BwsDAtW7ZMHo9HgwYN0iuvvKKlS5cqPj5ektSqVSs9/vjjys3N1ZAhQ1RaWqqlS5cqJCREktSvXz%2BNHz9eM2bM0JgxY9SpUydNnjzZeg4AAKD%2BCjHftd1YYIzR1q1btXHjRm3evFlRUVEaOHCgBg4cqJ/97Gf%2BXo5feDzf/4zdxbhcoeq94D3r271UXru/W0D263KFqkmTRiopORN0p5j9hYycISdnyKluZOSMjZxiYn5seVXOBOQarJCQEHXr1k3dunVTWVmZnn32WS1btkwrVqxQ586dNWrUKN12222BWBoAAMAPFrCL3IuKivTKK6/olVde0b59%2B9S5c2f98pe/1PHjx/XHP/5R27dv17Rp0wK1PAAAgO/N7wXr5Zdf1ssvv6wPPvhATZs21cCBA7VkyRK1bt3a%2B5wWLVpo3rx5FCwAABCU/F6wpk2bpp49e2rp0qW6%2BeabFRpa%2Bzr7Nm3aaMSIEf5eGgAAgBV%2BL1jvvvuumjRpotLSUm%2B52rlzpzp27KiwsDBJUufOndW5c2d/Lw0AAMAKv9%2Bm4fTp07r99tv11FNPecfGjRunAQMG6NixY/5eDgAAgHV%2BL1h//vOf9dOf/lT33nuvd%2BzVV19VixYtlJWV5e/lAAAAWOf3gvX3v/9dDz/8sM/f9mvatKkefPBBbdu2zd/LAQAAsM7vBcvlcunkyZO1xsvKyr7zHd0BAAAuR34vWDfffLPmzp2rf/7zn96xwsJCZWVlqUePHv5eDgAAgHV%2B/y3Chx56SPfee6/69OmjqKgoSdLJkyfVsWNHTZkyxd/LAQAAsM7vBatZs2Z66aWXtHXrVu3fv18ul0vXXHONbrrpJu8fXAYAAAhmAflTOWFhYerRowdvCQIAgHrJ7wXL4/Fo0aJF2rFjh86dO1frwva33nrL30sCAACwyu8Fa/r06dq1a5f69eunH//4x/7ePQAAwCXn94K1bds2rVy5Uqmpqf7eNQAAgF/4/TYNDRs2VLNmzfy9WwAAAL/xe8EaMGCAVq5cqerqan/vGgAAwC/8/hZhaWmp8vLy9Pbbb%2Bvqq69WeHi4z/zatWv9vSQAAACrAnKbhv79%2BwditwAAAH7h94KVlZXl710CAAD4ld%2BvwZKkoqIi5eTkaOLEifrXv/6l119/XYcOHQrEUgAAAKzze8H6/PPPdeedd%2Bqll17SG2%2B8obNnz%2BrVV1/V4MGD5Xa7/b0cAAAA6/xesB555BH16tVLmzdv1o9%2B9CNJ0qOPPqr09HQtWLDA38sBAACwzu8Fa8eOHbr33nt9/rCzy%2BXSfffdp927d/t7OQAAANb5vWDV1NSopqam1viZM2cUFhbm7%2BUAAABY5/eC1b17dy1fvtynZJWWlio7O1tdu3b193IAAACs83vBevjhh7Vr1y51795dFRUV%2Bn//7/%2BpZ8%2BeOnz4sB566CF/LwcAAMA6v98HKy4uThs3blReXp727NmjmpoaDRs2TAMGDFDjxo39vRwAAADrAnIn98jISN11112B2DUAAMAl5/eCNXLkyG%2Bd528RAgCAYOf3gtWyZUufx1VVVfr888%2B1b98%2BjRo1yt/LAQAAsO6y%2BVuES5cu1fHjx/28GgAAAPsC8rcIL2TAgAF67bXXAr0MAACAH%2ByyKVgfffQRNxoFAAD1wmVxkfvp06f1j3/8Q8OHD/f3cgAAAKzze8GKj4/3%2BTuEkvSjH/1II0aM0C9%2B8Qt/LwcAAMA6vxesRx55xOr2KisrNWjQIE2fPl1dunSRJM2dO1fPPvusz/OmT5%2BuESNGSJLy8vK0aNEieTwede/eXXPmzFHTpk0lScYYLVy4UBs2bFBNTY2GDBmiSZMmKTT0snk3FQAAXOb8XrC2b9/u%2BLk33njjt85XVFRo4sSJ2r9/v8/4wYMHNXHiRP3yl7/0jp2/S/zOnTs1bdo0zZo1S%2B3bt9e8efM0ZcoULV%2B%2BXJL0zDPPKC8vTzk5OaqqqtLkyZPVrFkzjR071vG6AQDAlc3vBeuee%2B7xvkVojPGOf3MsJCREe/bsueh2Dhw4oIkTJ/ps47yDBw9q7NixiomJqTW3bt063XHHHRo4cKAkaf78%2BerZs6cKCwt19dVXa%2B3atcrMzFRqaqokadKkSVq8eDEFCwAAOOb3972efPJJtWzZUosWLdL777%2Bv/Px8rV69Wj/72c/0wAMP6K233tJbb72lzZs3f%2Bt2PvzwQ3Xp0kXr16/3GT99%2BrROnDih1q1bX/B1brfbW54kqUWLFoqPj5fb7daJEyd07NgxnzNnKSkpOnLkiIqKir7/QQMAgCtKQG40OmPGDN18883esa5du2r27Nl68MEH9Zvf/MbRdi72G4cHDx5USEiInnzySb377ruKjo7Wvffe6327sKioSLGxsT6vadasmY4fPy6PxyNJPvPNmzeXJB0/frzW6wAAAC7E7wWrqKio1p/Lkb66RqqkpOQHb//QoUMKCQlRmzZtNGLECG3fvl3Tp09X48aN1bt3b5WXlys8PNznNeHh4aqsrFR5ebn38dfnpK8upneqqKjIW9bOc7kaWi9oYWHBdeG9yxWY9Z7PKdjy8icycoacnCGnupGRM8Gck98LVlJSkh599FH95S9/8V54XlpaquzsbN10000/ePsDBw5Uz549FR0dLUlq3769PvvsMz3//PPq3bu3IiIiapWlyspKRUZG%2BpSpiIgI778lKTIy0vEa1q9fr5ycHJ%2BxjIwMZWZmfu/jqg%2BaNGkU0P1HRTn/HF6pyMgZcnKGnOpGRs4EY05%2BL1h//OMfNXLkSN18881q3bq1jDH67LPPFBMTo7Vr1/7g7YeEhHjL1Xlt2rTRtm3bJElxcXEqLi72mS8uLlZMTIzi4uIkSR6PR61atfL%2BW9IFL5i/mKFDhyo9Pd1nzOVqqJKSM9/tYOoQbI3e9vE7FRYWqqioSJ08Wabq6pqArOFyR0bOkJMz5FQ3MnLGRk6B%2BuHe7wWrbdu2evXVV5WXl6eDBw9Kkn71q1%2BpX79%2B3%2Bks0cUsXrxYH330kVavXu0d27t3r9q0aSNJSkxMVH5%2BvgYNGiRJOnbsmI4dO6bExETFxcUpPj5e%2Bfn53oKVn5%2Bv%2BPj47/T2XmxsbK3nezynVFV1ZX8TBfr4q6trAr6Gyx0ZOUNOzpBT3cjImWDMye8FS5Kuuuoq3XXXXTp8%2BLCuvvpqSV/dzd2Gnj17asWKFVq1apV69%2B6tLVu2aOPGjd6zY8OGDdM999yjpKQkJSQkaN68ebr11lu96xg2bJgWLFign/zkJ5KkhQsXasyYMVbWBgAArgx%2BL1jn75T%2B7LPP6ty5c3rjjTf02GOPKTIyUjNnzvzBRatTp05avHixlixZosWLF6tly5ZauHChkpOTJUnJycmaPXu2lixZoi%2B//FLdunXTnDlzvK8fO3as/vWvf2nChAkKCwvTkCFDNHr06B%2B0JgAAcGUJMRe6U%2BcltHbtWj311FP6wx/%2BoNmzZ2vTpk36%2BOOPNWvWLP3nf/6n/vCHP/hzOX7j8Zyyvk2XK1S9F7xnfbuXymv3dwvIfl2uUDVp0kglJWeC7hSzv5CRM%2BTkDDnVjYycsZFTTMyPLa/KGb9fJb1%2B/XrNmDFDgwYN8t69vW/fvpo7d642bdrk7%2BUAAABY5/eCdfjwYXXo0KHWePv27WvdOwoAACAY%2Bb1gtWzZUh9//HGt8Xfffdd7oTkAAEAw8/tF7mPHjtWsWbPk8XhkjNH777%2Bv9evX69lnn9XDDz/s7%2BUAAABY5/eCNXjwYFVVVemJJ55QeXm5ZsyYoaZNm%2Br%2B%2B%2B/XsGHD/L0cAAAA6/xesPLy8nT77bdr6NCh%2BuKLL2SMUbNmzfy9DAAAgEvG79dgzZ4923sxe9OmTSlXAACg3vF7wWrdurX27dvn790CAAD4jd/fImzfvr0mTZqklStXqnXr1oqIiPCZz8rK8veSAAAArPJ7wfr000%2BVkpIiSdz3CgAA1Et%2BKVjz58/XhAkT1LBhQz377LP%2B2CUAAEDA%2BOUarGeeeUZlZWU%2BY%2BPGjVNRUZE/dg8AAOBXfilYF/p70tu3b1dFRYU/dg8AAOBXfv8tQgAAgPqOggUAAGCZ3wpWSEiIv3YFAAAQUH67TcPcuXN97nl17tw5ZWdnq1GjRj7P4z5YAAAg2PmlYN1444217nmVnJyskpISlZSU%2BGMJAAAAfuOXgsW9rwAAwJWEi9wBAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFgW9AWrsrJS/fv31wcffOAdKyws1OjRo5WUlKS%2Bfftqy5YtPq/ZunWr%2Bvfvr8TERI0cOVKFhYU%2B86tXr1aPHj2UnJysqVOnqqyszC/HAgAA6oegLlgVFRV64IEHtH//fu%2BYMUYZGRlq3ry5cnNzNWDAAE2YMEFHjx6VJB09elQZGRkaNGiQNmzYoKZNm%2Bq%2B%2B%2B6TMUaS9MYbbygnJ0ezZ8/WmjVr5Ha7lZ2dHZDjAwAAwSloC9aBAwd0991365///KfP%2BLZt21RYWKjZs2erbdu2Gj9%2BvJKSkpSbmytJevHFF3XDDTdozJgxuvbaa5WVlaUjR47oww8/lCStXbtWo0aNUs%2BePdWpUyfNmjVLubm5nMUCAACOBW3B%2BvDDD9WlSxetX7/eZ9ztduv6669Xw4YNvWMpKSkqKCjwzqempnrnIiMj1bFjRxUUFKi6uloff/yxz3xSUpLOnTunvXv3XuIjAgAA9YUr0Av4voYPH37BcY/Ho9jYWJ%2BxZs2a6fjx43XOnzyV5eNnAAAWRElEQVR5UhUVFT7zLpdL0dHR3tcDAADUJWgL1sWUlZUpPDzcZyw8PFyVlZV1zpeXl3sfX%2Bz1ThQVFcnj8fiMuVwNaxW7HyosLLhOQLpcgVnv%2BZyCLS9/IiNnyMkZcqobGTkTzDnVu4IVERGh0tJSn7HKyko1aNDAO//NslRZWamoqChFRER4H39zPjIy0vEa1q9fr5ycHJ%2BxjIwMZWZmOt5GfdSkSaOA7j8qyvnn8EpFRs6QkzPkVDcyciYYc6p3BSsuLk4HDhzwGSsuLvaePYqLi1NxcXGt%2BQ4dOig6OloREREqLi5W27ZtJUlVVVUqLS1VTEyM4zUMHTpU6enpPmMuV0OVlJz5Pod0UcHW6G0fv1NhYaGKiorUyZNlqq6uCcgaLndk5Aw5OUNOdSMjZ2zkFKgf7utdwUpMTNSKFStUXl7uPWuVn5%2BvlJQU73x%2Bfr73%2BWVlZdq9e7cmTJig0NBQJSQkKD8/X126dJEkFRQUyOVyqX379o7XEBsbW%2BvtQI/nlKqqruxvokAff3V1TcDXcLkjI2fIyRlyqhsZOROMOQXXKRAH0tLS1KJFC02ZMkX79%2B/XihUrtHPnTg0ZMkSSNHjwYO3YsUMrVqzQ/v37NWXKFLVq1cpbqIYPH65Vq1Zp8%2BbN2rlzp2bOnKm77777O71FCAAArmz1rmCFhYVp2bJl8ng8GjRokF555RUtXbpU8fHxkqRWrVrp8ccfV25uroYMGaLS0lItXbpUISEhkqR%2B/fpp/PjxmjFjhsaMGaNOnTpp8uTJgTwkAAAQZELM%2BVuY45LyeE5Z36bLFareC96zvt1L5bX7uwVkvy5XqJo0aaSSkjNBd4rZX8jIGXJyhpzqRkbO2MgpJubHllflTL07gwUAABBoFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWFZvC9abb76p6667zucjMzNTkrR7927dddddSkxM1ODBg7Vr1y6f1%2Bbl5alXr15KTExURkaGvvjii0AcAgAACFL1tmAdOHBAPXv21JYtW7wfc%2BfO1dmzZzVu3Dilpqbqb3/7m5KTkzV%2B/HidPXtWkrRz505NmzZNEyZM0Pr163Xy5ElNmTIlwEcDAACCSb0tWAcPHlS7du0UExPj/YiKitKrr76qiIgIPfjgg2rbtq2mTZumRo0a6fXXX5ckrVu3TnfccYcGDhyo9u3ba/78%2BXrnnXdUWFgY4CMCAADBol4XrNatW9cad7vdSklJUUhIiCQpJCREnTt3VkFBgXc%2BNTXV%2B/wWLVooPj5ebrfbL%2BsGAADBr14WLGOMPv30U23ZskV9%2BvRRr169tGDBAlVWVsrj8Sg2Ntbn%2Bc2aNdPx48clSUVFRd86DwAAUBdXoBdwKRw9elRlZWUKDw/XokWLdPjwYc2dO1fl5eXe8a8LDw9XZWWlJKm8vPxb550oKiqSx%2BPxGXO5GtYqbj9UWFhw9WOXKzDrPZ9TsOXlT2TkDDk5Q051IyNngjmnelmwWrZsqQ8%2B%2BEBXXXWVQkJC1KFDB9XU1Gjy5MlKS0urVZYqKyvVoEEDSVJERMQF5yMjIx3vf/369crJyfEZy8jI8P4W45WqSZNGAd1/VJTzz%2BGVioycISdnyKluZORMMOZULwuWJEVHR/s8btu2rSoqKhQTE6Pi4mKfueLiYu/Zpbi4uAvOx8TEON730KFDlZ6e7jPmcjVUScmZ73IIdQq2Rm/7%2BJ0KCwtVVFSkTp4sU3V1TUDWcLkjI2fIyRlyqhsZOWMjp0D9cF8vC9Z7772nSZMm6e233/aeedqzZ4%2Bio6OVkpKip556SsYYhYSEyBijHTt26Le//a0kKTExUfn5%2BRo0aJAk6dixYzp27JgSExMd7z82NrbW24EezylVVV3Z30SBPv7q6pqAr%2BFyR0bOkJMz5FQ3MnImGHMKrlMgDiUnJysiIkJ//OMfdejQIb3zzjuaP3%2B%2Bfv3rX%2Bv222/XyZMnNW/ePB04cEDz5s1TWVmZ7rjjDknSsGHD9PLLL%2BvFF1/U3r179eCDD%2BrWW2/V1VdfHeCjAgAAwaJeFqzGjRtr1apV%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%2BrFF1/UU089pY4dO6pjx47av3%2B/nnvuOQoWAABwhLcIv2Hv3r2qqqpScnKydywlJUVut1s1NTUBXBkAAAgWnMH6Bo/HoyZNmig8PNw71rx5c1VUVKi0tFRNmzatcxtFRUXyeDw%2BYy5XQ8XGxlpda1hYcPVjlysw6z2fU7Dl5U9k5Aw5OUNOdSMjZ4I5JwrWN5SVlfmUK0nex5WVlY62sX79euXk5PiMTZgwQb/73e/sLPLfioqKNOon%2BzV06FDr5a0%2BKSoq0po1K8npW5CRM%2BTkDDnVjYycCeacgq8SXmIRERG1itT5xw0aNHC0jaFDh%2Bpvf/ubz8fQoUOtr9Xj8SgnJ6fW2TL4Iqe6kZEz5OQMOdWNjJwJ5pw4g/UNcXFxKikpUVVVlVyur%2BLxeDxq0KCBoqKiHG0jNjY26Jo2AACwhzNY39ChQwe5XC4VFBR4x/Lz85WQkKDQUOICAAB1ozF8Q2RkpAYOHKiZM2dq586d2rx5s55%2B%2BmmNHDky0EsDAABBImzmzJkzA72Iy03Xrl21e/duLVy4UO%2B//75%2B%2B9vfavDgwYFe1gU1atRIaWlpatSoUaCXclkjp7qRkTPk5Aw51Y2MnAnWnEKMMSbQiwAAAKhPeIsQAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFK0hVVFRo6tSpSk1NVffu3fX0008HeklWVVZWqn///vrggw%2B8Y4WFhRo9erSSkpLUt29fbdmyxec1W7duVf/%2B/ZWYmKiRI0eqsLDQZ3716tXq0aOHkpOTNXXqVJWVlXnn6sqzrn3704kTJ5SZmam0tDT16NFDWVlZqqiocLTOKyUjSfr88881duxYJScn69Zbb9XKlSu9c%2BR0YePGjdPDDz/sfbx7927dddddSkxM1ODBg7Vr1y6f5%2Bfl5alXr15KTExURkaGvvjiC%2B%2BcMUYLFixQ165dlZaWpvnz56umpsY7X1JSot/97ndKTk5Wenq6Xn75ZZ9t17Vvf3vzzTd13XXX%2BXxkZmY6WuuVklNlZaVmzZqlG2%2B8UT//%2Bc/16KOP6vy9zK/IjAyC0uzZs82dd95pdu3aZf77v//bJCcnm9deey3Qy7KivLzcZGRkmHbt2plt27YZY4ypqakxd955p5k4caI5cOCAefLJJ01iYqI5cuSIMcaYI0eOmKSkJLNq1Sqzb98%2B8/vf/97079/f1NTUGGOMef31101KSor5n//5H%2BN2u03fvn3NrFmzvPv8tjzr2rc/1dTUmLvvvtv8%2Bte/Nvv27TPbt283vXv3No888ggZfU11dbW57bbbzMSJE82nn35q3n77bdO5c2fzyiuvkNNF5OXlmXbt2pmHHnrIGGPMmTNnTLdu3cwjjzxiDhw4YObMmWN%2B/vOfmzNnzhhjjHG73aZTp07mpZdeMnv27DEjRoww48aN825v1apV5pZbbjHbt28377//vunevbtZuXKld378%2BPFm1KhR5h//%2BIf561//am644Qbjdrsd7TsQli1bZsaPH2%2BKioq8H19%2B%2BSU5fc306dPNbbfdZtxut9m6davp0qWLef7556/YjChYQejMmTMmISHBWz6MMWbp0qVmxIgRAVyVHfv37ze/%2BMUvzJ133ulTsLZu3WqSkpJ8vilGjRpllixZYowxZtGiRT7Hf/bsWZOcnOx9/fDhw73PNcaY7du3m06dOpmzZ8/WmWdd%2B/anAwcOmHbt2hmPx%2BMd27Rpk%2BnevTsZfc2JEyfM73//e3Pq1CnvWEZGhvnTn/5EThdQUlJibr75ZjN48GBvwXrxxRdNenq6t1jW1NSY3r17m9zcXGOMMZMnT/Y%2B1xhjjh49aq677jrzz3/%2B0xhjzC233OJ9rjHGbNy40fTs2dMYY8znn39u2rVrZwoLC73zU6dOdbzvQJg4caJZuHBhrXFy%2BkpJSYm5/vrrzQcffOAdW758uXn44Yev2Ix4izAI7d27V1VVVUpOTvaOpaSkyO12%2B5w2DUYffvihunTpovXr1/uMu91uXX/99WrYsKF3LCUlRQUFBd751NRU71xkZKQ6duyogoICVVdX6%2BOPP/aZT0pK0rlz57R3794686xr3/4UExOjlStXqnnz5j7jp0%2BfJqOviY2N1aJFi9S4cWMZY5Sfn6/t27crLS2NnC7gL3/5iwYMGKBrrrnGO%2BZ2u5WSkqKQkBBJUkhIiDp37nzRnFq0aKH4%2BHi53W6dOHFCx44d04033uidT0lJ0ZEjR1RUVCS3260WLVqoVatWPvMfffSRo30HwsGDB9W6deta4%2BT0lfz8fDVu3FhpaWnesXHjxikrK%2BuKzYiCFYQ8Ho%2BaNGmi8PBw71jz5s1VUVGh0tLSAK7shxs%2BfLimTp2qyMhIn3GPx6PY2FifsWbNmun48eN1zp88eVIVFRU%2B8y6XS9HR0Tp%2B/Hideda1b3%2BKiopSjx49vI9ramq0bt06de3alYwuIj09XcOHD1dycrL69OlDTt/w/vvv6%2B9//7vuu%2B8%2Bn/G61lpUVHTReY/HI0k%2B8%2Bd/KDg/f6HXnjhxwtG%2B/c0Yo08//VRbtmxRnz591KtXLy1YsECVlZXk9G%2BFhYVq2bKlNm7cqNtvv13/8R//oaVLl6qmpuaKzch1yfcA68rKynz%2BAy7J%2B7iysjIQS7rkLnbM54/32%2BbLy8u9jy80b4z51jzr2ncgZWdna/fu3dqwYYNWr15NRhewZMkSFRcXa%2BbMmcrKyuJr6WsqKir0pz/9STNmzFCDBg185upaa3l5%2BXfK6bvkcLnldPToUe%2BaFi1apMOHD2vu3LkqLy8np387e/asPv/8c73wwgvKysqSx%2BPRjBkzFBkZecVmRMEKQhEREbW%2BOM4//uZ/JOuLiIiIWmfnKisrvcd7sUyioqIUERHhffzN%2BcjISFVXV39rnnXtO1Cys7O1Zs0aPfbYY2rXrh0ZXURCQoKkr8rEpEmTNHjwYJ/f%2BpOu3JxycnJ0ww03%2BJwVPe9iOdSVU2RkpM//AL%2BZWWRk5PfedqByatmypT744ANdddVVCgkJUYcOHVRTU6PJkycrLS2NnPTVmdzTp09r4cKFatmypaSviunzzz%2Bvn/70p1dkRrxFGITi4uJUUlKiqqoq75jH41GDBg0UFRUVwJVdOnFxcSouLvYZKy4u9p76vdh8TEyMoqOjFRER4TNfVVWl0tJSxcTE1JlnXfsOhDlz5uiZZ55Rdna2%2BvTpI4mMvrnvzZs3%2B4xdc801OnfunGJiYsjp3/7rv/5LmzdvVnJyspKTk7Vp0yZt2rRJycnJP%2BjrKS4uTpK8b%2B98/d/n5y/22m/bdiC/56Kjo73X8UhS27ZtVVFR8YO%2BnupTTjExMYqIiPCWK0n62c9%2BpmPHjl2xX0sUrCDUoUMHuVwun4v08vPzlZCQoNDQ%2BvkpTUxM1CeffOI9XSx9dcyJiYne%2Bfz8fO9cWVmZdu/ercTERIWGhiohIcFnvqCgQC6XS%2B3bt68zz7r27W85OTl64YUX9Oijj6pfv37ecTL6/x0%2BfFgTJkzwXochSbt27VLTpk2VkpJCTv/27LPPatOmTdq4caM2btyo9PR0paena%2BPGjUpMTNRHH33kvY%2BRMUY7duy4aE7Hjh3TsWPHlJiYqLi4OMXHx/vM5%2BfnKz4%2BXrGxsUpKStKRI0d8roPJz89XUlKSd9vftm9/e%2B%2B999SlSxefM5979uxRdHS094LqKz2nxMREVVRU6NNPP/WOHTp0SC1btrxyv5Yu%2Be8p4pKYPn266devn3G73ebNN980nTt3Nm%2B88Uagl2XV12/TUFVVZfr27Wvuv/9%2Bs2/fPrN8%2BXKTlJTkvX9QYWGhSUhIMMuXL/feu%2BjOO%2B/0/mpuXl6e6dy5s3nzzTeN2%2B02/fr1M3PmzPHu69vyrGvf/nTgwAHToUMH89hjj/ncj6eoqIiMvqaqqsoMGjTIjBkzxuzfv9%2B8/fbb5uc//7lZvXo1OX2Lhx56yPvr7adOnTJdu3Y1c%2BbMMfv37zdz5swx3bp1895iYseOHaZjx47mr3/9q/feRePHj/dua/ny5aZ79%2B5m27ZtZtu2baZ79%2B7m6aef9s6PGTPGjBgxwuzZs8f89a9/NQkJCd57F9W1b387deqU6dGjh3nggQfMwYMHzdtvv226d%2B9uVqxYQU5fM27cODN06FCzZ88e8%2B6775quXbuaNWvWXLEZUbCC1NmzZ82DDz5okpKSTPfu3c0zzzwT6CVZ9/WCZYwxn332mfnVr35lbrjhBtOvXz/zf//3fz7Pf/vtt81tt91mOnXqZEaNGuW9h8p5y5cvNzfddJNJSUkxU6ZMMeXl5d65uvKsa9/%2Bsnz5ctOuXbsLfjhZ55WQ0XnHjx83GRkZpnPnzqZbt27miSee8JYkcrqwrxcsY766AeTAgQNNQkKCGTJkiPnkk098np%2Bbm2tuueUWk5SUZDIyMswXX3zhnauqqjJ//vOfTWpqqunSpYvJzs725m%2BMMcXFxWb8%2BPEmISHBpKenm02bNvlsu659%2B9u%2BffvM6NGjTVJSkunWrZt5/PHHvcdDTl85efKkmTx5sklKSjI33XTTFZ9RiDH/Pm8GAAAAK%2BrnBTsAAAABRMECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGX/H5cqYL%2BBxWcUAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-603067566021566046”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>278</td> <td class=”number”>8.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-30.099407437116625</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7839555030772787</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.63651925237697</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0078713358341</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.29537905097039</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-55.06549827843521</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.537191558696197</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-23.175864713673935</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2815)</td> <td class=”number”>2815</td> <td class=”number”>87.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-603067566021566046”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-99.94242310762233</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.8895369395067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.08895762460443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.78266718621464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8282.707749903071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11232.759410102526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26128.162530164445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171961.8566608403</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>592130.5637640554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HHNV10”>LACCESS_HHNV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3115</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>661.07</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>16334</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1409573836035336826”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1409573836035336826,#minihistogram-1409573836035336826”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1409573836035336826”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1409573836035336826”

aria-controls=”quantiles-1409573836035336826” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1409573836035336826” aria-controls=”histogram-1409573836035336826”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1409573836035336826” aria-controls=”common-1409573836035336826”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1409573836035336826” aria-controls=”extreme-1409573836035336826”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1409573836035336826”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>18.665</td>

</tr> <tr>

<th>Q1</th> <td>118.22</td>

</tr> <tr>

<th>Median</th> <td>320.27</td>

</tr> <tr>

<th>Q3</th> <td>706.12</td>

</tr> <tr>

<th>95-th percentile</th> <td>2540.6</td>

</tr> <tr>

<th>Maximum</th> <td>16334</td>

</tr> <tr>

<th>Range</th> <td>16334</td>

</tr> <tr>

<th>Interquartile range</th> <td>587.9</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1122.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.698</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.813</td>

</tr> <tr>

<th>Mean</th> <td>661.07</td>

</tr> <tr>

<th>MAD</th> <td>618.56</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1237</td>

</tr> <tr>

<th>Sum</th> <td>2070500</td>

</tr> <tr>

<th>Variance</th> <td>1260000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1409573836035336826”>

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TCtBDhAAAAD9kFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABs5njBuv3227VmzRodO3bM6YcGAABwhOMFKyUlRQsXLlSfPn30%2B9//Xps3b5YxxullAAAANBjHC9bDDz%2Bsd999VwsWLFBISIgefPBB3XDDDXrqqaf0%2BeefO70cAAAA23ka40GDgoLUu3dv9e7dWxUVFXrhhRe0YMECLV68WD179tTo0aN14403NsbSAAAALlujFCxJKikp0WuvvabXXntNn332mXr27Klbb71Vhw4d0mOPPaZt27ZpypQpjbU8AACAS%2BZ4wdqwYYM2bNigrVu3qlWrVho%2BfLieffZZdejQwdqmbdu2mj17NgULAAAEJMcL1pQpU9SvXz/Nnz9f1113nYKDzz4NrGPHjho1apTTSwMAALCF4wXr/fffV8uWLVVeXm6Vq507d6p79%2B4KCQmRJPXs2VM9e/Z0emkAAAC2cPy3CI8fP65Bgwbpr3/9qzU2duxYDRs2TMXFxU4vBwAAwHaOF6w//elPuvLKK3XvvfdaY6%2B//rratm2rjIwMp5cDAABgO8cL1r/%2B9S89%2BuijioyMtMZatWqlRx55RFu2bHF6OQAAALZzvGB5PB4dPXr0rPGKigqu6A4AAFzB8YJ13XXXadasWfrqq6%2BssaKiImVkZKhv375OLwcAAMB2jv8W4cSJE3XvvffqpptuUkREhCTp6NGj6t69uyZNmuT0cgAAAGzneMFq3bq1Xn31VX3wwQcqKCiQx%2BPRz3/%2Bc1177bUKCgpyejkAAAC2a5Q/lRMSEqK%2BfftySBAAALiS4wXL5/Pp6aef1vbt23Xq1KmzTmx/5513nF4SAACArRwvWFOnTtWuXbs0ZMgQ/fSnP3X64QEAABqc4wVry5YtWrJkiZKSkpx%2BaAAAAEc4fpmGpk2bqnXr1k4/LAAAgGMcL1jDhg3TkiVLVFtb6/RDAwAAOMLxQ4Tl5eXKycnRP//5T11xxRUKDQ31m1%2B5cqXTSwIAALBVo1ymYejQoY3xsAAAAI5wvGBlZGQ4/ZAAAACOcvwcLEkqKSlRVlaWHn74YX3zzTd68803tX///sZYCgAAgO0cL1hffvmlbrnlFr366qt66623dPLkSb3%2B%2BusaMWKE8vPznV4OAACA7RwvWE888YQGDBigTZs26Sc/%2BYkk6cknn1T//v01d%2B5cp5cDAABgO8cL1vbt23Xvvff6/WFnj8ejBx54QLt3777o/VVXV2vo0KHaunWrNTZr1ixdddVVfl%2BrVq2y5nNycjRgwAB5vV6lpaXpyJEj1pwxRnPnzlVKSoqSk5M1Z84c1dXVXWJaAADwY%2BT4Se51dXXnLCwnTpxQSEjIRe2rqqpKDz/8sAoKCvzGCwsL9fDDD%2BvWW2%2B1xpo3by5J2rlzp6ZMmaLp06crNjZWs2fP1qRJk7Ro0SJJ0rJly5STk6OsrCzV1NRowoQJat26te67776LjQoAAH6kHP8Eq0%2BfPlq0aJFfySovL1dmZqZSUlLqvZ99%2B/bpjjvu0FdffXXWXGFhobp166bIyEjrKzw8XJK0atUq3XzzzRo%2BfLhiY2M1Z84cvffeeyoqKpL07XW40tPTlZSUpJSUFP3hD3/Qiy%2B%2BeJmpAQDAj4njBevRRx/Vrl271KdPH1VVVel3v/ud%2BvXrp6%2B//loTJ06s934%2B%2Bugj9erVS9nZ2X7jx48f1%2BHDh9WhQ4dz3i8/P9/v7yC2bdtWMTExys/P1%2BHDh1VcXKxrrrnGmk9MTNSBAwdUUlJycUEBAMCPluOHCKOjo7V%2B/Xrl5OTo008/VV1dne68804NGzbMOoxXH3fdddc5xwsLCxUUFKSFCxfq/fffV4sWLXTvvfdahwtLSkoUFRXld5/WrVvr0KFD8vl8kuQ336ZNG0nSoUOHzrrf%2BZSUlFj7Os3jaVrv%2B9dXSEijXGXjknk8F17v6UyBlu1C3JjLjZkkd%2BYiU%2BBwYy43ZqqPRrmSe3h4uG6//fYG2ff%2B/fsVFBSkjh07atSoUdq2bZumTp2q5s2ba%2BDAgaqsrDzrz/OEhoaqurpalZWV1u3vzknfnkxfX9nZ2crKyvIbS0tLU3p6%2BqXGcoWWLZvVe9uIiPAGXEnjcWMuN2aS3JmLTIHDjbncmOn7OF6w7r777u%2Bdv9y/RTh8%2BHD169dPLVq0kCTFxsbqiy%2B%2B0OrVqzVw4ECFhYWdVZaqq6sVHh7uV6bCwsKs/5ZkncNVHyNHjlT//v39xjyepiorO3HJuc4l0H4aqE/%2BkJBgRUSE6%2BjRCtXWuue3N92Yy42ZJHfmIlPgcGOuxs50MT/c28nxgtWuXTu/2zU1Nfryyy/12WefafTo0Ze9/6CgIKtcndaxY0dt2bJF0reHKEtLS/3mS0tLFRkZqejoaEmSz%2BdT%2B/btrf%2BWpMjIyHqvISoq6qzDgT7fMdXUuOPFcqkuJn9tbZ0rv19uzOXGTJI7c5EpcLgxlxszfZ8fzN8inD9/vg4dOnTZ%2B3/mmWe0Y8cOLV%2B%2B3Brbs2ePOnbsKEnyer3Kzc1VamqqJKm4uFjFxcXyer2Kjo5WTEyMcnNzrYKVm5urmJgY28%2BfAgAA7vWDOcY0bNgwvfHGG5e9n379%2Bmnbtm1aunSpvvrqK/3tb3/T%2BvXrNWbMGEnSnXfeqQ0bNmjt2rXas2ePHnnkEd1www264oorrPm5c%2Bdq69at2rp1q%2BbNm3fBw5oAAADf1SgnuZ/Ljh07LvpCo%2BfSo0cPPfPMM3r22Wf1zDPPqF27dpo3b54SEhIkSQkJCZoxY4aeffZZ/fvf/1bv3r01c%2BZM6/733XefvvnmG40fP14hISG67bbbdM8991z2ugAAwI/HD%2BIk9%2BPHj2vv3r3nvfTChezdu9fv9oABAzRgwIDzbp%2BammodIjxTSEiIJk2apEmTJl3SWgAAABwvWDExMX5/h1CSfvKTn2jUqFH65S9/6fRyAAAAbOd4wXriiSecfkgAAABHOV6wtm3bVu9tv/snawAAAAKF4wXr17/%2BtXWI0BhjjZ85FhQUpE8//dTp5QEAAFw2xwvWwoULNWvWLE2YMEHJyckKDQ3Vxx9/rBkzZujWW2/V4MGDnV4SAACArRy/DlZGRoamTZumm266SS1btlSzZs2UkpKiGTNmaPXq1WrXrp31BQAAEIgcL1glJSXnLE/NmzdXWVmZ08sBAACwneMFKz4%2BXk8%2B%2BaSOHz9ujZWXlyszM1PXXnut08sBAACwnePnYD322GO6%2B%2B67dd1116lDhw4yxuiLL75QZGSkVq5c6fRyAAAAbOd4werUqZNef/115eTkqLCwUJL0q1/9SkOGDFF4eLjTywEAALBdo/wtwp/97Ge6/fbb9fXXX1t/ZPknP/lJYywFAADAdo6fg2WM0dy5c3XNNddo6NChOnTokCZOnKgpU6bo1KlTTi8HAADAdo4XrBdeeEEbNmzQH//4R4WGhkr69o8zb9q0SVlZWU4vBwAAwHaOF6zs7GxNmzZNqamp1tXbBw8erFmzZmnjxo1OLwcAAMB2jhesr7/%2BWl27dj1rPDY2Vj6fz%2BnlAAAA2M7xgtWuXTt9/PHHZ42///771gnvAAAAgczx3yK87777NH36dPl8Phlj9OGHHyo7O1svvPCCHn30UaeXAwAAYDvHC9aIESNUU1Oj5557TpWVlZo2bZpatWqlhx56SHfeeafTywEAALCd4wUrJydHgwYN0siRI3XkyBEZY9S6dWunlwEAANBgHD8Ha8aMGdbJ7K1ataJcAQAA13G8YHXo0EGfffaZ0w8LAADgGMcPEcbGxuoPf/iDlixZog4dOigsLMxvPiMjw%2BklAQAA2MrxgvX5558rMTFRkrjuFQAAcCVHCtacOXM0fvx4NW3aVC%2B88IITDwkAANBoHDkHa9myZaqoqPAbGzt2rEpKSpx4eAAAAEc5UrCMMWeNbdu2TVVVVU48PAAAgKMc/y1CAAAAt6NgAQAA2MyxghUUFOTUQwEAADQqxy7TMGvWLL9rXp06dUqZmZlq1qyZ33ZcBwsAAAQ6RwrWNddcc9Y1rxISElRWVqaysjInlgAAAOAYRwoW174CAAA/JpzkDgAAYDMKFgAAgM0oWAAAADajYAEAANiMggUAAGAzChYAAIDNKFgAAAA2o2ABAADYjIIFAABgs4AvWNXV1Ro6dKi2bt1qjRUVFemee%2B5RfHy8Bg8erM2bN/vd54MPPtDQoUPl9Xp19913q6ioyG9%2B%2BfLl6tu3rxISEjR58mRVVFQ4kgUAALhDQBesqqoq/f73v1dBQYE1ZoxRWlqa2rRpo3Xr1mnYsGEaP368Dh48KEk6ePCg0tLSlJqaqpdfflmtWrXSAw88IGOMJOmtt95SVlaWZsyYoRUrVig/P1%2BZmZmNkg8AAASmgC1Y%2B/bt0x133KGvvvrKb3zLli0qKirSjBkz1KlTJ40bN07x8fFat26dJGnt2rW6%2BuqrNWbMGHXu3FkZGRk6cOCAPvroI0nSypUrNXr0aPXr1089evTQ9OnTtW7dOj7FAgAA9RawBeujjz5Sr169lJ2d7Teen5%2Bvbt26qWnTptZYYmKi8vLyrPmkpCRrLjw8XN27d1deXp5qa2v18ccf%2B83Hx8fr1KlT2rNnTwMnAgAAbuFp7AVcqrvuuuuc4z6fT1FRUX5jrVu31qFDhy44f/ToUVVVVfnNezwetWjRwro/AADAhQRswTqfiooKhYaG%2Bo2Fhoaqurr6gvOVlZXW7fPdvz5KSkrk8/n8xjyepmcVu8sVEhJYH0B6PBde7%2BlMgZbtQtyYy42ZJHfmIlPgcGMuN2aqD9cVrLCwMJWXl/uNVVdXq0mTJtb8mWWpurpaERERCgsLs26fOR8eHl7vNWRnZysrK8tvLC0tTenp6fXehxu1bNms3ttGRNT/%2Bx1I3JjLjZkkd%2BYiU%2BBwYy43Zvo%2BritY0dHR2rdvn99YaWmp9elRdHS0SktLz5rv2rWrWrRoobCwMJWWlqpTp06SpJqaGpWXlysyMrLeaxg5cqT69%2B/vN%2BbxNFVZ2YlLiXRegfbTQH3yh4QEKyIiXEePVqi2ts6BVTnDjbncmElyZy4yBQ435mrsTBfzw72dXFewvF6vFi9erMrKSutTq9zcXCUmJlrzubm51vYVFRXavXu3xo8fr%2BDgYMXFxSk3N1e9evWSJOXl5cnj8Sg2Nrbea4iKijrrcKDPd0w1Ne54sVyqi8lfW1vnyu%2BXG3O5MZPkzlxkChxuzOXGTN8nsD4CqYfk5GS1bdtWkyZNUkFBgRYvXqydO3fqtttukySNGDFC27dv1%2BLFi1VQUKBJkyapffv2VqG66667tHTpUm3atEk7d%2B7U448/rjvuuOOiDhECAIAfN9cVrJCQEC1YsEA%2Bn0%2Bpqal67bXXNH/%2BfMXExEiS2rdvr7/85S9at26dbrvtNpWXl2v%2B/PkKCgqSJA0ZMkTjxo3TtGnTNGbMGPXo0UMTJkxozEgAACDAuOIQ4d69e/1uX3nllVq1atV5t7/%2B%2But1/fXXn3d%2B7NixGjt2rG3rAwAAPy6u%2BwQLAACgsVGwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGzm2oL19ttv66qrrvL7Sk9PlyTt3r1bt99%2Bu7xer0aMGKFdu3b53TcnJ0cDBgyQ1%2BtVWlqajhw50hgRAABAgHJtwdq3b5/69eunzZs3W1%2BzZs3SyZMnNXbsWCUlJemVV15RQkKCxo0bp5MnT0qSdu7cqSlTpmj8%2BPHKzs7W0aNHNWnSpEZOAwAAAolrC1ZhYaG6dOmiyMhI6ysiIkKvv/66wsLC9Mgjj6hTp06aMmWKmjVrpjfffFOStGrVKt18880aPny4YmNjNWfOHL333nsqKipq5EQAACBQuLpgdejQ4azx/Px8JSYmKigoSJIUFBSknj17Ki8vz5pPSkoBCzoLAAAUHUlEQVSytm/btq1iYmKUn5/vyLoBAEDg8zT2AhqCMUaff/65Nm/erEWLFqm2tlaDBg1Senq6fD6ffv7zn/tt37p1axUUFEiSSkpKFBUVddb8oUOH6v34JSUl8vl8fmMeT9Oz9nu5QkICqx97PBde7%2BlMgZbtQtyYy42ZJHfmIlPgcGMuN2aqD1cWrIMHD6qiokKhoaF6%2Bumn9fXXX2vWrFmqrKy0xr8rNDRU1dXVkqTKysrvna%2BP7OxsZWVl%2BY2lpaVZJ9n/WLVs2aze20ZEhDfgShqPG3O5MZPkzlxkChxuzOXGTN/HlQWrXbt22rp1q372s58pKChIXbt2VV1dnSZMmKDk5OSzylJ1dbWaNGkiSQoLCzvnfHh4/Z8YI0eOVP/%2B/f3GPJ6mKis7cYmJzi3QfhqoT/6QkGBFRITr6NEK1dbWObAqZ7gxlxszSe7MRabA4cZcjZ3pYn64t5MrC5YktWjRwu92p06dVFVVpcjISJWWlvrNlZaWWofvoqOjzzkfGRlZ78eOioo663Cgz3dMNTXueLFcqovJX1tb58rvlxtzuTGT5M5cZAocbszlxkzfJ7A%2BAqmn//3f/1WvXr1UUVFhjX366adq0aKFEhMTtWPHDhljJH17vtb27dvl9XolSV6vV7m5udb9iouLVVxcbM0DAABciCsLVkJCgsLCwvTYY49p//79eu%2B99zRnzhzdf//9GjRokI4eParZs2dr3759mj17tioqKnTzzTdLku68805t2LBBa9eu1Z49e/TII4/ohhtu0BVXXNHIqQAAQKBwZcFq3ry5li5dqiNHjmjEiBGaMmWKRo4cqfvvv1/NmzfXokWLlJubq9TUVOXn52vx4sVq2rSppG/L2YwZMzR//nzdeeed%2BtnPfqaMjIxGTgQAAAKJa8/B6ty5s5YtW3bOuR49eujVV189731TU1OVmpraUEsDAAAu58pPsAAAABoTBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbeRp7AfjxuPnp/2vsJVyUNx7q3dhLAAAEKD7BAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbMYfewbOgz9ODQC4VHyCBQAAYDMK1jlUVVVp8uTJSkpKUp8%2BffT888839pIAAEAA4RDhOcyZM0e7du3SihUrdPDgQU2cOFExMTEaNGhQYy8NAAAEAArWGU6ePKm1a9fqr3/9q7p3767u3buroKBAL774IgULAADUCwXrDHv27FFNTY0SEhKsscTERC1cuFB1dXUKDuaoKn6YAu2k/EDCLxAAuFgUrDP4fD61bNlSoaGh1libNm1UVVWl8vJytWrV6oL7KCkpkc/n8xvzeJoqKirK1rWGhFD2ACd4PIH9Wjv9XuGm9ww3ZpLcmcuNmeqDgnWGiooKv3IlybpdXV1dr31kZ2crKyvLb2z8%2BPF68MEH7Vnk/1NSUqLR/1GgkSNH2l7eGktJSYmys7NdlUlyZy43ZpLcmaukpEQrViwhUwBwYy43ZqqPH1edrIewsLCzitTp202aNKnXPkaOHKlXXnnF72vkyJG2r9Xn8ykrK%2BusT8sCmRszSe7M5cZMkjtzkSlwuDGXGzPVB59gnSE6OlplZWWqqamRx/Ptt8fn86lJkyaKiIio1z6ioqJ%2BVC0dAAD44xOsM3Tt2lUej0d5eXnWWG5uruLi4jjBHQAA1AuN4Qzh4eEaPny4Hn/8ce3cuVObNm3S888/r7vvvruxlwYAAAJEyOOPP/54Yy/ihyYlJUW7d%2B/WvHnz9OGHH%2Bq3v/2tRowY0djLOqdmzZopOTlZzZo1a%2Byl2MaNmSR35nJjJsmducgUONyYy42ZLiTIGGMaexEAAABuwiFCAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGxGwQIAALAZBQsAAMBmFKwAVVVVpcmTJyspKUl9%2BvTR888/39hLOsvhw4eVnp6u5ORk9e3bVxkZGaqqqpIkFRUV6Z577lF8fLwGDx6szZs3%2B933gw8%2B0NChQ%2BX1enX33XerqKjIb3758uXq27evEhISNHnyZFVUVDiW67SxY8fq0UcftW7v3r1bt99%2Bu7xer0aMGKFdu3b5bZ%2BTk6MBAwbI6/UqLS1NR44cseaMMZo7d65SUlKUnJysOXPmqK6uzrEs1dXVmj59uq655hr94he/0JNPPqnT1yAO5FzFxcUaN26cevbsqf79%2B2v58uXWXKDlqq6u1tChQ7V161ZrrCFfR069x5wrV15env7rv/5LCQkJuummm7R27dqAynWuTKcdO3ZMffv21SuvvOI3fjnPt7KyMj344INKSEhQ//79tWHDBkcyHTx4UL/5zW/k9Xo1cOBAvf766wGVqcEZBKQZM2aYW265xezatcv84x//MAkJCeaNN95o7GVZ6urqzB133GHuv/9%2B89lnn5lt27aZgQMHmieeeMLU1dWZW265xTz88MNm3759ZuHChcbr9ZoDBw4YY4w5cOCAiY%2BPN0uXLjWfffaZ%2Be///m8zdOhQU1dXZ4wx5s033zSJiYnmf/7nf0x%2Bfr4ZPHiwmT59uqP5cnJyTJcuXczEiRONMcacOHHC9O7d2zzxxBNm3759ZubMmeYXv/iFOXHihDHGmPz8fNOjRw/z6quvmk8//dSMGjXKjB071trf0qVLzfXXX2%2B2bdtmPvzwQ9OnTx%2BzZMkSx/JMnTrV3HjjjSY/P9988MEHplevXmb16tUBn%2BuOO%2B4wDz30kPn888/N22%2B/bbxer/nHP/4RcLkqKytNWlqa6dKli9myZYsxxjT468iJ95hz5SopKTFJSUlm3rx55vPPPzc5OTkmLi7OvPvuuwGR61yZvmvq1KmmS5cuZt26ddbY5T7fxo0bZ0aPHm327t1rXnrpJXP11Veb/Pz8Bs106tQpM3ToUPPb3/7WFBYWmtWrV5vu3bubvXv3BkQmJ1CwAtCJEydMXFyc34t3/vz5ZtSoUY24Kn/79u0zXbp0MT6fzxrbuHGj6dOnj/nggw9MfHy89Y%2BZMcaMHj3aPPvss8YYY55%2B%2Bmm/LCdPnjQJCQlW3rvuusva1hhjtm3bZnr06GFOnjzZ0LGMMcaUlZWZ6667zowYMcIqWGvXrjX9%2B/e33uTr6urMwIEDrTfRCRMmWNsaY8zBgwfNVVddZb766itjjDHXX3%2B93xvu%2BvXrTb9%2B/RzL061bN7N161ZrbNGiRebRRx8N6Fzl5eWmS5cu1hu%2BMcaMHz/eTJ8%2BPaByFRQUmF/%2B8pfmlltu8fsHriFfR068x5wv19/%2B9jczaNAgv22nTp1qfv/73//gc50v03fXMnDgQNO7d2%2B/58/lPN%2B%2B/PJL06VLF1NUVGTNT5482W9/DZFp06ZNJjEx0Rw7dsza9ne/%2B51Zs2bNDz6TUzhEGID27NmjmpoaJSQkWGOJiYnKz8939PDL94mMjNSSJUvUpk0bv/Hjx48rPz9f3bp1U9OmTa3xxMRE5eXlSZLy8/OVlJRkzYWHh6t79%2B7Ky8tTbW2tPv74Y7/5%2BPh4nTp1Snv27GngVN/685//rGHDhunnP/%2B5NZafn6/ExEQFBQVJkoKCgtSzZ8/zZmrbtq1iYmKUn5%2Bvw4cPq7i4WNdcc401n5iYqAMHDqikpKTB8%2BTm5qp58%2BZKTk62xsaOHauMjIyAztWkSROFh4frlVde0alTp7R//35t375dXbt2DahcH330kXr16qXs7Gy/8YZ8HTnxHnO%2BXKdPJzjT8ePHf/C5zpdJ%2BvYQ29SpUzVt2jSFhob6zV3O8y0/P19t27ZV%2B/bt/eZ37Nhx2Xm%2BL9NHH32ka6%2B9Vs2bN7fGFixYoJEjR/7gMznF09gLwMXz%2BXxq2bKl34u0TZs2qqqqUnl5uVq1atWIq/tWRESE%2Bvbta92uq6vTqlWrlJKSIp/Pp6ioKL/tW7durUOHDknS984fPXpUVVVVfvMej0ctWrSw7t%2BQPvzwQ/3rX//Sxo0b9fjjj1vjPp/Pr3CdXnNBQYEkqaSk5LyZfD6fJPnNny6mhw4dOut%2BdisqKlK7du20fv16LVy4UKdOnVJqaqp%2B97vfBXSusLAwTZs2TTNnztTKlStVW1ur1NRU3X777XrnnXcCJtddd911zvGGfB0FBwc3%2BHvM%2BXK1b9/e7x/Wb775Rn//%2B9/14IMP/uBznS%2BTJC1cuFDdunVTnz59zpq7nOfb%2Bb4fhw8fvuQc33W%2BTKffN%2BbOnasNGzaoZcuWSk9P14ABA37wmZxCwQpAFRUVZ/0EdPp2dXV1YyzpgjIzM7V79269/PLLWr58%2BTnXf3rt58tXXV2tyspK6/b57t9Qqqqq9Mc//lHTpk1TkyZN/Oa%2Bb82SVFlZeVGZnPz/efLkSX355Zdas2aNMjIy5PP5NG3aNIWHhwd0LkkqLCxUv379dO%2B996qgoEAzZ87UtddeG/C5pAs/5y7ndWSM%2BUG8x1RWVurBBx9UmzZtrE9GAjHXvn37tGbNGr322mvnnL%2Bc59uFngcN5eTJk3r11Vc1ePBgLVy4UFu3blV6erqys7MVFxcXkJnsRsEKQGFhYWc90U7fPvMf/h%2BCzMxMrVixQk899ZS6dOmisLAwlZeX%2B21TXV1trf18%2BSIiIhQWFmbdPnM%2BPDy8AVNIWVlZuvrqq/0%2BmTvtfGu%2BUKbw8HC/N5Yz8zV0Junbn/CPHz%2BuefPmqV27dpK%2B/e2g1atX68orrwzYXB9%2B%2BKFefvllvffee2rSpIni4uJ0%2BPBhPffcc7riiisCNtdpDfk6qq2tbfT3mBMnTuiBBx7QF198ob/97W/W9zbQchlj9Nhjjyk9Pf2sUyZOu5zn24XeexpKSEiIWrRooccff1zBwcHq3r27/vWvf%2Bmll15SXFxcQGayG%2BdgBaDo6GiVlZWppqbGGvP5fGrSpIkiIiIacWVnmzlzppYtW6bMzEzddNNNkr5df2lpqd92paWl1kfC55uPjIxUixYtFBYW5jdfU1Oj8vJyRUZGNmiWv//979q0aZMSEhKUkJCgjRs3auPGjUpISLisTNHR0ZJkfWz%2B3f9u6EynHyMsLMwqV5L0n//5nyouLg7oXLt27dKVV17p96bcrVs3HTx4MKBzndaQr6PGfo85fvy47rvvPhUUFGjFihXq0KGDNRdouQ4ePKgdO3boz3/%2Bs/XecfDgQf3xj3/U/ffff8FMF3q%2Bfd99G1JUVJQ6dOig4OD/v0acft%2BQAjOT3ShYAahr167yeDzWyazStycqx8XF%2BT3ZG1tWVpbWrFmjJ598UkOGDLHGvV6vPvnkE%2BtjYunb9Xu9Xms%2BNzfXmquoqNDu3bvl9XoVHBysuLg4v/m8vDx5PB7FxsY2aJ4XXnhBGzdu1Pr167V%2B/Xr1799f/fv31/r16%2BX1erVjxw7r2lHGGG3fvv28mYqLi1VcXCyv16vo6GjFxMT4zefm5iomJqbBz1M6vbaqqip9/vnn1tj%2B/fvVrl27gM4VFRWlL7/80u8n4f3796t9%2B/YBneu0hnwdNeZ7TF1dncaPH6%2Bvv/5aL7zwgjp37uw3H2i5oqOj9Y9//MN631i/fr2ioqKUnp6u2bNnnzPTxTzf4uPjdeDAAb9zUHNzcxUfH98geU7zer0qKChQbW2tNVZYWGj9oBaImWzXaL%2B/iMsydepUM2TIEJOfn2/efvtt07NnT/PWW2819rIs%2B/btM127djVPPfWUKSkp8fuqqakxgwcPNg899JD57LPPzKJFi0x8fLx1/Z6ioiITFxdnFi1aZF3n5pZbbrF%2BpT4nJ8f07NnTvP322yY/P98MGTLEzJw50/GMEydOtH5t%2BNixYyYlJcXMnDnTFBQUmJkzZ5revXtbv0K/fft20717d/PSSy9Z14QZN26cta9FixaZPn36mC1btpgtW7aYPn36mOeff96xLGPHjjUjR440n376qXn//fdNSkqKWbFiRUDnOnr0qOndu7eZMGGC2b9/v3nnnXdMcnKyWb16dcDm%2Bu6vyTf068jJ95jv5srOzjaxsbHm3Xff9XvfKCsrC6hc57sOljHG9OvXz%2B8SBZf7fBszZowZNWqU%2BfTTT81LL71k4uLiGuSaUd/NdOzYMdOnTx8zdepU88UXX5hVq1aZbt26mV27dgVUpoZEwQpQJ0%2BeNI888oiJj483ffr0McuWLWvsJflZtGiR6dKlyzm/jDHmiy%2B%2BML/61a/M1VdfbYYMGWL%2B7//%2Bz%2B/%2B//znP82NN95oevToYUaPHm1dO%2BW7%2B7/22mtNYmKimTRpkqmsrHQs22nfLVjGfHthveHDh5u4uDhz2223mU8%2B%2BcRv%2B3Xr1pnrr7/exMfHm7S0NHPkyBFrrqamxvzpT38ySUlJplevXiYzM9P6B8MJR48eNRMmTDDx8fHm2muvNX/5y1%2Bsxw/kXAUFBeaee%2B4xPXv2NAMGDDDLli0L6Fxn/qPdkK8jJ99jvptrzJgx53zf%2BO61qgIh18UULGMu7/lWWlpqxo0bZ%2BLi4kz//v3Nxo0bHclUUFBgPf9uvPHGs4pqIGRqSEHG/L/PyAEAAGCLH84JOwAAAC5BwQIAALAZBQsAAMBmFCwAAACbUbAAAABsRsECAACwGQULAADAZhQsAAAAm1GwAAAAbEbBAgAAsBkFCwAAwGYULAAAAJtRsAAAAGz2/wES33NjetjKJAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1409573836035336826”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1233.55979709597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>112.065380839541</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>957.677673913419</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>193.181529933526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3150.6949512274</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>329.812011624733</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>341.98406489593</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>800.788733405248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>277.616486268864</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3104)</td> <td class=”number”>3104</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1409573836035336826”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0899097505334794</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.249055350564959</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.51227935496778</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.623552825694787</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11074.4328249868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12394.9195636111</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13813.4935438988</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13907.3064750753</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16334.4231756195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HHNV15”><s>LACCESS_HHNV15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_HHNV10”><code>LACCESS_HHNV10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.97302</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_HISP15”>LACCESS_HISP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3062</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2332.7</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>263440</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5708198883812863217”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5708198883812863217,#minihistogram-5708198883812863217”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5708198883812863217”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5708198883812863217”

aria-controls=”quantiles-5708198883812863217” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5708198883812863217” aria-controls=”histogram-5708198883812863217”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5708198883812863217” aria-controls=”common-5708198883812863217”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5708198883812863217” aria-controls=”extreme-5708198883812863217”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5708198883812863217”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.899</td>

</tr> <tr>

<th>Q1</th> <td>31.162</td>

</tr> <tr>

<th>Median</th> <td>136.78</td>

</tr> <tr>

<th>Q3</th> <td>883.65</td>

</tr> <tr>

<th>95-th percentile</th> <td>8461.8</td>

</tr> <tr>

<th>Maximum</th> <td>263440</td>

</tr> <tr>

<th>Range</th> <td>263440</td>

</tr> <tr>

<th>Interquartile range</th> <td>852.49</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12181</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.2217</td>

</tr> <tr>

<th>Kurtosis</th> <td>250.98</td>

</tr> <tr>

<th>Mean</th> <td>2332.7</td>

</tr> <tr>

<th>MAD</th> <td>3449.5</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>14.263</td>

</tr> <tr>

<th>Sum</th> <td>7261800</td>

</tr> <tr>

<th>Variance</th> <td>148370000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5708198883812863217”>

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uzYsXK5XJo2bZpKS0t9%2Brdu3aq0tDQlJSVpwYIFqqqq8tu8AABA8AvKgGWMUWZmpqqqqvTMM89o9erV%2Butf/6rHHntMxhhlZGSoa9euys/P17hx4zRnzhwdPXpUknT06FFlZGRowoQJev7559W5c2fde%2B%2B9MsZIkl5//XXl5uYqKytL27Ztk9vtVk5OTiCnCwAAgkxQBqzDhw%2BrqKhI2dnZ6tOnj1JSUpSZmamCggLt2rVLpaWlysrKUu/evTV79mwlJiYqPz9fkvTcc89pwIABmjlzpvr06aPs7GwdOXJE77//viRp%2B/btmj59utLT0zVw4EAtWbJE%2Bfn5nMUCAADNFpQBKzo6Wps3b1bXrl192k%2BfPi23261rr71W7du397YnJyerqKhIkuR2u5WSkuLti4iIUP/%2B/VVUVKT6%2Bnp9%2BOGHPv2JiYk6d%2B6cDhw40MKzAgAAbUVQBqzIyEilpaV5Xzc0NGjHjh0aMmSIPB6PYmJifN7fpUsXHT9%2BXJIu2X/q1CnV1NT49DudTkVFRXm3BwAAaIoz0AOwIScnR/v379fzzz%2BvrVu3KiwszKc/LCxMtbW1kqSqqqqL9ldXV3tfX2z75igrK5PH4/FpczrbNwp2lys0NLjysdPZesd7vpbBVtPWhjraQy3toZb2UMvmC/qAlZOTo23btmn16tXq27evwsPDVVlZ6fOe2tpatWvXTpIUHh7eKCzV1tYqMjJS4eHh3tff7o%2BIiGj2mPLy8pSbm%2BvTlpGRoczMzGbvoy3q1KlDoIfQpMjI5v93xsVRR3uopT3U0h5q2bSgDlhLly7Vs88%2Bq5ycHN18882SpNjYWB06dMjnfeXl5d6zR7GxsSovL2/U369fP0VFRSk8PFzl5eXq3bu3JKmurk6VlZWKjo5u9rgmT56s4cOH%2B7Q5ne1VUXHmO8/xUoLtE4Tt%2BdsUGhqiyMgInTpVpfr6hkAPJ2hRR3uopT3U0p5grGWgPtwHbcDKzc3VH/7wBz366KMaNWqUt93lcmnTpk2qrq72nrUqLCxUcnKyt7%2BwsND7/qqqKu3fv19z5sxRSEiIEhISVFhYqMGDB0uSioqK5HQ6FR8f3%2ByxxcTENLoc6PF8pbq64FiMLSUY5l9f3xAU42ztqKM91NIeamkPtWxacJ0C%2BX8lJSVav369fvnLXyo5OVkej8f7lZqaqm7dumn%2B/PkqLi7Wpk2btHfvXk2aNEmSNHHiRO3Zs0ebNm1ScXGx5s%2Bfrx49engD1ZQpU7Rlyxa9%2Beab2rt3rxYvXqw77rjjO10iBAAAP2xBeQbrL3/5i%2Brr6/XEE0/oiSee8On75JNPtH79ei1cuFATJkzQT37yE61bt05xcXGSpB49eujxxx/Xww8/rHXr1ikpKUnr1q2Tw%2BGQJI0ZM0ZHjhzRokWLVFtbq5/%2B9KeaN2%2Be3%2BcIAACCl8Ocf4S5n9x%2B%2B%2B2aOHGixowZoyuuuMKfhw4oj%2Bcr6/t0OkM0cuU71vfbUl67b2igh3BRTmeIOnXqoIqKM5z2vgzU0R5qaQ%2B1tCcYaxkdHZis4fdLhEOGDNGGDRs0bNgw/eY3v9HOnTvl54wHAADQovwesObOnau//vWvWr9%2BvUJDQ/XrX/9aN910k1avXq1PP/3U38MBAACwLiD3YDkcDg0dOlRDhw5VVVWVnn76aa1fv16bNm3SoEGDNH36dP30pz8NxNAAAAAuW8Buci8rK9PLL7%2Bsl19%2BWQcPHtSgQYN022236fjx4/rd736n3bt3a%2BHChYEaHgAAwPfm94D10ksv6aWXXtJ7772nzp07a/z48Vq7dq169uzpfU%2B3bt20fPlyAhYAAAhKfg9YCxcuVHp6utatW6cbbrhBISGNbwPr1auXpk6d6u%2BhAQAAWOH3gPX222%2BrU6dOqqys9IarvXv3qn///goNDZUkDRo0SIMGDfL30AAAAKzw%2B28Rnj59WqNGjdLvf/97b9s999yjcePG6dixY/4eDgAAgHV%2BD1gPP/ywfvKTn%2Bjuu%2B/2tr366qvq1q2bsrOz/T0cAAAA6/wesP73f/9Xv/3tbxUdHe1t69y5sx544AHt2rXL38MBAACwzu8By%2Bl06tSpU43aq6qqeKI7AABoE/wesG644QYtW7ZM//jHP7xtpaWlys7OVlpamr%2BHAwAAYJ3ff4vwwQcf1N13362bb75ZkZGRkqRTp06pf//%2Bmj9/vr%2BHAwAAYJ3fA1aXLl30wgsv6N1331VxcbGcTqeuvvpqXX/99XI4HP4eDgAAgHUB%2BVM5oaGhSktL45IgAABok/wesDwejx577DHt2bNH586da3Rj%2B1/%2B8hd/DwkAAMAqvweshx56SPv27dOYMWN0xRVX%2BPvwAAAALc7vAWvXrl3avHmzUlJS/H1oAAAAv/D7Yxrat2%2BvLl26%2BPuwAAAAfuP3gDVu3Dht3rxZ9fX1/j40AACAX/j9EmFlZaUKCgr0t7/9TVdddZXCwsJ8%2Brdv3%2B7vIQEAAFgVkMc0jB07NhCHBQAA8Au/B6zs7Gx/HxIAAMCv/H4PliSVlZUpNzdXc%2BfO1T//%2BU/9%2Bc9/1uHDhwMxFAAAAOv8HrA%2B//xz3XrrrXrhhRf0%2Buuv6%2BzZs3r11Vc1ceJEud1ufw8HAADAOr8HrEceeUQjRozQm2%2B%2BqR/96EeSpEcffVTDhw/XypUr/T0cAAAA6/wesPbs2aO7777b5w87O51O3Xvvvdq/f7%2B/hwMAAGCd3wNWQ0ODGhoaGrWfOXNGoaGh/h4OAACAdX4PWMOGDdPGjRt9QlZlZaVycnI0ZMgQfw8HAADAOr8HrN/%2B9rfat2%2Bfhg0bppqaGv3rv/6r0tPT9cUXX%2BjBBx/093AAAACs8/tzsGJjY/Xiiy%2BqoKBAH3/8sRoaGnTnnXdq3Lhx6tixo7%2BHAwAAYF1AnuQeERGh22%2B/PRCHBgAAaHF%2BD1jTpk27ZD9/ixAAAAQ7vwes7t27%2B7yuq6vT559/roMHD2r69On%2BHg4AAIB1reZvEa5bt07Hjx/382gAAADsC8jfIryQcePG6bXXXgv0MAAAAC5bqwlYH3zwAQ8aBQAAbUKruMn99OnT%2BuSTTzRlyhR/DwcAAMA6v5/BiouLU/fu3X2%2BBgwYoKVLl36vB43W1tZq7Nixeu%2B997xty5Yt0zXXXOPztWPHDm9/QUGBRowYIZfLpYyMDJ08edLbZ4zRypUrNWTIEKWmpmrFihUX/NM%2BAAAAF%2BP3M1iPPPKItX3V1NRo7ty5Ki4u9mkvKSnR3Llzddttt3nbzj/EdO/evVq4cKGWLFmi%2BPh4LV%2B%2BXPPnz9fGjRslSU899ZQKCgqUm5ururo6zZs3T126dNGsWbOsjRsAALRtfg9Yu3fvbvZ7r7vuuov2HTp0SHPnzpUxplFfSUmJZs2apejo6EZ9O3bs0C233KLx48dLklasWKH09HSVlpbqqquu0vbt25WZmamUlBRJ0v333681a9YQsAAAQLP5PWDdddddcjgckuQTjr7d5nA49PHHH190P%2B%2B//74GDx6sf//3f1diYqK3/fTp0zpx4oR69ux5we3cbrd%2B%2Bctfel9369ZNcXFxcrvdCgsL07Fjx3yCXXJyso4cOaKysjLFxMR89wkDAIAfHL8HrA0bNmjZsmWaN2%2BeUlNTFRYWpg8//FBZWVm67bbbNHr06Gbt52I3xJeUlMjhcGjDhg16%2B%2B23FRUVpbvvvtt7ufBCQalLly46fvy4PB6PJPn0d%2B3aVZJ0/PjxZgessrIy777OczrbWw9ooaGt5pdAm8XpbL3jPV/LYKtpa0Md7aGW9lBLe6hl8wXkQaOLFi3SDTfc4G0bMmSIsrKy9MADD/icXfo%2BDh8%2BLIfDoV69emnq1KnavXu3HnroIXXs2FEjR45UdXW1wsLCfLYJCwtTbW2tqqurva%2B/2Sd9fTN9c%2BXl5Sk3N9enLSMjQ5mZmd93Wm1Cp04dAj2EJkVGRgR6CG0CdbSHWtpDLe2hlk3ze8AqKytr9OdypK9vQq%2BoqLjs/Y8fP17p6emKioqSJMXHx%2Buzzz7Ts88%2Bq5EjRyo8PLxRWKqtrVVERIRPmAoPD/f%2BW/r6D1Q31%2BTJkzV8%2BHCfNqezvSoqznzveV1IsH2CsD1/m0JDQxQZGaFTp6pUX89vjX5f1NEeamkPtbQnGGsZqA/3fg9YiYmJevTRR/Wf//mf3t/sq6ysVE5Ojq6//vrL3r/D4fCGq/N69eqlXbt2SZJiY2NVXl7u019eXq7o6GjFxsZKkjwej3r06OH9t6QL3jB/MTExMY0uB3o8X6muLjgWY0sJhvnX1zcExThbO%2BpoD7W0h1raQy2b5vdTIL/73e9UVFSkG264QRMmTNBtt93m/S2%2BRYsWXfb%2B16xZoxkzZvi0HThwQL169ZIkuVwuFRYWevuOHTumY8eOyeVyKTY2VnFxcT79hYWFiouL4wZ3AADQbH4/g9W7d2%2B9%2BuqrKigoUElJiSTp5z//ucaMGfOdLsNdTHp6ujZt2qQtW7Zo5MiR2rlzp1588UVt375dknTnnXfqrrvuUmJiohISErR8%2BXLddNNNuuqqq7z9K1eu1I9//GNJ0qpVqzRz5szLHhcAAPjh8HvAkqQrr7xSt99%2Bu7744gtvsPnRj35kZd8DBw7UmjVrtHbtWq1Zs0bdu3fXqlWrlJSUJElKSkpSVlaW1q5dqy%2B//FJDhw7V0qVLvdvPmjVL//znPzVnzhyFhoZq0qRJjc6IAQAAXIrDXOhJnS3IGKNVq1bp6aef1rlz5/T6669r9erVioiI0OLFi60FrdbG4/nK%2Bj6dzhCNXPmO9f22lNfuGxroIVyU0xmiTp06qKLiDPcVXAbqaA%2B1tIda2hOMtYyOviIgx/X7PVhPP/20XnrpJf3Hf/yH97f2RowYoTfffLPRow0AAACCkd8DVl5enhYtWqQJEyZ4n94%2BevRoLVu2TK%2B88oq/hwMAAGCd3wPWF198oX79%2BjVqj4%2BPb/T0cwAAgGDk94DVvXt3ffjhh43a3377be8N7wAAAMHM779FOGvWLC1ZskQej0fGGP39739XXl6enn76af32t7/193AAAACs83vAmjhxourq6vTEE0%2BourpaixYtUufOnXXffffpzjvv9PdwAAAArPN7wCooKNCoUaM0efJknTx5UsYYdenSxd/DAAAAaDF%2BvwcrKyvLezN7586dCVcAAKDN8XvA6tmzpw4ePOjvwwIAAPiN3y8RxsfH6/7779fmzZvVs2dPhYeH%2B/RnZ2f7e0gAAABW%2BT1gffrpp0pOTpYknnsFAADaJL8ErBUrVmjOnDlq3769nn76aX8cEgAAIGD8cg/WU089paqqKp%2B2e%2B65R2VlZf44PAAAgF/5JWAZYxq17d69WzU1Nf44PAAAgF/5/bcIAQAA2joCFgAAgGV%2BC1gOh8NfhwIAAAgovz2mYdmyZT7PvDp37pxycnLUoUMHn/fxHCwAABDs/BKwrrvuukbPvEpKSlJFRYUqKir8MQQAAAC/8UvA4tlXAADgh4Sb3AEAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALAv6gFVbW6uxY8fqvffe87aVlpZqxowZSkxM1OjRo7Vz506fbd59912NHTtWLpdL06ZNU2lpqU//1q1blZaWpqSkJC1YsEBVVVV%2BmQsAAGgbgjpg1dTU6De/%2BY2Ki4u9bcYYZWRkqGvXrsrPz9e4ceM0Z84cHT16VJJ09OhRZWRkaMKECXr%2B%2BefVuXNn3XvvvTLGSJJef/115ebmKisrS9u2bZPb7VZOTk5A5gcAAIJT0AasQ4cO6Y477tA//vEPn/Zdu3aptLRUWVlZ6t27t2bPnq3ExETl5%2BdLkp577jkNGDBAM2fOVJ8%2BfZSdna0jR47o/ffflyRt375d06dPV3p6ugYOHKglS5YoPz%2Bfs1gAAKDZgjZgvf/%2B%2Bxo8eLDy8vJ82t1ut6699lq1b9/e25acnKyioiJvf0pKircvIiJC/fv3V1FRkerr6/Xhhx/69CcmJurcuXM6cOBAC88IAAC0Fc5AD%2BD7mjJlygXbPR6PYmJifNq6dOmi48ePN9l/6tQp1dTU%2BPQ7nU5FRUV5twcAAGhK0Aasi6mqqlJYWJhPW1hYmGpra5vsr66u9r6%2B2PbNUVZWJo/H49PmdLZvFOwuV2hocJ2AdDpb73jP1zLYatraUEd7qKU91NIeatk5phlLAAAUqElEQVR8bS5ghYeHq7Ky0qettrZW7dq18/Z/OyzV1tYqMjJS4eHh3tff7o%2BIiGj2GPLy8pSbm%2BvTlpGRoczMzGbvoy3q1KlDoIfQpMjI5v93xsVRR3uopT3U0h5q2bQ2F7BiY2N16NAhn7by8nLv2aPY2FiVl5c36u/Xr5%2BioqIUHh6u8vJy9e7dW5JUV1enyspKRUdHN3sMkydP1vDhw33anM72qqg4832mdFHB9gnC9vxtCg0NUWRkhE6dqlJ9fUOghxO0qKM91NIeamlPMNYyUB/u21zAcrlc2rRpk6qrq71nrQoLC5WcnOztLyws9L6/qqpK%2B/fv15w5cxQSEqKEhAQVFhZq8ODBkqSioiI5nU7Fx8c3ewwxMTGNLgd6PF%2Bpri44FmNLCYb519c3BMU4WzvqaA%2B1tIda2kMtmxZcp0CaITU1Vd26ddP8%2BfNVXFysTZs2ae/evZo0aZIkaeLEidqzZ482bdqk4uJizZ8/Xz169PAGqilTpmjLli168803tXfvXi1evFh33HHHd7pECAAAftjaXMAKDQ3V%2BvXr5fF4NGHCBL388stat26d4uLiJEk9evTQ448/rvz8fE2aNEmVlZVat26dHA6HJGnMmDGaPXu2Fi1apJkzZ2rgwIGaN29eIKcEAACCjMOcf4Q5WpTH85X1fTqdIRq58h3r%2B20pr903NNBDuCinM0SdOnVQRcUZTntfBupoD7W0h1raE4y1jI6%2BIiDHbXNnsAAAAAKNgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlrXZgPXGG2/ommuu8fnKzMyUJO3fv1%2B33367XC6XJk6cqH379vlsW1BQoBEjRsjlcikjI0MnT54MxBQAAECQarMB69ChQ0pPT9fOnTu9X8uWLdPZs2d1zz33KCUlRX/605%2BUlJSk2bNn6%2BzZs5KkvXv3auHChZozZ47y8vJ06tQpzZ8/P8CzAQAAwaTNBqySkhL17dtX0dHR3q/IyEi9%2BuqrCg8P1wMPPKDevXtr4cKF6tChg/785z9Lknbs2KFbbrlF48ePV3x8vFasWKG33npLpaWlAZ4RAAAIFm06YPXs2bNRu9vtVnJyshwOhyTJ4XBo0KBBKioq8vanpKR439%2BtWzfFxcXJ7Xb7ZdwAACD4OQM9gJZgjNGnn36qnTt3auPGjaqvr9eoUaOUmZkpj8ejq6%2B%2B2uf9Xbp0UXFxsSSprKxMMTExjfqPHz/e7OOXlZXJ4/H4tDmd7Rvt93KFhgZXPnY6W%2B94z9cy2Gra2lBHe6ilPdTSHmrZfG0yYB09elRVVVUKCwvTY489pi%2B%2B%2BELLli1TdXW1t/2bwsLCVFtbK0mqrq6%2BZH9z5OXlKTc316ctIyPDe5P9D1WnTh0CPYQmRUZGBHoIbQJ1tIda2kMt7aGWTWuTAat79%2B567733dOWVV8rhcKhfv35qaGjQvHnzlJqa2igs1dbWql27dpKk8PDwC/ZHRDR/MU2ePFnDhw/3aXM626ui4sz3nNGFBdsnCNvztyk0NESRkRE6dapK9fUNgR5O0KKO9lBLe6ilPcFYy0B9uG%2BTAUuSoqKifF737t1bNTU1io6OVnl5uU9feXm59/JdbGzsBfujo6ObfeyYmJhGlwM9nq9UVxcci7GlBMP86%2BsbgmKcrR11tIda2kMt7aGWTQuuUyDN9M4772jw4MGqqqrytn388ceKiopScnKyPvjgAxljJH19v9aePXvkcrkkSS6XS4WFhd7tjh07pmPHjnn7AQAAmtImA1ZSUpLCw8P1u9/9TocPH9Zbb72lFStW6Be/%2BIVGjRqlU6dOafny5Tp06JCWL1%2Buqqoq3XLLLZKkO%2B%2B8Uy%2B99JKee%2B45HThwQA888IBuuukmXXXVVQGeFQAACBZtMmB17NhRW7Zs0cmTJzVx4kQtXLhQkydP1i9%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%2B/furuLhYzzzzDAELAAA0CwHrWw4cOKC6ujolJSV525KTk7VhwwY1NDQoJISrqt/XLY/9T6CH8J28dt/QQA8BABCkCFjf4vF41KlTJ4WFhXnbunbtqpqaGlVWVqpz585N7qOsrEwej8enzelsr5iYGKtjDQ0l7LWkYAuEb9yfFugheNcka/PyUUt7qKU91LL5CFjfUlVV5ROuJHlf19bWNmsfeXl5ys3N9WmbM2eOfv3rX9sZ5P8rKyvT9B8Xa/LkydbD2w9NWVmZ8vLyqOVlKisr07Ztm6mjBdTSHmppD7VsPiLot4SHhzcKUudft2vXrln7mDx5sv70pz/5fE2ePNn6WD0ej3JzcxudLcN3Ry3toI72UEt7qKU91LL5OIP1LbGxsaqoqFBdXZ2czq/L4/F41K5dO0VGRjZrHzExMSR7AAB%2BwDiD9S39%2BvWT0%2BlUUVGRt62wsFAJCQnc4A4AAJqFxPAtERERGj9%2BvBYvXqy9e/fqzTff1JNPPqlp06YFemgAACBIhC5evHhxoAfR2gwZMkT79%2B/XqlWr9Pe//12/%2BtWvNHHixEAP64I6dOig1NRUdejQIdBDCXrU0g7qaA%2B1tIda2kMtm8dhjDGBHgQAAEBbwiVCAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUErCBVU1OjBQsWKCUlRcOGDdOTTz4Z6CEFzBtvvKFrrrnG5yszM1OStH//ft1%2B%2B%2B1yuVyaOHGi9u3b57NtQUGBRowYIZfLpYyMDJ08edLbZ4zRypUrNWTIEKWmpmrFihVqaGjw9ldUVOjXv/61kpKSNHz4cL300kv%2BmXALqK2t1dixY/Xee%2B9520pLSzVjxgwlJiZq9OjR2rlzp8827777rsaOHSuXy6Vp06aptLTUp3/r1q1KS0tTUlKSFixYoKqqKm9fU%2Bu3qWO3Zheq5bJlyxqt0R07dnj7W3IdNvU90NqcOHFCmZmZSk1NVVpamrKzs1VTUyOJNfldXaqWrEk/MAhKWVlZ5tZbbzX79u0z//Vf/2WSkpLMa6%2B9FuhhBcT69evN7NmzTVlZmffryy%2B/NGfOnDFDhw41jzzyiDl06JBZunSp%2BZd/%2BRdz5swZY4wxbrfbDBw40Lzwwgvm448/NlOnTjX33HOPd79btmwxN954o9m9e7f5%2B9//boYNG2Y2b97s7Z89e7aZPn26%2BeSTT8wf//hHM2DAAON2u/0%2B/8tVXV1tMjIyTN%2B%2Bfc2uXbuMMcY0NDSYW2%2B91cydO9ccOnTIbNiwwbhcLnPkyBFjjDFHjhwxiYmJZsuWLebgwYPm3/7t38zYsWNNQ0ODMcaYP//5zyY5Odn893//t3G73Wb06NFmyZIl3mNeav02dezW7EK1NMaYGTNmmI0bN/qs0bNnzxpjWnYdNvU90No0NDSYO%2B64w/ziF78wBw8eNLt37zYjR440jzzyCGvyO7pULY1hTfoDASsInTlzxiQkJPj8AF%2B3bp2ZOnVqAEcVOHPnzjWrVq1q1P7cc8%2BZ4cOHe3/ANjQ0mJEjR5r8/HxjjDHz5s0zDz74oPf9R48eNddcc435xz/%2BYYwx5sYbb/S%2B1xhjXnzxRZOenm6MMebzzz83ffv2NaWlpd7%2BBQsW%2BOwvGBQXF5uf/exn5tZbb/UJBe%2B%2B%2B65JTEz0%2BaE3ffp0s3btWmOMMY899pjPejt79qxJSkrybj9lyhTve40xZvfu3WbgwIHm7NmzTa7fpo7dWl2slsYYk5aWZt55550LbteS67Cp74HW5tChQ6Zv377G4/F421555RUzbNgw1uR3dKlaGsOa9AcuEQahAwcOqK6uTklJSd625ORkud1un9O0PxQlJSXq2bNno3a3263k5GQ5HA5JksPh0KBBg1RUVOTtT0lJ8b6/W7duiouLk9vt1okTJ3Ts2DFdd9113v7k5GQdOXJEZWVlcrvd6tatm3r06OHT/8EHH7TQLFvG%2B%2B%2B/r8GDBysvL8%2Bn3e1269prr1X79u29bcnJyRetXUREhPr376%2BioiLV19frww8/9OlPTEzUuXPndODAgSbXb1PHbq0uVsvTp0/rxIkTF1yjUsuuw6a%2BB1qb6Ohobd68WV27dvVpP336NGvyO7pULVmT/uEM9ADw3Xk8HnXq1ElhYWHetq5du6qmpkaVlZXq3LlzAEfnX8YYffrpp9q5c6c2btyo%2Bvp6jRo1SpmZmfJ4PLr66qt93t%2BlSxcVFxdLksrKyhQTE9Oo//jx4/J4PJLk03/%2BB9X5/gtte%2BLECetzbElTpky5YPvF5nf8%2BPEm%2B0%2BdOqWamhqffqfTqaioKB0/flwhISGXXL9NHbu1ulgtS0pK5HA4tGHDBr399tuKiorS3Xffrdtuu01Sy67Dpr4HWpvIyEilpaV5Xzc0NGjHjh0aMmQIa/I7ulQtWZP%2BQcAKQlVVVT4/CCR5X9fW1gZiSAFz9OhRbz0ee%2BwxffHFF1q2bJmqq6svWqfzNaqurr5of3V1tff1N/ukr2vc1L6DXVPzu1T/hWr3zX5jzCXXb1ur7eHDh%2BVwONSrVy9NnTpVu3fv1kMPPaSOHTtq5MiRLboOg72WOTk52r9/v55//nlt3bqVNXkZvlnLjz76iDXpBwSsIBQeHt5oMZ5/3a5du0AMKWC6d%2B%2Bu9957T1deeaUcDof69eunhoYGzZs3T6mpqRes0/kaXayOERERPj8wwsPDvf%2BWvr70cLFt20r9w8PDVVlZ6dPWnNpFRkY2qtc3%2ByMiIlRfX3/J9dvUsYPN%2BPHjlZ6erqioKElSfHy8PvvsMz377LMaOXJki67DYF6nOTk52rZtm1avXq2%2BffuyJi/Dt2vZp08f1qQfcA9WEIqNjVVFRYXq6uq8bR6PR%2B3atVNkZGQARxYYUVFR3uv5ktS7d2/V1NQoOjpa5eXlPu8tLy/3nr6OjY29YH90dLRiY2MlyXs6/Jv/Pt9/sW3bgovNrzm1i4qKUnh4uE9/XV2dKisrvbW71Ppt6tjBxuFweP9Hdl6vXr28l0xach0Gay2XLl2qp556Sjk5Obr55pslsSa/rwvVkjXpHwSsINSvXz85nU6fmwILCwuVkJCgkJAf1n/Sd955R4MHD/Z5ns3HH3%2BsqKgo742VxhhJX9%2BvtWfPHrlcLkmSy%2BVSYWGhd7tjx47p2LFjcrlcio2NVVxcnE9/YWGh4uLiFBMTo8TERB05csTnHozCwkIlJia29JT9wuVy6aOPPvJeDpC%2Bnt/FaldVVaX9%2B/fL5XIpJCRECQkJPv1FRUVyOp2Kj49vcv02dexgs2bNGs2YMcOn7cCBA%2BrVq5ekll2HLpfrkt8DrVFubq7%2B8Ic/6NFHH9WYMWO87azJ7%2B5itWRN%2BknAfn8Rl%2BWhhx4yY8aMMW6327zxxhtm0KBB5vXXXw/0sPzuq6%2B%2BMmlpaeY3v/mNKSkpMX/729/MsGHDzKZNm8xXX31lhgwZYpYuXWqKi4vN0qVLzdChQ72/ar1nzx7Tv39/88c//tH7rJfZs2d7971x40YzbNgws2vXLrNr1y4zbNgw8%2BSTT3r7Z86caaZOnWo%2B/vhj88c//tEkJCQE5XOwzvvmowXq6urM6NGjzX333WcOHjxoNm7caBITE73P/SktLTUJCQlm48aN3mcO3Xrrrd5fvS4oKDCDBg0yb7zxhnG73WbMmDFm6dKl3mNdav02dexg8M1aut1uc%2B2115rNmzebzz//3DzzzDNmwIABZs%2BePcaYll2HTX0PtDaHDh0y/fr1M6tXr/Z5PlNZWRlr8ju6VC1Zk/5BwApSZ8%2BeNQ888IBJTEw0w4YNM0899VSghxQwBw8eNDNmzDCJiYlm6NCh5vHHH/f%2BUHW73Wb8%2BPEmISHBTJo0yXz00Uc%2B2%2Bbn55sbb7zRJCYmmoyMDHPy5ElvX11dnXn44YdNSkqKGTx4sMnJyfHu1xhjysvLzezZs01CQoIZPny4eeWVV/wz4Rby7Wc3ffbZZ%2BbnP/%2B5GTBggBkzZoz5n//5H5/3/%2B1vfzM//elPzcCBA8306dO9z8g5b%2BPGjeb66683ycnJZv78%2Baa6utrb19T6berYrd23a/nGG2%2BYW2%2B91SQkJJhRo0Y1%2BjDUkuuwqe%2BB1mTjxo2mb9%2B%2BF/wyhjX5XTRVS9Zky3MY8//n6QAAAGDFD%2BuGHQAAAD8gYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAsv8DCycIr2qGUcgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5708198883812863217”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>780.101462879589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.8746741015684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>972.640103023006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>192.730928686085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>63.1425761251288</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>196.624887172406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254.521274996716</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.1522246400029</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>280.835001175464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3051)</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5708198883812863217”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0004283733724151</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013897542376071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002359034217136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0036824005073868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>187655.499169663</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>217460.415981226</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>246274.004039954</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254482.018349479</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>263440.500069803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_LOWI10”><s>LACCESS_LOWI10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_POP15”><code>LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.907</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_LOWI15”><s>LACCESS_LOWI15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_LOWI10”><code>LACCESS_LOWI10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98469</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_MULTIR15”>LACCESS_MULTIR15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3076</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1293.9</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>115780</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6478210240925563503”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6478210240925563503,#minihistogram-6478210240925563503”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6478210240925563503”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6478210240925563503”

aria-controls=”quantiles-6478210240925563503” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6478210240925563503” aria-controls=”histogram-6478210240925563503”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6478210240925563503” aria-controls=”common-6478210240925563503”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6478210240925563503” aria-controls=”extreme-6478210240925563503”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6478210240925563503”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>3.857</td>

</tr> <tr>

<th>Q1</th> <td>33.394</td>

</tr> <tr>

<th>Median</th> <td>132.64</td>

</tr> <tr>

<th>Q3</th> <td>659.32</td>

</tr> <tr>

<th>95-th percentile</th> <td>5400.9</td>

</tr> <tr>

<th>Maximum</th> <td>115780</td>

</tr> <tr>

<th>Range</th> <td>115780</td>

</tr> <tr>

<th>Interquartile range</th> <td>625.93</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5423.5</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.1916</td>

</tr> <tr>

<th>Kurtosis</th> <td>222.49</td>

</tr> <tr>

<th>Mean</th> <td>1293.9</td>

</tr> <tr>

<th>MAD</th> <td>1802.3</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>13.067</td>

</tr> <tr>

<th>Sum</th> <td>4027900</td>

</tr> <tr>

<th>Variance</th> <td>29415000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6478210240925563503”>

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1GwAAAALEbBAgAAsFjAFqxTp04pIyNDycnJGjRokLKyslRdXS1JKioq0r333qv4%2BHgNGzZMu3fvdnvtnj17NGLECNntdo0fP15FRUVu8%2BvXr9egQYOUkJCgWbNmqbKy0mvHBQAAAl9AFixjjDIyMlRZWannn39eTz75pN5880099dRTMsYoPT1dbdu21fbt2zVy5EhNnTpVJ06ckCSdOHFC6enpGj16tLZt26bWrVvrgQcekDFGkvTaa68pJydHmZmZ2rBhgxwOh7Kzs315uAAAIMAEZME6duyYCgoKlJWVpa5duyopKUkZGRnKy8vT3r17VVRUpMzMTHXp0kVTpkxRfHy8tm/fLknaunWrrrvuOk2cOFFdu3ZVVlaWjh8/rvfee0%2BStHHjRk2YMEEpKSnq3bu35s%2Bfr%2B3bt3MWCwAAeCwgC1ZUVJTWrl2rtm3buo2fPn1aDodDPXv2VPPmzV3jiYmJKigokCQ5HA4lJSW55sLDw9WrVy8VFBSorq5OH3zwgdt8fHy8zp07p8OHD1/lowIAAE2FzdcL%2BC4iIiI0aNAg1%2BP6%2Bnpt3rxZ/fr1k9PpVHR0tNvz27Rpo5MnT0rSZecrKipUXV3tNm%2Bz2RQZGel6vSdKSkrkdDrdxmy25g32%2B32FhARWP7bZfLfe81kFWma%2BQl6eIyvPkVXjkJfn/DGrgCxYF8rOztahQ4e0bds2rV%2B/XqGhoW7zoaGhqqmpkSRVVlZecr6qqsr1%2BFKv90Rubq5ycnLcxtLT05WRkeHxNpqiVq1a%2BHoJiogI9/USAgp5eY6sPEdWjUNenvOnrAK%2BYGVnZ2vDhg168skn1a1bN4WFham8vNztOTU1NWrWrJkkKSwsrEFZqqmpUUREhMLCwlyPL5wPD/f8Ny0tLU2pqaluYzZbc5WVnfF4G57wp6buCauPvzFCQoIVERGuiopK1dXV%2B2wdgYK8PEdWniOrxiEvz10uK1/95z6gC9aCBQu0ZcsWZWdn65ZbbpEkxcTE6MiRI27PKy0tdV2ei4mJUWlpaYP5Hj16KDIyUmFhYSotLVWXLl0kSbW1tSovL1dUVJTH64qOjm5wOdDp/Fq1tT/svyD%2BcPx1dfV%2BsY5AQV6eIyvPkVXjkJfn/CmrwDoF8i05OTl64YUX9MQTT2j48OGucbvdrg8//NB1uU%2BS8vPzZbfbXfP5%2BfmuucrKSh06dEh2u13BwcGKi4tzmy8oKJDNZlP37t29cFQAAKApCMiCdfToUa1cuVK/%2BtWvlJiYKKfT6fpKTk5Wu3btNHPmTBUWFmrNmjU6cOCAxo4dK0kaM2aM9u/frzVr1qiwsFAzZ85Uhw4d1LdvX0nS3XffrXXr1mnXrl06cOCA5s2bpzvvvLNRlwgBAMAPW0BeInzjjTdUV1enVatWadWqVW5zH3/8sVauXKnZs2dr9OjRuuaaa7RixQrFxsZKkjp06KCnn35ajz32mFasWKGEhAStWLFCQUFBkqThw4fr%2BPHjmjt3rmpqanTzzTdr%2BvTpXj9GAAAQuILM%2BVuY46pyOr%2B2fJs2W7CGLPm75du9Wl59aIDP9m2zBatVqxYqKzvjN9fn/Rl5eY6sPEdWjUNenrtcVlFRP/bJmrx%2BifCOO%2B7QCy%2B8oK%2B/tr5wAAAA%2BAOvF6x%2B/frpmWee0cCBA/Wb3/xGu3fvFifRAABAU%2BL1gjVt2jS9%2BeabWrlypUJCQvTggw/qpptu0pNPPqlPP/3U28sBAACwnE8%2B5B4UFKQBAwZowIABqqys1KZNm7Ry5UqtWbNGffr00YQJE3TzzTf7YmkAAADfm8%2B%2Bi7CkpEQvv/yyXn75ZX3yySfq06ePbr/9dp08eVK//e1vtW/fPs2ePdtXywMAAPjOvF6wXnrpJb300kt699131bp1a40aNUrLly9Xp06dXM9p166dFi1aRMECAAAByesFa/bs2UpJSdGKFSt0ww03KDi44cfAOnfurHHjxnl7aQAAAJbwesF6%2B%2B231apVK5WXl7vK1YEDB9SrVy%2BFhIRIkvr06aM%2Bffp4e2kAAACW8Pp3EZ4%2BfVpDhw7V73//e9fY5MmTNXLkSBUXF3t7OQAAAJbzesF67LHHdM011%2Bi%2B%2B%2B5zjb3yyitq166dsrKyvL0cAAAAy3m9YP3jH//Qo48%2BqqioKNdY69at9cgjj2jv3r3eXg4AAIDlvF6wbDabKioqGoxXVlZyR3cAANAkeL1g3XDDDVq4cKH%2B%2Bc9/usaKioqUlZWlQYMGeXs5AAAAlvP6dxHOmDFD9913n2655RZFRERIkioqKtSrVy/NnDnT28sBAACwnNcLVps2bfTiiy9qz549KiwslM1m07XXXqv%2B/fsrKCjI28sBAACwnE9%2BVE5ISIgGDRrEJUEAANAkeb1gOZ1OPfXUU9q/f7/OnTvX4IPtb7zxhreXBAAAYCmvF6w5c%2Bbo4MGDGj58uH784x97e/cAAABXndcL1t69e7V27VolJSV5e9cAAABe4fXbNDRv3lxt2rTx9m4BAAC8xusFa%2BTIkVq7dq3q6uq8vWsAAACv8PolwvLycuXl5elvf/ubOnbsqNDQULf5jRs3entJAAAAlvLJbRpGjBjhi90CAAB4hdcLVlZWlrd3CQAA4FVe/wyWJJWUlCgnJ0fTpk3Tl19%2Bqb/85S86duyYL5YCAABgOa8XrM8//1y33XabXnzxRb322ms6e/asXnnlFY0ZM0YOh8PbywEAALCc1wvW448/rsGDB2vXrl360Y9%2BJEl64oknlJqaqiVLlnh7OQAAAJbzesHav3%2B/7rvvPrcf7Gyz2fTAAw/o0KFD3l4OAACA5bxesOrr61VfX99g/MyZMwoJCfH2cgAAACzn9YI1cOBArV692q1klZeXKzs7W/369fP2cgAAACzn9YL16KOP6uDBgxo4cKCqq6v1n//5n0pJSdEXX3yhGTNmeHs5AAAAlvP6fbBiYmK0Y8cO5eXl6aOPPlJ9fb3uuusujRw5Ui1btvT2cgAAACznkzu5h4eH64477vDFrgEAAK46rxes8ePHX3aen0UIAAACndcLVvv27d0e19bW6vPPP9cnn3yiCRMmeHs5AAAAlvObn0W4YsUKnTx50surAQAAsJ5PfhbhxYwcOVKvvvqqr5cBAADwvflNwXr//fe50SgAAGgS/OJD7qdPn9bHH3%2Bsu%2B%2B%2Bu9Hbq6mp0ejRozVnzhz17dtXkrRw4UJt2rTJ7Xlz5szRuHHjJEl5eXl66qmn5HQ6NXDgQC1YsECtW7eWJBljtHTpUm3btk319fUaO3asHn74YQUH%2B00XBQAAfs7rBSs2Ntbt5xBK0o9%2B9CONGzdOv/jFLxq1rerqak2bNk2FhYVu40ePHtW0adN0%2B%2B23u8bO32PrwIEDmj17tubPn6/u3btr0aJFmjlzplavXi1Jeu6555SXl6ecnBzV1tZq%2BvTpatOmjSZNmvRdDhcAAPwAeb1gPf7445Zs58iRI5o2bZqMMQ3mjh49qkmTJikqKqrB3ObNm3Xrrbdq1KhRkqTFixcrJSVFRUVF6tixozZu3KiMjAwlJSVJkh5%2B%2BGEtW7aMggUAADzm9YK1b98%2Bj597/fXXX3LuvffeU9%2B%2BffX//t//U3x8vGv89OnTOnXqlDp16nTR1zkcDv3qV79yPW7Xrp1iY2PlcDgUGhqq4uJit/0mJibq%2BPHjKikpUXR0tMdrBwAAP1xeL1j33HOP6xLht88%2BXTgWFBSkjz766JLbudTntY4ePaqgoCA988wzevvttxUZGan77rvPdbnwYkWpTZs2OnnypJxOpyS5zbdt21aSdPLkSY8LVklJiWtb59lszS0vaCEhgfW5MJvNd%2Bs9n1WgZeYr5OU5svIcWTUOeXnOH7PyesF65plntHDhQk2fPl3JyckKDQ3VBx98oMzMTN1%2B%2B%2B0aNmzY99r%2BsWPHFBQUpM6dO2vcuHHat2%2Bf5syZo5YtW2rIkCGqqqpSaGio22tCQ0NVU1Ojqqoq1%2BNvz0nffJjeU7m5ucrJyXEbS09PV0ZGxnc9rCahVasWvl6CIiLCfb2EgEJeniMrz5FV45CX5/wpK5/caHTu3Lm64YYbXGP9%2BvVTZmamHnnkEbfLd9/FqFGjlJKSosjISElS9%2B7d9dlnn2nLli0aMmSIwsLCGpSlmpoahYeHu5WpsLAw16%2Blb35%2BoqfS0tKUmprqNmazNVdZ2ZnvfFwX409N3RNWH39jhIQEKyIiXBUVlaqrq/fZOgIFeXmOrDxHVo1DXp67XFa%2B%2Bs%2B91wtWSUlJgx%2BXI33zXX5lZWXfe/tBQUGucnVe586dtXfvXklSTEyMSktL3eZLS0sVFRWlmJgYSZLT6VSHDh1cv5Z00Q/MX0p0dHSDy4FO59eqrf1h/wXxh%2BOvq6v3i3UECvLyHFl5jqwah7w8509Zef0USHx8vJ544gmdPn3aNVZeXq7s7Gz179//e29/2bJluvfee93GDh8%2BrM6dO0uS7Ha78vPzXXPFxcUqLi6W3W5XTEyMYmNj3ebz8/MVGxvLB9wBAIDHvH4G67e//a3Gjx%2BvG264QZ06dZIxRp999pmioqK0cePG7739lJQUrVmzRuvWrdOQIUO0e/du7dixw7Xtu%2B66S/fcc4/i4%2BMVFxenRYsW6aabblLHjh1d80uWLNFPf/pTSdLSpUs1ceLE770uAADww%2BH1gtWlSxe98sorysvL09GjRyVJv/zlLzV8%2BPBGfc7pUnr37q1ly5Zp%2BfLlWrZsmdq3b6%2BlS5cqISFBkpSQkKDMzEwtX75c//rXvzRgwAAtWLDA9fpJkybpyy%2B/1NSpUxUSEqKxY8c2OCMGAABwOUHmYnfq9IKamhp98cUXrjNHP/rRj3yxDK9xOr%2B2fJs2W7CGLPm75du9Wl59aIDP9m2zBatVqxYqKzvjN9fn/Rl5eY6sPEdWjUNenrtcVlFRP/bJmrz%2BGSxjjJYsWaLrr79eI0aM0MmTJzVjxgzNnj1b586d8/ZyAAAALOf1grVp0ya99NJL%2Bt3vfue6LcLgwYO1a9euBveOAgAACEReL1i5ubmaO3euRo8e7bp7%2B7Bhw7Rw4ULt3LnT28sBAACwnNcL1hdffKEePXo0GO/evXuDHy8DAAAQiLxesNq3b68PPvigwfjbb7/t%2BsA7AABAIPP6bRomTZqk%2BfPny%2Bl0yhijd955R7m5udq0aZMeffRRby8HAADAcl4vWGPGjFFtba1WrVqlqqoqzZ07V61bt9ZDDz2ku%2B66y9vLAQAAsJzXC1ZeXp6GDh2qtLQ0ffXVVzLGqE2bNt5eBgAAwFXj9c9gZWZmuj7M3rp1a8oVAABocrxesDp16qRPPvnE27sFAADwGq9fIuzevbsefvhhrV27Vp06dVJYWJjbfFZWlreXBAAAYCmvF6xPP/1UiYmJksR9rwAAQJPklYK1ePFiTZ06Vc2bN9emTZu8sUsAAACf8cpnsJ577jlVVla6jU2ePFklJSXe2D0AAIBXeaVgGWMajO3bt0/V1dXe2D0AAIBXef27CAEAAJo6ChYAAIDFvFawgoKCvLUrAAAAn/LabRoWLlzods%2Brc%2BfOKTs7Wy1atHB7HvfBAgAAgc4rBev6669vcM%2BrhIQElZWVqayszBtLAAAA8BqvFCzufQUAAH5I%2BJA7AACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUCvmDV1NRoxIgRevfdd11jRUVFuvfeexUfH69hw4Zp9%2B7dbq/Zs2ePRowYIbvdrvHjx6uoqMhtfv369Ro0aJASEhI0a9YsVVZWeuVYAABA0xDQBau6ulq/%2Bc1vVFhY6Bozxig9PV1t27bV9u3bNXLkSE2dOlUnTpyQJJ04cULp6ekaPXq0tm3bptatW%2BuBBx6QMUaS9NprryknJ0eZmZnasGGDHA6HsrOzfXJ8AAAgMAVswTpy5IjuvPNO/fOf/3Qb37t3r4qKipSZmakuXbpoypQpio%2BP1/bt2yVJW7du1XXXXaeJEyeqa9euysrK0vHjx/Xee%2B9JkjZu3KgJEyYoJSVFvXv31vz587V9%2B3bOYgEAAI8FbMF677331LdvX%2BXm5rqNOxwO9ezZU82bN3eNJSYmqqCgwDWflJTkmgsPD1evXr1UUFCguro6ffDBB27z8fHxOnfunA4fPnyVjwgAADQVNl8v4Lu6%2B%2B67LzrudDoVHR3tNtamTRudPHnyivMVFRWqrq52m7fZkWX6AAAWgUlEQVTZbIqMjHS93hMlJSVyOp1uYzZb8wb7/b5CQgKrH9tsvlvv%2BawCLTNfIS/PkZXnyKpxyMtz/phVwBasS6msrFRoaKjbWGhoqGpqaq44X1VV5Xp8qdd7Ijc3Vzk5OW5j6enpysjI8HgbTVGrVi18vQRFRIT7egkBhbw8R1aeI6vGIS/P%2BVNWTa5ghYWFqby83G2spqZGzZo1c81fWJZqamoUERGhsLAw1%2BML58PDPf9NS0tLU2pqqtuYzdZcZWVnPN6GJ/ypqXvC6uNvjJCQYEVEhKuiolJ1dfU%2BW0egIC/PkZXnyKpxyMtzl8vKV/%2B5b3IFKyYmRkeOHHEbKy0tdV2ei4mJUWlpaYP5Hj16KDIyUmFhYSotLVWXLl0kSbW1tSovL1dUVJTHa4iOjm5wOdDp/Fq1tT/svyD%2BcPx1dfV%2BsY5AQV6eIyvPkVXjkJfn/CmrwDoF4gG73a4PP/zQdblPkvLz82W3213z%2Bfn5rrnKykodOnRIdrtdwcHBiouLc5svKCiQzWZT9%2B7dvXcQAAAgoDW5gpWcnKx27dpp5syZKiws1Jo1a3TgwAGNHTtWkjRmzBjt379fa9asUWFhoWbOnKkOHTqob9%2B%2Bkr758Py6deu0a9cuHThwQPPmzdOdd97ZqEuEAADgh63JFayQkBCtXLlSTqdTo0eP1ssvv6wVK1YoNjZWktShQwc9/fTT2r59u8aOHavy8nKtWLFCQUFBkqThw4drypQpmjt3riZOnKjevXtr%2BvTpvjwkAAAQYILM%2BVuY46pyOr%2B2fJs2W7CGLPm75du9Wl59aIDP9m2zBatVqxYqKzvjN9fn/Rl5eY6sPEdWjUNenrtcVlFRP/bJmprcGSwAAABfo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYrMkWrNdff10/%2B9nP3L4yMjIkSYcOHdIdd9whu92uMWPG6ODBg26vzcvL0%2BDBg2W325Wenq6vvvrKF4cAAAACVJMtWEeOHFFKSop2797t%2Blq4cKHOnj2ryZMnKykpSX/605%2BUkJCgKVOm6OzZs5KkAwcOaPbs2Zo6dapyc3NVUVGhmTNn%2BvhoAABAIGmyBevo0aPq1q2boqKiXF8RERF65ZVXFBYWpkceeURdunTR7Nmz1aJFC/3lL3%2BRJG3evFm33nqrRo0ape7du2vx4sV66623VFRU5OMjAgAAgaJJF6xOnTo1GHc4HEpMTFRQUJAkKSgoSH369FFBQYFrPikpyfX8du3aKTY2Vg6HwyvrBgAAgc/m6wVcDcYYffrpp9q9e7dWr16turo6DR06VBkZGXI6nbr22mvdnt%2BmTRsVFhZKkkpKShQdHd1g/uTJkx7vv6SkRE6n023MZmveYLvfV0hIYPVjm8136z2fVaBl5ivk5Tmy8hxZNQ55ec4fs2qSBevEiROqrKxUaGionnrqKX3xxRdauHChqqqqXOPfFhoaqpqaGklSVVXVZec9kZubq5ycHLex9PR014fsf6hatWrh6yUoIiLc10sIKOTlObLyHFk1Dnl5zp%2ByapIFq3379nr33Xf1k5/8REFBQerRo4fq6%2Bs1ffp0JScnNyhLNTU1atasmSQpLCzsovPh4Z7/pqWlpSk1NdVtzGZrrrKyM9/xiC7On5q6J6w%2B/sYICQlWRES4KioqVVdX77N1BAry8hxZeY6sGoe8PHe5rHz1n/smWbAkKTIy0u1xly5dVF1draioKJWWlrrNlZaWui7fxcTEXHQ%2BKirK431HR0c3uBzodH6t2tof9l8Qfzj%2Burp6v1hHoCAvz5GV58iqccjLc/6UVWCdAvHQ3//%2Bd/Xt21eVlZWusY8%2B%2BkiRkZFKTEzU%2B%2B%2B/L2OMpG8%2Br7V//37Z7XZJkt1uV35%2Bvut1xcXFKi4uds0DAABcSZMsWAkJCQoLC9Nvf/tbHTt2TG%2B99ZYWL16s%2B%2B%2B/X0OHDlVFRYUWLVqkI0eOaNGiRaqsrNStt94qSbrrrrv00ksvaevWrTp8%2BLAeeeQR3XTTTerYsaOPjwoAAASKJlmwWrZsqXXr1umrr77SmDFjNHv2bKWlpen%2B%2B%2B9Xy5YttXr1auXn52v06NFyOBxas2aNmjdvLumbcpaZmakVK1borrvu0k9%2B8hNlZWX5%2BIgAAEAgCTLnr5XhqnI6v7Z8mzZbsIYs%2Bbvl271aXn1ogM/2bbMFq1WrFiorO%2BM31%2Bf9GXl5jqw8R1aNQ16eu1xWUVE/9smamuQZLAAAAF%2BiYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMgnUR1dXVmjVrlpKSkjRw4EA9%2B%2Byzvl4SAAAIIDZfL8AfLV68WAcPHtSGDRt04sQJzZgxQ7GxsRo6dKivlxbQbn3qf329hEZ59aEBvl4CACBAUbAucPbsWW3dulW///3v1atXL/Xq1UuFhYV6/vnnKVgAAMAjXCK8wOHDh1VbW6uEhATXWGJiohwOh%2Brr6324MgAAECg4g3UBp9OpVq1aKTQ01DXWtm1bVVdXq7y8XK1bt77iNkpKSuR0Ot3GbLbmio6OtnStISH046sp0C5pvv7wIMu2df7PFn/GroysPEdWjUNenvPHrChYF6isrHQrV5Jcj2tqajzaRm5urnJyctzGpk6dqgcffNCaRf6fkpISTfhpodLS0iwvb01NSUmJcnNzycpDJSUl2rBhLXl5gKw8R1aNQ16e88es/Kfq%2BYmwsLAGRer842bNmnm0jbS0NP3pT39y%2B0pLS7N8rU6nUzk5OQ3OlqEhsmoc8vIcWXmOrBqHvDznj1lxBusCMTExKisrU21trWy2b%2BJxOp1q1qyZIiIiPNpGdHS03zRoAADgfZzBukCPHj1ks9lUUFDgGsvPz1dcXJyCg4kLAABcGY3hAuHh4Ro1apTmzZunAwcOaNeuXXr22Wc1fvx4Xy8NAAAEiJB58%2BbN8/Ui/E2/fv106NAhLV26VO%2B8845%2B/etfa8yYMb5e1kW1aNFCycnJatGiha%2BX4vfIqnHIy3Nk5Tmyahzy8py/ZRVkjDG%2BXgQAAEBTwiVCAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFKwAVV1drVmzZikpKUkDBw7Us88%2B6%2BslXTWnTp1SRkaGkpOTNWjQIGVlZam6ulqSVFRUpHvvvVfx8fEaNmyYdu/e7fbaPXv2aMSIEbLb7Ro/fryKiorc5tevX69BgwYpISFBs2bNUmVlpWsu0DOePHmyHn30UdfjQ4cO6Y477pDdbteYMWN08OBBt%2Bfn5eVp8ODBstvtSk9P11dffeWaM8ZoyZIl6tevn5KTk7V48WLV19e75svKyvTggw8qISFBqampeumll67%2BAVqgpqZG8%2BfP1/XXX6%2Bf//zneuKJJ3T%2B3svk1VBxcbGmTJmiPn36KDU1VevXr3fNkdc3ampqNGLECL377ruuMV%2B%2BT11p3750sawKCgr0H//xH0pISNAtt9yirVu3ur0moLIyCEiZmZnmtttuMwcPHjR//etfTUJCgnn11Vd9vSzL1dfXmzvvvNPcf//95pNPPjH79u0zQ4YMMY8//ripr683t912m5k2bZo5cuSIeeaZZ4zdbjfHjx83xhhz/PhxEx8fb9atW2c%2B%2BeQT81//9V9mxIgRpr6%2B3hhjzF/%2B8heTmJho/ud//sc4HA4zbNgwM3/%2BfNe%2BAznjvLw8061bNzNjxgxjjDFnzpwxAwYMMI8//rg5cuSIWbBggfn5z39uzpw5Y4wxxuFwmN69e5sXX3zRfPTRR2bcuHFm8uTJru2tW7fO3HjjjWbfvn3mnXfeMQMHDjRr1651zU%2BZMsVMmDDBfPzxx%2BaPf/yjue6664zD4fDuQX8Hc%2BbMMTfffLNxOBxmz549pm/fvmbLli3kdQl33nmneeihh8ynn35qXn/9dWO3281f//pX8vo/VVVVJj093XTr1s3s3bvXGGN8%2Bj51pX370sWyKikpMUlJSWbp0qXm008/NXl5eSYuLs68%2BeabxpjAy4qCFYDOnDlj4uLiXH8ojTFmxYoVZty4cT5c1dVx5MgR061bN%2BN0Ol1jO3fuNAMHDjR79uwx8fHxrjdxY4yZMGGCWb58uTHGmKeeesotk7Nnz5qEhARXbnfffbfrucYYs2/fPtO7d29z9uzZgM64rKzM3HDDDWbMmDGugrV161aTmprqeiOqr683Q4YMMdu3bzfGGDN9%2BnTXc40x5sSJE%2BZnP/uZ%2Bec//2mMMebGG290PdcYY3bs2GFSUlKMMcZ8/vnnplu3bqaoqMg1P2vWLLft%2BaOysjLTs2dP8%2B6777rGVq9ebR599FHyuojy8nLTrVs38/HHH7vGpk6daubPn09expjCwkLzi1/8wtx2221upcGX71NX2revXCqrP/zhD2bo0KFuz50zZ475zW9%2BY4wJvKy4RBiADh8%2BrNraWiUkJLjGEhMT5XA43E6rNwVRUVFau3at2rZt6zZ%2B%2BvRpORwO9ezZU82bN3eNJyYmqqCgQJLkcDiUlJTkmgsPD1evXr1UUFCguro6ffDBB27z8fHxOnfunA4fPhzQGf/3f/%2B3Ro4cqWuvvdY15nA4lJiYqKCgIElSUFCQ%2BvTpc8ms2rVrp9jYWDkcDp06dUrFxcW6/vrrXfOJiYk6fvy4SkpK5HA41K5dO3Xo0MFt/v3337/ah/q95Ofnq2XLlkpOTnaNTZ48WVlZWeR1Ec2aNVN4eLj%2B9Kc/6dy5czp27Jj279%2BvHj16kJek9957T3379lVubq7buC/fp660b1%2B5VFbnPwJyodOnT0sKvKwoWAHI6XSqVatWCg0NdY21bdtW1dXVKi8v9%2BHKrBcREaFBgwa5HtfX12vz5s3q16%2BfnE6noqOj3Z7fpk0bnTx5UpIuO19RUaHq6mq3eZvNpsjISJ08eTJgM37nnXf0j3/8Qw888IDb%2BJWyKikpueS80%2BmUJLf584X3/PzFXnvq1ClrDuoqKSoqUvv27bVjxw4NHTpU//7v/64VK1aovr6evC4iLCxMc%2BfOVW5urux2u2699VbdcMMNuuOOO8hL0t13361Zs2YpPDzcbdyX71NX2revXCqrDh06KD4%2B3vX4yy%2B/1J///Gf1799fUuBlZfvOr4TPVFZWuv0hkeR6XFNT44sleU12drYOHTqkbdu2af369RfN4XwGl8qppqZGVVVVrscXmzfGBFzG1dXV%2Bt3vfqe5c%2BeqWbNmbnOXy0KSqqqqGpXVt7O40rb91dmzZ/X555/rhRdeUFZWlpxOp%2BbOnavw8HDyuoSjR48qJSVF9913nwoLC7VgwQL179%2BfvC7jSuu/mu9TgZxdVVWVHnzwQbVt21ZpaWmSAi8rClYACgsLa/Cbfv7xhf%2BwNiXZ2dnasGGDnnzySXXr1k1hYWENzibV1NS4MrhUThEREQoLC3M9vnA%2BPDxcdXV1AZdxTk6OrrvuOrczfuddKosrZRUeHu72JnRhbuHh4Vfctr%2By2Ww6ffq0li5dqvbt20uSTpw4oS1btuiaa64hrwu888472rZtm9566y01a9ZMcXFxOnXqlFatWqWOHTuS1yX48n3qSvv2V2fOnNEDDzygzz77TH/4wx9cZ7oCLSsuEQagmJgYlZWVqba21jXmdDrVrFkzRURE%2BHBlV8%2BCBQv03HPPKTs7W7fccoukb3IoLS11e15paanrNO%2Bl5qOiohQZGamwsDC3%2BdraWpWXlysqKiogM/7zn/%2BsXbt2KSEhQQkJCdq5c6d27typhISE75VVTEyMJLku5Xz71%2BfnL/VafxYVFaWwsDBXuZKkf/u3f1NxcTF5XcTBgwd1zTXXuP2D07NnT504cYK8LsOX71NX2rc/On36tCZNmqTCwkJt2LBBnTp1cs0FWlYUrADUo0cP2Ww2tw/f5efnKy4uTsHBTe%2B3NCcnRy%2B88IKeeOIJDR8%2B3DVut9v14Ycfuk4NS9/kYLfbXfP5%2BfmuucrKSh06dEh2u13BwcGKi4tzmy8oKJDNZlP37t0DMuNNmzZp586d2rFjh3bs2KHU1FSlpqZqx44dstvtev/99133eDLGaP/%2B/ZfMqri4WMXFxbLb7YqJiVFsbKzbfH5%2BvmJjYxUdHa34%2BHgdP37c7bMK%2Bfn5bp%2Bl8Ed2u13V1dX69NNPXWPHjh1T%2B/btyesioqOj9fnnn7udBTh27Jg6dOhAXpfhy/epK%2B3b39TX12vq1Kn64osvtGnTJnXt2tVtPuCy%2Bs7ffwifmjNnjhk%2BfLhxOBzm9ddfN3369DGvvfaar5dluSNHjpgePXqYJ5980pSUlLh91dbWmmHDhpmHHnrIfPLJJ2b16tUmPj7edd%2BSoqIiExcXZ1avXu26Z8ptt93m%2BlbyvLw806dPH/P6668bh8Nhhg8fbhYsWODad6BnPGPGDNe3sn/99demX79%2BZsGCBaawsNAsWLDADBgwwPUtyfv37ze9evUyf/zjH133KZoyZYprW6tXrzYDBw40e/fuNXv37jUDBw40zz77rGt%2B4sSJZty4ceajjz4yf/zjH01cXJxf3qfoQpMnTzZpaWnmo48%2BMm%2B//bbp16%2Bf2bBhA3ldREVFhRkwYICZPn26OXbsmHnjjTdMcnKy2bJlC3ld4Nu3HvDl%2B9SV9u0Pvp1Vbm6u6d69u3nzzTfd3uvLysqMMYGXFQUrQJ09e9Y88sgjJj4%2B3gwcONA899xzvl7SVbF69WrTrVu3i34ZY8xnn31mfvnLX5rrrrvODB8%2B3Pzv//6v2%2Bv/9re/mZtvvtn07t3bTJgwwXXfnW9vv3///iYxMdHMnDnTVFVVueYCPeNvFyxjvrnZ46hRo0xcXJwZO3as%2BfDDD92ev337dnPjjTea%2BPh4k56ebr766ivXXG1trXnsscdMUlKS6du3r8nOzna9qRljTGlpqZkyZYqJi4szqampZufOnVf/AC1QUVFhpk%2BfbuLj403//v3N008/7Tou8mqosLDQ3HvvvaZPnz5m8ODB5rnnniOvi/h2aTDGt%2B9TV9q3r307q4kTJ170vf7b974KpKyCjPm/c7oAAACwhH9%2BmAQAACCAUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACL/X9HHe/le0UQrgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6478210240925563503”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>138.308562242177</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1443.3933485213</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>232.978171267488</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16251.0407088714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>269.46526443334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.13923115398529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102.512523248839</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.2061345196998</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3065)</td> <td class=”number”>3065</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6478210240925563503”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.78710449493e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.30253205902e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000632869778201</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0017772159771994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>73145.3450067915</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94996.7956478329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102836.110546633</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113699.486120656</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>115781.021512587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHASIAN15”>LACCESS_NHASIAN15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2836</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>696.05</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>76998</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3690287157680281778”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3690287157680281778,#minihistogram-3690287157680281778”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3690287157680281778”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3690287157680281778”

aria-controls=”quantiles-3690287157680281778” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3690287157680281778” aria-controls=”histogram-3690287157680281778”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3690287157680281778” aria-controls=”common-3690287157680281778”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3690287157680281778” aria-controls=”extreme-3690287157680281778”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3690287157680281778”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3.3309</td>

</tr> <tr>

<th>Median</th> <td>16.554</td>

</tr> <tr>

<th>Q3</th> <td>130.89</td>

</tr> <tr>

<th>95-th percentile</th> <td>2954.4</td>

</tr> <tr>

<th>Maximum</th> <td>76998</td>

</tr> <tr>

<th>Range</th> <td>76998</td>

</tr> <tr>

<th>Interquartile range</th> <td>127.56</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3471.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.988</td>

</tr> <tr>

<th>Kurtosis</th> <td>182.87</td>

</tr> <tr>

<th>Mean</th> <td>696.05</td>

</tr> <tr>

<th>MAD</th> <td>1109</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.465</td>

</tr> <tr>

<th>Sum</th> <td>2166800</td>

</tr> <tr>

<th>Variance</th> <td>12054000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3690287157680281778”>

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ItRsAAAACxGwQIAALCYzxasc%2BfOKTk5WfHx8erTp49SU1NVUVEhScrLy9O4ceMUHR2tgQMHateuXR7b7t69W4MHD5bT6dSYMWOUl5fnMb9%2B/Xr16dNHMTExmjFjhsrKyryWCwAA%2BD6fLFjGGCUnJ6usrEyvvPKKnn/%2Bef31r3/VkiVLZIxRUlKSWrZsqW3btmnIkCGaPHmyzp49K0k6e/askpKSNGzYMG3dulXNmzfX448/LmOMJOmdd95Renq6UlJStGHDBuXk5CgtLa0%2B4wIAAB/jkwXrxIkTys7OVmpqqjp06KC4uDglJycrMzNTe/bsUV5enlJSUtS%2BfXtNmjRJ0dHR2rZtmyRpy5Ytuv322zV%2B/Hh16NBBqampOnPmjD799FNJ0saNGzV27FglJCSoW7dumjt3rrZt28ZRLAAAUGc%2BWbDCwsK0Zs0atWzZ0mP8woULysnJUZcuXdS4cWP3eGxsrLKzsyVJOTk5iouLc88FBwera9euys7OVnV1tQ4cOOAxHx0drcuXL%2BvIkSM3OBUAALALR30v4HqEhISoT58%2B7sc1NTXatGmTevbsKZfLpfDwcI/nt2jRQgUFBZL0T%2BdLS0tVUVHhMe9wOBQaGurevi4KCwvlcrk8xhyOxrVe98cKCPCtfuxwXNt6r%2BTztZzXgoz2YPeMds8nkdEOGlo%2BnyxY35WWlqZDhw5p69atWr9%2BvQIDAz3mAwMDVVlZKUkqKyu76nx5ebn78dW2r4uMjAylp6d7jCUlJSk5ObnO%2B7CjZs2aXNd2ISHBFq%2Bk4SGjPdg9o93zSWS0g4aSz%2BcLVlpamjZs2KDnn39eHTt2VFBQkEpKSjyeU1lZqUaNGkmSgoKCapWlyspKhYSEKCgoyP34u/PBwXX/wEaOHKnExESPMYejsYqLL9Z5H3XRUFp6XV1r/oAAf4WEBKu0tEzV1TU3aFX1i4z2YPeMds8nkdEOrpbven%2B4/7F8umDNmzdPmzdvVlpamvr37y9JioiI0LFjxzyeV1RU5D49FxERoaKiolrznTt3VmhoqIKCglRUVKT27dtLkqqqqlRSUqKwsLA6rys8PLzW6UCX67yqquz3DX0trjd/dXWN7d87MtqD3TPaPZ9ERjtoKPl86xDIt6Snp%2BvVV1/Vc889p0GDBrnHnU6nPv/8c/fpPknKysqS0%2Bl0z2dlZbnnysrKdOjQITmdTvn7%2BysqKspjPjs7Ww6HQ506dfJCKgAAYAc%2BWbCOHz%2BuFStW6Fe/%2BpViY2PlcrncX/Hx8WrVqpWmT5%2Bu3NxcrV69Wvv379eIESMkScOHD9e%2Bffu0evVq5ebmavr06WrTpo169OghSRo1apTWrl2rnTt3av/%2B/ZozZ44eeuihazpFCAAA/rX55CnC9957T9XV1Vq5cqVWrlzpMffFF19oxYoVmjlzpoYNG6ZbbrlFy5cvV2RkpCSpTZs2euGFF/TMM89o%2BfLliomJ0fLly%2BXn5ydJGjRokM6cOaPZs2ersrJS9957r6ZOner1jAAAwHf5mSu3MMcN5XKdt3yfDoe/%2Bi36yPL93ihvP9Hrmp7vcPirWbMmKi6%2B2CDOp98IZLQHu2e0ez6JjHZwtXxhYT%2Btl/V4/RThgw8%2BqFdffVXnz1tfOAAAABoCrxesnj176sUXX1Tv3r31u9/9Trt27RIH0QAAgJ14vWBNmTJFf/3rX7VixQoFBAToN7/5je655x49//zzOnnypLeXAwAAYLl6ucjdz89PvXr1Uq9evVRWVqaXX35ZK1as0OrVq9W9e3eNHTtW9957b30sDQAA4Eert98iLCws1Jtvvqk333xTR48eVffu3fXAAw%2BooKBAv//977V3717NnDmzvpYHAABw3bxesN544w298cYb%2BuSTT9S8eXMNHTpUy5YtU9u2bd3PadWqlRYsWEDBAgAAPsnrBWvmzJlKSEjQ8uXLddddd8nfv/ZlYO3atdPo0aO9vTQAAABLeL1gffjhh2rWrJlKSkrc5Wr//v3q2rWrAgICJEndu3dX9%2B7dvb00AAAAS3j9twgvXLigAQMG6I9//KN7bOLEiRoyZIjy8/O9vRwAAADLeb1gPfPMM7rlllv06KOPusfeeusttWrVSqmpqd5eDgAAgOW8XrD%2B9re/6emnn1ZYWJh7rHnz5nrqqae0Z88eby8HAADAcl4vWA6HQ6WlpbXGy8rKuKM7AACwBa8XrLvuukvz58/X3//%2Bd/dYXl6eUlNT1adPH28vBwAAwHJe/y3CadOm6dFHH1X//v0VEhIiSSotLVXXrl01ffp0by8HAADAcl4vWC1atNDrr7%2Bu3bt3Kzc3Vw6HQ7feeqvuvPNO%2Bfn5eXs5AAAAlquXP5UTEBCgPn36cEoQAADYktcLlsvl0pIlS7Rv3z5dvny51oXt7733nreXBAAAYCmvF6xZs2bp4MGDGjRokH760596%2B%2BUBAABuOK8XrD179mjNmjWKi4vz9ksDAAB4hddv09C4cWO1aNHC2y8LAADgNV4vWEOGDNGaNWtUXV3t7ZcGAADwCq%2BfIiwpKVFmZqbef/993XzzzQoMDPSY37hxo7eXBAAAYKl6uU3D4MGD6%2BNlAQAAvMLrBSs1NdXbLwkAAOBVXr8GS5IKCwuVnp6uKVOm6B//%2BIf%2B/Oc/68SJE/WxFAAAAMt5vWCdOnVK999/v15//XW98847unTpkt566y0NHz5cOTk53l4OAACA5bxesJ599ln17dtXO3fu1E9%2B8hNJ0nPPPafExEQtWrTI28sBAACwnNcL1r59%2B/Too496/GFnh8Ohxx9/XIcOHfL2cgAAACzn9YJVU1OjmpqaWuMXL15UQECAt5cDAABgOa8XrN69e2vVqlUeJaukpERpaWnq2bOnt5cDAABgOa8XrKeffloHDx5U7969VVFRof/4j/9QQkKCTp8%2BrWnTpnl7OQAAAJbz%2Bn2wIiIitH37dmVmZurw4cOqqanRww8/rCFDhqhp06beXg4AAIDl6uVO7sHBwXrwwQfr46UBAABuOK8XrDFjxvzTef4WIQAA8HVeL1itW7f2eFxVVaVTp07p6NGjGjt2rLeXAwAAYLkG87cIly9froKCAi%2BvBgAAwHr18rcIv8%2BQIUP09ttv1/cyAAAAfrQGU7A%2B%2B%2BwzbjQKAABsoUFc5H7hwgV98cUXGjVq1DXvr7KyUsOGDdOsWbPUo0cPSdL8%2BfP18ssvezxv1qxZGj16tCQpMzNTS5YskcvlUu/evTVv3jw1b95ckmSM0eLFi7V161bV1NRoxIgRevLJJ%2BXv32C6KAAAaOC8XrAiIyM9/g6hJP3kJz/R6NGj9Ytf/OKa9lVRUaEpU6YoNzfXY/z48eOaMmWKHnjgAffYlXts7d%2B/XzNnztTcuXPVqVMnLViwQNOnT9eqVaskSevWrVNmZqbS09NVVVWlqVOnqkWLFpowYcL1xAUAAP%2BCvF6wnn32WUv2c%2BzYMU2ZMkXGmFpzx48f14QJExQWFlZrbtOmTbrvvvs0dOhQSdLChQuVkJCgvLw83Xzzzdq4caOSk5MVFxcnSXryySe1dOlSChYAAKgzrxesvXv31vm5d9xxx1XnPv30U/Xo0UP/%2BZ//qejoaPf4hQsXdO7cObVt2/Z7t8vJydGvfvUr9%2BNWrVopMjJSOTk5CgwMVH5%2BvsfrxsbG6syZMyosLFR4eHid1w4AAP51eb1gPfLII%2B5ThN8%2B%2BvTdMT8/Px0%2BfPiq%2B7na9VrHjx%2BXn5%2BfXnzxRX344YcKDQ3Vo48%2B6j5d%2BH1FqUWLFiooKJDL5ZIkj/mWLVtKkgoKCupcsAoLC937usLhaGx5QQsI8K3rwhyOa1vvlXy%2BlvNakNEe7J7R7vkkMtpBQ8vn9YL14osvav78%2BZo6dari4%2BMVGBioAwcOKCUlRQ888IAGDhz4o/Z/4sQJ%2Bfn5qV27dho9erT27t2rWbNmqWnTpurXr5/Ky8sVGBjosU1gYKAqKytVXl7ufvztOembi%2BnrKiMjQ%2Bnp6R5jSUlJSk5Ovt5YttCsWZPr2i4kJNjilTQ8ZLQHu2e0ez6JjHbQUPLVy41GZ8%2Berbvuuss91rNnT6WkpOipp57yOH13PYYOHaqEhASFhoZKkjp16qQvv/xSmzdvVr9%2B/RQUFFSrLFVWVio4ONijTAUFBbn/LX3z9xPrauTIkUpMTPQYczgaq7j44nXn%2Bj4NpaXX1bXmDwjwV0hIsEpLy1RdXXODVlW/yGgPds9o93wSGe3gavmu94f7H8vrBauwsLDWn8uRvvktv%2BLi4h%2B9fz8/P3e5uqJdu3bas2ePJCkiIkJFRUUe80VFRQoLC1NERIQkyeVyqU2bNu5/S/reC%2BavJjw8vNbpQJfrvKqq7PcNfS2uN391dY3t3zsy2oPdM9o9n0RGO2go%2Bbx%2BCCQ6OlrPPfecLly44B4rKSlRWlqa7rzzzh%2B9/6VLl2rcuHEeY0eOHFG7du0kSU6nU1lZWe65/Px85efny%2Bl0KiIiQpGRkR7zWVlZioyM5AJ3AABQZ14/gvX73/9eY8aM0V133aW2bdvKGKMvv/xSYWFh2rhx44/ef0JCglavXq21a9eqX79%2B2rVrl7Zv3%2B7e98MPP6xHHnlE0dHRioqK0oIFC3TPPffo5ptvds8vWrRIP/vZzyRJixcv1vjx43/0ugAAwL8Orxes9u3b66233lJmZqaOHz8uSfrlL3%2BpQYMGXdN1TlfTrVs3LV26VMuWLdPSpUvVunVrLV68WDExMZKkmJgYpaSkaNmyZfr666/Vq1cvzZs3z739hAkT9I9//EOTJ09WQECARowYUeuIGAAAwD/jZ77vTp1eUFlZqdOnT7uPHP3kJz%2Bpj2V4jct13vJ9Ohz%2B6rfoI8v3e6O8/USva3q%2Bw%2BGvZs2aqLj4YoM4n34jkNEe7J7R7vkkMtrB1fKFhf20Xtbj9WuwjDFatGiR7rjjDg0ePFgFBQWaNm2aZs6cqcuXL3t7OQAAAJbzesF6%2BeWX9cYbb%2BgPf/iD%2B7YIffv21c6dO2vdOwoAAMAXeb1gZWRkaPbs2Ro2bJj77u0DBw7U/PnztWPHDm8vBwAAwHJeL1inT59W586da4136tSp1p%2BXAQAA8EVeL1itW7fWgQMHao1/%2BOGH7gveAQAAfJnXb9MwYcIEzZ07Vy6XS8YYffzxx8rIyNDLL7%2Bsp59%2B2tvLAQAAsJzXC9bw4cNVVVWllStXqry8XLNnz1bz5s31xBNP6OGHH/b2cgAAACzn9YKVmZmpAQMGaOTIkfrqq69kjFGLFi28vQwAAIAbxuvXYKWkpLgvZm/evDnlCgAA2I7XC1bbtm119OhRb78sAACA13j9FGGnTp305JNPas2aNWrbtq2CgoI85lNTU729JAAAAEt5vWCdPHlSsbGxksR9rwAAgC15pWAtXLhQkydPVuPGjfXyyy974yUBAADqjVeuwVq3bp3Kyso8xiZOnKjCwkJvvDwAAIBXeaVgGWNqje3du1cVFRXeeHkAAACv8vpvEQIAANgdBQsAAMBiXitYfn5%2B3nopAACAeuW12zTMnz/f455Xly9fVlpampo0aeLxPO6DBQAAfJ1XCtYdd9xR655XMTExKi4uVnFxsTeWAAAA4DVeKVjc%2BwoAAPwr4SJ3AAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIv5fMGqrKzU4MGD9cknn7jH8vLyNG7cOEVHR2vgwIHatWuXxza7d%2B/W4MGD5XQ6NWbMGOXl5XnMr1%2B/Xn369FFMTIxmzJihsrIyr2QBAAD24NMFq6KiQr/73e%2BUm5vrHjPGKCkpSS1bttS2bds0ZMgQTZ48WWfPnpUknT17VklJSRo2bJi2bt2q5s2b6/HHH5cxRpL0zjvvKD09XSkpKdqwYYNycnKUlpZWL/kAAIBv8tmCdezYMT300EP6%2B9//7jG%2BZ88e5eXlKSUlRe3bt9ekSZMUHR2tbdu2SZK2bNmi22%2B/XePHj1eHDh2UmpqqM2fO6NNPP5Ukbdy4UWPHjlVCQoK6deumuXPnatu2bRzFAgAAdeazBevTTz9Vjx49lJGR4TGek5OjLl26qHHjxu6x2NhYZWdnu%2Bfj4uLcc8HBweratauys7NVXV2tAwcOeMxHR0fr8uXLOnLkyA1OBAAA7MJR3wu4XqNGjfrecZfLpfDwcI%2BxFi1aqKCg4AfnS0tLVVFR4THvcDgUGhrq3r4uCgsL5XK5PMZDwz%2BvAAAXmElEQVQcjsa1XvfHCgjwrX7scFzbeq/k87Wc14KM9mD3jHbPJ5HRDhpaPp8tWFdTVlamwMBAj7HAwEBVVlb%2B4Hx5ebn78dW2r4uMjAylp6d7jCUlJSk5ObnO%2B7CjZs2aXNd2ISHBFq%2Bk4SGjPdg9o93zSWS0g4aSz3YFKygoSCUlJR5jlZWVatSokXv%2Bu2WpsrJSISEhCgoKcj/%2B7nxwcN0/sJEjRyoxMdFjzOForOLii3XeR100lJZeV9eaPyDAXyEhwSotLVN1dc0NWlX9IqM92D2j3fNJZLSDq%2BW73h/ufyzbFayIiAgdO3bMY6yoqMh9ei4iIkJFRUW15jt37qzQ0FAFBQWpqKhI7du3lyRVVVWppKREYWFhdV5DeHh4rdOBLtd5VVXZ7xv6Wlxv/urqGtu/d2S0B7tntHs%2BiYx20FDy%2BdYhkDpwOp36/PPP3af7JCkrK0tOp9M9n5WV5Z4rKyvToUOH5HQ65e/vr6ioKI/57OxsORwOderUyXshAACAT7NdwYqPj1erVq00ffp05ebmavXq1dq/f79GjBghSRo%2BfLj27dun1atXKzc3V9OnT1ebNm3Uo0cPSd9cPL927Vrt3LlT%2B/fv15w5c/TQQw9d0ylCAADwr812BSsgIEArVqyQy%2BXSsGHD9Oabb2r58uWKjIyUJLVp00YvvPCCtm3bphEjRqikpETLly%2BXn5%2BfJGnQoEGaNGmSZs%2BerfHjx6tbt26aOnVqfUYCAAA%2BxhbXYH3xxRcej2%2B55RZt2rTpqs%2B/%2B%2B67dffdd191fuLEiZo4caJl6wMAAP9abHcECwAAoL5RsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxm24L17rvv6rbbbvP4Sk5OliQdOnRIDz74oJxOp4YPH66DBw96bJuZmam%2BffvK6XQqKSlJX331VX1EAAAAPsq2BevYsWNKSEjQrl273F/z58/XpUuXNHHiRMXFxem1115TTEyMJk2apEuXLkmS9u/fr5kzZ2ry5MnKyMhQaWmppk%2BfXs9pAACAL7FtwTp%2B/Lg6duyosLAw91dISIjeeustBQUF6amnnlL79u01c%2BZMNWnSRH/%2B858lSZs2bdJ9992noUOHqlOnTlq4cKE%2B%2BOAD5eXl1XMiAADgK2xdsNq2bVtrPCcnR7GxsfLz85Mk%2Bfn5qXv37srOznbPx8XFuZ/fqlUrRUZGKicnxyvrBgAAvs9R3wu4EYwxOnnypHbt2qVVq1apurpaAwYMUHJyslwul2699VaP57do0UK5ubmSpMLCQoWHh9eaLygoqPPrFxYWyuVyeYw5HI1r7ffHCgjwrX7scFzbeq/k87Wc14KM9mD3jHbPJ5HRDhpaPlsWrLNnz6qsrEyBgYFasmSJTp8%2Brfnz56u8vNw9/m2BgYGqrKyUJJWXl//T%2BbrIyMhQenq6x1hSUpL7Ivt/Vc2aNbmu7UJCgi1eScNDRnuwe0a755PIaAcNJZ8tC1br1q31ySef6KabbpKfn586d%2B6smpoaTZ06VfHx8bXKUmVlpRo1aiRJCgoK%2Bt754OC6f2AjR45UYmKix5jD0VjFxRevM9H3aygtva6uNX9AgL9CQoJVWlqm6uqaG7Sq%2BkVGe7B7Rrvnk8hoB1fLd70/3P9YtixYkhQaGurxuH379qqoqFBYWJiKioo85oqKityn7yIiIr53PiwsrM6vHR4eXut0oMt1XlVV9vuGvhbXm7%2B6usb27x0Z7cHuGe2eTyKjHTSUfL51CKSOPvroI/Xo0UNlZWXuscOHDys0NFSxsbH67LPPZIyR9M31Wvv27ZPT6ZQkOZ1OZWVlubfLz89Xfn6%2Bex4AAOCH2LJgxcTEKCgoSL///e914sQJffDBB1q4cKEee%2BwxDRgwQKWlpVqwYIGOHTumBQsWqKysTPfdd58k6eGHH9Ybb7yhLVu26MiRI3rqqad0zz336Oabb67nVAAAwFfYsmA1bdpUa9eu1VdffaXhw4dr5syZGjlypB577DE1bdpUq1atUlZWloYNG6acnBytXr1ajRs3lvRNOUtJSdHy5cv18MMP66abblJqamo9JwIAAL7EttdgdejQQevWrfveuW7duun111%2B/6rbDhg3TsGHDbtTSAACAzdnyCBYAAEB9omABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIL1PSoqKjRjxgzFxcWpd%2B/eeumll%2Bp7SQAAwIc46nsBDdHChQt18OBBbdiwQWfPntW0adMUGRmpAQMG1PfSfNp9S/63vpdwTd5%2Bold9LwEA4KMoWN9x6dIlbdmyRX/84x/VtWtXde3aVbm5uXrllVcoWAAAoE44RfgdR44cUVVVlWJiYtxjsbGxysnJUU1NTT2uDAAA%2BAqOYH2Hy%2BVSs2bNFBgY6B5r2bKlKioqVFJSoubNm//gPgoLC%2BVyuTzGHI7GCg8Pt3StAQH04xvJ105p%2BpJ3n%2BxT30uw1JX/Fu3636Td80lktIOGlo%2BC9R1lZWUe5UqS%2B3FlZWWd9pGRkaH09HSPscmTJ%2Bs3v/mNNYv8f4WFhRr7s1yNHDnS8vLWEBQWFiojI8O2%2BSQy2kVhYaE2bFhj24x2zyeR0Q4aWr6GUfMakKCgoFpF6srjRo0a1WkfI0eO1GuvvebxNXLkSMvX6nK5lJ6eXutomV3YPZ9ERruwe0a755PIaAcNLR9HsL4jIiJCxcXFqqqqksPxzdvjcrnUqFEjhYSE1Gkf4eHhDaI9AwCA%2BsERrO/o3LmzHA6HsrOz3WNZWVmKioqSvz9vFwAA%2BGE0hu8IDg7W0KFDNWfOHO3fv187d%2B7USy%2B9pDFjxtT30gAAgI8ImDNnzpz6XkRD07NnTx06dEiLFy/Wxx9/rF//%2BtcaPnx4fS/rezVp0kTx8fFq0qRJfS/lhrB7PomMdmH3jHbPJ5HRDhpSPj9jjKnvRQAAANgJpwgBAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsHxURUWFZsyYobi4OPXu3VsvvfRSfS/pqiorKzV48GB98skn7rG8vDyNGzdO0dHRGjhwoHbt2uWxze7duzV48GA5nU6NGTNGeXl5HvPr169Xnz59FBMToxkzZqisrMw956335ty5c0pOTlZ8fLz69Omj1NRUVVRU2CafJJ06dUoTJkxQTEyM7rnnHq1Zs8Y9Z5eMV0ycOFFPP/20%2B/GhQ4f04IMPyul0avjw4Tp48KDH8zMzM9W3b185nU4lJSXpq6%2B%2Bcs8ZY7Ro0SL17NlT8fHxWrhwoWpqatzzxcXF%2Bs1vfqOYmBglJibqjTfeuGG53n33Xd12220eX8nJybbKWFlZqblz5%2BqOO%2B7Qz3/%2Bcz333HO6cg9tO2R87bXXan2Gt912mzp16mSbjPn5%2BZo0aZK6d%2B%2BuxMRErV%2B/3j3ns/kMfFJKSoq5//77zcGDB81f/vIXExMTY95%2B%2B%2B36XlYt5eXlJikpyXTs2NHs2bPHGGNMTU2Nuf/%2B%2B82UKVPMsWPHzIsvvmicTqc5c%2BaMMcaYM2fOmOjoaLN27Vpz9OhR89vf/tYMHjzY1NTUGGOM%2BfOf/2xiY2PN//zP/5icnBwzcOBAM3fuXPdreuO9qampMQ899JB57LHHzNGjR83evXtNv379zLPPPmuLfMYYU11dbe69914zZcoUc/LkSfP%2B%2B%2B%2Bb7t27mzfffNM2Ga/IzMw0HTt2NNOmTTPGGHPx4kXTq1cv8%2Byzz5pjx46ZefPmmZ///Ofm4sWLxhhjcnJyTLdu3czrr79uDh8%2BbEaPHm0mTpzo3t/atWvN3Xffbfbu3Ws%2B/vhj07t3b7NmzRr3/KRJk8zYsWPNF198Yf70pz%2BZ22%2B/3eTk5NyQbCtWrDCTJk0yhYWF7q%2Bvv/7aVhlnzZpl7r33XpOTk2N2795tevToYTZv3mybjGVlZR6f39mzZ02/fv3MggULbJPxoYceMk888YQ5efKkeffdd43T6TR/%2BctffDofBcsHXbx40URFRbkLizHGLF%2B%2B3IwePboeV1Vbbm6u%2BcUvfmHuv/9%2Bj4K1e/duEx0d7f4PxBhjxo4da5YtW2aMMWbJkiUeWS5dumRiYmLc248aNcr9XGOM2bt3r%2BnWrZu5dOmS196bY8eOmY4dOxqXy%2BUe27Fjh%2Bndu7ct8hljzLlz58xvf/tbc/78efdYUlKS%2BcMf/mCbjMYYU1xcbO666y4zfPhwd8HasmWLSUxMdBfCmpoa069fP7Nt2zZjjDFTp051P9cYY86ePWtuu%2B028/e//90YY8zdd9/tfq4xxmzfvt0kJCQYY4w5deqU6dixo8nLy3PPz5gxw2N/VpoyZYpZvHhxrXG7ZCwuLjZdunQxn3zyiXts1apV5umnn7ZNxu968cUXTd%2B%2BfU1FRYUtMpaUlJiOHTuaL774wj02efJkM3fuXJ/OxylCH3TkyBFVVVUpJibGPRYbG6ucnByPQ5/17dNPP1WPHj2UkZHhMZ6Tk6MuXbqocePG7rHY2FhlZ2e75%2BPi4txzwcHB6tq1q7Kzs1VdXa0DBw54zEdHR%2Bvy5cs6cuSI196bsLAwrVmzRi1btvQYv3Dhgi3ySVJ4eLiWLFmipk2byhijrKws7d27V/Hx8bbJKEn/9V//pSFDhujWW291j%2BXk5Cg2NlZ%2Bfn6SJD8/P3Xv3v2q%2BVq1aqXIyEjl5OTo3Llzys/P1x133OGx/jNnzqiwsFA5OTlq1aqV2rRp4zH/2WefWZ5Nko4fP662bdvWGrdLxqysLDVt2lTx8fHusYkTJyo1NdU2Gb%2BtpKREf/zjHzVlyhQFBgbaImOjRo0UHBys1157TZcvX9aJEye0b98%2Bde7c2afzUbB8kMvlUrNmzRQYGOgea9mypSoqKlRSUlKPK/M0atQozZgxQ8HBwR7jLpdL4eHhHmMtWrRQQUHBD86XlpaqoqLCY97hcCg0NFQFBQVee29CQkLUp08f9%2BOamhpt2rRJPXv2tEW%2B70pMTNSoUaMUExOj/v372ybjxx9/rL/97W96/PHHPcZ/KF9hYeFV510ulyR5zF8p4lfmv2/bc%2BfOWRPqW4wxOnnypHbt2qX%2B/furb9%2B%2BWrRokSorK22TMS8vT61bt9b27ds1YMAA/fu//7uWL1%2Bumpoa22T8ts2bNys8PFwDBgyQZI/v1aCgIM2ePVsZGRlyOp267777dNddd%2BnBBx/06XwOS/YCryorK/P4Px9J7seVlZX1saRrcrX1X1n7P5svLy93P/6%2BeWNMvbw3aWlpOnTokLZu3ar169fbLt%2ByZctUVFSkOXPmKDU11RafYUVFhf7whz9o9uzZatSokcfcD%2BUrLy%2B/pnzfXv8P7dtKZ8%2Bedb/ekiVLdPr0ac2fP1/l5eW2yXjp0iWdOnVKr776qlJTU%2BVyuTR79mwFBwfbJuMVxhht2bJFjz32mHvMLhmPHz%2BuhIQEPfroo8rNzdW8efN05513%2BnQ%2BCpYPCgoKqvUNcOXxd/%2BPoiEKCgqqdSSisrLSvfar5QsJCVFQUJD78Xfng4ODVV1d7fX3Ji0tTRs2bNDzzz%2Bvjh072i6fJEVFRUn6ppQ8%2BeSTGj58uMdv/V1Zhy9lTE9P1%2B233%2B5xJPKKq63/h/IFBwd7/A/4d7MGBwf/4L6t1Lp1a33yySe66aab5Ofnp86dO6umpkZTp05VfHy8LTI6HA5duHBBixcvVuvWrSV9Uyw3b96sW265xRYZrzhw4IDOnTunQYMGucfs8L368ccfa%2BvWrfrggw/UqFEjRUVF6dy5c1q5cqVuvvlmn83HKUIfFBERoeLiYlVVVbnHXC6XGjVqpJCQkHpcWd1ERESoqKjIY6yoqMh9qPZq82FhYQoNDVVQUJDHfFVVlUpKShQWFub192bevHlat26d0tLS1L9/f1vlKyoq0s6dOz3Gbr31Vl2%2BfFlhYWE%2Bn/G///u/tXPnTsXExCgmJkY7duzQjh07FBMT86M%2Bw4iICPeav71%2BSe75q217I4SGhrqvX5Gk9u3bq6Ki4kd9hg0pY1hYmIKCgtzlSpL%2B7d/%2BTfn5%2Bbb6HCXpo48%2BUlxcnG666Sb3mB0yHjx4ULfccotHsenSpYvOnj3r0/koWD6oc%2BfOcjgc7ov8pG8u9IyKipK/f8P/SJ1Opz7//HP34Vvpm/U7nU73fFZWlnuurKxMhw4dktPplL%2B/v6Kiojzms7Oz5XA41KlTJ6%2B%2BN%2Bnp6Xr11Vf13HPPefxEaZd8p0%2Bf1uTJkz2uRzh48KCaN2%2Bu2NhYn8/48ssva8eOHdq%2Bfbu2b9%2BuxMREJSYmavv27XI6nfrss8/c91Iyxmjfvn1XzZefn6/8/Hw5nU5FREQoMjLSYz4rK0uRkZEKDw9XdHS0zpw5476G5Mp8dHS0Zdmu%2BOijj9SjRw%2BPo42HDx9WaGio%2B2JeX8/odDpVUVGhkydPusdOnDih1q1b2%2BZzvGL//v3q3r27x5gdMoaHh%2BvUqVMeR5NOnDihNm3a%2BHY%2BS34XEV43a9YsM2jQIJOTk2Peffdd0717d/POO%2B/U97Ku6tu3aaiqqjIDBw40TzzxhDl69KhZtWqViY6Odt9DKS8vz0RFRZlVq1a576F0//33u39NNzMz03Tv3t28%2B%2B67JicnxwwaNMjMmzfP/VreeG%2BOHTtmOnfubJ5//nmP%2B9MUFhbaIp8x33xOw4YNM%2BPHjze5ubnm/fffNz//%2Bc/N%2BvXrbZPx26ZNm%2Bb%2B9ezz58%2Bbnj17mnnz5pnc3Fwzb94806tXL/dtKfbt22e6du1q/vSnP7nvvTNp0iT3vlatWmV69%2B5t9uzZY/bs2WN69%2B5tXnrpJff8%2BPHjzejRo83hw4fNn/70JxMVFXVD7i10/vx506dPH/O73/3OHD9%2B3Lz//vumd%2B/eZvXq1bbJaIwxEydONCNHjjSHDx82H374oenZs6fZsGGDrTIaY0xCQoLJzMz0GLNDxtLSUtOrVy8zdepUc%2BLECfPee%2B%2BZ%2BPh4s3nzZp/OR8HyUZcuXTJPPfWUiY6ONr179zbr1q2r7yX9U98uWMYY8%2BWXX5pf/vKX5vbbbzeDBg0y//u//%2Bvx/Pfff9/ce%2B%2B9plu3bmbs2LHue5pcsWrVKnPnnXea2NhYM336dFNeXu6e88Z7s2rVKtOxY8fv/bJDvisKCgpMUlKS6d69u%2BnVq5dZuXKluyTZJeMV3y5YxnxzA8OhQ4eaqKgoM2LECPP55597PH/btm3m7rvvNtHR0SYpKcl89dVX7rmqqirzzDPPmLi4ONOjRw%2BTlpbmft%2BMMaaoqMhMmjTJREVFmcTERLNjx44bluvo0aNm3LhxJjo62vTq1cu88MIL7rXYJWNpaamZOnWqiY6ONnfeeactMxpjTFRUlPnwww9rjdshY25urhk3bpzp3r276du3r1m3bp3Pf4Z%2Bxvz/cTcAAABYouFfsAMAAOBjKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDF/g9D5FG97E23mwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3690287157680281778”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002980232</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000001490116</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999992549419</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000000186265</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2825)</td> <td class=”number”>2831</td> <td class=”number”>88.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3690287157680281778”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0010663634166122</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011702766641974</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012762330006808</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0017601195722818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>40484.6192974398</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40907.3637539073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41894.6005324653</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>74726.7598218825</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76998.3485618334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHNA15”>LACCESS_NHNA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2922</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>225.84</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>40351</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1088506075320450917”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1088506075320450917,#minihistogram-1088506075320450917”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1088506075320450917”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1088506075320450917”

aria-controls=”quantiles-1088506075320450917” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1088506075320450917” aria-controls=”histogram-1088506075320450917”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1088506075320450917” aria-controls=”common-1088506075320450917”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1088506075320450917” aria-controls=”extreme-1088506075320450917”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1088506075320450917”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>5.2623</td>

</tr> <tr>

<th>Median</th> <td>20.806</td>

</tr> <tr>

<th>Q3</th> <td>100.88</td>

</tr> <tr>

<th>95-th percentile</th> <td>794.64</td>

</tr> <tr>

<th>Maximum</th> <td>40351</td>

</tr> <tr>

<th>Range</th> <td>40351</td>

</tr> <tr>

<th>Interquartile range</th> <td>95.616</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1376.3</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.0942</td>

</tr> <tr>

<th>Kurtosis</th> <td>480.57</td>

</tr> <tr>

<th>Mean</th> <td>225.84</td>

</tr> <tr>

<th>MAD</th> <td>322.25</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.888</td>

</tr> <tr>

<th>Sum</th> <td>703050</td>

</tr> <tr>

<th>Variance</th> <td>1894300</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1088506075320450917”>

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A8%2B%2BECDBw%2BW0%2BnUmDFjVFhY6DW/bt06JSUlKS4uTtOnT1dZWZnPcgEAAPuzZcEyxig9PV1lZWV66aWXtHjxYv3lL3/RkiVLZIxRWlqaWrZsqS1btmjIkCGaPHmyTpw4IUk6ceKE0tLSlJqaqs2bN6t58%2BZ69NFHZYyRJL311lvKyclRZmam1q9fL5fLpezs7IaMCwAAbMaWBevo0aPKz89XVlaWOnTooISEBKWnp2vHjh3avXu3CgsLlZmZqfbt22vSpEmKjY3Vli1bJEmvvPKKunbtqvHjx6tDhw7KysrS8ePH9fHHH0uSNmzYoLFjxyo5OVndunXTnDlztGXLFs5iAQCAOrNlwYqIiNCaNWvUsmVLr/Fz587J5XLpjjvuUOPGjT3j8fHxys/PlyS5XC4lJCR45kJDQ9WlSxfl5%2Berurpan3zyidd8bGysLl68qIMHD9ZzKgAA4C9sWbDCwsKUlJTkuV1TU6NNmzapZ8%2BecrvdioyM9Lp/ixYtdOrUKUm65vzZs2dVUVHhNe9wOBQeHu55PAAAwPU4GnoBVsjOztaBAwe0efNmrVu3TsHBwV7zwcHBqqyslCSVlZVddb68vNxz%2B2qPr4uioiK53W6vMYejca1id7OCguzVjx2Ouq33Ui675bsectkLueyFXPbir7kuZ/uClZ2drfXr12vx4sXq2LGjQkJCVFpa6nWfyspKNWrUSJIUEhJSqyxVVlYqLCxMISEhntvfng8NDa3zmnJzc5WTk%2BM1lpaWpvT09Dofwx81a9bkhu4fFlb3z7mdkMteyGUv5LIXf80l2bxgzZ07Vy%2B//LKys7PVv39/SVJUVJQOHz7sdb/i4mLP2aOoqCgVFxfXmu/cubPCw8MVEhKi4uJitW/fXpJUVVWl0tJSRURE1HldI0aMUEpKiteYw9FYJSXnbzjjtdit%2Bdc1f1BQoMLCQnX2bJmqq2vqeVW%2BQy57IZe9kMtefJnrRv9zbxXbFqycnBz98Y9/1DPPPKMBAwZ4xp1Op1avXq3y8nLPWau8vDzFx8d75vPy8jz3Lysr04EDBzR58mQFBgYqJiZGeXl56tGjhyQpPz9fDodDnTp1qvPaIiMja70c6HZ/o6oq//ni%2BC5uNH91dY1ffs7IZS/kshdy2Yu/5pJsepH7kSNHtGLFCv3yl79UfHy83G635yMxMVGtWrVSRkaGCgoKtHr1au3bt0/Dhw%2BXJA0bNkx79%2B7V6tWrVVBQoIyMDLVp08ZTqEaNGqW1a9dq586d2rdvn2bPnq2HHnrohl4iBAAAP2w%2BP4P14IMPatiwYRo0aJB%2B/OMff6djvPPOO6qurtbKlSu1cuVKr7nPP/9cK1as0IwZM5Samqqf/vSnWr58uaKjoyVJbdq00bPPPqunnnpKy5cvV1xcnJYvX66AgABJ0qBBg3T8%2BHHNmjVLlZWVuvfeezVt2rSbCw0AAH5QAsyltzD3kUWLFmn79u0qKSnRv//7vys1NVW9evXyFBx/5XZ/Y/kxHY5A9Vv4vuXHrS9vPNarTvdzOALVrFkTlZSc96tTx%2BSyF3LZC7nsxZe5IiK%2B28mcm%2BXzlwinTJmiv/zlL1qxYoWCgoL061//Wvfcc48WL16sL774wtfLAQAAsFyDXOQeEBCgXr16qVevXiorK9PGjRu1YsUKrV69Wt27d9fYsWN17733NsTSAAAAblqD/RRhUVGRXnvtNb322ms6dOiQunfvrp///Oc6deqUfve732nPnj2aMWNGQy0PAADgO/N5wXr11Vf16quv6qOPPlLz5s01dOhQLVu2TG3btvXcp1WrVpo/fz4FCwAA2JLPC9aMGTOUnJys5cuXq0%2BfPgoMrH0ZWLt27TR69GhfLw0AAMASPi9Yf/3rX9WsWTOVlpZ6ytW%2BffvUpUsXBQUFSZK6d%2B%2Bu7t27%2B3ppAAAAlvD5TxGeO3dOAwYM0PPPP%2B8ZmzhxooYMGaKTJ0/6ejkAAACW83nBeuqpp/TTn/5UjzzyiGfs9ddfV6tWrZSVleXr5QAAAFjO5wXrb3/7m5588kmvX57cvHlzPf7449q9e7evlwMAAGA5nxcsh8Ohs2fP1hovKyuTj99UHgAAoF74vGD16dNH8%2BbN0z/%2B8Q/PWGFhobKyspSUlOTr5QAAAFjO5z9F%2BMQTT%2BiRRx5R//79FRYWJkk6e/asunTpooyMDF8vBwAAwHI%2BL1gtWrTQ1q1b9cEHH6igoEAOh0O33Xab7rrrLr//hc8AAOCHoUF%2BVU5QUJCSkpJ4SRAAAPglnxcst9utJUuWaO/evbp48WKtC9vfeecdXy8JAADAUj4vWDNnztT%2B/fs1aNAg/fjHP/b10wMAANQ7nxes3bt3a82aNUpISPD1UwMAAPiEz9%2BmoXHjxmrRooWvnxYAAMBnfF6whgwZojVr1qi6utrXTw0AAOATPn%2BJsLS0VDt27NC7776rW2%2B9VcHBwV7zGzZs8PWSAAAALNUgb9MwePDghnhaAAAAn/B5wcrKyvL1UwIAAPiUz6/BkqSioiLl5ORoypQp%2Buc//6k333xTR48ebYilAAAAWM7nBeurr77S/fffr61bt%2Bqtt97ShQsX9Prrr2vYsGFyuVy%2BXg4AAIDlfF6wnn76afXt21c7d%2B7Uj370I0nSM888o5SUFC1cuNDXywEAALCczwvW3r179cgjj3j9YmeHw6FHH31UBw4c8PVyAAAALOfzglVTU6Oamppa4%2BfPn1dQUJCvlwMAAGA5nxes3r17a9WqVV4lq7S0VNnZ2erZs6evlwMAAGA5nxesJ598Uvv371fv3r1VUVGh//zP/1RycrKOHTumJ554wtfLAQAAsJzP3wcrKipK27Zt044dO/TZZ5%2BppqZGI0eO1JAhQ9S0aVNfLwcAAMByDfJO7qGhoXrwwQcb4qkBAADqnc8L1pgxY645z%2B8iBAAAdufzgtW6dWuv21VVVfrqq6906NAhjR071tfLAQAAsNz35ncRLl%2B%2BXKdOnfLxagAAAKzXIL%2BL8EqGDBmiN954o6GXAQAAcNO%2BNwXr73//O280CgAA/ML34iL3c%2BfO6fPPP9eoUaN8vRwAAADL%2BbxgRUdHe/0eQkn60Y9%2BpNGjR%2BuBBx7w9XIAAAAs5/OC9fTTT1t6vMrKSqWmpmrmzJnq0aOHJGnevHnauHGj1/1mzpyp0aNHS5J27NihJUuWyO12q3fv3po7d66aN28uSTLGaNGiRdq8ebNqamo0fPhwTZ06VYGB35tXUwEAwPeczwvWnj176nzfO%2B%2B885rzFRUVmjJligoKCrzGjxw5oilTpujnP/%2B5Z%2BzSu8Tv27dPM2bM0Jw5c9SpUyfNnz9fGRkZWrVqlSTpxRdf1I4dO5STk6OqqipNmzZNLVq00IQJE%2Bq8bgAA8MPm84L18MMPe14iNMZ4xr89FhAQoM8%2B%2B%2Byqxzl8%2BLCmTJnidYxLjhw5ogkTJigiIqLW3KZNm3Tfffdp6NChkqQFCxYoOTlZhYWFuvXWW7Vhwwalp6crISFBkjR16lQtXbqUggUAAOrM5697Pffcc2rdurWWLFmiDz/8UHl5eVq3bp3%2B7d/%2BTb/97W/1zjvv6J133tHOnTuveZyPP/5YPXr0UG5urtf4uXPndPr0abVt2/aKj3O5XJ7yJEmtWrVSdHS0XC6XTp8%2BrZMnT3qdOYuPj9fx48dVVFT03UMDAIAflAZ5o9FZs2apT58%2BnrGePXsqMzNTjz/%2BuH75y1/W6ThX%2B4nDI0eOKCAgQM8995z%2B%2Bte/Kjw8XI888ojn5cKioiJFRkZ6PaZFixY6deqU3G63JHnNt2zZUpJ06tSpWo%2B7mqKiIs%2BxLnE4Gtf58XUVFGSv68Icjrqt91Iuu%2BW7HnLZC7nshVz24q%2B5LufzglVUVFTr1%2BVI/7pGqqSk5KaPf/ToUQUEBKhdu3YaPXq09uzZo5kzZ6pp06bq16%2BfysvLFRwc7PWY4OBgVVZWqry83HP78jnpXxfT11Vubq5ycnK8xtLS0pSenv5dY/mFZs2a3ND9w8JC62klDYtc9kIueyGXvfhrLqkBClZsbKyeeeYZ/eEPf/BceF5aWqrs7GzdddddN338oUOHKjk5WeHh4ZKkTp066csvv9TLL7%2Bsfv36KSQkpFZZqqysVGhoqFeZCgkJ8fxZkkJD6/6XYMSIEUpJSfEaczgaq6Tk/HfOdSV2a/51zR8UFKiwsFCdPVum6uqael6V75DLXshlL%2BSyF1/mutH/3FvF5wXrd7/7ncaMGaM%2Bffqobdu2Msboyy%2B/VEREhDZs2HDTxw8ICPCUq0vatWun3bt3S5KioqJUXFzsNV9cXKyIiAhFRUVJktxut9q0aeP5s6QrXjB/NZGRkbVeDnS7v1FVlf98cXwXN5q/urrGLz9n5LIXctkLuezFX3NJDXCRe/v27fX6669rypQpio2NVVxcnGbMmKFXX31VP/nJT276%2BEuXLtW4ceO8xg4ePKh27dpJkpxOp/Ly8jxzJ0%2Be1MmTJ%2BV0OhUVFaXo6Giv%2Bby8PEVHR1t%2B/RQAAPBfPj%2BDJUm33HKLHnzwQR07dky33nqrpH%2B9m7sVkpOTtXr1aq1du1b9%2BvXTrl27tG3bNs/ZsZEjR%2Brhhx9WbGysYmJiNH/%2BfN1zzz2edYwcOVILFy70lL1FixZp/PjxlqwNAAD8MPi8YF16p/SNGzfq4sWLeuutt7R48WKFhoZq9uzZN120unXrpqVLl2rZsmVaunSpWrdurUWLFikuLk6SFBcXp8zMTC1btkxff/21evXqpblz53oeP2HCBP3zn//U5MmTFRQUpOHDh9c6IwYAAHAtPi9YGzdu1Kuvvqrf//73yszMlCT17dtXc%2BbMUcuWLfVf//VfN3zMzz//3Ot237591bdv36vePzU1VampqVecCwoKUkZGhjIyMm54HQAAAFIDXIOVm5urWbNmKTU11fPu7QMHDtS8efO0fft2Xy8HAADAcj4vWMeOHVPnzp1rjXfq1KnWm3MCAADYkc8LVuvWrfXJJ5/UGv/rX//qudAcAADAznx%2BDdaECRM0Z84cud1uGWP04YcfKjc3Vxs3btSTTz7p6%2BUAAABYzucFa9iwYaqqqtLKlStVXl6uWbNmqXnz5nrsscc0cuRIXy8HAADAcj4vWDt27NCAAQM0YsQInTlzRsYYtWjRwtfLAAAAqDc%2BvwYrMzPTczF78%2BbNKVcAAMDv%2BLxgtW3bVocOHfL10wIAAPiMz18i7NSpk6ZOnao1a9aobdu2CgkJ8ZrPysry9ZIAAAAs5fOC9cUXXyg%2BPl6SeN8rAADgl3xSsBYsWKDJkyercePG2rhxoy%2BeEgAAoMH45BqsF198UWVlZV5jEydOVFFRkS%2BeHgAAwKd8UrCMMbXG9uzZo4qKCl88PQAAgE/5/KcIAQAA/B0FCwAAwGI%2BK1gBAQG%2BeioAAIAG5bO3aZg3b57Xe15dvHhR2dnZatKkidf9eB8sAABgdz4pWHfeeWet97yKi4tTSUmJSkpKfLEEAAAAn/FJweK9rwAAwA8JF7kDAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDHbF6zKykoNHjxYH330kWessLBQ48aNU2xsrAYOHKhdu3Z5PeaDDz7Q4MGD5XQ6NWbMGBUWFnrNr1u3TklJSYqLi9P06dNVVlbmkywAAMA/2LpgVVRU6Le//a0KCgo8Y8YYpaWlqWXLltqyZYuGDBmiyZMn68SJE5KkEydOKC0tTampqdq8ebOaN2%2BuRx99VMYYSdJbb72lnJwcZWZmav369XK5XMrOzm6QfAAAwJ5sW7AOHz6shx56SP/4xz%2B8xnfv3q3CwkJlZmaqffv2mjRpkmJjY7VlyxZJ0iuvvKKuXbtq/Pjx6tChg7KysnT8%2BHF9/PHHkqQNGzZo7NixSk5OVrdu3TRnzhxt2bKFs1gAAKDObFuwPv74Y/Xo0UO5uble4y6XS3fccYcaN27sGYuPj1d%2Bfr5nPiEhwTMXGhqqLl26KD8/X9XV1frkk0%2B85mNjY3Xx4kUdPHiwnhMBAAB/4WjoBXxXo0aNuuK42%2B1WZGSk11iLFi106tSp686fPXtWFRUVXvMOh0Ph4eGex9dFUVGR3G6315jD0bjW896soCB79WOHo27rvZTLbvmuh1z2Qi57IZe9%2BGuuy9m2YF1NWVmZgoODvcaCg4Nwcn5YAAAWOElEQVRVWVl53fny8nLP7as9vi5yc3OVk5PjNZaWlqb09PQ6H8MfNWvW5IbuHxYWWk8raVjkshdy2Qu57MVfc0l%2BWLBCQkJUWlrqNVZZWalGjRp55r9dliorKxUWFqaQkBDP7W/Ph4bW/S/BiBEjlJKS4jXmcDRWScn5Oh%2BjLuzW/OuaPygoUGFhoTp7tkzV1TX1vCrfIZe9kMteyGUvvsx1o/%2B5t4rfFayoqCgdPnzYa6y4uNjz8lxUVJSKi4trzXfu3Fnh4eEKCQlRcXGx2rdvL0mqqqpSaWmpIiIi6ryGyMjIWi8Hut3fqKrKf744vosbzV9dXeOXnzNy2Qu57IVc9uKvuSQbX%2BR%2BNU6nU59%2B%2Bqnn5T5JysvLk9Pp9Mzn5eV55srKynTgwAE5nU4FBgYqJibGaz4/P18Oh0OdOnXyXQgAAGBrflewEhMT1apVK2VkZKigoECrV6/Wvn37NHz4cEnSsGHDtHfvXq1evVoFBQXKyMhQmzZt1KNHD0n/unh%2B7dq12rlzp/bt26fZs2froYceuqGXCAEAwA%2Bb3xWsoKAgrVixQm63W6mpqXrttde0fPlyRUdHS5LatGmjZ599Vlu2bNHw4cNVWlqq5cuXKyAgQJI0aNAgTZo0SbNmzdL48ePVrVs3TZs2rSEjAQAAm/GLa7A%2B//xzr9s//elPtWnTpqve/%2B6779bdd9991fmJEydq4sSJlq0PAAD8sPjdGSwAAICGRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAs5rcF6%2B2339btt9/u9ZGeni5JOnDggB588EE5nU4NGzZM%2B/fv93rsjh071LdvXzmdTqWlpenMmTMNEQEAANiU3xasw4cPKzk5Wbt27fJ8zJs3TxcuXNDEiROVkJCgP//5z4qLi9OkSZN04cIFSdK%2Bffs0Y8YMTZ48Wbm5uTp79qwyMjIaOA0AALATvy1YR44cUceOHRUREeH5CAsL0%2Buvv66QkBA9/vjjat%2B%2BvWbMmKEmTZrozTfflCRt2rRJ9913n4YOHapOnTppwYIFeu%2B991RYWNjAiQAAgF34dcFq27ZtrXGXy6X4%2BHgFBARIkgICAtS9e3fl5%2Bd75hMSEjz3b9WqlaKjo%2BVyuXyybgAAYH9%2BWbCMMfriiy%2B0a9cu9e/fX3379tXChQtVWVkpt9utyMhIr/u3aNFCp06dkiQVFRVdcx4AAOB6HA29gPpw4sQJlZWVKTg4WEuWLNGxY8c0b948lZeXe8YvFxwcrMrKSklSeXn5NefroqioSG6322vM4Whcq7jdrKAge/Vjh6Nu672Uy275rodc9kIueyGXvfhrrsv5ZcFq3bq1PvroI91yyy0KCAhQ586dVVNTo2nTpikxMbFWWaqsrFSjRo0kSSEhIVecDw0NrfPz5%2BbmKicnx2ssLS3N81OMP1TNmjW5ofuHhdX9c24n5LIXctkLuezFX3NJflqwJCk8PNzrdvv27VVRUaGIiAgVFxd7zRUXF3vOLkVFRV1xPiIios7PPWLECKWkpHiNORyNVVJy/kYiXJfdmn9d8wcFBSosLFRnz5apurqmnlflO%2BSyF3LZC7nsxZe5bvQ/91bxy4L1/vvva%2BrUqXr33Xc9Z54%2B%2B%2BwzhYeHKz4%2BXs8//7yMMQoICJAxRnv37tWvfvUrSZLT6VReXp5SU1MlSSdPntTJkyfldDrr/PyRkZG1Xg50u79RVZX/fHF8Fzeav7q6xi8/Z%2BSyF3LZC7nsxV9zSX56kXtcXJxCQkL0u9/9TkePHtV7772nBQsW6Be/%2BIUGDBigs2fPav78%2BTp8%2BLDmz5%2BvsrIy3XfffZKkkSNH6tVXX9Urr7yigwcP6vHHH9c999yjW2%2B9tYFTAQAAu/DLgtW0aVOtXbtWZ86c0bBhwzRjxgyNGDFCv/jFL9S0aVOtWrXKc5bK5XJp9erVaty4saR/lbPMzEwtX75cI0eO1C233KKsrKwGTgQAAOzEL18ilKQOHTroxRdfvOJct27dtHXr1qs%2BNjU11fMSIQAAwI3yyzNYAAAADYmCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEK1hVUVFRo%2BvTpSkhIUO/evfXCCy809JIAAICNOBp6Ad9HCxYs0P79%2B7V%2B/XqdOHFCTzzxhKKjozVgwICGXhoAALABCta3XLhwQa%2B88oqef/55denSRV26dFFBQYFeeuklChYAAKgTXiL8loMHD6qqqkpxcXGesfj4eLlcLtXU1DTgygAAgF1wButb3G63mjVrpuDgYM9Yy5YtVVFRodLSUjVv3vy6xygqKpLb7fYaczgaKzIy0tK1BgXZqx/ft%2BT/GnoJN%2BTtqUmWHu/Sftlt366HXPZCLnshl31RsL6lrKzMq1xJ8tyurKys0zFyc3OVk5PjNTZ58mT9%2Bte/tmaR/19RUZHG/qRAI0aMsLy8NaSioiLl5ub6Za7169eQyybIZS/kshd/zXU5/62O31FISEitInXpdqNGjep0jBEjRujPf/6z18eIESMsX6vb7VZOTk6ts2V2Ry57IZe9kMteyGVfnMH6lqioKJWUlKiqqkoOx78%2BPW63W40aNVJYWFidjhEZGem3jRwAAFwfZ7C%2BpXPnznI4HMrPz/eM5eXlKSYmRoGBfLoAAMD10Ri%2BJTQ0VEOHDtXs2bO1b98%2B7dy5Uy%2B88ILGjBnT0EsDAAA2ETR79uzZDb2I75uePXvqwIEDWrRokT788EP96le/0rBhwxp6WVfUpEkTJSYmqkmTJg29FEuRy17IZS/kshdy2VOAMcY09CIAAAD8CS8RAgAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2DZVEVFhaZPn66EhAT17t1bL7zwQkMv6Yrefvtt3X777V4f6enpkqQDBw7owQcflNPp1LBhw7R//36vx%2B7YsUN9%2B/aV0%2BlUWlqazpw545kzxmjhwoXq2bOnEhMTtWDBAtXU1NR7nsrKSg0ePFgfffSRZ6ywsFDjxo1TbGysBg4cqF27dnk95oMPPtDgwYPldDo1ZswYFRYWes2vW7dOSUlJiouL0/Tp01VWVuaZ89U%2BXynXvHnzau3dpk2bPPM3sz8lJSX69a9/rbi4OKWkpOjVV1%2B1NM/p06eVnp6uxMREJSUlKSsrSxUVFZLsvV/XymXn/frqq680YcIExcXF6Z577tGaNWs8c3ber%2Btls/OeXTJx4kQ9%2BeSTntv1%2BX3dV5ksY2BLmZmZ5v777zf79%2B83//M//2Pi4uLMG2%2B80dDLqmXFihVm0qRJpqioyPPx9ddfm/Pnz5tevXqZp59%2B2hw%2BfNjMnTvX/OxnPzPnz583xhjjcrlMt27dzNatW81nn31mRo8ebSZOnOg57tq1a83dd99t9uzZYz788EPTu3dvs2bNmnrNUl5ebtLS0kzHjh3N7t27jTHG1NTUmPvvv99MmTLFHD582Dz33HPG6XSa48ePG2OMOX78uImNjTVr1641hw4dMr/5zW/M4MGDTU1NjTHGmDfffNPEx8eb//3f/zUul8sMHDjQzJkzx/OcvtjnK%2BUyxphx48aZVatWee3dhQsXjDE3vz%2BTJk0yY8eONZ9//rn505/%2BZLp27WpcLpcleWpqasxDDz1kfvGLX5hDhw6ZPXv2mH79%2Bpmnn37a1vt1rVzG2He/qqurzb333mumTJlivvjiC/Puu%2B%2Ba7t27m9dee83W%2B3W9bMbYd88u2bFjh%2BnYsaN54oknjDGm3r%2Bv%2ByKTlShYNnT%2B/HkTExPj9Y/h8uXLzejRoxtwVVc2ZcoUs2jRolrjr7zyiklJSfF8I6ypqTH9%2BvUzW7ZsMcYYM23aNM8XrTHGnDhxwtx%2B%2B%2B3mH//4hzHGmLvvvttzX2OM2bZtm0lOTq63HAUFBeaBBx4w999/v1cR%2BeCDD0xsbKznG4gxxowdO9YsW7bMGGPMkiVLvPblwoULJi4uzvP4UaNGee5rjDF79uwx3bp1MxcuXPDJPl8tlzHGJCUlmffff/%2BKj7uZ/fnqq69Mx44dTWFhoWd%2B%2BvTpXse7GYcPHzYdO3Y0brfbM7Z9%2B3bTu3dvW%2B/XtXIZY9/9On36tPnNb35jvvnmG89YWlqa%2Bf3vf2/r/bpeNmPsu2fGGFNSUmL69Oljhg0b5jlufX5f90Umq/ESoQ0dPHhQVVVViouL84zFx8fL5XL55GWyG3HkyBG1bdu21rjL5VJ8fLwCAgIkSQEBAerevbvy8/M98wkJCZ77t2rVStHR0XK5XDp9%2BrROnjypO%2B%2B80zMfHx%2Bv48ePq6ioqF5yfPzxx%2BrRo4dyc3Nr5bjjjjvUuHFjr7VcLUdoaKi6dOmi/Px8VVdX65NPPvGaj42N1cWLF3Xw4EGf7PPVcp07d06nT5%2B%2B4t5dKdeN7I/L5VKrVq3Upk0br/m///3vlmSKiIjQmjVr1LJly1qZ7Lxf18pl5/2KjIzUkiVL1LRpUxljlJeXpz179igxMdHW%2B3W9bHbeM0n6wx/%2BoCFDhui2227zWnN9fV/3RSarUbBsyO12q1mzZgoODvaMtWzZUhUVFSotLW3AlXkzxuiLL77Qrl271L9/f/Xt21cLFy5UZWWl3G63IiMjve7fokULnTp1SpJUVFR01Xm32y1JXvOX/tG59HirjRo1StOnT1doaKjX%2BPVyXGv%2B7Nmzqqio8Jp3OBwKDw/35Kzvfb5ariNHjiggIEDPPfec%2BvTpowceeEBbt271zN/M/lztc3L69GlLMoWFhSkpKclzu6amRps2bVLPnj1tvV/XymXn/bpcSkqKRo0apbi4OPXv39/W%2B3W9bHbesw8//FB/%2B9vf9Oijj3qN1%2Bf3dV/%2BPbSKo6EXgBtXVlbm9U1Bkud2ZWVlQyzpik6cOOFZ65IlS3Ts2DHNmzdP5eXlV81waf3l5eVXnS8vL/fcvnxO8n3%2B6%2BW41vyVclw%2Bb4xpsH0%2BevSoAgIC1K5dO40ePVp79uzRzJkz1bRpU/Xr1%2B%2Bm9ud6nzOrZWdn68CBA9q8ebPWrVvnN/t1ea5PP/3UL/Zr2bJlKi4u1uzZs5WVleVXX1/fztalSxdb7llFRYV%2B//vfa9asWWrUqJHXXH1%2BX/f19w0rULBsKCQkpNZfqku3v/0XviG1bt1aH330kW655RYFBASoc%2BfOqqmp0bRp05SYmHjFDJfWf7WMoaGhXl90ISEhnj9LqnUmpr6FhITU%2Bt9uXXKEhYXVWvvl86Ghoaqurm6wfR46dKiSk5MVHh4uSerUqZO%2B/PJLvfzyy%2BrXr99N7c/VHlsfmbKzs7V%2B/XotXrxYHTt29Jv9%2BnauDh06%2BMV%2BxcTESPrXP%2BJTp07VsGHDvH7q79vPbZf9kmpn27t3ry33LCcnR127dvU6m3rJ9Z73%2B5qpvvASoQ1FRUWppKREVVVVnjG3261GjRopLCysAVdWW3h4uOf1eElq3769KioqFBERoeLiYq/7FhcXe04BR0VFXXE%2BIiJCUVFRkuQ5pXz5nyMiIuolx9VcbZ11yREeHq6QkBCv%2BaqqKpWWlnpyNtQ%2BBwQEeL7xX9KuXTvP6fib2Z9rPdZKc%2BfO1Ysvvqjs7Gz179//muu2035dKZed96u4uFg7d%2B70Grvtttt08eLFm/o%2B8X3Yr2tlO3funC337L//%2B7%2B1c%2BdOxcXFKS4uTtu3b9f27dsVFxd3U19fDf33sD5QsGyoc%2BfOcjgcngsHJSkvL08xMTEKDPz%2BbOn777%2BvHj16eP0P9LPPPlN4eLjn4kRjjKR/Xa%2B1d%2B9eOZ1OSZLT6VReXp7ncSdPntTJkyfldDoVFRWl6Ohor/m8vDxFR0fXeo2%2BvjmdTn366aee09uX1nK1HGVlZTpw4ICcTqcCAwMVExPjNZ%2Bfny%2BHw6FOnTo16D4vXbpU48aN8xo7ePCg2rVrd8VcN7I/sbGxOn78uNf1cnl5eYqNjbVs/Tk5OfrjH/%2BoZ555RoMGDfKM232/rpbLzvt17NgxTZ482etamv3796t58%2BaKj4%2B39X5dK9vGjRttuWcbN27U9u3btW3bNm3btk0pKSlKSUnRtm3b5HQ66%2B37ui%2B%2Bb1iuoX58ETdn5syZZtCgQcblcpm3337bdO/e3bz11lsNvSwv33zzjUlKSjK//e1vzZEjR8y7775revfubVavXm2%2B%2BeYb07NnTzN37lxTUFBg5s6da3r16uX5cey9e/eaLl26mD/96U%2Be90uZNGmS59irVq0yvXv3Nrt37za7d%2B82vXv3Ni%2B88IJPcl3%2BdgZVVVVm4MCB5rHHHjOHDh0yq1atMrGxsZ736SksLDQxMTFm1apVnvfpuf/%2B%2Bz0/xrxjxw7TvXt38/bbbxuXy2UGDRpk5s6d63kuX%2B7z5blcLpe54447zJo1a8xXX31lXnrpJdO1a1ezd%2B9eY8zN78/48ePN6NGjzWeffWb%2B9Kc/mZiYGMvez%2Bbw4cOmc%2BfOZvHixV7vL1RUVGTr/bpWLjvvV1VVlUlNTTXjx483BQUF5t133zU/%2B9nPzLp162y9X9fLZuc9u9wTTzzheauE%2Bv6%2B7qtMVqFg2dSFCxfM448/bmJjY03v3r3Niy%2B%2B2NBLuqJDhw6ZcePGmdjYWNOrVy/z7LPPer75uVwuM3ToUBMTE2OGDx9uPv30U6/Hbtmyxdx9990mNjbWpKWlmTNnznjmqqqqzFNPPWUSEhJMjx49THZ2tue49e3b7xf15Zdfmv/4j/8wXbt2NYMGDTL/93//53X/d99919x7772mW7duZuzYsZ73fLlk1apV5q677jLx8fEmIyPDlJeXe%2BZ8uc/fzvX222%2Bb%2B%2B%2B/38TExJgBAwbU%2BofnZvanuLjYTJo0ycTExJiUlBSzfft2y3KsWrXKdOzY8Yofxth3v66Xy677ZYwxp06dMmlpaaZ79%2B6mV69eZuXKlZ7nt%2Bt%2B1SWbnffskssLljH1%2B33dV5msEmDM/z%2BXBwAAAEt8fy7YAQAA8BMULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGL/D%2BY03iKgL4FeAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1088506075320450917”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000002980232</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000004470348</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.99999998137355</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2911)</td> <td class=”number”>2912</td> <td class=”number”>90.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1088506075320450917”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0024060137802735</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0044683939777315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005254138906821</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0065442048944533</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>18956.7011005882</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25152.1782851138</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27048.7705948986</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36272.9042347345</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40350.8004293314</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_NHPI15”>LACCESS_NHPI15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1829</td>

</tr> <tr>

<th>Unique (%)</th> <td>57.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>32.305</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>21387</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>36.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1343491515740730316”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1343491515740730316,#minihistogram-1343491515740730316”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1343491515740730316”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1343491515740730316”

aria-controls=”quantiles-1343491515740730316” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1343491515740730316” aria-controls=”histogram-1343491515740730316”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1343491515740730316” aria-controls=”common-1343491515740730316”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1343491515740730316” aria-controls=”extreme-1343491515740730316”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1343491515740730316”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>7.1206</td>

</tr> <tr>

<th>95-th percentile</th> <td>71.12</td>

</tr> <tr>

<th>Maximum</th> <td>21387</td>

</tr> <tr>

<th>Range</th> <td>21387</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.1206</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>436.61</td>

</tr> <tr>

<th>Coef of variation</th> <td>13.515</td>

</tr> <tr>

<th>Kurtosis</th> <td>1885.6</td>

</tr> <tr>

<th>Mean</th> <td>32.305</td>

</tr> <tr>

<th>MAD</th> <td>51.668</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>40.47</td>

</tr> <tr>

<th>Sum</th> <td>100570</td>

</tr> <tr>

<th>Variance</th> <td>190630</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1343491515740730316”>

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vfvr/T0dGVlZam2tlaSVFpaqqlTpyopKUnDhw/X9u3bvbbdsWOHRo4cqcTERE2ePFmlpaVe8xs3blR6erqSk5M1d%2B5cVVdX%2B2xdAAAg%2BAVlwDLGKDMzU9XV1Xr%2B%2Bef1%2BOOP63/%2B53%2B0cuVKGWOUkZGhzp07Kz8/X6NGjdLMmTN15MgRSdKRI0eUkZGhMWPGaOvWrerYsaPuu%2B8%2BGWMkSW%2B//bZycnK0aNEibdq0SS6XS9nZ2f5cLgAACDJBGbAOHTqkoqIiZWVl6eqrr1ZqaqoyMzNVUFCgnTt3qrS0VIsWLVLPnj01Y8YMJSUlKT8/X5L00ksv6brrrtO0adN09dVXKysrS4cPH9Ynn3wiSdq8ebOmTJmiwYMHq2/fvlq4cKHy8/M5iwUAAJotKANWdHS01q9fr86dO3uNnzp1Si6XS9dee63atm3rGU9JSVFRUZEkyeVyKTU11TMXERGhPn36qKioSA0NDfrss8%2B85pOSknT27Fnt37//Eq8KAAC0FkEZsCIjI5Wenu553NjYqC1btmjgwIFyu92KiYnxen6nTp1UVlYmSd85f%2BLECdXW1nrNO51ORUVFebYHAAD4Pk5/F2BDdna29u3bp61bt2rjxo0KCwvzmg8LC1NdXZ0kqbq6%2BoLzNTU1nscX2r45ysvL5Xa7vcaczrZNgl1LhYYGVz52OoOr3ot1ri/B1p/Wjr4EJvoSmOhLywV9wMrOztamTZv0%2BOOPq1evXgoPD1dVVZXXc%2Brq6tSmTRtJUnh4eJOwVFdXp8jISIWHh3sef3s%2BIiKi2TXl5eUpJyfHaywjI0OZmZnN3kdr1KFDO3%2BX4FORkc1/z8B36Etgoi%2BBib5cvKAOWIsXL9YLL7yg7Oxs3XrrrZKk2NhYHTx40Ot5FRUVnrNHsbGxqqioaDLfu3dvRUVFKTw8XBUVFerZs6ckqb6%2BXlVVVYqOjm52XePHj9eQIUO8xpzOtqqsPP2D1/hdgu0nC9vrD1ShoSGKjIzQiRPVamho9Hc5%2BCf6EpjoS2BqTX3x1w/3QRuwcnJy9OKLL%2Bqxxx7TsGHDPOOJiYnKzc1VTU2N56xVYWGhUlJSPPOFhYWe51dXV2vfvn2aOXOmQkJClJCQoMLCQg0YMECSVFRUJKfTqfj4%2BGbXFhMT0%2BTjQLf7pOrrg/tN2lKX2/obGhovuzUHA/oSmOhLYKIvF8/np0DuvPNOvfjiizp58uRF76OkpERr167Vr371K6WkpMjtdnu%2B%2Bvfvry5dumjOnDkqLi5Wbm6u9uzZo3HjxkmSxo4dq927dys3N1fFxcWaM2eOunXr5glUEydO1IYNG7Rt2zbt2bNHCxYs0F133fWDPiIEAACXN58HrIEDB%2Bqpp55SWlqaHnjgAW3fvt1zk8/mevfdd9XQ0KAnn3xSaWlpXl%2BhoaFau3at3G63xowZo9dee01r1qxRXFycJKlbt2564oknlJ%2Bfr3Hjxqmqqkpr1qyRw%2BGQJI0YMUIzZszQI488omnTpqlv376aPXu29e8DAABovRzmh6YbC4wx2rFjh1555RVt27ZNkZGRGj16tEaPHq2f/OQnvi7HJ9zuiz9jdyFOZ4iGLv/Q%2Bn4vlTfvH%2BTvEnzC6QxRhw7tVFl5mlPrAYS%2BBCb6EphaU1%2Bio6/wy%2Bv65Rosh8OhQYMGadCgQaqurtZzzz2ntWvXKjc3V/369dOUKVP0s5/9zB%2BlAQAAtJjfLnIvLy/Xa6%2B9ptdee00HDhxQv379dMcdd6isrEy///3vtWvXLs2bN89f5QEAAFw0nwesV199Va%2B%2B%2Bqo%2B/vhjdezYUaNHj9bq1avVvXt3z3O6dOmipUuXErAAAEBQ8nnAmjdvngYPHqw1a9boxhtvVEhI0%2Bvse/TooUmTJvm6NAAAACt8HrA%2B%2BOADdejQQVVVVZ5wtWfPHvXp00ehoaGSpH79%2Bqlfv36%2BLg0AAMAKn9%2Bm4dSpUxo2bJiefvppz9i9996rUaNG6ejRo74uBwAAwDqfB6w//OEP%2Bpd/%2BRfdc889nrE33nhDXbp0UVZWlq/LAQAAsM7nAev//u//9Lvf/c7rd/t17NhRDz30kHbu3OnrcgAAAKzzecByOp06ceJEk/Hq6uoffEd3AACAQOTzgHXjjTdqyZIl%2Btvf/uYZKy0tVVZWltLT031dDgAAgHU%2B/1%2BEDz/8sO655x7deuutioyMlCSdOHFCffr00Zw5c3xdDgAAgHU%2BD1idOnXSyy%2B/rB07dqi4uFhOp1M//elPdcMNN3h%2B4TIAAEAw88uvygkNDVV6ejofCQIAgFbJ5wHL7XZr5cqV2r17t86ePdvkwvZ3333X1yUBAABY5fOANX/%2BfO3du1cjRozQFVdc4euXBwAAuOR8HrB27typ9evXKzU11dcvDQAA4BM%2Bv01D27Zt1alTJ1%2B/LAAAgM/4PGCNGjVK69evV0NDg69fGgAAwCd8/hFhVVWVCgoK9N577%2Bmqq65SWFiY1/zmzZt9XRIAAIBVfrlNw8iRI/3xsgAAAD7h84CVlZXl65cEAADwKZ9fgyVJ5eXlysnJ0axZs/T3v/9db731lg4dOuSPUgAAAKzzecD68ssvdfvtt%2Bvll1/W22%2B/rTNnzuiNN97Q2LFj5XK5fF0OAACAdT4PWI8%2B%2BqhuueUWbdu2TT/60Y8kSY899piGDBmi5cuX%2B7ocAAAA63wesHbv3q177rnH6xc7O51O3Xfffdq3b5%2BvywEAALDO5wGrsbFRjY2NTcZPnz6t0NBQX5cDAABgnc8DVlpamtatW%2BcVsqqqqpSdna2BAwf6uhwAAADrfB6wfve732nv3r1KS0tTbW2t/v3f/12DBw/WV199pYcfftjX5QAAAFjn8/tgxcbG6pVXXlFBQYE%2B//xzNTY2asKECRo1apTat2/v63IAAACs88ud3CMiInTnnXf646UBAAAuOZ8HrMmTJ3/nPL%2BLEAAABDufB6yuXbt6Pa6vr9eXX36pAwcOaMqUKb4uBwAAwLqA%2BV2Ea9asUVlZmY%2BrAQAAsM8vv4vwfEaNGqU333zT32UAAAC0WMAErE8//ZQbjQIAgFYhIC5yP3XqlP7yl79o4sSJvi4HAADAOp8HrLi4OK/fQyhJP/rRjzRp0iT9/Oc/93U5AAAA1vk8YD366KNW91dXV6cxY8Zo/vz5GjBggCRpyZIleu6557yeN3/%2BfE2aNEmSVFBQoJUrV8rtdistLU2LFy9Wx44dJUnGGK1YsUJbt25VY2Ojxo0bpwcffFAhIQHzaSoAAAhwPg9Yu3btavZzr7/%2B%2Bu%2Bcr62t1axZs1RcXOw1XlJSolmzZumOO%2B7wjJ27S/yePXs0b948LVy4UPHx8Vq6dKnmzJmjdevWSZKeffZZFRQUKCcnR/X19Zo9e7Y6deqk6dOnN7tuAABwefN5wLr77rs9HxEaYzzj3x5zOBz6/PPPL7ifgwcPatasWV77OKekpETTp09XdHR0k7ktW7botttu0%2BjRoyVJy5Yt0%2BDBg1VaWqqrrrpKmzdvVmZmplJTUyVJDz74oFatWkXAAgAAzebzz72eeuopde3aVStXrtRHH32kwsJCbdy4UT/5yU/0wAMP6N1339W7776rbdu2fed%2BPvnkEw0YMEB5eXle46dOndKxY8fUvXv3827ncrk84UmSunTpori4OLlcLh07dkxHjx71OnOWkpKiw4cPq7y8/OIXDQAALit%2BudHoI488ohtvvNEzNnDgQC1atEgPPfSQfvWrXzVrPxf6H4clJSVyOBx66qmn9MEHHygqKkr33HOP5%2BPC8vJyxcTEeG3TqVMnlZWVye12S5LXfOfOnSVJZWVlTbYDAAA4H58HrPLy8ia/Lkf6xzVSlZWVLd7/oUOH5HA41KNHD02aNEm7du3S/Pnz1b59ew0dOlQ1NTUKCwvz2iYsLEx1dXWqqanxPP7mnPSPi%2Bmbq7y83BPWznE621oPaKGhwXXhvdMZXPVerHN9Cbb%2BtHb0JTDRl8BEX1rO5wErKSlJjz32mP7zP//Tc%2BF5VVWVsrOzdcMNN7R4/6NHj9bgwYMVFRUlSYqPj9cXX3yhF154QUOHDlV4eHiTsFRXV6eIiAivMBUeHu75syRFREQ0u4a8vDzl5OR4jWVkZCgzM/Oi19UadOjQzt8l%2BFRkZPPfM/Ad%2BhKY6Etgoi8Xz%2BcB6/e//70mT56sG2%2B8Ud27d5cxRl988YWio6O1efPmFu/f4XB4wtU5PXr00M6dOyVJsbGxqqio8JqvqKhQdHS0YmNjJUlut1vdunXz/FnSeS%2BYv5Dx48dryJAhXmNOZ1tVVp7%2BYYv5HsH2k4Xt9Qeq0NAQRUZG6MSJajU0NPq7HPwTfQlM9CUwtaa%2B%2BOuHe58HrJ49e%2BqNN95QQUGBSkpKJEm/%2BMUvNGLEiB90luhCVq1apU8//VQbN270jO3fv189evSQJCUmJqqwsFBjxoyRJB09elRHjx5VYmKiYmNjFRcXp8LCQk/AKiwsVFxc3A/6eC8mJqbJ893uk6qvD%2B43aUtdbutvaGi87NYcDOhLYKIvgYm%2BXDyfByxJuvLKK3XnnXfqq6%2B%2B0lVXXSXpH3dzt2Hw4MHKzc3Vhg0bNHToUG3fvl2vvPKK5%2BzYhAkTdPfddyspKUkJCQlaunSpbr75Zk8dEyZM0PLly/XjH/9YkrRixQpNmzbNSm0AAODy4POAde5O6c8995zOnj2rt99%2BW48//rgiIiK0YMGCFgetvn37atWqVVq9erVWrVqlrl27asWKFUpOTpYkJScna9GiRVq9erW%2B/vprDRo0SIsXL/ZsP336dP3973/XzJkzFRoaqnHjxmnq1KktqgkAAFxeHOZ8d%2Bq8hDZv3qynn35av/3tb7Vo0SK9/vrr%2Buyzz7Rw4UL927/9m37729/6shyfcbtPWt%2Bn0xmiocs/tL7fS%2BXN%2Bwf5uwSfcDpD1KFDO1VWnubUegChL4GJvgSm1tSX6Ogr/PK6Pr9KOi8vT4888ojGjBnjuXv78OHDtWTJEr3%2B%2Buu%2BLgcAAMA6nwesr776Sr17924yHh8f3%2BTeUQAAAMHI5wGra9eu%2Buyzz5qMf/DBB54LzQEAAIKZzy9ynz59uhYuXCi32y1jjD766CPl5eXpueee0%2B9%2B9ztflwMAAGCdzwPW2LFjVV9fryeffFI1NTV65JFH1LFjR91///2aMGGCr8sBAACwzucBq6CgQMOGDdP48eN1/PhxGWPUqVMnX5cBAABwyfj8GqxFixZ5Lmbv2LEj4QoAALQ6Pg9Y3bt314EDB3z9sgAAAD7j848I4%2BPj9eCDD2r9%2BvXq3r27wsPDveazsrJ8XRIAAIBVPg9Yf/3rX5WSkiJJ3PcKAAC0Sj4JWMuWLdPMmTPVtm1bPffcc754SQAAAL/xyTVYzz77rKqrq73G7r33XpWXl/vi5QEAAHzKJwHrfL9PeteuXaqtrfXFywMAAPiUz/8XIQAAQGtHwAIAALDMZwHL4XD46qUAAAD8yme3aViyZInXPa/Onj2r7OxstWvXzut53AcLAAAEO58ErOuvv77JPa%2BSk5NVWVmpyspKX5QAAADgMz4JWNz7CgAAXE64yB0AAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGVBH7Dq6uo0cuRIffzxx56x0tJSTZ06VUlJSRo%2BfLi2b9/utc2OHTs0cuRIJSYmavLkySotLfWa37hxo9LT05WcnKy5c%2BequrraJ2sBAACtQ1AHrNraWj3wwAMqLi72jBljlJGRoc6dOys/P1%2BjRo3SzJkzdeTIEUnSkSNHlJGRoTFjxmjr1q3q2LGj7rvvPhljJElvv/22cnJytGjRIm3atEkul0vZ2dl%2BWR8AAAhOQRuwDh48qLvuukt/%2B9vfvMZ37typ0tJSLVq0SD179tSMGTOUlJSk/Px8SdJLL72k6667TtOmTdPVV1%2BtrKwsHT58WJ988okkafPmzZoyZYoGDx6svn37auHChcrPz%2BcsFgAAaLagDViffPKJBgwYoLy8PK9xl8ula6%2B9Vm3btvWMpaSkqKioyDOfmprqmYuIiFCfPn1UVFSkhoYGffbZZ17zSUlJOnv2rPbv33%2BJVwQAAFoLp78LuFgTJ04877jb7VZMTIzXWKdOnVRWVva98ydOnFBtba3XvNPpVFRUlGd7AACA7xO0AetCqqurFRYW5jUWFhamurq6752vqanxPL7Q9s1RXl4ut9vtNeZ0tm0S7FoqNDSufKWIAAAUCElEQVS4TkA6ncFV78U615dg609rR18CE30JTPSl5VpdwAoPD1dVVZXXWF1dndq0aeOZ/3ZYqqurU2RkpMLDwz2Pvz0fERHR7Bry8vKUk5PjNZaRkaHMzMxm76M16tChnb9L8KnIyOa/Z%2BA79CUw0ZfARF8uXqsLWLGxsTp48KDXWEVFhefsUWxsrCoqKprM9%2B7dW1FRUQoPD1dFRYV69uwpSaqvr1dVVZWio6ObXcP48eM1ZMgQrzGns60qK09fzJIuKNh%2BsrC9/kAVGhqiyMgInThRrYaGRn%2BXg3%2BiL4GJvgSm1tQXf/1w3%2BoCVmJionJzc1VTU%2BM5a1VYWKiUlBTPfGFhoef51dXV2rdvn2bOnKmQkBAlJCSosLBQAwYMkCQVFRXJ6XQqPj6%2B2TXExMQ0%2BTjQ7T6p%2BvrgfpO21OW2/oaGxstuzcGAvgQm%2BhKY6MvFC65TIM3Qv39/denSRXPmzFFxcbFyc3O1Z88ejRs3TpI0duxY7d69W7m5uSouLtacOXPUrVs3T6CaOHGiNmzYoG3btmnPnj1asGCB7rrrrh/0ESEAALi8tbqAFRoaqrVr18rtdmvMmDF67bXXtGbNGsXFxUmSunXrpieeeEL5%2BfkaN26cqqqqtGbNGjkcDknSiBEjNGPGDD3yyCOaNm2a%2Bvbtq9mzZ/tzSQAAIMg4zLlbmOOScrtPWt%2Bn0xmiocs/tL7fS%2BXN%2Bwf5uwSfcDpD1KFDO1VWnubUegChL4GJvgSm1tSX6Ogr/PK6re4MFgAAgL8RsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgWasNWO%2B8846uueYar6/MzExJ0r59%2B3TnnXcqMTFRY8eO1d69e722LSgo0C233KLExERlZGTo%2BPHj/lgCAAAIUq02YB08eFCDBw/W9u3bPV9LlizRmTNndO%2B99yo1NVV/%2BtOflJycrBkzZujMmTOSpD179mjevHmaOXOm8vLydOLECc2ZM8fPqwEAAMGk1QaskpIS9erVS9HR0Z6vyMhIvfHGGwoPD9dDDz2knj17at68eWrXrp3eeustSdKWLVt02223afTo0YqPj9eyZcv0/vvvq7S01M8rAgAAwaJVB6zu3bs3GXe5XEpJSZHD4ZAkORwO9evXT0VFRZ751NRUz/O7dOmiuLg4uVwun9QNAACCX6sMWMYY/fWvf9X27dt166236pZbbtHy5ctVV1cnt9utmJgYr%2Bd36tRJZWVlkqTy8vLvnAcAAPg%2BTn8XcCkcOXJE1dXVCgsL08qVK/XVV19pyZIlqqmp8Yx/U1hYmOrq6iRJNTU13znfHOXl5XK73V5jTmfbJsGtpUJDgysfO53BVe/FOteXYOtPa0dfAhN9CUz0peVaZcDq2rWrPv74Y1155ZVyOBzq3bu3GhsbNXv2bPXv379JWKqrq1ObNm0kSeHh4eedj4iIaPbr5%2BXlKScnx2ssIyPD878YL1cdOrTzdwk%2BFRnZ/PcMfIe%2BBCb6Epjoy8VrlQFLkqKiorwe9%2BzZU7W1tYqOjlZFRYXXXEVFhefsUmxs7Hnno6Ojm/3a48eP15AhQ7zGnM62qqw8/UOW8L2C7ScL2%2BsPVKGhIYqMjNCJE9VqaGj0dzn4J/oSmOhLYGpNffHXD/etMmB9%2BOGHevDBB/Xee%2B95zjx9/vnnioqKUkpKip5%2B%2BmkZY%2BRwOGSM0e7du/XrX/9akpSYmKjCwkKNGTNGknT06FEdPXpUiYmJzX79mJiYJh8Hut0nVV8f3G/Slrrc1t/Q0HjZrTkY0JfARF8CE325eMF1CqSZkpOTFR4ert///vc6dOiQ3n//fS1btky//OUvNWzYMJ04cUJLly7VwYMHtXTpUlVXV%2Bu2226TJE2YMEGvvvqqXnrpJe3fv18PPfSQbr75Zl111VV%2BXhUAAAgWrTJgtW/fXhs2bNDx48c1duxYzZs3T%2BPHj9cvf/lLtW/fXuvWrfOcpXK5XMrNzVXbtm0l/SOcLVq0SGvWrNGECRN05ZVXKisry88rAgAAwcRhjDH%2BLuJy4HaftL5PpzNEQ5d/aH2/l8qb9w/ydwk%2B4XSGqEOHdqqsPM2p9QBCXwITfQlMrakv0dFX%2BOV1W%2BUZLAAAAH8iYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQSs86itrdXcuXOVmpqqtLQ0PfPMM/4uCQAABBGnvwsIRMuWLdPevXu1adMmHTlyRA8//LDi4uI0bNgwf5cGAACCAAHrW86cOaOXXnpJTz/9tPr06aM%2BffqouLhYzz//PAELAAA0Cx8Rfsv%2B/ftVX1%2Bv5ORkz1hKSopcLpcaGxv9WBkAAAgWnMH6FrfbrQ4dOigsLMwz1rlzZ9XW1qqqqkodO3b83n2Ul5fL7XZ7jTmdbRUTE2O11tDQ4MrHTmdw1XuxzvUl2PrT2tGXwERfAhN9aTkC1rdUV1d7hStJnsd1dXXN2kdeXp5ycnK8xmbOnKnf/OY3dor8p/Lyck35cbHGjx9vPbzh4pWXl2vTpvX0JcDQl8BEXwITfWk5oum3hIeHNwlS5x63adOmWfsYP368/vSnP3l9jR8/3nqtbrdbOTk5Tc6Wwb/oS2CiL4GJvgQm%2BtJynMH6ltjYWFVWVqq%2Bvl5O5z%2B%2BPW63W23atFFkZGSz9hETE0PiBwDgMsYZrG/p3bu3nE6nioqKPGOFhYVKSEhQSAjfLgAA8P1IDN8SERGh0aNHa8GCBdqzZ4%2B2bdumZ555RpMnT/Z3aQAAIEiELliwYIG/iwg0AwcO1L59%2B7RixQp99NFH%2BvWvf62xY8f6u6zzateunfr376927dr5uxR8A30JTPQlMNGXwERfWsZhjDH%2BLgIAAKA14SNCAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUErCBVW1uruXPnKjU1VWlpaXrmmWf8XVKr9M477%2Biaa67x%2BsrMzJQk7du3T3feeacSExM1duxY7d2712vbgoIC3XLLLUpMTFRGRoaOHz/umTPGaPny5Ro4cKD69%2B%2BvZcuWqbGx0adrC0Z1dXUaOXKkPv74Y89YaWmppk6dqqSkJA0fPlzbt2/32mbHjh0aOXKkEhMTNXnyZJWWlnrNb9y4Uenp6UpOTtbcuXNVXV3tmeM4a57z9WXJkiVNjp0tW7Z45ltyfFRWVuo3v/mNkpOTNWTIEL366qu%2BWWiQOHbsmDIzM9W/f3%2Blp6crKytLtbW1kjhefMogKC1atMjcfvvtZu/evea//uu/THJysnnzzTf9XVars3btWjNjxgxTXl7u%2Bfr666/N6dOnzaBBg8yjjz5qDh48aBYvXmz%2B9V//1Zw%2BfdoYY4zL5TJ9%2B/Y1L7/8svn888/NpEmTzL333uvZ74YNG8xNN91kdu3aZT766COTlpZm1q9f769lBoWamhqTkZFhevXqZXbu3GmMMaaxsdHcfvvtZtasWebgwYPmqaeeMomJiebw4cPGGGMOHz5skpKSzIYNG8yBAwfMf/zHf5iRI0eaxsZGY4wxb731lklJSTH//d//bVwulxk%2BfLhZuHCh5zU5zr7f%2BfpijDFTp04169at8zp2zpw5Y4xp%2BfExY8YMM2XKFPOXv/zF/PGPfzTXXXedcblcvlt0AGtsbDR33XWX%2BeUvf2kOHDhgdu3aZYYOHWoeffRRjhcfI2AFodOnT5uEhASvv8zWrFljJk2a5MeqWqdZs2aZFStWNBl/6aWXzJAhQzx/8TQ2NpqhQ4ea/Px8Y4wxs2fPNg8//LDn%2BUeOHDHXXHON%2Bdvf/maMMeamm27yPNcYY1555RUzePDgS7mUoFZcXGx%2B/vOfm9tvv93rH/IdO3aYpKQkT7A1xpgpU6aY1atXG2OMWblypddxcebMGZOcnOzZfuLEiZ7nGmPMrl27TN%2B%2Bfc2ZM2c4zprhQn0xxpj09HTz4Ycfnne7lhwfX375penVq5cpLS31zM%2BdO9drf5ezgwcPml69ehm32%2B0Ze/31101aWhrHi4/xEWEQ2r9/v%2Brr65WcnOwZS0lJkcvl4mMmy0pKStS9e/cm4y6XSykpKXI4HJIkh8Ohfv36qaioyDOfmprqeX6XLl0UFxcnl8ulY8eO6ejRo7r%2B%2Bus98ykpKTp8%2BLDKy8sv7YKC1CeffKIBAwYoLy/Pa9zlcunaa69V27ZtPWMpKSkX7ENERIT69OmjoqIiNTQ06LPPPvOaT0pK0tmzZ7V//36Os2a4UF9OnTqlY8eOnffYkVp2fLhcLnXp0kXdunXzmv/000/tLi5IRUdHa/369ercubPX%2BKlTpzhefMzp7wLww7ndbnXo0EFhYWGesc6dO6u2tlZVVVXq2LGjH6trPYwx%2Butf/6rt27dr3bp1amho0LBhw5SZmSm3262f/vSnXs/v1KmTiouLJUnl5eWKiYlpMl9WVia32y1JXvPn/jIsKytrsh2kiRMnnnfc7XZf8Pv8ffMnTpxQbW2t17zT6VRUVJTKysoUEhLCcfY9LtSXkpISORwOPfXUU/rggw8UFRWle%2B65R3fccYeklh0fF%2BrpsWPHrK0rmEVGRio9Pd3zuLGxUVu2bNHAgQM5XnyMgBWEqqurvd7EkjyP6%2Brq/FFSq3TkyBHP93rlypX66quvtGTJEtXU1FywB%2Be%2B/zU1NRecr6mp8Tz%2B5pxE/36o7%2BvDd82frw/fnDfGcJxdpEOHDsnhcKhHjx6aNGmSdu3apfnz56t9%2B/YaOnRoi46P7%2Bs5vGVnZ2vfvn3aunWrNm7cyPHiQwSsIBQeHt7kDXvucZs2bfxRUqvUtWtXffzxx7ryyivlcDjUu3dvNTY2avbs2erfv/95e3Du%2B3%2BhHkVERHj9pRMeHu75s/SPU/JovvDwcFVVVXmNNacPkZGRTb7335yPiIhQQ0MDx9lFGj16tAYPHqyoqChJUnx8vL744gu98MILGjp0aIuOjwttS0%2Bays7O1qZNm/T444%2BrV69eHC8%2BxjVYQSg2NlaVlZWqr6/3jLndbrVp00aRkZF%2BrKz1iYqK8lxnJUk9e/ZUbW2toqOjVVFR4fXciooKz%2Bnz2NjY885HR0crNjZWkjwfhXzzz9HR0ZdkHa3Vhb7PzelDVFSUwsPDvebr6%2BtVVVXl6RPH2cVxOByecHVOjx49PB/jteT4%2BK5t8f8tXrxYzz77rLKzs3XrrbdK4njxNQJWEOrdu7ecTqfnwkRJKiwsVEJCgkJCaKktH374oQYMGOB1n5fPP/9cUVFRnotqjTGS/nG91u7du5WYmChJSkxMVGFhoWe7o0eP6ujRo0pMTFRsbKzi4uK85gsLCxUXF8f1Vz9QYmKi/vznP3s%2BvpD%2B8b28UB%2Bqq6u1b98%2BJSYmKiQkRAkJCV7zRUVFcjqdio%2BP5zhrgVWrVmnq1KleY/v371ePHj0ktez4SEpK0uHDhz3XDZ2bT0pKurSLCiI5OTl68cUX9dhjj2nEiBGecY4XH/Pr/2HERZs/f74ZMWKEcblc5p133jH9%2BvUzb7/9tr/LalVOnjxp0tPTzQMPPGBKSkrMe%2B%2B9Z9LS0kxubq45efKkGThwoFm8eLEpLi42ixcvNoMGDfL89%2Bfdu3ebPn36mD/%2B8Y%2Be%2B/zMmDHDs%2B9169aZtLQ0s3PnTrNz506TlpZmnnnmGX8tNah883YA9fX1Zvjw4eb%2B%2B%2B83Bw4cMOvWrTNJSUme%2B/qUlpaahIQEs27dOs99fW6//XbP7TUKCgpMv379zDvvvGNcLpcZMWKEWbx4see1OM6a75t9cblc5tprrzXr1683X375pXn%2B%2BefNddddZ3bv3m2MafnxMW3aNDNp0iTz%2Beefmz/%2B8Y8mISGB%2B2D908GDB03v3r3N448/7nUPsvLyco4XHyNgBakzZ86Yhx56yCQlJZm0tDTz7LPP%2BrukVunAgQNm6tSpJikpyQwaNMg88cQTnr9sXC6XGT16tElISDDjxo0zf/7zn722zc/PNzfddJNJSkoyGRkZ5vjx4565%2Bvp684c//MGkpqaaAQMGmOzsbM9%2B8d2%2Bfb%2BlL774wvziF78w1113nRkxYoT53//9X6/nv/fee%2BZnP/uZ6du3r5kyZYrnXkvnrFu3ztxwww0mJSXFzJkzx9TU1HjmOM6a79t9eeedd8ztt99uEhISzLBhw5r8Q9uS46OiosLMmDHDJCQkmCFDhpjXX3/90i8wSKxbt8706tXrvF/GcLz4ksOYf37GAQAAACsu0w9GAQAALh0CFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAs%2B3%2Bv6O1YOKhW6AAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1343491515740730316”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999970197678</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.00000002980232</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999940395355</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.00000002980232</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.999999985098839</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1818)</td> <td class=”number”>1829</td> <td class=”number”>57.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1343491515740730316”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.3930619692e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0003300452954136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005951898056082</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006521593895741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2727.39028008784</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3573.3913626684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4297.90149323097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8272.19155922628</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21387.0630610889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_POP10”>LACCESS_POP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>20192</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>886070</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5870594304503869633”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5870594304503869633,#minihistogram-5870594304503869633”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5870594304503869633”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5870594304503869633”

aria-controls=”quantiles-5870594304503869633” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5870594304503869633” aria-controls=”histogram-5870594304503869633”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5870594304503869633” aria-controls=”common-5870594304503869633”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5870594304503869633” aria-controls=”extreme-5870594304503869633”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5870594304503869633”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>252.7</td>

</tr> <tr>

<th>Q1</th> <td>1668.9</td>

</tr> <tr>

<th>Median</th> <td>4100.8</td>

</tr> <tr>

<th>Q3</th> <td>12962</td>

</tr> <tr>

<th>95-th percentile</th> <td>101570</td>

</tr> <tr>

<th>Maximum</th> <td>886070</td>

</tr> <tr>

<th>Range</th> <td>886070</td>

</tr> <tr>

<th>Interquartile range</th> <td>11293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>51387</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5449</td>

</tr> <tr>

<th>Kurtosis</th> <td>55.718</td>

</tr> <tr>

<th>Mean</th> <td>20192</td>

</tr> <tr>

<th>MAD</th> <td>25315</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.1021</td>

</tr> <tr>

<th>Sum</th> <td>63241000</td>

</tr> <tr>

<th>Variance</th> <td>2640600000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5870594304503869633”>

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tWbNm6fTp05Kk06dPKy0tTWPHjtX27dvVvn17PfDAAzLGSJJee%2B01ZWdna%2BnSpdq8ebNcLpeysrJ8ubsAACDABGTAOnHihAoKCpSZmakbbrhBSUlJSk9PV15envbv36%2BioiItXbpUPXr00MyZMxUfH68dO3ZIkl588UXdeOONmjZtmm644QZlZmbq1KlTevfddyVJW7Zs0ZQpU5SSkqJ%2B/fppyZIl2rFjB0exAABAkwVkwIqKitKGDRvUsWNHj/Fz587J5XKpT58%2Bat26tTWemJiogoICSZLL5VJSUpI1Fx4err59%2B6qgoED19fX64IMPPObj4%2BN18eJFHT16tJn3CgAAtBQBGbAiIiI0ZMgQ63FDQ4O2bt2qgQMHyu12Kzo62uP5HTp0UElJiSR943xlZaVqamo85h0OhyIjI63lAQAArsTh6wLskJWVpSNHjmj79u3atGmTQkNDPeZDQ0NVW1srSaqqqvra%2Berqauvx1y3fFKWlpXK73R5jDkfrRsHuuwoJCax87HAEVr1NdakPgdaPlohe%2BAf64D/ohe8EfMDKysrS5s2b9cQTT6hnz54KCwtTRUWFx3Nqa2vVqlUrSVJYWFijsFRbW6uIiAiFhYVZj786Hx4e3uSacnNzlZ2d7TGWlpam9PT0Jq%2BjJWrXro2vS2hWERFN/xtB86IX/oE%2B%2BA964X0BHbAyMjK0bds2ZWVl6Y477pAkxcTE6NixYx7PKysrs44excTEqKysrNF87969FRkZqbCwMJWVlalHjx6SpLq6OlVUVCgqKqrJdU2YMEGpqakeYw5Ha5WXn7/qffwmgfaOxO799xchIcGKiAhXZWWV6usbfF3ONY1e%2BAf64D/ohe/e3AdswMrOztYLL7ygxx9/XMOHD7fGnU6ncnJyVF1dbR21ys/PV2JiojWfn59vPb%2BqqkpHjhzRrFmzFBwcrLi4OOXn52vAgAGSpIKCAjkcDvXq1avJtUVHRzc6Heh2f666umvzj/uSlr7/9fUNLX4fAwW98A/0wX/QC%2B8LrEMg/3L8%2BHE99dRT%2Bs///E8lJibK7XZbP8nJyerUqZPmzZunwsJC5eTk6NChQxo/frwkady4cTp48KBycnJUWFioefPmqUuXLlagmjhxojZu3Kjdu3fr0KFDWrx4se69996rOkUIAACubQF5BOuNN95QfX291q1bp3Xr1nnMffTRR3rqqae0YMECjR07Vtddd53Wrl2r2NhYSVKXLl30m9/8Ro8%2B%2BqjWrl2rhIQErV27VkFBQZKkkSNH6tSpU1q0aJFqa2t1%2B%2B23a86cOV7fRwAAELiCzKVbmKNZud2f275OhyNYw1a%2BZft6m8urDw7ydQnNwuEIVrt2bVRefp5D8D5GL/wDffAf9EKKivq%2BT7YbkKcIAQAA/BkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbeT1g3XPPPXrhhRf0%2Bef23xcKAADAH3g9YA0cOFBPP/20Bg8erF/%2B8pfau3evuNcpAABoSbwesGbPnq2//OUveuqppxQSEqJf/OIXuu222/TEE0/ok08%2B8XY5AAAAtvPJdxEGBQVp0KBBGjRokKqqqvTcc8/pqaeeUk5Ojvr3768pU6bo9ttv90VpAAAA35nPvuy5tLRUf/jDH/SHP/xBH3/8sfr376%2B7775bJSUl%2BtWvfqUDBw5owYIFvioPAADgW/N6wHr55Zf18ssv65133lH79u01ZswYrVmzRt26dbOe06lTJy1fvpyABQAAApLXA9aCBQuUkpKitWvX6pZbblFwcOPLwLp3765JkyZ5uzQAAABbeD1gvfnmm2rXrp0qKiqscHXo0CH17dtXISEhkqT%2B/furf//%2B3i4NAADAFl7/FOG5c%2Bc0fPhw/fa3v7XGZsyYodGjR6u4uNjb5QAAANjO6wHr0Ucf1XXXXaf777/fGnvllVfUqVMnZWZmerscAAAA23k9YL333nt65JFHFBUVZY21b99eDz/8sPbv3%2B/tcgAAAGzn9YDlcDhUWVnZaLyqqoo7ugMAgBbB6wHrlltu0bJly/T3v//dGisqKlJmZqaGDBni7XIAAABs5/VPEc6dO1f333%2B/7rjjDkVEREiSKisr1bdvX82bN8/b5QAAANjO6wGrQ4cOeumll7Rv3z4VFhbK4XDo%2Buuv180336ygoCBvlwMAAGA7n3xVTkhIiIYMGcIpQQAA0CJ5PWC53W49%2BeSTOnjwoC5evNjowvY33njD2yUBAADYyusBa%2BHChTp8%2BLBGjhyp73//%2B97ePAAAQLPzesDav3%2B/NmzYoKSkJG9vGgAAwCu8fpuG1q1bq0OHDt7eLAAAgNd4PWCNHj1aGzZsUH19vbc3DQAA4BVeP0VYUVGhvLw8/fWvf1XXrl0VGhrqMb9lyxZvlwQAAGArn9ymYdSoUb7YLAAAgFd4PWBlZmZ6e5MAAABe5fVrsCSptLRU2dnZmj17tv7xj3/oj3/8o06cOOGLUgAAAGzn9YB18uRJ3XXXXXrppZf02muv6cKFC3rllVc0btw4uVwub5cDAABgO68HrMcee0xDhw7V7t279b3vfU%2BS9Pjjjys1NVUrV670djkAAAC283rAOnjwoO6//36PL3Z2OBx64IEHdOTIEW%2BXAwAAYDuvB6yGhgY1NDQ0Gj9//rxCQkK8XQ4AAIDtvB6wBg8erPXr13uErIqKCmVlZWngwIHeLgcAAMB2Xg9YjzzyiA4fPqzBgwerpqZG//Vf/6WUlBR99tlnmjt3rrfLAQAAsJ3X74MVExOjnTt3Ki8vTx9%2B%2BKEaGhp03333afTo0Wrbtq23ywEAALCdT%2B7kHh4ernvuuccXmwYAAGh2Xg9YkydP/sb5q/0uwtraWo0dO1YLFy7UgAEDJEnLli3Tc8895/G8hQsXatKkSZKkvLw8Pfnkk3K73Ro8eLAyMjLUvn17SZIxRqtWrdL27dvV0NCg8ePH66GHHlJwsE/uyQoAAAKQ1wNW586dPR7X1dXp5MmT%2BvjjjzVlypSrWldNTY1mz56twsJCj/Hjx49r9uzZuvvuu62xS6cfDx06pAULFmjJkiXq1auXli9frnnz5mn9%2BvWSpGeffVZ5eXnKzs5WXV2d5syZow4dOmj69OnfZncBAMA1yG%2B%2Bi3Dt2rUqKSlp8nqOHTum2bNnyxjTaO748eOaPn26oqKiGs1t3bpVd955p8aMGSNJWrFihVJSUlRUVKSuXbtqy5YtSk9PV1JSkiTpoYce0urVqwlYAACgyfzmvNfo0aP16quvNvn57777rgYMGKDc3FyP8XPnzunMmTPq1q3bZZdzuVxWeJKkTp06KTY2Vi6XS2fOnFFxcbFuuukmaz4xMVGnTp1SaWnp1e0QAAC4ZvnkIvfLef/996/qRqMTJ0687Pjx48cVFBSkp59%2BWm%2B%2B%2BaYiIyN1//33W6cLS0tLFR0d7bFMhw4dVFJSIrfbLUke8x07dpQklZSUNFru65SWllrrusThaN3k5ZsqJMRv8nGTOByBVW9TXepDoPWjJaIX/oE%2B%2BA964Tt%2BcZH7uXPn9NFHH31taLoaJ06cUFBQkLp3765JkybpwIEDWrhwodq2bathw4apurpaoaGhHsuEhoaqtrZW1dXV1uMvz0lfXEzfVLm5ucrOzvYYS0tLU3p6%2BrfdrRahXbs2vi6hWUVEhPu6BPwLvfAP9MF/0Avv83rAio2N9fgeQkn63ve%2Bp0mTJuk//uM/vvP6x4wZo5SUFEVGRkqSevXqpU8//VTbtm3TsGHDFBYW1igs1dbWKjw83CNMhYWFWb9LX9xaoqkmTJig1NRUjzGHo7XKy89/6/26nEB7R2L3/vuLkJBgRUSEq7KySvX1jb8GCt5DL/wDffAf9MJ3b%2B69HrAee%2ByxZl1/UFCQFa4u6d69u/bv3y/pixudlpWVecyXlZUpKipKMTExkiS3260uXbpYv0u67AXzXyc6OrrR6UC3%2B3PV1V2bf9yXtPT9r69vaPH7GCjohX%2BgD/6DXnif1wPWgQMHmvzcL19s3lSrV6/W%2B%2B%2B/r02bNlljR48eVffu3SVJTqdT%2Bfn5Gjt2rCSpuLhYxcXFcjqdiomJUWxsrPLz862AlZ%2Bfr9jYWNuvnwIAAC2X1wPWT3/6U%2BsU4ZdvsfDVsaCgIH344YdXvf6UlBTl5ORo48aNGjZsmPbu3audO3daNzC977779NOf/lTx8fGKi4vT8uXLddttt6lr167W/MqVK/XDH/5QkrRq1SpNmzbt2%2B8wAAC45ng9YD399NNatmyZ5syZo%2BTkZIWGhuqDDz7Q0qVLdffdd2vEiBHfaf39%2BvXT6tWrtWbNGq1evVqdO3fWqlWrlJCQIElKSEjQ0qVLtWbNGv3zn//UoEGDlJGRYS0/ffp0/eMf/9CsWbMUEhKi8ePHa%2BrUqd%2BpJgAAcG0JMpe7U2czuuOOO7RgwQLdcsstHuPvvfeeHn74Yf35z3/2Zjle43Z/bvs6HY5gDVv5lu3rbS6vPjjI1yU0C4cjWO3atVF5%2BXmucfAxeuEf6IP/oBdSVNT3fbJdr38MrbS0tNHX5UhffJVNeXm5t8sBAACwndcDVnx8vB5//HGdO3fOGquoqFBWVpZuvvlmb5cDAABgO69fg/WrX/1KkydP1i233KJu3brJGKNPP/1UUVFR1oXoAAAAgczrAatHjx565ZVXlJeXp%2BPHj0uSfvKTn2jkyJFXdTNPAAAAf%2BWT7yL8wQ9%2BoHvuuUefffaZdXuE733ve74oBQAAwHZevwbLGKOVK1fqpptu0qhRo1RSUqK5c%2BdqwYIFunjxorfLAQAAsJ3XA9Zzzz2nl19%2BWb/%2B9a%2Bt7/4bOnSodu/e3egLkgEAAAKR1wNWbm6uFi1apLFjx1p3bx8xYoSWLVumXbt2ebscAAAA23k9YH322Wfq3bt3o/FevXpZX6wMAAAQyLwesDp37qwPPvig0fibb75pXfAOAAAQyLz%2BKcLp06dryZIlcrvdMsbo7bffVm5urp577jk98sgj3i4HAADAdl4PWOPGjVNdXZ3WrVun6upqLVq0SO3bt9eDDz6o%2B%2B67z9vlAAAA2M7rASsvL0/Dhw/XhAkTdPbsWRlj1KFDB2%2BXAQAA0Gy8fg3W0qVLrYvZ27dvT7gCAAAtjtcDVrdu3fTxxx97e7MAAABe4/VThL169dJDDz2kDRs2qFu3bgoLC/OYz8zM9HZJAAAAtvJ6wPrkk0%2BUmJgoSdz3CgAAtEheCVgrVqzQrFmz1Lp1az333HPe2CQAAIDPeOUarGeffVZVVVUeYzNmzFBpaak3Ng8AAOBVXglYxphGYwcOHFBNTY03Ng8AAOBVXv8UIQAAQEtHwAIAALCZ1wJWUFCQtzYFAADgU167TcOyZcs87nl18eJFZWVlqU2bNh7P4z5YAAAg0HklYN10002N7nmVkJCg8vJylZeXe6MEAAAAr/FKwOLeVwAA4FrCRe4AAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2C/iAVVtbq1GjRumdd96xxoqKijR16lTFx8drxIgR2rt3r8cy%2B/bt06hRo%2BR0OjV58mQVFRV5zG/atElDhgxRQkKC5s%2Bfr6qqKq/sCwAAaBkCOmDV1NTol7/8pQoLC60xY4zS0tLUsWNH7dixQ6NHj9asWbN0%2BvRpSdLp06eVlpamsWPHavv27Wrfvr0eeOABGWMkSa%2B99pqys7O1dOlSbd68WS6XS1lZWT7ZPwAAEJgCNmAdO3ZM9957r/7%2B9797jO/fv19FRUVaunSpevTooZkzZyo%2BPl47duyQJL344ou68cYbNW3aNN1www3KzMzUqVOn9O6770qStmzZoilTpiglJUX9%2BvXTkiVLtGPHDo5iAQCAJgvYgPXuu%2B9qwIABys3N9Rh3uVzq06ePWrdubY0lJiaqoKDAmk9KSrLmwsPD1bdvXxUUFKi%2Bvl4ffPCBx3x8fLwuXryoo0ePNvMeAQCAlsLh6wK%2BrYkTJ1523O12Kzo62mOsQ4cOKikpueJ8ZWWlampqPOYdDociIyOt5QEAAK4kYAPW16mqqlJoaKjHWGhoqGpra684X11dbT3%2BuuWborS0VG6322PM4WjdKNh9VyEhgXUA0uEIrHqb6lIfAq0fLRG98A/0wX/QC99pcQErLCxMFRUVHmO1tbVq1aqVNf/VsFRbW6uIiAiFhYVZj786Hx4e3uQacnM95l1FAAAUnElEQVRzlZ2d7TGWlpam9PT0Jq%2BjJWrXro2vS2hWERFN/xtB86IX/oE%2B%2BA964X0tLmDFxMTo2LFjHmNlZWXW0aOYmBiVlZU1mu/du7ciIyMVFhamsrIy9ejRQ5JUV1eniooKRUVFNbmGCRMmKDU11WPM4Wit8vLz32aXvlagvSOxe//9RUhIsCIiwlVZWaX6%2BgZfl3NNoxf%2BgT74D3rhuzf3LS5gOZ1O5eTkqLq62jpqlZ%2Bfr8TERGs%2BPz/fen5VVZWOHDmiWbNmKTg4WHFxccrPz9eAAQMkSQUFBXI4HOrVq1eTa4iOjm50OtDt/lx1ddfmH/clLX3/6%2BsbWvw%2BBgp64R/og/%2BgF94XWIdAmiA5OVmdOnXSvHnzVFhYqJycHB06dEjjx4%2BXJI0bN04HDx5UTk6OCgsLNW/ePHXp0sUKVBMnTtTGjRu1e/duHTp0SIsXL9a99957VacIAQDAta3FBayQkBA99dRTcrvdGjt2rP7whz9o7dq1io2NlSR16dJFv/nNb7Rjxw6NHz9eFRUVWrt2rYKCgiRJI0eO1MyZM7Vo0SJNmzZN/fr105w5c3y5SwAAIMAEmUu3MEezcrs/t32dDkewhq18y/b1NpdXHxzk6xKahcMRrHbt2qi8/DyH4H2MXvgH%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%2BePRUVFWX9RERE6JVXXlFYWJgefvhh9ejRQwsWLFCbNm30xz/%2BUZK0detW3XnnnRozZox69eqlFStWaM%2BePSoqKvLxHgEAgEDRogNWt27dGo27XC4lJiYqKChIkhQUFKT%2B/furoKDAmk9KSrKe36lTJ8XGxsrlcnmlbgAAEPgcvi6gORhj9Mknn2jv3r1av3696uvrNXz4cKWnp8vtduv666/3eH6HDh1UWFgoSSotLVV0dHSj%2BZKSkiZvv7S0VG6322PM4WjdaL3fVUhIYOVjhyOw6m2qS30ItH60RPTCP9AH/0EvfKdFBqzTp0%2BrqqpKoaGhevLJJ/XZZ59p2bJlqq6utsa/LDQ0VLW1tZKk6urqb5xvitzcXGVnZ3uMpaWlWRfZX6vatWvj6xKaVUREuK9LwL/QC/9AH/wHvfC%2BFhmwOnfurHfeeUc/%2BMEPFBQUpN69e6uhoUFz5sxRcnJyo7BUW1urVq1aSZLCwsIuOx8e3vQ/zgkTJig1NdVjzOForfLy899yjy4v0N6R2L3//iIkJFgREeGqrKxSfX2Dr8u5ptEL/0Af/Ae98N2b%2BxYZsCQpMjLS43GPHj1UU1OjqKgolZWVecyVlZVZp%2B9iYmIuOx8VFdXkbUdHRzc6Heh2f666umvzj/uSlr7/9fUNLX4fAwW98A/0wX/QC%2B8LrEMgTfTWW29pwIABqqqqssY%2B/PBDRUZGKjExUe%2B//76MMZK%2BuF7r4MGDcjqdkiSn06n8/HxrueLiYhUXF1vzAAAAV9IiA1ZCQoLCwsL0q1/9SidOnNCePXu0YsUK/exnP9Pw4cNVWVmp5cuX69ixY1q%2BfLmqqqp05513SpLuu%2B8%2Bvfzyy3rxxRd19OhRPfzww7rtttvUtWtXH%2B8VAAAIFC0yYLVt21YbN27U2bNnNW7cOC1YsEATJkzQz372M7Vt21br169Xfn6%2Bxo4dK5fLpZycHLVu3VrSF%2BFs6dKlWrt2re677z794Ac/UGZmpo/3CAAABJIgc%2BlcGZqV2/257et0OII1bOVbtq%2B3ubz64CBfl9AsHI5gtWvXRuXl57nGwcfohX%2BgD/6DXkhRUd/3yXZb5BEsAAAAXyJgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYzOHrAnDtuPPJ//N1CVfl1QcH%2BboEAECA4ggWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM34FOFl1NTUaMmSJfrTn/6kVq1aadq0aZo2bZqvy4KX8alHAMC3RcC6jBUrVujw4cPavHmzTp8%2Brblz5yo2NlbDhw/3dWkAACAAELC%2B4sKFC3rxxRf129/%2BVn379lXfvn1VWFio559/noAFAACahGuwvuLo0aOqq6tTQkKCNZaYmCiXy6WGhgYfVgYAAAIFR7C%2Bwu12q127dgoNDbXGOnbsqJqaGlVUVKh9%2B/ZXXEdpaancbrfHmMPRWtHR0bbWGhJCPsb/F0jXjL3%2B0JBmWe%2Bl/ybs/m9j2Mq3bF1fc2uu17epmqsPuHr0wncIWF9RVVXlEa4kWY9ra2ubtI7c3FxlZ2d7jM2aNUu/%2BMUv7CnyX0pLSzXlh4WaMGGC7eENTVdaWqrc3Fz64AdKS0u1efMG23vx3nIuD7gazdUHXD164TtE2q8ICwtrFKQuPW7VqlWT1jFhwgT9/ve/9/iZMGGC7bW63W5lZ2c3OloG76IP/oNe%2BAf64D/ohe9wBOsrYmJiVF5errq6OjkcX7w8brdbrVq1UkRERJPWER0dzTsFAACuYRzB%2BorevXvL4XCooKDAGsvPz1dcXJyCg3m5AADAlZEYviI8PFxjxozR4sWLdejQIe3evVvPPPOMJk%2Be7OvSAABAgAhZvHjxYl8X4W8GDhyoI0eOaNWqVXr77bf185//XOPGjfN1WZfVpk0bJScnq02bNr4u5ZpGH/wHvfAP9MF/0AvfCDLGGF8XAQAA0JJwihAAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBK0DV1NRo/vz5SkpK0uDBg/XMM8/4uqSAcObMGaWnpys5OVlDhgxRZmamampqJElFRUWaOnWq4uPjNWLECO3du9dj2X379mnUqFFyOp2aPHmyioqKPOY3bdqkIUOGKCEhQfPnz1dVVZU1d6V%2BXWnbLd2MGTP0yCOPWI%2BPHDmie%2B65R06nU%2BPGjdPhw4c9np%2BXl6ehQ4fK6XQqLS1NZ8%2BeteaMMVq5cqUGDhyo5ORkrVixQg0NDdZ8eXm5fvGLXyghIUGpqal6%2BeWXPdZ9pW23RLW1tVqyZIluuukm/fjHP9bjjz%2BuS/egphfeU1xcrJkzZ6p///5KTU3Vpk2brDn6EIAMAtLSpUvNXXfdZQ4fPmz%2B9Kc/mYSEBPPqq6/6uiy/1tDQYO69917zs5/9zHz88cfmwIEDZtiwYeaxxx4zDQ0N5q677jKzZ882x44dM08//bRxOp3m1KlTxhhjTp06ZeLj483GjRvNxx9/bP77v//bjBo1yjQ0NBhjjPnjH/9oEhMTzZ///GfjcrnMiBEjzJIlS6xtf1O/rrTtli4vL8/07NnTzJ071xhjzPnz582gQYPMY489Zo4dO2YyMjLMj3/8Y3P%2B/HljjDEul8v069fPvPTSS%2BbDDz80kyZNMjNmzLDWt3HjRnPrrbeaAwcOmLffftsMHjzYbNiwwZqfOXOmmTJlivnoo4/M7373O3PjjTcal8vVpG23VAsXLjS33367cblcZt%2B%2BfWbAgAFm27Zt9MLL7r33XvPggw%2BaTz75xLz%2B%2BuvG6XSaP/3pT/QhQBGwAtD58%2BdNXFyc2b9/vzW2du1aM2nSJB9W5f%2BOHTtmevbsadxutzW2a9cuM3jwYLNv3z4THx/v8X8aU6ZMMWvWrDHGGPPkk096vL4XLlwwCQkJVg8mTpxoPdcYYw4cOGD69etnLly4cMV%2BXWnbLVl5ebm55ZZbzLhx46yA9eKLL5rU1FQrvDY0NJhhw4aZHTt2GGOMmTNnjvVcY4w5ffq0%2BdGPfmT%2B/ve/G2OMufXWW63nGmPMzp07TUpKijHGmJMnT5qePXuaoqIia37%2B/PlN3nZLVF5ebvr06WPeeecda2z9%2BvXmkUceoRdeVFFRYXr27Gk%2B%2Bugja2zWrFlmyZIl9CFAcYowAB09elR1dXVKSEiwxhITE%2BVyuTwO%2B8JTVFSUNmzYoI4dO3qMnzt3Ti6XS3369FHr1q2t8cTERBUUFEiSXC6XkpKSrLnw8HD17dtXBQUFqq%2Bv1wcffOAxHx8fr4sXL%2Bro0aNX7NeVtt2S/c///I9Gjx6t66%2B/3hpzuVxKTExUUFCQJCkoKEj9%2B/f/2l506tRJsbGxcrlcOnPmjIqLi3XTTTdZ84mJiTp16pRKS0vlcrnUqVMndenSxWP%2B/fffb9K2W6L8/Hy1bdtWycnJ1tiMGTOUmZlJL7yoVatWCg8P1%2B9//3tdvHhRJ06c0MGDB9W7d2/6EKAIWAHI7XarXbt2Cg0NtcY6duyompoaVVRU%2BLAy/xYREaEhQ4ZYjxsaGrR161YNHDhQbrdb0dHRHs/v0KGDSkpKJOkb5ysrK1VTU%2BMx73A4FBkZqZKSkiv260rbbqnefvttvffee3rggQc8xq/0epSWln7tvNvtliSP%2BUuB%2BtL85ZY9c%2BZMk7bdEhUVFalz587auXOnhg8frn//93/X2rVr1dDQQC%2B8KCwsTIsWLVJubq6cTqfuvPNO3XLLLbrnnnvoQ4By%2BLoAXL2qqiqPf6wlWY9ra2t9UVJAysrK0pEjR7R9%2B3Zt2rTpsq/ppdfz617z2tpaVVdXW48vN2%2BM%2BcZ%2BfdO6W6qamhr9%2Bte/1qJFi9SqVSuPuSu9HtXV1VfVi6t5ra/FXly4cEEnT57UCy%2B8oMzMTLndbi1atEjh4eH0wsuOHz%2BulJQU3X///SosLFRGRoZuvvlm%2BhCgCFgBKCwsrNEf96XHX/3HCpeXlZWlzZs364knnlDPnj0VFhbW6OhfbW2t9Xp%2B3WseERGhsLAw6/FX58PDw1VfX/%2BN/brStlui7Oxs3XjjjR5HFC/5utf6Sr0IDw/3%2BIfjq30JDw//1utuyb1wOBw6d%2B6cVq1apc6dO0uSTp8%2BrW3btum6666jF17y9ttva/v27dqzZ49atWqluLg4nTlzRuvWrVPXrl3pQwDiFGEAiomJUXl5uerq6qwxt9utVq1aKSIiwoeVBYaMjAw9%2B%2ByzysrK0h133CHpi9e0rKzM43llZWXWofGvm4%2BKilJkZKTCwsI85uvq6lRRUaGoqKgr9utK226J/vd//1e7d%2B9WQkKCEhIStGvXLu3atUsJCQnfqRcxMTGSZJ0W%2BfLvl%2Ba/btlvWndL7kVUVJTCwsKscCVJ//Zv/6bi4mJ64UWHDx/Wdddd5xFc%2BvTpo9OnT9OHAEXACkC9e/eWw%2BHwuMgwPz9fcXFxCg6mpd8kOztbL7zwgh5//HGNHDnSGnc6nfrb3/5mHU6XvnhNnU6nNZ%2Bfn2/NVVVV6ciRI3I6nQoODlZcXJzHfEFBgRwOh3r16nXFfl1p2y3Rc889p127dmnnzp3auXOnUlNTlZqaqp07d8rpdOr999%2B37sNkjNHBgwe/thfFxcUqLi6W0%2BlUTEyMYmNjPebz8/MVGxur6OhoxcfH69SpUx7Xj%2BTn5ys%2BPt5a9zdtuyVyOp2qqanRJ598Yo2dOHFCnTt3phdeFB0drZMnT3ocLTpx4oS6dOlCHwKVTz67iO9s4cKFZuTIkcblcpnXX3/d9O/f37z22mu%2BLsuvHTt2zPTu3ds88cQTprS01OOnrq7OjBgxwjz44IPm448/NuvXrzfx8fHWvaiKiopMXFycWb9%2BvXUfrLvuusv66HJeXp7p37%2B/ef31143L5TIjR440GRkZ1ra/qV9X2va1YO7cudbHwj///HMzcOBAk5GRYQoLC01GRoYZNGiQdRuLgwcPmr59%2B5rf/e531j1/Zs6caa1r/fr1ZvDgwWb//v1m//79ZvDgweaZZ56x5qdNm2YmTZpkPvzwQ/O73/3OxMXFWff8udK2W6oZM2aYCRMmmA8//NC8%2BeabZuDAgWbz5s30wosqKyvNoEGDzJw5c8yJEyfMG2%2B8YZKTk822bdvoQ4AiYAWoCxcumIcfftjEx8ebwYMHm2effdbXJfm99evXm549e172xxhjPv30U/OTn/zE3HjjjWbkyJHm//7v/zyW/%2Btf/2puv/12069fPzNlyhTrHjNfXv/NN99sEhMTzbx580x1dbU1d6V%2BXWnbLd2XA5YxX9w4ccyYMSYuLs6MHz/e/O1vf/N4/o4dO8ytt95q4uPjTVpamjl79qw1V1dXZx599FGTlJRkBgwYYLKysqwgbIwxZWVlZubMmSYuLs6kpqaaXbt2eaz7SttuiSorK82cOXNMfHy8ufnmm81vfvMb6zWjF95TWFhopk6davr372%2BGDh1qnn32WfoQwIKM%2BddxPwAAANiCC3YAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABs9v8AQ0qqJg/yemAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5870594304503869633”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>641.000000070198</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2939.75031268952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>740.834081486508</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1035.31608657788</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39853.8469149006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3038.85778064322</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2346.71934712042</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2189.89111615632</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>101.416347637773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5870594304503869633”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00296986475586891</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0441346429288387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0955020561814308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.312787592411041</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>492281.154146267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>503753.083526738</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>537897.612200725</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>613459.048963576</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>886068.668386497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_POP15”><s>LACCESS_POP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_POP10”><code>LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99406</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SENIORS10”><s>LACCESS_SENIORS10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_CHILD15”><code>LACCESS_CHILD15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91878</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SENIORS15”><s>LACCESS_SENIORS15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_SENIORS10”><code>LACCESS_SENIORS10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9937</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_SNAP15”><s>LACCESS_SNAP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_HHNV15”><code>LACCESS_HHNV15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90806</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_LACCESS_WHITE15”><s>LACCESS_WHITE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_LACCESS_SENIORS15”><code>LACCESS_SENIORS15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95929</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MEDHHINC15”>MEDHHINC15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2994</td>

</tr> <tr>

<th>Unique (%)</th> <td>93.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>48591</td>

</tr> <tr>

<th>Minimum</th> <td>22894</td>

</tr> <tr>

<th>Maximum</th> <td>125900</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5082273777267735938”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5082273777267735938,#minihistogram5082273777267735938”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5082273777267735938”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5082273777267735938”

aria-controls=”quantiles5082273777267735938” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5082273777267735938” aria-controls=”histogram5082273777267735938”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5082273777267735938” aria-controls=”common5082273777267735938”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5082273777267735938” aria-controls=”extreme5082273777267735938”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5082273777267735938”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>22894</td>

</tr> <tr>

<th>5-th percentile</th> <td>32596</td>

</tr> <tr>

<th>Q1</th> <td>40393</td>

</tr> <tr>

<th>Median</th> <td>46786</td>

</tr> <tr>

<th>Q3</th> <td>54134</td>

</tr> <tr>

<th>95-th percentile</th> <td>71912</td>

</tr> <tr>

<th>Maximum</th> <td>125900</td>

</tr> <tr>

<th>Range</th> <td>103010</td>

</tr> <tr>

<th>Interquartile range</th> <td>13741</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12357</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25431</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.4056</td>

</tr> <tr>

<th>Mean</th> <td>48591</td>

</tr> <tr>

<th>MAD</th> <td>9135.8</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3657</td>

</tr> <tr>

<th>Sum</th> <td>152140000</td>

</tr> <tr>

<th>Variance</th> <td>152700000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5082273777267735938”>

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bevbfPmzWrcuLHS0tKcng4AAIB1jgeszz77TFOmTFFUVJSvrUGDBpo0aZK2b9/u9HQAAACsczxguVwuFRUVndVeUlLCHd0BAECt4HjA6tWrlx577DF99913vracnBylpaWpZ8%2BeTk8HAADAOsc/RTh58mTdfffduvnmmxUZGSlJKioqUvv27TV16lSnpwMAAGCd4wGrYcOGevXVV7V161ZlZ2fL5XLp2muvVbdu3RQUFOT0dAAAAKwLyFflBAcHq2fPnpwSBAAAtZLjAcvr9WrhwoXauXOnTp48edaF7e%2B//77TUwIAALDK8YD18MMPa/fu3RowYICuvPJKp4cHAAC45BwPWNu3b9eyZcuUlJTk9NAAAACOcPw2DREREWrYsKHTwwIAADjG8YA1aNAgLVu2TKdOnXJ6aAAAAEc4foqwsLBQmzZt0j/%2B8Q81b95cISEhfv0vvfSS01MCAACwKiC3aRg4cGAghgUAAHCE4wErLS3N6SEBAAAc5fg1WJKUl5en9PR0PfDAA/rnP/%2Bpt956SwcOHAjEVAAAAKxzPGAdOnRIt9xyi1599VW9/fbbKi4u1ubNmzV06FB5PB6npwMAAGCd4wHr8ccfV58%2BffTee%2B/piiuukCQtWLBAycnJevLJJ52eDgAAgHWOB6ydO3fq7rvv9vtiZ5fLpXHjxmnPnj3WxikvL9fMmTPVqVMnXXfddVqwYIHva3n27Nmj4cOHy%2B12a%2BjQodq9e7ffups2bVKfPn3kdruVmpqqY8eOWZsXAACo/RwPWJWVlaqsrDyr/cSJEwoODrY2zmOPPaatW7fq%2Beef1/z58/XKK68oIyNDxcXFGjNmjJKSkrR%2B/XrFx8dr7NixKi4uliTt2rVL06dP1/jx45WRkaGioiJNnTrV2rwAAEDt53jA6tGjh5YsWeIXsgoLCzVv3jx17drVyhiFhYVat26dZs%2BerY4dO6pbt24aPXq0PB6PNm/erNDQUE2aNEmtWrXS9OnTVbduXb311luSpJdffln9%2BvXT4MGD1aZNG82dO1dbtmxRTk6OlbkBAIDaz/GANWXKFO3evVs9evRQWVmZ7r33XvXu3Vvff/%2B9Jk%2BebGWMzMxM1atXT507d/a1jRkzRmlpafJ4PEpMTPSdogwKClJCQoKysrIkSR6Px%2B97Ehs3bqwmTZpwAT4AAKg2x%2B%2BDFRMTow0bNmjTpk366quvVFlZqREjRmjQoEGqV6%2BelTFycnLUtGlTbdiwQYsXL9bJkyc1ZMgQ3XvvvfJ6vbr22mv9lm/YsKGys7Ml/esWEtHR0Wf1HzlypNrj5%2BXlyev1%2BrW5XBFnPe%2BZgoPr%2BP0GLoTLden3G/ZR%2B6ipXdTTPmp6cQJyJ/fw8HANHz78kj1/cXGxDh06pNWrVystLU1er1czZsxQeHi4SkpKzvp6npCQEJWXl0uSSktLz9tfHRkZGUpPT/drS01N1YQJE6q1fmRkeLXHAk6rX7%2BuY2Oxj9pHTe2invZR0wvjeMAaOXLkefttfBehy%2BXS8ePHNX/%2BfDVt2lSSdPjwYa1atUotWrQ4KyyVl5crLCxMkhQaGnrO/vDw6u9YKSkpSk5OPmNOESooOHHe9YKD6ygyMlxFRSU6dersDwIA51PV/mUD%2B6h91NQu6mlfTa%2Bpk398/pTjAet04DmtoqJChw4d0r59%2BzRq1CgrY0RFRSk0NNRvrGuuuUa5ubnq3Lmz8vPz/ZbPz8/3nb6LiYk5Z39UVFS1x4%2BOjj7rdKDX%2B6MqKqq3Y546VVntZYHTnNxn2Efto6Z2UU/7qOmFuWy%2Bi3DRokUXdJ3T%2BbjdbpWVlengwYO65pprJEkHDhxQ06ZN5Xa79dxzz8kYo6CgIBljtHPnTt1zzz2%2BdTMzMzVkyBBJUm5urnJzc%2BV2u63MDQAA1H6XzRVrgwYN0ptvvmnluX7zm9/ohhtu0NSpU7V371797//%2Br5YuXaoRI0aob9%2B%2BKioq0pw5c7R//37NmTNHJSUl6tevnyRpxIgR2rhxo9asWaO9e/dq0qRJuuGGG9S8eXMrcwMAALXfZROwPv/8c6s3Gn3yySf1b//2bxoxYoQmT56s22%2B/XXfeeafq1aunJUuW%2BI5SeTweLV26VBEREZKk%2BPh4zZo1S4sWLdKIESN01VVX/exRNwAAgHO5LC5yP378uL7%2B%2Bmvddttt1sa58sorNXfu3HP2dezYUa%2B%2B%2BurPrjtkyBDfKUIAAIAL5XjAatKkid/3EErSFVdcoTvuuEO/%2B93vnJ4OAACAdY4HrMcff9zpIQEAABzleMDasWNHtZft1KnTJZwJAADApeF4wLrzzjt9pwiNMb72M9uCgoL01VdfOT09AACAX8zxgLV48WI99thjevDBB9W5c2eFhIToiy%2B%2B0KxZs/T73/9e/fv3d3pKAAAAVjl%2Bm4a0tDTNmDFDN998s%2BrXr6%2B6deuqa9eumjVrllatWqWmTZv6fgAAAGoixwNWXl7eOcNTvXr1VFBQ4PR0AAAArHM8YMXFxWnBggU6fvy4r62wsFDz5s1Tt27dnJ4OAACAdY5fg/XQQw9p5MiR6tWrl1q2bCljjL799ltFRUXppZdecno6AAAA1jkesFq1aqXNmzdr06ZN%2BuabbyRJt99%2BuwYMGKDw8HCnpwMAAGCd4wFLkq666ioNHz5c33//ve9LlK%2B44opATAUAAMA6x6/BMsboySefVKdOnTRw4EAdOXJEkydP1vTp03Xy5EmnpwMAAGCd4wFr5cqV2rhxox555BGFhIRIkvr06aP33ntP6enpTk8HAADAOsdPEWZkZGjGjBm68cYbNXv2bElS//79dcUVVygtLU3/8z//4/SUgFqh38KPAz2FantzYvdATwEALinHj2B9//33atu27Vntbdq0kdfrdXo6AAAA1jkesJo2baovvvjirPYPP/zQd8E7AABATeb4KcI//vGPmjlzprxer4wx2rZtmzIyMrRy5UpNmTLF6ekAAABY53jAGjp0qCoqKvTss8%2BqtLRUM2bMUIMGDTRx4kSNGDHC6ekAAABY53jA2rRpk/r27auUlBQdO3ZMxhg1bNjQ6WkAAABcMo5fgzVr1izfxewNGjQgXAEAgFrH8YDVsmVL7du3z%2BlhAQAAHOP4KcI2bdroT3/6k5YtW6aWLVsqNDTUrz8tLc3pKQEAAFjleMA6ePCgEhMTJYn7XgEAgFrJkYA1d%2B5cjR8/XhEREVq5cqUTQwIAAASMI9dgvfjiiyopKfFrGzNmjPLy8pwYHgAAwFGOBCxjzFltO3bsUFlZmRPDAwAAOMrxTxECAADUdgQsAAAAyxwLWEFBQU4NBQAAEFCO3abhscce87vn1cmTJzVv3jzVrVvXbznugwUAAGo6RwJWp06dzrrnVXx8vAoKClRQUODEFAAAABzjSMDi3lcAAODXhIvcAQAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACyr9QFrzJgxmjJliu/xnj17NHz4cLndbg0dOlS7d%2B/2W37Tpk3q06eP3G63UlNTdezYMaenDAAAarhaHbDeeOMNbdmyxfe4uLhYY8aMUVJSktavX6/4%2BHiNHTtWxcXFkqRdu3Zp%2BvTpGj9%2BvDIyMlRUVKSpU6cGavoAAKCGqrUBq7CwUHPnzlVsbKyvbfPmzQoNDdWkSZPUqlUrTZ8%2BXXXr1tVbb70lSXr55ZfVr18/DR48WG3atNHcuXO1ZcsW5eTkBGozAABADVRrA9YTTzyhQYMG6dprr/W1eTweJSYmKigoSJIUFBSkhIQEZWVl%2BfqTkpJ8yzdu3FhNmjSRx%2BNxdvIAAKBGq5UBa9u2bfrss880btw4v3av16vo6Gi/toYNG%2BrIkSOSpLy8vPP2AwAAVIcr0BOwraysTI888ohmzJihsLAwv76SkhKFhIT4tYWEhKi8vFySVFpaet7%2B6srLy5PX6/Vrc7kizgpvZwoOruP3G6itXC728dN43dtFPe2jphen1gWs9PR0dejQQT179jyrLzQ09KywVF5e7gtiP9cfHh5%2BQXPIyMhQenq6X1tqaqomTJhQrfUjIy9sPKCmqV%2B/bqCncNnhdW8X9bSPml6YWhew3njjDeXn5ys%2BPl6SfIHp7bff1sCBA5Wfn%2B%2B3fH5%2Bvu/IUkxMzDn7o6KiLmgOKSkpSk5O9mtzuSJUUHDivOsFB9dRZGS4iopKdOpU5QWNCdQkVb0Wfk143dtFPe2r6TUN1B90tS5grVy5UhUVFb7HTz75pCTpT3/6k3bs2KHnnntOxhgFBQXJGKOdO3fqnnvukSS53W5lZmZqyJAhkqTc3Fzl5ubK7XZf0Byio6PPOh3o9f6oiorq7ZinTlVWe1mgJmL/Phuve7uop33U9MLUuoDVtGlTv8d16/4rubZo0UINGzbU/PnzNWfOHP3hD3/Q6tWrVVJSon79%2BkmSRowYoTvvvFNxcXGKjY3VnDlzdMMNN6h58%2BaObwcAAKi5flVXrNWrV09LlizxHaXyeDxaunSpIiIiJEnx8fGaNWuWFi1apBEjRuiqq65SWlpagGcNAABqmiBjjAn0JH4NvN4fq1zG5aqj%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%2Bvw4cOSpMOHDys1NVVDhgzR2rVr1aBBA40bN07GmABvFQAAqClcgZ7ApXDgwAFlZWXp448/VqNGjSRJEyZM0BNPPKFevXopJydHq1evVkREhFq1aqVt27Zp3bp1uu%2B%2B%2B7RmzRp16NBBo0ePliSlpaWpe/fu%2BvTTT9WlS5dAbhYAAKghauURrKioKC1btswXrk47fvy4PB6P2rVrp4iICF97YmKisrKyJEkej0dJSUm%2BvvDwcLVv397XDwAAUJVaeQQrMjJSPXv29D2urKzUyy%2B/rK5du8rr9So6Otpv%2BYYNG%2BrIkSOSVGV/deTl5cnr9fq1uVwRZz3vmYKD6/j9Bmorl4t9/DRe93ZRT/uo6cWplQHrTPPmzdOePXu0du1aLV%2B%2BXCEhIX79ISEhKi8vlySVlJSct786MjIylJ6e7teWmpqqCRMmVGv9yMjwao8F1ET169cN9BQuO7zu7aKe9lHTC1PrA9a8efO0YsUKPfXUU2rdurVCQ0NVWFjot0x5ebnCwsIkSaGhoWeFqfLyckVGRlZ7zJSUFCUnJ/u1uVwRKig4cd71goPrKDIyXEVFJTp1qrLa4wE1TVWvhV8TXvd2UU/7anpNA/UHXa0OWLNnz9aqVas0b9483XzzzZKkmJgY7d%2B/32%2B5/Px83%2Bm7mJgY5efnn9Xftm3bao8bHR191ulAr/dHVVRUb8c8daqy2ssCNRH799l43dtFPe2jphem1p5QTU9P1%2BrVq7VgwQINGDDA1%2B52u/Xll1%2BqtLTU15aZmSm32%2B3rz8zM9PWVlJRoz549vn4AAICq1MqA9c033%2BiZZ57Rf/3XfykxMVFer9f307lzZzVu3FhTp05Vdna2li5dql27dmnYsGGSpKFDh2rnzp1aunSpsrOzNXXqVDVr1oxbNAAAgGqrlQHr/fff16lTp/Tss8%2BqR48efj/BwcF65pln5PV6NWTIEL322mtatGiRmjRpIklq1qyZnn76aa1bt07Dhg1TYWGhFi1apKCgoABvFQAAqCmCDLcod4TX%2B2OVy7hcdVS/fl0VFJyo9nnufgs//qVTAxz35sTugZ7CZeNiXvf4edTTvppe06ioKwMybq08ggUAABBIBCwAAADLCFilXELBAAAPMElEQVQAAACWEbAAAAAsI2ABAABYRsACAACwrFZ/VQ6Ay1NNu70It5UAcKE4ggUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYJkr0BMAgMtdv4UfB3oKF%2BTNid0DPQXgV48jWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMlegJwAAsKvfwo8DPYVqe3Ni90BPAbgkOIIFAABgGQELAADAMk4RnkNZWZlmzpypd955R2FhYRo9erRGjx4d6GkBQK1Tk05nSpzSRPURsM5h7ty52r17t1asWKHDhw9r8uTJatKkifr27RvoqQEAgBqAgHWG4uJirVmzRs8995zat2%2Bv9u3bKzs7W3/9618JWAAAoFq4BusMe/fuVUVFheLj431tiYmJ8ng8qqysDODMAABATcERrDN4vV7Vr19fISEhvrZGjRqprKxMhYWFatCgQZXPkZeXJ6/X69fmckUoOjr6vOsFB9fx%2Bw0AuLzUtGvGapJ3/9Qz0FOwioB1hpKSEr9wJcn3uLy8vFrPkZGRofT0dL%2B28ePH67777jvvenl5eVqxYplSUlKqDGOnfTaH05Y/Jy8vTxkZGRdUT5wfNbWPmtpFPe2jpheHQyVnCA0NPStInX4cFhZWredISUnR%2BvXr/X5SUlKqXM/r9So9Pf2so1%2B4ONTTPmpqHzW1i3raR00vDkewzhATE6OCggJVVFTI5fpXebxer8LCwhQZGVmt54iOjiblAwDwK8YRrDO0bdtWLpdLWVlZvrbMzEzFxsaqTh3KBQAAqkZiOEN4eLgGDx6sRx99VLt27dJ7772nF154QSNHjgz01AAAQA0R/Oijjz4a6Elcbrp27ao9e/Zo/vz52rZtm%2B655x4NHTrUkbHr1q2rzp07q27duo6MV9tRT/uoqX3U1C7qaR81vXBBxhgT6EkAAADUJpwiBAAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwLLk6NGjmjBhgjp37qyePXsqLS1NZWVlkqScnBzdddddiouLU//%2B/fXRRx/5rbt161YNHDhQbrdbI0eOVE5Ojl//8uXL1bNnT8XHx2vatGkqKSnx9ZWVlWnatGlKSkpSjx499MILL1z6jXXYmDFjNGXKFN/jPXv2aPjw4XK73Ro6dKh2797tt/ymTZvUp08fud1upaam6tixY74%2BY4yefPJJde3aVZ07d9bcuXNVWVnp6y8oKNB9992n%2BPh4JScna%2BPGjZd%2BAx1SXl6umTNnqlOnTrruuuu0YMECnb7PMDW9OLm5uRo7dqwSEhKUnJys5cuX%2B/qo6YUpLy/XwIED9cknn/jaAvneWdXYNcG5apqVlaU//OEPio%2BP180336w1a9b4rUNNLTL4xSorK82tt95q/vM//9Ps27fP7Nixw9x4443m8ccfN5WVleaWW24xDzzwgNm/f79ZvHixcbvd5ocffjDGGPPDDz%2BYuLg48/zzz5t9%2B/aZ//7v/zYDBw40lZWVxhhj3nrrLZOYmGj%2B/ve/G4/HY/r3729mzpzpG3vWrFnmlltuMbt37zbvvPOOiY%2BPN2%2B%2B%2BWZA6nApbNq0ybRu3dpMnjzZGGPMiRMnTPfu3c3jjz9u9u/fb2bPnm2uu%2B46c%2BLECWOMMR6Px3Ts2NG8%2Buqr5quvvjJ33HGHGTNmjO/5nn/%2BeXP99debHTt2mG3btpkePXqYZcuW%2BfrHjh1rRo0aZb7%2B%2BmvzyiuvmA4dOhiPx%2BPsRl8iDz/8sLnpppuMx%2BMxW7duNV26dDGrVq2ipr/ArbfeaiZOnGgOHjxo3n33XeN2u80777xDTS9QaWmpSU1NNa1btzbbt283xpiAvndWNXZNcK6a5uXlmaSkJDN//nxz8OBBs2nTJhMbG2s%2B%2BOADYww1tY2AZcH%2B/ftN69atjdfr9bW9/vrrpkePHmbr1q0mLi7O98ZqjDGjRo0yf/nLX4wxxixcuNDccccdvr7i4mITHx/ve0HcdtttvmWNMWbHjh2mY8eOpri42Jw4ccLExsb6ljXGmEWLFvk9X01WUFBgevXqZYYOHeoLWGvWrDHJycm%2BF3xlZaW58cYbzbp164wxxjz44IO%2BZY0x5vDhw%2Bbf//3fzXfffWeMMeb666/3LWuMMRs2bDC9e/c2xhhz6NAh07p1a5OTk%2BPrnzZtmt/z1VQFBQWmXbt25pNPPvG1LVmyxEyZMoWaXqTCwkLTunVr8/XXX/vaxo8fb2bOnElNL0B2drb53e9%2BZ2655Ra/MBDI986qxr7c/VxN//a3v5m%2Bffv6Lfvwww%2Bb%2B%2B%2B/3xhDTW3jFKEFUVFRWrZsmRo1auTXfvz4cXk8HrVr104RERG%2B9sTERGVlZUmSPB6PkpKSfH3h4eFq3769srKydOrUKX3xxRd%2B/XFxcTp58qT27t2rvXv3qqKiQvHx8X7P7fF4/E4n1FRPPPGEBg0apGuvvdbX5vF4lJiYqKCgIElSUFCQEhISfraejRs3VpMmTeTxeHT06FHl5uaqU6dOvv7ExET98MMPysvLk8fjUePGjdWsWTO//s8///xSb%2Boll5mZqXr16qlz586%2BtjFjxigtLY2aXqSwsDCFh4dr/fr1OnnypA4cOKCdO3eqbdu21PQCfPrpp%2BrSpYsyMjL82gP53lnV2Je7n6vp6ctXznT8%2BHFJ1NQ2ApYFkZGR6tmzp%2B9xZWWlXn75ZXXt2lVer1fR0dF%2Byzds2FBHjhyRpPP2FxUVqayszK/f5XLp6quv1pEjR%2BT1elW/fn2FhIT4%2Bhs1aqSysjIVFhZeik11zLZt2/TZZ59p3Lhxfu1V1TMvL%2B9n%2B71eryT59Z8Oxaf7z7Xu0aNH7WxUAOXk5Khp06basGGD%2Bvbtq//4j//QokWLVFlZSU0vUmhoqGbMmKGMjAy53W7169dPvXr10vDhw6npBbjttts0bdo0hYeH%2B7UH8r2zqrEvdz9X02bNmikuLs73%2BJ///KfeeOMNdevWTRI1tc0V6AnURvPmzdOePXu0du1aLV%2B%2B3G%2BHk6SQkBCVl5dLkkpKSn62v7S01Pf4XP3GmHP2SfI9f01UVlamRx55RDNmzFBYWJhf3/nqJUmlpaUXVM%2Bf1quq567JiouLdejQIa1evVppaWnyer2aMWOGwsPDqekv8M0336h37966%2B%2B67lZ2drdmzZ6tbt27U1IKqtvNSvnf%2BGmpcWlqq%2B%2B67T40aNVJKSookamobAcuyefPmacWKFXrqqafUunVrhYaGnnU0qby83BccQkNDz9rBysvLFRkZqdDQUN/jM/vDw8N16tSpc/ZJOiuY1CTp6enq0KGD31HB036uXlXVMzw83O/FfmZtw8PDq3zumszlcun48eOaP3%2B%2BmjZtKkk6fPiwVq1apRYtWlDTi7Bt2zatXbtWW7ZsUVhYmGJjY3X06FE9%2B%2Byzat68OTX9hQL53lnV2DXdiRMnNG7cOH377bf629/%2B5jvSRU3t4hShRbNnz9aLL76oefPm6eabb5YkxcTEKD8/32%2B5/Px836HSn%2BuPiorS1VdfrdDQUL/%2BiooKFRYWKioqSjExMSooKFBFRYWv3%2Bv1KiwsTJGRkZdqMy%2B5N954Q%2B%2B9957i4%2BMVHx%2Bv119/Xa%2B//rri4%2BN/UT1jYmIkyXcK5qf/fbr/59at6aKiohQaGuoLV5J0zTXXKDc3l5pepN27d6tFixZ%2B/0C0a9dOhw8fpqYWBPK9s6qxa7Ljx4/rj3/8o7Kzs7VixQq1bNnS10dN7SJgWZKenq7Vq1drwYIFGjBggK/d7Xbryy%2B/9B1elf51wbHb7fb1Z2Zm%2BvpKSkq0Z88eud1u1alTR7GxsX79WVlZcrlcatOmjdq2bSuXy%2BV3kWBmZqZiY2NVp07N/V%2B7cuVKvf7669qwYYM2bNig5ORkJScna8OGDXK73fr88899928yxmjnzp0/W8/c3Fzl5ubK7XYrJiZGTZo08evPzMxUkyZNFB0drbi4OP3www9%2B1wRkZmb6XbNQU7ndbpWVlengwYO%2BtgMHDqhp06bU9CJFR0fr0KFDfn%2B1HzhwQM2aNaOmFgTyvbOqsWuqyspKjR8/Xt9//71Wrlyp3/72t3791NSygH1%2BsRbZv3%2B/adu2rXnqqadMXl6e309FRYXp37%2B/mThxotm3b59ZsmSJiYuL8937Iycnx8TGxpolS5b47jtyyy23%2BD7evWnTJpOQkGDeffdd4/F4zIABA8zs2bN9Yz/88MNmwIABxuPxmHfffdckJCSYt99%2BOyB1uFQmT57s%2Bwj6jz/%2BaLp27Wpmz55tsrOzzezZs0337t19H/3duXOnad%2B%2BvXnllVd89xcaO3as77mWLFlievToYbZv3262b99uevToYV544QVf/%2BjRo80dd9xhvvrqK/PKK6%2BY2NjYGn1/oZ8aM2aMSUlJMV999ZX58MMPTdeuXc2KFSuo6UUqKioy3bt3Nw8%2B%2BKA5cOCAef/9903nzp3NqlWrqOlF%2BuktBQL53lnV2DXJT2uakZFh2rRpYz744AO/f6cKCgqMMdTUNgKWBUuWLDGtW7c%2B548xxnz77bfm9ttvNx06dDADBgwwH3/8sd/6//jHP8xNN91kOnbsaEaNGuW7F85Pn79bt24mMTHRTJ061ZSWlvr6iouLzaRJk0xcXJzp0aOHefHFFy/59jrtpwHLmH/dpHHw4MEmNjbWDBs2zHz55Zd%2By69bt85cf/31Ji4uzqSmpppjx475%2BioqKsyf//xnk5SUZLp06WLmzZvne/Mwxpj8/HwzduxYExsba5KTk83rr79%2B6TfQIUVFRebBBx80cXFxplu3bubpp5/2bTs1vTjZ2dnmrrvuMgkJCaZPnz7mxRdfpKa/wE/DgDGBfe%2Bsauya4qc1HT169Dn/nfrpva%2BoqT1Bxvz/Y9gAAACwouZeqAMAAHCZImABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLL/B%2BUId1ytVJDJAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5082273777267735938”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>37085.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42120.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57862.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48127.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44221.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43428.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>49664.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>57367.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46067.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50335.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2983)</td> <td class=”number”>3105</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5082273777267735938”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>22894.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23014.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23341.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23968.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24001.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>109926.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>110224.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>112844.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>122092.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_METRO13”>METRO13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6024318621693063846”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-6024318621693063846”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6024318621693063846”

aria-controls=”quantiles-6024318621693063846” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6024318621693063846” aria-controls=”histogram-6024318621693063846”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6024318621693063846” aria-controls=”common-6024318621693063846”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6024318621693063846” aria-controls=”extreme-6024318621693063846”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6024318621693063846”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48332</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3005</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7186</td>

</tr> <tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>MAD</th> <td>0.46705</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.53147</td>

</tr> <tr>

<th>Sum</th> <td>1164</td>

</tr> <tr>

<th>Variance</th> <td>0.2336</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6024318621693063846”>

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vlZSUuMPaWQ5HY9MDWkiIvQ5AOhz2qfdsb%2B3WYzugt9aiv9aht9YJxN4GXMAaMWKE%2BvXrp6ioKElSQkKC9u/fr1deeUUDBw5UWFhYvbDkcrkUHh7uEabCwsLc/y/JfQ2XN3JycpSdne0xlp6eroyMjP94XoEgOrqJv0tosMhI7//d0TD01lr01zr01jqB1NuAC1hBQUHucHVW27ZttXnzZklSfHy8SktLPZaXlpYqNjZW8fHxkiSn06lWrVq5/1%2BSYmNjva4hLS1N/fv39xhzOBqrrOxUwybzM%2ByW9M2ev5VCQoIVGRmuiopK1dZymw4z0Vtr0V/r0FvrWNlbf/1xH3ABa%2BnSpfr888/14osvusd2796ttm3bSpISExOVm5ur1NRUSdKRI0d05MgRJSYmKj4%2BXi1atFBubq47YOXm5qpFixYNOr0XFxdXb32n84Rqai7sN6Qd519bW2fLuu2A3lqL/lqH3lonkHobcAGrX79%2BWr58uVauXKmBAwfqo48%2B0ptvvqk1a9ZIksaMGaPbbrtNXbt2VefOnbVw4UL17dtXrVu3di9fsmSJfvWrX0mSHn30Ud11111%2Bmw8AALCfgAtYXbp00dKlS/Xkk09q6dKlatmypR599FElJSVJkpKSkjRv3jw9%2BeST%2Bvbbb9WrVy/Nnz/fvf348eP1zTffaNKkSQoJCdFNN92kO%2B64w0%2BzAQAAdhRkGIbh7yIuBE7nCdP36XAEa%2BCSD03fr1Xe/mMvf5fgNYcjWNHRTVRWdipgDlefL%2BitteivdeitdazsbWzsxabuz1v2ukoaAADABghYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmMz2AcvlcmnYsGHasmWLeywvL0%2B33HKLkpKSNGjQIK1bt85jm9/%2B9re64oorPB5ffvmlJMkwDC1ZskQ9e/ZU9%2B7dtXjxYtXV1fl0TgAAwN4c/i7gl6iurtaUKVNUWFjoHnM6nbrnnns0ZswYPfLII9q5c6dmzJih2NhY9e3bV7W1tdq/f7/Wrl2rNm3auLeLjo6WJK1atUobN25Udna2ampqNHXqVMXExGj8%2BPG%2Bnh4AALAp2wasvXv3asqUKTIMw2N806ZNatasme677z5JUps2bbRlyxb99a9/Vd%2B%2BfXXw4EGdOXNGXbp0UVhYWL39rlmzRhkZGUpJSZEk3X///Vq6dCkBCwAAeM22pwi3bt2qHj16KCcnx2O8T58%2BWrRoUb31T548Kem7YNa8efNzhqtjx47pyJEjuvrqq91jycnJOnTokEpKSkyeAQAACFS2PYI1duzYc463atVKrVq1cj//5ptv9Le//U2TJ0%2BWJBUVFemiiy7Svffeqx07duiyyy7TtGnT1KVLFzmdTklSXFyce/tmzZpJko4ePeox/lNKSkrc%2BzrL4Wjs9fbeCgmxVz52OOxT79ne2q3HdkBvrUV/rUNvrROIvbVtwPJGVVWVJk%2BerGbNmiktLU2S9NVXX%2Bnbb7/V6NGjlZGRoVdffVXjxo3TW2%2B9paqqKklSaGioex9n/9/lcnn9ujk5OcrOzvYYS09PV0ZGxi%2Bdkq1FRzfxdwkNFhkZ7u8SAha9tRb9tQ69tU4g9dbnAWv06NEaNWqUhg4dqosvvtiy1zl16pQmTpyo/fv3609/%2BpPCw7/7R5s/f76qqqoUEREhSZozZ44%2B%2B%2BwzbdiwQddcc42k78LU2VOIZ4PV2e29kZaWpv79%2B3uMORyNVVZ26hfP6/vslvTNnr%2BVQkKCFRkZroqKStXW8ilSM9Fba9Ff69Bb61jZW3/9ce/zgNWzZ089%2B%2ByzWrRoka677jqlpqaqV69eCgoKMu01Tp48qbvvvltff/21Vq9e7fFpQYfD4Q5XkhQUFKS2bdvq2LFjio%2BPl/TdJxHPnmY8e6ovNjbW69ePi4urdzrQ6TyhmpoL%2Bw1px/nX1tbZsm47oLfWor/WobfWCaTe%2BvwQyJQpU/Tuu%2B/q6aefVkhIiCZPnqy%2Bffvq8ccf11dfffWL919XV6dJkybp4MGDeumll9S%2BfXuP5bfddpvH6bu6ujrt2bNHbdu2VXx8vFq0aKHc3Fz38tzcXLVo0cL066cAAEDg8ss1WEFBQerVq5d69eqlyspKvfTSS3r66ae1fPlydevWTePGjdP111//H%2B37tdde05YtW/TMM88oMjLSfQTqoosuUlRUlPr3769ly5apY8eOuuyyy7RmzRqdOHFCI0eOlCSNGTNGS5Ys0a9%2B9StJ0qOPPqq77rrLnIkDAIALgt8uci8pKdFf/vIX/eUvf9GXX36pbt26aeTIkTp69KgefPBBbdu2TZmZmQ3e7zvvvKO6ujrde%2B%2B9HuPdu3fXSy%2B9pDvuuEPV1dVasGCBSktLlZiYqFWrVrlPG44fP17ffPONJk2apJCQEN1000264447zJgyAAC4QAQZP7xTp8U2bNigDRs2aMuWLWratKlGjBihUaNGeVwn9dprr2nhwoX6/PPPfVmapZzOE6bv0%2BEI1sAlH5q%2BX6u8/cde/i7Baw5HsKKjm6is7FTAXA9wvqC31qK/1qG31rGyt7Gx1n2g7qf4/AhWZmam%2BvXrp2XLlunaa69VcHD9y8Datm2rW2%2B91delAQAAmMLnAeuDDz5QdHS0ysvL3eGqoKBAnTp1UkhIiCSpW7du6tatm69LAwAAMIXPP0V48uRJDR48WCtWrHCPTZgwQcOHD9eRI0d8XQ4AAIDpfB6wHn74YV166aW688473WNvvfWWmjdvfs7vEAQAALAbnwesTz/9VA888IDHjTubNm2qadOmafPmzb4uBwAAwHQ%2BD1gOh0MVFRX1xisrK%2BXjDzQCAABYwucB69prr9WCBQv09ddfu8eKi4u1aNEi9enTx9flAAAAmM7nnyKcPn267rzzTg0aNEiRkZGSpIqKCnXq1EkzZszwdTkAAASsIU/8H3%2BX4LVPFw72dwmm8nnAiomJ0RtvvKGPP/5YhYWFcjgcuvzyy/XrX//a1C98BgAA8Be/fFVOSEiI%2BvTpwylBAAAQkHwesJxOp5544gl99tlnOnPmTL0L2//1r3/5uiQAAABT%2BTxgzZo1Szt27NDQoUN18cX%2B%2BX4gAAAAK/k8YG3evFnPP/%2B8UlJSfP3SAAAAPuHz2zQ0btxYMTExvn5ZAAAAn/F5wBo%2BfLief/551dbW%2BvqlAQAAfMLnpwjLy8u1ceNGvffee2rdurVCQ0M9lq9Zs8bXJQEAAJjKL7dpGDZsmD9eFgAAwCd8HrAWLVrk65cEAADwKZ9fgyVJJSUlys7O1pQpU/TNN9/o73//u/bt2%2BePUgAAAEzn84B14MAB3XjjjXrjjTf0zjvv6PTp03rrrbc0atQo5efn%2B7ocAAAA0/k8YD3yyCMaMGCANm3apIsuukiS9Nhjj6l///5asmSJr8sBAAAwnc8D1meffaY777zT44udHQ6HJk6cqF27dvm6HAAAANP5PGDV1dWprq6u3vipU6cUEhLi63IAAABM5/OA1bt3bz333HMeIau8vFxZWVnq2bOnr8sBAAAwnc8D1gMPPKAdO3aod%2B/eqq6u1h/%2B8Af169dPBw8e1PTp031dDgAAgOl8fh%2Bs%2BPh4vfnmm9q4caO%2B%2BOIL1dXVacyYMRo%2BfLgiIiJ8XQ4AAIDp/HIn9/DwcI0ePdofLw0AAGA5nwes22%2B//SeXN/S7CF0ul1JTUzVr1iz16NFDklRcXKxZs2YpLy9PLVq00MyZM9W7d2/3Nh9//LEefvhhFRcXKzExUQsXLlTr1q3dy1988UWtXLlSJ0%2Be1JAhQzRr1iyFh4c3qC4AAHDh8vk1WC1btvR4xMfHq6qqSgUFBUpKSmrQvqqrq3XfffepsLDQPWYYhtLT09WsWTOtX79ew4cP16RJk3T48GFJ0uHDh5Wenq7U1FS99tpratq0qSZOnCjDMCRJ77zzjrKzszVv3jytXr1a%2Bfn5ysrKMq8BAAAg4J0330W4bNkyHT161Ov97N27V1OmTHEHo7M2b96s4uJi/fnPf1bjxo3Vrl07ffLJJ1q/fr0mT56sdevW6aqrrtJdd93lrqdXr17aunWrevTooTVr1mjcuHHq16%2BfJGnu3LkaP368pk6dylEsAADgFb98F%2BG5DB8%2BXG%2B//bbX658NRDk5OR7j%2Bfn5uvLKK9W4cWP3WHJysvLy8tzLU1JS3MvCw8PVqVMn5eXlqba2Vtu3b/dY3rVrV505c0a7d%2B/%2BT6cGAAAuMH65yP1cPv/88wbdaHTs2LHnHHc6nYqLi/MYi4mJcR8d%2B6nlFRUVqq6u9ljucDgUFRXVoKNrJSUlcjqdHmMOR%2BN6r/tLhYScN/nYKw6Hfeo921u79dgO6K216K916K31Aqm358VF7idPntSePXt%2BNDQ1RGVlpUJDQz3GQkND5XK5fnZ5VVWV%2B/mPbe%2BNnJwcZWdne4ylp6crIyPD630EoujoJv4uocEiIzktbBV6ay36ax16a51A6q3PA1aLFi08vodQki666CLdeuut%2Bu1vf/uL9x8WFqby8nKPMZfLpUaNGrmX/zAsuVwuRUZGKiwszP38h8sbcv1VWlqa%2Bvfv7zHmcDRWWdkpr/fhDbslfbPnb6WQkGBFRoaroqJStbX1v9oJ/zl6ay36ax16az0reuuvP%2B59HrAeeeQRS/cfHx%2BvvXv3eoyVlpa6T8/Fx8ertLS03vKOHTsqKipKYWFhKi0tVbt27SRJNTU1Ki8vV2xsrNc1xMXF1Tsd6HSeUE3Nhf2GtOP8a2vrbFm3HdBba9Ff69Bb6wRSb30esLZt2%2Bb1uldffXWD95%2BYmKjly5erqqrKfdQqNzdXycnJ7uW5ubnu9SsrK7Vr1y5NmjRJwcHB6ty5s3Jzc9331MrLy5PD4VBCQkKDawEAABcmnwes2267zX2K8Pu3WPjhWFBQkL744osG77979%2B5q3ry5ZsyYoYkTJ%2Brdd99VQUGB%2B/YQo0aN0sqVK7V8%2BXL169dPy5YtU6tWrdyBauzYsZo9e7Y6dOiguLg4zZkzRzfffDO3aAAAAF7zecB69tlntWDBAk2dOlXdu3dXaGiotm/frnnz5mnkyJG64YYbftH%2BQ0JC9PTTTyszM1Opqam69NJLtWzZMrVo0UKS1KpVKz311FN6%2BOGHtWzZMiUlJWnZsmXugDd06FAdOnRIs2fPlsvl0vXXX6%2BpU6f%2B4nkDAIALR5Dxwzt1WmzQoEHKzMzUtdde6zH%2B6aefatq0afr3v//ty3J8xuk8Yfo%2BHY5gDVzyoen7tcrbf%2Bzl7xK85nAEKzq6icrKTgXM9QDnC3prLfprHTv2dsgT/8ffJXjt04WDLeltbOzFpu7PWz7/GFpJSYlatmxZbzwiIkJlZWW%2BLgcAAMB0Pg9YXbt21WOPPaaTJ0%2B6x8rLy5WVlaVf//rXvi4HAADAdD6/BuvBBx/U7bffrmuvvVZt2rSRYRjav3%2B/YmNjtWbNGl%2BXAwAAYDqfB6x27drprbfe0saNG1VUVCRJ%2Bt3vfqehQ4fyST0AABAQ/PJdhJdccolGjx6tgwcPqnXr1pK%2Bu5s7AABAIPD5NViGYWjJkiW6%2BuqrNWzYMB09elTTp09XZmamzpw54%2BtyAAAATOfzgPXSSy9pw4YNeuihh9xfqjxgwABt2rSp3hckAwAA2JHPA1ZOTo5mz56t1NRU9809b7jhBi1YsEB//etffV0OAACA6XwesA4ePKiOHTvWG09ISJDT6fR1OQAAAKbzecBq2bKltm/fXm/8gw8%2BcF/wDgAAYGc%2B/xTh%2BPHjNXfuXDmdThmGoU8%2B%2BUQ5OTl66aWX9MADD/i6HAAAANP5PGCNGjVKNTU1euaZZ1RVVaXZs2eradOm%2BuMf/6gxY8b4uhwAAADT%2BTxgbdy4UYMHD1ZaWpqOHz8uwzAUExPj6zIAAAAs4/NrsObNm%2Be%2BmL1p06aEKwAAEHB8HrDatGmjL7/80tcvCwAA4DM%2BP0WYkJCg%2B%2B%2B/X88//7zatGmjsLAwj%2BWLFi3ydUkAAACm8nnA%2Buqrr5ScnCxJ3PcKAAAEJJ8ErMWLF2vSpElq3LixXnrpJV%2B8JAAAgN/45BqsVatWqbKy0mNswoQJKikp8cXLAwAA%2BJRPApZhGPXGtm3bpuowFRDbAAAXG0lEQVTqal%2B8PAAAgE/5/FOEAAAAgY6ABQAAYDKfBaygoCBfvRQAAIBf%2Bew2DQsWLPC459WZM2eUlZWlJk2aeKzHfbAAAIDd%2BSRgXX311fXueZWUlKSysjKVlZX5ogQAAACf8UnA4t5XAADgQhKQF7m//vrruuKKK%2Bo9EhISJEl/%2BMMf6i1799133du/%2BOKL6tOnj5KSkjRz5sx69/ACAAD4KT7/qhxfuOGGG9SnTx/385qaGo0bN059%2B/aVJBUVFSkrK0u//vWv3etccsklkqR33nlH2dnZysrKUkxMjGbMmKGsrCzNnj3bp3MAAAD2FZBHsBo1aqTY2Fj34y9/%2BYsMw9D9998vl8ulgwcPqnPnzh7rhIaGSpLWrFmjcePGqV%2B/furSpYvmzp2r9evXcxQLAAB4LSAD1veVl5drxYoVmjJlikJDQ7Vv3z4FBQWpdevW9datra3V9u3blZKS4h7r2rWrzpw5o927d/uybAAAYGMBH7BeeeUVxcXFafDgwZKkffv2KSIiQtOmTVPv3r1100036f3335ckVVRUqLq6WnFxce7tHQ6HoqKidPToUb/UDwAA7Ccgr8E6yzAMrVu3Tnfffbd7bN%2B%2BfaqqqlLv3r01YcIE/fOf/9Qf/vAH5eTkqFmzZpLkPl14VmhoqFwul9evW1JSUu%2B2FA5HY4/gZoaQEHvlY4fDPvWe7a3demwH9NZa9Nc69NZ6gdTbgA5Y27dv17FjxzR06FD32MSJE3Xbbbe5L2pPSEjQzp079eqrr%2Bp//ud/JKlemHK5XAoPD/f6dXNycpSdne0xlp6eroyMjP90KgEhOrrJz690nomM9P7fHQ1Db61Ff61Db60TSL0N6ID14YcfKiUlxR2mJCk4ONjjuSS1bdtWe/fuVVRUlMLCwlRaWqp27dpJ%2Bu4TiOXl5YqNjfX6ddPS0tS/f3%2BPMYejscrKTv2C2dRnt6Rv9vytFBISrMjIcFVUVKq2ts7f5QQUemst%2Bmsdems9K3rrrz/uAzpgFRQUqFu3bh5jDzzwgIKCgjy%2Bkmf37t3q0KGDgoOD1blzZ%2BXm5qpHjx6SpLy8PDkcDvc9tLwRFxdX73Sg03lCNTUX9hvSjvOvra2zZd12QG%2BtRX%2BtQ2%2BtE0i9tdchkAYqLCzU5Zdf7jHWv39//fWvf9Wbb76pAwcOKDs7W7m5ubr11lslSWPHjtXKlSu1adMmFRQUaM6cObr55psbdIoQAABc2AL6CFZpaakiIyM9xq6//no99NBDeuaZZ3T48GG1b99ezz//vFq1aiVJGjp0qA4dOqTZs2fL5XLp%2Buuv19SpU/1RPgAAsKmADlgFBQXnHB89erRGjx79o9tNmDBBEyZMsKosAAAQ4AL6FCEAAIA/ELAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwWcAGrH/%2B85%2B64oorPB4ZGRmSpF27dmn06NFKTEzUqFGjtGPHDo9tN27cqAEDBigxMVHp6ek6fvy4P6YAAABsKmAD1t69e9WvXz999NFH7seCBQt0%2BvRpTZgwQSkpKXr99deVlJSke%2B%2B9V6dPn5YkFRQUKDMzU5MmTVJOTo4qKio0Y8YMP88GAADYScAGrKKiInXo0EGxsbHuR2RkpN566y2FhYVp2rRpateunTIzM9WkSRP9/e9/lyStXbtWQ4YM0YgRI5SQkKDFixfr/fffV3FxsZ9nBAAA7CKgA1abNm3qjefn5ys5OVlBQUGSpKCgIHXr1k15eXnu5SkpKe71mzdvrhYtWig/P98ndQMAAPsLyIBlGIa%2B%2BuorffTRRxo0aJAGDBigJUuWyOVyyel0Ki4uzmP9mJgYHT16VJJUUlLyk8sBAAB%2BjsPfBVjh8OHDqqysVGhoqJ544gkdPHhQCxYsUFVVlXv8%2B0JDQ%2BVyuSRJVVVVP7ncGyUlJXI6nR5jDkfjesHtlwoJsVc%2BdjjsU%2B/Z3tqtx3ZAb61Ff61Db60XSL0NyIDVsmVLbdmyRZdccomCgoLUsWNH1dXVaerUqerevXu9sORyudSoUSNJUlhY2DmXh4eHe/36OTk5ys7O9hhLT093f4rxQhUd3cTfJTRYZKT3/%2B5oGHprLfprHXprnUDqbUAGLEmKioryeN6uXTtVV1crNjZWpaWlHstKS0vdR5fi4%2BPPuTw2Ntbr105LS1P//v09xhyOxiorO9WQKfwsuyV9s%2BdvpZCQYEVGhquiolK1tXX%2BLieg0Ftr0V/r0FvrWdFbf/1xH5AB68MPP9T999%2Bv9957z33k6YsvvlBUVJSSk5O1YsUKGYahoKAgGYahzz77TL///e8lSYmJicrNzVVqaqok6ciRIzpy5IgSExO9fv24uLh6pwOdzhOqqbmw35B2nH9tbZ0t67YDemst%2BmsdemudQOqtvQ6BeCkpKUlhYWF68MEHtW/fPr3//vtavHix7r77bg0ePFgVFRVauHCh9u7dq4ULF6qyslJDhgyRJI0ZM0YbNmzQunXrtHv3bk2bNk19%2B/ZV69at/TwrAABgFwEZsCIiIrRy5UodP35co0aNUmZmptLS0nT33XcrIiJCzz33nPsoVX5%2BvpYvX67GjRtL%2Bi6czZs3T8uWLdOYMWN0ySWXaNGiRX6eEQAAsJOAPEUoSe3bt9eqVavOuaxLly564403fnTb1NRU9ylCAACAhgrII1gAAAD%2BRMACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAEbsI4dO6aMjAx1795dffr00aJFi1RdXS1JWrBgga644gqPx9q1a93bbty4UQMGDFBiYqLS09N1/Phxf00DAADYkMPfBVjBMAxlZGQoMjJSL7/8sr799lvNnDlTwcHBmj59uoqKijRlyhSNHDnSvU1ERIQkqaCgQJmZmZo7d64SEhK0cOFCzZgxQ88995y/pgMAAGwmII9g7du3T3l5eVq0aJHat2%2BvlJQUZWRkaOPGjZKkoqIiXXnllYqNjXU/wsPDJUlr167VkCFDNGLECCUkJGjx4sV6//33VVxc7M8pAQAAGwnIgBUbG6vnn39ezZo18xg/efKkTp48qWPHjqlNmzbn3DY/P18pKSnu582bN1eLFi2Un59vZckAACCABOQpwsjISPXp08f9vK6uTmvXrlXPnj1VVFSkoKAgPfvss/rggw8UFRWlO%2B%2B80326sKSkRHFxcR77i4mJ0dGjR71%2B/ZKSEjmdTo8xh6Nxvf3%2BUiEh9srHDod96j3bW7v12A7orbXor3XorfUCqbcBGbB%2BKCsrS7t27dJrr72mnTt3KigoSG3bttWtt96qbdu2adasWYqIiNDAgQNVVVWl0NBQj%2B1DQ0Plcrm8fr2cnBxlZ2d7jKWnpysjI8OU%2BdhVdHQTf5fQYJGR4f4uIWDRW2vRX%2BvQW%2BsEUm8DPmBlZWVp9erVevzxx9WhQwe1b99e/fr1U1RUlCQpISFB%2B/fv1yuvvKKBAwcqLCysXphyuVzua7S8kZaWpv79%2B3uMORyNVVZ26pdP6HvslvTNnr%2BVQkKCFRkZroqKStXW1vm7nIBCb61Ff61Db61nRW/99cd9QAes%2BfPn65VXXlFWVpYGDRokSQoKCnKHq7Patm2rzZs3S5Li4%2BNVWlrqsby0tFSxsbFev25cXFy904FO5wnV1FzYb0g7zr%2B2ts6WddsBvbUW/bUOvbVOIPXWXodAGiA7O1t//vOf9dhjj2no0KHu8aVLl%2BqOO%2B7wWHf37t1q27atJCkxMVG5ubnuZUeOHNGRI0eUmJjok7oBAID9BWTAKioq0tNPP6177rlHycnJcjqd7ke/fv20bds2rVy5Ul9//bX%2B9Kc/6c0339Rdd90lSRozZow2bNigdevWaffu3Zo2bZr69u2r1q1b%2B3lWAADALgLyFOG//vUv1dbW6plnntEzzzzjsWzPnj1aunSpnnzySS1dulQtW7bUo48%2BqqSkJElSUlKS5s2bpyeffFLffvutevXqpfnz5/tjGgAAwKYCMmBNmDBBEyZM%2BNHlAwYM0IABA350eWpqqlJTU60oDQAAXAAC8hQhAACAPxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkB6xyqq6s1c%2BZMpaSkqHfv3nrhhRf8XRIAALARh78LOB8tXrxYO3bs0OrVq3X48GFNnz5dLVq00ODBg/1dGgAAsAEC1g%2BcPn1a69at04oVK9SpUyd16tRJhYWFevnllwlYAADAK5wi/IHdu3erpqZGSUlJ7rHk5GTl5%2Berrq7Oj5UBAAC74AjWDzidTkVHRys0NNQ91qxZM1VXV6u8vFxNmzb92X2UlJTI6XR6jDkcjRUXF2dqrSEh9srHDod96j3bW7v12A7orbXor3XorfUCqbcErB%2BorKz0CFeS3M9dLpdX%2B8jJyVF2drbH2KRJkzR58mRzivx/SkpKNO5XhUpLSzM9vF3oSkpKtHr18/TWAvTWWvTXOnbs7acL7XFpS0lJiZ566ilb9fbnBE5UNElYWFi9IHX2eaNGjbzaR1paml5//XWPR1pamum1Op1OZWdn1ztahl%2BO3lqH3lqL/lqH3lonEHvLEawfiI%2BPV1lZmWpqauRwfNcep9OpRo0aKTIy0qt9xMXFBUwCBwAADccRrB/o2LGjHA6H8vLy3GO5ubnq3LmzgoNpFwAA%2BHkkhh8IDw/XiBEjNGfOHBUUFGjTpk164YUXdPvtt/u7NAAAYBMhc%2BbMmePvIs43PXv21K5du/Too4/qk08%2B0e9//3uNGjXK32WdU5MmTdS9e3c1adLE36UEHHprHXprLfprHXprnUDrbZBhGIa/iwAAAAgknCIEAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwDrPVVdXa%2BbMmUpJSVHv3r31wgsv/Oi6u3bt0ujRo5WYmKhRo0Zpx44dPqzUfhrS2/fee0/Dhw9XUlKSbrzxRv3rX//yYaX205DennXw4EElJSVpy5YtPqjQ3hrS3z179mjMmDHq0qWLbrzxRm3evNmHldpPQ3r7z3/%2BU0OGDFFSUpLGjBmjnTt3%2BrBS%2B3K5XBo2bNhPvtcD4veZgfPavHnzjBtvvNHYsWOH8Y9//MNISkoy3n777XrrnTp1yujVq5fxyCOPGHv37jXmz59vXHPNNcapU6f8ULU9eNvbL774wujUqZOxevVqY//%2B/cbatWuNTp06GV988YUfqrYHb3v7fePHjzc6dOhgbN682UdV2pe3/a2oqDCuueYa48EHHzT2799vLF261EhOTjZKS0v9ULU9eNvbL7/80ujcubPxxhtvGAcOHDDmzp1r9OrVyzh9%2BrQfqraPqqoqIz09/Sff64Hy%2B4yAdR47deqU0blzZ48fwmXLlhm33nprvXXXrVtn9O/f36irqzMMwzDq6uqMgQMHGuvXr/dZvXbSkN5mZWUZ48eP9xi76667jMcee8zyOu2oIb09a8OGDcYtt9xCwPJCQ/q7evVqY8CAAUZNTY17LDU11Xjvvfd8UqvdNKS3q1atMkaOHOl%2BfuLECaNDhw5GQUGBT2q1o8LCQuO3v/2tceONN/7kez1Qfp9xivA8tnv3btXU1CgpKck9lpycrPz8fNXV1Xmsm5%2Bfr%2BTkZAUFBUmSgoKC1K1bN%2BXl5fm0ZrtoSG9Hjhyp%2B%2B%2B/v94%2BTpw4YXmddtSQ3kpSWVmZsrKyNG/ePF%2BWaVsN6e/WrVt13XXXKSQkxD22fv16/eY3v/FZvXbSkN5GRUVp7969ys3NVV1dnV5//XVFRETov/7rv3xdtm1s3bpVPXr0UE5Ozk%2BuFyi/zxz%2BLgA/zul0Kjo6WqGhoe6xZs2aqbq6WuXl5WratKnHupdffrnH9jExMSosLPRZvXbSkN62a9fOY9vCwkJ98sknuuWWW3xWr500pLeS9Mgjj2jkyJFq3769r0u1pYb0t7i4WF26dNGsWbP073//Wy1bttT06dOVnJzsj9LPew3p7Q033KB///vfGjt2rEJCQhQcHKznnntOl1xyiT9Kt4WxY8d6tV6g/D7jCNZ5rLKy0uONLsn93OVyebXuD9fDdxrS2%2B87fvy4Jk%2BerG7duum6666ztEa7akhvP/74Y%2BXm5mrixIk%2Bq8/uGtLf06dPa/ny5YqNjdWKFSt09dVXa/z48Tpy5IjP6rWThvS2rKxMTqdTs2fP1quvvqrhw4drxowZ%2Buabb3xWb6AKlN9nBKzzWFhYWL0fqLPPGzVq5NW6P1wP32lIb88qLS3VuHHjZBiGnnzySQUH8/Y5F297W1VVpdmzZ%2Buhhx7i57QBGvKzGxISoo4dOyojI0NXXnmlpk6dqjZt2mjDhg0%2Bq9dOGtLbJUuWqEOHDvrd736nq666SvPnz1d4eLjWr1/vs3oDVaD8PuM3xHksPj5eZWVlqqmpcY85nU41atRIkZGR9dYtLS31GCstLVVcXJxParWbhvRWko4dO6bf/e53crlcWrNmTb3TXPj/vO1tQUGBiouLlZGRoaSkJPd1L/fcc49mz57t87rtoiE/u7GxsWrbtq3HWJs2bTiC9SMa0tudO3cqISHB/Tw4OFgJCQk6fPiwz%2BoNVIHy%2B4yAdR7r2LGjHA6Hx4V9ubm56ty5c72jJ4mJifr8889lGIYkyTAMffbZZ0pMTPRpzXbRkN6ePn1ad999t4KDg7V27VrFx8f7ulxb8ba3Xbp00T/%2B8Q%2B9%2Beab7ockLViwQP/93//t87rtoiE/u127dtWePXs8xvbt26eWLVv6pFa7aUhv4%2BLiVFRU5DH21VdfqVWrVj6pNZAFyu8zAtZ5LDw8XCNGjNCcOXNUUFCgTZs26YUXXtDtt98u6bu/rKqqqiRJgwcPVkVFhRYuXKi9e/dq4cKFqqys1JAhQ/w5hfNWQ3r73HPP6euvv9b//u//upc5nU4%2BRfgjvO1to0aNdOmll3o8pO/%2Beo2JifHnFM5rDfnZveWWW7Rnzx499dRTOnDggJYuXari4mINHz7cn1M4bzWktzfffLNeffVVvfnmmzpw4ICWLFmiw4cPa%2BTIkf6cgm0F5O8zv94kAj/r9OnTxrRp04yuXbsavXv3NlatWuVe1qFDB4/7guTn5xsjRowwOnfubNx0003Gzp07/VCxfXjb20GDBhkdOnSo95g%2BfbqfKj//NeTn9vu4D5Z3GtLfTz/91Bg5cqRx1VVXGcOHDze2bt3qh4rtoyG9ffXVV43BgwcbXbt2NcaMGWPs2LHDDxXb0w/f64H4%2ByzIMP7fMTgAAACYglOEAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvu/OxCIi45H6CMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6024318621693063846”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6024318621693063846”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MILK_PRICE10”>MILK_PRICE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>36</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.97257</td>

</tr> <tr>

<th>Minimum</th> <td>0.72199</td>

</tr> <tr>

<th>Maximum</th> <td>1.3951</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2559765057699542336”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2559765057699542336,#minihistogram2559765057699542336”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2559765057699542336”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2559765057699542336”

aria-controls=”quantiles2559765057699542336” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2559765057699542336” aria-controls=”histogram2559765057699542336”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2559765057699542336” aria-controls=”common2559765057699542336”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2559765057699542336” aria-controls=”extreme2559765057699542336”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2559765057699542336”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.72199</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.76951</td>

</tr> <tr>

<th>Q1</th> <td>0.87364</td>

</tr> <tr>

<th>Median</th> <td>0.9703</td>

</tr> <tr>

<th>Q3</th> <td>1.082</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1367</td>

</tr> <tr>

<th>Maximum</th> <td>1.3951</td>

</tr> <tr>

<th>Range</th> <td>0.67309</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.20838</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.12024</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.12363</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.6628</td>

</tr> <tr>

<th>Mean</th> <td>0.97257</td>

</tr> <tr>

<th>MAD</th> <td>0.099678</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.14003</td>

</tr> <tr>

<th>Sum</th> <td>3018.9</td>

</tr> <tr>

<th>Variance</th> <td>0.014458</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2559765057699542336”>

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Hjx7VoEGDZLFY9Oyzz8rhcMhkMsnhcKisrEy33367c9/S0lKlpKRIko4fP67jx4/LYrG4ffy4uLh2lwNttm/U0tL1E6W1te2M2/izQK9fCpwedFVjoPSgJ/lD/3ge0ANfr9%2B3L3B24rzzztO0adOUk5Ojw4cP691331VRUZHS09M1c%2BZM1dfXKy8vT0eOHFFeXp7sdruSk5MlSenp6dq5c6e2bt2qw4cPKzs7W9OmTeMWDQAAwG1%2BGbAkae3atTr33HOVnp6u%2B%2B67TwsWLNANN9ygyMhIbdiwwXmWymq1qqioSBEREZKkhIQErVixQoWFhUpPT1ffvn2Vn5/v4WoAAIAv8ctLhJJ09tlna/Xq1R3OjRs3Tjt27Oh035SUFOclQgAAgO7y2zNYAAAAnkLAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADOZ1AWv%2B/PnasmWLvvnmG08vBQAA4CfxuoA1adIkPfPMM5oyZYruvvtu7du3Tw6Ho9uP86c//UkXXnihy0dWVpYk6dChQ5o/f74sFotSU1N18OBBl3137dqlGTNmyGKxKCMjQydPnjSkNgAAEBi8LmDdc889%2Bstf/qKnnnpKwcHBWrp0qaZNm6bHH39cX3zxhduPc%2BTIEV1xxRXat2%2Bf82PVqlVqaGjQokWLlJSUpJKSEiUkJGjx4sVqaGiQJFVUVCg3N1eZmZkqLi5WfX29cnJyeqpcAADgh7wuYEmSyWTS5MmTtWbNGu3fv18LFizQpk2bNGvWLC1YsEB//OMfz/gYn3/%2BuS644ALFxsY6P6KiorR7926FhoYqOztbw4cPV25urvr06aM9e/ZIkjZv3qzk5GTNnTtXI0eO1OrVq7V3715VVVX1dNkAAMBPeGXAkqTq6mpt3LhRaWlpWrdunS666CKtWLFCkyZN0oMPPqi8vLwu9//88881bNiwduNWq1WJiYkymUySfghz48ePV3l5uXM%2BKSnJuf2AAQM0cOBAWa1W44oDAAB%2BzezpBZxu586d2rlzpz744ANFR0dr7ty5euKJJ1zC0oABA5SXl6fc3NwOH8PhcOiLL77Qvn37tGHDBrW2tmrmzJnKysqSzWbTiBEjXLaPiYlRZWWlpB%2BCXVxcXLv5EydOuF1DdXW1bDaby5jZHNHucU8JDg5y%2BRxoAr1%2BKfB6YDa3rzPQetCTOuqvr%2BB5QA/8pX6vC1i5ubm64oorVFhYqKlTpyooqH2DzzvvPP3P//xPp49x7Ngx2e12hYSEaP369frqq6%2B0atUqNTY2Osd/LCQkRM3NzZKkxsbGLufdUVxcrIKCApexjIwM55vsOxMVFe72MfxRoNcvBU4P%2BvXr0%2BlcoPSgJ3XVX1/B84Ae%2BHr9Xhew3nnnHfXr1091dXXOcFVRUaHRo0crODhYkjR%2B/HiNHz%2B%2B08cYNGiQPvjgA/Xt21cmk0mjRo1SW1ubfvOb32jChAntwlJzc7PCwsIkSaGhoR3Oh4e7/w%2Bdlpam6dOnu4yZzRGqrf2uw%2B2Dg4MUFRWu%2Bnq7Wlvb3D6Ovwj0%2BqXA60FH3wuB1oOe1NlrjS/geUAPjK7fUz9weF3A%2Bvbbb5Wenq4rr7xS2dnZkqRFixapf//%2BevbZZzVgwAC3Huecc85x%2BXr48OFqampSbGysampqXOZqamqcl%2B/i4%2BM7nI%2BNjXW7hri4uHaXA222b9TS0vUTpbW17Yzb%2BLNAr18KnB50VWOg9KAn%2BUP/eB7QA1%2Bv3%2BsucP7v//6vhg4dqptvvtk5tnv3bg0YMED5%2BfluPca7776riRMnym63O8f%2B9re/6ZxzzlFiYqI%2B%2BeQT5721HA6HysrKZLFYJEkWi0WlpaXO/Y4fP67jx4875wEAAM7E6wLWxx9/rPvvv9/ljFF0dLSys7P1/vvvu/UYCQkJCg0N1YMPPqijR49q7969Wr16tW699VbNnDlT9fX1ysvL05EjR5SXlye73a7k5GRJUnp6unbu3KmtW7fq8OHDys7O1rRp0zRkyJAeqRcAAPgfrwtYZrNZ9fX17cbtdrvbd3SPjIzU7373O508eVKpqanKzc1VWlqabr31VkVGRmrDhg0qLS1VSkqKrFarioqKFBERIemHcLZixQoVFhYqPT1dffv2dfvMGQAAgOSF78GaOnWqVq1apXXr1uncc8%2BVJFVVVSk/P1%2BXXXaZ249z/vnn6/nnn%2B9wbty4cdqxY0en%2B6akpCglJaV7CwcAAPj/eV3Auu%2B%2B%2B3TzzTfrqquuUlRUlCSpvr5eo0eP5k/WAAAAn%2BB1ASsmJkY7duzQ/v37VVlZKbPZrBEjRujSSy913n0dAADAm3ldwJKk4OBgXXbZZd26JAgAAOAtvC5g2Ww2rV%2B/XmVlZfr%2B%2B%2B/bvbH97bff9tDKAAAA3ON1Aeu3v/2tDh48qKuvvlpnn322p5cDAADQbV4XsN5//31t3LhRSUlJnl4KAADAT%2BJ198GKiIhQTEyMp5cBAADwk3ldwJozZ442btyo1tZWTy8FAADgJ/G6S4R1dXXatWuX/vrXv2rIkCEKCQlxmX/xxRc9tDIAAAD3eF3AkqTZs2d7egkAAAA/mdcFLP7uHwAA8HVe9x4sSaqurlZBQYHuuece/fvf/9aePXt09OhRTy8LAADALV4XsL788kv98pe/1I4dO/Tmm2%2BqoaFBu3fvVmpqqqxWq6eXBwAAcEZeF7AeeeQRzZgxQ2%2B99ZbOOussSdK6des0ffp0rV271sOrAwAAODOvC1hlZWW6%2BeabXf6ws9ls1pIlS3To0CEPrgwAAMA9Xhew2tra1NbW1m78u%2B%2B%2BU3BwsAdWBAAA0D1eF7CmTJmiDRs2uISsuro6rVmzRpMmTfLgygAAANzjdQHr/vvv18GDBzVlyhQ1NTXpjjvu0BVXXKGvvvpK9913n6eXBwAAcEZedx%2Bs%2BPh4vfrqq9q1a5f%2B9re/qa2tTenp6ZozZ44iIyM9vTwAAIAz8rqAJUnh4eGaP3%2B%2Bp5eBAJe8/j1PLwEA4KO8LmAtXLiwy3n%2BFiEAAPB2XhewBg0a5PJ1S0uLvvzyS/3jH//QjTfe6KFVAQAAuM/rAlZnf4uwsLBQJ06c6OXVAAAAdJ/X/RZhZ%2BbMmaM33njD08sAAAA4I58JWJ988gk3GgUAAD7B6y4RdvQm92%2B//VZ///vfdf3113f78RYtWqTo6Gg98sgjkqRDhw7poYce0j/%2B8Q%2BNGDFCDz/8sMaMGePcfteuXVq/fr1sNpumTJmilStXKjo6%2BqcXBAAAAo7XncEaOHCgBg0a5PIxZswYrVy5sts3Gv3DH/6gvXv3Or9uaGjQokWLlJSUpJKSEiUkJGjx4sVqaGiQJFVUVCg3N1eZmZkqLi5WfX29cnJyDK0PAAD4P687g3XqTNPPVVdXp9WrV2vs2LHOsd27dys0NFTZ2dkymUzKzc3VO%2B%2B8oz179iglJUWbN29WcnKy5s6dK0lavXq1rrjiClVVVWnIkCGGrAsAAPg/rwtYH330kdvbXnLJJZ3OPfroo5ozZ46qq6udY1arVYmJiTKZTJIkk8mk8ePHq7y8XCkpKbJarbrtttuc2w8YMEADBw6U1WolYAEAALd5XcC64YYbnAHI4XA4x08fM5lM%2Btvf/tbhYxw4cEAff/yxXn/9dS1fvtw5brPZNGLECJdtY2JiVFlZKUmqrq5WXFxcu3luDwEAALrD6wLWM888o1WrVuk3v/mNJkyYoJCQEH366adasWKFrrnmGs2aNavL/ZuamvTQQw9p2bJlCgsLc5mz2%2B0KCQlxGQsJCVFzc7MkqbGxsct5d1VXV8tms7mMmc0R7cLbKcHBQS6fA02g1x%2BIzOb2/9Y8D4zTUX99Bc8DeuAv9XtdwMrPz9eyZcs0depU59ikSZO0YsUKZWdnu1zC60hBQYHGjBmjyy67rN1caGhou7DU3NzsDGKdzYeHh3erhuLiYhUUFLiMZWRkKCsrq8v9oqK6dxx/E%2Bj1B5J%2B/fp0Osfz4Ofrqr%2B%2BgucBPfD1%2Br0uYFVXV7f7czmSFBkZqdra2jPu/4c//EE1NTVKSEiQJGdgevPNNzV79mzV1NS4bF9TU%2BM8sxQfH9/hfGxsbLdqSEtL0/Tp013GzOYI1dZ%2B1%2BH2wcFBiooKV329Xa2tbd06lj8I9PoDUUffCzwPjNPZa40v4HlAD4yu31M/cHhdwLr44ou1bt06Pfroo4qMjJT0w28ErlmzRpdeeukZ93/ppZfU0tLi/Hrt2rWSpHvvvVcfffSRnn32WTkcDplMJjkcDpWVlen222%2BXJFksFpWWliolJUWSdPz4cR0/flwWi6VbNcTFxbW7HGizfaOWlq6fKK2tbWfcxp8Fev2BpKt/Z54HP58/9I/nAT3w9fq9LmA9%2BOCDWrhwoaZOnaphw4bJ4XDon//8p2JjY/Xiiy%2Becf/Tz3716fNDch06dKhiYmL02GOPKS8vT9ddd522bNkiu92u5ORkSVJ6erpuuOEGXXzxxRo7dqzy8vI0bdo0foMQAAB0i9cFrOHDh2v37t3atWuXPv/8c0nSggULdPXVV3f7vVCni4yM1IYNG/TQQw/plVde0YUXXqiioiJFRERIkhISErRixQo98cQT%2Bs9//qPJkydr5cqVP7smAAAQWLwuYElS3759NX/%2BfH311VfOs0dnnXXWT3qs029cOm7cOO3YsaPT7VNSUpyXCAEAAH4Kr/sdSIfDobVr1%2BqSSy7R7NmzdeLECd13333Kzc3V999/7%2BnlAQAAnJHXBayXXnpJO3fu1EMPPeS8J9WMGTP01ltvtbv1AQAAgDfyuoBVXFysZcuWKSUlxXn39lmzZmnVqlV6/fXXPbw6AACAM/O6gPXVV19p1KhR7cZHjhzZ7u7oAAAA3sjrAtagQYP06aeftht/5513uF0CAADwCV73W4S//vWv9fDDD8tms8nhcOjAgQMqLi7WSy%2B9pPvvv9/TywMAADgjrwtYqampamlp0dNPP63GxkYtW7ZM0dHRuuuuu5Senu7p5QEAAJyR1wWsXbt2aebMmUpLS9PJkyflcDgUExPj6WUBAAC4zeveg7VixQrnm9mjo6MJVwAAwOd43RmsYcOG6R//%2BIdGjBjh6aUA6CHJ69/z9BIAoEd5XcAaOXKk7r33Xm3cuFHDhg1TaGioy3x%2Bfr6HVgYAAOAerwtYX3zxhRITEyWJ%2B14BAACf5BUBa/Xq1crMzFRERIReeuklTy8HAADgZ/GKN7k///zzstvtLmOLFi1SdXW1h1YEAADw03lFwHI4HO3GPvroIzU1NXlgNQAAAD%2BPVwQsAAAAf0LAAgAAMJjXBCyTyeTpJQAAABjCK36LUJJWrVrlcs%2Br77//XmvWrFGfPn1ctuM%2BWAAAwNt5RcC65JJL2t3zKiEhQbW1taqtrfXQqgAAAH4arwhY3PsKAAD4E695DxYAAIC/IGABAAAYjIAFAABgMAIWAACAwfw2YH355Zf69a9/rYSEBE2bNk0bN250zlVVVemmm27SxRdfrFmzZmnfvn0u%2B%2B7fv1%2BzZ8%2BWxWLRwoULVVVV1dvLBwAAPswvA1ZbW5sWLVqkfv36aceOHXr44Yf19NNP6/XXX5fD4VBGRob69%2B%2Bv7du3a86cOcrMzNSxY8ckSceOHVNGRoZSUlK0bds2RUdHa8mSJR3%2BvUQAAICOeMVtGoxWU1OjUaNGafny5YqMjNSwYcN06aWXqrS0VP3791dVVZW2bNmiiIgIDR8%2BXAcOHND27du1dOlSbd26VWPGjNEtt9wi6Ycbm06ePFkffvihJk6c6OHKAACAL/DLM1hxcXFav369IiMj5XA4VFpaqo8%2B%2BkgTJkyQ1WrVRRddpIiICOf2iYmJKi8vlyRZrVYlJSU558LDwzV69GjnPAAAwJn4ZcD6senTp%2Bv6669XQkKCrrrqKtlsNsXFxblsExMToxMnTkjSGecBAADOxC8vEf7YE088oZqaGi1fvlz5%2Bfmy2%2B0KCQlx2SYkJETNzc2SdMZ5d1RXV7f70z9mc0S74HZKcHCQy%2BdAE%2Bj1A0Yzm333e4nXA3rgL/X7fcAaO3asJKmpqUn33nuvUlNTZbfbXbZpbm5WWFiYJCk0NLRdmGpublZUVJTbxywuLlZBQYHLWEZGhrKysrrcLyoq3O1j%2BKNArx8wSr9%2BfTy9hJ%2BN1wN64Ov1%2B2Wu/tOHAAAVy0lEQVTAqqmpUXl5uWbMmOEcGzFihL7//nvFxsbq6NGj7bY/dXYpPj5eNTU17eZHjRrl9vHT0tI0ffp0lzGzOUK1td91uH1wcJCiosJVX29Xa2ub28fxF4FeP2C0zl5rfAGvB/TA6Po99QOHXwasr776SpmZmdq7d6/i4%2BMlSQcPHlR0dLQSExP13HPPqbGx0XnWqrS0VImJiZIki8Wi0tJS52PZ7XYdOnRImZmZbh8/Li6u3eVAm%2B0btbR0/URpbW074zb%2BLNDrB4ziD99HvB7QA1%2Bv37cvcHZi7NixGj16tB544AEdOXJEe/fu1Zo1a3T77bdrwoQJGjBggHJyclRZWamioiJVVFRo3rx5kqTU1FSVlZWpqKhIlZWVysnJ0eDBg7lFAwAAcJtfBqzg4GA99dRTCg8PV1pamnJzc3XDDTdo4cKFzjmbzaaUlBS99tprKiws1MCBAyVJgwcP1pNPPqnt27dr3rx5qqurU2FhoUwmk4erAgAAvsIvLxFKP7yX6vQ3mp8ydOhQbd68udN9L7/8cl1%2B%2BeU9tTQAAODn/DZgwfskr3/P00sAAKBX%2BOUlQgAAAE8iYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjNs0AICf8aVborxx12RPLwHoEZzBAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgfhuwvv76a2VlZWnChAm67LLLlJ%2Bfr6amJklSVVWVbrrpJl188cWaNWuW9u3b57Lv/v37NXv2bFksFi1cuFBVVVWeKAEAAPgovwxYDodDWVlZstvt%2Bv3vf6/HH39cf/nLX7R%2B/Xo5HA5lZGSof//%2B2r59u%2BbMmaPMzEwdO3ZMknTs2DFlZGQoJSVF27ZtU3R0tJYsWSKHw%2BHhqgAAgK8we3oBPeHo0aMqLy/Xe%2B%2B9p/79%2B0uSsrKy9Oijj2rq1KmqqqrSli1bFBERoeHDh%2BvAgQPavn27li5dqq1bt2rMmDG65ZZbJEn5%2BfmaPHmyPvzwQ02cONGTZQEAAB/hlwErNjZWGzdudIarU7799ltZrVZddNFFioiIcI4nJiaqvLxckmS1WpWUlOScCw8P1%2BjRo1VeXu6VASt5/XueXgIAADiNXwasqKgoXXbZZc6v29ratHnzZk2aNEk2m01xcXEu28fExOjEiROSdMZ5d1RXV8tms7mMmc0R7R73lODgIJfPABAozGbX1z1eD%2BmBv9TvlwHrdGvWrNGhQ4e0bds2vfDCCwoJCXGZDwkJUXNzsyTJbrd3Oe%2BO4uJiFRQUuIxlZGQoKyury/2iosLdPgYA%2BIN%2B/fp0OM7rIT3w9fr9PmCtWbNGmzZt0uOPP64LLrhAoaGhqqurc9mmublZYWFhkqTQ0NB2Yaq5uVlRUVFuHzMtLU3Tp093GTObI1Rb%2B12H2wcHBykqKlz19Xa1tra5fRwA8HWnvy7yekgPjK6/sxDf0/w6YK1cuVIvv/yy1qxZo6uuukqSFB8fryNHjrhsV1NT47x8Fx8fr5qamnbzo0aNcvu4cXFx7S4H2mzfqKWl6ydKa2vbGbcBAH/S2Wser4f0wNfr9%2B0LnF0oKCjQli1btG7dOl199dXOcYvFos8%2B%2B0yNjY3OsdLSUlksFud8aWmpc85ut%2BvQoUPOeQAAgDPxy4D1%2Beef66mnntJtt92mxMRE2Ww258eECRM0YMAA5eTkqLKyUkVFRaqoqNC8efMkSampqSorK1NRUZEqKyuVk5OjwYMHe%2BVvEAIAAO/klwHr7bffVmtrq55%2B%2BmlNmTLF5SM4OFhPPfWUbDabUlJS9Nprr6mwsFADBw6UJA0ePFhPPvmktm/frnnz5qmurk6FhYUymUwergoAAPgKk4NblPcKm%2B2bTufM5iD169dHtbXfdft6M/fBAuDL3rhrssvXP%2Bf10F8Eeg%2BMrj829mwDVtV9fnkGCwAAwJMIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMH8PmA1Nzdr9uzZ%2BuCDD5xjVVVVuummm3TxxRdr1qxZ2rdvn8s%2B%2B/fv1%2BzZs2WxWLRw4UJVVVX19rIBAIAP8%2BuA1dTUpLvvvluVlZXOMYfDoYyMDPXv31/bt2/XnDlzlJmZqWPHjkmSjh07poyMDKWkpGjbtm2Kjo7WkiVL5HA4PFUGAADwMX4bsI4cOaJrr71W//rXv1zG33//fVVVVWnFihUaPny4Fi9erIsvvljbt2%2BXJG3dulVjxozRLbfcovPPP1/5%2Bfn6v//7P3344YeeKAMAAPggvw1YH374oSZOnKji4mKXcavVqosuukgRERHOscTERJWXlzvnk5KSnHPh4eEaPXq0cx4AAOBMzJ5eQE%2B5/vrrOxy32WyKi4tzGYuJidGJEyfcmgcAADgTvw1YnbHb7QoJCXEZCwkJUXNzs1vz7qiurpbNZnMZM5sj2gW3U4KDg1w%2BA0CgMJtdX/d4PaQH/lJ/wAWs0NBQ1dXVuYw1NzcrLCzMOX96mGpublZUVJTbxyguLlZBQYHLWEZGhrKysrrcLyoq3O1jAIA/6NevT4fjvB7SA1%2BvP%2BACVnx8vI4cOeIyVlNT4zy7FB8fr5qamnbzo0aNcvsYaWlpmj59usuY2Ryh2trvOtw%2BODhIUVHhqq%2B3q7W1ze3jAICvO/11kddDemB0/Z2F%2BJ4WcAHLYrGoqKhIjY2NzrNWpaWlSkxMdM6XlpY6t7fb7Tp06JAyMzPdPkZcXFy7y4E22zdqaen6idLa2nbGbQDAn3T2msfrIT3w9fp9%2BwLnTzBhwgQNGDBAOTk5qqysVFFRkSoqKjRv3jxJUmpqqsrKylRUVKTKykrl5ORo8ODBmjhxoodXDgAAfEXABazg4GA99dRTstlsSklJ0WuvvabCwkINHDhQkjR48GA9%2BeST2r59u%2BbNm6e6ujoVFhbKZDJ5eOUAAMBXBMQlwr///e8uXw8dOlSbN2/udPvLL79cl19%2BeU8vCwAA%2BKmAO4MFAADQ0whYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGMzs6QUAAAJX8vr3PL2EbnnjrsmeXgJ8BGewAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAGrA01NTXrggQeUlJSkKVOm6LnnnvP0kgAAgA/hbxF2YPXq1Tp48KA2bdqkY8eO6b777tPAgQM1c%2BZMTy8NAAD4AALWaRoaGrR161Y9%2B%2ByzGj16tEaPHq3Kykr9/ve/J2ABAAC3ELBOc/jwYbW0tCghIcE5lpiYqGeeeUZtbW0KCuKqKgDANySvf8/TS3DbG3dN9vQSDEXAOo3NZlO/fv0UEhLiHOvfv7%2BamppUV1en6OjoMz5GdXW1bDaby5jZHKG4uLgOtw8ODnL5DADwTmZzz79OB%2Br/Cad66y/1E7BOY7fbXcKVJOfXzc3Nbj1GcXGxCgoKXMYyMzO1dOnSDrevrq7Wpk0blZaW1mkI68zHeb5/2bK6ulrFxcU/qX5/QQ/ogUQPJHog/bz/E07ni/9HGFm/J/l2POwBoaGh7YLUqa/DwsLceoy0tDSVlJS4fKSlpXW6vc1mU0FBQbuzXoEi0OuX6IFEDyR6INEDiR74S/2cwTpNfHy8amtr1dLSIrP5h/bYbDaFhYUpKirKrceIi4vz6dQNAAB%2BHs5gnWbUqFEym80qLy93jpWWlmrs2LG8wR0AALiFxHCa8PBwzZ07V8uXL1dFRYXeeustPffcc1q4cKGnlwYAAHxE8PLly5d7ehHeZtKkSTp06JAee%2BwxHThwQLfffrtSU1N79Jh9%2BvTRhAkT1KdPnx49jrcK9PoleiDRA4keSPRAogf%2BUL/J4XA4PL0IAAAAf8IlQgAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYASsXtLU1KQHHnhASUlJmjJlip577rkOt7vhhht04YUXtvvIycnp5RUby936JelPf/qTkpOTlZCQoPT0dH322We9uNKe050e7Nu3T7/61a%2BUkJCgm266SUePHu3Flfa85uZmzZ49Wx988EGn2xw6dEjz58%2BXxWJRamqqDh482Isr7Hnu9OCUjz/%2BWFdeeWUvrKp3udODv/71r5ozZ44SEhL0y1/%2BUm%2B//XYvrrBnuVP/a6%2B9pquuukrjxo3Tddddp4qKil5cYc/rzvfBV199pYSEBLe29QYErF6yevVqHTx4UJs2bdJDDz2kgoIC7dmzp912Tz75pPbt2%2Bf8KCws1FlnnaXrr7/eA6s2jrv1V1ZW6p577tHixYu1c%2BdOjRo1SosXL5bdbvfAqo3VnR4sXrxYV155pbZv366LLrpIN954o7777jsPrNp4TU1Nuvvuu1VZWdnpNg0NDVq0aJGSkpJUUlKihIQELV68WA0NDb240p7jTg9O%2Bfvf/64777xT/vZHN9zpweHDh5WZmanU1FS9%2Buqruu6663TnnXfq8OHDvbjSnuFO/R9//LFyc3O1ZMkS/eEPf1BCQoJuu%2B22gHot%2BLHly5f71GsAAasXNDQ0aOvWrcrNzdXo0aP1X//1X7r11lv1%2B9//vt2255xzjmJjYxUbG6vo6Gg9/vjjuvXWWzV27FgPrNwY3an/vffe04gRIzR37lyde%2B65uvvuu2Wz2XTkyBEPrNw43enByy%2B/rISEBN15550677zz9Jvf/EZnn322Xn/9dQ%2Bs3FhHjhzRtddeq3/9619dbrd7926FhoYqOztbw4cPV25urvr06dNhIPU17vZAkrZs2aLrrrtOMTExvbCy3uNuD3bt2qVJkyZp4cKFGjp0qBYsWKCJEyfqjTfe6KWV9gx367fZbFqyZInmzJmjIUOGKCMjQ3V1dfr88897aaU9pzvfB9IPZ/J8LVgSsHrB4cOH1dLSooSEBOdYYmKirFar2traOt2vpKRE//nPf3Tbbbf1xjJ7THfqP%2Becc3TkyBGVlpaqra1NJSUlioyM1LnnntvbyzZUd3pQVVWlcePGOb82mUy64IILVF5e3mvr7SkffvihJk6cqOLi4i63s1qtSkxMlMlkkvRDD8aPHx9QPZCkd955R48%2B%2Bqhuuummnl9YL3K3B9dcc43uvffeduPffPNNTy2tV7hbf3Jysu644w5JUmNjo1544QXFxMRo%2BPDhvbHMHtWd74Pa2lqtWbNGK1as6IWVGcfs6QUEApvNpn79%2BikkJMQ51r9/fzU1Namurk7R0dHt9nE4HNq4caMWLlzo039NXOpe/bNmzdKf//xnXX/99QoODlZQUJA2bNigvn37emLphulOD/r376%2Bvv/7aZf8TJ074fA8kuX2p22azacSIES5jMTExbl9K8Gbdudz/1FNPSfrhhy1/4m4PTg8SlZWVOnDggK677rqeWFav6e5bPg4cOKBbbrlFDodDa9eu9fn/E6Tu9eCRRx7RNddco/PPP78HV2Q8zmD1Arvd7vIfqyTn183NzR3u88EHH%2BjEiRO69tpre3x9Pa079dfW1spms2nZsmV65ZVXNGfOHOXk5Ojf//53r623J3SnB8nJyXrzzTf1l7/8RS0tLdqxY4c%2B/fRTff/99722Xk/rrF%2Bdfb/A/508eVJLly7V%2BPHj/fIN/105//zzVVJSoqysLN1///1%2BcSbXXfv371dpaamWLFni6aV0GwGrF4SGhrb7j%2BHU12FhYR3u8%2Babb2rq1Kk655xzenx9Pa079a9du1YXXHCBFixYoDFjxmjlypUKDw/X9u3be229PaE7PZg6daoyMjK0dOlSjR07Vjt37tScOXMUGRnZa%2Bv1tM761dn3C/xbTU2NbrzxRjkcDj3xxBMKCgqs/7r69%2B%2BvUaNGacmSJZo8ebK2bNni6SX1isbGRi1btkwPPfSQT37vB9az1EPi4%2BNVW1urlpYW55jNZlNYWJiioqI63Ofdd9/1m5/SulP/Z599ppEjRzq/DgoK0siRI3Xs2LFeW29P6O5z4I477lBZWZn27dunF154Qd99950GDRrUm0v2qPj4eNXU1LiM1dTUKC4uzkMrgqd8/fXXWrBggZqbm/Xiiy92%2BJYKf1VRUdHuNjXDhw9XbW2th1bUuyoqKlRVVaWsrCwlJCQ438N62223admyZR5e3ZkRsHrBqFGjZDabXU7rlpaWauzYsR3%2BJHby5ElVVVUpMTGxN5fZY7pTf1xcXLvfkPniiy80ePDgXllrT%2BlOD3bt2qW8vDyFhIQoJiZGjY2N%2BuCDDzRx4sTeXrbHWCwWffLJJ85bEzgcDpWVlclisXh4ZehNDQ0NuvXWWxUUFKTNmzcrPj7e00vqVdu2bdO6detcxj777DOdd955HlpR7xo3bpz%2B%2BMc/6tVXX3V%2BSNKqVat05513enh1Z0bA6gXh4eGaO3euli9froqKCr311lt67rnntHDhQkk/nMlobGx0bl9ZWanQ0FCfDxWndKf%2Ba6%2B9Vq%2B88opeffVVffnll1q7dq2OHTuma665xpMl/Gzd6cGwYcO0ZcsW/fGPf9Q///lP3XPPPRowYICmTp3qyRJ63I97MHPmTNXX1ysvL09HjhxRXl6e7Ha7kpOTPbzKnnX6a0Eg%2BnEPNmzYoH/961969NFHnXM2m83nf4uwKz%2BuPy0tTe%2B//742bdqkf/7zn3riiSdUUVHhd79VerpTPQgLC9PQoUNdPqQfznD7wq1LCFi9JCcnR6NHj9aNN96ohx9%2BWEuXLtV///d/S5KmTJmi3bt3O7f997//raioKOevqPsDd%2BufNWuWfvvb32rDhg2aO3euysrKtGnTJp/4ZjoTd3swZswYLV%2B%2BXI888ohSUlIk/fAfjb%2B/7%2BTHPYiMjNSGDRtUWlqqlJQUWa1WFRUVKSIiwsOr7FmnvxYEoh/34M0331RjY6Pmz5%2BvKVOmOD/y8vI8vMqe8%2BP6R48erYKCAm3btk2/%2BtWvtHfvXv3ud7/z%2BzN5/vJ9YHL42%2B2BAQAAPMy/fyQGAADwAAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgsP8Pzc0v7ms29ZIAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2559765057699542336”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9485941</td> <td class=”number”>438</td> <td class=”number”>13.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.082025</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.795895</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.095938</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9703046</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.873642</td> <td class=”number”>200</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9400685</td> <td class=”number”>169</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.136671</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.062503</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.053402</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (25)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2559765057699542336”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.7219861</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7460754</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7695072</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.795895</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8388169</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.163639</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.191763</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.216942</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.217211</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.395075</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_MILK_SODA_PRICE10”><s>MILK_SODA_PRICE10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_MILK_PRICE10”><code>MILK_PRICE10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91655</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants FY 2009”><s>National School Lunch Program participants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2015”><code>WIC participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96736</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants FY 2011”><s>National School Lunch Program participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2009”><code>National School Lunch Program participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99972</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2012”><s>National School Lunch Program participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants FY 2011”><code>National School Lunch Program participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99994</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2013”><s>National School Lunch Program participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2012”><code>National School Lunch Program participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99978</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2014”><s>National School Lunch Program participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2013”><code>National School Lunch Program participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_National School Lunch Program participants, FY 2015”><s>National School Lunch Program participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2014”><code>National School Lunch Program participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99976</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRES07”>ORCHARD_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>776</td>

</tr> <tr>

<th>Unique (%)</th> <td>24.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>619</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1933.3</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>471820</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-306408118052712388”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-306408118052712388,#minihistogram-306408118052712388”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-306408118052712388”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-306408118052712388”

aria-controls=”quantiles-306408118052712388” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-306408118052712388” aria-controls=”histogram-306408118052712388”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-306408118052712388” aria-controls=”common-306408118052712388”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-306408118052712388” aria-controls=”extreme-306408118052712388”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-306408118052712388”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>13</td>

</tr> <tr>

<th>Median</th> <td>54</td>

</tr> <tr>

<th>Q3</th> <td>232.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>4262.5</td>

</tr> <tr>

<th>Maximum</th> <td>471820</td>

</tr> <tr>

<th>Range</th> <td>471820</td>

</tr> <tr>

<th>Interquartile range</th> <td>219.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16332</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.4476</td>

</tr> <tr>

<th>Kurtosis</th> <td>462.14</td>

</tr> <tr>

<th>Mean</th> <td>1933.3</td>

</tr> <tr>

<th>MAD</th> <td>3245.7</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>19.379</td>

</tr> <tr>

<th>Sum</th> <td>5009300</td>

</tr> <tr>

<th>Variance</th> <td>266730000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-306408118052712388”>

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x427vGgAAwBWuB6zU1FQtW7ZMvXr10n//939r8%2BbNMsa4XQYAAEC9cT1gTZo0Sf/4xz%2B0ZMkShYWF6a677tJVV12lBQsW6IsvvnC7HAAAAOs8gdhpSEiIevbsqZ49e6qsrEzPPPOMlixZohUrVqhr164aM2aMrr766kCUBgAA8KsFJGBJUlFRkV599VW9%2Buqr2rNnj7p27aobb7xRhYWFuv/%2B%2B7V161ZNnz49UOUBAAD8Yq4HrFdeeUWvvPKKPvzwQ7Vo0UKDBw/WokWL1K5dO%2Bc1rVu31ty5cwlYAAAgKLkesKZPn67evXtr8eLFuuKKKxQaeu5lYO3bt9eoUaPcLg0AAMAK1wPWe%2B%2B9p%2BbNm6u0tNQJV9u3b1fnzp0VFhYmSeratau6du3qdmkAAABWuP5bhCdOnNA111yjJ554whkbP368brjhBh0%2BfNjtcgAAAKxzPWA9/PDD%2BuMf/6jbb7/dGXvttdfUunVrZWZm/uztVVZWatCgQfrwww%2BdsTlz5uiSSy7xe6xdu9aZz8nJUd%2B%2BfeX1epWenq6jR486c8YYzZ8/X6mpqerevbvmzZunmpqaX7haAABwPnL9FOG///1vvfDCC4qOjnbGWrRooXvvvVe33HLLz9pWRUWFJk2apL179/qN79%2B/X5MmTdKNN97ojDVt2lTSd6cjp0%2BfrpkzZyo%2BPl5z587V1KlTtXz5cknSU089pZycHGVnZ6uqqkqTJ09Wy5YtNW7cuF%2B6ZAAAcJ5x/QiWx%2BPRsWPHzhkvKyv7WXd037dvn26%2B%2BWZ9/fXX58zt379fl156qaKjo51HZGSkJGnt2rW69tprNXjwYMXHx2vevHl69913VVBQIElas2aNMjIylJKSotTUVN1zzz169tlnf%2BFqAQDA%2Bcj1gHXFFVdozpw5fsGooKBAmZmZSktLq/N2PvroI/Xo0UPr1q3zGz9x4oSOHDnid9uH78vPz1dKSorzvHXr1oqLi1N%2Bfr6OHDmiw4cPq1u3bs58cnKyDh48qKKiojrXBgAAzm%2BunyKcMmWKbr/9dvXv319RUVGSpGPHjqlz586aOnVqnbczcuTIHxzfv3%2B/QkJCtGzZMr333ntq1qyZbr/9dud0YVFRkWJiYvze07JlSxUWFsrn80mS33yrVq0kSYWFhee8DwAA4Ie4HrBatmypl19%2BWe%2B//7727t0rj8ejiy66SJdffrlCQkJ%2B9fY///xzhYSEOPfS2rp1qx544AE1bdpU/fr1U3l5ucLDw/3eEx4ersrKSpWXlzvPvz8nfXcxfV0VFRU5Ye0Mj6ex9YAWFub6AchfxeMJrnrPdqbfwdb3YEbP3UW/3UfPG66A/KmcsLAwpaWl/axTgnU1ePBg9e7dW82aNZMkxcfH68svv9Rzzz2nfv36KSIi4pywVFlZqcjISL8wFRER4fxbknMNV12sW7dO2dnZfmPp6enKyMj4xetqCJo3bxLoEqyIiqr71wLsoOfuot/uo%2BcNj%2BsBy%2Bfz6fHHH9e2bdt0%2BvTpcy5sf/vtt3/V9kNCQpxwdUb79u21ZcsWSVJsbKyKi4v95ouLixUdHa3Y2FinxrZt2zr/luT3W4%2B1GT58uPr06eM35vE0VknJyZ%2B3mFoE2088ttfvtrCwUEVFRerYsTJVV3PrDjfQc3fRb/fR8/oXqB/uXQ9YDzzwgHbs2KGBAwfq97//vfXtL1y4UB9//LFWr17tjO3evVvt27eXJHm9XuXm5mrIkCGSpMOHD%2Bvw4cPyer2KjY1VXFyccnNznYCVm5uruLi4n3V6LyYm5pzX%2B3zHVVV1fn/zNJT1V1fXNJi1BAt67i767T563vC4HrC2bNmilStX%2Bv0mn029e/fWihUrtGrVKvXr10%2BbN2/Whg0btGbNGknSiBEjdOuttyoxMVEJCQmaO3eurrrqKl144YXO/Pz58/WHP/xBkvToo49q7Nix9VIrAABomFwPWI0bN1bLli3rbftdunTRwoULtWjRIi1cuFBt2rTRo48%2BqqSkJElSUlKSZs2apUWLFunbb79Vz549NXv2bOf948aN0zfffKOJEycqLCxMw4YN02233VZv9QIAgIYnxPycu3ta8Ne//lXHjh3TrFmznD/ufD7w%2BY5b36bHE6p%2B8/9pfbv15fW7ewa6hF/F4wlV8%2BZNVFJykkP5LqHn7qLf7qPn9S862v7lSHXh%2BhGs0tJS5eTk6J133tGFF154zi0TzpzKAwAACFYBuU3DoEGDArFbAAAAV7gesDIzM93eJQAAgKsCciOloqIiZWdna9KkSfrmm2/0xhtv6PPPPw9EKQAAANa5HrC%2B%2BuorXXfddXr55Zf15ptv6tSpU3rttdc0dOhQ5efnu10OAACAda4HrEceeUR9%2B/bVpk2b9Lvf/U6S9Nhjj6lPnz6aP3%2B%2B2%2BUAAABY53rA2rZtm26//Xa/P%2Bzs8Xh05513aufOnW6XAwAAYJ3rAaumpkY1Nefe6%2BPkyZPn1X2xAABAw%2BV6wOrVq5eWL1/uF7JKS0uVlZWl1NRUt8sBAACwzvWAdd9992nHjh3q1auXKioq9J//%2BZ/q3bu3Dhw4oClTprhdDgAAgHWu3wcrNjZWGzZsUE5Ojnbt2qWamhqNGDFCN9xwg5o2bep2OQAAANYF5E7ukZGRuummmwKxawAAgHrnesAaPXr0T87ztwgBAECwcz1gtWnTxu95VVWVvvrqK%2B3Zs0djxoxxuxwAAADrfjN/i3Dx4sUqLCx0uRoAAAD7AvK3CH/IDTfcoNdffz3QZQAAAPxqv5mA9fHHH3OjUQAA0CD8Ji5yP3HihD777DONHDnS7XIAAACscz1gxcXF%2Bf0dQkn63e9%2Bp1GjRun66693uxwAAADrXA9YjzzyiNu7BAAAcJXrAWvr1q11fm23bt3qsRIAAID64XrAuvXWW51ThMYYZ/zssZCQEO3atcvt8gAAAH411wPWsmXLNGfOHE2ePFndu3dXeHi4PvnkE82aNUs33nijBgwY4HZJAAAAVrl%2Bm4bMzEzNmDFD/fv3V/PmzdWkSROlpqZq1qxZeu6559SmTRvnAQAAEIxcD1hFRUU/GJ6aNm2qkpISt8sBAACwzvWAlZiYqMcee0wnTpxwxkpLS5WVlaXLL7/c7XIAAACsc/0arPvvv1%2BjR4/WFVdcoXbt2skYoy%2B//FLR0dFas2aN2%2BUAAABY53rA6tChg1577TXl5ORo//79kqRbbrlFAwcOVGRkpNvlAAAAWOd6wJKkCy64QDfddJMOHDigCy%2B8UNJ3d3MHAABoCFy/BssYo/nz56tbt24aNGiQCgsLNWXKFE2fPl2nT592uxwAAADrXA9YzzzzjF555RU9%2BOCDCg8PlyT17dtXmzZtUnZ2ttvlAAAAWOd6wFq3bp1mzJihIUOGOHdvHzBggObMmaONGze6XQ4AAIB1rgesAwcOqFOnTueMx8fHy%2BfzuV0OAACAda4HrDZt2uiTTz45Z/y9995zLngHAAAIZq7/FuG4ceM0c%2BZM%2BXw%2BGWP0wQcfaN26dXrmmWd03333uV0OAACAda4HrKFDh6qqqkpLly5VeXm5ZsyYoRYtWujuu%2B/WiBEj3C4HAADAOtcDVk5Ojq655hoNHz5cR48elTFGLVu2dLsMAACAeuP6NVizZs1yLmZv0aIF4QoAADQ4rgesdu3aac%2BePW7vFgAAwDWunyKMj4/XPffco5UrV6pdu3aKiIjwm8/MzHS7JAAAAKtcD1hffPGFkpOTJYn7XgEAgAbJlYA1b948TZw4UY0bN9Yzzzzjxi4BAAACxpVrsJ566imVlZX5jY0fP15FRUVu7B4AAMBVrgQsY8w5Y1u3blVFRYUbuwcAAHCV679FCAAA0NARsAAAACxzLWCFhIS4tSsAAICAcu02DXPmzPG759Xp06eVlZWlJk2a%2BL2O%2B2ABAIBg58oRrG7dusnn8%2BnAgQPOIykpSSUlJX5jBw4c%2BNnbrqys1KBBg/Thhx86YwUFBbrtttuUmJioAQMGaPPmzX7vef/99zVo0CB5vV6NHj1aBQUFfvOrV69WWlqakpKSNG3atHN%2BAxIAAOCnuHIEq77ufVVRUaFJkyZp7969zpgxRunp6erYsaPWr1%2BvTZs2aeLEiXrttdcUFxenQ4cOKT09XXfddZfS0tK0ePFi3XnnnXr11VcVEhKiN998U9nZ2crKylLLli01depUZWVlacaMGfWyBgAA0PAE7UXu%2B/bt080336yvv/7ab3zLli0qKCjQrFmz1KFDB02YMEGJiYlav369JOnFF1/UZZddprFjx%2Briiy9WZmamDh48qI8%2B%2BkiStGbNGo0ZM0a9e/dWly5dNHPmTK1fv56jWAAAoM6CNmB99NFH6tGjh9atW%2Bc3np%2Bfr0svvVSNGzd2xpKTk5WXl%2BfMp6SkOHORkZHq3Lmz8vLyVF1drU8%2B%2BcRvPjExUadPn9bu3bvreUUAAKChcP1vEdoycuTIHxz3%2BXyKiYnxG2vZsqUKCwtrnT927JgqKir85j0ej5o1a%2Ba8vy6KiorO%2BTuLHk/jc/b7a4WFBVc%2B9niCq96znel3sPU9mNFzd9Fv99HzhitoA9aPKSsrU3h4uN9YeHi4Kisra50vLy93nv/Y%2B%2Bti3bp1ys7O9htLT09XRkZGnbfREDVv3qT2FwWBqKjIQJdw3qHn7qLf7qPnDU%2BDC1gREREqLS31G6usrFSjRo2c%2BbPDUmVlpaKiopzbSPzQfGRk3b/4hw8frj59%2BviNeTyNVVJyss7bqItg%2B4nH9vrdFhYWqqioSB07Vqbq6ppAl3NeoOfuot/uo%2Bf1L1A/3De4gBUbG6t9%2B/b5jRUXFzun52JjY1VcXHzOfKdOndSsWTNFRESouLhYHTp0kCRVVVWptLRU0dHRda4hJibmnNOBPt9xVVWd3988DWX91dU1DWYtwYKeu4t%2Bu4%2BeNzzBdQikDrxerz799FPndJ8k5ebmyuv1OvO5ubnOXFlZmXbu3Cmv16vQ0FAlJCT4zefl5cnj8Sg%2BPt69RQAAgKDW4AJW9%2B7d1bp1a02dOlV79%2B7VihUrtH37dg0bNkySNHToUG3btk0rVqzQ3r17NXXqVLVt21Y9evSQ9N3F86tWrdKmTZu0fft2PfTQQ7r55pt/1ilCAABwfmtwASssLExLliyRz%2BfTkCFD9Oqrr2rx4sWKi4uTJLVt21Z/%2B9vftH79eg0bNkylpaVavHix87cSBw4cqAkTJmjGjBkaO3asunTposmTJwdySQAAIMiEGGNMoIs4H/h8x61v0%2BMJVb/5/7S%2B3fry%2Bt09A13Cr%2BLxhKp58yYqKTnJtRIuoefuot/uo%2Bf1Lzr69wHZb4M7ggUAABBoBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGBZgw1Yb731li655BK/R0ZGhiRp586duummm%2BT1ejV06FDt2LHD7705OTnq27evvF6v0tPTdfTo0UAsAQAABKkGG7D27dun3r17a/Pmzc5jzpw5OnXqlMaPH6%2BUlBT9/e9/V1JSkiZMmKBTp05JkrZv367p06dr4sSJWrdunY4dO6apU6cGeDUAACCYNNiAtX//fnXs2FHR0dHOIyoqSq%2B99poiIiJ07733qkOHDpo%2BfbqaNGmiN954Q5K0du1aXXvttRo8eLDi4%2BM1b948vfvuuyooKAjwigAAQLBo0AGrXbt254zn5%2BcrOTlZISEhkqSQkBB17dpVeXl5znxKSorz%2BtatWysuLk75%2Bfmu1A0AAIJfgwxYxhh98cUX2rx5s/r376%2B%2Bfftq/vz5qqyslM/nU0xMjN/rW7ZsqcLCQklSUVHRT84DAADUxhPoAurDoUOHVFZWpvDwcD3%2B%2BOM6cOCA5syZo/Lycmf8%2B8LDw1VZWSlJKi8v/8n5uigqKpLP5/Mb83ganxPcfq2wsODKxx5PcNV7tjP9Dra%2BBzN67i767T563nA1yIDVpk0bffjhh7rggguS9mALAAAPQElEQVQUEhKiTp06qaamRpMnT1b37t3PCUuVlZVq1KiRJCkiIuIH5yMjI%2Bu8/3Xr1ik7O9tvLD093fktxvNV8%2BZNAl2CFVFRdf9agB303F302330vOFpkAFLkpo1a%2Bb3vEOHDqqoqFB0dLSKi4v95oqLi52jS7GxsT84Hx0dXed9Dx8%2BXH369PEb83gaq6Tk5M9ZQq2C7Sce2%2Bt3W1hYqKKiInXsWJmqq2sCXc55gZ67i367j57Xv0D9cN8gA9Y///lP3XPPPXrnnXecI0%2B7du1Ss2bNlJycrCeeeELGGIWEhMgYo23btunPf/6zJMnr9So3N1dDhgyRJB0%2BfFiHDx%2BW1%2But8/5jYmLOOR3o8x1XVdX5/c3TUNZfXV3TYNYSLOi5u%2Bi3%2B%2Bh5wxNch0DqKCkpSREREbr//vv1%2Beef691339W8efN0xx136JprrtGxY8c0d%2B5c7du3T3PnzlVZWZmuvfZaSdKIESP0yiuv6MUXX9Tu3bt177336qqrrtKFF14Y4FUBAIBg0SADVtOmTbVq1SodPXpUQ4cO1fTp0zV8%2BHDdcccdatq0qZYvX%2B4cpcrPz9eKFSvUuHFjSd%2BFs1mzZmnx4sUaMWKELrjgAmVmZgZ4RQAAIJiEGGNMoIs4H/h8x61v0%2BMJVb/5/7S%2B3fry%2Bt09A13Cr%2BLxhKp58yYqKTnJoXyX0HN30W/30fP6Fx39%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%2B9ePfvsswQsAABQJwSss%2BzevVtVVVVKSkpyxpKTk7Vs2TLV1NQoNJSzqr/UtY//K9Al/Cyv390z0CUAAIIUAessPp9PzZs3V3h4uDPWqlUrVVRUqLS0VC1atKh1G0VFRfL5fH5jHk9jxcTEWK01LIywV588Hv/%2Bnuk3fXcPPXcX/XYfPW%2B4CFhnKSsr8wtXkpznlZWVddrGunXrlJ2d7Tc2ceJE3XXXXXaK/P%2BKioo05g97NXz4cOvhDecqKirS00%2BvpN8uoufuot/uo%2BcNF5H5LBEREecEqTPPGzVqVKdtDB8%2BXH//%2B9/9HsOHD7deq8/nU3Z29jlHy1A/6Lf76Lm76Lf76HnDxRGss8TGxqqkpERVVVXyeL5rj8/nU6NGjRQVFVWnbcTExPCTCAAA5zGOYJ2lU6dO8ng8ysvLc8Zyc3OVkJDABe4AAKBOSAxniYyM1ODBg/XQQw9p%2B/bt2rRpk5588kmNHj060KUBAIAgEfbQQw89FOgifmtSU1O1c%2BdOPfroo/rggw/05z//WUOHDg10WT%2BoSZMm6t69u5o0aRLoUs4L9Nt99Nxd9Nt99LxhCjHGmEAXAQAA0JBwihAAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQErSFVUVGjatGlKSUlRr1699OSTTwa6pN%2BsyspKDRo0SB9%2B%2BKEzVlBQoNtuu02JiYkaMGCANm/e7Pee999/X4MGDZLX69Xo0aNVUFDgN7969WqlpaUpKSlJ06ZNU1lZmTNX22dT276D1ZEjR5SRkaHu3bsrLS1NmZmZqqiokES/68tXX32lcePGKSkpSVdddZVWrlzpzNHz%2BjV%2B/Hjdd999zvOdO3fqpptuktfr1dChQ7Vjxw6/1%2Bfk5Khv377yer1KT0/X0aNHnTljjObPn6/U1FR1795d8%2BbNU01NjTNfUlKiu%2B66S0lJSerTp49eeeUVv23Xtm8EiEFQmjVrlrnuuuvMjh07zP/%2B7/%2BapKQk8/rrrwe6rN%2Bc8vJyk56ebjp27Gi2bNlijDGmpqbGXHfddWbSpElm3759ZtmyZcbr9ZqDBw8aY4w5ePCgSUxMNKtWrTJ79uwxf/nLX8ygQYNMTU2NMcaYN954wyQnJ5v/%2B7//M/n5%2BWbAgAFm5syZzj5/6rOpbd/Bqqamxtx8883mjjvuMHv27DFbt241/fr1M4888gj9rifV1dXm6quvNpMmTTJffPGFeeedd0zXrl3Nq6%2B%2BSs/rWU5OjunYsaOZMmWKMcaYkydPmp49e5pHHnnE7Nu3z8yePdv86U9/MidPnjTGGJOfn2%2B6dOliXn75ZbNr1y4zatQoM378eGd7q1atMldeeaXZunWr%2BeCDD0yvXr3MypUrnfkJEyaYMWPGmM8%2B%2B8y88MIL5rLLLjP5%2Bfl12jcCh4AVhE6ePGkSEhKcwGCMMYsXLzajRo0KYFW/PXv37jXXX3%2B9ue666/wC1vvvv28SExP9/gMaM2aMWbRokTHGmMcff9yvl6dOnTJJSUnO%2B0eOHOm81hhjtm7darp06WJOnTpV62dT276D1b59%2B0zHjh2Nz%2BdzxjZu3Gh69epFv%2BvJkSNHzF/%2B8hdz/PhxZyw9Pd08%2BOCD9LwelZSUmCuuuMIMHTrUCVgvvvii6dOnjxNQa2pqTL9%2B/cz69euNMcZMnjzZea0xxhw6dMhccskl5uuvvzbGGHPllVc6rzXGmA0bNpjevXsbY4z56quvTMeOHU1BQYEzP23atDrvG4HDKcIgtHv3blVVVSkpKckZS05OVn5%2Bvt9h5fPdRx99pB49emjdunV%2B4/n5%2Bbr00kvVuHFjZyw5OVl5eXnOfEpKijMXGRmpzp07Ky8vT9XV1frkk0/85hMTE3X69Gnt3r271s%2Bmtn0Hq%2BjoaK1cuVKtWrXyGz9x4gT9ricxMTF6/PHH1bRpUxljlJubq61bt6p79%2B70vB799a9/1Q033KCLLrrIGcvPz1dycrJCQkIkSSEhIerateuP9rt169aKi4tTfn6%2Bjhw5osOHD6tbt27OfHJysg4ePKiioiLl5%2BerdevWatu2rd/8xx9/XKd9I3AIWEHI5/OpefPmCg8Pd8ZatWqliooKlZaWBrCy35aRI0dq2rRpioyM9Bv3%2BXyKiYnxG2vZsqUKCwtrnT927JgqKir85j0ej5o1a6bCwsJaP5va9h2soqKilJaW5jyvqanR2rVrlZqaSr9d0KdPH40cOVJJSUnq378/Pa8nH3zwgf7973/rzjvv9Buvbc1FRUU/Ou/z%2BSTJb/7MDypn5n/ovUeOHKnTvhE4BKwgVFZW5vefmyTneWVlZSBKCio/1r8zvfup%2BfLycuf5D83X9tnUtu%2BGIisrSzt37tR//dd/0W8XLFq0SMuWLdOuXbuUmZlJz%2BtBRUWFHnzwQc2YMUONGjXym6ttzeXl5T%2Br3z%2Bnnw213w2BJ9AF4OeLiIg455vnzPOzv/FxroiIiHOO9FVWVjq9%2B7H%2BRkVFKSIiwnl%2B9nxkZKSqq6t/8rOpbd8NQVZWlp5%2B%2BmktWLBAHTt2pN8uSEhIkPRdCLjnnns0dOhQv9/6k%2Bj5r5Wdna3LLrvM70jtGT/Wz9r6HRkZ6Remzu59ZGTkL952sPe7IeAIVhCKjY1VSUmJqqqqnDGfz6dGjRopKioqgJUFh9jYWBUXF/uNFRcXO4fZf2w%2BOjpazZo1U0REhN98VVWVSktLFR0dXetnU9u%2Bg93s2bP11FNPKSsrS/3795dEv%2BtLcXGxNm3a5Dd20UUX6fTp04qOjqbnlv3P//yPNm3apKSkJCUlJWnjxo3auHGjkpKSftXXeGxsrCQ5pwq//%2B8z8z/23p/adrD3uyEgYAWhTp06yePx%2BF3EmJubq4SEBIWG8pHWxuv16tNPP3UOzUvf9c/r9Trzubm5zlxZWZl27twpr9er0NBQJSQk%2BM3n5eXJ4/EoPj6%2B1s%2Bmtn0Hs%2BzsbD3//PN67LHHNHDgQGecftePAwcOaOLEic61OJK0Y8cOtWjRQsnJyfTcsmeeeUYbN27Uhg0btGHDBvXp00d9%2BvTRhg0b5PV69fHHH8sYI%2Bm7%2B1pt27btR/t9%2BPBhHT58WF6vV7GxsYqLi/Obz83NVVxcnGJiYpSYmKiDBw/6XVOVm5urxMREZ9s/tW8EUEB/hxG/2AMPPGAGDhxo8vPzzVtvvWW6du1q3nzzzUCX9Zv1/ds0VFVVmQEDBpi7777b7NmzxyxfvtwkJiY69%2BkpKCgwCQkJZvny5c49gq677jrn16BzcnJM165dzVtvvWXy8/PNwIEDzezZs519/dRnU9u%2Bg9W%2BfftMp06dzIIFC0xRUZHfg37Xj6qqKjNkyBAzduxYs3fvXvPOO%2B%2BYP/3pT2b16tX03AVTpkxxbpVw/Phxk5qaambPnm327t1rZs%2BebXr27OncqmLbtm2mc%2BfO5oUXXnDugzVhwgRnW8uXLze9evUyW7ZsMVu2bDG9evUyTz75pDM/duxYM2rUKLNr1y7zwgsvmISEBOc%2BWLXtG4FDwApSp06dMvfee69JTEw0vXr1Mk899VSgS/pN%2B37AMsaYL7/80txyyy3msssuMwMHDjT/%2Bte//F7/zjvvmKuvvtp06dLFjBkzxrlfzRnLly83l19%2BuUlOTjZTp0415eXlzlxtn01t%2Bw5Gy5cvNx07dvzBhzH0u74UFhaa9PR007VrV9OzZ0%2BzdOlSJyTR8/r1/YBlzHc3Ex08eLBJSEgww4YNM59%2B%2Bqnf69evX2%2BuvPJKk5iYaNLT083Ro0eduaqqKvPwww%2BblJQU06NHD5OVleV8jsYYU1xcbCZMmGASEhJMnz59zMaNG/22Xdu%2BERghxvz/44oAAACwggt2AAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMCy/wddeIwfuYZ58AAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-306408118052712388”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (765)</td> <td class=”number”>1966</td> <td class=”number”>61.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>619</td> <td class=”number”>19.3%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-306408118052712388”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>187613.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>191155.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>274351.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>407208.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>471825.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRES12”><s>ORCHARD_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_ACRES07”><code>ORCHARD_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99592</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRESPTH07”>ORCHARD_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2234</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>619</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>26.027</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2943.1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5284335655492024258”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5284335655492024258,#minihistogram-5284335655492024258”
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Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5284335655492024258”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5284335655492024258”

aria-controls=”quantiles-5284335655492024258” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5284335655492024258” aria-controls=”histogram-5284335655492024258”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5284335655492024258” aria-controls=”common-5284335655492024258”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5284335655492024258” aria-controls=”extreme-5284335655492024258”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5284335655492024258”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.33058</td>

</tr> <tr>

<th>Median</th> <td>1.3103</td>

</tr> <tr>

<th>Q3</th> <td>5.2492</td>

</tr> <tr>

<th>95-th percentile</th> <td>111.15</td>

</tr> <tr>

<th>Maximum</th> <td>2943.1</td>

</tr> <tr>

<th>Range</th> <td>2943.1</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.9186</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>133.33</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1229</td>

</tr> <tr>

<th>Kurtosis</th> <td>214.59</td>

</tr> <tr>

<th>Mean</th> <td>26.027</td>

</tr> <tr>

<th>MAD</th> <td>40.939</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>12.754</td>

</tr> <tr>

<th>Sum</th> <td>67435</td>

</tr> <tr>

<th>Variance</th> <td>17777</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5284335655492024258”>

<img 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EbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGBZ0APWjTfeqBdffFGHDx8O9qEBAACCIugBq0%2BfPlqyZInS09N1//33a%2BPGjTLGBHsYAAAAjSboAWvSpEn6xz/%2BoUWLFikyMlL33nuvrr76aj3zzDP65ptvgj0cAAAA69yhOKjL5VLfvn3Vt29fVVZW6oUXXtCiRYu0bNky9ejRQ%2BPHj9e1114biqEBAAD8YiEJWJJUVlam1157Ta%2B99pp27typHj166IYbblBpaakeffRRbd68WVOnTg3V8AAAAM5Z0APWq6%2B%2BqldffVWffvqpWrdurREjRmjBggXq0KGD/zXt2rXTrFmzCFgAACAsBT1gTZ06Vf3799fChQt15ZVXKiKi4WVgHTt21Lhx44I9NAAAACuCHrA%2B/PBDtWrVShUVFf5wtXXrVnXr1k2RkZGSpB49eqhHjx7BHhoAAIAVQf8U4ZEjRzR48GD97W9/87fddddduv7661VSUvKz91dTU6Nhw4bp008/9bfNnDlTF198ccBjzZo1/v68vDxdc8018ng8ysjI0IEDB/x9xhjNmTNHffr0Ua9evTR79mzV19ef42wBAMBvUdAD1pNPPqk//vGPuu222/xtb7zxhtq1a6esrKyfta/q6mrdf//9KiwsDGgvKirSpEmTtHHjRv9j1KhRkn58t2zq1KmaOHGicnNzdejQIU2ZMsW/7fPPP6%2B8vDzl5ORowYIFev311/X888//ghkDAIDfmqAHrP/7v//Tww8/rPj4eH9b69at9eCDD2rTpk2O97Nr1y7ddNNN%2Bte//tWgr6ioSJdcconi4%2BP9j5iYGEnSmjVrdN1112nEiBHq0qWLZs%2BerQ8%2B%2BEDFxcWSpNWrVyszM1NpaWnq06ePHnjgAa1du/YXzhoAAPyWBD1gud1uHTp0qEF7ZWXlz7qj%2B2effabevXsrNzc3oP3IkSPav39/wKcST%2Bb1epWWluZ/3q5dOyUlJcnr9Wr//v0qKSlRz549/f2pqanau3evysrKHI8NAAD8tgX9Ivcrr7xSM2fO1NNPP61/%2B7d/kyQVFxcrKytL/fr1c7yfsWPHnra9qKhILpdLS5Ys0Ycffqi4uDjddtttuuGGGyT9eP%2BthISEgG3atGmj0tJS%2BXw%2BSQrob9u2rSSptLS0wXZnUlZW5t/XCW53c8fbOxUZGV7f1e12h2a8J%2BoUbvUKNurkDHVyhjo5Q52cC7daBT1gPfTQQ7rttts0aNAgxcbGSpIOHTqkbt26BVwLda52794tl8vlv9XD5s2bNW3aNLVs2VIDBw5UVVWVoqKiAraJiopSTU2Nqqqq/M9P7pN%2BvJjeqdzcXOXk5AS0ZWRkKDMz81yn1SS0atUipMePjY0J6fHDBXVyhjo5Q52coU7OhUutgh6w2rRpo5dfflkff/yxCgsL5Xa7deGFF%2Bryyy%2BXy%2BX6xfsfMWKE%2Bvfvr7i4OElSly5d9O2332rdunUaOHCgoqOjG4SlmpoaxcTEBISp6Oho/58l%2Ba/hcmLMmDEaMGBAQJvb3VwHDx4953mdTrik%2BBNsz9%2BpyMgIxcbG6NChStXV8YnQM6FOzlAnZ6iTM9TJuXOtVah%2BuQ/JV%2BVERkaqX79%2BP%2BuUoFMul8sfrk7o2LGj/wL6xMRElZeXB/SXl5crPj5eiYmJkiSfz6fzzz/f/2dJARfl/5SEhIQGpwN9vsOqrf1t//CEev51dfUhH0M4oE7OUCdnqJMz1Mm5cKlV0AOWz%2BfTvHnztGXLFh0/frzBhe3vvffeL9r//Pnz9fnnn2vlypX%2Bth07dqhjx46SJI/Ho/z8fI0cOVKSVFJSopKSEnk8HiUmJiopKUn5%2Bfn%2BgJWfn6%2BkpCTr108BAICmK%2BgBa9q0adq2bZuGDh2q3//%2B99b3379/fy1btkwrVqzQwIEDtXHjRr3yyitavXq1JOnmm2/WLbfcouTkZHXv3l2zZs3S1VdfrQsuuMDfP2fOHP3hD3%2BQJM2dO1e333679XECAICmK%2BgBa9OmTVq%2BfHnArRJsuuyyyzR//nwtWLBA8%2BfPV/v27TV37lylpKRIklJSUjR9%2BnQtWLBAP/zwg/r27asZM2b4t7/jjjv0/fffa%2BLEiYqMjNTo0aM1YcKERhkrAABomlzm59x8yoIrrrhCa9eu1b//%2B78H87Ah5/Mdtr5PtztCA%2Bd8ZH2/jeXN%2B/qG5Lhud4RatWqhgwePhsV5%2B1ChTs5QJ2eokzPUyblzrVV8vP2zZU4E/WNo119/vZYvX666urpgHxoAACAogn6KsKKiQnl5eXr//fd1wQUXNLgn1YlrpQAAAMJVSG7TMGzYsFAcFgAAICiCHrCysrKCfUgAAICgCsmtwMvKypSTk6NJkybp%2B%2B%2B/11tvvaXdu3eHYigAAADWBT1gfffddxo%2BfLhefvllvf322zp27JjeeOMNjRo1Sl6vN9jDAQAAsC7oAeupp57SNddco3fffVe/%2B93vJElPP/20BgwYoDlz5gR7OAAAANYFPWBt2bJFt912W8AXO7vdbt1zzz3avn17sIcDAABgXdADVn19verrG94g7OjRo4qMjAz2cAAAAKwLesBKT0/X0qVLA0JWRUWFsrOz1adPn2APBwAAwLqgB6yHH35Y27ZtU3p6uqqrq/Vf//Vf6t%2B/v/bs2aOHHnoo2MMBAACwLuj3wUpMTNQrr7yivLw8ffXVV6qvr9fNN9%2Bs66%2B/Xi1btgz2cAAAAKwLyZ3cY2JidOONN4bi0AAAAI0u6AHr1ltvPWs/30UIAADCXdADVvv27QOe19bW6rvvvtPOnTs1fvz4YA8HAADAul/NdxEuXLhQpaWlQR4NAACAfSH5LsLTuf766/Xmm2%2BGehgAAAC/2K8mYH3%2B%2BefcaBQAADQJv4qL3I8cOaKvv/5aY8eODfZwAAAArAt6wEpKSgr4HkJJ%2Bt3vfqdx48bpT3/6U7CHAwAAYF3QA9ZTTz0V7EMCAAAEVdAD1ubNmx2/tmfPno04EgAAgMYR9IB1yy23%2BE8RGmP87ae2uVwuffXVV8EeHgAAwC8W9IC1ZMkSzZw5U5MnT1avXr0UFRWlL774QtOnT9cNN9ygIUOGBHtIAAAAVgX9Ng1ZWVl67LHHNGjQILVq1UotWrRQnz59NH36dK1bt07t27f3PwAAAMJR0ANWWVnZacNTy5YtdfDgwWAPBwAAwLqgB6zk5GQ9/fTTOnLkiL%2BtoqJC2dnZuvzyy4M9HAAAAOuCfg3Wo48%2BqltvvVVXXnmlOnToIGOMvv32W8XHx2v16tXBHg4AAIB1QQ9YnTp10htvvKG8vDwVFRVJkv785z9r6NChiomJCfZwAAAArAt6wJKk8847TzfeeKP27NmjCy64QNKPd3MHAABoCoJ%2BDZYxRnPmzFHPnj01bNgwlZaW6qGHHtLUqVN1/PjxYA8HAADAuqAHrBdeeEGvvvqq/vrXvyoqKkqSdM011%2Bjdd99VTk5OsIcDAABgXdADVm5urh577DGNHDnSf/f2IUOGaObMmXr99deDPRwAAADrgh6w9uzZo65duzZo79Kli3w%2BX7CHAwAAYF3QA1b79u31xRdfNGj/8MMP/Re8AwAAhLOgf4rwjjvu0BNPPCGfzydjjD755BPl5ubqhRde0MMPPxzs4QAAAFgX9IA1atQo1dbWavHixaqqqtJjjz2m1q1b67777tPNN98c7OEAAABYF/SAlZeXp8GDB2vMmDE6cOCAjDFq06ZNsIcBAADQaIJ%2BDdb06dP9F7O3bt2acAUAAJqcoAesDh06aOfOncE%2BLAAAQNAE/RRhly5d9MADD2j58uXq0KGDoqOjA/qzsrKCPSQAAACrgh6wvvnmG6WmpkoS970CAABNUlAC1uzZszVx4kQ1b95cL7zwQjAOCQAAEDJBuQbr%2BeefV2VlZUDbXXfdpbKysmAcHgAAIKiCErCMMQ3aNm/erOrq6mAcHgAAIKiC/ilCAACApo6ABQAAYFnQApbL5QrWoQAAAEIqaLdpmDlzZsA9r44fP67s7Gy1aNEi4HU/9z5YNTU1GjlypKZNm6bevXtLkoqLizVt2jQVFBQoKSlJjzzyiNLT0/3bfPzxx3ryySdVXFwsj8ejWbNm6YILLvD3r1y5UitWrNCRI0d03XXXadq0aYqJiTmXaQMAgN%2BgoLyD1bNnT/l8Pu3Zs8f/SElJ0cGDBwPa9uzZ87P2W11drfvvv1%2BFhYX%2BNmOMMjIy1LZtW23YsEHXX3%2B9Jk6cqH379kmS9u3bp4yMDI0cOVLr169X69atdc899/gvxH/77beVk5Oj6dOna9WqVfJ6vcrOzrZXDAAA0OQF5R2sxrj31a5duzRp0qQGn1DctGmTiouL9eKLL6p58%2Bbq1KmTPvnkE23YsEH33nuvXnrpJV166aW6/fbbJf34jlnfvn312WefqXfv3lq9erXGjx%2Bv/v37S5KeeOIJ3XHHHZo8eTLvYgEAAEfC9iL3E4EoNzc3oN3r9eqSSy5R8%2BbN/W2pqakqKCjw96elpfn7YmJi1K1bNxUUFKiurk5ffPFFQH9ycrKOHz%2BuHTt2NPKMAABAUxH0r8qxZezYsadt9/l8SkhICGhr06aNSktLf7L/0KFDqq6uDuh3u92Ki4vzb%2B9EWVlZg68BcrubNzjuLxUZGV752O0OzXhP1Cnc6hVs1MkZ6uQMdXKGOjkXbrUK24B1JpWVlYqKigpoi4qKUk1NzU/2V1VV%2BZ%2BfaXsncnNzlZOTE9CWkZGhzMxMx/toilq1avHTL2pEsbGc4nWCOjlDnZyhTs5QJ%2BfCpVZNLmBFR0eroqIioK2mpkbNmjXz958almpqahQbG%2Bv/lOPp%2Bn/O9VdjxozRgAEDAtrc7uY6ePCo4304ES4p/gTb83cqMjJCsbExOnSoUnV19SEZQzigTs5QJ2eokzPUyblzrVWofrlvcgErMTFRu3btCmgrLy/3n55LTExUeXl5g/6uXbsqLi5O0dHRKi8vV6dOnSRJtbW1qqioUHx8vOMxJCQkNDgd6PMdVm3tb/uHJ9Tzr6urD/kYwgF1coY6OUOdnKFOzoVLrcLrLRAHPB6PvvzyS//pPknKz8%2BXx%2BPx9%2Bfn5/v7KisrtX37dnk8HkVERKh79%2B4B/QUFBXK73erSpUvwJgEAAMJakwtYvXr1Urt27TRlyhQVFhZq2bJl2rp1q0aPHi1JGjVqlLZs2aJly5apsLBQU6ZM0fnnn%2B%2B/SenYsWO1YsUKvfvuu9q6dasef/xx3XTTTdyiAQAAONbkAlZkZKQWLVokn8%2BnkSNH6rXXXtPChQuVlJQkSTr//PP17LPPasOGDRo9erQqKiq0cOFC/1f5DB06VHfffbcee%2Bwx3X777brssss0efLkUE4JAACEGZc59U6daBQ%2B32Hr%2B3S7IzRwzkfW99tY3ryvb0iO63ZHqFWrFjp48GhYnLcPFerkDHVyhjo5Q52cO9daxcf/vhFHdWZN7h0sAACAUCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAljXZgPXOO%2B/o4osvDnhkZmZKkrZv364bb7xRHo9Ho0aN0rZt2wK2zcvL0zXXXCOPx6OMjAwdOHAgFFMAAABhqskGrF27dql///7auHGj/zFz5kwdO3ZMd911l9LS0vT3v/9dKSkpuvvuu3Xs2DFJ0tatWzV16lRNnDhRubm5OnTokKZMmRLi2QAAgHDSZANWUVGROnfurPj4eP8jNjZWb7zxhqKjo/Xggw%2BqU6dOmjp1qlq0aKG33npLkrRmzRpdd911GjFihLp06aLZs2frgw8%2BUHFxcYhnBAAAwkWTDlgdOnRo0O71epWamiqXyyVJcrlc6tGjhwoKCvz9aWlp/te3a9dOSUlJ8nq9QRk3AAAIf%2B5QD6AxGGP0zTffaOPGjVq6dKnq6uo0ePAjfZvkAAAQzklEQVRgZWZmyufz6cILLwx4fZs2bVRYWChJKisrU0JCQoP%2B0tJSx8cvKyuTz%2BcLaHO7mzfY7y8VGRle%2BdjtDs14T9Qp3OoVbNTJGerkDHVyhjo5F261apIBa9%2B%2BfaqsrFRUVJTmzZunPXv2aObMmaqqqvK3nywqKko1NTWSpKqqqrP2O5Gbm6ucnJyAtoyMDP9F9r9VrVq1COnxY2NjQnr8cEGdnKFOzlAnZ6iTc%2BFSqyYZsNq3b69PP/1U5513nlwul7p27ar6%2BnpNnjxZvXr1ahCWampq1KxZM0lSdHT0aftjYpz/hY4ZM0YDBgwIaHO7m%2BvgwaPnOKPTC5cUf4Lt%2BTsVGRmh2NgYHTpUqbq6%2BpCMIRxQJ2eokzPUyRnq5Ny51ipUv9w3yYAlSXFxcQHPO3XqpOrqasXHx6u8vDygr7y83H/6LjEx8bT98fHxjo%2BdkJDQ4HSgz3dYtbW/7R%2BeUM%2B/rq4%2B5GMIB9TJGerkDHVyhjo5Fy61Cq%2B3QBz66KOP1Lt3b1VWVvrbvvrqK8XFxSk1NVWff/65jDGSfrxea8uWLfJ4PJIkj8ej/Px8/3YlJSUqKSnx9wMAAPyUJhmwUlJSFB0drUcffVS7d%2B/WBx98oNmzZ%2BvOO%2B/U4MGDdejQIc2aNUu7du3SrFmzVFlZqeuuu06SdPPNN%2BvVV1/VSy%2B9pB07dujBBx/U1VdfrQsuuCDEswIAAOGiSQasli1basWKFTpw4IBGjRqlqVOnasyYMbrzzjvVsmVLLV26VPn5%2BRo5cqS8Xq%2BWLVum5s2bS/oxnE2fPl0LFy7UzTffrPPOO09ZWVkhnhEAAAgnLnPiXBkalc932Po%2B3e4IDZzzkfX9NpY37%2BsbkuO63RFq1aqFDh48Ghbn7UOFOjlDnZyhTs5QJ%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%2BhHsLP8uZ9fUM9BABAmCJgneLYsWN66aWX9Le//U3dunVTt27dVFhYqLVr1xKwAACAI5wiPMWOHTtUW1urlJQUf1tqaqq8Xq/q6%2BtDODIAABAueAfrFD6fT61atVJUVJS/rW3btqqurlZFRYVat279k/soKyuTz%2BcLaHO7myshIcHqWCMjyceNKdxOab7zQL9ftP2J9cS6Ojvq5Ax1coY6ORdutSJgnaKysjIgXEnyP6%2BpqXG0j9zcXOXk5AS0TZw4Uffee6%2BdQf5/ZWVlGv%2BHQo0ZM8Z6eGtKysrKlJubS51%2BQllZmVatWk6dfgJ1coY6OUOdnAu3WoVHDAyi6OjoBkHqxPNmzZo52seYMWP097//PeAxZswY62P1%2BXzKyclp8G4ZAlEnZ6iTM9TJGerkDHVyLtxqxTtYp0hMTNTBgwdVW1srt/vH8vh8PjVr1kyxsbGO9pGQkBAW6RoAADQO3sE6RdeuXeV2u1VQUOBvy8/PV/fu3RURQbkAAMBPIzGcIiYmRiNGjNDjjz%2BurVu36t1339Vzzz2nW2%2B9NdRDAwAAYSLy8ccffzzUg/i16dOnj7Zv3665c%2Bfqk08%2B0X/%2B539q1KhRoR7WabVo0UK9evVSixYtQj2UXzXq5Ax1coY6OUOdnKFOzoVTrVzGGBPqQQAAADQlnCIEAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAClPV1dV65JFHlJaWpvT0dD333HOhHlJIvPPOO7r44osDHpmZmZKk7du368Ybb5TH49GoUaO0bdu2gG3z8vJ0zTXXyOPxKCMjQwcOHAjFFBpVTU2Nhg0bpk8//dTfVlxcrAkTJig5OVlDhgzRxo0bA7b5%2BOOPNWzYMHk8Ht16660qLi4O6F%2B5cqX69eunlJQUPfLII6qsrAzKXBrT6eo0c%2BbMBmtrzZo1/v6zrR9jjObMmaM%2BffqoV69emj17turr64M6J5v279%2BvzMxM9erVS/369VNWVpaqq6slsZ5OdrY6sZ4Cfffdd7rjjjuUkpKiq6%2B%2BWsuXL/f3NZk1ZRCWpk%2BfboYPH262bdtm/ud//sekpKSYN998M9TDCrpFixaZu%2B%2B%2B25SVlfkfP/zwgzl69Kjp27eveeqpp8yuXbvMjBkzzBVXXGGOHj1qjDHG6/Wayy67zLz88svmq6%2B%2BMuPGjTN33XVXiGdjV1VVlcnIyDCdO3c2mzZtMsYYU19fb4YPH24mTZpkdu3aZZYsWWI8Ho/Zu3evMcaYvXv3muTkZLNixQqzc%2BdO85e//MUMGzbM1NfXG2OMeeutt0xqaqr53//9X%2BP1es2QIUPME088EbI52nC6OhljzIQJE8zSpUsD1taxY8eMMT%2B9flasWGGuuuoqs3nzZvPJJ5%2BY9PR0s3z58qDPzYb6%2Bnpz0003mTvvvNPs3LnTbN682QwcONA89dRTrKeTnK1OxrCeTlZXV2euvfZaM2nSJPPNN9%2BY999/3/To0cO89tprTWpNEbDC0NGjR0337t0D/jNYuHChGTduXAhHFRqTJk0yc%2BfObdD%2B0ksvmQEDBvh/6Orr683AgQPNhg0bjDHGTJ482Tz00EP%2B1%2B/bt89cfPHF5l//%2BldwBt7ICgsLzZ/%2B9CczfPjwgODw8ccfm%2BTkZH/QNMaY8ePHmwULFhhjjJk3b17AOjp27JhJSUnxbz927Fj/a40xZvPmzeayyy7z/0cRbs5UJ2OM6devn/noo49Ou91PrZ%2BrrrrKv9aMMeaVV14x/fv3b6RZNK5du3aZzp07G5/P5297/fXXTXp6OuvpJGerkzGsp5Pt37/f/OUvfzGHDx/2t2VkZJi//vWvTWpNcYowDO3YsUO1tbVKSUnxt6Wmpsrr9Yb928Y/V1FRkTp06NCg3ev1KjU1VS6XS5LkcrnUo0cPFRQU%2BPvT0tL8r2/Xrp2SkpLk9XqDMu7G9tlnn6l3797Kzc0NaPd6vbrkkkvUvHlzf1tqauoZ6xITE6Nu3bqpoKBAdXV1%2BuKLLwL6k5OTdfz4ce3YsaORZ9Q4zlSnI0eOaP/%2B/addW9LZ18/%2B/ftVUlKinj17%2BvtTU1O1d%2B9elZWVNco8GlN8fLyWL1%2Butm3bBrQfOXKE9XSSs9WJ9RQoISFB8%2BbNU8uWLWWMUX5%2BvjZv3qxevXo1qTXlDvoR8Yv5fD61atVKUVFR/ra2bduqurpaFRUVat26dQhHFzzGGH3zzTfauHGjli5dqrq6Og0ePFiZmZny%2BXy68MILA17fpk0bFRYWSpLKysqUkJDQoL%2B0tDRo429MY8eOPW27z%2Bc767zP1n/o0CFVV1cH9LvdbsXFxYVt3c5Up6KiIrlcLi1ZskQffvih4uLidNttt%2BmGG26QdPb14/P5JCmg/8R/uqWlpQ22%2B7WLjY1Vv379/M/r6%2Bu1Zs0a9enTh/V0krPVifV0ZgMGDNC%2BffvUv39/DRo0SE8%2B%2BWSTWVMErDBUWVkZEK4k%2BZ/X1NSEYkghsW/fPn8t5s2bpz179mjmzJmqqqo6Y41O1Keqquqs/U3VT9XlbP1VVVX%2B52favqnYvXu3XC6XOnbsqHHjxmnz5s2aNm2aWrZsqYEDB551/ZyuTk3p5zM7O1vbt2/X%2BvXrtXLlStbTGZxcpy%2B//JL1dAYLFixQeXm5Hn/8cWVlZTWpf6MIWGEoOjq6wWI58bxZs2ahGFJItG/fXp9%2B%2BqnOO%2B88uVwude3aVfX19Zo8ebJ69ep12hqdqM%2BZahgTExO08YdCdHS0KioqAtqc1CU2NlbR0dH%2B56f2N7W6jRgxQv3791dcXJwkqUuXLvr222%2B1bt06DRw48Kzr5%2BT//E6tWbjXKTs7W6tWrdIzzzyjzp07s57O4NQ6XXTRRaynM%2BjevbukHz8Z/8ADD2jUqFENPvUXrmuKa7DCUGJiog4ePKja2lp/m8/nU7NmzRQbGxvCkQVfXFyc/zorSerUqZOqq6sVHx%2Bv8vLygNeWl5f73zpOTEw8bX98fHzjDzqEzjRvJ3WJi4tTdHR0QH9tba0qKiqaXN1cLpf/P8MTOnbsqP3790s6e50SExMlyX9q5%2BQ/h3OdZsyYoeeff17Z2dkaNGiQJNbT6ZyuTqynQOXl5Xr33XcD2i688EIdP378F/3b/WtbUwSsMNS1a1e53W7/RX%2BSlJ%2Bfr%2B7duysi4rfzV/rRRx%2Bpd%2B/eAb/tfPXVV4qLi1Nqaqo%2B//xzGWMk/Xi91pYtW%2BTxeCRJHo9H%2Bfn5/u1KSkpUUlLi72%2BqPB6PvvzyS/9b6dKPa%2BdMdamsrNT27dvl8XgUERGh7t27B/QXFBTI7XarS5cuwZtEEMyfP18TJkwIaNuxY4c6duwo6ezrJzExUUlJSQH9%2Bfn5SkpKCtvrZXJycvTiiy/q6aef1tChQ/3trKdAZ6oT6ynQnj17NHHiRH/AlKRt27apdevWSk1NbTprKuifW4QV06ZNM0OHDjVer9e88847pkePHubtt98O9bCC6vDhw6Zfv37m/vvvN0VFReb999836enpZtmyZebw4cOmT58%2BZsaMGaawsNDMmDHD9O3b1//R3y1btphu3bqZ//7v//bfd%2Bbuu%2B8O8Ywax8m3H6itrTVDhgwx9913n9m5c6dZunSpSU5O9t9jpri42HTv3t0sXbrUf4%2BZ4cOH%2B293kZeXZ3r06GHeeecd4/V6zdChQ82MGTNCNjebTq6T1%2Bs1l1xyiVm%2BfLn57rvvzNq1a82ll15qtmzZYoz56fWzdOlSk56ebjZt2mQ2bdpk0tPTzXPPPReSef1Su3btMl27djXPPPNMwD2cysrKWE8nOVudWE%2BBamtrzciRI83tt99uCgsLzfvvv2%2BuuOIKs3Llyia1pghYYerYsWPmwQcfNMnJySY9Pd08//zzoR5SSOzcudNMmDDBJCcnm759%2B5pnn33W/4Pm9XrNiBEjTPfu3c3o0aPNl19%2BGbDthg0bzFVXXWWSk5NNRkaGOXDgQCim0OhOvb/Tt99%2Ba/785z%2BbSy%2B91AwdOtT885//DHj9%2B%2B%2B/b6699lpz2WWXmfHjxze4N9jSpUvN5ZdfblJTU82UKVNMVVVVUObR2E6t0zvvvGOGDx9uunfvbgYPHtzgF5izrZ/a2lrz5JNPmrS0NNO7d2%2BTnZ3tX5fhZunSpaZz586nfRjDejrhp%2BrEegpUWlpqMjIyTI8ePUzfvn3N4sWL/XNqKmvKZcz/P4cCAAAAK347F%2BwAAAAECQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJb9P/ouPZ3kDI5%2BAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5284335655492024258”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6005645306588193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5086395533086847</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0431723989003414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5136826375268515</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.541057621761947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7037402671846886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>172.9202830818534</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3981125480601189</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.6364764267990073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2223)</td> <td class=”number”>2223</td> <td class=”number”>69.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>619</td> <td class=”number”>19.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5284335655492024258”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0036862890419237775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005715254586015535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.007714127653384408</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009053962749733756</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1512.6675739062455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1591.6457811194653</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2488.3511269276396</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2588.838883888389</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2943.0600420448136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_ACRESPTH12”><s>ORCHARD_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_ACRESPTH07”><code>ORCHARD_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96957</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_FARMS07”>ORCHARD_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>234</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>37.657</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4685</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>11.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6993930423616494428”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-6993930423616494428”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6993930423616494428”

aria-controls=”quantiles-6993930423616494428” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6993930423616494428” aria-controls=”histogram-6993930423616494428”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6993930423616494428” aria-controls=”common-6993930423616494428”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6993930423616494428” aria-controls=”extreme-6993930423616494428”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6993930423616494428”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>3</td>

</tr> <tr>

<th>Median</th> <td>9</td>

</tr> <tr>

<th>Q3</th> <td>22</td>

</tr> <tr>

<th>95-th percentile</th> <td>108.25</td>

</tr> <tr>

<th>Maximum</th> <td>4685</td>

</tr> <tr>

<th>Range</th> <td>4685</td>

</tr> <tr>

<th>Interquartile range</th> <td>19</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>186.84</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9616</td>

</tr> <tr>

<th>Kurtosis</th> <td>289.24</td>

</tr> <tr>

<th>Mean</th> <td>37.657</td>

</tr> <tr>

<th>MAD</th> <td>47.799</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.217</td>

</tr> <tr>

<th>Sum</th> <td>115830</td>

</tr> <tr>

<th>Variance</th> <td>34909</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6993930423616494428”>

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aehQ4fK7XZr7NixKi4u9plfv369UlNTlZiYqFmzZqmqqsq2dQEAgMAXkAHLsixlZGSoqqpKzz//vJ566in95S9/0ZIlS2RZltLT09WuXTvl5eVp2LBhmjJlio4cOSJJOnLkiNLT0zVixAi99NJLatu2re6//35ZliVJeuutt5STk6PMzExt2LBBRUVFys7O9udyAQBAgAnIgPXZZ5%2BpsLBQWVlZ6tKli5KTk5WRkaH8/Hxt375dxcXFyszMVOfOnTV58mQlJCQoLy9PkrR582Zde%2B21mjBhgrp06aKsrCwdPnxYH3zwgSRp48aNGjdunNLS0tSzZ0/NmzdPeXl5HMUCAABNFpABy%2BVyae3atWrXrp3P%2BIkTJ1RUVKRrrrlGLVu29I4nJSWpsLBQklRUVKTk5GTvXEREhHr06KHCwkLV19drz549PvMJCQk6ffq09u/f38yrAgAAwSIgA1ZkZKRSU1O9zxsaGrRp0yb16dNHHo9HMTExPttHR0ertLRUkv7p/PHjx1VTU%2BMz73Q6FRUV5X09AADAj3H6uwATsrOztW/fPr300ktav369wsLCfObDwsJUW1srSaqqqvrB%2Berqau/zH3p9U5SVlcnj8fiMOZ0tGwW7CxUaGlj52OkMrHrP5UzPA633gYye24%2Be24%2BeB5%2BAD1jZ2dnasGGDnnrqKXXt2lXh4eGqrKz02aa2tlYtWrSQJIWHhzcKS7W1tYqMjFR4eLj3%2BdnzERERTa4pNzdXOTk5PmPp6enKyMho8j6CUZs2rfxdgjGRkU3/eYAZ9Nx%2B9Nx%2B9Dx4BHTAmj9/vl544QVlZ2dr0KBBkqTY2FgdPHjQZ7vy8nLv0aPY2FiVl5c3mu/evbuioqIUHh6u8vJyde7cWZJUV1enyspKuVyuJtc1evRo9e/f32fM6WypioqT573GfybQPumYXr8/hIaGKDIyQsePV6m%2BvsHf5VwS6Ln96Ln96Hnz8deH%2B4ANWDk5OXrxxRf15JNP6pZbbvGOu91urVmzRtXV1d6jVgUFBUpKSvLOFxQUeLevqqrSvn37NGXKFIWEhCg%2BPl4FBQXq3bu3JKmwsFBOp1PdunVrcm0xMTGNTgd6PN%2Bqru7S/qUJpvXX1zcE1XoCAT23Hz23Hz0PHoF1COT/HTp0SCtWrNCvf/1rJSUlyePxeB8pKSlq3769Zs6cqQMHDmjNmjXavXu3Ro0aJUkaOXKkdu3apTVr1ujAgQOaOXOmOnTo4A1UY8aM0bp167R161bt3r1bc%2BfO1Z133nlepwgBAMClLSCPYP35z39WfX29Vq5cqZUrV/rMffLJJ1qxYoVmz56tESNG6Oc//7mWL1%2BuuLg4SVKHDh30hz/8QY899piWL1%2BuxMRELV%2B%2BXA6HQ5I0ZMgQHT58WHPmzFFtba1uvvlmTZ8%2B3fY1AgCAwOWwztzCHM3K4/nW%2BD6dzhANXPxX4/ttLm882NffJVwwpzNEbdq0UkXFSQ7j24Se24%2Be24%2BeNx%2BX63K/vG9AniIEAAC4mNkesO644w69%2BOKL%2BvZb80d0AAAALga2B6w%2Bffpo1apV6tevnx566CFt27ZNnKUEAADBxPaANXXqVP3lL3/RihUrFBoaqgceeEA33XSTnnrqKX3%2B%2Bed2lwMAAGCcX/6K0OFwqG/fvurbt6%2Bqqqr03HPPacWKFVqzZo169eqlcePG6eabb/ZHaQAAABfMb7dpKCsr06uvvqpXX31Vn376qXr16qXbb79dpaWl%2Bt3vfqedO3dq9uzZ/ioPAADgJ7M9YL3yyit65ZVXtGPHDrVt21bDhw/XsmXL1LFjR%2B827du318KFCwlYAAAgINkesGbPnq20tDQtX75cN9xwg0JCGl8G1qlTJ9199912lwYAAGCE7QHr3XffVZs2bVRZWekNV7t371aPHj0UGhoqSerVq5d69epld2kAAABG2P5XhCdOnNAtt9yip59%2B2js2adIkDRs2TCUlJXaXAwAAYJztAeuxxx7Tz3/%2Bc91zzz3esddff13t27dXVlaW3eUAAAAYZ3vA%2Bvvf/65HHnlELpfLO9a2bVs9/PDD2r59u93lAAAAGGd7wHI6nTp%2B/Hij8aqqKu7oDgAAgoLtAeuGG27QggUL9NVXX3nHiouLlZWVpdTUVLvLAQAAMM72vyKcMWOG7rnnHg0aNEiRkZGSpOPHj6tHjx6aOXOm3eUAAAAYZ3vAio6O1ssvv6z33ntPBw4ckNPp1FVXXaXrr79eDofD7nIAAACM88tX5YSGhio1NZVTggAAICjZHrA8Ho%2BWLFmiXbt26fTp040ubP/zn/9sd0kAAABG2R6wHn30Ue3du1dDhgzR5ZdfbvfbAwAANDvbA9b27du1du1aJScn2/3WAAAAtrD9Ng0tW7ZUdHS03W8LAABgG9sD1rBhw7R27VrV19fb/dYAAAC2sP0UYWVlpfLz8/X222/ryiuvVFhYmM/8xo0b7S4JAADAKL/cpmHo0KH%2BeFsAAABb2B6wsrKy7H5LAAAAW9l%2BDZYklZWVKScnR1OnTtU//vEPvfnmm/rss8/8UQoAAIBxtgesL7/8UrfddptefvllvfXWWzp16pRef/11jRw5UkVFRXaXAwAAYJztAevxxx/XgAEDtHXrVl122WWSpCeffFL9%2B/fX4sWL7S4HAADAONsD1q5du3TPPff4fLGz0%2BnU/fffr3379tldDgAAgHG2B6yGhgY1NDQ0Gj958qRCQ0PtLgcAAMA42wNWv379tHr1ap%2BQVVlZqezsbPXp08fucgAAAIyzPWA98sgj2rt3r/r166eamhr9x3/8h9LS0vT1119rxowZdpcDAABgnO33wYqNjdWWLVuUn5%2Bvjz/%2BWA0NDbrrrrs0bNgwtW7d2u5yAAAAjPPLndwjIiJ0xx13%2BOOtAQAAmp3tAWvs2LH/dJ7vIgQAAIHO9oB1xRVX%2BDyvq6vTl19%2BqU8//VTjxo2zuxwAAADjLprvIly%2BfLlKS0ttrgYAAMA8v3wX4bkMGzZMb7zxhr/LAAAAuGAXTcD68MMPf9KNRmtrazV06FDt2LHDO7ZgwQJdffXVPo9NmzZ55/Pz8zVgwAC53W6lp6fr2LFj3jnLsrR48WL16dNHKSkpWrRo0TlvjAoAAPBDLoqL3E%2BcOKFPPvlEY8aMOa991dTUaOrUqTpw4IDP%2BKFDhzR16lTdfvvt3rEzt4DYvXu3Zs%2BerXnz5qlbt25auHChZs6cqdWrV0uSnn32WeXn5ysnJ0d1dXWaPn26oqOjNXHixPNdKgAAuETZHrDi4uJ8vodQki677DLdfffd%2BsUvftHk/Rw8eFBTp06VZVmN5g4dOqSJEyfK5XI1mtu0aZNuvfVWDR8%2BXJK0aNEipaWlqbi4WFdeeaU2btyojIwMJScnS5KmTZumpUuXErAAAECT2R6wHn/8cSP7%2BeCDD9S7d2/99re/VUJCgnf8xIkTOnr0qDp27HjO1xUVFenXv/6193n79u0VFxenoqIihYWFqaSkRNddd513PikpSYcPH1ZZWZliYmKM1A4AAIKb7QFr586dTd72%2B0HnbD90OvHQoUNyOBxatWqV3n33XUVFRemee%2B7xni48V1CKjo5WaWmpPB6PJPnMt2vXTpJUWlpKwAIAAE1ie8D61a9%2B5T1F%2BP3Te2ePORwOffzxx%2Be9/88%2B%2B0wOh0OdOnXS3XffrZ07d%2BrRRx9V69atNXDgQFVXVyssLMznNWFhYaqtrVV1dbX3%2BffnpO8upm%2BqsrIyb1g7w%2BlsaTyghYZeNH%2Bj0CROZ2DVey5neh5ovQ9k9Nx%2B9Nx%2B9Dz42B6wVq1apQULFmj69OlKSUlRWFiY9uzZo8zMTN1%2B%2B%2B0aPHjwBe1/%2BPDhSktLU1RUlCSpW7du%2BuKLL/TCCy9o4MCBCg8PbxSWamtrFRER4ROmwsPDvf%2BWvvt6n6bKzc1VTk6Oz1h6eroyMjJ%2B8rqCQZs2rfxdgjGRkU3/eYAZ9Nx%2B9Nx%2B9Dx4%2BOVGo3PmzNENN9zgHevTp48yMzP18MMP%2B1wf9VM4HA5vuDqjU6dO2r59u6Tvvmy6vLzcZ768vFwul0uxsbGSJI/How4dOnj/LemcF8z/kNGjR6t///4%2BY05nS1VUnDy/xfyIQPukY3r9/hAaGqLIyAgdP16l%2Bnpu32EHem4/em4/et58/PXh3vaAVVZW1ujrcqTvbqNQUVFxwftfunSpPvzwQ61fv947tn//fnXq1EmS5Ha7VVBQoBEjRkiSSkpKVFJSIrfbrdjYWMXFxamgoMAbsAoKChQXF3dep/diYmIabe/xfKu6ukv7lyaY1l9f3xBU6wkE9Nx%2B9Nx%2B9Dx42H4IJCEhQU8%2B%2BaROnDjhHausrFR2drauv/76C95/Wlqadu7cqXXr1umrr77Sf/3Xf2nLli2aMGGCJOmuu%2B7SK6%2B8os2bN2v//v16%2BOGHddNNN%2BnKK6/0zi9evFg7duzQjh079MQTT/zoF1QDAAB8n%2B1HsH73u99p7NixuuGGG9SxY0dZlqUvvvhCLpdLGzduvOD99%2BzZU0uXLtWyZcu0dOlSXXHFFXriiSeUmJgoSUpMTFRmZqaWLVumb775Rn379tX8%2BfO9r584caL%2B8Y9/aMqUKQoNDdWoUaM0fvz4C64LAABcOhzWue7U2cy%2B%2BeYb5efn69ChQ5Kka665RkOGDDmvC8kDjcfzrfF9Op0hGrj4r8b321zeeLCvv0u4YE5niNq0aaWKipMcxrcJPbcfPbcfPW8%2BLtflfnlf249gSdLPfvYz3XHHHfr666%2B9p%2BYuu%2Bwyf5QCAABgnO3XYJ35MuXrrrtOQ4cOVWlpqWbMmKHZs2fr9OnTdpcDAABgnO0B67nnntMrr7yi3//%2B9977Tg0YMEBbt25tdO8oAACAQGR7wMrNzdWcOXM0YsQI793bBw8erAULFui1116zuxwAAADjbA9YX3/9tbp3795ovFu3bo2%2BXgYAACAQ2R6wrrjiCu3Zs6fR%2BLvvvuu94B0AACCQ2f5XhBMnTtS8efPk8XhkWZbef/995ebm6rnnntMjjzxidzkAAADG2R6wRo4cqbq6Oq1cuVLV1dWaM2eO2rZtqwcffFB33XWX3eUAAAAYZ3vAys/P1y233KLRo0fr2LFjsixL0dHRdpcBAADQbGy/BiszM9N7MXvbtm0JVwAAIOjYHrA6duyoTz/91O63BQAAsI3tpwi7deumadOmae3aterYsaPCw8N95rOysuwuCQAAwCjbA9bnn3%2BupKQkSeK%2BVwAAICjZErAWLVqkKVOmqGXLlnruuefseEsAAAC/seUarGeffVZVVVU%2BY5MmTVJZWZkdbw8AAGArWwKWZVmNxnbu3Kmamho73h4AAMBWtv8VIQAAQLAjYAEAABhmW8ByOBx2vRUAAIBf2XabhgULFvjc8%2Br06dPKzs5Wq1atfLbjPlgAACDQ2RKwrrvuukb3vEpMTFRFRYUqKirsKAEAAMA2tgQs7n0FAAAuJVzkDgAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwLOADVm1trYYOHaodO3Z4x4qLizV%2B/HglJCRo8ODB2rZtm89r3nvvPQ0dOlRut1tjx45VcXGxz/z69euVmpqqxMREzZo1S1VVVbasBQAABIeADlg1NTV66KGHdODAAe%2BYZVlKT09Xu3btlJeXp2HDhmnKlCk6cuSIJOnIkSNKT0/XiBEj9NJLL6lt27a6//77ZVmWJOmtt95STk6OMjMztWHDBhUVFSk7O9sv6wMAAIEpYAPWwYMHdeedd%2Bqrr77yGd%2B%2BfbuKi4uVmZmpzp07a/LkyUpISFBeXp4kafPmzbr22ms1YcIEdenSRVlZWTp8%2BLA%2B%2BOADSdLGjRs1btw4paWlqWfPnpo3b57y8vI4igUAAJosYAPWBx98oN69eys3N9dnvKioSNdcc41atmzpHUtKSlJhYaF3Pjk52TsXERGhHj16qLCwUPX19dqzZ4/PfEJCgk6fPq39%2B/c384oAAECwcPq7gJ9qzJgx5xz3eDyKiYnxGYuOjlZpaemPzh8/flw1NTU%2B806nU1FRUd7XAwAA/JiADVg/pKqqSmFhYT5jYWFhqq2t/dH56upq7/Mfen1TlJWVyePx%2BIw5nS0bBbsLFRoaWAcgnc7AqvdczvQ80HofyOi5/ei5/eh58Am6gBUeHq7KykqfsdraWrVo0cI7f3ZYqq2tVWRkpMLDw73Pz56PiIhocg25ubnKycnxGUtPT1dGRkaT9xEDGMLtAAATAUlEQVSM2rRp5e8SjImMbPrPA8yg5/aj5/aj58Ej6AJWbGysDh486DNWXl7uPXoUGxur8vLyRvPdu3dXVFSUwsPDVV5ers6dO0uS6urqVFlZKZfL1eQaRo8erf79%2B/uMOZ0tVVFx8qcs6QcF2icd0%2Bv3h9DQEEVGRuj48SrV1zf4u5xLAj23Hz23Hz1vPv76cB90AcvtdmvNmjWqrq72HrUqKChQUlKSd76goMC7fVVVlfbt26cpU6YoJCRE8fHxKigoUO/evSVJhYWFcjqd6tatW5NriImJaXQ60OP5VnV1l/YvTTCtv76%2BIajWEwjouf3ouf3oefAIrEMgTZCSkqL27dtr5syZOnDggNasWaPdu3dr1KhRkqSRI0dq165dWrNmjQ4cOKCZM2eqQ4cO3kA1ZswYrVu3Tlu3btXu3bs1d%2B5c3Xnnned1ihAAAFzagi5ghYaGasWKFfJ4PBoxYoReffVVLV%2B%2BXHFxcZKkDh066A9/%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%2Bnp6Tp27Jg/lgAAAAJU0AasgwcPKi0tTdu2bfM%2BFixYoFOnTmnSpElKTk7WH//4RyUmJmry5Mk6deqUJGn37t2aPXu2pkyZotzcXB0/flwzZ87082oAAEAgCdqAdejQIXXt2lUul8v7iIyM1Ouvv67w8HA9/PDD6ty5s2bPnq1WrVrpzTfflCRt2rRJt956q4YPH65u3bpp0aJFeuedd1RcXOznFQEAgEAR1AGrY8eOjcaLioqUlJQkh8MhSXI4HOrVq5cKCwu988nJyd7t27dvr7i4OBUVFdlSNwAACHxBGbAsy9Lnn3%2Bubdu2adCgQRowYIAWL16s2tpaeTwexcTE%2BGwfHR2t0tJSSVJZWdk/nQcAAPgxTn8X0ByOHDmiqqoqhYWFacmSJfr666%2B1YMECVVdXe8e/LywsTLW1tZKk6urqfzrfFGVlZfJ4PD5jTmfLRsHtQoWGBlY%2BdjoDq95zOdPzQOt9IKPn9qPn9qPnwScoA9YVV1yhHTt26Gc/%2B5kcDoe6d%2B%2BuhoYGTZ8%2BXSkpKY3CUm1trVq0aCFJCg8PP%2Bd8REREk98/NzdXOTk5PmPp6enev2K8VLVp08rfJRgTGdn0nweYQc/tR8/tR8%2BDR1AGLEmKioryed65c2fV1NTI5XKpvLzcZ668vNx7dCk2Nvac8y6Xq8nvPXr0aPXv399nzOlsqYqKk%2BezhB8VaJ90TK/fH0JDQxQZGaHjx6tUX9/g73IuCfTcfvTcfvS8%2Bfjrw31QBqy//vWvmjZtmt5%2B%2B23vkaePP/5YUVFRSkpK0tNPPy3LsuRwOGRZlnbt2qX77rtPkuR2u1VQUKARI0ZIkkpKSlRSUiK3293k94%2BJiWl0OtDj%2BVZ1dZf2L00wrb%2B%2BviGo1hMI6Ln96Ln96HnwCKxDIE2UmJio8PBw/e53v9Nnn32md955R4sWLdK9996rW265RcePH9fChQt18OBBLVy4UFVVVbr11lslSXfddZdeeeUVbd68Wfv379fDDz%2Bsm266SVdeeaWfVwUAAAJFUAas1q1ba926dTp27JhGjhyp2bNna/To0br33nvVunVrrV692nuUqqioSGvWrFHLli0lfRfOMjMztXz5ct1111362c9%2BpqysLD%2BvCAAABBKHZVmWv4u4FHg83xrfp9MZooGL/2p8v83ljQf7%2BruEC%2BZ0hqhNm1aqqDjJYXyb0HP70XP70fPm43Jd7pf3DcojWAAAAP5EwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwzqGmpkazZs1ScnKy%2BvXrp2eeecbfJQEAgADi9HcBF6NFixZp79692rBhg44cOaIZM2YoLi5Ot9xyi79LAwAAAYCAdZZTp05p8%2BbNevrpp9WjRw/16NFDBw4c0PPPP0/AAgAATULAOsv%2B/ftVV1enxMRE71hSUpJWrVqlhoYGhYRwVvWnunXJ3/xdwnl548G%2B/i4BABCgCFhn8Xg8atOmjcLCwrxj7dq1U01NjSorK9W2bdsf3UdZWZk8Ho/PmNPZUjExMUZrDQ0l7DWnQAuEgeR/pqU2edszP%2Bf8vNuHntuPngcfAtZZqqqqfMKVJO/z2traJu0jNzdXOTk5PmNTpkzRAw88YKbI/1dWVqZx/3JAo0ePNh7ecG5lZWXKzc2l5zYqKyvThg1r6bmN6Ln96HnwISqfJTw8vFGQOvO8RYsWTdrH6NGj9cc//tHnMXr0aOO1ejwe5eTkNDpahuZDz%2B1Hz%2B1Hz%2B1Hz4MPR7DOEhsbq4qKCtXV1cnp/K49Ho9HLVq0UGRkZJP2ERMTwycQAAAuYRzBOkv37t3ldDpVWFjoHSsoKFB8fDwXuAMAgCYhMZwlIiJCw4cP19y5c7V7925t3bpVzzzzjMaOHevv0gAAQIAInTt37lx/F3Gx6dOnj/bt26cnnnhC77//vu677z6NHDnS32WdU6tWrZSSkqJWrVr5u5RLBj23Hz23Hz23Hz0PLg7Lsix/FwEAABBMOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyAFaBqamo0a9YsJScnq1%2B/fnrmmWf8XVLAqq2t1dChQ7Vjxw7vWHFxscaPH6%2BEhAQNHjxY27Zt83nNe%2B%2B9p6FDh8rtdmvs2LEqLi72mV%2B/fr1SU1OVmJioWbNmqaqqypa1XOyOHj2qjIwMpaSkKDU1VVlZWaqpqZFEz5vLl19%2BqYkTJyoxMVE33XST1q5d652j581v0qRJeuSRR7zP9%2B3bpzvuuENut1sjR47U3r17fbbPz8/XgAED5Ha7lZ6ermPHjnnnLMvS4sWL1adPH6WkpGjRokVqaGiwbS04TxYCUmZmpnXbbbdZe/futf77v//bSkxMtN544w1/lxVwqqurrfT0dKtr167W9u3bLcuyrIaGBuu2226zpk6dah08eNBatWqV5Xa7rcOHD1uWZVmHDx%2B2EhISrHXr1lmffvqp9Zvf/MYaOnSo1dDQYFmWZb355ptWUlKS9b//%2B79WUVGRNXjwYGvevHl%2BW%2BPFoqGhwbrzzjute%2B%2B91/r000%2BtnTt3WgMHDrQef/xxet5M6uvrrZtvvtmaOnWq9fnnn1tvv/221atXL%2BvVV1%2Bl5zbIz8%2B3unbtas2YMcOyLMs6efKk1bdvX%2Bvxxx%2B3Dh48aM2fP9/6t3/7N%2BvkyZOWZVlWUVGR1bNnT%2Bvll1%2B2Pv74Y%2Bvuu%2B%2B2Jk2a5N3funXrrBtvvNHauXOn9f7771v9%2BvWz1q5d65e14ccRsALQyZMnrfj4eG8gsCzLWr58uXX33Xf7sarAc%2BDAAesXv/iFddttt/kErPfee89KSEjw/k/Psixr3Lhx1rJlyyzLsqwlS5b49PrUqVNWYmKi9/VjxozxbmtZlrVz506rZ8%2Be1qlTp%2BxY1kXr4MGDVteuXS2Px%2BMde%2B2116x%2B/frR82Zy9OhR6ze/%2BY317bffesfS09Ot3//%2B9/S8mVVUVFg33HCDNXLkSG/A2rx5s9W/f39vSG1oaLAGDhxo5eXlWZZlWdOnT/dua1mWdeTIEevqq6%2B2vvrqK8uyLOvGG2/0bmtZlrVlyxYrLS3NriXhPHGKMADt379fdXV1SkxM9I4lJSWpqKiIw8Xn4YMPPlDv3r2Vm5vrM15UVKRrrrlGLVu29I4lJSWpsLDQO5%2BcnOydi4iIUI8ePVRYWKj6%2Bnrt2bPHZz4hIUGnT5/W/v37m3lFFzeXy6W1a9eqXbt2PuMnTpyg580kJiZGS5YsUevWrWVZlgoKCrRz506lpKTQ82b2n//5nxo2bJiuuuoq71hRUZGSkpLkcDgkSQ6HQ7169frBnrdv315xcXEqKirS0aNHVVJSouuuu847n5SUpMOHD6usrMymVeF8ELACkMfjUZs2bRQWFuYda9eunWpqalRZWenHygLLmDFjNGvWLEVERPiMezwexcTE%2BIxFR0ertLT0R%2BePHz%2Bumpoan3mn06moqCjv6y9VkZGRSk1N9T5vaGjQpk2b1KdPH3pug/79%2B2vMmDFKTEzUoEGD6Hkzev/99/X3v/9d999/v8/4j/W8rKzsB%2Bc9Ho8k%2Bcyf%2BbBCzy9OBKwAVFVV5ROuJHmf19bW%2BqOkoPJD/T3T2382X11d7X3%2BQ6/Hd7Kzs7Vv3z799re/pec2WLZsmVatWqWPP/5YWVlZ9LyZ1NTU6Pe//73mzJmjFi1a%2BMz9WM%2Brq6vPq%2Bf8f//i5vR3ATh/4eHhjX6hzjw/%2Bxca5y88PLzRkcDa2lpvb3%2Bo/5GRkQoPD/c%2BP3v%2B7CNll7Ls7Gxt2LBBTz31lLp27UrPbRAfHy/puwAwbdo0jRw5stFf/dHzC5eTk6Nrr73W52jtGT/U0x/reUREhE%2BYOrv/l3rPL1YcwQpAsbGxqqioUF1dnXfM4/GoRYsWioyM9GNlwSE2Nlbl5eU%2BY%2BXl5d5D8z8073K5FBUVpfDwcJ/5uro6VVZWyuVyNX/xAWD%2B/Pl69tlnlZ2drUGDBkmi582lvLxcW7du9Rm76qqrdPr0ablcLnreDP70pz9p69atSkxMVGJiol577TW99tprSkxMvKCf89jYWEnynir8/r8v9Z5frAhYAah79%2B5yOp3eCyMlqaCgQPHx8QoJ4T/phXK73froo4%2B8h%2BSl7/rrdru98wUFBd65qqoq7du3T263WyEhIYqPj/eZLywslNPpVLdu3exbxEUqJydHL774op588kkNGTLEO07Pm8fXX3%2BtKVOm6OjRo96xvXv3qm3btkpKSqLnzeC5557Ta6%2B9pi1btmjLli3q37%2B/%2Bvfvry1btsjtduvDDz%2BUZVmSvruv1a5du36w5yUlJSopKZHb7VZsbKzi4uJ85gsKChQXF9foui1cHELnzp07199F4PxcdtllKikp0QsvvKBrr71We/bsUXZ2tqZOnarOnTv7u7yAlJOTo9tvv10dOnRQXFyc8vPzVVhYqM6dOysvL09/%2BtOftHDhQl1%2B%2BeXq0KGDFi9erNDQUEVGRiorK0v19fWaNm2aHA6HWrRooSeffFKdO3fWiRMn9Oijj2rQoEH693//d38v068OHTqkhx56SJMmTdKgQYN06tQp7%2BOqq66i583A5XLpnXfe0d/%2B9jf16NFDe/bs0fz58zV58mTdeuut9LwZREZGKioqyvt49913FRYWplGjRulf//VftXbtWpWWliouLk4rV67U/v37lZmZqcsuu0zt2rVTVlaWXC6XQkNDNWfOHHXt2lW//OUvJX13SnDVqlXq0aOHDh8%2BrHnz5mn8%2BPE%2Bf1GOi4ifbxOBn%2BjUqVPWww8/bCUkJFj9%2BvWznn32WX%2BXFNC%2Bfx8sy7KsL774wvrlL39pXXvttdaQIUOsv/3tbz7bv/3229bNN99s9ezZ0xo3bpz3PjVnrF692rr%2B%2BuutpKQka%2BbMmVZ1dbUt67iYrV692urates5H5ZFz5tLaWmplZ6ebvXq1cvq27evtXLlSu99mOh585sxY4bPva2Kioqs4cOHW/Hx8daoUaOsjz76yGf7vLw868Ybb7QSEhKs9PR069ixY965uro667HHHrOSk5Ot3r17W9nZ2d7/lrj4OCzr/49VAgAAwAgu2AEAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4Px%2BR5JfQ4JZYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6993930423616494428”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>147</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>132</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>127</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (223)</td> <td class=”number”>1472</td> <td class=”number”>45.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6993930423616494428”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2334.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3124.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3671.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4008.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4685.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_ORCHARD_FARMS12”><s>ORCHARD_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_ORCHARD_FARMS07”><code>ORCHARD_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99031</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_AGRITRSM_OPS_07_12”>PCH_AGRITRSM_OPS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>445</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>15.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>496</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>75.616</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>3900</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1857391993600425660”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1857391993600425660,#minihistogram1857391993600425660”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1857391993600425660”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1857391993600425660”

aria-controls=”quantiles1857391993600425660” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1857391993600425660” aria-controls=”histogram1857391993600425660”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1857391993600425660” aria-controls=”common1857391993600425660”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1857391993600425660” aria-controls=”extreme1857391993600425660”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1857391993600425660”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-28.571</td>

</tr> <tr>

<th>Median</th> <td>25</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>400</td>

</tr> <tr>

<th>Maximum</th> <td>3900</td>

</tr> <tr>

<th>Range</th> <td>4000</td>

</tr> <tr>

<th>Interquartile range</th> <td>128.57</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>194.75</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5755</td>

</tr> <tr>

<th>Kurtosis</th> <td>68.329</td>

</tr> <tr>

<th>Mean</th> <td>75.616</td>

</tr> <tr>

<th>MAD</th> <td>117.27</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.4671</td>

</tr> <tr>

<th>Sum</th> <td>205220</td>

</tr> <tr>

<th>Variance</th> <td>37927</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1857391993600425660”>

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RsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyvxes22%2B/Xa%2B88oqOHj3q76cGAADwC78XrK5du2rRokXq3r27Hn74YW3YsEHGGH8vAwAAoNb4vWCNGzdOf//737VgwQKFhYXpj3/8o2688UbNnj1b33333QUfr7y8XAMGDNCmTZu8Y9OnT9dVV13l87V69WrvfHZ2tnr16iW32620tDQdPHjQO2eM0axZs9S1a1elpKRo5syZqqqqurTQAADgV8UViCcNCQlRt27d1K1bN5WUlOjFF1/UggULtGTJEnXu3Fn33nuvbrrppvMep6ysTOPGjVN%2Bfr7PeEFBgcaNG6dbb73VO9awYUNJ0tatWzV58mRNmTJF7dq104wZMzRx4kQtXrxYkrR8%2BXJlZ2crKytLFRUVGj9%2BvJo0aaJRo0ZZ/BMAAABOFpCCJUlFRUV688039eabb2rnzp3q3Lmzbr31Vu3fv1%2BPP/64Nm/erMmTJ5/18bt27dK4cePOeHqxoKBAo0aNUkxMTLW51atX6%2Babb9bgwYMlSTNnzlSPHj1UWFioyy%2B/XKtWrVJ6erqSk5MlSY888ojmzp1LwQIAADXm94L1xhtv6I033tCmTZvUuHFjDR48WPPmzVPLli2992nWrJlmzJhxzoL1%2Beefq0uXLvqv//ovJSQkeMePHTumAwcO%2BBzvl/Ly8vSHP/zB57ni4%2BOVl5en8PBw7du3T9dee613PikpSXv27FFRUZFiY2MvPjgAAPjV8HvBmjx5snr06KH58%2Bfr%2BuuvV2ho9Y%2BBtWrVSnfdddc5jzNixIgzjhcUFCgkJESLFi3Sxx9/rOjoaN13333e04VnKkpNmjTR/v375fF4JMlnvmnTppKk/fv3U7AAAECN%2BL1gffzxx2rUqJEOHz7sLVdbt25Vhw4dFBYWJknq3LmzOnfufFHH//bbbxUSEuItaZs3b9YTTzyhhg0bqnfv3iotLVV4eLjPY8LDw1VeXq7S0lLv7V/OST9/mL6mioqKvGXtFJer/q%2B%2BoLlc9v9NRVhYqM%2BvTuLUbE7NJTk3m1NzSWQLRsGSy%2B8F69ixYxo%2BfLj%2B/d//XY8%2B%2BqgkafTo0WratKn%2B8pe/qFmzZpd0/MGDB6tHjx6Kjo6WJLVr107ff/%2B9Xn75ZfXu3VsRERHVylJ5ebkiIyN9ylRERIT395IUGRlZ4zWsWbNGWVlZPmNpaWlKT0%2B/6FxO0KhRg1o7dlRUzfcn2Dg1m1NzSc7N5tRcEtmCUV3P5feC9fTTT%2BuKK67Qfffd5x17%2B%2B23NWHCBGVkZGjevHmXdPyQkBBvuTqlVatW2rhxoyQpLi5OxcXFPvPFxcWKiYlRXFycJMnj8ahFixbe30s64wfmz2bYsGHq2bOnz5jLVV%2BHDh2/sDAOUxv5w8JCFRUVqSNHSlRZ6azLaTg1m1NzSc7N5tRcEtmC0YXmqs2/3J%2BL3wvWP//5T/31r3/1KSyNGzfWo48%2BqjvvvPOSjz937lx98cUXWrFihXdsx44datWqlSTJ7XYrJydHQ4YMkSTt27dP%2B/btk9vtVlxcnOLj45WTk%2BMtWDk5OYqPj7%2Bg03uxsbHV7u/xHFVFhXO%2BwS9GbeavrKxy7J%2BvU7M5NZfk3GxOzSWRLRjV9Vx%2BL1gul0tHjhypNl5SUmLliu49evTQkiVLtGzZMvXu3VsbNmzQunXrtGrVKknS8OHDdffddyshIUEdO3bUjBkzdOONN%2Bryyy/3zs%2BaNUu/%2B93vJEnPPvus7r///kteFwAA%2BPXwe8G6/vrrNX36dD333HP6t3/7N0lSYWGhMjIylJqaesnH79Spk%2BbOnat58%2BZp7ty5at68uZ599lklJiZKkhITEzV16lTNmzdPP/30k7p166Zp06Z5Hz9q1Cj9%2BOOPGjt2rMLCwnTbbbdp5MiRl7wuAADw6xFi/PyDAH/88Ufdd999ys/PV1RUlCTpyJEj6tChgxYuXHhBn3UKJh5P7fxw65vn/KNWjlsb3nmom/VjulyhatSogQ4dOl6n3yq%2BGE7N5tRcknOzOTWXRLZgdKG5YmJ%2B64dVVef3d7CaNGmi119/XZ9%2B%2Bqny8/Plcrl05ZVX6rrrrlNISIi/lwMAAGBdQH5UTlhYmFJTU62cEgQAAKhr/F6wPB6P5syZoy1btujkyZPVPtj%2BwQcf%2BHtJAAAAVvm9YD3xxBPatm2b%2Bvfvr9/%2BNjDnRQEAAGqT3wvWxo0btXTpUiUnJ/v7qQEAAPzC7z/Ip379%2BmrSpIm/nxYAAMBv/F6wBg0apKVLl6qystLfTw0AAOAXfj9FePjwYWVnZ%2BvDDz/U5Zdf7v0By6ecuuI6AABAsArIZRoGDBgQiKcFAADwC78XrIyMDH8/JQAAgF/5/TNYklRUVKSsrCyNGzdOP/74o9599119%2B%2B23gVgKAACAdX4vWD/88IMGDhyo119/Xe%2B9955OnDiht99%2BW0OHDlVeXp6/lwMAAGCd3wvWM888o169emn9%2BvX6zW9%2BI0l67rnn1LNnT82aNcvfywEAALDO7wVry5Ytuu%2B%2B%2B3x%2BsLPL5dKDDz6o7du3%2B3s5AAAA1vm9YFVVVamqqqra%2BPHjxxUWFubv5QAAAFjn94LVvXt3LV682KdkHT58WJmZmeratau/lwMAAGCd3wvWY489pm3btql79%2B4qKyvTf/7nf6pHjx7avXu3JkyY4O/lAAAAWOf362DFxcVp3bp1ys7O1tdff62qqioNHz5cgwYNUsOGDf29HAAAAOsCciX3yMhI3X777YF4agAAgFrn94J1zz33nHOen0UIAACCnd8LVvPmzX1uV1RU6IcfftDOnTt17733%2Bns5AAAA1tWZn0U4f/587d%2B/38%2BrAQAAsC8gP4vwTAYNGqR33nkn0MsAAAC4ZHWmYH3xxRdcaBQAADhCnfiQ%2B7Fjx/TNN99oxIgR/l4OAACAdX4vWPHx8T4/h1CSfvOb3%2Biuu%2B7SLbfc4u/lAAAAWOf3gvXMM8/4%2BykBAAD8yu8Fa/PmzTW%2B77XXXluLKwEAAKgdfi9Yd999t/cUoTHGO376WEhIiL7%2B%2Bmt/Lw8AAOCS%2Bb1gLVq0SNOnT9f48eOVkpKi8PBwffnll5o6dapuvfVW9evXz99LAgAAsMrvl2nIyMjQk08%2BqT59%2BqhRo0Zq0KCBunbtqqlTp%2Brll19W8%2BbNvV8AAADByO8Fq6io6IzlqWHDhjp06JC/lwMAAGCd3wtWQkKCnnvuOR07dsw7dvjwYWVmZuq6667z93IAAACs8/tnsB5//HHdc889uv7669WyZUsZY/T9998rJiZGq1at8vdyAAAArPN7wWrdurXefvttZWdnq6CgQJJ05513qn///oqMjPT3cgAAAKzze8GSpMsuu0y33367du/ercsvv1zSz1dzBwAAcAK/fwbLGKNZs2bp2muv1YABA7R//35NmDBBkydP1smTJ/29HAAAAOv8XrBefPFFvfHGG/rzn/%2Bs8PBwSVKvXr20fv16ZWVl%2BXs5AAAA1vm9YK1Zs0ZPPvmkhgwZ4r16e79%2B/TR9%2BnS99dZb/l4OAACAdX4vWLt371b79u2rjbdr104ej8ffywEAALDO7wWrefPm%2BvLLL6uNf/zxx94PvAMAAAQzv/8rwlGjRmnKlCnyeDwyxuizzz7TmjVr9OKLL%2Bqxxx7z93IAAACs83vBGjp0qCoqKrRw4UKVlpbqySefVOPGjfXQQw9p%2BPDh/l4OAACAdX4vWNnZ2erbt6%2BGDRumgwcPyhijJk2a%2BHsZAAAAtcbvn8GaOnWq98PsjRs3plwBAADH8XvBatmypXbu3OnvpwUAAPAbv58ibNeunR555BEtXbpULVu2VEREhM98RkaGv5cEAABgld8L1nfffaekpCRJ4rpXAADAkfxSsGbOnKmxY8eqfv36evHFF/3xlAAAAAHjl89gLV%2B%2BXCUlJT5jo0ePVlFRkT%2BeHgAAwK/8UrCMMdXGNm/erLKysks%2Bdnl5uQYMGKBNmzZ5xwoLCzVy5EglJCSoX79%2B2rBhg89jPv30Uw0YMEBut1v33HOPCgsLfeZXrFih1NRUJSYmatKkSdXKIQAAwLn4/V8R2lRWVqaHH35Y%2Bfn53jFjjNLS0tS0aVOtXbtWgwYN0tixY7V3715J0t69e5WWlqYhQ4botddeU%2BPGjfXggw96S%2BB7772nrKwsTZ06VStXrlReXp4yMzMDkg8AAASnoC1Yu3bt0h133KF//etfPuMbN25UYWGhpk6dqtatW2vMmDFKSEjQ2rVrJUmvvvqqrrnmGt1///1q06aNMjIytGfPHn3%2B%2BeeSpFWrVunee%2B9Vjx491KlTJ02ZMkVr167lXSwAAFBjfitYISEhVo/3%2Beefq0uXLlqzZo3PeF5enq6%2B%2BmrVr1/fO5aUlKTc3FzvfHJysncuMjJSHTp0UG5uriorK/Xll1/6zCckJOjkyZPasWOH1fUDAADn8ttlGqZPn%2B5zzauTJ08qMzNTDRo08LlfTa%2BDNWLEiDOOezwexcbG%2Bow1adJE%2B/fvP%2B/8kSNHVFZW5jPvcrkUHR3tfTwAAMD5%2BKVgXXvttdWueZWYmKhDhw7p0KFDVp%2BrpKRE4eHhPmPh4eEqLy8/73xpaan39tkeXxNFRUXV8rpc9asVu18bl8v%2BG6ZhYaE%2BvzqJU7M5NZfk3GxOzSWRLRgFSy6/FCx/XvsqIiJChw8f9hkrLy9XvXr1vPOnl6Xy8nJFRUV532E703xkZGSN17BmzRplZWX5jKWlpSk9Pb3Gx3CiRo0anP9OFykqqub7E2ycms2puSTnZnNqLolswaiu5/L7ldxrW1xcnHbt2uUzVlxc7H33KC4uTsXFxdXm27dvr%2BjoaEVERKi4uFitW7eWJFVUVOjw4cOKiYmp8RqGDRumnj17%2Boy5XPV16NDxi4nkGLWRPywsVFFRkTpypESVlVXWjx9ITs3m1FySc7M5NZdEtmB0oblq8y/35%2BK4guV2u7VkyRKVlpZ637XKycnx/nget9utnJwc7/1LSkq0fft2jR07VqGhoerYsaNycnLUpUsXSVJubq5cLpfatWtX4zXExsZWOx3o8RxVRYVzvsEvRm3mr6yscuyfr1OzOTWX5NxsTs0lkS0Y1fVcdfsE5kVISUlRs2bNNHHiROXn52vJkiXaunWrbrvtNknS0KFDtWXLFi1ZskT5%2BfmaOHGiWrRo4S1UI0aM0LJly7R%2B/Xpt3bpVTz31lO64444LOkUIAAB%2B3RxXsMLCwrRgwQJ5PB4NGTJEb775pubPn6/4%2BHhJUosWLfT8889r7dq1uu2223T48GHNnz/fexmJ/v37a8yYMXryySd1//33q1OnTho/fnwgIwEAgCDjiFOE33zzjc/tK664QqtXrz7r/W%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%2B%2B21FRETo0UcfVevWrTV58mQ1aNBA7777riRp9erVuvnmmzV48GC1a9dOM2fO1EcffaTCwsIAJwIAAMHC0QWrZcuW1cbz8vKUlJSkkJAQSVJISIg6d%2B6s3Nxc73xycrL3/s2aNVN8fLzy8vL8sm4AABD8XIFeQG0wxui7777Thg0btHjxYlVWVqpv375KT0%2BXx%2BPRlVde6XP/Jk2aKD8/X5JUVFSk2NjYavP79%2B%2Bv8fMXFRXJ4/H4jLkCi%2BBFAAARxElEQVRc9asd99fG5bLf58PCQn1%2BdRKnZnNqLsm52ZyaSyJbMAqWXI4sWHv37lVJSYnCw8M1Z84c7d69W9OnT1dpaal3/JfCw8NVXl4uSSotLT3nfE2sWbNGWVlZPmNpaWneD9n/WjVq1KDWjh0VFVlrxw40p2Zzai7JudmcmksiWzCq67kcWbCaN2%2BuTZs26bLLLlNISIjat2%2BvqqoqjR8/XikpKdXKUnl5uerVqydJioiIOON8ZGTNN3LYsGHq2bOnz5jLVV%2BHDh2/yETOUBv5w8JCFRUVqSNHSlRZWWX9%2BIHk1GxOzSU5N5tTc0lkC0YXmqs2/3J/Lo4sWJIUHR3tc7t169YqKytTTEyMiouLfeaKi4u9p%2B/i4uLOOB8TE1Pj546Nja12OtDjOaqKCud8g1%2BM2sxfWVnl2D9fp2Zzai7JudmcmksiWzCq67nq9gnMi/TJJ5%2BoS5cuKikp8Y59/fXXio6OVlJSkr744gsZYyT9/HmtLVu2yO12S5LcbrdycnK8j9u3b5/27dvnnQcAADgfRxasxMRERURE6PHHH9e3336rjz76SDNnztQDDzygvn376siRI5oxY4Z27dqlGTNmqKSkRDfffLMkafjw4XrjjTf06quvaseOHXr00Ud144036vLLLw9wKgAAECwcWbAaNmyoZcuW6eDBgxo6dKgmT56sYcOG6YEHHlDDhg21ePFi5eTkaMiQIcrLy9OSJUtUv359ST%2BXs6lTp2r%2B/PkaPny4LrvsMmVkZAQ4EQAACCaO/QxWmzZttHz58jPOderUSa%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%2BHj17ds30EuDH9085x%2BBXsIFeeehboFeAgDg/6FgnebEiRN69dVX9Ze//EUdOnRQhw4dlJ%2Bfr5deeomCBQAAaoRThKfZsWOHKioqlJiY6B1LSkpSXl6eqqqqArgyAAAQLHgH6zQej0eNGjVSeHi4d6xp06YqKyvT4cOH1bhx4/Meo6ioSB6Px2fM5aqv2NhY6%2BsFTgmmU5r/nNFXYWHO%2B/vdqUxOy%2BbUXBLZglGw5KJgnaakpMSnXEny3i4vL6/RMdasWaOsrCyfsbFjx%2BqPf/yjnUX%2Bwj9n1Oy0ZVFRkdasWaNhw4Y5qug5NZfk3GxFRUV6/vnnHZdL%2BjnbypVLHZfNqbkksgWjYMlVt%2BtfAERERFQrUqdu16tXr0bHGDZsmP72t7/5fA0bNsz6Wi%2BEx%2BNRVlZWtXfWgp1Tc0nOzebUXJJzszk1l0S2YBQsuXgH6zRxcXE6dOiQKioq5HL9/Mfj8XhUr149RUVF1egYsbGxdbpVAwCA2sU7WKdp3769XC6XcnNzvWM5OTnq2LGjQkP54wIAAOdHYzhNZGSkBg8erKeeekpbt27V%2BvXr9cILL%2Biee%2B4J9NIAAECQCHvqqaeeCvQi6pquXbtq%2B/btevbZZ/XZZ5/pP/7jPzR06NBAL%2BuSNWjQQCkpKWrQoEGgl2KVU3NJzs3m1FySc7M5NZdEtmAUDLlCjDEm0IsAAABwEk4RAgAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYDlcWVmZJk2apOTkZHXv3l0vvPBCoJdUY%2B%2B//76uuuoqn6/09HRJ0vbt23X77bfL7XZr6NCh2rZtm89js7Oz1atXL7ndbqWlpengwYOBiFBNeXm5BgwYoE2bNnnHCgsLNXLkSCUkJKhfv37asGGDz2M%2B/fRTDRgwQG63W/fcc48KCwt95lesWKHU1FQlJiZq0qRJKikp8UuWXzpTrunTp1fbv9WrV3vnz7VHxhjNmjVLXbt2VUpKimbOnKmqqiq/Zjpw4IDS09OVkpKi1NRUZWRkqKysTFJw79m5cgX7nv3www8aNWqUEhMTdeONN2rp0qXeuWDeM%2Bnc2YJ9304ZPXq0HnvsMe/tS3mdrxO5DBxt6tSpZuDAgWbbtm3mf//3f01iYqJ55513Ar2sGlmwYIEZM2aMKSoq8n799NNP5vjx46Zbt27mmWeeMbt27TLTpk0zv//9783x48eNMcbk5eWZTp06mddff918/fXX5q677jKjR48OcBpjSktLTVpammnbtq3ZuHGjMcaYqqoqM3DgQDNu3Diza9cus2jRIuN2u82ePXuMMcbs2bPHJCQkmGXLlpmdO3eaP/3pT2bAgAGmqqrKGGPMu%2B%2B%2Ba5KSksz//d//mby8PNOvXz8zZcqUgOcyxpiRI0eaxYsX%2B%2BzfiRMnjDHn36Nly5aZG264wWzevNl89tlnpnv37mbp0qV%2By1RVVWXuuOMO88ADD5idO3eazZs3m969e5tnnnkmqPfsXLmMCe49q6ysNDfddJMZN26c%2Be6778yHH35oOnfubN58882g3rPzZTMmuPftlOzsbNO2bVszYcIEY4y55Nf5upCLguVgx48fNx07dvT5n978%2BfPNXXfdFcBV1dy4cePMs88%2BW2381VdfNT179vS%2B%2BFVVVZnevXubtWvXGmOMGT9%2BvPc/UmOM2bt3r7nqqqvMv/71L/8s/Azy8/PNLbfcYgYOHOhTRD799FOTkJDgfdEwxph7773XzJs3zxhjzJw5c3z268SJEyYxMdH7%2BBEjRnjva4wxmzdvNp06dfK%2BuNa2s%2BUyxpjU1FTzySefnPFx59ujG264wbufxhizbt0606NHj1pKUd2uXbtM27Ztjcfj8Y699dZbpnv37kG9Z%2BfKZUxw79mBAwfMn/70J3P06FHvWFpamvnzn/8c1HtmzLmzGRPc%2B2aMMYcOHTLXX3%2B9GTp0qHetl/o6XxdycYrQwXbs2KGKigolJiZ6x5KSkpSXlxewt4AvREFBgVq2bFltPC8vT0lJSQoJCZEkhYSEqHPnzsrNzfXOJycne%2B/frFkzxcfHKy8vzy/rPpPPP/9cXbp00Zo1a3zG8/LydPXVV6t%2B/fresaSkpLNmiYyMVIcOHZSbm6vKykp9%2BeWXPvMJCQk6efKkduzYUcuJfna2XMeOHdOBAwfOuH/SuffowIED2rdvn6699lrvfFJSkvbs2aOioqJayXG6mJgYLV26VE2bNvUZP3bsWFDv2blyBfuexcbGas6cOWrYsKGMMcrJydHmzZuVkpIS1Ht2vmzBvm%2BS9N///d8aNGiQrrzySp91X%2BzrfF3JRcFyMI/Ho0aNGik8PNw71rRpU5WVlenw4cMBXNn5GWP03XffacOGDerTp4969eqlWbNmqby8XB6PR7GxsT73b9Kkifbv3y9JKioqOud8IIwYMUKTJk1SZGSkz/j5spxr/siRIyorK/OZd7lcio6O9lvWs%2BUqKChQSEiIFi1apOuvv1633HKLXn/9de/8ufbI4/FIks/8qULgr1xRUVFKTU313q6qqtLq1avVtWvXoN6zc%2BUK9j37pZ49e2rEiBFKTExUnz59gnrPTnd6tmDft88%2B%2B0z//Oc/9eCDD/qMX8rrfF3IJUkuvz0T/K6kpMSnXEny3i4vLw/Ekmps79693vXPmTNHu3fv1vTp01VaWnrWXKcylZaWnnO%2BLjlflnPNl5aWem%2Bf7fGB8u233yokJEStWrXSXXfdpc2bN%2BuJJ55Qw4YN1bt373Pu0ZlyBfr7NjMzU9u3b9drr72mFStWOGbPfpnrq6%2B%2BcsyezZs3T8XFxXrqqaeUkZHhqP/OTs/WoUOHoN23srIy/fnPf9aTTz6pevXq%2Bcxdyut8oHOdQsFysIiIiGrfTKdun/7NXNc0b95cmzZt0mWXXaaQkBC1b99eVVVVGj9%2BvFJSUs6Y61Sms%2BU%2B/V2WuiAiIqLau4k1yRIVFaWIiAjv7dPnA5118ODB6tGjh6KjoyVJ7dq10/fff6%2BXX35ZvXv3Puce/fKF8PSMgciVmZmplStXavbs2Wrbtq1j9uz0XG3atHHMnnXs2FHSz/8Df%2BSRRzR06NBq/%2BovGPdMqp5ty5YtQbtvWVlZuuaaa3zeVT3lbOuuyet8oHOdwilCB4uLi9OhQ4dUUVHhHfN4PKpXr56ioqICuLKaiY6O9p5/l6TWrVurrKxMMTExKi4u9rlvcXGx9%2B3guLi4M87HxMTU/qIv0NnWWpMs0dHRioiI8JmvqKjQ4cOHA541JCTE%2B4J/SqtWrXTgwAFJ584VFxcnSd63%2BX/5e3/nmjZtmpYvX67MzEz16dNHkjP27Ey5gn3PiouLtX79ep%2BxK6%2B8UidPnryk14y6sGfnynbs2LGg3bf/%2BZ//0fr165WYmKjExES99dZbeuutt5SYmHhJ/50FOtcpFCwHa9%2B%2BvVwul/dDgZKUk5Ojjh07KjS0bm/9J598oi5duvj8rfPrr79WdHS0kpKS9MUXX8gYI%2Bnnz2tt2bJFbrdbkuR2u5WTk%2BN93L59%2B7Rv3z7vfF3idrv11Vdfed/Sln7eo7NlKSkp0fbt2%2BV2uxUaGqqOHTv6zOfm5srlcqldu3b%2BC3EGc%2BfO1ciRI33GduzYoVatWkk69x7FxcUpPj7eZz4nJ0fx8fHVPnNRm7KysvTKK6/oueeeU//%2B/b3jwb5nZ8sV7Hu2e/dujR071lssJGnbtm1q3LixkpKSgnrPzpXtxRdfDNp9e/HFF/XWW29p3bp1WrdunXr27KmePXtq3bp1crvdF/06H%2BhcXn79N4vwuyeeeML079/f5OXlmffff9907tzZvPfee4Fe1nkdPXrUpKammocfftgUFBSYDz/80HTv3t0sWbLEHD161HTt2tVMmzbN5Ofnm2nTpplu3bp5/wn2li1bTIcOHcxf//pX7/VRxowZE%2BBE/79fXs6goqLC9OvXzzz00ENm586dZvHixSYhIcF7fZ7CwkLTsWNHs3jxYu/1eQYOHOj9p8vZ2dmmc%2BfO5v333zd5eXmmf//%2BZtq0aQHPlZeXZ66%2B%2BmqzdOlS88MPP5iXXnrJXHPNNWbLli3GmPPv0eLFi0337t3Nxo0bzcaNG0337t3NCy%2B84Lcsu3btMu3btzezZ8/2ubZQUVFRUO/ZuXIF%2B55VVFSYIUOGmPvvv9/k5%2BebDz/80Pz%2B9783K1asCOo9O1%2B2YN%2B3X5owYYL30guX%2BjpfF3JRsBzuxIkT5tFHHzUJCQmme/fuZvny5YFeUo3t3LnTjBw50iQkJJhu3bqZ559/3vuCl5eXZwYPHmw6duxobrvtNvPVV1/5PHbt2rXmhhtuMAkJCSYtLc0cPHgwEBHO6PTrRX3//ffmzjvvNNdcc43p37%2B/%2Bcc//uFz/w8//NDcdNNNplOnTubee%2B%2Btdj2vxYsXm%2Buuu84kJSWZiRMnmtLSUr/kON3pud5//30zcOBA07FjR9O3b99qxf5ce1RRUWGefvppk5ycbLp06WIyMzO9e%2B8PixcvNm3btj3jlzHBu2fnyxXMe2aMMfv37zdpaWmmc%2BfOplu3bmbhwoXeNQTrnp1yrmzBvm%2Bn/LJgGXNpr/N1IVeIMf/v/TcAAABYUbc/iAMAABCEKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsOz/A3NHTpdxJ6L3AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1857391993600425660”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>145</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (434)</td> <td class=”number”>1682</td> <td class=”number”>52.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>496</td> <td class=”number”>15.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1857391993600425660”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.75</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_AGRITRSM_RCT_07_12”>PCH_AGRITRSM_RCT_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1122</td>

</tr> <tr>

<th>Unique (%)</th> <td>35.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>60.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1945</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>232.45</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>20557</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-376342095483727567”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-376342095483727567”

aria-controls=”quantiles-376342095483727567” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-376342095483727567” aria-controls=”histogram-376342095483727567”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-376342095483727567” aria-controls=”common-376342095483727567”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-376342095483727567” aria-controls=”extreme-376342095483727567”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-376342095483727567”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-90.503</td>

</tr> <tr>

<th>Q1</th> <td>-40.783</td>

</tr> <tr>

<th>Median</th> <td>22.768</td>

</tr> <tr>

<th>Q3</th> <td>146.76</td>

</tr> <tr>

<th>95-th percentile</th> <td>1036.3</td>

</tr> <tr>

<th>Maximum</th> <td>20557</td>

</tr> <tr>

<th>Range</th> <td>20657</td>

</tr> <tr>

<th>Interquartile range</th> <td>187.54</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1026.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.4176</td>

</tr> <tr>

<th>Kurtosis</th> <td>163.95</td>

</tr> <tr>

<th>Mean</th> <td>232.45</td>

</tr> <tr>

<th>MAD</th> <td>358.47</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.081</td>

</tr> <tr>

<th>Sum</th> <td>294040</td>

</tr> <tr>

<th>Variance</th> <td>1054400</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-376342095483727567”>

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ywuczgg89Cg30KfjRozANWJL05ZdfasCAAbr%2B%2ButVVFSkOXPm6MILL1RlZaWioqJ8to2KilJNTY0kqaqq6hfnGyM/P195eXk%2BY9nZ2crJyTnJ1YSHNm1aBrqEoBAXFxPoEvAr6FFooE/Brzn3KCwD1qZNm7RmzRq9//77atGihZKSkrR//3498cQTOvPMMxuEpZqaGrVo0ULSj2e/jjcfE9P4b5KxY8cqMzPTZ8zpjFV5%2BeGTXFF4aO7rj4yMUFxcjA4cqFRdXX2gy8Fx0KPQQJ%2BCXzD1KFD/uA/LgLV9%2B3adddZZ3tAkSeeff74WL16stLQ0lZWV%2BWxfVlbmvXyXmJh43Pn4%2BPhG7z8hIaHB5UCP56Bqa5v3D4Lmvv5j6urq%2BVoEOXoUGuhT8GvOPQrLi6MJCQn69ttvfc5EffXVV%2BrcubNcLpc%2B/fRTGWMkScYYbd26VS6XS5LkcrlUUFDgfd6%2Bffu0b98%2B7zwAAMCv8XvAGjNmjFatWqWDBw822T4yMzN12mmn6e6779bXX3%2Btv/zlL1q8eLGuvfZaDRo0SAcOHNC8efO0e/duzZs3T5WVlRo8eLAkady4cXr11Ve1evVq7dy5U1OnTtUll1yiM888s8nqBQAA4cXvAatfv35avHix0tPTNWXKFH344Yfes0m2nH766Vq%2BfLk8Ho9Gjx6t3Nxc3XTTTRo7dqxatWqlJUuWqKCgQFlZWXK73Vq6dKliY2MlSSkpKZo9e7YWLVqkcePG6YwzzlBubq7V%2BgAAQHhzGNvpphGMMdq4caNeeeUVbdiwQXFxcRo5cqRGjhyps88%2B29/l%2BIXH0zRn7AYv/KhJXrcpvHVr/0CXEFBOZ4TatGmp8vLDzfY9CcGOHoUG%2BhT8gqlH8fGnB2S/AXmTu8PhUP/%2B/dW/f39VVlZq5cqVevzxx7V06VL17t1bEyZM0GWXXRaI0gAAAE5ZwH6LsLS0VK%2B99ppee%2B017dq1S71799bvfvc7lZSU6O6779aWLVs0Y8aMQJUHAABw0vwesF599VW9%2Buqr2rx5s9q2bauRI0fqz3/%2Bs7p06eLdpkOHDpo3bx4BCwAAhCS/B6wZM2ZowIABWrRokS666CJFRDR8n/0555yja665xt%2BlAQAAWOH3gPXBBx%2BoTZs2qqio8Iarbdu2qUePHoqMjJQk9e7dW7179/Z3aQAAAFb4/TYNhw4d0qBBg/Tkk096xyZOnKgRI0Zo3759/i4HAADAOr8HrPvvv19nnXWWrr/%2Beu/Ym2%2B%2BqQ4dOnC/KQAAEBb8HrD%2B9re/6a677vL5235t27bV1KlT9fHHH/u7HAAAAOv8HrCcTqcOHDjQYLyystL6Hd0BAAACwe8B66KLLtLcuXP13XffeceKi4uVm5urjIwMf5cDAABgnd9/i/DOO%2B/U9ddfr8svv1xxcXGSpAMHDqhHjx6aNm2av8sBAACwzu8Bq127dnr55Ze1ceNGFRUVyel06txzz9WFF14oh8Ph73IAAACsC8ifyomMjFRGRgaXBAEAQFjye8DyeDxauHChtm7dqqNHjzZ4Y/u7777r75IAAACs8nvAuueee7R9%2B3YNHTpUp59%2Bur93DwAA0OT8HrA%2B/vhjLVu2TGlpaf7eNQAAgF/4/TYNsbGxateunb93CwAA4Dd%2BD1gjRozQsmXLVFdX5%2B9dAwAA%2BIXfLxFWVFRo3bp1eu%2B993TmmWcqKirKZ/7ZZ5/1d0kAAABWBeQ2DcOGDQvEbgEAAPzC7wErNzfX37sEAADwK7%2B/B0uSSktLlZeXp9tvv13/%2BMc/9Pbbb%2Burr74KRCkAAADW%2BT1gffvttxo%2BfLhefvllvfPOOzpy5IjefPNNjRo1Sm6329/lAAAAWOf3gPXAAw9o4MCB2rBhg0477TRJ0iOPPKLMzEw99NBD/i4HAADAOr8HrK1bt%2Br666/3%2BcPOTqdTN998s3bs2OHvcgAAAKzze8Cqr69XfX19g/HDhw8rMjLS3%2BUAAABY5/eAlZ6eriVLlviErIqKCi1YsED9%2BvXzdzkAAADW%2BT1g3XXXXdq%2BfbvS09NVXV2tm266SQMGDND333%2BvO%2B%2B809/lAAAAWOf3%2B2AlJibqlVde0bp16/TFF1%2Bovr5e48aN04gRI9SqVSt/lwMAAGBdQO7kHhMTozFjxgRi1wAAAE3O7wFr/PjxvzjP3yIEAAChzu8Bq1OnTj6Pa2tr9e2332rXrl2aMGGCv8sBAACwLmj%2BFuGiRYtUUlLi52oAAADsC8jfIjyeESNG6K233gp0GQAAAKcsaALWp59%2Byo1GAQBAWAiKN7kfOnRIf//733X11Vf7uxwAAADr/B6wOnbs6PN3CCXptNNO0zXXXKMrrrjC3%2BUAAABY5/eA9cADD/hlPzU1NcrNzdW6det02mmnafTo0brtttvkcDi0Y8cO3Xvvvdq1a5fOPfdczZo1Sz179vQ%2Bd926dVq4cKE8Ho/S09M1Z84ctW3b1i91AwCA0Of3gLVly5ZGb3vBBRec9H7mzp2rzZs366mnntLhw4d12223qWPHjrriiis0ceJEDR8%2BXA888IBeeOEFTZo0SevXr1dsbKy2bdumGTNmaNasWerWrZvmzZunadOmacmSJSddCwAAaF78HrCuvfZa7yVCY4x3/KdjDodDX3zxxUnto6KiQmvXrtUzzzyjXr16SZJuuOEGud1uOZ1ORUdHa%2BrUqXI4HJoxY4Y%2B%2BOADvf3228rKytJzzz2nwYMHa%2BTIkZKk%2BfPna8CAASouLtaZZ5550usGAADNh99/i3Dx4sXq1KmTFi5cqE2bNqmgoEDLly/X2WefrSlTpujdd9/Vu%2B%2B%2Bqw0bNpz0PgoKCtSqVSv16dPHOzZx4kTl5ubK7XYrNTXVG%2BgcDod69%2B6twsJCSZLb7VZaWpr3eR06dFDHjh3ldrtPuh4AANC8BORGozNnztRFF13kHevXr59mz56tqVOn6sYbbzzlfRQXF6tTp0565ZVXtHjxYh09elRZWVm66aab5PF4dO655/ps365dOxUVFUmSSktLlZCQ0GD%2BRG6CWlpaKo/H4zPmdMY2eN3mxukMmruCBERkZITPZwQfehQa6FPwo0cBCFilpaUN/lyOJLVq1Url5eVW9nHkyBF9%2B%2B23WrVqlXJzc%2BXxeDRz5kzFxMSosrJSUVFRPttHRUWppqZGklRVVfWL842Rn5%2BvvLw8n7Hs7Gzl5OSc5IrCQ5s2LQNdQlCIi4sJdAn4FfQoNNCn4Nece%2BT3gJWcnKxHHnlEDz74oFq1aiXpx/dMLViwQBdeeKGVfTidTh06dEgPP/ywN8zt3btXL7zwgs4666wGYammpkYtWrSQJEVHRx93Piam8d8kY8eOVWZm5k9qilV5%2BeGTWU7YaO7rj4yMUFxcjA4cqFRdXX2gy8Fx0KPQQJ%2BCXzD1KFD/uPd7wLr77rs1fvx4XXTRRerSpYuMMfrmm28UHx%2BvZ5991so%2B4uPjFR0d7XOm7Oyzz9a%2BffvUp08flZWV%2BWxfVlbmvXyXmJh43Pn4%2BPhG7z8hIaHB5UCP56Bqa5v3D4Lmvv5j6urq%2BVoEOXoUGuhT8GvOPfL7xdGuXbvqzTff1O23367k5GSlpKRoxowZevXVV/Vv//ZvVvbhcrlUXV2tr7/%2B2jv21VdfqVOnTnK5XPr000%2B9v61ojNHWrVvlcrm8zy0oKPA%2Bb9%2B%2Bfdq3b593HgAA4Nf4/QyWJJ1xxhkaM2aMvv/%2Be%2B%2BtD0477TRrr3/OOefokksu0bRp03TffffJ4/Fo6dKluummmzRo0CA9/PDDmjdvnq666iqtWrVKlZWVGjx4sCRp3Lhxuvbaa5WcnKykpCTNmzdPl1xyCbdoAAAAjeb3M1jGGD300EO64IILNGzYMJWUlOjOO%2B/UjBkzdPToUWv7eeihh/Tv//7vGjdunO688079/ve/17XXXqtWrVppyZIlKigoUFZWltxut5YuXarY2FhJUkpKimbPnq1FixZp3LhxOuOMM5Sbm2utLgAAEP78fgZr5cqVevXVV3Xvvfdq9uzZkqSBAwdq1qxZat%2B%2BvW677TYr%2Bzn99NM1f/7848716tVLL7/88s8%2BNysrS1lZWVbqAAAAzY/fz2Dl5%2Bdr5syZysrK8t7sc8iQIZo7d65ef/11f5cDAABgnd8D1vfff6/u3bs3GO/WrVuDm3MCAACEIr8HrE6dOumzzz5rMP7BBx/wRnIAABAW/P4erP/8z//UrFmz5PF4ZIzRpk2blJ%2Bfr5UrV%2Bquu%2B7ydzkAAADW%2BT1gjRo1SrW1tXriiSdUVVWlmTNnqm3btrr11ls1btw4f5cDAABgnd8D1rp16zRo0CCNHTtWP/zwg4wxateunb/LAAAAaDJ%2Bfw/W7NmzvW9mb9u2LeEKAACEHb8HrC5dumjXrl3%2B3i0AAIDf%2BP0SYbdu3fSnP/1Jy5YtU5cuXRQdHe0zz13TAQBAqPN7wPr666%2BVmpoqSdz3CgAAhCW/BKz58%2Bdr8uTJio2N1cqVK/2xSwAAgIDxy3uwnnnmGVVWVvqMTZw4UaWlpf7YPQAAgF/5JWAZYxqMbdmyRdXV1f7YPQAAgF/5/bcIAQAAwh0BCwAAwDK/BSyHw%2BGvXQEAAASU327TMHfuXJ97Xh09elQLFixQy5YtfbbjPlgAACDU%2BSVgXXDBBQ3ueZWSkqLy8nKVl5f7owQAAAC/8UvA4t5XAACgOeFN7gAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALAv7gDVx4kTddddd3sc7duzQmDFj5HK5NGrUKG3fvt1n%2B3Xr1mngwIFyuVzKzs7WDz/84O%2BSAQBAiAvrgPXGG2/o/fff9z4%2BcuSIJk6cqLS0NL300ktKSUnRpEmTdOTIEUnStm3bNGPGDE2ePFn5%2Bfk6cOCApk2bFqjyAQBAiArbgFVRUaH58%2BcrKSnJO/bmm28qOjpaU6dOVdeuXTVjxgy1bNlSb7/9tiTpueee0%2BDBgzVy5Eh169ZN8%2BfP1/vvv6/i4uJALQMAAISgsA1YDz74oEaMGKFzzz3XO%2BZ2u5WamiqHwyFJcjgc6t27twoLC73zaWlp3u07dOigjh07yu12%2B7d4AAAQ0sIyYG3atEl/%2B9vfdPPNN/uMezweJSQk%2BIy1a9dOJSUlkqTS0tJfnAcAAGgMZ6ALsK26ulr33nuvZs6cqRYtWvjMVVZWKioqymcsKipKNTU1kqSqqqpfnG%2Bs0tJSeTwenzGnM7ZBeGtunM6wzPONFhkZ4fMZwYcehQb6FPzoURgGrLy8PPXs2VMZGRkN5qKjoxuEpZqaGm8Q%2B7n5mJiYE6ohPz9feXl5PmPZ2dnKyck5odcJN23atAx0CUEhLu7Evp/gf/QoNNCn4NecexR2AeuNN95QWVmZUlJSJMkbmN555x0NGzZMZWVlPtuXlZV5zywlJiYedz4%2BPv6Eahg7dqwyMzN9xpzOWJWXHz6h1wk3zX39kZERiouL0YEDlaqrqw90OTgOehQa6FPwC6YeBeof92EXsFauXKna2lrv44ceekiS9Kc//UlbtmzRk08%2BKWOMHA6HjDHaunWr/vjHP0qSXC6XCgoKlJWVJUnat2%2Bf9u3bJ5fLdUI1JCQkNLgc6PEcVG1t8/5B0NzXf0xdXT1fiyBHj0IDfQp%2BzblHYRewOnXq5PO4Zcsfk%2BtZZ52ldu3a6eGHH9a8efN01VVXadWqVaqsrNTgwYMlSePGjdO1116r5ORkJSUlad68ebrkkkt05pln%2Bn0dAAAgdDWrd5%2B1atVKS5Ys8Z6lcrvdWrp0qWJjYyVJKSkpmj17thYtWqRx48bpjDPOUG5uboCrBgAAocZhjDGBLqI58HgONsnrDl74UZO8blN469b%2BgS4hoJzOCLVp01Ll5Yeb7SnzYEePQgN9Cn7B1KP4%2BNMDst9mdQYLAADAHwhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJbVU5FJAAAREUlEQVQRsAAAACwjYAEAAFhGwAIAALAsbAPW/v37lZOToz59%2BigjI0O5ubmqrq6WJBUXF%2Bu6665TcnKyhgwZog8//NDnuRs3btSwYcPkcrk0fvx4FRcXB2IJAAAgRIVlwDLGKCcnR5WVlXr%2B%2Bef16KOP6v/%2B7/%2B0cOFCGWOUnZ2t9u3ba%2B3atRoxYoQmT56svXv3SpL27t2r7OxsZWVlac2aNWrbtq1uvvlmGWMCvCoAABAqnIEuoCl89dVXKiws1EcffaT27dtLknJycvTggw/qoosuUnFxsVatWqXY2Fh17dpVmzZt0tq1a3XLLbdo9erV6tmzp2644QZJUm5urvr3769PPvlEffv2DeSyAABAiAjLM1jx8fFatmyZN1wdc%2BjQIbndbp1//vmKjY31jqempqqwsFCS5Ha7lZaW5p2LiYlRjx49vPMAAAC/JiwDVlxcnDIyMryP6%2Bvr9dxzz6lfv37yeDxKSEjw2b5du3YqKSmRpF%2BdBwAA%2BDVheYnwpxYsWKAdO3ZozZo1Wr58uaKionzmo6KiVFNTI0mqrKz8xfnGKC0tlcfj8RlzOmMbBLfmxukMyzzfaJGRET6fEXzoUWigT8GPHjWDgLVgwQKtWLFCjz76qM477zxFR0eroqLCZ5uamhq1aNFCkhQdHd0gTNXU1CguLq7R%2B8zPz1deXp7PWHZ2tnJyck5yFeGhTZuWgS4hKMTFxQS6BPwKehQa6FPwa849CuuANWfOHL3wwgtasGCBLr/8cklSYmKidu/e7bNdWVmZ9%2BxSYmKiysrKGsx379690fsdO3asMjMzfcaczliVlx8%2BmWWEjea%2B/sjICMXFxejAgUrV1dUHuhwcBz0KDfQp%2BAVTjwL1j/uwDVh5eXlatWqVHnnkEQ0aNMg77nK5tHTpUlVVVXnPWhUUFCg1NdU7X1BQ4N2%2BsrJSO3bs0OTJkxu974SEhAaXAz2eg6qtbd4/CJr7%2Bo%2Bpq6vnaxHk6FFooE/Brzn3KCwvjn755Zd6/PHHdeONNyo1NVUej8f70adPH3Xo0EHTpk1TUVGRli5dqm3btmn06NGSpFGjRmnr1q1aunSpioqKNG3aNHXu3JlbNAAAgEYLy4D17rvvqq6uTk888YTS09N9PiIjI/X444/L4/EoKytLr732mhYtWqSOHTtKkjp37qzHHntMa9eu1ejRo1VRUaFFixbJ4XAEeFUAACBUOAy3KPcLj%2Bdgk7zu4IUfNcnrNoW3bu0f6BICyumMUJs2LVVefrjZnjIPdvQoNNCn4BdMPYqPPz0g%2Bw3LM1gAAACBRMACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWMdRXV2t6dOnKy0tTenp6Xr66acDXRIAAAghzkAXEIzmz5%2Bv7du3a8WKFdq7d6/uvPNOdezYUYMGDQp0aSFt8MKPAl3CCXnr1v6BLgEAEKIIWD9x5MgRrV69Wk8%2B%2BaR69OihHj16qKioSM8//zwBCwAANAqXCH9i586dqq2tVUpKincsNTVVbrdb9fX1AawMAACECs5g/YTH41GbNm0UFRXlHWvfvr2qq6tVUVGhtm3b/uprlJaWyuPx%2BIw5nbFKSEiwXi%2BaTqhd0lz/p4xAl9Bolz7010CXcEJC6WvbHERGRvh8RvChRwSsBiorK33ClSTv45qamka9Rn5%2BvvLy8nzGJk%2BerFtuucVOkf/ib/MGqbS0VPn5%2BRo7diwhLojRp//f3%2BYF5%2BV2ehQaSktLtWLFMvoUxOgRlwgbiI6ObhCkjj1u0aJFo15j7Nixeumll3w%2Bxo4da73WYzwej/Ly8hqcNUNwoU/Bjx6FBvoU/OgRZ7AaSExMVHl5uWpra%2BV0/vjl8Xg8atGiheLi4hr1GgkJCc02sQMAAM5gNdC9e3c5nU4VFhZ6xwoKCpSUlKSICL5cAADg15EYfiImJkYjR47Ufffdp23btmnDhg16%2BumnNX78%2BECXBgAAQkTkfffdd1%2Bgiwg2/fr1044dO/Twww9r06ZN%2BuMf/6hRo0YFuqxf1LJlS/Xp00ctW7YMdCn4BfQp%2BNGj0ECfgl9z75HDGGMCXQQAAEA44RIhAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICVoirrq7W9OnTlZaWpvT0dD399NOBLqlZWL9%2BvX7729/6fOTk5EiSduzYoTFjxsjlcmnUqFHavn27z3PXrVungQMHyuVyKTs7Wz/88IN3zhijhx56SP369VOfPn00f/581dfX%2B3Vtoa6mpkbDhg3T5s2bvWPFxcW67rrrlJycrCFDhujDDz/0ec7GjRs1bNgwuVwujR8/XsXFxT7zy5cvV0ZGhlJSUjR9%2BnRVVlZ65zgGT87x%2BjR37twGx9Vzzz3nnT%2BVY6e8vFy33HKLUlJSlJmZqVdffdU/Cw1B%2B/fvV05Ojvr06aOMjAzl5uaqurpaEsfSCTEIabNnzzbDhw8327dvN//7v/9rUlJSzFtvvRXossLe448/biZNmmRKS0u9H//85z/N4cOHTf/%2B/c0DDzxgdu/ebebMmWP%2B4z/%2Bwxw%2BfNgYY4zb7Ta9evUyL7/8svniiy/MNddcYyZOnOh93aeeespcfPHFZsuWLWbTpk0mPT3dLFu2LFDLDDlVVVUmOzvbnHfeeebjjz82xhhTX19vhg8fbm6//Xaze/dus3jxYuNyucyePXuMMcbs2bPHJCcnm6eeesrs2rXL/Nd//ZcZNmyYqa%2BvN8YY8/bbb5vU1FTzl7/8xbjdbjNkyBAza9Ys7z45Bk/c8fpkjDHXXXedWbJkic9xdeTIEWPMqR87kyZNMhMmTDB///vfzYsvvmh69uxp3G63/xYdIurr682VV15p/vCHP5hdu3aZLVu2mEsvvdQ88MADHEsniIAVwg4fPmySkpJ8fkAtWrTIXHPNNQGsqnm4/fbbzcMPP9xgfPXq1SYzM9P7A6W%2Bvt5ceumlZu3atcYYY%2B644w5z5513erffu3ev%2Be1vf2u%2B%2B%2B47Y4wxF198sXdbY4x55ZVXzIABA5pyKWGjqKjIXHHFFWb48OE%2B/%2BPeuHGjSU5O9oZcY4yZMGGC%2BfOf/2yMMWbhwoU%2Bx8yRI0dMSkqK9/lXX321d1tjjNmyZYvp1auXOXLkCMfgSfi5PhljTEZGhvnrX/963OedyrHz7bffmvPOO88UFxd756dPn%2B7zevjR7t27zXnnnWc8Ho937PXXXzfp6ekcSyeIS4QhbOfOnaqtrVVKSop3LDU1VW63m8tKTezLL79Uly5dGoy73W6lpqbK4XBIkhwOh3r37q3CwkLvfFpamnf7Dh06qGPHjnK73dq/f7/27dunCy64wDufmpqqPXv2qLS0tGkXFAY%2B%2BeQT9e3bV/n5%2BT7jbrdb559/vmJjY71jqampP9uTmJgY9ejRQ4WFhaqrq9Nnn33mM5%2BcnKyjR49q586dHIMn4ef6dOjQIe3fv/%2B4x5V0aseO2%2B1Whw4d1LlzZ5/5Tz/91O7iwkB8fLyWLVum9u3b%2B4wfOnSIY%2BkEOQNdAE6ex%2BNRmzZtFBUV5R1r3769qqurVVFRobZt2wawuvBljNHXX3%2BtDz/8UEuWLFFdXZ0GDRqknJwceTwenXvuuT7bt2vXTkVFRZKk0tJSJSQkNJgvKSmRx%2BORJJ/5Yz/kSkpKGjwPvq6%2B%2Burjjns8np/9mv/a/IEDB1RdXe0z73Q61bp1a5WUlCgiIoJj8AT9XJ%2B%2B/PJLORwOLV68WB988IFat26t66%2B/Xr/73e8kndqx83M93r9/v7V1hYu4uDhlZGR4H9fX1%2Bu5555Tv379OJZOEAErhFVWVvp8M0ryPq6pqQlESc3C3r17vV/7hQsX6vvvv9fcuXNVVVX1sz051o%2Bqqqqfna%2BqqvI%2B/tc5iX6eil/ryS/NH68n/zpvjOEYtOSrr76Sw%2BHQOeeco2uuuUZbtmzRPffco1atWunSSy89pWPn174H8PMWLFigHTt2aM2aNVq%2BfDnH0gkgYIWw6OjoBt94xx63aNEiECU1C506ddLmzZt1xhlnyOFwqHv37qqvr9cdd9yhPn36HLcnx/rxcz2LiYnx%2BWESHR3t/W/px1PtODnR0dGqqKjwGWtMT%2BLi4hr04V/nY2JiVFdXxzFoyciRIzVgwAC1bt1aktStWzd98803euGFF3TppZee0rHzc8%2BlR79swYIFWrFihR599FGdd955HEsniPdghbDExESVl5ertrbWO%2BbxeNSiRQvFxcUFsLLw17p1a%2B/7rCSpa9euqq6uVnx8vMrKyny2LSsr854WT0xMPO58fHy8EhMTJcl7ueNf/zs%2BPr5J1tEc/NzXvDE9ad26taKjo33ma2trVVFR4e0Zx6AdDofDG66OOeecc7yX8U7l2Pml5%2BL45syZo2eeeUYLFizQ5ZdfLolj6UQRsEJY9%2B7d5XQ6vW8wlKSCggIlJSUpIoLWNpW//vWv6tu3r8/9W7744gu1bt3a%2B8ZZY4ykH9%2BvtXXrVrlcLkmSy%2BVSQUGB93n79u3Tvn375HK5lJiYqI4dO/rMFxQUqGPHjrz/6hS4XC59/vnn3ksU0o9f15/rSWVlpXbs2CGXy6WIiAglJSX5zBcWFsrpdKpbt24cgxb993//t6677jqfsZ07d%2Bqcc86RdGrHTnJysvbs2eN9r9Cx%2BeTk5KZdVIjKy8vTqlWr9Mgjj2jo0KHecY6lExTQ32HEKbvnnnvM0KFDjdvtNuvXrze9e/c277zzTqDLCmsHDx40GRkZZsqUKebLL7807733nklPTzdLly41Bw8eNP369TNz5swxRUVFZs6cOaZ///7eX2veunWr6dGjh3nxxRe99/KZNGmS97WXLFli0tPTzccff2w%2B/vhjk56ebp5%2B%2BulALTVk/euv/9fW1pohQ4aYW2%2B91ezatcssWbLEJCcne%2B/dU1xcbJKSksySJUu89%2B4ZPny491Yb69atM7179zbr1683brfbDB061MyZM8e7L47Bk/evfXK73eb88883y5YtM99%2B%2B615/vnnTc%2BePc3WrVuNMad%2B7Nxwww3mmmuuMV988YV58cUXTVJSEvfBOo7du3eb7t27m0cffdTnfmSlpaUcSyeIgBXijhw5YqZOnWqSk5NNenq6eeaZZwJdUrOwa9cuc91115nk5GTTv39/89hjj3l/iLjdbjNy5EiTlJRkRo8ebT7//HOf565du9ZcfPHFJjk52WRnZ5sffvjBO1dbW2vuv/9%2Bk5aWZvr27WsWLFjgfV003k/vr/TNN9%2BY3//%2B96Znz55m6NCh5qOPPvLZ/r333jOXXXaZ6dWrl5kwYYL33krHLFmyxFx44YUmNTXVTJs2zVRVVXnnOAZP3k/7tH79ejN8%2BHCTlJRkBg0a1OB/rqdy7JSVlZlJkyaZpKQkk5mZaV5//fWmX2AIWrJkiTnvvPOO%2B2EMx9KJcBjz/13LAAAAgBUhemETAAAgeBGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGDZ/wMjctTPS5tlVAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-376342095483727567”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>450.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1111)</td> <td class=”number”>1191</td> <td class=”number”>37.1%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1945</td> <td class=”number”>60.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-376342095483727567”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.85714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.07692307692307</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.93877551020408</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10158.333333333332</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10585.714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11661.111111111111</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20557.14285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_ACRESPTH_07_12”><s>PCH_BERRY_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_BERRY_ACRES_07_12”><code>PCH_BERRY_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99863</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_ACRES_07_12”>PCH_BERRY_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>667</td>

</tr> <tr>

<th>Unique (%)</th> <td>20.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1939</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>55.641</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>4400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8607911888265368165”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8607911888265368165,#minihistogram8607911888265368165”
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</div> <div class=”row collapse col-md-12” id=”descriptives8607911888265368165”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8607911888265368165”

aria-controls=”quantiles8607911888265368165” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8607911888265368165” aria-controls=”histogram8607911888265368165”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8607911888265368165” aria-controls=”common8607911888265368165”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8607911888265368165” aria-controls=”extreme8607911888265368165”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8607911888265368165”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-78.49</td>

</tr> <tr>

<th>Q1</th> <td>-30.602</td>

</tr> <tr>

<th>Median</th> <td>4.318</td>

</tr> <tr>

<th>Q3</th> <td>66.667</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>4400</td>

</tr> <tr>

<th>Range</th> <td>4500</td>

</tr> <tr>

<th>Interquartile range</th> <td>97.269</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>222.51</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.9991</td>

</tr> <tr>

<th>Kurtosis</th> <td>142.18</td>

</tr> <tr>

<th>Mean</th> <td>55.641</td>

</tr> <tr>

<th>MAD</th> <td>102.95</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>9.373</td>

</tr> <tr>

<th>Sum</th> <td>70720</td>

</tr> <tr>

<th>Variance</th> <td>49513</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8607911888265368165”>

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mItWbJEl19%2BuTwej4KCgny2DQoKUl1dnSSppqbm3843R25urrKzs33G0tPTNXv27J%2B4Gv8QEdHW7hJsFR4eancJrR49sB89sB89sIZfBqxdu3bphRde0Ntvv62QkBD17dtXx48f1%2BOPP66LL764SViqq6tTSEiIpG/Ofp1rPjS0%2BW/ItLQ0JScn%2B4w5nWGqrDz9E1fkH1rr%2BgMDAxQeHqrqao8aGhrtLqdVogf2owf2a609sOube78MWAcPHtQll1ziDU2S1KtXL61evVqJiYmqqKjw2b6iosJ7%2BS4mJuac81FRUc3ef3R0dJPLgW73SdXXt5439Lm09vU3NDS2%2BmNgN3pgP3pgP3pgDb%2B8EBsdHa0vvvjC50zUp59%2Bqs6dO8vlcunDDz%2BUYRiSJMMwtG/fPrlcLkmSy%2BVSfn6%2B93mlpaUqLS31zgMAAPwQvwxYycnJatOmjf7617/qs88%2B09///netXr1aN9xwg0aOHKnq6motW7ZMhw8f1rJly%2BTxeDRq1ChJ0qRJk/TSSy9p06ZNOnTokObOnauhQ4fq4osvtnlVAACgpfDLgHXBBRdo/fr1crvdmjBhgjIzM3XbbbcpLS1N7dq105o1a5Sfn6%2BUlBQVFhZq7dq1CgsLkyTFx8dr8eLFysnJ0aRJk3ThhRcqMzPT5hUBAICWxGF8e60M55XbffK8vO6oVe%2Bdl9c9H169fbDdJdjC6QxQRERbVVae5nMPNqEH9qMH9mutPYiKusCW/frlGSwAAAA7WR6wJk6cqI0bN%2BrkyfNzRgcAAMBulgesQYMGafXq1RoyZIjuuOMOvfvuu%2BIqJQAA8CeWB6w5c%2BboH//4hx577DEFBgZq1qxZGjp0qB5%2B%2BGF99tlnVpcDAABgOltuNOpwODR48GANHjxYHo9HGzZs0GOPPaa1a9eqf//%2Bmjp1qq666io7SgMAAPjZbLuTe3l5ubZu3aqtW7fqk08%2BUf/%2B/XXttdeqrKxMf/3rX7V3714tWLDArvIAAAB%2BMssD1ksvvaSXXnpJu3fvVocOHTR%2B/Hg98sgj6tKli3ebTp06admyZQQsAADQIlkesBYsWKBhw4YpJydHV1xxhQICmn4M7Ne//rWuv/56q0sDAAAwheUB65133lFERISqqqq84Wr//v3q3bu3AgMDJUn9%2B/dX//79rS4NAADAFJb/FOGpU6c0cuRIPfHEE96xadOmady4cSotLbW6HAAAANNZHrDuv/9%2BXXLJJbrpppu8Y6%2B88oo6derE7/wDAAB%2BwfKA9cEHH%2Bjuu%2B9WVFSUd6xDhw6aO3eu3n//favLAQAAMJ3lAcvpdKq6urrJuMfj4Y7uAADAL1gesK644gotXbpUX375pXespKREmZmZSkpKsrocAAAA01n%2BU4Tz5s3TTTfdpBEjRig8PFySVF1drd69eysjI8PqcgAAAExnecCKjIzUiy%2B%2BqJ07d6q4uFhOp1PdunXT5ZdfLofDYXU5AAAAprPlV%2BUEBgYqKSmJS4IAAMAvWR6w3G63Vq1apX379uns2bNNPtj%2B5ptvWl0SAACAqSwPWPfcc48OHjyoMWPG6IILLrB69wAAAOed5QHr/fff17p165SYmGj1rgEAACxh%2BW0awsLCFBkZafVuAQAALGN5wBo3bpzWrVunhoYGq3cNAABgCcsvEVZVVWn79u166623dPHFFysoKMhn/tlnn7W6JAAAAFPZcpuGsWPH2rFbAAAAS1gesDIzM63eJQAAgKUs/wyWJJWXlys7O1tz5szRP//5T7322mv69NNP7SgFAADAdJYHrC%2B%2B%2BEJXX321XnzxRb3%2B%2Bus6c%2BaMXnnlFaWmpqqwsNDqcgAAAExnecBavny5hg8frh07dqhNmzaSpJUrVyo5OVkPPvig1eUAAACYzvKAtW/fPt10000%2Bv9jZ6XRqxowZKioqsrocAAAA01kesBobG9XY2Nhk/PTp0woMDLS6HAAAANNZHrCGDBmiNWvW%2BISsqqoqZWVladCgQVaXAwAAYDrLA9bdd9%2BtgwcPasiQIaqtrdVtt92mYcOG6auvvtK8efOsLgcAAMB0lt8HKyYmRlu2bNH27dv18ccfq7GxUZMmTdK4cePUrl07q8sBAAAwnS13cg8NDdXEiRPt2DUAAMB5Z3nAmjJlyr%2Bd53cRAgCAls7ygHXRRRf5PK6vr9cXX3yhTz75RFOnTrW6HAAAANP9Yn4XYU5OjsrKyiyuBgAAwHy2/C7Ccxk3bpxeffVVu8sAAAD42X4xAevDDz809UajdXV1WrRokS677DL99re/1cqVK2UYhiSpqKhIEydOlMvlUmpqqg4ePOjz3O3bt2v48OFyuVxKT0/XiRMnTKsLAAD4v1/Eh9xPnTql//u//9PkyZNN28/SpUu1e/duPfnkkzp9%2BrT%2B8pe/KDY2Vtdcc42mTZumq6%2B%2BWsuXL9fzzz%2Bv6dOn64033lBYWJj279%2BvBQsWaNGiRerRo4eWLVumjIwMrVmzxrTaAACAf7M8YMXGxvr8HkJJatOmja6//npdc801puyjqqpKeXl5evrpp9WvXz9J0s0336zCwkI5nU4FBwdr7ty5cjgcWrBggd555x299tprSklJ0XPPPadRo0Zp/PjxkqQVK1Zo2LBhKikp0cUXX2xKfQAAwL9ZHrCWL19%2B3veRn5%2Bvdu3aacCAAd6xadOmSZLuueceJSQkeEOew%2BFQ//79VVBQoJSUFBUWFuqWW27xPq9Tp06KjY1VYWEhAQsAADSL5QFr7969zd72sssu%2B0n7KCkp0UUXXaQtW7Zo9erVOnv2rFJSUnTbbbfJ7XarW7duPttHRkaquLhYklReXq7o6Ogm8/yEIwAAaC7LA9YNN9zgPXv07YfOJTUZczgc%2Bvjjj3/SPs6cOaMvvvhCGzduVGZmptxutxYuXKjQ0FB5PB4FBQX5bB8UFKS6ujpJUk1Nzb%2Bdb47y8nK53W6fMaczrElwa22czl/Mz1RYKjAwwOcrrEcP7EcP7EcPrGV5wFq9erWWLl2qu%2B66SwMGDFBQUJAOHDigxYsX69prr9Xo0aN/9j6cTqdOnTqlhx56yHtj02PHjun555/XJZdc0iQs1dXVKSQkRJIUHBx8zvnQ0NBm7z83N1fZ2dk%2BY%2Bnp6Zo9e/ZPWY7fiIhoa3cJtgoPb/57COcHPbAfPbAfPbCGLTcaXbhwoa644grv2KBBg7R48WLNnTvX5/NPP1VUVJSCg4N97hr/q1/9SqWlpRowYIAqKip8tq%2BoqPCeXYqJiTnnfFRUVLP3n5aWpuTkZJ8xpzNMlZWnf%2BxS/EprXX9gYIDCw0NVXe1RQ0Oj3eW0SvTAfvTAfq21B3Z9c295wCovL2/y63IkqV27dqqsrDRlHy6XS7W1tfrss8/0q1/9SpL06aef6qKLLpLL5dITTzwhwzDkcDhkGIb27dunW2%2B91fvc/Px8paSkSJJKS0tVWloql8vV7P1HR0c3uRzodp9UfX3reUOfS2tff0NDY6s/BnajB/ajB/ajB9aw/EJsXFycVq5cqVOnTnnHqqqqlJWVpcsvv9yUffz617/W0KFDlZGRoUOHDul///d/tXbtWk2aNEkjR45UdXW1li1bpsOHD2vZsmXyeDwaNWqUJGnSpEl66aWXtGnTJh06dEhz587V0KFD%2BQlCAADQbA7jXz9pboEjR45oypQp8ng86tKliwzD0Oeff66oqCg9%2B%2Byz%2Bo//%2BA9T9nPy5EktWbJEb7zxhkJDQzV58mSlp6fL4XBo//79uvfee3XkyBH95je/0aJFi9SrVy/vczdv3qxHHnlEX3/9tQYPHqwlS5YoIiLiZ9Xjdp/8uUs6p1Gr3jsvr3s%2BvHr7YLtLsIXTGaCIiLaqrDzNd402oQf2owf2a609iIq6wJb9Wh6wJOnrr7/W9u3bdeTIEUlSr169NGbMmB/1QfKWhoBFwGpt/1P7JaEH9qMH9mutPbArYFn%2BGSxJuvDCCzVx4kR99dVX3ktvbdq0saMUAAAA01n%2BGSzDMPTggw/qsssu09ixY1VWVqZ58%2BZpwYIFOnv2rNXlAAAAmM7ygLVhwwa99NJLuvfee7039Bw%2BfLh27NjR5N5RAAAALZHlASs3N1cLFy5USkqK9%2B7to0eP1tKlS7Vt2zarywEAADCd5QHrq6%2B%2BUs%2BePZuM9%2BjRo8mvlwEAAGiJLA9YF110kQ4cONBk/J133uFeUwAAwC9Y/lOEf/zjH7Vo0SK53W4ZhqFdu3YpNzdXGzZs0N133211OQAAAKazPGClpqaqvr5ejz/%2BuGpqarRw4UJ16NBBt99%2BuyZNmmR1OQAAAKazPGBt375dI0eOVFpamk6cOCHDMBQZGWl1GQAAAOeN5Z/BWrx4sffD7B06dCBcAQAAv2N5wOrSpYs%2B%2BeQTq3cLAABgGcsvEfbo0UN33nmn1q1bpy5duig4ONhnPjMz0%2BqSAAAATGV5wPrss8%2BUkJAgSdz3CgAA%2BCVLAtaKFSs0c%2BZMhYWFacOGDVbsEgAAwDaWfAbr6aeflsfj8RmbNm2aysvLrdg9AACApSwJWIZhNBnbu3evamtrrdg9AACApSz/KUIAAAB/R8ACAAAwmWUBy%2BFwWLUrAAAAW1l2m4alS5f63PPq7NmzysrKUtu2bX224z5YAACgpbMkYF122WVN7nkVHx%2BvyspKVVZWWlECAACAZSwJWNz7CgAAtCZ8yB0AAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwmd8HrGnTpunuu%2B/2Pi4qKtLEiRPlcrmUmpqqgwcP%2Bmy/fft2DR8%2BXC6XS%2Bnp6Tpx4oTVJQMAgBbOrwPWyy%2B/rLffftv7%2BMyZM5o2bZoSExO1efNmxcfHa/r06Tpz5owkaf/%2B/VqwYIFmzpyp3NxcVVdXKyMjw67yAQBAC%2BW3AauqqkorVqxQ3759vWOvvPKKgoODNXfuXHXt2lULFixQ27Zt9dprr0mSnnvuOY0aNUrjx49Xjx49tGLFCr399tsqKSmxaxkAAKAF8tuA9cADD2jcuHHq1q2bd6ywsFAJCQlyOBySJIfDof79%2B6ugoMA7n5iY6N2%2BU6dOio2NVWFhobXFAwCAFs0vA9auXbv0wQcfaMaMGT7jbrdb0dHRPmORkZEqKyuTJJWXl//beQAAgOZw2l2A2Wpra3Xvvfdq4cKFCgkJ8ZnzeDwKCgryGQsKClJdXZ0kqaam5t/ON1d5ebncbrfPmNMZ1iS8tTZOp1/m%2BR8UGBjg8xXWowf2owf2owfW8ruAlZ2drT59%2BigpKanJXHBwcJOwVFdX5w1i3zcfGhr6o2rIzc1Vdna2z1h6erpmz579o17H30REtLW7BFuFh/%2B49xHMRw/sRw/sRw%2Bs4XcB6%2BWXX1ZFRYXi4%2BMlyRuYXn/9dY0dO1YVFRU%2B21dUVHjPLMXExJxzPioq6kfVkJaWpuTkZJ8xpzNMlZWnf9Tr%2BJvWuv7AwACFh4equtqjhoZGu8tpleiB/eiB/VprD%2Bz65t7vAtaGDRtUX1/vffzggw9Kku68807t3btXTzzxhAzDkMPhkGEY2rdvn2699VZJksvlUn5%2BvlJSUiRJpaWlKi0tlcvl%2BlE1REdHN7kc6HafVH1963lDn0trX39DQ2OrPwZ2owf2owf2owfW8LuAddFFF/k8btv2m%2BR6ySWXKDIyUg899JCWLVum3//%2B99q4caM8Ho9GjRolSZo0aZJuuOEGxcXFqW/fvlq2bJmGDh2qiy%2B%2B2PJ1AACAlqtVfdKtXbt2WrNmjfcsVWFhodauXauwsDBJUnx8vBYvXqycnBxNmjRJF154oTIzM22uGgAAtDQOwzAMu4toDdzuk%2BfldUeteu%2B8vO758Ortg%2B0uwRZOZ4AiItqqsvI0p%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%2BoQ4cOmjFjhgzDsHlVAACgpXDaXcD58Omnn6qgoEDvvfeeOnbsKEmaPXu2HnjgAV1xxRUqKSnRxo0bFRYWpq5du2rXrl3Ky8vTrFmztGnTJvXp00c333yzJCkzM1ODBw/Wnj17NHDgQDuXBQAAWgi/PIMVFRWldevWecPVt06dOqXCwkL16tVLYWFh3vGEhAQVFBRIkgoLC5WYmOidCw0NVe/evb3zAAAAP8QvA1Z4eLiSkpK8jxsbG/Xcc89p0KBBcrvdio6O9tk%2BMjJSZWVlkvSD8wAAAD/ELy8RfldWVpaKior0wgsvaP369QoKCvKZDwoKUl1dnSTJ4/H82/nmKC8vl9vt9hlzOsOaBLfWxun0yzz/gwIDA3y%2Bwnr0wH70wH70wFp%2BH7CysrL0zDPP6OGHH1b37t0VHBysqqoqn23q6uoUEhIiSQoODm4Spurq6hQeHt7sfebm5io7O9tnLD09XbNnz/6Jq/APERFt7S7BVuHhoXaX0OrRA/vRA/vRA2v4dcBasmSJnn/%2BeWVlZWnEiBGSpJiYGB0%2BfNhnu4qKCu/ZpZiYGFVUVDSZ79mzZ7P3m5aWpuTkZJ8xpzNMlZWnf8oy/EZrXX9gYIDCw0NVXe1RQ0Oj3eW0SvTAfvTAfq21B3Z9c%2B%2B3ASs7O1sbN27UypUrNXLkSO%2B4y%2BXS2rVrVVNT4z1rlZ%2Bfr4SEBO98fn6%2Bd3uPx6OioiLNnDmz2fuOjo5ucjnQ7T6p%2BvrW84Y%2Bl9a%2B/oaGxlZ/DOxGD%2BxHD%2BxHD6zhlxdijxw5oscee0y33HKLEhIS5Ha7vX8GDBigTp06KSMjQ8XFxVq7dq3279%2BvCRMmSJJSU1O1b98%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%2BO9YwkJCSosLFRjY6ONlQEAgJaCM1jf4Xa7FRERoaCgIO9Yx44dVVtbq6qqKnXo0OEHX6O8vFxut9tnzOkMU3R0tOn1At9qSWfc3rgzydL9BQYG%2BHyF9eiB/eiBtQhY3%2BHxeHzClSTv47q6uma9Rm5urrKzs33GZs6cqVmzZplT5L/4YNkPX7YsLy9Xbm6u0tLSCHk24Pjbr7y8XM88s44e2Ige2I8eWIsY%2Bx3BwcFNgtS3j0NCQpr1Gmlpadq8ebPPn7S0NNNrbS63263s7OwmZ9VgDY6//eiB/eiB/eiBtTiD9R0xMTGqrKxUfX29nM5vDo/b7VZISIjCw8Ob9RrR0dF8dwAAQCvGGazv6Nmzp5xOpwoKCrxj%2Bfn56tu3rwICOFwAAOCHkRi%2BIzQ0VOPHj9d9992n/fv3a8eOHXrqqac0ZcoUu0sDAAAtROB99913n91F/NIMGjRIRUVFeuihh7Rr1y7deuutSk1Ntbusn6Vt27YaMGCA2rZta3cprRLH3370wH70wH70wDoOwzAMu4sAAADwJ1wiBAAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsDyY7W1tZo/f74SExM1ZMgQPfXUU3aX5Ffq6uo0duxY7d692ztWUlKiG2%2B8UXFxcRo9erTeffddn%2Bfs3LlTY8eOlcvl0pQpU1RSUuIzv379eiUlJSk%2BPl7z58%2BXx%2BOxZC0tyfHjxzV79mwNGDBASUlJyszMVG1trSSOv5W%2B%2BOIL/fGPf1R8fLyGDh2qdevWeefog7WmTZumu%2B%2B%2B2/u4qKhIEydOlMvlUmpqqg4ePOiz/fbt2zV8%2BHC5XC6lp6frxIkT3jnDMPTggw9q0KBBGjBggFasWKHGxkbL1uJXDPitxYsXG1dffbVx8OBB43/%2B53%2BM%2BPh449VXX7W7LL9QU1NjpKenG927dzfef/99wzAMo7Gx0bj66quNOXPmGIcPHzZWr15tuFwu4%2BjRo4ZhGMbRo0eNuLg448knnzQ%2B%2BeQT489//rMxduxYo7Gx0TAMw3jttdeMhIQE4%2B9//7tRWFhojB492li0aJFta/wlamxsNK677jrjT3/6k/HJJ58Ye/fuNX73u98Zy5cv5/hbqKGhwbjqqquMOXPmGJ999pnx1ltvGf379ze2bt1KHyy2fft2o3v37sa8efMMwzCM06dPG4MHDzaWL19uHD582FiyZInx29/%2B1jh9%2BrRhGIZRWFho9OvXz3jxxReNjz/%2B2Lj%2B%2BuuNadOmeV/vySefNK688kpj7969xq5du4whQ4YY69ats2VtLR0By0%2BdPn3a6Nu3r/cff8MwjJycHOP666%2B3sSr/UFxcbFxzzTXG1Vdf7ROwdu7cacTFxXn/R2YYhjF16lTjkUceMQzDMFatWuVz/M%2BcOWPEx8d7nz958mTvtoZhGHv37jX69etnnDlzxopltQiHDx82unfvbrjdbu/Ytm3bjCFDhnD8LXT8%2BHHjz3/%2Bs3Hy5EnvWHp6unHvvffSBwtVVlYaV1xxhZGamuoNWJs2bTKSk5O9gbWxsdH43e9%2BZ%2BTl5RmGYRh33XWXd1vDMIxjx44Zv/nNb4wvv/zSMAzDuPLKK73bGoZhbNmyxRg2bJhVS/IrXCL0U4cOHVJ9fb3i4%2BO9YwkJCSosLOR078%2B0Z88eDRw4ULm5uT7jhYWF6tWrl8LCwrxjCQkJKigo8M4nJiZ650JDQ9W7d28VFBSooaFBBw4c8JmPi4vT2bNndejQofO8opYjKipK69atU8eOHX3GT506xfG3UHR0tFatWqV27drJMAzl5%2Bdr7969GjBgAH2w0AMPPKBx48apW7du3rHCwkIlJCTI4XBIkhwOh/r37/%2B9x79Tp06KjY1VYWGhjh8/rtLSUl122WXe%2BYSEBB09elTl5eUWrcp/ELD8lNvtVkREhIKCgrxjHTt2VG1traqqqmysrOWbPHmy5s%2Bfr9DQUJ9xt9ut6Ohon7HIyEiVlZX94Hx1dbVqa2t95p1Op9q3b%2B99PqTw8HAlJSV5Hzc2Nuq5557ToEGDOP42SU5O1uTJkxUfH68RI0bQB4vs2rVLH3zwgWbMmOEz/kPHv7y8/Hvn3W63JPnMf/vNDMf/xyNg%2BSmPx%2BMTriR5H9fV1dlRkt/7vmP%2B7fH%2Bd/M1NTXex9/3fDSVlZWloqIi/eUvf%2BH42%2BSRRx7R6tWr9fHHHyszM5M%2BWKC2tlb33nuvFi5cqJCQEJ%2B5Hzr%2BNTU1P%2Br48%2B/GT%2Be0uwCcH8HBwU3%2BQnz7%2BLt/IWGO4ODgJmcH6%2BrqvMf7%2B3oSHh6u4OBg7%2BPvzn/3TBm%2BkZWVpWeeeUYPP/ywunfvzvG3Sd%2B%2BfSV984/%2BnXfeqdTU1CY/9UcfzJWdna0%2Bffr4nM391vcd3x86/qGhoT5h6ru94Pj/eJzB8lMxMTGqrKxUfX29d8ztdiskJETh4eE2Vua/YmJiVFFR4TNWUVHhPd3%2BffNRUVFq3769goODfebr6%2BtVVVWlqKio8198C7NkyRI9/fTTysrK0ogRIyRx/K1UUVGhHTt2%2BIx169ZNZ8%2BeVVRUFH04z15%2B%2BWXt2LFD8fHxio%2BP17Zt27Rt2zbFx8f/rL8HMTExkuS9VPiv/83x//EIWH6qZ8%2Becjqd3g82SlJ%2Bfr769u2rgADafj64XC599NFH3tPs0jfH3OVyeefz8/O9cx6PR0VFRXK5XAoICFDfvn195gsKCuR0OtWjRw/rFtECZGdna%2BPGjVq5cqXGjBnjHef4W%2Berr77SzJkzdfz4ce/YwYMH1aFDByUkJNCH82zDhg3atm2btmzZoi1btig5OVnJycnasmWLXC6XPvzwQxmGIemb%2B1rt27fve49/aWmpSktL5XK5FBMTo9jYWJ/5/Px8xcbGNvncFn5Y4H333Xef3UXAfG3atFFpaamef/559enTRwcOHFBWVpbmzJmjrl272l2e38jOzta1116rzp07KzY2Vtu3b1dBQYG6du2qvLw8vfzyy1q2bJkuuOACde7cWQ8%2B%2BKACAwMVHh6uzMxMNTQ06M4775TD4VBISIhWrlyprl276tSpU7rnnns0YsQI/dd//Zfdy/zFOHLkiO644w5NmzZNI0aM0JkzZ7x/unXrxvG3SFRUlN5%2B%2B22999576t27tw4cOKAlS5Zo%2BvTpGjVqFH04z8LDw9W%2BfXvvn3feeUdBQUGaMGGC/vM//1Pr1q1TWVmZYmNj9fjjj%2BvQoUNavHix2rRpo44dOyozM1NRUVEKDAzUwoUL1b17d/3hD3%2BQ9M0lwdWrV6t37946evSoFi1apBtvvNHnJ9LRTDbfJgLn0ZkzZ4y5c%2BcacXFxxpAhQ4ynn37a7pL8zr/eB8swDOPzzz83/vCHPxh9%2BvQxxowZY7z33ns%2B27/11lvGVVddZfTr18%2BYOnWq994z31qzZo1x%2BeWXGwkJCUZGRoZRU1NjyTpaijVr1hjdu3c/5x/D4PhbqayszEhPTzf69%2B9vDB482Hj88ce9916iD9aaN2%2Bez72tCgsLjfHjxxt9%2B/Y1JkyYYHz00Uc%2B2%2Bfl5RlXXnmlERcXZ6SnpxsnTpzwztXX1xv333%2B/kZiYaAwcONDIysry9hU/jsMw/v95RAAAAJiCD%2BMAAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACY7P8B7Y2lkBzFbaYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8607911888265368165”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (656)</td> <td class=”number”>1014</td> <td class=”number”>31.6%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1939</td> <td class=”number”>60.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8607911888265368165”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.48818897637796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.42105263157895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.10526315789474</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.9090909090909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2033.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_BERRY_FARMS_07_12”>PCH_BERRY_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>432</td>

</tr> <tr>

<th>Unique (%)</th> <td>13.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>977</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>44.479</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1496596464507647441”>

</div> <div class=”col-md-12 text-right”>

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aria-controls=”quantiles1496596464507647441” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1496596464507647441” aria-controls=”histogram1496596464507647441”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1496596464507647441” aria-controls=”common1496596464507647441”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1496596464507647441” aria-controls=”extreme1496596464507647441”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1496596464507647441”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-32</td>

</tr> <tr>

<th>Median</th> <td>14.286</td>

</tr> <tr>

<th>Q3</th> <td>77.778</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr>

<th>Range</th> <td>1800</td>

</tr> <tr>

<th>Interquartile range</th> <td>109.78</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>136.04</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.0585</td>

</tr> <tr>

<th>Kurtosis</th> <td>24.465</td>

</tr> <tr>

<th>Mean</th> <td>44.479</td>

</tr> <tr>

<th>MAD</th> <td>86.28</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.5841</td>

</tr> <tr>

<th>Sum</th> <td>99321</td>

</tr> <tr>

<th>Variance</th> <td>18506</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1496596464507647441”>

<img 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yDwhbw%2BW7BqorS0VGPHjlXjxo2VmpoqSSopKVFoaKjbdqGhoSovL1dpaanr8qnmayorK0uZmZluY2lpaUpPTz%2BbGH6jQYO63l5CjUVGhnt7CR4VaHmlwMscaHklMgeC2pzXbwvWkSNHNHr0aH333Xf65z//qfDw409CWFhYtbJUXl6uyMhIhYWFuS6fPH/i%2BjWRmpqqXr16uY3ZbBE6ePDI2UTxG76QPyQkWJGR4SouLlFlZZW3l3PeBVpeKfAyB1peicyBkPlM8nrrh3u/LFiHDx/Wn//8Z/3www968cUX1aJFC9dcTEyMCgsL3bYvLCxU27ZtVb9%2BfYWFhamwsFCtWrWSJFVUVKioqEhRUVE1vv/o6OhqhwOdzkOqqPD/T/rf4kv5KyurfGq95yrQ8kqBlznQ8kpkDgS1OW/tPXh5lqqqqjRmzBj9%2BOOPWrp0qS677DK3ebvdruzsbNflkpISbdu2TXa7XcHBwYqLi3Obz8nJkc1mU5s2bTyWAQAA%2BDa/K1ivv/66NmzYoEcffVSRkZFyOp1yOp0qKiqSJA0cOFCbN2/WokWLlJubq4kTJ6pZs2bq3LmzpONvnn/uuef0/vvva8uWLZoyZYpuvfXWMzpECAAAApvfHSJcu3atqqqqNGrUKLfxTp06aenSpWrWrJmefvppPfbYY5o3b54SEhI0b948BQUFSZL69eunvXv3avLkySovL9e1116r%2B%2B67zxtRAACAjwoyJ05hjvPK6Tx0Xm6379zPz8vtng/v3NPV20v4XTZbsBo0qKuDB4/U2uP6Vgq0vFLgZQ60vBKZAyHzmeSNirrQQ6ty53eHCAEAALyNggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMY8XrEGDBunVV1/VoUOHPH3XAAAAHuHxgtWlSxctWLBA3bp107hx4/TZZ5/JGOPpZQAAAJw3Hi9Y48eP14cffqj58%2BcrJCREY8eOVY8ePfTkk09qz549nl4OAACA5WzeuNOgoCB17dpVXbt2VUlJiZYuXar58%2Bdr0aJF6tixo%2B644w5de%2B213lgaAADAOfNKwZKkgoICvfHGG3rjjTe0Y8cOdezYUTfffLP279%2Bvhx56SJs2bdKkSZO8tTwAAICz5vGCtXr1aq1evVobNmxQw4YNNWDAAD311FNq0aKFa5smTZpoxowZFCwAAOCTPP4erEmTJqlu3bqaN2%2BePv74Y40fP96tXElSy5Ytdfvtt9fo9srLy9W/f39t2LDBNZaXl6fhw4crPj5e119/vT777DO363zxxRfq37%2B/7Ha7hg0bpry8PLf5F154QcnJyUpISNCDDz6okpKSswsLAAACkscL1ieffKKnnnpKdrtdwcHH737Lli2qrKx0bdOxY0eNHz/%2Bd2%2BrrKxM48aNU25urmvMGKO0tDQ1btxYK1as0E033aQxY8Zo3759kqR9%2B/YpLS1NKSkpev3119WwYUONHj3a9ZuMa9euVWZmpqZNm6YXX3xRDodDs2bNsvIhAAAAfs7jBevw4cPq06ePnn32WdfYyJEjddNNNyk/P7/Gt7Nz507deuut%2BuGHH9zG169fr7y8PE2bNk2tWrXSqFGjFB8frxUrVkiSli9frvbt22vEiBG67LLLlJGRob1792rjxo2SpJdeekl33HGHevbsqQ4dOmjq1KlasWIFe7EAAECNebxgPfbYY7r44ot15513usbefvttNWnSRBkZGTW%2BnY0bN6pz587KyspyG3c4HLr88ssVERHhGktMTFROTo5rPikpyTUXHh6udu3aKScnR5WVlfr666/d5uPj43Xs2DFt3779jLMCAIDA5PE3uX/55Zd67bXXFBUV5Rpr2LChJkyYoNtuu63GtzNkyJBTjjudTkVHR7uNNWrUSPv37//d%2BeLiYpWVlbnN22w21a9f33X9migoKJDT6XQbs9kiqt1voLHZav9fZgoJCXb76O8CLa8UeJkDLa9E5kDgC3k9XrBsNpuKi4urjZeUlFhyRveSkhKFhoa6jYWGhqq8vPx350tLS12XT3f9msjKylJmZqbbWFpamtLT02t8G/6oQYO63l5CjUVGhnt7CR4VaHmlwMscaHklMgeC2pzX4wXr6quv1qOPPqonnnhC//M//yPp%2BG/9ZWRkKDk5%2BZxvPywsTEVFRW5j5eXlqlOnjmv%2B5LJUXl6uyMhIhYWFuS6fPB8eXvMnMTU1Vb169XIbs9kidPDgkRrfhj/yhfwhIcGKjAxXcXGJKiurvL2c8y7Q8kqBlznQ8kpkDoTMZ5LXWz/ce7xg3X///brzzjt13XXXKTIyUpJUXFysdu3aaeLEied8%2BzExMdq5c6fbWGFhoevwXExMjAoLC6vNt23bVvXr11dYWJgKCwvVqlUrSVJFRYWKiorcDmn%2Bnujo6GqHA53OQ6qo8P9P%2Bt/iS/krK6t8ar3nKtDySoGXOdDySmQOBLU5r8cLVqNGjbRq1Sp98cUXys3Nlc1m06WXXqorr7xSQUFB53z7drtdixYtUmlpqWuvVXZ2thITE13z2dnZru1LSkq0bds2jRkzRsHBwYqLi1N2drY6d%2B4sScrJyZHNZlObNm3OeW0AACAweOVP5YSEhCg5OdmSQ4In69Spk5o0aaKJEydq9OjR%2BvDDD7VlyxbXbygOHDhQzz33nBYtWqSePXtq3rx5atasmatQDRkyRJMnT1br1q0VHR2tKVOm6NZbbz2jQ4QAACCwebxgOZ1OzZ07V5s3b9axY8eqvbH9gw8%2BOKfbDwkJ0fz58zVp0iSlpKTo4osv1rx58xQbGytJatasmZ5%2B%2Bmk99thjmjdvnhISEjRv3jzX3rN%2B/fpp7969mjx5ssrLy3XttdfqvvvuO6c1AQCAwOLxgvXwww9r69at6tevny688EJLbvPbb791u3zxxRdr2bJlp92%2Be/fu6t69%2B2nnR44cqZEjR1qyNgAAEHg8XrDWr1%2BvxYsXu53MEwAAwJ94/AxdERERatSokafvFgAAwGM8XrBuuukmLV682O2POwMAAPgTjx8iLCoq0po1a/TRRx%2BpefPm1c6a/tJLL3l6SQAAAJbyymka%2Bvfv7427BQAA8AiPF6wT56MCAADwV175M9QFBQXKzMzU%2BPHj9dNPP%2Bndd9/V7t27vbEUAAAAy3m8YH3//fe64YYbtGrVKq1du1ZHjx7V22%2B/rYEDB8rhcHh6OQAAAJbzeMF6/PHH1bt3b73//vu64IILJElPPPGEevXqpdmzZ3t6OQAAAJbzeMHavHmz7rzzTrc/7Gyz2TR69Ght27bN08sBAACwnMcLVlVVlaqqqqqNHzlyRCEhIZ5eDgAAgOU8XrC6deumhQsXupWsoqIizZo1S126dPH0cgAAACzn8YL1wAMPaOvWrerWrZvKysp09913q2fPnvrxxx91//33e3o5AAAAlvP4ebBiYmL0r3/9S2vWrNE333yjqqoqDR48WDfddJPq1avn6eUAAABYzitncg8PD9egQYO8cdcAAADnnccL1rBhw35znr9FCAAAfJ3HC1bTpk3dLldUVOj777/Xjh07dMcdd3h6OQAAAJarNX%2BLcN68edq/f7%2BHVwMAAGA9r/wtwlO56aab9M4773h7GQAAAOes1hSsr776ihONAgAAv1Ar3uR%2B%2BPBhffvttxoyZIinlwMAAGA5jxes2NhYt79DKEkXXHCBbr/9dt14442eXg4AAIDlPF6wHn/8cU/fJQAAgEd5vGBt2rSpxtteccUV53ElAAAA54fHC9bQoUNdhwiNMa7xk8eCgoL0zTffnPX95Ofna8qUKdq0aZPq16%2BvYcOGafjw4ZKkbdu26ZFHHtGOHTt06aWXaurUqWrfvr3rumvWrNHcuXPldDrVrVs3TZ8%2BXQ0bNjzrtQAAgMDi8d8iXLBggZo2baq5c%2Bdq3bp1ys7O1gsvvKBLLrlE48aN0wcffKAPPvhA77///jndzz333KOIiAitXLlSDz74oObOnav33ntPR48e1ciRI5WUlKSVK1cqISFBo0aN0tGjRyVJW7Zs0aRJkzRmzBhlZWWpuLhYEydOtCI6AAAIEB4vWBkZGZo8ebKuu%2B46NWjQQHXr1lWXLl00bdo0vfLKK2ratKnr39n65ZdflJOTo7vvvlstWrRQ7969lZycrHXr1untt99WWFiYJkyYoFatWmnSpEmqW7eu3n33XUnSsmXL1LdvXw0YMEBt2rTRzJkz9fHHHysvL8%2BqhwAAAPg5jxesgoKCU5anevXq6eDBg5bcR506dRQeHq6VK1fq2LFj2r17tzZv3qy2bdvK4XAoMTHRdUgyKChIHTt2VE5OjiTJ4XAoKSnJdVtNmjRRbGysHA6HJWsDAAD%2Bz%2BMFKz4%2BXk888YQOHz7sGisqKtKsWbN05ZVXWnIfYWFhmjx5srKysmS329W3b19dffXVGjRokJxOp6Kjo922b9SokevP9BQUFPzmPAAAwO/x%2BJvcH3roIQ0bNkxXX321WrRoIWOMvvvuO0VFRemll16y7H527dqlnj176s4771Rubq6mT5%2BuK6%2B8UiUlJQoNDXXbNjQ0VOXl5ZKk0tLS35yviYKCAjmdTrcxmy2iWnELNDZbrfnDAacVEhLs9tHfBVpeKfAyB1peicyBwBfyerxgtWrVSm%2B//bbWrFmjXbt2SZJuu%2B029evXT%2BHh4Zbcx7p16/T666/r448/Vp06dRQXF6cDBw7omWeeUfPmzauVpfLyctWpU0fS8b1fp5o/k7VlZWUpMzPTbSwtLU3p6elnmcg/NGhQ19tLqLHISGs%2BF31FoOWVAi9zoOWVyBwIanNejxcsSbrooos0aNAg/fjjj2revLmk42dzt8rWrVt18cUXu0qTJF1%2B%2BeVasGCBkpKSVFhY6LZ9YWGha%2B9STEzMKeejoqJqfP%2Bpqanq1auX25jNFqGDB4%2BcaRS/4gv5Q0KCFRkZruLiElVWVnl7OeddoOWVAi9zoOWVyBwImc8kr7d%2BuPd4wTLGaM6cOVq6dKmOHTumtWvX6sknn1R4eLimTJliSdGKjo7W999/r/Lyctfhvt27d6tZs2ay2%2B169tlnZYxRUFCQjDHavHmz/vrXv0qS7Ha7srOzlZKSIun4%2BbTy8/Nlt9vP6P5PPhzodB5SRYX/f9L/Fl/KX1lZ5VPrPVeBllcKvMyBllcicyCozXk9fvBy6dKlWr16tR555BFX%2Bendu7fef//9aofVzlavXr10wQUX6KGHHtKePXv0n//8RwsWLNDQoUPVp08fFRcXa8aMGdq5c6dmzJihkpIS9e3bV5I0ePBgrV69WsuXL9f27ds1YcIE9ejRw7WnDQAA4Pd4vGBlZWVp8uTJSklJcZ0q4frrr9ejjz6qN99805L7uPDCC/XCCy/I6XTqlltuUUZGhu6%2B%2B26lpqaqXr16WrhwoWsvlcPh0KJFixQRESFJSkhI0LRp0zRv3jwNHjxYF110kTIyMixZFwAACAweP0T4448/qm3bttXG27RpU%2B03787FpZdeqiVLlpxyrkOHDlq1atVpr5uSkuI6RAgAAHCmPL4Hq2nTpvr666%2BrjX/yyScchgMAAH7B43uw7rrrLk2dOlVOp1PGGK1bt05ZWVlaunSpHnjgAU8vBwAAwHIeL1gDBw5URUWFnnnmGZWWlmry5Mlq2LCh7rnnHg0ePNjTywEAALCcxwvWmjVr1KdPH6Wmpurnn3%2BWMUaNGjXy9DIAAADOG4%2B/B2vatGmuN7M3bNiQcgUAAPyOxwtWixYttGPHDk/fLQAAgMd4/BBhmzZtdO%2B992rx4sVq0aKFwsLC3OY55xQAAPB1Hi9Ye/bsUWJioiRZet4rAACA2sIjBWvmzJkaM2aMIiIitHTpUk/cJQAAgNd45D1YS5YsUUlJidvYyJEjVVBQ4Im7BwAA8CiPFCxjTLWxTZs2qayszBN3DwAA4FEe/y1CAAAAf0fBAgAAsJjHClZQUJCn7goAAMCrPHaahkcffdTtnFfHjh3TrFmzVLduXbftOA8WAADwdR4pWFdccUW1c14lJCTo4MGDOnjwoCeWAAAA4DEeKVic%2BwoAAAQS3uQOAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFjMbwtWeXm5pk6dqiuuuEJXXXWVnnjiCRljJEnbtm3ToEGDZLfbNXDgQG3%2BLiWXAAAdnUlEQVTdutXtumvWrFHv3r1lt9uVlpamn3/%2B2RsRAACAj/LbgvXoo4/qiy%2B%2B0HPPPac5c%2BbotddeU1ZWlo4ePaqRI0cqKSlJK1euVEJCgkaNGqWjR49KkrZs2aJJkyZpzJgxysrKUnFxsSZOnOjlNAAAwJd47I89e1JRUZFWrFihJUuWqEOHDpKkESNGyOFwyGazKSwsTBMmTFBQUJAmTZqkTz75RO%2B%2B%2B65SUlK0bNky9e3bVwMGDJAkzZw5Uz179lReXp6aN2/uzVgAAMBH%2BOUerOzsbNWrV0%2BdOnVyjY0cOVIZGRlyOBxKTExUUFCQJCkoKEgdO3ZUTk6OJMnhcCgpKcl1vSZNmig2NlYOh8OzIQAAgM/yyz1YeXl5atq0qf71r39pwYIFOnbsmFJSUnT33XfL6XTq0ksvddu%2BUaNGys3NlSQVFBQoOjq62vz%2B/ftrfP8FBQVyOp1uYzZbRLXbDTQ2W%2B3v8yEhwW4f/V2g5ZUCL3Og5ZXIHAh8Ia9fFqyjR4/q%2B%2B%2B/16uvvqqMjAw5nU5NnjxZ4eHhKikpUWhoqNv2oaGhKi8vlySVlpb%2B5nxNZGVlKTMz020sLS1N6enpZ5nIPzRoUNfbS6ixyMhwby/BowItrxR4mQMtr0TmQFCb8/plwbLZbDp8%2BLDmzJmjpk2bSpL27dunV155RRdffHG1slReXq46depIksLCwk45Hx5e8ycxNTVVvXr1OmlNETp48MjZxPEbvpA/JCRYkZHhKi4uUWVllbeXc94FWl4p8DIHWl6JzIGQ%2BUzyeuuHe78sWFFRUQoLC3OVK0m65JJLlJ%2Bfr06dOqmwsNBt%2B8LCQtfhu5iYmFPOR0VF1fj%2Bo6Ojqx0OdDoPqaLC/z/pf4sv5a%2BsrPKp9Z6rQMsrBV7mQMsrkTkQ1Oa8tffg5Tmw2%2B0qKyvTnj17XGO7d%2B9W06ZNZbfb9dVXX7nOiWWM0ebNm2W3213Xzc7Odl0vPz9f%2Bfn5rnkAAIDf45cFq2XLlurRo4cmTpyo7du369NPP9WiRYs0ePBg9enTR8XFxZoxY4Z27typGTNmqKSkRH379pUkDR48WKtXr9by5cu1fft2TZgwQT169OAUDQAAoMb8smBJ0uzZs/U///M/Gjx4sO6//37ddtttGjp0qOrVq6eFCxcqOztbKSkpcjgcWrRokSIiIiRJCQkJmjZtmubNm6fBgwfroosuUkZGhpfTAAAAX%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%2Brdd9%2BVJC1btkx9%2B/bVgAED1KZNG82cOVMff/yx8vLyvBUDAAD4IL8tWH//%2B99100036dJLL3WNORwOJSYmKigoSJIUFBSkjh07KicnxzWflJTk2r5JkyaKjY2Vw%2BHw7OIBAIBP88v3YK1bt05ffvml3nzzTU2ZMsU17nQ63QqXJDVq1Ei5ubmSpIKCAkVHR1eb379//xndf0FBgZxOp9uYzRZR7bZRu9lsfvvzh0tISLDbx0AQaJkDLa9E5kDgC3n9rmCVlZXpkUce0eTJk1WnTh23uZKSEoWGhrqNhYaGqry8XJJUWlr6m/M1lZWVpczMTLextLQ0paenn9HtwLsaNKjr7SV4TGRkuLeX4HGBljnQ8kpkDgS1Oa/fFazMzEy1b99eycnJ1ebCwsKqlaXy8nJXETvdfHj4mT2Bqamp6tWrl9uYzRahgwePnNHtwLsC4fkKCQlWZGS4iotLVFlZ5e3leESgZQ60vBKZAyHzmeT11g/Lflew3nrrLRUWFiohIUGSXIVp7dq16t%2B/vwoLC922LywsdB26i4mJOeV8VFTUGa0hOjq62uFAp/OQKir8/5PenwTS81VZWRVQeaXAyxxoeSUyB4LanNfvCtbSpUtVUVHhujx79mxJ0r333qtNmzbp2WeflTFGQUFBMsZo8%2BbN%2Butf/ypJstvtys7OVkpKiiQpPz9f%2Bfn5stvtng8CAAB8lt8VrKZNm7pdrlv3%2BK7Biy%2B%2BWI0aNdKcOXM0Y8YM/elPf9Krr76qkpIS9e3bV5I0ePBgDR06VPHx8YqLi9OMGTPUo0cPNW/e3OM5AACA76q9b78/D%2BrVq6eFCxe69lI5HA4tWrRIERERkqSEhARNmzZN8%2BbN0%2BDBg3XRRRcpIyPDy6sGAAC%2Bxu/2YJ3sxJ/IOaFDhw5atWrVabdPSUlxHSIEAAA4GwG1BwsAAMATKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxvy1YBw4cUHp6ujp16qTk5GRlZGSorKxMkpSXl6fhw4crPj5e119/vT777DO3637xxRfq37%2B/7Ha7hg0bpry8PG9EAAAAPsovC5YxRunp6SopKdHLL7%2BsJ598Uh9%2B%2BKHmzp0rY4zS0tLUuHFjrVixQjfddJPGjBmjffv2SZL27duntLQ0paSk6PXXX1fDhg01evRoGWO8nAoAAPgKm7cXcD7s3r1bOTk5%2Bvzzz9W4cWNJUnp6uv7%2B97/r6quvVl5enl599VVFRESoVatWWrdunVasWKGxY8dq%2BfLlat%2B%2BvUaMGCFJysjIUNeuXbVx40Z17tzZm7EAAICP8Ms9WFFRUVq8eLGrXJ1w%2BPBhORwOXX755YqIiHCNJyYmKicnR5LkcDiUlJTkmgsPD1e7du1c8wAAAL/HLwtWZGSkkpOTXZerqqq0bNkydenSRU6nU9HR0W7bN2rUSPv375ek350HAAD4PX55iPBks2bN0rZt2/T666/rhRdeUGhoqNt8aGioysvLJUklJSW/OV8TBQUFcjqdbmM2W0S14obazWbzy58/3ISEBLt9DASBljnQ8kpkDgS%2BkNfvC9asWbP04osv6sknn1Tr1q0VFhamoqIit23Ky8tVp04dSVJYWFi1MlVeXq7IyMga32dWVpYyMzPdxtLS0pSenn6WKeANDRrU9fYSPCYyMtzbS/C4QMscaHklMgeC2pzXrwvW9OnT9corr2jWrFm67rrrJEkxMTHauXOn23aFhYWuvUsxMTEqLCysNt%2B2bdsa329qaqp69erlNmazRejgwSNnEwNeEgjPV0hIsCIjw1VcXKLKyipvL8cjAi1zoOWVyBwImc8kr7d%2BWPbbgpWZmalXX31VTzzxhPr06eMat9vtWrRokUpLS117rbKzs5WYmOiaz87Odm1fUlKibdu2acyYMTW%2B7%2Bjo6GqHA53OQ6qo8P9Pen8SSM9XZWVVQOWVAi9zoOWVyBwIanPe2nvw8hzs2rVL8%2BfP11/%2B8hclJibK6XS6/nXq1ElNmjTRxIkTlZubq0WLFmnLli265ZZbJEkDBw7U5s2btWjRIuXm5mrixIlq1qwZp2gAAAA15pcF64MPPlBlZaWeeeYZdevWze1fSEiI5s%2BfL6fTqZSUFL3xxhuaN2%2BeYmNjJUnNmjXT008/rRUrVuiWW25RUVGR5s2bp6CgIC%2BnAgAAviLIcIpyj3A6D52X2%2B079/PzcruQ3rmnq7eXcN7ZbMFq0KCuDh48Umt3s1st0DIHWl6JzIGQ%2BUzyRkVd6KFVufPLPVgAAADeRMECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYjZvLwCorfrO/dzbSzgj79zT1dtLAAD8P%2BzBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGGdyP4WysjJNnTpV//73v1WnTh2NGDFCI0aM8PaygN/kS2ee56zzAPwdBesUZs6cqa1bt%2BrFF1/Uvn37dP/99ys2NlZ9%2BvTx9tIAAIAPoGCd5OjRo1q%2BfLmeffZZtWvXTu3atVNubq5efvllChYAAKgR3oN1ku3bt6uiokIJCQmuscTERDkcDlVVVXlxZQAAwFewB%2BskTqdTDRo0UGhoqGuscePGKisrU1FRkRo2bPi7t1FQUCCn0%2Bk2ZrNFKDo62vL1Ar7Il94v5oveuzfZ9f%2BQkGC3j7XNNbM/9fYSzsivH9vapLY/z1bzhbwUrJOUlJS4lStJrsvl5eU1uo2srCxlZma6jY0ZM0Zjx461ZpG/8uWM83fYsqCgQFlZWUpNTQ2YchhomQMtrxR4mQsKCvTii4trbd7z8RoWaM%2BxVPufZ6v5Qt7aW/28JCwsrFqROnG5Tp06NbqN1NRUrVy50u1famqq5Ws935xOpzIzM6vtjfNngZY50PJKgZc50PJKZA4EvpCXPVgniYmJ0cGDB1VRUSGb7fjD43Q6VadOHUVGRtboNqKjo2ttowYAAOcfe7BO0rZtW9lsNuXk5LjGsrOzFRcXp%2BBgHi4AAPD7aAwnCQ8P14ABAzRlyhRt2bJF77//vp5//nkNGzbM20sDAAA%2BImTKlClTvL2I2qZLly7atm2b5syZo3Xr1umvf/2rBg4c6O1leUXdunXVqVMn1a1b19tL8ZhAyxxoeaXAyxxoeSUyB4LanjfIGGO8vQgAAAB/wiFCAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCycUllZmR588EElJSWpW7duev755729pHN24MABpaenq1OnTkpOTlZGRobKysokSY8%2B%2Bqj%2B%2BMc/uv1btmyZ67pr1qxR7969ZbfblZaWpp9//tlbMWrsvffeq5YpPT1dkrRt2zYNGjRIdrtdAwcO1NatW92u64t5V65cWS3vH//4R7Vp00aSdPfdd1eb%2B/DDD13Xf%2BGFF5ScnKyEhAQ9%2BOCDKikp8VaUGikvL1f//v21YcMG11heXp6GDx%2Bu%2BPh4XX/99frss8/crvPFF1%2Bof//%2BstvtGjZsmPLy8tzma/NjcKq8OTk5%2BtOf/qSEhARdd911Wr58udt1brzxxmrP%2BY4dOyRJxhjNnj1bXbp0UadOnTRz5kxVVVV5NNPvOVXmc3mtqu2ZT877wAMPnPJr%2Btd/ui4pKana/JEjRyTVgu9jBjiFadOmmRtuuMFs3brV/Pvf/zYJCQnmnXfe8fayzlpVVZW59dZbzZ///GezY8cOs2nTJnPNNdeYxx9/3BhjzPDhw83ChQtNQUGB69/Ro0eNMcY4HA7ToUMHs2rVKvPNN9%2BY22%2B/3YwcOdKbcWpk/vz5ZtSoUW6ZfvnlF3PkyBHTtWtX8/jjj5udO3ea6dOnm6uuusocOXLEGOO7eUtKStyy7tu3z1xzzTVmxowZxhhjrrnmGrN69Wq3bcrKyowxxrz77rsmMTHR/Oc//zEOh8Ncf/31ZurUqd6M85tKS0tNWlqaad26tVm/fr0x5vjn%2BA033GDGjx9vdu7caRYsWGDsdrvZu3evMcaYvXv3mvj4ePPcc8%2BZHTt2mP/zf/6P6d%2B/v6mqqjLG1O7H4FR5CwoKTFJSkpkzZ47Zs2ePWbNmjYmLizMffvihMcaYiooKExcXZzZu3Oj2nB87dswYY8xzzz1nunfvbjZt2mTWrVtnunXrZhYvXuytiNWcKrMx5/ZaVZsznypvcXGxW86vvvrKtG/f3rz33nvGGGP2799vWrdubX744Qe37U58Tnv7%2BxgFC9UcOXLExMXFuX1Rz5s3z9x%2B%2B%2B1eXNW52blzp2ndurVxOp2usTfffNN069bNGGNMcnKy%2BfTTT0953fvuu8/cf//9rsv79u0zf/zjH80PP/xwfhd9jsaPH2/mzJlTbXz58uWmV69erhehqqoqc80115gVK1YYY3w378kWLFhgevfubcrKykxZWZlp27at2b179ym3HTJkiHnqqadclzdt2mQ6dOjg%2BsZVm%2BTm5pobb7zR3HDDDW7fjL744gsTHx/vKsrGGHPHHXe4cs2dO9fta/jo0aMmISHBdf3a%2BhicLu8///lP06dPH7dtH374YTNu3DhjjDHfffedadOmjSktLT3l7Xbv3t31OW%2BMMf/6179Mz549z1OKM3O6zMac22tVbc38W3l/bcSIEebee%2B91Xf78889N165dT7ltbfg%2BxiFCVLN9%2B3ZVVFQoISHBNZaYmCiHw1GrdiefiaioKC1evFiNGzd2Gz98%2BLAOHz6sAwcOqEWLFqe8rsPhUFJSkutykyZNFBsbK4fDcT6XfM527dp1ykwOh0OJiYkKCgqSJAUFBaljx47Kyclxzfti3l8rKirSs88%2Bq/Hjxys0NFS7d%2B9WUFCQmjdvXm3byspKff31126Z4%2BPjdezYMW3fvt2Ty66RjRs3qnPnzsrKynIbdzgcuvzyyxUREeEaS0xMPO3zGh4ernbt2iknJ6dWPwany3viMP/JDh8%2BLEnauXOnmjRporCwsGrbHDhwQPn5%2BbriiitcY4mJidq7d68KCgosTnDmTpf5XF6ranPm0%2BX9tXXr1mnTpk0aN26ca2znzp265JJLTrl9bfg%2BZvPIvcCnOJ1ONWjQQKGhoa6xxo0bq6ysTEVFRWrYsKEXV3d2IiMjlZyc7LpcVVWlZcuWqUuXLtq1a5eCgoK0YMECffLJJ6pfv77uvPNO3XzzzZKkgoICRUdHu91eo0aNtH//fo9mOBPGGO3Zs0efffaZFi5cqMrKSvXp00fp6elyOp269NJL3bZv1KiRcnNzJflm3pO98sorio6OVp8%2BfSRJu3fvVr169TRhwgRt3LhRf/jDHzR27Fh1795dxcXFKisrc8tss9lUv379Wpl5yJAhpxx3Op2/%2Bbz91nxtfgxOl7dZs2Zq1qyZ6/JPP/2kt956S2PHjpV0/AeMCy64QKNGjdLWrVt1ySWXaMKECerQoYOcTqckueU98cPX/v37qz1Onna6zOfyWlWbM58u768tWrRIN998s5o0aeIa27Vrl0pKSjR06FDt2bNHbdu21YMPPqhLLrmkVnwfYw8WqikpKXH7pJTkulxeXu6NJVlu1qxZ2rZtm/72t7%2B59m60bNlSixYt0qBBg/Twww/rvffekySVlpae8vGozY/Fvn37XM/j3Llzdf/99%2BvNN9/UzJkzT/v8nsjji3l/zRij5cuX6/bbb3eN7d69W6WlperWrZsWL16s7t276%2B6779bXX3%2Bt0tJSSfLpzNLpv25PZPiteV9/DEpLSzV27Fg1btxYqampkqQ9e/bol19%2B0aBBg7Ro0SK1atVKd9xxh/Lz80%2BZ1xde487ltcpXM0vHf3lj/fr1Gjp0qNv47t279csvv%2Bjuu%2B/W/PnzVadOHQ0fPlyHDx%2BuFd/H2IOFasLCwqp9Ap64XKdOHW8syVKzZs3Siy%2B%2BqCeffFKtW7fWZZddpp49e6p%2B/fqSpDZt2ui7777TK6%2B8omuuuea0j0d4eLg3ll8jTZs21YYNG3TRRRcpKChIbdu2VVVVle677z516tTplHlOPLe%2BmPfXvv76ax04cED9%2BvVzjY0ePVpDhw7VRRddJOn4c/zf//5Xr732mv72t79Jqv6i60uZpePPW1FRkdtYTZ7XyMhI12E0X3wMjhw5otGjR%2Bu7777TP//5T9d6p0%2BfrtLSUtWrV0%2BSNGXKFG3evFmrV6/WVVddJel4vpOz1%2Ba8AwYMOOvXql%2BXC1/KLElr165V27Ztq%2B15f%2B6553Ts2DHVrVtXkjR79mx1795dH374Ya34PsYeLFQTExOjgwcPqqKiwjXmdDpVp04dRUZGenFl52769OlasmSJZs2apeuuu07S8fcgnXjBOqFly5Y6cOCApOOPR2Fhodt8YWGhoqKiPLPos1S/fn3X%2B6wkqVWrViorK1NUVNQp85w4ROCreU/49NNPlZSU5CpTkhQcHOx2Wfr/z3H9%2BvUVFhbmlrmiokJFRUU%2Bk1k6/fNWk%2BfVVx%2BDw4cP66677lJubq5efPFFt/cm2Ww2V7mS5Nrzc%2BDAAcXExEiS67DZr/9fm/Oey2uVr2aWjn9N/%2B///m%2B18dDQUFe5ko7/ENGsWTPXc%2Bzt72MULFTTtm1b2Ww215tjJSk7O1txcXEKDvbdT5nMzEy9%2BuqreuKJJ9z2bvzjH//Q8OHD3bbdvn27WrZsKUmy2%2B3Kzs52zeXn5ys/P192u90j6z4bn376qTp37ux2HqNvvvlG9evXV2Jior766isZYyQdP6S2efNmVx5fzPtrW7ZsUceOHd3GHnjgAU2cONFt7MRzHBwcrLi4OLfMOTk5stlsrnNo%2BQK73a7//ve/rkNB0vGv29M9ryUlJdq2bZvsdrtPPgZVVVUaM2aMfvzxRy1dulSXXXaZ2/zQoUOVmZnptv23336rli1bKiYmRrGxsW55s7OzFRsb6/X3X/2Wc3mt8tXMxhh9/fXX1b6mjTHq3bu3Vq5c6Ro7evSovv/%2Be7Vs2bJ2fB/z2O8rwqc8/PDDpl%2B/fsbhcJj33nvPdOzY0axdu9bbyzprO3fuNG3btjVPPvmk2/lSCgoKjMPhMJdffrlZvHix%2Bf77783LL79s2rdvbzZv3myMMWbz5s2mXbt25rXXXnOdW2bUqFFeTvTbDh06ZJKTk824cePMrl27zEcffWS6detmFi1aZA4dOmS6dOlipk%2BfbnJzc8306dNN165dXb/e74t5f61nz55mzZo1bmNr16417dq1M6tWrTLfffedefrpp02HDh1MXl6eMcaYNWvWmI4dO5r33nvPOBwO069fPzN9%2BnRvLP%2BM/PpX2isqKsz1119v7rnnHrNjxw6zcOFCEx8f7zoPVl5enomLizMLFy50nQfrhhtucJ2uwxceg1/nzcrKMm3atDEffvih29fzwYMHjTHGPP/88yYxMdG8//77ZteuXeaRRx4xV111lTl06JAxxpiFCxeabt26mfXr15v169ebbt26meeff95r2U7n15nP9bXKFzKffJqGvLw807p1a1NQUFBt2%2BnTp5sePXqY9evXmx07dpi0tDTTv39/U1FRYYzx/vcxChZO6ejRo2bChAkmPj7edOvWzSxZssTbSzonCxcuNK1btz7lP2OMee%2B998wNN9xg4uLiTJ8%2Bfap9Ea5YscJ0797dxMfHm7S0NPPzzz97I8YZ2bFjhxk%2BfLiJj483Xbt2NU8//bTrm6nD4TADBgwwcXFx5pZbbjH//e9/3a7ri3lPiIuLM5988km18ddee81ce%2B21pn379ubmm282GzdudJtfuHChufLKK01iYqKZOHHiac%2BfVJuc/M3ou%2B%2B%2BM7fddptp37696devn/n888/dtv/oo4/Mtddeazp06GDuuOOOauc2q%2B2Pwa/zjhgx4pRfzyfOc1RVVWWeeeYZ06NHD9O%2BfXtz2223mW%2B//dZ1WxUVFeaxxx4zSUlJpnPnzmbWrFmur4/a5OTn%2BFxeq3wh88l5c3JyTOvWrV0nBf610tJSk5GRYbp27WrsdrsZNWqU2bdvn2ve29/Hgoz5f8cJAAAAYAnffUMNAABALUXBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALPZ/AaKjhZ7xQKvEAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1496596464507647441”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>218</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>47</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (421)</td> <td class=”number”>1234</td> <td class=”number”>38.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1496596464507647441”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.33333333333334</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-81.81818181818183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CACFP_09_15”>PCH_CACFP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.15572</td>

</tr> <tr>

<th>Minimum</th> <td>-0.37468</td>

</tr> <tr>

<th>Maximum</th> <td>0.92495</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6079056147456134218”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6079056147456134218,#minihistogram6079056147456134218”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6079056147456134218”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6079056147456134218”

aria-controls=”quantiles6079056147456134218” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6079056147456134218” aria-controls=”histogram6079056147456134218”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6079056147456134218” aria-controls=”common6079056147456134218”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6079056147456134218” aria-controls=”extreme6079056147456134218”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6079056147456134218”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-0.37468</td>

</tr> <tr>

<th>5-th percentile</th> <td>-0.18379</td>

</tr> <tr>

<th>Q1</th> <td>0.035319</td>

</tr> <tr>

<th>Median</th> <td>0.12288</td>

</tr> <tr>

<th>Q3</th> <td>0.22377</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.76085</td>

</tr> <tr>

<th>Maximum</th> <td>0.92495</td>

</tr> <tr>

<th>Range</th> <td>1.2996</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.18845</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.24843</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5954</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.0908</td>

</tr> <tr>

<th>Mean</th> <td>0.15572</td>

</tr> <tr>

<th>MAD</th> <td>0.1811</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0035</td>

</tr> <tr>

<th>Sum</th> <td>487.72</td>

</tr> <tr>

<th>Variance</th> <td>0.061719</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6079056147456134218”>

<img 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ne8D605/%2BpLPOOkvXXXede2zjxo3q2rWrcnNz7S4HAADAONsD1kcffaS77rpLMTEx7rHOnTtrxowZ2rx5s93lAAAAGGd7wHI4HKqtrW01Xl9fzxXdAQBAQLA9YF100UWaN2%2BevvnmG/dYeXm5cnNzNWTIEGPbaWxs1P3336/zzz9fF154oRYvXuwOcDt27NCECRPkdDo1btw4bd%2B%2B3WPdDRs2aMSIEXI6ncrKytKBAweM1QUAAAKf7QHrzjvvVGNjoy655BINGjRIgwYN0u9%2B9zsdPXpUM2fONLadefPm6f3339fjjz%2BuRYsW6dlnn1VhYaHq6uo0depUpaSkaN26dUpMTNS0adNUV1cn6ftPNM6aNUvTp09XYWGhamtrjdYFAAACn%2B2XaYiOjtbzzz%2Bv999/X2VlZXI4HDrnnHN0wQUXKCgoyMg2ampqVFRUpCeffFIDBgyQJE2ZMkWlpaVyOBwKCwvTjBkzFBQUpFmzZumdd97RK6%2B8ooyMDK1evVrp6ekaM2aMJGnBggUaNmyYysvL1bNnTyP1AQCAwOaTr8oJCQnRkCFDjJ4S/LHi4mJ17NhRAwcOdI9NnTpVkjR79mwlJye7w1xQUJCSkpJUUlKijIwMlZaW6oYbbnCv17VrV3Xr1k2lpaUELAAA0Ca2ByyXy6UlS5Zo69atOnr0aKs3tr/xxhv/9jbKy8vVvXt3vfDCC1q%2BfLmOHj2qjIwM3XTTTXK5XDrnnHM8lo%2BOjlZZWZmk778jMTY2ttX8/v37/%2B26AADAqcH2gDV79mxt375dl112mU4//fR22UZdXZ2%2B/vprrVmzRrm5uXK5XJozZ44iIiJUX1%2Bv0NBQj%2BVDQ0PV2NgoSTpy5MgJ59uisrJSLpfLY8zhiGwV3E4kJCTY4/epjF54CoR%2BOBxmag%2BEXphCL1rQixb0wndsD1ibN2/WihUrlJKS0m7bcDgcOnTokBYtWqTu3btLkvbt26dnnnlGZ511Vquw1NjYqPDwcElSWFjYcecjIiLavP3CwkLl5%2Bd7jGVlZSk7O9vrxxIV1fbtBjp64cmf%2B9GpUwej9%2BfPvTCNXrSgFy3ohf1sD1iRkZGKjo5u123ExMQoLCzMHa4k6Ve/%2BpUqKio0cOBAVVVVeSxfVVXlProUFxd33PkfXxj1l2RmZmr48OEeYw5HpKqrD7f5PkJCghUVFaHa2nodO9bc5vUCEb3wFAj98Oa5cCKB0AtT6EULetGCXph/QddWtges0aNHa8WKFcrJyXF/ubNpTqdTDQ0N2r17t371q19Jknbt2qXu3bvL6XTqsccek2VZCgoKkmVZ2rp1q2688Ub3usXFxcrIyJAkVVRUqKKiQk6ns83bj42NbXU60OU6qKYm73fuY8ea/6X1AhG98OTP/fjtwv/zdQleefnWVF%2BX0Gb%2BvF%2BYRi9a0Av72R6wampqtGHDBr311lvq2bNnq/c7Pf300//2Ns4%2B%2B2wNHTpUM2fO1H333SeXy6WCggLddNNNGjlypBYtWqT58%2Bfryiuv1Jo1a1RfX6/09HRJ0sSJEzVp0iQlJCQoPj5e8%2BfP19ChQ/kEIQAAaDOfXKZh1KhR7b6NhQsXau7cuZo4caIiIiJ09dVXa9KkSQoKCtKjjz6qe%2B%2B9V88%2B%2B6z%2B8z//UwUFBYqMjJQkJSYmKicnR0uXLtU///lPpaamau7cue1eLwAACBxBFl8AaAuX66BXyzscwerUqYOqqw%2Bf8od16YWnn%2BtH%2BpL3fFhVYPOHU4Q8T1rQixb0QoqJaZ8rFvwSn3xus7KyUvn5%2BfrjH/%2Bo7777Tq%2B88op27drli1IAAACMsz1gff3117r88sv1/PPP69VXX1VdXZ02btyocePGqbS01O5yAAAAjLM9YD3wwAMaMWKEXn/9dZ122mmSpMWLF2v48OFauHCh3eUAAAAYZ3vA2rp1q6677jqPL3Z2OBy6%2BeabtWPHDrvLAQAAMM72gNXc3Kzm5tZvtDt8%2BHC7XRcLAADATrYHrLS0ND366KMeIaumpkZ5eXkaPHiw3eUAAAAYZ3vAuuuuu7R9%2B3alpaWpoaFBN910k4YNG6a9e/fqzjvvtLscAAAA42y/0GhcXJxeeOEFbdiwQZ999pmam5s1ceJEjR49Wh07drS7HAAAAON8ciX3iIgITZgwwRebBgAAaHe2B6zJkyefcN7EdxECAAD4ku0Bq3v37h63m5qa9PXXX%2BvLL7/Utddea3c5AAAAxtkesHJzc487vmzZMu3fv9/magAAAMzzyXcRHs/o0aP18ssv%2B7oMAACAf9tJE7A%2B/vhjLjQKAAACwknxJvdDhw7piy%2B%2B0FVXXWV3OQAAAMbZHrC6devm8T2EknTaaafpmmuu0X/913/ZXQ4AAIBxtgesBx54wO5NAgAA2Mr2gLVly5Y2L3v%2B%2Bee3YyUAAADtw/aANWnSJPcpQsuy3OM/HQsKCtJnn31md3kAAAD/NtsD1vLlyzVv3jzdcccdGjhwoEJDQ/XJJ58oJydHY8eO1aWXXmp3SQAAAEbZfpmG3NxczZkzR5dccok6deqkDh06aPDgwcrJydEzzzyj7t27u38AAAD8ke0Bq7Ky8rjhqWPHjqqurra7HAAAAONsD1gJCQlavHixDh065B6rqalRXl6eLrjgArvLAQAAMM7292Ddc889mjx5si666CL16tVLlmVpz549iomJ0dNPP213OQAAAMbZHrB69%2B6tjRs3asOGDfrqq68kSVdffbUuu%2BwyRURE2F0OAACAcbYHLEk644wzNGHCBO3du1c9e/aU9P3V3IGTSfqS93xdAgDAT9n%2BHizLsrRw4UKdf/75GjVqlPbv368777xTs2bN0tGjR%2B0uBwAAwDjbA9aqVau0fv163XvvvQoNDZUkjRgxQq%2B//rry8/PtLgcAAMA42wNWYWGh5syZo4yMDPfV2y%2B99FLNmzdPf/vb3%2BwuBwAAwDjbA9bevXvVr1%2B/VuN9%2B/aVy%2BWyuxwAAADjbA9Y3bt31yeffNJq/J133nG/4R0AAMCf2f4pwuuvv17333%2B/XC6XLMvSpk2bVFhYqFWrVumuu%2B6yuxwAAADjbA9Y48aNU1NTkx555BEdOXJEc%2BbMUefOnXXrrbdq4sSJdpcDAABgnO0Ba8OGDRo5cqQyMzN14MABWZal6Ohou8sAAABoN7a/BysnJ8f9ZvbOnTsTrgAAQMCxPWD16tVLX375pd2bBQAAsI3tpwj79u2r22%2B/XStWrFCvXr0UFhbmMZ%2Bbm2t3SQAAAEbZHrB2796t5ORkSeK6VwAAICDZErAWLFig6dOnKzIyUqtWrbJjkwAAAD5jy3uwnnzySdXX13uMTZ06VZWVlXZsHgAAwFa2BCzLslqNbdmyRQ0NDXZsHgAAwFa2f4oQAAAg0BGwAAAADLMtYAUFBdm1KQAAAJ%2By7TIN8%2BbN87jm1dGjR5WXl6cOHTp4LMd1sAAAgL%2BzJWCdf/75ra55lZiYqOrqalVXV9tRAgAAgG1sCVhc%2BwoAAJxKAv5N7lOnTtVdd93lvr1jxw5NmDBBTqdT48aN0/bt2z2W37Bhg0aMGCGn06msrCwdOHDA7pIBAICfC%2BiA9dJLL%2Bntt992366rq9PUqVOVkpKidevWKTExUdOmTVNdXZ0kadu2bZo1a5amT5%2BuwsJC1dbWaubMmb4qHwAA%2BKmADVg1NTVasGCB4uPj3WMbN25UWFiYZsyYod69e2vWrFnq0KGDXnnlFUnS6tWrlZ6erjFjxqhv375asGCB3n77bZWXl/vqYQAAAD8UsAHrwQcf1OjRo3XOOee4x0pLS5WcnOy%2BZERQUJCSkpJUUlLink9JSXEv37VrV3Xr1k2lpaX2Fg8AAPyabZdpsNOmTZv00Ucf6W9/%2B5vuu%2B8%2B97jL5fIIXJIUHR2tsrIySVJlZaViY2Nbze/fv9%2Br7VdWVrb61KTDEdnqvk8kJCTY4/epjF7A1xyOk3/f43nSgl60oBe%2BE3ABq6GhQffee6/mzJmj8PBwj7n6%2BnqFhoZ6jIWGhqqxsVGSdOTIkRPOt1VhYaHy8/M9xrKyspSdne3V/UhSVFSE1%2BsEKnoBX%2BnUqcMvL3SS4HnSgl60oBf2C7iAlZ%2Bfr/POO09DhgxpNRcWFtYqLDU2NrqD2M/NR0R4t2NmZmZq%2BPDhHmMOR6Sqqw%2B3%2BT5CQoIVFRWh2tp6HTvW7NX2Aw29gK9589z1FZ4nLehFC3rhuxdIARewXnrpJVVVVSkxMVGS3IHp1Vdf1ahRo1RVVeWxfFVVlfvUXVxc3HHnY2JivKohNja21elAl%2Bugmpq837mPHWv%2Bl9YLRPQCvuJP%2Bx3Pkxb0ogW9sF/ABaxVq1apqanJfXvhwoWSpNtvv11btmzRY489JsuyFBQUJMuytHXrVt14442SJKfTqeLiYmVkZEiSKioqVFFRIafTaf8DAQAAfivgAlb37t09bv/wXYdnnXWWoqOjtWjRIs2fP19XXnml1qxZo/r6eqWnp0uSJk6cqEmTJikhIUHx8fGaP3%2B%2Bhg4dqp49e9r%2BOAAAgP86pT5W0LFjRz366KPuo1SlpaUqKChQZGSkpO%2B/HzEnJ0fLli3TxIkTdcYZZ/Dl0wAAwGtBlmVZvi7iVOByHfRqeYcjWJ06dVB19eFT/ry5r3qRvuQ927aFk9vLt6b6uoRfxN%2BMFvSiBb2QYmJO98l2T6kjWAAAAHYgYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMIevCwAAmJW%2B5D1fl9BmL9%2Ba6usSgHbBESwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzOHrAnDqSF/ynq9LAADAFhzBAgAAMIyABQAAYFjABqxvv/1W2dnZGjhwoIYMGaLc3Fw1NDRIksrLy/X73/9eCQkJuvTSS/Xuu%2B96rPv%2B%2B%2B9r1KhRcjqdmjx5ssrLy33xEAAAgJ8KyIBlWZays7NVX1%2Bvv/zlL3rooYf05ptvasmSJbIsS1lZWerSpYuKioo0evRoTZ8%2BXfv27ZMk7du3T1lZWcrIyNBzzz2nzp076%2Babb5ZlWT5%2BVAAAwF8E5Jvcd%2B3apZKSEr333nvq0qWLJCk7O1sPPvigLrroIpWXl2vNmjWKjIxU7969tWnTJhUVFemWW27R2rVrdd5552nKlCmSpNzcXKWmpurDDz/UoEGDfPmwAACAnwjII1gxMTFasWKFO1z94NChQyotLdW5556ryMhI93hycrJKSkokSaWlpUpJSXHPRUREqH///u55AACAXxKQASsqKkpDhgxx325ubtbq1as1ePBguVwuxcbGeiwfHR2t/fv3S9IvzgMAAPySgDxF%2BFN5eXnasWOHnnvuOT311FMKDQ31mA8NDVVjY6Mkqb6%2B/oTzbVFZWSmXy%2BUx5nBEtgpuJxISEuzxG4DvOBwn//PQX/9mtEdv/bUX7YFe%2BE7AB6y8vDytXLlSDz30kPr06aOwsDDV1NR4LNPY2Kjw8HBJUlhYWKsw1djYqKioqDZvs7CwUPn5%2BR5jWVlZys7O9rr%2BqKgIr9cBYFanTh18XUKb%2BdvfjPbsrb/1oj3RC/sFdMCaO3eunnnmGeXl5emSSy6RJMXFxWnnzp0ey1VVVbmPLsXFxamqqqrVfL9%2B/dq83czMTA0fPtxjzOGIVHX14TbfR0hIsKKiIlRbW69jx5rbvB4A87x57vrKj/9m%2BJP26C1/P1vQC9%2B9QArYgJWfn681a9Zo8eLFGjlypHvc6XSqoKBAR44ccR%2B1Ki4uVnJysnu%2BuLjYvXx9fb127Nih6dOnt3nbsbGxrU4HulwH1dTk/c597Fjzv7QeAHP86Tnob/%2BJtmdv%2BfvZgl7YLyBPyn711Vd6%2BOGHdcMNNyg5OVkul8v9M3DgQHXt2lUzZ85UWVmZCgoKtG3bNo0fP16SNG7cOG3dulUFBQUqKyvTzJkz1aNHDy7RAAAA2iwgA9Ybb7yhY8eO6ZFHHlFaWprHT0hIiB5%2B%2BGG5XC5lZGToxRdf1LJly9StWzdJUo8ePfTnP/9ZRUVFGj9%2BvGqEXCxBAAANXElEQVRqarRs2TIFBQX5%2BFEBAAB/EZCnCKdOnaqpU6f%2B7PxZZ52l1atX/%2Bz8xRdfrIsvvrg9SgMAAKeAgDyCBQAA4EsELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgWkJ8iBACT0pe85%2BsSAPgZjmABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjm8HUBAAD4i/Ql7/m6BK%2B8dvsQX5dwyuIIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhXMkdAIAA9duF/%2BfrEtrs5VtTfV2CURzBAgAAMIwjWAAAn/G37/YD2oqA5ef44wQAwMmHU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgHUdDQ4PuvvtupaSkKC0tTU888YSvSwIAAH6E7yI8jgULFmj79u1auXKl9u3bpzvvvFPdunXTyJEjfV0aAADwAwSsn6irq9PatWv12GOPqX///urfv7/Kysr0l7/8hYAFAADahFOEP/H555%2BrqalJiYmJ7rHk5GSVlpaqubnZh5UBAAB/wRGsn3C5XOrUqZNCQ0PdY126dFFDQ4NqamrUuXPnX7yPyspKuVwujzGHI1KxsbFtriMkJNjjNwAAgczhCKz/7whYP1FfX%2B8RriS5bzc2NrbpPgoLC5Wfn%2B8xNn36dN1yyy1trqOyslIrV65QZmbmCYPZR/MD/7RlZWWlCgsLf7EXpwr60YJetKAXLehFC3rhO4EVFw0ICwtrFaR%2BuB0eHt6m%2B8jMzNS6des8fjIzM72qw%2BVyKT8/v9WRsFMRvfBEP1rQixb0ogW9aEEvfIcjWD8RFxen6upqNTU1yeH4vj0ul0vh4eGKiopq033ExsbySgEAgFMYR7B%2Bol%2B/fnI4HCopKXGPFRcXKz4%2BXsHBtAsAAPwyEsNPREREaMyYMbrvvvu0bds2vf7663riiSc0efJkX5cGAAD8RMh99913n6%2BLONkMHjxYO3bs0KJFi7Rp0ybdeOONGjdunO11dOjQQQMHDlSHDh1s3/bJhl54oh8t6EULetGCXrSgF74RZFmW5esiAAAAAgmnCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbBOEpZlaeHChRo8eLAGDhyoBQsWqLm5%2BRfXO3jwoIYMGaJ169bZUKU9vO1FSUmJrrzySiUmJuqSSy7R2rVrbay2fTQ0NOjuu%2B9WSkqK0tLS9MQTT/zssjt27NCECRPkdDo1btw4bd%2B%2B3cZK2583vXjrrbc0evRoJSYm6vLLL9cbb7xhY6Xtz5te/GDv3r1KTEzUBx98YEOF9vGmF1988YUmTpyoAQMG6PLLL9fmzZttrLT9edOL1157Tenp6UpMTNTEiRP16aef2ljpKcbCSeHxxx%2B3Lr74YmvLli3Wpk2brLS0NGvFihW/uN7s2bOtPn36WEVFRTZUaQ9velFZWWmlpKRYixYtsnbv3m1t2LDBio%2BPt9588017izYsJyfHuvzyy63t27dbf//7363ExETr5ZdfbrXc4cOHrdTUVOuBBx6wdu7cac2dO9e68MILrcOHD/ug6vbR1l589tlnVv/%2B/a2VK1dae/bssVavXm3179/f%2Buyzz3xQdftoay9%2B7Prrr7f69Oljbd682aYq7dHWXtTW1loXXnihdc8991h79uyx/ud//sdKTk62qqqqfFB1%2B2hrL7788ksrPj7eev75562vv/7auv/%2B%2B63U1FSrrq7OB1UHPgLWSeLiiy/2CEkvvPCCNWzYsBOus2XLFuu3v/2tlZqaGlABy5te/PWvf7VGjhzpMTZ79mzrtttua9ca29Phw4et%2BPh4j/8Qly1bZl1zzTWtll27dq01fPhwq7m52bIsy2pubrZ%2B%2B9vfBsz%2B4E0v8vLyrOuvv95jbMqUKdbixYvbvU47eNOLH6xfv9668sorAy5gedOLlStXWiNGjLCamprcYxkZGdZbb71lS63tzZtePPnkk9bYsWPdtw8ePGj16dPH2rZtmy21nmo4RXgS%2BPbbb1VRUaHzzz/fPZacnKx//OMfqqysPO46jY2Nmj17tubMmaPQ0FC7Sm133vZiyJAhys3NbTV%2B6NChdq2zPX3%2B%2BedqampSYmKieyw5OVmlpaWtTpWWlpYqOTlZQUFBkqSgoCAlJSWppKTE1prbize9GDt2rG6//fZW93Hw4MF2r9MO3vRCkqqrq5WXl6ecnBw7y7SFN7348MMP9Zvf/EYhISHusaKiIl188cW21duevOnFmWeeqZ07d6q4uFjNzc1at26dOnbsqP/4j/%2Bwu%2BxTAgHrJOByuSRJsbGx7rEuXbpIkvbv33/cdZYvX65zzz1XaWlp7V%2BgjbztRY8ePZSQkOC%2B/d133%2Bmll17SBRdc0M6Vth%2BXy6VOnTp5BOcuXbqooaFBNTU1rZb9ca8kKTo6%2Bmf3G3/jTS969%2B6tvn37um%2BXlZVp06ZNfr0v/Jg3vZCkBx54QGPHjtWvf/1rO8u0hTe9KC8vV%2BfOnTV79mylpqbqiiuuUHFxsd0ltxtvenHppZdq6NChuuqqq3TeeedpwYIFWrp0qc444wy7yz4lOHxdwKniyJEj%2Bvbbb487V1dXJ0keT5Af/t3Y2Nhq%2BZ07d2rNmjV68cUX26HS9meyFz%2B931tuuUVdunRRZmamoWrtV19f3%2Bqo5M/14OeW/aVe%2BQtvevFjBw4c0C233KKkpCT95je/adca7eJNL95//30VFxdrw4YNttVnJ296UVdXp4KCAk2ePFmPPfaYXnrpJV1//fV6%2BeWX1bVrV9tqbi/e9KK6uloul0tz5syR0%2BnUM888o5kzZ%2Br5559XdHS0bTWfKghYNiktLdXkyZOPO3fHHXdI%2Bv7JEBYW5v63JEVERHgsa1mW7rnnHmVnZ7uP7PgbU734scOHD%2Bvmm2/Wnj179Ne//vWEy57swsLCWv1h/OF2eHh4m5b96XL%2Bypte/KCqqkrXXXedLMvS0qVLFRwcGAfq29qLI0eOaM6cObr33nsDZj/4KW/2i5CQEPXr10/Z2dmSpHPPPVfvvfee1q9frxtvvNGegtuRN71YuHCh%2BvTpo6uvvlqSNHfuXKWnp6uoqEhTp061p%2BBTCAHLJoMGDdIXX3xx3Llvv/1WeXl5crlc6tGjh6SWU2UxMTEey%2B7bt08ff/yxvvjiCz344IOSvn8Fc%2B%2B992rjxo1asWJFOz4KM0z14geHDh3SH/7wB33zzTdauXKlevXq1S512yUuLk7V1dVqamqSw/H9U9Tlcik8PFxRUVGtlq2qqvIYq6qqanXa0F950wvp%2B/3nh/D%2B9NNPq3PnzrbW257a2ott27apvLzcHSh%2BcMMNN2jMmDEB8Z4sb/aLmJgYnX322R5jvXr1UkVFhW31tidvevHpp59q0qRJ7tvBwcHq27ev9u3bZ2vNp4rAeGnn5%2BLi4tStWzeP9wUUFxerW7durf6jjIuL09///ne98MIL7p/Y2FhlZ2dr/vz5dpdunDe9kKTm5mZNnz5de/fu1apVqwLi/Sb9%2BvWTw%2BHweKN6cXGx4uPjWx2NcTqd%2Bvjjj2VZlqTvj3Bu3bpVTqfT1prbize9qKur0x/%2B8AcFBwdr9erViouLs7vcdtXWXgwYMKDV3whJmjdvnv77v//b9rrbgzf7RUJCQqsXdLt27VL37t1tqbW9edOL2NhYffXVVx5ju3fvdr%2BYhWG%2B/RAjfvDoo49aaWlp1ubNm63NmzdbaWlp1hNPPOGe/%2B6776xDhw4dd91hw4YFzMfyLcu7XhQWFlp9%2B/a13nzzTauystL9U11d7avyjZg9e7Z12WWXWaWlpdZrr71mJSUlWa%2B%2B%2BqplWd9f%2B6u%2Bvt6yrO8/Zj148GBr7ty5VllZmTV37lwrNTU1oK6D1dZeLF682BowYIBVWlrqsS/U1tb6snyj2tqLnwq0yzRYVtt7sXfvXishIcFaunSptWfPHmvJkiVWQkKCtX//fl%2BWb1Rbe/HSSy%2B5r4O1Z88eKy8vL%2BCuCXYyIWCdJJqamqw//elPVkpKijVo0CArLy/PfW0jy/o%2BRC1duvS46wZawPKmF1OmTLH69OnT6udE1wbyB3V1ddaMGTOshIQEKy0tzXryySfdcz%2B9sGxpaak1ZswYKz4%2B3ho/frz16aef%2BqDi9tPWXlxyySXH3RfuvPNOH1Vunjf7xY8FYsDyphcfffSRNXbsWOu8886zRo8ebX344Yc%2BqLj9eNOLZ5991ho5cqSVkJBgTZw40dq%2BfbsPKj41BFnW/z%2B3AAAAACN4DxYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/ADcN0NIyCWE/AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6079056147456134218”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.504188649377471</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.037501523481868304</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06979255283625063</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2237696603925874</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0486443082022352</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03531898846845771</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13831482126624062</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.11938814094361927</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16494664181334517</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16679358189872617</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6079056147456134218”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.37468134376752626</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.30016237495327425</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.20829892508883452</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.18378842957478048</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.16617867453602786</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5064546537266801</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5515094754631577</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7608535025514243</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7667722877815244</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9249518226067124</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CONVSPTH_09_14”><s>PCH_CONVSPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_CONVS_09_14”><code>PCH_CONVS_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98861</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CONVS_09_14”>PCH_CONVS_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>738</td>

</tr> <tr>

<th>Unique (%)</th> <td>23.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.7404</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>350</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>16.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3669189683092617535”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3669189683092617535,#minihistogram3669189683092617535”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3669189683092617535”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3669189683092617535”

aria-controls=”quantiles3669189683092617535” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3669189683092617535” aria-controls=”histogram3669189683092617535”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3669189683092617535” aria-controls=”common3669189683092617535”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3669189683092617535” aria-controls=”extreme3669189683092617535”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3669189683092617535”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-33.333</td>

</tr> <tr>

<th>Q1</th> <td>-11.765</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>11.765</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>350</td>

</tr> <tr>

<th>Range</th> <td>450</td>

</tr> <tr>

<th>Interquartile range</th> <td>23.529</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>30.596</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.165</td>

</tr> <tr>

<th>Kurtosis</th> <td>24.301</td>

</tr> <tr>

<th>Mean</th> <td>2.7404</td>

</tr> <tr>

<th>MAD</th> <td>18.449</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.1309</td>

</tr> <tr>

<th>Sum</th> <td>8531</td>

</tr> <tr>

<th>Variance</th> <td>936.13</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3669189683092617535”>

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KksvlUk1Njfv5f/ZJZy%2Bmb6qysjJ3WPue3R5qekALDAzw%2BImWzW5vmdsB%2B4H1qIH1qEHz43cBa/To0Ro0aJDCw8MlSd27d9eXX36pl156SUOGDFFwcHCjsORyuRQSEuIRpoKDg93/luS%2BhqspcnJylJWV5dGWmpqqtLS0n71ePyYsrOlzg/%2BKiGht9RQsxX5gPWpgPWrQfPhdwLLZbO5w9b0uXbrok08%2BkSTFxMSovLzco7%2B8vFwOh0MxMTGSJKfTqY4dO7r/LUkOh6PJc0hJSVFycrJHm90eqoqKUxe2Mj8hMDBAYWEhqqqqVn09t5Jo6czevnwF%2B4H1qIH1qMH5WfXHp98FrBUrVujTTz/VCy%2B84G47ePCgunTpIkmKjY1Vbm6uxowZI0k6duyYjh07ptjYWMXExKhDhw7Kzc11B6zc3Fx16NDhgk7vRUdHN3q903lCdXWXZqOvr2%2B4ZGPDd7T0bYD9wHrUwHrUoPnwu4A1aNAgrVmzRuvWrdOQIUO0Y8cOvfbaa9qwYYMkafz48br99tsVFxenXr16acmSJRo4cKA6derk7l%2B2bJmuuOIKSdITTzyhO%2B%2B807L1AQAAvsfvAtZ1112nFStWaOXKlVqxYoWuvPJKPfHEE4qPj5ckxcfHa9GiRVq5cqW%2B%2B%2B479e/fX4899pj7/VOnTtU333yj6dOnKzAwUOPGjdOUKVMsWhsAAOCLbIZhGFZPoiVwOk%2BYPqbdHqCIiNaqqDjFIeFLYMTyD62ewgV564H%2BVk/BEuwH1qMG1qMG5%2BdwtLVkuXyeEwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJP5fMByuVwaNWqUdu7c6W7Ly8vTf//3fys%2BPl7Dhg3T5s2bPd7z%2B9//Xtdcc43H4/PPP5ckGYahZcuWqV%2B/furTp4%2BWLl2qhoYGr64TAADwbXZvL/CWW27R2LFjNXLkSLVt2/aixqqtrdXMmTNVWFjobnM6nbr77rs1fvx4/elPf9L%2B/fuVnp4uh8OhgQMHqr6%2BXl9%2B%2BaU2btyozp07u98XEREhSXr%2B%2Bee1bds2ZWVlqa6uTrNmzVJkZKSmTp16UXMFAAAth9ePYPXr109PP/20BgwYoAcffFA7duyQYRgXPE5RUZFuvfVWff311x7t27dvV1RUlB588EF17txZI0eO1OjRo/XGG29Ikg4fPqwzZ87ouuuuk8PhcD/s9rNZc8OGDUpLS1NiYqL69eunhx56SJs2bbr4FQcAAC2G1wPWzJkz9e6772rVqlUKDAzUjBkzNHDgQD311FP64osvmjzOrl271LdvX%2BXk5Hi0JyUlKSMjo9HrT548KelsMGvfvr2Cg4Mbvaa0tFTHjh3T9ddf725LSEjQkSNHVFZW1uS5AQCAls3rpwglyWazqX///urfv7%2Bqq6v14osvatWqVVqzZo169%2B6tyZMna%2BjQoT86xoQJE36wvWPHjurYsaP7%2BTfffKO///3vmjFjhiSpuLhYl112me69917t27dPV111lWbPnq3rrrtOTqdTkhQdHe1%2Bf1RUlCTp%2BPHjHu0/pqyszD3W9%2Bz20Ca/v6kCAwM8fqJls9tb5nbAfmA9amA9atD8WBKwpLMh5PXXX9frr7%2Buzz//XL1799bNN9%2Bs48eP65FHHtHu3bs1b968i1pGTU2NZsyYoaioKKWkpEiSvvjiC3333Xe65ZZblJaWpr/%2B9a%2BaPHmy3nzzTdXU1EiSgoKC3GN8/2%2BXy9Xk5ebk5CgrK8ujLTU1VWlpaRe1PucTFhZyScaFb4mIaG31FCzFfmA9amA9atB8eD1gbd26VVu3btXOnTvVrl07jR49WitXrvS44Lx9%2B/ZasmTJRQWsU6dOadq0afryyy/1l7/8RSEhZze6xx57TDU1NWrTpo0kacGCBdqzZ4%2B2bt2q//qv/5J0Nkx9fwrx%2B2D1/fubIiUlRcnJyR5tdnuoKipO/ez1%2BSGBgQEKCwtRVVW16uv5pGNLZ/b25SvYD6xHDaxHDc7Pqj8%2BvR6w5s2bp0GDBik7O1s33HCDAgIaH87s0qWLJk6c%2BLOXcfLkSd111136%2BuuvtX79eo/wZrfb3eFKOnu6skuXLiotLVVMTIyks59E/P404/en%2BhwOR5OXHx0d3eh0oNN5QnV1l2ajr69vuGRjw3e09G2A/cB61MB61KD58HrAev/99xUREaHKykp3uCooKFDPnj0VGBgoSerdu7d69%2B79s8ZvaGjQ9OnTdfjwYb344ovq2rWrR//tt9%2Buvn37avr06e7Xf/bZZ7rtttsUExOjDh06KDc31x2wcnNz1aFDB9OvnwIAAP7L6wHr5MmTGj9%2BvH77299q9uzZkqR77rlHUVFRevbZZ9W%2BffuLGv%2BVV17Rzp07tXr1aoWFhbmPQF122WUKDw9XcnKysrOz1aNHD1111VXasGGDTpw4oZtvvlmSNH78eC1btkxXXHGFJOmJJ57QnXfeeVFzAgAALYvXA9bjjz%2BuX/ziF7rjjjvcbW%2B%2B%2BabmzJmjjIwMrVy58qLGf%2Bedd9TQ0KB7773Xo71Pnz568cUXNWXKFNXW1mrx4sUqLy9XbGysnn/%2Befdpw6lTp%2Bqbb77R9OnTFRgYqHHjxmnKlCkXNScAANCy2Iyfc5fPi5CYmKi//vWv6tKli0d7YWGhbrvtNu3atcub0/Eap/OE6WPa7QGKiGitiopTnHO/BEYs/9DqKVyQtx7ob/UULMF%2BYD1qYD1qcH4Ox8V9a8zP5fUbZtjtdlVVVTVqr66u/ll3dAcAAGhuvB6wbrjhBi1evNjjK25KSkqUkZGhpKQkb08HAADAdF6/BmvOnDm64447NGzYMIWFhUmSqqqq1LNnT6Wnp3t7OgAAAKbzesCKjIzU3/72N3300UcqLCyU3W7X1VdfrV//%2Btey2Wzeng4AAIDpLPmqnMDAQCUlJXFKEAAA%2BCWvByyn06nly5drz549OnPmTKML2//5z396e0oAAACm8nrA%2BuMf/6h9%2B/Zp5MiRatvWmo9OAgAAXEpeD1iffPKJ1q5dq8TERG8vGgAAwCu8fpuG0NBQRUZGenuxAAAAXuP1gHXTTTdp7dq1qq%2Bv9/aiAQAAvMLrpwgrKyu1bds2/etf/1KnTp0UFBTk0b9hwwZvTwkAAMBUltymYdSoUVYsFgAAwCu8HrAyMjK8vUgAAACv8vo1WJJUVlamrKwszZw5U998843efvttHTp0yIqpAAAAmM7rAeurr77S7373O/3tb3/TO%2B%2B8o9OnT%2BvNN9/U2LFjlZ%2Bf7%2B3pAAAAmM7rAetPf/qTBg8erO3bt%2Buyyy6TJD355JNKTk7WsmXLvD0dAAAA03k9YO3Zs0d33HGHxxc72%2B12TZs2TQcOHPD2dAAAAEzn9YDV0NCghoaGRu2nTp1SYGCgt6cDAABgOq8HrAEDBuiZZ57xCFmVlZXKzMxUv379vD0dAAAA03k9YD388MPat2%2BfBgwYoNraWt1///0aNGiQDh8%2BrDlz5nh7OgAAAKbz%2Bn2wYmJi9Nprr2nbtm3697//rYaGBo0fP1433XST2rRp4%2B3pAAAAmM6SO7mHhITolltusWLRAAAAl5zXA9akSZN%2BtJ/vIgQAAL7O6wHryiuv9HheV1enr776Sp9//rkmT57s7ekAAACYrtl8F2F2draOHz/u5dkAAACYz5LvIvwhN910k9566y2rpwEAAHDRmk3A%2BvTTT3/WjUZdLpdGjRqlnTt3uttKSko0ZcoUxcXF6cYbb9SOHTs83vPRRx9p1KhRio2N1aRJk1RSUuLR/8ILLygpKUnx8fGaO3euqqurf95KAQCAFqlZXOR%2B8uRJffbZZ5owYcIFjVVbW6uZM2eqsLDQ3WYYhlJTU9WtWzdt2bJF27dv1/Tp0/Xmm2%2BqQ4cOOnr0qFJTUzVjxgwlJSUpOztb06ZN0%2Buvvy6bzaZ33nlHWVlZyszMVGRkpNLT05WZman58%2Bdf9LoDAICWwesBq0OHDh7fQyhJl112mSZOnKjf//73TR6nqKhIM2fOlGEYHu2ffPKJSkpK9PLLLys0NFRdu3bVxx9/rC1btmjGjBnavHmzfvWrX%2BnOO%2B%2BUdPaasP79%2B2vXrl3q27evNmzYoMmTJ2vQoEGSpIULF2rq1KmaNWuWQkJCLnLtAQBAS%2BD1gPWnP/3JlHG%2BD0R/%2BMMfFBcX527Pz8/Xtddeq9DQUHdbQkKC8vLy3P2JiYnuvpCQEPXs2VN5eXlKTEzU3r17NX36dHd/XFyczpw5o4MHDyo%2BPt6UuQMAAP/m9YC1e/fuJr/2%2BuuvP2/f%2BU4nOp1ORUdHe7RFRka6P6H4Y/1VVVWqra316Lfb7QoPD7%2BgTziWlZXJ6XR6tNntoY2We7ECAwM8fqJls9tb5nbAfmA9amA9atD8eD1g3X777e5ThP95eu/cNpvNpn//%2B98XPH51dbWCgoI82oKCguRyuX6yv6amxv38fO9vipycHGVlZXm0paamKi0trcljXIiwME5dQoqIaG31FCzFfmA9amA9atB8eD1gPf3001q8eLFmzZqlPn36KCgoSHv37tWiRYt0880368Ybb7yo8YODg1VZWenR5nK51KpVK3f/uWHJ5XIpLCxMwcHB7ufn9l/I9VcpKSlKTk72aLPbQ1VRcarJYzRFYGCAwsJCVFVVrfr6BlPHhu8xe/vyFewH1qMG1qMG52fVH5%2BW3Gh0/vz5uuGGG9xt/fr106JFizR79mzdfffdFzV%2BTEyMioqKPNrKy8vdp%2BdiYmJUXl7eqL9Hjx4KDw9XcHCwysvL1bVrV0ln7zRfWVkph8PR5DlER0c3Oh3odJ5QXd2l2ejr6xsu2djwHS19G2A/sB41sB41aD68frK2rKys0dflSFKbNm1UUVFx0ePHxsZq//797tN9kpSbm6vY2Fh3f25urruvurpaBw4cUGxsrAICAtSrVy%2BP/ry8PNntdnXv3v2i5wYAAFoGrwesuLg4Pfnkkzp58qS7rbKyUpmZmfr1r3990eP36dNH7du3V3p6ugoLC7VmzRoVFBRo3LhxkqSxY8dqz549WrNmjQoLC5Wenq6OHTuqb9%2B%2Bks5ePL9u3Tpt375dBQUFWrBggW699VZu0QAAAJrM66cIH3nkEU2aNEk33HCDOnfuLMMw9OWXX8rhcGjDhg0XPX5gYKBWrVqlefPmacyYMfrFL36h7OxsdejQQZLUsWNH/fnPf9bjjz%2Bu7OxsxcfHKzs7232R/ciRI3XkyBHNnz9fLpdLQ4cO1axZsy56XgAAoOWwGefeqdMLvvvuO23btk3FxcWSpGuvvVYjR47066NETucJ08e02wMUEdFaFRWnOOd%2BCYxY/qHVU7ggbz3Q3%2BopWIL9wHrUwHrU4PwcjraWLNfrR7Ak6fLLL9ctt9yiw4cPq1OnTpLO3s0dAADAH3j9GizDMLRs2TJdf/31GjVqlI4fP645c%2BZo3rx5OnPmjLenAwAAYDqvB6wXX3xRW7du1aOPPuq%2BoefgwYO1ffv2RjfnBAAA8EVeD1g5OTmaP3%2B%2BxowZ476w/MYbb9TixYv1xhtveHs6AAAApvN6wDp8%2BLB69OjRqL179%2B6Nvr8PAADAF3k9YF155ZXau3dvo/b333/ffcE7AACAL/P6pwinTp2qhQsXyul0yjAMffzxx8rJydGLL76ohx9%2B2NvTAQAAMJ3XA9bYsWNVV1en1atXq6amRvPnz1e7du30wAMPaPz48d6eDgAAgOm8HrC2bdum4cOHKyUlRd9%2B%2B60Mw1BkZKS3pwEAAHDJeP0arEWLFrkvZm/Xrh3hCgAA%2BB2vB6zOnTvr888/9/ZiAQAAvMbrpwi7d%2B%2Buhx56SGvXrlXnzp0VHBzs0Z%2BRkeHtKQEAAJjK6wHriy%2B%2BUEJCgiRx3ysAAOCXvBKwli5dqunTpys0NFQvvviiNxYJAABgGa9cg/X888%2Brurrao%2B2ee%2B5RWVmZNxYPAADgVV4JWIZhNGrbvXu3amtrvbF4AAAAr/L6pwgBAAD8HQELAADAZF4LWDabzVuLAgAAsJTXbtOwePFij3tenTlzRpmZmWrdurXH67gPFgAA8HVeCVjXX399o3texcfHq6KiQhUVFd6YApqBEcs/tHoKAAB4hVcCFve%2BAgAALQkXuQMAAJhGYimgAAAW5ElEQVSMgAUAAGAyAhYAAIDJ/DJgvfrqq7rmmmsaPbp37y5Juv/%2B%2Bxv1vfvuu%2B73v/DCC0pKSlJ8fLzmzp3b6Gt%2BAAAAfozXbtPgTTfeeKOSkpLcz%2Bvq6jR58mQNHDhQklRcXKzMzEz9%2Bte/dr/m8ssvlyS98847ysrKUmZmpiIjI5Wenq7MzEzNnz/fq%2BsAAAB8l18ewWrVqpUcDof78frrr8swDD300ENyuVw6fPiwevXq5fGaoKAgSdKGDRs0efJkDRo0SNddd50WLlyoLVu2cBQLAAA0mV8GrP9UWVmpZ599VjNnzlRQUJAOHTokm82mTp06NXptfX299u7dq8TERHdbXFyczpw5o4MHD3pz2gAAwIf5fcB66aWXFB0dreHDh0uSDh06pDZt2mj27NkaMGCAxo0bp/fee0%2BSVFVVpdraWkVHR7vfb7fbFR4eruPHj1syfwAA4Hv88hqs7xmGoc2bN%2Buuu%2B5ytx06dEg1NTUaMGCA7rnnHv3jH//Q/fffr5ycHEVFRUmS%2B3Th94KCguRyuZq83LKyskZ3rrfbQz2CmxkCAwM8fqJls9tb5nbAfmA9amA9atD8%2BHXA2rt3r0pLSzVy5Eh327Rp03T77be7L2rv3r279u/fr7/%2B9a/6wx/%2BIEmNwpTL5VJISEiTl5uTk6OsrCyPttTUVKWlpf3cVflRYWFNnxv8V0RE659%2BkR9jP7AeNbAeNWg%2B/DpgffDBB0pMTHSHKUkKCAjweC5JXbp0UVFRkcLDwxUcHKzy8nJ17dpV0tlPIFZWVsrhcDR5uSkpKUpOTvZos9tDVVFx6iLWprHAwACFhYWoqqpa9fUNpo4N32P29uUr2A%2BsRw2sRw3Oz6o/Pv06YBUUFKh3794ebQ8//LBsNpsyMjLcbQcPHlS3bt0UEBCgXr16KTc3V3379pUk5eXlyW63u%2B%2Bh1RTR0dGNTgc6nSdUV3dpNvr6%2BoZLNjZ8R0vfBtgPrEcNrEcNmg%2B/PllbWFioq6%2B%2B2qMtOTlZb7zxhl577TV99dVXysrKUm5uriZOnChJmjBhgtatW6ft27eroKBACxYs0K233npBpwgBAEDL5tdHsMrLyxUWFubRNnToUD366KNavXq1jh49ql/%2B8pdau3atOnbsKEkaOXKkjhw5ovnz58vlcmno0KGaNWuWFdMHAAA%2BymYYhmH1JFoCp/OE6WPa7QGKiGitiopTPnFIeMTyD62egl9764H%2BVk/BEr62H/gjamA9anB%2BDkdbS5br16cIAQAArEDAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMJnfBqx//OMfuuaaazweaWlpkqQDBw7olltuUWxsrMaOHat9%2B/Z5vHfbtm0aPHiwYmNjlZqaqm%2B//daKVQAAAD7KbwNWUVGRBg0apB07drgfixcv1unTp3XPPfcoMTFRr776quLj43Xvvffq9OnTkqSCggLNmzdP06dPV05OjqqqqpSenm7x2gAAAF/itwGruLhY3bp1k8PhcD/CwsL05ptvKjg4WLNnz1bXrl01b948tW7dWm%2B//bYkaePGjRoxYoRGjx6t7t27a%2BnSpXrvvfdUUlJi8RoBAABf4dcBq3Pnzo3a8/PzlZCQIJvNJkmy2Wzq3bu38vLy3P2JiYnu17dv314dOnRQfn6%2BV%2BYNAAB8n18GLMMw9MUXX2jHjh0aNmyYBg8erGXLlsnlcsnpdCo6Otrj9ZGRkTp%2B/Lgkqays7Ef7AQAAford6glcCkePHlV1dbWCgoK0fPlyHT58WIsXL1ZNTY27/T8FBQXJ5XJJkmpqan60vynKysrkdDo92uz20EbB7WIFBgZ4/ETLZre3zO2A/cB61MB61KD58cuAdeWVV2rnzp26/PLLZbPZ1KNHDzU0NGjWrFnq06dPo7DkcrnUqlUrSVJwcPAP9oeEhDR5%2BTk5OcrKyvJoS01NdX%2BK0WxhYU2fG/xXRERrq6dgKfYD61ED61GD5sMvA5YkhYeHezzv2rWramtr5XA4VF5e7tFXXl7uProUExPzg/0Oh6PJy05JSVFycrJHm90eqoqKUxeyCj8pMDBAYWEhqqqqVn19g6ljw/eYvX35CvYD61ED61GD87Pqj0%2B/DFgffPCBHnroIf3rX/9yH3n697//rfDwcCUkJOjZZ5%2BVYRiy2WwyDEN79uzRfffdJ0mKjY1Vbm6uxowZI0k6duyYjh07ptjY2CYvPzo6utHpQKfzhOrqLs1GX1/fcMnGhu9o6dsA%2B4H1qIH1qEHz4Zcna%2BPj4xUcHKxHHnlEhw4d0nvvvaelS5fqrrvu0vDhw1VVVaUlS5aoqKhIS5YsUXV1tUaMGCFJGj9%2BvLZu3arNmzfr4MGDmj17tgYOHKhOnTpZvFYAAMBX%2BGXAatOmjdatW6dvv/1WY8eO1bx585SSkqK77rpLbdq00TPPPOM%2BSpWfn681a9YoNDRU0tlwtmjRImVnZ2v8%2BPG6/PLLlZGRYfEaAQAAX2IzDMOwehItgdN5wvQx7fYARUS0VkXFKZ84JDxi%2BYdWT8GvvfVAf6unYAlf2w/8ETWwHjU4P4ejrSXL9csjWAAAAFYiYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJjMbwNWaWmp0tLS1KdPHyUlJSkjI0O1tbWSpMWLF%2Buaa67xeGzcuNH93m3btmnw4MGKjY1Vamqqvv32W6tWAwAA%2BCC71RO4FAzDUFpamsLCwrRp0yZ99913mjt3rgICAjRnzhwVFxdr5syZuvnmm93vadOmjSSpoKBA8%2BbN08KFC9W9e3ctWbJE6enpeuaZZ6xaHQAA4GP88gjWoUOHlJeXp4yMDP3yl79UYmKi0tLStG3bNklScXGxrr32WjkcDvcjJCREkrRx40aNGDFCo0ePVvfu3bV06VK99957KikpsXKVAACAD/HLgOVwOLR27VpFRUV5tJ88eVInT55UaWmpOnfu/IPvzc/PV2Jiovt5%2B/bt1aFDB%2BXn51/KKQMAAD/il6cIw8LClJSU5H7e0NCgjRs3ql%2B/fiouLpbNZtPTTz%2Bt999/X%2BHh4brjjjvcpwvLysoUHR3tMV5kZKSOHz/e5OWXlZXJ6XR6tNntoY3GvViBgQEeP9Gy2e0tcztgP7AeNbAeNWh%2B/DJgnSszM1MHDhzQK6%2B8ov3798tms6lLly6aOHGidu/erT/%2B8Y9q06aNhgwZopqaGgUFBXm8PygoSC6Xq8nLy8nJUVZWlkdbamqq0tLSTFmfc4WFhVySceFbIiJaWz0FS7EfWI8aWI8aNB9%2BH7AyMzO1fv16PfXUU%2BrWrZt%2B%2BctfatCgQQoPD5ckde/eXV9%2B%2BaVeeuklDRkyRMHBwY3ClMvlcl%2Bj1RQpKSlKTk72aLPbQ1VRceriV%2Bg/BAYGKCwsRFVV1aqvbzB1bPges7cvX8F%2BYD1qYD1qcH5W/fHp1wHrscce00svvaTMzEwNGzZMkmSz2dzh6ntdunTRJ598IkmKiYlReXm5R395ebkcDkeTlxsdHd3odKDTeUJ1dZdmo6%2Bvb7hkY8N3tPRtgP3AetTAetSg%2BfDbk7VZWVl6%2BeWX9eSTT2rkyJHu9hUrVmjKlCkerz148KC6dOkiSYqNjVVubq6779ixYzp27JhiY2O9Mm8AAOD7/DJgFRcXa9WqVbr77ruVkJAgp9PpfgwaNEi7d%2B/WunXr9PXXX%2Bsvf/mLXnvtNd15552SpPHjx2vr1q3avHmzDh48qNmzZ2vgwIHq1KmTxWsFAAB8hV%2BeIvznP/%2Bp%2Bvp6rV69WqtXr/bo%2B%2Byzz7RixQqtXLlSK1as0JVXXqknnnhC8fHxkqT4%2BHgtWrRIK1eu1Hfffaf%2B/fvrscces2I1AACAj7IZhmFYPYmWwOk8YfqYdnuAIiJaq6LilE%2Bccx%2Bx/EOrp%2BDX3nqgv9VTsISv7Qf%2BiBpYjxqcn8PR1pLl%2BuUpQgAAACsRsAAAAExGwAIAADAZAQsAAMBkfvkpQqAl8qUPEbTUC/IBtBwcwQIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATGa3egIAWp4Ryz%2B0egoX5K0H%2Bls9BQA%2BhiNYP6C2tlZz585VYmKiBgwYoOeee87qKQEAAB/CEawfsHTpUu3bt0/r16/X0aNHNWfOHHXo0EHDhw%2B3emqNJM572%2BopAACAcxCwznH69Glt3rxZzz77rHr27KmePXuqsLBQmzZtapYBCwAAND%2BcIjzHwYMHVVdXp/j4eHdbQkKC8vPz1dDQYOHMAACAr%2BAI1jmcTqciIiIUFBTkbouKilJtba0qKyvVrl27nxyjrKxMTqfTo81uD1V0dLSpcw0MJB8D3uBrF%2BX7kn88lGT1FC7IkGUfWD2FC%2BJrv19/QsA6R3V1tUe4kuR%2B7nK5mjRGTk6OsrKyPNqmT5%2BuGTNmmDPJ/1NWVqbJVxQqJSXF9PCGpikrK1NOTg41sBA1sF5LqsH/W9I8LxVpSTXwFRwCOUdwcHCjIPX981atWjVpjJSUFL366qsej5SUFNPn6nQ6lZWV1ehoGbyHGliPGliPGliPGjQ/HME6R0xMjCoqKlRXVye7/eyvx%2Bl0qlWrVgoLC2vSGNHR0fwFAQBAC8YRrHP06NFDdrtdeXl57rbc3Fz16tVLAQH8ugAAwE8jMZwjJCREo0eP1oIFC1RQUKDt27frueee06RJk6yeGgAA8BGBCxYsWGD1JJqbfv366cCBA3riiSf08ccf67777tPYsWOtntYPat26tfr06aPWrVtbPZUWixpYjxpYjxpYjxo0LzbDMAyrJwEAAOBPOEUIAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgOVDDMPQnXfeqVdffdWjvaKiQjNmzFB8fLySk5O1detWj/4DBw7olltuUWxsrMaOHat9%2B/Z5c9p%2Bq7a2VnPnzlViYqIGDBig5557zuop%2BS2Xy6VRo0Zp586d7raSkhJNmTJFcXFxuvHGG7Vjxw6P93z00UcaNWqUYmNjNWnSJJWUlHh72n6htLRUaWlp6tOnj5KSkpSRkaHa2lpJ1MBbvvrqK02dOlXx8fEaOHCg1q5d6%2B6jBs0XActHNDQ0aPHixfrwww8b9aWnp%2BvEiRPKycnR/fffr0ceeUQFBQWSpNOnT%2Buee%2B5RYmKiXn31VcXHx%2Bvee%2B/V6dOnvb0Kfmfp0qXat2%2Bf1q9fr0cffVRZWVl6%2B%2B23rZ6W36mtrdWDDz6owsJCd5thGEpNTVVUVJS2bNmim266SdOnT9fRo0clSUePHlVqaqrGjBmjV155Re3atdO0adPEF1dcGMMwlJaWpurqam3atElPPfWU3n33XS1fvpwaeElDQ4PuueceRURE6G9/%2B5sWLlyo1atX64033qAGzZ2BZu/48ePGxIkTjYEDBxqJiYnGli1b3H1fffWV0a1bN6OkpMTdNnfuXGPOnDmGYRjG5s2bjeTkZKOhocEwDMNoaGgwhgwZ4jEGLtypU6eMXr16GZ988om7LTs725g4caKFs/I/hYWFxu9//3vjd7/7ndGtWzf37/ujjz4y4uLijFOnTrlfO3nyZGPlypWGYRjG8uXLPWpx%2BvRpIz4%2B3qNe%2BGlFRUVGt27dDKfT6W574403jAEDBlADLyktLTX%2B53/%2Bxzhx4oS7LTU11Xj00UepQTPHESwfsH//frVv315btmxR27ZtPfry8/PVvn17dezY0d2WkJCgTz/91N2fkJAgm80mSbLZbOrdu7fy8vK8twJ%2B6ODBg6qrq1N8fLy7LSEhQfn5%2BWpoaLBwZv5l165d6tu3r3Jycjza8/Pzde211yo0NNTdlpCQ4N6u8/PzlZiY6O4LCQlRz5492e4vkMPh0Nq1axUVFeXRfvLkSWrgJdHR0Vq%2BfLnatGkjwzCUm5ur3bt3q0%2BfPtSgmbNbPQH8tOTkZCUnJ/9gn9PpVHR0tEdbZGSkSktL3f1XX311o/7/PN2CC%2Bd0OhUREaGgoCB3W1RUlGpra1VZWal27dpZODv/MWHChB9sP992f/z48Sb1o2nCwsKUlJTkft7Q0KCNGzeqX79%2B1MACycnJOnr0qAYNGqRhw4bp8ccfpwbNGAGrGaipqXEHonM5HA6Pv07OVV1d7fE/eUkKCgqSy%2BVqUj9%2BnvP9XiXxu/UCtntrZGZm6sCBA3rllVf0wgsvUAMvW7lypcrLy7VgwQJlZGSwHzRzBKxmID8/X5MmTfrBvuzsbA0ePPi87w0ODm60s7hcLrVq1apJ/fh5zvd7lcTv1guCg4NVWVnp0daU7T4sLMxrc/Q3mZmZWr9%2BvZ566il169aNGligV69eks5%2B8OOhhx7S2LFjVV1d7fEaatB8ELCagb59%2B%2Bqzzz77We%2BNiYlReXm5R1t5ebkcDseP9p972BgXJiYmRhUVFaqrq5PdfnY3cjqdatWqFf/x8oKYmBgVFRV5tP3ndn2%2B7b5Hjx5em6M/eeyxx/TSSy8pMzNTw4YNk0QNvKW8vFx5eXkef2hfffXVOnPmjBwOhw4dOtTo9dSgeeAidx8XFxenI0eOeJxTz83NVVxcnCQpNjZWn376qftjuYZhaM%2BePYqNjbVkvv6iR48estvtHheL5ubmqlevXgoIYLe61GJjY7V//37V1NS423Jzc93bdWxsrHJzc9191dXVOnDgANv9z5CVlaWXX35ZTz75pEaOHOlupwbecfjwYU2fPt3jMpJ9%2B/apXbt2SkhIoAbNGP8n8HGdOnXSgAEDNGvWLB08eFCbN2/Wtm3bdNttt0mShg8frqqqKi1ZskRFRUVasmSJqqurNWLECItn7ttCQkI0evRoLViwQAUFBdq%2Bfbuee%2B65857qhbn69Omj9u3bKz09XYWFhVqzZo0KCgo0btw4SdLYsWO1Z88erVmzRoWFhUpPT1fHjh3Vt29fi2fuW4qLi7Vq1SrdfffdSkhIkNPpdD%2BogXf06tVLPXv21Ny5c1VUVKT33ntPmZmZuu%2B%2B%2B6hBc2fxbSJwgQYNGtToHlbl5eXGvffea/Tq1ctITk423njjDY/%2B/Px8Y/To0UavXr2McePGGfv37/fmlP3W6dOnjdmzZxtxcXHGgAEDjOeff97qKfm1/7wPlmEYxpdffmncdtttxq9%2B9Stj5MiRxocffujx%2Bn/961/G0KFDjeuuu86YPHmy8fXXX3t7yj7vmWeeMbp16/aDD8OgBt5y/PhxIzU11ejdu7fRv39/Y/Xq1e57G1KD5stmGNzSFQAAwEycIgQAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADDZ/wcSPYiwl7A%2BQQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3669189683092617535”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>542</td> <td class=”number”>16.9%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.285714286</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-12.5</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (727)</td> <td class=”number”>2094</td> <td class=”number”>65.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3669189683092617535”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-57.142857143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_CSA_07_12”>PCH_CSA_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>235</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>27.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>866</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.355</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2123370312409071368”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2123370312409071368,#minihistogram-2123370312409071368”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2123370312409071368”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2123370312409071368”

aria-controls=”quantiles-2123370312409071368” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2123370312409071368” aria-controls=”histogram-2123370312409071368”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2123370312409071368” aria-controls=”common-2123370312409071368”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2123370312409071368” aria-controls=”extreme-2123370312409071368”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2123370312409071368”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-83.333</td>

</tr> <tr>

<th>Median</th> <td>-33.333</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>300</td>

</tr> <tr>

<th>Maximum</th> <td>1700</td>

</tr> <tr>

<th>Range</th> <td>1800</td>

</tr> <tr>

<th>Interquartile range</th> <td>133.33</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>153.55</td>

</tr> <tr>

<th>Coef of variation</th> <td>13.522</td>

</tr> <tr>

<th>Kurtosis</th> <td>21.862</td>

</tr> <tr>

<th>Mean</th> <td>11.355</td>

</tr> <tr>

<th>MAD</th> <td>97.209</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.6487</td>

</tr> <tr>

<th>Sum</th> <td>26617</td>

</tr> <tr>

<th>Variance</th> <td>23578</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2123370312409071368”>

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u3bt6tBgwYaO3asmjdvLqfTKUmKjIx0rV%2BrVi1J0rFjx9zGAQAArsZnC1Z5FBQUaOTIkapVq5b69esnSdq3b59Onjypvn37KiUlRe%2B//74GDRqkTz/9VAUFBZKk4OBg1zYu/r%2BoqKjcj5uTk%2BMqaxc5HJVtX9Acjoq/wzQoKNDtX39nt7yS/TLbLa9EZjvwhbx%2BW7DOnj2r4cOHa//%2B/XrvvfcUGhoqSZo6daoKCgpUtWpVSdLkyZO1efNmrVq1SnfddZekC2Xq4iHEi8Xq4vrlkZaWptTUVLex5ORkpaSkXHcuX1a9ehVvT6HcwsLK/3r7A7vlleyX2W55JTLbQUXO65cF68yZM3rqqad04MABLV261O3dgg6Hw1WuJCkgIEAxMTHKzs5WVFSUpAvvRLx4mPHinqiIiIhyP36/fv3UuXNntzGHo7Jyc8/%2B1kh%2BwRfyBwUFKiwsVKdO5aukxP8vz2G3vJL9Mtstr0RmO2S%2Blrze%2BuXe7wpWaWmpRowYoUOHDmnZsmVq2LCh2/KBAweqdevWGjFihOv%2BP/74ox577DFFRUUpOjpa6enproKVnp6u6Ojoazq8FxkZWeb%2BTudpFRf7/yf9L/Gl/CUlpT413%2Btlt7yS/TLbLa9EZjuoyHn9rmD9/e9/14YNG/TGG28oLCzMtQfqpptuUnh4uDp37qx58%2BapadOmatCggd555x2dPn1avXv3liT1799fs2fP1i233CJJmjNnjp588kmv5QEAAL7H7wrWmjVrVFpaqmHDhrmNt2rVSsuWLdPgwYNVWFioadOm6fjx44qPj9dbb73lOmw4ZMgQ/fzzzxoxYoSCgoL08MMPa/DgwV5IAgAAfFWAufxKnbghnM7TN2S7973yzQ3Z7o3w2TNtvT2FX%2BVwBKp69SrKzT1bYXc7W8lueSX7ZbZbXonMdsh8LXkjIm720KzcVdz3NwIAAPgoChYAAIDFKFgAAAAW83jB6tu3r/72t7/p9Okbc04SAACAt3m8YLVp00bz589Xu3btNGrUKH399dfiPHsAAOBPPF6wRo8erXXr1un1119XUFCQRo4cqY4dO%2Brll1/Wvn37PD0dAAAAy3nlOlgBAQFq27at2rZtq/z8fC1btkyvv/66Fi5cqJYtW2rQoEG65557vDE1AACA6%2Ba1C43m5OToH//4h/7xj39o9%2B7datmypXr37q1jx47pz3/%2BszZt2qRJkyZ5a3oAAAC/mccL1qpVq7Rq1Spt2LBBNWrUUK9evfTqq6%2B6/UHm2rVra/r06RQsAADgkzxesCZNmqROnTpp3rx5uvvuuxUYWPY0sJiYGA0YMMDTUwMAALCExwvWl19%2BqerVqysvL89VrrZu3apmzZopKChIktSyZUu1bNnS01MDAACwhMffRXjmzBl169ZNb775pmts6NChevDBB3X06FFPTwcAAMByHi9Yf/3rX/Vf//VfeuKJJ1xjn376qWrXrq0ZM2Z4ejoAAACW83jB%2Br//%2Bz%2BNHz9eERERrrEaNWpo7NixWr9%2BvaenAwAAYDmPFyyHw6FTp06VGc/Pz%2BeK7gAAwC94vGDdfffdmjZtmg4cOOAaO3jwoGbMmKH27dt7ejoAAACW8/i7CMeNG6cnnnhC9957r8LCwiRJp06dUrNmzTRhwgRPTwcAAMByHi9YNWvW1EcffaRvv/1We/bskcPh0K233qo777xTAQEBnp4OAACA5bzyp3KCgoLUvn17DgkCAAC/5PGC5XQ69corr2jz5s06f/58mRPb//3vf3t6SgAAAJbyeMF67rnntH37dvXo0UM333yzpx8eAADghvN4wVq/fr0WLVqkpKQkTz80AACAR3j8Mg2VK1dWzZo1Pf2wAAAAHuPxgvXggw9q0aJFKikp8fRDAwAAeITHDxHm5eVp9erV%2Bs9//qN69eopODjYbfk777zj6SkBAABYyiuXaejZs6c3HhYAAMAjPF6wZsyY4emHBAAA8CiPn4MlSTk5OUpNTdXo0aP1888/61//%2Bpf27t3rjakAAABYzuMF66efftL999%2Bvjz76SGvWrNG5c%2Bf06aef6qGHHlJGRoanpwMAAGA5jxesF198UV26dNHnn3%2Bum266SZL00ksvqXPnzpo9e/Y1b6%2BoqEg9e/bUhg0bXGMHDx7U4MGD1aJFC3Xv3l1ff/212zrffvutevbsqfj4eD3%2B%2BOM6ePCg2/K3335b7du3V0JCgiZOnKj8/PzfkBQAANiVxwvW5s2b9cQTT7j9YWeHw6Hhw4dr586d17StwsJCjRo1Snv27HGNGWOUnJysWrVq6YMPPtCDDz6oESNG6MiRI5KkI0eOKDk5WX369NHf//531ahRQ8OHD3f9yZ41a9YoNTVVU6ZM0dKlS5WRkaFZs2ZZkBwAANiFxwtWaWmpSktLy4yfPXtWQUFB5d5OZmamHnnkER04cMBtfP369Tp48KCmTJmihg0batiwYWrRooU%2B%2BOADSdLKlSt1%2B%2B2368knn1SjRo00Y8YMHT58WBs3bpR04TIRgwYNUqdOndS8eXO98MIL%2BuCDD9iLBQAAys3jBatdu3ZasGCBW8nKy8vTrFmz1KZNm3JvZ%2BPGjWrdurXS0tLcxjMyMnTbbbepcuXKrrHExERt2bLFtfzSP9MTGhqqZs2aacuWLSopKdG2bdvclrdo0ULnz5/Xrl27rjkrAACwJ49fpmH8%2BPF6/PHH1a5dOxUWFurpp5/W4cOHFR4erhdffLHc23n00UevOO50OhUZGek2VrNmTR07duxXl586dUqFhYVuyx0Oh8LDw13rl0dOTo6cTqfbmMNRuczj2o3D4ZU3rV6ToKBAt3/9nd3ySvbLbLe8EpntwBfyerxgRUVF6eOPP9bq1av1ww8/qLS0VP3799eDDz6oqlWrXvf28/Pzy1wdPjg4WEVFRb%2B6vKCgwHX7auuXR1pamlJTU93GkpOTlZKSUu5t%2BKPq1at4ewrlFhYW6u0peJTd8kr2y2y3vBKZ7aAi5/XKldxDQ0PVt2/fG7LtkJAQ5eXluY0VFRWpUqVKruWXl6WioiKFhYUpJCTEdfvy5aGh5X8R%2B/Xrp86dO7uNORyVlZt7ttzb8Ee%2BkD8oKFBhYaE6dSpfJSVlzxX0N3bLK9kvs93ySmS2Q%2BZryeutX%2B49XrAef/zxX1x%2BvX%2BLMCoqSpmZmW5jx48fdx2ei4qK0vHjx8ssb9q0qcLDwxUSEqLjx4%2BrYcOGkqTi4mLl5eUpIiKi3HOIjIwsczjQ6Tyt4mL//6T/Jb6Uv6Sk1Kfme73slleyX2a75ZXIbAcVOa/HD17WqVPH7SMqKkoFBQXaunWrEhISrnv78fHx2rFjh%2BtwnySlp6crPj7etTw9Pd21LD8/Xzt37lR8fLwCAwMVFxfntnzLli1yOByKjY297rkBAAB7qDB/i3DevHnXdCL51bRq1Uq1a9fWhAkTNHz4cK1bt05bt251Pe5DDz2kxYsXa%2BHCherUqZPmzZununXrqnXr1pIunDz//PPPq3HjxoqMjNTkyZP1yCOPXNMhQgAAYG8V5vT7Bx98UJ999tl1bycoKEivv/66nE6n%2BvTpo3/84x%2BaN2%2BeoqOjJUl169bVa6%2B9pg8%2B%2BEAPP/yw8vLyNG/ePNeFT3v06KFhw4bp%2Beef15NPPqnmzZtrzJgx1z0vAABgH145yf1Kvv/%2B%2B2u60OilfvzxR7fb//Vf/6Xly5df9f4dOnRQhw4drrp86NChGjp06G%2BaCwAAQIU4yf3MmTP68ccfr3ptKwAAAF/i8YIVHR3t9ncIJemmm27SgAED9MADD3h6OgAAAJbzeMG6lqu1AwAA%2BCKPF6xNmzaV%2B7533HHHDZwJAADAjeHxgjVw4EDXIUJjjGv88rGAgAD98MMPnp4eAADAdfN4wZo/f76mTZumMWPGqFWrVgoODta2bds0ZcoU9e7dW927d/f0lAAAACzl8etgzZgxQ88//7zuvfdeVa9eXVWqVFGbNm00ZcoUrVixwu0q7wAAAL7I4wUrJyfniuWpatWqys3N9fR0AAAALOfxgtWiRQu99NJLOnPmjGssLy9Ps2bN0p133unp6QAAAFjO4%2Bdg/fnPf9bjjz%2Buu%2B%2B%2BW/Xr15cxRvv371dERITeeecdT08HAADAch4vWA0bNtSnn36q1atXKysrS5L02GOPqUePHvxBZQAA4Be88rcIq1Wrpr59%2B%2BrQoUOqV6%2BepAtXcwcAAPAHHj8Hyxij2bNn64477lDPnj117NgxjRs3TpMmTdL58%2Bc9PR0AAADLebxgLVu2TKtWrdJf/vIXBQcHS5K6dOmizz//XKmpqZ6eDgAAgOU8XrDS0tL0/PPPq0%2BfPq6rt3fv3l3Tpk3TJ5984unpAAAAWM7jBevQoUNq2rRpmfHY2Fg5nU5PTwcAAMByHi9YderU0bZt28qMf/nll64T3gEAAHyZx99FOGTIEL3wwgtyOp0yxui7775TWlqali1bpvHjx3t6OgAAAJbzeMF66KGHVFxcrDfeeEMFBQV6/vnnVaNGDT3zzDPq37%2B/p6cDAABgOY8XrNWrV6tbt27q16%2BfTpw4IWOMatas6elpAAAA3DAePwdrypQprpPZa9SoQbkCAAB%2Bx%2BMFq379%2Btq9e7enHxYAAMBjPH6IMDY2Vs8%2B%2B6wWLVqk%2BvXrKyQkxG35jBkzPD0lAAAAS3m8YO3bt0%2BJiYmSxHWvAACAX/JIwZo5c6ZGjBihypUra9myZZ54SAAAAK/xyDlYb731lvLz893Ghg4dqpycHE88PAAAgEd5pGAZY8qMbdq0SYWFhZ54eAAAAI/y%2BLsIAQAA/J1fFqwPP/xQTZo0KfMRGxsrSXr66afLLFu3bp1r/bffflvt27dXQkKCJk6cWObwJgAAwC/x2LsIAwICPPVQ6t69u9q3b%2B%2B6XVxcrEGDBqljx46SpKysLM2aNUt33nmn6z7VqlWTJK1Zs0apqamaNWuWatasqQkTJmjWrFl6/vnnPTZ/AADg2zxWsKZNm%2BZ2zavz589r1qxZqlKlitv9rLgOVqVKlVSpUiXX7QULFsgYo2effVZFRUU6dOiQ4uLiFBERUWbdd955R4MGDVKnTp0kSS%2B88IKGDBmiMWPGKDQ09LrnBgAA/J9HCtYdd9xR5ppXCQkJys3NVW5u7g197Ly8PL355puaNm2agoODtWvXLgUEBKhevXpl7ltSUqJt27ZpxIgRrrEWLVro/Pnz2rVrlxISEm7oXAEAgH/wSMHy5rWvVqxYocjISHXr1k2StHfvXlWtWlVjx47Vxo0bdcstt2jkyJHq0KGDTp06pcLCQkVGRrrWdzgcCg8P17Fjx7wVAQAA%2BBiPX8ndk4wxWrlypZ566inX2N69e1VQUKB27dpp6NChWrt2rZ5%2B%2BmmlpaWpVq1akqTg4GC37QQHB6uoqKjcj5uTk1Nmj53DUdmtuNmRw1Hx31MRFBTo9q%2B/s1teyX6Z7ZZXIrMd%2BEJevy5Y27ZtU3Z2tnr06OEaGz58uAYOHOg6qT02NlY7duzQ%2B%2B%2B/rz/96U%2BSVKZMFRUVXdP5V2lpaUpNTXUbS05OVkpKym%2BN4heqV6/y63eqIMLC7HW%2Bnd3ySvbLbLe8EpntoCLn9euC9dVXXykpKclVpiQpMDDQ7bYkxcTEKDMzU%2BHh4QoJCdHx48fVsGFDSRfegZiXl3fFE%2BKvpl%2B/furcubPbmMNRWbm5Z68jje/zhfxBQYEKCwvVqVP5Kikp9fZ0bji75ZXsl9lueSUy2yHzteT11i/3fl2wtm7dqpYtW7qNjR8/XgEBAW7vVty1a5caN26swMBAxcXFKT09Xa1bt5YkbdmyRQ6Hw3UNrfKIjIwsczjQ6Tyt4mL//6T/Jb6Uv6Sk1Kfme73slleyX2a75ZXIbAcVOW/FPXhpgT179ujWW291G%2BvcubM%2B%2BeQTffzxx/rpp5%2BUmpqq9PR0DRgwQJL06KOPavHixfr888%2B1detWTZ48WY888giXaAAAAOXm13uwjh8/rrCwMLexe%2B65R3/5y1/0xhtv6MiRI2rUqJEWLVqkunXrSpJ69Oihw4cP6/nnn1dRUZHuuecejRkzxhvTBwAAPsqvC9bWrVuvON63b1/17dv3qusNHTpUQ4cOvVHTAgAAfs6vDxECAAB4AwULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGLuNUD4AAAaHElEQVQULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsJjfFqy1a9eqSZMmbh8pKSmSpJ07d6pv376Kj4/XQw89pO3bt7utu3r1anXp0kXx8fFKTk7WiRMnvBEBAAD4KL8tWJmZmerUqZO%2B/vpr18e0adN07tw5DR06VElJSfrwww%2BVkJCgYcOG6dy5c5KkrVu3atKkSRoxYoTS0tJ06tQpTZgwwctpAACAL/HbgpWVlaXGjRsrIiLC9REWFqZPP/1UISEhGjt2rBo2bKhJkyapSpUq%2Bte//iVJWr58ue677z716tVLsbGxmjlzpr744gsdPHjQy4kAAICv8OuCVb9%2B/TLjGRkZSkxMVEBAgCQpICBALVu21JYtW1zLk5KSXPevXbu2oqOjlZGR4ZF5AwAA3%2Bfw9gRuBGOM9u3bp6%2B//loLFixQSUmJunXrppSUFDmdTt16661u969Zs6b27NkjScrJyVFkZGSZ5ceOHSv34%2Bfk5MjpdLqNORyVy2zXbhyOit/ng4IC3f71d3bLK9kvs93ySmS2A1/I65cF68iRI8rPz1dwcLBeeeUVHTp0SNOmTVNBQYFr/FLBwcEqKiqSJBUUFPzi8vJIS0tTamqq21hycrLrJHu7ql69irenUG5hYaHenoJH2S2vZL/MdssrkdkOKnJevyxYderU0YYNG1StWjUFBASoadOmKi0t1ZgxY9SqVasyZamoqEiVKlWSJIWEhFxxeWho%2BV/Efv36qXPnzm5jDkdl5eae/Y2J/IMv5A8KClRYWKhOncpXSUmpt6dzw9ktr2S/zHbLK5HZDpmvJa%2B3frn3y4IlSeHh4W63GzZsqMLCQkVEROj48eNuy44fP%2B46fBcVFXXF5REREeV%2B7MjIyDKHA53O0you9v9P%2Bl/iS/lLSkp9ar7Xy255Jftltlteicx2UJHzVtyDl9fhq6%2B%2BUuvWrZWfn%2B8a%2B%2BGHHxQeHq7ExER9//33MsZIunC%2B1ubNmxUfHy9Jio%2BPV3p6umu9o0eP6ujRo67lAAAAv8YvC1ZCQoJCQkL05z//WXv37tUXX3yhmTNn6qmnnlK3bt106tQpTZ8%2BXZmZmZo%2Bfbry8/N13333SZL69%2B%2BvVatWaeXKldq1a5fGjh2rjh07ql69el5OBQAAfIVfFqyqVatq8eLFOnHihB566CFNmjRJ/fr101NPPaWqVatqwYIFSk9PV58%2BfZSRkaGFCxeqcuXKki6UsylTpmjevHnq37%2B/qlWrphkzZng5EQAA8CV%2Bew5Wo0aN9NZbb11xWfPmzfXRRx9ddd0%2BffqoT58%2BN2pqAADAz/nlHiwAAABvomABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFjM4e0JwD7ue%2BUbb0/hmnz2TFtvTwEA4KPYgwUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMb8tWNnZ2UpJSVGrVq3Uvn17zZgxQ4WFhZKkadOmqUmTJm4fy5cvd627evVqdenSRfHx8UpOTtaJEye8FQMAAPggv7xMgzFGKSkpCgsL07vvvquTJ09q4sSJCgwM1Lhx45SVlaXRo0erd%2B/ernWqVq0qSdq6dasmTZqkF154QbGxsZo%2BfbomTJigBQsWeCsOAADwMX65B2vv3r3asmWLZsyYoUaNGikpKUkpKSlavXq1JCkrK0u33XabIiIiXB%2BhoaGSpOXLl%2Bu%2B%2B%2B5Tr169FBsbq5kzZ%2BqLL77QwYMHvRkJAAD4EL8sWBEREVq0aJFq1arlNn7mzBmdOXNG2dnZql%2B//hXXzcjIUFJSkut27dq1FR0drYyMjBs5ZQAA4Ef8smCFhYWpffv2rtulpaVavny52rRpo6ysLAUEBGj%2B/Pm6%2B%2B679cADD%2Bijjz5y3TcnJ0eRkZFu26tZs6aOHTvmsfkDAADf5pfnYF1u1qxZ2rlzp/7%2B979rx44dCggIUExMjAYMGKBNmzbpueeeU9WqVdW1a1cVFBQoODjYbf3g4GAVFRWV%2B/FycnLkdDrdxhyOymWKGyo2h8Mvf/9wExQU6PavHdgts93ySmS2A1/I6/cFa9asWVq6dKlefvllNW7cWI0aNVKnTp0UHh4uSYqNjdX%2B/fu1YsUKde3aVSEhIWXKVFFRkescrfJIS0tTamqq21hycrJSUlKuPxA8pnr1Kt6egseEhZX/89tf2C2z3fJKZLaDipzXrwvW1KlTtWLFCs2aNUv33nuvJCkgIMBVri6KiYnR%2BvXrJUlRUVE6fvy42/Ljx48rIiKi3I/br18/de7c2W3M4ais3NyzvyUGvMQOr1dQUKDCwkJ16lS%2BSkpKvT0dj7BbZrvllchsh8zXktdbvyz7bcFKTU3V3/72N7300kvq1q2ba3zu3Ln6/vvv9fbbb7vGdu3apZiYGElSfHy80tPT1adPH0nS0aNHdfToUcXHx5f7sSMjI8scDnQ6T6u42P8/6f2JnV6vkpJSW%2BWV7JfZbnklMttBRc5bcQ9eXoesrCy9/vrr%2BsMf/qDExEQ5nU7XR6dOnbRp0yYtXrxYBw4c0HvvvaePP/5YTz75pCSpf//%2BWrVqlVauXKldu3Zp7Nix6tixo%2BrVq%2BflVAAAwFf45R6sf//73yopKdEbb7yhN954w23Zjz/%2BqLlz5%2BrVV1/V3LlzVadOHc2ZM0cJCQmSpISEBE2ZMkWvvvqqTp48qbZt22rq1KneiAEAAHxUgDHGeHsSduB0nr4h273vlW9uyHYhffZMW29P4YZzOAJVvXoV5eaerbC72a1mt8x2yyuR2Q6ZryVvRMTNHpqVO788RAgAAOBNFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIOb08AqKjue%2BUbb0/hmnz2TFtvTwEA8P%2BwBwsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi3GZBsBP%2BNJlJbikBAB/xx6sKygsLNTEiROVlJSkdu3aacmSJd6eEgAA8CHswbqCmTNnavv27Vq6dKmOHDmicePGKTo6Wt26dfP21AAAgA%2BgYF3m3LlzWrlypd588001a9ZMzZo10549e/Tuu%2B9SsAAAQLlwiPAyu3btUnFxsRISElxjiYmJysjIUGlpqRdnBgAAfAV7sC7jdDpVvXp1BQcHu8Zq1aqlwsJC5eXlqUaNGr%2B6jZycHDmdTrcxh6OyIiMjLZ8v4It86YR8X7T22fau/wcFBbr9awdk9n%2B%2BkJeCdZn8/Hy3ciXJdbuoqKhc20hLS1Nqaqrb2IgRIzRy5EhrJnmJ/5t%2B4w5b5uTkKC0tTf369bNNObRbZrvlleyXOScnR0uXLrJNXonMdsjsC3krbvXzkpCQkDJF6uLtSpUqlWsb/fr104cffuj20a9fP8vneqM5nU6lpqaW2Rvnz%2ByW2W55Jftltlteicx24At52YN1maioKOXm5qq4uFgOx4Wnx%2Bl0qlKlSgoLCyvXNiIjIytsowYAADcee7Au07RpUzkcDm3ZssU1lp6erri4OAUG8nQBAIBfR2O4TGhoqHr16qXJkydr69at%2Bvzzz7VkyRI9/vjj3p4aAADwEUGTJ0%2Be7O1JVDRt2rTRzp07NWfOHH333Xf64x//qIceesjb0/KKKlWqqFWrVqpSpYq3p%2BIxdstst7yS/TLbLa9EZjuo6HkDjDHG25MAAADwJxwiBAAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsHCFRUWFmrixIlKSkpSu3bttGTJEm9P6bplZ2crJSVFrVq1Uvv27TVjxgwVFhZKkqZNm6YmTZq4fSxfvty17urVq9WlSxfFx8crOTlZJ06c8FaMclu7dm2ZTCkpKZKknTt3qm/fvoqPj9dDDz2k7du3u63ri3k//PDDMnmbNGmi2NhYSdLTTz9dZtm6detc67/99ttq3769EhISNHHiROXn53srSrkUFRWpZ8%2Be2rBhg2vs4MGDGjx4sFq0aKHu3bvr66%2B/dlvn22%2B/Vc%2BePRUfH6/HH39cBw8edFtekZ%2BDK%2BXdsmWLfv/73yshIUH33nuvVq5c6bbOAw88UOY13717tyTJGKPZs2erTZs2atWqlWbOnKnS0lKPZvo1V8p8Pd%2BrKnrmy/OOHz/%2Bil/Tl/7puqSkpDLLz549K6kC/BwzwBVMmTLF3H///Wb79u3mf/7nf0xCQoL57LPPvD2t36y0tNQ88sgj5qmnnjK7d%2B82mzZtMl27djUvvviiMcaYwYMHmwULFpicnBzXx7lz54wxxmRkZJjmzZubjz76yPzwww9mwIABZujQod6MUy6vv/66GTZsmFumkydPmrNnz5q2bduaF1980WRmZpqpU6eau%2B66y5w9e9YY47t58/Pz3bIeOXLEdO3a1UyfPt0YY0zXrl3NqlWr3O5TWFhojDHmX//6l0lMTDT/%2B7//azIyMkz37t3NCy%2B84M04v6igoMAkJyebxo0bm/Xr1xtjLnyO33///Wb06NEmMzPTzJ8/38THx5vDhw8bY4w5fPiwadGihVm8eLHZvXu3%2Be///m/Ts2dPU1paaoyp2M/BlfLm5OSYpKQkM2fOHLNv3z6zevVqExcXZ9atW2eMMaa4uNjExcWZjRs3ur3m58%2BfN8YYs3jxYtOhQwezadMm891335l27dqZRYsWeStiGVfKbMz1fa%2BqyJmvlPfUqVNuOb///ntz%2B%2B23m7Vr1xpjjDl27Jhp3LixOXDggNv9Ln5Oe/vnGAULZZw9e9bExcW5fVHPmzfPDBgwwIuzuj6ZmZmmcePGxul0usY%2B%2BeQT065dO2OMMe3btzdfffXVFdcdM2aMGTdunOv2kSNHTJMmTcyBAwdu7KSv0%2BjRo82cOXPKjK9cudJ07tzZ9U2otLTUdO3a1XzwwQfGGN/Ne7n58%2BebLl26mMLCQlNYWGiaNm1q9u7de8X7Pvroo%2BbVV1913d60aZNp3ry56wdXRbJnzx7zwAMPmPvvv9/th9G3335rWrRo4SrKxhgzaNAgV65XXnnF7Wv43LlzJiEhwbV%2BRX0Orpb3vffeM926dXO773PPPWdGjRpljDFm//79JjY21hQUFFxxux06dHB9zhtjzMcff2w6dep0g1Jcm6tlNub6vldV1My/lPdSTz75pHn22Wddt7/55hvTtm3bK963Ivwc4xAhyti1a5eKi4uVkJDgGktMTFRGRkaF2p18LSIiIrRo0SLVqlXLbfzMmTM6c%2BaMsrOzVb9%2B/Suum5GRoaSkJNft2rVrKzo6WhkZGTdyytctKyvripkyMjKUmJiogIAASVJAQIBatmypLVu2uJb7Yt5L5eXl6c0339To0aMVHBysvXv3KiAgQPXq1Stz35KSEm3bts0tc4sWLXT%2B/Hnt2rXLk9Mul40bN6p169ZKS0tzG8/IyNBtt92mypUru8YSExOv%2BrqGhoaqWbNm2rJlS4V%2BDq6W9%2BJh/sudOXNGkpSZmanatWsrJCSkzH2ys7N19OhR3XHHHa6xxMREHT58WDk5ORYnuHZXy3w936sqcuar5b3Ud999p02bNmnUqFGusczMTDVo0OCK968IP8ccHnkU%2BBSn06nq1asrODjYNVarVi0VFhYqLy9PNWrU8OLsfpuwsDC1b9/edbu0tFTLly9XmzZtlJWVpYCAAM2fP19ffvmlwsPD9cQTT6h3796SpJycHEVGRrptr2bNmjp27JhHM1wLY4z27dunr7/%2BWgsWLFBJSYm6deumlJQUOZ1O3XrrrW73r1mzpvbs2SPJN/NebsWKFYqMjFS3bt0kSXv37lXVqlU1duxYbdy4UbfccotGjhypDh066NSpUyosLHTL7HA4FB4eXiEzP/roo1ccdzqdv/i6/dLyivwcXC1v3bp1VbduXdftn3/%2BWf/85z81cuRISRd%2Bwbjppps0bNgwbd%2B%2BXQ0aNNDYsWPVvHlzOZ1OSXLLe/GXr2PHjpV5njztapmv53tVRc58tbyXWrhwoXr37q3atWu7xrKyspSfn6%2BBAwdq3759atq0qSZOnKgGDRpUiJ9j7MFCGfn5%2BW6flJJct4uKirwxJcvNmjVLO3fu1J/%2B9CfX3o2YmBgtXLhQffv21XPPPae1a9dKkgoKCq74fFTk5%2BLIkSOu1/GVV17RuHHj9Mknn2jmzJlXfX0v5vHFvJcyxmjlypUaMGCAa2zv3r0qKChQu3bttGjRInXo0EFPP/20tm3bpoKCAkny6czS1b9uL2b4peW%2B/hwUFBRo5MiRqlWrlvr16ydJ2rdvn06ePKm%2Bfftq4cKFatiwoQYNGqSjR49eMa8vfI%2B7nu9VvppZuvDmjfXr12vgwIFu43v37tXJkyf19NNP6/XXX1elSpU0ePBgnTlzpkL8HGMPFsoICQkp8wl48XalSpW8MSVLzZo1S0uXLtXLL7%2Bsxo0bq1GjRurUqZPCw8MlSbGxsdq/f79WrFihrl27XvX5CA0N9cb0y6VOnTrasGGDqlWrpoCAADVt2lSlpaUaM2aMWrVqdcU8F19bX8x7qW3btik7O1s9evRwjQ0fPlwDBw5UtWrVJF14jXfs2KH3339ff/rTnySV/abrS5mlC69bXl6e21h5XtewsDDXYTRffA7Onj2r4cOHa//%2B/Xrvvfdc8506daoKCgpUtWpVSdLkyZO1efNmrVq1SnfddZekC/kuz16R8/bq1es3f6%2B6tFz4UmZJWrNmjZo2bVpmz/vixYt1/vx5ValSRZI0e/ZsdejQQevWrasQP8fYg4UyoqKilJubq%2BLiYteY0%2BlUpUqVFBYW5sWZXb%2BpU6fqrbfe0qxZs3TvvfdKunAO0sVvWBfFxMQoOztb0oXn4/jx427Ljx8/roiICM9M%2BjcKDw93nWclSQ0bNlRhYaEiIiKumOfiIQJfzXvRV199paSkJFeZkqTAwEC329L//xqHh4crJCTELXNxcbHy8vJ8JrN09detPK%2Brrz4HZ86c0ZAhQ7Rnzx4tXbrU7dwkh8PhKleSXHt%2BsrOzFRUVJUmuw2aX/r8i572e71W%2Bmlm68DX9u9/9rsx4cHCwq1xJF36JqFu3rus19vbPMQoWymjatKkcDofr5FhJSk9PV1xcnAIDffdTJjU1VX/729/00ksvue3dmDt3rgYPHux23127dikmJkaSFB8fr/T0dNeyo0eP6ujRo4qPj/fIvH%2BLr776Sq1bt3a7jtEPP/yg8PBwJSYm6vvvv5cxRtKFQ2qbN2925fHFvJfaunWrWrZs6TY2fvx4TZgwwW3s4mscGBiouLg4t8xbtmyRw%2BFwXUPLF8THx2vHjh2uQ0HSha/bq72u%2Bfn52rlzp%2BLj433yOSgtLdWIESN06NAhLVu2TI0aNXJbPnDgQKWmprrd/8cff1RMTIyioqIUHR3tljc9PV3R0dFeP//ql1zP9ypfzWyM0bZt28p8TRtj1KVLF3344YeusXPnzumnn35STExMxfg55rH3K8KnPPfcc6ZHjx4mIyPDrF271rRs2dKsWbPG29P6zTIzM03Tpk3Nyy%2B/7Ha9lJycHJORkWFuu%2B02s2jRIvPTTz%2BZd99919x%2B%2B%2B1m8%2BbNxhhjNm/ebJo1a2bef/9917Vlhg0b5uVEv%2Bz06dOmffv2ZtSoUSYrK8v85z//Me3atTMLFy40p0%2BfNm3atDFTp041e/bsMVOnTjVt27Z1vb3fF/NeqlOnTmb16tVuY2vWrDHNmjUzH330kdm/f7957bXXTPPmzc3BgweNMcasXr3atGzZ0qxdu9ZkZGSYHj16mKlTp3pj%2Btfk0re0FxcXm%2B7du5tnnnnG7N692yxYsMC0aNHCdR2sgwcPmri4OLNgwQLXdbDuv/9%2B1%2BU6fOE5uDRvWlqaiY2NNevWrXP7es7NzTXGGLNkyRKTmJhoPv/8c5OVlWX%2B8pe/mLvuusucPn3aGGPMggULTLt27cz69evN%2BvXrTbt27cySJUu8lu1qLs18vd%2BrfCHz5ZdpOHjwoGncuLHJyckpc9%2BpU6eajh07mvXr15vdu3eb5ORk07NnT1NcXGyM8f7PMQoWrujcuXNm7NixpkWLFqZdu3bmrbfe8vaUrsuCBQtM48aNr/hhjDFr1641999/v4mLizPdunUr80X4wQcfmA4dOpgWLVqY5ORkc%2BLECW/EuCa7d%2B82gwcPNi1atDBt27Y1r732muuHaUZGhunVq5eJi4szDz/8sNmxY4fbur6Y96K4uDjz5Zdflhl///33zT333GNuv/1207t3b7Nx40a35QsWLDB33nmnSUxMNBMmTLjq9ZMqkst/GO3fv9889thj5vbbbzc9evQw33zzjdv9//Of/5h77rnHNG/e3AwaNKjMtc0q%2BnNwad4nn3zyil/PF69zVFpaat544w3TsWNHc/vtt5vHHnvM/Pjjj65tFRcXm7/%2B9a8mKSnJtG7d2syaNcv19VGRXP4aX8/3Kl/IfHneLVu2mMaNG7suCnypgoICM2PGDNO2bVsTHx9vhg0bZo4cOeJa7u2fYwHG/L/jBAAAALCE755QAwAAUEFRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIv9f9f8pemLxmLKAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2123370312409071368”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>535</td> <td class=”number”>16.7%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>219</td> <td class=”number”>6.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>127</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>52</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (224)</td> <td class=”number”>950</td> <td class=”number”>29.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>866</td> <td class=”number”>27.0%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2123370312409071368”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>535</td> <td class=”number”>16.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.73684210526315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.11764705882352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.85714285714286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_DIRSALES_07_12”>PCH_DIRSALES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2347</td>

</tr> <tr>

<th>Unique (%)</th> <td>73.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>16.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>529</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>54.31</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>11585</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6981697669053326207”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6981697669053326207,#minihistogram6981697669053326207”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6981697669053326207”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6981697669053326207”

aria-controls=”quantiles6981697669053326207” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6981697669053326207” aria-controls=”histogram6981697669053326207”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6981697669053326207” aria-controls=”common6981697669053326207”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6981697669053326207” aria-controls=”extreme6981697669053326207”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6981697669053326207”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-77.907</td>

</tr> <tr>

<th>Q1</th> <td>-33.333</td>

</tr> <tr>

<th>Median</th> <td>5.2486</td>

</tr> <tr>

<th>Q3</th> <td>64.205</td>

</tr> <tr>

<th>95-th percentile</th> <td>293.09</td>

</tr> <tr>

<th>Maximum</th> <td>11585</td>

</tr> <tr>

<th>Range</th> <td>11685</td>

</tr> <tr>

<th>Interquartile range</th> <td>97.538</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>313.12</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.7654</td>

</tr> <tr>

<th>Kurtosis</th> <td>738.22</td>

</tr> <tr>

<th>Mean</th> <td>54.31</td>

</tr> <tr>

<th>MAD</th> <td>103.83</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>22.614</td>

</tr> <tr>

<th>Sum</th> <td>145600</td>

</tr> <tr>

<th>Variance</th> <td>98042</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6981697669053326207”>

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ACWUbAAAAAso2ABAABY5veCdd111%2BnZZ5/VN9984%2B%2BnBgAA8Au/F6zU1FQ99thj6tu3r26//XZt27ZNxhh/LwMAAKDe%2BL1gTZs2TW%2B%2B%2BaYeeeQRhYeH69Zbb9UVV1yhZcuW6bPPPvP3cgAAAKxzBeJJw8LC1KdPH/Xp00cVFRVat26dHnnkEa1cuVI9e/bUDTfcoCuvvDIQSwMAAPjZAlKwJKm4uFgvvviiXnzxRX366afq2bOnrrnmGh05ckR33323du7cqdmzZwdqeQAAAD%2BZ3wvWli1btGXLFu3YsUMtW7bUyJEj9dBDD6lDhw7e%2B7Rp00YLFy6kYAEAgJDk94I1e/Zs9e/fX8uXL9fll1%2BuRo1OfxtYx44dNWHCBH8vDQAAwAq/F6x33nlHLVq0UFlZmbdc7d69W926dVN4eLgkqWfPnurZs6e/lwYAAGCF33%2BK8Pjx47rqqqv0%2BOOPe8emTJmiESNGqKioyN/LAQAAsM7vBeu%2B%2B%2B7ThRdeqBtvvNE79vLLL6tNmzbKyMjw93IAAACs83vB%2Btvf/qa77rpLMTEx3rGWLVvqzjvv1Pbt2/29HAAAAOv8XrBcLpfKy8tPG6%2BoqOCK7gAAoEHwe8G6/PLLtWDBAn355ZfescLCQmVkZKhfv37%2BXg4AAIB1fv8pwhkzZujGG2/U4MGDFR0dLUkqLy9Xt27dNHPmTH8vBwAAwDq/F6xWrVrphRde0HvvvaeCggK5XC796le/0mWXXaawsDB/LwcAAMC6gPyqnPDwcPXr149TggAAoEHy%2B3uwPB6PZs%2BerSFDhmjgwIH67W9/6/Pxz6qurtbw4cO1Y8cO79iCBQt08cUX%2B3ysX7/eO5%2BTk6OBAwfK7XYrLS1Nx44d884ZY7R48WKlpqYqJSVFixYtUl1d3c/baQAAcF7x%2BxGse%2B65R3v27NGwYcP0y1/%2B8mdtq6qqStOmTVNBQYHP%2BIEDBzRt2jRdc8013rFmzZpJ%2Bu6q8bNnz9bcuXPVpUsXLVy4UDNnztSKFSskSU8%2B%2BaRycnKUlZWlmpoaTZ8%2BXa1atdLkyZN/1loBAMD5w%2B8Fa/v27Vq1apWSk5N/1nb279%2BvadOmnfHSDgcOHNDkyZN9rrX1vfXr12vIkCEaOXKkJGnRokXq37%2B/CgsL1b59e61du1bp6ene9d1xxx168MEHKVgAAMAxv58ibNKkiVq1avWzt/PBBx%2Bod%2B/eys7O9hk/fvy4jh49qg4dOpzxcfn5%2BT7lrk2bNoqPj1d%2Bfr6OHj2qoqIi9erVyzuflJSkQ4cOqbi4%2BGevGQAAnB/8fgRrxIgRWrVqlebNm%2Bf95c4/xfjx4884fuDAAYWFhemxxx7TO%2B%2B8o%2BbNm%2BvGG2/0ni4sLi5WbGysz2NatWqlI0eOyOPxSJLPfOvWrSVJR44cOe1xZ1NcXOzd1vdcriaOH99QuVx%2B7/NnFB7eyOczzo6snCMrZ8jJObJyLhiz8nvBKisrU05Ojt566y21b99eERERPvNr1679Wds/ePCgwsLC1LFjR02YMEE7d%2B7UPffco2bNmmnQoEGqrKw87TkjIiJUXV2tyspK7%2B0fzknfvZneqezsbGVlZfmMpaWlKT09/afuVoPQokXTQC/BR3R0VKCXEDLIyjmycoacnCMr54Ipq4BcpmH48OH1tu2RI0eqf//%2Bat68uSSpS5cu%2Bvzzz7VhwwYNGjRIkZGRp5Wl6upqRUVF%2BZSpyMhI758lKSrK%2BV/a2LFjNWDAAJ8xl6uJSktP/OT9agiCZf/DwxspOjpK5eUVqq3lJ0TPhaycIytnyMk5snLuXFkF6pt7vxesjIyMet1%2BWFiYt1x9r2PHjt5fJB0XF6eSkhKf%2BZKSEsXExCguLk7Sd5eSaNeunffPks74hvmziY2NPe10oMfzjWpqzu//IMG2/7W1dUG3pmBFVs6RlTPk5BxZORdMWQXkZGVxcbGysrI0bdo0ff3113r11Vd18OBBK9t%2B8MEHNXHiRJ%2Bxffv2qWPHjpIkt9ut3Nxc71xRUZGKiorkdrsVFxen%2BPh4n/nc3FzFx8ef9%2B%2BfAgAAzvm9YH3xxRe6%2Buqr9cILL%2Bi1117TyZMn9fLLL2v06NHKz8//2dvv37%2B/du7cqdWrV%2BvLL7/UM888o82bN2vSpEmSpHHjxmnLli3auHGj9u3bpzvvvFNXXHGF2rdv751fvHixduzYoR07dmjJkiW6/vrrf/a6AADA%2BcPvBev%2B%2B%2B/XwIEDtXXrVv3iF7%2BQJC1dulQDBgzQ4sWLf/b2e/TooQcffFBbtmzR8OHDtW7dOi1ZskSJiYmSpMTERM2bN0/Lly/XuHHjdMEFF/ictpw8ebKGDh2qqVOn6k9/%2BpNGjBhx2hExAACAc/H7e7B27dqlp59%2B2ucXO7tcLt1yyy0aM2bMT9rmJ5984nN74MCBGjhw4FnvP2rUKI0aNeqMc%2BHh4Zo5c6Zmzpz5k9YCAADg9yNYdXV1Z/zdfidOnPhZ18UCAAAIFn4vWH379tWKFSt8SlZZWZkyMzOVmprq7%2BUAAABY5/eCddddd2nPnj3q27evqqqq9B//8R/q37%2B/vvrqK82YMcPfywEAALDO7%2B/BiouL0%2BbNm5WTk6OPP/5YdXV1GjdunEaMGKFmzZr5ezkAAADWBeRK7lFRUbruuusC8dQAAAD1zu8F68euKfVzfxchAABAoPm9YLVt29bndk1Njb744gt9%2BumnuuGGG/y9HAAAAOuC5ncRLl%2B%2BXEeOHPHzagAAAOwLyO8iPJMRI0bolVdeCfQyAAAAfragKVh///vfudAoAABoEILiTe7Hjx/XJ598ovHjx/t7OQAAANb5vWDFx8f7/B5CSfrFL36hCRMm6He/%2B52/lwMAAGCd3wvW/fff7%2B%2BnBAAA8Cu/F6ydO3c6vm%2BvXr3qcSUAAAD1w%2B8F6w9/%2BIP3FKExxjt%2B6lhYWJg%2B/vhjfy8PAADgZ/N7wXrssce0YMECTZ8%2BXSkpKYqIiNCHH36oefPm6ZprrtHQoUP9vSQAAACr/H6ZhoyMDM2ZM0eDBw9WixYt1LRpU6WmpmrevHnasGGD2rZt6/0AAAAIRX4vWMXFxWcsT82aNVNpaam/lwMAAGCd3wtWQkKCli5dquPHj3vHysrKlJmZqcsuu8zfywEAALDO7%2B/Buvvuu3X99dfr8ssvV4cOHWSM0eeff66YmBitXbvW38sBAACwzu8Fq1OnTnr55ZeVk5OjAwcOSJJ%2B//vfa9iwYYqKivL3cgAAAKzze8GSpAsuuEDXXXedvvrqK7Vv317Sd1dzBwAAaAj8/h4sY4wWL16sXr16afjw4Tpy5IhmzJih2bNn69tvv/X3cgAAAKzze8Fat26dtmzZoj//%2Bc%2BKiIiQJA0cOFBbt25VVlaWv5cDAABgnd8LVnZ2tubMmaNRo0Z5r94%2BdOhQLViwQC%2B99JK/lwMAAGCd3wvWV199pa5du5423qVLF3k8Hn8vBwAAwDq/F6y2bdvqww8/PG38nXfe8b7hHQAAIJT5/acIJ0%2BerLlz58rj8cgYo/fff1/Z2dlat26d7rrrLn8vBwAAwDq/F6zRo0erpqZGjz76qCorKzVnzhy1bNlSt912m8aNG%2Bfv5QAAAFjn94KVk5Ojq666SmPHjtWxY8dkjFGrVq38vQwAAIB64/f3YM2bN8/7ZvaWLVtSrgAAQIPj94LVoUMHffrpp/5%2BWgAAAL/x%2BynCLl266I477tCqVavUoUMHRUZG%2BsxnZGT4e0kAAABW%2Bb1gffbZZ0pKSpIkrnsFAAAaJL8UrEWLFmnq1Klq0qSJ1q1b54%2BnBAAACBi/vAfrySefVEVFhc/YlClTVFxc7I%2BnBwAA8Cu/FCxjzGljO3fuVFVVlT%2BeHgAAwK/8/lOEAAAADR0FCwAAwDK/FaywsDB/PRUAAEBA%2Be0yDQsWLPC55tW3336rzMxMNW3a1Od%2BXAcLAACEOr8UrF69ep12zavExESVlpaqtLTUH0sAAADwG78ULK59BQAAzie8yR0AAMAyChYAAIBlIV%2BwqqurNXz4cO3YscM7VlhYqIkTJyohIUFDhw7Vtm3bfB7z3nvvafjw4XK73br%2B%2ButVWFjoM//UU0%2BpX79%2BSkxM1KxZs067Cj0AAMC5hHTBqqqq0u23366CggLvmDFGaWlpat26tTZt2qQRI0Zo6tSpOnz4sCTp8OHDSktL06hRo/Tcc8%2BpZcuWuuWWW7xXm3/ttdeUlZWlefPmac2aNcrPz1dmZmZA9g8AAISmkC1Y%2B/fv15gxY/Tll1/6jG/fvl2FhYWaN2%2BeOnXqpJtvvlkJCQnatGmTJGnjxo269NJLNWnSJF100UXKyMjQoUOH9MEHH0iS1q5dqxtuuEH9%2B/dXjx49NHfuXG3atImjWAAAwLGQLVgffPCBevfurezsbJ/x/Px8XXLJJWrSpIl3LCkpSXl5ed755ORk71xUVJS6deumvLw81dbW6sMPP/SZT0hI0Lfffqt9%2B/bV8x4BAICGwm8XGrVt/PjxZxz3eDyKjY31GWvVqpWOHDnyo/Pl5eWqqqrymXe5XGrevLn38QAAAD8mZAvW2VRUVCgiIsJnLCIiQtXV1T86X1lZ6b19tsc7UVxcfNqFVV2uJqcVu/ONyxUcB0zDwxv5fMbZkZVzZOUMOTlHVs4FY1YNrmBFRkaqrKzMZ6y6ulqNGzf2zp9alqqrqxUdHe39VT5nmo%2BKinK8huzsbGVlZfmMpaWlKT093fE2GqIWLZr%2B%2BJ38KDra%2Bd/p%2BY6snCMrZ8jJObJyLpiyanAFKy4uTvv37/cZKykp8R49iouLU0lJyWnzXbt2VfPmzRUZGamSkhJ16tRJklRTU6OysjLFxMQ4XsPYsWM1YMAAnzGXq4lKS0/8lF1qMIJl/8PDGyk6Okrl5RWqra0L9HKCGlk5R1bOkJNzZOXcubIK1Df3Da5gud1urVy5UpWVld6jVrm5uUpKSvLO5%2Bbmeu9fUVGhvXv3aurUqWrUqJG6d%2B%2Bu3Nxc9e7dW5KUl5cnl8ulLl26OF5DbGzsaacDPZ5vVFNzfv8HCbb9r62tC7o1BSuyco6snCEn58jKuWDKKnhOVlqSkpKiNm3aaObMmSooKNDKlSu1e/duXXvttZKk0aNHa9euXVq5cqUKCgo0c%2BZMtWvXzluoxo8fr9WrV2vr1q3avXu37r33Xo0ZM%2BafOkUIAADObw2uYIWHh%2BuRRx6Rx%2BPRqFGj9OKLL2r58uWKj4%2BXJLVr104PP/ywNm3apGuvvVZlZWVavny5wsLCJEnDhg3TzTffrDlz5mjSpEnq0aOHpk%2BfHshdAgAAISbMfH8Jc9Qrj%2BebetnukAferZft1odXbusT6CVI%2Bu6nGVu0aKrS0hNBcyg5WJGVc2TlDDk5R1bOnSurmJhfBmRNDe4IFgAAQKBRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYFmDLVivv/66Lr74Yp%2BP9PR0SdLevXt13XXXye12a/To0dqzZ4/PY3NycjRw4EC53W6lpaXp2LFjgdgFAAAQohpswdq/f7/69%2B%2Bvbdu2eT8WLFigkydPasqUKUpOTtbzzz%2BvxMRE3XzzzTp58qQkaffu3Zo9e7amTp2q7OxslZeXa%2BbMmQHeGwAAEEoabME6cOCAOnfurJiYGO9HdHS0Xn75ZUVGRurOO%2B9Up06dNHv2bDVt2lSvvvqqJGn9%2BvUaMmSIRo4cqS5dumjRokV6%2B%2B23VVhYGOA9AgAAoaJBF6wOHTqcNp6fn6%2BkpCSFhYVJksLCwtSzZ0/l5eV555OTk733b9OmjeLj45Wfn%2B%2BXdQMAgNDXIAuWMUafffaZtm3bpsGDB2vgwIFavHixqqur5fF4FBsb63P/Vq1a6ciRI5Kk4uLic84DAAD8GFegF1AfDh8%2BrIqKCkVEROiBBx7QV199pQULFqiystI7/kMRERGqrq6WJFVWVp5z3oni4mJ5PB6fMZeryWku4DXwAAAQ/UlEQVTF7XzjcgVHnw8Pb%2BTzGWdHVs6RlTPk5BxZOReMWTXIgtW2bVvt2LFDF1xwgcLCwtS1a1fV1dVp%2BvTpSklJOa0sVVdXq3HjxpKkyMjIM85HRUU5fv7s7GxlZWX5jKWlpXl/ivF81aJF00AvwUd0tPO/0/MdWTlHVs6Qk3Nk5VwwZdUgC5YkNW/e3Od2p06dVFVVpZiYGJWUlPjMlZSUeI8uxcXFnXE%2BJibG8XOPHTtWAwYM8BlzuZqotPTEP7MLDU6w7H94eCNFR0epvLxCtbV1gV5OUCMr58jKGXJyjqycO1dWgfrmvkEWrP/5n//RHXfcobfeest75Onjjz9W8%2BbNlZSUpMcff1zGGIWFhckYo127dumPf/yjJMntdis3N1ejRo2SJBUVFamoqEhut9vx88fGxp52OtDj%2BUY1Nef3f5Bg2//a2rqgW1OwIivnyMoZcnKOrJwLpqyC52SlRYmJiYqMjNTdd9%2BtgwcP6u2339aiRYt000036aqrrlJ5ebkWLlyo/fv3a%2BHChaqoqNCQIUMkSePGjdOWLVu0ceNG7du3T3feeaeuuOIKtW/fPsB7BQAAQkWDLFjNmjXT6tWrdezYMY0ePVqzZ8/W2LFjddNNN6lZs2ZasWKF9yhVfn6%2BVq5cqSZNmkj6rpzNmzdPy5cv17hx43TBBRcoIyMjwHsEAABCSZgxxgR6EecDj%2BebetnukAferZft1odXbusT6CVI%2Bu6nGVu0aKrS0hNBcyg5WJGVc2TlDDk5R1bOnSurmJhfBmRNDfIIFgAAQCBRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIK1hlUVVVp1qxZSk5OVt%2B%2BffXEE08EekkAACCEuAK9gGC0aNEi7dmzR2vWrNHhw4c1Y8YMxcfH66qrrgr00gAAQAigYJ3i5MmT2rhxox5//HF169ZN3bp1U0FBgZ5%2B%2BmkKFgAAcIRThKfYt2%2BfampqlJiY6B1LSkpSfn6%2B6urqArgyAAAQKjiCdQqPx6MWLVooIiLCO9a6dWtVVVWprKxMLVu2/NFtFBcXy%2BPx%2BIy5XE0UGxtrfb2hZMgD7wZ6Cf%2BU1%2B/oF%2BglBFx4eCOfzzg7snKGnJwjK%2BeCMSsK1ikqKip8ypUk7%2B3q6mpH28jOzlZWVpbP2NSpU3XrrbfaWeQP/G3h/5%2B2LC4uVnZ2tsaOHXvel7lzISfniouLtWbNKrJygKycISfnyMq5YMwqeKpekIiMjDytSH1/u3Hjxo62MXbsWD3//PM%2BH2PHjrW%2B1lN5PB5lZWWddvQMvsjJObJyjqycISfnyMq5YMyKI1iniIuLU2lpqWpqauRyfRePx%2BNR48aNFR0d7WgbsbGxQdOgAQCA/3EE6xRdu3aVy%2BVSXl6edyw3N1fdu3dXo0bEBQAAfhyN4RRRUVEaOXKk7r33Xu3evVtbt27VE088oeuvvz7QSwMAACEi/N5777030IsINqmpqdq7d6%2BWLFmi999/X3/84x81evToQC/LkaZNmyolJUVNmzYN9FKCGjk5R1bOkZUz5OQcWTkXbFmFGWNMoBcBAADQkHCKEAAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBasBqKqq0qxZs5ScnKy%2BffvqiSeeCPSS/Obo0aNKT09XSkqK%2BvXrp4yMDFVVVUmSCgsLNXHiRCUkJGjo0KHatm2bz2Pfe%2B89DR8%2BXG63W9dff70KCwt95p966in169dPiYmJmjVrlioqKvy2X/VtypQpuuuuu7y39%2B7dq%2Buuu05ut1ujR4/Wnj17fO6fk5OjgQMHyu12Ky0tTceOHfPOGWO0ePFipaamKiUlRYsWLVJdXZ3f9qU%2BVFdXa%2B7cuerVq5d%2B/etfa%2BnSpfr%2Bmsxk9f%2BKiop08803q2fPnhowYICeeuop7xw5fae6ulrDhw/Xjh07vGP1%2BdoUyl8PzpRVXl6e/u3f/k2JiYkaPHiwNm7c6POYoM7KIOTNmzfPXH311WbPnj3mr3/9q0lMTDSvvPJKoJdV7%2Brq6syYMWPMTTfdZD799FOzc%2BdOM2jQIHP//feburo6c/XVV5tp06aZ/fv3m8cee8y43W5z6NAhY4wxhw4dMgkJCWb16tXm008/NX/605/M8OHDTV1dnTHGmFdffdUkJSWZ//7v/zb5%2Bflm6NChZu7cuYHcXWtycnJM586dzYwZM4wxxpw4ccL06dPH3H///Wb//v1m/vz55te//rU5ceKEMcaY/Px806NHD/PCCy%2BYjz/%2B2EyYMMFMmTLFu73Vq1eb3/zmN2bnzp3m/fffN3379jWrVq0KyL7Zcs8995grr7zS5Ofnm/fee8/07t3bbNiwgaxOMWbMGHPbbbeZzz77zLz%2B%2BuvG7Xabv/71r%2BT0fyorK01aWprp3Lmz2b59uzHG1PtrU6h%2BPThTVsXFxSY5OdksWbLEfPbZZyYnJ8d0797dvPnmm8aY4M%2BKghXiTpw4Ybp37%2B79B2mMMcuXLzcTJkwI4Kr8Y//%2B/aZz587G4/F4x1566SXTt29f895775mEhATvC7oxxtxwww3moYceMsYY88ADD/hkdPLkSZOYmOjNcfz48d77GmPMzp07TY8ePczJkyfre7fqVWlpqbn88svN6NGjvQVr48aNZsCAAd4Xpbq6OjNo0CCzadMmY4wx06dP997XGGMOHz5sLr74YvPll18aY4z5zW9%2B472vMcZs3rzZ9O/f31%2B7ZF1paam55JJLzI4dO7xjK1asMHfddRdZ/UBZWZnp3Lmz%2BeSTT7xjU6dONXPnziUnY0xBQYH53e9%2BZ66%2B%2Bmqf0lCfr02h%2BvXgbFk988wz5qqrrvK57z333GNuv/12Y0zwZ8UpwhC3b98%2B1dTUKDEx0TuWlJSk/Pz8kD2k7lRMTIxWrVql1q1b%2B4wfP35c%2Bfn5uuSSS9SkSRPveFJSkvLy8iRJ%2Bfn5Sk5O9s5FRUWpW7duysvLU21trT788EOf%2BYSEBH377bfat29fPe9V/frP//xPjRgxQr/61a%2B8Y/n5%2BUpKSlJYWJgkKSwsTD179jxrVm3atFF8fLzy8/N19OhRFRUVqVevXt75pKQkHTp0SMXFxX7aK7tyc3PVrFkzpaSkeMemTJmijIwMsvqBxo0bKyoqSs8//7y%2B/fZbHTx4ULt27VLXrl3JSdIHH3yg3r17Kzs722e8Pl%2BbQvXrwdmy%2Bv5tH6c6fvy4pODPioIV4jwej1q0aKGIiAjvWOvWrVVVVaWysrIArqz%2BRUdHq1%2B/ft7bdXV1Wr9%2BvVJTU%2BXxeBQbG%2Btz/1atWunIkSOSdM758vJyVVVV%2Bcy7XC41b97c%2B/hQ9P777%2Btvf/ubbrnlFp/xH8uquLj4rPMej0eSfOa/L7yhmlVhYaHatm2rzZs366qrrtJvf/tbLV%2B%2BXHV1dWT1A5GRkZozZ46ys7Pldrs1ZMgQXX755bruuuvISdL48eM1a9YsRUVF%2BYzX52tTqH49OFtW7dq1U0JCgvf2119/rb/85S%2B67LLLJAV/Vi4rW0HAVFRU%2BPwDkeS9XV1dHYglBUxmZqb27t2r5557Tk899dQZc/k%2Bk7PlVl1drcrKSu/tsz0%2B1FRVVenPf/6z5syZo8aNG/vMnSsLSaqsrPynsgr1f38nT57UF198oWeffVYZGRnyeDyaM2eOoqKiyOoUBw4cUP/%2B/XXjjTeqoKBA8%2BfP12WXXUZO5/Bj2fyc1yZjTIP9elBZWalbb71VrVu31tixYyUFf1YUrBAXGRl52j%2BG72%2Bf%2BoW0IcvMzNSaNWu0bNkyde7cWZGRkad9F1JdXe3N5Gy5RUdHKzIy0nv71PlTv8MKFVlZWbr00kt9jvh972xZ/FhWUVFRPi9Ip%2BYWqlm5XC4dP35cS5YsUdu2bSVJhw8f1oYNG3ThhReS1f95//339dxzz%2Bntt99W48aN1b17dx09elSPPvqo2rdvT05nUZ%2BvTbW1tQ3y68GJEyd0yy236PPPP9czzzzj/XcQ7FlxijDExcXFqbS0VDU1Nd4xj8ejxo0bKzo6OoAr85/58%2BfrySefVGZmpgYPHizpu1xKSkp87ldSUuI9XHy2%2BZiYGDVv3lyRkZE%2B8zU1NSorK1NMTEw97039%2BMtf/qKtW7cqMTFRiYmJeumll/TSSy8pMTHxZ2UVFxcnSd7TOj/8c6hmFRMTo8jISG%2B5kqR//dd/VVFREVn9wJ49e3ThhRf6fDG65JJLdPjwYXI6h/p8bWqIXw%2BOHz%2BuyZMnq6CgQGvWrFGHDh28c8GeFQUrxHXt2lUul8v7Bknpuzfpdu/eXY0aNfy/3qysLD377LNaunSphg0b5h13u9366KOPvIeJpe9ycbvd3vnc3FzvXEVFhfbu3Su3261GjRqpe/fuPvN5eXlyuVzq0qWLH/bKvnXr1umll17S5s2btXnzZg0YMEADBgzQ5s2b5Xa79fe//917nSdjjHbt2nXWrIqKilRUVCS32624uDjFx8f7zOfm5io%2BPv6090aECrfbraqqKn322WfesYMHD6pt27Zk9QOxsbH64osvfI4CHDx4UO3atSOnc6jP16aG9vWgrq5OU6dO1VdffaV169bpoosu8pkP%2Bqys/TwiAuaee%2B4xw4YNM/n5%2Beb11183PXv2NK%2B99lqgl1Xv9u/fb7p27WqWLVtmiouLfT5qamrM0KFDzW233WY%2B/fRTs2LFCpOQkOC91kxhYaHp3r27WbFihff6KVdffbX3x8pzcnJMz549zeuvv27y8/PNsGHDzPz58wO5u1bNmDHD%2B2Py33zzjUlNTTXz5883BQUFZv78%2BaZPnz7eHyPftWuX6datm/mv//ov7zWLbr75Zu%2B2VqxYYfr27Wu2b99utm/fbvr27WueeOKJgOyXLVOmTDFjx441H3/8sXnnnXdMamqqWbNmDVn9QHl5uenTp4%2BZPn26OXjwoHnjjTdMSkqK2bBhAzmd4oeXHqjv16ZQ/3rww6yys7NNly5dzJtvvunz%2Bl5aWmqMCf6sKFgNwMmTJ82dd95pEhISTN%2B%2Bfc2TTz4Z6CX5xYoVK0znzp3P%2BGGMMZ9//rn5/e9/by699FIzbNgw8%2B677/o8/q233jJXXnml6dGjh7nhhhu81%2BD54fYvu%2Bwyk5SUZGbOnGkqKyv9tm/17YcFy5jvLvw4cuRI0717d3Pttdeajz76yOf%2BmzZtMr/5zW9MQkKCSUtLM8eOHfPO1dTUmPvuu88kJyeb3r17m8zMTO8LXKgqLy8306dPNwkJCeayyy4zDz/8sHefyOr/FRQUmIkTJ5qePXuagQMHmieffJKczuCHpcGY%2Bn1tCvWvBz/MatKkSWd8ff/htaqCOaswY/7vGC4AAACsCL2TsgAAAEGOggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAy/4XgQFV9mXUfY4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6981697669053326207”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.66666666666666</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2336)</td> <td class=”number”>2575</td> <td class=”number”>80.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>529</td> <td class=”number”>16.5%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6981697669053326207”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.03588143525741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.90721649484536</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.49122807017544</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3144.4444444444443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5355.555555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11584.615384615385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_DIRSALES_FARMS_07_12”>PCH_DIRSALES_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1277</td>

</tr> <tr>

<th>Unique (%)</th> <td>39.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>194</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>16.348</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1500</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2657375089200015643”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2657375089200015643”

aria-controls=”quantiles2657375089200015643” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2657375089200015643” aria-controls=”histogram2657375089200015643”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2657375089200015643” aria-controls=”common2657375089200015643”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2657375089200015643” aria-controls=”extreme2657375089200015643”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-60</td>

</tr> <tr>

<th>Q1</th> <td>-22.222</td>

</tr> <tr>

<th>Median</th> <td>1.1912</td>

</tr> <tr>

<th>Q3</th> <td>32.059</td>

</tr> <tr>

<th>95-th percentile</th> <td>125</td>

</tr> <tr>

<th>Maximum</th> <td>1500</td>

</tr> <tr>

<th>Range</th> <td>1600</td>

</tr> <tr>

<th>Interquartile range</th> <td>54.281</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>87.291</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.3394</td>

</tr> <tr>

<th>Kurtosis</th> <td>72.496</td>

</tr> <tr>

<th>Mean</th> <td>16.348</td>

</tr> <tr>

<th>MAD</th> <td>45.663</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.4638</td>

</tr> <tr>

<th>Sum</th> <td>49307</td>

</tr> <tr>

<th>Variance</th> <td>7619.7</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2657375089200015643”>

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GAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMr8XrOuuu07PPvusjhw54u%2BHBgAA8Au/F6yUlBQtX75cAwcO1B/%2B8Adt2bJFxhh/jwEAANBo/F6wpkyZorfffltPPPGEwsLCdNddd%2BmKK67QY489pi%2B//NLf4wAAAFjnCsSDhoSEaMCAARowYIAqKir09NNP64knntDKlSvVt29f3XzzzbryyisDMRoAAMB5C0jBkqTi4mK9/PLLevnll/XFF1%2Bob9%2B%2Buuaaa3Tw4EE98MAD2rZtm2bMmBGo8QAAAH40vxesl156SS%2B99JI%2B/PBDtWnTRmPGjNHSpUvVuXNn73Xat2%2Bv%2BfPnU7AAAEBQ8nvBmjFjhlJTU7Vs2TJddtllCg2tfxjYRRddpBtvvNHfowEAAFjh94L17rvvqnXr1iorK/OWqx07dqhXr14KCwuTJPXt21d9%2B/b192gAAABW%2BP2nCI8ePaphw4bpT3/6k3dt0qRJGj16tIqKivw9DgAAgHV%2BL1gPP/ywLrzwQt16663etVdffVXt27dXRkaGv8cBAACwzu8F6x//%2BIfuu%2B8%2BRUdHe9fatGmje%2B%2B9V1u3bvX3OAAAANb5vWC5XC6Vl5fXW6%2BoqOCM7gAAwBH8XrAuu%2BwyzZs3T//85z%2B9a4WFhcrIyNCgQYN%2B8P1VV1dr1KhR%2BvDDD71r8%2BbN08UXX%2BzzsWHDBu9%2BTk6OhgwZIrfbrbS0NB0%2BfNi7Z4zRwoULlZKSouTkZC1YsEB1dXU/Mi0AAPgp8vtPEU6bNk233nqrrrrqKkVFRUmSysvL1atXL02fPv0H3VdVVZWmTJmigoICn/W9e/dqypQpuuaaa7xrLVu2lPTdTyzOmDFDs2fPVvfu3TV//nxNnz5dK1askCStWbNGOTk5ysrKUk1NjaZOnaq2bdtq4sSJ5xMbAAD8hPi9YLVt21Yvvvii3n//fRUUFMjlcumXv/ylLr30UoWEhDT4fvbs2aMpU6ac9m3FvXv3auLEiT7HeZ20YcMGDR8%2BXGPGjJEkLViwQKmpqSosLFSnTp20fv16paenKykpSZJ0zz33aMmSJRQsAADQYAH5VTlhYWEaNGjQj3pL8KSPPvpI/fv313/9138pPj7eu3706FEdOnTI58zw35efn6/f/e533svt27dXXFyc8vPzFR4erqKiIvXr18%2B7n5iYqP3796u4uFgxMTE/el4AAPDT4feC5fF4tHjxYm3fvl0nTpyo9wrUW2%2B91aD7mTBhwmnX9%2B7dq5CQEC1fvlzvvvuuWrVqpVtvvdX7duHpilLbtm118OBBeTweSfLZb9eunSTp4MGDDS5YxcXF3vs6yeVq/pMvaC6X3w/5a5CwsFCfP53Cqbkk52Zzai7JudmcmktydjZ/8HvBmjlzpnbu3KmRI0fq5z//ufX737dvn0JCQry/bmfbtm2aOXOmWrZsqaFDh6qyslLh4eE%2BtwkPD1d1dbUqKyu9l7%2B/J313MH1DZWdnKysry2ctLS1N6enpPzaWI7Ru3SLQI5xVVFRkoEdoFE7NJTk3m1NzSc7N5tRckrOzNSa/F6ytW7dq1apV3mOcbBszZoxSU1PVqlUrSVL37t311VdfaePGjRo6dKgiIiLqlaXq6mpFRkb6lKmIiAjv3yUpMrLhT7Dx48dr8ODBPmsuV3OVlh770bmcoKnmDwsLVVRUpMrLK1Rb65yfGHVqLsm52ZyaS3JuNqfmkpyTLVDf3Pu9YDVv3lxt27ZttPsPCQnxlquTLrroIu9JTGNjY1VSUuKzX1JSoujoaMXGxkr67m3Mjh07ev8u6bQHzJ9JTExMvbcDPZ4jqqkJ3ieoDU09f21tXZOf8cdwai7Judmcmktybjan5pKcna0x%2Bf2N1dGjR2vVqlWqra1tlPtfsmSJbrnlFp%2B13bt366KLLpIkud1u5ebmeveKiopUVFQkt9ut2NhYxcXF%2Bezn5uYqLi7uJ3/8FAAAaDi/v4JVVlamnJwc/f3vf1enTp3qHQ%2B1fv3687r/1NRUrVy5UqtXr9bQoUO1ZcsWbd682Xu/N9xwg377298qPj5evXv31vz583XFFVeoU6dO3v2FCxfqF7/4hSRp0aJFuu22285rJgAA8NMSkNM0jBo1qtHuu0%2BfPlqyZImWLl2qJUuWqEOHDlq0aJESEhIkSQkJCZozZ46WLl2qf/3rXxowYIDmzp3rvf3EiRP17bffavLkyQoLC9O1115b7xUxAACAswkx/AJAv/B4jjTK/Q5f/F6j3G9jeO3uAYEe4bRcrlC1bt1CpaXHHHWcgVNzSc7N5tRcknOzOTWX5Jxs0dH2z1jQEAE5uUVxcbGysrI0ZcoUffvtt/rrX/%2Bqffv2BWIUAAAA6/xesL7%2B%2BmtdffXVevHFF/X666/r%2BPHjevXVVzVu3Djl5%2Bf7exwAAADr/F6wHnnkEQ0ZMkRvvvmmfvazn0mSHn30UQ0ePFgLFy709zgAAADW%2Bb1gbd%2B%2BXbfeeqvPL3Z2uVy68847tWvXLn%2BPAwAAYJ3fC1ZdXZ3q6uofLHfs2DGFhYX5exwAAADr/F6wBg4cqBUrVviUrLKyMmVmZiolJcXf4wAAAFjn94J13333aefOnRo4cKCqqqr0%2B9//Xqmpqfrmm280bdo0f48DAABgnd9PNBobG6vNmzcrJydHn332merq6nTDDTdo9OjRatmypb/HAQAAsC4gZ3KPjIzUddddF4iHBgAAaHR%2BL1g33XTTWffP93cRAgAABJrfC1aHDh18LtfU1Ojrr7/WF198oZtvvtnf4wAAAFjn94KVkZFx2vVly5bp4MGDfp4GAADAvoD8LsLTGT16tF577bVAjwEAAHDemkzB%2BvjjjznRKAAAcIQmcZD70aNH9fnnn2vChAn%2BHgcAAMA6vxesuLg4n99DKEk/%2B9nPdOONN%2BrXv/61v8cBAACwzu8F65FHHvH3QwIAAPiV3wvWtm3bGnzdfv36NeIkAAAAjcPvBeu3v/2t9y1CY4x3/dS1kJAQffbZZ/4eDwAA4Lz5vWAtX75c8%2BbN09SpU5WcnKzw8HB98sknmjNnjq655hqNGDHC3yMBAABY5ffTNGRkZGjWrFm66qqr1Lp1a7Vo0UIpKSmaM2eONm7cqA4dOng/AAAAgpHfC1ZxcfFpy1PLli1VWlrq73EAAACs83vBio%2BP16OPPqqjR49618rKypSZmalLL73U3%2BMAAABY5/djsB544AHddNNNuuyyy9S5c2cZY/TVV18pOjpa69ev9/c4AAAA1vm9YHXp0kWvvvqqcnJytHfvXknSb37zG40cOVKRkZH%2BHgcAAMA6vxcsSbrgggt03XXX6ZtvvlGnTp0kfXc2dwAAACfw%2BzFYxhgtXLhQ/fr106hRo3Tw4EFNmzZNM2bM0IkTJ/w9DgAAgHV%2BL1hPP/20XnrpJT344IMKDw%2BXJA0ZMkRvvvmmsrKy/D0OAACAdX4vWNnZ2Zo1a5bGjh3rPXv7iBEjNG/ePL3yyiv%2BHgcAAMA6vxesb775Rj169Ki33r17d3k8Hn%2BPAwAAYJ3fC1aHDh30ySef1Ft/9913vQe8AwAABDO//xThxIkTNXv2bHk8Hhlj9MEHHyg7O1tPP/207rvvPn%2BPAwAAYJ3fC9a4ceNUU1OjJ598UpWVlZo1a5batGmju%2B%2B%2BWzfccIO/xwEAALDO7wUrJydHw4YN0/jx43X48GEZY9S2bVt/jwEAANBo/H4M1pw5c7wHs7dp04ZyBQAAHMfvBatz58764osv/P2wAAAAfuP3twi7d%2B%2Bue%2B65R6tWrVLnzp0VERHhs5%2BRkeHvkQAAAKzye8H68ssvlZiYKEmc9woAADiSXwrWggULNHnyZDVv3lxPP/20Px4SAAAgYPxyDNaaNWtUUVHhszZp0iQVFxf74%2BEBAAD8yi8FyxhTb23btm2qqqryx8MDAAD4ld9/ihAAAMDpKFgAAACW%2Ba1ghYSE%2BOuhAAAAAspvp2mYN2%2BezzmvTpw4oczMTLVo0cLnepwHCwAABDu/FKx%2B/frVO%2BdVQkKCSktLVVpa6o8RAAAA/MYvBasxz31VXV2tsWPHaubMmerfv78kqbCwUDNnzlReXp7i4uJ0//33a%2BDAgd7bvP/%2B%2B3r44YdVWFgot9ut%2BfPnq1OnTt79tWvXavXq1Tp69KiGDx%2BumTNnKjIystEyAAAAZwnqg9yrqqr0hz/8QQUFBd41Y4zS0tLUrl07bdq0SaNHj9bkyZN14MABSdKBAweUlpamsWPH6vnnn1ebNm105513ek8l8frrrysrK0tz5szRunXrlJ%2Bfr8zMzIDkAwAAwSloC9aePXt0/fXX65///KfP%2BtatW1VYWKg5c%2BaoS5cuuuOOOxQfH69NmzZJkp577jldcskluu2229S1a1dlZGRo//79%2BuijjyRJ69ev180336zU1FT16dNHs2fP1qZNm%2BqdKBUAAOBMgrZgffTRR%2Brfv7%2Bys7N91vPz89WzZ081b97cu5aYmKi8vDzvflJSkncvMjJSvXr1Ul5enmpra/XJJ5/47MfHx%2BvEiRPavXt3IycCAABO4fdf9mzLhAkTTrvu8XgUExPjs9a2bVsdPHjwnPvl5eWqqqry2Xe5XGrVqpX39gAAAOcStAXrTCoqKhQeHu6zFh4erurq6nPuV1ZWei%2Bf6fYNUVxcXO%2BnJl2u5vWK3U%2BNy9U0XzANCwv1%2BdMpnJpLcm42p%2BaSnJvNqbkkZ2fzB8cVrIiICJWVlfmsVVdXq1mzZt79U8tSdXW1oqKivOfpOt3%2BD/kpwuzsbGVlZfmspaWlKT09vcH34UStW7c495UCKCrKmT8p6tRcknOzOTWX5NxsTs0lOTtbY3JcwYqNjdWePXt81kpKSryvHsXGxqqkpKTefo8ePdSqVStFRESopKREXbp0kSTV1NSorKxM0dHRDZ5h/PjxGjx4sM%2Bay9VcpaXHfkwkx2iq%2BcPCQhUVFany8grV1tYFehxrnJpLcm42p%2BaSnJvNqbkk52QL1Df3jitYbrdbK1euVGVlpfdVq9zcXCUmJnr3c3NzvdevqKjQrl27NHnyZIWGhqp3797Kzc31nlMrLy9PLpdL3bt3b/AMMTEx9d4O9HiOqKYmeJ%2BgNjT1/LW1dU1%2Bxh/Dqbkk52Zzai7JudmcmktydrbG5Lg3VpOTk9W%2BfXtNnz5dBQUFWrlypXbs2KFrr71WkjRu3Dht375dK1euVEFBgaZPn66OHTt6C9WECRO0evVqvfnmm9qxY4ceeughXX/99ZxoFAAANJjjClZYWJieeOIJeTwejR07Vi%2B//LKWLVumuLg4SVLHjh31%2BOOPa9OmTbr22mtVVlamZcuWeX8Z9ciRI3XHHXdo1qxZuu2229SnTx9NnTo1kJEAAECQCTEnT2GORuXxHGmU%2Bx2%2B%2BL1Gud/G8NrdAwI9wmm5XKFq3bqFSkuPOeplcKfmkpybzam5JOdmc2ouyTnZoqN/HpDHddwrWAAAAIFGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJY5tmC98cYbuvjii30%2B0tPTJUm7du3SddddJ7fbrXHjxmnnzp0%2Bt83JydGQIUPkdruVlpamw4cPByICAAAIUo4tWHv27FFqaqq2bNni/Zg3b56OHz%2BuSZMmKSkpSS%2B88IISEhJ0xx136Pjx45KkHTt2aMaMGZo8ebKys7NVXl6u6dOnBzgNAAAIJo4tWHv37lW3bt0UHR3t/YiKitKrr76qiIgI3XvvverSpYtmzJihFi1a6K9//askacOGDRo%2BfLjGjBmj7t27a8GCBXrnnXdUWFgY4EQAACBYOLpgde7cud56fn6%2BEhMTFRISIkkKCQlR3759lZeX591PSkryXr99%2B/aKi4tTfn6%2BX%2BYGAADBzxXoARqDMUZffvmltmzZohUrVqjxASO2AAASR0lEQVS2tlbDhg1Tenq6PB6PfvnLX/pcv23btiooKJAkFRcXKyYmpt7%2BwYMHG/z4xcXF8ng8PmsuV/N69/tT43I1zT4fFhbq86dTODWX5NxsTs0lOTebU3NJzs7mD44sWAcOHFBFRYXCw8O1ePFiffPNN5o3b54qKyu9698XHh6u6upqSVJlZeVZ9xsiOztbWVlZPmtpaWneg%2Bx/qlq3bhHoEc4qKioy0CM0Cqfmkpybzam5JOdmc2ouydnZGpMjC1aHDh304Ycf6oILLlBISIh69Oihuro6TZ06VcnJyfXKUnV1tZo1ayZJioiIOO1%2BZGTDn2Djx4/X4MGDfdZcruYqLT32IxM5Q1PNHxYWqqioSJWXV6i2ti7Q41jj1FySc7M5NZfk3GxOzSU5J1ugvrl3ZMGSpFatWvlc7tKli6qqqhQdHa2SkhKfvZKSEu/bd7Gxsafdj46ObvBjx8TE1Hs70OM5opqa4H2C2tDU89fW1jX5GX8Mp%2BaSnJvNqbkk52Zzai7J2dkakyPfWP3f//1f9e/fXxUVFd61zz77TK1atVJiYqI%2B/vhjGWMkfXe81vbt2%2BV2uyVJbrdbubm53tsVFRWpqKjIuw8AAHAujixYCQkJioiI0AMPPKB9%2B/bpnXfe0YIFC3T77bdr2LBhKi8v1/z587Vnzx7Nnz9fFRUVGj58uCTphhtu0EsvvaTnnntOu3fv1r333qsrrrhCnTp1CnAqAAAQLBxZsFq2bKnVq1fr8OHDGjdunGbMmKHx48fr9ttvV8uWLbVixQrl5uZq7Nixys/P18qVK9W8eXNJ35WzOXPmaNmyZbrhhht0wQUXKCMjI8CJAABAMHHsMVhdu3bVmjVrTrvXp08fvfjii2e87dixYzV27NjGGg0AADicI1/BAgAACCQKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBljv1VOWh6hi9%2BL9Aj/CCv3T0g0CMAAIIUr2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIK1mlUVVXp/vvvV1JSkgYOHKinnnoq0CMBAIAg4gr0AE3RggULtHPnTq1bt04HDhzQtGnTFBcXp2HDhgV6NPjR8MXvBXqEH%2BS1uwcEegQAwP%2BjYJ3i%2BPHjeu655/SnP/1JvXr1Uq9evVRQUKBnnnmGggUAABqEtwhPsXv3btXU1CghIcG7lpiYqPz8fNXV1QVwMgAAECx4BesUHo9HrVu3Vnh4uHetXbt2qqqqUllZmdq0aXPO%2ByguLpbH4/FZc7maKyYmxvq8wEnB9pZmMHnjnkF%2BeZywsFCfP53EqdmcmktydjZ/oGCdoqKiwqdcSfJerq6ubtB9ZGdnKysry2dt8uTJuuuuu%2BwM%2BT3/mO%2Bfty2Li4uVnZ2t8ePHO64oOjWbU3NJzs1WXFysdetWOS6X5NxsTs0lOTubP1BLTxEREVGvSJ283KxZswbdx/jx4/XCCy/4fIwfP976rP7k8XiUlZVV75U5J3BqNqfmkpybzam5JOdmc2ouydnZ/IFXsE4RGxur0tJS1dTUyOX67j%2BPx%2BNRs2bNFBUV1aD7iImJoe0DAPATxitYp%2BjRo4dcLpfy8vK8a7m5uerdu7dCQ/nPBQAAzo3GcIrIyEiNGTNGDz30kHbs2KE333xTTz31lG666aZAjwYAAIJE2EMPPfRQoIdoalJSUrRr1y4tWrRIH3zwgf7jP/5D48aNC/RYAdeiRQslJyerRYsWgR7FOqdmc2ouybnZnJpLcm42p%2BaSnJ2tsYUYY0yghwAAAHAS3iIEAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBwjlVVVXp/vvvV1JSkgYOHKinnnoq0CM1yKFDh5Senq7k5GQNGjRIGRkZqqqqkiQVFhbqlltuUXx8vEaMGKEtW7b43Pb999/XqFGj5Ha7ddNNN6mwsDAQERpk0qRJuu%2B%2B%2B7yXd%2B3apeuuu05ut1vjxo3Tzp07fa6fk5OjIUOGyO12Ky0tTYcPH/b3yGdVXV2t2bNnq1%2B/fvrVr36lRx99VCfPhxzM2YqKinTHHXeob9%2B%2BGjx4sNauXevdC9Zc1dXVGjVqlD788EPv2vl%2Bbq1du1aDBg1SQkKC7r//flVUVPgly/edLldeXp7%2B/d//XQkJCbrqqqv03HPP%2BdwmGHJJp8920pEjRzRo0CC98MILPutne/4ZY7Rw4UKlpKQoOTlZCxYsUF1dXaPnCAoGOIc5c%2BaYq6%2B%2B2uzcudP87W9/MwkJCea1114L9FhnVVdXZ66//npz%2B%2B23my%2B%2B%2BMJs27bNDB061DzyyCOmrq7OXH311WbKlClmz549Zvny5cbtdpv9%2B/cbY4zZv3%2B/iY%2BPN6tXrzZffPGF%2Bc///E8zatQoU1dXF%2BBU9eXk5Jhu3bqZadOmGWOMOXbsmBkwYIB55JFHzJ49e8zcuXPNr371K3Ps2DFjjDH5%2BfmmT58%2B5sUXXzSfffaZufHGG82kSZMCGaGemTNnmiuvvNLk5%2Beb999/3/Tv399s3Lgx6LNdf/315u677zZffvmleeONN4zb7TZ/%2B9vfgjZXZWWlSUtLM926dTNbt241xpjz/tz661//ahITE83//M//mPz8fDNixAgze/bsgOcqLi42SUlJZtGiRebLL780OTk5pnfv3ubtt98OmlxnyvZ9M2fONN26dTObNm3yrp3r%2Bbd69Wpz%2BeWXm23btpkPPvjADBw40KxatcoveZo6ChbO6tixY6Z3794%2Bn4zLli0zN954YwCnOrc9e/aYbt26GY/H41175ZVXzMCBA837779v4uPjvf%2BAGWPMzTffbJYuXWqMMWbx4sU%2B%2BY4fP24SEhJO%2BwUpkEpLS81ll11mxo0b5y1Yzz33nBk8eLD3C3tdXZ0ZOnSo9wvm1KlTvdc1xpgDBw6Yiy%2B%2B2Pzzn//0f4DTKC0tNT179jQffvihd23FihXmvvvuC%2BpsZWVlplu3bubzzz/3rk2ePNnMnj07KHMVFBSYX//61%2Bbqq6/2%2Bcf6fD%2B3JkyY4L2uMcZs27bN9OnTxxw/ftwfsc6Y689//rMZNmyYz3Vnzpxp/vCHPxhjmn4uY86c7fszDR061AwYMMCnYJ3r%2BXf55Zf7XH/z5s0mNTW1kdMEB94ixFnt3r1bNTU1SkhI8K4lJiYqPz%2B/Sb8MHB0drVWrVqldu3Y%2B60ePHlV%2Bfr569uyp5s2be9cTExOVl5cnScrPz1dSUpJ3LzIyUr169fLuNxV//OMfNXr0aP3yl7/0ruXn5ysxMVEhISGSpJCQEPXt2/eM2dq3b6%2B4uDjl5%2Bf7d/gzyM3NVcuWLZWcnOxdmzRpkjIyMoI6W7NmzRQZGakXXnhBJ06c0L59%2B7R9%2B3b16NEjKHN99NFH6t%2B/v7Kzs33Wz%2Bdzq7a2Vp988onPfnx8vE6cOKHdu3c3cqLvnCnXyUMMTnX06FFJTT%2BXdOZs0ndvG86cOVOzZs1SeHi4z97Znn%2BHDh1SUVGR%2BvXr591PTEzU/v37VVxc3HhhgoQr0AOgafN4PGrdurXPJ127du1UVVWlsrIytWnTJoDTnVlUVJQGDRrkvVxXV6cNGzYoJSVFHo9HMTExPtdv27atDh48KEnn3G8KPvjgA/3jH//QK6%2B8ooceesi77vF4fAqX9N3sBQUFkqTi4uImna2wsFAdOnTQ5s2btXz5cp04cUJjx47V73//%2B6DOFhERoVmzZmnu3Llav369amtrNXbsWF133XV66623gi7XhAkTTrt%2BPp9b5eXlqqqq8tl3uVxq1aqV37KeKVfHjh3VsWNH7%2BVvv/1Wf/nLX3TXXXdJavq5pDNnk6Tly5erZ8%2BeGjhwYL29sz3/PB6PJPnsn/ym9uDBg/Vu91NDwcJZVVRU1PuO5uTl6urqQIz0o2RmZmrXrl16/vnntXbt2tNmOpnnTJmbSt6qqio9%2BOCDmjVrlpo1a%2Bazd67ZKysrm3S248eP6%2Buvv9azzz6rjIwMeTwezZo1S5GRkUGfbe/evUpNTdWtt96qgoICzZ07V5deemnQ5/q%2Bc2U5235lZaX38plu3xRUVlbqrrvuUrt27TR%2B/HhJwZ1rz549evbZZ/Xyyy%2Bfdv9sz7/TZQvGfx8aCwULZxUREVHvE%2BXk5VP/cW%2BqMjMztW7dOj322GPq1q2bIiIiVFZW5nOd6upqb54zZY6KivLbzGeTlZWlSy65xOcVupPONPu5skVGRjbewD%2BAy%2BXS0aNHtWjRInXo0EGSdODAAW3cuFEXXnhh0Gb74IMP9Pzzz%2Budd95Rs2bN1Lt3bx06dEhPPvmkOnXqFLS5TnU%2Bn1sRERHey6fuN5Wsx44d05133qmvvvpKf/7zn71zBWsuY4weeOABpaen1zuc4qSzPf%2B%2BX6ZOzRnobE0Bx2DhrGJjY1VaWqqamhrvmsfjUbNmzZpM4TibuXPnas2aNcrMzNRVV10l6btMJSUlPtcrKSnxvpx9pv3o6Gj/DH0Of/nLX/Tmm28qISFBCQkJeuWVV/TKK68oISEh6LNFR0crIiLCW64k6d/%2B7d9UVFQU1Nl27typCy%2B80Oebkp49e%2BrAgQNBnetU55OlVatWioiI8NmvqalRWVlZk8h69OhRTZw4UQUFBVq3bp06d%2B7s3QvWXAcOHNDHH3%2BsP/7xj96vJwcOHNCDDz6o22%2B/XdLZs8XGxkqS963C7/890NmaAgoWzqpHjx5yuVw%2BB3jn5uaqd%2B/eCg1t2k%2BfrKwsPfvss3r00Uc1cuRI77rb7dann37qfXlb%2Bi6T2%2B327ufm5nr3KioqtGvXLu9%2BoD399NN65ZVXtHnzZm3evFmDBw/W4MGDtXnzZrndbn388cfe80YZY7R9%2B/YzZisqKlJRUVGTyeZ2u1VVVaUvv/zSu7Zv3z516NAhqLPFxMTo66%2B/9nklYN%2B%2BferYsWNQ5zrV%2BXxuhYaGqnfv3j77eXl5crlc6t69u/9CnEZdXZ0mT56sb775Rk8//bS6du3qsx%2BsuWJjY/W3v/3N%2B7Vk8%2BbNiomJUXp6uubPny/p7M%2B/2NhYxcXF%2Bezn5uYqLi7uJ3/8lSTOg4Vzmzlzphk5cqTJz883b7zxhunbt695/fXXAz3WWe3Zs8f06NHDPPbYY6a4uNjno6amxowYMcLcfffd5osvvjArVqww8fHx3nP1FBYWmt69e5sVK1Z4z2lz9dVXN8nzYBljzLRp07w/Rn3kyBGTkpJi5s6dawoKCszcuXPNgAEDvD82v337dtOrVy/z3//9395z2txxxx2BHL%2BeSZMmmfHjx5vPPvvMvPvuuyYlJcWsW7cuqLOVl5ebAQMGmKlTp5p9%2B/aZt956yyQnJ5uNGzcGdS5jjM%2BP/J/v51ZOTo7p27eveeONN0x%2Bfr4ZOXKkmTt3bsBzZWdnm%2B7du5u3337b52tJaWlp0OU6NdupUlNTfU67cK7n34oVK8zAgQPN1q1bzdatW83AgQPNU0891egZggEFC%2Bd0/Phxc%2B%2B995r4%2BHgzcOBAs2bNmkCPdE4rVqww3bp1O%2B2HMcZ89dVX5je/%2BY255JJLzMiRI817773nc/u///3v5sorrzR9%2BvQxN998c5M4l9KZfL9gGfPdiQHHjBljevfuba699lrz6aef%2Blx/06ZN5vLLLzfx8fEmLS3NHD582N8jn1V5ebmZOnWqiY%2BPN5deeql5/PHHvf9QBXO2goICc8stt5i%2BffuaIUOGmDVr1jgi16n/WJ/v59aKFSvMpZdeahITE8306dNNZWWlX3Kc6vu5brvtttN%2BLfn%2Bua%2BCJZcxP6xgGXP2519NTY15%2BOGHTVJSkunfv7/JzMxsst%2BM%2BluIMf//ujQAAACsaNoH0QAAAAQhChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALPs/0%2BB3G%2BwKTQEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2657375089200015643”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>148</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1266)</td> <td class=”number”>2561</td> <td class=”number”>79.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>194</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2657375089200015643”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.66666666666666</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FFRPTH_09_14”><s>PCH_FFRPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FFR_09_14”><code>PCH_FFR_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9901</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FFR_09_14”>PCH_FFR_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>855</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>124</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>5.6048</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>17.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1951600146950039389”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1951600146950039389,#minihistogram-1951600146950039389”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1951600146950039389”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1951600146950039389”

aria-controls=”quantiles-1951600146950039389” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1951600146950039389” aria-controls=”histogram-1951600146950039389”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1951600146950039389” aria-controls=”common-1951600146950039389”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1951600146950039389” aria-controls=”extreme-1951600146950039389”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1951600146950039389”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-40</td>

</tr> <tr>

<th>Q1</th> <td>-7.1429</td>

</tr> <tr>

<th>Median</th> <td>1.3992</td>

</tr> <tr>

<th>Q3</th> <td>16.667</td>

</tr> <tr>

<th>95-th percentile</th> <td>57.143</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>23.81</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>34.369</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.1321</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.021</td>

</tr> <tr>

<th>Mean</th> <td>5.6048</td>

</tr> <tr>

<th>MAD</th> <td>21.221</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.319</td>

</tr> <tr>

<th>Sum</th> <td>17296</td>

</tr> <tr>

<th>Variance</th> <td>1181.2</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1951600146950039389”>

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1544QV9%2BumnioiI0D333OM6XVheXq7o6Gi3xzRv3lwHDhyQw%2BGQJLftLVq0kCQdOHCgwePOpLy83LWvk%2Bz20EY/vrECAwPc/rQKq%2BbyNLvdd98vK68Z2fyPVXNJ1s1mxVx%2BW7DOZPfu3bLZbIqLi9PIkSO1efNmPfbYYwoLC1Pv3r1VXV2toKAgt8cEBQXJ6XSqurradfuX26SfP0zfWHl5ecrNzXUby8jIUGZm5oXGOqvw8BCP7NfXrJrLUyIjm/p6CpZeM7L5H6vmkqybzUq5LFewBg0apJ49eyoiIkKSFB8fr%2B%2B%2B%2B06rVq1S7969FRwc3KAsOZ1OhYSEuJWp4OBg139Lcn2GqzGGDRum9PR0tzG7PVSVlccvONfpBAYGKDw8REeOVKmuzjo/6WjVXJ5m9uvrfFh5zcjmf6yaS7JuNk/m8tU/Pi1XsGw2m6tcnRQXF6cNGzZIkmJiYlRRUeG2vaKiQlFRUYqJiZEkORwOtWrVyvXfkhQVFdXoOURHRzc4HehwHFVtrWfeDHV19R7bty9ZNZenXArfKyuvGdn8j1VzSdbNZqVc1jnZ%2BX8WLFigMWPGuI3t3LlTcXFxkqTExETl5%2Be7tu3fv1/79%2B9XYmKiYmJiFBsb67Y9Pz9fsbGxpn9%2BCgAAWJfljmD17NlTS5cu1fLly9W7d299/vnneuedd7RixQpJ0vDhw3X33XcrKSlJCQkJeuKJJ9SjRw9dffXVru3z5s3TlVdeKUl6%2BumnNXbsWJ/lAQAA/sdyBatjx45asGCBFi5cqAULFuiqq67S008/reTkZElScnKyZs2apYULF%2Brw4cPq1q2bZs%2Be7Xr8vffeqx9//FHjx49XYGCg7rzzzgZHxAAAAM7GZhiG4etJ/Bo4HEdN36fdHqDIyKaqrDxumXPW0qWT67b5//HZc1%2BIDx7q5rPnvlTWzBPI5n%2BsmkuybjZP5oqKutzU/TWW5T6DBQAA4GsULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGR%2BX7CcTqcGDBigjRs3usYKCgr0P//zP0pOTtatt96q1atXuz3m97//vdq1a%2Bf2tWvXLkmSYRiaN2%2Beunbtqs6dO2vu3Lmqr6/3aiYAAODf7N5%2BwqFDh2rIkCHq37%2B/Lr/88ovaV01NjbKyslRUVOQaczgcuv/%2B%2BzV8%2BHA99dRT2r59u6ZMmaKoqCj16NFDdXV1%2Bu6777Ry5Uq1bt3a9bjIyEhJ0ssvv6x169YpNzdXtbW1mjhxopo3b6577733ouYKAAB%2BPbx%2BBKtr16564YUX1L17dz388MP6/PPPZRjGee%2BnuLhYd911l3744Qe38fXr16tFixZ6%2BOGH1bp1a/Xv31%2BDBg3Se%2B%2B9J0nas2ePTpw4oY4dOyoqKsr1Zbf/3DVXrFihzMxMpaamqmvXrnrkkUf0%2BuuvX3xwAADwq%2BH1gpWVlaV///vfev755xUYGKgJEyaoR48eevbZZ/Xtt982ej%2BbNm1Sly5dlJeX5zaelpam7OzsBvc/duyYpJ%2BLWcuWLRUcHNzgPmVlZdq/f79uuukm11hKSor27t2r8vLyRs8NAAD8unn9FKEk2Ww2devWTd26dVNVVZVee%2B01Pf/881q6dKk6deqk0aNHq0%2BfPmfdx4gRI0473qpVK7Vq1cp1%2B8cff9Q//vEPTZgwQZJUUlKiyy67TOPGjdO2bdt07bXXatKkSerYsaMcDockKTo62vX4Fi1aSJIOHDjgNg4AAHAmPilYklReXq53331X7777rnbt2qVOnTrpjjvu0IEDB/TXv/5Vmzdv1rRp0y7qOaqrqzVhwgS1aNFCw4YNkyR9%2B%2B23Onz4sIYOHarMzEy9%2BeabGj16tN5//31VV1dLkoKCglz7OPnfTqfzvLKdLGsn2e2hphe0wMAAtz%2Btwqq5PM1u9933y8prRjb/Y9VcknWzWTGX1wvW2rVrtXbtWm3cuFHNmjXToEGDtHDhQrcPnLds2VJPPPHERRWs48eP68EHH9R3332nv/3tbwoJCZEkzZ49W9XV1QoLC5MkzZgxQ1u2bNHatWv129/%2BVtLPZerkKcSTxerk4xsjLy9Pubm5bmMZGRnKzMy84DxnEx7e%2BLn5E6vm8pTIyKa%2BnoKl14xs/sequSTrZrNSLq8XrGnTpqlnz55atGiRbr75ZgUENGyrcXFxGjly5AU/x7Fjx3Tffffphx9%2B0KuvvupW3ux2u6tcST%2BfroyLi1NZWZliYmIk/fyTiCdPM548EhUVFdXo5x82bJjS09Pdxuz2UFVWHr/QSKcVGBig8PAQHTlSpbo661xKwqq5PM3s19f5sPKakc3/WDWXZN1snszlq398er1gffrpp4qMjNShQ4dc5Wrr1q3q0KGDAgMDJUmdOnVSp06dLmj/9fX1Gj9%2BvPbs2aPXXntNbdq0cdt%2B9913q0uXLho/frzr/t98843%2B8Ic/KCYmRrGxscrPz3cVrPz8fMXGxp7X6b3o6OgG93c4jqq21jNvhrq6eo/t25esmstTLoXvlZXXjGz%2Bx6q5JOtms1IurxesY8eOafjw4frd736nSZMmSZIeeOABtWjRQi%2B%2B%2BKJatmx5Uft/6623tHHjRi1evFjh4eGuI1CXXXaZIiIilJ6erkWLFql9%2B/a69tprtWLFCh09elR33HGHJGn48OGaN2%2BerrzySknS008/rbFjx17UnAAAwK%2BL1wvWk08%2BqWuuuUb33HOPa%2Bz999/X5MmTlZ2drYULF17U/j/88EPV19dr3LhxbuOdO3fWa6%2B9pjFjxqimpkZz5sxRRUWFEhMT9fLLL7tOG95777368ccfNX78eAUGBurOO%2B/UmDFjLmpOAADg18VmXMhVPi9Camqq3nzzTcXFxbmNFxUV6Q9/%2BIM2bdrkzel4jcNx1PR92u0BioxsqsrK45Y5pCpdOrlum/8fnz33hfjgoW4%2Be%2B5LZc08gWz%2Bx6q5JOtm82SuqKiL%2B60xF8rrPw9pt9t15MiRBuNVVVUXdEV3AACAS43XC9bNN9%2BsOXPmuP2Km9LSUmVnZystLc3b0wEAADCd1z%2BDNXnyZN1zzz269dZbFR4eLkk6cuSIOnTooClTpnh7OgAAAKbzesFq3ry53n77bX3xxRcqKiqS3W7Xddddp9/85jey2Wzeng4AAIDpfPKrcgIDA5WWlsYpQQAAYEleL1gOh0Pz58/Xli1bdOLEiQYfbP/Xv/7l7SkBAACYyusF67HHHtO2bdvUv39/XX65b350EgAAwJO8XrA2bNigZcuWKTU11dtPDQAA4BVev0xDaGiomjdv7u2nBQAA8BqvF6zbb79dy5YtU11dnbefGgAAwCu8forw0KFDWrdunT7%2B%2BGNdffXVCgoKctu%2BYsUKb08JAADAVD65TMOAAQN88bQAAABe4fWClZ2d7e2nBAAA8CqvfwZLksrLy5Wbm6usrCz9%2BOOP%2Buc//6ndu3f7YioAAACm83rB%2Bv777zVw4EC9/fbb%2BvDDD/XTTz/p/fff15AhQ1RYWOjt6QAAAJjO6wXrqaeeUq9evbR%2B/XpddtllkqRnnnlG6enpmjdvnrenAwAAYDqvF6wtW7bonnvucfvFzna7XQ8%2B%2BKB27Njh7ekAAACYzusFq76%2BXvX19Q3Gjx8/rsDAQG9PBwAAwHReL1jdu3fXkiVL3ErWoUOHlJOTo65du3p7OgAAAKbzesF69NFHtW3bNnXv3l01NTX605/%2BpJ49e2rPnj2aPHmyt6cDAABgOq9fBysmJkbvvPOO1q1bp6%2B//lr19fUaPny4br/9doWFhXl7OgAAAKbzyZXcQ0JCNHToUF88NQAAgMd5vWCNGjXqrNv5XYQAAMDfeb1gXXXVVW63a2tr9f3332vXrl0aPXq0t6cDAABgukvmdxEuWrRIBw4c8PJsAAAAzOeT30V4Orfffrs%2B%2BOADX08DAADgol0yBeu///3vBV1o1Ol0asCAAdq4caNrrLS0VGPGjFFSUpL69eunzz//3O0xX3zxhQYMGKDExESNGjVKpaWlbttfeeUVpaWlKTk5WVOnTlVVVdWFhQIAAL9Kl8SH3I8dO6ZvvvlGI0aMOK991dTUKCsrS0VFRa4xwzCUkZGhtm3bas2aNVq/fr3Gjx%2Bv999/X7Gxsdq3b58yMjI0YcIEpaWladGiRXrwwQf17rvvymaz6cMPP1Rubq5ycnLUvHlzTZkyRTk5OZo%2BffpFZwcAAL8OXi9YsbGxbr%2BHUJIuu%2BwyjRw5Ur///e8bvZ/i4mJlZWXJMAy38Q0bNqi0tFRvvPGGQkND1aZNG3355Zdas2aNJkyYoNWrV%2BvGG2/U2LFjJf38mbBu3bpp06ZN6tKli1asWKHRo0erZ8%2BekqSZM2fq3nvv1cSJExUSEnKR6QEAwK%2BB1wvWU089Zcp%2BThaiv/zlL0pKSnKNFxYW6oYbblBoaKhrLCUlRQUFBa7tqamprm0hISHq0KGDCgoKlJqaqq%2B%2B%2Bkrjx493bU9KStKJEye0c%2BdOJScnmzJ3AABgbV4vWJs3b270fW%2B66aYzbjvT6USHw6Ho6Gi3sebNm7t%2BQvFs248cOaKamhq37Xa7XREREfyEIwAAaDSvF6y7777bdYrwl6f3Th2z2Wz6%2Buuvz3v/VVVVCgoKchsLCgqS0%2Bk85/bq6mrX7TM9vjHKy8vlcDjcxuz20AbF7mIFBga4/WkVVs3laXa7775fVl4zsvkfq%2BaSrJvNirm8XrBeeOEFzZkzRxMnTlTnzp0VFBSkr776SrNmzdIdd9yhfv36XdT%2Bg4ODdejQIbcxp9OpJk2auLafWpacTqfCw8MVHBzsun3q9vP5/FVeXp5yc3PdxjIyMpSZmdnofZyP8HBrfjbMqrk8JTKyqa%2BnYOk1I5v/sWouybrZrJTLJxcanT59um6%2B%2BWbXWNeuXTVr1ixNmjRJ999//0XtPyYmRsXFxW5jFRUVrqNHMTExqqioaLC9ffv2ioiIUHBwsCoqKtSmTRtJP19p/tChQ4qKimr0HIYNG6b09HS3Mbs9VJWVxy8k0hkFBgYoPDxER45Uqa6u3tR9%2B5JVc3ma2a%2Bv82HlNSOb/7FqLsm62TyZy1f/%2BPR6wSovL2/w63IkKSwsTJWVlRe9/8TERC1dulTV1dWuo1b5%2BflKSUlxbc/Pz3fdv6qqSjt27ND48eMVEBCghIQE5efnq0uXLpKkgoIC2e12xcfHN3oO0dHRDU4HOhxHVVvrmTdDXV29x/btS1bN5SmXwvfKymtGNv9j1VySdbNZKZfXT3YmJSXpmWee0bFjx1xjhw4dUk5Ojn7zm99c9P47d%2B6sli1basqUKSoqKtLSpUu1detW3XnnnZKkIUOGaMuWLVq6dKmKioo0ZcoUtWrVylWoRowYoeXLl2v9%2BvXaunWrZsyYobvuuotLNAAAgEbz%2BhGsv/71rxo1apRuvvlmtW7dWoZh6LvvvlNUVJRWrFhx0fsPDAzU888/r2nTpmnw4MG65pprtGjRIsXGxkqSWrVqpeeee05PPvmkFi1apOTkZC1atMj1Ifv%2B/ftr7969mj59upxOp/r06aOJEyde9LwAAMCvh8049UqdXnD48GGtW7dOJSUlkqQbbrhB/fv3t/RRIofjqOn7tNsDFBnZVJWVxy1zSFW6dHLdNv8/PnvuC/HBQ9189tyXypp5Atn8j1VzSdbN5slcUVGXm7q/xvL6ESxJuuKKKzR06FDt2bNHV199taSfr%2BYOAABgBV7/DJZhGJo3b55uuukmDRgwQAcOHNDkyZM1bdo0nThxwtvTAQAAMJ3XC9Zrr72mtWvX6vHHH3dd0LNXr15av359g2tHAQAA%2BCOvF6y8vDxNnz5dgwcPdn2wvF%2B/fpozZ47ee%2B89b08HAADAdF4vWHv27FH79u0bjMfHxzf49TIAAAD%2ByOsF66qrrtJXX33VYPzTTz91feAdAADAn3n9pwjvvfdezZw5Uw6HQ4Zh6Msvv1ReXp5ee%2B01Pfroo96eDgAAgOm8XrCGDBmi2tpaLV68WNXV1Zo%2BfbqaNWumhx56SMOHD/f2dAAAAEzn9YK1bt069e3bV8OGDdPBgwdlGIaaN2/u7WkAAAB4jNc/gzVr1izXh9mbNWtGuQIAAJbj9YLVunVr7dq1y9tPCwAA4DUWkCBlAAAcLElEQVReP0UYHx%2BvRx55RMuWLVPr1q0VHBzstj07O9vbUwIAADCV1wvWt99%2Bq5SUFEniulcAAMCSvFKw5s6dq/Hjxys0NFSvvfaaN54SAADAZ7zyGayXX35ZVVVVbmMPPPCAysvLvfH0AAAAXuWVgmUYRoOxzZs3q6amxhtPDwAA4FVe/ylCAAAAq6NgAQAAmMxrBctms3nrqQAAAHzKa5dpmDNnjts1r06cOKGcnBw1bdrU7X5cBwsAAPg7rxSsm266qcE1r5KTk1VZWanKykpvTAEAAMBrvFKwuPYVAAD4NeFD7gAAACajYAEAAJiMggUAAGAyChYAAIDJLFmw/v73v6tdu3YNvuLj4yVJf/rTnxps%2B/e//%2B16/CuvvKK0tDQlJydr6tSpDX6PIgAAwNl47TpY3tSvXz%2BlpaW5btfW1mr06NHq0aOHJKmkpEQ5OTn6zW9%2B47rPFVdcIUn68MMPlZubq5ycHDVv3lxTpkxRTk6Opk%2Bf7tUMAADAf1nyCFaTJk0UFRXl%2Bnr33XdlGIYeeeQROZ1O7dmzRwkJCW73CQoKkiStWLFCo0ePVs%2BePdWxY0fNnDlTa9as4SgWAABoNEsWrF86dOiQXnzxRWVlZSkoKEi7d%2B%2BWzWbT1Vdf3eC%2BdXV1%2Buqrr5SamuoaS0pK0okTJ7Rz505vThsAAPgxS54i/KVVq1YpOjpaffv2lSTt3r1bYWFhmjRpkjZt2qQrr7xSEyZM0C233KIjR46opqZG0dHRrsfb7XZFRETowIEDjX7O8vLyBleut9tD3fZrhsDAALc/rcKquTzNbvfd98vKa0Y2/2PVXJJ1s1kxl6ULlmEYWr16te677z7X2O7du1VdXa3u3bvrgQce0EcffaQ//elPysvLU4sWLSTJdbrwpKCgIDmdzkY/b15ennJzc93GMjIylJmZeRFpziw8PMQj%2B/U1q%2BbylMjIpue%2Bk4dZec3I5n%2BsmkuybjYr5bJ0wfrqq69UVlam/v37u8YefPBB3X333a4PtcfHx2v79u1688039Ze//EWSGpQpp9OpkJDGL/qwYcOUnp7uNma3h6qy8viFRjmtwMAAhYeH6MiRKtXV1Zu6b1%2Byai5PM/v1dT6svGZk8z9WzSVZN5snc/nqH5%2BWLlifffaZUlNTXWVKkgICAtxuS1JcXJyKi4sVERGh4OBgVVRUqE2bNpJ%2B/gnEQ4cOKSoqqtHPGx0d3eB0oMNxVLW1nnkz1NXVe2zfvmTVXJ5yKXyvrLxmZPM/Vs0lWTeblXJZ52TnaWzdulWdOnVyG3v00Uc1ZcoUt7GdO3cqLi5OAQEBSkhIUH5%2BvmtbQUGB7Ha76xpaAAAA52LpglVUVKTrrrvObSw9PV3vvfee3nnnHX3//ffKzc1Vfn6%2BRo4cKUkaMWKEli9frvXr12vr1q2aMWOG7rrrrvM6RQgAAH7dLH2KsKKiQuHh4W5jffr00eOPP67Fixdr3759uv7667Vs2TK1atVKktS/f3/t3btX06dPl9PpVJ8%2BfTRx4kRfTB8AAPgpSxesrVu3nnZ86NChGjp06Bkf98ADD%2BiBBx7w1LQAAIDFWfoUIQAAgC9QsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNZtmB99NFHateundtXZmamJGnHjh0aOnSoEhMTNWTIEG3bts3tsevWrVOvXr2UmJiojIwMHTx40BcRAACAn7L7egKeUlxcrJ49e2r27NmuseDgYP3000964IEHNHDgQD311FNatWqVxo0bp48%2B%2BkihoaHaunWrpk2bppkzZyo%2BPl5PPPGEpkyZoiVLlvgwDXBut83/j6%2Bn0GgfPNTN11MAAI%2By7BGskpIStW3bVlFRUa6v8PBwvf/%2B%2BwoODtakSZPUpk0bTZs2TU2bNtU///lPSdLKlSt12223adCgQYqPj9fcuXP1ySefqLS01MeJAACAv7B0wWrdunWD8cLCQqWkpMhms0mSbDabOnXqpIKCAtf21NRU1/1btmyp2NhYFRYWemXeAADA/1myYBmGoW%2B//Vaff/65br31VvXq1Uvz5s2T0%2BmUw%2BFQdHS02/2bN2%2BuAwcOSJLKy8vPuh0AAOBcLPkZrH379qmqqkpBQUGaP3%2B%2B9uzZozlz5qi6uto1/ktBQUFyOp2SpOrq6rNub4zy8nI5HA63Mbs9tEFxu1iBgQFuf1qFVXPh/7Pb/Wdtrfx6tGo2q%2BaSrJvNirksWbCuuuoqbdy4UVdccYVsNpvat2%2Bv%2Bvp6TZw4UZ07d25QlpxOp5o0aSLp5w/Cn257SEhIo58/Ly9Pubm5bmMZGRmun2I0W3h44%2BfmT6yaC1JkZFNfT%2BG8Wfn1aNVsVs0lWTeblXJZsmBJUkREhNvtNm3aqKamRlFRUaqoqHDbVlFR4Tq6FBMTc9rtUVFRjX7uYcOGKT093W3Mbg9VZeXx84lwToGBAQoPD9GRI1Wqq6s3dd%2B%2BZNVc%2BP/Mfi94kpVfj1bNZtVcknWzeTKXr/5BZ8mC9dlnn%2BmRRx7Rxx9/7Dry9PXXXysiIkIpKSl68cUXZRiGbDabDMPQli1b9Mc//lGSlJiYqPz8fA0ePFiStH//fu3fv1%2BJiYmNfv7o6OgGpwMdjqOqrfXMm6Gurt5j%2B/Ylq%2BaC/HJdrfx6tGo2q%2BaSrJvNSrmsc7LzF5KTkxUcHKy//vWv2r17tz755BPNnTtX9913n/r27asjR47oiSeeUHFxsZ544glVVVXptttukyQNHz5ca9eu1erVq7Vz505NmjRJPXr00NVXX%2B3jVAAAwF9YsmCFhYVp%2BfLlOnjwoIYMGaJp06Zp2LBhuu%2B%2B%2BxQWFqYlS5a4jlIVFhZq6dKlCg0NlfRzOZs1a5YWLVqk4cOH64orrlB2draPEwEAAH9iyVOEknT99dfr5ZdfPu22jh076u233z7jYwcPHuw6RQgAAHC%2BLHkECwAAwJcoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDK7ryeAX4/b5v/H11MAAMArOIIFAABgMssWrLKyMmVmZqpz585KS0tTdna2ampqJElz5sxRu3bt3L5Wrlzpeuy6devUq1cvJSYmKiMjQwcPHvRVDAAA4IcseYrQMAxlZmYqPDxcr7/%2Bug4fPqypU6cqICBAkydPVklJibKysnTHHXe4HhMWFiZJ2rp1q6ZNm6aZM2cqPj5eTzzxhKZMmaIlS5b4Kg4AAPAzljyCtXv3bhUUFCg7O1vXX3%2B9UlNTlZmZqXXr1kmSSkpKdMMNNygqKsr1FRISIklauXKlbrvtNg0aNEjx8fGaO3euPvnkE5WWlvoyEgAA8COWLFhRUVFatmyZWrRo4TZ%2B7NgxHTt2TGVlZWrduvVpH1tYWKjU1FTX7ZYtWyo2NlaFhYWenDIAALAQS54iDA8PV1pamut2fX29Vq5cqa5du6qkpEQ2m00vvPCCPv30U0VEROiee%2B5xnS4sLy9XdHS02/6aN2%2BuAwcONPr5y8vL5XA43Mbs9tAG%2B71YgYEBbn8C/sJu95/XrJXfZ1bNZtVcknWzWTGXJQvWqXJycrRjxw699dZb2r59u2w2m%2BLi4jRy5Eht3rxZjz32mMLCwtS7d29VV1crKCjI7fFBQUFyOp2Nfr68vDzl5ua6jWVkZCgzM9OUPKcKDw/xyH4BT4mMbOrrKZw3K7/PrJrNqrkk62azUi7LF6ycnBy9%2BuqrevbZZ9W2bVtdf/316tmzpyIiIiRJ8fHx%2Bu6777Rq1Sr17t1bwcHBDcqU0%2Bl0fUarMYYNG6b09HS3Mbs9VJWVxy8%2B0C8EBgYoPDxER45Uqa6u3tR9A55k9nvBk6z8PrNqNqvmkqybzZO5fPUPOksXrNmzZ2vVqlXKycnRrbfeKkmy2WyucnVSXFycNmzYIEmKiYlRRUWF2/aKigpFRUU1%2Bnmjo6MbnA50OI6qttYzb4a6unqP7RvwBH98vVr5fWbVbFbNJVk3m5VyWedk5ylyc3P1xhtv6JlnnlH//v1d4wsWLNCYMWPc7rtz507FxcVJkhITE5Wfn%2B/atn//fu3fv1%2BJiYlemTcAAPB/lixYJSUlev7553X//fcrJSVFDofD9dWzZ09t3rxZy5cv1w8//KC//e1veueddzR27FhJ0vDhw7V27VqtXr1aO3fu1KRJk9SjRw9dffXVPk4FAAD8hSVPEf7rX/9SXV2dFi9erMWLF7tt%2B%2Babb7RgwQItXLhQCxYs0FVXXaWnn35aycnJkqTk5GTNmjVLCxcu1OHDh9WtWzfNnj3bFzEAAICfshmGYfh6Er8GDsdR0/dptwcoMrKpKiuP%2B8U5a37ZM0764KFuvp5Co/nb%2B%2Bx8WDWbVXNJ1s3myVxRUZebur/GsuQpQgAAAF%2BiYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMruvJ4CLkzrtn76eAmB5/vY%2B%2B%2BChbr6eAvCrxxEsAAAAk1GwAAAATEbBAgAAMBmfwQLgdbfN/4%2BvpwAAHsURLAAAAJNRsAAAAExGwTqNmpoaTZ06VampqerevbteeuklX08JAAD4ET6DdRpz587Vtm3b9Oqrr2rfvn2aPHmyYmNj1bdvX19PDQAA%2BAEK1il%2B%2BuknrV69Wi%2B%2B%2BKI6dOigDh06qKioSK%2B//joFCwAANAoF6xQ7d%2B5UbW2tkpOTXWMpKSl64YUXVF9fr4AAzqoCgFn87SdKuUo%2BGouCdQqHw6HIyEgFBQW5xlq0aKGamhodOnRIzZo1O%2Bc%2BysvL5XA43Mbs9lBFR0ebOtfAQMoegIb8rbT4E7vdt3/vnvx732p//1sxFwXrFFVVVW7lSpLrttPpbNQ%2B8vLylJub6zY2fvx4TZgwwZxJ/p/y8nKNvrJIw4YNM728%2BVJ5ebny8vIsl0uybjar5pLI5o%2Bsmkv6Odurry6zXDYr5rJOVTRJcHBwgyJ18naTJk0atY9hw4bp73//u9vXsGHDTJ%2Brw%2BFQbm5ug6Nl/s6quSTrZrNqLols/siquSTrZrNiLo5gnSImJkaVlZWqra2V3f7zt8fhcKhJkyYKDw9v1D6io6Mt08ABAMD54wjWKdq3by%2B73a6CggLXWH5%2BvhISEviAOwAAaBQawylCQkI0aNAgzZgxQ1u3btX69ev10ksvadSoUb6eGgAA8BOBM2bMmOHrSVxqunbtqh07dujpp5/Wl19%2BqT/%2B8Y8aMmSIr6d1Wk2bNlXnzp3VtGlTX0/FVFbNJVk3m1VzSWTzR1bNJVk3m9Vy2QzDMHw9CQAAACvhFCEAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKlh8xDENjx47V3//%2Bd7fxyspKTZgwQcnJyUpPT9fatWvdtu/YsUNDhw5VYmKihgwZom3btnlz2o22Y8cOtWvXzu1r8ODBru2lpaUaM2aMkpKS1K9fP33%2B%2Bec%2BnO35q6mp0dSpU5Wamqru3bvrpZde8vWULshHH33UYJ0yMzMl%2Bc9r7VROp1MDBgzQxo0bXWPner198cUXGjBggBITEzVq1CiVlpZ6e9qNcrpsc%2BbMabCGK1eudG1ft26devXqpcTERGVkZOjgwYO%2BmPpplZWVKTMzU507d1ZaWpqys7NVU1Mjyf/X7GzZ/HnNvv/%2Be917771KTk5Wjx49tGzZMtc2f1%2BzszLgF%2Brq6oxZs2YZbdu2NdasWeO2bdy4ccbo0aONb775xnjzzTeNG2%2B80SgsLDQMwzCOHz9udOvWzXjqqaeM4uJiY/bs2cZvf/tb4/jx476IcVZr1641br/9dqO8vNz1dfDgQcMwDKO%2Bvt4YOHCgkZWVZRQXFxsvvPCCkZiYaOzdu9fHs268WbNmGQMHDjS2bdtm/O///q%2BRnJxsfPDBB76e1nl7/vnnjXHjxrmt0%2BHDh/3qtfZL1dXVRkZGhtG2bVtjw4YNhmGc%2B/W2d%2B9eIykpyVi%2BfLmxa9cu489//rMxYMAAo76%2B3pdRGjhdNsMwjDFjxhhLlixxW8OffvrJMAzDKCwsNDp27Gi8/fbbxtdff22MHDnSeOCBB3wVwU19fb1x1113Gffdd5%2Bxa9cuY/PmzUbv3r2Np556yu/X7GzZDMN/16yurs7o06ePkZWVZXz77bfGxx9/bHTq1Ml49913/X7NzoWC5QcOHDhgjBw50ujRo4eRmprqVrC%2B//57o23btkZpaalrbOrUqcbkyZMNwzCM1atXG%2Bnp6a4XZH19vdG7d%2B8GJe1S8MwzzxgPP/zwabd98cUXRlJSktv/rEePHm0sXLjQW9O7KMePHzcSEhLc/ie3aNEiY%2BTIkT6c1YXJysoynn766Qbj/vRaO6moqMj4/e9/bwwcONCthJzr9TZ//ny3tfvpp5%2BM5ORkt/X1tTNlMwzDSEtLMz777LPTPm7ixImuvz8MwzD27dtntGvXzvjhhx88PudzKS4uNtq2bWs4HA7X2HvvvWd0797d79fsbNkMw3/XrKyszPjzn/9sHD161DWWkZFhPP74436/ZufCKUI/sH37drVs2VJr1qzR5Zdf7ratsLBQLVu2VKtWrVxjKSkp%2Bu9//%2BvanpKSIpvNJkmy2Wzq1KmTCgoKvBegkUpKStS6devTbissLNQNN9yg0NBQ11hKSsolmeN0du7cqdraWiUnJ7vGUlJSVFhYqPr6eh/O7PydaZ386bV20qZNm9SlSxfl5eW5jZ/r9VZYWKjU1FTXtpCQEHXo0OGSynqmbMeOHVNZWdlZ32u/zNayZUvFxsaqsLDQk9NtlKioKC1btkwtWrRwGz927Jjfr9nZsvnzmkVHR2v%2B/PkKCwuTYRjKz8/X5s2b1blzZ79fs3Ox%2B3oCOLf09HSlp6efdpvD4VB0dLTbWPPmzVVWVubaft111zXYXlRU5JnJXoSSkhLV19dr4MCBOnr0qG6%2B%2BWZNmjRJYWFhZ8x54MABH832/DgcDkVGRiooKMg11qJFC9XU1OjQoUNq1qyZD2fXeIZh6Ntvv9Xnn3%2BuJUuWqK6uTn379lVmZqZfvdZOGjFixGnHz/V684fX45mylZSUyGaz6YUXXtCnn36qiIgI3XPPPbrjjjskSeXl5ZdstvDwcKWlpblu19fXa%2BXKleratavfr9nZsvnzmv1Senq69u3bp549e%2BrWW2/Vk08%2B6ddrdi4UrEtAdXW1qxCdKioqyq3dn6qqqsrtf9qSFBQUJKfT2ajt3nS2nM2aNVNpaalatWqlJ598UkeOHFF2drYmTpyoxYsXX1I5LsSZ5i/JbzJI0r59%2B1xZ5s%2Bfrz179mjOnDmqrq72%2BzX6JX96X52v3bt3y2azKS4uTiNHjtTmzZv12GOPKSwsTL1791Z1dbXfZMvJydGOHTv01ltv6ZVXXrHUmv0y2/bt2y2xZgsXLlRFRYVmzJih7OxsS7/PJArWJaGwsFCjRo067bZFixapV69eZ3xscHBwgxeb0%2BlUkyZNGrXdm86Vc8OGDQoODtZll10mSXrqqac0ZMgQlZWVKTg4WIcOHXJ7jK9yXIgzrYMkv8kgSVdddZU2btyoK664QjabTe3bt1d9fb0mTpyozp07XzKvtYt1rtfbmdYzPDzca3O8UIMGDVLPnj0VEREhSYqPj9d3332nVatWqXfv3mfMFhIS4ovpnlFOTo5effVVPfvss2rbtq2l1uzUbNdff70l1iwhIUHSzz9R/cgjj2jIkCGqqqpyu4%2B/rtnpULAuAV26dNE333xzQY%2BNiYlRRUWF21hFRYWioqLOuv3Uw67ecL4527RpI%2BnnH12OiYlRcXGx23Zf5bgQMTExqqysVG1trez2n992DodDTZo08Zu/LE46%2BZf8SW3atFFNTY2ioqIumdfaxTrX6%2B1M76v27dt7bY4XymazNVjDuLg4bdiwQdK5/065FMyePVurVq1STk6Obr31VknWWbPTZfPnNauoqFBBQYHbgYLrrrtOJ06cUFRUlHbv3t3g/v62ZmfCh9z9XFJSkvbu3et2Tjo/P19JSUmSpMTERP33v/%2BVYRiSfv4MzZYtW5SYmOiT%2BZ5JcXGxkpOT3a5x8vXXX8tut%2Buaa65RYmKitm/frurqatf2/Pz8Sy7HmbRv3152u93tw5n5%2BflKSEhQQID/vA0/%2B%2BwzdenSxe1fnV9//bUiIiJcP1xxqb/WGuNcr7fExETl5%2Be7tlVVVWnHjh1%2BkXXBggUaM2aM29jOnTsVFxcnqWG2/fv3a//%2B/ZdMttzcXL3xxht65pln1L9/f9e4FdbsTNn8ec327Nmj8ePHu308ZNu2bWrWrJlSUlL8fs3Oypc/wojz17NnzwY/9j527Fhj5MiRxtdff228%2BeabRkJCgus6WEePHjW6du1qzJ492ygqKjJmz55tdOvW7ZK7NlFdXZ1x%2B%2B23u67ntXnzZqNfv37G448/bhiGYdTW1hr9%2BvUzHnroIWPXrl3GkiVLjKSkJL%2B6DtZjjz1m9O/f3ygsLDQ%2B%2Bugjo1OnTsaHH37o62mdl6NHjxppaWnGww8/bJSUlBgff/yx0b17d2Pp0qV%2B81o7k19eyuBcr7fS0lIjISHBWLJkiev6PAMHDrxkr8/zy2yFhYXGDTfcYCxbtsz4/vvvjddff9248cYbjS1bthiGYRhbtmwxOnToYLz55puuayqNGzfOl9N3KS4uNtq3b288%2B%2ByzbteDKi8v9/s1O1s2f16z2tpaY/DgwcbYsWONoqIi4%2BOPPzZ%2B%2B9vfGq%2B88orfr9m5ULD8zOkKVkVFhTFu3DgjISHBSE9PN9577z237YWFhcagQYOMhIQE48477zS2b9/uzSk32r59%2B4yMjAwjNTXV6Ny5szF79myjpqbGtf27774z/vCHPxg33nij0b9/f%2BM///mPD2d7/n766Sdj0qRJRlJSktG9e3fj5Zdf9vWULsiuXbuMMWPGGElJSUa3bt2M5557zvUXnr%2B81k7n1GtFnev19vHHHxt9%2BvQxOnbsaIwePfqSuObQmZya7aOPPjIGDhxoJCQkGH379m1Q9NesWWPccsstRlJSkpGRkeG64K%2BvLVmyxGjbtu1pvwzDv9fsXNn8dc0M4%2BdrOWZkZBidOnUyunXrZixevNj1d4Y/r9m52Azj/47nAwAAwBT%2B8%2BEPAAAAP0HBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATPb/AJ4JHOEPZas4AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1951600146950039389”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>547</td> <td class=”number”>17.0%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>71</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (844)</td> <td class=”number”>2030</td> <td class=”number”>63.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>124</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1951600146950039389”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>183.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FMRKTPTH_09_16”><s>PCH_FMRKTPTH_09_16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FMRKT_09_16”><code>PCH_FMRKT_09_16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99444</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FMRKT_09_16”>PCH_FMRKT_09_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>126</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>19.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>628</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>37.589</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1600</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2407243385513258423”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2407243385513258423,#minihistogram-2407243385513258423”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2407243385513258423”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2407243385513258423”

aria-controls=”quantiles-2407243385513258423” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2407243385513258423” aria-controls=”histogram-2407243385513258423”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2407243385513258423” aria-controls=”common-2407243385513258423”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2407243385513258423” aria-controls=”extreme-2407243385513258423”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2407243385513258423”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>200</td>

</tr> <tr>

<th>Maximum</th> <td>1600</td>

</tr> <tr>

<th>Range</th> <td>1700</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>99.005</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.6339</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.191</td>

</tr> <tr>

<th>Mean</th> <td>37.589</td>

</tr> <tr>

<th>MAD</th> <td>62.163</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.4185</td>

</tr> <tr>

<th>Sum</th> <td>97055</td>

</tr> <tr>

<th>Variance</th> <td>9801.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2407243385513258423”>

<img 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2B9e/fKz89PsbGxuu%2B%2B%2B7RlyxY9%2BeSTCgkJUd%2B%2BfVVVVaXAwECn5wQGBqqmpkZVVVWO%2Bz/fJp05mb6xcnJylJ2d7TSWnp6ujIyMi43lFcLDm7l7CpckNDTY3VNwK/KT35f5en6JNbhQXlewBg0apF69eiksLEySFBcXp3379mnVqlXq27evgoKCGpSlmpoaBQcHO5WpoKAgx58lOc7haoy0tDT17t3bacxma6ry8pMXncsbeGr%2BgAB/hYYG69ixStXV%2Bd5vlJKf/OT33fyS56%2BBu36497qC5efn5yhXZ8XGxmrjxo2SpOjoaJWVlTltLysrU2RkpKKjoyVJdrtdrVu3dvxZkiIjIxs9h6ioqAaHA%2B3246qt9bwvTCt5ev66unqPz3ApyE9%2B8vtufok1uFBed0B1/vz5GjVqlNPY7t27FRsbK0lKSEhQXl6eY9vhw4d1%2BPBhJSQkKDo6WjExMU7b8/LyFBMT4/PnTwEAgMbzunewevXqpSVLlmjZsmXq27evNmzYoPfff18rVqyQJA0bNkwjRoxQYmKi4uPjNWvWLN16661q06aNY/vcuXN19dVXS5KeffZZPfjgg27LAwAAPI/XFazOnTtr/vz5euGFFzR//ny1atVKzz77rJKSkiRJSUlJmj59ul544QX99NNP6tGjh2bMmOF4/kMPPaQff/xRY8eOVUBAgO65554G74gBAAD8K37GGOPuSfgCu/34ZdnvHfP%2BcVn2ezl8/FgPd0/hoths/goPb6by8pM%2Bef4B%2BclPft/NL3n%2BGkRGXumW1/W6c7AAAADcjYIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMY8vWDU1NRo4cKA2bdrkGMvPz9fvfvc7JSUl6fbbb9fq1audnvPb3/5WHTp0cLrt2bNHkmSM0dy5c9W9e3d17dpVc%2BbMUX19vUszAQAAz2Zz9wQuRXV1tTIzM1VYWOgYs9vt%2BsMf/qBhw4bpmWee0c6dOzV58mRFRkbq1ltvVV1dnfbt26eVK1eqbdu2jueFh4dLkl599VXl5uYqOztbtbW1mjBhglq0aKGHHnrI1fEAAICHcvk7WEOHDtVbb72l48ePX9J%2BioqKdO%2B99%2BqHH35wGl%2B/fr0iIiI0fvx4tW3bVgMGDNCgQYP04YcfSpIOHDig06dPq3PnzoqMjHTcbLYzXXPFihXKyMhQSkqKunfvrscff1xvvPHGJc0VAAD4FpcXrO7du2vRokXq2bOnxo8frw0bNsgYc8H72bx5s7p166acnByn8dTUVM2ePbvB40%2BcOCHpTDFr2bKlgoKCGjympKREhw8f1o033ugYS05O1sGDB1VaWnrBcwQAAL7J5YcIMzMzNX78eH311Vd6//33NW7cOIWGhmrQoEEaNGiQrrnmmkbtZ/jw4eccb926tVq3bu24/%2BOPP%2Bqvf/2rxo0bJ0kqLi7WFVdcoTFjxmjHjh265pprNHHiRHXu3Fl2u12SFBUV5Xh%2BRESEJOnIkSNO4/9KaWmpY19n2WxNG/18b2WzeeYpfwEB/k4ffQ35yf/zj77G1/NLrMHFcss5WH5%2BfurRo4d69OihyspKvf7663rppZe0ZMkSdenSRSNHjtRtt912ya9TVVWlcePGKSIiQmlpaZKk7777Tj/99JOGDh2qjIwMvf322xo5cqQ%2B%2BugjVVVVSZICAwMd%2Bzj755qamka/bk5OjrKzs53G0tPTlZGRcamRPFp4eDN3T%2BGShIYGu3sKbkV%2B8vsyX88vsQYXym0nuZeWluqDDz7QBx98oD179qhLly4aPHiwjhw5ov/%2B7//Wli1bNHXq1Ive/8mTJ/Xoo49q3759evPNNxUcfOYLY8aMGaqqqlJISIgkadq0adq6davWrl2rm2%2B%2BWdKZMnX2EOLZYnX2%2BY2Rlpam3r17O43ZbE1VXn7yovN4A0/NHxDgr9DQYB07Vqm6Ot/7jVLyk5/8vptf8vw1cNcP9y4vWGvXrtXatWu1adMmNW/eXIMGDdILL7zg9Bt9LVu21KxZsy66YJ04cUIPP/ywfvjhBy1fvtxp3zabzVGupDPvpsXGxqqkpETR0dGSzvwm4tnDjGcP9UVGRjb69aOiohocDrTbj6u21vO%2BMK3k6fnr6uo9PsOlID/5ye%2B7%2BSXW4EK5/IDq1KlT1axZMy1YsECff/65MjMznQqQJMXGxuq%2B%2B%2B67qP3X19dr7NixOnDggF5//XW1b9/eafuIESOcDt/V19fr22%2B/VWxsrKKjoxUTE6O8vDzH9ry8PMXExPj8%2BVMAAKDxXP4O1hdffKHw8HBVVFTI3/9Mv9u2bZs6deqkgIAASVKXLl3UpUuXi9r/O%2B%2B8o02bNmnhwoUKDQ11vAN1xRVXKCwsTL1799aCBQvUsWNHXXPNNVqxYoWOHz%2BuwYMHS5KGDRumuXPn6uqrr5YkPfvss3rwwQcvNTYAAPAhLi9YJ06c0LBhw/Sb3/xGEydOlCSNHj1aERERevnll9WyZctL2v%2B6detUX1%2BvMWPGOI137dpVr7/%2BukaNGqXq6mrNnDlTZWVlSkhI0Kuvvuo4bPjQQw/pxx9/1NixYxUQEKB77rlHo0aNuqQ5AQAA3%2BJnLuYiVJfgj3/8o%2Brr6zVr1izHeU1Hjx7VpEmTFBwcrBdeeMGV03EZu/3SLqx6PnfM%2B8dl2e/l8PFjPdw9hYtis/krPLyZystP%2BuT5B%2BQnP/l9N7/k%2BWsQGXmlW17X5edg/d///Z%2BeeOIJp5PGmzdvrokTJ2rjxo2ung4AAIDlXF6wbDabjh071mC8srLyoq7oDgAA8Gvj8oL1n//5n5o5c6bT/yG4f/9%2BzZ49W6mpqa6eDgAAgOVcfpL7pEmT9MADD%2Bj2229XaGioJOnYsWPq1KmTJk%2Be7OrpAAAAWM7lBatFixZ677339NVXX6mwsFA2m03XXnutbrrpJvn5%2Bbl6OgAAAJZzy3%2BVExAQoNTUVA4JAgAAr%2BTygmW32zVv3jxt3bpVp0%2BfbnBi%2B9///ndXTwkAAMBSLi9YTz75pHbs2KEBAwboyivdc20KAACAy8nlBWvjxo1aunSpUlJSXP3SAAAALuHyyzQ0bdpULVq0cPXLAgAAuIzLC9Zdd92lpUuXqq6uztUvDQAA4BIuP0RYUVGh3NxcffbZZ2rTpo0CAwOdtq9YscLVUwIAALCUWy7TMHDgQHe8LAAAgEu4vGDNnj3b1S8JAADgUi4/B0uSSktLlZ2drczMTP3444/629/%2Bpr1797pjKgAAAJZzecH6/vvvdeedd%2Bq9997TunXrdOrUKX300UcaMmSICgoKXD0dAAAAy7m8YD3zzDPq06eP1q9fryuuuEKS9Nxzz6l3796aO3euq6cDAABgOZcXrK1bt%2BqBBx5w%2Bo%2BdbTabHn30Ue3atcvV0wEAALCcywtWfX296uvrG4yfPHlSAQEBrp4OAACA5VxesHr27KnFixc7layKigplZWWpe/furp4OAACA5VxesJ544gnt2LFDPXv2VHV1tR555BH16tVLBw4c0KRJk1w9HQAAAMu5/DpY0dHRev/995Wbm6tvvvlG9fX1GjZsmO666y6FhIS4ejoAAACWc8uV3IODgzV06FB3vDQAAMBl5/KCdf/99//L7fxfhAAAwNO5vGC1atXK6X5tba2%2B//577dmzRyNHjnT1dAAAACz3q/m/CBcsWKAjR464eDYAAADWc8v/RXgud911lz7%2B%2BOMLfl5NTY0GDhyoTZs2Ocb279%2BvUaNGKTExUf3799eGDRucnvPVV19p4MCBSkhI0P3336/9%2B/c7bX/ttdeUmpqqpKQkTZkyRZWVlRcXCgAA%2BKRfTcH65z//ecEXGq2urtb48eNVWFjoGDPGKD09XREREVqzZo3uuusujR07VocOHZIkHTp0SOnp6br77rv1zjvvqHnz5nr00UdljJEkrVu3TtnZ2Zo%2BfbqWL1%2BugoICZWVlWRcUAAB4vV/FSe4nTpzQt99%2Bq%2BHDhzd6P0VFRcrMzHQUo7M2btyo/fv366233lLTpk3Vrl07ff3111qzZo3GjRun1atX64YbbtCDDz4o6cwhyx49emjz5s3q1q2bVqxYoZEjR6pXr16SpKeffloPPfSQJkyYoODg4EtIDgAAfIXL38GKiYlRq1atnG433HCDZsyYcUEXGj1biHJycpzGCwoKdP3116tp06aOseTkZOXn5zu2p6SkOLYFBwerU6dOys/PV11dnbZv3%2B60PTExUadPn9bu3bsvNjIAAPAxLn8H65lnnrFkP%2Bd7t8tutysqKspprEWLFo4T6P/V9mPHjqm6utppu81mU1hY2AWdgF9aWiq73e40ZrM1bfC6vsZm%2B9Uckb4gAQH%2BTh99DfnJ//OPvsbX80uswcVyecHasmVLox974403XvD%2BKysrFRgY6DQWGBiompqaf7u9qqrKcf98z2%2BMnJwcZWdnO42lp6crIyOj0fvwRuHhzdw9hUsSGurbh4jJT35f5uv5JdbgQrm8YI0YMUJ%2Bfn6S5HT%2B1C/H/Pz89M0331zw/oOCglRRUeE0VlNToyZNmji2/7Is1dTUKDQ0VEFBQY77v9x%2BIedfpaWlqXfv3k5jNltTlZefbPQ%2BvJGn5g8I8FdoaLCOHatUXV39v3%2BClyE/%2Bcnvu/klz18Dd/1w7/KCtWjRIs2cOVMTJkxQ165dFRgYqO3bt2v69OkaPHiw%2Bvfvf0n7j46OVlFRkdNYWVmZ4/BcdHS0ysrKGmzv2LGjwsLCFBQUpLKyMrVr107SmQuhVlRUKDIystFziIqKanA40G4/rtpaz/vCtJKn56%2Brq/f4DJeC/OQnv%2B/ml1iDC%2BXyA6qzZ8/WU089pdtvv13h4eFq1qyZunfvrunTp2vVqlVOJ79fjISEBO3cudNxuE%2BS8vLylJCQ4Niel5fn2FZZWaldu3YpISFB/v7%2Bio%2BPd9qen58vm82muLi4i0wMAAB8jcsLVmlp6TnLU0hIiMrLyy95/127dlXLli01efJkFRYWasmSJdq2bZvuueceSdKQIUO0detWLVmyRIWFhZo8ebJat26tbt26STpz8vyyZcu0fv16bdu2TdOmTdO9997LJRoAAECjubxgJSYm6rnnntOJEyccYxUVFcrKytJNN910yfsPCAjQSy%2B9JLvdrrvvvlsffPCBFixYoJiYGElS69at9eKLL2rNmjW65557VFFRoQULFjjOARswYIDGjBmjp556Sg8%2B%2BKA6d%2B6sCRMmXPK8AACA7/Azv7xS52VWXFys%2B%2B%2B/X5WVlWrbtq2MMdq3b58iIyO1YsUKXX311a6cjsvY7ccvy37vmPePy7Lfy%2BHjx3q4ewoXxWbzV3h4M5WXn/TJ8w/IT37y%2B25%2ByfPXIDLySre8rstPcm/Xrp0%2B%2Bugj5ebmqri4WJL0%2B9//XgMGDOAwHAAA8AouL1iSdNVVV2no0KE6cOCA2rRpI0m64oor3DEVAAAAy7n8HCxjjObOnasbb7xRAwcO1JEjRzRp0iRNnTpVp0%2BfdvV0AAAALOfygvX6669r7dq1%2BvOf/%2By4YnqfPn20fv36Blc/BwAA8EQuL1g5OTl66qmndPfddzt%2Bc69///6aOXOmPvzwQ1dPBwAAwHIuL1gHDhxQx44dG4zHxcU1%2BA%2BSAQAAPJHLC1arVq20ffv2BuNffPGF44R3AAAAT%2Bby3yJ86KGH9PTTT8tut8sYo6%2B//lo5OTl6/fXX9cQTT7h6OgAAAJZzecEaMmSIamtrtXDhQlVVVempp55S8%2BbN9dhjj2nYsGGung4AAIDlXF6wcnNz1a9fP6Wlpeno0aMyxqhFixaungYAAMBl4/JzsKZPn%2B44mb158%2BaUKwAA4HVcXrDatm2rPXv2uPplAQAAXMblhwjj4uL0%2BOOPa%2BnSpWrbtq2CgoKcts%2BePdvVUwIAALCUywvWd999p%2BTkZEniulcAAMAruaRgzZkzR2PHjlXTpk31%2Buuvu%2BIlAQAA3MYl52C9%2BuqrqqysdBobPXq0SktLXfHyAAAALuWSgmWMaTC2ZcsWVVdXu%2BLlAQAAXMrlv0UIAADg7ShYAAAAFnNZwfLz83PVSwEAALiVyy7TMHPmTKdrXp0%2BfVpZWVlq1qyZ0%2BO4DhYAAPB0LilYN954Y4PcvYqEAAAceElEQVRrXiUlJam8vFzl5eWumAIAAIDLuKRgce0rAADgSzjJHQAAwGIULAAAAItRsAAAACzmlQXr3XffVYcOHRrc4uLiJEmPPPJIg22ffvqp4/mvvfaaUlNTlZSUpClTpjT4b34AAAD%2BFZddpsGV%2Bvfvr9TUVMf92tpajRw5Urfeeqskqbi4WFlZWbrpppscj7nqqqskSevWrVN2draysrLUokULTZ48WVlZWXrqqadcmgEAAHgur3wHq0mTJoqMjHTcPvjgAxlj9Pjjj6umpkYHDhxQfHy802MCAwMlSStWrNDIkSPVq1cvde7cWU8//bTWrFnDu1gAAKDRvLJg/VxFRYVefvllZWZmKjAwUHv37pWfn5/atGnT4LF1dXXavn27UlJSHGOJiYk6ffq0du/e7cppAwAAD%2BaVhwh/btWqVYqKilK/fv0kSXv37lVISIgmTpyozZs36%2Bqrr9a4ceN0yy236NixY6qurlZUVJTj%2BTabTWFhYTpy5EijX7O0tLTBhVVttqZO%2B/VFNptn9vmAAH%2Bnj76G/OT/%2BUdf4%2Bv5JdbgYnl1wTLGaPXq1Xr44YcdY3v37lVVVZV69uyp0aNH65NPPtEjjzyinJwcRURESJLjcOFZgYGBqqmpafTr5uTkKDs722ksPT1dGRkZl5DG84WHN/v3D/oVCw0NdvcU3Ir85Pdlvp5fYg0ulFcXrO3bt6ukpEQDBgxwjD366KMaMWKE46T2uLg47dy5U2%2B//bb%2B9Kc/SVKDMlVTU6Pg4MZ/YaWlpal3795OYzZbU5WXn7zYKF7BU/MHBPgrNDRYx45Vqq6u3t3TcTnyk5/8vptf8vw1cNcP915dsL788kulpKQ4ypQk%2Bfv7O92XpNjYWBUVFSksLExBQUEqKytTu3btJJ35DcSKigpFRkY2%2BnWjoqIaHA6024%2BrttbzvjCt5On56%2BrqPT7DpSA/%2Bcnvu/kl1uBCefUB1W3btqlLly5OY0888YQmT57sNLZ7927FxsbK399f8fHxysvLc2zLz8%2BXzWZzXEMLAADg3/HqglVYWKhrr73Waax379768MMP9f777%2Bv7779Xdna28vLydN9990mShg8frmXLlmn9%2BvXatm2bpk2bpnvvvfeCDhECAADf5tWHCMvKyhQaGuo0dtttt%2BnPf/6zFi5cqEOHDql9%2B/ZaunSpWrduLUkaMGCADh48qKeeeko1NTW67bbbNGHCBHdMHwAAeCivLljbtm075/jQoUM1dOjQ8z5v9OjRGj169OWaFgAA8HJefYgQAADAHShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFvPagvXJJ5%2BoQ4cOTreMjAxJ0q5duzR06FAlJCRoyJAh2rFjh9Nzc3Nz1adPHyUkJCg9PV1Hjx51RwQAAOChvLZgFRUVqVevXtqwYYPjNnPmTJ06dUqjR49WSkqK3n33XSUlJWnMmDE6deqUJGnbtm2aOnWqxo4dq5ycHB07dkyTJ092cxoAAOBJvLZgFRcX67rrrlNkZKTjFhoaqo8%2B%2BkhBQUGaOHGi2rVrp6lTp6pZs2b629/%2BJklauXKl7rjjDg0aNEhxcXGaM2eOPv/8c%2B3fv9/NiQAAgKfw6oLVtm3bBuMFBQVKTk6Wn5%2BfJMnPz09dunRRfn6%2BY3tKSorj8S1btlRMTIwKCgpcMm8AAOD5bO6ewOVgjNF3332nDRs2aPHixaqrq1O/fv2UkZEhu92ua6%2B91unxLVq0UGFhoSSptLRUUVFRDbYfOXKk0a9fWloqu93uNGazNW2wX19js3lmnw8I8Hf66GvIT/6ff/Q1vp5fYg0ullcWrEOHDqmyslKBgYGaN2%2BeDhw4oJkzZ6qqqsox/nOBgYGqqamRJFVVVf3L7Y2Rk5Oj7Oxsp7H09HTHSfa%2BKjy8mbuncElCQ4PdPQW3Ij/5fZmv55dYgwvllQWrVatW2rRpk6666ir5%2BfmpY8eOqq%2Bv14QJE9S1a9cGZammpkZNmjSRJAUFBZ1ze3Bw47%2Bw0tLS1Lt3b6cxm62pystPXmQi7%2BCp%2BQMC/BUaGqxjxypVV1fv7um4HPnJT37fzS95/hq464d7ryxYkhQWFuZ0v127dqqurlZkZKTKysqctpWVlTkO30VHR59ze2RkZKNfOyoqqsHhQLv9uGprPe8L00qenr%2Burt7jM1wK8pOf/L6bX2INLpRXFqwvv/xSjz/%2BuD777DPHO0/ffPONwsLClJycrJdfflnGGPn5%2BckYo61bt%2BqPf/yjJCkhIUF5eXm6%2B%2B67JUmHDx/W4cOHlZCQ4LY83uKOef9w9xQuyMeP9XD3FAAAHsorz1hLSkpSUFCQ/vu//1t79%2B7V559/rjlz5ujhhx9Wv379dOzYMc2aNUtFRUWaNWuWKisrdccdd0iShg0bprVr12r16tXavXu3Jk6cqFtvvVVt2rRxcyoAAOApvLJghYSEaNmyZTp69KiGDBmiqVOnKi0tTQ8//LBCQkK0ePFix7tUBQUFWrJkiZo2bSrpTDmbPn26FixYoGHDhumqq67S7Nmz3ZwIAAB4Ej9jjHH3JHyB3X78suzX0w67eZKzhwhtNn%2BFhzdTeflJnzz/gPzkJ7/v5pc8fw0iI690y%2Bt65TtYAAAA7kTBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsJjXFqySkhJlZGSoa9euSk1N1ezZs1VdXS1Jmjlzpjp06OB0W7lypeO5ubm56tOnjxISEpSenq6jR4%2B6KwYAAPBANndP4HIwxigjI0OhoaF644039NNPP2nKlCny9/fXpEmTVFxcrMzMTA0ePNjxnJCQEEnStm3bNHXqVD399NOKi4vTrFmzNHnyZC1evNhdcQAAgIfxynew9u7dq/z8fM2ePVvt27dXSkqKMjIylJubK0kqLi7W9ddfr8jISMctODhYkrRy5UrdcccdGjRokOLi4jRnzhx9/vnn2r9/vzsjAQAAD%2BKVBSsyMlJLly5VRESE0/iJEyd04sQJlZSUqG3btud8bkFBgVJSUhz3W7ZsqZiYGBUUFFzOKQMAAC/ilYcIQ0NDlZqa6rhfX1%2BvlStXqnv37iouLpafn58WLVqkL774QmFhYXrggQcchwtLS0sVFRXltL8WLVroyJEjjX790tJS2e12pzGbrWmD/eLXzWY78/NHQIDzR19DfvL//KOv8fX8EmtwsbyyYP1SVlaWdu3apXfeeUc7d%2B6Un5%2BfYmNjdd9992nLli168sknFRISor59%2B6qqqkqBgYFOzw8MDFRNTU2jXy8nJ0fZ2dlOY%2Bnp6crIyLAkD1wjPLyZ0/3Q0GA3zeTXgfzk92W%2Bnl9iDS6U1xesrKwsLV%2B%2BXM8//7yuu%2B46tW/fXr169VJYWJgkKS4uTvv27dOqVavUt29fBQUFNShTNTU1jnO0GiMtLU29e/d2GrPZmqq8/OSlB4LLnP18BQT4KzQ0WMeOVaqurt7Ns3I98pOf/L6bX/L8NfjlD8uu4tUFa8aMGVq1apWysrJ0%2B%2B23S5L8/Pwc5eqs2NhYbdy4UZIUHR2tsrIyp%2B1lZWWKjIxs9OtGRUU1OBxotx9Xba3nfWH6sl9%2Bvurq6n36c0h%2B8pPfd/NLrMGF8toDqtnZ2Xrrrbf03HPPacCAAY7x%2BfPna9SoUU6P3b17t2JjYyVJCQkJysvLc2w7fPiwDh8%2BrISEBJfMGwAAeD6vLFjFxcV66aWX9Ic//EHJycmy2%2B2OW69evbRlyxYtW7ZMP/zwg9588029//77evDBByVJw4YN09q1a7V69Wrt3r1bEydO1K233qo2bdq4ORUAAPAUXnmI8O9//7vq6uq0cOFCLVy40Gnbt99%2Bq/nz5%2BuFF17Q/Pnz1apVKz377LNKSkqSJCUlJWn69Ol64YUX9NNPP6lHjx6aMWOGO2IAAAAP5WeMMe6ehC%2Bw249flv3eMe8fl2W/kD5%2BrIekM5drCA9vpvLykz55/gH5yU9%2B380vef4aREZe6ZbX9cpDhAAAAO5EwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYjZ3TwD4tbpj3j/cPYUL8vFjPdw9BQDA/8M7WAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIJ1DtXV1ZoyZYpSUlLUs2dPvfLKK%2B6eEgAA8CBcB%2Bsc5syZox07dmj58uU6dOiQJk2apJiYGPXr18/dUwMAAB6AgvULp06d0urVq/Xyyy%2BrU6dO6tSpkwoLC/XGG29QsAAAQKNQsH5h9%2B7dqq2tVVJSkmMsOTlZixYtUn19vfz9OaqKXydPuvI8V50H4O0oWL9gt9sVHh6uwMBAx1hERISqq6tVUVGh5s2b/9t9lJaWym63O43ZbE0VFRVl%2BXwBT%2BRJZVCS/m9WPwUE%2BOYPV2dzk98380uswcWiYP1CZWWlU7mS5LhfU1PTqH3k5OQoOzvbaWzs2LEaN26cNZP8mf%2BbdXkPW5aWlionJ0dpaWk%2BWxB9fQ3IX6oXX3zRp/MvX76U/D6aX2INLhZ19BeCgoIaFKmz95s0adKofaSlpendd991uqWlpVk%2BV1ew2%2B3Kzs5u8I6cL/H1NSA/%2Bcnvu/kl1uBi8Q7WL0RHR6u8vFy1tbWy2c4sj91uV5MmTRQaGtqofURFRdHyAQDwYbyD9QsdO3aUzWZTfn6%2BYywvL0/x8fGc4A4AABqFxvALwcHBGjRokKZNm6Zt27Zp/fr1euWVV3T//fe7e2oAAMBDBEybNm2auyfxa9O9e3ft2rVLzz77rL7%2B%2Bmv98Y9/1JAhQ9w9Lbdp1qyZunbtqmbNmrl7Km7j62tAfvKT33fzS6zBxfAzxhh3TwIAAMCbcIgQAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQvnVV1drSlTpiglJUU9e/bUK6%2B84u4pWa6kpEQZGRnq2rWrUlNTNXv2bFVXV0uSZs6cqQ4dOjjdVq5c6Xhubm6u%2BvTpo4SEBKWnp%2Bvo0aPuinHRPvnkkwYZMzIyJEm7du3S0KFDlZCQoCFDhmjHjh1Oz/X0/O%2B%2B%2B26D7B06dFBcXJwk6ZFHHmmw7dNPP3U8/7XXXlNqaqqSkpI0ZcoUVVZWuivKBaupqdHAgQO1adMmx9j%2B/fs1atQoJSYmqn///tqwYYPTc7766isNHDhQCQkJuv/%2B%2B7V//36n7Z60HufKn5%2Bfr9/97ndKSkrS7bffrtWrVzs957e//W2Dr4c9e/ZIkowxmjt3rrp3766uXbtqzpw5qq%2Bvd2mmC3WuNbiU73meuAaXnQHOY/r06ebOO%2B80O3bsMP/zP/9jkpKSzMcff%2BzuaVmmvr7e3Hvvvebhhx82e/bsMVu2bDF9%2B/Y1zzzzjDHGmFGjRpnFixeb0tJSx%2B3UqVPGGGMKCgpM586dzXvvvWe%2B%2BeYbc99995nRo0e7M85Feemll8yYMWOcMv7000/m5MmTpkePHuaZZ54xRUVFZsaMGebmm282J0%2BeNMZ4R/7Kykqn3IcOHTJ9%2B/Y1s2bNMsYY07dvX7N27Vqnx1RXVxtjjPnb3/5mkpOTzf/%2B7/%2BagoIC079/f/P000%2B7M06jVVVVmfT0dHPdddeZjRs3GmPO/F248847TWZmpikqKjKLFi0yCQkJ5uDBg8YYYw4ePGgSExPNsmXLzJ49e8x//dd/mYEDB5r6%2BnpjjGetx7nyl5aWmpSUFPPss8%2Ba7777zuTm5pr4%2BHjz6aefGmOMqa2tNfHx8Wbz5s1OXw%2BnT582xhizbNkyc8stt5gtW7aYr7/%2B2vTs2dMsXbrUXRH/rXOtgTGX9j3P09bAFShYOKeTJ0%2Ba%2BPh4p798CxYsMPfdd58bZ2WtoqIic9111xm73e4Y%2B/DDD03Pnj2NMcakpqaaL7/88pzPnTBhgpk0aZLj/qFDh0yHDh3MDz/8cHknbbHMzEzz7LPPNhhfvXq16d27t%2BMf0Pr6etO3b1%2BzZs0aY4z35P%2B5RYsWmT59%2Bpjq6mpTXV1tOnbsaPbu3XvOxw4fPty88MILjvtbtmwxnTt3dvxj9GtVWFhofvvb35o777zT6R/Xr776yiQmJjoKtDHGjBw50pFx3rx5Tn/3T506ZZKSkhzP95T1OF/%2BN9980/Tr18/psU8%2B%2BaQZP368McaYffv2mbi4OFNVVXXO/d5yyy2OvxvGGPP%2B%2B%2B%2BbXr16XaYUl%2BZ8a2DMpX3P86Q1cBUOEeKcdu/erdraWiUlJTnGkpOTVVBQ4DVv%2B0ZGRmrp0qWKiIhwGj9x4oROnDihkpIStW3b9pzPLSgoUEpKiuN%2By5YtFRMTo4KCgss5ZcsVFxefM2NBQYGSk5Pl5%2BcnSfLz81OXLl2Un5/v2O4N%2Bc%2BqqKjQyy%2B/rMzMTAUGBmrv3r3y8/NTmzZtGjy2rq5O27dvd8qfmJio06dPa/fu3a6c9gXbvHmzunXrppycHKfxgoICXX/99WratKljLDk5%2Bbyf7%2BDgYHXq1En5%2BfketR7ny3/29IBfOnHihCSpqKhILVu2VFBQUIPHlJSU6PDhw7rxxhsdY8nJyTp48KBKS0stTnDpzrcGl/I9z9PWwFVs7p4Afp3sdrvCw8MVGBjoGIuIiFB1dbUqKirUvHlzN87OGqGhoUpNTXXcr6%2Bv18qVK9W9e3cVFxfLz89PixYt0hdffKGwsDA98MADGjx4sCSptLRUUVFRTvtr0aKFjhw54tIMl8IYo%2B%2B%2B%2B04bNmzQ4sWLVVdXp379%2BikjI0N2u13XXnut0%2BNbtGihwsJCSd6R/%2BdWrVqlqKgo9evXT5K0d%2B9ehYSEaOLEidq8ebOuvvpqjRs3TrfccouOHTum6upqp/w2m01hYWG/%2BvzDhw8/57jdbv%2BXn89/td2T1uN8%2BVu3bq3WrVs77v/444/661//qnHjxkk684PIFVdcoTFjxmjHjh265pprNHHiRHXu3Fl2u12SnPKf/aHtyJEjDdbN3c63BpfyPc/T1sBVKFg4p8rKSqdyJclxv6amxh1TuuyysrK0a9cuvfPOO9q5c6f8/PwUGxur%2B%2B67T1u2bNGTTz6pkJAQ9e3bV1VVVedcH09am0OHDjk%2Bz/PmzdOBAwc0c%2BZMVVVVnffzfzafN%2BQ/yxij1atX6%2BGHH3aM7d27V1VVVerZs6dGjx6tTz75RI888ohycnIc/3B4S37p/H/fz%2Bb5V9urqqoc98/3fE9SVVWlcePGKSIiQmlpaZKk7777Tj/99JOGDh2qjIwMvf322xo5cqQ%2B%2Buijc%2Bb3xO%2BVZ9%2B1vZjved6yBlajYOGcgoKCGvzFOHu/SZMm7pjSZZWVlaXly5fr%2Beef13XXXaf27durV69eCgsLkyTFxcVp3759WrVqlfr27Xve9QkODnbH9C9Kq1attGnTJl111VXy8/NTx44dVV9frwkTJqhr167nzHf2c%2B8N%2Bc/avn27SkpKNGDAAMfYo48%2BqhEjRuiqq66SdObzv3PnTr399tv605/%2BJKnhPxyeml868/msqKhwGmvM5zs0NNRx2Mwb1uPkyZN69NFHtW/fPr355puO%2Bc%2BYMUNVVVUKCQmRJE2bNk1bt27V2rVrdfPNN0s6k/eXa%2BFJ%2BQcNGnTR3/N%2BXqY8eQ2sxjlYOKfo6GiVl5ertrbWMWa329WkSROFhoa6cWbWmzFjhl599VVlZWXp9ttvl3TmnKOz32jOio2NVUlJiaQz61NWVua0vaysTJGRka6ZtEXCwsIc51lJUrt27VRdXa3IyMhz5jv7Vr%2B35JekL7/8UikpKY4yJUn%2B/v5O96X///MfFhamoKAgp/y1tbWqqKjwyPzS%2BT%2Bfjfl8e8t6nDhxQg899JAKCwu1fPlyp3ORbDabo1xJcrzTU1JSoujoaElyHCb7%2BZ89Kf%2BlfM/zljWwGgUL59SxY0fZbDbHSa6SlJeXp/j4ePn7e8%2BXTXZ2tt566y0999xzTu9gzJ8/X6NGjXJ67O7duxUbGytJSkhIUF5enmPb4cOHdfjwYSUkJLhk3lb48ssv1a1bN6frFX3zzTcKCwtTcnKy/vnPf8oYI%2BnMYbStW7c68nlD/rO2bdumLl26OI098cQTmjx5stPY2c%2B/v7%2B/4uPjnfLn5%2BfLZrM5rqHlaRISErRz507HoR7pzN/3832%2BKysrtWvXLiUkJHjFetTX12vs2LE6cOCAXn/9dbVv395p%2B4gRI5Sdne30%2BG%2B//VaxsbGKjo5WTEyMU/68vDzFxMR41LlHl/I9z1vWwHLu/SVG/Jo9%2BeSTZsCAAaagoMB88sknpkuXLmbdunXunpZlioqKTMeOHc3zzz/vdN2X0tJSU1BQYK6//nqzdOlS8/3335s33njD3HDDDWbr1q3GGGO2bt1qOnXqZN5%2B%2B23HNWHGjBnj5kQX5vjx4yY1NdWMHz/eFBcXm88%2B%2B8z07NnTLFmyxBw/ftx0797dzJgxwxQWFpoZM2aYHj16OH6N3xvyn9WrVy%2BTm5vrNLZu3TrTqVMn895775l9%2B/aZF1980XTu3Nns37/fGGNMbm6u6dKli/nkk09MQUGBGTBggJkxY4Y7pn/Rfv4r%2BrW1taZ///7mscceM3v27DGLFy82iYmJjutg7d%2B/38THx5vFixc7roN15513Oi7j4Ynr8fP8OTk5Ji4uznz66adO3wfKy8uNMca88sorJjk52axfv94UFxebP//5z%2Bbmm282x48fN8YYs3jxYtOzZ0%2BzceNGs3HjRtOzZ0/zyiuvuC1bY/18DS71e56nrsHlRMHCeZ06dcpMnDjRJCYmmp49e5pXX33V3VOy1OLFi8111113zpsxxnzyySfmzjvvNPHx8aZfv34NyuWaNWvMLbfcYhITE016ero5evSoO2Jckj179phRo0aZxMRE06NHD/Piiy86/tEsKCgwgwYNMvHx8eaee%2B4xO3fudHquN%2BQ3xpj4%2BHjzxRdfNBh/%2B%2B23zW233WZuuOEGM3jwYLN582an7YsXLzY33XSTSU5ONpMnTz7vNZJ%2BrX55DaR9%2B/aZ3//%2B9%2BaGG24wAwYMMP/4xz%2BcHv/ZZ5%2BZ2267zXTu3NmMHDmywTXPPG09fp7/wQcfPOf3gbPX/qqvrzcLFy40t956q7nhhhvM73//e/Ptt9869lVbW2v%2B8pe/mJSUFNOtWzeTlZXl%2BHv0a/bLr4FL%2BZ7nqWtwOfkZ8/%2BOAQAAAMAS3nMyDQAAwK8EBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALDY/wcG9lBAFU6y1gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2407243385513258423”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1505</td> <td class=”number”>46.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>282</td> <td class=”number”>8.8%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.3333333333333</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (115)</td> <td class=”number”>320</td> <td class=”number”>10.0%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>628</td> <td class=”number”>19.6%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2407243385513258423”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.6666666666667</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:92%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-45.4545454545455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>750.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_ACRESPTH_07_12”><s>PCH_FRESHVEG_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FRESHVEG_ACRES_07_12”><code>PCH_FRESHVEG_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99841</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_ACRES_07_12”>PCH_FRESHVEG_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>880</td>

</tr> <tr>

<th>Unique (%)</th> <td>27.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>67.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2160</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>18.501</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1933.3</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3396042149264950923”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives3396042149264950923”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3396042149264950923”

aria-controls=”quantiles3396042149264950923” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3396042149264950923” aria-controls=”histogram3396042149264950923”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3396042149264950923” aria-controls=”common3396042149264950923”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3396042149264950923” aria-controls=”extreme3396042149264950923”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3396042149264950923”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-85</td>

</tr> <tr>

<th>Q1</th> <td>-43.139</td>

</tr> <tr>

<th>Median</th> <td>-6.8323</td>

</tr> <tr>

<th>Q3</th> <td>38.374</td>

</tr> <tr>

<th>95-th percentile</th> <td>182.98</td>

</tr> <tr>

<th>Maximum</th> <td>1933.3</td>

</tr> <tr>

<th>Range</th> <td>2033.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>81.513</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>137.11</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.4108</td>

</tr> <tr>

<th>Kurtosis</th> <td>83.308</td>

</tr> <tr>

<th>Mean</th> <td>18.501</td>

</tr> <tr>

<th>MAD</th> <td>68.614</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.3649</td>

</tr> <tr>

<th>Sum</th> <td>19426</td>

</tr> <tr>

<th>Variance</th> <td>18798</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3396042149264950923”>

<img 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udXAyAACAynFdwYqIiFBBQUGF9aKiojM%2BozsAAIATXFewrrnmGk2ePFnff/%2B9fy0nJ0fp6elKSUlxcDIAAIDKcd1vEY4dO1a33XabrrvuOsXGxkqSCgoK1KpVK40bN87h6QAAAE7PdQWrdu3aWrp0qVavXq3s7GxFRESoWbNmuuqqqxQWFub0eAAAAKfluoIlSeHh4UpJSeEtQQAAEJRcV7B8Pp9mzpyp9evX68iRIxU%2B2P7RRx85NBkAAEDluK5gPfTQQ9q0aZN69eqlCy%2B80OlxAAAAzpjrCtbatWs1f/58JSUlOT0KAADAWXHdaRpiYmJUu3Ztp8cAAAA4a64rWH379tX8%2BfN19OhRp0cBAAA4K657izA/P1/Lly/Xxx9/rMaNGysyMjJg%2B0svveTQZAAAAJXjuoIlSb1793Z6BAAAgLPmuoKVnp7u9AgAAADnxHWfwZKkvLw8zZo1S/fcc4/%2B9a9/6f3339eOHTucHgsAAKBSXFewdu/erT59%2Bmjp0qX64IMPVFhYqHfffVf9%2B/eX1%2Bt1ejwAAIDTcl3Beuyxx9StWzd9%2BOGHuuCCCyRJjz/%2BuLp27arp06c7PB0AAMDpua5grV%2B/XrfddlvAP%2BwcERGhO%2B%2B8U5s3b3ZwMgAAgMpxXcEqLy9XeXl5hfXDhw8rPDzcgYkAAADOjOsKVnJysubOnRtQsvLz8zVt2jR16NDBwckAAAAqx3UF6/7779emTZuUnJyskpISjRgxQl26dNEPP/ygsWPHOj0eAADAabnuPFj16tXTm2%2B%2BqeXLl%2Bvbb79VeXm5Bg0apL59%2B6p69epOjwcAAHBaritYkhQdHa0BAwY4PQYAAMBZcV3BuuWWW065nX%2BLEAAAuJ3rClbDhg0DLpeVlWn37t3aunWrbr31VoemAgAAqDzXFayT/VuEs2fP1t69e3/jaQAAAM6c636L8GT69u2r9957z%2BkxAAAATitoCtbXX3/NiUYBAEBQcN1bhCf6kPuhQ4f03Xff6aabbnJgIgAAgDPjuoLVoEGDgH%2BHUJIuuOAC3Xzzzbr%2B%2BusdmgoAAKDyXFewHnvsMadHAAAAOCeuK1jr1q2r9HWvuOKK8zgJAADA2XFdwRoyZIj/LUJjjH/912thYWH69ttvf/sBAQAATsN1BWvOnDmaPHmy7rvvPrVv316RkZHauHGjJk6cqBtuuEE9e/Z0ekQAAIBTct1pGtLT0zVhwgRdd911qlmzpqpVq6YOHTpo4sSJevXVV9WwYUP/FwAAgBu5rmDl5eWdsDxVr15dBw8edGAiAACAM%2BO6gtW2bVs9/vjjOnTokH8tPz9f06ZN01VXXeXgZAAAAJXjus9gPfjgg7rlllt0zTXXqEmTJjLGaNeuXapbt65eeuklp8cDAAA4LdcVrEsvvVTvvvuuli9fru3bt0uSBg8erF69eik6Otrh6QAAAE7PdQVLki666CINGDBAP/zwgxo3bizp2NncAQAAgoHrPoNljNH06dN1xRVXqHfv3tq7d6/Gjh2r8ePH68iRI06PBwAAcFquK1gvv/yyli1bpocffliRkZGSpG7duunDDz/UrFmzHJ4OAADg9FxXsDIyMjRhwgSlpqb6z97es2dPTZ48WW%2B//bbD0wEAAJye6wrWDz/8oJYtW1ZYb9GihXw%2BnwMTAQAAnBnXFayGDRtq48aNFdZXrVrl/8A7AACAm7nutwjvuOMOPfroo/L5fDLGaM2aNcrIyNDLL7%2Bs%2B%2B%2B/3%2BnxAAAATst1Bat///4qKyvTM888o%2BLiYk2YMEG1atXSXXfdpUGDBjk9HgAAwGm5rmAtX75c3bt318CBA3XgwAEZY1S7dm2nxwIAAKg0130Ga%2BLEif4Ps9eqVeucy9WwYcMC3lrcvHmzBgwYII/Ho/79%2B2vTpk0B11%2B%2BfLm6desmj8ejtLQ0HThw4Jy%2BPwAA%2BP1xXcFq0qSJtm7dauW%2B3nnnHX3yySf%2By4WFhRo2bJiSkpK0ZMkSJSQkaPjw4SosLJQkbdiwQePHj9fIkSOVkZGhgoICjRs3zsosAADg98N1bxG2aNFC9957r%2BbPn68mTZooKioqYHt6enql7ic/P19Tp05VfHy8f%2B3dd99VVFSUxowZo7CwMI0fP16rVq3S%2B%2B%2B/r9TUVC1cuFA9evRQv379JElTp05Vly5dlJOTw28wAgCASnPdEaydO3cqMTFR1apVk8/n0w8//BDwVVl/%2B9vf1LdvXzVr1sy/5vV6lZiY6D%2BBaVhYmNq1a6esrCz/9qSkJP/169evrwYNGsjr9VpKBwAAfg9ccQRr6tSpGjlypGJiYvTyyy%2Bf8/2tWbNGX331ld5%2B%2B2098sgj/nWfzxdQuCSpdu3ays7OliTl5eUpLi6uwva9e/ee0ffPy8urcFLUiIiYCvf9exMR4VyfDw%2BvEvDfUBGKuUIxk0SuYBKKmaTQzOXmTK4oWC%2B88ILuuOMOxcTE%2BNeGDRumyZMnn3EpKSkp0cMPP6wJEyaoatWqAduKior8/77hcZGRkSotLZUkFRcXn3J7ZWVkZFT4dxPT0tI0evToM7qfUFOzZjWnR1BsbLTTI5wXoZgrFDNJ5AomoZhJCs1cbszkioJljKmwtm7dOpWUlJzxfc2aNUutW7dWSkpKhW1RUVEVylJpaam/iJ1se3T0mT1wAwcOVNeuXQPWIiJidPDg4TO6n1DjZP7w8CqKjY1WQUGRjh4td2wO20IxVyhmksgVTEIxkxSauSqTyam/3LuiYNn0zjvvaP/%2B/UpISJAkf2H64IMP1Lt3b%2B3fvz/g%2Bvv37/cfJatXr94Jt9etW/eMZoiLi6tw5M3n%2B1llZaHxA3223JD/6NFyV8xhWyjmCsVMErmCSShmkkIzlxszhVzBevnll1VWVua/PH36dEnSvffeq3Xr1unZZ5%2BVMUZhYWEyxmj9%2BvX685//LEnyeDzKzMxUamqqJCk3N1e5ubnyeDy/fRAAABC0XFOwjv9m37lq2LBhwOVq1Y4dGrz44otVu3ZtzZgxQ1OmTNGf/vQnvfbaayoqKlKPHj0kSYMGDdKQIUPUtm1bxcfHa8qUKercuTOnaAAAAGfENQVr8uTJAee8OnLkiKZNm%2BYvSMdV9jxYJ1K9enXNnTtXDz/8sF5//XVddtllmjdvnv/D9QkJCZo4caKefPJJ/fTTT%2BrYsaMmTZp01t8PAAD8PrmiYF1xxRUVTmuQkJCggwcP6uDBg%2Bd034899ljA5TZt2mjp0qUnvX5qaqr/LUIAAICz4YqCZePcVwAAAG7hvjNzAQAABDkKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJaFbMHat2%2BfRo8erfbt2yslJUXp6ekqKSmRJOXk5Gjo0KFq27atevbsqc8%2B%2ByzgtqtXr1bv3r3l8Xh0yy23KCcnx4kIAAAgSIVkwTLGaPTo0SoqKtIrr7yiJ554QitXrtTMmTNljFFaWprq1KmjxYsXq2/fvho5cqT27NkjSdqzZ4/S0tKUmpqqN954Q7Vq1dKdd94pY4zDqQAAQLCIcHqA82HHjh3KysrS559/rjp16kiSRo8erb/97W%2B65pprlJOTo9dee00xMTG69NJLtWbNGi1evFijRo3SokWL1Lp1a91%2B%2B%2B2SpPT0dHXs2FFffvmlrrzySidjAQCAIBGSR7Dq1q2r%2BfPn%2B8vVcYcOHZLX69Xll1%2BumJgY/3piYqKysrIkSV6vV0lJSf5t0dHRatWqlX87AADA6YTkEazY2FilpKT4L5eXl2vhwoXq0KGDfD6f4uLiAq5fu3Zt7d27V5JOu70y8vLy5PP5AtYiImIq3O/vTUSEc30%2BPLxKwH9DRSjmCsVMErmCSShmkkIzl5szhWTB%2BrVp06Zp8%2BbNeuONN/Tiiy8qMjIyYHtkZKRKS0slSUVFRafcXhkZGRmaNWtWwFpaWppGjx59lglCQ82a1ZweQbGx0U6PcF6EYq5QzCSRK5iEYiYpNHO5MVPIF6xp06ZpwYIFeuKJJ9S8eXNFRUUpPz8/4DqlpaWqWrWqJCkqKqpCmSotLVVsbGylv%2BfAgQPVtWvXgLWIiBgdPHj4LFOEBifzh4dXUWxstAoKinT0aLljc9gWirlCMZNErmASipmk0MxVmUxO/eU%2BpAvWpEmT9Oqrr2ratGm67rrrJEn16tXTtm3bAq63f/9%2B/9t39erV0/79%2Bytsb9myZaW/b1xcXIW3A32%2Bn1VWFho/0GfLDfmPHi13xRy2hWKuUMwkkSuYhGImKTRzuTGT%2B960tGTWrFl67bXX9Pjjj6tXr17%2BdY/Ho2%2B%2B%2BUbFxcX%2BtczMTHk8Hv/2zMxM/7aioiJt3rzZvx0AAOB0QrJgbd%2B%2BXU8//bT%2B%2B7//W4mJifL5fP6v9u3bq379%2Bho3bpyys7M1b948bdiwQTfeeKMkqX///lq/fr3mzZun7OxsjRs3To0aNeIUDQAAoNJCsmB99NFHOnr0qJ555hklJycHfIWHh%2Bvpp5%2BWz%2BdTamqq3nrrLc2ePVsNGjSQJDVq1EhPPfWUFi9erBtvvFH5%2BfmaPXu2wsLCHE4FAACCRUh%2BBmvYsGEaNmzYSbdffPHFWrhw4Um3d%2BrUSZ06dTofowEAgN%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%2Bujk6PAAD4P7xF%2BCtbtmxRWVmZEhIS/GuJiYnyer0qLy93cDIAABAsOIL1Kz6fTzVr1lRkZKR/rU6dOiopKVF%2Bfr5q1ap12vvIy8uTz%2BcLWIuIiFFcXJz1eYHjgu2IWzBZcW/KOd9HeHiVgP%2BGilDMFUqZrp3%2BqdMjVNrZPM/c/FhRsH6lqKgooFxJ8l8uLS2t1H1kZGRo1qxZAWsjR47UqFGj7Az5C19Nsfu2ZV5enjIyMjRw4MCQKoTkCh6hmEk6lmvBgvnkCgKhlOmXrxGh%2BNxy82PlvsrnsKioqApF6vjlqlWrVuo%2BBg4cqCVLlgR8DRw40Pqs54PP59OsWbMqHIELduQKHqGYSSJXMAnFTFJo5nJzJo5g/Uq9evV08OBBlZWVKSLi2B%2BPz%2BdT1apVFRsbW6n7iIuLc12TBgAAvx2OYP1Ky5YtFRERoaysLP9aZmam4uPjVaUKf1wAAOD0aAy/Eh0drX79%2BumRRx7Rhg0b9OGHH%2Br555/XLbfc4vRoAAAgSIQ/8sgjjzg9hNt06NBBmzdv1owZM7RmzRr9%2Bc9/Vv/%2B/Z0e6zdTrVo1tW/fXtWqVXN6FKvIFTxCMZNErmASipmk0Mzl1kxhxhjj9BAAAAChhLcIAQAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAC35gg6AAAOhElEQVQAAJZRsOBXUlKiBx54QElJSUpOTtbzzz/v9EiVsm/fPo0ePVrt27dXSkqK0tPTVVJSIkmaPHmyLrvssoCvhQsX%2Bm%2B7fPlydevWTR6PR2lpaTpw4IBTMSpYsWJFhdlHjx4tSdq8ebMGDBggj8ej/v37a9OmTQG3dWOuJUuWVMhz2WWXqUWLFpKkESNGVNi2cuVK/%2B1ffPFFpaSkKCEhQQ888ICKioqciuJXWlqq3r1764svvvCv5eTkaOjQoWrbtq169uypzz77LOA2q1evVu/eveXxeHTLLbcoJycnYLsbcp4oV1ZWlv70pz8pISFB1113nRYtWhRwm%2Buvv77C47d161ZJkjFG06dPV4cOHdS%2BfXtNnTpV5eXljmc6l/2DGzJJFXPdf//9J3ye/fKfe0tKSqqw/fDhw5KcfR041b48KJ9XBvg/EydONH369DGbNm0y//jHP0xCQoJ57733nB7rlMrLy81//ud/mv/6r/8yW7duNevWrTPXXnuteeyxx4wxxgwdOtTMnTvX5OXl%2Bb8KCwuNMcZ4vV7Tpk0bs3TpUvPtt9%2Bam2%2B%2B2QwbNszJOAGefvppM3z48IDZf/rpJ3P48GHTsWNH89hjj5lt27aZSZMmmauvvtocPnzYGOPeXEVFRQFZ9uzZY6699lozZcoUY4wx1157rVm2bFnAdUpKSowxxrz//vsmMTHR/POf/zRer9f07NnTPProo07GMcXFxSYtLc00b97crF271hhz7OexT58%2B5p577jHbtm0zc%2BbMMR6Px/z444/GGGN%2B/PFH07ZtW/Pcc8%2BZrVu3mv/5n/8xvXv3NuXl5cYYd%2BQ8Ua68vDyTlJRkZsyYYXbu3GmWL19u4uPjzcqVK40xxpSVlZn4%2BHjz5ZdfBjx%2BR44cMcYY89xzz5lOnTqZdevWmTVr1pjk5GQzf/58RzMZc277B6cznSxXQUFBQJ6vv/7atG7d2qxYscIYY8zevXtN8%2BbNzffffx9wveM/g069DpxqXx6szysKFowxxhw%2BfNjEx8cH7Hxmz55tbr75ZgenOr1t27aZ5s2bG5/P5197%2B%2B23TXJysjHGmJSUFPPpp5%2Be8Lb33XefGTt2rP/ynj17zGWXXWa%2B//778zt0Jd1zzz1mxowZFdYXLVpkunbt6t95lJeXm2uvvdYsXrzYGOP%2BXMfNmTPHdOvWzZSUlJiSkhLTsmVLs2PHjhNe96abbjJPPvmk//K6detMmzZt/C%2BGv7Xs7Gxz/fXXmz59%2BgS8uK1evdq0bdvWX3aNMebWW2/1zz5z5syA51RhYaFJSEjw397pnCfL9fe//91079494LoPPfSQufvuu40xxuzatcu0aNHCFBcXn/B%2BO3Xq5P/5NMaYN99803Tp0uU8pQh0skzGnNv%2BwclMxpw61y/dfvvt5t577/Vf/vzzz03Hjh1PeF0nXwdOtS8P1ucVbxFCkrRlyxaVlZUpISHBv5aYmCiv1%2BvIYe/Kqlu3rubPn686deoErB86dEiHDh3Svn371KRJkxPe1uv1KikpyX%2B5fv36atCggbxe7/kcudK2b99%2Bwtm9Xq8SExMVFhYmSQoLC1O7du2UlZXl3%2B7mXJKUn5%2BvZ599Vvfcc48iIyO1Y8cOhYWFqXHjxhWue/ToUW3cuDEgU9u2bXXkyBFt2bLltxzb78svv9SVV16pjIyMgHWv16vLL79cMTEx/rXExMSTPjbR0dFq1aqVsrKyXJHzZLmOv13za4cOHZIkbdu2TfXr11dUVFSF6%2Bzbt0%2B5ubm64oor/GuJiYn68ccflZeXZzlBRSfLdC77B6czSSfP9Utr1qzRunXrdPfdd/vXtm3bpksuueSE13fydeBU%2B/JgfV5FnNd7R9Dw%2BXyqWbOmIiMj/Wt16tRRSUmJ8vPzVatWLQenO7nY2FilpKT4L5eXl2vhwoXq0KGDtm/frrCwMM2ZM0erVq1SjRo1dNttt%2BmGG26QJOXl5SkuLi7g/mrXrq29e/f%2BphlOxBijnTt36rPPPtPcuXN19OhRde/eXaNHj5bP51OzZs0Crl%2B7dm1lZ2dLcneu41599VXFxcWpe/fukqQdO3aoevXqGjNmjL788kv927/9m0aNGqVOnTqpoKBAJSUlAZkiIiJUo0YNxzLddNNNJ1z3%2BXyn/LM/1XY35DxZrkaNGqlRo0b%2By//617/0zjvvaNSoUZKO/WXgggsu0PDhw7Vp0yZdcsklGjNmjNq0aSOfzydJAbmOv4ju3bu3wp%2BHbSfLdC77B6czSSfP9Uvz5s3TDTfcoPr16/vXtm/frqKiIg0ZMkQ7d%2B5Uy5Yt9cADD%2BiSSy5x9HXgVPvyYH1ecQQLkqSioqKAJ5Uk/%2BXS0lInRjor06ZN0%2BbNm/WXv/zFf1SkadOmmjdvngYMGKCHHnpIK1askCQVFxefMLMb8u7Zs8f/mMycOVNjx47V22%2B/ralTp570sTo%2Bt5tzScfK46JFi3TzzTf713bs2KHi4mIlJydr/vz56tSpk0aMGKGNGzequLhYklyd6bjTPTan2h4sOYuLizVq1CjVqVNHAwcOlCTt3LlTP/30kwYMGKB58%2Bbp0ksv1a233qrc3NwT5nLDvuVc9g9uzfRLOTk5Wrt2rYYMGRKwvmPHDv30008aMWKEnn76aVWtWlVDhw7VoUOHXPU68Mt9ebA%2BrziCBUlSVFRUhR%2B245erVq3qxEhnbNq0aVqwYIGeeOIJNW/eXH/4wx/UpUsX1ahRQ5LUokUL7dq1S6%2B%2B%2Bqquvfbak2aOjo52YvwADRs21BdffKGLLrpIYWFhatmypcrLy3Xfffepffv2J5z7%2BOPk5lyStHHjRu3bt0%2B9evXyr915550aMmSILrroIknHHqtvvvlGr7/%2Buv7yl79IqriDd1Om46KiopSfnx%2BwVpnHJjY21v/2mptzHj58WHfeead27dqlv//97/65Jk2apOLiYlWvXl2S9Mgjj2j9%2BvVatmyZrr76aknHcvw6o5O5%2BvXrd9b7h1%2BWDjdl%2BqUPPvhALVu2rHC0%2B7nnntORI0dUrVo1SdL06dPVqVMnrVy50jWvA7/elwfr84ojWJAk1atXTwcPHlRZWZl/zefzqWrVqoqNjXVwssqZNGmSXnjhBU2bNk3XXXedpGOfTTq%2B8zyuadOm2rdvn6Rjmffv3x%2Bwff/%2B/apbt%2B5vM/Rp1KhRw/85K0m69NJLVVJSorp1655w7uOHwN2e69NPP1VSUpK/TElSlSpVAi5L//%2BxqlGjhqKiogIylZWVKT8/3zWZjjvZn31lHhu35zx06JDuuOMOZWdna8GCBQGfXYqIiPCXK0n%2BI0P79u1TvXr1JMn/ttov/9/JXOeyf3Brpl/69NNP9R//8R8V1iMjI/3lSjpWTho1auR/rJx%2BHTjRvjxYn1cULEiSWrZsqYiICP%2BHBiUpMzNT8fHxqlLF3T8ms2bN0muvvabHH3884KjI//7v/2ro0KEB192yZYuaNm0qSfJ4PMrMzPRvy83NVW5urjwez28y96l8%2BumnuvLKKwPO1fLtt9%2BqRo0aSkxM1Ndffy1jjKRjb7mtX7/eP7ebc0nShg0b1K5du4C1%2B%2B%2B/X%2BPGjQtYO/5YValSRfHx8QGZsrKyFBER4T%2BHllt4PB598803/rclpGPPo5M9NkVFRdq8ebM8Ho%2Brc5aXl2vkyJH64Ycf9PLLL%2BsPf/hDwPYhQ4Zo1qxZAdf/7rvv1LRpU9WrV08NGjQIyJWZmakGDRr8Jp9VOplz2T%2B4NdNxxhht3LixwvPMGKNu3bppyZIl/rXCwkLt3r1bTZs2dfx14GT78qB9Xp3X31FEUHnooYdMr169jNfrNStWrDDt2rUzH3zwgdNjndK2bdtMy5YtzRNPPBFwTpe8vDzj9XrN5ZdfbubPn292795tXnnlFdO6dWuzfv16Y4wx69evN61atTKvv/66/zw3w4cPdzjRMT///LNJSUkxd999t9m%2Bfbv5%2BOOPTXJyspk3b575%2BeefTYcOHcykSZNMdna2mTRpkunYsaP/V5jdnMsYY7p06WKWL18esPbBBx%2BYVq1amaVLl5pdu3aZp556yrRp08bk5OQYY4xZvny5adeunVmxYoXxer2mV69eZtKkSU6MX8Evf0W%2BrKzM9OzZ09x1111m69atZu7cuaZt27b%2B8/Xk5OSY%2BPh4M3fuXP/5evr06eM/5Yabcv4yV0ZGhmnRooVZuXJlwHPs4MGDxhhjnn/%2BeZOYmGg%2B/PBDs337dvPwww%2Bbq6%2B%2B2vz888/GGGPmzp1rkpOTzdq1a83atWtNcnKyef755x3NdK77B7dk%2BnUuY479nDVv3tzk5eVVuO6kSZNM586dzdq1a83WrVtNWlqa6d27tykrKzPGOPc6cKp9ebA%2BryhY8CssLDRjxowxbdu2NcnJyeaFF15weqTTmjt3rmnevPkJv4wxZsWKFaZPnz4mPj7edO/evcKOYvHixaZTp06mbdu2Ji0tzRw4cMCJGCe0detWM3ToUNO2bVvTsWNH89RTT/l3GF6v1/Tr18/Ex8ebG2%2B80XzzzTcBt3Vzrvj4eLNq1aoK66%2B//rr54x//aFq3bm1uuOEG8%2BWXXwZsnzt3rrnqqqtMYmKiGTdu3EnPufRb%2B/WL265du8zgwYNN69atTa9evcznn38ecP2PP/7Y/PGPfzRt2rQxt956a4Xzk7kl5y9z3X777Sd8jh0/91B5ebl55plnTOfOnU3r1q3N4MGDzXfffee/r7KyMvPXv/7VJCUlmSuvvNJMmzbN/7PsVCZjzm3/4JZMxlTMlZWVZZo3b%2B4/Ue8vFRcXm/T0dNOxY0fj8XjM8OHDzZ49e/zbnXodON2%2BPBifV2HG/N/7DAAAALDC3R%2BuAQAACEIULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABY9v8A0mz%2BV6/6amYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3396042149264950923”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (869)</td> <td class=”number”>950</td> <td class=”number”>29.6%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2160</td> <td class=”number”>67.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3396042149264950923”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.3568281938326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.84210526315789</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.10894941634241</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.11764705882352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>750.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1400.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1887.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1933.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FRESHVEG_FARMS_07_12”><s>PCH_FRESHVEG_FARMS_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_VEG_FARMS_07_12”><code>PCH_VEG_FARMS_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96454</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FSRPTH_09_14”><s>PCH_FSRPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_FSR_09_14”><code>PCH_FSR_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98863</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_FSR_09_14”>PCH_FSR_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>928</td>

</tr> <tr>

<th>Unique (%)</th> <td>28.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>109</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.5323</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>12.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2994957149492151241”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-2994957149492151241”>

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<li role=”presentation” class=”active”><a href=”#quantiles-2994957149492151241”

aria-controls=”quantiles-2994957149492151241” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2994957149492151241” aria-controls=”histogram-2994957149492151241”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2994957149492151241” aria-controls=”common-2994957149492151241”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2994957149492151241” aria-controls=”extreme-2994957149492151241”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2994957149492151241”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-40</td>

</tr> <tr>

<th>Q1</th> <td>-10.345</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>15</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>25.345</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.865</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.472</td>

</tr> <tr>

<th>Kurtosis</th> <td>14.59</td>

</tr> <tr>

<th>Mean</th> <td>4.5323</td>

</tr> <tr>

<th>MAD</th> <td>20.908</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2386</td>

</tr> <tr>

<th>Sum</th> <td>14055</td>

</tr> <tr>

<th>Variance</th> <td>1146.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2994957149492151241”>

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IcLy8vLFR0d7XGfNm3aaO/evXI6nZLksb1t27aSpL179za6HwAAwIkEbME6mR07dshmsykuLk5jxozRxo0b9fDDDys8PFwDBgxQTU2NQkJCPO4TEhIil8ulmpoa9/Vfb5N%2B%2BTB9U5WXl7vL2jF2e0vTC1pwcJDHn1Zh1VzeZrf77%2Btl5TUjW%2BCxai7JutmsmMtyBWvo0KHq16%2BfIiMjJUldunTRDz/8oFdffVUDBgxQaGhoo7LkcrkUFhbmUaZCQ0Pdf5fk/gxXU%2BTl5Sk3N9djLD09XRkZGWed61QiIpo%2Bt0Bi1VzeEhXVyt9TsPSakS3wWDWXZN1sVspluYJls9nc5eqYuLg4rV%2B/XpIUExOjiooKj%2B0VFRVyOByKiYmRJDmdTrVv3979d0lyOBxNnsPIkSOVlpbmMWa3t1Rl5eEzC3MawcFBiogI04ED1aqvt86pJKyay9vMfn6dCSuvGdkCj1VzSdbN5s1c/vrm03IFa/78%2Bfrqq6%2B0bNky99i2bdsUFxcnSUpISFB%2Bfr6GDRsmSdqzZ4/27NmjhIQExcTEKDY2Vvn5%2Be6ClZ%2Bfr9jY2DM6vBcdHd3o9k7nQdXVeefFUF/f4LV9%2B5NVc3lLc/haWXnNyBZ4rJpLsm42K%2BWyXMHq16%2BfFi9erKVLl2rAgAH67LPP9NZbb2nFihWSpFGjRunWW29VYmKi4uPj9dhjj6lv37666KKL3Nvnzp2r3/3ud5KkJ598Urfffrvf8gAAgMBjuYJ1xRVXaP78%2BXrmmWc0f/58XXjhhXryySeVlJQkSUpKStLMmTP1zDPPaP/%2B/erdu7dmzZrlvv8dd9yhn3/%2BWRMnTlRwcLBuuukmjR8/3k9pAABAILIZhmH4exK/BU7nQdP3abcHKSqqlSorD1vmLVWp%2BeS6bt5//fbYZ%2BO9%2B3r77bGby5p5A9kCj1VzSdbN5s1cDsf5pu6vqazz85AAAADNBAULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZD4vWCNGjNBrr72mgwcPmrI/l8ulIUOGaMOGDe6xgoIC/b//9/%2BUlJSka6%2B9VqtWrfK4z5///Gd17tzZ47J9%2B3ZJkmEYmjt3rnr16qUePXpozpw5amhoMGWuAADgt8Hu6wfs1auXnn/%2BeWVnZ%2BtPf/qThg0bpt69e8tms53xvmpra5WZmamioiL3mNPp1F133aVRo0bpiSee0DfffKOsrCw5HA717dtX9fX1%2BuGHH7Ry5Up16NDBfb%2BoqChJ0ksvvaS1a9cqNzdXdXV1mjx5stq0aaM77rjjnLMDAIDfBp%2B/g5WZman//Oc/eu655xQcHKxJkyapb9%2B%2Bevrpp/X99983eT/FxcW6%2Beab9dNPP3mMr1u3Tm3bttX999%2BvDh06aPDgwRo6dKjeeecdSdLOnTt19OhRXXHFFXI4HO6L3f5L11yxYoUyMjKUkpKiXr166YEHHtArr7xi3hcAAABYnl8%2Bg2Wz2dS7d2/l5OTo888/1y233KLly5dr0KBBuuWWW/Tvf//7tPv48ssv1bNnT%2BXl5XmMp6amKjs7u9HtDx06JOmXYtauXTuFhoY2uk1ZWZn27NmjK6%2B80j2WnJysXbt2qby8/ExjAgCA3yifHyI8pry8XG%2B//bbefvttbd%2B%2BXd27d9eNN96ovXv36m9/%2B5s2btyoadOmnfT%2Bo0ePPuF4%2B/bt1b59e/f1n3/%2BWf/85z81adIkSVJJSYnOO%2B88TZgwQVu2bNHvf/97TZkyRVdccYWcTqckKTo62n3/tm3bSpL27t3rMX66bMf2dYzd3rLJ92%2Bq4OAgjz%2Btwqq5vM1u99/Xy8prRrbAY9VcknWzWTGXzwvWmjVrtGbNGm3YsEGtW7fW0KFD9cwzz3h8Hqpdu3Z67LHHTlmwmqKmpkaTJk1S27ZtNXLkSEnS999/r/3792vEiBHKyMjQ66%2B/rnHjxundd99VTU2NJCkkJMS9j2N/d7lcTX7cvLw85ebmeoylp6crIyPjnPKcTEREmFf2629WzeUtUVGt/D0FS68Z2QKPVXNJ1s1mpVw%2BL1jTpk1Tv379tGDBAl111VUKCmrcVuPi4jRmzJhzepzDhw/r3nvv1Q8//KC///3vCgv7ZdFmzZqlmpoahYeHS5JmzJihTZs2ac2aNfrjH/8o6ZcydewQ4rFidez%2BTTFy5EilpaV5jNntLVVZeficMh0vODhIERFhOnCgWvX11vlJR6vm8jazn19nwsprRrbAY9VcknWzeTOXv7759HnB%2BuSTTxQVFaWqqip3udq8ebO6deum4OBgSVL37t3VvXv3s36MQ4cO6c4779RPP/2k5cuXe7w7Zrfb3eVK%2BuXzYHFxcSorK1NMTIykX34S8dhhxmOH%2BhwOR5MfPzo6utHhQKfzoOrqvPNiqK9v8Nq%2B/cmqubylOXytrLxmZAs8Vs0lWTeblXL5/GDnoUOHNHDgQL3wwgvusbvvvls33HCD9uzZc877b2ho0MSJE7Vz5069/PLLuvTSSz2233rrrR6H7xoaGvTdd98pLi5OMTExio2NVX5%2Bvnt7fn6%2BYmNjTf/8FAAAsC6fF6zHH39cF198sW677Tb32Lvvvqt27dqd8Kf/ztQbb7yhDRs2aPbs2YqIiJDT6ZTT6VRVVZUkKS0tTcuWLdOHH36oHTt2aObMmTp48KBuvPFGSdKoUaM0d%2B5cbdiwQRs2bNCTTz6psWPHnvO8AADAb4fPDxH%2B7//%2Br15//XWPQ26tW7fWlClTdMstt5zz/t9//301NDRowoQJHuM9evTQyy%2B/rPHjx6u2tlazZ89WRUWFEhIS9NJLL7kPG95xxx36%2BeefNXHiRAUHB%2Bumm27S%2BPHjz3leAADgt8PnBctut%2BvAgQONxqurq2UYxlnt87vvvnP/fenSpae8rc1m0z333KN77rnnhNuDg4OVlZWlrKyss5oLAACAzw8RXnXVVZo9e7bHGdhLS0uVnZ2t1NRUX08HAADAdD5/B%2BvBBx/UbbfdpmuvvVYRERGSpAMHDqhbt268awQAACzB5wWrTZs2%2Bsc//qHPP/9cRUVFstvtuuSSS/SHP/zhrH7hMwAAQHPjl1%2BVExwcrNTUVA4JAgAAS/J5wXI6nZo3b542bdqko0ePNvpg%2B4cffujrKQEAAJjK5wXr4Ycf1pYtWzR48GCdf/75vn54AAAAr/N5wVq/fr2WLFmilJQUXz80AACAT/j8NA0tW7ZUmzZtfP2wAAAAPuPzgnXDDTdoyZIlqq%2Bv9/VDAwAA%2BITPDxFWVVVp7dq1%2Buijj3TRRRcpJCTEY/uKFSt8PSUAAABT%2BeU0DUOGDPHHwwIAAPiEzwtWdna2rx8SAADAp3z%2BGSxJKi8vV25urjIzM/Xzzz/rX//6l3bs2OGPqQAAAJjO5wXrxx9/1PXXX69//OMfev/993XkyBG9%2B%2B67Gj58uAoLC309HQAAANP5vGA98cQT6t%2B/v9atW6fzzjtPkvTUU08pLS1Nc%2BfO9fV0AAAATOfzgrVp0ybddtttHr/Y2W63695779XWrVt9PR0AAADT%2BbxgNTQ0qKGhodH44cOHFRwc7OvpAAAAmM7nBatPnz5atGiRR8mqqqpSTk6OevXq5evpAAAAmM7nBeuhhx7Sli1b1KdPH9XW1uovf/mL%2BvXrp507d%2BrBBx/09XQAAABM5/PzYMXExOitt97S2rVr9e2336qhoUGjRo3SDTfcoPDwcF9PBwAAwHR%2BOZN7WFiYRowY4Y%2BHBgAA8DqfF6yxY8eecju/ixAAAAQ6nxesCy%2B80ON6XV2dfvzxR23fvl3jxo3z9XQAAABM12x%2BF%2BGCBQu0d%2B9eH88GAADAfH75XYQncsMNN%2Bi9997z9zQAAADOWbMpWF999dVZnWjU5XJpyJAh2rBhg3ustLRU48ePV2JiogYNGqTPPvvM4z6ff/65hgwZooSEBI0dO1alpaUe25ctW6bU1FQlJSVp6tSpqq6uPrtQAADgN6lZfMj90KFD%2Bu677zR69Ogz2ldtba0yMzNVVFTkHjMMQ%2Bnp6erUqZNWr16tdevWaeLEiXr33XcVGxur3bt3Kz09XZMmTVJqaqoWLFige%2B%2B9V2%2B//bZsNpvef/995ebmKicnR23atFFWVpZycnI0ffr0c84OAAB%2BG3xesGJjYz1%2BD6EknXfeeRozZoz%2B/Oc/N3k/xcXFyszMlGEYHuPr169XaWmpXnvtNbVs2VIdO3bUF198odWrV2vSpElatWqVLr/8ct1%2B%2B%2B2SfvlMWO/evfXll1%2BqZ8%2BeWrFihcaNG6d%2B/fpJkh599FHdcccdmjx5ssLCws4xPQAA%2BC3wecF64oknTNnPsUL0P//zP0pMTHSPFxYW6rLLLlPLli3dY8nJySooKHBvT0lJcW8LCwtTt27dVFBQoJSUFH399deaOHGie3tiYqKOHj2qbdu2KSkpyZS5AwAAa/N5wdq4cWOTb3vllVeedNvJDic6nU5FR0d7jLVp08b9E4qn2n7gwAHV1tZ6bLfb7YqMjOQnHAEAQJP5vGDdeuut7kOEvz68d/yYzWbTt99%2Be8b7r66uVkhIiMdYSEiIXC7XabfX1NS4r5/s/k1RXl4up9PpMWa3t2xU7M5VcHCQx59WYdVc3ma3%2B%2B/rZeU1I1vgsWouybrZrJjL5wXr%2Beef1%2BzZszV58mT16NFDISEh%2BvrrrzVz5kzdeOONGjRo0DntPzQ0VFVVVR5jLpdLLVq0cG8/viy5XC5FREQoNDTUff347Wfy%2Bau8vDzl5uZ6jKWnpysjI6PJ%2BzgTERHW/GyYVXN5S1RUK39PwdJrRrbAY9VcknWzWSmXX040On36dF111VXusV69emnmzJmaMmWK7rrrrnPaf0xMjIqLiz3GKioq3O8excTEqKKiotH2rl27KjIyUqGhoaqoqFDHjh0l/XKm%2BaqqKjkcjibPYeTIkUpLS/MYs9tbqrLy8NlEOqng4CBFRITpwIFq1dc3mLpvf7JqLm8z%2B/l1Jqy8ZmQLPFbNJVk3mzdz%2BeubT58XrPLy8ka/LkeSwsPDVVlZec77T0hI0OLFi1VTU%2BN%2B1yo/P1/Jycnu7fn5%2Be7bV1dXa%2BvWrZo4caKCgoIUHx%2Bv/Px89ezZU5JUUFAgu92uLl26NHkO0dHRjQ4HOp0HVVfnnRdDfX2D1/btT1bN5S3N4Wtl5TUjW%2BCxai7JutmslMvnBzsTExP11FNP6dChQ%2B6xqqoq5eTk6A9/%2BMM5779Hjx5q166dsrKyVFRUpMWLF2vz5s266aabJEnDhw/Xpk2btHjxYhUVFSkrK0vt27d3F6rRo0dr6dKlWrdunTZv3qwZM2bo5ptv5hQNAACgyXz%2BDtbf/vY3jR07VldddZU6dOggwzD0ww8/yOFwaMWKFee8/%2BDgYD333HOaNm2ahg0bposvvlgLFixQbGysJKl9%2B/Z69tln9fjjj2vBggVKSkrSggUL3B%2ByHzx4sHbt2qXp06fL5XLpmmuu0eTJk895XgAA4LfDZhx/pk4f2L9/v9auXauSkhJJ0mWXXabBgwdb%2Bl0ip/Og6fu024MUFdVKlZWHLfOWqtR8cl03779%2Be%2Byz8d59vf322M1lzbyBbIHHqrkk62bzZi6H43xT99dUPn8HS5IuuOACjRgxQjt37tRFF10k6ZezuQMAAFiBzz%2BDZRiG5s6dqyuvvFJDhgzR3r179eCDD2ratGk6evSor6cDAABgOp8XrJdffllr1qzRI4884j6hZ//%2B/bVu3bpG544CAAAIRD4vWHl5eZo%2BfbqGDRvm/mD5oEGDNHv2bL3zzju%2Bng4AAIDpfF6wdu7cqa5duzYa79KlS6NfLwMAABCIfF6wLrzwQn399deNxj/55BP3B94BAAACmc9/ivCOO%2B7Qo48%2BKqfTKcMw9MUXXygvL08vv/yyHnroIV9PBwAAwHQ%2BL1jDhw9XXV2dFi5cqJqaGk2fPl2tW7fWfffdp1GjRvl6OgAAAKbzecFau3atBg4cqJEjR2rfvn0yDENt2rTx9TQAAAC8xuefwZo5c6b7w%2BytW7emXAEAAMvxecHq0KGDtm/f7uuHBQAA8BmfHyLs0qWLHnjgAS1ZskQdOnRQaGiox/bs7GxfTwkAAMC8DP14AAAcNElEQVRUPi9Y33//vZKTkyWJ814BAABL8knBmjNnjiZOnKiWLVvq5Zdf9sVDAgAA%2BI1PPoP10ksvqbq62mPs7rvvVnl5uS8eHgAAwKd8UrAMw2g0tnHjRtXW1vri4QEAAHzK5z9FCAAAYHUULAAAAJP5rGDZbDZfPRQAAIBf%2Bew0DbNnz/Y459XRo0eVk5OjVq1aedyO82ABAIBA55OCdeWVVzY651VSUpIqKytVWVnpiykAAAD4jE8KFue%2BAgAAvyV8yB0AAMBkFCwAAACTUbAAAABMRsECAAAwmSUL1ptvvqnOnTs3unTp0kWS9Je//KXRtv/85z/u%2By9btkypqalKSkrS1KlTG/0eRQAAgFPx2XmwfGnQoEFKTU11X6%2Brq9O4cePUt29fSVJJSYlycnL0hz/8wX2bCy64QJL0/vvvKzc3Vzk5OWrTpo2ysrKUk5Oj6dOn%2BzQDAAAIXJZ8B6tFixZyOBzuy9tvvy3DMPTAAw/I5XJp586dio%2BP97hNSEiIJGnFihUaN26c%2BvXrpyuuuEKPPvqoVq9ezbtYAACgySxZsH6tqqpKL7zwgjIzMxUSEqIdO3bIZrPpoosuanTb%2Bvp6ff3110pJSXGPJSYm6ujRo9q2bZsvpw0AAAKYJQ8R/tqrr76q6OhoDRw4UJK0Y8cOhYeHa8qUKfryyy/1u9/9TpMmTdLVV1%2BtAwcOqLa2VtHR0e772%2B12RUZGau/evU1%2BzPLy8kZnrrfbW3rs1wzBwUEef1qFVXN5m93uv6%2BXldeMbIHHqrkk62azYi5LFyzDMLRq1Srdeeed7rEdO3aopqZGffr00d13360PPvhAf/nLX5SXl6e2bdtKkvtw4TEhISFyuVxNfty8vDzl5uZ6jKWnpysjI%2BMc0pxcRESYV/brb1bN5S1RUa1OfyMvs/KakS3wWDWXZN1sVspl6YL19ddfq6ysTIMHD3aP3Xvvvbr11lvdH2rv0qWLvvnmG73%2B%2Buv6n//5H0lqVKZcLpfCwpq%2B6CNHjlRaWprHmN3eUpWVh882ygkFBwcpIiJMBw5Uq76%2BwdR9%2B5NVc3mb2c%2BvM2HlNSNb4LFqLsm62byZy1/ffFq6YH366adKSUlxlylJCgoK8rguSXFxcSouLlZkZKRCQ0NVUVGhjh07SvrlJxCrqqrkcDia/LjR0dGNDgc6nQdVV%2BedF0N9fYPX9u1PVs3lLc3ha2XlNSNb4LFqLsm62ayUyzoHO09g8%2BbN6t69u8fYQw89pKysLI%2Bxbdu2KS4uTkFBQYqPj1d%2Bfr57W0FBgex2u/scWgAAAKdj6YJVVFSkSy65xGMsLS1N77zzjt566y39%2BOOPys3NVX5%2BvsaMGSNJGj16tJYuXap169Zp8%2BbNmjFjhm6%2B%2BeYzOkQIAAB%2B2yx9iLCiokIREREeY9dcc40eeeQRLVy4ULt379all16qJUuWqH379pKkwYMHa9euXZo%2BfbpcLpeuueYaTZ482R/TBwAAAcrSBWvz5s0nHB8xYoRGjBhx0vvdfffduvvuu701LQAAYHGWPkQIAADgDxQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZJYtWB988IE6d%2B7sccnIyJAkbd26VSNGjFBCQoKGDx%2BuLVu2eNx37dq16t%2B/vxISEpSenq59%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%2Bfl5Sk3N9djLD093f1TjGaLiGj63AKJVXN5S1RUK39PwdJrRrbAY9VcknWzWSmXJQuWJEVGRnpc79ixo2pra%2BVwOFRRUeGxraKiwv3uUkxMzAm3OxyOJj/2yJEjlZaW5jFmt7dUZeXhM4lwWsHBQYqICNOBA9Wqr28wdd/%2BZNVc3mb28%2BtMWHnNyBZ4rJpLsm42b%2Bby1zeflixYn376qR544AF99NFH7neevv32W0VGRio5OVkvvPCCDMOQzWaTYRjatGmT7rnnHklSQkKC8vPzNWzYMEnSnj17tGfPHiUkJDT58aOjoxsdDnQ6D6quzjsvhvr6Bq/t25%2BsmstbmsPXysprRrbAY9VcknWzWSmXdQ52/kpSUpJCQ0P1t7/9TTt27NDHH3%2BsOXPm6M4779TAgQN14MABPfbYYyouLtZjjz2m6upqXXfddZKkUaNGac2aNVq1apW2bdumKVOmqG/fvrrooov8nAoAAAQKSxas8PBwLV26VPv27dPw4cM1bdo0jRw5UnfeeafCw8O1aNEi97tUhYWFWrx4sVq2bCnpl3I2c%2BZMLViwQKNGjdIFF1yg7OxsPycCAACBxGYYhuHvSfwWOJ0HTd%2Bn3R6kqKhWqqw8bJm3VKXmk%2Bu6ef/122Nb3Xv39fb3FJqsuTwfvcGq2ayaS7JuNm/mcjjON3V/TWXJd7AAAAD8iYIFAABgMgoWAACAyShYAAAAJqNgAQAAmMySJxpF88RP5QEAfit4BwsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMJllC1ZZWZkyMjLUo0cPpaamKjs7W7W1tZKk2bNnq3Pnzh6XlStXuu%2B7du1a9e/fXwkJCUpPT9e%2Bffv8FQMAAAQgu78n4A2GYSgjI0MRERF65ZVXtH//fk2dOlVBQUF68MEHVVJSoszMTN14443u%2B4SHh0uSNm/erGnTpunRRx9Vly5d9NhjjykrK0uLFi3yVxwAABBgLPkO1o4dO1RQUKDs7GxdeumlSklJUUZGhtauXStJKikp0WWXXSaHw%2BG%2BhIWFSZJWrlyp6667TkOHDlWXLl00Z84cffzxxyotLfVnJAAAEEAsWbAcDoeWLFmitm3beowfOnRIhw4dUllZmTp06HDC%2BxYWFiolJcV9vV27doqNjVVhYaE3pwwAACzEkocIIyIilJqa6r7e0NCglStXqlevXiopKZHNZtPzzz%2BvTz75RJGRkbrtttvchwvLy8sVHR3tsb82bdpo7969TX788vJyOZ1OjzG7vWWj/Z6r4OAgjz%2BBQGG3B85z1sqvM6tms2ouybrZrJjLkgXreDk5Odq6daveeOMNffPNN7LZbIqLi9OYMWO0ceNGPfzwwwoPD9eAAQNUU1OjkJAQj/uHhITI5XI1%2BfHy8vKUm5vrMZaenq6MjAxT8hwvIiLMK/sFvCUqqpW/p3DGrPw6s2o2q%2BaSrJvNSrksX7BycnK0fPlyPf300%2BrUqZMuvfRS9evXT5GRkZKkLl266IcfftCrr76qAQMGKDQ0tFGZcrlc7s9oNcXIkSOVlpbmMWa3t1Rl5eFzD/QrwcFBiogI04ED1aqvbzB134A3mf1a8CYrv86sms2quSTrZvNmLn99Q2fpgjVr1iy9%2BuqrysnJ0bXXXitJstls7nJ1TFxcnNavXy9JiomJUUVFhcf2iooKORyOJj9udHR0o8OBTudB1dV558VQX9/gtX0D3hCIz1crv86sms2quSTrZrNSLusc7DxObm6uXnvtNT311FMaPHiwe3z%2B/PkaP368x223bdumuLg4SVJCQoLy8/Pd2/bs2aM9e/YoISHBJ/MGAACBz5IFq6SkRM8995zuuusuJScny%2Bl0ui/9%2BvXTxo0btXTpUv3000/6%2B9//rrfeeku33367JGnUqFFas2aNVq1apW3btmnKlCnq27evLrroIj%2BnAgAAgcKShwg//PBD1dfXa%2BHChVq4cKHHtu%2B%2B%2B07z58/XM888o/nz5%2BvCCy/Uk08%2BqaSkJElSUlKSZs6cqWeeeUb79%2B9X7969NWvWLH/EAAAAAcpmGIbh70n8FjidB03fp90epKioVqqsPBwQx6yvm/dff08BzcR79/X29xSaLNBeZ2fCqtmsmkuybjZv5nI4zjd1f01lyUOEAAAA/kTBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwmd3fEwCA5i5l2r/8PYUz8t59vf09BeA3j4IFwOeum/dff08BALyKghXgAu07awAAfgv4DBYAAIDJKFgnUFtbq6lTpyolJUV9%2BvTRiy%2B%2B6O8pAQCAAMIhwhOYM2eOtmzZouXLl2v37t168MEHFRsbq4EDB/p7agAAIABQsI5z5MgRrVq1Si%2B88IK6deumbt26qaioSK%2B88goFCwAANAkF6zjbtm1TXV2dkpKS3GPJycl6/vnn1dDQoKAgjqoCgFkC7SdKOQUGmoqCdRyn06moqCiFhIS4x9q2bava2lpVVVWpdevWp91HeXm5nE6nx5jd3lLR0dGmzjU4mLIHoLFAKy2BxG7377%2B7x/7db%2Bq//wPmfurN6Zjqfx8baKn/1yhYx6murvYoV5Lc110uV5P2kZeXp9zcXI%2BxiRMnatKkSeZM8v%2BUl5dr3O%2BKNHLkSNPLmz%2BVl5crLy/Pcrkk62azai6JbIHIqrmkX7ItX76kydn%2B97HA%2BGhLeXm5nn32WUutmXWqoklCQ0MbFalj11u0aNGkfYwcOVJvvvmmx2XkyJGmz9XpdCo3N7fRu2WBzqq5JOtms2ouiWyByKq5JOtms2Iu3sE6TkxMjCorK1VXVye7/Zcvj9PpVIsWLRQREdGkfURHR1umgQMAgDPHO1jH6dq1q%2Bx2uwoKCtxj%2Bfn5io%2BP5wPuAACgSWgMxwkLC9PQoUM1Y8YMbd68WevWrdOLL76osWPH%2BntqAAAgQATPmDFjhr8n0dz06tVLW7du1ZNPPqkvvvhC99xzj4YPH%2B7vaZ1Qq1at1KNHD7Vq1crfUzGVVXNJ1s1m1VwS2QKRVXNJ1s1mtVw2wzAMf08CAADASjhECAAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIVQAzD0O23364333zTY7yyslKTJk1SUlKS0tLStGbNGo/tW7du1YgRI5SQkKDhw4dry5Ytvpx2k23dulWdO3f2uAwbNsy9vbS0VOPHj1diYqIGDRqkzz77zI%2BzPXO1tbWaOnWqUlJS1KdPH7344ov%2BntJZ%2BeCDDxqtU0ZGhqTAea4dz%2BVyaciQIdqwYYN77HTPt88//1xDhgxRQkKCxo4dq9LSUl9Pu0lOlG327NmN1nDlypXu7WvXrlX//v2VkJCg9PR07du3zx9TP6GysjJlZGSoR48eSk1NVXZ2tmprayUF/pqdKlsgr9mPP/6oO%2B64Q0lJSerbt6%2BWLFni3hboa3ZKBgJCfX29MXPmTKNTp07G6tWrPbZNmDDBGDdunPHdd98Zr7/%2BunH55ZcbhYWFhmEYxuHDh43evXsbTzzxhFFcXGzMmjXL%2BOMf/2gcPnzYHzFOac2aNcYNN9xglJeXuy/79u0zDMMwGhoajOuvv97IzMw0iouLjeeff95ISEgwdu3a5edZN93MmTON66%2B/3tiyZYvx73//20hKSjLee%2B89f0/rjD333HPGhAkTPNZp//79AfVc%2B7WamhojPT3d6NSpk7F%2B/XrDME7/fNu1a5eRmJhoLF261Ni%2Bfbvx17/%2B1RgyZIjR0NDgzyiNnCibYRjG%2BPHjjUWLFnms4ZEjRwzDMIzCwkLjiiuuMP7xj38Y3377rTFmzBjj7rvv9lcEDw0NDcbNN99s3Hnnncb27duNjRs3GgMGDDCeeOKJgF%2BzU2UzjMBds/r6euOaa64xMjMzje%2B//9746KOPjO7duxtvv/12wK/Z6VCwAsDevXuNMWPGGH379jVSUlI8CtaPP/5odOrUySgtLXWPTZ061XjwwQcNwzCMVatWGWlpae4nZENDgzFgwIBGJa05eOqpp4z777//hNs%2B//xzIzEx0eM/63HjxhnPPPOMr6Z3Tg4fPmzEx8d7/Ce3YMECY8yYMX6c1dnJzMw0nnzyyUbjgfRcO6aoqMj485//bFx//fUeJeR0z7d58%2BZ5rN2RI0eMpKQkj/X1t5NlMwzDSE1NNT799NMT3m/y5Mnufz8MwzB2795tdO7c2fjpp5%2B8PufTKS4uNjp16mQ4nU732DvvvGP06dMn4NfsVNkMI3DXrKyszPjrX/9qHDx40D2Wnp5uPPLIIwG/ZqfDIcIA8M0336hdu3ZavXq1zj//fI9thYWFateundq3b%2B8eS05O1ldffeXenpycLJvNJkmy2Wzq3r27CgoKfBegiUpKStShQ4cTbissLNRll12mli1buseSk5ObZY4T2bZtm%2Brq6pSUlOQeS05OVmFhoRoaGvw4szN3snUKpOfaMV9%2B%2BaV69uypvLw8j/HTPd8KCwuVkpLi3hYWFqZu3bo1q6wny3bo0CGVlZWd8rX262zt2rVTbGysCgsLvTndJnE4HFqyZInatm3rMX7o0KGAX7NTZQvkNYuOjta8efMUHh4uwzCUn5%2BvjRs3qkePHgG/Zqdj9/cEcHppaWlKS0s74Tan06no6GiPsTZt2qisrMy9/ZJLLmm0vaioyDuTPQclJSVqaGjQ9ddfr4MHD%2Bqqq67SlClTFB4eftKce/fu9dNsz4zT6VRUVJRCQkLcY23btlVtba2qqqrUunVrP86u6QzD0Pfff6/PPvtMixYtUn19vQYOHKiMjIyAeq4dM3r06BOOn%2B75FgjPx5NlKykpkc1m0/PPP69PPvlEkZGRuu2223TjjTdKksrLy5tttoiICKWmprqvNzQ0aOXKlerVq1fAr9mpsgXymv1aWlqadu/erX79%2Bunaa6/V448/HtBrdjoUrGagpqbGXYiO53A4PNr98aqrqz3%2B05akkJAQuVyuJm33pVPlbN26tUpLS9W%2BfXs9/vjjOnDggLKzszV58mQtXLiwWeU4Gyebv6SAySBJu3fvdmeZN2%2Bedu7cqdmzZ6umpibg1%2BjXAul1daZ27Nghm82muLg4jRkzRhs3btTDDz%2Bs8PBwDRgwQDU1NQGTLScnR1u3btUbb7yhZcuWWWrNfp3tm2%2B%2BscSaPfPMM6qoqNCMGTOUnZ1t6deZRMFqFgoLCzV27NgTbluwYIH69%2B9/0vuGhoY2erK5XC61aNGiSdt96XQ5169fr9DQUJ133nmSpCeeeELDhw9XWVmZQkNDVVVV5XEff%2BU4GydbB0kBk0GSLrzwQm3YsEEXXHCBbDabunbtqoaGBk2ePFk9evRoNs%2B1c3W659vJ1jMiIsJnczxbQ4cOVb9%2B/RQZGSlJ6tKli3744Qe9%2BuqrGjBgwEmzhYWF%2BWO6J5WTk6Ply5fr6aefVqdOnSy1Zsdnu/TSSy2xZvHx8ZJ%2B%2BYnqBx54QMOHD1d1dbXHbQJ1zU6EgtUM9OzZU999991Z3TcmJkYVFRUeYxUVFXI4HKfcfvzbrr5wpjk7duwo6ZcfXY6JiVFxcbHHdn/lOBsxMTGqrKxUXV2d7PZfXnZOp1MtWrQImH8sjjn2j/wxHTt2VG1trRwOR7N5rp2r0z3fTva66tq1q8/meLZsNlujNYyLi9P69eslnf7flOZg1qxZevXVV5WTk6Nrr71WknXW7ETZAnnNKioqVFBQ4PFGwSWXXKKjR4/K4XBox44djW4faGt2MnzIPcAlJiZq165dHsek8/PzlZiYKElKSEjQV199JcMwJP3yGZpNmzYpISHBL/M9meLiYiUlJXmc4%2BTbb7%2BV3W7XxRdfrISEBH3zzTeqqalxb8/Pz292OU6ma9eustvtHh/OzM/PV3x8vIKCAudl%2BOmnn6pnz54e33V%2B%2B%2B23ioyMdP9wRXN/rjXF6Z5vCQkJys/Pd2%2Brrq7W1q1bAyLr/PnzNX78eI%2Bxbdu2KS4uTlLjbHv27NGePXuaTbbc3Fy99tpreuqppzR48GD3uBXW7GTZAnnNdu7cqYkTJ3p8PGTLli1q3bq1kpOTA37NTsmfP8KIM9evX79GP/Z%2B%2B%2B23G2PGjDG%2B/fZb4/XXXzfi4%2BPd58E6ePCg0atXL2PWrFlGUVGRMWvWLKN3797N7txE9fX1xg033OA%2Bn9fGjRuNQYMGGY888ohhGIZRV1dnDBo0yLjvvvuM7du3G4sWLTISExMD6jxYDz/8sDF48GCjsLDQ%2BOCDD4zu3bsb77//vr%2BndUYOHjxopKamGvfff79RUlJifPTRR0afPn2MxYsXB8xz7WR%2BfSqD0z3fSktLjfj4eGPRokXu8/Ncf/31zfb8PL/OVlhYaFx22WXGkiVLjB9//NF45ZVXjMsvv9zYtGmTYRiGsWnTJqNbt27G66%2B/7j6n0oQJE/w5fbfi4mKja9euxtNPP%2B1xPqjy8vKAX7NTZQvkNaurqzOGDRtm3H777UZRUZHx0UcfGX/84x%2BNZcuWBfyanQ4FK8CcqGBVVFQYEyZMMOLj4420tDTjnXfe8dheWFhoDB061IiPjzduuukm45tvvvHllJts9%2B7dRnp6upGSkmL06NHDmDVrllFbW%2Bve/sMPPxi33HKLcfnllxuDBw82/vvf//pxtmfuyJEjxpQpU4zExESjT58%2BxksvveTvKZ2V7du3G%2BPHjzcSExON3r17G88%2B%2B6z7H7xAea6dyPHnijrd8%2B2jjz4yrrnmGuOKK64wxo0b1yzOOXQyx2f74IMPjOuvv96Ij483Bg4c2Kjor1692rj66quNxMREIz093X3CX39btGiR0alTpxNeDCOw1%2Bx02QJ1zQzjl3M5pqenG927dzd69%2B5tLFy40P1vRiCv2enYDOP/3s8HAACAKQLnwx8AAAABgoIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACY7P8D3D45b9XSNnYAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2994957149492151241”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>401</td> <td class=”number”>12.5%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>52</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666667</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (917)</td> <td class=”number”>2228</td> <td class=”number”>69.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2994957149492151241”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_FARMS_07_12”>PCH_GHVEG_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>147</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>62.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2012</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>93.699</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1900</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5220474690065710856”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAA5xJREFUeJzt3LtLa1kYhvE3IoKCdrkgFtrlD5CANgYbg8aASIwWioVNUKxsRNI5Vt5Ae20sLGws5ICQdGJkGivRRgsRk6iIUQtvazqZM8fzMceJxpl5fuXeSfbXPKzFZu94nHNOAN5UUe4BgK%2BsstwD/FXz1Ldf/s7vv0U%2BYBKAFQQwEQhgIBDAQCCAgUAAA4EABgIBDAQCGAgEMBAIYCAQwEAggIFAAAOBAAYCAQwEAhgIBDAQCGAgEMBAIICBQAADgQAGAgEMBAIYvtwfx70HfzaHj8IKAhgIBDAQCGAgEMBAIICBQAADgQAGAgEMBAIYCAQwEAhgIBDAQCCAgUAAA4EAhv/E%2ByDvwTsk%2BDtYQQADgQCG/%2B0W6z1%2BdVvGluzfjxUEMLCCfKDPuhHAyvZxPM45V%2B4hAEs%2Bn9f6%2BroSiYR8Pt%2BnXpstFr68QqGg5eVlFQqFT782gQAGAgEMBAIYCARfntfr1djYmLxe76dfm7tYgIEVBDAQCGAgEMBAIICBQAADgQAGAgEMBIKyWVlZUTQaVTQa1eTkpB4eHnR0dKS%2Bvj5FIhGNj4/r/v5eknR7e6tkMqnOzk719vbq5OTk9Xfm5%2BcViUTU0dGhdDpd2iEdUAb7%2B/suGo26u7s79/Ly4iYmJtzKyoqLxWIum80655xbXFx0s7Ozzjnnpqen3dLSknPOuZ2dHZdIJJxzzm1vb7vh4WH3%2BPjocrmca29vd9fX1yWbkxUEZVFXV6dUKqWamhp5PB4Fg0EdHh6qWCwqFApJkuLxuLa2tiRJmUxGPT09kqSWlhYVCgWdnZ0pnU4rFoupsrJSPp9PoVBImUymZHMSCMqisbHxNYTLy0utra2pqalJfr//9TM%2Bn0%2B5XE6SlMvlfjh3fn7%2B0%2BOlQiAoq9PTUw0NDSkej6u5ufmH8x6PR5Lk3nhksKKi4qfHS4VAUDYHBwcaGBhQf3%2B/ksmkAoHAd28NFgoFBQIBSZLf73/znN/vVz6f/%2B74n1eUf4pAUBZXV1caGRlRKpXS4OCgJKm%2Bvl7V1dXa29uTJG1sbKitrU2SFA6HtbGxIUnKZrOqqalRIBBQOBzW5uamnp6edHFxod3dXbW2tpZsTh53R1ksLCxodXVVjY2Nr8fC4bC6urqUSqVULBbV0NCgubk51dbW6ubmRlNTUzo%2BPlZVVZVmZmYUDAblnNP8/LzS6bSen581Ojqq7u7uks1JIICBLRZgIBDAQCCA4Q/x5O2UPDSfNAAAAABJRU5ErkJggg%3D%3D”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5220474690065710856”

aria-controls=”quantiles-5220474690065710856” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5220474690065710856” aria-controls=”histogram-5220474690065710856”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5220474690065710856” aria-controls=”common-5220474690065710856”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5220474690065710856” aria-controls=”extreme-5220474690065710856”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5220474690065710856”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>166.67</td>

</tr> <tr>

<th>95-th percentile</th> <td>450</td>

</tr> <tr>

<th>Maximum</th> <td>1900</td>

</tr> <tr>

<th>Range</th> <td>2000</td>

</tr> <tr>

<th>Interquartile range</th> <td>191.67</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>201.7</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1526</td>

</tr> <tr>

<th>Kurtosis</th> <td>16.936</td>

</tr> <tr>

<th>Mean</th> <td>93.699</td>

</tr> <tr>

<th>MAD</th> <td>135.13</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.0691</td>

</tr> <tr>

<th>Sum</th> <td>112250</td>

</tr> <tr>

<th>Variance</th> <td>40683</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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<img 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2u5WVlaXExERJUn5%2BvvLz8%2BV2u%2Bv89aOiomqcDvR6T6qy0lnfuJcqUPJXVVUHzCxWc2o2p%2BaSnJvNqbkk52Zzai67OO%2BEq6TY2FiFhobq17/%2BtQ4ePKgPPvhACxcu1N13363hw4eruLhYCxYsUG5urhYsWKDS0lKNGDFCkjRhwgStXbtWK1eu1L59%2BzRr1iwNGjRI7dq1szkVAACoLxxZsBo3bqwXX3xRx48f17hx45SSkqKkpCTdfffdaty4sZYtW%2BY7SuXxeJSRkaHw8HBJZ8vZ3LlzlZ6ergkTJqhJkyZKTU21OREAAKhPHHmKUJKuvvpqvfzyy7Vu69mzp9asWXPe6yYmJvpOEQIAAFwqRx7BAgAAsBMFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIsFXMEaP3683njjDZ08edLuUQAAAL6XgCtY/fr109KlS5WQkKD7779fmzdvljHG7rEAAADqLOAK1gMPPKCNGzdqyZIlCg4O1owZMzRo0CA999xzOnTokN3jAQAAXJTL7gFqExQUpP79%2B6t///4qLS3Vq6%2B%2BqiVLligjI0O9e/fWnXfeqZ/%2B9Kd2jwkAAFCrgCxYklRQUKC33npLb731lvbv36/evXvrlltu0dGjR/XrX/9aO3bsUEpKit1jAgAA1BBwBWvt2rVau3atPv74YzVv3lxjx47V7373O3Xo0MG3T6tWrbRgwQIKFgAACEgBV7BSUlI0ePBgpaen68Ybb1SDBjVfJtaxY0fdcccdNkwHAABwcQFXsD788EM1a9ZMRUVFvnK1a9cude/eXcHBwZKk3r17q3fv3naOCQAAcF4B91uEp06d0vDhw/XCCy/41qZMmaIxY8YoPz/fxskAAADqJuAK1m9%2B8xu1b99ev/jFL3xr69evV6tWrZSammrjZAAAAHUTcAXrH//4hx5%2B%2BGFFRkb61po3b65Zs2Zp27ZtNk4GAABQNwFXsFwul4qLi2usl5aW8o7uAACgXgi4gnXjjTdq/vz5%2BvLLL31reXl5Sk1N1YABA2ycDAAAoG4C7rcIZ8%2BerV/84hcaNmyYIiIiJEnFxcXq3r275syZY/N0AAAAFxdwBatFixZas2aNtmzZopycHLlcLnXu3FnXX3%2B9goKC7B4PAADgogKuYElScHCwBgwYwClBAABQLwVcwfJ6vVq8eLF27typM2fO1Hhh%2B9///vdLur0pU6aoefPmeuqppyRJe/fu1eOPP679%2B/erc%2BfOevLJJ9WjRw/f/uvWrdPixYvl9XqVkJCgefPmqXnz5pcfDAAA/GgE3IvcH330UX3wwQe68cYbNXbsWN1yyy1%2BH5fib3/7mz744APf5ZKSEk2ZMkXx8fFavXq1YmNjNXXqVJWUlEg6%2B47xKSkpmj59ujIzM1VcXMzrvgAAwCULuCNY27Zt0/LlyxUfH39Zt1NUVKSFCxcqJibGt7Z%2B/XqFhoZq1qxZCgoKUkpKij788EO98847SkxM1IoVKzRixAiNHTtWkrRw4UINHjxYeXl5ateu3WXNAwAAfjwC7ghWeHi4WrRocdm389vf/lZjxoxR586dfWsej0dxcXG%2BF8sHBQWpd%2B/eys7O9m3/drFr1aqVWrduLY/Hc9nzAACAH4%2BAK1hjxozR8uXLVVVV9b1vY%2BvWrfrHP/6hadOm%2Ba17vV5FRUX5rbVo0UJHjx6VJBUUFFxwOwAAQF0E3CnCoqIirVu3Tu%2B//77atWunkJAQv%2B1//OMfL3j98vJyPf7443rsscfUsGFDv22lpaU1bi8kJEQVFRWSpLKysgtur6uCggJ5vV6/NZcrvEZ5%2B7Fxuezt88HBDfw%2BO4lTszk1l%2BTcbE7NJTk3m1NzSfZmCriCJUmjR4/%2B3tdNS0tTjx49an2Lh9DQ0BplqaKiwlfEzrc9LCzskmbIzMxUWlqa31pycrJmzpx5SbfjNM2aNbJ7BElSRMSl3Z/1iVOzOTWX5NxsTs0lOTebU3PZJeAKVmpq6mVd/29/%2B5sKCwsVGxsrSb7C9O6772r06NEqLCz027%2BwsNB3ZCk6OrrW7d/%2Bw9N1kZSUpCFDhvituVzhOnHi9CXdjtPYnT84uIEiIsJUXFyqqqpqW2exmlOzOTWX5NxsTs0lOTebU3NJ32SzQ8AVLOnsKbY333xThw4d0iOPPKIdO3aoS5cu6tix40Wv%2B%2Bqrr6qystJ3%2Bemnn5YkPfjgg9qxY4deeOEFGWMUFBQkY4x27type%2B65R5LkdruVlZWlxMRESVJ%2Bfr7y8/Pldrsvaf6oqKgapwO93pOqrHTWN%2B6lCpT8VVXVATOL1Zyazam5JOdmc2ouybnZnJrLLgF3wvXw4cO6%2BeabtWbNGr377rsqKSnR%2BvXrNW7cuDr9Nl%2BbNm3Uvn1730ejRo3UqFEjtW/fXsOHD1dxcbEWLFig3NxcLViwQKWlpRoxYoQkacKECVq7dq1Wrlypffv2adasWRo0aBBv0QAAAC5JwBWsp556SkOHDtV7772nn/zkJ5KkZ599VkOGDPEdjfq%2BGjdurGXLlvmOUnk8HmVkZCg8PFySFBsbq7lz5yo9PV0TJkxQkyZNLvuUJQAA%2BPEJuFOEO3fu1Guvveb3h51dLpemTZumn//855d8e%2Bf%2BRM45PXv21Jo1a867f2Jiou8UIQAAwPcRcEewqqurVV1d8xzw6dOnFRwcbMNEAAAAlybgClZCQoKWLVvmV7KKioq0aNEi9evXz8bJAAAA6ibgCtbDDz%2BsPXv2KCEhQeXl5br33ns1ePBgffXVV5o9e7bd4wEAAFxUwL0GKzo6Wn/5y1%2B0bt06ffbZZ6qurtaECRM0ZswYNW7c2O7xAAAALirgCpYkhYWFafz48XaPAQAA8L0EXMGaNGnSBbdf7G8RAgAA2C3gClabNm38LldWVurw4cPav3%2B/7rzzTpumAgAAqLuAK1jne2PP9PR0HT169AeeBgAA4NIF3G8Rns%2BYMWP09ttv2z0GAADARdWbgvXJJ5/wRqMAAKBeCLhThLW9yP3UqVP6/PPPddttt9kwEQAAwKUJuILVunVrv79DKEk/%2BclPdMcdd%2BhnP/uZTVMBAADUXcAVrO/%2BcWYAAID6JuAK1o4dO%2Bq873XXXXcFJwEAAPh%2BAq5gTZw40XeK0BjjW//uWlBQkD777LMffkAAAICLCLiCtXTpUs2fP18PPfSQ%2BvTpo5CQEO3evVtz587VLbfcopEjR9o9IgAAwAUF3Ns0pKam6rHHHtOwYcPUrFkzNWrUSP369dPcuXP1%2Buuvq02bNr4PAACAQBRwBaugoKDW8tS4cWOdOHHChokAAAAuTcAVrF69eunZZ5/VqVOnfGtFRUVatGiRrr/%2BehsnAwAAqJuAew3Wr3/9a02aNEk33nijOnToIGOMvvjiC0VGRuqPf/yj3eMBAABcVMAVrE6dOmn9%2BvVat26dDhw4IEm6/fbbNWrUKIWFhdk8HQAAwMUFXMGSpCZNmmj8%2BPH66quv1K5dO0ln380d9duIxR/ZPcIlefu%2B/naPAACopwLuNVjGGD399NO67rrrNHr0aB09elSzZ89WSkqKzpw5Y/d4AAAAFxVwBevVV1/V2rVr9fjjjyskJESSNHToUL333ntKS0uzeToAAICLC7iClZmZqccee0yJiYm%2Bd28fOXKk5s%2Bfr7/%2B9a82TwcAAHBxAVewvvrqK3Xr1q3GeteuXeX1em2YCAAA4NIEXMFq06aNdu/eXWP9ww8/9L3gHQAAIJAF3G8R/vKXv9STTz4pr9crY4y2bt2qzMxMvfrqq3r44YftHg8AAOCiAq5gjRs3TpWVlfr973%2BvsrIyPfbYY2revLnuu%2B8%2BTZgwwe7xAAAALirgCta6des0fPhwJSUl6fjx4zLGqEWLFnaPBQAAUGcB9xqsuXPn%2Bl7M3rx5c8oVAACodwKuYHXo0EH79%2B%2B/7Ns5fPiwfvnLXyo2NlaDBg3S8uXLfdvy8vI0efJk9erVSyNHjtTmzZv9rrtlyxaNHj1abrdbkyZNUl5e3mXPAwAAfjwC7hRh165d9eCDD2r58uXq0KGDQkND/banpqZe9Daqq6s1ZcoUxcTEaM2aNTp8%2BLDuv/9%2BRUdHa/To0UpOTlaXLl20atUqvffee5o%2BfbrWr1%2Bv1q1b68iRI0pOTtaMGTM0YMAApaena9q0aXrrrbd878sFAABwIQFXsA4dOqS4uDhJ%2Bt7ve1VYWKhu3brpiSeeUOPGjdWhQwddf/31ysrKUsuWLZWXl6c33nhD4eHh6tSpk7Zu3apVq1ZpxowZWrlypXr06KG77rpL0tlC179/f23fvl19%2B/a1LCcAAHCugChYCxcu1PTp0xUeHq5XX331sm8vKipKixcvlnT2bxvu3LlTO3bs0OOPPy6Px6Nrr71W4eHhvv3j4uKUnZ0tSfJ4PIqPj/dtCwsLU/fu3ZWdnU3BAgAAdRIQr8F6%2BeWXVVpa6rc2ZcoUFRQUXPZtDxkyRLfddptiY2M1bNgweb1eRUVF%2Be3TokULHT16VJIuuh0AAOBiAuIIljGmxtqOHTtUXl5%2B2bf9u9/9ToWFhXriiSeUmpqq0tJS3x%2BRPickJEQVFRWSdNHtdVFQUFDj9KbLFV6juCGwuVwB8e%2BPOgkObuD32Smcmktybjan5pKcm82puSR7MwVEwbqSYmJiJEnl5eV68MEHNW7cuBpHyyoqKtSwYUNJUmhoaI0yVVFRoYiIiDp/zczMTKWlpfmtJScna%2BbMmd8nAmzSrFkju0e4ZBERYXaPcEU4NZfk3GxOzSU5N5tTc9nFkQWrsLBQ2dnZGjp0qG%2Btc%2BfOOnPmjCIjI3Xw4MEa%2B587uhQdHa3CwsIa22v7A9Tnk5SUpCFDhvituVzhOnHi9KVGgY3q0/0VHNxAERFhKi4uVVVVtd3jWMapuSTnZnNqLsm52ZyaS/ommx0CpmBZ%2BRYIX331laZPn64PPvhA0dHRkqQ9e/aoefPmiouL00svvaSysjLfUausrCzfby663W5lZWX5bqu0tFR79%2B7V9OnT6/z1o6KiapwO9HpPqrLSWd%2B4Tlcf76%2Bqqup6OffFODWX5NxsTs0lOTebU3PZJWAK1vz58/3e8%2BrMmTNatGiRGjXyP01Tl/fBiomJUffu3fXII49ozpw5%2Bvrrr7Vo0SLdc8896tOnj1q1aqU5c%2BZo2rRp2rhxo3bt2uW73XHjxunFF19URkaGBg8erPT0dLVt25bfIAQAAHUWEAXruuuuq/Gi8NjYWJ04cUInTpy45NsLDg7WkiVLNG/ePCUlJSksLEwTJ07UpEmTFBQUpCVLliglJUWJiYlq37690tPT1bp1a0lS27Zt9fzzz%2Bs3v/mN0tPTFRsbq/T0dN5kFAAA1FmQqe1X%2BGA5r/fkFbndEYs/uiK3C%2Bnt%2B/rbPUKduVwN1KxZI504cdpRh/idmktybjan5pKcm82puaRvstnBeb%2BTCQAAYDMKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWc2zBOnbsmGbOnKk%2BffpowIABSk1NVXl5uSQpLy9PkydPVq9evTRy5Eht3rzZ77pbtmzR6NGj5Xa7NWnSJOXl5dkRAQAA1FOOLFjGGM2cOVOlpaV67bXX9Nxzz2njxo1avHixjDFKTk5Wy5YttWrVKo0ZM0bTp0/XkSNHJElHjhxRcnKyEhMT9ec//1nNmzfXtGnTZIyxORUAAKgvXHYPcCUcPHhQ2dnZ%2Buijj9SyZUtJ0syZM/Xb3/5WN954o/Ly8vTGG28oPDxcnTp10tatW7Vq1SrNmDFDK1euVI8ePXTXXXdJklJTU9W/f39t375dffv2tTMWAACoJxx5BCsyMlLLly/3latzTp06JY/Ho2uvvVbh4eG%2B9bi4OGVnZ0uSPB6P4uPjfdvCwsLUvXt333YAAICLceQRrIiICA0YMMB3ubq6WitWrFC/fv3k9XoVFRXlt3%2BLFi109OhRSbro9rpVhWEMAAAYL0lEQVQoKCiQ1%2Bv1W3O5wmvcLgKby1V//v0RHNzA77NTODWX5NxsTs0lOTebU3NJ9mZyZMH6rkWLFmnv3r3685//rD/84Q8KCQnx2x4SEqKKigpJUmlp6QW310VmZqbS0tL81pKTkzVz5szvmQB2aNaskd0jXLKIiDC7R7ginJpLcm42p%2BaSnJvNqbns4viCtWjRIr3yyit67rnn1KVLF4WGhqqoqMhvn4qKCjVs2FCSFBoaWqNMVVRUKCIios5fMykpSUOGDPFbc7nCdeLE6e%2BZAnaoT/dXcHADRUSEqbi4VFVV1XaPYxmn5pKcm82puSTnZnNqLumbbHZwdMGaN2%2BeXn/9dS1atEjDhg2TJEVHRys3N9dvv8LCQt/pu%2BjoaBUWFtbY3q1btzp/3aioqBqnA73ek6qsdNY3rtPVx/urqqq6Xs59MU7NJTk3m1NzSc7N5tRcdnHeCdf/k5aWpjfeeEPPPvusRo0a5Vt3u9369NNPVVZW5lvLysqS2%2B32bc/KyvJtKy0t1d69e33bAQAALsaRBevAgQNasmSJ/uM//kNxcXHyer2%2Bjz59%2BqhVq1aaM2eOcnJylJGRoV27dunWW2%2BVJI0bN047d%2B5URkaGcnJyNGfOHLVt25a3aAAAAHXmyIL197//XVVVVfr973%2BvhIQEv4/g4GAtWbJEXq9XiYmJeuutt5Senq7WrVtLktq2bavnn39eq1at0q233qqioiKlp6crKCjI5lQAAKC%2BCDK8RfkPwus9eUVud8Tij67I7UJ6%2B77%2Bdo9QZy5XAzVr1kgnTpx21GsonJpLcm42p%2BaSnJvNqbmkb7LZwZFHsAAAAOxEwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYo4vWBUVFRo9erQ%2B/vhj31peXp4mT56sXr16aeTIkdq8ebPfdbZs2aLRo0fL7XZr0qRJysvL%2B6HHBgAA9ZijC1Z5ebnuv/9%2B5eTk%2BNaMMUpOTlbLli21atUqjRkzRtOnT9eRI0ckSUeOHFFycrISExP15z//Wc2bN9e0adNkjLErBgAAqGccW7Byc3P185//XF9%2B%2BaXf%2BrZt25SXl6e5c%2BeqU6dOmjp1qnr16qVVq1ZJklauXKkePXrorrvu0tVXX63U1FR9/fXX2r59ux0xAABAPeTYgrV9%2B3b17dtXmZmZfusej0fXXnutwsPDfWtxcXHKzs72bY%2BPj/dtCwsLU/fu3X3bAQAALsZl9wBXym233VbrutfrVVRUlN9aixYtdPTo0Tptr4uCggJ5vV6/NZcrvMbtIrC5XPXn3x/BwQ38PjuFU3NJzs3m1FySc7M5NZdkbybHFqzzKS0tVUhIiN9aSEiIKioq6rS9LjIzM5WWlua3lpycrJkzZ37PqWGHZs0a2T3CJYuICLN7hCvCqbkk52Zzai7JudmcmssuP7qCFRoaqqKiIr%2B1iooKNWzY0Lf9u2WqoqJCERERdf4aSUlJGjJkiN%2BayxWuEydOf8%2BpYYf6dH8FBzdQRESYiotLVVVVbfc4lnFqLsm52ZyaS3JuNqfmkr7JZocfXcGKjo5Wbm6u31phYaHv9F10dLQKCwtrbO/WrVudv0ZUVFSN04Fe70lVVjrrG9fp6uP9VVVVXS/nvhin5pKcm82puSTnZnNqLrv86AqW2%2B1WRkaGysrKfEetsrKyFBcX59uelZXl27%2B0tFR79%2B7V9OnTbZkX9hmx%2BCO7R7gk/1gw3O4RAAD/x3mvaLuIPn36qFWrVpozZ45ycnKUkZGhXbt26dZbb5UkjRs3Tjt37lRGRoZycnI0Z84ctW3bVn379rV5cgAAUF/86ApWcHCwlixZIq/Xq8TERL311ltKT09X69atJUlt27bV888/r1WrVunWW29VUVGR0tPTFRQUZPPkAACgvvhRnCL8/PPP/S63b99eK1asOO/%2BAwcO1MCBA6/0WAAAwKF%2BdEewAAAArjQKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMZfdAwCwRnzKO3aPUGdv39ff7hEA4IriCBYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWLUoLy/XI488ovj4eCUkJOill16yeyQAAFCP8KdyarFw4ULt2bNHr7zyio4cOaLZs2erdevWGj58uN2jAY4wYvFHdo9wSfjTPgAuFQXrO0pKSrRy5Uq98MIL6t69u7p3766cnBy99tprFCwAAFAnFKzv2LdvnyorKxUbG%2Btbi4uL09KlS1VdXa0GDTirCiCw1acjhBwdhFNRsL7D6/WqWbNmCgkJ8a21bNlS5eXlKioqUvPmzS96GwUFBfJ6vX5rLle4oqKiLJ8XwJXncln/D6vg4AZ%2Bn3%2BsrsT/2yslOLiB4lPesXsMx9rw4ADLb9POxxcF6ztKS0v9ypUk3%2BWKioo63UZmZqbS0tL81qZPn64ZM2ZYM%2BS3/GOB9actCwoKlJmZqaSkJEeVQqfmkpybzam5pLPZXnll%2BRXJdiWeF%2BrK6ffZnf%2Ba47hsTr/PrtTj7GLqzz8dfiChoaE1itS5yw0bNqzTbSQlJWn16tV%2BH0lJSZbPeqV4vV6lpaXVOApX3zk1l%2BTcbE7NJTk3m1NzSc7N5tRckr3ZOIL1HdHR0Tpx4oQqKyvlcp393%2BP1etWwYUNFRETU6TaioqIc968AAABQdxzB%2Bo5u3brJ5XIpOzvbt5aVlaWYmBhe4A4AAOqExvAdYWFhGjt2rJ544gnt2rVL7733nl566SVNmjTJ7tEAAEA9EfzEE088YfcQgaZfv37au3evnnnmGW3dulX33HOPxo0bZ/dYP6hGjRqpT58%2BatSokd2jWMqpuSTnZnNqLsm52ZyaS3JuNqfmkuzLFmSMMT/oVwQAAHA4ThECAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYMGnvLxcjzzyiOLj45WQkKCXXnrJ7pHq7NixY5o5c6b69OmjAQMGKDU1VeXl5ZKk%2BfPn65prrvH7WLFihe%2B669at09ChQ%2BV2u5WcnKzjx4/bFaNWGzZsqDH/zJkzJUl79%2B7V%2BPHj5Xa7NW7cOO3Zs8fvuoGabfXq1TUyXXPNNeratask6d57762xbePGjb7r/%2BEPf9CAAQMUGxurRx55RKWlpXZF8VNRUaHRo0fr448/9q3l5eVp8uTJ6tWrl0aOHKnNmzf7XWfLli0aPXq03G63Jk2apLy8PL/tgZC1tlzZ2dn693//d8XGxmrYsGFauXKl33V%2B9rOf1bgP9%2B/fL0kyxujpp59Wv3791KdPHy1cuFDV1dU/aKZzast2Oc8ZgZzt4YcfrvVx9%2B0/BRcfH19j%2B%2BnTpyXZ/zPiQs/zAfk4M8D/mTt3rrn55pvNnj17zH//93%2Bb2NhY8/bbb9s91kVVV1ebn//85%2Bbuu%2B82%2B/fvNzt27DA33XSTeeqpp4wxxkyePNksW7bMFBQU%2BD5KSkqMMcZ4PB7Ts2dPs2bNGvPZZ5%2BZO%2B64w0yZMsXOODUsWbLETJ061W/%2Bf/7zn%2Bb06dOmf//%2B5qmnnjK5ublm3rx55oYbbjCnT582xgR2ttLSUr88R44cMTfddJNZsGCBMcaYm266yaxdu9Zvn/LycmOMMe%2B8846Ji4sz//M//2M8Ho8ZOXKkefLJJ%2B2MY4wxpqyszCQnJ5suXbqYbdu2GWPOfm/efPPN5oEHHjC5ublm6dKlxu12m6%2B//toYY8zXX39tevXqZV588UWzf/9%2B85//%2BZ9m9OjRprq62hgTGFlry1VQUGDi4%2BPNM888Yw4dOmTWrVtnYmJizMaNG40xxlRWVpqYmBizfft2v/vwzJkzxhhjXnzxRTNw4ECzY8cOs3XrVpOQkGCWL1/%2Bg%2BY6XzZjLu85I5CzFRcX%2B2X65JNPTI8ePcyGDRuMMcYcPXrUdOnSxXz55Zd%2B%2B537frTzZ8SFnucD9XFGwYIxxpjTp0%2BbmJgYvyeZ9PR0c8cdd9g4Vd3k5uaaLl26GK/X61v761//ahISEowxxgwYMMBs2rSp1us%2B9NBDZvbs2b7LR44cMddcc4358ssvr%2BzQl%2BCBBx4wzzzzTI31lStXmiFDhvieJKqrq81NN91kVq1aZYypH9nOWbp0qRk6dKgpLy835eXlplu3bubgwYO17nvbbbeZ3/3ud77LO3bsMD179vT9ALRDTk6O%2BdnPfmZuvvlmvx9oW7ZsMb169fKVXmOMufPOO33zL1682O8xVlJSYmJjY33Xtzvr%2BXL96U9/MsOHD/fb99FHHzX333%2B/McaYL774wnTt2tWUlZXVersDBw70fZ8aY8xf/vIXM3jw4CuUonbny2bM5T1nBHq2b7vrrrvMgw8%2B6Lv80Ucfmf79%2B9e6r90/Iy70PB%2BojzNOEUKStG/fPlVWVio2Nta3FhcXJ4/HY9vh7bqKjIzU8uXL1bJlS7/1U6dO6dSpUzp27Jg6dOhQ63U9Ho/i4%2BN9l1u1aqXWrVvL4/FcyZEvyYEDB2qd3%2BPxKC4uTkFBQZKkoKAg9e7dW9nZ2b7tgZ5NkoqKivTCCy/ogQceUEhIiA4ePKigoCC1a9euxr5VVVXavXu3X65evXrpzJkz2rdv3w85tp/t27erb9%2B%2ByszM9Fv3eDy69tprFR4e7luLi4s7730UFham7t27Kzs7OyCyni/XudMz33Xq1ClJUm5urlq1aqXQ0NAa%2Bxw7dkz5%2Bfm67rrrfGtxcXH6%2BuuvVVBQYHGC8ztftst5zgj0bN%2B2detW7dixQ/fff79vLTc3V1dddVWt%2B9v9M%2BJCz/OB%2BjhzXda14Rher1fNmjVTSEiIb61ly5YqLy9XUVGRmjdvbuN0FxYREaEBAwb4LldXV2vFihXq16%2BfDhw4oKCgIC1dulQffvihmjZtql/84he65ZZbJEkFBQWKioryu70WLVro6NGjP2iG8zHG6NChQ9q8ebOWLVumqqoqDR8%2BXDNnzpTX61Xnzp399m/RooVycnIkBX62c15//XVFRUVp%2BPDhkqSDBw%2BqcePGmjVrlrZv365//dd/1YwZMzRw4EAVFxervLzcL5fL5VLTpk1tzXXbbbfVuu71ei94H1xoeyBkPV%2Butm3bqm3btr7L//u//6u//e1vmjFjhqSz/yj4yU9%2BoqlTp2rPnj266qqrNGvWLPXs2VNer1eS/HKd%2B6F59OjRGv8/rpTzZbuc54xAz/ZtGRkZuuWWW9SqVSvf2oEDB1RaWqqJEyfq0KFD6tatmx555BFdddVVtv%2BMuNDzfKA%2BzjiCBUlSaWmp3wNHku9yRUWFHSN9b4sWLdLevXv1q1/9ync0pGPHjsrIyND48eP16KOPasOGDZKksrKyWnMHSuYjR4747pvFixdr9uzZ%2Butf/6qFCxee9z47N3ugZ5POFsiVK1fqjjvu8K0dPHhQZWVlSkhI0PLlyzVw4EDde%2B%2B92r17t8rKyiQp4HOdc7H76ELb60vWsrIyzZgxQy1btlRSUpIk6dChQ/rnP/%2Bp8ePHKyMjQ506ddKdd96p/Pz8WnMF0nPN5TxnBHq2c/Ly8rRt2zZNnDjRb/3gwYP65z//qXvvvVdLlixRw4YNNXnyZJ06dSrgfkZ8%2B3k%2BUB9nHMGCJCk0NLTGN9O5yw0bNrRjpO9l0aJFeuWVV/Tcc8%2BpS5cuuvrqqzV48GA1bdpUktS1a1d98cUXev3113XTTTedN3dYWJgd49fQpk0bffzxx2rSpImCgoLUrVs3VVdX66GHHlKfPn1qnf3c/RXo2SRp9%2B7dOnbsmEaNGuVbmzZtmiZOnKgmTZpIOnufffrpp3rzzTf1q1/9SlLNJ/RAy3VOaGioioqK/Nbqch9FRET4Tq8FctbTp09r2rRp%2BuKLL/SnP/3JN9e8efNUVlamxo0bS5KeeOIJ7dy5U2vXrtUNN9wg6WyO72YMhFxjx4793s8Z3y4cgZjtnHfffVfdunWrcQT8xRdf1JkzZ9SoUSNJ0tNPP62BAwdq48aNAfUz4rvP84H6OOMIFiRJ0dHROnHihCorK31rXq9XDRs2VEREhI2T1d28efP08ssva9GiRRo2bJiks69LOvdEeU7Hjh117NgxSWdzFxYW%2Bm0vLCxUZGTkDzN0HTRt2tT3OitJ6tSpk8rLyxUZGVnr7OcOddeHbJs2bVJ8fLyvTElSgwYN/C5L39xnTZs2VWhoqF%2BuyspKFRUVBVSuc853H9TlPgr0rKdOndIvf/lL5eTk6JVXXvF7zZLL5fKVK0m%2BI0LHjh1TdHS0JPlOp337vwMh1%2BU8ZwR6tnM2bdqkf/u3f6uxHhIS4itX0tli0rZtW9/9Fgg/I2p7ng/UxxkFC5Kkbt26yeVy%2BV4UKElZWVmKiYlRgwaB/22SlpamN954Q88%2B%2B6zf0ZD/%2Bq//0uTJk/323bdvnzp27ChJcrvdysrK8m3Lz89Xfn6%2B3G73DzL3xWzatEl9%2B/b1e0%2BWzz77TE2bNlVcXJw%2B%2BeQTGWMknT3dtnPnTt/sgZ5Nknbt2qXevXv7rT388MOaM2eO39q5%2B6xBgwaKiYnxy5WdnS2Xy%2BV7D61A4na79emnn/pOQ0hnH1fnu49KS0u1d%2B9eud3ugM5aXV2t6dOn66uvvtKrr76qq6%2B%2B2m/7xIkTlZaW5rf/559/ro4dOyo6OlqtW7f2y5WVlaXWrVv/YK9RupDLec4I9GzS2eeJ3bt313jcGWM0dOhQrV692rdWUlKiw4cPq2PHjgHxM%2BJ8z/MB%2Bzi7rN9BhKM8%2BuijZtSoUcbj8ZgNGzaY3r17m3fffdfusS4qNzfXdOvWzTz33HN%2B791SUFBgPB6Pufbaa83y5cvN4cOHzWuvvWZ69Ohhdu7caYwxZufOnaZ79%2B7mzTff9L2nzdSpU21O9I2TJ0%2BaAQMGmPvvv98cOHDAvP/%2B%2ByYhIcFkZGSYkydPmn79%2Bpl58%2BaZnJwcM2/ePNO/f3/fryoHejZjjBk8eLBZt26d39q7775runfvbtasWWO%2B%2BOIL8/zzz5uePXuavLw8Y4wx69atM7179zYbNmwwHo/HjBo1ysybN8%2BO8Wv17V%2BLr6ysNCNHjjT33Xef2b9/v1m2bJnp1auX7/158vLyTExMjFm2bJnv/Xluvvlm31tvBFLWb%2BfKzMw0Xbt2NRs3bvR7vJ04ccIYY8xLL71k4uLizHvvvWcOHDhgHn/8cXPDDTeYkydPGmOMWbZsmUlISDDbtm0z27ZtMwkJCeall16yJdd3s13uc0YgZzPm7Pdcly5dTEFBQY19582bZwYNGmS2bdtm9u/fb5KTk83o0aNNZWWlMcbenxEXep4P1McZBQs%2BJSUlZtasWaZXr14mISHBvPzyy3aPVCfLli0zXbp0qfXDGGM2bNhgbr75ZhMTE2OGDx9e4wlh1apVZuDAgaZXr14mOTnZHD9%2B3I4Y57V//34zefJk06tXL9O/f3/z/PPP%2B54YPB6PGTt2rImJiTG33nqr%2BfTTT/2uG%2BjZYmJizIcfflhj/c033zQ//elPTY8ePcwtt9xitm/f7rd92bJl5vrrrzdxcXFmzpw5532/JTt89wfaF198YW6//XbTo0cPM2rUKPPRRx/57f/%2B%2B%2B%2Bbn/70p6Znz57mzjvvrPE%2BZYGS9du57rrrrlofb%2Bfea6i6utr8/ve/N4MGDTI9evQwt99%2Bu/n88899t1VZWWl%2B85vfmPj4eNO3b1%2BzaNEi3/e0Hb57n13Oc0agZ8vOzjZdunTxvXHvt5WVlZnU1FTTv39/43a7zdSpU82RI0d82%2B38GXGx5/lAfJwFGfN/5xcAAABgicB/cQ0AAEA9Q8ECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAs9v8BieUWuXPwluQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5220474690065710856”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>187</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (136)</td> <td class=”number”>409</td> <td class=”number”>12.7%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2012</td> <td class=”number”>62.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5220474690065710856”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.71428571428571</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.33333333333334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1200.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1500.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_SQFTPTH_07_12”><s>PCH_GHVEG_SQFTPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_GHVEG_SQFT_07_12”><code>PCH_GHVEG_SQFT_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99987</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GHVEG_SQFT_07_12”>PCH_GHVEG_SQFT_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>325</td>

</tr> <tr>

<th>Unique (%)</th> <td>10.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>89.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>2870</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>293.41</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>27641</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4485425408946348220”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4485425408946348220,#minihistogram-4485425408946348220”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4485425408946348220”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4485425408946348220”

aria-controls=”quantiles-4485425408946348220” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4485425408946348220” aria-controls=”histogram-4485425408946348220”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4485425408946348220” aria-controls=”common-4485425408946348220”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4485425408946348220” aria-controls=”extreme-4485425408946348220”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4485425408946348220”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-93.897</td>

</tr> <tr>

<th>Q1</th> <td>5.1369</td>

</tr> <tr>

<th>Median</th> <td>91.527</td>

</tr> <tr>

<th>Q3</th> <td>247.16</td>

</tr> <tr>

<th>95-th percentile</th> <td>932.4</td>

</tr> <tr>

<th>Maximum</th> <td>27641</td>

</tr> <tr>

<th>Range</th> <td>27741</td>

</tr> <tr>

<th>Interquartile range</th> <td>242.03</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1552.9</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.2926</td>

</tr> <tr>

<th>Kurtosis</th> <td>285.97</td>

</tr> <tr>

<th>Mean</th> <td>293.41</td>

</tr> <tr>

<th>MAD</th> <td>368.76</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>16.301</td>

</tr> <tr>

<th>Sum</th> <td>99759</td>

</tr> <tr>

<th>Variance</th> <td>2411400</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4485425408946348220”>

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HnzZklSbGysiouLPeaLi4sVHR1d68%2BNiYmpcTrQ5Tqlysr6t7DP5a/7X1VV7be1%2BTt65x365j165z16d3UF7AnZrKwsvf3225o7d64GDRrkHp8/f77Gjh3r8do9e/YoLi5OkuRwOJSXl%2BeeO3r0qI4ePSqHw3FV6gYAAHVfQAas/Px8LVq0SH/729%2BUlJQkl8vlfvTp00e5ublasWKFfvzxR7311lt67733NH78eEnSXXfdpXXr1mnVqlXas2ePHnvsMd1yyy269tprfbxXAACgrgjIU4SfffaZqqqqtHjxYi1evNhj7vvvv9f8%2BfO1YMECzZ8/Xy1atNALL7ygxMRESVJiYqJmzJihBQsW6Oeff1aPHj00c%2BZMX%2BwGAACoo2zGGOPrIuoDl%2BvUFdnubfP%2BdUW2eyV89FAPX5fgwW4PUuPGDVVScobrEi4RvfMOffMevfNefe9ddPSfffK5AXmKEAAAwJcIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFjM7wLWnXfeqbffflunTp3ydSkAAABe8buA1b17dy1ZskQ9e/bUww8/rI0bN8oY4%2BuyAAAAas3vAtbkyZP1%2Beefa9GiRQoODtaDDz6oW265RS%2B%2B%2BKJ%2B%2BOEHX5cHAABwUXZfF3A%2BNptNPXr0UI8ePVRaWqo33nhDixYt0rJly9SlSxfdc889uvXWW31dJgAAwHn5ZcCSpKKiIr3//vt6//33tXfvXnXp0kXDhw9XYWGhnnzySeXm5mratGm%2BLhMAAKAGvwtY69at07p167RlyxZFRUVp2LBhWrBggVq1auV%2BTbNmzTR79mwCFgAA8Et%2Bdw3WtGnT1LBhQy1cuFAbNmzQ5MmTPcKVJMXFxWn06NF/uJ1jx44pPT1dycnJ6tWrlzIyMlReXi5JKigo0NixY5WQkKCBAwdq48aNHu/dtGmTBg8eLIfDoTFjxqigoMDSfQQAAIHN7wLWl19%2BqQULFsjhcCgo6NfyduzYoaqqKvdrunTposmTJ19wG8YYpaenq7S0VG%2B%2B%2BaZefPFFff7555o3b56MMUpLS1PTpk21Zs0aDR06VJMmTdKRI0ckSUeOHFFaWppGjBih1atXKyoqSg888AC/yQgAAGrN7wLW6dOnNWDAAL388svusQkTJmjo0KE6evRorbZx4MABbd%2B%2BXRkZGWrTpo26du2q9PR0rV%2B/Xps3b1ZBQYFmzJih1q1ba%2BLEiUpISNCaNWskSatWrVLnzp01fvx4tWnTRhkZGfrpp5%2B0devWK7K/AAAg8PhdwHr22Wd13XXXady4ce6xDz/8UM2aNVNGRkatthEdHa3ly5eradOmHuOnT5%2BW0%2BlUx44dFR4e7h5PSkrS9u3bJUlOp1Ndu3Z1z4WFhalTp07ueQAAgIvxu4D1v//7v3riiScUHR3tHouKitJjjz2mzZs312obERER6tWrl/t5dXW1srOz1b17d7lcLsXExHi8vkmTJiosLJSki84DAABcjN/9FqHdbtfJkydrjJeWlnp9HVRmZqZ27dql1atX67XXXlNISIjHfEhIiCoqKtyf80fztVFUVCSXy%2BUxZreH1whu9Y3d7l95Pjg4yOMrao/eeYe%2BeY/eeY/e%2BYbfBaybb75Zs2bN0ty5c/Vv//Zvkn79rb%2BMjAyPo1K1lZmZqddff10vvvii2rZtq9DQUJ04ccLjNRUVFWrQoIEkKTQ0tEaYqqioUERERK0/MycnR1lZWR5jaWlpSk9Pv%2BT6A0njxg19XcJ5RUSE%2BbqEOoveeYe%2BeY/eeY/eXV1%2BF7Aef/xxjRs3Tv3793eHmpMnT6pTp06aMmXKJW1r5syZWrlypTIzM9W/f39JUmxsrPbv3%2B/xuuLiYvfRpdjYWBUXF9eY79ChQ60/NzU1VSkpKR5jdnu4SkrOXFL9gcbf9j84OEgREWE6ebJUVVXVvi6nTqF33qFv3qN33qvvvfPVD/d%2BF7CaNGmitWvXatOmTdq3b5/sdruuv/563XTTTbLZbLXeTlZWlt5%2B%2B23NnTtXAwYMcI87HA4tW7ZMZWVl7qNWeXl5SkpKcs/n5eW5X19aWqpdu3Zp0qRJtf7smJiYGqcDXa5Tqqysfwv7XP66/1VV1X5bm7%2Bjd96hb96jd96jd1eX3wUsSQoODlavXr28OiUoSfn5%2BVq0aJEmTJigpKQkj%2BuhkpOT1axZM02ZMkUPPPCAPv/8c%2B3YscP9G4ojR47UihUrtGzZMvXp00cLFy5Uy5Yt1a1bN0v2DQAABD6/C1gul0vz5s3Ttm3b9Msvv9S4sP2zzz676DY%2B%2B%2BwzVVVVafHixVq8eLHH3Pfff69FixZp2rRpGjFihK677jotXLhQzZs3lyS1bNlSL730kp599lktXLhQiYmJWrhw4SUdPQMAAPWb3wWsp556Sjt37tSgQYP05z//2attTJgwQRMmTLjg/HXXXafs7OwLzvfu3Vu9e/f26rMBAAD8LmBt3rxZy5cv97jZJwAAQF3idzfFCA8PV5MmTXxdBgAAgNf8LmANHTpUy5cv9/jjzgAAAHWJ350iPHHihNavX68vvvhC1157bY27qv/jH//wUWUAAAC143cBS5IGDx7s6xIAAAC85ncB67f7UQEAANRVfncNlvTrH0vOysrS5MmT9X//93/6%2BOOPdeDAAV%2BXBQAAUCt%2BF7AOHTqkIUOGaO3atfrnP/%2Bps2fP6sMPP9TIkSPldDp9XR4AAMBF%2BV3Aeu6559S3b199%2Bumn%2BtOf/iRJmjt3rlJSUvT888/7uDoAAICL87uAtW3bNo0bN87jT9PY7XY98MAD2rVrlw8rAwAAqB2/C1jV1dWqrq75177PnDmj4OBgH1QEAABwafwuYPXs2VNLly71CFknTpxQZmamunfv7sPKAAAAasfvAtYTTzyhnTt3qmfPniovL9d//Md/qE%2BfPjp8%2BLAef/xxX5cHAABwUX53H6zY2Fi99957Wr9%2BvXbv3q3q6mrdddddGjp0qBo1auTr8gAAAC7K7wKWJIWFhenOO%2B/0dRkAAABe8buANWbMmD%2Bc528RAgAAf%2Bd3AatFixYezysrK3Xo0CHt3btX99xzj4%2BqAgAAqD2/C1gX%2BluECxcuVGFh4VWuBgAA4NL53W8RXsjQoUP10Ucf%2BboMAACAi6ozAeubb77hRqMAAKBO8LtThOe7yP306dP6/vvvNWrUKB9UBAAAcGn8LmA1b97c4%2B8QStKf/vQnjR49WrfffruPqgIAAKg9vwtYzz33nK9LAAAAuCx%2BF7Byc3Nr/dobb7zxClYCAADgHb8LWHfffbf7FKExxj3%2B%2BzGbzabdu3df/QIBAAAuwu8C1pIlSzRr1iw9%2BuijSk5OVkhIiL799lvNmDFDw4cP18CBA31dIgAAwB/yu9s0ZGRk6Omnn1b//v3VuHFjNWzYUN27d9eMGTO0cuVKtWjRwv0AAADwR34XsIqKis4bnho1aqSSkhIfVAQAAHBp/C5gJSQkaO7cuTp9%2BrR77MSJE8rMzNRNN93kw8oAAABqx%2B%2BuwXryySc1ZswY3XzzzWrVqpWMMTp48KCio6P1j3/8w9flAQAAXJTfBazWrVvrww8/1Pr165Wfny9J%2Butf/6pBgwYpLCzMx9UBAABcnN8FLEm65pprdOedd%2Brw4cO69tprJf16N3cAAIC6wO%2BuwTLG6Pnnn9eNN96owYMHq7CwUI8//rimTZumX375xdflAQAAXJTfBaw33nhD69at09///neFhIRIkvr27atPP/1UWVlZl7y9iooKDR48WFu2bHGPzZo1S%2B3atfN4ZGdnu%2BfXr1%2Bvvn37yuFwKC0tTcePH7/8HQMAAPWG3wWsnJwcPf300xoxYoT77u0DBw7UrFmz9MEHH1zStsrLy/Xwww9r3759HuP5%2BfmaPHmyNm7c6H6MHDlSkrRjxw5NmzZNkyZNUk5Ojk6ePKkpU6ZYs3MAAKBe8LtrsA4fPqwOHTrUGG/fvr1cLlett7N//35NnjzZ48/t/CY/P1/33nuvoqOja8xlZ2frtttu07BhwyRJc%2BbMUZ8%2BfVRQUOC%2BHgwAAOCP%2BN0RrBYtWujbb7%2BtMf7ll19eUsDZunWrunXrppycHI/x06dP69ixY2rVqtV53%2Bd0OtW1a1f382bNmql58%2BZyOp21/mwAAFC/%2Bd0RrHvvvVfPPPOMXC6XjDH6%2BuuvlZOTozfeeENPPPFErbczatSo847n5%2BfLZrNpyZIl%2BvLLLxUZGalx48Zp%2BPDhkn69k3xMTIzHe5o0aaLCwkLvdwoAANQrfhewRo4cqcrKSi1evFhlZWV6%2BumnFRUVpYceekh33XXXZW//wIEDstlsiouL0%2BjRo5Wbm6unnnpKjRo1Ur9%2B/VRWVua%2BuP43ISEhqqioqPVnFBUV1TidabeH1whu9Y3d7l8HTIODgzy%2BovbonXfom/fonffonW/4XcBav369BgwYoNTUVB0/flzGGDVp0sSy7Q8bNkx9%2BvRRZGSkpF%2Bv7Tp48KBWrlypfv36KTQ0tEaYqqiouKSbnObk5NT4jce0tDSlp6df/g7UYY0bN/R1CecVEcENbL1F77xD37xH77xH764uvwtYM2bM0FtvvaVrrrlGUVFRlm/fZrO5w9Vv4uLitHnzZklSbGysiouLPeaLi4vPe0H8haSmpiolJcVjzG4PV0nJGS%2BrDgz%2Btv/BwUGKiAjTyZOlqqqq9nU5dQq98w598x698159752vfrj3u4DVqlUr7d27V9dff/0V2f78%2BfP1zTff6LXXXnOP7dmzR3FxcZIkh8OhvLw8jRgxQpJ09OhRHT16VA6Ho9afERMTU%2BN0oMt1SpWV9W9hn8tf97%2Bqqtpva/N39M479M179M579O7q8ruA1b59ez3yyCNavny5WrVqpdDQUI/5jIyMy9p%2Bnz59tGzZMq1YsUL9%2BvXTxo0b9d5777n/kPRdd92lu%2B%2B%2BWwkJCYqPj9fs2bN1yy23cIsGAABQa34XsH744QclJSVJ0iXd96q2brjhBs2fP18LFizQ/Pnz1aJFC73wwgtKTEyUJCUmJmrGjBlasGCBfv75Z/Xo0UMzZ860vA4AABC4bOZ8d%2BK8yubMmaNJkyYpPDzc16VcMS7XqSuy3dvm/euKbPdK%2BOihHr4uwYPdHqTGjRuqpOQMh80vEb3zDn3zHr3zXn3vXXT0n33yuX7xO5uvvvqqSktLPcYmTJigoqIiH1UEAADgPb8IWOc7iJabm6vy8nIfVAMAAHB5/CJgAQAABBICFgAAgMX8JmDZbDZflwAAAGAJv7lNw6xZszzuefXLL78oMzNTDRt63oH1cu%2BDBQAAcKX5RcC68cYba9zzKjExUSUlJSopKfFRVQAAAN7xi4D1xhtv%2BLoEAAAAy/jNNVgAAACBgoAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgsYAPWBUVFRo8eLC2bNniHisoKNDYsWOVkJCggQMHauPGjR7v2bRpkwYPHiyHw6ExY8aooKDgapcNAADqsIAOWOXl5Xr44Ye1b98%2B95gxRmlpaWratKnWrFmjoUOHatKkSTpy5Igk6ciRI0pLS9OIESO0evVqRUVF6YEHHpAxxle7AQAA6piADVj79%2B/XX/7yF/34448e45s3b1ZBQYFmzJih1q1ba%2BLEiUpISNCaNWskSatWrVLnzp01fvx4tWnTRhkZGfrpp5%2B0detWX%2BwGAACogwI2YG3dulXdunVTTk6Ox7jT6VTHjh0VHh7uHktKStL27dvd8127dnXPhYWFqVOnTu55AACAi7H7uoArZdSoUecdd7lciomJ8Rhr0qSJCgsLazVfG0VFRXK5XB5jdnt4je3WN3a7f%2BX54OAgj6%2BoPXrnHfrmPXrnPXrnGwEbsC6ktLRUISEhHmMhISGqqKio1Xxt5OTkKCsry2MsLS1N6enpXlYdGBo3bujrEs4rIiJrLodHAAASpUlEQVTM1yXUWfTOO/TNe/TOe/Tu6qp3ASs0NFQnTpzwGKuoqFCDBg3c878PUxUVFYqIiKj1Z6SmpiolJcVjzG4PV0nJGS%2BrDgz%2Btv/BwUGKiAjTyZOlqqqq9nU5dQq98w598x698159752vfrivdwErNjZW%2B/fv9xgrLi52n76LjY1VcXFxjfkOHTrU%2BjNiYmJqnA50uU6psrL%2BLexz%2Bev%2BV1VV%2B21t/o7eeYe%2BeY/eeY/eXV317oSsw%2BHQd999p7KyMvdYXl6eHA6Hez4vL889V1paql27drnnAQAALqbeBazk5GQ1a9ZMU6ZM0b59%2B7Rs2TLt2LFDd9xxhyRp5MiR2rZtm5YtW6Z9%2B/ZpypQpatmypbp16%2BbjygEAQF1R7wJWcHCwFi1aJJfLpREjRuj999/XwoUL1bx5c0lSy5Yt9dJLL2nNmjW64447dOLECS1cuFA2m83HlQMAgLqiXlyD9f3333s8v%2B6665SdnX3B1/fu3Vu9e/e%2B0mUBAIAAVe%2BOYAEAAFxpBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGL1NmB98sknateunccjPT1dkrRr1y7deeedcjgcGjlypHbu3OnjagEAQF1SbwPW/v371adPH23cuNH9mDVrls6ePasJEyaoa9euevfdd5WYmKiJEyfq7Nmzvi4ZAADUEfU2YOXn56tt27aKjo52PyIiIvThhx8qNDRUjz32mFq3bq1p06apYcOG%2Bvjjj31dMgAAqCPqdcBq1apVjXGn06mkpCTZbDZJks1mU5cuXbR9%2B/arXCEAAKir6mXAMsbohx9%2B0MaNG9W/f3/17dtXzz//vCoqKuRyuRQTE%2BPx%2BiZNmqiwsNBH1QIAgLrG7usCfOHIkSMqLS1VSEiI5s2bp8OHD2vWrFkqKytzj58rJCREFRUVtd5%2BUVGRXC6Xx5jdHl4juNU3drt/5fng4CCPr6g9eucd%2BuY9euc9eucb9TJgtWjRQlu2bNE111wjm82mDh06qLq6Wo8%2B%2BqiSk5NrhKmKigo1aNCg1tvPyclRVlaWx1haWpr7txTrq8aNG/q6hPOKiAjzdQl1Fr3zDn3zHr3zHr27uuplwJKkyMhIj%2BetW7dWeXm5oqOjVVxc7DFXXFx8SUefUlNTlZKS4jFmt4erpOSM9wUHAH/b/%2BDgIEVEhOnkyVJVVVX7upw6hd55h755j955r773zlc/3NfLgPXVV1/pkUce0RdffKGwsF8T/e7duxUZGamkpCS9/PLLMsbIZrPJGKNt27bp/vvvr/X2Y2JiagQyl%2BuUKivr38I%2Bl7/uf1VVtd/W5u/onXfom/fonffo3dVVL0/IJiYmKjQ0VE8%2B%2BaQOHDigDRs2aM6cObrvvvs0YMAAnTx5UrNnz9b%2B/fs1e/ZslZaW6rbbbvN12QAAoI6olwGrUaNGWrFihY4fP66RI0dq2rRpSk1N1X333adGjRpp6dKlysvL04gRI%2BR0OrVs2TKFh4f7umwAAFBH1MtThJLUpk0bvfrqq%2Bedu%2BGGG7R27dqrXBEAAAgU9fIIFgAAwJVEwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgB6zzKy8s1depUde3aVT179tQrr7zi65IAAEAdYvd1Af5ozpw52rlzp15//XUdOXJEjz/%2BuJo3b64BAwb4ujQAAFAHELB%2B5%2BzZs1q1apVefvllderUSZ06ddK%2Bffv05ptvErAu023z/uXrEi7JRw/18HUJAIA6ilOEv7Nnzx5VVlYqMTHRPZaUlCSn06nq6mofVgYAAOoKjmD9jsvlUuPGjRUSEuIea9q0qcrLy3XixAlFRUVddBtFRUVyuVweY3Z7uGJiYiyvF1eO3c7PHxcSHBzk8RW1Q9%2B8R%2B%2B80%2B/5r3xdQq198kgvX5dgKQLW75SWlnqEK0nu5xUVFbXaRk5OjrKysjzGJk2apAcffNCaIs/x4X92UU5OjlJTUwlwl6ioqIjeeamoqEivv76c3l0i%2BuY9eued/509gO91PsKPAr8TGhpaI0j99rxBgwa12kZqaqreffddj0dqaqrltUq/HnHLysqqccQMF0fvvEfvvEPfvEfvvEfvfIMjWL8TGxurkpISVVZWym7/tT0ul0sNGjRQRERErbYRExPDTwkAANRjHMH6nQ4dOshut2v79u3usby8PMXHxysoiHYBAICLIzH8TlhYmIYNG6bp06drx44d%2BvTTT/XKK69ozJgxvi4NAADUEcHTp0%2Bf7usi/E337t21a9cuvfDCC/r66691//33a%2BTIkb4u64IaNmyo5ORkNWzY0Nel1Dn0znv0zjv0zXv0znv07uqzGWOMr4sAAAAIJJwiBAAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsCqw8rLyzV16lR17dpVPXv21CuvvOLrknzmk08%2BUbt27Twe6enpkqRdu3bpzjvvlMPh0MiRI7Vz506P965fv159%2B/aVw%2BFQWlqajh8/7p4zxuj5559X9%2B7dlZycrDlz5qi6uvqq7tuVUlFRocGDB2vLli3usYKCAo0dO1YJCQkaOHCgNm7c6PGeTZs2afDgwXI4HBozZowKCgo85l977TX16tVLiYmJmjp1qkpLS91zgbRez9e7WbNm1ViD2dnZ7vnLWWclJSV68MEHlZiYqJSUFK1bt%2B7q7KhFjh07pvT0dCUnJ6tXr17KyMhQeXm5JNbcxfxR71hzfs6gzpoxY4YZMmSI2blzp/nv//5vk5iYaD766CNfl%2BUTixYtMhMnTjRFRUXux88//2zOnDljevToYZ577jmzf/9%2BM3PmTPPv//7v5syZM8YYY5xOp7nhhhvM2rVrze7du83o0aPNhAkT3NtdsWKF6d27t8nNzTVff/216dmzp1m%2BfLmvdtMyZWVlJi0tzbRt29Zs3rzZGGNMdXW1GTJkiJk8ebLZv3%2B/WbJkiXE4HOann34yxhjz008/mYSEBLNixQqzd%2B9e85//%2BZ9m8ODBprq62hhjzMcff2ySkpLM//zP/xin02kGDhxonnnmGfdnBsp6PV/vjDFm7NixZunSpR5r8OzZs8aYy19nEydONPfcc4/5/vvvzTvvvGM6d%2B5snE7n1dvpy1BdXW3%2B8pe/mPvuu8/s3bvX5Obmmn79%2BpnnnnuONXcRf9Q7Y1hz/o6AVUedOXPGxMfHe3yDX7hwoRk9erQPq/KdyZMnmxdeeKHG%2BKpVq0xKSor7G3J1dbXp16%2BfWbNmjTHGmEcffdQ8/vjj7tcfOXLEtGvXzvz444/GGGN69%2B7tfq0xxrz33numT58%2BV3JXrrh9%2B/aZ22%2B/3QwZMsQjJGzatMkkJCS4w6cxxtxzzz1mwYIFxhhj5s2b57G%2Bzp49axITE93vHzVqlPu1xhiTm5trbrjhBnP27NmAWa8X6p0xxvTq1ct89dVX533f5ayzQ4cOmbZt25qCggL3/NSpUz2258/2799v2rZta1wul3vsgw8%2BMD179mTNXcQf9c4Y1py/4xRhHbVnzx5VVlYqMTHRPZaUlCSn0xkwp7AuRX5%2Bvlq1alVj3Ol0KikpSTabTZJks9nUpUsXbd%2B%2B3T3ftWtX9%2BubNWum5s2by%2Bl06tixYzp69KhuvPFG93xSUpJ%2B%2BuknFRUVXdkduoK2bt2qbt26KScnx2Pc6XSqY8eOCg8Pd48lJSVdsFdhYWHq1KmTtm/frqqqKn377bce8wkJCfrll1%2B0Z8%2BegFmvF%2Brd6dOndezYsfOuQeny1pnT6VSzZs3UsmVLj/lvvvnG2p27QqKjo7V8%2BXI1bdrUY/z06dOsuYv4o96x5vyf3dcFwDsul0uNGzdWSEiIe6xp06YqLy/XiRMnFBUV5cPqri5jjH744Qdt3LhRS5cuVVVVlQYMGKD09HS5XC5df/31Hq9v0qSJ9u3bJ0kqKipSTExMjfnCwkK5XC5J8pj/7RtdYWFhjffVFaNGjTrvuMvlumAvLjZ/8uRJlZeXe8zb7XZFRkaqsLBQQUFBAbFeL9S7/Px82Ww2LVmyRF9%2B%2BaUiIyM1btw4DR8%2BXNLlrbML9f3YsWOW7deVFBERoV69ermfV1dXKzs7W927d2fNXcQf9Y415/8IWHVUaWmpxzcOSe7nFRUVvijJZ44cOeLux7x583T48GHNmjVLZWVlF%2BzTbz0qKyu74HxZWZn7%2BblzUmD2%2BGK9%2BqP58/Xq3HljTECv1wMHDshmsykuLk6jR49Wbm6unnrqKTVq1Ej9%2BvW7rHV2sf8vdU1mZqZ27dql1atX67XXXmPNXYJze/fdd9%2Bx5vwcAauOCg0NrbHYf3veoEEDX5TkMy1atNCWLVt0zTXXyGazqUOHDqqurtajjz6q5OTk8/bptx5dqI9hYWEe33BCQ0Pd/y39eqoi0ISGhurEiRMeY7XpVURERI3%2BnDsfFhamqqqqgF6vw4YNU58%2BfRQZGSlJat%2B%2BvQ4ePKiVK1eqX79%2Bl7XOLvTeuti3zMxMvf7663rxxRfVtm1b1twl%2BH3v2rRpw5rzc1yDVUfFxsaqpKRElZWV7jGXy6UGDRooIiLCh5X5RmRkpPs6K0lq3bq1ysvLFR0dreLiYo/XFhcXuw9/x8bGnnc%2BOjpasbGxkuQ%2BnH7uf0dHR1%2BR/fClC/WiNr2KjIxUaGiox3xlZaVOnDjh7mUgr1ebzeb%2Bh%2B43cXFx7lMql7PO/ui9dcnMmTP16quvKjMzU/3795fEmqut8/WONef/CFh1VIcOHWS3290Xg0pSXl6e4uPjFRRUv/63fvXVV%2BrWrZvH/W92796tyMhI94WZxhhJv16vtW3bNjkcDkmSw%2BFQXl6e%2B31Hjx7V0aNH5XA4FBsbq%2BbNm3vM5%2BXlqXnz5nX2%2Bqs/4nA49N1337lPH0i/7u%2BFelVaWqpdu3bJ4XAoKChI8fHxHvPbt2%2BX3W5X%2B/btA369zp8/X2PHjvUY27Nnj%2BLi4iRd3jpLSEjQTz/95L4u6bf5hISEK7tTFsrKytLbb7%2BtuXPnatCgQe5x1tzFXah3rLk6wKe/w4jL8tRTT5lBgwYZp9NpPvnkE9OlSxfzz3/%2B09dlXXWnTp0yvXr1Mg8//LDJz883X3zxhenZs6dZtmyZOXXqlOnevbuZOXOm2bdvn5k5c6bp0aOH%2B9fCt23bZjp16mTeeecd971iJk6c6N720qVLTc%2BePc3mzZvN5s2bTc%2BePc0rr7ziq1213Lm3GqisrDQDBw40Dz30kNm7d69ZunSpSUhIcN%2BTqKCgwMTHx5ulS5e670k0ZMgQ9y0w1q9fb7p06WI%2B%2BeQT43Q6zaBBg8zMmTPdnxVo6/Xc3jmdTtOxY0ezfPlyc%2BjQIfPmm2%2Bazp07m23bthljLn%2BdjR8/3owePdrs3r3bvPPOOyY%2BPr7O3JNo//79pkOHDubFF1/0uF9TUVERa%2B4i/qh3rDn/R8Cqw86ePWsee%2Bwxk5CQYHr27GleffVVX5fkM3v37jVjx441CQkJpkePHuall15yfxN2Op1m2LBhJj4%2B3txxxx3mu%2B%2B%2B83jvmjVrTO/evU1CQoJJS0szx48fd89VVlaaZ5991nTt2tV069bNZGZmurcbCH5/L6eDBw%2Bav/71r6Zz585m0KBB5l//%2BpfH67/44gtz6623mhtuuMHcc8897nvq/Gbp0qXmpptuMklJSWbKlCmmrKzMPRdo6/X3vfvkk0/MkCFDTHx8vBkwYECNf8gvZ50VFxebiRMnmvj4eJOSkmI%2B%2BOCDK7%2BDFlm6dKlp27bteR/GsOb%2ByMV6x5rzbzZj/v%2B5EwAAAFii7pyIBgAAqCMIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMX%2BHzXM0uAgRYkGAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4485425408946348220”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>159.98360655737704</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>171.60493827160494</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-45.36423841059602</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>344.91148433953697</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.004825468446455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-26.141754094387316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-6.315789473684211</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.07610729881472</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (314)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2870</td> <td class=”number”>89.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4485425408946348220”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.57604878950286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.17075428042573</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.05833333333334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-79.96918335901387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2266.447368421053</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2983.333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3536.238709677419</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3774.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27641.176470588238</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GROCPTH_09_14”><s>PCH_GROCPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_GROC_09_14”><code>PCH_GROC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99149</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_GROC_09_14”>PCH_GROC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>415</td>

</tr> <tr>

<th>Unique (%)</th> <td>12.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>93</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.9576</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>29.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7813442492329845932”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7813442492329845932,#minihistogram7813442492329845932”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7813442492329845932”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7813442492329845932”

aria-controls=”quantiles7813442492329845932” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7813442492329845932” aria-controls=”histogram7813442492329845932”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7813442492329845932” aria-controls=”common7813442492329845932”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7813442492329845932” aria-controls=”extreme7813442492329845932”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7813442492329845932”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50</td>

</tr> <tr>

<th>Q1</th> <td>-20</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>6.6429</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>26.643</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.615</td>

</tr> <tr>

<th>Coef of variation</th> <td>-17.171</td>

</tr> <tr>

<th>Kurtosis</th> <td>13.687</td>

</tr> <tr>

<th>Mean</th> <td>-1.9576</td>

</tr> <tr>

<th>MAD</th> <td>21.072</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.9976</td>

</tr> <tr>

<th>Sum</th> <td>-6101.9</td>

</tr> <tr>

<th>Variance</th> <td>1130</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7813442492329845932”>

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43rx5czVv3tx5%2B/vvv9ff//53TZw4UZKUl5ena665RuPHj9euXbt0ww03aMqUKWrfvr0cDockKSIiwvn4xo0bS5JOnDjhMg4AAHAxPluwaqO0tFQTJ05U48aNlZycLEk6ePCgfvjhBw0bNkypqal6/fXXNWrUKL333nsqLS2VJAUEBDjX8dN/l5eX1/p5CwoKnGXtJ3Z7fdMLmr%2B/n8u/VmHVXO5mt3vv%2B2XlbUY232PVXJJ1s1kxl2UL1tmzZ/Xggw/q0KFD%2Butf/6qgoCBJ0uzZs1VaWqrg4GBJ0syZM7V9%2B3Zt2LBBv/vd7yT9WKZ%2BOoT4U7H66fG1kZmZqYyMDJexlJQUpaamXnWumoSE1H5uvsSqudwlLKyBt6dg6W1GNt9j1VySdbNZKZclC9aZM2f0wAMP6LvvvtOqVatcPi1ot9ud5UqSbDaboqKilJ%2Bfr8jISEk/fhLxp8OMP70TFR4eXuvnT05OVlJSksuY3V5fRUVnrzRSjfz9/RQSEqRTp0pUWWmdS0lYNZe7mb1/XQ4rbzOy%2BR6r5pKsm82dubz1y6flClZVVZUmTJigI0eO6NVXX1WrVq1clt93333q1KmTJkyY4Lz/N998o9///veKjIxU06ZNlZWV5SxYWVlZatq06WUd3ouIiKh2f4fjtCoq3PNiqKysctu6vcmqudylLnyvrLzNyOZ7rJpLsm42K%2BWyXMF64403tGXLFi1dulQhISHOd6CuueYahYaGKikpSUuWLFHbtm11ww03aPXq1Tp9%2BrQGDx4sSRo%2BfLgWLFig66%2B/XpL09NNPa8yYMV7LAwAAfI/lCtYHH3ygqqoqjR8/3mW8Y8eOevXVVzV69GiVlZVpzpw5KiwsVGxsrF5%2B%2BWXnYcOxY8fq%2B%2B%2B/14QJE%2BTv76%2B7775bo0eP9kISAADgq2zGhVfqhFs4HKdNX6fd7qewsAYqKjprmbdUpbqTq8/CL7z23Ffi/Ye6eO2568o2cwey%2BR6r5pKsm82ducLDrzV1fbVlnc9DAgAA1BEULAAAAJNRsAAAAEzm8YI1bNgw/e1vf9Pp0%2BafkwQAAFAXeLxgde7cWS%2B88IK6du2qSZMmadOmTeI8ewAAYCUeL1hpaWn6%2BOOP9fzzz8vf318TJ05U9%2B7d9eyzz%2BrgwYOeng4AAIDpvHIdLJvNpi5duqhLly4qKSnRq6%2B%2Bqueff17Lly9Xhw4dNGrUKN1xxx3emBoAAMBV89qFRgsKCvTOO%2B/onXfe0f79%2B9WhQwcNHjxYJ06c0F/%2B8hdt27ZN06dP99b0AAAArpjHC9aGDRu0YcMGbdmyRQ0bNtSgQYO0ePFilz/I3KRJE82dO5eCBQAAfJLHC9b06dPVo0cPLVmyRLfddpv8/KqfBhYVFaURI0Z4emoAAACm8HjB%2BuyzzxQWFqbi4mJnudq5c6fatWsnf39/SVKHDh3UoUMHT08NAADAFB7/FOGZM2fUu3dvvfjii86xcePGaeDAgTp%2B/LinpwMAAGA6jxesJ598Ur/5zW90//33O8fee%2B89NWnSROnp6Z6eDgAAgOk8XrD%2B85//6NFHH1V4eLhzrGHDhpoyZYo2b97s6ekAAACYzuMFy26369SpU9XGS0pKuKI7AACwBI8XrNtuu01z5szRd9995xw7fPiw0tPT1a1bN09PBwAAwHQe/xThI488ovvvv1933nmnQkJCJEmnTp1Su3btNHXqVE9PBwAAwHQeL1iNGjXSW2%2B9pS%2B//FI5OTmy2%2B268cYb9dvf/lY2m83T0wEAADCdV/5Ujr%2B/v7p168YhQQAAYEkeL1gOh0MLFy7U9u3bdf78%2BWontv/rX//y9JQAAABM5fGC9dhjj2nXrl3q16%2Bfrr32Wk8/PQAAgNt5vGBt3rxZK1asUGJioqefGgAAwCM8fpmG%2BvXrq1GjRp5%2BWgAAAI/xeMEaOHCgVqxYocrKSk8/NQAAgEd4/BBhcXGxNm7cqE8%2B%2BUQtWrRQQECAy/LVq1d7ekoAAACm8splGvr37%2B%2BNpwUAAPAIjxes9PR0Tz8lAACAR3n8HCxJKigoUEZGhtLS0vT999/rH//4hw4cOOCNqQAAAJjO4wXr22%2B/1YABA/TWW2/pgw8%2B0Llz5/Tee%2B9p6NChys7O9vR0AAAATOfxgvXUU0%2BpZ8%2Be%2Buijj3TNNddIkp555hklJSVpwYIFl72%2B8vJy9e/fX1u2bHGOHT58WKNHj1ZcXJz69u2rTZs2uTzmyy%2B/VP/%2B/RUbG6uRI0fq8OHDLstfeeUVdevWTfHx8Zo2bZpKSkquICkAAPi18njB2r59u%2B6//36XP%2Bxst9v14IMPas%2BePZe1rrKyMk2aNEk5OTnOMcMwlJKSosaNG2v9%2BvUaOHCgJkyYoGPHjkmSjh07ppSUFA0ZMkRvvPGGGjZsqAcffND5J3s%2B%2BOADZWRkaNasWVq1apWys7M1f/58E5IDAIBfC48XrKqqKlVVVVUbP3v2rPz9/Wu9ntzcXN1zzz367rvvXMY3b96sw4cPa9asWWrVqpXGjx%2BvuLg4rV%2B/XpK0bt063XLLLRozZoxuuukmpaen6%2BjRo9q6daukHy8TMWrUKPXo0UPt27fXE088ofXr1/MuFgAAqDWPF6yuXbtq2bJlLiWruLhY8%2BfPV%2BfOnWu9nq1bt6pTp07KzMx0Gc/OztbNN9%2Bs%2BvXrO8cSEhK0Y8cO5/Kf/5meoKAgtWvXTjt27FBlZaW%2B/vprl%2BVxcXE6f/689u3bd9lZAQDAr5PHL9Pw6KOPauTIkeratavKysr0pz/9SUePHlVoaKieeuqpWq/n3nvvrXHc4XAoIiLCZaxRo0Y6ceLEJZefOnVKZWVlLsvtdrtCQ0Odj6%2BNgoICORwOlzG7vX61571a/v5%2BLv9ahVVzuZvd7r3vl5W3Gdl8j1VzSdbNZsVcHi9YkZGRevvtt7Vx40bt3btXVVVVGj58uAYOHKjg4OCrXn9JSUm1q8MHBASovLz8kstLS0udty/2%2BNrIzMxURkaGy1hKSopSU1NrvY7LERIS5Jb1eptVc7lLWFgDb0/B0tuMbL7Hqrkk62azUi6vXMk9KChIw4YNc8u6AwMDVVxc7DJWXl6uevXqOZdfWJbKy8sVEhKiwMBA5%2B0LlwcF1X6jJycnKykpyWXMbq%2BvoqKztV5Hbfj7%2BykkJEinTpWosrL6eW2%2Byqq53M3s/etyWHmbkc33WDWXZN1s7szlrV8%2BPV6wRo4c%2BYvLr/ZvEUZGRio3N9dlrLCw0Hl4LjIyUoWFhdWWt23bVqGhoQoMDFRhYaFatWolSaqoqFBxcbHCw8NrPYeIiIhqhwMdjtOqqHDPi6Gysspt6/Ymq%2BZyl7rwvbLyNiOb77FqLsm62ayUy%2BMHO5s1a%2BbyFRkZqdLSUu3cuVPx8fFXvf7Y2Fjt3r3bebhPkrKyshQbG%2BtcnpWV5VxWUlKiPXv2KDY2Vn5%2BfoqJiXFZvmPHDtntdkVHR1/13AAAwK9DnflbhEuWLLmsE8kvpmPHjmrSpImmTp2qBx98UB9//LF27tzpfN6hQ4dq5cqVWr58uXr06KElS5aoefPm6tSpk6QfT56fMWOGWrdurYiICM2cOVP33HPPZR0iBAAAv2515nT9gQMH6v3337/q9fj7%2B%2Bv555%2BXw%2BHQkCFD9M4772jJkiVq2rSpJKl58%2BZ67rnntH79et19990qLi7WkiVLnBc%2B7devn8aPH68ZM2ZozJgxat%2B%2BvSZPnnzV8wIAAL8eXjnJvSZfffXVZV1o9Oe%2B%2BeYbl9u/%2Bc1vtGbNmove//bbb9ftt99%2B0eXjxo3TuHHjrmguAAAAdeIk9zNnzuibb7656LWtAAAAfInHC1bTpk1d/g6hJF1zzTUaMWKE7rrrLk9PBwAAwHQeL1iXc7V2AAAAX%2BTxgrVt27Za3/fWW29140wAAADcw%2BMF67777nMeIjQMwzl%2B4ZjNZtPevXs9PT0AAICr5vGC9cILL2jOnDmaPHmyOnbsqICAAH399deaNWuWBg8erL59%2B3p6SgAAAKby%2BHWw0tPTNWPGDN15550KCwtTgwYN1LlzZ82aNUtr1651uco7AACAL/J4wSooKKixPAUHB6uoqMjT0wEAADCdxwtWXFycnnnmGZ05c8Y5VlxcrPnz5%2Bu3v/2tp6cDAABgOo%2Bfg/WXv/xFI0eO1G233aaWLVvKMAwdOnRI4eHhWr16taenAwAAYDqPF6xWrVrpvffe08aNG5WXlydJ%2Bv3vf69%2B/frxB5UBAIAleOVvEV533XUaNmyYjhw5ohYtWkj68WruAAAAVuDxc7AMw9CCBQt06623qn///jpx4oQeeeQRTZ8%2BXefPn/f0dAAAAEzn8YL16quvasOGDXr88ccVEBAgSerZs6c%2B%2BugjZWRkeHo6AAAApvN4wcrMzNSMGTM0ZMgQ59Xb%2B/btqzlz5ujdd9/19HQAAABM5/GCdeTIEbVt27baeHR0tBwOh6enAwAAYDqPF6xmzZrp66%2B/rjb%2B2WefOU94BwAA8GUe/xTh2LFj9cQTT8jhcMgwDP373/9WZmamXn31VT366KOeng4AAIDpPF6whg4dqoqKCi1dulSlpaWaMWOGGjZsqIceekjDhw/39HQAAABM5/GCtXHjRvXu3VvJyck6efKkDMNQo0aNPD0NAAAAt/H4OVizZs1ynszesGFDyhUAALAcjxesli1bav/%2B/Z5%2BWgAAAI/x%2BCHC6OhoPfzww1qxYoVatmypwMBAl%2BXp6emenhIAAICpPF6wDh48qISEBEniulcAAMCSPFKw5s2bpwkTJqh%2B/fp69dVXPfGUAAAAXuORc7BefvlllZSUuIyNGzdOBQUFnnh6AAAAj/JIwTIMo9rYtm3bVFZW5omnBwAA8CiPf4oQAADA6ixZsN588021adOm2ld0dLQk6U9/%2BlO1ZR9//LHz8a%2B88oq6deum%2BPh4TZs2rdrhTQAAgF/isU8R2mw2Tz2V%2Bvbtq27dujlvV1RUaNSoUerevbskKS8vT/Pnz9dvf/tb532uu%2B46SdIHH3ygjIwMzZ8/X40aNdLUqVM1f/58zZgxw2PzBwAAvs1jBWvOnDku17w6f/685s%2BfrwYNGrjcz4zrYNWrV0/16tVz3l62bJkMw9DDDz%2Bs8vJyHTlyRDExMQoPD6/22NWrV2vUqFHq0aOHJOmJJ57Q2LFjNXnyZAUFBV313AAAgPV5pGDdeuut1a55FR8fr6KiIhUVFbn1uYuLi/Xiiy9qzpw5CggI0L59%2B2Sz2dSiRYtq962srNTXX3%2BtCRMmOMfi4uJ0/vx57du3T/Hx8W6dKwAAsAaPFCxvXvtq7dq1ioiIUO/evSVJBw4cUHBwsKZMmaKtW7fq%2Buuv18SJE3X77bfr1KlTKisrU0REhPPxdrtdoaGhOnHihLciAAAAH%2BPxK7l7kmEYWrdunR544AHn2IEDB1RaWqquXbtq3Lhx%2BvDDD/WnP/1JmZmZaty4sSQpICDAZT0BAQEqLy%2Bv9fMWFBRUe8fObq/vUtzM4O/v5/KvVVg1l7vZ7d77fll5m5HN91g1l2TdbFbMZemC9fXXXys/P1/9%2BvVzjj344IO67777nCe1R0dHa/fu3Xr99df1f//3f5JUrUyVl5df1vlXmZmZysjIcBlLSUlRamrqlUb5RSEh1jw3zKq53CUsrMGl7%2BRmVt5mZPM9Vs0lWTeblXJZumB9/vnnSkxMdJYpSfLz83O5LUlRUVHKzc1VaGioAgMDVVhYqFatWkn68ROIxcXFNZ4QfzHJyclKSkpyGbPb66uo6OxVpKnO399PISFBOnWqRJWVVaau25usmsvdzN6/LoeVtxnZfI9Vc0nWzebOXN765dPSBWvnzp3q0KGDy9ijjz4qm83m8mnFffv2qXXr1vLz81NMTIyysrLUqVMnSdKOHTtkt9ud19CqjYiIiGqHAx2O06qocM%2BLobKyym3r9iar5nKXuvC9svI2I5vvsWouybrZrJTLOgc7a5CTk6Mbb7zRZSwpKUnvvvuu3n77bX377bfKyMhQVlaWRowYIUm69957tXLlSn300UfauXOnZs6cqXvuuYdLNAAAgFqz9DtYhYWFCgkJcRm744479Pjjj2vp0qU6duyYbrrpJq1YsULNmzeXJPXr109Hjx7VjBkzVF5erjvuuEOTJ0/2xvQBAICPsnTB2rlzZ43jw4YN07Bhwy76uHHjxmncuHHumhYAALA4Sx8iBAAA8AYKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyS3%2BKEHVLn4VfeHsKAAB4BO9gAQAAmIyCBQAAYDIKFgA/AZ3QAAAXpklEQVQAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAySxbsD788EO1adPG5Ss1NVWStGfPHg0bNkyxsbEaOnSodu3a5fLYjRs3qmfPnoqNjVVKSopOnjzpjQgAAMBHWbZg5ebmqkePHtq0aZPza86cOTp37pzGjRunxMREvfnmm4qPj9f48eN17tw5SdLOnTs1ffp0TZgwQZmZmTp16pSmTp3q5TQAAMCXWLZg5eXlqXXr1goPD3d%2BhYSE6L333lNgYKCmTJmiVq1aafr06WrQoIH%2B8Y9/SJLWrFmjPn36aNCgQYqOjta8efP06aef6vDhw15OBAAAfIWlC1bLli2rjWdnZyshIUE2m02SZLPZ1KFDB%2B3YscO5PDEx0Xn/Jk2aqGnTpsrOzvbIvAEAgO%2Bze3sC7mAYhg4ePKhNmzZp2bJlqqysVO/evZWamiqHw6Ebb7zR5f6NGjVSTk6OJKmgoEARERHVlp84caLWz19QUCCHw%2BEyZrfXr7beq%2BXv7%2BfyL37d7Hbv7QdW3hfJ5nusmkuybjYr5rJkwTp27JhKSkoUEBCghQsX6siRI5ozZ45KS0ud4z8XEBCg8vJySVJpaekvLq%2BNzMxMZWRkuIylpKQ4T7I3W0hIkFvWC98SFtbA21Ow9L5INt9j1VySdbNZKZclC1azZs20ZcsWXXfddbLZbGrbtq2qqqo0efJkdezYsVpZKi8vV7169SRJgYGBNS4PCqr9Rk9OTlZSUpLLmN1eX0VFZ68wUc38/f0UEhKkU6dKVFlZZeq64XvM3r8uh5X3RbL5HqvmkqybzZ25vPXLpyULliSFhoa63G7VqpXKysoUHh6uwsJCl2WFhYXOw3eRkZE1Lg8PD6/1c0dERFQ7HOhwnFZFhXteDJWVVW5bN3xHXdgHrLwvks33WDWXZN1sVsplnYOdP/P555%2BrU6dOKikpcY7t3btXoaGhSkhI0FdffSXDMCT9eL7W9u3bFRsbK0mKjY1VVlaW83HHjx/X8ePHncsBAAAuxZIFKz4%2BXoGBgfrLX/6iAwcO6NNPP9W8efP0wAMPqHfv3jp16pTmzp2r3NxczZ07VyUlJerTp48kafjw4dqwYYPWrVunffv2acqUKerevbtatGjh5VQAAMBXWLJgBQcHa%2BXKlTp58qSGDh2q6dOnKzk5WQ888ICCg4O1bNkyZWVlaciQIcrOztby5ctVv359ST%2BWs1mzZmnJkiUaPny4rrvuOqWnp3s5EQAA8CWWPQfrpptu0ssvv1zjsvbt2%2Butt9666GOHDBmiIUOGuGtqAADA4iz5DhYAAIA3UbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACT2b09AQDm6LPwC29Podbef6iLt6cAAG7FO1gAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYzLIFKz8/X6mpqerYsaO6deum9PR0lZWVSZLmzJmjNm3auHytWbPG%2BdiNGzeqZ8%2Beio2NVUpKik6ePOmtGAAAwAfZvT0BdzAMQ6mpqQoJCdFrr72mH374QdOmTZOfn58eeeQR5eXlKS0tTYMHD3Y%2BJjg4WJK0c%2BdOTZ8%2BXU888YSio6M1d%2B5cTZ06VcuWLfNWHAAA4GMs%2BQ7WgQMHtGPHDqWnp%2Bumm25SYmKiUlNTtXHjRklSXl6ebr75ZoWHhzu/goKCJElr1qxRnz59NGjQIEVHR2vevHn69NNPdfjwYW9GAgAAPsSSBSs8PFwrVqxQ48aNXcbPnDmjM2fOKD8/Xy1btqzxsdnZ2UpMTHTebtKkiZo2bars7Gx3ThkAAFiIJQtWSEiIunXr5rxdVVWlNWvWqHPnzsrLy5PNZtMLL7yg2267TXfddZfeeust530LCgoUERHhsr5GjRrpxIkTHps/AADwbZY8B%2BtC8%2BfP1549e/TGG29o9%2B7dstlsioqK0ogRI7Rt2zY99thjCg4OVq9evVRaWqqAgACXxwcEBKi8vLzWz1dQUCCHw%2BEyZrfXr1bcrpa/v5/Lv4CvsNt9Z5%2B18uvMqtmsmkuybjYr5rJ8wZo/f75WrVqlZ599Vq1bt9ZNN92kHj16KDQ0VJIUHR2tQ4cOae3aterVq5cCAwOrlany8nLnOVq1kZmZqYyMDJexlJQUpaamXn2gGoSE1H5uQF0QFtbA21O4bFZ%2BnVk1m1VzSdbNZqVcli5Ys2fP1tq1azV//nzdeeedkiSbzeYsVz%2BJiorS5s2bJUmRkZEqLCx0WV5YWKjw8PBaP29ycrKSkpJcxuz2%2BioqOnslMS7K399PISFBOnWqRJWVVaauG3Ans18L7mTl15lVs1k1l2TdbO7M5a1f6CxbsDIyMvS3v/1NzzzzjHr37u0cX7Rokb766iu98sorzrF9%2B/YpKipKkhQbG6usrCwNGTJEknT8%2BHEdP35csbGxtX7uiIiIaocDHY7Tqqhwz4uhsrLKbesG3MEX91crv86sms2quSTrZrNSLusc7PyZvLw8Pf/88/rDH/6ghIQEORwO51ePHj20bds2rVy5Ut99953%2B%2Bte/6u2339aYMWMkScOHD9eGDRu0bt067du3T1OmTFH37t3VokULL6cCAAC%2BwpLvYP3rX/9SZWWlli5dqqVLl7os%2B%2Babb7Ro0SItXrxYixYtUrNmzfT0008rPj5ekhQfH69Zs2Zp8eLF%2BuGHH9SlSxfNnj3bGzEAAICPshmGYXh7Er8GDsdp09dpt/spLKyBiorO%2BsRbqn0WfuHtKaCOeP%2BhLt6eQq352uvsclg1m1VzSdbN5s5c4eHXmrq%2B2rLkIUIAAABvomABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyu7cnAAB1XeL0f3h7Cpfl/Ye6eHsKwK8eBcvH%2BdoPfgAAfg04RAgAAGAyChYAAIDJKFgAAAAmo2ABAACYjJPca1BWVqYnnnhC//znP1WvXj2NGTNGY8aM8fa0AMvos/ALb08BANyKglWDefPmadeuXVq1apWOHTumRx55RE2bNlXv3r29PTUAAOADKFgXOHfunNatW6cXX3xR7dq1U7t27ZSTk6PXXnuNggUAAGqFc7AusG/fPlVUVCg%2BPt45lpCQoOzsbFVVVXlxZgAAwFfwDtYFHA6HwsLCFBAQ4Bxr3LixysrKVFxcrIYNG15yHQUFBXI4HC5jdnt9RUREmDpXf3/6MYDq7Pba/Wz46WeIN3%2BW9Frwudee%2B0p8%2BHA3rz7/5W4zX/r%2B/mdub0v9f42CdYGSkhKXciXJebu8vLxW68jMzFRGRobL2IQJEzRx4kRzJvlfBQUFGnV9jpKTk00vb95UUFCgzMxMy%2BWSrJvNqrkk62dbtWqFV7P9Z675p16wzf4/d3x/3aGgoEDPPfecpbaZdaqiSQIDA6sVqZ9u16tXr1brSE5O1ptvvunylZycbPpcHQ6HMjIyqr1b5uusmkuybjar5pLI5ousmkuybjYr5uIdrAtERkaqqKhIFRUVstt//PY4HA7Vq1dPISEhtVpHRESEZRo4AAC4fLyDdYG2bdvKbrdrx44dzrGsrCzFxMTIz49vFwAAuDQawwWCgoI0aNAgzZw5Uzt37tRHH32kl156SSNHjvT21AAAgI/wnzlz5kxvT6Ku6dy5s/bs2aOnn35a//73v/XHP/5RQ4cO9fa0atSgQQN17NhRDRo08PZUTGXVXJJ1s1k1l0Q2X2TVXJJ1s1ktl80wDMPbkwAAALASDhECAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYPkQwzA0ZswYvfnmmy7jRUVFmjhxouLj45WUlKQNGza4LN%2BzZ4%2BGDRum2NhYDR06VLt27fLktGttz549atOmjcvXkCFDnMsPHz6s0aNHKy4uTn379tWmTZu8ONvLV1ZWpmnTpikxMVFdu3bVSy%2B95O0pXZEPP/yw2nZKTU2V5Dv72oXKy8vVv39/bdmyxTl2qf3tyy%2B/VP/%2B/RUbG6uRI0fq8OHDnp52rdSUbc6cOdW24Zo1a5zLN27cqJ49eyo2NlYpKSk6efKkN6Zeo/z8fKWmpqpjx47q1q2b0tPTVVZWJsn3t9kvZfPlbfbtt99q7Nixio%2BPV/fu3bVixQrnMl/fZr/IgE%2BorKw0Zs2aZbRu3dpYv369y7Lx48cbo0aNMr755hvj9ddfN2655RYjOzvbMAzDOHv2rNGlSxfjqaeeMnJzc43Zs2cbv/vd74yzZ896I8Yv2rBhgzFw4ECjoKDA%2BXXy5EnDMAyjqqrKGDBggJGWlmbk5uYaL7zwghEbG2scPXrUy7OuvVmzZhkDBgwwdu3aZfzzn/804uPjjffff9/b07pszz//vDF%2B/HiX7fTDDz/41L72c6WlpUZKSorRunVrY/PmzYZhXHp/O3r0qBEXF2esXLnS2L9/v/HnP//Z6N%2B/v1FVVeXNKNXUlM0wDGP06NHGsmXLXLbhuXPnDMMwjOzsbKN9%2B/bGW2%2B9Zezdu9cYMWKEMW7cOG9FcFFVVWXcc889xgMPPGDs37/f2LZtm9GrVy/jqaee8vlt9kvZDMN3t1llZaVxxx13GGlpacbBgweNTz75xOjQoYPxzjvv%2BPw2uxQKlg84ceKEMWLECKN79%2B5GYmKiS8H69ttvjdatWxuHDx92jk2bNs145JFHDMMwjHXr1hlJSUnOHbKqqsro1atXtZJWFzzzzDPGpEmTalz25ZdfGnFxcS7/sx41apSxePFiT03vqpw9e9aIiYlx%2BZ/ckiVLjBEjRnhxVlcmLS3NePrpp6uN%2B9K%2B9pOcnBzjrrvuMgYMGOBSQi61vy1cuNBl2507d86Ij4932b7edrFshmEY3bp1Mz7//PMaHzd58mTnzw/DMIxjx44Zbdq0Mb777ju3z/lScnNzjdatWxsOh8M59u677xpdu3b1%2BW32S9kMw3e3WX5%2BvvHnP//ZOH36tHMsJSXFePzxx31%2Bm10Khwh9wO7du9WkSROtX79e1157rcuy7OxsNWnSRM2bN3eOJSQk6KuvvnIuT0hIkM1mkyTZbDZ16NBBO3bs8FyAWsrLy1PLli1rXJadna2bb75Z9evXd44lJCTUyRw12bdvnyoqKhQfH%2B8cS0hIUHZ2tqqqqrw4s8t3se3kS/vaT7Zu3apOnTopMzPTZfxS%2B1t2drYSExOdy4KCgtSuXbs6lfVi2c6cOaP8/PxffK39PFuTJk3UtGlTZWdnu3O6tRIeHq4VK1aocePGLuNnzpzx%2BW32S9l8eZtFRERo4cKFCg4OlmEYysrK0rZt29SxY0ef32aXYvf2BHBpSUlJSkpKqnGZw%2BFQRESEy1ijRo2Un5/vXH7jjTdWW56Tk%2BOeyV6FvLw8VVVVacCAATp9%2BrRuu%2B02TZkyRcHBwRfNeeLECS/N9vI4HA6FhYUpICDAOda4cWOVlZWpuLhYDRs29OLsas8wDB08eFCbNm3SsmXLVFlZqd69eys1NdWn9rWf3HvvvTWOX2p/84X98WLZ8vLyZLPZ9MILL%2Bizzz5TaGio7r//fg0ePFiSVFBQUGezhYSEqFu3bs7bVVVVWrNmjTp37uzz2%2ByXsvnyNvu5pKQkHTt2TD169NCdd96pJ5980qe32aVQsOqA0tJSZyG6UHh4uEu7v1BJSYnL/7QlKSAgQOXl5bVa7km/lLNhw4Y6fPiwmjdvrieffFKnTp1Senq6Jk%2BerKVLl9apHFfiYvOX5DMZJOnYsWPOLAsXLtSRI0c0Z84clZaW%2Bvw2%2Bjlfel1drgMHDshmsykqKkojRozQtm3b9Nhjjyk4OFi9evVSaWmpz2SbP3%2B%2B9uzZozfeeEOvvPKKpbbZz7Pt3r3bEtts8eLFKiws1MyZM5Wenm7p15lEwaoTsrOzNXLkyBqXLVmyRD179rzoYwMDA6vtbOXl5apXr16tlnvSpXJu3rxZgYGBuuaaayRJTz31lIYOHar8/HwFBgaquLjY5THeynElLrYdJPlMBklq1qyZtmzZouuuu042m01t27ZVVVWVJk%2BerI4dO9aZfe1qXWp/u9j2DAkJ8dgcr9SgQYPUo0cPhYaGSpKio6N16NAhrV27Vr169bpotqCgIG9M96Lmz5%2BvVatW6dlnn1Xr1q0ttc0uzHbTTTdZYpvFxMRI%2BvET1Q8//LCGDh2qkpISl/v46jarCQWrDujUqZO%2B%2BeabK3psZGSkCgsLXcYKCwsVHh7%2Bi8svfNvVEy43Z6tWrST9%2BNHlyMhI5ebmuiz3Vo4rERkZqaKiIlVUVMhu//Fl53A4VK9ePZ/5YfGTn37I/6RVq1YqKytTeHh4ndnXrtal9reLva7atm3rsTleKZvNVm0bRkVFafPmzZIu/TOlLpg9e7bWrl2r%2BfPn684775RknW1WUzZf3maFhYXasWOHyxsFN954o86fP6/w8HAdOHCg2v19bZtdDCe5%2B7i4uDgdPXrU5Zh0VlaW4uLiJEmxsbH66quvZBiGpB/Podm%2BfbtiY2O9Mt%2BLyc3NVXx8vMs1Tvbu3Su73a7f/OY3io2N1e7du1VaWupcnpWVVedyXEzbtm1lt9tdTs7MyspSTEyM/Px852X4%2Beefq1OnTi6/de7du1ehoaHOD1fU9X2tNi61v8XGxiorK8u5rKSkRHv27PGJrIsWLdLo0aNdxvbt26eoqChJ1bMdP35cx48frzPZMjIy9Le//U3PPPOM%2BvXr5xy3wja7WDZf3mZHjhzRhAkTXE4P2bVrlxo2bKiEhASf32a/yJsfYcTl69GjR7WPvY8ZM8YYMWKEsXfvXuP11183YmJinNfBOn36tNG5c2dj9uzZRk5OjjF79myjS5cude7aRJWVlcbAgQOd1/Patm2b0bdvX%2BPxxx83DMMwKioqjL59%2BxoPPfSQsX//fmPZsmVGXFycT10H67HHHjP69etnZGdnGx9%2B%2BKHRoUMH44MPPvD2tC7L6dOnjW7duhmTJk0y8vLyjE8%2B%2BcTo2rWrsXz5cp/Z1y7m55cyuNT%2BdvjwYSMmJsZYtmyZ8/o8AwYMqLPX5/l5tuzsbOPmm282VqxYYXz77bfGa6%2B9Ztxyyy3G9u3bDcMwjO3btxvt2rUzXn/9dec1lcaPH%2B/N6Tvl5uYabdu2NZ599lmX60EVFBT4/Db7pWy%2BvM0qKiqMIUOGGGPGjDFycnKMTz75xPjd735nvPLKKz6/zS6FguVjaipYhYWFxvjx442YmBgjKSnJePfdd12WZ2dnG4MGDTJiYmKMu%2B%2B%2B29i9e7cnp1xrx44dM1JSUozExESjY8eOxuzZs42ysjLn8kOHDhm///3vjVtuucXo16%2Bf8cUXX3hxtpfv3LlzxpQpU4y4uDija9euxssvv%2BztKV2R/fv3G6NHjzbi4uKMLl26GM8995zzB56v7Gs1ufBaUZfa3z755BPjjjvuMNq3b2%2BMGjWqTlxz6GIuzPbhhx8aAwYMMGJiYozevXtXK/rr1683br/9diMuLs5ISUlxXvDX25YtW2a0bt26xi/D8O1tdqlsvrrNDOPHazmmpKQYHTp0MLp06WIsXbrU%2BTPDl7fZpdgM47/v5wMAAMAUvnPyBwAAgI%2BgYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACb7fx3p1IMHehM8AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7813442492329845932”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>961</td> <td class=”number”>29.9%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>144</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>129</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>86</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.666666667</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (404)</td> <td class=”number”>1261</td> <td class=”number”>39.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7813442492329845932”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_HHNV_10_15”>PCH_LACCESS_HHNV_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3113</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>91</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>33.086</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>43191</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6282510798294712146”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6282510798294712146,#minihistogram-6282510798294712146”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6282510798294712146”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6282510798294712146”

aria-controls=”quantiles-6282510798294712146” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6282510798294712146” aria-controls=”histogram-6282510798294712146”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6282510798294712146” aria-controls=”common-6282510798294712146”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6282510798294712146” aria-controls=”extreme-6282510798294712146”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6282510798294712146”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-46.422</td>

</tr> <tr>

<th>Q1</th> <td>-14.628</td>

</tr> <tr>

<th>Median</th> <td>4.7531</td>

</tr> <tr>

<th>Q3</th> <td>28.984</td>

</tr> <tr>

<th>95-th percentile</th> <td>100.94</td>

</tr> <tr>

<th>Maximum</th> <td>43191</td>

</tr> <tr>

<th>Range</th> <td>43291</td>

</tr> <tr>

<th>Interquartile range</th> <td>43.612</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>789.88</td>

</tr> <tr>

<th>Coef of variation</th> <td>23.873</td>

</tr> <tr>

<th>Kurtosis</th> <td>2863.6</td>

</tr> <tr>

<th>Mean</th> <td>33.086</td>

</tr> <tr>

<th>MAD</th> <td>61.353</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>52.642</td>

</tr> <tr>

<th>Sum</th> <td>103200</td>

</tr> <tr>

<th>Variance</th> <td>623910</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6282510798294712146”>

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%2BV2uzV9%2BnSVlZX5zG/YsEHDhw9XamqqFi5cqJqaGr%2BtCwAAhL6QDFjGGGVnZ6umpkZPP/20Hn74Yf3P//yPVq1aJWOMsrKy1K1bNxUWFmrcuHGaO3euDh8%2BLEk6fPiwsrKyNGHCBD3//PPq0qWLbrvtNhljJEmvvfaa8vPzlZOTo40bN8rj8SgvLy%2BQywUAACEmJAPWxx9/rJKSEuXm5uriiy9Wenq6srOzVVRUpJ07d6qsrEw5OTnq06eP5syZo5SUFBUWFkqSnnvuOV166aWaOXOmLr74YuXm5urQoUN65513JEmbNm3SjBkzNGLECA0cOFBLlixRYWEhZ7EAAECLhWTAio%2BP17p169StWzef8ZMnT8rj8eiSSy5Rx44dnfG0tDSVlJRIkjwej9LT05256OhoDRgwQCUlJWpsbNR7773nM5%2BSkqIzZ85o//79rbwqAADQVrgCXcC3ERMTo%2BHDhzuvm5qatGXLFg0ZMkRer1cJCQk%2B7%2B/atavKy8sl6Wvnjx8/rrq6Op95l8ul2NhYZ/uWqKiokNfr9RlzuTo2O25743KFZJ4/bxER4T6fEVj0I/jQk%2BBDT%2BwLyYD1ZXl5edq3b5%2Bef/55bdiwQZGRkT7zkZGRqq%2BvlyTV1NScc762ttZ5fa7tW6KgoED5%2Bfk%2BY1lZWcrOzm7xPtqiuLhOgS7Br2JiogNdAr6AfgQfehJ86Ik9IR%2Bw8vLytHHjRj388MPq27evoqKiVF1d7fOe%2Bvp6dejQQZIUFRXVLCzV19crJiZGUVFRzusvz0dHt/ybbvLkycrMzPQZc7k6qqrqVIv30Ra1l/VHRIQrJiZax4/XqLGxKdDltHv0I/jQk%2BDTlnsSqB/uQzpgLV26VM8884zy8vI0atQoSVJiYqIOHDjg877Kykrn8lxiYqIqKyubzffv31%2BxsbGKiopSZWWl%2BvTpI0lqaGhQdXW14uPjW1xXQkJCs8uBXu8JNTS0rW/a89Xe1t/Y2NTu1hzM6EfwoSfBh57Y4/eLrTfeeKOeffZZnThx4jvtJz8/X88%2B%2B6weeughjRkzxhl3u916//33nct9klRcXCy32%2B3MFxcXO3M1NTXat2%2Bf3G63wsPDlZyc7DNfUlIil8ulfv36fad6AQBA%2B%2BH3gDVkyBA98cQTGjZsmH75y19q%2B/btzjOoWurgwYN67LHH9O///u9KS0uT1%2Bt1PjIyMtS9e3ctWLBApaWlWrt2rfbs2aNJkyZJkiZOnKjdu3dr7dq1Ki0t1YIFC9SjRw8NHjxYkjR16lStX79e27Zt0549e7R48WLddNNN53WJEAAAtG9h5nzTjQXGGO3YsUNbt27Vtm3bFBMTo/Hjx2v8%2BPH6t3/7t2/cfu3atVq5cuVXzn344Yf69NNPtWjRInk8Hv3whz/UwoUL9eMf/9h5z5tvvqlf//rXKi8vV2pqqpYuXaqePXv67H/Dhg2qr6/XNddco/vvv9%2B5P%2Bvb8nq/2xm7c7lu1V9bZb%2Bt4dU7hga6BL9wucIVF9dJVVWnONUeBOhH8KEnwact9yQ%2B/vsBOW5AAtYX1dTUaPPmzXrsscdUV1enQYMGacaMGbrmmmsCWZZ1BCwCFgKDfgQfehJ82nJPAhWwAnaTe0VFhV566SW99NJL%2BuijjzRo0CDdcMMNKi8v169%2B9Svt2rVLixYtClR5AAAA35rfA9aLL76oF198UW%2B//ba6dOmi8ePH69FHH1WvXr2c93Tv3l3Lly8nYAEAgJDk94C1aNEijRgxQqtXr9YVV1yh8PDm99n37t1b06ZN83dpAAAAVvg9YP3lL39RXFycqqurnXC1Z88eDRgwQBEREZKkQYMGadCgQf4uDQAAwAq/P6bh5MmTuvbaa/W73/3OGZs9e7bGjRunI0eO%2BLscAAAA6/wesH7961/rhz/8oW699VZn7JVXXlH37t2Vm5vr73IAAACs83vA%2Btvf/qZ77rnH50/PdOnSRXfddZd27tzp73IAAACs83vAcrlcOn78eLPxmpqa836iOwAAQDDye8C64oortGzZMv3jH/9wxsrKypSbm6vhw4f7uxwAAADr/P5bhHfffbduvfVWjRo1SjExMZKk48ePa8CAAVqwYIG/ywEAALDO7wGra9eueuGFF7Rjxw6VlpbK5XLpoosu0uWXX66wsDB/lwMAAGBdQP5UTkREhIYPH84lQQAA0Cb5PWB5vV6tWrVKu3fv1pkzZ5rd2P6nP/3J3yUBAABY5feAde%2B992rv3r0aM2aMvv/9wPyFawAAgNbk94C1c%2BdOrVu3Tunp6f4%2BNAAAgF/4/TENHTt2VNeuXf19WAAAAL/xe8AaN26c1q1bp8bGRn8fGgAAwC/8fomwurpaRUVFeuONN9SzZ09FRkb6zG/atMnfJQEAAFgVkMc0jB07NhCHBQAA8Au/B6zc3Fx/HxIAAMCv/H4PliRVVFQoPz9f8%2BbN0z//%2BU/98Y9/1McffxyIUgAAAKzze8D69NNPdf311%2BuFF17Qa6%2B9ptOnT%2BuVV17RxIkT5fF4/F0OAACAdX4PWA888ICuvvpqbdu2Td/73vckSQ899JAyMzP14IMP%2BrscAAAA6/wesHbv3q1bb73V5w87u1wu3Xbbbdq3b5%2B/ywEAALDO7wGrqalJTU1NzcZPnTqliIgIf5cDAABgnd8D1rBhw7RmzRqfkFVdXa28vDwNGTLE3%2BUAAABY5/eAdc8992jv3r0aNmyY6urq9B//8R8aMWKEPvvsM919993%2BLgcAAMA6vz8HKzExUVu3blVRUZE%2B%2BOADNTU1acqUKRo3bpw6d%2B7s73IAAACsC8iT3KOjo3XjjTcG4tAAAACtzu8Ba/r06V87z98iBAAAoc7vAevCCy/0ed3Q0KBPP/1UH330kWbMmOHvcgAAAKwLmr9FuHr1apWXl/u5GgAAAPsC8rcIv8q4ceP06quvBroMAACA7yxoAta7777Lg0YBAECbEBQ3uZ88eVIffvihpk6d6u9yAAAArPN7wEpKSvL5O4SS9L3vfU/Tpk3TT37yE3%2BXAwAAYJ3fA9YDDzzg70MCAAD4ld8D1q5du1r83ssuu%2Bwb31NfX68JEybo3nvv1eDBgyVJy5Yt0%2BbNm33ed%2B%2B992ratGmSpKKiIq1atUper1fDhg3T0qVL1aVLF0mSMUYrV67U888/r6amJk2aNEl33nmnwsOD5nY1AAAQ5PwesG6%2B%2BWbnEqExxhn/8lhYWJg%2B%2BOCDr91XXV2d5s2bp9LSUp/xgwcPat68ebrhhhucsbN/hmfPnj1atGiRlixZon79%2Bmn58uVasGCB1qxZI0l66qmnVFRUpPz8fDU0NGj%2B/Pnq2rWrZs2a9R1XDgAA2gu/B6wnnnhCy5Yt0/z585WRkaHIyEi99957ysnJ0Q033KDRo0e3aD8HDhzQvHnzfELaWQcPHtSsWbMUHx/fbG7Lli267rrrNH78eEnSihUrNGLECJWVlalnz57atGmTsrOzlZ6eLkm688479cgjjxCwAABAi/n9uldubq7uu%2B8%2BjRo1SnFxcerUqZOGDBminJwcPfPMM7rwwgudj6/zzjvvaPDgwSooKPAZP3nypI4ePapevXp95XYej8cJT5LUvXt3JSUlyePx6OjRozpy5IjPpcm0tDQdOnRIFRUV337RAACgXfH7GayKioqvDE%2BdO3dWVVVVi/dzrkc6HDx4UGFhYXriiSf0l7/8RbGxsbr11ludy4UVFRVKSEjw2aZr164qLy%2BX1%2BuVJJ/5bt26SZLKy8ubbXcuFRUVzr7Ocrk6tnj7tsrlah/3sUVEhPt8RmDRj%2BBDT4IPPbHP7wErJSVFDz30kH7zm98490VVV1crLy9Pl19%2B%2BXfe/8cff6ywsDD17t1b06ZN065du3Tvvfeqc%2BfOGjlypGpraxUZGemzTWRkpOrr61VbW%2Bu8/uKc9PnN9C1VUFCg/Px8n7GsrCxlZ2d/22W1CXFxnQJdgl/FxEQHugR8Af0IPvQk%2BNATe/wesH71q19p%2BvTpuuKKK9SrVy8ZY/TJJ58oPj5emzZt%2Bs77Hz9%2BvEaMGKHY2FhJUr9%2B/fTJJ5/omWee0ciRIxUVFdUsLNXX1ys6OtonTEVFRTn/lqTo6JZ/002ePFmZmZk%2BYy5XR1VVnfrW62oL2sv6IyLCFRMTrePHa9TY2BTocto9%2BhF86Enwacs9CdQP934PWH369NErr7yioqIiHTx4UJL005/%2BVGPGjDmvEHMuYWFhTrg6q3fv3tq5c6ckKTExUZWVlT7zlZWVio%2BPV2JioiTJ6/WqR48ezr8lfeUN8%2BeSkJDQ7HKg13tCDQ1t65v2fLW39Tc2NrW7NQcz%2BhF86EnwoSf2%2BD1gSdIFF1ygG2%2B8UZ999pl69uwp6fOnudvwyCOP6N1339WGDRucsf3796t3796SJLfbreLiYk2YMEGSdOTIER05ckRut1uJiYlKSkpScXGxE7CKi4uVlJTU7u%2BfAgAALef3gHX2QZ6bN2/WmTNn9Nprr%2Bnhhx9WdHS0Fi9e/J2D1ogRI7R27VqtX79eI0eO1Pbt27V161bn8uOUKVN08803KyUlRcnJyVq%2BfLmuuuoqJ%2BhNmTJFDz74oH7wgx9IklauXKmZM2d%2Bt0UDAIB2xe%2B/LrB582a9%2BOKLuv/%2B%2B517nq6%2B%2Bmpt27at2Y3h38bAgQP1yCOP6MUXX9TYsWO1efNmrVy5UqmpqZKk1NRU5eTkaPXq1ZoyZYouuOAC5ebmOtvPmjVLo0eP1ty5c3X77bdr3LhxuuWWW75zXQAAoP0IM1/1pM5WNGbMGN1xxx0aOXKkUlNT9dJLL6lnz556/fXXlZubqz//%2Bc/%2BLMdvvN4TrbLf61b9tVX22xpevWNooEvwC5crXHFxnVRVdYp7GYIA/Qg%2B9CT4tOWexMd/PyDH9fsZrM8%2B%2B0z9%2B/dvNt6vX79mz44CAAAIRX4PWBdeeKHee%2B%2B9ZuN/%2BctfnPugAAAAQpnfb3KfNWuWlixZIq/XK2OM3nrrLRUUFGjz5s265557/F0OAACAdX4PWBMnTlRDQ4Mef/xx1dbW6r777lOXLl10xx13aMqUKf4uBwAAwDq/B6yioiJde%2B21mjx5so4dOyZjjLp27ervMgAAAFqN3%2B/BysnJcW5m79KlC%2BEKAAC0OX4PWL169dJHH33k78MCAAD4jd8vEfbr10933nmn1q1bp169ejl/VPmsLz70EwAAIBT5PWD9/e9/V1pamiTx3CsAANAm%2BSVgrVixQnPnzlXHjh21efNmfxwSAAAgYPxyD9ZTTz2lmpoan7HZs2eroqLCH4cHAADwK78ErK/6c4e7du1SXV2dPw4PAADgV37/LUIAAIC2joAFAABgmd8CVlhYmL8OBQAAEFB%2Be0zDsmXLfJ55debMGeXl5alTp04%2B7%2BM5WAAAINT5JWBddtllzZ55lZqaqqqqKlVVVfmjBAAAAL/xS8Di2VcAAKA94SZ3AAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsC/mAVV9fr7Fjx%2Brtt992xsrKynTLLbcoJSVFo0eP1vbt23222bFjh8aOHSu3263p06errKzMZ37Dhg0aPny4UlNTtXDhQtXU1PhlLQAAoG0I6YBVV1enX/7ylyotLXXGjDHKyspSt27dVFhYqHHjxmnu3Lk6fPiwJOnw4cPKysrShAkT9Pzzz6tLly667bbbZIyRJL322mvKz89XTk6ONm7cKI/Ho7y8vICsDwAAhKaQDVgHDhzQTTfdpH/84x8%2B4zt37lRZWZlycnLUp08fzZkzRykpKSosLJQkPffcc7r00ks1c%2BZMXXzxxcrNzdWhQ4f0zjvvSJI2bdqkGTNmaMSIERo4cKCWLFmiwsJCzmIBAIAWC9mA9c4772jw4MEqKCjwGfd4PLrkkkvUsWNHZywtLU0lJSXOfHp6ujMXHR2tAQMGqKSkRI2NjXrvvfd85lNSUnTmzBnt37%2B/lVcEAADaClegC/i2pk6d%2BpXjXq9XCQkJPmNdu3ZVeXn5N84fP35cdXV1PvMul0uxsbHO9i1RUVEhr9frM%2BZydWx23PbG5QrZPH9eIiLCfT4jsOhH8KEnwYee2BeyAetcampqFBkZ6TMWGRmp%2Bvr6b5yvra11Xp9r%2B5YoKChQfn6%2Bz1hWVpays7NbvI%2B2KC6uU6BL8KuYmOhAl4AvoB/Bh54EH3qdqk%2BDAAATIUlEQVRiT5sLWFFRUaqurvYZq6%2BvV4cOHZz5L4el%2Bvp6xcTEKCoqynn95fno6JZ/002ePFmZmZk%2BYy5XR1VVnWrxPtqi9rL%2BiIhwxcRE6/jxGjU2NgW6nHaPfgQfehJ82nJPAvXDfZsLWImJiTpw4IDPWGVlpXN5LjExUZWVlc3m%2B/fvr9jYWEVFRamyslJ9%2BvSRJDU0NKi6ulrx8fEtriEhIaHZ5UCv94QaGtrWN%2B35am/rb2xsandrDmb0I/jQk%2BBDT%2Bxpcxdb3W633n//fedynyQVFxfL7XY788XFxc5cTU2N9u3bJ7fbrfDwcCUnJ/vMl5SUyOVyqV%2B/fv5bBAAACGltLmBlZGSoe/fuWrBggUpLS7V27Vrt2bNHkyZNkiRNnDhRu3fv1tq1a1VaWqoFCxaoR48eGjx4sKTPb55fv369tm3bpj179mjx4sW66aabzusSIQAAaN/aXMCKiIjQY489Jq/XqwkTJuill17S6tWrlZSUJEnq0aOHfvvb36qwsFCTJk1SdXW1Vq9erbCwMEnSmDFjNGfOHN13332aOXOmBg4cqPnz5wdySQAAIMSEmbOPMEer8npPtMp%2Br1v111bZb2t49Y6hgS7BL1yucMXFdVJV1SnuZQgC9CP40JPg05Z7Eh///YAct82dwQIAAAg0AhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALGuzAev111/Xj370I5%2BP7OxsSdK%2Bfft04403yu12a%2BLEidq7d6/PtkVFRbr66qvldruVlZWlY8eOBWIJAAAgRLXZgHXgwAGNGDFC27dvdz6WLVum06dPa/bs2UpPT9cf/vAHpaamas6cOTp9%2BrQkac%2BePVq0aJHmzp2rgoICHT9%2BXAsWLAjwagAAQChpswHr4MGD6tu3r%2BLj452PmJgYvfLKK4qKitJdd92lPn36aNGiRerUqZP%2B%2BMc/SpK2bNmi6667TuPHj1e/fv20YsUKvfnmmyorKwvwigAAQKho0wGrV69ezcY9Ho/S0tIUFhYmSQoLC9OgQYNUUlLizKenpzvv7969u5KSkuTxePxSNwAACH2uQBfQGowx%2Bvvf/67t27drzZo1amxs1LXXXqvs7Gx5vV5ddNFFPu/v2rWrSktLJUkVFRVKSEhoNl9eXt7i41dUVMjr9fqMuVwdm%2B23vXG52mye9xEREe7zGYFFP4IPPQk%2B9MS%2BNhmwDh8%2BrJqaGkVGRmrVqlX67LPPtGzZMtXW1jrjXxQZGan6%2BnpJUm1t7dfOt0RBQYHy8/N9xrKyspyb7NuruLhOgS7Br2JiogNdAr6AfgQfehJ86Ik9bTJgXXjhhXr77bd1wQUXKCwsTP3791dTU5Pmz5%2BvjIyMZmGpvr5eHTp0kCRFRUV95Xx0dMu/6SZPnqzMzEyfMZero6qqTn3LFbUN7WX9ERHhiomJ1vHjNWpsbAp0Oe0e/Qg%2B9CT4tOWeBOqH%2BzYZsCQpNjbW53WfPn1UV1en%2BPh4VVZW%2BsxVVlY6l%2B8SExO/cj4%2BPr7Fx05ISGh2OdDrPaGGhrb1TXu%2B2tv6Gxub2t2agxn9CD70JPjQE3va5MXW//3f/9XgwYNVU1PjjH3wwQeKjY1VWlqa3n33XRljJH1%2Bv9bu3bvldrslSW63W8XFxc52R44c0ZEjR5x5AACAb9ImA1ZqaqqioqL0q1/9Sh9//LHefPNNrVixQj/72c907bXX6vjx41q%2BfLkOHDig5cuXq6amRtddd50kacqUKXrxxRf13HPPaf/%2B/brrrrt01VVXqWfPngFeFQAACBVtMmB17txZ69ev17FjxzRx4kQtWrRIkydP1s9%2B9jN17txZa9asUXFxsSZMmCCPx6O1a9eqY8eOkj4PZzk5OVq9erWmTJmiCy64QLm5uQFeEQAACCVh5uy1MrQqr/dEq%2Bz3ulV/bZX9toZX7xga6BL8wuUKV1xcJ1VVneJehiBAP4IPPQk%2Bbbkn8fHfD8hx2%2BQZLAAAgEAiYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgLWV6irq9PChQuVnp6uYcOG6cknnwx0SQAAIIS4Al1AMFqxYoX27t2rjRs36vDhw7r77ruVlJSka6%2B9NtClAQCAEEDA%2BpLTp0/rueee0%2B9%2B9zsNGDBAAwYMUGlpqZ5%2B%2BmkCFgAAaBEuEX7J/v371dDQoNTUVGcsLS1NHo9HTU1NAawMAACECs5gfYnX61VcXJwiIyOdsW7duqmurk7V1dXq0qXLN%2B6joqJCXq/XZ8zl6qiEhATr9YYSl6t95PmIiHCfzwgs%2BhF86EnwoSf2EbC%2BpKamxidcSXJe19fXt2gfBQUFys/P9xmbO3eufvGLX9gp8gteuX2QCgoKNHny5HYf4IJFRUWFNm5cR0%2BCBP0IPvQk%2BNAT%2B4iqXxIVFdUsSJ193aFDhxbtY/LkyfrDH/7g8zF58mTrtUqfn3HLz89vdsYMgUNPggv9CD70JPjQE/s4g/UliYmJqqqqUkNDg1yuz788Xq9XHTp0UExMTIv2kZCQwE8AAAC0Y5zB%2BpL%2B/fvL5XKppKTEGSsuLlZycrLCw/lyAQCAb0Zi%2BJLo6GiNHz9eixcv1p49e7Rt2zY9%2BeSTmj59eqBLAwAAISJi8eLFiwNdRLAZMmSI9u3bp5UrV%2Bqtt97Sz3/%2Bc02cODHQZZ1Tp06dlJGRoU6dOgW6FPw/ehJc6EfwoSfBh57YFWaMMYEuAgAAoC3hEiEAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgJWCKurq9PChQuVnp6uYcOG6cknnwx0SW1GfX29xo4dq7ffftsZKysr0y233KKUlBSNHj1a27dv99lmx44dGjt2rNxut6ZPn66ysjKf%2BQ0bNmj48OFKTU3VwoULVVNT48zRy6929OhRZWdnKyMjQ8OHD1dubq7q6uok0Y9A%2BfTTTzVr1iylpqbqqquu0rp165w5ehJ4s2fP1j333OO83rdvn2688Ua53W5NnDhRe/fu9Xl/UVGRrr76arndbmVlZenYsWPOnDFGDz74oIYMGaKMjAytWLFCTU1NznxVVZV%2B8YtfKDU1VZmZmXrxxRdbf4GhxCBk5eTkmOuvv97s3bvX/Pd//7dJTU01r776aqDLCnm1tbUmKyvL9O3b1%2BzcudMYY0xTU5O5/vrrzbx588yBAwfME088Ydxutzl06JAxxphDhw6ZlJQUs379evPRRx%2BZ22%2B/3YwdO9Y0NTUZY4z54x//aNLS0syf//xn4/F4zOjRo82SJUucY9LL5pqamsxNN91kfvazn5mPPvrI7Nq1y4wcOdI88MAD9CNAGhsbzTXXXGPmzZtn/v73v5s33njDDBo0yLz00kv0JAgUFRWZvn37mrvvvtsYY8ypU6fM0KFDzQMPPGAOHDhgli5dan784x%2BbU6dOGWOM8Xg8ZuDAgeaFF14wH3zwgZk2bZqZPXu2s7/169ebK6%2B80uzatcu89dZbZtiwYWbdunXO/Jw5c8yMGTPMhx9%2BaH7/%2B9%2BbSy%2B91Hg8Hv8uOogRsELUqVOnTHJyshMAjDFm9erVZtq0aQGsKvSVlpaan/zkJ%2Bb666/3CVg7duwwKSkpzv8wGWPMjBkzzKOPPmqMMWbVqlU%2BX/vTp0%2Bb1NRUZ/upU6c67zXGmF27dpmBAwea06dP08tzOHDggOnbt6/xer3O2Msvv2yGDRtGPwLk6NGj5vbbbzcnTpxwxrKyssz9999PTwKsqqrKXHHFFWbixIlOwHruuedMZmamE2KbmprMyJEjTWFhoTHGmPnz5zvvNcaYw4cPmx/96EfmH//4hzHGmCuvvNJ5rzHGbN261YwYMcIYY8ynn35q%2Bvbta8rKypz5hQsX%2BuyvveMSYYjav3%2B/GhoalJqa6oylpaXJ4/H4nMLF%2BXnnnXc0ePBgFRQU%2BIx7PB5dcskl6tixozOWlpamkpISZz49Pd2Zi46O1oABA1RSUqLGxka99957PvMpKSk6c%2BaM9u/fTy/PIT4%2BXuvWrVO3bt18xk%2BePEk/AiQhIUGrVq1S586dZYxRcXGxdu3apYyMDHoSYL/5zW80btw4XXTRRc6Yx%2BNRWlqawsLCJElhYWEaNGjQOXvSvXt3JSUlyePx6OjRozpy5Iguu%2BwyZz4tLU2HDh1SRUWFPB6Punfvrh49evjMv/vuu6291JBBwApRXq9XcXFxioyMdMa6deumuro6VVdXB7Cy0DZ16lQtXLhQ0dHRPuNer1cJCQk%2BY127dlV5efk3zh8/flx1dXU%2B8y6XS7GxsSovL6eX5xATE6Phw4c7r5uamrRlyxYNGTKEfgSBzMxMTZ06VampqRo1ahQ9CaC33npLf/vb33Tbbbf5jH9TTyoqKs457/V6Jcln/uwPO2fnv2rbo0eP2llUG0DAClE1NTU%2B/2MjyXldX18fiJLatHN9vc9%2Brb9uvra21nn9VfP0smXy8vK0b98%2B/ed//if9CAKPPvqonnjiCX3wwQfKzc2lJwFSV1en%2B%2B%2B/X/fdd586dOjgM/dNPamtrT2vnnzxa/5N%2B4bkCnQB%2BHaioqKafSOfff3l/8jw3UVFRTX7Sbm%2Bvt75Wp%2BrHzExMYqKinJef3k%2BOjpajY2N9PIb5OXlaePGjXr44YfVt29f%2BhEEkpOTJX3%2Bf/B33nmnJk6c6PNbfxI98Yf8/HxdeumlPmd7zzrX1/ybehIdHe0Tpr7cn%2Bjo6G/cNziDFbISExNVVVWlhoYGZ8zr9apDhw6KiYkJYGVtU2JioiorK33GKisrnVPk55qPj49XbGysoqKifOYbGhpUXV2t%2BPh4evkNli5dqqeeekp5eXkaNWqUJPoRKJWVldq2bZvP2EUXXaQzZ84oPj6engTAf/3Xf2nbtm1KTU1VamqqXn75Zb388stKTU39Tv%2BdJCYmSpJzqfCL/z47f65t8TkCVojq37%2B/XC6Xc7OiJBUXFys5OVnh4bTVNrfbrffff985bS59/vV2u93OfHFxsTNXU1Ojffv2ye12Kzw8XMnJyT7zJSUlcrlc6tevH738Gvn5%2BXr22Wf10EMPacyYMc44/QiMzz77THPnzvW5z2bv3r3q0qWL0tLS6EkAbN68WS%2B//LK2bt2qrVu3KjMzU5mZmdq6davcbrfeffddGWMkff5cq927d5%2BzJ0eOHNGRI0fkdruVmJiopKQkn/ni4mIlJSUpISFBKSkpOnTokHM/19n5lJQUP608BAT4txjxHdx7771mzJgxxuPxmNdff90MGjTIvPbaa4Euq8344mMaGhoazOjRo80dd9xhPvroI7NmzRqTkpLiPOOnrKzMJCcnmzVr1jjP%2BLn%2B%2BuudX48uKioygwYNMq%2B//rrxeDxmzJgxZunSpc6x6GVzBw4cMP379zcPP/ywqaio8PmgH4HR0NBgJkyYYGbOnGlKS0vNG2%2B8YX784x%2BbDRs20JMgcffddzuPSjhx4oQZMmSIWbp0qSktLTVLly41Q4cOdR6lsXv3bjNgwADz%2B9//3nkO1pw5c5x9rVmzxgwbNszs3LnT7Ny50wwbNsw8%2BeSTzvzMmTPNtGnTzAcffGB%2B//vfm%2BTkZJ6D9QUErBB2%2BvRpc9ddd5mUlBQzbNgw89RTTwW6pDbliwHLGGM%2B%2BeQT89Of/tRceumlZsyYMeavf/2rz/vfeOMNc80115iBAweaGTNmOM%2BSOWvNmjXm8ssvN2lpaWbBggWmtrbWmaOXza1Zs8b07dv3Kz%2BMoR%2BBUl5ebrKyssygQYPM0KFDzeOPP%2B6EJHoSeF8MWMZ8/jDR8ePHm%2BTkZDNp0iTz/vvv%2B7y/sLDQXHnllSYlJcVkZWWZY8eOOXMNDQ3m17/%2BtUlPTzeDBw82eXl5Tq%2BNMaaystLMmTPHJCcnm8zMTPPyyy%2B3/gJDSJgx/3/uEAAAAFa074vXAAAArYCABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADL/g9JmjJbjHipVgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6282510798294712146”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.145756979844002</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-24.130394091201644</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.123585725463743</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.975448491610585</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-18.115846071191786</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.56100758898044</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.53604079131686</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.366185591722498</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3102)</td> <td class=”number”>3102</td> <td class=”number”>96.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>91</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6282510798294712146”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.3923858227572</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.86540134812407</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.43642778059058</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-92.62676300352646</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1414.9742534899246</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1418.1396799536421</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2566.029297037542</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7527.600269913827</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43191.275083753615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_LOWI_10_15”><s>PCH_LACCESS_LOWI_10_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_LACCESS_POP_10_15”><code>PCH_LACCESS_POP_10_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99999</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_POP_10_15”>PCH_LACCESS_POP_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2854</td>

</tr> <tr>

<th>Unique (%)</th> <td>88.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>104</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7205.4</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>22084000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-759480183400498517”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAuRJREFUeJzt2jFIonEcxvGnkIaWoIggA3FqEwIhCFoa4ogMqk2aqzUcEm4LbotqKGhxCGoI2oS4d4jmxKEhiDCi5toaIjO8Tc6re65AfaX7fuAFff2rv8Gv7x%2Bxo1qtVgXgXZ1hDwC0s0jYA/wp%2Bf3np59T/PGtCZMAXEEAi0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAg0AAo6NarVbDHgJoV1xBAINAAINAAINA0BTlclnT09M6Ozv70PqJiQkNDw%2B/Oba3t5s8qRcJ9d3xJT0/PyuTyahUKn34OUdHR3p9fa3dD4JAW1tbmp2dbcaIH0YgaKjr62tlMhl99sfR3t7e2u3Hx0ft7OxodXVV0Wi00SN%2BClssNFShUNDo6KgODw/fPFYsFjU3N6dEIqFUKqUgCN59jVwup/7%2Bfs3Pzzd73H/iCoKGSqfT756/v7/X0tKSVlZWND4%2BrvPzc2WzWfX19SmZTNbWPT09aX9/X2tra%2BrsDP/7m0DQEgcHBxobG9PCwoIkKRaL6fLyUnt7e3WBHB8fq7u7W5OTk2GNWodA0BI3Nzc6PT3VyMhI7dzLy4vi8XjduiAINDU1pUikPT6a7TEFvrxKpaJUKqXl5eW687%2BHUC6XVSgUtLi42Orx/ir8TR7%2BC/F4XHd3d4rFYrXj5ORE%2BXy%2Btubq6kqVSkWJRCLESesRCFoinU7r4uJCm5ubur29VT6f18bGhgYHB2trSqWShoaG1NXVFeKk9dhioSWi0ah2d3e1vr6uXC6ngYEBZbNZzczM1NY8PDyop6cnxCnf4u/ugMEWCzAIBDAIBDAIBDAIBDAIBDAIBDAIBDAIBDAIBDB%2BAc/XpbypYp3KAAAAAElFTkSuQmCC”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-759480183400498517,#minihistogram-759480183400498517”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-759480183400498517”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-759480183400498517”

aria-controls=”quantiles-759480183400498517” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-759480183400498517” aria-controls=”histogram-759480183400498517”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-759480183400498517” aria-controls=”common-759480183400498517”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-759480183400498517” aria-controls=”extreme-759480183400498517”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-759480183400498517”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-50.821</td>

</tr> <tr>

<th>Q1</th> <td>-11.029</td>

</tr> <tr>

<th>Median</th> <td>-0.066231</td>

</tr> <tr>

<th>Q3</th> <td>7.2319</td>

</tr> <tr>

<th>95-th percentile</th> <td>82.44</td>

</tr> <tr>

<th>Maximum</th> <td>22084000</td>

</tr> <tr>

<th>Range</th> <td>22084000</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.261</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>396270</td>

</tr> <tr>

<th>Coef of variation</th> <td>54.996</td>

</tr> <tr>

<th>Kurtosis</th> <td>3105.5</td>

</tr> <tr>

<th>Mean</th> <td>7205.4</td>

</tr> <tr>

<th>MAD</th> <td>14356</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>55.725</td>

</tr> <tr>

<th>Sum</th> <td>22380000</td>

</tr> <tr>

<th>Variance</th> <td>157030000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-759480183400498517”>

<img src=”data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3X9UVPW%2B//HXwBwQNRIQSNR1TNMwQ0BI7SqddGVaWhpaXL1mpSet4LAqf5RafhX1cq5o/gitSEtNV3GUfroq17XVqVxlejBAM0%2BIpw7lD4aC8Ac/BOb7R9dZjVhhfdgzA8/HWrOW8/ns2fP%2B%2BGYvX7Nnu7E5nU6nAAAAYIyfpwsAAABobQhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwu6cLaCscjlPG9%2BnnZ1NoaAd9//0ZNTY6je8fzUcvvAN98B70wnu09V6Eh1/mkfflDJYP8/OzyWazyc/P5ulS2jx64R3og/egF96DXngGAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADLN7ugD8Ponz3/V0Cc32zsNDPF0CAACW4AwWAACAYQQsAAAAwwhYAAAAhhGwAAAADPPZgPX1119r2rRpio%2BP14033qj169e75kpLS3XvvfcqLi5Ot956q3bv3u322o8//lhjxoxRbGyspkyZotLSUrf5jRs3KikpSfHx8Zo3b56qq6stWRMAAGgdfDJgNTY2avr06QoJCdFrr72mRYsW6ZlnntFbb70lp9Op1NRUde7cWXl5eRo7dqzS0tJ07NgxSdKxY8eUmpqq5ORkbd%2B%2BXaGhoXrooYfkdDolSTt37lR2drYyMjK0adMmFRYWKisry5PLBQAAPsYnA1Z5ebn69u2rhQsXqkePHvrTn/6k66%2B/Xvn5%2BdqzZ49KS0uVkZGhXr16acaMGYqLi1NeXp4kadu2bbr22ms1depU9e7dW5mZmfr222%2B1d%2B9eSdLmzZt1zz33aNiwYerfv78WLVqkvLw8zmIBAIBm88mAFRERoVWrVqljx45yOp3Kz8/Xvn37NHDgQBUWFuqaa65R%2B/btXdsnJCSooKBAklRYWKjExETXXFBQkPr166eCggI1NDTowIEDbvNxcXE6d%2B6cDh8%2BbN0CAQCAT/PJgPVTw4cP16RJkxQfH6%2BRI0fK4XAoIiLCbZuwsDCdOHFCkn5xvqqqSrW1tW7zdrtdnTp1cr0eAADg1/j8ndzXrFmj8vJyLVy4UJmZmaqurlZAQIDbNgEBAaqrq5OkX5yvqalxPf%2B51zdHWVmZHA6H25jd3r5JsPu9/P19Kx/b7b5V76U43wtf60lrQx%2B8B73wHvTCM3w%2BYMXExEiSamtrNWvWLI0fP77J9VJ1dXVq166dJCkwMLBJWKqrq1NwcLACAwNdzy%2BcDwoKanZNubm5ys7OdhtLTU1Venp6s/fRGoWEdPB0CS0uOLj5PydoOfTBe9AL70EvrOWTAau8vFwFBQW66aabXGNXXXWVzp07p/DwcB09erTJ9ufPHkVGRqq8vLzJfN%2B%2BfdWpUycFBgaqvLxcvXr1kiTV19ersrJS4eHhza4vJSVFw4cPdxuz29urouLMJa3z1/japxHT6/cm/v5%2BCg4OUlVVtRoaGj1dTptFH7wHvfAebb0Xnvpw75MB65tvvlFaWpo%2B%2BOADRUZGSpIOHjyo0NBQJSQk6IUXXlBNTY3rrFV%2Bfr4SEhIkSbGxscrPz3ftq7q6WocOHVJaWpr8/PwUExOj/Px8DRo0SJJUUFAgu92u6OjoZtcXERHR5OtAh%2BOU6uvb3g/2T7WF9Tc0NLaJdXo7%2BuA96IX3oBfW8q1TIP8nJiZG/fr107x583TkyBF98MEHysrK0gMPPKCBAweqS5cumjt3roqLi5WTk6OioiJNmDBBkjR%2B/Hjt379fOTk5Ki4u1ty5c9WtWzdXoJo0aZI2bNigXbt2qaioSAsXLtRdd911SV8RAgCAts0nA5a/v7/WrVunoKAgpaSkaP78%2Bbr77rs1ZcoU15zD4VBycrLefPNNrV27VlFRUZKkbt266emnn1ZeXp4mTJigyspKrV27VjabTZI0evRozZgxQwsWLNDUqVPVv39/zZ4925PLBQAAPsbmPH8Lc7Qoh%2BOU8X3a7X4asfwj4/ttKe88PMTTJbQYu91PISEdVFFxhlPwHkQfvAe98B5tvRfh4Zd55H198gwWAACANyNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwnw1YJ0%2BeVHp6ugYOHKikpCRlZmaqtrZWkrRkyRJdffXVbo8tW7a4Xrtjxw7ddNNNio2NVWpqqr7//nvXnNPp1PLlyzV48GANHDhQy5YtU2Njo%2BXrAwAAvsvu6QJ%2BC6fTqfT0dAUHB2vr1q364YcfNG/ePPn5%2Bemxxx5TSUmJZs6cqTvuuMP1mo4dO0qSioqKNH/%2BfC1atEjR0dFaunSp5s6dq%2Beee06S9OKLL2rHjh3Kzs5WfX29Zs%2BerbCwME2bNs0jawUAAL7HJ89gHT16VAUFBcrMzFTv3r2VmJio9PR07dixQ5JUUlKia665RuHh4a5HUFCQJGnLli265ZZbNG7cOEVHR2vZsmX64IMPVFpaKknavHmz0tPTlZiYqMGDB2vWrFnaunWrx9YKAAB8j08GrPDwcK1fv16dO3d2Gz99%2BrROnz6tkydPqkePHhd9bWFhoRITE13Pu3TpoqioKBUWFurkyZM6fvy4rrvuOtd8QkKCvv32W5WVlbXIWgAAQOvjkwErODhYSUlJrueNjY3asmWLBg8erJKSEtlsNj377LO64YYbdPvtt%2Bu1115zbVtWVqaIiAi3/YWFhenEiRNyOByS5DZ/PsSdOHGiJZcEAABaEZ%2B8ButCWVlZOnTokLZv367PP/9cNptNPXv21OTJk7Vv3z49%2BeST6tixo0aMGKGamhoFBAS4vT4gIEB1dXWqqalxPf/pnCTV1dU1u56ysjJXWDvPbm/fJNj9Xv7%2BvpWP7XbfqvdSnO%2BFr/WktaEP3oNeeA964Rk%2BH7CysrK0adMmrVy5Un369FHv3r01bNgwderUSZIUHR2tr776Si%2B//LJGjBihwMDAJmGprq5OQUFBbmEqMDDQ9WdJrmu4miM3N1fZ2dluY6mpqUpPT//N62wNQkI6eLqEFhcc3PyfE7Qc%2BuA96IX3oBfW8umAtXjxYr388svKysrSyJEjJUk2m80Vrs7r2bOn9uzZI0mKjIxUeXm523x5ebnCw8MVGRkpSXI4HOrWrZvrz9KP1301V0pKioYPH%2B42Zre3V0XFmUtY3a/ztU8jptfvTfz9/RQcHKSqqmo1NHBbD0%2BhD96DXniPtt4LT32499mAlZ2drVdeeUVPPfWURo0a5RpfvXq1PvvsM23cuNE1dvjwYfXs2VOSFBsbq/z8fCUnJ0uSjh8/ruPHjys2NlaRkZGKiopSfn6%2BK2Dl5%2BcrKirqkr7ei4iIaLK9w3FK9fVt7wf7p9rC%2BhsaGtvEOr0dffAe9MJ70Atr%2BWTAKikp0bp16zR9%2BnQlJCS4Xe80bNgw5eTkaMOGDRoxYoR2796t119/XZs3b5YkTZw4UXfffbfi4uIUExOjpUuX6sYbb1T37t1d88uXL9cVV1whSVqxYoWmTp1q/SIBAIDP8smA9d5776mhoUHPPPOMnnnmGbe5f/7zn1q9erXWrFmj1atXq2vXrlqxYoXi4%2BMlSfHx8crIyNCaNWv0ww8/aMiQIVq8eLHr9dOmTdN3332ntLQ0%2Bfv7a8KECbr33nutXB4AAPBxNqfT6fR0EW2Bw3HK%2BD7tdj%2BNWP6R8f22lHceHuLpElqM3e6nkJAOqqg4wyl4D6IP3oNeeI%2B23ovw8Ms88r6%2BdZU0AACADyBgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwnw1YJ0%2BeVHp6ugYOHKikpCRlZmaqtrZWklRaWqp7771XcXFxuvXWW7V7926313788ccaM2aMYmNjNWXKFJWWlrrNb9y4UUlJSYqPj9e8efNUXV1t2boAAIDv88mA5XQ6lZ6erurqam3dulUrV67U%2B%2B%2B/r1WrVsnpdCo1NVWdO3dWXl6exo4dq7S0NB07dkySdOzYMaWmpio5OVnbt29XaGioHnroITmdTknSzp07lZ2drYyMDG3atEmFhYXKysry5HIBAICP8cmAdfToURUUFCgzM1O9e/dWYmKi0tPTtWPHDu3Zs0elpaXKyMhQr169NGPGDMXFxSkvL0%2BStG3bNl177bWaOnWqevfurczMTH377bfau3evJGnz5s265557NGzYMPXv31%2BLFi1SXl4eZ7EAAECz%2BWTACg8P1/r169W5c2e38dOnT6uwsFDXXHON2rdv7xpPSEhQQUGBJKmwsFCJiYmuuaCgIPXr108FBQVqaGjQgQMH3Obj4uJ07tw5HT58uIVXBQAAWgufDFjBwcFKSkpyPW9sbNSWLVs0ePBgORwORUREuG0fFhamEydOSNIvzldVVam2ttZt3m63q1OnTq7XAwAA/Bq7pwswISsrS4cOHdL27du1ceNGBQQEuM0HBASorq5OklRdXf2z8zU1Na7nP/f65igrK5PD4XAbs9vbNwl2v5e/v2/lY7vdt%2Bq9FOd74Ws9aW3og/egF96DXniGzwesrKwsbdq0SStXrlSfPn0UGBioyspKt23q6urUrl07SVJgYGCTsFRXV6fg4GAFBga6nl84HxQU1OyacnNzlZ2d7TaWmpqq9PT0Zu%2BjNQoJ6eDpElpccHDzf07QcuiD96AX3oNeWMunA9bixYv18ssvKysrSyNHjpQkRUZG6siRI27blZeXu84eRUZGqry8vMl837591alTJwUGBqq8vFy9evWSJNXX16uyslLh4eHNrislJUXDhw93G7Pb26ui4swlr/GX%2BNqnEdPr9yb%2B/n4KDg5SVVW1GhoaPV1Om0UfvAe98B5tvRee%2BnDvswErOztbr7zyip566imNGjXKNR4bG6ucnBzV1NS4zlrl5%2BcrISHBNZ%2Bfn%2B/avrq6WocOHVJaWpr8/PwUExOj/Px8DRo0SJJUUFAgu92u6OjoZtcWERHR5OtAh%2BOU6uvb3g/2T7WF9Tc0NLaJdXo7%2BuA96IX3oBfWsvwUyJ133qlXXnlFp06d%2Bs37KCkp0bp163T//fcrISFBDofD9Rg4cKC6dOmiuXPnqri4WDk5OSoqKtKECRMkSePHj9f%2B/fuVk5Oj4uJizZ07V926dXMFqkmTJmnDhg3atWuXioqKtHDhQt11112X9BUhAABo2ywPWIMHD9azzz6roUOH6tFHH9Xu3btdN/lsrvfee08NDQ165plnNHToULeHv7%2B/1q1bJ4fDoeTkZL355ptau3atoqKiJEndunXT008/rby8PE2YMEGVlZVau3atbDabJGn06NGaMWOGFixYoKlTp6p///6aPXu28b8HAADQetmcl5puDHA6nfr444/1%2Buuva9euXQoODta4ceM0btw4XXnllVaXYwmH47efsfs5drufRiz/yPh%2BW8o7Dw/xdAktxm73U0hIB1VUnOEUvAfRB%2B9BL7xHW%2B9FePhlHnlfj1yDZbPZNGTIEA0ZMkTV1dV66aWXtG7dOuXk5GjAgAG65557dPPNN3uiNAAAgN/NYxe5l5WV6c0339Sbb76pL7/8UgMGDNAdd9yhEydO6IknntC%2Bffs0f/58T5UHAADwm1kesN544w298cYb%2BvTTTxUaGqpx48ZpzZo16tGjh2ubLl26aOnSpQQsAADgkywPWPPnz9ewYcO0du1a3XDDDfLza3qdfc%2BePTV58mSrSwMAADDC8oD14YcfKiQkRJWVla5wVVRUpH79%2Bsnf31%2BSNGDAAA0YMMDq0gAAAIyw/DYNp0%2Bf1qhRo/T888%2B7xqZPn66xY8fq%2BPHjVpcDAABgnOUB67//%2B7/1xz/%2BUffdd59r7O2331aXLl2UmZlpdTkAAADGWR6w/vGPf%2Bjxxx93%2B91%2BoaGhmjNnjvbs2WN1OQAAAMZZHrDsdruqqqqajFdXV1/yHd0BAAC8keUB64YbbtCSJUv073//2zVWWlqqzMxMJSUlWV0OAACAcZb/L8LHHntM9913n0aOHKng4GBJUlVVlfr166e5c%2BdaXQ4AAIBxlgessLAwvfbaa/r4449VXFwsu92uq666Stdff73rFy4DAAD4Mo/8qhx/f38lJSXxlSAAAGiVLA9YDodDq1at0v79%2B3Xu3LkmF7a/9957VpcEAABglOUB68knn9TBgwc1evRoXXbZZVa/PQAAQIuzPGDt2bNH69evV2JiotVvDQAAYAnLb9PQvn17hYWFWf22AAAAlrE8YI0dO1br169XQ0OD1W8NAABgCcu/IqysrNSOHTv097//Xd27d1dAQIDb/ObNm60uCQAAwCiP3KZhzJgxnnhbAAAAS1gesDIzM61%2BSwAAAEtZfg2WJJWVlSk7O1szZ87Ud999p3fffVdHjx71RCkAAADGWR6wvv76a91222167bXXtHPnTp09e1Zvv/22xo8fr8LCQqvLAQAAMM7ygPXXv/5VN910k3bt2qU//OEPkqSnnnpKw4cP1/Lly60uBwAAwDjLA9b%2B/ft13333uf1iZ7vdroceekiHDh2yuhwAAADjLA9YjY2NamxsbDJ%2B5swZ%2Bfv7W10OAACAcZYHrKFDh%2Bq5555zC1mVlZXKysrS4MGDrS4HAADAOMsD1uOPP66DBw9q6NChqq2t1YMPPqhhw4bpm2%2B%2B0WOPPWZ1OQAAAMZZfh%2BsyMhIvf7669qxY4e%2B%2BOILNTY2auLEiRo7dqw6duxodTkAAADGeeRO7kFBQbrzzjs98dYAAAAtzvKANWXKlF%2Bc53cRAgAAX2d5wOratavb8/r6en399df68ssvdc8991hdDgAAgHFe87sI165dqxMnTlhcDQAAgHke%2BV2EFzN27Fi98847ni4DAADgd/OagPXZZ59xo1EAANAqeMVF7qdPn9Y///lPTZo0yepyAAAAjLM8YEVFRbn9HkJJ%2BsMf/qDJkyfr9ttvt7ocAAAA4ywPWH/961%2BN7q%2Burk7Jycl68sknNWjQIEnSkiVL9NJLL7lt9%2BSTT2ry5MmSpB07dmjVqlVyOBwaOnSoFi9erNDQUEmS0%2BnUihUrtH37djU2NmrChAmaNWuW/Py85ttUAADg5SwPWPv27Wv2ttddd90vztfW1mrmzJkqLi52Gy8pKdHMmTN1xx13uMbO3yW%2BqKhI8%2BfP16JFixQdHa2lS5dq7ty5eu655yRJL774onbs2KHs7GzV19dr9uzZCgsL07Rp05pdNwAAaNssD1h333236ytCp9PpGr9wzGaz6YsvvvjZ/Rw5ckQzZ85028d5JSUlmjZtmsLDw5vMbdmyRbfccovGjRsnSVq2bJmGDRum0tJSde/eXZs3b1Z6eroSExMlSbNmzdLq1asJWAAAoNks/97r2WefVdeuXbVq1Sp98sknys/P18aNG3XllVfq0Ucf1Xvvvaf33ntPu3bt%2BsX97N27V4MGDVJubq7b%2BOnTp3Xy5En16NHjoq8rLCx0hSdJ6tKli6KiolRYWKiTJ0/q%2BPHjbmfOEhIS9O2336qsrOy3LxoAALQpHrnR6IIFC3TDDTe4xgYPHqyMjAzNmTNH999/f7P283P/47CkpEQ2m03PPvusPvzwQ3Xq1En33Xef6%2BvCsrIyRUREuL0mLCxMJ06ckMPhkCS3%2Bc6dO0uSTpw40eR1AAAAF2N5wCorK2vy63KkH6%2BRqqio%2BN37P3r0qGw2m3r27KnJkydr3759evLJJ9WxY0eNGDFCNTU1CggIcHtNQECA6urqVFNT43r%2B0znpx4vpm6usrMwV1s6z29sbD2j%2B/r514b3d7lv1XorzvfC1nrQ29MF70AvvQS88w/KAFRcXp6eeekr/8z//47rwvLKyUllZWbr%2B%2But/9/7HjRunYcOGqVOnTpKk6OhoffXVV3r55Zc1YsQIBQYGNglLdXV1CgoKcgtTgYGBrj9LUlBQULNryM3NVXZ2tttYamqq0tPTf/O6WoOQkA6eLqHFBQc3/%2BcELYc%2BeA964T3ohbUsD1hPPPGEpkyZohtuuEE9evSQ0%2BnUV199pfDwcG3evPl3799ms7nC1Xk9e/bUnj17JEmRkZEqLy93my8vL1d4eLgiIyMlSQ6HQ926dXP9WdJFL5j/OSkpKRo%2BfLjbmN3eXhUVZy5tMb/C1z6NmF6/N/H391NwcJCqqqrV0NDo6XLaLPrgPeiF92jrvfDUh3vLA1avXr309ttva8eOHSopKZEk/dd//ZdGjx59SWeJfs7q1av12WefaePGja6xw4cPq2fPnpKk2NhY5efnKzk5WZJ0/PhxHT9%2BXLGxsYqMjFRUVJTy8/NdASs/P19RUVGX9PVeREREk%2B0djlOqr297P9g/1RbW39DQ2CbW6e3og/egF96DXljL8oAlSZdffrnuvPNOffPNN%2BrevbukH%2B/mbsKwYcOUk5OjDRs2aMSIEdq9e7def/1119mxiRMn6u6771ZcXJxiYmK0dOlS3Xjjja46Jk6cqOXLl%2BuKK66QJK1YsUJTp041UhsAAGgbLA9Y5%2B%2BU/tJLL%2BncuXPauXOnVq5cqaCgIC1cuPB3B63%2B/ftr9erVWrNmjVavXq2uXbtqxYoVio%2BPlyTFx8crIyNDa9as0Q8//KAhQ4Zo8eLFrtdPmzZN3333ndLS0uTv768JEybo3nvv/V01AQCAtsXmvNidOlvQ5s2b9fzzz%2BuRRx5RRkaG3nrrLR04cECLFi3Sf/7nf%2BqRRx6xshzLOBynjO/TbvfTiOUfGd9vS3nn4SGeLqHF2O1%2BCgnpoIqKM5yC9yD64D3ohfdo670ID7/MI%2B9r%2BVXSubm5WrBggZKTk113b7/11lu1ZMkSvfXWW1aXAwAAYJzlAeubb75R3759m4xHR0c3uXcUAACAL7I8YHXt2lUHDhxoMv7hhx%2B6LjQHAADwZZZf5D5t2jQtWrRIDodDTqdTn3zyiXJzc/XSSy/p8ccft7ocAAAA4ywPWOPHj1d9fb2eeeYZ1dTUaMGCBQoNDdXDDz%2BsiRMnWl0OAACAcZYHrB07dmjUqFFKSUnR999/L6fTqbCwMKvLAAAAaDGWX4OVkZHhupg9NDSUcAUAAFodywNWjx499OWXX1r9tgAAAJax/CvC6OhozZo1S%2BvXr1ePHj0UGBjoNp%2BZmWl1SQAAAEZZHrD%2B9a9/KSEhQZK47xUAAGiVLAlYy5YtU1pamtq3b6%2BXXnrJircEAADwGEuuwXrxxRdVXV3tNjZ9%2BnSVlZVZ8fYAAACWsiRgXez3Se/bt0%2B1tbVWvD0AAIClLP9fhAAAAK0dAQsAAMAwywKWzWaz6q0AAAA8yrLbNCxZssTtnlfnzp1TVlaWOnTo4LYd98ECAAC%2BzpKAdd111zW551V8fLwqKipUUVFhRQkAAACWsSRgce8rAADQlnCROwAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw3w%2BYNXV1WnMmDH69NNPXWOlpaW69957FRcXp1tvvVW7d%2B92e83HH3%2BsMWPGKDY2VlOmTFFpaanb/MaNG5WUlKT4%2BHjNmzdP1dXVlqwFAAC0Dj4dsGpra/Xoo4%2BquLjYNeZ0OpWamqrOnTsrLy9PY8eOVVpamo4dOyZJOnbsmFJTU5WcnKzt27crNDRUDz30kJxOpyRp586dys7OVkZGhjZt2qTCwkJlZWV5ZH0AAMA3%2BWzAOnLkiO666y79%2B9//dhvfs2ePSktLlZGRoV69emnGjBmKi4tTXl6eJGnbtm269tprNXXqVPXu3VuZmZn69ttvtXfvXknS5s2bdc8992jYsGHq37%2B/Fi1apLy8PM5iAQCAZvPZgLV3714NGjRIubm5buOFhYW65ppr1L59e9dYQkKCCgoKXPOJiYmuuaCgIPXr108FBQVqaGjQgQMH3Obj4uJ07tw5HT58uIVXBAAAWgu7pwv4rSZNmnTRcYfDoYiICLexsLAwnThx4lfnq6qqVFtb6zZvt9vVqVMn1%2BsBAAB%2Bjc8GrJ9TXV2tgIAAt7GAgADV1dX96nxNTY3r%2Bc%2B9vjnKysrkcDjcxuz29k2C3e/l7%2B9bJyDtdt%2Bq91Kc74Wv9aS1oQ/eg154D3rhGa0uYAUKVZ6KAAAQOUlEQVQGBqqystJtrK6uTu3atXPNXxiW6urqFBwcrMDAQNfzC%2BeDgoKaXUNubq6ys7PdxlJTU5Went7sfbRGISEdPF1CiwsObv7PCVoOffAe9MJ70AtrtbqAFRkZqSNHjriNlZeXu84eRUZGqry8vMl837591alTJwUGBqq8vFy9evWSJNXX16uyslLh4eHNriElJUXDhw93G7Pb26ui4sxvWdLP8rVPI6bX7038/f0UHBykqqpqNTQ0erqcNos%2BeA964T3aei889eG%2B1QWs2NhY5eTkqKamxnXWKj8/XwkJCa75/Px81/bV1dU6dOiQ0tLS5Ofnp5iYGOXn52vQoEGSpIKCAtntdkVHRze7hoiIiCZfBzocp1Rf3/Z%2BsH%2BqLay/oaGxTazT29EH70EvvAe9sJZvnQJphoEDB6pLly6aO3euiouLlZOTo6KiIk2YMEGSNH78eO3fv185OTkqLi7W3Llz1a1bN1egmjRpkjZs2KBdu3apqKhICxcu1F133XVJXxECAIC2rdUFLH9/f61bt04Oh0PJycl68803tXbtWkVFRUmSunXrpqefflp5eXmaMGGCKisrtXbtWtlsNknS6NGjNWPGDC1YsEBTp05V//79NXv2bE8uCQAA%2BBib8/wtzNGiHI5Txvdpt/tpxPKPjO%2B3pbzz8BBPl9Bi7HY/hYR0UEXFGU7BexB98B70wnu09V6Eh1/mkfdtdWewAAAAPI2ABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDWm3A%2Bt///V9dffXVbo/09HRJ0qFDh3TnnXcqNjZW48eP18GDB91eu2PHDt10002KjY1Vamqqvv/%2Be08sAQAA%2BKhWG7COHDmiYcOGaffu3a7HkiVLdPbsWU2fPl2JiYl69dVXFR8frxkzZujs2bOSpKKiIs2fP19paWnKzc1VVVWV5s6d6%2BHVAAAAX9JqA1ZJSYn69Omj8PBw1yM4OFhvv/22AgMDNWfOHPXq1Uvz589Xhw4d9O6770qStmzZoltuuUXjxo1TdHS0li1bpg8%2B%2BEClpaUeXhEAAPAVrTpg9ejRo8l4YWGhEhISZLPZJEk2m00DBgxQQUGBaz4xMdG1fZcuXRQVFaXCwkJL6gYAAL6vVQYsp9Opf/3rX9q9e7dGjhypm266ScuXL1ddXZ0cDociIiLctg8LC9OJEyckSWVlZb84DwAA8Gvsni6gJRw7dkzV1dUKCAjQqlWr9M0332jJkiWqqalxjf9UQECA6urqJEk1NTW/ON8cZWVlcjgcbmN2e/smwe338vf3rXxst/tWvZfifC98rSetDX3wHvTCe9ALz2iVAatr16769NNPdfnll8tms6lv375qbGzU7NmzNXDgwCZhqa6uTu3atZMkBQYGXnQ%2BKCio2e%2Bfm5ur7Oxst7HU1FTX/2Jsq0JCOni6hBYXHNz8nxO0HPrgPeiF96AX1mqVAUuSOnXq5Pa8V69eqq2tVXh4uMrLy93mysvLXWeXIiMjLzofHh7e7PdOSUnR8OHD3cbs9vaqqDhzKUv4Vb72acT0%2Br2Jv7%2BfgoODVFVVrYaGRk%2BX02bRB%2B9BL7xHW%2B%2BFpz7ct8qA9dFHH2nWrFn6%2B9//7jrz9MUXX6hTp05KSEjQ888/L6fTKZvNJqfTqf379%2BuBBx6QJMXGxio/P1/JycmSpOPHj%2Bv48eOKjY1t9vtHREQ0%2BTrQ4Til%2Bvq294P9U21h/Q0NjW1ind6OPngPeuE96IW1fOsUSDPFx8crMDBQTzzxhI4ePaoPPvhAy5Yt05///GeNGjVKVVVVWrp0qY4cOaKlS5equrpat9xyiyRp4sSJeuONN7Rt2zYdPnxYc%2BbM0Y033qju3bt7eFUAAMBXtMqA1bFjR23YsEHff/%2B9xo8fr/nz5yslJUV//vOf1bFjRz333HOus1SFhYXKyclR%2B/btJf0YzjIyMrR27VpNnDhRl19%2BuTIzMz28IgAA4EtsTqfT6eki2gKH45Txfdrtfhqx/CPj%2B20p7zw8xNMltBi73U8hIR1UUXGGU/AeRB%2B8B73wHm29F%2BHhl3nkfVvlGSwAAABPImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErIuora3VvHnzlJiYqKFDh%2BqFF17wdEkAAMCH2D1dgDdatmyZDh48qE2bNunYsWN67LHHFBUVpVGjRnm6NAAA4AMIWBc4e/astm3bpueff179%2BvVTv379VFxcrK1btxKwAABAs/AV4QUOHz6s%2Bvp6xcfHu8YSEhJUWFioxsZGD1YGAAB8BWewLuBwOBQSEqKAgADXWOfOnVVbW6vKykqFhob%2B6j7KysrkcDjcxuz29oqIiDBaq7%2B/b%2BVju9236r0U53vhaz1pbeiD96AX3oNeeAYB6wLV1dVu4UqS63ldXV2z9pGbm6vs7Gy3sbS0NP3lL38xU%2BT/KSsr0z1XFCslJcV4eMOlKSsr06ZN6%2BmFh9EH70EvvAe98Azi7AUCAwObBKnzz9u1a9esfaSkpOjVV191e6SkpBiv1eFwKDs7u8nZMliPXngH%2BuA96IX3oBeewRmsC0RGRqqiokL19fWy23/863E4HGrXrp2Cg4ObtY%2BIiAg%2BJQAA0IZxBusCffv2ld1uV0FBgWssPz9fMTEx8vPjrwsAAPw6EsMFgoKCNG7cOC1cuFBFRUXatWuXXnjhBU2ZMsXTpQEAAB/hv3DhwoWeLsLbDB48WIcOHdKKFSv0ySef6IEHHtD48eM9XdZFdejQQQMHDlSHDh08XUqbRy%2B8A33wHvTCe9AL69mcTqfT00UAAAC0JnxFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgOXlamtrNW/ePCUmJmro0KF64YUXfnbbQ4cO6c4771RsbKzGjx%2BvgwcPWlhp63cpvXjwwQd19dVXuz3ef/99C6tt/erq6jRmzBh9%2BumnP7sNx4Q1mtMLjomWdfLkSaWnp2vgwIFKSkpSZmamamtrL7otx4U1CFhebtmyZTp48KA2bdqk//f//p%2Bys7P17rvvNtnu7Nmzmj59uhITE/Xqq68qPj5eM2bM0NmzZz1QdevU3F5IUklJibKysrR7927XY8iQIRZX3HrV1tbq0UcfVXFx8c9uwzFhjeb0QuKYaElOp1Pp6emqrq7W1q1btXLlSr3//vtatWpVk205LizkhNc6c%2BaMMyYmxrlnzx7X2Nq1a52TJ09usu22bducw4cPdzY2NjqdTqezsbHROWLECGdeXp5l9bZml9KL2tpaZ9%2B%2BfZ1Hjx61ssQ2o7i42Hn77bc7b7vtNmefPn3cevJTHBMtr7m94JhoWUeOHHH26dPH6XA4XGNvvfWWc%2BjQoU225biwDmewvNjhw4dVX1%2Bv%2BPh411hCQoIKCwvV2Njotm1hYaESEhJks9kkSTabTQMGDFBBQYGlNbdWl9KLo0ePymazqXv37laX2Sbs3btXgwYNUm5u7i9uxzHR8prbC46JlhUeHq7169erc%2BfObuOnT59usi3HhXXsni4AP8/hcCgkJEQBAQGusc6dO6u2tlaVlZUKDQ112/aqq65ye31YWNivnrZH81xKL44ePaqOHTtqzpw52rt3r6644gr95S9/0Z/%2B9CdPlN7qTJo0qVnbcUy0vOb2gmOiZQUHByspKcn1vLGxUVu2bNHgwYObbMtxYR3OYHmx6upqt3/QJbme19XVNWvbC7fDb3MpvTh69Khqamo0dOhQrV%2B/Xn/605/04IMP6sCBA5bVC44Jb8IxYa2srCwdOnRIjzzySJM5jgvrcAbLiwUGBjb5oT//vF27ds3a9sLt8NtcSi8eeugh3X333br88sslSdHR0fr888/1t7/9TTExMdYUDI4JL8IxYZ2srCxt2rRJK1euVJ8%2BfZrMc1xYhzNYXiwyMlIVFRWqr693jTkcDrVr107BwcFNti0vL3cbKy8vV0REhCW1tnaX0gs/Pz/XPyTn9ezZUydPnrSkVvyIY8J7cExYY/HixXrxxReVlZWlkSNHXnQbjgvrELC8WN%2B%2BfWW3290uPszPz1dMTIz8/NxbFxsbq88%2B%2B0xOp1PSj/9td//%2B/YqNjbW05tbqUnrx%2BOOPa%2B7cuW5jhw8fVs%2BePS2pFT/imPAeHBMtLzs7W6%2B88oqeeuopjR49%2Bme347iwDgHLiwUFBWncuHFauHChioqKtGvXLr3wwguaMmWKpB/PoNTU1EiSRo0apaqqKi1dulRHjhzR0qVLVV1drVtuucWTS2g1LqUXw4cP11tvvaXXX39dX3/9tbKzs5Wfn6/Jkyd7cgltAseE9%2BCYsE5JSYnWrVun%2B%2B%2B/XwkJCXI4HK6HxHHhMZ68RwR%2B3dmzZ51z5sxxxsXFOYcOHep88cUXXXN9%2BvRxu3dJYWGhc9y4cc6YmBjnhAkTnJ9//rkHKm69LqUXf/vb35w333yz89prr3Xecccdzr1793qg4tbvwnsvcUx4zq/1gmOi5Tz33HPOPn36XPThdHJceIrN6fy/84QAAAAwgq8IAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAA4DXq6uo0ZswYffrpp83afvjw4br66qubPLKzs1u40l/GL3sGAABeoba2VjNnzlRxcXGzX7N9%2B3Y1NDS4nu/cuVOrVq3SHXfc0RIlNhsBCwAAeNyRI0c0c%2BZMXer9z0NDQ11/PnXqlNauXavHHntMXbt2NV3iJeErQgAA4HF79%2B7VoEGDlJub22TuH//4h5KTk9W/f3/ddttt2rlz50X3sWHDBoWHh2v8%2BPEtXe6v4gwWAADwuEmTJl103OFwaMaMGXrkkUeUlJSkgoICPf744woLC1NiYqJru%2Brqam3ZskUZGRny8/P8%2BSMCFgAA8Fpbt27Vf/zHf2jy5MmSpD/%2B8Y/64osvtGnTJreA9fbbb6t9%2B/a6%2BeabPVWqGwIWAADwWkePHtX777%2Bv%2BPh419i5c%2Bd05ZVXum23c%2BdO3XrrrbLbvSPa/H8t9zYhhm6NAwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-759480183400498517”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-13.02214348099776</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2795109011547191</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-32.911122307261365</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.778184617906631</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9264387408107527</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>108.78233620264051</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.908729681773295</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-28.130063456816035</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2843)</td> <td class=”number”>2843</td> <td class=”number”>88.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-759480183400498517”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-99.9396082716682</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-98.4748552102837</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.81572711751195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-96.31525184384067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4684.809925818614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15117.3265672713</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26147.419425788114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>198601.9453146752</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22083757.707267486</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_LACCESS_SENIORS_10_15”><s>PCH_LACCESS_SENIORS_10_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_LACCESS_LOWI_10_15”><code>PCH_LACCESS_LOWI_10_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99981</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_NSLP_09_15”>PCH_NSLP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.85431</td>

</tr> <tr>

<th>Minimum</th> <td>-1.6969</td>

</tr> <tr>

<th>Maximum</th> <td>0.52972</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2787613498682281767”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2787613498682281767”

aria-controls=”quantiles-2787613498682281767” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2787613498682281767” aria-controls=”histogram-2787613498682281767”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2787613498682281767” aria-controls=”common-2787613498682281767”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2787613498682281767” aria-controls=”extreme-2787613498682281767”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-1.6969</td>

</tr> <tr>

<th>5-th percentile</th> <td>-1.3805</td>

</tr> <tr>

<th>Q1</th> <td>-1.0448</td>

</tr> <tr>

<th>Median</th> <td>-0.83969</td>

</tr> <tr>

<th>Q3</th> <td>-0.66507</td>

</tr> <tr>

<th>95-th percentile</th> <td>-0.27875</td>

</tr> <tr>

<th>Maximum</th> <td>0.52972</td>

</tr> <tr>

<th>Range</th> <td>2.2266</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.37972</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.33679</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.39423</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.9196</td>

</tr> <tr>

<th>Mean</th> <td>-0.85431</td>

</tr> <tr>

<th>MAD</th> <td>0.25071</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.36881</td>

</tr> <tr>

<th>Sum</th> <td>-2675.7</td>

</tr> <tr>

<th>Variance</th> <td>0.11343</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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Bg1VcXGxjZQAAAF3juID18ccf6%2BGHH1ZcXJx/bODAgVq0aJE%2B/PBDGysDAADoGscFLLfbrcbGxg7jzc3N3NEdAAD0Co4LWJMmTdKyZcv0t7/9zT9WXV2t4uJiTZw40cbKAAAAusZxf0W4ePFi3X333br55psVExMjSWpsbNSoUaNUUFBgc3UAAACdc1zAio2N1SuvvKIPPvhABw8elNvt1ne/%2B13deOONcrlcdpcHAADQKccFLEkKDQ3VxIkTOSUIAAB6JccFLJ/Pp5UrV2rPnj366quvOlzY/oc//MGmygAAALrGcQHrJz/5iaqqqnTrrbfqyiuvtLscAACAbnNcwPrwww/17LPPKi0tze5SAAAALorjbtMQFRWl2NhYu8sAAAC4aI4LWNOmTdOzzz6rs2fP2l0KAADARXHcKcKGhgZt27ZN//Vf/6WhQ4cqLCwsYPpvfvMbmyoDAADoGscFLEmaOnWq3SUAAABcNMcFrOLiYrtLAAAAuCSOuwZLkmpra1VWVqYHH3xQ//M//6M33nhDR44csbssAACALnFcwDp69Khuu%2B02vfLKK3rzzTfV1NSk7du3a8aMGfJ6vXaXBwAA0CnHBazHH39cU6ZM0Y4dO3TFFVdIkp544gllZGRo%2BfLlNlcHAADQOccFrD179ujuu%2B8O%2BGBnt9ut%2B%2B67T/v377exMgAAgK5xXMBqb29Xe3t7h/HTp08rNDTUhooAAAC6x3EBa8KECXrmmWcCQlZDQ4NKS0s1btw4GysDAADoGscFrIcfflhVVVWaMGGCWlpaNH/%2BfKWnp%2BuLL77Q4sWL7S4PAACgU467D1ZCQoK2bNmibdu26S9/%2BYva29uVm5uradOmKTo62u7yAAAAOuW4gCVJkZGRmjVrlt1lAAAAXBTHBay5c%2BdecDqfRQgAAJzOcQFryJAhAY/b2tp09OhRffrpp7rzzjttqgoAAKDrHBewvu2zCMvLy1VTU9PD1QAAAHSf4/6K8NtMmzZNr7/%2But1lAAAAdKrXBKw//elP3GgUAAD0Co47RXi%2Bi9xPnTqlv/71r/rXf/1XGyoCAADoHscFrMTExIDPIZSkK664QrNnz9YPfvADm6oCAADoOscFrMcff9zuEgAAAC6J4wLW7t27uzzvmDFjLmMlAAAAF8dxAWvOnDn%2BU4SWZfnHzx1zuVz6y1/%2B0vMFAgAAdMJxAWvVqlVatmyZHnroIY0dO1ZhYWH685//rKVLl%2Br222/XLbfcYneJAAAAF%2BS42zQUFxdryZIluvnmmzVgwAD169dP48aN09KlS7V%2B/XoNGTLE/wUAAOBEjgtYtbW15w1P0dHRqq%2Bvt6EiAACA7nFcwBo9erSeeOIJnTp1yj/W0NCg0tJS3XjjjTZWBgAA0DWOuwbr0Ucf1dy5czVp0iQNGzZMlmXp888/V1xcnH7zm9/YXR4AAECnHBewrrnmGm3fvl3btm3T4cOHJUl33HGHbr31VkVGRtpcHQAAQOccF7AkqX///po1a5a%2B%2BOILDR06VNLXd3MHAADoDRx3DZZlWVq%2BfLnGjBmjqVOnqqamRosXL1ZhYaG%2B%2Buoru8sDAADolOMC1tq1a7V161b99Kc/VVhYmCRpypQp2rFjh8rKymyuDgAAoHOOC1gVFRVasmSJsrOz/Xdvv%2BWWW7Rs2TK99tprNlcHAADQOccFrC%2B%2B%2BEIjR47sMD5ixAj5fL5uLy8vL08PP/yw//H%2B/fs1a9YseTwezZgxQ1VVVQHzb9u2TVOmTJHH41F%2Bfr5OnjzZ/ZUAAAB9muMC1pAhQ/TnP/%2B5w/i7777rv%2BC9q37/%2B9/rnXfe8T9uampSXl6e0tLStHnzZiUnJ2vevHlqamqSJO3bt0%2BFhYVasGCBKioq1NjYqIKCgktbIQAA0Oc47q8If/jDH%2BpnP/uZfD6fLMvSrl27VFFRobVr1wYciepMQ0ODSkpKlJSU5B/bvn27wsPDtWjRIrlcLhUWFurdd9/VG2%2B8oezsbK1bt05ZWVmaPn26JKmkpETp6emqrq7udrgDAAB9l%2BMC1owZM9TW1qann35aZ86c0ZIlSzRw4EA98MADys3N7fJyfvnLX2ratGmqra31j3m9XqWmpvqv7XK5XEpJSdHevXuVnZ0tr9erH/3oR/75Bw8erMTERHm9XgIWAADoMscFrG3btikzM1M5OTk6efKkLMtSbGxst5axa9cuffzxx3rttdf02GOP%2Bcd9Pp%2B%2B%2B93vBswbGxurgwcPSvr6cxDj4%2BM7TK%2BpqenW89fW1na4Xsztjuqw7GAVGhoS8C/Q27ndF78tsz84A31whr7UB8cFrKVLl%2Bp3v/ud%2Bvfvr4EDB3b7%2B1taWvTTn/5US5YsUURERMC05uZm/60fvhEWFqbW1lZJ0pkzZy44vasqKio63FIiPz9fCxcu7NZyeruYGO68j%2BAwYEC/S14G%2B4Mz0Adn6At9cFzAGjZsmD799NMOR5q6qqysTNdff70mTpzYYVp4eHiHsNTa2uoPYt82vbsf0ZOTk6OMjIyAMbc7SvX1p7u1nN4qNDREMTGRamxs1tmz7XaXA1yyS9l32R%2BcgT44gx19MPEL0sVwXMAaMWKEfvzjH%2BvZZ5/VsGHDFB4eHjC9uLj4gt//%2B9//XnV1dUpOTpYkf2B68803NXXqVNXV1QXMX1dX5z91l5CQcN7pcXFx3VqH%2BPj4DqcDfb4v1dbWt3bqs2fb%2B9w6IziZ2I7ZH5yBPjhDX%2BiD4wLWZ599ptTUVEm6qPterV27Vm1tbf7Hy5cvlyT9%2BMc/1u7du7VmzRpZliWXyyXLsrRnzx7de%2B%2B9kiSPx6PKykplZ2dLko4fP67jx4/L4/Fc6moBAIA%2BxBEBq6SkRAsWLFBUVJTWrl17ScsaMmRIwON%2B/b4%2BNHj11VcrNjZWK1asUFFRkf7lX/5FGzZsUHNzs7KysiRJubm5mjNnjkaPHq2kpCQVFRVp8uTJ/AUhAADoFkdcxv/888%2Brubk5YCwvLy/gFgsmREdH65lnnvEfpfJ6vVq9erWioqIkScnJyVq6dKnKy8uVm5ur/v37d3pKEgAA4FyOOIJlWVaHsd27d6ulpeWSl/34448HPP7e976nV1555Vvnz87O9p8iBAAAuBiOOIIFAAAQTAhYAAAAhjkmYH3z8TUAAAC9nSOuwZKkZcuWBdzz6quvvlJpaan/rwC/wUXnAADA6RwRsMaMGdPhnlfJycmqr69XfX29TVUBAABcHEcErEu99xUAAICTOOYaLAAAgGBBwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMEd82DP6hqyVO%2B0uAQCAHsERLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmNvuAgAAZmWt3Gl3CV32%2BgPj7S4BuCw4ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/iwZwDoRG/68GQAzsARLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhQRuwTpw4oYULF2rs2LGaOHGiiouL1dLSIkmqrq7WXXfdpdGjR%2BuWW27R%2B%2B%2B/H/C9H3zwgaZOnSqPx6O5c%2BequrrajlUAAAC9VFAGLMuytHDhQjU3N%2Bu3v/2tnnzySb399ttauXKlLMtSfn6%2BBg0apE2bNmnatGlasGCBjh07Jkk6duyY8vPzlZ2drZdfflkDBw7UfffdJ8uybF4rAADQWwTlR%2BUcOXJEe/fu1c6dOzVo0CBJ0sKFC/XLX/5SkyZNUnV1tTZs2KCoqChdc8012rVrlzZt2qT7779fGzdu1PXXX6977rlHklRcXKzx48frj3/8o2644QY7VwsAAPQSQXkEKy4uTs8%2B%2B6w/XH3j1KlT8nq9uu666xQVFeUfT01N1d69eyVJXq9XaWlp/mmRkZEaNWqUfzoAAEBngjJgxcTEaOLEif7H7e3tWrduncaNGyefz6f4%2BPiA%2BWNjY1VTUyNJnU4HAADoTFCeIjxXaWmp9u/fr5dfflkvvPCCwsLCAqaHhYWptbVVktTc3HzB6V1RW1srn88XMOZ2R3UIbsEqNDQk4F8A%2BDZud8%2B8T/C%2B5Ax9qQ9BH7BKS0v14osv6sknn9S1116r8PBwNTQ0BMzT2tqqiIgISVJ4eHiHMNXa2qqYmJguP2dFRYXKysoCxvLz87Vw4cKLXIveKSYm0u4SADjcgAH9evT5eF9yhr7Qh6AOWD//%2Bc%2B1fv16lZaW6uabb5YkJSQk6NChQwHz1dXV%2BY8uJSQkqK6ursP0kSNHdvl5c3JylJGRETDmdkepvv70xaxGrxMaGqKYmEg1Njbr7Nl2u8sB4GA99b7I%2B5Iz2NGHng7x3wjagFVWVqYNGzboiSeeUGZmpn/c4/Fo9erVOnPmjP%2BoVWVlpVJTU/3TKysr/fM3Nzdr//79WrBgQZefOz4%2BvsPpQJ/vS7W19a2d%2BuzZ9j63zgC6p6ffI3hfcoa%2B0IegPAl6%2BPBhPfXUU/rRj36k1NRU%2BXw%2B/9fYsWM1ePBgFRQU6ODBg1q9erX27dunmTNnSpJmzJihPXv2aPXq1Tp48KAKCgp01VVXcYsGAADQZUEZsP7whz/o7NmzevrppzVhwoSAr9DQUD311FPy%2BXzKzs7Wq6%2B%2BqvLyciUmJkqSrrrqKv3Hf/yHNm3apJkzZ6qhoUHl5eVyuVw2rxUAAOgtXBa3KO8RPt%2BXdpfQY9zuEA0Y0E/19acDDgFnrdxpY1UAnOj1B8b3yPN82/sSepYdfYiLu7JHnudcQXkECwAAwE4ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGue0uAJcma%2BVOu0sAAADn4AgWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGue0uAAAAXB5ZK3faXUKXvf7AeLtLMIojWOfR0tKiRx55RGlpaZowYYKee%2B45u0sCAAC9CEewzqOkpERVVVV68cUXdezYMS1evFiJiYnKzMy0uzQAANALELDO0dTUpI0bN2rNmjUaNWqURo0apYMHD%2Bq3v/0tAQsAAHQJpwjPceDAAbW1tSk5Odk/lpqaKq/Xq/b2dhsrAwAAvQVHsM7h8/k0YMAAhYWF%2BccGDRqklpYWNTQ0aODAgZ0uo7a2Vj6fL2DM7Y5SfHy88XoBoDdzu3vm9/zQ0JCAfy/WTcvfM1EOzqOntoWeQsA6R3Nzc0C4kuR/3Nra2qVlVFRUqKysLGBswYIFuv/%2B%2B80U%2Bf/5uMh5py1ra2tVUVGhnJwcQqWN6IMz0AdnqK2t1YsvPnvJfXDie25v0pf2h%2BCKiwaEh4d3CFLfPI6IiOjSMnJycrR58%2BaAr5ycHOO1OpXP51NZWVmHo3joWfTBGeiDM9AHZ%2BhLfeAI1jkSEhJUX1%2BvtrY2ud1fvzw%2Bn08RERGKiYnp0jLi4%2BODPpkDAIBvxxGsc4wcOVJut1t79%2B71j1VWViopKUkhIbxcAACgcySGc0RGRmr69Ol67LHHtG/fPu3YsUPPPfec5s6da3dpAACglwh97LHHHrO7CKcZN26c9u/frxUrVmjXrl269957NWPGDLvL6lX69eunsWPHql%2B/fnaX0qfRB2egD85AH5yhr/TBZVmWZXcRAAAAwYRThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgwxrIs3XPPPdq8efMF51u2bJn%2B4R/%2BIeBr3bp1PVRl8OtqH6qrq3XXXXdp9OjRuuWWW/T%2B%2B%2B/3UIXBzbIsLV/atlgiAAAGy0lEQVS%2BXOPGjdPYsWNVUlKi9vb2b52f/cGclpYWPfLII0pLS9OECRP03HPPfeu8%2B/fv16xZs%2BTxeDRjxgxVVVX1YKXBrTt9mD9/foft/%2B233%2B7Bai8ft90FIDi0t7erqKhIO3fu1NSpUy847%2BHDh/Xggw/q9ttv949FR0df7hL7hK72wbIs5efn69prr9WmTZu0Y8cOLViwQNu3b1diYmIPVhx8nn/%2BeW3btk1lZWVqa2vTQw89pNjYWP3whz887/zsD%2BaUlJSoqqpKL774oo4dO6bFixcrMTFRmZmZAfM1NTUpLy9Pt912mx5//HGtX79e8%2BbN01tvvaWoqCibqg8eXe2D9PX2X1paqhtvvNE/1r9//54s9/KxgEtUU1NjzZ4925o8ebKVlpZmbdq06YLzT5w40Xrvvfd6qLq%2Bozt9%2BOCDD6zRo0dbp0%2Bf9o/deeed1q9%2B9aueKDWo/dM//VPAa79lyxYrPT39W%2BdnfzDj9OnTVlJSkvXhhx/6x8rLy63Zs2d3mHfjxo1WRkaG1d7eblmWZbW3t1s33XRTp%2B9d6Fx3%2BtDS0mKNHDnSOnLkSE%2BW2GM4RYhL9sknn2jw4MHatGmTrrzyygvOe%2BrUKZ04cULDhg3rmeL6kO70wev16rrrrgv4bT01NVV79%2B693GUGtRMnTuj48eMaM2aMfyw1NVX//d//rdra2g7zsz%2BYc%2BDAAbW1tSk5Odk/lpqaKq/X2%2BEUrdfrVWpqqlwulyTJ5XIpJSWF7d%2BA7vThyJEjcrlcGjp0aE%2BX2SMIWLhkGRkZKikp0cCBAzud9/Dhw3K5XFq1apUmTZqkH/zgB3rllVd6oMrg150%2B%2BHw%2BxcfHB4zFxsaqpqbmcpXXJ/h8PkkKeG0HDRokSed9bdkfzPH5fBowYIDCwsL8Y4MGDVJLS4saGho6zMv2f3l0pw9HjhxRdHS0Fi1apAkTJmjmzJl65513errky4ZrsNCpM2fO6MSJE%2BedFhcX161rFr75jWX48OGaPXu2du/erZ/85CeKjo7WTTfdZKrkoGSyD83NzQFvgJIUFham1tbWS6qxL7hQH5qamiQp4LX95v/ne23ZH8z5tm1a6vjas/1fPt3pw5EjR3TmzBlNmDBBeXl5euuttzR//nxVVFQoKSmpx2q%2BXAhY6JTX69XcuXPPO628vFxTpkzp8rKmT5%2Bu9PR0/d3f/Z0kacSIEfr888%2B1fv16fqB0wmQfwsPDO/w22draqoiIiEuqsS%2B4UB8eeughSV%2B/luHh4f7/S1JkZGSH%2BdkfzAkPD%2B/wA/ybx%2Bdu1982L9v/petOH%2B677z7NmTPHf1H7iBEj9Mknn%2Bill14iYKFvuOGGG/TXv/7VyLJcLpf/h8k3hg8frg8//NDI8oOZyT4kJCTo0KFDAWN1dXUdTpugowv14cSJEyotLZXP59NVV10l6f%2BdNoyLi%2BswP/uDOQkJCaqvr1dbW5vc7q9/tPl8PkVERCgmJqbDvHV1dQFjbP9mdKcPISEhHf5icPjw4R3em3orrsFCj/r3f/933XXXXQFjBw4c0PDhw%2B0pqI/yeDz65JNPdObMGf9YZWWlPB6PjVX1fgkJCUpMTFRlZaV/rLKyUomJief94c3%2BYM7IkSPldrsDLlSvrKxUUlKSQkICf9R5PB796U9/kmVZkr6%2BbcmePXvY/g3oTh8efvhhFRQUBIwF0/ZPwMJld/LkSZ0%2BfVqSlJ6ert27d%2BvXv/61/va3v%2Bl3v/udtmzZonvuucfmKoPf/9%2BHsWPHavDgwSooKNDBgwe1evVq7du3TzNnzrS5yt4vNzdXy5cv10cffaSPPvpIK1asCDilyP5weURGRmr69Ol67LHHtG/fPu3YsUPPPfec/7X3%2BXz%2BXygyMzPV2NiooqIiHTp0SEVFRWpublZWVpadqxAUutOHjIwMvfbaa9qyZYuOHj2qsrIyVVZWavbs2Xaugjl23ycCwSU9Pb3DvWTS09MD7q/01ltvWbfddpuVlJRkZWZmWm%2B%2B%2BWZPlxn0utKHzz//3Lrjjjus66%2B/3rr11lutnTt39nSZQamtrc36xS9%2BYaWlpVk33HCDVVpa6r/fkmWxP1xOTU1N1qJFi6zRo0dbEyZMsJ5//nn/tGuvvTZgn/B6vdb06dOtpKQka%2BbMmdYnn3xiQ8XBqTt9eOmll6zvf//71vXXX2/dfvvt1h//%2BEcbKr48XJb1f4%2BRAgAAwAhOEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8HLGugsqxw5psAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2787613498682281767”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.8292550292280048</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.032756923426117</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.1669174732523278</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.3597551462116115</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8534170176788809</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7846266669309205</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2787497928955176</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2498273848264496</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8930345156151489</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7381798930509191</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2787613498682281767”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.6968605681597184</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.5197014184803113</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.511422340362337</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4023717697451108</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.380526082488867</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2318128380258475</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.15074467934100255</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18146206578609814</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.400809876497803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.529722771676318</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_ACRESPTH_07_12”><s>PCH_ORCHARD_ACRESPTH_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_ORCHARD_ACRES_07_12”><code>PCH_ORCHARD_ACRES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99826</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_ACRES_07_12”>PCH_ORCHARD_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1609</td>

</tr> <tr>

<th>Unique (%)</th> <td>50.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>37.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1192</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.148</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>3712.5</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5483967703527970060”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5483967703527970060,#minihistogram5483967703527970060”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5483967703527970060”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5483967703527970060”

aria-controls=”quantiles5483967703527970060” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5483967703527970060” aria-controls=”histogram5483967703527970060”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5483967703527970060” aria-controls=”common5483967703527970060”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5483967703527970060” aria-controls=”extreme5483967703527970060”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5483967703527970060”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-72.167</td>

</tr> <tr>

<th>Q1</th> <td>-31.092</td>

</tr> <tr>

<th>Median</th> <td>-2.6667</td>

</tr> <tr>

<th>Q3</th> <td>37.886</td>

</tr> <tr>

<th>95-th percentile</th> <td>188.15</td>

</tr> <tr>

<th>Maximum</th> <td>3712.5</td>

</tr> <tr>

<th>Range</th> <td>3812.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>68.978</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>152.63</td>

</tr> <tr>

<th>Coef of variation</th> <td>6.0692</td>

</tr> <tr>

<th>Kurtosis</th> <td>213.16</td>

</tr> <tr>

<th>Mean</th> <td>25.148</td>

</tr> <tr>

<th>MAD</th> <td>67.675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.365</td>

</tr> <tr>

<th>Sum</th> <td>50749</td>

</tr> <tr>

<th>Variance</th> <td>23296</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5483967703527970060”>

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u01YcIEbd26VQ888ICaN2%2BuQYMGqbKyUmFhYT7rhIWFqbq6WpWVld7n310mfXszfUPl5uYqJyfHZyw9PV0ZGRk/NpYtREc388t%2BIiMj/LKfQHNCTjLagxMySs7I6YSMVrFdwRo5cqT69%2B%2BvqKgoSVJCQoK%2B%2BOILPf/88xo0aJDCw8PrlaXq6mpFRET4lKnw8HDvnyV57%2BFqiLFjx2rAgAE%2BY253U5WVnfjRueygsfOHhoYoMjJCR49WqLbWvu/8dEJOMtqDEzJKzsgZzBn99Y/7M9muYLlcLm%2B5Oq19%2B/bavHmzJCkuLk6lpaU%2By0tLSxUTE6O4uDhJksfjUdu2bb1/lqSYmJgGzyE2Nrbe5UCP55hqaoLrh9Jq/spfW1vniO%2B1E3KS0R6ckFFyRk4nZLSK7S6mLlq0SJMnT/YZ2717t9q3by9JSkxMVF5ennfZoUOHdOjQISUmJiouLk7x8fE%2By/Py8hQfH%2B/4%2B6cAAEDD2e4MVv/%2B/bV8%2BXKtXLlSgwYN0nvvvaeXXnpJa9askSSNGzdON954o5KSktStWzc9%2BOCD6tevny6%2B%2BGLv8gULFugXv/iFJOmRRx7RzTffHLA8AAAg%2BNiuYHXv3l2LFi3S448/rkWLFqlNmzZ65JFHlJycLElKTk7WnDlz9Pjjj%2Bubb75Rnz59NHfuXO/6t9xyi77%2B%2BmtNmTJFoaGhuv766%2BudEQMAADgflzHGBHoSTuDxHGuU7V698D%2BNst3G8PpdfRp1%2B253iKKjm6ms7ISt7xFwQk4y2oMTMkrOyBnMGWNiLgzIfm13DxYAAECgUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwWNAXrOrqao0YMUJbtmzxjuXn5%2Bu//uu/lJycrCFDhmjdunU%2B6/z2t79V586dfR6fffaZJMkYowULFqh3797q2bOn5s%2Bfr7q6Or9mAgAAwc0d6An8FFVVVcrMzFRhYaF3zOPx6Pe//73GjRunhx9%2BWB9//LGmTZummJgY9evXT7W1tfriiy%2B0du1atWvXzrtedHS0JGnVqlXasGGDcnJyVFNTo6lTp6ply5a65ZZb/B0PAAAEqaAtWHv27FFmZqaMMT7jmzZtUqtWrXT33XdLktq1a6ctW7bo1VdfVb9%2B/bR//36dOnVK3bt3V3h4eL3trlmzRhkZGUpNTZUk3XPPPVq0aBEFCwAANFjQXiL88MMP1atXL%2BXm5vqMp6WlKSsrq97rjx8/LunbYta6deuzlqvi4mIdOnRIV1xxhXcsJSVFBw4cUElJicUJAACAXQXtGazx48efdbxt27Zq27at9/nXX3%2Btf/7zn7rzzjslSUVFRbrgggt0%2B%2B23a%2BfOnbrkkkt07733qnv37vJ4PJKk2NhY7/qtWrWSJB0%2BfNhn/HxKSkq82zrN7W7a4PXtyu1u3D4fGhri89WunJCTjPbghIySM3I6IaPVgrZgNURlZaXuvPNOtWrVSmPHjpUkff755/rmm280ZswYZWRk6O9//7smTZqk1157TZWVlZKksLAw7zZO/7m6urrB%2B83NzVVOTo7PWHp6ujIyMn5qpKAWHd3ML/uJjIzwy34CzQk5yWgPTsgoOSOnEzJaxe8Fa8yYMRo9erSGDx%2BuCy%2B8sNH2c%2BLECd1xxx364osv9Ne//lUREd/%2BUMydO1eVlZVq3ry5JGnWrFnatm2bXn75Zf3qV7%2BS9G2ZOn0J8XSxOr1%2BQ4wdO1YDBgzwGXO7m6qs7MRPzhXMGjt/aGiIIiMjdPRohWpr7fvOTyfkJKM9OCGj5IycwZzRX/%2B4P5PfC1bv3r315JNPKisrS7/5zW80atQo9enTRy6Xy7J9HD9%2BXLfeequ%2B%2BuorrV692ufdgm6321uuJMnlcql9%2B/YqLi5WXFycpG/fiXj6MuPpS30xMTEN3n9sbGy9y4EezzHV1ATXD6XV/JW/trbOEd9rJ%2BQkoz04IaPkjJxOyGgVv19MzczM1JtvvqklS5YoNDRUd955p/r166fHHntMn3/%2B%2BU/efl1dnaZMmaL9%2B/fr2WefVceOHX2W33jjjT6X7%2Brq6vTpp5%2Bqffv2iouLU3x8vPLy8rzL8/LyFB8f7/j7pwAAQMMF5B4sl8ulPn36qE%2BfPqqoqNCzzz6rJUuWaPny5erRo4cmTZqkwYMH/6htv/DCC9qyZYuWLl2qyMhI7xmoCy64QFFRURowYIAWL16sLl266JJLLtGaNWt07NgxXXfddZKkcePGacGCBfrFL34hSXrkkUd08803WxMcAAA4QsBuci8pKdErr7yiV155RZ999pl69Oih6667TocPH9af//xnbd26VTNmzPjB2924caPq6up0%2B%2B23%2B4z37NlTzz77rCZPnqyqqirNmzdPpaWlSkxM1KpVq7yXDW%2B55RZ9/fXXmjJlikJDQ3X99ddr8uTJVkQGAAAO4TJnflJnI3v55Zf18ssva8uWLWrRooVGjhyp0aNH%2B9wn9cILL%2BjBBx/URx995M%2BpNSqP51ijbPfqhf9plO02htfv6tOo23e7QxQd3UxlZSdsfY%2BAE3KS0R6ckFFyRs5gzhgT03hvqDsfv5/BmjFjhvr376/Fixfr17/%2BtUJC6t8G1r59e02YMMHfUwMAALCE3wvWO%2B%2B8o%2BjoaJWXl3vL1fbt29W1a1eFhoZKknr06KEePXr4e2oAAACW8Pu7CI8fP66hQ4fqqaee8o7ddtttuvbaa3Xo0CF/TwcAAMByfi9YDz30kH75y1/qpptu8o699tprat269Vl/hyAAAECw8XvB%2Br//%2Bz/df//9Ph/c2aJFC917773avHmzv6cDAABgOb8XLLfbraNHj9Ybr6iokJ/f0AgAANAo/F6wfv3rX2vevHn66quvvGP79u1TVlaW0tLS/D0dAAAAy/n9XYT33XefbrrpJg0ZMkSRkZGSpKNHj6pr166aNm2av6cDAABgOb8XrJYtW%2BrFF1/U%2B%2B%2B/r8LCQrndbl166aW68sorLf2FzwAAAIESkF%2BVExoaqrS0NC4JAgAAW/J7wfJ4PFq4cKG2bdumU6dO1bux/d///re/pwQAAGApvxesBx54QDt37tTw4cN14YWB%2Bf1AAAAAjcnvBWvz5s1asWKFUlNT/b1rAAAAv/D7xzQ0bdpULVu29PduAQAA/MbvBevaa6/VihUrVFtb6%2B9dAwAA%2BIXfLxGWl5drw4YNeuutt3TxxRcrLCzMZ/maNWv8PSUAAABLBeRjGkaMGBGI3QIAAPiF3wtWVlaWv3cJAADgV36/B0uSSkpKlJOTo8zMTH399df617/%2Bpb179wZiKgAAAJbze8H68ssvdc011%2BjFF1/Uxo0bdfLkSb322msaPXq0CgoK/D0dAAAAy/m9YD388MMaOHCgNm3apAsuuECS9Oijj2rAgAFasGCBv6cDAABgOb8XrG3btummm27y%2BcXObrdbd9xxh3bt2uXv6QAAAFjO7wWrrq5OdXV19cZPnDih0NBQf08HAADAcn4vWH379tWyZct8SlZ5ebmys7PVu3dvf08HAADAcn4vWPfff7927typvn37qqqqSn/84x/Vv39/7d%2B/X/fdd5%2B/pwMAAGA5v38OVlxcnF566SVt2LBBn3zyierq6jRu3Dhde%2B21at68ub%2BnAwAAYLmAfA5WRESExowZo5kzZ2rWrFn63e9%2B96PLVXV1tUaMGKEtW7Z4x/bt26fJkycrKSlJw4YN03vvveezzvvvv68RI0YoMTFREydO1L59%2B3yWP/PMM0pLS1NycrKmT5%2BuioqKHzU3AADgTH4/gzVx4sTzLv8hv4uwqqpKmZmZKiws9I4ZY5Senq5OnTpp/fr12rRpk6ZMmaLXXntN8fHxOnjwoNLT03XnnXcqLS1Nixcv1h133KFXXnlFLpdLGzduVE5OjrKzs9WyZUtNmzZN2dnZmjlz5o/ODAAAnMXvZ7DatGnj84iLi1NlZaW2b9%2Bu5OTkBm9nz549uuGGG/TVV1/5jG/evFn79u3TnDlz1KFDB91%2B%2B%2B1KSkrS%2BvXrJUnr1q3T5ZdfrptvvlkdO3ZUVlaWDhw4oA8//FDStwVv0qRJ6t%2B/v7p3767Zs2dr/fr1nMUCAAAN9rP5XYSLFy/W4cOHG7ydDz/8UL169dJ///d/KykpyTteUFCgyy67TE2bNvWOpaSkKD8/37s8NTXVuywiIkJdu3ZVfn6%2BUlNTtWPHDk2ZMsW7PCkpSadOndLu3bt/UAEEAADOFZB7sM7m2muv1euvv97g148fP17Tp09XRESEz7jH41FsbKzPWMuWLb3l7XzLjx49qqqqKp/lbrdbUVFRP6j8AQAAZ/P7Gaxz%2Beijjyz5oNGKigqFhYX5jIWFham6uvp7l1dWVnqfn2v9higpKZHH4/EZc7ub1it2TuN2N26fDw0N8flqV07ISUZ7cEJGyRk5nZDRaj%2BLm9yPHz%2BuTz/9VOPHj//J2w8PD1d5ebnPWHV1tZo0aeJdfmZZqq6uVmRkpMLDw73Pz1x%2B5pmy88nNzVVOTo7PWHp6ujIyMhq8DTuKjm7ml/1ERjb8WAUzJ%2BQkoz04IaPkjJxOyGgVvxes%2BPh4n99DKEkXXHCBJkyYoN/%2B9rc/eftxcXHas2ePz1hpaan37FFcXJxKS0vrLe/SpYuioqIUHh6u0tJSdejQQZJUU1Oj8vJyxcTENHgOY8eO1YABA3zG3O6mKis78WMi2UZj5w8NDVFkZISOHq1QbW39X8dkF07ISUZ7cEJGyRk5gzmjv/5xfya/F6yHH364UbefmJio5cuXq7Ky0nvWKi8vTykpKd7leXl53tdXVFRo165dmjJlikJCQtStWzfl5eWpV69ekqT8/Hy53W4lJCQ0eA6xsbH1Lgd6PMdUUxNcP5RW81f%2B2to6R3yvnZCTjPbghIySM3I6IaNV/F6wtm7d2uDXXnHFFT94%2Bz179lTr1q01bdo03XHHHXrzzTe1fft277sXR48erZUrV2r58uXq37%2B/Fi9erLZt23oL1fjx4zVz5kx16tRJsbGxmjVrlm644YYfdIkQAAA4m98L1o033ui9RGiM8Y6fOeZyufTJJ5/84O2HhoZqyZIlmjFjhkaNGqVf/vKXWrx4seLj4yVJbdu21RNPPKGHHnpIixcvVnJyshYvXuzd//Dhw3XgwAHNnDlT1dXVGjx4sKZOnfqTMgMAAGdxme%2B2HD946623NG/ePE2dOlU9e/ZUWFiYduzYoTlz5ui6667TsGHDvK9t06aNP6fWqDyeY42y3asX/qdRttsYXr%2BrT6Nu3%2B0OUXR0M5WVnbD1KWwn5CSjPTgho%2BSMnMGcMSbmwoDs1%2B/vt8zKytLMmTM1ZMgQRUdHq1mzZurdu7fmzJmj559/3udT3gEAAIKR3wtWSUnJWctT8%2BbNVVZW5u/pAAAAWM7vBSspKUmPPvqojh8/7h0rLy9Xdna2rrzySn9PBwAAwHJ%2Bv8n9z3/%2BsyZOnKhf//rXateunYwx%2BuKLLxQTE6M1a9b4ezoAAACW83vB6tChg1577TVt2LBBRUVFkqTf/e53Gj58OB%2BFAAAAbCEgv4vwoosu0pgxY7R//35dfPHFkr79NHcAAAA78Ps9WMYYLViwQFdccYVGjBihw4cP67777tOMGTN06tQpf08HAADAcn4vWM8%2B%2B6xefvll/eUvf1FYWJgkaeDAgdq0aVO9X5AMAAAQjPxesHJzczVz5kyNGjXK%2B%2Bnpw4YN07x58/Tqq6/6ezoAAACW83vB2r9/v7p06VJvPCEhQR6Px9/TAQAAsJzfC1abNm20Y8eOeuPvvPOO94Z3AACAYOb3dxHecsstmj17tjwej4wx%2BuCDD5Sbm6tnn31W999/v7%2BnAwAAYDm/F6zRo0erpqZGS5cuVWVlpWbOnKkWLVrorrvu0rhx4/w9HQAAAMv5vWBt2LBBQ4cO1dixY3XkyBEZY9SyZUt/TwMAAKDR%2BP0erDlz5nhvZm/RogXlCgAA2I7fC1a7du302Wef%2BXu3AAAAfuP3S4QJCQm65557tGLFCrVr107h4eE%2By7Oysvw9JQAAAEv5vWB9/vnnSklJkSQ%2B9woAANiSXwrW/PnzNWXKFDVt2lTPPvusP3YJAAAQMH65B2vVqlWqqKik2TvLAAAbF0lEQVTwGbvttttUUlLij90DAAD4lV8KljGm3tjWrVtVVVXlj90DAAD4ld/fRQgAAGB3FCwAAACL%2Ba1guVwuf%2B0KAAAgoPz2MQ3z5s3z%2BcyrU6dOKTs7W82aNfN5HZ%2BDBQAAgp1fCtYVV1xR7zOvkpOTVVZWprKyMn9MAQAAwG/8UrD47CsAAOAktrzJ/R//%2BIc6d%2B5c75GQkCBJ%2BuMf/1hv2Ztvvuld/5lnnlFaWpqSk5M1ffr0ep/hBQAAcD5%2B/1U5/jBs2DClpaV5n9fU1GjSpEnq16%2BfJKmoqEjZ2dm68sorva%2B56KKLJEkbN25UTk6OsrOz1bJlS02bNk3Z2dmaOXOmXzMAAIDgZcszWE2aNFFMTIz38corr8gYo3vuuUfV1dXav3%2B/unXr5vOasLAwSdKaNWs0adIk9e/fX927d9fs2bO1fv16zmIBAIAGs2XB%2Bq7y8nI99dRTyszMVFhYmPbu3SuXy6WLL7643mtra2u1Y8cOpaameseSkpJ06tQp7d6925/TBgAAQcz2Bev5559XbGyshg4dKknau3evmjdvrnvvvVd9%2B/bV9ddfr7fffluSdPToUVVVVSk2Nta7vtvtVlRUlA4fPhyQ%2BQMAgOBjy3uwTjPGaN26dbr11lu9Y3v37lVlZaX69u2r2267TW%2B88Yb%2B%2BMc/Kjc3V61atZIk7%2BXC08LCwlRdXd3g/ZaUlNT7WAq3u6lPcXMit7tx%2B3xoaIjPV7tyQk4y2oMTMkrOyOmEjFazdcHasWOHiouLNXz4cO/YHXfcoRtvvNF7U3tCQoI%2B/vhj/f3vf9d///d/S1K9MlVdXa2IiIgG7zc3N1c5OTk%2BY%2Bnp6crIyPixUWwhOrrZ97/IApGRDT9WwcwJOcloD07IKDkjpxMyWsXWBevdd99Vamqqt0xJUkhIiM9zSWrfvr327NmjqKgohYeHq7S0VB06dJD07TsQy8vLFRMT0%2BD9jh07VgMGDPAZc7ubqqzsxE9IE/waO39oaIgiIyN09GiFamvrGnVfgeSEnGS0BydklJyRM5gz%2Busf92eydcHavn27evTo4TN2//33y%2BVy%2BfxKnt27d6tTp04KCQlRt27dlJeXp169ekmS8vPz5Xa7vZ%2Bh1RCxsbH1Lgd6PMdUUxNcP5RW81f%2B2to6R3yvnZCTjPbghIySM3I6IaNVbH0xtbCwUJdeeqnP2IABA/Tqq6/qpZde0pdffqmcnBzl5eVpwoQJkqTx48dr5cqV2rRpk7Zv365Zs2bphhtu%2BEGXCAEAgLPZ%2BgxWaWmpIiMjfcYGDx6sv/zlL1q6dKkOHjyojh07asWKFWrbtq0kafjw4Tpw4IBmzpyp6upqDR48WFOnTg3E9AEAQJCydcHavn37WcfHjBmjMWPGnHO92267TbfddltjTQsAANicrS8RAgAABAIFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAIvZtmC98cYb6ty5s88jIyNDkrRr1y6NGTNGiYmJGj16tHbu3Omz7oYNGzRw4EAlJiYqPT1dR44cCUQEAAAQpGxbsPbs2aP%2B/fvrvffe8z7mzZunkydP6rbbblNqaqr%2B8Y9/KDk5WbfffrtOnjwpSdq%2BfbtmzJihKVOmKDc3V0ePHtW0adMCnAYAAAQT2xasoqIiderUSTExMd5HZGSkXnvtNYWHh%2Bvee%2B9Vhw4dNGPGDDVr1kz/%2Bte/JElr167V1VdfrZEjRyohIUHz58/X22%2B/rX379gU4EQAACBa2Lljt2rWrN15QUKCUlBS5XC5JksvlUo8ePZSfn%2B9dnpqa6n1969atFR8fr4KCAr/MGwAABD93oCfQGIwx%2Bvzzz/Xee%2B9p2bJlqq2t1dChQ5WRkSGPx6NLL73U5/UtW7ZUYWGhJKmkpESxsbH1lh8%2BfLjB%2By8pKZHH4/EZc7ub1tuu07jdjdvnQ0NDfL7alRNyktEenJBRckZOJ2S0mi0L1sGDB1VRUaGwsDAtXLhQ%2B/fv17x581RZWekd/66wsDBVV1dLkiorK8%2B7vCFyc3OVk5PjM5aenu69yd6poqOb%2BWU/kZERftlPoDkhJxntwQkZJWfkdEJGq9iyYLVp00ZbtmzRRRddJJfLpS5duqiurk5Tp05Vz54965Wl6upqNWnSRJIUHh5%2B1uUREQ3/oRo7dqwGDBjgM%2BZ2N1VZ2YkfmcgeGjt/aGiIIiMjdPRohWpr6xp1X4HkhJxktAcnZJSckTOYM/rrH/dnsmXBkqSoqCif5x06dFBVVZViYmJUWlrqs6y0tNR7%2BS4uLu6sy2NiYhq879jY2HqXAz2eY6qpCa4fSqv5K39tbZ0jvtdOyElGe3BCRskZOZ2Q0Sq2vJj67rvvqlevXqqoqPCOffLJJ4qKilJKSoo%2B%2BugjGWMkfXu/1rZt25SYmChJSkxMVF5enne9Q4cO6dChQ97lAAAA38eWBSs5OVnh4eH685//rL179%2Brtt9/W/Pnzdeutt2ro0KE6evSoHnzwQe3Zs0cPPvigKioqdPXVV0uSxo0bp5dfflnr1q3T7t27de%2B996pfv366%2BOKLA5wKAAAEC1sWrObNm2vlypU6cuSIRo8erRkzZmjs2LG69dZb1bx5cy1btkx5eXkaNWqUCgoKtHz5cjVt2lTSt%2BVszpw5Wrx4scaNG6eLLrpIWVlZAU4EAACCiW3vwerYsaNWrVp11mXdu3fXiy%2B%2BeM51R40apVGjRjXW1AAAgM3Z8gwWAABAIFGwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsJhtC1ZxcbEyMjLUs2dPpaWlKSsrS1VVVZKkefPmqXPnzj6PtWvXetfdsGGDBg4cqMTERKWnp%2BvIkSOBigEAAIKQO9ATaAzGGGVkZCgyMlLPPfecvvnmG02fPl0hISG67777VFRUpMzMTF133XXedZo3by5J2r59u2bMmKHZs2crISFBDz74oKZNm6Zly5YFKg4AAAgytjyDtXfvXuXn5ysrK0sdO3ZUamqqMjIytGHDBklSUVGRLrvsMsXExHgfERERkqS1a9fq6quv1siRI5WQkKD58%2Bfr7bff1r59%2BwIZCQAABBFbFqyYmBitWLFCrVq18hk/fvy4jh8/ruLiYrVr1%2B6s6xYUFCg1NdX7vHXr1oqPj1dBQUFjThkAANiILS8RRkZGKi0tzfu8rq5Oa9euVe/evVVUVCSXy6Unn3xS77zzjqKionTTTTd5LxeWlJQoNjbWZ3stW7bU4cOHG7z/kpISeTwenzG3u2m97TqN2924fT40NMTnq105IScZ7cEJGSVn5HRCRqvZsmCdKTs7W7t27dILL7ygjz/%2BWC6XS%2B3bt9eECRO0detWPfDAA2revLkGDRqkyspKhYWF%2BawfFham6urqBu8vNzdXOTk5PmPp6enKyMiwJE%2Bwio5u5pf9REZG%2BGU/geaEnGS0BydklJyR0wkZrWL7gpWdna3Vq1frscceU6dOndSxY0f1799fUVFRkqSEhAR98cUXev755zVo0CCFh4fXK1PV1dXee7QaYuzYsRowYIDPmNvdVGVlJ356oCDW2PlDQ0MUGRmho0crVFtb16j7CiQn5CSjPTgho%2BSMnMGc0V//uD%2BTrQvW3Llz9fzzzys7O1tDhgyRJLlcLm%2B5Oq19%2B/bavHmzJCkuLk6lpaU%2By0tLSxUTE9Pg/cbGxta7HOjxHFNNTXD9UFrNX/lra%2Bsc8b12Qk4y2oMTMkrOyOmEjFax7cXUnJwc/e1vf9Ojjz6q4cOHe8cXLVqkyZMn%2B7x29%2B7dat%2B%2BvSQpMTFReXl53mWHDh3SoUOHlJiY6Jd5AwCA4GfLglVUVKQlS5bo97//vVJSUuTxeLyP/v37a%2BvWrVq5cqW%2B%2Buor/fWvf9VLL72km2%2B%2BWZI0btw4vfzyy1q3bp12796te%2B%2B9V/369dPFF18c4FQAACBY2PIS4b///W/V1tZq6dKlWrp0qc%2ByTz/9VIsWLdLjjz%2BuRYsWqU2bNnrkkUeUnJwsSUpOTtacOXP0%2BOOP65tvvlGfPn00d%2B7cQMQAAABBymWMMYGehBN4PMcaZbtXL/xPo2y3Mbx%2BV59G3b7bHaLo6GYqKzth63sEnJCTjPbghIySM3IGc8aYmAsDsl9bXiIEAAAIJAoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxdyBngCc4%2BqF/wn0FH6Q1%2B/qE%2BgpAACCFGewzqKqqkrTp09Xamqq%2Bvbtq6effjrQUwIAAEGEM1hnMX/%2BfO3cuVOrV6/WwYMHdd999yk%2BPl5Dhw4N9NQAAEAQoGCd4eTJk1q3bp2eeuopde3aVV27dlVhYaGee%2B45ChYAAGgQLhGeYffu3aqpqVFycrJ3LCUlRQUFBaqrqwvgzAAAQLDgDNYZPB6PoqOjFRYW5h1r1aqVqqqqVF5erhYtWnzvNkpKSuTxeHzG3O6mio2NtXy%2BaDzBdlP%2BG/ekBXoKlgkNDfH5akdktA8n5HRCRqtRsM5QUVHhU64keZ9XV1c3aBu5ubnKycnxGZsyZYruvPNOayb5Hf/3YMMuW5aUlCg3N1djx461bdFzQkbJGTlLSkq0evUKMgY5J2SUnJHTCRmtRhU9Q3h4eL0idfp5kyZNGrSNsWPH6h//%2BIfPY%2BzYsZbP9YfweDzKycmpd2bNTpyQUXJGTjLagxMySs7I6YSMVuMM1hni4uJUVlammpoaud3ffns8Ho%2BaNGmiyMjIBm0jNjaWhg8AgINxBusMXbp0kdvtVn5%2BvncsLy9P3bp1U0gI3y4AAPD9aAxniIiI0MiRIzVr1ixt375dmzZt0tNPP62JEycGemoAACBIhM6aNWtWoCfxc9O7d2/t2rVLjzzyiD744AP94Q9/0OjRowM9rZ%2BsWbNm6tmzp5o1axboqTQaJ2SUnJGTjPbghIySM3I6IaOVXMYYE%2BhJAAAA2AmXCAEAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwHKCqqkrTp09Xamqq%2Bvbtq6effjrQU/pR3njjDXXu3NnnkZGRIUnatWuXxowZo8TERI0ePVo7d%2B70WXfDhg0aOHCgEhMTlZ6eriNHjgQiwjlVV1drxIgR2rJli3ds3759mjx5spKSkjRs2DC99957Puu8//77GjFihBITEzVx4kTt27fPZ/kzzzyjtLQ0JScna/r06aqoqPBLlnM5W8Z58%2BbVO6Zr1671Lj/fcTPGaMGCBerdu7d69uyp%2BfPnq66uzq%2BZTisuLlZGRoZ69uyptLQ0ZWVlqaqqSpJ9juP5MtrlOErSl19%2BqVtuuUXJycnq16%2BfVqxY4V1ml2N5vox2OpYBZ2B7c%2BbMMddcc43ZuXOn%2BZ//%2BR%2BTnJxsXn/99UBP6wdbsmSJuf32201JSYn38c0335gTJ06YPn36mIcfftjs2bPHzJ071/zqV78yJ06cMMYYU1BQYLp3725efPFF88knn5gJEyaY2267LcBp/n%2BVlZUmPT3ddOrUyWzevNkYY0xdXZ255pprTGZmptmzZ4958sknTWJiojlw4IAxxpgDBw6YpKQks3LlSvPZZ5%2BZP/3pT2bEiBGmrq7OGGPMv/71L5OSkmL%2B93//1xQUFJhhw4aZ2bNn/6wyGmPM5MmTzbJly3yO6cmTJ40x33/cVq5caa666iqzdetW88EHH5i%2BffuaFStW%2BD1bXV2dueGGG8ytt95qPvvsM7N161YzaNAg8/DDD9vmOJ4vozH2OI7GGFNbW2sGDx5sMjMzzeeff27eeust06NHD/PKK6/Y5lieL6Mx9jmWPwcULJs7ceKE6datm8//1BYvXmwmTJgQwFn9OJmZmeaRRx6pN75u3TozYMAA719kdXV1ZtCgQWb9%2BvXGGGOmTp1q7rvvPu/rDx48aDp37my%2B%2Buor/0z8PAoLC81vf/tbc8011/iUj/fff98kJSV5S6IxxkyaNMk8/vjjxhhjFi5c6HMMT548aZKTk73rjx8/3vtaY4zZunWr6d69u/cvSn86V0ZjjElLSzPvvvvuWdf7vuN21VVXeY%2BxMca89NJLpn///o2U4tz27NljOnXqZDwej3fs1VdfNX379rXNcTxfRmPscRyNMaa4uNj86U9/MseOHfOOpaenm7/85S%2B2OZbny2iMfY7lzwGXCG1u9%2B7dqqmpUXJysncsJSVFBQUFQXfqtqioSO3atas3XlBQoJSUFLlcLkmSy%2BVSjx49lJ%2Bf712emprqfX3r1q0VHx%2BvgoICv8z7fD788EP16tVLubm5PuMFBQW67LLL1LRpU%2B9YSkrKOTNFRESoa9euys/PV21trXbs2OGzPCkpSadOndLu3bsbOVF958p4/PhxFRcXn/WYSuc/bsXFxTp06JCuuOIK7/KUlBQdOHBAJSUljZLjXGJiYrRixQq1atXKZ/z48eO2OY7ny2iX4yhJsbGxWrhwoZo3by5jjPLy8rR161b17NnTNsfyfBntdCx/DtyBngAal8fjUXR0tMLCwrxjrVq1UlVVlcrLy9WiRYsAzq7hjDH6/PPP9d5772nZsmWqra3V0KFDlZGRIY/Ho0svvdTn9S1btlRhYaEkqaSkRLGxsfWWHz582G/zP5fx48efddzj8Zx3zudbfvToUVVVVfksd7vdioqKCkjmc2UsKiqSy%2BXSk08%2BqXfeeUdRUVG66aabdN1110k6/3HzeDyS5LP89P/8Dx8%2BXG%2B9xhQZGam0tDTv87q6Oq1du1a9e/e2zXE8X0a7HMczDRgwQAcPHlT//v01ZMgQPfTQQ7Y4lt91ZsadO3fa8lgGCgXL5ioqKnzKlSTv8%2Brq6kBM6Uc5ePCgN8vChQu1f/9%2BzZs3T5WVlefMeDpfZWXleZf/HH1fpvMtr6ys9D4/1/o/B3v37pXL5VL79u01YcIEbd26VQ888ICaN2%2BuQYMGnfe4nS3jz%2BXnOjs7W7t27dILL7ygZ555xpbH8bsZP/74Y1sex8cff1ylpaWaNWuWsrKybPnf5JkZu3btastjGSgULJsLDw%2Bv98N9%2BnmTJk0CMaUfpU2bNtqyZYsuuugiuVwudenSRXV1dZo6dap69ux51oyn853rexAREeG3%2Bf9Q4eHhKi8v9xlrSKbIyEiFh4d7n5%2B5/OeUeeTIkerfv7%2BioqIkSQkJCfriiy/0/PPPa9CgQec9bt/9i/vMvIHMmJ2drdWrV%2Buxxx5Tp06dbHkcz8zYsWNH2x1HSerWrZukb9%2BFfc8992j06NH13vUX7MfyzIzbtm2z5bEMFO7Bsrm4uDiVlZWppqbGO%2BbxeNSkSRNFRkYGcGY/XFRUlPc%2BK0nq0KGDqqqqFBMTo9LSUp/XlpaWek9Jx8XFnXV5TExM40/6RzrXnBuSKSoqSuHh4T7La2pqVF5e/rPK7HK5vH%2BRn9a%2BfXsVFxdLOn/GuLg4SfJelvjunwOVce7cuVq1apWys7M1ZMgQSfY7jmfLaKfjWFpaqk2bNvmMXXrppTp16tRP%2Bnvm53Qsz5fx%2BPHjtjmWPwcULJvr0qWL3G6390ZMScrLy1O3bt0UEhI8h//dd99Vr169fP4F%2BcknnygqKkopKSn66KOPZIyR9O39Wtu2bVNiYqIkKTExUXl5ed71Dh06pEOHDnmX/xwlJibq448/9p52l749bufKVFFRoV27dikxMVEhISHq1q2bz/L8/Hy53W4lJCT4L8T3WLRokSZPnuwztnv3brVv317S%2BY9bXFyc4uPjfZbn5eUpPj4%2BIPd65OTk6G9/%2B5seffRRDR8%2B3Dtup%2BN4rox2Oo779%2B/XlClTvIVCknbu3KkWLVooJSXFFsfyfBmfffZZ2xzLn4WAvocRfvHAAw%2BY4cOHm4KCAvPGG2%2BYHj16mI0bNwZ6Wj/IsWPHTFpamrn77rtNUVGReeutt0zfvn3N8uXLzbFjx0zv3r3N3LlzTWFhoZk7d67p06eP9%2B3U27ZtM127djV///vfvZ/dcvvttwc4UX3f/QiDmpoaM2zYMHPXXXeZzz77zCxbtswkJSV5P3Nn3759plu3bmbZsmXez9y55pprvB9VsWHDBtOjRw/zxhtvmIKCAjN8%2BHAzd%2B7cgGU77bsZCwoKzGWXXWZWrFhhvvzyS/Pcc8%2BZyy%2B/3Gzbts0Y8/3HbdmyZaZv375m8%2BbNZvPmzaZv377m6aef9numPXv2mC5dupjHHnvM57ODSkpKbHMcz5fRLsfRmG//uxs1apS5%2BeabTWFhoXnrrbfMr371K/PMM8/Y5lieL6OdjuXPAQXLAU6ePGnuvfdek5SUZPr27WtWrVoV6Cn9KJ999pmZPHmySUpKMn369DFPPPGE9y%2BvgoICM3LkSNOtWzdz/fXXm48//thn3fXr15urrrrKJCUlmfT0dHPkyJFARDivMz8j6osvvjC/%2B93vzOWXX26GDx9u/vOf//i8/q233jKDBw823bt3N5MmTar3uV7Lli0zV155pUlJSTHTpk0zlZWVfslxPmdmfOONN8w111xjunXrZoYOHVqv%2BJ/vuNXU1JiHHnrIpKamml69epns7Gzvz4M/LVu2zHTq1OmsD2PscRy/L6MdjuNphw8fNunp6aZHjx6mT58%2BZunSpd752OFYGnP%2BjHY6loHmMub/XVcBAACAJYLnJhwAAIAgQcECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAs9v8BDdYyaB4sAbUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5483967703527970060”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>30</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666666666664</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.11111111111111</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.66666666666666</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1598)</td> <td class=”number”>1879</td> <td class=”number”>58.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1192</td> <td class=”number”>37.1%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5483967703527970060”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.92405063291139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.89312977099237</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.24324324324324</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1383.6448598130842</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1434.4827586206898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1700.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3712.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_ORCHARD_FARMS_07_12”>PCH_ORCHARD_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>727</td>

</tr> <tr>

<th>Unique (%)</th> <td>22.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>15.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>493</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.1518</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1000</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>6.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7838975310417437374”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7838975310417437374,#minihistogram7838975310417437374”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7838975310417437374”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7838975310417437374”

aria-controls=”quantiles7838975310417437374” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7838975310417437374” aria-controls=”histogram7838975310417437374”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7838975310417437374” aria-controls=”common7838975310417437374”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7838975310417437374” aria-controls=”extreme7838975310417437374”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7838975310417437374”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-81.925</td>

</tr> <tr>

<th>Q1</th> <td>-36</td>

</tr> <tr>

<th>Median</th> <td>-8.3333</td>

</tr> <tr>

<th>Q3</th> <td>26.923</td>

</tr> <tr>

<th>95-th percentile</th> <td>143.17</td>

</tr> <tr>

<th>Maximum</th> <td>1000</td>

</tr> <tr>

<th>Range</th> <td>1100</td>

</tr> <tr>

<th>Interquartile range</th> <td>62.923</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>82.3</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.508</td>

</tr> <tr>

<th>Kurtosis</th> <td>21.758</td>

</tr> <tr>

<th>Mean</th> <td>7.1518</td>

</tr> <tr>

<th>MAD</th> <td>51.331</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.417</td>

</tr> <tr>

<th>Sum</th> <td>19432</td>

</tr> <tr>

<th>Variance</th> <td>6773.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7838975310417437374”>

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5ebkef/xxtWjRQklJSZKkgwcP6qefftLw4cOVmpqqV199VaNGjdJbb72l8vJySVJQUJC5jbN/rqysrPPrFhQUmGHtLLu98SUf0Ox2n91h6nGBgQEuX1F39K5%2B6J/76J37/LF3fhuwTp06pccee0zfffed/v73vyskJESSNHv2bJWXlys0NFSSNGPGDO3YsUOvv/66fv/730v6OUydPYR4NlidfX5dZGVlKTMz02UsOTlZqamp9V6XLwsPb%2BLtEnxOWFjd/93BFb2rH/rnPnrnPn/qnV8GrNLSUj3yyCP64YcftGrVKpd3C9rtdjNcSZLNZlP79u11/PhxtWzZUtLP70Q8e5jx7J4oh8NR59dPSkpSYmKiy5jd3lhFRafcXZJfuNTXfyECAwMUFhaikpIyVVdzmZALQe/qh/65j965ryF7561f7v0uYNXU1CglJUWHDx/WmjVr1KFDB5f5%2B%2B67T927d1dKSor5%2BG%2B%2B%2BUb33HOPWrZsqcjISGVnZ5sBKzs7W5GRkRd0eC8iIqLW453Ok6qqurS/4S719bujurqGvrmJ3tUP/XMfvXOfP/XO7wLWa6%2B9pq1bt%2Br5559XWFiYuQfqd7/7nZo2barExEQtXrxYnTp10lVXXaXVq1fr5MmTuvPOOyVJI0aM0Pz583XFFVdIkhYsWKCHHnrIa%2BsBAAC%2Bx%2B8C1jvvvKOamhqNGTPGZbxbt25as2aNHnjgAVVUVGjOnDkqLCxUdHS0VqxYYR42fPjhh/Xjjz8qJSVFgYGBuuuuu/TAAw94YSUAAMBX2YxfX6kTDcLpPNkg2x2w8NMG2W5DeHtsT2%2BX4DPs9gCFhzdRUdEpv9ld7in0rn7on/vonfsasncOx2WWbq%2Bu/Of9kAAAABcJAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFjM4wFr%2BPDheuWVV3TyZMN8%2BDEAAIC3eTxg9ejRQy%2B88IJ69eqlcePG6ZNPPpFhGJ4uAwAAoMF4PGCNHz9eH3zwgZ577jkFBgbq8ccfV58%2BffTMM8/o4MGDni4HAADAcnZvvKjNZlPPnj3Vs2dPlZWVac2aNXruuee0dOlSxcXFadSoUbr11lu9URoAAEC9eSVgSVJBQYHeeOMNvfHGG/r2228VFxenO%2B%2B8U8eOHdNf/vIXbd%2B%2BXVOnTvVWeQAAAG7zeMB6/fXX9frrr2vr1q1q1qyZhgwZomeffVbt2rUzH9OqVSvNnTuXgAUAAHySxwPW1KlT1bdvXy1evFg33XSTAgJqnwbWvn173XvvvZ4uDQAAwBIeD1gfffSRwsPDVVxcbIarXbt2qXPnzgoMDJQkxcXFKS4uztOlAQAAWMLj7yIsLS1V//799eKLL5pjo0eP1uDBg5Wfn%2B/pcgAAACzn8YD11FNP6corr9SDDz5ojr311ltq1aqV0tPTPV0OAACA5TwesP79739r8uTJcjgc5lizZs00ceJEffHFF54uBwAAwHIeD1h2u10lJSW1xsvKyriiOwAA8AseD1g33XST5syZox9%2B%2BMEcO3TokNLT09W7d29PlwMAAGA5j7%2BLcNKkSXrwwQd12223KSwsTJJUUlKizp07Ky0tzdPlAAAAWM7jAat58%2Bb6xz/%2Boc8%2B%2B0y5ubmy2%2B26%2BuqrdeONN8pms3m6HAAAAMt55aNyAgMD1bt3bw4JAgAAv%2BTxgOV0OrVw4ULt2LFDZ86cqXVi%2B/vvv%2B/pkgAAACzl8YA1bdo07dmzRwMHDtRll13m6ZcHAABocB4PWF988YWWLVumhIQES7ZXWVmpoUOHatq0aerevbukn9%2BVOG3aNO3cuVORkZGaMmWKevXqZT7ns88%2B01NPPaVDhw4pOjpac%2BfOVdu2bc35lStXavny5SotLdWAAQM0bdo0hYSEWFIvAADwfx6/TEPjxo3VvHlzS7ZVUVGhcePGKTc31xwzDEPJyclq0aKFNmzYoMGDByslJUVHjx6VJB09elTJyckaOnSoXnvtNTVr1kyPPfaYeajynXfeUWZmpmbNmqVVq1YpJydHGRkZltQLAAAuDR4PWIMHD9ayZctUXV1dr%2B3s379fd999t8v1tKSf95AdOnRIs2bNUocOHTRmzBjFxMRow4YNkqT169fr%2Buuv10MPPaRrrrlG6enpOnLkiLZt2yZJWr16tUaNGqW%2Bffuqa9eumjlzpjZs2KCysrJ61QsAAC4dHj9EWFxcrC1btuhf//qX2rZtq6CgIJf51atX12k727ZtU/fu3fXnP/9ZMTEx5nhOTo6uu%2B46NW7c2ByLj4/Xzp07zflfHp4MCQlR586dtXPnTiUkJGj37t1KSUkx52NiYnTmzBnt27dPsbGxbq0ZAABcWrxymYZBgwbVexsjR44857jT6VRERITLWPPmzXXs2LHzzpeUlKiiosJl3m63q2nTpubzAQAAzsfjASs9Pb1Bt19WVlZrr1hQUJAqKyvPO19eXm7e/63n10VBQYGcTqfLmN3euFawu9TY7R4/Iu2zAgMDXL6i7uhd/dA/99E79/lj77yyB6ugoECvvvqqDh48qClTpmj79u3q2LGj2rdvX%2B9tBwcHq7i42GWssrJSjRo1Mud/HZYqKysVFham4OBg8/6v5y/kXYRZWVnKzMx0GUtOTlZqamqdt%2BGPwsObeLsEnxMWxrtX3UXv6of%2BuY/euc%2BfeufxgPX999/r7rvvVmhoqI4fP66xY8fqrbfeUlpamlauXKno6Oh6bb9ly5bav3%2B/y1hhYaG596hly5YqLCysNd%2BpUyc1bdpUwcHBKiwsVIcOHSRJVVVVKi4ulsPhqHMNSUlJSkxMdBmz2xurqOiUO0vyG5f6%2Bi9EYGCAwsJCVFJSpurqGm%2BX41PoXf3QP/fRO/c1ZO%2B89cu9xwPWX//6V/Xr109z5sxRXFycJOnpp5/WpEmTNH/%2BfK1Zs6Ze24%2BOjtbSpUtVXl5u7rXKzs5WfHy8OZ%2BdnW0%2BvqysTHv37lVKSooCAgLUpUsXZWdnm9fU2rlzp%2Bx2u6KioupcQ0RERK3DgU7nSVVVXdrfcJf6%2Bt1RXV1D39xE7%2BqH/rmP3rnPn3rn8YOdO3bs0IMPPujywc52u12PPfaY9u7dW%2B/td%2BvWTa1atVJaWppyc3O1dOlS7dq1S3fddZckadiwYdqxY4eWLl2q3NxcpaWlqU2bNmagGjlypJYvX6733ntPu3bt0owZM3T33XdzoVEAAFBnHg9YNTU1qqmpnU5PnTqlwMDAem8/MDBQzz33nJxOp4YOHao33nhDixcvVmRkpCSpTZs2%2Btvf/qYNGzborrvuUnFxsRYvXmwGvoEDB2rMmDGaPn26HnroIXXt2lUTJkyod10AAODS4fFDhL169dKSJUtcro5eXFysjIwM9ejRw61tfvPNNy73r7zySq1du/Y3H3/zzTfr5ptv/s350aNHa/To0W7VAgAA4PE9WJMnT9aePXvUq1cvVVRU6NFHH1Xfvn11%2BPBhTZo0ydPlAAAAWM7je7BatmypTZs2acuWLfr6669VU1OjESNGaPDgwQoNDfV0OQAAAJbzynWwQkJCNHz4cG%2B8NAAAQIPzeMC6//77/%2BN8XT%2BLEAAA4GLl8YDVunVrl/tVVVX6/vvv9e2332rUqFGeLgcAAMByF81nES5evJgPVAYAAH7hovlUxcGDB%2Bvtt9/2dhkAAAD1dtEErC%2B//NKSC40CAAB420Vxkntpaam%2B%2BeYbjRw50tPlAAAAWM7jASsyMtLlcwgl6Xe/%2B53uvfde3XHHHZ4uBwAAwHIeD1h//etfPf2SAAAAHuXxgLV9%2B/Y6P/aGG25owEoAAAAahscD1n333WceIjQMwxz/9ZjNZtPXX3/t6fIAAADqzeMB64UXXtCcOXM0YcIEdevWTUFBQdq9e7dmzZqlO%2B%2B8U7fffrunSwIAALCUxy/TkJ6erunTp%2Bu2225TeHi4mjRpoh49emjWrFlat26dWrdubd4AAAB8kccDVkFBwTnDU2hoqIqKijxdDgAAgOU8HrBiYmL09NNPq7S01BwrLi5WRkaGbrzxRk%2BXAwAAYDmPn4P1l7/8Rffff79uuukmtWvXToZh6LvvvpPD4dDq1as9XQ4AAIDlPB6wOnTooLfeektbtmxRXl6eJOmee%2B7RwIEDFRIS4ulyAAAALOfxgCVJl19%2BuYYPH67Dhw%2Brbdu2kn6%2BmjsAAIA/8Pg5WIZhaP78%2Bbrhhhs0aNAgHTt2TJMmTdLUqVN15swZT5cDAABgOY8HrDVr1uj111/Xk08%2BqaCgIElSv3799N577ykzM9PT5QAAAFjO4wErKytL06dP19ChQ82rt99%2B%2B%2B2aM2eONm/e7OlyAAAALOfxgHX48GF16tSp1nhUVJScTqenywEAALCcxwNW69attXv37lrjH330kXnCOwAAgC/z%2BLsIH374Yc2cOVNOp1OGYejzzz9XVlaW1qxZo8mTJ3u6HAAAAMt5PGANGzZMVVVVev7551VeXq7p06erWbNmGjt2rEaMGOHpcgAAACzn8YC1ZcsW9e/fX0lJSTpx4oQMw1Dz5s09XQYAAECD8fg5WLNmzTJPZm/WrBnhCgAA%2BB2PB6x27drp22%2B/9fTLAgAAeIzHDxFGRUXpiSee0LJly9SuXTsFBwe7zKenp3u6JAAAAEt5PGAdPHhQ8fHxktRg173auHGj0tLSao3bbDbt27dPjz76qP7v//7PZe6FF15Q3759JUkrV67U8uXLVVpaqgEDBmjatGl8EDUAAKgzjwSsefPmKSUlRY0bN9aaNWsa/PVuv/129e7d27xfVVWlUaNGqU%2BfPpKkvLw8ZWRk6MYbbzQfc/nll0uS3nnnHWVmZiojI0PNmzdXWlqaMjIyNH369AavGwAA%2BAePnIO1YsUKlZWVuYyNHj1aBQUFDfJ6jRo1ksPhMG9vvPGGDMPQE088ocrKSh0%2BfFhdunRxeczZz0VcvXq1Ro0apb59%2B6pr166aOXOmNmzYUKt%2BAACA3%2BKRgGUYRq2x7du3q6KiosFfu7i4WC%2B%2B%2BKLGjx%2BvoKAgHThwQDab7ZxXja%2Burtbu3buVkJBgjsXExOjMmTPat29fg9cKAAD8g8fPwfK0devWKSIiQv3795ckHThwQKGhoZo4caK2bdumK664Qo8//rhuvvlmlZSUqKKiQhEREebz7Xa7mjZtqmPHjtX5NQsKCmqdX2a3N3bZ7qXIbvf4m1Z9VmBggMtX1B29qx/65z565z5/7J1fByzDMLR%2B/Xo98sgj5tiBAwdUXl6uXr16afTo0Xr33Xf16KOPKisrSy1atJAk83DhWUFBQaqsrKzz62ZlZSkzM9NlLDk5WampqfVYje8LD2/i7RJ8TlgYb65wF72rH/rnPnrnPn/qnccCls1m89RLmXbv3q3jx49r4MCB5thjjz2m%2B%2B67zzypPSoqSl999ZVeffVV/fnPf5akWmGqsrLygt5FmJSUpMTERJcxu72xiopOubsUv5Aw9Z/eLuGCvPtE7/M/qIEEBgYoLCxEJSVlqq6u8Vodvoje1Q/9cx%2B9c19D9s5bv9x7LGDNmTPH5ZpXZ86cUUZGhpo0cV24ldfB%2Bvjjj5WQkGCGKUkKCAhwuS9J7du31/79%2B9W0aVMFBwersLBQHTp0kPTzOxCLi4vlcDjq/LoRERG1Dgc6nSdVVcU3nC%2B5GP6%2BqqtrLoo6fBG9qx/65z565z5/6p1HAtYNN9xQ65yk2NhYFRUVqaioqMFed9euXYqLi3MZmzx5smw2m0uQ27dvnzp27KiAgAB16dJF2dnZ6t69uyRp586dstvtioqKarA6AQCAf/FIwPLEta/OJTc3V3fccYfLWGJiosaNG6fu3bsrNjZWmzdvVnZ2tmbNmiVJGjlypKZPn66OHTsqIiJCM2bM0N13382FRgEAQJ359UnuhYWFCgsLcxm79dZb9eSTT%2Br555/X0aNHdc0112jZsmVq06aNJGngwIE6cuSIpk%2BfrsrKSt16662aMGGCN8oHAAA%2Bymac6yJVsJzTebJBtjtg4acNsl1Ib4/t6bXXttsDFB7eREVFp/zmfARPoXf1Q//cR%2B/c15C9czgus3R7deU/F5wAAAC4SBCwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAACxIRHjAAAVFklEQVQALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACzmtwHr3Xff1bXXXutyS01NlSTt3btXw4cPV3R0tIYNG6Y9e/a4PHfLli3q16%2BfoqOjlZycrBMnTnhjCQAAwEf5bcDav3%2B/%2Bvbtq08%2B%2BcS8zZkzR6dPn9bo0aOVkJCgjRs3KjY2VmPGjNHp06clSbt27dLUqVOVkpKirKwslZSUKC0tzcurAQAAvsRvA1ZeXp46duwoh8Nh3sLCwvTWW28pODhYEydOVIcOHTR16lQ1adJE//znPyVJa9eu1YABAzRkyBBFRUVp3rx5%2BvDDD3Xo0CEvrwgAAPgKvw5Y7dq1qzWek5Oj%2BPh42Ww2SZLNZlNcXJx27txpzickJJiPb9WqlSIjI5WTk%2BORugEAgO%2Bze7uAhmAYhg4ePKhPPvlES5YsUXV1tfr376/U1FQ5nU5dffXVLo9v3ry5cnNzJUkFBQWKiIioNX/s2LE6v35BQYGcTqfLmN3euNZ2cXGz2733%2B0dgYIDLV9Qdvasf%2Buc%2Beuc%2Bf%2BydXwaso0ePqqysTEFBQVq4cKEOHz6sOXPmqLy83Bz/paCgIFVWVkqSysvL/%2BN8XWRlZSkzM9NlLDk52TzJHr4hPLyJt0tQWFiIt0vwWfSufuif%2B%2Bid%2B/ypd34ZsFq3bq2tW7fq8ssvl81mU6dOnVRTU6MJEyaoW7dutcJSZWWlGjVqJEkKDg4%2B53xISN3/0pOSkpSYmOgyZrc3VlHRKTdXBG/w5t9XYGCAwsJCVFJSpurqGq/V4YvoXf3QP/fRO/c1ZO%2B89cuyXwYsSWratKnL/Q4dOqiiokIOh0OFhYUuc4WFhebhu5YtW55z3uFw1Pm1IyIiah0OdDpPqqqKbzhfcjH8fVVX11wUdfgielc/9M999M59/tQ7/znY%2BQsff/yxunfvrrKyMnPs66%2B/VtOmTRUfH68vv/xShmFI%2Bvl8rR07dig6OlqSFB0drezsbPN5%2Bfn5ys/PN%2BcBAADOxy8DVmxsrIKDg/WXv/xFBw4c0Icffqh58%2BbpkUceUf/%2B/VVSUqK5c%2Bdq//79mjt3rsrKyjRgwABJ0ogRI/T6669r/fr12rdvnyZOnKg%2Bffqobdu2Xl4VAADwFX4ZsEJDQ7V8%2BXKdOHFCw4YN09SpU5WUlKRHHnlEoaGhWrJkibKzszV06FDl5ORo6dKlaty4saSfw9msWbO0ePFijRgxQpdffrnS09O9vCIAAOBLbMbZY2VoUE7nyQbZ7oCFnzbIdiG9Pban117bbg9QeHgTFRWd8pvzETyF3tUP/XMfvXNfQ/bO4bjM0u3VlV/uwQIAAPAmAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxfw2YB0/flypqanq1q2bevfurfT0dFVUVEiS5syZo2uvvdbltnbtWvO5W7ZsUb9%2B/RQdHa3k5GSdOHHCW8sAAAA%2ByO7tAhqCYRhKTU1VWFiYXn75Zf3000%2BaMmWKAgICNGnSJOXl5Wn8%2BPG68847zeeEhoZKknbt2qWpU6dq5syZioqK0ty5c5WWlqYlS5Z4azkAAMDH%2BOUerAMHDmjnzp1KT0/XNddco4SEBKWmpmrLli2SpLy8PF133XVyOBzmLSQkRJK0du1aDRgwQEOGDFFUVJTmzZunDz/8UIcOHfLmkgAAgA/xy4DlcDi0bNkytWjRwmW8tLRUpaWlOn78uNq1a3fO5%2Bbk5CghIcG836pVK0VGRionJ6chSwYAAH7ELwNWWFiYevfubd6vqanR2rVr1aNHD%2BXl5clms%2BmFF17QTTfdpDvuuEP/%2BMc/zMcWFBQoIiLCZXvNmzfXsWPHPFY/AADwbX55DtavZWRkaO/evXrttdf01VdfyWazqX379rr33nu1fft2TZs2TaGhobrllltUXl6uoKAgl%2BcHBQWpsrKyzq9XUFAgp9PpMma3N64V3HBxs9u99/tHYGCAy1fUHb2rH/rnPnrnPn/snd8HrIyMDK1atUrPPPOMOnbsqGuuuUZ9%2B/ZV06ZNJUlRUVH67rvvtG7dOt1yyy0KDg6uFaYqKyvNc7TqIisrS5mZmS5jycnJSk1Nrf%2BC4DHh4U28XYLCwur%2B7w6u6F390D/30Tv3%2BVPv/DpgzZ49W%2BvWrVNGRoZuu%2B02SZLNZjPD1Vnt27fXF198IUlq2bKlCgsLXeYLCwvlcDjq/LpJSUlKTEx0GbPbG6uo6JQ7y4CXePPvKzAwQGFhISopKVN1dY3X6vBF9K5%2B6J/76J37GrJ33vpl2W8DVmZmpl555RU9/fTT6t%2B/vzm%2BaNEiffnll1q5cqU5tm/fPrVv316SFB0drezsbA0dOlSSlJ%2Bfr/z8fEVHR9f5tSMiImodDnQ6T6qqim84X3Ix/H1VV9dcFHX4InpXP/TPffTOff7UO/852PkLeXl5eu655/SnP/1J8fHxcjqd5q1v377avn27li9frh9%2B%2BEF///vftWnTJj300EOSpBEjRuj111/X%2BvXrtW/fPk2cOFF9%2BvRR27ZtvbwqAADgK/xyD9b777%2Bv6upqPf/883r%2B%2Bedd5r755hstWrRIzz77rBYtWqTWrVtrwYIFio2NlSTFxsZq1qxZevbZZ/XTTz%2BpZ8%2Bemj17tjeWAQAAfJTNMAzD20VcCpzOkw2y3QELP22Q7UJ6e2xPr7223R6g8PAmKio65Te7yz2F3tUP/XMfvXNfQ/bO4bjM0u3VlV8eIgQAAPAmAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxezeLgC4WA1Y%2BKm3S7ggb4/t6e0SAAD/H3uwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGB%2BVA/gJX/poHz7WB4C/Yw8WAACAxQhY51BRUaEpU6YoISFBvXr10ksvveTtkgAAgA/hEOE5zJs3T3v27NGqVat09OhRTZo0SZGRkerfv7%2B3SwMAAD6AgPUrp0%2Bf1vr16/Xiiy%2Bqc%2BfO6ty5s3Jzc/Xyyy8TsAAAQJ0QsH5l3759qqqqUmxsrDkWHx%2BvF154QTU1NQoI4KgqUF%2B%2BdEK%2BxEn5AC4cAetXnE6nwsPDFRQUZI61aNFCFRUVKi4uVrNmzc67jYKCAjmdTpcxu72xIiIiLK8XQMPztUCIhvPuE71/cy4wMMDlK%2BrOH3tHwPqVsrIyl3AlybxfWVlZp21kZWUpMzPTZSwlJUWPP/64NUX%2Bwr/nXpyHLQsKCpSVlaWkpCSCpRvon/voXf3QP/cVFBRo1apl9M4N/tg7/4mKFgkODq4VpM7eb9SoUZ22kZSUpI0bN7rckpKSLK/1YuZ0OpWZmVlrTx7qhv65j97VD/1zH71znz/2jj1Yv9KyZUsVFRWpqqpKdvvP7XE6nWrUqJHCwsLqtI2IiAi/SeAAAODCsQfrVzp16iS73a6dO3eaY9nZ2erSpQsnuAMAgDohMfxKSEiIhgwZohkzZmjXrl1677339NJLL%2Bn%2B%2B%2B/3dmkAAMBHBM6YMWOGt4u42PTo0UN79%2B7VggUL9Pnnn%2Bu///u/NWzYMG%2BX5XOaNGmibt26qUmTJt4uxSfRP/fRu/qhf%2B6jd%2B7zt97ZDMMwvF0EAACAP%2BEQIQAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWLFdRUaEpU6YoISFBvXr10ksvveTtki4qx48fV2pqqrp166bevXsrPT1dFRUVkqRDhw7pgQceUExMjG6//XZ98sknLs/97LPPNGjQIEVHR%2Bv%2B%2B%2B/XoUOHvLGEi8Lo0aM1efJk8/7evXs1fPhwRUdHa9iwYdqzZ4/L47ds2aJ%2B/fopOjpaycnJOnHihKdL9qrKykrNnDlTN9xwg37/%2B9/r6aef1tnrTNO788vPz9eYMWMUFxenxMRErVy50pyjf%2BdWWVmpQYMGaevWreZYfX/GrVy5Ur1791ZsbKymTJmisrIyj6zFHQQsWG7evHnas2ePVq1apSeffFKZmZn65z//6e2yLgqGYSg1NVVlZWV6%2BeWX9cwzz%2BiDDz7QwoULZRiGkpOT1aJFC23YsEGDBw9WSkqKjh49Kkk6evSokpOTNXToUL322mtq1qyZHnvsMV2KH8bw5ptv6sMPPzTvnz59WqNHj1ZCQoI2btyo2NhYjRkzRqdPn5Yk7dq1S1OnTlVKSoqysrJUUlKitLQ0b5XvFXPmzNFnn32m5cuXa8GCBXr11VeVlZVF7%2Bpo7Nixaty4sTZu3KgpU6Zo4cKFevfdd%2Bnfb6ioqNC4ceOUm5trjtX3Z9w777yjzMxMzZo1S6tWrVJOTo4yMjK8sr46MQALnTp1yujSpYvxxRdfmGOLFy827r33Xi9WdfHYv3%2B/0bFjR8PpdJpjmzdvNnr16mV89tlnRkxMjHHq1ClzbtSoUcazzz5rGIZhLFy40KWPp0%2BfNmJjY116fSkoKioybrrpJmPYsGHGpEmTDMMwjPXr1xuJiYlGTU2NYRiGUVNTY9xyyy3Ghg0bDMMwjAkTJpiPNQzDOHr0qHHttdcaP/zwg%2BcX4AVFRUXGddddZ2zdutUcW7JkiTF58mR6VwfFxcVGx44djW%2B%2B%2BcYcS0lJMWbOnEn/ziE3N9e44447jD/%2B8Y9Gx44dzZ9R9f0ZN3LkSPOxhmEY27dvN7p27WqcPn3aE8u6YOzBgqX27dunqqoqxcbGmmPx8fHKyclRTU2NFyu7ODgcDi1btkwtWrRwGS8tLVVOTo6uu%2B46NW7c2ByPj4/Xzp07JUk5OTlKSEgw50JCQtS5c2dz/lLxv//7vxo8eLCuvvpqcywnJ0fx8fGy2WySJJvNpri4uN/sXatWrRQZGamcnBzPFu8l2dnZCg0NVbdu3cyx0aNHKz09nd7VQaNGjRQSEqKNGzfqzJkzOnDggHbs2KFOnTrRv3PYtm2bunfvrqysLJfx%2BvyMq66u1u7du13mY2JidObMGe3bt6%2BBV%2BQeAhYs5XQ6FR4erqCgIHOsRYsWqqioUHFxsRcruziEhYWpd%2B/e5v2amhqtXbtWPXr0kNPpVEREhMvjmzdvrmPHjknSeecvBZ9//rn%2B/e9/67HHHnMZP19vCgoKLuneHTp0SK1bt9amTZvUv39//eEPf9DixYtVU1ND7%2BogODhY06dPV1ZWlqKjozVgwADddNNNGj58OP07h5EjR2rKlCkKCQlxGa/Pz7iSkhJVVFS4zNvtdjVt2vSi7aXd2wXAv5SVlbmEK0nm/crKSm%2BUdFHLyMjQ3r179dprr2nlypXn7N3Zvv1Wby%2BVvlZUVOjJJ5/U9OnT1ahRI5e58/WmvLz8ku7d6dOn9f333%2BuVV15Renq6nE6npk%2BfrpCQEHpXR3l5eerbt68efPBB5ebmavbs2brxxhvp3wU4X6/%2B03x5ebl5/7eef7EhYMFSwcHBtf6xn73/6/8UL3UZGRlatWqVnnnmGXXs2FHBwcG19vJVVlaaffut3oaFhXmsZm/KzMzU9ddf77IH8Kzf6s35evfr37D9ld1uV2lpqRYsWKDWrVtL%2BvmE4nXr1unKK6%2Bkd%2Bfx%2Beef67XXXtOHH36oRo0aqUuXLjp%2B/Lief/55tW3blv7VUX1%2BxgUHB5v3fz1/sfaSQ4SwVMuWLVVUVKSqqipzzOl0qlGjRpdMEKiL2bNna8WKFcrIyNBtt90m6efeFRYWujyusLDQ3CX%2BW/MOh8MzRXvZm2%2B%2Bqffee0%2BxsbGKjY3V5s2btXnzZsXGxtK783A4HAoODjbDlSRdddVVys/Pp3d1sGfPHl155ZUuvyRed911Onr0KP27APXpVdOmTRUcHOwyX1VVpeLi4ou2lwQsWKpTp06y2%2B0uJ15nZ2erS5cuCgjgn5v0856YV155RU8//bQGDhxojkdHR%2Burr74yd4VLP/cuOjranM/OzjbnysrKtHfvXnPe361Zs0abN2/Wpk2btGnTJiUmJioxMVGbNm1SdHS0vvzyS/Pt3IZhaMeOHb/Zu/z8fOXn518yvYuOjlZFRYUOHjxojh04cECtW7emd3UQERGh77//3mXvyYEDB9SmTRv6dwHq8zMuICBAXbp0cZnfuXOn7Ha7oqKiPLeIC%2BHNtzDCP02bNs0YOHCgkZOTY7z77rtGXFyc8c4773i7rIvC/v37jU6dOhnPPPOMUVBQ4HKrqqoybr/9dmPs2LHGt99%2BayxZssSIiYkxjhw5YhiGYRw6dMjo0qWLsWTJEuPbb781/ud//sf44x//aL49/FIzadIk8%2B3vJ0%2BeNHr06GHMnj3byM3NNWbPnm307NnTfDv4jh07jM6dOxuvvvqq8fXXXxv33nuvMWbMGG%2BW73GjR482kpKSjK%2B//tr46KOPjB49ehirVq2id3VQUlJi9OzZ05gwYYJx4MAB4/333ze6detmrFu3jv6dxy8v01Dfn3Fbtmwx4uLijHfffdfIyckxBg4caMyePdtrazsfAhYsd/r0aWPixIlGTEyM0atXL2PFihXeLumisWTJEqNjx47nvBmGYXz33XfGPffcY1x//fXGwIEDjU8//dTl%2Bf/617%2BMW2%2B91ejatasxatQov76Wzvn8MmAZhmHk5OQYQ4YMMbp06WLcddddxldffeXy%2BA0bNhg333yzERMTYyQnJxsnTpzwdMleVVJSYkyYMMGIiYkxbrzxRuNvf/ub%2BR8XvTu/3Nxc44EHHjDi4uKMfv36GStWrKB/dfDLgGUY9f8Zt2TJEuPGG2804uPjjbS0NKO8vNwj63CHzTAuwctAAwAANCBOigEAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALDY/wPIb2TKRsoy0AAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7838975310417437374”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>223</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>118</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>114</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>85</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-40.0</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (716)</td> <td class=”number”>1865</td> <td class=”number”>58.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>493</td> <td class=”number”>15.4%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7838975310417437374”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>114</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.06493506493506</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-91.00719424460432</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.9090909090909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>450.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>500.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>600.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>700.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_DIRSALES_07_12”><s>PCH_PC_DIRSALES_07_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_DIRSALES_07_12”><code>PCH_DIRSALES_07_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99896</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_SNAPBEN_10_15”>PCH_PC_SNAPBEN_10_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3051</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>158</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-4.9067</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>166.49</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1607543783047924978”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1607543783047924978,#minihistogram-1607543783047924978”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1607543783047924978”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1607543783047924978”

aria-controls=”quantiles-1607543783047924978” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1607543783047924978” aria-controls=”histogram-1607543783047924978”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1607543783047924978” aria-controls=”common-1607543783047924978”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1607543783047924978” aria-controls=”extreme-1607543783047924978”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1607543783047924978”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-27.823</td>

</tr> <tr>

<th>Q1</th> <td>-15.55</td>

</tr> <tr>

<th>Median</th> <td>-6.5059</td>

</tr> <tr>

<th>Q3</th> <td>3.8755</td>

</tr> <tr>

<th>95-th percentile</th> <td>22.646</td>

</tr> <tr>

<th>Maximum</th> <td>166.49</td>

</tr> <tr>

<th>Range</th> <td>266.49</td>

</tr> <tr>

<th>Interquartile range</th> <td>19.425</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>17.043</td>

</tr> <tr>

<th>Coef of variation</th> <td>-3.4735</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.727</td>

</tr> <tr>

<th>Mean</th> <td>-4.9067</td>

</tr> <tr>

<th>MAD</th> <td>12.497</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0591</td>

</tr> <tr>

<th>Sum</th> <td>-14975</td>

</tr> <tr>

<th>Variance</th> <td>290.47</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1607543783047924978”>

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o4MCBKi0tlZ%2Bfn8t7/Pz8ZLfbVVpa6nz%2Bn33SrxfTV1dGRobS09Nd2hITE5WUlFTTzbqsoKCAOlluQ0Md3SckpLGnh3BNYy6aQR3NoI4143UBa/jw4YqLi1NwcLAkKTIyUocOHdJ7772ngQMHyt/fv0pYstvtCggIcAlT/v7%2Bzn9Lcl7DVR0JCQmKj493abNaA1VYeKbG23Uxvr4%2BCgoKUHFxiSoq%2BKRjTVFH9zO9L3gL5qIZ1NEMb6mjp/6g87qAZbFYnOHqvIiICG3dulWSFB4eroKCApf%2BgoIChYaGKjw8XJJks9nUtm1b578lKTQ0tNpjCAsLq3I60GY7pfLyupmgFRWVdbbshoQ6ug91vjzmohnU0QzqWDNed2J18eLFmjBhgkvb/v37FRERIUmKiopSZmams%2B/YsWM6duyYoqKiFB4ertatW7v0Z2ZmqnXr1savnwIAAN7L645gxcXFadmyZVqxYoUGDhyoLVu26MMPP9SqVaskSWPGjNEDDzyg7t27q2vXrpo/f7769%2B%2Bvdu3aOfsXLlyo66%2B/XpL0wgsvaOLEiR7bHgAAUP94XcDq1q2bFi9erJdeekmLFy9WmzZt9MILLyg6OlqSFB0drblz5%2Bqll17SL7/8oj59%2BujZZ591vn/SpEn6%2BeefNWXKFPn6%2Buqee%2B6pckQMAADgciwOh8Ph6UE0BDbbKePLtFp9FBLSWIWFZzg/XgveUsfBi7729BCq7ZPH%2Bnh6CNckb5mLnkYdzfCWOoaGNvXIer3uGiwAAABPI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwrN4HLLvdrqFDh2rbtm3OtqysLP33f/%2B3oqOjddddd2nNmjUu7/n973%2BvTp06uTx%2B%2BOEHSZLD4dDChQvVu3dv9ezZUwsWLFBlZaVbtwkAANRvVk8PoDbKysqUnJysAwcOONtsNpv%2B8Ic/aMyYMXr%2B%2Bee1d%2B9ezZgxQ6Ghoerfv78qKip06NAhrV69Wu3bt3e%2BLyQkRJL05ptvasOGDUpPT1d5ebmmTZumFi1aaNKkSe7ePAAAUE/V24CVk5Oj5ORkORwOl/ZNmzapZcuWevzxxyVJ7du317Zt2/TRRx%2Bpf//%2BOnz4sM6dO6du3brJ39%2B/ynJXrVqlpKQkxcbGSpKeeOIJLV68mIAFAACqrd6eIty%2Bfbt69eqljIwMl/Z%2B/fopNTW1yutPnz4t6ddg1qpVq4uGqxMnTujYsWO67bbbnG0xMTE6cuSI8vPzDW8BAADwVvX2CNbYsWMv2t62bVu1bdvW%2Bfznn3/W3//%2Bd02dOlWSlJubq%2Buuu06PPPKI9uzZoxtvvFHTp09Xt27dZLPZJElhYWHO97ds2VKSdPz4cZf2y8nPz3cu6zyrNbDa768uX18fl5%2BoGeroflYrtb4Y5qIZ1NEM6lg7bg9Yo0eP1qhRozRkyBA1bdq0TtdVWlqqqVOnqmXLlkpISJAk/fjjj/rll180evRoJSUl6S9/%2BYvGjx%2Bvjz/%2BWKWlpZIkPz8/5zLO/9tut1d7vRkZGUpPT3dpS0xMVFJSUm036aKCggLqZLkNDXV0n5CQxp4ewjWNuWgGdTSDOtaM2wNW79699eqrryo1NVW//e1vNXLkSPXp00cWi8Xoes6cOaPJkyfr0KFDevfddxUQ8OsEefbZZ1VaWqomTZpIkubMmaOdO3dq/fr1%2Bq//%2Bi9Jv4ap86cQzwer8%2B%2BvjoSEBMXHx7u0Wa2BKiw8U%2Bvt%2Bk%2B%2Bvj4KCgpQcXGJKir4pGNNUUf3M70veAvmohnU0QxvqaOn/qBze8BKTk7W448/rm%2B%2B%2BUYffvihpk6dqqCgIA0fPlzDhw/XjTfeWOt1nD59Wg899JB%2B%2BuknrVy50uXTglar1RmuJMlisSgiIkInTpxQeHi4pF8/iXj%2BNOP5U32hoaHVXn9YWFiV04E22ymVl9fNBK2oqKyzZTck1NF9qPPlMRfNoI5mUMea8ciJVYvFoj59%2BigtLU3ffPON7rvvPq1cuVJ333237rvvPv3v//5vjZddWVmpKVOm6PDhw3r77bd18803u/Q/8MADLqfvKisr9f333ysiIkLh4eFq3bq1MjMznf2ZmZlq3bq18eunAACA9/LYRe75%2Bfn629/%2Bpr/97W/64Ycf1KNHD40YMULHjx/X008/rR07diglJeWql7t27Vpt27ZNS5cuVVBQkPMI1HXXXafg4GDFx8dryZIl6ty5s2688UatWrVKp06d0ogRIyRJY8aM0cKFC3X99ddLkl544QVNnDjR3IYDAACv5/aAtX79eq1fv17btm1T8%2BbNNXz4cL300ksup/FatWql%2BfPn1yhgbdy4UZWVlXrkkUdc2nv27Km3335bEyZMUFlZmebNm6eCggJFRUXpzTffdJ42nDRpkn7%2B%2BWdNmTJFvr6%2BuueeezRhwoTabDIAAGhgLI4L79RZx2699VbFxcVp1KhRuv322%2BXjU/Us5c6dO7V582YlJye7c2h1ymY7ZXyZVquPQkIaq7DwDOfHa8Fb6jh40deeHkK1ffJYH08P4ZrkLXPR06ijGd5Sx9DQur1jwaW4/QjWl19%2BqZCQEBUVFTnD1a5du9SlSxf5%2BvpKknr06KEePXq4e2gAAABGuP0i99OnT2vQoEFavny5s%2B3hhx/WsGHDdOzYMXcPBwAAwDi3B6znnntON9xwgx588EFn28cff6xWrVpd9CtuAAAA6hu3B6x//vOfeuqpp1zuK9W8eXNNnz5dW7dudfdwAAAAjHN7wLJarSouLq7SXlJSIjdfbw8AAFAn3B6wbr/9ds2bN08//fSTsy0vL0%2Bpqanq16%2Bfu4cDAABgnNs/Rfjkk0/qwQcf1F133aWgoCBJUnFxsbp06aIZM2a4ezgAAADGuT1gtWjRQn/961/1zTff6MCBA7Jarbrpppv0m9/8xvgXPgMAAHiCR74qx9fXV/369eOUIAAA8EpuD1g2m02LFi3Szp07de7cuSoXtn/%2B%2BefuHhIAAIBRbg9Ys2bN0p49ezRkyBA1beqZ29cDAADUJbcHrK1bt%2Br1119XbGysu1cNAADgFm6/TUNgYKBatGjh7tUCAAC4jdsD1rBhw/T666%2BroqLC3asGAABwC7efIiwqKtKGDRv0j3/8Q%2B3atZOfn59L/6pVq9w9JAAAAKM8cpuGoUOHemK1AAAAbuH2gJWamuruVQIAALiV26/BkqT8/Hylp6crOTlZP//8sz799FMdPHjQE0MBAAAwzu0B69///rd%2B97vf6a9//as2btyos2fP6uOPP9aoUaOUnZ3t7uEAAAAY5/aA9fzzz2vAgAHatGmTrrvuOknSiy%2B%2BqPj4eC1cuNDdwwEAADDO7QFr586devDBB12%2B2NlqtWry5Mnat2%2Bfu4cDAABgnNsDVmVlpSorK6u0nzlzRr6%2Bvu4eDgAAgHFuD1h9%2B/bVa6%2B95hKyioqKlJaWpt69e7t7OAAAAMa5PWA99dRT2rNnj/r27auysjI9%2BuijiouL0%2BHDh/Xkk0%2B6ezgAAADGuf0%2BWOHh4frwww%2B1YcMGfffdd6qsrNSYMWM0bNgwNWnSxN3DAQAAMM4jd3IPCAjQ6NGjPbFqAACAOuf2gDVu3LjL9l/tdxHa7XaNHDlSs2bNUq9evSRJeXl5mjVrlrKystS6dWvNnDlTffv2db7nm2%2B%2B0XPPPae8vDxFRUVp/vz5ateunbP/rbfe0ooVK3T69GkNHjxYs2bNUkBAwFWNCwAANFxuvwarTZs2Lo/w8HCVlpZq165dio6OvqpllZWV6fHHH9eBAwecbQ6HQ4mJiWrZsqXWrVunYcOGacqUKTp69Kgk6ejRo0pMTNTIkSO1du1aNW/eXJMnT5bD4ZAkbdy4Uenp6Zo7d65Wrlyp7OxspaWlmSsAAADwetfMdxEuWbJEx48fr/ZycnJylJyc7AxG523dulV5eXl6//33FRgYqA4dOujbb7/VunXrNHXqVK1Zs0a33nqrJk6c6BxPnz59tH37dvXq1UurVq3S%2BPHjFRcXJ0l65plnNGnSJE2bNo2jWAAAoFo88l2EFzNs2DB98skn1X79%2BUCUkZHh0p6dna1bbrlFgYGBzraYmBhlZWU5%2B2NjY519AQEB6tKli7KyslRRUaHdu3e79Hfv3l3nzp3T/v37a7ppAACggfHIRe4X869//euqbjQ6duzYi7bbbDaFhYW5tLVo0cJ5dOxy/cXFxSorK3Ppt1qtCg4OvqqjawAAoGG7Ji5yP336tL7//vtLhqarUVJSIj8/P5c2Pz8/2e32K/aXlpY6n1/q/dWRn58vm83m0ma1BlYJdrXl6%2Bvj8hM1Qx3dz2ql1hfDXDSDOppBHWvH7QGrdevWLt9DKEnXXXed7r//fv3%2B97%2Bv9fL9/f1VVFTk0ma329WoUSNn/4VhyW63KygoSP7%2B/s7nF/ZfzfVXGRkZSk9Pd2lLTExUUlJStZdxNYKCuDbMBOroPiEhjT09hGsac9EM6mgGdawZtwes559/vk6XHx4erpycHJe2goIC59Gj8PBwFRQUVOnv3LmzgoOD5e/vr4KCAnXo0EGSVF5erqKiIoWGhlZ7DAkJCYqPj3dps1oDVVh4piabdEm%2Bvj4KCgpQcXGJKiqqfr8jqoc6up/pfcFbMBfNoI5meEsdPfUHndsD1o4dO6r92ttuu%2B2qlx8VFaVly5aptLTUedQqMzNTMTExzv7MzEzn60tKSrRv3z5NmTJFPj4%2B6tq1qzIzM5331MrKypLValVkZGS1xxAWFlbldKDNdkrl5XUzQSsqKuts2Q0JdXQf6nx5zEUzqKMZ1LFm3B6wHnjgAecpwv%2B8xcKFbRaLRd99991VL79nz55q1aqVZsyYocmTJ2vz5s3atWuX8/YQo0aN0ooVK7Rs2TLFxcVpyZIlatu2rTNQjR07VrNnz1bHjh0VFhamOXPm6N577%2BUWDQAAoNrcHrBeffVVzZs3T9OmTVPPnj3l5%2Ben3bt3a%2B7cuRoxYoTuvvvuWi3f19dXr7zyilJSUjRy5EjdcMMNWrJkiVq3bi1Jatu2rV5%2B%2BWU999xzWrJkiaKjo7VkyRJnwBsyZIiOHDmi2bNny263684779S0adNqvd0AAKDhsDguvFNnHbvrrruUkpKi22%2B/3aX9n//8p6ZPn67/%2B7//c%2Bdw3MZmO2V8mVarj0JCGquw8AyHb2vBW%2Bo4eNHXnh5CtX3yWB9PD%2BGa5C1z0dOooxneUsfQ0KYeWa/bP3uZn5%2BvNm3aVGlv0qSJCgsL3T0cAAAA49wesLp3764XX3xRp0%2BfdrYVFRUpLS1Nv/nNb9w9HAAAAOPcfg3W008/rXHjxun2229X%2B/bt5XA4dOjQIYWGhmrVqlXuHg4AAIBxbg9YHTp00Mcff6wNGzYoNzdXknTfffdpyJAhfFIPAAB4BY98F2GzZs00evRoHT58WO3atZP0693cAQAAvIHbr8FyOBxauHChbrvtNg0dOlTHjx/Xk08%2BqZSUFJ07d87dwwEAADDO7QHr7bff1vr16/WnP/3J%2BaXKAwYM0KZNm6p8fx8AAEB95PaAlZGRodmzZ2vkyJHOm3vefffdmjdvnj766CN3DwcAAMA4twesw4cPq3PnzlXaIyMjZbPZ3D0cAAAA49wesNq0aaPdu3dXaf/yyy%2BdF7wDAADUZ27/FOGkSZP0zDPPyGazyeFw6Ntvv1VGRobefvttPfXUU%2B4eDgAAgHFuD1ijRo1SeXm5li5dqtLSUs2ePVvNmzfXY489pjFjxrh7OAAAAMa5PWBt2LBBgwYNUkJCgk6ePCmHw6EWLVq4exgAAAB1xu3XYM2dO9d5MXvz5s0JVwAAwOu4PWC1b99eP/zwg7tXCwAA4DZuP0UYGRmpJ554Qq%2B//rrat28vf39/l/7U1FR3DwkAAMAotwesH3/8UTExMZLEfa8AAIBXckvAWrBggaZMmaLAwEC9/fbb7lglAACAx7jlGqw333xTJSUlLm0PP/yw8vPz3bF6AAAAt3JLwHI4HFXaduzYobKyMnesHgAAwK3c/ilCAAAAb0fAAgAAMMxtActisbhrVQAAAB7ltts0zJs3z%2BWeV%2BfOnVNaWpoaN27s8jrugwUAAOo7twSs2267rco9r6Kjo1VYWKjCwkLdP/rUAAAY%2B0lEQVR3DAEAAMBt3BKwuPcVAABoSLjIHQAAwDCvDFgffPCBOnXqVOURGRkpSXr00Uer9G3evNn5/rfeekv9%2BvVTdHS0Zs6cWeUmqQAAAJfj9u8idIe7775b/fr1cz4vLy/X%2BPHj1b9/f0lSbm6u0tLS9Jvf/Mb5mmbNmkmSNm7cqPT0dKWlpalFixaaMWOG0tLSNHv2bLduAwAAqL%2B88ghWo0aNFBoa6nz87W9/k8Ph0BNPPCG73a7Dhw%2Bra9euLq/x8/OTJK1atUrjx49XXFycunXrpmeeeUbr1q3jKBYAAKg2rwxY/6moqEjLly9XcnKy/Pz8dPDgQVksFrVr167KaysqKrR7927FxsY627p3765z585p//797hw2AACox7zyFOF/eu%2B99xQWFqZBgwZJkg4ePKgmTZpo%2BvTp2r59u66//npNnTpVd9xxh4qLi1VWVqawsDDn%2B61Wq4KDg3X8%2BPFqrzM/P7/KbSms1kCX5Zrg6%2Bvj8hM1Qx3dz2ql1hfDXDSDOppBHWvHqwOWw%2BHQmjVr9NBDDznbDh48qNLSUvXt21cPP/ywPvvsMz366KPKyMhQy5YtJcl5uvA8Pz8/2e32aq83IyND6enpLm2JiYlKSkqqxdZcWlBQQJ0st6Ghju4TEtL4yi9qwJiLZlBHM6hjzXh1wNq9e7dOnDihIUOGONsmT56sBx54wHlRe2RkpPbu3au//OUv%2Bp//%2BR9JqhKm7Ha7AgKqP8ESEhIUHx/v0ma1Bqqw8ExNN%2BWifH19FBQUoOLiElVUVBpddkNCHd3P9L7gLZiLZlBHM7yljp76g86rA9ZXX32l2NhYZ5iSJB8fH5fnkhQREaGcnBwFBwfL399fBQUF6tChg6RfP4FYVFSk0NDQaq83LCysyulAm%2B2UysvrZoJWVFTW2bIbEuroPtT58piLZlBHM6hjzXj1idVdu3apR48eLm1PPfWUZsyY4dK2f/9%2BRUREyMfHR127dlVmZqazLysrS1ar1XkPLQAAgCvx6oB14MAB3XTTTS5t8fHx%2Buijj/Thhx/q3//%2Bt9LT05WZman7779fkjR27FitWLFCmzZt0q5duzRnzhzde%2B%2B9V3WKEAAANGxefYqwoKBAQUFBLm133nmn/vSnP2np0qU6evSobr75Zr3%2B%2Butq27atJGnIkCE6cuSIZs%2BeLbvdrjvvvFPTpk3zxPABAEA9ZXE4HA5PD6IhsNlOGV%2Bm1eqjkJDGKiw8w/nxWvCWOg5e9LWnh1BtnzzWx9NDuCZ5y1z0NOpohrfUMTS0qUfW69WnCAEAADyBgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOsnh4AgIZn8KKvPT2Eq/LJY308PQQA9QxHsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY1waszz77TJ06dXJ5JCUlSZL27dun0aNHKyoqSqNGjdKePXtc3rthwwYNGDBAUVFRSkxM1MmTJz2xCQAAoJ7y2oCVk5OjuLg4bdmyxfmYN2%2Bezp49q4cfflixsbH64IMPFB0drUceeURnz56VJO3atUspKSmaMmWKMjIyVFxcrBkzZnh4awAAQH3itQErNzdXHTt2VGhoqPMRFBSkjz/%2BWP7%2B/po%2Bfbo6dOiglJQUNW7cWJ9%2B%2BqkkafXq1Ro8eLCGDx%2BuyMhILViwQF988YXy8vI8vEUAAKC%2B8OqA1b59%2Byrt2dnZiomJkcVikSRZLBb16NFDWVlZzv7Y2Fjn61u1aqXWrVsrOzvbLeMGAAD1n1cGLIfDoR9//FFbtmzRXXfdpQEDBmjhwoWy2%2B2y2WwKCwtzeX2LFi10/PhxSVJ%2Bfv5l%2BwEAAK7EK%2B/kfvToUZWUlMjPz0%2BLFi3S4cOHNW/ePJWWljrb/5Ofn5/sdrskqbS09LL91ZGfny%2BbzebSZrUGVgluteXr6%2BPyEzVDHXElVqt75gZz0QzqaAZ1rB2vDFht2rTRtm3b1KxZM1ksFnXu3FmVlZWaNm2aevbsWSUs2e12NWrUSJLk7%2B9/0f6AgIBqrz8jI0Pp6ekubYmJic5PMZoWFFT9seHSqCMuJSSksVvXx1w0gzqaQR1rxisDliQFBwe7PO/QoYPKysoUGhqqgoICl76CggLn0aXw8PCL9oeGhlZ73QkJCYqPj3dps1oDVVh45mo24Yp8fX0UFBSg4uISVVRUGl12Q0IdcSWm991LYS6aQR3N8JY6uvsPpPO8MmB99dVXeuKJJ/SPf/zDeeTpu%2B%2B%2BU3BwsGJiYrR8%2BXI5HA5ZLBY5HA7t3LlTf/zjHyVJUVFRyszM1MiRIyVJx44d07FjxxQVFVXt9YeFhVU5HWiznVJ5ed1M0IqKyjpbdkNCHXEp7p4XzEUzqKMZ1LFmvPLEanR0tPz9/fX000/r4MGD%2BuKLL7RgwQI99NBDGjRokIqLizV//nzl5ORo/vz5Kikp0eDBgyVJY8aM0fr167VmzRrt379f06dPV//%2B/dWuXTsPbxUAAKgvvDJgNWnSRCtWrNDJkyc1atQopaSkKCEhQQ899JCaNGmi1157zXmUKjs7W8uWLVNgYKCkX8PZ3LlztWTJEo0ZM0bNmjVTamqqh7cIAADUJxaHw%2BHw9CAaApvtlPFlWq0%2BCglprMLCMxy%2BrQVvqePgRV97eghe65PH%2BrhlPd4yFz2NOprhLXUMDW3qkfV65REsAAAATyJgAQAAGEbAAgAAMIyABQAAYJhX3gcLMIGLxgEANcURLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjXBqwTJ04oKSlJPXv2VL9%2B/ZSamqqysjJJ0rx589SpUyeXx%2BrVq53v3bBhgwYMGKCoqCglJibq5MmTntoMAABQD1k9PYC64HA4lJSUpKCgIL3zzjv65ZdfNHPmTPn4%2BOjJJ59Ubm6ukpOTNWLECOd7mjRpIknatWuXUlJS9MwzzygyMlLz58/XjBkz9Nprr3lqcwAAQD3jlUewDh48qKysLKWmpurmm29WbGyskpKStGHDBklSbm6ubrnlFoWGhjofAQEBkqTVq1dr8ODBGj58uCIjI7VgwQJ98cUXysvL8%2BQmAQCAesQrA1ZoaKhef/11tWzZ0qX99OnTOn36tE6cOKH27dtf9L3Z2dmKjY11Pm/VqpVat26t7OzsuhwyAADwIl55ijAoKEj9%2BvVzPq%2BsrNTq1avVu3dv5ebmymKx6NVXX9WXX36p4OBgPfjgg87Thfn5%2BQoLC3NZXosWLXT8%2BPFqrz8/P182m82lzWoNrLLc2vL19XH5iZqhjrgSq9U9c4O5aAZ1NIM61o5XBqwLpaWlad%2B%2BfVq7dq327t0ri8WiiIgI3X///dqxY4dmzZqlJk2aaODAgSotLZWfn5/L%2B/38/GS326u9voyMDKWnp7u0JSYmKikpycj2XCgoKKBOltvQUEdcSkhIY7euj7loBnU0gzrWjNcHrLS0NK1cuVJ//vOf1bFjR918882Ki4tTcHCwJCkyMlKHDh3Se%2B%2B9p4EDB8rf379KmLLb7c5rtKojISFB8fHxLm1Wa6AKC8/UfoP%2Bg6%2Bvj4KCAlRcXKKKikqjy25IqCOuxPS%2BeynMRTOooxneUkd3/4F0nlcHrGeffVbvvfee0tLSdNddd0mSLBaLM1ydFxERoa1bt0qSwsPDVVBQ4NJfUFCg0NDQaq83LCysyulAm%2B2UysvrZoJWVFTW2bIbEuqIS3H3vGAumkEdzaCONeO1J1bT09P1/vvv68UXX9SQIUOc7YsXL9aECRNcXrt//35FRERIkqKiopSZmensO3bsmI4dO6aoqCi3jBsAANR/XhmwcnNz9corr%2BgPf/iDYmJiZLPZnI%2B4uDjt2LFDK1as0E8//aR3331XH374oSZOnChJGjNmjNavX681a9Zo//79mj59uvr376927dp5eKsAAEB94ZWnCD///HNVVFRo6dKlWrp0qUvf999/r8WLF%2Bull17S4sWL1aZNG73wwguKjo6WJEVHR2vu3Ll66aWX9Msvv6hPnz569tlnPbEZAACgnrI4HA6HpwfRENhsp4wv02r1UUhIYxUWnuH8eC1cqo6DF33twVHhWvLJY33csh72aTOooxneUsfQ0KYeWa9XniIEAADwJAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYVZPDwAArnWDF33t6SFclU8e6%2BPpIQANHkewLqKsrEwzZ85UbGys%2BvbtqzfeeMPTQwIAAPUIR7AuYsGCBdqzZ49Wrlypo0eP6sknn1Tr1q01aNAgTw8NAADUAwSsC5w9e1Zr1qzR8uXL1aVLF3Xp0kUHDhzQO%2B%2B8Q8ACAADVQsC6wP79%2B1VeXq7o6GhnW0xMjF599VVVVlbKx4ezqgCubfXpmjGuF4O3ImBdwGazKSQkRH5%2Bfs62li1bqqysTEVFRWrevPkVl5Gfny%2BbzebSZrUGKiwszOhYfX19FJvyqdFlAoA71acwKEmfPdHP00NwG19fH5efuDoErAuUlJS4hCtJzud2u71ay8jIyFB6erpL25QpUzR16lQzg/x/8vPzNf76A0pISDAe3hqS/Px8ZWRkUMdaoo61Rw3NoI5m5Ofna%2BXK16ljDRFLL%2BDv718lSJ1/3qhRo2otIyEhQR988IHLIyEhwfhYbTab0tPTqxwtw9WhjmZQx9qjhmZQRzOoY%2B1wBOsC4eHhKiwsVHl5uazWX8tjs9nUqFEjBQUFVWsZYWFhpH0AABowjmBdoHPnzrJarcrKynK2ZWZmqmvXrlzgDgAAqoXEcIGAgAANHz5cc%2BbM0a5du7Rp0ya98cYbGjdunKeHBgAA6gnfOXPmzPH0IK41vXv31r59%2B/TCCy/o22%2B/1R//%2BEeNGjXK08O6qMaNG6tnz55q3Lixp4dSr1FHM6hj7VFDM6ijGdSx5iwOh8Ph6UEAAAB4E04RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYNUjDodDEydO1AcffODSXlhYqKlTpyo6Olrx8fFav369S/%2B%2Bffs0evRoRUVFadSoUdqzZ487h33N2rdvnzp16uTyGDlypLM/Ly9PEyZMUPfu3XX33Xdry5YtHhzttausrEwzZ85UbGys%2BvbtqzfeeMPTQ6oXPvvssyrzLykpSRL7bHXY7XYNHTpU27Ztc7ZdaZ/95ptvNHToUEVFRWncuHHKy8tz97CvORer47x586rMzdWrVzv7N2zYoAEDBigqKkqJiYk6efKkJ4Z%2BzSNg1ROVlZWaN2%2Bevv766yp9M2bM0KlTp5SRkaFHH31UTz/9tHbt2iVJOnv2rB5%2B%2BGHFxsbqgw8%2BUHR0tB555BGdPXvW3ZtwzcnJyVHnzp21ZcsW52PFihWSfg2ziYmJatmypdatW6dhw4ZpypQpOnr0qIdHfe1ZsGCB9uzZo5UrV%2BpPf/qT0tPT9emnn3p6WNe8nJwcxcXFucy/efPmsc9WQ1lZmR5//HEdOHDA2Xalffbo0aNKTEzUyJEjtXbtWjVv3lyTJ09WQ/4yk4vVUZJyc3OVnJzsMjfPf13crl27lJKSoilTpigjI0PFxcWaMWOGJ4Z/zbN6egC4shMnTuiJJ57Q4cOHFRQU5NL3008/afPmzfr888/Vtm1bdezYUVlZWXr33XfVrVs3ffzxx/L399f06dNlsViUkpKiL7/8Up9%2B%2BqnL0ZqGKDc3Vx06dFBoaGiVvq1btyovL0/vv/%2B%2BAgMD1aFDB3377bdat26dpk6d6oHRXpvOnj2rNWvWaPny5erSpYu6dOmiAwcO6J133tGgQYM8PbxrWm5urjp27Fhl/q1du5Z99jJycnKUnJxcJRhdaZ9ds2aNbr31Vk2cOFGSlJqaqj59%2Bmj79u3q1auXJzbFoy5VR%2BnXuTlp0qSL/m5cvXq1Bg8erOHDh0v69Q%2BsuLg45eXlqV27dnU%2B7vqEI1j1wN69e9WqVSutW7dOTZs2denLzs5Wq1at1LZtW2dbTEyM/vWvfzn7Y2JiZLFYJEkWi0U9evRQVlaW%2BzbgGpWbm6v27dtftC87O1u33HKLAgMDnW0xMTHU7QL79%2B9XeXm5oqOjnW0xMTHKzs5WZWWlB0d27bvU/GOfvbzzgSgjI8Ol/Ur7bHZ2tmJjY519AQEB6tKlS4Ot66XqePr0aZ04ceKyvxv/s46tWrVS69atlZ2dXZfDrZc4glUPxMfHKz4%2B/qJ9NptNYWFhLm0tWrTQiRMnnP033XRTlf4LDwk3RLm5uaqsrNTvfvc7nTp1SrfffrumT5%2BuJk2aXLKux48f99Bor002m00hISHy8/NztrVs2VJlZWUqKipS8%2BbNPTi6a5fD4dCPP/6oLVu26LXXXlNFRYUGDRqkpKQk9tkrGDt27EXbr7TPsk%2B7ulQdc3NzZbFY9Oqrr%2BrLL79UcHCwHnzwQY0YMUKSlJ%2BfTx2riYB1DSgtLXUGoguFhoa6/EV2oZKSEpf/3CTJz89Pdru9Wv3e7HJ1bd68ufLy8tS2bVs999xzKi4uVmpqqqZNm6alS5c26LpdjUvVSRK1uoyjR486a7do0SIdPnxY8%2BbNU2lpKXOvhvhdaMbBgwdlsVgUERGh%2B%2B%2B/Xzt27NCsWbPUpEkTDRw4UKWlpdSxmghY14Ds7GyNGzfuon1LlizRgAEDLvlef3//KhPbbrerUaNG1er3Zleq69atW%2BXv76/rrrtOkvT8889r1KhROnHihPz9/VVUVOTynoZSt6txqfkliVpdRps2bbRt2zY1a9ZMFotFnTt3VmVlpaZNm6aePXs22H22Nq60z15qrl54XWtDN3z4cMXFxSk4OFiSFBkZqUOHDum9997TwIEDL1nHgIAATwz3mkbAugb06tVL33//fY3eGx4eroKCApe2goIC58WJl%2Bq/8BCvN7raunbo0EHSrx8qCA8PV05Ojkt/Q6nb1QgPD1dhYaHKy8tltf7668Rms6lRo0b8x3UF5/8DO69Dhw4qKytTaGhog91na%2BNK%2B%2Bylfhd27tzZbWOsDywWS5W5GRERoa1bt0q68v85%2BP9xkXs91717dx05csTl/HdmZqa6d%2B8uSYqKitK//vUv5ydFHA6Hdu7cqaioKI%2BM91qRk5Oj6Ohol/vgfPfdd7JarbrhhhsUFRWlvXv3qrS01NmfmZnZ4Ot2oc6dO8tqtbpcKJyZmamuXbvKx4dfL5fy1VdfqVevXiopKXG2fffddwoODnZ%2BSIV99upcaZ%2BNiopSZmams6%2BkpET79u2jrhdYvHixJkyY4NK2f/9%2BRURESKpax2PHjunYsWPU8SL4DVjPtWvXTn379tW0adO0f/9%2BrVmzRhs2bNB9990nSRo0aJCKi4s1f/585eTkaP78%2BSopKdHgwYM9PHLPioiI0A033KBZs2bphx9%2B0D//%2BU/NmjVLo0ePVrNmzdSzZ0%2B1atVKM2bM0IEDB7Rs2TLt2rVL99xzj6eHfk0JCAjQ8OHDNWfOHO3atUubNm3SG2%2B8cclTs/hVdHS0/P399fTTT%2BvgwYP64osvtGDBAj300EPsszV0pX121KhR2rlzp5YtW6YDBw5oxowZatu2bYO8RcPlxMXFaceOHVqxYoV%2B%2Buknvfvuu/rwww%2Bdt7cYM2aM1q9frzVr1mj//v2aPn26%2Bvfvzy0aLsaBeiUuLs6xbt06l7aCggLHI4884ujatasjPj7e8dFHH7n0Z2dnO4YPH%2B7o2rWr45577nHs3bvXnUO%2BZh09etSRmJjoiI2NdfTs2dPx7LPPOsrKypz9hw4dctx3332OW2%2B91TFkyBDH119/7cHRXrvOnj3rmD59uqN79%2B6Ovn37Ot58801PD6le%2BOGHHxwTJkxwdO/e3dGnTx/Hyy%2B/7KisrHQ4HOyz1dWxY0fH1q1bnc%2BvtM/%2B4x//cNx5552Obt26OcaPH%2B/46aef3D3ka9KFdfzss88cv/vd7xxdu3Z1DBo0yLFx40aX169bt85xxx13OLp37%2B5ITEx0nDx50t1DrhcsDkcDvo0tAABAHeAUIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9v8BskXHrfHdG9sAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1607543783047924978”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.682493475205764</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.29771772747727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-13.57801767815846</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.387415467318316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.073673528673588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.130195221490796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.691265469605406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.770588291605267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.072969571232555</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3040)</td> <td class=”number”>3040</td> <td class=”number”>94.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1607543783047924978”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-82.71216451839076</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.52902111790639</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-57.204265324055385</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-56.604511109806644</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.02247160480412</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.11795059490913</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>102.32075078491461</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113.7509175434304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>166.49403029539158</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_PC_WIC_REDEMP_08_12”>PCH_PC_WIC_REDEMP_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1938</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>39.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1273</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-7.956</td>

</tr> <tr>

<th>Minimum</th> <td>-99.498</td>

</tr> <tr>

<th>Maximum</th> <td>249.95</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7531946681391505552”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7531946681391505552,#minihistogram-7531946681391505552”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7531946681391505552”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7531946681391505552”

aria-controls=”quantiles-7531946681391505552” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7531946681391505552” aria-controls=”histogram-7531946681391505552”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7531946681391505552” aria-controls=”common-7531946681391505552”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7531946681391505552” aria-controls=”extreme-7531946681391505552”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7531946681391505552”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-99.498</td>

</tr> <tr>

<th>5-th percentile</th> <td>-28.396</td>

</tr> <tr>

<th>Q1</th> <td>-17.196</td>

</tr> <tr>

<th>Median</th> <td>-8.9853</td>

</tr> <tr>

<th>Q3</th> <td>-0.4449</td>

</tr> <tr>

<th>95-th percentile</th> <td>14.959</td>

</tr> <tr>

<th>Maximum</th> <td>249.95</td>

</tr> <tr>

<th>Range</th> <td>349.45</td>

</tr> <tr>

<th>Interquartile range</th> <td>16.751</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>15.969</td>

</tr> <tr>

<th>Coef of variation</th> <td>-2.0072</td>

</tr> <tr>

<th>Kurtosis</th> <td>39.096</td>

</tr> <tr>

<th>Mean</th> <td>-7.956</td>

</tr> <tr>

<th>MAD</th> <td>10.761</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.9396</td>

</tr> <tr>

<th>Sum</th> <td>-15411</td>

</tr> <tr>

<th>Variance</th> <td>255</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7531946681391505552”>

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nG/0NBQuVwuVVdXu283Nt9UeXl5ysnJ8RjLzMxUVlbW%2BcQ4o8jIcNO36S%2BsnL0xbdq09PUSmpWV97dVs1s1t2Td7IGUO2AL1okTJ3T77bfryy%2B/1H//938rPPyHnRYWFtagLLlcLkVGRiosLMx9%2B9T5Hx/fFBkZGUpNTfUYs9kiVF5%2B4nyiNCokJFiRkeGqrKxSXV29adv1B1bOfiZmvr8uJFbe31bNbtXcknWzN2duX33zGZAF6/jx4/rjH/%2Bor776Sk8//bS6dOninouJiVFZWZnH/cvKytSjRw%2B1bt1aYWFhKisrU9euXSVJtbW1qqioUFRUVJOfPzo6usHpQKfzmGprzf9iqaurb5bt%2BgMrZ29MoL8WVt7fVs1u1dySdbMHUu7AOdn5f%2Brr6zVt2jR9/fXXWrduna688kqPebvdrvz8fPftqqoq7du3T3a7XcHBwYqLi/OY3717t2w2m7p37%2B61DAAAwL8FXMF6/vnntWPHDi1atEiRkZFyOp1yOp2qqKiQJI0aNUq7du3SqlWrVFBQoNmzZ6tTp07q27evpB8%2BPP/kk0/q9ddf1549ezR//nyNHTv2nE4RAgAAawu4U4Svvvqq6uvrNXXqVI/x5ORkrVu3Tp06ddKjjz6qv/zlL8rNzVVCQoJyc3MVFBQkSRo2bJgOHz6sefPmyeVy6Te/%2BY3uvvtuX0QBAAB%2BKsj48RLmaFZO5zFTt2ezBatNm5YqLz8RMOerm8pb2Yc88m6zbbs5vHxHf18voVnwXrdedqvmlqybvTlzR0VdbOr2mirgThECAAD4GgULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwmd8XLJfLpbS0NO3YscM9VlRUpEmTJik%2BPl5Dhw7VO%2B%2B84/GY7du3Ky0tTXa7XRMmTFBRUZHH/FNPPaWUlBQlJCRozpw5qqqq8koWAAAQGPy6YNXU1GjGjBkqKChwjxmGoczMTLVv316bNm3SiBEjNG3aNB05ckSSdOTIEWVmZio9PV3PP/%2B82rZtq9tvv12GYUiSXn31VeXk5GjBggV6%2Bumn5XA4tHTpUp/kAwAA/snrBWvMmDHasGGDjh079rO2U1hYqLFjx%2Bqrr77yGH///fdVVFSkBQsWqGvXrpo6dari4%2BO1adMmSdLGjRvVq1cvTZ48WVdeeaWys7N1%2BPBh7dy5U5K0du1aTZw4UQMHDlTv3r11//33a9OmTRzFAgAATeb1gtWvXz%2BtWLFCAwYM0IwZM/TOO%2B%2B4jx6di507d6pv377Ky8vzGHc4HLrqqqsUERHhHktMTNTu3bvd80lJSe658PBw9ezZU7t371ZdXZ0%2B/vhjj/n4%2BHh9//332r9//zmvEQAAWJPN20945513asaMGdq%2Bfbv%2B8Y9/aPr06YqMjNTIkSM1cuRIXXbZZU3azo033tjouNPpVHR0tMdYu3btVFJSctb5yspK1dTUeMzbbDa1bt3a/XgAAICz8XrBkqSgoCD1799f/fv3V1VVldatW6fHHntMq1atUp8%2BfTRx4kT95je/Oa9tV1VVKTQ01GMsNDRULpfrrPPV1dXu26d7fFOUlpbK6XR6jNlsEQ2K3c8REhLs8beVWDn7mdhsgfl6WHl/WzW7VXNL1s0eiLl9UrCkH0rICy%2B8oBdeeEEHDhxQnz599Lvf/U4lJSX685//rA8%2B%2BEBz58495%2B2GhYWpoqLCY8zlcqlFixbu%2BVPLksvlUmRkpMLCwty3T50PDw9v8hry8vKUk5PjMZaZmamsrKwmb6OpIiObvq5AY%2BXsjWnTpqWvl9CsrLy/rZrdqrkl62YPpNxeL1hbt27V1q1btWPHDrVt21YjR47U8uXL1aVLF/d9OnTooMWLF59XwYqJiVFhYaHHWFlZmfvoUUxMjMrKyhrM9%2BjRQ61bt1ZYWJjKysrUtWtXSVJtba0qKioUFRXV5DVkZGQoNTXVY8xmi1B5%2BYlzznM6ISHBiowMV2Vllerq6k3brj%2BwcvYzMfP9dSGx8v62anar5pasm705c/vqm0%2BvF6y5c%2Bdq4MCBys3N1bXXXqvg4IaHAy%2B//HLddNNN57V9u92uVatWqbq62n3UKj8/X4mJie75/Px89/2rqqq0b98%2BTZs2TcHBwYqLi1N%2Bfr769u0rSdq9e7dsNpu6d%2B/e5DVER0c3OB3odB5Tba35Xyx1dfXNsl1/YOXsjQn018LK%2B9uq2a2aW7Ju9kDK7fWC9dZbb6lNmzaqqKhwl6s9e/aoZ8%2BeCgkJkST16dNHffr0Oa/tJycnq0OHDpo9e7Zuv/12/e///q/27Nmj7OxsSdKoUaP05JNPatWqVe6i16lTJ3ehuvHGGzVv3jx169ZN0dHRmj9/vsaOHXtOpwgBAIC1ef3TZMePH9fgwYP1xBNPuMemTJmiESNGqLi4%2BGdvPyQkRI899picTqfS09P1wgsvKDc3V7GxsZKkTp066dFHH9WmTZs0evRoVVRUKDc3V0FBQZKkYcOGaerUqZo3b54mT56s3r176%2B677/7Z6wIAANYRZJzPRah%2Bhttuu0319fVavHix%2B3NNR48e1axZsxQeHq7ly5d7czle43T%2BvAurnspmC1abNi1VXn4iYA6nNpW3sg955N1m23ZzePmO/r5eQrPgvW697FbNLVk3e3Pmjoq62NTtNZXXj2B9%2BOGHuueeezw%2BNN62bVvNnDlT77//vreXAwAAYDqvFyybzabKysoG41VVVed1RXcAAIALjdcL1rXXXqtFixZ5/A7BoqIiZWdnKyUlxdvLAQAAMJ3Xf4pw1qxZuvnmm3XDDTcoMjJSklRZWamePXtq9uzZ3l4OAACA6bxesNq1a6ctW7Zo%2B/btKigokM1m0xVXXKFrrrnG/ZN8AAAA/swnvyonJCREKSkpnBIEAAAByesFy%2Bl06pFHHtGuXbv0/fffN/hg%2B7///W9vLwkAAMBUXi9Y9957r/bu3athw4bp4ot9c20KAACA5uT1gvX%2B%2B%2B9r9erVSkpK8vZTAwAAeIXXL9MQERGhdu3aeftpAQAAvMbrBWvEiBFavXq16urqvP3UAAAAXuH1U4QVFRXatm2b3njjDXXu3FmhoaEe82vXrvX2kgAAAEzlk8s0pKWl%2BeJpAQAAvMLrBSs7O9vbTwkAAOBVXv8MliSVlpYqJydHd955p7799lu98sor%2Bvzzz32xFAAAANN5vWAdOnRIw4cP15YtW/Tqq6/q5MmTeumllzRq1Cg5HA5vLwcAAMB0Xi9YDzzwgAYNGqTXX39dF110kSTpoYceUmpqqpYtW%2Bbt5QAAAJjO6wVr165duvnmmz1%2BsbPNZtPtt9%2Buffv2eXs5AAAApvN6waqvr1d9fX2D8RMnTigkJMTbywEAADCd1wvWgAEDtHLlSo%2BSVVFRoaVLl6pfv37eXg4AAIDpvF6w7rnnHu3du1cDBgxQTU2N/vSnP2ngwIH6%2BuuvNWvWLG8vBwAAwHRevw5WTEyM/vGPf2jbtm369NNPVV9fr3HjxmnEiBFq1aqVt5cDAABgOp9cyT08PFxjxozxxVMDAAA0O68XrAkTJpxxnt9FCAAA/J3XC1bHjh09btfW1urQoUM6cOCAJk6c6O3lAAAAmO6C%2BV2Eubm5Kikp8fJqAAAAzOeT30XYmBEjRujll182bXvFxcWaOnWq%2BvTpo9TUVD311FPuuX379mnMmDGy2%2B0aNWqU9u7d6/HYbdu2adCgQbLb7crMzNTRo0dNWxcAAAh8F0zB%2Buijj0y90Ogdd9yhiIgIbd68WXPmzNEjjzyi1157TSdPntSUKVOUlJSkzZs3KyEhQVOnTtXJkyclSXv27NHcuXM1bdo05eXlqbKyUrNnzzZtXQAAIPBdEB9yP378uD777DPdeOONpjzHd999p927d2vhwoXq0qWLunTpopSUFL333nv67rvvFBYWppkzZyooKEhz587VW2%2B9pVdeeUXp6elav369hgwZopEjR0qSlixZooEDB6qoqEidO3c2ZX0AACCwef0IVmxsrDp27Ojxp1evXlq4cKFpFxpt0aKFwsPDtXnzZn3//ff6/PPPtWvXLvXo0UMOh0OJiYnu34UYFBSkPn36aPfu3ZIkh8OhpKQk97Y6dOig2NhYORwOU9YGAAACn9ePYD3wwAPN/hxhYWGaN2%2BeFi5cqLVr16qurk7p6ekaM2aM/v3vf%2BuKK67wuH%2B7du1UUFAgSSotLVV0dHSDeT6ADwAAmsrrBeuDDz5o8n2vvvrq836egwcPauDAgbr55ptVUFCghQsX6pprrlFVVZVCQ0M97hsaGiqXyyVJqq6uPuN8U5SWlsrpdHqM2WwRDYrbzxESEuzxt5VYOfuZ2GyB%2BXpYeX9bNbtVc0vWzR6Iub1esMaPH%2B8%2BPWcYhnv81LGgoCB9%2Bumn5/Uc7733np5//nm9%2BeabatGiheLi4vTNN9/o8ccfV%2BfOnRuUJZfLpRYtWkj64ehXY/Ph4eFNfv68vDzl5OR4jGVmZiorK%2Bu88pxJZGTT1xVorJy9MW3atPT1EpqVlfe3VbNbNbdk3eyBlNvrBWvFihVatGiR7r77biUnJys0NFQff/yxFixYoN/97ncaOnToz36OvXv36tJLL3WXJkm66qqrtGLFCiUlJamsrMzj/mVlZe6jSzExMY3OR0VFNfn5MzIylJqa6jFms0WovPzEuUY5rZCQYEVGhquyskp1dfWmbdcfWDn7mZj5/rqQWHl/WzW7VXNL1s3enLl99c2nTy40Om/ePF177bXusX79%2BmnBggWaOXOmbr311p/9HNHR0Tp06JBcLpf7dN/nn3%2BuTp06yW6364knnpBhGAoKCpJhGNq1a5duu%2B02SZLdbld%2Bfr7S09Ml/XA9reLiYtnt9nN6/lNPBzqdx1Rba/4XS11dfbNs1x9YOXtjAv21sPL%2Btmp2q%2BaWrJs9kHJ7/WRnaWlpg1%2BXI0mtWrVSeXm5Kc%2BRmpqqiy66SH/%2B85/1xRdf6H/%2B53%2B0YsUKjR8/XoMHD1ZlZaUWL16swsJCLV68WFVVVRoyZIgkady4cdq6das2btyo/fv3a%2BbMmbruuuu4RAMAAGgyrxes%2BPh4PfTQQzp%2B/Lh7rKKiQkuXLtU111xjynNcfPHFeuqpp%2BR0OjV69GhlZ2frT3/6kzIyMtSqVSutXLnSfZTK4XBo1apVioiIkCQlJCRowYIFys3N1bhx43TJJZec9tf7AAAANCbI%2BOknzb3g4MGDmjBhgqqqqtSlSxcZhqEvv/xSUVFRWrt2rX7xi194czle43QeM3V7Nluw2rRpqfLyEwFzOLWpvJV9yCPvNtu2m8PLd/T39RKaBe9162W3am7JutmbM3dU1MWmbq%2BpvP4ZrK5du%2Bqll17Stm3bdPDgQUnSH/7wBw0bNuycflIPAADgQuX1giVJl1xyicaMGaOvv/7a/dmmiy66yBdLAQAAMJ3XP4NlGIaWLVumq6%2B%2BWmlpaSopKdGsWbM0d%2B5cff/9995eDgAAgOm8XrDWrVunrVu36r777nNfQmHQoEF6/fXXG1ycEwAAwB95vWDl5eVp3rx5Sk9Pd1%2B9fejQoVq0aJFefPFFby8HAADAdF4vWF9//bV69OjRYLx79%2B4Nfn8fAACAP/J6werYsaM%2B/vjjBuNvvfUWF/MEAAABwes/RXjLLbfo/vvvl9PplGEYeu%2B995SXl6d169bpnnvu8fZyAAAATOf1gjVq1CjV1tbq8ccfV3V1tebNm6e2bdvqjjvu0Lhx47y9HAAAANN5vWBt27ZNgwcPVkZGho4ePSrDMNSuXTtvLwMAAKDZeP0zWAsWLHB/mL1t27aUKwAAEHC8XrC6dOmiAwcOePtpAQAAvMbrpwi7d%2B%2Buu%2B66S6tXr1aXLl0UFhbmMZ%2Bdne3tJQEAAJjK6wXriy%2B%2BUGJioiRx3SsAABCQvFKwlixZomnTpikiIkLr1q3zxlMCAAD4jFc%2Bg7VmzRpVVVV5jE2ZMkWlpaXeeHoAAACv8krBMgyjwdgHH3ygmpoabzw9AACAV3n9pwgBAAACHQULAADAZF4rWEFBQd56KgAAAJ/y2mUaFi1a5HHNq%2B%2B//15Lly5Vy5YtPe7HdbAAAIC/80rBuvrqqxtc8yohIUHl5eUqLy/3xhIAAAC8xisFi2tfAQAAK%2BFD7gC%2B8DS4AAAZ10lEQVQAACajYAEAAJiMggUAAGCygC1YLpdL999/v66%2B%2Bmr96le/0kMPPeS%2Bovy%2Bffs0ZswY2e12jRo1Snv37vV47LZt2zRo0CDZ7XZlZmbq6NGjvogAAAD8VMAWrEWLFmn79u168skn9eCDD%2Bq5555TXl6eTp48qSlTpigpKUmbN29WQkKCpk6dqpMnT0qS9uzZo7lz52ratGnKy8tTZWWlZs%2Be7eM0AADAn3jtOljeVFFRoU2bNmnNmjXq3bu3JGny5MlyOByy2WwKCwvTzJkzFRQUpLlz5%2Bqtt97SK6%2B8ovT0dK1fv15DhgzRyJEjJUlLlizRwIEDVVRUpM6dO/syFgAA8BMBeQQrPz9frVq1UnJysntsypQpys7OlsPhUGJiovvK8kFBQerTp492794tSXI4HEpKSnI/rkOHDoqNjZXD4fBuCAAA4LcCsmAVFRWpY8eO%2Bsc//qHBgwfrP/7jP5Sbm6v6%2Bno5nU5FR0d73L9du3YqKSmRJJWWlp5xHgAA4GwC8hThyZMndejQIW3YsEHZ2dlyOp2aN2%2BewsPDVVVVpdDQUI/7h4aGyuVySZKqq6vPON8UpaWlDa5cb7NFNChuP0dISLDH31Zi5exnYrMF5uth5f1t1exWzS1ZN3sg5g7IgmWz2XT8%2BHE9%2BOCD6tixoyTpyJEjevbZZ3XppZc2KEsul0stWrSQJIWFhTU6Hx4e3uTnz8vLU05OjsdYZmamsrKyzifOGUVGNn1dgcbK2RvTpk3Ls9/Jj1l5f1s1u1VzS9bNHki5A7JgRUVFKSwszF2uJOmyyy5TcXGxkpOTVVZW5nH/srIy99GlmJiYRuejoqKa/PwZGRlKTU31GLPZIlRefuJco5xWSEiwIiPDVVlZpbq6etO26w%2BsnP1MzHx/XUisvL%2Btmt2quSXrZm/O3L765jMgC5bdbldNTY2%2B%2BOILXXbZZZKkzz//XB07dpTdbtcTTzwhwzAUFBQkwzC0a9cu3Xbbbe7H5ufnKz09XZJUXFys4uJi2e32Jj9/dHR0g9OBTucx1daa/8VSV1ffLNv1B1bO3phAfy2svL%2Btmt2quSXrZg%2Bk3IFzsvMnLr/8cl133XWaPXu29u/fr7ffflurVq3SuHHjNHjwYFVWVmrx4sUqLCzU4sWLVVVVpSFDhkiSxo0bp61bt2rjxo3av3%2B/Zs6cqeuuu45LNAAAgCYLyIIlScuWLdP/%2B3//T%2BPGjdOsWbP0hz/8QePHj1erVq20cuVK91Eqh8OhVatWKSIiQpKUkJCgBQsWKDc3V%2BPGjdMll1yi7OxsH6cBAAD%2BJCBPEUrSxRdfrCVLljQ617t3b23ZsuW0j01PT3efIgQAADhXAXsECwAAwFcoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACYL%2BII1ZcoU3XPPPe7b%2B/bt05gxY2S32zVq1Cjt3bvX4/7btm3ToEGDZLfblZmZqaNHj3p7yQAAwM8FdMH65z//qTfffNN9%2B%2BTJk5oyZYqSkpK0efNmJSQkaOrUqTp58qQkac%2BePZo7d66mTZumvLw8VVZWavbs2b5aPgAA8FMBW7AqKiq0ZMkSxcXFucdeeuklhYWFaebMmeratavmzp2rli1b6pVXXpEkrV%2B/XkOGDNHIkSPVvXt3LVmyRG%2B%2B%2BaaKiop8FQMAAPihgC1Yf/3rXzVixAhdccUV7jGHw6HExEQFBQVJkoKCgtSnTx/t3r3bPZ%2BUlOS%2Bf4cOHRQbGyuHw%2BHdxQMAAL9m8/UCmsN7772nDz/8UC%2B%2B%2BKLmz5/vHnc6nR6FS5LatWungoICSVJpaamio6MbzJeUlJzT85eWlsrpdHqM2WwRDbb9c4SEBHv8bSVWzn4mNltgvh5W3t9WzW7V3JJ1swdi7oArWDU1Nbrvvvs0b948tWjRwmOuqqpKoaGhHmOhoaFyuVySpOrq6jPON1VeXp5ycnI8xjIzM5WVlXVO22mKyMhw07fpL6ycvTFt2rT09RKalZX3t1WzWzW3ZN3sgZQ74ApWTk6OevXqpZSUlAZzYWFhDcqSy%2BVyF7HTzYeHn9sOz8jIUGpqqseYzRah8vIT57SdMwkJCVZkZLgqK6tUV1dv2nb9gZWzn4mZ768LiZX3t1WzWzW3ZN3szZnbV998BlzB%2Buc//6mysjIlJCRIkrswvfrqq0pLS1NZWZnH/cvKytyn7mJiYhqdj4qKOqc1REdHNzgd6HQeU22t%2BV8sdXX1zbJdf2Dl7I0J9NfCyvvbqtmtmluybvZAyh1wBWvdunWqra113162bJkk6a677tIHH3ygJ554QoZhKCgoSIZhaNeuXbrtttskSXa7Xfn5%2BUpPT5ckFRcXq7i4WHa73ftBAACA3wq4gtWxY0eP2y1b/nBo8NJLL1W7du304IMPavHixfr973%2BvDRs2qKqqSkOGDJEkjRs3TuPHj1d8fLzi4uK0ePFiXXfddercubPXcwAAAP8VOB/Xb4JWrVpp5cqV7qNUDodDq1atUkREhCQpISFBCxYsUG5ursaNG6dLLrlE2dnZPl41AADwNwF3BOtUDzzwgMft3r17a8uWLae9f3p6uvsUIQAAwPmw1BEsAAAAb6BgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmCxgC9Y333yjrKwsJScnKyUlRdnZ2aqpqZEkFRUVadKkSYqPj9fQoUP1zjvveDx2%2B/btSktLk91u14QJE1RUVOSLCAAAwE/ZfL2A5mAYhrKyshQZGalnnnlG3333nebMmaPg4GDNnDlTmZmZ6tatmzZt2qTXX39d06ZN00svvaTY2FgdOXJEmZmZmj59ulJSUpSbm6vbb79dL7zwgoKCgnwdDTitIY%2B86%2BslNNnLd/T39RIAoFkFZMH6/PPPtXv3br377rtq3769JCkrK0t//etfde2116qoqEgbNmxQRESEunbtqvfee0%2BbNm3S9OnTtXHjRvXq1UuTJ0%2BWJGVnZ6t///7auXOn%2Bvbt68tYAADATwTkKcKoqCitXr3aXa5%2BdPz4cTkcDl111VWKiIhwjycmJmr37t2SJIfDoaSkJPdceHi4evbs6Z4HAAA4m4A8ghUZGamUlBT37fr6eq1fv179%2BvWT0%2BlUdHS0x/3btWunkpISSTrrfFOUlpbK6XR6jNlsEQ22%2B3OEhAR7/G0lVs4eKGy2pu87K%2B9vq2a3am7JutkDMXdAFqxTLV26VPv27dPzzz%2Bvp556SqGhoR7zoaGhcrlckqSqqqozzjdFXl6ecnJyPMYyMzOVlZV1nglOLzIy3PRt%2BgsrZ/d3bdq0POfHWHl/WzW7VXNL1s0eSLkDvmAtXbpUTz/9tB5%2B%2BGF169ZNYWFhqqio8LiPy%2BVSixYtJElhYWENypTL5VJkZGSTnzMjI0OpqakeYzZbhMrLT5xnioZCQoIVGRmuysoq1dXVm7Zdf2Dl7IHiXL4WrLy/rZrdqrkl62Zvztzn8w2dGQK6YC1cuFDPPvusli5dqhtuuEGSFBMTo8LCQo/7lZWVuU/fxcTEqKysrMF8jx49mvy80dHRDU4HOp3HVFtr/hdLXV19s2zXH1g5u787n/1m5f1t1exWzS1ZN3sg5Q6ck52nyMnJ0YYNG/TQQw9p2LBh7nG73a5PPvlE1dXV7rH8/HzZ7Xb3fH5%2BvnuuqqpK%2B/btc88DAACcTUAWrIMHD%2Bqxxx7TrbfeqsTERDmdTvef5ORkdejQQbNnz1ZBQYFWrVqlPXv2aPTo0ZKkUaNGadeuXVq1apUKCgo0e/ZsderUiUs0AACAJgvIgvXvf/9bdXV1evzxxzVgwACPPyEhIXrsscfkdDqVnp6uF154Qbm5uYqNjZUkderUSY8%2B%2Bqg2bdqk0aNHq6KiQrm5uVxkFAAANFlAfgZrypQpmjJlymnnL730Uq1fv/6087/%2B9a/161//ujmWBgAALCAgj2ABAAD4EgULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATGbz9QJgHUMeedfXSwAAwCs4ggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKViNqamo0Z84cJSUlacCAAfr73//u6yUBAAA/wq/KacSSJUu0d%2B9ePf300zpy5IhmzZql2NhYDR482NdLAwAAfoCCdYqTJ09q48aNeuKJJ9SzZ0/17NlTBQUFeuaZZyhYgEn87fdSvnxHf18vAYCf4RThKfbv36/a2lolJCS4xxITE%2BVwOFRfX%2B/DlQEAAH/BEaxTOJ1OtWnTRqGhoe6x9u3bq6amRhUVFWrbtu1Zt1FaWiqn0%2BkxZrNFKDo62rR1hoQEe/wNoPn42xG31%2B5K8fUSfhYr//tm1eyBmJuCdYqqqiqPciXJfdvlcjVpG3l5ecrJyfEYmzZtmqZPn27OIvVDiXv66dXKyMgwtbg1pw8Xm3OKtbS0VHl5eX6V3QzktlZuybrZ/fHfN7NYNXsg5g6cqmiSsLCwBkXqx9stWrRo0jYyMjK0efNmjz8ZGRmmrtPpdConJ6fBkTIrsGp2clsrt2Td7FbNLVk3eyDm5gjWKWJiYlReXq7a2lrZbD%2B8PE6nUy1atFBkZGSTthEdHR0wDRwAAJw7jmCdokePHrLZbNq9e7d7LD8/X3FxcQoO5uUCAABnR2M4RXh4uEaOHKn58%2Bdrz549ev311/X3v/9dEyZM8PXSAACAnwiZP3/%2BfF8v4kLTr18/7du3Tw8%2B%2BKDee%2B893XbbbRo1apSvl9VAy5YtlZycrJYtW/p6KV5n1ezktlZuybrZrZpbsm72QMsdZBiG4etFAAAABBJOEQIAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNg%2BQnDMDR58mRt3rzZY7y8vFzTp09XQkKCUlNTtXXrVo/5ffv2acyYMbLb7Ro1apT27t3rzWWbat%2B%2BffrlL3/p8Sc9Pd09X1RUpEmTJik%2BPl5Dhw7VO%2B%2B848PVmqumpkZz5sxRUlKSBgwYoL///e%2B%2BXlKzeO211xrs46ysLEmB9V7%2BKZfLpbS0NO3YscM9drb38vbt25WWlia73a4JEyaoqKjI28v%2B2RrLvWjRogb7f/369e75bdu2adCgQbLb7crMzNTRo0d9sfTz9s033ygrK0vJyclKSUlRdna2ampqJAX2Pj9T7oDe5wYueHV1dcaCBQuMbt26GZs2bfKYmzp1qjFx4kTjs88%2BM5577jmjV69ehsPhMAzDME6cOGH079/feOCBB4zCwkJj4cKFxq9%2B9SvjxIkTvojxs23dutUYMWKEUVpa6v5z9OhRwzAMo76%2B3hg%2BfLhx5513GoWFhcaKFSsMu91uHD582MerNseCBQuM4cOHG3v37jX%2B9a9/GQkJCcbLL7/s62WZ7rHHHjOmTp3qsY%2B/%2B%2B67gHsv/6i6utrIzMw0unXrZrz//vuGYZz9vXz48GEjPj7eePLJJ40DBw4Y//mf/2mkpaUZ9fX1voxyThrLbRiGMWnSJGPlypUe%2B//kyZOGYRiGw%2BEwevfubWzZssX49NNPjZtuusmYMmWKryKcs/r6emPs2LHGH//4R%2BPAgQPGBx98YFx//fXGAw88END7/Ey5DSOw9zkF6wJXUlJi3HTTTcZ1111nJCUleRSsQ4cOGd26dTOKiorcY3PmzDFmzZplGIZhbNy40UhNTXV/EdbX1xvXX399g5LmLx566CFjxowZjc5t377diI%2BP9/gf7sSJE43ly5d7a3nN5sSJE0ZcXJzH/4hyc3ONm266yYerah533nmn8eCDDzYYD7T3smEYRkFBgfHb3/7WGD58uEfRONt7%2BZFHHvHY9ydPnjQSEhI83h8XstPlNgzDSElJMd5%2B%2B%2B1GH3f33Xe7/20zDMM4cuSI8ctf/tL46quvmn3NZigsLDS6detmOJ1O99iLL75oDBgwIKD3%2BZlyG0Zg73NOEV7gPvnkE3Xo0EGbNm3SxRdf7DHncDjUoUMHderUyT2WmJiojz76yD2fmJiooKAgSVJQUJD69Omj3bt3ey%2BAiQ4ePKguXbo0OudwOHTVVVcpIiLCPZaYmOi3WX9q//79qq2tVUJCgnssMTFRDodD9fX1PlyZ%2BU63jwPtvSxJO3fuVN%2B%2BfZWXl%2Bcxfrb3ssPhUFJSknsuPDxcPXv29JvX4nS5jx8/rm%2B%2B%2BeaMX%2BM/zd2hQwfFxsbK4XA053JNExUVpdWrV6t9%2B/Ye48ePHw/ofX6m3IG%2Bz22%2BXgDOLDU1VampqY3OOZ1ORUdHe4y1a9dO33zzjXv%2BiiuuaDBfUFDQPIttZgcPHlR9fb2GDx%2BuY8eO6dprr9XMmTPVqlWr074WJSUlPlqteZxOp9q0aaPQ0FD3WPv27VVTU6OKigq1bdvWh6szj2EY%2BuKLL/TOO%2B9o5cqVqqur0%2BDBg5WVlRVw72VJuvHGGxsdP9t72d/f66fLffDgQQUFBWnFihV666231Lp1a91888363e9%2BJ0kqLS3169yRkZFKSUlx366vr9f69evVr1%2B/gN7nZ8od6PucguVj1dXV7kJ0qqioKI/vaE5VVVXl8T9dSQoNDZXL5WrS/IXmTK9F27ZtVVRUpE6dOukvf/mLKisrlZ2drbvvvluPP/6432U9F6fLJikg8v3oyJEj7qyPPPKIvv76ay1atEjV1dUBvX9PFWhf1031%2BeefKygoSJdffrluuukmffDBB7r33nvVqlUrXX/99aqurg6o3EuXLtW%2Bffv0/PPP66mnnrLMPv9p7k8%2B%2BSSg9zkFy8ccDocmTJjQ6Fxubq4GDRp02seGhYU1eKO5XC61aNGiSfMXmrO9Fu%2B//77CwsJ00UUXSZIeeOABjRo1St98843CwsJUUVHh8ZgLOeu5ON1%2BlBQQ%2BX7UsWNH7dixQ5dccomCgoLUo0cP1dfX6%2B6771ZycrJfvZd/jrO9l0/3foiMjPTaGpvDyJEjNXDgQLVu3VqS1L17d3355Zd69tlndf311582d3h4uC%2BW%2B7MsXbpUTz/9tB5%2B%2BGF169bNMvv81NxXXnllQO9zCpaP9e3bV5999tl5PTYmJkZlZWUeY2VlZYqKijrj/KmHXC8U5/padO3aVdIPPwIcExOjwsJCj/kLOeu5iImJUXl5uWpra2Wz/fAl63Q61aJFC7/7B/ZsfvyH9kddu3ZVTU2NoqKi/Oq9/HOc7b18uq/rHj16eG2NzSEoKKjB/r/88sv1/vvvSzr7v3f%2BYuHChXr22We1dOlS3XDDDZKssc8byx3o%2B5wPufux%2BPh4HT582ON8dH5%2BvuLj4yVJdrtdH330kQzDkPTDZ1x27dolu93uk/X%2BHIWFhUpISPC49sunn34qm82mSy%2B9VHa7XZ988omqq6vd8/n5%2BX6Z9VQ9evSQzWbz%2BEBrfn6%2B4uLiFBwcOF/Cb7/9tvr27auqqir32KeffqrWrVu7f3gjEN7LZ3O297Ldbld%2Bfr57rqqqSvv27fP71%2BJvf/ubJk2a5DG2f/9%2BXX755ZIa5i4uLlZxcbFf5c7JydGGDRv00EMPadiwYe7xQN/np8sd8Pvcpz/DiHMycODABj%2BWPnnyZOOmm24yPv30U%2BO5554z4uLi3NfBOnbsmNGvXz9j4cKFRkFBgbFw4UKjf//%2BfnntoLq6OmPEiBHua3598MEHxtChQ4377rvPMAzDqK2tNYYOHWrccccdxoEDB4yVK1ca8fHxAXMdrHvvvdcYNmyY4XA4jNdee83o06eP8eqrr/p6WaY6duyYkZKSYsyYMcM4ePCg8cYbbxgDBgwwVq1aFVDv5cb89HIFZ3svFxUVGXFxccbKlSvd10QaPny4X1wT6VQ/ze1wOIyrrrrKWL16tXHo0CHjmWeeMXr16mXs2rXLMAzD2LVrl9GzZ0/jueeec18TaerUqb5c/jkpLCw0evToYTz88MMe13wqLS0N6H1%2BptyBvs8pWH6ksYJVVlZmTJ061YiLizNSU1ONF1980WPe4XAYI0eONOLi4ozRo0cbn3zyiTeXbKojR44YmZmZRlJSkpGcnGwsXLjQqKmpcc9/%2BeWXxh/%2B8AejV69exrBhw4x3333Xh6s118mTJ42ZM2ca8fHxxoABA4w1a9b4eknN4sCBA8akSZOM%2BPh4o3///sajjz7q/p9IIL2XT3Xq9aDO9l5%2B4403jN/85jdG7969jYkTJ/rNdYFOdWru1157zRg%2BfLgRFxdnDB48uME3EZs2bTJ%2B/etfG/Hx8UZmZqb7QsP%2BYOXKlUa3bt0a/WMYgbvPz5Y7kPd5kGH83zF3AAAAmCJwPsABAABwgaBgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJvv/ADEUSLQrAwI1AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7531946681391505552”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-18.78033</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-38.21899</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-8.161306</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.718587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.58828</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.7779</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.155752</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-6.134289</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-9.662831</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-11.70654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1927)</td> <td class=”number”>1927</td> <td class=”number”>60.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1273</td> <td class=”number”>39.7%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7531946681391505552”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-99.4984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-70.83068</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.84319</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-61.54203</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.67674</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>75.29898</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.18424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>78.47611</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.01101</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>249.9504</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_RECFACPTH_09_14”><s>PCH_RECFACPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_RECFAC_09_14”><code>PCH_RECFAC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9968</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_RECFAC_09_14”>PCH_RECFAC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>355</td>

</tr> <tr>

<th>Unique (%)</th> <td>11.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>201</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.7627</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3130282275200960531”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3130282275200960531,#minihistogram-3130282275200960531”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3130282275200960531”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3130282275200960531”

aria-controls=”quantiles-3130282275200960531” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3130282275200960531” aria-controls=”histogram-3130282275200960531”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3130282275200960531” aria-controls=”common-3130282275200960531”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3130282275200960531” aria-controls=”extreme-3130282275200960531”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3130282275200960531”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>71.092</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr>

<th>Range</th> <td>500</td>

</tr> <tr>

<th>Interquartile range</th> <td>25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>50.374</td>

</tr> <tr>

<th>Coef of variation</th> <td>-8.7413</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.47</td>

</tr> <tr>

<th>Mean</th> <td>-5.7627</td>

</tr> <tr>

<th>MAD</th> <td>30.968</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5426</td>

</tr> <tr>

<th>Sum</th> <td>-17340</td>

</tr> <tr>

<th>Variance</th> <td>2537.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3130282275200960531”>

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cXIvLgS7XYTU2WvMF2dTUbNm%2BETgCbQ2wbq1Ff61Db60TTL0NqoAVHx%2Bvv/zlL15j48eP1/jx4/XrX/9akmS321VYWKjMzExJUnl5ucrLy2W32xUfH6%2BEhAQVFhZ6AlZhYaESEhJM//wUAAAIXkEVsGw2my655JIWYx06dPCcnRo7dqzGjx%2BvpKQkJSYmav78%2BRo4cKC6du3qmV%2B0aJEuvvhiSdLixYs1ceJE3x4IAAAIaEEVsFojOTlZc%2BfO1dKlS/Xdd9%2BpX79%2Bmjdvnmd%2B0qRJ%2Bvbbb5WTk6OwsDCNGjVKt956q/8KBgAAASfEMAzD30WcD1yuw6bv02YLVUxMW1VXHw2aa9bnCpstVNctetvfZZyWV%2B/u5%2B8SWoV1ay36ax16ax0rexsbe6Gp%2B2utwPtRKQAAgHMcAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMFfMByu93KyMjQrl27PGNFRUX67W9/q%2BTkZN1www1av3691zY7duxQRkaG7Ha7JkyYoLKyMq/55557TgMGDFBycrJmzJihuro6nxwLAAAIDgEdsBoaGjRt2jSVlJR4xlwul26//XalpaVp8%2BbNys3N1bx58/S3v/1NknTgwAFlZ2crMzNTGzZsUPv27TV16lQZhiFJev3111VQUKC5c%2Bdq1apVcjqdWrhwoT8ODwAABKiADVilpaUaM2aMvvrqK6/xN954Qx07dtS0adPUrVs3DRs2TCNGjNDWrVslSevXr9eVV16piRMn6he/%2BIXy8/P1zTffaPfu3ZKk1atX65ZbbtGgQYN01VVXac6cOdq4cSNnsQAAQKsFbMDavXu3%2BvTpI4fD4TU%2BYMAA5efnt3j%2BkSNHJElOp1Opqame8cjISPXq1UtFRUVqamrSRx995DWflJSk48ePa//%2B/RYdCQAACDY2fxdwpsaNG3fS8S5duqhLly6ex99%2B%2B61efvll3XXXXZK%2Bv4QYFxfntU2HDh1UUVGh2tpaNTQ0eM3bbDZddNFFqqiosOAoAABAMArYgNUa9fX1uuuuu9SxY0dlZWVJkurq6hQeHu71vPDwcLndbtXX13sen2y%2BtSorK%2BVyubzGbLaoFsHubIWFhXr9DfMEYk9ttsComXVrLfprHXprnWDsbdAGrKNHj2rq1Kn6xz/%2Bof/%2B7/9WZGSkJCkiIqJFWHK73YqOjlZERITn8YnzP2zfGg6HQwUFBV5j2dnZys3NPZNDOaXo6NbXhuAVE9PW3yWcFtatteivdeitdYKpt0EZsI4cOaLf/e53%2Buqrr7Rq1Sp169bNMxcfH6%2Bqqiqv51dVValnz5666KKLFBERoaqqKl122WWSpMbGRtXU1Cg2NrbVr5%2BVlaX09HSvMZstStXVR8/8oE4iLCxU0dGRqq2tU1NTs6n7Pt8F4rsos9eXVVi31qK/1qG31rGyt/568xl0Aau5uVk5OTn6%2BuuvtWbNGk9Q%2BoHdbldhYaHncV1dnfbt26ecnByFhoYqMTFRhYWF6tOnj6Tv76lls9nUo0ePVtcQFxfX4nKgy3VYjY3WfEE2NTVbtm8EjkBbA6xba9Ff69Bb6wRTbwPvbfopbNiwQbt27dLDDz%2Bs6OhouVwuuVwu1dTUSJJGjhypPXv2aPny5SopKVFeXp66dOniCVTjxo3Tn/70J73xxhsqLi7W7NmzNWbMmNO6RAgAAM5vQXcG6/XXX1dzc7OmTJniNZ6WlqY1a9aoS5cuevzxx/X73/9ey5YtU3JyspYtW6aQkBBJ0rBhw/TNN99o1qxZcrvduv7663Xffff541AAAECACjF%2BuIU5LOVyHTZ9nzZbqGJi2qq6%2BmjQnFI9V9hsobpu0dv%2BLuO0vHp3P3%2BX0CqsW2vRX%2BvQW%2BtY2dvY2AtN3V9rBd0lQgAAAH8jYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJjM5wFr9OjRWrdunQ4fPuzrlwYAAPAJnwesvn376qmnnlL//v01bdo0vfPOOzIMw9dlAAAAWMbnAeuee%2B7RX//6Vz3xxBMKCwvTXXfdpYEDB%2Bqxxx7TF1984etyAAAATGfzx4uGhISoX79%2B6tevn%2Brq6rRmzRo98cQTWr58uXr37q1bbrlF119/vT9KAwAAOGt%2BCViSVFlZqZdeekkvvfSSPv30U/Xu3Vu/%2Bc1vVFFRoQcffFDvv/%2B%2BZs6c6a/yAAAAzpjPA9aWLVu0ZcsW7dq1S%2B3bt9eIESO0dOlSdevWzfOcTp06af78%2BQQsAAAQkHwesGbOnKlBgwZp2bJluvbaaxUa2vJjYJdeeqluvvlmX5cGAABgCp8HrLfeeksxMTGqqanxhKvi4mL16tVLYWFhkqTevXurd%2B/evi4NAADAFD7/KcIjR47oxhtv1DPPPOMZmzx5soYPH67y8nJflwMAAGA6nwes3//%2B97rkkkt02223ecZeeeUVderUSfn5%2Bb4uBwAAwHQ%2BD1gffPCBHnjgAcXGxnrG2rdvr%2BnTp%2Bu999477f253W5lZGRo165dnrGysjLdeuutSkpK0tChQ/XOO%2B94bbNjxw5lZGTIbrdrwoQJKisr85p/7rnnNGDAACUnJ2vGjBmqq6s77boAAMD5y%2BcBy2azqba2tsV4XV3dad/RvaGhQdOmTVNJSYlnzDAMZWdnq2PHjtq4caOGDx%2BunJwcHThwQJJ04MABZWdnKzMzUxs2bFD79u01depUz2u//vrrKigo0Ny5c7Vq1So5nU4tXLjwLI4YAACcb3wesK699lo9/PDD%2BuqrrzxjZWVlys/P14ABA1q9n9LSUo0ZM8ZrP5L03nvvqaysTHPnztVll12mKVOmKCkpSRs3bpQkrV%2B/XldeeaUmTpyoX/ziF8rPz9c333yj3bt3S5JWr16tW265RYMGDdJVV12lOXPmaOPGjZzFAgAArebzgHX//ffL7XbrhhtuUJ8%2BfdSnTx9df/31On78uPLy8lq9n927d6tPnz5yOBxe406nU1dccYWioqI8YykpKSoqKvLMp6ameuYiIyPVq1cvFRUVqampSR999JHXfFJSko4fP679%2B/ef6SEDAIDzjM9v09ChQwdt3rxZO3bsUElJiWw2my6//HJdc801CgkJafV%2Bxo0bd9Jxl8uluLi4Fq9ZUVFxyvna2lo1NDR4zdtsNl100UWe7VujsrJSLpfLa8xmi2rxumcrLCzU62%2BYJxB7arMFRs2sW2vRX%2BvQW%2BsEY2/98qtywsLCNGDAgNO6JNhadXV1Cg8P9xoLDw%2BX2%2B0%2B5Xx9fb3n8Y9t3xoOh0MFBQVeY9nZ2crNzW31Pk5HdHSkJftFYImJaevvEk4L69Za9Nc69NY6wdRbnwcsl8ulJUuWaM%2BePTp%2B/HiLD7a/%2BeabZ7X/iIgI1dTUeI253W61adPGM39iWHK73YqOjlZERITn8YnzkZGt/4%2BelZWl9PR0rzGbLUrV1UdbvY/WCAsLVXR0pGpr69TU1Gzqvs93gfguyuz1ZRXWrbXor3XorXWs7K2/3nz6PGD913/9l/bu3athw4bpwgsvNH3/8fHxKi0t9RqrqqryXJ6Lj49XVVVVi/mePXvqoosuUkREhKqqqnTZZZdJkhobG1VTU%2BN1W4lTiYuLa3E50OU6rMZGa74gm5qaLds3AkegrQHWrbXor3XorXWCqbc%2BD1jvvfeeVqxY4fVBcjPZ7XYtX75c9fX1nrNWhYWFSklJ8cwXFhZ6nl9XV6d9%2B/YpJydHoaGhSkxMVGFhofr06SNJKioqks1mU48ePSypFwAABB%2BfXweJiopShw4dLNt/WlqaOnXqpLy8PJWUlGj58uUqLi7WqFGjJEkjR47Unj17tHz5cpWUlCgvL09dunTxBKpx48bpT3/6k9544w0VFxdr9uzZGjNmzGldIgQAAOc3nwes4cOHa8WKFWpqarJk/2FhYXriiSfkcrmUmZmpl156ScuWLVNCQoIkqUuXLnr88ce1ceNGjRo1SjU1NVq2bJnnJxiHDRumKVOmaNasWZo4caKuuuoq3XfffZbUCgAAglOIcbq3Tz9LeXl52rZtm6Kjo9W1a9cWP7G3evVqX5bjMy7XYdP3abOFKiamraqrjwbNNetzhc0WqusWve3vMk7Lq3f383cJrcK6tRb9tQ69tY6VvY2NNf/z3q3hl9s0ZGRk%2BONlAQAAfMLnASs/P9/XLwkAAOBTfrnZT2VlpQoKCnTPPffo22%2B/1WuvvabPP//cH6UAAACYzucB68svv9SvfvUrbd68Wa%2B//rqOHTumV155RSNHjpTT6fR1OQAAAKbzecD6wx/%2BoMGDB%2BuNN97QBRdcIEl69NFHlZ6erkWLFvm6HAAAANP5PGDt2bNHt912m9cvdrbZbJo6dar27dvn63IAAABM5/OA1dzcrObmlj%2BCefToUYWFhfm6HAAAANP5PGD1799fTz/9tFfIqqmp0cKFC9W3b19flwMAAGA6nwesBx54QHv37lX//v3V0NCgO%2B%2B8U4MGDdLXX3%2Bt%2B%2B%2B/39flAAAAmM7n98GKj4/Xiy%2B%2BqG3btunvf/%2B7mpubNXbsWA0fPlzt2rXzdTkAAACm88ud3CMjIzV69Gh/vDQAAIDlfB6wJkyY8JPzwfq7CAEAwPnD5wGrc%2BfOXo8bGxv15Zdf6tNPP9Utt9zi63IAAABMd878LsJly5apoqLCx9UAAACYzy%2B/i/Bkhg8frldffdXfZQAAAJy1cyZgffjhh9xoFAAABIVz4kPuR44c0SeffKJx48b5uhwAAADT%2BTxgJSQkeP0eQkm64IILdPPNN%2BvXv/61r8sBAAAwnc8D1h/%2B8AdfvyQAAIBP%2BTxgvf/%2B%2B61%2B7tVXX21hJQAAANbwecAaP3685xKhYRie8RPHQkJC9Pe//93X5QEAAJw1nwesp556Sg8//LDuu%2B8%2BpaWlKTw8XB999JHmzp2r3/zmNxo6dKivSwIAADCVz2/TkJ%2Bfr1mzZumGG25QTEyM2rZtq759%2B2ru3Ll64YUX1LlzZ88fAACAQOTzgFVZWXnS8NSuXTtVV1eb9jrl5eWaMmWKevfurfT0dD333HOeuX379mn06NGy2%2B0aOXKk9u7d67Xttm3bNHjwYNntdmVnZ%2BvQoUOm1QUAAIKfzwNWUlKSHn30UR05csQzVlNTo4ULF%2Bqaa64x7XXuvvtuRUVFadOmTZoxY4aWLFmi//mf/9GxY8c0efJkpaamatOmTUpOTtaUKVN07NgxSVJxcbFmzpypnJwcORwO1dbWKi8vz7S6AABA8PP5Z7AefPBBTZgwQddee626desmwzD0j3/8Q7GxsVq9erUpr/Hdd9%2BpqKhI8%2BbNU7du3dStWzcNGDBAO3fu1HfffaeIiAhNnz5dISEhmjlzpt566y299tpryszM1Nq1azVkyBCNGDFCkrRgwQINGjRIZWVl6tq1qyn1AQCA4ObzM1iXXXaZXnnlFd1zzz1KSkpScnKyZs6cqS1btujiiy825TXatGmjyMhIbdq0ScePH9fnn3%2BuPXv2qGfPnnI6nUpJSfH81GJISIh69%2B6toqIiSZLT6VRqaqpnX506dVJCQoKcTqcptQEAgODn8zNYkvSzn/1Mo0eP1tdff%2B05K3TBBReYtv%2BIiAjNmjVL8%2BbN0%2BrVq9XU1KTMzEyNHj1ab775pi6//HKv53fo0EElJSWSvv%2BMWFxcXIv5iooK0%2BoDAADBzecByzAMLV68WGvWrNHx48f1%2Buuv67HHHlNkZKRmz55tWtD67LPPNGjQIN12220qKSnRvHnzdM0116iurk7h4eFezw0PD5fb7ZYk1dfX/%2BR8a1RWVsrlcnmN2WxRLYLb2QoLC/X6G%2BYJxJ7abIFRM%2BvWWvTXOvTWOsHYW58HrDVr1mjLli166KGHNHfuXEnS4MGDNWfOHHXs2FH/%2BZ//edavsXPnTm3YsEHbt29XmzZtlJiYqIMHD%2BrJJ59U165dW4Qlt9utNm3aSPr%2B7NfJ5iMjI1v9%2Bg6HQwUFBV5j2dnZys3NPcMj%2BmnR0a2vDcErJqatv0s4Laxba9Ff69Bb6wRTb30esBwOh2bNmqXrrrtO8%2BbNkyQNHTpUF1xwgfLz800JWHv37tUll1ziCU2SdMUVV%2Bipp55Samph46kcAAAbf0lEQVSqqqqqvJ5fVVXlObsUHx9/0vnY2NhWv35WVpbS09O9xmy2KFVXHz3dQ/lJYWGhio6OVG1tnZqamk3d9/kuEN9Fmb2%2BrMK6tRb9tQ69tY6VvfXXm0%2BfB6yvv/5aPXv2bDHeo0ePFpfVzlRcXJy%2B/PJLud1uz%2BW%2Bzz//XF26dJHdbtczzzwjwzAUEhIiwzC0Z88e3XHHHZIku92uwsJCZWZmSvr%2Bflrl5eWy2%2B2n9fonXg50uQ6rsdGaL8impmbL9o3AEWhrgHVrLfprHXprnWDqrc/fpnfu3FkfffRRi/G33nrLtNsgpKen64ILLtCDDz6oL774Qv/7v/%2Brp556SuPHj9eNN96o2tpazZ8/X6WlpZo/f77q6uo0ZMgQSdLYsWO1ZcsWrV%2B/Xvv379f06dM1cOBAbtEAAABazednsCZNmqQ5c%2BbI5XLJMAzt3LlTDodDa9as0QMPPGDKa1x44YV67rnnNH/%2BfI0aNUrt27fXnXfeqaysLIWEhOjpp5/WQw89pD//%2Bc/65S9/qeXLlysqKkqSlJycrLlz52rp0qX67rvv1K9fP8%2BlTAAAgNYIMQzD8PWLOhwOPfnkk55bH7Rv31633367brvtNl%2BX4jMu12HT92mzhSompq2qq48GzSnVc4XNFqrrFr3t7zJOy6t39/N3Ca3CurUW/bUOvbWOlb2Njb3Q1P21ls/PYG3btk033nijsrKydOjQIRmGoQ4dOvi6DAAAAMv4/DNYc%2BfO9XyYvX379oQrAAAQdHwesLp166ZPP/3U1y8LAADgMz6/RNijRw/de%2B%2B9WrFihbp166aIiAiv%2Bfz8fF%2BXBAAAYCqfB6wvvvhCKSkpkmTafa8AAADOJT4JWAsWLFBOTo6ioqK0Zs0aX7wkAACA3/jkM1grV65UXV2d19jkyZNVWVnpi5cHAADwKZ8ErJPdauv9999XQ0ODL14eAADApwLvN9oCAACc4whYAAAAJvNZwAoJCfHVSwEAAPiVz27T8PDDD3vd8%2Br48eNauHCh2rZt6/U87oMFAAACnU8C1tVXX93inlfJycmqrq5WdXW1L0oAAADwGZ8ELO59BQAAzid8yB0AAMBkPv9VOTh/DVnyrr9LAADAJziDBQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYLGgDltvt1pw5c3T11VfrX//1X/Xoo4/KMAxJ0r59%2BzR69GjZ7XaNHDlSe/fu9dp227ZtGjx4sOx2u7Kzs3Xo0CF/HAIAAAhQQRuwHn74Ye3YsUN/%2BtOftHjxYv35z3%2BWw%2BHQsWPHNHnyZKWmpmrTpk1KTk7WlClTdOzYMUlScXGxZs6cqZycHDkcDtXW1iovL8/PRwMAAAJJUP6qnJqaGm3cuFErV67UVVddJUmaOHGinE6nbDabIiIiNH36dIWEhGjmzJl666239NprrykzM1Nr167VkCFDNGLECEnSggULNGjQIJWVlalr167%2BPCwAABAggvIMVmFhodq1a6e0tDTP2OTJk5Wfny%2Bn06mUlBSFhIRIkkJCQtS7d28VFRVJkpxOp1JTUz3bderUSQkJCXI6nb49CAAAELCC8gxWWVmZOnfurBdffFFPPfWUjh8/rszMTN15551yuVy6/PLLvZ7foUMHlZSUSJIqKysVFxfXYr6ioqLVr19ZWSmXy%2BU1ZrNFtdjv2QoLC/X6G%2Bc3my0w1gHr1lr01zr01jrB2NugDFjHjh3Tl19%2BqXXr1ik/P18ul0uzZs1SZGSk6urqFB4e7vX88PBwud1uSVJ9ff1PzreGw%2BFQQUGB11h2drZyc3PP8Ih%2BWnR0pCX7RWCJiWnr7xJOC%2BvWWvTXOvTWOsHU26AMWDabTUeOHNHixYvVuXNnSdKBAwf0wgsv6JJLLmkRltxut9q0aSNJioiIOOl8ZGTr/6NnZWUpPT39hJqiVF199EwO50eFhYUqOjpStbV1ampqNnXfCDxmry%2BrsG6tRX%2BtQ2%2BtY2Vv/fXmMygDVmxsrCIiIjzhSpJ%2B/vOfq7y8XGlpaaqqqvJ6flVVlefyXXx8/EnnY2NjW/36cXFxLS4HulyH1dhozRdkU1OzZftG4Ai0NcC6tRb9tQ69tU4w9TZ4Lnb%2BE7vdroaGBn3xxReesc8//1ydO3eW3W7Xhx9%2B6LknlmEY2rNnj%2Bx2u2fbwsJCz3bl5eUqLy/3zAMAAJxKUAasSy%2B9VAMHDlReXp7279%2Bvt99%2BW8uXL9fYsWN14403qra2VvPnz1dpaanmz5%2Bvuro6DRkyRJI0duxYbdmyRevXr9f%2B/fs1ffp0DRw4kFs0AACAVgvKgCVJixYt0r/8y79o7Nixuv/%2B%2B3XTTTdp/PjxateunZ5%2B%2BmkVFhYqMzNTTqdTy5cvV1RUlCQpOTlZc%2BfO1bJlyzR27Fj97Gc/U35%2Bvp%2BPBgAABJIQ44drZbCUy3XY9H3abKGKiWmr6uqjAXHNesiSd/1dQlB79e5%2B/i6hVQJt3QYa%2BmsdemsdK3sbG3uhqftrraA9gwUAAOAvBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAEwW9AFr8uTJeuCBBzyP9%2B3bp9GjR8tut2vkyJHau3ev1/O3bdumwYMHy263Kzs7W4cOHfJ1yQAAIMAFdcB6%2BeWXtX37ds/jY8eOafLkyUpNTdWmTZuUnJysKVOm6NixY5Kk4uJizZw5Uzk5OXI4HKqtrVVeXp6/ygcAAAEqaANWTU2NFixYoMTERM/YK6%2B8ooiICE2fPl2XXXaZZs6cqbZt2%2Bq1116TJK1du1ZDhgzRiBEj1KNHDy1YsEDbt29XWVmZvw4DAAAEoKANWI888oiGDx%2Buyy%2B/3DPmdDqVkpKikJAQSVJISIh69%2B6toqIiz3xqaqrn%2BZ06dVJCQoKcTqdviwcAAAEtKAPWzp079cEHH2jq1Kle4y6XS3FxcV5jHTp0UEVFhSSpsrLyJ%2BcBAABaw%2BbvAszW0NCghx56SLNmzVKbNm285urq6hQeHu41Fh4eLrfbLUmqr6//yfnWqqyslMvl8hqz2aJahLezFRYW6vU3zm82W2CsA9atteivdeitdYKxt0EXsAoKCnTllVdqwIABLeYiIiJahCW32%2B0JYj82HxkZeVo1OBwOFRQUeI1lZ2crNzf3tPbTWtHRp1cfglNMTFt/l3BaWLfWor/WobfWCabeBl3Aevnll1VVVaXk5GRJ8gSm119/XRkZGaqqqvJ6flVVlefMUnx8/EnnY2NjT6uGrKwspaene43ZbFGqrj56Wvs5lbCwUEVHR6q2tk5NTc2m7huBx%2Bz1ZRXWrbXor3XorXWs7K2/3nwGXcBas2aNGhsbPY8XLVokSbr33nv1/vvv65lnnpFhGAoJCZFhGNqzZ4/uuOMOSZLdbldhYaEyMzMlSeXl5SovL5fdbj%2BtGuLi4lpcDnS5Dqux0ZovyKamZsv2jcARaGuAdWst%2BmsdemudYOpt0AWszp07ez1u2/b75HrJJZeoQ4cOWrx4sebPn6/f/va3Wrdunerq6jRkyBBJ0tixYzV%2B/HglJSUpMTFR8%2BfP18CBA9W1a1efHwcAAAhcwfNpslZo166dnn76ac9ZKqfTqeXLlysqKkqSlJycrLlz52rZsmUaO3asfvaznyk/P9/PVQMAgEATYhiG4e8izgcu12HT92mzhSompq2qq48GxCnVIUve9XcJQe3Vu/v5u4RWCbR1G2jor3XorXWs7G1s7IWm7q%2B1zqszWAAAAL5AwAIAADAZAQsAAMBkQfdThOeb1Jmv%2BbsEAABwAs5gAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmC9qAdfDgQeXm5iotLU0DBgxQfn6%2BGhoaJEllZWW69dZblZSUpKFDh%2Bqdd97x2nbHjh3KyMiQ3W7XhAkTVFZW5o9DAAAAASooA5ZhGMrNzVVdXZ2ef/55PfbYY/rrX/%2BqJUuWyDAMZWdnq2PHjtq4caOGDx%2BunJwcHThwQJJ04MABZWdnKzMzUxs2bFD79u01depUGYbh56MCAACBwubvAqzw%2Beefq6ioSO%2B%2B%2B646duwoScrNzdUjjzyia6%2B9VmVlZVq3bp2ioqJ02WWXaefOndq4caPuuusurV%2B/XldeeaUmTpwoScrPz1e/fv20e/du9enTx5%2BHBQAAAkRQnsGKjY3VihUrPOHqB0eOHJHT6dQVV1yhqKgoz3hKSoqKiookSU6nU6mpqZ65yMhI9erVyzMPAABwKkF5Bis6OloDBgzwPG5ubtbatWvVt29fuVwuxcXFeT2/Q4cOqqiokKRTzrdGZWWlXC6X15jNFtViv2crLCwo8zHOkM0WGOvhh3XL%2BrUG/bUOvbVOMPY2KAPWiRYuXKh9%2B/Zpw4YNeu655xQeHu41Hx4eLrfbLUmqq6v7yfnWcDgcKigo8BrLzs5Wbm7uGR4BcGoxMW39XcJpiY6O9HcJQY3%2BWofeWieYehv0AWvhwoVatWqVHnvsMXXv3l0RERGqqanxeo7b7VabNm0kSRERES3ClNvtVnR0dKtfMysrS%2Bnp6V5jNluUqquPnuFRnFwwJX2cPbPXl1XCwkIVHR2p2to6NTU1%2B7ucoEN/rUNvrWNlb/315jOoA9a8efP0wgsvaOHChbrhhhskSfHx8SotLfV6XlVVlefyXXx8vKqqqlrM9%2BzZs9WvGxcX1%2BJyoMt1WI2NfEHCOoG2vpqamgOu5kBCf61Db60TTL0N2lMgBQUFWrdunR599FENGzbMM2632/Xxxx%2Brvr7eM1ZYWCi73e6ZLyws9MzV1dVp3759nnkAAIBTCcqA9dlnn%2BmJJ57Q7bffrpSUFLlcLs%2BftLQ0derUSXl5eSopKdHy5ctVXFysUaNGSZJGjhypPXv2aPny5SopKVFeXp66dOnCLRoAAECrBWXAevPNN9XU1KQnn3xS/fv39/oTFhamJ554Qi6XS5mZmXrppZe0bNkyJSQkSJK6dOmixx9/XBs3btSoUaNUU1OjZcuWKSQkxM9HBQAAAkWIwS3KfcLlOmz6Pm22UF236G3T94vA9Ord/fxdQqvYbKGKiWmr6uqjQfNZi3MJ/bUOvbWOlb2Njb3Q1P21VlCewQIAAPAnAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJgvq30UInE%2BGLHnX3yW02gfzb/R3CQBgKc5gAQAAmIyABQAAYDICFgAAgMkIWAAAACbjQ%2B4AfC515mv%2BLuG0BMov0gZw7uAMFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyAdRINDQ2aMWOGUlNT1b9/fz377LP%2BLgkAAAQQftnzSSxYsEB79%2B7VqlWrdODAAd1///1KSEjQjTfe6O/SAOCUAumXafOLtBGsCFgnOHbsmNavX69nnnlGvXr1Uq9evVRSUqLnn3%2BegAUAAFqFS4Qn2L9/vxobG5WcnOwZS0lJkdPpVHNzsx8rAwAAgYIzWCdwuVyKiYlReHi4Z6xjx45qaGhQTU2N2rdvf8p9VFZWyuVyeY3ZbFGKi4sztdawMPIx4AtDlrzr7xKCls0WOP8fCwsLDajLr4Hmg/k3BtX3NQLWCerq6rzClSTPY7fb3ap9OBwOFRQUeI3l5OTorrvuMqfI/1NZWalbLi5RVlaW6eHtfFdZWSmHw0FvLUBvrUV/rcP/c61TWVmpxx9/PKh6GzxR0SQREREtgtQPj9u0adOqfWRlZWnTpk1ef7Kyskyv1eVyqaCgoMXZMpw9emsdemst%2BmsdemudYOwtZ7BOEB8fr%2BrqajU2Nspm%2B749LpdLbdq0UXR0dKv2ERcXFzQJHAAAnD7OYJ2gZ8%2BestlsKioq8owVFhYqMTFRoaG0CwAAnBqJ4QSRkZEaMWKEZs%2BereLiYr3xxht69tlnNWHCBH%2BXBgAAAkTY7NmzZ/u7iHNN3759tW/fPi1evFg7d%2B7UHXfcoZEjR/q7rJNq27at0tLS1LZtW3%2BXEnTorXXorbXor3XorXWCrbchhmEY/i4CAAAgmHCJEAAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQErgBiGoYkTJ2rTpk1e49XV1brrrruUnJys9PR0bdmyxWt%2B3759Gj16tOx2u0aOHKm9e/f6suyA09DQoBkzZig1NVX9%2B/fXs88%2B6%2B%2BSAo7b7VZGRoZ27drlGSsrK9Ott96qpKQkDR06VO%2B8847XNjt27FBGRobsdrsmTJigsrIyX5d9Tjt48KByc3OVlpamAQMGKD8/Xw0NDZLorRm%2B/PJLTZo0ScnJyRo4cKBWrFjhmaO/5pk8ebIeeOABz%2BNTfX/atm2bBg8eLLvdruzsbB06dMjXJZ8xAlaAaG5u1sMPP6x33323xVxeXp4OHz4sh8OhO%2B%2B8Uw8%2B%2BKCKi4slSceOHdPkyZOVmpqqTZs2KTk5WVOmTNGxY8d8fQgBY8GCBdq7d69WrVqlhx56SAUFBXrttdf8XVbAaGho0LRp01RSUuIZMwxD2dnZ6tixozZu3Kjhw4crJydHBw4ckCQdOHBA2dnZyszM1IYNG9S%2BfXtNnTpV/KKJ7xmGodzcXNXV1en555/XY489pr/%2B9a9asmQJvTVBc3OzJk%2BerJiYGG3evFlz5szRk08%2Bqa1bt9JfE7388svavn275/Gpvj8VFxdr5syZysnJkcPhUG1trfLy8vxV/ukzcM6rqKgwbr75ZmPgwIFGamqqsXHjRs/cl19%2BaXTv3t0oKyvzjM2YMcO4//77DcMwjPXr1xvp6elGc3OzYRiG0dzcbFx33XVe%2B8D/d/ToUSMxMdF47733PGPLli0zbr75Zj9WFThKSkqMX//618avfvUro3v37p4%2B7tixw0hKSjKOHj3qee4tt9xiLF261DAMw1iyZIlXj48dO2YkJyd7/Xc4n5WWlhrdu3c3XC6XZ2zr1q1G//796a0JDh48aPzHf/yHcfjwYc9Ydna28dBDD9Ffk1RXVxvXXnutMXLkyFZ/f7rvvvs8zzUMwzhw4IDxy1/%2B0vjqq698fwBngDNYAeDjjz9Wp06dtHHjRl144YVec06nU506dVKXLl08YykpKfrwww898ykpKQoJCZEkhYSEqHfv3ioqKvLdAQSQ/fv3q7GxUcnJyZ6xlJQUOZ1ONTc3%2B7GywLB792716dNHDofDa9zpdOqKK65QVFSUZywlJcWzDp1Op1JTUz1zkZGR6tWrF%2Bv0/8TGxmrFihXq2LGj1/iRI0forQni4uK0ZMkStWvXToZhqLCwUO%2B//77S0tLor0keeeQRDR8%2BXJdffrln7FTfn07sbadOnZSQkCCn0%2Bnb4s8QASsApKena8GCBWrfvn2LOZfLpbi4OK%2BxDh066ODBgz85X1FRYV3BAczlcikmJkbh4eGesY4dO6qhoUE1NTV%2BrCwwjBs3TjNmzFBkZKTX%2BKnWIev0p0VHR2vAgAGex83NzVq7dq369u1Lb02Wnp6ucePGKTk5WTfccAP9NcHOnTv1wQcfaOrUqV7jp%2BpdZWVlQPfW5u8CINXX13sC0YliY2O93jmdqK6uzisMSFJ4eLjcbner5uHtx/oliZ6dBdapuRYuXKh9%2B/Zpw4YNeu655%2BitiZYuXaqqqirNnj1b%2Bfn5rN2z1NDQoIceekizZs1SmzZtvOZO1bv6%2BvqA7i0B6xzgdDo1YcKEk84tW7ZMgwcP/tFtIyIiWiw2t9vtWcinmoe3H%2BuXJHp2FiIiIlqcAWzNOo2OjvZZjYFi4cKFWrVqlR577DF1796d3posMTFR0vfB4N5779XIkSNVV1fn9Rz623oFBQW68sorvc7A/uBMv3%2BdeIb8XEXAOgf06dNHn3zyyRltGx8fr6qqKq%2BxqqoqxcbG/uT8iadd8b34%2BHhVV1ersbFRNtv3Xx4ul0tt2rThf5hnIT4%2BXqWlpV5j/7wOf2yd9uzZ02c1BoJ58%2BbphRde0MKFC3XDDTdIordmqKqqUlFRkdeb2csvv1zHjx9XbGysPv/88xbPp7%2Bt8/LLL6uqqsrzudYfAtPrr7%2BujIyMn/z%2BdKrvb%2Bc6PoMV4JKSkvTNN994XZMuLCxUUlKSJMlut%2BvDDz/0/MiwYRjas2eP7Ha7X%2Bo91/Xs2VM2m83rA6qFhYVKTExUaChfLmfKbrfr448/Vn19vWessLDQsw7tdrsKCws9c3V1ddq3bx/r9J8UFBRo3bp1evTRRzVs2DDPOL09e19//bVycnK8Pqqxd%2B9etW/fXikpKfT3LKxZs0Zbt27Viy%2B%2BqBdffFHp6elKT0/Xiy%2B%2BeMrvTyf2try8XOXl5QHTW75jBLiuXbuqf//%2Buu%2B%2B%2B7R//36tX79e27Zt00033SRJuvHGG1VbW6v58%2BertLRU8%2BfPV11dnYYMGeLnys9NkZGRGjFihGbPnq3i4mK98cYbevbZZ3/0Ei5aJy0tTZ06dVJeXp5KSkq0fPlyFRcXa9SoUZKkkSNHas%2BePVq%2BfLlKSkqUl5enLl26qE%2BfPn6u/Nzw2Wef6YknntDtt9%2BulJQUuVwuzx96e/YSExPVq1cvzZgxQ6Wlpdq%2BfbsWLlyoO%2B64g/6epc6dO%2BuSSy7x/Gnbtq3atm2rSy655JTfn8aOHastW7Zo/fr12r9/v6ZPn66BAweqa9eufj6qVvLrTSJw2gYNGtTiHlZVVVXGlClTjMTERCM9Pd3YunWr17zT6TRGjBhhJCYmGqNGjTI%2B/vhjX5YccI4dO2ZMnz7dSEpKMvr372%2BsXLnS3yUFpH%2B%2BD5ZhGMY//vEP46abbjKuvPJKY9iwYca7777r9fy//e1vxvXXX29cddVVxi233BIw97rxhaefftro3r37Sf8YBr01Q0VFhZGdnW307t3b6Nevn/Hkk0967s9Ef81z//33e93b6lTfnzZu3Gj827/9m5GUlGRkZ2cbhw4d8nXJZyzEMLjdLAAAgJm4RAgAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGCy/wefLTV%2BFKP03gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3130282275200960531”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1285</td> <td class=”number”>40.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>245</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>191</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>96</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (344)</td> <td class=”number”>834</td> <td class=”number”>26.0%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>201</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3130282275200960531”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>245</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.714285714</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>166.666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_REDEMP_SNAPS_12_16”>PCH_REDEMP_SNAPS_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2860</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>10.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>351</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-19.942</td>

</tr> <tr>

<th>Minimum</th> <td>-93.354</td>

</tr> <tr>

<th>Maximum</th> <td>405.98</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6628396157896533339”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6628396157896533339,#minihistogram6628396157896533339”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6628396157896533339”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6628396157896533339”

aria-controls=”quantiles6628396157896533339” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6628396157896533339” aria-controls=”histogram6628396157896533339”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6628396157896533339” aria-controls=”common6628396157896533339”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6628396157896533339” aria-controls=”extreme6628396157896533339”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6628396157896533339”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-93.354</td>

</tr> <tr>

<th>5-th percentile</th> <td>-45.427</td>

</tr> <tr>

<th>Q1</th> <td>-32.324</td>

</tr> <tr>

<th>Median</th> <td>-22.087</td>

</tr> <tr>

<th>Q3</th> <td>-10.496</td>

</tr> <tr>

<th>95-th percentile</th> <td>11.866</td>

</tr> <tr>

<th>Maximum</th> <td>405.98</td>

</tr> <tr>

<th>Range</th> <td>499.34</td>

</tr> <tr>

<th>Interquartile range</th> <td>21.827</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>20.923</td>

</tr> <tr>

<th>Coef of variation</th> <td>-1.0492</td>

</tr> <tr>

<th>Kurtosis</th> <td>71.923</td>

</tr> <tr>

<th>Mean</th> <td>-19.942</td>

</tr> <tr>

<th>MAD</th> <td>13.965</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6556</td>

</tr> <tr>

<th>Sum</th> <td>-57015</td>

</tr> <tr>

<th>Variance</th> <td>437.77</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6628396157896533339”>

<img 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jYAEAABhme8C666679Oqrr%2BrUqVN2bxoAAMAWtgesnj176tlnn1Xv3r31yCOPaMeOHbIsy%2B4yAAAAvMb2gDV16lS9//77WrFihYKCgvTQQw%2Bpb9%2B%2BWrx4sb766iu7ywEAADDOJ5/BCggIUK9evZSenq6dO3dq3LhxWrt2rQYPHqxx48bpf/7nf%2Bq9rqqqKg0dOlS7d%2B92j82fP1833nijx2P9%2BvXu%2BaysLPXv319Op1PJyck6efKke86yLC1atEg9e/ZUYmKiFi5cqNraWjM7DgAArgoOX224qKhIb7zxht544w19%2BeWX6t69u%2B68804VFhbq8ccf1549ezRr1qxfXEdlZaWmTp2q/Px8j/HDhw9r6tSpuvPOO91jzZo1kyTl5eVp1qxZmjt3rjp37qwFCxYoNTVVzz33nCTppZdeUlZWljIyMlRdXa1p06apZcuWmjhxouEOAACAxsr2gLV161Zt3bpVu3fvVosWLTR8%2BHAtW7ZM7du3d7%2BmdevWWrBgwS8GrEOHDmnq1KkX/PzW4cOHNXHiREVGRtaZW79%2BvQYNGqThw4dLkhYuXKh%2B/fqpoKBA7dq107p165SSkqKEhARJ0qOPPqqlS5cSsAAAQL3Zfolw1qxZCgsL0/Lly7V9%2B3ZNnTrVI1xJUocOHXTPPff84no%2B%2BeQT9ejRQ5mZmR7jp0%2Bf1okTJ%2Bqs8ye5ubnu8CT9GOZiYmKUm5urEydO6Pjx47r11lvd8/Hx8Tp69KiKiooubUcBAMBVy/YzWB9%2B%2BKEiIiJUWlqqwMAf811eXp66du2qoKAgSVL37t3VvXv3X1zP2LFjLzh%2B%2BPBhBQQE6Nlnn9WHH36o5s2b6/7773dfLiwqKlJUVJTHMi1btlRhYaFcLpckecy3atVKklRYWFhnOQAAgAuxPWCdPn1aY8aM0b/9279p%2BvTpkqRJkyapVatWev7559W6desGrf/IkSMKCAhwnwXbs2ePnnjiCTVr1kwDBgxQRUWFgoODPZYJDg5WVVWVKioq3M//eU768cP09VVUVOQOaz9xOJpedkALCgr0%2BAl7OBz0u6E4dr2H3noX/fWeq6W3tgesP/3pT7ruuut0//33u8feeustzZgxQ2lpaVq2bFmD1j98%2BHD169dPzZs3lyR17txZX3/9tTZs2KABAwYoJCSkTliqqqpSaGioR5gKCQlx/1mSQkND611DZmamMjIyPMaSk5OVkpJy2fslSeHh9a8BDRcREebrEhoNjl3vobfeRX%2B9p7H31vaA9fe//13//d//7fEB9BYtWmj69OkaN25cg9cfEBDgDlc/6dChg3bt2iVJio6OVnFxscd8cXGxIiMjFR0dLUlyuVxq27at%2B8%2BSLviB%2BZ8zevRoJSUleYw5HE1VUnLm0nbm/wUFBSo8PFRlZeWqqeGWEXa53L8v/APHrvfQW%2B%2Biv95jd2999cuy7QHL4XCorKysznh5ebmRO7ovXbpUn376qdasWeMeO3jwoDp06CBJcjqdys7O1ogRIyRJx48f1/Hjx%2BV0OhUdHa2YmBhlZ2e7A1Z2drZiYmIu6fJeVFRUnde7XKdUXd2wA6mmprbB60D90WtzOHa9h956F/31nsbeW9svgN52222aP3%2B%2Bvv32W/dYQUGB0tLS1KdPnwavv1%2B/ftqzZ49eeOEFffvtt/qv//ovbdmyRRMmTJAkjRkzRlu3btXGjRt18OBBTZ8%2BXX379lW7du3c84sWLdLu3bu1e/duPf300xo/fnyD6wIAAFcP289gzZgxQ/fff78GDhyo8PBwSVJZWZm6du2q1NTUBq//lltu0dKlS7Vs2TItXbpUbdq00dNPP624uDhJUlxcnObNm6dly5bphx9%2BUK9evfTUU0%2B5l584caK%2B//57TZkyRUFBQRo1apTuu%2B%2B%2BBtcFAACuHgGWD75puaamRjt37lR%2Bfr4cDoeuv/56/frXv1ZAQIDdpdjG5Tp12cs6HIGKiAhTSckZvz6dOmjJR74u4ZK8/XAvX5fg9xrLsXslorfeRX%2B9x%2B7eRkZe4/VtXIhPvionKChIffr0MXJJEAAA4Epje8ByuVxasmSJ9u7dq3PnztX5YPt7771nd0kAAABG2R6wnnjiCe3bt09DhgzRNdf45rQdAACAN9kesHbt2qXVq1d7fB8gAABAY2L7bRqaNm2qli1b2r1ZAAAA29gesIYNG6bVq1erpqbG7k0DAADYwvZLhKWlpcrKytIHH3ygdu3a1fni5XXr1tldEgAAgFE%2BuU3D0KFDfbFZAAAAW9gesNLS0uzeJAAAgK1s/wyWJBUVFSkjI0NTp07V999/r3feeUdHjhzxRSkAAADG2R6wvvnmG/32t7/V66%2B/rnfffVdnz57VW2%2B9pZEjRyo3N9fucgAAAIyzPWD9%2Bc9/Vv/%2B/bVt2zb96le/kiQ988wzSkpK0qJFi%2BwuBwAAwDjbA9bevXt1//33e3yxs8Ph0IMPPqgDBw7YXQ4AAIBxtges2tpa1dbW/fbsM2fOKCgoyO5yAAAAjLM9YPXu3VvPPfecR8gqLS1Venq6evbsaXc5AAAAxtkesB577DHt27dPvXv3VmVlpf74xz%2BqX79%2B%2Bu677zRjxgy7ywEAADDO9vtgRUdHa8uWLcrKytLnn3%2Bu2tpajRkzRsOGDVOzZs3sLgcAAMA4n9zJPTQ0VHfddZcvNg0AAOB1tges8ePH/%2BI830UIAAD8ne0Bq02bNh7Pq6ur9c033%2BjLL7/Uvffea3c5AAAAxl0x30W4fPlyFRYW2lwNAACAeT75LsILGTZsmN5%2B%2B21flwEAANBgV0zA%2BvTTT7nRKAAAaBSuiA%2B5nz59Wl988YXGjh1rdzkAAADG2R6wYmJiPL6HUJJ%2B9atf6Z577tEdd9xhdzkAAADG2R6w/vznP9u9SQAAAFvZHrD27NlT79feeuutXqwEAADAO2wPWL/73e/clwgty3KPnz8WEBCgzz//3O7yAAAAGsz2gPXss89q/vz5mjZtmhITExUcHKzPPvtM8%2BbN05133qnBgwfbXRIAAIBRtt%2BmIS0tTbNnz9bAgQMVERGhsLAw9ezZU/PmzdOGDRvUpk0b9wMAAMAf2R6wioqKLhiemjVrppKSErvLAQAAMM72gBUbG6tnnnlGp0%2Bfdo%2BVlpYqPT1dv/71r%2B0uBwAAwDjbP4P1%2BOOPa/z48brtttvUvn17WZalr7/%2BWpGRkVq3bp3d5QAAABhne8Dq2LGj3nrrLWVlZenw4cOSpHHjxmnIkCEKDQ21uxwAAADjbA9YknTttdfqrrvu0nfffad27dpJ%2BvFu7gAAAI2B7Z/BsixLixYt0q233qqhQ4eqsLBQM2bM0KxZs3Tu3Dm7ywEAADDO9oD18ssva%2BvWrXryyScVHBwsSerfv7%2B2bdumjIwMu8sBAAAwzvaAlZmZqdmzZ2vEiBHuu7cPHjxY8%2BfP15tvvml3OQAAAMbZHrC%2B%2B%2B47denSpc54586d5XK57C4HAADAONsDVps2bfTZZ5/VGf/www/dH3gHAADwZ7b/K8KJEydq7ty5crlcsixLH3/8sTIzM/Xyyy/rscces7scAAAA42wPWCNHjlR1dbVWrlypiooKzZ49Wy1atNDDDz%2BsMWPG2F0OAACAcbYHrKysLN1%2B%2B%2B0aPXq0Tp48Kcuy1LJlS7vLAAAA8BrbP4M1b94894fZW7RoQbgCAACNju0Bq3379vryyy/t3iwAAIBtbL9E2LlzZz366KNavXq12rdvr5CQEI/5tLQ0u0sCAAAwyvaA9dVXXyk%2BPl6SuO8VAABolGwJWAsXLtSUKVPUtGlTvfzyy3ZsEgAAwGds%2BQzWSy%2B9pPLyco%2BxSZMmqaioyI7NAwAA2MqWgGVZVp2xPXv2qLKy0o7NAwAA2Mr2f0VoWlVVlYYOHardu3e7xwoKCnTfffcpNjZWgwcP1o4dOzyW2blzp4YOHSqn06nx48eroKDAY37NmjXq06eP4uLiNHPmzDpn3wAAAH6JXwesyspKPfLII8rPz3ePWZal5ORktWrVSps3b9awYcM0ZcoUHTt2TJJ07NgxJScna8SIEdq0aZNatGihBx980H2W7d1331VGRobmzZuntWvXKjc3V%2Bnp6T7ZPwAA4J9sC1gBAQFG13fo0CHdfffd%2Bvbbbz3Gd%2B3apYKCAs2bN08dO3bU5MmTFRsbq82bN0uSNm7cqJtvvlkTJkzQDTfcoLS0NB09elSffPKJJGndunW699571a9fP91yyy2aO3euNm/ezFksAABQb7bdpmH%2B/Pke97w6d%2B6c0tPTFRYW5vG6%2Bt4H65NPPlGPHj30H//xH4qNjXWP5%2Bbm6qabblLTpk3dY/Hx8crJyXHPJyQkuOdCQ0PVtWtX5eTkKCEhQZ999pmmTJnino%2BNjdW5c%2Bd08OBBxcXFXdpOAwCAq5ItAevWW2%2Btc8%2BruLg4lZSUqKSk5LLWOXbs2AuOu1wuRUVFeYy1bNlShYWFF50vKytTZWWlx7zD4VDz5s3dy9dHUVFRnf11OJrW2W59BQUFevyEPRwO%2Bt1QHLveQ2%2B9i/56z9XSW1sClp33viovL1dwcLDHWHBwsKqqqi46X1FR4X7%2Bc8vXR2ZmpjIyMjzGkpOTlZKSUu91XEh4eGiDlseliYgIu/iLUC8cu95Db72L/npPY%2B%2Bt7Xdy97aQkBCVlpZ6jFVVValJkybu%2BfPDUlVVlcLDw92XMC80Hxpa/wNh9OjRSkpK8hhzOJqqpORMvdfxz4KCAhUeHqqysnLV1NRe1jpw6S737wv/wLHrPfTWu%2Biv99jdW1/9stzoAlZ0dLQOHTrkMVZcXOy%2BPBcdHa3i4uI68126dFHz5s0VEhKi4uJidezYUZJUXV2t0tJSRUZG1ruGqKioOpcDXa5Tqq5u2IFUU1Pb4HWg/ui1ORy73kNvvYv%2Bek9j722juwDqdDq1f/9%2B9%2BU%2BScrOzpbT6XTPZ2dnu%2BfKy8t14MABOZ1OBQYGqlu3bh7zOTk5cjgc6ty5s307AQAA/FqjC1iJiYlq3bq1UlNTlZ%2Bfr1WrVikvL0%2BjRo2SJI0cOVJ79%2B7VqlWrlJ%2Bfr9TUVLVt21Y9evSQ9OOH51944QVt27ZNeXl5mjNnju6%2B%2B%2B5LukQIAACubo0uYAUFBWnFihVyuVwaMWKE3njjDS1fvlwxMTGSpLZt2%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%2B9re/6cYbb/R4pKSkSJIOHDigu%2B66S06nUyNHjtS%2Bffs8ls3KylL//v3ldDqVnJyskydP%2BmIXAACAn2q0AevQoUPq16%2BfduzY4X7Mnz9fZ8%2Be1aRJk5SQkKDXXntNcXFxmjx5ss6ePStJysvL06xZszRlyhRlZmaqrKxMqampPt4bAADgTxptwDp8%2BLA6deqkyMhI9yM8PFxvvfWWQkJCNH36dHXs2FGzZs1SWFiY3nnnHUnS%2BvXrNWjQIA0fPlydO3fWwoULtX37dhUUFPh4jwAAgL9o1AGrffv2dcZzc3MVHx%2BvgIAASVJAQIC6d%2B%2BunJwc93xCQoL79a1bt1ZMTIxyc3NtqRsAAPi/RhmwLMvSV199pR07dmjgwIHq37%2B/Fi1apKqqKrlcLkVFRXm8vmXLliosLJQkFRUV/eI8AADAxTh8XYA3HDt2TOXl5QoODtaSJUv03Xffaf78%2BaqoqHCP/7Pg4GBVVVVJkioqKn5xvj6Kiorkcrk8xhyOpnWCW30FBQV6/IQ9HA763VAcu95Db72L/nrP1dLbRhmw2rRpo927d%2Bvaa69VQECAunTpotraWk2bNk2JiYlVotc4AAAOV0lEQVR1wlJVVZWaNGkiSQoJCbngfGhoaL23n5mZqYyMDI%2Bx5ORk979ivFzh4fWvAQ0XERHm6xIaDY5d76G33kV/vaex97ZRBixJat68ucfzjh07qrKyUpGRkSouLvaYKy4udp9dio6OvuB8ZGRkvbc9evRoJSUleYw5HE1VUnLmUnbBLSgoUOHhoSorK1dNTe1lrQOX7nL/vvAPHLveQ2%2B9i/56j9299dUvy40yYP3v//6vHn30UX3wwQfuM0%2Bff/65mjdvrvj4eD3//POyLEsBAQGyLEt79%2B7VAw88IElyOp3Kzs7WiBEjJEnHjx/X8ePH5XQ66739qKioOpcDXa5Tqq5u2IFUU1Pb4HWg/ui1ORy73kNvvYv%2Bek9j722jvAAaFxenkJAQPf744zpy5Ii2b9%2BuhQsX6ve//71uv/12lZWVacGCBTp06JAWLFig8vJyDRo0SJI0ZswYbd26VRs3btTBgwc1ffp09e3bV%2B3atfPxXgEAAH/RKANWs2bN9MILL%2BjkyZMaOXKkZs2apdGjR%2Bv3v/%2B9mjVrpueee859lio3N1erVq1S06ZNJf0YzubNm6fly5drzJgxuvbaa5WWlubjPQIAAP4kwLIsy9dFXA1crlOXvazDEaiIiDCVlJzx69Opg5Z85OsSLsnbD/fydQl%2Br7Ecu1cieutd9Nd77O5tZOQ1Xt/GhTTKM1gAAAC%2BRMACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHP4ugDgSjVoyUe%2BLuGSvP1wL1%2BXAAD4fwQsP%2BdvIQAAgKsBlwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsC6gsrJSM2fOVEJCgnr37q0XX3zR1yUBAAA/4vB1AVeihQsXat%2B%2BfVq7dq2OHTumGTNmKCYmRrfffruvSwMAAH6AgHWes2fPauPGjXr%2B%2BefVtWtXde3aVfn5%2BXrllVcIWAAAoF64RHiegwcPqrq6WnFxce6x%2BPh45ebmqra21oeVAQAAf8EZrPO4XC5FREQoODjYPdaqVStVVlaqtLRULVq0uOg6ioqK5HK5PMYcjqaKioq6rJqCggI9fgIXMmjJR74uod7%2B9mgfX5fg9/j/gnfRX%2B%2B5WnpLwDpPeXm5R7iS5H5eVVVVr3VkZmYqIyPDY2zKlCl66KGHLqumoqIirV27WqNHj64T0v6%2BgMuWDVFUVKTMzMwL9hYNR3%2B955f%2Bv4CGo7/ec7X0tnHHx8sQEhJSJ0j99LxJkyb1Wsfo0aP12muveTxGjx592TW5XC5lZGTUOSuGhqO33kV/vYfeehf99Z6rpbecwTpPdHS0SkpKVF1dLYfjx/a4XC41adJE4eHh9VpHVFRUo07lAADgl3EG6zxdunSRw%2BFQTk6Oeyw7O1vdunVTYCDtAgAAF0diOE9oaKiGDx%2BuOXPmKC8vT9u2bdOLL76o8ePH%2B7o0AADgJ4LmzJkzx9dFXGl69uypAwcO6Omnn9bHH3%2BsBx54QCNHjvRpTWFhYUpMTFRYWJhP62iM6K130V/vobfeRX%2B952robYBlWZaviwAAAGhMuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyAdQWyLEsTJkzQa6%2B95jFeUlKihx56SHFxcUpKStLWrVs95g8cOKC77rpLTqdTI0eO1L59%2B%2Bws2%2B9UVlZq5syZSkhIUO/evfXiiy/6uiS/VFVVpaFDh2r37t3usYKCAt13332KjY3V4MGDtWPHDo9ldu7cqaFDh8rpdGr8%2BPEqKCiwu%2Bwr2okTJ5SSkqLExET16dNHaWlpqqyslERvTfjmm280ceJExcXFqW/fvlq9erV7jv6aM2nSJD322GPu5xd7j8rKylL//v3ldDqVnJyskydP2l2yUQSsK0xtba3mz5%2Bvjz76qM5camqqTp06pczMTP3xj3/U448/rry8PEnS2bNnNWnSJCUkJOi1115TXFycJk%2BerLNnz9q9C35j4cKF2rdvn9auXasnn3xSGRkZeuedd3xdll%2BprKzUI488ovz8fPeYZVlKTk5Wq1attHnzZg0bNkxTpkzRsWPHJEnHjh1TcnKyRowYoU2bNqlFixZ68MEHxZdK/MiyLKWkpKi8vFyvvPKKFi9erPfff19LliyhtwbU1tZq0qRJioiI0Ouvv665c%2Bdq5cqVevPNN%2BmvQX/961%2B1fft29/OLvUfl5eVp1qxZmjJlijIzM1VWVqbU1FRflW%2BGhStGYWGhdc8991h9%2B/a1EhISrM2bN7vnvvnmG6tTp05WQUGBe2zmzJnWjBkzLMuyrI0bN1pJSUlWbW2tZVmWVVtbaw0YMMBjHfiHM2fOWN26dbN27drlHlu%2BfLl1zz33%2BLAq/5Kfn2/dcccd1m9/%2B1urU6dO7l7u3LnTio2Ntc6cOeN%2B7b333mstW7bMsizLWrJkiUefz549a8XFxXn8XVzNDh06ZHXq1MlyuVzusTfffNPq3bs3vTXgxIkT1r//%2B79bp06dco8lJydbTz75JP01pKSkxLrtttuskSNH1vs9atq0ae7XWpZlHTt2zLrxxhutb7/91v4dMIQzWFeQ/fv3q3Xr1tq8ebOuueYaj7nc3Fy1bt1abdu2dY/Fx8fr008/dc/Hx8crICBAkhQQEKDu3bsrJyfHvh3wIwcPHlR1dbXi4uLcY/Hx8crNzVVtba0PK/Mfn3zyiXr06KHMzEyP8dzcXN10001q2rSpeyw%2BPt59LObm5iohIcE9Fxoaqq5du3Ks/r/IyEitXr1arVq18hg/ffo0vTUgKipKS5YsUbNmzWRZlrKzs7Vnzx4lJibSX0P%2B8z//U8OGDdP111/vHrvYe9T5vW3durViYmKUm5trb/EGEbCuIElJSVq4cKFatGhRZ87lcikqKspjrGXLljpx4sQvzhcWFnqvYD/mcrkUERGh4OBg91irVq1UWVmp0tJSH1bmP8aOHauZM2cqNDTUY/xixyLH6i8LDw9Xnz593M9ra2u1fv169ezZk94alpSUpLFjxyouLk4DBw6kvwZ8/PHH%2Bvvf/64HH3zQY/xivSsqKmp0vXX4uoCrSUVFhTsQnS8yMtLjt6bzlZeXe4QBSQoODlZVVVW95uHp5/oliZ41EMeqWenp6Tpw4IA2bdqkNWvW0FuDli1bpuLiYs2ZM0dpaWkcuw1UWVmpJ598UrNnz1aTJk085i7Wu4qKikbXWwKWjXJzczV%2B/PgLzi1fvlz9%2B/f/2WVDQkLqHGhVVVXug/hi8/D0c/2SRM8aKCQkpM5ZwPocq%2BHh4bbV6C/S09O1du1aLV68WJ06daK3hnXr1k3Sj8Hg0Ucf1ciRI1VeXu7xGvpbfxkZGbr55ps9zsD%2B5HLfw84/Q%2B5PCFg26tGjh7744ovLWjY6OlrFxcUeY8XFxYqMjPzF%2BfNPueJH0dHRKikpUXV1tRyOH/8zcLlcatKkCf%2BzbKDo6GgdOnTIY%2Byfj8WfO1a7dOliW43%2B4KmnntKGDRuUnp6ugQMHSqK3JhQXFysnJ8fjF9rrr79e586dU2RkpI4cOVLn9fS3fv7617%2BquLjY/dnWnwLTu%2B%2B%2Bq6FDh/7ie9TF3uP8EZ/B8hOxsbE6evSox/Xo7OxsxcbGSpKcTqc%2B/fRT9z8XtixLe/fuldPp9Em9V7ouXbrI4XB4fDg1Oztb3bp1U2Ag/1k0hNPp1P79%2B1VRUeEey87Odh%2BLTqdT2dnZ7rny8nIdOHCAY/WfZGRk6NVXX9UzzzyjIUOGuMfpbcN99913mjJlisfHNfbt26cWLVooPj6e/jbAyy%2B/rDfffFNbtmzRli1blJSUpKSkJG3ZsuWi71Hn9/b48eM6fvy4X/eWdxI/0a5dO/Xu3VvTpk3TwYMHtXHjRmVlZWncuHGSpNtvv11lZWVasGCBDh06pAULFqi8vFyDBg3yceVXptDQUA0fPlxz5sxRXl6etm3bphdffPFnL%2BGi/hITE9W6dWulpqYqPz9fq1atUl5enkaNGiVJGjlypPbu3atVq1YpPz9fqampatu2rXr06OHjyq8Mhw8f1ooVK/SHP/xB8fHxcrlc7ge9bbhu3bqpa9eumjlzpg4dOqTt27crPT1dDzzwAP1toDZt2ui6665zP8LCwhQWFqbrrrvuou9RY8aM0datW7Vx40YdPHhQ06dPV9%2B%2BfdWuXTsf71UD%2BPQmEfhZ/fr1q3MPq%2BLiYmvy5MlWt27drKSkJOvNN9/0mM/NzbWGDx9udevWzRo1apS1f/9%2BO0v2O2fPnrWmT59uxcbGWr1797ZeeuklX5fkt/75PliWZVlff/21NW7cOOvmm2%2B2hgwZYn300Ucer//ggw%2Bs3/zmN9Ytt9xi3XvvvX59rxvTnnvuOatTp04XfFgWvTWhsLDQSk5Otrp372716tXLWrlypfv%2BTPTXnBkzZnjc2%2Bpi71GbN2%2B2/vVf/9WKjY21kpOTrZMnT9pdslEBlsUtaAEAAEziEiEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPZ/N6aZ869pgLkAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6628396157896533339”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-31.382259185584143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.766414693954195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.727560652703104</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.66110047898739</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.7861944103078</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-24.25459242659929</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.872452066960705</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-23.756639958918576</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.49355335018637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-22.139024954817586</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2849)</td> <td class=”number”>2849</td> <td class=”number”>88.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6628396157896533339”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-93.3539482297769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-85.90756052007816</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.82452044481902</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-67.7326095706506</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-67.3805592808927</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>107.43031920457118</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>146.6120379870547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>175.46705483314022</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>227.4641245673449</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>405.9816390895147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_REDEMP_WICS_08_12”>PCH_REDEMP_WICS_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1938</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>39.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1273</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-3.5951</td>

</tr> <tr>

<th>Minimum</th> <td>-98.127</td>

</tr> <tr>

<th>Maximum</th> <td>254.62</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6910550592952981612”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6910550592952981612,#minihistogram6910550592952981612”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6910550592952981612”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6910550592952981612”

aria-controls=”quantiles6910550592952981612” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6910550592952981612” aria-controls=”histogram6910550592952981612”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6910550592952981612” aria-controls=”common6910550592952981612”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6910550592952981612” aria-controls=”extreme6910550592952981612”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6910550592952981612”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-98.127</td>

</tr> <tr>

<th>5-th percentile</th> <td>-34.5</td>

</tr> <tr>

<th>Q1</th> <td>-17.359</td>

</tr> <tr>

<th>Median</th> <td>-5.6912</td>

</tr> <tr>

<th>Q3</th> <td>7.6577</td>

</tr> <tr>

<th>95-th percentile</th> <td>33.164</td>

</tr> <tr>

<th>Maximum</th> <td>254.62</td>

</tr> <tr>

<th>Range</th> <td>352.75</td>

</tr> <tr>

<th>Interquartile range</th> <td>25.016</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>22.289</td>

</tr> <tr>

<th>Coef of variation</th> <td>-6.1998</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.001</td>

</tr> <tr>

<th>Mean</th> <td>-3.5951</td>

</tr> <tr>

<th>MAD</th> <td>16.148</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.6249</td>

</tr> <tr>

<th>Sum</th> <td>-6963.7</td>

</tr> <tr>

<th>Variance</th> <td>496.79</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6910550592952981612”>

<img 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fql27dh7Pb9asmfLy8iRJBQUFioqKqjF//PjxWu%2B/oKBALpfLY8xuD6ux3SsVFFTP4%2B9AEIg9Xy673X/em0Bdb/qmb38XSD37ZcCSpIMHD6pPnz669957lZeXpzlz5ujWW29VaWmpgoODPZ4bHBysiooKSVJZWdlF52sjKytLGRkZHmOpqalKS0u7wm7OLzw81Oj26oJA7Lm2mjZt6OsSjAvU9abvwBKIfQdCz34ZsLZu3ao1a9bo448/VoMGDRQTE6MTJ07ohRde0PXXX18jLFVUVKhBgwaSfjz7db750NDaHwwpKSlKSkryGLPbw3Ty5Jkr7MhTUFA9hYeHqri4VFVV1Ua2ea0LxJ4vl6nj61oQqOtN3/Tt73zRs6%2B%2B%2BfR6wBoxYoSGDRumgQMHqnHjxldlH3v27NENN9zgDk2SdPPNN2vx4sVKSEhQYWGhx/MLCwvdl%2B%2Bio6PPOx8ZGVnr/UdFRdW4HOhynVJlpdmDqaqq2vg2r3WB2HNt%2BeP7EqjrTd%2BBJRD7DoSevX4RtEePHlq8eLF69eqlKVOm6LPPPpNlWUb3ERUVpcOHD3uciTp06JBat24th8OhL774wr1Py7K0a9cuORwOSZLD4VB2drb7dceOHdOxY8fc8wAAAJfi9YD14IMP6u9//7uef/55BQUFafLkyerdu7eeffZZffPNN0b2kZSUpPr16%2BuRRx7RN998o//7v//T4sWLdc8996hfv34qLi7WvHnzdODAAc2bN0%2BlpaXq37%2B/JGnkyJHasGGDVq9erX379mnq1Knq3bu3rr/%2BeiO1AQAA/%2BeTj/HbbDb17NlTCxcu1JYtW3T33XdrxYoVGjBggO6%2B%2B2797W9/%2B0Xbb9y4sZYvXy6Xy6Xhw4crPT1d999/v1JSUtSoUSMtWbJE2dnZSk5OltPp1NKlSxUWFiZJiouL0%2BzZs5WZmamRI0fquuuuU3p6uom2AQBAgPDZh9wLCgr01ltv6a233tL%2B/fvVtWtX/e53v9Px48f1yCOPaOfOnZo5c%2BYVb79du3Z65ZVXzjvXpUsXrV%2B//oKvTU5OVnJy8hXvGwAABDavB6wNGzZow4YN2r59uyIiIjR06FD95S9/UZs2bdzPadGihebNm/eLAhYAAICveD1gzZw5U3369FFmZqZuu%2B021atX8yrljTfeqFGjRnm7NAAAACO8HrA%2B%2BeQTNW3aVEVFRe5wtXv3bnXq1ElBQUGSpK5du6pr167eLg0AAMAIr3/I/fTp0%2BrXr59efPFF99j48eM1ZMgQHTt2zNvlAAAAGOf1gPXEE0/ohhtu0L333usee/fdd9WiRQt%2BWg8AAPgFrwesf/zjH3r44Yc97oweERGhqVOnatu2bd4uBwAAwDivByy73a7i4uIa46Wlpcbv6A4AAOALXg9Yt912m%2BbOnavvvvvOPZafn6/09HQlJiZ6uxwAAADjvP5ThNOmTdO9996rO%2B64Q%2BHh4ZKk4uJiderUSdOnT/d2OQAAAMZ5PWA1a9ZM69ev15YtW5SXlye73a527drp1ltvlc1m83Y5AAAAxvnkV%2BUEBQUpMTGRS4IAAMAveT1guVwuLVq0SLt27dLZs2drfLD9ww8/9HZJAAAARnk9YP35z3/Wnj17NHDgQDVu3NjbuwcAALjqvB6wtm3bpmXLlikhIcHbuwYAAPAKr9%2BmISwsTM2aNfP2bgEAALzG6wFryJAhWrZsmaqqqry9awAAAK/w%2BiXCoqIibdy4UR999JGuv/56BQcHe8y/%2Buqr3i4JAADAKJ/cpmHQoEG%2B2C0AAIBXeD1gpaene3uXAAAAXuX1z2BJUkFBgTIyMvTggw/qn//8p95//30dOnTIF6UAAAAY5/WAdfjwYQ0ePFjr16/Xpk2bVFJSonfffVfDhg2T0%2Bn0djkAAADGeT1gzZ8/X3379tXmzZtVv359SdIzzzyjpKQkPfXUU94uBwAAwDivB6xdu3bp3nvv9fjFzna7XRMnTlRubq63ywEAADDO6wGrurpa1dXVNcbPnDmjoKAgb5cDAABgnNcDVq9evbRkyRKPkFVUVKSFCxeqR48e3i4HAADAOK8HrIcfflh79uxRr169VF5ervvvv199%2BvTR999/r2nTpnm7HAAAAOO8fh%2Bs6Oho/fWvf9XGjRu1d%2B9eVVdXa%2BTIkRoyZIgaNWrk7XIAAACM88md3ENDQzVixAhf7BoAAOCq83rAGj169EXn%2BV2EAACgrvN6wGrVqpXH48rKSh0%2BfFj79%2B/XmDFjvF0OAACAcdfM7yLMzMzU8ePHvVwNAACAeT75XYTnM2TIEL333nu%2BLgMAAOAXu2YC1hdffMGNRgEAgF%2B4Jj7kfvr0aX399de66667vF0OAACAcV4PWC1btvT4PYSSVL9%2BfY0aNUq//e1vvV0OAACAcV4PWPPnz/f2LgEAALzK6wFr586dtX7uLbfcchUrAQAAuDq8HrDuuece9yVCy7Lc4%2BeO2Ww27d2794r3U1FRofT0dG3cuFH169fX8OHD9d///d%2By2WzKzc3Vo48%2Bqv3796tdu3Z6/PHH1blzZ/drN27cqEWLFsnlcqlXr16aM2eOIiIirrgWAAAQWLz%2BU4SLFy9Wq1attGjRIm3dulXZ2dlavny5fvWrX2nKlCn68MMP9eGHH2rz5s2/aD9z587Vli1b9NJLL%2Bnpp5/Wm2%2B%2BqaysLJWUlGj8%2BPFKSEjQunXrFBcXpwkTJqikpESStHv3bs2cOVOTJk1SVlaWiouLNX36dBOtAwCAAOGTG43OmjVLt912m3usR48emj17tqZOnar77rvvF%2B%2BjqKhIa9eu1SuvvKIuXbpIksaNGyen0ym73a6QkBBNnTpVNptNM2fO1CeffKL3339fycnJWrVqlfr376%2BhQ4dKkhYsWKA%2BffooPz9f119//S%2BuDQAA%2BD%2Bvn8EqKCio8etyJKlRo0Y6efKkkX1kZ2erUaNG6tatm3ts/PjxSk9Pl9PpVHx8vPuSpM1mU9euXZWTkyNJcjqdSkhIcL%2BuRYsWatmypZxOp5HaAACA//P6GazY2Fg988wzevLJJ9WoUSNJP55xWrhwoW699VYj%2B8jPz1erVq3017/%2BVYsXL9bZs2eVnJys%2B%2B%2B/Xy6XS%2B3atfN4frNmzZSXlyfpxwAYFRVVY/5yfo1PQUGBXC6Xx5jdHlZju1cqKKiex9%2BBIBB7vlx2u/%2B8N4G63vRN3/4ukHr2esB65JFHNHr0aN12221q06aNLMvSt99%2Bq8jISL366qtG9lFSUqLDhw/rjTfeUHp6ulwul2bNmqXQ0FCVlpYqODjY4/nBwcGqqKiQJJWVlV10vjaysrKUkZHhMZaamqq0tLQr7Oj8wsNDjW6vLgjEnmuradOGvi7BuEBdb/oOLIHYdyD07PWA1bZtW7377rvauHGjDh48KEm6%2B%2B67NXDgQIWGmnnD7Xa7Tp8%2Braefftp9OfLo0aN6/fXXdcMNN9QISxUVFWrQoIEkKSQk5Lzzl1NbSkqKkpKSzqkpTCdPnrmSdmoICqqn8PBQFReXqqqq2sg2r3WB2PPlMnV8XQsCdb3pm779nS969tU3n14PWJJ03XXXacSIEfr%2B%2B%2B/dHxyvX7%2B%2Bse1HRkYqJCTE47Nev/rVr3Ts2DF169ZNhYWFHs8vLCx0X76Ljo4%2B73xkZGSt9x8VFVXjcqDLdUqVlWYPpqqqauPbvNYFYs%2B15Y/vS6CuN30HlkDsOxB69vpFUMuy9NRTT%2BmWW27RoEGDdPz4cU2bNk0zZ87U2bNnjezD4XCovLxc33zzjXvs0KFDatWqlRwOh7744gv3/bYsy9KuXbvkcDjcr83Ozna/7tixYzp27Jh7HgAA4FK8HrBWrlypDRs26NFHH3V/1qlv377avHlzjc8tXakbb7xRvXv31vTp07Vv3z59%2BumnWrp0qUaOHKl%2B/fqpuLhY8%2BbN04EDBzRv3jyVlpaqf//%2BkqSRI0dqw4YNWr16tfbt26epU6eqd%2B/e3KIBAADUmtcDVlZWlmbNmqXk5GT3rRIGDBiguXPn6u233za2n6eeekr/9m//ppEjR2ratGm6%2B%2B67dc8996hRo0ZasmSJsrOzlZycLKfTqaVLlyosLEySFBcXp9mzZyszM1MjR47Uddddp/T0dGN1AQAA/%2Bf1z2B9//336tixY43xDh061Li1wS/RuHFjLViw4LxzXbp00fr16y/42uTkZCUnJxurBQAABBavn8Fq1aqVvvzyyxrjn3zyCZfhAACAX/D6Gaz//M//1OOPPy6XyyXLsrR161ZlZWVp5cqVevjhh71dDgAAgHFeD1jDhg1TZWWlXnjhBZWVlWnWrFmKiIjQAw88oJEjR3q7HAAAAOO8HrA2btyofv36KSUlRT/88IMsy1KzZs28XQYAAMBV4/XPYM2ePdv9YfaIiAjCFQAA8DteD1ht2rTR/v37vb1bAAAAr/H6JcIOHTroT3/6k5YtW6Y2bdooJCTEY557TgEAgLrO6wHrm2%2B%2BUXx8vCQZve8VAADAtcIrAWvBggWaNGmSwsLCtHLlSm/sEgAAwGe88hmsV155RaWlpR5j48ePV0FBgTd2DwAA4FVeCViWZdUY27lzp8rLy72xewAAAK/y%2Bk8RAgAA%2BDsCFgAAgGFeC1g2m81buwIAAPApr92mYe7cuR73vDp79qwWLlyohg0bejyP%2B2ABAIC6zisB65Zbbqlxz6u4uDidPHlSJ0%2Be9EYJAAAAXuOVgMW9rwAAQCDhQ%2B4AAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhm93UBV9v48eMVERGh%2BfPnS5Jyc3P16KOPav/%2B/WrXrp0ef/xxde7c2f38jRs3atGiRXK5XOrVq5fmzJmjiIgIX5UP1Fr/RZ/7uoRae%2B%2BBnr4uAQCuKr8%2Bg/XOO%2B/o448/dj8uKSnR%2BPHjlZCQoHXr1ikuLk4TJkxQSUmJJGn37t2aOXOmJk2apKysLBUXF2v69Om%2BKh8AANRRfhuwioqKtGDBAsXExLjH3n33XYWEhGjq1Klq27atZs6cqYYNG%2Br999%2BXJK1atUr9%2B/fX0KFD1aFDBy1YsEAff/yx8vPzfdUGAACog/w2YD355JMaMmSI2rVr5x5zOp2Kj4%2BXzWaTJNlsNnXt2lU5OTnu%2BYSEBPfzW7RooZYtW8rpdHq3eAAAUKf5ZcDaunWr/vGPf2jixIke4y6XS1FRUR5jzZo10/HjxyVJBQUFF50HAACoDb/7kHt5ebkeffRRzZo1Sw0aNPCYKy0tVXBwsMdYcHCwKioqJEllZWUXna%2BtgoICuVwujzG7PaxGeLtSQUH1PP4OBIHYsz%2Bz2y%2B%2BjoG63vRN3/4ukHr2u4CVkZGhzp07KzExscZcSEhIjbBUUVHhDmIXmg8NDb2sGrKyspSRkeExlpqaqrS0tMvazqWEh19eXf4gEHv2R02bNqzV8wJ1vek7sARi34HQs98FrHfeeUeFhYWKi4uTJHdg2rRpkwYNGqTCwkKP5xcWFrrPLEVHR593PjIy8rJqSElJUVJSkseY3R6mkyfPXNZ2LiQoqJ7Cw0NVXFyqqqpqI9u81gViz/7sUl8Lgbre9E3f/s4XPdf2GzrT/C5grVy5UpWVle7HTz31lCTpT3/6k3bu3KkXX3xRlmXJZrPJsizt2rVLf/zjHyVJDodD2dnZSk5OliQdO3ZMx44dk8PhuKwaoqKialwOdLlOqbLS7MFUVVVtfJvXukDs2R/Vdg0Ddb3pO7AEYt%2BB0LPfBaxWrVp5PG7Y8MfkesMNN6hZs2Z6%2BumnNW/ePP3%2B97/XG2%2B8odLSUvXv31%2BSNHLkSN1zzz2KjY1VTEyM5s2bp969e%2Bv666/3eh8AAKDu8v9Pmf1Mo0aNtGTJEvdZKqfTqaVLlyosLEySFBcXp9mzZyszM1MjR47Uddddp/T0dB9XDQAA6hq/O4N1rp9%2BRc5PunTpovXr11/w%2BcnJye5LhAAAAFcioM5gAQAAeAMBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEYkdMvkAAARrklEQVTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG%2BW3AOnHihNLS0tStWzclJiYqPT1d5eXlkqT8/HyNHTtWsbGxGjBggD777DOP127ZskWDBg2Sw%2BHQ6NGjlZ%2Bf74sWAABAHeWXAcuyLKWlpam0tFSvvfaann32Wf3973/XokWLZFmWUlNT1bx5c61du1ZDhgzRpEmTdPToUUnS0aNHlZqaquTkZK1Zs0YRERGaOHGiLMvycVcAAKCusPu6gKvh0KFDysnJ0eeff67mzZtLktLS0vTkk0/qtttuU35%2Bvt544w2FhYWpbdu22rp1q9auXavJkydr9erV6ty5s8aNGydJSk9PV8%2BePbVjxw51797dl20BAIA6wi/PYEVGRmrZsmXucPWT06dPy%2Bl06uabb1ZYWJh7PD4%2BXjk5OZIkp9OphIQE91xoaKg6derkngcAALgUvzyDFR4ersTERPfj6upqrVq1Sj169JDL5VJUVJTH85s1a6bjx49L0iXna6OgoEAul8tjzG4Pq7HdKxUUVM/j70AQiD37M7v94usYqOtN3/Tt7wKpZ78MWOdauHChcnNztWbNGi1fvlzBwcEe88HBwaqoqJAklZaWXnS%2BNrKyspSRkeExlpqaqrS0tCvs4PzCw0ONbq8uCMSe/VHTpg1r9bxAXW/6DiyB2Hcg9Oz3AWvhwoVasWKFnn32WbVv314hISEqKiryeE5FRYUaNGggSQoJCakRpioqKhQeHl7rfaakpCgpKcljzG4P08mTZ66wC09BQfUUHh6q4uJSVVVVG9mmN9z%2B1Ke%2BLgHXiEt9LdTVY/yXom/69ne%2B6Lm239CZ5tcBa86cOXr99de1cOFC3XHHHZKk6OhoHThwwON5hYWF7st30dHRKiwsrDHfsWPHWu83KiqqxuVAl%2BuUKivNHkxVVdXGtwl4Q22P20A9xuk7sARi34HQs99eBM3IyNAbb7yhZ555RgMHDnSPOxwOffXVVyorK3OPZWdny%2BFwuOezs7Pdc6WlpcrNzXXPAwAAXIpfBqyDBw/q%2Beef13333af4%2BHi5XC73n27duqlFixaaPn268vLytHTpUu3evVvDhw%2BXJA0bNky7du3S0qVLlZeXp%2BnTp6t169bcogEAANSaXwasDz/8UFVVVXrhhRfUq1cvjz9BQUF6/vnn5XK5lJycrLfeekuZmZlq2bKlJKl169Z67rnntHbtWg0fPlxFRUXKzMyUzWbzcVcAAKCusFncotwrXK5TxrZlt9dT06YNdfLkmTp1Dbv/os99XQKuEe890POi83X1GP%2Bl6Ju%2B/Z0veo6MbOyV/ZzLL89gAQAA%2BBIBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyz%2B7oAAIGn/6LPfV3CZXnvgZ6%2BLgFAHcMZLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAes8ysvLNWPGDCUkJKhXr156%2BeWXfV0SAACoQ7gP1nksWLBAe/bs0YoVK3T06FFNmzZNLVu2VL9%2B/XxdGgAAqAMIWOcoKSnR6tWr9eKLL6pTp07q1KmT8vLy9NprrxGwAABArXCJ8Bz79u1TZWWl4uLi3GPx8fFyOp2qrq72YWUAAKCu4AzWOVwul5o2barg4GD3WPPmzVVeXq6ioiJFRERcchsFBQVyuVweY3Z7mKKioozUGBRUz/337U99amSbAC6srv1qnw/%2BlOjrEi7Lz/9NCySB2Hcg9UzAOkdpaalHuJLkflxRUVGrbWRlZSkjI8NjbNKkSZo8ebKRGgsKCrRixTKlpKToH/MC47JlQUGBsrKylJKSYiyo1gX0Td%2BB4Of/ptG3fwuknv0/Ql6mkJCQGkHqp8cNGjSo1TZSUlK0bt06jz8pKSnGanS5XMrIyKhxlsyfBWLPEn3Td2Cg78DpO5B65gzWOaKjo3Xy5ElVVlbKbv/x7XG5XGrQoIHCw8NrtY2oqCi/T%2BYAAODCOIN1jo4dO8putysnJ8c9lp2drZiYGNWrx9sFAAAujcRwjtDQUA0dOlSPPfaYdu/erc2bN%2Bvll1/W6NGjfV0aAACoI4Iee%2Byxx3xdxLWmR48eys3N1dNPP62tW7fqj3/8o4YNG%2Bbrsjw0bNhQ3bp1U8OGDX1ditcEYs8SfdN3YKDvwOk7UHq2WZZl%2BboIAAAAf8IlQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKw6wLIsjRs3TuvWrfMYP3nypCZPnqy4uDglJSVpw4YNHvO5ubkaMWKEHA6Hhg0bpj179nizbCNyc3P161//2uNPcnKyez4/P19jx45VbGysBgwYoM8%2B%2B8yH1ZpVXl6uGTNmKCEhQb169dLLL7/s65Kuig8%2B%2BKDGGqelpUnyj2P4XBUVFRo0aJC2b9/uHrvUcbxlyxYNGjRIDodDo0ePVn5%2BvrfL/sXO1/fcuXNrrP2qVavc8xs3blTfvn3lcDiUmpqqH374wRelX7YTJ04oLS1N3bp1U2JiotLT01VeXi7Jv9f6Yn3761pflIVrWlVVlTV79myrffv21tq1az3mJkyYYI0ZM8b6%2BuuvrTfffNPq3Lmz5XQ6LcuyrDNnzlg9e/a05s%2Bfbx04cMCaM2eO9e///u/WmTNnfNHGFduwYYM1ZMgQq6CgwP3nhx9%2BsCzLsqqrq63BgwdbDz74oHXgwAFr8eLFlsPhsI4cOeLjqs2YPXu2NXjwYGvPnj3W3/72NysuLs567733fF2Wcc8//7w1YcIEjzX%2B17/%2B5TfH8M%2BVlZVZqampVvv27a1t27ZZlnXp4/jIkSNWbGys9dJLL1n79%2B%2B3/uu//ssaNGiQVV1d7ctWLsv5%2BrYsyxo7dqy1ZMkSj7UvKSmxLMuynE6n1aVLF2v9%2BvXW3r17rVGjRlnjx4/3VQu1Vl1dbd15553WH/7wB2v//v3Wzp07rdtvv92aP3%2B%2BX6/1xfq2LP9c60shYF3Djh8/bo0aNcrq3bu3lZCQ4BGwDh8%2BbLVv397Kz893j82YMcOaNm2aZVmWtXr1aispKcn9hVldXW3dfvvtNULate6ZZ56xpkyZct65LVu2WLGxsR7/4Y4ZM8b6y1/%2B4q3yrpozZ85YMTExHv8ZZWZmWqNGjfJhVVfHgw8%2BaD399NM1xv3lGP5JXl6e9dvf/tYaPHiwR9C41HG8aNEij3UvKSmx4uLiPI6Na9mF%2BrYsy0pMTLQ%2B/fTT877uoYcecv97ZlmWdfToUevXv/619d133131mn%2BJAwcOWO3bt7dcLpd77O2337Z69erl12t9sb4tyz/X%2BlK4RHgN%2B%2Bqrr9SiRQutXbtWjRs39phzOp1q0aKFWrdu7R6Lj4/XF1984Z6Pj4%2BXzWaTJNlsNnXt2lU5OTnea8CAgwcPqk2bNuedczqduvnmmxUWFuYei4%2BPr3M9ns%2B%2BfftUWVmpuLg491h8fLycTqeqq6t9WJl5F1pjfzmGf7Jjxw51795dWVlZHuOXOo6dTqcSEhLcc6GhoerUqVOdeR8u1Pfp06d14sSJi359/7zvFi1aqGXLlnI6nVez3F8sMjJSy5YtU/PmzT3GT58%2B7ddrfbG%2B/XWtL8Xu6wJwYUlJSUpKSjrvnMvlUlRUlMdYs2bNdOLECfd8u3btaszn5eVdnWKvkoMHD6q6ulqDBw/WqVOndNttt2nq1Klq1KjRBd%2BD48eP%2B6hac1wul5o2barg4GD3WPPmzVVeXq6ioiJFRET4sDpzLMvSN998o88%2B%2B0xLlixRVVWV%2BvXrp7S0NL85hn9y1113nXf8UsdxXT/OL9T3wYMHZbPZtHjxYn3yySdq0qSJ7r33Xv3ud7%2BTJBUUFNTJvsPDw5WYmOh%2BXF1drVWrVqlHjx5%2BvdYX69tf1/pSCFg%2BVFZW5g5E54qMjPT4LudcpaWlHv/5SlJwcLAqKipqNX%2BtuNh7EBERofz8fLVu3VpPPPGEiouLlZ6eroceekgvvPBCnenxSlyoN0l%2B0d9Pjh496u510aJF%2Bv777zV37lyVlZX59fr%2BnL98LV%2BuQ4cOyWaz6cYbb9SoUaO0c%2BdO/fnPf1ajRo10%2B%2B23q6yszC/6XrhwoXJzc7VmzRotX748YNb6531/9dVXAbHW5yJg%2BZDT6dTo0aPPO5eZmam%2Bffte8LUhISE1Dr6Kigo1aNCgVvPXiku9B9u2bVNISIjq168vSZo/f76GDRumEydOKCQkREVFRR6vuRZ7vBIXWj9JftHfT1q1aqXt27fruuuuk81mU8eOHVVdXa2HHnpI3bp1qxPH8C91qeP4QsdCeHi412q8GoYOHao%2BffqoSZMmkqQOHTro22%2B/1euvv67bb7/9gn2Hhob6otwrsnDhQq1YsULPPvus2rdvHzBrfW7fN910k9%2Bv9fkQsHyoe/fu%2Bvrrr6/otdHR0SosLPQYKywsVGRk5EXnzz0N62uX%2Bx60bdtW0o8/DhwdHa0DBw54zF%2BLPV6J6OhonTx5UpWVlbLbf/wydblcatCgQZ37x/ZSfvpH9ydt27ZVeXm5IiMj68Qx/Etd6ji%2B0Ndyx44dvVbj1WCz2Wqs/Y033qht27ZJuvS/cde6OXPm6PXXX9fChQt1xx13SAqMtT5f3/6%2B1hfCh9zrqNjYWB05csTjGnV2drZiY2MlSQ6HQ1988YUsy5L042dddu3aJYfD4ZN6r8SBAwcUFxfncR%2BYvXv3ym6364YbbpDD4dBXX32lsrIy93x2dnad6vFCOnbsKLvd7vHh1uzsbMXExKhePf/5sv3000/VvXt3lZaWusf27t2rJk2auH9ooy4fw7VxqePY4XAoOzvbPVdaWqrc3Nw6/z78z//8j8aOHesxtm/fPt14442SavZ97NgxHTt2rE70nZGRoTfeeEPPPPOMBg4c6B7397W%2BUN/%2BvNYX5dOfYUSt9enTp8aPp48bN84aNWqUtXfvXuvNN9%2B0YmJi3PfBOnXqlNWjRw9rzpw5Vl5enjVnzhyrZ8%2BedeoeQlVVVdaQIUPc9/rauXOnNWDAAOvRRx%2B1LMuyKisrrQEDBlgPPPCAtX//fmvJkiVWbGys39wH689//rM1cOBAy%2Bl0Wh988IHVtWtXa9OmTb4uy6hTp05ZiYmJ1pQpU6yDBw9aH330kdWrVy9r6dKlfnEMX8jPb1dwqeM4Pz/fiomJsZYsWeK%2BN9LgwYPrxL2RzvXzvp1Op3XzzTdby5Ytsw4fPmy99tprVufOna1du3ZZlmVZu3btsjp16mS9%2Beab7nsjTZgwwZfl18qBAwesjh07Ws8%2B%2B6zHPZ8KCgr8eq0v1re/rvWlELDqiPMFrMLCQmvChAlWTEyMlZSUZL399tse806n0xo6dKgVExNjDR8%2B3Prqq6%2B8WbIRR48etVJTU62EhASrW7du1pw5c6zy8nL3/LfffmvdfffdVufOna2BAwdan3/%2BuQ%2BrNaukpMSaOnWqFRsba/Xq1ct65ZVXfF3SVbF//35r7NixVmxsrNWzZ0/rueeec/%2BH4g/H8Pmcez%2BoSx3HH330kfWb3/zG6tKlizVmzJg6e3%2Bgc/v%2B4IMPrMGDB1sxMTFWv379anwDsXbtWus//uM/rNjYWCs1NdV9k%2BFr2ZIlS6z27duf949l%2Be9aX6pvf1zrS7FZ1v8//w4AAAAj/OfDHAAAANcIAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPt/gnvEPIq/OhcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6910550592952981612”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-16.67612</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.6952</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.139538</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.35116</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.91461</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.48456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.64413</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-12.98182</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-5.848339</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.236936</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1927)</td> <td class=”number”>1927</td> <td class=”number”>60.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1273</td> <td class=”number”>39.7%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6910550592952981612”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-98.12657</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-68.79174</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-64.7597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-63.54496</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-56.97397</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>94.64105</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>104.2254</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>131.2125</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.3263</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>254.6232</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SBP_09_15”>PCH_SBP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.68642</td>

</tr> <tr>

<th>Minimum</th> <td>-0.13397</td>

</tr> <tr>

<th>Maximum</th> <td>4.2368</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1051173535919161120”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1051173535919161120,#minihistogram1051173535919161120”
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</div> <div class=”row collapse col-md-12” id=”descriptives1051173535919161120”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1051173535919161120”

aria-controls=”quantiles1051173535919161120” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1051173535919161120” aria-controls=”histogram1051173535919161120”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1051173535919161120” aria-controls=”common1051173535919161120”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1051173535919161120” aria-controls=”extreme1051173535919161120”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1051173535919161120”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-0.13397</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.044096</td>

</tr> <tr>

<th>Q1</th> <td>0.39306</td>

</tr> <tr>

<th>Median</th> <td>0.65002</td>

</tr> <tr>

<th>Q3</th> <td>0.86191</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.2584</td>

</tr> <tr>

<th>Maximum</th> <td>4.2368</td>

</tr> <tr>

<th>Range</th> <td>4.3708</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.46886</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.5472</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.79718</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.664</td>

</tr> <tr>

<th>Mean</th> <td>0.68642</td>

</tr> <tr>

<th>MAD</th> <td>0.3521</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.7143</td>

</tr> <tr>

<th>Sum</th> <td>2149.9</td>

</tr> <tr>

<th>Variance</th> <td>0.29943</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1051173535919161120”>

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4FvrhW%2BiHb6EfLRNwASshIUFHjhzxGquqqvLsPUpISFBVVVWz%2Bd69e%2BuSSy5RWFiYqqqq1KNHD0lSQ0ODampqFBcX1%2BIaMjMzNWzYMK8xhyNS1dWnf84mGRUSEqyoqAjV1tapsbHJ7nJ8mhX9oh%2B%2BhX74FvrhW/y1H1Z%2BWP6ugAtYSUlJKiws1NmzZz17rYqLi5WSkuKZLy4u9ty/rq5Ohw4dUnZ2toKDg5WYmKji4mINGDBAklRSUiKHw6FevXq1uIb4%2BPhmhwOdzpNqaPCdN2RjY5NP1eOLrHx96IdvoR%2B%2BhX74FvrRMgF3IDUtLU0dO3ZUbm6uysrKVFhYqAMHDmjcuHGSpLFjx2r//v0qLCxUWVmZcnNz1aVLF0%2BgmjRpkp588knt2LFDBw4c0Pz58zVhwoQLOkQIAADatoALWCEhIVq1apWcTqcyMjL04osvauXKlerUqZMkqUuXLnr00Ue1ZcsWjRs3TjU1NVq5cqWCgoIkSdddd52mT5%2BuefPmaerUqbr88st1zz332LlJAADAzwS5v72EOVqV03nS7hIkffPNuOjodqquPm35Lt6RK962dL1f6pU7B7X6Gnb2A83RD99CP3yLv/YjLu4iW9YNuD1YAAAAdiNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZnnAGj9%2BvDZu3KiTJ09avTQAAIAlLA9YAwcO1OrVqzV48GDNnDlTb731ltxut9VlAAAAtBrLA9Zdd92lf/7zn1q1apVCQkI0Y8YMDRkyRA8//LA%2B/fRTq8sBAAAwzmHHokFBQRo0aJAGDRqkuro6rV%2B/XqtWrVJhYaH69%2B%2BvG2%2B8UVdffbUdpQEAAPxitgQsSaqsrNSLL76oF198UR999JH69%2B%2Bv66%2B/XhUVFbrvvvu0b98%2BzZ07167yAAAAfjbLA9YLL7ygF154QXv27FFMTIzGjBmjRx55RN27d/fcp2PHjlqyZAkBCwAA%2BCXLA9bcuXM1dOhQrVy5UldddZWCg5ufBvab3/xGf/rTn6wuDQAAwAjLA9abb76p6Oho1dTUeMLVgQMH1KdPH4WEhEiS%2Bvfvr/79%2B1tdGgAAgBGWf4vw1KlTGjFihJ544gnP2LRp0zR69GidOHHC6nIAAACMszxgPfDAA%2BrWrZtuvvlmz9jLL7%2Bsjh07Ki8vz%2BpyAAAAjLM8YL377ru69957FRcX5xmLiYnRrFmz9M4771hdDgAAgHGWByyHw6Ha2tpm43V1dVzRHQAABATLA9ZVV12lxYsX69ixY56x8vJy5eXlKT093epyAAAAjLP8W4SzZ8/WzTffrGuuuUZRUVGSpNraWvXp00e5ublWlwMAAGCc5QErNjZWzz//vHbt2qWysjI5HA717NlTV155pYKCgoytc%2BLECc2fP1/79u3TJZdcoilTpuimm26SJB06dEj333%2B/PvroI/Xs2VMLFixQ3759PY/dvn27VqxYIafTqcGDB2vRokWKiYkxVhsAAAhslh8ilKSQkBClp6dr6tSpmjJlin73u98ZDVeSdOeddyoyMlJbt27VnDlztGLFCv3f//2fzpw5o2nTpik1NVVbt25VcnKypk%2BfrjNnzkj65ppcc%2BfOVXZ2toqKilRbW8ueNQAAcEEs34PldDq1YsUK7d%2B/X%2BfOnWt2Yvvrr7/%2Bi9f4%2BuuvVVJSokWLFql79%2B7q3r270tPTtXv3bn399dcKCwvTrFmzFBQUpLlz5%2BrNN9/Uq6%2B%2BqoyMDG3YsEEjR47UmDFjJElLly7V0KFDVV5erq5du/7i2gAAQOCzPGD99a9/1cGDB3XdddfpoosuapU1wsPDFRERoa1bt%2Bquu%2B5SeXm59u/frzvvvFOlpaVKSUnx7DELCgpS//79VVJSooyMDJWWlurWW2/1PFfHjh3VqVMnlZaWErAAAECLWB6w3nnnHa1Zs0apqamttkZYWJjmzZunRYsWad26dWpsbFRGRobGjx%2Bv119/XT179vS6f2xsrMrKyiRJlZWVio%2BPbzZfUVHR4vUrKyvldDq9xhyOyGbPa4eQkGCvn/hhDkfrv0b0w7fQD99CP3wL/bgwlgesyMhIxcbGtvo6H3/8sYYOHaqbb75ZZWVlWrRoka688krV1dUpNDTU676hoaFyuVySpLNnz/7ofEsUFRWpoKDAaywrK0s5OTk/c2vMi4qKsLsEnxcd3c6yteiHb6EfvoV%2B%2BBb60TKWB6zRo0drzZo1WrhwoeePO5u2e/dubd68WTt37lR4eLgSExP1xRdf6LHHHlPXrl2bhSWXy6Xw8HBJ3%2Bz9Ot98RETL31CZmZkaNmyY15jDEanq6tM/c4vMCQkJVlRUhGpr69TY2GR3OT7Nin7RD99CP3wL/fAt/toPKz8sf5flAaumpkbbt2/XG2%2B8oa5duzbbW7Ru3bpfvMbBgwfVrVs3T2iSpMsuu0yrV69WamqqqqqqvO5fVVXlOXyXkJBw3vnv/mmfnxIfH9/scKDTeVINDb7zhmxsbPKpenyRla8P/fAt9MO30A/fQj9axvKAJUmjRo1q1eePj4/X0aNH5XK5PAHuk08%2BUZcuXZSUlKQnnnhCbrdbQUFBcrvd2r9/v2677TZJUlJSkoqLi5WRkSHpm%2BtpnThxQklJSa1aMwAACByWB6y8vLxWX2PYsGHKz8/Xfffdp9tvv12ffvqpVq9erb/85S8aMWKEli9friVLluiGG27Qxo0bVVdXp5EjR0qSJk6cqMmTJ6tfv35KTEzUkiVLNGTIEL5BCAAAWsyWrwJUVlaqoKBAd911l7788ku9%2Buqr%2BuSTT4w9/0UXXaRnnnlGTqdT48aNU15enm6//XZlZmaqffv2evzxxz17qUpLS1VYWKjIyEhJUnJyshYuXKiVK1dq4sSJuvjiiy0JhQAAIHAEub9/pc9WdvToUU2YMEHt27fXF198oVdeeUX5%2Bfn617/%2BpWeeeSZgD8U5nSftLkHSN5ceiI5up%2Brq05YfQx%2B54m1L1/ulXrlzUKuvYWc/0Bz98C30w7f4az/i4lrnmps/xfI9WH/72980fPhw7dixQ7/61a8kSQ899JCGDRumZcuWWV0OAACAcZYHrP379%2Bvmm2/2%2BtuDDodDd9xxhw4dOmR1OQAAAMZZfpJ7U1OTmpqa71o8ffp0q10XC/g5OKQJAPi5LN%2BDNXjwYD3%2B%2BONeIaumpkb5%2BfkaOHCg1eUAAAAYZ3nAuvfee3Xw4EENHjxY9fX1uv322zV06FB99tlnmj17ttXlAAAAGGf5IcKEhAT9/e9/1/bt2/XBBx%2BoqalJEydO1OjRo9W%2BfXurywEAADDOliu5R0REaPz48XYsDQAA0OosD1hTpkz50XkTf4sQAADATpYHrM6dO3vdbmho0NGjR/XRRx/pxhtvtLocAAAA43zmbxGuXLlSFRUVFlcDAABgni1/i/B8Ro8erVdeecXuMgAAAH4xnwlY7733HhcaBQAAAcEnTnI/deqUPvzwQ02aNMnqcgAAAIyzPGB16tTJ6%2B8QStKvfvUr/elPf9If/vAHq8sBAAAwzvKA9be//c3qJQEAACxlecDat29fi%2B97xRVXtGIlAAAArcPygDV58mTPIUK32%2B0Z//5YUFCQPvjgA6vLAwAA%2BMUsD1irV6/W4sWLdc899ygtLU2hoaF6//33tXDhQl1//fW69tprrS4JAADAKMsv05CXl6d58%2BbpmmuuUXR0tNq1a6eBAwdq4cKFeu6559S5c2fPPwAAAH9kecCqrKw8b3hq3769qqurrS4HAADAOMsDVr9%2B/fTQQw/p1KlTnrGamhrl5%2BfryiuvtLocAAAA4yw/B%2Bu%2B%2B%2B7TlClTdNVVV6l79%2B5yu93697//rbi4OK1bt87qcgAAAIyzPGD16NFDL7/8srZv366PP/5YkvTHP/5R1113nSIiIqwuBwAAwDjLA5YkXXzxxRo/frw%2B%2B%2Bwzde3aVdI3V3MHAAAIBJafg%2BV2u7Vs2TJdccUVGjVqlCoqKjR79mzNnTtX586ds7ocAAAA4ywPWOvXr9cLL7yg%2B%2B%2B/X6GhoZKk4cOHa8eOHSooKLC6HAAAAOMsD1hFRUWaN2%2BeMjIyPFdvv/baa7V48WJt27bN6nIAAACMszxgffbZZ%2Brdu3ez8V69esnpdFpdDgAAgHGWB6zOnTvr/fffbzb%2B5ptvek54BwAA8GeWf4vwlltu0YIFC%2BR0OuV2u7V7924VFRVp/fr1uvfee60uBwAAwDjLA9bYsWPV0NCgxx57TGfPntW8efMUExOjO%2B%2B8UxMnTrS6HAAAAOMsP0S4fft2jRgxQm%2B88YZ27dqlt99%2BW7t27dLNN99sdB2Xy6UFCxboiiuu0O9%2B9zs99NBDcrvdkqRDhw5p/PjxSkpK0tixY3Xw4MFmNQ4fPlxJSUnKysrSV199ZbQ2AAAQ2CwPWAsXLvSczB4TE6PY2NhWWWfx4sXatWuXnnzySS1fvlz/%2B7//q6KiIp05c0bTpk1Tamqqtm7dquTkZE2fPl1nzpyRJB04cEBz585Vdna2ioqKVFtbq9zc3FapEQAABCbLDxF2795dH330kXr27Nlqa9TU1GjLli16%2Bumndfnll0uSpk6dqtLSUjkcDoWFhWnWrFkKCgrS3Llz9eabb%2BrVV19VRkaGNmzYoJEjR2rMmDGSpKVLl2ro0KEqLy/nJHwAANAilgesXr166e6779aaNWvUvXt3hYWFec3n5eX94jWKi4vVvn17paWlecamTZsmSfrrX/%2BqlJQUzzW4goKC1L9/f5WUlCgjI0OlpaW69dZbPY/r2LGjOnXqpNLSUgIWAABoEcsD1qeffqqUlBRJarXrXpWXl6tz5876%2B9//rtWrV%2BvcuXPKyMjQ7bffLqfT2WzvWWxsrMrKyiRJlZWVio%2BPbzZfUVHRKrUCAIDAY0nAWrp0qbKzsxUZGan169e3%2BnpnzpzR0aNHtXHjRuXl5cnpdGrevHmKiIhQXV2d50/0fCs0NFQul0uSdPbs2R%2Bdb4nKyspm4dHhiGwW3OwQEhLs9ROBw%2BGgp78Uvx%2B%2BhX74FvpxYSwJWE8//bRuueUWRUZGesamTZumxYsXt0rocDgcOnXqlJYvX67OnTtLko4fP67nnntO3bp1axaWXC6XwsPDJUlhYWHnnY%2BIiGjx%2BkVFRc3%2BrmJWVpZycnJ%2Bzua0iqiolm8P/EN0dDu7SwgY/H74FvrhW%2BhHy1gSsL69PMJ37du3T/X19a2yXlxcnMLCwjzhSpJ%2B/etf68SJE0pLS1NVVZXX/auqqjxBLyEh4bzzcXFxLV4/MzNTw4YN8xpzOCJVXX36QjfFuJCQYEVFRai2tk6NjU12lwODfOH95e/4/fAt9MO3%2BGs/7Prwafk5WFZISkpSfX29Pv30U/3617%2BWJH3yySfq3LmzkpKS9MQTT8jtdisoKEhut1v79%2B/Xbbfd5nlscXGxMjIyJEknTpzQiRMnlJSU1OL14%2BPjm%2B2ZczpPqqHBd96QjY1NPlUPfjn6aQ6/H76FfvgW%2BtEyAXkg9Te/%2BY2GDBmi3NxcHT58WP/6179UWFioiRMnasSIEaqtrdWSJUt05MgRLVmyRHV1dRo5cqQkaeLEiXrhhRe0adMmHT58WLNmzdKQIUP4BiEAAGgxywLWt5dFsMqyZcv0n//5n5o4caJmz56tP/7xj5o8ebLat2%2Bvxx9/3LOXqrS0VIWFhZ7zw5KTk7Vw4UKtXLlSEydO1MUXX2zk0hEAAKDtCHKf7wQpw3r16qVrr73W65pX27Zt07Bhw9Sunfex0UANM07nSbtLkPTNN82io9upuvq05bt4R65429L12ppX7hxkdwl%2Bz87fDzRHP3yLv/YjLu4iW9a15BysK664otllC5KTk1VdXa3q6morSgAAALCMJQHLimtfAQAA%2BIqAPMkdAADATgQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBLLjSK1sOfnwEAwPewBwsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwL%2BIA1bdo03XvvvZ7bhw4d0vjx45WUlKSxY8fq4MFHU/OxAAAPcElEQVSDXvffvn27hg8frqSkJGVlZemrr76yumQAAODnAjpgvfTSS9q5c6fn9pkzZzRt2jSlpqZq69atSk5O1vTp03XmzBlJ0oEDBzR37lxlZ2erqKhItbW1ys3Ntat8AADgpwI2YNXU1Gjp0qVKTEz0jL388ssKCwvTrFmz1KNHD82dO1ft2rXTq6%2B%2BKknasGGDRo4cqTFjxqhXr15aunSpdu7cqfLycrs2AwAA%2BKGADVgPPvigRo8erZ49e3rGSktLlZKSoqCgIElSUFCQ%2Bvfvr5KSEs98amqq5/4dO3ZUp06dVFpaam3xAADArznsLqA17N69W%2B%2B%2B%2B662bdum%2BfPne8adTqdX4JKk2NhYlZWVSZIqKysVHx/fbL6iouKC1q%2BsrJTT6fQaczgimz03YJLDEbCflywTEhLs9RP2oh%2B%2BhX5cmIALWPX19br//vs1b948hYeHe83V1dUpNDTUayw0NFQul0uSdPbs2R%2Bdb6mioiIVFBR4jWVlZSknJ%2BeCnge4ENHR7ewuIWBERUXYXQK%2Bg374FvrRMgEXsAoKCtS3b1%2Blp6c3mwsLC2sWllwulyeI/dB8RMSFvZkyMzM1bNgwrzGHI1LV1acv6HmAC8H765cLCQlWVFSEamvr1NjYZHc5bR798C3%2B2g%2B7PnwGXMB66aWXVFVVpeTkZEnyBKbXXntNo0aNUlVVldf9q6qqPIfuEhISzjsfFxd3QTXEx8c3OxzodJ5UQ4P/vCHhf3h/mdPY2MTr6UPoh2%2BhHy0TcAFr/fr1amho8NxetmyZJOnuu%2B/Wvn379MQTT8jtdisoKEhut1v79%2B/XbbfdJklKSkpScXGxMjIyJEknTpzQiRMnlJSUZP2GAAAAvxVwAatz585et9u1%2B2bXYLdu3RQbG6vly5dryZIluuGGG7Rx40bV1dVp5MiRkqSJEydq8uTJ6tevnxITE7VkyRINGTJEXbt2tXw7AACA/2pTXwVo3769Hn/8cc9eqtLSUhUWFioyMlKSlJycrIULF2rlypWaOHGiLr74YuXl5dlcNQAA8DdBbrfbbXcRbYHTebJVnnfkirdb5Xnhf165c5DdJfg9hyNY0dHtVF19mnNMfAD98C3%2B2o%2B4uItsWbdN7cECAACwAgELAADAMAIWAACAYQH3LUKgrfKn8/E4XwxAoGMPFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYFbMD64osvlJOTo7S0NKWnpysvL0/19fWSpPLyct10003q16%2Bfrr32Wr311ltej921a5dGjRqlpKQkTZkyReXl5XZsAgAA8FMBGbDcbrdycnJUV1enZ599Vg8//LD%2B%2Bc9/asWKFXK73crKylKHDh20ZcsWjR49WtnZ2Tp%2B/Lgk6fjx48rKylJGRoY2b96smJgY3XHHHXK73TZvFQAA8BcOuwtoDZ988olKSkr09ttvq0OHDpKknJwcPfjgg7rqqqtUXl6ujRs3KjIyUj169NDu3bu1ZcsWzZgxQ5s2bVLfvn01depUSVJeXp4GDRqkvXv3asCAAXZuFgAA8BMBuQcrLi5Oa9as8YSrb506dUqlpaW67LLLFBkZ6RlPSUlRSUmJJKm0tFSpqameuYiICPXp08czDwAA8FMCMmBFRUUpPT3dc7upqUkbNmzQwIED5XQ6FR8f73X/2NhYVVRUSNJPzgMAAPyUgDxE%2BH35%2Bfk6dOiQNm/erGeeeUahoaFe86GhoXK5XJKkurq6H51vicrKSjmdTq8xhyOyWXAD2iqHwzc/24WEBHv9hL3oh2%2BhHxcm4ANWfn6%2B1q5dq4cffli//e1vFRYWppqaGq/7uFwuhYeHS5LCwsKahSmXy6WoqKgWr1lUVKSCggKvsaysLOXk5PzMrQACS3R0O7tL%2BFFRURF2l4DvoB%2B%2BhX60TEAHrEWLFum5555Tfn6%2BrrnmGklSQkKCjhw54nW/qqoqz96lhIQEVVVVNZvv3bt3i9fNzMzUsGHDvMYcjkhVV5/%2BOZsBBBxf/V0ICQlWVFSEamvr1NjYZHc5bR798C3%2B2g%2B7PtAFbMAqKCjQxo0b9dBDD2nEiBGe8aSkJBUWFurs2bOevVbFxcVKSUnxzBcXF3vuX1dXp0OHDik7O7vFa8fHxzc7HOh0nlRDg/%2B8IYHW5Ou/C42NTT5fY1tCP3wL/WiZgDyQ%2BvHHH2vVqlW69dZblZKSIqfT6fmXlpamjh07Kjc3V2VlZSosLNSBAwc0btw4SdLYsWO1f/9%2BFRYWqqysTLm5uerSpQuXaAAAAC0WkAHr9ddfV2Njox577DENHjzY619ISIhWrVolp9OpjIwMvfjii1q5cqU6deokSerSpYseffRRbdmyRePGjVNNTY1WrlypoKAgm7cKAAD4iyA3lyi3hNN5slWed%2BSKt1vleYHW9Mqdg%2Bwu4bwcjmBFR7dTdfVpDoH4APrhW/y1H3FxF9mybkDuwQIAALATAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYw%2B4CAABt18gVb9tdwgV55c5BdpcAP8EeLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYl2k4j/r6ei1YsED/%2BMc/FB4erqlTp2rq1Kl2lwUEDL6aDyDQEbDOY%2BnSpTp48KDWrl2r48ePa/bs2erUqZNGjBhhd2kAAMAPELC%2B58yZM9q0aZOeeOIJ9enTR3369FFZWZmeffZZAhYAwK/4097iQNtTzDlY33P48GE1NDQoOTnZM5aSkqLS0lI1NTXZWBkAAPAX7MH6HqfTqejoaIWGhnrGOnTooPr6etXU1CgmJuYnn6OyslJOp9NrzOGIVHx8vPF6AbQ%2Bh4PPovgG74XWE2ivLQHre%2Brq6rzClSTPbZfL1aLnKCoqUkFBgddYdna2ZsyYYabI73h3yYUdtqysrFRRUZEyMzMJfD6AfvgW%2BmG9H/vfMPrxy13o/0f8GPpxYQIrLhoQFhbWLEh9ezs8PLxFz5GZmamtW7d6/cvMzDRe68/hdDpVUFDQbA8b7EE/fAv98C30w7fQjwvDHqzvSUhIUHV1tRoaGuRwfPPyOJ1OhYeHKyoqqkXPER8fT7oHAKANYw/W9/Tu3VsOh0MlJSWeseLiYiUmJio4mJcLAAD8NBLD90RERGjMmDGaP3%2B%2BDhw4oB07duipp57SlClT7C4NAAD4iZD58%2BfPt7sIXzNw4EAdOnRIy5cv1%2B7du3Xbbbdp7NixdpdlTLt27ZSWlqZ27drZXQpEP3wN/fAt9MO30I%2BWC3K73W67iwAAAAgkHCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAakPq6%2Bs1Z84cpaamavDgwXrqqafsLgmSXC6XRo0apT179thdSpv1xRdfKCcnR2lpaUpPT1deXp7q6%2BvtLqvNOnr0qG655RYlJydryJAhWrNmjd0l4f%2BbNm2a7r33XrvL8AsOuwuAdZYuXaqDBw9q7dq1On78uGbPnq1OnTppxIgRdpfWZtXX1%2Buuu%2B5SWVmZ3aW0WW63Wzk5OYqKitKzzz6rr7/%2BWnPmzFFwcLBmz55td3ltTlNTk6ZNm6bExEQ9//zzOnr0qGbOnKmEhAT993//t93ltWkvvfSSdu7cqeuvv97uUvwCe7DaiDNnzmjTpk2aO3eu%2BvTpo9///vf685//rGeffdbu0tqsI0eOaMKECTp27JjdpbRpn3zyiUpKSpSXl6dLL71UqampysnJ0fbt2%2B0urU2qqqpS7969NX/%2BfHXv3l3/9V//pSuvvFLFxcV2l9am1dTUaOnSpUpMTLS7FL9BwGojDh8%2BrIaGBiUnJ3vGUlJSVFpaqqamJhsra7v27t2rAQMGqKioyO5S2rS4uDitWbNGHTp08Bo/deqUTRW1bfHx8VqxYoXat28vt9ut4uJi7du3T2lpaXaX1qY9%2BOCDGj16tHr27Gl3KX6DQ4RthNPpVHR0tEJDQz1jHTp0UH19vWpqahQTE2NjdW3TpEmT7C4BkqKiopSenu653dTUpA0bNmjgwIE2VgVJGjZsmI4fP66hQ4fqmmuusbucNmv37t169913tW3bNs2fP9/ucvwGe7DaiLq6Oq9wJclz2%2BVy2VES4JPy8/N16NAh/eUvf7G7lDbvkUce0erVq/XBBx8oLy/P7nLapPr6et1///2aN2%2BewsPD7S7Hr7AHq40ICwtrFqS%2Bvc0vDfCN/Px8rV27Vg8//LB%2B%2B9vf2l1Om/ft%2BT719fW6%2B%2B67NWvWrGYfFNG6CgoK1LdvX6%2B9vGgZAlYbkZCQoOrqajU0NMjh%2BKbtTqdT4eHhioqKsrk6wH6LFi3Sc889p/z8fA5H2aiqqkolJSUaPny4Z6xnz546d%2B6cTp06xekMFnvppZdUVVXlOX/32w/mr732mt577z07S/N5BKw2onfv3nI4HCopKVFqaqokqbi4WImJiQoO5kgx2raCggJt3LhRDz30EJctsdlnn32m7Oxs7dy5UwkJCZKkgwcPKiYmhnBlg/Xr16uhocFze9myZZKku%2B%2B%2B266S/AYBq42IiIjQmDFjNH/%2BfD3wwAOqrKzUU089xXkNaPM%2B/vhjrVq1StOmTVNKSoqcTqdnLi4uzsbK2qbExET16dNHc%2BbMUW5urj7//HPl5%2Bfrtttus7u0Nqlz585et9u1aydJ6tatmx3l%2BBUCVhuSm5ur%2BfPn68Ybb1T79u01Y8YMXX311XaXBdjq9ddfV2Njox577DE99thjXnMffvihTVW1XSEhIVq1apUWLVqkzMxMRUREaPLkyZoyZYrdpQEXJMjtdrvtLgIAACCQcPINAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABj2/wA2FjwULg0gowAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1051173535919161120”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.7572224013779127</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.39305586107764334</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4240674302214602</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8619116579790758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6787441323913024</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5616871155140446</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0191839651297627</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6265330254922419</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3343375363551049</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2553703762687425</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1051173535919161120”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.13397324917975917</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.1177125976924489</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.11492635956941344</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.10415315822124027</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:78%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03103797836582345</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3978601063838512</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.699209651935957</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7248856799309928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0418616197607866</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.236801603228066</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SFSP_09_15”>PCH_SFSP_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.094027</td>

</tr> <tr>

<th>Minimum</th> <td>-1.615</td>

</tr> <tr>

<th>Maximum</th> <td>0.84929</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5654523232308775013”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5654523232308775013,#minihistogram5654523232308775013”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5654523232308775013”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5654523232308775013”

aria-controls=”quantiles5654523232308775013” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5654523232308775013” aria-controls=”histogram5654523232308775013”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5654523232308775013” aria-controls=”common5654523232308775013”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5654523232308775013” aria-controls=”extreme5654523232308775013”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5654523232308775013”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-1.615</td>

</tr> <tr>

<th>5-th percentile</th> <td>-0.37704</td>

</tr> <tr>

<th>Q1</th> <td>-0.041263</td>

</tr> <tr>

<th>Median</th> <td>0.11501</td>

</tr> <tr>

<th>Q3</th> <td>0.26852</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.46222</td>

</tr> <tr>

<th>Maximum</th> <td>0.84929</td>

</tr> <tr>

<th>Range</th> <td>2.4643</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.30978</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.28236</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.0029</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.3315</td>

</tr> <tr>

<th>Mean</th> <td>0.094027</td>

</tr> <tr>

<th>MAD</th> <td>0.20701</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.24314</td>

</tr> <tr>

<th>Sum</th> <td>294.49</td>

</tr> <tr>

<th>Variance</th> <td>0.079726</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5654523232308775013”>

<img 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5fMbs9pg2r3u2CQ8P8/kI8%2Bixf9Ff%2BJvd7t%2B1xRoOHSEZsCRp//79ysjI0A033KDy8nItWbJEl1xyidxutyIiIny2jYiIUFNTkySpoaHhF%2Bfbo6ioSAUFBT5jOTk5mj9//hkejbViY6MDXULIo8f%2BRX/hL3FxnSzZD2s4%2BIVkwNq6davWrVunjz76SFFRUUpOTtbhw4f15JNPqnfv3m3CUlNTk6KioiQdP/t1svno6PYv9uzsbGVmZvqM2e0xqqk5eoZHZI3w8DDFxkarrs6tlpbWQJcTkuixf9Ff%2BJu/v46zhs2zKhSfKCQD1q5du3T%2B%2Bed7Q5MkDRw4UCtXrlRaWpqqq6t9tq%2BurvZevktKSjrpfEJCQrv3n5iY2OZyoMv1o5qbg%2BOTpaWlNWhqDVb02L/oL/zFqnXFGg5%2BIXmRNzExUV9//bXPmagDBw6oV69ecjgc%2Buyzz%2BTxeCRJHo9HO3fulMPhkCQ5HA6VlJR4n3fo0CEdOnTIOw8AAHAqIRmwMjMzdc455%2Bjuu%2B/Wl19%2Bqf/5n//RypUrde2112rcuHGqq6vTsmXLtG/fPi1btkxut1vjx4%2BXJE2fPl1vvPGG1q5dq71792rBggUaM2aMevfuHeCjAgAAwSIkA1aXLl30wgsvyOVyaerUqcrLy9OcOXOUnZ2tzp0766mnnlJJSYmysrLkdDq1atUqxcTESJJSUlK0ePFiFRYWavr06Tr33HOVl5cX4CMCAADBxOb56VqZRaZNm6YpU6ZowoQJ6tKli5W7DiiX68dAl3BKdnuY4uI6qabmKNf%2B/YQe%2Bxf9PW78is2BLiFkvXvLCL%2B%2BPmvYvISEwGQNy89gDR8%2BXCtXrtTIkSN166236pNPPpHFGQ8AAMCvLA9Yt912mz788EM98cQTCg8P180336wxY8bo0Ucf1Zdffml1OQAAAMYF5DYNNptNI0aM0IgRI%2BR2u7VmzRo98cQTWrVqlYYOHarrrrtOl156aSBKAwAA%2BJcF7D5YVVVVevPNN/Xmm2/qiy%2B%2B0NChQ3XllVeqsrJSd999t3bs2KGFCxcGqjwAAIAzZnnAeuONN/TGG29o27Zt6tq1qyZPnqzHHntMffr08W7TvXt3LVu2jIAFAACCkuUBa%2BHChcrIyFBhYaFGjx6tsLC2bwPr27evrrnmGqtLAwAAMMLygPXxxx8rLi5OtbW13nBVVlamQYMGKTw8XJI0dOhQDR061OrSAAAAjLD8twiPHDmicePG6emnn/aOzZo1S5MmTdKhQ4esLgcAAMA4ywPW/fffr/PPP1833HCDd%2Bydd95R9%2B7duWM6AAAICZYHrL/97W%2B68847lZCQ4B3r2rWrFixYoE8//dTqcgAAAIyzPGDZ7XbV1dW1GXe73dzRHQAAhATLA9bo0aO1dOlSffPNN96xiooK5eXladSoUVaXAwAAYJzlv0V4xx136IYbbtBll12m2NhYSVJdXZ0GDRqk3Nxcq8sBAAAwzvKAFR8fr9dee01btmxReXm57Ha7LrjgAl1yySWy2WxWlwMAAGBcQP5UTnh4uEaNGsUlQQAAEJIsD1gul0srVqzQzp07dezYsTZvbP/ggw%2BsLgkAAMAoywPWH//4R%2B3atUsTJkxQly5drN49AACA31kesD799FM988wzSktLs3rXAAAAlrD8Ng0xMTGKj4%2B3ercAAACWsTxgTZo0Sc8884xaWlqs3jUAAIAlLL9EWFtbqw0bNugvf/mLevfurYiICJ/5F1980eqSAAAAjArIbRomTpwYiN0CAABYwvKAlZeXZ/UuAQAALGX5e7AkqaqqSgUFBbrtttv0z3/%2BU%2B%2B9954OHDgQiFIAAACMszxgff3117riiiv02muvaePGjaqvr9c777yjKVOmyOl0Wl0OAACAcZYHrAceeEBjx47Vpk2bdM4550iSHnnkEWVmZuqhhx6yuhwAAADjLA9YO3fu1A033ODzh53tdrvmzp2rPXv2WF0OAACAcZYHrNbWVrW2trYZP3r0qMLDw60uBwAAwDjLA9bIkSP11FNP%2BYSs2tpa5efna/jw4VaXAwAAYJzlAevOO%2B/Url27NHLkSDU2NmrOnDnKyMjQt99%2BqzvuuMPqcgAAAIyz/D5YSUlJev3117Vhwwb9/e9/V2trq6ZPn65Jkyapc%2BfOVpcDAABgXEDu5B4dHa1p06YFYtcAAAB%2BZ3nAmjFjxi/O87cIAQBAsLM8YPXs2dPncXNzs77%2B%2Bmt98cUXuu6666wuBwAAwLiz5m8RFhYWqrKy0uJqAAAAzAvI3yI8mUmTJundd98NdBkAAAD/srMmYH322WfcaBQAAISEs%2BJN7keOHNE//vEPXX311cb209TUpLy8PG3YsEHnnHOOpk6dqv/6r/%2BSzWbTnj17dM899%2BiLL77QBRdcoPvuu0%2BDBw/2PnfDhg1asWKFXC6XRo4cqSVLlqhr167GagMAAKHN8jNYPXr0UM%2BePX3%2BDR48WEuWLDF6o9GlS5dqy5YtevbZZ/Xwww/r1VdfVVFRkerr6zVr1iylpaVp/fr1SklJ0ezZs1VfXy9JKisr08KFCzVv3jwVFRWprq5Oubm5xuoCAAChz/IzWA888IDf91FbW6vi4mI9//zzuuiiiyRJM2fOlNPplN1uV2RkpBYsWCCbzaaFCxfq448/1nvvvaesrCy99NJLGj9%2BvCZPnixJWr58uTIyMlRRUaHevXv7vXYAABD8LA9YO3bsaPe2F1988Rnto6SkRJ07d1Z6erp3bNasWZKkP/7xj0pNTZXNZpMk2Ww2DR06VKWlpcrKypLT6dSNN97ofV737t3Vo0cPOZ1OAhYAAGgXywPWtdde6w03Ho/HO37imM1m09///vcz2kdFRYV69uyp119/XStXrtSxY8eUlZWlOXPmyOVy6YILLvDZPj4%2BXuXl5ZKkqqoqJSYmtpnnFhIAAKC9LA9YK1eu1NKlS3X77bcrPT1dERER%2Bvzzz7V48WJdeeWVuvzyy//lfdTX1%2Bvrr7/WK6%2B8ory8PLlcLi1atEjR0dFyu92KiIjw2T4iIkJNTU2SpIaGhl%2Bcb4%2Bqqiq5XC6fMbs9pk1wO9uEh4f5fIR59Ni/6C/8zW7379piDYeOgNxodNGiRRo9erR3bPjw4Vq8eLEWLFjgc3nuTNntdh05ckQPP/yw987xBw8e1Msvv6zzzz%2B/TVhqampSVFSUJCkyMvKk89HR0e3ef1FRkQoKCnzGcnJyNH/%2B/DM5HMvFxrb/WHFm6LF/0V/4S1xcJ0v2wxoOfpYHrKqqqjZ/LkeSOnfurJqaGiP7SEhIUGRkpM9%2BfvWrX%2BnQoUNKT09XdXW1z/bV1dXes0tJSUknnU9ISGj3/rOzs5WZmekzZrfHqKbm6OkeiqXCw8MUGxutujq3WlpaA11OSKLH/kV/4W/%2B/jrOGjbPqlB8IssD1pAhQ/TII4/owQcfVOfOnSUd/62//Px8XXLJJUb24XA41NjYqC%2B//FK/%2BtWvJEkHDhxQz5495XA49PTTT8vj8chms8nj8Wjnzp266aabvM8tKSlRVlaWJOnQoUM6dOiQHA5Hu/efmJjY5nKgy/WjmpuD45OlpaU1aGoNVvTYv%2Bgv/MWqdcUaDn6WX%2BS9%2B%2B67VVpaqtGjRysrK0tXXnml9zYIixYtMrKPvn37asyYMcrNzdXevXv117/%2BVatWrdL06dM1btw41dXVadmyZdq3b5%2BWLVsmt9ut8ePHS5KmT5%2BuN954Q2vXrtXevXu1YMECjRkzht8gBAAA7Wbz/N9f5bPIDz/8oA0bNmj//v2SpIEDB2rChAmn9T6nU/nxxx%2B1ZMkSvf/%2B%2B4qOjtbVV1%2BtnJwc2Ww2lZWV6Z577tH%2B/fv1m9/8Rvfdd58GDhzofe769ev12GOP6YcfftCIESO0ZMkSxcXF/Uv1uFw//quH5Hd2e5ji4jqppuYoPzn5CT32L/p73PgVmwNdQsh695YRfn191rB5CQldArLfgAQs6fgbx7/99lvvmaFzzjknEGVYhoAFiR77G/09joDlPwSs4BOogGX5JUKPx6OHHnpIF198sSZOnKjKykrdcccdWrhwoY4dO2Z1OQAAAMZZHrDWrFmjN954Q/fcc4/3flNjx47Vpk2b2tzaAAAAIBhZHrCKioq0aNEiZWVlee/efvnll2vp0qV66623rC4HAADAOMsD1rfffqsBAwa0Ge/fv3%2Bbu58DAAAEI8sDVs%2BePfX555%2B3Gf/444%2B5FQIAAAgJlt9o9Pe//73uu%2B8%2BuVwueTwebd26VUVFRVqzZo3uvPNOq8sBAAAwzvKANWXKFDU3N%2BvJJ59UQ0ODFi1apK5du%2BqWW27R9OnTrS4HAADAOMsD1oYNGzRu3DhlZ2fr%2B%2B%2B/l8fjUXx8vNVlAAAA%2BI3l78FavHix983sXbt2JVwBAICQY3nA6tOnj7744gurdwsAAGAZyy8R9u/fX3/4wx/0zDPPqE%2BfPoqMjPSZz8vLs7okAAAAoywPWF9%2B%2BaVSU1MlifteAQCAkGRJwFq%2BfLnmzZunmJgYrVmzxopdAgAABIwl78F6/vnn5Xa7fcZmzZqlqqoqK3YPAABgKUsClsfjaTO2Y8cONTY2WrF7AAAAS1n%2BW4QAAAChjoAFAABgmGUBy2azWbUrAACAgLLsNg1Lly71uefVsWPHlJ%2Bfr06dOvlsx32wAABAsLMkYF188cVt7nmVkpKimpoa1dTUWFECAACAZSwJWNz7CgAAdCS8yR0AAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYZb9sWcACFbjV2wOdAkAggxnsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhIR%2BwZs2apTvvvNP7eM%2BePZo2bZocDoemTJmiXbt2%2BWy/YcMGjR07Vg6HQzk5Ofr%2B%2B%2B%2BtLhkAAAS5kA5Yb7/9tj766CPv4/r6es2aNUtpaWlav369UlJSNHv2bNXX10uSysrKtHDhQs2bN09FRUWqq6tTbm5uoMoHAABBKmQDVm1trZYvX67k5GTv2DvvvKPIyEgtWLBA/fr108KFC9WpUye99957kqSXXnpJ48eP1%2BTJk9W/f38tX75cH330kSoqKgJ1GAAAIAiFbMB68MEHNWnSJF1wwQXeMafTqdTUVNlsNkmSzWbT0KFDVVpa6p1PS0vzbt%2B9e3f16NFDTqfT2uIBAEBQC8mAtXXrVv3tb3/T3LlzfcZdLpcSExN9xuLj41VZWSlJqqqq%2BsV5AACA9gi5v0XY2Nioe%2B65R4sWLVJUVJTPnNvtVkREhM9YRESEmpqaJEkNDQ2/ON9eVVVVcrlcPmN2e0yb8Ha2CQ8P8/kI8%2Bixf9Ff%2BJvd7t%2B1xRoOHSEXsAoKCjR48GCNGjWqzVxkZGSbsNTU1OQNYj83Hx0dfVo1FBUVqaCgwGcsJydH8%2BfPP63XCZTY2NM7Xpw%2Beuxf9Bf%2BEhfXyZL9sIaDX8gFrLffflvV1dVKSUmRJG9g2rhxoyZOnKjq6mqf7aurq71nlpKSkk46n5CQcFo1ZGdnKzMz02fMbo9RTc3R03odq4WHhyk2Nlp1dW61tLQGupyQRI/9i/7C3/z9dZw1bJ5VofhEIRew1qxZo%2BbmZu/jhx56SJL0hz/8QTt27NDTTz8tj8cjm80mj8ejnTt36qabbpIkORwOlZSUKCsrS5J06NAhHTp0SA6H47RqSExMbHM50OX6Uc3NwfHJ0tLSGjS1Bit67F/0F/5i1bpiDQe/kAtYPXv29HncqdPx5Hr%2B%2BecrPj5eDz/8sJYtW6bf/e53euWVV%2BR2uzV%2B/HhJ0vTp03XttddqyJAhSk5O1rJlyzRmzBj17t3b8uMAAADBq0O9i65z58566qmnvGepnE6nVq1apZiYGElSSkqKFi9erMLCQk2fPl3nnnuu8vLyAlw1AAAINjaPx%2BMJdBEdgcv1Y6BLOCW7PUxxcZ1UU3OUU9N%2BQo/9y1/9Hb9is7HXQnB795YRfn19vkaYl5DQJSD77VBnsAAAAKxAwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyzB7oAAACCxfgVmwNdwml595YRgS6hw%2BIMFgAAgGEELAAAAMMIWAAAAIYRsAB9YvlKAAAOK0lEQVQAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFrIB6/Dhw5o/f77S09M1atQo5eXlqbGxUZJUUVGh66%2B/XkOGDNHll1%2BuTz75xOe5W7Zs0cSJE%2BVwODRjxgxVVFQE4hAAAECQCsmA5fF4NH/%2BfLndbv3pT3/So48%2Bqg8//FArVqyQx%2BNRTk6OunXrpuLiYk2aNEnz5s3TwYMHJUkHDx5UTk6OsrKytG7dOnXt2lVz586Vx%2BMJ8FEBAIBgYQ90Af5w4MABlZaWavPmzerWrZskaf78%2BXrwwQc1evRoVVRU6JVXXlFMTIz69eunrVu3qri4WDfffLPWrl2rwYMHa%2BbMmZKkvLw8jRgxQtu3b9ewYcMCeVgAACBIhOQZrISEBD3zzDPecPWTI0eOyOl0auDAgYqJifGOp6amqrS0VJLkdDqVlpbmnYuOjtagQYO88wAAAKcSkmewYmNjNWrUKO/j1tZWvfTSSxo%2BfLhcLpcSExN9to%2BPj1dlZaUknXK%2BPaqqquRyuXzG7PaYNq97tgkPD/P5CPPosX/RX8CX3c7nQqCEZMA6UX5%2Bvvbs2aN169bphRdeUEREhM98RESEmpqaJElut/sX59ujqKhIBQUFPmM5OTmaP3/%2BGR6BtWJjowNdQsijx/5Ff4Hj4uI6BbqEDivkA1Z%2Bfr5Wr16tRx99VBdeeKEiIyNVW1vrs01TU5OioqIkSZGRkW3CVFNTk2JjY9u9z%2BzsbGVmZvqM2e0xqqk5eoZHYY3w8DDFxkarrs6tlpbWQJcTkuixf9FfwNfZ/n3HCoEKmSEdsJYsWaKXX35Z%2Bfn5uuyyyyRJSUlJ2rdvn8921dXV3st3SUlJqq6ubjM/YMCAdu83MTGxzeVAl%2BtHNTcHxxf8lpbWoKk1WNFj/6K/wHF8HgROyF6cLSgo0CuvvKJHHnlEEyZM8I47HA7t3r1bDQ0N3rGSkhI5HA7vfElJiXfO7XZrz5493nkAAIBTCcmAtX//fj3xxBO68cYblZqaKpfL5f2Xnp6u7t27Kzc3V%2BXl5Vq1apXKyso0depUSdKUKVO0c%2BdOrVq1SuXl5crNzVWvXr24RQMAAGi3kAxYH3zwgVpaWvTkk09q5MiRPv/Cw8P1xBNPyOVyKSsrS2%2B%2B%2BaYKCwvVo0cPSVKvXr30%2BOOPq7i4WFOnTlVtba0KCwtls9kCfFQAACBY2DzcotwSLtePgS7hlOz2MMXFdVJNzVGu2/sJPfYvf/V3/IrNxl4LsNK7t4wIdAkBl5DQJSD7DckzWAAAAIFEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYfZAFwCg4xm/YnOgSwAAv%2BIMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjGbxECABCiguk3dt%2B9ZUSgSzCKM1gAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH2QBcAwIzxKzYHugQAwP/iDBYAAIBhBCwAAADDCFgn0djYqLvuuktpaWkaOXKknnvuuUCXBAAAggjvwTqJ5cuXa9euXVq9erUOHjyoO%2B64Qz169NC4ceMCXRoAAAgCBKwT1NfXa%2B3atXr66ac1aNAgDRo0SOXl5frTn/5EwAIAAO3CJcIT7N27V83NzUpJSfGOpaamyul0qrW1NYCVAQCAYMEZrBO4XC7FxcUpIiLCO9atWzc1NjaqtrZWXbt2PeVrVFVVyeVy%2BYzZ7TFKTEw0Xq9J4eFhPh9hHj0GgJOz20Pr6yIB6wRut9snXEnyPm5qamrXaxQVFamgoMBnbN68ebr55pvNFOknVVVVWr36GWVnZ5/1YTBY%2BbPHf1vGJeyqqioVFRWxhv2IHvsX/Q0doRUXDYiMjGwTpH56HBUV1a7XyM7O1vr1633%2BZWdnG6/VNJfLpYKCgjZn32AOPfYv%2But/9Ni/6G/o4AzWCZKSklRTU6Pm5mbZ7cfb43K5FBUVpdjY2Ha9RmJiIj95AADQgXEG6wQDBgyQ3W5XaWmpd6ykpETJyckKC6NdAADg1EgMJ4iOjtbkyZN17733qqysTJs2bdJzzz2nGTNmBLo0AAAQJMLvvffeewNdxNlm%2BPDh2rNnjx5%2B%2BGFt3bpVN910k6ZMmRLosizRqVMnpaenq1OnToEuJWTRY/%2Biv/5Hj/2L/oYGm8fj8QS6CAAAgFDCJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsyOPxaObMmVq/fv0vbrd06VL95je/8fn30ksvWVRlcGtvjysqKnT99ddryJAhuvzyy/XJJ59YVGFw8ng8euihhzR8%2BHClp6dr%2BfLlam1t/dntWcPt09jYqLvuuktpaWkaOXKknnvuuZ/dds%2BePZo2bZocDoemTJmiXbt2WVhpcDqd/s6ZM6fNmv3www8trBZnyh7oAhBYra2tWrZsmTZv3qyJEyf%2B4rb79%2B/XbbfdpiuvvNI71rlzZ3%2BXGPTa22OPx6OcnBxdeOGFKi4u1qZNmzRv3jy988476tGjh4UVB4/nn39eGzZsUEFBgZqbm3X77bcrPj5ev//970%2B6PWu4fZYvX65du3Zp9erVOnjwoO644w716NFD48aN89muvr5es2bN0hVXXKEHHnhAL7/8smbPnq33339fMTExAar%2B7Nfe/krH12x%2Bfr4uueQS79i5555rZbk4Ux50WJWVlZ5rrrnGM2bMGE9aWpqnuLj4F7cfNWqU569//atF1YWG0%2Bnxli1bPEOGDPEcPXrUO3bdddd5HnvsMStKDUr/9m//5tPT119/3ZORkfGz27OGT%2B3o0aOe5ORkz6effuodKyws9FxzzTVttl27dq0nMzPT09ra6vF4PJ7W1lbPb3/721N%2BLenITqe/jY2NngEDBngOHDhgZYkwhEuEHdju3bvVvXt3FRcXq0uXLr%2B47ZEjR3T48GH16dPHmuJCxOn02Ol0auDAgT4/%2Baempqq0tNTfZQalw4cP69ChQ7r44ou9Y6mpqfruu%2B9UVVXVZnvWcPvs3btXzc3NSklJ8Y6lpqbK6XS2ufzqdDqVmpoqm80mSbLZbBo6dChr9hecTn8PHDggm82m3r17W10mDCBgdWCZmZlavny5unbtespt9%2B/fL5vNppUrV2r06NH693//d7322msWVBncTqfHLpdLiYmJPmPx8fGqrKz0V3lBzeVySZJPz7p16yZJJ%2B0Za7h9XC6X4uLiFBER4R3r1q2bGhsbVVtb22Zb1uzpOZ3%2BHjhwQJ07d9aCBQs0cuRITZ06VR999JHVJeMM8R6sENbQ0KDDhw%2BfdC4hIeG03iPx009Sffv21TXXXKMdO3boj3/8ozp37qzf/va3pkoOOiZ77Ha7fb7oSlJERISampr%2BpRqD2S/1t76%2BXpJ8evbT/0/WM9Zw%2B/zcOpTa9pU1e/pOp78HDhxQQ0ODRo4cqVmzZun999/XnDlzVFRUpOTkZMtqxpkhYIUwp9OpGTNmnHSusLBQY8eObfdrTZ48WRkZGTrvvPMkSf3799dXX32ll19%2BuUN/czLZ48jIyDY/wTY1NSkqKupfqjGY/VJ/b7/9dknHexQZGen9vyRFR0e32Z413D6RkZFtvtH/9PjEtfhz23bkNXsqp9PfuXPn6tprr/W%2Bqb1///7avXu3Xn31VQJWECBghbBhw4bpH//4h5HXstls3m9MP%2Bnbt68%2B/fRTI68frEz2OCkpSfv27fMZq66ubnMJpiP5pf4ePnxY%2Bfn5crlc6tWrl6T/f9kwISGhzfas4fZJSkpSTU2NmpubZbcf/xbhcrkUFRWl2NjYNttWV1f7jHX0NXsqp9PfsLCwNr8x2Ldv3zZfJ3B24j1YaJf//u//1vXXX%2B8ztnfvXvXt2zcwBYUgh8Oh3bt3q6GhwTtWUlIih8MRwKrOXklJSerRo4dKSkq8YyUlJerRo8dJv8GzhttnwIABstvtPm9ULykpUXJyssLCfL9lOBwOffbZZ/J4PJKO32pk586drNlfcDr9vfPOO5Wbm%2BszxpoNHgQs/Kzvv/9eR48elSRlZGRox44devbZZ/XNN9/oz3/%2Bs15//XXNnDkzwFUGt//b4/T0dHXv3l25ubkqLy/XqlWrVFZWpqlTpwa4yrPX9OnT9dBDD2nbtm3atm2bHn74YZ9Liqzh0xcdHa3Jkyfr3nvvVVlZmTZt2qTnnnvO21eXy%2BX9IWDcuHGqq6vTsmXLtG/fPi1btkxut1vjx48P5CGc1U6nv5mZmXrrrbf0%2Buuv6%2Buvv1ZBQYFKSkp0zTXXBPIQ0F6Bvk8Ezg4ZGRlt7l2TkZHhcw%2Bm999/33PFFVd4kpOTPePGjfNs3LjR6jKDWnt6/NVXX3n%2B4z/%2BwzN48GDPhAkTPJs3b7a6zKDS3Nzsuf/%2B%2Bz1paWmeYcOGefLz8733ZPJ4WMNnqr6%2B3rNgwQLPkCFDPCNHjvQ8//zz3rkLL7zQZx07nU7P5MmTPcnJyZ6pU6d6du/eHYCKg8vp9PfVV1/1XHrppZ7Bgwd7rrzySs/27dsDUDHOhM3j%2Bd9zuwAAADCCS4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYNj/AyosNrpff59sAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5654523232308775013”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.2319788970337091</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.29466099351100317</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026741720927772383</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5360765660553791</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08475386705376708</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3457474230037112</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1605192270115351</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06284676825651125</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19071844546134248</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.26852081390561167</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5654523232308775013”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.6150463789400007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7475382495502649</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5360765660553791</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3770388194174602</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.2817160725294582</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.46221921943258293</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4771634313341099</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8040334711354716</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8166099613606475</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8492897607778729</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SLHOUSE_07_12”>PCH_SLHOUSE_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>7.6%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>243</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.4926</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>76.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4397382840691352821”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4397382840691352821,#minihistogram-4397382840691352821”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4397382840691352821”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4397382840691352821”

aria-controls=”quantiles-4397382840691352821” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4397382840691352821” aria-controls=”histogram-4397382840691352821”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4397382840691352821” aria-controls=”common-4397382840691352821”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4397382840691352821” aria-controls=”extreme-4397382840691352821”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4397382840691352821”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>35.689</td>

</tr> <tr>

<th>Coef of variation</th> <td>-6.4976</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.2094</td>

</tr> <tr>

<th>Mean</th> <td>-5.4926</td>

</tr> <tr>

<th>MAD</th> <td>17.7</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.31452</td>

</tr> <tr>

<th>Sum</th> <td>-16297</td>

</tr> <tr>

<th>Variance</th> <td>1273.7</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4397382840691352821”>

<img 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wAAwDIKFgAAgGUULAAAAMv8XrAefPBBvfHGGzp16pS/7xoAAMAv/F6w2rZtq4ULF6p9%2B/Z66qmntH79ehlj/L0MAACASuP3gjV69Gj94x//0Pz58xUaGqonnnhCHTt21KxZs/Tdd99d8fGKi4vVo0cPbdy40Ts2ZcoU3XbbbT5vK1eu9M6vW7dOnTt3ltvtVkpKio4dO%2BadM8Zo%2BvTpatu2rZKSkjRt2jSVlZVdW2gAAPCb4grEnYaEhKhdu3Zq166dCgoK9Nprr2n%2B/PlavHixWrdurSFDhuiee%2B657HGKioo0evRoZWdn%2B4zn5ORo9OjReuCBB7xjNWrUkCRt27ZNEyZM0MSJE9W8eXNNnTpV48eP16JFiyRJr776qtatW6eMjAyVlJRozJgxqlOnjoYPH27xMwAAAJwsIAVLkvLy8vT%2B%2B%2B/r/fff1969e9W6dWs98MADOnLkiJ599llt2rRJEyZMuOjt9%2B3bp9GjR1/w9GJOTo6GDx%2BuqKiocnMrV67Ufffdp969e0uSpk2bpuTkZOXm5urmm2/WihUrlJqaqsTEREnS008/rTlz5lCwAABAhfm9YK1du1Zr167Vxo0bVbt2bfXu3Vtz585Vo0aNvB9Tr149TZ069ZIF65tvvlGbNm30P//zP4qLi/OOnz59WkePHvU53q9lZWXpj3/8o8991a9fX1lZWQoLC9Phw4d15513eucTEhJ08OBB5eXlKTo6%2BuqDAwCA3wy/F6wJEyYoOTlZ8%2BbN01133aUqVcq/DKxx48YaNGjQJY8zcODAC47n5OQoJCRECxcu1JdffqmaNWvq0Ucf9Z4uvFBRqlOnjo4cOSKPxyNJPvN169aVJB05coSCBQAAKsTvBevLL79UrVq1dOLECW%2B52rZtm1q2bKnQ0FBJUuvWrdW6deurOv7%2B/fsVEhLiLWmbNm3Sc889pxo1aqhLly4qLCxUWFiYz23CwsJUXFyswsJC7/u/npN%2BfjF9ReXl5XnL2i9crmrWC1poaBWfP53Cqbkqm8sVuM%2BXk/eMbMHHqbkk52ZzYi6/F6zTp09rwIAB%2Bs///E%2BNHTtWkjRixAjVrVtXf/nLX1SvXr1rOn7v3r2VnJysmjVrSpKaN2%2Bu77//XqtWrVKXLl0UHh5eriwVFxcrIiLCp0yFh4d7/y5JERERFV7D6tWrlZGR4TOWkpKi1NTUq851KZGRFV9bMHFqrspSq1b1QC/B0XtGtuDj1FySc7M5KZffC9ZLL72kW265RY8%2B%2Bqh37MMPP9S4ceOUlpamuXPnXtPxQ0JCvOXqF40bN9aGDRskSTExMcrPz/eZz8/PV1RUlGJiYiRJHo9HDRs29P5d0gVfMH8x/fv3V6dOnXzGXK5qOn78zJWFuYzQ0CqKjIzQyZMFKi11zqUknJqrstl%2BfF0JJ%2B8Z2YKPU3NJzs1WmbkC9Z9Pvxes//3f/9Wbb77pU1hq166tsWPH6uGHH77m48%2BZM0dbtmzRsmXLvGO7d%2B9W48aNJUlut1uZmZnq06ePJOnw4cM6fPiw3G63YmJiVL9%2BfWVmZnoLVmZmpurXr39Fp/eio6PLfbzHc0olJZXzxVBaWlZpxw4kp%2BaqLNfD58rJe0a24OPUXJJzszkpl98Llsvl0smTJ8uNFxQUWLmie3JyshYvXqylS5eqS5cuWr9%2Bvd577z2tWLFCkjRgwAA98sgjiouLU2xsrKZOnaqOHTvq5ptv9s5Pnz5dv/vd7yRJM2bM0LBhw655XQAA4LfD7wXrrrvu0pQpUzRz5kz9x3/8hyQpNzdXaWlp6tChwzUfv1WrVpozZ47mzp2rOXPmqEGDBpoxY4bi4%2BMlSfHx8Zo0aZLmzp2rn376Se3atdPkyZO9tx8%2BfLh%2B/PFHjRo1SqGhoerXr5%2BGDh16zesCAAC/HSHGz78I8Mcff9Sjjz6q7OxsRUZGSpJOnjypli1basGCBVf0Wqdg4vHY/%2BXWLlcV1apVXcePn3HMU6rS9ZPrvtn/DNh9X42PnmwXsPu%2BXvasMpAt%2BDg1l%2BTcbJWZKyrqRqvHqyi/P4NVp04dvfvuu/r666%2BVnZ0tl8ulW2%2B9Vb///e8VEhLi7%2BUAAABYF5BflRMaGqoOHTpYOSUIAABwvfF7wfJ4PJo9e7Y2b96sc%2BfOlXth%2B2effebvJQEAAFjl94L13HPPafv27erevbtuvDEw50UBAAAqk98L1oYNG7RkyRIlJib6%2B64BAAD8wu%2B/9KdatWqqU6eOv%2B8WAADAb/xesHr16qUlS5aotLTU33cNAADgF34/RXjixAmtW7dOn3/%2BuW6%2B%2BWbvL1j%2BxS9XXAcAAAhWAblMQ48ePQJxtwAAAH7h94KVlpbm77sEAADwK7%2B/BkuS8vLylJGRodGjR%2BvHH3/U3/72N%2B3fvz8QSwEAALDO7wXrhx9%2BUM%2BePfXuu%2B/q448/1tmzZ/Xhhx%2Bqb9%2B%2BysrK8vdyAAAArPN7wXr55ZfVuXNnffrpp7rhhhskSTNnzlSnTp00ffp0fy8HAADAOr8XrM2bN%2BvRRx/1%2BcXOLpdLjz/%2BuHbu3Onv5QAAAFjn94JVVlamsrKycuNnzpxRaGiov5cDAABgnd8LVvv27bVo0SKfknXixAmlp6erbdu2/l4OAACAdX4vWH/%2B85%2B1fft2tW/fXkVFRfrv//5vJScn68CBAxo3bpy/lwMAAGCd36%2BDFRMTo/fee0/r1q3Trl27VFZWpgEDBqhXr16qUaOGv5cDAABgXUCu5B4REaEHH3wwEHcNAABQ6fxesAYPHnzJeX4XIQAACHZ%2BL1gNGjTweb%2BkpEQ//PCD9u7dqyFDhvh7OQAAANZdN7%2BLcN68eTpy5IifVwMAAGBfQH4X4YX06tVLH330UaCXAQAAcM2um4K1ZcsWLjQKAAAc4bp4kfvp06e1Z88eDRw40N/LAQAAsM7vBat%2B/fo%2Bv4dQkm644QYNGjRI999/v7%2BXAwAAYJ3fC9bLL7/s77sEAADwK78XrE2bNlX4Y%2B%2B8885KXAkAAEDl8HvBeuSRR7ynCI0x3vHzx0JCQrRr1y5/Lw8AAOCa%2Bb1gLVy4UFOmTNGYMWOUlJSksLAwffvtt5o0aZIeeOABdevWzd9LAgAAsMrvl2lIS0vT888/r3vvvVe1atVS9erV1bZtW02aNEmrVq1SgwYNvG8AAADByO8FKy8v74LlqUaNGjp%2B/Li/lwMAAGCd3wtWXFycZs6cqdOnT3vHTpw4ofT0dP3%2B97/393IAAACs8/trsJ599lkNHjxYd911lxo1aiRjjL7//ntFRUVpxYoV/l4OAACAdX4vWE2aNNGHH36odevWKScnR5L08MMPq3v37oqIiPD3cgAAAKzze8GSpJtuukkPPvigDhw4oJtvvlnSz1dzBwAAcAK/vwbLGKPp06frzjvvVI8ePXTkyBGNGzdOEyZM0Llz5/y9HAAAAOv8XrBee%2B01rV27Vi%2B88ILCwsIkSZ07d9ann36qjIwMfy8HAADAOr8XrNWrV%2Bv5559Xnz59vFdv79atm6ZMmaIPPvjA38sBAACwzu8F68CBA2rRokW58ebNm8vj8fh7OQAAANb5vWA1aNBA3377bbnxL7/80vuCdwAAgGDm958iHD58uCZOnCiPxyNjjP71r39p9erVeu211/TnP//Z38sBAACwzu8Fq2/fviopKdGCBQtUWFio559/XrVr19aTTz6pAQMG%2BHs5AAAA1vm9YK1bt05du3ZV//79dezYMRljVKdOHX8vAwAAoNL4/TVYkyZN8r6YvXbt2pQrAADgOH4vWI0aNdLevXv9fbcAAAB%2B4/dThM2bN9fTTz%2BtJUuWqFGjRgoPD/eZT0tL8/eSAAAArPJ7wfruu%2B%2BUkJAgSVz3CgAAOJJfCta0adM0atQoVatWTa%2B99po/7hIAACBg/PIarFdffVUFBQU%2BYyNGjFBeXp4/7h4AAMCv/FKwjDHlxjZt2qSioqJrPnZxcbF69OihjRs3esdyc3M1dOhQxcXFqVu3blq/fr3Pbb7%2B%2Bmv16NFDbrdbgwcPVm5urs/8smXL1KFDB8XHx%2BuZZ54pVw4BAAAuxe8/RWhTUVGRnnrqKWVnZ3vHjDFKSUlR3bp1tWbNGvXq1UujRo3SoUOHJEmHDh1SSkqK%2BvTpo7ffflu1a9fW448/7i2BH3/8sTIyMjRp0iQtX75cWVlZSk9PD0g%2BAAAQnIK2YO3bt08PPfSQ/v3vf/uMb9iwQbm5uZo0aZKaNGmikSNHKi4uTmvWrJEkvfXWW7rjjjs0bNgwNW3aVGlpaTp48KC%2B%2BeYbSdKKFSs0ZMgQJScnq1WrVpo4caLWrFnDs1gAAKDC/FawQkJCrB7vm2%2B%2BUZs2bbR69Wqf8aysLN1%2B%2B%2B2qVq2adywhIUFbt271zicmJnrnIiIi1LJlS23dulWlpaX69ttvfebj4uJ07tw57d692%2Br6AQCAc/ntMg1TpkzxuebVuXPnlJ6erurVq/t8XEWvgzVw4MALjns8HkVHR/uM1alTR0eOHLns/MmTJ1VUVOQz73K5VLNmTe/tAQAALscvBevOO%2B8sd82r%2BPh4HT9%2BXMePH7d6XwUFBQoLC/MZCwsLU3Fx8WXnCwsLve9f7PYVkZeXVy6vy1WtXLG7VqGhVXz%2BdAqn5qpsLlfgPl9O3jOyBR%2Bn5pKcm82JufxSsPx57avw8HCdOHHCZ6y4uFhVq1b1zp9floqLixUZGel9hu1C8xERERVew%2BrVq5WRkeEzlpKSotTU1Aof40pERlZ8bcHEqbkqS61a1S//QZXMyXtGtuDj1FySc7M5KZffr%2BRe2WJiYrRv3z6fsfz8fO%2BzRzExMcrPzy8336JFC9WsWVPh4eHKz89XkyZNJEklJSU6ceKEoqKiKryG/v37q1OnTj5jLlc1HT9%2B5moiXVRoaBVFRkbo5MkClZaWWT12IDk1V2Wz/fi6Ek7eM7IFH6fmkpybrTJzBeo/n44rWG63W4sXL1ZhYaH3WavMzEzvr%2Bdxu93KzMz0fnxBQYF27typUaNGqUqVKoqNjVVmZqbatGkjSdq6datcLpeaN29e4TVER0eXOx3o8ZxSSUnlfDGUlpZV2rEDyam5Ksv18Lly8p6RLfg4NZfk3GxOyuWck53/LykpSfXq1dP48eOVnZ2txYsXa9u2berXr58kqW/fvtq8ebMWL16s7OxsjR8/Xg0bNvQWqoEDB2rp0qX69NNPtW3bNr344ot66KGHrugUIQAA%2BG1zXMEKDQ3V/Pnz5fF41KdPH73//vuaN2%2Be6tevL0lq2LChXnnlFa1Zs0b9%2BvXTiRMnNG/ePO9lJLp3766RI0fq%2Beef17Bhw9SqVSuNGTMmkJEAAECQccQpwj179vi8f8stt2jlypUX/fi7775bd99990XnR4wYoREjRlhbHwAA%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%2B5ScnKz169d736ZMmaKzZ89qxIgRSkxM1DvvvKP4%2BHiNHDlSZ8%2BelSRt27ZNEyZM0KhRo7R69WqdPHlS48ePD3AaAAAQTBxbsHJyctSsWTNFRUV53yIjI/Xhhx8qPDxcY8eOVZMmTTRhwgRVr15df/vb3yRJK1eu1H333afevXurefPmmjZtmr744gvl5uYGOBEAAAgWji5YjRo1KjeelZWlhIQEhYSESJJCQkLUunVrbd261TufmJjo/fh69eqpfv36ysrK8su6AQBA8HMFegGVwRij7777TuvXr9eiRYtUWlqqrl27KjU1VR6PR7feeqvPx9epU0fZ2dmSpLy8PEVHR5ebP3I883qFAAARmklEQVTkSIXvPy8vTx6Px2fM5apW7rjXKjS0is%2BfTuHUXJXN5Qrc58vJe0a24OPUXJJzszkxlyML1qFDh1RQUKCwsDDNnj1bBw4c0JQpU1RYWOgd/7WwsDAVFxdLkgoLCy85XxGrV69WRkaGz1hKSor3Rfa2RUZGVMpxA82puSpLrVrVA70ER%2B8Z2YKPU3NJzs3mpFyOLFgNGjTQxo0bddNNNykkJEQtWrRQWVmZxowZo6SkpHJlqbi4WFWrVpUkhYeHX3A%2BIqLim96/f3916tTJZ8zlqqbjx89cZaILCw2tosjICJ08WaDS0jKrxw4kp%2BaqbLYfX1fCyXtGtuDj1FySc7NVZq5A/efTkQVLkmrWrOnzfpMmTVRUVKSoqCjl5%2Bf7zOXn53tP38XExFxwPioqqsL3HR0dXe50oMdzSiUllfPFUFpaVmnHDiSn5qos18Pnysl7Rrbg49RcknOzOSmXc052/spXX32lNm3aqKCgwDu2a9cu1axZUwkJCdqyZYuMMZJ%2Bfr3W5s2b5Xa7JUlut1uZmZne2x0%2BfFiHDx/2zgMAAFyOIwtWfHy8wsPD9eyzz2r//v364osvNG3aND322GPq2rWrTp48qalTp2rfvn2aOnWqCgoKdN9990mSBgwYoLVr1%2Bqtt97S7t27NXbsWHXs2FE333xzgFMBAIBg4ciCVaNGDS1dulTHjh1T3759NWHCBPXv31%2BPPfaYatSooUWLFikzM1N9%2BvRRVlaWFi9erGrVqkn6uZxNmjRJ8%2BbN04ABA3TTTTcpLS0twIkAAEAwcexrsJo2bapXX331gnOtWrXSu%2B%2B%2Be9Hb9unTR3369KmspQEAAIdz5DNYAAAAgUTBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADLKFgAAACWUbAAAAAso2ABAABYRsECAACwjIIFAABgGQULAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYJkr0AvAtUmc8LdAL6HCPnqyXaCXAACAX/AMFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQIAALCMggUAAGAZBQsAAMAyChYAAIBlFCwAAADL%2BFU58Jv7Zv8z0EsAAMAveAYLAADAMgoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkXGgWAy0ic8LdAL%2BGKfPRku0AvAfjN4xksAAAAyyhYAAAAlnGK8AKKioo0ceJE/f3vf1fVqlU1bNgwDRs2LNDLAi4pmH7XI6ewADgdBesCpk2bpu3bt2v58uU6dOiQxo0bp/r166tr166BXhoAAAgCFKzznD17Vm%2B99Zb%2B8pe/qGXLlmrZsqWys7P1%2BuuvU7AAAECFULDOs3v3bpWUlCg%2BPt47lpCQoIULF6qsrExVqvCyNQCwJZhObUuc3kbFUbDO4/F4VKtWLYWFhXnH6tatq6KiIp04cUK1a9e%2B7DHy8vLk8Xh8xlyuaoqOjra61tBQyh6CU7D9oxpsXK6KfW/45XsI30sqrqKf28ri1D1zYi4K1nkKCgp8ypUk7/vFxcUVOsbq1auVkZHhMzZq1Cg98cQTdhb5//Ly8jTkd9nq37%2B/9fIWSHl5eVq9erXjcknOzebUXJLzsy1fviSg2f53qv2XXrBnwceJuZxTFS0JDw8vV6R%2Beb9q1aoVOkb//v31zjvv%2BLz179/f%2Blo9Ho8yMjLKPVsW7JyaS3JuNqfmksgWjJyaS3JuNifm4hms88TExOj48eMqKSmRy/Xzp8fj8ahq1aqKjIys0DGio6Md08ABAMCV4xms87Ro0UIul0tbt271jmVmZio2NpYXuAMAgAqhMZwnIiJCvXv31osvvqht27bp008/1V//%2BlcNHjw40EsDAABBIvTFF198MdCLuN60bdtWO3fu1IwZM/Svf/1L//Vf/6W%2BffsGelkXVL16dSUlJal69eqBXopVTs0lOTebU3NJZAtGTs0lOTeb03KFGGNMoBcBAADgJJwiBAAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACyjYAEAAFhGwQoixhgNGzZM77zzjs/48ePH9cQTTyg%2BPl6dOnXS2rVrfeZ37typBx98UG63W3379tX27dv9uewK27lzp2677Taftz59%2Bnjnc3NzNXToUMXFxalbt25av359AFd75YqKivTMM88oMTFR7du311//%2BtdAL%2BmqfPLJJ%2BX2KTU1VVLwPNbOV1xcrB49emjjxo3escs93r7%2B%2Bmv16NFDbrdbgwcPVm5urr%2BXXSEXyjZlypRye7hy5Urv/Lp169S5c2e53W6lpKTo2LFjgVj6BR09elSpqalKSkpShw4dlJaWpqKiIknBv2eXyhbMe/bDDz9o%2BPDhio%2BPV8eOHbVkyRLvXLDv2SUZBIXS0lIzadIk06xZM7NmzRqfuZEjR5ohQ4aYPXv2mDfffNPccccdJisryxhjzJkzZ0y7du3Myy%2B/bPbt22cmT55s/vCHP5gzZ84EIsYlrV271vTq1cvk5eV5344dO2aMMaasrMz07NnTjB492uzbt88sXLjQuN1uc/DgwQCvuuImTZpkevbsabZv327%2B/ve/m/j4ePPRRx8FellXbP78%2BWbkyJE%2B%2B/TTTz8F1WPt1woLC01KSopp1qyZ2bBhgzHm8o%2B3gwcPmri4OLN06VKzd%2B9e86c//cn06NHDlJWVBTJKORfKZowxQ4cONYsWLfLZw7NnzxpjjMnKyjKtWrUy7777rtm1a5cZNGiQGTFiRKAi%2BCgrKzMPPfSQeeyxx8zevXvNpk2bTJcuXczLL78c9Ht2qWzGBO%2BelZaWmnvuuceMHj3afPfdd%2Bbzzz83rVu3Nu%2B//37Q79nlULCCwJEjR8ygQYNMx44dTWJiok/B%2BuGHH0yzZs1Mbm6ud%2ByZZ54x48aNM8YY89Zbb5lOnTp5H5BlZWWmS5cu5Ura9WDmzJnmqaeeuuDc119/beLi4nz%2BsR4yZIiZO3euv5Z3Tc6cOWNiY2N9/pGbN2%2BeGTRoUABXdXVGjx5tZsyYUW48mB5rv8jOzjb333%2B/6dmzp08Judzjbfbs2T57d/bsWRMfH%2B%2Bzv4F2sWzGGNOhQwfz1VdfXfB2Y8aM8X7/MMaYQ4cOmdtuu838%2B9//rvQ1X86%2BfftMs2bNjMfj8Y598MEHpn379kG/Z5fKZkzw7tnRo0fNn/70J3Pq1CnvWEpKinnhhReCfs8uh1OEQWDHjh2qV6%2Be1qxZoxtvvNFnLisrS/Xq1VPDhg29YwkJCdqyZYt3PiEhQSEhIZKkkJAQtW7dWlu3bvVfgArKyclRo0aNLjiXlZWl22%2B/XdWqVfOOJSQkXJc5LmT37t0qKSlRfHy8dywhIUFZWVkqKysL4Mqu3MX2KZgea7/45ptv1KZNG61evdpn/HKPt6ysLCUmJnrnIiIi1LJly%2Bsq68WynT59WkePHr3k19qvs9WrV0/169dXVlZWZS63QqKiorRkyRLVrVvXZ/z06dNBv2eXyhbMexYdHa3Zs2erRo0aMsYoMzNTmzZtUlJSUtDv2eW4Ar0AXF6nTp3UqVOnC855PB5FR0f7jNWpU0dHjx71zt96663l5rOzsytnsdcgJydHZWVl6tmzp06dOqW77rpLY8eOVY0aNS6a88iRIwFa7ZXxeDyqVauWwsLCvGN169ZVUVGRTpw4odq1awdwdRVnjNF3332n9evXa9GiRSotLVXXrl2VmpoaVI%2B1XwwcOPCC45d7vAXD4/Fi2XJychQSEqKFCxfqyy%2B/VM2aNfXoo4/qgQcekCTl5eVdt9kiIyPVoUMH7/tlZWVauXKl2rZtG/R7dqlswbxnv9apUycdOnRIycnJuvfee/XSSy8F9Z5dDgXrOlBYWOgtROeLioryaffnKygo8PlHW5LCwsJUXFxcoXl/ulTO2rVrKzc3Vw0bNtRLL72kkydPKi0tTWPGjNGCBQuuqxxX42LrlxQ0GSTp0KFD3iyzZ8/WgQMHNGXKFBUWFgb9Hv1aMH1dXan9%2B/crJCREjRs31qBBg7Rp0yY999xzqlGjhrp06aLCwsKgyZaenq6dO3fq7bff1rJlyxy1Z7/OtmPHDkfs2dy5c5Wfn68XX3xRaWlpjv46kyhY14WsrCwNHjz4gnPz5s1T586dL3rb8PDwcg%2B24uJiVa1atULz/nS5nBs2bFB4eLhuuOEGSdLLL7%2Bsvn376ujRowoPD9eJEyd8bhOoHFfjYvsgKWgySFKDBg20ceNG3XTTTQoJCVGLFi1UVlamMWPGKCkp6bp5rF2ryz3eLrafkZGRflvj1erdu7eSk5NVs2ZNSVLz5s31/fffa9WqVerSpctFs0VERARiuReVnp6u5cuXa9asWWrWrJmj9uz8bE2bNnXEnsXGxkr6%2BSeqn376afXt21cFBQU%2BHxOse3YhFKzrQJs2bbRnz56rum1MTIzy8/N9xvLz8xUVFXXJ%2BfOfdvWHK83ZpEkTST//6HJMTIz27dvnMx%2BoHFcjJiZGx48fV0lJiVyun7/sPB6PqlatGjTfLH7xyzf5XzRp0kRFRUWKioq6bh5r1%2Bpyj7eLfV21aNHCb2u8WiEhIeX2sHHjxtqwYYOky39PuR5MnjxZq1atUnp6uu69915JztmzC2UL5j3Lz8/X1q1bfZ4ouPXWW3Xu3DlFRUVp//795T4%2B2PbsYniRe5CLi4vTwYMHfc5JZ2ZmKi4uTpLkdru1ZcsWGWMk/fwams2bN8vtdgdkvRezb98%2BxcfH%2B1zjZNeuXXK5XLrlllvkdru1Y8cOFRYWeuczMzOvuxwX06JFC7lcLp8XZ2ZmZio2NlZVqgTPl%2BFXX32lNm3a%2BPyvc9euXapZs6b3hyuu98daRVzu8eZ2u5WZmemdKygo0M6dO4Mi65w5czR06FCfsd27d6tx48aSymc7fPiwDh8%2BfN1ky8jI0BtvvKGZM2eqe/fu3nEn7NnFsgXznh04cECjRo3yeXnI9u3bVbt2bSUkJAT9nl1SIH%2BEEVcuOTm53I%2B9Dxs2zAwaNMjs2rXLvPnmmyY2NtZ7HaxTp06Ztm3bmsmTJ5vs7GwzefJk065du%2Bvu2kSlpaWmV69e3ut5bdq0yXTr1s288MILxhhjSkpKTLdu3cyTTz5p9u7daxYtWmTi4uKC6jpYzz33nOnevbvJysoyn3zyiWndurX5%2BOOPA72sK3Lq1CnToUMH89RTT5mcnBzz%2Beefm/bt25vFixcHzWPtYn59KYPLPd5yc3NNbGysWbRokff6PD179rxur8/z62xZWVnm9ttvN0uWLDE//PCDef31180dd9xhNm/ebIwxZvPmzaZly5bmzTff9F5TaeTIkYFcvte%2BfftMixYtzKxZs3yuB5WXlxf0e3apbMG8ZyUlJaZPnz5m2LBhJjs723z%2B%2BefmD3/4g1m2bFnQ79nlULCCzIUKVn5%2Bvhk5cqSJjY01nTp1Mh988IHPfFZWlundu7eJjY01/fr1Mzt27PDnkivs0KFDJiUlxSQmJpqkpCQzefJkU1RU5J3//vvvzcMPP2zuuOMO0717d/PPf/4zgKu9cmfPnjVjx441cXFxpn379ubVV18N9JKuyt69e83QoUNNXFycadeunXnllVe83/CC5bF2IedfK%2Bpyj7fPP//c3HPPPaZVq1ZmyJAh18U1hy7m/GyffPKJ6dmzp4mNjTVdu3YtV/TXrFlj7r77bhMXF2dSUlK8F/wNtEWLFplmzZpd8M2Y4N6zy2UL1j0z5udrOaakpJjWrVubdu3amQULFni/ZwTznl1OiDH//3w%2BAAAArAieF38AAAAECQoWAACAZRQsAAAAyyhYAAAAllGwAAAALKNgAQAAWEbBAgAAsIyCBQAAYBkFCwAAwDIKFgAAgGUULAAAAMsoWAAAAJZRsAAAACz7P3M%2BjTtLz8hgAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4397382840691352821”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2464</td> <td class=”number”>76.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>243</td> <td class=”number”>7.6%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4397382840691352821”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.42857142857143</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>68</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAPSPTH_12_16”><s>PCH_SNAPSPTH_12_16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SNAPS_12_16”><code>PCH_SNAPS_12_16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98953</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAPS_12_16”>PCH_SNAPS_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2655</td>

</tr> <tr>

<th>Unique (%)</th> <td>82.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>107</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.5041</td>

</tr> <tr>

<th>Minimum</th> <td>-76.471</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>3.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6344979124048633033”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6344979124048633033,#minihistogram6344979124048633033”
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</div> <div class=”row collapse col-md-12” id=”descriptives6344979124048633033”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6344979124048633033”

aria-controls=”quantiles6344979124048633033” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6344979124048633033” aria-controls=”histogram6344979124048633033”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6344979124048633033” aria-controls=”common6344979124048633033”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6344979124048633033” aria-controls=”extreme6344979124048633033”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6344979124048633033”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-76.471</td>

</tr> <tr>

<th>5-th percentile</th> <td>-18.004</td>

</tr> <tr>

<th>Q1</th> <td>-1.6829</td>

</tr> <tr>

<th>Median</th> <td>6.3075</td>

</tr> <tr>

<th>Q3</th> <td>17.157</td>

</tr> <tr>

<th>95-th percentile</th> <td>41.954</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr>

<th>Range</th> <td>1176.5</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.84</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>29.747</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.13</td>

</tr> <tr>

<th>Kurtosis</th> <td>588.69</td>

</tr> <tr>

<th>Mean</th> <td>9.5041</td>

</tr> <tr>

<th>MAD</th> <td>14.772</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>17.101</td>

</tr> <tr>

<th>Sum</th> <td>29491</td>

</tr> <tr>

<th>Variance</th> <td>884.91</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6344979124048633033”>

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QZmamamtr9cwzz2j16tX629/%2BpjVr1sgwDKWnp6tbt27Kz8/XpEmTlJGRobKyMklSWVmZ0tPTNWXKFO3YsUNdunTRPffcI8MwJEmvvvqqcnNzlZWVpa1bt8rr9SonJyeQywUAAEEmKAPWsWPHVFRUpOzsbF1zzTVKSUlRZmamCgoKtG/fPpWWliorK0t9%2B/bV3LlzlZiYqPz8fEnSc889p2uvvVZ33XWXrrnmGmVnZ%2Bv48eN6%2B%2B23JUlPPfWU7rjjDo0cOVKDBg3S0qVLlZ%2Bfz1EsAABgWVAGrJiYGG3atEndunXzGz99%2BrS8Xq8GDBigyMhIczw5OVlFRUWSJK/Xq5SUFHMuIiJCAwcOVFFRkZqamvTuu%2B/6zScmJurcuXM6fPhwK68KAAC0FUEZsKKiopSWlmY%2Bbm5u1rZt2zR06FD5fD7Fxsb6bd%2B1a1edOHFCkr5xvqamRvX19X7zLpdLnTt3Np8PAABwMa5AF2CHnJwcHTp0SDt27NCWLVsUHh7uNx8eHq6GhgZJUm1t7QXn6%2BrqzMcXer4VFRUV8vl8fmMuV2SLYNfeuFxBmecvWVhYqN93XBi9soY%2BWUOfrKFPzgj6gJWTk6OtW7dq9erV6tevn9xut6qrq/22aWhoUIcOHSRJbre7RVhqaGhQVFSU3G63%2Bfj8%2BYiICMs15eXlKTc3128sPT1dmZmZlvfRFkVHdwx0CY6KirL%2Bnmnv6JU19Mka%2BmQNfWpdQR2wli1bpu3btysnJ0c33XSTJCkuLk5Hjhzx266ystI8ehQXF6fKysoW8/3791fnzp3ldrtVWVmpvn37SpIaGxtVXV2tmJgYy3VNnz5do0aN8htzuSJVVXXmktfYlrSX9YeFhSoqKkI1NbVqamoOdDmXNXplDX2yhj5Z0976FKhf7oM2YOXm5urZZ5/VY489prFjx5rjHo9HGzduVF1dnXnUqrCwUMnJyeZ8YWGhuX1tba0OHTqkjIwMhYaGKiEhQYWFhRoyZIgkqaioSC6XS/Hx8ZZri42NbXE60Of7XI2Nbf%2BN/E3a2/qbmprb3Zq/LXplDX2yhj5ZQ59aV1CegD169KieeOIJ/eIXv1BycrJ8Pp/5lZqaqu7du2vBggUqKSnRxo0bVVxcrGnTpkmSpk6dqgMHDmjjxo0qKSnRggUL1KtXLzNQzZgxQ5s3b9auXbtUXFysJUuW6LbbbrukU4QAAKB9C8ojWK%2B//rqampq0fv16rV%2B/3m/ugw8%2B0BNPPKFFixZpypQpuuqqq7Ru3Tr16NFDktSrVy/97ne/0yOPPKJ169YpKSlJ69atU0hIiCRpwoQJOn78uBYvXqyGhgbdeOONmjdvnuNrBAAAwSvE%2BPIW5mhVPt/nrbLfcWveaJX9toZX7h0W6BIc4XKFKjq6o6qqznD4/SLolTX0yRr6ZE1761NMzBUBed2gPEUIAABwOSNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNHA9Yt956q5599ll9/nnr3HgTAAAg0BwPWEOHDtXvf/97DR8%2BXL/%2B9a%2B1Z88ecTN5AADQljgesO677z797W9/0xNPPKGwsDD96le/0g033KDVq1fro48%2BcrocAAAA2wXkjz2HhIRo2LBhGjZsmGpra/X000/riSee0MaNGzV48GDdcccduvHGGwNRGgAAwHcWkIAlSRUVFfrzn/%2BsP//5z/rwww81ePBg3XLLLTpx4oR%2B85vfaP/%2B/Vq0aFGgygMAAPjWHA9YL774ol588UW99dZb6tKliyZPnqy1a9eqd%2B/e5jbdu3fXihUrCFgAACAoOR6wFi1apJEjR2rdunUaMWKEQkNbXgbWp08fzZw50%2BnSAAAAbOF4wPrHP/6h6OhoVVdXm%2BGquLhYAwcOVFhYmCRp8ODBGjx4sNOlAQAA2MLxTxGePn1aY8eO1R/%2B8AdzbM6cOZo0aZLKy8udLgcAAMB2jgesRx55RFdddZXuvPNOc%2Bzll19W9%2B7dlZ2d7XQ5AAAAtnM8YP3zn//Ugw8%2BqJiYGHOsS5cueuCBB7Rv3z6nywEAALCd4wHL5XKppqamxXhtbS13dAcAAG2C4wFrxIgRWr58uT799FNzrLS0VNnZ2UpLS3O6HAAAANs5/inC%2BfPn684779RNN92kqKgoSVJNTY0GDhyoBQsWOF0OAACA7RwPWF27dtULL7ygvXv3qqSkRC6XS1dffbWuv/56hYSEOF0OAACA7QLyp3LCwsKUlpbGKUEAANAmOR6wfD6f1qxZowMHDujcuXMtLmx//fXXnS4JAADAVo4HrIceekgHDx7UhAkTdMUVVzj98gAAAK3O8YC1b98%2Bbdq0SSkpKU6/NAAAgCMcv01DZGSkunbt6vTLAgAAOMbxgDVp0iRt2rRJTU1NTr80AACAIxw/RVhdXa2CggL9/e9/15VXXqnw8HC/%2BaeeesrpkgAAAGwVkNs0TJw4MRAvCwAA4AjHA1Z2drbTLwkAAOAox6/BkqSKigrl5ubqvvvu07/%2B9S/99a9/1bFjxwJRCgAAgO0cD1iffPKJfvKTn%2BiFF17Qq6%2B%2BqrNnz%2Brll1/W1KlT5fV6nS4HAADAdo4HrN/%2B9rcaPXq0du3ape9973uSpMcee0yjRo3So48%2B6nQ5AAAAtnM8YB04cEB33nmn3x92drlcuueee3To0CGnywEAALCd4wGrublZzc3NLcbPnDmjsLAwp8sBAACwneMBa/jw4dqwYYNfyKqurlZOTo6GDh3qdDkAAAC2czxgPfjggzp48KCGDx%2Bu%2Bvp6/dd//ZdGjhypzz77TPPnz3e6HAAAANs5fh%2BsuLg47dy5UwUFBXr//ffV3Nys22%2B/XZMmTVKnTp2cLgcAAMB2AbmTe0REhG699VZb9tXQ0KApU6booYce0pAhQyRJy5cv19NPP%2B233UMPPaSZM2dKkgoKCrRmzRr5fD4NHz5cy5YtU5cuXSRJhmFo1apV2rFjh5qbmzVt2jTdf//9Cg0NyC3DAABAEHI8YM2aNesb5y/lbxHW19frvvvuU0lJid/40aNHdd999%2BmWW24xx748OlZcXKxFixZp6dKlio%2BP14oVK7RgwQJt2LBBkvTkk0%2BqoKBAubm5amxs1Lx589S1a1fdfffdlusCAADtm%2BOHZXr27On3FRcXp7q6OhUXFyspKcnyfo4cOaLbbrtNn376aYu5o0ePasCAAYqJiTG/IiIiJEnbtm3TuHHjNHnyZMXHx2vlypXavXu3SktLJX0R8DIzM5WSkqKhQ4fq/vvv1zPPPGPP4gEAQLtw2fwtwnXr1unEiROW9/P2229ryJAh%2Bn//7/8pMTHRHD99%2BrROnjyp3r17f%2B3zvF6vfvGLX5iPu3fvrh49esjr9So8PFzl5eW67rrrzPnk5GQdP35cFRUVio2NtVwfAABovy6bC4smTZqkV155xfL2M2bM0MKFC80jU186evSoQkJC9Pvf/14jRozQzTffrBdeeMGc/7qg1LVrV504cUI%2Bn0%2BS/Oa7desmSZcU/gAAQPsWkIvcv84777xjy41Gjx07ppCQEPXp00czZ87U/v379dBDD6lTp04aM2aM6urqFB4e7vec8PBwNTQ0qK6uznz81Tnpi4vpraqoqDDD2pdcrsh2fwTM5bps8nyrCgsL9fuOC6NX1tAna%2BiTNfTJGZfFRe6nT5/WBx98oBkzZnzn/U%2BePFkjR45U586dJUnx8fH6%2BOOPtX37do0ZM0Zut7tFWGpoaFBERIRfmHK73ea/JbU4UvZN8vLylJub6zeWnp6uzMzMb72utiA6umOgS3BUVJT190x7R6%2BsoU/W0Cdr6FPrcjxg9ejRw%2B/vEErS9773Pc2cOVM333zzd95/SEiIGa6%2B1KdPH%2B3bt0/SF/fhqqys9JuvrKxUTEyM4uLiJEk%2Bn0%2B9evUy/y1JMTExlmuYPn26Ro0a5TfmckWqqurMpS2mjWkv6w8LC1VUVIRqamrV1NTyz0Lh/9Ara%2BiTNfTJmvbWp0D9cu94wPrtb3/bqvt//PHH9c4772jLli3m2OHDh9WnTx9JksfjUWFhoaZMmSJJKi8vV3l5uTwej%2BLi4tSjRw8VFhaaAauwsFA9evS4pNN7sbGxLbb3%2BT5XY2PbfyN/k/a2/qam5na35m%2BLXllDn6yhT9bQp9bleMDav3%2B/5W2/%2Bmk%2Bq0aOHKmNGzdq8%2BbNGjNmjPbs2aOdO3ea99e6/fbb9bOf/UyJiYlKSEjQihUrdMMNN%2BjKK6805x999FH94Ac/kCStWrVKd9111yXXAQAA2i/HA9bPfvYz8xShYRjm%2BPljISEhev/99y95/4MGDdLjjz%2ButWvX6vHHH1fPnj21atUq8x5bSUlJysrK0tq1a/Xvf/9bw4YN07Jly8zn33333frXv/6ljIwMhYWFadq0aZo9e/a3XS4AAGiHQoyvphwH/P3vf9fy5cs1b948paamKjw8XO%2B%2B%2B66ysrJ0yy23aPz48ea2PXv2dLK0VuXzfd4q%2Bx235o1W2W9reOXeYYEuwREuV6iiozuqquoMh98vgl5ZQ5%2BsoU/WtLc%2BxcRcEZDXdfwzmtnZ2Vq8eLFuuukmRUdHq2PHjho6dKiysrK0fft2v7u8AwAABCPHA1ZFRcXXhqdOnTqpqqrK6XIAAABs53jASkxM1GOPPabTp0%2BbY9XV1crJydH111/vdDkAAAC2c/wi99/85jeaNWuWRowYod69e8swDH388ceKiYkxP%2BkHAAAQzBwPWH379tXLL7%2BsgoICHT16VJL005/%2BVBMmTLiku6UDAABcrgLytwi///3v69Zbb9Vnn31m3n/qe9/7XiBKAQAAsJ3j12AZhqFHH31U1113nSZOnKgTJ05o/vz5WrRokc6dO%2Bd0OQAAALZzPGA9/fTTevHFF/Xwww%2Bbf1x59OjR2rVrV4s/kAwAABCMHA9YeXl5Wrx4saZMmWLevX38%2BPFavny5XnrpJafLAQAAsJ3jAeuzzz5T//79W4zHx8fL5/M5XQ4AAIDtHA9YPXv21Lvvvtti/B//%2BId5wTsAAEAwc/xThHfffbeWLl0qn88nwzD05ptvKi8vT08//bQefPBBp8sBAACwneMBa%2BrUqWpsbNT69etVV1enxYsXq0uXLrr33nt1%2B%2B23O10OAACA7RwPWAUFBRo7dqymT5%2BuU6dOyTAMde3a1ekyAAAAWo3j12BlZWWZF7N36dKFcAUAANocxwNW79699eGHHzr9sgAAAI5x/BRhfHy87r//fm3atEm9e/eW2%2B32m8/Ozna6JAAAAFs5HrA%2B%2BugjJScnSxL3vQIAAG2SIwFr5cqVysjIUGRkpJ5%2B%2BmknXhIAACBgHLkG68knn1Rtba3f2Jw5c1RRUeHEywMAADjKkYBlGEaLsf3796u%2Bvt6JlwcAAHCU458iBAAAaOsIWAAAADZzLGCFhIQ49VIAAAAB5dhtGpYvX%2B53z6tz584pJydHHTt29NuO%2B2ABAIBg50jAuu6661rc8yopKUlVVVWqqqpyogQAAADHOBKwuPcVAABoT7jIHQAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGZBH7AaGho0ceJEvfXWW%2BZYaWmpZs%2BercTERI0fP1579uzxe87evXs1ceJEeTwezZo1S6WlpX7zW7ZsUVpampKSkrRw4ULV1tY6shYAANA2BHXAqq%2Bv169//WuVlJSYY4ZhKD09Xd26dVN%2Bfr4mTZqkjIwMlZWVSZLKysqUnp6uKVOmaMeOHerSpYvuueceGYYhSXr11VeVm5urrKwsbd26VV6vVzk5OQFZHwAACE5BG7COHDmi2267TZ9%2B%2Bqnf%2BL59%2B1RaWqqsrCz17dtXc%2BfOVWJiovLz8yVJzz33nK699lrddddduuaaa5Sdna3jx4/r7bffliQ99dRTuuOOOzRy5EgNGjRIS5cuVX5%2BPkexAACAZUEbsN5%2B%2B20NGTJEeXl5fuNer1cDBgxQZGSkOZacnKyioiJzPiUlxZyLiIjQwIEDVVRUpKamJr377rt%2B84mJiTp37pwOHz7cyisCAABthSvQBXxbM2bM%2BNpxn8%2Bn2NhYv7GuXbvqxIkTF52vqalRfX2937zL5VLnzp3N51tRUVEhn8/nN%2BZyRbZ43fbG5QraPH9JwsJC/b7jwuiVNfTJGvpkDX1yRtAGrAupra1VeHi431h4eLgaGhouOl9XV2c%2BvtDzrcjLy1Nubq7fWHp6ujIzMy1RN4NWAAATeUlEQVTvoy2Kju4Y6BIcFRUVEegSgga9soY%2BWUOfrKFPravNBSy3263q6mq/sYaGBnXo0MGcPz8sNTQ0KCoqSm6323x8/nxEhPU34vTp0zVq1Ci/MZcrUlVVZyzvoy1qL%2BsPCwtVVFSEampq1dTUHOhyLmv0yhr6ZA19sqa99SlQv9y3uYAVFxenI0eO%2BI1VVlaap%2Bfi4uJUWVnZYr5///7q3Lmz3G63Kisr1bdvX0lSY2OjqqurFRMTY7mG2NjYFqcDfb7P1djY9t/I36S9rb%2BpqbndrfnbolfW0Cdr6JM19Kl1tbkTsB6PR%2B%2B99555uk%2BSCgsL5fF4zPnCwkJzrra2VocOHZLH41FoaKgSEhL85ouKiuRyuRQfH%2B/cIgAAQFBrcwErNTVV3bt314IFC1RSUqKNGzequLhY06ZNkyRNnTpVBw4c0MaNG1VSUqIFCxaoV69eGjJkiKQvLp7fvHmzdu3apeLiYi1ZskS33XbbJZ0iBAAA7VubC1hhYWF64okn5PP5NGXKFP35z3/WunXr1KNHD0lSr1699Lvf/U75%2BfmaNm2aqqurtW7dOoWEhEiSJkyYoLlz52rx4sW66667NGjQIM2bNy%2BQSwIAAEEmxPjyFuZoVT7f562y33Fr3miV/baGV%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%2BeSUlJWnu3Lk6e/asJKm4uFiLFi1SRkaG8vLyVFNTowULFgR4NQAAIJi02YB19OhR9evXTzExMeZXVFSUXn75Zbndbj3wwAPq27evFi1apI4dO%2Bqvf/2rJGnbtm0aN26cJk%2BerPj4eK1cuVK7d%2B9WaWlpgFcEAACCRZsOWL17924x7vV6lZycrJCQEElSSEiIBg8erKKiInM%2BJSXF3L579%2B7q0aOHvF6vI3UDAIDg5wp0Aa3BMAx99NFH2rNnjzZs2KCmpiaNHTtWmZmZ8vl8uvrqq/2279q1q0pKSiRJFRUVio2NbTF/4sQJy69fUVEhn8/nN%2BZyRbbYb3vjcrXZPO8nLCzU7zsujF5ZQ5%2BsoU/W0CdntMmAVVZWptraWoWHh2vNmjX67LPPtHz5ctXV1ZnjXxUeHq6GhgZJUl1d3TfOW5GXl6fc3Fy/sfT0dPMi%2B/YqOrpjoEtwVFRURKBLCBr0yhr6ZA19soY%2Bta42GbB69uypt956S9///vcVEhKi/v37q7m5WfPmzVNqamqLsNTQ0KAOHTpIktxu99fOR0RYfyNOnz5do0aN8htzuSJVVXXmW66obWgv6w8LC1VUVIRqamrV1NQc6HIua/TKGvpkDX2ypr31KVC/3LfJgCVJnTt39nvct29f1dfXKyYmRpWVlX5zlZWV5um7uLi4r52PiYmx/NqxsbEtTgf6fJ%2BrsbHtv5G/SXtbf1NTc7tb87dFr6yhT9bQJ2voU%2Btqkydg//d//1dDhgxRbW2tOfb%2B%2B%2B%2Brc%2BfOSk5O1jvvvCPDMCR9cb3WgQMH5PF4JEkej0eFhYXm88rLy1VeXm7OAwAAXEybDFhJSUlyu936zW9%2Bo2PHjmn37t1auXKlfv7zn2vs2LGqqanRihUrdOTIEa1YsUK1tbUaN26cJOn222/Xiy%2B%2BqOeee06HDx/WAw88oBtuuEFXXnllgFcFAACCRZsMWJ06ddLmzZt16tQpTZ06VYsWLdL06dP185//XJ06ddKGDRtUWFioKVOmyOv1auPGjYqMjJT0RTjLysrSunXrdPvtt%2Bv73/%2B%2BsrOzA7wiAAAQTEKML8%2BVoVX5fJ%2B3yn7HrXmjVfbbGl65d1igS3CEyxWq6OiOqqo6w/UNF0GvrKFP1tAna9pbn2JirgjI67bJI1gAAACBRMACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbOYKdAFoP8ateSPQJVySV%2B4dFugSAABBiiNYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjID1Nerr67Vw4UKlpKRo%2BPDh%2BuMf/xjokgAAQBDhbxF%2BjZUrV%2BrgwYPaunWrysrKNH/%2BfPXo0UNjx44NdGkAACAIELDOc/bsWT333HP6wx/%2BoIEDB2rgwIEqKSnRM888Q8ACAACWcIrwPIcPH1ZjY6OSkpLMseTkZHm9XjU3NwewMgAAECw4gnUen8%2Bn6OhohYeHm2PdunVTfX29qqur1aVLl4vuo6KiQj6fz2/M5YpUbGys7fWi9Yxb80agS7gkr92fFugSLllYWKjfd3w9%2BmQNfbKGPjmDgHWe2tpav3AlyXzc0NBgaR95eXnKzc31G8vIyNCvfvUre4r8in%2BucO60ZUVFhfLy8jR9%2BnTC4jegT9ZVVFRo69ZN9Ooi6JM19Mka%2BuQM4ut53G53iyD15eMOHTpY2sf06dP1/PPP%2B31Nnz7d9lqd5vP5lJub2%2BLoHPzRJ%2BvolTX0yRr6ZA19cgZHsM4TFxenqqoqNTY2yuX6oj0%2Bn08dOnRQVFSUpX3ExsbyWwEAAO0YR7DO079/f7lcLhUVFZljhYWFSkhIUGgo7QIAABdHYjhPRESEJk%2BerCVLlqi4uFi7du3SH//4R82aNSvQpQEAgCARtmTJkiWBLuJyM3ToUB06dEirVq3Sm2%2B%2BqV/%2B8peaOnVqoMu6LHTs2FGpqanq2LFjoEu5rNEn6%2BiVNfTJGvpkDX1qfSGGYRiBLgIAAKAt4RQhAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYsqa%2Bv18KFC5WSkqLhw4frj3/8Y6BLCoiTJ08qMzNTqampSktLU3Z2turr6yVJpaWlmj17thITEzV%2B/Hjt2bPH77l79%2B7VxIkT5fF4NGvWLJWWlgZiCY6bM2eOHnzwQfPxoUOHdOutt8rj8Wjq1Kk6ePCg3/YFBQUaPXq0PB6P0tPTderUKadLdlRDQ4OWLl2q6667Tj/60Y/02GOP6cv7P9Or/1NeXq65c%2Bdq8ODBGjVqlLZs2WLO0acv3kcTJ07UW2%2B9ZY59159JW7ZsUVpampKSkrRw4ULV1tY6spa2goAFS1auXKmDBw9q69atevjhh5Wbm6u//vWvgS7LUYZhKDMzU7W1tXrmmWe0evVq/e1vf9OaNWtkGIbS09PVrVs35efna9KkScrIyFBZWZkkqaysTOnp6ZoyZYp27NihLl266J577lFb/0MKf/nLX7R7927z8dmzZzVnzhylpKTo%2BeefV1JSkubOnauzZ89KkoqLi7Vo0SJlZGQoLy9PNTU1WrBgQaDKd8Ty5cu1d%2B9ebd68WatWrdKf/vQn5eXl0avz3HvvvYqMjNTzzz%2BvhQsXas2aNXrttdfok774BfjXv/61SkpKzLHv%2BjPp1VdfVW5urrKysrR161Z5vV7l5OQEZH1BywAu4syZM0ZCQoKxb98%2Bc2zdunXGzJkzA1iV844cOWL069fP8Pl85thLL71kDB8%2B3Ni7d6%2BRmJhonDlzxpy74447jLVr1xqGYRhr1qzx69fZs2eNpKQkv562NVVVVcaIESOMqVOnGvPnzzcMwzCee%2B45Y9SoUUZzc7NhGIbR3NxsjBkzxsjPzzcMwzDmzZtnbmsYhlFWVmb88Ic/ND799FPnF%2BCAqqoqY8CAAcZbb71ljm3YsMF48MEH6dVXVFdXG/369TM%2B%2BOADcywjI8NYunRpu%2B9TSUmJcfPNNxs/%2BclPjH79%2Bpk/U77rz6QZM2aY2xqGYezfv98YNGiQcfbsWSeW1SZwBAsXdfjwYTU2NiopKckcS05OltfrVXNzcwArc1ZMTIw2bdqkbt26%2BY2fPn1aXq9XAwYMUGRkpDmenJysoqIiSZLX61VKSoo5FxERoYEDB5rzbdH//M//aNKkSbr66qvNMa/Xq%2BTkZIWEhEiSQkJCNHjw4Av2qXv37urRo4e8Xq%2BzxTuksLBQnTp1Umpqqjk2Z84cZWdn06uv6NChgyIiIvT888/r3LlzOnbsmA4cOKD%2B/fu3%2Bz69/fbbGjJkiPLy8vzGv8vPpKamJr377rt%2B84mJiTp37pwOHz7cyitqOwhYuCifz6fo6GiFh4ebY926dVN9fb2qq6sDWJmzoqKilJaWZj5ubm7Wtm3bNHToUPl8PsXGxvpt37VrV504cUKSLjrf1rz55pv65z//qXvuucdv/GJ9qKioaFd9Ki0tVc%2BePbVz506NHTtW//mf/6l169apubmZXn2F2%2B3W4sWLlZeXJ4/Ho3HjxmnEiBG69dZb232fZsyYoYULFyoiIsJv/Lv8TKqpqVF9fb3fvMvlUufOndtM35zgCnQBuPzV1tb6hStJ5uOGhoZAlHRZyMnJ0aFDh7Rjxw5t2bLla3v0ZX8u1MO22L/6%2Bno9/PDDWrx4sTp06OA3d7E%2B1NXVtZs%2BSV9ck/bJJ5/o2WefVXZ2tnw%2BnxYvXqyIiAh6dZ6jR49q5MiRuvPOO1VSUqJly5bp%2Buuvp08XcLG%2BfNN8XV2d%2BfhCz8fFEbBwUW63u8V/VF8%2BPv9/oO1FTk6Otm7dqtWrV6tfv35yu90tjuY1NDSY/blQD6Oiohyr2Sm5ubm69tpr/Y72felCfbhYn87/7bytcLlcOn36tFatWqWePXtK%2BuLi4%2B3bt%2Buqq66iV/%2B/N998Uzt27NDu3bvVoUMHJSQk6OTJk1q/fr2uvPJK%2BvQ1vsvPJLfbbT4%2Bf76t981OnCLERcXFxamqqkqNjY3mmM/nU4cOHdpkQLiYZcuW6cknn1ROTo5uuukmSV/0qLKy0m%2B7yspK8xD7heZjYmKcKdpBf/nLX7Rr1y4lJSUpKSlJL730kl566SUlJSXRp/PExMTI7Xab4UqS/uM//kPl5eX06isOHjyoq666yu8XugEDBqisrIw%2BXcB36Uvnzp3ldrv95hsbG1VdXd3m%2B2YnAhYuqn///nK5XH4XZBcWFiohIUGhoe3rLZSbm6tnn31Wjz32mCZMmGCOezwevffee%2BahdemLHnk8HnO%2BsLDQnKutrdWhQ4fM%2Bbbk6aef1ksvvaSdO3dq586dGjVqlEaNGqWdO3fK4/HonXfeMT8KbhiGDhw4cME%2BlZeXq7y8vE32SfpivfX19froo4/MsWPHjqlnz5706itiY2P1ySef%2BB1ROXbsmHr16kWfLuC7/EwKDQ1VQkKC33xRUZFcLpfi4%2BOdW0SwC%2BRHGBE8HnroIWPChAmG1%2Bs1XnvtNWPw4MHGq6%2B%2BGuiyHHXkyBGjf//%2BxurVq42Kigq/r8bGRmP8%2BPHGvffea3z44YfGhg0bjMTEROP48eOGYRhGaWmpkZCQYGzYsMH48MMPjf/%2B7/82fvKTn5gfLW/L5s%2Bfb35M/vPPPzeGDh1qLFu2zCgpKTGWLVtmDBs2zPwo%2BYEDB4yBAwcaf/rTn4z333/fmDlzpjF37txAlt/q5syZY0yfPt14//33jX/84x/G0KFDja1bt9Krr6ipqTGGDRtmzJs3zzh27Jjx%2BuuvG6mpqcb27dvp01d89TYN3/VnUkFBgTF48GDjtddeM7xerzFhwgRj2bJlAVtbMCJgwZKzZ88aDzzwgJGYmGgMHz7cePLJJwNdkuM2bNhg9OvX72u/DMMwPv74Y%2BOnP/2pce211xoTJkww3njjDb/n//3vfzduvPFGY9CgQcYdd9zRZu7DczFfDViGYRher9eYPHmykZCQYEybNs147733/LbPz883fvzjHxuJiYlGenq6cerUKadLdlRNTY0xb948IzEx0bj%2B%2BuuN3/3ud%2Bb/5OjV/ykpKTFmz55tDB482Bg9erTx5JNP0qfzfDVgGcZ3/5m0YcMG4/rrrzeSk5ONBQsWGHV1dY6so60IMYw2fitpAAAAh7WvC2gAAAAcQMACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABs9v8B9AZR2NddB4sAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6344979124048633033”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-14.2857142857143</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.3333333333333</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2644)</td> <td class=”number”>2873</td> <td class=”number”>89.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>107</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6344979124048633033”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-76.4705882352941</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-72.093023255814</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-68.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.6666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>176.923076923077</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>241.666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SNAP_12_16”>PCH_SNAP_12_16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.8106</td>

</tr> <tr>

<th>Minimum</th> <td>-4.8657</td>

</tr> <tr>

<th>Maximum</th> <td>2.0182</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4058363906456357286”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4058363906456357286,#minihistogram-4058363906456357286”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4058363906456357286”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4058363906456357286”

aria-controls=”quantiles-4058363906456357286” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4058363906456357286” aria-controls=”histogram-4058363906456357286”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4058363906456357286” aria-controls=”common-4058363906456357286”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4058363906456357286” aria-controls=”extreme-4058363906456357286”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4058363906456357286”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-4.8657</td>

</tr> <tr>

<th>5-th percentile</th> <td>-4.3623</td>

</tr> <tr>

<th>Q1</th> <td>-2.9504</td>

</tr> <tr>

<th>Median</th> <td>-1.7087</td>

</tr> <tr>

<th>Q3</th> <td>-1.1284</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.4874</td>

</tr> <tr>

<th>Maximum</th> <td>2.0182</td>

</tr> <tr>

<th>Range</th> <td>6.8838</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.822</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.4481</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.79978</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.19106</td>

</tr> <tr>

<th>Mean</th> <td>-1.8106</td>

</tr> <tr>

<th>MAD</th> <td>1.1248</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.0013237</td>

</tr> <tr>

<th>Sum</th> <td>-5670.9</td>

</tr> <tr>

<th>Variance</th> <td>2.097</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4058363906456357286”>

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/qW6urqNHr0aDU0NAQs39DQ4D99l5KSct75pKSkoB8/OTm53elAt/tTnTvX8R2ntbXtktazCrv33xE8T52nq59bO%2B/3du5dsnf/VuvdWic8/8XpdKqlpUUffvihf%2BzYsWPq16%2BfnE6n3nvvPf89sXw%2Bnw4cOCCn0%2Blft7Ky0r9eXV2d6urq/PMAAAAXY8mA9e1vf1sTJkxQQUGBDh8%2BrD//%2Bc8qLy9XTk6OJk%2BerKamJhUVFenIkSMqKiqSx%2BPRlClTJEk5OTnauXOnNm/erMOHD2vRokWaMGECt2gAAABBs2TAkqRHHnlE3/zmN5WTk6PFixfrlltu0ezZsxUfH68nn3xSlZWVysrKksvlUnl5uWJjYyVJaWlpWrZsmcrKypSTk6MePXqouLjY5G4AAEB3YslrsCTp8ssvV0lJyXnnrrrqKm3fvv1r183KylJWVlZnlQYAACzOskewAAAAzELAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgIRewZs2apU2bNunTTz81uxQAAIBLEnIBa8yYMVq7dq3GjRune%2B%2B9V/v27ZPP5zO7LAAAgKCFXMBasGCB/vjHP2r16tWKiIjQ/PnzNWHCBD3%2B%2BOP68MMPzS4PAADgohxmF3A%2BYWFhGjt2rMaOHSuPx6Pnn39eq1evVnl5uUaNGqXbbrtN3//%2B980uEwAA4LxCMmBJUn19vV588UW9%2BOKL%2BuCDDzRq1Cj98Ic/1MmTJ3X//ffrnXfeUWFhodllAgAAtBNyAWvnzp3auXOn3nrrLSUmJmr69Ol64oknNHDgQP8yffr0UVFR0QUD1u9//3vl5eUFjE2aNElPPPGEqqur9cADD%2BiDDz7Q4MGD9eCDD2r48OH%2B5Xbt2qVVq1bJ7XZr3LhxWr58uRITEw3vFQAAWFPIXYNVWFiouLg4lZWVae/evVqwYEFAuJKkb3/727r11lsvuJ0jR47ohhtu0L59%2B/xfK1asUHNzs%2BbOnauMjAxt27ZNaWlpmjdvnpqbmyVJBw8eVGFhofLy8lRRUaGmpiYVFBR0VrsAAMCCQu4I1uuvv66ePXuqsbFR4eFf5L%2BDBw9q2LBhioiIkCSNGjVKo0aNuuB2jh49qu9%2B97tKSkoKGN%2ByZYuioqK0aNEihYWFqbCwUK%2B//rpeffVVZWVlacOGDZoyZYqmT58uSSopKdENN9yg2tpaDRgwoBM6BgAAVhNyR7A%2B%2B%2BwzTZ48WevWrfOPzZ07V9OmTVNdXV3Q2zl69Gi7I1%2BS5HK5lJ6errCwMElfXFA/atQoVVVV%2BeczMjL8y/fp00d9%2B/aVy%2BW6xI4AAIDdhFzAeuihh5Samqrbb7/dP7Z792716dNHxcXFQW3D5/Ppww8/1L59%2BzRp0iRNnDhRjzzyiLxer9xut5KTkwOW79Wrl06ePCnpi4vrLzQPAABwMSF3ivDdd9/Vb3/724BTe4mJiVq0aJFuueWWoLZx4sQJeTweRUZGatWqVTp%2B/LhWrFihM2fO%2BMf/XWRkpLxeryTpzJkzF5wPRn19vdxud8CYwxHbLrhdSEREeMB3u7F7/5fC4eC56ixd9dzaeb%2B3c%2B%2BSvfu3au8hF7AcDoeamprajXs8nqDv6N6vXz%2B99dZb6tGjh8LCwjR06FC1tbXp5z//uUaPHt0uLHm9XkVHR0uSoqKizjsfExMTdA8VFRUqLS0NGMvNzVV%2Bfn7Q2/hSQkLwj2tFdu%2B/I3r2jDO7BMvq6ufWzvu9nXuX7N2/1XoPuYB13XXXacWKFXrsscf0zW9%2BU5JUW1ur4uJijR8/PujtfOMb3wj4edCgQWppaVFSUpIaGhoC5hoaGvxHl1JSUs47/9WL5S8kOztbmZmZAWMOR6xOn/486G1ERIQrISFGTU0etba2Bb2eVdi9/0vRkf0LHdNVz62d93s79y7Zu//O7t2sXz5DLmAtXrxYt99%2BuyZNmqSEhARJUlNTk4YNGxb07RL%2B/Oc/a%2BHChfrTn/7kP/L0/vvv6xvf%2BIbS09O1bt06%2BXw%2BhYWFyefz6cCBA7rzzjslSU6nU5WVlcrKypIk1dXVqa6uTk6nM%2BgekpOT250OdLs/1blzHd9xWlvbLmk9q7B7/x3B89R5uvq5tfN%2Bb%2BfeJXv3b7XeQy5g9erVS9u3b9ebb76pmpoaORwODR48WNdee63/f/5dTFpamqKionT//fcrNzdXtbW1Kikp0U9/%2BlNNnjxZjz76qIqKivSjH/1ImzZtksfj0ZQpUyRJOTk5mj17tkaOHKkRI0aoqKhIEyZM4BYNAAAgaCEXsCQpIiJC48eP79ApwX8XHx%2Bv3/zmN3rooYc0Y8YMxcXF6Uc/%2BpF%2B%2BtOfKiwsTE8%2B%2BaQeeOAB/fa3v9UVV1yh8vJyxcbGSvoinC1btkxPPPGEPvnkE40dO1bLly83sj0AAGBxIRew3G63Vq1apQMHDujs2bPtLmzfs2dPUNv5zne%2Bo6effvq8c1dddZW2b9/%2BtetmZWX5TxECAAB0VMgFrF/%2B8pc6dOiQbrrpJl1%2B%2BeVmlwMAANBhIRew/vKXv2j9%2BvUBd1MHAADoTkLurl6xsbHq1auX2WUAAABcspALWNOmTdP69evV2tpqdikAAACXJOROETY2NmrXrl3605/%2BpAEDBrT7szXPPfecSZUBAAAEJ%2BQCliRNnTrV7BIAAAAuWcgFrOLiYrNLAAAA%2BI%2BE3DVYklRfX6/S0lItWLBA//znP/Xqq6/q2LFjZpcFAAAQlJALWB999JF%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%2B09///nfdfPPNJlQEAADQMSEXsPr27Rvwdwgl6bLLLtOtt96q//qv/zKpKgAAgOCFXMD61a9%2BZXYJAAAA/5GQC1jvvPNO0MteffXVnVgJAADApQm5gDV79mz/KUKfz%2Bcf/%2BpYWFiY3n///Ytub%2B7cuUpMTPQfGauurtYDDzygDz74QIMHD9aDDz6o4cOH%2B5fftWuXVq1aJbfbrXHjxmn58uVKTEw0rD8AAGB9Ife/CNeuXat%2B/fpp1apV2r9/vyorK/XMM8/oW9/6lu69917t2bNHe/bs0WuvvXbRbb388svau3ev/%2Bfm5mbNnTtXGRkZ2rZtm9LS0jRv3jw1NzdLkg4ePKjCwkLl5eWpoqJCTU1NKigo6LReAQCANYVcwCouLtaSJUs0adIk9ezZU3FxcRozZoyWLVumjRs3ql%2B/fv6vC2lsbFRJSYlGjBjhH9u9e7eioqK0aNEiDRo0SIWFhYqLi9Orr74qSdqwYYOmTJmi6dOna8iQISopKdHevXtVW1vbqT0DAABrCbmAVV9ff97wFB8fr9OnTwe9nYcffljTpk3T4MGD/WMul0vp6en%2B041hYWEaNWqUqqqq/PMZGRn%2B5fv06aO%2BffvK5XJdajsAAMCGQu4arJEjR%2Bqxxx7Tww8/rPj4eElfHI1auXKlrr322qC2sX//fr377rt66aWXtHTpUv%2B42%2B0OCFyS1KtXL9XU1Ej6ItwlJye3m%2B/on%2Bipr6%2BX2%2B0OGHM4Yttt%2B0IiIsIDvtuN3fu/FA4HzxW%2B0B33Bbu/5%2B3cv1V7D7mAdf/992vOnDm67rrrNHDgQPl8Pv3v//6vkpKS9Nxzz110/ZaWFj3wwANasmSJoqOjA%2BY8Ho8iIyMDxiIjI%2BX1eiVJZ86cueB8sCoqKlRaWhowlpubq/z8/A5tR5ISEmI6vI6V2L3/jujZM87sEhAiuvO%2BYPf3vJ37t1rvIRewBg0apN27d2vXrl06evSoJOmWW27RTTfdpJiYiz/5paWlGj58uMaPH99uLioqql1Y8nq9/iD2dfPBPO6/y87OVmZmZsCYwxGr06c/D3obERHhSkiIUVOTR62tbR16fCuwe/%2BXoiP7F6ytO%2B4Ldn/P27n/zu7drF84Qi5gSVKPHj00a9YsHT9%2BXAMGDJD0xd3cg/Hyyy%2BroaFBaWlpkuQPTL/73e80depUNTQ0BCzf0NDgP3WXkpJy3vmkpKQO1Z%2BcnNzudKDb/anOnev4jtPa2nZJ61mF3fvvCJ4nfKk77wt2f8/buX%2Br9R5yJzx9Pp8eeeQRXX311Zo6dapOnjypxYsXq7CwUGfPnr3o%2Bs8//7xeeukl7dixQzt27FBmZqYyMzO1Y8cOOZ1Ovffee/57afl8Ph04cEBOp1OS5HQ6VVlZ6d9WXV2d6urq/PMAAADBCLmA9fzzz2vnzp164IEH/NdDTZw4Ua%2B99lq765rOp1%2B/fkpNTfV/xcXFKS4uTqmpqZo8ebKamppUVFSkI0eOqKioSB6PR1OmTJEk5eTkaOfOndq8ebMOHz6sRYsWacKECf6jaAAAAMEIuYBVUVGhJUuWKCsry387hRtvvFErVqzQSy%2B99B9tOz4%2BXk8%2B%2BaQqKyuVlZUll8ul8vJyxcbGSpLS0tK0bNkylZWVKScnRz169FBxcfF/3BMAALCXkLsG6/jx4xo6dGi78SFDhrS79UEwvvrHo6%2B66ipt3779a5fPyspSVlZWhx8HAADgSyF3BKtfv37661//2m789ddf51QdAADoFkLuCNZPfvITPfjgg3K73fL5fNq/f78qKir0/PPP6xe/%2BIXZ5QEAAFxUyAWsGTNm6Ny5c1qzZo3OnDmjJUuWKDExUffcc49ycnLMLg8AAOCiQi5g7dq1S5MnT1Z2drY%2B/vhj%2BXw%2B9erVy%2ByyAAAAghZy12AtW7bMfzF7YmIi4QoAAHQ7IRewBg4cqA8%2B%2BMDsMgAAAC5ZyJ0iHDJkiBYuXKj169dr4MCBioqKCpjnvlQAACDUhVzA%2BvDDD5Weni5Jl3TfKwAAALOFRMAqKSlRXl6eYmNj9fzzz5tdDgAAwH8kJK7Bevrpp%2BXxeALG5s6dq/r6epMqAgAAuHQhEbB8Pl%2B7sXfeeUctLS0mVAMAAPCfCYmABQAAYCUELAAAAIOFTMAKCwszuwQAAABDhMT/IpSkFStWBNzz6uzZs1q5cqXi4uICluM%2BWAAAINSFRMC6%2Buqr293zKi0tTadPn9bp06dNqgoAAODShETA4t5XAGBPU1a9YXYJHfLKPWPNLgHdRMhcgwUAAGAVBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADGbZgPXRRx/pJz/5idLS0jRhwgStX7/eP1dbW6sf//jHGjlypG688Ubt27cvYN0333xTU6dOldPp1Jw5c1RbW9vV5QMAgG7MkgGrra1Nc%2BfOVc%2BePbV9%2B3Y9%2BOCDWrNmjV566SX5fD7l5uaqd%2B/e2rp1q6ZNm6a8vDydOHFCknTixAnl5uYqKytLW7ZsUWJiou6%2B%2B275fD6TuwIAAN1FSNzJ3WgNDQ0aOnSoli5dqvj4eA0cOFDXXnutKisr1bt3b9XW1mrTpk2KjY3VoEGDtH//fm3dulXz58/X5s2bNXz4cN1xxx2Svvjbh2PHjtXbb7%2Bta665xuTOAABAd2DJI1jJyclatWqV4uPj5fP5VFlZqXfeeUejR4%2BWy%2BXSlVdeqdjYWP/y6enpqqqqkiS5XC5lZGT452JiYjRs2DD/PAAAwMVYMmD9u8zMTN18881KS0vTpEmT5Ha7lZycHLBMr169dPLkSUm66DwAAMDFWPIU4b974okn1NDQoKVLl6q4uFgej0eRkZEBy0RGRsrr9UrSReeDUV9fL7fbHTDmcMS2C24XEhERHvDdbuze/6VwOHiu8AX2hc7TWc%2BtnT/zrNq75QPWiBEjJEluWdy7AAAURklEQVQtLS1auHChZsyYIY/HE7CM1%2BtVdHS0JCkqKqpdmPJ6vUpISAj6MSsqKlRaWhowlpubq/z8/A7Xn5AQ0%2BF1rMTu/XdEz55xZpeAEMG%2B0Hk6%2B7m182ee1Xq3ZMBqaGhQVVWVJk6c6B8bPHiwzp49q6SkJB07dqzd8l8eXUpJSVFDQ0O7%2BaFDhwb9%2BNnZ2crMzAwYczhidfr050FvIyIiXAkJMWpq8qi1tS3o9azC7v1fio7sX7A29oXO01nPrZ0/8zq7d7N%2B4bBkwDp%2B/Ljy8vK0d%2B9epaSkSJIOHTqkxMREpaen66mnntKZM2f8R60qKyuVnp4uSXI6naqsrPRvy%2BPxqLq6Wnl5eUE/fnJycrvTgW73pzp3ruM7Tmtr2yWtZxV2778jeJ7wJfaFztPZz62dP/Os1ru1Tnj%2By4gRIzRs2DDdd999OnLkiPbu3auVK1fqzjvv1OjRo9WnTx8VFBSopqZG5eXlOnjwoGbOnClJmjFjhg4cOKDy8nLV1NSooKBA/fv35xYNAAAgaJYMWBEREVq9erViYmKUnZ2twsJCzZ49W3PmzPHPud1uZWVl6cUXX1RZWZn69u0rSerfv79%2B/etfa%2BvWrZo5c6YaGxtVVlamsLAwk7sCAADdhSVPEUpfXEv11QvNv5SamqoNGzZ87brXX3%2B9rr/%2B%2Bs4qDQAAWJwlj2ABAACYiYAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDDL3qYBAOxqyqo3zC4BsD2OYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAbjjz13c93pj7q%2Bcs9Ys0sAAKBLcAQLAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGCWDVinTp1Sfn6%2BRo8erfHjx6u4uFgtLS2SpNraWv34xz/WyJEjdeONN2rfvn0B67755puaOnWqnE6n5syZo9raWjNaAAAA3ZQlA5bP51N%2Bfr48Ho9eeOEFPf744/rjH/%2BoVatWyefzKTc3V71799bWrVs1bdo05eXl6cSJE5KkEydOKDc3V1lZWdqyZYsSExN19913y%2BfzmdwVAADoLix5H6xjx46pqqpKb7zxhnr37i1Jys/P18MPP6zrrrtOtbW12rRpk2JjYzVo0CDt379fW7du1fz587V582YNHz5cd9xxhySpuLhYY8eO1dtvv61rrrnGzLYAAEA3YckjWElJSVq/fr0/XH3ps88%2Bk8vl0pVXXqnY2Fj/eHp6uqqqqiRJLpdLGRkZ/rmYmBgNGzbMPw8AAHAxljyClZCQoPHjx/t/bmtr04YNGzRmzBi53W4lJycHLN%2BrVy%2BdPHlSki46H4z6%2Bnq53e6AMYcjtt12LyQiIjzguxU4HMH3YsX%2BO1tHnl8Al6az3md2/syzau%2BWDFhftXLlSlVXV2vLli165plnFBkZGTAfGRkpr9crSfJ4PBecD0ZFRYVKS0sDxnJzc5Wfn9/h2hMSYjq8Tqjq2TOuw%2BtYqf/OdinPL4CO6ez3mZ0/86zWu%2BUD1sqVK/Xss8/q8ccf13e/%2B11FRUWpsbExYBmv16vo6GhJUlRUVLsw5fV6lZCQEPRjZmdnKzMzM2DM4YjV6dOfB72NiIhwJSTEqKnJo9bWtqDXC2V277%2BzdeT5BXBpOut9ZufPvM7u3axfPi0dsJYvX66NGzdq5cqVmjRpkiQpJSVFR44cCViuoaHBf/ouJSVFDQ0N7eaHDh0a9OMmJye3Ox3odn%2Bqc%2Bc6vuO0trZd0nqhyO79dzaeJ6Dzdfb7zM6feVbr3VonPP9NaWmpNm3apMcee0w33XSTf9zpdOpvf/ubzpw54x%2BrrKyU0%2Bn0z1dWVvrnPB6Pqqur/fMAAAAXY8mAdfToUa1evVo/%2B9nPlJ6eLrfb7f8aPXq0%2BvTpo4KCAtXU1Ki8vFwHDx7UzJkzJUkzZszQgQMHVF5erpqaGhUUFKh///7cogEAAATNkgFrz549am1t1Zo1azRu3LiAr4iICK1evVput1tZWVl68cUXVVZWpr59%2B0qS%2Bvfvr1//%2BtfaunWrZs6cqcbGRpWVlSksLMzkrgAAQHdhyWuw5s6dq7lz537tfGpqqjZs2PC189dff72uv/76zigNAADYgCWPYAEAAJiJgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBHGYXAMAYU1a9YXYJAIB/4QgWAACAwQhYAAAABiNgAQAAGIyABQAAYDAuckeX4SJsAIBdcAQLAADAYAQsAAAAgxGwAAAADGb5gOX1ejV16lS99dZb/rHa2lr9%2BMc/1siRI3XjjTdq3759Aeu8%2Beabmjp1qpxOp%2BbMmaPa2tquLhsAAHRjlg5YLS0tuvfee1VTU%2BMf8/l8ys3NVe/evbV161ZNmzZNeXl5OnHihCTpxIkTys3NVVZWlrZs2aLExETdfffd8vl8ZrUBAAC6GcsGrCNHjui///u/9Y9//CNg/C9/%2BYtqa2u1bNkyDRo0SPPmzdPIkSO1detWSdLmzZs1fPhw3XHHHfrOd76j4uJi/d///Z/efvttM9oAAADdkGUD1ttvv61rrrlGFRUVAeMul0tXXnmlYmNj/WPp6emqqqryz2dkZPjnYmJiNGzYMP88AADAxVj2Plg333zzecfdbreSk5MDxnr16qWTJ08GNQ8AAHAxlg1YX8fj8SgyMjJgLDIyUl6vN6j5YNTX18vtdgeMORyx7YLbhUREhAd8BwCYz%2BHonM9kO3/mW7V32wWsqKgoNTY2Box5vV5FR0f7578aprxerxISEoJ%2BjIqKCpWWlgaM5ebmKj8/v8P1JiTEdHgdAEDn6NkzrlO3b%2BfPfKv1bruAlZKSoiNHjgSMNTQ0%2BI8upaSkqKGhod380KFDg36M7OxsZWZmBow5HLE6ffrzoLcRERGuhIQYNTV51NraFvR6AIDO05HP8Y6w82d%2BZ/fe2aH469guYDmdTpWXl%2BvMmTP%2Bo1aVlZVKT0/3z1dWVvqX93g8qq6uVl5eXtCPkZyc3O50oNv9qc6d6/iO09radknrAQCM19mfx3b%2BzLda79Y64RmE0aNHq0%2BfPiooKFBNTY3Ky8t18OBBzZw5U5I0Y8YMHThwQOXl5aqpqVFBQYH69%2B%2Bva665xuTKAQBAd2G7gBUREaHVq1fL7XYrKytLL774osrKytS3b19JUv/%2B/fXrX/9aW7du1cyZM9XY2KiysjKFhYWZXDkAAOgubHGK8O9//3vAz6mpqdqwYcPXLn/99dfr%2Buuv7%2ByyAACARdnuCBYAAEBnI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDBb3AcLAAAjTFn1htkldMgr94w1uwTb4ggWAACAwQhYAAAABiNgAQAAGIyABQAAYDAucgcAwKK600X5v1843uwSDMURLAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAErPNoaWnRfffdp4yMDI0bN05PPfWU2SUBAIBuxGF2AaGopKREhw4d0rPPPqsTJ05o8eLF6tu3ryZPnmx2aQAAoBsgYH1Fc3OzNm/erHXr1mnYsGEaNmyYampq9MILLxCwAABAUDhF%2BBWHDx/WuXPnlJaW5h9LT0%2BXy%2BVSW1ubiZUBAIDugiNYX%2BF2u9WzZ09FRkb6x3r37q2WlhY1NjYqMTHxotuor6%2BX2%2B0OGHM4YpWcnBx0HRER4QHfAQCwMqv9e0fA%2BgqPxxMQriT5f/Z6vUFto6KiQqWlpQFjeXl5mj9/ftB11NfX69ln1ys7O/uCwezdImuetqyvr1dFRcVF%2B7cierdn75K9%2B7dz75K9%2B/%2By9zNnRlmqd2vFRQNERUW1C1Jf/hwdHR3UNrKzs7Vt27aAr%2Bzs7A7V4Xa7VVpa2u5ImF3YuX96t2fvkr37t3Pvkr37t2rvHMH6ipSUFJ0%2BfVrnzp2Tw/HF0%2BN2uxUdHa2EhISgtpGcnGypFA4AADqGI1hfMXToUDkcDlVVVfnHKisrNWLECIWH83QBAICLIzF8RUxMjKZPn66lS5fq4MGDeu211/TUU09pzpw5ZpcGAAC6iYilS5cuNbuIUDNmzBhVV1fr0Ucf1f79%2B3XnnXdqxowZXV5HXFycRo8erbi4uC5/7FBg5/7p3Z69S/bu3869S/bu34q9h/l8Pp/ZRQAAAFgJpwgBAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsEJcdXW1rrjiioCvrKwss8syxYMPPqjZs2ebXUaX%2Bec//6n8/Hylp6dr7NixWrlypc6dO2d2WV2iqalJhYWF%2Bt73vqcxY8boF7/4hZqamswuq8v5fD7dcccd2rZtm9mldKqWlhbdd999ysjI0Lhx4/TUU0%2BZXVKX83q9mjp1qt566y2zS%2BlSp06dUn5%2BvkaPHq3x48eruLhYLS0tZpdlCIfZBeDCjhw5oqFDh2rdunX%2BMYfDfi/bgQMHtHHjRl199dVml9JlFi5cqLCwMFVUVKixsVELFy7U5ZdfrjvvvNPs0jrdAw88oH/84x8qLy9XWFiYli5dqvvvv19PPPGE2aV1mba2NhUVFemNN97Q1KlTzS6nU5WUlOjQoUN69tlndeLECS1evFh9%2B/bV5MmTzS6tS7S0tGjBggWqqakxu5Qu5fP5lJ%2Bfr4SEBL3wwgv65JNPdN999yk8PFyLFy82u7z/mP3%2Bpe5mjh49qkGDBikpKcnsUkzj9Xq1ZMkSjRw50uxSuozX61WvXr00f/58paamSpImTZqkyspKkyvrfM3Nzfrd736njRs3avjw4ZKk%2B%2B67T7fccotaWloUFRVlcoWd79SpU1q4cKGOHz%2BuhIQEs8vpVM3Nzdq8ebPWrVunYcOGadiwYaqpqdELL7xgi4B15MgRLViwQHb8q3XHjh1TVVWV3njjDfXu3VuSlJ%2Bfr4cfftgSAYtThCHu6NGjGjhwoNllmKq8vFxXXHGFxo4da3YpXSYyMlKPPPKIP1zV1NToD3/4g0aPHm1yZZ0vPDxca9eu1dChQwPGW1tb9fnnn5tUVdf629/%2Bpj59%2Bmjr1q26/PLLzS6nUx0%2BfFjnzp1TWlqafyw9PV0ul0ttbW0mVtY13n77bV1zzTWqqKgwu5Qul5SUpPXr1/vD1Zc%2B%2B%2BwzkyoyFkewQtzRo0fV1tamH/zgB/r000913XXXadGiRYqPjze7tC5x9OhRbdy4UTt37tTGjRvNLscUt956q9555x0NGzZMt9xyi9nldLro6Ghdd911AWPPPfecrrjiCiUmJppUVdfKzMxUZmam2WV0CbfbrZ49eyoyMtI/1rt3b7W0tKixsdHyr/nNN99sdgmmSUhI0Pjx4/0/t7W1acOGDRozZoyJVRmHgGWyM2fO6NSpU%2BedS0xMVG1trfr376%2BHHnpITU1NKi4u1s9//nOtWbOmiyvtHBfqPykpSUuWLNH8%2BfPb/YZjBRfrPTY2VpJ0//3365NPPtGKFSt07733au3atV1ZZqcItndJ2rBhg1555RWtX7%2B%2Bq8rrdB3p3%2Bo8Hk9AuJLk/9nr9ZpREkyycuVKVVdXa8uWLWaXYggClslcLpfmzJlz3rmysjL95S9/UVRUlC677DJJ0q9%2B9SvNmDFDp06dUkpKSleW2iku1P%2BCBQvU2tqq7OzsLq6qa1zstZ84caIkaciQIZKkhx56SDNnztTx48fVv3//LquzMwTb%2BwsvvKAVK1aooKBA48aN68oSO1Ww/dtBVFRUuyD15c/R0dFmlAQTrFy5Us8%2B%2B6wef/xxffe73zW7HEMQsEx2zTXX6O9//3vQyw8aNEiSLBOwLtT/7NmzdejQIY0aNUqSdPbsWbW2tiotLU0vv/yy%2Bvbt25WlGu5CvX/22WfavXu3Jk%2BerPDwLy6VHDx4sCTp9OnT3T5gBbPf/%2BY3v1FJSYkWLVqk2267rYsq6xodfd9bWUpKik6fPq1z5875/4e02%2B1WdHS05S/wxxeWL1%2BujRs3auXKlZo0aZLZ5RiGi9xD2JEjR5SWlqba2lr/2Pvvvy%2BHw%2BG/%2BNnKHnnkEb388svasWOHduzYoR/96EcaPny4duzYoeTkZLPL61Qej0f/8z//I5fL5R/729/%2BpoiICH3rW98ysbKusX37dpWUlKigoEA/%2BclPzC4HnWjo0KFyOByqqqryj1VWVmrEiBH%2BXy5gXaWlpdq0aZMee%2Bwx3XTTTWaXYyj23hD27W9/W6mpqfrlL3%2BpDz74QO%2B%2B%2B65%2B%2BctfatasWerRo4fZ5XW6lJQUpaam%2Br969Oih6OhopaamWv5eYElJSfr%2B97%2Bv5cuXq7q6Wu%2B%2B%2B64KCwt16623Wv4/ODQ2NmrZsmX64Q9/qJtuuklut9v/1draanZ5MFhMTIymT5%2BupUuX6uDBg3rttdf01FNPfe0pVFjH0aNHtXr1av3sZz9Tenp6wHvdCqz9r1Q3Fx4erjVr1qioqEi33HKLwsPD9YMf/ECLFi0yuzR0gYceekgPPfSQbr/9dknS9OnTtWDBApOr6nxvvPGGmpubtX37dm3fvj1gbs%2BePd3%2B9CjaKygo0NKlS3XbbbcpPj5e8%2BfP1/e//32zy0In27Nnj1pbW7VmzZp2/3HLCqfQw3x2vLsZAABAJ%2BIUIQAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMH%2BHzazJVVJ2P7NAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4058363906456357286”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-1.708690387971247</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.9803494601194487</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4358604025986246</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.8656865219835925</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.8051481646864413</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.947113431694829</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03446033376004998</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.5205929978670714</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.2745595423513532</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.362260747189428</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4058363906456357286”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-4.8656865219835925</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.819767060392673</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.362260747189428</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:79%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.699569351713766</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.612576324823852</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.48739945519058203</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8099985931559885</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0344030631389174</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6980611560603478</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0181617256231856</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SPECSPTH_09_14”><s>PCH_SPECSPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SPECS_09_14”><code>PCH_SPECS_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99719</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SPECS_09_14”>PCH_SPECS_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>288</td>

</tr> <tr>

<th>Unique (%)</th> <td>9.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>8.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>266</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-5.6471</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4102646360590900156”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4102646360590900156,#minihistogram4102646360590900156”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4102646360590900156”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4102646360590900156”

aria-controls=”quantiles4102646360590900156” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4102646360590900156” aria-controls=”histogram4102646360590900156”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4102646360590900156” aria-controls=”common4102646360590900156”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4102646360590900156” aria-controls=”extreme4102646360590900156”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4102646360590900156”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-100</td>

</tr> <tr>

<th>Q1</th> <td>-25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>400</td>

</tr> <tr>

<th>Range</th> <td>500</td>

</tr> <tr>

<th>Interquartile range</th> <td>25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>52.646</td>

</tr> <tr>

<th>Coef of variation</th> <td>-9.3227</td>

</tr> <tr>

<th>Kurtosis</th> <td>8.2296</td>

</tr> <tr>

<th>Mean</th> <td>-5.6471</td>

</tr> <tr>

<th>MAD</th> <td>31.307</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5582</td>

</tr> <tr>

<th>Sum</th> <td>-16625</td>

</tr> <tr>

<th>Variance</th> <td>2771.6</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4102646360590900156”>

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aGurxvNDQULlcLtXV1bkfn2%2B%2BpQoLC5Wfn%2B8xlpWVpZycnEs5jAuKigq3ZLvwL9HRkb4u4aKwbq1Ff61Db60TSL0N2IB1%2BvRpTZ48WX/729/03//93woP/%2B4fLSwsrFlYcrlcioqKUlhYmPvxufPfv74lMjMzlZaW5jFms0Wouvr0pRzKDwoJCVZUVLhqamrV2Nhk6ravdv74Lsrs9WUV1q216K916K11rOytr958BmTAOnXqlP7t3/5NX331lVavXq3OnTu75%2BLj41VVVeXx/KqqKvXo0UPXX3%2B9wsLCVFVVpS5dukiSGhoadOLECcXGxrZ4/3Fxcc0uBzqdJ9XQYM0XZGNjk2Xbhv/wtzXAurUW/bUOvbVOIPXW/96mX0BTU5Oys7P19ddfa82aNfr5z3/uMW%2B321VUVOR%2BXFtbq4MHD8putys4OFiJiYke88XFxbLZbOrevbvXjgEAAPi3gAtYGzZs0N69e/XMM88oKipKTqdTTqdTJ06ckCSNGjVK%2B/fv14oVK1RaWqrc3Fx16NBBffv2lfTdh%2Bd/%2B9vfaufOnSopKdGsWbN01113XdQlQgAAcHULuEuEO3bsUFNTkyZNmuQx3qdPH61Zs0YdOnTQ888/r1//%2BtcqKChQcnKyCgoKFBQUJEkaPny4jh49qhkzZsjlcum2227TE0884YtDAQAAfirI%2BP4W5rCU03nS9G3abMGKjo5UdfXpgLlmfaWw2YJ168I/%2BbqMi/LWo/19XUKLsG6tRX%2BtQ2%2BtY2VvY2OvNXV7LRVwlwgBAAB8jYAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDKvB6w777xT69at08mTJ729awAAAK/wesDq16%2Bfli9frgEDBmjKlCl6//33ZRiGt8sAAACwjNcD1mOPPaZ3331Xy5YtU0hIiB555BENGjRIixcv1pdffuntcgAAAExn88VOg4KC1L9/f/Xv31%2B1tbVas2aNli1bphUrVqh379669957ddttt/miNAAAgMvmk4AlSZWVlXrjjTf0xhtv6LPPPlPv3r31r//6r6qoqNDTTz%2Btjz76SNOnT/dVeQAAAJfM6wFry5Yt2rJli/bu3as2bdpo5MiRWrp0qTp37ux%2BTrt27TRv3jwCFgAA8Ete/wzW9OnTFRkZqYKCAu3atUuPPfaYR7iSpJ/97Ge65557WrQ9l8ul9PR07d271z1WVlam%2B%2B67T0lJSbrjjjv0/vvve7xm9%2B7dSk9Pl91u1/jx41VWVuYx//LLL2vgwIFKTk7WtGnTVFtbe2kHCwAArkpeD1jvvfeeli5dKrvdruDg73ZfUlKixsZG93N69%2B6txx577ILbqq%2Bv15QpU1RaWuoeMwxDWVlZatu2rTZu3KgRI0YoOztbx44dkyQdO3ZMWVlZysjI0IYNG9SmTRtNnjzZ/ZOMO3bsUH5%2BvubMmaPVq1fL4XBowYIFZrYAAAAEOK8HrFOnTmno0KF66aWX3GMTJ07UiBEjVF5e3uLtHD58WHfddZe%2B%2Buorj/EPP/xQZWVlmjNnjrp06aJJkyYpKSlJGzdulCStX79evXr10oQJE/Tzn/9ceXl5Onr0qPbt2ydJeuWVV3Tvvfdq8ODBuvHGGzV79mxt3LiRs1gAAKDFvB6wfv3rX6tTp066//773WPbt29Xu3btlJeX1%2BLt7Nu3T3379lVhYaHHuMPh0A033KCIiAj3WEpKioqLi93zqamp7rnw8HD17NlTxcXFamxs1J///GeP%2BaSkJJ09e1aHDh266GMFAABXJ69/yP3jjz/W73//e8XGxrrH2rRpo6lTp%2Bruu%2B9u8XbGjh173nGn06m4uDiPsZiYGFVUVFxwvqamRvX19R7zNptN119/vfv1LVFZWSmn0%2BkxZrNFNNvv5QoJCfb4G%2Bbxx57abP5RM%2BvWWvTXOvTWOoHYW68HLJvNppqammbjtbW1ptzRvba2VqGhoR5joaGhcrlcF5yvq6tzP/6h17dEYWGh8vPzPcaysrKUk5PT4m1cjKiocEu2C/8SHR3p6xIuCuvWWvTXOvTWOoHUW68HrFtuuUXPPPOMnnvuOf3kJz%2BR9N1P/eXl5WngwIGXvf2wsDCdOHHCY8zlcqlVq1bu%2BXPDksvlUlRUlMLCwtyPz50PD2/5P3pmZqbS0tI8xmy2CFVXn27xNloiJCRYUVHhqqmpVWNjk6nbvtr547sos9eXVVi31qK/1qG31rGyt7568%2Bn1gPXkk0/q/vvv1%2B23366oqChJUk1NjXr27Knc3NzL3n58fLwOHz7sMVZVVeW%2BPBcfH6%2Bqqqpm8z169ND111%2BvsLAwVVVVqUuXLpKkhoYGnThxwuOS5oXExcU1uxzodJ5UQ4M1X5CNjU2WbRv%2Bw9/WAOvWWvTXOvTWOoHUW68HrJiYGG3evFm7d%2B9WaWmpbDabunbtqptvvllBQUGXvX273a4VK1aorq7OfdaqqKhIKSkp7vmioiL382tra3Xw4EFlZ2crODhYiYmJKioqUt%2B%2BfSVJxcXFstls6t69%2B2XXBgAArg4%2B%2BVU5ISEhGjhwoCmXBM/Vp08ftWvXTrm5uZo8ebLeffddlZSUuH9CcdSoUfrtb3%2BrFStWaPDgwSooKFCHDh3cgWrs2LGaMWOGunXrpri4OM2aNUt33XXXRV0iBAAAVzevByyn06klS5Zo//79Onv2bLMPtr/zzjuXtf2QkBAtW7ZM06dPV0ZGhjp16qSCggIlJCRIkjp06KDnn39ev/71r1VQUKDk5GQVFBS4z54NHz5cR48e1YwZM%2BRyuXTbbbfpiSeeuKyaAADA1SXIMONH9y7CQw89pAMHDmj48OG69tprm81nZ2d7sxyvcTpPmr5Nmy1Y0dGRqq4%2BHTDXrK8UNluwbl34J1%2BXcVHeerS/r0toEdatteivdeitdazsbWxs86zhDV4/g/Xhhx9q5cqVHjfzBAAACCRe/1n0iIgIxcTEeHu3AAAAXuP1gDVixAitXLnS45c7AwAABBKvXyI8ceKEtm3bpj/%2B8Y/q2LFjs7umv/LKK94uCQAAwFQ%2BuU1Denq6L3YLAADgFV4PWN/fjwoAACBQ%2BeQXrlVWVio/P1%2BPPfaYvvnmG7399tv64osvfFEKAACA6bwesI4cOaJf/OIX2rx5s3bs2KEzZ85o%2B/btGjVqlBwOh7fLAQAAMJ3XA9ZvfvMbDRkyRDt37tQ111wjSXruueeUlpamhQsXerscAAAA03k9YO3fv1/333%2B/xy92ttlsmjx5sg4ePOjtcgAAAEzn9YDV1NSkpqbmt8E/ffq0QkJCvF0OAACA6bwesAYMGKAXX3zRI2SdOHFCCxYsUL9%2B/bxdDgAAgOm8HrCeeuopHThwQAMGDFB9fb0efvhhDR48WF9//bWefPJJb5cDAABgOq/fBys%2BPl6vv/66tm3bpr/%2B9a9qamrSmDFjNGLECLVu3drb5QAAAJjOJ3dyDw8P15133umLXQMAAFjO6wFr/PjxPzrP7yIEAAD%2BzusBq3379h6PGxoadOTIEX322We69957vV0OAACA6a6Y30VYUFCgiooKL1cDAABgPp/8LsLzGTFihN566y1flwEAAHDZrpiA9cknn3CjUQAAEBCuiA%2B5nzp1Sp9%2B%2BqnGjh3r7XIAAABM5/WAlZCQ4PF7CCXpmmuu0T333KNf/vKX3i4HAADAdF4PWL/5zW%2B8vUsAAACv8nrA%2Buijj1r83JtuusnCSgAAAKzh9YA1btw49yVCwzDc4%2BeOBQUF6a9//esl76e8vFyzZs3SRx99pOuvv17jx4/XfffdJ0k6ePCgZs6cqc8%2B%2B0xdu3bV7Nmz1atXL/drt23bpiVLlsjpdGrAgAGaO3eu2rRpc8m1AACAq4vXf4pw%2BfLlat%2B%2BvZYsWaI9e/aoqKhIL7/8sn76059qypQpeuedd/TOO%2B9o586dl7WfRx99VBEREdq0aZOmTZumJUuW6H/%2B53905swZTZw4Uampqdq0aZOSk5M1adIknTlzRpJUUlKi6dOnKzs7W4WFhaqpqVFubq4Zhw4AAK4SXg9YeXl5mjFjhm6//XZFR0crMjJS/fr105w5c/Taa6%2Bpffv27j%2BX6u9//7uKi4v18MMPq3PnzhoyZIgGDhyoPXv2aPv27QoLC9PUqVPVpUsXTZ8%2BXZGRkXr77bclSWvXrtWwYcM0cuRIde/eXfPnz9euXbtUVlZmVgsAAECA83rAqqysPG94at26taqrq03ZR6tWrRQeHq5Nmzbp7Nmz%2BuKLL7R//3716NFDDodDKSkp7kuSQUFB6t27t4qLiyVJDodDqamp7m21a9dOCQkJcjgcptQGAAACn9cDVlJSkp577jmdOnXKPXbixAktWLBAN998syn7CAsL04wZM1RYWCi73a5hw4bplltu0Z133imn06m4uDiP58fExLh/TU9lZeWPzgMAAFyI1z/k/vTTT2v8%2BPG65ZZb1LlzZxmGob/97W%2BKjY3VK6%2B8Ytp%2BPv/8cw0ePFj333%2B/SktLNXfuXN18882qra1VaGiox3NDQ0PlcrkkSXV1dT863xKVlZVyOp0eYzZbRLPgdrlCQoI9/oZ5/LGnNpt/1My6tRb9tQ69tU4g9tbrAatLly7avn27tm3bps8//1ySdPfdd2v48OEKDw83ZR979uzRhg0btGvXLrVq1UqJiYk6fvy4XnjhBXXs2LFZWHK5XGrVqpWk785%2BnW/%2BYmorLCxUfn6%2Bx1hWVpZycnIu8Yh%2BXFSUOX2Df4uOjvR1CReFdWst%2BmsdemudQOqt1wOWJF133XW688479fXXX6tjx46Svrubu1kOHDigTp06uUOTJN1www1avny5UlNTVVVV5fH8qqoq99ml%2BPj4887Hxsa2eP%2BZmZlKS0vzGLPZIlRdffpiD%2BVHhYQEKyoqXDU1tWpsbDJ121c7f3wXZfb6sgrr1lr01zr01jpW9tZXbz69HrAMw9CiRYu0Zs0anT17Vjt27NDixYsVHh6uWbNmmRK04uLidOTIEblcLvflvi%2B%2B%2BEIdOnSQ3W7XSy%2B9JMMwFBQUJMMwtH//fj300EOSJLvdrqKiImVkZEj67n5a5eXlstvtF7X/cy8HOp0n1dBgzRdkY2OTZduG//C3NcC6tRb9tQ69tU4g9dbrb9PXrFmjLVu2aObMme7wM2TIEO3cubPZZbVLlZaWpmuuuUZPP/20vvzyS/3v//6vli9frnHjxmno0KGqqanRvHnzdPjwYc2bN0%2B1tbUaNmyYJGnMmDHasmWL1q9fr0OHDmnq1KkaNGiQ%2B0wbAADAhXg9YBUWFmrGjBnKyMhw3yrhjjvu0DPPPKOtW7easo9rr71WL7/8spxOp0aPHq28vDw9/PDDyszMVOvWrfXiiy%2B6z1I5HA6tWLFCERERkqTk5GTNmTNHBQUFGjNmjK677jrl5eWZUhcAALg6eP0S4ddff60ePXo0G%2B/evXuzn7y7HF27dtWqVavOO3fjjTdq8%2BbNP/jajIwM9yVCAACAi%2BX1M1jt27fXn//852bj7733HpfhAABAQPD6GawHHnhAs2fPltPplGEY2rNnjwoLC7VmzRo99dRT3i4HAADAdF4PWKNGjVJDQ4NeeOEF1dXVacaMGWrTpo0effRRjRkzxtvlAAAAmM7rAWvbtm0aOnSoMjMz9e2338owDMXExHi7DAAAAMt4/TNYc%2BbMcX%2BYvU2bNoQrAAAQcLwesDp37qzPPvvM27sFAADwGq9fIuzevbsef/xxrVy5Up07d1ZYWJjHPPecAgAA/s7rAevLL79USkqKJJl63ysAAIArhVcC1vz585Wdna2IiAitWbPGG7sEAADwGa98BmvVqlWqra31GJs4caIqKyu9sXsAAACv8krAMgyj2dhHH32k%2Bvp6b%2BweAADAq7z%2BU4QAAACBjoAFAABgMq8FrKCgIG/tCgAAwKe8dpuGZ555xuOeV2fPntWCBQsUGRnp8TzugwUAAPydVwLWTTfd1OyeV8nJyaqurlZ1dbU3SgAAAPAarwQs7n0FAACuJnzIHQAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwmdd%2BVY63uVwu5eXladu2bbrmmms0evRo/cd//IeCgoJ08OBBzZw5U5999pm6du2q2bNnq1evXu7Xbtu2TUuWLJHT6dSAAQNdsp4FAAAX%2BklEQVQ0d%2B5ctWnTxodHExiGLfnA1yUAAOAVAXsG65lnntHu3bv129/%2BVosWLdLvf/97FRYW6syZM5o4caJSU1O1adMmJScna9KkSTpz5owkqaSkRNOnT1d2drYKCwtVU1Oj3NxcHx8NAADwJwF5BuvEiRPauHGjVq1apRtvvFGSNGHCBDkcDtlsNoWFhWnq1KkKCgrS9OnT9d577%2Bntt99WRkaG1q5dq2HDhmnkyJGSpPnz52vw4MEqKytTx44dfXlYAADATwTkGayioiK1bt1affr0cY9NnDhReXl5cjgcSklJUVBQkCQpKChIvXv3VnFxsSTJ4XAoNTXV/bp27dopISFBDofDuwcBAAD8VkCewSorK1P79u31%2Buuva/ny5Tp79qwyMjL08MMPy%2Bl0qmvXrh7Pj4mJUWlpqSSpsrJScXFxzeYrKipavP/Kyko5nU6PMZstotl2L1dISLDH37i62Wz%2BsQ5Yt9aiv9aht9YJxN4GZMA6c%2BaMjhw5onXr1ikvL09Op1MzZsxQeHi4amtrFRoa6vH80NBQuVwuSVJdXd2PzrdEYWGh8vPzPcaysrKUk5NziUf046Kiwi3ZLvxLdHSkr0u4KKxba9Ff69Bb6wRSbwMyYNlsNp06dUqLFi1S%2B/btJUnHjh3Ta6%2B9pk6dOjULSy6XS61atZIkhYWFnXc%2BPLzl/%2BiZmZlKS0s7p6YIVVefvpTD%2BUEhIcGKigpXTU2tGhubTN02/I/Z68sqrFtr0V/r0FvrWNlbX735DMiAFRsbq7CwMHe4kqSf/vSnKi8vV58%2BfVRVVeXx/KqqKvflu/j4%2BPPOx8bGtnj/cXFxzS4HOp0n1dBgzRdkY2OTZduG//C3NcC6tRb9tQ69tU4g9TZwLnb%2BA7vdrvr6en355ZfusS%2B%2B%2BELt27eX3W7XJ598IsMwJEmGYWj//v2y2%2B3u1xYVFblfV15ervLycvc8AADAhQRkwPrZz36mQYMGKTc3V4cOHdKf/vQnrVixQmPGjNHQoUNVU1OjefPm6fDhw5o3b55qa2s1bNgwSdKYMWO0ZcsWrV%2B/XocOHdLUqVM1aNAgbtEAAABaLCADliQtXLhQP/nJTzRmzBg9%2BeSTuvvuuzVu3Di1bt1aL774ooqKipSRkSGHw6EVK1YoIiJCkpScnKw5c%2BaooKBAY8aM0XXXXae8vDwfHw0AAPAnQcb318pgKafzpOnbtNmCFR0dqerq035xzZpflWOttx7t7%2BsSWsTf1q2/ob/WobfWsbK3sbHXmrq9lgrYM1gAAAC%2BQsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTBXzAmjhxop566in344MHD%2BrOO%2B%2BU3W7XqFGjdODAAY/nb9u2TUOGDJHdbldWVpa%2B/fZbb5cMAAD8XEAHrDfffFO7du1yPz5z5owmTpyo1NRUbdq0ScnJyZo0aZLOnDkjSSopKdH06dOVnZ2twsJC1dTUKDc311flAwAAPxWwAevEiROaP3%2B%2BEhMT3WPbt29XWFiYpk6dqi5dumj69OmKjIzU22%2B/LUlau3athg0bppEjR6p79%2B6aP3%2B%2Bdu3apbKyMl8dBgAA8EMBG7CeffZZjRgxQl27dnWPORwOpaSkKCgoSJIUFBSk3r17q7i42D2fmprqfn67du2UkJAgh8Ph3eIBAIBfs/m6ACvs2bNHH3/8sbZu3apZs2a5x51Op0fgkqSYmBiVlpZKkiorKxUXF9dsvqKi4qL2X1lZKafT6TFms0U02/blCgkJ9vgbVzebzT/WAevWWvTXOvTWOoHY24ALWPX19Zo5c6ZmzJihVq1aeczV1tYqNDTUYyw0NFQul0uSVFdX96PzLVVYWKj8/HyPsaysLOXk5FzUdloqKircku3Cv0RHR/q6hIvCurUW/bUOvbVOIPU24AJWfn6%2BevXqpYEDBzabCwsLaxaWXC6XO4j90Hx4%2BMX9g2dmZiotLc1jzGaLUHX16YvazoWEhAQrKipcNTW1amxsMnXb8D9mry%2BrsG6tRX%2BtQ2%2BtY2VvffXmM%2BAC1ptvvqmqqiolJydLkjsw7dixQ%2Bnp6aqqqvJ4flVVlfvSXXx8/HnnY2NjL6qGuLi4ZpcDnc6Tamiw5guysbHJsm3Df/jbGmDdWov%2BWofeWieQehtwAWvNmjVqaGhwP164cKEk6fHHH9dHH32kl156SYZhKCgoSIZhaP/%2B/XrooYckSXa7XUVFRcrIyJAklZeXq7y8XHa73fsHAgAA/FbABaz27dt7PI6M/O7UYKdOnRQTE6NFixZp3rx5%2BtWvfqV169aptrZWw4YNkySNGTNG48aNU1JSkhITEzVv3jwNGjRIHTt29PpxAAAA/xU4H9dvgdatW%2BvFF190n6VyOBxasWKFIiIiJEnJycmaM2eOCgoKNGbMGF133XXKy8vzcdUAAMDfBBmGYfi6iKuB03nS9G3abMGKjo5UdfVpv7hmPWzJB74uIaC99Wh/X5fQIv62bv0N/bUOvbWOlb2Njb3W1O211FV1BgsAAMAbAu4zWFeb1Olv%2B7oEAABwDs5gAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgsoANWMePH1dOTo769OmjgQMHKi8vT/X19ZKksrIy3XfffUpKStIdd9yh999/3%2BO1u3fvVnp6uux2u8aPH6%2BysjJfHAIAAPBTARmwDMNQTk6Oamtr9eqrr2rx4sV69913tWTJEhmGoaysLLVt21YbN27UiBEjlJ2drWPHjkmSjh07pqysLGVkZGjDhg1q06aNJk%2BeLMMwfHxUAADAX9h8XYAVvvjiCxUXF%2BuDDz5Q27ZtJUk5OTl69tlndcstt6isrEzr1q1TRESEunTpoj179mjjxo165JFHtH79evXq1UsTJkyQJOXl5al///7at2%2Bf%2Bvbt68vDAgAAfiIgz2DFxsZq5cqV7nD1vVOnTsnhcOiGG25QRESEezwlJUXFxcWSJIfDodTUVPdceHi4evbs6Z4HAAC4kIAMWFFRURo4cKD7cVNTk9auXat%2B/frJ6XQqLi7O4/kxMTGqqKiQpAvOAwAAXEhAXiI814IFC3Tw4EFt2LBBL7/8skJDQz3mQ0ND5XK5JEm1tbU/Ot8SlZWVcjqdHmM2W0Sz4Ha5QkICMh/jEtls/rEevl%2B3rF9r0F/r0FvrBGJvAz5gLViwQKtXr9bixYvVrVs3hYWF6cSJEx7PcblcatWqlSQpLCysWZhyuVyKiopq8T4LCwuVn5/vMZaVlaWcnJxLPArgwqKjI31dwkWJigr3dQkBjf5ah95aJ5B6G9ABa%2B7cuXrttde0YMEC3X777ZKk%2BPh4HT582ON5VVVV7rNL8fHxqqqqajbfo0ePFu83MzNTaWlpHmM2W4Sqq09fymH8oEBK%2Brh8Zq8vq4SEBCsqKlw1NbVqbGzydTkBh/5ah95ax8re%2BurNZ8AGrPz8fK1bt07PPfechg4d6h632%2B1asWKF6urq3GetioqKlJKS4p4vKipyP7%2B2tlYHDx5UdnZ2i/cdFxfX7HKg03lSDQ18QcI6/ra%2BGhub/K5mf0J/rUNvrRNIvQ3IUyCff/65li1bpgcffFApKSlyOp3uP3369FG7du2Um5ur0tJSrVixQiUlJRo9erQkadSoUdq/f79WrFih0tJS5ebmqkOHDtyiAQAAtFhABqx33nlHjY2NeuGFFzRgwACPPyEhIVq2bJmcTqcyMjL0xhtvqKCgQAkJCZKkDh066Pnnn9fGjRs1evRonThxQgUFBQoKCvLxUQEAAH8RZHCLcq9wOk%2Bavk2bLVi3LvyT6duFf3rr0f6%2BLqFFbLZgRUdHqrr6dMBcCriS0F/r0FvrWNnb2NhrTd1eSwXkGSwAAABfImABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkC9ncRArhypU5/29clXBR/uYkrgCsHZ7AAAABMRsACAAAwGZcIgQAxbMkHvi4BAPB/OIMFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMpuvC7gS1dfXa/bs2frDH/6gVq1aacKECZowYYKvywKAFkmd/ravS2ixtx7t7%2BsSAEsQsM5j/vz5OnDggFavXq1jx47pySefVEJCgoYOHerr0gAAgB8gYJ3jzJkzWr9%2BvV566SX17NlTPXv2VGlpqV599VUCFgAAaBEC1jkOHTqkhoYGJScnu8dSUlK0fPlyNTU1KTiYj60BV5thSz7wdQm4QvjT5VeJS7C%2BRMA6h9PpVHR0tEJDQ91jbdu2VX19vU6cOKE2bdpccBuVlZVyOp0eYzZbhOLi4kytNSSEsAfAv9ls/vP/MX/8f64/vTn4eN5Qv%2BzxDyFgnaO2ttYjXElyP3a5XC3aRmFhofLz8z3GsrOz9cgjj5hT5P%2BprKzUvf9UqszMTNPD29WusrJShYWF9NYC9NZa9Nc6/D/XOpWVlXr%2B%2BecDqreBExVNEhYW1ixIff%2B4VatWLdpGZmamNm3a5PEnMzPT9FqdTqfy8/ObnS3D5aO31qG31qK/1qG31gnE3nIG6xzx8fGqrq5WQ0ODbLbv2uN0OtWqVStFRUW1aBtxcXEBk8ABAMDF4wzWOXr06CGbzabi4mL3WFFRkRITE/mAOwAAaBESwznCw8M1cuRIzZo1SyUlJdq5c6d%2B97vfafz48b4uDQAA%2BImQWbNmzfJ1EVeafv366eDBg1q0aJH27Nmjhx56SKNGjfJ1WecVGRmpPn36KDIy0telBBx6ax16ay36ax16a51A622QYRiGr4sAAAAIJFwiBAAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsDyI4ZhaMKECdq0aZPHeHV1tR555BElJycrLS1NW7Zs8Zg/ePCg7rzzTtntdo0aNUoHDhzwZtl%2Bp76%2BXtOmTVNqaqoGDBig3/3ud74uye%2B4XC6lp6dr79697rGysjLdd999SkpK0h133KH333/f4zW7d%2B9Wenq67Ha7xo8fr7KyMm%2BXfUU7fvy4cnJy1KdPHw0cOFB5eXmqr6%2BXRG/NcOTIET3wwANKTk7WoEGDtHLlSvcc/TXPxIkT9dRTT7kfX%2Bj707Zt2zRkyBDZ7XZlZWXp22%2B/9XbJl4yA5Seampr0zDPP6IMPPmg2l5ubq5MnT6qwsFAPP/ywnn76aZWUlEiSzpw5o4kTJyo1NVWbNm1ScnKyJk2apDNnznj7EPzG/PnzdeDAAa1evVozZ85Ufn6%2B3n77bV%2BX5Tfq6%2Bs1ZcoUlZaWuscMw1BWVpbatm2rjRs3asSIEcrOztaxY8ckSceOHVNWVpYyMjK0YcMGtWnTRpMnTxa/aOI7hmEoJydHtbW1evXVV7V48WK9%2B%2B67WrJkCb01QVNTkyZOnKjo6Ght3rxZs2fP1gsvvKCtW7fSXxO9%2Beab2rVrl/vxhb4/lZSUaPr06crOzlZhYaFqamqUm5vrq/IvnoErXkVFhXHPPfcYgwYNMlJTU42NGze6544cOWJ069bNKCsrc49NmzbNePLJJw3DMIz169cbaWlpRlNTk2EYhtHU1GTceuutHtvA/3f69GkjMTHR%2BPDDD91jBQUFxj333OPDqvxHaWmp8ctf/tL4xS9%2BYXTr1s3dx927dxtJSUnG6dOn3c%2B99957jaVLlxqGYRhLlizx6PGZM2eM5ORkj3%2BHq9nhw4eNbt26GU6n0z22detWY8CAAfTWBMePHzf%2B/d//3Th58qR7LCsry5g5cyb9NUl1dbVxyy23GKNGjWrx96cnnnjC/VzDMIxjx44Z//zP/2x89dVX3j%2BAS8AZLD/wl7/8Re3atdPGjRt17bXXesw5HA61a9dOHTp0cI%2BlpKTok08%2Bcc%2BnpKQoKChIkhQUFKTevXuruLjYewfgRw4dOqSGhgYlJye7x1JSUuRwONTU1OTDyvzDvn371LdvXxUWFnqMOxwO3XDDDYqIiHCPpaSkuNehw%2BFQamqqey48PFw9e/Zknf6f2NhYrVy5Um3btvUYP3XqFL01QVxcnJYsWaLWrVvLMAwVFRXpo48%2BUp8%2BfeivSZ599lmNGDFCXbt2dY9d6PvTub1t166dEhIS5HA4vFv8JSJg%2BYG0tDTNnz9fbdq0aTbndDoVFxfnMRYTE6Pjx4//6HxFRYV1Bfsxp9Op6OhohYaGusfatm2r%2Bvp6nThxwoeV%2BYexY8dq2rRpCg8P9xi/0Dpknf64qKgoDRw40P24qalJa9euVb9%2B/eitydLS0jR27FglJyfr9ttvp78m2LNnjz7%2B%2BGNNnjzZY/xCvausrPTr3tp8XQCkuro6dyA6V2xsrMc7p3PV1tZ6hAFJCg0NlcvlatE8PP1QvyTRs8vAOjXXggULdPDgQW3YsEEvv/wyvTXR0qVLVVVVpVmzZikvL4%2B1e5nq6%2Bs1c%2BZMzZgxQ61atfKYu1Dv6urq/Lq3BKwrgMPh0Pjx4887V1BQoCFDhvzga8PCwpotNpfL5V7IF5qHpx/qlyR6dhnCwsKanQFsyTqNioryWo3%2BYsGCBVq9erUWL16sbt260VuTJSYmSvouGDz%2B%2BOMaNWqUamtrPZ5Df1suPz9fvXr18jgD%2B71L/f517hnyKxUB6wrQt29fffrpp5f02vj4eFVVVXmMVVVVKTY29kfnzz3tiu/Ex8erurpaDQ0Nstm%2B%2B/JwOp1q1aoV/8O8DPHx8Tp8%2BLDH2D%2Buwx9apz169PBajf5g7ty5eu2117RgwQLdfvvtkuitGaqqqlRcXOzxZrZr1646e/asYmNj9cUXXzR7Pv1tmTfffFNVVVXuz7V%2BH5h27Nih9PT0H/3%2BdKHvb1c6PoPl55KSknT06FGPa9JFRUVKSkqSJNntdn3yySfuHxk2DEP79%2B%2BX3W73Sb1Xuh49eshms3l8QLWoqEiJiYkKDubL5VLZ7Xb95S9/UV1dnXusqKjIvQ7tdruKiorcc7W1tTp48CDr9B/k5%2Bdr3bp1eu655zR8%2BHD3OL29fF9//bWys7M9Pqpx4MABtWnTRikpKfT3MqxZs0Zbt27V66%2B/rtdff11paWlKS0vT66%2B/fsHvT%2Bf2try8XOXl5X7TW75j%2BLmOHTtqwIABeuKJJ3To0CGtX79e27Zt09133y1JGjp0qGpqajRv3jwdPnxY8%2BbNU21trYYNG%2Bbjyq9M4eHhGjlypGbNmqWSkhLt3LlTv/vd737wEi5apk%2BfPmrXrp1yc3NVWlqqFStWqKSkRKNHj5YkjRo1Svv379eKFStUWlqq3NxcdejQQX379vVx5VeGzz//XMuWLdODDz6olJQUOZ1O9x96e/kSExPVs2dPTZs2TYcPH9auXbu0YMECPfTQQ/T3MrVv316dOnVy/4mMjFRkZKQ6dep0we9PY8aM0ZYtW7R%2B/XodOnRIU6dO1aBBg9SxY0cfH1UL%2BfQmEbhogwcPbnYPq6qqKmPSpElGYmKikZaWZmzdutVj3uFwGCNHjjQSExON0aNHG3/5y1%2B8WbLfOXPmjDF16lQjKSnJGDBggLFq1Spfl%2BSX/vE%2BWIZhGH/729%2BMu%2B%2B%2B2%2BjVq5cxfPhw44MPPvB4/h//%2BEfjtttuM2688Ubj3nvv9Zt73XjDiy%2B%2BaHTr1u28fwyD3pqhoqLCyMrKMnr37m3079/feOGFF9z3Z6K/5nnyySc97m11oe9PGzduNP7lX/7FSEpKMrKysoxvv/3W2yVfsiDD4HazAAAAZuISIQAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMn%2BHz2WlD09prlPAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4102646360590900156”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1435</td> <td class=”number”>44.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>116</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>57</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.666666667</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (277)</td> <td class=”number”>685</td> <td class=”number”>21.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>266</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4102646360590900156”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>264</td> <td class=”number”>8.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-83.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-80.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-71.428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>225.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>350.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>400.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SUPERCPTH_09_14”><s>PCH_SUPERCPTH_09_14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_SUPERC_09_14”><code>PCH_SUPERC_09_14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99027</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_SUPERC_09_14”>PCH_SUPERC_09_14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>72</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>6.7%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>216</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6.7204</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>80.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3568554342101042653”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3568554342101042653,#minihistogram-3568554342101042653”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3568554342101042653”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3568554342101042653”

aria-controls=”quantiles-3568554342101042653” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3568554342101042653” aria-controls=”histogram-3568554342101042653”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3568554342101042653” aria-controls=”common-3568554342101042653”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3568554342101042653” aria-controls=”extreme-3568554342101042653”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3568554342101042653”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>50</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>26.425</td>

</tr> <tr>

<th>Coef of variation</th> <td>3.932</td>

</tr> <tr>

<th>Kurtosis</th> <td>30.95</td>

</tr> <tr>

<th>Mean</th> <td>6.7204</td>

</tr> <tr>

<th>MAD</th> <td>12.605</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.7892</td>

</tr> <tr>

<th>Sum</th> <td>20121</td>

</tr> <tr>

<th>Variance</th> <td>698.26</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3568554342101042653”>

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bAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYJnfA9bDDz%2Bsd955R2fPnvX3SwMAAPiF3wNW27ZttXDhQrVv315PP/20Nm3aJGOMv8sAAACoMH4PWGPGjNE//vEPzZ8/XyEhIXriiSfUsWNHzZ49W4cOHfJ3OQAAANa5A/GiLpdL7dq1U7t27ZSfn6%2B3335b8%2BfP1%2BLFi9W6dWsNHTpU999/fyBKAwAAuGEBCViSlJubqw8//FAffvih9u3bp9atW%2BvBBx/U8ePH9dxzz2nr1q2aNGlSoMoDAAC4bn4PWOvXr9f69eu1ZcsW1axZU3379tVrr72mhg0beh9Tp04dTZs2jYAFAACCkt8D1qRJk5SUlKR58%2Bbp3nvvVaVKZd8G1qhRIw0ePNjfpQEAAFjh94D15ZdfqkaNGjp9%2BrQ3XO3YsUMtWrRQSEiIJKl169Zq3bq1v0sDAACwwu%2BfIjx37py6deumN954wzs2cuRI9enTR8eOHfN3OQAAANb5PWC98soruv322/Xoo496xz7%2B%2BGPVqVNHqamp/i4HAADAOr8HrP/93//Vs88%2Bq8jISO9YzZo1NW7cOG3evNnf5QAAAFjn94Dldrt15syZMuP5%2Bflc0R0AADiC3wPWvffeq6lTp%2Bqnn37yjuXk5Cg1NVUdOnTwdzkAAADW%2Bf1ThOPHj9ejjz6qrl27KiIiQpJ05swZtWjRQhMmTPB3OQAAANb5PWDVqlVL69at09dff639%2B/fL7Xbrjjvu0J/%2B9Ce5XC5/lwMAAGBdQH5VTkhIiDp06MApQQAA4Eh%2Bfw%2BWx%2BPRpEmT1L17d3Xu3Fn//u//7nO7VkVFRerVq5e2bNniHZs6daruvPNOn9vKlSu98xs2bFDnzp0VExOj5ORknTx50jtnjNGMGTPUtm1bJSYmavr06SotLb2xpgEAwB%2BK349gPf/889q5c6d69uypW2%2B99Ya2VVhYqDFjxmj//v0%2B49nZ2RozZowefPBB71i1atUk/XrV%2BEmTJunll19Ws2bNNG3aNE2YMEGLFi2SJL311lvasGGD0tPTVVxcrLFjx6pWrVoaMWLEDdUKAAD%2BOPwesDZv3qwlS5YoISHhhrZz4MABjRkz5pKXdsjOztaIESN8rrX1m5UrV6p79%2B7q27evJGn69OlKSkpSTk6OGjRooBUrViglJcVb3zPPPKO5c%2BcSsAAAQLn5/RRhlSpVVKtWrRvezjfffKM2bdooIyPDZ/zcuXM6ceKEGjZseMnnZWVl%2BYS7OnXqqG7dusrKytKJEyd07Ngx3XPPPd75%2BPh4HTlyRLm5uTdcMwAA%2BGPwe8Dq06ePlixZopKSkhvazqBBgzRx4kSFh4f7jGdnZ8vlcmnhwoW699579cADD2jdunXe%2BdzcXEVFRfk8p1atWjp%2B/Lg8Ho8k%2BczXrl1bknT8%2BPEbqhcAAPxx%2BP0U4enTp7VhwwZt3LhRDRo0UGhoqM/8ihUrbmj7Bw8elMvlUqNGjTR48GBt3bpVzz//vKpVq6YuXbqooKCgzGuGhoaqqKhIBQUF3vu/n5N%2BfTN9eeXm5nrD2m/c7iplgt2NCgmp5POnUzi1r4rmdgfu6%2BXkNaO34OPUviTn9ubEvgJymYZevXpV2Lb79u2rpKQkVa9eXZLUrFkz/fDDD1q9erW6dOmisLCwMmGpqKhI4eHhPmEqLCzM%2B3dJZY6UXUlGRobS09N9xpKTk5WSknLdfV1JRET5awsmTu2rotSoUTXQJTh6zegt%2BDi1L8m5vTmpL78HrNTU1Ardvsvl8oar3zRq1Mj7i6Sjo6OVl5fnM5%2BXl6fIyEhFR0dL%2BvVSEvXr1/f%2BXdIl3zB/OQMGDFCnTp18xtzuKjp16vy1NXMVISGVFBERrjNn8lVS4pxLSTi1r4pme/%2B6Fk5eM3oLPk7tS3JubxXZV6D%2B8xmQI1i5ubl69913dejQIU2cOFFbt25V06ZN1ahRoxve9ty5c/Xtt99q2bJl3rE9e/Z4tx0TE6PMzEz169dPknTs2DEdO3ZMMTExio6OVt26dZWZmekNWJmZmapbt%2B41nd6Liooq83iP56yKiyvmm6GkpLTCth1ITu2rotwMXysnrxm9BR%2Bn9iU5tzcn9eX3k50//vijevfurXXr1unTTz/VhQsX9PHHH6t///7Kysq64e0nJSVp69atWrp0qX766Sf913/9lz744AMNHz5ckjRw4ECtX79ea9as0Z49ezRu3Dh17NhRDRo08M7PmDFDW7Zs0ZYtWzRz5kwNGTLkhusCAAB/HH4PWK%2B%2B%2Bqo6d%2B6szz//XLfccoskadasWerUqZNmzJhxw9tv1aqV5s6dq/Xr16tXr156%2B%2B23NXPmTMXFxUmS4uLiNHnyZM2bN08DBw7Ubbfd5nPacsSIEerRo4dGjx6tJ598Un369NGwYcNuuC4AAPDH4fdThNu2bdOqVat8frGz2%2B3W448/rkceeeS6trl3716f%2B507d1bnzp0v%2B/h%2B/fp5TxFeLCQkRBMmTNCECROuqxYAAAC/H8EqLS295O/2O3/%2BvEJCQvxdDgAAgHV%2BD1jt27fXokWLfELW6dOnlZaWprZt2/q7HAAAAOv8HrCeffZZ7dy5U%2B3bt1dhYaH%2B8z//U0lJSTp8%2BLDGjx/v73IAAACs8/t7sKKjo/XBBx9ow4YN2r17t0pLSzVw4ED16dNH1apV83c5AAAA1gXkOljh4eF6%2BOGHA/HSAAAAFc7vAetq15S60d9FCAAAEGh%2BD1j16tXzuV9cXKwff/xR%2B/bt09ChQ/1dDgAAgHU3ze8inDdvno4fP%2B7nagAAAOzz%2B6cIL6dPnz765JNPAl0GAADADbtpAta3337LhUYBAIAj3BRvcj937pz27t2rQYMG%2BbscAAAA6/wesOrWrevzewgl6ZZbbtHgwYP1wAMP%2BLscAAAA6/wesF599VV/vyQAAIBf%2BT1gbd26tdyPveeeeyqwEgAAgIrh94D117/%2B1XuK0BjjHb94zOVyaffu3f4uDwAA4Ib5PWAtXLhQU6dO1dixY5WYmKjQ0FB99913mjx5sh588EH16NHD3yUBAABY5ffLNKSmpuqFF15Q165dVaNGDVWtWlVt27bV5MmTtXr1atWrV897AwAACEZ%2BD1i5ubmXDE/VqlXTqVOn/F0OAACAdX4PWLGxsZo1a5bOnTvnHTt9%2BrTS0tL0pz/9yd/lAAAAWOf392A999xzGjJkiO699141bNhQxhj98MMPioyM1IoVK/xdDgAAgHV%2BD1iNGzfWxx9/rA0bNig7O1uS9Je//EU9e/ZUeHi4v8sBAACwzu8BS5Juu%2B02Pfzwwzp8%2BLAaNGgg6deruQMAADiB39%2BDZYzRjBkzdM8996hXr146fvy4xo8fr0mTJumXX37xdzkAAADW%2BT1gvf3221q/fr1efPFFhYaGSpI6d%2B6szz//XOnp6f4uBwAAwDq/B6yMjAy98MIL6tevn/fq7T169NDUqVP10Ucf%2BbscAAAA6/wesA4fPqzmzZuXGW/WrJk8Ho%2B/ywEAALDO7wGrXr16%2Bu6778qMf/nll943vAMAAAQzv3%2BKcMSIEXr55Zfl8XhkjNG//vUvZWRk6O2339azzz7r73IAAACs83vA6t%2B/v4qLi7VgwQIVFBTohRdeUM2aNfXUU09p4MCB/i4HAADAOr8HrA0bNqhbt24aMGCATp48KWOMatWq5e8yAAAAKozf34M1efJk75vZa9asSbgCAACO4/eA1bBhQ%2B3bt8/fLwsAAOA3fj9F2KxZMz3zzDNasmSJGjZsqLCwMJ/51NRUf5cEAABgld8D1qFDhxQfHy9JXPcKAAA4kl8C1vTp0zV69GhVqVJFb7/9tj9eEgAAIGD88h6st956S/n5%2BT5jI0eOVG5urj9eHgAAwK/8ErCMMWXGtm7dqsLCQn%2B8PAAAgF/5/VOEAAAATkfAAgAAsMxvAcvlcvnrpQAAAALKb5dpmDp1qs81r3755RelpaWpatWqPo/jOlgAACDY%2BSVg3XPPPWWueRUXF6dTp07p1KlT/igBAADAb/wSsLj2FQAA%2BCPhTe4AAACWEbAAAAAsC/qAVVRUpF69emnLli3esZycHA0bNkyxsbHq0aOHNm3a5POcr7/%2BWr169VJMTIyGDBminJwcn/lly5apQ4cOiouL08SJE8tchR4AAOBKgjpgFRYW6umnn9b%2B/fu9Y8YYJScnq3bt2lq7dq369Omj0aNH6%2BjRo5Kko0ePKjk5Wf369dN7772nmjVr6vHHH/debf7TTz9Venq6Jk%2BerOXLlysrK0tpaWkB6Q8AAASnoA1YBw4c0COPPKKffvrJZ3zz5s3KycnR5MmT1bhxY40aNUqxsbFau3atJGnNmjW6%2B%2B67NXz4cDVp0kSpqak6cuSIvvnmG0nSihUrNHToUCUlJalVq1Z6%2BeWXtXbtWo5iAQCAcgvagPXNN9%2BoTZs2ysjI8BnPysrSXXfdpSpVqnjH4uPjtX37du98QkKCdy48PFwtWrTQ9u3bVVJSou%2B%2B%2B85nPjY2Vr/88ov27NlTwR0BAACn8NuFRm0bNGjQJcc9Ho%2BioqJ8xmrVqqXjx49fdf7MmTMqLCz0mXe73apevbr3%2BQAAAFcTtAHrcvLz8xUaGuozFhoaqqKioqvOFxQUeO9f7vnlkZubW%2BbCqm53lTLB7kaFhFTy%2BdMpnNpXRXO7A/f1cvKa0VvwcWpfknN7c2JfjgtYYWFhOn36tM9YUVGRKleu7J2/OCwVFRUpIiLC%2B6t8LjUfHh5e7hoyMjKUnp7uM5acnKyUlJRyb%2BNaRESUv7Zg4tS%2BKkqNGlWv/qAK5uQ1o7fg49S%2BJOf25qS%2BHBewoqOjdeDAAZ%2BxvLw879Gj6Oho5eXllZlv3ry5qlevrrCwMOXl5alx48aSpOLiYp0%2BfVqRkZHlrmHAgAHq1KmTz5jbXUWnTp2/npYuKySkkiIiwnXmTL5KSkqtbjuQnNpXRbO9f10LJ68ZvQUfp/YlObe3iuwrUP/5dFzAiomJ0eLFi1VQUOA9apWZman4%2BHjvfGZmpvfx%2Bfn52rVrl0aPHq1KlSqpZcuWyszMVJs2bSRJ27dvl9vtVrNmzcpdQ1RUVJnTgR7PWRUXV8w3Q0lJaYVtO5Cc2ldFuRm%2BVk5eM3oLPk7tS3Jub07qyzknO/9PYmKi6tSpowkTJmj//v1avHixduzYoYceekiS1L9/f23btk2LFy/W/v37NWHCBNWvX98bqAYNGqSlS5fq888/144dO/TSSy/pkUceuaZThAAA4I/NcQErJCRE8%2BfPl8fjUb9%2B/fThhx9q3rx5qlu3riSpfv36ev3117V27Vo99NBDOn36tObNmyeXyyVJ6tmzp0aNGqUXXnhBw4cPV6tWrTR27NhAtgQAAIKMI04R7t271%2Bf%2B7bffrpUrV1728ffdd5/uu%2B%2B%2By86PHDlSI0eOtFYfAAD4Y3HcESwAAIBAI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyxwaszz77THfeeafPLSUlRZK0a9cuPfzww4qJiVH//v21c%2BdOn%2Bdu2LBBnTt3VkxMjJKTk3Xy5MlAtAAAAIKUYwPWgQMHlJSUpE2bNnlvU6dO1YULFzRy5EglJCTo/fffV1xcnEaNGqULFy5Iknbs2KFJkyZp9OjRysjI0JkzZzRhwoQAdwMAAIKJYwNWdna2mjZtqsjISO8tIiJCH3/8scLCwjRu3Dg1btxYkyZNUtWqVfXf//3fkqSVK1eqe/fu6tu3r5o1a6bp06friy%2B%2BUE5OToA7AgAAwcLRAathw4ZlxrOyshQfHy%2BXyyVJcrlcat26tbZv3%2B6dT0hI8D6%2BTp06qlu3rrKysvxSNwAACH6ODFjGGB06dEibNm1S165d1blzZ82YMUNFRUXyeDyKiooPyWt6AAARfUlEQVTyeXytWrV0/PhxSVJubu4V5wEAAK7GHegCKsLRo0eVn5%2Bv0NBQzZkzR4cPH9bUqVNVUFDgHf%2B90NBQFRUVSZIKCgquOF8eubm58ng8PmNud5Uywe1GhYRU8vnTKZzaV0VzuwP39XLymtFb8HFqX5Jze3NiX44MWPXq1dOWLVt02223yeVyqXnz5iotLdXYsWOVmJhYJiwVFRWpcuXKkqSwsLBLzoeHh5f79TMyMpSenu4zlpyc7P0Uo20REeWvLZg4ta%2BKUqNG1UCX4Og1o7fg49S%2BJOf25qS%2BHBmwJKl69eo%2B9xs3bqzCwkJFRkYqLy/PZy4vL897dCk6OvqS85GRkeV%2B7QEDBqhTp04%2BY253FZ06df5aWriqkJBKiogI15kz%2BSopKbW67UByal8Vzfb%2BdS2cvGb0Fnyc2pfk3N4qsq9A/efTkQHrq6%2B%2B0jPPPKONGzd6jzzt3r1b1atXV3x8vN544w0ZY%2BRyuWSM0bZt2/S3v/1NkhQTE6PMzEz169dPknTs2DEdO3ZMMTEx5X79qKioMqcDPZ6zKi6umG%2BGkpLSCtt2IDm1r4pyM3ytnLxm9BZ8nNqX5NzenNSXc052/k5cXJzCwsL03HPP6eDBg/riiy80ffp0PfbYY%2BrWrZvOnDmjadOm6cCBA5o2bZry8/PVvXt3SdLAgQO1fv16rVmzRnv27NG4cePUsWNHNWjQIMBdAQCAYOHIgFWtWjUtXbpUJ0%2BeVP/%2B/TVp0iQNGDBAjz32mKpVq6ZFixZ5j1JlZWVp8eLFqlKliqRfw9nkyZM1b948DRw4ULfddptSU1MD3BEAAAgmjjxFKElNmjTRW2%2B9dcm5Vq1aad26dZd9br9%2B/bynCAEAAK6VI49gAQAABBIBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFjmDnQBAOzoPuefgS6h3D55ql2gSwCACsURLAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAs43cRAsBVJEz670CXcE34XY9A4HEECwAAwDICFgAAgGUErEsoLCzUxIkTlZCQoPbt2%2BvNN98MdEkAACCI8B6sS5g%2Bfbp27typ5cuX6%2BjRoxo/frzq1q2rbt26Bbo0AAAQBAhYF7lw4YLWrFmjN954Qy1atFCLFi20f/9%2BrVq1ioAFWNJ9zj8DXQIAVCgC1kX27Nmj4uJixcXFecfi4%2BO1cOFClZaWqlIlzqpeL/5RBXCxYPu5wCc0UV4ErIt4PB7VqFFDoaGh3rHatWursLBQp0%2BfVs2aNa%2B6jdzcXHk8Hp8xt7uKoqKirNYaElLJ508AkIIvtAQTtzuwP2%2Bd%2BnPfiX0RsC6Sn5/vE64kee8XFRWVaxsZGRlKT0/3GRs9erSeeOIJO0X%2Bn9zcXC1fvkQDBgywHt4qwv9OK98p1tzcXGVkZARNX9fCqb05tS%2BJ3oKRU/uSgu/nfnk5sS/nREVLwsLCygSp3%2B5Xrly5XNsYMGCA3n//fZ/bgAEDrNfq8XiUnp5e5mhZsHNqX5Jze3NqXxK9BSOn9iU5tzcn9sURrItER0fr1KlTKi4ultv965fH4/GocuXKioiIKNc2oqKiHJPAAQDAteMI1kWaN28ut9ut7du3e8cyMzPVsmVL3uAOAADKhcRwkfDwcPXt21cvvfSSduzYoc8//1xvvvmmhgwZEujSAABAkAh56aWXXgp0ETebtm3bateuXZo5c6b%2B9a9/6W9/%2B5v69%2B8f6LIuqWrVqkpMTFTVqlUDXYpVTu1Lcm5vTu1Lordg5NS%2BJOf25rS%2BXMYYE%2BgiAAAAnIRThAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFhBxBij4cOH6/333/cZP3XqlJ544gnFxcWpU6dOWr9%2Bvc/8rl279PDDDysmJkb9%2B/fXzp07/Vl2ue3atUt33nmnz61fv37e%2BZycHA0bNkyxsbHq0aOHNm3aFMBqr11hYaEmTpyohIQEtW/fXm%2B%2B%2BWagS7oun332WZl1SklJkRQ8%2B9rFioqK1KtXL23ZssU7drX97euvv1avXr0UExOjIUOGKCcnx99ll8uleps6dWqZNVy5cqV3fsOGDercubNiYmKUnJyskydPBqL0Szpx4oRSUlKUmJioDh06KDU1VYWFhZKCf82u1Fswr9mPP/6oESNGKC4uTh07dtSSJUu8c8G%2BZldkEBRKSkrM5MmTTdOmTc3atWt95kaNGmWGDh1q9u7da959911z9913m6ysLGOMMefPnzft2rUzr776qjlw4ICZMmWK%2BfOf/2zOnz8fiDauaP369aZPnz4mNzfXezt58qQxxpjS0lLTu3dvM2bMGHPgwAGzcOFCExMTY44cORLgqstv8uTJpnfv3mbnzp3mf/7nf0xcXJz55JNPAl3WNZs/f74ZNWqUzzr9/PPPQbWv/V5BQYFJTk42TZs2NZs3bzbGXH1/O3LkiImNjTVLly41%2B/btM08%2B%2BaTp1auXKS0tDWQrZVyqN2OMGTZsmFm0aJHPGl64cMEYY0xWVpZp1aqVWbdundm9e7cZPHiwGTlyZKBa8FFaWmoeeeQR89hjj5l9%2B/aZrVu3mi5duphXX3016NfsSr0ZE7xrVlJSYu6//34zZswYc%2BjQIbNx40bTunVr8%2BGHHwb9ml0NASsIHD9%2B3AwePNh07NjRJCQk%2BASsH3/80TRt2tTk5OR4xyZOnGjGjx9vjDFmzZo1plOnTt4dsrS01HTp0qVMSLsZzJo1yzz99NOXnPv6669NbGyszz/WQ4cONa%2B99pq/yrsh58%2BfNy1btvT5R27evHlm8ODBAazq%2BowZM8bMnDmzzHgw7Wu/2b9/v3nggQdM7969fULI1fa3OXPm%2BKzdhQsXTFxcnM/6BtrlejPGmA4dOpivvvrqks8bO3as9%2BeHMcYcPXrU3Hnnneann36q8Jqv5sCBA6Zp06bG4/F4xz766CPTvn37oF%2BzK/VmTPCu2YkTJ8yTTz5pzp496x1LTk42L774YtCv2dVwijAIfP/996pTp47Wrl2rW2%2B91WcuKytLderUUf369b1j8fHx%2Bvbbb73z8fHxcrlckiSXy6XWrVtr%2B/bt/mugnLKzs9WwYcNLzmVlZemuu%2B5SlSpVvGPx8fE3ZR%2BXsmfPHhUXFysuLs47Fh8fr6ysLJWWlgawsmt3uXUKpn3tN998843atGmjjIwMn/Gr7W9ZWVlKSEjwzoWHh6tFixY3Va%2BX6%2B3cuXM6ceLEFb/Xft9bnTp1VLduXWVlZVVkueUSGRmpJUuWqHbt2j7j586dC/o1u1JvwbxmUVFRmjNnjqpVqyZjjDIzM7V161YlJiYG/ZpdjTvQBeDqOnXqpE6dOl1yzuPxKCoqymesVq1aOnHihHf%2BjjvuKDO/f//%2Biin2BmRnZ6u0tFS9e/fW2bNnde%2B992rcuHGqVq3aZfs8fvx4gKq9Nh6PRzVq1FBoaKh3rHbt2iosLNTp06dVs2bNAFZXfsYYHTp0SJs2bdKiRYtUUlKibt26KSUlJaj2td8MGjTokuNX29%2BCYX%2B8XG/Z2dlyuVxauHChvvzyS1WvXl2PPvqoHnzwQUlSbm7uTdtbRESEOnTo4L1fWlqqlStXqm3btkG/ZlfqLZjX7Pc6deqko0ePKikpSV27dtUrr7wS1Gt2NQSsm0BBQYE3EF0sMjLSJ91fLD8/3%2BcfbUkKDQ1VUVFRueb96Up91qxZUzk5Oapfv75eeeUVnTlzRqmpqRo7dqwWLFhwU/VxPS5Xv6Sg6UGSjh496u1lzpw5Onz4sKZOnaqCgoKgX6PfC6bvq2t18OBBuVwuNWrUSIMHD9bWrVv1/PPPq1q1aurSpYsKCgqCpre0tDTt2rVL7733npYtW%2BaoNft9b99//70j1uy1115TXl6eXnrpJaWmpjr6%2B0wiYN0UsrKyNGTIkEvOzZs3T507d77sc8PCwsrsbEVFRapcuXK55v3pan1u3rxZYWFhuuWWWyRJr776qvr3768TJ04oLCxMp0%2Bf9nlOoPq4HpdbB0lB04Mk1atXT1u2bNFtt90ml8ul5s2bq7S0VGPHjlViYuJNs6/dqKvtb5dbz4iICL/VeL369u2rpKQkVa9eXZLUrFkz/fDDD1q9erW6dOly2d7Cw8MDUe5lpaWlafny5Zo9e7aaNm3qqDW7uLcmTZo4Ys1atmwp6ddPVD/zzDPq37%2B/8vPzfR4TrGt2KQSsm0CbNm20d%2B/e63pudHS08vLyfMby8vIUGRl5xfmLD7v6w7X22bhxY0m/fnQ5OjpaBw4c8JkPVB/XIzo6WqdOnVJxcbHc7l%2B/7TwejypXrhw0Pyx%2B89sP%2Bd80btxYhYWFioyMvGn2tRt1tf3tct9XzZs391uN18vlcpVZw0aNGmnz5s2Srv4z5WYwZcoUrV69Wmlpaeratask56zZpXoL5jXLy8vT9u3bfQ4U3HHHHfrll18UGRmpgwcPlnl8sK3Z5fAm9yAXGxurI0eO%2BJyTzszMVGxsrCQpJiZG3377rYwxkn59D822bdsUExMTkHov58CBA4qLi/O5xsnu3bvldrt1%2B%2B23KyYmRt9//70KCgq885mZmTddH5fTvHlzud1unzdnZmZmqmXLlqpUKXi%2BDb/66iu1adPG53%2Bdu3fvVvXq1b0frrjZ97XyuNr%2BFhMTo8zMTO9cfn6%2Bdu3aFRS9zp07V8OGDfMZ27Nnjxo1aiSpbG/Hjh3TsWPHbpre0tPT9c4772jWrFnq2bOnd9wJa3a53oJ5zQ4fPqzRo0f7vD1k586dqlmzpuLj44N%2Bza4okB9hxLVLSkoq87H34cOHm8GDB5vdu3ebd99917Rs2dJ7HayzZ8%2Batm3bmilTppj9%2B/ebKVOmmHbt2t101yYqKSkxffr08V7Pa%2BvWraZHjx7mxRdfNMYYU1xcbHr06GGeeuops2/fPrNo0SITGxsbVNfBev75503Pnj1NVlaW%2Beyzz0zr1q3Np59%2BGuiyrsnZs2dNhw4dzNNPP22ys7PNxo0bTfv27c3ixYuDZl%2B7nN9fyuBq%2B1tOTo5p2bKlWbRokff6PL17975pr8/z%2B96ysrLMXXfdZZYsWWJ%2B/PFHs2rVKnP33Xebbdu2GWOM2bZtm2nRooV59913vddUGjVqVCDL9zpw4IBp3ry5mT17ts/1oHJzc4N%2Bza7UWzCvWXFxsenXr58ZPny42b9/v9m4caP585//bJYtWxb0a3Y1BKwgc6mAlZeXZ0aNGmVatmxpOnXqZD766COf%2BaysLNO3b1/TsmVL89BDD5nvv//enyWX29GjR01ycrJJSEgwiYmJZsqUKaawsNA7/8MPP5i//OUv5u677zY9e/Y0//znPwNY7bW7cOGCGTdunImNjTXt27c3b731VqBLui779u0zw4YNM7GxsaZdu3bm9ddf9/7AC5Z97VIuvlbU1fa3jRs3mvvvv9%2B0atXKDB069Ka45tDlXNzbZ599Znr37m1atmxpunXrVibor1271tx3330mNjbWJCcney/4G2iLFi0yTZs2veTNmOBes6v1FqxrZsyv13JMTk42rVu3Nu3atTMLFizw/swI5jW7Gpcx/3c8HwAAAFYEz5s/AAAAggQBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACW/X%2BSzlM7j0PW4wAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3568554342101042653”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2596</td> <td class=”number”>80.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.333333333</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.666666667</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.666666667</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.285714286</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (61)</td> <td class=”number”>136</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>216</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3568554342101042653”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.333333333</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133.333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_ACRESPTH_07_12”>PCH_VEG_ACRESPTH_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2243</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>968</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.4568</td>

</tr> <tr>

<th>Minimum</th> <td>-43.114</td>

</tr> <tr>

<th>Maximum</th> <td>13.396</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3926377798283418134”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives3926377798283418134”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3926377798283418134”

aria-controls=”quantiles3926377798283418134” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3926377798283418134” aria-controls=”histogram3926377798283418134”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3926377798283418134” aria-controls=”common3926377798283418134”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3926377798283418134” aria-controls=”extreme3926377798283418134”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3926377798283418134”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-43.114</td>

</tr> <tr>

<th>5-th percentile</th> <td>-9.0041</td>

</tr> <tr>

<th>Q1</th> <td>-3.5725</td>

</tr> <tr>

<th>Median</th> <td>-0.87767</td>

</tr> <tr>

<th>Q3</th> <td>1.2833</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.2241</td>

</tr> <tr>

<th>Maximum</th> <td>13.396</td>

</tr> <tr>

<th>Range</th> <td>56.509</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.8558</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.2378</td>

</tr> <tr>

<th>Coef of variation</th> <td>-2.9089</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.7643</td>

</tr> <tr>

<th>Mean</th> <td>-1.4568</td>

</tr> <tr>

<th>MAD</th> <td>3.1555</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.1795</td>

</tr> <tr>

<th>Sum</th> <td>-3266.2</td>

</tr> <tr>

<th>Variance</th> <td>17.959</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3926377798283418134”>

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u3bp2nTpmnDhg0aO3aspk2bpv/%2B7//%2BWe8fGRmpqKgobdu2TefOndOnn36qgwcPql%2B/fnI4HEpJSVFISIinlsGDB6u4uFiS5HA4lJqa6nmvrl27qlu3bnI4HD%2BrJgAA0H7YrFq4vLxcL7/8sl5%2B%2BWV9/PHHGjx4sK699lqdPHlSixYt0nvvvaecnJxWvXdERIQWL16s5cuXa%2BPGjWpoaFBGRoamTJmi119/XX369PF6fmxsrEpLSz11xcfHN5k/efKkT9vmdDq9xmy26Cbv29aFhYV6/cb50TPf0bPWoW/WsNnaV7/Zz1rP7wFr%2B/bt2r59u95991117txZEydO1MMPP6xevXp5ntO1a1etXLmy1QFLkj755BONHDlSt9xyi0pLS7V8%2BXJdeeWVqq2tVXh4uNdzw8PD5XK5JElnz579yfmWKCgoUF5entdYVlaW5s2b18qtsVZMTJTVJQQceuY7etY69M2/OnXqYHUJlmA/853fA1ZOTo5GjhypdevW6aqrrlJoaNNU/Otf/1o33nhjq9fYv3%2B/tm7dqjfffFORkZEaOHCgvvrqK/35z39Wjx49moQll8ulyMhISd%2Bd/WpuPiqq5TtXZmam0tPTvcZstmhVVp5p5RZZIywsVDExUaqurlVDQ6PV5QQEeuY7etY69M0agXYc/7mCYT%2BzKhT7PWC99dZb6tSpk6qqqjzhqqSkRP3791dYWJgkafDgwRo8eHCr1zh06JB69uzpCU2SdPnll2v9%2BvVKTU1VRUWF1/MrKio8l%2B8SEhKanY%2BLi2vx%2BvHx8U0uBzqdp1RfH5g7Z0NDY8DWbhV65jt61jr0zb/aa6/Zz3zn94uqp0%2Bf1ujRo/X44497xmbNmqUJEyboxIkTRtaIj4/X559/7nUm6tNPP1ViYqLsdrs%2B%2BOADz60h3G63Dh48KLvdLkmy2%2B0qKiryvO7EiRM6ceKEZx4AAOB8/B6w7r//fvXs2VO33HKLZ2zXrl3q2rWrcnNzjayRnp6uCy64QIsWLdJnn32mv/71r1q/fr2mT5%2Bu0aNHq7q6WitXrtTRo0e1cuVK1dbWasyYMZKkqVOnavv27dqyZYuOHDmiBQsW6Oqrr1aPHj2M1AYAAIKf3wPW%2B%2B%2B/r3vvvdfrklvnzp21YMECvfPOO0bWuPDCC/XMM8/I6XRq8uTJys3N1a233qrMzEx17NhRjz32mIqKipSRkSGHw6H8/HxFR0dLkpKTk7Vs2TKtW7dOU6dO1UUXXWQs%2BAEAgPbB75/Bstlsqq6ubjJeW1tr9I7uffr00dNPP93s3KBBg/Tiiy/%2B6GszMjKUkZFhrBYAANC%2B%2BP0M1lVXXaUVK1boiy%2B%2B8IyVlZUpNzdXI0aM8Hc5AAAAxvn9DNY999yjW265Rddcc41iYmIkSdXV1erfvz/f%2BQcAAIKC3wNWbGysXnzxRe3bt0%2BlpaWy2Wzq06ePrrzySs/X1wAAAAQyS74qJywsTCNGjOCSIAAACEp%2BD1hOp1Nr167VwYMHde7cuSYfbH/99df9XRIAAIBRfg9Y//mf/6lDhw5p3LhxuvDCC/29PAAAwC/O7wHrnXfe0RNPPKHU1FR/Lw0AAOAXfr9NQ3R0tGJjY/29LAAAgN/4PWBNmDBBTzzxhBoaGvy9NAAAgF/4/RJhVVWVduzYob/97W/q0aOHwsPDveY3btzo75IAAACMsuQ2DePHj7diWQAAAL/we8Dii5MBAECw8/tnsCSpvLxceXl5uvPOO/X111/rtdde06effmpFKQAAAMb5PWB9/vnn%2Brd/%2Bze9%2BOKL2r17t2pqarRr1y5NmjRJDofD3%2BUAAAAY5/eA9cc//lGjRo3Snj17dMEFF0iSHnzwQaWnp2vNmjX%2BLgcAAMA4vwesgwcP6pZbbvH6YmebzabbbrtNhw8f9nc5AAAAxvk9YDU2NqqxsbHJ%2BJkzZxQWFubvcgAAAIzze8AaPny4HnvsMa%2BQVVVVpdWrV2vo0KH%2BLgcAAMA4vwese%2B%2B9V4cOHdLw4cNVV1enW2%2B9VSNHjtSxY8d0zz33%2BLscAAAA4/x%2BH6yEhAS99NJL2rFjh/7%2B97%2BrsbFRU6dO1YQJE9SxY0d/lwMAAGCcJXdyj4qK0pQpU6xYGgAA4Bfn94A1Y8aMn5znuwgBAECg83vA6t69u9fj%2Bvp6ff755/r444910003%2BbscAAAA49rMdxGuW7dOJ0%2Be9HM1AAAA5lnyXYTNmTBhgl599VWrywAAAPjZ2kzA%2BuCDD7jRKAAACApt4kPup0%2Bf1j/%2B8Q/dcMMN/i4HAADAOL8HrG7dunl9D6EkXXDBBbrxxhv17//%2B7/4uBwAAwDi/B6w//vGP/l4SAADAr/wesN57770WP/eKK674BSsBAAD4Zfg9YE2fPt1zidDtdnvGfzgWEhKiv//97/4uDwAA4Gfze8Bav369VqxYobvvvltpaWkKDw/Xhx9%2BqGXLlunaa6/V2LFj/V0SAACAUX6/TUNubq4WL16sa665Rp06dVKHDh00dOhQLVu2TJs2bVL37t09PwAAAIHI7wGrvLy82fDUsWNHVVZW%2BrscAAAA4/wesJKSkvTggw/q9OnTnrGqqiqtXr1aV155pb/LAQAAMM7vAWvRokUqLi7WVVddpYyMDF177bUaOXKkysrKtHjxYmPruFwuLV26VFdccYV%2B85vf6MEHH/R8gP7w4cOaMmWK7Ha7Jk2apEOHDnm9dseOHRo1apTsdruysrL0zTffGKsLAAAEP78HrN69e2vXrl268847lZSUpOTkZOXk5Gj79u36l3/5F2PrrFixQvv27dOTTz6pBx54QH/5y19UUFCgmpoazZo1S6mpqdq2bZuSk5M1e/Zs1dTUSJJKSkqUk5OjOXPmqKCgQNXV1crOzjZWFwAACH5%2B/ytCSbrooos0ZcoUHTt2TD169JD03d3cTamqqlJhYaGefvppDRo0SJI0c%2BZMORwO2Ww2RUREaMGCBQoJCVFOTo7eeustvfbaa8rIyNBzzz2nMWPGaOLEiZKkVatWec6wfV8rAADAT/H7GSy32601a9boiiuu0Pjx43Xy5Endc889ysnJ0blz54ysUVRUpI4dOyotLc0zNmvWLOXm5srhcCglJcVz362QkBANHjxYxcXFkiSHw6HU1FTP67p27apu3brJ4XAYqQ0AAAQ/v5/BevbZZ7V9%2B3bdd999WrZsmSRp1KhRWrp0qbp06aI77rjjZ69RVlam7t2766WXXtL69et17tw5ZWRk6NZbb5XT6VSfPn28nh8bG6vS0lJJ3/2VY3x8fJP5kydPtnj98vJyOZ1OrzGbLbrJ%2B7Z1YWGhXr9xfvTMd/SsdeibNWy29tVv9rPW83vAKigo0OLFi/Wv//qvWr58uSRp7NixuuCCC5Sbm2skYNXU1Ojzzz/X5s2blZubK6fTqcWLFysqKkq1tbUKDw/3en54eLhcLpck6ezZsz853xIFBQXKy8vzGsvKytK8efNauUXWiomJsrqEgEPPfEfPWoe%2B%2BVenTh2sLsES7Ge%2B83vAOnbsmPr169dkvG/fvk3O%2BrSWzWbT6dOn9cADD3juuXX8%2BHFt2rRJPXv2bBKWXC6XIiMjJUkRERHNzkdFtXznyszMVHp6%2Bg9qilZl5ZnWbI5lwsJCFRMTperqWjU0NFpdTkCgZ76jZ61D36wRaMfxnysY9jOrQrHfA1b37t314YcfKjEx0Wv8rbfeMvYh8ri4OEVERHjd0PRXv/qVTpw4obS0NFVUVHg9v6KiwnP5LiEhodn5uLi4Fq8fHx/f5HKg03lK9fWBuXM2NDQGbO1WoWe%2Bo2etQ9/8q732mv3Md36/qPq73/1OS5cu1caNG%2BV2u7V//36tWbNGq1at0vTp042sYbfbVVdXp88%2B%2B8wz9umnn6p79%2B6y2%2B364IMPPPfEcrvdOnjwoOx2u%2Be1RUVFntedOHFCJ06c8MwDAACcj98D1qRJk3THHXfoqaee0tmzZ7V48WJt27ZNt99%2Bu6ZOnWpkjV//%2Bte6%2BuqrlZ2drSNHjuh///d/lZ%2Bfr6lTp2r06NGqrq7WypUrdfToUa1cuVK1tbUaM2aMJGnq1Knavn27tmzZoiNHjmjBggW6%2BuqruUUDAABoMb9fItyxY4dGjx6tzMxMffPNN3K73YqNjTW%2Bzpo1a7R8%2BXJNnTpVUVFRmjZtmqZPn66QkBA99thjuu%2B%2B%2B/SXv/xFl112mfLz8xUdHS1JSk5O1rJly/Twww/r22%2B/1bBhwzwfxgcAAGiJEPf318r8JC0tTS%2B88EKTWyUEO6fzlNUl%2BMxmC1WnTh1UWXmGa%2B8tRM98R89aJ1j6NmbtXqtL8Mmrtw%2BzugS/Cob9LC7uQkvW9fslwl69eunjjz/297IAAAB%2B4/dLhH379tVdd92lJ554Qr169VJERITXfG5urr9LAgAAMMrvAeuzzz5TSkqKJBm77xUAAEBb4peAtWrVKs2ZM0fR0dF69tln/bEkAACAZfzyGaynn35atbW1XmOzZs1SeXm5P5YHAADwK78ErOb%2BUPG9995TXV2dP5YHAADwK74eGwAAwDACFgAAgGF%2BC1ghISH%2BWgoAAMBSfrtNw4oVK7zueXXu3DmtXr1aHTp08Hoe98ECAACBzi8B64orrmhyz6vk5GRVVlaqsrLSHyUAAAD4jV8CFve%2BAgAA7QkfcgcAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhtmsLgAAYNaYtXutLgFo9ziDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADAs6APWrFmzdO%2B993oeHz58WFOmTJHdbtekSZN06NAhr%2Bfv2LFDo0aNkt1uV1ZWlr755ht/lwwAAAJcUAesnTt36s033/Q8rqmp0axZs5Samqpt27YpOTlZs2fPVk1NjSSppKREOTk5mjNnjgoKClRdXa3s7GyrygcAAAEqaANWVVWVVq1apYEDB3rGdu3apYiICC1YsEC9e/dWTk6OOnTooNdee02S9Nxzz2nMmDGaOHGi%2Bvbtq1WrVunNN99UWVmZVZsBAAACUNAGrD/96U%2BaMGGC%2BvTp4xlzOBxKSUlRSEiIJCkkJESDBw9WcXGxZz41NdXz/K5du6pbt25yOBz%2BLR4AAAS0oPyy5/379%2Bv999/XK6%2B8oiVLlnjGnU6nV%2BCSpNjYWJWWlkqSysvLFR8f32T%2B5MmTPq1fXl4up9PpNWazRTd577YuLCzU6zfOj575jp61Dn2zhs3WvvrNftZ6QRew6urqdN9992nx4sWKjIz0mqutrVV4eLjXWHh4uFwulyTp7NmzPznfUgUFBcrLy/May8rK0rx583x6n7YiJibK6hICDj3zHT1rHfrmX506dbC6BEuwn/ku6AJWXl6eBgwYoBEjRjSZi4iIaBKWXC6XJ4j92HxUlG87VmZmptLT073GbLZoVVae8el9rBYWFqqYmChVV9eqoaHR6nICAj3zHT1rHfpmjUA7jv9cwbCfWRWKgy5g7dy5UxUVFUpOTpYkT2DavXu3xo8fr4qKCq/nV1RUeC7dJSQkNDsfFxfnUw3x8fFNLgc6nadUXx%2BYO2dDQ2PA1m4VeuY7etY69M2/2muv2c98F3QB69lnn1V9fb3n8Zo1ayRJd911l9577z09/vjjcrvdCgkJkdvt1sGDB/WHP/xBkmS321VUVKSMjAxJ0okTJ3TixAnZ7Xb/bwgAAAhYQRewunfv7vW4Q4fvTg327NlTsbGxeuCBB7Ry5Updf/312rx5s2prazVmzBhJ0tSpUzV9%2BnQlJSVp4MCBWrlypa6%2B%2Bmr16NHD79sBAAACV7v6s4COHTvqscce85ylcjgcys/PV3R0tCQpOTlZy5Yt07p16zR16lRddNFFys3NtbhqAAAQaELcbrfb6iLaA6fzlNUl%2BMxmC1WnTh1UWXmGa%2B8tRM98R89a56f6NmbtXouqCn6v3j7M6hL8Khj%2BfcbFXWjJuu3qDBYAAIA/BN1nsAAA%2BKUE2tnB9nbGrS3hDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQt4IZaeAAAOUklEQVQAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYUEbsL766ivNmzdPaWlpGjFihHJzc1VXVydJKisr080336ykpCSNHTtWb7/9ttdr9%2B3bp/Hjx8tut2vGjBkqKyuzYhMAAECACsqA5Xa7NW/ePNXW1ur555/XQw89pDfeeENr166V2%2B1WVlaWunTposLCQk2YMEFz5szR8ePHJUnHjx9XVlaWMjIytHXrVnXu3Fm33Xab3G63xVsFAAAChc3qAn4Jn376qYqLi7V371516dJFkjRv3jz96U9/0lVXXaWysjJt3rxZ0dHR6t27t/bv36/CwkLNnTtXW7Zs0YABAzRz5kxJUm5uroYNG6YDBw5oyJAhVm4WAAAIEEF5BisuLk5PPPGEJ1x97/Tp03I4HLr88ssVHR3tGU9JSVFxcbEkyeFwKDU11TMXFRWl/v37e%2BYBAADOJyjPYMXExGjEiBGex42NjXruuec0dOhQOZ1OxcfHez0/NjZWJ0%2BelKTzzrdEeXm5nE6n15jNFt3kfdu6sLBQr984P3rmO3rWOvQNLWGz/bz9g/2s9YIyYP3Q6tWrdfjwYW3dulXPPPOMwsPDvebDw8PlcrkkSbW1tT853xIFBQXKy8vzGsvKytK8efNauQXWiomJsrqEgEPPfEfPWoe%2B4ad06tTByPuwn/ku6APW6tWrtWHDBj300EO69NJLFRERoaqqKq/nuFwuRUZGSpIiIiKahCmXy6WYmJgWr5mZman09HSvMZstWpWVZ1q5FdYICwtVTEyUqqtr1dDQaHU5AYGe%2BY6etQ59Q0v83P/uBMN%2BZipk%2BiqoA9by5cu1adMmrV69Wtdcc40kKSEhQUePHvV6XkVFhefyXUJCgioqKprM9%2BvXr8XrxsfHN7kc6HSeUn19YO6cDQ2NAVu7VeiZ7%2BhZ69A3/BRT%2Bwb7me%2BC9qJqXl6eNm/erAcffFDjxo3zjNvtdn300Uc6e/asZ6yoqEh2u90zX1RU5Jmrra3V4cOHPfMAAADnE5QB65NPPtGjjz6q3//%2B90pJSZHT6fT8pKWlqWvXrsrOzlZpaany8/NVUlKiyZMnS5ImTZqkgwcPKj8/X6WlpcrOzlZiYiK3aAAAAC0WlAHr9ddfV0NDg/785z9r%2BPDhXj9hYWF69NFH5XQ6lZGRoZdfflnr1q1Tt27dJEmJiYl65JFHVFhYqMmTJ6uqqkrr1q1TSEiIxVsFAAACRYibW5T7hdN5yuoSfGazhapTpw6qrDzDtfcWome%2Bo2et81N9G7N2r0VVoa159fZhP%2Bv1wfDvMy7uQkvWDcozWAAAAFYiYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMxmdQEA0NaNWbvX6hIABBjOYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwmlFXV6eFCxcqNTVVw4cP11NPPWV1SQAAIIDYrC6gLVq1apUOHTqkDRs26Pjx47rnnnvUrVs3jR492urSAABAACBg/UBNTY22bNmixx9/XP3791f//v1VWlqq559/noAFGDJm7V6rSwDahUD6t/bq7cOsLsEoLhH%2BwJEjR1RfX6/k5GTPWEpKihwOhxobGy2sDAAABArOYP2A0%2BlUp06dFB4e7hnr0qWL6urqVFVVpc6dO5/3PcrLy%2BV0Or3GbLZoxcfHG6/3lxQWFur1G%2BdHzwCgdWy24DpuErB%2BoLa21itcSfI8drlcLXqPgoIC5eXleY3NmTNHc%2BfONVOkn5SXl2vDhieUmZkZcOHQKvSsZd5f%2BX%2BX28vLy1VQUEDPfETffEfPfEfPWi%2B44qIBERERTYLU948jIyNb9B6ZmZnatm2b109mZqbxWn9pTqdTeXl5Tc7G4cfRM9/Rs9ahb76jZ76jZ63HGawfSEhIUGVlperr62Wzfdcep9OpyMhIxcTEtOg94uPjSfoAALRjnMH6gX79%2Bslms6m4uNgzVlRUpIEDByo0lHYBAIDzIzH8QFRUlCZOnKglS5aopKREe/bs0VNPPaUZM2ZYXRoAAAgQYUuWLFlidRFtzdChQ3X48GE98MAD2r9/v/7whz9o0qRJVpdliQ4dOigtLU0dOnSwupSAQc98R89ah775jp75jp61Tojb7XZbXQQAAEAw4RIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFn7S0qVLNX36dK%2BxsrIy3XzzzUpKStLYsWP19ttvW1Rd2/H1119r3rx5SklJ0bBhw7R69WrV19d75isrKzV37lwlJycrPT1d27dvt7DatqG6ulo5OTn6zW9%2Bo6FDh%2Bree%2B9VdXW1Z56e/Ti3262ZM2dq27ZtXuP0rHl1dXVauHChUlNTNXz4cD311FNWl9SmuVwujR8/Xu%2B%2B%2B65njOO%2B7whY%2BFEHDx7Upk2bvMbcbreysrLUpUsXFRYWasKECZozZ46OHz9uUZVtw1133aXTp0%2BroKBA//Vf/6WdO3fqiSee8MxnZ2fr1KlTKigo0K233qpFixappKTEwoqtd9999%2BnIkSPKz8/Xk08%2BqU8%2B%2BUSLFi3yzNOz5jU2NmrFihXau3dvkzl61rxVq1bp0KFD2rBhg%2B677z7l5eXptddes7qsNqmurk7z589XaWmpZ4zjfuvYrC4AbZPL5dLixYuVlJTkNf7OO%2B%2BorKxMmzdvVnR0tHr37q39%2B/ersLBQc%2BfOtahaa7lcLsXGxmru3Lnq2bOnJOmaa65RUVGRJOmLL77QG2%2B8oddff12JiYm69NJLVVxcrBdeeEGDBg2ysnTL1NTUaPfu3dq0aZMGDBggSVq4cKGmTZumuro6ffXVV/SsGV999ZXuuusuHTt2TDExMV5z7GfNq6mp0ZYtW/T444%2Brf//%2B6t%2B/v0pLS/X8889r9OjRVpfXphw9elR33nmnfvgNehz3W4czWGhWfn6%2BLrvsMg0bNsxr3OFw6PLLL1d0dLRnLCUlRcXFxf4usc0IDw/XmjVrPOGqtLRUf/3rX5WWlibpu5517dpViYmJntekpKTogw8%2BsKTetiA0NFTr169Xv379vMYbGhp05swZevYjPvroI3Xt2lWFhYW68MILveboWfOOHDmi%2Bvp6JScne8ZSUlLkcDjU2NhoYWVtz4EDBzRkyBAVFBR4jXPcbx3OYKGJTz75RJs2bdL27dubXCJ0Op2Kj4/3GouNjdXJkyf9WWKbdeONN%2Bq9995T//79NW3aNEk/3rOvvvrKihLbhMjISF111VVeYxs3btRll12mzp0707MfkZ6ervT09Gbn6FnznE6nOnXqpPDwcM9Yly5dVFdXp6qqKnXu3NnC6tqWG264odlxjvutQ8Bqh86ePfujB924uDgtXrxYc%2BfOVZcuXZrM19bWeh2opO/O4Lhcrl%2Bk1rbifD37/v/sFi1apG%2B//VYrVqzQ/PnztX79enrWjP%2B/Z5L03HPP6dVXX/V8bo2eNfXDnv1Qe%2B3Z%2BfxYXyS1%2B960FPtW6xCw2iGHw6EZM2Y0O3fnnXeqoaFBmZmZzc5HRESoqqrKa8zlcikyMtJ4nW3JT/Vs3bp1GjVqlCSpb9%2B%2BkqT7779fkydP1rFjxxQREdHkQETP/q9nzz//vFasWKHs7GwNHz5ckuhZM/7/njWnvfbsfH6sL5LafW9aqr0e938uAlY7NGTIEP3jH/9odm769Ok6dOiQBg8eLEk6d%2B6cGhoalJycrJ07dyohIUFHjx71ek1FRUWT08fB5qd6dvr0ae3atUujR49WaOh3H2vs06ePpO/%2BbD4hIUEVFRVer6moqFBcXNwvW7TFfqpn33vyySe1atUqLViwQDfddJNnnJ75rr327HwSEhJUWVmp%2Bvp62Wzf/SfP6XQqMjKyyR8KoHnt9bj/c/Ehd3hZs2aNdu7cqZdeekkvvfSSrr/%2Beg0YMEAvvfSS4uPjZbfb9dFHH%2Bns2bOe1xQVFclut1tYtbVqa2t1xx13yOFweMY%2B%2BugjhYWF6Ve/%2BpWSkpL05Zdfen1eoaioqMlfaLY3L774olatWqXs7Gz97ne/85qjZ76jZ83r16%2BfbDab1weyi4qKNHDgQM//EOGncdxvHfYueElISFDPnj09PxdddJEiIyPVs2dP2Ww2paWlqWvXrsrOzlZpaany8/NVUlKiyZMnW126ZeLi4vTb3/5Wy5cv1%2BHDh/X%2B%2B%2B8rJydHN954ozp27KgePXpo%2BPDhuvvuu3XkyBFt2bJFO3bs8HwIvj2qqqrSsmXLdO2112rcuHFyOp2en4aGBnrWCvSseVFRUZo4caKWLFmikpIS7dmzR0899dSPXopFUxz3W8kN/ISHH37YfeONN3qN/fOf/3RPmzbNPWDAAPe4cePce/futai6tqO6utp97733utPS0txpaWnu%2B%2B%2B/311XV%2BeZr6iocM%2BePds9cOBAd3p6uvuVV16xsFrr7dixw33ppZc2%2B1NWVuZ2u%2BnZ%2BYwcOdJdWFjoNUbPmldTU%2BNesGCBOykpyT18%2BHD3008/bXVJbd6ll17qfueddzyPOe77LsTt/sEdxQAAAPCzcIkQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7f1WI3Zx0eNp/AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3926377798283418134”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.1156263091746883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.548062973510286</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.6563057153429522</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.780922176140511</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-4.73644935893087</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-7.021709449426987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-3.2539974091761596</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.4870623967317869</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.7429535997487655</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.09903487709397067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2232)</td> <td class=”number”>2232</td> <td class=”number”>69.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>968</td> <td class=”number”>30.2%</td> <td>

<div class=”bar” style=”width:43%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3926377798283418134”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-43.113541641571715</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-26.345885267068947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-21.258723334847897</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.120450277775085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-19.92289766679134</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>10.44776119402985</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.892116182572611</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.847672778561352</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.188528443817589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.39568562377434</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_ACRES_07_12”>PCH_VEG_ACRES_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1718</td>

</tr> <tr>

<th>Unique (%)</th> <td>53.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>59.285</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>22109</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram988701394962913140”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives988701394962913140,#minihistogram988701394962913140”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives988701394962913140”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles988701394962913140”

aria-controls=”quantiles988701394962913140” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram988701394962913140” aria-controls=”histogram988701394962913140”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common988701394962913140” aria-controls=”common988701394962913140”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme988701394962913140” aria-controls=”extreme988701394962913140”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles988701394962913140”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-76.544</td>

</tr> <tr>

<th>Q1</th> <td>-29.93</td>

</tr> <tr>

<th>Median</th> <td>6.6667</td>

</tr> <tr>

<th>Q3</th> <td>60.615</td>

</tr> <tr>

<th>95-th percentile</th> <td>275.47</td>

</tr> <tr>

<th>Maximum</th> <td>22109</td>

</tr> <tr>

<th>Range</th> <td>22209</td>

</tr> <tr>

<th>Interquartile range</th> <td>90.545</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>521.24</td>

</tr> <tr>

<th>Coef of variation</th> <td>8.7922</td>

</tr> <tr>

<th>Kurtosis</th> <td>1560.2</td>

</tr> <tr>

<th>Mean</th> <td>59.285</td>

</tr> <tr>

<th>MAD</th> <td>108.21</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>37.151</td>

</tr> <tr>

<th>Sum</th> <td>121950</td>

</tr> <tr>

<th>Variance</th> <td>271690</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram988701394962913140”>

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iiouLzXWdZ7c3rffyvspu99o8Xy/%2B/n5OP%2BEe1MFzUAvPQB3cw2sD1sUcPHhQNptN7du316hRo7Rz50498cQTat68ufr166eKigoFBAQ4LRMQEKCqqipVVFSYz3/cJ/1wMX19ZWVlKTMz06ktNTVVaWlpv3ZaPiE0tJm7h%2BASwcFB7h4CRB08CbXwDNTBtXwuYA0dOlR9%2BvRRSEiIJCkqKkqHDh3Syy%2B/rH79%2BikwMLBOWKqqqlJQUJBTmAoMDDT/W5J5DVd9pKSkKDk52anNbm%2Bq0tIzv3pevsDX5%2B/v76fg4CCdPFmumhr%2B8tRdqIPnoBaeobHXwV2/3PtcwLLZbGa4Oq99%2B/bavn27JCkiIkIlJSVO/SUlJQoLC1NERIQkyeFwqG3btuZ/S1JYWFi9xxAeHl7ndKDDcUrV1Y3vg/1jjWX%2BNTW1jWaunow6eA5q4Rmog2v53AnZRYsWaezYsU5t%2B/fvV/v27SVJMTExys7ONvuOHTumY8eOKSYmRhEREYqMjHTqz87OVmRkZKO/fgoAANSfzx3B6tOnj1asWKFVq1apX79%2B2rZtm958802tXbtWkjRixAjdfffdio2NVXR0tJ588kn17t1bV155pdk/b948/eY3v5EkzZ8/X/fee6/b5gMAALyPzwWsrl27atGiRVq8eLEWLVqkNm3aaP78%2BYqLi5MkxcXFaebMmVq8eLG%2B//579erVS7NmzTKXv%2B%2B%2B%2B/Tdd99p/Pjx8vf31%2B23317niBgAAMDPsRmGYbh7EI2Bw3GqQdY7cOEnDbLehvDuw73cPYQGZbf7KTS0mUpLz3CdgxtRB89BLTxDY69DWNjlbtmuz12DBQAA4G4ELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALObygHXHHXfolVde0alTp1y9aQAAAJdwecDq2bOnnnvuOSUmJuqRRx7Rtm3bZBiGq4cBAADQYFwesNLT0/X%2B%2B%2B9r6dKl8vf314QJE9S7d28tWLBAX3/99b%2B9vqqqKg0ZMkQ7duww23JycvSf//mfiouL080336wNGzY4LfOHP/xBnTp1cnocOHBAkmQYhubNm6eePXuqe/fumjt3rmpray9t0gAAoFGxu2OjNptNvXr1Uq9evVReXq5169Zp6dKlWrFihbp166YxY8aof//%2Bv7ieyspKpaenKy8vz2xzOBx64IEHNGLECD399NP64osvNHnyZIWFhal3796qqanRoUOHtH79erVr185cLjQ0VJL04osvavPmzcrMzFR1dbUmTpyoli1b6r777rP8fQAAAL7JLQFLkoqLi/XWW2/prbfe0oEDB9StWzcNGzZMx48f11/%2B8hft3LlTU6dOvejy%2Bfn5Sk9Pr3N6cevWrWrVqpUeeeQRSVK7du20Y8cOvf322%2Brdu7cOHz6sc%2BfOqWvXrgoMDKyz3rVr1yotLU0JCQmSpEcffVSLFi0iYAEAgHpzecDatGmTNm3apB07dqhFixYaOnSoFi9e7HQ0qXXr1nryySd/NmB9/vnn6tGjh/785z8rNjbWbE9KSlLnzp3rvP706dOSfghmrVu3vmC4Kioq0rFjx3T99debbfHx8Tpy5IiKi4sVHh7%2Ba6YMAAAaGZcHrKlTp6pPnz5asmSJbrzxRvn51b0MrH379ho1atTPrmfkyJEXbG/btq3atm1rPv/uu%2B/097//XRMmTJAkFRQU6LLLLtO4ceO0d%2B9eXXXVVZo0aZK6du0qh8MhSU5BqlWrVpKk48ePE7AAAEC9uDxgffTRRwoNDVVZWZkZrnbv3q0uXbrI399fktStWzd169btkrdVUVGhCRMmqFWrVkpJSZEkff311/r%2B%2B%2B91xx13KC0tTa%2B%2B%2BqrGjBmjd955RxUVFZKkgIAAcx3n/7uqqqre2y0uLjbD2nl2e9NGH9Dsdt%2B%2B7Zq/v5/TT7gHdfAc1MIzUAf3cHnAOn36tEaMGKHf//73mjRpkiTpwQcfVKtWrfT888%2BrdevWlmznzJkzeuihh3To0CH97W9/U1BQkCRp1qxZqqioUPPmzSVJ06dP165du7Rp0yb97ne/k/RDmDp/CvF8sDq/fH1kZWUpMzPTqS01NVVpaWmXPC9vFhrazN1DcIng4Pp/VtBwqIPnoBaegTq4lssD1lNPPaXf/va3uueee8y2d955R4899pjmzJmjxYsXX/I2Tp8%2Brfvvv1/ffvut1qxZ43R9l91uN8OV9MNfNLZv315FRUWKiIiQ9MNfIp4/zXj%2BSFRYWFi9t5%2BSkqLk5GSnNru9qUpLz/zaKfkEX5%2B/v7%2BfgoODdPJkuWpquLWHu1AHz0EtPENjr4O7frl3ecD6v//7P7366qtOgaVFixaaNGmS7rrrrktef21trcaPH6/Dhw9r3bp16tChg1P/3XffrR49emj8%2BPHm67/66ivdddddioiIUGRkpLKzs82AlZ2drcjIyH/r9F54eHid1zscp1Rd3fg%2B2D/WWOZfU1PbaObqyaiD56AWnoE6uJbLA5bdbtfJkyfrtJeXl1tyR/fXXntNO3bs0LJlyxQcHGwegbrssssUEhKi5ORkLVmyRJ07d9ZVV12ltWvX6tSpUxo2bJgkacSIEZo3b55%2B85vfSJLmz5%2Bve%2B%2B995LHBQAAGg%2BXB6wbb7xRs2fP1jPPPKP/%2BI//kCQVFhZqzpw5SkpKuuT1b9myRbW1tRo3bpxTe/fu3bVu3TqNHTtWlZWVmj17tkpKShQTE6MXX3zRPG1433336bvvvtP48ePl7%2B%2Bv22%2B/XWPHjr3kcQEAgMbDZrj4iwC/%2B%2B473XPPPcrLy1NwcLAk6eTJk%2BrSpYuWLVv2b13r5E0cjob5cuuBCz9pkPU2hHcf7uXuITQou91PoaHNVFp6hsPwbkQdPAe18AyNvQ5hYZe7ZbsuP4LVsmVLvfHGG/r000%2BVl5cnu92uq6%2B%2BWjfccINsNpurhwMAAGA5t3xVjr%2B/v5KSkiw5JQgAAOBpXB6wHA6HFi5cqF27duncuXN1Lmz/5z//6eohAQAAWMrlAeuJJ57Q3r17NXjwYF1%2BuXvOiwIAADQklwes7du3a%2BXKlUpISHD1pgEAAFzC5V9M1LRpU7Vs2dLVmwUAAHAZlwesW2%2B9VStXrlRNTY2rNw0AAOASLj9FWFZWps2bN%2BuDDz7QlVdeqYCAAKf%2BtWvXunpIAAAAlnLLbRqGDBnijs0CAAC4hMsD1pw5c1y9SQAAAJdy%2BTVYklRcXKzMzEylp6fru%2B%2B%2B0z/%2B8Q8dPHjQHUMBAACwnMsD1jfffKNbbrlFb7zxhrZs2aKzZ8/qnXfe0fDhw5Wbm%2Bvq4QAAAFjO5QHr6aefVt%2B%2BfbV161ZddtllkqRnnnlGycnJmjdvnquHAwAAYDmXB6xdu3bpnnvucfpiZ7vdroceekj79u1z9XAAAAAs5/KAVVtbq9ra2jrtZ86ckb%2B/v6uHAwAAYDmXB6zExEQtX77cKWSVlZUpIyNDPXv2dPVwAAAALOfygPX4449r7969SkxMVGVlpf70pz%2BpT58%2BOnz4sB577DFXDwcAAMByLr8PVkREhN58801t3rxZX375pWprazVixAjdeuutat68uauHAwAAYDm33Mk9KChId9xxhzs2DQAA0OBcHrBGjx79s/18FyEAAPB2Lg9Ybdq0cXpeXV2tb775RgcOHNCYMWNcPRwAAADLecx3ES5ZskTHjx938WgAAACs55bvIryQW2%2B9Ve%2B%2B%2B667hwEAAHDJPCZg/etf/%2BJGowAAwCd4xEXup0%2Bf1ldffaWRI0e6ejgAAACWc/kRrMjISLVp08bpcd1112nWrFm/6kajVVVVGjJkiHbs2GG2FRYWauzYsYqNjdWgQYO0bds2p2U%2B/fRTDRkyRDExMRo9erQKCwud%2BlevXq2kpCTFxcVpypQpKi8v/3WTBQAAjZLLj2A9/fTTlq2rsrJS6enpysvLM9sMw1Bqaqo6duyojRs3auvWrRo/frzeeecdRUZG6ujRo0pNTdWECROUlJSkJUuW6KGHHtJbb70lm82mLVu2KDMzUxkZGWrZsqUmT56sjIwMTZs2zbJxAwAA3%2BbygLVz5856v/b666%2B/aF9%2Bfr7S09NlGIZT%2B/bt21VYWKhXXnlFTZs2VYcOHfTZZ59p48aNmjBhgjZs2KDrrrtO9957r6Qf/qqxV69e%2Bvzzz9WjRw%2BtXbtWY8aMUZ8%2BfSRJM2bM0H333aeJEycqKCjoV8wYAAA0Ni4PWHfffbdsNpskOYWjn7bZbDZ9%2BeWXF13P%2BUD05z//WbGxsWZ7bm6urr32WjVt2tRsi4%2BPV05OjtmfkJBg9gUFBalLly7KyclRQkKC9uzZo/Hjx5v9sbGxOnfunPbv36%2B4uLhLmToAAGgkXB6wnnvuOc2ePVsTJ05U9%2B7dFRAQoD179mjmzJkaNmyYBg0aVK/1XOyCeIfDofDwcKe2li1bmvfY%2Brn%2BkydPqrKy0qnfbrcrJCSEe3QBAIB6c8uNRqdNm6Ybb7zRbOvZs6dmzpypSZMm6YEHHrik9ZeXlysgIMCpLSAgQFVVVb/YX1FRYT6/2PL1UVxcLIfD4dRmtzetE%2BwaG7vdY%2B4K0iD8/f2cfsI9qIPnoBaegTq4h8sDVnFxcZ2vy5Gk5s2bq7S09JLXHxgYqLKyMqe2qqoqNWnSxOz/aViqqqpScHCwAgMDzec/7f93rr/KyspSZmamU1tqaqrS0tLqvQ5fFBrazN1DcIngYK7V8wTUwXNQC89AHVzL5QErNjZWzzzzjP77v/9bzZs3lySVlZUpIyNDN9xwwyWvPyIiQvn5%2BU5tJSUl5tGjiIgIlZSU1Onv3LmzQkJCFBgYqJKSEnXo0EHSD9%2BVWFZWprCwsHqPISUlRcnJyU5tdntTlZae%2BTVT8hm%2BPn9/fz8FBwfp5Mly1dTUuns4jRZ18BzUwjM09jq465d7lwesv/zlLxo9erRuvPFGtWvXToZh6NChQwoLC9PatWsvef0xMTFasWKFKioqzKNW2dnZio%2BPN/uzs7PN15eXl2vfvn0aP368/Pz8FB0drezsbPXo0UOSlJOTI7vdrqioqHqPITw8vM7pQIfjlKqrG98H%2B8cay/xramobzVw9GXXwHNTCM1AH13J5wOrQoYPeeecdbd68WQUFBZKku%2B66S4MHD7bkNgjdu3dX69atNXnyZD300EN6//33tXv3bvNLpocPH65Vq1ZpxYoV6tOnj5YsWaK2bduagWrkyJGaNm2aOnbsqPDwcE2fPl133nknt2gAAAD15vKAJUlXXHGF7rjjDh0%2BfFhXXnmlJOmyyy6zZN3%2B/v5aunSppk6dqttuu02//e1vtWTJEkVGRkqS2rZtq2effVZPPfWUlixZori4OC1ZssS8TcTgwYN15MgRTZs2TVVVVerfv78mTpxoydgAAEDjYDN%2BeqfOBmYYhubPn69169bp3Llz2rJlixYsWKCgoCBNnz7dsqDlaRyOUw2y3oELP2mQ9TaEdx/u5e4hNCi73U%2Bhoc1UWnqGw/BuRB08B7XwDI29DmFhl7tluy7/m81169Zp06ZN%2Butf/2reDqFv377aunVrnb%2B8AwAA8EYuD1hZWVmaNm2abrvtNvO03KBBgzR79my9/fbbrh4OAACA5VwesA4fPqzOnTvXaY%2BKiqpzc04AAABv5PKA1aZNG%2B3Zs6dO%2B0cffWRe8A4AAODNXP5XhPfdd59mzJghh8MhwzD02WefKSsrS%2BvWrdPjjz/u6uEAAABYzuUBa/jw4aqurtayZctUUVGhadOmqUWLFnr44Yc1YsQIVw8HAADAci4PWJs3b9aAAQOUkpKiEydOyDAMtWzZ0tXDAAAAaDAuvwZr5syZ5sXsLVq0IFwBAACf4/KA1a5dOx04cMDVmwUAAHAZl58ijIqK0qOPPqqVK1eqXbt2CgwMdOo//52BAAAA3srlAevrr79WfHy8JHHfKwAA4JNcErDmzp2r8ePHq2nTplq3bp0rNgkAAOA2LrkG68UXX1R5eblT24MPPqji4mJXbB4AAMClXBKwDMOo07Zz505VVla6YvMAAAAu5fK/IgQAAPB1BCwAAACLuSxg2Ww2V20KAADArVx2m4bZs2c73fPq3LlzysjIULNmzZxex32wAACAt3NJwLr%2B%2Buvr3PMqLi5OpaWlKi0tdcUQAAAAXMYlAYt7XwEAgMaEi9wBAAAsRsACAACwGAELAAD33IH1AAAZb0lEQVTAYgQsAAAAixGwAAAALOaTAev1119Xp06d6jyioqIkSX/605/q9L3//vvm8qtXr1ZSUpLi4uI0ZcqUOl9UDQAA8HNcdqNRVxo0aJCSkpLM59XV1RozZox69%2B4tSSooKFBGRoZuuOEG8zVXXHGFJGnLli3KzMxURkaGWrZsqcmTJysjI0PTpk1z6RwAAID38skjWE2aNFFYWJj5eOutt2QYhh599FFVVVXp8OHDio6OdnpNQECAJGnt2rUaM2aM%2BvTpo65du2rGjBnauHEjR7EAAEC9%2BWTA%2BrGysjI9//zzSk9PV0BAgA4ePCibzaYrr7yyzmtramq0Z88eJSQkmG2xsbE6d%2B6c9u/f78phAwAAL%2BbzAevll19WeHi4BgwYIEk6ePCgmjdvrkmTJikxMVG33367PvzwQ0nSyZMnVVlZqfDwcHN5u92ukJAQHT9%2B3C3jBwAA3scnr8E6zzAMbdiwQffff7/ZdvDgQVVUVCgxMVEPPvig3nvvPf3pT39SVlaWWrVqJUnm6cLzAgICVFVVVe/tFhcX1/nuRbu9qVNwa4zsdt/O8/7%2Bfk4/4R7UwXNQC89AHdzDpwPWnj17VFRUpMGDB5ttDz30kO6%2B%2B27zovaoqCh98cUXevXVV/XnP/9ZkuqEqaqqKgUFBdV7u1lZWcrMzHRqS01NVVpa2q%2Bdik8IDW3m7iG4RHBw/T8raDjUwXNQC89AHVzLpwPWxx9/rISEBDNMSZKfn5/Tc0lq37698vPzFRISosDAQJWUlKhDhw6SfvgLxLKyMoWFhdV7uykpKUpOTnZqs9ubqrT0zCXMxvv5%2Bvz9/f0UHBykkyfLVVNT6%2B7hNFrUwXNQC8/Q2Ovgrl/ufTpg7d69W926dXNqe/zxx2Wz2TRnzhyzbf/%2B/erYsaP8/PwUHR2t7Oxs9ejRQ5KUk5Mju91u3kOrPsLDw%2BucDnQ4Tqm6uvF9sH%2Bsscy/pqa20czVk1EHz0EtPAN1cC2fPiGbl5enq6%2B%2B2qktOTlZb7/9tt5880198803yszMVHZ2tkaNGiVJGjlypFatWqWtW7dq9%2B7dmj59uu68885/6xQhAABo3Hz6CFZJSYmCg4Od2vr376%2B//vWvWrZsmY4ePaprrrlGK1euVNu2bSVJgwcP1pEjRzRt2jRVVVWpf//%2BmjhxojuGDwAAvJTNMAzD3YNoDByOUw2y3oELP2mQ9TaEdx/u5e4hNCi73U%2Bhoc1UWnqGw/BuRB08B7XwDI29DmFhl7tluz59ihAAAMAdCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFfDZgvffee%2BrUqZPTIy0tTZK0b98%2B3XHHHYqJidHw4cO1d%2B9ep2U3b96svn37KiYmRqmpqTpx4oQ7pgAAALyUzwas/Px89enTR9u2bTMfs2fP1tmzZ/Xggw8qISFBr7/%2BuuLi4jRu3DidPXtWkrR7925NnTpV48ePV1ZWlk6ePKnJkye7eTYAAMCb%2BGzAKigoUMeOHRUWFmY%2BgoOD9c477ygwMFCTJk1Shw4dNHXqVDVr1kz/%2BMc/JEnr16/XwIEDNXToUEVFRWnu3Ln68MMPVVhY6OYZAQAAb%2BHTAatdu3Z12nNzcxUfHy%2BbzSZJstls6tatm3Jycsz%2BhIQE8/WtW7dWZGSkcnNzXTJuAADg/XwyYBmGoa%2B//lrbtm3TzTffrL59%2B2revHmqqqqSw%2BFQeHi40%2Btbtmyp48ePS5KKi4t/th8AAOCX2N09gIZw9OhRlZeXKyAgQAsXLtThw4c1e/ZsVVRUmO0/FhAQoKqqKklSRUXFz/bXR3FxsRwOh1Ob3d60TnBrbOx2n8zzJn9/P6efcA/q4DmohWegDu7hkwGrTZs22rFjh6644grZbDZ17txZtbW1mjhxorp3714nLFVVValJkyaSpMDAwAv2BwUF1Xv7WVlZyszMdGpLTU01/4qxsQoNbebuIbhEcHD9PytoONTBc1ALz0AdXMsnA5YkhYSEOD3v0KGDKisrFRYWppKSEqe%2BkpIS8%2BhSRETEBfvDwsLqve2UlBQlJyc7tdntTVVaeubfmYLP8fX5%2B/v7KTg4SCdPlqumptbdw2m0qIPnoBaeobHXwV2/3PtkwPr444/16KOP6oMPPjCPPH355ZcKCQlRfHy8nn/%2BeRmGIZvNJsMwtGvXLv3xj3%2BUJMXExCg7O1u33XabJOnYsWM6duyYYmJi6r398PDwOqcDHY5Tqq5ufB/sH2ss86%2BpqW00c/Vk1MFzUAvPQB1cyydPyMbFxSkwMFB/%2BctfdPDgQX344YeaO3eu7r//fg0YMEAnT57Uk08%2Bqfz8fD355JMqLy/XwIEDJUkjRozQpk2btGHDBu3fv1%2BTJk1S7969deWVV7p5VgAAwFv4ZMBq3ry5Vq1apRMnTmj48OGaOnWqUlJSdP/996t58%2BZavny5eZQqNzdXK1asUNOmTSX9EM5mzpypJUuWaMSIEbriiis0Z84cN88IAAB4E5thGIa7B9EYOBynGmS9Axd%2B0iDrbQjvPtzL3UNoUHa7n0JDm6m09AyH4d2IOngOauEZGnsdwsIud8t2ffIIFgAAgDsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIv5bMAqKipSWlqaunfvrqSkJM2ZM0eVlZWSpNmzZ6tTp05Oj/Xr15vLbt68WX379lVMTIxSU1N14sQJd00DAAB4Ibu7B9AQDMNQWlqagoOD9dJLL%2Bn777/XlClT5Ofnp8cee0wFBQVKT0/XsGHDzGWaN28uSdq9e7emTp2qGTNmKCoqSk8%2B%2BaQmT56s5cuXu2s6AADAy/jkEayDBw8qJydHc%2BbM0TXXXKOEhASlpaVp8%2BbNkqSCggJde%2B21CgsLMx9BQUGSpPXr12vgwIEaOnSooqKiNHfuXH344YcqLCx055QAAIAX8cmAFRYWppUrV6pVq1ZO7adPn9bp06dVVFSkdu3aXXDZ3NxcJSQkmM9bt26tyMhI5ebmNuSQAQCAD/HJgBUcHKykpCTzeW1trdavX6%2BePXuqoKBANptNzz33nG688Ub94Q9/0BtvvGG%2Btri4WOHh4U7ra9mypY4fP%2B6y8QMAAO/mk9dg/VRGRob27dun1157TV988YVsNpvat2%2BvUaNGaefOnXriiSfUvHlz9evXTxUVFQoICHBaPiAgQFVVVfXeXnFxsRwOh1Ob3d60TnBrbOx2n8zzJn9/P6efcA/q4DmohWegDu7h8wErIyNDa9as0YIFC9SxY0ddc8016tOnj0JCQiRJUVFROnTokF5%2B%2BWX169dPgYGBdcJUVVWVeY1WfWRlZSkzM9OpLTU1VWlpaZc%2BIS8WGtrM3UNwieDg%2Bn9W0HCog%2BegFp6BOriWTwesWbNm6eWXX1ZGRoZuvvlmSZLNZjPD1Xnt27fX9u3bJUkREREqKSlx6i8pKVFYWFi9t5uSkqLk5GSnNru9qUpLz/yaafgMX5%2B/v7%2BfgoODdPJkuWpqat09nEaLOngOauEZGnsd3PXLvc8GrMzMTL3yyit65plnNGDAALN90aJF%2Bte//qXVq1ebbfv371f79u0lSTExMcrOztZtt90mSTp27JiOHTummJiYem87PDy8zulAh%2BOUqqsb3wf7xxrL/GtqahvNXD0ZdfAc1MIzUAfX8skTsgUFBVq6dKkeeOABxcfHy%2BFwmI8%2Bffpo586dWrVqlb799lv97W9/05tvvql7771XkjRixAht2rRJGzZs0P79%2BzVp0iT17t1bV155pZtnBQAAvIVPHsH65z//qZqaGi1btkzLli1z6vvqq6%2B0aNEiLV68WIsWLVKbNm00f/58xcXFSZLi4uI0c%2BZMLV68WN9//7169eqlWbNmuWMaAADAS9kMwzDcPYjGwOE41SDrHbjwkwZZb0N49%2BFe7h5Cg7Lb/RQa2kylpWc4DO9G1MFzUAvP0NjrEBZ2uVu265OnCAEAANyJgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAtYFVFZWasqUKUpISFBiYqJeeOEFdw8JAAB4Ebu7B%2BCJ5s6dq71792rNmjU6evSoHnvsMUVGRmrAgAHuHhoAAPACBKyfOHv2rDZs2KDnn39eXbp0UZcuXZSXl6eXXnqJgAUAAOqFU4Q/sX//flVXVysuLs5si4%2BPV25urmpra904MgAA4C04gvUTDodDoaGhCggIMNtatWqlyspKlZWVqUWLFr%2B4juLiYjkcDqc2u72pwsPDLR%2BvN7HbfTvP%2B/v7Of2Ee1AHz0EtPAN1cA8C1k%2BUl5c7hStJ5vOqqqp6rSMrK0uZmZlObePHj9eECROsGeSP/N%2BTA1RcXKysrCylpKQ0%2BhDnTsXFxVqzZiV1cDPq4DmohWegDu5BnP2JwMDAOkHq/PMmTZrUax0pKSl6/fXXnR4pKSmWj/U8h8OhzMzMOkfN4FrUwTNQB89BLTwDdXAPjmD9REREhEpLS1VdXS27/Ye3x%2BFwqEmTJgoODq7XOsLDw/ktAQCARowjWD/RuXNn2e125eTkmG3Z2dmKjo6Wnx9vFwAA%2BGUkhp8ICgrS0KFDNX36dO3evVtbt27VCy%2B8oNGjR7t7aAAAwEv4T58%2Bfbq7B%2BFpevbsqX379mn%2B/Pn67LPP9Mc//lHDhw9397B%2BVrNmzdS9e3c1a9bM3UNp1KiDZ6AOnoNaeAbq4Ho2wzAMdw8CAADAl3CKEAAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAHLy1VWVmrKlClKSEhQYmKiXnjhBXcPySe899576tSpk9MjLS1NkrRv3z7dcccdiomJ0fDhw7V3716nZTdv3qy%2BffsqJiZGqampOnHihNlnGIbmzZunnj17qnv37po7d65qa2tdOjdvUFVVpSFDhmjHjh1mW2FhocaOHavY2FgNGjRI27Ztc1rm008/1ZAhQxQTE6PRo0ersLDQqX/16tVKSkpSXFycpkyZovLycrOP/ejiLlSL2bNn19k/1q9fb/Zfyj5QWlqqCRMmKC4uTsnJydq0aZNrJuqhioqKlJaWpu7duyspKUlz5sxRZWWlJPYJj2fAq82cOdO45ZZbjL179xr/8z//Y8TFxRnvvvuuu4fl9ZYuXWqMGzfOKC4uNh/ff/%2B9cebMGaNXr17G008/beTn5xuzZs0yfve73xlnzpwxDMMwcnNzja5duxpvvPGG8eWXXxqjRo0yHnzwQXO9q1atMm666SZj586dxmeffWYkJiYaK1eudNc0PVJFRYWRmppqdOzY0di%2BfbthGIZRW1tr3HLLLUZ6erqRn59vPPfcc0ZMTIxx5MgRwzAM48iRI0ZsbKyxatUq48CBA8Z//dd/GUOGDDFqa2sNwzCMf/zjH0Z8fLzxv//7v0Zubq4xaNAgY8aMGeY22Y8u7EK1MAzDGDt2rLF8%2BXKn/ePs2bOGYVz6PjBu3DhjzJgxxldffWW8%2BuqrxnXXXWfk5ua6btIepLa21rjzzjuN%2B%2B%2B/3zhw4ICxc%2BdOo1%2B/fsbTTz/NPuEFCFhe7MyZM0Z0dLTTP3xLliwxRo0a5cZR%2BYb09HRj/vz5ddo3bNhgJCcnm/9I1dbWGv369TM2btxoGIZhTJw40XjsscfM1x89etTo1KmT8e233xqGYRg33XST%2BVrDMIw333zT6NOnT0NOxavk5eUZf/jDH4xbbrnF6X/qn376qREbG2sGWcMwjDFjxhiLFy82DMMwFi5c6PS5P3v2rBEXF2cuP3LkSPO1hmEYO3fuNLp27WqcPXuW/egiLlYLwzCMpKQk4%2BOPP77gcpeyD3zzzTdGx44djcLCQrN/ypQpTutrTPLz842OHTsaDofDbHv77beNxMRE9gkvwClCL7Z//35VV1crLi7ObIuPj1dubi6nnS5RQUGB2rVrV6c9NzdX8fHxstlskiSbzaZu3bopJyfH7E9ISDBf37p1a0VGRio3N1dFRUU6duyYrr/%2BerM/Pj5eR44cUXFxccNOyEt8/vnn6tGjh7Kyspzac3Nzde2116pp06ZmW3x8/EXf96CgIHXp0kU5OTmqqanRnj17nPpjY2N17tw57d%2B/n/3oIi5Wi9OnT6uoqOiC%2B4d0aftAbm6uWrdurbZt2zr1/%2Btf/7J2cl4iLCxMK1euVKtWrZzaT58%2BzT7hBezuHgB%2BPYfDodDQUAUEBJhtrVq1UmVlpcrKytSiRQs3js57GYahr7/%2BWtu2bdPy5ctVU1OjAQMGKC0tTQ6HQ1dffbXT61u2bKm8vDxJUnFxscLDw%2Bv0Hz9%2BXA6HQ5Kc%2Bs//w3n8%2BPE6yzVGI0eOvGC7w%2BG46Pv6S/0nT55UZWWlU7/dbldISIiOHz8uPz8/9qMLuFgtCgoKZLPZ9Nxzz%2Bmjjz5SSEiI7rnnHg0bNkzSpe0DF6tjUVGRZfPyJsHBwUpKSjKf19bWav369erZsyf7hBcgYHmx8vJypx1Akvm8qqrKHUPyCUePHjXf24ULF%2Brw4cOaPXu2KioqLvqen3%2B/KyoqLtpfUVFhPv9xn0S9fskvve8/13%2Bh9/3H/YZhsB/9Gw4ePCibzab27dtr1KhR2rlzp5544gk1b95c/fr1u6R94Jfq3NhlZGRo3759eu2117R69Wr2CQ9HwPJigYGBdT7s5583adLEHUPyCW3atNGOHTt0xRVXyGazqXPnzqqtrdXEiRPVvXv3C77n59/vi9UkKCjI6R%2BowMBA87%2BlHw7f4%2BICAwNVVlbm1Faf9z04OLjOe/3j/qCgINXU1LAf/RuGDh2qPn36KCQkRJIUFRWlQ4cO6eWXX1a/fv0uaR%2B42LLU4YdwtWbNGi1YsEAdO3Zkn/ACXIPlxSIiIlRaWqrq6mqzzeFwqEmTJgoODnbjyLxfSEiIeZ2VJHXo0EGVlZUKCwtTSUmJ02tLSkrMQ%2B0REREX7A8LC1NERIQkmadJfvzfYWFhDTIPX3Gx97U%2B73tISIgCAwOd%2Bqurq1VWVmbWhf2o/mw2mxmuzmvfvr15Gu9S9oGfW7YxmzVrll588UVlZGTo5ptvlsQ%2B4Q0IWF6sc%2BfOstvt5kWNkpSdna3o6Gj5%2BVHaX%2Bvjjz9Wjx49nO4J8%2BWXXyokJMS84NYwDEk/XK%2B1a9cuxcTESJJiYmKUnZ1tLnfs2DEdO3ZMMTExioiIUGRkpFN/dna2IiMjuf7qF8TExOiLL74wT21IP7x3F3vfy8vLtW/fPsXExMjPz0/R0dFO/Tk5ObLb7YqKimI/%2BjctWrRIY8eOdWrbv3%2B/2rdvL%2BnS9oHY2FgdOXLEvI7ofH9sbGzDTsqDZWZm6pVXXtEzzzyjwYMHm%2B3sE17ArX/DiEv2xBNPGIMHDzZyc3ON9957z%2BjWrZuxZcsWdw/Lq506dcpISkoyHnnkEaOgoMD44IMPjMTERGPFihXGqVOnjJ49exqzZs0y8vLyjFmzZhm9evUy/1R6165dRpcuXYxXX33VvAfQuHHjzHUvX77cSExMNLZv325s377dSExMNF544QV3TdWj/fjWANXV1cagQYOMhx9%2B2Dhw4ICxfPlyIzY21rznT2FhoREdHW0sX77cvOfPLbfcYt5OY/PmzUa3bt2M9957z8jNzTUGDx5szJo1y9wW%2B9HP%2B3EtcnNzjWuvvdZYuXKl8c033xgvvfSScd111xm7du0yDOPS94F7773XGDVqlPHll18ar776qhEdHd1o74OVn59vdO7c2ViwYIHTPceKi4vZJ7wAAcvLnT171pg0aZIRGxtrJCYmGi%2B%2B%2BKK7h%2BQTDhw4YIwdO9aIjY01evXqZTz77LPmP0y5ubnG0KFDjejoaOP22283vvjiC6dlN27caNx0001GbGyskZqaapw4ccLsq66uNp566ikjISHB6NGjh5GRkWGuF85%2Beu%2BlQ4cOGXfddZdx3XXXGYMHDzY%2B%2BeQTp9d/8MEHRv/%2B/Y2uXbsaY8aMMe%2B7dN7y5cuNG264wYiPjzcmT55sVFRUmH3sRz/vp7V47733jFtuucWIjo42BgwYUOd/vJeyD5SUlBjjxo0zoqOjjeTkZOPtt99u%2BAl6qOXLlxsdO3a84MMw2Cc8nc0w/v%2B5DgAAAFiCk6kAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAW%2B390ZwgBwqb3TgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common988701394962913140”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>150.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1707)</td> <td class=”number”>1918</td> <td class=”number”>59.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1153</td> <td class=”number”>35.9%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme988701394962913140”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-97.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.34883720930233</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-95.08196721311475</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-94.9685534591195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1600.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1988.888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2470.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3066.666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22109.090909090908</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_VEG_FARMS_07_12”>PCH_VEG_FARMS_07_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>836</td>

</tr> <tr>

<th>Unique (%)</th> <td>26.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>13.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>420</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>22.921</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>1800</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4602455764671563893”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4602455764671563893,#minihistogram-4602455764671563893”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4602455764671563893”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4602455764671563893”

aria-controls=”quantiles-4602455764671563893” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4602455764671563893” aria-controls=”histogram-4602455764671563893”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4602455764671563893” aria-controls=”common-4602455764671563893”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4602455764671563893” aria-controls=”extreme-4602455764671563893”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4602455764671563893”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-80</td>

</tr> <tr>

<th>Q1</th> <td>-31.25</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>40</td>

</tr> <tr>

<th>95-th percentile</th> <td>200</td>

</tr> <tr>

<th>Maximum</th> <td>1800</td>

</tr> <tr>

<th>Range</th> <td>1900</td>

</tr> <tr>

<th>Interquartile range</th> <td>71.25</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>112.41</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9041</td>

</tr> <tr>

<th>Kurtosis</th> <td>40.469</td>

</tr> <tr>

<th>Mean</th> <td>22.921</td>

</tr> <tr>

<th>MAD</th> <td>65.45</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.6547</td>

</tr> <tr>

<th>Sum</th> <td>63951</td>

</tr> <tr>

<th>Variance</th> <td>12636</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4602455764671563893”>

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oVjPicnR3379lVMTIxSU1N14MABx5wxRnPmzFHv3r3Vs2dPzZ49W7W1tS7NBAAAPJvN3Qu4EJWVlRo/frx27tzpNF5QUKDx48dryJAhjrHmzZtLkjZv3qypU6fqmWeeUVRUlGbNmqXJkydr8eLFkqRXX31VOTk5yszMVHV1tSZMmKBWrVpp7NixrgsGAAA8msfuwdq1a5duv/12/fjjj3XmCgoKdOWVVyosLMzxERwcLElasWKFbrrpJg0ePFhRUVGaPXu2/vWvf6mwsFCStHz5cqWlpSkhIUG9e/fW448/rjfeeMOl2QAAgGdzecEaPny43nzzTR0%2BfPiCHmfDhg3q1auXsrOzncaPHDmi/fv3q0OHDvVul5%2Bfr4SEBMftNm3aKDIyUvn5%2Bdq/f7%2BKiop01VVXOebj4%2BO1d%2B9elZSUXNB6AQCA73D5IcLevXtr0aJFSk9P1/XXX6%2BhQ4cqMTFRfn5%2B5/Q4I0eOrHe8oKBAfn5%2BWrRokT799FOFhobqnnvucRwuLCkpUXh4uNM2rVq1UnFxsex2uyQ5zbdu3VqSVFxcXGe7MykpKXE81kk2W9MGb%2B%2BtbLbGtcM0IMDf6U9v5Qs5yeg9fCGnL2SUfCfnmbi8YI0fP16PPfaYvvjiC7333nt65JFHFBISosGDB2vw4MG67LLLLujxd%2B/eLT8/P3Xs2FF33XWXNm7cqGnTpql58%2Bbq16%2BfKioqFBgY6LRNYGCgqqqqVFFR4bh96px04mT6hsrOzlZmZqbTWGpqqtLS0s43lldo0aKZu5dQr5CQYHcvwSV8IScZvYcv5PSFjJLv5DydW05y9/PzU2JiohITE1VeXq7XX39dCxcuVFZWlnr06KHRo0frhhtuOK/HHjx4sFJSUhQaGipJioqK0p49e7Ry5Ur169dPQUFBdcpSVVWVgoODncpUUFCQ4%2B%2BSHOdwNcSIESPUp08fpzGbrakOHjx6Xpm8RWPLHxDgr5CQYB06VK6aGu/9SVFfyElG7%2BELOX0ho9R4crrrP/du%2BynCkpIS/fWvf9Vf//pX7dixQz169NCQIUNUXFysJ598Uhs3btTUqVPP%2BXH9/Pwc5eqkjh07av369ZKkiIgIlZaWOs2XlpYqLCxMERERkiS73a527do5/i5JYWFhDV5DeHh4ncOBdvthVVd77xdSQzTW/DU1tY12bVbyhZxk9B6%2BkNMXMkq%2Bk/N0Li9Ya9as0Zo1a/TVV1%2BpZcuWGjx4sF588UWnk9LbtGmjWbNmnVfBmj9/vr7%2B%2Bmu99tprjrHt27erY8eOkqSYmBjl5uZq6NChkqSioiIVFRUpJiZGERERioyMVG5urqNg5ebmKjIy0ufPnwIAAA3n8oI1depUpaSkaMGCBbrmmmvk71/35LeT50%2Bdj5SUFGVlZWnp0qXq16%2Bf1q1bp/fee0/Lly%2BXJN1xxx0aNWqUYmNjFR0drVmzZum6665T%2B/btHfNz5szRb3/7W0nS3Llzde%2B9955nWgAA4ItcXrA%2B/fRTtWjRQmVlZY5ytXnzZnXt2lUBAQGSpB49eqhHjx7n9fjdu3fX/Pnz9eKLL2r%2B/Plq27at5s6dq7i4OElSXFycpk%2BfrhdffFG//PKLEhMTNWPGDMf2Y8eO1c8//6xx48YpICBAt912m8aMGXNhoQEAgE/xM8YYVz7hjz/%2BqPvvv1/XX3%2B9Jk6cKOnEpRtat26tP//5z2rTpo0rl%2BMydvuFXffrTG6a9/lFedyL4cNHE929BCc2m79atGimgwePevX5Ab6Qk4zewxdy%2BkJGqfHkDAv7jVue1%2BUXp3j22Wf1u9/9Tvfcc49j7IMPPlCbNm2Unp7u6uUAAABYzuUF69///reeeOIJp5/Ka9mypSZOnOj4ST8AAABP5vKCZbPZdOjQoTrj5eXlcvHRSgAAgIvC5QXrmmuu0cyZM51%2BSXNhYaHS09OVnJzs6uUAAABYzuU/RThp0iTdc889uvHGGxUSEiJJOnTokLp27arJkye7ejkAAACWc3nBatWqld5991198cUX2rlzp2w2m37/%2B9/rD3/4wzn/wmcAAIDGyC2/KicgIEDJyckcEgQAAF7J5QXLbrdr3rx52rRpk44fP17nxPa///3vrl4SAACApVxesKZNm6atW7dq4MCB%2Bs1v3HPxLwAAgIvJ5QVr/fr1WrJkiRISElz91AAAAC7h8ss0NG3aVK1atXL10wIAALiMywvWrbfeqiVLlqimpsbVTw0AAOASLj9EWFZWppycHP3zn/9U%2B/btFRgY6DS/fPlyVy8JAADAUm65TMOgQYPc8bQAAAAu4fKClZ6e7uqnBAAAcCmXn4MlSSUlJcrMzNT48eP1888/629/%2B5t2797tjqUAAABYzuUF64cfftDNN9%2Bsd999Vx999JGOHTumDz74QMOGDVN%2Bfr6rlwMAAGA5lxes5557Tn379tXHH3%2BsSy65RJL0/PPPq0%2BfPpozZ46rlwMAAGA5lxesTZs26Z577nH6xc42m00PP/ywtm3b5urlAAAAWM7lBau2tla1tbV1xo8ePaqAgABXLwcAAMByLi9YSUlJWrx4sVPJKisrU0ZGhnr37u3q5QAAAFjO5QXriSee0NatW5WUlKTKyko99NBDSklJ0U8//aRJkya5ejkAAACWc/l1sCIiIvTee%2B8pJydH3377rWpra3XHHXfo1ltvVfPmzV29HAAAAMu55UruwcHBGj58uDueGgAA4KJzecG6%2B%2B67zzrP7yIEAACezuUFq23btk63q6ur9cMPP2jHjh0aPXq0q5cDAABguUbzuwgXLFig4uJiF68GAADAem75XYT1ufXWW/Xhhx%2B6exkAAAAXrNEUrK%2B//poLjQIAAK/QKE5yP3LkiL777juNHDnS1csBAACwnMsLVmRkpNPvIZSkSy65RHfddZduueUWVy8HAADAci4vWM8995yrnxIAAMClXF6wNm7c2OD7XnXVVRdxJQAAABeHywvWqFGjHIcIjTGO8dPH/Pz89O2337p6eQAAABfM5QVr0aJFmjlzpiZMmKCePXsqMDBQW7Zs0fTp0zVkyBANGDDA1UsCAACwlMsv05Cenq6nnnpKN954o1q0aKFmzZqpd%2B/emj59ulauXKm2bds6PgAAADyRywtWSUlJveWpefPmOnjwoKuXAwAAYDmXF6zY2Fg9//zzOnLkiGOsrKxMGRkZ%2BsMf/uDq5QAAAFjO5edgPfnkk7r77rt1zTXXqEOHDjLGaM%2BePQoLC9Py5ctdvRwAAADLubxgderUSR988IFycnJUUFAgSbrzzjs1cOBABQcHu3o5AAAAlnN5wZKkSy%2B9VMOHD9dPP/2k9u3bSzpxNXcAAABv4PJzsIwxmjNnjq666ioNGjRIxcXFmjRpkqZOnarjx4%2B7ejkAAACWc3nBev3117VmzRr96U9/UmBgoCSpb9%2B%2B%2Bvjjj5WZmenq5QAAAFjO5QUrOztbTz31lIYOHeq4evuAAQM0c%2BZMvf/%2B%2B65eDgAAgOVcXrB%2B%2BukndenSpc54VFSU7Ha7q5cDAABgOZcXrLZt22rLli11xj/99FPHCe8AAACezOU/RTh27Fg988wzstvtMsboyy%2B/VHZ2tl5//XU98cQTrl4OAACA5VxesIYNG6bq6mq9/PLLqqio0FNPPaWWLVvq0Ucf1R133OHq5QAAAFjO5QUrJydH/fv314gRI3TgwAEZY9SqVStXLwMAAOCicfk5WNOnT3eczN6yZUvKFQAA8DouL1gdOnTQjh07XP20AAAALuPyQ4RRUVF6/PHHtWTJEnXo0EFBQUFO8%2Bnp6ef0eFVVVRo6dKimTZumXr16SZIKCws1bdo05eXlKTIyUlOmTFFSUpJjmy%2B%2B%2BELPPvusCgsLFRMTo1mzZjn9BONrr72mpUuX6siRI7rppps0bdo0fk8iAABoMJfvwfr%2B%2B%2B8VHx%2BvZs2ayW6366effnL6OBeVlZV67LHHtHPnTseYMUapqalq3bq1Vq9erVtvvVXjxo3Tvn37JEn79u1Tamqqhg4dqrffflstW7bUww8/LGOMJOmjjz5SZmampk%2BfrmXLlik/P18ZGRnW/QMAAACv55I9WLNnz9a4cePUtGlTvf7665Y85q5duzR%2B/HhHMTpp/fr1Kiws1JtvvqmmTZuqU6dO%2BvLLL7V69Wo98sgjWrVqlbp166Z7771X0ok9ZomJidqwYYN69eql5cuXa/To0UpJSZEkPfPMMxo7dqwmTJjAXiwAANAgLtmD9eqrr6q8vNxp7IEHHlBJScl5P%2BbJQpSdne00np%2BfryuvvFJNmzZ1jMXHxysvL88xn5CQ4JgLDg5W165dlZeXp5qaGm3ZssVpPjY2VsePH9f27dvPe60AAMC3uGQP1ul7mSRp48aNqqysPO/HHDlyZL3jdrtd4eHhTmOtWrVScXHxr84fOnRIlZWVTvM2m02hoaGO7QEAAH6Ny09yv9jKy8sVGBjoNBYYGKiqqqpfna%2BoqHDcPtP2DVFSUlLn9yrabE3rFDtfY7O5/JS/swoI8Hf601v5Qk4yeg9fyOkLGSXfyXkmXlewgoKCVFZW5jRWVVWlJk2aOOZPL0tVVVUKCQlx/ERjffPncv5Vdna2MjMzncZSU1OVlpbW4MfwRi1aNHP3EuoVEuIb59b5Qk4yeg9fyOkLGSXfyXk6lxUsPz8/lzxPRESEdu3a5TRWWlrq2HsUERGh0tLSOvNdunRRaGiogoKCVFpaqk6dOkmSqqurVVZWprCwsAavYcSIEerTp4/TmM3WVAcPHj2fSF6jseUPCPBXSEiwDh0qV01NrbuXc9H4Qk4yeg9fyOkLGaXGk9Nd/7l3WcGaOXOm0zWvjh8/royMDDVr5hz8XK%2BDdbqYmBhlZWWpoqLCsdcqNzdX8fHxjvnc3FzH/cvLy7Vt2zaNGzdO/v7%2Bio6OVm5uruOaWnl5ebLZbIqKimrwGsLDw%2BscDrTbD6u62nu/kBqiseavqalttGuzki/kJKP38IWcvpBR8p2cp3NJwbrqqqvqnJMUFxengwcP6uDBg5Y%2BV8%2BePdWmTRtNnjxZDz/8sD755BNt3rzZUdyGDRumpUuXKisrSykpKVqwYIHatWvnKFQjR47UU089pc6dOys8PFxPP/20br/9di7RAAAAGswlBcuqa181REBAgBYuXKipU6dq6NCh%2Bt3vfqcFCxYoMjJSktSuXTu99NJLevbZZ7VgwQLFxcVpwYIFjkOYAwcO1N69e/XUU0%2BpqqpKN9xwgyZMmOCy9QMAAM/nZ%2Bq7hgIsZ7cfviiPe9O8zy/K414MHz6a6O4lOLHZ/NWiRTMdPHjUq3df%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%2BQkJC9MEHHygoKEgTJ05Up06dNHXqVDVr1kx/%2B9vfJEkrVqzQTTfdpMGDBysqKkqzZ8/Wv/71LxUWFro5EQAA8BReXbA6dOhQZzw/P1/x8fHy8/OTJPn5%2BalHjx7Ky8tzzCckJDju36ZNG0VGRio/P98l6wYAAJ7P5u4FXAzGGH3//fdat26dFi9erJqaGvXv319paWmy2%2B36/e9/73T/Vq1aaefOnZKkkpIShYeH15kvLi5u8POXlJTIbrc7jdlsTes8rq%2Bx2RpXnw8I8Hf601v5Qk4yeg9fyOkLGSXfyXkmXlmw9u0FbZKrAAAUVUlEQVTbp/LycgUGBmrevHn66aefNHPmTFVUVDjGTxUYGKiqqipJUkVFxVnnGyI7O1uZmZlOY6mpqY6T7H1VixbN3L2EeoWEBLt7CS7hCznJ6D18IacvZJR8J%2BfpvLJgtW3bVl999ZUuvfRS%2Bfn5qUuXLqqtrdWECRPUs2fPOmWpqqpKTZo0kSQFBQXVOx8c3PBPkBEjRqhPnz5OYzZbUx08ePQ8E3mHxpY/IMBfISHBOnSoXDU1te5ezkXjCznJ6D18IacvZJQaT053/efeKwuWJIWGhjrd7tSpkyorKxUWFqbS0lKnudLSUsfhu4iIiHrnw8LCGvzc4eHhdQ4H2u2HVV3tvV9IDdFY89fU1DbatVnJF3KS0Xv4Qk5fyCj5Ts7TeeWB0c8%2B%2B0y9evVSeXm5Y%2Bzbb79VaGio4uPj9fXXX8sYI%2BnE%2BVqbNm1STEyMJCkmJka5ubmO7YqKilRUVOSYBwAA%2BDVeWbDi4uIUFBSkJ598Urt379a//vUvzZ49W/fdd5/69%2B%2BvQ4cOadasWdq1a5dmzZql8vJy3XTTTZKkO%2B64Q2vWrNGqVau0fft2TZw4Udddd53at2/v5lQAAMBTeGXBat68uZYuXaoDBw5o2LBhmjp1qkaMGKH77rtPzZs31%2BLFi5Wbm6uhQ4cqPz9fWVlZatq0qaQT5Wz69OlasGCB7rjjDl166aVKT093cyIAAOBJvPYcrMsvv1yvvvpqvXPdu3fXu%2B%2B%2Be8Zthw4dqqFDh16spQEAAC/nlXuwAAAA3ImCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxChYAAIDFKFgAAAAWo2ABAABYjIIFAABgMQoWAACAxShYAAAAFqNgAQAAWIyCBQAAYDEKFgAAgMUoWAAAABajYAEAAFiMggUAAGAxm7sXAN9x07zP3b2Ec/Lho4nuXgIAwEOxBwsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsBgFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALGZz9wKAxuqmeZ%2B7ewnn5MNHE929BADA/2EPFgAAgMUoWPWorKzUlClTlJCQoKSkJL3yyivuXhIAAPAgHCKsx%2BzZs7V161YtW7ZM%2B/bt06RJkxQZGan%2B/fu7e2kAAMADULBOc%2BzYMa1atUp//vOf1bVrV3Xt2lU7d%2B7UG2%2B8QcECAAANQsE6zfbt21VdXa24uDjHWHx8vBYtWqTa2lr5%2B3NUFY2TJ52Uzwn5ALwdBes0drtdLVq0UGBgoGOsdevWqqysVFlZmVq2bPmrj1FSUiK73e40ZrM1VXh4uOXrBTyRJ5VBSVr7eLK7l6CAAH%2BnP72VL%2BT0hYyS7%2BQ8EwrWacrLy53KlSTH7aqqqgY9RnZ2tjIzM53Gxo0bp0ceecSaRZ7i37MuzmHLkpISZWdna8SIEV5bDH0ho%2BQbOX0l47JlS7w6o%2BQbOX0ho%2BQ7Oc/EN2vlWQQFBdUpUidvN2nSpEGPMWLECL3zzjtOHyNGjLB8rReT3W5XZmZmnT1x3sQXMkq%2BkZOM3sMXcvpCRsl3cp4Je7BOExERoYMHD6q6ulo224l/HrvdriZNmigkJKRBjxEeHu6TbR0AAJzAHqzTdOnSRTabTXl5eY6x3NxcRUdHc4I7AABoEBrDaYKDgzV48GA9/fTT2rx5sz7%2B%2BGO98soruvvuu929NAAA4CECnn766afdvYjGpnfv3tq2bZvmzp2rL7/8Uv/5n/%2BpYcOGuXtZLtesWTP17NlTzZo1c/dSLhpfyCj5Rk4yeg9fyOkLGSXfyVkfP2OMcfciAAAAvAmHCAEAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwUEdlZaWmTJmihIQEJSUl6ZVXXnH3ks7Z/v37lZaWpp49eyo5OVnp6emqrKyUJM2cOVNXXHGF08eKFSsc2%2Bbk5Khv376KiYlRamqqDhw44K4Yv2rt2rV1sqSlpUmStm3bpuHDhysmJkbDhg3T1q1bnbb1hJzvvPNOnXxXXHGFoqKiJEkPPfRQnblPPvnEsf1rr72m5ORkxcXFacqUKSovL3dXlDOqqqrSoEGD9NVXXznGCgsLNWbMGMXGxmrAgAFat26d0zZffPGFBg0apJiYGN19990qLCx0mm9suevLmJeXp//4j/9QXFycbrzxRq1atcppm1tuuaXOa7tjxw5JkjFGc%2BbMUe/evdWzZ0/Nnj1btbW1Ls10uvoyXsh7TWPMKNXN%2BcQTT9T7NXrqr5dLSEioM3/06FFJ3vH95owMcJrp06ebm2%2B%2B2WzdutX87//%2Br4mLizMffvihu5fVYLW1teb222839913n9mxY4fZuHGj6devn3nuueeMMcaMGTPGLF682JSUlDg%2Bjh07ZowxJj8/33Tv3t28%2B%2B675ttvvzV33XWXeeCBB9wZ56wWLlxoHnzwQacsv/zyizl69KhJTEw0zz33nNm1a5eZMWOGufrqq83Ro0eNMZ6Ts7y83Cnbvn37TL9%2B/cysWbOMMcb069fPrFmzxuk%2BlZWVxhhj/va3v5n4%2BHjzj3/8w%2BTn55sBAwaYZ555xp1x6qioqDCpqammc%2BfOZv369caYE5%2B/N998sxk/frzZtWuXWbRokYmJiTF79%2B41xhizd%2B9eExsba5YuXWp27Nhh/uu//ssMGjTI1NbWGmMaX%2B76MpaUlJiEhAQzd%2B5c8/3335ucnBwTHR1tPvnkE2OMMdXV1SY6Otps2LDB6bU9fvy4McaYpUuXmmuvvdZs3LjRfPnllyYpKcksWbLEXRHrzWjMhb3XNLaMxtSf89ChQ075vv76a9OtWzezdu1aY4wxxcXFpnPnzubHH390ut/Jz1dP/35zNhQsODl69KiJjo52epNYsGCBueuuu9y4qnOza9cu07lzZ2O32x1j77//vklKSjLGGJOcnGw%2B%2B%2ByzeredMGGCmTRpkuP2vn37zBVXXGF%2B/PHHi7vo8zR%2B/Hgzd%2B7cOuOrVq0yffr0cbyJ1dbWmn79%2BpnVq1cbYzwv50mLFi0yffv2NZWVlaaystJ06dLF7N69u977jhw50rz44ouO2xs3bjTdu3d3fINzt507d5pbbrnF3HzzzU7fsL744gsTGxvrKMPGGDN69GhHlnnz5jl9PR47dszExcU5tm9Muc%2BU8S9/%2BYvp37%2B/032nTZtmHnvsMWOMMXv27DFRUVGmoqKi3se99tprHZ/Lxhjz3nvvmZSUlIuU4uzOlNGYC3uvaUwZjTl7zlPde%2B%2B95vHHH3fc/vzzz01iYmK99/WG7zdnwyFCONm%2Bfbuqq6sVFxfnGIuPj1d%2Bfn6j2D3dEGFhYVqyZIlat27tNH7kyBEdOXJE%2B/fvV4cOHerdNj8/XwkJCY7bbdq0UWRkpPLz8y/mks9bQUFBvVny8/MVHx8vPz8/SZKfn5969OihvLw8x7wn5ZSksrIy/fnPf9b48eMVGBio3bt3y8/PT%2B3bt69z35qaGm3ZssUpY2xsrI4fP67t27e7ctlntGHDBvXq1UvZ2dlO4/n5%2BbryyivVtGlTx1h8fPwZX7vg4GB17dpVeXl5jS73mTKePGx/uiNHjkiSdu3apTZt2igoKKjOffbv36%2BioiJdddVVjrH4%2BHjt3btXJSUlFif4dWfKeCHvNY0to3TmnKf68ssvtXHjRj322GOOsV27dumyyy6r9/7e8P3mbGzuXgAaF7vdrhYtWigwMNAx1rp1a1VWVqqsrEwtW7Z04%2BoaJiQkRMnJyY7btbW1WrFihXr37q2CggL5%2Bflp0aJF%2BvTTTxUaGqp77rlHQ4YMkSSVlJQoPDzc6fFatWql4uJil2ZoCGOMvv/%2Be61bt06LFy9WTU2N%2Bvfvr7S0NNntdv3%2B9793un%2BrVq20c%2BdOSZ6V86SVK1cqPDxc/fv3lyTt3r1bzZs318SJE7Vhwwb99re/1SOPPKJrr71Whw4dUmVlpVNGm82m0NDQRpNx5MiR9Y7b7fazvjZnm29suc%2BUsV27dmrXrp3j9s8//6z/%2BZ//0SOPPCLpxH8cLrnkEj344IPaunWrLrvsMk2cOFHdu3eX3W6XJKeMJ/8zVVxcXOff5mI7U8YLea9pbBmlM%2Bc8VVZWloYMGaI2bdo4xgoKClReXq5Ro0bp%2B%2B%2B/V5cuXTRlyhRddtllXvH95mzYgwUn5eXlTp/skhy3q6qq3LGkC5aRkaFt27bpj3/8o2OvR8eOHZWVlaXhw4dr2rRpWrt2rSSpoqKi3vyNMfu%2Bffscr9e8efM0adIkvf/%2B%2B5o9e/YZX8eTOTwpp3SiTK5atUp33XWXY2z37t2qqKhQUlKSlixZomuvvVYPPfSQtmzZooqKCknyqIwn/dprd7Z5T8xdUVGhRx55RK1bt9aIESMkSd9//71%2B%2BeUXDR8%2BXFlZWerUqZNGjx6toqKiejM2xveoC3mv8ZSMpyosLNT69es1atQop/Hdu3frl19%2B0UMPPaSFCxeqSZMmGjNmjI4cOeKV329OxR4sOAkKCqrziX3ydpMmTdyxpAuSkZGhZcuW6YUXXlDnzp11%2BeWXKyUlRaGhoZKkqKgo7dmzRytXrlS/fv3OmD84ONgdyz%2Brtm3b6quvvtKll14qPz8/denSRbW1tZowYYJ69uxZb46Tr6En5ZSkLVu2aP/%2B/Ro4cKBj7OGHH9aoUaN06aWXSjrxWn7zzTd666239Mc//lFS3TfpxpzxpKCgIJWVlTmNNeS1CwkJcRxS85TcR48e1cMPP6w9e/boL3/5i2ONM2bMUEVFhZo3by5Jevrpp7Vp0yatWbNGV199taQTmU7P25gyDh48%2BLzfa04tGY0546k%2B%2BugjdenSpc6e86VLl%2Br48eNq1qyZJGnOnDm69tpr9cknn3jd95vTsQcLTiIiInTw4EFVV1c7xux2u5o0aaKQkBA3ruzczZgxQ6%2B%2B%2BqoyMjJ04403SjpxLtLJN7yTOnbsqP3790s6kb%2B0tNRpvrS0VGFhYa5Z9DkKDQ11nGclSZ06dVJlZaXCwsLqzXHy0IKn5fzss8%2BUkJDgKFOS5O/v73Rb%2Bv%2BvZWhoqIKCgpwyVldXq6ysrNFmPOlMr01DXjtPyn3kyBGNHTtWO3fu1LJly5zOVbLZbI5yJcmxJ2j//v2KiIiQJMdhtFP/3pgyXsh7jadkPNVnn32m66%2B/vs54YGCgo1xJJ/6D0K5dO8dr6S3fb%2BpDwYKTLl26yGazOU6olaTc3FxFR0fL399zPl0yMzP15ptv6vnnn3fa6zF//nyNGTPG6b7bt29Xx44dJUkxMTHKzc11zBUVFamoqEgxMTEuWfe5%2BOyzz9SrVy%2Bnaxx9%2B%2B23Cg0NVXx8vL7%2B%2BmsZYySdOMS2adMmRw5PyilJmzdvVo8ePZzGnnjiCU2ePNlp7ORr6e/vr%2BjoaKeMeXl5stlsjmtoNVYxMTH65ptvHIeJpBNfg2d67crLy7Vt2zbFxMR4TO7a2lqNGzdOP/30k15//XVdfvnlTvOjRo1SZmam0/2/%2B%2B47dezYUREREYqMjHTKmJubq8jISLecm3QmF/Je4ykZTzLGaMuWLXW%2BRo0x6tu3r9555x3H2LFjx/TDDz%2BoY8eOXvP95ozc%2BSOMaJymTZtmBg4caPLz883atWtNjx49zEcffeTuZTXYrl27TJcuXcwLL7zgdN2VkpISk5%2Bfb6688kqzZMkS88MPP5g33njDdOvWzWzatMkYY8ymTZtM165dzVtvveW4Ns2DDz7o5kT1O3z4sElOTjaPPfaYKSgoMP/85z9NUlKSycrKMocPHza9e/c2M2bMMDt37jQzZswwiYmJjh/996ScxhiTkpJicnJynMY%2B%2Bugj07VrV/Puu%2B%2BaPXv2mJdeesl0797dFBYWGmOMycnJMT169DBr1641%2Bfn5ZuDAgWbGjBnuWP6vOvXH3qurq82AAQPMo48%2Banbs2GEWL15sYmNjHdfBKiwsNNHR0Wbx4sWO62DdfPPNjktyNNbcp2bMzs42UVFR5pNPPnH6%2Bjx48KAxxphXXnnFxMfHm48//tgUFBSYP/3pT%2Bbqq682hw8fNsYYs3jxYpOUlGTWr19v1q9fb5KSkswrr7zitmwnnZrxQt9rGmtGY0ydyzQUFhaazp07m5KSkjr3nTFjhrnuuuvM%2BvXrzY4dO0xqaqoZNGiQqa6uNsZ4/vebs6FgoY5jx46ZiRMnmtjYWJOUlGReffVVdy/pnCxevNh07ty53g9jjFm7dq25%2BeabTXR0tOnfv3%2BdL%2BbVq1eba6%2B91sTGxprU1FRz4MABd8RokB07dpgxY8aY2NhYk5iYaF566SXHN9r8/HwzePBgEx0dbW677TbzzTffOG3rSTmjo6PNp59%2BWmf8rbfeMjfccIPp1q2bGTJkiNmwYYPT/OLFi80f/vAHEx8fbyZPnnzG6yq52%2BnfsPbs2WPuvPNO061bNzNw4EDz%2BeefO93/n//8p7nhhhtM9%2B7dzejRo%2Btcv6wx5j4147333lvv1%2BfJ6x/V1taal19%2B2Vx33XWmW7du5s477zTfffed47Gqq6vNs88%2BaxISEkyvXr1MRkaG4/PenU5/HS/kvaaxZjSmbs68vDzTuXNnx0V%2BT1VRUWHS09NNYmKiiYmJMQ8%2B%2BKDZt2%2BfY97Tv9%2BcjZ8x/3cMAQAAAJbwgoOcAAAAjQsFCwAAwGIULAAAAItRsAAAACxGwQIAALAYBQsAAMBiFCwAAACLUbAAAAAsRsECAACwGAULAADAYhQsAAAAi1GwAAAALEbBAgAAsNj/A7ujpNU6QJjLAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4602455764671563893”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>73</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333333333333</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>200.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>41</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666666666666</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (825)</td> <td class=”number”>2030</td> <td class=”number”>63.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>420</td> <td class=”number”>13.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4602455764671563893”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-93.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-90.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-88.88888888888889</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-87.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>900.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1000.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1800.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WICSPTH_08_12”><s>PCH_WICSPTH_08_12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCH_WICS_08_12”><code>PCH_WICS_08_12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98279</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WICS_08_12”>PCH_WICS_08_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>353</td>

</tr> <tr>

<th>Unique (%)</th> <td>11.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-1.9004</td>

</tr> <tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7028536371170449859”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7028536371170449859,#minihistogram-7028536371170449859”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-7028536371170449859”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7028536371170449859”

aria-controls=”quantiles-7028536371170449859” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7028536371170449859” aria-controls=”histogram-7028536371170449859”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7028536371170449859” aria-controls=”common-7028536371170449859”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7028536371170449859” aria-controls=”extreme-7028536371170449859”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7028536371170449859”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-100</td>

</tr> <tr>

<th>5-th percentile</th> <td>-41.842</td>

</tr> <tr>

<th>Q1</th> <td>-12.5</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>39.3</td>

</tr> <tr>

<th>Maximum</th> <td>300</td>

</tr> <tr>

<th>Range</th> <td>400</td>

</tr> <tr>

<th>Interquartile range</th> <td>12.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>25.823</td>

</tr> <tr>

<th>Coef of variation</th> <td>-13.589</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.303</td>

</tr> <tr>

<th>Mean</th> <td>-1.9004</td>

</tr> <tr>

<th>MAD</th> <td>15.686</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0166</td>

</tr> <tr>

<th>Sum</th> <td>-5915.9</td>

</tr> <tr>

<th>Variance</th> <td>666.85</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7028536371170449859”>

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XffBbocAAAA64Lyq3LCwsLUv39/9e/fX5WVlXrllVe0YMECLVq0SH369NGkSZN09dVXB6M0AACAXy1ov4uwtLRU7777rt59911t375dffr00TXXXKP9%2B/fr/vvv1/r16zVr1qxglQcAAHDOAh6wVq5cqZUrV2rdunWKjY3VmDFj9Mwzz6hLly6%2Bj%2BnQoYMeeeQRAhYAAAhJAQ9Ys2bNUnp6uvLz83XllVeqRYuGbwPr2rWrJkyYEOjSAAAArAh4wPrss8/Utm1bVVRU%2BMLVxo0b1atXL4WHh0uS%2BvTpoz59%2BgS6NAAAACsC/lOER44c0dChQ/XCCy/4xm699VaNHj1a%2B/btC3Q5AAAA1gU8YD366KO66KKLdNNNN/nG3n//fXXo0EE5OTmBLgcAAMC6gAes//3f/9W9996ruLg431hsbKzuvvturV27NtDlAAAAWBfwgOVyuXTo0KEG45WVldzRHQAAOELAA9aVV16puXPn6scff/SNlZSUKCcnRwMHDgx0OQAAANYF/KcI77nnHt10000aMmSIoqOjJUmHDh1Sr169NHPmzECXAwAAYF3AA1a7du30zjvv6Msvv9SOHTvkcrl08cUX6/e//73CwsICXQ4AAIB1QflVOeHh4Ro4cCCXBAEAgCMFPGB5vV7Nnz9fGzZs0PHjxxu8sf0f//hHoEsCAACwKuAB64EHHtCmTZs0YsQInX/%2B%2BYHePQAAQJMLeMBau3atFi9erNTU1EDvGgAAICACfpuGVq1aqV27doHeLQAAQMAEPGCNHj1aixcvVl1dXaB3DQAAEBABv0RYUVGhVatWafXq1brwwgsVERHhN79s2bJAlwQAAGBVUG7TMHLkyGDsFgAAICACHrBycnICvUsAAICACvh7sCSptLRUeXl5mj59uv71r3/p73//u3bt2hWMUgAAAKwLeMD64YcfNGrUKL3zzjv68MMPdezYMb3//vsaN26cPB5PoMsBAACwLuAB67HHHtOgQYP08ccf67zzzpMkPfnkk8rIyNDjjz8e6HIAAACsC3jA2rBhg2666Sa/X%2Bzscrl02223acuWLYEuBwAAwLqAB6z6%2BnrV19c3GD969KjCw8MDXQ4AAIB1AQ9YAwYM0MKFC/1CVkVFhXJzc9WvX79AlwMAAGBdwAPWvffeq02bNmnAgAGqrq7Wf/3Xfyk9PV27d%2B/WPffcE%2BhyAAAArAv4fbASEhL0t7/9TatWrdLWrVtVX1%2BvG264QaNHj1abNm0CXQ4AAIB1QbmTe1RUlK677rpg7BoAAKDJBTxgTZw48bTz/C5CAAAQ6gIesDp16uT3vLa2Vj/88IO2b9%2BuSZMmBbocAAAA65rN7yLMz8/X/v37A1wNAACAfUH5XYQnM3r0aH3wwQfBLgMAAOBXazYB6%2Buvv%2BZGowAAwBGaxZvcjxw5om%2B//VY33nhjoMsBAACwLuABq2PHjn6/h1CSzjvvPE2YMEF//OMfA10OAACAdQEPWI899ligdwkAABBQAQ9Y69evb/THXnHFFU1YCQAAQNMIeMD685//7LtEaIzxjZ84FhYWpq1btwa6PAAAgF8t4AHr%2Beef19y5czVjxgylpaUpIiJC33zzjbKzs3XNNddo%2BPDhgS4JAADAqoDfpiEnJ0ezZ8/WkCFD1LZtW7Vu3Vr9%2BvVTdna2XnvtNXXq1Mn3AAAACEUBD1ilpaUnDU9t2rRReXl5oMsBAACwLuABKykpSU8%2B%2BaSOHDniG6uoqFBubq5%2B//vfB7ocAAAA6wL%2BHqz7779fEydO1JVXXqkuXbrIGKPvv/9ecXFxWrZsWaDLAQAAsC7gAatbt256//33tWrVKu3cuVOS9Kc//UkjRoxQVFRUoMsBAACwLuABS5IuuOACXXfdddq9e7cuvPBCST/dzR0AAMAJAv4eLGOMHn/8cV1xxRUaOXKk9u/fr3vuuUezZs3S8ePHA10OAACAdQEPWK%2B88opWrlypBx98UBEREZKkQYMG6eOPP1ZeXt5Zb6%2BmpkYjR47UunXrfGMlJSWaPHmykpKSNHz4cK1Zs8bvNV9%2B%2BaVGjhwpt9utiRMnqqSkxG9%2ByZIlGjhwoJKTk3XfffepsrLyHDoFAAC/VQEPWAUFBZo9e7bGjh3ru3v78OHDNXfuXL333ntnta3q6mrdeeed2rFjh2/MGKPMzEy1b99eK1as0OjRozVt2jTt3btXkrR3715lZmZq7NixeuuttxQbG6vbbrvNdwf5Dz/8UHl5ecrOztbSpUvl8XiUm5trqXsAAPBbEPCAtXv3bvXs2bPBeI8ePeT1ehu9neLiYl1//fX68ccf/cbXrl2rkpISZWdnq1u3bpo6daqSkpK0YsUKSdKbb76pyy%2B/XFOmTNEll1yinJwc7dmzR1999ZUkadmyZZo0aZLS09PVu3dvPfzww1qxYgVnsQAAQKMFPGB16tRJ33zzTYPxzz77zPeG98b46quv1LdvXxUUFPiNezweXXbZZWrVqpVvLCUlRUVFRb751NRU31xUVJR69eqloqIi1dXV6ZtvvvGbT0pK0vHjx7Vt27ZG1wYAAH7bAv5ThDfffLMefvhheb1eGWP0z3/%2BUwUFBXrllVd07733Nno7N95440nHvV6v4uPj/cbatWun/fv3n3H%2B0KFDqq6u9pt3uVyKiYnxvb4xSktLG5yNc7laNdjvrxUe3sLvT6dwal9NzeUK3ufLyWtGb6HHqX1Jzu3NiX0FPGCNGzdOtbW1eu6551RVVaXZs2crNjZWd9xxh2644YZfvf3Kykrfm%2Bd/FhERoZqamjPOV1VV%2BZ6f6vWNUVBQ0OAN%2B5mZmcrKymr0Ns5GdLQz7x/m1L6aStu2rYNdgqPXjN5Cj1P7kpzbm5P6CnjAWrVqlYYOHarx48fr4MGDMsaoXbt21rYfGRmpiooKv7Gamhq1bNnSN39iWKqpqVF0dLQiIyN9z0%2BcP5uboI4fP14ZGRl%2BYy5XK5WXH230NhojPLyFoqOjdOhQperq6q1uO5ic2ldTs318nQ0nrxm9hR6n9iU5t7em7CtY//kMeMDKzs7Wf//3f%2BuCCy5QbGys9e0nJCSouLjYb6ysrMx3eS4hIUFlZWUN5nv27KmYmBhFRkaqrKxM3bp1kyTV1taqoqJCcXFxja4hPj6%2BweVAr/ewamub5ouhrq6%2BybYdTE7tq6k0h8%2BVk9eM3kKPU/uSnNubk/oK%2BMXOLl26aPv27U22fbfbrc2bN/su90lSYWGh3G63b76wsNA3V1lZqS1btsjtdqtFixZKTEz0my8qKpLL5VKPHj2arGYAAOAsAT%2BD1aNHD911111avHixunTp4rss97OcnJxftf20tDR16NBBM2fO1G233aZPPvlEGzdu9G133LhxevHFF7Vo0SKlp6crPz9fnTt3Vt%2B%2BfSX99Ob52bNnq3v37oqPj9dDDz2k66%2B/nt%2BTCAAAGi3gAeu7775TSkqKJJ3Vfa8aKzw8XAsWLNCsWbM0duxYXXTRRcrPz1fHjh0lSZ07d9azzz6rRx99VPn5%2BUpOTlZ%2Bfr7vpqcjRozQnj17NHv2bNXU1Ojqq6/WjBkzrNcJAACcK8z8fAvzJjRv3jxNmzbN795UvzVe72Hr23S5Wqht29YqLz/qmGvWUvPpa9j8L4K273PxwR39g7bv5rJmTYHeQo9T%2B5Kc21tT9hUXd77V7TVWQN6D9fLLLze4E/qtt96q0tLSQOweAAAgoAISsE52kmz9%2BvWqrq4OxO4BAAACyjm3TAUAAGgmCFgAAACWBSxg/fxTegAAAE4XsNs0zJ071%2B%2BeV8ePH1dubq5at/a/hf2vvQ8WAABAsAUkYF1xxRUN7nmVnJys8vJylZeXB6IEAACAgAlIwHrllVcCsRsAAIBmgTe5AwAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALHNswProo4906aWX%2Bj2ysrIkSVu2bNF1110nt9utcePGadOmTX6vXbVqlQYNGiS3263MzEwdPHgwGC0AAIAQ5diAVVxcrPT0dK1Zs8b3mDt3ro4dO6Zbb71Vqampevvtt5WcnKypU6fq2LFjkqSNGzdq1qxZmjZtmgoKCnTo0CHNnDkzyN0AAIBQ4tiAtXPnTnXv3l1xcXG%2BR3R0tN5//31FRkbq7rvvVrdu3TRr1iy1bt1af//73yVJy5cv17BhwzRmzBj16NFD8%2BbN06effqqSkpIgdwQAAEKFowNWly5dGox7PB6lpKQoLCxMkhQWFqY%2BffqoqKjIN5%2Bamur7%2BA4dOqhjx47yeDwBqRsAAIQ%2BRwYsY4y%2B%2B%2B47rVmzRkOGDNGgQYP0%2BOOPq6amRl6vV/Hx8X4f365dO%2B3fv1%2BSVFpaetp5AACAM3EFu4CmsHfvXlVWVioiIkLz58/X7t27NXfuXFVVVfnGfykiIkI1NTWSpKqqqtPON0Zpaam8Xq/fmMvVqkFw%2B7XCw1v4/ekUTu2rqblcwft8OXnN6C30OLUvybm9ObEvRwasTp06ad26dbrgggsUFhamnj17qr6%2BXjNmzFBaWlqDsFRTU6OWLVtKkiIjI086HxUV1ej9FxQUKC8vz28sMzPT91OMtkVHN762UOLUvppK27atg12Co9eM3kKPU/uSnNubk/pyZMCSpJiYGL/n3bp1U3V1teLi4lRWVuY3V1ZW5ju7lJCQcNL5uLi4Ru97/PjxysjI8BtzuVqpvPzo2bRwRuHhLRQdHaVDhypVV1dvddvB5NS%2Bmprt4%2BtsOHnN6C30OLUvybm9NWVfwfrPpyMD1ueff6677rpLq1ev9p152rp1q2JiYpSSkqIXXnhBxhiFhYXJGKMNGzboL3/5iyTJ7XarsLBQY8eOlSTt27dP%2B/btk9vtbvT%2B4%2BPjG1wO9HoPq7a2ab4Y6urqm2zbweTUvppKc/hcOXnN6C30OLUvybm9Oakv51zs/IXk5GRFRkbq/vvv165du/Tpp59q3rx5uuWWWzR06FAdOnRIjzzyiIqLi/XII4%2BosrJSw4YNkyTdcMMNWrlypd58801t27ZNd999t6666ipdeOGFQe4KAACECkcGrDZt2ujFF1/UwYMHNW7cOM2aNUvjx4/XLbfcojZt2mjhwoW%2Bs1Qej0eLFi1Sq1atJP0UzrKzs5Wfn68bbrhBF1xwgXJycoLcEQAACCWOvEQoSZdccolefvnlk8717t1b77zzzilfO3bsWN8lQgAAgLPlyDNYAAAAweTYM1hofobN/yLYJQAAEBCcwQIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALHMFuwAAdgyb/0WwS2i0D%2B7oH%2BwSAKBJcQYLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACxzBbsA/Dqps/4e7BIAAMAJOIMFAABgGQHrJKqrq3XfffcpNTVVAwYM0EsvvRTskgAAQAjhEuFJzJs3T5s2bdLSpUu1d%2B9e3XPPPerYsaOGDh0a7NIARxg2/4tgl%2BBoH9zRP9glAL95BKwTHDt2TG%2B%2B%2BaZeeOEF9erVS7169dKOHTv06quvErAAAECjcInwBNu2bVNtba2Sk5N9YykpKfJ4PKqvrw9iZQAAIFSM7Q2WAAANGUlEQVRwBusEXq9Xbdu2VUREhG%2Bsffv2qq6uVkVFhWJjY8%2B4jdLSUnm9Xr8xl6uV4uPjrdYaHk4%2BBtCQy9W47w0/fw9x2vcSp/YlObc3J/ZFwDpBZWWlX7iS5HteU1PTqG0UFBQoLy/Pb2zatGm6/fbb7RT5/0pLSzXpdzs0fvx46%2BEtmEpLS1VQUOC4viTn9ubUviTn97Z06WLH9ebUviTn9ubEvpwTFS2JjIxsEKR%2Bft6yZctGbWP8%2BPF6%2B%2B23/R7jx4%2B3XqvX61VeXl6Ds2Whzql9Sc7tzal9SfQWipzal%2BTc3pzYF2ewTpCQkKDy8nLV1tbK5frp0%2BP1etWyZUtFR0c3ahvx8fGOSeAAAODscQbrBD179pTL5VJRUZFvrLCwUImJiWrRgk8XAAA4MxLDCaKiojRmzBg99NBD2rhxoz7%2B%2BGO99NJLmjhxYrBLAwAAISL8oYceeijYRTQ3/fr105YtW/TEE0/on//8p/7yl79o3LhxwS7rpFq3bq20tDS1bt062KVY5dS%2BJOf25tS%2BJHoLRU7tS3Jub07rK8wYY4JdBAAAgJNwiRAAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQErhBhjNGXKFL399tt%2B4%2BXl5br99tuVnJysjIwMrVy50m9%2By5Ytuu666%2BR2uzVu3Dht2rQpkGU32pYtW3TppZf6PcaOHeubLykp0eTJk5WUlKThw4drzZo1Qaz27FVXV%2Bu%2B%2B%2B5TamqqBgwYoJdeeinYJZ2Tjz76qME6ZWVlSQqdY%2B1ENTU1GjlypNatW%2BcbO9Px9uWXX2rkyJFyu92aOHGiSkpKAl12o5yst7lz5zZYw%2BXLl/vmV61apUGDBsntdiszM1MHDx4MRukndeDAAWVlZSktLU0DBw5UTk6OqqurJYX%2Bmp2ut1Besx9%2B%2BEE333yzkpOTddVVV2nx4sW%2BuVBfs9MyCAl1dXUmOzvbdO/e3axYscJvburUqWbSpEnm22%2B/NW%2B88Ya5/PLLjcfjMcYYc/ToUdO/f3/z2GOPmeLiYjNnzhzzhz/8wRw9ejQYbZzWypUrzejRo01paanvcfDgQWOMMfX19WbUqFFm%2BvTppri42Dz//PPG7XabPXv2BLnqxsvOzjajRo0ymzZtMv/zP/9jkpOTzQcffBDsss7aggULzNSpU/3W6d///ndIHWu/VFVVZTIzM0337t3N2rVrjTFnPt727NljkpKSzIsvvmi2b99u/vrXv5qRI0ea%2Bvr6YLbSwMl6M8aYyZMnm4ULF/qt4bFjx4wxxng8HtO7d2/zzjvvmK1bt5oJEyaYW2%2B9NVgt%2BKmvrzfXX3%2B9ueWWW8z27dvN%2BvXrzeDBg81jjz0W8mt2ut6MCd01q6urM1dffbWZPn26%2Be6778zq1atNnz59zLvvvhvya3YmBKwQsH//fjNhwgRz1VVXmdTUVL%2BA9cMPP5ju3bubkpIS39h9991n7rnnHmOMMW%2B%2B%2BabJyMjwHZD19fVm8ODBDUJac/Dkk0%2BaO%2B%2B886RzX375pUlKSvL7x3rSpEnmmWeeCVR5v8rRo0dNYmKi3z9y%2Bfn5ZsKECUGs6txMnz7dPPHEEw3GQ%2BlY%2B9mOHTvMH//4RzNq1Ci/EHKm423%2B/Pl%2Ba3fs2DGTnJzst77BdqrejDFm4MCB5vPPPz/p62bMmOH7/mGMMXv37jWXXnqp%2BfHHH5u85jMpLi423bt3N16v1zf23nvvmQEDBoT8mp2uN2NCd80OHDhg/vrXv5rDhw/7xjIzM82DDz4Y8mt2JlwiDAGbN29Whw4dtGLFCp1//vl%2Bcx6PRx06dFDnzp19YykpKfr666998ykpKQoLC5MkhYWFqU%2BfPioqKgpcA420c%2BdOdenS5aRzHo9Hl112mVq1auUbS0lJaZZ9nMy2bdtUW1ur5ORk31hKSoo8Ho/q6%2BuDWNnZO9U6hdKx9rOvvvpKffv2VUFBgd/4mY43j8ej1NRU31xUVJR69erVrHo9VW9HjhzRgQMHTvu19sveOnTooI4dO8rj8TRluY0SFxenxYsXq3379n7jR44cCfk1O11vobxm8fHxmj9/vtq0aSNjjAoLC7V%2B/XqlpaWF/JqdiSvYBeDMMjIylJGRcdI5r9er%2BPh4v7F27drpwIEDvvmLL764wfyOHTuapthfYefOnaqvr9eoUaN0%2BPBhXXnllbr77rvVpk2bU/a5f//%2BIFV7drxer9q2bauIiAjfWPv27VVdXa2KigrFxsYGsbrGM8bou%2B%2B%2B05o1a7Rw4ULV1dVp6NChysrKCqlj7Wc33njjScfPdLyFwvF4qt527typsLAwPf/88/rss88UExOjm266Sddcc40kqbS0tNn2Fh0drYEDB/qe19fXa/ny5erXr1/Ir9npegvlNfuljIwM7d27V%2Bnp6RoyZIgeffTRkF6zMyFgNQNVVVW%2BQHSiuLg4v3R/osrKSr9/tCUpIiJCNTU1jZoPpNP1GRsbq5KSEnXu3FmPPvqoDh06pJycHM2YMUPPPfdcs%2BrjXJyqfkkh04Mk7d2719fL/PnztXv3bs2dO1dVVVUhv0a/FEpfV2dr165dCgsLU9euXTVhwgStX79eDzzwgNq0aaPBgwerqqoqZHrLzc3Vli1b9NZbb2nJkiWOWrNf9rZ582ZHrNkzzzyjsrIyPfTQQ8rJyXH015lEwGoWPB6PJk6ceNK5/Px8DRo06JSvjYyMbHCw1dTUqGXLlo2aD6Qz9bl27VpFRkbqvPPOkyQ99thjGjdunA4cOKDIyEhVVFT4vSZYfZyLU62DpJDpQZI6deqkdevW6YILLlBYWJh69uyp%2Bvp6zZgxQ2lpac3mWPu1znS8nWo9o6OjA1bjuRozZozS09MVExMjSerRo4e%2B//57vfbaaxo8ePApe4uKigpGuaeUm5urpUuX6qmnnlL37t0dtWYn9nbJJZc4Ys0SExMl/fQT1XfddZfGjRunyspKv48J1TU7GQJWM9C3b199%2B%2B235/TahIQElZWV%2BY2VlZUpLi7utPMnnnYNhLPts1u3bpJ%2B%2BtHlhIQEFRcX%2B80Hq49zkZCQoPLyctXW1srl%2BunLzuv1qmXLliHzzeJnP3%2BT/1m3bt1UXV2tuLi4ZnOs/VpnOt5O9XXVs2fPgNV4rsLCwhqsYdeuXbV27VpJZ/6e0hzMmTNHr732mnJzczVkyBBJzlmzk/UWymtWVlamoqIivxMFF198sY4fP664uDjt2rWrwceH2pqdCm9yD3FJSUnas2eP3zXpwsJCJSUlSZLcbre%2B/vprGWMk/fQemg0bNsjtdgel3lMpLi5WcnKy3z1Otm7dKpfLpYsuukhut1ubN29WVVWVb76wsLDZ9XEqPXv2lMvl8ntzZmFhoRITE9WiReh8GX7%2B%2Befq27ev3/86t27dqpiYGN8PVzT3Y60xznS8ud1uFRYW%2BuYqKyu1ZcuWkOj16aef1uTJk/3Gtm3bpq5du0pq2Nu%2Bffu0b9%2B%2BZtNbXl6eXn/9dT355JMaMWKEb9wJa3aq3kJ5zXbv3q1p06b5vT1k06ZNio2NVUpKSsiv2WkF80cYcfbS09Mb/Nj7lClTzIQJE8zWrVvNG2%2B8YRITE333wTp8%2BLDp16%2BfmTNnjtmxY4eZM2eO6d%2B/f7O7N1FdXZ0ZPXq0735e69evN8OHDzcPPvigMcaY2tpaM3z4cHPHHXeY7du3m4ULF5qkpKSQug/WAw88YEaMGGE8Ho/56KOPTJ8%2BfcyHH34Y7LLOyuHDh83AgQPNnXfeaXbu3GlWr15tBgwYYBYtWhQyx9qp/PJWBmc63kpKSkxiYqJZuHCh7/48o0aNarb35/llbx6Px1x22WVm8eLF5ocffjCvvvqqufzyy82GDRuMMcZs2LDB9OrVy7zxxhu%2BeypNnTo1mOX7FBcXm549e5qnnnrK735QpaWlIb9mp%2BstlNestrbWjB071kyZMsXs2LHDrF692vzhD38wS5YsCfk1OxMCVog5WcAqKyszU6dONYmJiSYjI8O89957fvMej8eMGTPGJCYmmmuvvdZs3rw5kCU32t69e01mZqZJTU01aWlpZs6cOaa6uto3//3335s//elP5vLLLzcjRowwX3zxRRCrPXvHjh0zd999t0lKSjIDBgwwL7/8crBLOifbt283kydPNklJSaZ///7m2Wef9X3DC5Vj7WROvFfUmY631atXm6uvvtr07t3bTJo0qVncc%2BhUTuzto48%2BMqNGjTKJiYlm6NChDYL%2BihUrzH/8x3%2BYpKQkk5mZ6bvhb7AtXLjQdO/e/aQPY0J7zc7UW6iumTE/3csxMzPT9OnTx/Tv398899xzvu8ZobxmZxJmzP%2BfzwcAAIAVofPmDwAAgBBBwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZf8HcDadXtspqtsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7028536371170449859”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1325</td> <td class=”number”>41.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-25.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-20.0</td> <td class=”number”>112</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-33.33333</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-50.0</td> <td class=”number”>86</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-16.66667</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.66667</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>42</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (342)</td> <td class=”number”>1100</td> <td class=”number”>34.3%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7028536371170449859”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-100.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-75.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-73.33334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-66.66666</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-60.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>120.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>125.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>300.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCH_WIC_09_15”>PCH_WIC_09_15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>-0.59653</td>

</tr> <tr>

<th>Minimum</th> <td>-2.4915</td>

</tr> <tr>

<th>Maximum</th> <td>0.027258</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6090276145418952310”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6090276145418952310,#minihistogram-6090276145418952310”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6090276145418952310”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6090276145418952310”

aria-controls=”quantiles-6090276145418952310” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6090276145418952310” aria-controls=”histogram-6090276145418952310”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6090276145418952310” aria-controls=”common-6090276145418952310”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6090276145418952310” aria-controls=”extreme-6090276145418952310”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6090276145418952310”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>-2.4915</td>

</tr> <tr>

<th>5-th percentile</th> <td>-2.4915</td>

</tr> <tr>

<th>Q1</th> <td>-0.65445</td>

</tr> <tr>

<th>Median</th> <td>-0.51373</td>

</tr> <tr>

<th>Q3</th> <td>-0.36604</td>

</tr> <tr>

<th>95-th percentile</th> <td>-0.14835</td>

</tr> <tr>

<th>Maximum</th> <td>0.027258</td>

</tr> <tr>

<th>Range</th> <td>2.5187</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.28841</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.47735</td>

</tr> <tr>

<th>Coef of variation</th> <td>-0.80021</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.783</td>

</tr> <tr>

<th>Mean</th> <td>-0.59653</td>

</tr> <tr>

<th>MAD</th> <td>0.25839</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-3.0479</td>

</tr> <tr>

<th>Sum</th> <td>-1868.3</td>

</tr> <tr>

<th>Variance</th> <td>0.22786</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6090276145418952310”>

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NdssEJm2AWslJQU7d6922essrLSe3suJSVFlZWVTea7dOmic889V9HR0aqsrFSnTp0kSfX19aqurlZSUlKLa0hOTm5yO9DlOqD6erMLqaGh0fg%2BcRS99R966x/0Fc05nddEuK/ZsLsB6nA49Omnn3pv90lScXGxHA6Hd764uNg753a7tXPnTjkcDkVERCg1NdVnvqSkRHa7XZ07dw7cSQAAgJAWdgGrR48eatu2rfLy8lRaWqolS5Zo%2B/btGjVqlCRp5MiR2rZtm5YsWaLS0lLl5eWpffv26tmzp6SjH55ftmyZNm3apO3bt2vmzJm65pprTuoWIQAAOLOFXcCKjIzUE088IZfLpREjRui1117TokWL1K5dO0lS%2B/bt9fjjj2vt2rUaNWqUqqurtWjRItlsNknSkCFDNGnSJE2fPl0TJkzQZZddprvvvjuYpwQAAEKMzfrpEebwK5frgLF92e0RSkhopaqqQ2F9/zoY6K3/0Fv/CPW%2BhtqXJ4ea0/HLngO9ZpOSzvb7MZoTdlewAAAAgo2ABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEBD1ijR4/W6tWrdeDAgUAfGgAAICACHrB69eqlxYsXq0%2BfPrrjjjv07rvvyrKsQJcBAADgNwEPWHfeeaf%2B/ve/64knnlBkZKRuu%2B029e/fX48%2B%2Bqi%2B/PLLQJcDAABgnD0YB7XZbOrdu7d69%2B4tt9utlStX6oknntCSJUvUrVs3jR8/Xr///e%2BDURoAAMAvFpSAJUkVFRV67bXX9Nprr%2Bnzzz9Xt27ddPXVV6u8vFz333%2B/tm7dqmnTpgWrPAAAgFMW8FuEf/7znzVhwgRlZWVpxYoV%2Bu1vf6sNGzbohRde0OjRo3Xbbbdp6tSpWrNmzS86zr59%2BzRp0iR169ZNAwYM0LPPPuud27lzp0aPHi2Hw6GRI0dqx44dPu9dv369Bg4cKIfDoZycHP3www%2B/qBYAAHBmCXjAmjZtmlq1aqVFixbp7bff1p133qmOHTv6bHPBBRfohhtu%2BEXHuf322xUXF6d169bpvvvu02OPPaa//vWvOnz4sCZOnKjMzEytW7dO6enpmjRpkg4fPixJ2r59u6ZNm6YpU6aoqKhINTU1ysvL%2B0W1AACAM0vAbxG%2B8847SkhIUHV1tSIijua77du3q2vXroqMjJQkdevWTd26dTvlY/z4448qKSnR7Nmz1bFjR3Xs2FF9%2B/bV%2B%2B%2B/rx9//FHR0dGaOnWqbDabpk2bpnfeeUcbNmzQiBEjtGrVKg0ePFjDhw%2BXJBUUFCgrK0tlZWXq0KHDL28AAAAIewG/gnXw4EENGjRITz/9tHds4sSJGjZsmPbt22fkGDExMYqNjdW6det05MgRffHFF9q2bZu6dOkip9OpjIwM2Ww2SUc/cN%2BtWzeVlJRIkpxOpzIzM737atu2rdq1ayen02mkNgAAEP4CHrAeeughnX/%2B%2Bbrpppu8Y2%2B%2B%2Babatm2r/Px8I8eIjo7W9OnTVVRUJIfDocGDB6tfv34aPXq0XC6XkpOTfbZPTExUeXm5pKMfvj/RPAAAwM8J%2BC3Cjz76SC%2B99JKSkpK8Y61bt9bUqVN1/fXXGzvOnj17lJWVpZtuukmlpaWaPXu2Lr/8crndbkVFRflsGxUVJY/HI0mqra094XxLVFRUyOVy%2BYzZ7XFNgtupioyM8PkV5tBb/6G3/kFfcSJ2%2B%2Bm3Ls6UNRvwgGW321VTU9Nk3O12G3ui%2B/vvv681a9bo7bffVkxMjFJTU7V//349%2BeST6tChQ5Ow5PF4FBMTI%2Bno1a/m5mNjY1t8/KKiIhUWFvqM5eTkKDc39xTPqHnx8S2vCSeH3voPvfUP%2BormJCS0CnYJxxXuazbgAatfv36aM2eOHnnkEf3nf/6nJKmsrEz5%2Bfnq27evkWPs2LFD559/vjc0SdIll1yixYsXKzMzU5WVlT7bV1ZWeq8upaSkNDv/71fcfk52drYGDBjgM2a3x6mq6tDJnkqzIiMjFB8fq5oatxoaGo3sE0fRW/%2Bht/5BX3Eipv7eMSnQazZYITPgAeuee%2B7RTTfdpCuuuELx8fGSpJqaGnXt2tXY4xCSk5P19ddfy%2BPxeG/3ffHFF2rfvr0cDoeefvppWZYlm80my7K0bds23XLLLZIkh8Oh4uJijRgxQtLR52nt27dPDofjpI5/7O1Al%2BuA6uvNLqSGhkbj%2B8RR9NZ/6K1/0Fc053ReE%2BG%2BZgMesBITE/XKK69o8%2BbNKi0tld1u14UXXqjLL7/c%2By/7fqkBAwZo3rx5uv/%2B%2B3Xrrbfqyy%2B/1OLFi/U///M/GjRokBYsWKC5c%2Bfq2muv1erVq%2BV2uzV48GBJ0pgxYzR27FilpaUpNTVVc%2BfOVf/%2B/XlEAwAAaLGgfFVOZGSk%2Bvbta%2ByW4LHOPvtsPfvss5o7d65GjRql1q1b69Zbb1V2drZsNpueeuopzZgxQy%2B99JIuvvhiLVmyRHFxcZKk9PR0zZo1SwsXLtSPP/6o3r17a/bs2X6pEwAAhCebZeqT5S3kcrn02GOPadu2bTpy5EiTD7b/7W9/C2Q5AeNyHTC2L7s9QgkJrVRVdSisL68GA731H3rrH6He18GPvRfsEsLaW7f3DnYJTQR6zSYlne33YzQn4FewHnjgAe3YsUNDhgzR2WcH56QBAAD8KeAB64MPPtDSpUt9npYOAAAQTgL%2BlK%2B4uDglJiYG%2BrAAAAABE/CANWzYMC1dulQNDQ2BPjQAAEBABPwWYXV1tdavX69//OMf6tChQ5OvpXnuuecCXRIAAIBRQXlMw9ChQ4NxWAAAgIAIeMDKz88P9CEBAAACKihfZV1RUaHCwkLdeeed%2Bv7777VhwwZ98cUXwSgFAADAuIAHrK%2B//lp/%2BMMf9Morr2jjxo06fPiw3nzzTY0cOVJOpzPQ5QAAABgX8ID1pz/9SQMHDtSmTZv0q1/9SpL0yCOPaMCAAZo/f36gywEAADAu4AFr27Ztuummm3y%2B2Nlut2vy5MnauXNnoMsBAAAwLuABq7GxUY2NTb976NChQ4qMjAx0OQAAAMYFPGD16dNHTz31lE/Iqq6u1rx589SrV69AlwMAAGBcwAPWvffeqx07dqhPnz6qq6vTrbfeqqysLH377be65557Al0OAACAcQF/DlZKSopeffVVrV%2B/Xv/85z/V2NioMWPGaNiwYTrrrLMCXQ4AAIBxQXmSe2xsrEaPHh2MQwMAAPhdwAPWuHHjTjjPdxECAIBQF/CAdd555/m8rq%2Bv19dff63PP/9c48ePD3Q5AAAAxp0230W4aNEilZeXB7gaAAAA84LyXYTNGTZsmN56661glwEAAPCLnTYB6%2BOPP%2BZBowAAICycFh9yP3jwoD777DNdd911gS4HAADAuIAHrHbt2vl8D6Ek/epXv9INN9ygq666KtDlAAAAGBfwgPWnP/0p0IcEAAAIqIAHrK1bt7Z42%2B7du/uxEgAAAP8IeMAaO3as9xahZVne8WPHbDab/vnPfwa6PAAAgF8s4AFr8eLFmjNnju6%2B%2B2716NFDUVFR%2BuSTTzRr1ixdffXVuvLKKwNdEgAAgFEBf0xDfn6%2Bpk%2BfriuuuEIJCQlq1aqVevXqpVmzZunFF1/Ueeed5/0BAAAIRQEPWBUVFc2Gp7POOktVVVWBLgcAAMC4gAestLQ0PfLIIzp48KB3rLq6WvPmzdPll18e6HIAAACMC/hnsO6//36NGzdO/fr1U8eOHWVZlr766islJSXpueeeC3Q5AAAAxgU8YHXq1Elvvvmm1q9frz179kiSrr/%2Beg0ZMkSxsbGBLgcAAMC4gAcsSTrnnHM0evRoffvtt%2BrQoYOko09zBwAACAcB/wyWZVmaP3%2B%2BunfvrqFDh6q8vFz33HOPpk2bpiNHjgS6HAAAAOMCHrBWrlypP//5z5oxY4aioqIkSQMHDtSmTZtUWFho7Dgej0cPPvigunfvrt/85jd65JFHvA8x3blzp0aPHi2Hw6GRI0dqx44dPu9dv369Bg4cKIfDoZycHP3www/G6gIAAOEv4AGrqKhI06dP14gRI7xPb7/yyis1Z84cvf7668aOM2fOHG3evFnLli3TggUL9NJLL6moqEiHDx/WxIkTlZmZqXXr1ik9PV2TJk3S4cOHJUnbt2/XtGnTNGXKFBUVFammpkZ5eXnG6gIAAOEv4J/B%2Bvbbb9WlS5cm4507d5bL5TJyjOrqaq1du1bLly/XZZddJkmaMGGCnE6n7Ha7oqOjNXXqVNlsNk2bNk3vvPOONmzYoBEjRmjVqlUaPHiwhg8fLkkqKChQVlaWysrKvJ8XAwAAOJGAX8E677zz9MknnzQZf%2Bedd4wFmOLiYp111lnq0aOHd2zixInKz8%2BX0%2BlURkaG9%2BqZzWZTt27dVFJSIklyOp3KzMz0vq9t27Zq166dnE6nkdoAAED4C/gVrJtvvlkPPvigXC6XLMvS%2B%2B%2B/r6KiIq1cuVL33nuvkWOUlZXpvPPO06uvvqrFixfryJEjGjFihG699Va5XC5deOGFPtsnJiaqtLRU0tEnzScnJzeZLy8vb/HxKyoqmlyNs9vjmuz3VEVGRvj8CnPorf/QW/%2BgrzgRu/30WxdnypoNeMAaOXKk6uvr9eSTT6q2tlbTp09X69atdfvtt2vMmDFGjnH48GF9/fXXWr16tfLz8%2BVyuTR9%2BnTFxsbK7XZ7P1z/k6ioKHk8HklSbW3tCedboqioqMkH9nNycpSbm3uKZ9S8%2BHieG%2BYv9NZ/6K1/0Fc0JyGhVbBLOK5wX7MBD1jr16/XoEGDlJ2drR9%2B%2BEGWZSkxMdHoMex2uw4ePKgFCxZ4v/dw7969evHFF3X%2B%2Bec3CUsej0cxMTGSpOjo6GbnT%2BYhqNnZ2RowYMAxNcWpqurQqZxOE5GREYqPj1VNjVsNDY1G9omj6K3/0Fv/oK84EVN/75gU6DUbrJAZ8IA1a9YsvfDCCzrnnHPUunVrvxwjKSlJ0dHRPl8q/V//9V/at2%2BfevToocrKSp/tKysrvbfvUlJSmp1PSkpq8fGTk5Ob3A50uQ6ovt7sQmpoaDS%2BTxxFb/2H3voHfUVzTuc1Ee5rNuA3QDt27KjPP//cr8dwOByqq6vTl19%2B6R374osvdN5558nhcOjjjz/2PhPLsixt27ZNDofD%2B97i4mLv%2B/bt26d9%2B/Z55wEAAH5OwK9gde7cWXfddZeWLl2qjh07Kjo62mc%2BPz//Fx/jggsuUP/%2B/ZWXl6eZM2fK5XJpyZIluvXWWzVo0CAtWLBAc%2BfO1bXXXqvVq1fL7XZr8ODBkqQxY8Zo7NixSktLU2pqqubOnav%2B/fvziAYAANBiAQ9YX375pTIyMiTJ2HOvmjN//nzNnj1bY8aMUWxsrK6//nqNHTtWNptNTz31lGbMmKGXXnpJF198sZYsWaK4uDhJUnp6umbNmqWFCxfqxx9/VO/evTV79my/1QkAAMKPzfrpXpkfFRQUaMqUKd4QcyZyuQ4Y25fdHqGEhFaqqjoU1vevg4He%2Bg%2B99Y9Q7%2Bvgx94Ldglh7a3bewe7hCYCvWaTks72%2BzGaE5DPYC1fvlxut9tnbOLEiaqoqAjE4QEAAAIqIAGruYtkW7duVV1dXSAODwAAEFDh/RhVAACAICBgAQAAGBawgPXTlysDAACEu4A9pmHOnDk%2Bz7w6cuSI5s2bp1atfB9hb%2BI5WAAAAMEUkIDVvXv3Js%2B8Sk9PV1VVlaqqqgJRAgAAQMAEJGCtXLkyEIcBAAA4LfAhdwAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMPCPmBNnDhR9957r/f1zp07NXr0aDkcDo0cOVI7duzw2X79%2BvUaOHCgHA6HcnJy9MMPPwS6ZAAAEOLCOmC98cYbevvtt72vDx8%2BrIkTJyozM1Pr1q1Tenq6Jk2apMOHD0uStm/frmnTpmnKlCkqKipSTU2N8vLyglU%2BAAAIUWEbsKqrq1VQUKDU1FTv2Jtvvqno6GhNnTrACppnAAAR2ElEQVRVnTp10rRp09SqVStt2LBBkrRq1SoNHjxYw4cPV%2BfOnVVQUKC3335bZWVlwToNAAAQgsI2YD388MMaNmyYLrzwQu%2BY0%2BlURkaGbDabJMlms6lbt24qKSnxzmdmZnq3b9u2rdq1ayen0xnY4gEAQEgLy4D1/vvv66OPPtLkyZN9xl0ul5KTk33GEhMTVV5eLkmqqKg44TwAAEBL2INdgGl1dXWaMWOGpk%2BfrpiYGJ85t9utqKgon7GoqCh5PB5JUm1t7QnnW6qiokIul8tnzG6PaxLeTlVkZITPrzCH3voPvfUP%2BooTsdtPv3VxpqzZsAtYhYWFuvTSS9W3b98mc9HR0U3Cksfj8Qax483HxsaeVA1FRUUqLCz0GcvJyVFubu5J7efnxMefXF1oOXrrP/TWP%2BgrmpOQ0CrYJRxXuK/ZsAtYb7zxhiorK5Weni5J3sC0ceNGDR06VJWVlT7bV1ZWeq8spaSkNDuflJR0UjVkZ2drwIABPmN2e5yqqg6d1H6OJzIyQvHxsaqpcauhodHIPnEUvfUfeusf9BUnYurvHZMCvWaDFTLDLmCtXLlS9fX13tfz58%2BXJN11113aunWrnn76aVmWJZvNJsuytG3bNt1yyy2SJIfDoeLiYo0YMUKStG/fPu3bt08Oh%2BOkakhOTm5yO9DlOqD6erMLqaGh0fg%2BcRS99R966x/0Fc05nddEuK/ZsAtY5513ns/rVq2OJtfzzz9fiYmJWrBggebOnatrr71Wq1evltvt1uDBgyVJY8aM0dixY5WWlqbU1FTNnTtX/fv3V4cOHQJ%2BHgAAIHSF9yfMjnHWWWfpqaee8l6lcjqdWrJkieLi4iRJ6enpmjVrlhYtWqQxY8bonHPOUX5%2BfpCrBgAAocZmWZYV7CLOBC7XAWP7stsjlJDQSlVVh8L68mow0Fv/obf%2BEep9HfzYe8EuIay9dXvvYJfQRKDXbFLS2X4/RnPOqCtYAAAAgRB2n8ECgDMdV4WA4OMKFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwsA1Y%2B/fvV25urnr06KG%2BffsqPz9fdXV1kqSysjLdeOONSktL05VXXql3333X572bN2/W0KFD5XA4NG7cOJWVlQXjFAAAQIgKy4BlWZZyc3Pldrv1/PPP69FHH9Xf//53PfbYY7IsSzk5OWrTpo3Wrl2rYcOGacqUKdq7d68kae/evcrJydGIESO0Zs0atW7dWpMnT5ZlWUE%2BKwAAECrswS7AH7744guVlJTovffeU5s2bSRJubm5evjhh9WvXz%2BVlZVp9erViouLU6dOnfT%2B%2B%2B9r7dq1uu222/Tyyy/r0ksv1YQJEyRJ%2Bfn56t27t7Zs2aKePXsG87QAAECICMuAlZSUpKVLl3rD1U8OHjwop9OpSy65RHFxcd7xjIwMlZSUSJKcTqcyMzO9c7GxseratatKSkoIWACAkDL4sfeCXUKLvXV772CXYFRYBqz4%2BHj17dvX%2B7qxsVGrVq1Sr1695HK5lJyc7LN9YmKiysvLJeln51uioqJCLpfLZ8xuj2uy31MVGRnh8yvMobf%2BQ2/9g74iXNjt4bWGwzJgHWvevHnauXOn1qxZo2effVZRUVE%2B81FRUfJ4PJIkt9t9wvmWKCoqUmFhoc9YTk6OcnNzT/EMmhcfH2t0f/gXeus/9NY/6CtCXUJCq2CXYFTYB6x58%2BZpxYoVevTRR3XRRRcpOjpa1dXVPtt4PB7FxMRIkqKjo5uEKY/Ho/j4%2BBYfMzs7WwMGDPAZs9vjVFV16BTPwldkZITi42NVU%2BNWQ0OjkX3iKHrrP/TWP%2BgrwoWpvyOPFazgFtYBa/bs2XrxxRc1b948XXHFFZKklJQU7d6922e7yspK7%2B27lJQUVVZWNpnv0qVLi4%2BbnJzc5Hagy3VA9fVm//BraGg0vk8cRW/9h976B31FqAu39RteNzz/TWFhoVavXq1HHnlEQ4YM8Y47HA59%2Bumnqq2t9Y4VFxfL4XB454uLi71zbrdbO3fu9M4DAAD8nLAMWHv27NETTzyhP/7xj8rIyJDL5fL%2B9OjRQ23btlVeXp5KS0u1ZMkSbd%2B%2BXaNGjZIkjRw5Utu2bdOSJUtUWlqqvLw8tW/fnn9BCAAAWiwsA9bf/vY3NTQ06Mknn1SfPn18fiIjI/XEE0/I5XJpxIgReu2117Ro0SK1a9dOktS%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%2BJkELCO4Xa7fcKVJO9rj8fTon0UFRWpsLDQZ2zKlCm67bbbjNRYUVGhFSuWKjs7Wx/N5balSf/eW1OBGEeFcm9P5//OKioqVFRUFJJ9Pd3RW/84U/oaXnHRgOjo6CZB6qfXMTExLdpHdna21q1b5/OTnZ1trEaXy6XCwsImV8nwy9Fb/6G3/kFf/Yfe%2BseZ0leuYB0jJSVFVVVVqq%2Bvl91%2BtD0ul0sxMTGKj49v0T6Sk5PDOpUDAIAT4wrWMbp06SK73a6SkhLvWHFxsVJTUxURQbsAAMDPIzEcIzY2VsOHD9fMmTO1fft2bdq0Sc8884zGjRsX7NIAAECIiJw5c%2BbMYBdxuunVq5d27typBQsW6P3339ctt9yikSNHBrssH61atVKPHj3UqlWrYJcSduit/9Bb/6Cv/kNv/eNM6KvNsiwr2EUAAACEE24RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYIWAmpoaTZs2Tb/5zW/Uq1cv3XvvvaqpqTnu9nPmzNHFF1/s87Nq1aoAVhw6Tra3ZWVluvHGG5WWlqYrr7xS7777bgCrDU2WZWnChAlat27dCbdj3Z6clvaVNdtylmVp/vz56tWrl3r06KGCggI1NjYed3vW7PHV1dXpvvvuU2Zmpvr06aNnnnnmuNvu3LlTo0ePlsPh0MiRI7Vjx44AVuo/BKwQMGPGDO3atUtLlizRsmXLtGfPHt1///3H3X7Pnj2688479e6773p/Trev%2BjldnExvLctSTk6O2rRpo7Vr12rYsGGaMmWK9u7dG%2BCqQ0djY6PmzJmj995772e3Zd22XEv7ypo9OcuXL9f69etVWFiohQsX6vXXX9fy5cuPuz1r9vgKCgq0Y8cOrVixQjNmzFBhYaE2bNjQZLvDhw9r4sSJyszM1Lp165Senq5Jkybp8OHDQajaMAuntUOHDlldunSxSkpKvGPbtm2zunTpYtXW1jb7nr59%2B1r/93//F6gSQ9bJ9nbz5s1WWlqadejQIe/Y%2BPHjrYULFwak3lBTXl5u3XDDDVb//v2tzMxMa%2B3atSfcnnXbMifTV9bsyfntb3/r089XX33VysrKOu72rNnmHTp0yEpNTbU%2B%2BOAD79iiRYusG264ocm2L7/8sjVgwACrsbHRsizLamxstH73u9/97J8XoYArWKe5iIgILV68WF26dPEZb2ho0KFDh5psf/DgQe3fv18dO3YMUIWh62R763Q6dckllyguLs47lpGRoZKSEr/XGoo%2B/fRTtW3bVmvXrtXZZ599wm1Zty13Mn1lzbbc/v37tW/fPnXv3t07lpGRoe%2B%2B%2B04VFRVNtmfNHt%2BuXbtUX1%2Bv9PR071hGRoacTmeTW65Op1MZGRmy2WySJJvNpm7duoXFGiVgneZiYmLUr18/RUVFeceee%2B45XXzxxWrdunWT7ffs2SObzabFixerX79%2Buuqqq/TKK68EsuSQcbK9dblcSk5O9hlLTExUeXm532sNRQMGDFBBQUGzvTwW67blTqavrNmWc7lckuTTrzZt2khSs/1izR6fy%2BVSQkKCz5%2Btbdq0UV1dnaqrq5tsG65r1B7sAiDV1tZq//79zc4lJSX5/N/nqlWr9NZbb2np0qXNbv/FF1/IZrPpggsu0A033KCtW7fqgQce0FlnnaXf/e53fqn/dGayt2632%2BcPDEmKioqSx%2BMxV3AIOZne/hzW7b%2BY7Ctr1teJevvTZ37%2BvV8//b65frFmj%2B94605q2stwXqMErNOA0%2BnUuHHjmp1btGiRBg4cKEl6/vnnNWfOHOXl5alPnz7Nbj98%2BHBlZWXp3HPPlSR17txZX331lV588cUz8j96k72Njo5u8n9fHo9HMTExZosOES3tbUuwbv/FZF9Zs75O1Nu7775b0tH%2BREdHe38vSbGxsU22Z80eX3R0dJOA9NPrY9fe8bYNhzVKwDoN9OzZU5999tkJt1m2bJkKCgo0depUjR8//rjb2Ww273/wP7ngggv0wQcfGKk11JjsbUpKinbv3u0zVllZ2eTy9pmiJb1tKdbtv5jsK2vW14l6u3//fs2bN08ul0vt27eX9K/bhklJSU22Z80eX0pKiqqqqlRfXy%2B7/WjMcLlciomJUXx8fJNtKysrfcbCZY3yGawQ8Morr6igoEB5eXm6%2BeabT7jt//7v/%2BrGG2/0Gdu1a5cuuOACP1YYuk6mtw6HQ59%2B%2Bqlqa2u9Y8XFxXI4HP4uM%2Byxbv2DNdtyKSkpateunYqLi71jxcXFateuXbN/2bNmj69Lly6y2%2B0%2BH1QvLi5WamqqIiJ8Y4fD4dDHH38sy7IkHX20yLZt28JijRKwTnPV1dWaNWuWrr76ag0ZMkQul8v709DQIEn64YcfvP/qLSsrS1u3btWyZcv0zTff6IUXXtCrr76qCRMmBPM0Tksn29sePXqobdu2ysvLU2lpqZYsWaLt27dr1KhRwTyNkMW69Q/W7KkbM2aM5s%2Bfrw8//FAffvihFixY4HNLkTXbMrGxsRo%2BfLhmzpyp7du3a9OmTXrmmWe8vXS5XN7QP2jQINXU1Gju3LnavXu35s6dK7fbrcGDBwfzFMwI9nMicGLr16%2B3LrroomZ/ysrKLMuyrKysLJ/n2vz1r3%2B1/vCHP1ipqanWoEGDrI0bNwar/NPaqfT2q6%2B%2Bsq6//nrr0ksvtYYMGWK99957wSo/pGRlZTV5rg3r9pdrSV9Zsy1XX19vPfTQQ1ZmZqbVs2dPa968ed7nM1kWa/ZkHD582Jo6daqVlpZm9enTx1q%2BfLl37qKLLvJZt06n0xo%2BfLiVmppqjRo1yvr000%2BDULF5Nsv6/9flAAAAYAS3CAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsP8HDih2pRmAO%2BsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6090276145418952310”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.7777581929748312</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-2.4914751705058906</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.3660385278387144</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.6606871436162249</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.29216362776089877</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5727577363531964</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.47485297220779166</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.4539179868851524</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.48279832271787315</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.5023841583305733</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6090276145418952310”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-2.4914751705058906</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-1.0306852483372513</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.8420947050758385</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7870816411765578</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.7777581929748312</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>-0.19070303995662163</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.14834674182593544</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.13700748954855402</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>-0.03610139992422701</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027258200990513792</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_18YOUNGER10”>PCT_18YOUNGER10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3132</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.417</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>40.127</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4365994445941400486”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4365994445941400486”

aria-controls=”quantiles-4365994445941400486” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4365994445941400486” aria-controls=”histogram-4365994445941400486”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4365994445941400486” aria-controls=”common-4365994445941400486”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4365994445941400486” aria-controls=”extreme-4365994445941400486”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4365994445941400486”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>18.275</td>

</tr> <tr>

<th>Q1</th> <td>21.435</td>

</tr> <tr>

<th>Median</th> <td>23.333</td>

</tr> <tr>

<th>Q3</th> <td>25.102</td>

</tr> <tr>

<th>95-th percentile</th> <td>29.155</td>

</tr> <tr>

<th>Maximum</th> <td>40.127</td>

</tr> <tr>

<th>Range</th> <td>40.127</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.6667</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.346</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.14288</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.5178</td>

</tr> <tr>

<th>Mean</th> <td>23.417</td>

</tr> <tr>

<th>MAD</th> <td>2.4612</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33491</td>

</tr> <tr>

<th>Sum</th> <td>73343</td>

</tr> <tr>

<th>Variance</th> <td>11.196</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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cX5jcXExKi0tPS881VVVaqtrfWbd7lcatu2re/5TVFWViaPx%2BM35nJFNTruxQoLC/X77BRO7Utybm9O78tuXK7z1%2B30NaMv%2B3Bqb7YNWGdTXV2t8PBwv7Hw8HB5vd7zztfU1Pgen%2B35TZGfn6%2B8vDy/saysLGVnZzd5H80RHR3ZIvsNNqf2JTm3N6f2ZTft2rVu8rZOXTP6sh%2Bn9ea4gBUREaHKykq/Ma/Xq1atWvnmTw9LXq9X0dHRioiI8D0%2BfT4ysukLn5mZqfT0dL8xlytKFRUnmryPpggLC1V0dKSqqqpVX99gdN/B5NS%2BJOf25vS%2B7KYp32ucvmb0ZR8t3VtzfuAwyXEBKz4%2BXgcOHPAbKy8v912ei4%2BPV3l5eaP5Hj16qG3btoqIiFB5ebm6desmSaqrq1NlZaViY2ObXENcXFyjy4EezzHV1bXMF0V9fUOL7TuYnNqX5NzenNqX3TRnDZy6ZvRlP07rzVkXPCW53W59/PHHvst9klRQUCC32%2B2bLygo8M1VV1dr3759crvdCg0NVWJiot98YWGhXC6XunfvHrgmAACArTkuYPXt21cdO3bUzJkzVVxcrOXLl2vPnj0aN26cJGns2LHavXu3li9fruLiYs2cOVOdO3dWv379JH375vnnnntO27dv1549ezR37lzdcMMNzbpECAAAftgcF7DCwsL09NNPy%2BPxKCMjQ6%2B%2B%2BqqWLFmihIQESVLnzp311FNPaePGjRo3bpwqKyu1ZMkShYSESJJGjhypKVOmaM6cOZo0aZJ69%2B6te%2B%2B9N5gtAQAAmwmxvruFOVqUx3PM%2BD5drlC1a9daFRUnHHXd2ql9Sc7tzel9pc5%2BPdilONYf7xpgdH9Ofy06rS%2Bp5XuLjb3E%2BD6bwnFnsAAAAIKNgAUAAGBYwAPW%2BPHjtW7dOh07Zv6SGQAAwPdBwANW//79tXTpUg0cOFDTpk3TO%2B%2B8I94GBgAAnCTgAevuu%2B/Wn//8Zz399NMKCwvTnXfeqcGDB%2Bvxxx/XZ599FuhyAAAAjAvKndxDQkI0YMAADRgwQNXV1VqzZo2efvppLV%2B%2BXH369NEtt9yin//858EoDQAA4KIF7U/llJWV6dVXX9Wrr76qTz75RH369NEvfvELlZaW6v7779f777%2Bv2bNnB6s8AACACxbwgPXKK6/olVde0c6dO9W%2BfXuNGTNGTz75pLp27erbpmPHjlq4cCEBCwAA2FLAA9bs2bM1ZMgQLVmyRIMGDVJoaOO3gV1%2B%2BeW66aabAl0aAACAEQEPWH/5y1/Url07VVZW%2BsLVnj171LNnT4WFhUmS%2BvTpoz59%2BgS6NAAAACMC/luEx48f1/Dhw/Xss8/6xiZPnqzRo0fryJEjgS4HAADAuIAHrN///ve67LLLdNttt/nGtm7dqo4dOyonJyfQ5QAAABgX8ID1wQcf6L777lNsbKxvrH379po%2Bfbree%2B%2B9QJcDAABgXMADlsvlUlVVVaPx6upq7ugOAAAcIeABa9CgQVqwYIG%2B%2BOIL31hJSYlycnKUlpYW6HIAAACMC/hvEc6YMUO33Xabhg0bpujoaElSVVWVevbsqZkzZwa6HAAAAOMCHrBiYmK0adMmvfvuuyouLpbL5dIVV1yhH//4xwoJCQl0OQAAAMYF5U/lhIWFKS0tjUuCAADAkQIesDwej5544gnt3r1bp06davTG9j/96U%2BBLgkAAMCogAesBx54QHv37tXIkSN1ySWXBPrwAAAALS7gAeu9997TihUrlJqaGuhDAwAABETAb9MQFRWlmJiYQB8WAAAgYAIesEaPHq0VK1aovr4%2B0IcGAAAIiIBfIqysrNSWLVv05ptvqkuXLgoPD/ebf/755wNdEgAAgFFBuU3DqFGjgnFYAACAgAh4wMrJyQn0IQEAAAIq4O/BkqSysjLl5eXp7rvv1j/%2B8Q%2B9/vrr%2BvTTT4NRCgAAgHEBD1iHDh3Sddddp02bNumNN97QyZMntXXrVo0dO1ZFRUWBLgcAAMC4gAeshx9%2BWEOHDtX27dv1ox/9SJL02GOPKT09XYsXLzZ2nCNHjmjKlCnq06eP0tPTtWrVKt/cvn37NH78eLndbo0dO1Z79%2B71e%2B6WLVs0dOhQud1uZWVl6euvvzZWFwAAcL6AB6zdu3frtttu8/vDzi6XS3fccYf27dtn7Dh33XWXoqKi9NJLL2nWrFl64okntG3bNp08eVKTJ09WamqqXnrpJSUnJ2vKlCk6efKkJGnPnj2aPXu2pk6dqvz8fFVVVWnmzJnG6gIAAM4X8IDV0NCghoaGRuMnTpxQWFiYkWN88803Kiws1O23366uXbtq6NChSktL044dO7R161ZFRERo%2BvTp6tatm2bPnq3WrVvr9ddflyStXbtWI0aM0JgxY9S9e3ctWrRIb731lkpKSozUBgAAnC/gAWvgwIFatmyZX8iqrKxUbm6u%2Bvfvb%2BQYrVq1UmRkpF566SWdOnVKn376qXbv3q0ePXqoqKhIKSkpvjNoISEh6tOnjwoLCyVJRUVFfn/Gp2PHjkpISOD9YQAAoMkCHrDuu%2B8%2B7d27VwMHDlRtba1uv/12DRkyRF9%2B%2BaVmzJhh5BgRERGaM2eO8vPz5Xa7NWLECA0aNEjjx4%2BXx%2BNRXFyc3/YxMTEqLS2V9O1vOJ5rHgAA4HwCfh%2Bs%2BPh4vfzyy9qyZYv%2B9re/qaGhQRMmTNDo0aPVpk0bY8c5ePCghgwZottuu03FxcWaP3%2B%2BfvzjH6u6urrR3ePDw8Pl9XolSTU1Neecb4qysjJ5PB6/MZcrqlFwu1hhYaF%2Bn53CqX1Jzu3N6X2h5bhcZv%2BNnf5adFpfknN7C8qd3CMjIzV%2B/PgW2/%2BOHTu0YcMGvfXWW2rVqpUSExN19OhRPfPMM%2BrSpUujsOT1etWqVStJ3579OtN8ZGRkk4%2Bfn5%2BvvLw8v7GsrCxlZ2dfYEfnFh3d9NrsxKl9Sc7tzal9oeW0a9e6Rfbr1NeiU/uSnNdbwAPWzTfffM55E3%2BLcO/evbrssst8oUmSrr76ai1dulSpqakqLy/32768vNx3dik%2BPv6M87GxsU0%2BfmZmptLT0/3GXK4oVVScaG4r5xQWFqro6EhVVVWrvr7xLw7YlVP7kpzbm9P7Qsvh%2B2LTOLUvqeV7a6kQfz4BD1idOnXye1xXV6dDhw7pk08%2B0S233GLkGHFxcTp06JC8Xq/vct%2Bnn36qzp07y%2B1269lnn5VlWQoJCZFlWdq9e7d%2B%2B9vfSpLcbrcKCgqUkZEh6dv7aR05ckRut7tZxz/9cqDHc0x1dS3zRVFf39Bi%2Bw4mp/YlObc3p/aFlsP3xeZxal%2BS83r73vwtwiVLlhh7I3l6erpyc3N1//336/bbb9dnn32mpUuX6ne/%2B52GDx%2BuRx99VAsXLtQvf/lLrVu3TtXV1RoxYoQkacKECZo4caKSkpKUmJiohQsXavDgwerSpYuR2gAAgPN9b95RNnr0aP3xj380sq9LLrlEq1atksfj0bhx45STk6Pbb79dmZmZatOmjZYtW%2BY7S1VUVKTly5crKipKkpScnKx58%2BZpyZIlmjBhgi699FL%2BQDUAAGiWoLzJ/Uw%2B/PBDYzcalaQrrrhCK1euPONc7969tWnTprM%2BNyMjw3eJEAAAoLm%2BF29yP378uP7%2B97/rxhtvDHQ5AAAAxgU8YCUkJPj9HUJJ%2BtGPfqSbbrpJ119/faDLAQAAMC7gAevhhx8O9CEBAAACKuAB6/3332/yttdcc00LVgIAANAyAh6wJk6c6LtEaFmWb/z0sZCQEP3tb38LdHkAAAAXLeABa%2BnSpVqwYIHuvfde9e3bV%2BHh4froo480b948/eIXv9C1114b6JIAAACMCvh9sHJycjRnzhwNGzZM7dq1U%2BvWrdW/f3/NmzdPL774ojp16uT7AAAAsKOAB6yysrIzhqc2bdqooqIi0OUAAAAYF/CAlZSUpMcee0zHjx/3jVVWVio3N1c//vGPA10OAACAcQF/D9b999%2Bvm2%2B%2BWYMGDVLXrl1lWZY%2B//xzxcbG6vnnnw90OQAAAMYFPGB169ZNW7du1ZYtW3Tw4EFJ0q9%2B9SuNHDlSkZGRgS4HAADAuKD8LcJLL71U48eP15dffqkuXbpI%2BvZu7gAAAE4Q8PdgWZalxYsX65prrtGoUaNUWlqqGTNmaPbs2Tp16lSgywEAADAu4AFrzZo1euWVV/Tggw8qPDxckjR06FBt375deXl5gS4HAADAuIAHrPz8fM2ZM0cZGRm%2Bu7dfe%2B21WrBggTZv3hzocgAAAIwLeMD68ssv1aNHj0bj3bt3l8fjCXQ5AAAAxgU8YHXq1EkfffRRo/G//OUvvje8AwAA2FnAf4vw17/%2BtR566CF5PB5ZlqUdO3YoPz9fa9as0X333RfocgAAAIwLeMAaO3as6urq9Mwzz6impkZz5sxR%2B/btddddd2nChAmBLgcAAMC4gAesLVu2aPjw4crMzNTXX38ty7IUExMT6DIAAABaTMDfgzVv3jzfm9nbt29PuAIAAI4T8IDVtWtXffLJJ4E%2BLAAAQMAE/BJh9%2B7ddc8992jFihXq2rWrIiIi/OZzcnICXRIAAIBRAQ9Yn332mVJSUiSJ%2B14BAABHCkjAWrRokaZOnaqoqCitWbMmEIcEAAAImoC8B2vlypWqrq72G5s8ebLKysoCcXgAAICACkjAsiyr0dj777%2Bv2traQBweAAAgoAL%2BW4QAAABOR8ACAAAwLGABKyQkJFCHkiR5vV499NBDuuaaa/STn/xEjz32mO9S5b59%2BzR%2B/Hi53W6NHTtWe/fu9Xvuli1bNHToULndbmVlZenrr78OaO0AAMDeAnabhgULFvjd8%2BrUqVPKzc1V69at/bYzdR%2BsBQsWaOfOnXruued04sQJ/e53v1NCQoKuv/56TZ48Wdddd50efvhhvfjii5oyZYq2bdumqKgo7dmzR7Nnz9ZDDz2k7t27a%2BHChZo5c6aWLVtmpC4AAOB8AQlY11xzTaN7XiUnJ6uiokIVFRXGj1dZWamNGzdq5cqV6t27tyRp0qRJKioqksvlUkREhKZPn66QkBDNnj1bf/nLX/T6668rIyNDa9eu1YgRIzRmzBhJ395iYsiQISopKVGXLl2M1woAAJwnIAEr0Pe%2BKigoUJs2bdS3b1/f2OTJkyVJDzzwgFJSUnyXLENCQtSnTx8VFhYqIyNDRUVF%2Bs1vfuN7XseOHZWQkKCioiICFgAAaJKA38k9EEpKStSpUye9/PLLWrp0qU6dOqWMjAzdfvvt8ng8uuKKK/y2j4mJUXFxsSSprKxMcXFxjeZLS0ubfPyysrJGZ%2BxcrqhG%2B71YYWGhfp%2Bdwql9Sc7tzel9oeW4XGb/jZ3%2BWnRaX5Jze3NkwDp58qQOHTqkdevWKScnRx6PR3PmzFFkZKSqq6sVHh7ut314eLi8Xq8kqaam5pzzTZGfn6%2B8vDy/saysLGVnZ19gR%2BcWHR3ZIvsNNqf2JTm3N6f2hZbTrl3r8290AZz6WnRqX5LzenNkwHK5XDp%2B/LgeffRRderUSZJ0%2BPBhvfjii7rssssahSWv16tWrVpJkiIiIs44HxnZ9IXPzMxUenr6aTVFqaLixIW0c1ZhYaGKjo5UVVW16usbjO47mJzal%2BTc3pzeF1oO3xebxql9SS3fW0uF%2BPNxZMCKjY1VRESEL1xJ0n/8x3/oyJEj6tu3r8rLy/22Ly8v912%2Bi4%2BPP%2BN8bGxsk48fFxfX6HKgx3NMdXUt80VRX9/QYvsOJqf2JTm3N6f2hZbD98XmcWpfkvN6c9YFz39yu92qra3VZ5995hv79NNP1alTJ7ndbn344Ye%2Be2JZlqXdu3fL7Xb7nltQUOB73pEjR3TkyBHfPAAAwPk4MmBdfvnlGjx4sGbOnKn9%2B/fr7bff1vLlyzVhwgQNHz5cVVVVWrhwoQ4cOKCFCxequrpaI0aMkCRNmDBBr7zyitavX6/9%2B/dr%2BvTpGjx4ML9BCAAAmsyRAUuSFi9erH//93/XhAkTNGPGDP3qV7/SxIkT1aZNGy1btkwFBQW%2B2zIsX75cUVFRkr69P9e8efO0ZMkSTZgwQZdeeqmxm58CAIAfBke%2BB0uSLrnkEi1atOiMc71799amTZvO%2BtyMjAxlZGS0VGkAAMDhHHsGCwAAIFgIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGHcRqdyAAAUJElEQVQELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjm%2BIA1efJk3Xfffb7H%2B/bt0/jx4%2BV2uzV27Fjt3bvXb/stW7Zo6NChcrvdysrK0tdffx3okgEAgM05OmC99tpreuutt3yPT548qcmTJys1NVUvvfSSkpOTNWXKFJ08eVKStGfPHs2ePVtTp05Vfn6%2BqqqqNHPmzGCVDwAAbMqxAauyslKLFi1SYmKib2zr1q2KiIjQ9OnT1a1bN82ePVutW7fW66%2B/Lklau3atRowYoTFjxqh79%2B5atGiR3nrrLZWUlASrDQAAYEOODViPPPKIRo8erSuuuMI3VlRUpJSUFIWEhEiSQkJC1KdPHxUWFvrmU1NTfdt37NhRCQkJKioqCmzxAADA1lzBLqAl7NixQx988IE2b96suXPn%2BsY9Ho9f4JKkmJgYFRcXS5LKysoUFxfXaL60tLRZxy8rK5PH4/Ebc7miGu37YoWFhfp9dgqn9iU5tzen94WW43KZ/Td2%2BmvRaX1Jzu3NcQGrtrZWDz74oObMmaNWrVr5zVVXVys8PNxvLDw8XF6vV5JUU1Nzzvmmys/PV15ent9YVlaWsrOzm7WfpoqOjmyR/QabU/uSnNubU/tCy2nXrnWL7Nepr0Wn9iU5rzfHBay8vDz16tVLaWlpjeYiIiIahSWv1%2BsLYmebj4xs3qJnZmYqPT3db8zlilJFxYlm7ed8wsJCFR0dqaqqatXXNxjddzA5tS/Jub05vS%2B0HL4vNo1T%2B5JavreWCvHn47iA9dprr6m8vFzJycmS5AtMb7zxhkaNGqXy8nK/7cvLy32X7uLj4884Hxsb26wa4uLiGl0O9HiOqa6uZb4o6usbWmzfweTUviTn9ubUvtBy%2BL7YPE7tS3Jeb44LWGvWrFFdXZ3v8eLFiyVJ99xzj95//309%2B%2ByzsixLISEhsixLu3fv1m9/%2B1tJktvtVkFBgTIyMiRJR44c0ZEjR%2BR2uwPfCAAAsC3HBaxOnTr5PW7d%2BttTg5dddpliYmL06KOPauHChfrlL3%2BpdevWqbq6WiNGjJAkTZgwQRMnTlRSUpISExO1cOFCDR48WF26dAl4HwAAwL6c9Zb982jTpo2WLVvmO0tVVFSk5cuXKyoqSpKUnJysefPmacmSJZowYYIuvfRS5eTkBLlqAABgN447g3W6hx9%2B2O9x7969tWnTprNun5GR4btECAAAcCF%2BUGewAAAAAsHxZ7AAfP%2BMeOKvwS4BAFoUZ7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5tiAdfToUWVnZ6tv375KS0tTTk6OamtrJUklJSW69dZblZSUpGuvvVbvvPOO33PfffddjRo1Sm63WzfffLNKSkqC0QIAALApRwYsy7KUnZ2t6upqvfDCC3r88cf15z//WU888YQsy1JWVpY6dOigjRs3avTo0Zo6daoOHz4sSTp8%2BLCysrKUkZGhDRs2qH379rrjjjtkWVaQuwIAAHbhCnYBLeHTTz9VYWGh/vrXv6pDhw6SpOzsbD3yyCMaNGiQSkpKtG7dOkVFRalbt27asWOHNm7cqDvvvFPr169Xr169NGnSJElSTk6OBgwYoF27dqlfv37BbAsAANiEI89gxcbGasWKFb5w9Z3jx4%2BrqKhIV199taKionzjKSkpKiwslCQVFRUpNTXVNxcZGamePXv65gEAAM7HkQErOjpaaWlpvscNDQ1au3at%2BvfvL4/Ho7i4OL/tY2JiVFpaKknnnQcAADgfR14iPF1ubq727dunDRs2aNWqVQoPD/ebDw8Pl9frlSRVV1efc74pysrK5PF4/MZcrqhGwe1ihYWF%2Bn12Cqf2JTm3N6f2hZbncpl9zTj1tejUviTn9ub4gJWbm6vVq1fr8ccf11VXXaWIiAhVVlb6beP1etWqVStJUkRERKMw5fV6FR0d3eRj5ufnKy8vz28sKytL2dnZF9jFuUVHR7bIfoPNqX1Jzu3NqX2h5bRr17pF9uvU16JT%2B5Kc15ujA9b8%2BfP14osvKjc3V8OGDZMkxcfH68CBA37blZeX%2B84uxcfHq7y8vNF8jx49mnzczMxMpaen%2B425XFGqqDhxIW2cVVhYqKKjI1VVVa36%2Bgaj%2Bw4mp/YlObc3p/aFlpc6%2B/Vgl9As2%2B5JO/9GLcDJX2Mt3VtLhfjzcWzAysvL07p16/TYY49p%2BPDhvnG3263ly5erpqbGd9aqoKBAKSkpvvmCggLf9tXV1dq3b5%2BmTp3a5GPHxcU1uhzo8RxTXV3LfFHU1ze02L6Dyal9Sc7tzal9Ad8J9uvbyV9jTuvNWRc8/%2BngwYN6%2Bumn9Zvf/EYpKSnyeDy%2Bj759%2B6pjx46aOXOmiouLtXz5cu3Zs0fjxo2TJI0dO1a7d%2B/W8uXLVVxcrJkzZ6pz587cogEAADSZIwPWn/70J9XX1%2BuZZ57RwIED/T7CwsL09NNPy%2BPxKCMjQ6%2B%2B%2BqqWLFmihIQESVLnzp311FNPaePGjRo3bpwqKyu1ZMkShYSEBLkrAABgFyEWtygPCI/nmPF9ulyhateutSoqTjjqtKpT%2B5Kc21tz%2BxrxxF8DUBVg3h/vGhCU4zr1e4fU8r3Fxl5ifJ9N4cgzWAAAAMFEwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjmCnYBAADYxYgn/hrsEprlj3cNCHYJP1icwQIAADCMM1iAQ9jtJ2sAcDLOYAEAABhGwDqD2tpazZo1S6mpqRo4cKD%2B8Ic/BLskAABgI1wiPINFixZp7969Wr16tQ4fPqwZM2YoISFBw4cPD3ZpAADABghYpzl58qTWr1%2BvZ599Vj179lTPnj1VXFysF154gYAFAACahEuEp9m/f7/q6uqUnJzsG0tJSVFRUZEaGhqCWBkAALALzmCdxuPxqF27dgoPD/eNdejQQbW1taqsrFT79u3Pu4%2BysjJ5PB6/MZcrSnFxcUZrDQsL9fvsFN%2BXvn62%2BO2gHh8ALpadfrv4/834abBLMIqAdZrq6mq/cCXJ99jr9TZpH/n5%2BcrLy/Mbmzp1qu68804zRf5TWVmZVq9eoczMTOPhLZi%2BL319sND8JeGysjLl5%2BcHvTfT6Mt%2BnNobfdnPd73V1PRxVG/OOvVhQERERKMg9d3jVq1aNWkfmZmZeumll/w%2BMjMzjdfq8XiUl5fX6GyZ3Tm1L8m5vdGX/Ti1N/qyH6f2xhms08THx6uiokJ1dXVyub795/F4PGrVqpWio6ObtI%2B4uDhHpXAAANA8nME6TY8ePeRyuVRYWOgbKygoUGJiokJD%2BecCAADnR2I4TWRkpMaMGaO5c%2Bdqz5492r59u/7whz/o5ptvDnZpAADAJsLmzp07N9hFfN/0799f%2B/bt06OPPqodO3bot7/9rcaOHRvsss6odevW6tu3r1q3bh3sUoxyal%2BSc3ujL/txam/0ZT9O7C3Esiwr2EUAAAA4CZcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsGyqtrZWs2bNUmpqqgYOHKg//OEPwS7JiG3btuk///M//T6ys7ODXdYF83q9GjVqlHbu3OkbKykp0a233qqkpCRde%2B21euedd4JY4YU7U28LFixotH5r164NYpVNd/ToUWVnZ6tv375KS0tTTk6OamtrJdl7zc7Vl53XS5IOHTqkX//610pOTtbgwYO1YsUK35yd1%2Bxcfdl9zb4zefJk3Xfffb7H%2B/bt0/jx4%2BV2uzV27Fjt3bs3iNWZ4Qp2AbgwixYt0t69e7V69WodPnxYM2bMUEJCgoYPHx7s0i7KgQMHNGTIEM2fP983FhEREcSKLlxtba3uvvtuFRcX%2B8Ysy1JWVpauuuoqbdy4Udu3b9fUqVO1detWJSQkBLHa5jlTb5J08OBB3X333frFL37hG2vTpk2gy2s2y7KUnZ2t6OhovfDCC/rmm280a9YshYaGavr06bZds3P1NWPGDNuulyQ1NDRo8uTJSkxM1KZNm3To0CFNmzZN8fHxGjVqlG3X7Fx9XXfddbZes%2B%2B89tpreuutt3w9nDx5UpMnT9Z1112nhx9%2BWC%2B%2B%2BKKmTJmibdu2KSoqKsjVXgQLtnPixAkrMTHReu%2B993xjS5YssW666aYgVmXG3XffbT366KPBLuOiFRcXW9dff7113XXXWVdddZVvrd59910rKSnJOnHihG/bW265xXryySeDVWqzna03y7KstLQ06%2B233w5idRfmwIED1lVXXWV5PB7f2ObNm62BAwfaes3O1Zdl2Xe9LMuyjh49av33f/%2B3dezYMd9YVlaW9eCDD9p6zc7Vl2XZe80sy7IqKiqsQYMGWWPHjrVmzJhhWZZlrV%2B/3kpPT7caGhosy7KshoYG62c/%2B5m1cePGYJZ60bhEaEP79%2B9XXV2dkpOTfWMpKSkqKipSQ0NDECu7eAcPHlTXrl2DXcZF27Vrl/r166f8/Hy/8aKiIl199dV%2BP5WlpKSosLAw0CVesLP1dvz4cR09etSW6xcbG6sVK1aoQ4cOfuPHjx%2B39Zqdqy87r5ckxcXF6YknnlCbNm1kWZYKCgr0/vvvq2/fvrZes3P1Zfc1k6RHHnlEo0eP1hVXXOEbKyoqUkpKikJCQiRJISEh6tOnjy3W61wIWDbk8XjUrl07hYeH%2B8Y6dOig2tpaVVZWBrGyi2NZlj777DO98847GjZsmIYOHarFixfL6/UGu7Rmu/HGGzVr1ixFRkb6jXs8HsXFxfmNxcTEqLS0NJDlXZSz9Xbw4EGFhIRo6dKlGjRokK6//npt2rQpSFU2T3R0tNLS0nyPGxoatHbtWvXv39/Wa3auvuy8XqdLT0/XjTfeqOTkZA0bNszWa/avTu/L7mu2Y8cOffDBB7rjjjv8xp2yXqfjPVg2VF1d7ReuJPke2zGMfOfw4cO%2B3p544gl9%2BeWXWrBggWpqanT//fcHuzwjzrZ2dl6373z66acKCQnR5Zdfrptuuknvv/%2B%2BHnjgAbVp00Y/%2B9nPgl1es%2BTm5mrfvn3asGGDVq1a5Zg1%2B9e%2BPv74Y8es15NPPqny8nLNnTtXOTk5jvk6O72vnj172nbNamtr9eCDD2rOnDlq1aqV35xT1ut0BCwbioiIaPTC%2B%2B7x6S9cO%2BnUqZN27typSy%2B9VCEhIerRo4caGhp07733aubMmQoLCwt2iRctIiKi0VlGr9dr63X7zpgxYzRkyBC1bdtWktS9e3d9/vnnevHFF7/33/z/VW5urlavXq3HH39cV111lWPW7PS%2BrrzySkeslyQlJiZK%2BvZ/4vfcc4/Gjh2r6upqv23suGan97V7927brlleXp569erld0b1O2f7f5rd1ut0XCK0ofj4eFVUVKiurs435vF41KpVK0VHRwexsovXtm1b33V4SerWrZtqa2v1zTffBLEqc%2BLj41VeXu43Vl5e3uj0uB2FhIT4vvF/5/LLL9fRo0eDVFHzzZ8/XytXrlRubq6GDRsmyRlrdqa%2B7L5e5eXl2r59u9/YFVdcoVOnTik2Nta2a3auvo4fP27bNXvttde0fft2JScnKzk5WZs3b9bmzZuVnJzsiK%2BxMyFg2VCPHj3kcrn83gBYUFCgxMREhYbad0nffvtt9evXz%2B8nz7/97W9q27at2rdvH8TKzHG73fr4449VU1PjGysoKJDb7Q5iVWb8z//8j2699Va/sf379%2Bvyyy8PTkHNlJeXp3Xr1umxxx7TyJEjfeN2X7Oz9WX39fryyy81depUv3Cxd%2B9etW/fXikpKbZds3P1tWbNGtuu2Zo1a7R582a9/PLLevnll5Wenq709HS9/PLLcrvd%2BvDDD2VZlqRv34%2B7e/duW6zXOQX1dxhxwR544AFr5MiRVlFRkbVt2zarT58%2B1htvvBHssi7KsWPHrLS0NGvatGnWwYMHrTfffNMaOHCgtXz58mCXdlH%2B9VYGdXV11rXXXmvddddd1ieffGItW7bMSkpKsr766qsgV3lh/rW3oqIi6%2Bqrr7ZWrFhhHTp0yHrhhResXr16Wbt37w5yled34MABq0ePHtbjjz9ulZWV%2BX3Yec3O1Zed18uyvv1aysjIsCZNmmQVFxdbb775pvWTn/zEWrVqla3X7Fx92X3N/tWMGTN8t2k4duyY1b9/f2v%2B/PlWcXGxNX/%2BfGvAgAF%2Bt9mwIwKWTZ08edKaPn26lZSUZA0cONBauXJlsEsy4pNPPrFuvfVWKykpyRowYID11FNP%2Be6NYlen3yvq888/t371q19ZvXr1skaOHGn99a9/DWJ1F%2Bf03rZt22Zdd911VmJiojV8%2BHDbhP5ly5ZZV1111Rk/LMu%2Ba3a%2Bvuy6Xt8pLS21srKyrD59%2BlgDBgywnnnmGd/3C7uumWWduy%2B7r9l3/jVgWda3P6CNGTPGSkxMtMaNG2d9/PHHQazOjBDL%2Buc5OQAAABhh3zfsAAAAfE8RsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8HWiLXKLcqIYkAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4365994445941400486”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.077120822622106</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.337390642829973</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.804739490342477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.57856567284448</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.839405421160013</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.565349168058393</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.713755918064606</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.79842744817727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.60522242412006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.036039584461626</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3121)</td> <td class=”number”>3121</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4365994445941400486”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.11153928494969</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.980096673301109</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.139165009940358</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.975609756097562</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>36.54852171868571</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.44876049189928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.09318100678329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.22594142259414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.12692467748648</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_65OLDER10”>PCT_65OLDER10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3131</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.894</td>

</tr> <tr>

<th>Minimum</th> <td>3.4706</td>

</tr> <tr>

<th>Maximum</th> <td>43.385</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5838772362810170826”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5838772362810170826,#minihistogram-5838772362810170826”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5838772362810170826”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5838772362810170826”

aria-controls=”quantiles-5838772362810170826” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5838772362810170826” aria-controls=”histogram-5838772362810170826”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5838772362810170826” aria-controls=”common-5838772362810170826”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5838772362810170826” aria-controls=”extreme-5838772362810170826”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5838772362810170826”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.4706</td>

</tr> <tr>

<th>5-th percentile</th> <td>9.7045</td>

</tr> <tr>

<th>Q1</th> <td>13.128</td>

</tr> <tr>

<th>Median</th> <td>15.57</td>

</tr> <tr>

<th>Q3</th> <td>18.207</td>

</tr> <tr>

<th>95-th percentile</th> <td>23.44</td>

</tr> <tr>

<th>Maximum</th> <td>43.385</td>

</tr> <tr>

<th>Range</th> <td>39.914</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.0793</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.1837</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.26322</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.2147</td>

</tr> <tr>

<th>Mean</th> <td>15.894</td>

</tr> <tr>

<th>MAD</th> <td>3.2221</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.60325</td>

</tr> <tr>

<th>Sum</th> <td>49780</td>

</tr> <tr>

<th>Variance</th> <td>17.503</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5838772362810170826”>

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5ouHDh%2Bupp54KrKWmpmr06NE6ePCg3eMAAAAYZ3vB%2Bvd//3ddddVVuvnmmwNrmzdvVqtWrZSZmWn3OAAAAMbZXrA%2B/vhjzZkzR7GxsYG15s2ba9asWdqxY4fd4wAAABhne8Hyer0qLy%2Bvs15RUcE7ugMAAFewvWANGjRIixcv1j//%2Bc/AWnFxsTIzMzVw4EC7xwEAADDO9p8inD17tm6%2B%2BWYNGzZMMTExkqTy8nJ17dpVc%2BfOtXscAAAA42wvWC1atNDGjRv1/vvvq6ioSF6vVx07dlS/fv3k8XjsHgcAAMC4kPyqnLCwMA0cOJCnBAEAgCvZXrD8fr%2BWLVum/Px8HT9%2BvM4L29988027RwIAADDK9oL1l7/8Rbt27dJ1112nyy%2B/3O7TAwAAXHS2F6wdO3Zo1apV6tWrl92nBgAAsIXtb9MQHR2tFi1a2H1aAAAA29hesEaPHq1Vq1appqbG7lMDAADYwvanCMvKyrRp0ya9/fbbatu2rcLDw4OOP/vss3aPBAAAYFRI3qZh1KhRoTgtAACALWwvWJmZmXafEgAAwFa2vwZLkkpKSpSdna2ZM2fqf//3f7Vlyxbt27fP6Dmqqqp0//3365prrtFvf/tbPfLII4H33Nq9e7fGjx8vn8%2BnsWPHateuXUGfu2nTJg0dOlQ%2Bn09paWn6/vvvjc4GAADczfaCtX//fl1//fXauHGjtm7dqqNHj2rz5s0aO3asCgsLjZ1n8eLFev/99/XXv/5VDz/8sJ5//nnl5ubq6NGjSk1NVa9evbRhwwYlJiZq%2BvTpOnr0qCRp586dmjdvntLT05Wbm6vy8nJ%2BRyIAADgnthesBx54QEOHDtUbb7yhyy67TJL0yCOPKDk5WQ899JCRc5SVlWn9%2BvVatGiRunfvrn79%2BmnatGkqLCzU5s2bFRERoVmzZqlDhw6aN2%2BeGjdurC1btkiS1qxZoxEjRmjMmDHq3Lmzli5dqm3btqm4uNjIbAAAwP1sL1j5%2Bfm6%2Beabg36xs9fr1W233abdu3cbOUdeXp6aNGmi3r17B9ZSU1OVmZmpwsJCJSUlBc7v8XjUs2dPFRQUSJIKCwuD3gS1VatWat26tdGrawAAwN1sL1i1tbWqra2ts/7TTz8pLCzMyDmKi4vVpk0bvfTSSxo%2BfLj%2B5V/%2BRcuXL1dtba38fr/i4uKCbt%2BiRQsdOnRI0s%2BvDzvTcQAAgLOx/acIBwwYoJUrVyorKyuwVlZWpqysLPXt29fIOY4ePar9%2B/dr3bp1yszMlN/v1/z58xUVFaWKioo6770VHh6uqqoqSdKxY8fOeLw%2BSkpK5Pf7g9a83ug6xa0hCAtrFPS3W7g1l1t4vafeF7fum1tzSWRzIrfmkhpWNtsL1pw5czRlyhQNGDBAlZWVmjFjhr799ltdccUVeuCBB4ycw%2Bv16siRI3r44YfVpk0bSdKBAwe0du1aXXXVVXXKUlVVlSIjIyVJERERpzweFRVV7/Pn5uYqOzs7aC0tLU0ZGRnnE8cWMTH1z%2Bckbs3ldM2aNT7jcbfum1tzSWRzIrfmkhpGNtsLVnx8vF566SVt2rRJn3/%2BuWprazVx4kSNHj1aTZo0MXKO2NhYRUREBMqVJP3qV7/SwYMH1bt3b5WWlgbdvrS0NHB1KT4%2B/pTHY2Nj633%2BCRMmKDk5OWjN643W4cM/nWuUiy4srJFiYqJUXl6hmpq6T906lVtzucXpHgtu3Te35pLI5kRuzSWdOtvZvqG7WELyTu5RUVEaP378Rbt/n8%2BnyspKffXVV/rVr34lSdq3b5/atGkjn8%2Bnp556SpZlyePxyLIs5efn69Zbbw18bl5enlJSUiRJBw8e1MGDB%2BXz%2Bep9/ri4uDpPB/r9P6q6uuF%2BIdfU1Dbo%2Bc6XW3M53dn2xK375tZcEtmcyK25pIaRzfaCNWXKlDMeN/G7CNu3b6/Bgwdr7ty5WrBggfx%2Bv3JycjRjxgwNHz5cDz/8sJYsWaI//vGPWrdunSoqKjRixAhJ0sSJEzV58mT16NFDCQkJWrJkiQYPHqy2bdte8FwAAODSYHvB%2BuXTdpJUXV2t/fv368svv9RNN91k7DwPPfSQFi1apIkTJyoqKko33nijJk%2BeLI/Ho5UrV%2Bq%2B%2B%2B7T888/r9/85jfKyclRdHS0JCkxMVELFy7UY489ph9%2B%2BEH9%2B/fXokWLjM0FAADcr8H8LsLly5cbfSuEyy%2B/XEuXLj3lse7du2vjxo2n/dyUlJTAU4QAAADnKvQ/x/j/Ro8erb///e%2BhHgMAAOCCNZiC9cknnxh7o1EAAIBQahAvcj9y5Ij%2B8Y9/aNKkSXaPAwAAYJztBat169ZBv4dQki677DL96U9/0u9//3u7xwEAADDO9oJl6t3aAQAAGirbC9ZHH31U79tec801F3ESAACAi8P2gnXivagkybKswPrJax6PR59//rnd4wEAAFww2wvWihUrtHjxYt19993q3bu3wsPD9emnn2rhwoW64YYbNHLkSLtHAgAAMMr2t2nIzMzU/PnzNWzYMDVr1kyNGzdW3759tXDhQq1du1Zt2rQJ/AEAAHAi2wtWSUnJKctTkyZNdPjwYbvHAQAAMM72gtWjRw898sgjOnLkSGCtrKxMWVlZ6tevn93jAAAAGGf7a7DuvfdeTZkyRYMGDVK7du1kWZa%2B/vprxcbG6tlnn7V7HAAAAONsL1gdOnTQ5s2btWnTJu3du1eSdOONN%2Bq6665TVFSU3eMAAAAYZ3vBkqSmTZtq/Pjx%2Buabb9S2bVtJP7%2BbOwAAgBvY/hosy7L00EMP6ZprrtGoUaN06NAhzZ49W/PmzdPx48ftHgcAAMA42wvW6tWr9fLLL%2Bu%2B%2B%2B5TeHi4JGno0KF64403lJ2dbfc4AAAAxtlesHJzczV//nylpKQE3r195MiRWrx4sV599VW7xwEAADDO9oL1zTffqEuXLnXWO3fuLL/fb/c4AAAAxtlesNq0aaNPP/20zvo777wTeME7AACAk9n%2BU4S33HKL7r//fvn9flmWpe3btys3N1erV6/WnDlz7B4HAADAONsL1tixY1VdXa0nn3xSx44d0/z589W8eXPdcccdmjhxot3jAAAAGGd7wdq0aZOGDx%2BuCRMm6Pvvv5dlWWrRooXdYwAAAFw0tr8Ga%2BHChYEXszdv3pxyBQAAXMf2gtWuXTt9%2BeWXdp8WAADANrY/Rdi5c2fdddddWrVqldq1a6eIiIig45mZmXaPBAAAYJTtBeurr75SUlKSJPG%2BVwAAwJVsKVhLly5Venq6oqOjtXr1ajtOCQAAEDK2vAbrb3/7myoqKoLWUlNTVVJSYsfpAQAAbGVLwbIsq87aRx99pMrKSjtODwAAYCvbf4oQAADA7ShYAAAAhtlWsDwej12nAgAACCnb3qZh8eLFQe95dfz4cWVlZalx48ZBt%2BN9sAAAgNPZUrCuueaaOu95lZiYqMOHD%2Bvw4cN2jAAAAGAbWwoW730FAAAuJbzIHQAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCY6wtWamqq5syZE/h49%2B7dGj9%2BvHw%2Bn8aOHatdu3YF3X7Tpk0aOnSofD6f0tLS9P3339s9MgAAcDhXF6zXXntN27ZtC3x89OhRpaamqlevXtqwYYMSExM1ffp0HT16VJK0c%2BdOzZs3T%2Bnp6crNzVV5ebnmzp0bqvEBAIBDubZglZWVaenSpUpISAisbd68WREREZo1a5Y6dOigefPmqXHjxtqyZYskac2aNRoxYoTGjBmjzp07a%2BnSpdq2bZuKi4tDFQMAADiQLb%2BLMBQefPBBjR49WiUlJYG1wsJCJSUlyePxSJI8Ho969uypgoICpaSkqLCwUH/%2B858Dt2/VqpVat26twsJCtW3b1vYMgFuNWPZeqEc4J3%2B/o3%2BoRwDgMK4sWNu3b9fHH3%2BsV199VQsWLAis%2B/1%2BdezYMei2LVq0UFFRkSSppKREcXFxdY4fOnTonM5fUlIiv98ftOb1Rte574YgLKxR0N9u4dZcCA2v98K%2Bjtz89Ug253FrLqlhZXNdwaqsrNR9992n%2BfPnKzIyMuhYRUWFwsPDg9bCw8NVVVUlSTp27NgZj9dXbm6usrOzg9bS0tKUkZFxTvdjp5iYqFCPcFG4NRfs1axZYyP34%2BavR7I5j1tzSQ0jm%2BsKVnZ2trp166aBAwfWORYREVGnLFVVVQWK2OmOR0Wd20ZNmDBBycnJQWteb7QOH/7pnO7HDmFhjRQTE6Xy8grV1NSGehxj3JoLoXGhj103fz2SzXncmks6dTZT3yCdK9cVrNdee02lpaVKTEyUpEBh2rp1q0aNGqXS0tKg25eWlgaeuouPjz/l8djY2HOaIS4urs7TgX7/j6qubrhfyDU1tQ16vvPl1lywl6mvITd/PZLNedyaS2oY2VxXsFavXq3q6urAxw899JAk6a677tJHH32kp556SpZlyePxyLIs5efn69Zbb5Uk%2BXw%2B5eXlKSUlRZJ08OBBHTx4UD6fz/4gAADAsVxXsNq0aRP0cePGP18avOqqq9SiRQs9/PDDWrJkif74xz9q3bp1qqio0IgRIyRJEydO1OTJk9WjRw8lJCRoyZIlGjx4MD9BCAAAzknoX2ZvoyZNmmjlypWBq1SFhYXKyclRdHS0JCkxMVELFy7U8uXLNXHiRDVt2lSZmZkhnhoAADiN665gneyBBx4I%2Brh79%2B7auHHjaW%2BfkpISeIoQAADgfFxSV7AAAADs4PorWGg4nPbu3QAAnC%2BuYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADHNtwfruu%2B%2BUkZGh3r17a%2BDAgcrMzFRlZaUkqbi4WFOnTlWPHj00cuRIvfvuu0Gf%2B/7772vUqFHy%2BXyaMmWKiouLQxEBAAA4lCsLlmVZysjIUEVFhZ577jk9%2Buijeuutt7Rs2TJZlqW0tDS1bNlS69ev1%2BjRo5Wenq4DBw5Ikg4cOKC0tDSlpKToxRdfVPPmzXXbbbfJsqwQpwIAAE7hDfUAF8O%2BfftUUFCg9957Ty1btpQkZWRk6MEHH9SgQYNUXFysdevWKTo6Wh06dND27du1fv163X777XrhhRfUrVs3TZs2TZKUmZmp/v3768MPP1SfPn1CGQsAADiEK69gxcbGatWqVYFydcKRI0dUWFioq6%2B%2BWtHR0YH1pKQkFRQUSJIKCwvVq1evwLGoqCh17do1cBwAAOBsXFmwYmJiNHDgwMDHtbW1WrNmjfr27Su/36%2B4uLig27do0UKHDh2SpLMeBwAAOBtXPkV4sqysLO3evVsvvviinn76aYWHhwcdDw8PV1VVlSSpoqLijMfro6SkRH6/P2jN642uU9wagrCwRkF/A6jL672wx4ebH2dkcx635pIaVjbXF6ysrCw988wzevTRR9WpUydFRESorKws6DZVVVWKjIyUJEVERNQpU1VVVYqJian3OXNzc5WdnR20lpaWpoyMjPNMcfHFxESFegSgwWrWrLGR%2B3Hz44xszuPWXFLDyObqgrVo0SKtXbtWWVlZGjZsmCQpPj5ee/bsCbpdaWlp4OpSfHy8SktL6xzv0qVLvc87YcIEJScnB615vdE6fPin84lxUYWFNVJMTJTKyytUU1Mb6nGABulCH7tufpyRzXncmks6dTZT3yCdK9cWrOzsbK1bt06PPPKIhg8fHlj3%2BXzKycnRsWPHAlet8vLylJSUFDiel5cXuH1FRYV2796t9PT0ep87Li6uztOBfv%2BPqq5uuF/INTW1DXo%2BIJRMPTbc/Dgjm/O4NZfUMLKF/knKi2Dv3r164okn9Oc//1lJSUny%2B/2BP71791arVq00d%2B5cFRUVKScnRzt37tS4ceMkSWPHjlV%2Bfr5ycnJUVFSkuXPn6sorr%2BQtGgAAQL25smC9%2Beabqqmp0ZNPPqkBAwYE/QkLC9MTTzwhv9%2BvlJQUvfLKK1q%2BfLlat24tSbryyiv1%2BON7aYroAAAMyUlEQVSPa/369Ro3bpzKysq0fPlyeTyeEKcCAABO4bF4i3Jb%2BP0/hnqEU/J6G6lZs8Y6fPini345dcSy9y7q/QMXy9/v6H9Bn2/n48xuZHMet%2BaSTp0tNvbykMziyitYAAAAoUTBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwb6gHAICGzmm/qPxCfzk1gAvHFSwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBh3lAPgAszYtl7oR4BAACchCtYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBg/RXgKlZWVuv/%2B%2B/Vf//VfioyM1LRp0zRt2rRQjwUA9eKkny7%2B%2Bx39Qz0CcFFQsE5h6dKl2rVrl5555hkdOHBAs2fPVuvWrTV8%2BPBQjwYAAByAgnWSo0eP6oUXXtBTTz2lrl27qmvXrioqKtJzzz1HwQIAAPVCwTrJF198oerqaiUmJgbWkpKStGLFCtXW1qpRI162BgCmOOnpTImnNFF/FKyT%2BP1%2BNWvWTOHh4YG1li1bqrKyUmVlZWrevPlZ76OkpER%2Bvz9ozeuNVlxcnPF5AQD28Xqd/012WFijoL/dpCFlo2CdpKKiIqhcSQp8XFVVVa/7yM3NVXZ2dtBaenq6br/9djND/sLHSy7sacuSkhLl5uZqwoQJriqAbs0lkc2J3JpLIpsTlZSU6JlnVrkul9SwsoW%2B4jUwERERdYrUiY8jIyPrdR8TJkzQhg0bgv5MmDDB%2BKwm%2BP1%2BZWdn17ni5nRuzSWRzYncmksimxO5NZfUsLJxBesk8fHxOnz4sKqrq%2BX1/vyfx%2B/3KzIyUjExMfW6j7i4uJA3ZwAAEDpcwTpJly5d5PV6VVBQEFjLy8tTQkICL3AHAAD1QmM4SVRUlMaMGaMFCxZo586deuONN/Sf//mfmjJlSqhHAwAADhG2YMGCBaEeoqHp27evdu/erYcffljbt2/XrbfeqrFjx4Z6rIumcePG6t27txo3bhzqUYxyay6JbE7k1lwS2ZzIrbmkhpPNY1mWFdIJAAAAXIanCAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAuUa%2B//rp%2B85vfBP3JyMgI9VgXpKqqSqNGjdIHH3wQWCsuLtbUqVPVo0cPjRw5Uu%2B%2B%2B24IJzx/p8q2ePHiOnu4Zs2aEE5Zf999950yMjLUu3dvDRw4UJmZmaqsrJTk/D07UzYn75kk7d%2B/X7fccosSExM1ePBgrVq1KnDMyft2plxO37NfSk1N1Zw5cwIf7969W%2BPHj5fP59PYsWO1a9euEE53/k7ONWPGjDp79tZbb9k%2Bl9f2M6JB2LNnj4YMGaJFixYF1iIiIkI40YWprKzUzJkzVVRUFFizLEtpaWnq1KmT1q9frzfeeEPp6enavHmzWrduHcJpz82psknS3r17NXPmTN1www2BtSZNmtg93jmzLEsZGRmKiYnRc889px9%2B%2BEH33HOPGjVqpFmzZjl6z86Ubfbs2Y7dM0mqra1VamqqEhIStHHjRu3fv1933nmn4uPjNWrUKMfu25lyXX/99Y7es1967bXXtG3btkCOo0ePKjU1Vddff70eeOABrV27VtOnT9frr7%2Bu6OjoEE9bfyfnkn7%2BtzErK0v9%2BvULrDVt2tT22ShYl6i9e/eqU6dOio2NDfUoF2zPnj2aOXOmTv6tTzt27FBxcbHWrVun6OhodejQQdu3b9f69et1%2B%2B23h2jac3O6bNLPe3jLLbc4bg/37dungoICvffee2rZsqUkKSMjQw8%2B%2BKAGDRrk6D07U7YTBcuJeyZJpaWl6tKlixYsWKAmTZqoXbt26tevn/Ly8tSyZUvH7tuZcp0oWE7dsxPKysq0dOlSJSQkBNY2b96siIgIzZo1Sx6PR/PmzdM777yjLVu2KCUlJYTT1t%2BpclVVVembb75RQkJCyPeMpwgvUXv37lW7du1CPYYRH374ofr06aPc3Nyg9cLCQl199dVB340lJSWpoKDA7hHP2%2BmyHTlyRN99950j9zA2NlarVq0KFJATjhw54vg9O1M2J%2B%2BZJMXFxWnZsmVq0qSJLMtSXl6ePvroI/Xu3dvR%2B3amXE7fsxMefPBBjR49Wh07dgysFRYWKikpSR6PR5Lk8XjUs2dPR%2BzZCafKtW/fPnk8HrVt2zaEk/2MgnUJsixLX331ld59910NGzZMQ4cO1UMPPaSqqqpQj3ZeJk2apHvuuUdRUVFB636/X3FxcUFrLVq00KFDh%2Bwc74KcLtvevXvl8Xi0YsUKDRo0SL///e%2B1cePGEE15bmJiYjRw4MDAx7W1tVqzZo369u3r%2BD07UzYn79nJkpOTNWnSJCUmJmrYsGGO37cTTs7lhj3bvn27Pv74Y912221B607fs9Pl2rdvn5o0aaJZs2ZpwIABGjdunLZt2xaSGSlYl6ADBw6ooqJC4eHhWrZsmWbPnq1XX31VS5cuDfVoRp3I%2BEvh4eGOLZK/dOK7tPbt2ysnJ0fjx4/XX/7yF73%2B%2BuuhHu2cZWVlaffu3fq3f/s31%2B3ZL7O5ac8ee%2BwxrVixQp9//rkyMzNds28n53L6nlVWVuq%2B%2B%2B7T/PnzFRkZGXTMyXt2plz79u3TsWPHNGDAAK1atUrXXnutZsyYoU8//dT2OXkN1iWoTZs2%2BuCDD9S0aVN5PB516dJFtbW1uvvuuzV37lyFhYWFekQjIiIiVFZWFrRWVVVV5wHpRGPGjNGQIUN0xRVXSJI6d%2B6sr7/%2BWmvXrtW//uu/hni6%2BsvKytIzzzyjRx99VJ06dXLVnp2c7de//rUr9kxS4DUvlZWVuuuuuzR27FhVVFQE3caJ%2B3Zyrvz8fEfvWXZ2trp16xZ0VfWEiIiIOmXKKXt2ply33XabJk%2BeHHhRe%2BfOnfXZZ5/p%2BeefD3qtlh0oWJeoE/9gnNChQwdVVlbqhx9%2BUPPmzUM0lVnx8fHas2dP0FppaWmdy%2BJO5PF46uxh%2B/bttWPHjhBNdO4WLVqktWvXKisrS8OGDZPknj07VTan71lpaakKCgo0dOjQwFrHjh11/PhxxcbGat%2B%2BfXVu74R9O1OuI0eO1Pn30El79tprr6m0tFSJiYmSFChUW7du1ahRo1RaWhp0e6fs2ZlyffLJJ3V%2BYrB9%2B/Z1/l2xA08RXoL%2B53/%2BR3369An6jvPzzz/XFVdc4ZpyJUk%2Bn0%2BfffaZjh07FljLy8uTz%2BcL4VRm/Md//IemTp0atPbFF1%2Boffv2oRnoHGVnZ2vdunV65JFHdN111wXW3bBnp8vm9D375ptvlJ6eru%2B%2B%2By6wtmvXLjVv3lxJSUmO3bcz5Vq9erWj92z16tV69dVX9dJLL%2Bmll15ScnKykpOT9dJLL8nn8%2BmTTz4J/ISyZVnKz893xJ6dKdecOXM0d%2B7coNuHbM8sXHJ%2B/PFHa%2BDAgdadd95p7d2713r77betAQMGWDk5OaEe7YJ16tTJ2rFjh2VZllVdXW2NHDnSuuOOO6wvv/zSWrlypdWjRw/r22%2B/DfGU5%2BeX2QoLC62rr77aWrVqlbV//37rueees7p162bl5%2BeHeMqz27Nnj9WlSxfr0UcftUpKSoL%2BOH3PzpTNyXtmWT8/nlJSUqxp06ZZRUVF1ttvv2399re/tZ5%2B%2BmlH79uZcjl9z042e/Zsa/bs2ZZl/fz/gb59%2B1qLFi2yioqKrEWLFln9%2B/e3fvrppxBPee5%2BmWvr1q1W165drY0bN1pff/219fjjj1vdu3e3iouLbZ%2BLgnWJ%2BvLLL62pU6daPXr0sPr37289/vjjVm1tbajHumC/LCGWZVlff/21deONN1rdunWzrrvuOuu9994L4XQX5uRsr7/%2BunX99ddbCQkJ1vDhw62tW7eGcLr6W7lypdWpU6dT/rEsZ%2B/Z2bI5dc9OOHTokJWWlmb17NnT6t%2B/v/Xkk08G/t1w8r6dKZfT9%2ByXfllELOvnb9TGjBljJSQkWOPGjbM%2B%2B%2ByzEE53/k7O9fzzz1u/%2B93vrG7dulk33HCD9eGHH4ZkLo9lneIdDAEAAHDeeA0WAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2f6OiBGgs0Dj6AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5838772362810170826”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.845484221980414</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.601101494885917</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.93617358610224</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.239792984473835</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.397019947128094</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.870607751470367</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.831190185083688</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.12906774681519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.704046932164328</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.21773905591659</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3120)</td> <td class=”number”>3120</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5838772362810170826”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.4705988131631003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7277003638945594</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.262990455991517</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.934734161095192</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.573316283034953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>32.213066628874536</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.61750207428376</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.9622641509434</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.12906774681519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.38471419396275</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_CACFP09”>PCT_CACFP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.2225</td>

</tr> <tr>

<th>Minimum</th> <td>0.38792</td>

</tr> <tr>

<th>Maximum</th> <td>2.5525</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2384475760931757377”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2384475760931757377,#minihistogram2384475760931757377”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2384475760931757377”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2384475760931757377”

aria-controls=”quantiles2384475760931757377” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2384475760931757377” aria-controls=”histogram2384475760931757377”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2384475760931757377” aria-controls=”common2384475760931757377”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2384475760931757377” aria-controls=”extreme2384475760931757377”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2384475760931757377”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.38792</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.69396</td>

</tr> <tr>

<th>Q1</th> <td>0.95401</td>

</tr> <tr>

<th>Median</th> <td>1.2247</td>

</tr> <tr>

<th>Q3</th> <td>1.4249</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.0079</td>

</tr> <tr>

<th>Maximum</th> <td>2.5525</td>

</tr> <tr>

<th>Range</th> <td>2.1646</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.47085</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.39711</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.32484</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.5928</td>

</tr> <tr>

<th>Mean</th> <td>1.2225</td>

</tr> <tr>

<th>MAD</th> <td>0.3003</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0943</td>

</tr> <tr>

<th>Sum</th> <td>3828.8</td>

</tr> <tr>

<th>Variance</th> <td>0.1577</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2384475760931757377”>

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1MNarVy/NmTNHb7zxho2VAQAAhMZxAcvtdqupqanDuN/v54ruAACgW3BcwBo1apQWLVqk//73v4GxAwcOqLS0VCNHjrSxMgAAgNA47lOEc%2BfO1a233qqxY8cqKSlJktTU1KQhQ4Zo3rx5NlcHAABwdo4LWCkpKfrLX/6i119/XTU1NXK73brssst09dVXy%2BVy2V0eAADAWTkuYElSdHS0Ro4cySlBAADQLTkuYPl8Pi1btkw7d%2B7UiRMnOryx/aWXXrKpMgAAgNA4LmD95je/UVVVla6//npdeOGFdpcDAADQaY4LWG%2B88YbWrFmj7Oxsu0sBAAA4J467TENCQoJSUlLsLgMAAOCcOS5gTZgwQWvWrNHJkyftLgUAAOCcOO4UYWNjo7Zt26a///3v6t%2B/v2JiYoK2P/744zZVBgAAEBrHBSxJGj9%2BvN0lAAAAnDPHBazS0lK7SwAAAPhKHPceLEmqq6tTeXm57rjjDv3vf//Tc889p9raWrvLAgAACInjAtb%2B/ft1ww036C9/%2BYuef/55NTc3a/v27Zo8ebK8Xq/d5QEAAJyV4wLWfffdpzFjxujFF1/UBRdcIElasmSJcnNzdf/999tcHQAAwNk5LmDt3LlTt956a9AXO7vdbt12222qrq62sTIAAIDQOC5gtbe3q729vcP4sWPHFB0dbUNFAAAAneO4gJWTk6NVq1YFhazGxkaVlZVp%2BPDhNlYGAAAQGscFrDvvvFNVVVXKyclRS0uLZs6cqWuvvVYfffSR5s6da3d5AAAAZ%2BW462Clp6dr8%2BbN2rZtm95//321t7eroKBAEyZMUI8ePewuDwAA4KwcF7AkKT4%2BXlOmTLG7DAAAgHPiuIA1bdq0M27nuwgBAIDTOS5g9evXL%2Bh2W1ub9u/frw8%2B%2BEA/%2BtGPbKoKAAAgdI4LWF/2XYTLly/X4cOHu7gaAACAznPcpwi/zIQJE/Tss8/aXQYAAMBZdZuA9c4773ChUQAA0C047hTh6d7kfvToUf373//WD3/4QxsqAgAA6BzHBay%2BffsGfQ%2BhJF1wwQW65ZZb9P3vf9%2BmqgAAAELnuIB133332V0CAADAV%2BK4gPXWW2%2BFvO9VV111HisBAAA4N44LWFOnTg2cIrQsKzB%2B6pjL5dL777/f9QUCAACcheMC1sqVK7Vo0SL9%2Bte/1rBhwxQTE6N3331XCxcu1I033qjrrrvO7hIBAADOyHGXaSgtLdX8%2BfM1duxYJScnKzExUcOHD9fChQv15JNPql%2B/foEfAAAAJ3JcwKqrqztteOrRo4caGhpsqAgAAKBzHBewrrzySi1ZskRHjx4NjDU2NqqsrExXX321jZUBAACExnHvwbr77rs1bdo0jRo1SgMGDJBlWfrPf/6j1NRUPf7443aXBwAAcFaOC1iXXnqptm/frm3btmnfvn2SpJtvvlnXX3%2B94uPjba4OAADg7BwXsCSpZ8%2BemjJlij766CP1799f0mdXcwcAAOgOHPceLMuydP/99%2Buqq67S%2BPHjdfjwYc2dO1fFxcU6ceKE3eUBAACcleMC1tq1a7Vlyxbdc889iomJkSSNGTNGL774osrLyzv9eNOnT9edd94ZuF1dXa0pU6bI4/Fo8uTJqqqqCtp/27ZtGjNmjDwejwoLC3XkyJGvdkAAACDiOC5gVVRUaP78%2BZo0aVLg6u3XXXedFi1apK1bt3bqsf7617/qlVdeCdxubm7W9OnTlZ2drU2bNikjI0MzZsxQc3OzJGn37t0qLi7WrFmzVFFRoaamJs2bN8/cwQEAgIjguID10UcfafDgwR3GBw0aJJ/PF/LjNDY2avHixRo6dGhgbPv27YqNjdWcOXN06aWXqri4WImJiXruueckSevWrVNeXp4mTpyoQYMGafHixXrllVd04MCBr35gAAAgYjguYPXr10/vvvtuh/FXX3018Ib3UPz%2B97/XhAkTdNlllwXGvF6vsrKyAitjLpdLmZmZ2rVrV2B7dnZ2YP8%2Bffqob9%2B%2B8nq953o4AAAgAjnuU4Q//elP9dvf/lY%2Bn0%2BWZWnHjh2qqKjQ2rVrg95LdSY7duzQ22%2B/ra1bt2rBggWBcZ/PFxS4JCklJUU1NTWSPruKfFpaWofthw8f7tQx1NXVdVhtc7sTOjy2U0VHRwX9BiKd291xLjBPnIeeOE8k98RxAWvy5Mlqa2vTgw8%2BqOPHj2v%2B/Pnq1auXZs%2BerYKCgrPev6WlRffcc4/mz5%2BvuLi4oG1%2Bvz/wxvnPxcTEqLW1VZJ0/PjxM24PVUVFRYc35BcWFqqoqKhTj2O3pCSuOwZIUnJy4pduY544Dz1xnkjsieMC1rZt2zRu3Djl5%2BfryJEjsixLKSkpId%2B/vLxcV1xxhUaOHNlhW2xsbIew1NraGghiX7a9sxc4zc/PV25ubtCY252ghoZjnXocu0RHRykpKV5NTX6dPNludzmA7U43d5knzkNPnMcJPTnTH0jnk%2BMC1sKFC/XnP/9ZPXv2VK9evTp9/7/%2B9a%2Bqr69XRkaGJAUC0/PPP6/x48ervr4%2BaP/6%2BvrAqbv09PTTbk9NTe1UDWlpaR1OB/p8n6qtrXtN%2BJMn27tdzcD5cKZ5wDxxHnriPJHYE8edFB0wYIA%2B%2BOCDc77/2rVrtXXrVm3evFmbN29Wbm6ucnNztXnzZnk8Hr3zzjuyLEvSZxc13blzpzwejyTJ4/GosrIy8FiHDh3SoUOHAtsBAABC4bgVrEGDBulXv/qV1qxZowEDBig2NjZoe2lp6Rnv369fv6DbiYmfLQ1efPHFSklJ0QMPPKCSkhL94Ac/0Pr16%2BX3%2B5WXlydJKigo0NSpU3XllVdq6NChKikp0ejRozv16UUAAADHBawPP/xQWVlZktSp616FokePHlq1apXuuecePfXUU7r88su1evVqJSQkSJIyMjK0cOFC/eEPf9Ann3yiESNG6N577zVaAwAACH8u6/PzZTZavHixZs2aFQg64cjn%2B9TuEkLmdkcpOTlRDQ3HIu6c%2BRflLXvN7hLgEM/OHtFhjHniPPTEeZzQk9TUC215Xke8B%2BuRRx6R3%2B8PGps%2Bfbrq6upsqggAAODcOSJgnW4R7a233lJLS4sN1QAAAHw1jghYAAAA4YSABQAAYJhjAtbnX8AMAADQ3TnmMg2LFi0KuubViRMnVFZWFriO1efOdh0sAAAAuzkiYF111VUdrnmVkZGhhoYGNTQ02FQVAADAuXFEwFq7dq3dJQAAABjjmPdgAQAAhAsCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGOeLLngHAyfKWvWZ3CZ3y7OwRdpcARDxWsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYb3JHl%2BlubxQGAOBcsYIFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvbgPXxxx%2BrqKhIw4YN08iRI1VaWqqWlhZJ0oEDB/TjH/9YV155pa677jr985//DLrv66%2B/rvHjx8vj8WjatGk6cOCAHYcAAAC6qbAMWJZlqaioSH6/X0888YSWLl2ql19%2BWcuWLZNlWSosLFTv3r21ceNGTZgwQbNmzdLBgwclSQcPHlRhYaEmTZqkp59%2BWr169dJtt90my7JsPioAANBduO0u4Hyora3Vrl279Nprr6l3796SpKKiIv3%2B97/XqFGjdODAAa1fv14JCQm69NJLtWPHDm3cuFG33367NmzYoCuuuEI/%2BclPJEmlpaUaMWKE3nzzTX3729%2B287AAAEA3EZYrWKmpqVqzZk0gXH3u6NGj8nq9%2BuY3v6mEhITAeFZWlnbt2iVJ8nq9ys7ODmyLj4/XkCFDAtsBAADOJiwDVlJSkkaOHBm43d7ernXr1mn48OHy%2BXxKS0sL2j8lJUWHDx%2BWpLNuBwAAOJuwPEV4qrKyMlVXV%2Bvpp5/Wo48%2BqpiYmKDtMTExam1tlST5/f4zbg9FXV2dfD5f0JjbndAhuDlVdHRU0G8A3YvbHZlzl9cu54nknoR9wCorK9Njjz2mpUuXauDAgYqNjVVjY2PQPq2trYqLi5MkxcbGdghTra2tSkpKCvk5KyoqVF5eHjRWWFiooqKiczwKeyQlxdtdAoBzkJycaHcJtuK1y3kisSdhHbDuvfdePfnkkyorK9PYsWMlSenp6dq7d2/QfvX19YHVpfT0dNXX13fYPnjw4JCfNz8/X7m5uUFjbneCGhqOncthdLno6CglJcWrqcmvkyfb7S4HQCd1l9ca03jtch4n9MSuPzjCNmCVl5dr/fr1WrJkicaNGxcY93g8Wr16tY4fPx5YtaqsrFRWVlZge2VlZWB/v9%2Bv6upqzZo1K%2BTnTktL63A60Of7VG1t3WvCnzzZ3u1qBqCIn7e8djlPJPYkLE%2BK7tu3TytWrNDPfvYzZWVlyefzBX6GDRumPn36aN68eaqpqdHq1au1e/du3XTTTZKkyZMna%2BfOnVq9erVqamo0b948XXTRRVyiAQAAhCwsA9ZLL72kkydP6sEHH1ROTk7QT3R0tFasWCGfz6dJkybpmWee0fLly9W3b19J0kUXXaQ//vGP2rhxo2666SY1NjZq%2BfLlcrlcNh8VAADoLlwWlyjvEj7fp3aXEDK3O0rJyYlqaDhmdEk3b9lrxh4LwJd7dvYIu0uwxfl67cK5c0JPUlMvtOV5w3IFCwAAwE4ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjb7gIAAGblLXvN7hJC9uzsEXaXAJwXrGABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw7hMAwAAIepOl8CQuAyGnVjBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD3HYXAAAAzo%2B8Za/ZXULInp09wu4SjGIFCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjU4TdXHf6hAgAAJGCgAUAsA1/JCJccYoQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgnUZLS4vuuusuZWdnKycnRw8//LDdJQEAgG6EyzScxuLFi1VVVaXHHntMBw8e1Ny5c9W3b1%2BNGzfO7tIAAEA3QMA6RXNzszZs2KCHHnpIQ4YM0ZAhQ1RTU6MnnniCgAUAAELCKcJT7NmzR21tbcrIyAiMZWVlyev1qr293cbKAABAd8EK1il8Pp%2BSk5MVExMTGOvdu7daWlrU2NioXr16nfUx6urq5PP5gsbc7gSlpaUZrxcAgHDgdofXmg8B6xR%2Bvz8oXEkK3G5tbQ3pMSoqKlReXh40NmvWLN1%2B%2B%2B1mivyCt0vMn7asq6tTRUWF8vPzCYUOQU%2Bch544Dz1xnkjuSXjFRQNiY2M7BKnPb8fFxYX0GPn5%2Bdq0aVPQT35%2BvvFazxefz6fy8vIOq3CwDz1xHnriPPTEeSK5J6xgnSI9PV0NDQ1qa2uT2/3ZP4/P51NcXJySkpJCeoy0tLSIS%2BoAAOD/YwXrFIMHD5bb7dauXbsCY5WVlRo6dKiiovjnAgAAZ0diOEV8fLwmTpyoBQsWaPfu3XrxxRf18MMPa9q0aXaXBgAAuonoBQsWLLC7CKcZPny4qqur9cADD2jHjh36%2Bc9/rsmTJ9tdVpdKTEzUsGHDlJiYaHcp%2BD/0xHnoifPQE%2BeJ1J64LMuy7C4CAAAgnHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAStCtbS06K677lJ2drZycnL08MMPf%2Bm%2BM2fO1OWXXx708/LLL3dhtZGltbVV48eP17/%2B9a8v3ae6ulpTpkyRx%2BPfu3AqAAAE/0lEQVTR5MmTVVVV1YUVRp5QesI86Roff/yxioqKNGzYMI0cOVKlpaVqaWk57b7Mk/OvM/2ItDnitrsA2GPx4sWqqqrSY489poMHD2ru3Lnq27evxo0b12Hfffv2qaysTFdffXVgrGfPnl1ZbsRoaWnRHXfcoZqami/dp7m5WdOnT9cNN9yg%2B%2B67T08%2B%2BaRmzJihF154QQkJCV1YbWQIpScS86QrWJaloqIiJSUl6YknntAnn3yiu%2B66S1FRUZo7d27QvsyT868z/ZAicI5YiDjHjh2zhg4dar3xxhuBseXLl1u33HJLh31bWlqswYMHW7W1tV1ZYkSqqamxvv/971s33HCDNXDgwKD%2BfNGGDRus3Nxcq7293bIsy2pvb7e%2B%2B93vWhs3buzKciNCqD1hnnSNvXv3WgMHDrR8Pl9gbOvWrVZOTk6HfZkn519n%2BhGJc4RThBFoz549amtrU0ZGRmAsKytLXq9X7e3tQfvW1tbK5XKpf//%2BXV1mxHnzzTf17W9/WxUVFWfcz%2Bv1KisrSy6XS5LkcrmUmZmpXbt2dUWZESXUnjBPukZqaqrWrFmj3r17B40fPXq0w77Mk/OvM/2IxDnCKcII5PP5lJycrJiYmMBY79691dLSosbGRvXq1SswXltbqx49emjOnDl688039bWvfU233367rrnmGjtKD2s//OEPQ9rP5/PpsssuCxpLSUk56yksdF6oPWGedI2kpCSNHDkycLu9vV3r1q3T8OHDO%2BzLPDn/OtOPSJwjrGBFIL/fHxSuJAVut7a2Bo3X1tbq%2BPHjysnJ0Zo1a3TNNddo5syZevfdd7usXgT7sv6d2jt0HeaJPcrKylRdXa1f/OIXHbYxT7remfoRiXOEFawIFBsb2%2BFF5vPbcXFxQeO33Xabpk6dGngj4qBBg/Tee%2B/pqaee0tChQ7umYAT5sv6d2jt0HeZJ1ysrK9Njjz2mpUuXauDAgR22M0%2B61tn6EYlzhBWsCJSenq6Ghga1tbUFxnw%2Bn%2BLi4pSUlBS0b1RUVIdPeVxyySX6%2BOOPu6RWdJSenq76%2Bvqgsfr6eqWlpdlUEZgnXevee%2B/VI488orKyMo0dO/a0%2BzBPuk4o/YjEOULAikCDBw%2BW2%2B0OerNnZWWlhg4dqqio4P8Sd955p%2BbNmxc0tmfPHl1yySVdUis68ng8euedd2RZlqTPPiq9c%2BdOeTwemyuLXMyTrlNeXq7169dryZIluv766790P%2BZJ1wi1H5E4RwhYESg%2BPl4TJ07UggULtHv3br344ot6%2BOGHNW3aNEmfrWYdP35ckpSbm6utW7dq8%2BbN2r9/v8rLy1VZWalbbrnFzkOIOF/sybhx49TU1KSSkhLt3btXJSUl8vv9ysvLs7nKyMI86Xr79u3TihUr9LOf/UxZWVny%2BXyBH4l50tU604%2BInCP2XiUCdmlubrbmzJljXXnllVZOTo71yCOPBLYNHDgw6FoxTz31lPW9733PuuKKK6wbb7zRevPNN22oOLKces2lU3vi9XqtiRMnWkOHDrVuuukm67333rOjzIhytp4wT86/VatWWQMHDjztj2UxT7paZ/sRaXPEZVn/t34KAAAAIzhFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/T%2Be24QVFl%2BxZAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2384475760931757377”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.2247449813177163</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.424865129052576</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7474066265022029</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0607742541746126</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0504410797016492</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6784053340012424</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9615961818095771</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.358123605408616</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2477990967652708</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9540117028315566</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2384475760931757377”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.3879179065372472</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5308832022847476</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5354170205537625</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6260184849149406</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6862035668256398</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.773987916176593</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:93%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7820142308066007</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.007890843824686</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2861831028170134</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5525474457519897</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_CACFP15”>PCT_CACFP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.3782</td>

</tr> <tr>

<th>Minimum</th> <td>0.56811</td>

</tr> <tr>

<th>Maximum</th> <td>2.3593</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8453719771828531746”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8453719771828531746,#minihistogram-8453719771828531746”
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</div> <div class=”row collapse col-md-12” id=”descriptives-8453719771828531746”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8453719771828531746”

aria-controls=”quantiles-8453719771828531746” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8453719771828531746” aria-controls=”histogram-8453719771828531746”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8453719771828531746” aria-controls=”common-8453719771828531746”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8453719771828531746” aria-controls=”extreme-8453719771828531746”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8453719771828531746”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.56811</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.7939</td>

</tr> <tr>

<th>Q1</th> <td>1.0991</td>

</tr> <tr>

<th>Median</th> <td>1.3297</td>

</tr> <tr>

<th>Q3</th> <td>1.7046</td>

</tr> <tr>

<th>95-th percentile</th> <td>2.2827</td>

</tr> <tr>

<th>Maximum</th> <td>2.3593</td>

</tr> <tr>

<th>Range</th> <td>1.7912</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.60555</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.42491</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.30831</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.0087477</td>

</tr> <tr>

<th>Mean</th> <td>1.3782</td>

</tr> <tr>

<th>MAD</th> <td>0.33115</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.68721</td>

</tr> <tr>

<th>Sum</th> <td>4316.5</td>

</tr> <tr>

<th>Variance</th> <td>0.18055</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8453719771828531746”>

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B/LirHwAAoD4BGbASEhIUHh6uv/71rzp8%2BLA%2B/fRTLV68WA888IBGjRql0tJSLVq0SIcOHdKiRYvkdDo1evRoSdLkyZO1efNmrV%2B/XgcPHtScOXM0dOhQtW/f3stVAQAAfxGQAatp06Z65ZVXdPLkSU2YMEHp6elKSUnRAw88oKZNm2rVqlXKyclRcnKy7Ha7srKyFBUVJelcOJs/f75WrFihyZMnq1mzZsrIyPByRQAAwJ8E7GewfvOb3%2Bi1116rs693797atGnTJZ%2BbnJys5OTkxpoaAAAIcAF5BgsAAMCbCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvO5gDVp0iStXbtWP//8s7enAgAAcEV8LmANGDBAL730kgYNGqQ///nP2r59uwzD8Pa0AAAAPOZzAesvf/mLPv74Y61cuVKhoaGaOXOmhg4dqmXLlunIkSPenh4AAEC9rN6eQF0sFosGDhyogQMHyul06s0339TKlSuVlZWlvn376g9/%2BINuueUWb08TAACgTj4ZsCSpqKhIW7Zs0ZYtW/Tdd9%2Bpb9%2B%2Buv3221VYWKi//vWv2rNnj9LT0709TQAAgFp8LmBt3rxZmzdv1q5du9SyZUuNHz9ey5cvV8eOHV1jWrdurUWLFhGwAACAT/K5gJWenq5hw4ZpxYoVuummmxQSUvtjYp06ddLdd9/thdkBAADUz%2BcC1meffaYWLVqopKTEFa7y8vLUs2dPhYaGSpL69u2rvn37enOaAAAAl%2BRzv0V4%2BvRpjRo1Si%2B//LKrbdq0aRo3bpyOHz/uxZkBAAB4xucC1t/%2B9jd16NBB9957r6tt27Ztat26tTIyMrw4MwAAAM/4XMD66quvNHfuXMXGxrraWrZsqTlz5ujLL7/04swAAAA843MBy2q1qrS0tFa70%2Bnkju4AAMAv%2BFzAuummm7Rw4UL98MMPrraCggJlZGRo8ODBXpwZAACAZ3wuYD322GOqrKzUyJEj1b9/f/Xv31%2B33HKLzp49q7S0tAZvb9q0aZo7d67r8f79%2BzVp0iTZbDZNmDBB%2B/btcxu/detWjRgxQjabTampqTp58uRV1wQAAIKLzwWsmJgYbdq0SVlZWZo%2BfbpSU1P1yiuvaP369W6fy/LEe%2B%2B9p08//dT1uKysTNOmTVNSUpI2btyohIQETZ8%2BXWVlZZLO3Q4iPT1dM2bMUHZ2tkpLS68o1AEAgODmc/fBkqTQ0FANHjz4qi4JlpSUaPHixerVq5erbdu2bQoPD9ecOXNksViUnp6uzz77TO%2B//76Sk5O1Zs0ajR49WuPHj5ckLV68WMOGDVNBQYHat29/1XUBAIDg4HMBy%2BFw6Pnnn9fevXt19uzZWh9s/%2BijjzzazjPPPKNx48apqKjI1Wa325WYmCiLxSLp3B%2BV7tu3r3Jzc5WcnCy73a4HH3zQNb5169Zq06aN7HY7AQsAAHjM5wLWf//3f2vfvn269dZb9ctf/vKKtrFz50599dVXevfddzVv3jxXu8PhUJcuXdzGxsTEKD8/X9K5PzAdFxdXq7%2BwsPCK5gEAAIKTzwWsL7/8UqtXr1ZSUtIVPb%2BiokJPPvmknnjiCUVERLj1OZ1OhYWFubWFhYWpsrJSklReXn7Zfk8VFRXJ4XC4tVmtUW7hLTQ0xO17MAjGmhEYrNbLH7Mc21euvvfWlwTjfg7Gms3icwErKipKMTExV/z8zMxMXX/99XV%2Bfis8PLxWWKqsrHQFsUv1R0ZGNmgO2dnZyszMdGtLTU3VrFmzao2Njm7YtgNBMNYM/9aiRROPxnFsN5yn760vCcb9HIw1Xy2fC1jjxo3T6tWrNX/%2BfNcfd26I9957T8XFxUpISJAkV2D64IMPNHbsWBUXF7uNLy4udp1Zio%2BPr7O/ob%2B9mJKSouHDh7u1Wa1ROnXqjOtxaGiIoqMjVVrqVHV1TYO276%2BCsWYEhgvXbl04tq9cfe%2BtLwnG/RwINXsrxPtcwCopKdHWrVv1ySefqH379rUu2b3xxhuXff6bb76pqqoq1%2BNnn31WkjR79mzt2bNHL7/8sgzDkMVikWEY2rt3rx566CFJks1mU05OjpKTkyVJx48f1/Hjx2Wz2RpUQ1xcXK3PcjkcP6uqqvbBWV1dU2d7IAvGmuHfPD1eObYbzh/fr2Dcz8FY89XyuYAlSWPHjr3i57Zt29btcZMm55Jrhw4dFBMTo%2Beee06LFi3S73//e61du1ZOp1OjR4%2BWJE2ePFlTpkxRnz591KtXLy1atEhDhw7lNwgBAECD%2BFzAysjIaLRtN23aVKtWrdKTTz6pdevWqVu3bsrKylJUVJQkKSEhQfPnz9fy5cv1008/aeDAgVqwYEGjzQcAAAQmnwtY0rnfwlu3bp2OHDmixx9/XHv27FHXrl3VqVOnBm/r6aefdnvcu3dvbdq06ZLjk5OTXZcIAQAAroTP/d7l0aNHddttt2nTpk364IMPVFZWpm3btmnChAmy2%2B3enh4AAEC9fC5gPf300xoxYoQ%2B/PBD/eIXv5AkLV26VMOHD3d9YB0AAMCX%2BVzA2rt3r%2B69917Xn7ORJKvVqkceeUT79%2B/34swAAAA843MBq6amRjU1tX8V9MyZM1d0XywAAIBrzecC1qBBg7Rq1Sq3kFVSUqIlS5ZowIABXpwZAACAZ3wuYM2dO1f79u3ToEGDVFFRoYcffljDhg3Tjz/%2BqMcee8zb0wMAAKiXz92mIT4%2BXu%2B88462bt2qAwcOqKamRpMnT9a4cePUtGlTb08PAACgXj4XsCQpMjJSkyZN8vY0AAAArojPBaypU6detr%2B%2Bv0UIAADgbT4XsC7%2BW4JVVVU6evSovvvuO/3hD3/w0qwAAAA853MB61J/i3DFihUqLCy8xrMBAABoOJ/7LcJLGTdunP75z396exoAAAD18puA9fXXX3OjUQAA4Bd87hJhXR9yP336tP7973/rzjvv9MKMAAAAGsbnAlabNm3c/g6hJP3iF7/Q3Xffrd/97ndemhUAAIDnfC5gPf30096eAgAAwFXxuYC1Z88ej8fecMMNjTgTAACAK%2BNzAWvKlCmuS4SGYbjaL26zWCw6cODAtZ8gAABAPXwuYL300ktauHChHn30UfXr109hYWH65ptvNH/%2BfN1%2B%2B%2B0aM2aMt6cIAABwWT53m4aMjAw98cQTGjlypFq0aKEmTZpowIABmj9/vt566y21bdvW9QUAAOCLfC5gFRUV1RmemjZtqlOnTnlhRgAAAA3jcwGrT58%2BWrp0qU6fPu1qKykp0ZIlS3TjjTd6cWYAAACe8bnPYP31r3/V1KlTddNNN6ljx44yDEP/%2Bc9/FBsbqzfeeMPb0wMAAKiXzwWszp07a9u2bdq6dau%2B//57SdJdd92lW2%2B9VZGRkV6eHQAAQP18LmBJUrNmzTRp0iT9%2BOOPat%2B%2BvaRzd3MHAADwBz73GSzDMPTss8/qhhtu0NixY1VYWKjHHntM6enpOnv2rLenBwAAUC%2BfC1hvvvmmNm/erCeffFJhYWGSpBEjRujDDz9UZmaml2cHAABQP58LWNnZ2XriiSeUnJzsunv7mDFjtHDhQr377rtenh0AAED9fC5g/fjjj%2BrRo0et9u7du8vhcHhhRgAAAA3jcwGrbdu2%2Buabb2q1f/bZZ64PvAMAAPgyn/stwvvvv19PPfWUHA6HDMPQzp07lZ2drTfffFNz58719vQAAADq5XMBa8KECaqqqtKLL76o8vJyPfHEE2rZsqX%2B%2BMc/avLkyd6eHgAAQL187hLh1q1bNWrUKH3yySfasWOHvvjiC%2B3YsUP33ntvg7Zz9OhR3X///UpISNDQoUO1evVqV19BQYHuuece9enTR2PGjNH27dvdnrtjxw6NHTtWNptNU6dOVUFBgSm1AQCA4OBzAWv%2B/PmuD7O3bNlSMTExDd5GTU2Npk2bphYtWmjTpk166qmn9OKLL%2Brdd9%2BVYRhKTU1Vq1attGHDBo0bN04zZszQsWPHJEnHjh1TamqqkpOT9fbbb6tly5Z65JFHZBiGqXUCAIDA5XOXCDt27KjvvvtOXbp0ueJtFBcXq0ePHpo3b56aNm2qjh076sYbb1ROTo5atWqlgoICrV27VlFRUercubN27typDRs2aObMmVq/fr2uv/563XfffZKkjIwMDRw4ULt371b//v3NKhMAAAQwnwtY3bt31%2BzZs7V69Wp17NhR4eHhbv0ZGRn1biMuLk7PP/%2B8pHN3ht%2B7d6/27NmjJ598Una7Xdddd52ioqJc4xMTE5WbmytJstvtSkpKcvVFRkaqZ8%2Beys3NJWABAACP%2BFzAOnLkiBITEyXJlPteDR8%2BXMeOHdOwYcM0cuRI/e1vf1NcXJzbmJiYGBUWFrpe83L9nigqKqo1d6s1ym27oaEhbt%2BDQTDWjMBgtV7%2BmOXYvnL1vbe%2BJBj3czDWbBafCFiLFy/WjBkzFBUVpTfffNPUbS9fvlzFxcWaN2%2BeMjIy5HQ6XX%2BC57ywsDBVVlZKUr39nsjOzq71Z31SU1M1a9asWmOjoyM93m6gCMaa4d9atGji0TiO7Ybz9L31JcG4n4Ox5qvlEwHrtdde0/333%2B922W7atGlauHBhrbNJDdWrVy9JUkVFhWbPnq0JEybI6XS6jamsrFRERIQkKTw8vFaYqqysVHR0tMevmZKSouHDh7u1Wa1ROnXqjOtxaGiIoqMjVVrqVHV1TYNq8lfBWDMCw4Vrty4c21euvvfWlwTjfg6Emr0V4n0iYNX1G3p79uxRRUXFFW2vuLhYubm5GjFihKutS5cuOnv2rGJjY3X48OFa488Hufj4eBUXF9fqr%2BvP91xKXFxcrWDocPysqqraB2d1dU2d7YEsGGuGf/P0eOXYbjh/fL%2BCcT8HY81XKyAvqv7444%2BaMWOGTpw44Wrbt2%2BfWrZsqcTERH377bcqLy939eXk5Mhms0mSbDabcnJyXH1Op1P79%2B939QMAANQnIANWr1691LNnTz3%2B%2BOM6dOiQPv30Uy1ZskQPPfSQ%2BvXrp9atWystLU35%2BfnKyspSXl6eJk6cKOncneT37t2rrKws5efnKy0tTe3ateM3CAEAgMd8JmBZLBbTthUaGqqVK1cqMjJSKSkpSk9P15QpUzR16lRXn8PhUHJysrZs2aIVK1aoTZs2kqR27drphRde0IYNGzRx4kSVlJRoxYoVps4PAAAENp/4DJYkLVy40O2eV2fPntWSJUvUpIn7h9M8uQ%2BWdO6zVBf/Jt95HTp00Jo1ay753CFDhmjIkCEevQ4AAMDFfCJg3XDDDbXuG5WQkKBTp07p1KlTXpoVAAD%2BbfTzX3h7Ch775x8HensKpvKJgGX2va8AAAC8yWc%2BgwUAABAoCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvOJ2zQgONz87OfengIAANcEZ7AAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADBZwAasEydOaNasWerXr58GDx6sjIwMVVRUSJIKCgp0zz33qE%2BfPhozZoy2b9/u9twdO3Zo7Nixstlsmjp1qgoKCrxRAgAA8FMBGbAMw9CsWbPkdDr1j3/8Q8uWLdPHH3%2Bs559/XoZhKDU1Va1atdKGDRs0btw4zZgxQ8eOHZMkHTt2TKmpqUpOTtbbb7%2Btli1b6pFHHpFhGF6uCgAA%2BAurtyfQGA4fPqzc3Fx98cUXatWqlSRp1qxZeuaZZ3TTTTepoKBAa9euVVRUlDp37qydO3dqw4YNmjlzptavX6/rr79e9913nyQpIyNDAwcO1O7du9W/f39vlgUAAPxEQJ7Bio2N1erVq13h6rzTp0/LbrfruuuuU1RUlKs9MTFRubm5kiS73a6kpCRXX2RkpHr27OnqBwAAqE9ABqzo6GgNHjzY9bimpkZr1qzRgAED5HA4FBcX5zY%2BJiZGhYWFklRvPwAAQH0C8hLhxZYsWaL9%2B/fr7bff1t///neFhYW59YeFhamyslKS5HQ6L9vviaKiIjkcDrc2qzXKLbiFhoa4fQfgu6zWy69T1vOVq%2B%2B99SXs58Y%2Bil6KAAATdklEQVTlT8eCJwI%2BYC1ZskSvv/66li1bpq5duyo8PFwlJSVuYyorKxURESFJCg8PrxWmKisrFR0d7fFrZmdnKzMz060tNTVVs2bNqjU2OjrS4%2B0C8I4WLZp4NI713HCevre%2BhP3cOPzxWLicgA5YCxYs0FtvvaUlS5Zo5MiRkqT4%2BHgdOnTIbVxxcbHr7FJ8fLyKi4tr9ffo0cPj101JSdHw4cPd2qzWKJ06dcb1ODQ0RNHRkSotdaq6uqZBdQG4ti5cu3VhPV%2B5%2Bt5bX8J%2BblyNdSx4K7gFbMDKzMzU2rVrtXTpUo0aNcrVbrPZlJWVpfLyctdZq5ycHCUmJrr6c3JyXOOdTqf279%2BvGTNmePzacXFxtT7H5XD8rKqq2guyurqmznYAvsPTNcp6bjh/fL/Yz40j0N7TwLrg%2Bf99//33WrlypR588EElJibK4XC4vvr166fWrVsrLS1N%2Bfn5ysrKUl5eniZOnChJmjBhgvbu3ausrCzl5%2BcrLS1N7dq14xYNAADAYwEZsD766CNVV1frxRdf1KBBg9y%2BQkNDtXLlSjkcDiUnJ2vLli1asWKF2rRpI0lq166dXnjhBW3YsEETJ05USUmJVqxYIYvF4uWqAACAvwjIS4TTpk3TtGnTLtnfoUMHrVmz5pL9Q4YM0ZAhQxpjagAAIAgE5BksAAAAbyJgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJAj5gVVZWauzYsdq1a5erraCgQPfcc4/69OmjMWPGaPv27W7P2bFjh8aOHSubzaapU6eqoKDgWk8bAAD4sYAOWBUVFfrzn/%2Bs/Px8V5thGEpNTVWrVq20YcMGjRs3TjNmzNCxY8ckSceOHVNqaqqSk5P19ttvq2XLlnrkkUdkGIa3ygAAAH4mYAPWoUOHdMcdd%2BiHH35wa//yyy9VUFCg%2BfPnq3Pnzpo%2Bfbr69OmjDRs2SJLWr1%2Bv66%2B/Xvfdd59%2B85vfKCMjQ//3f/%2Bn3bt3e6MMAADghwI2YO3evVv9%2B/dXdna2W7vdbtd1112nqKgoV1tiYqJyc3Nd/UlJSa6%2ByMhI9ezZ09UPAABQH6u3J9BY7rzzzjrbHQ6H4uLi3NpiYmJUWFjoUb8nioqK5HA43Nqs1ii37YaGhrh9B%2BC7rNbLr1PW85Wr7731JeznxuVPx4InAjZgXYrT6VRYWJhbW1hYmCorKz3q90R2drYyMzPd2lJTUzVr1qxaY6OjIz3eLgDvaNGiiUfjWM8N5%2Bl760vYz43DH4%2BFywm6gBUeHq6SkhK3tsrKSkVERLj6Lw5TlZWVio6O9vg1UlJSNHz4cLc2qzVKp06dcT0ODQ1RdHSkSkudqq6uaWgZAK6hC9duXVjPV66%2B99aXsJ8bV2MdC94KbkEXsOLj43Xo0CG3tuLiYtflu/j4eBUXF9fq79Gjh8evERcXV%2Bsyo8Pxs6qqai/I6uqaOtsB%2BA5P1yjrueH88f1iPzeOQHtPgy5g2Ww2ZWVlqby83HXWKicnR4mJia7%2BnJwc13in06n9%2B/drxowZXplvfUY//4W3pwAAAC4SWJ8o80C/fv3UunVrpaWlKT8/X1lZWcrLy9PEiRMlSRMmTNDevXuVlZWl/Px8paWlqV27durfv7%2BXZw4AAPxF0AWs0NBQrVy5Ug6HQ8nJydqyZYtWrFihNm3aSJLatWunF154QRs2bNDEiRNVUlKiFStWyGKxeHnmAADAXwTFJcJ///vfbo87dOigNWvWXHL8kCFDNGTIkMaeFgAACFBBdwYLAACgsRGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADBZUNwHCwAAMySlv%2B/tKcBPcAYLAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExm9fYEAMDXjX7%2BC29PAYCf4QwWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICVh0qKir0%2BOOPKykpSYMGDdKrr77q7SkBAAA/wm8R1mHx4sXat2%2BfXn/9dR07dkyPPfaY2rRpo1GjRnl7agAAwA8QsC5SVlam9evX6%2BWXX1bPnj3Vs2dP5efn6x//%2BAcBCwAAeIRLhBc5ePCgqqqqlJCQ4GpLTEyU3W5XTU2NF2cGAAD8BWewLuJwONSiRQuFhYW52lq1aqWKigqVlJSoZcuW9W6jqKhIDofDrc1qjVJcXJzrcWhoiNt3AAhGVqv//Azk53Xj8qdjwRMErIs4nU63cCXJ9biystKjbWRnZyszM9OtbcaMGZo5c6brcVFRkV5/fbVSUlLcgldDfbXIfy5bFhUVKTs7%2B6pr9ifUTM2BKlhr/sOv8oOu5mDbz2YJrLhogvDw8FpB6vzjiIgIj7aRkpKijRs3un2lpKS4jXE4HMrMzKx1piuQUXNwoObgQM3BIRhrNgtnsC4SHx%2BvU6dOqaqqSlbrubfH4XAoIiJC0dHRHm0jLi6OpA8AQBDjDNZFevToIavVqtzcXFdbTk6OevXqpZAQ3i4AAFA/EsNFIiMjNX78eM2bN095eXn68MMP9eqrr2rq1KnenhoAAPATofPmzZvn7Un4mgEDBmj//v167rnntHPnTj300EOaMGGC6a/TpEkT9evXT02aNDF9276KmoMDNQcHag4OwVizGSyGYRjengQAAEAg4RIhAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyApaJKioq9PjjjyspKUmDBg3Sq6%2B%2BesmxDz/8sLp16%2Bb29fHHH7v6//73v2vw4MFKSEjQ448/LqfTeS1KaDBPa54yZUqtert166a0tDRJ0k8//VSrr3///teylAarrKzU2LFjtWvXrkuO2b9/vyZNmiSbzaYJEyZo3759bv1bt27ViBEjZLPZlJqaqpMnTzb2tK%2BKJzV/8sknGjdunBISEnTbbbfpo48%2BcutPSkqqta/PnDnT2FO/Yp7UHCjr%2Bbz6ag6k9XzixAnNmjVL/fr10%2BDBg5WRkaGKioo6xwbCem5IvYG2lq85A6aZP3%2B%2Bcdtttxn79u0z/ud//sdISEgw/vnPf9Y59uabbzY2b95sFBUVub4qKioMwzCM999/30hMTDT%2B93//17Db7caYMWOMp5566lqW4jFPaz516pRbrf/617%2BMnj17Gnl5eYZhGMZXX31l9OvXz21McXHxtS7HY%2BXl5UZqaqrRtWtX48svv6xzzJkzZ4yBAwcaTz/9tHHo0CFjwYIFxm9/%2B1vjzJkzhmEYht1uN3r37m1s2rTJOHDggHH33Xcb06ZNu5ZlNIgnNR84cMDo2bOn8frrrxv/%2Bc9/jDVr1hg9e/Y0Dhw4YBiGYRQWFhpdu3Y1fvjhB7d9XVNTcy1L8ZgnNRtG4Kxnw/Cs5kBZzzU1NcYdd9xhPPDAA8Z3331n7Nmzx7j55puNp59%2ButbYQFjPDak30NayNxCwTHLmzBmjV69ebj%2BQVqxYYdx99921xlZUVBg9evQwDh8%2BXOe27rzzTmP58uWux3v27DF69%2B5tlJWVmT/xq9CQmi9UVVVljBkzxli2bJmrbd26dUZKSkqjzdVM%2Bfn5xu9%2B9zvjtttuu%2Bw/QuvXrzeGDx/u%2BoFTU1Nj3HzzzcaGDRsMwzCMRx991Hjsscdc448dO2Z069bN%2BOGHHxq/iAbytOYlS5YY999/v1vbfffdZyxdutQwDMP44osvjIEDBzb6fM3gac2Bsp4Nw/OaL%2BTP6/nQoUNG165dDYfD4Wp79913jUGDBtUaGwjruSH1BtJa9hYuEZrk4MGDqqqqUkJCgqstMTFRdrtdNTU1bmMPHz4si8Wi9u3b19pOdXW1vvnmGyUlJbna%2BvTpo7Nnz%2BrgwYONV8AVaEjNF9q4caN%2B%2BuknPfjgg662Q4cOqWPHjo05XdPs3r1b/fv3V3Z29mXH2e12JSYmymKxSJIsFov69u2r3NxcV/%2BF%2B7l169Zq06aN7HZ7403%2BCnla8%2B23367Zs2fXav/5558lndvPv/71rxtljmbztOZAWc%2BS5zVfyJ/Xc2xsrFavXq1WrVq5tZ8%2BfbrW2EBYzw2pN5DWsrdYvT2BQOFwONSiRQuFhYW52lq1aqWKigqVlJSoZcuWrvbDhw%2BradOmmjNnjnbv3q1f/epXmjlzpoYMGaLS0lJVVFQoLi7ONd5qtap58%2BYqLCy8pjXVpyE1n2cYhlavXq2pU6e6/WX277//XlVVVZo4caJOnDihpKQkpaWlub0PvuLOO%2B/0aJzD4VCXLl3c2mJiYpSfny9JKioqqlVfTEyMz%2B1nyfOaO3fu7PY4Pz9fO3fu1O9//3tJ5/az0%2BnUlClTdOTIEfXo0UOPP/64T/6g9rTmQFnPkuc1n%2Bfv6zk6OlqDBw92Pa6pqdGaNWs0YMCAWmMDYT03pN5AWsvewhkskzidTregIcn1uLKy0q398OHDKi8v16BBg7R69WoNGTJEDz/8sL755huVl5e7PffCbV28HW9rSM3n7dq1S4WFhbrjjjvc2g8fPqzTp08rLS1Ny5YtU1FRkR566CFVV1c3zuSvgUu9P%2Bffm/Lycr/Yz1fq5MmTmjlzpvr27av/%2Bq//knRuP//00096%2BOGHtXLlSkVEROiee%2B6p83/Q/iJQ1vOVCLT1vGTJEu3fv19/%2BtOfavUF4nq%2BXL0XCpa1bDbOYJkkPDy81kI6/zgiIsKt/ZFHHtGUKVPUrFkzSVL37t317bffat26da4Dva5tRUZGNtb0r0hDaj7vgw8%2B0E033aTmzZu7tb/33nuyWCyu5y1fvlyDBg2S3W5X3759G2H2je9S78/5Gi/V72v7%2BUoUFxfr3nvvlWEYWr58uUJCzv1f7pVXXtHZs2ddZzueffZZDRkyRB9//LFuu%2B02b075igXKer4SgbSelyxZotdff13Lli1T165da/UH2nqur97zgmktm40zWCaJj4/XqVOnVFVV5WpzOByKiIhQdHS029iQkBDXD%2BPzOnXqpBMnTqh58%2BYKDw9XcXGxq6%2BqqkolJSWKjY1t3CIaqCE1n/f555%2B7/gd0ocjISLdQFhMTo%2BbNm%2BvEiRPmT/waiY%2BPd9uP0rkfVucvI1yq39f2c0OdOHFCd911lyorK/XGG2%2B4XSoOCwtzu5QUHh6udu3a%2BfV%2BDpT1fCUCZT0vWLBAr732mpYsWaKRI0fWOSaQ1rMn9UrBt5bNRsAySY8ePWS1Wl0feJSknJwc9erVy5X4z5s7d67rfjHnHTx4UJ06dVJISIh69eqlnJwcV19ubq6sVqu6d%2B/euEU0UENqls6dZi4oKFBiYqJb%2B%2BnTp3XDDTfoyy%2B/dLWdOHFCp06dUqdOnRqvgEZms9n09ddfyzAMSec%2Br7J3717ZbDZX/4X7%2Bfjx4zp%2B/Lir3x%2BVlZXpgQceUEhIiNasWaP4%2BHhXn2EYGjFihDZu3Og2/ujRo369nwNlPTdUoKznzMxMrV27VkuXLtWtt956yXGBsp49rTcY17LZCFgmiYyM1Pjx4zVv3jzl5eXpww8/1KuvvqqpU6dKOndm5/znMYYPH653331X77zzjo4eParMzEzl5OTo7rvvlnTug6avvPKKPvzwQ%2BXl5WnevHm64447fO5Uc0Nqls59SPL8/3Iu1LRpUyUmJiojI0N5eXn69ttv9ac//UmDBw9Wt27drmlNV%2BvCmkeNGqXS0lItWrRIhw4d0qJFi%2BR0OjV69GhJ0uTJk7V582atX79eBw8e1Jw5czR06NA6fxvNl11Y86pVq/TDDz/omWeecfU5HA79/PPPslgsGjp0qF544QXt2rVL%2Bfn5mjNnjn71q19pyJAh3iyhwQJxPdcnENfz999/r5UrV%2BrBBx9UYmKi63h1OBySAm89N6TeYFnLjcpb94cIRGVlZcacOXOMPn36GIMGDTJee%2B01V1/Xrl1d90sxjHP3ibnllluM66%2B/3rj99tuN3bt3u21r1apVxo033mgkJiYaaWlpRnl5%2BbUqo0EaUvN77713yfumlJSUGHPnzjX69%2B9vJCQkGLNnzzZKSkoae/pX7eJ7BV1cs91uN8aPH2/06tXLmDhxovHtt9%2B6PX/Dhg3GkCFDjD59%2BhipqanGyZMnr9ncr9Tlah45cqTRtWvXWl/n7w9UXl5uZGRkGAMHDjRsNpsxffp049ixY16poyHq28%2BBsp4vVF/NgbCeV61aVefx2rVrV8MwAm89N6TeQF3L15LFMP7/%2BU4AAACYgkuEAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvt/6S5EgdBXrbwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8453719771828531746”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.7289336306951872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3873636055707077</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8171991793384535</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2845439145672</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0990853879038844</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7137243224697</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0999110030758177</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2387354644649968</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.412745738578616</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1208052847302827</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8453719771828531746”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5681124377128487</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6091323979652933</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6232658886522023</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6841797133340548</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7053017348165783</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.1129116830561117</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.238598341499091</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2827348294475462</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.344248520663155</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3593099847589114</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_DIABETES_ADULTS08”>PCT_DIABETES_ADULTS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>127</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.9147</td>

</tr> <tr>

<th>Minimum</th> <td>3</td>

</tr> <tr>

<th>Maximum</th> <td>18.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7572841748232844834”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-7572841748232844834”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7572841748232844834”

aria-controls=”quantiles-7572841748232844834” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7572841748232844834” aria-controls=”histogram-7572841748232844834”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7572841748232844834” aria-controls=”common-7572841748232844834”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7572841748232844834” aria-controls=”extreme-7572841748232844834”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7572841748232844834”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3</td>

</tr> <tr>

<th>5-th percentile</th> <td>6.7</td>

</tr> <tr>

<th>Q1</th> <td>8.5</td>

</tr> <tr>

<th>Median</th> <td>9.8</td>

</tr> <tr>

<th>Q3</th> <td>11.3</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.445</td>

</tr> <tr>

<th>Maximum</th> <td>18.2</td>

</tr> <tr>

<th>Range</th> <td>15.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.0578</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.20755</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.21918</td>

</tr> <tr>

<th>Mean</th> <td>9.9147</td>

</tr> <tr>

<th>MAD</th> <td>1.6329</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.18334</td>

</tr> <tr>

<th>Sum</th> <td>31053</td>

</tr> <tr>

<th>Variance</th> <td>4.2345</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7572841748232844834”>

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mAdPHhQTz31lCIjIz1rrVq10vTp0/Xuu%2B9amAwAAKB%2BfK5gORwOlZWV1VmvqKjgiu4AAKBJ8LmCNWjQIC1YsECffvqpZ62wsFBZWVlKSEiwMBkAAED9%2BNy7CGfMmKFHH31UDzzwgMLDwyVJZWVl6tGjhzIyMixOBwAAcHM%2BV7AiIiK0detW7d%2B/XydOnJDD4dC9996r/v37y8/Pz%2Bp4AAAAN%2BVzBUuSAgIClJCQwCFBAADQJPlcwXK5XFq%2BfLkOHz6sq1ev1jmx/c9//rNFyQAAAOrH5wrWb37zGx09elQPPvig7r77bqvjAAAANJjPFax3331Xa9asUVxcnNVRAAAAbonPXaYhNDRUERERVscAAAC4ZT5XsEaNGqU1a9aopqbG6igAAAC3xOcOEZaWlmrHjh3629/%2Bpg4dOigwMNBr%2B7p16yxKBgAAUD8%2BV7AkaeTIkVZHAAAAuGU%2BV7CysrKsjgAAAHBbfO4cLEkqLi5Wdna2fvWrX%2BmLL77Q7t27dfr0aatjAQAA1IvPFayzZ8/qv//7v7V161a98cYbKi8v165duzRmzBgVFBRYHQ8AAOCmfK5g/e53v9OwYcO0Z88e3XXXXZKkZcuWaejQoVqyZInF6QAAAG7O5wrW4cOH9eijj3p9sLPD4dDjjz%2BuY8eOWZgMAACgfnyuYNXW1qq2trbO%2BuXLlxUQEGBBIgAAgIbxuYI1cOBAPffcc14lq7S0VIsXL1a/fv0sTAYAAFA/PlewnnrqKR09elQDBw5UZWWlpkyZoiFDhuizzz7TjBkzrI4HAABwUz53Hazo6Gi9%2Buqr2rFjh/7xj3%2BotrZWKSkpGjVqlFq0aGF1PAAAgJvyuYIlSSEhIRo3bpzVMQAAAG6JzxWsRx555Fu381mEAADA1/lcwWrXrp3X19XV1Tp79qw%2B%2BeQT/exnP7MoFQAAQP35XMG60WcRrlixQufPn7/DaQAAABrO595FeCOjRo3S66%2B/bnUMAACAm2oyBevDDz/kQqMAAKBJ8LlDhNc7yf3SpUv6%2BOOP9ZOf/MSCRAAAAA3jcwWrbdu2Xp9DKEl33XWXfvrTn%2BqHP/yhRakAAADqz%2BcK1u9%2B9zurIwAAANwWnytYH3zwQb1v27dv30ZMAgAAcGt8rmCNHz/ec4jQ7XZ71q9d8/Pz0z/%2B8Y87HxAAAOAmfK5g5eTkaMGCBfqf//kfxcfHKzAwUB999JHmzZunH/3oRxoxYoTVEQEAAL6Vz12mISsrS7NmzdIDDzygli1bKiwsTP369dO8efP08ssvq127dp5fAAAAvsjnClZxcfF1y1OLFi108eJFCxIBAAA0jM8VrF69emnZsmW6dOmSZ620tFSLFy9W//79LUwGAABQPz53DtbMmTP1yCOPaNCgQerYsaPcbrf%2B%2Bc9/KjIyUuvWrbM6HgAAwE35XMHq0qWLdu3apR07dujUqVOSpIcfflgPPvigQkJCLE4HAABwcz5XsCTpnnvu0bhx4/TZZ5%2BpQ4cOkr6%2BmjsAAEBT4HPnYLndbi1ZskR9%2B/bVyJEjdf78ec2YMUOZmZm6evWq1fEAAABuyucK1vr167Vt2zbNnj1bgYGBkqRhw4Zpz549ys7OtjgdAADAzflcwcrLy9OsWbOUlJTkuXr7iBEjtGDBAm3fvr3B9zdx4kQ99dRTnq%2BPHTumcePGyel0asyYMTp69KjX7Xfs2KFhw4bJ6XQqNTVVFy5cuL2BAABAs%2BNzBeuzzz5Tt27d6qx37dpVLperQfe1c%2BdO7d271/N1eXm5Jk6cqLi4OG3ZskWxsbGaNGmSysvLJUlHjhxRZmam0tLSlJeXp7KyMmVkZNzeQAAAoNnxuYLVrl07ffTRR3XW33rrLc8J7/VRWlqqRYsWqWfPnp61Xbt2KSgoSNOnT1eXLl2UmZmpsLAw7d69W5L04osvKjExUaNHj1bXrl21aNEi7d27V4WFhbc/GAAAaDZ8rmD94he/0Ny5c7Vu3Tq53W4dOHBAS5Ys0aJFizR%2B/Ph638/TTz%2BtUaNG6d577/WsFRQUqE%2BfPp5Dj35%2Bfurdu7fy8/M92%2BPi4jy3b9Omjdq2bauCggJD0wEAgObA5y7TMGbMGFVXV2vVqlW6cuWKZs2apVatWmnatGlKSUmp130cOHBABw8e1Pbt2zVnzhzPusvl8ipckhQREaETJ05I%2BvpjeqKioupsP3/%2BfINmKC4urnM40%2BEIrXPfTVFAgL/X73bUHGaEvTkc9vu32xyel8xoLz5XsHbs2KHhw4crOTlZFy5ckNvtVkRERL2/v7KyUrNnz9asWbMUHBzsta2iosLzzsRvBAYGqqqqSpJ05cqVb91eX3l5eXXe8Ziamqr09PQG3Y8vCw%2B3/0Vfm8OMsKeWLcOsjtBomsPzkhntwecK1rx58/THP/5R99xzj1q1atXg78/OztZ9992nhISEOtuCgoLqlKWqqipPEbvR9oZeQT45OVlDhw71WnM4QnXx4uUG3Y8vCgjwV3h4iMrKKlRTU2t1nEbRHGaEvdnhteZazeF5yYyNw6ofOHyuYHXs2FGffPJJnUN59bVz506VlJQoNjZWkjyF6Y033tDIkSNVUlLidfuSkhLPobvo6Ojrbo%2BMjGxQhqioqDqHA12ur1RdbZ8nTE1Nra3muZ7mMCPsyc7/bpvD85IZ7cHnClbXrl315JNPas2aNerYsaOCgoK8tmdlZX3r969fv17V1dWer5csWSJJevLJJ/XBBx9o9erVcrvd8vPzk9vt1uHDhzV58mRJktPp1KFDh5SUlCRJKioqUlFRkZxOp8kRAQCAzflcwTpz5oz69OkjSQ2%2B7pX09WUe/lVY2Ne7BmNiYhQREaGlS5dq4cKFeuihh7RhwwZVVFQoMTFRkpSSkqLx48erV69e6tmzpxYuXKjBgwc36PIQAAAAPlGwFi1apLS0NIWGhmr9%2BvWN9jgtWrTQc889p9mzZ%2BuVV17Rf/zHfyg3N1ehoaGSpNjYWM2bN0/PPvusvvzySw0YMEDz589vtDwAAMCe/Nxut9vqEN26ddO%2Bffu83i04ceJELViwwBaXNpC%2BPgfLDhwOf7VsGaaLFy/b9vh5U50xcfk7VkeAj3h92gCrIxjXVJ%2BXDcGMjSMy8u478jjX8okLUVyv433wwQeqrKy0IA0AAMDt8YmCBQAAYCcULAAAAMN8pmB98/mAAAAATZ1PvItQkhYsWOB1zaurV69q8eLFnsssfONm18ECAACwmk8UrL59%2B9a55lVsbKwuXryoixcvWpQKAADg1vhEwWrMa18BAADcaT5zDhYAAIBdULAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOYTn0UIADAncfk7Vkeot9enDbA6AtAo2IMFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGOawOAPiqHyx52%2BoIAIAmij1YAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhti1Yn3/%2BudLT0xUfH6%2BEhARlZWWpsrJSklRYWKif//zn6tWrl0aMGKF9%2B/Z5fe/%2B/fs1cuRIOZ1OPfLIIyosLLRiBAAA0ETZsmC53W6lp6eroqJCL730kp555hn99a9/1fLly%2BV2u5WamqrWrVtr8%2BbNGjVqlNLS0nTu3DlJ0rlz55SamqqkpCRt2rRJrVq10uOPPy63223xVAAAoKlwWB2gMZw%2BfVr5%2Bfl655131Lp1a0lSenq6nn76aQ0aNEiFhYXasGGDQkND1aVLFx04cECbN2/W1KlTtXHjRt13332aMGGCJCkrK0sDBgzQ%2B%2B%2B/r%2B9973tWjgUAAJoIW%2B7BioyM1Jo1azzl6huXLl1SQUGBunfvrtDQUM96nz59lJ%2BfL0kqKChQXFycZ1tISIh69Ojh2Q4AAHAzttyDFR4eroSEBM/XtbW1evHFF9WvXz%2B5XC5FRUV53T4iIkLnz5%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%2BX5FR8frzZt2igjI0MnTpxQbm6ujhw5orFjx0qSxowZo8OHDys3N1cnTpxQRkaG2rdvzyUaAABAvdmyYP35z39WTU2NVq1apYEDB3r9CggI0MqVK%2BVyuZSUlKTXXntNK1asUNu2bSVJ7du31%2B9//3tt3rxZY8eOVWlpqVasWCE/Pz%2BLpwIAAE2Fn5tLlN8RLtdXVkcwwuHwV8uWYbp48bJtj59/M2Nc5m6rowC29/q0AfW6XXN67WFGsyIj774jj3MtW%2B7BAgAAsBIFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjmsDoAAKD5Slz%2BjtURGuT1aQOsjoAmgj1YAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZxJXfcMU3tis0AANwq9mABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDA%2B7BkAgHpqah9a//q0AVZHaLbYgwUAAGAYe7Cuo7KyUnPnztWbb76p4OBgTZgwQRMmTLA61nU1tZ%2BmAABoDihY17Fo0SIdPXpUL7zwgs6dO6cZM2aobdu2Gj58uNXRAABAE0DBukZ5ebk2btyo1atXq0ePHurRo4dOnDihl156iYIFAADqhYJ1jePHj6u6ulqxsbGetT59%2BignJ0e1tbXy9%2Be0NQBA09CUTiOx2wn5FKxruFwutWzZUoGBgZ611q1bq7KyUqWlpWrVqtVN76O4uFgul8trzeEIVVRUlPG8AADYgcNhrx0YFKxrVFRUeJUrSZ6vq6qq6nUfeXl5ys7O9lpLS0vT1KlTzYT8FwcX3tnDlsXFxcrLy1NycrJtCyMz2kdzmLM5zCg1jzmZ0V7sVRcNCAoKqlOkvvk6ODi4XveRnJysLVu2eP1KTk42ntUKLpdL2dnZdfbQ2Qkz2kdzmLM5zCg1jzmZ0V7Yg3WN6OhoXbx4UdXV1XI4vv7jcblcCg4OVnh4eL3uIyoqyvbNHAAA3Bh7sK7RrVs3ORwO5efne9YOHTqknj17coI7AACoFxrDNUJCQjR69GjNmTNHR44c0Z49e/R///d/euSRR6yOBgAAmoiAOXPmzLE6hK/p16%2Bfjh07pqVLl%2BrAgQOaPHmyxowZY3UsnxEWFqb4%2BHiFhYVZHaXRMKN9NIc5m8OMUvOYkxntw8/tdrutDgEAAGAnHCIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBQoNNnDhRTz31lNUxGkVVVZXmzp2rvn376vvf/750b%2BMrAAAIvElEQVSWLVsmu12Lt6ioSJMmTVLv3r01dOhQrV271upIRlVVVWnkyJF67733PGuFhYX6%2Bc9/rl69emnEiBHat2%2BfhQlv3/VmzM/P10MPPaTY2Fg98MAD2rhxo4UJb9/1ZvzGV199pYSEBG3ZssWCZGZdb85z587psccek9Pp1A9%2B8APt2rXLwoS373ozHjx4UElJSerVq5dGjRql/fv3W5iwcVCw0CA7d%2B7U3r17rY7RaBYsWKD9%2B/frD3/4g5YuXapXXnlFeXl5Vscyatq0aQoNDdWWLVv061//WsuXL9ef/vQnq2MZUVlZqSeeeEInTpzwrLndbqWmpqp169bavHmzRo0apbS0NJ07d87CpLfuejO6XC499thjio%2BP19atW5Wenq758%2Bfrb3/7m3VBb8P1ZvxXixcvVnFx8R1OZd715qyurtakSZPkcDi0detW/eIXv9D06dP1ySefWJj01l1vxi%2B%2B%2BEKTJ0/WiBEjtH37diUmJurxxx/X%2BfPnLUxqHgUL9VZaWqpFixapZ8%2BeVkdpFKWlpdq8ebPmz5%2Bv%2B%2B%2B/X/3799eECRNUUFBgdTRjvvzyS%2BXn52vKlCnq2LGjhg0bpoSEBB04cMDqaLft5MmT%2BvGPf6xPP/3Ua/3dd99VYWGh5s2bpy5dumjSpEnq1auXNm/ebFHSW3ejGffs2aPWrVvriSeeUMeOHfXggw9q9OjR2r59u0VJb92NZvzGwYMH9e677yoyMvIOJzPrRnPu3btXRUVFWrx4sTp37qyHHnpIgwYN0ocffmhR0lt3oxkPHz6sgIAA/fKXv1SHDh00efJkBQUFKT8/36KkjYOChXp7%2BumnNWrUKN17771WR2kUhw4dUosWLRQfH%2B9ZmzhxorKysixMZVZwcLBCQkK0ZcsWXb16VadPn9bhw4fVrVs3q6Pdtvfff1/f%2B9736uxxLCgoUPfu3RUaGupZ69OnT5N8Mb/RjAkJCdf9d3rp0qU7Fc2YG80ofX2o6Te/%2BY1mzZqlwMBAC9KZc6M533//ffXv318tWrTwrK1cuVLJycl3OuJtu9GM3/nOd1RaWqo333xTbrdbe/bs0eXLl/Xd737XoqSNw2F1ADQNBw4c0MGDB7V9%2B3bNmTPH6jiNorCwUO3atdOrr76qnJwcXb16VUlJSZoyZYr8/e3xs0hQUJBmzZql%2BfPna926daqpqVFSUpLGjRtndbTb9pOf/OS66y6XS1FRUV5rERERTfJwxI1mbN%2B%2Bvdq3b%2B/5%2BosvvtDOnTs1derUOxXNmBvNKEk5OTnq3r27Bg4ceAcTNY4bzfnN69CSJUu0bds2tWzZUunp6Ro2bNgdTnj7bjRjXFycHn74YaWnp8vf3181NTXKyspS586d73DCxmWP/zXQqCorKzV79mzNmjVLwcHBVsdpNOXl5Tp79qw2bNigrKwszZgxQ%2BvXr7fdSeCnTp3SkCFDlJeXp6ysLO3evVuvvfaa1bEaTUVFRZ29HYGBgaqqqrIoUeO6cuWKpk6dqtatWzfJvR43cvLkSW3YsEEZGRlWR2lU5eXl2rp1q8rKypSTk6PRo0crPT1dH330kdXRjLl8%2BbIKCwuVlpamjRs3avLkyVqwYIFOnTpldTSj2IOFm8rOztZ9992nhIQEq6M0KofDoUuXLmnp0qVq166dpK/fzfPyyy9rwoQJFqcz48CBA9q0aZP27t2r4OBg9ezZU59//rlWrVqlH/7wh1bHaxRBQUEqLS31WquqqrLlDwuXL1/W448/rn/%2B85/64x//qJCQEKsjGeF2uzVz5kylp6erdevWVsdpVAEBAfrOd76jOXPmyN/fXz169NDBgwf1yiuv2Ob81zVr1sjtdistLU2S1KNHDx05ckTr1q3T3LlzLU5nDgULN7Vz506VlJQoNjZWkjw/%2Bb/xxhtN8sTLG4mMjFRQUJCnXElSp06dVFRUZGEqs44ePaqYmBivctG9e3fl5ORYmKpxRUdH6%2BTJk15rJSUldQ4bNnWXLl3SL3/5S3366ad64YUX1LFjR6sjGXPu3Dl9%2BOGH%2Bvjjj/X0009L%2BnrP5OzZs7Vr1y6tWbPG4oTmREVFyc/Pz%2Bu0hE6dOunjjz%2B2MJVZf//739W1a1evtW7dut3wXaNNFQULN7V%2B/XpVV1d7vl6yZIkk6cknn7QqUqNwOp2qrKzUmTNn1KlTJ0nS6dOnvQpXUxcVFaWzZ8%2BqqqrKc9js9OnTXufv2I3T6VRubq6uXLniKZaHDh1Snz59LE5mTm1trdLS0vTZZ59p/fr16tKli9WRjIqOjtabb77ptTZ%2B/HiNHz/edntenU6nVq1apZqaGgUEBEj6%2BrC%2B3V6Hrv2hx46vQ5yDhZtq166dYmJiPL/CwsIUFhammJgYq6MZ1blzZw0ePFgZGRk6fvy43n77beXm5iolJcXqaMYMHTpUd911l2bOnKkzZ87oL3/5i3JycjR%2B/HirozWa%2BPh4tWnTRhkZGTpx4oRyc3N15MgRjR071upoxmzatEnvvfeeFixYoPDwcLlcLrlcrjqHRpsqh8Ph9RoUExMjh8OhiIgIRUdHWx3PqJEjR6q2tlZz587V2bNn9dJLL%2Bntt9/Wj3/8Y6ujGTNu3Di99dZbWrt2rQoLC7V27Vrt27fvW9/g0BSxBwv4F0uWLNH8%2BfOVkpKikJAQPfzww7YqH3fffbfWrl2rhQsXauzYsWrVqpWmTJliq5OhrxUQEKCVK1cqMzNTSUlJiomJ0YoVK9S2bVuroxnzxhtvqLa2VpMmTfJaj4%2BP1/r16y1KhVvRokULPf/885ozZ45Gjhyptm3b6plnnlGPHj2sjmZMr1699Pvf/17PPvus/vd//1edOnVSbm6u/v3f/93qaEb5ue32OSAAAAAW4xAhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2/wA7FK1chf5IJgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7572841748232844834”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.5</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.7</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.4</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.9</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.2</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.1</td> <td class=”number”>66</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.6</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.1</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (116)</td> <td class=”number”>2451</td> <td class=”number”>76.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7572841748232844834”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.6</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>16.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.1</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_DIABETES_ADULTS13”>PCT_DIABETES_ADULTS13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>151</td>

</tr> <tr>

<th>Unique (%)</th> <td>4.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.246</td>

</tr> <tr>

<th>Minimum</th> <td>3.3</td>

</tr> <tr>

<th>Maximum</th> <td>23.5</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram9146610846711778998”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives9146610846711778998,#minihistogram9146610846711778998”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives9146610846711778998”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles9146610846711778998”

aria-controls=”quantiles9146610846711778998” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram9146610846711778998” aria-controls=”histogram9146610846711778998”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common9146610846711778998” aria-controls=”common9146610846711778998”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme9146610846711778998” aria-controls=”extreme9146610846711778998”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles9146610846711778998”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.3</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.5</td>

</tr> <tr>

<th>Q1</th> <td>9.5</td>

</tr> <tr>

<th>Median</th> <td>11.1</td>

</tr> <tr>

<th>Q3</th> <td>12.9</td>

</tr> <tr>

<th>95-th percentile</th> <td>15.5</td>

</tr> <tr>

<th>Maximum</th> <td>23.5</td>

</tr> <tr>

<th>Range</th> <td>20.2</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.4</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4784</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.22039</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.3569</td>

</tr> <tr>

<th>Mean</th> <td>11.246</td>

</tr> <tr>

<th>MAD</th> <td>1.9646</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.30933</td>

</tr> <tr>

<th>Sum</th> <td>35222</td>

</tr> <tr>

<th>Variance</th> <td>6.1425</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram9146610846711778998”>

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GHD1KNHDx0/ftzYeqZPn67NmzdryZIlmj17tlatWqWkpCRduHBBw4YNU4sWLbR27VpFRUVp%2BPDhunDhgqRLYW/ixIkaPXq0kpKSdPbsWcXFxRmrCwAABD7bA9Z//dd/6bbbbtMTTzzhGduwYYNq1Kih%2BPh4I%2BvIzMzUmjVrNG3aNDVr1kz33HOPhg4dqpSUFG3YsEEhISEaP3686tevr4kTJ6pChQr6%2BOOPJUnLly9Xly5d1LNnTzVs2FAzZ87U559/rrS0NCO1AQCAwGd7wPrmm2/03HPPqVq1ap6xKlWqaPz48dq6dauRdezcuVMVK1ZUy5YtPWPDhg1TfHy8UlJSFB0dLYfDIenSIyOaN2%2Bu5ORkSVJKSopatGjh%2Bb4aNWqoZs2aSklJMVIbAAAIfLYHLKfTqbNnzxYaz8rKMvZE97S0NNWqVUvvvfeeOnfurD/96U%2BaN2%2BeCgoK5Ha7FRER4TW/atWqOnHihKRLj4%2B42nIAAIBrsf2nCO%2B9915Nnz5dc%2BbM0f/7f/9P0qVAFB8fr5iYGCPruHDhgo4ePaqVK1cqPj5ebrdbkyZNUmhoqLKyshQcHOw1Pzg4WLm5uZKk7Ozsqy4vifT0dLndbq8xpzPMK7gFBZXz%2BjNQ0BcCkdNZNrZ7oH4O6cu/BGpfptkesCZMmKAnnnhCDzzwgMLDwyVJZ8%2BeVePGjY3dTO50OnXu3DnNnj1btWrVkiQdO3ZMK1as0G233VYoLOXm5qp8%2BfKSpJCQkCKXh4aGlnj9SUlJSkhI8BqLjY3V2LFjC80NDy/5%2B/oT%2BkIgqVy5gq9L8BKon0P68i%2BB2pcptgesqlWr6t1339XmzZuVmpoqp9OpO%2B64Q/fcc4/nvqjfq1q1agoJCfGEK0m6/fbbdfz4cbVs2VIZGRle8zMyMjxnl6pXr17k8svvGbuW/v37q2PHjl5jTmeYTp8%2B73kdFFRO4eGhOns2S/n5BSV%2B77KOvhCILt93fSlQP4f05V/8rS9f/QfJJ78qJygoSDExMcYuCV7J5XIpJydHR44c0e233y5JOnz4sGrVqiWXy6VFixbJsiw5HA5ZlqVdu3ZpxIgRnu/duXOnevfuLUk6fvy4jh8/LpfLVeL1R0REFLqPy%2B3%2BRXl5hT%2BI%2BfkFRY77O/pCIClr2zxQP4f05V8CtS9TbA9Ybrdbc%2BfO1a5du3Tx4sVCN7Z/%2Bumnv3sd9erVU/v27RUXF6fJkyfL7XYrMTFRI0eOVOfOnTV79mzNmDFDjzzyiFauXKmsrCx16dJFkjRgwAANHDhQkZGRatq0qWbMmKH27durTp06v7suAABwY7A9YP3tb3/T3r179eCDD%2Brmm28utfW8/PLLmjZtmgYMGKDQ0FA99thjGjhwoBwOhxYuXKgXX3xRq1at0p133qnExESFhYVJkqKiojR16lS9%2BuqrOnPmjNq0aaNp06aVWp0AACDw2B6wtm7dqsWLF3s9a6o03HzzzZo5c2aRy5o1a6Z333232O/t3bu35xIhAADA9bL9ZyzDwsJUtWpVu1cLAABgG9sDVo8ePbR48WKvX%2B4MAAAQSGy/RJiZman169frX//6l%2BrUqVPooZ5vvfWW3SUBAAAY5ZPHNHTr1s0XqwUAALCF7QErPj7e7lUCAADYyidnsNLT07Vq1SodOXJEzz//vHbs2KEGDRqoXr16vigHKFKXuV/7ugQAgJ%2By/Sb3o0ePqnv37nr33Xf1ySef6MKFC9qwYYP69OmjlJQUu8sBAAAwzvaA9dJLL6lTp07atGmTbrrpJknSnDlz1LFjR7388st2lwMAAGCc7QFr165deuKJJ7x%2BsbPT6dSoUaO0b98%2Bu8sBAAAwzvaAVVBQoIKCwr8c8vz58woKCrK7HAAAAONsD1ht27bVwoULvUJWZmamZs2apdatW9tdDgAAgHG2B6znnntOe/fuVdu2bZWTk6ORI0eqQ4cO%2BumnnzRhwgS7ywEAADDO9sc0VK9eXe%2B9957Wr1%2Bv7777TgUFBRowYIB69OihihUr2l0OAACAcT55DlZoaKj69evni1UDAACUOtsD1qBBg666nN9FCAAA/J3tAatWrVper/Py8nT06FEdOHBAgwcPtrscAAAA48rM7yKcN2%2BeTpw4YXM1AAAA5tn%2BU4TF6dGjhz766CNflwEAAPC7lZmA9e9//5sHjQIAgIBQJm5yP3funL7//ns9%2BuijdpcDAABgnO0Bq2bNml6/h1CSbrrpJj3%2B%2BON66KGH7C4HAADAONsD1ksvvWT3KgEAAGxle8DasWNHiefefffdpVgJAABA6bA9YA0cONBzidCyLM/4lWMOh0Pfffed3eUBAAD8brYHrAULFmj69Ol69tln1bJlSwUHB2vPnj2aOnWqevXqpa5du9pdEgAAgFG2P6YhPj5ekyZN0gMPPKDKlSurQoUKat26taZOnaoVK1aoVq1ani8AAAB/ZHvASk9PLzI8VaxYUadPn7a7HAAAAONsD1iRkZGaM2eOzp075xnLzMzUrFmzdM8999hdDgAAgHG234P1wgsvaNCgQbr33ntVt25dWZalH374QdWqVdNbb71ldzkAAADG2R6w6tevrw0bNmj9%2BvU6dOiQJOmxxx7Tgw8%2BqNDQULvLAQAAMM72gCVJt9xyi/r166effvpJderUkXTpae4AAACBwPZ7sCzL0ssvv6y7775b3bp104kTJzRhwgRNnDhRFy9etLscAAAA42wPWMuWLdO6dev04osvKjg4WJLUqVMnbdq0SQkJCXaXAwAAYJztASspKUmTJk1S7969PU9v79q1q6ZPn64PPvjA7nIAAACMsz1g/fTTT2rUqFGh8YYNG8rtdttdDgAAgHG2B6xatWppz549hca/%2BOILzw3vAAAA/sz2nyL885//rClTpsjtdsuyLG3ZskVJSUlatmyZnnvuObvLAQAAMM72gNWnTx/l5eXp9ddfV3Z2tiZNmqQqVaroqaee0oABA%2BwuBwAAwDjbA9b69evVuXNn9e/fX6dOnZJlWapatardZQBAiXWZ%2B7WvS7guHz3VxtclADc82%2B/Bmjp1qudm9ipVqhCuAABAwLE9YNWtW1cHDhywe7UAAAC2sf0SYcOGDfXMM89o8eLFqlu3rkJCQryWx8fH210SAACAUbYHrCNHjig6OlqSeO4VAAAISLYErJkzZ2r06NEKCwvTsmXL7FglAACAz9hyD9Ybb7yhrKwsr7Fhw4YpPT3djtUDAADYypaAZVlWobEdO3YoJyfHjtUDAADYyvafIgQAAAh0BCwAAADDbAtYDofDrlV5GTZsmNfvONy3b5/69esnl8ulPn36aO/evV7z169fr06dOsnlcik2NlanTp2yu2QAAODnbHtMw/Tp072eeXXx4kXNmjVLFSpU8Jpn8jlYH374oT7//HP16tVLknThwgUNGzZM3bt310svvaQVK1Zo%2BPDh2rhxo8LCwrR7925NnDhRU6ZMUcOGDTVjxgzFxcVp4cKFxmoCAACBz5aAdffddxd65lVUVJROnz6t06dPl8o6MzMzNXPmTDVt2tQztmHDBoWEhGj8%2BPFyOByaOHGivvjiC3388cfq3bu3li9fri5duqhnz56SLj1eokOHDkpLS1OdOnVKpU4AABB4bAlYvnj21d///nf16NHD61EQKSkpio6O9lyudDgcat68uZKTk9W7d2%2BlpKToySef9MyvUaOGatasqZSUFAIWAAAosYC8yX3Lli365ptvNGrUKK9xt9utiIgIr7GqVavqxIkTkqT09PSrLgcAACgJ239VTmnLycnRiy%2B%2BqEmTJql8%2BfJey7KyshQcHOw1FhwcrNzcXElSdnb2VZeXVHp6eqFLok5nmFd4Cwoq5/VnoAjUvgB/4nT61/4XqMcN%2BrqxBVzASkhIUJMmTRQTE1NoWUhISKGwlJub6wlixS0PDQ29rhqSkpKUkJDgNRYbG6uxY8cWmhsefn3v7S8CtS/AH1SuXOHak8qgQD1u0NeNKeAC1ocffqiMjAxFRUVJkicwffLJJ%2BrWrZsyMjK85mdkZHjOLFWvXr3I5dWqVbuuGvr376%2BOHTt6jTmdYTp9%2BrzndVBQOYWHh%2Brs2Szl5xdc1/uXZYHaF%2BBPLj/W%2BINAPW7QV9ngq/9wBFzAWrZsmfLy8jyvX375ZUnSM888ox07dmjRokWyLEsOh0OWZWnXrl0aMWKEJMnlcmnnzp3q3bu3JOn48eM6fvy4XC7XddUQERFR6F4ut/sX5eUV/iDm5xcUOe7vArUvwB/4674XqMcN%2BroxBVzAqlWrltfrX5%2Bzddttt6lq1aqaPXu2ZsyYoUceeUQrV65UVlaWunTpIkkaMGCABg4cqMjISDVt2lQzZsxQ%2B/bt%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%2BcnBxJUlpamoYMGaLIyEh17dpVX331ldf3bt68Wd26dZPL5dKgQYOUlpbmixYAAICfCsiAZVmWxo4dq6ysLP3jH//QK6%2B8os8%2B%2B0xz586VZVmKjY3VrbfeqjVr1qhHjx4aPXq0jh07Jkk6duyYYmNj1bt3b73zzjuqUqWKRo0aJcuyfNwVAADwF05fF1AaDh8%2BrOTkZH399de69dZbJUljx47V3//%2Bd917771KS0vTypUrFRYWpvr162vLli1as2aNxowZo9WrV6tJkyYaOnSoJCk%2BPl5t2rTR9u3b1apVK1%2B2BQAA/ERAnsGqVq2aFi9e7AlXvzp37pxSUlJ01113KSwszDMeHR2t5ORkSVJKSopatGjhWRYaGqrGjRt7lgMAAFxLQAas8PBwxcTEeF4XFBRo%2BfLlat26tdxutyIiIrzmV61aVSdOnJCkay4HAAC4loC8RHilWbNmad%2B%2BfXrnnXe0dOlSBQcHey0PDg5Wbm6uJCkrK%2Buqy0siPT1dbrfba8zpDPMKbkFB5bz%2BDBSB2hfgT5xO/9r/AvW4QV83toAPWLNmzdKbb76pV155RQ0aNFBISIgyMzO95uTm5qp8%2BfKSpJCQkEJhKjc3V%2BHh4SVeZ1JSkhISErzGYmNjNXbs2EJzw8NDS/y%2B/iRQ%2BwL8QeXKFXxdwm8SqMcN%2BroxBXTAmjZtmlasWKFZs2bpgQcekCRVr15dBw8e9JqXkZHhObtUvXp1ZWRkFFreqFGjEq%2B3f//%2B6tixo9eY0xmm06fPe14HBZVTeHiozp7NUn5%2BwXX1VZYFal%2BAP7n8WOMPAvW4QV9lg6/%2BwxGwASshIUErV67UnDlz1LlzZ8%2B4y%2BVSYmKisrOzPWetdu7cqejoaM/ynTt3euZnZWVp3759Gj16dInXHRERUeg%2BLrf7F%2BXlFf4g5ucXFDnu7wK1L8Af%2BOu%2BF6jHDfq6MQXkBdRDhw5p/vz5evLJJxUdHS232%2B35atmypWrUqKG4uDilpqYqMTFRu3fvVt%2B%2BfSVJffr00a5du5SYmKjU1FTFxcWpdu3aPKIBAACUWEAGrE8//VT5%2Bfl6/fXX1bZtW6%2BvoKAgzZ8/X263W71799b777%2BvefPmqWbNmpKk2rVr67XXXtOaNWvUt29fZWZmat68eXI4HD7uCgAA%2BAuHxSPKbeF2/%2BL12uksp8qVK%2Bj06fMBdYo1kPrqMvdrX5cA/CYfPdXG1yVcl0A6blyOvsqGatVu9sl6A/IMFgAAgC8RsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsCObCbQAAAQsklEQVQCAAAwjIAFAABgGAELAADAMAIWAACAYU5fFwAAMKvL3K99XUKJffRUG1%2BXAJQKzmABAAAYRsACAAAwjIAFAABgGAELAADAMG5yh2386cZbAAB%2BD85gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHP6ugAAwI2ry9yvfV3CdfnoqTa%2BLgF%2BgjNYAAAAhhGwAAAADCNgAQAAGMY9WEXIycnRlClT9L//%2B78qX768hg4dqqFDh/q6rCL52/0LAADcCAhYRZg5c6b27t2rN998U8eOHdOECRNUs2ZNde7c2delAQAAP0DAusKFCxe0evVqLVq0SI0bN1bjxo2Vmpqqf/zjHwQsAABQItyDdYX9%2B/crLy9PUVFRnrHo6GilpKSooKDAh5UBAAB/wRmsK7jdblWuXFnBwcGesVtvvVU5OTnKzMxUlSpVrvke6enpcrvdXmNOZ5giIiI8r4OCynn9CQAo%2B7jvtfRsfCbG1yUYRcC6QlZWlle4kuR5nZubW6L3SEpKUkJCgtfY6NGjNWbMGM/r9PR0vfnmYvXv398reF2vb2aUrcuW6enpSkpK%2Bt19lTX05V/oy7/Ql38J1L5M4/TJFUJCQgoFqV9fly9fvkTv0b9/f61du9brq3///l5z3G63EhISCp3p8nf05V/oy7/Ql3%2BhrxsbZ7CuUL16dZ0%2BfVp5eXlyOi/99bjdbpUvX17h4eEleo%2BIiAhSPQAANzDOYF2hUaNGcjqdSk5O9ozt3LlTTZs2Vbly/HUBAIBrIzFcITQ0VD179tTkyZO1e/dubdq0Sf/zP/%2BjQYMG%2Bbo0AADgJ4ImT5482ddFlDWtW7fWvn37NHv2bG3ZskUjRoxQnz59jK%2BnQoUKatmypSpUqGD8vX2JvvwLffkX%2BvIv9HXjcliWZfm6CAAAgEDCJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsUnLy5EmNHTtWLVu2VExMjOLj45WTk1Pk3JEjR%2BrOO%2B/0%2Bvrss89srrhkNm7cWKjWsWPHFjl38%2BbN6tatm1wulwYNGqS0tDSbqy25tWvXFurrzjvvVMOGDYuc/9BDDxWae%2BDAAZurvrrc3Fx169ZN27Zt84ylpaVpyJAhioyMVNeuXfXVV19d9T3Wr1%2BvTp06yeVyKTY2VqdOnSrtsq%2BpqL6Sk5P1yCOPKCoqSg888IBWr1591fdo0aJFoe13/vz50i79qorqa/r06YXqXL58ebHvsXTpUsXExCgqKkrPP/%2B8srKy7Cj9qq7s67nnnityXyvu15GdOXOm0NxWrVrZ2YLH1Y7r/r5vXa23QNi/fMKCcQUFBdbDDz9s/eUvf7EOHDhg7dixw7rvvvusl156qcj59913n7Vu3TorPT3d85WTk2Nz1SUzf/58a/jw4V61njlzptC8n3/%2B2YqMjLSWLFliHThwwPrP//xPq1u3blZBQYEPqr62rKwsr56OHTtm3XfffdaMGTMKzc3Ly7OaNm1qbd%2B%2B3et7Ll686IPKi5adnW3FxsZaDRo0sLZu3WpZ1qXPZffu3a1x48ZZBw8etBYsWGC5XC7r559/LvI9UlJSrGbNmlnvvvuu9d1331mPP/64NWzYMDvbKKSovtLT060WLVpYs2fPto4cOWKtX7/eatq0qfXZZ58V%2BR4nTpywGjRoYP34449e28%2BXn82i%2BrIsyxoyZIi1cOFCrzovXLhQ5Ht8/PHHVnR0tPXPf/7TSklJsbp27WpNmTLFrhaKVFRfZ8%2Be9ern3//%2Bt9WkSRNr48aNRb7HN998Y7Vs2dLrezIyMuxsw7Ksqx/X/X3fulpvgbB/%2BQoBqxQcPHjQatCggeV2uz1jH3zwgdW2bdtCc3NycqxGjRpZhw8ftrPE32zcuHHW7Nmzrzlv7ty51uOPP%2B55feHCBSsqKsrrH4%2BybMGCBVanTp2KDLo//PCD1bBhQys7O9sHlV1bamqq9dBDD1ndu3f3%2Bodt8%2BbNVmRkpHX%2B/HnP3MGDB1uvvvpqke/z7LPPWhMmTPC8PnbsmHXnnXdaP/74Y%2Bk2UIzi%2Bnr77betzp07e83929/%2BZj399NNFvs/XX39ttWnTptTrLani%2BrIsy4qJibG%2B/PLLEr3Po48%2B6rUtd%2BzYYTVr1qzYQFbartbX5YYOHWo988wzxb7PqlWrrP79%2B5dWmSV2teO6v%2B9bV%2BvN3/cvX%2BISYSmoVq2aFi9erFtvvdVr/Ny5c4XmHj58WA6HQ3Xq1LGrvN/l0KFDqlu37jXnpaSkqEWLFp7XoaGhaty4sZKTk0uxOjMyMzO1aNEijRs3TsHBwYWWHzx4UDVq1FBISIgPqru27du3q1WrVkpKSvIaT0lJ0V133aWwsDDPWHR0dLHb5MptWKNGDdWsWVMpKSmlU/g1FNfXr5czrlTU/iZd2n633357qdT4WxTX17lz53Ty5MkS7W/5%2Bfnas2eP1/aKjIzUxYsXtX//ftMll0hxfV1uy5Yt2rFjh55%2B%2Buli5xw8eLBEfwel7WrHdX/ft67Wm7/vX77k9HUBgSg8PFwxMTGe1wUFBVq%2BfLlat25daO7hw4dVsWJFjR8/Xtu3b9cf/vAHjRkzRu3atbOz5BKxLEtHjhzRV199pYULFyo/P1%2BdO3fW2LFjCwURt9utiIgIr7GqVavqxIkTdpb8m6xYsUIRERHq3LlzkcsPHTqkm266ScOHD9fevXt1%2B%2B23a/z48WrWrJnNlRbt0UcfLXL8erdJenp6mdqGxfVVu3Zt1a5d2/P6//7v//Thhx9qzJgxRc4/dOiQsrKyNHDgQB05ckSNGjXS888/77N/FIrr69ChQ3I4HFqwYIG%2B%2BOILVapUSU888YR69epVaO7Zs2eVk5Pjtb2cTqcqVapU5rbX5RITE9WrVy/VqFGj2DmHDh1SXl6e%2Bvbtq5MnT6pFixaKi4sr9NksbVc7rvv7vnW13vx9//IlzmDZYNasWdq3b5/%2B%2Bte/Flp2%2BPBhZWdnq23btlq8eLHatWunkSNHas%2BePT6o9OqOHTumrKwsBQcHa%2B7cuZowYYI%2B%2BOADzZw5s9DcX%2BddLjg4WLm5uXaV%2B5tYlqXVq1fr8ccfL3bOkSNHdObMGfXr10%2BJiYmqX7%2B%2BBg8erOPHj9tY6fW73m2SnZ3td9swOztbY8aM0a233qr%2B/fsXOefw4cM6c%2BaMRo4cqfnz56t8%2BfIaMmRIsf8j95Vfz27Xq1dPiYmJ6tevn/72t79p48aNheZmZ2dLkl9tr7S0NG3dulUDBw686rzDhw/r3LlziouL0yuvvKL09HSNGDFC%2Bfn5NlVatMuP64G2bxX3b1Yg7V924AxWKZs1a5befPNNvfLKK2rQoEGh5aNGjdLAgQN1yy23SJIaNmyob7/9VqtWrVLTpk3tLveqatWqpW3btumWW26Rw%2BFQo0aNVFBQoGeffVZxcXEKCgryzA0JCSl0sMjNzVV4eLjdZV%2BXPXv26OTJk3rwwQeLnTNt2jRlZ2erYsWKkqTJkydr165dWrdunUaMGGFXqdctJCREmZmZXmO5ubkqX758sfOL2oahoaGlVuPvcf78eY0aNUo//PCD3n777WLrXLJkiS5evKgKFSpIkl5%2B%2BWW1a9dOn332mbp3725nyVfVs2dPdejQQZUqVZJ06djwww8/aMWKFbrvvvu85v56udqfttcnn3yiRo0a6Y477rjqvA8//FAOh8PzOX311VfVtm1bpaSkqHnz5naUWsiVx/VA2reK%2Bzcr0PYvO3AGqxRNmzZNb7zxhmbNmqUHHnigyDnlypXzhKtf1atXTydPnrSjxOtWqVIlORwOz%2Bv69esrJydHZ86c8ZpXvXp1ZWRkeI1lZGSoWrVqttT5W3355Zdq0aJFoW1yOafT6QlXkjxnGcrqNvtVcdukuEst/rQNz507pz//%2Bc9KTU3Vm2%2B%2BedV7doKDgz0Hf%2BnSP3a1a9cuc9vP4XB4wtWvivucVapUSSEhIV7bKy8vT5mZmWVye0mX9rU//elP15wXGhrqFVSqVq2qSpUq%2BWx7FXVcD5R9q7h/swJx/7IDAauUJCQkaOXKlZozZ85Vz4Y899xziouL8xrbv3%2B/6tWrV9olXrcvv/xSrVq18nq2znfffadKlSqpSpUqXnNdLpd27tzpeZ2VlaV9%2B/bJ5XLZVu9vsXv37mv%2Br3jgwIFKSEjwvC4oKND3339fJrfZ5Vwul7799lvP5SRJ2rlzZ7Hb5MptePz4cR0/frzMbcOCggKNHj1aP/30k5YtW6Y//vGPxc61LEudOnXS2rVrPWMXLlzQ0aNHy9z2%2B%2B///m8NGTLEa6y4Y0O5cuXUtGlTr%2B2VnJwsp9NZ7LPcfMmyLO3Zs%2Bea%2B9q5c%2Bd09913a%2BvWrZ6xkydP6vTp0z7ZXsUd1wNh3yqut0Ddv%2BxAwCoFhw4d0vz58/Xkk08qOjpabrfb8yVdutn41x2xY8eO%2BuCDD/Tee%2B/p6NGjSkhI0M6dO696D5CvREVFKSQkRC%2B88IIOHz6szz//XDNnztRf/vIX5efny%2B12e0579%2BnTR7t27VJiYqJSU1MVFxen2rVr%2B%2BwBgSWVmppa6JLFlb117NhRS5cu1aeffqrDhw9r6tSp%2BuWXX4q8%2BbgsadmypWrUqKG4uDilpqYqMTFRu3fvVt%2B%2BfSVdukThdrs997YMGDBA69at0%2BrVq7V//36NHz9e7du3L3M/8frOO%2B9o27Ztmj59usLDwz372q%2BXbC7vy%2BFwqH379nrttde0bds2paamavz48frDH/5Q5n6wpEOHDtqxY4eWLFmiH3/8UW%2B//bbee%2B89DR06VNKl%2B2F%2BPaZIl24qX7JkiTZt2qTdu3dr8uTJevjhh8vEZacr/fzzzzp//nyRlwcv76tixYqKjo5WfHy8du/erW%2B//VZ//etfFRMTozvvvNPWmq92XPf3fetqvQXq/mULnz4kIkAtXLjQatCgQZFflmVZDRo0sNasWeOZv2rVKuv%2B%2B%2B%2B3mjRpYvXq1cvavn27r0q/pgMHDlhDhgyxIiMjrTZt2livvfaaVVBQYKWlpRV61s2//vUv6/7777eaNWtmDR482GfPeLkeTZs2tb744guvsSt7KygosF5//XWrffv2VpMmTazHHnvM%2Bv77731R7jVduU1%2B%2BOEH67HHHrOaNGliPfjgg9bXX3/tWbZ161arQYMGVlpammdszZo1Vrt27azIyEgrNjbWOnXqlK31F%2BfyvoYOHVrkvvbrc9iu7Cs7O9uKj4%2B32rRpY7lcLmv48OHWsWPHfNbL5a7cXhs3brS6d%2B9uNW3a1OrcubP1ySefeJatWbPGc0z51cKFC6177rnHio6OtuLi4srMs9qu7Cs5Odlq0KBBkc%2BZu7KvzMxM67nnnrNatWplRUVFWc8884yVmZlpS92Xu9Zx3Z/3rav1Fkj7l90clmVZvg55AAAAgYRLhAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8HcS1acUvIYkgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common9146610846711778998”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.5</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.5</td> <td class=”number”>61</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.2</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>59</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.9</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.8</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.6</td> <td class=”number”>54</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.6</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (140)</td> <td class=”number”>2562</td> <td class=”number”>79.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme9146610846711778998”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.1</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:67%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>20.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_ANMLPROD16”>PCT_FMRKT_ANMLPROD16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>109</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>53.554</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>19.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6661491698383619048”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives6661491698383619048”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6661491698383619048”

aria-controls=”quantiles6661491698383619048” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6661491698383619048” aria-controls=”histogram6661491698383619048”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6661491698383619048” aria-controls=”common6661491698383619048”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6661491698383619048” aria-controls=”extreme6661491698383619048”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6661491698383619048”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.441</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.75515</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.5251</td>

</tr> <tr>

<th>Mean</th> <td>53.554</td>

</tr> <tr>

<th>MAD</th> <td>35.75</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.16655</td>

</tr> <tr>

<th>Sum</th> <td>120120</td>

</tr> <tr>

<th>Variance</th> <td>1635.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6661491698383619048”>

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RX9%2Bht74TTL0NrEMgXnrrrbc0atQouVwu99gnn3yia6%2B9VklJSfrwww9lGIakb6/XKisrk91ulyTZ7XaVlpa6X1dTU6Oamhr3PAAAQHuCMmAlJCQoPDxcjzzyiI4ePap9%2B/Zp6dKluueeezRp0iQ1NDQoPz9fR44cUX5%2BvlwulyZPnixJysjI0Pbt27Vx40YdPnxY8%2BbN04QJE9S7d2%2BLVwUAAAJFUAasjh076oUXXtCpU6eUlpamvLw8paen65577lHHjh21evVqlZaWKjU1VQ6HQ8XFxYqKipL0bThbvHixioqKlJGRoU6dOqmgoMDiFQEAgEAStNdg/eQnP9HatWvPOzd8%2BHBt3br1gq9NTU1Vamqqr0oDAABBLiiPYAEAAFiJgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYzO8C1qxZs7RhwwZ99dVXVpcCAABwSfwuYI0ePVrPPfecxowZo/vvv1/79%2B9333UdAAAgEPhdwHrggQf0%2Buuva9WqVQoNDdV9992nCRMm6KmnntKxY8esLg8AAKBdfnmj0ZCQEN1444268cYb5XK5tG7dOq1atUrFxcVKTEzUHXfcoV/84hdWlwkAAHBefhmwJKm2tlY7duzQjh079I9//EOJiYn69a9/rZMnT%2BqRRx7R%2B%2B%2B/r7y8PKvLBAAAaMPvAtb27du1fft2vfvuu%2BrSpYtmzJihp59%2BWn379nVv0717d%2BXn5xOwAACAX/K7gJWXl6ebbrpJRUVFGjdunDp0aHuZ2HXXXafbbrvNguoAAADa53cB680331Tnzp1VX1/vDlcVFRUaMmSIQkNDJUmJiYlKTEy0skwAAIAL8rvfIjxz5owmTZqk559/3j2WmZmp6dOnq6amxsLKAAAAvON3AesPf/iD%2BvTpo7vuuss9tnv3bnXv3l0FBQUWVgYAAOAdvwtYH3zwgRYsWKDY2Fj3WJcuXTRv3jy98847FlYGAADgHb8LWDabTQ0NDW3GXS4Xd3QHAAABwe8C1rhx47RkyRJ9/vnn7rHq6moVFBRo7NixFlYGAADgHb/7LcL58%2Bfrrrvu0s0336yYmBhJUkNDg4YMGaLc3FyLqwMAAGif3wWsrl27auvWrTpw4ICqqqpks9nUv39/3XDDDQoJCbG6PAAAgHb5XcCSpNDQUI0dO5ZTggAAICD5XcByOp1auXKlysrK9M0337S5sP3vf/%2B7RZUBAAB4x%2B8C1v/8z//o0KFDmjp1qn70ox9ZXQ4AAMBF87uA9c4772jNmjVKTk62uhQAAIBL4ne3aYiKilLXrl2tLgMAAOCS%2BV3Amj59utasWaOWlharSwEAALgkfneKsL6%2BXrt27dIbb7yh3r17KywszGP%2BT3/6k0WVAQAAeMfvApYkTZs2zeoSAAAALpnfBayCggKrSwAAALgsfncNliTV1taqsLBQDzzwgP7v//5Pe/bs0dGjRy9pX5mZmVqwYIH7eWVlpWbNmiW73a60tDQdOnTIY/tdu3Zp4sSJstvtysrK0qlTpy5rLQAA4OrjdwHr%2BPHj%2BuUvf6mtW7fqr3/9qxobG7V7926lpaXJ4XBc1L5ee%2B017du3z/28sbFRmZmZSk5O1pYtW5SQkKA5c%2BaosbFRklRRUaG8vDxlZ2erpKREDQ0N/P1DAABw0fwuYP3xj3/UxIkTtXfvXl1zzTWSpBUrViglJUVPPvmk1/upr6/X0qVLNWzYMPfY7t27FR4ernnz5qlfv37Ky8tTdHS09uzZI0lav369Jk%2BerBkzZmjgwIFaunSp9u3bp%2BrqanMXCQAAgprfBayysjLdddddHn/Y2Wazae7cuaqsrPR6P0888YSmT5%2Bu/v37u8ccDoeSkpLc%2Bw4JCVFiYqLKy8vd8/9%2Bg9Pu3burR48eF33kDAAAXN387iL31tZWtba2thn/%2BuuvFRoa6tU%2BDh48qA8%2B%2BEA7d%2B7UokWL3ONOp9MjcElS165dVVVVJenba7/i4uLazJ88efKi1lBbWyun0%2BkxZrNFtdn35QoN9bt8/INstsCp97veBlqPAwG99S366zv01veCqbd%2BF7DGjBmj1atXa9myZe6x%2Bvp6LVu2TKNHj2739U1NTXr00Ue1cOFCRUREeMy5XK4299UKCwtTc3OzJOns2bM/OO%2BtkpISFRYWeoxlZWUpJyfnovYTbDp3jra6hIsWExNpdQlBi976Fv31HXrrO8HUW78LWAsWLNDs2bM1ZswYNTU16Xe/%2B53%2B9a9/6dprr9Uf//jHdl9fWFiooUOHauzYsW3mwsPD24Sl5uZmdxC70Hxk5MX9h6enpyslJcVjzGaL0unTX1/UftoTaEnf7PX7UmhoB8XERKqhwaWWlrZHVHHp6K1v0V/fobe%2B54veWvXDvd8FrPj4eG3btk27du3SJ598otbWVmVkZGj69Onq2LFju69/7bXXVFdXp4SEBElyB6a//vWvmjZtmurq6jy2r6urc5%2B6i4%2BPP%2B98bGzsRa0hLi6uzelAp/MrnTt3dX9BBuL6W1paA7LuQEBvfYv%2B%2Bg699Z1g6q3fBSxJioyM1KxZsy7ptevWrdO5c%2Bfcz7/7zcMHH3xQ77//vp5//nkZhqGQkBAZhqGysjLde%2B%2B9kiS73a7S0lKlpqZKkmpqalRTUyO73X6ZKwIAAFcTvwtYs2fP/sH59v4WYc%2BePT2eR0d/e2iwT58%2B6tq1q5YvX678/Hz993//tzZs2CCXy6XJkydLkjIyMnT77bdrxIgRGjZsmPLz8zVhwgT17t37MlYEAACuNn53EU/Pnj09HvHx8Tp79qwqKircp/0uVceOHbV69Wr3USqHw6Hi4mJFRUVJkhISErR48WIVFRUpIyNDnTp14k/3AACAi%2BZ3R7AuFGiKioou%2BnYJktpcGD98%2BHBt3br1gtunpqa6TxECAABcCr87gnUh06dP11/%2B8herywAAAGhXwASsDz/80OsbjQIAAFjJ704Rnu8i9zNnzujTTz/VLbfcYkFFAAAAF8fvAlaPHj08/g6hJF1zzTW67bbb9Ktf/cqiqgAAALzndwHLm7u1AwAA%2BDO/C1jvv/%2B%2B19tef/31PqwEAADg0vhdwLr99tvdpwgNw3CPf38sJCREn3zyyZUvEAAAoB1%2BF7Cee%2B45LVmyRA899JBGjhypsLAwffTRR1q8eLF%2B/etfa8qUKVaXCAAA8IP87jYNBQUFWrhwoW6%2B%2BWZ17txZ0dHRGj16tBYvXqxXX33V4y7vAAAA/sjvAlZtbe15w1PHjh11%2BvRpCyoCAAC4OH4XsEaMGKEVK1bozJkz7rH6%2BnotW7ZMN9xwg4WVAQAAeMfvrsF65JFHNHv2bI0bN059%2B/aVYRj65z//qdjYWP3pT3%2ByujwAAIB2%2BV3A6tevn3bv3q1du3bps88%2BkyTdeuutmjp1qiIjIy2uDgAAoH1%2BF7AkqVOnTpo1a5a%2B%2BOIL9e7dW9K3d3MHAAAIBH53DZZhGHryySd1/fXXa9q0aTp58qTmz5%2BvvLw8ffPNN1aXBwAA0C6/C1jr1q3T9u3b9eijjyosLEySNHHiRO3du1eFhYUWVwcAANA%2BvwtYJSUlWrhwoVJTU913b58yZYqWLFminTt3WlwdAABA%2B/wuYH3xxRcaNGhQm/GBAwfK6XRaUBEAAMDF8buA1bNnT3300Udtxt988033Be8AAAD%2BzO9%2Bi/A3v/mNHnvsMTmdThmGoYMHD6qkpETr1q3TggULrC4PAACgXX4XsNLS0nTu3Dk9%2B%2ByzOnv2rBYuXKguXbro97//vTIyMqwuDwAAoF1%2BF7B27dqlSZMmKT09XadOnZJhGOratavVZQEAAHjN767BWrx4sfti9i5duhCuAABAwPG7gNW3b1/94x//sLoMAACAS%2BZ3pwgHDhyoBx98UGvWrFHfvn0VHh7uMV9QUGBRZQAAAN7xu4B17NgxJSUlSRL3vQIAAAHJLwLW0qVLlZ2draioKK1bt87qcgAAAC6LX1yDtXbtWrlcLo%2BxzMxM1dbWWlQRAADApfOLgGUYRpux999/X01NTRZUAwAAcHn8ImD5wvHjx/Wb3/xGCQkJmjBhgtasWeOeq66u1p133qkRI0ZoypQp2r9/v8drDxw4oGnTpslut2v27Nmqrq6%2B0uUDAIAA5hfXYJmttbVVmZmZGjZsmLZu3arjx4/r/vvvV3x8vKZNm6asrCwNGDBAmzdv1t69e5Wdna3du3erR48eOnHihLKysnTfffdp7NixKioq0ty5c7Vjxw6FhIRYvTQAFpi88m2rS7goH%2BRPsroE4KrnNwHLzPBSV1enQYMGadGiRerYsaP69u2rG264QaWlperWrZuqq6u1YcMGRUVFqV%2B/fjp48KA2b96s%2B%2B67Txs3btTQoUN19913S/r2thA33nij3nvvPY0aNcq0GgEAQPDym4C1ZMkSj3teffPNN1q2bJmio6M9tvPmPlhxcXFauXKlpG%2Bv7yorK9P777%2BvRx99VA6HQ4MHD1ZUVJR7%2B6SkJJWXl0uSHA6HkpOT3XORkZEaMmSIysvLCVgAAMArfhGwrr/%2B%2Bjb3vEpISNDp06d1%2BvTpy9p3SkqKTpw4oZtuukk333yz/vCHPyguLs5jm65du%2BrkyZOSvr331g/Ne6O2trbNemy2qDb7vVyhoYF1CZ3NFjj1ftfbQOtxIKC3Vwb9NR%2Bfu74XTL31i4Dly3tfPf3006qrq9OiRYtUUFAgl8ulsLAwj23CwsLU3NwsSe3Oe6OkpESFhYUeY1lZWcrJybnEVQSHzp2j29/Iz8TERFpdQtCit75Ff32H3vpOMPXWLwKWLw0bNkyS1NTUpAcffFBpaWlt7rnV3NysiIgISVJ4eHibMNXc3KyYmBivP2Z6erpSUlI8xmy2KJ0%2B/fWlLOGCAi3pm71%2BXwoN7aCYmEg1NLjU0tJqdTlBhd5eGfTXfHzu%2Bp4vemvVD/dBGbDq6upUXl6uiRMnusf69%2B%2Bvb775RrGxsTp69Gib7b87fRcfH6%2B6uro284MGDfL648fFxbU5Heh0fqVz567uL8hAXH9LS2tA1h0I6K1v0V/fobe%2BE0y9DaxDIF764osvlJ2drS%2B//NI9dujQIXXp0kVJSUn6%2BOOPdfbsWfdcaWmp7Ha7JMlut6u0tNQ953K5VFlZ6Z4HAABoT1AGrGHDhmnIkCF6%2BOGHdeTIEe3bt0/Lli3Tvffeq5EjR6p79%2B7Kzc1VVVWViouLVVFRoZkzZ0qS0tLSVFZWpuLiYlVVVSk3N1e9evXiNwgBAIDXgjJghYaGatWqVYqMjFR6erry8vJ0%2B%2B23a/bs2e45p9Op1NRU7dixQ0VFRerRo4ckqVevXnrmmWe0efNmzZw5U/X19SoqKuImowAAwGtBeQ2W9O21VN//Tb7v9OnTR%2BvXr7/ga8ePH6/x48f7qjQAABDkgvIIFgAAgJUIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJgjZgffnll8rJydHIkSM1duxYFRQUqKmpSZJUXV2tO%2B%2B8UyNGjNCUKVO0f/9%2Bj9ceOHBA06ZNk91u1%2BzZs1VdXW3FEgAAQIAKyoBlGIZycnLkcrn0yiuv6KmnntLrr7%2BulStXyjAMZWVlqVu3btq8ebOmT5%2Bu7OxsnThxQpJ04sQJZWVlKTU1VZs2bVKXLl00d%2B5cGYZh8aoAAECgsFldgC8cPXpU5eXlevvtt9WtWzdJUk5Ojp544gmNGzdO1dXV2rBhg6KiotSvXz8dPHhQmzdv1n333aeNGzdq6NChuvvuuyVJBQUFuvHGG/Xee%2B9p1KhRVi4LAAAEiKA8ghUbG6s1a9a4w9V3zpw5I4fDocGDBysqKso9npSUpPLyckmSw%2BFQcnKyey4yMlJDhgxxzwMAALQnKI9gxcTEaOzYse7nra2tWr9%2BvUaPHi2n06m4uDiP7bt27aqTJ09KUrvz3qitrZXT6fQYs9mi2uz3coWGBlY%2BttkCp97vehtoPQ4E9PbKoL/m43PX94Kpt0EZsL5v2bJlqqys1KZNm/TSSy8pLCzMYz4sLEzNzc2SJJfL9YPz3igpKVFhYaHHWFZWlnJyci5xBcGhc%2Bdoq0u4aDExkVaXELTorW/RX9/IRstvAAAQq0lEQVSht74TTL0N%2BoC1bNkyvfzyy3rqqac0YMAAhYeHq76%2B3mOb5uZmRURESJLCw8PbhKnm5mbFxMR4/THT09OVkpLiMWazRen06a8vcRXnF2hJ3%2Bz1%2B1JoaAfFxESqocGllpZWq8sJKvT2yqC/5uNz1/d80VurfrgP6oD1%2BOOP69VXX9WyZct08803S5Li4%2BN15MgRj%2B3q6urcp%2B/i4%2BNVV1fXZn7QoEFef9y4uLg2pwOdzq907tzV/QUZiOtvaWkNyLoDAb31LfrrO/TWd4Kpt4F1COQiFBYWasOGDVqxYoWmTp3qHrfb7fr444919uxZ91hpaansdrt7vrS01D3ncrlUWVnpngcAAGhPUAaszz77TKtWrdJvf/tbJSUlyel0uh8jR45U9%2B7dlZubq6qqKhUXF6uiokIzZ86UJKWlpamsrEzFxcWqqqpSbm6uevXqxS0aAACA14IyYP39739XS0uLnn32WY0ZM8bjERoaqlWrVsnpdCo1NVU7duxQUVGRevToIUnq1auXnnnmGW3evFkzZ85UfX29ioqKFBISYvGqAABAoAjKa7AyMzOVmZl5wfk%2Bffpo/fr1F5wfP368xo8f74vSAADAVSAoj2ABAABYKSiPYAHA1Sw5b4/VJXjtL7%2B/0eoSAJ/gCBYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYLOgDVnNzs6ZNm6Z3333XPVZdXa0777xTI0aM0JQpU7R//36P1xw4cEDTpk2T3W7X7NmzVV1dfaXLBgAAASyoA1ZTU5Puv/9%2BVVVVuccMw1BWVpa6deumzZs3a/r06crOztaJEyckSSdOnFBWVpZSU1O1adMmdenSRXPnzpVhGFYtAwAABJigDVhHjhzRf/3Xf%2Bnzzz/3GH/nnXdUXV2txYsXq1%2B/fpozZ45GjBihzZs3S5I2btyooUOH6u6779ZPfvITFRQU6F//%2Bpfee%2B89K5YBAAACUNAGrPfee0%2BjRo1SSUmJx7jD4dDgwYMVFRXlHktKSlJ5ebl7Pjk52T0XGRmpIUOGuOcBAADaY7O6AF%2B55ZZbzjvudDoVFxfnMda1a1edPHnSq3lv1NbWyul0eozZbFFt9nu5QkMDKx/bbIFT73e9DbQeBwJ6i3/H%2BwL%2BXTD1NmgD1oW4XC6FhYV5jIWFham5udmreW%2BUlJSosLDQYywrK0s5OTmXWHVw6Nw52uoSLlpMTKTVJQQteguJ9wV4CqbeXnUBKzw8XPX19R5jzc3NioiIcM9/P0w1NzcrJibG64%2BRnp6ulJQUjzGbLUqnT399iVWfX6AlfbPX70uhoR0UExOphgaXWlparS7HKz9/8i2rS/DaB/mTAqq38B3eF/DvfNFbq0L8VRew4uPjdeTIEY%2Bxuro69%2Bm7%2BPh41dXVtZkfNGiQ1x8jLi6uzelAp/MrnTt3dX9BBuL6W1paA7LuQEBvIfG%2BAE/B1NvAOgRiArvdro8//lhnz551j5WWlsput7vnS0tL3XMul0uVlZXueQAAgPZcdQFr5MiR6t69u3Jzc1VVVaXi4mJVVFRo5syZkqS0tDSVlZWpuLhYVVVVys3NVa9evTRq1CiLKwcAAIHiqgtYoaGhWrVqlZxOp1JTU7Vjxw4VFRWpR48ekqRevXrpmWee0ebNmzVz5kzV19erqKhIISEhFlcOAAACxVVxDdann37q8bxPnz5av379BbcfP368xo8f7%2BuyAABAkLoqAhYAwD9NXvm21SVclA/yJ1ldAgLEVXeKEAAAwNcIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAm4zYNAAB4KTlvj9UlIEBwBAsAAMBkHMECLoCfVAEAl4ojWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJuMgdwBXHLxAACHYcwQIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAEzGH3vGFTN55dtWlwAAwBXBESwAAACTEbDOo6mpSQ8//LCSk5M1ZswYvfjii1aXBAAAAginCM9j6dKlOnTokF5%2B%2BWWdOHFC8%2BfPV48ePTRp0iSrSwMAAAGAgPU9jY2N2rhxo55//nkNGTJEQ4YMUVVVlV555RUCFgAA8AqnCL/n8OHDOnfunBISEtxjSUlJcjgcam1ttbAyAAAQKDiC9T1Op1OdO3dWWFiYe6xbt25qampSfX29unTp0u4%2Bamtr5XQ6PcZstijFxcWZWmtoKPkYABA8gun7GgHre1wul0e4kuR%2B3tzc7NU%2BSkpKVFhY6DGWnZ2t%2B%2B67z5wi/3%2B1tbW64/9VKT093fTwdrWrra1VSUkJvfUBeutb9Nd36K3v1NbW6plnngmq3gZPVDRJeHh4myD13fOIiAiv9pGenq4tW7Z4PNLT002v1el0qrCwsM3RMlw%2Beus79Na36K/v0FvfCcbecgTre%2BLj43X69GmdO3dONtu37XE6nYqIiFBMTIxX%2B4iLiwuaBA4AAC4eR7C%2BZ9CgQbLZbCovL3ePlZaWatiwYerQgXYBAID2kRi%2BJzIyUjNmzNCiRYtUUVGhvXv36sUXX9Ts2bOtLg0AAASI0EWLFi2yugh/M3r0aFVWVmr58uU6ePCg7r33XqWlpVld1nlFR0dr5MiRio6OtrqUoENvfYfe%2Bhb99R166zvB1tsQwzAMq4sAAAAIJpwiBAAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsAKUE1NTXr44YeVnJysMWPG6MUXX7S6pID15ZdfKicnRyNHjtTYsWNVUFCgpqYmSVJ1dbXuvPNOjRgxQlOmTNH%2B/fstrjZwZWZmasGCBe7nlZWVmjVrlux2u9LS0nTo0CELqws8zc3Neuyxx3T99dfrZz/7mVasWKHv7htNby9fTU2N5syZo8TERKWkpOill15yz9HfS9Pc3Kxp06bp3XffdY%2B19x574MABTZs2TXa7XbNnz1Z1dfWVLvuSEbAC1NKlS3Xo0CG9/PLLevTRR1VYWKg9e/ZYXVbAMQxDOTk5crlceuWVV/TUU0/p9ddf18qVK2UYhrKystStWzdt3rxZ06dPV3Z2tk6cOGF12QHntdde0759%2B9zPGxsblZmZqeTkZG3ZskUJCQmaM2eOGhsbLawysCxZskQHDhzQCy%2B8oOXLl%2BvPf/6zSkpK6K1Jfv/73ysqKkpbtmzRww8/rJUrV%2Bpvf/sb/b1ETU1Nuv/%2B%2B1VVVeUea%2B899sSJE8rKylJqaqo2bdqkLl26aO7cuQqYP0BjIOB8/fXXxrBhw4x33nnHPVZUVGTcdtttFlYVmI4cOWIMGDDAcDqd7rGdO3caY8aMMQ4cOGCMGDHC%2BPrrr91zd9xxh/H0009bUWrAOn36tDFu3DgjLS3NmD9/vmEYhrFx40YjJSXFaG1tNQzDMFpbW42f//znxubNm60sNWCcPn3aGDx4sPHuu%2B%2B6x1avXm0sWLCA3pqgvr7eGDBggPHpp5%2B6x7Kzs43HHnuM/l6Cqqoq41e/%2BpXxy1/%2B0hgwYID7e1d777ErV670%2BL7W2NhoJCQkeHzv82ccwQpAhw8f1rlz55SQkOAeS0pKksPhUGtrq4WVBZ7Y2FitWbNG3bp18xg/c%2BaMHA6HBg8erKioKPd4UlKSysvLr3SZAe2JJ57Q9OnT1b9/f/eYw%2BFQUlKSQkJCJEkhISFKTEykt14qLS1Vx44dNXLkSPdYZmamCgoK6K0JIiIiFBkZqS1btuibb77R0aNHVVZWpkGDBtHfS/Dee%2B9p1KhRKikp8Rhv7z3W4XAoOTnZPRcZGakhQ4YETK8JWAHI6XSqc%2BfOCgsLc49169ZNTU1Nqq%2Bvt7CywBMTE6OxY8e6n7e2tmr9%2BvUaPXq0nE6n4uLiPLbv2rWrTp48eaXLDFgHDx7UBx98oLlz53qM09vLU11drZ49e2rbtm2aNGmS/vM//1NFRUVqbW2ltyYIDw/XwoULVVJSIrvdrsmTJ2vcuHGaNWsW/b0Et9xyix5%2B%2BGFFRkZ6jLfXy0Dvtc3qAnDxXC6XR7iS5H7e3NxsRUlBY9myZaqsrNSmTZv00ksvnbfP9Ng7TU1NevTRR7Vw4UJFRER4zF3oc5jeeqexsVHHjx/Xhg0bVFBQIKfTqYULFyoyMpLemuSzzz7TTTfdpLvuuktVVVV6/PHHdcMNN9BfE7XXy0DvNQErAIWHh7f5BPvu%2Bfe/kcF7y5Yt08svv6ynnnpKAwYMUHh4eJsjgs3NzfTYS4WFhRo6dKjHEcLvXOhzmN56x2az6cyZM1q%2BfLl69uwp6dsLgl999VX16dOH3l6mgwcPatOmTdq3b58iIiI0bNgwffnll3r22WfVu3dv%2BmuS9t5jL/Q%2BERMTc8VqvBycIgxA8fHxOn36tM6dO%2BceczqdioiICJhPPH/z%2BOOPa%2B3atVq2bJluvvlmSd/2ua6uzmO7urq6NoescX6vvfaa9u7dq4SEBCUkJGjnzp3auXOnEhIS6O1lio2NVXh4uDtcSdKPf/xj1dTU0FsTHDp0SH369PEITYMHD9aJEyfor4na6%2BWF5mNjY69YjZeDgBWABg0aJJvN5nGhX2lpqYYNG6YOHfgvvViFhYXasGGDVqxYoalTp7rH7Xa7Pv74Y509e9Y9VlpaKrvdbkWZAWfdunXauXOntm3bpm3btiklJUUpKSnatm2b7Ha7PvzwQ/evWxuGobKyMnrrJbvdrqamJh07dsw9dvToUfXs2ZPemiAuLk7Hjx/3OHpy9OhR9erVi/6aqL33WLvdrtLSUvecy%2BVSZWVlwPSa78YBKDIyUjNmzNCiRYtUUVGhvXv36sUXX9Ts2bOtLi3gfPbZZ1q1apV%2B%2B9vfKikpSU6n0/0YOXKkunfvrtzcXFVVVam4uFgVFRWaOXOm1WUHhJ49e6pPnz7uR3R0tKKjo9WnTx9NmjRJDQ0Nys/P15EjR5Sfny%2BXy6XJkydbXXZAuO666zRhwgTl5ubq8OHDeuutt1RcXKyMjAx6a4KUlBRdc801euSRR3Ts2DH97//%2Br5577jndfvvt9NdE7b3HpqWlqaysTMXFxaqqqlJubq569eqlUaNGWVy5l6y8RwQuXWNjozFv3jxjxIgRxpgxY4y1a9daXVJAWr16tTFgwIDzPgzDMP75z38at956qzF06FBj6tSpxttvv21xxYFr/vz57vtgGYZhOBwOY8aMGcawYcOMmTNnGh9//LGF1QWehoYG46GHHjJGjBhh3HDDDcYzzzzjvjcTvb18VVVVxp133mkkJiYaEydONNauXUt/TfDv98EyjPbfY9944w3jF7/4hTF8%2BHDjjjvuMD7//PMrXfIlCzGMQLklKgAAQGDgFCEAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJ/j9hi9aUFlYvJQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6661491698383619048”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>277</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (98)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6661491698383619048”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>91.6666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>732</td> <td class=”number”>22.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_BAKED16”>PCT_FMRKT_BAKED16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>108</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>57.31</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>17.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-499964232867529857”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-499964232867529857”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-499964232867529857”

aria-controls=”quantiles-499964232867529857” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-499964232867529857” aria-controls=”histogram-499964232867529857”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-499964232867529857” aria-controls=”common-499964232867529857”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-499964232867529857” aria-controls=”extreme-499964232867529857”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-499964232867529857”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>64.286</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.24</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.70215</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.4492</td>

</tr> <tr>

<th>Mean</th> <td>57.31</td>

</tr> <tr>

<th>MAD</th> <td>35.654</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.3264</td>

</tr> <tr>

<th>Sum</th> <td>128550</td>

</tr> <tr>

<th>Variance</th> <td>1619.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-499964232867529857”>

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Oe/%2BMpXvqLZs2erqKhIR44c0RtvvKGKigrl5OQoPT1dHR0dKikp0dGjR1VSUiK3262MjAxJUk5Ojnbv3q3t27fryJEjKiws1OzZs7lEAwAA8FlIBixJeuSRR/SlL31JOTk5WrZsmW655RbdeuutiomJ0aZNmzxHqZxOpyoqKhQdHS1JSkpK0qpVq1ReXq6cnBwNGjRIpaWlfl4NAAAIJjbjk%2B%2BPgaVcrg9M36fdPkCDBw9UW9uZkDmkGijorXXorbXor3WCsbcZG//g7xJ89m5JuiW9jY//gqn781XIHsECAADwFwIWAACAyULyU4RXkpTi/f4uwWf/c%2B8N/i4BAIDPBUewAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMFnABa/Hixdq2bZs%2B%2BOADf5cCAABwSQIuYE2fPl1PPfWUZsyYoR/%2B8Ic6ePCgDMO46P288sor%2BtrXvuZ1KygokCTV19dr8eLFcjgcysrKUl1dnddj9%2B7dq7lz58rhcCgvL0%2BnT582ZW0AAODKEHAB60c/%2BpF%2B//vf64knnlBYWJjuuecezZ49W48%2B%2BqhOnDjh836OHj2qOXPm6ODBg57bmjVr1NnZqdzcXKWkpGjXrl1KSkrS0qVL1dnZKUmqra1VcXGx8vPzVVlZqY6ODhUVFVm1XAAAEIICLmBJks1m0w033KB169bp0KFDuuWWW/Tss89q3rx5uuWWW/Sb3/ym330cO3ZM48aNU3x8vOcWGxurffv2KSIiQoWFhRozZoyKi4s1cOBA7d%2B/X5K0detWZWRkaOHChRo/frzWrl2rAwcOqLGx0eplAwCAEBGQAUuSWlpatGXLFmVnZ2vDhg2aOHGiVq1apenTp%2BvBBx9USUnJZz7%2B2LFjuvrqq/uMO51OJScny2azSfo4zE2bNk01NTWe%2BZSUFM/2w4YN0/Dhw%2BV0Os1bHAAACGl2fxfwabt379bu3bv11ltvaciQIVq4cKEee%2Bwxr7A0bNgwlZSUqLi4%2BLz7MAxDJ06c0MGDB7Vp0yb19PQoPT1dBQUFcrlcGjt2rNf2cXFxamhokPRxsEtISOgzf%2BrUKXMXCgAAQlbABazi4mLNmTNH5eXlmjVrlgYM6HuQ7Stf%2BYq%2B853vXHAfTU1NcrvdCg8P18aNG/Xee%2B9pzZo1Onv2rGf8X4WHh6u7u1uSdPbs2c%2Bc90VLS4tcLpfXmN0e3Se4Xa6wsIA9AHlednvw1PtJb4Otx8GA3lqL/lqH3lovlHobcAHr9ddf1%2BDBg9Xe3u4JV7W1tZo0aZLCwsIkSdOmTdO0adMuuI8RI0borbfe0qBBg2Sz2TRhwgT19vbqxz/%2BsVJTU/uEpe7ubkVGRkqSIiIizjsfFRXl8xoqKytVVlbmNZaXl%2Bf5FOOVavDggf4u4aLFxvr%2B946LQ2%2BtRX%2BtQ2%2BtE0q9DbiA9eGHHyonJ0ff%2BMY3VFhYKEnKzc3V0KFDtXnzZg0bNsyn/Xzxi1/0uj9mzBh1dXUpPj5era2tXnOtra2eo0uJiYnnnY%2BPj/d5DdnZ2UpLS/Mas9uj1dZ2xud9%2BCLYkr7Z67dSWNgAxcZGqaPDrZ6eXn%2BXE1LorbXor3XorfWs6K2//nMfcAHrJz/5iUaPHq077rjDM7Zv3z4tW7ZMpaWleuyxx/rdxxtvvKH77rtPr732mufI01/%2B8hd98YtfVHJysjZv3izDMGSz2WQYhqqrq3XXXXdJkhwOh6qqqpSZmSlJam5uVnNzsxwOh89rSEhI6HM60OX6QOfOXdk/kMG4/p6e3qCsOxjQW2vRX%2BvQW%2BuEUm8D7hDIu%2B%2B%2Bq/vvv9/riNGQIUNUWFioN99806d9JCUlKSIiQg8%2B%2BKCOHz%2BuAwcOaO3atfre976n9PR0dXR0qKSkREePHlVJSYncbrcyMjIkSTk5Odq9e7e2b9%2BuI0eOqLCwULNnz9aoUaMsWS8AAAg9ARew7Ha7Ojo6%2Boy73W6fr%2BgeExOjn/3sZzp9%2BrSysrJUXFys7Oxsfe9731NMTIw2bdrkOUrldDpVUVGh6OhoSR%2BHs1WrVqm8vFw5OTkaNGiQSktLTV0jAAAIbQF3inDWrFlas2aNNmzYoC996UuSpMbGRpWWlmrmzJk%2B7%2BerX/2qnn766fPOXXPNNXrhhRcu%2BNjMzEzPKUIAAICLFXABa9myZbrjjjt00003KTY2VpLU0dGhSZMm8ZU1AAAgKARcwIqLi9MLL7ygQ4cOqaGhQXa7XWPHjtX111/vufo6AABAIAu4gCVJYWFhmjlz5kWdEgQAAAgUARewXC6XNm7cqOrqan300Ud93tj%2B29/%2B1k%2BVAQAA%2BCbgAtZ///d/q66uTjfffLO%2B8IUv%2BLscAACAixZwAevNN9/Uli1blJKS4u9SAAAALknAXQcrOjpacXFx/i4DAADgkgVcwFqwYIG2bNminp4ef5cCAABwSQLuFGF7e7v27t2r1157TaNGjVJ4eLjX/C9%2B8Qs/VQYAAOCbgAtYkjR//nx/lwAAAHDJAi5g8b1/AAAg2AXce7AkqaWlRWVlZfrRj36k//u//9P%2B/ft1/Phxf5cFAADgk4ALWCdPntQ3v/lNvfDCC/r1r3%2Btzs5O7du3T1lZWXI6nf4uDwAAoF8BF7B%2B%2BtOfau7cuXr11Vd11VVXSZI2bNigtLQ0PfLII36uDgAAoH8BF7Cqq6t1xx13eH2xs91u19133636%2Bno/VgYAAOCbgAtYvb296u3t7TN%2B5swZhYWF%2BaEiAACAixNwAWvGjBnatGmTV8hqb2/XunXrNH36dD9WBgAA4JuAC1j333%2B/6urqNGPGDHV1dekHP/iB5syZo/fee0/Lli3zd3kAAAD9CrjrYCUmJurFF1/U3r179Ze//EW9vb3KycnRggULFBMT4%2B/yAAAA%2BhVwAUuSoqKitHjxYn%2BXAQAAcEkCLmAtWbLkM%2Bf5LkIAABDoAi5gjRgxwuv%2BuXPndPLkSf3tb3/Tbbfd5qeqAAAAfBdwAetC30VYXl6uU6dOfc7VAAAAXLyA%2BxThhSxYsED/8z//4%2B8yAAAA%2BhU0AeuPf/wjFxoFAABBIeBOEZ7vTe4ffvih/vrXv%2Brb3/62HyoCAAC4OAF3BGv48OEaMWKE123y5MlavXr1JV1oNDc3V/fff7/nfn19vRYvXiyHw6GsrCzV1dV5bb93717NnTtXDodDeXl5On369GWvCQAAXFkC7gjWT3/6U9P29fLLL%2BvAgQP61re%2BJUnq7OxUbm6uvvnNb%2BqnP/2pnn/%2BeS1dulSvvPKKoqOjVVtbq%2BLiYq1cuVLjx49XSUmJioqKtGnTJtNqAgAAoS/gAtY777zj87bXXnvtBefa29u1du1aTZkyxTO2b98%2BRUREqLCwUDabTcXFxXr99de1f/9%2BZWZmauvWrcrIyNDChQslSWvXrtWcOXPU2NioUaNGXfqiAADAFSXgAtatt94qm80mSTIMwzP%2B6TGbzaa//OUvF9zPww8/rAULFqilpcUz5nQ6lZyc7NmXzWbTtGnTVFNTo8zMTDmdTn3/%2B9/3bD9s2DANHz5cTqeTgAUAAHwWcAHrqaee0po1a/TjH/9YqampCg8P15/%2B9CetWrVK3/rWtzRv3rx%2B93H48GG9%2B%2B672rNnj1asWOEZd7lcGjt2rNe2cXFxamhokCS1tLQoISGhz/zFXn%2BrpaVFLpfLa8xuj%2B6z78sVFhZwb6H7THZ78NT7SW%2BDrcfBgN5ai/5ah95aL5R6G3ABq7S0VMuXL9esWbM8Y9OnT9eqVatUWFjodYTpfLq6uvTQQw9p%2BfLlioyM9Jpzu90KDw/3GgsPD1d3d7ck6ezZs58576vKykqVlZV5jeXl5amgoOCi9hNqBg8e6O8SLlpsbJS/SwhZ9NZa9Nc69NY6odTbgAtYLS0tfb4uR5JiYmLU1tbW7%2BPLyso0efJkzZw5s89cREREn7DU3d3tCWIXmo%2BKuri/8OzsbKWlpXmN2e3Rams7c1H76U%2BwJX2z12%2BlsLABio2NUkeHWz09vf4uJ6TQW2vRX%2BvQW%2BtZ0Vt//ec%2B4ALW1KlTtWHDBj388MOKiYmR9PEb1tetW6frr7%2B%2B38e//PLLam1tVVJSkiR5AtOvf/1rzZ8/X62trV7bt7a2ek7dJSYmnnc%2BPj7%2BotaQkJDQ53Sgy/WBzp27sn8gg3H9PT29QVl3MKC31qK/1qG31gml3gZcwHrwwQe1ZMkSzZo1S1dffbUMw9Df//53xcfH6xe/%2BEW/j3/uued07tw5z/1HHnlEknTffffpnXfe0ebNm2UYhmw2mwzDUHV1te666y5JksPhUFVVlTIzMyVJzc3Nam5ulsPhsGClAAAgVAVcwBozZoz27dunvXv36tixY5KkW265RTfffLNPp%2Bo%2BfXpx4MCPDw2OHj1acXFxWr9%2BvUpKSvRf//Vf2rZtm9xutzIyMiRJOTk5uvXWWzV16lRNmTJFJSUlmj17Np8gBAAAFyXgApYkDRo0SIsXL9Z7773nCTdXXXXVZe83JiZGmzZt0kMPPaRf/epX%2BtrXvqaKigpFR0dLkpKSkrRq1So99thj%2Buc//6kbbrhBq1evvuznBQAAV5aAC1iGYWj9%2BvV67rnn9NFHH%2BnXv/61Hn30UUVFRWnFihUXHbQ%2BfWX4a665Ri%2B88MIFt8/MzPScIgQAALgUAfcxtOeee067d%2B/WQw895Llkwty5c/Xqq6/2ufQBAABAIAq4gFVZWanly5crMzPTc8X1efPmac2aNdqzZ4%2BfqwMAAOhfwAWs9957TxMmTOgzPn78%2BD5XRwcAAAhEARewRowYoT/96U99xl9//XU%2BzQcAAIJCwL3J/bvf/a5Wrlwpl8slwzB0%2BPBhVVZW6rnnntP999/v7/IAAAD6FXABKysrS%2BfOndOTTz6ps2fPavny5RoyZIjuvfde5eTk%2BLs8AACAfgVcwNq7d6/S09OVnZ2t06dPyzAMxcXF%2BbssAAAAnwXce7BWrVrleTP7kCFDCFcAACDoBFzAuvrqq/W3v/3N32UAAABcsoA7RTh%2B/Hjdd9992rJli66%2B%2BmpFRER4zZeWlvqpMgAAAN8EXMA6ceKEkpOTJYnrXgEAgKAUEAFr7dq1ys/PV3R0tJ577jl/lwMAAHBZAuI9WE8//bTcbrfXWG5urlpaWvxUEQAAwKULiIBlGEafsXfeeUddXV1%2BqAYAAODyBETAAgAACCUELAAAAJMFTMCy2Wz%2BLgEAAMAUAfEpQklas2aN1zWvPvroI61bt04DBw702o7rYAEAgEAXEAHr2muv7XPNq6SkJLW1tamtrc1PVQEAAFyagAhYXPsKAACEkoB5DxYAAECoIGABAACYjIAFAABgMgIWAACAyQhYAAAAJgvZgHXy5El997vfVVJSkmbPnq0tW7Z45hobG3X77bdr6tSpmjdvng4ePOj12EOHDmn%2B/PlyOBxasmSJGhsbP%2B/yAQBAEAvJgNXb26vc3FwNHjxYL7zwglauXKknn3xSe/bskWEYysvL09ChQ7Vz504tWLBA%2Bfn5ampqkiQ1NTUpLy9PmZmZ2rFjh4YMGaK77777vF9IDQAAcD4BcR0ss7W2tmrChAlasWKFYmJidPXVV%2Bv6669XVVWVhg4dqsbGRm3btk3R0dEaM2aMDh8%2BrJ07d%2Bqee%2B7R9u3bNXnyZN15552SPr5y/A033KC3335b1113nZ9XBgAAgkFIHsFKSEjQxo0bFRMTI8MwVFVVpXfeeUepqalyOp2aOHGioqOjPdsnJyerpqZGkuR0OpWSkuKZi4qK0qRJkzzzAAAA/QnJI1j/Ki0tTU1NTZozZ45uuukm/eQnP1FCQoLXNnFxcTp16pQkyeVyfea8L1paWvp89Y/dHt1nv5crLCy48rHdHjz1ftLbYOtxMAjG3v7bI2/4u4SL8m5JelD1N1gE42s32IRSb0M%2BYD322GNqbW3VihUrVFpaKrfbrfDwcK9twsPD1d3dLUn9zvuisrJSZWVlXmN5eXkqKCi4xFWEhsGDB/a/UYCJjY3ydwkhi95ai/5ah95aJ5R6G/IBa8qUKZKkrq4u3XfffcrKypLb7fbapru7W5GRkZKkiIiIPmGqu7tbsbGxPj9ndna20tLSvMbs9mi1tZ25lCVcULAlfbPXb6WwsAGKjY1SR4dbPT29/i4npNDbzwf9NR%2BvXetZ0VvHPgRcAAAUdElEQVR//ec%2BJANWa2urampqNHfuXM/Y2LFj9dFHHyk%2BPl7Hjx/vs/0np%2B8SExPV2traZ37ChAk%2BP39CQkKf04Eu1wc6d%2B7K/oEMxvX39PQGZd3BgN5ai/5ah95aJ5R6G1yHQHz03nvvKT8/X%2B%2B//75nrK6uTkOGDFFycrL%2B/Oc/6%2BzZs565qqoqORwOSZLD4VBVVZVnzu12q76%2B3jMPAADQn5AMWFOmTNGkSZP0wAMP6OjRozpw4IDWrVunu%2B66S6mpqRo2bJiKiorU0NCgiooK1dbWatGiRZKkrKwsVVdXq6KiQg0NDSoqKtLIkSO5RAMAAPBZSAassLAwPfHEE4qKilJ2draKi4t16623asmSJZ45l8ulzMxMvfTSSyovL9fw4cMlSSNHjtTjjz%2BunTt3atGiRWpvb1d5eblsNpufVwUAAIJFSL4HS/r4vVSf/iTfJ0aPHq2tW7de8LE33nijbrzxRqtKAwAAIS4kj2ABAAD4EwELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwWcgGrPfff18FBQVKTU3VzJkzVVpaqq6uLklSY2Ojbr/9dk2dOlXz5s3TwYMHvR576NAhzZ8/Xw6HQ0uWLFFjY6M/lgAAAIJUSAYswzBUUFAgt9utX/7yl3r00Uf1%2B9//Xhs3bpRhGMrLy9PQoUO1c%2BdOLViwQPn5%2BWpqapIkNTU1KS8vT5mZmdqxY4eGDBmiu%2B%2B%2BW4Zh%2BHlVAAAgWNj9XYAVjh8/rpqaGv3hD3/Q0KFDJUkFBQV6%2BOGHNWvWLDU2Nmrbtm2Kjo7WmDFjdPjwYe3cuVP33HOPtm/frsmTJ%2BvOO%2B%2BUJJWWluqGG27Q22%2B/reuuu86fywIAAEEiJI9gxcfHa8uWLZ5w9YkPP/xQTqdTEydOVHR0tGc8OTlZNTU1kiSn06mUlBTPXFRUlCZNmuSZBwAA6E9IBqzY2FjNnDnTc7%2B3t1dbt27V9OnT5XK5lJCQ4LV9XFycTp06JUn9zgMAAPQnJE8Rftq6detUX1%2BvHTt26JlnnlF4eLjXfHh4uLq7uyVJbrf7M%2Bd90dLSIpfL5TVmt0f3CW6XKywsuPKx3R489X7S22DrcTCgt58P%2Bms%2BXrvWC6XehnzAWrdunZ599lk9%2BuijGjdunCIiItTe3u61TXd3tyIjIyVJERERfcJUd3e3YmNjfX7OyspKlZWVeY3l5eWpoKDgElcRGgYPHujvEi5abGyUv0sIWfTWWvTXOvTWOqHU25AOWKtXr9bzzz%2BvdevW6aabbpIkJSYm6ujRo17btba2eo4uJSYmqrW1tc/8hAkTfH7e7OxspaWleY3Z7dFqaztzKcu4oGBL%2Bmav30phYQMUGxuljg63enp6/V1OSKG3nw/6az5eu9azorf%2B%2Bs99yAassrIybdu2TRs2bFB6erpn3OFwqKKiQmfPnvUctaqqqlJycrJnvqqqyrO92%2B1WfX298vPzfX7uhISEPqcDXa4PdO7clf0DGYzr7%2BnpDcq6gwG9tRb9tQ69tU4o9Ta4DoH46NixY3riiSf0/e9/X8nJyXK5XJ5bamqqhg0bpqKiIjU0NKiiokK1tbVatGiRJCkrK0vV1dWqqKhQQ0ODioqKNHLkSC7RAAAAfBaSAeu3v/2tenp69OSTT2rGjBlet7CwMD3xxBNyuVzKzMzUSy%2B9pPLycg0fPlySNHLkSD3%2B%2BOPauXOnFi1apPb2dpWXl8tms/l5VQAAIFiE5CnC3Nxc5ebmXnB%2B9OjR2rp16wXnb7zxRt14441WlAYAAK4AIXkECwAAwJ8IWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJQvJThABwJUsp3u/vEnz2P/fe4O8SAEtwBAsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATBbyAau7u1vz58/XW2%2B95RlrbGzU7bffrqlTp2revHk6ePCg12MOHTqk%2BfPny%2BFwaMmSJWpsbPy8ywYAAEEspANWV1eXfvjDH6qhocEzZhiG8vLyNHToUO3cuVMLFixQfn6%2BmpqaJElNTU3Ky8tTZmamduzYoSFDhujuu%2B%2BWYRj%2BWgYAAAgyIRuwjh49qv/8z//UP/7xD6/xN998U42NjVq1apXGjBmjpUuXaurUqdq5c6ckafv27Zo8ebLuvPNOffWrX1Vpaan%2B93//V2%2B//bY/lgEAAIJQyAast99%2BW9ddd50qKyu9xp1OpyZOnKjo6GjPWHJysmpqajzzKSkpnrmoqChNmjTJMw8AANAfu78LsMq3v/3t8467XC4lJCR4jcXFxenUqVM%2BzQMAAPQnZAPWhbjdboWHh3uNhYeHq7u726d5X7S0tMjlcnmN2e3RfYLb5QoLC64DkHZ78NT7SW%2BDrcfBgN7iX2Vs/IO/S7go75ak89q1UCj19ooLWBEREWpvb/ca6%2B7uVmRkpGf%2B02Gqu7tbsbGxPj9HZWWlysrKvMby8vJUUFBwiVWHhsGDB/q7hIsWGxvl7xJCFr1FsOK1a51Q6u0VF7ASExN19OhRr7HW1lbP0aXExES1trb2mZ8wYYLPz5Gdna20tDSvMbs9Wm1tZy6x6vMLtqRv9vqtFBY2QLGxUerocKunp9ff5YQUeotgx2vXOlb01l//ub/iApbD4VBFRYXOnj3rOWpVVVWl5ORkz3xVVZVne7fbrfr6euXn5/v8HAkJCX1OB7pcH%2BjcuSv7BzIY19/T0xuUdQcDeotgxWvXOqHU2%2BA6BGKC1NRUDRs2TEVFRWpoaFBFRYVqa2u1aNEiSVJWVpaqq6tVUVGhhoYGFRUVaeTIkbruuuv8XDkAAAgWV1zACgsL0xNPPCGXy6XMzEy99NJLKi8v1/DhwyVJI0eO1OOPP66dO3dq0aJFam9vV3l5uWw2m58rBwAAweKKOEX417/%2B1ev%2B6NGjtXXr1gtuf%2BONN%2BrGG2%2B0uiwAABCiroiABVwJgunj7u%2BWpPu7BOCSpBTv93cJCBJX3ClCAAAAqxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMxlflABfAV2IAAC4VR7AAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATManCAF87viEJoBQxxEsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkXGj0PLq6urRy5Ur95je/UWRkpO68807deeed/i4r6GVs/IO/SwAA4HNBwDqPtWvXqq6uTs8%2B%2B6yampq0bNkyDR8%2BXOnp6f4uDQAABAEC1qd0dnZq%2B/bt2rx5syZNmqRJkyapoaFBv/zlLwlYAADAJ7wH61OOHDmic%2BfOKSkpyTOWnJwsp9Op3t5eP1YGAACCBUewPsXlcmnw4MEKDw/3jA0dOlRdXV1qb2/XkCFD%2Bt1HS0uLXC6X15jdHq2EhARTaw0LIx8DAEJHKP27RsD6FLfb7RWuJHnud3d3%2B7SPyspKlZWVeY3l5%2BfrnnvuMafI/19LS4tu%2B38Nys7ONj28XelaWlpUWVlJby1Ab61Ff61Db63T0tKixx9/PKR6GzpR0SQRERF9gtQn9yMjI33aR3Z2tnbt2uV1y87ONr1Wl8ulsrKyPkfLcPnorXXorbXor3XorXVCsbccwfqUxMREtbW16dy5c7LbP26Py%2BVSZGSkYmNjfdpHQkJCyCRwAABw8TiC9SkTJkyQ3W5XTU2NZ6yqqkpTpkzRgAG0CwAA9I/E8ClRUVFauHChVqxYodraWr366qv6%2Bc9/riVLlvi7NAAAECTCVqxYscLfRQSa6dOnq76%2BXuvXr9fhw4d11113KSsry99lndfAgQOVmpqqgQMH%2BruUkENvrUNvrUV/rUNvrRNqvbUZhmH4uwgAAIBQwilCAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBKwg1dXVpQceeEApKSmaMWOGfv7zn/u7pKD1/vvvq6CgQKmpqZo5c6ZKS0vV1dUlSWpsbNTtt9%2BuqVOnat68eTp48KCfqw1eubm5uv/%2B%2Bz336%2BvrtXjxYjkcDmVlZamurs6P1QWf7u5urVy5Utdee62%2B/vWva8OGDfrkutH09vI1Nzdr6dKlmjZtmtLS0vTMM8945ujvpenu7tb8%2BfP11ltvecb6%2Bx176NAhzZ8/Xw6HQ0uWLFFjY%2BPnXfYlI2AFqbVr16qurk7PPvusHnroIZWVlWn//v3%2BLivoGIahgoICud1u/fKXv9Sjjz6q3//%2B99q4caMMw1BeXp6GDh2qnTt3asGCBcrPz1dTU5O/yw46L7/8sg4cOOC539nZqdzcXKWkpGjXrl1KSkrS0qVL1dnZ6ccqg8uaNWt06NAh/exnP9P69ev1q1/9SpWVlfTWJPfee6%2Bio6O1a9cuPfDAA9q4caNeeeUV%2BnuJurq69MMf/lANDQ2esf5%2BxzY1NSkvL0%2BZmZnasWOHhgwZorvvvltB8wU0BoLOmTNnjClTphhvvvmmZ6y8vNz4zne%2B48eqgtPRo0eNcePGGS6XyzO2Z88eY8aMGcahQ4eMqVOnGmfOnPHM3XbbbcZjjz3mj1KDVltbmzFr1iwjKyvLWLZsmWEYhrF9%2B3YjLS3N6O3tNQzDMHp7e41/%2B7d/M3bu3OnPUoNGW1ubMXHiROOtt97yjG3atMm4//776a0J2tvbjXHjxhl//etfPWP5%2BfnGypUr6e8laGhoMP7jP/7D%2BOY3v2mMGzfO829Xf79jN27c6PXvWmdnp5GUlOT1b18g4whWEDpy5IjOnTunpKQkz1hycrKcTqd6e3v9WFnwiY%2BP15YtWzR06FCv8Q8//FBOp1MTJ05UdHS0Zzw5OVk1NTWfd5lB7eGHH9aCBQs0duxYz5jT6VRycrJsNpskyWazadq0afTWR1VVVYqJiVFqaqpnLDc3V6WlpfTWBJGRkYqKitKuXbv00Ucf6fjx46qurtaECRPo7yV4%2B%2B23dd1116mystJrvL/fsU6nUykpKZ65qKgoTZo0KWh6TcAKQi6XS4MHD1Z4eLhnbOjQoerq6lJ7e7sfKws%2BsbGxmjlzpud%2Bb2%2Bvtm7dqunTp8vlcikhIcFr%2B7i4OJ06derzLjNoHT58WO%2B%2B%2B67uvvtur3F6e3kaGxs1YsQIvfjii0pPT9c3vvENlZeXq7e3l96aICIiQsuXL1dlZaUcDocyMjI0a9YsLV68mP5egm9/%2B9t64IEHFBUV5TXeXy%2BDvdd2fxeAi%2Bd2u73ClSTP/e7ubn%2BUFDLWrVun%2Bvp67dixQ88888x5%2B0yPfdPV1aWHHnpIy5cvV2RkpNfchV7D9NY3nZ2dOnnypLZt26bS0lK5XC4tX75cUVFR9NYkx44d05w5c3THHXeooaFBq1ev1vXXX09/TdRfL4O91wSsIBQREdHnBfbJ/U//QwbfrVu3Ts8%2B%2B6weffRRjRs3ThEREX2OCHZ3d9NjH5WVlWny5MleRwg/caHXML31jd1u14cffqj169drxIgRkj5%2BQ/Dzzz%2Bv0aNH09vLdPjwYe3YsUMHDhxQZGSkpkyZovfff19PPvmkRo0aRX9N0t/v2Av9noiNjf3carwcnCIMQomJiWpra9O5c%2Bc8Yy6XS5GRkUHzwgs0q1ev1tNPP61169bppptukvRxn1tbW722a21t7XPIGuf38ssv69VXX1VSUpKSkpK0Z88e7dmzR0lJSfT2MsXHxysiIsITriTpy1/%2Bspqbm%2BmtCerq6jR69Giv0DRx4kQ1NTXRXxP118sLzcfHx39uNV4OAlYQmjBhgux2u9cb/aqqqjRlyhQNGMBf6cUqKyvTtm3btGHDBt18882ecYfDoT//%2Bc86e/asZ6yqqkoOh8MfZQad5557Tnv27NGLL76oF198UWlpaUpLS9OLL74oh8OhP/7xj56PWxuGoerqanrrI4fDoa6uLp04ccIzdvz4cY0YMYLemiAhIUEnT570Onpy/PhxjRw5kv6aqL/fsQ6HQ1VVVZ45t9ut%2Bvr6oOk1/xoHoaioKC1cuFArVqxQbW2tXn31Vf385z/XkiVL/F1a0Dl27JieeOIJff/731dycrJcLpfnlpqaqmHDhqmoqEgNDQ2qqKhQbW2tFi1a5O%2Byg8KIESM0evRoz23gwIEaOHCgRo8erfT0dHV0dKikpERHjx5VSUmJ3G63MjIy/F12UPjKV76i2bNnq6ioSEeOHNEbb7yhiooK5eTk0FsTpKWl6aqrrtKDDz6oEydO6He/%2B52eeuop3XrrrfTXRP39js3KylJ1dbUqKirU0NCgoqIijRw5Utddd52fK/eRP68RgUvX2dlpFBYWGlOnTjVmzJhhPP300/4uKSht2rTJGDdu3HlvhmEYf//7341bbrnFmDx5snHzzTcbf/jDH/xccfBatmyZ5zpYhmEYTqfTWLhwoTFlyhRj0aJFxp///Gc/Vhd8Ojo6jB//%2BMfG1KlTjeuvv954/PHHPddmoreXr6Ghwbj99tuNadOmGXPnzjWefvpp%2BmuCf70OlmH0/zv2tddeM/793//duOaaa4zbbrvN%2BMc//vF5l3zJbIYRLJdEBQAACA6cIgQAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADDZ/wdt8rAUKbajawAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-499964232867529857”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>810</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>576</td> <td class=”number”>17.9%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>266</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>35</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (97)</td> <td class=”number”>229</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-499964232867529857”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>576</td> <td class=”number”>17.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>92.3076923076923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>810</td> <td class=”number”>25.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_CREDIT16”>PCT_FMRKT_CREDIT16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>105</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>49.269</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>22.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4910282130601061349”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4910282130601061349,#minihistogram4910282130601061349”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4910282130601061349”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4910282130601061349”

aria-controls=”quantiles4910282130601061349” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4910282130601061349” aria-controls=”histogram4910282130601061349”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4910282130601061349” aria-controls=”common4910282130601061349”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4910282130601061349” aria-controls=”extreme4910282130601061349”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4910282130601061349”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>50</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>100</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>40.55</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.82304</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.5614</td>

</tr> <tr>

<th>Mean</th> <td>49.269</td>

</tr> <tr>

<th>MAD</th> <td>35.84</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.0057969</td>

</tr> <tr>

<th>Sum</th> <td>110510</td>

</tr> <tr>

<th>Variance</th> <td>1644.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4910282130601061349”>

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%2B/cTExB6nA12ub3T06On9BRmK6z92rDsk6w4F9Daw6G/g0NvACafehtYWiJ%2BSk5MVFRWlu%2B66SwcOHNDOnTu1cuVK3XjjjcrMzFRHR4dKSkq0f/9%2BlZSUyO12a/r06ZKk3Nxcbd26VZs2bdK%2Bffu0aNEiTZkyRSNHjrR4VQAAIFSEZcDq37%2B/nnrqKR06dEjZ2dkqLi5WTk6ObrzxRvXv319r16717lI5nU5VVFQoNjZW0j/D2fLly1VeXq7c3FwNGDBApaWlFq8IAACEkrA8RShJ55xzjp555pnjzl1wwQV68cUXT/jcrKws7ylCAACAvgrLHSwAAAArEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAEwWdAFr3rx52rhxo7755hurSwEAADgpQRewLr74Yj3xxBOaOHGibrvtNu3atUuGYVhdFgAAgN%2BCLmDdfvvtev3117VmzRpFRETolltu0ZQpU/TII4/o888/t7o8AACAXtmtLuB4bDabLr30Ul166aVyu91av3691qxZo4qKCqWkpOjaa6/Vb3/7W6vLBAAAOK6gDFiS1NLSom3btmnbtm369NNPlZKSot///vc6ePCg7rrrLr3//vsqLi62ukwAAIAegi5gbd26VVu3btW7776rQYMGac6cOXr00Ud1xhlneB8zdOhQlZSUELAAAEBQCrqAVVxcrKlTp6q8vFyTJ09Wv3493yZ21lln6eqrr7agOgAAgN4FXcB68803NXDgQLW3t3vDVV1dncaNG6eIiAhJUkpKilJSUqwsEwAA4ISC7q8IDx8%2BrMzMTK1bt847lpeXp9mzZ6u5udnCygAAAPwTdAHr/vvv16hRo3T99dd7x1599VUNHTpUpaWlFlYGAADgn6ALWB988IGWLFmihIQE79igQYO0aNEivfPOOxZWBgAA4J%2BgC1h2u10dHR09xt1uN1d0BwAAISHoAtbkyZO1YsUKffnll96xxsZGlZaWatKkSRZWBgAA4J%2Bg%2ByvCxYsX6/rrr9fll1%2Bu%2BPh4SVJHR4fGjRunoqIii6sDAADoXdAFrMGDB%2BvFF1/U7t271dDQILvdrrPPPluXXHKJbDab1eUBAAD0KugCliRFRERo0qRJnBIEAAAhKegClsvl0urVq1VTU6Pvvvuuxxvb//a3v1lUGQAAgH%2BCLmD94Q9/0N69ezVz5kz9/Oc/t7ocAACAPgu6gPXOO%2B/oySefVFpamtWlAAAAnJSgu0xDbGysBg8ebHUZAAAAJy3oAtbs2bP15JNP6tixY6YcLy8vT0uWLPHer6%2Bv17x58%2BRwOJSdna29e/f6PH779u2aNm2aHA6H8vPzdejQIVPqAAAAp4%2BgC1jt7e3atm2bJk%2BerH/7t3/T/PnzfW598corr2jnzp3e%2B52dncrLy1NaWpq2bNmi5ORkLViwQJ2dnZKkuro6FRcXq6CgQJWVlero6ODaWwAAoM%2BC7j1YkjRr1qxTPkZ7e7tWrlyp8ePHe8deffVVRUVFadGiRbLZbCouLtabb76pHTt2KCsrSxs2bND06dM1Z84cSdLKlSs1depUNTY2auTIkadcEwAAOD0EXcAqLS015TgPPPCAZs%2BerZaWFu%2BY0%2BlUamqq94KlNptNKSkpqq2tVVZWlpxOp2666Sbv44cOHaphw4bJ6XQSsAAAgN%2BC7hShJLW0tKisrEy33367/vd//1c7duzQgQMH/H7%2Bnj179MEHH2jhwoU%2B4y6XS4mJiT5jgwcP1sGDB72v%2B2PzAAAA/gi6HawvvvhCV1xxhfr376%2Bvv/5at956q1599VUVFRXp2WeflcPh%2BNHnd3V16e6779ayZcsUHR3tM%2Bd2uxUZGekzFhkZKY/HI0k6cuTIj877q6WlRS6Xy2fMbo/tEd5OVUREUObjE7LbQ6fe73sbaj0OBfQ2sOhv4NDbwAun3gZdwPrjH/%2BoadOmacWKFUpJSZEkPfzww1q8eLEefPBBrV%2B//kefX1ZWpvPPP/%2B4/2YnKiqqR1jyeDzeIHai%2BZiYmD6tobKyUmVlZT5j%2Bfn5Kiws7NNxws3AgXFWl9Bn8fF9%2B9jDf/Q2sOhv4NDbwAmn3gZdwKqpqdHzzz/v84%2Bd7Xa7Fi5cqCuuuKLX57/yyitqbW1VcnKyJHkD03/9139p1qxZam1t9Xl8a2urd2cpKSnpuPMJCQl9WkNOTo4yMjJ8xuz2WLW1fdun4/Qm1JK%2B2esPpIiIfoqPj1FHh1vHjnVbXU5YobeBRX8Dh94GXiB6a9Uv90EXsLq7u9Xd3bO53377rSIiInp9/vr163X06FHv/QcffFCSdMcdd%2Bj999/XunXrZBiGbDabDMNQTU2Nbr75ZkmSw%2BFQdXW1srKyJEnNzc1qbm7u9bTkDyUmJvY4HehyfaOjR0/vL8hQXP%2BxY90hWXcooLeBRX8Dh94GTjj1Nui2QCZOnKi1a9f6hKz29natWrVKF198ca/PHz58uEaNGuW9xcXFKS4uTqNGjVJmZqY6OjpUUlKi/fv3q6SkRG63W9OnT5ck5ebmauvWrdq0aZP27dunRYsWacqUKfwFIQAA6JOgC1hLlizR3r17NXHiRHV1dek//uM/NHXqVH311VdavHjxKR27f//%2BWrt2rXeXyul0qqKiQrGxsZKk5ORkLV%2B%2BXOXl5crNzdWAAQNMu2wEAAA4fdgMwzCsLuKH3G63tm/fro8//ljd3d0655xzNHv2bPXv39/q0k6ay/WN6ce02/vpNw%2B%2BZfpxA%2BUvt15qdQl%2Bs9v7aeDAOLW1fRs229XBgt4GFv0NnFDs7fTVb1tdgt8%2BKMkMSG8TEn5u6vH8FXTvwZKkmJgYzZs3z%2BoyAAAATkrQBaze/t/gn/70p5%2BoEgAAgJMTdAFr%2BPDhPvePHj2qL774Qp9%2B%2BqmuvfZai6oCAADwX9AFrBO9qby8vJx/WQMAAEJC0P0V4YnMnj1bf/nLX6wuAwAAoFchE7D%2B/ve/%2B3WhUQAAAKsF3SnC473J/fDhw/rkk0905ZVXWlARAABA3wRdwBo2bJjP/yGUpJ/97Ge6%2Buqr9bvf/c6iqgAAAPwXdAHrj3/8o9UlAAAAnJKgC1jvv/%2B%2B34%2B98MILA1gJAADAyQm6gHXNNdd4TxH%2B3//i88Mxm82mjz/%2B%2BKcvEAAAoBdBF7CeeOIJrVixQnfeeafS09MVGRmpDz/8UMuXL9fvf/97zZgxw%2BoSAQAAflTQXaahtLRUy5Yt0%2BWXX66BAwcqLi5OF198sZYvX64XXnhBw4cP994AAACCUdAFrJaWluOGp/79%2B6utrc2CigAAAPom6ALWhAkT9PDDD%2Bvw4cPesfb2dq1atUqXXHKJhZUBAAD4J%2Bjeg3XXXXdp/vz5mjx5ss444wwZhqF//OMfSkhI0J/%2B9CerywMAAOhV0AWs0aNH69VXX9X27dv12WefSZKuuuoqzZw5UzExMRZXBwAA0LugC1iSNGDAAM2bN09fffWVRo4cKemfV3MHAAAIBUH3HizDMPTggw/qwgsv1KxZs3Tw4EEtXrxYxcXF%2Bu6776wuDwAAoFdBF7DWr1%2BvrVu36u6771ZkZKQkadq0aXrttddUVlZmcXUAAAC9C7qAVVlZqWXLlikrK8t79fYZM2ZoxYoVevnlly2uDgAAoHdBF7C%2B%2BuorjR07tsf4ueeeK5fLZUFFAAAAfRN0AWv48OH68MMPe4y/%2Beab3je8AwAABLOg%2ByvCf//3f9e9994rl8slwzC0Z88eVVZWav369VqyZInV5QEAAPQq6AJWdna2jh49qscff1xHjhzRsmXLNGjQIN16663Kzc21ujwAAIBeBV3A2r59uzIzM5WTk6NDhw7JMAwNHjzY6rIAAAD8FnTvwVq%2BfLn3zeyDBg0iXAEAgJATdAHrjDPO0Keffmp1GQAAACct6E4Rnnvuubrjjjv05JNP6owzzlBUVJTPfGlpqUWVAQAA%2BCfoAtbnn3%2Bu1NRUSeK6VwAAICQFRcBauXKlCgoKFBsbq/Xr15tyzC%2B%2B%2BELLly9XTU2NBgwYoKuvvlo33nijJKmxsVF/%2BMMfVFtbq2HDhmnp0qWaOHGi97m7d%2B/W/fffr8bGRjkcDpWUlHANLgAA4LegeA/WM888I7fb7TOWl5enlpaWkzped3e38vLyNHDgQL344ou699579fjjj%2Bvll1%2BWYRjKz8/XkCFDVFVVpdmzZ6ugoEBNTU2SpKamJuXn5ysrK0ubN2/WoEGDtHDhQhmGccrrBAAAp4eg2ME6Xnh5//331dXVdVLHa21t1dixY3XPPfeof//%2BOuOMM3TJJZeourpaQ4YMUWNjozZu3KjY2FiNHj1ae/bsUVVVlW655RZt2rRJ559/vm644QZJ/3zP16WXXqr33ntPF1100SmtEwAAnB6CYgfLbImJiVq9erX69%2B8vwzBUXV2t999/X%2Bnp6XI6nTrvvPMUGxvrfXxqaqpqa2slSU6nU2lpad65mJgYjRs3zjsPAADQm6DYwQqkjIwMNTU1aerUqbr88st1//33KzEx0ecxgwcP1sGDByX98431Pzbvj5aWlh5v0LfbY3sc91RFRIRWPrbbQ6fe73sbaj0OBfQ2sOhv4NDbwAun3gZNwLLZbAE57qOPPqrW1lbdc889Ki0tldvtVmRkpM9jIiMj5fF4JKnXeX9UVlaqrKzMZyw/P1%2BFhYUnuYrwMHBgnNUl9Fl8fIzVJYQtehtY9Ddw6G3ghFNvgyZgrVixwueaV999951WrVqluDjfH8p9vQ7W%2BPHjJUldXV264447lJ2d3eMN9R6PR9HR0ZKkqKioHmHK4/EoPj7e79fMyclRRkaGz5jdHqu2tm/7VHtvQi3pm73%2BQIqI6Kf4%2BBh1dLh17Fi31eWEFXobWPQ3cOht4AWit1b9ch8UAevCCy/scUotOTlZbW1tamtr6/PxWltbVVtbq2nTpnnHzj77bH333XdKSEjQgQMHejz%2B%2B9N3SUlJam1t7TE/duxYv18/MTGxx%2BlAl%2BsbHT16en9BhuL6jx3rDsm6QwG9DSz6Gzj0NnDCqbdBEbDMuvbV97766isVFBRo586dSkpKkiTt3btXgwYNUmpqqp5%2B%2BmkdOXLEu2tVXV3tvbipw%2BFQdXW191hut1v19fUqKCgwtUYAoWP66retLqFPPijJtLoE4LQXWueY/DR%2B/HiNGzdOS5cu1f79%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%2BtwsJCpaena9KkSSotLVVXV5ckqbGxUdddd50mTJigGTNmaNeuXT7P3b17t2bNmiWHw6H58%2BersbHRiiUAAIAQFZYByzAMFRYWyu126/nnn9cjjzyi119/XatXr5ZhGMrPz9eQIUNUVVWl2bNnq6CgQE1NTZKkpqYm5efnKysrS5s3b9agQYO0cOFCGYZh8aoAAECosFtdQCAcOHBAtbW1evvttzVkyBBJUmFhoR544AFNnjxZjY2N2rhxo2JjYzV69Gjt2bNHVVVVuuWWW7Rp0yadf/75uuGGGyRJpaWluvTSS/Xee%2B/poosusnJZAAAgRITlDlZCQoKefPJJb7j63uHDh%2BV0OnXeeecpNjbWO56amqra2lpJktPpVFpamncuJiZG48aN884DAAD0Jix3sOLj4zVp0iTv/e7ubm3YsEEXX3yxXC6XEhMTfR4/ePBgHTx4UJJ6nfdHS0uLXC6Xz5jdHtvjuKcqIiK08rHdHjr1ft/bUOtxKKC3Pw36az4%2BdwMvnHoblgHrh1atWqX6%2Bnpt3rxZzz77rCIjI33mIyOAC3o7AAAQ9ElEQVQj5fF4JElut/tH5/1RWVmpsrIyn7H8/HwVFhae5ArCw8CBcVaX0Gfx8TFWlxC26G1g0d/AobeBE069DfuAtWrVKj333HN65JFHNGbMGEVFRam9vd3nMR6PR9HR0ZKkqKioHmHK4/EoPj7e79fMyclRRkaGz5jdHqu2tm9PchXHF2pJ3%2Bz1B1JERD/Fx8eoo8OtY8e6rS4nrNDbnwb9NR%2Bfu4EXiN5a9ct9WAes%2B%2B67Ty%2B88IJWrVqlyy%2B/XJKUlJSk/fv3%2BzyutbXVe/ouKSlJra2tPebHjh3r9%2BsmJib2OB3ocn2jo0dP7y/IUFz/sWPdIVl3KKC3gUV/A4feBk449Ta0tkD6oKysTBs3btTDDz%2BsmTNnescdDoc%2B%2BugjHTlyxDtWXV0th8Phna%2BurvbOud1u1dfXe%2BcBAAB6E5YB67PPPtOaNWt00003KTU1VS6Xy3tLT0/X0KFDVVRUpIaGBlVUVKiurk5z586VJGVnZ6umpkYVFRVqaGhQUVGRRowYwSUaAACA38IyYP3tb3/TsWPH9Pjjj2vixIk%2Bt4iICK1Zs0Yul0tZWVnatm2bysvLNWzYMEnSiBEj9Nhjj6mqqkpz585Ve3u7ysvLZbPZLF4VAAAIFWH5Hqy8vDzl5eWdcH7UqFHasGHDCecvu%2BwyXXbZZYEoDQAAnAbCcgcLAADASgQsAAAAk4XlKUIAOJ2lFe%2BwugS//eXWS60uAQgIdrAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZGEfsDwej2bNmqV3333XO9bY2KjrrrtOEyZM0IwZM7Rr1y6f5%2BzevVuzZs2Sw%2BHQ/Pnz1djY%2BFOXDQAAQlhYB6yuri7ddtttamho8I4ZhqH8/HwNGTJEVVVVmj17tgoKCtTU1CRJampqUn5%2BvrKysrR582YNGjRICxculGEYVi0DAACEmLANWPv379cVV1yhL7/80mf8nXfeUWNjo5YvX67Ro0drwYIFmjBhgqqqqiRJmzZt0vnnn68bbrhB55xzjkpLS/U///M/eu%2B996xYBgAACEF2qwsIlPfee08XXXSR/vM//1MTJkzwjjudTp133nmKjY31jqWmpqq2ttY7n5aW5p2LiYnRuHHjVFtbq4suuuinWwDQR9NXv211CX77oCTT6hIAIKDCNmBdeeWVxx13uVxKTEz0GRs8eLAOHjzo17w/Wlpa5HK5fMbs9tgexz1VERGhtQFpt4dOvd/3NtR6HEroLSS%2BL8BXOPU2bAPWibjdbkVGRvqMRUZGyuPx%2BDXvj8rKSpWVlfmM5efnq7Cw8CSrDg8DB8ZZXUKfxcfHWF1C2KK3kKTfPPiW1SX0yQclmXzuBlA49fa0C1hRUVFqb2/3GfN4PIqOjvbO/zBMeTwexcfH%2B/0aOTk5ysjI8Bmz22PV1vbtSVZ9fKGW9M1efyBFRPRTfHyMOjrcOnas2%2BpywhK9RajiczdwAtFbq365P%2B0CVlJSkvbv3%2B8z1tra6j19l5SUpNbW1h7zY8eO9fs1EhMTe5wOdLm%2B0dGjp/cXZCiu/9ix7pCsOxTQW4QqPncDJ5x6G1pbICZwOBz66KOPdOTIEe9YdXW1HA6Hd766uto753a7VV9f750HAADozWkXsNLT0zV06FAVFRWpoaFBFRUVqqur09y5cyVJ2dnZqqmpUUVFhRoaGlRUVKQRI0bwF4QAAMBvp13AioiI0Jo1a%2BRyuZSVlaVt27apvLxcw4YNkySNGDFCjz32mKqqqjR37ly1t7ervLxcNpvN4soBAECoOC3eg/XJJ5/43B81apQ2bNhwwsdfdtlluuyyywJdFgAACFOn3Q4WAABAoBGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAEx2WlymATgZacU7rC4BABCi2MECAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJNxHSwAAPzE9fHgLwIWgJ8cP6QAhDtOEQIAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACbjnz3jJzN99dtWlwAAwE%2BCHSwAAACTEbCOo6urS0uXLlVaWpomTpyop59%2B2uqSAABACOEU4XGsXLlSe/fu1XPPPaempiYtXrxYw4YNU2ZmptWlAQCAEEDA%2BoHOzk5t2rRJ69at07hx4zRu3Dg1NDTo%2BeefJ2ABAAC/cIrwB/bt26ejR48qOTnZO5aamiqn06nu7m4LKwMAAKGCHawfcLlcGjhwoCIjI71jQ4YMUVdXl9rb2zVo0KBej9HS0iKXy%2BUzZrfHKjEx0dRaIyLIxwCA8BFOP9cIWD/gdrt9wpUk732Px%2BPXMSorK1VWVuYzVlBQoFtuucWcIv9/LS0tuvb/NSgnJ8f08Ha6a2lpUWVlJb0NAHobWPQ3cOht4LS0tOixxx4Lq96GT1Q0SVRUVI8g9f396Ohov46Rk5OjLVu2%2BNxycnJMr9XlcqmsrKzHbhlOHb0NHHobWPQ3cOht4IRjb9nB%2BoGkpCS1tbXp6NGjstv/2R6Xy6Xo6GjFx8f7dYzExMSwSeAAAKDv2MH6gbFjx8put6u2ttY7Vl1drfHjx6tfP9oFAAB6R2L4gZiYGM2ZM0f33HOP6urq9Nprr%2Bnpp5/W/PnzrS4NAACEiIh77rnnHquLCDYXX3yx6uvr9dBDD2nPnj26%2BeablZ2dbXVZxxUXF6f09HTFxcVZXUrYobeBQ28Di/4GDr0NnHDrrc0wDMPqIgAAAMIJpwgBAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsEJUV1eXli5dqrS0NE2cOFFPP/201SWFrK%2B//lqFhYVKT0/XpEmTVFpaqq6uLklSY2OjrrvuOk2YMEEzZszQrl27LK42dOXl5WnJkiXe%2B/X19Zo3b54cDoeys7O1d%2B9eC6sLPR6PR/fee68uvPBC/epXv9LDDz%2Bs768bTW9PXXNzsxYsWKCUlBRlZGTo2Wef9c7R35Pj8Xg0a9Ysvfvuu96x3r7H7t69W7NmzZLD4dD8%2BfPV2Nj4U5d90ghYIWrlypXau3evnnvuOd19990qKyvTjh07rC4r5BiGocLCQrndbj3//PN65JFH9Prrr2v16tUyDEP5%2BfkaMmSIqqqqNHv2bBUUFKipqcnqskPOK6%2B8op07d3rvd3Z2Ki8vT2lpadqyZYuSk5O1YMECdXZ2WlhlaFmxYoV2796tp556Sg899JD%2B/Oc/q7Kykt6a5NZbb1VsbKy2bNmipUuXavXq1frrX/9Kf09SV1eXbrvtNjU0NHjHevse29TUpPz8fGVlZWnz5s0aNGiQFi5cqJD5BzQGQs63335rjB8/3njnnXe8Y%2BXl5cbVV19tYVWhaf/%2B/caYMWMMl8vlHXv55ZeNiRMnGrt37zYmTJhgfPvtt965a6%2B91nj00UetKDVktbW1GZMnTzays7ONxYsXG4ZhGJs2bTIyMjKM7u5uwzAMo7u72/jNb35jVFVVWVlqyGhrazPOO%2B8849133/WOrV271liyZAm9NUF7e7sxZswY45NPPvGOFRQUGPfeey/9PQkNDQ3G7373O%2BNf//VfjTFjxnh/dvX2PXb16tU%2BP9c6OzuN5ORkn599wYwdrBC0b98%2BHT16VMnJyd6x1NRUOZ1OdXd3W1hZ6ElISNCTTz6pIUOG%2BIwfPnxYTqdT5513nmJjY73jqampqq2t/anLDGkPPPCAZs%2BerbPPPts75nQ6lZqaKpvNJkmy2WxKSUmht36qrq5W//79lZ6e7h3Ly8tTaWkpvTVBdHS0YmJitGXLFn333Xc6cOCAampqNHbsWPp7Et577z1ddNFFqqys9Bnv7Xus0%2BlUWlqady4mJkbjxo0LmV4TsEKQy%2BXSwIEDFRkZ6R0bMmSIurq61N7ebmFloSc%2BPl6TJk3y3u/u7taGDRt08cUXy%2BVyKTEx0efxgwcP1sGDB3/qMkPWnj179MEHH2jhwoU%2B4/T21DQ2Nmr48OF66aWXlJmZqV//%2BtcqLy9Xd3c3vTVBVFSUli1bpsrKSjkcDk2fPl2TJ0/WvHnz6O9JuPLKK7V06VLFxMT4jPfWy1Dvtd3qAtB3brfbJ1xJ8t73eDxWlBQ2Vq1apfr6em3evFnPPvvscftMj/3T1dWlu%2B%2B%2BW8uWLVN0dLTP3Ik%2Bh%2Bmtfzo7O/XFF19o48aNKi0tlcvl0rJlyxQTE0NvTfLZZ59p6tSpuv7669XQ0KD77rtPl1xyCf01UW%2B9DPVeE7BCUFRUVI9PsO/v//AHGfy3atUqPffcc3rkkUc0ZswYRUVF9dgR9Hg89NhPZWVlOv/88312CL93os9heusfu92uw4cP66GHHtLw4cMl/fMNwS%2B88IJGjRpFb0/Rnj17tHnzZu3cuVPR0dEaP368vv76az3%2B%2BOMaOXIk/TVJb99jT/R9Ij4%2B/ier8VRwijAEJSUlqa2tTUePHvWOuVwuRUdHh8wnXrC577779Mwzz2jVqlW6/PLLJf2zz62trT6Pa21t7bFljeN75ZVX9Nprryk5OVnJycl6%2BeWX9fLLLys5OZnenqKEhARFRUV5w5UknXnmmWpubqa3Jti7d69GjRrlE5rOO%2B88NTU10V8T9dbLE80nJCT8ZDWeCgJWCBo7dqzsdrvPG/2qq6s1fvx49evHh7SvysrKtHHjRj388MOaOXOmd9zhcOijjz7SkSNHvGPV1dVyOBxWlBly1q9fr5dfflkvvfSSXnrpJWVkZCgjI0MvvfSSHA6H/v73v3v/3NowDNXU1NBbPzkcDnV1denzzz/3jh04cEDDhw%2BntyZITEzUF1984bN7cuDAAY0YMYL%2Bmqi377EOh0PV1dXeObfbrfr6%2BpDpNT%2BNQ1BMTIzmzJmje%2B65R3V1dXrttdf09NNPa/78%2BVaXFnI%2B%2B%2BwzrVmzRjfddJNSU1Plcrm8t/T0dA0dOlRFRUVqaGhQRUWF6urqNHfuXKvLDgnDhw/XqFGjvLe4uDjFxcVp1KhRyszMVEdHh0pKSrR//36VlJTI7XZr%2BvTpVpcdEs466yxNmTJFRUVF2rdvn9566y1VVFQoNzeX3pogIyNDP/vZz3TXXXfp888/13//93/riSee0DXXXEN/TdTb99js7GzV1NSooqJCDQ0NKioq0ogRI3TRRRdZXLmfrLxGBE5eZ2ensWjRImPChAnGxIkTjWeeecbqkkLS2rVrjTFjxhz3ZhiG8Y9//MO46qqrjPPPP9%2BYOXOm8fbbb1tccehavHix9zpYhmEYTqfTmDNnjjF%2B/Hhj7ty5xkcffWRhdaGno6PDuPPOO40JEyYYl1xyifHYY495r81Eb09dQ0ODcd111xkpKSnGtGnTjGeeeYb%2BmuD/XgfLMHr/HvvGG28Yv/3tb40LLrjAuPbaa40vv/zypy75pNkMI1QuiQoAABAaOEUIAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgsv8PPuoJn6hsMAcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4910282130601061349”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>726</td> <td class=”number”>22.6%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:66%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>268</td> <td class=”number”>8.3%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>84</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (94)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4910282130601061349”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>726</td> <td class=”number”>22.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.69230769230769</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.0232558139535</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.3333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.8717948717949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>636</td> <td class=”number”>19.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_FRVEG16”>PCT_FMRKT_FRVEG16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>111</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>62.482</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>15.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6754511895821364706”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-6754511895821364706”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6754511895821364706”

aria-controls=”quantiles-6754511895821364706” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6754511895821364706” aria-controls=”histogram-6754511895821364706”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6754511895821364706” aria-controls=”common-6754511895821364706”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6754511895821364706” aria-controls=”extreme-6754511895821364706”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6754511895821364706”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>33.333</td>

</tr> <tr>

<th>Median</th> <td>75</td>

</tr> <tr>

<th>Q3</th> <td>100</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>66.667</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>39.538</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.63279</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.255</td>

</tr> <tr>

<th>Mean</th> <td>62.482</td>

</tr> <tr>

<th>MAD</th> <td>34.897</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.54443</td>

</tr> <tr>

<th>Sum</th> <td>140150</td>

</tr> <tr>

<th>Variance</th> <td>1563.3</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6754511895821364706”>

<img 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ieffKKFCxd6vbFnt27dNH/%2BfH344Yc2TgYAAOCbgCtYDodDDQ0N7dabmpoUYC94BAAAuKSAK1ijR4/W8uXL9Y9//MOzVlVVpYKCAo0aNcrGyQAAAHwTcK8iXLBggWbPnq377rtPsbGxkqSGhgYNHDhQubm5Nk8HAADQsYArWHFxcXr77bd18OBBVVZWyuFw6I477tDw4cOv6AOfAQAA7BJwBUuSwsPDNWrUKC4JAgCAoBRwBcvtdmv16tUqKyvThQsX2j2x/b333rNpMgAAAN8EXMH6r//6L5WXl2vChAm6%2BWZ7Pj8IAADgWgRcwfrwww/16quvKjU11e5RAAAArkrAvU1DTEyM4uLi7B4DAADgqgVcwZo0aZJeffVVXbx40e5RAAAArkrAXSKsr6/Xnj179Pvf/159%2BvRRRESE1/bXX3/dpskAAAB8E3AFS5ImTpxo9wgAAABXLeAKVkFBgd0jAAAAXJOAew6WJNXU1KioqEg/%2B9nP9L//%2B7/at2%2BfTpw4YfdYAAAAPgm4gnXq1Cn94Ac/0Ntvv61f//rXamxs1N69e5WVlSWXy2X3eAAAAB0KuIL1zDPPaNy4cdq/f79uuukmSdKqVauUlpam5557zubpAAAAOhZwBausrEyzZ8/2%2BmBnh8OhRx99VBUVFTZOBgAA4JuAK1htbW1qa2trt37u3DmFh4fbMBEAAMCVCbiCNXLkSK1bt86rZNXX16uwsFDDhg2zcTIAAADfBFzBWrhwocrLyzVy5Eg1NzfrkUce0dixY/X5559rwYIFdo8HAADQoYB7H6wePXpo586d2rNnj/7617%2Bqra1N06dP16RJk9SlSxe7xwMAAOhQwBUsSYqOjtbUqVPtHgMAAOCqBFzBmjVr1rdu57MIAQBAoAu4gpWYmOh1u7W1VadOndJnn32mBx54wKapAAAAfBdwBetyn0W4Zs0anTlz5jpPE/hS8/bZPYLPfvX4CLtHAADgugi4VxFezqRJk/SrX/3K7jEAAAA6FDQF69NPP%2BWNRgEAQFAIuEuEl3qS%2B9mzZ/W3v/1NP/7xj22YCAAA4MoEXMHq1auX1%2BcQStJNN92k%2B%2B%2B/Xz/84Q9tmgoAAMB3AVewnnnmGbtHAAAAuCYBV7A%2B/vhjn%2B87ZMgQP04CAABwdQKuYM2cOdNzidCyLM/6N9fCwsL017/%2B9foPCAAA0IGAK1hr167V8uXL9Ytf/EJDhw5VRESE/vznP2vp0qX60Y9%2BpPHjx9s9IgAAwLcKuLdpKCgo0OLFi3Xfffepa9eu6ty5s4YNG6alS5fqzTffVGJioucLAAAgEAVcwaqpqblkeerSpYvq6upsmAgAAODKBFzBGjx4sFatWqWzZ8961urr61VYWKjhw4fbOBkAAIBvAu45WE8%2B%2BaRmzZql0aNH65ZbbpFlWfr73/%2Bu%2BPh4vf7663aPBwAA0KGAK1i333679u7dqz179uj48eOSpBkzZmjChAmKjo62eToAAICOBVzBkqTvfOc7mjp1qj7//HP16dNH0r/ezR0AACAYBNxzsCzL0nPPPachQ4Zo4sSJOnPmjBYsWKC8vDxduHDB7vEAAAA6FHAFa9OmTdq1a5eWLFmiiIgISdK4ceO0f/9%2BFRUV2TwdAABAxwKuYJWUlGjx4sXKzMz0vHv7%2BPHjtXz5cu3evdvm6QAAADoWcAXr888/V//%2B/dut33nnnXK73TZMBAAAcGUCrmAlJibqz3/%2Bc7v1999/3/OEdwAAgEAWcK8i/OlPf6qnn35abrdblmXp0KFDKikp0aZNm7Rw4UK7xwMAAOhQwBWsrKwstba26uWXX9b58%2Be1ePFidevWTY8//rimT59u93gAAAAdCriCtWfPHqWnp2vatGn68ssvZVmW4uLi7B4LAADAZwH3HKylS5d6nszerVs3yhUAAAg6AVewbrnlFn322WfG9pedne313K2KigpNnTpVTqdTWVlZKi8v97r/nj17NG7cODmdTuXk5OjLL780NgsAALgxBFzBuvPOO/Xzn/9cmZmZeuKJJ5Sbm%2Bv1dSXeffddHThwwHO7sbFR2dnZSk1N1Y4dO5SUlKQ5c%2BaosbFRknTkyBHl5eVp7ty5KikpUUNDwxU/JgAAQMA9B%2BvkyZNKSUmRpGt636v6%2BnqtWLFCgwYN8qzt3btXkZGRmj9/vsLCwpSXl6f3339f%2B/btU2ZmpjZv3qyMjAxNnjxZkrRixQqNHTtWVVVVvEUEAADwWUAUrBUrVmju3LmKiYnRpk2bjOzz2Wef1aRJk1RTU%2BNZc7lcSklJ8bxDfFhYmJKTk3X48GFlZmbK5XLpoYce8ty/Z8%2Be6tWrl1wuFwULAAD4LCAK1vr16/XTn/5UMTExnrXs7GwtX75cCQkJV7y/Q4cO6ZNPPtHu3bv11FNPedbdbrfuuOMOr/vGxcWpsrJSklRTU9Pu8eLi4nTmzJkrevyampp2Z98cjpirOpZvEx4ecFd4v5XDETzzfp1tsGUcDMjWv8jXf8jW/0Ip24AoWJZltVv7%2BOOP1dzcfMX7am5u1pIlS7R48WJFRUV5bWtqavJ8gPTXIiIi1NLSIkk6f/78t273VUlJSbsPps7JydG8efOuaD%2BhpmvXznaPcMViY6PtHiFkka1/ka//kK3/hFK2AVGwTCoqKtJdd92lUaNGtdsWGRnZriy1tLR4itjltkdHX9kf%2BLRp05SWlua15nDEqK7u3BXtpyPB1vRNH78/hYd3UmxstBoamnTxYpvd44QUsvUv8vUfsvU/f2Rr1//ch1zBevfdd1VbW6ukpCRJ8hSmX//615o4caJqa2u97l9bW%2Bu5dNejR49Lbo%2BPj7%2BiGRISEtpdDnS7/6nW1hv7H2QwHv/Fi21BOXcwIFv/Il//IVv/CaVsA6Zgff3E82u1adMmtba2em4/99xzkqSf//zn%2Bvjjj/XKK6/IsiyFhYXJsiyVlZXp4YcfliQ5nU6VlpYqMzNTklRdXa3q6mo5nU4jswEAgBtDwBSs5cuXKzIy0nP7woULKiwsVOfO3qf2CgoKvnU/iYmJXre//v6%2BffsqLi5OK1euVH5%2Bvv7zP/9TW7ZsUVNTkzIyMiRJ06dP18yZMzV48GANGjRI%2Bfn5GjNmDK8gBAAAVyQgCtaQIUPaveouKSlJdXV1qqurM/Y4Xbp00bp167RkyRK99dZb%2Bu53v6vi4mLPqxeTkpK0dOlSvfDCC/rqq680YsQILVu2zNjjAwCAG0NAFCxT7311Kc8884zX7bvvvltvv/32Ze%2BfmZnpuUQIAABwNYLrZWgAAABBgIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADHPYPQAABLqM1R/YPcIV%2BSQ/3e4RgBseZ7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGBYyBasL774QvPmzdPQoUM1atQoFRQUqLm5WZJUVVWlBx98UIMHD9b48eP1xz/%2B0et7Dx48qIkTJ8rpdGrWrFmqqqqy4xAAAECQCsmCZVmW5s2bp6amJr3xxht6/vnn9bvf/U6rV6%2BWZVnKyclR9%2B7dtX37dk2aNElz587V6dOnJUmnT59WTk6OMjMztW3bNnXr1k2PPvqoLMuy%2BagAAECwcNg9gD%2BcOHFChw8f1gcffKDu3btLkubNm6dnn31Wo0ePVlVVlbZs2aKYmBjdfvvtOnTokLZv367HHntMW7du1V133aWf/OQnkqSCggKNGDFCf/rTn3TPPffYeVgAACBIhOQZrPj4eL366quecvW1s2fPyuVyacCAAYqJifGsp6Sk6PDhw5Ikl8ul1NRUz7bo6GgNHDjQsx0AAKAjIVmwYmNjNWrUKM/ttrY2bd68WcOGDZPb7VZCQoLX/ePi4nTmzBlJ6nA7AABAR0LyEuE3FRYWqqKiQtu2bdOGDRsUERHhtT0iIkItLS2SpKampm/d7ouamhq53W6vNYcjpl1xu1bh4cHVjx2O4Jn362yDLeNgQLbXB/max99d/wulbEO%2BYBUWFmrjxo16/vnn1a9fP0VGRqq%2Bvt7rPi0tLYqKipIkRUZGtitTLS0tio2N9fkxS0pKVFRU5LWWk5OjefPmXeVRhIauXTvbPcIVi42NtnuEkEW2/kW%2B/kO2/hNK2YZ0wVq2bJnefPNNFRYW6r777pMk9ejRQ8eOHfO6X21trefsUo8ePVRbW9tue//%2B/X1%2B3GnTpiktLc1rzeGIUV3duas5jMsKtqZv%2Bvj9KTy8k2Jjo9XQ0KSLF9vsHiekkO31Qb7m8XfX//yRrV3/cx%2ByBauoqEhbtmzRqlWrlJ6e7ll3Op0qLi7W%2BfPnPWetSktLlZKS4tleWlrquX9TU5MqKio0d%2B5cnx87ISGh3eVAt/ufam29sf9BBuPxX7zYFpRzBwOy9S/y9R%2By9Z9Qyja4ToH46Pjx43rppZf00EMPKSUlRW632/M1dOhQ9ezZU7m5uaqsrFRxcbGOHDmiKVOmSJKysrJUVlam4uJiVVZWKjc3V7179%2BYtGgAAgM9CsmC99957unjxol5%2B%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%2BvV7du3TrcR01Njdxut9eawxGjhIQEo7OGh9OPAQChI5R%2Br1GwvqGpqcmrXEny3G5pafFpHyUlJSoqKvJamzt3rh577DEzQ/5/NTU1euDfKjVt2jTj5e1GV1NTo5KSErL1A7L1L/L1H7L1n5qaGr344oshlW3oVEVDIiMj2xWpr29HRUX5tI9p06Zpx44dXl/Tpk0zPqvb7VZRUVG7s2W4dmTrP2TrX%2BTrP2TrP6GYLWewvqFHjx6qq6tTa2urHI5/xeN2uxUVFaXY2Fif9pGQkBAyDRwAAFw5zmB9Q//%2B/eVwOHT48GHPWmlpqQYNGqROnYgLAAB0jMbwDdHR0Zo8ebKeeuopHTlyRPv379drr72mWbNm2T0aAAAIEuFPPfXUU3YPEWiGDRumiooKrVy5UocOHdLDDz%2BsrKwsu8e6pM6dO2vo0KHq3Lmz3aOEHLL1H7L1L/L1H7L1n1DLNsyyLMvuIQAAAEIJlwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwglRzc7MWLVpGSlkkAAAH7UlEQVSk1NRUjRw5Uq%2B99prdIwWtL774QvPmzdPQoUM1atQoFRQUqLm5WZJUVVWlBx98UIMHD9b48eP1xz/%2B0eZpg1d2drYWLlzouV1RUaGpU6fK6XQqKytL5eXlNk4XfFpaWvT0009ryJAh%2Bt73vqdVq1bp6/eNJttrV11drTlz5ig5OVlpaWnasGGDZxv5Xp2WlhZNnDhRH330kWeto5%2BxBw8e1MSJE%2BV0OjVr1ixVVVVd77GvGgUrSK1YsULl5eXauHGjlixZoqKiIu3bt8/usYKOZVmaN2%2Bempqa9MYbb%2Bj555/X7373O61evVqWZSknJ0fdu3fX9u3bNWnSJM2dO1enT5%2B2e%2Byg8%2B677%2BrAgQOe242NjcrOzlZqaqp27NihpKQkzZkzR42NjTZOGVyWL1%2BugwcP6pe//KVWrlypt956SyUlJWRryOOPP66YmBjt2LFDixYt0urVq/Wb3/yGfK9Sc3OznnjiCVVWVnrWOvoZe/r0aeXk5CgzM1Pbtm1Tt27d9OijjypoPoDGQtA5d%2B6cNWjQIOvDDz/0rK1Zs8a6//77bZwqOB07dszq16%2Bf5Xa7PWu7d%2B%2B2Ro4caR08eNAaPHiwde7cOc%2B2Bx54wHrhhRfsGDVo1dXVWaNHj7aysrKsBQsWWJZlWVu3brXS0tKstrY2y7Isq62tzfr3f/93a/v27XaOGjTq6uqsAQMGWB999JFnbd26ddbChQvJ1oD6%2BnqrX79%2B1t/%2B9jfP2ty5c62nn36afK9CZWWl9cMf/tD6wQ9%2BYPXr18/zu6ujn7GrV6/2%2Br3W2NhoJSUlef3uC2ScwQpCR48eVWtrq5KSkjxrKSkpcrlcamtrs3Gy4BMfH69XX31V3bt391o/e/asXC6XBgwYoJiYGM96SkqKDh8%2BfL3HDGrPPvusJk2apDvuuMOz5nK5lJKSorCwMElSWFiYkpOTydZHpaWl6tKli4YOHepZy87OVkFBAdkaEBUVpejoaO3YsUMXLlzQiRMnVFZWpv79%2B5PvVfjTn/6ke%2B65RyUlJV7rHf2MdblcSk1N9WyLjo7WwIEDgyZrClYQcrvd6tq1qyIiIjxr3bt3V3Nzs%2Brr622cLPjExsZq1KhRntttbW3avHmzhg0bJrfbrYSEBK/7x8XF6cyZM9d7zKB16NAhffLJJ3r00Ue91sn22lRVVSkxMVE7d%2B5Uenq67r33Xq1Zs0ZtbW1ka0BkZKQWL16skpISOZ1OZWRkaPTo0Zo6dSr5XoUf//jHWrRokaKjo73WO8oy2LN22D0ArlxTU5NXuZLkud3S0mLHSCGjsLBQFRUV2rZtmzZs2HDJnMnYN83NzVqyZIkWL16sqKgor22X%2BztMtr5pbGzUqVOntGXLFhUUFMjtdmvx4sWKjo4mW0OOHz%2BusWPHavbs2aqsrNSyZcs0fPhw8jWooyyDPWsKVhCKjIxs9xfs69vf/EUG3xUWFmrjxo16/vnn1a9fP0VGRrY7I9jS0kLGPioqKtJdd93ldYbwa5f7O0y2vnE4HDp79qxWrlypxMRESf96QvCbb76pvn37ku01OnTokLZt26YDBw4oKipKgwYN0hdffKGXX35Zffr0IV9DOvoZe7mfE7GxsddtxmvBJcIg1KNHD9XV1am1tdWz5na7FRUVFTR/8QLNsmXLtH79ehUWFuq%2B%2B%2B6T9K%2Bca2trve5XW1vb7pQ1Lu3dd9/V/v37lZSUpKSkJO3evVu7d%2B9WUlIS2V6j%2BPh4RUZGesqVJN16662qrq4mWwPKy8vVt29fr9I0YMAAnT59mnwN6ijLy22Pj4%2B/bjNeCwpWEOrfv78cDofXE/1KS0s1aNAgderEH%2BmVKioq0pYtW7Rq1SpNmDDBs%2B50OvWXv/xF58%2Bf96yVlpbK6XTaMWbQ2bRpk3bv3q2dO3dq586dSktLU1pamnbu3Cmn06lPP/3U83Jry7JUVlZGtj5yOp1qbm7WyZMnPWsnTpxQYmIi2RqQkJCgU6dOeZ09OXHihHr37k2%2BBnX0M9bpdKq0tNSzrampSRUVFUGTNb%2BNg1B0dLQmT56sp556SkeOHNH%2B/fv12muvadasWXaPFnSOHz%2Bul156SQ899JBSUlLkdrs9X0OHDlXPnj2Vm5uryspKFRcX68iRI5oyZYrdYweFxMRE9e3b1/PVuXNnde7cWX379lV6eroaGhqUn5%2BvY8eOKT8/X01NTcrIyLB77KBw2223acyYMcrNzdXRo0f1hz/8QcXFxZo%2BfTrZGpCWlqabbrpJTz75pE6ePKnf/va3Wrt2rWbOnEm%2BBnX0MzYrK0tlZWUqLi5WZWWlcnNz1bt3b91zzz02T%2B4jO98jAlevsbHRmj9/vjV48GBr5MiR1vr16%2B0eKSitW7fO6tev3yW/LMuy/v73v1szZsyw7rrrLmvChAnWBx98YPPEwWvBggWe98GyLMtyuVzW5MmTrUGDBllTpkyx/vKXv9g4XfBpaGiwfvGLX1iDBw%2B2hg8fbr344oue92Yi22tXWVlpPfjgg1ZycrI1btw4a/369eRrwP99HyzL6vhn7O9//3vrP/7jP6y7777beuCBB6x//OMf13vkqxZmWcHylqgAAADBgUuEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wPTV2V7zTqONQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6754511895821364706”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>927</td> <td class=”number”>28.9%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>488</td> <td class=”number”>15.2%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>246</td> <td class=”number”>7.7%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>63</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (100)</td> <td class=”number”>235</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6754511895821364706”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>488</td> <td class=”number”>15.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.14285714285714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.75</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>94.7368421052632</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.3488372093023</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6521739130435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>927</td> <td class=”number”>28.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_OTHERFOOD16”><s>PCT_FMRKT_OTHERFOOD16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_FMRKT_FRVEG16”><code>PCT_FMRKT_FRVEG16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90806</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_SFMNP16”>PCT_FMRKT_SFMNP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>102</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.93</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2581301964502564054”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2581301964502564054,#minihistogram-2581301964502564054”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2581301964502564054”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2581301964502564054”

aria-controls=”quantiles-2581301964502564054” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2581301964502564054” aria-controls=”histogram-2581301964502564054”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2581301964502564054” aria-controls=”common-2581301964502564054”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2581301964502564054” aria-controls=”extreme-2581301964502564054”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2581301964502564054”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>36.796</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.419</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.38095</td>

</tr> <tr>

<th>Mean</th> <td>25.93</td>

</tr> <tr>

<th>MAD</th> <td>31.569</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0779</td>

</tr> <tr>

<th>Sum</th> <td>58161</td>

</tr> <tr>

<th>Variance</th> <td>1353.9</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2581301964502564054”>

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1vtNzqampqq2tbTPfq1cvnXvuuYqOjlZtba3OP/98SVJzc7Pq6%2BuVnJzc7hpSUlLanA50uw%2BqufnM/oIMxeNvaWkNybpDAb21F/21D721Tzj1NrSWQNrB6XTqvffe853uk6SysjI5nU7ffFlZmW/O4/Fo7969cjqd6tChg9LS0vzmy8vL5XA4dNFFFwXuIAAAQEgLu4CVnZ2tzp07a/r06aqoqNCKFSu0e/dujR07VpI0ZswY7dq1SytWrFBFRYWmT5%2Bubt26qV%2B/fpK%2BuXj%2BiSee0NatW7V7927NmTNH11xzzUmdIgQAAGe2sAtYkZGRevjhh%2BV2u5Wbm6sXXnhBy5YtU5cuXSRJ3bp100MPPaQNGzZo7Nixqq%2Bv17JlyxQRESFJGjFihKZMmaJZs2Zp8uTJuuSSS3TnnXcG85AAAECIibCO3cIctnK7DxrfpsPRQb9Y9A/j27XLy7cOCHYJ7eZwdFBiYrzq6g6HzfUAPxT01l701z701j529jY5%2BWyj22uvsFvBAgAACDYCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYFPGCNGzdO69at08GDBwO9awAAgIAIeMDq37%2B/li9froEDB%2Bq2227T66%2B/LsuyAl0GAACAbQIesG6//Xa9%2BuqrevjhhxUZGalbbrlFgwcP1gMPPKCPP/440OUAAAAY5wjGTiMiIjRgwAANGDBAHo9Ha9as0cMPP6wVK1aob9%2B%2BmjRpkq688spglAYAAHDaghKwJKmmpkYvvPCCXnjhBX344Yfq27evfv3rX6u6ulr33nuv3nrrLc2cOTNY5QEAAJyygJ8i/Mtf/qLJkydryJAhWr16ta644gpt2bJFf/rTnzRu3Djdcsstuuuuu7R%2B/frT2k9VVZWmTJmivn37KicnR6tWrfLN7d27V%2BPGjZPT6dSYMWO0Z88ev9du3rxZQ4cOldPpVEFBgb766qvTqgUAAJxZAh6wZs6cqfj4eC1btkzbtm3T7bffrp49e/o957zzztNvfvOb09rPrbfeqri4OG3cuFEzZszQ0qVL9de//lWNjY3Kz89XVlaWNm7cqIyMDE2ZMkWNjY2SpN27d2vmzJmaNm2aSktL1dDQoOnTp59WLQAA4MwS8FOEr732mhITE1VfX68OHb7Jd7t371afPn0UGRkpSerbt6/69u17yvv4%2BuuvVV5ernnz5qlnz57q2bOnBg0apB07dujrr79WdHS07rrrLkVERGjmzJl67bXXtGXLFuXm5mrt2rUaPny4Ro8eLUlauHChhgwZosrKSnXv3v30GwAAAMJewFewDh06pGHDhumxxx7zjeXn52vUqFGqqqoyso%2BYmBjFxsZq48aNOnr0qPbv369du3apV69ecrlcyszMVEREhKRvLrjv27evysvLJUkul0tZWVm%2BbXXu3FldunSRy%2BUyUhsAAAh/AQ9Yf/jDH9SjRw/deOONvrGXXnpJnTt3VlFRkZF9REdHa9asWSotLZXT6dTw4cN1%2BeWXa9y4cXK73UpJSfF7flJSkqqrqyV9c/H9980DAACcSMBPEb799tt69tlnlZyc7Bvr2LGj7rrrLl1//fXG9vPRRx9pyJAhuvHGG1VRUaF58%2Bbpsssuk8fjUVRUlN9zo6Ki5PV6JUlHjhz53vn2qKmpkdvt9htzOOLaBLfTFRkZWu905HCETr3HehtqPQ4F9NZe9Nc%2B9NY%2B4djbgAcsh8OhhoaGNuMej8fYHd137Nih9evXa9u2bYqJiVFaWpq%2B/PJLPfLII%2BrevXubsOT1ehUTEyPpm9Wv483Hxsa2e/%2BlpaUqKSnxGysoKFBhYeEpHlF4SEyMD3YJJy0hof2fd5wcemsv%2BmsfemufcOptwAPW5Zdfrvnz52vJkiX6j//4D0lSZWWlioqKNGjQICP72LNnj3r06OELTZLUu3dvLV%2B%2BXFlZWaqtrfV7fm1trW91KTU19bjz/3fF7UTy8vKUk5PjN%2BZwxKmu7vDJHsr3CrWkb/r47RQZ2UEJCbFqaPCopaU12OWEFXprL/prH3prHzt7G6xf7gMesO6%2B%2B27deOONuuqqq5SQkCBJamhoUJ8%2BfYzdDiElJUWffvqpvF6v73Tf/v371a1bNzmdTj322GOyLEsRERGyLEu7du3STTfdJElyOp0qKytTbm6upG/up1VVVSWn03lS%2B//26UC3%2B6Cam8/sL8hQPP6WltaQrDsU0Ft70V/70Fv7hFNvAx6wkpKS9Oc//1nbt29XRUWFHA6HLrjgAl122WW%2Bv%2Bw7XTk5OSouLta9996rm2%2B%2BWR9//LGWL1%2Bu//7v/9awYcO0ePFiLViwQNdee63WrVsnj8ej4cOHS5LGjx%2BvCRMmKD09XWlpaVqwYIEGDx7MLRoAAEC7BeWtciIjIzVo0CBjpwS/7eyzz9aqVau0YMECjR07Vh07dtTNN9%2BsvLw8RURE6NFHH9Xs2bP17LPP6mc/%2B5lWrFihuLg4SVJGRobmzp2rBx98UF9//bUGDBigefPm2VInAAAITxGWqSvL28ntdmvp0qXatWuXjh492ubC9r/97W%2BBLCdg3O6DxrfpcHTQLxb9w/h27fLyrQOCXUK7ORwdlJgYr7q6w2GzXP1DQW/tRX/tQ2/tY2dvk5PPNrq99gr4Ctbvf/977dmzRyNGjNDZZwfnoAEAAOwU8ID1xhtv6PHHH/e7WzoAAEA4Cfjf%2BcfFxSkpKSnQuwUAAAiYgAesUaNG6fHHH1dLS0ugdw0AABAQAT9FWF9fr82bN%2Bvvf/%2B7unfv3uZtaZ566qlAlwQAAGBUUG7TMHLkyGDsFgAAICACHrCKiooCvUsAAICACsqb2dXU1KikpES33367/v3vf2vLli3av39/MEoBAAAwLuAB69NPP9Uvf/lL/fnPf9Yrr7yixsZGvfTSSxozZoxcLlegywEAADAu4AHrj3/8o4YOHaqtW7fqRz/6kSRpyZIlysnJ0aJFiwJdDgAAgHEBD1i7du3SjTfe6PfGzg6HQ1OnTtXevXsDXQ4AAIBxAQ9Yra2tam1t%2Bz5Dhw8fVmRkZKDLAQAAMC7gAWvgwIF69NFH/UJWfX29iouL1b9//0CXAwAAYFzAA9Y999yjPXv2aODAgWpqatLNN9%2BsIUOG6PPPP9fdd98d6HIAAACMC/h9sFJTU/X8889r8%2BbN%2Bte//qXW1laNHz9eo0aN0llnnRXocgAAAIwLyp3cY2NjNW7cuGDsGgAAwHYBD1gTJ0783nneixAAAIS6gAesrl27%2Bj1ubm7Wp59%2Bqg8//FCTJk0KdDkAAADG/WDei3DZsmWqrq4OcDUAAADmBeW9CI9n1KhRevnll4NdBgAAwGn7wQSsd955hxuNAgCAsPCDuMj90KFD%2BuCDD3TdddcFuhwAAADjAh6wunTp4vc%2BhJL0ox/9SL/5zW/0q1/9KtDlAAAAGBfwgPXHP/4x0LsEAAAIqIAHrLfeeqvdz7300kttrAQAAMAeAQ9YEyZM8J0itCzLN/7tsYiICP3rX/8KdHkAAACnLeABa/ny5Zo/f77uvPNOZWdnKyoqSu%2B%2B%2B67mzp2rX//617r66qsDXRIAAIBRAb9NQ1FRkWbNmqWrrrpKiYmJio%2BPV//%2B/TV37lw988wz6tq1q%2B8DAAAgFAU8YNXU1Bw3PJ111lmqq6sLdDkAAADGBTxgpaena8mSJTp06JBvrL6%2BXsXFxbrssssCXQ4AAIBxAb8G695779XEiRN1%2BeWXq2fPnrIsS5988omSk5P11FNPBbocAAAA4wIesM4//3y99NJL2rx5sz766CNJ0vXXX68RI0YoNjY20OUAAAAYF/CAJUnnnHOOxo0bp88//1zdu3eX9M3d3AEAAMJBwK/BsixLixYt0qWXXqqRI0equrpad999t2bOnKmjR48GuhwAAADjAh6w1qxZo7/85S%2BaPXu2oqKiJElDhw7V1q1bVVJSYmw/Xq9X9913ny699FL9/Oc/15IlS3w3Md27d6/GjRsnp9OpMWPGaM%2BePX6v3bx5s4YOHSqn06mCggJ99dVXxuoCAADhL%2BABq7S0VLNmzVJubq7v7u1XX3215s%2Bfr02bNhnbz/z587V9%2B3Y98cQTWrx4sZ599lmVlpaqsbFR%2Bfn5ysrK0saNG5WRkaEpU6aosbFRkrR7927NnDlT06ZNU2lpqRoaGjR9%2BnRjdQEAgPAX8GuwPv/8c/Xq1avN%2BEUXXSS3221kH/X19dqwYYNWrlypSy65RJI0efJkuVwuORwORUdH66677lJERIRmzpyp1157TVu2bFFubq7Wrl2r4cOHa/To0ZKkhQsXasiQIaqsrPRdLwYAAPB9Ar6C1bVrV7377rttxl977TVjAaasrExnnXWWsrOzfWP5%2BfkqKiqSy%2BVSZmamb/UsIiJCffv2VXl5uSTJ5XIpKyvL97rOnTurS5cucrlcRmoDAADhL%2BArWL/97W913333ye12y7Is7dixQ6WlpVqzZo3uueceI/uorKxU165d9fzzz2v58uU6evSocnNzdfPNN8vtduuCCy7we35SUpIqKiokfXOn%2BZSUlDbz1dXV7d5/TU1Nm9U4hyOuzXZPV2RkwPPxaXE4QqfeY70NtR6HAnprL/prH3prn3DsbcAD1pgxY9Tc3KxHHnlER44c0axZs9SxY0fdeuutGj9%2BvJF9NDY26tNPP9W6detUVFQkt9utWbNmKTY2Vh6Px3dx/TFRUVHyer2SpCNHjnzvfHuUlpa2uWC/oKBAhYWFp3hE4SExMT7YJZy0hATuzWYXemsv%2BmsfemufcOptwAPW5s2bNWzYMOXl5emrr76SZVlKSkoyug%2BHw6FDhw5p8eLFvvc9PHDggJ555hn16NGjTVjyer2KiYmRJEVHRx93/mRugpqXl6ecnJxv1RSnurrDp3I43ynUkr7p47dTZGQHJSTEqqHBo5aW1mCXE1borb3or33orX3s7G2wfrkPeMCaO3eu/vSnP%2Bmcc85Rx44dbdlHcnKyoqOj/d5U%2Bic/%2BYmqqqqUnZ2t2tpav%2BfX1tb6Tt%2BlpqYedz45Obnd%2B09JSWlzOtDtPqjm5jP7CzIUj7%2BlpTUk6w4F9NZe9Nc%2B9NY%2B4dTbgC%2BB9OzZUx9%2B%2BKGt%2B3A6nWpqatLHH3/sG9u/f7%2B6du0qp9Opd955x3dPLMuytGvXLjmdTt9ry8rKfK%2BrqqpSVVWVbx4AAOBEAr6CddFFF%2BmOO%2B7Q448/rp49eyo6Otpvvqio6LT3cd5552nw4MGaPn265syZI7fbrRUrVujmm2/WsGHDtHjxYi1YsEDXXnut1q1bJ4/Ho%2BHDh0uSxo8frwkTJig9PV1paWlasGCBBg8ezC0aAABAuwU8YH388cfKzMyUJGP3vTqeRYsWad68eRo/frxiY2N1/fXXa8KECYqIiNCjjz6q2bNn69lnn9XPfvYzrVixQnFxcZKkjIwMzZ07Vw8%2B%2BKC%2B/vprDRgwQPPmzbOtTgAAEH4irGPnymy0cOFCTZs2zRdizkRu90Hj23Q4OugXi/5hfLt2efnWAcEuod0cjg5KTIxXXd3hsLke4IeC3tqL/tqH3trHzt4mJ59tdHvtFZBrsFauXCmPx%2BM3lp%2Bfr5qamkDsHgAAIKACErCOt0j21ltvqampKRC7BwAACKjQupESAABACCBgAQAAGBawgHXszZUBAADCXcBu0zB//ny/e14dPXpUxcXFio/3v4W9iftgAQAABFNAAtall17a5p5XGRkZqqurU11dXSBKAAAACJiABKw1a9YEYjcAAAA/CFzkDgAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYFjYB6z8/Hzdc889vsd79%2B7VuHHj5HQ6NWbMGO3Zs8fv%2BZs3b9bQoUPldDpVUFCgr776KtAlAwCAEBfWAevFF1/Utm3bfI8bGxuVn5%2BvrKwsbdy4URkZGZoyZYoaGxslSbt379bMmTM1bdo0lZaWqqGhQdOnTw9W%2BQAAIESFbcBmHQYdAAAS10lEQVSqr6/XwoULlZaW5ht76aWXFB0drbvuukvnn3%2B%2BZs6cqfj4eG3ZskWStHbtWg0fPlyjR4/WRRddpIULF2rbtm2qrKwM1mEAAIAQFLYB6/7779eoUaN0wQUX%2BMZcLpcyMzMVEREhSYqIiFDfvn1VXl7um8/KyvI9v3PnzurSpYtcLldgiwcAACEtLAPWjh079Pbbb2vq1Kl%2B4263WykpKX5jSUlJqq6uliTV1NR87zwAAEB7OIJdgGlNTU2aPXu2Zs2apZiYGL85j8ejqKgov7GoqCh5vV5J0pEjR753vr1qamrkdrv9xhyOuDbh7XRFRoZWPnY4QqfeY70NtR6HAnprL/prH3prn3DsbdgFrJKSEl188cUaNGhQm7no6Og2Ycnr9fqC2HfNx8bGnlQNpaWlKikp8RsrKChQYWHhSW0n3CQmxge7hJOWkHByn3u0H721F/21D721Tzj1NuwC1osvvqja2lplZGRIki8wvfLKKxo5cqRqa2v9nl9bW%2BtbWUpNTT3ufHJy8knVkJeXp5ycHL8xhyNOdXWHT2o7JxJqSd/08dspMrKDEhJi1dDgUUtLa7DLCSv01l701z701j529jZYv9yHXcBas2aNmpubfY8XLVokSbrjjjv01ltv6bHHHpNlWYqIiJBlWdq1a5duuukmSZLT6VRZWZlyc3MlSVVVVaqqqpLT6TypGlJSUtqcDnS7D6q5%2Bcz%2BggzF429paQ3JukMBvbUX/bUPvbVPOPU27AJW165d/R7Hx3%2BTXHv06KGkpCQtXrxYCxYs0LXXXqt169bJ4/Fo%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%2BuqrWrp0qSzLUkFBgTp16qQNGzZo1KhRmjZtmg4cOCBJOnDggAoKCpSbm6v169erY8eOmjp1qizLCvJRAQCAUOEIdgF22L9/v8rLy/XPf/5TnTp1kiQVFhbq/vvv1%2BWXX67KykqtW7dOcXFxOv/887Vjxw5t2LBBt9xyi5577jldfPHFmjx5siSpqKhIAwYM0M6dO9WvX79gHhYAAAgRYbmClZycrMcff9wXro45dOiQXC6Xevfurbi4ON94ZmamysvLJUkul0tZWVm%2BudjYWPXp08c3DwAAcCJhuYKVkJCgQYMG%2BR63trZq7dq16t%2B/v9xut1JSUvyen5SUpOrqakk64Xx71NTUyO12%2B405HHFttnu6IiNDKx87HKFT77HehlqPQwG9tRf9tQ%2B9tV849TYsA9a3FRcXa%2B/evVq/fr1WrVqlqKgov/moqCh5vV5Jksfj%2Bd759igtLVVJSYnfWEFBgQoLC0/xCMJDYmJ8sEs4aQkJscEuIWzRW3vRX/vQW/uEU2/DPmAVFxdr9erVeuCBB3ThhRcqOjpa9fX1fs/xer2KiYmRJEVHR7cJU16vVwkJCe3eZ15ennJycvzGHI441dUdPsWjOL5QS/qmj99OkZEdlJAQq4YGj1paWoNdTliht/aiv/aht/azo7fB%2BuU%2BrAPWvHnz9Mwzz6i4uFhXXXWVJCk1NVX79u3ze15tba3v9F1qaqpqa2vbzPfq1avd%2B01JSWlzOtDtPqjm5jP7CzIUj7%2BlpTUk6w4F9NZe9Nc%2B9NY%2B4dTb0FoCOQklJSVat26dlixZohEjRvjGnU6n3nvvPR05csQ3VlZWJqfT6ZsvKyvzzXk8Hu3du9c3DwAAcCJhuYL10Ucf6eGHH1Z%2Bfr4yMzP9LjjPzs5W586dNX36dE2dOlWvvvqqdu/eraKiIknSmDFj9MQTT2jFihUaMmSIli1bpm7dunGLBuAMNnzpP4Ndwkl5e8GwYJcAnPHCcgXrb3/7m1paWvTII49o4MCBfh%2BRkZF6%2BOGH5Xa7lZubqxdeeEHLli1Tly5dJEndunXTQw89pA0bNmjs2LGqr6/XsmXLFBEREeSjAgAAoSIsV7Dy8/OVn5//nfM9evTQ2rVrv3P%2Biiuu0BVXXGFHaQAA4AwQlitYAAAAwUTAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzBHsAgCYMXzpP4NdQru9vWBYsEsAAFuxggUAAGAYK1gAEGayZm4Jdgnt9vKtA4JdAmALAhbwHULphxQA4IeFU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIvcETChdJ8mAABOBytYAAAAhhGwAAAADCNgAQAAGMY1WAACjpu44phQuzaT99FEe7GCBQAAYBgBCwAAwDACFgAAgGEErONoamrSjBkzlJWVpYEDB%2BrJJ58MdkkAACCEcJH7cSxcuFB79uzR6tWrdeDAAd19993q0qWLhg3j4kYAOJPxBxpoLwLWtzQ2Nuq5557TY489pj59%2BqhPnz6qqKjQ008/TcACAADtwinCb3n//ffV3NysjIwM31hmZqZcLpdaW1uDWBkAAAgVrGB9i9vtVmJioqKionxjnTp1UlNTk%2Brr69WxY8cTbqOmpkZut9tvzOGIU0pKitFaIyPJxwCA8BFOP9cIWN/i8Xj8wpUk32Ov19uubZSWlqqkpMRvbNq0abrlllvMFPn/1dTUaNKPK5SXl2c8vJ3pampqVFpaSm9tQG/tRX/tQ2/tU1NTo4ceeiisehs%2BUdGQ6OjoNkHq2OOYmJh2bSMvL08bN270%2B8jLyzNeq9vtVklJSZvVMpw%2Bemsfemsv%2BmsfemufcOwtK1jfkpqaqrq6OjU3N8vh%2BKY9brdbMTExSkhIaNc2UlJSwiaBAwCAk8cK1rf06tVLDodD5eXlvrGysjKlpaWpQwfaBQAATozE8C2xsbEaPXq05syZo927d2vr1q168sknNXHixGCXBgAAQkTknDlz5gS7iB%2Ba/v37a%2B/evVq8eLF27Nihm266SWPGjAl2WccVHx%2Bv7OxsxcfHB7uUsENv7UNv7UV/7UNv7RNuvY2wLMsKdhEAAADhhFOEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWCGqqalJM2bMUFZWlgYOHKgnn3wy2CWFrC%2B//FKFhYXKzs7WoEGDVFRUpKamJklSZWWlbrjhBqWnp%2Bvqq6/W66%2B/HuRqQ1d%2Bfr7uuece3%2BO9e/dq3LhxcjqdGjNmjPbs2RPE6kKP1%2BvVfffdp0svvVQ///nPtWTJEh27bzS9PX1VVVWaMmWK%2Bvbtq5ycHK1atco3R39Pjdfr1ciRI/Xmm2/6xk70PXb79u0aOXKknE6nJk6cqMrKykCXfcoIWCFq4cKF2rNnj1avXq3Zs2erpKREW7ZsCXZZIceyLBUWFsrj8ejpp5/WAw88oFdffVVLly6VZVkqKChQp06dtGHDBo0aNUrTpk3TgQMHgl12yHnxxRe1bds23%2BPGxkbl5%2BcrKytLGzduVEZGhqZMmaLGxsYgVhla5s%2Bfr%2B3bt%2BuJJ57Q4sWL9eyzz6q0tJTeGnLrrbcqLi5OGzdu1IwZM7R06VL99a9/pb%2BnqKmpSbfddpsqKip8Yyf6HnvgwAEVFBQoNzdX69evV8eOHTV16lSFzBvQWAg5hw8fttLS0qw33njDN7Zs2TLrN7/5TRCrCk379u2zLrzwQsvtdvvGNm3aZA0cONDavn27lZ6ebh0%2BfNg3N2nSJOvBBx8MRqkhq66uzrr88sutMWPGWHfffbdlWZb13HPPWTk5OVZra6tlWZbV2tpq/eIXv7A2bNgQzFJDRl1dndW7d2/rzTff9I09%2Buij1j333ENvDaivr7cuvPBC64MPPvCNTZs2zbrvvvvo7ymoqKiwfvWrX1m//OUvrQsvvND3s%2BtE32OXLl3q93OtsbHRysjI8PvZ90PGClYIev/999Xc3KyMjAzfWGZmplwul1pbW4NYWehJTk7W448/rk6dOvmNHzp0SC6XS71791ZcXJxvPDMzU%2BXl5YEuM6Tdf//9GjVqlC644ALfmMvlUmZmpiIiIiRJERER6tu3L71tp7KyMp111lnKzs72jeXn56uoqIjeGhATE6PY2Fht3LhRR48e1f79%2B7Vr1y716tWL/p6CnTt3ql%2B/fiotLfUbP9H3WJfLpaysLN9cbGys%2BvTpEzK9JmCFILfbrcTEREVFRfnGOnXqpKamJtXX1wexstCTkJCgQYMG%2BR63trZq7dq16t%2B/v9xut1JSUvyen5SUpOrq6kCXGbJ27Niht99%2BW1OnTvUbp7enp7KyUl27dtXzzz%2BvYcOG6T//8z%2B1bNkytba20lsDoqOjNWvWLJWWlsrpdGr48OG6/PLLNW7cOPp7Cq677jrNmDFDsbGxfuMn6mWo99oR7AJw8jwej1%2B4kuR77PV6g1FS2CguLtbevXu1fv16rVq16rh9psft09TUpNmzZ2vWrFmKiYnxm/uu/8P0tn0aGxv16aefat26dSoqKpLb7dasWbMUGxtLbw356KOPNGTIEN14442qqKjQvHnzdNlll9Ffg07Uy1DvNQErBEVHR7f5D3bs8bd/kKH9iouLtXr1aj3wwAO68MILFR0d3WZF0Ov10uN2Kikp0cUXX%2By3QnjMd/0fprft43A4dOjQIS1evFhdu3aV9M0Fwc8884x69OhBb0/Tjh07tH79em3btk0xMTFKS0vTl19%2BqUceeUTdu3env4ac6Hvsd32fSEhICFiNp4NThCEoNTVVdXV1am5u9o253W7FxMSEzH%2B8H5p58%2BZp5cqVKi4u1lVXXSXpmz7X1tb6Pa%2B2trbNkjWO78UXX9TWrVuVkZGhjIwMbdq0SZs2bVJGRga9PU3JycmKjo72hStJ%2BslPfqKqqip6a8CePXvUo0cPv9DUu3dvHThwgP4adKJeftd8cnJywGo8HQSsENSrVy85HA6/C/3KysqUlpamDh34lJ6skpISrVu3TkuWLNGIESN8406nU%2B%2B9956OHDniGysrK5PT6QxGmSFnzZo12rRpk55//nk9//zzysnJUU5Ojp5//nk5nU698847vj%2B3tixLu3btorft5HQ61dTUpI8//tg3tn//fnXt2pXeGpCSkqJPP/3Ub/Vk//796tatG/016ETfY51Op8rKynxzHo9He/fuDZle89M4BMXGxmr06NGaM2eOdu/era1bt%2BrJJ5/UxIkTg11ayPnoo4/08MMP63e/%2B50yMzPldrt9H9nZ2ercubOmT5%2BuiooKrVixQrt379bYsWODXXZI6Nq1q3r06OH7iI%2BPV3x8vHr06KFhw4apoaFBCxYs0L59%2B7RgwQJ5PB4NHz482GWHhPPOO0%2BDBw/W9OnT9f777%2Bsf//iHVqxYofHjx9NbA3JycvSjH/1I9957rz7%2B%2BGP97//%2Br5YvX64JEybQX4NO9D12zJgx2rVrl1asWKGKigpNnz5d3bp1U79%2B/YJceTsF8x4ROHWNjY3WXXfdZaWnp1sDBw60Vq5cGeySQtKjjz5qXXjhhcf9sCzL%2BuSTT6zrr7/euvjii60RI0ZY//znP4Nccei6%2B%2B67fffBsizLcrlc1ujRo620tDRr7Nix1nvvvRfE6kJPQ0ODdeedd1rp6enWZZddZj300EO%2BezPR29NXUVFh3XDDDVbfvn2toUOHWitXrqS/Bvzf%2B2BZ1om/x/7973%2B3rrzySuuSSy6xJk2aZH322WeBLvmURVhWqNwSFQAAIDRwihAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPt/nvk7yU8j9nAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2581301964502564054”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1329</td> <td class=”number”>41.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>160</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (91)</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2581301964502564054”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1329</td> <td class=”number”>41.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.26315789473684</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.6976744186046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>315</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_SNAP16”>PCT_FMRKT_SNAP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>108</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>24.174</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>40.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6145166046958716616”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6145166046958716616”

aria-controls=”quantiles-6145166046958716616” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6145166046958716616” aria-controls=”histogram-6145166046958716616”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6145166046958716616” aria-controls=”common-6145166046958716616”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6145166046958716616” aria-controls=”extreme-6145166046958716616”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6145166046958716616”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>50</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>50</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>34.372</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4218</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.046684</td>

</tr> <tr>

<th>Mean</th> <td>24.174</td>

</tr> <tr>

<th>MAD</th> <td>28.736</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.1933</td>

</tr> <tr>

<th>Sum</th> <td>54223</td>

</tr> <tr>

<th>Variance</th> <td>1181.4</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6145166046958716616”>

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/7nsrb/v//7vyopKdH999%2BvHj16aNSoURo%2BfLj27dun1157TeHh4Zo1a5Z69uypefPmqU2bNnr99dclSZs2bdKYMWM0ceJE9enTR8uWLdOePXtUXl5u4tABAMAVwG8XuVdUVGjt2rVKT0/XihUr1K9fPy1atEhDhw7Vo48%2BqqVLl17ytiMiIhQZGant27fr3LlzOnr0qIqLi9W3b185HA4lJiYqJCRE0jdhb/DgwSopKZEkORwOJSUlubfVuXNndenSRQ6H4/IOGAAAXDFsvt7hyy%2B/rJdffln79%2B9X%2B/btNXHiRP3%2B979Xjx493M/p3Lmzli5dqnnz5l3SPsLDwzV//nwtXrxYzz//vBobG5WamqrJkyfrjTfeUK9evTye36FDB5WVlUn6JvjFxsY2mz916lSL919RUSGn0%2BkxZrNFNdvu5QoNDaz/BGqzBU693/Y20HocCOitd9Ff76G33hOMvfV5wJo3b55Gjhyp/Px83XTTTWrVqnkzr732Wt15552XtZ9PPvlEI0eO1D333KOysjItXrxYN9xwg1wul8LCwjyeGxYWpvr6eknS2bNn/%2BV8SxQWFiovL89jLDMzU1lZWZd4NMGhXbs2/i7hokVHR/q7hKBFb72L/noPvfWeYOqtzwPW22%2B/rXbt2qm6utodrg4ePKj%2B/fsrNDRUkjR48GANHjz4kvexb98%2Bbd26VXv27FFERIQGDhyoL7/8Uk8//bS6devWLCzV19crIiJC0jerX%2Bebj4xs%2BTc9PT1dKSkpHmM2W5Sqqs5c4hGdX6AlfdPH702hoa0UHR2pmhqXGhub/F1OUKG33kV/vYfeeo83e%2BuvP%2B59HrBOnz6tKVOm6Gc/%2B5lmzZolScrIyFDHjh31zDPPqHPnzpe9j0OHDql79%2B7u0CRJ/fr10%2BrVq5WUlKTKykqP51dWVrpP33Xq1Om88zExMS3ef2xsbLPTgU7n12pouLJ/IAPx%2BBsbmwKy7kBAb72L/noPvfWeYOqtz5dAfvvb36p79%2B6655573GOvvfaaOnfurJycHCP7iI2N1fHjxz1Woo4ePaq4uDjZ7XZ9%2BOGH7ntvWZal4uJi2e12SZLdbldRUZH7dSdPntTJkyfd8wAAABfi84D1wQcfaM6cOR4rQu3bt9esWbP03nvvGdlHSkqKWrdurUcffVTHjh3TX/7yF61evVpTp07V6NGjVVNTo6VLl%2BrIkSNaunSpXC6XxowZI0maMmWKXn75ZW3ZskWHDx/WrFmzNGLECHXr1s1IbQAAIPj5PGDZbDbV1NQ0G3e5XMbu6H7VVVdp/fr1cjqdmjRpknJycnT//fcrPT1dbdu21Zo1a1RUVKTU1FQ5HA4VFBQoKipKkpSQkKBFixYpPz9fU6ZM0dVXX21sZQ0AAFwZfH4N1k033aQlS5ZoxYoV%2BtGPfiRJKi8vV05OjoYPH25sP7169dK6devOOzdo0CC99NJL3/va1NRUpaamGqsFAABcWXwesGbPnq177rlHt9xyi6KjoyVJNTU16t%2B/Px9JAwAAgoLPA1aHDh300ksvae/evSorK5PNZlOvXr10ww03uO%2BuDgAAEMh8HrAkKTQ0VMOHDzd6ShAAAOCHwucBy%2Bl0atWqVSouLta5c%2BeaXdj%2Bxhtv%2BLokAAAAo3wesP7rv/5Lhw4d0rhx43TVVVf5evcAAABe5/OA9d5772nt2rVKSkry9a4BAAB8wuf3wYqKilKHDh18vVsAAACf8XnAmjBhgtauXavGxkZf7xoAAMAnfH6KsLq6Wjt37tRbb72lbt26KSwszGP%2B%2Beef93VJAAAARvnlNg3jx4/3x24BAAB8wucBi8/1AwAAwc7n12BJUkVFhfLy8vTwww/rn//8p15//XUdPXrUH6UAAAAY5/OAdfz4cf3iF7/QSy%2B9pF27dqm2tlavvfaa0tLS5HA4fF0OAACAcT4PWL/73e80atQo7d69W61bt5YkrVixQikpKXriiSd8XQ4AAIBxPg9YxcXFuueeezw%2B2Nlms2nGjBkqLS31dTkAAADG%2BTxgNTU1qampqdn4mTNnFBoa6utyAAAAjPN5wBo2bJjWrFnjEbKqq6uVm5uroUOH%2BrocAAAA43wesObMmaNDhw5p2LBhqqur0/3336%2BRI0fq888/1%2BzZs31dDgAAgHE%2Bvw9Wp06dtGPHDu3cuVN/%2B9vf1NTUpClTpmjChAlq27atr8sBAAAwzi93co%2BMjNTkyZP9sWsAAACv83nAmjZt2r%2Bc57MIAQBAoPN5wOratavH44aGBh0/flz/%2BMc/dNddd/m6HAAAAON%2BMJ9FmJ%2Bfr1OnTvm4GgAAAPP88lmE5zNhwgT96U9/8ncZAAAAl%2B0HE7A%2B/PBDbjQKAACCwg/iIvfTp0/r73//u26//XZflwMAAGCczwNWly5dPD6HUJJat26tO%2B%2B8U7/85S99XQ4AAIBxPg9Yv/vd73y9SwAAAJ/yecB6//33W/zc66%2B/3ouVAAAAeIfPA9bUqVPdpwgty3KPf3csJCREf/vb33xdHgAAwGXzecBavXq1lixZokceeUTJyckKCwvTRx99pEWLFunf//3fNXbsWF%2BXBAAAYJTPb9OQk5Oj%2BfPn65ZbblG7du3Upk0bDR06VIsWLdKLL76orl27ur8AAAACkc8DVkVFxXnDU9u2bVVVVeXrcgAAAIzzecCKj4/XihUrdPr0afdYdXW1cnNzdcMNN/i6HAAAAON8fg3Wo48%2BqmnTpummm25Sjx49ZFmWPv30U8XExOj555/3dTkAAADG%2BXwFq2fPnnrttdf08MMPKz4%2BXgkJCZo3b55efvll/du//Zux/dTX12vhwoW6/vrr9ZOf/EQrVqxw/w/F0tJSTZ48WXa7XWlpaTp06JDHa3fu3KlRo0bJbrcrMzNTX331lbG6AABA8PPLZxFeffXVmjx5su68805lZ2drwoQJioyMNLqPJUuWaO/evXr22We1fPly/eEPf1BhYaFqa2uVkZGhpKQkbd%2B%2BXQkJCZo%2Bfbpqa2slSQcPHtS8efM0c%2BZMFRYWqqamRtnZ2UZrAwAAwc3npwgty9Ly5cu1ceNGnTt3Trt27dLKlSsVGRmpBQsWqHXr1pe9j%2Brqam3btk3r1q3ToEGDJEn33nuvHA6HbDabwsPDNWvWLIWEhGjevHl6%2B%2B239frrrys1NVWbNm3SmDFjNHHiREnSsmXLNHLkSJWXl6tbt26XXRsAAAh%2BPl/B2rhxo15%2B%2BWU99thjCgsLkySNGjVKu3fvVl5enpF9FBUVqW3btkpOTnaPZWRkKCcnRw6HQ4mJie4bm4aEhGjw4MEqKSmRJDkcDiUlJblf17lzZ3Xp0kUOh8NIbQAAIPj5fAWrsLBQ8%2BfP180336zFixdLksaOHavWrVsrJydHv/71ry97H%2BXl5eratat27Nih1atX69y5c0pNTdX9998vp9OpXr16eTy/Q4cOKisrk/TNbSRiY2ObzZ86darF%2B6%2BoqJDT6fQYs9mimm33coWG%2BuUM7yWz2QKn3m97G2g9DgT01rvor/fQW%2B8Jxt76PGB9/vnn6tu3b7PxPn36NAsll6q2tlbHjx/X5s2blZOTI6fTqfnz5ysyMlIul8u9cvatsLAw1dfXS5LOnj37L%2BdborCwsNlqXGZmprKysi7xiIJDu3Zt/F3CRYuONnttIP4feutd9Nd76K33BFNvfR6wunbtqo8%2B%2BkhxcXEe42%2B//baxa5xsNptOnz6t5cuXu29qeuLECb344ovq3r17s7BUX1%2BviIgISVJ4ePh55y/mIvz09HSlpKR8p6YoVVWduZTD%2BV6BlvRNH783hYa2UnR0pGpqXGpsbPJ3OUGF3noX/fUeeus93uytv/6493nA%2Bo//%2BA8tXLhQTqdTlmVp3759Kiws1MaNGzVnzhwj%2B4iJiVF4eLjHHeN//OMf6%2BTJk0pOTlZlZaXH8ysrK92n7zp16nTe%2BZiYmBbvPzY2ttnpQKfzazU0XNk/kIF4/I2NTQFZdyCgt95Ff72H3npPMPXW50sgaWlp%2BvWvf63nnntOZ8%2Be1fz587V9%2B3Y9%2BOCDmjJlipF92O121dXV6dixY%2B6xo0ePqmvXrrLb7frwww/d98SyLEvFxcWy2%2B3u1xYVFblfd/LkSZ08edI9DwAAcCE%2BX8HauXOnRo8erfT0dH311VeyLEsdOnQwuo9rr71WI0aMUHZ2thYsWCCn06mCggLdf//9Gj16tJYvX66lS5fqtttu0%2BbNm%2BVyuTRmzBhJ0pQpUzR16lTFx8dr4MCBWrp0qUaMGMEtGgAAQIv5fAVr0aJF7ovZ27dvbzxcfeuJJ57Qj370I02ZMkWzZ8/WHXfcoalTp6pt27Zas2aNioqKlJqaKofDoYKCAkVFRUmSEhIStGjRIuXn52vKlCm6%2BuqrlZOT45UaAQBAcPL5ClaPHj30j3/8o9mtEky76qqrtGzZsvPODRo0SC%2B99NL3vjY1NVWpqaneKg0AAAQ5nwesPn366De/%2BY3Wrl2rHj16KDw83GOe1SIAABDofB6wjh07psTEREkydt8rAACAHxKfBKxly5Zp5syZioqK0saNG32xSwAAAL/xyUXu69atk8vl8hjLyMhQRUWFL3YPAADgUz4JWN/ec%2Br/9/7776uurs4XuwcAAPCpwPqsFQAAgABAwAIAADDMZwErJCTEV7sCAADwK5/dpmHJkiUe97w6d%2B6ccnNz1aaN56dccx8sAAAQ6HwSsK6//vpm97xKSEhQVVWVqqqqfFECAACAz/gkYHHvKwAAcCXhIncAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwoA9YGRkZmjNnjvtxaWmpJk%2BeLLvdrrS0NB06dMjj%2BTt37tSoUaNkt9uVmZmpr776ytclAwCAABfUAevVV1/Vnj173I9ra2uVkZGhpKQkbd%2B%2BXQkJCZo%2Bfbpqa2slSQcPHtS8efM0c%2BZMFRYWqqamRtnZ2f4qHwAABKigDVjV1dVatmyZBg4c6B577bXXFB4erlmzZqlnz56aN2%2Be2rRpo9dff12StGnTJo0ZM0YTJ05Unz59tGzZMu3Zs0fl5eX%2BOgwAABCAgjZgPf7445owYYJ69erlHnM4HEpMTFRISIgkKSQkRIMHD1ZJSYl7Pikpyf38zp07q0uXLnI4HL4tHgAABLSgDFj79u3TBx98oBkzZniMO51OxcbGeox16NBBp06dkiRVVFT8y3kAAICWsPm7ANPq6ur02GOPaf78%2BYqIiPCYc7lcCgsL8xgLCwtTfX29JOns2bP/cr6lKioq5HQ6PcZstqhm4e1yhYYGVj622QKn3m97G2g9DgT01rvor/fQW%2B8Jxt4GXcDKy8vTgAEDNHz48GZz4eHhzcJSfX29O4h933xkZORF1VBYWKi8vDyPsczMTGVlZV3UdoJNu3Zt/F3CRYuOvrjvPVqO3noX/fUeeus9wdTboAtYr776qiorK5WQkCBJ7sC0a9cujR8/XpWVlR7Pr6ysdK8sderU6bzzMTExF1VDenq6UlJSPMZstihVVZ25qO1cSKAlfdPH702hoa0UHR2pmhqXGhub/F1OUKG33kV/vYfeeo83e%2BuvP%2B6DLmBt3LhRDQ0N7sdPPPGEJOk3v/mN3n//fT3zzDOyLEshISGyLEvFxcX61a9%2BJUmy2%2B0qKipSamqqJOnkyZM6efKk7Hb7RdUQGxvb7HSg0/m1Ghqu7B/IQDz%2BxsamgKw7ENBb76K/3kNvvSeYeht0Aatr164ej9u0%2BSa5du/eXR06dNDy5cu1dOlS3Xbbbdq8ebNcLpfGjBkjSZoyZYqmTp2q%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%2BvLLL5WVlaXk5GQNHz5cOTk5qqurkySVl5fr7rvvVnx8vMaOHat33nnH47V79%2B7V%2BPHjZbfbNW3aNJWXl/vjEAAAQIAKyoBlWZaysrLkcrn0wgsvaOXKlXrzzTe1atUqWZalzMxMdezYUdu2bdOECRM0c%2BZMnThxQpJ04sQJZWZmKjU1VVu3blX79u01Y8YMWZbl56MCAACBIihvNHr06FGVlJTo3XffVceOHSVJWVlZevzxx3XTTTepvLxcmzdvVlRUlHr27Kl9%2B/Zp27ZteuCBB7RlyxYNGDBA9957ryQpJydHN954ow4cOKAhQ4b487AAAECACMoVrJiYGK1du9Ydrr51%2BvRpORwO9evXT1FRUe7xxMRElZSUSJIcDoeSkpLcc5GRkerfv797HgAA4EKCcgUrOjpaw4cPdz9uamrSpk2bNHToUDmdTsXGxno8v0OHDjp16pQkXXC%2BJSoqKuR0Oj3GbLaoZtu9XKGhgZWPbbbAqffb3gZajwMBvfUu%2Bus99Nb7gqm3QRmwvis3N1elpaXaunWr1q9fr7CwMI/5sLAw1dfXS5JcLte/nG%2BJwsJC5eXleYxlZmYqKyvrEo8gOLRr18bfJVy06OhIf5cQtOitd9Ff76G33hNMvQ36gJWbm6sNGzZo5cqV6t27t8LDw1VdXe3xnPr6ekVEREiSwsPDm4Wp%2Bvp6RUdHt3if6enpSklJ8Riz2aJUVXXmEo/i/AIt6Zs%2Bfm8KDW2l6OhI1dS41NjY5O9yggq99S766z301vu80Vt//XEf1AFr8eLFevHFF5Wbm6tbbrlFktSpUycdOXLE43mVlZXu03edOnVSZWVls/m%2Bffu2eL%2BxsbHNTgc6nV%2BroeHK/oEMxONvbGwKyLoDQSD1dsyqd/1dwkX5YOnogOpvoKG33hNMvQ2sJZCLkJeXp82bN2vFihUaN26ce9xut%2Bvjjz/W2bNn3WNFRUWy2%2B3u%2BaKiIvecy%2BVSaWmpex4AAOBCgjJgffLJJ3rqqad03333KTExUU6n0/2VnJyszp07Kzs7W2VlZSooKNDBgwc1adIkSVJaWpqKi4tVUFCgsrIyZWdnKy4ujls0AACAFgvKgPXGG2%2BosbFRTz/9tIYNG%2BbxFRoaqqeeekpOp1Opqan64x//qPz8fHXp0kWSFBcXpyeffFLbtm3TpEmTVF1drfz8fIWEhPj5qAAAQKAIymuwMjIylJGR8b3z3bt316ZNm753/qc//al%2B%2BtOfeqM0AABwBQjKFSwAAAB/ImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCwoP4sQMCFp3uv%2BLuGi/OnBG/1dAgDg/2IFCwAAwDBWsIAgMWbVu/4uocU%2BWDra3yUAgFexggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjBuNAkCQCaSPeeIjnhCsWMECAAAwjBUs%2BEwgfZQLAACXgxUsAAAAwwhYAAAAhhGwAAAADOMaLAA%2BF0j/yw0ALgUrWAAAAIYRsAAAAAwjYAEAABjGNVgAAL8JtPvjfbB0tL9LQIBgBQsAAMAwAhYAAIBhBKzzqKur09y5c5WUlKRhw4bpueee83dJAAAggHAN1nksW7ZMhw4d0oYNG3TixAnNnj1bXbp00ejRnHsHgCsZ93BDSxGwvqO2tlZbtmzRM888o/79%2B6t///4qKyvTCy%2B8QMACAAAtwinC7zh8%2BLAaGhqUkJDgHktMTJTD4VBTU5MfKwMAAIGCFazvcDqdateuncLCwtxjHTt2VF1dnaqrq9W%2BffsLbqOiokJOp9NjzGaLUmxsrNFaQ0PJxwCA4BFMv9cIWN/hcrk8wpUk9%2BP6%2BvoWbaOwsFB5eXkeYzNnztQDDzxgpsj/q6KiQnf9W5nS09ONh7crXUVFhQoLC%2BmtF9Bb76K/3kNvvaeiokJPPvlkUPU2eKKiIeHh4c2C1LePIyIiWrSN9PR0bd%2B%2B3eMrPT3deK1Op1N5eXnNVstw%2Beit99Bb76K/3kNvvScYe8sK1nd06tRJVVVVamhokM32TXucTqciIiIUHR3dom3ExsYGTQIHAAAXjxWs7%2Bjbt69sNptKSkrcY0VFRRo4cKBataJdAADgwkgM3xEZGamJEydqwYIFOnjwoHbv3q3nnntO06ZN83dpAAAgQIQuWLBggb%2BL%2BKEZOnSoSktLtXz5cu3bt0%2B/%2BtWvlJaW5u%2ByzqtNmzZKTk5WmzZt/F1K0KG33kNvvYv%2Beg%2B99Z5g622IZVmWv4sAAAAIJpwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwApQdXV1mjt3rpKSkjRs2DA999xz/i4pYH355ZfKyspScnKyhg8frpycHNXV1UmSysvLdffddys%2BPl5jx47VO%2B%2B84%2BdqA1dGRobmzJnjflxaWqrJkyfLbrcrLS1Nhw4d8mN1gae%2Bvl4LFy7U9ddfr5/85CdasWKFvr1vNL29fCdPntT06dM1ePBgpaSkaP369e45%2Bntp6uvrNX78eO3fv989dqH32L1792r8%2BPGy2%2B2aNm2aysvLfV32JSNgBahly5bp0KFD2rBhgx577DHl5eXp9ddf93dZAceyLGVlZcnlcumFF17QypUr9eabb2rVqlWyLEuZmZnq2LGjtm3bpgkTJmjmzJk6ceKEv8sOOK%2B%2B%2Bqr27NnjflxbW6uMjAwlJSVp%2B/btSkhI0PTp01VbW%2BvHKgPLkiVLtHfvXj377LNavny5/vCHP6iwsJDeGvLggw8qKipK27dv19y5c7Vq1Sr9%2Bc9/pr%2BXqK6uTg899JDKysrcYxd6jz1x4oQyMzOVmpqqrVu3qn379poxY4YC5gNoLAScM2fOWAMHDrTee%2B8991h%2Bfr515513%2BrGqwHTkyBGrd%2B/eltPpdI%2B98sor1rBhw6y9e/da8fHx1pkzZ9xzd911l/X73//eH6UGrKqqKuumm26y0tLSrNmzZ1uWZVlbtmyxUlJSrKamJsuyLKupqcm6%2BeabrW3btvmz1IBRVVVl9evXz9q/f797bM2aNdacOXPorQHV1dVW7969rb///e/usZkzZ1oLFy6kv5egrKzM%2BuUvf2n94he/sHr37u3%2B3XWh99hVq1Z5/F6rra21EhISPH73/ZCxghWADh8%2BrIaGBiUkJLjHEhMT5XA41NTU5MfKAk9MTIzWrl2rjh07eoyfPn1aDodD/fr1U1RUlHs8MTFRJSUlvi4zoD3%2B%2BOOaMGGCevXq5R5zOBxKTExUSEiIJCkkJESDBw%2Bmty1UVFSktm3bKjk52T2WkZGhnJwcemtARESEIiMjtX37dp07d05Hjx5VcXGx%2BvbtS38vwYEDBzRkyBAVFhZ6jF/oPdbhcCgpKck9FxkZqf79%2BwdMrwlYAcjpdKpdu3YKCwtzj3Xs2FF1dXWqrq72Y2WBJzo6WsOHD3c/bmpq0qZNmzR06FA5nU7FxsZ6PL9Dhw46deqUr8sMWPv27dMHH3ygGTNmeIzT28tTXl6url27aseOHRo9erR%2B9rOfKT8/X01NTfTWgPDwcM2fP1%2BFhYWy2%2B0aM2aMbrrpJk2ePJn%2BXoLbb79dc%2BfOVWRkpMf4hXoZ6L22%2BbsAXDyXy%2BURriS5H9fX1/ujpKCRm5ur0tJSbd26VevXrz9vn%2Blxy9TV1emxxx7T/PnzFRER4TH3ff%2BG6W3L1NbW6vjx49q8ebNycnLkdDo1f/58RUZG0ltDPvnkE40cOVL33HOPysrKtHjxYt1www3016AL9TLQe03ACkDh4eHN/oF9%2B/i7v8jQcrm5udqwYYNWrlyp3r17Kzw8vNmKYH19PT1uoby8PA0YMMBjhfBb3/dvmN62jM1m0%2BnTp7V8%2BXJ17dpV0jcXBL/44ovq3r07vb1M%2B/bt09atW7Vnzx5FRERo4MCB%2BvLLL/X000%2BrW7du9NeQC73Hft/7RHR0tM9qvBycIgxAnTp1UlVVlRoaGtxjTqdTERERAfMP74dm8eLFWrdunXJzc3XLLbdI%2BqbPlZWVHs%2BrrKxstmSN83v11Ve1e/duJSQkKCEhQa%2B88opeeeUVJSQk0NvLFBMTo/DwcHe4kqQf//jHOnnyJL014NChQ%2BrevbtHaOrXr59OnDhBfw26UC%2B/bz4mJsZnNV4OAlYA6tu3r2w2m8eFfkVFRRo4cKBateJberHy8vK0efNmrVixQuPGjXOP2%2B12ffzxxzp79qx7rKioSHa73R9lBpyNGzfqlVde0Y4dO7Rjxw6lpKQoJSVFO3bskN1u14cffuj%2B79aWZam4uJjetpDdblddXZ2OHTvmHjt69Ki6du1Kbw2IjY3V8ePHPVZPjh49qri4OPpr0IXeY%2B12u4qKitxzLpdLpaWlAdNrfhsHoMjISE2cOFELFizQwYMHtXv3bj333HOaNm2av0sLOJ988omeeuop3XfffUpMTJTT6XR/JScnq3PnzsrOzlZZWZkKCgp08OBBTZo0yd9lB4SuXbuqe/fu7q82bdqoTZs26t69u0aPHq2amhotXbpUR44c0dKlS%2BVyuTRmzBh/lx0Qrr32Wo0YMULZ2dk6fPiw/vrXv6qgoEBTpkyhtwakpKSodevWevTRR3Xs2DH95S9/0erVqzV16lT6a9CF3mPT0tJUXFysgoIClZWVKTs7W3FxcRoyZIifK28hf94jApeutrbWmjVrlhUfH28NGzbMWrdunb9LCkhr1qyxevfufd4vy7KsTz/91LrjjjusAQMGWOPGjbPeffddP1ccuGbPnu2%2BD5ZlWZbD4bAmTpxoDRw40Jo0aZL18ccf%2B7G6wFNTU2M98sgjVnx8vHXDDTdYTz75pPveTPT28pWVlVl33323NXjwYGvUqFHWunXr6K8B//99sCzrwu%2Bxb731lvXzn//cGjRokHXXXXdZn332ma9LvmQhlhUot0QFAAAIDJwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/g%2B0sntkwT9SIQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6145166046958716616”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>180</td> <td class=”number”>5.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>43</td> <td class=”number”>1.3%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (97)</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:75%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6145166046958716616”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1289</td> <td class=”number”>40.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.55555555555556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>83.3333333333333</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.8888888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>253</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_WIC16”>PCT_FMRKT_WIC16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>105</td>

</tr> <tr>

<th>Unique (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21.917</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>44.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-7992237162331611729”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-7992237162331611729,#minihistogram-7992237162331611729”
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</div> <div class=”row collapse col-md-12” id=”descriptives-7992237162331611729”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-7992237162331611729”

aria-controls=”quantiles-7992237162331611729” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-7992237162331611729” aria-controls=”histogram-7992237162331611729”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-7992237162331611729” aria-controls=”common-7992237162331611729”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-7992237162331611729” aria-controls=”extreme-7992237162331611729”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-7992237162331611729”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>42.857</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>42.857</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>33.963</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5496</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.26311</td>

</tr> <tr>

<th>Mean</th> <td>21.917</td>

</tr> <tr>

<th>MAD</th> <td>28.291</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.305</td>

</tr> <tr>

<th>Sum</th> <td>49160</td>

</tr> <tr>

<th>Variance</th> <td>1153.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-7992237162331611729”>

<img 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XF6uwsDBgLCcnR7m5uS3eRiRq0yYh1CWcs8TEuFCXELHorb3or33orX0iqbcRF7CcTqdqa2sDxnw%2Bn2JjY/3z3w1LPp9PiYmJcjqd/sffnY%2BLa/knPTs7W5mZmQFjDke8ampOtHgbLRFuSd/08dspOrqVEhPjVFfnVWNjU6jLiSj01l701z701j529jZUv9xHXMBKTU3VoUOHAsaqq6v9p%2BdSU1NVXV3dbL5Xr1667LLL5HQ6VV1drW7dukmSGhoaVFtbq%2BTk5BbXkJKS0ux0oMdzTA0NF/cXZDgef2NjU1jWHQ7orb3or33orX0iqbfhtQTSAi6XSx999JH/dJ8klZSUyOVy%2BedLSkr8c16vVwcPHpTL5VKrVq3Ur1%2B/gPnS0lI5HA717NkzeAcBAADCWsQFrIEDB6p9%2B/bKy8tTWVmZioqKtH//fk2YMEGSNH78eO3bt09FRUUqKytTXl6eOnXqpEGDBkn65uL5Z599Vjt27ND%2B/fs1f/583XTTTed0ihAAAFzcIi5gRUdH68knn5TH41FWVpZee%2B01rVy5Uh06dJAkderUSU888YS2bNmiCRMmqLa2VitXrlRUVJQkafTo0Zo%2Bfbrmzp2rO%2B64Q1deeaXuu%2B%2B%2BUB4SAAAIM1HWt7cwh608nmPGt%2BlwtNJPl/3J%2BHbt8uY9Q0JdQos5HK3Upk2CampORMz1AD8U9NZe9Nc%2B9NY%2BdvY2OflSo9trqYhbwQIAAAg1AhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAt6wJo4caI2btyoY8eOBXvXAAAAQRH0gDV48GCtWrVKQ4cO1axZs/Tuu%2B/KsqxglwEAAGCboAes//mf/9Ef/vAHPfnkk4qOjtbdd9%2Bt4cOH67HHHtORI0eCXQ4AAIBxjlDsNCoqSkOGDNGQIUPk9Xq1fv16PfnkkyoqKtKAAQM0depUXXfddaEoDQAA4IKFJGBJUlVVlV577TW99tpr%2BuSTTzRgwAD9/Oc/V2VlpR566CG9//77mjNnTqjKAwAAOG9BD1ivvvqqXn31Ve3Zs0dt27bVuHHj9Pjjj6tr167%2B57Rv3175%2BfkELAAAEJaCHrDmzJmjESNGaOXKlbrmmmvUqlXzy8CuuOIK/eIXvwh2aQAAAEYEPWC98847atOmjWpra/3hav/%2B/erTp4%2Bio6MlSQMGDNCAAQOCXRoAAIARQf8rwuPHj2vkyJF6%2Bumn/WPTpk3T2LFjVVFREexyAAAAjAt6wPrNb36jLl266Pbbb/ePvfHGG2rfvr0KCgqCXQ4AAIBxQQ9YH3zwgR588EElJyf7x9q2bav7779f7733XrDLAQAAMC7oAcvhcKiurq7ZuNfr5Y7uAAAgIgQ9YF1zzTVavHixPv/8c/9YeXm5CgoKNGzYsGCXAwAAYFzQ/4rwgQce0O23367rr79eiYmJkqS6ujr16dNHeXl5wS4HAADAuKAHrKSkJL388svatWuXysrK5HA41L17d1199dWKiooKdjkAAADGheStcqKjozVs2DBOCQIAgIgU9IDl8Xi0YsUK7du3T6dPn252Yfvvfve7YJcEAABgVNAD1q9//WsdOHBAo0eP1qWXXmrbfioqKjR//ny9//77uuyyyzRlyhTddtttkqSDBw9q3rx5%2BuSTT9S9e3ctWLBAffv29b92%2B/btWrFihTwej4YOHapFixapbdu2ttUKAAAiS9AD1nvvvadnnnlGGRkZtu7nnnvuUYcOHbR161YdOnRI9957rzp27KghQ4Zo2rRp%2BtnPfqaHH35YL774oqZPn67f/va3io%2BP1/79%2BzVnzhwtWLBAPXv2VH5%2BvvLy8rR69Wpb6wUAAJEj6LdpiI%2BPV1JSkq37%2BOc//6nS0lLddddd6tq1q6699loNGzZMu3fv1htvvCGn06n7779f3bp105w5c5SQkKC33npLkrRhwwaNGjVK48aNU8%2BePbVkyRLt3LlT5eXlttYMAAAiR9AD1tixY/XMM8%2BosbHRtn3ExsYqLi5OW7du1enTp3X48GHt27dPvXr1ktvtVnp6uv8vFqOiojRgwACVlpZKktxud8DqWvv27dWhQwe53W7b6gUAAJEl6KcIa2trtX37dv3xj39U586dFRMTEzD//PPPX/A%2BnE6n5s6dq0WLFun5559XY2OjsrKyNHHiRP3ud79T9%2B7dA56flJSksrIySVJVVZVSUlKazVdWVl5wXQAA4OIQkts0jBkzxvZ9fPrppxoxYoRuv/12lZWVadGiRbr66qvl9XqbhbqYmBj5fD5J0qlTp/7lfEtUVVXJ4/EEjDkc8c2C24WKjg76AuQFcTjCp95vextuPQ4H9NZe9Nc%2B9NY%2BkdjboAesgoIC2/exe/dubd68WTt37lRsbKz69eunr776Sk899ZQ6d%2B7cLCz5fD7FxsZK%2Bmb160zzcXFxLd5/cXGxCgsLA8ZycnKUm5t7nkcUGdq0SQh1CecsMbHln3ecG3prL/prH3prn0jqbUhWsKqqqvTSSy/pyJEjmj17tt5//3316NFDV1xxhZHtHzhwQF26dPGHJknq3bu3Vq1apYyMDFVXVwc8v7q62r%2B6lJqaesb55OTkFu8/OztbmZmZAWMOR7xqak6c66H8S%2BGW9E0fv52io1spMTFOdXVeNTY2hbqciEJv7UV/7UNv7WNnb0P1y33QA9Znn32mm266Sa1bt9ZXX32le%2B65R2%2B88Yby8vK0du1auVyuC95HSkqKPvvsM/l8Pv/pvsOHD6tTp05yuVx6%2BumnZVmWoqKiZFmW9u3bpzvvvFOS5HK5VFJSoqysLEnf3E%2BroqLinOpKSUlpdjrQ4zmmhoaL%2BwsyHI%2B/sbEpLOsOB/TWXvTXPvTWPpHU26AvgTz88MO69tprtWPHDl1yySWSpEcffVSZmZlatmyZkX1kZmbqkksu0UMPPaQjR47o97//vVatWqXJkydr5MiRqqurU35%2Bvg4dOqT8/Hx5vV6NGjVKkjRp0iS9%2Buqr2rRpkz7%2B%2BGPdf//9Gj58uDp37mykNgAAEPmCHrD27dun22%2B/PeCNnR0Oh2bMmKGDBw8a2cell16qtWvXyuPxaMKECSooKNBdd92l7OxstW7dWqtXr/avUrndbhUVFSk%2BPl6SlJaWpoULF2rlypWaNGmSfvSjHwXlujEAABA5gn6KsKmpSU1NzZf/Tpw4oejoaGP76d69u9asWXPGuSuvvFIvv/zy9742KyvLf4oQAADgXAV9BWvo0KFavXp1QMiqra3V0qVLNXjw4GCXAwAAYFzQA9aDDz6oAwcOaOjQoaqvr9ddd92lESNG6IsvvtADDzwQ7HIAAACMC/opwtTUVL3yyivavn27/vrXv6qpqUmTJk3S2LFj1bp162CXAwAAYFxI7oMVFxeniRMnhmLXAAAAtgt6wJoyZcq/nDfxXoQAAAChFPSA1bFjx4DHDQ0N%2Buyzz/TJJ59o6tSpwS4HAADAuB/MexGuXLlSlZWVQa4GAADAvB/Mm9mNHTtWb775ZqjLAAAAuGA/mID1l7/8xeiNRgEAAELlB3GR%2B/Hjx/W3v/1Nt9xyS7DLAQAAMC7oAatDhw4B70MoSZdccol%2B8Ytf6MYbbwx2OQAAAMYFPWA9/PDDwd4lAABAUAU9YL3//vstfu5VV11lYyUAAAD2CHrAmjx5sv8UoWVZ/vHvjkVFRemvf/1rsMsDAAC4YEEPWKtWrdLixYt13333aeDAgYqJidGHH36ohQsX6uc//7luuOGGYJcEAABgVNBv01BQUKC5c%2Bfq%2BuuvV5s2bZSQkKDBgwdr4cKFevHFF9WxY0f/BwAAQDgKesCqqqo6Y3hq3bq1ampqgl0OAACAcUEPWP3799ejjz6q48eP%2B8dqa2u1dOlSXX311cEuBwAAwLigX4P10EMPacqUKbrmmmvUtWtXWZalv//970pOTtbzzz8f7HIAAACMC3rA6tatm9544w1t375dn376qSTp1ltv1ejRoxUXFxfscgAAAIwLesCSpB/96EeaOHGivvjiC3Xu3FnSN3dzBwAAiARBvwbLsiwtW7ZMV111lcaMGaPKyko98MADmjNnjk6fPh3scgAAAIwLesBav369Xn31Vc2bN08xMTGSpGuvvVY7duxQYWFhsMsBAAAwLugBq7i4WHPnzlVWVpb/7u033HCDFi9erG3btgW7HAAAAOOCHrC%2B%2BOIL9erVq9l4z5495fF4gl0OAACAcUEPWB07dtSHH37YbPydd97xX/AOAAAQzoL%2BV4S//OUvtWDBAnk8HlmWpd27d6u4uFjr16/Xgw8%2BGOxyAAAAjAt6wBo/frwaGhr01FNP6dSpU5o7d67atm2re%2B65R5MmTQp2OQAAAMYFPWBt375dI0eOVHZ2tr7%2B%2BmtZlqWkpKRglwEAAGCboF%2BDtXDhQv/F7G3btiVcAQCAiBP0gNW1a1d98sknwd4tAABA0AT9FGHPnj1177336plnnlHXrl3ldDoD5gsKCozsx%2BfzqaCgQNu3b9cll1yiCRMm6L//%2B78VFRWlgwcPat68efrkk0/UvXt3LViwQH379vW/dvv27VqxYoU8Ho%2BGDh2qRYsWqW3btkbqAgAAkS/oK1hHjhxRenq6EhIS5PF49MUXXwR8mLJ48WLt2rVLzz77rJYvX66XXnpJxcXFOnnypKZNm6aMjAxt3bpVaWlpmj59uk6ePClJ2r9/v%2BbMmaOZM2equLhYdXV1ysvLM1YXAACIfEFZwVqyZIlmzpyp%2BPh4rV%2B/3vb91dbWasuWLVqzZo2uvPJKSdIdd9wht9sth8Mhp9Op%2B%2B%2B/X1FRUZozZ47eeecdvfXWW8rKytKGDRs0atQojRs3zl/7iBEjVF5ezn26AABAiwRlBWvNmjXyer0BY9OmTVNVVZUt%2ByspKVHr1q01cODAgP0VFBTI7XYrPT3d/zY9UVFRGjBggEpLSyVJbrdbGRkZ/te1b99eHTp0kNvttqVWAAAQeYKygmVZVrOx999/X/X19bbsr7y8XB07dtQrr7yiVatW6fTp08rKytJdd90lj8ej7t27Bzw/KSlJZWVlkqSqqiqlpKQ0m6%2BsrGzx/quqqpq97Y/DEd9suxcqOjroZ3gviMMRPvV%2B29tw63E4oLf2or/2obf2icTeBv0i92A4efKkPvvsM23cuFEFBQXyeDyaO3eu4uLi5PV6FRMTE/D8mJgY%2BXw%2BSdKpU6f%2B5XxLFBcXq7CwMGAsJydHubm553lEkaFNm4RQl3DOEhPjQl1CxKK39qK/9qG39omk3kZkwHI4HDp%2B/LiWL1%2Bujh07SpKOHj2qF198UV26dGkWlnw%2Bn2JjYyVJTqfzjPNxcS3/pGdnZyszM/M7NcWrpubE%2BRzO9wq3pG/6%2BO0UHd1KiYlxqqvzqrGxKdTlRBR6ay/6ax96ax87exuqX%2B6DFrC%2BveYpGJKTk%2BV0Ov3hSpIuv/xyVVRUaODAgaqurg54fnV1tf/0XWpq6hnnk5OTW7z/lJSUZqcDPZ5jami4uL8gw/H4GxubwrLucEBv7UV/7UNv7RNJvQ1awFq8eHHAPa9Onz6tpUuXKiEhMFmauA%2BWy%2BVSfX29jhw5ossvv1ySdPjwYXXs2FEul0tPP/20LMtSVFSULMvSvn37dOedd/pfW1JSoqysLElSRUWFKioq5HK5LrguAABwcQhKwLrqqquaXfSdlpammpoa1dTUGN/fFVdcoeHDhysvL0/z58%2BXx%2BNRUVGR7rrrLo0cOVLLly9Xfn6%2Bbr75Zm3cuFFer1ejRo2SJE2aNEmTJ09W//791a9fP%2BXn52v48OHcogEAALRYUAJWMO599V3Lli3TokWLNGnSJMXFxenWW2/V5MmTFRUVpdWrV2vevHl66aWX9O///u8qKipSfHy8pG%2BC38KFC/X444/rn//8p4YMGaJFixYFvX4AABC%2Boqwz3UMBxnk8x4xv0%2BFopZ8u%2B5Px7drlzXuGhLqEFnM4WqlNmwTV1JyImOsBfijorb3or33orX3s7G1y8qVGt9dS4fVnaAAAAGGAgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQvNQZLrAAATT0lEQVQAAMAwAhYAAIBhBCwAAADDCFgAAACGRXzAmjZtmh588EH/44MHD2rixIlyuVwaP368Dhw4EPD87du369prr5XL5VJOTo6%2B/vrrYJcMAADCXEQHrNdff107d%2B70Pz558qSmTZumjIwMbd26VWlpaZo%2BfbpOnjwpSdq/f7/mzJmjmTNnqri4WHV1dcrLywtV%2BQAAIExFbMCqra3VkiVL1K9fP//YG2%2B8IafTqfvvv1/dunXTnDlzlJCQoLfeekuStGHDBo0aNUrjxo1Tz549tWTJEu3cuVPl5eWhOgwAABCGIjZgPfLIIxo7dqy6d%2B/uH3O73UpPT1dUVJQkKSoqSgMGDFBpaal/PiMjw//89u3bq0OHDnK73cEtHgAAhLWIDFi7d%2B/WBx98oBkzZgSMezwepaSkBIwlJSWpsrJSklRVVfUv5wEAAFrCEeoCTKuvr9e8efM0d%2B5cxcbGBsx5vV7FxMQEjMXExMjn80mSTp069S/nW6qqqkoejydgzOGIbxbeLlR0dHjlY4cjfOr9trfh1uNwQG/tRX/tQ2/tE4m9jbiAVVhYqL59%2B2rYsGHN5pxOZ7Ow5PP5/EHs%2B%2Bbj4uLOqYbi4mIVFhYGjOXk5Cg3N/ecthNp2rRJCHUJ5ywx8dw%2B92g5emsv%2BmsfemufSOptxAWs119/XdXV1UpLS5Mkf2B6%2B%2B23NWbMGFVXVwc8v7q62r%2BylJqaesb55OTkc6ohOztbmZmZAWMOR7xqak6c03bOJtySvunjt1N0dCslJsaprs6rxsamUJcTUeitveivfeitfezsbah%2BuY%2B4gLV%2B/Xo1NDT4Hy9btkySdO%2B99%2Br999/X008/LcuyFBUVJcuytG/fPt15552SJJfLpZKSEmVlZUmSKioqVFFRIZfLdU41pKSkNDsd6PEcU0PDxf0FGY7H39jYFJZ1hwN6ay/6ax96a59I6m3EBayOHTsGPE5I%2BCa5dunSRUlJSVq%2BfLny8/N18803a%2BPGjfJ6vRo1apQkadKkSZo8ebL69%2B%2Bvfv36KT8/X8OHD1fnzp2DfhwAACB8hdc5pgvUunVrrV692r9K5Xa7VVRUpPj4eElSWlqaFi5cqJUrV2rSpEn60Y9%2BpIKCghBXDQAAwk2UZVlWqIu4GHg8x4xv0%2BFopZ8u%2B5Px7drlzXuGhLqEFnM4WqlNmwTV1JyImOXqHwp6ay/6ax96ax87e5ucfKnR7bXURbWCBQAAEAwELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYI9QF2OWrr75Sfn6%2B3nvvPTmdTt1www2aNWuWnE6nysvL9etf/1qlpaXq0KGDZs%2BeraFDh/pfu2vXLv3mN79ReXm5XC6X8vPz1blz5xAeDQAA527Uij%2BHuoQW%2ByB/ZKhLMCoiV7Asy1Jubq68Xq9eeOEFPfbYY/rDH/6gFStWyLIs5eTkqF27dtqyZYvGjh2rmTNn6ujRo5Kko0ePKicnR1lZWdq8ebPatm2rGTNmyLKsEB8VAAAIFxG5gnX48GGVlpbqz3/%2Bs9q1aydJys3N1SOPPKJrrrlG5eXl2rhxo%2BLj49WtWzft3r1bW7Zs0d13361Nmzapb9%2B%2BuuOOOyRJBQUFGjJkiPbu3atBgwaF8rAAAECYiMgVrOTkZD3zzDP%2BcPWt48ePy%2B12q3fv3oqPj/ePp6enq7S0VJLkdruVkZHhn4uLi1OfPn388wAAAGcTkStYiYmJGjZsmP9xU1OTNmzYoMGDB8vj8SglJSXg%2BUlJSaqsrJSks863RFVVlTweT8CYwxHfbLsXKjo6vPKxwxE%2B9X7b23DrcTigt/aiv/aht/aLpN5GZMD6rqVLl%2BrgwYPavHmz1q5dq5iYmID5mJgY%2BXw%2BSZLX6/2X8y1RXFyswsLCgLGcnBzl5uae5xFEhjZtEkJdwjlLTIwLdQkRi97ai/7ah97aJ5J6G/EBa%2BnSpVq3bp0ee%2Bwx9ejRQ06nU7W1tQHP8fl8io2NlSQ5nc5mYcrn8ykxMbHF%2B8zOzlZmZmbAmMMRr5qaE%2Bd5FGcWbknf9PHbKTq6lRIT41RX51VjY1Ooy4ko9NZe9Nc%2B9NZ%2BdvQ2VL/cR3TAWrRokV588UUtXbpU119/vSQpNTVVhw4dCnhedXW1//Rdamqqqqurm8336tWrxftNSUlpdjrQ4zmmhoaL%2BwsyHI%2B/sbEpLOsOB/TWXvTXPvTWPpHU2/BaAjkHhYWF2rhxox599FGNHj3aP%2B5yufTRRx/p1KlT/rGSkhK5XC7/fElJiX/O6/Xq4MGD/nkAAICziciA9emnn%2BrJJ5/Ur371K6Wnp8vj8fg/Bg4cqPbt2ysvL09lZWUqKirS/v37NWHCBEnS%2BPHjtW/fPhUVFamsrEx5eXnq1KkTt2gAAAAtFpEB63e/%2B50aGxv11FNPaejQoQEf0dHRevLJJ%2BXxeJSVlaXXXntNK1euVIcOHSRJnTp10hNPPKEtW7ZowoQJqq2t1cqVKxUVFRXiowIAAOEiIq/BmjZtmqZNm/a98126dNGGDRu%2Bd/4nP/mJfvKTn9hRGgAAuAhE5AoWAABAKEXkChYAmBROb5grRd6b5gLhiBUsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwR6gLAGDGqBV/DnUJLfZB/shQlwAAtiJgAd8jY85boS4BOC/h9H/3zXuGhLoEwBacIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD%2BCtCBE043UYAAIALwQoWAACAYaxgAQBCJtxWtrlJLlqKFSwAAADDWMECEHThdKdxADgfrGABAAAYRsACAAAwjIB1BvX19Zo9e7YyMjI0dOhQPffcc6EuCQAAhBGuwTqDJUuW6MCBA1q3bp2OHj2qBx54QB06dNDIkfz1CABczLh%2BEC1FwPqOkydPatOmTXr66afVp08f9enTR2VlZXrhhRcIWAAAoEU4RfgdH3/8sRoaGpSWluYfS09Pl9vtVlNTUwgrAwAA4YIVrO/weDxq06aNYmJi/GPt2rVTfX29amtr1bZt27Nuo6qqSh6PJ2DM4YhXSkqK0Vqjo8nHAIDIEUk/1whY3%2BH1egPClST/Y5/P16JtFBcXq7CwMGBs5syZuvvuu80U%2Bf%2BrqqrS1H8rU3Z2tvHwdrGrqqpScXExvbUBvbUX/bUPvbVPVVWVnnjiiYjqbeRERUOcTmezIPXt49jY2BZtIzs7W1u3bg34yM7ONl6rx%2BNRYWFhs9UyXDh6ax96ay/6ax96a59I7C0rWN%2BRmpqqmpoaNTQ0yOH4pj0ej0exsbFKTExs0TZSUlIiJoEDAIBzxwrWd/Tq1UsOh0OlpaX%2BsZKSEvXr10%2BtWtEuAABwdiSG74iLi9O4ceM0f/587d%2B/Xzt27NBzzz2nKVOmhLo0AAAQJqLnz58/P9RF/NAMHjxYBw8e1PLly7V7927deeedGj9%2BfKjLOqOEhAQNHDhQCQkJoS4l4tBb%2B9Bbe9Ff%2B9Bb%2B0Rab6Msy7JCXQQAAEAk4RQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACVpiqr6/X7NmzlZGRoaFDh%2Bq5554LdUlh66uvvlJubq4GDhyoYcOGqaCgQPX19ZKk8vJy3Xbbberfv79uuOEGvfvuuyGuNnxNmzZNDz74oP/xwYMHNXHiRLlcLo0fP14HDhwIYXXhx%2BfzacGCBbrqqqv0H//xH3r00Uf17X2j6e2Fq6io0PTp0zVgwABlZmZq7dq1/jn6e358Pp/GjBmjPXv2%2BMfO9j12165dGjNmjFwul6ZMmaLy8vJgl33eCFhhasmSJTpw4IDWrVunefPmqbCwUG%2B99Vaoywo7lmUpNzdXXq9XL7zwgh577DH94Q9/0IoVK2RZlnJyctSuXTtt2bJFY8eO1cyZM3X06NFQlx12Xn/9de3cudP/%2BOTJk5o2bZoyMjK0detWpaWlafr06Tp58mQIqwwvixcv1q5du/Tss89q%2BfLleumll1RcXExvDbnnnnsUHx%2BvrVu3avbs2VqxYoV%2B%2B9vf0t/zVF9fr1mzZqmsrMw/drbvsUePHlVOTo6ysrK0efNmtW3bVjNmzFDYvAGNhbBz4sQJq1%2B/ftZ7773nH1u5cqX1i1/8IoRVhadDhw5ZPXr0sDwej39s27Zt1tChQ61du3ZZ/fv3t06cOOGfmzp1qvX444%2BHotSwVVNTY11zzTXW%2BPHjrQceeMCyLMvatGmTlZmZaTU1NVmWZVlNTU3WT3/6U2vLli2hLDVs1NTUWL1797b27NnjH1u9erX14IMP0lsDamtrrR49elh/%2B9vf/GMzZ860FixYQH/PQ1lZmXXjjTdaP/vZz6wePXr4f3ad7XvsihUrAn6unTx50kpLSwv42fdDxgpWGPr444/V0NCgtLQ0/1h6errcbreamppCWFn4SU5O1jPPPKN27doFjB8/flxut1u9e/dWfHy8fzw9PV2lpaXBLjOsPfLIIxo7dqy6d%2B/uH3O73UpPT1dUVJQkKSoqSgMGDKC3LVRSUqLWrVtr4MCB/rFp06apoKCA3hoQGxuruLg4bd26VadPn9bhw4e1b98%2B9erVi/6eh71792rQoEEqLi4OGD/b91i3262MjAz/XFxcnPr06RM2vSZghSGPx6M2bdooJibGP9auXTvV19ertrY2hJWFn8TERA0bNsz/uKmpSRs2bNDgwYPl8XiUkpIS8PykpCRVVlYGu8ywtXv3bn3wwQeaMWNGwDi9vTDl5eXq2LGjXnnlFY0cOVL/%2BZ//qZUrV6qpqYneGuB0OjV37lwVFxfL5XJp1KhRuuaaazRx4kT6ex5uueUWzZ49W3FxcQHjZ%2BtluPfaEeoCcO68Xm9AuJLkf%2Bzz%2BUJRUsRYunSpDh48qM2bN2vt2rVn7DM9bpn6%2BnrNmzdPc%2BfOVWxsbMDc9/0fprctc/LkSX322WfauHGjCgoK5PF4NHfuXMXFxdFbQz799FONGDFCt99%2Bu8rKyrRo0SJdffXV9Negs/Uy3HtNwApDTqez2X%2Bwbx9/9wcZWm7p0qVat26dHnvsMfXo0UNOp7PZiqDP56PHLVRYWKi%2BffsGrBB%2B6/v%2BD9PblnE4HDp%2B/LiWL1%2Bujh07SvrmguAXX3xRXbp0obcXaPfu3dq8ebN27typ2NhY9evXT1999ZWeeuopde7cmf4acrbvsd/3fSIxMTFoNV4IThGGodTUVNXU1KihocE/5vF4FBsbGzb/8X5oFi1apDVr1mjp0qW6/vrrJX3T5%2Brq6oDnVVdXN1uyxpm9/vrr2rFjh9LS0pSWlqZt27Zp27ZtSktLo7cXKDk5WU6n0x%2BuJOnyyy9XRUUFvTXgwIED6tKlS0Bo6t27t44ePUp/DTpbL79vPjk5OWg1XggCVhjq1auXHA5HwIV%2BJSUl6tevn1q14lN6rgoLC7Vx40Y9%2BuijGj16tH/c5XLpo48%2B0qlTp/xjJSUlcrlcoSgz7Kxfv17btm3TK6%2B8oldeeUWZmZnKzMzUK6%2B8IpfLpb/85S/%2BP7e2LEv79u2jty3kcrlUX1%2BvI0eO%2BMcOHz6sjh070lsDUlJS9NlnnwWsnhw%2BfFidOnWivwad7Xusy%2BVSSUmJf87r9ergwYNh02t%2BGoehuLg4jRs3TvPnz9f%2B/fu1Y8cOPffcc5oyZUqoSws7n376qZ588kn96le/Unp6ujwej/9j4MCBat%2B%2BvfLy8lRWVqaioiLt379fEyZMCHXZYaFjx47q0qWL/yMhIUEJCQnq0qWLRo4cqbq6OuXn5%2BvQoUPKz8%2BX1%2BvVqFGjQl12WLjiiis0fPhw5eXl6eOPP9af/vQnFRUVadKkSfTWgMzMTF1yySV66KGHdOTIEf3%2B97/XqlWrNHnyZPpr0Nm%2Bx44fP1779u1TUVGRysrKlJeXp06dOmnQoEEhrryFQnmPCJy/kydPWvfff7/Vv39/a%2BjQodaaNWtCXVJYWr16tdWjR48zfliWZf3973%2B3br31Vqtv377W6NGjrT//%2Bc8hrjh8PfDAA/77YFmWZbndbmvcuHFWv379rAkTJlgfffRRCKsLP3V1ddZ9991n9e/f37r66qutJ554wn9vJnp74crKyqzbbrvNGjBggHXttddaa9asob8G/L/3wbKss3%2BP/eMf/2hdd9111pVXXmlNnTrV%2Bvzzz4Nd8nmLsqxwuSUqAABAeOAUIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9v8Bg2EzVU2cpTcAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-7992237162331611729”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>222</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>74</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (94)</td> <td class=”number”>216</td> <td class=”number”>6.7%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-7992237162331611729”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1416</td> <td class=”number”>44.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.34782608695652</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.66666666666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>85.7142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.5</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>90.6976744186046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>222</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FMRKT_WICCASH16”>PCT_FMRKT_WICCASH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>84</td>

</tr> <tr>

<th>Unique (%)</th> <td>2.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>30.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>967</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>10.538</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>53.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7769925982644222794”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7769925982644222794,#minihistogram7769925982644222794”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7769925982644222794”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7769925982644222794”

aria-controls=”quantiles7769925982644222794” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7769925982644222794” aria-controls=”histogram7769925982644222794”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7769925982644222794” aria-controls=”common7769925982644222794”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7769925982644222794” aria-controls=”extreme7769925982644222794”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7769925982644222794”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>66.667</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>24.286</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3045</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.0387</td>

</tr> <tr>

<th>Mean</th> <td>10.538</td>

</tr> <tr>

<th>MAD</th> <td>16.352</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.592</td>

</tr> <tr>

<th>Sum</th> <td>23638</td>

</tr> <tr>

<th>Variance</th> <td>589.81</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7769925982644222794”>

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QQkKcdQLS5XLOvKezdVrGTkC29iJf%2B5CtfQIx24AtWCdOnNC4ceP07bff6m9/%2B5vCw8MlSY899pjKysoUFRUlSZo5c6a2b9%2But99%2BWzfccIOkX8rU6Y8QTxer06%2BvjezsbGVlZXmtpaamKj09/YKPy8liYiL9PcI5i46u/a87zg3Z2ot87UO29gmkbAOyYB0/flz33nuvvv/%2Bey1fvtzrXwu6XC5PuZKkoKAgtWjRQkePHlXDhg0l/fIvEU9/zHj6TFRsbGyt3z8lJUXJycleay5XhIqKTpzvIZ2R05q%2B6eO3U0hIsKKjw1VSUqqqKm7TYRLZ2ot87UO29rEzW3/95T7gClZ1dbXS0tJ08OBBrVy5Ui1btvTaPmzYMHXq1ElpaWme53/99de666671LBhQzVu3Fg5OTmegpWTk6PGjRuf08d7cXFxNZ7vdh9TZeUf%2BzekE4%2B/qqrakXM7Adnai3ztQ7b2CaRsA65gvfHGG9qyZYuee%2B45RUdHe85AXXTRRapbt66Sk5O1aNEitWnTRpdffrlWrFihY8eO6fbbb5ckDRkyRPPnz9ell14qSVqwYIFGjRrlt%2BMBAADOE3AF68MPP1R1dbXGjh3rtd6xY0etXLlSI0eOVHl5uebMmaPCwkLFx8dr2bJlno8NR48erR9//FFpaWkKCQnRHXfcoZEjR/rhSAAAgFMFWb%2B%2BUyds4XYfM75PlytYveZvML5fu7z/QBd/j1BrLlewYmIiVVR0ImBOV/9ekK29yNc%2BZGsfO7ONjb3Y6P5qy1lXSQMAADgABQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjPC9bgwYP12muv6dixY75%2BawAAAJ/wecHq3Lmznn/%2BeXXt2lUPPvigNm7cKMuyfD0GAACAbXxesCZMmKCPP/5Yzz77rEJCQjR%2B/Hh1795dTz75pPbv3%2B/rcQAAAIxz%2BeNNg4KC1KVLF3Xp0kWlpaVauXKlnn32WS1ZskQdOnTQiBEjdPPNN/tjNAAAgAvml4IlSQUFBXrnnXf0zjvv6JtvvlGHDh10%2B%2B2368iRI3r44Ye1bds2TZ8%2B3V/jAQAAnDefF6y3335bb7/9trZs2aJ69eppwIABevrpp9W8eXPPcxo1aqS5c%2BdSsAAAgCP5vGBNnz5dPXr00KJFi3TjjTcqOLjmZWAtWrTQ3Xff7evRAAAAjPB5wfr0008VExOj4uJiT7nauXOn2rZtq5CQEElShw4d1KFDB1%2BPBgAAYITP/xXh8ePH1bt3b73wwguetTFjxqh///46fPiwr8cBAAAwzucF669//asuu%2Bwy3XPPPZ619957T40aNVJGRoavxwEAADDO5wXrX//6l6ZMmaLY2FjPWr169TRp0iRt3rzZ1%2BMAAAAY5/OC5XK5VFJSUmO9tLSUO7oDAICA4POCdeONN2rOnDn6/vvvPWsHDhxQRkaGunXr5utxAAAAjPP5vyKcPHmy7rnnHt1yyy2Kjo6WJJWUlKht27aaOnWqr8cBAAAwzudnsOrXr6%2B///3vWrJkicaOHavU1FQtXbpUq1ev9rouq7YqKirUr18/bdmyxbN24MABjRw5Uu3bt9ett96qjRs3er3ms88%2BU79%2B/RQfH6/hw4frwIEDXttffvlldevWTQkJCZo2bZpKS0vP72ABAMAfks8LliSFhISoW7duGjVqlIYPH64bbrhBQUFB57yf8vJyPfjgg8rLy/OsWZal1NRUNWjQQG%2B%2B%2Bab69%2B%2BvtLQ0HTp0SJJ06NAhpaamauDAgXrjjTdUr149jRs3znP914cffqisrCzNnj1by5cvV25urjIzM80cOAAA%2BEPw%2BUeEbrdbTz31lLZv365Tp07VuLD9H//4R632s3fvXk2YMKHG6zdv3qwDBw7otddeU0REhFq2bKlNmzbpzTff1Pjx47V69Wpdc801GjVqlCQpIyNDXbp00datW9WpUyetWLFCI0aMUI8ePSRJs2bN0ujRozVx4kSFh4cbSAAAAAQ6nxesRx55RLt27VLfvn118cUXn/d%2BTheiv/zlL2rfvr1nPTc3V1dffbUiIiI8a4mJidqxY4dne1JSkmdbeHi42rZtqx07digpKUlffPGF0tLSPNvbt2%2BvU6dOac%2BePUpISDjveQEAwB%2BHzwvW5s2b9eKLL3qVnPMxdOjQM6673W7FxcV5rdWvX19Hjhw56/aSkhKVl5d7bXe5XKpbt67n9QAAAGfj84IVERGh%2BvXr27b/0tJShYaGeq2FhoaqoqLirNvLyso8j3/r9bVRUFAgt9vtteZyRdQodhcqJMQvl9CdN5fLOfOeztZpGTsB2dqLfO1DtvYJxGx9XrD69%2B%2BvF198UbNnz/b8cGeTwsLCVFxc7LVWUVGhOnXqeLb/uixVVFQoOjpaYWFhnse/3n4u119lZ2crKyvLay01NVXp6em13kcgiomJ9PcI5yw6muvu7EK29iJf%2B5CtfQIpW58XrOLiYq1bt07//Oc/1axZsxpni1asWHFB%2B2/YsKH27t3rtVZYWOg5e9SwYUMVFhbW2N6mTRvVrVtXYWFhKiwsVMuWLSVJlZWVKi4uPqdbSKSkpCg5OdlrzeWKUFHRifM5pN/ktKZv%2BvjtFBISrOjocJWUlKqqqtrf4wQUsrUX%2BdqHbO1jZ7b%2B%2Bsu9zwuWJPXr18%2B2fcfHx2vJkiUqKyvznLXKyclRYmKiZ3tOTo7n%2BaWlpdq9e7fS0tIUHBysdu3aKScnR506dZIk7dixQy6XS61bt671DHFxcTU%2BDnS7j6my8o/9G9KJx19VVe3IuZ2AbO1FvvYhW/sEUrY%2BL1gZGRm27r9jx45q1KiRpk6dqnHjxunjjz/Wzp07Pe87aNAgLV26VEuWLFGPHj20aNEiNW3a1FOohg4dqhkzZqhVq1aKi4vTzJkzdeedd3KLBgAAUGt%2B%2BYypoKBAWVlZmjBhgn788Ud98MEH2rdvn5F9h4SE6Nlnn5Xb7dbAgQP1zjvvaNGiRWrcuLEkqWnTpnrmmWf05ptv6o477lBxcbEWLVrkudFp3759NXbsWM2YMUOjRo3Stddeq4kTJxqZDQAA/DEEWb%2B%2BU6fNvvvuO915552KiorS0aNH9f777yszM1MbNmzQyy%2B/rPj4eF%2BO4zNu9zHj%2B3S5gtVr/gbj%2B7XL%2Bw908fcIteZyBSsmJlJFRScC5nT17wXZ2ot87UO29rEz29jY87/n5oXw%2BRmsxx9/XD179tT69et10UUXSZKeeOIJJScna/78%2Bb4eBwAAwDifF6zt27frnnvu8frZgy6XS%2BPGjdPu3bt9PQ4AAIBxPi9Y1dXVqq6uefrvxIkTttwXCwAAwNd8XrC6du2qxYsXe5Ws4uJiZWZmqnPnzr4eBwAAwDifF6wpU6Zo165d6tq1q8rLy3X//ferR48eOnjwoCZPnuzrcQAAAIzz%2BX2wGjZsqDVr1mjdunX66quvVF1drSFDhqh///6Kiory9TgAAADG%2BeVO7uHh4Ro8eLA/3hoAAMB2Pi9Yw4cP/z%2B3X%2BjPIgQAAPA3nxesJk2aeD2urKzUd999p2%2B%2B%2BUYjRozw9TgAAADG/W5%2BFuGiRYt05MgRH08DAABgnl9%2BFuGZ9O/fX%2B%2B//76/xwAAALhgv5uC9fnnn3OjUQAAEBB%2BFxe5Hz9%2BXF9//bWGDh3q63EAAACM83nBaty4sdfPIZSkiy66SHfffbduu%2B02X48DAABgnM8L1uOPP%2B7rtwQAAPApnxesbdu21fq51113nY2TAAAA2MPnBWvYsGGejwgty/Ks/3otKChIX331la/HAwAAuGA%2BL1jPP/%2B85syZo4kTJ6pjx44KDQ3VF198odmzZ%2Bv222/Xrbfe6uuRAAAAjPL5bRoyMjI0Y8YM3XLLLYqJiVFkZKQ6d%2B6s2bNn69VXX1WTJk08XwAAAE7k84JVUFBwxvIUFRWloqIiX48DAABgnM8LVvv27fXEE0/o%2BPHjnrXi4mJlZmbq%2Buuv9/U4AAAAxvn8GqyHH35Yw4cP14033qjmzZvLsix9%2B%2B23io2N1YoVK3w9DgAAgHE%2BL1gtW7bUe%2B%2B9p3Xr1ik/P1%2BSdNddd6lv374KDw/39TgAAADG%2BbxgSdIll1yiwYMH6%2BDBg2rWrJmkX%2B7mDgAAEAh8fg2WZVmaP3%2B%2BrrvuOvXr109HjhzR5MmTNX36dJ06dcrX4wAAABjn84K1cuVKvf3223r00UcVGhoqSerZs6fWr1%2BvrKwsX48DAABgnM8LVnZ2tmbMmKGBAwd67t5%2B6623as6cOVq7dq2vxwEAADDO5wXr4MGDatOmTY311q1by%2B12%2B3ocAAAA43xesJo0aaIvvviixvqnn37queAdAADAyXz%2BrwhHjx6tWbNmye12y7Isbdq0SdnZ2Vq5cqWmTJni63EAAACM83nBGjRokCorK/Xcc8%2BprKxMM2bMUL169fTAAw9oyJAhvh4HAADAOJ8XrHXr1ql3795KSUnRTz/9JMuyVL9%2BfV%2BPAQAAYBufX4M1e/Zsz8Xs9erVs6VcvfXWW7rqqqtqfLVu3VqSdP/999fY9vHHH3te//LLL6tbt25KSEjQtGnTVFpaanxGAAAQuHx%2BBqt58%2Bb65ptvdMUVV9j2Hrfeequ6devmeVxZWakRI0aoe/fukqT8/PwaP1z6kksukSR9%2BOGHysrKUmZmpurXr6%2BpU6cqMzNTM2bMsG1eAAAQWHxesFq3bq2HHnpIL774opo3b66wsDCv7RkZGRf8HnXq1FGdOnU8jxcvXizLsvTQQw%2BpoqJCBw8eVLt27RQbG1vjtStWrNCIESPUo0cPSdKsWbM0evRoTZw4kZ%2BVCAAAasXnBWv//v1KTEyUJJ/c96q4uFgvvPCC5syZo9DQUO3Zs0dBQUFnvCVEVVWVvvjiC6WlpXnW2rdvr1OnTmnPnj1KSEiwfV4AAOB8PilY8%2BbNU1pamiIiIrRy5UpfvKXHq6%2B%2Bqri4OPXu3VuStG/fPkVFRWnSpEnaunWrLr30Uo0fP1433XSTSkpKVF5erri4OM/rXS6X6tatqyNHjtT6PQsKCmqUR5crwmu/JoSE%2BPwSugvicjln3tPZOi1jJyBbe5GvfcjWPoGYrU8K1rJlyzR69GhFRER41saMGaM5c%2BYYLx3/zrIsrV69Wvfee69nbd%2B%2BfSorK1PXrl01ZswYffTRR7r//vuVnZ2tBg0aSJLnZySeFhoaqoqKilq/b3Z2do2fq5iamqr09PQLOBrni4mJ9PcI5yw6mo%2BF7UK29iJf%2B5CtfQIpW58ULMuyaqxt27ZN5eXltr7vF198oaNHj6pv376etXHjxmnYsGGei9pbt26tL7/8Uq%2B//rr%2B8pe/SFKNMlVRUXFO11%2BlpKQoOTnZa83lilBR0YnzPZQzclrTN338dgoJCVZ0dLhKSkpVVVXt73ECCtnai3ztQ7b2sTNbf/3l3ufXYPnShg0blJSU5ClTkhQcHOz1WJJatGihvXv3qm7dugoLC1NhYaFatmwp6Zd/gVhcXHzGC%2BJ/S1xcXI0zc273MVVW/rF/Qzrx%2BKuqqh05txOQrb3I1z5ka59AytZZp0DO0c6dO9WhQwevtSlTpmjq1Klea3v27FGLFi0UHBysdu3aKScnx7Ntx44dcrlcnntoAQAAnI3PClZQUJCv3sojLy%2Bvxv22kpOTtXbtWq1Zs0bfffedsrKylJOTo7vvvluSNHToUC1dulTr16/Xzp07NXPmTN15553cogEAANSazz4inDNnjtc9r06dOqXMzExFRnp/NmriPlinFRYWKjo62mvt5ptv1qOPPqrnnntOhw4d0pVXXqkXX3xRTZs2lST17dtXP/zwg2bMmKGKigrdfPPNmjhxorGZAABA4AuyznQFumHDhg2r9XN9fRsHX3G7jxnfp8sVrF7zNxjfr13ef6CLv0eoNZcrWDExkSoqOhEw1wP8XpCtvcjXPmRrHzuzjY292Oj%2BassnZ7ACtTQBAACcSUBf5A4AAOAPFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAAA3K0JpAAAUA0lEQVTDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLGAL1kcffaSrrrrK6ys9PV2StHv3bg0ePFjx8fEaNGiQdu3a5fXadevWqWfPnoqPj1dqaqp%2B%2BuknfxwCAABwqIAtWHv37lWPHj20ceNGz9ecOXN08uRJjRkzRklJSXrrrbeUkJCgsWPH6uTJk5KknTt3avr06UpLS1N2drZKSko0depUPx8NAABwkoAtWPn5%2BWrVqpViY2M9X9HR0XrvvfcUFhamSZMmqWXLlpo%2BfboiIyP1wQcfSJJWrVqlPn36aMCAAWrdurXmzZunTz75RAcOHPDzEQEAAKcI6ILVvHnzGuu5ublKTExUUFCQJCkoKEgdOnTQjh07PNuTkpI8z2/UqJEaN26s3Nxcn8wNAACcz%2BXvAexgWZb279%2BvjRs3avHixaqqqlLv3r2Vnp4ut9utK664wuv59evXV15eniSpoKBAcXFxNbYfOXKk1u9fUFAgt9vtteZyRdTY74UKCXFWP3a5nDPv6WydlrETkK29yNc%2BZGufQMw2IAvWoUOHVFpaqtDQUD311FM6ePCg5syZo7KyMs/6vwsNDVVFRYUkqays7P/cXhvZ2dnKysryWktNTfVcZP9HFRMT6e8Rzll0dLi/RwhYZGsv8rUP2donkLINyILVpEkTbdmyRZdccomCgoLUpk0bVVdXa%2BLEierYsWONslRRUaE6depIksLCws64PTy89r/oKSkpSk5O9lpzuSJUVHTiPI/ozJzW9E0fv51CQoIVHR2ukpJSVVVV%2B3ucgEK29iJf%2B5CtfezM1l9/uQ/IgiVJdevW9XrcsmVLlZeXKzY2VoWFhV7bCgsLPR/fNWzY8IzbY2Nja/3ecXFxNT4OdLuPqbLyj/0b0onHX1VV7ci5nYBs7UW%2B9iFb%2BwRSts46BVJLGzZsUKdOnVRaWupZ%2B%2Bqrr1S3bl0lJibq888/l2VZkn65Xmv79u2Kj4%2BXJMXHxysnJ8fzusOHD%2Bvw4cOe7QAAAGcTkAUrISFBYWFhevjhh7Vv3z598sknmjdvnu6991717t1bJSUlmjt3rvbu3au5c%2BeqtLRUffr0kSQNGTJEb7/9tlavXq09e/Zo0qRJ6t69u5o1a%2BbnowIAAE4RkAUrKipKS5cu1U8//aRBgwZp%2BvTpSklJ0b333quoqCgtXrxYOTk5GjhwoHJzc7VkyRJFRERI%2BqWczZ49W4sWLdKQIUN0ySWXKCMjw89HBAAAnCTIOv1ZGWzldh8zvk%2BXK1i95m8wvl%2B7vP9AF3%2BPUGsuV7BiYiJVVHQiYK4H%2BL0gW3uRr33I1j52Zhsbe7HR/dVWQJ7BAgAA8CcKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADAsYAvW0aNHlZ6ero4dO6pbt27KyMhQeXm5JGnOnDm66qqrvL5WrVrlee26devUs2dPxcfHKzU1VT/99JO/DgMAADiQy98D2MGyLKWnpys6OlqvvPKKfv75Z02bNk3BwcGaPHmy8vPzNWHCBN1%2B%2B%2B2e10RFRUmSdu7cqenTp2vWrFlq3bq15s6dq6lTp2rx4sX%2BOhwAAOAwAXkGa9%2B%2BfdqxY4cyMjJ05ZVXKikpSenp6Vq3bp0kKT8/X1dffbViY2M9X%2BHh4ZKkVatWqU%2BfPhowYIBat26tefPm6ZNPPtGBAwf8eUgAAMBBArJgxcbG6sUXX1SDBg281o8fP67jx4/r6NGjat68%2BRlfm5ubq6SkJM/jRo0aqXHjxsrNzbVzZAAAEEACsmBFR0erW7dunsfV1dVatWqVOnfurPz8fAUFBen555/XjTfeqNtuu01///vfPc8tKChQXFyc1/7q16%2BvI0eO%2BGx%2BAADgbAF5DdavZWZmavfu3XrjjTf05ZdfKigoSC1atNDdd9%2Btbdu26ZFHHlFUVJR69eqlsrIyhYaGer0%2BNDRUFRUVtX6/goICud1urzWXK6JGcbtQISHO6scul3PmPZ2t0zJ2ArK1F/nah2ztE4jZBnzByszM1PLly/Xkk0%2BqVatWuvLKK9WjRw/VrVtXktS6dWt9%2B%2B23evXVV9WrVy%2BFhYXVKFMVFRWea7RqIzs7W1lZWV5rqampSk9Pv/ADcrCYmEh/j3DOoqNr/%2BuOc0O29iJf%2B5CtfQIp24AuWI899pheffVVZWZm6pZbbpEkBQUFecrVaS1atNDmzZslSQ0bNlRhYaHX9sLCQsXGxtb6fVNSUpScnOy15nJFqKjoxPkcxm9yWtM3ffx2CgkJVnR0uEpKSlVVVe3vcQIK2dqLfO1DtvaxM1t//eU%2BYAtWVlaWXnvtNT3xxBPq3bu3Z33hwoX6/PPP9fLLL3vW9uzZoxYtWkiS4uPjlZOTo4EDB0qSDh8%2BrMOHDys%2BPr7W7x0XF1fj40C3%2B5gqK//YvyGdePxVVdWOnNsJyNZe5GsfsrVPIGXrrFMgtZSfn69nn31W9913nxITE%2BV2uz1fPXr00LZt27R06VJ9//33%2Btvf/qY1a9Zo1KhRkqQhQ4bo7bff1urVq7Vnzx5NmjRJ3bt3V7Nmzfx8VAAAwCkC8gzWP/7xD1VVVem5557Tc88957Xt66%2B/1sKFC/X0009r4cKFatKkiRYsWKCEhARJUkJCgmbPnq2nn35aP//8s7p06aLHHnvMH4cBAAAcKsiyLMvfQ/wRuN3HjO/T5QpWr/kbjO/XLu8/0MXfI9SayxWsmJhIFRWdCJjT1b8XZGsv8rUP2drHzmxjYy82ur/aCsiPCAEAAPyJggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjL3wMAv1dJ0z/w9wjn5P0Huvh7BADA/%2BIMFgAAgGGcwQIAIED1eep//D1Crf1rbm9/j2AUBQsAzsJJf0hJgfcHFeBEfEQIAABgGAULAADAMAoWAACAYRQsAAAAw7jIHT7jtAuFncZJ%2BXIRNoBAxxksAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCdQbl5eWaNm2akpKS1LVrV7300kv%2BHgkAADgIt2k4g3nz5mnXrl1avny5Dh06pMmTJ6tx48bq3Zt/Wg7g9y9p%2Bgf%2BHqHW3n%2Bgi79HAGxBwfqVkydPavXq1XrhhRfUtm1btW3bVnl5eXrllVcoWIAhTioAAHA%2BKFi/smfPHlVWViohIcGzlpiYqOeff17V1dUKDuZTVQAwxUk3yJW4SS5qj4L1K263WzExMQoNDfWsNWjQQOXl5SouLla9evXOuo%2BCggK53W6vNZcrQnFxcUZnDQmh7AGAr/G91z6BlC0F61dKS0u9ypUkz%2BOKiopa7SM7O1tZWVlea2lpaRo/fryZIf9XQUGBRlyap5SUFOPl7Y%2BuoKBA2dnZZGsDsrUX%2BdqnoKBAzzzzjKOydcoZNydmezaBUxUNCQsLq1GkTj%2BuU6dOrfaRkpKit956y%2BsrJSXF%2BKxut1tZWVk1zpbhwpGtfcjWXuRrH7K1TyBmyxmsX2nYsKGKiopUWVkpl%2BuXeNxut%2BrUqaPo6Oha7SMuLi5gGjgAADh3nMH6lTZt2sjlcmnHjh2etZycHLVr144L3AEAQK3QGH4lPDxcAwYM0MyZM7Vz506tX79eL730koYPH%2B7v0QAAgEOEzJw5c6a/h/i96dy5s3bv3q0FCxZo06ZN%2BvOf/6xBgwb5e6wzioyMVMeOHRUZGenvUQIO2dqHbO1FvvYhW/sEWrZBlmVZ/h4CAAAgkPARIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwCpZDlZeXa9q0aUpKSlLXrl310ksv%2BXskxzp69KjS09PVsWNHdevWTRkZGSovL5ckHThwQCNHjlT79u116623auPGjX6e1rnGjBmjKVOmeB7v3r1bgwcPVnx8vAYNGqRdu3b5cTrnqaio0KxZs3Tdddfphhtu0BNPPKHT940m2wt3%2BPBhjR07Vh06dFBycrJefvllzzbyPT8VFRXq16%2BftmzZ4lk72/fYzz77TP369VN8fLyGDx%2BuAwcO%2BHrs80bBcqh58%2BZp165dWr58uR599FFlZWXpgw8%2B8PdYjmNZltLT01VaWqpXXnlFTz75pD7%2B%2BGM99dRTsixLqampatCggd588031799faWlpOnTokL/Hdpx3331Xn3zyiefxyZMnNWbMGCUlJemtt95SQkKCxo4dq5MnT/pxSmeZM2eOPvvsMy1dulQLFizQ66%2B/ruzsbLI15IEHHlBERITeeustTZs2TU899ZQ%2B%2Bugj8j1P5eXlevDBB5WXl%2BdZO9v32EOHDik1NVUDBw7UG2%2B8oXr16mncuHFyzA%2BgseA4J06csNq1a2dt3rzZs7Zo0SLr7rvv9uNUzrR3716rVatWltvt9qytXbvW6tq1q/XZZ59Z7du3t06cOOHZNmLECOvpp5/2x6iOVVRUZN14443WoEGDrMmTJ1uWZVmrV6%2B2kpOTrerqasuyLKu6utrq1auX9eabb/pzVMcoKiqyrr76amvLli2etcWLF1tTpkwhWwOKi4utVq1aWV9//bVnLS0tzZo1axb5noe8vDzrtttus/7jP/7DatWqlefPrrN9j33qqae8/lw7efKklZCQ4PVn3%2B8ZZ7AcaM%2BePaqsrFRCQoJnLTExUbm5uaqurvbjZM4TGxurF198UQ0aNPBaP378uHJzc3X11VcrIiLCs56YmKgdO3b4ekxH%2B6//%2Bi/1799fV1xxhWctNzdXiYmJCgoKkiQFBQWpQ4cOZFtLOTk5ioqKUseOHT1rY8aMUUZGBtkaUKdOHYWHh%2Butt97SqVOntG/fPm3fvl1t2rQh3/OwdetWderUSdnZ2V7rZ/sem5ubq6SkJM%2B28PBwtW3b1jFZU7AcyO12KyYmRqGhoZ61Bg0aqLy8XMXFxX6czHmio6PVrVs3z%2BPq6mqtWrVKnTt3ltvtVlxcnNfz69evryNHjvh6TMfatGmT/vWvf2ncuHFe62R7YQ4cOKAmTZpozZo16t27t/70pz9p0aJFqq6uJlsDwsLCNGPGDGVnZys%2BPl59%2BvTRjTfeqMGDB5PveRg6dKimTZum8PBwr/WzZen0rF3%2BHgDnrrS01KtcSfI8rqio8MdIASMzM1O7d%2B/WG2%2B8oZdffvmMOZNx7ZSXl%2BvRRx/VjBkzVKdOHa9tv/X/MNnWzsmTJ/Xdd9/ptddeU0ZGhtxut2bMmKHw8HCyNSQ/P189evTQPffco7y8PD322GO6/vrrydegs2Xp9KwpWA4UFhZW43%2Bw049//QcZai8zM1PLly/Xk08%2BqVatWiksLKzGGcGKigoyrqWsrCxdc801XmcIT/ut/4fJtnZcLpeOHz%2BuBQsWqEmTJpJ%2BuSD41Vdf1WWXXUa2F2jTpk1644039Mknn6hOnTpq166djh49queee07NmjUjX0PO9j32t75PREdH%2B2zGC8FHhA7UsGFDFRUVqbKy0rPmdrtVp04dx/yP93vz2GOPadmyZcrMzNQtt9wi6ZecCwsLvZ5XWFhY45Q1zuzdd9/V%2BvXrlZCQoISEBK1du1Zr165VQkIC2V6g2NhYhYWFecqVJF1%2B%2BeU6fPgw2Rqwa9cuXXbZZV6l6eqrr9ahQ4fI16CzZflb22NjY30244WgYDlQmzZt5HK5vC70y8nJUbt27RQczC/pucrKytJrr72mJ554Qn379vWsx8fH68svv1RZWZlnLScnR/Hx8f4Y03FWrlyptWvXas2aNVqzZo2Sk5OVnJysNWvWKD4%2BXp9//rnnn1tblqXt27eTbS3Fx8ervLxc%2B/fv96zt27dPTZo0IVsD4uLi9N1333mdPdm3b5%2BaNm1Kvgad7XtsfHy8cnJyPNtKS0u1e/dux2TNn8YOFB4ergEDBmjmzJnauXOn1q9fr5deeknDhw/392iOk5%2Bfr2effVb33XefEhMT5Xa7PV8dO3ZUo0aNNHXqVOXl5WnJkiXauXOn7rjjDn%2BP7QhNmjTRZZdd5vmKjIxUZGSkLrvsMvXu3VslJSWaO3eu9u7dq7lz56q0tFR9%2BvTx99iO0KJFC3Xv3l1Tp07Vnj17tGHDBi1ZskRDhgwhWwOSk5N10UUX6eGHH9b%2B/fv13//933r%2B%2Bec1bNgw8jXobN9jBw0apO3bt2vJkiXKy8vT1KlT1bRpU3Xq1MnPk9eSP%2B8RgfN38uRJa9KkSVb79u2trl27WsuWLfP3SI60ePFiq1WrVmf8sizL%2Bvbbb6277rrLuuaaa6y%2Bffta//M//%2BPniZ1r8uTJnvtgWZZl5ebmWgMGDLDatWtn3XHHHdaXX37px%2Bmcp6SkxJo4caLVvn176/rrr7eeeeYZz72ZyPbC5eXlWSNHjrQ6dOhg9ezZ01q2bBn5GvDv98GyrLN/j/3nP/9p3Xzzzda1115rjRgxwvr%2B%2B%2B99PfJ5C7Isp9wSFQAAwBn4iBAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPt/ATQ1KY/7bQoAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7769925982644222794”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>90</td> <td class=”number”>2.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.3333333333333</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.6666666666667</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.6666666666667</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.2857142857143</td> <td class=”number”>13</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.5</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (73)</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>967</td> <td class=”number”>30.1%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7769925982644222794”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1730</td> <td class=”number”>53.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2258064516129</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.57142857142857</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.63636363636364</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>73.9130434782609</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.8571428571429</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>98</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FREE_LUNCH09”>PCT_FREE_LUNCH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3044</td>

</tr> <tr>

<th>Unique (%)</th> <td>94.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>41.304</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>99.478</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2104237901639508353”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2104237901639508353,#minihistogram2104237901639508353”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2104237901639508353”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2104237901639508353”

aria-controls=”quantiles2104237901639508353” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2104237901639508353” aria-controls=”histogram2104237901639508353”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2104237901639508353” aria-controls=”common2104237901639508353”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2104237901639508353” aria-controls=”extreme2104237901639508353”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2104237901639508353”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>17.981</td>

</tr> <tr>

<th>Q1</th> <td>29.525</td>

</tr> <tr>

<th>Median</th> <td>40.092</td>

</tr> <tr>

<th>Q3</th> <td>50.833</td>

</tr> <tr>

<th>95-th percentile</th> <td>69.829</td>

</tr> <tr>

<th>Maximum</th> <td>99.478</td>

</tr> <tr>

<th>Range</th> <td>99.478</td>

</tr> <tr>

<th>Interquartile range</th> <td>21.308</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16.219</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39268</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.626</td>

</tr> <tr>

<th>Mean</th> <td>41.304</td>

</tr> <tr>

<th>MAD</th> <td>12.783</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.62192</td>

</tr> <tr>

<th>Sum</th> <td>126270</td>

</tr> <tr>

<th>Variance</th> <td>263.07</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2104237901639508353”>

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DanA12u73TihH%2B%2BEU%2BebPXbtgEoZL%2B/ODb4F/31n3Dobeif5DyF1NRURUVF6Z577tHevXv19ttva%2BHChbr55ps1btw4NTU1acGCBdqzZ48WLFggt9ut7OxsSVJ%2Bfr7Wr1%2Bv1atXa/fu3Zo5c6ZGjx6tvn37BnivAABAqAjLgNW1a1c99dRTOnLkiHJzc1VSUqK8vDzdfPPN6tq1q5YtW%2BZdpXI6naqoqFBsbKykf4WzefPmqby8XPn5%2BerWrZtKS0sDvEcAACCU2CzLsgJdxC%2BBy/Wd8W3a7V2UkBCnhoZjIb%2BUGoyyl7wX6BIQJF69/fJAl9ApHBv8i/76jz96m5R0jpHtdFZYrmABAAAEEgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOCLmBNmTJFq1at0nffmf/jyAAAAD%2BHoAtYl156qZ544gmNGDFCd9xxh7Zs2SLLsgJdFgAAQIcFXcD661//qjfffFNLly5VRESEbrvtNo0ePVqPPPKI9u3bF%2BjyAAAA2mUPdAGnYrPZdPnll%2Bvyyy%2BX2%2B3WihUrtHTpUlVUVCgtLU3XX3%2B9/vCHPwS6TAAAgFMKyoAlSXV1ddqwYYM2bNigL7/8UmlpafrTn/6kQ4cO6Z577tFHH32kkpKSQJcJAADQRtAFrPXr12v9%2BvX64IMP1L17d02aNEmPPvqo%2Bvfv731Mz549tWDBAgIWAAAISkEXsEpKSjRmzBiVl5dr1KhR6tKl7cfEzj//fF177bUBqA4AAKB9QRew3nnnHSUkJKixsdEbrnbu3KkhQ4YoIiJCkpSWlqa0tLRAlgkAAPCTgu63CI8ePapx48bpySef9I5NmzZNEydO1MGDBwNYGQAAQMcEXcB64IEH1K9fP914443esc2bN6tnz54qLS0NYGUAAAAdE3QB6%2BOPP9bdd9%2BtpKQk71j37t01c%2BZMvf/%2B%2BwGsDAAAoGOC7jNYdrtdTU1NbcbdbjdXdAcQENlL3gt0CZ3y%2Bp0jA10C8IsXdCtYo0aN0vz58/X11197x2pra1VaWqqRIzloAACA4Bd0K1izZs3SjTfeqCuvvFLx8fGSpKamJg0ZMkTFxcUBrg4AAKB9QRewEhMT9fLLL2vr1q2qqamR3W7XBRdcoMsuu0w2my3Q5QEAALQr6AKWJEVERGjkyJGcEgQAACEp6AKWy%2BXSkiVLtH37dn3//fdtPtj%2Bj3/8I0CVAQAAdEzQBaz//u//1q5du3TVVVfpnHPOCXQ5AAAAnRZ0Aev999/X8uXLlZGREehSAAAAzkjQXaYhNjZWiYmJxrY3bdo03X333d771dXVmjJlihwOh3Jzc7Vr1y6fx2/atEljx46Vw%2BFQQUGBjhw5YqwWAADwyxB0AWvixIlavny5Tp48edbbeuWVV/T222977zc3N2vatGnKyMjQ2rVrlZqaqunTp6u5uVnSv/6odElJiQoLC1VZWammpiYuDQEAADot6E4RNjY2atOmTXrrrbfUt29fRUZG%2Bsz//e9/7/B2Fi5cqKFDh3rHNm/erKioKM2cOVM2m00lJSV655139NprryknJ0crV65Udna2Jk2aJElauHChxowZo9raWvXt29fcTgIAgLAWdAFLkiZMmHDW23jwwQc1ceJE1dXVececTqfS09O919Oy2WxKS0vTjh07lJOTI6fTqVtuucX7%2BJ49e6pXr15yOp0ELAAA0GFBF7BKS0vPehvbtm3Txx9/rI0bN2ru3LnecZfLpQsuuMDnsYmJiaqpqZEk1dXVKTk5uc38oUOHOvX6dXV1crlcPmN2e2ybbZ%2BtiIguPl8BQOLY4G/013/CqbdBF7CkfwWUF198Ufv27dPs2bP10UcfaeDAgTr//PPbfW5LS4vuvfdezZkzR9HR0T5zbre7zSnHyMhIeTweSdLx48dPO99RlZWVKisr8xkrKChQUVFRp7bTUfHxMX7ZLoDQ9MMxgWODf9Ff/wmH3gZdwNq/f7/%2B/Oc/q2vXrjp8%2BLBuv/12bd68WcXFxXr22WflcDhO%2B/yysjJddNFFp7wKfFRUVJuw5PF4vEHsp%2BZjYjr3D52Xl6esrCyfMbs9Vg0Nxzq1nfZERHRRfHyMmprcOnmy1ei2AYSupiY3xwY/4tjrP/7obUJCnJHtdFbQBay//e1vGjt2rObPn6%2B0tDRJ0sMPP6xZs2bpoYce0ooVK077/FdeeUX19fVKTU2VJG9g%2Bp//%2BR9NmDBB9fX1Po%2Bvr6/3nrpLSUk55XxSUlKn9iE5ObnN6UCX6zudOOGfb8STJ1v9tm0AoeeHH0wcG/yL/vpPOPQ26E5ybt%2B%2BXTfeeKPPH3a22%2B2aMWOGqqur233%2BihUrtHHjRq1bt07r1q1TVlaWsrKytG7dOjkcDn3yySfeP79jWZa2b9/uXRVzOByqqqrybuvgwYM6ePBgu6tmAAAA/y7oVrBaW1vV2to2tR47dkwRERHtPr93794%2B9%2BPi/rU02K9fPyUmJmrx4sVasGCB/uu//kurVq2S2%2B1Wdna2JCk/P1/XXXedhg0bpqFDh2rBggUaPXo0v0EIAAA6JehWsEaMGKFly5b5hKzGxkYtWrRIl1566Vltu2vXrlq2bJmqqqq8l2WoqKhQbGysJCk1NVXz5s1TeXm58vPz1a1bNyO/1QgAAH5ZbNYP58uCxOHDhzV16lR99913amxs1Pnnn69vv/1W5557rlauXNlmhSpUuFzfGd%2Bm3d5FCQlxamg4FvLnqoNR9pL3Al0CcEZev3MkxwY/4tjrP/7obVLSOUa201lBd4owJSVF69at06ZNm/T555%2BrtbVV%2Bfn5mjhxorp27Rro8gAAANoVdAFLkmJiYjRlypRAlwEAAHBGgi5gTZ069bTzHf1bhAAAAIESdAHrx5%2BxOnHihPbv368vv/xS119/fYCqAgAA6LigC1g/9Vt75eXlnf6bgAAAAIEQdJdp%2BCkTJ07Uq6%2B%2BGugyAAAA2hUyAeuTTz7p0IVGAQAAAi3oThGe6kPuR48e1RdffKGrr746ABUBAAB0TtAFrF69evn8HUJJ%2BtWvfqVrr71Wf/zjHwNUFQAAQMcFXcD629/%2BFugSAAAAzkrQBayPPvqow4%2B95JJL/FgJAADAmQm6gHXdddd5TxH%2B%2B59J/PGYzWbT559//vMXCAAA0I6gC1hPPPGE5s%2Bfr7vuukuZmZmKjIzUp59%2Bqnnz5ulPf/qTxo8fH%2BgSAQAATivoLtNQWlqqOXPm6Morr1RCQoLi4uJ06aWXat68eXrhhRfUu3dv7w0AACAYBV3AqqurO2V46tq1qxoaGgJQEQAAQOcEXcAaNmyYHn74YR09etQ71tjYqEWLFumyyy4LYGUAAAAdE3Sfwbrnnns0depUjRo1Sv3795dlWfrnP/%2BppKQk/f3vfw90eQAAAO0KuoA1YMAAbd68WZs2bdJXX30lSbrmmmt01VVXKSYmJsDVAQAAtC/oApYkdevWTVOmTNE333yjvn37SvrX1dwBAABCQdB9BsuyLD300EO65JJLNGHCBB06dEizZs1SSUmJvv/%2B%2B0CXBwAA0K6gC1grVqzQ%2BvXrde%2B99yoyMlKSNHbsWL3xxhsqKysLcHUAAADtC7qAVVlZqTlz5ignJ8d79fbx48dr/vz52rhxY4CrAwAAaF/QBaxvvvlGgwYNajN%2B4YUXyuVyBaAiAACAzgm6gNW7d299%2Bumnbcbfeecd7wfeAQAAglnQ/RbhX/7yF913331yuVyyLEvbtm1TZWWlVqxYobvvvjvQ5QEAALQr6AJWbm6uTpw4occff1zHjx/XnDlz1L17d91%2B%2B%2B3Kz88PdHkAAADtCrqAtWnTJo0bN055eXk6cuSILMtSYmJioMsCAADosKD7DNa8efO8H2bv3r074QoAAIScoAtY/fv315dffhnoMgAAAM5Y0J0ivPDCC3XnnXdq%2BfLl6t%2B/v6KionzmS0tLA1QZAABAxwTdCta%2BffuUnp6uuLg4uVwuffPNNz63jtq/f7/%2B8pe/KDU1VaNHj9by5cu9c7W1tbrhhhs0bNgwjR8/Xlu2bPF57tatWzVhwgQ5HA5NnTpVtbW1xvYPAACEv6BYwVq4cKEKCwsVGxurFStWnPX2WltbNW3aNA0dOlQvv/yy9u/frzvuuEMpKSmaMGGCCgoKNHDgQK1Zs0ZvvPGGCgsLtXnzZvXq1UsHDhxQQUGBbrvtNo0cOVLl5eWaMWOGNmzY4L2yPAAAwOkExQrWM888I7fb7TM2bdo01dXVndH26uvrNWjQIM2dO1f9%2B/fXFVdcocsuu0xVVVV6//33VVtbq3nz5mnAgAGaPn26hg0bpjVr1kiSVq9erYsuukg33XSTfv3rX6u0tFTffvutPvzww7PeTwAA8MsQFAHLsqw2Yx999JFaWlrOaHvJyclasmSJunbtKsuyVFVVpY8%2B%2BkiZmZlyOp0aPHiwYmNjvY9PT0/Xjh07JElOp1MZGRneuZiYGA0ZMsQ7DwAA0J6gOEXoT1lZWTpw4IDGjBmjK6%2B8Ug888ICSk5N9HpOYmKhDhw5Jklwu12nnO6Kurq7N302022PbbPdsRUR08fkKABLHBn%2Bjv/4TTr0N%2B4D16KOPqr6%2BXnPnzlVpaancbrciIyN9HhMZGSmPxyNJ7c53RGVlpcrKynzGCgoKVFRUdIZ7cXrx8TF%2B2S6A0PTDMYFjg3/RX/8Jh94GTcDy1wfIhw4dKklqaWnRnXfeqdzc3Daf9/J4PIqOjpYkRUVFtQlTHo9H8fHxHX7NvLw8ZWVl%2BYzZ7bFqaDh2JrvwkyIiuig%2BPkZNTW6dPNlqdNsAQldTk5tjgx9x7PUff/Q2ISHOyHY6K2gC1vz5832uefX9999r0aJFiovzbUxHroNVX1%2BvHTt2aOzYsd6xCy64QN9//72SkpK0d%2B/eNo//4fRdSkqK6uvr28wPGjSow/uSnJzc5nSgy/WdTpzwzzfiyZOtfts2gNDzww8mjg3%2BRX/9Jxx6GxQnOS%2B55JI217xKTU1VQ0PDGV0H65tvvlFhYaEOHz7sHdu1a5e6d%2B%2Bu9PR0ffbZZzp%2B/Lh3rqqqSg6HQ5LkcDhUVVXlnXO73aqurvbOAwAAtCcoVrBMXPvq3w0dOlRDhgzR7NmzVVxcrG%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%2B%2B9px49ekiSioqK9OCDD2rUqFGqra3VqlWrFBsbqwEDBmjbtm1as2aNbrvtNq1evVoXXXSRbrrpJklSaWmpLr/8cn344YcaPnx4IHcLAACEiLBcwUpKStLy5cu94eoHR48eldPp1ODBgxUbG%2BsdT09P144dOyRJTqdTGRkZ3rmYmBgNGTLEOw8AANCesAxY8fHxGjlypPd%2Ba2urVq5cqUsvvVQul0vJyck%2Bj09MTNShQ4ckqd15AACA9oTlKcIfW7Rokaqrq/XSSy/p2WefVWRkpM98ZGSkPB6PJMntdp92viPq6urkcrl8xuz22DbB7WxFRHTx%2BQoAocZuD73jF8de/wke/XuYAAAQ0ElEQVSn3oZ9wFq0aJGee%2B45PfLIIxo4cKCioqLU2Njo8xiPx6Po6GhJUlRUVJsw5fF4FB8f3%2BHXrKysVFlZmc9YQUGBioqKznAvTi8%2BPsYv2wUAf0tIiAt0CWeMY6//hENvwzpg3X///XrhhRe0aNEiXXnllZKklJQU7dmzx%2Bdx9fX13tWllJQU1dfXt5kfNGhQh183Ly9PWVlZPmN2e6waGo6dyW78pIiILoqPj1FTk1snT7Ya3bY//P6hdwNdAoAgY/q4%2BHMItWNvKPFHbwMV4sM2YJWVlWnVqlV6%2BOGHNW7cOO%2B4w%2BFQRUWFjh8/7l21qqqqUnp6une%2BqqrK%2B3i3263q6moVFhZ2%2BLWTk5PbnA50ub7TiRP%2B%2BUY8ebLVb9sGAH8K5WMXx17/CYfehv5JzlP46quvtHTpUt1yyy1KT0%2BXy%2BXy3jIzM9WzZ08VFxerpqZGFRUV2rlzpyZPnixJys3N1fbt21VRUaGamhoVFxerT58%2BXKIBAAB0WFgGrH/84x86efKkHn/8cY0YMcLnFhERoaVLl8rlciknJ0cbNmxQeXm5evXqJUnq06ePHnvsMa1Zs0aTJ09WY2OjysvLZbPZArxXAAAgVNgsLlH%2Bs3C5vjO%2BTbu9ixIS4tTQcCwkllKzl7wX6BIABJlXb7880CV0Wqgde0OJP3qblHSOke10VliuYAEAAAQSAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC/uA5fF4NGHCBH3wwQfesdraWt1www0aNmyYxo8fry1btvg8Z%2BvWrZowYYIcDoemTp2q2tran7tsAAAQwsI6YLW0tOiOO%2B5QTU2Nd8yyLBUUFKhHjx5as2aNJk6cqMLCQh04cECSdODAARUUFCgnJ0cvvfSSunfvrhkzZsiyrEDtBgAACDFhG7D27NmjP//5z/r66699xt9//33V1tZq3rx5GjBggKZPn65hw4ZpzZo1kqTVq1froosu0k033aRf//rXKi0t1bfffqsPP/wwELsBAABCUNgGrA8//FDDhw9XZWWlz7jT6dTgwYMVGxvrHUtPT9eOHTu88xkZGd65mJgYDRkyxDsPAADQHnugC/CXq6%2B%2B%2BpTjLpdLycnJPmOJiYk6dOhQh%2BYBAADaE7YB66e43W5FRkb6jEVGRsrj8XRoviPq6urkcrl8xuz22DbB7WxFRHTx%2BQoAocZuD73jF8de/wmn3v7iAlZUVJQaGxt9xjwej6Kjo73zPw5THo9H8fHxHX6NyspKlZWV%2BYwVFBSoqKjoDKs%2Bvfj4GL9sFwD8LSEhLtAlnDGOvf4TDr39xQWslJQU7dmzx2esvr7eu7qUkpKi%2Bvr6NvODBg3q8Gvk5eUpKyvLZ8xuj1VDw7EzrPrUIiK6KD4%2BRk1Nbp082Wp02wDwczB9XPw5cOz1H3/0NlAh/hcXsBwOhyoqKnT8%2BHHvqlVVVZXS09O981VVVd7Hu91uVVdXq7CwsMOvkZyc3OZ0oMv1nU6c8M834smTrX7bNgD4Uygfuzj2%2Bk849Db0T3J2UmZmpnr27Kni4mLV1NSooqJCO3fu1OTJkyVJubm52r59uyoqKlRTU6Pi4mL16dNHw4cPD3DlAAAgVPziVrAiIiK0dOlSlZSUKCcnR/369VN5ebl69eolSerTp48ee%2BwxPfDAAyovL1dqaqrKy8tls9kCXDkAhJ/sJe8FuoROefX2ywNdAkLELyJgffHFFz73%2B/Xrp5UrV/7k46%2B44gpdccUV/i4LAACEqV/cKUIAAAB/I2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwe6ALAAAA/pG95L1Al9Bhr95%2BeaBLMIoVLAAAAMNYwQIAoINCaUUIgcUKFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjQ%2B4hLqPktUCXAAAAfoQVLAAAAMMIWAAAAIYRsE6hpaVFs2fPVkZGhkaMGKGnn3460CUBAIAQwmewTmHhwoXatWuXnnvuOR04cECzZs1Sr169NG7cuECXBgAAQgAB60eam5u1evVqPfnkkxoyZIiGDBmimpoaPf/88wQsAADQIZwi/JHdu3frxIkTSk1N9Y6lp6fL6XSqtbU1gJUBAIBQwQrWj7hcLiUkJCgyMtI71qNHD7W0tKixsVHdu3dvdxt1dXVyuVw%2BY3Z7rJKTk43WGhFBPgYAhAe7vYv351o4/HwjYP2I2%2B32CVeSvPc9Hk%2BHtlFZWamysjKfscLCQt12221mivx/6urqdP3/V6O8vDzj4Q3/6m9lZSX99QN661/017/or//U1dXpueeWh0VvQz8iGhYVFdUmSP1wPzo6ukPbyMvL09q1a31ueXl5xmt1uVwqKytrs1oGM%2Biv/9Bb/6K//kV//SecessK1o%2BkpKSooaFBJ06ckN3%2Br/a4XC5FR0crPj6%2BQ9tITk4O%2BeQNAADOHCtYPzJo0CDZ7Xbt2LHDO1ZVVaWhQ4eqSxfaBQAA2kdi%2BJGYmBhNmjRJc%2BfO1c6dO/XGG2/o6aef1tSpUwNdGgAACBERc%2BfOnRvoIoLNpZdequrqai1evFjbtm3Trbfeqtzc3ECXdUpxcXHKzMxUXFxcoEsJS/TXf%2Bitf9Ff/6K//hMuvbVZlmUFuggAAIBwwilCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErBDV0tKi2bNnKyMjQyNGjNDTTz8d6JJC1uHDh1VUVKTMzEyNHDlSpaWlamlpkSTV1tbqhhtu0LBhwzR%2B/Hht2bIlwNWGtmnTpunuu%2B/23q%2BurtaUKVPkcDiUm5urXbt2BbC60OTxeHTffffpkksu0W9/%2B1s9/PDD%2BuH60fT37B08eFDTp09XWlqasrKy9Oyzz3rn6O%2BZ8Xg8mjBhgj744APvWHvH2q1bt2rChAlyOByaOnWqamtrf%2B6yO42AFaIWLlyoXbt26bnnntO9996rsrIyvfbaa4EuK%2BRYlqWioiK53W49//zzeuSRR/Tmm29qyZIlsixLBQUF6tGjh9asWaOJEyeqsLBQBw4cCHTZIemVV17R22%2B/7b3f3NysadOmKSMjQ2vXrlVqaqqmT5%2Bu5ubmAFYZeubPn6%2BtW7fqqaee0uLFi/Xiiy%2BqsrKS/hpy%2B%2B23KzY2VmvXrtXs2bO1ZMkSvf766/T3DLW0tOiOO%2B5QTU2Nd6y9Y%2B2BAwdUUFCgnJwcvfTSS%2BrevbtmzJihoP9DNBZCzrFjx6yhQ4da77//vnesvLzcuvbaawNYVWjas2ePNXDgQMvlcnnHNm7caI0YMcLaunWrNWzYMOvYsWPeueuvv9569NFHA1FqSGtoaLBGjRpl5ebmWrNmzbIsy7JWr15tZWVlWa2trZZlWVZra6v1%2B9//3lqzZk0gSw0pDQ0N1uDBg60PPvjAO7Zs2TLr7rvvpr8GNDY2WgMHDrS%2B%2BOIL71hhYaF133330d8zUFNTY/3xj3%2B0/vM//9MaOHCg92dYe8faJUuW%2BPx8a25utlJTU31%2BBgYjVrBC0O7du3XixAmlpqZ6x9LT0%2BV0OtXa2hrAykJPUlKSli9frh49eviMHz16VE6nU4MHD1ZsbKx3PD09XTt27Pi5ywx5Dz74oCZOnKgLLrjAO%2BZ0OpWeni6bzSZJstlsSktLo7%2BdUFVVpa5duyozM9M7Nm3aNJWWltJfA6KjoxUTE6O1a9fq%2B%2B%2B/1969e7V9%2B3YNGjSI/p6BDz/8UMOHD1dlZaXPeHvHWqfTqYyMDO9cTEyMhgwZEvS9JmCFIJfLpYSEBEVGRnrHevTooZaWFjU2NgawstATHx%2BvkSNHeu%2B3trZq5cqVuvTSS%2BVyuZScnOzz%2BMTERB06dOjnLjOkbdu2TR9//LFmzJjhM05/z15tba169%2B6tdevWady4cfrd736n8vJytba20l8DoqKiNGfOHFVWVsrhcCg7O1ujRo3SlClT6O8ZuPrqqzV79mzFxMT4jLfXy1DttT3QBaDz3G63T7iS5L3v8XgCUVLYWLRokaqrq/XSSy/p2WefPWWf6XHHtbS06N5779WcOXMUHR3tM/dT72P623HNzc3av3%2B/Vq1apdLSUrlcLs2ZM0cxMTH015CvvvpKY8aM0Y033qiamhrdf//9uuyyy%2BivQe31MlR7TcAKQVFRUW3eWD/c//EPMXTcokWL9Nxzz%2BmRRx7RwIEDFRUV1WZF0OPx0ONOKCsr00UXXeSzSviDn3of09%2BOs9vtOnr0qBYvXqzevXtL%2BtcHgl944QX169eP/p6lbdu26aWXXtLbb7%2Bt6OhoDR06VIcPH9bjjz%2Buvn370l9D2jvW/tSxIj4%2B/mer8UxwijAEpaSkqKGhQSdOnPCOuVwuRUdHB/0bLljdf//9euaZZ7Ro0SJdeeWVkv7V5/r6ep/H1dfXt1mqxk975ZVX9MYbbyg1NVWpqanauHGjNm7cqNTUVPprQFJSkqKiorzhSpLOO%2B88HTx4kP4asGvXLvXr188nNA0ePFgHDhygvwa118ufmk9KSvrZajwTBKwQNGjQINntdp8P%2BFVVVWno0KHq0oV/0s4qKyvTqlWr9PDDD%2Buqq67yjjscDn322Wc6fvy4d6yqqkoOhyMQZYakFStWaOPGjVq3bp3WrVunrKwsZWVlad26dXI4HPrkk0%2B8v2ptWZa2b99OfzvB4XCopaVF%2B/bt847t3btXvXv3pr8GJCcna//%2B/T6rJ3v37lWfPn3or0HtHWsdDoeqqqq8c263W9XV1UHfa34ah6CYmBhNmjRJc%2BfO1c6dO/XGG2/o6aef1tSpUwNdWsj56quvtHTpUt1yyy1KT0%2BXy%2BXy3jIzM9WzZ08VFxerpqZGFRUV2rlzpyZPnhzoskNG79691a9fP%2B8tLi5OcXFx6tevn8aNG6empiYtWLBAe/bs0YIFC%2BR2u5WdnR3oskPG%2Beefr9GjR6u4uFi7d%2B/Wu%2B%2B%2Bq4qKCuXn59NfA7KysvSrX/1K99xzj/bt26f//d//1RNPPKHrrruO/hrU3rE2NzdX27dvV0VFhWpqalRcXKw%2Bffpo%2BPDhAa68HYG8RgTOXHNzszVz5kxr2LBh1ogRI6xnnnkm0CWFpGXLllkDBw485c2yLOuf//yndc0111gXXXSRddVVV1nvvfdegCsObbNmzfJeB8uyLMvpdFqTJk2yhg4dak2ePNn67LPPAlhdaGpqarLuuusua9iwYdZll11mPfbYY95rM9Hfs1dTU2PdcMMNVlpamjV27FjrmWeeob8G/Pt1sCyr/WPtW2%2B9Zf3hD3%2BwLr74Yuv666%2B3vv7665%2B75E6zWVawXwoVAAAgtHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2B/8Bz4ouWaM5n94AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2104237901639508353”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.0</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.52941176470588</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>38.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.069930069930066</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.807692307692307</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.835051546391753</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.60923623445826</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3033)</td> <td class=”number”>3034</td> <td class=”number”>94.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2104237901639508353”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.45045045045045046</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7882182119892139</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2222222222222223</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7227999650441315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.29865575686733</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.3050193050193</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.31714719271623</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.3908067195865</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.47765525246662</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_FREE_LUNCH14”>PCT_FREE_LUNCH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2864</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>314</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>45.969</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6074419953544331960”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives6074419953544331960”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6074419953544331960”

aria-controls=”quantiles6074419953544331960” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6074419953544331960” aria-controls=”histogram6074419953544331960”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6074419953544331960” aria-controls=”common6074419953544331960”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6074419953544331960” aria-controls=”extreme6074419953544331960”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6074419953544331960”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>20.581</td>

</tr> <tr>

<th>Q1</th> <td>33.332</td>

</tr> <tr>

<th>Median</th> <td>44.134</td>

</tr> <tr>

<th>Q3</th> <td>55.676</td>

</tr> <tr>

<th>95-th percentile</th> <td>81.454</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>22.344</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>18.046</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39257</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.60302</td>

</tr> <tr>

<th>Mean</th> <td>45.969</td>

</tr> <tr>

<th>MAD</th> <td>14.006</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.68882</td>

</tr> <tr>

<th>Sum</th> <td>133130</td>

</tr> <tr>

<th>Variance</th> <td>325.66</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6074419953544331960”>

<img 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111%2BvsWPHqqmpScXFxdqzZ4%2BKi4vldrs1btw4SdK0adP08ssva%2B3atdq9e7fmzJmjkSNHqlevXkHeKwAAEC4iMmB16tRJjz32mL7//nvl5eWpqKhIU6dO1fXXX69OnTpp1apV3rNUTqdTZWVlSkhIkPRDOFu0aJFWrlypadOmqXPnziopKQnyHgEAgHBisyzLCnYRpwOX64DxNe32KCUnJ6qh4VDEnFINFeHY23Er3g92CQgRnxSPDavXbrgIx%2BNCuAhkb1NTf2V0PX9F5BksAACAYCJgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCzkAtaUKVO0Zs0aHThwINilAAAAnJSQC1hDhw7VI488ouHDh%2Bvmm2/Wli1bZFlWsMsCAADwW8gFrFtuuUVvv/22HnroIUVHR%2BvGG2/UyJEjdf/99%2Bubb74JdnkAAAAdsge7gGOx2WwaNmyYhg0bJrfbrWeeeUYPPfSQysrKNGTIEF199dW65JJLgl0mAADAMYVkwJKkuro6vfLKK3rllVf01VdfaciQIfrjH/%2Bo/fv364477tDHH3%2BsoqKiYJcJAADQTsgFrJdfflkvv/yyPvzwQ6WkpGjSpEl64IEHdOaZZ3q36datm4qLiwlYAAAgJIVcwCoqKtKoUaO0cuVKXXzxxYqKav82sbPOOktXXnmlX%2BvNmDFDKSkpuvvuuyVJlZWVWrBggb766iv17dtXCxcu1MCBA73bb9y4UStWrJDL5dLw4cO1ePFipaSkmNk5hJWsok3BLgEAEKZC7k3u7777rh544AE5HA5vuNq5c6eOHj3q3WbIkCG65ZZbOlzrtdde0%2BbNm72Pm5ubNWPGDGVlZWnDhg3KyMjQzJkz1dzc7H2eoqIiFRQUqLy8XE1NTSosLDS8hwAAINKFXMA6ePCgxo4dq0cffdQ7NmPGDE2cOFG1tbV%2Br9PY2KilS5dq0KBB3rHXX39dsbGxmjNnjvr06aOioiIlJiZq06YfzlQ8%2B%2ByzGjdunCZNmqRzzjlHS5cu1ebNm1VdXW1uBwEAQMQLuYB11113qXfv3rr22mu9Y6%2B//rq6deumkpISv9e55557NHHiRPXt29c75nQ6lZmZKZvNJumH31YcMmSIduzY4Z3Pysrybt%2BtWzd1795dTqfzVHcLAACcRkIuYH3yySe6/fbblZqa6h1LSUnRnDlz9MEHH/i1xrZt2/TJJ59o1qxZPuMul0tpaWk%2BY126dNH%2B/fsl/fCbi8ebBwAA8EfIvcndbrerqamp3bjb7fbrju4tLS1asGCB5s%2Bfr7i4uHZrxMTE%2BIzFxMTI4/FIkg4fPnzceX/V1dXJ5XL5jNntCe3C26mKjo7y%2BQxz6CnCHa9h8zjmBk4k9jbkAtbFF1%2BsJUuWaPny5frNb34jSaqurlZJSYlGjBjR4deXlpZq4MCBx9w2Nja2XVjyeDzeIPZz8/Hx8Se0D%2BXl5SotLfUZy8/P1%2BzZs09oHX8lJZ1YfQAiH8eFwKG3gRNJvQ25gDV37lxde%2B21GjNmjJKSkiRJTU1NGjBggF%2B/0ffaa6%2Bpvr5eGRkZkuQNTH/96181YcIE1dfX%2B2xfX1/vPbOUnp5%2BzPl/vlzpj6lTpyonJ8dnzG5PUEPDoRNapyPR0VFKSopXU5NbR4%2B2GV37dBdJ/4vC6YnjgnkccwMnkL1NTk40up6/Qi5gdenSRS%2B%2B%2BKK2bt2qqqoq2e129e3bVxdeeKH3zenH88wzz6i1tdX7%2BN5775Uk3Xrrrfr444/16KOPyrIs2Ww2WZal7du364YbbpAkORwOVVRUKDc3V5JUW1ur2tpaORyOE9qHtLS0dpcDXa4Dam0NzDfk0aNtAVsbQHjiuBA49DZwIqm3IRewJCk6OlojRozw65LgT/Xo0cPncWLiD8m1d%2B/e6tKli%2B677z4VFxfr3//937VmzRq53W6NGzdOkjRt2jRdddVVGjx4sAYNGqTi4mKNHDlSvXr1OvWdAgAAp42QC1gul0srVqzQ9u3bdeTIkXZvbP/b3/520mt36tRJq1at0oIFC/TCCy/o7LPPVllZmRISEiRJGRkZWrRokR544AH97//%2Br4YNG6bFixef0v4AAIDTT8gFrP/4j//Qrl27NH78eP3qV7865fV%2B/BM5PzrvvPP04osv/uz2ubm53kuEAAAAJyPkAtYHH3yg1atX%2B9zwEwAAIJyE3K9KJSQkqEuXLsEuAwAA4KSFXMCaOHGiVq9e7fPHnQEAAMJJyF0ibGxs1MaNG/XOO%2B%2BoV69e7e6s/vTTTwepMgAAAP%2BEXMCSpAkTJgS7BAAAgJMWcgGrpKQk2CUAAACckpB7D5b0wx9LLi0t1S233KL/%2BZ//0aZNm7R3795glwUAAOCXkAtY%2B/bt02WXXaYXX3xRf/3rX9Xc3KzXX39deXl5cjqdwS4PAACgQyEXsO6%2B%2B26NHj1ab731ls444wxJ0vLly5WTk%2BP9u4IAAAChLOQC1vbt23Xttdf6/GFnu92uWbNmqbKyMoiVAQAA%2BCfkAlZbW5va2tr/Je1Dhw4pOjo6CBUBAACcmJALWMOHD9eqVat8QlZjY6OWLVumoUOHBrEyAAAA/4RcwLr99tu1a9cuDR8%2BXC0tLfrLX/6iUaNG6dtvv9XcuXODXR4AAECHQu4%2BWOnp6XrppZe0ceNGffHFF2pra9O0adM0ceJEderUKdjlAQAAdCjkApYkxcfHa8qUKcEuAwAA4KSEXMCaPn36cef5W4QAACDUhVzA6tGjh8/j1tZW7du3T1999ZWuvvrqIFUFAADgv5ALWD/3twhXrlyp/fv3/8LVAAAAnLiQ%2By3CnzNx4kS98cYbwS4DAACgQ2ETsD799FNuNAoAAMJCyF0iPNab3A8ePKgvv/xSl19%2BeRAqAgAAODEhF7C6d%2B/u83cIJemMM87QlVdeqT/84Q9BqgoAAMB/IRew7r777mCXAAAAcEpCLmB9/PHHfm97/vnnB7ASAACAkxNyAeuqq67yXiK0LMs7/tMxm82mL7744pcvEAAAoAMhF7AeeeQRLVmyRLfddpuys7MVExOjzz77TIsWLdIf//hHXXrppcEuEQAA4LhC7jYNJSUlmj9/vsaMGaPk5GQlJiZq6NChWrRokZ5//nn16NHD%2BwEAABCKQi5g1dXVHTM8derUSQ0NDUGoCAAA4MSEXMAaPHiwli9froMHD3rHGhsbtWzZMl144YVBrAwAAMA/IfcerDvuuEPTp0/XxRdfrDPPPFOWZenvf/%2B7UlNT9fTTTwe7PAAAgA6FXMDq06ePXn/9dW3cuFFff/21JOmKK67Q%2BPHjFR8fH%2BTqAAAAOhZyAUuSOnfurClTpujbb79Vr169JP1wN3cAAIBwEHLvwbIsS/fee6/OP/98TZgwQfv379fcuXNVVFSkI0eOBLs8AACADoVcwHrmmWf08ssva8GCBYqJiZEkjR49Wm%2B99ZZKS0uDXB0AAEDHQi5glZeXa/78%2BcrNzfXevf3SSy/VkiVL9Oqrr/q9zr59%2B/SnP/1JGRkZGjlypFavXu2dq66u1jXXXKPBgwfr0ksv1ZYtW3y%2BduvWrZowYYIcDoemT5%2Bu6upqMzsHAABOCyEXsL799lv179%2B/3fg555wjl8vl1xptbW2aMWOGkpOT9eKLL2rhwoV6%2BOGH9eqrr8qyLOXn56tr165av369Jk6cqIKCAtXU1EiSampqlJ%2Bfr9zcXK1bt04pKSmaNWuWz5/tAQAAOJ6Qe5N7jx499Nlnn6lnz54%2B4%2B%2B%2B%2B673De8dqa%2BvV//%2B/XXnnXeqU6dOOvPMM3XhhReqoqJCXbt2VXV1tdasWaOEhAT16dNH27Zt0/r163XjjTdq7dq1GjhwoK677jpJP9xZftiwYfroo490wQUXGN9fAAAQeULuDNaf/vQnLVy4UE8//bQsy9K2bdt07733aunSpbrqqqv8WiMtLU0rVqxQp06dZFmWKioq9PHHHys7O1tOp1PnnnuuEhISvNtnZmZqx44dkiSn06msrCzvXHx8vAYMGOCdBwAA6EjIncHKy8tTa2urHn74YR0%2BfFjz589XSkqKbrrpJk2bNu2E18vJyVFNTY1GjRqlMWPG6K677lJaWprPNl26dNH%2B/fslSS6X67jz/qirq2t3OdNuT2i37qmKjo7y%2BQxz6CnCHa9h8zjmBk4k9jbkAtbGjRs1duxYTZ06Vd9//70sy1KXLl1Oer0HHnhA9fX1uvPOO1VSUiK32%2B397cQfxcTEyOPxSFKH8/4oLy9v9xuP%2Bfn5mj179knuxfElJXEDVgC%2BOC4EDr0NnEjqbcgFrEWLFuk///M/1blzZ6WkpJzyeoMGDZIktbS06NZbb1VeXp7cbrfPNh6PR3FxcZKk2NjYdmHK4/EoKSnJ7%2BecOnWqcnJyfMbs9gQ1NBw6mV34WdHRUUpKildTk1tHj7YZXft0F0n/i8LpieOCeRxzAyeQvU1OTjS6nr9CLmCdeeaZ%2Buqrr9S3b9%2BTXqO%2Bvl47duzQ6NGjvWN9%2B/bVkSNHlJqaqr1797bb/sfLd%2Bnp6aqvr283f6zfbPw5aWlp7S4HulwH1NoamG/Io0fbArY2gPDEcSFw6G3gRFJvQy5gnXPOObr11lu1evVqnXnmmYqNjfWZLykp6XCNb7/9VgUFBdq8ebPS09MlSbt27VJKSooyMzP1%2BOOP6/Dhw96zVhUVFcrMzJQkORwOVVRUeNdyu92qrKxUQUGBqV0EAAARLuSug3zzzTfKzMxUYmKiXC6Xvv32W58PfwwaNEgDBgzQvHnztGfPHm3evFnLli3TDTfcoOzsbHXr1k2FhYWqqqpSWVmZdu7cqcmTJ0v64U3227dvV1lZmaqqqlRYWKiePXtyiwYAAOA3mxUCd9BcunSpCgoKfG6dcKq%2B%2B%2B47LV68WNu2bVN8fLyuvPJKzZw5UzabTfv27VNRUZGcTqd69%2B6tefPm6aKLLvJ%2B7ebNm3XXXXdp//79ysjI0OLFi/2%2BB9fPcbkOnOoutWO3Ryk5OVENDYci5pRqqLDbo/T7e98LdhnASfmkeCzHhQDgmBs4gextauqvjK7nr5AIWP3799eWLVt8fltwxowZWrJkifFbGwQLASu8ELAQzghYgcExN3AiMWCFxCXCY2W8jz/%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%2B5ublat26dUlJSNGvWLFmWFeS9AgAA4cIe7AICYe/evdqxY4fef/99de3aVZI0e/Zs3XPPPbr44otVXV2tNWvWKCEhQX369NG2bdu0fv163XjjjVq7dq0GDhyo6667TpJUUlKiYcOG6aOPPtIFF1wQzN0CAABhIiLPYKWmpmr16tXecPWjgwcPyul06txzz1VCQoJ3PDMzUzt27JAkOZ1OZWVleefi4%2BM1YMAA7zwAAEBHIvIMVlJSkkaMGOF93NbWpmeffVZDhw6Vy%2BVSWlqaz/ZdunTR/v37JanDeX/U1dXJ5XL5jNntCe3WPVXR0VE%2Bn2EOPQV%2BGXZ7%2BHyvccwNnEjsbUQGrJ9atmyZKisrtW7dOj355JOKiYnxmY%2BJiZHH45Ekud3u4877o7y8XKWlpT5j%2Bfn5mj179knuwfElJcVDGAliAAAQ1ElEQVQHZF0ACLTk5MRgl3DCOOYGTiT1NuID1rJly/TUU0/p/vvvV79%2B/RQbG6vGxkafbTwej%2BLi4iRJsbGx7cKUx%2BNRUlKS3885depU5eTk%2BIzZ7QlqaDh0kntxbNHRUUpKildTk1tHj7YZXft0F0n/iwJCmenjYiBxzA2cQPY2WCE%2BogPW4sWL9fzzz2vZsmUaM2aMJCk9PV179uzx2a6%2Bvt57%2BS49PV319fXt5vv37%2B/386alpbW7HOhyHVBra2C%2BIY8ebQvY2gAQSOF47OKYGziR1NuI/W96aWmp1qxZo%2BXLl2v8%2BPHecYfDoc8//1yHDx/2jlVUVMjhcHjnKyoqvHNut1uVlZXeeQAAgI5EZMD6%2Buuv9dBDD%2BnPf/6zMjMz5XK5vB/Z2dnq1q2bCgsLVVVVpbKyMu3cuVOTJ0%2BWJOXl5Wn79u0qKytTVVWVCgsL1bNnT27RAAAA/BaRAetvf/ubjh49qocffljDhw/3%2BYiOjtZDDz0kl8ul3NxcvfLKK1q5cqW6d%2B8uSerZs6cefPBBrV%2B/XpMnT1ZjY6NWrlwpm80W5L0CAADhwmZxi/JfhMt1wPiadnuUkpMT1dBwKGKuWYcKuz1Kv7/3vWCXAUS8N24aFuwS/MYxN3AC2dvU1F8ZXc9fEXkGCwAAIJgIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwe7ALAACcvsateD/YJZyQT4rHBrsEhAkCFn4x4XYgBQDgZEX8JUKPx6MJEyboww8/9I5VV1frmmuu0eDBg3XppZdqy5YtPl%2BzdetWTZgwQQ6HQ9OnT1d1dfUvXTYAAAhjER2wWlpadPPNN6uqqso7ZlmW8vPz1bVrV61fv14TJ05UQUGBampqJEk1NTXKz89Xbm6u1q1bp5SUFM2aNUuWZQVrNwAAQJiJ2IC1Z88e/du//Zv%2B8Y9/%2BIx/8MEHqq6u1qJFi9SnTx/NnDlTgwcP1vr16yVJa9eu1cCBA3XdddfpX/7lX1RSUqL//u//1kcffRSM3QAAAGEoYgPWRx99pAsuuEDl5eU%2B406nU%2Beee64SEhK8Y5mZmdqxY4d3PisryzsXHx%2BvAQMGeOcBAAA6ErFvcr/88suPOe5yuZSWluYz1qVLF%2B3fv9%2BveQAAgI5EbMD6OW63WzExMT5jMTEx8ng8fs37o66uTi6Xy2fMbk9oF9xOVXR0lM9nAEDgccw1LxJ/np12ASs2NlaNjY0%2BYx6PR3Fxcd75n4Ypj8ejpKQkv5%2BjvLxcpaWlPmP5%2BfmaPXv2SVZ9fElJ8QFZFwDQHsfcwImk3p52ASs9PV179uzxGauvr/eeXUpPT1d9fX27%2Bf79%2B/v9HFOnTlVOTo7PmN2eoIaGQydZ9bFFR0cpKSleTU1uHT3aZnRtAMCxccw1L5A/z5KTE42u56/TLmA5HA6VlZXp8OHD3rNWFRUVyszM9M5XVFR4t3e73aqsrFRBQYHfz5GWltbucqDLdUCtrYH5hjx6tC1gawMAfHHMDZxI6m3kXOz0U3Z2trp166bCwkJVVVWprKxMO3fu1OTJkyVJeXl52r59u8rKylRVVaXCwkL17NlTF1xwQZArBwAA4eK0C1jR0dF66KGH5HK5lJubq1deeUUrV65U9%2B7dJUk9e/bUgw8%2BqPXr12vy5MlqbGzUypUrZbPZglw5AAAIF6fFJcIvv/zS53Hv3r317LPP/uz2v/vd7/S73/0u0GUBAIAIddqdwQIAAAi00%2BIMFgAAp6NxK94Pdgl%2B%2B6R4bLBLMIozWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMN7kDAOCnrKJNwS4BYYIzWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZg10ATk1W0aZglwAAAH6CM1gAAACGEbCOoaWlRfPmzVNWVpaGDx%2Buxx9/PNglAQCAMMIlwmNYunSpdu3apaeeeko1NTWaO3euunfvrrFjxwa7NAAAEAYIWD/R3NystWvX6tFHH9WAAQM0YMAAVVVV6bnnniNgAQAAv3CJ8Cd2796t1tZWZWRkeMcyMzPldDrV1tYWxMoAAEC44AzWT7hcLiUnJysmJsY71rVrV7W0tKixsVEpKSkdrlFXVyeXy%2BUzZrcnKC0tzWit0dHkYwBA5Iikn2sErJ9wu90%2B4UqS97HH4/FrjfLycpWWlvqMFRQU6MYbbzRT5P%2Bpq6vT1f%2BvSlOnTjUe3k53dXV1Ki8vp7cBQG8Di/4GDr0NnLq6Oj344IMR1dvIiYqGxMbGtgtSPz6Oi4vza42pU6dqw4YNPh9Tp041XqvL5VJpaWm7s2U4dfQ2cOhtYNHfwKG3gROJveUM1k%2Bkp6eroaFBra2tstt/aI/L5VJcXJySkpL8WiMtLS1iEjgAADhxnMH6if79%2B8tut2vHjh3esYqKCg0aNEhRUbQLAAB0jMTwE/Hx8Zo0aZLuvPNO7dy5U2%2B99ZYef/xxTZ8%2BPdilAQCAMBF955133hnsIkLN0KFDVVlZqfvuu0/btm3TDTfcoLy8vGCXdUyJiYnKzs5WYmJisEuJOPQ2cOhtYNHfwKG3gRNpvbVZlmUFuwgAAIBIwiVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErDDV0tKiefPmKSsrS8OHD9fjjz8e7JLC1nfffafZs2crOztbI0aMUElJiVpaWiRJ1dXVuuaaazR48GBdeuml2rJlS5CrDV8zZszQ7bff7n1cWVmpKVOmyOFwKC8vT7t27QpideHH4/Fo4cKFOv/883XRRRdp%2BfLl%2BvG%2B0fT21NXW1mrmzJkaMmSIcnJy9OSTT3rn6O/J8Xg8mjBhgj788EPvWEfH2K1bt2rChAlyOByaPn26qqurf%2BmyTxoBK0wtXbpUu3bt0lNPPaUFCxaotLRUmzZtCnZZYceyLM2ePVtut1vPPfec7r//fr399ttasWKFLMtSfn6%2BunbtqvXr12vixIkqKChQTU1NsMsOO6%2B99po2b97sfdzc3KwZM2YoKytLGzZsUEZGhmbOnKnm5uYgVhlelixZoq1bt%2Bqxxx7TfffdpxdeeEHl5eX01pCbbrpJCQkJ2rBhg%2BbNm6cVK1bozTffpL8nqaWlRTfffLOqqqq8Yx0dY2tqapSfn6/c3FytW7dOKSkpmjVrlsLmD9BYCDuHDh2yBg0aZH3wwQfesZUrV1pXXnllEKsKT3v27LH69etnuVwu79irr75qDR8%2B3Nq6das1ePBg69ChQ965q6%2B%2B2nrggQeCUWrYamhosC6%2B%2BGIrLy/Pmjt3rmVZlrV27VorJyfHamtrsyzLstra2qzf//731vr164NZathoaGiwzj33XOvDDz/0jq1atcq6/fbb6a0BjY2NVr9%2B/awvv/zSO1ZQUGAtXLiQ/p6Eqqoq6w9/%2BIN12WWXWf369fP%2B7OroGLtixQqfn2vNzc1WRkaGz8%2B%2BUMYZrDC0e/dutba2KiMjwzuWmZkpp9Optra2IFYWflJTU7V69Wp17drVZ/zgwYNyOp0699xzlZCQ4B3PzMzUjh07fukyw9o999yjiRMnqm/fvt4xp9OpzMxM2Ww2SZLNZtOQIUPorZ8qKirUqVMnZWdne8dmzJihkpISemtAXFyc4uPjtWHDBh05ckR79%2B7V9u3b1b9/f/p7Ej766CNdcMEFKi8v9xnv6BjrdDqVlZXlnYuPj9eAAQPCptcErDDkcrmUnJysmJgY71jXrl3V0tKixsbGIFYWfpKSkjRixAjv47a2Nj377LMaOnSoXC6X0tLSfLbv0qWL9u/f/0uXGba2bdumTz75RLNmzfIZp7enprq6Wj169NBLL72ksWPH6l//9V%2B1cuVKtbW10VsDYmNjNX/%2BfJWXl8vhcGjcuHG6%2BOKLNWXKFPp7Ei6//HLNmzdP8fHxPuMd9TLce20PdgE4cW632ydcSfI%2B9ng8wSgpYixbtkyVlZVat26dnnzyyWP2mR77p6WlRQsWLND8%2BfMVFxfnM/dzr2F665/m5mbt27dPa9asUUlJiVwul%2BbPn6/4%2BHh6a8jXX3%2BtUaNG6dprr1VVVZUWL16sCy%2B8kP4a1FEvw73XBKwwFBsb2%2B4F9uPjn/4gg/%2BWLVump556Svfff7/69eun2NjYdmcEPR4PPfZTaWmpBg4c6HOG8Ec/9xqmt/6x2%2B06ePCg7rvvPvXo0UPSD28Ifv7559W7d296e4q2bdumdevWafPmzYqLi9OgQYP03Xff6eGHH1avXr3oryEdHWN/7jiRlJT0i9V4KrhEGIbS09PV0NCg1tZW75jL5VJcXFzYvPBCzeLFi/XEE09o2bJlGjNmjKQf%2BlxfX%2B%2BzXX19fbtT1ji21157TW%2B99ZYyMjKUkZGhV199Va%2B%2B%2BqoyMjLo7SlKTU1VbGysN1xJ0m9/%2B1vV1tbSWwN27dql3r17%2B4Smc889VzU1NfTXoI56%2BXPzqampv1iNp4KAFYb69%2B8vu93u80a/iooKDRo0SFFR/JOeqNLSUq1Zs0bLly/X%2BPHjveMOh0Off/65Dh8%2B7B2rqKiQw%2BEIRplh55lnntGrr76ql156SS%2B99JJycnKUk5Ojl156SQ6HQ59%2B%2Bqn3160ty9L27dvprZ8cDodaWlr0zTffeMf27t2rHj160FsD0tLStG/fPp%2BzJ3v37lXPnj3pr0EdHWMdDocqKiq8c263W5WVlWHTa34ah6H4%2BHhNmjRJd955p3bu3Km33npLjz/%2BuKZPnx7s0sLO119/rYceekh//vOflZmZKZfL5f3Izs5Wt27dVFhYqKqqKpWVlWnnzp2aPHlysMsOCz169FDv3r29H4mJiUpMTFTv3r01duxYNTU1qbi4WHv27FFxcbHcbrfGjRsX7LLDwllnnaWRI0eqsLBQu3fv1nvvvaeysjJNmzaN3hqQk5OjM844Q3fccYe%2B%2BeYb/dd//ZceeeQRXXXVVfTXoI6OsXl5edq%2BfbvKyspUVVWlwsJC9ezZUxdccEGQK/dTMO8RgZPX3NxszZkzxxo8eLA1fPhw64knngh2SWFp1apVVr9%2B/Y75YVmW9fe//9264oorrIEDB1rjx4%2B33n///SBXHL7mzp3rvQ%2BWZVmW0%2Bm0Jk2aZA0aNMiaPHmy9fnnnwexuvDT1NRk3XbbbdbgwYOtCy%2B80HrwwQe992ait6euqqrKuuaaa6whQ4ZYo0ePtp544gn6a8A/3wfLsjo%2Bxr7zzjvWJZdcYp133nnW1Vdfbf3jH//4pUs%2BaTbLCpdbogIAAIQHLhECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGH/H%2B0mSmDQv2ffAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6074419953544331960”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.93103448275862</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.76923076923077</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.064935064935064</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.44444444444444</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.714285714285715</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.97093257973355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2853)</td> <td class=”number”>2853</td> <td class=”number”>88.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6074419953544331960”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6753720311422096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.821683309557775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.583850931677018</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.909444039815489</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>99.89432898908066</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.91009889121966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.91431019708654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.96734800496311</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_HISP10”>PCT_HISP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3132</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.3036</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>95.745</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2411692941505832663”>

</div> <div class=”col-md-12 text-right”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2411692941505832663”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2411692941505832663”

aria-controls=”quantiles-2411692941505832663” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2411692941505832663” aria-controls=”histogram-2411692941505832663”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2411692941505832663” aria-controls=”common-2411692941505832663”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2411692941505832663” aria-controls=”extreme-2411692941505832663”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2411692941505832663”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.79737</td>

</tr> <tr>

<th>Q1</th> <td>1.5928</td>

</tr> <tr>

<th>Median</th> <td>3.2855</td>

</tr> <tr>

<th>Q3</th> <td>8.2574</td>

</tr> <tr>

<th>95-th percentile</th> <td>36.963</td>

</tr> <tr>

<th>Maximum</th> <td>95.745</td>

</tr> <tr>

<th>Range</th> <td>95.745</td>

</tr> <tr>

<th>Interquartile range</th> <td>6.6646</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>13.21</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5908</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.524</td>

</tr> <tr>

<th>Mean</th> <td>8.3036</td>

</tr> <tr>

<th>MAD</th> <td>8.1762</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.2645</td>

</tr> <tr>

<th>Sum</th> <td>26007</td>

</tr> <tr>

<th>Variance</th> <td>174.49</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2411692941505832663”>

<img 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d85mZmRo8eLCcTqcSExN16tQp95xlWVq4cKHi4uIUGxurBQsWqK6uzkitAADg2mB7wIqLi9Pq1as1YMAAPfbYY9q7d68sy7qsfVVVVemxxx5TXl6ex3h%2Bfr4ef/xx7d271/0YM2aMJCk3N1czZsxQUlKSMjIyVF5erpSUFPe2GzZsUGZmptLT07V8%2BXLt2rVLGzZsuPwFAwCAa47tAevxxx/XO%2B%2B8o5UrVyogIEB/%2BMMfdMcdd2jJkiX6/PPPG7yfY8eO6Z577tFXX31Vby4/P1%2B9evVSaGio%2BxEcHCxJ2rx5s4YNG6ZRo0apR48eWrBggfbs2aOCggJJ0qZNm5ScnKyYmBjFxcXpiSee0JYtW8wsHgAAXBO8cg2Wn5%2BfbrvtNqWlpWnfvn269957tXHjRt11112699579T//8z%2BX3McHH3yg/v37KyMjw2P89OnTOnnypLp06XLR7XJychQTE%2BN%2B3r59e0VERCgnJ0cnT55UYWGh%2BvXr556Pjo7WN998o%2BLi4stbLAAAuOY4vPXGxcXFeu211/Taa6/ps88%2BU9%2B%2BfXX33XerqKhITz/9tA4ePKgZM2b85PYTJky46Hh%2Bfr78/Py0evVqvfvuu2rVqpUmTZqku%2B%2B%2B2/2%2BYWFhHtu0bdtWRUVFcrlckuQx365dO0lSUVFRve1%2Bbm3n93Wew9G8wds3VECAb31GweHwrXovdL7XvtZzX0Sv7UOv7UOv7XU19Nv2gPXqq6/q1Vdf1YEDB9SmTRuNGjVKy5cv9zji1L59e82fP/9nA9ZPOX78uPz8/NS1a1fdd999OnjwoGbOnKmWLVtqyJAhqqysVGBgoMc2gYGBqq6uVmVlpfv5hXPSDxfTN1RGRobS09M9xhITE5WcnPyL19OUtG7dwtslXLGQkGBvl3DNoNf2odf2odf28ma/bQ9YM2bM0KBBg7RixQrdfvvt8vevny7Ph6PLMWrUKA0aNEitWrWSJPXo0UNffPGFtm7dqiFDhigoKKheWKqurlZwcLBHmAoKCnL/WZL7Gq6GSEhIUHx8vMeYw9FcpaVnLmtNP8XX/iVkev12CgjwV0hIsMrLK1Rby6dKGxO9tg%2B9tg%2B9tteF/fZWyLI9YL377rtq3bq1ysrK3OEqNzdXvXv3VkBAgCSpb9%2B%2B6tu372Xt38/Pzx2uzuvatav2798vSQoPD1dJSYnHfElJiUJDQxUeHi5Jcrlc6tixo/vPkhQaGtrgGsLCwuqdDnS5vldNzbX9l6oprL%2B2tq5JrMMX0Gv70Gv70Gt7eTPM2n4I5PTp0xo6dKj%2B9Kc/uccmT56skSNHqrCw8Ir3v2zZMj344IMeY0ePHlXXrl0lSU6nU1lZWe65wsJCFRYWyul0Kjw8XBERER7zWVlZioiIMH79FAAAaLpsD1jPPfecOnfurEmTJrnHXn/9dbVv316pqalXvP9Bgwbp4MGDWr9%2Bvb766iv993//t3bu3KmHHnpIkjR%2B/Hi9%2Buqr2rZtm44ePaonn3xSd9xxhzp16uSeX7hwoQ4cOKADBw5o0aJFmjhx4hXXBQAArh22nyL85z//qT//%2Bc8ep9zatGmjJ598Uvfee%2B8V7/%2BWW27RsmXLtHz5ci1btkwdOnTQokWLFBUVJUmKiorSnDlztHz5cn333Xe67bbbNHfuXPf2Dz/8sL799lslJSUpICBAY8eOrXdEDAAA4OfYHrAcDofKy8vrjVdUVFz2Hd0//fRTj%2BeDBw/W4MGDf/L1o0eP1ujRoy86FxAQoJSUFI%2B7uwMAAPwStp8ivP322zVv3jyPO7AXFBQoNTVVAwcOtLscAAAA42w/gjVt2jRNmjRJd955p0JCQiRJ5eXl6t27N0eNAABAk2B7wGrbtq3%2B8pe/aN%2B%2BfcrLy5PD4dANN9ygW2%2B9VX5%2BfnaXAwAAYJxXflVOQECABg4cyClBAADQJNkesFwul5YuXapDhw7p3Llz9S5sf/vtt%2B0uCQAAwCjbA9bMmTN1%2BPBhDR8%2BXP/yL/9i99sDAAA0OtsD1v79%2B7Vu3TrFxMTY/dYAAAC2sP02Dc2bN1fbtm3tflsAAADb2B6wRo4cqXXr1qm2ttbutwYAALCF7acIy8rKlJmZqb///e/q1KmTAgMDPeY3bdpkd0kAAABGeeU2DSNGjPDG2wIAANjC9oCVmppq91sCAADYyvZrsCSpuLhY6enpevzxx/Xtt9/qzTff1PHjx71RCgAAgHG2B6wvv/xSv/nNb/SXv/xFb731ls6ePavXX39dY8aMUU5Ojt3lAAAAGGd7wPrjH/%2BowYMHa/fu3bruuuskSYsXL1Z8fLwWLlxodzkAAADG2R6wDh06pEmTJnn8YmeHw6FHHnlER44csbscAAAA42wPWHV1daqrq6s3fubMGQUEBNhdDgAAgHG2B6wBAwZozZo1HiGrrKxMaWlpiouLs7scAAAA42wPWE899ZQOHz6sAQMGqKqqSv/5n/%2BpQYMG6euvv9a0adPsLgcAAMA42%2B%2BDFR4erp07dyozM1OffPKJ6urqNH78eI0cOVItW7a0uxwAAADjvHIn9%2BDgYI0bN84bbw0AANDobA9YEydO/Nl5fhchAADwdbYHrA4dOng8r6mp0ZdffqnPPvtMDzzwgN3lAAAAGHfV/C7CFStWqKioyOZqAAAAzPPK7yK8mJEjR%2BqNN97wdhkAAABX7KoJWB9%2B%2BCE3GgUAAE3CVXGR%2B%2BnTp/Xpp59qwoQJdpcDAABgnO0BKyIiwuP3EErSddddp/vuu0%2B//e1v7S4HAADAONsD1h//%2BEe73xIAAMBWtgesgwcPNvi1/fr1a8RKAAAAGoftAev%2B%2B%2B93nyK0LMs9/uMxPz8/ffLJJ3aXBwAAcMVsD1irV6/WvHnzNHXqVMXGxiowMFAfffSR5syZo7vvvlt33XWX3SUBAAAYZfttGlJTUzVr1izdeeedat26tVq0aKG4uDjNmTNHW7duVYcOHdwPAAAAX2R7wCouLr5oeGrZsqVKS0vtLgcAAMA42wNWZGSkFi9erNOnT7vHysrKlJaWpltvvdXucgAAAIyz/Rqsp59%2BWhMnTtTtt9%2BuLl26yLIsffHFFwoNDdWmTZvsLgcAAMA42wNWt27d9PrrryszM1P5%2BfmSpHvvvVfDhw9XcHCw3eUAAAAYZ3vAkqTrr79e48aN09dff61OnTpJ%2BuFu7gAAAE2B7ddgWZalhQsXql%2B/fhoxYoSKioo0bdo0zZgxQ%2BfOnbO7HAAAAONsD1gvvfSSXn31VT3zzDMKDAyUJA0ePFi7d%2B9Wenq63eUAAAAYZ3vAysjI0KxZszR69Gj33dvvuusuzZs3T7t27bK7HAAAAONsD1hff/21evbsWW%2B8R48ecrlcdpcDAABgnO0Bq0OHDvroo4/qjb/77rvuC94BAAB8me2fInz44Yc1e/ZsuVwuWZal999/XxkZGXrppZf01FNP2V0OAACAcbYHrDFjxqimpkarVq1SZWWlZs2apTZt2ujRRx/V%2BPHj7S4HAADAONsDVmZmpoYOHaqEhASdOnVKlmWpbdu2dpcBAADQaGy/BmvOnDnui9nbtGlDuAIAAE2O7QGrS5cu%2Buyzz%2Bx%2BWwAAANvYfoqwR48eeuKJJ7Ru3Tp16dJFQUFBHvOpqal2lwQAAGCU7QHr888/V3R0tCRx3ysAANAk2RKwFixYoKSkJDVv3lwvvfSS0X1XV1dr9OjRmjlzpvr37y9JKigo0MyZM5Wdna2IiAhNnz5dAwYMcG%2Bzb98%2BPffccyooKJDT6dT8%2BfM97sH14osvav369Tp9%2BrSGDRummTNnKjg42GjdAACg6bLlGqwNGzaooqLCY2zy5MkqLi6%2Bov1WVVXpscceU15ennvMsiwlJiaqXbt22rFjh0aOHKmkpCSdOHFCknTixAklJiZq9OjR2r59u9q0aaNHHnlElmVJkt566y2lp6drzpw52rhxo3JycpSWlnZFdQIAgGuLLQHrfHi50MGDB1VVVXXZ%2Bzx27JjuueceffXVVx7j%2B/fvV0FBgebMmaNu3bppypQpioyM1I4dOyRJ27Zt080336yHHnpIN954o1JTU/XNN9/ogw8%2BkCRt2rRJDzzwgAYNGqRbbrlFs2fP1o4dO%2BoFRAAAgJ9i%2B6cITfnggw/Uv39/ZWRkeIzn5OSoV69eat68uXssOjpa2dnZ7vmYmBj3XHBwsHr37q3s7GzV1tbqo48%2B8piPjIzUuXPndPTo0UZeEQAAaCpsv8jdlAkTJlx03OVyKSwszGOsbdu2KioquuR8eXm5qqqqPOYdDodatWrl3h4AAOBSbAtYfn5%2BtrxPRUWFAgMDPcYCAwNVXV19yfnKykr385/aviGKi4vrfULS4WheL9hdqYAA3zoA6XD4Vr0XOt9rX%2Bu5L6LX9qHX9qHX9roa%2Bm1bwJo3b57HPa/OnTuntLQ0tWjRwuN1V3ofrKCgIJWVlXmMVVdXq1mzZu75H4el6upqhYSEuOu72Pwv%2BRRhRkaG0tPTPcYSExOVnJzc4H00Ra1bt7j0i65yISF8mtQu9No%2B9No%2B9Npe3uy3LQGrX79%2B9Y7oREVFqbS0VKWlpUbfKzw8XMeOHfMYKykpcR89Cg8PV0lJSb35nj17qlWrVgoKClJJSYm6desmSaqpqVFZWZlCQ0MbXENCQoLi4%2BM9xhyO5iotPXM5S/pJvvYvIdPrt1NAgL9CQoJVXl6h2to6b5fTpNFr%2B9Br%2B9Bre13Yb2%2BFLFsClul7X/0cp9OptWvXqrKy0n3UKisry31zU6fTqaysLPfrKyoqdOTIESUlJcnf3199%2BvRRVlaW%2B55a2dnZcjgc6tGjR4NrCAsLq3c60OX6XjU11/Zfqqaw/trauiaxDl9Ar%2B1Dr%2B1Dr%2B3lzTDrW4dAGiA2Nlbt27dXSkqK8vLytHbtWuXm5mrs2LGSpDFjxujQoUNau3at8vLylJKSoo4dO7oD1YQJE7R%2B/Xrt3r1bubm5evbZZ3XPPfdwo1EAANBgTS5gBQQEaOXKlXK5XBo9erRee%2B01rVixQhEREZKkjh076vnnn9eOHTs0duxYlZWVacWKFe6L8IcPH64pU6Zo1qxZeuihh3TLLbdo6tSp3lwSAADwMX7Wxe4CCuNcru%2BN79Ph8NeQhe8Z329jeePR27xdwmVzOPzVunULlZae4fB%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%2BV0OpWYmKhTp055YwkAAMBHNdmAdezYMQ0aNEh79%2B51P%2BbNm6ezZ89q8uTJiomJ0SuvvKKoqChNmTJFZ8%2BelSTl5uZqxowZSkpKUkZGhsrLy5WSkuLl1QAAAF/SZANWfn6%2BunfvrtDQUPcjJCREr7/%2BuoKCgvTkk0%2BqW7dumjFjhlq0aKE333xTkrR582YNGzZMo0aNUo8ePbRgwQLt2bNHBQUFXl4RAADwFU06YHXp0qXeeE5OjqKjo%2BXn5ydJ8vPzU9%2B%2BfZWdne2ej4mJcb%2B%2Bffv2ioiIUE5Oji11AwAA3%2BfwdgGNwbIsff7559q7d6/WrFmj2tpaDR06VMnJyXK5XLrhhhs8Xt%2B2bVvl5eVJkoqLixUWFlZvvqioqMHvX1xcLJfL5THmcDSvt98rFRDgW/nY4fCtei90vte%2B1nNfRK/tQ6/tQ6/tdTX0u0kGrBMnTqiiokKBgYFaunSpvv76a82bN0%2BVlZXu8QsFBgaqurpaklRZWfmz8w2RkZGh9PR0j7HExET3RfbXqtatW3i7hCsWEhLs7RKuGfTaPvTaPvTaXt7sd5MMWB06dNCBAwd0/fXXy8/PTz179lRdXZ2mTp2q2NjYemGpurpazZq%2BQi3NAAANmklEQVQ1kyQFBQVddD44uOH/kxISEhQfH%2B8x5nA0V2npmctc0cX52r%2BETK/fTgEB/goJCVZ5eYVqa%2Bu8XU6TRq/tQ6/tQ6/tdWG/vRWymmTAkqRWrVp5PO/WrZuqqqoUGhqqkpISj7mSkhL36bvw8PCLzoeGhjb4vcPCwuqdDnS5vldNzbX9l6oprL%2B2tq5JrMMX0Gv70Gv70Gt7eTPM%2BtYhkAZ677331L9/f1VUVLjHPvnkE7Vq1UrR0dH68MMPZVmWpB%2Bu1zp06JCcTqckyel0Kisry71dYWGhCgsL3fMAAACX0iQDVlRUlIKCgvT000/r%2BPHj2rNnjxYsWKDf/e53Gjp0qMrLyzV//nwdO3ZM8%2BfPV0VFhYYNGyZJGj9%2BvF599VVt27ZNR48e1ZNPPqk77rhDnTp18vKqAACAr2iSAatly5Zav369Tp06pTFjxmjGjBlKSEjQ7373O7Vs2VJr1qxRVlaWRo8erZycHK1du1bNmzeX9EM4mzNnjlasWKHx48fr%2BuuvV2pqqpdXBAAAfImfdf5cGRqVy/W98X06HP4asvA94/ttLG88epu3S7hsDoe/WrduodLSM1w/0cjotX3otX3otb0u7Le3PsHeJI9gAQAAeBMBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMc3i7AFw7hi39h7dL%2BEXeePQ2b5cAAPBRHMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5vB2AcDVatjSf3i7hF/kjUdv83YJAID/wxEsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBi3aQCaCF%2B6rQS3lADQ1HEECwAAwDACFgAAgGGcIgRgO186nSlxShPAL0fAAoBLIBAC%2BKU4RXgRVVVVmj59umJiYjRgwAC98MIL3i4JAAD4EI5gXcSCBQt0%2BPBhbdy4USdOnNC0adMUERGhoUOHers0AADgAwhYP3L27Flt27ZNf/rTn9S7d2/17t1beXl52rJlCwELAAA0CAHrR44ePaqamhpFRUW5x6Kjo7V69WrV1dXJ35%2BzqgCubr50zZivXS/mS731Nb72tXApBKwfcblcat26tQIDA91j7dq1U1VVlcrKytSmTZtL7qO4uFgul8tjzOForrCwMKO1BgQQ9gD4NgILznM4zP1MO//z0Zs/JwlYP1JRUeERriS5n1dXVzdoHxkZGUpPT/cYS0pK0h/%2B8AczRf6f4uJiPfCrPCUkJBgPb/BUXFysjIwMem0Dem0fem0fem2v4uJibdy4TgkJCQoJCfZKDRwC%2BZGgoKB6Qer882bNmjVoHwkJCXrllVc8HgkJCcZrdblcSk9Pr3e0DObRa/vQa/vQa/vQa3tdDf3mCNaPhIeHq7S0VDU1NXI4fmiPy%2BVSs2bNFBIS0qB9hIWF8S8UAACuYRzB%2BpGePXvK4XAoOzvbPZaVlaU%2BffpwgTsAAGgQEsOPBAcHa9SoUXr22WeVm5ur3bt364UXXtDEiRO9XRoAAPARAc8%2B%2B%2Byz3i7iahMXF6cjR45o0aJFev/99/Uf//EfGjNmjLfLuqgWLVooNjZWLVq08HYpTR69tg%2B9tg%2B9tg%2B9tpe3%2B%2B1nWZbllXcGAABoojhFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgOWjqqqqNH36dMXExGjAgAF64YUXvF1Sk3Hy5EklJycrNjZWAwcOVGpqqqqqqiRJBQUFevDBBxUZGam77rpLe/fu9XK1TcfkyZP11FNPuZ8fOXJE48aNk9Pp1JgxY3T48GEvVuf7qqurNXv2bPXr10//9m//psWLF%2Bv8fabptVmFhYWaMmWK%2Bvbtq/j4eL344ovuOXptTnV1tUaMGKEDBw64xy71PXrfvn0aMWKEnE6nJk6cqIKCgkarj4DloxYsWKDDhw9r48aNeuaZZ5Senq4333zT22X5PMuylJycrIqKCm3ZskVLlizRO%2B%2B8o6VLl8qyLCUmJqpdu3basWOHRo4cqaSkJJ04ccLbZfu8v/71r9qzZ4/7%2BdmzZzV58mTFxMTolVdeUVRUlKZMmaKzZ896sUrfNm/ePO3bt0/r16/XokWL9Oc//1kZGRn0uhE8%2Buijat68uV555RVNnz5dS5cu1d/%2B9jd6bVBVVZUee%2Bwx5eXluccu9T36xIkTSkxM1OjRo7V9%2B3a1adNGjzzyiBrtF9pY8Dlnzpyx%2BvTpY%2B3fv989tmLFCuu%2B%2B%2B7zYlVNw7Fjx6zu3btbLpfLPbZr1y5rwIAB1r59%2B6zIyEjrzJkz7rkHHnjAWr58uTdKbTJKS0ut22%2B/3RozZow1bdo0y7Isa9u2bVZ8fLxVV1dnWZZl1dXVWUOGDLF27NjhzVJ9VmlpqdWrVy/rwIED7rE1a9ZYTz31FL02rKyszOrevbv16aefuseSkpKs2bNn02tD8vLyrN/%2B9rfWb37zG6t79%2B7un4WX%2Bh69dOlSj5%2BTZ8%2BetaKiojx%2BlprEESwfdPToUdXU1CgqKso9Fh0drZycHNXV1XmxMt8XGhqqdevWqV27dh7jp0%2BfVk5Ojnr16qXmzZu7x6Ojo5WdnW13mU3Kf/3Xf2nkyJG64YYb3GM5OTmKjo6Wn5%2BfJMnPz099%2B/al15cpKytLLVu2VGxsrHts8uTJSk1NpdeGNWvWTMHBwXrllVd07tw5HT9%2BXIcOHVLPnj3ptSEffPCB%2Bvfvr4yMDI/xS32PzsnJUUxMjHsuODhYvXv3brT%2BE7B8kMvlUuvWrRUYGOgea9eunaqqqlRWVubFynxfSEiIBg4c6H5eV1enzZs3Ky4uTi6XS2FhYR6vb9u2rYqKiuwus8l4//339c9//lOPPPKIxzi9NqugoEAdOnTQzp07NXToUP37v/%2B7VqxYobq6OnptWFBQkGbNmqWMjAw5nU4NGzZMt99%2Bu8aNG0evDZkwYYKmT5%2Bu4OBgj/FL9dfu/jsaZa9oVBUVFR7hSpL7eXV1tTdKarLS0tJ05MgRbd%2B%2BXS%2B%2B%2BOJF%2B07PL09VVZWeeeYZzZo1S82aNfOY%2B6mvcXp9ec6ePasvv/xSL7/8slJTU%2BVyuTRr1iwFBwfT60aQn5%2BvQYMGadKkScrLy9PcuXN166230utGdqn%2B2t1/ApYPCgoKqvcFcf75j39Q4fKlpaVp48aNWrJkibp3766goKB6Rwirq6vp%2BWVKT0/XzTff7HHE8Lyf%2Bhqn15fH4XDo9OnTWrRokTp06CDphwt%2Bt27dqs6dO9Nrg95//31t375de/bsUbNmzdSnTx%2BdPHlSq1atUqdOneh1I7rU9%2Bif%2Br4SEhLSKPVwitAHhYeHq7S0VDU1Ne4xl8ulZs2aNdoXyrVm7ty52rBhg9LS0nTnnXdK%2BqHvJSUlHq8rKSmpd8gZDfPXv/5Vu3fvVlRUlKKiorRr1y7t2rVLUVFR9Nqw0NBQBQUFucOVJP3rv/6rCgsL6bVhhw8fVufOnT1CU69evXTixAl63cgu1d%2Bfmg8NDW2UeghYPqhnz55yOBweF%2BZlZWWpT58%2B8vfnf%2BmVSk9P18svv6zFixdr%2BPDh7nGn06mPP/5YlZWV7rGsrCw5nU5vlOnzXnrpJe3atUs7d%2B7Uzp07FR8fr/j4eO3cuVNOp1Mffvih%2B%2BPTlmXp0KFD9PoyOZ1OVVVV6fPPP3ePHT9%2BXB06dKDXhoWFhenLL7/0OFJy/PhxdezYkV43skt9j3Y6ncrKynLPVVRU6MiRI43Wf34a%2B6Dg4GCNGjVKzz77rHJzc7V792698MILmjhxordL83n5%2BflauXKlfv/73ys6Oloul8v9iI2NVfv27ZWSkqK8vDytXbtWubm5Gjt2rLfL9kkdOnRQ586d3Y8WLVqoRYsW6ty5s4YOHary8nLNnz9fx44d0/z581VRUaFhw4Z5u2yf1LVrV91xxx1KSUnR0aNH9d5772nt2rUaP348vTYsPj5e1113nZ5%2B%2Bml9/vnn%2Bt///V%2BtXr1a999/P71uZJf6Hj1mzBgdOnRIa9euVV5enlJSUtSxY0f179%2B/cQpqlJs/oNGdPXvWevLJJ63IyEhrwIAB1oYNG7xdUpOwZs0aq3v37hd9WJZlffHFF9a9995r3Xzzzdbw4cOtf/zjH16uuOmYNm2a%2Bz5YlmVZOTk51qhRo6w%2BffpYY8eOtT7%2B%2BGMvVuf7ysvLralTp1qRkZHWrbfeaj3//PPu%2BzHRa7Py8vKsBx980Orbt681ePBga8OGDfS6kVx4HyzLuvT36L///e/Wr3/9a%2BuWW26xHnjgAeurr75qtNr8LKuxbmEKAABwbeIUIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9v8A8jPDB5rGfOwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2411692941505832663”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9012256669069935</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.2923570283271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2209125919010386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.235764235764236</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.250355618776672</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3161869110623465</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.04456672239668</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.570057581573896</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.672131147540984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.419080843318671</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3121)</td> <td class=”number”>3121</td> <td class=”number”>97.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2411692941505832663”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1310615989515072</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19064378941193721</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24875621890547264</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.29239766081871343</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>93.33713796547296</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.86828808769376</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.67989973828745</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.6846214407558</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.74477435438507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_HSPA15”>PCT_HSPA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>31</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>37.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1196</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>25.547</td>

</tr> <tr>

<th>Minimum</th> <td>19.5</td>

</tr> <tr>

<th>Maximum</th> <td>32.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3684040795230938841”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3684040795230938841,#minihistogram3684040795230938841”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3684040795230938841”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3684040795230938841”

aria-controls=”quantiles3684040795230938841” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3684040795230938841” aria-controls=”histogram3684040795230938841”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3684040795230938841” aria-controls=”common3684040795230938841”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3684040795230938841” aria-controls=”extreme3684040795230938841”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3684040795230938841”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>19.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>20.2</td>

</tr> <tr>

<th>Q1</th> <td>24.3</td>

</tr> <tr>

<th>Median</th> <td>25.3</td>

</tr> <tr>

<th>Q3</th> <td>26.8</td>

</tr> <tr>

<th>95-th percentile</th> <td>30.9</td>

</tr> <tr>

<th>Maximum</th> <td>32.2</td>

</tr> <tr>

<th>Range</th> <td>12.7</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.8892</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.11309</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.0011864</td>

</tr> <tr>

<th>Mean</th> <td>25.547</td>

</tr> <tr>

<th>MAD</th> <td>2.1316</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.11987</td>

</tr> <tr>

<th>Sum</th> <td>51452</td>

</tr> <tr>

<th>Variance</th> <td>8.3475</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3684040795230938841”>

<img 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jVFvrVkNDo93l%2BAV6YkY/5PPepSe%2B6IkvemJmVz/O9gNSSwu4gPXGG2%2BourpaiYmJkuQNTH/5y180fvx4VVdXm7avrq72nrqLi4s743xMTMx51RAbG%2BtzOtDlOqr6ev99gzU0NPp1fXagJ2bB3I%2Bz7Xcw9%2BRs6IkvemIWLP0IuIC1atUq1dfXe28vXrxYkjRjxgxt375dy5cvl2EYCgkJkWEY2rFjh6ZOnSpJcjqdKikpUWpqqiTp4MGDOnjwoJxOZ%2BvvCAAAaLP87pNmkyZN0po1a3T06NELun/37t3Vs2dP71eHDh3UoUMH9ezZU2PHjlVtba0WLFig8vJyLViwQG63W%2BPGjZMkZWRkaP369Vq7dq2%2B/PJLzZw5U9dff70uueQSK3cRAAAEOL8LWEOGDNGyZcs0bNgw3XPPPdqyZYsMw7DksaOiovTcc895V6nKyspUWFioyMhISVJiYqLmzZungoICZWRkqGPHjsrLy7PkuQEAQPAIMaxKLxYyDENbt27Va6%2B9ps2bNys6OloTJ07UxIkT1bt3b7vLuyAu14WtyLU0h6OdOnXqoCNHjgfFOfHmoCdmLdGPcUs%2BtORxWsubdw813eY14oue%2BKInZnb1IybmZ632XD/ll5/BCgkJ0dChQzV06FC53W6tWrVKS5cuVWFhoQYPHqxbb71VN9xwg91lAgAAnJFfBizpHxfr3LBhgzZs2KA9e/Zo8ODB%2Bs///E8dOnRIDz74oLZv367c3Fy7ywQAAPDhdwFr/fr1Wr9%2BvT7%2B%2BGN17txZEydO1NNPP61evXp5t%2BnWrZsWLFhAwAIAAH7J7wJWbm6uRo4cqYKCAl133XVq1873c/iXXnqpbr75ZhuqAwAAODe/C1gffPCBOnXqpJqaGm%2B42rlzpxISEhQaGipJGjx4sAYPHmxnmQAAAGfld5dpOHbsmMaOHavly5d7xzIzMzVhwgQdPHjQxsoAAACax%2B8C1qOPPqqePXvq9ttv945t2rRJ3bp145pUAACgTfC7gPXpp5/q/vvvN/39v86dO2vmzJn66KOPbKwMAACgefwuYDkcDtXW1vqMu91uy67oDgAA0JL8LmBdd911mj9/vr777jvvWEVFhfLy8jR8%2BHAbKwMAAGgev/stwvvuu0%2B33367xowZo%2BjoaElSbW2tEhISNGvWLJurAwAAODe/C1hdunTRunXrtHXrVu3du1cOh0OXXXaZrrnmGoWEhNhdHgAAwDn5XcCSpNDQUA0fPpxTggAAoE3yu4Dlcrm0ZMkS7dixQ6dOnfL5YPs777xjU2UAAADN43cBa/bs2dq1a5d%2B8Ytf6Gc/%2B5nd5QAAAJw3vwtYH330kVasWKHk5GS7SwEAALggfneZhsjISHXp0sXuMgAAAC6Y3wWsCRMmaMWKFWpoaLC7FAAAgAvid6cIa2pqtHHjRr333nu65JJLFBYWZpp/6aWXbKoMQLAat%2BRDu0s4L2/ePdTuEoCg53cBS5LGjx9vdwkAAAAXzO8CVl5ent0lAAAA/FP87jNYklRVVaX8/Hzde%2B%2B9%2Bvvf/6633npL%2B/bts7ssAACAZvG7gLV//3798pe/1Lp16/SXv/xFJ06c0KZNm5SWlqaysjK7ywMAADgnvwtYjz32mEaPHq3NmzfroosukiQ9%2BeSTGjVqlBYvXmxzdQAAAOfmdwFrx44duv32201/2NnhcGjatGnavXu3jZUBAAA0j98FrMbGRjU2NvqMHz9%2BXKGhoTZUBAAAcH78LmANGzZMzz33nClk1dTUaNGiRRoyZIiNlQEAADSP3wWs%2B%2B%2B/X7t27dKwYcNUV1en3/3udxo5cqQOHDig%2B%2B67z%2B7yAAAAzsnvroMVFxen1157TRs3btQXX3yhxsZGZWRkaMKECYqKirK7PAAAgHPyu4AlSREREZo0aZLdZQAAAFwQvwtYkydPbnKev0UIAAD8nd8FrO7du5tu19fXa//%2B/dqzZ49uvfVWm6oCAABoPr8LWGf7W4QFBQU6dOhQK1cDAABw/vzutwjPZsKECXrzzTftLgMAAOCc2kzA%2Buyzz7jQKAAAaBP87hThmT7kfuzYMX311Vf69a9/bUNFAAAA58fvAlZ8fLzp7xBK0kUXXaSbb75Z//Ef/2FTVQAAAM3ndwHrscces%2BRx9u/fr3nz5mnHjh3q2LGjbr75Zk2ZMkWSVFFRodmzZ6u0tFTx8fF64IEHNGzYMO99t27dqkcffVQVFRVyOp1asGCBLrnkEkvqAgAAgc/vAtb27dubve1VV111xvHGxkZlZmZq4MCBWrdunfbv36977rlHcXFxGj9%2BvLKystS3b18VFxdr8%2BbNys7O1qZNmxQfH6/KykplZWVp%2BvTpGj58uAoKCjRt2jRt2LDBZ2UNAADgTPwuYN1yyy3eIGMYhnf89LGQkBB98cUXZ3yM6upq9e/fX3PnzlVUVJR69eqla665RiUlJeratasqKiq0Zs0aRUZGqk%2BfPtq2bZuKi4s1ffp0rV27VldccYXuuOMOSf%2B4bMTQoUP1ySef6Oqrr27JXQcAAAHC736LcNmyZerevbuWLFmibdu2qaSkRC%2B88IJ69%2B6te%2B65R%2B%2B8847eeecdbd68%2BayPERsbqyVLligqKkqGYaikpETbt29XSkqKysrKNGDAAEVGRnq3T0pKUmlpqSSprKxMycnJ3rmIiAglJCR45wEAAM7F71aw8vLyNGfOHF133XXesSFDhmjevHmaOXOmfvvb357X440aNUqVlZUaOXKkxowZo0cffVSxsbGmbbp06eK9iKnL5WpyvjmqqqrkcrlMYw5HpM/j%2BoPQ0Hamf0FPTkc/2h6Ho/W/V7xOfNETs2Drh98FrKqqKp8/lyNJUVFROnLkyHk/3tNPP63q6mrNnTtXeXl5crvdCgsLM20TFhYmj8cjSeecb46ioiLl5%2BebxrKyspSTk3Pe9beW6OgIu0vwO/TEjH60HZ06dbDtuXmd%2BKInZsHSD78LWIMGDdKTTz6pxx9/XFFRUZKkmpoaLVq0SNdcc815P97AgQMlSXV1dZoxY4bS0tLkdrtN23g8HrVv316SFB4e7hOmPB6PoqOjm/2c6enpGjVqlGnM4YjUkSPHz7v%2BlhYa2k7R0RGqrXWroaHR7nL8Aj0xox9tjx3HGl4nvuiJmV39sOsHDr8LWA8%2B%2BKAmT56s6667Tr169ZJhGPr2228VExOjl156qVmPUV1drdLSUo0ePdo7dtlll%2BnUqVOKiYnRvn37fLb/8fRdXFycqqurfeb79%2B/f7H2IjY31OR3och1Vfb3/vsEaGhr9uj470BMz%2BtF22Pl94nXii56YBUs//O5EaJ8%2BfbRp0ybde%2B%2B9GjRokBITE5Wbm6v169frX/7lX5r1GAcOHFB2drZ%2B%2BOEH79iuXbvUuXNnJSUl6fPPP9fJkye9cyUlJXI6nZIkp9OpkpIS75zb7dbu3bu98wAAAOfidytYktSxY0dNmjRJBw4c8F7g86KLLmr2/QcOHKiEhAQ98MADmjVrlr7//nstWrRIU6dOVUpKirp166ZZs2Zp2rRpevfdd7Vz507l5eVJktLS0vT888%2BrsLBQI0eOVEFBgXr06MElGgAAQLP53QqWYRhavHixrrrqKo0fP16HDh3Sfffdp9zcXJ06dapZjxEaGqqlS5cqIiJC6enpys3N1S233KLJkyd751wul1JTU7VhwwYVFBQoPj5ektSjRw8988wzKi4u1k033aSamhoVFBRwkVEAANBsfreCtWrVKq1fv14PPfSQ5s2bJ0kaPXq0Hn74YXXt2lW///3vm/U4cXFxPr/J96OePXtq9erVZ73viBEjNGLEiPMvHgAAQH64glVUVKQ5c%2BYoNTXVu2p04403av78%2BXr99ddtrg4AAODc/C5gHThw4Iy/sdevXz%2Bfi3cCAAD4I78LWN27d9ff/vY3n/EPPvjA%2B4F3AAAAf%2BZ3n8G688479fDDD8vlcskwDG3btk1FRUVatWqV7r//frvLAwAAOCe/C1hpaWmqr6/Xs88%2Bq5MnT2rOnDnq3Lmz7r77bmVkZNhdHgAAwDn5XcDauHGjxo4dq/T0dB0%2BfFiGYahLly52lwUAANBsfvcZrHnz5nk/zN65c2fCFQAAaHP8LmD16tVLe/bssbsMAACAC%2BZ3pwj79eunGTNmaMWKFerVq5fCw8NN8z/%2BSRsAAAB/5XcB65tvvlFSUpIkcd0rAADQJvlFwFq4cKGys7MVGRmpVatW2V0OAADAP8UvPoO1cuVKud1u01hmZqaqqqpsqggAAODC%2BUXAMgzDZ2z79u2qq6uzoRoAAIB/jl8ELAAAgEBCwAIAALCY3wSskJAQu0sAAACwhF/8FqEkzZ8/33TNq1OnTmnRokXq0KGDaTuugwUAAPydXwSsq666yueaV4mJiTpy5IiOHDliU1UAAAAXxi8CFte%2BAgAAgcRvPoMFAAAQKAhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMb/4Y88A/nnjlnxodwkAgP/DChYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQI2YP3www/KyclRSkqKhg8frry8PNXV1UmSKioqdNttt2nQoEG68cYbtWXLFtN9t27dqvHjx8vpdGry5MmqqKiwYxcAAEAbFZAByzAM5eTkyO12649//KOeeuopvfvuu1qyZIkMw1BWVpa6du2q4uJiTZgwQdnZ2aqsrJQkVVZWKisrS6mpqXrllVfUuXNnTZs2TYZh2LxXAACgrQjI62Dt27dPpaWl%2BvDDD9W1a1dJUk5Ojh5//HFdd911qqio0Jo1axQZGak%2Bffpo27ZtKi4u1vTp07V27VpdccUVuuOOOyRJeXl5Gjp0qD755BNdffXVdu4WAABoIwJyBSsmJkYrVqzwhqsfHTt2TGVlZRowYIAiIyO940lJSSotLZUklZWVKTk52TsXERGhhIQE7zwAAMC5BGTAio6O1vDhw723GxsbtXr1ag0ZMkQul0uxsbGm7bt06aJDhw5J0jnnAQAAziUgTxGebtGiRdq9e7deeeUVvfDCCwoLCzPNh4WFyePxSJLcbneT881RVVUll8tlGnM4In2Cmz8IDW1n%2Bhf0BG2fw9H6r13eN77oiVmw9SPgA9aiRYv04osv6qmnnlLfvn0VHh6umpoa0zYej0ft27eXJIWHh/uEKY/Ho%2Bjo6GY/Z1FRkfLz801jWVlZysnJucC9aHnR0RF2l%2BB36Anaqk6dOtj23LxvfNETs2DpR0AHrEceeUQvv/yyFi1apDFjxkiS4uLiVF5ebtquurrau7oUFxen6upqn/n%2B/fs3%2B3nT09M1atQo05jDEakjR45fyG60qNDQdoqOjlBtrVsNDY12l%2BMX6AnaOjuONbxvfNETM7v6YdcPHAEbsPLz87VmzRo9%2BeSTGjt2rHfc6XSqsLBQJ0%2Be9K5alZSUKCkpyTtfUlLi3d7tdmv37t3Kzs5u9nPHxsb6nA50uY6qvt5/32ANDY1%2BXZ8d6AnaKjtft7xvfNETs2DpR0AGrK%2B//lpLly5VZmamkpKSTJ%2BHSklJUbdu3TRr1ixNmzZN7777rnbu3Km8vDxJUlpamp5//nkVFhZq5MiRKigoUI8ePbhEAwC0gHFLPrS7hPPy5t1D7S4BbURAftLsnXfeUUNDg5599lkNGzbM9BUaGqqlS5fK5XIpNTVVGzZsUEFBgeLj4yVJPXr00DPPPKPi4mLddNNNqqmpUUFBgUJCQmzeKwAA0FYE5ApWZmamMjMzzzrfs2dPrV69%2BqzzI0aM0IgRI1qiNAAAEAQCcgULAADATgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsJjD7gLwzxm35EO7S2i2N%2B8eancJAAC0ClawAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALOawuwAAANqKcUs%2BtLuE8/Lm3UPtLiFosYIFAABgMVaw0Gra2k9%2B/ztjuN0lAADaKFawAAAALEbAAgAAsBgBCwAAwGIELAAAAIsFfMDyeDwaP368Pv74Y%2B9YRUWFbrvtNg0aNEg33nijtmzZYrpl6ikeAAAOYUlEQVTP1q1bNX78eDmdTk2ePFkVFRWtXTYAAGjDAjpg1dXV6Z577tHevXu9Y4ZhKCsrS127dlVxcbEmTJig7OxsVVZWSpIqKyuVlZWl1NRUvfLKK%2BrcubOmTZsmwzDs2g0AANDGBGzAKi8v169%2B9St99913pvGPPvpIFRUVmjdvnvr06aO77rpLgwYNUnFxsSRp7dq1uuKKK3THHXfo8ssvV15enr7//nt98sknduwGAABogwI2YH3yySe6%2BuqrVVRUZBovKyvTgAEDFBkZ6R1LSkpSaWmpdz45Odk7FxERoYSEBO88AADAuQTshUZ//etfn3Hc5XIpNjbWNNalSxcdOnSoWfMAAADnErAB62zcbrfCwsJMY2FhYfJ4PM2ab46qqiq5XC7TmMMR6RPc4N9CQwN2gRcBzuFo/dfuj%2B8X3jf%2BxY7XwtkE22sk6AJWeHi4ampqTGMej0ft27f3zp8epjwej6Kjo5v9HEVFRcrPzzeNZWVlKScn5wKrhh2ioyPsLgG4IJ06dbDtuXnf%2BBc7XwtnEyyvkaALWHFxcSovLzeNVVdXe1eX4uLiVF1d7TPfv3//Zj9Henq6Ro0aZRpzOCJ15MjxC6wadqitdauhodHuMoDzZsexJjS0naKjI3jf%2BBl/%2Bn/HrteIXSEz6AKW0%2BlUYWGhTp486V21KikpUVJSkne%2BpKTEu73b7dbu3buVnZ3d7OeIjY31OR3och1VfT0HnbakoaGR7xnaJDtft7xv/Is/fi%2BC5TUSHCdCfyIlJUXdunXTrFmztHfvXhUWFmrnzp266aabJElpaWnasWOHCgsLtXfvXs2aNUs9evTQ1VdfbXPlAACgrQi6gBUaGqqlS5fK5XIpNTVVGzZsUEFBgeLj4yVJPXr00DPPPKPi4mLddNNNqqmpUUFBgUJCQmyuHAAAtBVBcYrwq6%2B%2BMt3u2bOnVq9efdbtR4wYoREjRrR0WQAAIEAF3QoWAABASwuKFSzgQvx88V/tLgEA0EaxggUAAGAxAhYAAIDFCFgAAAAW4zNYAAAEqHFLPrS7hGZ78%2B6hdpdgKVawAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACzGbxECQIBpS785BgQqVrAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAHrDOrq6vTAAw8oOTlZw4YN0x/%2B8Ae7SwIAAG2Iw%2B4C/NHChQu1a9cuvfjii6qsrNR9992n%2BPh4jR071u7SAABAG0DAOs2JEye0du1aLV%2B%2BXAkJCUpISNDevXv1xz/%2BkYAFAACahVOEp/nyyy9VX1%2BvxMRE71hSUpLKysrU2NhoY2UAAKCtYAXrNC6XS506dVJYWJh3rGvXrqqrq1NNTY06d%2B58zseoqqqSy%2BUyjTkckYqNjbW8XgAAAoHDEVhrPgSs07jdblO4kuS97fF4mvUYRUVFys/PN41lZ2dr%2BvTp1hT5E58u%2BOdOW1ZVVamoqEjp6ekEwP9DT8zohy964oue%2BKInZsHWj8CKixYIDw/3CVI/3m7fvn2zHiM9PV2vvvqq6Ss9Pd3yWq3gcrmUn5/vs%2BIWzOiJGf3wRU980RNf9MQs2PrBCtZp4uLidOTIEdXX18vh%2BEd7XC6X2rdvr%2Bjo6GY9RmxsbFCkcwAAcGasYJ2mf//%2BcjgcKi0t9Y6VlJRo4MCBateOdgEAgHMjMZwmIiJCEydO1Ny5c7Vz505t3rxZf/jDHzR58mS7SwMAAG1E6Ny5c%2BfaXYS/GTJkiHbv3q0nnnhC27Zt09SpU5WWlmZ3WS2mQ4cOSklJUYcOHewuxW/QEzP64Yue%2BKInvuiJWTD1I8QwDMPuIgAAAAIJpwgBAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsILIDz/8oJycHKWkpGj48OHKy8tTXV2dJKmiokK33XabBg0apBtvvFFbtmyxudqW11Q/SktL9V//9V9KTEzUmDFjtHbtWpurbR1N9eRHR48e1fDhw/Xqq6/aVGXraqonlZWV%2Bu1vfyun06mf//zn2rRpk83Vto6mevLpp58qNTVVgwYN0oQJE7R161abq215%2B/fv15133qnExERdf/31WrFihXcuGI%2BtUtM9CZrjq4Gg0NjYaPzqV78ypkyZYuzZs8fYvn278fOf/9x47LHHjMbGRuOXv/ylce%2B99xrl5eXGsmXLDKfTaXz//fd2l91imupHVVWVkZycbDzxxBPGN998Y2zcuNEYOHCg8e6779pddotqqic/NXv2bKNv375GcXGxTZW2nqZ6curUKWP8%2BPHG1KlTja%2B//tp4%2BeWXjYSEBOOrr76yu%2BwW1VRPqqurjaSkJGP58uXGd999Zzz77LOG0%2Bk0Dh48aHfZLaahocG44YYbjHvvvdf45ptvjPfee88YPHiwsWHDhqA8thpG0z0JpuMrAStIlJeXG3379jVcLpd37PXXXzeGDRtmbN261Rg0aJBx/Phx79ytt95qPP3003aU2iqa6sef/vQnY%2BzYsabtZ8%2Bebdxzzz2tXWaraqonP/rxP9OhQ4cGRcBqqiebN282kpKSjKNHj3rnfve73xlr1qyxo9RW01RP3n77bSMlJcW0fUpKivHmm2%2B2dpmt5ocffjD%2B%2B7//2/Q6yMrKMh566KGgPLYaRtM9CabjK6cIg0RMTIxWrFihrl27msaPHTumsrIyDRgwQJGRkd7xpKQklZaWtnaZraapfvx4yuN0x44da63ybNFUTyTJ4/Fo9uzZmjNnjsLCwuwosdU11ZNPPvlE11xzjaKiorzjS5cuVXp6emuX2aqa6snFF1%2Bsmpoavf322zIMQ5s3b9bx48fVt29fm6ptebGxsVqyZImioqJkGIZKSkq0fft2paSkBOWxVWq6J8F0fHXYXQBaR3R0tIYPH%2B693djYqNWrV2vIkCFyuVyKjY01bd%2BlSxcdOnSotctsNU31o0ePHurRo4d37u9//7veeOMNTZ8%2B3Y5SW01TPZGkZcuWacCAARo2bJhdJba6pnpSUVGh7t27a/HixVq/fr06deqknJwcjR492saKW15TPUlOTtZvfvMb5eTkqF27dmpoaFBeXp4uvfRSGytuPaNGjVJlZaVGjhypMWPG6NFHHw26Y%2BvpTu9JaGho0BxfWcEKUosWLdLu3bv1%2B9//Xm6322dFIiwsTB6Px6bqWt9P%2B/FTJ0%2Be1PTp09W1a9eAX5k43U97Ul5erjVr1mjWrFl2l2Wrn/bkxIkTWrdunWpra7Vs2TJNnDhROTk5%2Btvf/mZ3ma3qpz05fvy4KioqlJ2drbVr12rq1KmaP3%2B%2Bvv76a7vLbBVPP/20li1bpi%2B%2B%2BEJ5eXkcW%2BXbk58K9OMrK1hBaNGiRXrxxRf11FNPqW/fvgoPD1dNTY1pG4/Ho/bt29tUYes6vR8/On78uKZNm6Zvv/1Wf/rTnxQREWFjla3rpz25/PLLlZGRoZycHJ/TQsHk9NdJaGioLr74Ys2dO1ft2rVTQkKCPv30U/35z3/WwIED7S63VZzekyVLlsgwDGVnZ0uSEhIStHPnTr300kt6%2BOGHba625f34fa%2Brq9OMGTOUlpYmt9tt2iaYjq2Sb09mzpypsLCwoDi%2BsoIVZB555BGtXLlSixYt0pgxYyRJcXFxqq6uNm1XXV3ts7QdiM7UD%2Bkfnwe48847tXfvXr344ovq1auXfUW2stN7UllZqc8%2B%2B0yPP/64EhMTlZiYqMrKSj300EOaMmWK3eW2ijO9TmJjY9WrVy%2B1a/f/D6O9e/fWwYMH7SqzVZ2pJ59//rn69etn2q5///6qrKy0o8RWUV1drc2bN5vGLrvsMp06dUoxMTFBeWxtqifHjh0LmuMrASuI5Ofna82aNXryySf1i1/8wjvudDr1%2Beef6%2BTJk96xkpISOZ1OO8psNWfrR2Njo7Kzs3XgwAGtWrVKl19%2BuY1Vtq4z9SQuLk5vv/22XnvtNe9XbGyscnJytGDBApsrbnlNvW/27t2rhoYG79jXX3%2Bt7t2721FmqzpbT2JjY1VeXm7adt%2B%2BfabP3ASaAwcOKDs7Wz/88IN3bNeuXercubOSkpKC8tjaVE8uvvji4Dm%2B2vtLjGgt5eXlRv/%2B/Y2nnnrKqKqqMn3V19cbN954o3H33Xcbe/bsMZ577jlj0KBBAX2tlqb6UVRUZPTr18949913TeNHjhyxu%2BwW1VRPTjdy5MiguUzD2Xpy9OhRY9iwYcbs2bONb7/91li9erUxYMAAY9euXXaX3aKa6slnn31m9O/f31i5cqXx3XffGStXrjQSEhKMPXv22F12i6mvrzdSU1ONO%2B64w9i7d6/x3nvvGddee63xwgsvBOWx1TCa7kkwHV9DDMMw7A55aHmFhYV64oknzjj31Vdfaf/%2B/crNzVVZWZl69uypBx54QNdee20rV9l6murHsGHDzni15ZSUFK1ataqlS7PNuV4jPzVq1ChlZ2crNTW1NUqzzbl6Ul5errlz56qsrEzx8fG69957dcMNN7Ryla3rXD1555139PTTT%2Bu7775T7969NWPGjIA%2Blkj/uLL9I488om3btikiIkI333yz7rrrLoWEhATdsfVHZ%2BvJlClTgub4SsACAACwGJ/BAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsNj/A23Z9DyHQ68cAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3684040795230938841”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>25.299999999999997</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.099999999999994</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.200000000000003</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.400000000000006</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.799999999999997</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.299999999999997</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.900000000000006</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.700000000000003</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.599999999999994</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (20)</td> <td class=”number”>871</td> <td class=”number”>27.1%</td> <td>

<div class=”bar” style=”width:73%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1196</td> <td class=”number”>37.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3684040795230938841”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>19.5</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.200000000000003</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.299999999999997</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.900000000000006</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.200000000000003</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>28.700000000000003</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:60%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.599999999999994</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.700000000000003</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.900000000000006</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.2</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_BLACK15”>PCT_LACCESS_BLACK15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2926</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.8853</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>50.136</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-460642124343488382”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-460642124343488382,#minihistogram-460642124343488382”
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</div> <div class=”row collapse col-md-12” id=”descriptives-460642124343488382”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-460642124343488382”

aria-controls=”quantiles-460642124343488382” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-460642124343488382” aria-controls=”histogram-460642124343488382”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-460642124343488382” aria-controls=”common-460642124343488382”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-460642124343488382” aria-controls=”extreme-460642124343488382”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-460642124343488382”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.057349</td>

</tr> <tr>

<th>Median</th> <td>0.25723</td>

</tr> <tr>

<th>Q3</th> <td>1.6926</td>

</tr> <tr>

<th>95-th percentile</th> <td>9.4192</td>

</tr> <tr>

<th>Maximum</th> <td>50.136</td>

</tr> <tr>

<th>Range</th> <td>50.136</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.6352</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.1454</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1989</td>

</tr> <tr>

<th>Kurtosis</th> <td>27.426</td>

</tr> <tr>

<th>Mean</th> <td>1.8853</td>

</tr> <tr>

<th>MAD</th> <td>2.3677</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.4807</td>

</tr> <tr>

<th>Sum</th> <td>5868.8</td>

</tr> <tr>

<th>Variance</th> <td>17.184</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-460642124343488382”>

<img 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wAAwDAKFgAAgGEULAAAAMN8XrBuvvlmPf/88zp58qSvXxoAAMAnfF6wMjMz9cQTT2jAgAG67777tGvXLlmW5esxAAAAbOPzgjVr1iy9%2B%2B67Wrt2rUJDQ/WnP/1J119/vVasWKEvv/zS1%2BMAAAAY5/THizocDvXv31/9%2B/dXfX29Nm/erLVr12r9%2BvXq27evpk6dqt/97nf%2BGA0AAOCC%2BaVgSVJ1dbVeffVVvfrqq/riiy/Ut29f3XTTTaqqqtJDDz2kvXv3av78%2Bf4aDwAA4BfzecF65ZVX9Morr2jPnj3q2LGjxo4dq9WrV6tbt26ex3Tu3FlLliyhYAEAgIDk84I1f/58DR48WGvWrNGgQYMUEtL6MrDu3bvr1ltv9fVoAAAARvi8YL333nuKiYlRXV2dp1yVlpaqd%2B/eCg0NlST17dtXffv29fVoAAAARvj8pwhPnTqlESNG6KmnnvKszZgxQ2PGjNHRo0d9PQ4AAIBxPi9Yf/7zn3X55Zfr9ttv96y9/vrr6ty5s3Jzc309DgAAgHE%2BL1j//Oc/9eCDDyouLs6z1rFjRz3wwAPavXu3r8cBAAAwzucFy%2Bl06sSJE63W6%2Bvr%2BUR3AAAQFHxesAYNGqScnBz961//8qxVVlYqNzdXAwcO9PU4AAAAxvn8pwjnzJmj22%2B/XcOHD1dUVJQk6cSJE%2Brdu7fmzp3r63EAAACM83nBio2N1csvv6wPPvhAZWVlcjqduuKKK/Tb3/5WDofD1%2BMAAAAY55dflRMaGqqBAwfyliAAAAhKPi9YLpdLK1eu1L59%2B3T27NlWF7a/8847vh4JAADAKJ8XrIcfflj79%2B/XqFGjdMkll/j65QEAAGzn84K1e/dubdiwQenp6b5%2BaQAAAJ/w%2Bcc0tG/fXrGxsb5%2BWQAAAJ/xecEaM2aMNmzYoObmZl%2B/NAAAgE/4/C3Curo6FRYW6u9//7suu%2BwyhYWFeW3ftGmTr0cCAAAwyudnsCRp9OjRGjRokP77v/9bXbp08bqdL7fbrdGjR2vPnj2etZycHF111VVety1btni2FxYWaujQoUpOTlZWVpaOHz/u2WZZlpYtW6bMzExlZGRo6dKlamlpubADBgAAFxWfn8HKzc01tq/GxkbNmjVLZWVlXusVFRWaNWuWbrrpJs9ahw4dJEmlpaWaP3%2B%2BFi1apKSkJC1ZskRz587Vk08%2BKUl69tlnVVhYqPz8fDU1NWn27NmKjY3V9OnTjc0NAACCm1/OYFVXVys/P1%2BzZs3St99%2BqzfffFOHDh06r32Ul5frlltu8fqdhudUVFTo6quvVlxcnOcWEREhSdqyZYtGjhypsWPHKikpSUuXLtXOnTtVWVkp6fu3KLOzs5Wenq7MzEzdf//92rp164UfNAAAuGj4vGB9/fXX%2Bv3vf6%2BXX35Zb731ls6cOaPXX39d48ePV0lJSZv389FHH6lfv34qKCjwWj916pSOHTumbt26/ejzSkpKvD4ionPnzkpMTFRJSYmOHTumo0eP6rrrrvNsT0tL0zfffKPq6urzO1AAAHDR8vlbhH/5y180dOhQ5eTkqG/fvpKkxx57THPmzNGyZcu0efPmNu1n8uTJP7peUVEhh8OhJ554Qu%2B9956io6N1%2B%2B23e94urK6uVnx8vNdzYmNjVVVVJZfLJUle2zt16iRJqqqqavW8n1JdXe3Z1zlOZ/s2P7%2BtQkP9cgLyF3M6A2fec9kGWsaBgGztQ7b2IVv7BGu2Pi9Y%2B/bt09atW71%2BsbPT6dQ999yjW2655YL3f%2BjQITkcDnXv3l233nqr9u7dq4cfflgdOnTQsGHD1NDQ0OonF8PCwuR2u9XQ0OC5/%2B/bpO8vpm%2BrgoIC5efne61lZWUpOzv7lx5WUIiJifT3COctKirC3yMELbK1D9nah2ztE2zZ%2BrxgtbS0/OhP5Z0%2BfVqhoaEXvP%2BxY8dq8ODBio6OliQlJSXpq6%2B%2B0rZt2zRs2DCFh4e3Kktut1sRERFeZSo8PNzzZ0mea7jaYuLEiRoyZIjXmtPZXrW1p3/xcf2YQGv7po/fTqGhIYqKitCJE/VqbuanSE0iW/uQrX3I1j52Z%2Buvf9z7vGANGDBATz75pPLy8jxrdXV1ysvLU2Zm5gXv3%2BFweMrVOd27d9fu3bslSQkJCaqpqfHaXlNTo7i4OCUkJEj6/hdSd%2B3a1fNnSYqLi2vzDPHx8a3eDnS5Tqqp6eL%2BpgzE429ubgnIuQMB2dqHbO1DtvYJtmx9fgrkwQcf1P79%2BzVgwAA1Njbqf/7nfzR48GAdPnxYc%2BbMueD9r1q1StOmTfNaO3jwoLp37y5JSk5OVlFRkWfb0aNHdfToUSUnJyshIUGJiYle24uKipSYmGj8%2BikAABC8fH4GKyEhQTt27FBhYaE%2B%2B%2BwztbS0aNKkSRozZozns6ouxODBg7V%2B/Xo9/fTTGjZsmHbt2qUdO3Z4PiF%2B0qRJuu2225SSkqI%2BffpoyZIluv7663XZZZd5ti9btky/%2Bc1vJEnLly/XHXfcccFzAQCAi4fPC5b0/fVMN998sy37vvbaa7Vq1SqtXr1aq1atUpcuXbR8%2BXKlpqZKklJTU7V48WKtXr1a3333nfr3769HH33U8/zp06fr22%2B/1cyZMxUaGqoJEya0OiMGAADwnzgsy7J8%2BYJTpkz5j9uD9XcRulwnje/T6QzRsGXvG9%2BvXd64t7%2B/R2gzpzNEMTGRqq09HVTXBPwakK19yNY%2BZGsfu7ONi7vE%2BD7bwudnsH74%2Bwabmpr09ddf64svvtDUqVN9PQ4AAIBxv5rfRbhmzRpVVVX5eBoAAADzfjUfpDRmzBi98cYb/h4DAADggv1qCtbHH39s5INGAQAA/M3nbxH%2B2EXup06d0ueff/6Tv18QAAAgkPi8YCUmJnr9HkJJ%2Bq//%2Bi/deuutuvHGG309DgAAgHE%2BL1h/%2BctffP2SAAAAPuXzgrV37942P/a6666zcRIAAAB7%2BLxg3XbbbZ63CP/9M05/uOZwOPTZZ5/5ejwAAIAL5vOC9cQTTygnJ0ezZ89WRkaGwsLC9Mknn2jx4sW66aabdMMNN/h6JAAAAKN8/jENubm5WrBggYYPH66YmBhFRkYqMzNTixcv1rZt29SlSxfPDQAAIBD5vGBVV1f/aHnq0KGDamtrfT0OAACAcT4vWCkpKXrsscd06tQpz1pdXZ3y8vL029/%2B1tfjAAAAGOfza7AeeughTZkyRYMGDVK3bt1kWZa%2B%2BuorxcXFadOmTb4eBwAAwDifF6wePXro9ddfV2FhoSoqKiRJf/jDHzRq1ChFRET4ehwAAADjfF6wJOnSSy/VzTffrMOHD%2Buyyy6T9P2nuQMAAAQDn1%2BDZVmWli1bpuuuu06jR49WVVWV5syZo/nz5%2Bvs2bO%2BHgcAAMA4nxeszZs365VXXtEjjzyisLAwSdLQoUP19ttvKz8/39fjAAAAGOfzglVQUKAFCxZo3Lhxnk9vv%2BGGG5STk6PXXnvN1%2BMAAAAY5/OCdfjwYfXq1avVelJSklwul6/HAQAAMM7nBatLly765JNPWq2/9957ngveAQAAApnPf4pw%2BvTpWrRokVwulyzL0ocffqiCggJt3rxZDz74oK/HAQAAMM7nBWv8%2BPFqamrSunXr1NDQoAULFqhjx4669957NWnSJF%2BPAwAAYJzPC1ZhYaFGjBihiRMn6vjx47IsS7Gxsb4eAwAAwDY%2BvwZr8eLFnovZO3bsSLkCAABBx%2BcFq1u3bvriiy98/bIAAAA%2B4/O3CJOSknT//fdrw4YN6tatm8LDw7225%2Bbm%2BnokAAAAo3xesL788kulpaVJEp97BQAAgpJPCtbSpUs1c%2BZMtW/fXps3b/bFSwIAAPiNT67BevbZZ1VfX%2B%2B1NmPGDFVXV/vi5QEAAHzKJwXLsqxWa3v37lVjY6MvXh4AAMCnfP5ThAAAAMGOggUAAGCYzwqWw%2BHw1UsBAAD4lc8%2BpiEnJ8frM6/Onj2rvLw8RUZGej2Oz8ECAACBzicF67rrrmv1mVepqamqra1VbW2tL0YAAADwGZ8ULD77CgAAXEy4yB0AAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYFvAFy%2B12a/To0dqzZ49nrbKyUtOmTVNKSopuuOEG7dq1y%2Bs5H3zwgUaPHq3k5GRNmTJFlZWVXtufe%2B45DRw4UKmpqZo3b57q6%2Bt9ciwAACA4BHTBamxs1H333aeysjLPmmVZysrKUqdOnbR9%2B3aNGTNGM2fO1JEjRyRJR44cUVZWlsaNG6cXX3xRHTt21D333CPLsiRJb731lvLz87V48WJt3LhRJSUlysvL88vxAQCAwBSwBau8vFy33HKL/vWvf3mt7969W5WVlVq8eLF69Oihu%2B66SykpKdq%2Bfbsk6YUXXtA111yjO%2B64Q1deeaVyc3P1zTff6KOPPpIkbdq0SVOnTtXgwYN17bXXatGiRdq%2BfTtnsQAAQJsFbMH66KOP1K9fPxUUFHitl5SU6Oqrr1b79u09a2lpaSouLvZsT09P92yLiIhQ7969VVxcrObmZn3yySde21NSUnT27FkdPHjQ5iMCAADBwme/7Nm0yZMn/%2Bi6y%2BVSfHy811psbKyqqqp%2BdvuJEyfU2Njotd3pdCo6OtrzfAAAgJ8TsAXrp9TX1yssLMxrLSwsTG63%2B2e3NzQ0eO7/1PPborq6utUvt3Y627cqdhcqNDSwTkA6nYEz77lsAy3jQEC29iFb%2B5CtfYI126ArWOHh4aqrq/Nac7vdateunWf7D8uS2%2B1WVFSUwsPDPfd/uD0iIqLNMxQUFCg/P99rLSsrS9nZ2W3eRzCKiYn09wjnLSqq7V93nB%2BytQ/Z2ods7RNs2QZdwUpISFB5ebnXWk1NjefsUUJCgmpqalpt79Wrl6KjoxUeHq6amhr16NFDktTU1KS6ujrFxcW1eYaJEydqyJAhXmtOZ3vV1p7%2BJYf0kwKt7Zs%2BfjuFhoYoKipCJ07Uq7m5xd/jBBWytQ/Z2ods7WN3tv76x33QFazk5GStX79eDQ0NnrNWRUVFSktL82wvKiryPL6%2Bvl4HDhzQzJkzFRISoj59%2BqioqEj9%2BvWTJBUXF8vpdCopKanNM8THx7d6O9DlOqmmpov7mzIQj7%2B5uSUg5w4EZGsfsrUP2don2LINrFMgbZCRkaHOnTtr7ty5Kisr0/r161VaWqoJEyZIksaPH699%2B/Zp/fr1Kisr09y5c9W1a1dPoZo8ebKefvppvf322yotLdXChQt1yy23nNdbhAAA4OIWdAUrNDRUa9eulcvl0rhx4/Tqq69qzZo1SkxMlCR17dpVjz/%2BuLZv364JEyaorq5Oa9askcPhkCSNGjVKd911lxYsWKA77rhD1157rWbPnu3PQwIAAAHGYZ37CHPYyuU6aXyfTmeIhi173/h%2B7fLGvf39PUKbOZ0hiomJVG3t6aA6Zf1rQLb2IVv7kK197M42Lu4S4/tsi6A7gwUAAOBvFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIYFbcH629/%2Bpquuusrrlp2dLUk6cOCAbr75ZiUnJ2v8%2BPHav3%2B/13MLCws1dOhQJScnKysrS8ePH/fHIQAAgAAVtAWrvLxcgwcP1q5duzy3nJwcnTlzRjNmzFB6erpeeuklpaam6q677tKZM2ckSaWlpZo/f75mzpypgoICnThxQnPnzvXz0QAAgEAStAWroqJCPXv2VFxcnOcWFRWl119/XeHh4XrggQfUo0cPzZ8/X5GRkXrzzTclSVu2bNHIkSM1duxYJSUlaenSpdq5c6cqKyv9fEQAACBQBHXB6tatW6v1kpISpaWlyeFwSJIcDof69u2r4uJiz/b09HTP4zt37qzExESVlJT4ZG4AABD4grJgWZalL7/8Urt27dLw4cM1dOhQLVu2TG63Wy6XS/Hx8V6Pj42NVVVVlSSpurr6P24HAAD4OU5/D2CHI0eOqL6%2BXmGsK/wuAAANHklEQVRhYVq5cqUOHz6snJwcNTQ0eNb/XVhYmNxutySpoaHhP25vi%2BrqarlcLq81p7N9q%2BJ2oUJDA6sfO52BM%2B%2B5bAMt40BAtvYhW/uQrX2CNdugLFhdunTRnj17dOmll8rhcKhXr15qaWnR7NmzlZGR0aosud1utWvXTpIUHh7%2Bo9sjIiLa/PoFBQXKz8/3WsvKyvL8FOPFKiYm0t8jnLeoqLZ/3XF%2ByNY%2BZGsfsrVPsGUblAVLkqKjo73u9%2BjRQ42NjYqLi1NNTY3XtpqaGs/ZpYSEhB/dHhcX1%2BbXnjhxooYMGeK15nS2V23t6fM5hJ8VaG3f9PHbKTQ0RFFRETpxol7NzS3%2BHieokK19yNY%2BZGsfu7P11z/ug7Jgvf/%2B%2B7r//vv197//3XPm6bPPPlN0dLTS0tL01FNPybIsORwOWZalffv26e6775YkJScnq6ioSOPGjZMkHT16VEePHlVycnKbXz8%2BPr7V24Eu10k1NV3c35SBePzNzS0BOXcgIFv7kK19yNY%2BwZZtYJ0CaaPU1FSFh4froYce0qFDh7Rz504tXbpUd955p0aMGKETJ05oyZIlKi8v15IlS1RfX6%2BRI0dKkiZNmqRXXnlFL7zwgg4ePKgHHnhA119/vS677DI/HxUAAAgUQVmwOnTooKefflrHjx/X%2BPHjNX/%2BfE2cOFF33nmnOnTooCeffNJzlqqkpETr169X%2B/btJX1fzhYvXqw1a9Zo0qRJuvTSS5Wbm%2BvnIwIAAIHEYVmW5e8hLgYu10nj%2B3Q6QzRs2fvG92uXN%2B7t7%2B8R2szpDFFMTKRqa08H1SnrXwOytQ/Z2ods7WN3tnFxlxjfZ1sE5RksAAAAf6JgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGOb09wC4eIxc%2BQ9/j3Be/rlkhL9HAAAEKM5gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhTn8PAPxapc9/098jnJc37u3v7xEAAP8fZ7AAAAAMo2ABAAAYRsECAAAwjIL1IxobGzVv3jylp6drwIABeuaZZ/w9EgAACCBc5P4jli5dqv3792vjxo06cuSI5syZo8TERI0YMcLfowEAgABAwfqBM2fO6IUXXtBTTz2l3r17q3fv3iorK9PWrVspWPhVG7nyH/4eoc3%2BuYTvJQDBjbcIf%2BDgwYNqampSamqqZy0tLU0lJSVqaWnx42QAACBQcAbrB1wul2JiYhQWFuZZ69SpkxobG1VXV6eOHTv%2B7D6qq6vlcrm81pzO9oqPjzc6a2go/RiBKdA%2BYwz2%2Bdv9A/09Qpuc%2B/uWv3fNC9ZsKVg/UF9f71WuJHnuu93uNu2joKBA%2Bfn5XmszZ87Un/70JzND/n/V1dWa%2BpsyTZw40Xh5u9hVV1eroKCAbG1AtvYhW/tUV1dr48YNZGuDYM02uOqiAeHh4a2K1Ln77dq1a9M%2BJk6cqJdeesnrNnHiROOzulwu5efntzpbhgtHtvYhW/uQrX3I1j7Bmi1nsH4gISFBtbW1ampqktP5fTwul0vt2rVTVFRUm/YRHx8fVC0cAACcH85g/UCvXr3kdDpVXFzsWSsqKlKfPn0UEkJcAADg59EYfiAiIkJjx47VwoULVVpaqrffflvPPPOMpkyZ4u/RAABAgAhduHDhQn8P8WuTmZmpAwcOaPny5frwww919913a/z48f4e60dFRkYqIyNDkZGR/h4l6JCtfcjWPmRrH7K1TzBm67Asy/L3EAAAAMGEtwgBAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAlRjY6PmzZun9PR0DRgwQM8884y/Rwp4brdbo0eP1p49ezxrlZWVmjZtmlJSUnTDDTdo165dfpww8Bw7dkzZ2dnKyMjQwIEDlZubq8bGRklke6G%2B/vprTZ8%2BXampqbr%2B%2Buu1YcMGzzayNWPGjBl68MEHPfcPHDigm2%2B%2BWcnJyRo/frz279/vx%2BkC09/%2B9jddddVVXrfs7GxJwZcvBStALV26VPv379fGjRv1yCOPKD8/X2%2B%2B%2Baa/xwpYjY2Nuu%2B%2B%2B1RWVuZZsyxLWVlZ6tSpk7Zv364xY8Zo5syZOnLkiB8nDRyWZSk7O1v19fXaunWrVqxYoXfffVcrV64k2wvU0tKiGTNmKCYmRi%2B//LIWLVqkdevW6bXXXiNbQ/76179q586dnvtnzpzRjBkzlJ6erpdeekmpqam66667dObMGT9OGXjKy8s1ePBg7dq1y3PLyckJznwtBJzTp09bffr0sXbv3u1ZW7NmjXXrrbf6carAVVZWZt14443W73//e6tnz56eXD/44AMrJSXFOn36tOexU6dOtVavXu2vUQNKeXm51bNnT8vlcnnWXnvtNWvAgAFke4GOHTtm/e///q918uRJz1pWVpb1yCOPkK0BtbW11qBBg6zx48dbc%2BbMsSzLsl544QVryJAhVktLi2VZltXS0mINGzbM2r59uz9HDTizZs2yli9f3mo9GPPlDFYAOnjwoJqampSamupZS0tLU0lJiVpaWvw4WWD66KOP1K9fPxUUFHitl5SU6Oqrr1b79u09a2lpaSouLvb1iAEpLi5OGzZsUKdOnbzWT506RbYXKD4%2BXitXrlSHDh1kWZaKioq0d%2B9eZWRkkK0B//d//6cxY8boiiuu8KyVlJQoLS1NDodDkuRwONS3b19yPU8VFRXq1q1bq/VgzJeCFYBcLpdiYmIUFhbmWevUqZMaGxtVV1fnx8kC0%2BTJkzVv3jxFRER4rbtcLsXHx3utxcbGqqqqypfjBayoqCgNHDjQc7%2BlpUVbtmxRZmYm2Ro0ZMgQTZ48WampqRo%2BfDjZXqAPP/xQ//znP3XPPfd4rZPrhbMsS19%2B%2BaV27dql4cOHa%2BjQoVq2bJncbndQ5uv09wA4f/X19V7lSpLnvtvt9sdIQemncibjXyYvL08HDhzQiy%2B%2BqOeee45sDVm9erVqamq0cOFC5ebm8t/tBWhsbNQjjzyiBQsWqF27dl7byPXCHTlyxJPjypUrdfjwYeXk5KihoSEo86VgBaDw8PBW/9Gdu//DvxTwy4WHh7c6I%2Bh2u8n4F8jLy9PGjRu1YsUK9ezZk2wN6tOnj6Tvy8H999%2Bv8ePHq76%2B3usxZNs2%2Bfn5uuaaa7zOvJ7zU3/vkmvbdenSRXv27NGll14qh8OhXr16qaWlRbNnz1ZGRkbQ5UvBCkAJCQmqra1VU1OTnM7vv4Qul0vt2rVTVFSUn6cLHgkJCSovL/daq6mpaXUaG//Zo48%2Bqm3btikvL0/Dhw%2BXRLYXqqamRsXFxRo6dKhn7YorrtDZs2cVFxenQ4cOtXo82f68v/71r6qpqfFc33ruf/hvvfWWRo8erZqaGq/Hk%2Bv5i46O9rrfo0cPNTY2Ki4uLujy5RqsANSrVy85nU6vi/%2BKiorUp08fhYTwJTUlOTlZn376qRoaGjxrRUVFSk5O9uNUgSU/P1/PP/%2B8HnvsMY0aNcqzTrYX5vDhw5o5c6aOHTvmWdu/f786duyotLQ0sv2FNm/erNdee007duzQjh07NGTIEA0ZMkQ7duxQcnKyPv74Y1mWJen764n27dtHrufh/fffV79%2B/bzOsH722WeKjo5WWlpa0OXL/40DUEREhMaOHauFCxeqtLRUb7/9tp555hlNmTLF36MFlYyMDHXu3Flz585VWVmZ1q9fr9LSUk2YMMHfowWEiooKrV27Vn/84x%2BVlpYml8vluZHthenTp4969%2B6tefPmqby8XDt37lReXp7uvvtusr0AXbp00eWXX%2B65RUZGKjIyUpdffrlGjBihEydOaMmSJSovL9eSJUtUX1%2BvkSNH%2BnvsgJGamqrw8HA99NBDOnTokHbu3KmlS5fqzjvvDM58/fkZEfjlzpw5Yz3wwANWSkqKNWDAAOvZZ5/190hB4d8/B8uyLOurr76y/vCHP1jXXHONNWrUKOsf//iHH6cLLE8%2B%2BaTVs2fPH71ZFtleqKqqKisrK8vq27ev1b9/f2vdunWezxAiWzPmzJnj%2BRwsy7KskpISa%2BzYsVafPn2sCRMmWJ9%2B%2BqkfpwtMX3zxhTVt2jQrJSXF6t%2B/v/X44497/rsNtnwdlvX/z8cBAADACN4iBAAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADD/h%2Bsyqn4IUqGXwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-460642124343488382”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8070571373242753</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8017713481333834</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07532033866983129</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14341323313692583</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.416874787275482</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.042444821358068444</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.219054664133509</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04007183864993543</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.17315777276231314</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2915)</td> <td class=”number”>2915</td> <td class=”number”>90.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-460642124343488382”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>189</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0455696392644135e-10</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4126198468572791e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.738483421165433e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.216050325461762e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>34.37088606749391</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.494038484447195</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.56869361046067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>40.791256943332556</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.136323722588436</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_CHILD10”><s>PCT_LACCESS_CHILD10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.96149</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_CHILD15”><s>PCT_LACCESS_CHILD15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95773</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HHNV10”>PCT_LACCESS_HHNV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3115</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.1235</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>60.871</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2154843902565798468”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2154843902565798468,#minihistogram-2154843902565798468”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2154843902565798468”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2154843902565798468”

aria-controls=”quantiles-2154843902565798468” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2154843902565798468” aria-controls=”histogram-2154843902565798468”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2154843902565798468” aria-controls=”common-2154843902565798468”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2154843902565798468” aria-controls=”extreme-2154843902565798468”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2154843902565798468”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.67693</td>

</tr> <tr>

<th>Q1</th> <td>1.6091</td>

</tr> <tr>

<th>Median</th> <td>2.5693</td>

</tr> <tr>

<th>Q3</th> <td>3.8001</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.253</td>

</tr> <tr>

<th>Maximum</th> <td>60.871</td>

</tr> <tr>

<th>Range</th> <td>60.871</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.191</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.9534</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.94555</td>

</tr> <tr>

<th>Kurtosis</th> <td>120.75</td>

</tr> <tr>

<th>Mean</th> <td>3.1235</td>

</tr> <tr>

<th>MAD</th> <td>1.6502</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.0895</td>

</tr> <tr>

<th>Sum</th> <td>9782.8</td>

</tr> <tr>

<th>Variance</th> <td>8.7227</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2154843902565798468”>

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PNNZWZmavHixdq0aZMKCgqUkZHhy8MFAAB%2Bxi8L1uHDh5Wfn6/09HRdccUVSkxMVFpamrZt26Zdu3apuLhYixcvVrdu3TRjxgzFxcUpNzdXkvTCCy/oqquu0rRp03TFFVcoPT1dR48e1QcffCBJeuaZZzRlyhQNGTJEV199tRYtWqTc3FzOYgEAgCbzy4IVFRWl9evXq3379h7jp06dUkFBgXr16qVWrVq5xxMSEpSfny9JKigoUGJionsuLCxMvXv3Vn5%2Bvurq6vTRRx95zMfFxens2bM6cODAL3xUAACgufDLghUeHq5Bgwa5n9fX12vz5s3q37%2B/nE6noqOjPV7frl07lZSUSJLX%2BcrKStXU1HjMOxwORUREuN8PAADwYxy%2BXoAJGRkZ2r9/v1588UVt3LhRISEhHvMhISFyuVySpKqqqkbnq6ur3c8be39TlJaWyul0eow5HK0aFLufKzjYv/qxw2Hves/l42852YFsvCMf78incWTjXSDl4/cFKyMjQ5s2bdKqVavUvXt3hYaGqqKiwuM1LpdLLVu2lCSFhoY2KEsul0vh4eEKDQ11Pz9/PiwsrMlrysnJUWZmpsdYSkqK0tLSmryN5igysrVP9hse3vS/u0BDNt6Rj3fk0ziy8S4Q8vHrgvXwww9ry5YtysjI0IgRIyRJMTExOnjwoMfrysrK3GePYmJiVFZW1mC%2BZ8%2BeioiIUGhoqMrKytStWzdJUm1trSoqKhQVFdXkdU2YMEFDhw71GHM4Wqm8/PQFH6M3/vYdgOnj/zHBwS0UHh6mysoq1dXV27rvXzuy8Y58vCOfxpGNd77Ix1ff3PttwcrMzNTzzz%2BvlStXauTIke7x2NhYZWdnq7q62n3WKi8vTwkJCe75vLw89%2Burqqq0f/9%2BpaamqkWLFurTp4/y8vLUr18/SVJ%2Bfr4cDod69OjR5LVFR0c3%2BDjQ6Typ2trA/sfmq%2BOvq6sP%2BOwbQzbekY935NM4svEuEPLxr1Mg/3To0CGtXbtWd911lxISEuR0Ot2PpKQkdejQQXPnzlVRUZGys7NVWFio8ePHS5KSk5O1d%2B9eZWdnq6ioSHPnzlXnzp3dhWrSpEnasGGDtm/frsLCQi1cuFC33HLLBX1ECAAAAptfnsF66623VFdXp3Xr1mndunUec59%2B%2BqnWrl2r%2BfPna9y4cbrsssuUlZWljh07SpI6d%2B6sxx57TI888oiysrIUHx%2BvrKwsBQUFSZJGjRqlo0ePasGCBXK5XPrtb3%2BrWbNm2X6MAADAfwVZ525hjl%2BU03nS%2BDYdjhYavvxd49v9pfzl3gG27s/haKHIyNYqLz/d7E9FXyiy8Y58vCOfxpGNd77IJyrqYlv2cz6//IgQAADg14yCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDbC9YN998s55//nmdPHnS7l0DAADYwvaC1b9/fz3%2B%2BOMaOHCg7rvvPu3cuVOWZdm9DAAAgF%2BM7QXr/vvv19tvv621a9cqODhYf/jDHzR48GCtWrVKn3/%2Bud3LAQAAMM7hi50GBQVpwIABGjBggKqqqvTss89q7dq1ys7OVt%2B%2BfTVlyhT99re/9cXSAAAAfjafFCxJKi0t1auvvqpXX31Vn332mfr27aubbrpJJSUlevDBB7Vnzx7Nnz/fV8sDAAD4yWwvWK%2B88opeeeUV7d69W23bttXYsWO1Zs0adenSxf2aDh06aOnSpRQsAADgl2wvWPPnz9eQIUOUlZWl6667Ti1aNLwMrGvXrrr11lvtXhoAAIARthesd955R5GRkaqoqHCXq8LCQvXu3VvBwcGSpL59%2B6pv3752Lw0AAMAI23%2BK8NSpUxo5cqSefPJJ99j06dM1ZswYHT9%2B3O7lAAAAGGd7wXrkkUd02WWX6fbbb3ePvf766%2BrQoYPS09PtXg4AAIBxthesf/zjH5ozZ46ioqLcY23bttUDDzygXbt22b0cAAAA42wvWA6HQ5WVlQ3Gq6qquKM7AABoFmwvWNddd52WLFmir776yj1WXFys9PR0DRo0yO7lAAAAGGf7TxHOnj1bt99%2Bu0aMGKHw8HBJUmVlpXr37q25c%2BfavRwAAADjbC9Y7dq108svv6z33ntPRUVFcjgcuvzyy3XttdcqKCjI7uUAAAAY55NflRMcHKxBgwbxkSAAAGiWbC9YTqdTq1ev1t69e3X27NkGF7a/9dZbdi8JAADAKNsL1h//%2BEft27dPo0aN0sUXX2z37gEAAH5xthesXbt2af369UpMTLR71wAAALaw/TYNrVq1Urt27ezeLQAAgG1sL1hjxozR%2BvXrVVdXZ/euAQAAbGH7R4QVFRXatm2b/va3v%2BnSSy9VSEiIx/wzzzxj95IAAACM8sltGkaPHu2L3QIAANjC9oKVnp5u9y4BAABsZfs1WJJUWlqqzMxM3X///fr666/1xhtv6PDhw75YCgAAgHG2F6wvv/xSv/vd7/Tyyy/rzTff1JkzZ/T6668rOTlZBQUFdi8HAADAONsL1p/%2B9CcNGzZM27dv10UXXSRJWrlypYYOHarly5fbvRwAAADjbC9Ye/fu1e233%2B7xi50dDofuuece7d%2B/3%2B7lAAAAGGd7waqvr1d9fX2D8dOnTys4OPiCt%2BdyuTR69Gjt3r3bPbZkyRJdeeWVHo/Nmze757dt26Zhw4YpNjZWKSkp%2Buabb9xzlmVp%2BfLl6t%2B/v5KSkrRs2bIfXC8AAEBjbC9YAwcO1BNPPOFRWioqKpSRkaH%2B/ftf0LZqamp03333qaioyGP80KFDuv/%2B%2B7Vz5073Izk5WZJUWFio%2BfPnKzU1VTk5OaqsrNTcuXPd73366ae1bds2ZWZmas2aNXrttdf09NNP/4wjBgAAgcb2gjVnzhzt27dPAwcOVE1Njf7rv/5LQ4YM0ZEjRzR79uwmb%2BfgwYO65ZZb9NVXXzWYO3TokHr16qWoqCj3IywsTJK0efNmXX/99Ro7dqx69OihZcuWaceOHSouLpb03Y1O09LSlJiYqP79%2B2vmzJl67rnnzBw8AAAICLbfBysmJkZbt27Vtm3b9Mknn6i%2Bvl4TJ07UmDFj1KZNmyZv54MPPlC/fv30P//zP4qLi3OPnzp1SidOnFCXLl1%2B8H0FBQW666673M87dOigjh07qqCgQCEhITp%2B/LiuueYa93xCQoKOHj2q0tJSRUdHX/gBAwCAgOOTO7mHhYXp5ptv/lnbmDRp0g%2BOHzp0SEFBQXr88cf1zjvvKCIiQrfffrtuuukmSfrBotSuXTuVlJTI6XRKksd8%2B/btJUklJSUULAAA0CS2F6zJkyd7nf%2B5v4vw8OHDCgoKUteuXXXrrbdqz549%2BuMf/6g2bdpo%2BPDhqq6ubvD7D0NCQuRyuVRdXe1%2B/v056buL6ZuqtLTUXdbOcThaGS9owcE%2BuU/sT%2BZw2Lvec/n4W052IBvvyMc78mkc2XgXSPnYXrA6derk8by2tlZffvmlPvvsM02ZMuVnb3/s2LEaMmSIIiIiJEk9evTQF198oS1btmj48OEKDQ1tUJZcLpfCwsI8ylRoaKj7z5Lc13A1RU5OjjIzMz3GUlJSlJaW9pOPqzmIjGztk/2Ghzf97y7QkI135OMd%2BTSObLwLhHx%2BNb%2BLMCsrSyUlJT97%2B0FBQe5ydU7Xrl21a9cuSd9dA1ZWVuYxX1ZWpqioKMXExEiSnE6nOnfu7P6zJEVFRTV5DRMmTNDQoUM9xhyOViovP31hB/Mj/O07ANPH/2OCg1soPDxMlZVVqqvjVhvfRzbekY935NM4svHOF/n46pt7n1yD9UPGjBmjsWPH6uGHH/5Z23n00Uf14YcfauPGje6xAwcOqGvXrpKk2NhY5eXlady4cZKk48eP6/jx44qNjVVMTIw6duyovLw8d8HKy8tTx44dL%2Bjjvejo6AavdzpPqrY2sP%2Bx%2Ber46%2BrqAz77xpCNd%2BTjHfk0jmy8C4R8fjUF68MPP/xJNxo935AhQ5Sdna0NGzZo%2BPDh2rlzp7Zu3eq%2BtmvixIm67bbbFBcXpz59%2Bmjp0qUaPHiwLr30Uvf88uXL9Zvf/EaStGLFCk2bNu1nrwsAAASOX8VF7qdOndKnn37a6E8GXoirr75ajz76qNasWaNHH31UnTp10ooVKxQfHy9Jio%2BP1%2BLFi7VmzRp9%2B%2B23GjBggMdZszvuuENff/21UlNTFRwcrPHjx2vq1Kk/e10AACBwBFmWZdm5wzlz5nj8HkJJuuiiixQXF6cbb7xRDsev5qSaUU7nSePbdDhaaPjyd41v95fyl3sH2Lo/h6OFIiNbq7z8dLM/FX2hyMY78vGOfBpHNt75Ip%2BoqItt2c/5bG8zf/rTn%2BzeJQAAgK1sL1h79uxp8mu/f0d1AAAAf2F7wbrtttvcHxF%2B/9PJ88eCgoL0ySef2L08AACAn832gvX4449ryZIlmjVrlpKSkhQSEqKPPvpIixcv1k033aQbbrjB7iUBAAAYZfudKtPT07VgwQKNGDFCkZGRat26tfr376/Fixdry5Yt6tSpk/sBAADgj2wvWKWlpT9Yntq0aaPy8nK7lwMAAGCc7QUrLi5OK1eu1KlTp9xjFRUVysjI0LXXXmv3cgAAAIyz/RqsBx98UJMnT9Z1112nLl26yLIsffHFF4qKinLfbR0AAMCf2V6wunXrptdff13btm3ToUOHJEm///3vNWrUKIWFNf/frg0AAJo/n9w2/ZJLLtHNN9%2BsI0eOuH8H4EUXXeSLpQAAABhn%2BzVYlmVp%2BfLluuaaazR69GiVlJRo9uzZmj9/vs6ePWv3cgAAAIyzvWA9%2B%2ByzeuWVV/TQQw8pJCREkjRs2DBt375dmZmZdi8HAADAONsLVk5OjhYsWKBx48a5795%2Bww03aMmSJXrttdfsXg4AAIBxthesI0eOqGfPng3Ge/ToIafTafdyAAAAjLO9YHXq1EkfffRRg/F33nnHfcE7AACAP7P9pwjvuOMOLVq0SE6nU5Zl6f3331dOTo6effZZzZkzx%2B7lAAAAGGd7wUpOTlZtba3WrVun6upqLViwQG3bttW9996riRMn2r0cAAAA42wvWNu2bdPIkSM1YcIEffPNN7IsS%2B3atbN7GQAAAL8Y26/BWrx4sfti9rZt21KuAABAs2N7werSpYs%2B%2B%2Bwzu3cLAABgG9s/IuzRo4dmzpyp9evXq0uXLgoNDfWYT09Pt3tJAAAARtlesD7//HMlJCRIEve9AgAAzZItBWvZsmVKTU1Vq1at9Oyzz9qxSwAAAJ%2Bx5Rqsp59%2BWlVVVR5j06dPV2lpqR27BwAAsJUtBcuyrAZje/bsUU1NjR27BwAAsJXtP0UIAADQ3FGwAAAADLOtYAUFBdm1KwAAAJ%2By7TYNS5Ys8bjn1dmzZ5WRkaHWrVt7vI77YAEAAH9nS8G65pprGtzzKj4%2BXuXl5SovL7djCQAAALaxpWBx7ysAABBIuMgdAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGF%2BX7BcLpdGjx6t3bt3u8eKi4s1depUxcXF6YYbbtDOnTs93vPee%2B9p9OjRio2N1eTJk1VcXOwxv3HjRg0aNEjx8fGaN2%2BeqqqqbDkWAADQPPh1waqpqdF9992noqIi95hlWUpJSVH79u2Vm5urMWPGKDU1VceOHZMkHTt2TCkpKRo3bpxefPFFtW3bVvfcc48sy5Ikvfnmm8rMzNTixYu1adMmFRQUKCMjwyfHBwAA/JPfFqyDBw/qlltu0VdffeUxvmvXLhUXF2vx4sXq1q2bZsyYobi4OOXm5kqSXnjhBV111VWaNm2arrjiCqWnp%2Bvo0aP64IMPJEnPPPOMpkyZoiFDhujqq6/WokWLlJuby1ksAADQZH5bsD744AP169dPOTk5HuMFBQXq1auXWrVq5R5LSEhQfn6%2Bez4xMdE9FxYWpt69eys/P191dXX66KOPPObj4uJ09uxZHThw4Bc%2BIgAA0Fw4fL2An2rSpEk/OO50OhUdHe0x1q5dO5WUlPzofGVlpWpqajzmHQ6HIiIi3O8HAAD4MX5bsBpTVVWlkJAQj7GQkBC5XK4fna%2BurnY/b%2Bz9TVFaWiqn0%2Bkx5nC0alDsfq7gYP86Aelw2Lvec/n4W052IBvvyMc78mkc2XgXSPk0u4IVGhqqiooKjzGXy6WWLVu6588vSy6XS%2BHh4QoNDXU/P38%2BLCysyWvIycmomOvNAAASdUlEQVRRZmamx1hKSorS0tKavI3mKDKytU/2Gx7e9L%2B7QEM23pGPd%2BTTOLLxLhDyaXYFKyYmRgcPHvQYKysrc589iomJUVlZWYP5nj17KiIiQqGhoSorK1O3bt0kSbW1taqoqFBUVFST1zBhwgQNHTrUY8zhaKXy8tM/5ZAa5W/fAZg%2B/h8THNxC4eFhqqysUl1dva37/rUjG%2B/IxzvyaRzZeOeLfHz1zX2zK1ixsbHKzs5WdXW1%2B6xVXl6eEhIS3PN5eXnu11dVVWn//v1KTU1VixYt1KdPH%2BXl5alfv36SpPz8fDkcDvXo0aPJa4iOjm7wcaDTeVK1tYH9j81Xx19XVx/w2TeGbLwjH%2B/Ip3Fk410g5ONfp0CaICkpSR06dNDcuXNVVFSk7OxsFRYWavz48ZKk5ORk7d27V9nZ2SoqKtLcuXPVuXNnd6GaNGmSNmzYoO3bt6uwsFALFy7ULbfcckEfEQIAgMDW7ApWcHCw1q5dK6fTqXHjxunVV19VVlaWOnbsKEnq3LmzHnvsMeXm5mr8%2BPGqqKhQVlaWgoKCJEmjRo3SjBkztGDBAk2bNk1XX321Zs2a5ctDAgAAfibIOncLc/yinM6TxrfpcLTQ8OXvGt/uL%2BUv9w6wdX8ORwtFRrZWefnpZn8q%2BkKRjXfk4x35NI5svPNFPlFRF9uyn/M1uzNYAAAAvkbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAY1mwL1l//%2BlddeeWVHo%2B0tDRJ0v79%2B3XzzTcrNjZWycnJ2rdvn8d7t23bpmHDhik2NlYpKSn65ptvfHEIAADATzXbgnXw4EENGTJEO3fudD%2BWLFmiM2fOaPr06UpMTNRLL72k%2BPh4zZgxQ2fOnJEkFRYWav78%2BUpNTVVOTo4qKys1d%2B5cHx8NAADwJ822YB06dEjdu3dXVFSU%2BxEeHq7XX39doaGheuCBB9StWzfNnz9frVu31htvvCFJ2rx5s66//nqNHTtWPXr00LJly7Rjxw4VFxf7%2BIgAAIC/aNYFq0uXLg3GCwoKlJCQoKCgIElSUFCQ%2Bvbtq/z8fPd8YmKi%2B/UdOnRQx44dVVBQYMu6AQCA/2uWBcuyLH3%2B%2BefauXOnRowYoWHDhmn58uVyuVxyOp2Kjo72eH27du1UUlIiSSotLfU6DwAA8GMcvl7AL%2BHYsWOqqqpSSEiIVq9erSNHjmjJkiWqrq52j39fSEiIXC6XJKm6utrrfFOUlpbK6XR6jDkcrRoUt58rONi/%2BrHDYe96z%2BXjbznZgWy8Ix/vyKdxZONdIOXTLAtWp06dtHv3bl1yySUKCgpSz549VV9fr1mzZikpKalBWXK5XGrZsqUkKTQ09Afnw8LCmrz/nJwcZWZmeoylpKS4f4oxUEVGtvbJfsPDm/53F2jIxjvy8Y58Gkc23gVCPs2yYElSRESEx/Nu3bqppqZGUVFRKisr85grKytzn12KiYn5wfmoqKgm73vChAkaOnSox5jD0Url5acv5BB%2BlL99B2D6%2BH9McHALhYeHqbKySnV19bbu%2B9eObLwjH%2B/Ip3Fk450v8vHVN/fNsmC9%2B%2B67mjlzpv72t7%2B5zzx98sknioiIUEJCgp588klZlqWgoCBZlqW9e/fq7rvvliTFxsYqLy9P48aNkyQdP35cx48fV2xsbJP3Hx0d3eDjQKfzpGprA/sfm6%2BOv66uPuCzbwzZeEc%2B3pFP48jGu0DIx79OgTRRfHy8QkND9eCDD%2Brw4cPasWOHli1bpjvvvFMjR45UZWWlli5dqoMHD2rp0qWqqqrS9ddfL0maOHGiXnnlFb3wwgs6cOCAHnjgAQ0ePFiXXnqpj48KAAD4i2ZZsNq0aaMNGzbom2%2B%2BUXJysubPn68JEybozjvvVJs2bfTEE0%2B4z1IVFBQoOztbrVq1kvRdOVu8eLGysrI0ceJEXXLJJUpPT/fxEQEAAH8SZFmW5etFBAKn86TxbTocLTR8%2BbvGt/tL%2Bcu9A2zdn8PRQpGRrVVefrrZn4q%2BUGTjHfl4Rz6NIxvvfJFPVNTFtuznfM3yDBYAAIAvUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwh68XgMBx/eq/%2B3oJF%2BQv9w7w9RIAAH6KM1gAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwfoBNTU1mjdvnhITEzVw4EA99dRTvl4SAADwIw5fL%2BDXaNmyZdq3b582bdqkY8eOafbs2erYsaNGjhzp66UBAAA/QME6z5kzZ/TCCy/oySefVO/evdW7d28VFRXpueeeo2ABAIAmoWCd58CBA6qtrVV8fLx7LCEhQY8//rjq6%2BvVogWfqgaK61f/3ddLuCB/uXeAr5cAAPgnCtZ5nE6nIiMjFRIS4h5r3769ampqVFFRobZt2/7oNkpLS%2BV0Oj3GHI5Wio6ONrrW4GDKHv7F4Wj618O5rx2%2Bhn4Y%2BXhHPo0jG%2B8CKR8K1nmqqqo8ypUk93OXy9WkbeTk5CgzM9NjLDU1VX/4wx/MLPKfSktLNeU3RZowYYLx8tYclJaWKicnh3x%2BQGlpqTZtWk82jSAf78incWTjXSDl0/wr5AUKDQ1tUKTOPW/ZsmWTtjFhwgS99NJLHo8JEyYYX6vT6VRmZmaDs2X4Dvk0jmy8Ix/vyKdxZONdIOXDGazzxMTEqLy8XLW1tXI4vovH6XSqZcuWCg8Pb9I2oqOjm30zBwAAjeMM1nl69uwph8Oh/Px891heXp769OnDBe4AAKBJaAznCQsL09ixY7Vw4UIVFhZq%2B/bteuqppzR58mRfLw0AAPiJ4IULFy709SJ%2Bbfr376/9%2B/drxYoVev/993X33XcrOTnZ18v6Qa1bt1ZSUpJat27t66X8KpFP48jGO/LxjnwaRzbeBUo%2BQZZlWb5eBAAAQHPCR4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYfqqmpkbz5s1TYmKiBg4cqKeeesrXS/I5l8ul0aNHa/fu3e6x4uJiTZ06VXFxcbrhhhu0c%2BdOH67QN06cOKG0tDQlJSVp0KBBSk9PV01NjSTykaQvv/xSd9xxh%2BLj4zV48GCtX7/ePUc%2B/zJ9%2BnTNmTPH/Xz//v26%2BeabFRsbq%2BTkZO3bt8%2BHq/ONv/71r7ryyis9HmlpaZLIR/ruv8mLFi3SNddco3//93/XypUrde7e5oGQDwXLTy1btkz79u3Tpk2b9NBDDykzM1NvvPGGr5flMzU1NbrvvvtUVFTkHrMsSykpKWrfvr1yc3M1ZswYpaam6tixYz5cqb0sy1JaWpqqqqr03HPPadWqVXr77be1evVq8pFUX1%2Bv6dOnKzIyUi%2B//LIWLVqkdevW6bXXXiOf7/nzn/%2BsHTt2uJ%2BfOXNG06dPV2Jiol566SXFx8drxowZOnPmjA9Xab%2BDBw9qyJAh2rlzp/uxZMkS8vmnJUuW6L333tOGDRu0YsUK/d///Z9ycnICJx8Lfuf06dNWnz59rF27drnHsrKyrFtvvdWHq/KdoqIi68Ybb7R%2B97vfWd27d3fn8t5771lxcXHW6dOn3a%2BdMmWKtWbNGl8t1XYHDx60unfvbjmdTvfYa6%2B9Zg0cOJB8LMs6ceKE9d///d/WyZMn3WMpKSnWQw89RD7/VF5ebl133XVWcnKyNXv2bMuyLOuFF16whg4datXX11uWZVn19fXW8OHDrdzcXF8u1Xb333%2B/tWLFigbj5PPd102vXr2s3bt3u8eeeOIJa86cOQGTD2ew/NCBAwdUW1ur%2BPh491hCQoIKCgpUX1/vw5X5xgcffKB%2B/fopJyfHY7ygoEC9evVSq1at3GMJCQnKz8%2B3e4k%2BExUVpfXr16t9%2B/Ye46dOnSIfSdHR0Vq9erXatGkjy7KUl5enPXv2KCkpiXz%2B6X//9381ZswYXX755e6xgoICJSQkKCgoSJIUFBSkvn37Blw2hw4dUpcuXRqMk4%2BUl5enNm3aKCkpyT02ffp0paenB0w%2BFCw/5HQ6FRkZqZCQEPdY%2B/btVVNTo4qKCh%2BuzDcmTZqkefPmKSwszGPc6XQqOjraY6xdu3YqKSmxc3k%2BFR4erkGDBrmf19fXa/Pmzerfvz/5nGfo0KGaNGmS4uPjNWLECPKR9P777%2Bsf//iH7rnnHo9xsvnu4/fPP/9cO3fu1IgRIzRs2DAtX75cLpeLfPTd9YudOnXS1q1bNXLkSP3nf/6nsrKyVF9fHzD5OHy9AFy4qqoqj3Ilyf3c5XL5Ykm/So3lFMgZZWRkaP/%2B/XrxxRe1ceNG8vmeNWvWqKysTAsXLlR6enrAf/3U1NTooYce0oIFC9SyZUuPuUDPRpKOHTvmzmH16tU6cuSIlixZourqavLRd9fpffnll3r%2B%2BeeVnp4up9OpBQsWKCwsLGDyoWD5odDQ0AZfiOeen/8fwkAWGhra4Iyey%2BUK2IwyMjK0adMmrVq1St27dyef8/Tp00fSd8Vi5syZSk5OVlVVlcdrAimfzMxMXXXVVR5nQM9p7L9BgZKNJHXq1Em7d%2B/WJZdcoqCgIPXs2VP19fWaNWuWkpKSAj4fh8OhU6dOacWKFerUqZOk70rpli1bdNlllwVEPhQsPxQTE6Py8nLV1tbK4fjur9DpdKply5YKDw/38ep%2BPWJiYnTw4EGPsbKysganpgPBww8/rC1btigjI0MjRoyQRD7Sd8ebn5%2BvYcOGuccuv/xynT17VlFRUTp8%2BHCD1wdKPn/%2B859VVlbmvtbz3P8Q33zzTY0ePVplZWUerw%2BkbM6JiIjweN6tWzfV1NQoKioq4POJiopSaGiou1xJ0r/927/p%2BPHjSkpKCoh8uAbLD/Xs2VMOh8PjgsC8vDz16dNHLVrwV3pObGysPv74Y1VXV7vH8vLyFBsb68NV2S8zM1PPP/%2B8Vq5cqVGjRrnHyUc6cuSIUlNTdeLECffYvn371LZtWyUkJAR0Ps8%2B%2B6xee%2B01bd26VVu3btXQoUM1dOhQbd26VbGxsfrwww/d9zSyLEt79%2B4NmGwk6d1331W/fv08znJ%2B8sknioiIUEJCQsDnExsbq5qaGn3%2B%2BefuscOHD6tTp04B8/XD/439UFhYmMaOHauFCxeqsLBQ27dv11NPPaXJkyf7emm/KklJSerQoYPmzp2roqIiZWdnq7CwUOPHj/f10mxz6NAhrV27VnfddZcSEhLkdDrdD/L57mPB3r17a968eTp48KB27NihjIwM3X333QGfT6dOnXTZZZe5H61bt1br1q112WWXaeTIkaqsrNTSpUt18OBBLV26VFVVVbr%2B%2But9vWzbxMfHKzQ0VA8%2B%2BKAOHz6sHTt2aNmyZbrzzjvJR1LXrl01ePBgzZ07VwcOHNC7776r7OxsTZw4MXDy8d0dIvBznDlzxnrggQesuLg4a%2BDAgdbTTz/t6yX9Knz/PliWZVlffPGF9fvf/9666qqrrFGjRll///vffbg6%2Bz3xxBNW9%2B7df/BhWeRjWZZVUlJipaSkWH379rUGDBhgrVu3zn1/HvL5l9mzZ7vvg2VZllVQUGCNHTvW6tOnjzV%2B/Hjr448/9uHqfOOzzz6zpk6dasXFxVkDBgywHnvsMffXDvlYVmVlpTVr1iwrLi7OuvbaawMunyDL%2Buc5OgAAABjBR4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYNj/A2cMo14fk/PVAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2154843902565798468”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.087808198318207</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.803064768439417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7370731642297117</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.776464742317004</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4459863892231977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.6875418151397845</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.137768995705271</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9145233685396565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8917347824530615</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3104)</td> <td class=”number”>3104</td> <td class=”number”>96.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2154843902565798468”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.003213955098514565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.012559523477809332</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.014004634039482773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.032745074701054845</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.67099663318213</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.80599000365968</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.17823056001488</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.54214106795225</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.871017417782205</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HHNV15”>PCT_LACCESS_HHNV15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3123</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>80</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.2582</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>53.515</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5745889724983956224”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-5745889724983956224”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5745889724983956224”

aria-controls=”quantiles-5745889724983956224” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5745889724983956224” aria-controls=”histogram-5745889724983956224”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5745889724983956224” aria-controls=”common-5745889724983956224”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5745889724983956224” aria-controls=”extreme-5745889724983956224”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5745889724983956224”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.76964</td>

</tr> <tr>

<th>Q1</th> <td>1.683</td>

</tr> <tr>

<th>Median</th> <td>2.6716</td>

</tr> <tr>

<th>Q3</th> <td>4.0342</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.4171</td>

</tr> <tr>

<th>Maximum</th> <td>53.515</td>

</tr> <tr>

<th>Range</th> <td>53.515</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.3512</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0043</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.92209</td>

</tr> <tr>

<th>Kurtosis</th> <td>93.439</td>

</tr> <tr>

<th>Mean</th> <td>3.2582</td>

</tr> <tr>

<th>MAD</th> <td>1.6933</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.2297</td>

</tr> <tr>

<th>Sum</th> <td>10198</td>

</tr> <tr>

<th>Variance</th> <td>9.026</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5745889724983956224”>

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MmTO%2BfmkAAACf8HnB6tu3r5599ln1799fjzzyiPbt2yfLsnw9BgAAwFXj84I1a9YsffDBB1q3bp2Cg4P1xz/%2BUQMHDtTKlSv19ddf%2B3ocAAAA4%2Bz%2BeFGbzaZ%2B/fqpX79%2Bqq6u1tatW7Vu3Tpt2LBBvXv31tSpU/Wb3/zGH6MBAAB4zS8FS5LKysr05ptv6s0339RXX32l3r1766677lJpaakef/xxHTx4UAsWLPDXeAAAAFfM5wXrjTfe0BtvvKEDBw6obdu2GjNmjNasWaPOnTu7H9O%2BfXstXbqUggUAAAKSzwvWggULNGjQIK1du1YDBgxQUFDjj4F16dJFkydP9vVoAAAARvi8YP3tb39TVFSUKisr3eWqoKBAPXv2VHBwsCSpd%2B/e6t27t69HAwAAMMLnP0V49uxZDR8%2BXM8//7x7bcaMGRo9erROnjzp63EAAACM83nB%2BtOf/qQbbrhBv//9791rb7/9ttq3b6/MzExfjwMAAGCczwvWP/7xDz322GOKiYlxr7Vt21aPPvqo9u/f7%2BtxAAAAjPN5wbLb7aqqqmq0Xl1dzRXdAQBAi%2BDzgjVgwABlZGTon//8p3utpKREmZmZSk1N9fU4AAAAxvn8pwjnzp2r3//%2B9xo2bJgiIiIkSVVVVerZs6fmzZvn63EAAACM83nBio6O1uuvv66PPvpIhYWFstvtuvHGG/XrX/9aNpvN1%2BMAAAAY55dflRMcHKzU1FTeEgQAAC2SzwuW0%2BnUqlWrdOjQIV24cKHRB9vff/99X48EAABglM8L1hNPPKHDhw9r5MiRuu6663z98gAAAFedzwvW/v37tXHjRiUnJ/v6pQEAAHzC55dpaN26taKjo339sgAAAD7j84I1evRobdy4UfX19b5%2BaQAAAJ/wecGqrKzUm2%2B%2BqQEDBug///M/NWXKFI/b5XK5XBo1apQOHDjgXsvIyNDNN9/scdu2bZt7%2B%2B7duzVkyBA5HA6lpaXp9OnT7m2WZWn58uXq27evUlJStGzZMjU0NHh30AAA4Jril8s0jBo1ysh%2BamtrNWvWLBUWFnqsFxcXa9asWbrrrrvca23atJEkFRQUaMGCBVq8eLG6d%2B%2BupUuXat68eXruueckSS%2B99JJ2796t7Oxs1dXVac6cOYqOjtb06dONzAwAAFo%2BnxeszMxMI/spKirSrFmzfvb3FxYXF2v69Okev1D6om3btmnEiBEaM2aMJGnZsmUaNGiQSkpK1KlTJ23ZskXp6enuD%2BHPnj1bq1evpmABAIAm8/lbhJJUVlam7OxszZo1S99//73effddHTt27LL28cknn6hPnz7KycnxWD979qxOnTqlzp07/%2Bzz8vPzPX6CsX379oqPj1d%2Bfr5OnTqlkydP6vbbb3dvT0pK0vHjx1VWVnZZ8wEAgGuXzwvWt99%2Bq9/%2B9rd6/fXX9d577%2Bn8%2BfN6%2B%2B23NW7cOOXn5zd5P5MmTdL8%2BfMVFhbmsV5cXCybzaZnn31WAwYM0J133qnXX3/dvb2srEyxsbEez4mOjlZpaamcTqckeWxv166dJKm0tPSyjxUAAFybfP4W4Z///GcNGTJEGRkZ6t27tyTp6aef1ty5c7V8%2BXJt3brVq/0fO3ZMNptNXbp00eTJk3Xw4EE98cQTatOmjYYOHaqamhqFhIR4PCckJEQul0s1NTXu%2B/%2B6Tfrxw/RNVVZW5i5rF9ntrRsVO28FB/vlBOQVs9ub37wXMwy0LJsbcvQeGZpBjmaQo/d8XrAOHTqk7du3e/xiZ7vdroceekj33HOP1/sfM2aMBg0apMjISElS9%2B7d9c0332jHjh0aOnSoQkNDG5Ull8ulsLAwjzIVGhrq/rOkRmfK/p2cnBxlZ2d7rKWlpSk9Pf2Kj6sliIoK9/cIlxQR0fS/X1waOXqPDM0gRzPI8cr5vGA1NDT87GUPzp07p%2BDgYK/3b7PZ3OXqoi5dumj//v2SpLi4OJWXl3tsLy8vV0xMjOLi4iT9%2BPsSO3bs6P6zpJ/9wPylTJgwQYMHD/ZYs9tbq6Li3OUdzC8ItO8sTB%2B/CcHBQYqICFNVVbXq67kcx5UiR%2B%2BRoRnkaEZLytFf39z7vGD1799fzz33nLKystxrlZWVysrKUt%2B%2Bfb3e/%2BrVq/Xpp59q06ZN7rWjR4%2BqS5cukiSHw6Hc3FyNHTtWknTy5EmdPHlSDodDcXFxio%2BPV25urrtg5ebmKj4%2B/rLe3ouNjW30eKfzjOrqAvuL1FvN%2Bfjr6xua9XyBghy9R4ZmkKMZ5HjlfF6wHnvsMU2ZMkX9%2B/dXbW2t/uu//kvHjx9XZGSk/vznP3u9/0GDBmnDhg164YUXNHToUO3bt0%2B7du3Sli1bJEkTJ07Uvffeq4SEBPXq1UtLly7VwIED1alTJ/f25cuX61e/%2BpUkacWKFbrvvvu8ngsAAFw7fF6w4uLitGvXLu3evVtffPGFGhoaNHHiRI0ePdp9MVBv3HbbbVq9erXWrFmj1atXq0OHDlqxYoUSExMlSYmJiVqyZInWrFmjH374Qf369dNTTz3lfv706dP1/fffa%2BbMmQoODtb48eM1bdo0r%2BcCAADXDpv1c1fqhHFO5xnj%2B7TbgzR0%2BYfG93u1vPNwP3%2BP0IjdHqSoqHBVVJzjNLgXyNF7ZGgGOZrRknKMibnOL6/r8zNYv/T7Bi%2B%2BlQcAABCofF6wOnTo4HG/rq5O3377rb766itNnTrV1%2BMAAAAY12x%2BF%2BHatWu5WjoAAGgRms2FlEaPHq133nnH32MAAAB4rdkUrE8//dTIhUYBAAD8rVl8yP3s2bP68ssvNWnSJF%2BPAwAAYJzPC1Z8fLzH7yGUpP/4j//Q5MmTdeedd/p6HAAAAON8XrBMXK0dAACgOfN5wTp48GCTH3v77bdfxUkAAACuDp8XrHvvvdf9FuG/XkT%2Bp2s2m01ffPGFr8cDAADwms8L1rPPPquMjAzNmTNHKSkpCgkJ0WeffaYlS5borrvu0h133OHrkQAAAIzy%2BWUaMjMztXDhQg0bNkxRUVEKDw9X3759tWTJEu3YsUMdOnRw3wAAAAKRzwtWWVnZz5anNm3aqKKiwtfjAAAAGOfzgpWQkKCnn35aZ8%2Beda9VVlYqKytLv/71r309DgAAgHE%2B/wzW448/rilTpmjAgAHq3LmzLMvSN998o5iYGG3ZssXX4wAAABjn84LVtWtXvf3229q9e7eKi4slSb/73e80cuRIhYWF%2BXocAAAA43xesCTp%2Buuv1913363vvvtOnTp1kvTj1dwBAABaAp9/BsuyLC1fvly33367Ro0apdLSUs2dO1cLFizQhQsXfD0OAACAcT4vWFu3btUbb7yhJ598UiEhIZKkIUOGaM%2BePcrOzvb1OAAAAMb5vGDl5ORo4cKFGjt2rPvq7XfccYcyMjL01ltv%2BXocAAAA43xesL777jv16NGj0Xr37t3ldDp9PQ4AAIBxPi9YHTp00GeffdZo/W9/%2B5v7A%2B8AAACBzOc/RTh9%2BnQtXrxYTqdTlmXp448/Vk5OjrZu3arHHnvM1%2BMAAAAY5/OCNW7cONXV1Wn9%2BvWqqanRwoUL1bZtWz388MOaOHGir8cBAAAwzucFa/fu3Ro%2BfLgmTJig06dPy7IsRUdH%2B3oMAACAq8bnn8FasmSJ%2B8Psbdu2pVwBAIAWx%2BcFq3Pnzvrqq698/bIAAAA%2B4/O3CLt3767Zs2dr48aN6ty5s0JDQz22Z2Zm%2BnokAAAAo3xesL7%2B%2BmslJSVJEte9AgAALZJPCtayZcs0c%2BZMtW7dWlu3bvXFSwIAAPiNTz6D9dJLL6m6utpjbcaMGSorK/PFywMAAPiUTwqWZVmN1g4ePKja2lpfvDwAAIBP%2BfynCAEAAFo6ChYAAIBhPitYNpvNVy8FAADgVz67TENGRobHNa8uXLigrKwshYeHezyO62ABAIBA55OCdfvttze65lViYqIqKipUUVHhixEAAAB8xicFi2tfAQCAawkfcgcAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGBXzBcrlcGjVqlA4cOOBeKykp0bRp05SQkKA77rhD%2B/bt83jORx99pFGjRsnhcGjKlCkqKSnx2L5p0yalpqYqMTFR8%2BfPV3V1tU%2BOBQAAtAwBXbBqa2v1yCOPqLCw0L1mWZbS0tLUrl077dy5U6NHj9bMmTN14sQJSdKJEyeUlpamsWPH6tVXX1Xbtm310EMPybIsSdJ7772n7OxsLVmyRJs3b1Z%2Bfr6ysrL8cnwAACAwBWzBKioq0j333KN//vOfHuv79%2B9XSUmJlixZoq5du%2BqBBx5QQkKCdu7cKUl65ZVXdOutt%2Bq%2B%2B%2B7TTTfdpMzMTB0/flyffPKJJGnLli2aOnWqBg0apNtuu02LFy/Wzp07OYsFAACaLGAL1ieffKI%2BffooJyfHYz0/P1%2B33HKLWrdu7V5LSkpSXl6ee3tycrJ7W1hYmHr27Km8vDzV19frs88%2B89iekJCgCxcu6OjRo1f5iAAAQEvhs1/2bNqkSZN%2Bdt3pdCo2NtZjLTo6WqWlpb%2B4vaqqSrW1tR7b7Xa7IiMj3c9virKyska/e9Fub93odb0VHBxY/dhub37zXsww0LJsbsjRe2RoBjmaQY7eC9iCdSnV1dUKCQnxWAsJCZHL5frF7TU1Ne77l3p%2BU%2BTk5Cg7O9tjLS0tTenp6U3eR0sUFRXu7xEuKSIizN8jtAjk6D0yNIMczSDHK9fiClZoaKgqKys91lwul1q1auXe/tOy5HK5FBERodDQUPf9n24PC2v6F9mECRM0ePBgjzW7vbUqKs41eR9NEWjfWZg%2BfhOCg4MUERGmqqpq1dc3%2BHucgEWO3iNDM8jRjJaUo7%2B%2BuW9xBSsuLk5FRUUea%2BXl5e635%2BLi4lReXt5oe48ePRQZGanQ0FCVl5era9eukqS6ujpVVlYqJiamyTPExsY2ejvQ6TyjurrA/iL1VnM%2B/vr6hmY9X6AgR%2B%2BRoRnkaAY5XrnAOgXSBA6HQ59//rn77T5Jys3NlcPhcG/Pzc11b6uurtaRI0fkcDgUFBSkXr16eWzPy8uT3W5X9%2B7dfXcQAAAgoLW4gpWSkqL27dtr3rx5Kiws1IYNG1RQUKDx48dLksaNG6dDhw5pw4YNKiws1Lx589SxY0f16dNH0o8fnn/hhRe0Z88eFRQUaNGiRbrnnnsu6y1CAABwbWtxBSs4OFjr1q2T0%2BnU2LFj9eabb2rt2rWKj4%2BXJHXs2FHPPPOMdu7cqfHjx6uyslJr166VzWaTJI0cOVIPPPCAFi5cqPvuu0%2B33Xab5syZ489DAgAAAcZmXbyEOa4qp/OM8X3a7UEauvxD4/u9Wt55uJ%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%2Bf14wZM5ScnKzXXntNiYmJeuCBB3T%2B/HlJUkFBgRYsWKCZM2cqJydHVVVVmjdvnp%2BPBgAABJIWW7CKi4vVrVs3xcTEuG8RERF6%2B%2B23FRoaqkcffVRdu3bVggULFB4ernfffVeStG3bNo0YMUJjxoxR9%2B7dtWzZMu3du1clJSV%2BPiIAABAoWnTB6ty5c6P1/Px8JSUlyWazSZJsNpt69%2B6tvLw89/bk5GT349u3b6/4%2BHjl5%2Bf7ZG4AABD4WmTBsixLX3/9tfbt26dhw4ZpyJAhWr58uVwul5xOp2JjYz0eHx0drdLSUklSWVnZv90OAADwS%2Bz%2BHuBqOHHihKqrqxUSEqJVq1bpu%2B%2B%2BU0ZGhmpqatzr/yokJEQul0uSVFNT82%2B3N0VZWZmcTqfp7lr5AAAM3UlEQVTHmt3eulFx81ZwcGD1Y7u9%2Bc17McNAy7K5IUfvkaEZ5GgGOXqvRRasDh066MCBA7r%2B%2Butls9nUo0cPNTQ0aM6cOUpJSWlUllwul1q1aiVJCg0N/dntYWFhTX79nJwcZWdne6ylpaW5f4rxWhUVFe7vES4pIqLpf7%2B4NHL0HhmaQY5mkOOVa5EFS5IiIyM97nft2lW1tbWKiYlReXm5x7by8nL32aW4uLif3R4TE9Pk154wYYIGDx7ssWa3t1ZFxbnLOYRfFGjfWZg%2BfhOCg4MUERGmqqpq1dc3%2BHucgEWO3iNDM8jRjJaUo7%2B%2BuW%2BRBevDDz/U7Nmz9b//%2B7/uM09ffPGFIiMjlZSUpOeff16WZclms8myLB06dEgPPvigJMnhcCg3N1djx46VJJ08eVInT56Uw%2BFo8uvHxsY2ejvQ6TyjurrA/iL1VnM%2B/vr6hmY9X6AgR%2B%2BRoRnkaAY5XrnAOgXSRImJiQoNDdXjjz%2BuY8eOae/evVq2bJnuv/9%2BDR8%2BXFVVVVq6dKmKioq0dOlSVVdXa8SIEZKkiRMn6o033tArr7yio0eP6tFHH9XAgQPVqVMnPx8VAAAIFC2yYLVp00YvvPCCTp8%2BrXHjxmnBggWaMGGC7r//frVp00bPPfec%2ByxVfn6%2BNmzYoNatW0v6sZwtWbJEa9eu1cSJE3X99dcrMzPTz0cEAAACic2yLMvfQ1wLnM4zxvdptwdp6PIPje/3annn4X7%2BHqERuz1IUVHhqqg4x2lwL5Cj98jQDHI0oyXlGBNznV9et0WewQIAAPAnChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAw%2Bz%2BHgDXjhGr/u7vES5Lc/zdiQCAwMAZLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFKyfUVtbq/nz5ys5OVn9%2B/fXiy%2B%2B6O%2BRAABAALH7e4DmaNmyZTp8%2BLA2b96sEydOaO7cuYqPj9fw4cP9PRoAAAgAFKyfOH/%2BvF555RU9//zz6tmzp3r27KnCwkJt376dgnWNGbHq7/4e4bK883A/f48AAPg/vEX4E0ePHlVdXZ0SExPda0lJScrPz1dDQ4MfJwMAAIGCM1g/4XQ6FRUVpZCQEPdau3btVFtbq8rKSrVt2/YX91FWVian0%2BmxZre3VmxsrNFZg4Ppx/j/AumM219np/p7hGbn4r9n/l17hxzNIEfvUbB%2Borq62qNcSXLfd7lcTdpHTk6OsrOzPdZmzpypP/7xj2aG/D9lZWWa%2BqtCTZgwwXh5u1aUlZUpJyeHDL1Ejt4rKyvT5s0bydBL5GgGOXqPavoToaGhjYrUxfutWrVq0j4mTJig1157zeM2YcIE47M6nU5lZ2c3OluGpiNDM8jRe2RoBjmaQY7e4wzWT8TFxamiokJ1dXWy23%2BMx%2Bl0qlWrVoqIiGjSPmJjY2n8AABcwziD9RM9evSQ3W5XXl6eey03N1e9evVSUBBxAQCAX0Zj%2BImwsDCNGTNGixYtUkFBgfbs2aMXX3xRU6ZM8fdoAAAgQAQvWrRokb%2BHaG769u2rI0eOaMWKFfr444/14IMPaty4cf4e62eFh4crJSVF4eHh/h4lYJGhGeToPTI0gxzNIEfv2CzLsvw9BAAAQEvCW4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAaq2tlbz589XcnKy%2BvfvrxdffNHfIwUMl8ulUaNG6cCBA%2B61kpISTZs2TQkJCbrjjju0b98%2BP07YvJ06dUrp6elKSUlRamqqMjMzVVtbK4kcm%2Brbb7/V9OnTlZiYqIEDB2rjxo3ubWR4%2BWbMmKHHHnvMff/IkSO6%2B%2B675XA4NG7cOB0%2BfNiP0zVvf/3rX3XzzTd73NLT0yWRo7coWAFq2bJlOnz4sDZv3qwnn3xS2dnZevfdd/09VrNXW1urRx55RIWFhe41y7KUlpamdu3aaefOnRo9erRmzpypEydO%2BHHS5smyLKWnp6u6ulrbt2/XypUr9cEHH2jVqlXk2EQNDQ2aMWOGoqKi9Prrr2vx4sVav3693nrrLTK8An/5y1%2B0d%2B9e9/3z589rxowZSk5O1muvvabExEQ98MADOn/%2BvB%2BnbL6Kioo0aNAg7du3z33LyMggRxMsBJxz585ZvXr1svbv3%2B9eW7t2rTV58mQ/TtX8FRYWWnfeeaf129/%2B1urWrZs7v48%2B%2BshKSEiwzp07537s1KlTrTVr1vhr1GarqKjI6tatm%2BV0Ot1rb731ltW/f39ybKJTp05Z//3f/22dOXPGvZaWlmY9%2BeSTZHiZKioqrAEDBljjxo2z5s6da1mWZb3yyivW4MGDrYaGBsuyLKuhocEaOnSotXPnTn%2BO2mzNmjXLWrFiRaN1cvQeZ7AC0NGjR1VXV6fExET3WlJSkvLz89XQ0ODHyZq3Tz75RH369FFOTo7Hen5%2Bvm655Ra1bt3avZaUlKS8vDxfj9jsxcTEaOPGjWrXrp3H%2BtmzZ8mxiWJjY7Vq1Sq1adNGlmUpNzdXBw8eVEpKChlepv/5n//R6NGjdeONN7rX8vPzlZSUJJvNJkmy2Wzq3bs3GV5CcXGxOnfu3GidHL1HwQpATqdTUVFRCgkJca%2B1a9dOtbW1qqys9ONkzdukSZM0f/58hYWFeaw7nU7FxsZ6rEVHR6u0tNSX4wWEiIgIpaamuu83NDRo27Zt6tu3LzlegcGDB2vSpElKTEzUsGHDyPAyfPzxx/rHP/6hhx56yGOdDJvOsix9/fXX2rdvn4YNG6YhQ4Zo%2BfLlcrlc5GiA3d8D4PJVV1d7lCtJ7vsul8sfIwW0S%2BVJlr8sKytLR44c0auvvqpNmzaR42Vas2aNysvLtWjRImVmZvK12ES1tbV68skntXDhQrVq1cpjGxk23YkTJ9x5rVq1St99950yMjJUU1NDjgZQsAJQaGhooy/yi/d/%2Bh8b/LLQ0NBGZ/5cLhdZ/oKsrCxt3rxZK1euVLdu3cjxCvTq1UvSj4Vh9uzZGjdunKqrqz0eQ4aNZWdn69Zbb/U4m3rRpf77SIaNdejQQQcOHND1118vm82mHj16qKGhQXPmzFFKSgo5eomCFYDi4uJUUVGhuro62e0//hU6nU61atVKERERfp4u8MTFxamoqMhjrby8vNHpcfx/Tz31lHbs2KGsrCwNGzZMEjk2VXl5ufLy8jRkyBD32o033qgLFy4oJiZGx44da/R4MvT0l7/8ReXl5e7PoV4sAu%2B9955GjRql8vJyj8eT4aVFRkZ63O/atatqa2sVExNDjl7iM1gBqEePHrLb7R4fNszNzVWvXr0UFMRf6eVyOBz6/PPPVVNT417Lzc2Vw%2BHw41TNV3Z2tl5%2B%2BWU9/fTTGjlypHudHJvmu%2B%2B%2B08yZM3Xq1Cn32uHDh9W2bVslJSWRYRNs3bpVb731lnbt2qVdu3Zp8ODBGjx4sHbt2iWHw6FPP/1UlmVJ%2BvFzRocOHSLDn/Hhhx%2BqT58%2BHmdNv/jiC0VGRiopKYkcvcT/jQNQWFiYxowZo0WLFqmgoEB79uzRiy%2B%2BqClTpvh7tICUkpKi9u3ba968eSosLNSGDRtUUFCg8ePH%2B3u0Zqe4uFjr1q3TH/7wByUlJcnpdLpv5Ng0vXr1Us%2BePTV//nwVFRVp7969ysrK0oMPPkiGTdShQwfdcMMN7lt4eLjCw8N1ww03aPjw4aqqqtLSpUtVVFSkpUuXqrq6WiNGjPD32M1OYmKiQkND9fjjj%2BvYsWPau3evli1bpvvvv58cTfDnNSJw5c6fP289%2BuijVkJCgtW/f3/rpZde8vdIAeVfr4NlWZb1zTffWL/73e%2BsW2%2B91Ro5cqT197//3Y/TNV/PPfec1a1bt5%2B9WRY5NlVpaamVlpZm9e7d2%2BrXr5%2B1fv169/WGyPDyzZ07130dLMuyrPz8fGvMmDFWr169rPHjx1uff/65H6dr3r766itr2rRpVkJCgtWvXz/rmWeecX8tkqN3bJb1f%2Bf/AAAAYARvEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYf8PWKSmvBntASEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5745889724983956224”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9040073130323529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0807507257585356</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0354787039594395</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.172217619557041</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.125516036726128</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.35239984632855</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.888540455691249</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8237595827618455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0038830556794247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3112)</td> <td class=”number”>3112</td> <td class=”number”>96.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>80</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5745889724983956224”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0018013317443853999</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005858824349547977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020729707271763757</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02077190988971637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.24343009503053</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.955790874295914</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>49.19482970211735</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.38088680450114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.51506658816814</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_HISP15”>PCT_LACCESS_HISP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3062</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.0253</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>79.324</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2773596382068090474”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2773596382068090474,#minihistogram-2773596382068090474”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2773596382068090474”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2773596382068090474”

aria-controls=”quantiles-2773596382068090474” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2773596382068090474” aria-controls=”histogram-2773596382068090474”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2773596382068090474” aria-controls=”common-2773596382068090474”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2773596382068090474” aria-controls=”extreme-2773596382068090474”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2773596382068090474”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.020982</td>

</tr> <tr>

<th>Q1</th> <td>0.19757</td>

</tr> <tr>

<th>Median</th> <td>0.58063</td>

</tr> <tr>

<th>Q3</th> <td>1.6804</td>

</tr> <tr>

<th>95-th percentile</th> <td>8.0931</td>

</tr> <tr>

<th>Maximum</th> <td>79.324</td>

</tr> <tr>

<th>Range</th> <td>79.324</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.4828</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.0385</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.4877</td>

</tr> <tr>

<th>Kurtosis</th> <td>67.234</td>

</tr> <tr>

<th>Mean</th> <td>2.0253</td>

</tr> <tr>

<th>MAD</th> <td>2.3136</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8912</td>

</tr> <tr>

<th>Sum</th> <td>6304.9</td>

</tr> <tr>

<th>Variance</th> <td>25.387</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2773596382068090474”>

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dLJkyeVnJys0aNHa/v27WrVqpUeeOABWZYlSXrjjTeUkZGhhQsXavPmzcrLy1N6erov4wIAAJuxZcE6duyYcnNzlZaWpq5duyo%2BPl6pqanavXu39u/fr6KiIi1cuFBdunTRtGnTFBMTox07dkiStm3bpuuvv1733nuvunbtqrS0NJ04cUIffvihJGnLli2aNGmSEhMTdcMNN2jBggXasWMHR7EAAECD2bJghYeHa/369WrTpo3H%2BJkzZ5SXl6eePXsqJCTEPR4XF6fc3FxJUl5enuLj491zwcHB6tWrl3Jzc1VbW6tPPvnEYz4mJkbnz5/XkSNHLnMqAADQVNiyYIWGhmrAgAHu53V1ddq6dav69u0rp9OpiIgIj9e3bt1axcXFkvRP5ysqKlRdXe0x73A4FBYW5t4eAADgX3H4egEmpKen6/Dhw9q%2Bfbs2bdqkwMBAj/nAwEC5XC5JUmVl5UXnq6qq3M8vtn1DlJSUyOl0eow5HCH1it0vFRBgr37scDR8vRey2S1jQ5DNnshmT2Szp6aQzfYFKz09XZs3b9ZTTz2lbt26KSgoSOXl5R6vcblcatasmSQpKCioXllyuVwKDQ1VUFCQ%2B/lP54ODgxu8puzsbGVkZHiMJScnKzU1tcH7aIpatmze6G1CQxv%2BdbcbstkT2eyJbPZk52y2LliLFi3SCy%2B8oPT0dA0ZMkSSFBkZqaNHj3q8rrS01H30KDIyUqWlpfXme/ToobCwMAUFBam0tFRdunSRJNXU1Ki8vFzh4eENXte4ceOUlJTkMeZwhKis7GyjM/4zdmv2jckfEOCv0NBgVVRUqra27jKuyvvIZk9ksyey2ZPJbJfyl3sTbFuwMjIy9OKLL%2BrJJ5/U0KFD3ePR0dHKyspSVVWV%2B6hVTk6O4uLi3PM5OTnu11dWVurw4cNKSUmRv7%2B/oqKilJOToz59%2BkiScnNz5XA41L179wavLSIiot7pQKfze9XUNK0fgMa6lPy1tXVN9utGNnsimz2RzZ7snM1eh0D%2BX2FhoVavXq0//vGPiouLk9PpdD8SEhLUtm1bzZ49WwUFBcrKylJ%2Bfr7Gjh0rSRozZowOHjyorKwsFRQUaPbs2erQoYO7UE2YMEEbNmzQnj17lJ%2Bfr/nz5%2BvOO%2B9s1ClCAADw62bLI1hvvfWWamtrtWbNGq1Zs8Zj7vPPP9fq1as1d%2B5cjR49Wh07dlRmZqbatWsnSerQoYOefvppPfHEE8rMzFRsbKwyMzPl5%2BcnSRo%2BfLhOnDihefPmyeVy6dZbb9XMmTO9nhEAANiXn3XhFua4rJzO743v0%2BHw1%2BBl7xrf7%2BXy1wf7Nfi1Doe/WrZsrrKys7Y9PHwxZLMnstkT2ezJZLbw8N8YWlXj2PIUIQAAwJWMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM83rBuuOOO/Tiiy/q%2B%2B/N3xcKAADgSuD1gtW3b1%2BtXbtW/fv310MPPaR9%2B/aJe50CAICmxOsFa/r06Xr77be1evVqBQQE6E9/%2BpMGDhyop556Sl9%2B%2BaW3lwMAAGCcTz6L0M/PT/369VO/fv1UWVmp5557TqtXr1ZWVpZ69%2B6tSZMm6dZbb/XF0gAAAH4xn33Yc0lJiV599VW9%2Buqr%2BuKLL9S7d2/dfvvtKi4u1qOPPqoDBw5o7ty5vloeAADAJfN6wXrllVf0yiuv6IMPPlCrVq00atQorVq1Sp06dXK/pm3btlqyZAkFCwAA2JLXC9bcuXOVmJiozMxM3XzzzfL3r38ZWOfOnXXXXXd5e2kAAABGeL1gvfPOO2rZsqXKy8vd5So/P1%2B9evVSQECAJKl3797q3bu3t5cGAABghNd/i/DMmTMaOnSonnnmGffY1KlTNXLkSJ06dcrbywEAADDO6wXriSeeUMeOHXXPPfe4x1577TW1bdtWaWlp3l4OAACAcV4vWB999JEeeeQRhYeHu8datWqlhx9%2BWPv37/f2cgAAAIzzesFyOByqqKioN15ZWckd3QEAQJPg9YJ18803a/HixfrHP/7hHisqKlJaWpoGDBjg7eUAAAAY5/XfIpw1a5buueceDRkyRKGhoZKkiooK9erVS7Nnz/b2cgAAAIzzesFq3bq1Xn75Zb333nsqKCiQw%2BHQtddeq5tuukl%2Bfn7eXg4AAIBxPvmonICAAA0YMIBTggAAoEnyesFyOp1asWKFDh48qPPnz9e7sP2tt97y9pIAAACM8nrBeuyxx3To0CENHz5cv/nNb7z99gAAAJed1wvW/v37tX79esXHx3v7rQEAALzC67dpCAkJUevWrb39tgAAAF7j9YI1cuRIrV%2B/XrW1td5%2BawAAAK/w%2BinC8vJy7d69W3//%2B991zTXXKDAw0GN%2By5Yt3l4SAACAUT65TcOIESN88bYAAABe4fWClZaW5u23BAAA8CqvX4MlSSUlJcrIyND06dP1zTff6PXXX9exY8d8sRQAAADjvF6wvv76a/3ud7/Tyy%2B/rDfeeEPnzp3Ta6%2B9pjFjxigvL8/bywEAADDO6wXrz3/%2BswYNGqQ9e/boqquukiQ9%2BeSTSkpK0rJly7y9HAAAAOO8XrAOHjyoe%2B65x%2BODnR0Ohx544AEdPnzY28sBAAAwzusFq66uTnV1dfXGz549q4CAAG8vBwAAwDivF6z%2B/ftr3bp1HiWrvLxc6enp6tu3r7eXAwAAYJzXC9YjjzyiQ4cOqX///qqurtZ//ud/KjExUcePH9esWbO8vRwAAADjvH4frMjISO3cuVO7d%2B/WZ599prq6Oo0fP14jR45UixYtvL0cAAAA43xyJ/fg4GDdcccdvnhrAACAy87rBWvixIn/dL6xn0Xocrk0evRoPfbYY%2BrTp48kafHixXruuec8XvfYY4/prrvukiTt3r1bK1askNPpVP/%2B/bVo0SK1atVKkmRZlpYvX67t27errq5OY8eO1YwZM%2BTv75N7sgIAABvyesFq3769x/Oamhp9/fXX%2BuKLLzRp0qRG7au6ulrTp09XQUGBx3hhYaGmT5%2Bu22%2B/3T124fRjfn6%2B5s6dqwULFqh79%2B5asmSJZs%2BerXXr1kmSNm7cqN27dysjI0M1NTWaOXOmWrdurSlTplxKXAAA8Ct0xXwWYWZmpoqLixu8n6NHj2r69OmyLKveXGFhoaZMmaLw8PB6c1u3btWwYcM0atQoSdLSpUuVmJiooqIiXXPNNdqyZYtSU1MVHx8vSZoxY4ZWrlxJwQIAAA12xZz3GjlypP761782%2BPUffvih%2BvTpo%2BzsbI/xM2fO6PTp0%2BrUqdPPbpeXl%2BcuT5LUtm1btWvXTnl5eTp9%2BrROnTqlG2%2B80T0fFxenEydOqKSkpHGBAADAr5ZPLnL/OR9//HGjbjQ6YcKEnx0vLCyUn5%2Bf1q5dq3feeUdhYWG655573KcLS0pKFBER4bFN69atVVxcLKfTKUke823atJEkFRcX19vuYkpKStz7usDhCGnw9g0VEHDF9OMGcTgavt4L2eyWsSHIZk9ksyey2VNTyHZFXOR%2B5swZff755xctTY1x7Ngx%2Bfn5qXPnzrrrrrt04MABPfbYY2rRooUGDx6sqqoqBQYGemwTGBgol8ulqqoq9/Mfz0k/XEzfUNnZ2crIyPAYS05OVmpq6qXGahJatmze6G1CQ4Mvw0quDGSzJ7LZE9nsyc7ZvF6w2rVr5/E5hJJ01VVX6a677tLvf//7X7z/UaNGKTExUWFhYZKk7t2766uvvtILL7ygwYMHKygoqF5ZcrlcCg4O9ihTQUFB7j9LP9xaoqHGjRunpKQkjzGHI0RlZWcvOdfPsVuzb0z%2BgAB/hYYGq6KiUrW19T9ayc7IZk9ksyey2ZPJbJfyl3sTvF6w/vznP1/W/fv5%2BbnL1QWdO3fW/v37Jf1wo9PS0lKP%2BdLSUoWHhysyMlKS5HQ61aFDB/efJf3sBfMXExERUe90oNP5vWpqmtYPQGNdSv7a2rom%2B3Ujmz2RzZ7IZk92zub1gnXgwIEGv/bHF5s31MqVK/Xxxx9r06ZN7rEjR46oc%2BfOkqTo6Gjl5ORo9OjRkqRTp07p1KlTio6OVmRkpNq1a6ecnBx3wcrJyVG7du2MXz8FAACaLq8XrLvvvtt9ivDHt1j46Zifn58%2B%2B%2ByzRu8/MTFRWVlZ2rBhgwYPHqx9%2B/Zp586d7huYjh8/XnfffbdiYmIUFRWlJUuWaODAgbrmmmvc88uWLdNvf/tbSdLy5ct17733XnpgAADwq%2BP1grV27VotXrxYM2fOVEJCggIDA/XJJ59o4cKFuv3223Xbbbf9ov3fcMMNWrlypVatWqWVK1eqffv2Wr58uWJjYyVJsbGxWrhwoVatWqXvvvtO/fr106JFi9zbT5kyRd98841SUlIUEBCgsWPHavLkyb9oTQAA4NfFz/q5O3VeRkOGDNHcuXN18803e4x/9NFHevjhh/W3v/3Nm8vxGqfze%2BP7dDj8NXjZu8b3e7n89cF%2BDX6tw%2BGvli2bq6zsrG3Pv18M2eyJbPZENnsymS08/DeGVtU4Xv81tJKSknoflyP98FE2ZWVl3l4OAACAcV4vWDExMXryySd15swZ91h5ebnS09N10003eXs5AAAAxnn9GqxHH31UEydO1M0336xOnTrJsix99dVXCg8Pd1%2BIDgAAYGdeL1hdunTRa6%2B9pt27d6uwsFCS9Ic//EHDhw9v1M08AQAArlQ%2B%2BSzCq6%2B%2BWnfccYeOHz/uvj3CVVdd5YulAAAAGOf1a7Asy9KyZct04403asSIESouLtasWbM0d%2B5cnT9/3tvLAQAAMM7rBeu5557TK6%2B8oscff9z92X%2BDBg3Snj176n1AMgAAgB15vWBlZ2dr3rx5Gj16tPvu7bfddpsWL16sXbt2eXs5AAAAxnm9YB0/flw9evSoN969e3f3BysDAADYmdcLVvv27fXJJ5/UG3/nnXfcF7wDAADYmdd/i3DKlClasGCBnE6nLMvS%2B%2B%2B/r%2BzsbD333HN65JFHvL0cAAAA47xesMaMGaOamhqtWbNGVVVVmjdvnlq1aqUHH3xQ48eP9/ZyAAAAjPN6wdq9e7eGDh2qcePG6dtvv5VlWWrdurW3lwEAAHDZeP0arIULF7ovZm/VqhXlCgAANDleL1idOnXSF1984e23BQAA8BqvnyLs3r27ZsyYofXr16tTp04KCgrymE9LS/P2kgAAAIzyesH68ssvFRcXJ0nc9woAADRJXilYS5cuVUpKikJCQvTcc8954y0BAAB8xivXYG3cuFGVlZUeY1OnTlVJSYk33h4AAMCrvFKwLMuqN3bgwAFVV1d74%2B0BAAC8yuu/RQgAANDUUbAAAAAM81rB8vPz89ZbAQAA%2BJTXbtOwePFij3tenT9/Xunp6WrevLnH67gPFgAAsDuvFKwbb7yx3j2vYmNjVVZWprKyMm8sAQAAwGu8UrC49xUAAPg14SJ3AAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDM9gXL5XJpxIgR%2BuCDD9xjRUVFmjx5smJiYnTbbbdp3759Htu89957GjFihKKjozVx4kQVFRV5zG/atEkDBgxQbGys5syZo8rKSq9kAQAATYOtC1Z1dbUeeughFRQUuMcsy1JycrLatGmjHTt2aOTIkUpJSdHJkyclSSdPnlRycrJGjx6t7du3q1WrVnrggQdkWZYk6Y033lBGRoYWLlyozZs3Ky8vT%2Bnp6T7JBwAA7Mm2Bevo0aO688479Y9//MNjfP/%2B/SoqKtLChQvVpUsXTZs2TTExMdqxY4ckadu2bbr%2B%2But17733qmvXrkpLS9OJEyf04YcfSpK2bNmiSZMmKTExUTfccIMWLFigHTt2cBQLAAA0mG0L1ocffqg%2BffooOzvbYzwvL089e/ZUSEiIeywuLk65ubnu%2Bfj4ePdccHCwevXqpdzcXNXW1uqTTz7xmI%2BJidH58%2Bd15MiRy5wIAAA0FQ5fL%2BBSTZgw4WfHnU6nIiIiPMZat26t4uLifzlfUVGh6upqj3mHw6GwsDD39gAAAP%2BKbQvWxVRWViowMNBjLDAwUC6X61/OV1VVuZ9fbPuGKCkpkdPp9BhzOELqFbtfKiDAXgcgHY6Gr/dCNrtlbAiy2RPZ7Ils9tQUsjW5ghUUFKTy8nKPMZfLpWbNmrnnf1qWXC6XQkNDFRQU5H7%2B0/ng4OAGryE7O1sZGRkeY8nJyUpNTW1GUIDSAAATq0lEQVTwPpqili2bN3qb0NCGf93thmz2RDZ7Ips92TlbkytYkZGROnr0qMdYaWmp%2B%2BhRZGSkSktL68336NFDYWFhCgoKUmlpqbp06SJJqqmpUXl5ucLDwxu8hnHjxikpKcljzOEIUVnZ2UuJdFF2a/aNyR8Q4K/Q0GBVVFSqtrbuMq7K%2B8hmT2SzJ7LZk8lsl/KXexOaXMGKjo5WVlaWqqqq3EetcnJyFBcX557Pyclxv76yslKHDx9WSkqK/P39FRUVpZycHPXp00eSlJubK4fDoe7duzd4DREREfVOBzqd36umpmn9ADTWpeSvra1rsl83stkT2eyJbPZk52z2OgTSAAkJCWrbtq1mz56tgoICZWVlKT8/X2PHjpUkjRkzRgcPHlRWVpYKCgo0e/ZsdejQwV2oJkyYoA0bNmjPnj3Kz8/X/PnzdeeddzbqFCEAAPh1a3IFKyAgQKtXr5bT6dTo0aP16quvKjMzU%2B3atZMkdejQQU8//bR27NihsWPHqry8XJmZmfLz85MkDR8%2BXNOmTdO8efN077336oYbbtDMmTN9GQkAANhMkzhF%2BPnnn3s879ixo7Zu3XrR199yyy265ZZbLjo/depUTZ061dj6AADAr0uTO4IFAADgaxQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCwJluw3nzzTV133XUej9TUVEnS4cOHdccddyg6OlpjxozRoUOHPLbdvXu3Bg0apOjoaCUnJ%2Bvbb7/1RQQAAGBTTbZgHT16VImJidq3b5/7sXjxYp07d05Tp05VfHy8XnrpJcXGxmratGk6d%2B6cJCk/P19z585VSkqKsrOzVVFRodmzZ/s4DQAAsJMmW7AKCwvVrVs3hYeHux%2BhoaF67bXXFBQUpIcfflhdunTR3Llz1bx5c73%2B%2BuuSpK1bt2rYsGEaNWqUunfvrqVLl2rv3r0qKirycSIAAGAXTbpgderUqd54Xl6e4uLi5OfnJ0ny8/NT7969lZub656Pj493v75t27Zq166d8vLyvLJuAABgfw5fL%2BBysCxLX375pfbt26d169aptrZWQ4cOVWpqqpxOp6699lqP17du3VoFBQWSpJKSEkVERNSbLy4ubvD7l5SUyOl0eow5HCH19vtLBQTYqx87HA1f74VsdsvYEGSzJ7LZE9nsqSlka5IF6%2BTJk6qsrFRgYKBWrFih48ePa/HixaqqqnKP/1hgYKBcLpckqaqq6p/ON0R2drYyMjI8xpKTk90X2f9atWzZvNHbhIYGX4aVXBnIZk9ksyey2ZOdszXJgtW%2BfXt98MEHuvrqq%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%2BnItDQAANHFN8ggWAACAL1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjm8PUC8OsxbMX/%2BnoJjfLXB/v5egkAAJviCBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2D9jOrqas2ZM0fx8fHq37%2B/nn32WV8vCQAA2Ai/Rfgzli5dqkOHDmnz5s06efKkZs2apXbt2mno0KG%2BXhoAALABCtZPnDt3Ttu2bdMzzzyjXr16qVevXiooKNDzzz9PwfqV4bYSAIBLRcH6iSNHjqimpkaxsbHusbi4OK1du1Z1dXXy9%2BesKq5MdiqElEEATR0F6yecTqdatmypwMBA91ibNm1UXV2t8vJytWrV6l/uo6SkRE6n02PM4QhRRESE0bUGBFD2YE92KoN29OaMAY16/YX/ljTF/6aQzZ6aQjYK1k9UVlZ6lCtJ7ucul6tB%2B8jOzlZGRobHWEpKiv70pz%2BZWeT/Kykp0aTfFmjcuHHGy5uvlZSUKDs7m2w2QzZ7Kikp0ebN68lmM2S7stm3Gl4mQUFB9YrUhefNmjVr0D7GjRunl156yeMxbtw442t1Op3KyMiod7SsKSCbPZHNnshmT2S7snEE6yciIyNVVlammpoaORw/fHmcTqeaNWum0NDQBu0jIiLCto0bAAD8chzB%2BokePXrI4XAoNzfXPZaTk6OoqCgucAcAAA1CY/iJ4OBgjRo1SvPnz1d%2Bfr727NmjZ599VhMnTvT10gAAgE0EzJ8/f76vF3Gl6du3rw4fPqzly5fr/fff1/33368xY8b4elk/q3nz5kpISFDz5s19vRTjyGZPZLMnstkT2a5cfpZlWb5eBAAAQFPCKUIAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsm6qurtacOXMUHx%2Bv/v3769lnn/X1kn4xl8ulESNG6IMPPnCPFRUVafLkyYqJidFtt92mffv2%2BXCFjXf69GmlpqYqISFBAwYMUFpamqqrqyXZP9vXX3%2BtKVOmKDY2VgMHDtT69evdc3bPdsHUqVP1yCOPuJ8fPnxYd9xxh6KjozVmzBgdOnTIh6u7NG%2B%2B%2Baauu%2B46j0dqaqok%2B%2BdzuVxasGCBbrzxRv37v/%2B7nnzySV24l7ads7300kv1vmfXXXedunfvLsne2STp1KlTmjZtmnr37q2kpCRt2rTJPWfnbBQsm1q6dKkOHTqkzZs36/HHH1dGRoZef/11Xy/rklVXV%2Buhhx5SQUGBe8yyLCUnJ6tNmzbasWOHRo4cqZSUFJ08edKHK204y7KUmpqqyspKPf/883rqqaf09ttva8WKFbbPVldXp6lTp6ply5Z6%2BeWXtWDBAq1Zs0a7du2yfbYL/vKXv2jv3r3u5%2BfOndPUqVMVHx%2Bvl156SbGxsZo2bZrOnTvnw1U23tGjR5WYmKh9%2B/a5H4sXL24S%2BRYvXqz33ntPGzZs0PLly/U///M/ys7Otn22C39JufD4%2B9//ro4dO2rixIm2zyZJDz74oEJCQvTSSy9pzpw5WrFihd588037Z7NgO2fPnrWioqKs/fv3u8cyMzOtu%2B66y4erunQFBQXW73//e%2Bt3v/ud1a1bN3eu9957z4qJibHOnj3rfu2kSZOsVatW%2BWqpjXL06FGrW7dultPpdI/t2rXL6t%2B/v%2B2znT592vqv//ov6/vvv3ePJScnW48//rjts1mWZZWVlVk333yzNWbMGGvWrFmWZVnWtm3brKSkJKuurs6yLMuqq6uzBg8ebO3YscOXS2206dOnW8uXL683bvd8ZWVlVs%2BePa0PPvjAPbZu3TrrkUcesX22n1q7dq01aNAgq7q62vbZysvLrW7dulmff/65eywlJcVasGCB7bNxBMuGjhw5opqaGsXGxrrH4uLilJeXp7q6Oh%2Bu7NJ8%2BOGH6tOnj7Kzsz3G8/Ly1LNnT4WEhLjH4uLilJub6%2B0lXpLw8HCtX79ebdq08Rg/c%2BaM7bNFRERoxYoVatGihSzLUk5Ojg4cOKCEhATbZ5Ok//7v/9bIkSN17bXXusfy8vIUFxcnPz8/SZKfn5969%2B5tq1ySVFhYqE6dOtUbt3u%2BnJwctWjRQgkJCe6xqVOnKi0tzfbZfqy8vFzPPPOMpk%2BfrsDAQNtna9asmYKDg/XSSy/p/PnzOnbsmA4ePKgePXrYPhsFy4acTqdatmypwMBA91ibNm1UXV2t8vJyH67s0kyYMEFz5sxRcHCwx7jT6VRERITHWOvWrVVcXOzN5V2y0NBQDRgwwP28rq5OW7duVd%2B%2BfW2f7ceSkpI0YcIExcbGasiQIbbP9v777%2Bujjz7SAw884DFu91zSD6etv/zyS%2B3bt09DhgzRoEGDtGzZMrlcLtvnKyoqUvv27bVz504NHTpU//Ef/6HMzEzV1dXZPtuPvfDCC4qIiNDQoUMl2f/fy6CgIM2bN0/Z2dmKjo7WsGHDdPPNN%2BuOO%2B6wfTaHrxeAxqusrPQoV5Lcz10uly%2BWdFlcLKddM6anp%2Bvw4cPavn27Nm3a1GSyrVq1SqWlpZo/f77S0tJs/X2rrq7W448/rnnz5qlZs2Yec3bOdcHJkyfdOVasWKHjx49r8eLFqqqqsn2%2Bc%2BfO6euvv9aLL76otLQ0OZ1OzZs3T8HBwbbPdoFlWdq2bZvuu%2B8%2B91hTyFZYWKjExETdc889Kigo0KJFi3TTTTfZPhsFy4aCgoLq/Qt24flP/6dgZ0FBQfWOyLlcLltmTE9P1%2BbNm/XUU0%2BpW7duTSpbVFSUpB/KyYwZMzRmzBhVVlZ6vMYu2TIyMnT99dd7HHm84GI/d3bIdUH79u31wQcf6Oqrr5afn5969Oihuro6zZw5UwkJCbbO53A4dObMGS1fvlzt27eX9EOhfOGFF9SxY0dbZ7vgk08%2B0enTpzV8%2BHD3mN3/vXz//fe1fft27d27V82aNVNUVJROnz6tNWvW6JprrrF1Nk4R2lBkZKTKyspUU1PjHnM6nWrWrJlCQ0N9uDKzIiMjVVpa6jFWWlpa75DxlW7RokXauHGj0tPTNWTIEEn2z1ZaWqo9e/Z4jF177bU6f/68wsPDbZvtL3/5i/bs2aPY2FjFxsZq165d2rVrl2JjY23/PbsgLCzMfU2LJHXp0kXV1dW2/r5JP1zzGBQU5C5XkvRv//ZvOnXqVJP53r377ruKj4/X1Vdf7R6ze7ZDhw6pY8eOHqWpZ8%2BeOnnypO2zUbBsqEePHnI4HB4X%2BuXk5CgqKkr%2B/k3nWxodHa1PP/1UVVVV7rGcnBxFR0f7cFWNk5GRoRdffFFPPvmkx9867Z7t%2BPHjSklJ0enTp91jhw4dUqtWrRQXF2fbbM8995x27dqlnTt3aufOnUpKSlJSUpJ27typ6Ohoffzxx%2B77KlmWpYMHD9oi1wXvvvuu%2BvTp43GE8bPPPlNYWJji4uJsnS86OlrV1dX68ssv3WPHjh1T%2B/btm8T3TpLy8/PVu3dvjzG7Z4uIiNDXX3/tcaTq2LFj6tChg%2B2zNZ3/G/%2BKBAcHa9SoUZo/f77y8/O1Z88ePfvss5o4caKvl2ZUQkKC2rZtq9mzZ6ugoEBZWVnKz8/X2LFjfb20BiksLNTq1av1xz/%2BUXFxcXI6ne6H3bNFRUWpV69emjNnjo4ePaq9e/cqPT1d999/v62ztW/fXh07dnQ/mjdvrubNm6tjx44aOnSoKioqtGTJEh09elRLlixRZWWlhg0b5utlN1hsbKyCgoL06KOP6tixY9q7d6%2BWLl2q%2B%2B67z/b5OnfurIEDB2r27Nk6cuSI3n33XWVlZWn8%2BPG2z3ZBQUGBx2%2B2SrJ9tqSkJF111VV69NFH9eWXX%2Bpvf/ub1q5dq7vvvtv22bgPlk2dO3fOevjhh62YmBirf//%2B1saNG329JCN%2BfB8sy7Ksr776yvrDH/5gXX/99dbw4cOt//3f//Xh6hpn3bp1Vrdu3X72YVn2zmZZllVcXGwlJydbvXv3tvr162etWbPGfb8au2e7YNasWe77YFmWZeXl5VmjRo2yoqKirLFjx1qffvqpD1d3ab744gtr8uTJVkxMjNWvXz/r6aefdn/f7J6voqLCmjlzphUTE2PddNNNTSqbZVlWVFSU9c4779Qbt3u2goICa/LkyVbv3r2tQYMGWRs3bmwS3zc/y/r/Y28AAAAwglOEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wFxuR30%2BuUFMgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2773596382068090474”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.121867826880777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.30216693920349025</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5188319908750236</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5198711385818262</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6911343106393006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08480165721152727</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.186520475918503</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7724189823898375</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4024793759503564</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3051)</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2773596382068090474”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.738483421165433e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.424255111408063e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.748209735538758e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.28321450127231e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>52.522198670029475</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.69058885809977</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0256808684387</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.81178871697067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>79.32396806273532</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_LOWI10”><s>PCT_LACCESS_LOWI10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90295</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_LOWI15”><s>PCT_LACCESS_LOWI15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.90394</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_MULTIR15”>PCT_LACCESS_MULTIR15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3077</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1525</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>27.121</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2557506884002386254”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2557506884002386254,#minihistogram2557506884002386254”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2557506884002386254”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2557506884002386254”

aria-controls=”quantiles2557506884002386254” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2557506884002386254” aria-controls=”histogram2557506884002386254”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2557506884002386254” aria-controls=”common2557506884002386254”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2557506884002386254” aria-controls=”extreme2557506884002386254”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2557506884002386254”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.029079</td>

</tr> <tr>

<th>Q1</th> <td>0.22101</td>

</tr> <tr>

<th>Median</th> <td>0.57244</td>

</tr> <tr>

<th>Q3</th> <td>1.318</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1694</td>

</tr> <tr>

<th>Maximum</th> <td>27.121</td>

</tr> <tr>

<th>Range</th> <td>27.121</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.097</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8599</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6138</td>

</tr> <tr>

<th>Kurtosis</th> <td>41.427</td>

</tr> <tr>

<th>Mean</th> <td>1.1525</td>

</tr> <tr>

<th>MAD</th> <td>1.0507</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.1297</td>

</tr> <tr>

<th>Sum</th> <td>3587.7</td>

</tr> <tr>

<th>Variance</th> <td>3.4592</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2557506884002386254”>

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IAFAABgGAELAADAMAIWAACAYZYHrDvvvFM7duzQt99%2Ba/WqAQAALGF5wEpOTtb69et1880365FHHtG%2BffvkcrmsLgMAAKDVWB6w5syZo7/%2B9a9at26dAgMD9dBDD%2BmWW27R008/rc8//9zqcgAAAIyz%2B2KlNptNQ4cO1dChQ1VbW6uXXnpJ69atU25urgYPHqx7771Xt956qy9KAwAA8JpPApYkVVRU6LXXXtNrr72mzz77TIMHD9b48eNVXl6uhQsX6tChQ8rIyPBVeQAAAFfM8oD16quv6tVXX9WBAwfUuXNnjRs3TmvWrFGvXr3cr%2BnWrZuWL19OwAIAAH7J8oCVkZGh1NRUrV27VsOGDVNAQPPLwHr37q2pU6daXRoAAIARlgesd955RxEREaqurnaHq%2BLiYg0YMECBgYGSpMGDB2vw4MFWlwYAAGCE5d8iPHfunEaPHq3nn3/ePXb//ffrjjvu0KlTp6wuBwAAwDjLA9ZTTz2l6667TjNmzHCP7dmzR926dVNmZqbV5QAAABhnecD6xz/%2Boccff1yRkZHusc6dO%2Buxxx7T/v37rS4HAADAOMsDlt1uV01NTbPx2tpa7ugOAADaBcsD1rBhw7Rs2TJ99dVX7rGysjJlZmYqJSXF6nIAAACMs/xbhPPmzdOMGTM0atQohYWFSZJqamo0YMAAzZ8/3%2BpyAAAAjLM8YHXp0kUvv/yy3nvvPZWUlMhut%2Btf/uVf9Otf/1o2m83qcgAAAIzzyU/lBAYGKiUlhVOCAACgXbI8YDkcDj3zzDM6fPiwvv/%2B%2B2YXtr/11ltWlwQAAGCU5QHriSee0EcffaQxY8bommuusXr1AAAArc7ygLV//35t2LBBiYmJVq8aAADAEpbfpiE0NFRdunSxerUAAACWsTxg3XHHHdqwYYMaGxutXjUAAIAlLD9FWF1drfz8fP3tb39Tz549FRQU5DG/ZcsWq0sCAAAwyie3aRg7dqwvVgsAAGAJywNWZmam1asEAACwlOXXYElSRUWFcnJyNGfOHH3zzTd64403dPz4cV%2BUAgAAYJzlAevLL7/Uv/3bv%2Bnll1/Wm2%2B%2BqfPnz2vPnj2aOHGiioqKfvbynE6nxo4dqwMHDrjHli1bpuuvv97jsXXrVvd8fn6%2BRowYobi4OKWlpenMmTPuOZfLpRUrVig5OVlJSUnKyspSU1OTdxsNAACuKpYHrN///vcaMWKE9u7dq1/84heSpFWrVmn48OFasWLFz1pWfX29HnnkEZWUlHiMl5aWas6cOdq3b5/7MXHiRElScXGxMjIyNHv2bOXl5ammpsbjR6ZffPFF5efnKycnR2vWrNGf/vQnvfjii15uNQAAuJpYHrAOHz6sGTNmePyws91u14MPPqgjR460eDnHjh3TXXfdpa%2B%2B%2BqrZXGlpqW644QZFRka6HyEhIZKkrVu36rbbbtO4ceMUGxurrKwsvf322yorK5P0w7cY09PTlZiYqOTkZD366KPatm2bl1sNAACuJpYHrKampkuecvvuu%2B8UGBjY4uUcPHhQQ4YMUV5ensf4uXPndPr0afXq1euS7ysqKvK4i3y3bt0UExOjoqIinT59WqdOndJNN93knk9ISNCJEydUUVHR4toAAMDVzfJvEd5888167rnnlJ2d7R6rrq5Wdna2kpOTW7ycu%2B%2B%2B%2B5LjpaWlstlsWr9%2Bvd555x2Fh4drxowZGj9%2BvKQfLrCPioryeE%2BXLl1UXl4uh8MhSR7zXbt2lSSVl5c3e9/lVFRUuJd1gd0e2uL3t1RgoE%2B%2Bo3DF7Pa2Ve%2BF/vlbH9sSeugd%2Buc9eugd%2Btd6LA9Yjz/%2BuKZNm6abb75Z9fX1%2Bs///E%2BdOHFC4eHh%2Bv3vf%2B/18o8fPy6bzabevXtr6tSpOnTokJ544gl16tRJI0eOVF1dXbObmwYFBcnpdKqurs79/J/npB8upm%2BpvLw85eTkeIylpaUpPT39SjerXYiI6OjrEi4pLCzE1yX4PXroHfrnPXroHfpnnuUBKzo6Wq%2B88ory8/P1ySefqKmpSVOmTNEdd9yhTp06eb38cePGKTU1VeHh4ZKk2NhYffHFF9q%2BfbtGjhyp4ODgZmHJ6XQqJCTEI0wFBwe7/yzJfQ1XS0yePFnDhw/3GLPbQ1VV9d0Vb9el%2BNsnDtPb763AwACFhYWopqZWjY18U/RK0EPv0D/v0UPvXA3989WHe5/cyT0kJER33nlnqyzbZrO5w9UFvXv31v79%2ByX9EPAqKys95isrKxUZGano6GhJksPhUI8ePdx/lqTIyMgW1xAVFdXsdKDD8a0aGtrnzttSbXX7Gxub2mxt/oIeeof%2BeY8eeof%2BmWd5wJo2bdqPznv7W4SrV6/WBx98oE2bNrnHjh49qt69e0uS4uLiVFBQoAkTJkiSTp06pVOnTikuLk7R0dGKiYlRQUGBO2AVFBQoJibG%2BPVTAACg/bI8YHXv3t3jeUNDg7788kt99tlnuvfee71efmpqqnJzc7Vx40aNHDlS%2B/bt0yuvvOIOblOmTNE999yjQYMGaeDAgVq%2BfLluueUW9ezZ0z2/YsUK/fKXv5QkrVy5UjNnzvS6LgAAcPVoM79FuHbtWpWXl3u9/BtvvFGrV6/WmjVrtHr1anXv3l0rV65UfHy8JCk%2BPl5LlizRmjVrdPbsWQ0dOlRLly51v3/WrFn65ptvNHv2bAUGBmrSpEmaPn2613UBAICrh83lcrl8XYQkff311xo3bpz%2B8Y9/%2BLqUVuFwfGt8mXZ7gEaueNf4clvL6w8P9XUJHuz2AEVEdFRV1Xdce3CF6KF36J/36KF3rob%2BRUZe45P1tpmvoX3wwQc/60ajAAAAbVWbuMj93Llz%2BvTTTy9781AAAAB/YnnAiomJ8fgdQkn6xS9%2BoalTp%2Bo3v/mN1eUAAAAYZ3nAMnG3dgAAgLbM8oB16NChFr/2n390GQAAwF9YHrDuuece9ynCf/4C48VjNptNn3zyidXlAQAAeM3ygLV%2B/XotW7ZMc%2BfOVVJSkoKCgvThhx9qyZIlGj9%2BvG6//XarSwIAADDK8ts0ZGZmatGiRRo1apQiIiLUsWNHJScna8mSJdq%2Bfbu6d%2B/ufgAAAPgjywNWRUXFJcNTp06dVFVVZXU5AAAAxlkesAYNGqRVq1bp3Llz7rHq6mplZ2fr17/%2BtdXlAAAAGGf5NVgLFy7UtGnTNGzYMPXq1Usul0tffPGFIiMj3T/IDAAA4M8sD1h9%2BvTRnj17lJ%2Bfr9LSUknSf/zHf2jMmDEKCQmxuhwAAADjLA9YknTttdfqzjvv1Ndff62ePXtK%2BuFu7gAAAO2B5ddguVwurVixQjfddJPGjh2r8vJyzZs3TxkZGfr%2B%2B%2B%2BtLgcAAMA4ywPWSy%2B9pFdffVX//d//raCgIEnSiBEjtHfvXuXk5FhdDgAAgHGWB6y8vDwtWrRIEyZMcN%2B9/fbbb9eyZcv0pz/9yepyAAAAjLM8YH399dfq379/s/HY2Fg5HA6rywEAADDO8oDVvXt3ffjhh83G33nnHfcF7wAAAP7M8m8Rzpo1S08%2B%2BaQcDodcLpfef/995eXl6aWXXtLjjz9udTkAAADGWR6wJk6cqIaGBj377LOqq6vTokWL1LlzZz388MOaMmWK1eUAAAAYZ3nAys/P1%2BjRozV58mSdOXNGLpdLXbp0sboMAACAVmP5NVhLlixxX8zeuXNnwhUAAGh3LA9YvXr10meffWb1agEAACxj%2BSnC2NhYPfroo9qwYYN69eql4OBgj/nMzEyrSwIAADDK8oD1%2BeefKyEhQZK47xUAAGiXLAlYWVlZmj17tkJDQ/XSSy9ZsUoAAACfseQarBdffFG1tbUeY/fff78qKiqsWD0AAIClLAlYLper2dihQ4dUX19vxeoBAAAsZfm3CAEAANo7AhYAAIBhlgUsm81m1aoAAAB8yrLbNCxbtszjnlfff/%2B9srOz1bFjR4/XcR8sAADg7ywJWDfddFOze17Fx8erqqpKVVVVVpQAAABgGUsCFve%2BAgAAVxMucgcAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5vcBy%2Bl0auzYsTpw4IB7rKysTNOnT9egQYN0%2B%2B23a9%2B%2BfR7vee%2B99zR27FjFxcVp2rRpKisr85jftGmTUlJSFB8frwULFqi2ttaSbQEAAO2DXwes%2Bvp6PfLIIyopKXGPuVwupaWlqWvXrtq9e7fuuOMOzZ49WydPnpQknTx5UmlpaZowYYJ27dqlzp0768EHH5TL5ZIkvfnmm8rJydGSJUu0efNmFRUVKTs72yfbBwAA/JPfBqxjx47prrvu0ldffeUxvn//fpWVlWnJkiXq06ePHnjgAQ0aNEi7d%2B%2BWJO3cuVO/%2BtWvNHPmTPXt21eZmZk6ceKEDh48KEnasmWL7r33XqWmpurGG2/Uk08%2Bqd27d3MUCwAAtJjfBqyDBw9qyJAhysvL8xgvKirSDTfcoNDQUPdYQkKCCgsL3fOJiYnuuZCQEA0YMECFhYVqbGzUhx9%2B6DE/aNAgff/99zp69GgrbxEAAGgvLPmx59Zw9913X3Lc4XAoKirKY6xLly4qLy//yfmamhrV19d7zNvtdoWHh7vfDwAA8FP8NmBdTm1trYKCgjzGgoKC5HQ6f3K%2Brq7O/fxy72%2BJiooKORwOjzG7PbRZsPNWYKB/HYC029tWvRf65299bEvooXfon/fooXfoX%2BtpdwErODhY1dXVHmNOp1MdOnRwz18clpxOp8LCwhQcHOx%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%2BsMf/qDdu3dr0qRJqq6u1tq1a2Wz2SRJY8aM0QMPPKBFixZp5syZuvHGGzV37lxfbhIAAPAzNteFW5ijVTkc3xpfpt0eoJEr3jW%2B3Nby%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%2BUfHx8XrggQd0/vx5SVJxcbEyMjI0e/Zs5eXlqaamRvPnz/fx1gAAAH/SbgNWaWmp%2BvXrp8jISPcjLCxMe/bsUXBwsB577DH16dNHGRkZ6tixo9544w1J0tatW3Xbbbdp3Lhxio2NVVZWlt5%2B%2B22VlZX5eIsAAIC/aNcBq1evXs3Gi4qKlJCQIJvNJkmy2WwaPHiwCgsL3fOJiYnu13fr1k0xMTEqKiqypG4AAOD/2mXAcrlc%2Bvzzz7Vv3z6NGjVKI0aM0IoVK%2BR0OuVwOBQVFeXx%2Bi5duqi8vFySVFFR8aPzAAAAP8Xu6wJaw8mTJ1VbW6ugoCA988wz%2Bvrrr7Vs2TJW2sKbAAAMDElEQVTV1dW5x/9ZUFCQnE6nJKmuru5H51uioqJCDofDY8xuD20W3LwVGOhf%2Bdhub1v1Xuifv/WxLaGH3qF/3qOH3qF/raddBqzu3bvrwIEDuvbaa2Wz2dS/f381NTVp7ty5SkpKahaWnE6nOnToIEkKDg6%2B5HxISEiL15%2BXl6ecnByPsbS0NPe3GK9WEREdfV3CJYWFtfzvFpdGD71D/7xHD71D/8xrlwFLksLDwz2e9%2BnTR/X19YqMjFRlZaXHXGVlpfvoUnR09CXnIyMjW7zuyZMna/jw4R5jdnuoqqq%2B%2Bzmb8JP87ROH6e33VmBggMLCQlRTU6vGxiZfl%2BOX6KF36J/36KF3rob%2B%2BerDfbsMWO%2B%2B%2B64effRR/e1vf3Mfefrkk08UHh6uhIQEPf/883K5XLLZbHK5XDp8%2BLB%2B97vfSZLi4uJUUFCgCRMmSJJOnTqlU6dOKS4ursXrj4qKanY60OH4Vg0N7XPnbam2uv2NjU1ttjZ/QQ%2B9Q/%2B8Rw%2B9Q//M869DIC0UHx%2Bv4OBgLVy4UMePH9fbb7%2BtrKws3XfffRo9erRqamq0fPlyHTt2TMuXL1dtba1uu%2B02SdKUKVP06quvaufOnTp69Kgee%2Bwx3XLLLerZs6ePtwoAAPiLdhmwOnXqpI0bN%2BrMmTOaOHGiMjIyNHnyZN13333q1KmTnnvuOfdRqqKiIuXm5io0NFTSD%2BFsyZIlWrt2raZMmaJrr71WmZmZPt4iAADgT2wul8vl6yKuBg7Ht8aXabcHaOSKd40vt7W8/vBQX5fgwW4PUERER1VVfceh8StED71D/7xHD71zNfQvMvIan6y3XR7BAgAA8CUCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDC7rwvA1eO2Z/7u6xJ%2BltcfHurrEgAAfoojWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhtl9XUBbVF9fryeffFJ//vOf1aFDB82cOVMzZ870dVmw2G3P/N3XJfwsrz881NclAAD%2BHwHrErKysvTRRx9p8%2BbNOnnypObNm6eYmBiNHj3a16UBAAA/QMC6yPnz57Vz5049//zzGjBggAYMGKCSkhJt27aNgAUAAFqEgHWRo0ePqqGhQfHx8e6xhIQErV%2B/Xk1NTQoI4LI1tE3%2BdEqT05kA2jsC1kUcDociIiIUFBTkHuvatavq6%2BtVXV2tzp07/%2BQyKioq5HA4PMbs9lBFRUUZrTUwkLAH/%2BRPYdAf/eXRFF%2BXYJkL/w7y7%2BGVoX%2Bth4B1kdraWo9wJcn93Ol0tmgZeXl5ysnJ8RibPXu2HnroITNF/r%2BKigrd%2B8sSTZ482Xh4uxpUVFQoLy%2BP/nmBHnqH/nmvoqJCmzdvoIdXiP61HiLrRYKDg5sFqQvPO3To0KJlTJ48WX/84x89HpMnTzZeq8PhUE5OTrOjZWgZ%2Buc9eugd%2Buc9eugd%2Btd6OIJ1kejoaFVVVamhoUF2%2Bw/tcTgc6tChg8LCwlq0jKioKD4JAABwFeMI1kX69%2B8vu92uwsJC91hBQYEGDhzIBe4AAKBFSAwXCQkJ0bhx47R48WIVFxdr7969euGFFzRt2jRflwYAAPxE4OLFixf7uoi2Jjk5WUeOHNHKlSv1/vvv63e/%2B50mTpzo67IuqWPHjkpKSlLHjh19XYpfon/eo4feoX/eo4feoX%2Btw%2BZyuVy%2BLgIAAKA94RQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAClp%2Bqr6/XggULlJiYqJtvvlkvvPCCr0vyK3/5y190/fXXezzS09N9XZZfcDqdGjt2rA4cOOAeKysr0/Tp0zVo0CDdfvvt2rdvnw8rbNsu1b9ly5Y12x%2B3bt3qwyrbntOnTys9PV1JSUlKSUlRZmam6uvrJbH/tdSP9ZB90Dy7rwvAlcnKytJHH32kzZs36%2BTJk5o3b55iYmI0evRoX5fmF44dO6bU1FQtXbrUPRYcHOzDivxDfX295syZo5KSEveYy%2BVSWlqa%2BvXrp927d2vv3r2aPXu29uzZo5iYGB9W2/Zcqn%2BSVFpaqjlz5mj8%2BPHusU6dOlldXpvlcrmUnp6usLAwbdu2TWfPntWCBQsUEBCgxx57jP2vBX6sh/PmzWMfbAUELD90/vx57dy5U88//7wGDBigAQMGqKSkRNu2bSNgtVBpaan69eunyMhIX5fiN44dO6Y5c%2Bbo4l/X2r9/v8rKyrRjxw6FhoaqT58%2Bev/997V792499NBDPqq27blc/6Qf9sdZs2axP17G8ePHVVhYqL///e/q2rWrJCk9PV3/8z//o2HDhrH/tcCP9fBCwGIfNItThH7o6NGjamhoUHx8vHssISFBRUVFampq8mFl/qO0tFS9evXydRl%2B5eDBgxoyZIjy8vI8xouKinTDDTcoNDTUPZaQkKDCwkKrS2zTLte/c%2BfO6fTp0%2ByPPyIyMlIbNmxwB4MLzp07x/7XQj/WQ/bB1sERLD/kcDgUERGhoKAg91jXrl1VX1%2Bv6upqde7c2YfVtX0ul0uff/659u3bp%2Beee06NjY0aPXq00tPTPXoKT3ffffclxx0Oh6KiojzGunTpovLycivK8huX619paalsNpvWr1%2Bvd955R%2BHh4ZoxY4bHqZqrXVhYmFJSUtzPm5qatHXrViUnJ7P/tdCP9ZB9sHUQsPxQbW1tsyBw4bnT6fRFSX7l5MmT7h4%2B88wz%2Bvrrr7Vs2TLV1dVp4cKFvi7P71xuf2RfbJnjx4/LZrOpd%2B/emjp1qg4dOqQnnnhCnTp10siRI31dXpuUnZ2tI0eOaNeuXdq0aRP73xX45x5%2B/PHH7IOtgIDlh4KDg5v943HheYcOHXxRkl/p3r27Dhw4oGuvvVY2m039%2B/dXU1OT5s6dq/nz5yswMNDXJfqV4OBgVVdXe4w5nU72xRYaN26cUlNTFR4eLkmKjY3VF198oe3bt/Of2yVkZ2dr8%2BbNevrpp9WvXz/2vytwcQ/79u3LPtgKuAbLD0VHR6uqqkoNDQ3uMYfDoQ4dOigsLMyHlfmP8PBw2Ww29/M%2Bffqovr5eZ8%2Be9WFV/ik6OlqVlZUeY5WVlc1O2%2BDSbDab%2Bz%2B2C3r37q3Tp0/7qKK2a%2BnSpXrxxReVnZ2tUaNGSWL/%2B7ku1UP2wdZBwPJD/fv3l91u97iIs6CgQAMHDlRAAH%2BlP%2BXdd9/VkCFDVFtb6x775JNPFB4ezvVrVyAuLk4ff/yx6urq3GMFBQWKi4vzYVX%2BY/Xq1Zo%2BfbrH2NGjR9W7d2/fFNRG5eTkaMeOHVq1apXGjBnjHmf/a7nL9ZB9sHXwv7EfCgkJ0bhx47R48WIVFxdr7969euGFFzRt2jRfl%2BYX4uPjFRwcrIULF%2Br48eN6%2B%2B23lZWVpfvuu8/XpfmlpKQkdevWTfPnz1dJSYlyc3NVXFysSZMm%2Bbo0v5CamqpDhw5p48aN%2Buqrr/S///u/euWVVzRz5kxfl9ZmlJaWat26dfrtb3%2BrhIQEORwO94P9r2V%2BrIfsg63D5rrUTVnQ5tXW1mrx4sX685//rE6dOmnWrFnNPoHg8kpKSvTUU0%2BpsLBQHTt21L//%2B78rLS3N47QhLu/666/Xli1bNGTIEEnSl19%2BqYyMDBUVFem6667TggUL9K//%2Bq8%2BrrLturh/e/fu1Zo1a/TFF1%2Boe/fu%2Bq//%2Bi/deuutPq6y7cjNzdXKlSsvOffpp5%2By/7XAT/WQfdA8AhYAAIBhnCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BD84/vfBFYxhWAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2557506884002386254”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8043776284454447</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4736572024798587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16187731751297074</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.12845496497855796</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.36235922873307247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.140702061098989</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8768511999938557</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3120137344415888</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8693333683503464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3066)</td> <td class=”number”>3066</td> <td class=”number”>95.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2557506884002386254”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>38</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.394831152199691e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.1873108393812605e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.161115441987928e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0793447726680707e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.91188201497283</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.234372136300316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.77323422389639</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.356415968257945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>27.12065186150593</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHASIAN15”>PCT_LACCESS_NHASIAN15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2885</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.24886</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>25.088</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>7.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-914163921270426143”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-914163921270426143,#minihistogram-914163921270426143”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-914163921270426143”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-914163921270426143”

aria-controls=”quantiles-914163921270426143” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-914163921270426143” aria-controls=”histogram-914163921270426143”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-914163921270426143” aria-controls=”common-914163921270426143”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-914163921270426143” aria-controls=”extreme-914163921270426143”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-914163921270426143”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.026974</td>

</tr> <tr>

<th>Median</th> <td>0.0855</td>

</tr> <tr>

<th>Q3</th> <td>0.24907</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.96994</td>

</tr> <tr>

<th>Maximum</th> <td>25.088</td>

</tr> <tr>

<th>Range</th> <td>25.088</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.22209</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.69393</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.7885</td>

</tr> <tr>

<th>Kurtosis</th> <td>578</td>

</tr> <tr>

<th>Mean</th> <td>0.24886</td>

</tr> <tr>

<th>MAD</th> <td>0.2679</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>18.998</td>

</tr> <tr>

<th>Sum</th> <td>774.69</td>

</tr> <tr>

<th>Variance</th> <td>0.48154</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-914163921270426143”>

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Vmjx5slJSUnTzzTdr165dPq999913NWrUKCUnJ2vSpEmqqqrymd%2B4caMGDx6s1NRUzZkzR/X19X5bFwAAsD9bBiyPx6Ps7GzV19dr69atWrFihf76179q5cqV8ng8ysrKUqdOnVRcXKzRo0dr%2BvTpOnbsmCTp2LFjysrK0tixY7V9%2B3Z17NhR999/vzwejyTpjTfeUEFBgXJzc7Vp0yaVlZUpPz8/kMsFAAA2Y8uAdfjwYZWWliovL089e/ZUenq6srOztWPHDu3evVtVVVXKzc1Vjx49NG3aNKWkpKi4uFiStG3bNl111VWaMmWKevbsqby8PB09elR79uyRJG3evFl33nmnMjIy1K9fPy1cuFDFxcUcxQIAAG1my4AVFxen9evXq1OnTj7jp0%2BfVllZma688kpFRkZ6x9PS0lRaWipJKisrU3p6uncuIiJCffr0UWlpqZqbm/XBBx/4zKekpOjMmTM6ePCgxasCAADBwpYBKyoqSoMHD/Y%2Bbmlp0ZYtWzRw4EC5XC7Fx8f7PD82NlYnTpyQpB%2BdP3XqlBobG33mnU6noqOjva8HAAD4Kc5AF2BCfn6%2BDhw4oO3bt2vjxo0KCwvzmQ8LC5Pb7ZYk1dfX/%2BB8Q0OD9/EPvb4tqqur5XK5fMaczshWwe5ChYbaKx87nfap92xv7dZjO6C31qG31qG31gnW3to%2BYOXn52vTpk1asWKFevXqpfDwcNXV1fk8x%2B12q127dpKk8PDwVmHJ7XYrKipK4eHh3sffn4%2BIiGhzTUVFRSooKPAZy8rKUnZ2dpu3EYxiYtoHuoTzFhXV9u87zg%2B9tQ69tQ69tU6w9dbWAWvRokV6/vnnlZ%2Bfr%2BHDh0uSEhISdOjQIZ/n1dTUeI8eJSQkqKamptV87969FR0drfDwcNXU1KhHjx6SpKamJtXV1SkuLq7NdWVmZmro0KE%2BY05npGprvz3vNf4Yu6V90%2Bu3UmhoiKKiInTqVL2am1sCXU5QobfWobfWobfWsbq3gfrl3rYBq6CgQC%2B88IIef/xxjRgxwjuenJyswsJCNTQ0eI9alZSUKC0tzTtfUlLifX59fb0OHDig6dOnKyQkRH379lVJSYkGDBggSSotLZXT6VRSUlKba4uPj291OtDl%2BkZNTRf3D6Ud19/c3GLLuu2A3lqH3lqH3lon2Hprr0Mg/6eyslJr167VPffco7S0NLlcLu9X//791blzZ%2BXk5KiiokKFhYUqLy/X%2BPHjJUnjxo3Tvn37VFhYqIqKCuXk5Khr167eQHX77bdrw4YN2rlzp8rLy7VgwQLddttt53WKEAAAXNz8fgTr1ltv1bhx4zRy5EhdcsklP2sbb775ppqbm7Vu3TqtW7fOZ%2B7jjz/W2rVrNXfuXI0dO1aXX3651qxZo8TERElS165d9cQTT%2Bixxx7TmjVrlJqaqjVr1sjhcEiSRo4cqaNHj2r%2B/Plyu9268cYbNWvWrAtbNAAAuKg4PGdvYe4ny5cv16uvvqra2lr9%2B7//u8aOHatBgwZ5A06wcrm%2BMb5NpzNEw5a9Y3y7Vnn9wUGBLqHNnM4QxcS0V23tt0F1yPqXgN5ah95ah95ax%2BrexsX9vIM5F8rvpwhnzJihv/71r1q7dq1CQ0P1wAMP6Prrr9eKFSv06aef%2BrscAAAA4wJykbvD4dCgQYM0aNAg1dfX67nnntPatWtVWFioq6%2B%2BWnfeeaduvPHGQJQGAABwwQL2V4TV1dV65ZVX9Morr%2BiTTz7R1VdfrVtuuUUnTpzQo48%2Bqr1792ru3LmBKg8AAOBn83vAevnll/Xyyy/r/fffV8eOHTVmzBitXr1a3bp18z6nc%2BfOWrJkCQELAADYkt8D1ty5c5WRkaE1a9ZoyJAhCglpfRlY9%2B7dNXHiRH%2BXBgAAYITfA9bbb7%2BtmJgY1dXVecNVeXm5%2BvTpo9DQUEnS1VdfrauvvtrfpQEAABjh978iPH36tEaMGKGnn37aO3bvvfdq9OjROn78uL/LAQAAMM7vAeuxxx7T5Zdfrrvuuss79tprr6lz587Ky8vzdzkAAADG%2BT1g/c///I8eeeQRnw9P7tixox5%2B%2BGHt3r3b3%2BUAAAAY5/eA5XQ6derUqVbj9fX18vNN5QEAACzh94A1ZMgQLV68WF988YV3rKqqSnl5eRo8eLC/ywEAADDO739FOHv2bN11110aPny4oqKiJEmnTp1Snz59lJOT4%2B9yAAAAjPN7wIqNjdWLL76od999VxUVFXI6nfrNb36ja6%2B9Nug/8BkAAFwcAvJROaGhoRo8eDCnBAEAQFDye8ByuVxauXKl9u3bpzNnzrS6sP3NN9/0d0kAAABG%2BT1gzZs3T/v379fIkSN1ySWX%2BPvtAQAALOf3gLV7926tX79e6enp/n5rAAAAv/D7bRoiIyMVGxvr77cFAADwG78HrNGjR2v9%2BvVqbm7291sDAAD4hd9PEdbV1WnHjh3629/%2Bpssuu0xhYWE%2B85s3b/Z3SQAAAEYF5DYNo0aNCsTbAgAA%2BIXfA1ZeXp6/3xIAAMCv/H4NliRVV1eroKBAM2bM0Jdffqk///nPOnz4cCBKAQAAMM7vAevzzz/Xf/zHf%2BjFF1/UG2%2B8oe%2B%2B%2B06vvfaaxo0bp7KyMn%2BXAwAAYJzfA9Yf/vAH3XDDDdq5c6d%2B9atfSZIef/xxDR06VMuWLfN3OQAAAMb5PWDt27dPd911l88HOzudTt1///06cOCAv8sBAAAwzu8Bq6WlRS0tLa3Gv/32W4WGhvq7HAAAAOP8HrCuu%2B46PfXUUz4hq66uTvn5%2BRo4cKC/ywEAADDO7wHrkUce0f79%2B3XdddepsbFR//mf/6mMjAwdOXJEs2fP9nc5AAAAxvn9PlgJCQl66aWXtGPHDn300UdqaWnRhAkTNHr0aHXo0MHf5QAAABgXkDu5R0RE6NZbbw3EWwMAAFjO7wFr0qRJPzrPZxECAAC783vA6tKli8/jpqYmff755/rkk0905513%2BrscAAAA434xn0W4Zs0anThxws/VAAAAmBeQzyI8l9GjR%2Bv1118PdBkAAAAX7BcTsP7xj39wo1EAABAUfhEXuZ8%2BfVoff/yxbr/9dn%2BXAwAAYJzfA1ZiYqLP5xBK0q9%2B9StNnDhRv/3tb/1dDgAAgHF%2BD1h/%2BMMfjG7P7XZr7NixmjdvngYMGCBJWrx4sZ577jmf582bN08TJ06UJO3YsUMrV66Uy%2BXSddddp0WLFqljx46SJI/Ho%2BXLl2v79u1qaWnR%2BPHjNXPmTIWE/GLOpgIAgF84vwesvXv3tvm511xzzY/ONzY2asaMGaqoqPAZr6ys1IwZM3TLLbd4x87eJb68vFxz587VwoULlZSUpCVLlignJ0dPPfWUJOnZZ5/Vjh07VFBQoKamJs2aNUuxsbGaOnVqm%2BsGAAAXN78HrDvuuMN7itDj8XjHvz/mcDj00Ucf/eB2Dh06pBkzZvhs46zKykpNnTpVcXFxrea2bNmim266SWPGjJEkLV26VBkZGaqqqtJll12mzZs3Kzs7W%2Bnp6ZKkmTNnatWqVQQsAADQZn4/7/Xkk0%2BqS5cuWrlypd577z2VlJRo48aN%2Bpd/%2BRf9/ve/15tvvqk333xTO3fu/NHt7NmzRwMGDFBRUZHP%2BOnTp3Xy5El169btnK8rKyvzhidJ6ty5sxITE1VWVqaTJ0/q%2BPHjPkfO0tLSdPToUVVXV//8RQMAgItKQG40On/%2BfA0ZMsQ7NnDgQOXm5urhhx/WPffc06bt/NBfHFZWVsrhcOjJJ5/U22%2B/rejoaN11113e04XV1dWKj4/3eU1sbKxOnDghl8slST7znTp1kiSdOHGi1et%2BSHV1tXdbZzmdkW1%2BfVuFhtrrujCn0z71nu2t3XpsB/TWOvTWOvTWOsHaW78HrOrq6lYflyP98xqp2traC97%2B4cOH5XA41L17d02cOFF79%2B7VvHnz1KFDBw0bNkwNDQ0KCwvzeU1YWJjcbrcaGhq8j///nPTPi%2BnbqqioSAUFBT5jWVlZys7O/rnLCgoxMe0DXcJ5i4qKCHQJQYveWofeWofeWifYeuv3gJWSkqLHH39cf/zjH70XntfV1Sk/P1/XXnvtBW9/zJgxysjIUHR0tCQpKSlJn332mZ5//nkNGzZM4eHhrcKS2%2B1WRESET5gKDw/3/luSIiLa/o3PzMzU0KFDfcaczkjV1n77s9d1LnZL%2B6bXb6XQ0BBFRUXo1Kl6NTe3BLqcoEJvrUNvrUNvrWN1bwP1y73fA9ajjz6qSZMmaciQIerWrZs8Ho8%2B%2B%2BwzxcXFafPmzRe8fYfD4Q1XZ3Xv3l27d%2B%2BWJCUkJKimpsZnvqamRnFxcUpISJAkuVwude3a1ftvSee8YP6HxMfHtzod6HJ9o6ami/uH0o7rb25usWXddkBvrUNvrUNvrRNsvfX7IZAePXrotdde04wZM5SSkqLU1FTNnTtXL7/8sn79619f8PZXrVqlyZMn%2B4wdPHhQ3bt3lyQlJyerpKTEO3f8%2BHEdP35cycnJSkhIUGJios98SUmJEhMTjV8/BQAAgpffj2BJ0qWXXqpbb71VR44c0WWXXSbpn3dzNyEjI0OFhYXasGGDhg0bpl27dumll17yHh2bMGGC7rjjDqWkpKhv375asmSJrr/%2Bem8dEyZM0LJly7xhb/ny5ZoyZYqR2gAAwMXB7wHr7J3Sn3vuOZ05c0ZvvPGGVqxYoYiICC1YsOCCg1a/fv20atUqrV69WqtWrVKXLl20fPlypaamSpJSU1OVm5ur1atX6%2Buvv9agQYO0aNEi7%2BunTp2qL7/8UtOnT1doaKjGjx/f6ogYAADAj3F4znWnTgtt3rxZTz/9tB566CHl5ubq1Vdf1QcffKCFCxfqd7/7nR566CF/luM3Ltc3xrfpdIZo2LJ3jG/XKq8/OCjQJbSZ0xmimJj2qq39NqiuCfgloLfWobfWobfWsbq3cXGXGN9mW/j9GqyioiLNnz9fY8eO9d69/eabb9bixYv16quv%2BrscAAAA4/wesI4cOaLevXu3Gk9KSmp1c04AAAA78nvA6tKliz744INW42%2B//bb3QnMAAAA78/tF7lOnTtXChQvlcrnk8Xj03nvvqaioSM8995weeeQRf5cDAABgnN8D1rhx49TU1KR169apoaFB8%2BfPV8eOHfXggw9qwoQJ/i4HAADAOL8HrB07dmjEiBHKzMzUV199JY/Ho9jYWH%2BXAQAAYBm/X4OVm5vrvZi9Y8eOhCsAABB0/B6wunXrpk8%2B%2BcTfbwsAAOA3fj9FmJSUpJkzZ2r9%2BvXq1q2bwsPDfebz8vL8XRIAAIBRfg9Yn376qdLS0iSJ%2B14BAICg5JeAtXTpUk2fPl2RkZF67rnn/PGWAAAAAeOXa7CeffZZ1dfX%2B4zde%2B%2B9qq6u9sfbAwAA%2BJVfAta5Pk967969amxs9MfbAwAA%2BJXf/4oQAAAg2BGwAAAADPNbwHI4HP56KwAAgIDy220aFi9e7HPPqzNnzig/P1/t27f3eR73wQIAAHbnl4B1zTXXtLrnVWpqqmpra1VbW%2BuPEgAAAPzGLwGLe18BAICLCRe5AwAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzPYBy%2B12a9SoUXr//fe9Y1VVVZo8ebJSUlJ08803a9euXT6veffddzVq1CglJydr0qRJqqqq8pnfuHGjBg8erNTUVM2ZM0f19fV%2BWQsAAAgOtg5YjY2N%2Bv3vf6%2BKigrvmMfjUVZWljp16qTi4mKNHj1a06dP17FjxyRJx44dU1ZWlsaOHavt27erY8eOuv/%2B%2B%2BXxeCRJb7zxhgoKCpSbm6tNmzaprKxM%2Bfn5AVkfAACwJ9sGrEOHDum2227TF1984TO%2Be/duVVVVKTc3Vz169NC0adOUkpKi4uJiSdK2bdt01VVXacqUKerZs6fy8vJ09OhR7dmzR5K0efNm3XnnncrIyFC/fv20cOFCFRcXcxQLAAC0mW0D1p49ezRgwAAVFRX5jJeVlenKK69UZGSkdywtLU2lpaXe%2BfT0dO9cRESE%2BvTpo9LSUjU3N%2BuDDz7wmU9JSdGZM2d08OBBi1cEAACChTPQBfxct99%2B%2BznHXS6X4uPjfcZiY2N14sSJn5w/deqUGhsbfeadTqeio6O9r2%2BL6upquVwunzGnM7LV%2B16o0FB75WOn0z71nu2t3XpsB/TWOvTrQLPfAAAQV0lEQVTWOvTWOsHaW9sGrB9SX1%2BvsLAwn7GwsDC53e6fnG9oaPA%2B/qHXt0VRUZEKCgp8xrKyspSdnd3mbQSjmJj2gS7hvEVFRQS6hKBFb61Db61Db60TbL0NuoAVHh6uuro6nzG326127dp5578fltxut6KiohQeHu59/P35iIi2f%2BMzMzM1dOhQnzGnM1K1td%2B2eRttYbe0b3r9VgoNDVFUVIROnapXc3NLoMsJKvTWOvTWOvTWOlb3NlC/3AddwEpISNChQ4d8xmpqaryn5xISElRTU9Nqvnfv3oqOjlZ4eLhqamrUo0cPSVJTU5Pq6uoUFxfX5hri4%2BNbnQ50ub5RU9PF/UNpx/U3N7fYsm47oLfWobfWobfWCbbe2usQSBskJyfrww8/9J7uk6SSkhIlJyd750tKSrxz9fX1OnDggJKTkxUSEqK%2Bffv6zJeWlsrpdCopKcl/iwAAALYWdAGrf//%2B6ty5s3JyclRRUaHCwkKVl5dr/PjxkqRx48Zp3759KiwsVEVFhXJyctS1a1cNGDBA0j8vnt%2BwYYN27typ8vJyLViwQLfddtt5nSIEAAAXt6ALWKGhoVq7dq1cLpfGjh2rV155RWvWrFFiYqIkqWvXrnriiSdUXFys8ePHq66uTmvWrJHD4ZAkjRw5UtOmTdP8%2BfM1ZcoU9evXT7NmzQrkkgAAgM04PGdvYQ5LuVzfGN%2Bm0xmiYcveMb5dq7z%2B4KBAl9BmTmeIYmLaq7b226C6JuCXgN5ah95ah95ax%2BrexsVdYnybbRF0R7AAAAACjYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOCNmD95S9/0RVXXOHzlZ2dLUk6cOCAbr31ViUnJ2vcuHHav3%2B/z2t37NihG264QcnJycrKytJXX30ViCUAAACbCtqAdejQIWVkZGjXrl3er8WLF%2Bu7777Tvffeq/T0dP3pT39Samqqpk2bpu%2B%2B%2B06SVF5errlz52r69OkqKirSqVOnlJOTE%2BDVAAAAOwnagFVZWalevXopLi7O%2BxUVFaXXXntN4eHhevjhh9WjRw/NnTtX7du315///GdJ0pYtW3TTTTdpzJgxSkpK0tKlS/XWW2%2BpqqoqwCsCAAB2EdQBq1u3bq3Gy8rKlJaWJofDIUlyOBy6%2BuqrVVpa6p1PT0/3Pr9z585KTExUWVmZX%2BoGAAD2F5QBy%2BPx6NNPP9WuXbs0fPhw3XDDDVq2bJncbrdcLpfi4%2BN9nh8bG6sTJ05Ikqqrq390HgAA4Kc4A12AFY4dO6b6%2BnqFhYVp5cqVOnLkiBYvXqyGhgbv%2BP8XFhYmt9stSWpoaPjR%2Bbaorq6Wy%2BXyGXM6I1sFtwsVGmqvfOx02qfes721W4/tgN5ah95ah95aJ1h7G5QBq0uXLnr//fd16aWXyuFwqHfv3mppadGsWbPUv3//VmHJ7XarXbt2kqTw8PBzzkdERLT5/YuKilRQUOAzlpWV5f0rxotVTEz7QJdw3qKi2v59x/mht9aht9aht9YJtt4GZcCSpOjoaJ/HPXr0UGNjo%2BLi4lRTU%2BMzV1NT4z26lJCQcM75uLi4Nr93Zmamhg4d6jPmdEaqtvbb81nCT7Jb2je9fiuFhoYoKipCp07Vq7m5JdDlBBV6ax16ax16ax2rexuoX%2B6DMmC98847mjlzpv72t795jzx99NFHio6OVlpamp5%2B%2Bml5PB45HA55PB7t27dP9913nyQpOTlZJSUlGjt2rCTp%2BPHjOn78uJKTk9v8/vHx8a1OB7pc36ip6eL%2BobTj%2BpubW2xZtx3QW%2BvQW%2BvQW%2BsEW2/tdQikjVJTUxUeHq5HH31Uhw8f1ltvvaWlS5fq7rvv1ogRI3Tq1CktWbJEhw4d0pIlS1RfX6%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%2Bnp6bruuuv0zDPPBLokAABgI85AF/BLtHTpUu3fv1%2BbNm3SsWPHNHv2bCUmJmrEiBGBLg0AANgAAet7vvvuO23btk1PP/20%2BvTpoz59%2BqiiokJbt24lYAEAgDbhFOH3HDx4UE1NTUpNTfWOpaWlqaysTC0tLQGsDAAA2AVHsL7H5XIpJiZGYWFh3rFOnTqpsbFRdXV16tix409uo7q6Wi6Xy2fM6YxUfHy80VpDQ%2B2Vj29a%2BfdAl3Be/mfJCNv12A7O9pTemkdvrUNvrROsvSVgfU99fb1PuJLkfex2u9u0jaKiIhUUFPiMTZ8%2BXQ888ICZIv9PdXW17vx1hTIzM42Ht4tddXW1nnjiCXprgerqam3atJ7eWoDeWofeWidYextccdGA8PDwVkHq7ON27dq1aRuZmZn605/%2B5POVmZlpvFaXy6WCgoJWR8tw4eitdeitdeitdeitdYK1txzB%2Bp6EhATV1taqqalJTuc/2%2BNyudSuXTtFRUW1aRvx8fFBlcIBAMD54QjW9/Tu3VtOp1OlpaXesZKSEvXt21chIbQLAAD8NBLD90RERGjMmDFasGCBysvLtXPnTj3zzDOaNGlSoEsDAAA2EbpgwYIFgS7il2bgwIE6cOCAli9frvfee0/33Xefxo0bF%2Biyzql9%2B/bq37%2B/2rdvH%2BhSgg69tQ69tQ69tQ69tU4w9tbh8Xg8gS4CAAAgmHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAcumGhsbNWfOHKWnp%2Bu6667TM888E%2BiSgsZf/vIXXXHFFT5f2dnZgS7L1txut0aNGqX333/fO1ZVVaXJkycrJSVFN998s3bt2hXACu3rXL1dvHhxq314y5YtAazSPk6ePKns7Gz1799fgwcPVl5enhobGyWxz16oH%2BttMO6zzkAXgJ9n6dKl2r9/vzZt2qRjx45p9uzZSkxM1IgRIwJdmu0dOnRIGRkZWrRokXcsPDw8gBXZW2Njo2bMmKGKigrvmMfjUVZWlnr16qXi4mLt3LlT06dP12uvvabExMQAVmsv5%2BqtJFVWVmrGjBm65ZZbvGMdOnTwd3m24/F4lJ2draioKG3dulVff/215syZo5CQED388MPssxfgx3o7e/bsoNxnCVg29N1332nbtm16%2Bumn1adPH/Xp00cVFRXaunUrAcuAyspK9erVS3FxcYEuxfYOHTqkGTNm6PufyLV7925VVVXphRdeUGRkpHr06KH33ntPxcXFeuCBBwJUrb38UG%2Blf%2B7DU6dOZR8%2BT4cPH1Zpaan%2B/ve/q1OnTpKk7Oxs/fGPf9SQIUPYZy/Aj/X2bMAKtn2WU4Q2dPDgQTU1NSk1NdU7lpaWprKyMrW0tASwsuBQWVmpbt26BbqMoLBnzx4NGDBARUVFPuNlZWW68sorFRkZ6R1LS0tTaWmpv0u0rR/q7enTp3Xy5En24Z8hLi5O69ev9waAs06fPs0%2Be4F%2BrLfBus9yBMuGXC6XYmJiFBYW5h3r1KmTGhsbVVdXp44dOwawOnvzeDz69NNPtWvXLj311FNqbm7WiBEjlJ2d7dNvtM3tt99%2BznGXy6X4%2BHifsdjYWJ04ccIfZQWFH%2BptZWWlHA6HnnzySb399tuKjo7WXXfd5XPqBecWFRWlwYMHex%2B3tLRoy5YtGjhwIPvsBfqx3gbrPkvAsqH6%2BvpW/9mffex2uwNRUtA4duyYt78rV67UkSNHtHjxYjU0NOjRRx8NdHlB44f2YfbfC3f48GE5HA51795dEydO1N69ezVv3jx16NBBw4YNC3R5tpKfn68DBw5o%2B/bt2rhxI/usQf%2B/tx9%2B%2BGFQ7rMELBsKDw9v9UN99nG7du0CUVLQ6NKli95//31deumlcjgc6t27t1paWjRr1izl5OQoNDQ00CUGhfDwcNXV1fmMud1u9l8DxowZo4yMDEVHR0uSkpKS9Nlnn%2Bn555%2B39X9W/pafn69NmzZpxYoV6tWrF/usQd/vbc%2BePYNyn%2BUaLBtKSEhQbW2tmpqavGMul0vt2rVTVFRUACsLDtHR0XI4HN7HPXr0UGNjo77%2B%2BusAVhVcEhISVFNT4zNWU1PT6hQMzp/D4fD%2BR3VW9%2B7ddfLkyQBVZD%2BLFi3Ss88%2Bq/z8fA0fPlwS%2B6wp5%2BptsO6zBCwb6t27t5xOp8/FlSUlJerbt69CQviWXoh33nlHAwYMUH19vXfso48%2BUnR0NNe2GZScnKwPP/xQDQ0N3rGSkhIlJycHsKrgsGrVKk2ePNln7ODBg%2BrevXtgCrKZgoICvfDCC3r88cc1cuRI7zj77IX7od4G6z7L/8Y2FBERoTFjxmjBggUqLy/Xzp079cwzz2jSpEmBLs32UlNTFR4erkcffVSHDx/WW2%2B9paVLl%2Bruu%2B8OdGlBpX///urcubNycnJUUVGhwsJClZeXa/z48YEuzfYyMjK0d%2B9ebdiwQV988YX%2B67/%2BSy%2B99JKmTJkS6NJ%2B8SorK7V27Vrdc889SktLk8vl8n6xz16YH%2BttsO6zDs%2B5bqKCX7z6%2BnotWLBA//3f/60OHTpo6tSprX4DwM9TUVGhxx57TKWlpWrfvr1%2B97vfKSsry%2Be0Ic7fFVdcoc2bN2vAgAGSpM8//1xz585VWVmZLr/8cs2ZM0f/%2Bq//GuAq7en7vd25c6dWr16tzz77TF26dNFDDz2kG2%2B8McBV/vIVFhZq%2BfLl55z7%2BOOP2WcvwE/1Nhj3WQIWAACAYZwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD/hdv2Z5bQIPsdQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-914163921270426143”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00488471482991773</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2726639462994582</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0837245293810234</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03153662117278969</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03212077351235999</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03427592152288209</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.20402215352700614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13850379492086456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5651772464780063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2874)</td> <td class=”number”>2874</td> <td class=”number”>89.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-914163921270426143”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>230</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.546316229191107e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.235394888818366e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1382045863177702e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8029219907524265e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.971474256436248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.839510182141182</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.623275578940993</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.223929168093157</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.087551491157946</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHNA15”>PCT_LACCESS_NHNA15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2945</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.748</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>83.844</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>5.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3056168768204097166”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-3056168768204097166”

aria-controls=”quantiles-3056168768204097166” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3056168768204097166” aria-controls=”histogram-3056168768204097166”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3056168768204097166” aria-controls=”common-3056168768204097166”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3056168768204097166” aria-controls=”extreme-3056168768204097166”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3056168768204097166”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.027008</td>

</tr> <tr>

<th>Median</th> <td>0.071824</td>

</tr> <tr>

<th>Q3</th> <td>0.18568</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.7475</td>

</tr> <tr>

<th>Maximum</th> <td>83.844</td>

</tr> <tr>

<th>Range</th> <td>83.844</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.15867</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.4721</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.9788</td>

</tr> <tr>

<th>Kurtosis</th> <td>142.75</td>

</tr> <tr>

<th>Mean</th> <td>0.748</td>

</tr> <tr>

<th>MAD</th> <td>1.1582</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.135</td>

</tr> <tr>

<th>Sum</th> <td>2328.5</td>

</tr> <tr>

<th>Variance</th> <td>20</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3056168768204097166”>

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gcPHnR476FDhzRmzBiFh4drypQpKi4udpjfunWrhgwZosjISM2fP19VVVVOWxcAAPB8HhmwbDabkpOTVVVVpRdeeEGrV6/Wu%2B%2B%2BqzVr1shmsykxMVHt2rVTTk6Oxo4dq6SkJJ09e1aSdPbsWSUmJmr8%2BPHas2eP2rZtq0ceeUQ2m02S9NZbbykjI0Opqanatm2b8vPzlZ6e7srlAgAAD%2BORAevUqVPKy8tTWlqaunfvrujoaCUnJ2vfvn06fPiwiouLlZqaqm7dumn27NmKiIhQTk6OJGn37t266667NGPGDHXv3l1paWk6c%2BaMPvroI0nS9u3bNXXqVMXGxqpfv35avHixcnJyOIsFAACazCMDVnBwsDZv3qx27do5jF%2B4cEH5%2Bfnq3bu3AgIC7ONRUVHKy8uTJOXn5ys6Oto%2B5%2B/vrz59%2BigvL0/19fX65JNPHOYjIiJ0%2BfJlnThx4iavCgAA3Cosri7glwgMDNSQIUPs2w0NDdq5c6cGDhwoq9WqkJAQh9cHBQWppKREkn5yvrKyUjU1NQ7zFotFrVu3tr%2B/KUpLS2W1Wh3GLJaARse9UT4%2BnpWPLRbPqvdGXemPp/WpuaA/7o3%2BuDf6c30eGbCulp6eruPHj2vPnj3aunWrfH19HeZ9fX1VW1srSaqqqrrmfHV1tX37Wu9viuzsbGVkZDiMJSYmKjk5ucn7uBW1adPS1SW4RGCgv6tLwE%2BgP%2B6N/rg3%2BnNtHh%2Bw0tPTtW3bNq1evVo9evSQn5%2BfKioqHF5TW1urFi1aSJL8/PwahaXa2loFBgbKz8/Pvn31vL9/0z9ECQkJiouLcxizWAJUXn6xyftoCk/75mB6/e7Ox8dbgYH%2BqqysUn19g6vLwVXoj3ujP%2B7Nk/rjqi/3Hh2wlixZol27dik9PV333XefJCk0NFQnT550eF1ZWZn98lxoaKjKysoazffq1UutW7eWn5%2BfysrK1K1bN0lSXV2dKioqFBwc3OS6QkJCGl0OtFq/V12de38Ib7bmuv76%2BoZmu3ZPQH/cG/1xb/Tn2jzrFMg/ycjI0IsvvqhVq1Zp9OjR9vHw8HB9%2Bumn9st9kpSbm6vw8HD7fG5urn2uqqpKx48fV3h4uLy9vdW3b1%2BH%2Bby8PFksFvXs2dMJqwIAALcCjwxYRUVFWr9%2BvR5%2B%2BGFFRUXJarXaf2JiYtS%2BfXulpKSosLBQWVlZKigo0IQJEyRJ8fHxOnr0qLKyslRYWKiUlBR17NhRAwYMkCRNmjRJW7Zs0f79%2B1VQUKBFixbpwQcf/FmXCAEAQPPmkZcI33nnHdXX12vDhg3asGGDw9znn3%2Bu9evXa8GCBRo/frw6d%2B6szMxMhYWFSZI6duyoZ599Vn/%2B85%2BVmZmpyMhIZWZmysvLS5I0evRonTlzRgsXLlRtba1%2B%2B9vfau7cuU5fIwAA8FxetiuPMHeSBx54QPHx8Ro9erRuv/12Zx7apazW743v02Lx1vAV7xvf783yxqODXF2CU1ks3mrTpqXKyy9yj4Iboj/ujf64N0/qT3Cwa7KG0y8RDhw4UBs3btTgwYP1pz/9SQcPHpSTMx4AAMBN5fSANWfOHL377rtav369fHx89Mc//lHDhg3T6tWr9eWXXzq7HAAAAONccg%2BWl5eXBg0apEGDBqmqqko7duzQ%2BvXrlZWVpf79%2B2vq1Kn67W9/64rSAAAAbpjLbnIvLS3Vq6%2B%2BqldffVVffPGF%2Bvfvr/vvv18lJSV68skndeTIES1YsMBV5QEAAPxiTg9Yr7zyil555RV9%2BOGHatu2rcaNG6d169apS5cu9te0b99ey5YtI2ABAACP5PSAtWDBAsXGxiozM1NDhw6Vt3fj28C6du2qyZMnO7s0AAAAI5wesN577z21adNGFRUV9nBVUFCgPn36yMfHR5LUv39/9e/f39mlAQAAGOH0f0V44cIFjRgxQn/961/tY7NmzdLYsWN17tw5Z5cDAABgnNMD1p///Gd17txZ06dPt4%2B9/vrrat%2B%2BvdLS0pxdDgAAgHFOD1h///vf9cQTTyg4ONg%2B1rZtWz3%2B%2BOM6fPiws8sBAAAwzukBy2KxqLKystF4VVUVT3QHAAC3BKcHrKFDh2rp0qX6xz/%2BYR8rLi5WWlqahgwZ4uxyAAAAjHP6vyKcN2%2Bepk%2Bfrvvuu0%2BBgYGSpMrKSvXp00cpKSnOLgcAAMA4pwesoKAgvfzyyzp06JAKCwtlsVh0xx136J577pGXl5ezywEAADDOJb8qx8fHR0OGDOGSIAAAuCU5PWBZrVatWbNGR48e1eXLlxvd2P7OO%2B84uyQAAACjnB6wnnrqKR07dkyjR4/W7bff7uzDAwAA3HROD1iHDx/W5s2bFR0d7exDAwAAOIXTH9MQEBCgoKAgZx8WAADAaZwesMaOHavNmzervr7e2YcGAABwCqdfIqyoqNC%2Bffv0f//3f%2BrUqZN8fX0d5rdv3%2B7skgAAAIxyyWMaxowZ44rDAgAAOIXTA1ZaWpqzDwkAAOBUTr8HS5JKS0uVkZGhOXPm6JtvvtGbb76pU6dOuaIUAAAA45wesE6fPq1///d/18svv6y33npLly5d0uuvv674%2BHjl5%2Bc7uxwAAADjnB6wnnnmGd17773av3%2B/brvtNknSqlWrFBcXpxUrVji7HAAAAOOcHrCOHj2q6dOnO/xiZ4vFokceeUTHjx93djkAAADGOT1gNTQ0qKGhodH4xYsX5ePj4%2BxyAAAAjHN6wBo8eLA2bdrkELIqKiqUnp6ugQMHOrscAAAA45wesJ544gkdO3ZMgwcPVk1Njf7zP/9TsbGx%2BvrrrzVv3jxnlwMAAGCc05%2BDFRoaqr1792rfvn367LPP1NDQoIkTJ2rs2LFq1aqVs8sBAAAwziVPcvf399cDDzzgikMDAADcdE4PWFOmTPnJeX4XIQAA8HROD1gdOnRw2K6rq9Pp06f1xRdfaOrUqc4uBwAAwDi3%2BV2EmZmZKikpcXI1AAAA5rnkdxH%2BmLFjx%2BqNN95wdRkAAAA3zG0C1scff8yDRgEAwC3BLW5yv3Dhgj7//HNNmjTpZ%2B%2BvtrZW48eP11NPPaUBAwZIkpYuXaodO3Y4vO6pp57S5MmTJUn79u3TmjVrZLVaNXjwYC1ZskRt27aVJNlsNq1cuVJ79uxRQ0ODJkyYoMcee0ze3m6TRQEAgJtzesAKCwtz%2BD2EknTbbbdp8uTJ%2Bo//%2BI%2Bfta%2BamhrNmTNHhYWFDuNFRUWaM2eO7r//fvvYlWdsFRQUaMGCBVq8eLF69uypZcuWKSUlRZs2bZIkPf/889q3b58yMjJUV1enuXPnKigoSDNnzvwlywUAAM2Q0wPWM888Y2Q/J0%2Be1Jw5c2Sz2RrNFRUVaebMmQoODm40t3PnTo0cOVLjxo2TJC1fvlyxsbEqLi5Wp06dtH37diUnJys6OlqS9Nhjj2nt2rUELAAA0GROD1hHjhxp8mvvvvvua8599NFHGjBggP7rv/5LERER9vELFy7o/Pnz6tKly4%2B%2BLz8/Xw8//LB9u3379goLC1N%2Bfr58fX117tw5h%2BNGRUXpzJkzKi0tVUhISJNrBwAAzZfTA9ZDDz1kv0T4z2efrh7z8vLSZ599ds39XOt%2BraKiInl5eWnjxo1677331Lp1a02fPt1%2BufDHglJQUJBKSkpktVolyWG%2BXbt2kqSSkhICFgAAaBKnB6yNGzdq6dKlmjt3rmJiYuTr66tPPvlEqampuv/%2B%2BzVq1Kgb2v%2BpU6fk5eWlrl27avLkyTpy5IieeuoptWrVSsOHD1d1dbV8fX0d3uPr66va2lpVV1fbt/95TvrhZvqmKi0ttYe1KyyWAOMBzcfHs268t1g8q94bdaU/ntan5oL%2BuDf6497oz/W55EGjCxcu1NChQ%2B1jAwcOVGpqqh5//HGHy3e/xLhx4xQbG6vWrVtLknr27KmvvvpKu3bt0vDhw%2BXn59coLNXW1srf398hTPn5%2Bdn/LP3w%2BxObKjs7WxkZGQ5jiYmJSk5O/sXruhW0adPS1SW4RGBg0z87cD76497oj3ujP9fm9IBVWlra6NflSD/8K7/y8vIb3r%2BXl5c9XF3RtWtXHT58WJIUGhqqsrIyh/mysjIFBwcrNDRUkmS1WtWxY0f7nyX96A3z15KQkKC4uDiHMYslQOXlF3/eYq7D0745mF6/u/Px8VZgoL8qK6tUX9/g6nJwFfrj3uiPe/Ok/rjqy73TA1ZERIRWrVqlv/zlL/ZHJ1RUVCg9PV333HPPDe9/7dq1%2Bvjjj7V161b72IkTJ9S1a1dJUnh4uHJzczV%2B/HhJ0rlz53Tu3DmFh4crNDRUYWFhys3NtQes3NxchYWF/azLeyEhIY1eb7V%2Br7o69/4Q3mzNdf319Q3Ndu2egP64N/rj3ujPtTk9YD355JOaMmWKhg4dqi5dushms%2Bmrr75ScHCwtm/ffsP7j42NVVZWlrZs2aLhw4fr4MGD2rt3r33fEydO1EMPPaSIiAj17dtXy5Yt07Bhw9SpUyf7/IoVK/TrX/9akrRy5UrNmDHjhusCAADNh9MDVrdu3fT6669r3759KioqkiT97ne/0%2BjRo3/WfU7X0q9fP61du1br1q3T2rVr1aFDB61cuVKRkZGSpMjISKWmpmrdunX67rvvNGjQIC1ZssT%2B/pkzZ%2Bqbb75RUlKSfHx8NGHCBE2bNu2G6wIAAM2Hl%2B3HntTpBLW1tfr666/tZ45uu%2B02V5ThNFbr98b3abF4a/iK943v92Z549FBri7BqSwWb7Vp01Ll5Rc5he6G6I97oz/uzZP6Exx8u0uO6/S7pG02m1asWKG7775bY8aMUUlJiebNm6cFCxbo8uXLzi4HAADAOKcHrB07duiVV17R008/bX8swr333qv9%2B/c3erQBAACAJ3J6wMrOztbChQs1fvx4%2B9PbR40apaVLl%2Bq1115zdjkAAADGOT1gff311%2BrVq1ej8Z49ezZ6%2BjkAAIAncnrA6tChgz755JNG4%2B%2B99579hncAAABP5vTHNMycOVOLFy%2BW1WqVzWbTBx98oOzsbO3YsUNPPPGEs8sBAAAwzukBKz4%2BXnV1ddqwYYOqq6u1cOFCtW3bVo8%2B%2BqgmTpzo7HIAAACMc3rA2rdvn0aMGKGEhAR9%2B%2B23stlsCgoKcnYZAAAAN43T78FKTU2138zetm1bwhUAALjlOD1gdenSRV988YWzDwsAAOA0Tr9E2LNnTz322GPavHmzunTpIj8/P4f5tLQ0Z5cEAABglNMD1pdffqmoqChJ4rlXAADgluSUgLV8%2BXIlJSUpICBAO3bscMYhAQAAXMYp92A9//zzqqqqchibNWuWSktLnXF4AAAAp3JKwLLZbI3Gjhw5opqaGmccHgAAwKmc/q8IAQAAbnUELAAAAMOcFrC8vLycdSgAAACXctpjGpYuXerwzKvLly8rPT1dLVu2dHgdz8ECAACezikB6%2B677270zKvIyEiVl5ervLzcGSUAAAA4jVMCFs%2B%2BAgAAzQk3uQMAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObxAau2tlZjxozRhx9%2BaB8rLi7WtGnTFBERoVGjRungwYMO7zl06JDGjBmj8PBwTZkyRcXFxQ7zW7du1ZAhQxQZGan58%2BerqqrKKWsBAAC3Bo8OWDU1NfrTn/6kwsJC%2B5jNZlNiYqLatWunnJwcjR07VklJSTp79qwk6ezZs0pMTNT48eO1Z88etW3bVo888ohsNpsk6a233lJGRoZSU1O1bds25efnKz093SXrAwAAnsljA9bJkyf14IMP6h//%2BIfD%2BOHDh1VcXKzU1FR169ZNs2fPVkREhHJyciRJu3fv1l133aUZM2aoe/fuSktL05kzZ/TRRx9JkrZv366pU6cqNjZW/fr10%2BLFi5WTk8NZLAAA0GQeG7A%2B%2BugjDRgwQNnZ2Q7j%2Bfn56t27twICAuxjUVFRysvLs89HR0fb5/z9/dWnTx/l5eWpvr5en3zyicN8RESELl%2B%2BrBMnTtzkFQEAgFuFxdUF/FKTJk360XGr1aqQkBCHsaCgIJWUlFx3vrKyUjU1NQ7zFotFrVu3tr8fAADgejw2YF1LVVWVfH19HcZ8fX1VW1t73fnq6mr79rXe3xSlpaWyWq0OYxZLQKNgd6N8fDzrBKTF4ln13qgr/fG0PjUX9Me90R/3Rn%2Bu75YLWH5%2BfqqoqHAYq62tVYsWLezzV4el2tpaBQYGys/Pz7599by/v3%2BuyKYSAAAQ2UlEQVSTa8jOzlZGRobDWGJiopKTk5u8j1tRmzYtXV2CSwQGNv2zA%2BejP%2B6N/rg3%2BnNtt1zACg0N1cmTJx3GysrK7GePQkNDVVZW1mi%2BV69eat26tfz8/FRWVqZu3bpJkurq6lRRUaHg4OAm15CQkKC4uDiHMYslQOXlF3/Jkq7J0745mF6/u/Px8VZgoL8qK6tUX9/g6nJwFfrj3uiPe/Ok/rjqy/0tF7DCw8OVlZWl6upq%2B1mr3NxcRUVF2edzc3Ptr6%2BqqtLx48eVlJQkb29v9e3bV7m5uRowYIAkKS8vTxaLRT179mxyDSEhIY0uB1qt36uuzr0/hDdbc11/fX1Ds127J6A/7o3%2BuDf6c22edQqkCWJiYtS%2BfXulpKSosLBQWVlZKigo0IQJEyRJ8fHxOnr0qLKyslRYWKiUlBR17NjRHqgmTZqkLVu2aP/%2B/SooKNCiRYv04IMP/qxLhAAAoHm75QKWj4%2BP1q9fL6vVqvHjx%2BvVV19VZmamwsLCJEkdO3bUs88%2Bq5ycHE2YMEEVFRXKzMyUl5eXJGn06NGaPXu2Fi5cqBkzZqhfv36aO3euK5cEAAA8jJftyiPMcVNZrd8b36fF4q3hK943vt%2Bb5Y1HB7m6BKeyWLzVpk1LlZdf5BS6G6I/7o3%2BuDdP6k9w8O0uOe4tdwYLAADA1QhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDYLRuw3n77bd15550OP8nJyZKk48eP64EHHlB4eLji4%2BN17Ngxh/fu27dP9957r8LDw5WYmKhvv/3WFUsAAAAe6pYNWCdPnlRsbKwOHjxo/1m6dKkuXbqkWbNmKTo6Wi%2B99JIiIyM1e/ZsXbp0SZJUUFCgBQsWKCkpSdnZ2aqsrFRKSoqLVwMAADzJLRuwioqK1KNHDwUHB9t/AgMD9frrr8vPz0%2BPP/64unXrpgULFqhly5Z68803JUk7d%2B7UyJEjNW7cOPXs2VPLly/XgQMHVFxc7OIVAQAAT3FLB6wuXbo0Gs/Pz1dUVJS8vLwkSV5eXurfv7/y8vLs89HR0fbXt2/fXmFhYcrPz3dK3QAAwPNZXF3AzWCz2fTll1/q4MGD2rRpk%2Brr6zVixAglJyfLarXqjjvucHh9UFCQCgsLJUmlpaUKCQlpNF9SUtLk45eWlspqtTqMWSwBjfZ7o3x8PCsfWyyeVe%2BNutIfT%2BtTc0F/3Bv9cW/05/puyYB19uxZVVVVydfXV2vWrNHXX3%2BtpUuXqrq62j7%2Bz3x9fVVbWytJqq6u/sn5psjOzlZGRobDWGJiov0m%2B%2BaqTZuWri7BJQID/V1dAn4C/XFv9Me90Z9ruyUDVocOHfThhx/qV7/6lby8vNSrVy81NDRo7ty5iomJaRSWamtr1aJFC0mSn5/fj877%2Bzf9Q5SQkKC4uDiHMYslQOXlF3/hin6cp31zML1%2Bd%2Bfj463AQH9VVlapvr7B1eXgKvTHvdEf9%2BZJ/XHVl/tbMmBJUuvWrR22u3XrppqaGgUHB6usrMxhrqyszH75LjQ09Efng4ODm3zskJCQRpcDrdbvVVfn3h/Cm625rr%2B%2BvqHZrt0T0B/3Rn/cG/25Ns86BdJE77//vgYMGKCqqir72GeffabWrVsrKipKH3/8sWw2m6Qf7tc6evSowsPDJUnh4eHKzc21v%2B/cuXM6d%2B6cfR4AAOB6bsmAFRkZKT8/Pz355JM6deqUDhw4oOXLl%2Bv3v/%2B9RowYocrKSi1btkwnT57UsmXLVFVVpZEjR0qSJk6cqFdeeUW7d%2B/WiRMn9Pjjj2vYsGHq1KmTi1cFAAA8xS0ZsFq1aqUtW7bo22%2B/VXx8vBYsWKCEhAT9/ve/V6tWrbRp0ybl5uZq/Pjxys/PV1ZWlgICAiT9EM5SU1OVmZmpiRMn6le/%2BpXS0tJcvCIAAOBJvGxXrpXhprJavze%2BT4vFW8NXvG98vzfLG48OcnUJTmWxeKtNm5YqL7/IPQpuiP64N/rj3jypP8HBt7vkuLfkGSwAAABXImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErB9RU1Oj%2BfPnKzo6WoMHD9Zzzz3n6pIAAIAHsbi6AHe0fPlyHTt2TNu2bdPZs2c1b948hYWFacSIEa4uDQAAeAAC1lUuXbqk3bt3669//av69OmjPn36qLCwUC%2B88AIBCwAANAkB6yonTpxQXV2dIiMj7WNRUVHauHGjGhoa5O3NVdVfauSa/8/VJfwsbzw6yNUlAAA8FAHrKlarVW3atJGvr699rF27dqqpqVFFRYXatm173X2UlpbKarU6jFksAQoJCTFaq48PYe9m8rRA6EnefmyIq0uw//3h75F7oj/ujf5cHwHrKlVVVQ7hSpJ9u7a2tkn7yM7OVkZGhsNYUlKS/vjHP5op8v8pLS3V1F8XKiEhwXh4w40rLS1VdnY2/XFTpaWl2rZtM/1xU/THvdGf6yN6XsXPz69RkLqy3aJFiybtIyEhQS%2B99JLDT0JCgvFarVarMjIyGp0tg3ugP%2B6N/rg3%2BuPe6M/1cQbrKqGhoSovL1ddXZ0slh/%2B81itVrVo0UKBgYFN2kdISAiJHgCAZowzWFfp1auXLBaL8vLy7GO5ubnq27cvN7gDAIAmITFcxd/fX%2BPGjdOiRYtUUFCg/fv367nnntOUKVNcXRoAAPAQPosWLVrk6iLczcCBA3X8%2BHGtXLlSH3zwgf7whz8oPj7e1WX9qJYtWyomJkYtW7Z0dSn4EfTHvdEf90Z/3Bv9%2BWleNpvN5uoiAAAAbiVcIgQAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsDyUDU1NZo/f76io6M1ePBgPffcc64uqVk7f/68kpOTFRMToyFDhigtLU01NTWSpOLiYk2bNk0REREaNWqUDh486OJqm69Zs2bpiSeesG8fP35cDzzwgMLDwxUfH69jx465sLrmq7a2VosXL9bdd9%2Bt3/zmN1q1apWuPAObHrneuXPnNHv2bPXv319xcXHaunWrfY7%2BXBsBy0MtX75cx44d07Zt2/T0008rIyNDb775pqvLapZsNpuSk5NVVVWlF154QatXr9a7776rNWvWyGazKTExUe3atVNOTo7Gjh2rpKQknT171tVlNzt/%2B9vfdODAAfv2pUuXNGvWLEVHR%2Bull15SZGSkZs%2BerUuXLrmwyuZp6dKlOnTokLZs2aKVK1fqv//7v5WdnU2P3MSjjz6qgIAAvfTSS5o/f77WrFmjt99%2Bm/5cjw0e5%2BLFi7a%2BffvaDh8%2BbB/LzMy0TZ482YVVNV8nT5609ejRw2a1Wu1jr732mm3w4MG2Q4cO2SIiImwXL160z02dOtW2bt06V5TabJWXl9uGDh1qi4%2BPt82bN89ms9lsu3fvtsXFxdkaGhpsNpvN1tDQYBs%2BfLgtJyfHlaU2O%2BXl5bbevXvbPvzwQ/vYpk2bbE888QQ9cgMVFRW2Hj162D7//HP7WFJSkm3x4sX05zo4g%2BWBTpw4obq6OkVGRtrHoqKilJ%2Bfr4aGBhdW1jwFBwdr8%2BbNateuncP4hQsXlJ%2Bfr969eysgIMA%2BHhUVpby8PGeX2az95S9/0dixY3XHHXfYx/Lz8xUVFSUvLy9JkpeXl/r3709vnCw3N1etWrVSTEyMfWzWrFlKS0ujR26gRYsW8vf310svvaTLly/r1KlTOnr0qHr16kV/roOA5YGsVqvatGkjX19f%2B1i7du1UU1OjiooKF1bWPAUGBmrIkCH27YaGBu3cuVMDBw6U1WpVSEiIw%2BuDgoJUUlLi7DKbrQ8%2B%2BEB///vf9cgjjziM0xv3UFxcrA4dOmjv3r0aMWKE/u3f/k2ZmZlqaGigR27Az89PCxcuVHZ2tsLDwzVy5EgNHTpUDzzwAP25DourC8DPV1VV5RCuJNm3a2trXVES/kl6erqOHz%2BuPXv2aOvWrT/aK/rkHDU1NXr66ae1cOFCtWjRwmHuWn%2BP6I1zXbp0SadPn9aLL76otLQ0Wa1WLVy4UP7%2B/vTITRQVFSk2NlbTp09XYWGhlixZonvuuYf%2BXAcBywP5%2Bfk1%2BgBf2b76fyJwrvT0dG3btk2rV69Wjx495Ofn1%2BisYm1tLX1ykoyMDN11110OZxivuNbfI3rjXBaLRRcuXNDKlSvVoUMHSdLZs2e1a9cude7cmR652AcffKA9e/bowIEDatGihfr27avz589rw4YN6tSpE/35CQQsDxQaGqry8nLV1dXJYvmhhVarVS1atFBgYKCLq2u%2BlixZol27dik9PV333XefpB96dfLkSYfXlZWVNTqtjpvjb3/7m8rKyuz3K175n8Fbb72lMWPGqKyszOH19Mb5goOD5efnZw9XkvQv//IvOnfunGJiYuiRix07dkydO3d2CE29e/fWxo0bFR0dTX9%2BAvdgeaBevXrJYrE43EiYm5urvn37ytublrpCRkaGXnzxRa1atUqjR4%2B2j4eHh%2BvTTz9VdXW1fSw3N1fh4eGuKLPZ2bFjh1577TXt3btXe/fuVVxcnOLi4rR3716Fh4fr448/tj9vyWaz6ejRo/TGycLDw1VTU6Mvv/zSPnbq1Cl16NCBHrmBkJAQnT592uFM1alTp9SxY0f6cx3839gD%2Bfv7a9y4cVq0aJEKCgq0f/9%2BPffcc5oyZYqrS2uWioqKtH79ej388MOKioqS1Wq1/8TExKh9%2B/ZKSUlRYWGhsrKyVFBQoAkTJri67GahQ4cO6ty5s/2nZcuWatmypTp37qwRI0aosrJSy5Yt08mTJ7Vs2TJVVVVp5MiRri67WenatauGDRumlJQUnThxQu%2B//76ysrI0ceJEeuQG4uLidNttt%2BnJJ5/Ul19%2Bqf/93//Vxo0b9dBDD9Gf6/CyXYme8ChVVVVatGiR/ud//ketWrXSzJkzNW3aNFeX1SxlZWVp5cqVPzr3%2Beef6/Tp01qwYIHy8/PVuXNnzZ8/X7/5zW%2BcXCUk2Z/i/swzz0iSCgoK9PTTT6uoqEh33nmnFi9erN69e7uyxGbp%2B%2B%2B/15IlS/T222/L399fkyZNUmJiory8vOiRG7gSngoKCtS2bVv97ne/09SpU%2BnPdRCwAAAADOMSIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY9v8D0i4W/bIoUasAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3056168768204097166”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0782175353377071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03119727129711379</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.12931822163007417</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.303971750687346</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0065836701937614</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.056038890236844795</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0700982012084883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09722336493813279</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012974468757656987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2934)</td> <td class=”number”>2934</td> <td class=”number”>91.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3056168768204097166”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.306834924921786e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4952758075379475e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.893685272581147e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4010577934910866e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>58.44887942118595</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>60.86846305401751</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.05211511345065</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>71.4387974379141</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.84421028494457</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_NHPI15”>PCT_LACCESS_NHPI15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1940</td>

</tr> <tr>

<th>Unique (%)</th> <td>60.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.019193</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.4695</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>36.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-949793576456894477”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-949793576456894477”

aria-controls=”quantiles-949793576456894477” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-949793576456894477” aria-controls=”histogram-949793576456894477”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-949793576456894477” aria-controls=”common-949793576456894477”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-949793576456894477” aria-controls=”extreme-949793576456894477”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.0032411</td>

</tr> <tr>

<th>Q3</th> <td>0.013016</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.059681</td>

</tr> <tr>

<th>Maximum</th> <td>4.4695</td>

</tr> <tr>

<th>Range</th> <td>4.4695</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.013016</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.1351</td>

</tr> <tr>

<th>Coef of variation</th> <td>7.0392</td>

</tr> <tr>

<th>Kurtosis</th> <td>725.15</td>

</tr> <tr>

<th>Mean</th> <td>0.019193</td>

</tr> <tr>

<th>MAD</th> <td>0.025167</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>25.235</td>

</tr> <tr>

<th>Sum</th> <td>59.748</td>

</tr> <tr>

<th>Variance</th> <td>0.018253</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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iohYvXqympiaPrQsAAPg%2BnwxYTqdTWVlZampq0q5du/TEE0/o//7v/7R27Vo5nU5lZGSoT58%2BKi4u1qRJk5SZmamamhpJUk1NjTIyMjRlyhTt2bNH4eHhuv/%2B%2B%2BV0OiVJe/fuVUFBgXJzc7Vt2zaVl5crPz/fyuUCAAAf45MB69SpUyorK1NeXp4GDx6s5ORkZWVlqaSkRIcOHVJ1dbVyc3M1aNAgzZs3TwkJCSouLpYk7d69W9ddd51mz56twYMHKy8vT2fOnNHhw4clSdu3b9esWbOUlpamYcOGadmyZSouLuYsFgAA6DSfDFiRkZHavHmz%2BvTp4zZ%2B4cIFlZeX69prr1XPnj1d40lJSSorK5MklZeXKzk52TUXHBysoUOHqqysTG1tbXrvvffc5hMSEnTx4kWdOHHiMq8KAAB0FT4ZsEJCQpSamup63d7erp07d2rEiBGy2%2B2Kiopye39ERITOnTsnSd8539jYqJaWFrd5m82m0NBQ1/YAAADfx2Z1ASbk5%2Bfr%2BPHj2rNnj7Zu3arAwEC3%2BcDAQDkcDklSU1PTt843Nze7Xn/b9p1RW1sru93uNmaz9ewQ7H6qgADfysc2m2/V2xmXeuBrvehq6IN3oA/Wowfew%2BcDVn5%2BvrZt26YnnnhC11xzjYKCgtTQ0OD2HofDoR49ekiSgoKCOoQlh8OhkJAQBQUFuV5/fT44OLjTNRUVFamgoMBtLCMjQ1lZWZ3eR1cUFtbL6hIum5CQzn8%2BcPnQB%2B9AH6xHD6zn0wFr%2BfLlev7555Wfn69x48ZJkqKjo3Xy5Em399XV1bnOHkVHR6uurq7D/JAhQxQaGqqgoCDV1dVp0KBBkqTW1lY1NDQoMjKy03Wlp6dr9OjRbmM2W0/V13/5g9f4XXztG4rp9XuDgAB/hYQEq7GxSW1t7VaX023RB%2B9AH6xHDzqy6su9zwasgoICvfDCC3r88cc1fvx413h8fLwKCwvV3NzsOmtVWlqqpKQk13xpaanr/U1NTTp%2B/LgyMzPl7%2B%2BvuLg4lZaWavjw4ZKksrIy2Ww2xcbGdrq2qKioDpcD7fYv1NravT/sXXn9bW3tXXp9voI%2BeAf6YD16YD2PnwK5/fbb9cILL%2BiLL7740fuoqqrSxo0b9bvf/U5JSUmy2%2B2un5SUFPXt21fZ2dmqrKxUYWGhKioqNG3aNEnS1KlTdfToURUWFqqyslLZ2dnq37%2B/K1DNmDFDW7Zs0b59%2B1RRUaGcnBzdcccdP%2BgSIQAA6N48HrBGjBihp556SqNGjdJDDz2kgwcPuh7y2Vn79%2B9XW1ubNm3apFGjRrn9BAQEaOPGjbLb7ZoyZYpeeeUVbdiwQTExMZKk/v3768knn1RxcbGmTZumhoYGbdiwQX5%2BfpKkCRMmaN68eVq6dKlmz56tYcOGaeHChcb/OwAAgK7Lz/lD040BTqdTb731ll566SXt27dPISEhmjx5siZPnqxf/vKXni7HI%2Bz2H3/G7tvYbP4au/pN4/u9XF5/YKTVJRhns/krLKyX6uu/5HS8heiDd6AP1qMHHUVGXmHJcS25B8vPz08jR47UyJEj1dTUpB07dmjjxo0qLCzU9ddfr1mzZuk3v/mNFaUBAAD8ZJbd5F5bW6tXXnlFr7zyij788ENdf/31uu2223Tu3Dn94Q9/0JEjR7RkyRKrygMAAPjRPB6wXn75Zb388st65513FB4ersmTJ2v9%2BvUaMGCA6z19%2B/bVypUrCVgAAMAneTxgLVmyRGlpadqwYYNuuukm%2Bft3vM9%2B4MCBuvPOOz1dGgAAgBEeD1hvvPGGwsLC1NDQ4ApXFRUVGjp0qAICAiRJ119/va6//npPlwYAAGCExx/TcOHCBY0fP17PPPOMa2zu3LmaNGmSzp496%2BlyAAAAjPN4wHrsscf0i1/8Qvfcc49r7LXXXlPfvn2Vl5fn6XIAAACM83jA%2Bvvf/65HHnnE7W/7hYeH6%2BGHH9ahQ4c8XQ4AAIBxHg9YNptNjY2NHcabmpp%2B8BPdAQAAvJHHA9ZNN92kFStW6JNPPnGNVVdXKy8vT6mpqZ4uBwAAwDiP/xbhokWLdM8992jcuHEKCQmRJDU2Nmro0KHKzs72dDkAAADGeTxgRURE6MUXX9Rbb72lyspK2Ww2XX311brxxhtdf3AZAADAl1nyp3ICAgKUmprKJUEAANAleTxg2e12rV27VkePHtXFixc73Ni%2Bf/9%2BT5cEAABglMcD1qOPPqpjx45pwoQJuuKKKzx9eAAAgMvO4wHr0KFD2rx5s5KTkz19aAAAAI/w%2BGMaevbsqYiICE8fFgAAwGM8HrAmTZqkzZs3q62tzdOHBgAA8AiPXyJsaGhQSUmJ/va3v%2Bmqq65SYGCg2/z27ds9XRIAAIBRljymYeLEiVYcFgAAwCM8HrDy8vI8fUgAAACP8vg9WJJUW1urgoICzZ8/X59%2B%2Bqn%2B%2Bte/6tSpU1aUAgAAYJzHA9bHH3%2Bs3/72t3rxxRe1d%2B9effXVV3rttdc0depUlZeXe7ocAAAA4zwesP74xz9qzJgx2rdvn372s59Jkh5//HGNHj1aq1ev9nQ5AAAAxnk8YB09elT33HOP2x92ttlsuv/%2B%2B3X8%2BHFPlwMAAGCcxwNWe3u72tvbO4x/%2BeWXCggI8HQ5AAAAxnk8YI0aNUpPP/20W8hqaGhQfn6%2BRowY4elyAAAAjPN4wHrkkUd07NgxjRo1Si0tLfrv//5vpaWl6fTp01q0aJGnywEAADDO48/Bio6O1ksvvaSSkhK9//77am9v1/Tp0zVp0iT17t3b0%2BUAAAAYZ8mT3IODg3X77bdbcWgAAIDLzuMBa%2BbMmd85z98iBAAAvs7jAatfv35ur1tbW/Xxxx/rww8/1KxZszxdDgAAgHFe87cIN2zYoHPnznm4GgAAAPMs%2BVuE32TSpEl6/fXXrS4DAADgJ/OagPXuu%2B/yoFEAANAleMVN7hcuXNAHH3ygGTNmeLocAAAA4zwesGJiYtz%2BDqEk/exnP9Odd96p//iP//B0OQAAAMZ5PGD98Y9/NLo/h8OhKVOm6NFHH9Xw4cMlSStWrNCOHTvc3vfoo4/qzjvvlCSVlJRo7dq1stvtGjVqlJYvX67w8HBJktPp1Jo1a7Rnzx61t7dr2rRpWrBggfz9veZqKgAA8HIeD1hHjhzp9HtvuOGG75xvaWnR/PnzVVlZ6TZeVVWl%2BfPn67bbbnONXXpKfEVFhZYsWaJly5YpNjZWK1euVHZ2tp5%2B%2BmlJ0nPPPaeSkhIVFBSotbVVCxcuVEREhObMmdPpugEAQPfm8YB11113uS4ROp1O1/jXx/z8/PT%2B%2B%2B9/635Onjyp%2BfPnu%2B3jkqqqKs2ZM0eRkZEd5nbu3KlbbrlFkydPliStWrVKaWlpqq6u1lVXXaXt27crKytLycnJkqQFCxZo3bp1BCwAANBpHr/u9dRTT6lfv35au3at3n77bZWWlmrr1q365S9/qYceekj79%2B/X/v37tW/fvu/cz%2BHDhzV8%2BHAVFRW5jV%2B4cEHnz5/XgAEDvnG78vJyV3iSpL59%2ByomJkbl5eU6f/68zp4963bmLCkpSWfOnFFtbe2PXzQAAOhWLHnQ6NKlS3XTTTe5xkaMGKHc3Fw9/PDD%2Bt3vftep/XzbbxxWVVXJz89PTz31lN544w2FhobqnnvucV0urK2tVVRUlNs2EREROnfunOx2uyS5zffp00eSdO7cuQ7bAQAAfBOPB6za2toOfy5H%2Btc9UvX19T95/6dOnZKfn58GDhyoO%2B%2B8U0eOHNGjjz6q3r17a%2BzYsWpublZgYKDbNoGBgXI4HGpubna9/vc56V8303dWbW2tK6xdYrP1NB7QAgJ868Z7m8236u2MSz3wtV50NfTBO9AH69ED7%2BHxgJWQkKDHH39cf/rTn1w3njc0NCg/P1833njjT97/5MmTlZaWptDQUElSbGysPvroIz3//PMaO3asgoKCOoQlh8Oh4OBgtzAVFBTk%2BrckBQcHd7qGoqIiFRQUuI1lZGQoKyvrR6%2BrKwgL62V1CZdNSEjnPx%2B4fOiDd6AP1qMH1vN4wPrDH/6gmTNn6qabbtKAAQPkdDr10UcfKTIyUtu3b//J%2B/fz83OFq0sGDhyoQ4cOSZKio6NVV1fnNl9XV6fIyEhFR0dLkux2u/r37%2B/6t6RvvGH%2B26Snp2v06NFuYzZbT9XXf/nDFvM9fO0biun1e4OAAH%2BFhASrsbFJbW3tVpfTbdEH70AfrEcPOrLqy73HA9agQYP02muvqaSkRFVVVZKk//qv/9KECRN%2B0Fmib7Nu3Tq9%2B%2B672rp1q2vsxIkTGjhwoCQpPj5epaWlmjJliiTp7NmzOnv2rOLj4xUdHa2YmBiVlpa6AlZpaaliYmJ%2B0OW9qKioDu%2B3279Qa2v3/rB35fW3tbV36fX5CvrgHeiD9eiB9TwesCTpyiuv1O23367Tp0/rqquukvSvp7mbkJaWpsLCQm3ZskVjx47VwYMH9dJLL7nOjk2fPl133XWXEhISFBcXp5UrV%2Brmm2921TF9%2BnStXr1aP//5zyVJa9as0ezZs43UBgAAugePB6xLT0rfsWOHLl68qL179%2BqJJ55QcHCwcnJyfnLQGjZsmNatW6f169dr3bp16tevn9asWaPExERJUmJionJzc7V%2B/Xp9/vnnGjlypJYvX%2B7afs6cOfr000%2BVmZmpgIAATZs2TXffffdPqgkAAHQvfs5velLnZbR9%2B3Y988wzevDBB5Wbm6tXX31V7733npYtW6b//M//1IMPPujJcjzGbv/C%2BD5tNn%2BNXf2m8f1eLq8/MNLqEoyz2fwVFtZL9fVfcjreQvTBO9AH69GDjiIjr7DkuB6/S7qoqEhLly7VlClTXE9vv/XWW7VixQq9%2Buqrni4HAADAOI8HrNOnT2vIkCEdxmNjYzs8OwoAAMAXeTxg9evXT%2B%2B9916H8TfeeMN1ozkAAIAv8/hN7nPmzNGyZctkt9vldDr19ttvq6ioSDt27NAjjzzi6XIAAACM83jAmjp1qlpbW7Vp0yY1Nzdr6dKlCg8P1wMPPKDp06d7uhwAAADjPB6wSkpKNH78eKWnp%2Buzzz6T0%2BlURESEp8sAAAC4bDx%2BD1Zubq7rZvbw8HDCFQAA6HI8HrAGDBigDz/80NOHBQAA8BiPXyKMjY3VggULtHnzZg0YMEBBQUFu83l5eZ4uCQAAwCiPB6x//vOfSkpKkiSeewUAALokjwSsVatWKTMzUz179tSOHTs8cUgAAADLeOQerOeee05NTU1uY3PnzlVtba0nDg8AAOBRHglY3/T3pI8cOaKWlhZPHB4AAMCjPP5bhAAAAF0dAQsAAMAwjwUsPz8/Tx0KAADAUh57TMOKFSvcnnl18eJF5efnq1evXm7v4zlYAADA13kkYN1www0dnnmVmJio%2Bvp61dfXe6IEAAAAj/FIwOLZVwAAoDvhJncAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb5fMByOByaOHGi3nnnHddYdXW17r77biUkJOjWW2/VwYMH3bZ56623NHHiRMXHx2vmzJmqrq52m9%2B6datSU1OVmJioxYsXq6mpySNrAQAAXYNPB6yWlhY99NBDqqysdI05nU5lZGSoT58%2BKi4u1qRJk5SZmamamhpJUk1NjTIyMjRlyhTt2bNH4eHhuv/%2B%2B%2BV0OiVJe/fuVUFBgXJzc7Vt2zaVl5crPz/fkvUBAADf5LMB6%2BTJk7rjjjv0ySefuI0fOnRI1dXVys3N1aBBgzRv3jwlJCSouLhYkrR7925dd911mj17tgYPHqy8vDydOXNGhw8fliRt375ds2bNUlpamoYNG6Zly5apuLiYs1gAAKDTfDZgHT58WMOHD1dRUZHbeHl5ua699lr17NnTNZaUlKSysjLXfHJysmsuODhYQ4cOVVlZmdra2vTee%2B%2B5zSckJOjixYs6ceLEZV4RAADoKmxWF/BjzZgx4xvH7Xa7oqKi3MYiIiJ07ty5751vbGxUS0uL27zNZlNoaKhrewAAgO/jswHr2zQ1NSkwMNBtLDAwUA6H43vnm5ubXa%2B/bfvOqK2tld1udxuz2Xp2CHY/VUCAb52AtNl8q97OuNQDX%2BtFV0MfvAN9sB498B5dLmAFBQWpoaHBbczhcKhHjx6u%2Ba%2BHJYfDoZCQEAUFBblef30%2BODieyB6YAAAOXklEQVS40zUUFRWpoKDAbSwjI0NZWVmd3kdXFBbWy%2BoSLpuQkM5/PnD50AfvQB%2BsRw%2Bs1%2BUCVnR0tE6ePOk2VldX5zp7FB0drbq6ug7zQ4YMUWhoqIKCglRXV6dBgwZJklpbW9XQ0KDIyMhO15Cenq7Ro0e7jdlsPVVf/%2BWPWdK38rVvKKbX7w0CAvwVEhKsxsYmtbW1W11Ot0UfvAN9sB496MiqL/ddLmDFx8ersLBQzc3NrrNWpaWlSkpKcs2Xlpa63t/U1KTjx48rMzNT/v7%2BiouLU2lpqYYPHy5JKisrk81mU2xsbKdriIqK6nA50G7/Qq2t3fvD3pXX39bW3qXX5yvog3egD9ajB9bzrVMgnZCSkqK%2BffsqOztblZWVKiwsVEVFhaZNmyZJmjp1qo4eParCwkJVVlYqOztb/fv3dwWqGTNmaMuWLdq3b58qKiqUk5OjO%2B644wddIgQAAN1blwtYAQEB2rhxo%2Bx2u6ZMmaJXXnlFGzZsUExMjCSpf//%2BevLJJ1VcXKxp06apoaFBGzZskJ%2BfnyRpwoQJmjdvnpYuXarZs2dr2LBhWrhwoZVLAgAAPsbPeekR5ris7PYvjO/TZvPX2NVvGt/v5fL6AyOtLsE4m81fYWG9VF//JafjLUQfvAN9sB496Cgy8gpLjtvlzmABAABYjYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMO6bMD63//9X/3qV79y%2B8nKypIkHT9%2BXLfffrvi4%2BM1depUHTt2zG3bkpISjRkzRvHx8crIyNBnn31mxRIAAICP6rIB6%2BTJk0pLS9PBgwddPytWrNBXX32luXPnKjk5WX/%2B85%2BVmJioefPm6auvvpIkVVRUaMmSJcrMzFRRUZEaGxuVnZ1t8WoAAIAv6bIBq6qqStdcc40iIyNdPyEhIXrttdcUFBSkhx9%2BWIMGDdKSJUvUq1cv/fWvf5Uk7dy5U7fccosmT56s2NhYrVq1SgcOHFB1dbXFKwIAAL6iSwesAQMGdBgvLy9XUlKS/Pz8JEl%2Bfn66/vrrVVZW5ppPTk52vb9v376KiYlReXm5R%2BoGAAC%2Br0sGLKfTqX/%2B8586ePCgxo0bpzFjxmj16tVyOByy2%2B2Kiopye39ERITOnTsnSaqtrf3OeQAAgO9js7qAy6GmpkZNTU0KDAzU2rVrdfr0aa1YsULNzc2u8X8XGBgoh8MhSWpubv7O%2Bc6ora2V3W53G7PZenYIbj9VQIBv5WObzbfq7YxLPfC1XnQ19ME70Afr0QPv0SUDVr9%2B/fTOO%2B/oyiuvlJ%2Bfn4YMGaL29nYtXLhQKSkpHcKSw%2BFQjx49JElBQUHfOB8cHNzp4xcVFamgoMBtLCMjw/VbjN1VWFgvq0u4bEJCOv/5wOVDH7wDfbAePbBelwxYkhQaGur2etCgQWppaVFkZKTq6urc5urq6lxnl6Kjo79xPjIystPHTk9P1%2BjRo93GbLaeqq//8ocs4Xv52jcU0%2Bv3BgEB/goJCVZjY5Pa2tqtLqfbog/egT5Yjx50ZNWX%2By4ZsN58800tWLBAf/vb31xnnt5//32FhoYqKSlJzzzzjJxOp/z8/OR0OnX06FHdd999kqT4%2BHiVlpZqypQpkqSzZ8/q7Nmzio%2BP7/Txo6KiOlwOtNu/UGtr9/6wd%2BX1t7W1d%2Bn1%2BQr64B3og/XogfV86xRIJyUmJiooKEh/%2BMMfdOrUKR04cECrVq3Svffeq/Hjx6uxsVErV67UyZMntXLlSjU1NemWW26RJE2fPl0vv/yydu/erRMnTujhhx/WzTffrKuuusriVQEAAF/RJQNW7969tWXLFn322WeaOnWqlixZovT0dN17773q3bu3nn76addZqvLychUWFqpnz56S/hXOcnNztWHDBk2fPl1XXnml8vLyLF4RAADwJX5Op9NpdRHdgd3%2BhfF92mz%2BGrv6TeP7vVxef2Ck1SUYZ7P5Kyysl%2Brrv%2BR0vIXog3egD9ajBx1FRl5hyXG75BksAAAAKxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAtY3aGlp0eLFi5WcnKxRo0bp2WeftbokAADgQ2xWF%2BCNVq1apWPHjmnbtm2qqanRokWLFBMTo/Hjx1tdGgAA8AEErK/56quvtHv3bj3zzDMaOnSohg4dqsrKSu3atYuABQAAOoVLhF9z4sQJtba2KjEx0TWWlJSk8vJytbe3W1gZAADwFZzB%2Bhq73a6wsDAFBga6xvr06aOWlhY1NDQoPDz8e/dRW1sru93uNmaz9VRUVJTRWgMCfCsf22y%2BVW9nXOqBr/Wiq6EP3oE%2BWI8eeA8C1tc0NTW5hStJrtcOh6NT%2BygqKlJBQYHbWGZmpn7/%2B9%2BbKfL/q62t1ayfVyo9Pd14eEPn1NbWatu2zfTAYvTBO9AH69ED70HE/ZqgoKAOQerS6x49enRqH%2Bnp6frzn//s9pOenm68VrvdroKCgg5ny%2BA59MA70AfvQB%2BsRw%2B8B2ewviY6Olr19fVqbW2Vzfav/zx2u109evRQSEhIp/YRFRXFNwcAALoxzmB9zZAhQ2Sz2VRWVuYaKy0tVVxcnPz9%2Bc8FAAC%2BH4nha4KDgzV58mTl5OSooqJC%2B/bt07PPPquZM2daXRoAAPARATk5OTlWF%2BFtRowYoePHj2vNmjV6%2B%2B23dd9992nq1KlWl/WNevXqpZSUFPXq1cvqUroteuAd6IN3oA/Wowfewc/pdDqtLgIAAKAr4RIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAClo9qaWnR4sWLlZycrFGjRunZZ5%2B1uqRuy%2BFwaOLEiXrnnXesLqVbOn/%2BvLKyspSSkqLU1FTl5eWppaXF6rK6lY8//lhz5sxRYmKibr75Zm3evNnqkrq9uXPn6pFHHrG6jG7NZnUB%2BHFWrVqlY8eOadu2baqpqdGiRYsUExOj8ePHW11at9LS0qL58%2BersrLS6lK6JafTqaysLIWEhGjXrl36/PPPtXjxYvn7%2B2vRokVWl9cttLe3a%2B7cuYqLi9OLL76ojz/%2BWA899JCio6P129/%2B1uryuqW//OUvOnDggG677TarS%2BnWOIPlg7766ivt3r1bS5Ys0dChQzV27Fjde%2B%2B92rVrl9WldSsnT57UHXfcoU8%2B%2BcTqUrqtU6dOqaysTHl5eRo8eLCSk5OVlZWlkpISq0vrNurq6jRkyBDl5ORowIAB%2BvWvf60bb7xRpaWlVpfWLTU0NGjVqlWKi4uzupRuj4Dlg06cOKHW1lYlJia6xpKSklReXq729nYLK%2BteDh8%2BrOHDh6uoqMjqUrqtyMhIbd68WX369HEbv3DhgkUVdT9RUVFau3atevfuLafTqdLSUh05ckQpKSlWl9Yt/elPf9KkSZN09dVXW11Kt8clQh9kt9sVFhamwMBA11ifPn3U0tKihoYGhYeHW1hd9zFjxgyrS%2Bj2QkJClJqa6nrd3t6unTt3asSIERZW1X2NHj1aNTU1SktL07hx46wup9t5%2B%2B239fe//12vvvqqcnJyrC6n2%2BMMlg9qampyC1eSXK8dDocVJQFeIT8/X8ePH9eDDz5odSnd0vr16/XUU0/p/fffV15entXldCstLS36n//5Hy1dulQ9evSwuhyIM1g%2BKSgoqEOQuvSa/7HQXeXn52vbtm164okndM0111hdTrd06b6flpYWLViwQA8//HCHL4O4PAoKCnTddde5ndGFtQhYPig6Olr19fVqbW2VzfavFtrtdvXo0UMhISEWVwd43vLly/X8888rPz%2BfS1MeVldXp7KyMo0ZM8Y1dvXVV%2BvixYu6cOECtyx4yF/%2B8hfV1dW57s299KV77969evfdd60srdsiYPmgIUOGyGazqaysTMnJyZKk0tJSxcXFyd%2Bfq77oXgoKCvTCCy/o8ccf5zElFjh9%2BrQyMzN14MABRUdHS5KOHTum8PBwwpUH7dixQ62tra7Xq1evliQtWLDAqpK6PQKWDwoODtbkyZOVk5Ojxx57TLW1tXr22We55wHdTlVVlTZu3Ki5c%2BcqKSlJdrvdNRcZGWlhZd1HXFychg4dqsWLFys7O1tnzpxRfn6%2B7rvvPqtL61b69evn9rpXr16SpF/84hdWlAMRsHxWdna2cnJyNGvWLPXu3Vu///3v9Zvf/MbqsgCP2r9/v9ra2rRp0yZt2rTJbe6DDz6wqKruJSAgQBs3btTy5cuVnp6u4OBg3XXXXZo5c6bVpQGW8nM6nU6riwAAAOhKuGEHAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAz7f2FXj6xlfb6sAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-949793576456894477”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01194681129579588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.033801395162232704</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.04615207326629525</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.021842555455531307</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.004196810313842147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000855791228875384</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009380600387265877</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005490639325721949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.874654190620268e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1929)</td> <td class=”number”>1929</td> <td class=”number”>60.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-949793576456894477”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1175</td> <td class=”number”>36.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3248328670178477e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.585739759080649e-07</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.580018192056047e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.69923180266681e-06</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.8126888317341692</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.24369555207724</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.775812478674561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.065210356214455</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.469546279819035</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_POP10”>PCT_LACCESS_POP10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.538</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram225996071812710512”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives225996071812710512,#minihistogram225996071812710512”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives225996071812710512”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles225996071812710512”

aria-controls=”quantiles225996071812710512” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram225996071812710512” aria-controls=”histogram225996071812710512”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common225996071812710512” aria-controls=”common225996071812710512”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme225996071812710512” aria-controls=”extreme225996071812710512”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles225996071812710512”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1.9934</td>

</tr> <tr>

<th>Q1</th> <td>10.852</td>

</tr> <tr>

<th>Median</th> <td>19.671</td>

</tr> <tr>

<th>Q3</th> <td>29.578</td>

</tr> <tr>

<th>95-th percentile</th> <td>63.96</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>18.727</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>20.233</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.85959</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.3486</td>

</tr> <tr>

<th>Mean</th> <td>23.538</td>

</tr> <tr>

<th>MAD</th> <td>13.626</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.1205</td>

</tr> <tr>

<th>Sum</th> <td>73720</td>

</tr> <tr>

<th>Variance</th> <td>409.36</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram225996071812710512”>

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TZOBgAA4JuAC6yjR49q3rx5io%2BP9xzr3Lmz5syZow8%2B%2BMDGyQAAAHwTcIHlcDhUX19/zfHGxkbe0R0AAASFgAusUaNGacmSJfrHP/7hOVZZWamCggKNHDnSxskAAAB8E3D/inDu3LmaPn26HnjgAcXFxUmS6uvrNWDAAOXm5to8HQAAQNsCLrC6dOmiX/3qVzp8%2BLAqKirkcDh0zz33aNiwYQoLC7N7PAAAgDYFXGBJUnh4uEaOHMlLggAAICgFXGC53W6tXr1apaWlunLlyjXf2P673/3OpskAAAB8E3CB9R//8R8qKyvTQw89pNtvv93ucQAAANot4ALrgw8%2B0MaNG5Wammr3KAAAADck4N6mISYmRl26dLF7DAAAgBsWcIE1fvx4bdy4UVevXrV7FAAAgBsScC8R1tXVad%2B%2BffrDH/6gXr16KSIiwuv8q6%2B%2BatNkAAAAvgm4wJKkcePG2T0CAADADQu4wCooKLB7BAAAgJsScN%2BDJUnV1dUqLCzUT3/6U/3P//yPDhw4oNOnT9s9FgAAgE8CLrDOnj2r73//%2B/rVr36l3/zmN2poaND%2B/fuVmZkpl8tl93gAAABtCrjA%2BsUvfqGxY8fq4MGDuu222yRJK1euVFpampYvX27zdAAAAG0LuMAqLS3V9OnTvX6ws8Ph0MyZM1VeXm7jZAAAAL4JuMBqbW1Va2vrNccvXbqk8PBwGyYCAABon4ALrBEjRmj9%2BvVekVVXV6dly5Zp6NChNk4GAADgm4ALrHnz5qmsrEwjRoxQU1OTnnzySY0ZM0affPKJ5s6da/d4AAAAbQq498Hq1q2bdu/erX379umvf/2rWltblZWVpfHjx6tjx452jwcAANCmgAssSYqOjtbkyZPtHgMAAOCGBFxgTZs27WvP87MIAQBAoAu4wEpMTPT6uKWlRWfPntXHH3%2BsRx991KapAAAAfBdwgfVVP4twzZo1On/%2B/Dc8DQAAQPsF3L8i/Crjx4/Xr3/9a7vHAAAAaFPQBNaf/vQn3mgUAAAEhYB7ifB63%2BR%2B8eJF/e1vf9OPfvQjGyYCAABon4ALrB49enj9HEJJuu222/TII4/oBz/4gU1TAQAA%2BC7gAusXv/iF3SMAAADclIALrI8%2B%2Bsjn2w4ePNiPkwAAANyYgAusqVOnel4itCzLc/zLx8LCwvTXv/71mx8QAACgDQEXWOvWrdOSJUv085//XEOGDFFERIT%2B/Oc/a9GiRfrhD3%2BoBx980O4RAQAAvlbAvU1DQUGB5s%2BfrwceeECdOnVSbGyshg4dqkWLFun1119XYmKi5xcAAEAgCrjAqq6uvm48dezYUbW1tTZMBAAA0D4BF1iDBg3SypUrdfHiRc%2Bxuro6LVu2TMOGDbNxMgAAAN8E3PdgPfvss5o2bZpGjRqlPn36yLIs/f3vf1d8fLxeffVVu8cDAABoU8AF1t133639%2B/dr3759OnXqlCTp4Ycf1kMPPaTo6GibpwMAAGhbwAWWJN1xxx2aPHmyPvnkE/Xq1UvS5%2B/mDgAAEAwC7nuwLMvS8uXLNXjwYI0bN07nz5/X3LlzlZeXpytXrtg9HgAAQJsCLrC2bNmiPXv2aMGCBYqIiJAkjR07VgcPHlRhYWG77y87O1vz5s3zfFxeXq7JkyfL6XQqMzNTZWVlXrfft2%2Bfxo4dK6fTqZycHF24cOHmHhAAALjlBFxgFRcXa/78%2BZo4caLn3dsffPBBLVmyRHv37m3Xfb311ls6dOiQ5%2BOGhgZlZ2crNTVVu3btUlJSkmbMmKGGhgZJ0vHjx5WXl6dZs2apuLhY9fX1ys3NNffgAADALSHgAuuTTz5Rv379rjl%2B7733yu12%2B3w/dXV1Wrp0qQYOHOg5tn//fkVGRmrOnDm6%2B%2B67lZeXp9jYWB04cECStHXrVmVkZGjChAm69957tXTpUh06dEiVlZU3/8AAAMAtI%2BACKzExUX/%2B85%2BvOf7OO%2B94vuHdF88//7zGjx%2Bve%2B65x3PM5XIpJSXFc2UsLCxMycnJOnbsmOd8amqq5/bdu3dXjx495HK5bvThAACAW1DABda///u/a%2BHChXr11VdlWZaOHDmi5cuXa%2BnSpZo6dapP93HkyBEdPXpUM2fO9DrudruVkJDgdaxLly46f/68pM/fRf7rzgMAAPgi4N6mITMzUy0tLXrppZd0%2BfJlzZ8/X507d9bTTz%2BtrKysNj%2B/qalJCxYs0Pz58xUVFeV1rrGx0fON81%2BIiIhQc3OzJOny5ctfe95X1dXV17yc6XDEXBNvNys8POD6GDZyOILj%2BfDF85bnr3%2BwX/9ht/4TirsNuMDat2%2Bf0tPTNWXKFF24cEGWZalLly4%2Bf35hYaHuu%2B8%2BjRw58ppzkZGR18RSc3OzJ8S%2B6nx73%2BC0uLj4mn/xmJOTo9mzZ7frfoD26NQp1u4R2iUujjcO9if26z/s1n9CabcBF1iLFi3Sf/3Xf%2BmOO%2B5Q586d2/35b731lmpqapSUlCRJnmD6zW9%2Bo3Hjxqmmpsbr9jU1NZ4rS926dbvu%2Bfj4%2BHbNMGXKFKWlpXkdczhiVFt7qV3305ZQKn3cPNPPL38JD%2B%2BguLho1dc36urVVrvHCTns13/Yrf/4c7d2/cdnwAVWnz599PHHH3t9c3p7bNmyRS0tLZ6Ply9fLkn62c9%2Bpo8%2B%2BkgbNmyQZVkKCwuTZVkqLS3VE088IUlyOp0qKSnRxIkTJUlVVVWqqqqS0%2Bls1wwJCQnXvBzodn%2Bmlhb%2BQMJ/gu35dfVqa9DNHEzYr/%2BwW/8Jpd0GXGDde%2B%2B9%2BtnPfqaNGzeqT58%2BioyM9DpfUFDwtZ%2BfmJjo9XFs7Ofl2rt3b3Xp0kUrVqxQfn6%2B/u3f/k3btm1TY2OjMjIyJElZWVmaOnWqBg0apIEDByo/P1%2BjR49u179eBAAACLjAOnPmjFJSUiSpXe975YuOHTtq/fr1WrBggd544w3967/%2Bq4qKihQTEyNJSkpK0qJFi/TCCy/on//8p4YPH67FixcbnQHwl4zV79s9gs%2BO5qfbPQIA%2BFWYZVmW3UMsXbpUs2bN8oROKHK7PzN%2Bnw5HB313%2BbvG7xfwt6P56aqtvRQyLwUEEoejgzp1imW/fsBu/cefu42Pv93o/fkqIL5LetOmTWpsbPQ6lp2drerqapsmAgAAuHEBEVjXu4j20UcfqampyYZpAAAAbk5ABBYAAEAoIbAAAAAMC5jA%2BuIHMAMAAAS7gHmbhiVLlni959WVK1e0bNkyz/tYfaGt98ECAACwW0AE1uDBg695z6ukpCTV1taqtrbWpqkAAABuTEAE1pYtW%2BweAQAAwJiA%2BR4sAACAUEFgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGBaygfXpp59q9uzZGjJkiEaOHKmCggI1NTVJkiorK/XYY49p0KBBevDBB/Xee%2B95fe7hw4c1btw4OZ1OTZs2TZWVlXY8BAAAEKRCMrAsy9Ls2bPV2Nio1157TatWrdLbb7%2Bt1atXy7Is5eTkqGvXrtq5c6fGjx%2BvWbNm6dy5c5Kkc%2BfOKScnRxMnTtSOHTvUuXNnzZw5U5Zl2fyoAABAsHDYPYA/nD59WseOHdP777%2Bvrl27SpJmz56t559/XqNGjVJlZaW2bdummJgY3X333Tpy5Ih27typp556Stu3b9d9992nxx9/XJJUUFCg4cOH649//KPuv/9%2BOx8WAAAIEiF5BSs%2BPl4bN270xNUXLl68KJfLpf79%2BysmJsZzPCUlRceOHZMkuVwupaames5FR0drwIABnvMAAABtCcnAiouL08iRIz0ft7a2auvWrRo6dKjcbrcSEhK8bt%2BlSxedP39ekto8DwAA0JaQfInwy5YtW6by8nLt2LFDmzdvVkREhNf5iIgINTc3S5IaGxu/9rwvqqur5Xa7vY45HDHXhNvNCg8PyT7GLYLnr398sVf2ax679Z9Q3G3IB9ayZcv0yiuvaNWqVerbt68iIyNVV1fndZvm5mZFRUVJkiIjI6%2BJqebmZsXFxfn8exYXF6uwsNDrWE5OjmbPnn2DjwIIPXFx0XaPENLYr/%2BwW/8Jpd2GdGAtXrxYr7/%2BupYtW6YHHnhAktStWzedPHnS63Y1NTWeq0vdunVTTU3NNef79evn8%2B87ZcoUpaWleR1zOGJUW3vpRh7GVwql0setp76%2BUVevtto9RsgJD%2B%2BguLho9usH7NZ//LnbTp1ijd6fr0I2sAoLC7Vt2zatXLlS6enpnuNOp1NFRUW6fPmy56pVSUmJUlJSPOdLSko8t29sbFR5eblmzZrl8%2B%2BdkJBwzcuBbvdnamnhDyTwhatXW/kz4Ufs13/Yrf%2BE0m5D8hLIqVOntHbtWv34xz9WSkqK3G6359eQIUPUvXt35ebmqqKiQkVFRTp%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%2Bn27R2iXo/npdo8A3PK4ggUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYPyoHAEJMat4Bu0fw2a%2BfHm73CIBfEFjX0dTUpIULF%2Bq///u/FRUVpccff1yPP/643WMBQMjh5zwiVBFY17F06VKVlZXplVde0blz5zR37lz16NFD6en8wQKAW1kwXR2EvQisL2loaND27du1YcMGDRgwQAMGDFBFRYVee%2B01AgsAAPiEb3L/khMnTqilpUVJSUmeYykpKXK5XGptbbVxMgAAECy4gvUlbrdbnTp1UkREhOdY165d1dTUpLq6OnXu3LnN%2B6iurpbb7fY65nDEKCEhweis4eH0MQAgdITS32sE1pc0NjZ6xZUkz8fNzc0%2B3UdxcbEKCwu9js2aNUtPPfWUmSH/V3V1tR79fxWaMmWK8Xi71VVXV6u4uJjd%2BgG79S/26z/s1n%2Bqq6v14osvhtRuQycVDYmMjLwmpL74OCoqyqf7mDJlinbt2uX1a8qUKcZndbvdKiwsvOZqGW4eu/Ufdutf7Nd/2K3/hOJuuYL1Jd26dVNtba1aWlrkcHy%2BHrfbraioKMXFxfl0HwkJCSFT4AAAoP24gvUl/fr1k8Ph0LFjxzzHSkpKNHDgQHXowLoAAEDbKIYviY6O1oQJE/Tcc8/p%2BPHjOnjwoF5%2B%2BWVNmzbN7tEAAECQCH/uueees3uIQDN06FCVl5drxYoVOnLkiJ544gllZmbaPdZ1xcbGasiQIYqNjbV7lJDDbv2H3foX%2B/Ufdus/obbbMMuyLLuHAAAACCW8RAgAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgRWkmppZGd5WAAAH%2BklEQVSa9Mwzzyg1NVUjRozQyy%2B/bPdIQevTTz/V7NmzNWTIEI0cOVIFBQVqamqSJFVWVuqxxx7ToEGD9OCDD%2Bq9996zedrglZ2drXnz5nk%2BLi8v1%2BTJk%2BV0OpWZmamysjIbpws%2Bzc3NWrhwoQYPHqxvf/vbWrlypb5432h2e/Oqqqo0Y8YMJScnKy0tTZs3b/acY783prm5WePGjdOHH37oOdbW19jDhw9r3LhxcjqdmjZtmiorK7/psW8YgRWkli5dqrKyMr3yyitasGCBCgsLdeDAAbvHCjqWZWn27NlqbGzUa6%2B9plWrVuntt9/W6tWrZVmWcnJy1LVrV%2B3cuVPjx4/XrFmzdO7cObvHDjpvvfWWDh065Pm4oaFB2dnZSk1N1a5du5SUlKQZM2aooaHBximDy5IlS3T48GH98pe/1IoVK/TGG2%2BouLiY3Rry9NNPKyYmRrt27dIzzzyj1atX67e//S37vUFNTU36yU9%2BooqKCs%2Bxtr7Gnjt3Tjk5OZo4caJ27Nihzp07a%2BbMmQqaH0BjIehcunTJGjhwoPXBBx94jq1Zs8Z65JFHbJwqOJ08edLq27ev5Xa7Pcf27t1rjRgxwjp8%2BLA1aNAg69KlS55zjz76qPXCCy/YMWrQqq2ttUaNGmVlZmZac%2BfOtSzLsrZv326lpaVZra2tlmVZVmtrq/Xd737X2rlzp52jBo3a2lqrf//%2B1ocffug5tn79emvevHns1oC6ujqrb9%2B%2B1t/%2B9jfPsVmzZlkLFy5kvzegoqLC%2BsEPfmB9//vft/r27ev5u6utr7GrV6/2%2BnutoaHBSkpK8vq7L5BxBSsInThxQi0tLUpKSvIcS0lJkcvlUmtrq42TBZ/4%2BHht3LhRXbt29Tp%2B8eJFuVwu9e/fXzExMZ7jKSkpOnbs2Dc9ZlB7/vnnNX78eN1zzz2eYy6XSykpKQoLC5MkhYWFKTk5md36qKSkRB07dtSQIUM8x7Kzs1VQUMBuDYiKilJ0dLR27dqlK1eu6PTp0yotLVW/fv3Y7w344x//qPvvv1/FxcVex9v6GutyuZSamuo5Fx0drQEDBgTNrgmsIOR2u9WpUydFRER4jnXt2lVNTU2qq6uzcbLgExcXp5EjR3o%2Bbm1t1datWzV06FC53W4lJCR43b5Lly46f/78Nz1m0Dpy5IiOHj2qmTNneh1ntzensrJSiYmJ2r17t9LT0/Wd73xHa9asUWtrK7s1IDIyUvPnz1dxcbGcTqcyMjI0atQoTZ48mf3egB/96Ed65plnFB0d7XW8rV0G%2B64ddg%2BA9mtsbPSKK0mej5ubm%2B0YKWQsW7ZM5eXl2rFjhzZv3nzdPbNj3zQ1NWnBggWaP3%2B%2BoqKivM591XOY3fqmoaFBZ8%2Be1bZt21RQUCC326358%2BcrOjqa3Rpy6tQpjRkzRtOnT1dFRYUWL16sYcOGsV%2BD2tplsO%2BawApCkZGR1zzBvvj4y3%2BRwXfLli3TK6%2B8olWrVqlv376KjIy85opgc3MzO/ZRYWGh7rvvPq8rhF/4qucwu/WNw%2BHQxYsXtWLFCiUmJkr6/BuCX3/9dfXu3Zvd3qQjR45ox44dOnTokKKiojRw4EB9%2Bumneumll9SrVy/2a0hbX2O/6utEXFzcNzbjzeAlwiDUrVs31dbWqqWlxXPM7XYrKioqaJ54gWbx4sXatGmTli1bpgceeEDS53uuqanxul1NTc01l6xxfW%2B99ZYOHjyopKQkJSUlae/evdq7d6%2BSkpLY7U2Kj49XZGSkJ64k6c4771RVVRW7NaCsrEy9e/f2iqb%2B/fvr3Llz7Negtnb5Vefj4%2BO/sRlvBoEVhPr16yeHw%2BH1jX4lJSUaOHCgOnTg/9L2Kiws1LZt27Ry5Uo99NBDnuNOp1N/%2BctfdPnyZc%2BxkpISOZ1OO8YMOlu2bNHevXu1e/du7d69W2lpaUpLS9Pu3bvldDr1pz/9yfPPrS3LUmlpKbv1kdPpVFNTk86cOeM5dvr0aSUmJrJbAxISEnT27FmvqyenT59Wz5492a9BbX2NdTqdKikp8ZxrbGxUeXl50Oyav42DUHR0tCZMmKDnnntOx48f18GDB/Xyyy9r2rRpdo8WdE6dOqW1a9fqxz/%2BsVJSUuR2uz2/hgwZou7duys3N1cVFRUqKirS8ePHNWnSJLvHDgqJiYnq3bu351dsbKxiY2PVu3dvpaenq76%2BXvn5%2BTp58qTy8/PV2NiojIwMu8cOCnfddZdGjx6t3NxcnThxQu%2B%2B%2B66KioqUlZXFbg1IS0vTbbfdpmeffVZnzpzR73//e61bt05Tp05lvwa19TU2MzNTpaWlKioqUkVFhXJzc9WzZ0/df//9Nk/uIzvfIwI3rqGhwZozZ441aNAga8SIEdamTZvsHikorV%2B/3urbt%2B91f1mWZf3973%2B3Hn74Yeu%2B%2B%2B6zHnroIev999%2B3eeLgNXfuXM/7YFmWZblcLmvChAnWwIEDrUmTJll/%2BctfbJwu%2BNTX11s///nPrUGDBlnDhg2zXnzxRc97M7Hbm1dRUWE99thjVnJysjV27Fhr06ZN7NeA//s%2BWJbV9tfYP/zhD9b3vvc961vf%2Bpb16KOPWv/4xz%2B%2B6ZFvWJhlBctbogIAAAQHXiIEAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAw7P8Ds3dcVR2iVY4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common225996071812710512”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.633605132861428</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.00526201334661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0725893951893353</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.46845778160539</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36.696235585931966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.1064742023219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.95946099612089</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.74880484541413</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.615616591206695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme225996071812710512”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.944829763351498e-05</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00012611350341420783</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00023948474105398398</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0005359863967977933</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.00000059283695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000074171741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000094378623</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000095859588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000097149982</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_POP15”>PCT_LACCESS_POP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3106</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>23.042</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4061474521306398390”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4061474521306398390,#minihistogram4061474521306398390”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4061474521306398390”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4061474521306398390”

aria-controls=”quantiles4061474521306398390” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4061474521306398390” aria-controls=”histogram4061474521306398390”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4061474521306398390” aria-controls=”common4061474521306398390”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4061474521306398390” aria-controls=”extreme4061474521306398390”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4061474521306398390”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.2369</td>

</tr> <tr>

<th>Q1</th> <td>10.93</td>

</tr> <tr>

<th>Median</th> <td>19.177</td>

</tr> <tr>

<th>Q3</th> <td>28.823</td>

</tr> <tr>

<th>95-th percentile</th> <td>58.107</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>17.893</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.539</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.84799</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.8339</td>

</tr> <tr>

<th>Mean</th> <td>23.042</td>

</tr> <tr>

<th>MAD</th> <td>13.119</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.1814</td>

</tr> <tr>

<th>Sum</th> <td>71730</td>

</tr> <tr>

<th>Variance</th> <td>381.79</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4061474521306398390”>

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B9enTh3dUBwAAQSHgAuvo0aNatGiRYmNjvcd69OihBQsW6P3337dxMgAAgI4JuMByOp1qaGhoc7ypqYl3dAcAAEEh4AJr3LhxWrZsmf71r395j1VVVSk/P19jx461cTIAAICOCbh/Rbhw4ULNnj1b9913n2JiYiRJDQ0NGjp0qHJycmyeDgAAoH0BF1g9e/bU66%2B/rsOHD6uyslJOp1N33HGHRo8eLYfDYfd4AAAA7Qq4wJKksLAwjR07lpcEAQBAUAq4wPJ4PFq7dq3Kysp05cqVNt/Y/oc//MGmyQAAADom4ALrP//zP1VeXq5Jkybp5ptvtnscAACATgu4wHr//fe1adMmJScn2z0KAADAdQm4t2mIjo5Wz5497R4DAADgugVcYE2ZMkWbNm3StWvX7B4FAADgugTcS4T19fXat2%2Bf/vSnP6l///4KDw/3Of/KK6/YNBkAAEDHBFxgSdLkyZPtHgEAAOC6BVxg5efn2z0CAADADQm478GSpJqaGhUUFOjnP/%2B5/ud//kcHDhzQ6dOn7R4LAACgQwIusM6ePasf/OAHev311/W73/1OjY2N2r9/vzIyMuR2u%2B0eDwAAoF0BF1jPPfecJk6cqIMHD%2Bqmm26SJK1evVopKSlauXKlzdMBAAC0L%2BACq6ysTLNnz/b5wc5Op1Nz585VRUWFjZMBAAB0TMAFVmtrq1pbW9scv3TpksLCwmyYCAAAoHMCLrDGjBmjDRs2%2BERWfX29VqxYoVGjRtk4GQAAQMcEXGAtWrRI5eXlGjNmjJqbm/XYY49pwoQJ%2Bvjjj7Vw4UK7xwMAAGhXwL0PVu/evbV7927t27dPf//739Xa2qrMzExNmTJF3bp1s3s8AACAdgVcYElSVFSUpk%2BfbvcYAAAA1yXgAmvWrFlfe56fRQgAAAJdwAVWfHy8z8dXr17V2bNn9dFHH%2Bmhhx6yaSoAAICOC7jA%2BqqfRVhYWKjz589/w9MAAAB0XsD9K8KvMmXKFP32t7%2B1ewwAAIB2BU1g/eUvf%2BGNRgEAQFAIuJcIv%2Byb3C9evKh//OMf%2BvGPf2zDRAAAAJ0TcIHVt29fn59DKEk33XSTHnzwQf3whz%2B0aSoAAICOC7jAeu655%2BweAQAA4IYEXGB9%2BOGHHb7tiBEj/DgJAADA9Qm4wJo5c6b3JULLsrzHv3jM4XDo73//%2Bzc/IAAAQDsCLrDWr1%2BvZcuW6Ze//KVGjhyp8PBw/fWvf9XSpUv1ox/9SPfff7/dIwIAAHytgHubhvz8fC1evFj33Xefunfvrq5du2rUqFFaunSpXnvtNcXHx3t/AQAABKKAC6yampovjadu3bqprq7OhokAAAA6J%2BACa/jw4Vq9erUuXrzoPVZfX68VK1Zo9OjRNk4GAADQMQH3PVhPPfWUZs2apXHjxmngwIGyLEv//Oc/FRsbq1deecXu8QAAANoVcIF1%2B%2B23a//%2B/dq3b59OnTolSXrggQc0adIkRUVF2TwdAABA%2BwIusCTplltu0fTp0/Xxxx%2Brf//%2Bkj57N3cAAIBgEHDfg2VZllauXKkRI0Zo8uTJOn/%2BvBYuXKjc3FxduXLF7vEAAADaFXCBtXXrVu3Zs0dPP/20wsPDJUkTJ07UwYMHVVBQYPN0AAAA7Qu4wCouLtbixYuVnp7ufff2%2B%2B%2B/X8uWLdPevXttng4AAKB9ARdYH3/8sQYPHtzm%2BJ133imPx2PDRAAAAJ0TcIEVHx%2Bvv/71r22Ov/32295veAcAAAhkARdYP/nJT7RkyRK98sorsixLR44c0cqVK7V8%2BXLNnDmz0/eXlZWlRYsWeT%2BuqKjQ9OnT5XK5lJGRofLycp/b79u3TxMnTpTL5VJ2drYuXLhww48JAAB8uwRcYGVkZOg//uM/9NJLL%2Bny5ctavHixSkpK9MQTTygzM7NT9/Xmm2/q0KFD3o8bGxuVlZWl5ORklZSUKCEhQXPmzFFjY6Mk6fjx48rNzdW8efNUXFyshoYG5eTkGH18AAAg9AXc%2B2Dt27dPqampmjFjhi5cuCDLstSzZ89O3099fb2WL1%2BuYcOGeY/t379fERERWrBggRwOh3Jzc/X222/rwIEDSk9P17Zt25SWlqapU6dKkpYvX64JEyaoqqqKlycBAECHBdwVrKVLl3q/mb1Hjx7XFVeS9Pzzz2vKlCm64447vMfcbreSkpK8/zrR4XAoMTFRx44d855PTk723r5Pnz7q27ev3G739T4cAADwLRRwgTVw4EB99NFHN3QfR44c0dGjRzV37lyf4x6PR3FxcT7HevbsqfPnz0uSampqvvY8AABARwTcS4R33nmnfvGLX2jTpk0aOHCgIiIifM7n5%2Bd/7e9vbm7W008/rcWLFysyMtLnXFNTk/fNSz8XHh6ulpYWSdLly5e/9nxH1dTUtHlLCaczuk283aiwsIDrY9jI6QyO58Pnz1uev/7Bfv2H3fpPKO424ALrzJkzSkpKkqTret%2BrgoIC3XXXXRo7dmybcxEREW1iqaWlxRtiX3W%2Bsz9kuri4uM27zmdnZ2v%2B/Pmduh%2BgM7p372r3CJ0SE8MPb/cn9us/7NZ/Qmm3ARFYy5cv17x58xQdHa2tW7fe0H29%2Beabqq2tVUJCgiR5g%2Bl3v/udJk%2BerNraWp/b19bWeq8s9e7d%2B0vPx8bGdmqGGTNmKCUlxeeY0xmturpLnbqf9oRS6ePGmX5%2B%2BUtYWBfFxESpoaFJ16612j1OyGG//sNu/cefu7XrPz4DIrA2b96sn/zkJ4qOjvYey8rK0rJlyzr9strWrVt19epV78crV66UJP3iF7/Qhx9%2BqI0bN8qyLDkcDlmWpbKyMj366KOSJJfLpdLSUqWnp0uSqqurVV1dLZfL1akZ4uLi2szt8Xyqq1f5Awn/Cbbn17VrrUE3czBhv/7Dbv0nlHYbEIFlWVabYx9%2B%2BKGam5s7fV/x8fE%2BH3ft%2Blm5DhgwQD179tSqVauUl5enf//3f9f27dvV1NSktLQ0SVJmZqZmzpyp4cOHa9iwYcrLy9P48eN5iwYAANAp36rXmLp166YNGzZ4r1K53W4VFRV5r5wlJCRo6dKlKiwsVGZmpm655ZZ2v6keAADgiwLiCpY/Pffccz4f33333Xr99de/8vbp6enelwgBAACuR8Bcwfr8zT8BAACCXcBcwVq2bJnPe15duXJFK1as8H4P1ed4yQ4AAAS6gAisESNGtHnPq4SEBNXV1amurs6mqQAAAK5PQATWjb73FQAAQCAJmO/BAgAACBUEFgAAgGEEFgAAgGEB8T1YAG5c2tr37B6hw47mpdo9AgD4FVewAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADAvZwPrkk080f/58jRw5UmPHjlV%2Bfr6am5slSVVVVXr44Yc1fPhw3X///Xr33Xd9fu/hw4c1efJkuVwuzZo1S1VVVXY8BAAAEKRCMrAsy9L8%2BfPV1NSkV199VWvWrNFbb72ltWvXyrIsZWdnq1evXtq1a5emTJmiefPm6dy5c5Kkc%2BfOKTs7W%2Bnp6dq5c6d69OihuXPnyrIsmx8VAAAIFk67B/CH06dP69ixY3rvvffUq1cvSdL8%2BfP1/PPPa9y4caqqqtL27dsVHR2t22%2B/XUeOHNGuXbv0%2BOOPa8eOHbrrrrv0yCOPSJLy8/N177336s9//rPuueceOx8WAAAIEiF5BSs2NlabNm3yxtXnLl68KLfbrSFDhig6Otp7PCkpSceOHZMkud1uJScne89FRUVp6NCh3vMAAADtCcnAiomJ0dixY70ft7a2atu2bRo1apQ8Ho/i4uJ8bt%2BzZ0%2BdP39ekto9DwAA0J6QfInwi1asWKGKigrt3LlTW7ZsUXh4uM/58PBwtbS0SJKampq%2B9nxH1NTUyOPx%2BBxzOqPbhNuNCgsLyT7GtwTPX//4fK/s1zx26z%2BhuNuQD6wVK1bo5Zdf1po1azRo0CBFRESovr7e5zYtLS2KjIyUJEVERLSJqZaWFsXExHT4cxYXF6ugoMDnWHZ2tubPn3%2BdjwIIPTExUXaPENLYr/%2BwW/8Jpd2GdGA9%2B%2Byzeu2117RixQrdd999kqTevXvr5MmTPrerra31Xl3q3bu3amtr25wfPHhwhz/vjBkzlJKS4nPM6YxWXd2l63kYXymUSh/fPg0NTbp2rdXuMUJOWFgXxcREsV8/YLf%2B48/ddu/e1ej9dVTIBlZBQYG2b9%2Bu1atXKzU11Xvc5XKpqKhIly9f9l61Ki0tVVJSkvd8aWmp9/ZNTU2qqKjQvHnzOvy54%2BLi2rwc6PF8qqtX%2BQMJfO7atVb%2BTPgR%2B/Ufdus/obTbkLwEcurUKa1bt04//elPlZSUJI/H4/01cuRI9enTRzk5OaqsrFRRUZGOHz%2BuadOmSZIyMjJUVlamoqIiVVZWKicnR/369eMtGgAAQIeFZGD94Q9/0LVr1/Tiiy9qzJgxPr/CwsK0bt06eTwepaen64033lBhYaH69u0rSerXr59eeOEF7dq1S9OmTVN9fb0KCwvlcDhsflQAACBYOCzeovwb4fF8avw%2Bnc4u%2Bt7Kd4zfL%2BBvR/NSVVd3KWReCggkTmcXde/elf36Abv1H3/uNjb2ZqP311EheQULAADATgQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYU67BwDw7ZOce8DuETrlt0/ca/cIAIIMV7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAM441GAQAIUWlr37N7hA47mpdq9whGcQULAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMKfdAwBAoEtb%2B57dI3TK0bxUu0cAvvW4ggUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYPyoHAEJMcu4Bu0fosN8%2Bca/dIwB%2BQWB9iebmZi1ZskT//d//rcjISD3yyCN65JFH7B4LAEJOsP2cR6CjCKwvsXz5cpWXl%2Bvll1/WuXPntHDhQvXt21epqfwAVQAA0D4C6wsaGxu1Y8cObdy4UUOHDtXQoUNVWVmpV199lcACAAAdwje5f8GJEyd09epVJSQkeI8lJSXJ7XartbXVxskAAECw4ArWF3g8HnXv3l3h4eHeY7169VJzc7Pq6%2BvVo0ePdu%2BjpqZGHo/H55jTGa24uDijs4aF0ccAgNARSn%2BvEVhf0NTU5BNXkrwft7S0dOg%2BiouLVVBQ4HNs3rx5evzxx80M%2Bf/U1NToof9TqRkzZhiPt2%2B7mpoaFRcXs1s/YLf%2BxX79h936T01NjV544YWQ2m3opKIhERERbULq848jIyM7dB8zZsxQSUmJz68ZM2YYn9Xj8aigoKDN1TLcOHbrP%2BzWv9iv/7Bb/wnF3XIF6wt69%2B6turo6Xb16VU7nZ%2BvxeDyKjIxUTExMh%2B4jLi4uZAocAAB0HlewvmDw4MFyOp06duyY91hpaamGDRumLl1YFwAAaB/F8AVRUVGaOnWqnnnmGR0/flwHDx7USy%2B9pFmzZtk9GgAACBJhzzzzzDN2DxFoRo0apYqKCq1atUpHjhzRo48%2BqoyMDLvH%2BlJdu3bVyJEj1bVrV7tHCTns1n/YrX%2BxX/9ht/4Tart1WJZl2T0EAABAKOElQgAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMIrCDV3NysJ598UsnJyRozZoxeeuklu0cKWp988onmz5%2BvkSNHauzYscrPz1dzc7MkqaqqSg8//LAMiSclAAAHx0lEQVSGDx%2Bu%2B%2B%2B/X%2B%2B%2B%2B67N0wavrKwsLVq0yPtxRUWFpk%2BfLpfLpYyMDJWXl9s4XfBpaWnRkiVLNGLECH3nO9/R6tWr9fn7RrPbG1ddXa05c%2BYoMTFRKSkp2rJli/cc%2B70%2BLS0tmjx5sj744APvsfa%2Bxh4%2BfFiTJ0%2BWy%2BXSrFmzVFVV9U2Pfd0IrCC1fPlylZeX6%2BWXX9bTTz%2BtgoICHThwwO6xgo5lWZo/f76ampr06quvas2aNXrrrbe0du1aWZal7Oxs9erVS7t27dKUKVM0b948nTt3zu6xg86bb76pQ4cOeT9ubGxUVlaWkpOTVVJSooSEBM2ZM0eNjY02Thlcli1bpsOHD%2BvXv/61Vq1apd/85jcqLi5mt4Y88cQTio6OVklJiZ588kmtXbtWv//979nvdWpubtbPfvYzVVZWeo%2B19zX23Llzys7OVnp6unbu3KkePXpo7ty5CpofQGMh6Fy6dMkaNmyY9f7773uPFRYWWg8%2B%2BKCNUwWnkydPWoMGDbI8Ho/32N69e60xY8ZYhw8ftoYPH25dunTJe%2B6hhx6yfvWrX9kxatCqq6uzxo0bZ2VkZFgLFy60LMuyduzYYaWkpFitra2WZVlWa2ur9b3vfc/atWuXnaMGjbq6OmvIkCHWBx984D22YcMGa9GiRezWgPr6emvQoEHWP/7xD%2B%2BxefPmWUuWLGG/16GystL64Q9/aP3gBz%2BwBg0a5P27q72vsWvXrvX5e62xsdFKSEjw%2BbsvkHEFKwidOHFCV69eVUJCgvdYUlKS3G63WltbbZws%2BMTGxmrTpk3q1auXz/GLFy/K7XZryJAhio6O9h5PSkrSsWPHvukxg9rzzz%2BvKVOm6I477vAec7vdSkpKksPhkCQ5HA4lJiay2w4qLS1Vt27dNHLkSO%2BxrKws5efns1sDIiMjFRUVpZKSEl25ckWnT59WWVmZBg8ezH6vw5///Gfdc889Ki4u9jne3tdYt9ut5ORk77moqCgNHTo0aHZNYAUhj8ej7t27Kzw83HusV69eam5uVn19vY2TBZ%2BYmBiNHTvW%2B3Fra6u2bdumUaNGyePxKC4uzuf2PXv21Pnz57/pMYPWkSNHdPToUc2dO9fnOLu9MVVVVYqPj9fu3buVmpqq7373uyosLFRrayu7NSAiIkKLFy9WcXGxXC6X0tLSNG7cOE2fPp39Xocf//jHevLJJxUVFeVzvL1dBvuunXYPgM5ramryiStJ3o9bWlrsGClkrFixQhUVFdq5c6e2bNnypXtmxx3T3Nysp59%2BWosXL1ZkZKTPua96DrPbjmlsbNTZs2e1fft25efny%2BPxaPHixYqKimK3hpw6dUoTJkzQ7NmzVVlZqWeffVajR49mvwa1t8tg3zWBFYQiIiLaPME%2B//iLf5Gh41asWKGXX35Za9as0aBBgxQREdHmimBLSws77qCCggLdddddPlcIP/dVz2F22zFOp1MXL17UqlWrFB8fL%2Bmzbwh%2B7bXXNGDAAHZ7g44cOaKdO3fq0KFDioyM1LBhw/TJJ5/oxRdfVP/%2B/dmvIe19jf2qrxMxMTHf2Iw3gpcIg1Dv3r1VV1enq1eveo95PB5FRkYGzRMv0Dz77LPavHmzVqxYofvuu0/SZ3uura31uV1tbW2bS9b4cm%2B%2B%2BaYOHjyohIQEJSQkaO/evdq7d68SEhLY7Q2KjY1VRESEN64k6dZbb1V1dTW7NaC8vFwDBgzwiaYhQ4bo3Llz7Neg9nb5VedjY2O/sRlvBIEVhAYPHiyn0%2BnzjX6lpaUaNmyYunTh/9LOKigo0Pbt27V69WpNmjTJe9zlculvf/ubLl%2B%2B7D1WWloql8tlx5hBZ%2BvWrdq7d692796t3bt3KyUlRSkpKdq9e7dcLpf%2B8pe/eP%2B5tWVZKisrY7cd5HK51NzcrDNnzniPnT59WvHx8ezWgLi4OJ09e9bn6snp06fVr18/9mtQe19jXS6XSktLveeamppUUVERNLvmb%2BMgFBUVpalTp%2BqZZ57R8ePHdfDgQb300kuaNWuW3aMFnVOnTmndunX66U9/qqSkJHk8Hu%2BvkSNHqk%2BfPsrJyVFlZaWKiop0/PhxTZs2ze6xg0J8fLwGDBjg/dW1a1d17dpVAwYMUGpqqhoaGpSXl6eTJ08qLy9PTU1NSktLs3vsoHDbbbdp/PjxysnJ0YkTJ/TOO%2B%2BoqKhImZmZ7NaAlJQU3XTTTXrqqad05swZ/fGPf9T69es1c%2BZM9mtQe19jMzIyVFZWpqKiIlVWVionJ0f9%2BvXTPffcY/PkHWTne0Tg%2BjU2NloLFiywhg8fbo0ZM8bavHmz3SMFpQ0bNliDBg360l%2BWZVn//Oc/rQceeMC66667rEmTJlnvvfeezRMHr4ULF3rfB8uyLMvtdltTp061hg0bZk2bNs3629/%2BZuN0waehocH65S9/aQ0fPtwaPXq09cILL3jfm4nd3rjKykrr4YcfthITE62JEydamzdvZr8G/P/vg2VZ7X%2BN/dOf/mR9//vft%2B6%2B%2B27roYcesv71r3990yNfN4dlBctbogIAAAQHXiIEAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAw7P8C2I0viN1D1ZQAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4061474521306398390”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.158687217087312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.905375536615939</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.69408236774551</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.900998542829868</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.8781375304654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.82067368329241</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.40711440880959</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.4425962973110096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.992752803469884</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3095)</td> <td class=”number”>3095</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4061474521306398390”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00012611350341420783</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.003080740593036945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.010001508740149247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0135249805431423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.00000057203329</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000059283695</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000074171741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000095859588</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.00000097149982</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SENIORS10”><s>PCT_LACCESS_SENIORS10</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP10”><code>PCT_LACCESS_POP10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92085</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SENIORS15”><s>PCT_LACCESS_SENIORS15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_POP15”><code>PCT_LACCESS_POP15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91044</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_SNAP15”>PCT_LACCESS_SNAP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3101</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>97</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.8931</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>37.254</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4435120261960852264”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4435120261960852264,#minihistogram4435120261960852264”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4435120261960852264”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4435120261960852264”

aria-controls=”quantiles4435120261960852264” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4435120261960852264” aria-controls=”histogram4435120261960852264”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4435120261960852264” aria-controls=”common4435120261960852264”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4435120261960852264” aria-controls=”extreme4435120261960852264”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4435120261960852264”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.24561</td>

</tr> <tr>

<th>Q1</th> <td>1.1318</td>

</tr> <tr>

<th>Median</th> <td>2.1231</td>

</tr> <tr>

<th>Q3</th> <td>3.6644</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.7156</td>

</tr> <tr>

<th>Maximum</th> <td>37.254</td>

</tr> <tr>

<th>Range</th> <td>37.254</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5326</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0461</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0529</td>

</tr> <tr>

<th>Kurtosis</th> <td>25.315</td>

</tr> <tr>

<th>Mean</th> <td>2.8931</td>

</tr> <tr>

<th>MAD</th> <td>1.9113</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.8829</td>

</tr> <tr>

<th>Sum</th> <td>9006.2</td>

</tr> <tr>

<th>Variance</th> <td>9.2789</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4435120261960852264”>

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aP3%2B%2BXnjhBX377bfq37%2B/nn76af/9p0yZon/84x%2BaOnWqwsPDdffddzd5RgwAAOBiPJZlWXY%2B4Jtvvqk333xTO3fuVHx8vEaNGqUxY8YEvG/Vpk2btGDBAn322Wd2jtaifL7vjJ/T6w3T4EUfGj9vS3nrkf6XdD%2BvN0xt2rTWyZPfO/4aALI6j1tySu7J6packjOyJiSY/4W65rD9GazZs2crMzNTS5cu1S233KKwsKaXgZ17iwUAAIBQZHvB%2BuCDD9SmTRtVVVX5y9WePXvUs2dPhYeHS5J69%2B6t3r172z0aAACAEbb/FuHp06c1dOhQvfzyy/61Bx98UCNHjtTx48ftHgcAAMA42wvWM888o2uvvVaTJk3yr23dulXt27dXbm6u3eMAAAAYZ3vB%2Butf/6rHH3884I074%2BPjNWPGDP%2BbgQIAAIQy2wuW1%2BvVqVOnmqxXV1fL5l9oBAAAaBG2F6xbbrlFOTk5%2Bvvf/%2B5fKysrU25urgYOHGj3OAAAAMbZ/luEM2fO1KRJkzRkyBDFxsZKkk6dOqWePXtq1qxZdo8DAABgnO0Fq23btnrjjTf08ccfq6SkRF6vV9ddd51uvvlmY3/wGQAAIJiC8qdywsPDNXDgQF4SBAAAjmR7wfL5fFqyZIl2796ts2fPNrmw/c9//rPdIwEAABhle8F66qmntHfvXg0fPlxXXRWcvw8EAADQkmwvWDt27NCqVauUkZFh90MDAADYwva3aYiOjlbbtm3tflgAAADb2F6wRo4cqVWrVqmhocHuhwYAALCF7S8RVlVVacuWLXrvvfd0zTXXKCIiIuD42rVr7R4JAADAqKC8TcOIESOC8bAAAAC2sL1g5ebm2v2QAAAAtrL9GixJqqioUH5%2BvqZNm6Z//OMfevvtt3Xo0KFgjAIAAGCc7QXr8OHDuvPOO/XGG2/onXfe0ZkzZ7R161aNGTNGxcXFdo8DAABgnO0F63e/%2B51uu%2B02bdu2Tb/4xS8kSc8995wGDRqkRYsW2T0OAACAcbYXrN27d2vSpEkBf9jZ6/XqoYce0r59%2B%2BweBwAAwDjbC1ZjY6MaGxubrH///fcKDw%2B3exwAAADjbC9YAwYM0IoVKwJKVlVVlfLy8tSvXz%2B7xwEAADDO9oL1%2BOOPa%2B/evRowYIBqa2v1n//5n8rMzNSRI0c0c%2BZMu8cBAAAwzvb3wUpKStIf//hHbdmyRV9%2B%2BaUaGxs1btw4jRw5UjExMXaPAwAAYFxQ3sk9KipK99xzTzAeGgAAoMXZXrAmTJhw0eP8LUIAABDqbC9YHTp0CLhdX1%2Bvw4cP68CBA3rggQfsHgcAAMC4K%2BZvES5dulTl5eU2TwMAAGBeUP4W4YWMHDlSb731VrDHAAAAuGxXTMH67LPPeKNRAADgCFfERe6nT5/W3/72N40fP97ucQAAAIyzvWAlJycH/B1CSfrFL36h%2B%2B67T//2b/9m9zgAAADG2V6wfve739n9kAAAALayvWDt2rWr2Z970003teAkAAAALcP2gnX//ff7XyK0LMu/fv6ax%2BPRl19%2Bafd4AAAAl832grV8%2BXLl5ORo%2BvTp6tOnjyIiIvT5559r/vz5uuuuuzRs2DC7RwIAADDK9rdpyM3N1Zw5czRkyBC1adNGrVu3Vr9%2B/TR//ny99tpr6tChg/8DAAAgFNlesCoqKi5YnmJiYnTy5Em7xwEAADDO9oKVmpqq5557TqdPn/avVVVVKS8vTzfffLPd4wAAABhn%2BzVYTz75pCZMmKBbbrlFnTp1kmVZ%2Bvrrr5WQkKC1a9faPQ4AAIBxthesLl26aOvWrdqyZYtKS0slSb/%2B9a81fPhwRUVF2T0OAACAcbYXLEm6%2Buqrdc899%2BjIkSO65pprJP3wbu4AAABOYPs1WJZladGiRbrppps0YsQIlZeXa%2BbMmZo9e7bOnj1r9zgAAADG2V6w1q1bpzfffFP/9V//pYiICEnSbbfdpm3btik/P9/ucQAAAIyzvWAVFBRozpw5Gj16tP/d24cNG6acnBz9z//8j93jAAAAGGd7wTpy5Ii6d%2B/eZL1bt27y%2BXx2jwMAAGCc7QWrQ4cO%2Bvzzz5usf/DBB/4L3gEAAEKZ7b9FOGXKFM2bN08%2Bn0%2BWZemTTz5RQUGB1q1bp8cff9zucQAAAIyzvWCNGTNG9fX1eumll1RTU6M5c%2BYoPj5ejzzyiMaNG2f3OAAAAMbZXrC2bNmioUOHauzYsfrmm29kWZbatm1r9xgAAAAtxvZrsObPn%2B%2B/mD0%2BPv6yy1VdXZ1GjBihnTt3%2BtfKyso0ceJEpaamatiwYdq%2BfXvAfT7%2B%2BGONGDFCKSkpmjBhgsrKygKOr169WgMHDlRaWpqeeOIJVVdXX9aMAADAXWwvWJ06ddKBAweMnKu2tlaPPvqoSkpK/GuWZSkrK0vt2rXT5s2bNXLkSE2dOlXHjh2TJB07dkxZWVkaPXq0Nm3apPj4eD300EOyLEuS9M477yg/P1/z58/XmjVrVFxcrLy8PCPzAgAAd7D9JcJu3brpscce06pVq9SpUydFRkYGHM/NzW3WeQ4ePKhp06b5i9E5O3bsUFlZmV5//XVFR0erS5cu%2BuSTT7R582Y9/PDD2rhxo2688UZNnjzZ/3j9%2B/fXp59%2Bqr59%2B2rt2rV64IEHlJmZKUmaN2%2BepkyZounTp/O3EgEAQLPY/gzWV199pfT0dLVu3Vo%2Bn09HjhwJ%2BGiuc4WooKAgYL24uFg9evRQdHS0fy09PV1FRUX%2B4xkZGf5jUVFR6tmzp4qKitTQ0KDPP/884HhqaqrOnj2r/fv3X2pkAADgMrY8g7Vw4UJNnTpV0dHRWrdunZFzjh8//oLrPp9PiYmJAWtt27ZVeXn5Pz1%2B6tQp1dbWBhz3er2Ki4vz3x8AAOCfsaVgvfrqq5oyZUrAs0oPPvigcnJympSdy1VdXe3/G4fnREREqK6u7p8er6mp8d/%2Bqfs3R0VFRZN3pfd6o41nDQ%2B3/QnIy%2BL1Xtq853KGWt5LQVbncUtOyT1Z3ZJTcldW02wpWOdfJyVJu3btUm1trfHHioyMVFVVVcBaXV2dWrVq5T9%2Bflmqq6tTbGys/3qwCx3/OddfFRQUNPnD1VlZWcrOzm72OZyoTZvWl3X/2Fj3XANHVudxS07JPVndklNyV1ZTbL/IvaUlJSXp4MGDAWuVlZX%2BZ4%2BSkpJUWVnZ5Hj37t0VFxenyMhIVVZWqkuXLpKk%2Bvp6VVVVKSEhodkzjB07VoMGDQpY83qjdfLk95cS6SeF2k8Ul5o/PDxMsbFROnWqWg0NjYanurKQ1XncklNyT1a35JSckfVyf7i/VI4rWCkpKVq5cqVqamr8z1oVFhYqPT3df7ywsND/%2BdXV1dq3b5%2BmTp2qsLAw9erVS4WFherbt68kqaioSF6vV926dWv2DImJiU1eDvT5vlN9fWh%2Bc5pyufkbGhpd8zUkq/O4JafknqxuySm5K6sptj0F4vF4bHmcPn36qH379po1a5ZKSkq0cuVK7dmzR3fffbekH/5Uz%2B7du7Vy5UqVlJRo1qxZ6tixo79QjR8/Xq%2B88oq2bdumPXv2aO7cubr33nt5iwYAANBstj2DlZOTE/CeV2fPnlVeXp5atw586q6574P1U8LDw7Vs2TLNnj1bo0eP1rXXXqulS5cqOTlZktSxY0e9%2BOKLeuaZZ7R06VKlpaVp6dKl/gI4fPhwHT16VHPmzFFdXZ1uv/12TZ8%2B/bJmAgAA7uKxLnQFumH3339/sz/X1Ns4XGl8vu%2BMn9PrDdPgRR8aP29LeeuR/pd0P683TG3atNbJk987/ilqsjqPW3JK7snqlpySM7ImJFwVlMe15Rksp5YmAACACwmtX0MDAAAIARQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwzBvsAeAedyz5KNgj/CxvPdI/2CMAAEIUz2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYY4tWO%2B%2B%2B65uuOGGgI/s7GxJ0r59%2B3TPPfcoJSVFY8aM0d69ewPuu2XLFt12221KSUlRVlaWvvnmm2BEAAAAIcqxBevgwYPKzMzU9u3b/R85OTk6c%2BaMHnzwQWVkZOgPf/iD0tLS9Nvf/lZnzpyRJO3Zs0ezZ8/W1KlTVVBQoFOnTmnWrFlBTgMAAEKJYwtWaWmpunbtqoSEBP9HbGystm7dqsjISM2YMUNdunTR7Nmz1bp1a7399tuSpPXr1%2BuOO%2B7QqFGj1K1bNy1cuFDvv/%2B%2BysrKgpwIAACECkcXrE6dOjVZLy4uVnp6ujwejyTJ4/God%2B/eKioq8h/PyMjwf3779u2VnJys4uJiW%2BYGAAChz5Hv5G5Zlr766itt375dK1asUENDg4YOHars7Gz5fD5dd911AZ/ftm1blZSUSJIqKiqUmJjY5Hh5eXmzH7%2BiokI%2Bny9gzeuNbnLeyxUe7th%2BfEXweu3/%2Bp7bUzfsrVuyuiWn5J6sbskpuSuraY4sWMeOHVN1dbUiIiK0ZMkSHTlyRDk5OaqpqfGv/1hERITq6uokSTU1NRc93hwFBQXKz88PWMvKyvJfZI/Q0KZN66A9dmxsVNAe225uyeqWnJJ7srolp%2BSurKY4smB16NBBO3fu1NVXXy2Px6Pu3bursbFR06dPV58%2BfZqUpbq6OrVq1UqSFBkZecHjUVHN/%2BYaO3asBg0aFLDm9Ubr5MnvLzHRhfETRcsyvV/NER4eptjYKJ06Va2GhkbbH99ObsnqlpySe7K6JafkjKzB%2BmHZkQVLkuLi4gJud%2BnSRbW1tUpISFBlZWXAscrKSv/Ld0lJSRc8npCQ0OzHTkxMbPJyoM/3nerrQ/Ob062CuV8NDY2u%2BX5xS1a35JTck9UtOSV3ZTXFkU%2BBfPjhh%2Brbt6%2Bqq6v9a19%2B%2BaXi4uKUnp6uzz77TJZlSfrheq3du3crJSVFkpSSkqLCwkL//Y4fP67jx4/7jwMAAPwzjixYaWlpioyM1JNPPqlDhw7p/fff18KFC/Wb3/xGQ4cO1alTp7RgwQIdPHhQCxYsUHV1te644w5J0rhx4/Tmm29q48aN2r9/v2bMmKFbb71V11xzTZBTAQCAUOHIghUTE6NXXnlF33zzjcaMGaPZs2dr7Nix%2Bs1vfqOYmBitWLFChYWFGj16tIqLi7Vy5UpFR0dL%2BqGczZ8/X0uXLtW4ceN09dVXKzc3N8iJAABAKPFY514rQ4vy%2Bb4zfk6vN0yDF31o/Lz4wVuP9Lf9Mb3eMLVp01onT37v%2BOsd3JLVLTkl92R1S07JGVkTEq4KyuM68hksAACAYKJgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMG%2BwBwCuVHcs%2BSjYI/wsbz3SP9gjAAD%2BH89gAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIbxPlgXUFtbq3nz5ul///d/1apVK02ePFmTJ08O9ljARYXS%2B3bxnl0AnI6CdQELFy7U3r17tWbNGh07dkwzZ85UcnKyhg4dGuzRAABACKBgnefMmTPauHGjXn75ZfXs2VM9e/ZUSUmJNmzYQMECAADNQsE6z/79%2B1VfX6%2B0tDT/Wnp6upYvX67GxkaFhXHZGnC5QunlTImXNAH8fBSs8/h8PrVp00YRERH%2BtXbt2qnGy9hQAAAJV0lEQVS2tlZVVVWKj4//p%2BeoqKiQz%2BcLWPN6o5WYmGh01vBwyh5gh1ArhKHk3ccGBnuEn2Xwog%2BDPcLPcrlf33P/n%2BH/Nz8fBes81dXVAeVKkv92XV1ds85RUFCg/Pz8gLWpU6fq4YcfNjPk/6uoqNADvyzR2LFjjZe3K0lFRYUKCgocn1MiqxO5Jafkjqx/XTDUFTnPqaio0Jo1q1yR1TQq6XkiIyObFKlzt1u1atWsc4wdO1Z/%2BMMfAj7Gjh1rfFafz6f8/Pwmz5Y5jVtySmR1IrfklNyT1S05JXdlNY1nsM6TlJSkkydPqr6%2BXl7vD18en8%2BnVq1aKTY2tlnnSExMpOkDAOBiPIN1nu7du8vr9aqoqMi/VlhYqF69enGBOwAAaBYaw3mioqI0atQozZ07V3v27NG2bdv0%2B9//XhMmTAj2aAAAIESEz507d26wh7jS9OvXT/v27dOzzz6rTz75RP/xH/%2BhMWPGBHusC2rdurX69Omj1q1bB3uUFuWWnBJZncgtOSX3ZHVLTsldWU3yWJZlBXsIAAAAJ%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%2B2oXClYIOnPmjDZu3KiXX35ZPXv2VM%2BePVVSUqINGzY4rmCVlpaqa9euSkhICPYoxh08eFDTpk3T%2BX%2BtaseOHSorK9Prr7%2Bu6OhodenSRZ988ok2b96shx9%2BOEjTXrqfyin9sL9TpkxxxP4eOnRIRUVF%2Buijj9SuXTtJUnZ2tv77v/9bt9xyi2P29GI5zxUsp%2BxpZWWlunfvrrlz5yomJkadOnXSzTffrMLCQrVr184xeypdPOu5guWUfbULLxGGoP3796u%2Bvl5paWn%2BtfT0dBUXF6uxsTGIk5lXWlqqTp06BXuMFvHpp5%2Bqb9%2B%2BKigoCFgvLi5Wjx49FB0d7V9LT09XUVGR3SMa8VM5T58%2BrRMnTjhmfxMSErRq1Sp/6Tjn9OnTjtrTi%2BV02p4mJiZqyZIliomJkWVZKiws1K5du9SnTx9H7al08axO21e78AxWCPL5fGrTpo0iIiL8a%2B3atVNtba2qqqoUHx8fxOnMsSxLX331lbZv364VK1aooaFBQ4cOVXZ2dkD2UDV%2B/PgLrvt8PiUmJgastW3bVuXl5XaMZdxP5SwtLZXH49Hy5cv1wQcfKC4uTpMmTQp4CSKUxMbGauDAgf7bjY2NWr9%2Bvfr16%2BeoPb1YTqft6Y8NGjRIx44dU2ZmpoYMGaJnnnnGMXt6vvOz7t2717H72pIoWCGourq6ScE4d7uuri4YI7WIY8eO%2BbMuWbJER44cUU5OjmpqavTkk08Ge7wW81P766S9lX54qcnj8ahz58667777tGvXLj311FOKiYnR4MGDgz3eZcvLy9O%2Bffu0adMmrV692rF7%2BuOcX3zxhWP39IUXXlBlZaXmzp2r3NxcR/97en7Wnj17OnZfWxIFKwRFRkY2%2BZf43O1WrVoFY6QW0aFDB%2B3cuVNXX321PB6PunfvrsbGRk2fPl2zZs1SeHh4sEdsEZGRkaqqqgpYq6urc9TeStKoUaOUmZmpuLg4SVK3bt309ddf67XXXgv5/2jn5eVpzZo1Wrx4sbp27erYPT0/5/XXX%2B/YPe3Vq5ekH35h47HHHtOYMWNUXV0d8DlO2FOpadbdu3c7dl9bEtdghaCkpCSdPHlS9fX1/jWfz6dWrVopNjY2iJOZFxcXJ4/H47/dpUsX1dbW6ttvvw3iVC0rKSlJlZWVAWuVlZVNXo4IdR6Px/8f7HM6d%2B6sEydOBGkiM55%2B%2Bmm9%2BuqrysvL05AhQyQ5c08vlNNpe1pZWalt27YFrF133XU6e/asEhISHLWnF8t6%2BvRpR%2B2rXShYIah79%2B7yer0BF1MWFhaqV69eCgtzzpZ%2B%2BOGH6tu3b8BPiV9%2B%2BaXi4uIcc53ZhaSkpOiLL75QTU2Nf62wsFApKSlBnMq8559/XhMnTgxY279/vzp37hycgQzIz8/X66%2B/rueee07Dhw/3rzttT38qp9P29MiRI5o6dWpAkdi7d6/i4%2BOVnp7uqD29WNZ169Y5al9tYyEkPfXUU9bw4cOt4uJi691337V69%2B5tvfPOO8Eey6jvvvvOGjhwoPXoo49apaWl1nvvvWcNGDDAWrlyZbBHM65r167Wjh07LMuyrPr6emvYsGHWI488Yh04cMBasWKFlZqaah09ejTIU16%2BH%2BcsLi62evToYa1atco6fPiwtWHDBuvGG2%2B0du/eHeQpL83Bgwet7t27W4sXL7YqKioCPpy0pxfL6bQ9ra%2Bvt0aPHm1NnjzZKikpsd577z3rX/7lX6zVq1c7ak8t6%2BJZnbavdqFghagzZ85YM2bMsFJTU60BAwZYr776arBHahEHDhywJk6caKWmplr9%2B/e3XnzxRauxsTHYYxn34%2BJhWZb19ddfW7/%2B9a%2BtG2%2B80Ro%2BfLj10UcfBXE6c87P%2Be6771p33nmn1atXL2vo0KEh/UPCihUrrK5du17ww7Kcs6f/LKeT9tSyLKu8vNzKysqyevfubfXv39966aWX/P8NcsqennOxrE7bVzt4LOsC7/4HAACAS%2BacC3YAAACuEBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABj2f66Rwql18K%2B%2BAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4435120261960852264”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5768005767045246</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7390108700576679</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6982309627506187</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.090028092868416</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.898523723161081</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0690733021956598</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.3535797992116514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.151558099261077</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2236079065374443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3090)</td> <td class=”number”>3090</td> <td class=”number”>96.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4435120261960852264”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0004437389204020983</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0008080882371859443</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011150201499580523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0011809836054727893</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.334506176606347</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.167352189637803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.450721209209693</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.40852845002016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>37.25448848259154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LACCESS_WHITE15”><s>PCT_LACCESS_WHITE15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_LACCESS_SENIORS15”><code>PCT_LACCESS_SENIORS15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93066</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLFARM07”>PCT_LOCLFARM07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2679</td>

</tr> <tr>

<th>Unique (%)</th> <td>83.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>139</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>6.3292</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5466147093796603546”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5466147093796603546,#minihistogram-5466147093796603546”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5466147093796603546”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5466147093796603546”

aria-controls=”quantiles-5466147093796603546” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5466147093796603546” aria-controls=”histogram-5466147093796603546”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5466147093796603546” aria-controls=”common-5466147093796603546”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5466147093796603546” aria-controls=”extreme-5466147093796603546”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5466147093796603546”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.98308</td>

</tr> <tr>

<th>Q1</th> <td>2.6892</td>

</tr> <tr>

<th>Median</th> <td>4.5757</td>

</tr> <tr>

<th>Q3</th> <td>7.9447</td>

</tr> <tr>

<th>95-th percentile</th> <td>17.87</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>100</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.2555</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>5.9832</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.94533</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.622</td>

</tr> <tr>

<th>Mean</th> <td>6.3292</td>

</tr> <tr>

<th>MAD</th> <td>4.0131</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.148</td>

</tr> <tr>

<th>Sum</th> <td>19437</td>

</tr> <tr>

<th>Variance</th> <td>35.799</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5466147093796603546”>

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wxowZo23btun777/39EsDAAB4hMcDVp8%2BffT888%2BrX79%2Bevzxx7V3715ZluXpMgAAANzG4wFr2rRpeuedd7Rq1SoFBQXp0Ucf1R133KGlS5fqyy%2B/9HQ5AAAAxtm88aIBAQHq27ev%2Bvbtq6qqKr344otatWqVcnJy1LNnT02cOFF33nmnN0oDAAC4Yl4JWJJUWlqqV199Va%2B%2B%2Bqq%2B%2BOIL9ezZU/fcc49KSkr09NNP68MPP9SsWbO8VR4AAMAv5vGA9corr%2BiVV17RgQMH1KpVK40YMUIrVqxQx44dnY9p06aNFixYQMACAAA%2ByePnYM2aNUvh4eFauXKl9uzZo2nTprmEK0nq1KmT7r///iatV1tbq2HDhunAgQPOsfnz5%2Bvmm292uW3evNk5v2vXLg0cOFB2u12pqak6c%2BaMc86yLC1atEh9%2BvRRcnKyFi5cqIaGhivbaAAAcE3x%2BB6sd999Vy1btlRFRYUCA3/Id4cOHVL37t0VFBQkSerZs6d69ux52bVqamo0bdo0FRYWuowXFRVp2rRpuueee5xjzZs3d77WrFmzNGfOHMXFxWnBggXKyMjQmjVrJEnr16/Xrl27lJ2drbq6Ok2fPl2RkZGaMmWKke0HAAD%2Bz%2BN7sM6ePavBgwfrT3/6k3Ns6tSpGj58uE6dOtXkdY4dO6Z7771X//jHPxrNFRUVqVu3boqKinLeQkNDJUmbN2/WkCFDNGLECMXFxWnhwoXas2ePiouLJUmbNm1Senq6kpKS1KdPHz3xxBPasmXLFW41AAC4lng8YP3hD39Qhw4dNHnyZOfY66%2B/rjZt2igzM7PJ63zwwQfq3bu3cnNzXcbPnj2r06dPNzrseFFBQYGSkpKc99u0aaPY2FgVFBTo9OnTOnXqlHr16uWcT0xM1DfffKPS0tIm1wYAAK5tHj9E%2BNFHH%2BnPf/6zoqKinGOtWrXSk08%2Bqfvuu6/J64wfP/6S40VFRQoICNDzzz%2Bvd999Vy1atNDkyZOdhwtLS0sVHR3t8pzIyEiVlJTI4XBIkst869atJUklJSWNnvdTSktLnWtdZLOFNfn5TRUU5FufdGSz%2BU69F3vraz32BfTWveiv%2B9Bb9/HH3no8YNlsNlVWVjYar6qqMnJF9%2BPHjysgIMB5ovyHH36o3//%2B92revLkGDRqk6upqBQcHuzwnODhYtbW1qq6udt7/5znph5Ppmyo3N1fZ2dkuY6mpqUpPT/%2Blm%2BUXWrYM93YJP1tERKi3S/Bb9Na96K/70Fv38afeejxg3X777Zo/f76WLFmif/u3f5MkFRcXKzMzU/3797/i9UeMGKEBAwaoRYsWkqS4uDh99dVX2rp1qwYNGqSQkJBGYam2tlahoaEuYSokJMT5b0nOc7iaYuzYsUpJSXEZs9nCVF5%2B7hdv16X4WtI3vf3uFBQUqIiIUFVWVqm%2Bnr8iNYneuhf9dR966z7u7K23frn3eMCaMWOGJk%2BerLvuuksRERGSpMrKSnXv3l0ZGRlXvH5AQIAzXF3UqVMn7d%2B/X5IUExOjsrIyl/mysjJFRUUpJiZGkuRwONSuXTvnvyW5HNK8nOjo6EaHAx2O71VXd21/Q/ri9tfXN/hk3b6A3roX/XUfeus%2B/tRbjwesyMhI/eUvf9G%2BfftUWFgom82mG2%2B8UbfddpsCAgKueP3ly5fr448/1oYNG5xjR48eVadOnSRJdrtdeXl5GjlypCTp1KlTOnXqlOx2u2JiYhQbG6u8vDxnwMrLy1NsbKzx86cAAID/8spH5QQFBal///5GDgn%2B2IABA5STk6N169Zp0KBB2rt3r3bu3KlNmzZJksaNG6cHHnhA8fHx6tGjhxYsWKA77rhD7du3d84vWrRIv/rVryRJixcv1oMPPmi8TgAA4L88HrAcDoeWLVumgwcP6sKFC41ObH/77bevaP1bb71Vy5cv14oVK7R8%2BXK1bdtWixcvVkJCgiQpISFBc%2BfO1YoVK/Tdd9%2Bpb9%2B%2BmjdvnvP5U6ZM0bfffqu0tDQFBQVp9OjRmjRp0hXVBAAAri0Blok/3fsZfve73%2Bnw4cMaOnSorr/%2B%2BkbzaWlpnizHYxyO742vabMFatCi94yv6y5vPNbX2yU0mc0WqJYtw1Vefs5vzge4WtBb96K/7kNv3cedvY2Kapw1PMHje7D279%2BvtWvXulzsEwAAwJ94/O/8w8LCFBkZ6emXBQAA8BiPB6zhw4dr7dq1qq%2Bv9/RLAwAAeITHDxFWVFRo165d%2Bvvf/6727ds3uqr6xb/2AwAA8FVeuUzDsGHDvPGyAAAAHuHxgJWZmenplwQAAPAor3yYXWlpqbKzszVt2jR9%2B%2B23evPNN3X8%2BHFvlAIAAGCcxwPW119/rV//%2Btf6y1/%2Borfeekvnz5/X66%2B/rlGjRqmgoMDT5QAAABjn8YD1xz/%2BUQMHDtTu3bt13XXXSZKWLFmilJQULVq0yNPlAAAAGOfxgHXw4EFNnjzZ5YOdbTabHnnkER05csTT5QAAABjn8YDV0NCghobGl8E/d%2B6cgoKCPF0OAACAcR4PWP369dOaNWtcQlZFRYWysrLUp08fT5cDAABgnMcD1lNPPaXDhw%2BrX79%2Bqqmp0X/9139pwIABOnHihGbMmOHpcgAAAIzz%2BHWwYmJitHPnTu3atUufffaZGhoaNG7cOA0fPlzNmzf3dDkAAADGeeVK7qGhoRozZow3XhoAAMDtPB6wJkyY8C/n%2BSxCAADg6zwesNq2betyv66uTl9//bW%2B%2BOILTZw40dPlAAAAGHfVfBbhypUrVVJS4uFqAAAAzPPKZxFeyvDhw/XGG294uwwAAIArdtUErI8//pgLjQIAAL9wVZzkfvbsWX3%2B%2BecaP368p8sBAAAwzuMBKzY21uVzCCXpuuuu0/3336/f/OY3ni4HAADAOI8HrD/%2B8Y%2BefkkAAACP8njA%2BvDDD5v82F69ermxEgAAAPfweMB64IEHnIcILctyjv94LCAgQJ999pmnywMAALhiHg9Yzz//vObPn6/p06crOTlZwcHB%2BuSTTzR37lzdc889uvvuuz1dEgAAgFEev0xDZmamZs%2BerbvuukstW7ZUeHi4%2BvTpo7lz52rr1q1q27at8wYAAOCLPB6wSktLLxmemjdvrvLyck%2BXAwAAYJzHA1Z8fLyWLFmis2fPOscqKiqUlZWl2267zdPlAAAAGOfxc7CefvppTZgwQbfffrs6duwoy7L01VdfKSoqSps2bfJ0OQAAAMZ5PGB17txZr7/%2Bunbt2qWioiJJ0n333aehQ4cqNDTU0%2BUAAAAY5/GAJUk33HCDxowZoxMnTqh9%2B/aSfriaOwAAgD/w%2BDlYlmVp0aJF6tWrl4YNG6aSkhLNmDFDs2bN0oULFzxdDgAAgHEeD1gvvviiXnnlFT3zzDMKDg6WJA0cOFC7d%2B9Wdna2p8sBAAAwzuMBKzc3V7Nnz9bIkSOdV2%2B/%2B%2B67NX/%2BfL322mueLgcAAMA4jwesEydOqGvXro3G4%2BLi5HA4PF0OAACAcR4PWG3bttUnn3zSaPzdd991nvAOAADgyzz%2BV4RTpkzRnDlz5HA4ZFmW3n//feXm5urFF1/UU0895elyAAAAjPN4wBo1apTq6uq0evVqVVdXa/bs2WrVqpUee%2BwxjRs3ztPlAAAAGOfxgLVr1y4NHjxYY8eO1ZkzZ2RZliIjIz1dBgAAgNt4/BysuXPnOk9mb9WqFeEKAAD4HY8HrI4dO%2BqLL77w9MsCAAB4jMcPEcbFxemJJ57Q2rVr1bFjR4WEhLjMZ2ZmerokAAAAozwesL788kslJiZKEte9AgAAfskjAWvhwoVKS0tTWFiYXnzxRU%2B8JAAAgNd45Bys9evXq6qqymVs6tSpKi0t9cTLAwAAeJRHApZlWY3GPvzwQ9XU1Hji5QEAADzK439FCAAA4O98PmDV1tZq2LBhOnDggHOsuLhYkyZNUnx8vO6%2B%2B27t3bvX5Tn79u3TsGHDZLfbNWHCBBUXF7vMb9iwQf3791dCQoJmzpzZ6PAmAADAv%2BKxgBUQEGB8zZqaGj3%2B%2BOMqLCx0jlmWpdTUVLVu3Vo7duzQ8OHDlZaWppMnT0qSTp48qdTUVI0cOVLbt29Xq1at9MgjjzgPY7711lvKzs7W3LlztXHjRhUUFCgrK8t47QAAwH957DIN8%2BfPd7nm1YULF5SVlaXw8HCXxzX1OljHjh3TtGnTGp3ftX//fhUXF2vbtm0KCwtT586d9f7772vHjh169NFH9dJLL%2BmWW27Rgw8%2B6Hy9vn376oMPPlDv3r21adMmTZw4UQMGDJAkzZkzR1OmTNH06dMVGhp6JS0AAADXCI/swerVq5ccDodOnDjhvCUkJKi8vNxl7MSJE01e82Igys3NdRkvKChQt27dFBYW5hxLTExUfn6%2Bcz4pKck5Fxoaqu7duys/P1/19fX65JNPXObj4%2BN14cIFHT169JduPgAAuMZ4ZA%2BWO659NX78%2BEuOOxwORUdHu4xFRkaqpKTksvOVlZWqqalxmbfZbGrRooXz%2BQAAAJfj8Su5u1tVVZWCg4NdxoKDg1VbW3vZ%2Berqauf9n3p%2BU5SWlja6Sr3NFtYo2F2poCDf%2BhsFm8136r3YW1/rsS%2Bgt%2B5Ff92H3rqPP/bW7wJWSEiIKioqXMZqa2vVrFkz5/yPw1Jtba0iIiKc54hdav7nnH%2BVm5ur7Oxsl7HU1FSlp6c3eQ1/1LJl%2BOUfdJWJiOC8O3eht%2B5Ff92H3rqPP/XW7wJWTEyMjh075jJWVlbm3HsUExOjsrKyRvNdu3ZVixYtFBISorKyMnXu3FmSVFdXp4qKCkVFRTW5hrFjxyolJcVlzGYLU3n5uV%2ByST/J15K%2B6e13p6CgQEVEhKqyskr19Q3eLsev0Fv3or/uQ2/dx5299dYv934XsOx2u3JyclRdXe3ca5WXl%2Bf8gGm73a68vDzn46uqqnTkyBGlpaUpMDBQPXr0UF5ennr37i1Jys/Pl81mU1xcXJNriI6ObnQ40OH4XnV11/Y3pC9uf319g0/W7QvorXvRX/eht%2B7jT731rV0gTZCcnKw2bdooIyNDhYWFysnJ0aFDhzR69GhJ0qhRo3Tw4EHl5OSosLBQGRkZateunTNQjR8/XuvWrdPu3bt16NAhPfvss7r33nu5RAMAAGgyvwtYQUFBWrVqlRwOh0aOHKlXX31VK1euVGxsrCSpXbt2eu6557Rjxw6NHj1aFRUVWrlypfNCqEOHDtXDDz%2Bs2bNn68EHH9Stt96q6dOne3OTAACAjwmwLvVJzDDO4fje%2BJo2W6AGLXrP%2BLru8sZjfb1dQpPZbIFq2TJc5eXn/GZ39dWC3roX/XUfeus%2B7uxtVNT1RtdrKr/bgwUAAOBtBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhfhuw/va3v%2Bnmm292uaWnp0uSjhw5ojFjxshut2vUqFE6fPiwy3N37dqlgQMHym63KzU1VWfOnPHGJgAAAB/ltwHr2LFjGjBggPbu3eu8zZ8/X%2BfPn9fUqVOVlJSkl19%2BWQkJCXr44Yd1/vx5SdKhQ4c0a9YspaWlKTc3V5WVlcrIyPDy1gAAAF/itwGrqKhIXbp0UVRUlPMWERGh119/XSEhIXryySfVuXNnzZo1S%2BHh4XrzzTclSZs3b9aQIUM0YsQIxcXFaeHChdqzZ4%2BKi4u9vEUAAMBX%2BHXA6tixY6PxgoICJSYmKiAgQJIUEBCgnj17Kj8/3zmflJTkfHybNm0UGxurgoICj9QNAAB8n83bBbiDZVn68ssvtXfvXq1Zs0b19fUaPHiw0tPT5XA4dOONN7o8PjIyUoWFhZKk0tJSRUdHN5ovKSlp8uuXlpbK4XC4jNlsYY3WvVJBQb6Vj20236n3Ym99rce%2BgN66F/11H3rrPv7YW78MWCdPnlRVVZWCg4MeX5ReAAAN4UlEQVS1bNkynThxQvPnz1d1dbVz/J8FBwertrZWklRdXf0v55siNzdX2dnZLmOpqanOk%2ByvVS1bhnu7hJ8tIiLU2yX4LXrrXvTXfeit%2B/hTb/0yYLVt21YHDhzQDTfcoICAAHXt2lUNDQ2aPn26kpOTG4Wl2tpaNWvWTJIUEhJyyfnQ0Kb/p48dO1YpKSkuYzZbmMrLz/3CLbo0X0v6prffnYKCAhUREarKyirV1zd4uxy/Qm/di/66D711H3f21lu/3PtlwJKkFi1auNzv3LmzampqFBUVpbKyMpe5srIy5%2BG7mJiYS85HRUU1%2BbWjo6MbHQ50OL5XXd21/Q3pi9tfX9/gk3X7AnrrXvTXfeit%2B/hTb31rF0gTvffee%2Brdu7eqqqqcY5999platGihxMREffzxx7IsS9IP52sdPHhQdrtdkmS325WXl%2Bd83qlTp3Tq1CnnPAAAwOX4ZcBKSEhQSEiInn76aR0/flx79uzRwoUL9dvf/laDBw9WZWWlFixYoGPHjmnBggWqqqrSkCFDJEnjxo3TK6%2B8opdeeklHjx7Vk08%2BqTvuuEPt27f38lYBAABf4ZcBq3nz5lq3bp3OnDmjUaNGadasWRo7dqx%2B%2B9vfqnnz5lqzZo3y8vI0cuRIFRQUKCcnR2FhYZJ%2BCGdz587VypUrNW7cON1www3KzMz08hYBAABfEmBdPFYGt3I4vje%2Bps0WqEGL3jO%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%2Bf10svvaQ//elP6t69u7p3767CwkJt2bKFgAUAAJqEQ4Q/cvToUdXV1SkhIcE5lpiYqIKCAjU0NHixMgAA4CvYg/UjDodDLVu2VHBwsHOsdevWqqmpUUVFhVq1anXZNUpLS%2BVwOFzGbLYwRUdHG601KIh8jP9vyLL/9XYJTfbRgsF8/brJxb7SX/Porfv4Y28JWD9SVVXlEq4kOe/X1tY2aY3c3FxlZ2e7jKWlpenRRx81U%2BT/KS0t1cRfFWrs2LHGw9u1rrS0VLm5ufTWDUpLS/Xcc8/RWzcpLS3Vxo1r6a8b0Fv38cfe%2Bk9UNCQkJKRRkLp4v1mzZk1aY%2BzYsXr55ZddbmPHjjVeq8PhUHZ2dqO9Zbhy9NZ96K170V/3obfu44%2B9ZQ/Wj8TExKi8vFx1dXWy2X5oj8PhULNmzRQREdGkNaKjo/0mgQMAgJ%2BPPVg/0rVrV9lsNuXn5zvH8vLy1KNHDwUG0i4AAHB5JIYfCQ0N1YgRI/Tss8/q0KFD2r17t1544QVNmDDB26UBAAAfEfTss88%2B6%2B0irjZ9%2BvTRkSNHtHjxYr3//vv63e9%2Bp1GjRnm7rEsKDw9XcnKywsPDvV2K36G37kNv3Yv%2Bug%2B9dR9/622AZVmWt4sAAADwJxwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwPJRNTU1mjlzppKSktSvXz%2B98MIL3i7JZ50%2BfVrp6elKTk5W//79lZmZqZqaGklScXGxJk2apPj4eN19993au3evl6v1XVOnTtVTTz3lvH/kyBGNGTNGdrtdo0aN0uHDh71Yne%2Bpra3VnDlz1KtXL/3Hf/yHlixZoovXjaa3V%2B7UqVN6%2BOGH1bNnT6WkpGjDhg3OOfr7y9TW1mrYsGE6cOCAc%2Bxy77H79u3TsGHDZLfbNWHCBBUXF3u67F%2BMgOWjFi5cqMOHD2vjxo165plnlJ2drTfffNPbZfkcy7KUnp6uqqoqbdmyRUuXLtU777yjZcuWybIspaamqnXr1tqxY4eGDx%2ButLQ0nTx50ttl%2B5y//vWv2rNnj/P%2B%2BfPnNXXqVCUlJenll19WQkKCHn74YZ0/f96LVfqW%2BfPna9%2B%2BfVq3bp0WL16sP//5z8rNzaW3hjz22GMKCwvTyy%2B/rJkzZ2rZsmX629/%2BRn9/oZqaGj3%2B%2BOMqLCx0jl3uPfbkyZNKTU3VyJEjtX37drVq1UqPPPKIfOYDaCz4nHPnzlk9evSw9u/f7xxbuXKldf/993uxKt907Ngxq0uXLpbD4XCOvfbaa1a/fv2sffv2WfHx8da5c%2BeccxMnTrRWrFjhjVJ9Vnl5uXX77bdbo0aNsmbMmGFZlmW99NJLVkpKitXQ0GBZlmU1NDRYgwYNsnbs2OHNUn1GeXm51a1bN%2BvAgQPOsTVr1lhPPfUUvTWgoqLC6tKli/X55587x9LS0qw5c%2BbQ31%2BgsLDQ%2Bs1vfmP9%2Bte/trp06eL82XW599hly5a5/Fw7f/68lZCQ4PKz72rGHiwfdPToUdXV1SkhIcE5lpiYqIKCAjU0NHixMt8TFRWltWvXqnXr1i7jZ8%2BeVUFBgbp166awsDDneGJiovLz8z1dpk/77//%2Bbw0fPlw33nijc6ygoECJiYkKCAiQJAUEBKhnz570tony8vLUvHlzJScnO8emTp2qzMxMemtAs2bNFBoaqpdfflkXLlzQ8ePHdfDgQXXt2pX%2B/gIffPCBevfurdzcXJfxy73HFhQUKCkpyTkXGhqq7t27%2B0yvCVg%2ByOFwqGXLlgoODnaOtW7dWjU1NaqoqPBiZb4nIiJC/fv3d95vaGjQ5s2b1adPHzkcDkVHR7s8PjIyUiUlJZ4u02e9//77%2Buijj/TII4%2B4jNPbK1NcXKy2bdtq586dGjx4sP7zP/9TK1euVENDA701ICQkRLNnz1Zubq7sdruGDBmi22%2B/XWPGjKG/v8D48eM1c%2BZMhYaGuoxfrpe%2B3mubtwvAz1dVVeUSriQ579fW1nqjJL%2BRlZWlI0eOaPv27dqwYcMl%2B0yPm6ampkbPPPOMZs%2BerWbNmrnM/dTXML1tmvPnz%2Bvrr7/Wtm3blJmZKYfDodmzZys0NJTeGlJUVKQBAwZo8uTJKiws1Lx583TbbbfRX4Mu10tf7zUByweFhIQ0%2BgK7eP/HP8jQdFlZWdq4caOWLl2qLl26KCQkpNEewdraWnrcRNnZ2brllltc9hBe9FNfw/S2aWw2m86ePavFixerbdu2kn44IXjr1q3q0KEDvb1C77//vrZv3649e/aoWbNm6tGjh06fPq3Vq1erffv29NeQy73H/tT7REREhMdqvBIcIvRBMTExKi8vV11dnXPM4XCoWbNmPvOFd7WZN2%2Be1q9fr6ysLN11112SfuhzWVmZy%2BPKysoa7bLGpf31r3/V7t27lZCQoISEBL322mt67bXXlJCQQG%2BvUFRUlEJCQpzhSpL%2B/d//XadOnaK3Bhw%2BfFgdOnRwCU3dunXTyZMn6a9Bl%2BvlT81HRUV5rMYrQcDyQV27dpXNZnM50S8vL089evRQYCD/pT9Xdna2tm3bpiVLlmjo0KHOcbvdrk8//VTV1dXOsby8PNntdm%2BU6XNefPFFvfbaa9q5c6d27typlJQUpaSkaOfOnbLb7fr444%2Bdf25tWZYOHjxIb5vIbrerpqZGX375pXPs%2BPHjatu2Lb01IDo6Wl9//bXL3pPjx4%2BrXbt29Negy73H2u125eXlOeeqqqp05MgRn%2Bk1P419UGhoqEaMGKFnn31Whw4d0u7du/XCCy9owoQJ3i7N5xQVFWnVqlV66KGHlJiYKIfD4bwlJyerTZs2ysjIUGFhoXJycnTo0CGNHj3a22X7hLZt26pDhw7OW3h4uMLDw9WhQwcNHjxYlZWVWrBggY4dO6YFCxaoqqpKQ4YM8XbZPqFTp0664447lJGRoaNHj%2Bq9995TTk6Oxo0bR28NSElJ0XXXXaenn35aX375pf7nf/5Hzz//vB544AH6a9Dl3mNHjRqlgwcPKicnR4WFhcrIyFC7du3Uu3dvL1feRN68RgR%2BufPnz1tPPvmkFR8fb/Xr189av369t0vySWvWrLG6dOlyyZtlWdZXX31l3XfffdYtt9xiDR061Prf//1fL1fsu2bMmOG8DpZlWVZBQYE1YsQIq0ePHtbo0aOtTz/91IvV%2BZ7Kykpr%2BvTpVnx8vHXbbbdZzz33nPPaTPT2yhUWFlqTJk2yevbsaQ0cONBav349/TXgn6%2BDZVmXf4/9%2B9//bt15553Wrbfeak2cONH6xz/%2B4emSf7EAy/KVS6ICAAD4Bg4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/w9gxPRkVmrfRwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5466147093796603546”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.285714285714285</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.166666666666666</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.125</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.197802197802198</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.761904761904762</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.405405405405405</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.894736842105263</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2668)</td> <td class=”number”>2970</td> <td class=”number”>92.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>139</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5466147093796603546”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1949317738791423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19880715705765406</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23201856148491878</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24390243902439024</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>38.46153846153847</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.17647058823529</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.333333333333336</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLFARM12”>PCT_LOCLFARM12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2692</td>

</tr> <tr>

<th>Unique (%)</th> <td>83.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>138</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.1398</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1128625394673116066”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1128625394673116066,#minihistogram1128625394673116066”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1128625394673116066”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1128625394673116066”

aria-controls=”quantiles1128625394673116066” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1128625394673116066” aria-controls=”histogram1128625394673116066”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1128625394673116066” aria-controls=”common1128625394673116066”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1128625394673116066” aria-controls=”extreme1128625394673116066”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1128625394673116066”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.82297</td>

</tr> <tr>

<th>Q1</th> <td>2.7808</td>

</tr> <tr>

<th>Median</th> <td>4.9773</td>

</tr> <tr>

<th>Q3</th> <td>9.2813</td>

</tr> <tr>

<th>95-th percentile</th> <td>21.017</td>

</tr> <tr>

<th>Maximum</th> <td>80</td>

</tr> <tr>

<th>Range</th> <td>80</td>

</tr> <tr>

<th>Interquartile range</th> <td>6.5005</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.8079</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.95352</td>

</tr> <tr>

<th>Kurtosis</th> <td>9.1866</td>

</tr> <tr>

<th>Mean</th> <td>7.1398</td>

</tr> <tr>

<th>MAD</th> <td>4.8256</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3494</td>

</tr> <tr>

<th>Sum</th> <td>21933</td>

</tr> <tr>

<th>Variance</th> <td>46.348</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1128625394673116066”>

<img 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b1gTZkyRe%2B//76WLVum4OBgPfroo7rjjju0ZMkSHT9%2B3O5xAAAAjPME4kWDgoLUp08f9enTRxUVFVq3bp2WLVum7Oxs9ezZUxMmTNCdd94ZiNEAAACuWkAKliQVFxfrd7/7nX73u9/p888/V8%2BePXX33Xfr9OnTmjlzpvbt26cZM2YEajwAAIDvzfaC9eabb%2BrNN9/U3r171bJlS40YMUIvvPCCOnXq5HtMmzZttGDBAgoWAABwJNsL1owZM9S/f39lZWXp9ttvV6NGdU8Du/7663XffffZPRoAAIARthesDz74QC1atFBZWZmvXBUUFKhHjx4KDg6WJPXs2VM9e/a0ezQAAAAjbP9XhGfPntXgwYP18ssv%2B9YmT56s4cOH69SpU3aPAwAAYJztBevZZ59Vx44dNWnSJN/aW2%2B9pTZt2ig9Pd3ucQAAAIyzvWD9%2Bc9/1tNPP63IyEjfWsuWLfXkk09qz549do8DAABgnO0Fy%2BPxqLy8vM56RUUFV3QHAACuYHvBuv322zV//nz97W9/860VFRUpPT1d/fr1s3scAAAA42z/V4RPPfWUJk2apEGDBikiIkKSVF5erh49emjatGl2jwMAAGCc7QWrVatWeuONN7R7924VFhbK4/Hohhtu0G233aagoCC7xwEAADAuIL8qJzg4WP369eMjQQAA4Eq2Fyyv16ulS5dq//79unDhQp0T2//whz/YPRIAAIBRthes3/zmNzp48KCGDh2qn/zkJ3a/PAAAwDVne8Has2ePVq5cqcTERLtfGgAAwBa2X6YhPDxcrVq1svtlAQAAbGN7wRo%2BfLhWrlypixcv2v3SAAAAtrD9I8KysjJt375df/zjH9WhQweFhIT4bV%2B7dq3dIwEAABgVkMs0DBs2LBAvCwAAYAvbC1Z6errdLwkAAGAr28/BkqTi4mJlZmZqypQp%2Bvvf/6533nlHx44dC8QoAAAAxtlesL744gv97Gc/0xtvvKF3331X58%2Bf11tvvaVRo0YpPz/f7nEAAACMs71g/fa3v9WAAQO0Y8cOXXfddZKk5557TsnJyVq0aJHd4wAAABhne8Hav3%2B/Jk2a5PeLnT0ejx555BEdOnTI7nEAAACMs71g1dbWqra2ts76uXPnFBwcbPc4AAAAxtlesPr27asVK1b4layysjJlZGSod%2B/edo8DAABgnO0F6%2Bmnn9bBgwfVt29fVVVV6T//8z/Vv39/nThxQk899ZTd4wAAABhn%2B3WwoqOjtXXrVm3fvl2ffvqpamtrNXbsWA0fPlxNmza1exwAAADjAnIl97CwMI0ePToQLw0AAHDN2V6wxo8f/0%2B387sIAQCA09lesNq1a%2Bd3v6amRl988YU%2B//xzTZgwwe5xAAAAjPvB/C7CrKwsnT592uZpAAAAzAvI7yK8kuHDh%2Bvtt98O9BgAAABX7QdTsD755BMuNAoAAFzhB3GS%2B9mzZ/XZZ59p3Lhxdo8DAABgnO0Fq23btn6/h1CSrrvuOt133336%2Bc9/bvc4AAAAxtlesH7729/a/ZIAAAC2sr1g7du3r96PvfXWW6/hJAAAANeG7QXr/vvv931EaFmWb/3ytaCgIH366ad2jwcAAHDVbC9YL730kubPn6%2BpU6cqKSlJISEhOnDggObOnau7775bd911l90jAQAAGGX7ZRrS09M1a9YsDRo0SC1atFCTJk3Uu3dvzZ07Vxs2bFC7du18NwAAACeyvWAVFxdfsTw1bdpUpaWldo8DAABgnO0FKy4uTs8995zOnj3rWysrK1NGRoZuu%2B22Bu%2Bvurpaw4YN0969e31rRUVFmjhxouLi4nTXXXdp165dfs/ZvXu3hg0bptjYWI0fP15FRUV%2B21evXq1%2B/fopPj5e06dPV0VFRYPnAgAAP162F6yZM2cqLy9Pt99%2Bu0aOHKm7775b/fv3V1FRkWbNmtWgfVVVVenxxx9XYWGhb82yLKWkpKh169bavHmzhg8frtTUVJ08eVKSdPLkSaWkpGjkyJHatGmTWrZsqUceecR3cv27776rzMxMzZ07V2vWrFF%2Bfr4yMjLMfQEAAIDr2X6Se%2BfOnfXWW29p%2B/btOnr0qCTpF7/4hYYOHaqwsLB67%2BfIkSOaMmWK379ElKQ9e/aoqKhIr7/%2BusLDw9W5c2d99NFH2rx5sx599FFt3LhRN998sx544AFJ35wT1qdPH3388cfq1auX1q5dqwkTJqh///6SpDlz5ujBBx/U1KlTGzQfAAD48bK9YElSs2bNNHr0aJ04cUIdOnSQ9M3V3BviUiH69a9/rbi4ON96fn6%2BunfvrvDwcN9aQkKC8vLyfNsTExN928LCwtSjRw/l5eUpMTFRBw4cUGpqqm97XFycLly4oMOHDys%2BPv575QUAAD8uthcsy7K0ePFirVu3ThcuXNC7776rJUuWKCwsTLNnz6530fqu31vo9XoVFRXlt9aqVSudPn36X24vLy9XVVWV33aPx6PmzZv7ng8AAPCv2F6w1q1bpzfffFPPPPOM5s6dK0kaMGCA5syZo9atW%2BvXv/71Ve2/oqJCISEhfmshISGqrq7%2Bl9srKyt997/r%2BfVRXFwsr9frt%2BbxhNcpdlcrONj2U%2BiuisdTv3kv5XJavvpwaza35pLI5kRuzSW5N5sbc9lesHJycjRr1iwNHDhQ8%2BbNkyTddddduu6665Senn7VBSs0NFRlZWV%2Ba9XV1WrcuLFv%2B%2BVlqbq6WhEREQoNDfXdv3x7Q86/ysnJUWZmpt9aSkqK0tLS6r0PN2rRokmDHh8R4d5z3tyaza25JLI5kVtzSe7N5qZcthesEydOqFu3bnXWu3btWueoz/cRHR2tI0eO%2BK2VlJT4jh5FR0erpKSkzvZu3bqpefPmCg0NVUlJiTp37ixJqqmpUVlZmSIjI%2Bs9w5gxY5ScnOy35vGEq7T03PeJ9J2c1vTrmz84uJEiIsJUXl6hixdrr/FU9nJrNrfmksjmRG7NJbk327XM1dAf7k2xvWC1a9dOBw4cUPv27f3WP/jgA98J71cjNjZW2dnZqqys9B21ys3NVUJCgm97bm6u7/EVFRU6dOiQUlNT1ahRI8XExCg3N1e9evWSJOXl5cnj8ahr1671niEqKqrOx4Fe79eqqXHPN8P30dD8Fy/WuvZr5tZsbs0lkc2J3JpLcm82N%2BWy/RDIgw8%2BqDlz5mjt2rWyLEsfffSRFi1apIULF%2Br%2B%2B%2B%2B/6v0nJSWpTZs2mjZtmgoLC5Wdna2CggLdc889kqRRo0Zp//79ys7OVmFhoaZNm6b27dv7CtW4ceP0yiuvaMeOHSooKNDs2bN17733cokGAABQb7YfwRo1apRqamq0fPlyVVZWatasWWrZsqUee%2BwxjR079qr3HxwcrGXLlmnGjBkaOXKkOnbsqKysLLVt21aS1L59e7344ot69tlnlZWVpfj4eGVlZSkoKEiSNHToUH355ZeaNWuWqqurdeedd2rq1KlXPRcAAPjxCLIuv1LnNbZ9%2B3b169dPzZo101dffSXLstSqVSs7RwgIr/dr4/v0eBpp4KIPje/3Wnn7sT71epzH00gtWjRRaek51xwqvsSt2dyaSyKbE7k1l%2BTebNcyV2TkT4zur75s/4hw7ty5vpPZW7Zs%2BaMoVwAA4MfF9oLVqVMnff7553a/LAAAgG1sPwera9eueuKJJ7Ry5Up16tTJd%2B2pS9LT0%2B0eCQAAwCjbC9bx48d9l0wwcd0rAACAHxpbCtbChQuVmpqq8PBwrVu3zo6XBAAACBhbzsFatWqVKioq/NYmT56s4uJiO14eAADAVrYUrCtdCWLfvn2qqqqy4%2BUBAABs5axfZgcAAOAAFCwAAADDbCtYl34VDQAAgNvZdpmG%2BfPn%2B13z6sKFC8rIyFCTJk38Hsd1sAAAgNPZUrBuvfXWOte8io%2BPV2lpqUpLS%2B0YAQAAwDa2FCyufQUAAH5MOMkdAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMM8gR4APx5Dlv4p0CM0yNuP9Qn0CAAAh%2BIIFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDXFuw3nvvPd10001%2Bt7S0NEnSoUOHNHr0aMXGxmrUqFE6ePCg33O3b9%2BuAQMGKDY2VikpKfrqq68CEQEAADiUawvWkSNH1L9/f%2B3atct3mz9/vs6fP6/JkycrMTFRW7ZsUXx8vB5%2B%2BGGdP39eklRQUKAZM2YoNTVVOTk5Ki8v17Rp0wKcBgAAOIlrC9bRo0fVpUsXRUZG%2Bm4RERF66623FBoaqieffFKdO3fWjBkz1KRJE73zzjuSpPXr12vIkCEaMWKEunbtqoULF2rnzp0qKioKcCIAAOAUri5YnTp1qrOen5%2BvhIQEBQUFSZKCgoLUs2dP5eXl%2BbYnJib6Ht%2BmTRu1bdtW%2Bfn5tswNAACczxPoAa4Fy7J0/Phx7dq1SytWrNDFixc1ePBgpaWlyev16oYbbvB7fKtWrVRYWChJKi4uVlRUVJ3tp0%2BfrvfrFxcXy%2Bv1%2Bq15POF19nu1goNd249/EDwe81/fS%2B%2BZ2947t%2BaSyOZEbs0luTebG3O5smCdPHlSFRUVCgkJ0dKlS3XixAnNnz9flZWVvvVvCwkJUXV1tSSpsrLyn26vj5ycHGVmZvqtpaSk%2BE6yhzO0aNHkmu07IiLsmu07kNyaSyKbE7k1l%2BTebG7K5cqC1a5dO%2B3du1fNmjVTUFCQunXrptraWk2dOlVJSUl1ylJ1dbUaN24sSQoNDb3i9rCw%2Br/pY8aMUXJyst%2BaxxOu0tJz3zPRlbmp6f8QmX6/pG/es4iIMJWXV%2BjixVrj%2Bw8Ut%2BaSyOZEbs0luTfbtcx1LX9Y/mdcWbAkqXnz5n73O3furKqqKkVGRqqkpMRvW0lJie/ju%2Bjo6Ctuj4yMrPdrR0VF1fk40Ov9WjU17vlm%2BDG4lu/XxYu1rvzz4NZcEtmcyK25JPdmc1MuVx4C%2BfDDD9WrVy9VVFT41j799FM1b95cCQkJ%2BuSTT2RZlqRvztfav3%2B/YmNjJUmxsbHKzc31Pe/UqVM6deqUbzsAAMC/4sqCFR8fr9DQUM2cOVPHjh3Tzp07tXDhQj300EMaPHiwysvLtWDBAh05ckQLFixQRUWFhgwZIkkaO3as3nzzTW3cuFGHDx/Wk08%2BqTvuuEMdOnQIcCoAAOAUrixYTZs21SuvvKKvvvpKo0aN0owZMzRmzBg99NBDatq0qVasWKHc3FyNHDlS%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%2Bvbtq1dffTXQIwEAAAfhXxFewcKFC3Xw4EGtWbNGJ0%2Be1FNPPaW2bdtq8ODBgR4NAAA4AAXrMufPn9fGjRv18ssvq0ePHurRo4cKCwv12muvUbAAQ5x0SQlJ%2BvMCvvcBNAwfEV5whBjaAAAL3ElEQVTm8OHDqqmpUXx8vG8tISFB%2Bfn5qq2tDeBkAADAKTiCdRmv16sWLVooJCTEt9a6dWtVVVWprKxMLVu2/Jf7KC4ultfr9VvzeMIVFRVldNbgYPoxYIfEGe8EeoQGee%2BJfvV63KX/h7jt/yVuzSW5N5sbc1GwLlNRUeFXriT57ldXV9drHzk5OcrMzPRbS01N1aOPPmpmyH8oLi7WhJ8WasyYMcbLWyAVFxcrJyfHdbkk92Zzay7J/dnWrFnpumxuzSW5N5sbc7mnKhoSGhpap0hdut%2B4ceN67WPMmDHasmWL323MmDHGZ/V6vcrMzKxztMzp3JpLcm82t%2BaSyOZEbs0luTebG3NxBOsy0dHRKi0tVU1NjTyeb748Xq9XjRs3VkRERL32ERUV5ZoGDgAAGo4jWJfp1q2bPB6P8vLyfGu5ubmKiYlRo0Z8uQAAwL9GY7hMWFiYRowYodmzZ6ugoEA7duzQq6%2B%2BqvHjxwd6NAAA4BDBs2fPnh3oIX5oevfurUOHDmnx4sX66KOP9Ktf/UqjRo0K9FhX1KRJEyUlJalJkyaBHsUot%2BaS3JvNrbkksjmRW3NJ7s3mtlxBlmVZgR4CAADATfiIEAAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBcuhqqqqNH36dCUmJqpv37569dVXAz3SVamurtawYcO0d%2B9e31pRUZEmTpyouLg43XXXXdq1a1cAJ2y4M2fOKC0tTUlJSerXr5/S09NVVVUlydnZvvjiCz344IOKj4/XHXfcoZUrV/q2OTnXt02ePFlPP/207/6hQ4c0evRoxcbGatSoUTp48GAAp2u49957TzfddJPfLS0tTZLzs1VXV2vOnDm69dZb9e///u967rnndOn62U7OtmXLljrv2U033aSuXbtKcna2U6dO6eGHH1bPnj2VnJys1atX%2B7Y5OdflKFgOtXDhQh08eFBr1qzRM888o8zMTL3zzjuBHut7qaqq0uOPP67CwkLfmmVZSklJUevWrbV582YNHz5cqampOnnyZAAnrT/LspSWlqaKigq99tprWrJkid5//30tXbrU0dlqa2s1efJktWjRQm%2B88YbmzJmj5cuXa9u2bY7O9W2///3vtXPnTt/98%2BfPa/LkyUpMTNSWLVsUHx%2Bvhx9%2BWOfPnw/glA1z5MgR9e/fX7t27fLd5s%2Bf74ps8%2BfP1%2B7du/XKK69o8eLF%2Bp//%2BR/l5OQ4PtulH1Au3f74xz%2BqY8eOGj9%2BvOOzPfbYYwoPD9eWLVs0ffp0LV26VO%2B9957jc9VhwXHOnTtnxcTEWHv27PGtZWVlWffdd18Ap/p%2BCgsLrZ///OfWz372M6tLly6%2BTLt377bi4uKsc%2BfO%2BR47YcIE64UXXgjUqA1y5MgRq0uXLpbX6/Wtbdu2zerbt6%2Bjs505c8b6r//6L%2Bvrr7/2raWkpFjPPPOMo3NdUlpaat1%2B%2B%2B3WqFGjrKeeesqyLMvauHGjlZycbNXW1lqWZVm1tbXWwIEDrc2bNwdy1AaZMmWKtXjx4jrrTs9WWlpqde/e3dq7d69vbcWKFdbTTz/t%2BGyXe%2Bmll6wBAwZYVVVVjs5WVlZmdenSxfrss898a6mpqdacOXMcnetKOILlQIcPH1ZNTY3i4%2BN9awkJCcrPz1dtbW0AJ2u4jz/%2BWL169VJOTo7fen5%2Bvrp3767w8HDfWkJCgvLy8uwe8XuJjIzUypUr1bp1a7/1s2fPOjpbVFSUli5dqqZNm8qyLOXm5mrfvn1KSkpydK5L/vu//1vDhw/XDTfc4FvLz89XQkKCgoKCJElBQUHq2bOno3IdPXpUnTp1qrPu9Gy5ublq2rSpkpKSfGuTJ09Wenq647N9W1lZmV5%2B%2BWVNmTJFISEhjs7WuHFjhYWFacuWLbpw4YKOHTum/fv3q1u3bo7OdSUULAfyer1q0aKFQkJCfGutW7dWVVWVysrKAjhZw40bN07Tp09XWFiY37rX61VUVJTfWqtWrXT69Gk7x/veIiIi1K9fP9/92tparV%2B/Xr1793Z8tkuSk5M1btw4xcfHa9CgQY7P9dFHH%2BnPf/6zHnnkEb91p%2BeyLEvHjx/Xrl27NGjQIA0YMECLFi1SdXW147MVFRWpXbt22rp1qwYPHqz/%2BI//UFZWlmprax2f7ds2bNigqKgoDR48WJKz/0yGhoZq1qxZysnJUWxsrIYMGaLbb79do0ePdnSuK/EEegA0XEVFhV%2B5kuS7X11dHYiRjPuujE7Nl5GRoUOHDmnTpk1avXq1K7K98MILKikp0ezZs5Wenu7o96yqqkrPPPOMZs2apcaNG/ttc3IuSTp58qQvw9KlS3XixAnNnz9flZWVjs92/vx5ffHFF3r99deVnp4ur9erWbNmKSwszPHZLrEsSxs3btRDDz3kW3N6tqNHj6p///6aNGmSCgsLNW/ePN12222Oz3U5CpYDhYaG1vkDd%2Bn%2B5X85OFVoaGido3HV1dWOzJeRkaE1a9ZoyZIl6tKli2uyxcTESPqmnDzxxBMaNWqUKioq/B7jlFyZmZm6%2Beab/Y46XvJd329OyCVJ7dq10969e9WsWTMFBQWpW7duqq2t1dSpU5WUlOTobB6PR2fPntXixYvVrl07Sd8Uyg0bNqhjx46OznbJgQMHdObMGQ0dOtS35uQ/kx999JE2bdqknTt3qnHjxoqJidGZM2e0fPlydejQwbG5roSPCB0oOjpapaWlqqmp8a15vV41btxYERERAZzMnOjoaJWUlPitlZSU1Dl8/EM3b948rVq1ShkZGRo0aJAkZ2crKSnRjh07/NZuuOEGXbhwQZGRkY7N9fvf/147duxQfHy84uPjtW3bNm3btk3x8fGOfr8uad68ue%2B8Fknq3LmzqqqqHP2eSd%2Bc6xgaGuorV5L0b//2bzp16pQr3jdJ%2BvDDD5WYmKhmzZr51pyc7eDBg%2BrYsaNfaerevbtOnjzp6FxXQsFyoG7dusnj8fid%2BJebm6uYmBg1auSOtzQ2NlZ/%2BctfVFlZ6VvLzc1VbGxsAKdqmMzMTL3%2B%2But67rnn/H76dHK2EydOKDU1VWfOnPGtHTx4UC1btlRCQoJjc61bt07btm3T1q1btXXrViUnJys5OVlbt25VbGysPvnkE9%2B1lSzL0v79%2Bx2RS/rmL%2BhevXr5HV389NNP1bx5cyUkJDg6W2xsrKqqqnT8%2BHHf2rFjx9SuXTvHv2%2BXFBQUqGfPnn5rTs4WFRWlL774wu9I1bFjx9S%2BfXtH57oSd/xt/CMTFhamESNGaPbs2SooKNCOHTv06quvavz48YEezZikpCS1adNG06ZNU2FhobKzs1VQUKB77rkn0KPVy9GjR7Vs2TL98pe/VEJCgrxer%2B/m5GwxMTHq0aOHpk%2BfriNHjmjnzp3KyMjQr371K0fnateunTp27Oi7NWnSRE2aNFHHjh01ePBglZeXa8GCBTpy5IgWLFigiooKDRkyJNBj10t8fLxCQ0M1c%2BZMHTt2TDt37tTChQv10EMPOT7b9ddfrzvuuEPTpk3T4cOH9eGHHyo7O1tjx451fLZLCgsL/f5VqyRHZ0tOTtZ1112nmTNn6vjx4/rf//1fvfTSS7r//vsdneuKAnJxCFy18%2BfPW08%2B%2BaQVFxdn9e3b11q1alWgR7pq374OlmVZ1l//%2BlfrF7/4hXXzzTdbQ4cOtf70pz8FcLqGWbFihdWlS5cr3izL2dlOnz5tpaSkWD179rT69OljLV%2B%2B3HfdGifn%2BrannnrKdx0sy7Ks/Px8a8SIEVZMTIx1zz33WH/5y18COF3Dff7559bEiROtuLg4q0%2BfPtaLL77oe8%2Bcnq28vNyaOnWqFRcXZ912222uymZZlhUTE2N98MEHddadnK2wsNCaOHGi1bNnT2vAgAHWqlWrXPWeXRJkWf84FgcAAAAj%2BIgQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAz7f13NEJmpemuDAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1128625394673116066”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>9</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.952380952380952</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.084745762711865</td> <td class=”number”>6</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.88235294117647</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.225806451612903</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.555555555555555</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.761904761904762</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2681)</td> <td class=”number”>2950</td> <td class=”number”>91.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1128625394673116066”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16420361247947454</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16778523489932887</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1697792869269949</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18796992481203006</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>43.984962406015036</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.39252336448598</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.45454545454545</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLSALE07”>PCT_LOCLSALE07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2747</td>

</tr> <tr>

<th>Unique (%)</th> <td>85.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>13.0%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>416</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.98733</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>33.333</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2600154711536100996”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives2600154711536100996,#minihistogram2600154711536100996”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives2600154711536100996”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2600154711536100996”

aria-controls=”quantiles2600154711536100996” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2600154711536100996” aria-controls=”histogram2600154711536100996”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2600154711536100996” aria-controls=”common2600154711536100996”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2600154711536100996” aria-controls=”extreme2600154711536100996”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2600154711536100996”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.011757</td>

</tr> <tr>

<th>Q1</th> <td>0.080359</td>

</tr> <tr>

<th>Median</th> <td>0.29833</td>

</tr> <tr>

<th>Q3</th> <td>0.91001</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1991</td>

</tr> <tr>

<th>Maximum</th> <td>33.333</td>

</tr> <tr>

<th>Range</th> <td>33.333</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.82965</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.2085</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.2368</td>

</tr> <tr>

<th>Kurtosis</th> <td>53.84</td>

</tr> <tr>

<th>Mean</th> <td>0.98733</td>

</tr> <tr>

<th>MAD</th> <td>1.111</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.1046</td>

</tr> <tr>

<th>Sum</th> <td>2758.6</td>

</tr> <tr>

<th>Variance</th> <td>4.8773</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2600154711536100996”>

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AMMz2gnXbbbfpueee0zfffGP3SwMAANjC9oKVmpqqtWvXaujQoXrwwQe1Z88eWZZl9zQAAABaje0Fa8aMGfr73/%2Bu1atXKzg4WPfff79uuOEGLVu2TJ999pnd0wEAADDO6YsXdTgcGjJkiIYMGaKamho9%2B%2ByzWr16tdavX69Bgwbprrvu0o033uiLqQEAAPxiPilYklReXq6XX35ZL7/8sj799FMNGjRIt9xyi8rKyvToo49q//79mjt3rq%2BmBwAA8LPZXrBeeuklvfTSS9q3b586deqkCRMmaOXKlerZs6fnMV27dtXixYspWAAAwC/ZXrDmzp2rtLQ0rVq1Stdff72CgpqfBtarVy/dcccddk8NAADACNsL1ttvv62OHTuqqqrKU64OHjyoAQMGKDg4WJI0aNAgDRo0yO6pAQAAGGH7bxFWV1dr9OjReuaZZzxj99xzj8aPH69Tp07ZPR0AAADjbC9YTzzxhH7zm9/o7rvv9oy98sor6tq1q7Kzs%2B2eDgAAgHG2F6x//vOfevjhhxUVFeUZ69Spk2bNmqW9e/faPR0AAADjbC9YTqdTZ8%2BebTZeU1Pzs67oXl9fr3Hjxmnfvn2esUWLFumqq67yum3bts2zPT8/XyNGjFB8fLwyMjJ05swZzzbLsrR06VKlpqYqJSVFS5YsUVNT00%2BeFwAACFy2F6zrr79eixYt0pdffukZKy0tVXZ2toYNG/aT9lVXV6cHH3xQxcXFXuMlJSWaMWOG9uzZ47lNnDhR0oUT6ufOnavMzEzl5eXp7NmzmjNnjue5mzZtUn5%2BvnJzc7Vy5Ur99a9/1aZNm37BEQMAgEBje8GaPXu26uvrNWrUKA0ePFiDBw/WjTfeqPPnz3sVnR9z9OhR3X777V5Fza2kpET9%2B/dXVFSU5xYWFiZJ2rZtm8aMGaMJEyYoLi5OS5Ys0VtvvaXS0lJJ0tatW5WVlaXk5GSlpqbqoYce0vbt280cPAAACAi2X6ahc%2BfOeuGFF/Tuu%2B%2BquLhYTqdTV155pa677jo5HI4W7%2Bf999/X4MGD9V//9V9KSEjwjFdXV%2Bv06dNeFy79vqKiIv3xj3/03O/atatiY2NVVFSkkJAQnTp1Stdee61ne1JSkk6cOKHy8nJFR0f/9AMGAAABxydflRMcHKxhw4b95I8Ev2/KlCmXHC8pKZHD4dDatWv19ttvKzIyUnfffbduueUWSbpkUercubPKysrkcrkkyWt7ly5dJEllZWUULAAA0CK2FyyXy6Xly5frwIEDOn/%2BfLMT2994441ftP9jx47J4XB4rga/f/9%2BzZs3T%2BHh4Ro5cqRqa2sVEhLi9ZyQkBDV19ertrbWc//726QLJ9O3VHl5uaesuTmd7Y0XtOBg2z/h/UWcTvPzdWfgb1mYRg5k4EYOZOBGDr49dtsL1rx583To0CGNHTtW//Zv/2Z8/xMmTFBaWpoiIyMlSXFxcfr888%2B1Y8cOjRw5UqGhoc3KUn19vcLCwrzKVGhoqOfPkjzncLVEXl6ecnNzvcYyMjKUlZX1s4%2BrLejYsUOr7TsiouV/P20ZOZCBGzmQgRs5%2BIbtBWvv3r3asGGDkpOTW2X/DofDU67cevXq5bnGVkxMjCoqKry2V1RUKCoqSjExMZIuvMvWvXt3z58leV2368dMmjRJw4cP9xpzOtursvLbn3YwP8LffioxffzShQwiIsJ09myNGhsD93Ia5EAGbuRABm7k8F0GvmB7wWrfvr06d%2B7cavtfsWKFPvjgA23evNkzduTIEfXq1UuSFB8fr4KCAqWnp0uSTp06pVOnTik%2BPl4xMTGKjY1VQUGBp2AVFBQoNjb2J328Fx0d3ezxLtc3amgIzAXu1prH39jYFPD5SuQgkYEbOZCBGzn4hu1vgYwfP14bNmxQY2Njq%2Bw/LS1N%2B/fv18aNG/Xll1/qf//3f/Xiiy9q2rRpkqTJkyfrpZde0s6dO3XkyBHNmjVLN9xwg3r06OHZvnTpUu3bt0/79u3Tk08%2BqTvvvLNV5goAANom29/BqqqqUn5%2Bvt5880316NGj2QnnW7du/UX7v%2Baaa7RixQqtXLlSK1asULdu3fTkk08qMTFRkpSYmKgFCxZo5cqV%2BvrrrzVkyBAtXLjQ8/zp06frq6%2B%2BUmZmpoKDg3Xrrbdq6tSpv2hOAAAgsPjkMg3jxo0zur9PPvnE6/6IESM0YsSIH3x8enq65yPCiwUHB2vOnDk/6aKnAAAA32d7wcrOzrb7JQEAAGzlk19DKy8vV25urmbMmKGvvvpKf/vb33Ts2DFfTAUAAMA42wvWF198oZtvvlkvvPCCXnvtNZ07d06vvPKKJk6cqKKiIrunAwAAYJztBetPf/qTRowYod27d%2BtXv/qVJOmpp57S8OHDtXTpUrunAwAAYJztBevAgQO6%2B%2B67vb7Y2el06r777tPhw4ftng4AAIBxthespqYmNTU1v%2BDZt99%2Bq%2BDgYLunAwAAYJztBWvo0KFat26dV8mqqqpSTk6OUlNT7Z4OAACAcbYXrIcffliHDh3S0KFDVVdXp//8z/9UWlqajh8/rtmzZ9s9HQAAAONsvw5WTEyMXnzxReXn5%2Bvjjz9WU1OTJk%2BerPHjxys8PNzu6QAAABjnkyu5h4WF6bbbbvPFSwMAALQ62wvWj31x8i/9LkIAAABfs71gdevWzet%2BQ0ODvvjiC3366ae666677J4OAACAcf8y30W4atUqlZWV2TwbAAAA83zyXYSXMn78eL366qu%2BngYAAMAv9i9TsD744AMuNAoAANqEf4mT3Kurq/XJJ59oypQpdk8HAADAONsLVmxsrNf3EErSr371K91xxx367W9/a/d0AAAAjLO9YP3pT3%2By%2ByUBAABsZXvB2r9/f4sfe%2B2117biTAAAAFqH7QXr97//vecjQsuyPOMXjzkcDn388cd2Tw8AAOAXs71grV27VosWLdLMmTOVkpKikJAQffjhh1qwYIFuueUW3XTTTXZPCQAAwCjbL9OQnZ2txx57TKNGjVLHjh3VoUMHpaamasGCBdqxY4e6devmuQEAAPgj2wtWeXn5JctTeHi4Kisr7Z4OAACAcbYXrISEBD311FOqrq72jFVVVSknJ0fXXXed3dMBAAAwzvZzsB599FHdeeeduv7669WzZ09ZlqXPP/9cUVFR2rp1q93TAQAAMM72gtW7d2%2B98sorys/PV0lJiSTpd7/7ncaOHauwsDC7pwMAAGCc7QVLkq644grddtttOn78uHr06CHpwtXcAQAA2gLbz8GyLEtLly7Vtddeq3HjxqmsrEyzZ8/W3Llzdf78ebunAwAAYJztBevZZ5/VSy%2B9pP/%2B7/9WSEiIJGnEiBHavXu3cnNz7Z4OAACAcbYXrLy8PD322GNKT0/3XL39pptu0qJFi/TXv/7V7ukAAAAYZ3vBOn78uPr169dsPC4uTi6Xy%2B7pAAAAGGd7werWrZs%2B/PDDZuNvv/2254R3AAAAf2b7bxFOnz5djz/%2BuFwulyzL0nvvvae8vDw9%2B%2Byzevjhh%2B2eDgAAgHG2F6yJEyeqoaFBa9asUW1trR577DF16tRJDzzwgCZPnmz3dAAAAIyzvWDl5%2Bdr9OjRmjRpks6cOSPLstS5c2e7pwEAANBqbD8Ha8GCBZ6T2Tt16kS5AgAAbY7tBatnz5769NNP7X5ZAAAA29j%2BEWFcXJweeughbdiwQT179lRoaKjX9uzsbLunBAAAYJTtBeuzzz5TUlKSJHHdKwAA0CbZUrCWLFmizMxMtW/fXs8%2B%2B6wdLwkAAOAztpyDtWnTJtXU1HiN3XPPPSovL7fj5QEAAGxlS8GyLKvZ2P79%2B1VXV2fHywMAANjK9t8iBAAAaOsoWAAAAIbZVrAcDoddLwUAAOBTtl2mYdGiRV7XvDp//rxycnLUoUMHr8dxHSwAAODvbClY1157bbNrXiUmJqqyslKVlZV2TAEAAMA2thSs1rz2VX19vdLT0zVv3jwNHjxYklRaWqp58%2BapsLBQsbGxeuSRRzR06FDPc95991098cQTKi0tVXx8vBYvXqwePXp4tm/evFkbN25UdXW1xowZo3nz5iksLKzVjgEAALQtfn2Se11dnR588EEVFxd7xizLUkZGhrp06aJdu3Zp/PjxyszM1MmTJyVJJ0%2BeVEZGhtLT0/X888%2BrU6dOuu%2B%2B%2BzyXknjttdeUm5urBQsWaMuWLSoqKlJOTo5Pjg8AAPgnvy1YR48e1e23364vv/zSa3zv3r0qLS3VggUL1Lt3b917771KSEjQrl27JEk7d%2B7U1VdfrWnTpqlPnz7Kzs7WiRMn9P7770uStm7dqrvuuktpaWm65ppr9Pjjj2vXrl3NLpQKAADwQ/y2YL3//vsaPHiw8vLyvMaLiorUv39/tW/f3jOWlJSkwsJCz/bk5GTPtrCwMA0YMECFhYVqbGzUhx9%2B6LU9ISFB58%2Bf15EjR1r5iAAAQFth%2B5c9mzJlypRLjrtcLkVHR3uNde7cWWVlZT%2B6/ezZs6qrq/Pa7nQ6FRkZ6Xk%2BAADAj/HbgvVDampqFBIS4jUWEhKi%2Bvr6H91eW1vruf9Dz2%2BJ8vLyZr816XS2b1bsfqngYP96A9LpND9fdwb%2BloVp5EAGbuRABm7k4Ntjb3MFKzQ0VFVVVV5j9fX1ateunWf7xWWpvr5eERERnut0XWr7T/ktwry8POXm5nqNZWRkKCsrq8X7aIs6duzw4w/6mSIi%2BC1PiRwkMnAjBzJwIwffaHMFKyYmRkePHvUaq6io8Lx7FBMTo4qKimbb%2B/Xrp8jISIWGhqqiokK9e/eWJDU0NKiqqkpRUVEtnsOkSZM0fPhwrzGns70qK7/9OYf0g/ztpxLTxy9dyCAiIkxnz9aosbHJ%2BP79BTmQgRs5kIEbOXyXgS%2B0uYIVHx%2Bv9evXq7a21vOuVUFBgZKSkjzbCwoKPI%2BvqanR4cOHlZmZqaCgIA0cOFAFBQWea2oVFhbK6XQqLi6uxXOIjo5u9nGgy/WNGhoCc4G7tebxNzY2BXy%2BEjlIZOBGDmTgRg6%2B4V9vgbRASkqKunbtqjlz5qi4uFjr16/XwYMHdeutt0qSJk6cqAMHDmj9%2BvUqLi7WnDlz1L17d0%2BhmjJlijZu3Kjdu3fr4MGDmj9/vm6//XYuNAoAAFqszRWs4OBgrV69Wi6XS%2Bnp6Xr55Ze1atUqxcbGSpK6d%2B%2Bup59%2BWrt27dKtt96qqqoqrVq1yvNl1GPHjtW9996rxx57TNOmTdM111yjmTNn%2BvKQAACAn3FY7kuYo1W5XN8Y36fTGaSRS98xvt/W8uoDQ4zv0%2BkMUseOHVRZ%2BW1AvwVODmTgRg5k4EYO32XgC23uHSwAAABfo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADD2mzBev3113XVVVd53bKysiRJhw8f1m233ab4%2BHhNnDhRhw4d8npufn6%2BRowYofj4eGVkZOjMmTO%2BOAQAAOCn2mzBOnr0qNLS0rRnzx7PbdGiRTp37pzuueceJScn6y9/%2BYsSExN177336ty5c5KkgwcPau7cucrMzFReXp7Onj2rOXPm%2BPhoAACAP2mzBaukpER9%2B/ZVVFSU5xYREaFXXnlFoaGhmjVrlnr37q25c%2BeqQ4cO%2Btvf/iZJ2rZtm8aMGaMJEyYoLi5OS5Ys0VtvvaXS0lIfHxEAAPAXbbpg9ezZs9l4UVGRkpKS5HA4JEkOh0ODBg1SYWGhZ3tycrLn8V27dlVsbKyKiopsmTcAAPB/Tl9PoDVYlqXPPvtMe/bs0bp169TY2KjRo0crKytLLpdLV155pdfjO3furOLiYklSeXm5oqOjm20vKytr8euXl5fL5XJ5jTmd7Zvt95cKDvavfux0mp%2BvOwN/y8I0ciADN3IgAzdy8O2xt8mCdfLkSdXU1CgkJETLly/X8ePHtWjRItXW1nrGvy8kJET19fWSpNra2stub4m8t8TtAAANQklEQVS8vDzl5uZ6jWVkZHhOsg9UHTt2aLV9R0SEtdq%2B/Qk5kIEbOZCBGzn4RpssWN26ddO%2Bfft0xRVXyOFwqF%2B/fmpqatLMmTOVkpLSrCzV19erXbt2kqTQ0NBLbg8La/kCnTRpkoYPH%2B415nS2V2Xltz/ziC7N334qMX380oUMIiLCdPZsjRobm4zv31%2BQAxm4kQMZuJHDdxn4QpssWJIUGRnpdb93796qq6tTVFSUKioqvLZVVFR4Pr6LiYm55PaoqKgWv3Z0dHSzjwNdrm/U0BCYC9ytNY%2B/sbEp4POVyEEiAzdyIAM3cvAN/3oLpIXeeecdDR48WDU1NZ6xjz/%2BWJGRkUpKStIHH3wgy7IkXThf68CBA4qPj5ckxcfHq6CgwPO8U6dO6dSpU57tAAAAP6ZNFqzExESFhobq0Ucf1bFjx/TWW29pyZIl%2BsMf/qDRo0fr7NmzWrx4sY4eParFixerpqZGY8aMkSRNnjxZL730knbu3KkjR45o1qxZuuGGG9SjRw8fHxUAAPAXbbJghYeHa%2BPGjTpz5owmTpyouXPnatKkSfrDH/6g8PBwrVu3TgUFBUpPT1dRUZHWr1%2Bv9u3bS7pQzhYsWKBVq1Zp8uTJuuKKK5Sdne3jIwIAAP7EYbk/K0Orcrm%2BMb5PpzNII5e%2BY3y/reXVB4YY36fTGaSOHTuosvLbgD7HgBzIwI0cyMCNHL7LwBfa5DtYAAAAvkTBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhTl9PAIFjzPJ/%2BHoKP8mrDwzx9RQAAH6Kd7AAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADONK7pdQV1enxx9/XP/3f/%2Bndu3aadq0aZo2bZqvpwWb%2BduV5/%2B5eLSvpwAA%2BP8oWJewZMkSHTp0SFu2bNHJkyc1e/ZsxcbGavRo/gcGAAB%2BHAXrIufOndPOnTv1zDPPaMCAARowYICKi4u1fft2ChYAAGgRCtZFjhw5ooaGBiUmJnrGkpKStHbtWjU1NSkoiNPW8K8pee7ffD2FFuOLtAG0dRSsi7hcLnXs2FEhISGesS5duqiurk5VVVXq1KnTj%2B6jvLxcLpfLa8zpbK/o6Gijcw0OpuzBP/nb%2BW2vPzTM11NoMfe/C4H87wMZXEAOvj12CtZFampqvMqVJM/9%2Bvr6Fu0jLy9Pubm5XmOZmZm6//77zUzy/ysvL9ddvy7WpEmTjJc3f1FeXq68vLyAzkAiB4kM3MrLy7Vly4aAzoEMLiAH32YQuLX2B4SGhjYrUu777dq1a9E%2BJk2apL/85S9et0mTJhmfq8vlUm5ubrN3ywIJGVxADmTgRg5k4EYOvs2Ad7AuEhMTo8rKSjU0NMjpvBCPy%2BVSu3btFBER0aJ9REdHB%2BxPCwAAgHewmunXr5%2BcTqcKCws9YwUFBRo4cCAnuAMAgBahMVwkLCxMEyZM0Pz583Xw4EHt3r1bf/7zn3XnnXf6emoAAMBPBM%2BfP3%2B%2BryfxryY1NVWHDx/Wk08%2Bqffee0//8R//oYkTJ/p6WpfUoUMHpaSkqEOHDr6eis%2BQwQXkQAZu5EAGbuTguwwclmVZtr4iAABAG8dHhAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFh%2Bqq6uTo888oiSk5M1dOhQ/fnPf/b1lGz3%2Buuv66qrrvK6ZWVl%2BXpatqmvr9e4ceO0b98%2Bz1hpaammTp2qhIQE3XTTTdqzZ48PZ9j6LpXBokWLmq2Lbdu2%2BXCWreP06dPKyspSSkqKhg0bpuzsbNXV1UkKrHVwuRwCZS188cUXmj59uhITE3XDDTdow4YNnm2BtBYul4Mv1oKzVfeOVrNkyRIdOnRIW7Zs0cmTJzV79mzFxsZq9OjRvp6abY4ePaq0tDQtXLjQMxYaGurDGdmnrq5OM2bMUHFxsWfMsixlZGSob9%2B%2B2rVrl3bv3q3MzEy98sorio2N9eFsW8elMpCkkpISzZgxQ7fccotnLDw83O7ptSrLspSVlaWIiAht375dX3/9tR555BEFBQVp1qxZAbMOLpfD7NmzA2ItNDU16Z577tHAgQP1wgsv6IsvvtCDDz6omJgYjRs3LmDWwuVyuPnmm32yFihYfujcuXPauXOnnnnmGQ0YMEADBgxQcXGxtm/fHlAFq6SkRH379lVUVJSvp2Kro0ePasaMGbr4W6727t2r0tJSPffcc2rfvr169%2B6t9957T7t27dL999/vo9m2jh/KQLqwLqZPn96m18WxY8dUWFiof/zjH%2BrSpYskKSsrS//zP/%2Bj66%2B/PmDWweVycBestr4WKioq1K9fP82fP1/h4eHq2bOnrrvuOhUUFKhLly4BsxYul4O7YNm9FviI0A8dOXJEDQ0NSkxM9IwlJSWpqKhITU1NPpyZvUpKStSzZ09fT8N277//vgYPHqy8vDyv8aKiIvXv31/t27f3jCUlJamwsNDuKba6H8qgurpap0%2BfbvPrIioqShs2bPCUCrfq6uqAWgeXyyFQ1kJ0dLSWL1%2Bu8PBwWZalgoIC7d%2B/XykpKQG1Fi6Xg6/WAu9g%2BSGXy6WOHTsqJCTEM9alSxfV1dWpqqpKnTp18uHs7GFZlj777DPt2bNH69atU2Njo0aPHq2srCyvXNqiKVOmXHLc5XIpOjraa6xz584qKyuzY1q2%2BqEMSkpK5HA4tHbtWr399tuKjIzU3Xff7fWxQFsQERGhYcOGee43NTVp27ZtSk1NDah1cLkcAmUtfN/w4cN18uRJpaWladSoUXriiScCZi1838U5HDp0yCdrgYLlh2pqapqVCPf9%2Bvp6X0zJdidPnvTksHz5ch0/flyLFi1SbW2tHn30UV9Pzyd%2BaF0EypqQLnxk5HA41KtXL91xxx3av3%2B/5s2bp/DwcI0cOdLX02s1OTk5Onz4sJ5//nlt3rw5YNfB93P46KOPAm4trFy5UhUVFZo/f76ys7MD9t%2BEi3MYMGCAT9YCBcsPhYaGNvsPxH2/Xbt2vpiS7bp166Z9%2B/bpiiuukMPhUL9%2B/dTU1KSZM2dqzpw5Cg4O9vUUbRcaGqqqqiqvsfr6%2BoBZE5I0YcIEpaWlKTIyUpIUFxenzz//XDt27Giz/1PNycnRli1btGzZMvXt2zdg18HFOfTp0yfg1sLAgQMlXfgFkIceekgTJ05UTU2N12MCYS1cnMOBAwd8shY4B8sPxcTEqLKyUg0NDZ4xl8uldu3aKSIiwoczs1dkZKQcDofnfu/evVVXV6evv/7ah7PynZiYGFVUVHiNVVRUNPuIoC1zOByef0TdevXqpdOnT/toRq1r4cKF2rRpk3JycjRq1ChJgbkOLpVDoKyFiooK7d6922vsyiuv1Pnz5xUVFRUwa%2BFyOVRXV/tkLVCw/FC/fv3kdDq9TlQsKCjQwIEDFRQUGH%2Bl77zzjgYPHuz109nHH3%2BsyMjIgDgH7VLi4%2BP10Ucfqba21jNWUFCg%2BPh4H87KXitWrNDUqVO9xo4cOaJevXr5ZkKtKDc3V88995yeeuopjR071jMeaOvgh3IIlLVw/PhxZWZmepWFQ4cOqVOnTkpKSgqYtXC5HJ599lnfrAULfmnevHnW2LFjraKiIuv111%2B3Bg0aZL322mu%2BnpZtvvnmG2vYsGHWgw8%2BaJWUlFhvvvmmNXToUGv9%2BvW%2Bnpqt%2Bvbta%2B3du9eyLMtqaGiwbrrpJuuBBx6wPv30U2vdunVWQkKCdeLECR/PsnV9P4OioiKrf//%2B1oYNG6wvvvjC2r59u3X11VdbBw4c8PEszTp69KjVr18/a9myZVZ5ebnXLZDWweVyCJS10NDQYKWnp1vTpk2ziouLrTfffNP693//d2vz5s0BtRYul4Ov1gIFy0%2BdO3fOmjVrlpWQkGANHTrU2rRpk6%2BnZLtPP/3Umjp1qpWQkGANGTLEevrpp62mpiZfT8tW3y8XlmVZn3/%2BufW73/3Ouvrqq62xY8da//jHP3w4O3tcnMHrr79u3XzzzdbAgQOt0aNHt8kfPNatW2f17dv3kjfLCpx18GM5BMJasCzLKisrszIyMqxBgwZZQ4YMsdasWeP5tzBQ1oJlXT4HX6wFh2Vd4kp9AAAA%2BNkC44QdAAAAG1GwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGDY/wPN1Fvmf0B09gAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2600154711536100996”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4743323130125944</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.013085722386399573</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5539622184681585</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3481856264621076</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.957031869351514</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09557059546332235</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9538934199765707</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.3474178403755865</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3333449684107648</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2736)</td> <td class=”number”>2736</td> <td class=”number”>85.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>416</td> <td class=”number”>13.0%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2600154711536100996”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006504403481156743</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000989481808376953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0012369807773187204</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0013323873383231238</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>21.695859872611464</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.666666666666664</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.69404517453799</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.382502543234995</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.33333333333333</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_LOCLSALE12”>PCT_LOCLSALE12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2789</td>

</tr> <tr>

<th>Unique (%)</th> <td>86.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.1%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>357</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.0457</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>39.312</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1426016060875747966”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1426016060875747966,#minihistogram1426016060875747966”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1426016060875747966”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1426016060875747966”

aria-controls=”quantiles1426016060875747966” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1426016060875747966” aria-controls=”histogram1426016060875747966”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1426016060875747966” aria-controls=”common1426016060875747966”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1426016060875747966” aria-controls=”extreme1426016060875747966”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1426016060875747966”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.0059177</td>

</tr> <tr>

<th>Q1</th> <td>0.057424</td>

</tr> <tr>

<th>Median</th> <td>0.22488</td>

</tr> <tr>

<th>Q3</th> <td>0.79798</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.914</td>

</tr> <tr>

<th>Maximum</th> <td>39.312</td>

</tr> <tr>

<th>Range</th> <td>39.312</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.74056</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.6727</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.5558</td>

</tr> <tr>

<th>Kurtosis</th> <td>56.312</td>

</tr> <tr>

<th>Mean</th> <td>1.0457</td>

</tr> <tr>

<th>MAD</th> <td>1.2802</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.3992</td>

</tr> <tr>

<th>Sum</th> <td>2983.5</td>

</tr> <tr>

<th>Variance</th> <td>7.1435</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1426016060875747966”>

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MMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMzvBevuu%2B/Wyy%2B/rNOnT/v7UwMAAPiF3wtWcnKynn/%2BefXr108PPfSQdu7cKcuy/D0GAADANeP3gjVlyhT9/e9/17JlyxQcHKzf//73uu2227R48WJ9/vnn/h4HAADAOHcgPqnL5VLfvn3Vt29fVVZWav369Vq2bJlWrlypXr166b777tMvf/nLQIwGAADQZAEpWJJUVlamV199Va%2B%2B%2Bqo%2B/fRT9erVS3feeadKS0v16KOPas%2BePZo1a1agxgMAALhqfi9Y27dv1/bt27V79261bt1ao0aN0rPPPquOHTt679O2bVvNnz%2BfggUAAGzJ7wVr1qxZSk1N1dKlS9W/f38FBTV8G1inTp00fvx4f48GAABghN8L1j/%2B8Q%2B1atVKFRUV3nK1b98%2B9ejRQ8HBwZKkXr16qVevXv4eDQAAwAi//yvCM2fOaMiQIXrhhRe8a5MnT9bIkSN17Ngxf48DAABgnN8L1h//%2BEddf/31%2Bs1vfuNde/3119W2bVtlZWX5exwAAADj/F6w/vWvf%2BmRRx5RVFSUd61169aaNm2adu3a5e9xAAAAjPN7wXK73Tp16lSD9crKSq7oDgAAHMHvBat///6aN2%2BevvrqK%2B9aSUmJsrKylJKS4u9xAAAAjPP7vyKcPn26fvOb32jw4MGKiIiQJJ06dUo9evTQjBkz/D0OAACAcX4vWJGRkdq6daveffddFRUVye1264YbbtDPf/5zuVwuf48DAABgXEB%2BVU5wcLBSUlJ4SRAAADiS39%2BD5fF4NGvWLA0dOlQDBw7Uv//7v/t8XKmamhqNGDFCu3fv9q7NmzdPN954o8/Hhg0bvMfz8vI0cOBAxcXFKT09XSdOnPAesyxLCxcuVHJysnr37q0FCxaovr6%2BaaEBAMCPit/PYD322GPav3%2B/hg8frp/%2B9KdNeq7q6mpNmTJFRUVFPuvFxcWaMmWK7rzzTu9aeHi4pG%2BvGj9r1iw9%2BeST6tatm%2BbPn68ZM2ZoxYoVkqTVq1crLy9POTk5qq2t1dSpUxUZGalJkyY1aVYAAPDj4feCtWvXLq1atUpJSUlNep5Dhw5pypQpF720Q3FxsSZNmuRzra3zNmzYoKFDh2rUqFGSpAULFig1NVUlJSXq0KGD1q1bp8zMTO98Dz/8sJYsWULBAgAAjeb3lwibN2%2BuyMjIJj/P%2B%2B%2B/rz59%2Big3N9dn/cyZMzp%2B/Lg6dux40ccVFhb6lLu2bdsqNjZWhYWFOn78uI4dO6Zbb73VezwxMVFHjhxRWVlZk2cGAAA/Dn4vWCNHjtSqVatUV1fXpOcZN26cZs6cqbCwMJ/14uJiuVwuPf/88%2Brfv7/uuOMObd261Xu8rKxM0dHRPo%2BJjIxUaWmpPB6PJPkcb9OmjSSptLS0SfMCAIAfD7%2B/RFhRUaG8vDy99dZb6tChg0JCQnyOr1u3rknP/9lnn8nlcqlTp04aP3689uzZo8cee0zh4eEaNGiQqqqqGnzOkJAQ1dTUqKqqynv7u8ekb99M31hlZWXesnae2928QbFrquBgv/fjJnG7Gz/v%2BWx2y9hYTs7n5GySs/M5OZvk7HxOzibZM19ALtMwYsSIa/bco0aNUmpqqlq2bClJ6tatm7744gtt2rRJgwYNUmhoaIOyVFNTo7CwMJ8yFRoa6v2zpAZnyi4nNzdXOTk5Pmvp6enKzMy86lxO0KpViyt%2BTERE47/uduTkfE7OJjk7n5OzSc7O5%2BRskr3y%2Bb1gZWVlXdPnd7lc3nJ1XqdOnby/SDomJkbl5eU%2Bx8vLyxUVFaWYmBhJ315Kon379t4/S7roG%2BYvZcyYMRowYIDPmtvdXCdPnr2yMN/DTk1e0hXlDw4OUkREmE6dqlRdnfMuk%2BHkfE7OJjk7n5OzSc7O5%2BRsUtPyXc0P9yYE5AxWWVmZ/ud//keff/65Zs6cqT179qhr167q1KlTk597yZIl%2BuCDD7RmzRrv2sGDB73PHRcXp/z8fKWlpUmSjh07pmPHjikuLk4xMTGKjY1Vfn6%2Bt2Dl5%2BcrNjb2il7ei46ObnB/j%2Be0amud901/Ja4mf11dvaO/bk7O5%2BRskrPzOTmb5Ox8Ts4m2Suf30%2BBfPnll7r99tu1detWvfHGGzp37pxef/11jR49WoWFhU1%2B/tTUVO3Zs0cvvviivvrqK/33f/%2B3tm3bpokTJ0qSxo4dq%2B3bt2vz5s06ePCgpk2bpttuu00dOnTwHl%2B4cKF2796t3bt3a9GiRbr33nubPBcAAPjx8HvBeuqppzRw4EDt2LFDP/nJTyRJTz/9tAYMGKCFCxc2%2BflvueUWLVmyRNu3b9eIESO0fv16LVq0SAkJCZKkhIQEzZkzR0uXLtXYsWN13XXX%2BbxsOWnSJA0bNkwZGRn6wx/%2BoJEjR2rChAlNngsAAPx4%2BP0lwr1792rjxo0%2Bv9jZ7XbrgQce0D333HNVz/nJJ5/43B44cKAGDhx4yfunpaV5XyK8UHBwsGbMmKEZM2Zc1SwAAAB%2BP4NVX19/0d/td/bsWQUHB/t7HAAAAOP8XrD69eunFStW%2BJSsiooKZWdnKzk52d/jAAAAGOf3gvXII49o//796tevn6qrq/Wf//mfSk1N1eHDhzV9%2BnR/jwMAAGCc39%2BDFRMTo23btikvL08ff/yx6uvrNXbsWI0cOVLh4eH%2BHgcAAMC4gFwHKywsTHfffXcgPjUAAMA15/eC9X3XlGrq7yIEAAAINL8XrHbt2vncrq2t1ZdffqlPP/1U9913n7/HAQAAMO4H87sIly5dqtLSUj9PAwAAYN4P5rcFjxw5Un/5y18CPQYAAECT/WAK1gcffMCFRgEAgCP8IN7kfubMGX3yyScaN26cv8cBAAAwzu8FKzY21uf3EErST37yE40fP1533HGHv8cBAAAwzu8F66mnnvL3pwQAAPArvxesPXv2NPq%2Bt9566zWcBAAA4Nrwe8H69a9/7X2J0LIs7/qFay6XSx9//LG/xwMAAGgyvxes559/XvPmzdPUqVPVu3dvhYSE6MMPP9ScOXN05513atiwYf4eCQAAwCi/X6YhKytLs2fP1uDBg9WqVSu1aNFCycnJmjNnjjZt2qR27dp5PwAAAOzI7wWrrKzsouUpPDxcJ0%2Be9Pc4AAAAxvm9YMXHx%2Bvpp5/WmTNnvGsVFRXKzs7Wz3/%2Bc3%2BPAwAAYJzf34P16KOP6t5771X//v3VsWNHWZalL774QlFRUVq3bp2/xwEAADDO7wWrc%2BfOev3115WXl6fi4mJJ0q9%2B9SsNHz5cYWFh/h4HAADAOL8XLEm67rrrdPfdd%2Bvw4cPq0KGDpG%2Bv5g4AAOAEfn8PlmVZWrhwoW699VaNGDFCpaWlmj59umbNmqVvvvnG3%2BMAAAAY5/eCtX79em3fvl2PP/64QkJCJEkDBw7Ujh07lJOT4%2B9xAAAAjPN7wcrNzdXs2bOVlpbmvXr7sGHDNG/ePL322mv%2BHgcAAMA4vxesw4cPq3v37g3Wu3XrJo/H4%2B9xAAAAjPN7wWrXrp0%2B/PDDBuv/%2BMc/vG94BwAAsDO//yvCSZMm6cknn5TH45FlWXrvvfeUm5ur9evX65FHHvH3OAAAAMb5vWCNHj1atbW1Wr58uaqqqjR79my1bt1aDz74oMaOHevvcQAAAIzze8HKy8vTkCFDNGbMGJ04cUKWZSkyMtLfYwAAAFwzfn8P1pw5c7xvZm/dujXlCgAAOI7fC1bHjh316aef%2BvvTAgAA%2BI3fXyLs1q2bHn74Ya1atUodO3ZUaGioz/GsrCx/jwQAAGCU3wvW559/rsTEREniulcAAMCR/FKwFixYoIyMDDVv3lzr16/3x6cEAAAIGL%2B8B2v16tWqrKz0WZs8ebLKysr88ekBAAD8yi8Fy7KsBmt79uxRdXW1Pz49AACAX/n9XxECAAA4HQULAADAML8VLJfL5a9PBQAAEFB%2Bu0zDvHnzfK559c033yg7O1stWrTwuR/XwQIAAHbnl4J16623NrjmVUJCgk6ePKmTJ0/6YwQAAAC/8UvB4tpXAADgx4Q3uQMAABhGwQIAADDM9gWrpqZGI0aM0O7du71rJSUlmjBhguLj4zVs2DDt3LnT5zHvvvuuRowYobi4ON17770qKSnxOb5mzRqlpKQoISFBM2fObHAVegAAgMuxdcGqrq7WQw89pKKiIu%2BaZVlKT09XmzZttGXLFo0cOVIZGRk6evSoJOno0aNKT09XWlqaXnnlFbVu3VoPPPCA92rzb7zxhnJycjRnzhytXbtWhYWFys7ODkg%2BAABgT7YtWIcOHdI999yjr776ymd9165dKikp0Zw5c9S5c2fdf//9io%2BP15YtWyRJmzdv1s0336yJEyeqS5cuysrK0pEjR/T%2B%2B%2B9LktatW6f77rtPqampuuWWW/Tkk09qy5YtnMUCAACNZtuC9f7776tPnz7Kzc31WS8sLNRNN92k5s2be9cSExNVUFDgPZ6UlOQ9FhYWph49eqigoEB1dXX68MMPfY7Hx8frm2%2B%2B0cGDB69xIgAA4BR%2Bu9CoaePGjbvousfjUXR0tM9aZGSkSktLv/f4qVOnVF1d7XPc7XarZcuW3scDAAB8H9sWrEuprKxUSEiIz1pISIhqamq%2B93hVVZX39qUe3xhlZWUNLqzqdjdvUOyaKjjYXicg3e7Gz3s%2Bm90yNpaT8zk5m%2BTsfE7OJjk7n5OzSfbM57iCFRoaqoqKCp%2B1mpoaNWvWzHv8wrJUU1OjiIgI76/yudjxsLCwRs%2BQm5urnJwcn7X09HRlZmY2%2BjmcqFWrFt9/pwtERDT%2B625HTs7n5GySs/M5OZvk7HxOzibZK5/jClZMTIwOHTrks1ZeXu49exQTE6Py8vIGx7t3766WLVsqNDRU5eXl6ty5sySptrZWFRUVioqKavQMY8aM0YABA3zW3O7mOnny7NVEuiQ7NXlJV5Q/ODhIERFhOnWqUnV19ddwqsBwcj4nZ5Ocnc/J2SRn53NyNqlp%2Ba7mh3sTHFew4uLitHLlSlVVVXnPWuXn5ysxMdF7PD8/33v/yspKHThwQBkZGQoKClLPnj2Vn5%2BvPn36SJIKCgrkdrvVrVu3Rs8QHR3d4OVAj%2Be0amud901/Ja4mf11dvaO/bk7O5%2BRskrPzOTmb5Ox8Ts4m2SufvU6BNELv3r3Vtm1bzZgxQ0VFRVq5cqX27dunu%2B66S5I0evRo7d27VytXrlRRUZFmzJih9u3bewvVuHHj9OKLL2rHjh3at2%2BfnnjiCd1zzz1X9BIhAAD4cXNcwQoODtayZcvk8XiUlpamV199VUuXLlVsbKwkqX379nruuee0ZcsW3XXXXaqoqNDSpUvlcrkkScOHD9f999%2Bv2bNna%2BLEibrllls0derUQEYCAAA244iXCD/55BOf29dff702bNhwyfv/4he/0C9%2B8YtLHp88ebImT55sbD4AAPDj4rgzWAAAAIFGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGGOLVhvvvmmbrzxRp%2BPzMxMSdKBAwd09913Ky4uTqNHj9b%2B/ft9HpuXl6eBAwcqLi5O6enpOnHiRCAiAAAAm3JswTp06JBSU1O1c%2BdO78e8efN07tw5TZ48WUlJSfrTn/6khIQE3X///Tp37pwkad%2B%2BfZo1a5YyMjKUm5urU6dOacaMGQFOAwAA7MSxBau4uFhdu3ZVVFSU9yMiIkKvv/66QkNDNW3aNHXu3FmzZs1SixYt9Ne//lWStGHDBg0dOlSjRo1St27dtGDBAr399tsqKSkJcCIAAGAXji5YHTt2bLBeWFioxMREuVwuSZLL5VKvXr1UUFDgPZ6UlOS9f9skI7q6AAAPK0lEQVS2bRUbG6vCwkK/zA0AAOzPkQXLsix9/vnn2rlzpwYPHqyBAwdq4cKFqqmpkcfjUXR0tM/9IyMjVVpaKkkqKyu77HEAAIDv4w70ANfC0aNHVVlZqZCQED3zzDM6fPiw5s2bp6qqKu/6d4WEhKimpkaSVFVVddnjjVFWViaPx%2BOz5nY3b1Dcmio42F792O1u/Lzns9ktY2M5OZ%2BTs0nOzufkbJKz8zk5m2TPfI4sWO3atdPu3bt13XXXyeVyqXv37qqvr9fUqVPVu3fvBmWppqZGzZo1kySFhoZe9HhYWFijP39ubq5ycnJ81tLT073/ivHHqlWrFlf8mIiIxn/d7cjJ%2BZycTXJ2Pidnk5ydz8nZJHvlc2TBkqSWLVv63O7cubOqq6sVFRWl8vJyn2Pl5eXes0sxMTEXPR4VFdXozz1mzBgNGDDAZ83tbq6TJ89eSYTvZacmL%2BmK8gcHBykiIkynTlWqrq7%2BGk4VGE7O5%2BRskrPzOTmb5Ox8Ts4mNS3f1fxwb4IjC9Y777yjhx9%2BWG%2B99Zb3zNPHH3%2Bsli1bKjExUS%2B88IIsy5LL5ZJlWdq7d69%2B97vfSZLi4uKUn5%2BvtLQ0SdKxY8d07NgxxcXFNfrzR0dHN3g50OM5rdpa533TX4mryV9XV%2B/or5uT8zk5m%2BTsfE7OJjk7n5OzSfbKZ69TII2UkJCg0NBQPfroo/rss8/09ttva8GCBfrtb3%2BrIUOG6NSpU5o/f74OHTqk%2BfPnq7KyUkOHDpUkjR07Vtu3b9fmzZt18OBBTZs2Tbfddps6dOgQ4FQAAMAuHFmwwsPD9eKLL%2BrEiRMaPXq0Zs2apTFjxui3v/2twsPDtWLFCu9ZqsLCQq1cuVLNmzeX9G05mzNnjpYuXaqxY8fquuuuU1ZWVoATAQAAO3HkS4SS1KVLF61evfqix2655RZt3br1ko9NS0vzvkQIAABwpRx5BgsAACCQKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYJg70APgx2PoM/8M9AhX5C8P9g30CAAAm%2BIMFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBesiqqurNXPmTCUlJalfv3566aWXAj0SAACwES40ehELFizQ/v37tXbtWh09elTTp09XbGyshgwZEujR4EdcGBUAcLUoWBc4d%2B6cNm/erBdeeEE9evRQjx49VFRUpI0bN1KwAABAo/AS4QUOHjyo2tpaJSQkeNcSExNVWFio%2Bvr6AE4GAADsgjNYF/B4PGrVqpVCQkK8a23atFF1dbUqKirUunXr732OsrIyeTwenzW3u7mio6ONzhocTD/G/2e3lzRx7bz5cMpVPe783ylO/bvFyfmcnE2yZz4K1gUqKyt9ypUk7%2B2amppGPUdubq5ycnJ81jIyMvT73//ezJD/p6ysTPf9rEhjxowxXt4CraysTLm5uY7MJjk7n5OzSc7OV1ZWprVrVzkym%2BTsfE7OJtkzn32qoJ%2BEhoY2KFLnbzdr1qxRzzFmzBj96U9/8vkYM2aM8Vk9Ho9ycnIanC1zAidnk5ydz8nZJGfnc3I2ydn5nJxNsmc%2BzmBdICYmRidPnlRtba3c7m%2B/PB6PR82aNVNERESjniM6Oto2DRsAAJjHGawLdO/eXW63WwUFBd61/Px89ezZU0FBfLkAAMD3ozFcICwsTKNGjdITTzyhffv2aceOHXrppZd07733Bno0AABgE8FPPPHEE4Ee4ocmOTlZBw4c0KJFi/Tee%2B/pd7/7nUaPHh3osS6qRYsW6t27t1q0aBHoUYxzcjbJ2fmcnE1ydj4nZ5Ocnc/J2ST75XNZlmUFeggAAAAn4SVCAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULJuqrq7WzJkzlZSUpH79%2Bumll14K9EjGvPnmm7rxxht9PjIzMwM9VpPV1NRoxIgR2r17t3etpKREEyZMUHx8vIYNG6adO3cGcMKrd7Fs8%2BbNa7CPGzZsCOCUV%2B748ePKzMxU7969lZKSoqysLFVXV0uy/95dLpsT9u7LL7/UpEmTlJCQoNtuu02rVq3yHrP73l0umxP27rzJkyfrkUce8d4%2BcOCA7r77bsXFxWn06NHav39/AKf7fu5AD4Crs2DBAu3fv19r167V0aNHNX36dMXGxmrIkCGBHq3JDh06pNTUVM2dO9e7FhoaGsCJmq66ulpTpkxRUVGRd82yLKWnp6tr167asmWLduzYoYyMDL3%2B%2BuuKjY0N4LRX5mLZJKm4uFhTpkzRnXfe6V0LDw/393hXzbIsZWZmKiIiQhs3btTXX3%2BtmTNnKigoSNOmTbP13l0u2/Tp022/d/X19Zo8ebJ69uyprVu36ssvv9RDDz2kmJgYjRgxwtZ7d7lst99%2Bu%2B337rw///nPevvtt705zp07p8mTJ%2Bv222/XU089pU2bNun%2B%2B%2B/Xm2%2B%2BqebNmwd42kuwYDtnz561evbsae3atcu7tnTpUmv8%2BPEBnMqcKVOmWIsWLQr0GMYUFRVZd9xxh3X77bdbXbt29e7bu%2B%2B%2Ba8XHx1tnz5713ve%2B%2B%2B6znn322UCNesUulc2yLCslJcV65513Ajhd0xw6dMjq2rWr5fF4vGuvvfaa1a9fP9vv3eWyWZb99%2B748ePWH/7wB%2Bv06dPetfT0dOvxxx%2B3/d5dLptl2X/vLMuyTp48afXv398aPXq0NX36dMuyLGvz5s3WgAEDrPr6esuyLKu%2Bvt4aNGiQtWXLlkCOelm8RGhDBw8eVG1trRISErxriYmJKiwsVH19fQAnM6O4uFgdO3YM9BjGvP/%2B%2B%2BrTp49yc3N91gsLC3XTTTf5/PSVmJiogoICf4941S6V7cyZMzp%2B/Lit9zEqKkqrVq1SmzZtfNbPnDlj%2B727XDYn7F10dLSeeeYZhYeHy7Is5efna8%2BePerdu7ft9%2B5y2Zywd5L0X//1Xxo5cqRuuOEG71phYaESExPlcrkkSS6XS7169fpB7xsFy4Y8Ho9atWqlkJAQ71qbNm1UXV2tioqKAE7WdJZl6fPPP9fOnTs1ePBgDRw4UAsXLlRNTU2gR7tq48aN08yZMxUWFuaz7vF4FB0d7bMWGRmp0tJSf47XJJfKVlxcLJfLpeeff179%2B/fXHXfcoa1btwZoyqsTERGhlJQU7%2B36%2Bnpt2LBBycnJtt%2B7y2Vzwt5914ABAzRu3DglJCRo8ODBtt%2B777owmxP27r333tO//vUvPfDAAz7rdtw33oNlQ5WVlT7lSpL3tp2LiCQdPXrUm%2B%2BZZ57R4cOHNW/ePFVVVenRRx8N9HhGXWof7b6HkvTZZ5/J5XKpU6dOGj9%2BvPbs2aPHHntM4eHhGjRoUKDHuyrZ2dk6cOCAXnnlFa1Zs8ZRe/fdbB999JGj9u7ZZ59VeXm5nnjiCWVlZTnqv7sLs/Xo0cPWe1ddXa3HH39cs2fPVrNmzXyO2XHfKFg2FBoa2uCb6vztC78p7aZdu3bavXu3rrvuOrlcLnXv3l319fWaOnWqZsyYoeDg4ECPaExoaGiDM441NTW230NJGjVqlFJTU9WyZUtJUrdu3fTFF19o06ZNtviL/kLZ2dlau3atFi9erK5duzpq7y7M1qVLF0ftXc%2BePSV9%2Bz/vhx9%2BWKNHj1ZlZaXPfey6dxdm27t3r633LicnRzfffLPP2dXzLvX/vR/yvvESoQ3FxMTo5MmTqq2t9a55PB41a9ZMERERAZzMjJYtW3pfZ5ekzp07q7q6Wl9//XUApzIvJiZG5eXlPmvl5eUNToPbkcvl8v4lf16nTp10/PjxAE109ebOnavVq1crOztbgwcPluScvbtYNifsXXl5uXbs2OGzdsMNN%2Bibb75RVFSUrffuctnOnDlj673785//rB07dighIUEJCQl67bXX9NprrykhIcGW/81RsGyoe/fucrvdPm/uy8/PV8%2BePRUUZO8tfeedd9SnTx%2BfnzA//vhjtWzZUq1btw7gZObFxcXpo48%2BUlVVlXctPz9fcXFxAZzKjCVLlmjChAk%2BawcPHlSnTp0CM9BVysnJ0csvv6ynn35aw4cP9647Ye8ulc0Je3f48GFlZGT4FIv9%2B/erdevWSkxMtPXeXS7b%2BvXrbb1369ev12uvvaZt27Zp27ZtGjBggAYMGKBt27YpLi5OH3zwgSzLkvTt%2B3X37t37w963gP4bRly1xx57zBo%2BfLhVWFhovfnmm1avXr2sN954I9BjNdnp06etlJQU66GHHrKKi4utt956y%2BrXr5%2B1cuXKQI9mxHcvZVBbW2sNGzbMevDBB61PP/3UWrFihRUfH28dOXIkwFNene9mKywstG666SZr1apV1pdffmlt3LjRuvnmm629e/cGeMrGO3TokNW9e3dr8eLFVllZmc%2BH3ffuctmcsHe1tbVWWlqaNXHiRKuoqMh66623rH/7t3%2Bz1qxZY/u9u1w2J%2Bzdd02fPt17mYbTp09bycnJ1ty5c62ioiJr7ty5Vt%2B%2BfX0ut/FDQ8GyqXPnzlnTpk2z4uPjrX79%2BlmrV68O9EjGfPrpp9aECROs%2BPh4q2/fvtZzzz3nvfaJ3V14ragvvvjC%2BtWvfmXdfPPN1vDhw61//vOfAZyuaS7M9uabb1q333671bNnT2vIkCG2%2BwFgxYoVVteuXS/6YVn23rvvy2b3vbMsyyotLbXS09OtXr16WX379rWWL1/u/XvEzntnWZfP5oS9O%2B%2B7Bcuyvv3BbdSoUVbPnj2tu%2B66y/roo48CON33c1nW/51vAwAAgBH2fsMOAADADxAFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACG/T%2Bum34ug4IPRQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1426016060875747966”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.22002200220022</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02728200795578555</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.15152180596424963</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.10075097576050054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08313287109098627</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6336501584125396</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0947472688381975</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9248960955335714</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3565166569257744</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2778)</td> <td class=”number”>2778</td> <td class=”number”>86.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>357</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1426016060875747966”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00024394408801502694</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0006058219489292097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0007219955958268654</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0007621325145207414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>27.56183745583039</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.10693641618497</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.143021914648216</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.80246913580247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.311594202898554</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHASIAN10”>PCT_NHASIAN10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3105</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.1365</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>43.015</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6369817944120144907”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6369817944120144907,#minihistogram6369817944120144907”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6369817944120144907”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6369817944120144907”

aria-controls=”quantiles6369817944120144907” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6369817944120144907” aria-controls=”histogram6369817944120144907”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6369817944120144907” aria-controls=”common6369817944120144907”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6369817944120144907” aria-controls=”extreme6369817944120144907”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6369817944120144907”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.11225</td>

</tr> <tr>

<th>Q1</th> <td>0.26951</td>

</tr> <tr>

<th>Median</th> <td>0.46308</td>

</tr> <tr>

<th>Q3</th> <td>0.97933</td>

</tr> <tr>

<th>95-th percentile</th> <td>4.1366</td>

</tr> <tr>

<th>Maximum</th> <td>43.015</td>

</tr> <tr>

<th>Range</th> <td>43.015</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.70982</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.4731</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1761</td>

</tr> <tr>

<th>Kurtosis</th> <td>84.991</td>

</tr> <tr>

<th>Mean</th> <td>1.1365</td>

</tr> <tr>

<th>MAD</th> <td>1.1162</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.7865</td>

</tr> <tr>

<th>Sum</th> <td>3559.5</td>

</tr> <tr>

<th>Variance</th> <td>6.1162</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6369817944120144907”>

<img 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S0nTixAlJ0okTJ5SamqqJEyfq9ddfV%2BfOnfXggw/K4XBIkj744APl5uYqKytLmzdvVnFxsXJycjy5XAAA4GN8MmAdPXpURUVFys7OVu/evZWYmKj09HTt3LlTe/fuVXl5ubKystSrVy/Nnj1bcXFxKigokCRt375dN910k2bMmKHevXsrOztbx48f1759%2ByRJW7Zs0b333qthw4ZpwIABWrRokQoKCjiKBQAAWs0nA1ZERIQ2btyoLl26uIyfOXNGxcXF6tu3r4KDg53jCQkJKioqkiQVFxcrMTHRORcUFKR%2B/fqpqKhITU1N%2BuKLL1zm4%2BLidO7cOR0%2BfPgKrwoAALQVPhmwQkJClJyc7Hze3NysrVu3avDgwbLZbIqMjHR5fXh4uCoqKiTpn87X1taqoaHBZd5qtSo0NNT5fgAAgH/F6ukCTMjJydGhQ4f0%2Buuv6%2BWXX1ZAQIDLfEBAgOx2uySprq7uN%2Bfr6%2Budz3/r/a1RWVkpm83mMma1BrcIdpfL39%2B38rHV6lv1XorzPfG13rR19MX70BPvRF/M8fmAlZOTo82bN2vlypW64YYbFBgYqJqaGpfX2O12dejQQZIUGBjYIizZ7XaFhIQoMDDQ%2BfzC%2BaCgoFbXlJ%2Bfr9zcXJex1NRUpaent3obbVFYWEdPl%2BA2ISGt/7zAfeiL96En3om%2BXD6fDliLFy/Wq6%2B%2BqpycHI0aNUqSFBUVpSNHjri8rqqqynn0KCoqSlVVVS3m%2B/Tpo9DQUAUGBqqqqkq9evWSJDU2NqqmpkYRERGtrislJUXDhw93GbNag1Vd/fNFr/Gf8bWfMEyv3xv5%2B/spJCRItbV1ampq9nQ5%2BDv64n3oiXdqi33x1A/3PhuwcnNz9dprr%2BnZZ5/V6NGjneOxsbHKy8tTfX2986hVYWGhEhISnPOFhYXO19fV1enQoUNKS0uTn5%2Bf%2Bvfvr8LCQg0aNEiSVFRUJKvVqpiYmFbXFhkZ2eJ0oM32kxob28aH9VK1p/U3NTW3q/X6CvrifeiJd6Ivl8%2B3DoH8XVlZmdatW6f7779fCQkJstlszkdSUpK6du2qjIwMlZaWKi8vTyUlJZo8ebIkadKkSTpw4IDy8vJUWlqqjIwMde/e3Rmopk6dqk2bNmnXrl0qKSnRwoULddddd13UKUIAANC%2B%2BeQRrA8//FBNTU1av3691q9f7zL39ddfa926dcrMzNTEiRN13XXXae3atYqOjpYkde/eXc8995yefvpprV27VvHx8Vq7dq0sFoskaezYsTp%2B/LgWLFggu92u3//%2B95o3b57b1wgAAHyXxXH%2BFua4omy2n4xv02r108jlfzG%2B3SvlvYeHeLqEK85q9VNYWEdVV//M4XUvQl%2B8Dz3xTm2xLxERV3tkvz55ihAAAMCbEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmNsD1p133qnXXntNP/1k/rfqAAAAvIHbA9bgwYO1YcMG3XrrrXrkkUe0Z88ecacIAADQlrg9YM2ZM0cfffSR1q1bJ39/fz300EMaOnSoVq5cqW%2B%2B%2Bcbd5QAAABjnkTu5WywWDRkyREOGDFFdXZ1eeeUVrVu3Tnl5eRo4cKDuvfde/f73v/dEaQAAAJfNY1%2BVU1lZqbfffltvv/22/va3v2ngwIG64447VFFRoSeeeEL79%2B9XZmamp8oDAAC4ZG4PWG%2B99ZbeeustffbZZ%2BrcubMmTJigNWvWqEePHs7XdO3aVUuXLiVgAQAAn%2BT2gJWZmalhw4Zp7dq1uu222%2BTn1/IysJ49e%2Bruu%2B92d2kAAABGuD1gffzxxwoLC1NNTY0zXJWUlKhfv37y9/eXJA0cOFADBw50d2kAAABGuP23CM%2BcOaPRo0frhRdecI7NmjVL48eP18mTJ91dDgAAgHFuD1hPP/20rrvuOt13333OsXfffVddu3ZVdna2u8sBAAAwzu0B669//asef/xxRUREOMc6d%2B6sRx99VHv37nV3OQAAAMa5PWBZrVbV1ta2GK%2Brq%2BOO7gAAoE1we8C67bbbtGTJEn3//ffOsfLycmVnZys5Odnd5QAAABjn9t8ifOyxx3Tfffdp1KhRCgkJkSTV1taqX79%2BysjIcHc5AAAAxrk9YIWHh%2BvNN9/UJ598otLSUlmtVl1//fW65ZZbZLFY3F0OAACAcR75qhx/f38lJydzShAAALRJbg9YNptNq1at0oEDB3Tu3LkWF7Z/%2BOGH7i4JAADAKLcHrCeffFIHDx7U2LFjdfXVV7t79wAAAFec2wPW3r17tXHjRiUmJrp71wAAAG7h9ts0BAcHKzw83N27BQAAcBu3B6zx48dr48aNampqcveuAQAA3MLtpwhramq0c%2BdO/fnPf9a1116rgIAAl/ktW7a4uyQAAACjPHKbhnHjxnlitwAAAG7h9oCVnZ3t7l0CAAC4lduvwZKkyspK5ebmas6cOfrhhx/0/vvv6%2BjRo54oBQAAwDi3B6zvvvtO//7v/64333xTH3zwgc6ePat3331XkyZNUnFxsbvLAQAAMM7tAeuZZ57RiBEjtGvXLl111VWSpGeffVbDhw/X8uXL3V0OAACAcW4PWAcOHNB9993n8sXOVqtVDz74oA4dOuTucgAAAIxze8Bqbm5Wc3Nzi/Gff/5Z/v7%2B7i4HAADAOLcHrFtvvVXPP/%2B8S8iqqalRTk6OBg8e7O5yAAAAjHN7wHr88cd18OBB3XrrrWpoaNB//dd/adiwYTp27Jgee%2Bwxd5cDAABgnNvvgxUVFaUdO3Zo586d%2Buqrr9Tc3KwpU6Zo/Pjx6tSpk7vLAQAAMM4jd3IPCgrSnXfe6YldAwAAXHFuD1jTpk37p/N8FyEAAPB1br8Gq1u3bi6PqKgo1dfXq6SkRPHx8Re9PbvdrnHjxumzzz5zji1ZskQ33nijy2Pr1q3O%2BZ07d2rEiBGKjY1VamqqTp8%2B7ZxzOBxavny5Bg8erKSkJC1btuxXf%2BsRAADgt3jNdxGuXbtWFRUVF7WthoYGzZkzR6WlpS7jZWVlmjNnju644w7n2Pnru0pKSpSZmalFixYpJiZGS5cuVUZGhp5//nlJ0ksvvaSdO3cqNzdXjY2NmjdvnsLDwzVz5syLqg0AALRfHvkuwl8zfvx4vffee61%2B/ZEjR3TXXXfp%2B%2B%2B/bzFXVlamvn37KiIiwvkICgqSJG3dulW33367JkyYoJiYGC1btky7d%2B9WeXm5pF9OUaanpysxMVGDBw/W3LlztW3bNjOLBAAA7YLXBKzPP//8om40um/fPg0aNEj5%2Bfku42fOnNGpU6fUo0ePX31fcXGxEhMTnc%2B7du2q6OhoFRcX69SpUzp58qRuvvlm53xCQoKOHz%2BuysrKi1sQAABot7ziIvczZ87o66%2B/1tSpU1u9nd96bVlZmSwWizZs2KCPP/5YoaGhuu%2B%2B%2B5ynCysrKxUZGenynvDwcFVUVMhms0mSy3yXLl0kSRUVFS3eBwAA8GvcHrCio6NdvodQkq666irdfffd%2Bo//%2BI/L3v7Ro0dlsVjUs2dP3X333dq/f7%2BefPJJderUSSNHjlR9fb0CAgJc3hMQECC73a76%2Bnrn83%2Bck365mL61KisrnWHtPKs12HhA8/f3mgOQrWK1%2Bla9l%2BJ8T3ytN20dffE%2B9MQ70Rdz3B6wnnnmmSu6/QkTJmjYsGEKDQ2VJMXExOjbb7/Vq6%2B%2BqpEjRyowMLBFWLLb7QoKCnIJU4GBgc4/S3Jew9Ua%2Bfn5ys3NdRlLTU1Venr6Ja%2BrLQgL6%2BjpEtwmJKT1nxe4D33xPvTEO9GXy%2Bf2gLV///5Wv/Yfr4VqLYvF4gxX5/Xs2VN79%2B6V9Mud5Kuqqlzmq6qqFBERoaioKEmSzWZT9%2B7dnX%2BWpIiIiFbXkJKSouHDh7uMWa3Bqq7%2B%2BeIW8y/42k8Yptfvjfz9/RQSEqTa2jo1NXF7D29BX7wPPfFObbEvnvrh3u0B65577nGeInQ4HM7xC8csFou%2B%2Buqri97%2B6tWr9fnnn%2Bvll192jh0%2BfFg9e/aUJMXGxqqwsFATJ06UJJ08eVInT55UbGysoqKiFB0drcLCQmfAKiwsVHR09EWd3ouMjGzxepvtJzU2to0P66VqT%2BtvampuV%2Bv1FfTF%2B9AT70RfLp/bA9aGDRu0ZMkSzZs3T0lJSQoICNAXX3yhrKws3XHHHRozZsxlbX/YsGHKy8vTpk2bNHLkSO3Zs0c7duxw3iF%2BypQpuueeexQXF6f%2B/ftr6dKlGjp0qK699lrn/PLly/W73/1OkrRixQrNmDHj8hYNAADaFY/caHTBggW67bbbnGODBw9WVlaWHn30Ud1///2Xtf0BAwZo9erVWrNmjVavXq1u3bppxYoVzrvEx8fHKysrS2vWrNGPP/6oIUOGaPHixc73z5w5Uz/88IPS0tLk7%2B%2BvyZMna/r06ZdVEwAAaF/cHrAqKyvVrVu3FuOdOnVSdXX1JW3z66%2B/dnk%2BYsQIjRgx4jdfP3HiROcpwgv5%2B/srIyNDGRkZl1QLAACA26%2BSjouL07PPPqszZ844x2pqapSTk6NbbrnF3eUAAAAY5/YjWE888YSmTZum2267TT169JDD4dC3336riIgI53VSAAAAvsztAatXr1569913tXPnTpWVlUmS/vM//1Njx469qHtNAQAAeCu3ByxJuuaaa3TnnXfq2LFjzt/eu%2BqqqzxRCgAAgHFuvwbL4XBo%2BfLluvnmmzVu3DhVVFToscceU2Zmps6dO%2BfucgAAAIxze8B65ZVX9NZbb%2Bmpp55yfjXNiBEjtGvXrhZfLwMAAOCL3B6w8vPztWDBAk2cONF59/YxY8ZoyZIleuedd9xdDgAAgHFuD1jHjh1Tnz59WozHxMQ4v/cPAADAl7k9YHXr1k1ffPFFi/GPP/7YecE7AACAL3P7bxHOnDlTixYtks1mk8Ph0Keffqr8/Hy98sorevzxx91dDgAAgHFuD1iTJk1SY2Oj1q9fr/r6ei1YsECdO3fWww8/rClTpri7HAAAAOPcHrB27typ0aNHKyUlRadPn5bD4VB4eLi7ywAAALhi3H4NVlZWlvNi9s6dOxOuAABAm%2BP2gNWjRw/97W9/c/duAQAA3MbtpwhjYmI0d%2B5cbdy4UT169FBgYKDLfHZ2trtLAgAAMMrtAeubb75RQkKCJHHfKwAA0Ca5JWAtW7ZMaWlpCg4O1iuvvOKOXQIAAHiMW67Beumll1RXV%2BcyNmvWLFVWVrpj9wAAAG7lloDlcDhajO3fv18NDQ3u2D0AAIBbuf23CAEAANo6AhYAAIBhbgtYFovFXbsCAADwKLfdpmHJkiUu97w6d%2B6ccnJy1LFjR5fXcR8sAADg69wSsG6%2B%2BeYW97yKj49XdXW1qqur3VECAACA27glYHHvKwAA0J5wkTsAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMznA5bdbte4ceP02WefOcfKy8s1ffp0xcXFacyYMdqzZ4/Lez755BONGzdOsbGxmjZtmsrLy13mX375ZSUnJys%2BPl7z589XXV2dW9YCAADaBp8OWA0NDXrkkUdUWlrqHHM4HEpNTVWXLl1UUFCg8ePHKy0tTSdOnJAknThxQqmpqZo4caJef/11de7cWQ8%2B%2BKAcDock6YMPPlBubq6ysrK0efNmFRcXKycnxyPrAwAAvslnA9aRI0d011136fvvv3cZ37t3r8rLy5WVlaVevXpp9uzZiouLU0FBgSRp%2B/btuummmzRjxgz17t1b2dnZOn78uPbt2ydJ2rJli%2B69914NGzZMAwYM0KJFi1RQUMBRLAAA0Go%2BG7D27dunQYMGKT8/32W8uLhYffv2VXBwsHMsISFBRUVFzvnExETnXFBQkPr166eioiI1NTXpiy%2B%2BcJmPi4vTuXPndPjw4Su8IgAA0FZYPV3ApZo6deqvjttsNkVGRrqMhYeHq6Ki4l/O19bWqqGhwWXearUqNDTU%2BX4AAIB/xWcD1m%2Bpq6tTQECAy1hAQIDsdvu/nK%2Bvr3c%2B/633t0ZlZaVsNpvLmNUa3CLYXS5/f986AGm1%2Bla9l%2BJ8T3ytN20dffE%2B9MQ70Rdz2lzACgwMVE1NjcuY3W5Xhw4dnPMXhiW73a6QkBAFBgY6n184HxQU1Ooa8vPzlZub6zKWmpqq9PT0Vm%2BjLQoL6%2BjpEtwmJKT1nxe4D33xPvTEO9GXy9fmAlZUVJSOHDniMlZVVeU8ehQVFaWqqqpu82S4AAAP/klEQVQW83369FFoaKgCAwNVVVWlXr16SZIaGxtVU1OjiIiIVteQkpKi4cOHu4xZrcGqrv75Upb0m3ztJwzT6/dG/v5%2BCgkJUm1tnZqamj1dDv6OvngfeuKd2mJfPPXDfZsLWLGxscrLy1N9fb3zqFVhYaESEhKc84WFhc7X19XV6dChQ0pLS5Ofn5/69%2B%2BvwsJCDRo0SJJUVFQkq9WqmJiYVtcQGRnZ4nSgzfaTGhvbxof1UrWn9Tc1Nber9foK%2BuJ96Il3oi%2BXz7cOgbRCUlKSunbtqoyMDJWWliovL08lJSWaPHmyJGnSpEk6cOCA8vLyVFpaqoyMDHXv3t0ZqKZOnapNmzZp165dKikp0cKFC3XXXXdd1ClCAADQvrW5gOXv769169bJZrNp4sSJevvtt7V27VpFR0dLkrp3767nnntOBQUFmjx5smpqarR27VpZLBZJ0tixYzV79mwtWLBAM2bM0IABAzRv3jxPLgkAAPgYi%2BP8LcxxRdlsPxnfptXqp5HL/2J8u1fKew8P8XQJV5zV6qewsI6qrv6Zw%2BtehL54H3rindpiXyIirvbIftvcESwAAABPI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMPabMD63//9X914440uj/T0dEnSoUOHdOeddyo2NlaTJk3SwYMHXd67c%2BdOjRgxQrGxsUpNTdXp06c9sQQAAOCj2mzAOnLkiIYNG6Y9e/Y4H0uWLNHZs2c1a9YsJSYm6o033lB8fLxmz56ts2fPSpJKSkqUmZmptLQ05efnq7a2VhkZGR5eDQAA8CVtNmCVlZXphhtuUEREhPMREhKid999V4GBgXr00UfVq1cvZWZmqmPHjnr//fclSVu3btXtt9%2BuCRMmKCYmRsuWLdPu3btVXl7u4RUBAABf0aYDVo8ePVqMFxcXKyEhQRaLRZJksVg0cOBAFRUVOecTExOdr%2B/atauio6NVXFzslroBAIDva5MBy%2BFw6JtvvtGePXs0atQojRgxQsuXL5fdbpfNZlNkZKTL68PDw1VRUSFJqqys/KfzAAAA/4rV0wVcCSdOnFBdXZ0CAgK0atUqHTt2TEuWLFF9fb1z/B8FBATIbrdLkurr6//pfGtUVlbKZrO5jFmtwS2C2%2BXy9/etfGy1%2Bla9l%2BJ8T3ytN20dffE%2B9MQ70Rdz2mTA6tatmz777DNdc801slgs6tOnj5qbmzVv3jwlJSW1CEt2u10dOnSQJAUGBv7qfFBQUKv3n5%2Bfr9zcXJex1NRU528xtldhYR09XYLbhIS0/vMC96Ev3oeeeCf6cvnaZMCSpNDQUJfnvXr1UkNDgyIiIlRVVeUyV1VV5Ty6FBUV9avzERERrd53SkqKhg8f7jJmtQaruvrni1nCv%2BRrP2GYXr838vf3U0hIkGpr69TU1OzpcvB39MX70BPv1Bb74qkf7ttkwPrLX/6iuXPn6s9//rPzyNNXX32l0NBQJSQk6IUXXpDD4ZDFYpHD4dCBAwf0wAMPSJJiY2NVWFioiRMnSpJOnjypkydPKjY2ttX7j4yMbHE60Gb7SY2NbePDeqna0/qbmprb1Xp9BX3xPvTEO9GXy%2Bdbh0BaKT4%2BXoGBgXriiSd09OhR7d69W8uWLdMf/vAHjR49WrW1tVq6dKmOHDmipUuXqq6uTrfffrskacqUKXrrrbe0fft2HT58WI8%2B%2BqiGDh2qa6%2B91sOrAgAAvqJNBqxOnTpp06ZNOn36tCZNmqTMzEylpKToD3/4gzp16qTnn3/eeZSquLhYeXl5Cg4OlvRLOMvKytLatWs1ZcoUXXPNNcrOzvbwigAAgC%2BxOBwOh6eLaA9stp%2BMb9Nq9dPI5X8xvt0r5b2Hh3i6hCvOavVTWFhHVVf/zOF1L0JfvA898U5tsS8REVd7ZL9t8ggWAACAJxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyzeroAtB%2B3r/o/T5dwUd57eIinSwAA%2BCiOYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKxf0dDQoPnz5ysxMVG33nqrXnzxRU%2BXBAAAfAi3afgVy5Yt08GDB7V582adOHFCjz32mKKjozV69GhPlwYAAHwAAesCZ8%2Be1fbt2/XCCy%2BoX79%2B6tevn0pLS7Vt2zYCFgAAaBUC1gUOHz6sxsZGxcfHO8cSEhK0YcMGNTc3y8%2BPs6rtha/dGNWXcBNXAG0dAesCNptNYWFhCggIcI516dJFDQ0NqqmpUefOnf/lNiorK2Wz2VzGrNZgRUZGGq3V35%2BwB9/ka%2BH1f%2Bcme7qENuf831/8PeZd6Is5BKwL1NXVuYQrSc7ndru9VdvIz89Xbm6uy1haWpoeeughM0X%2BXWVlpe79XalSUlKMhzdcmsrKSuXn59MTL0NfvE9lZaU2b95IT7wMfTGHiHqBwMDAFkHq/PMOHTq0ahspKSl64403XB4pKSnGa7XZbMrNzW1xtAyeQ0%2B8E33xPvTEO9EXcziCdYGoqChVV1ersbFRVusv/3psNps6dOigkJCQVm0jMjKS5A8AQDvGEawL9OnTR1arVUVFRc6xwsJC9e/fnwvcAQBAq5AYLhAUFKQJEyZo4cKFKikp0a5du/Tiiy9q2rRpni4NAAD4CP%2BFCxcu9HQR3mbw4ME6dOiQVqxYoU8//VQPPPCAJk2a5OmyflXHjh2VlJSkjh07eroU/B098U70xfvQE%2B9EX8ywOBwOh6eLAAAAaEs4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIDloxoaGjR//nwlJibq1ltv1Ysvvujpktotu92ucePG6bPPPnOOlZeXa/r06YqLi9OYMWO0Z88eD1bYfpw6dUrp6elKSkpScnKysrOz1dDQIImeeNJ3332nmTNnKj4%2BXkOHDtXGjRudc/TF82bNmqXHH3/c%2BfzQoUO68847FRsbq0mTJungwYMerM53EbB81LJly3Tw4EFt3rxZTz31lHJzc/X%2B%2B%2B97uqx2p6GhQY888ohKS0udYw6HQ6mpqerSpYsKCgo0fvx4paWl6cSJEx6stO1zOBxKT09XXV2dtm3bppUrV%2Bqjjz7SqlWr6IkHNTc3a9asWQoLC9Obb76pRYsWaf369XrnnXfoixf405/%2BpN27dzufnz17VrNmzVJiYqLeeOMNxcfHa/bs2Tp79qwHq/RNVk8XgIt39uxZbd%2B%2BXS%2B88IL69eunfv36qbS0VNu2bdPo0aM9XV67ceTIEc2ZM0cXftvU3r17VV5ertdee03BwcHq1auXPv30UxUUFOihhx7yULVt39GjR1VUVKT/%2B7//U5cuXSRJ6enp%2BuMf/6jbbruNnnhIVVWV%2BvTpo4ULF6pTp07q0aOHbrnlFhUWFqpLly70xYNqamq0bNky9e/f3zn27rvvKjAwUI8%2B%2BqgsFosyMzP18ccf6/3339fEiRM9WK3v4QiWDzp8%2BLAaGxsVHx/vHEtISFBxcbGam5s9WFn7sm/fPg0aNEj5%2Bfku48XFxerbt6%2BCg4OdYwkJCSoqKnJ3ie1KRESENm7c6AxX5505c4aeeFBkZKRWrVqlTp06yeFwqLCwUPv371dSUhJ98bA//vGPGj9%2BvK6//nrnWHFxsRISEmSxWCRJFotFAwcOpCeXgIDlg2w2m8LCwhQQEOAc69KlixoaGlRTU%2BPBytqXqVOnav78%2BQoKCnIZt9lsioyMdBkLDw9XRUWFO8trd0JCQpScnOx83tzcrK1bt2rw4MH0xEsMHz5cU6dOVXx8vEaNGkVfPOjTTz/VX//6Vz344IMu4/TEHAKWD6qrq3MJV5Kcz%2B12uydKwj/4rf7QG/fKycnRoUOH9N///d/0xEusWbNGGzZs0FdffaXs7Gz64iENDQ166qmntGDBAnXo0MFljp6YwzVYPigwMLDFh/388wv/Y4H7BQYGtjiSaLfb6Y0b5eTkaPPmzVq5cqVuuOEGeuIlzl/r09DQoLlz52rSpEmqq6tzeQ19ufJyc3N10003uRzxPe%2B3/v9CTy4eAcsHRUVFqbq6Wo2NjbJaf2mhzWZThw4dFBIS4uHqEBUVpSNHjriMVVVVtTjsjitj8eLFevXVV5WTk6NRo0ZJoieeVFVVpaKiIo0YMcI5dv311%2BvcuXOKiIjQ0aNHW7yevlxZf/rTn1RVVeW8jvd8oPrggw80btw4VVVVubyenlwaThH6oD59%2BshqtbpcdFhYWKj%2B/fvLz4%2BWelpsbKy%2B/PJL1dfXO8cKCwsVGxvrwarah9zcXL322mt69tlnNXbsWOc4PfGcY8eOKS0tTadOnXKOHTx4UJ07d1ZCQgJ98YBXXnlF77zzjnbs2KEdO3Zo%2BPDhGj58uHbs2KHY2Fh9/vnnzt%2BOdjgcOnDgAD25BPzf2AcFBQVpwoQJWrhwoUpKSrRr1y69%2BOKLmjZtmqdLg6SkpCR17dpVGRkZKi0tVV5enkpKSjR58mRPl9amlZWVad26dbr//vuVkJAgm83mfNATz%2Bnfv7/69eun%2BfPn68iRI9q9e7dycnL0wAMP0BcP6datm6677jrno2PHjurYsaOuu%2B46jR49WrW1tVq6dKmOHDmipUuXqq6uTrfffruny/Y5FseFN/GBT6irq9PChQv1P//zP%2BrUqZNmzpyp6dOne7qsduvGG2/Uli1bNGjQIEm/3Lk6MzNTxcXFuu666zR//nz927/9m4erbNvy8vK0YsWKX537%2Buuv6YkHnTp1SosXL9ann36qoKAg3X333Zo9e7YsFgt98QLn7%2BL%2BzDPPSJJKSkr01FNPqaysTDfeeKMWLVqkvn37erJEn0TAAgAAMIxThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8HJGrlg2B8%2BrUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6369817944120144907”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1404494382022472</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4316546762589928</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19880715705765406</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1342281879194631</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21645021645021645</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5454545454545455</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.060441220912662436</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.15552099533437014</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.122888771420214</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3094)</td> <td class=”number”>3094</td> <td class=”number”>96.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6369817944120144907”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.010123506782749543</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.023629489603024575</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025691530358825036</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025886616619207874</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>30.251449523781133</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.738474957370784</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.99657863853409</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.43457497612225</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.014686211914096</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHBLACK10”>PCT_NHBLACK10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3094</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.7707</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>85.439</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram2170061607693888105”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives2170061607693888105”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles2170061607693888105”

aria-controls=”quantiles2170061607693888105” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram2170061607693888105” aria-controls=”histogram2170061607693888105”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common2170061607693888105” aria-controls=”common2170061607693888105”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme2170061607693888105” aria-controls=”extreme2170061607693888105”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles2170061607693888105”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.12959</td>

</tr> <tr>

<th>Q1</th> <td>0.40259</td>

</tr> <tr>

<th>Median</th> <td>1.9089</td>

</tr> <tr>

<th>Q3</th> <td>9.7899</td>

</tr> <tr>

<th>95-th percentile</th> <td>41.825</td>

</tr> <tr>

<th>Maximum</th> <td>85.439</td>

</tr> <tr>

<th>Range</th> <td>85.439</td>

</tr> <tr>

<th>Interquartile range</th> <td>9.3873</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>14.438</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6462</td>

</tr> <tr>

<th>Kurtosis</th> <td>5.207</td>

</tr> <tr>

<th>Mean</th> <td>8.7707</td>

</tr> <tr>

<th>MAD</th> <td>10.204</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2916</td>

</tr> <tr>

<th>Sum</th> <td>27470</td>

</tr> <tr>

<th>Variance</th> <td>208.46</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram2170061607693888105”>

<img 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Tp0/Xj370I0%2BUBgAA0Ggeu01DSUmJXn/9db3%2B%2Buv67LPP1K9fPz388MMqLi7Ws88%2Bq48%2B%2BkipqameKg8AAOCWWR6wXnvtNb322ms6duyYWrdurXHjxunFF19Uly5dXK9p166dVqxYQcACAAA%2ByfKAlZqaqmHDhikzM1NDhw6Vv3/9y8C6du2qRx991OrSAAAAjLA8YL3//vtq1aqVysvLXeEqPz9fvXv3VkBAgCSpX79%2B6tevn9WlAQAAGGH5pwgvX76sESNG6Ne//rVrbM6cORo7dqwuXrxodTkAAADGWR6wfvGLX6hz586aOXOma%2BzNN99Uu3btlJaWZnU5AAAAxlkesP785z/rmWeeUUREhGusdevWevrpp3X06FGrywEAADDO8oBls9lUUVFRb9zhcHBHdwAA0CRYHrCGDh2q5cuX669//atrrKioSGlpaRoyZIjV5QAAABhn%2BacIFyxYoJkzZ%2Bqhhx5SeHi4JKmiokK9e/dWSkqK1eUAAAAYZ3nAatOmjX7729/q8OHDKigokM1m09133637779ffn5%2BVpcDAABgnEe%2BKicgIEBDhgzhlCAAAGiSLA9Ydrtd69at0/Hjx3Xt2rV6F7b/4Q9/sLokAAAAoywPWD//%2Bc914sQJjR49WnfeeafVuwcAALjtLA9YR48e1aZNmxQXF2f1rgEAACxh%2BW0aQkND1aZNG6t3CwAAYBnLA9bYsWO1adMm1dbWWr1rAAAAS1h%2BirC8vFz79%2B/XH//4R3Xq1EmBgYFu89u2bbO6JAAAAKM8cpuGMWPGeGK3AAAAlrA8YKWlpVm9SwAAAEtZfg2WJJWUlCgjI0Pz5s3T3/72N7399ts6e/asJ0oBAAAwzvKAdf78ef3bv/2bfvvb3%2Bqdd97R1atX9eabb2rChAnKy8uzuhwAAADjLA9Yzz//vB588EEdOHBAd9xxhyRpzZo1SkhI0KpVq6wuBwAAwDjLA9bx48c1c%2BZMty92ttlsevzxx3Xy5EmrywEAADDO8oBVV1enurq6euNXrlxRQECA1eUAAAAYZ3nAGjx4sDZu3OgWssrLy5Wenq6BAwdaXQ4AAIBxlgesZ555RidOnNDgwYNVVVWl//zP/9SwYcP0%2Beefa8GCBVaXAwAAYJzl98GKiorSvn37tH//fn366aeqq6vTlClTNHbsWLVo0cLqcgAAAIzzyJ3cQ0JCNGnSJE/sGgAA4LazPGBNmzbtn87zXYQAAMDXWR6wOnTo4Pa8pqZG58%2Bf12effabp06dbXQ4AAIBxXvNdhJmZmSouLra4GgAAAPM88l2ENzN27Fi99dZbni4DAACg0bwmYH388cfcaBQAADQJXnGR%2B%2BXLl3X69GlNnTrV6nIAAACMszxgtW/f3u17CCXpjjvu0KOPPqp///d/t7ocAAAA4ywPWM8//7zVuwQAALCU5QHro48%2BavBr%2B/fvfxsrAQAAuD0sD1iPPfaY6xSh0%2Bl0jd845ufnp08//dTq8gAAABrN8oC1YcMGLV%2B%2BXPPnz1d8fLwCAwP1ySefaOnSpXr44Yc1atQoq0sCAAAwyvLbNKSlpWnRokV66KGH1KpVK4WFhWngwIFaunSpduzYoQ4dOrgeAAAAvsjygFVSUnLT8NSiRQuVlZVZXQ4AAIBxlgesvn37as2aNbp8%2BbJrrLy8XOnp6br//vutLgcAAMA4y6/BevbZZzVt2jQNHTpUXbp0kdPp1F/%2B8hdFRERo27ZtVpcDAABgnOUBq1u3bnrzzTe1f/9%2BnTlzRpL04x//WKNHj1ZISIjV5QAAABhnecCSpLvuukuTJk3S559/rk6dOkn6%2Bm7uAAAATYHl12A5nU6tWrVK/fv315gxY1RcXKwFCxYoNTVV165ds7ocAAAA4ywPWK%2B88opee%2B01PffccwoMDJQkPfjggzpw4IAyMjKsLgcAAMA4ywNWdna2Fi1apPHjx7vu3j5q1CgtX75cb7zxhtXlAAAAGGd5wPr888/Vs2fPeuM9evSQ3W63uhwAAADjLA9YHTp00CeffFJv/P3333dd8P5dVFdXa8yYMTp27JhrrKioSDNmzFDfvn01atQoHTp0yO09hw8f1pgxYxQdHa1p06apqKjIbX7Lli0aMmSIYmJitHDhQjkcju9cFwAAaL4sD1izZ8/WkiVLtG3bNjmdTh05ckSrVq3SypUr9dhjj32nbVVVVelnP/uZCgoKXGNOp1OJiYlq27at9uzZo7FjxyopKUkXLlyQJF24cEGJiYkaP368du/erdatW%2Bvxxx93fcn0O%2B%2B8o4yMDC1dulRbt25VXl6e0tPTzf0FAACAJs/y2zRMmDBBNTU1eumll1RZWalFixapdevWevLJJzVlypQGb6ewsFDz5s1zBaPrjh49qqKiIu3cuVOhoaHq1q2bjhw5oj179uiJJ57Qrl27dO%2B992rWrFmSvv5uxEGDBunDDz/UgAEDtG3bNk2fPl3Dhg2TJC1ZskSzZ8/W/PnzuU8XAABoEMtXsPbv368RI0boj3/8ow4fPqw//elPOnz4sGbOnPmdtnM9EGVnZ7uN5%2BXlqVevXgoNDXWNxcbGKjc31zUfFxfnmgsJCVHv3r2Vm5ur2tpaffLJJ27zffv21bVr13Tq1KlbOVwAANAMWb6CtXTpUv3Xf/2X7rrrLrVu3fqWtzN16tSbjtvtdkVGRrqNtWnTRsXFxd86X1FRoaqqKrd5m82mli1but7fECUlJfUu2LfZQuvtt7ECAizPx41is/lWvbfqel98rT9NHX3xTvTFO9GXxrM8YHXp0kWfffaZ7r777tuyfYfD4bq/1nWBgYGqrq7%2B1vnKykrX8296f0NkZ2fXu6dXYmKikpOTG7yNpqhVqzBPl2Cp8HBOKXsj%2BuKd6It3oi%2B3zvKA1aNHDz311FPatGmTunTpoqCgILf5tLS0Rm0/KChI5eXlbmPV1dUKDg52zd8YlqqrqxUeHu6q5Wbz3%2BX6q8mTJyshIcFtzGYLVVnZlQZvoyF87TcL08fvrQIC/BUeHqKKCodqa%2Bs8XQ7%2Bjr54J/rinZpSXzz1y73lAevcuXOKjY2VpNty36uoqCgVFha6jZWWlrpOz0VFRam0tLTefM%2BePdWyZUsFBQWptLRU3bp1kyTV1NSovLxcERERDa4hMjKy3ulAu/0r1dT49g9pYzW346%2BtrWt2x%2BwL6It3oi/eib7cOksC1sqVK5WUlKTQ0FC98sort3Vf0dHRysrKUmVlpWvVKicnxxXqoqOjlZOT43q9w%2BHQyZMnlZSUJH9/f/Xp00c5OTkaMGCAJCk3N1c2m009evS4rXUDAICmw5JzTL/5zW/q3axzzpw5KikpMb6v%2BPh4tWvXTikpKSooKFBWVpby8/M1ceJESV/fJuL48ePKyspSQUGBUlJS1LFjR1egmjp1qjZv3qwDBw4oPz9fixcv1iOPPMItGgAAQINZErBuvFeVJH300Ueqqqoyvq%2BAgACtX79edrtd48eP1%2Buvv67MzEy1b99ektSxY0f96le/0p49ezRx4kSVl5crMzPT9b2Io0eP1ty5c7Vo0SLNmjVL9913n%2BbPn2%2B8TgAA0HRZfg3W7XD69Gm35507d9b27du/8fU//OEP9cMf/vAb5%2BfMmaM5c%2BYYqw8AADQvvvUxNAAAAB9gWcC6fgoOAACgqbPsFOHy5cvd7nl17do1paenKyzM/f4Ujb0PFgAAgKdZErD69%2B9f755XMTExKisrU1lZmRUlAAAAWMaSgHW7730FAADgTbjIHQAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGFNNmC9%2B%2B67uueee9weycnJkqSTJ09q0qRJio6O1oQJE3TixAm39%2B7fv18PPvigoqOjlZiYqC%2B//NIThwAAAHxUkw1YhYWFGjZsmA4dOuR6LF%2B%2BXFevXtWcOXMUFxenvXv3KiYmRnPnztXVq1clSfn5%2BUpNTVVSUpKys7NVUVGhlJQUDx8NAADwJU02YJ05c0bdu3dXRESE6xEeHq4333xTQUFBevrpp9WtWzelpqYqLCxMb7/9tiRp%2B/btGjlypMaNG6cePXpo5cqVOnjwoIqKijx8RAAAwFc06YDVpUuXeuN5eXmKjY2Vn5%2BfJMnPz0/9%2BvVTbm6uaz4uLs71%2Bnbt2ql9%2B/bKy8uzpG4AAOD7bJ4u4HZwOp06d%2B6cDh06pI0bN6q2tlYjRoxQcnKy7Ha77r77brfXt2nTRgUFBZKkkpISRUZG1psvLi5u8P5LSkpkt9vdxmy20HrbbayAAN/Kxzabb9V7q673xdf609TRF%2B9EX7wTfWm8JhmwLly4IIfDocDAQK1bt06ff/65li9frsrKStf4PwoMDFR1dbUkqbKy8p/ON0R2drYyMjLcxhITE10X2TdXrVqFeboES4WHh3i6BNwEffFO9MU70Zdb1yQDVocOHXTs2DHddddd8vPzU8%2BePVVXV6f58%2BcrPj6%2BXliqrq5WcHCwJCkoKOim8yEhDf8hmzx5shISEtzGbLZQlZVducUjujlf%2B83C9PF7q4AAf4WHh6iiwqHa2jpPl4O/oy/eib54p6bUF0/9ct8kA5YktWzZ0u15t27dVFVVpYiICJWWlrrNlZaWuk7fRUVF3XQ%2BIiKiwfuOjIysdzrQbv9KNTW%2B/UPaWM3t%2BGtr65rdMfsC%2BuKd6It3oi%2B3zreWQBrogw8%2B0IABA%2BRwOFxjn376qVq2bKnY2Fh9/PHHcjqdkr6%2BXuv48eOKjo6WJEVHRysnJ8f1vosXL%2BrixYuueQAAgG/TJANWTEyMgoKC9Oyzz%2Brs2bM6ePCgVq5cqZ/85CcaMWKEKioqtGLFChUWFmrFihVyOBwaOXKkJGnKlCl67bXXtGvXLp06dUpPP/20HnjgAXXq1MnDRwUAAHxFkwxYLVq00ObNm/Xll19qwoQJSk1N1eTJk/WTn/xELVq00MaNG5WTk6Px48crLy9PWVlZCg0NlfR1OFu6dKkyMzM1ZcoU3XXXXUpLS/PwEQEAAF/i57x%2Brgy3ld3%2BlfFt2mz%2BGr7qA%2BPbvV3eenKQp0uwhM3mr1atwlRWdoVrF7wIffFO9MU7NaW%2BRETc6ZH9NskVLAAAAE8iYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGE2TxeA5mPkuj95uoTv5K0nB3m6BACAj2IFCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMs3m6AMBbjVz3J0%2BX8J289eQgT5cAAPg7VrAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMD5FCDQRvvSpRz7xCKCpYwULAADAMAIWAACAYQQsAAAAw7gGC4DlfOl6MYlrxgB8d6xgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGF8ihAAvgWfegTwXbGCBQAAYBgBCwAAwDBOEd5EVVWVlixZot///vcKDg7WrFmzNGvWLE%2BXBQAN4kunNN99aoinSwBuCwLWTaxcuVInTpzQ1q1bdeHCBS1YsEDt27fXiBEjPF0aAADwAQSsG1y9elW7du3Sr3/9a/Xu3Vu9e/dWQUGBXn31VQIWABg2fNUHni6hSeMDD57DNVg3OHXqlGpqahQTE%2BMai42NVV5enurq6jxYGQAA8BWsYN3AbrerVatWCgwMdI21bdtWVVVVKi8vV%2BvWrb91GyUlJbLb7W5jNluoIiMjjdYaEEA%2BBgB8M67H8xwC1g0cDodbuJLkel5dXd2gbWRnZysjI8NtLCkpSU888YSZIv/SIqJyAAAINElEQVSupKRE079XoMmTJxsPb7h1JSUlys7Opi9ehr54J/rinehL47EEcoOgoKB6Qer68%2BDg4AZtY/Lkydq7d6/bY/LkycZrtdvtysjIqLdaBs%2BiL96Jvngn%2BuKd6EvjsYJ1g6ioKJWVlammpkY229d/PXa7XcHBwQoPD2/QNiIjI0n8AAA0Y6xg3aBnz56y2WzKzc11jeXk5KhPnz7y9%2BevCwAAfDsSww1CQkI0btw4LV68WPn5%2BTpw4IBefvllTZs2zdOlAQAAHxGwePHixZ4uwtsMHDhQJ0%2Be1OrVq3XkyBH9x3/8hyZMmODpsm4qLCxM8fHxCgsL83Qp%2BAf0xTvRF%2B9EX7wTfWkcP6fT6fR0EQAAAE0JpwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwfFRVVZUWLlyouLg4DR48WC%2B//LKnS2qWLl26pOTkZMXHx2vIkCFKS0tTVVWVJKmoqEgzZsxQ3759NWrUKB06dMjD1TZPc%2BbM0TPPPON6fvLkSU2aNEnR0dGaMGGCTpw44cHqmpfq6motWbJE/fv31w9%2B8AOtWbNG1%2B91TV885%2BLFi5o7d6769eunhIQEbdmyxTVHX24dActHrVy5UidOnNDWrVv13HPPKSMjQ2%2B//bany2pWnE6nkpOT5XA49Oqrr2rt2rV67733tG7dOjmdTiUmJqpt27bas2ePxo4dq6SkJF24cMHTZTcrv/vd73Tw4EHX86tXr2rOnDmKi4vT3r17FRMTo7lz5%2Brq1aserLL5WL58uQ4fPqzNmzdr9erV%2Bu///m9lZ2fTFw978sknFRoaqr1792rhwoVat26d3n33XfrSWE74nCtXrjj79OnjPHr0qGssMzPT%2Beijj3qwquansLDQ2b17d6fdbneNvfHGG87Bgwc7Dx8%2B7Ozbt6/zypUrrrnp06c7X3zxRU%2BU2iyVlZU5hw4d6pwwYYJzwYIFTqfT6dy1a5czISHBWVdX53Q6nc66ujrn8OHDnXv27PFkqc1CWVmZs1evXs5jx465xjZu3Oh85pln6IsHlZeXO7t37%2B48ffq0aywpKcm5ZMkS%2BtJIrGD5oFOnTqmmpkYxMTGusdjYWOXl5amurs6DlTUvERER2rRpk9q2bes2fvnyZeXl5alXr14KDQ11jcfGxio3N9fqMputX/7ylxo7dqzuvvtu11heXp5iY2Pl5%2BcnSfLz81O/fv3oiwVycnLUokULxcfHu8bmzJmjtLQ0%2BuJBwcHBCgkJ0d69e3Xt2jWdPXtWx48fV8%2BePelLIxGwfJDdblerVq0UGBjoGmvbtq2qqqpUXl7uwcqal/DwcA0ZMsT1vK6uTtu3b9fAgQNlt9sVGRnp9vo2bdqouLjY6jKbpSNHjujPf/6zHn/8cbdx%2BuI5RUVF6tChg/bt26cRI0boX//1X5WZmam6ujr64kFBQUFatGiRsrOzFR0drZEjR2ro0KGaNGkSfWkkm6cLwHfncDjcwpUk1/Pq6mpPlARJ6enpOnnypHbv3q0tW7bctEf05/arqqrSc889p0WLFik4ONht7pv%2B7dCX2%2B/q1as6f/68du7cqbS0NNntdi1atEghISH0xcPOnDmjYcOGaebMmSooKNCyZct0//3305dGImD5oKCgoHo/4Nef3/gfCqyRnp6urVu3au3aterevbuCgoLqrSZWV1fTHwtkZGTo3nvvdVtdvO6b/u3Ql9vPZrPp8uXLWr16tTp06CBJunDhgnbs2KHOnTvTFw85cuSIdu/erYMHDyo4OFh9%2BvTRpUuX9NJLL6lTp070pREIWD4oKipKZWVlqqmpkc32dQvtdruCg4MVHh7u4eqan2XLlmnHjh1KT0/XQw89JOnrHhUWFrq9rrS0tN5yO8z73e9%2Bp9LSUtc1itf/g3jnnXc0ZswYlZaWur2evlgjIiJCQUFBrnAlSf/yL/%2BiixcvKj4%2Bnr54yIkTJ9S5c2e30NSrVy9t2LBBcXFx9KURuAbLB/Xs2VM2m83tQsOcnBz16dNH/v601EoZGRnauXOn1qxZo9GjR7vGo6Oj9b//%2B7%2BqrKx0jeXk5Cg6OtoTZTYrr7zyit544w3t27dP%2B/btU0JCghISErRv3z5FR0fr448/dt17yel06vjx4/TFAtHR0aqqqtK5c%2BdcY2fPnlWHDh3oiwdFRkbq/PnzbitVZ8%2BeVceOHelLI/G/sQ8KCQnRuHHjtHjxYuXn5%2BvAgQN6%2BeWXNW3aNE%2BX1qycOXNG69ev109/%2BlPFxsbKbre7HvHx8WrXrp1SUlJUUFCgrKws5efna%2BLEiZ4uu8nr0KGDOnfu7HqEhYUpLCxMnTt31ogRI1RRUaEVK1aosLBQK1askMPh0MiRIz1ddpPXtWtXPfDAA0pJSdGpU6f0wQcfKCsrS1OmTKEvHpSQkKA77rhDzz77rM6dO6f/%2BZ//0YYNG/TYY4/Rl0byc16PpvApDodDixcv1u9//3u1aNFCs2fP1owZMzxdVrOSlZWl1atX33Tu9OnTOn/%2BvFJTU5WXl6fOnTtr4cKF%2BsEPfmBxlbh%2BF/fnn39ekpSfn6/nnntOZ86c0T333KMlS5aoV69eniyx2fjqq6%2B0bNkyvfvuuwoJCdHUqVOVmJgoPz8/%2BuJB18NTfn6%2BWrdurR//%2BMeaPn06fWkkAhYAAIBhnCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BHyZqe4dzM/L2AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common2170061607693888105”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14534883720930233</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09633911368015415</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.17905102954341987</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07042253521126761</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.08743806470416789</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.20920502092050208</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.09359530881121</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.26232741617357</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3512805773775308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3083)</td> <td class=”number”>3083</td> <td class=”number”>96.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme2170061607693888105”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.015015015015015015</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.01720282126268708</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.022568269013766643</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.023512814483893724</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>81.24930901050304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.18814096587731</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.95134909886447</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.04831320283215</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.43877815169557</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHNA10”>PCT_NHNA10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3117</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.7811</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>86.319</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3914050097871472350”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3914050097871472350,#minihistogram-3914050097871472350”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3914050097871472350”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3914050097871472350”

aria-controls=”quantiles-3914050097871472350” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3914050097871472350” aria-controls=”histogram-3914050097871472350”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3914050097871472350” aria-controls=”common-3914050097871472350”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3914050097871472350” aria-controls=”extreme-3914050097871472350”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3914050097871472350”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.1092</td>

</tr> <tr>

<th>Q1</th> <td>0.19496</td>

</tr> <tr>

<th>Median</th> <td>0.30497</td>

</tr> <tr>

<th>Q3</th> <td>0.62471</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.8275</td>

</tr> <tr>

<th>Maximum</th> <td>86.319</td>

</tr> <tr>

<th>Range</th> <td>86.319</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42975</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>7.1799</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.0313</td>

</tr> <tr>

<th>Kurtosis</th> <td>72.445</td>

</tr> <tr>

<th>Mean</th> <td>1.7811</td>

</tr> <tr>

<th>MAD</th> <td>2.4846</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>7.9989</td>

</tr> <tr>

<th>Sum</th> <td>5578.3</td>

</tr> <tr>

<th>Variance</th> <td>51.551</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3914050097871472350”>

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957T6tWrZLdbldSUpI6dOignJwcjR07VsnJyTpz5owk6cyZM0pKStL48eO1e/dutWvXTg8%2B%2BKDsdrsk6e2331ZmZqbS0tK0ZcsW5efnKyMjw53LBQAAXsYrA9bJkyeVl5en9PR09ejRQzExMUpJSdHevXt16NAhFRcXKy0tTd27d9fs2bMVGRmpnJwcSdKuXbt02223acaMGerRo4fS09N1%2BvRpffzxx5KkrVu3aurUqYqLi1P//v21aNEi5eTkcBYLAAA0mVcGrNDQUG3cuFEdOnRwGr9w4YLy8/PVp08fBQUFOcajo6OVl5cnScrPz1dMTIxjLjAwUH379lVeXp7q6%2Bv16aefOs1HRkbq8uXLOn78%2BHVeFQAAaC68MmAFBwdr6NChjtcNDQ3avn27Bg0aJJvNprCwMKf3t2/fXiUlJZL0D%2BcrKytVU1PjNG%2BxWNS2bVvH9gAAAP%2BMxd0FmJCRkaFjx45p9%2B7d2rx5s/z9/Z3m/f39VVtbK0mqqqr6yfnq6mrH65/avilKS0tls9mcxiyWoEbB7pfy8/OufGyxeFe9P8eVnnhbb5ozeuJ56InnoSfmeX3AysjI0JYtW7Ry5Ur17NlTAQEBqqiocHpPbW2tWrVqJUkKCAhoFJZqa2sVHBysgIAAx%2Bur5wMDA5tcU3Z2tjIzM53GkpKSlJKS0uR9NEchIa3dXYLLBAc3/fMC16AnnoeeeB56Yo5XB6zFixdr586dysjI0N133y1JCg8P14kTJ5zeV1ZW5jh7FB4errKyskbzvXv3Vtu2bRUQEKCysjJ1795dklRXV6eKigqFhoY2ua7ExETFx8c7jVksQSovv3jNa/xHvO07DdPr90R%2Bfr4KDg5UZWWV6usb3F0ORE88ET3xPM25J%2B765t5rA1ZmZqZeeuklPfvssxoxYoRj3Gq1KisrS9XV1Y6zVrm5uYqOjnbM5%2BbmOt5fVVWlY8eOKTk5Wb6%2BvurXr59yc3M1cOBASVJeXp4sFot69erV5NrCwsIaXQ602c6rrq55fWivVUtaf319Q4tarzegJ56HnngeemKOd50C%2BZuioiKtW7dODzzwgKKjo2Wz2RxfsbGx6tixo1JTU1VYWKisrCwVFBRowoQJkqSEhAQdOXJEWVlZKiwsVGpqqjp37uwIVPfdd582bdqkffv2qaCgQAsXLtS99957TZcIAQBAy%2BaVZ7Deffdd1dfXa/369Vq/fr3T3BdffKF169Zp/vz5Gj9%2BvLp06aK1a9cqIiJCktS5c2c999xzevrpp7V27VpFRUVp7dq18vHxkSSNHj1ap0%2Bf1oIFC1RbW6vf/OY3mjNnjsvXCAAAvJeP/cojzHFd2Wznje/TYvHV8OUfGN/v9fLmQ4PdXcJ1Z7H4KiSktcrLL3Ka3UPQE89DTzxPc%2B5JaOiNbjmuV14iBAAA8GQELAAAAMMIWAAAAIYRsAAAAAxzecCaOHGiXnrpJZ0/b/6mbwAAAE/g8oA1aNAgbdiwQUOGDNHDDz%2BsAwcOiB9kBAAAzYnLA9Yjjzyi9957T%2BvWrZOfn5/%2B8Ic/aNiwYVq5cqW%2B%2BuorV5cDAABgnFseNOrj46PBgwdr8ODBqqqq0rZt27Ru3TplZWVpwIABmjp1qn7zm9%2B4ozQAAIBfzG1Pci8tLdVrr72m1157TV9%2B%2BaUGDBige%2B65RyUlJXriiSd0%2BPBhzZ8/313lAQAA/GwuD1ivvvqqXn31VX300Udq166dxo0bpzVr1qhr166O93Ts2FFLly4lYAEAAK/k8oA1f/58xcXFae3atbrzzjvl69v4NrBu3bpp8uTJri4NAADACJcHrPfff18hISGqqKhwhKuCggL17dtXfn5%2BkqQBAwZowIABri4NAADACJf/FOGFCxc0YsQI/elPf3KMzZo1S2PHjtXZs2ddXQ4AAIBxLg9YTz/9tLp06aLp06c7xt544w117NhR6enpri4HAADAOJcHrL/85S96/PHHFRoa6hhr166dHnvsMR06dMjV5QAAABjn8oBlsVhUWVnZaLyqqoonugMAgGbB5QHrzjvv1JIlS/TNN984xoqLi5Wenq6hQ4e6uhwAAADjXP5ThHPnztX06dN19913Kzg4WJJUWVmpvn37KjU11dXlAAAAGOfygNW%2BfXu98sorOnjwoAoLC2WxWHTLLbfojjvukI%2BPj6vLAQAAMM4tvyrHz89PQ4cO5ZIgAABollwesGw2m1atWqUjR47o8uXLjW5sf/fdd11dEgAAgFEuD1hPPvmkjh49qtGjR%2BvGG2909eEBAACuO5cHrEOHDmnjxo2KiYlx9aEBAABcwuWPaQgKClL79u1dfVgAAACXcXnAGjt2rDZu3Kj6%2BnpXHxoAAMAlXH6JsKKiQnv37tX//d//6eabb5a/v7/T/NatW11dEgAAgFFueUzDmDFj3HFYAAAAl3B5wEpPT3f1IQEAAFzK5fdgSVJpaakyMzP1yCOP6LvvvtNbb72lkydPuqMUAAAA41wesE6dOqV///d/1yuvvKK3335bly5d0htvvKGEhATl5%2Be7uhwAAADjXB6wnnnmGd11113at2%2BfbrjhBknSs88%2Bq/j4eC1fvtzV5QAAABjn8oB15MgRTZ8%2B3ekXO1ssFj344IM6duyYq8sBAAAwzuUBq6GhQQ0NDY3GL168KD8/P1eXAwAAYJzLA9aQIUP0/PPPO4WsiooKZWRkaNCgQa4uBwAAwDiXB6zHH39cR48e1ZAhQ1RTU6P//M//VFxcnL799lvNnTvX1eUAAAAY5/LnYIWHh2vPnj3au3evPv/8czU0NGjSpEkaO3as2rRp4%2BpyAAAAjHPLk9wDAwM1ceJEdxwaAADgunN5wJoyZco/nOd3EQIAAG/n8oDVqVMnp9d1dXU6deqUvvzyS02dOtXV5QAAABjnMb%2BLcO3atSopKbnm/dXW1mr8%2BPF68sknNXDgQEnSkiVLtG3bNqf3Pfnkk5o8ebIkae/evVq1apVsNpuGDBmixYsXq127dpIku92uFStWaPfu3WpoaNCECRP06KOPytfXLb9VCAAAeCGPSQ1jx47Vm2%2B%2BeU3b1NTU6OGHH1ZhYaHTeFFRkR555BEdOHDA8ZWQkCBJKigo0Pz585WcnKzs7GxVVlYqNTXVse2LL76ovXv3KjMzU2vWrNHrr7%2BuF1988ZcvEAAAtBgeE7A%2B%2BeSTa3rQ6IkTJ3Tvvffqm2%2B%2BaTRXVFSkPn36KDQ01PEVGBgoSdq%2BfbtGjhypcePGqVevXlq2bJn279%2Bv4uJiST/cA5aSkqKYmBgNGjRIjz76qHbs2GFmkQAAoEXwiJvcL1y4oC%2B%2B%2BEL33Xdfk/fz8ccfa%2BDAgfqv//ovRUZGOu3r3Llz6tq1649ul5%2BfrwceeMDxumPHjoqIiFB%2Bfr78/f119uxZ3X777Y756OhonT59WqWlpQoLC2tyfQAAoOVyecCKiIhw%2Bj2EknTDDTdo8uTJ%2Bo//%2BI8m7%2BenwlhRUZF8fHy0YcMGvf/%2B%2B2rbtq2mT5%2Bue%2B65R5J%2BNCi1b99eJSUlstlskuQ036FDB0lSSUkJAQsAADSJywPWM888c133f/LkSfn4%2BKhbt26aPHmyDh8%2BrCeffFJt2rTR8OHDVV1dLX9/f6dt/P39VVtbq%2Brqasfrv5%2BTfriZvqlKS0sdYe0KiyXIeEDz8/OYK7xNYrF4V70/x5WeeFtvmjN64nnoieehJ%2Ba5PGAdPny4ye/9%2B0t1TTVu3DjFxcWpbdu2kqRevXrp66%2B/1s6dOzV8%2BHAFBAQ0Cku1tbUKDAx0ClMBAQGOP0ty3MPVFNnZ2crMzHQaS0pKUkpKyjWvpzkJCWnt7hJcJji46Z8XuAY98Tz0xPPQE3NcHrDuv/9%2BxyVCu93uGL96zMfHR59//vk179/Hx8cRrq7o1q2bDh06JOmHX9VTVlbmNF9WVqbQ0FCFh4dLkmw2mzp37uz4sySFhoY2uYbExETFx8c7jVksQSovv3hti/knvO07DdPr90R%2Bfr4KDg5UZWWV6usb/vkGuO7oieehJ56nOffEXd/cuzxgbdiwQUuWLNGcOXMUGxsrf39/ffrpp0pLS9M999yjUaNG/aL9r169Wp988ok2b97sGDt%2B/Li6desmSbJarcrNzdX48eMlSWfPntXZs2dltVoVHh6uiIgI5ebmOgJWbm6uIiIirunyXlhYWKP322znVVfXvD6016olrb%2B%2BvqFFrdcb0BPPQ088Dz0xxy0PGl2wYIHuvPNOx9igQYOUlpamxx57zOkn/H6OuLg4ZWVladOmTRo%2BfLgOHDigPXv2OH4Fz6RJk3T//fcrMjJS/fr109KlSzVs2DDdfPPNjvnly5frV7/6lSRpxYoVmjFjxi%2BqCQAAtCwuD1ilpaWNfl2OJLVp00bl5eW/eP/9%2B/fX6tWrtWbNGq1evVqdOnXSihUrFBUVJUmKiopSWlqa1qxZo7/%2B9a8aPHiwFi9e7Nh%2B5syZ%2Bu6775ScnCw/Pz9NmDBB06ZN%2B8V1AQCAlsPH/vc3QrnA9OnTFRQUpD/%2B8Y9q06aNJKmiokKPPPKIAgICtG7dOleW4zI223nj%2B7RYfDV8%2BQfG93u9vPnQYHeXcN1ZLL4KCWmt8vKLnGb3EPTE89ATz9OcexIaeqNbjuvyM1hPPPGEpkyZojvvvFNdu3aV3W7X119/rdDQUMdlPAAAAG/m8oDVvXt3vfHGG9q7d6%2BKiookSb/97W81evToa3oUAgAAgKdyecCSpJtuukkTJ07Ut99%2B67i5/IYbbnBHKQAAAMa5/EFKdrtdy5cv1%2B23364xY8aopKREc%2BfO1fz583X58mVXlwMAAGCcywPWtm3b9Oqrr%2Bqpp55yPDn9rrvu0r59%2Bxo9/RwAAMAbuTxgZWdna8GCBRo/frzj6e2jRo3SkiVL9Prrr7u6HAAAAONcHrC%2B/fZb9e7du9F4r169Gv2CZAAAAG/k8oDVqVMnffrpp43G33//fccN7wAAAN7M5T9FOHPmTC1atEg2m012u10ffvihsrOztW3bNj3%2B%2BOOuLgcAAMA4lweshIQE1dXVaf369aqurtaCBQvUrl07PfTQQ5o0aZKrywEAADDO5QFr7969GjFihBITE/X999/Lbrerffv2ri4DAADgunH5PVhpaWmOm9nbtWtHuAIAAM2OywNW165d9eWXX7r6sAAAAC7j8kuEvXr10qOPPqqNGzeqa9euCggIcJpPT093dUkAAABGuTxgffXVV4qOjpYknnsFAACaJZcErGXLlik5OVlBQUHatm2bKw4JAADgNi65B%2BvFF19UVVWV09isWbNUWlrqisMDAAC4lEsClt1ubzR2%2BPBh1dTUuOLwAAAALuXynyIEAABo7ghYAAAAhrksYPn4%2BLjqUAAAAG7lssc0LFmyxOmZV5cvX1ZGRoZat27t9D6egwUAALydSwLW7bff3uiZV1FRUSovL1d5ebkrSgAAAHAZlwQsnn0FAABaEm5yBwAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM8/qAVVtbqzFjxuijjz5yjBUXF2vatGmKjIzUqFGjdODAAadtDh48qDFjxshqtWrKlCkqLi52mt%2B8ebOGDh2qqKgozZs3T1VVVS5ZCwAAaB68OmDV1NTo4YcfVmFhoWPMbrcrKSlJHTp0UE5OjsaOHavk5GSdOXNGknTmzBklJSVp/Pjx2r17t9q1a6cHH3xQdrtdkvT2228rMzNTaWlp2rJli/Lz85WRkeGW9QEAAO/ktQHrxIkTuvfee/XNN984jR86dEjFxcVKS0tT9%2B7dNXv2bEVGRionJ0eStGvXLt12222aMWOGevToofT0dJ0%2BfVoff/yxJGnr1q2aOnWq4uLi1L9/fy1atEg5OTmcxQIAAE3mtQHr448/1sCBA5Wdne00np%2Bfrz59%2BigoKMgxFh0drby8PMd8TEyMYy4wMFB9%2B/ZVXl6e6uvr9emnnzrNR0ZG6vLlyzp%2B/Ph1XhEAAGguLO4u4Oe67777fnTcZrMpLCzMaax9%2B/YqKSn5p/OVlZWqqalxmrdYLGrbtq1j%2B6YoLS2VzWZzGrNYghod95fy8/OufGyxeFe9P8eVnnhbb5ozeuJ56InnoSfmeW3A%2BilVVVXy9/d3GvP391dtbe0/na%2Burna8/qntmyI7O1uZmZlOY0lJSUpJSWnyPpqjkJDW7i7BZYKDA91dAq5CTzwPPfE89MScZhewAgICVFFR4TRdEWh4AAAQ40lEQVRWW1urVq1aOeavDku1tbUKDg5WQECA4/XV84GBTf/QJSYmKj4%2B3mnMYglSefnFJu%2BjKbztOw3T6/dEfn6%2BCg4OVGVllerrG9xdDkRPPBE98TzNuSfu%2Bua%2B2QWs8PBwnThxwmmsrKzMcXkuPDxcZWVljeZ79%2B6ttm3bKiAgQGVlZerevbskqa6uThUVFQoNDW1yDWFhYY0uB9ps51VX17w%2BtNeqJa2/vr6hRa3XG9ATz0NPPA89Mce7ToE0gdVq1Weffea43CdJubm5slqtjvnc3FzHXFVVlY4dOyar1SpfX1/169fPaT4vL08Wi0W9evVy3SIAAIBXa3YBKzY2Vh07dlRqaqoKCwuVlZWlgoICTZgwQZKUkJCgI0eOKCsrS4WFhUpNTVXnzp01cOBAST/cPL9p0ybt27dPBQUFWrhwoe69995rukQIAABatmYXsPz8/LRu3TrZbDaNHz9er732mtauXauIiAhJUufOnfXcc88pJydHEyZMUEVFhdauXSsfHx9J0ujRozV79mwtWLBAM2bMUP/%2B/TVnzhx3LgkAAHgZH/uVR5jjurLZzhvfp8Xiq%2BHLPzC%2B3%2BvlzYcGu7uE685i8VVISGuVl1/kPgYPQU88Dz3xPM25J6GhN7rluM3uDBYAAIC7EbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGHNNmC98847uvXWW52%2BUlJSJEnHjh3TxIkTZbValZCQoKNHjzptu3fvXt11112yWq1KSkrS999/744lAAAAL9VsA9aJEycUFxenAwcOOL6WLFmiS5cuadasWYqJidHLL7%2BsqKgozZ49W5cuXZIkFRQUaP78%2BUpOTlZ2drYqKyuVmprq5tUAAABv0mwDVlFRkXr27KnQ0FDHV3BwsN544w0FBAToscceU/fu3TV//ny1bt1ab731liRp%2B/btGjlypMaNG6devXpp2bJl2r9/v4qLi928IgAA4C2adcDq2rVro/H8/HxFR0fLx8dHkuTj46MBAwYoLy/PMR8TE%2BN4f8eOHRUREaH8/HyX1A0AALyfxd0FXA92u11fffWVDhw4oOeff1719fUaMWKEUlJSZLPZdMsttzi9v3379iosLJQklZaWKiwsrNF8SUlJk49fWloqm83mNGaxBDXa7y/l5%2Bdd%2Bdhi8a56f44rPfG23jRn9MTz0BPPQ0/Ma5YB68yZM6qqqpK/v79WrVqlb7/9VkuWLFF1dbVj/O/5%2B/urtrZWklRdXf0P55siOztbmZmZTmNJSUmOm%2BxbqpCQ1u4uwWWCgwPdXQKuQk88Dz3xPPTEnGYZsDp16qSPPvpIN910k3x8fNS7d281NDRozpw5io2NbRSWamtr1apVK0lSQEDAj84HBjb9Q5eYmKj4%2BHinMYslSOXlF3/min6ct32nYXr9nsjPz1fBwYGqrKxSfX2Du8uB6Iknoieepzn3xF3f3DfLgCVJbdu2dXrdvXt31dTUKDQ0VGVlZU5zZWVljst34eHhPzofGhra5GOHhYU1uhxos51XXV3z%2BtBeq5a0/vr6hha1Xm9ATzwPPfE89MQc7zoF0kQffPCBBg4cqKqqKsfY559/rrZt2yo6OlqffPKJ7Ha7pB/u1zpy5IisVqskyWq1Kjc317Hd2bNndfbsWcc8AADAP9MsA1ZUVJQCAgL0xBNP6OTJk9q/f7%2BWLVum3/3udxoxYoQqKyu1dOlSnThxQkuXLlVVVZVGjhwpSZo0aZJeffVV7dq1S8ePH9djjz2mYcOG6eabb3bzqgAAgLdolgGrTZs22rRpk77//nslJCRo/vz5SkxM1O9%2B9zu1adNGzz//vHJzczV%2B/Hjl5%2BcrKytLQUFBkn4IZ2lpaVq7dq0mTZqkm266Senp6W5eEQAA8CY%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%2BhD38P94WCL3JO48OdXcJ12T48g/cXUKT8f8WV3jbZ%2BGfIWBdpaqqyilcSXK8rq2tbdI%2BsrOzlZmZ6TSWnJysP/zhD2aK/JvS0lJN/VWhEhMTjYc3/DylpaXKzs6mJx6kJfbkL0s9%2B3aGv%2B9JSEhrd5dzTTz9/%2B3P1RL/nlxvnAK5SkBAQKMgdeV1q1atmrSPxMREvfzyy05fiYmJxmu12WzKzMxsdLYM7kNPPA898Tz0xPPQE/M4g3WV8PBwlZeXq66uThbLD/97bDabWrVqpeDg4CbtIywsjO8AAABowTiDdZXevXvLYrEoLy/PMZabm6t%2B/fpxgzsAAGgSEsNVAgMDNW7cOC1cuFAFBQXat2%2BfXnjhBU2ZMsXdpQEAAC/ht3DhwoXuLsLTDBo0SMeOHdOKFSv04Ycf6ve//70SEhLcXdaPat26tWJjY9W6tXfdKNqc0RPPQ088Dz3xPPTELB%2B73W53dxEAAADNCZcIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsLxUTU2N5s2bp5iYGA0ZMkQvvPCCu0tqcc6dO6eUlBTFxsZq6NChSk9PV01NjSSpuLhY06ZNU2RkpEaNGqUDBw64udqWZdasWXr88ccdr48dO6aJEyfKarUqISFBR48edWN1LUttba0WLVqk22%2B/Xb/%2B9a/17LPP6srzremLe5w9e1azZ8/WgAEDFB8fr82bNzvm6Ik5BCwvtWzZMh09elRbtmzRU089pczMTL311lvuLqvFsNvtSklJUVVVlXbs2KGVK1fqvffe06pVq2S325WUlKQOHTooJydHY8eOVXJyss6cOePusluEP//5z9q/f7/j9aVLlzRr1izFxMTo5ZdfVlRUlGbPnq1Lly65scqWY8mSJTp48KA2bdqkFStW6L//%2B7%2BVnZ1NX9zooYceUlBQkF5%2B%2BWXNmzdPq1at0jvvvENPTLPD61y8eNHer18/%2B6FDhxxja9eutU%2BePNmNVbUsJ06csPfs2dNus9kcY6%2B//rp9yJAh9oMHD9ojIyPtFy9edMxNnTrVvmbNGneU2qKUl5fb77zzTntCQoJ97ty5drvdbt%2B1a5c9Pj7e3tDQYLfb7faGhgb78OHD7Tk5Oe4stUUoLy%2B39%2BnTx/7RRx85xp5//nn7448/Tl/cpKKiwt6zZ0/7F1984RhLTk62L1q0iJ4YxhksL3T8%2BHHV1dUpKirKMRYdHa38/Hw1NDS4sbKWIzQ0VBs3blSHDh2cxi9cuKD8/Hz16dNHQUFBjvHo6Gjl5eW5uswW549//KPGjh2rW265xTGWn5%2Bv6Oho%2Bfj4SJJ8fHw0YMAA%2BuECubm5atOmjWJjYx1js2bNUnp6On1xk1atWikwMFAvv/yyLl%2B%2BrJMnT%2BrIkSPq3bs3PTGMgOWFbDabQkJC5O/v7xjr0KGDampqVFFR4cbKWo7g4GANHTrU8bqhoUHbt2/XoEGDZLPZFBYW5vT%2B9u3bq6SkxNVltigffvih/vKXv%2BjBBx90Gqcf7lNcXKxOnTppz549GjFihP7t3/5Na9euVUNDA31xk4CAAC1YsEDZ2dmyWq0aOXKk7rzzTk2cOJGeGGZxdwG4dlVVVU7hSpLjdW1trTtKavEyMjJ07Ngx7d69W5s3b/7R/tCb66empkZPPfWUFixYoFatWjnN/dTfF/px/V26dEmnTp3SSy%2B9pPT0dNlsNi1YsECBgYH0xY2KiooUFxen6dOnq7CwUIsXL9Ydd9xBTwwjYHmhgICARh/4K6%2Bv/scF119GRoa2bNmilStXqmfPngoICGh0JrG2tpbeXEeZmZm67bbbnM4qXvFTf1/ox/VnsVh04cIFrVixQp06dZIknTlzRjt37lSXLl3oixt8%2BOGH2r17t/bv369WrVqpX79%2BOnfunNavX6%2Bbb76ZnhhEwPJC4eHhKi8vV11dnSyWH1pos9nUqlUrBQcHu7m6lmXx4sXauXOnMjIydPfdd0v6oT8nTpxwel9ZWVmjU%2B8w589//rPKysoc9yVe%2BUfi7bff1pgxY1RWVub0fvrhGqGhoQoICHCEK0n6l3/5F509e1axsbH0xQ2OHj2qLl26OIWmPn36aMOGDYqJiaEnBnEPlhfq3bu3LBaL042Hubm56tevn3x9aamrZGZm6qWXXtKzzz6r0aNHO8atVqs%2B%2B%2BwzVVdXO8Zyc3NltVrdUWaLsG3bNr3%2B%2Buvas2eP9uzZo/j4eMXHx2vPnj2yWq365JNPHM9estvtOnLkCP1wAavVqpqaGn311VeOsZMnT6pTp070xU3CwsJ06tQppzNVJ0%2BeVOfOnemJYfxr7IUCAwM1btw4LVy4UAUFBdq3b59eeOEFTZkyxd2ltRhFRUVat26dHnjgAUVHR8tmszm%2BYmNj1bFjR6WmpqqwsFBZWVkqKCjQhAkT3F12s9WpUyd16dLF8dW6dWu1bt1aXbp00YgRI1RZWamlS5fqxIkTWrp0qaqqqjRy5Eh3l93sdevWTcOGDVNqaqqOHz%2BuDz74QFlZWZo0aRJ9cZP4%2BHjdcMMNeuKJJ/TVV1/pf//3f7Vhwwbdf//99MQwH/uVqAqvUlVVpYULF%2Bp//ud/1KZNG82cOVPTpk1zd1ktRlZWllasWPGjc1988YVOnTql%2BfPnKz8/X126dNG8efP061//2sVVtlxXnuL%2BzDPPSJIKCgr01FNPqaioSLfeeqsWLVqkPn36uLPEFuP8%2BfNavHix3nnnHQUGBuq%2B%2B%2B5TUlKSfHx86IubXAlPBQUFateunX77299q6tSp9MQwAhYAAIBhXCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2Bf5wBLEpNzs2aAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3914050097871472350”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5952380952380952</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5189990732159407</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.24096385542168677</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3019627579265224</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2098635886673662</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.28776978417266186</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21337126600284498</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3770589402659258</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.41265474552957354</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3106)</td> <td class=”number”>3107</td> <td class=”number”>96.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3914050097871472350”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.014654161781946071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020968756552736424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027883479858286313</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.028893383415197923</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>82.3744769874477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.61917357314994</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>82.66313508307248</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.60113421550095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.31918435289222</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHPI10”>PCT_NHPI10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2635</td>

</tr> <tr>

<th>Unique (%)</th> <td>82.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.080475</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>48.889</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>15.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4740466534399281851”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-4740466534399281851”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4740466534399281851”

aria-controls=”quantiles-4740466534399281851” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4740466534399281851” aria-controls=”histogram-4740466534399281851”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4740466534399281851” aria-controls=”common-4740466534399281851”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4740466534399281851” aria-controls=”extreme-4740466534399281851”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4740466534399281851”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.0099493</td>

</tr> <tr>

<th>Median</th> <td>0.022894</td>

</tr> <tr>

<th>Q3</th> <td>0.046435</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.18141</td>

</tr> <tr>

<th>Maximum</th> <td>48.889</td>

</tr> <tr>

<th>Range</th> <td>48.889</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.036486</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.94721</td>

</tr> <tr>

<th>Coef of variation</th> <td>11.77</td>

</tr> <tr>

<th>Kurtosis</th> <td>2267.6</td>

</tr> <tr>

<th>Mean</th> <td>0.080475</td>

</tr> <tr>

<th>MAD</th> <td>0.099499</td>

</tr> <tr class=”alert”>

<th>Skewness</th> <td>45.127</td>

</tr> <tr>

<th>Sum</th> <td>252.05</td>

</tr> <tr>

<th>Variance</th> <td>0.89722</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4740466534399281851”>

<img 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MsQXr7NmzSktLU1JSkvr27av09HRVVlZKkgoLCzVp0iTFxcVp6NCh2r9/v9dr33vvPQ0fPlyxsbGaMGGCCgsLvbZv3LhRffv2VXx8vObMmaPy8nLbjgsAADifIwuWZVlKS0tTeXm5XnjhBS1fvlxvv/22VqxYIcuylJKSolatWik7O1sjRoxQamqqTp8%2BLUk6ffq0UlJSNGrUKO3cuVMtWrTQww8/LMuyJEl79uxRZmamFixYoE2bNikvL08ZGRn%2BPFwAAOAwjixYJ0%2BeVG5urtLT09W5c2clJiYqLS1Nu3fv1oEDB1RYWKgFCxaoU6dOmjZtmuLi4pSdnS1J2rFjh2677TZNnjxZnTt3Vnp6ur766isdPHhQkrR582ZNnDhR/fv3V48ePTR//nxlZ2dzFgsAANSbIwtWZGSk1q9fr1atWnmtnz9/Xnl5ebr11lvVtGlTz3pCQoJyc3MlSXl5eUpMTPRsCw0NVffu3ZWbm6uamhp99NFHXtvj4uJ06dIlHTt2zMdHBQAAGguXvwdoiLCwMPXt29fzuLa2Vlu3blXv3r3ldrsVFRXl9fyWLVuqqKhIkv7t9nPnzqmystJru8vlUnh4uOf19VFcXCy32%2B215nI1rfO%2BVysoyFn92OVy1rzSvzJ2WtZOQsb2IGffI2Pfc1LGjixYP5SRkaGjR49q586d2rhxo4KDg722BwcHq6qqSpJUXl7%2Bo9srKio8j3/s9fWxfft2ZWZmeq2lpKQoLS2t3vtojCIimvl7hAYLCwv19wiNHhnbg5x9j4x9zwkZO75gZWRkaNOmTVq%2BfLm6dOmikJAQlZWVeT2nqqpKTZo0kSSFhITUKUtVVVUKCwtTSEiI5/EPt4eG1v%2BbOWbMGA0YMMBrzeVqqtLSC/XeR304ocF/n%2Bnjt0NQUKDCwkJ17ly5ampq/T1Oo0TG9iBn3yNj32tIxv76y73tBevee%2B9VcnKyhg0bphtuuOGq9rVw4UJt27ZNGRkZGjx4sCQpOjpax48f93peSUmJ5/JcdHS0SkpK6mzv1q2bwsPDFRISopKSEnXq1EmSVF1drbKyMkVGRtZ7rqioqDqXA93ub1RdfX3/wDn5%2BGtqah09vxOQsT3I2ffI2PeckLHtp0B69%2B6tZ599Vn369NGjjz6q/fv3ez4i4UpkZmbqxRdf1NNPP61hw4Z51mNjY/Xxxx97LvdJUk5OjmJjYz3bc3JyPNvKy8t19OhRxcbGKjAwUDExMV7bc3Nz5XK51LVr14YcLgAAuA7ZXrCmT5%2But99%2BW2vWrFFQUJB%2B%2B9vfql%2B/flq%2BfLk%2B//zzeu3jxIkTWrNmjX7zm98oISFBbrfb85WUlKTWrVtr9uzZKigoUFZWlvLz8zV69GhJUnJysg4fPqysrCwVFBRo9uzZateunXr16iVJGjdunDZs2KC9e/cqPz9f8%2BbN03333XdFlwgBAMD1LcBqyOkjg8rLy7VlyxatWbNGlZWV6tmzpyZOnKhf/OIXP/qarKwsLVu27LLbPv30U3355ZeaO3eu8vLydNNNN2nOnDn6j//4D89z9u3bpz/84Q8qKipSfHy8Fi5cqPbt23vtf%2BPGjaqqqtIvfvELPfXUU577sxrK7f7mql5/OS5XoAYtfdf4fn3ljUfu9PcIV8zlClRERDOVll645k9HOxUZ24OcfY%2BMfa8hGUdGXt3tSA3lt4JVXFysV199Va%2B%2B%2Bqo%2B%2B%2Bwz9ezZU/fcc4%2BKioq0ZcsWjRgxQnPnzvXHaD5BwaJg4fLI2B7k7Htk7HtOKli23%2BT%2Byiuv6JVXXtEHH3ygFi1aaOTIkVq1apU6dOjgeU7r1q21ePHiRlWwAADA9cP2gjV37lz1799fq1ev1l133aXAwLq3gXXs2FHjx4%2B3ezQAAAAjbC9Y77zzjiIiIlRWVuYpV/n5%2BerevbuCgoIkST179lTPnj3tHg0AAMAI2/8V4fnz5zVkyBD9%2Bc9/9qxNnTpVI0aM0JkzZ%2BweBwAAwDjbC9Yf/vAH3XTTTXrggQc8a6%2B//rpat26t9PR0u8cBAAAwzvaC9be//U2PP/641yejt2jRQo899pgOHDhg9zgAAADG2V6wXC6Xzp07V2e9vLy8QZ/oDgAAcK2xvWDdddddWrRokf7%2B97971goLC5Wenq6%2BffvaPQ4AAIBxtv8rwlmzZumBBx7Q4MGDFRYWJkk6d%2B6cunfvrtmzZ9s9DgAAgHG2F6yWLVvq5Zdf1nvvvaeCggK5XC7dfPPNuuOOOxQQEGD3OAAAAMbZXrAkKSgoSH379uWSIAAAaJRsL1hut1srVqzQ4cOHdenSpTo3tv/1r3%2B1eyQAAACjbC9YTzzxhI4cOaJhw4bphhv88wsYAQAAfMn2gnXgwAGtX79eiYmJdr81AACALWz/mIamTZuqZcuWdr8tAACAbWwvWCNGjND69etVU1Nj91sDAADYwvZLhGVlZdq9e7f%2B53/%2BR%2B3bt1dwcLDX9s2bN9s9EgAAgFF%2B%2BZiG4cOH%2B%2BNtAQAAbGF7wUpPT7f7LQEAAGxl%2Bz1YklRcXKzMzExNnz5d//jHP/Tmm2/q5MmT/hgFAADAONsL1pdffqlf/vKXevnll7Vnzx5dvHhRr7/%2BupKTk5WXl2f3OAAAAMbZXrD%2B%2BMc/auDAgdq7d69%2B9rOfSZKefvppDRgwQEuXLrV7HAAAAONsL1iHDx/WAw884PWLnV0ulx5%2B%2BGEdPXrU7nEAAACMs71g1dbWqra2ts76hQsXFBQUZPc4AAAAxtlesPr06aN169Z5layysjJlZGSod%2B/edo8DAABgnO0F6/HHH9eRI0fUp08fVVZW6r/%2B67/Uv39/nTp1SrNmzbJ7HAAAAONs/xys6Oho7dq1S7t379Ynn3yi2tpajR07ViNGjFDz5s3tHgcAAMA4v3ySe2hoqO69915/vDUAAIDP2V6wJkyY8G%2B387sIAQCA09lesNq2bev1uLq6Wl9%2B%2BaU%2B%2B%2BwzTZw40e5xAAAAjLtmfhfh6tWrVVRUZPM0AAAA5vnldxFezogRI/TGG2/4ewwAAICrds0UrA8//JAPGgUAAI3CNXGT%2B/nz5/Xpp59q3Lhxdo8DAABgnO0Fq02bNl6/h1CSfvazn2n8%2BPH61a9%2BZfc4AAAAxtlesP74xz/a/ZYAAAC2sr1gHTp0qN7Pvf3223/yOVVVVRo1apSeeOIJ9erVS5K0aNEibdmyxet5TzzxhMaPHy9J2r17t1asWCG3260%2Bffpo4cKFatGihSTJsiwtW7ZMO3fuVG1trUaPHq0ZM2YoMPCauV0NAABc42wvWPfff7/nEqFlWZ71H64FBATok08%2B%2Bbf7qqys1PTp01VQUOC1fuLECU2fPl333HOPZ%2B27X8OTn5%2BvuXPnav78%2BeratasWL16s2bNna926dZKk559/Xrt371ZmZqaqq6s1c%2BZMtWzZUlOmTLnKIwcAANcL2wvWs88%2Bq0WLFmnmzJlKSkpScHCwPvroIy1YsED33HOPhg4dWq/9HD9%2BXNOnT/cqad85ceKEpkyZosjIyDrbtm7dqrvvvlsjR46UJC1ZskT9%2B/dXYWGh2rdvr82bNystLU2JiYmSpBkzZmjlypUULAAAUG%2B2X/dKT0/Xk08%2BqcGDBysiIkLNmjVT7969tWDBAm3btk1t27b1fP07Bw8eVK9evbR9%2B3av9fPnz%2Bvs2bPq0KHDZV%2BXl5fnKU%2BS1Lp1a7Vp00Z5eXk6e/aszpw543VpMiEhQV999ZWKi4sbftAAAOC6YvsZrOLi4suWp%2BbNm6u0tLTe%2B/mxj3Q4ceKEAgIC9Oyzz%2Bqdd95ReHi4HnjgAc/lwuLiYkVFRXm9pmXLlioqKpLb7ZYkr%2B2tWrWSJBUVFdV5HQAAwOXYXrDi4uL09NNP609/%2BpPnvqiysjJlZGTojjvuuOr9nzx5UgEBAerYsaPGjx%2BvQ4cO6YknnlDz5s01aNAgVVRUKDg42Os1wcHBqqqqUkVFhefx97dJ395MX1/FxcWesvYdl6up8YIWFOSsG%2B9dLmfNK/0rY6dl7SRkbA9y9j0y9j0nZWx7wfr973%2BvCRMm6K677lKHDh1kWZa%2B%2BOILRUZGavPmzVe9/5EjR6p///4KDw%2BXJHXt2lVffPGFtm3bpkGDBikkJKROWaqqqlJoaKhXmQoJCfH8WZJCQ0PrPcP27duVmZnptZaSkqK0tLQGH1djEBHRzN8jNFhYWP2//2gYMrYHOfseGfueEzK2vWB16tRJr7/%2Bunbv3q0TJ05Ikn79619r2LBhV1RifkxAQICnXH2nY8eOOnDggCQpOjpaJSUlXttLSkoUGRmp6OhoSZLb7Va7du08f5Z02Rvmf8yYMWM0YMAArzWXq6lKSy9c2cH8BCc0%2BO8zffx2CAoKVFhYqM6dK1dNTa2/x2mUyNge5Ox7ZOx7DcnYX3%2B5t71gSdKNN96oe%2B%2B9V6dOnVL79u0lfftp7iasXLlSH374oTZu3OhZO3bsmDp27ChJio2NVU5OjkaNGiVJOnPmjM6cOaPY2FhFR0erTZs2ysnJ8RSsnJwctWnT5oou70VFRdV5vtv9jaqrr%2B8fOCcff01NraPndwIytgc5%2Bx4Z%2B54TMra9YH33QZ5btmzRpUuXtGfPHi1fvlyhoaGaN2/eVRet/v37KysrSxs2bNCgQYO0f/9%2B7dq1y3P5cezYsbr//vsVFxenmJgYLV68WP369fMUvbFjx2rp0qX6%2Bc9/LklatmyZJk%2BefHUHDQAAriu2X2PasmWLXnnlFT311FOee54GDhyovXv31rlvqSF69OihlStX6pVXXtHw4cO1ZcsWLVu2TPHx8ZKk%2BPh4LViwQKtXr9bYsWN14403Kj093fP6KVOmaOjQoUpNTdXvfvc7jRgxQpMmTbrquQAAwPUjwLrcJ3X60LBhw/TII49o0KBBio%2BP16uvvqr27dvrrbfeUnp6uv77v//bznFs43Z/Y3yfLlegBi191/h%2BfeWNR%2B709whXzOUKVEREM5WWXrjmT0c7FRnbg5x9j4x9ryEZR0be4OOpLs/2M1inTp1St27d6qx37dq1zkcbAAAAOJHtBatt27b66KOP6qy/8847nvugAAAAnMz2m9ynTJmi%2BfPny%2B12y7Isvf/%2B%2B9q%2Bfbu2bNmixx9/3O5xAAAAjLO9YCUnJ6u6ulpr165VRUWFnnzySbVo0UKPPPKIxo4da/c4AAAAxtlesHbv3q0hQ4ZozJgx%2Bvrrr2VZllq2bGn3GAAAAD5j%2Bz1YCxYs8NzM3qJFC8oVAABodGwvWB06dNBnn31m99sCAADYxvZLhF27dtWMGTO0fv16dejQwfNLlb/z/Q/9BAAAcCLbC9bnn3%2BuhIQESeJzrwAAQKNkS8FasmSJUlNT1bRpU23ZssWOtwQAAPAbW%2B7Bev7551VeXu61NnXqVBUXF9vx9gAAALaypWBd7tcdHjp0SJWVlXa8PQAAgK1s/1eEAAAAjR0FCwAAwDDbClZAQIBdbwUAAOBXtn1Mw6JFi7w%2B8%2BrSpUvKyMhQs2bNvJ7H52ABAACns6Vg3X777XU%2B8yo%2BPl6lpaUqLS21YwQAAADb2FKw%2BOwrAABwPeEmdwAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADHN8waqqqtLw4cP1wQcfeNYKCws1adIkxcXFaejQodq/f7/Xa9577z0NHz5csbGxmjBhggoLC722b9y4UX379lV8fLzmzJmj8vJyW44FAAA0Do4uWJWVlXr00UdVUFDgWbMsSykpKWrVqpWys7M1YsQIpaam6vTp05Kk06dPKyUlRaNGjdLOnTvVokULPfzww7IsS5K0Z88eZWZmasGCBdq0aZPy8vKUkZHhl%2BMDAADO5NiCdfz4cd133336%2B9//7rV%2B4MABFRYWasGCBerUqZOmTZumuLg4ZWdnS5J27Nih2267TZMnT1bnzp2Vnp6ur776SgcPHpQkbd68WRMnTlT//v3Vo0cPzZ8/X9nZ2ZzFAgAA9ebYgnXw4EH16tVL27dv91rPy8vTrbfeqqZNm3rWEhISlJub69memJjo2RYaGqru3bsrNzdXNTU1%2Buijj7y2x8XF6dKlSzp27JiPjwgAADQWLn8P0FDjxo277Lrb7VZUVJTXWsuWLVVUVPST28%2BdO6fKykqv7S6XS%2BHh4Z7XAwAA/BTHFqwfU15eruDgYK%2B14OBgVVVV/eT2iooKz%2BMfe33WeGgxAAARSElEQVR9FBcXy%2B12e625XE3rFLurFRTkrBOQLpez5pX%2BlbHTsnYSMrYHOfseGfuekzJudAUrJCREZWVlXmtVVVVq0qSJZ/sPy1JVVZXCwsIUEhLiefzD7aGhofWeYfv27crMzPRaS0lJUVpaWr330RhFRDTz9wgNFhZW/%2B8/GoaM7UHOvkfGvueEjBtdwYqOjtbx48e91kpKSjxnj6Kjo1VSUlJne7du3RQeHq6QkBCVlJSoU6dOkqTq6mqVlZUpMjKy3jOMGTNGAwYM8FpzuZqqtPRCQw7pRzmhwX%2Bf6eO3Q1BQoMLCQnXuXLlqamr9PU6jRMb2IGffI2Pfa0jG/vrLfaMrWLGxscrKylJFRYXnrFVOTo4SEhI823NycjzPLy8v19GjR5WamqrAwEDFxMQoJydHvXr1kiTl5ubK5XKpa9eu9Z4hKiqqzuVAt/sbVVdf3z9wTj7%2BmppaR8/vBGRsD3L2PTL2PSdk7KxTIPWQlJSk1q1ba/bs2SooKFBWVpby8/M1evRoSVJycrIOHz6srKwsFRQUaPbs2WrXrp2nUI0bN04bNmzQ3r17lZ%2Bfr3nz5um%2B%2B%2B67okuEAADg%2BtboClZQUJDWrFkjt9utUaNG6dVXX9Xq1avVpk0bSVK7du30zDPPKDs7W6NHj1ZZWZlWr16tgIAASdKwYcM0bdo0Pfnkk5o8ebJ69OihmTNn%2BvOQAACAwwRY332EOXzK7f7G%2BD5drkANWvqu8f36yhuP3OnvEa6YyxWoiIhmKi29cM2fjnYqMrYHOfseGfteQzKOjLzBx1NdXqM7gwUAAOBvFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGNZoC9Zbb72lW265xesrLS1NknT06FHde%2B%2B9io2NVXJyso4cOeL12t27d2vgwIGKjY1VSkqKvv76a38cAgAAcKhGW7COHz%2Bu/v37a//%2B/Z6vRYsW6eLFi5o6daoSExP10ksvKT4%2BXtOmTdPFixclSfn5%2BZo7d65SU1O1fft2nTt3TrNnz/bz0QAAACdptAXrxIkT6tKliyIjIz1fYWFhev311xUSEqLHHntMnTp10ty5c9WsWTO9%2BeabkqStW7fq7rvv1siRI9W1a1ctWbJE%2B/btU2FhoZ%2BPCAAAOEWjLlgdOnSos56Xl6eEhAQFBARIkgICAtSzZ0/l5uZ6ticmJnqe37p1a7Vp00Z5eXm2zA0AAJzP5e8BfMGyLH3%2B%2Befav3%2B/1q1bp5qaGg0ZMkRpaWlyu926%2BeabvZ7fsmVLFRQUSJKKi4sVFRVVZ3tRUVG937%2B4uFhut9trzeVqWme/VysoyFn92OVy1rzSvzJ2WtZOQsb2IGffI2Pfc1LGjbJgnT59WuXl5QoODtaKFSt06tQpLVq0SBUVFZ717wsODlZVVZUkqaKi4t9ur4/t27crMzPTay0lJcVzk/31KiKimb9HaLCwsFB/j9DokbE9yNn3yNj3nJBxoyxYbdu21QcffKAbb7xRAQEB6tatm2prazVz5kwlJSXVKUtVVVVq0qSJJCkkJOSy20ND6//NHDNmjAYMGOC15nI1VWnphQYe0eU5ocF/n%2Bnjt0NQUKDCwkJ17ly5ampq/T1Oo0TG9iBn3yNj32tIxv76y32jLFiSFB4e7vW4U6dOqqysVGRkpEpKSry2lZSUeC7fRUdHX3Z7ZGRkvd87KiqqzuVAt/sbVVdf3z9wTj7%2BmppaR8/vBGRsD3L2PTL2PSdk7KxTIPX07rvvqlevXiovL/esffLJJwoPD1dCQoI%2B/PBDWZYl6dv7tQ4fPqzY2FhJUmxsrHJycjyvO3PmjM6cOePZDgAA8FMaZcGKj49XSEiIfv/73%2BvkyZPat2%2BflixZogcffFBDhgzRuXPntHjxYh0/flyLFy9WeXm57r77bknS2LFj9corr2jHjh06duyYHnvsMfXr10/t27f381EBAACnaJQFq3nz5tqwYYO%2B/vprJScna%2B7cuRozZowefPBBNW/eXOvWrVNOTo5GjRqlvLw8ZWVlqWnTppK%2BLWcLFizQ6tWrNXbsWN14441KT0/38xEBAAAnCbC%2Bu1YGn3K7vzG%2BT5crUIOWvmt8v77yxiN3%2BnuEK%2BZyBSoioplKSy9c89f7nYqM7UHOvkfGvteQjCMjb/DxVJfXKM9gAQAA%2BBMFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAuo7KyUnPmzFFiYqL69Omj5557zt8jAQAAB3H5e4Br0ZIlS3TkyBFt2rRJp0%2Bf1qxZs9SmTRsNGTLE36MBAAAHoGD9wMWLF7Vjxw79%2Bc9/Vvfu3dW9e3cVFBTohRdeoGABAIB64RLhDxw7dkzV1dWKj4/3rCUkJCgvL0%2B1tbV%2BnAwAADgFZ7B%2BwO12KyIiQsHBwZ61Vq1aqbKyUmVlZWrRosVP7qO4uFhut9trzeVqqqioKKOzBgU5qx%2B7XM6aV/pXxk7L2knI2B7k7Htk7HtOypiC9QPl5eVe5UqS53FVVVW99rF9%2B3ZlZmZ6raWmpuq3v/2tmSH/X3FxsSb%2BvEBjxowxXt7wreLiYm3atJ6MfYiM7UHOvkfGvuekjK/9CmizkJCQOkXqu8dNmjSp1z7GjBmjl156yetrzJgxxmd1u93KzMysc7YM5pCx75GxPcjZ98jY95yUMWewfiA6OlqlpaWqrq6Wy/VtPG63W02aNFFYWFi99hEVFXXNN2sAAOA7nMH6gW7dusnlcik3N9ezlpOTo5iYGAUGEhcAAPhpNIYfCA0N1ciRIzVv3jzl5%2Bdr7969eu655zRhwgR/jwYAABwiaN68efP8PcS1pnfv3jp69KiWLVum999/Xw899JCSk5P9PdZlNWvWTElJSWrWrJm/R2m0yNj3yNge5Ox7ZOx7Tsk4wLIsy99DAAAANCZcIgQAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsFyqMrKSs2ZM0eJiYnq06ePnnvuOX%2BP1GhUVVVp%2BPDh%2BuCDDzxrhYWFmjRpkuLi4jR06FDt37/fjxM619mzZ5WWlqakpCT17dtX6enpqqyslETGpnz55ZeaMmWK4uPj1a9fP61fv96zjYzNmzp1qh5//HHP46NHj%2Bree%2B9VbGyskpOTdeTIET9O52xvvfWWbrnlFq%2BvtLQ0Sc7ImYLlUEuWLNGRI0e0adMmPfXUU8rMzNSbb77p77Ecr7KyUo8%2B%2BqgKCgo8a5ZlKSUlRa1atVJ2drZGjBih1NRUnT592o%2BTOo9lWUpLS1N5ebleeOEFLV%2B%2BXG%2B//bZWrFhBxobU1tZq6tSpioiI0Msvv6z58%2Bdr7dq1eu2118jYB/7yl79o3759nscXL17U1KlTlZiYqJdeeknx8fGaNm2aLl686Mcpnev48ePq37%2B/9u/f7/latGiRc3K24DgXLlywYmJirAMHDnjWVq9ebY0fP96PUzlfQUGB9atf/cr65S9/aXXp0sWT73vvvWfFxcVZFy5c8Dx34sSJ1qpVq/w1qiMdP37c6tKli%2BV2uz1rr732mtWnTx8yNuTs2bPW7373O%2Bubb77xrKWkpFhPPfUUGRtWWlpq3XXXXVZycrI1a9Ysy7Isa8eOHdaAAQOs2tpay7Isq7a21ho0aJCVnZ3tz1Eda/r06dayZcvqrDslZ85gOdCxY8dUXV2t%2BPh4z1pCQoLy8vJUW1vrx8mc7eDBg%2BrVq5e2b9/utZ6Xl6dbb71VTZs29awlJCQoNzfX7hEdLTIyUuvXr1erVq281s%2BfP0/GhkRFRWnFihVq3ry5LMtSTk6ODh06pKSkJDI27E9/%2BpNGjBihm2%2B%2B2bOWl5enhIQEBQQESJICAgLUs2dPMm6gEydOqEOHDnXWnZIzBcuB3G63IiIiFBwc7Flr1aqVKisrVVZW5sfJnG3cuHGaM2eOQkNDvdbdbreioqK81lq2bKmioiI7x3O8sLAw9e3b1/O4trZWW7duVe/evcnYBwYMGKBx48YpPj5egwcPJmOD3n//ff3tb3/Tww8/7LVOxuZYlqXPP/9c%2B/fv1%2BDBgzVw4EAtXbpUVVVVjsnZ5e8BcOXKy8u9ypUkz%2BOqqip/jNSo/VjeZH11MjIydPToUe3cuVMbN24kY8NWrVqlkpISzZs3T%2Bnp6fx3bEhlZaWeeuopPfnkk2rSpInXNjI25/Tp0548V6xYoVOnTmnRokWqqKhwTM4ULAcKCQmp8x/Sd49/%2BAOPqxcSElLnzGBVVRVZX4WMjAxt2rRJy5cvV5cuXcjYB2JiYiR9WwhmzJih5ORklZeXez2HjK9cZmambrvtNq%2Bzsd/5sf9tJuMr17ZtW33wwQe68cYbFRAQoG7duqm2tlYzZ85UUlKSI3KmYDlQdHS0SktLVV1dLZfr22%2Bh2%2B1WkyZNFBYW5ufpGp/o6GgdP37ca62kpKTOKWrUz8KFC7Vt2zZlZGRo8ODBksjYlJKSEuXm5mrgwIGetZtvvlmXLl1SZGSkTp48Wef5ZHxl/vKXv6ikpMRzD%2Bx3/0e/Z88eDR8%2BXCUlJV7PJ%2BOGCw8P93rcqVMnVVZWKjIy0hE5cw%2BWA3Xr1k0ul8vrhr6cnBzFxMQoMJBvqWmxsbH6%2BOOPVVFR4VnLyclRbGysH6dypszMTL344ot6%2BumnNWzYMM86GZtx6tQppaam6uzZs561I0eOqEWLFkpISCBjA7Zs2aLXXntNu3bt0q5duzRgwAANGDBAu3btUmxsrD788ENZliXp2/uIDh8%2BTMYN8O6776pXr15eZ10/%2BeQThYeHKyEhwRE58//GDhQaGqqRI0dq3rx5ys/P1969e/Xcc89pwoQJ/h6tUUpKSlLr1q01e/ZsFRQUKCsrS/n5%2BRo9erS/R3OUEydOaM2aNfrNb36jhIQEud1uzxcZmxETE6Pu3btrzpw5On78uPbt26eMjAw99NBDZGxI27ZtddNNN3m%2BmjVrpmbNmummm27SkCFDdO7cOS1evFjHjx/X4sWLVV5errvvvtvfYztOfHy8QkJC9Pvf/14nT57Uvn37tGTJEj344IPOydmfnxGBhrt48aL12GOPWXFxcVafPn2s559/3t8jNSrf/xwsy7KsL774wvr1r39t3XbbbdawYcOs//3f//XjdM60bt06q0uXLpf9siwyNqWoqMhKSUmxevbsad15553W2rVrPZ8XRMbmzZo1y/M5WJZlWXl5edbIkSOtmJgYa/To0dbHH3/sx%2Bmc7bPPPrMmTZpkxcXFWXfeeaf1zDPPeP5bdkLOAZb1/%2BfYAAAAYASXCAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8DPtCtA4fchX0AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4740466534399281851”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>484</td> <td class=”number”>15.1%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.018474043968224645</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0121921482565228</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.004950004950004951</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.03575898444484177</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02469135802469136</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.025106703489831784</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011473811026332397</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02663115845539281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.02254791431792559</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2624)</td> <td class=”number”>2630</td> <td class=”number”>81.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4740466534399281851”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>484</td> <td class=”number”>15.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002151462994836489</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002294156782674528</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002523468254769355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0025673940949935818</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.519771653425943</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.046828233531647</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.853778885774442</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.330296792180636</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>48.888888888888886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NHWHITE10”>PCT_NHWHITE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3133</td>

</tr> <tr>

<th>Unique (%)</th> <td>97.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>78.354</td>

</tr> <tr>

<th>Minimum</th> <td>2.8604</td>

</tr> <tr>

<th>Maximum</th> <td>99.163</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1437975978365723945”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1437975978365723945”

aria-controls=”quantiles-1437975978365723945” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1437975978365723945” aria-controls=”histogram-1437975978365723945”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1437975978365723945” aria-controls=”common-1437975978365723945”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1437975978365723945” aria-controls=”extreme-1437975978365723945”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1437975978365723945”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>2.8604</td>

</tr> <tr>

<th>5-th percentile</th> <td>37.787</td>

</tr> <tr>

<th>Q1</th> <td>66.995</td>

</tr> <tr>

<th>Median</th> <td>85.772</td>

</tr> <tr>

<th>Q3</th> <td>94.203</td>

</tr> <tr>

<th>95-th percentile</th> <td>97.334</td>

</tr> <tr>

<th>Maximum</th> <td>99.163</td>

</tr> <tr>

<th>Range</th> <td>96.303</td>

</tr> <tr>

<th>Interquartile range</th> <td>27.208</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>19.811</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25284</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.0049</td>

</tr> <tr>

<th>Mean</th> <td>78.354</td>

</tr> <tr>

<th>MAD</th> <td>15.983</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.2504</td>

</tr> <tr>

<th>Sum</th> <td>245400</td>

</tr> <tr>

<th>Variance</th> <td>392.47</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1437975978365723945”>

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qSgoCD1799fRUVFkqTi4mIlJSV5flfHjh3VqVMnFRcXG60RAAAELofdCz7wwAOaPn26du7cqc2bN%2Bu%2B%2B%2B5TdHS0xowZozFjxujyyy%2B/4DXCwsI0d%2B5cLVy4UC%2B88IIaGhqUmpqq8ePH65133tGVV17ptX27du1UWloqSSovL1dsbGyT%2BaNHjzZ7/fLycrlcLq8xhyOyye/9sZCQYK/vaBn02T702j702h70uWU5HP/qayD02vaAJX1/1GjQoEEaNGiQ3G631q5dqyeffFJ5eXnq37%2B/Jk2apBtuuOGC1vj88881dOhQ3XXXXSotLdXChQt17bXXyu12KzQ01Gvb0NBQ1dXVSZJOnjz5s/PNkZ%2Bfr5ycHK%2Bx9PR0ZWZmnnXf6OiIZq%2BD80ef7UOv7UOv7UGfW0abNlFNxvy51z4JWNL3R3lef/11vf766/rss8/Uv39//e53v9PRo0f10EMP6aOPPtKcOXPO63fv2rVLGzZs0I4dOxQeHq5%2B/frp22%2B/1VNPPaUuXbo0CUt1dXUKDw%2BX9P3Rr9PNR0Q0/4%2BclpamlJQUrzGHI1KVlSfOuE9ISLCioyNUXe1WQ0Njs9fCuaHP9qHX9qHX9qDPLevHr5Eme3264GYH2wPWa6%2B9ptdee027d%2B9W27ZtNWbMGD3%2B%2BOPq1q2bZ5uOHTtq8eLF5x2w9u3bp65du3pCkyT17t1bK1euVFJSkioqKry2r6io8Jy%2B69Chw2nnY2Jimr1%2BbGxsk9OBLtd3qq8/%2B4OkoaGxWdvhwtBn%2B9Br%2B9Bre9DnlnG6nvpzr20/ufnDf%2B3l5uZqx44deuCBB7zClSRdccUVuv322897jdjYWB06dMjrSNTBgwcVFxcnp9OpTz75xPPGesuyVFhYKKfTKUlyOp0qKCjw7HfkyBEdOXLEMw8AAHA2tges9957T48//ricTqeCg79ffu/evWpoaPBs079/fz3wwAPnvUZKSopatWqlhx56SF988YX%2B/Oc/a%2BXKlbrjjjs0fPhwVVdXa/HixTpw4IAWL14st9utESNGSJImTJig1157TevXr9f%2B/fs1Y8YMXX/99erSpcuF3XEAAPCLYXvAOn78uIYPH66nn37aMzZlyhSNHj1aR44cMbLGJZdcotWrV8vlcmncuHHKzs7Wvffeq7S0NLVu3VqrVq1SQUGBUlNTVVxcrLy8PEVGRkqSEhIStGDBAuXm5mrChAm69NJLlZ2dbaQuAADwyxBkmb4I1Vncc889amxs1OLFiz3vazp27JhmzpypiIgIPf7443aWYxuX6%2BcvrOpwBKtNmyhVVp7w2/PN/oA%2B24de24de28Mf%2BzxixQe%2BLqHZ3rp/kOdnk72OibnkQks7L7Yfwfr44481a9YsrzeNt23bVjNmzNCHH35odzkAAADG2R6wHA6Hqqurm4y73W7jV3QHAADwBdsD1nXXXadFixbpq6%2B%2B8oyVlZUpOztbQ4YMsbscAAAA42y/DtbMmTN111136cYbb1R0dLQkqbq6Wn369FFWVpbd5QAAABhne8Bq166dXn31Ve3cuVOlpaVyOBy68sorde2113o%2BgBkAAMCf%2BeSjckJCQjRkyBBOCQIAgIBke8ByuVxasWKFCgsLderUqSZvbH/nnXfsLgkAAMAo2wPWH/7wB%2B3bt08jR47UJZf45toUAAAALcn2gPXhhx/qmWeeUVJSkt1LAwAA2ML2yzRERkaqXbt2di8LAABgG9sD1ujRo/XMM894fbgzAABAILH9FGFVVZW2bt2qd999V126dFFoaKjX/AsvvGB3SQAAAEb55DINo0aN8sWyAAAAtrA9YGVnZ9u9JAAAgK1sfw%2BWJJWXlysnJ0cPPPCA/vGPf%2Bjtt9/WwYMHfVEKAACAcbYHrEOHDuk3v/mNXn31VW3btk01NTV68803NXbsWBUXF9tdDgAAgHG2B6w//vGPGjZsmLZv365WrVpJkpYvX66UlBQ9%2BuijdpcDAABgnO0Bq7CwUHfddZfXBzs7HA5NmzZNJSUldpcDAABgnO0Bq7GxUY2NjU3GT5w4oZCQELvLAQAAMM72gDV48GCtWrXKK2RVVVVp6dKlGjhwoN3lAAAAGGd7wJo1a5b27dunwYMHq7a2Vvfee6%2BGDh2qr7/%2BWjNnzrS7HAAAAONsvw5Whw4dtHnzZm3dulV/%2B9vf1NjYqAkTJmj06NFq3bq13eUAAAAY55MruUdERGj8%2BPG%2BWBoAAKDF2R6wJk6c%2BLPzfBYhAADwd7YHrM6dO3vdrq%2Bv16FDh/TZZ59p0qRJdpcDAABg3EXzWYS5ubk6evSozdUAAACY55PPIjyd0aNH66233vJ1GQAAABfsoglYn3zyCRcaBQAAAeGieJP78ePH9fe//1233nqr3eUAAAAYZ3vA6tSpk9fnEEpSq1atdPvtt%2Bu3v/2t3eUAAAAYZ3vA%2BuMf/2j3kgAAALayPWB99NFHzd72mmuuacFKAAAAWobtAeuOO%2B7wnCK0LMsz/tOxoKAg/e1vf7O7PAAAgAtme8BauXKlFi1apAcffFDJyckKDQ3Vp59%2BqgULFuh3v/udbrrpJrtLAgAAMMr2yzRkZ2dr7ty5uvHGG9WmTRtFRUVp4MCBWrBggV5%2B%2BWV17tzZ8wUAAOCPbA9Y5eXlpw1PrVu3VmVlpbF16urqNH/%2BfF1zzTX61a9%2BpeXLl3tOP5aUlGj8%2BPFyOp0aO3as9u3b57Xv1q1bNWzYMDmdTqWnp%2BvYsWPG6gIAAIHP9oAVHx%2Bv5cuX6/jx456xqqoqLV26VNdee62xdRYtWqSdO3fq2Wef1bJly/TKK68oPz9fNTU1mjJlipKSkrRp0yYlJCRo6tSpqqmpkSTt3btXc%2BbMUUZGhvLz81VdXa2srCxjdQEAgMBn%2B3uwHnroIU2cOFHXXXedunXrJsuy9OWXXyomJkYvvPCCkTWqqqq0ceNGPf/887r66qslSZMnT1ZxcbEcDofCwsI0Y8YMBQUFac6cOXrvvff09ttvKzU1VS%2B%2B%2BKJGjBihMWPGSJKWLFmioUOHqqysTF26dDFSHwAACGy2B6zu3bvrzTff1NatW/X5559Lkm677TaNHDlSERERRtYoKChQ69atlZyc7BmbMmWKJOkPf/iDEhMTPf%2B1GBQUpP79%2B6uoqEipqakqLi7W3Xff7dmvY8eO6tSpk4qLiwlYAACgWWwPWJJ06aWXavz48fr66689oaVVq1bGfn9ZWZk6d%2B6szZs3a%2BXKlTp16pRSU1N17733yuVy6corr/Tavl27diotLZX0/XvEYmNjm8wfPXrUWH0AACCw2R6wLMvSsmXLtHbtWp06dUrbtm3TY489poiICM2bN89I0KqpqdGhQ4e0bt06ZWdny%2BVyae7cuYqIiJDb7VZoaKjX9qGhoaqrq5MknTx58mfnm6O8vFwul8trzOGIbBLcfiwkJNjrO1oGfbYPvbYPvbYHfW5ZDse/%2BhoIvbY9YK1du1avvfaaHn74YS1YsECSNGzYMM2fP1/t27fXf/3Xf13wGg6HQ8ePH9eyZcs8/7F4%2BPBhvfzyy%2BratWuTsFRXV6fw8HBJUlhY2Gnnz%2BX0ZX5%2BvnJycrzG0tPTlZmZedZ9o6PNnCbFz6PP9qHX9qHX9qDPLaNNm6gmY/7ca9sDVn5%2BvubOnatf//rXWrhwoSTppptuUqtWrZSdnW0kYMXExCgsLMzrchCXX365jhw5ouTkZFVUVHhtX1FR4Tm61KFDh9POx8TENHv9tLQ0paSkeI05HJGqrDxxxn1CQoIVHR2h6mq3Ghoam70Wzg19tg%2B9tg%2B9tgd9blk/fo002evTBTc72B6wvv76a/Xq1avJeM%2BePZucVjtfTqdTtbW1%2BuKLL3T55ZdLkg4ePKjOnTvL6XTq6aeflmVZCgoKkmVZKiws1D333OPZt6CgQKmpqZKkI0eO6MiRI3I6nc1ePzY2tsnpQJfrO9XXn/1B0tDQ2KztcGHos33otX3otT3oc8s4XU/9ude2B6zOnTvr008/VVxcnNf4e%2B%2B9Z%2By/9K644gpdf/31ysrK0rx58%2BRyuZSXl6d7771Xw4cP17Jly7R48WLdcsstWrdundxut0aMGCFJmjBhgu644w7Fx8erX79%2BWrx4sa6//nr%2BgxAAoKQ5b/u6BPgJ2wPWf/zHf2j%2B/PlyuVyyLEu7du1Sfn6%2B1q5dq1mzZhlb59FHH9XChQs1YcIERURE6LbbbvN80PSqVav08MMP65VXXtG//du/KS8vT5GRkZKkhIQELViwQI8//rj%2B%2Bc9/atCgQZ5TmQAAAM0RZP3w%2BTE2ys/P11NPPeW59EHbtm11991366677rK7FNu4XN/97LzDEaw2baJUWXnCbw%2BH%2BgP6bB96bR96bQ%2BHI1i/fvT/fF1GwHrr/kGen00%2BpmNiLrnQ0s6L7Uewtm7dquHDhystLU3Hjh2TZVlq166d3WUAAAC0GNsvMLFgwQLPm9nbtm1LuAIAAAHH9oDVrVs3ffbZZ3YvCwAAYBvbTxH27NlTv//97/XMM8%2BoW7duCgsL85rPzs62uyQAAACjbA9YX3zxhRITEyXJ2HWvAAAALia2BKwlS5YoIyNDkZGRWrt2rR1LAgAA%2BIwt78F6/vnn5Xa7vcamTJmi8vJyO5YHAACwlS0B63SX2vroo49UW1trx/IAAAC2sv2/CAEAAAIdAQsAAMAw2wJWUFCQXUsBAAD4lG2XaVi0aJHXNa9OnTqlpUuXKioqyms7roMFAAD8nS0B65prrmlyzauEhARVVlaqsrLSjhIAAABsY0vA4tpXAADgl4Q3uQMAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMCPmBNmTJFs2bN8twuKSnR%2BPHj5XQ6NXbsWO3bt89r%2B61bt2rYsGFyOp1KT0/XsWPH7C4ZAAD4uYAOWG%2B88YZ27NjhuV1TU6MpU6YoKSlJmzZtUkJCgqZOnaqamhpJ0t69ezVnzhxlZGQoPz9f1dXVysrK8lX5AADATwVswKqqqtKSJUvUr18/z9ibb76psLAwzZgxQ927d9ecOXMUFRWlt99%2BW5L04osvasSIERozZox69uypJUuWaMeOHSorK/PV3QAAAH4oYAPWI488otGjR%2BvKK6/0jBUXFysxMVFBQUGSpKCgIPXv319FRUWe%2BaSkJM/2HTt2VKdOnVRcXGxv8QAAwK85fF1AS9i1a5c%2B/vhjbdmyRfPmzfOMu1wur8AlSe3atVNpaakkqby8XLGxsU3mjx49ek7rl5eXy%2BVyeY05HJFNfvePhYQEe31Hy6DP9qHX9qHX9qC/Lcvh%2BFd/A%2BExHXABq7a2Vg8//LDmzp2r8PBwrzm3263Q0FCvsdDQUNXV1UmSTp48%2BbPzzZWfn6%2BcnByvsfT0dGVmZp513%2BjoiHNaC%2BeHPtuHXtuHXsOftWkT1WTMnx/TARewcnJy1LdvXw0ZMqTJXFhYWJOwVFdX5wliZ5qPiDi3P3BaWppSUlK8xhyOSFVWnjjjPiEhwYqOjlB1tVsNDY3ntB6ajz7bh17bh17bw5%2BPpviDH79GmnxMny642SHgAtYbb7yhiooKJSQkSJInMG3btk2jRo1SRUWF1/YVFRWeU3cdOnQ47XxMTMw51RAbG9vkdKDL9Z3q68/%2BIGloaGzWdrgw9Nk%2B9No%2B9Br%2B7HSPXX9%2BTAdcwFq7dq3q6%2Bs9tx999FFJ0u9//3t99NFHevrpp2VZloKCgmRZlgoLC3XPPfdIkpxOpwoKCpSamipJOnLkiI4cOSKn02n/HQEAAH4r4AJW586dvW5HRX1/aLBr165q166dli1bpsWLF%2BuWW27RunXr5Ha7NWLECEnShAkTdMcddyg%2BPl79%2BvXT4sWLdf3116tLly623w8AAOC/flEnlFu3bq1Vq1Z5jlIVFxcrLy9PkZGRkqSEhAQtWLBAubm5mjBhgi699FJlZ2f7uGoAAOBvgizLsnxdxC%2BBy/Xdz847HMFq0yZKlZUn/PZ8sz%2Bgz/ah1/bx516PWPGBr0vAReKt%2Bwd5fjb5mI6JueRCSzsvv6gjWAAAAHYgYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQRiUD/uAAAPd0lEQVQsAAAAwxy%2BLgAAYNaIFR/4ugTgF48jWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHP4ugAAuNiNWPGBr0sA4GcC9gjWt99%2Bq8zMTCUnJ2vIkCHKzs5WbW2tJKmsrEx33nmn4uPjddNNN%2Bn999/32nfnzp0aNWqUnE6nJk6cqLKyMl/cBQAA4KcCMmBZlqXMzEy53W699NJLeuyxx/SXv/xFK1askGVZSk9PV/v27bVx40aNHj1aGRkZOnz4sCTp8OHDSk9PV2pqqjZs2KC2bdtq2rRpsizLx/cKAAD4i4A8RXjw4EEVFRXpgw8%2BUPv27SVJmZmZeuSRR3TdddeprKxM69atU2RkpLp3765du3Zp48aNuu%2B%2B%2B7R%2B/Xr17dtXkydPliRlZ2dr0KBB2rNnjwYMGODLuwUAAPxEQB7BiomJ0TPPPOMJVz84fvy4iouL1bt3b0VGRnrGExMTVVRUJEkqLi5WUlKSZy4iIkJ9%2BvTxzAMAAJxNQAas6OhoDRkyxHO7sbFRL774ogYOHCiXy6XY2Fiv7du1a6ejR49K0lnnAQAAziYgTxH%2B1NKlS1VSUqINGzZo9erVCg0N9ZoPDQ1VXV2dJMntdv/sfHOUl5fL5XJ5jTkckU2C24%2BFhAR7fUfLoM/2odcAzoXD8a/nikB4/gj4gLV06VKtWbNGjz32mHr06KGwsDBVVVV5bVNXV6fw8HBJUlhYWJMwVVdXp%2Bjo6GavmZ%2Bfr5ycHK%2Bx9PR0ZWZmnnXf6OiIZq%2BD80ef7UOvATRHmzZRTcb8%2BfkjoAPWwoUL9fLLL2vp0qW68cYbJUkdOnTQgQMHvLarqKjwHF3q0KGDKioqmsz36tWr2eumpaUpJSXFa8zhiFRl5Ykz7hMSEqzo6AhVV7vV0NDY7LVwbuizfeg1gHPx49dIk88fpwtudgjYgJWTk6N169Zp%2BfLlGj58uGfc6XQqLy9PJ0%2Be9By1KigoUGJiome%2BoKDAs73b7VZJSYkyMjKavXZsbGyT04Eu13eqrz/7g6ShobFZ2%2BHC0Gf70GsAzXG65wl/fv7w35ObP%2BPzzz/Xk08%2BqbvvvluJiYlyuVyer%2BTkZHXs2FFZWVkqLS1VXl6e9u7dq3HjxkmSxo4dq8LCQuXl5am0tFRZWVmKi4vjEg0AAKDZAjJgvfPOO2poaNBTTz2lwYMHe32FhIToySeflMvlUmpqql5//XXl5uaqU6dOkqS4uDg98cQT2rhxo8aNG6eqqirl5uYqKCjIx/cKAAD4iyCLS5TbwuX67mfnHY5gtWkTpcrKE357ONQf0Gf7BFKv%2BSxCoOW9df8gz88mnz9iYi650NLOS0AewQIAAPAlAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYFrBXcgdw8eKyBwACHUewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjSu5AgODq6ABw8eAIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5vB1AcDFasSKD3xdAgDAT3EECwAAwDCOYME2HBECAPxScAQLAADAMAIWAACAYQQsAAAAw3gP1mnU1tZq/vz5%2Bt///V%2BFh4dr8uTJmjx5sq/LOi3e1wQAwMWHgHUaS5Ys0b59%2B7RmzRodPnxYM2fOVKdOnTR8%2BHBflwYAAPwAAesnampqtH79ej399NPq06eP%2BvTpo9LSUr300ksELAAA0Cy8B%2Bsn9u/fr/r6eiUkJHjGEhMTVVxcrMbGRh9WBgAA/AVHsH7C5XKpTZs2Cg0N9Yy1b99etbW1qqqqUtu2bc/6O8rLy%2BVyubzGHI5IxcbGnnGfkJBgr%2B8AAPySOBz/ev0LhNdEAtZPuN1ur3AlyXO7rq6uWb8jPz9fOTk5XmMZGRm67777zrhPeXm51qx5RmlpaT8bxH7q48WctjwX5eXlys/PP%2Bc%2B49zRa/vQa3vQZ/uc72vixcR/o2ELCQsLaxKkfrgdHh7erN%2BRlpamTZs2eX2lpaX97D4ul0s5OTlNjnzBLPpsH3ptH3ptD/psn0DoNUewfqJDhw6qrKxUfX29HI7v2%2BNyuRQeHq7o6Ohm/Y7Y2Fi/TdwAAODCcQTrJ3r16iWHw6GioiLPWEFBgfr166fgYNoFAADOjsTwExERERozZozmzZunvXv3avv27Xruuec0ceJEX5cGAAD8RMi8efPm%2BbqIi83AgQNVUlKiZcuWadeuXbrnnns0duzYFl83KipKycnJioqKavG1fsnos33otX3otT3os338vddBlmVZvi4CAAAgkHCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAesiUFtbq9mzZyspKUmDBw/Wc8895%2BuSAsK3336rzMxMJScna8iQIcrOzlZtba0kqaysTHfeeafi4%2BN100036f333/dxtYFjypQpmjVrlud2SUmJxo8fL6fTqbFjx2rfvn0%2BrM7/1dXVaf78%2Bbrmmmv0q1/9SsuXL9cP14um1%2BYcOXJEU6dOVf/%2B/ZWSkqLVq1d75uizGXV1dRo1apR2797tGTvbc/POnTs1atQoOZ1OTZw4UWVlZXaX3WwErIvAkiVLtG/fPq1Zs0YPP/ywcnJy9Pbbb/u6LL9mWZYyMzPldrv10ksv6bHHHtNf/vIXrVixQpZlKT09Xe3bt9fGjRs1evRoZWRk6PDhw74u2%2B%2B98cYb2rFjh%2Bd2TU2NpkyZoqSkJG3atEkJCQmaOnWqampqfFilf1u0aJF27typZ599VsuWLdMrr7yi/Px8em3Y/fffr8jISG3atEmzZ8/WihUr9Kc//Yk%2BG1JbW6vp06ertLTUM3a25%2BbDhw8rPT1dqamp2rBhg9q2batp06bpov1AGgs%2BdeLECatfv37Whx9%2B6BnLzc21br/9dh9W5f8OHDhg9ejRw3K5XJ6xLVu2WIMHD7Z27txpxcfHWydOnPDMTZo0yXr88cd9UWrAqKystK677jpr7Nix1syZMy3Lsqz169dbKSkpVmNjo2VZltXY2Gj9%2Bte/tjZu3OjLUv1WZWWl1bt3b2v37t2esVWrVlmzZs2i1wZVVVVZPXr0sP7%2B9797xjIyMqz58%2BfTZwNKS0ut3/72t9ZvfvMbq0ePHp7Xv7M9N69YscLrtbGmpsZKSEjwev28mHAEy8f279%2Bv%2Bvp6JSQkeMYSExNVXFysxsZGH1bm32JiYvTMM8%2Boffv2XuPHjx9XcXGxevfurcjISM94YmKiioqK7C4zoDzyyCMaPXq0rrzySs9YcXGxEhMTFRQUJEkKCgpS//796fV5KigoUOvWrZWcnOwZmzJlirKzs%2Bm1QeHh4YqIiNCmTZt06tQpHTx4UIWFherVqxd9NmDPnj0aMGCA8vPzvcbP9txcXFyspKQkz1xERIT69Olz0faegOVjLpdLbdq0UWhoqGesffv2qq2tVVVVlQ8r82/R0dEaMmSI53ZjY6NefPFFDRw4UC6XS7GxsV7bt2vXTkePHrW7zICxa9cuffzxx5o2bZrXOL02q6ysTJ07d9bmzZs1fPhw/fu//7tyc3PV2NhIrw0KCwvT3LlzlZ%2BfL6fTqREjRui6667T%2BPHj6bMBt956q2bPnq2IiAiv8bP11t967/B1Ab90brfbK1xJ8tyuq6vzRUkBaenSpSopKdGGDRu0evXq0/acfp%2Bf2tpaPfzww5o7d67Cw8O95s70%2BKbX56empkaHDh3SunXrlJ2dLZfLpblz5yoiIoJeG/b5559r6NChuuuuu1RaWqqFCxfq2muvpc8t6Gy99bfeE7B8LCwsrMmD44fbP32xwvlZunSp1qxZo8cee0w9evRQWFhYk6ODdXV19Ps85eTkqG/fvl5HDH9wpsc3vT4/DodDx48f17Jly9S5c2dJ37/x9%2BWXX1bXrl3ptSG7du3Shg0btGPHDoWHh6tfv3769ttv9dRTT6lLly70uYWc7bn5TM8n0dHRttV4LjhF6GMdOnRQZWWl6uvrPWMul0vh4eEX7YPGnyxcuFDPP/%2B8li5dqhtvvFHS9z2vqKjw2q6ioqLJoWc0zxtvvKHt27crISFBCQkJ2rJli7Zs2aKEhAR6bVhMTIzCwsI84UqSLr/8ch05coReG7Rv3z517drVKzT17t1bhw8fps8t6Gy9PdN8TEyMbTWeCwKWj/Xq1UsOh8PrTXoFBQXq16%2BfgoP581yInJwcrVu3TsuXL9fIkSM9406nU3/961918uRJz1hBQYGcTqcvyvR7a9eu1ZYtW7R582Zt3rxZKSkpSklJ0ebNm%2BV0OvXJJ594/o3asiwVFhbS6/PkdDpVW1urL774wjN28OBBde7cmV4bFBsbq0OHDnkdLTl48KDi4uLocws623Oz0%2BlUQUGBZ87tdqukpOSi7T2v4D4WERGhMWPGaN68edq7d6%2B2b9%2Bu5557ThMnTvR1aX7t888/15NPPqm7775biYmJcrlcnq/k5GR17NhRWVlZKi0tVV5envbu3atx48b5umy/1LlzZ3Xt2tXzFRUVpaioKHXt2lXDhw9XdXW1Fi9erAMHDmjx4sVyu90aMWKEr8v2S1dccYWuv/56ZWVlaf/%2B/fq///s/5eXlacKECfTaoJSUFLVq1UoPPfSQvvjiC/35z3/WypUrdccdd9DnFnS25%2BaxY8eqsLBQeXl5Ki0tVVZWluLi4jRgwAAfV34GvrxGBL5XU1NjzZgxw4qPj7cGDx5sPf/8874uye%2BtWrXK6tGjx2m/LMuyvvzyS%2Bu2226z%2Bvbta40cOdL64IMPfFxx4Jg5c6bnOliWZVnFxcXWmDFjrH79%2Blnjxo2z/vrXv/qwOv9XXV1tPfjgg1Z8fLx17bXXWk888YTnmkz02pzS0lLrzjvvtPr3728NGzbMev755%2BlzC/jxdbAs6%2BzPze%2B%2B%2B651ww03WFdffbU1adIk66uvvrK75GYLsqyL9RKoAAAA/olThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8D6dUbCwtSEFwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1437975978365723945”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>57.856774858320456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.75822673567033</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>75.24600756946369</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>93.54163552100462</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.89225589225589</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.80296658074435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.5911582024114</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.37719656722517</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.44110860359561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>64.25179814528849</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3122)</td> <td class=”number”>3122</td> <td class=”number”>97.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1437975978365723945”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.8604076818165063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.3339459217591414</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.016861304290775</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.540806714053267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.14210301041518</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>98.63636363636363</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.67863720073665</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.68228404099561</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.90776699029125</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.16317991631799</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NSLP09”>PCT_NSLP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>11.13</td>

</tr> <tr>

<th>Minimum</th> <td>6.9543</td>

</tr> <tr>

<th>Maximum</th> <td>13.744</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1913276879794550963”>

</div> <div class=”col-md-12 text-right”>

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aria-controls=”quantiles1913276879794550963” role=”tab” data-toggle=”tab”>Statistics</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1913276879794550963”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>6.9543</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.9909</td>

</tr> <tr>

<th>Q1</th> <td>9.2751</td>

</tr> <tr>

<th>Median</th> <td>11.417</td>

</tr> <tr>

<th>Q3</th> <td>13.112</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.551</td>

</tr> <tr>

<th>Maximum</th> <td>13.744</td>

</tr> <tr>

<th>Range</th> <td>6.7895</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.8369</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.8825</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.16915</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.2579</td>

</tr> <tr>

<th>Mean</th> <td>11.13</td>

</tr> <tr>

<th>MAD</th> <td>1.6675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.32225</td>

</tr> <tr>

<th>Sum</th> <td>34858</td>

</tr> <tr>

<th>Variance</th> <td>3.5439</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1913276879794550963”>

<img 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xXcByOByqqKhoMF5VVdXkK7oDAABYwXYBa8iQIVq0aJH%2B/ve/e8aKi4uVnZ2tpKQkCysDAABoHNu9i3DevHmaOnWqRo4cqYiICElSRUWF%2Bvbtq8zMTIurAwAAuDrbBayoqCi99tpr2rdvn4qKiuRwOHTzzTfr9ttvV0BAgNXlAQAAXJXtApYkBQYGKikpiVOCAACgRbJdwHK5XFqxYoUOHjyoixcvNnhh%2B5/%2B9CeLKgMAAGgc2wWsX/7ylyosLNSYMWPUrp01n4ANAABwPWwXsN577z09//zzSkxMtLoUAACAa2K7yzSEhYUpKirK6jIAAACume0C1vjx4/X888%2Brrq7O6lIAAACuie1OEZaXl2v79u3as2ePunXrpqCgIK/tL7/8skWVAQAANI7tApYkjR071uoSAAAArpntAlZ2drbVJQAAAFwX270GS5JKS0u1cuVKzZkzR59//rl27typY8eOWV0WAABAo9guYJ04cUI/%2BtGP9Nprr2nXrl2qrKzUjh07lJqaqoKCAqvLAwAAuCrbBawnn3xSI0aM0O7du3XDDTdIkp5%2B%2BmkNHz5cy5Yts7g6AACAq7NdwDp48KCmTp3q9cHODodDDz74oA4fPmxhZQAAAI1ju4BVX1%2Bv%2Bvr6BuNfffWVAgMDLagIAACgaWwXsAYPHqznnnvOK2SVl5dr6dKlGjRokIWVAQAANI7tAtYjjzyiwsJCDR48WNXV1ZoxY4aGDRumkydPat68eVaXBwAAcFW2uw5WbGysfv/732v79u3629/%2Bpvr6eqWlpWn8%2BPFq27at1eUBAABcle0CliSFhoZq0qRJVpcBAABwTWwXsO66664rbuezCAEAgN3ZLmB16dLF635tba1OnDihTz/9VHfffbdFVQEAADSe7QLW5T6LMCcnR6dPn/ZxNQAAAE1nu3cRXs748eP1xhtvWF0GAADAVbWYgPXRRx9xoVEAANAi2O4U4aVe5H7%2B/Hn9z//8j372s59ZUBEAAEDT2C5gde7c2etzCCXphhtu0J133qlx48ZZVBUAAEDj2S5gPfnkk1aXAAAAcF1sF7A%2B%2BOCDRj/21ltvbcZKAAAAro3tAtaUKVM8pwjdbrdn/NtjAQEB%2Btvf/ub7AgEAAK7CdgFr9erVWrRokf793/9dAwcOVFBQkD7%2B%2BGMtXLhQP/7xjzV69GirSwQAALgi212mITs7WwsWLNDIkSPVoUMHhYeHa9CgQVq4cKFeffVVdenSxXMDAACwI9sFrNLS0kuGp7Zt2%2Brs2bMWVAQAANA0tgtYAwYM0NNPP63z5897xsrLy7V06VLdfvvtFlYGAADQOLZ7Ddb8%2BfN11113aciQIerRo4fcbrc%2B%2B%2BwzRUdH6%2BWXX7a6PAAAgKuyXcDq2bOnduzYoe3bt%2Bvo0aOSpJ///OcaM2aMQkNDLa4OAADg6mwXsCSpffv2mjRpkk6ePKlu3bpJ%2Bvpq7gAAAC2B7V6D5Xa7tWzZMt16660aO3asTp8%2BrXnz5ikrK0sXL160ujwAAICrsl3AWr9%2BvbZu3arHHntMQUFBkqQRI0Zo9%2B7dWrlypcXVAQAAXJ3tAlZubq4WLFiglJQUz9XbR48erUWLFukPf/iDxdUBAABcne0C1smTJ9W7d%2B8G47169ZLL5bKgIgAAgKaxXcDq0qWLPv744wbjb731lucF7wAAAHZmu3cR3nvvvfrVr34ll8slt9ut/fv3Kzc3V%2BvXr9cjjzxidXkAAABXZbuAlZqaqtraWj377LO6cOGCFixYoMjISM2ePVtpaWlWlwcAAHBVtjtFuH37do0aNUp79uzRvn379O6772rfvn2aOnXqNe1v2rRpXitfhw8f1qRJk%2BR0OpWamqrCwsIGxx8xYoScTqfS09P1xRdfXFc/AACg9bFdwFq4cKHnxeyRkZGKioq65n29/vrr2rt3r%2Bd%2BZWWlpk2bpsTEROXl5SkuLk7Tp09XZWWlJOnQoUPKyspSRkaGcnNzVVFRoczMzOtrCAAAtDq2C1g9evTQp59%2Bet37KS8v15IlS9SvXz/P2I4dOxQcHKy5c%2BeqZ8%2BeysrKUnh4uHbu3ClJ2rBhg5KTkzVhwgT16tVLS5Ys0d69e1VcXHzd9QAAgNbDdq/B6tWrlx5%2B%2BGE9//zz6tGjh4KDg722Z2dnN2o/Tz31lMaPH6/S0lLPWEFBgRISEjzX1woICFB8fLzy8/OVkpKigoIC3X///Z7Hd%2BrUSZ07d1ZBQQHvYAQAAI1muxWs48ePKyEhQeHh4XK5XDp58qTXrTH279%2BvDz/8UA8%2B%2BKDXuMvlUkxMjNdYVFSUTp8%2BLUkqLS294nYAAIDGsMUK1pIlS5SRkaGwsDCtX7/%2BuvZVXV2txx57TAsWLFBISIjXtqqqKs/H73wjKChINTU1kqQLFy5ccXtjlZaWNrgoqsMR1iC8mRQY2Mbra2tD/627f4k5oH/6/8evLZHDcX21220ObBGwXnzxRd17770KCwvzjE2bNk2LFi1qcihZuXKlvvvd7yopKanBtuDg4AZhqaamxhPELrc9NDS0STXk5uY2%2BNzE9PR0zZo1q0n7uRYREU2r1d/Qf%2BvuX2IO6J/%2BW6oOHcKN7Mcuc2CLgOV2uxuMffDBB6qurm7yvl5//XWVlZUpLi5OkjyBadeuXRo7dqzKysq8Hl9WVuYJcbGxsZfcHh0d3aQaJk%2BerOHDh3uNORxhOnv2qybtpykCA9soIiJUFRVVqqurb7bj2BX9t%2B7%2BpeadgzuWvW10f83tjw83/APT37X2nwF/6P96nyMvNwemgltT2SJgmbR%2B/XrV1tZ67i9btkyS9PDDD%2BuDDz7Q2rVr5Xa7FRAQILfbrYMHD%2BqBBx6QJDmdTh04cEApKSmSpJKSEpWUlMjpdDaphpiYmAYrby7XOdXWNv9/%2Brq6ep8cx67ov3X3LzEHklp1/639378l92%2BqbrvMgd8FrC5dunjdDw//Orl2795dUVFRWr58uRYvXqyf/vSn2rhxo6qqqpScnCxJSktL05QpUzRgwAD169dPixcv1tChQ3kHIQAAaBJ7vBJM8lw6oTm1bdtWzz33nGeVqqCgQGvWrPG89isuLk4LFy5UTk6O0tLS1L59%2B0ZfFgIAAOAbtlnBWrRokdc1ry5evKilS5d6VqC%2B0dTA8%2BSTT3rd79%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%2B%2BqjatGmjuXPnKj09Xbfccou2bNmi3bt3KyMjQzt27FDnzp116tQppaena%2BbMmUpKSlJOTo4efPBBbdu2TQEBAVa31qIlr3jX6hIAAPAJvwxYx44dU35%2Bvt5991117NhRkjRr1iw99dRTGjJkiIqLi7Vx40aFhYWpZ8%2Be2r9/v7Zs2aKZM2dq06ZN%2Bu53v6tf/OIXkqTs7Gx973vf01/%2B8hfddtttVrYFAABaCL88RRgdHa3nn3/eE66%2Bcf78eRUUFKhPnz4KCwvzjCckJCg/P1%2BSVFBQoMTERM%2B20NBQ9e3b17MdAADgavxyBSsiIkJJSUme%2B/X19dqwYYMGDRokl8ulmJgYr8dHRUXp9OnTknTV7QCuH6eLAfg7vwxY37Z06VIdPnxYmzdv1rp16xQUFOS1PSgoSDU1NZKkqqqqK25vjNLSUrlcLq8xhyOsQXAzKTCwjddXAK2Xw8HvgeZi17n1h%2BeA651bu82B3wespUuX6qWXXtIzzzyjW265RcHBwSovL/d6TE1NjUJCQiRJwcHBDcJUTU2NIiIiGn3M3NxcrVy50mssPT1ds2bNusYuGi8iIrTZjwHA3jp0CLe6BL9l97ltyc8BpubWLnPg1wHriSee0KuvvqqlS5dq5MiRkqTY2FgdOXLE63FlZWWe1aXY2FiVlZU12N67d%2B9GH3fy5MkaPny415jDEaazZ7%2B6ljYaJTCwjSIiQlVRUaW6uvpmOw4A%2B2vO3zWtnV3n1h%2BeA653bi83B1aFYr8NWCtXrtTGjRv19NNPa9SoUZ5xp9OpNWvW6MKFC55VqwMHDighIcGz/cCBA57HV1VV6fDhw8rIyGj0sWNiYhqcDnS5zqm2tvn/09fV1fvkOADsi98Bzcfuc9uSnwNM1W2XObDHiUrDjh49qlWrVun%2B%2B%2B9XQkKCXC6X5zZw4EB16tRJmZmZKioq0po1a3To0CFNnDhRkpSamqqDBw9qzZo1KioqUmZmprp27colGgAAQKP5ZcD605/%2BpLq6Oj377LMaPHiw1y0wMFCrVq2Sy%2BVSSkqKtm3bppycHHXu3FmS1LVrV/32t7/Vli1bNHHiRJWXlysnJ4eLjAIAgEbzy1OE06ZN07Rp0y67vXv37tqwYcNlt3//%2B9/X97///eYoDQAAtAJ%2BuYIFAABgJQIWAACAYX55ihAAWjOulA9YjxUsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIx3EQIA0Ei8QxONxQoWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIY5rC4A1yd5xbtWlwAAAL6FFSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYl1BdXa1HH31UiYmJGjx4sH73u99ZXRIAAGhB%2BKicS1iyZIkKCwv10ksv6dSpU5o3b546d%2B6sUaNGWV0aAABoAQhY31JZWalNmzZp7dq16tu3r/r27auioiK98sorBCwAANAonCL8lk8%2B%2BUS1tbWKi4vzjCUkJKigoED19fUWVgYAAFoKVrC%2BxeVyqUOHDgoKCvKMdezYUdXV1SovL1dkZORV91FaWiqXy%2BU15nCEKSYmxni9AAD4A4fj%2BtZ8AgPbeH21GgHrW6qqqrzClSTP/ZqamkbtIzc3VytXrvQay8jI0MyZM80U%2BQ8%2BXPz1acvS0lLl5uZq8uTJrTLI0X/r7l9iDuif/ltz/9LXc/DSS8/bZg7sEfNsJDg4uEGQ%2BuZ%2BSEhIo/YxefJk5eXled0mT55svNZ/5HK5tHLlygYrZ60F/bfu/iXmgP7pvzX3L9lvDljB%2BpbY2FidPXtWtbW1cji%2Bnh6Xy6WQkBBFREQ0ah8xMTG2SM8AAMAarGB9S%2B/eveVwOJSfn%2B8ZO3DggPr166c2bZguAABwdSSGbwkNDdWECRP0%2BOOP69ChQ9q9e7d%2B97vf6a677rK6NAAA0EIEPv74449bXYTdDBo0SIcPH9by5cu1f/9%2BPfDAA0pNTbW6rKsKDw/XwIEDFR4ebnUplqD/1t2/xBzQP/235v4le81BgNvtdltdBAAAgD/hFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAJWC5eXl6fvfOc7DW69evWyujSfKSkp0fTp0xUfH69zdL47AAAIDklEQVThw4dr3bp1VpfkU59//rlmzZqlxMRE3XHHHcrLy7O6JJ%2BoqanR2LFj9f7773vGiouLdc8992jAgAEaPXq03nnnHQsrbH6XmoNvHDt2THFxcRZU5TuX6j8/P18//elPFRcXp5EjR2rTpk0WVti8LtX/22%2B/rXHjxql///4aN26c9u7da2GFze9KPwPnzp1TUlKSZb8THZYcFcaMHj1aSUlJnvu1tbW6%2B%2B67NXToUOuK8rHZs2erc%2BfOysvL05EjR/Twww%2BrS5cuuuOOO6wurdm53W6lp6ervr5eL7/8ss6cOaN58%2Bapbdu2%2BuEPf2h1ec2murpac%2BbMUVFRkWfsm7m45ZZbtGXLFu3evVsZGRnasWOHOnfubGG1zeNSc/CN//3f/9WMGTNUXV1tQWW%2Bcan%2BXS6X7r//fqWlpenJJ5/UX//6V2VmZio6Otrvfideqv8TJ04oIyND//Zv/6Yf/OAH2r17t9LT07Vz50517drVwmqbx5V%2BBiRp6dKlKi0t9XFV/48VrBYuJCRE0dHRntu2bdvkdrv18MMPW12aT3z55ZfKz8/XjBkz1KNHD40YMUJJSUnav3%2B/1aX5RGFhoT766CMtX75cffr00bBhw3TffffphRdesLq0ZnPkyBH95Cc/0d///nev8ffee0/FxcVauHChevbsqenTp2vAgAHasmWLRZU2n8vNgSTt2rVLqampCg4OtqAy37hc/7t371bHjh310EMPqUePHhozZowmTJigP/zhDxZV2jwu1//p06f1k5/8RPfcc4%2B6deumqVOnKiwsTIcOHbKo0uZzpZ8BSfrwww/13nvvKTo62seV/T8Clh8pLy/X2rVrNWfOHAUFBVldjk%2BEhIQoNDRUeXl5unjxoo4dO6aDBw%2Bqd%2B/eVpfmE8XFxYqMjFS3bt08Y9/5zndUWFioixcvWlhZ8/nLX/6i2267Tbm5uV7jBQUF6tOnj8LCwjxjCQkJys/P93WJze5ycyBJe/bs0UMPPaRHHnnEgsp843L9JyUlKTs7u8Hjz58/76vSfOJy/d92223KysqSJF28eFGbNm1STU2N%2Bvfvb0WZzepKPwM1NTX65S9/qQULFlj6XMgpQj/y6quvKiYmRqNGjbK6FJ8JDg7WggUL9MQTT%2Bjll19WXV2dUlJSNGnSJKtL84mOHTvq3LlzqqqqUmhoqKSv/4qtra3VuXPnFBkZaXGF5v3sZz%2B75LjL5VJMTIzXWFRUlE6fPu2LsnzqcnMgyRMw9u3b56tyfO5y/Xft2tXrVNjnn3%2Bu119/XTNnzvRVaT5xpX9/6etThcnJyaqrq9OcOXP88vTgleZg9erV6tOnjwYPHuzDihpiBctPuN1ubdq0SXfeeafVpfjc0aNHNWzYMOXm5io7O1s7d%2B7Utm3brC7LJ5xOp2JiYvTEE0%2BosrJSJ06c0IsvvihJfruCdTlVVVUN/loNCgpSTU2NRRXBShcuXNDMmTPVsWNHTZ482epyfCoyMlKbN2/WggUL9Nvf/la7du2yuiSfOXLkiDZu3KjMzEyrS2EFy198/PHHOnPmjMaMGWN1KT61f/9%2Bbd68WXv37lVISIj69eunM2fO6Nlnn9W4ceOsLq/ZBQcHa8WKFZo9e7YSEhIUFRWl%2B%2B67T9nZ2Wrbtq3V5flUcHCwysvLvcZqamoUEhJiUUWwyldffaUHH3xQn332mf7zP//Ts7rbWrRr1059%2BvRRnz59dPToUW3YsEEjR460uqxm53a7NX/%2BfM2aNUsdO3a0uhxWsPzF22%2B/rcTERLVv397qUnyqsLBQ3bt393oS7dOnj06dOmVhVb7Vv39/vfnmm3rrrbe0Z88e3XjjjerQoYPCw8OtLs2nYmNjVVZW5jVWVlbW4LQh/Nv58%2Bd17733qqioSC%2B99JJ69OhhdUk%2BU1RUpA8//NBrrGfPnjp79qxFFfnWqVOn9NFHH%2Bmpp55SXFyc4uLidOrUKT322GO67777fF4PK1h%2B4tChQ4qPj7e6DJ%2BLiYnRiRMnVFNT4zk9dOzYMb98zcGllJeXa8aMGVq1apXn3TJ79uzRwIEDLa7M95xOp9asWaMLFy54AveBAweUkJBgcWXwlfr6emVkZOjkyZNav369evbsaXVJPvXnP/9ZeXl5euONNxQQECBJ%2Butf/6qbbrrJ4sp8IzY2Vv/93//tNTZlyhRNmTLFkjMarGD5iaKiIt18881Wl%2BFzw4cP1w033KD58%2Bfr%2BPHjevPNN7V69WpNmTLF6tJ84p/%2B6Z9UWVmppUuXqri4WJs2bdKWLVss%2BWvNagMHDlSnTp2UmZmpoqIirVmzRocOHdLEiROtLg0%2BsnnzZr3//vtatGiRIiIi5HK55HK5Gpw69lfjxo2Ty%2BXSsmXL9Nlnn%2BmVV17Rtm3bNH36dKtL8wmHw6Hu3bt73RwOh6KiohQbG%2Bv7enx%2BRDSLsrIyRUREWF2Gz7Vr107r1q3T4sWLNXHiREVGRmrGjBmt6kWtzzzzjB577DH96Ec/UteuXfWb3/zGL9%2BWfTWBgYFatWqVsrKylJKSou7duysnJ8cvLzKKS9u1a5fq6%2BsbBIqBAwdq/fr1FlXlO//8z/%2BsF154Qb/%2B9a%2B1YcMGdenSRb/5zW/Ut29fq0trlQLcbrfb6iIAAAD8CacIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCw/wNseYAhrC3RGwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1913276879794550963”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.131342681563643</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.134279038266653</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.54900610078667</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.229950165885779</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.77667438263873</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.647286187799049</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.898951226099808</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.250761015699586</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.112030629125861</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.990789126359896</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1913276879794550963”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.954297724061087</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:71%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.434083150867912</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.502499503897138</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.590923238935214</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.6672970895081125</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.131342681563643</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.134279038266653</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.229950165885779</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.551342827833837</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.743785560685042</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_NSLP15”><s>PCT_NSLP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_NSLP09”><code>PCT_NSLP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98444</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_OBESE_ADULTS08”>PCT_OBESE_ADULTS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>244</td>

</tr> <tr>

<th>Unique (%)</th> <td>7.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>28.932</td>

</tr> <tr>

<th>Minimum</th> <td>11.7</td>

</tr> <tr>

<th>Maximum</th> <td>43.7</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4290272488759641525”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4290272488759641525,#minihistogram-4290272488759641525”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4290272488759641525”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4290272488759641525”

aria-controls=”quantiles-4290272488759641525” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4290272488759641525” aria-controls=”histogram-4290272488759641525”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4290272488759641525” aria-controls=”common-4290272488759641525”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4290272488759641525” aria-controls=”extreme-4290272488759641525”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4290272488759641525”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>11.7</td>

</tr> <tr>

<th>5-th percentile</th> <td>22.2</td>

</tr> <tr>

<th>Q1</th> <td>27.2</td>

</tr> <tr>

<th>Median</th> <td>29.1</td>

</tr> <tr>

<th>Q3</th> <td>31</td>

</tr> <tr>

<th>95-th percentile</th> <td>34.6</td>

</tr> <tr>

<th>Maximum</th> <td>43.7</td>

</tr> <tr>

<th>Range</th> <td>32</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.8</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.7026</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.12798</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.2043</td>

</tr> <tr>

<th>Mean</th> <td>28.932</td>

</tr> <tr>

<th>MAD</th> <td>2.664</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.47329</td>

</tr> <tr>

<th>Sum</th> <td>90615</td>

</tr> <tr>

<th>Variance</th> <td>13.71</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4290272488759641525”>

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UrFkzz/yZYcnlcikqKkphYWGex2fOh4fX/z9WWlqaUlJSLGM2W4QOHz5W79fwVyEhwYqKCldFhVPV1TW%2BLsfn6IcV/TDDn3/WsEas6IdVXf3w1T84/C5gxcbGau/evZaxsrIyz%2BW52NhYlZWV1Zrv1q2bLr74YoWFhamsrEydO3eWJJ08eVLl5eWKjo6udw0xMTG1Lgc6HEd08iSL/5Tq6hr6cRr6YUU//phA6B1rxIp%2BWDWGfvj%2BIqVhdrtdX375pedynyTl5%2BfLbrd75vPz8z1zTqdTu3fvlt1uV3BwsOLi4izzBQUFstls6tq1q/cOAgAANGl%2BF7CSkpLUpk0bTZ06VUVFRcrNzdXOnTs1atQoSdLIkSO1Y8cO5ebmqqioSFOnTlX79u3Vu3dvSb9%2BeP6ll17Spk2btHPnTs2cOVM33njjOV0iBAAAgc3vAlZISIgWLlwoh8Oh1NRUvfnmm1qwYIHatm0rSWrfvr2ee%2B45rVmzRqNGjVJ5ebkWLFigoKAgSdLQoUM1ceJEzZgxQ%2BPHj9cVV1yhBx980JeHBAAAmpgg96lbmKNBORxHfF1Co2CzBatFi0gdPnzM59fHGwP6YdVY%2BzFk/se%2BLuGcvHNvX1%2BX0GAa6xrxFfphVVc/oqMv9EktfncGCwAAwNcIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjXA9bo0aO1cuVKHTlyxNu7BgAA8AqvB6w%2Bffpo0aJF6tevn%2B677z599NFHcrvd3i4DAACgwXg9YN1///364IMPtHDhQoWEhOiee%2B5R//799cwzz%2Bibb77xdjkAAADG2Xyx06CgIPXt21d9%2B/aV0%2BnUsmXLtHDhQuXm5qpXr1669dZbdc011/iiNAAAgD/MJwFLkkpLS/Xmm2/qzTff1Ndff61evXrphhtuUElJiR5%2B%2BGFt375d06dP91V5AAAA583rAeuNN97QG2%2B8oa1bt6ply5YaMWKEnn32WXXq1MmzTZs2bTRnzhwCFgAAaJK8HrCmT5%2BuAQMGaMGCBbr66qsVHFz7Y2CXXHKJbr75Zm%2BXBgAAYITXA9aHH36oFi1aqLy83BOudu7cqe7duyskJESS1KtXL/Xq1cvbpQEAABjh9d8iPHr0qAYPHqwXX3zRMzZhwgQNHz5chw4d8nY5AAAAxnk9YP3lL39Rx44ddfvtt3vGNmzYoDZt2igrK8vb5QAAABjn9YD12Wef6aGHHlJ0dLRnrGXLlpo8ebI%2B/fRTY/s5dOiQJk6cqF69eiklJUWvvPKKZ2737t0aPXq07Ha7Ro4cqV27dlmeu379eg0cOFB2u13p6en6%2BeefjdUFAAD8n9cDls1mU0VFRa1xp9Np9I7u9957ryIiIrR27VpNmzZN8%2BfP13vvvafjx49rwoQJSkxM1Nq1axUfH6%2BJEyfq%2BPHjkn79PNj06dOVkZGhvLw8VVRUaOrUqcbqAgAA/s/rAevqq6/W7Nmz9d1333nGiouLlZWVpeTkZCP7%2BOWXX1RQUKC7775bnTp10sCBA5WcnKwtW7Zow4YNCgsL0%2BTJk9W5c2dNnz5dkZGRevfddyVJy5cv15AhQzRixAh17dpVc%2BfO1ebNm1VcXGykNgAA4P%2B8HrCmTJkil8ulQYMGqXfv3urdu7euueYanThxwtiZombNmik8PFxr167ViRMntH//fu3YsUPdunVTYWGhEhISFBQUJOnXu8r36tVLBQUFkqTCwkIlJiZ6XqtNmzZq27atCgsLjdQGAAD8n9dv09CqVSutW7dOn3zyiYqKimSz2dSlSxddddVVntDzR4WFhWnGjBl6/PHHtXTpUlVXVys1NVWjR4/W%2B%2B%2B/ry5dutSqqaioSNKvd5iPiYmpNV9SUlLv/ZeWlsrhcFjGbLaIWq8biEJCgi3fAx39sKIfZths/ts/1ogV/bBqTP3wyZ/KCQkJUXJysrFLgnXZt2%2BfBgwYoNtvv11FRUV6/PHHddVVV8npdCo0NNSybWhoqFwulySpsrLyrPP1kZeXp5ycHMtYenq6MjMzz/No/E9UVLivS2hU6IcV/fhjWrSI9HUJDY41YkU/rBpDP7wesBwOh%2BbPn68dO3boxIkTtT7Y/v777//hfWzZskWrV6/W5s2b1axZM8XFxenHH3/U888/rw4dOtQKSy6XS82aNZP069mvuubDw%2Bv/HystLU0pKSmWMZstQocPHzvPI/IfISHBiooKV0WFU9XVNb4ux%2BfohxX9MMOff9awRqzoh1Vd/fDVPzi8HrAeeeQR7dq1S0OHDtWFF17YIPvYtWuXOnbs6AlNknT55Zdr0aJFSkxMVFlZmWX7srIyz%2BW72NjYOudPv63E74mJial1OdDhOKKTJ1n8p1RX19CP09APK/rxxwRC71gjVvTDqjH0w%2BsB69NPP9XixYstHyQ3LSYmRgcOHJDL5fJc7tu/f7/at28vu92uF198UW63W0FBQXK73dqxY4fuuusuSZLdbld%2Bfr5SU1Ml/Xo/rUOHDslutzdYvQAAwL94/VNgERERatWqVYPuIyUlRRdccIEefvhhffPNN/rf//1fLVq0SOPGjdPgwYNVUVGhOXPmaO/evZozZ46cTqeGDBkiSRozZozeeOMNrVq1Snv27NHkyZPVv39/dejQoUFrBgAA/sPrAWv48OFavHixqqurG2wfF154oV555RU5HA6NGjVKWVlZuvvuu5WWlqbmzZvrhRde8JylKiwsVG5uriIiIiRJ8fHxmjVrlhYsWKAxY8booosu4k/4AACAc%2BL1S4Tl5eVav369/v73v6tDhw61fmNv6dKlRvbTpUsXvfzyy3XOXXHFFVq3bt1vPjc1NdVziRAAAOBc%2BeQ2DcOGDfPFbgEAALzC6wGLy20AAMDf%2BeRWp6WlpcrJydH999%2Bvn376Se%2B%2B%2B67279/vi1IAAACM83rAOnDggK677jqtW7dOGzdu1PHjx7VhwwaNHDmSv/cHAAD8gtcD1hNPPKGBAwdq06ZNuuCCCyRJTz/9tFJSUjRv3jxvlwMAAGCc1wPWjh07dPvtt1v%2BsLPNZtOkSZO0e/dub5cDAABgnNcDVk1NjWpqat%2B%2B/tixYwoJCfF2OQAAAMZ5PWD169dPL7zwgiVklZeXKzs7W3369PF2OQAAAMZ5PWA99NBD2rVrl/r166eqqirdfffdGjBggL7//ntNmTLF2%2BUAAAAY5/X7YMXGxur111/X%2BvXr9dVXX6mmpkZjxozR8OHD1bx5c2%2BXAwAAYJxP7uQeHh6u0aNH%2B2LXAAAADc7rAeuWW24567ypv0UIAADgK14PWO3atbM8PnnypA4cOKCvv/5at956q7fLAQAAMK7R/C3CBQsWqKSkxMvVAAAAmOeTv0VYl%2BHDh%2Budd97xdRkAAAB/WKMJWJ9//jk3GgUAAH6hUXzI/ejRo/rHP/6hsWPHerscAAAA47wesNq2bWv5O4SSdMEFF%2Bjmm2/W9ddf7%2B1yAAAAjPN6wHriiSe8vUsAAACv8nrA2r59e723vfLKKxuwEgAAgIbh9YA1btw4zyVCt9vtGT9zLCgoSF999ZW3ywMAAPjDvB6wFi1apNmzZ%2BvBBx9UUlKSQkND9cUXX2jWrFm64YYbdO2113q7JABeNmT%2Bx74uAQAalNdv05CVlaUZM2Zo0KBBatGihSIjI9WnTx/NmjVLK1asULt27TxfAAAATZHXA1ZpaWmd4al58%2BY6fPiwt8sBAAAwzusBq2fPnnr66ad19OhRz1h5ebmys7N11VVXebscAAAA47z%2BGayHH35Yt9xyi66%2B%2Bmp16tRJbrdb3377raKjo7V06VJvlwMAAGCc1wNW586dtWHDBq1fv1779u2TJN10000aOnSowsPDvV0OAACAcV4PWJJ00UUXafTo0fr%2B%2B%2B/VoUMHSb/ezR0AAMAfeP0zWG63W/PmzdOVV16pYcOGqaSkRFOmTNH06dN14sQJb5cDAABgnNcD1rJly/TGG2/o0UcfVWhoqCRp4MCB2rRpk3JycrxdDgAAgHFeD1h5eXmaMWOGUlNTPXdvv/baazV79my99dZb3i4HAADAOK8HrO%2B//17dunWrNd61a1c5HA5vlwMAAGCc1wNWu3bt9MUXX9Qa//DDDz0feDfB5XLpscce05VXXql//dd/1dNPP%2B35O4e7d%2B/W6NGjZbfbNXLkSO3atcvy3PXr12vgwIGy2%2B1KT0/Xzz//bKwuAADg/7wesO644w499thjWrp0qdxut7Zs2aJ58%2BZp7ty5GjdunLH9zJ49W5988oleeuklPfXUU3rttdeUl5en48ePa8KECUpMTNTatWsVHx%2BviRMn6vjx45KknTt3avr06crIyFBeXp4qKio0depUY3UBAAD/5/XbNIwcOVInT57U888/r8rKSs2YMUMtW7bUvffeqzFjxhjZR3l5udasWaOXX35ZV1xxhSRp/PjxKiwslM1mU1hYmCZPnqygoCBNnz5dH374od59912lpqZq%2BfLlGjJkiEaMGCFJmjt3rgYMGKDi4mKjZ9gAAID/8nrAWr9%2BvQYPHqy0tDT9/PPPcrvdatWqldF95Ofnq3nz5kpKSvKMTZgwQZL0yCOPKCEhwfMB%2B6CgIPXq1UsFBQVKTU1VYWGh7rzzTs/z2rRpo7Zt26qwsJCABQAA6sXrlwhnzZrl%2BTB7y5YtjYcrSSouLla7du30%2Buuva/Dgwfr3f/93LViwQDU1NXI4HIqJibFs36pVK5WUlEj69Y9Rn20eAADg93j9DFanTp309ddfq0uXLg22j%2BPHj%2BvAgQNauXKlsrKy5HA4NGPGDIWHh8vpdHruv3VKaGioXC6XJKmysvKs8/VRWlpa6zcibbaIWsEtEIWEBFu%2BBzr6gYZgs/nveuI9Y0U/rBpTP7wesLp27aoHHnhAixcvVqdOnRQWFmaZz8rK%2BsP7sNlsOnr0qJ566im1a9dOknTw4EGtWLFCHTt2rBWWXC6XmjVrJkkKCwurc/5c/k5iXl5erZumpqenKzMz83wOxy9FRfF3J09HP2BSixaRvi6hwfGesaIfVo2hH14PWN98840SEhIkqcHuexUdHa2wsDBPuJKkP/3pTzp06JCSkpJUVlZm2b6srMxzdik2NrbO%2Bejo6HrvPy0tTSkpKZYxmy1Chw8fO9dD8TshIcGKigpXRYVT1dU1vi7H5%2BgHGkLi9Hd9XUK9vfdA8jltz3vGin5Y1dUPX/2DwysBa%2B7cucrIyFBERISWLVvW4Puz2%2B2qqqrSN998oz/96U%2BSpP3796tdu3ay2%2B168cUX5Xa7FRQUJLfbrR07duiuu%2B7yPDc/P1%2BpqamSpEOHDunQoUOy2%2B313n9MTEyty4EOxxGdPMniP6W6uoZ%2BnIZ%2BIFCd77rnPWNFP6waQz%2B8cpHy5ZdfltPptIxNmDBBpaWlDbK/Sy65RP3799fUqVO1Z88e/d///Z9yc3M1ZswYDR48WBUVFZozZ4727t2rOXPmyOl0asiQIZKkMWPG6I033tCqVau0Z88eTZ48Wf379%2Bc3CAEAQL15JWCduoP66bZv366qqqoG2%2Be8efP0z//8zxozZoymTJmim266SePGjVPz5s31wgsveM5SFRYWKjc3VxEREZKk%2BPh4zZo1SwsWLNCYMWN00UUXGflcGAAACBxe/wyWt1x44YWaO3dunXNXXHGF1q1b95vPTU1N9VwiBAAAOFe%2B/z1GAAAAP%2BO1gHXqzukAAAD%2BzmuXCGfPnm2559WJEyeUnZ2tyEjrr0/yeScAANDUeSVgXXnllbXueRUfH6/Dhw/r8OHD3igBAADAa7wSsLxx7ysAAIDGgg%2B5AwAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDC/D1gTJkzQQw895Hm8e/dujR49Wna7XSNHjtSuXbss269fv14DBw6U3W5Xenq6fv75Z2%2BXDAAAmji/Dlhvv/22Nm/e7Hl8/PhxTZgwQYmJiVq7dq3i4%2BM1ceJEHT9%2BXJK0c%2BdOTZ8%2BXRkZGcrLy1NMBP3MAAASpElEQVRFRYWmTp3qq/IBAEAT5bcBq7y8XHPnzlVcXJxnbMOGDQoLC9PkyZPVuXNnTZ8%2BXZGRkXr33XclScuXL9eQIUM0YsQIde3aVXPnztXmzZtVXFzsq8MAAABNkN8GrCeffFLDhw9Xly5dPGOFhYVKSEhQUFCQJCkoKEi9evVSQUGBZz4xMdGzfZs2bdS2bVsVFhZ6t3gAANCk2XxdQEPYsmWLPvvsM7311luaOXOmZ9zhcFgClyS1atVKRUVFkqTS0lLFxMTUmi8pKTmn/ZeWlsrhcFjGbLaIWq8diEJCgi3fAx39QKCz2c5t7fOesaIfVo2pH34XsKqqqvToo49qxowZatasmWXO6XQqNDTUMhYaGiqXyyVJqqysPOt8feXl5SknJ8cylp6erszMzHN6HX8WFRXu6xIaFfqBQNWiReR5PY/3jBX9sGoM/fC7gJWTk6MePXooOTm51lxYWFitsORyuTxB7Lfmw8PP7T9UWlqaUlJSLGM2W4QOHz52Tq/jj0JCghUVFa6KCqeqq2t8XY7P0Q8EunP9uch7xop%2BWNXVj/MN8X%2BU3wWst99%2BW2VlZYqPj5ckT2DauHGjhg0bprKyMsv2ZWVlnkt3sbGxdc5HR0efUw0xMTG1Lgc6HEd08iSL/5Tq6hr6cRr6gUB1vuue94wV/bBqDP3wu4C1bNkynTx50vN43rx5kqQHHnhA27dv14svvii3262goCC53W7t2LFDd911lyTJbrcrPz9fqampkqRDhw7p0KFDstvt3j8QAADQZPldwGrXrp3lcWTkr6cGO3bsqFatWumpp57SnDlz9Oc//1krV66U0%2BnUkCFDJEljxozRuHHj1LNnT8XFxWnOnDnq37%2B/OnTo4PXjAAAATZfvP2bvRc2bN9cLL7zgOUtVWFio3NxcRURESJLi4%2BM1a9YsLViwQGPGjNFFF12krKwsH1cNAACamiC32%2B32dRGBwOE44usSGgWbLVgtWkTq8OFjPr8%2B3hgEaj%2BGzP/Y1yWgkXjn3r7ntH2gvmd%2BC/2wqqsf0dEX%2BqSWgDqDBQAA4A0ELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwvw1YP/74ozIzM5WUlKTk5GRlZWWpqqpKklRcXKzbbrtNPXv21LXXXquPPvrI8txPPvlEw4YNk91u1y233KLi4mJfHAIAAGii/DJgud1uZWZmyul06tVXX9UzzzyjDz74QPPnz5fb7VZ6erpat26tNWvWaPjw4crIyNDBgwclSQcPHlR6erpSU1O1evVqtWzZUpMmTZLb7fbxUQEAgKbC5usCGsL%2B/ftVUFCgjz/%2BWK1bt5YkZWZm6sknn9TVV1%2Bt4uJirVy5UhEREercubO2bNmiNWvW6J577tGqVavUo0cPjR8/XpKUlZWlvn37atu2berdu7cvDwsAADQRfnkGKzo6WosXL/aEq1OOHj2qwsJCXX755YqIiPCMJyQkqKCgQJJUWFioxMREz1x4eLi6d%2B/umQcAAPg9fhmwoqKilJyc7HlcU1Oj5cuXq0%2BfPnI4HIqJibFs36pVK5WUlEjS784DAAD8Hr%2B8RHim7Oxs7d69W6tXr9Yrr7yi0NBQy3xoaKhcLpckyel0nnW%2BPkpLS%2BVwOCxjNltEreAWiEJCgi3fAx39QKCz2c5t7fOesaIfVo2pH34fsLKzs7VkyRI988wzuuyyyxQWFqby8nLLNi6XS82aNZMkhYWF1QpTLpdLUVFR9d5nXl6ecnJyLGPp6enKzMw8z6PwP1FR4b4uoVGhHwhULVpEntfzeM9Y0Q%2BrxtAPvw5Yjz/%2BuFasWKHs7GwNGjRIkhQbG6u9e/datisrK/OcXYqNjVVZWVmt%2BW7dutV7v2lpaUpJSbGM2WwROnz42Pkchl8JCQlWVFS4Kiqcqq6u8XU5Pkc/EOjO9eci7xkr%2BmFVVz/ON8T/UX4bsHJycrRy5Uo9/fTTGjx4sGfcbrcrNzdXlZWVnrNW%2Bfn5SkhI8Mzn5%2Bd7tnc6ndq9e7cyMjLqve%2BYmJhalwMdjiM6eZLFf0p1dQ39OA39QKA633XPe8aKflg1hn74/iJlA9i3b58WLlyoO%2B%2B8UwkJCXI4HJ6vpKQktWnTRlOnTlVRUZFyc3O1c%2BdOjRo1SpI0cuRI7dixQ7m5uSoqKtLUqVPVvn17btEAAADqzS8D1vvvv6/q6mo9//zz6tevn%2BUrJCRECxculMPhUGpqqt58800tWLBAbdu2lSS1b99ezz33nNasWaNRo0apvLxcCxYsUFBQkI%2BPCgAANBVBbm5R7hUOxxFfl9Ao2GzBatEiUocPH/P56dvGwGQ/hsz/2FBVgPe8c2/fc9qenyFW9MOqrn5ER1/ok1r88gwWAACALxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYJjf3mgUAND4NbXffj3X33pE4OIMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzObrAoDGasj8j31dAgCgieIMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwbjQKAEA9NbUbEL9zb19flxCwOIMFAABgGAELAADAMC4Rwmua2ql1AADOF2ew6lBVVaVp06YpMTFR/fr101//%2BldflwQAAJoQzmDVYe7cudq1a5eWLFmigwcPasqUKWrbtq0GDx7s69IAAEATQMA6w/Hjx7Vq1Sq9%2BOKL6t69u7p3766ioiK9%2BuqrBCwAQJPSlD6a4W%2B/8UjAOsOePXt08uRJxcfHe8YSEhK0aNEi1dTUKDi4cV1VbUpvHgAAAgUB6wwOh0MtWrRQaGioZ6x169aqqqpSeXm5WrZs%2BbuvUVpaKofDYRmz2SIUExNjvF4AAPyBzfbHT2CEhARbvvsSAesMTqfTEq4keR67XK56vUZeXp5ycnIsYxkZGbrnnnvMFHmaz%2BY0rcuWpaWlysvLU1paGoFT9ONM9MOKftRGT6zoh1VpaamWLFncKPrh%2B4jXyISFhdUKUqceN2vWrF6vkZaWprVr11q%2B0tLSjNfaFDkcDuXk5NQ6wxeo6IcV/bCiH7XREyv6YdWY%2BsEZrDPExsbq8OHDOnnypGy2X9vjcDjUrFkzRUVF1es1YmJifJ6cAQCA73AG6wzdunWTzWZTQUGBZyw/P19xcXGN7gPuAACgcSIxnCE8PFwjRozQzJkztXPnTm3atEl//etfdcstt/i6NAAA0ESEzJw5c6avi2hs%2BvTpo927d%2Bupp57Sli1bdNddd2nkyJG%2BLstvREZGKikpSZGRkb4upVGgH1b0w4p%2B1EZPrOiHVWPpR5Db7Xb7tAIAAAA/wyVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELDQ4l8ulYcOGaevWrZ6x2bNn61/%2B5V8sX8uXL/dhlQ3vxx9/VGZmppKSkpScnKysrCxVVVVJkoqLi3XbbbepZ8%2Beuvbaa/XRRx/5uNqGd7Z%2BBOL6OHDggO644w7Fx8erf//%2BWrx4sWcuENfH2foRiOvjdBMmTNBDDz3kebx7926NHj1adrtdI0eO1K5du3xYnfed2Y%2B777671vr44IMPvF6Xzet7RECpqqrS/fffr6KiIsv4vn37dP/99%2BuGG27wjDVv3tzb5XmN2%2B1WZmamoqKi9Oqrr%2BqXX37RtGnTFBwcrMmTJys9PV2XXXaZ1qxZo02bNikjI0MbNmxQ27ZtfV16gzhbP6ZMmRJw66OmpkYTJkxQXFyc1q1bpwMHDui%2B%2B%2B5TbGyshg0bFnDr42z9uO666wJufZzu7bff1ubNmz3Hfvz4cU2YMEHXXXednnjiCa1YsUITJ07Ue%2B%2B9p4iICB9X2/DO7If06/%2B/ZGdn66qrrvKMXXTRRV6vjYCFBrN3717df//9quuvMe3bt0933HGHoqOjfVCZ9%2B3fv18FBQX6%2BOOP1bp1a0lSZmamnnzySV199dUqLi7WypUrFRERoc6dO2vLli1as2aN7rnnHh9X3jDO1o9TASuQ1kdZWZm6deummTNnqnnz5urUqZOuuuoq5efnq3Xr1gG3Ps7Wj1MBK5DWxynl5eWaO3eu4uLiPGMbNmxQWFiYJk%2BerKCgIE2fPl0ffvih3n33XaWmpvqw2oZXVz9cLpe%2B//57xcXF%2BXx9cIkQDWbbtm3q3bu38vLyLONHjx7Vjz/%2BqE6dOvmmMB%2BIjo7W4sWLPWHilKNHj6qwsFCXX3655V%2BbCQkJKigo8HaZXnO2fgTi%2BoiJidH8%2BfPVvHlzud1u5efna/v27UpKSgrI9XG2fgTi%2BjjlySef1PDhw9WlSxfPWGFhoRISEhQUFCRJCgoKUq9evfx6fZxSVz/279%2BvoKAgdejQwYeV/YqAhQYzduxYTZs2TeHh4Zbxffv2KSgoSIsWLdLVV1%2Bt66%2B/XuvWrfNRld4RFRWl5ORkz%2BOamhotX75cffr0kcPhUExMjGX7Vq1aqaSkxNtles3Z%2BhGI6%2BN0KSkpGjt2rOLj4zVo0KCAXB%2BnO7Mfgbo%2BtmzZos8%2B%2B0yTJk2yjAfq%2Bvitfuzfv1/NmzfX5MmT1a9fP40aNUqbN2/2SY0ELHjdqX9hXHLJJcrNzdXo0aP1yCOP6L333vN1aV6TnZ2t3bt367/%2B67/kdDoVGhpqmQ8NDZXL5fJRdd53ej8CfX08%2B%2ByzWrRokb766itlZWUF/Po4sx%2BBuD6qqqr06KOPasaMGWrWrJllLhDXx9n6sX//flVWVqpfv35avHix/u3f/k133323vvjiC6/XyWew4HUjRozQgAEDdPHFF0uSunbtqm%2B//VYrVqzQf/zHf/i4uoaXnZ2tJUuW6JlnntFll12msLAwlZeXW7ZxuVy1fnD4qzP7cemllwb0%2Bjj1eZKqqio98MADGjlypJxOp2WbQFofZ/Zjx44dAbc%2BcnJy1KNHD8tZ31PCwsJqhSl/Xx9n68ekSZM0btw4z4fau3btqi%2B//FKvvfaa5bNa3kDAgtcFBQV5fjiecskll%2BjTTz/1UUXe8/jjj2vFihXKzs7WoEGDJEmxsbHau3evZbuysrJap/39UV39CMT1UVZWpoKCAg0cONAz1qVLF504cULR0dHav39/re39eX2crR9Hjx5Vy5YtLdv7%2B/p4%2B%2B23VVZWpvj4eEnyBKqNGzdq2LBhKisrs2zv7%2BvjbP34/PPPa/3G4CWXXFLrZ6w3cIkQXvff//3fuu222yxje/bs0SWXXOKbgrwkJydHK1eu1NNPP62hQ4d6xu12u7788ktVVlZ6xvLz82W3231Rptf8Vj8CcX18//33ysjI0I8//ugZ27Vrl1q2bKmEhISAWx9n68eyZcsCbn0sW7ZMb731ll5//XW9/vrrSklJUUpKil5//XXZ7XZ9/vnnnt/Wdrvd2rFjh1%2Bvj7P146GHHtLUqVMt2/tqfRCw4HUDBgzQ9u3b9dJLL%2Bm7777T3/72N73%2B%2BusaP368r0trMPv27dPChQt15513KiEhQQ6Hw/OVlJSkNm3aaOrUqSoqKlJubq527typUaNG%2BbrsBnO2fgTi%2BoiLi1P37t01bdo07d27V5s3b1Z2drbuuuuugFwfZ%2BtHIK6Pdu3aqWPHjp6vyMhIRUZGqmPHjho8eLAqKio0Z84c7d27V3PmzJHT6dSQIUN8XXaDOVs/UlJSPOHrwIEDysnJUX5%2Bvm6%2B%2BWbvF%2BoGvOCyyy5zf/rpp57H7733nvu6665zx8XFuQcPHuzeuHGjD6treC%2B88IL7sssuq/PL7Xa7v/32W/dNN93k7tGjh3vo0KHujz/%2B2McVN6zf60egrQ%2B32%2B0uKSlxp6enu3v16uXu27ev%2B/nnn3fX1NS43e7AWx9u99n7EYjr43RTpkxxT5kyxfO4sLDQPWLECHdcXJx71KhR7i%2B//NKH1Xnfmf147bXX3Ndcc427R48e7htuuMG9bds2n9QV5HbXcRdIAAAAnDcuEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8PrvkV2q9wVBUAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4290272488759641525”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>29.8</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.4</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.6</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.6</td> <td class=”number”>51</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.4</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.1</td> <td class=”number”>50</td> <td class=”number”>1.6%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.7</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.9</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.3</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.8</td> <td class=”number”>48</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (233)</td> <td class=”number”>2629</td> <td class=”number”>81.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4290272488759641525”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.9</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.2</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>41.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.2</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_OBESE_ADULTS13”>PCT_OBESE_ADULTS13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>267</td>

</tr> <tr>

<th>Unique (%)</th> <td>8.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>31.022</td>

</tr> <tr>

<th>Minimum</th> <td>11.8</td>

</tr> <tr>

<th>Maximum</th> <td>47.6</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1975906403624745538”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1975906403624745538,#minihistogram1975906403624745538”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1975906403624745538”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1975906403624745538”

aria-controls=”quantiles1975906403624745538” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1975906403624745538” aria-controls=”histogram1975906403624745538”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1975906403624745538” aria-controls=”common1975906403624745538”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1975906403624745538” aria-controls=”extreme1975906403624745538”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1975906403624745538”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>11.8</td>

</tr> <tr>

<th>5-th percentile</th> <td>23.055</td>

</tr> <tr>

<th>Q1</th> <td>28.3</td>

</tr> <tr>

<th>Median</th> <td>31.2</td>

</tr> <tr>

<th>Q3</th> <td>33.825</td>

</tr> <tr>

<th>95-th percentile</th> <td>38</td>

</tr> <tr>

<th>Maximum</th> <td>47.6</td>

</tr> <tr>

<th>Range</th> <td>35.8</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.525</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>4.5138</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.1455</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.90702</td>

</tr> <tr>

<th>Mean</th> <td>31.022</td>

</tr> <tr>

<th>MAD</th> <td>3.473</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.29067</td>

</tr> <tr>

<th>Sum</th> <td>97160</td>

</tr> <tr>

<th>Variance</th> <td>20.374</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1975906403624745538”>

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O9oD1z//8z%2BrUqZPuvvtu/9imTZvUtm1b5efn210OAACAcbYHrM8%2B%2B0xz586V2%2B32jyUnJ2v27Nnatm2b3eUAAAAYZ3vAioqKUk1NTaNxn8/HHd0BAIAj2B6whgwZosWLF%2Bu7777zj1VUVCg/P1%2BDBw%2B2uxwAAADjbP8U4Zw5c3T33Xfr%2BuuvV1JSkiSppqZGV199tXJzc%2B0uBwAAwDjbA1br1q31%2Buuv65NPPlF5ebmioqLUrVs3DRgwQC6Xy%2B5yAAAAjAvKn8qJjIzU4MGDOSUIAAAcyfaA5fV6tXTpUu3cuVOnT59udGH7X/7yF7tLAgAAMMr2gPX73/9eu3fv1siRI9WiRYtm209dXZ3y8/O1ceNGXXHFFRo3bpzuv/9%2BuVwu7dmzR4888oi%2B/vprdevWTY8%2B%2Bqh69erl/9mNGzdq6dKl8nq9GjRokB577DElJyc3W60AAMBZbA9Y27Zt06pVq9S3b99m3c/ixYv16aef6vnnn9cPP/yg%2B%2B%2B/X%2B3atdNNN92kKVOm6MYbb9Tjjz%2ButWvXaurUqXr//fcVHx%2BvXbt2af78%2BXr00UfVo0cP5eXlKTc3VytXrmzWegEAgHPYHrDi4%2BPVunXrZt1HdXW11q9frz/96U%2B65pprJEmTJ09WWVmZoqKiFBMTo9mzZ8vlcmn%2B/Pn66KOP9O677yo7O1tr1qxRVlaWxowZI0lasmSJhg0bpoqKCnXs2LFZ6wYAAM5g%2B32wRo8erVWrVunMmTPNto%2BSkhIlJiaqX79%2B/rEpU6YoPz9fZWVlysjI8H9i0eVyqU%2BfPiotLZUklZWVBRxda9u2rdq1a6eysrJmqxcAADiL7UewqqurtXHjRv3nf/6nOnbsqOjo6ID5l1566Rfvo6KiQu3bt9eGDRu0YsUKnT59WtnZ2Zo2bZq8Xq%2B6desW8PzWrVurvLxcklRZWamUlJRG80ePHv3FdQEAgPAQlNs0jBo1qlm3f/LkSR08eFDr1q1Tfn6%2BvF6vFixYoLi4OPl8vkahLjo6WnV1dZKkU6dOXXC%2BKSorK%2BX1egPG3G63YmOb76L%2By01kZETA93BAz8Cli4q6PF874fbaDrd%2B7WB7wMrPz2/2fURFRenEiRN66qmn1L59e0nS4cOHtXbtWnXq1KlRWKqrq1NsbKwkKSYm5rzzcXFxTd5/cXGxCgsLA8ZycnI0Y8aMn9NOSEtKavrvzSnoGWi6Vq0Sgl3CBYXbazvc%2Bm1OQTmCVVlZqZdfflnffPON5s2bpx07dqh79%2B7q0qWLke273W7FxMT4w5Uk/epXv9KRI0fUr18/VVVVBTy/qqrKf1owNTX1vPNut7vJ%2B58wYYIyMzMb1VRT49OZMw2X2k5IioyMUFJSHD07XDj2DLOOH/8h2CWcV7i9tp3cb7BCvO0B6%2BDBg7rllluUmJioY8eOadasWdq0aZNyc3P1wgsvyOPx/OJ9eDwe1dbW6ptvvtGvfvUrSdKBAwfUvn17eTwePffcc7IsSy6XS5ZlaefOnbr33nv9P1tSUqLs7GxJ0pEjR3TkyJFLqislJaXRdVzSj28k9fXOeuFezJkzDfQcBsKxZ5hxub9uwu21HW79NifbT7Y%2B/vjjGj58uD744ANdccUVkqSnn35amZmZevLJJ43so0uXLho6dKhyc3O1d%2B9e/dd//ZeKioo0ceJEjRgxQjU1NcrLy9O%2BffuUl5cnn8%2BnrKwsSdLEiRP1xhtv6JVXXtHevXs1e/ZsDR06lFs0AACAJrM9YO3cuVN33313wB92joqK0n333ac9e/YY28%2BTTz6pv/u7v9PEiRM1Z84c3XbbbbrjjjuUmJiolStX%2Bo9SlZWVqaioSPHx8ZKk9PR0LVq0SMuWLdPEiRPVsmVLW64bAwAAzmH7KcKGhgY1NDQ%2B/PjDDz8oMjLS2H5atGihJUuWnHfummuu0euvv/6TP5udne0/RQgAAHCpbD%2BCNWjQIK1cuTIgZFVXV6ugoED9%2B/e3uxwAAADjbA9Yc%2BfO1e7duzVo0CDV1tZq2rRpGjZsmA4dOqQ5c%2BbYXQ4AAIBxtp8iTE1N1YYNG7Rx40Z99dVXamho0MSJEzV69GglJibaXQ4AAIBxQbkPVlxcnMaPHx%2BMXQMAADQ72wPWpEmTLjhv4m8RAgAABJPtAetv764uSfX19Tp48KC%2B/vpr3XnnnXaXAwAAYNxl87cIly1bpqNHj9pcDQAAgHmXzZ/NHj16tN55551glwEAAPCLXTYB6/PPPzd6o1EAAIBguSwucj9x4oT%2B%2B7//W7feeqvd5QAAABhne8Bq165dwN8hlKQrrrhCt99%2Bu2666Sa7ywEAADDO9oD1%2BOOP271LAAAAW9kesHbs2NHk51577bXNWAkAAEDzsD1g3XHHHf5ThJZl%2BcfPHXO5XPrqq6/sLg8AAOAXsz1grVixQosXL9bvfvc79evXT9HR0friiy%2B0aNEi3XzzzbrhhhvsLgkAAMAo22/TkJ%2BfrwULFuj6669Xq1atlJCQoP79%2B2vRokVau3at2rdv7/8CAAAIRbYHrMrKyvOGp8TERB0/ftzucgAAAIyzPWD17t1bTz/9tE6cOOEfq66uVkFBgQYMGGB3OQAAAMbZfg3Www8/rEmTJmnIkCHq3LmzLMvSt99%2BK7fbrZdeesnucgAAAIyzPWB17dpVmzZt0saNG7V//35J0m233aaRI0cqLi7O7nIAAACMsz1gSVLLli01fvx4HTp0SB07dpT0493cAQAAnMD2a7Asy9KTTz6pa6%2B9VqNGjdLRo0c1Z84czZ8/X6dPn7a7HAAAAONsD1irV6/WG2%2B8oUceeUTR0dGSpOHDh%2BuDDz5QYWGh3eUAAAAYZ3vAKi4u1oIFC5Sdne2/e/sNN9ygxYsX66233rK7HAAAAONsD1iHDh1Sz549G4336NFDXq/X7nIAAACMsz1gtW/fXl988UWj8Y8%2B%2Bsh/wTsAAEAos/1ThPfcc48effRReb1eWZalrVu3qri4WKtXr9bcuXPtLgcAAMA42wPW2LFjVV9fr2effVanTp3SggULlJycrFmzZmnixIl2lwMAAGCc7QFr48aNGjFihCZMmKDvv/9elmWpdevWdpcBAADQbGy/BmvRokX%2Bi9mTk5MJVwAAwHFsD1idO3fW119/bfduAQAAbGP7KcIePXrooYce0qpVq9S5c2fFxMQEzOfn59tdEgAAgFG2B6xvvvlGGRkZksR9rwAAgCPZErCWLFminJwcxcfHa/Xq1XbsEgAAIGhsuQbrT3/6k3w%2BX8DYlClTVFlZacfuAQAAbGVLwLIsq9HYjh07VFtba8fuAQAAbGX7pwgBAACcjoAFAABgmG0By%2BVy2bUrAACAoLLtNg2LFy8OuOfV6dOnVVBQoISEhIDncR8sAAAQ6mwJWNdee22je16lp6fr%2BPHjOn78uB0lAAAA2MaWgMW9rwAAQDjhIncAAADDCFgAAACGEbAAAAAMc3zAmjJliubOnet/vGfPHo0fP14ej0djx47V7t27A56/ceNGDR8%2BXB6PR9OnT9f3339vd8kAACDEOTpgvf3229q8ebP/8cmTJzVlyhT17dtXr732mtLT0zV16lSdPHlSkrRr1y7Nnz9fOTk5Ki4uVk1NjXJzc4NVPgAACFGODVjV1dVasmSJ0tLS/GObNm1STEyMZs%2Bera5du2r%2B/PlKSEjQu%2B%2B%2BK0las2aNsrKyNGbMGPXo0UNLlizR5s2bVVFREaw2AABACHJswHriiSc0evRodevWzT9WVlamjIwM/13lXS6X%2BvTpo9LSUv983759/c9v27at2rVrp7KyMnuLBwAAIc22O7nbaevWrfrss8/01ltvaeHChf5xr9cbELgkqXXr1iovL5ckVVZWKiUlpdH80aNHL2n/lZWVjW6s6na7FRvb4pK2E8oiIyMCvocDegYuXVTU5fnaCbfXdrj1awfHBaza2lo98sgjWrBggWJjYwPmfD6foqOjA8aio6NVV1cnSTp16tQF55uquLhYhYWFAWM5OTmaMWPGJW3HCZKS4oJdgu3oGWi6Vq0SLv6kIAq313a49ducHBewCgsL1atXLw0ePLjRXExMTKOwVFdX5w9iPzUfF3dpL7gJEyYoMzMzYMztdqumxqczZxouaVuhKjIyQklJcfTscOHYM8w6fvyHYJdwXuH22nZyv8EK8Y4LWG%2B//baqqqqUnp4uSf7A9N5772nUqFGqqqoKeH5VVZX/tGBqaup5591u9yXVkJKS0uhUo/TjG0l9vbNeuBdz5kwDPYeBcOwZZlzur5twe22HW7/NyXEBa/Xq1aqvr/c/fvLJJyVJDz30kHbs2KHnnntOlmXJ5XLJsizt3LlT9957ryTJ4/GopKRE2dnZkqQjR47oyJEj8ng89jcCAABCluMCVvv27QMeJyT8eGiwU6dOat26tZ566inl5eXpn/7pn7Ru3Tr5fD5lZWVJkiZOnKg77rhDvXv3VlpamvLy8jR06FB17NjR9j4AAEDoCquPCyQmJmrlypX%2Bo1RlZWUqKipSfHy8JCk9PV2LFi3SsmXLNHHiRLVs2VL5%2BflBrhoAAIQal2VZVrCLCBfhdA1WVFSEWrVKoGeH%2B7k9Zy3d0oxVIZS8M2tgsEs4r3D79%2Bzkft3u4NwiKayOYAEAANiBgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYVHBLgCAGVlLtwS7BADA/%2BMIFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwxwasY8eOaebMmerXr58GDx6s/Px81dbWSpIqKip01113qXfv3rrhhhv08ccfB/zsJ598olGjRsnj8WjSpEmqqKgIRgsAACBEOTJgWZalmTNnyufz6c9//rOeeeYZffjhh1q6dKksy9L06dPVpk0brV%2B/XqNHj1ZOTo4OHz4sSTp8%2BLCmT5%2Bu7Oxsvfrqq0pOTtZ9990ny7KC3BUAAAgVjvxTOQcOHFBpaam2bNmiNm3aSJJmzpypJ554QkOGDFFFRYXWrVun%2BPh4de3aVVu3btX69es1Y8YMvfLKK%2BrVq5cmT54sScrPz9fAgQO1fft2XXfddcFsCwAcJ9T%2BxNM7swYGuwSECEcewXK73Vq1apU/XJ114sQJlZWV6aqrrlJ8fLx/PCMjQ6WlpZKksrIy9e3b1z8XFxenq6%2B%2B2j8PAABwMY48gpWUlKTBgwf7Hzc0NGjNmjXq37%2B/vF6vUlJSAp7funVrHT16VJIuOt8UlZWV8nq9AWNut1uxsS0utZWQFRkZEfA9HIRjz0C4iYpy5r9v3r/Mc2TAOldBQYH27NmjV199VS%2B88IKio6MD5qOjo1VXVydJ8vl8F5xviuLiYhUWFgaM5eTkaMaMGT%2Bzg9CVlBQX7BJsF449A%2BGiVauEYJfQrHj/MsfxAaugoEAvvviinnnmGXXv3l0xMTGqrq4OeE5dXZ1iY2MlSTExMY3CVF1dnZKSkpq8zwkTJigzMzNgzO12q6bGpzNnGn5mJ6ElMjJCSUlx9AzAUY4f/yHYJTQLJ79/BSsUOzpgPfbYY1q7dq0KCgp0/fXXS5JSU1O1b9%2B%2BgOdVVVX5Twumpqaqqqqq0XzPnj2bvN%2BUlJRGpxmlH/9h1tc764V7MWfONNAzAMdw%2Br9t3r/McezJ1sLCQq1bt05PP/20Ro4c6R/3eDz68ssvderUKf9YSUmJPB6Pf76kpMQ/5/P5tGfPHv88AADAxTgyYO3fv1/Lly/Xb3/7W2VkZMjr9fq/%2BvXrp7Zt2yo3N1fl5eUqKirSrl27NG7cOEnS2LFjtXPnThUVFam8vFy5ubnq0KEDt2gAAABN5siA9Ze//EVnzpzRs88%2Bq0GDBgV8RUZGavny5fJ6vcrOztabb76pZcuWqV27dpKkDh066I9//KPWr1%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%2Bqn//939XbGysJk%2BerMmTJwe7rJAXah9vBgDg5yJgnceSJUu0e/duvfjiizp8%2BLDmzJmjdu3aacSIEcEuDQAAhACXZVlWsIu4nJw8eVL9%2B/fXc889p%2Buuu06StHz5cm3dulWrV6/%2BRds%2BfvwH1dc3mCjTj6NCAAAnaK6borrdLZpluxfDNVjn2Lt3r%2Brr65Wenu4fy8jIUFlZmRoazIYjAADgTJwiPIfX61WrVq0UHR3tH2vTpo1qa2tVXV2t5OTki26jsrJSXq83YMztdis2NjgpGgCAy11UlLOO%2BRCwzuHz%2BQLClST/47q6uiZto7i4WIWFhQFj1157rZ5%2B%2BmmlpKSYKfT/fZZ3eV4XVllZqeLiYk2YMMF4z5creqZnp6Jn5/ccbv3awVlx0YCYmJhGQers49jY2CZtY8KECXrttdf8XwUFBdqxY0ejo1pO5vV6VVhYSM8OR8/hgZ6dL9z6tQNHsM6Rmpqq48ePq76%2BXlFRP/56vF6vYmNjlZSU1KRtpKSk8D8AAADCGEewztGzZ09FRUWptLTUP1ZSUqK0tDRFRPDrAgAAF0diOEdcXJzGjBmjhQsXateuXfrggw/0r//6r5o0aVKwSwMAACEicuHChQuDXcTlpn///tqzZ4%2Beeuopbd26Vffee6/Gjh37i7aZkJCgfv36KSEhwVCVlz96Dg/0HB7o2fnCrd/mxo1GAQAADOMUIQAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAlYzqaur06hRo/Tpp5/6xyoqKnTXXXepd%2B/euuGGG/Txxx8HsULzztfz4sWL9fd///cBX2vWrAlilb/csWPHNHPmTPXr10%2BDBw9Wfn6%2BamtrJTl3jS/UsxPX%2BKyDBw/qnnvuUXp6uoYOHapVq1b555y61hfq2clrLUlTpkzR3Llz/Y/37Nmj8ePHy%2BPxaOzYsdq9e3cQq2se5/Y8bdq0Rmv84YcfBrHC0BUV7AKcqLa2Vg8%2B%2BKDKy8v9Y5Zlafr06erevbvWr1%2BvDz74QDk5Odq0aZPatWsXxGrNOF/PkrR//349%2BOCDuvnmm/1jiYmJdpdnjGVZmjlzppKSkvTnP/9Zf/3rXzVv3jxFRERo9uzZjlzjC/U8Z84cx63xWQ0NDZoyZYrS0tL0%2Buuv6%2BDBg3rggQeUmpqqUaNGOXKtL9TzjTfe6Ni1lqS3335bmzdv9vd28uRJTZkyRTfeeKMef/xxrV27VlOnTtX777%2Bv%2BPj4IFdrxrk9Sz%2B%2BZxcUFGjAgAH%2BsZYtWwajvJBHwDJs3759evDBB3XuXyDatm2bKioqtG7dOsXHx6tr167aunWr1q9frxkzZgSpWjN%2Bqmfpx3%2Bs99xzj9xudxAqM%2B/AgQMqLS3Vli1b1KZNG0nSzJkz9cQTT2jIkCGOXOML9Xw2YDlpjc%2BqqqpSz549tXDhQiUmJqpz584aMGCASkpK1KZNG0eu9YV6PhuwnLjW1dXVWrJkidLS0vxjmzZtUkxMjGbPni2Xy6X58%2Bfro48%2B0rvvvqvs7OwgVmvG%2BXquq6vToUOHlJaW5rg1DgZOERq2fft2XXfddSouLg4YLysr01VXXRXwP5%2BMjAyVlpbaXaJxP9XziRMndOzYMXXu3Dk4hTUDt9utVatW%2BYPGWSdOnHDsGl%2BoZyeu8VkpKSlaunSpEhMTZVmWSkpKtGPHDvXr18%2Bxa32hnp281k888YRGjx6tbt26%2BcfKysqUkZEhl8slSXK5XOrTp0/Ir/FZ5%2Bv5wIEDcrlc6tixYxArcw4ClmG33nqr5s2bp7i4uIBxr9erlJSUgLHWrVvr6NGjdpbXLH6q5/3798vlcmnFihUaMmSIbrrpJr3%2B%2ButBqtKMpKQkDR482P%2B4oaFBa9asUf/%2B/R27xhfq2YlrfD6ZmZm69dZblZ6eruuvv96xa/23zu3ZqWu9detWffbZZ7rvvvsCxp28xj/V84EDB5SYmKjZs2dr0KBBGjdunDZv3hykKkMfAcsmPp9P0dHRAWPR0dGqq6sLUkXN7%2Bz/hrp06aKioiKNHz9ev//97/X%2B%2B%2B8HuzRjCgoKtGfPHt1///1hs8Z/23M4rLEk/eEPf9CKFSv01VdfKT8/PyzW%2BtyenbjWtbW1euSRR7RgwQLFxsYGzDl1jS/U84EDB3Tq1CkNGjRIq1at0j/8wz9o2rRp%2BuKLL4JUbWjjGiybxMTEqLq6OmCsrq6u0QvcScaMGaNhw4bpyiuvlCT16NFD3377rdauXat//Md/DHJ1v1xBQYFefPFFPfPMM%2BrevXtYrPG5Pf/617929BqfdfY6ldraWj300EMaO3asfD5fwHOcttbn9rxz507HrXVhYaF69eoVcIT2rJiYmEZhyglrfKGe77vvPt1xxx3%2Bi9p79OihL7/8Ui%2B//HLAtVpoGgKWTVJTU7Vv376AsaqqqkaHoJ3E5XL534zP6tKli7Zt2xakisx57LHHtHbtWhUUFOj666%2BX5Pw1Pl/PTl7jqqoqlZaWavjw4f6xbt266fTp03K73Tpw4ECj54f6Wl%2Bo5xMnTig5OTng%2BaG%2B1m%2B//baqqqqUnp4uSf5A9d5772nUqFGqqqoKeL4T1vhCPX/%2B%2BeeNPjHYpUuXRu9raBpOEdrE4/Hoyy%2B/1KlTp/xjJSUl8ng8Qayqef3Lv/yL7rrrroCxvXv3qkuXLsEpyJDCwkKtW7dOTz/9tEaOHOkfd/Ia/1TPTl1jSTp06JBycnJ07Ngx/9ju3buVnJysjIwMR671hXpevXq149Z69erVeuutt7RhwwZt2LBBmZmZyszM1IYNG%2BTxePT555/7Px1tWZZ27twZ8mt8oZ7nzp2r3NzcgOeH%2BhoHEwHLJv369VPbtm2Vm5ur8vJyFRUVadeuXRo3blywS2s2w4YN044dO/T888/ru%2B%2B%2B07/9279pw4YNmjx5crBL%2B9n279%2Bv5cuX67e//a0yMjLk9Xr9X05d4wv17MQ1PistLU1XX3215s2bp3379mnz5s0qKCjQvffe69i1vlDPTlzr9u3bq1OnTv6vhIQEJSQkqFOnThoxYoRqamqUl5enffv2KS8vTz6fT1lZWcEu%2Bxe5UM%2BZmZn%2B8HXw4EEVFhaqpKREt99%2Be7DLDk0Wmk337t2tbdu2%2BR9/%2B%2B231m233Wb16tXLGjlypLVly5YgVtc8zu35/ffft2688UYrLS3NGjFihPXee%2B8FsbpfbuXKlVb37t3P%2B2VZzlzji/XstDX%2BW0ePHrWmT59u9enTxxo4cKD17LPPWg0NDZZlOXOtLevCPTt5rS3LsubMmWPNmTPH/7isrMwaM2aMlZaWZo0bN8768ssvg1hd8zi355dfftn6zW9%2BY/Xq1cu6%2Beabre3btwexutDmsqzz3B0SAAAAPxunCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsP8DKuJLKVo5BJoAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1975906403624745538”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>32.6</td> <td class=”number”>40</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.9</td> <td class=”number”>37</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.1</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.3</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.8</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.5</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.1</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>31.3</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>29.1</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (256)</td> <td class=”number”>2785</td> <td class=”number”>86.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1975906403624745538”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>11.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>45.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_REDUCED_LUNCH09”>PCT_REDUCED_LUNCH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3000</td>

</tr> <tr>

<th>Unique (%)</th> <td>93.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>153</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>9.3546</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>45.601</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1406279981471199728”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1406279981471199728,#minihistogram1406279981471199728”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1406279981471199728”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1406279981471199728”

aria-controls=”quantiles1406279981471199728” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1406279981471199728” aria-controls=”histogram1406279981471199728”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1406279981471199728” aria-controls=”common1406279981471199728”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1406279981471199728” aria-controls=”extreme1406279981471199728”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1406279981471199728”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>4.6101</td>

</tr> <tr>

<th>Q1</th> <td>7.5314</td>

</tr> <tr>

<th>Median</th> <td>9.2415</td>

</tr> <tr>

<th>Q3</th> <td>11.065</td>

</tr> <tr>

<th>95-th percentile</th> <td>14.296</td>

</tr> <tr>

<th>Maximum</th> <td>45.601</td>

</tr> <tr>

<th>Range</th> <td>45.601</td>

</tr> <tr>

<th>Interquartile range</th> <td>3.5332</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.0894</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.33026</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.8084</td>

</tr> <tr>

<th>Mean</th> <td>9.3546</td>

</tr> <tr>

<th>MAD</th> <td>2.2743</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.85676</td>

</tr> <tr>

<th>Sum</th> <td>28597</td>

</tr> <tr>

<th>Variance</th> <td>9.5445</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1406279981471199728”>

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QTU2NYmJiWlxDWlqaUlJSvMZstghVV5/5OYf0g0JCghUVFa7aWqcaG5uMbhv%2Bx/T6upSx9q1F/61F/y%2BOVT98BlzAstvtys/P19mzZz1nrQoLC5WQkOCZLyws9Dzf6XTqyJEjysjIUHBwsPr166fCwkINHDhQklRUVCSbzaZevXq1uIbY2NhmlwMdjlNqaGidb4TGxqZW2zb8x%2BW4Blj71qL/1qL/l7aAu4CblJSkjh07KisrS6WlpcrPz1dJSYkmTJggSRo/frwOHTqk/Px8lZaWKisrS126dPEEqsmTJ%2BuZZ57R3r17VVJSogULFuiWW265qEuEAADg8hZwASskJESrV6%2BWw%2BFQamqqXn31Va1atUqdOnWSJHXp0kVPPvmktm3bpgkTJqimpkarVq1SUFCQJGn06NGaMWOG5s2bp2nTpum6667TAw88YOUhAQAAPxPk/v4W5mhVDscp49u02YIVHR2p6uozfnGaeNTKd6wuIaC9fu9gq0vwGX9b%2B4GG/luL/l%2BcmJgrLdlvwJ3BAgAAsBoBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObzgDVx4kRt3rxZp06d8vWuAQAAfMLnAWvQoEFas2aNhgwZotmzZ%2Bvtt9%2BW2%2B32dRkAAACtxucB67777tObb76p1atXKyQkRLNmzdLQoUP1%2BOOP6/PPP/d1OQAAAMbZrNhpUFCQBg8erMGDB8vpdGrjxo1avXq18vPzNWDAAN1222266aabrCgNAADgF7MkYElSZWWlXn31Vb366qv69NNPNWDAAP3hD39QRUWFHn74Yb3//vvKzs62qjwAAICfzecB65VXXtErr7yiAwcOqF27dho3bpyeeOIJde/e3fOcjh07aunSpQQsAADgl3wesLKzszVs2DCtWrVKN954o4KDm78N7Ne//rVuvfVWX5cGAABghM8D1ltvvaXo6GjV1NR4wlVJSYn69u2rkJAQSdKAAQM0YMAAX5cGAABghM9/i/D06dMaOXKk1q5d6xmbPn26xo4dqxMnTvi6HAAAAON8HrD%2B8pe/qFu3brrjjjs8Y7t27VLHjh2Vk5Pj63IAAACM83nA%2BuCDD/TQQw8pJibGM9auXTvNmTNH7733nrH9nDhxQjNmzNCAAQOUkpKi559/3jN35MgRTZw4UXa7XePHj9fhw4e9Xrtz504NHz5cdrtd6enp%2Bvbbb43VBQAAAp/PA5bNZlNtbW2zcafTafSO7vfee68iIiK0fft2zZ07VytXrtR//dd/qa6uTtOnT1diYqK2b9%2Bu%2BPh4zZgxQ3V1dZK%2Bez9Ydna2MjIyVFBQoNraWmVlZRmrCwAABD6fB6wbb7xRS5Ys0ZdffukZKy8vV05OjpKTk43s4%2B9//7uKiop0zz33qHv37ho%2BfLiSk5O1f/9%2B7dq1S2FhYZozZ4569Oih7OxsRUZGavfu3ZKkTZs2adSoURo3bpx69eqlZcuWad%2B%2BfSovLzdSGwAACHw%2BD1gPPvigXC6XRowYoYEDB2rgwIG66aabdO7cOWNnitq0aaPw8HBt375d586d07Fjx3To0CH17t1bxcXFSkhIUFBQkKTv7io/YMAAFRUVSZKKi4uVmJjo2VbHjh3VqVMnFRcXG6kNAAAEPp/fpqF9%2B/Z66aWX9O6776q0tFQ2m009e/bUDTfc4Ak9v1RYWJjmzZunxYsXa8OGDWpsbFRqaqomTpyoN954Qz179mxWU2lpqaTv7jAfGxvbbL6ioqLF%2B6%2BsrJTD4fAas9kimm33lwoJCfb6isubzXb5rAPWvrXov7Xov3%2Bw5KNyQkJClJycbOyS4IV89tlnGjZsmO644w6VlpZq8eLFuuGGG%2BR0OhUaGur13NDQULlcLknS2bNnf3S%2BJQoKCpSXl%2Bc1lp6erszMzJ95ND8uKiq8VbYL/xIdHWl1CT7H2rcW/bcW/b%2B0%2BTxgORwOrVy5UocOHdK5c%2BeavbH9jTfe%2BMX72L9/v7Zu3ap9%2B/apTZs26tevn06ePKmnnnpKXbt2bRaWXC6X2rRpI%2Bm7s18Xmg8Pb/lCTktLU0pKiteYzRah6uozP/OILiwkJFhRUeGqrXWqsbHJ6Lbhf0yvr0sZa99a9N9a9P/iWPXDp88D1p///GcdPnxYo0eP1pVXXtkq%2Bzh8%2BLC6devmCU2S1KdPH61Zs0aJiYmqqqryen5VVZXn8l1cXNwF5//xthI/JTY2ttnlQIfjlBoaWucbobGxqdW2Df9xOa4B1r616L%2B16P%2BlzecB67333tO6deu83khuWmxsrL744gu5XC7P5b5jx46pS5custvtWrt2rdxut4KCguR2u3Xo0CHdfffdkiS73a7CwkKlpqZK%2Bu5%2BWidOnJDdbm%2B1egEAQGDx%2BTvkIiIi1L59%2B1bdR0pKiq644go9/PDD%2Bvzzz/Xf//3fWrNmjaZMmaKRI0eqtrZWS5cuVVlZmZYuXSqn06lRo0ZJkiZNmqRXXnlFW7Zs0dGjRzVnzhwNHTpUXbt2bdWaAQBA4PB5wBo7dqzWrVunxsbGVtvHlVdeqeeff14Oh0MTJkxQTk6O7rnnHqWlpalt27Z6%2BumnPWepiouLlZ%2Bfr4iICElSfHy8Fi1apFWrVmnSpEm66qqr%2BAgfAABwUYLcJm%2Bf3gJZWVnauXOnoqKi1LVr12a/sbdhwwZfluMzDscp49u02YIVHR2p6uozfnEdftTKd6wuIaC9fu9gq0vwGX9b%2B4GG/luL/l%2BcmJjWeb/3T7HkNg1jxoyxYrcAAAA%2B4fOAxeU2AAAQ6Cy5DWxlZaXy8vJ033336ZtvvtHu3bt17NgxK0oBAAAwzucB64svvtC//uu/6qWXXtKePXtUV1enXbt2afz48XzeHwAACAg%2BD1iPPPKIhg8frr179%2BqKK66QJD322GNKSUnR8uXLfV0OAACAcT4PWIcOHdIdd9zh9cHONptNM2fO1JEjR3xdDgAAgHE%2BD1hNTU1qamr%2Ba6VnzpxRSEiIr8sBAAAwzucBa8iQIXr66ae9QlZNTY1yc3M1aNAgX5cDAABgnM8D1kMPPaTDhw9ryJAhqq%2Bv1z333KNhw4bpq6%2B%2B0oMPPujrcgAAAIzz%2BX2w4uLi9PLLL2vnzp36%2BOOP1dTUpEmTJmns2LFq27atr8sBAAAwzpI7uYeHh2vixIlW7BoAAKDV%2BTxgTZ069UfnA/WzCAEAwOXD5wGrc%2BfOXo8bGhr0xRdf6NNPP9Vtt93m63IAAACMu2Q%2Bi3DVqlWqqKjwcTUAAADmWfJZhBcyduxYvf7661aXAQAA8ItdMgHrww8/5EajAAAgIFwSb3I/ffq0PvnkE02ePNnX5QAAABjn84DVqVMnr88hlKQrrrhCt956q37/%2B9/7uhwAAADjfB6wHnnkEV/vEgAAwKd8HrDef//9Fj/3%2Buuvb8VKAAAAWofPA9aUKVM8lwjdbrdn/PyxoKAgffzxx74uDwAA4BfzecBas2aNlixZogceeEBJSUkKDQ3VRx99pEWLFukPf/iDbr75Zl%2BXBAAAYJTPb9OQk5OjefPmacSIEYqOjlZkZKQGDRqkRYsW6cUXX1Tnzp09fwAAAPyRzwNWZWXlBcNT27ZtVV1d7etyAAAAjPN5wOrfv78ee%2BwxnT592jNWU1Oj3Nxc3XDDDb4uBwAAwDifvwfr4Ycf1tSpU3XjjTeqe/fucrvd%2Btvf/qaYmBht2LDB1%2BUAAAAY5/OA1aNHD%2B3atUs7d%2B7UZ599Jkn605/%2BpNGjRys8PNzX5QAAABjn84AlSVdddZUmTpyor776Sl27dpX03d3cAQAAAoHP34Pldru1fPlyXX/99RozZowqKir04IMPKjs7W%2BfOnfN1OQAAAMb5PGBt3LhRr7zyiubPn6/Q0FBJ0vDhw7V3717l5eX5uhwAAADjfB6wCgoKNG/ePKWmpnru3n7zzTdryZIl2rFjh6/LAQAAMM7nAeurr75S7969m4336tVLDofD1%2BUAAAAY5/OA1blzZ3300UfNxt966y3PG95NcLlcWrhwoa6//nr97ne/02OPPeb5nMMjR45o4sSJstvtGj9%2BvA4fPuz12p07d2r48OGy2%2B1KT0/Xt99%2Ba6wuAAAQ%2BHwesO68804tXLhQGzZskNvt1v79%2B7V8%2BXItW7ZMU6ZMMbafJUuW6N1339UzzzyjFStW6D//8z9VUFCguro6TZ8%2BXYmJidq%2Bfbvi4%2BM1Y8YM1dXVSZJKSkqUnZ2tjIwMFRQUqLa2VllZWcbqAgAAgc/nt2kYP368Ghoa9NRTT%2Bns2bOaN2%2Be2rVrp3vvvVeTJk0yso%2Bamhpt27ZNzz33nK677jpJ0rRp01RcXCybzaawsDDNmTNHQUFBys7O1ltvvaXdu3crNTVVmzZt0qhRozRu3DhJ0rJlyzRs2DCVl5cbPcMGAAACl88D1s6dOzVy5EilpaXp22%2B/ldvtVvv27Y3uo7CwUG3btlVSUpJnbPr06ZKkP//5z0pISPC8wT4oKEgDBgxQUVGRUlNTVVxcrLvuusvzuo4dO6pTp04qLi4mYAEAgBbx%2BSXCRYsWed7M3q5dO%2BPhSpLKy8vVuXNnvfzyyxo5cqT%2B5V/%2BRatWrVJTU5McDodiY2O9nt%2B%2BfXtVVFRI%2Bu7DqH9sHgAA4Kf4/AxW9%2B7d9emnn6pnz56tto%2B6ujp98cUX2rx5s3JycuRwODRv3jyFh4fL6XR67r/1vdDQULlcLknS2bNnf3S%2BJSorK5v9RqTNFtEsuP1SISHBXl9xebPZLp91wNq3Fv23Fv33Dz4PWL169dL999%2BvdevWqXv37goLC/Oaz8nJ%2BcX7sNlsOn36tFasWKHOnTtLko4fP64XX3xR3bp1axaWXC6X2rRpI0kKCwu74PzFfE5iQUFBs5umpqenKzMz8%2Bcczk%2BKiuIzHCFFR0daXYLPsfatRf%2BtRf8vbT4PWJ9//rkSEhIkqdXuexUTE6OwsDBPuJKkX/3qVzpx4oSSkpJUVVXl9fyqqirP2aW4uLgLzsfExLR4/2lpaUpJSfEas9kiVF195mIP5UeFhAQrKipctbVONTY2Gd02/I/p9XUpY%2B1bi/5bi/5fHKt%2B%2BPRJwFq2bJkyMjIUERGhjRs3tvr%2B7Ha76uvr9fnnn%2BtXv/qVJOnYsWPq3Lmz7Ha71q5dK7fbraCgILndbh06dEh3332357WFhYVKTU2VJJ04cUInTpyQ3W5v8f5jY2ObXQ50OE6poaF1vhEaG5tabdvwH5fjGmDtW4v%2BW4v%2BX9p8cgH3ueeek9Pp9BqbPn26KisrW2V/v/71rzV06FBlZWXp6NGj%2Bt///V/l5%2Bdr0qRJGjlypGpra7V06VKVlZVp6dKlcjqdGjVqlCRp0qRJeuWVV7RlyxYdPXpUc%2BbM0dChQ/kNQgAA0GI%2BCVjf30H9H73//vuqr69vtX0uX75c//zP/6xJkybpwQcf1J/%2B9CdNmTJFbdu21dNPP%2B05S1VcXKz8/HxFRERIkuLj47Vo0SKtWrVKkyZN0lVXXWXkfWEAAODy4fP3YPnKlVdeqWXLll1w7rrrrtNLL730g69NTU31XCIEAAC4WPyOJwAAgGE%2BC1jf3zkdAAAg0PnsEuGSJUu87nl17tw55ebmKjLS%2B9cneb8TAADwdz4JWNdff32ze17Fx8erurpa1dXVvigBAADAZ3wSsHxx7ysAAIBLBW9yBwAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEBH7CmT5%2Buhx56yPP4yJEjmjhxoux2u8aPH6/Dhw97PX/nzp0aPny47Ha70tPT9e233/q6ZAAA4OcCOmC99tpr2rdvn%2BdxXV2dpk%2BfrsTERG3fvl3x8fGaMWOG6urqJEklJSXKzs5WRkaGCgoKVFtbq6ysLKvKBwAAfipgA1ZNTY2WLVumfv36ecZ27dqlsLAwzZkzRz169FB2drYiIyO1e/duSdKmTZs0atQojRs3Tr169dKyZcu0b98%2BlZeXW3UYAADNwAQrAAAQjElEQVTADwVswHr00Uc1duxY9ezZ0zNWXFyshIQEBQUFSZKCgoI0YMAAFRUVeeYTExM9z%2B/YsaM6deqk4uJi3xYPAAD8ms3qAlrD/v379cEHH2jHjh1asGCBZ9zhcHgFLklq3769SktLJUmVlZWKjY1tNl9RUXFR%2B6%2BsrJTD4fAas9kimm37lwoJCfb6isubzXb5rAPWvrXov7Xov38IuIBVX1%2Bv%2BfPna968eWrTpo3XnNPpVGhoqNdYaGioXC6XJOns2bM/Ot9SBQUFysvL8xpLT09XZmbmRW2npaKiwltlu/Av0dGRVpfgc6x9a9F/a9H/S1vABay8vDxde%2B21Sk5ObjYXFhbWLCy5XC5PEPuh%2BfDwi1vEaWlpSklJ8Rqz2SJUXX3morbzU0JCghUVFa7aWqcaG5uMbhv%2Bx/T6upSx9q1F/61F/y%2BOVT98BlzAeu2111RVVaX4%2BHhJ8gSmPXv2aMyYMaqqqvJ6flVVlefSXVxc3AXnY2JiLqqG2NjYZpcDHY5TamhonW%2BExsamVts2/MfluAZY%2B9ai/9ai/5e2gAtYGzduVENDg%2Bfx8uXLJUn333%2B/3n//fa1du1Zut1tBQUFyu906dOiQ7r77bkmS3W5XYWGhUlNTJUknTpzQiRMnZLfbfX8gAADAbwVcwOrcubPX48jI704NduvWTe3bt9eKFSu0dOlS/fGPf9TmzZvldDo1atQoSdKkSZM0ZcoU9e/fX/369dPSpUs1dOhQde3a1efHAQAA/Ndl9SsIbdu21dNPP%2B05S1VcXKz8/HxFRERIkuLj47Vo0SKtWrVKkyZN0lVXXaWcnByLqwYAAP4myO12u60u4nLgcJwyvk2bLVjR0ZGqrj7jF9fhR618x%2BoSAtrr9w62ugSf8be1H2jov7Xo/8WJibnSkv1eVmewAAAAfIGABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABgWsAHr5MmTyszMVFJSkpKTk5WTk6P6%2BnpJUnl5uW6//Xb1799fN998s95%2B%2B22v17777rsaM2aM7Ha7pk6dqvLycisOAQAA%2BKmADFhut1uZmZlyOp164YUX9Pjjj%2BvNN9/UypUr5Xa7lZ6erg4dOmjbtm0aO3asMjIydPz4cUnS8ePHlZ6ertTUVG3dulXt2rXTzJkz5Xa7LT4qAADgL2xWF9Aajh07pqKiIr3zzjvq0KGDJCkzM1OPPvqobrzxRpWXl2vz5s2KiIhQjx49tH//fm3btk2zZs3Sli1bdO2112ratGmSpJycHA0ePFgHDx7UwIEDrTwsAADgJwLyDFZMTIzWrVvnCVffO336tIqLi9WnTx9FRER4xhMSElRUVCRJKi4uVmJiomcuPDxcffv29cwDAAD8lIAMWFFRUUpOTvY8bmpq0qZNmzRo0CA5HA7FxsZ6Pb99%2B/aqqKiQpJ%2BcBwAA%2BCkBeYnwfLm5uTpy5Ii2bt2q559/XqGhoV7zoaGhcrlckiSn0/mj8y1RWVkph8PhNWazRTQLbr9USEiw11dc3my2y2cdsPatRf%2BtRf/9Q8AHrNzcXK1fv16PP/64fvOb3ygsLEw1NTVez3G5XGrTpo0kKSwsrFmYcrlcioqKavE%2BCwoKlJeX5zWWnp6uzMzMn3kUPy4qKrxVtgv/Eh0daXUJPsfatxb9txb9v7QFdMBavHixXnzxReXm5mrEiBGSpLi4OJWVlXk9r6qqynN2KS4uTlVVVc3me/fu3eL9pqWlKSUlxWvMZotQdfWZn3MYPygkJFhRUeGqrXWqsbHJ6Lbhf0yvr0sZa99a9N9a9P/iWPXDZ8AGrLy8PG3evFmPPfaYRo4c6Rm32%2B3Kz8/X2bNnPWetCgsLlZCQ4JkvLCz0PN/pdOrIkSPKyMho8b5jY2ObXQ50OE6poaF1vhEaG5tabdvwH//f8v%2B1uoQWe/3ewUa2w9q3Fv23Fv2/tAXkBdzPPvtMq1ev1l133aWEhAQ5HA7Pn6SkJHXs2FFZWVkqLS1Vfn6%2BSkpKNGHCBEnS%2BPHjdejQIeXn56u0tFRZWVnq0qULt2gAAAAtFpAB64033lBjY6OeeuopDRkyxOtPSEiIVq9eLYfDodTUVL366qtatWqVOnXqJEnq0qWLnnzySW3btk0TJkxQTU2NVq1apaCgIIuPCgAA%2BIsgN7co9wmH45TxbdpswYqOjlR19Rm/OE08auU7VpeAS8QvvUTob2s/0NB/a9H/ixMTc6Ul%2Bw3IM1gAAABWImABAAAYRsACAAAwjIAFAABgGAELAADAsIC90ejlIjF7t9UlAACA83AGCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWBdQX1%2BvuXPnKjExUUOGDNGzzz5rdUkAAMCP2Kwu4FK0bNkyHT58WOvXr9fx48f14IMPqlOnTho5cqTVpQEBYdTKd6wu4aK8fu9gq0sA4GcIWOepq6vTli1btHbtWvXt21d9%2B/ZVaWmpXnjhBQIWAABoES4Rnufo0aNqaGhQfHy8ZywhIUHFxcVqamqysDIAAOAvOIN1HofDoejoaIWGhnrGOnTooPr6etXU1Khdu3Y/uY3Kyko5HA6vMZstQrGxsUZrDQkhHwO%2B4G%2BXNP/r/uRW3f73//bwb5A16L9/IGCdx%2Bl0eoUrSZ7HLperRdsoKChQXl6e11hGRoZmzZplpsj/p7KyUrf9U6nS0tKMhzf8tMrKShUUFNB/C9B7a1VWVmr9%2BnX03yL03z8Qf88TFhbWLEh9/7hNmzYt2kZaWpq2b9/u9SctLc14rQ6HQ3l5ec3OlsE36L916L216L%2B16L9/4AzWeeLi4lRdXa2GhgbZbN%2B1x%2BFwqE2bNoqKimrRNmJjY/mpAgCAyxhnsM7Tu3dv2Ww2FRUVecYKCwvVr18/BQfTLgAA8NNIDOcJDw/XuHHjtGDBApWUlGjv3r169tlnNXXqVKtLAwAAfiJkwYIFC6wu4lIzaNAgHTlyRCtWrND%2B/ft19913a/z48VaXdUGRkZFKSkpSZGSk1aVclui/dei9tei/tej/pS/I7Xa7rS4CAAAgkHCJEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYActP1dfXa%2B7cuUpMTNSQIUP07LPPWl1SwHO5XBozZowOHDjgGSsvL9ftt9%2Bu/v376%2Babb9bbb79tYYWB6eTJk8rMzFRSUpKSk5OVk5Oj%2Bvp6SfTfF7744gvdeeedio%2BP19ChQ7Vu3TrPHP33nenTp%2Buhhx7yPD5y5IgmTpwou92u8ePH6/DhwxZWhwshYPmpZcuW6fDhw1q/fr3mz5%2BvvLw87d692%2BqyAlZ9fb1mz56t0tJSz5jb7VZ6ero6dOigbdu2aezYscrIyNDx48ctrDSwuN1uZWZmyul06oUXXtDjjz%2BuN998UytXrqT/PtDU1KTp06crOjpaL730khYuXKinnnpKO3bsoP8%2B9Nprr2nfvn2ex3V1dZo%2BfboSExO1fft2xcfHa8aMGaqrq7OwSpzPZnUBuHh1dXXasmWL1q5dq759%2B6pv374qLS3VCy%2B8oJEjR1pdXsApKyvTfffdp/M/Veq9995TeXm5Nm/erIiICPXo0UP79%2B/Xtm3bNGvWLIuqDSzHjh1TUVGR3nnnHXXo0EGSlJmZqUcffVQ33ngj/W9lVVVV6t27txYsWKC2bduqe/fuuuGGG1RYWKgOHTrQfx%2BoqanRsmXL1K9fP8/Yrl27FBYWpjlz5igoKEjZ2dl66623tHv3bqWmplpYLf4RZ7D80NGjR9XQ0KD4%2BHjPWEJCgoqLi9XU1GRhZYHp4MGDGjhwoAoKCrzGi4uL1adPH0VERHjGEhISVFRU5OsSA1ZMTIzWrVvnCVffO336NP33gdjYWK1cuVJt27aV2%2B1WYWGh3n//fSUlJdF/H3n00Uc1duxY9ezZ0zNWXFyshIQEBQUFSZKCgoI0YMAAen%2BJIWD5IYfDoejoaIWGhnrGOnTooPr6etXU1FhYWWCaPHmy5s6dq/DwcK9xh8Oh2NhYr7H27duroqLCl%2BUFtKioKCUnJ3seNzU1adOmTRo0aBD997GUlBRNnjxZ8fHxGjFiBP33gf379%2BuDDz7QzJkzvcbpvX8gYPkhp9PpFa4keR67XC4rSros/dDfA38HrSc3N1dHjhzRv//7v9N/H3viiSe0Zs0affzxx8rJyaH/ray%2Bvl7z58/XvHnz1KZNG685eu8feA%2BWHwoLC2v2jfT94/O/EdF6wsLCmp0xdLlc/B20ktzcXK1fv16PP/64fvOb39B/H/v%2BPUD19fW6//77NX78eDmdTq/n0H9z8vLydO2113qdwf3eD/0/gN5fWghYfiguLk7V1dVqaGiQzfbdX6HD4VCbNm0UFRVlcXWXj7i4OJWVlXmNVVVVNTt1j19u8eLFevHFF5Wbm6sRI0ZIov%2B%2BUFVVpaKiIg0fPtwz1rNnT507d04xMTE6duxYs%2BfTfzNee%2B01VVVVed5r%2B32g2rNnj8aMGaOqqiqv59P7Sw%2BXCP1Q7969ZbPZvN7QWFhYqH79%2Bik4mL9SX7Hb7fq///s/nT171jNWWFgou91uYVWBJy8vT5s3b9Zjjz2m0aNHe8bpf%2Bv76quvlJGRoZMnT3rGDh8%2BrHbt2ikhIYH%2Bt6KNGzdqx44devnll/Xyyy8rJSVFKSkpevnll2W32/Xhhx96frPZ7Xbr0KFD9P4Sw/%2BN/VB4eLjGjRunBQsWqKSkRHv37tWzzz6rqVOnWl3aZSUpKUkdO3ZUVlaWSktLlZ%2Bfr5KSEk2YMMHq0gLGZ599ptWrV%2Buuu%2B5SQkKCHA6H5w/9b339%2BvVT3759NXfuXJWVlWnfvn3Kzc3V3XffTf9bWefOndWtWzfPn8jISEVGRqpbt24aOXKkamtrtXTpUpWVlWnp0qVyOp0aNWqU1WXjHwS5z7%2B5D/yC0%2BnUggUL9Ne//lVt27bVnXfeqdtvv93qsgLeb3/7W23YsEEDBw6U9N1drrOzs1VcXKxu3bpp7ty5%2Bt3vfmdxlYEjPz9fK1asuODcJ598Qv994OTJk1q8eLH279%2Bv8PBw3XrrrZoxY4aCgoLovw99fxf3Rx55RJJUUlKi%2BfPn67PPPtNvf/tbLVy4UH369LGyRJyHgAUAAGAYlwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/H2ZearmtGs53AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1406279981471199728”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.11111111111111</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.333333333333334</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.090909090909092</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.987473903966595</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.179640718562874</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.846560846560847</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.377358490566039</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.643835616438356</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2989)</td> <td class=”number”>3012</td> <td class=”number”>93.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>153</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1406279981471199728”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>20</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.05743825387708214</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.061087354917532075</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1590264813662449</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.36120642947444465</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>23.776223776223777</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.817053886388518</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>28.82882882882883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45.60129620739318</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_REDUCED_LUNCH14”>PCT_REDUCED_LUNCH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2721</td>

</tr> <tr>

<th>Unique (%)</th> <td>84.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>314</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.6961</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>32.051</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3273108919730444796”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives3273108919730444796”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3273108919730444796”

aria-controls=”quantiles3273108919730444796” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3273108919730444796” aria-controls=”histogram3273108919730444796”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3273108919730444796” aria-controls=”common3273108919730444796”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3273108919730444796” aria-controls=”extreme3273108919730444796”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3273108919730444796”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>5.5174</td>

</tr> <tr>

<th>Median</th> <td>7.7375</td>

</tr> <tr>

<th>Q3</th> <td>9.91</td>

</tr> <tr>

<th>95-th percentile</th> <td>13.501</td>

</tr> <tr>

<th>Maximum</th> <td>32.051</td>

</tr> <tr>

<th>Range</th> <td>32.051</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.3926</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.7377</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.48567</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.5269</td>

</tr> <tr>

<th>Mean</th> <td>7.6961</td>

</tr> <tr>

<th>MAD</th> <td>2.837</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.27177</td>

</tr> <tr>

<th>Sum</th> <td>22288</td>

</tr> <tr>

<th>Variance</th> <td>13.971</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3273108919730444796”>

<img 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1a9d6x6ZMmaKRI0fq7NmzdpcDAABgnO0B6/e//71uv/12TZo0yTu2a9cuNWvWTJmZmXaXAwAAYJztlwj/9re/6Y033lB8fLx3LDY2VjNnztQ999xjdzmw0dDl%2B3xdAgAAtrD9DJbT6VRZWVmd8YqKCu7oDgAAAoLtAatfv35avHixvvzyS%2B9YYWGhMjMz1bdvX7vLAQAAMM72S4SzZs3SpEmTNHjwYMXExEiSysrK1KlTJ82ePdvucgAAAIyzPWDFxcVpx44d%2BvDDD1VQUCCn06l27dqpV69ecjgcdpcDAABgnE8%2BKic0NFR9%2B/blkiAAAAhItgcst9ut5cuX6%2BDBg7p06VKdN7b/%2Bc9/trskAAAAo2wPWL/73e90%2BPBhDR8%2BXLfeeqvduwcAAGhwtgesjz76SOvWrVNqaqrduwYAALCF7bdpiIqKUlxcnN27BQAAsI3tAWvkyJFat26dampq7N41AACALWy/RFhaWqqdO3fqv/7rv9SqVSuFhYVZ5l999VW7SwIAADDKJ7dpGDFihC92CwAAYAvbA1ZmZqYt%2B6mqqlJmZqZ27typW265RWPGjNG//du/yeFw6MiRI3ryySd17NgxtWvXTk899ZQ6d%2B7s/dmdO3dq%2BfLlcrvd6tOnjxYtWqTY2Fhb6gYAAP7P9vdgSVJRUZGys7M1Y8YM/e///q/effddnTx50ug%2BFi9erA8//FAvvfSSli1bpjfeeEM5OTkqLy/XlClTlJqaqu3btys5OVlTp05VeXm5JOnQoUOaO3eupk%2BfrpycHJWVlfERPgAA4LrYHrBOnz6tX/3qV9qxY4fee%2B89lZeXa9euXRo9erTy8/ON7KO0tFTbtm3TokWL1KVLF/Xq1UuTJ09Wfn6%2Bdu3apfDwcM2cOVNt27bV3LlzFR0drXfffVeStGnTJg0dOlSjRo1Shw4dtHTpUu3Zs0eFhYVGagMAAIHP9oD1hz/8QYMGDdLu3bt1yy23SJKeffZZDRw4UM8884yRfeTm5qpRo0bq3r27d2zKlCnKzMxUfn6%2BUlJSvJ976HA41K1bN%2BXl5UmS8vPzLffoatasmZo3b24s/AEAgMBne8A6ePCgJk2aZPlgZ6fTqYceekhHjhwxso/CwkK1aNFCb775poYMGaJ/%2Bqd/0ooVK1RbWyu3262EhATL8%2BPi4nTu3DlJ31%2B%2BvNY8AADAj7H9Te61tbWqra2tM/7dd98pNDTUyD7Ky8t1%2BvRpbd68WZmZmXK73Zo/f74iIyNVUVFR59YQYWFhqqqqkiRdvHjxmvP1UVRUJLfbbRlzOqPqBLebFRoaYvmO4OZ0WtcB68OKfljRDyv6YUU/bp7tAatPnz5avXq1srKyvGOlpaXKyspSz549jezD6XTqwoULWrZsmVq0aCFJOnPmjF5//XXdfvvtdcJSVVWVIiIiJEnh4eFXnI%2BMjKz3/nNycpSdnW0ZS09PV0ZGxo0czo%2BKial/bQhcTZpEX3Gc9WFFP6zohxX9sKIfN872gPXEE09owoQJ6tOnjyorK/Xggw/q73//uxo3bqw//OEPRvYRHx%2Bv8PBwb7iSpJ/97Gc6e/asunfvruLiYsvzi4uLvWeXEhMTrzgfHx9f7/2PGzdOAwcOtIw5nVEqKfnueg/lmkJDQxQTE6mysgrV1NQ9K4jgcvn6Yn1Y0Q8r%2BmFFP6wCqR9X%2B%2BWzodkesBITE/Xmm29q586d%2Bvzzz1VbW6vx48dr5MiRatSokZF9uFwuVVZW6tSpU/rZz34mSTp58qRatGghl8ultWvXyuPxyOFwyOPx6ODBg5o2bZr3Z3Nzc5WWliZJOnv2rM6ePSuXy1Xv/SckJNS5HOh2f6vq6oZZpDU1tQ22bfiPq60B1ocV/bCiH1b0w4p%2B3Dif3Mk9MjJSY8eObbDtt2nTRv3799fs2bO1YMECud1urVmzRg8%2B%2BKCGDBmiZcuWacmSJfrNb36jzZs3q6KiQkOHDpUkjR8/Xvfdd5%2B6du2qpKQkLVmyRP3791erVq0arF7AhKHL9/m6hHp755Hevi4BABqU7QFrwoQJ15w39VmEzzzzjBYtWqTx48crMjJS99xzj%2B677z45HA6tXr1aTz75pN544w394he/0Jo1axQVFSVJSk5O1sKFC/X888/rm2%2B%2BUe/evbVo0SIjNQEAgOBge8D6x/dFSVJ1dbVOnz6tY8eO6f777ze2n1tvvVVLly694lyXLl20Y8eOq/5sWlqa9xIhAADA9frJfBbhihUruNcUAAAICD%2BZG1yMHDlS77zzjq/LAAAAuGk/mYD13//938ZuNAoAAOBLP4k3uV%2B4cEH/8z//o7vvvtvucgAAAIyzPWA1b97c8jmEknTLLbfo3nvv1b/8y7/YXQ4AAIBxtgcsU3drBwAA%2BKmyPWAdOHCg3s%2B98847G7ASAACAhmF7wPrhZp%2BS5PF4vOOXjzkcDn3%2B%2Bed2lwcAAHDTbA9Yq1at0uLFi/X444%2Bre/fuCgsL06effqqFCxfqrrvu0rBhw%2BwuCQAAwCjbb9OQmZmp%2BfPna/DgwWrSpImio6PVs2dPLVy4UK%2B//rpatGjh/QIAAPBHtgesoqKiK4anRo0aqaSkxO5yAAAAjLM9YHXt2lXPPvusLly44B0rLS1VVlaWevXqZXc5AAAAxtn%2BHqx58%2BZpwoQJ6tevn1q3bi2Px6MvvvhC8fHxevXVV%2B0uBwAAwDjbA1bbtm21a9cu7dy5UydOnJAk3XPPPRo%2BfLgiIyPtLgcAAMA42wOWJN12220aO3asvvrqK7Vq1UrS93dzBwAACAS2vwfL4/HomWee0Z133qkRI0bo3LlzmjVrlubOnatLly7ZXQ4AAIBxtgesjRs36q233tKTTz6psLAwSdKgQYO0e/duZWdn210OAACAcbYHrJycHM2fP19paWneu7cPGzZMixcv1h//%2BEe7ywEAADDO9oD11VdfqWPHjnXGO3ToILfbbXc5AAAAxtkesFq0aKFPP/20zvgHH3zgfcM7AACAP7P9rwgfeOABPfXUU3K73fJ4PNq/f79ycnK0ceNGPfHEE3aXAwAAYJztAWv06NGqrq7Wiy%2B%2BqIsXL2r%2B/PmKjY3VI488ovHjx9tdDgAAgHG2B6ydO3dqyJAhGjdunL7%2B%2Bmt5PB7FxcXZXQYAAECDsf09WAsXLvS%2BmT02NpZwBQAAAo7tAat169Y6duyY3bsFAACwje2XCDt06KDHHntM69atU%2BvWrRUeHm6Zz8zMtLskAAAAo2wPWKdOnVJKSookcd8rAAAQkGwJWEuXLtX06dMVFRWljRs32rFLAAAAn7HlPVivvPKKKioqLGNTpkxRUVGRHbsHAACwlS0By%2BPx1Bk7cOCAKisr7dg9AACArWz/K0IAAIBAR8ACAAAwzLaA5XA47NoVAACAT9l2m4bFixdb7nl16dIlZWVlKTo62vI87oMFAAD8nS0B684776xzz6vk5GSVlJSopKTEjhIAAABsY0vA4t5XAAAgmPAmdwAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMCwgA9YU6ZM0RNPPOF9fOTIEY0dO1Yul0ujR4/W4cOHLc/fuXOnBg0aJJfLpfT0dH399dd2lwwAAPxcQAesP/3pT9qzZ4/3cXl5uaZMmaLU1FRt375dycnJmjp1qsrLyyVJhw4d0ty5czV9%2BnTl5OSorKxMs2fP9lX5AADATwVswCotLdXSpUuVlJTkHdu1a5fCw8M1c%2BZMtW3bVnPnzlV0dLTeffddSdKmTZs0dOhQjRo1Sh06dNDSpUu1Z88eFRYW%2BuowAACAHwrYgPX0009r5MiRateunXcsPz9fKSkpcjgckiSHw6Fu3bopLy/PO5%2Bamup9frNmzdS8eXPl5%2BfbWzwAAPBrtnwWod3279%2Bvv/3tb/rjH/%2BoBQsWeMfdbrclcElSXFycCgoKJElFRUVKSEioM3/u3Lnr2n9RUVGdD7d2OqPqbPtmhYaGWL4D/sLptH/N8nqxoh9W9MOKfty8gAtYlZWVevLJJzV//nxFRERY5ioqKhQWFmYZCwsLU1VVlSTp4sWL15yvr5ycHGVnZ1vG0tPTlZGRcV3bqa%2BYmMgG2S7QUJo0ifbZvnm9WNEPK/phRT9uXMAFrOzsbHXu3Fl9%2B/atMxceHl4nLFVVVXmD2NXmIyOvb4GNGzdOAwcOtIw5nVEqKfnuurbzY0JDQxQTE6mysgrV1NQa3TbQkEy/FuqD14sV/bCiH1aB1A9f/UIXcAHrT3/6k4qLi5WcnCxJ3sD03nvvacSIESouLrY8v7i42HvpLjEx8Yrz8fHx11VDQkJCncuBbve3qq5umEVaU1PbYNsGGoIv1yuvFyv6YUU/rOjHjQu4gLVx40ZVV1d7Hz/zzDOSpMcee0wHDhzQ2rVr5fF45HA45PF4dPDgQU2bNk2S5HK5lJubq7S0NEnS2bNndfbsWblcLvsPBAAA%2BK2AC1gtWrSwPI6O/v7U4O233664uDgtW7ZMS5Ys0W9%2B8xtt3rxZFRUVGjp0qCRp/Pjxuu%2B%2B%2B9S1a1clJSVpyZIl6t%2B/v1q1amX7cQAAAP8VVH8e0KhRI61evdp7lio/P19r1qxRVFSUJCk5OVkLFy7UihUrNH78eN12223KzMz0cdUAAMDfODwej8fXRQQDt/tb49t0OkPUpEm0Skq%2B84tr5EOX7/N1CfiJeOeR3rbv099eLw2NfljRD6tA6kd8/K0%2B2W9QncECAACwAwELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwp68LwM1Jnfuur0sAAACX4QwWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsIANWOfPn1dGRoa6d%2B%2Buvn37KjMzU5WVlZKkwsJCTZw4UV27dtWwYcO0d%2B9ey89%2B%2BOGHGjFihFwulyZMmKDCwkJfHAIAAPBTARmwPB6PMjIyVFFRoddee03PPfec/vKXv2j58uXyeDxKT09X06ZNtW3bNo0cOVLTp0/XmTNnJElnzpxRenq60tLStHXrVsXGxuqhhx6Sx%2BPx8VEBAAB/4fR1AQ3h5MmTysvL0759%2B9S0aVNJUkZGhp5%2B%2Bmn169dPhYWF2rx5s6KiotS2bVvt379f27Zt08MPP6wtW7aoc%2BfOmjx5siQpMzNTvXv31ieffKIePXr48rAAAICfCMgzWPHx8Vq3bp03XP3gwoULys/P1x133KGoqCjveEpKivLy8iRJ%2Bfn5Sk1N9c5FRkaqU6dO3nkAAIAfE5BnsGJiYtS3b1/v49raWm3atEk9e/aU2%2B1WQkKC5flxcXE6d%2B6cJP3ofH0UFRXJ7XZbxpzOqDrbvVmhoQGZjxEEnE771%2B4PrxdeN9%2BjH1b0w4p%2B3LyADFiXy8rK0pEjR7R161atX79eYWFhlvmwsDBVVVVJkioqKq45Xx85OTnKzs62jKWnpysjI%2BMGjwAILE2aRPts3zExkT7b908R/bCiH1b048YFfMDKysrShg0b9Nxzz6l9%2B/YKDw9XaWmp5TlVVVWKiIiQJIWHh9cJU1VVVYqJian3PseNG6eBAwdaxpzOKJWUfHeDR3Fl/GYBf2X6tVAfoaEhiomJVFlZhWpqam3f/08N/bCiH1aB1A9f/UIX0AFr0aJFev3115WVlaXBgwdLkhITE3X8%2BHHL84qLi72X7xITE1VcXFxnvmPHjvXeb0JCQp3LgW73t6qu9u9FCpjiy9dCTU0tr8V/QD%2Bs6IcV/bhxAXsKJDs7W5s3b9azzz6r4cOHe8ddLpc%2B%2B%2BwzXbx40TuWm5srl8vlnc/NzfXOVVRU6MiRI955AACAHxOQAevEiRNauXKlfvvb3yolJUVut9v71b17dzVr1kyzZ89WQUGB1qxZo0OHDmnMmDGSpNGjR%2BvgwYNas2aNCgoKNHv2bLVs2ZJbNAAAgHoLyID15z//WTU1NXrxxRfVp08fy1doaKhWrlwpt9uttLQ0vf3221qxYoWaN28uSWrZsqVeeOEFbdu2TWPGjFFpaalWrFghh8Ph46MCAAD%2BwuHhFuW2cLu/Nb5NpzNEv3zmr8a3CzS0dx7ydF68AAAK4ElEQVTpbfs%2Bnc4QNWkSrZKS73hPiejH5eiHVSD1Iz7%2BVp/sNyDPYAEAAPgSAQsAAMAwAhYAAIBhBCwAAADDAvpGowB%2BmoYu3%2BfrEq6LL96UD8C/cQYLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDCnrwsAgJ%2B6ocv3%2BbqE6/LOI719XQIQ9DiDBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWFdQWVmpOXPmKDU1VX369NHLL7/s65IAAIAf4a8Ir2Dp0qU6fPiwNmzYoDNnzmjWrFlq3ry5hgwZ4uvSAACAHyBgXaa8vFxbtmzR2rVr1alTJ3Xq1EkFBQV67bXXCFgA/II/3VaCW0ogUHGJ8DJHjx5VdXW1kpOTvWMpKSnKz89XbW2tDysDAAD%2BgjNYl3G73WrSpInCwsK8Y02bNlVlZaVKS0sVGxv7o9soKiqS2%2B22jDmdUUpISDBaa2go%2BRiAf/Ons22S9P5jfX1dwnX55TN/9XUJ9eZvvf0xBKzLVFRUWMKVJO/jqqqqem0jJydH2dnZlrHp06fr4YcfNlPk/ykqKtL9/69A48aNMx7e/FFRUZFycnLox/%2BhH1b0w4p%2BWAVqP/625Mbe2hKo/bATp0AuEx4eXidI/fA4IiKiXtsYN26ctm/fbvkaN26c8Vrdbreys7PrnC0LVvTDin5Y0Q8r%2BmFFP6zox83jDNZlEhMTVVJSourqajmd37fH7XYrIiJCMTEx9dpGQkICiR8AgCDGGazLdOzYUU6nU3l5ed6x3NxcJSUlKSSEdgEAgB9HYrhMZGSkRo0apQULFujQoUPavXu3Xn75ZU2YMMHXpQEAAD8RumDBggW%2BLuKnpmfPnjpy5IiWLVum/fv3a9q0aRo9erSvy7qi6Ohode/eXdHR0b4u5SeBfljRDyv6YUU/rOiHFf24OQ6Px%2BPxdREAAACBhEuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWH6qsrJSc%2BbMUWpqqvr06aOXX37Z1yX51Pvvv69f/OIXlq%2BMjAxfl2W7qqoqjRgxQh9//LF3rLCwUBMnTlTXrl01bNgw7d2714cV2utK/Vi8eHGdtbJp0yYfVtnwzp8/r4yMDHXv3l19%2B/ZVZmamKisrJQXn%2BrhWP4JxfZw%2BfVoPPPCAkpOT1b9/f61bt847F4zrwxSnrwvAjVm6dKkOHz6sDRs26MyZM5o1a5aaN2%2BuIUOG%2BLo0nzh%2B/LgGDBigRYsWecfCw8N9WJH9KisrNWPGDBUUFHjHPB6P0tPT1b59e23btk27d%2B/W9OnTtWvXLjVv3tyH1Ta8K/VDkk6cOKEZM2borrvu8o41atTI7vJs4/F4lJGRoZiYGL322mv65ptvNGfOHIWEhGjmzJlBtz6u1Y9Zs2YF3fqora3VlClTlJSUpB07duj06dN69NFHlZiYqBEjRgTd%2BjCJgOWHysvLtWXLFq1du1adOnVSp06dVFBQoNdeey1oA9aJEyfUvn17xcfH%2B7oUnzh%2B/LhmzJihyz/56qOPPlJhYaE2b96sqKgotW3bVvv379e2bdv08MMP%2B6jahne1fkjfr5UHHnggaNbKyZMnlZeXp3379qlp06aSpIyMDD399NPq169f0K2Pa/Xjh4AVTOujuLhYHTt21IIFC9SoUSO1bt1avXr1Um5urpo2bRp068MkLhH6oaNHj6q6ulrJycnesZSUFOXn56u2ttaHlfnOiRMn1Lp1a1%2BX4TOffPKJevTooZycHMt4fn6%2B7rjjDkVFRXnHUlJSlJeXZ3eJtrpaPy5cuKDz588H1VqJj4/XunXrvGHiBxcuXAjK9XGtfgTj%2BkhISNDy5cvVqFEjeTwe5ebm6sCBA%2BrevXtQrg%2BTOIPlh9xut5o0aaKwsDDvWNOmTVVZWanS0lLFxsb6sDr7eTwenTp1Snv37tXq1atVU1OjIUOGKCMjw9KjQHb33XdfcdztdishIcEyFhcXp3PnztlRls9crR8nTpyQw%2BHQqlWr9MEHH6hx48aaNGmS5XJQoImJiVHfvn29j2tra7Vp0yb17NkzKNfHtfoRjOvjHw0cOFBnzpzRgAEDNHjwYP3%2B978PuvVhEgHLD1VUVNQJDj88rqqq8kVJPnXmzBlvT5YvX66vvvpKixcv1sWLFzVv3jxfl%2BdTV1srwbhOpO8vDzkcDrVp00b33nuvDhw4oN/97ndq1KiRfvnLX/q6PFtkZWXpyJEj2rp1q9avXx/06%2BMf%2B/HZZ58F9fp4/vnnVVxcrAULFigzM5P/f9wkApYfCg8Pr7PAf3gcERHhi5J8qkWLFvr444912223yeFwqGPHjqqtrdXjjz%2Bu2bNnKzQ01Ncl%2Bkx4eLhKS0stY1VVVUG5TiRp1KhRGjBggBo3bixJ6tChg7744gu9/vrrQfEPaFZWljZs2KDnnntO7du3D/r1cXk/fv7znwf1%2BkhKSpL0/R%2BIPPbYYxo9erQqKioszwmm9XGzeA%2BWH0pMTFRJSYmqq6u9Y263WxEREYqJifFhZb7TuHFjORwO7%2BO2bduqsrJS33zzjQ%2Br8r3ExEQVFxdbxoqLi%2Buc9g8WDofD%2B4/nD9q0aaPz58/7qCL7LFq0SK%2B88oqysrI0ePBgScG9Pq7Uj2BcH8XFxdq9e7dlrF27drp06ZLi4%2BODdn2YQMDyQx07dpTT6bS80TA3N1dJSUkKCQm%2B/6R//etf1aNHD8tvWp9//rkaN24cdO9Hu5zL5dJnn32mixcvesdyc3Plcrl8WJXv/Pu//7smTpxoGTt69KjatGnjm4Jskp2drc2bN%2BvZZ5/V8OHDvePBuj6u1o9gXB9fffWVpk%2BfbgmRhw8fVmxsrFJSUoJyfZgSfP8aB4DIyEiNGjVKCxYs0KFDh7R79269/PLLmjBhgq9L84nk5GSFh4dr3rx5OnnypPbs2aOlS5fqX//1X31dms91795dzZo10%2BzZs1VQUKA1a9bo0KFDGjNmjK9L84kBAwbowIEDeumll/Tll1/qP/7jP/Tmm29q8uTJvi6twZw4cUIrV67Ub3/7W6WkpMjtdnu/gnF9XKsfwbg%2BkpKS1KlTJ82ZM0fHjx/Xnj17lJWVpWnTpgXl%2BjDKA79UXl7umTlzpqdr166ePn36eF555RVfl%2BRTx44d80ycONHTtWtXT%2B/evT0vvPCCp7a21tdl%2BUT79u09H330kffxF1984bnnnns8nTt39gwfPtyzb98%2BH1Znv8v78f7773t%2B9atfeZKSkjxDhgzxvPfeez6sruGtXr3a0759%2Byt%2BeTzBtz5%2BrB/Btj48Ho/n3LlznvT0dE%2B3bt08vXv39rz44ove/38G2/owyeHxXOFOfAAAALhhXCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMP%2BPzNHRvEC6tIoAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3273108919730444796”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>151</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.526315789473683</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.595959595959595</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.317073170731707</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.47945205479452</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.27027027027027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.545454545454546</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.60377358490566</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.267813267813267</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.233846790034116</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2710)</td> <td class=”number”>2725</td> <td class=”number”>84.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>314</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3273108919730444796”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>151</td> <td class=”number”>4.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.07354743809757293</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.09523809523809523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.11607048644085682</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.14278914802475012</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>22.29580573951435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.563218390804597</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.675324675324674</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>30.434782608695656</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>32.05128205128205</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SBP09”>PCT_SBP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.9531</td>

</tr> <tr>

<th>Minimum</th> <td>1.6614</td>

</tr> <tr>

<th>Maximum</th> <td>6.771</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1149332991915833027”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1149332991915833027,#minihistogram1149332991915833027”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1149332991915833027”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1149332991915833027”

aria-controls=”quantiles1149332991915833027” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1149332991915833027” aria-controls=”histogram1149332991915833027”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1149332991915833027” aria-controls=”common1149332991915833027”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1149332991915833027” aria-controls=”extreme1149332991915833027”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1149332991915833027”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.6614</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.2725</td>

</tr> <tr>

<th>Q1</th> <td>2.8289</td>

</tr> <tr>

<th>Median</th> <td>3.295</td>

</tr> <tr>

<th>Q3</th> <td>5.4277</td>

</tr> <tr>

<th>95-th percentile</th> <td>6.1692</td>

</tr> <tr>

<th>Maximum</th> <td>6.771</td>

</tr> <tr>

<th>Range</th> <td>5.1096</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.5987</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.3965</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.35328</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.1675</td>

</tr> <tr>

<th>Mean</th> <td>3.9531</td>

</tr> <tr>

<th>MAD</th> <td>1.232</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.48837</td>

</tr> <tr>

<th>Sum</th> <td>12381</td>

</tr> <tr>

<th>Variance</th> <td>1.9503</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1149332991915833027”>

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JgCVJ8%2BfP17/9278pLy9PM2bM0G9/%2B1tNmTJFcXFxWrRokWpqapSdnS2Hw6GqqirFxMRIklJTUzV79mxVVlYqLy9P3bt3V1lZmcVHAwAAgkmY519rZfArp/Nbq0swwmbrph49YnXgwHch/3mBrAXvW13CKXn93hFWlxBQzqZz9Uyhp%2BbRU/84tq9W/YZ1yF7BAgAAsAoBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmM3qAgAAZ6%2BsBe9bXcIpef3eEVaXgCDBFSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw0I2YP3973/XBRdc4PMoKiqSJG3dulWTJ0%2BW3W5XTk6OtmzZ4vPc9evXa8yYMbLb7SooKND%2B/futOAQAABCkQjZgbd%2B%2BXaNHj9Z7773nfcydO1eHDh1Sfn6%2BMjIytHr1aqWmpuqOO%2B7QoUOHJEl1dXUqKSlRYWGhqqur1draquLiYouPBgAABJOQDVg7duzQ%2Beefr8TERO8jPj5eGzZsUGRkpKZPn66BAweqpKREsbGxeuONNyRJy5YtU1ZWliZOnKjBgwdr3rx52rhxoxoaGiw%2BIgAAECxCOmANGDCg07jD4VB6errCwsIkSWFhYUpLS1Ntba13PiMjw7t97969lZKSIofDcUbqBgAAwS8kA5bH49GuXbv03nvvaezYsRozZozmz58vt9stp9OppKQkn%2B0TEhK0b98%2BSVJTU9OPzgMAAJyMzeoC/GHv3r1yuVyKiIjQggULtGfPHs2dO1eHDx/2jh8rIiJCbrdbknT48OEfne%2BKpqYmOZ1OnzGbLaZTcAtG4eHdfL4icNhs/EyOxblqHj01/z6jp/4RCH0NyYDVp08fffTRR%2BrevbvCwsI0ZMgQdXR06MEHH9Tw4cM7hSW3262oqChJUmRk5Anno6Oju7z/6upqVVRU%2BIwVFBR4f4sxFMTHd70fODN69Ii1uoSAxLlq3tncU3%2B9z87mnvqTlX0NyYAlSeeee67P9wMHDlRbW5sSExPV3NzsM9fc3Oy9upScnHzC%2BcTExC7vOzc3V5mZmT5jNluMDhz47lQOISCFh3dTfHy0Wltdam/vsLocHCMUzi%2BTOFfNo6fm32f01D%2BO7atVISskA9a7776radOm6e233/Zeefriiy907rnnKj09XYsXL5bH41FYWJg8Ho82b96sO%2B%2B8U5Jkt9tVU1Oj7OxsSVJjY6MaGxtlt9u7vP%2BkpKROy4FO57c6ejR03jzt7R0hdTyhgJ/HiXGumnc299Rfx30299SfrAytIbnom5qaqsjISD3yyCPauXOnNm7cqHnz5um2227TuHHj1NraqtLSUm3fvl2lpaVyuVzKysqSJOXl5Wnt2rVauXKltm3bpunTp2vUqFHq16%2BfxUcFAACCRcAFrMmTJ2vFihX69ttvT/s14uLi9Pzzz2v//v3KyclRSUmJcnNzddtttykuLk6LFi3yXqVyOByqqqpSTEyMpO/D2ezZs1VZWam8vDx1795dZWVlpg4PAACcBQJuifDyyy/Xc889p7KyMv3yl79Udna2RowY4b1vVVf94he/0AsvvHDCuYsvvlhr1qz5wedmZ2d7lwgBAABOVcBdwXrggQf0j3/8QwsXLlR4eLjuuecejRo1Sk8//bR27dpldXkAAAAnFXBXsKTv764%2BYsQIjRgxQi6XS0uXLtXChQtVVVWltLQ03XTTTfrVr35ldZkAAAAnFJABS/r%2BZp3r1q3TunXr9NVXXyktLU2/%2Bc1vtG/fPj3yyCP65JNPVFJSYnWZAAAAnQRcwFq7dq3Wrl2rjz76SD179tTEiRP1zDPP%2BPxdwd69e6u0tJSABQAAAlLABaySkhKNHj1alZWVuvrqq9WtW%2BePiZ133nm68cYbLagOAADg5AIuYL3zzjvq0aOHWlpavOGqrq5OQ4cOVXh4uCQpLS1NaWlpVpYJAADwgwLutwgPHjyocePGafHixd6x/Px8TZgwQY2NjRZWBgAA0DUBF7Aef/xx9e/fXzfffLN3bMOGDerduzc3/AQAAEEh4ALWp59%2Bqoceesjnjyv37NlT06dP14cffmhhZQAAAF0TcAHLZrOptbW107jL5ZLH47GgIgAAgFMTcAHr6quv1ty5c/XPf/7TO9bQ0KCysjKNHDnSwsoAAAC6JuB%2Bi3DGjBm6%2BeabNXbsWMXHx0uSWltbNXToUBUXF1tcHQAAwMkFXMBKSEjQmjVr9MEHH6i%2Bvl42m02DBg3SFVdcccp/8BkAAMAKARewJCk8PFwjR45kSRAAAASlgAtYTqdTCxYs0ObNm3XkyJFOH2x/6623LKoMAACgawIuYP3%2B97/Xli1bdN111%2BlnP/uZ1eUAAACcsoALWB9%2B%2BKGWLFmijIwMq0sBAAA4LQF3m4aYmBglJCRYXQYAAMBpC7iANWHCBC1ZskTt7e1WlwIAAHBaAm6JsKWlRevXr9fbb7%2Btfv36KSIiwmf%2Bz3/%2Bs0WVAQAAdE3ABSxJGj9%2BvNUlAAAAnLaAC1hlZWVWlwAAAPCTBNxnsCSpqalJFRUVeuCBB/R///d/euONN7Rz506rywIAAOiSgAtYu3fv1q9//WutWbNGf/vb33To0CFt2LBBOTk5cjgcVpcHAABwUgG3RPiHP/xBY8aM0dy5c5WWliZJeuqppzRjxgzNnz9fS5cutbhCIDBlLXjf6hK67PV7R1hdAgD4VcBdwdq8ebNuvvlmnz/sbLPZdPfdd2vr1q0WVgYAANA1ARewOjo61NHR0Wn8u%2B%2B%2BU3h4uAUVAQAAnJqAWyK86qqrtGjRIpWXl3vHWlpaVF5erssvv9zCyvBTBdMSFgAAP0XAXcF66KGHtGXLFl111VVqa2vTXXfdpdGjR2vPnj2aMWPGKb9efn6%2BHnroIe/3W7du1eTJk2W325WTk6MtW7b4bL9%2B/XqNGTNGdrtdBQUF2r9//08%2BJgAAcHYJuICVnJysV155Rffdd5%2Buv/56ZWRkaNq0aXr11VfVp0%2BfU3qt1157TRs3bvR%2Bf%2BjQIeXn5ysjI0OrV69Wamqq7rjjDh06dEiSVFdXp5KSEhUWFqq6ulqtra0qLi42enwAACD0BdwSoSRFR0dr8uTJP%2Bk1WlpaNG/ePA0bNsw7tmHDBkVGRmr69OkKCwtTSUmJ3nnnHb3xxhvKzs7WsmXLlJWVpYkTJ0qS5s2bp9GjR6uhoUH9%2BvX7SfUAAICzR8AFrKlTp/7ofFf/FuETTzyhCRMmqKmpyTvmcDiUnp7u/Q3FsLAwpaWlqba2VtnZ2XI4HLr99tu92/fu3VspKSlyOBwELAAA0GUBF7COXwY8evSodu/era%2B%2B%2Bko33XRTl15j06ZN%2BvTTT/Xqq69q1qxZ3nGn06lBgwb5bJuQkKD6%2BnpJ399BPikpqdP8vn37TukYmpqa5HQ6fcZstphOrx2MwsO7%2BXwFTofN5v/zh3PVPHpq/tylp/4RCH0NuID1Q3%2BLsLKysktBp62tTY8%2B%2BqhmzpypqKgonzmXy6WIiAifsYiICLndbknS4cOHf3S%2Bq6qrq1VRUeEzVlBQoKKiolN6nUAWHx9tdQkIYj16xJ6xfXGumnc299Rf5%2B7Z3FN/srKvARewfsiECRM0ceJEzZkz50e3q6io0EUXXaSRI0d2mouMjOwUltxutzeI/dB8dPSp/YByc3OVmZnpM2azxejAge9O6XUCUXh4N8XHR6u11aX29s73KwO64ky8FzhXzaOn5s9deuofx/bVqpAVNAHrs88%2B69KNRl977TU1NzcrNTVVkryB6W9/%2B5vGjx%2Bv5uZmn%2B2bm5u9S3fJycknnE9MTDylWpOSkjotBzqd3%2Bro0dB587S3d4TU8eDMOpPnDueqeWdzT/113GdzT/3JytAacAHrRB9yP3jwoL788kvdcMMNJ33%2B0qVLdfToUe/38%2BfPlyRNmzZNn3zyiRYvXiyPx6OwsDB5PB5t3rxZd955pyTJbrerpqZG2dnZkqTGxkY1NjbKbrebODQAAHCWCLiAlZKS4vN3CCXpnHPO0Y033qh///d/P%2Bnzj/%2BQfGzs9%2Bvl/fv3V0JCgp588kmVlpbq%2Buuv14oVK%2BRyuZSVlSVJysvL05QpU3TJJZdo2LBhKi0t1ahRo/gNQgAAcEoCLmD94Q9/8Ntrx8XFadGiRXr00Uf117/%2BVRdccIGqqqoUExMjSUpNTdXs2bP1zDPP6JtvvtGIESNO%2BpkvAACA4wVcwPrkk0%2B6vO2ll1560m2OD2wXX3yx1qxZ84PbZ2dne5cIAQAATkfABawpU6Z4lwg9Ho93/PixsLAwffHFF2e%2BQAAAgJMIuID13HPPae7cuXrwwQc1fPhwRURE6PPPP9fs2bP1m9/8Rtdee63VJQIAAPyogLt1bFlZmWbOnKmxY8eqR48eio2N1eWXX67Zs2dr%2BfLl6tOnj/cBAAAQiAIuYDU1NZ0wPMXFxenAgQMWVAQAAHBqAi5gXXLJJXrqqad08OBB71hLS4vKy8t1xRVXWFgZAABA1wTcZ7AeeeQRTZ06VVdffbUGDBggj8ej//mf/1FiYqL%2B/Oc/W10eAADASQVcwBo4cKA2bNig9evXa8eOHZKk3/72t7ruuutO%2BW8CAgAAWCHgApYkde/eXZMnT9aePXu8d1E/55xzLK4KAACgawLuM1gej0fz58/XpZdeqvHjx2vfvn2aMWOGSkpKdOTIEavLAwAAOKmAC1hLly7V2rVr9eijjyoiIkKSNGbMGL355puqqKiwuDoAAICTC7iAVV1drZkzZyo7O9t79/Zrr71Wc%2BfO1auvvmpxdQAAACcXcAFrz549GjJkSKfxwYMHy%2Bl0WlARAADAqQm4gNWnTx99/vnnncbfeecd7wfeAQAAAlnA/Rbhrbfeqscee0xOp1Mej0ebNm1SdXW1li5dqoceesjq8gAAAE4q4AJWTk6Ojh49qmeffVaHDx/WzJkz1bNnT917773Ky8uzujwAAICTCriAtX79eo0bN065ubnav3%2B/PB6PEhISrC4LAACgywLuM1izZ8/2fpi9Z8%2BehCsAABB0Ai5gDRgwQF999ZXVZQAAAJy2gFsiHDx4sKZNm6YlS5ZowIABioyM9JkvKyuzqDIAAICuCbiAtWvXLqWnp0sS970CAABBKSAC1rx581RYWKiYmBgtXbrU6nIAAAB%2BkoD4DNYLL7wgl8vlM5afn6%2BmpiaLKgIAADh9ARGwPB5Pp7FPPvlEbW1tFlQDAADw0wREwAIAAAglBCwAAADDAiZghYWFWV0CAACAEQHxW4SSNHfuXJ97Xh05ckTl5eWKjY312Y77YAEAgEAXEFewLr30UjmdTu3Zs8f7SE1N1YEDB3zG9uzZ0%2BXX3L17t2699ValpqZq1KhRWrJkiXeuoaFBv/vd73TJJZfo2muv1Xvvvefz3A8%2B%2BEDjx4%2BX3W7X1KlT1dDQYOxYAQBA6AuIK1im733V0dGh/Px8DRs2TGvWrNHu3bt1//33Kzk5WePHj1dBQYHOP/98rVq1Sm%2B%2B%2BaYKCwu1YcMGpaSkaO/evSooKNA999yjkSNHqrKyUnfffbfWrVvHMiYAAOiSgAhYpjU3N2vIkCGaNWuW4uLiNGDAAF1xxRWqqalRr1691NDQoBUrVigmJkYDBw7Upk2btGrVKt1zzz1auXKlLrroIt1yyy2Svl%2BSHDFihD7%2B%2BGNddtllFh8ZAAAIBgGxRGhaUlKSFixYoLi4OHk8HtXU1OiTTz7R8OHD5XA4dOGFFyomJsa7fXp6umprayVJDodDGRkZ3rno6GgNHTrUOw8AAHAyIRmwjpWZmakbbrhBqampGjt2rJxOp5KSkny2SUhI0L59%2ByTppPMAAAAnE5JLhMd65pln1NzcrFmzZqmsrEwul0sRERE%2B20RERMjtdkvSSee7oqmpqdMfqrbZYjoFt2AUHt7N5ytwOmw2/58/nKvm0VPz5y499Y9A6GvIB6xhw4ZJktra2jRt2jTl5OR0%2BruHbrdbUVFRkqTIyMhOYcrtdis%2BPr7L%2B6yurlZFRYXPWEFBgYqKik7nEAJSfHy01SUgiPXoEXvyjQzhXDXvbO6pv87ds7mn/mRlX0MyYDU3N6u2tlZjxozxjg0aNEhHjhxRYmKidu7c2Wn7f11dSk5OVnNzc6f5IUOGdHn/ubm5yszM9Bmz2WJ04MB3p3ooASc8vJv53Ky4AAASiklEQVTi46PV2upSe3uH1eUgSJ2J9wLnqnn01Py5S0/949i%2BWhWyQjJg7dmzR4WFhdq4caOSk5MlSVu2bFHPnj2Vnp6uP/3pTzp8%2BLD3qlVNTY3S09MlSXa7XTU1Nd7Xcrlc2rp1qwoLC7u8/6SkpE7LgU7ntzp6NHTePO3tHSF1PDizzuS5w7lq3tncU38d99ncU3%2ByMrSG5KLvsGHDNHToUD388MPavn27Nm7cqPLyct15550aPny4evfureLiYtXX16uqqkp1dXWaNGmSJCknJ0ebN29WVVWV6uvrVVxcrL59%2B3KLBgAA0GUhGbDCw8O1cOFCRUdHKzc3VyUlJZoyZYqmTp3qnXM6ncrOzta6detUWVmplJQUSVLfvn31xz/%2BUatWrdKkSZPU0tKiyspKbjIKAAC6LCSXCKXvP0t1/AfN/6V///5atmzZDz73mmuu0TXXXOOv0gAAQIgLyStYAAAAViJgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYyP6xZwAATMta8L7VJZyS1%2B8dYXUJZy2uYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYTarCwBw9sla8L7VJZyS1%2B8dYXUJAIIMV7AAAAAMC9mA9fXXX6uoqEjDhw/XyJEjVVZWpra2NklSQ0ODfve73%2BmSSy7Rtddeq/fee8/nuR988IHGjx8vu92uqVOnqqGhwYpDAAAAQSokA5bH41FRUZFcLpf%2B8pe/6Omnn9Y//vEPLViwQB6PRwUFBerVq5dWrVqlCRMmqLCwUHv37pUk7d27VwUFBcrOztbLL7%2Bsnj176u6775bH47H4qAAAQLAIyc9g7dy5U7W1tXr//ffVq1cvSVJRUZGeeOIJXX311WpoaNCKFSsUExOjgQMHatOmTVq1apXuuecerVy5UhdddJFuueUWSVJZWZlGjBihjz/%2BWJdddpmVhwUAAIJESAasxMRELVmyxBuu/uXgwYNyOBy68MILFRMT4x1PT09XbW2tJMnhcCgjI8M7Fx0draFDh6q2tpaABZyl%2BFA%2BgFMVkgErPj5eI0eO9H7f0dGhZcuW6fLLL5fT6VRSUpLP9gkJCdq3b58knXS%2BK5qamuR0On3GbLaYTq8bjMLDu/l8BRB4bDb/vD95/wcff50LgS4QztWQDFjHKy8v19atW/Xyyy/rxRdfVEREhM98RESE3G63JMnlcv3ofFdUV1eroqLCZ6ygoEBFRUWneQSBJz4%2B2uoSAPyAHj1i/fr6vP%2BDh7/PhUBn5bka8gGrvLxcL730kp5%2B%2Bmmdf/75ioyMVEtLi882brdbUVFRkqTIyMhOYcrtdis%2BPr7L%2B8zNzVVmZqbPmM0WowMHvjvNowgc4eHdFB8frdZWl9rbO6wuB8AJ%2BOvfGt7/wScU/rtzOo49V60KWSEdsObMmaPly5ervLxcY8eOlSQlJydr%2B/btPts1Nzd7l%2B%2BSk5PV3NzcaX7IkCFd3m9SUlKn5UCn81sdPRo6/yC1t3eE1PEAocTf703e/8HjbP85Wfk/AiG7OFtRUaEVK1boqaee0nXXXecdt9vt%2Bu///m8dPnzYO1ZTUyO73e6dr6mp8c65XC5t3brVOw8AAHAyIRmwduzYoYULF%2Br2229Xenq6nE6n9zF8%2BHD17t1bxcXFqq%2BvV1VVlerq6jRp0iRJUk5OjjZv3qyqqirV19eruLhYffv25TcIAQBAl4VkwHrrrbfU3t6uZ599VldddZXPIzw8XAsXLpTT6VR2drbWrVunyspKpaSkSJL69u2rP/7xj1q1apUmTZqklpYWVVZWKiwszOKjAgAAwSIkP4OVn5%2Bv/Pz8H5zv37%2B/li1b9oPz11xzja655hp/lAYAAM4CIXkFCwAAwEoELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADLNZXQB%2BmqwF71tdAgAAOA5XsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhIX%2BbBrfbrezsbP3%2B97/XZZddJklqaGjQ73//e9XW1iolJUUPP/ywrrrqKu9zPvjgAz3%2B%2BONqaGiQ3W5XaWmp%2BvXrZ9UhAABwWoLpVj6v3zvC6hKMCukrWG1tbbr//vtVX1/vHfN4PCooKFCvXr20atUqTZgwQYWFhdq7d68kae/evSooKFB2drZefvll9ezZU3fffbc8Ho9VhwEAAIJMyAas7du36z/%2B4z/0z3/%2B02f8ww8/VENDg2bPnq2BAwfqjjvu0CWXXKJVq1ZJklauXKmLLrpIt9xyi37xi1%2BorKxM//u//6uPP/7YisMAAABBKGQD1scff6zLLrtM1dXVPuMOh0MXXnihYmJivGPp6emqra31zmdkZHjnoqOjNXToUO88AADAyYTsZ7BuuOGGE447nU4lJSX5jCUkJGjfvn1dmgcAADiZkA1YP8TlcikiIsJnLCIiQm63u0vzXdHU1CSn0%2BkzZrPFdApuAOAPNpt/FifCw7v5fAVMMnneBsK5etYFrMjISLW0tPiMud1uRUVFeeePD1Nut1vx8fFd3kd1dbUqKip8xgoKClRUVHSaVQNA1/XoEevX14%2BPj/br6%2BPs5I/z1spz9awLWMnJydq%2BfbvPWHNzs/fqUnJyspqbmzvNDxkypMv7yM3NVWZmps%2BYzRajAwe%2BO82qAaDr/PVvTXh4N8XHR6u11aX29g6/7ANnL5Pn7bHnqlUh66wLWHa7XVVVVTp8%2BLD3qlVNTY3S09O98zU1Nd7tXS6Xtm7dqsLCwi7vIykpqdNyoNP5rY4e5R8kAP7n739r2ts7%2BPcMxvnjnLLyfwTOuoX04cOHq3fv3iouLlZ9fb2qqqpUV1enSZMmSZJycnK0efNmVVVVqb6%2BXsXFxerbt6/3JqUAAAAnc9YFrPDwcC1cuFBOp1PZ2dlat26dKisrlZKSIknq27ev/vjHP2rVqlWaNGmSWlpaVFlZqbCwMIsrBwAAweKsWCL88ssvfb7v37%2B/li1b9oPbX3PNNbrmmmv8XRYAAAhRZ90VLAAAAH8jYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7Kz4W4QAcDbJWvC%2B1SUAZz2uYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgnUBbW5sefvhhZWRk6KqrrtKf/vQnq0sCAABBxGZ1AYFo3rx52rJli1566SXt3btXM2bMUEpKisaNG2d1aQAAIAgQsI5z6NAhrVy5UosXL9bQoUM1dOhQ1dfX6y9/%2BQsBCwAAdAlLhMfZtm2bjh49qtTUVO9Yenq6HA6HOjo6LKwMAAAEC65gHcfpdKpHjx6KiIjwjvXq1UttbW1qaWlRz549T/oaTU1NcjqdPmM2W4ySkpKM1wsAQCiw2cxd8wkP7%2Bbz1QoErOO4XC6fcCXJ%2B73b7e7Sa1RXV6uiosJnrLCwUPfcc4%2BZIo/xaemZXbZsampSdXW1cnNzCYyG0FP/oK/m0VPz6Kl/NDU16aWXlig3N1fx8dGW1MAS4XEiIyM7Bal/fR8VFdWl18jNzdXq1at9Hrm5ucZrtYLT6VRFRUWnK3Q4ffTUP%2BirefTUPHrqH4HQV65gHSc5OVkHDhzQ0aNHZbN93x6n06moqCjFx8d36TWSkpL4PxEAAM5iXME6zpAhQ2Sz2VRbW%2Bsdq6mp0bBhw9StG%2B0CAAAnR2I4TnR0tCZOnKhZs2aprq5Ob775pv70pz9p6tSpVpcGAACCRPisWbNmWV1EoLn88su1detWPfnkk9q0aZPuvPNO5eTkWF1WwIiNjdXw4cMVGxtrdSkhg576B301j56aR0/9w%2Bq%2Bhnk8Ho8lewYAAAhRLBECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgoUu%2B/vprFRUVafjw4Ro5cqTKysrU1tZmdVlBb/fu3br11luVmpqqUaNGacmSJVaXFFLy8/P10EMPWV1GSPj73/%2BuCy64wOdRVFRkdVlBze1267HHHtOll16qK6%2B8Uk899ZS49/dPs3r16k7n6QUXXKDBgwef8VpsZ3yPCDoej0dFRUWKj4/XX/7yF33zzTd6%2BOGH1a1bN82YMcPq8oJWR0eH8vPzNWzYMK1Zs0a7d%2B/W/fffr%2BTkZP3617%2B2uryg99prr2njxo36zW9%2BY3UpIWH79u0aPXq05syZ4x2LjIy0sKLgN3fuXH300Ud6/vnn9d133%2Bm%2B%2B%2B5TSkqKrr/%2BeqtLC1rXXnutRo4c6f3%2B6NGjuummmzRq1KgzXgsBCye1c%2BdO1dbW6v3331evXr0kSUVFRXriiScIWD9Bc3OzhgwZolmzZikuLk4DBgzQFVdcoZqaGgLWT9TS0qJ58%2BZp2LBhVpcSMnbs2KHzzz9fiYmJVpcSElpaWrRq1Sq98MILuvjiiyVJt9xyixwOBwHrJ4iKilJUVJT3%2B0WLFsnj8WjatGlnvBYCFk4qMTFRS5Ys8Yarfzl48KBFFYWGpKQkLViwQNL3Vwk3b96sTz75RI8%2B%2BqjFlQW/J554QhMmTFBTU5PVpYSMHTt26Morr7S6jJBRU1OjuLg4DR8%2B3DuWn59vYUWhp6WlRYsXL9bcuXMVERFxxvfPZ7BwUvHx8T6XXDs6OrRs2TJdfvnlFlYVWjIzM3XDDTcoNTVVY8eOtbqcoLZp0yZ9%2Bumnuvvuu60uJWR4PB7t2rVL7733nsaOHasxY8Zo/vz5crvdVpcWtBoaGtSnTx%2B98sorGjdunH75y1%2BqsrJSHR0dVpcWMpYvX66kpCSNGzfOkv0TsHDKysvLtXXrVt13331WlxIynnnmGT333HP64osvVFZWZnU5QautrU2PPvqoZs6c6bNMgJ9m7969crlcioiI0IIFCzRjxgy9%2BuqrmjdvntWlBa1Dhw5p9%2B7dWrFihcrKyjRjxgwtXbpUL774otWlhQSPx6OVK1fqxhtvtKwGlghxSsrLy/XSSy/p6aef1vnnn291OSHjX58Vamtr07Rp0zR9%2BnRLLmkHu4qKCl100UU%2BV1zx0/Xp00cfffSRunfvrrCwMA0ZMkQdHR168MEHVVxcrPDwcKtLDDo2m00HDx7Uk08%2BqT59%2Bkj6PsguX75ct9xyi8XVBb/PP/9cX3/9ta677jrLaiBgocvmzJmj5cuXq7y8nGUsA5qbm1VbW6sxY8Z4xwYNGqQjR47o4MGD6tmzp4XVBafXXntNzc3NSk1NlSTvEtbf/vY3ffbZZ1aWFvTOPfdcn%2B8HDhyotrY2ffPNN5yrpyExMVGRkZHecCVJP//5z9XY2GhhVaHj3XffVUZGhrp3725ZDSwRoksqKiq0YsUKPfXUU5b%2BH0Eo2bNnjwoLC/X11197x7Zs2aKePXvyH6zTtHTpUr366qt65ZVX9MorrygzM1OZmZl65ZVXrC4tqL377ru67LLL5HK5vGNffPGFzj33XM7V02S329XW1qZdu3Z5x3bu3OkTuHD66urqlJaWZmkNBCyc1I4dO7Rw4ULdfvvtSk9Pl9Pp9D5w%2BoYNG6ahQ4fq4Ycf1vbt27Vx40aVl5frzjvvtLq0oNWnTx/179/f%2B4iNjVVsbKz69%2B9vdWlBLTU1VZGRkXrkkUe0c%2BdObdy4UfPmzdNtt91mdWlB67zzztOoUaNUXFysbdu26d1331VVVZXy8vKsLi0k1NfXa9CgQZbWwBIhTuqtt95Se3u7nn32WT377LM%2Bc19%2B%2BaVFVQW/8PBwLVy4UHPmzFFubq6io6M1ZcoUTZ061erSAB9xcXF6/vnn9fjjjysnJ0exsbG6/vrrCVg/0fz58zVnzhzl5eUpOjpav/3tbzVlyhSrywoJzc3Nio%2BPt7SGMA/35QcAADCKJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/Afm1FjJNrO23AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1149332991915833027”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.169233189071782</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.74437765147172</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.973591167370116</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5349036986282</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.936181228476279</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.416376141597667</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2724686723712626</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.913159996435304</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.811105318871648</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.454458158771867</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1149332991915833027”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.661363078723364</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9586799034075664</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9803074024152538</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.046139680874765</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.113931258359077</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.64811977054827</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.74437765147172</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.169233189071782</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.464341675826541</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.771011884839952</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SBP15”><s>PCT_SBP15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_SBP09”><code>PCT_SBP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92649</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SFSP09”>PCT_SFSP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.70337</td>

</tr> <tr>

<th>Minimum</th> <td>0.18804</td>

</tr> <tr>

<th>Maximum</th> <td>5.8864</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram530599098327662535”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives530599098327662535,#minihistogram530599098327662535”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives530599098327662535”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles530599098327662535”

aria-controls=”quantiles530599098327662535” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram530599098327662535” aria-controls=”histogram530599098327662535”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common530599098327662535” aria-controls=”common530599098327662535”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme530599098327662535” aria-controls=”extreme530599098327662535”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles530599098327662535”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.18804</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.32805</td>

</tr> <tr>

<th>Q1</th> <td>0.54888</td>

</tr> <tr>

<th>Median</th> <td>0.62553</td>

</tr> <tr>

<th>Q3</th> <td>0.7705</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.2821</td>

</tr> <tr>

<th>Maximum</th> <td>5.8864</td>

</tr> <tr>

<th>Range</th> <td>5.6983</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.22162</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37575</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.53422</td>

</tr> <tr>

<th>Kurtosis</th> <td>17.155</td>

</tr> <tr>

<th>Mean</th> <td>0.70337</td>

</tr> <tr>

<th>MAD</th> <td>0.23472</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.8958</td>

</tr> <tr>

<th>Sum</th> <td>2202.9</td>

</tr> <tr>

<th>Variance</th> <td>0.14119</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram530599098327662535”>

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oKChIjz/%2BuPr3768XX3xRhw8ftrscAAAA4xy5yD0gIEB9%2BvRRnz59VFFRodWrV2vJkiXKyclRjx499MADD%2BhXv/qVE6UBAABcMcd%2Bi7CkpETvvPOO3nnnHX399dfq0aOH7rrrLp04cULPPfecdu3apenTpztVHgAAwGWzPWC9/fbbevvtt7Vz5061atVKI0eO1Msvv6yOHTt6l2nbtq3mzZtHwAIAAH7J9oA1ffp0DRgwQIsXL9btt9%2BuwMC6l4H98EHNAAAA/sj2gPXRRx%2BpZcuWKi8v94arwsJCde3aVUFBQZKkHj16qEePHnaXBgAAYITtv0V45swZDRkyRK%2B99pp3bOLEiRoxYoSOHz9udzkAAADG2R6wnn/%2BeXXo0EEPPfSQd%2By9995T27ZtlZGRYXc5AAAAxtkesD7//HM9%2B%2Byzio6O9o61atVKzzzzjHbs2GF3OQAAAMbZHrBcLpdOnz5dZ7yiooI7ugMAgCbB9oB1%2B%2B23a%2B7cufrnP//pHSsuLlZGRob69etndzkAAADG2f5bhFOnTtVDDz2kwYMHKzIyUpJ0%2BvRpde3aVenp6XaXAwAAYJztAat169Z666239Omnn6qoqEgul0s33HCDbrvtNgUEBNhdDgAAgHGOfFROUFCQ%2BvXrxylBAADQJNkesNxutxYtWqTdu3frwoULdS5s/%2Btf/2p3SQAAAEbZHrB%2B97vfac%2BePRo2bJiuueYauzcPAABw1dkesHbs2KGlS5cqKSnJ7k0DAADYwvbbNISFhal169Z2bxYAAMA2tgesESNGaOnSpaqpqbF70wAAALaw/RRheXm5Nm/erL///e%2B67rrrFBwc7DO/atUqu0sCAAAwypHbNAwfPtyJzQIAANjC9oCVkZFh9yYBAABsZfs1WJJUUlKi7OxsTZ48Wf/617/0l7/8RYcOHXKiFAAAAONsD1hHjhzRr3/9a7311lv64IMPdO7cOb333nsaPXq0CgoK7C4HAADAONsD1gsvvKBBgwZpy5Yt%2BtnPfiZJWrhwoQYOHKj58%2BfbXQ4AAIBxtges3bt366GHHvL5YGeXy6XHHntMe/futbscAAAA42wPWLW1taqtra0zfvbsWQUFBdldDgAAgHG2B6y%2Bffvq1Vdf9QlZ5eXlysrKUu/eve0uBwAAwDjbA9azzz6rPXv2qG/fvqqsrNR///d/a8CAATp69KimTp1qdzkAAADG2X4frNjYWG3atEmbN2/Wvn37VFtbqzFjxmjEiBGKiIiwuxwAAADjHLmTe2hoqO655x4nNg0AAHDV2R6wxo0bd8l5PosQAAD4O9sDVrt27XyeV1dX68iRI/r666/1wAMP2F0OAACAcY3mswgXL16sEydO2FwNAACAeY58FmF9RowYoffff9/pMgAAAK5YowlYX3zxBTcaBQAATUKjuMj9zJkz%2BuqrrzR27Fi7ywEAADDO9oAVFxfn8zmEkvSzn/1M9913n/7zP//T7nIAAACMsz1gvfDCC3ZvEgAAwFa2B6xdu3Y1eNmePXtexUoAAACuDtsD1v333%2B89RejxeLzjPx4LCAjQvn377C4PAADgitkesF555RXNnTtXU6ZMUXJysoKDg/Xll19q9uzZuuuuu3TnnXfaXRIAAIBRtt%2BmISMjQzNmzNDgwYPVsmVLhYeHq3fv3po9e7bWrVundu3aeR8AAAD%2ByPaAVVJSUm94ioiIUFlZmd3lAAAAGGd7wOrevbsWLlyoM2fOeMfKy8uVlZWl2267ze5yAAAAjLP9GqznnntO48aN0%2B23366OHTvK4/HoH//4h6Kjo7Vq1Sq7ywEAADDO9oDVqVMnvffee9q8ebMOHjwoSfqv//ovDRs2TKGhoXaXAwAAYJztAUuSrr32Wt1zzz06evSorrvuOknf380dAACgKbD9GiyPx6P58%2BerZ8%2BeGj58uE6cOKGpU6dq%2BvTpunDhgt3lAAAAGGd7wFq9erXefvtt/f73v1dwcLAkadCgQdqyZYuys7PtLgcAAMA42wNWbm6uZsyYoVGjRnnv3n7nnXdq7ty5evfdd%2B0uBwAAwDjbA9bRo0fVpUuXOuOdO3eW2%2B22uxwAAADjbA9Y7dq105dfflln/KOPPvJe8A4AAODPbA9YEyZM0KxZs7Rq1Sp5PB5t375d8%2BfPV2Zmpu6//37L66uqqtLw4cO1c%2BdO71hxcbEefPBBde/eXXfeeae2bdvm85pPP/1Uw4cPV3x8vMaNG6fi4mKf%2BRUrVqhfv35KSEjQtGnTVFFRcXk7CwAAmiXbA9bo0aP15JNPavny5Tp//rxmzJihN998U0888YTGjBljaV2VlZV66qmnVFRU5B3zeDxKTU1VmzZttHHjRo0YMUKTJk3SsWPHJEnHjh1TamqqRo0apTfeeEOtWrXSY489Jo/HI0n64IMPlJ2drdmzZ2vlypUqKChQVlaWuQYAAIAmz/b7YG3evFlDhgxRSkqKTp06JY/Ho9atW1tez4EDBzR58mRvMPrBjh07VFxcrPXr1yssLEydOnXS9u3btXHjRj3%2B%2BOPasGGDbrnlFo0fP17S9x8%2B3adPH3322Wfq1auXVq1apQceeEADBgyQJM2aNUsTJkzQlClTuBEqAABoENuPYM2ePdt7MXurVq0uK1xJ8gai3Nxcn/GCggLdfPPNCgsL844lJiYqPz/fO5%2BUlOSdCw0NVdeuXZWfn6%2Bamhp9%2BeWXPvPdu3fXhQsXtH///suqEwAAND%2B2H8Hq2LGjvv76a91www1XtJ6xY8fWO%2B52uxUTE%2BMz1rp1a504ceIn50%2BfPq3KykqfeZfLpaioKO/rG6KkpKTOb0S6XGF1titJQUGBPl%2BbMpfLzD42p56ZQs%2BsoV/W0TNr6Jd1/tYz2wNW586d9fTTT2vp0qXq2LGjQkJCfOYzMjKuaP0VFRXeG5j%2BIDg4WFVVVT85f/78ee/zi72%2BIXJzc%2BvcNDU1NVVpaWkXfU1kZNM//diyZbjR9TWHnplGz6yhX9bRM2vol3X%2B0jPbA9bhw4eVmJgoSVflvlchISEqLy/3GauqqlKLFi288z8OS1VVVYqMjPSGvfrmrVx/lZKSooEDB/qMuVxhKis7W2fZoKBARUaG6vTpCtXU1DZ4G/6ovv2/HM2pZ6bQM2vol3X0zBr6Zd3l9sz0D/cNZUvAyszM1KRJkxQWFqbVq1df1W3FxsbqwIEDPmOlpaXe03OxsbEqLS2tM9%2BlSxdFRUUpJCREpaWl6tSpkySpurpa5eXlio6ObnANMTExdU4Hut3fqbr64m%2BImpraS843Bab3rzn0zDR6Zg39so6eWUO/rPOXntlyIvP111%2Bvcy%2BpiRMnqqSkxPi24uPj9b//%2B7/e032SlJeXp/j4eO98Xl6ed66iokJ79%2B5VfHy8AgMD1a1bN5/5/Px8uVwude7c2XitAACgabIlYP34VgqStGvXLlVWVhrfVnJystq2bav09HQVFRUpJydHhYWFuvvuuyV9fx%2Bu3bt3KycnR0VFRUpPT1f79u3Vq1cvSd9fPL9s2TJt2bJFhYWFmjlzpu69915u0QAAABrMPy7FtyAoKEhLliyR2%2B3WqFGj9M4772jx4sWKi4uTJLVv315//OMftXHjRt19990qLy/X4sWLvR88PWzYMD3yyCOaMWOGxo8fr1tvvVVTpkxxcpcAAICfsf0i96vhq6%2B%2B8nneoUMHrVmz5qLL/%2BIXv9AvfvGLi85PnDhREydONFYfAABoXmw7gvXDESIAAICmzrYjWHPnzvW559WFCxeUlZWl8HDfX5%2B80vtgAQAAOM2WgNWzZ88697xKSEhQWVmZysrK7CgBAADANrYErKt97ysAAIDGpMn9FiEAAIDTCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmMvpAtB8DF30idMlWPL%2BE32cLgEA4Kc4ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGFNNmB9%2BOGHuummm3weaWlpkqS9e/fqnnvuUXx8vEaPHq09e/b4vHbz5s0aNGiQ4uPjlZqaqlOnTjmxCwAAwE812YB14MABDRgwQNu2bfM%2B5s6dq3PnzmnixIlKSkrSm2%2B%2BqYSEBD3yyCM6d%2B6cJKmwsFDTp0/XpEmTlJubq9OnTys9Pd3hvQEAAP6kyQasgwcP6sYbb1R0dLT3ERkZqffee08hISF65pln1KlTJ02fPl3h4eH6y1/%2BIklas2aNhg4dqpEjR6pz587KzMzU1q1bVVxc7PAeAQAAf9GkA1bHjh3rjBcUFCgxMVEBAQGSpICAAPXo0UP5%2Bfne%2BaSkJO/ybdu2VVxcnAoKCmypGwAA%2BL8mGbA8Ho8OHz6sbdu2afDgwRo0aJDmz5%2Bvqqoqud1uxcTE%2BCzfunVrnThxQpJUUlJyyXkAAICf4nK6gKvh2LFjqqioUHBwsBYtWqSjR49q7ty5On/%2BvHf8/wsODlZVVZUk6fz585ecb4iSkhK53W6fMZcrrE5wk6SgoECfr2g8XK6m8z3hfWYN/bKOnllDv6zzt541yYDVrl077dy5U9dee60CAgLUpUsX1dbWasqUKUpOTq4TlqqqqtSiRQtJUkhISL3zoaGhDd5%2Bbm6usrOzfcZSU1O9v8VYn8jIhq8f9mjZMtzpEozjfWYN/bKOnllDv6zzl541yYAlSVFRUT7PO3XqpMrKSkVHR6u0tNRnrrS01Ht0KTY2tt756OjoBm87JSVFAwcO9BlzucJUVna2zrJBQYGKjAzV6dMVqqmpbfA2cPXV9/3yV7zPrKFf1tEza%2BiXdZfbM6d%2BWG6SAevjjz/W008/rb///e/eI0/79u1TVFSUEhMT9dprr8nj8SggIEAej0e7d%2B/Wo48%2BKkmKj49XXl6eRo0aJUk6fvy4jh8/rvj4%2BAZvPyYmps7pQLf7O1VXX/wNUVNTe8l52K8pfj94n1lDv6yjZ9bQL%2Bv8pWf%2BcSLTooSEBIWEhOi5557ToUOHtHXrVmVmZurhhx/WkCFDdPr0ac2bN08HDhzQvHnzVFFRoaFDh0qSxowZo7ffflsbNmzQ/v379cwzz6h///667rrrHN4rAADgL5pkwIqIiNCyZct06tQpjR49WtOnT1dKSooefvhhRURE6NVXX/UepSooKFBOTo7CwsIkfR/OZs%2BercWLF2vMmDG69tprlZGR4fAeAQAAfxLg8Xg8ThfRHLjd39U77nIFqmXLcJWVnb2sQ55DF31ypaXhIt5/oo/TJRhzpe%2Bz5oZ%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%2Bpa9eu6tq1q4qKirR27VoCFgAAaBBOEf7I/v37VV1drYSEBO9YYmKiCgoKVFtb62BlAADAX3AE60fcbrdatmyp4OBg71ibNm1UWVmp8vJytWrV6ifXUVJSIrfb7TPmcoUpJiamzrJBQYG/%2BT2tAAAIW0lEQVQ%2BX4HmwOVq3O93/l5aR8%2BsoV/W%2BVvPCFg/UlFR4ROuJHmfV1VVNWgdubm5ys7O9hmbNGmSHn/88TrLlpSUaOXKpUpJSak3gP2Uz%2Bc1v9OWJSUlys3NveyeNUf0zJor/XvZHNEza%2BiXdf7WM/%2BIgTYKCQmpE6R%2BeN6iRYsGrSMlJUVvvvmmzyMlJaXeZd1ut7Kzs%2Bsc8cLF0TPr6Jk19Ms6emYN/bLO33rGEawfiY2NVVlZmaqrq%2BVyfd8et9utFi1aKDIyskHriImJ8Yt0DQAArg6OYP1Ily5d5HK5lJ%2Bf7x3Ly8tTt27dFBhIuwAAwE8jMfxIaGioRo4cqZkzZ6qwsFBbtmzR8uXLNW7cOKdLAwAAfiJo5syZM50uorHp3bu39u7dqwULFmj79u169NFHNXr06Ku2vfDwcCUnJys8PPyqbaOpoWfW0TNr6Jd19Mwa%2BmWdP/UswOPxeJwuAgAAoCnhFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAKWgyorKzVt2jQlJSWpb9%2B%2BWr58udMl%2BY2qqioNHz5cO3fudLqURu3kyZNKS0tTcnKy%2BvXrp4yMDFVWVjpdVqN25MgRTZgwQQkJCerfv7%2BWLl3qdEl%2BY%2BLEiXr22WedLqPR%2B/DDD3XTTTf5PNLS0pwuq1GrqqrSrFmz1LNnT/3Hf/yHFi5cqMZ%2Bn3SX0wU0Z5mZmdqzZ49WrlypY8eOaerUqYqLi9OQIUOcLq1Rq6ys1OTJk1VUVOR0KY2ax%2BNRWlqaIiMjtXbtWn377beaNm2aAgMDNXXqVKfLa5Rqa2s1ceJEdevWTW%2B99ZaOHDmip556SrGxsfr1r3/tdHmN2p///Gdt3bpVd911l9OlNHoHDhzQgAEDNGfOHO9YSEiIgxU1fnPnztXOnTu1bNkynT17Vk8%2B%2BaTi4uL0m9/8xunSLoqA5ZBz585pw4YNeu2119S1a1d17dpVRUVFWrt2LQHrEg4cOKDJkyc3%2Bp9cGoNDhw4pPz9fn3zyidq0aSNJSktL0x/%2B8AcC1kWUlpaqS5cumjlzpiIiItSxY0fddtttysvLI2BdQnl5uTIzM9WtWzenS/ELBw8e1I033qjo6GinS/EL5eXl2rhxo15//XXdeuutkqTx48eroKCgUQcsThE6ZP/%2B/aqurlZCQoJ3LDExUQUFBaqtrXWwssbts88%2BU69evZSbm%2Bt0KY1edHS0li5d6g1XPzhz5oxDFTV%2BMTExWrRokSIiIuTxeJSXl6ddu3YpOTnZ6dIatT/84Q8aMWKEbrjhBqdL8QsHDx5Ux44dnS7Db%2BTl5SkiIsLn7%2BHEiROVkZHhYFU/jYDlELfbrZYtWyo4ONg71qZNG1VWVqq8vNzByhq3sWPHatq0aQoNDXW6lEYvMjJS/fr18z6vra3VmjVr1Lt3bwer8h8DBw7U2LFjlZCQoMGDBztdTqO1fft2ff7553rsscecLsUveDweHT58WNu2bdPgwYM1aNAgzZ8/X1VVVU6X1mgVFxerXbt22rRpk4YMGaJf/vKXWrx4caM/GEHAckhFRYVPuJLkfc5fNFwNWVlZ2rt3r5588kmnS/ELL7/8sl555RXt27ev0f%2Bk7JTKykr9/ve/14wZM9SiRQuny/ELx44d8/77v2jRIk2dOlXvvvuuMjMznS6t0Tp37pyOHDmi9evXKyMjQ1OnTtXq1au1YsUKp0u7JK7BckhISEidIPXDc/6hgmlZWVlauXKlXnzxRd14441Ol%2BMXfrieqLKyUk8//bSeeeaZOj8UNXfZ2dm65ZZbfI6U4tLatWunnTt36tprr1VAQIC6dOmi2tpaTZkyRenp6QoKCnK6xEbH5XLpzJkzWrBggdq1ayfp%2B6C6bt06jR8/3uHqLo6A5ZDY2FiVlZWpurpaLtf33wa3260WLVooMjLS4erQlMyZM0fr1q1TVlYWp7p%2BQmlpqfLz8zVo0CDv2A033KALFy7ozJkzatWqlYPVNT5//vOfVVpa6r2W9IcfEj/44AN98cUXTpbWqEVFRfk879SpkyorK/Xtt9/yHqtHdHS0QkJCvOFKkv793/9dx48fd7Cqn8YpQod06dJFLpdL%2Bfn53rG8vDx169ZNgYF8W2BGdna21q9fr4ULF2rYsGFOl9PoHT16VJMmTdLJkye9Y3v27FGrVq34j68eq1ev1rvvvqtNmzZp06ZNGjhwoAYOHKhNmzY5XVqj9fHHH6tXr16qqKjwju3bt09RUVG8xy4iPj5elZWVOnz4sHfs0KFDPoGrMeJ/coeEhoZq5MiRmjlzpgoLC7VlyxYtX75c48aNc7o0NBEHDx7UkiVL9Nvf/laJiYlyu93eB%2BrXrVs3de3aVdOmTdOBAwe0detWZWVl6dFHH3W6tEapXbt26tChg/cRHh6u8PBwdejQwenSGq2EhASFhIToueee06FDh7R161ZlZmbq4Ycfdrq0Ruv6669X//79lZ6erv379%2Bvjjz9WTk6OxowZ43RplxTg4YZCjqmoqNDMmTP1P//zP4qIiNCECRP04IMPOl2W37jpppu0atUq9erVy%2BlSGqWcnBwtWLCg3rmvvvrK5mr8x8mTJzVnzhxt375doaGhuu%2B%2B%2B/TII48oICDA6dIavR/u4v7CCy84XEnjVlRUpOeff175%2BfkKDw/Xb37zG6WmpvIeu4TvvvtOc%2BbM0YcffqjQ0FCNHTu20feMgAUAAGAYpwgBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/Aw82kCalsiJmAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common530599098327662535”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.74045179499467</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6964343323182298</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6571190433601138</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.2821175523218793</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5794995640976822</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6255261646398205</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3902974723728737</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.565692956015659</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3439659345394194</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.458097783221579</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme530599098327662535”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.18804453394277368</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.21300866222614862</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2548187491193588</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.25723950090558695</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.32805436859582837</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.2821175523218793</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4636424740312628</td> <td class=”number”>44</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1567211747594506</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.23432720176949</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.886365038680446</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SFSP15”>PCT_SFSP15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.79739</td>

</tr> <tr>

<th>Minimum</th> <td>0.22708</td>

</tr> <tr>

<th>Maximum</th> <td>4.2713</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3835641069297364486”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3835641069297364486,#minihistogram-3835641069297364486”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3835641069297364486”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3835641069297364486”

aria-controls=”quantiles-3835641069297364486” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3835641069297364486” aria-controls=”histogram-3835641069297364486”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3835641069297364486” aria-controls=”common-3835641069297364486”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3835641069297364486” aria-controls=”extreme-3835641069297364486”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3835641069297364486”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.22708</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.36983</td>

</tr> <tr>

<th>Q1</th> <td>0.54866</td>

</tr> <tr>

<th>Median</th> <td>0.72662</td>

</tr> <tr>

<th>Q3</th> <td>0.97127</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.4092</td>

</tr> <tr>

<th>Maximum</th> <td>4.2713</td>

</tr> <tr>

<th>Range</th> <td>4.0442</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42261</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.35377</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.44365</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.522</td>

</tr> <tr>

<th>Mean</th> <td>0.79739</td>

</tr> <tr>

<th>MAD</th> <td>0.26198</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.8328</td>

</tr> <tr>

<th>Sum</th> <td>2497.4</td>

</tr> <tr>

<th>Variance</th> <td>0.12515</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3835641069297364486”>

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u7dq%2BzsbAUHBysxMVHFxcUaMGCApG/uqeVyudSrV69W1xAXF9ficqDHc0SNjfZ/oZ7U1NTsqH5Ox0n9OXW96Mte6MtenNqXVRx3gfXll1/W9u3b9cgjjygqKkoej0cej0c1NTWSpDFjxmjnzp1aunSpSktLlZubqy5duvgC1cSJE/Xcc89py5Yt2r17t%2BbMmaPx48e36RIhAAD4fnPcDtZbb72l5uZmTZ061W88NTVVq1atUpcuXfSb3/xGv/71r1VYWKikpCQVFhYqKChIknT99dfryy%2B/1OzZs%2BX1enXNNdfol7/8ZSBaAQAANhVknLyFOc4pj%2BdIoEswhcsVrOjoSFVXH2vz1vHIRe%2Beo6rOjTfuGRToEv5l/8p6nc/oy17oy16c1lds7IUBOa/jLhECAAAEGgELAADAZAQsAAAAkxGwAAAATEbAAgAAMJnjbtPwfWO3n8wDAOD7gB0sAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMZvuA5fV6lZ6eru3bt/vGysrKdMstt6hfv3667rrr9Oc//9nvOe%2B9957S09Pldrs1efJklZWV%2Bc2/8MILGjJkiJKSkjRz5kzV1dVZ0gsAAHAGWweshoYG3XvvvSotLfWNGYahrKwsdezYUevWrdOoUaOUnZ2tQ4cOSZIOHTqkrKwsZWRk6OWXX1aHDh00bdo0GYYhSXrrrbdUUFCguXPnasWKFSopKVF%2Bfn5A%2BgMAAPZk24C1f/9%2BjR8/Xp9//rnf%2BPvvv6%2BysjLNnTtXPXr00NSpU9WvXz%2BtW7dOkrR27Vpdfvnluu222/TDH/5QeXl5%2BvLLL7Vjxw5J0sqVK3XzzTdr2LBh6tu3rx5%2B%2BGGtW7eOXSwAANBqtg1YO3bs0IABA1RUVOQ3XlJSossuu0wRERG%2BseTkZO3atcs3n5KS4psLDw9Xnz59tGvXLjU1Nenjjz/2m%2B/Xr59OnDihffv2neOOAACAU7gCXcDZmjhx4mnHPR6P4uLi/MZiYmJUUVHxnfO1tbVqaGjwm3e5XLr44ot9zwcAAPgutg1YZ1JXV6fQ0FC/sdDQUHm93u%2Bcr6%2Bv9z0%2B0/Nbo7KyUh6Px2/M5YpoEexwfnO5bLvB6xMSEuz32Snoy17oy16c2pfVHBewwsLCVFNT4zfm9XrVrl073/ypYcnr9SoqKkphYWG%2Bx6fOh4eHt7qGoqIiFRQU%2BI1lZWUpJyen1V8DgRcdHRnoEkwTFdX616%2Bd0Je90Je9OLUvqzguYMXHx2v//v1%2BY1VVVb7do/j4eFVVVbWY7927ty6%2B%2BGKFhYWpqqpKPXr0kCQ1NjaqpqZGsbGxra4hMzNTaWlpfmMuV4Sqq4%2BdTUsIECesV0hIsKKiwlVbW6empuZAl2Ma%2BrIX%2BrIXp/UVqG%2BWHRew3G63li5dqvr6et%2BuVXFxsZKTk33zxcXFvuPr6uq0d%2B9eZWdnKzg4WImJiSouLtaAAQMkSbt27ZLL5VKvXr1aXUNcXFyLy4EezxE1Ntr/hfp94qT1ampqdlQ/J9GXvdCXvTi1L6s47gJramqqOnXqpNzcXJWWlmrp0qXavXu3xo4dK0kaM2aMdu7cqaVLl6q0tFS5ubnq0qWLL1BNnDhRzz33nLZs2aLdu3drzpw5Gj9%2BfJsuEQIAgO83xwWskJAQLV68WB6PRxkZGXrttddUWFiohIQESVKXLl30m9/8RuvWrdPYsWNVU1OjwsJCBQUFSZKuv/56TZ06VbNnz9Ztt92mvn376pe//GUgWwIAADYTZJy8hTnOKY/nyDn5uiMXvXtOvi6kN%2B4ZFOgS/mUuV7CioyNVXX3MUVv99GUv9GUvTusrNvbCgJzXcTtYAAAAgWZ5wBo3bpzWrFmjI0fOzY4OAABAoFkesAYOHKglS5Zo8ODBuvfee/XnP/9ZXKUEAABOYnnAuu%2B%2B%2B/THP/5RixcvVkhIiO6%2B%2B24NHTpUTz75pD777DOrywEAADBdQO6DFRQUpEGDBmnQoEGqq6vTqlWrtHjxYi1dulT9%2B/fXzTffrGuuuSYQpQEAAPzLAnaj0crKSr322mt67bXX9Mknn6h///768Y9/rIqKCj344IP64IMPNGvWrECVBwAAcNYsD1gbNmzQhg0btH37dnXo0EGjR4/WU089pe7du/uO6dSpk%2BbPn0/AAgAAtmR5wJo1a5aGDRumwsJCXXXVVQoObvk2sEsuuUQ33XST1aUBAACYwvKA9c477yg6Olo1NTW%2BcLV792716dNHISEhkqT%2B/furf//%2BVpcGAABgCst/ivDo0aMaMWKEnn32Wd/YlClTNGrUKJWXl1tdDgAAgOksD1i//vWv1a1bN916662%2Bsc2bN6tTp07Ky8uzuhwAAADTWR6wPvzwQz3wwAOKjY31jXXo0EHTp0/X%2B%2B%2B/b3U5AAAAprM8YLlcLtXW1rYYr6ur447uAADAESwPWFdddZUeeeQRff75576xsrIy5eXlaciQIVaXAwAAYDrLf4pwxowZuvXWW3XttdcqKipKklRbW6s%2BffooNzfX6nIAAABMZ3nAiomJ0fr16/Xee%2B%2BptLRULpdLl156qa688koFBQVZXQ4AAIDpAvKrckJCQjRkyBAuCQIAAEeyPGB5PB4tWrRIO3fu1IkTJ1q8sf0Pf/iD1SUBAACYyvKA9atf/Up79uzR9ddfrwsvvNDq0wMAAJxzlges999/X8uWLVNKSorVpwYAALCE5bdpiIiIUExMjNWnBQAAsIzlAWvUqFFatmyZmpqarD41AACAJSy/RFhTU6NNmzbp7bffVteuXRUaGuo3v3LlSqtLAgAAMFVAbtOQnp4eiNMCAABYwvKAlZeXZ/UpAQAALGX5e7AkqbKyUgUFBbrvvvv01Vdf6c0339SBAwcCUQoAAIDpLA9YBw8e1A033KD169frrbfe0vHjx7V582aNGTNGJSUlVpcDAABgOssD1qOPPqrhw4dry5YtuuCCCyRJTzzxhNLS0rRw4UKrywEAADCd5QFr586duvXWW/1%2BsbPL5dK0adO0d%2B9e085TXl6uqVOnqn///kpLS9MLL7zgm9u7d6/GjRsnt9utMWPGaM%2BePX7P3bRpk4YPHy63262srCx9/fXXptUFAACcz/KA1dzcrObm5hbjx44dU0hIiGnnueeeexQREaFXXnlFM2fO1KJFi/T73/9ex48f15QpU5SSkqJXXnlFSUlJmjp1qo4fPy5J2r17t2bNmqXs7GwVFRWptrZWubm5ptUFAACcz/KANXjwYD3zzDN%2BIaumpkb5%2BfkaOHCgKef4xz/%2BoV27dumuu%2B5S9%2B7dNXz4cA0ZMkTbtm3T5s2bFRYWpunTp6tHjx6aNWuWIiMj9eabb0qSVq9erZEjR2r06NHq1auXFixYoK1bt6qsrMyU2gAAgPNZHrAeeOAB7dmzR4MHD1ZDQ4PuuusuDRs2TF988YVmzJhhyjnatWun8PBwvfLKKzpx4oQOHDignTt3qnfv3iopKVFycrLvEmVQUJD69%2B%2BvXbt2SZJKSkr8fk9ip06dlJCQwBvwAQBAq1l%2BH6z4%2BHi9%2Buqr2rRpk/7617%2BqublZEyZM0KhRo9S%2BfXtTzhEWFqbZs2dr3rx5WrlypZqampSRkaFx48bpD3/4gy699FK/42NiYlRaWirpm1tIxMXFtZivqKgwpTYAAOB8AbmTe3h4uMaNG3dOz/Hpp59q2LBhuvXWW1VaWqp58%2BbpyiuvVF1dXYtfzxMaGiqv1ytJqq%2Bv/9b51qisrJTH4/Ebc7kiWgQ3nN9croDcJs5UISHBfp%2Bdgr7shb7sxal9Wc3ygDV58uRvnTfjdxFu27ZNL7/8srZu3ap27dopMTFRhw8f1tNPP62uXbu2CEter1ft2rWT9M3u1%2Bnmw8PDW33%2BoqIiFRQU%2BI1lZWUpJyfnLDtCIERHRwa6BNNERbX%2B9Wsn9GUv9GUvTu3LKpYHrM6dO/s9bmxs1MGDB/XJJ5/o5ptvNuUce/bsUbdu3XyhSZIuu%2BwyLVmyRCkpKaqqqvI7vqqqyre7FB8ff9r52NjYVp8/MzNTaWlpfmMuV4Sqq4%2B1tRUEkBPWKyQkWFFR4aqtrVNTU8uf3rUr%2BrIX%2BrIXp/UVqG%2BWz5vfRVhYWGja%2B5zi4uJ08OBBeb1e3%2BW%2BAwcOqEuXLnK73Xr22WdlGIaCgoJkGIZ27typO%2B%2B8U5LkdrtVXFysjIwMSd/cT6u8vFxut7tN5z/1cqDHc0SNjfZ/oX6fOGm9mpqaHdXPSfRlL/RlL07tyyrnzQXWUaNG6Y033jDla6WlpemCCy7Qgw8%2BqM8%2B%2B0z/%2B7//qyVLlmjSpEkaMWKEamtrNX/%2BfO3fv1/z589XXV2dRo4cKUmaMGGCNmzYoLVr12rfvn2aPn26hg4dqq5du5pSGwAAcL7zJmB99NFHpt1o9MILL9QLL7wgj8ejsWPHKi8vT3fddZcyMzPVvn17PfPMM75dqpKSEi1dulQRERGSpKSkJM2dO1eFhYWaMGGCLrroojPuugEAAJzOefEm96NHj%2Bpvf/ubJk6caNp5Lr30Ui1fvvy0c3379tX69evP%2BNyMjAzfJUIAAIC2sjxgJSQk%2BP0eQkm64IILdNNNN%2BnGG2%2B0uhwAAADTWR6wHn30UatPCQAAYCnLA9YHH3zQ6mOvuOKKc1gJAADAuWF5wJo0aZLvEqFhGL7xU8eCgoL017/%2B1eryAAAA/mWWB6wlS5bokUce0S9/%2BUulpqYqNDRUH3/8sebOnasf//jHuu6666wuCQAAwFSW36YhLy9Ps2fP1rXXXqvo6GhFRkZq4MCBmjt3rl566SV17tzZ9wEAAGBHlgesysrK04an9u3bq7q62upyAAAATGd5wOrXr5%2BeeOIJHT161DdWU1Oj/Px8XXnllVaXAwAAYDrL34P14IMPavLkybrqqqvUvXt3GYahv//974qNjdXKlSutLgcAAMB0lgesHj16aPPmzdq0aZM%2B/fRTSdJPf/pTXX/99QoPD7e6HAAAANNZHrAk6aKLLtK4ceP0xRdf%2BH6J8gUXXBCIUgAAAExn%2BXuwDMPQwoULdcUVVyg9PV0VFRWaMWOGZs2apRMnTlhdDgAAgOksD1irVq3Shg0b9NBDDyk0NFSSNHz4cG3ZskUFBQVWlwMAAGA6ywNWUVGRZs%2BerYyMDN/d26%2B77jo98sgj2rhxo9XlAAAAmM7ygPXFF1%2Bod%2B/eLcZ79eolj8djdTkAAACmszxgde7cWR9//HGL8Xfeecf3hncAAAA7s/ynCG%2B//XY9/PDD8ng8MgxD27ZtU1FRkVatWqUHHnjA6nIAAABMZ3nAGjNmjBobG/X000%2Brvr5es2fPVocOHXTPPfdowoQJVpcDAABgOssD1qZNmzRixAhlZmbq66%2B/lmEYiomJsboMAACAc8by92DNnTvX92b2Dh06EK4AAIDjWB6wunfvrk8%2B%2BcTq0wIAAFjG8kuEvXr10v33369ly5ape/fuCgsL85vPy8uzuiQAAABTWR6wPvvsMyUnJ0sS970CAACOZEnAWrBggbKzsxUREaFVq1ZZcUoAAICAseQ9WMuXL1ddXZ3f2JQpU1RZWWnF6QEAACxlScAyDKPF2AcffKCGhgYrTg8AAGApy3%2BKEAAAwOkIWAAAACazLGAFBQVZdSpJktfr1cMPP6wrrrhCP/rRj/TEE0/4LlXu3btX48aNk9vt1pgxY7Rnzx6/527atEnDhw%2BX2%2B1WVlaWvv76a0trBwAA9mbZbRoeeeQRv3tenThxQvn5%2BYqMjPQ7zqz7YD3yyCPavn27nnvuOR07dky/%2BMUvlJCQoBtvvFFTpkzRDTfcoEcffVQvvfSSpk6dqt///veKiIjQ7t27NWvWLD388MPq1auX5s%2Bfr9zcXD3zzDOm1AUAAJzPkoB1xRVXtLjnVVJSkqqrq1VdXW36%2BWpqarRu3TotX75cffv2lSTddtttKikpkcvlUlhYmKZPn66goCDNmjVL77zzjt58801lZGRo9erVGjlypEaPHi3pm1tMDBs2TGVlZeratavptQIAAOexJGBZfe%2Br4uJitW/fXqmpqb6xKVOmSJJ%2B9atfKTk52XfJMigoSP3799euXbuUkZGhkpIS3XHHHb7nderUSQkJCSopKSFgAQCAVnHkm9zLysrUuXNnvfrqqxoxYoT%2B67/%2BS4WFhWpubpZT7ZOmAAAV7UlEQVTH41FcXJzf8TExMaqoqJAkVVZWfus8AADAd7H8V%2BVY4fjx4zp48KDWrFmjvLw8eTwezZ49W%2BHh4aqrq1NoaKjf8aGhofJ6vZKk%2Bvr6b51vjcrKyhaXRF2uiBbBDec3l8v%2B33%2BEhAT7fXYK%2BrIX%2BrIXp/ZlNUcGLJfLpaNHj%2Brxxx9X586dJUmHDh3SSy%2B9pG7durUIS16vV%2B3atZMkhYWFnXY%2BPDy81ecvKipSQUGB31hWVpZycnLOph0ESHR05HcfZBNRUa1//doJfdkLfdmLU/uyiiMDVmxsrMLCwnzhSpJ%2B8IMfqLy8XKmpqaqqqvI7vqqqyre7FB8ff9r52NjYVp8/MzNTaWlpfmMuV4Sqq4%2B1tRUEkBPWKyQkWFFR4aqtrVNTU3OgyzENfdkLfdmL0/oK1DfLjgxYbrdbDQ0N%2Buyzz/SDH/xAknTgwAF17txZbrdbzz77rAzDUFBQkAzD0M6dO3XnnXf6nltcXKyMjAxJUnl5ucrLy%2BV2u1t9/ri4uBaXAz2eI2pstP8L9fvESevV1NTsqH5Ooi97oS97cWpfVnHkBdZLLrlEQ4cOVW5urvbt26c//elPWrp0qSZMmKARI0aotrZW8%2BfP1/79%2BzV//nzV1dVp5MiRkqQJEyZow4YNWrt2rfbt26fp06dr6NCh/AQhAABoNUcGLElauHCh/v3f/10TJkzQjBkz9NOf/lSTJk1S%2B/bt9cwzz/h2qUpKSrR06VJFRERI%2Bub%2BXHPnzlVhYaEmTJigiy66yLSbnwIAgO%2BHIOPk74/BOeXxHDknX3fkonfPydeF9MY9gwJdwr/M5QpWdHSkqquPOWqrn77shb7sxWl9xcZeGJDzOnYHCwAAIFAIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyxwesKVOm6IEHHvA93rt3r8aNGye3260xY8Zoz549fsdv2rRJw4cPl9vtVlZWlr7%2B%2BmurSwYAADbn6ID1%2Buuva%2BvWrb7Hx48f15QpU5SSkqJXXnlFSUlJmjp1qo4fPy5J2r17t2bNmqXs7GwVFRWptrZWubm5gSofAADYlGMDVk1NjRYsWKDExETf2ObNmxUWFqbp06erR48emjVrliIjI/Xmm29KklavXq2RI0dq9OjR6tWrlxYsWKCtW7eqrKwsUG0AAAAbcmzAeuyxxzRq1ChdeumlvrGSkhIlJycrKChIkhQUFKT%2B/ftr165dvvmUlBTf8Z06dVJCQoJKSkqsLR4AANiaIwPWtm3b9OGHH2ratGl%2B4x6PR3FxcX5jMTExqqiokCRVVlZ%2B6zwAAEBruAJdgNkaGhr00EMPafbs2WrXrp3fXF1dnUJDQ/3GQkND5fV6JUn19fXfOt9alZWV8ng8fmMuV0SL8Ibzm8tl/%2B8/QkKC/T47BX3ZC33Zi1P7sprjAlZBQYEuv/xyDRkypMVcWFhYi7Dk9Xp9QexM8%2BHh4W2qoaioSAUFBX5jWVlZysnJadPXQWBFR0cGugTTREW17TVsF/RlL/RlL07tyyqOC1ivv/66qqqqlJSUJEm%2BwPTWW28pPT1dVVVVfsdXVVX5dpbi4%2BNPOx8bG9umGjIzM5WWluY35nJFqLr6WJu%2BDgLLCesVEhKsqKhw1dbWqampOdDlmIa%2B7IW%2B7MVpfQXqm2XHBaxVq1apsbHR93jhwoWSpPvvv18ffPCBnn32WRmGoaCgIBmGoZ07d%2BrOO%2B%2BUJLndbhUXFysjI0OSVF5ervLycrnd7jbVEBcX1%2BJyoMdzRI2N9n%2Bhfp84ab2ampod1c9J9GUv9GUvTu3LKo4LWJ07d/Z7HBn5TXLt1q2bYmJi9Pjjj2v%2B/Pn6yU9%2BojVr1qiurk4jR46UJE2YMEGTJk1Sv379lJiYqPnz52vo0KHq2rWr5X0AAAD7%2Bl69g619%2B/Z65plnfLtUJSUlWrp0qSIiIiRJSUlJmjt3rgoLCzVhwgRddNFFysvLC3DVAADAbhy3g3WqRx991O9x3759tX79%2BjMen5GR4btECAAAcDa%2BVztYAAAAViBgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJHH%2BjUeBsjVz0bqBLaJM37hkU6BIAAP%2BHHSwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMJljA9bhw4eVk5Oj1NRUDRkyRHl5eWpoaJAklZWV6ZZbblG/fv103XXX6c9//rPfc9977z2lp6fL7XZr8uTJKisrC0QLAADAphwZsAzDUE5Ojurq6vTiiy/qySef1B//%2BEctWrRIhmEoKytLHTt21Lp16zRq1ChlZ2fr0KFDkqRDhw4pKytLGRkZevnll9WhQwdNmzZNhmEEuCsAAGAXrkAXcC4cOHBAu3bt0rvvvquOHTtKknJycvTYY4/pqquuUllZmdasWaOIiAj16NFD27Zt07p163T33Xdr7dq1uvzyy3XbbbdJkvLy8jRo0CDt2LFDAwYMCGRbAADAJhy5gxUbG6tly5b5wtVJR48eVUlJiS677DJFRET4xpOTk7Vr1y5JUklJiVJSUnxz4eHh6tOnj28eAADguzgyYEVFRWnIkCG%2Bx83NzVq9erUGDhwoj8ejuLg4v%2BNjYmJUUVEhSd85DwAA8F0ceYnwVPn5%2Bdq7d69efvllvfDCCwoNDfWbDw0NldfrlSTV1dV963xrVFZWyuPx%2BI25XBEtghtgJper5fdLISHBfp%2Bdgr7shb7sxal9Wc3xASs/P18rVqzQk08%2BqZ49eyosLEw1NTV%2Bx3i9XrVr106SFBYW1iJMeb1eRUVFtfqcRUVFKigo8BvLyspSTk7OWXYBfLfo6MgzzkVFhVtYiXXoy17oy16c2pdVHB2w5s2bp5deekn5%2Bfm69tprJUnx8fHav3%2B/33FVVVW%2B3aX4%2BHhVVVW1mO/du3erz5uZmam0tDS/MZcrQtXVx86mDaBVTvf6CgkJVlRUuGpr69TU1ByAqs4N%2BrIX%2BrIXp/X1bd98nkuODVgFBQVas2aNnnjiCY0YMcI37na7tXTpUtXX1/t2rYqLi5WcnOybLy4u9h1fV1envXv3Kjs7u9XnjouLa3E50OM5osZG%2B79Qcf76ttdXU1OzI19/9GUv9GUvTu3LKo68wPrpp59q8eLFuuOOO5ScnCyPx%2BP7SE1NVadOnZSbm6vS0lItXbpUu3fv1tixYyVJY8aM0c6dO7V06VKVlpYqNzdXXbp04RYNAACg1RwZsP7whz%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%2Bqz59%2BqhPnz4qLS3Viy%2B%2BSMACAACtwiXCU%2Bzbt0%2BNjY1KSkryjSUnJ6ukpETNzc0BrAwAANgFO1in8Hg8io6OVmhoqG%2BsY8eOamhoUE1NjTp06PCdX6OyslIej8dvzOWKUFxcnOn1Ajj3XC5rvhcNCQn2%2B%2BwU9GUvTu3LagSsU9TV1fmFK0m%2Bx16vt1Vfo6ioSAUFBX5j2dnZuvvuu80p8p98ON/ay5aVlZUqKipSZmamowIjfdmLk/tasWIZfdkEfeHbEE9PERYW1iJInXzcrl27Vn2NzMxMvfLKK34fmZmZptcaCB6PRwUFBS126OyOvuyFvuyFvuzFqX1ZjR2sU8THx6u6ulqNjY1yub754/F4PGrXrp2ioqJa9TXi4uJI/QAAfI%2Bxg3WK3r17y%2BVyadeuXb6x4uJiJSYmKjiYPy4AAPDdSAynCA8P1%2BjRozVnzhzt3r1bW7Zs0fPPP6/JkycHujQAAGATIXPmzJkT6CLONwMHDtTevXv1%2BOOPa9u2bbrzzjs1ZsyYQJd13oiMjFRqaqoiIyMDXYqp6Mte6Mte6MtenNqXlYIMwzACXQQAAICTcIkQAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQstNDQ0aObMmUpJSdHgwYP1/PPPn/HYu%2B66S//xH//h9/HHP/7Rwmrbzuv1Kj09Xdu3bz/jMXv37tW4cePkdrs1ZswY7dmzx8IKz05r%2BrLTeh0%2BfFg5OTlKTU3VkCFDlJeXp4aGhtMea6f1aktfdlqvgwcP6vbbb1dSUpKGDh2qZcuWnfFYO61XW/qy03qdNGXKFD3wwANnnH/vvfeUnp4ut9utyZMnq6yszMLqbM4ATjF37lzjhhtuMPbs2WP87ne/M5KSkow33njjtMdeffXVxoYNG4zKykrfR0NDg8UVt159fb2RlZVl9OzZ03j//fdPe8yxY8eMQYMGGY8%2B%2Bqixf/9%2BY968ecaPfvQj49ixYxZX23qt6csw7LNezc3Nxvjx442f/exnxieffGJ88MEHxtVXX208%2BuijLY6103q1pS/DsM96NTU1Gddcc41x3333GZ999pnx9ttvG/379zdee%2B21Fsfaab3a0pdh2Ge9Ttq0aZPRs2dPY8aMGaed//LLL41%2B/foZzz33nPHJJ58YP//5z4309HSjubnZ4krtiYAFP8eOHTMSExP9/iddWFho3HTTTS2ObWhoMHr37m0cOHDAyhLPWmlpqXHjjTcaN9xww7cGkbVr1xppaWm%2Bf0Sam5uNq6%2B%2B2li3bp2V5bZaa/uy03rt37/f6Nmzp%2BHxeHxjGzduNAYPHtziWDutV1v6stN6HT582Pj5z39uHDlyxDeWlZVlPPTQQy2OtdN6taUvO62XYRhGdXW1cdVVVxljxow5Y8BatGiR37/9x48fN5KSkr71mzj8f1wihJ99%2B/apsbFRSUlJvrHk5GSVlJSoubnZ79gDBw4oKChIXbt2tbrMs7Jjxw4NGDBARUVF33pcSUmJkpOTFRQUJEkKCgpS//79tWvXLivKbLPW9mWn9YqNjdWyZcvUsWNHv/GjR4%2B2ONZO69WWvuy0XnFxcVq0aJHat28vwzBUXFysDz74QKmpqS2OtdN6taUvO62XJD322GMaNWqULr300jMeU1JSopSUFN/j8PBw9enT57xcq/MRAQt%2BPB6PoqOjFRoa6hvr2LGjGhoaVFNT43fsgQMH1L59e02fPl2DBw/W2LFjtXXrVqtLbrWJEydq5syZCg8P/9bjPB6P4uLi/MZiYmJUUVFxLss7a63ty07rFRUVpSFDhvgeNzc3a/Xq1Ro4cGCLY%2B20Xm3py07r9c/S0tI0ceJEJSUl6dprr20xb6f1%2Bmff1Zed1mvbtm368MMPNW3atG89zq5rdb4gYMFPXV2dX7iS5Hvs9Xr9xg8cOKD6%2BnoNHjxYy5Yt03/%2B53/qrrvu0scff2xZvefCmf4MTu3fbuy8Xvn5%2Bdq7d69%2B8YtftJiz83p9W192Xa%2BnnnpKS5Ys0V//%2Blfl5eW1mLfren1XX3ZZr4aGBj300EOaPXu22rVr963H2nWtzheuQBeA80tYWFiLvzwnH5/6l3HatGmaNGmSLrroIklSr1699Je//EW//e1vlZiYaE3B58CZ/gy%2B6x%2Bj851d1ys/P18rVqzQk08%2BqZ49e7aYt%2Bt6fVdfdl2vk7U1NDTo/vvv1/Tp0/3%2BJ23X9fquvuyyXgUFBbr88sv9dlLP5ExrFRUVda7KcxR2sOAnPj5e1dXVamxs9I15PB61a9euxV%2Bq4OBg3z8mJ11yySU6fPiwJbWeK/Hx8aqqqvIbq6qqarFVbjd2XK958%2BZp%2BfLlys/PP%2B1lGcme69Wavuy0XlVVVdqyZYvf2KWXXqoTJ060eH%2BZndarLX3ZZb1ef/11bdmyRUlJSUpKStLGjRu1ceNGv/fdnnSmtYqNjbWqXFsjYMFP79695XK5/N7EWFxcrMTERAUH%2B79cHnjgAeXm5vqN7du3T5dccokltZ4rbrdbH330kQzDkCQZhqGdO3fK7XYHuLJ/jd3Wq6CgQGvWrNETTzyh66%2B//ozH2W29WtuXndbriy%2B%2BUHZ2tl%2BY2LNnjzp06KAOHTr4HWun9WpLX3ZZr1WrVmnjxo169dVX9eqrryotLU1paWl69dVXWxzrdrtVXFzse1xXV6e9e/eel2t1PiJgwU94eLhGjx6tOXPmaPfu3dqyZYuef/55TZ48WdI3u1n19fWSvnnT58m/qAcPHlRBQYGKi4t10003BbKFs/LPfY0YMUK1tbWaP3%2B%2B9u/fr/nz56uurk4jR44McJVtZ9f1%2BvTTT7V48WLdcccdSk5Olsfj8X1I9l2vtvRlp/VKTExUnz59NHPmTO3fv19bt25Vfn6%2B7rzzTkn2Xa%2B29GWX9ercubO6devm%2B4iMjFRkZKS6deumpqYmeTwe32XBMWPGaOfOnVq6dKlKS0uVm5urLl26aMCAAQHuwiYCd4cInK%2BOHz9uTJ8%2B3ejXr58xePBgY/ny5b65nj17%2Bt2v5re//a1xzTXXGJdffrnx4x//2NixY0cAKm67U%2B8XdWpfJSUlxujRo43ExERj7Nixxl/%2B8pdAlNlm39WXXdbrmWeeMXr27HnaD8Ow73q1tS%2B7rJdhGEZFRYWRlZVl9O/f3xg0aJDx9NNP%2B%2B51Zdf1Moy29WWn9TppxowZvvtglZWVtfg35O233zauueYao2/fvsbNN99sfP7554Eq1XaCDOP/9mkBAABgCi4RAgAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmOz/AWC7ksBIae1EAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3835641069297364486”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.5084728979609608</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.991095325829233</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6838607642878862</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7460409862665002</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6642534311514493</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9712735876435317</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5508166993844088</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6285397242721703</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5346843800007619</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7266185971271907</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3835641069297364486”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.22707556512709456</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.23442951992987765</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3271212041399707</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3698291272142557</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4093313579253466</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3594365854286208</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:64%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4091829252091856</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:46%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6425310114450082</td> <td class=”number”>72</td> <td class=”number”>2.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.217284472239579</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.271318659740445</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SNAP12”>PCT_SNAP12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>15.157</td>

</tr> <tr>

<th>Minimum</th> <td>5.8664</td>

</tr> <tr>

<th>Maximum</th> <td>22.132</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5013706810478157220”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5013706810478157220,#minihistogram5013706810478157220”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5013706810478157220”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5013706810478157220”

aria-controls=”quantiles5013706810478157220” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5013706810478157220” aria-controls=”histogram5013706810478157220”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5013706810478157220” aria-controls=”common5013706810478157220”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5013706810478157220” aria-controls=”extreme5013706810478157220”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5013706810478157220”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>5.8664</td>

</tr> <tr>

<th>5-th percentile</th> <td>9.4956</td>

</tr> <tr>

<th>Q1</th> <td>12.566</td>

</tr> <tr>

<th>Median</th> <td>15.223</td>

</tr> <tr>

<th>Q3</th> <td>18.353</td>

</tr> <tr>

<th>95-th percentile</th> <td>20.667</td>

</tr> <tr>

<th>Maximum</th> <td>22.132</td>

</tr> <tr>

<th>Range</th> <td>16.265</td>

</tr> <tr>

<th>Interquartile range</th> <td>5.7874</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.6083</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.23806</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.77222</td>

</tr> <tr>

<th>Mean</th> <td>15.157</td>

</tr> <tr>

<th>MAD</th> <td>2.9336</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.15215</td>

</tr> <tr>

<th>Sum</th> <td>47473</td>

</tr> <tr>

<th>Variance</th> <td>13.02</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5013706810478157220”>

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WmpXC6X15jDEV4vvJkUHNzM67td2LWvxuBwWPszsutjZbd%2B/I3J563dn4P0FVj8LmBJ31%2B669Wrl3r16qWqqiqtWrVKS5cuVU5Ojrp3767f//73%2Bo//%2BI/T3jc7O1vXXXed%2BvTpU28uNDS0XliqqanxBLEzzYeFhTWo/tzcXGVnZ3uNpaamKj09vUH7OR%2BRkQ2rNVDYtS%2BTWrZsYXUJknis0DCN8by163OQvgKLXwYs6fuzQJs2bdKmTZv0%2Beefq3v37vrNb36jI0eO6NFHH9UHH3ygqVOn1rvfa6%2B9prKyMsXHx0uSJzC9%2BeabGjp0qMrKyry2Lysr85xZiouLO%2B18TExMg2pPSUlR//79vcYcjnAdPXq8QftpiODgZoqMDFNFRZVqa%2Bsa7ThNza59NYbGfH75wq6PlV1/u/YXJp%2B3dn4O0tf5s%2BqXT78LWBs3btTGjRu1c%2BdOtWrVSsOHD9eTTz6pDh06eLZp3bq15s6de9qAtWrVKp06dcpze/78%2BZKkhx56SB988IGeeeYZud1uBQUFye12a/fu3Zo4caIkyel0Kj8/X8nJyZKkw4cP6/Dhw3I6nQ3qITY2tt7lQJfrO5061fj/MGpr65rkOE3Nrn2Z5C8/Hx4rNERjPFfs%2Bhykr8DidwFr6tSp6tevn5YsWaKbbrpJzZrV/%2B3xiiuu0O9%2B97vT3r9t27Zet1u0%2BD65tm/fXlFRUVqwYIHmzp2r2267TWvWrFFVVZUGDx4sSRo9erTGjBmjbt26qWvXrpo7d6769u2ryy67zHCXAADAzvwuYL3zzjtq2bKlysvLPeFqz5496tKli4KDgyVJ3bt3V/fu3Ru874iICD399NOaPn26Xn75ZV1zzTXKyclReHi4JCk%2BPl6zZs3Sk08%2BqW%2B//Va9evXS7NmzzTUHAAB%2BFvwuYB07dkyjR4/Wr371K02ZMkXS9x93Ex0drWeeeUatW7du0P4ef/xxr9vXX3%2B9NmzYcMbtk5OTPZcIAQAAzoffvXrzscceU/v27XXXXXd5xrZs2aLWrVsrMzPTwsoAAAB843cB68MPP9Qjjzzi9Zd7rVq10pQpU/T%2B%2B%2B9bWBkAAIBv/C5gORwOVVRU1Buvqqpq8Du6AwAAWMHvAtZNN92kOXPm6N///rdnrLi4WJmZmad981AAAAB/43cvcn/44Yd11113aeDAgYqMjJQkVVRUqEuXLsrIyLC4OgAAgHPzu4AVFRWlDRs2aPv27SoqKpLD4dCVV16pG2%2B8UUFBQVaXBwAAcE5%2BF7AkKTg4WH369OGSIAAACEh%2BF7BcLpcWL16s3bt36%2BTJk/Ve2P72229bVBkAAIBv/C5g/fnPf9bevXt166236uKLL7a6HAAAgAbzu4D1/vvva8WKFUpMTLS6FACNZPDi96wuAQAald%2B9TUN4eLiioqKsLgMAAOC8%2BV3AGjZsmFasWKHa2lqrSwEAADgvfneJsLy8XJs3b9Y//vEPXXbZZQoJCfGaf%2BGFFyyqDAAAwDd%2BF7AkaejQoVaXAABAPYH2%2BsHX7%2B9ldQk/W34XsDIzM60uAQAA4IL43WuwJKm0tFTZ2dl68MEH9fXXX%2BuNN97QwYMHrS4LAADAJ34XsA4dOqT//M//1IYNG/Tmm2%2BqsrJSW7Zs0YgRI1RYWGh1eQAAAOfkdwHr8ccf14ABA/TWW2/poosukiQtXLhQ/fv31/z58y2uDgAA4Nz8LmDt3r1bd911l9cHOzscDk2aNEn79u2zsDIAAADf%2BF3AqqurU11dXb3x48ePKzg42IKKAAAAGsbvAlbv3r319NNPe4Ws8vJyZWVlqWfPnhZWBgAA4Bu/C1iPPPKI9u7dq969e6u6ulr33Xef%2BvXrpy%2B//FIPP/yw1eUBAACck9%2B9D1ZcXJxeeeUVbd68WZ9%2B%2Bqnq6uo0evRoDRs2TBEREVaXBwAAcE5%2BF7AkKSwsTKNGjbK6DAAAgPPidwFr7NixZ53nswgBAIC/87uA1bZtW6/bp06d0qFDh/T555/r97//vUVVAQAA%2BM7vAtaZPotwyZIlOnLkSBNXAwAA0HB%2B91eEZzJs2DC9/vrrVpcBAABwTgETsD766CPeaBQAAAQEv7tEeLoXuR87dkz//Oc/dfvtt1tQEQAAQMP4XcBq06aN1%2BcQStJFF12k3/3ud/r1r39tUVUAAAC%2B87uA9fjjj1tdAgAAwAXxu4D1wQcf%2BLztDTfc0IiVAAAAnB%2B/C1hjxozxXCJ0u92e8Z%2BOBQUF6dNPP236AgEAxgxe/J7VJQCNwu8C1vLlyzVnzhz98Y9/VFJSkkJCQvTxxx9r1qxZ%2Bs1vfqMhQ4ZYXSIAAMBZ%2Bd3bNGRmZmratGkaOHCgWrZsqRYtWqhnz56aNWuWXnrpJbVt29bzBQAA4I/8LmCVlpaeNjxFRETo6NGjPu/n0KFDuvvuuxUfH6%2B%2BfftqxYoVnrni4mLdeeed6tatm4YMGaJt27Z53Xf79u0aOnSonE6nxo4dq%2BLi4vNvCAAA/Oz4XcDq1q2bFi5cqGPHjnnGysvLlZWVpRtvvNGnfdTV1Wn8%2BPFq2bKlNmzYoJkzZ2rZsmV69dVX5Xa7lZqaqujoaOXl5WnYsGFKS0tTSUmJJKmkpESpqalKTk7WunXr1KpVK02aNMnr9WAAAABn43evwXr00Uc1duxY3XTTTerQoYPcbrf%2B9a9/KSYmRi%2B88IJP%2BygrK1OnTp00Y8YMRUREqEOHDrrxxhuVn5%2Bv6OhoFRcXa82aNQoPD1fHjh21Y8cO5eXlafLkyVq7dq2uu%2B46jRs3TtL3lyx79eqlXbt2qUePHo3ZOgAAsAm/C1gdO3bUli1btHnzZh04cECSdMcdd%2BjWW29VWFiYT/uIjY3V4sWLJX3/V4e7d%2B/WBx98oOnTp6uwsFCdO3dWeHi4Z/uEhAQVFBRIkgoLC5WYmOiZCwsLU5cuXVRQUEDAAgAAPvG7gCVJl1xyiUaNGqUvv/xSl112maTv3839fPTv318lJSXq16%2BfBg4cqMcee0yxsbFe20RFRenIkSOSJJfLddZ5X5SWlsrlcnmNORzh9fZrUnBwM6/vdmHXvhqDw2Htz4jHCvA/Vq8LZ2P3NcPvApbb7daCBQu0atUqnTx5Um%2B%2B%2BaYWLVqksLAwzZgxo8FB68knn1RZWZlmzJihzMxMVVVVKSQkxGubkJAQ1dTUSNI5532Rm5ur7Oxsr7HU1FSlp6c3qPbzERnp21m%2BQGPXvkxq2bKF1SVI4rEC/Im/rAtnY9c1w%2B8C1qpVq7Rx40ZNnz5ds2bNkiQNGDBAM2fOVHR0tP7whz80aH9du3aVJFVXV%2Buhhx7SiBEjVFVV5bVNTU2NmjdvLkkKDQ2tF6ZqamoUGRnp8zFTUlLUv39/rzGHI1xHjx5vUO0NERzcTJGRYaqoqFJtbV2jHaep2bWvxtCYzy9f8FgB/sfqdeFsmmrNsCpk%2Bl3Ays3N1bRp03TLLbdo9uzZkqQhQ4booosuUmZmpk8Bq6ysTAUFBRowYIBn7Morr9TJkycVExOjgwcP1tv%2Bh8t3cXFxKisrqzffqVMnn3uIjY2tdznQ5fpOp041/n86tbV1TXKcpmbXvkzyl58PjxXgPwLh36Jd1wy/u/D55ZdfnjbMXHvttfVe13S2faSlpemrr77yjO3du1etWrVSQkKCPvnkE504ccIzl5%2BfL6fTKUlyOp3Kz8/3zFVVVWnfvn2eeQAAgHPxu4DVtm1bffzxx/XG33nnHc8L3s%2Bla9eu6tKli/70pz9p//792rp1q7KysjRx4kQlJSWpdevWysjIUFFRkXJycrRnzx6NHDlSkjRixAjt3r1bOTk5KioqUkZGhtq1a8dfEAIAAJ/5XcC6%2B%2B67NXPmTL3wwgtyu93asWOH5s%2Bfr3nz5mnMmDE%2B7SM4OFhLly5VWFiYUlJSNHXqVI0ZM0Zjx471zLlcLiUnJ2vTpk1asmSJ2rRpI0lq166dnnrqKeXl5WnkyJEqLy/XkiVLPB82DQAAcC5%2B9xqsESNG6NSpU1q2bJlOnDihadOmqVWrVrr//vs1evRon/cTFxdX7y/5ftC%2BfXutXr36jPe9%2BeabdfPNNze4dgAAAMkPA9bmzZs1aNAgpaSk6JtvvpHb7VZUVJTVZQEAAPjM7y4Rzpo1y/Ni9latWhGuAABAwPG7gNWhQwd9/vnnVpcBAABw3vzuEuG1116rhx56SCtWrFCHDh0UGhrqNZ%2BZmWlRZQAAAL7xu4D1xRdfKCEhQZJ8ft8rAAAAf%2BIXAWvevHlKS0tTeHi4Vq1aZXU5AAAAF8QvXoO1cuXKep8POH78eJWWllpUEQAAwPnzi4DldrvrjX3wwQeqrq62oBoAAIAL4xcBCwAAwE4IWAAAAIb5TcDis/4AAIBd%2BMVfEUrSnDlzvN7z6uTJk8rKylKLFi28tuN9sAAAgL/zi4B1ww031HvPq/j4eB09elRHjx61qCoAAIDz4xcBi/e%2BAgAAduI3r8ECAACwCwIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPOLvyIEAADmDV78ntUl%2BOz1%2B3tZXYJRnMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAyzbcD66quvlJ6erqSkJPXp00eZmZmqrq6WJBUXF%2BvOO%2B9Ut27dNGTIEG3bts3rvtu3b9fQoUPldDo1duxYFRcXW9ECAAAIULYMWG63W%2Bnp6aqqqtKLL76oRYsW6e9//7sWL14st9ut1NRURUdHKy8vT8OGDVNaWppKSkokSSUlJUpNTVVycrLWrVunVq1aadKkSXK73RZ3BQAAAoXD6gIaw8GDB1VQUKD33ntP0dHRkqT09HQ98cQTuummm1RcXKw1a9YoPDxcHTt21I4dO5SXl6fJkydr7dq1uu666zRu3DhJUmZmpnr16qVdu3apR48eVrYFAAAChC3PYMXExGjFihWecPWDY8eOqbCwUJ07d1Z4eLhnPCEhQQUFBZKkwsJCJSYmeubCwsLUpUsXzzwAAMC52DJgRUZGqk%2BfPp7bdXV1Wr16tXr27CmXy6XY2Fiv7aOionTkyBFJOuc8AADAudjyEuFPZWVlad%2B%2BfVq3bp2ee%2B45hYSEeM2HhISopqZGklRVVXXWeV%2BUlpbK5XJ5jTkc4fWCm0nBwc28vtuFXftqDA6HtT8jHisAF8LqNcw02wesrKwsPf/881q0aJGuvvpqhYaGqry83GubmpoaNW/eXJIUGhpaL0zV1NQoMjLS52Pm5uYqOzvbayw1NVXp6enn2YXvIiPDGv0YVrBrXya1bNnC6hIk8VgBOD/%2BsoaZYuuANXv2bL300kvKysrSwIEDJUlxcXHav3%2B/13ZlZWWes0txcXEqKyurN9%2BpUyefj5uSkqL%2B/ft7jTkc4Tp69Pj5tOGT4OBmiowMU0VFlWpr6xrtOE3Nrn01hsZ8fvmCxwrAhWisNcyq4GbbgJWdna01a9Zo4cKFGjRokGfc6XQqJydHJ06c8Jy1ys/PV0JCgmc%2BPz/fs31VVZX27duntLQ0n48dGxtb73Kgy/WdTp1q/P90amvrmuQ4Tc2ufZnkLz8fHisA58Nu64a9Lnj%2BvwMHDmjp0qW69957lZCQIJfL5flKSkpS69atlZGRoaKiIuXk5GjPnj0aOXKkJGnEiBHavXu3cnJyVFRUpIyMDLVr1463aAAAAD6zZcB6%2B%2B23VVtbq2XLlql3795eX8HBwVq6dKlcLpeSk5O1adMmLVmyRG3atJEktWvXTk899ZTy8vI0cuRIlZeXa8mSJQoKCrK4KwAAEChseYlw/PjxGj9%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%2BXyGnM4wuvt16Tg4GZe3%2B3Crn0BALw5HPZa520bsM6kqqpKISEhXmMhISGqqanxad4Xubm5ys7O9hpLTU1Venr6eVbtu8jIsEY/hhXs2hcA4HstW7awugSjfnYBKzQ0VOXl5V5jNTU1at68uWf%2Bp2GqpqZGkZGRPh8jJSVF/fv39xpzOMJ19Ojx86z63IKDmykyMkwVFVWqra1rtOM0Nbv2BQDw1lj/R1oV3H52ASsuLk779%2B/3GisrK/NcvouLi1NZWVm9%2BU6dOvl8jNjY2HqXA12u73TqVOMHhNrauiY5TlOza18AgO/ZbY231wVPHzidTn3yySc6ceKEZyw/P19Op9Mzn5%2Bf75mrqqrSvn37PPMAAADn8rMLWElJSWrdurUyMjJUVFSknJwc7dmzRyNHjpQkjRgxQrt371ZOTo6KioqUkZGhdu3aqUePHhZXDgAAAsXPLmAFBwdr6dKlcrlcSk5O1qZNm7RkyRK1adNGktSuXTs99dRTysvL08iRI1VeXq4lS5YoKCjI4soBAECgCHLzFuVNwuX6rlH373A0U8uWLXT06HFbXce2sq/Bi99r0uMBwM/Z6/f3apT9xsRc3Cj7PZef3RksAACAxkbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYw%2BoC8PPBZ/sBAH4uOIMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwh9UF4MIMXvye1SUAAICf4AwWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAOo3q6mr96U9/UmJionr37q2//OUvVpcEAAACCG/TcBrz5s3T3r179fzzz6ukpEQPP/yw2rRpo0GDBlldGgAACAAErJ%2BorKzU2rVr9cwzz6hLly7q0qWLioqK9OKLLxKwAACAT7hE%2BBOfffaZTp06pfj4eM9YQkKCCgsLVVdXZ2FlAAAgUHAG6ydcLpdatmypkJAQz1h0dLSqq6tVXl6uVq1anXMfpaWlcrlcXmMOR7hiY2ON1wsAgB04HPY650PA%2BomqqiqvcCXJc7umpsanfeTm5io7O9trLC0tTZMnTzZT5I98OPf7y5alpaXKzc1VSkqKrYIcfQUOO/Yk0VcgsWNPEn0FKnvFRQNCQ0PrBakfbjdv3tynfaSkpGj9%2BvVeXykpKcZr/TGXy6Xs7Ox6Z84CHX0FDjv2JNFXILFjTxJ9BSrOYP1EXFycjh49qlOnTsnh%2BP7H43K51Lx5c0VGRvq0j9jYWFumcQAA4BvOYP1Ep06d5HA4VFBQ4BnLz89X165d1awZPy4AAHBuJIafCAsL0/DhwzVjxgzt2bNHb731lv7yl79o7NixVpcGAAACRPCMGTNmWF2Ev%2BnZs6f27dunBQsWaMeOHZo4caJGjBhhdVnn1KJFCyUlJalFixZWl2IUfQUOO/Yk0VcgsWNPEn0FoiC32%2B22uggAAAA74RIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAClg3U1NRo5syZuuGGG/TLX/5SCxculB3eP/bw4cOaMGGCunfvrv79%2B%2Bu5556zuqQLUlNTo6FDh2rnzp2eseLiYt15553q1q2bhgwZom3btllYYcOdrqeCggLddtttio%2BP18CBA7V27VoLKzw/p%2BvrB99995369Omj9evXW1DZhTldXyUlJbr33nvldDp1yy23aMuWLRZW2HCn6%2BnDDz9UcnKyunXrpmHDhmn79u0WVtgwX331ldLT05WUlKQ%2BffooMzNT1dXVkgJ3vThbT3ZYL86EgGUDc%2BbM0fbt2/Xss89qwYIFevnll5Wbm2t1WRfs/vvvV3h4uNavX68//elPWrx4sf72t79ZXdZ5qa6u1gMPPKCioiLPmNvtVmpqqqKjo5WXl6dhw4YpLS1NJSUlFlbqu9P15HK5dO%2B99yopKUkbNmxQenq6Zs%2BerX/84x/WFdpAp%2Bvrx7KyslRaWtrEVV240/V16tQpTZgwQQ6HQxs2bNDdd9%2BtKVOm6PPPP7ewUt%2Bdrqevv/5aEydO1JAhQ/Tqq69q8ODBmjRpko4cOWJhpb5xu91KT09XVVWVXnzxRS1atEh///vftXjx4oBdL87Wkx3Wi7NxWF0ALkx5ebny8vK0cuVKXX/99ZKkcePGqbCwULfddpvF1Z2/b7/9VgUFBZo9e7Y6dOigDh06qE%2BfPtqxY4duueUWq8trkP379%2BvBBx%2Bsd1bx/fffV3FxsdasWaPw8HB17NhRO3bsUF5eniZPnmxRtb45U09vvfWWoqOj9cADD0iSOnTooJ07d%2BrVV19V3759Lai0Yc7U1w8%2B/PBDvf/%2B%2B4qJiWniyi7MmfraunWrDh8%2BrJdeekkRERG64oor9M477%2Bijjz7S1VdfbVG1vjlTT7t371ZwcLDuueceSdLEiRO1cuVKFRQUaNCgQVaU6rODBw%2BqoKBA7733nqKjoyVJ6enpeuKJJ3TTTTcF5Hpxtp5%2B8YtfBPR6cS6cwQpw%2Bfn5ioiIUFJSkmds/PjxyszMtLCqC9e8eXOFhYVp/fr1OnnypA4ePKjdu3erU6dOVpfWYLt27VKPHj3qnVUsLCxU586dFR4e7hlLSEhQQUFBU5fYYGfq6YfT/z917NixpirtgpypL%2Bn7S1F//vOfNW3aNIWEhFhQ3fk7U1%2B7du3SjTfeqIiICM/Y0qVLlZKS0tQlNtiZerr00ktVXl6uv/71r3K73Xrrrbd0/Phxvw%2BMkhQTE6MVK1Z4gsgPjh07FrDrxdl6CvT14lw4gxXgiouL1bZtW73yyitavny5Tp48qeTkZN13331q1ixw83NoaKimTZum2bNn64UXXlBtba2Sk5M1atQoq0trsNtvv/204y6XS7GxsV5jUVFRAXEp40w9tWvXTu3atfPc/vrrr/Xaa6/59W/YP3amviRp%2BfLl6ty5s3r37t2EFZlxpr5%2BWD/mz5%2BvjRs3qmXLlkpPT9eAAQOauMKGO1NPiYmJuuOOO5Senq5mzZqptrZWmZmZuuKKK5q4woaLjIxUnz59PLfr6uq0evVq9ezZM2DXi7P1FOjrxbkE7v/AkCRVVlbq0KFDWrNmjTIzM/Xwww9r1apVAf%2BCcEk6cOCA%2BvXrp9zcXGVmZuqNN97Qpk2brC7LmKqqqnpnQkJCQlRTU2NRRWadOHFCkydPVnR0dECcETmb/fv3a82aNcrIyLC6FKMqKyu1YcMGVVRUaPny5Ro%2BfLjS09P18ccfW13aeTt%2B/LiKi4uVlpamtWvXauLEiZozZ44OHDhgdWkNlpWVpX379ukPf/iDbdaLH/f0Y3ZaL37AGawA53A4dOzYMS1YsEBt27aV9P1fBb300ksaN26cxdWdvx07dmjdunXaunWrmjdvrq5du%2Bqrr77SsmXL9Otf/9rq8owIDQ1VeXm511hNTY2aN29uUUXmHD9%2BXJMmTdK//vUv/fd//7fCwsKsLum8ud1uPfroo0pPT693mSPQBQcH69JLL9WMGTPUrFkzdenSRR9%2B%2BKFefvllde3a1eryzsuKFSvkdruVlpYmSerSpYv27NmjF154QTNnzrS4Ot9lZWXp%2Beef16JFi3T11VfbYr34aU8/sNN68WOcwQpwMTExCg0N9YQrSbr88st1%2BPBhC6u6cHv37lX79u29Fo/OnTv7/V/MNERcXJzKysq8xsrKyupdBgg0x44d0913362ioiI9//zz6tChg9UlXZCSkhJ99NFHeuKJJxQfH6/4%2BHiVlJRo%2BvTpnhdSB6rY2Fh16NDB6%2BUEgb5%2BfPLJJ7r22mu9xjp16hRQa8fs2bO1cuVKZWVlaeDAgZICf704XU%2BS/daLHyNgBTin06nq6mp98cUXnrGDBw96Ba5AFBsbq0OHDnmd/j548KDX9fpA53Q69cknn%2BjEiROesfz8fDmdTgurujB1dXVKS0vTl19%2BqVWrVumqq66yuqQLFhcXp7/%2B9a965ZVXPF%2BxsbFKT0/X3LlzrS7vgjidThUVFam2ttYzduDAgYBeP2JjY7V//36vsUBaO7Kzs7VmzRotXLhQt956q2c8kNeLM/Vkx/XixwhYAe6KK65Q3759lZGRoc8%2B%2B0zvvvuucnJyNHr0aKtLuyD9%2B/fXRRddpEcffVRffPGF/ud//kfLly/XmDFjrC7NmKSkJLVu3VoZGRkqKipSTk6O9uzZo5EjR1pd2nlbt26ddu7cqTlz5igyMlIul0sul6vepY1A4nA41L59e68vh8OhqKgoxcXFWV3eBRk6dKjq6uo0c%2BZMHTp0SC%2B%2B%2BKLeffdd/fa3v7W6tPM2atQovfPOO3ruuedUXFys5557Ttu2bTvrHzD4iwMHDmjp0qW69957lZCQ4Pn343K5Ana9OFtPdlwvfozXYNnA/PnzNXv2bI0ePVphYWG64447Aj6IXHzxxXruuec0d%2B5cjRw5Uq1atdJ9991nmxc/St%2B//mXp0qWaOnWqkpOT1b59ey1ZskRt2rSxurTz9uabb6qurk4TJkzwGk9KStKqVassqgpnEhERoZUrV2rGjBkaOnSo2rRpo0WLFqlLly5Wl3beunXrpqeeekpPPvmk/uu//kuXX365cnJyAuLsyNtvv63a2lotW7ZMy5Yt85r75z//GZDrxdl66t27t63XiyC3HT5TBQAAwI9wiRAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPs/%2BUZV/rrf8JMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5013706810478157220”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15.223026955891909</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.3952970890651</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.167430595356233</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.359663723562868</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.67025566509675</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.544125974995477</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.399272252077058</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.9611639450476</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.357911883394</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.666611514886178</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5013706810478157220”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>5.866367454814733</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.323863433830711</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.909464693629092</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.298301068286891</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.49562077236101</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>20.666611514886178</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.01309784781326</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.06563828868176</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.899881129769458</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:86%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>22.131516338899353</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_SNAP16”><s>PCT_SNAP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PCT_SNAP12”><code>PCT_SNAP12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91642</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_WIC09”>PCT_WIC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.9584</td>

</tr> <tr>

<th>Minimum</th> <td>1.3863</td>

</tr> <tr>

<th>Maximum</th> <td>5.0789</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1221090632167506541”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives1221090632167506541”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1221090632167506541”

aria-controls=”quantiles1221090632167506541” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1221090632167506541” aria-controls=”histogram1221090632167506541”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1221090632167506541” aria-controls=”common1221090632167506541”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1221090632167506541” aria-controls=”extreme1221090632167506541”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1221090632167506541”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.3863</td>

</tr> <tr>

<th>5-th percentile</th> <td>2.0317</td>

</tr> <tr>

<th>Q1</th> <td>2.5076</td>

</tr> <tr>

<th>Median</th> <td>2.7313</td>

</tr> <tr>

<th>Q3</th> <td>3.2861</td>

</tr> <tr>

<th>95-th percentile</th> <td>5.0789</td>

</tr> <tr>

<th>Maximum</th> <td>5.0789</td>

</tr> <tr>

<th>Range</th> <td>3.6926</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.77854</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.74607</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.25218</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.1355</td>

</tr> <tr>

<th>Mean</th> <td>2.9584</td>

</tr> <tr>

<th>MAD</th> <td>0.57053</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.1525</td>

</tr> <tr>

<th>Sum</th> <td>9265.8</td>

</tr> <tr>

<th>Variance</th> <td>0.55662</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1221090632167506541”>

<img 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uyzz%2BwuBwAAwDjbA5bb7VZlZWW9cZ/PxxXdAQCAI9gesIYPH65Fixbpu%2B%2B%2B84%2BVlpYqIyNDw4YNs7scAAAA42z/K8K5c%2Bfqtttu01VXXaW4uDhJUmVlpXr16qX09HS7ywEAADDO9oDVunVrvfrqq/r0009VVFQkt9utbt266ZJLLpHL5bK7HAAAAONsP0UoSRERERo2bJimTp2qKVOm6NJLLzUermpqavTQQw/p4osv1qWXXqonnnjC/x6vwsJCTZgwQR6PR6mpqdqxY0fAfTdt2qSRI0fK4/EoLS1NP/74o9HaAACAs9l%2BBMvr9Wrp0qXKy8vTsWPH6r2x/f333zeyn0WLFunzzz/Xs88%2BqyNHjuiPf/yj2rdvr%2Buuu07Tpk3Ttddeq0ceeUQvvfSSpk%2BfrnfffVexsbHavn275s%2Bfr4ceekg9evTQ4sWLlZ6erpUrVxqpCwAAOJ/tAeu//uu/tGPHDl1zzTX6zW9%2B0yT7qKio0Pr16/Xcc8%2Bpb9%2B%2BkqSpU6eqoKBAbrdbUVFRmjNnjlwul%2BbPn6%2BPPvpIb7/9tlJSUrRmzRqNHj1a48aNkyQtWbJEl19%2BuUpLS9WpU6cmqRcAADiL7QHrs88%2B06pVqzRgwIAm20dubq5atGihgQMH%2BsemTZsm6eeA179/f/8pSZfLpaSkJOXn5yslJUUFBQW6/fbb/fdr166d2rdvr4KCAgIWAABoENsDVmxsrFq3bt2k%2BygtLVWHDh302muvacWKFTp27JhSUlI0Y8YMeb1edevWLWD71q1bq6ioSJJUVlamhISEevMHDhxo8P7Lysrk9XoDxtzu2HqPGwwREc0C/nWycOw1VLjdZ1dvOK4tvToLvTqf7QFr7NixWrVqlRYuXOj/cGfTjh49qr1792rt2rXKyMiQ1%2BvVggULFBMTI5/Pp8jIyIDtIyMjVVNTI0mqqqo67XxD5OTkKCsrK2AsLS1Ns2fP/pUdmRcXFxPsEmwTTr2Gilatmht5nHBaW3p1Jnp1LtsDVkVFhTZt2qQPP/xQnTp1qhdmXnzxxbPeh9vt1uHDh/X444%2BrQ4cOkqR9%2B/bppZdeUufOneuFpZqaGkVHR0uSoqKiTjofE9PwH4yJEycqOTn5hJpidfDgkV/TjlEREc0UFxejykqfamvrgl1Okwq3XkPJ2T4Xwm1t6dV56NU%2Bpn6hayzbA5YkjRkzpkkfv02bNoqKivKHK0n67W9/q/3792vgwIEqLy8P2L68vNx/%2Bq5t27Ynnf/Xz048k4SEhHqnA73eQzp%2B/Nx5EtXW1p1T9TSlcOo1VJhaj3BaW3p1Jnp1LtsDVkZGRpPvw%2BPxqLq6Wnv27NFvf/tbSVJJSYk6dOggj8ejZ555RpZlyeVyybIs5eXl6Y477vDfNzc3VykpKZKk/fv3a//%2B/fJ4PE1eNwAAcIagnFcoKytTVlaW7r77bv3f//2f3n77bZWUlBh7/AsvvFAjRoxQenq6du7cqY8//ljZ2dmaNGmSRo0apcrKSi1evFjFxcVavHixfD6fRo8eLUmaNGmSNmzYoHXr1mnnzp2aM2eORowYwV8QAgCABrM9YO3du1fXXnutXn31Vb3zzjs6evSo3nzzTaWmpqqgoMDYfh577DH927/9myZNmqS5c%2Bfqpptu0uTJk9WiRQutXLnSf5SqoKBA2dnZio2NlSQlJiZq4cKFWrZsmSZNmqSWLVvactQNAAA4h8s68VLqTWzGjBmKj4/XokWLlJSUpI0bN6pdu3aaO3euysrKtHr1ajvLsY3XeyjYJUj6%2Bc/jW7VqroMHjzj%2BXHi49XrFYx8Hu4wGe%2BuuIWd1/3BbW3p1Hnq1T5s2TXNR8zOx/QhWXl6ebrvttoDPHnS73Zo5c6YKCwvtLgcAAMA42wNWXV2d6urqJ9gjR4402XWxAAAA7GR7wBo6dKhWrlwZELIqKiqUmZmpwYMH210OAACAcbYHrHnz5mnHjh0aOnSoqqurNWPGDF1%2B%2BeX6/vvvNXfuXLvLAQAAMM7262C1bdtWr732mjZt2qRvvvlGdXV1mjRpksaOHasWLVrYXQ4AAIBxQbmSe0xMjCZMmBCMXQMAADQ52wPWlClTTjtv4rMIAQAAgsn2gPWvnw8oScePH9fevXu1a9cu3XLLLXaXAwAAYNw581mEy5Yt04EDB2yuBgAAwLygfBbhyYwdO1ZvvfVWsMsAAAA4a%2BdMwPryyy%2B50CgAAHCEc%2BJN7ocPH9a3336rG2%2B80e5yAAAAjLM9YLVv3z7gcwgl6bzzztPNN9%2Bs6667zu5yAAAAjLM9YD3yyCN27xIAAMBWtgesbdu2NXjbiy%2B%2BuAkrAQAAaBq2B6zJkyf7TxFaluUfP3HM5XLpm2%2B%2Bsbs8AACAs2Z7wFqxYoUWLVqke%2B%2B9VwMHDlRkZKS%2B%2BuorLVy4UNdff72uvvpqu0sCAAAwyvbLNGRkZGjBggW66qqr1KpVKzVv3lyDBw/WwoUL9dJLL6lDhw7%2BLwAAgFBke8AqKys7aXhq0aKFDh48aHc5AAAAxtkesPr166cnnnhChw8f9o9VVFQoMzNTl1xyid3lAAAAGGf7e7Duv/9%2BTZkyRcOHD1eXLl1kWZb%2B8Y9/qE2bNnrxxRftLgcAAMA42wNW165d9eabb2rTpk3avXu3JOmmm27SNddco5iYGLvLAQAAMM72gCVJLVu21IQJE/T999%2BrU6dOkn6%2BmjsAAIAT2P4eLMuy9Nhjj%2Bniiy/WmDFjdODAAc2dO1fz58/XsWPH7C4HAADAONsD1urVq7VhwwY98MADioyMlCSNHDlS7733nrKysuwuBwAAwDjbA1ZOTo4WLFiglJQU/9Xbr776ai1atEivv/663eUAAAAYZ3vA%2Bv7779WzZ8964z169JDX67W7HAAAAONsD1gdOnTQV199VW/8o48%2B8r/hHQAAIJTZ/leEv/vd7/TQQw/J6/XKsixt2bJFOTk5Wr16tebNm2d3OQAAAMbZHrBSU1N1/PhxPf3006qqqtKCBQsUHx%2Bvu%2B66S5MmTbK7HAAAAONsD1ibNm3SqFGjNHHiRP3444%2ByLEutW7e2uwwAAIAmY/t7sBYuXOh/M3t8fDzhCgAAOI7tAatLly7atWuX3bsFAACwje2nCHv06KF77rlHq1atUpcuXRQVFRUwn5GRYXdJAAAARtkesPbs2aP%2B/ftLEte9AgAAjmRLwFqyZIlmzZql2NhYrV692o5dAgAABI0t78F67rnn5PP5AsamTZumsrIyO3YPAABgK1sClmVZ9ca2bdum6upqO3YPAABgK9v/ihAAAMDpCFgAAACG2RawXC6XXbsCAAAIKtsu07Bo0aKAa14dO3ZMmZmZat68ecB2XAcLAACEOlsC1sUXX1zvmleJiYk6ePCgDh48aEcJAAAAtrElYHHtKwAAEE54kzsAAIBhjg9Y06ZN07x58/y3CwsLNWHCBHk8HqWmpmrHjh0B22/atEkjR46Ux%2BNRWlqafvzxR7tLBgAAIc7RAeuNN97Q5s2b/bePHj2qadOmacCAAXrllVeUmJio6dOn6%2BjRo5Kk7du3a/78%2BZo1a5ZycnJUWVmp9PT0YJUPAABClGMDVkVFhZYsWaI%2Bffr4x958801FRUVpzpw56tq1q%2BbPn6/mzZvr7bffliStWbNGo0eP1rhx49SjRw8tWbJEmzdvVmlpabDaAAAAIcixAevRRx/V2LFj1a1bN/9YQUGB%2Bvfv778ml8vlUlJSkvLz8/3zAwYM8G/frl07tW/fXgUFBfYWDwAAQpojA9aWLVv0xRdfaObMmQHjXq9XCQkJAWOtW7fWgQMHJEllZWWnnQcAAGgI2y40apfq6mo98MADWrBggaKjowPmfD6fIiMjA8YiIyNVU1MjSaqqqjrtfEOVlZXVu%2B6X2x1bL7wFQ0REs4B/nexse73isY9NloN/MXrp34NdQqO8e8%2BwoO2b56wz0avzOS5gZWVlqXfv3ho2rP4LYlRUVL2wVFNT4w9ip5qPiYlpVA05OTnKysoKGEtLS9Ps2bMb9ThNKS6ucT2FsnDqFU2jVavmZ96oiYXTzzG9OlM49So5MGC98cYbKi8vV2JioiT5A9M777yjMWPGqLy8PGD78vJy/5Gltm3bnnS%2BTZs2japh4sSJSk5ODhhzu2N18OCRRj1OU4iIaKa4uBhVVvpUW1sX7HKaVDj1iqYVzOduOP0c06szBbvXYP2C5LiAtXr1ah0/ftx/%2B7HHHpMk3XPPPdq2bZueeeYZWZYll8sly7KUl5enO%2B64Q5Lk8XiUm5urlJQUSdL%2B/fu1f/9%2BeTyeRtWQkJBQ73Sg13tIx4%2BfO0%2Bi2tq6c6qephROvaJpnAs/P%2BH0c0yvzhROvUoODFgdOnQIuP3Lh0l37txZrVu31uOPP67FixfrP//zP7V27Vr5fD6NHj1akjRp0iRNnjxZ/fr1U58%2BfbR48WKNGDFCnTp1sr0PAAAQusLqHWctWrTQypUr/UepCgoKlJ2drdjYWEk/fwD1woULtWzZMk2aNEktW7ZURkZGkKsGAAChxnFHsE70yCOPBNzu27evXn311VNun5KS4j9FCAAA8GuE1REsAAAAOxCwAAAADCNgAQAAGOb492ABwNkKtSvPv3XXkGCXAIQ9jmABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDMHewCED5GL/17sEsAAMAWHMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMPcwS4AABC%2BRi/9e7BLaJS37hoS7BIQIjiCBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCYYwPWDz/8oNmzZ2vgwIEaNmyYMjIyVF1dLUkqLS3Vrbfeqn79%2Bunqq6/WJ598EnDfTz/9VGPGjJHH49GUKVNUWloajBYAAECIcmTAsixLs2fPls/n01//%2Blc9%2BeST%2BuCDD7R06VJZlqW0tDRdcMEFWr9%2BvcaOHatZs2Zp3759kqR9%2B/YpLS1NKSkpevnllxUfH6%2BZM2fKsqwgdwUAAEKFIz%2BLsKSkRPn5%2Bfr73/%2BuCy64QJI0e/ZsPfrooxo%2BfLhKS0u1du1axcbGqmvXrtqyZYvWr1%2BvO%2B%2B8U%2BvWrVPv3r01depUSVJGRoaGDBmirVu3atCgQcFsCwAAhAhHHsFq06aNVq1a5Q9Xvzh8%2BLAKCgp00UUXKTY21j/ev39/5efnS5IKCgo0YMAA/1xMTIx69erlnwcAADgTRx7BiouL07Bhw/y36%2BrqtGbNGg0ePFher1cJCQkB27du3VoHDhyQpDPON0RZWZm8Xm/AmNsdW%2B9xgyEiolnAvwCcx%2B3m%2Bd1UTH1vw%2Bm1OJx6/VeODFgnyszMVGFhoV5%2B%2BWU9//zzioyMDJiPjIxUTU2NJMnn8512viFycnKUlZUVMJaWlqbZs2f/yg7Mi4uLCXYJAJpIq1bNg12CY5n%2B3obTa3E49SqFQcDKzMzUCy%2B8oCeffFLdu3dXVFSUKioqArapqalRdHS0JCkqKqpemKqpqVFcXFyD9zlx4kQlJycHjLndsTp48Miv7MKciIhmiouLUWWlT7W1dcEuB0ATOBdea5zK1Pc2nF6Lg91rsH7hcHTAevjhh/XSSy8pMzNTV111lSSpbdu2Ki4uDtiuvLzcf/qubdu2Ki8vrzffs2fPBu83ISGh3ulAr/eQjh8/d55EtbV151Q9AMzhud10TH9vw%2Bm1OJx6lRz6JndJysrK0tq1a/XEE0/ommuu8Y97PB59/fXXqqqq8o/l5ubK4/H453Nzc/1zPp9PhYWF/nkAAIAzcWTA2r17t5YvX67bb79d/fv3l9fr9X8NHDhQ7dq1U3p6uoqKipSdna3t27dr/PjxkqTU1FTl5eUpOztbRUVFSk9PV8eOHblEAwAAaDBHBqz3339ftbW1evrppzV06NCAr4iICC1fvlxer1cpKSnauHGjli1bpvbt20uSOnbsqKeeekrr16/X%2BPHjVVFRoWXLlsnlcgW5KwAAECoc%2BR6sadOmadq0aaec79y5s9asWXPK%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%2B/VVQUKC6urogVgYAAEIFR7BO4PV61apVK0VGRvrHLrjgAlVXV6uiokLx8fFnfIyysjJ5vd6AMbc7VgkJCcbrBQDYx%2B02c1wiIqJZwL8w9709VxCwTuDz%2BQLClST/7ZqamgY9Rk5OjrKysgLGZs2apTvvvNNMkf/ii8WNO21ZVlamnJwcTZw40fGBj16dK5z6pVdnKisr0wsvrGryXhv7f0RTCKd1/VfOiosGREVF1QtSv9yOjo5u0GNMnDhRr7zySsDXxIkTjdf6a3i9XmVlZdU7wuZE9Opc4dQvvToTvTofR7BO0LZtWx08eFDHjx%2BX2/3zt8fr9So6OlpxcXENeoyEhISwSukAACAQR7BO0LNnT7ndbuXn5/vHcnNz1adPHzVrxrcLAACcGYnhBDExMRo3bpwefPBBbd%2B%2BXe%2B9957%2B%2B7//W1OmTAl2aQAAIEREPPjggw8Gu4hzzeDBg1VYWKjHH39cW7Zs0R133KHU1NRgl2VM8%2BbNNXDgQDVv3jzYpTQ5enWucOqXXp2JXp3NZVmWFewiAAAAnIRThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgOVlNTozFjxujzzz8/5TYzZszQf/zHfwR8ffDBBzZWeXZ%2B%2BOEHzZ49WwMHDtSwYcOUkZGh6urqk25bWFioCRMmyOPxKDU1VTt27LC52rPTmF5DfV337t2r3/3ud0pMTNSIESO0atWqU24b6usqNa7fUF/bX0ybNk3z5s075fynn36qMWPGyOPxaMqUKSotLbWxOrPO1Ot1111Xb0137dplY4Vn7913363Xw%2BzZs0%2B6rZPW9rQsOFJVVZWVlpZmde/e3frss89Oud0VV1xhbdiwwSorK/N/VVdX21jpr1dXV2fdcMMN1u9//3tr165d1rZt26wrrrjCeuSRR%2Bpte%2BTIEWvIkCHWI488YhUXF1sPP/ywdemll1pHjhwJQuWN15heLSu017W2tta68sorrbvvvtvas2eP9eGHH1pJSUnWxo0b620b6utqWY3r17JCe21/sWnTJqt79%2B7W3LlzTzr/z3/%2B0%2BrXr5/17LPPWrt27bL%2B8Ic/WGPGjLHq6upsrvTsnanX48ePW3369LG2bt0asKbHjh2zudKzs3z5cmv69OkBPfz000/1tnPS2p4JAcuBioqKrOuuu8669tprTxuwqqurrZ49e1olJSU2V2hGcXGx1b17d8vr9frHXn/9dWvo0KH1tl23bp2VnJzsfxLX1dVZV1xxhbV%2B/Xrb6j0bjek11Nf1hx9%2BsP7whz9Yhw4d8o%2BlpaVZDzzwQL1tQ31dLatx/Yb62lqWZR08eNAaPny4lZqaesrQsXTpUuvmm2/23z569KiVmJh42l8Wz0UN6fUf//iH1aNHD6uqqsrm6sy6%2B%2B67rccff/yM2zllbRuCU4QOtHXrVg0aNEg5OTmn3a6kpEQul0udOnWyqTKz2rRpo1WrVumCCy4IGD98%2BHC9bQsKCtS/f3%2B5XC5JksvlUlJSkvLz822p9Ww1ptdQX9eEhAQtXbpULVq0kGVZys3N1bZt2zRw4MB624b6ukqN6zfU11aSHn30UY0dO1bdunU75TYFBQUaMGCA/3ZMTIx69eoVUusqNazX4uJitWvXTlFRUTZWZt7u3bvVpUuXM27nlLVtCAKWA91444267777FBMTc9rtSkpK1KJFC82ZM0dDhw7V%2BPHjtXnzZpuqPHtxcXEaNmyY/3ZdXZ3WrFmjwYMH19vW6/UqISEhYKx169Y6cOBAk9dpQmN6DfV1/VfJycm68cYblZiYqKuuuqrefKiv64nO1G%2Bor%2B2WLVv0xRdfaObMmafdzgnr2tBed%2B/erfPOO0/Tp0/XkCFDdPPNN2v79u02VWmGZVnas2ePPvnkE1111VUaOXKkHnvsMdXU1NTb1glr21AErDBWUlKiqqoqDR06VKtWrdJll12mGTNm6Kuvvgp2ab9KZmamCgsL9cc//rHenM/nU2RkZMBYZGTkSV8AQsHpenXSuv7lL3/RihUr9M033ygjI6PevNPW9Uz9hvLaVldX64EHHtCCBQsUHR192m1DfV0b0%2BuePXv0008/acKECcrOzlbXrl11yy23aP/%2B/TZVe/b27dvnX7OlS5dq7ty5ev3117VkyZJ624b62jaGO9gFIHhmzpypyZMnq2XLlpKkHj166Ouvv9bf/vY39enTJ8jVNU5mZqZeeOEFPfnkk%2BrevXu9%2BaioqHpP4JqamjO%2B%2BJ2LztSrk9b1l3qrq6t1zz33aM6cOQEvzk5aV%2BnM/Yby2mZlZal3794BR2JP5VTrGhcX11TlGdWYXh9%2B%2BGFVVVWpRYsWkqQHH3xQeXl52rBhg%2B64446mLtWIDh066PPPP1fLli3lcrnUs2dP1dXV6d5771V6eroiIiL824b62jYGASuMNWvWzP9C/YsLL7xQxcXFQaro13n44Yf10ksvKTMz86SnVSSpbdu2Ki8vDxgrLy%2Bvd6j6XNeQXkN9XcvLy5Wfn6%2BRI0f6x7p166Zjx47p8OHDio%2BP9487YV0b028or%2B0bb7yh8vJyJSYmSpL/P9l33nlHX375ZcC2p1rXnj172lPsWWpMr2632x%2BupJ/fR3jhhRfqhx9%2BsK9gA84///yA2127dlV1dbV%2B%2BumnBj1nQ2VtG4NThGFs3rx5Sk9PDxjbuXOnLrzwwiBV1HhZWVlau3atnnjiCV1zzTWn3M7j8ejLL7%2BUZVmSfn7PQF5enjwej12lnrWG9hrq6/r9999r1qxZAf/B7NixQ/Hx8QEv1JIz1rUx/Yby2q5evVqvv/66XnvtNb322mtKTk5WcnKyXnvttXrbejwe5ebm%2Bm/7fD4VFhaGzLo2ptfJkycrKyvLf7uurk7ffvttSKzpLz7%2B%2BGMNGjRIPp/PP/bNN9/o/PPPP%2BlzNpTXtjEIWGHG6/WqqqpK0s9vqP3lRWDv3r3KyspSbm6ubr755iBX2TC7d%2B/W8uXLdfvtt6t///7yer3%2BLymw11GjRqmyslKLFy9WcXGxFi9eLJ/Pp9GjRwezhQZrTK%2Bhvq59%2BvRRr169dN9996m4uFibN29WZmam/3SJk9ZValy/oby2HTp0UOfOnf1fzZs3V/PmzdW5c2fV1tbK6/X6j/SkpqYqLy9P2dnZKioqUnp6ujp27KhBgwYFuYuGaUyvycnJev755/X%2B%2B%2B%2BrpKRECxcu1KFDh3T99dcHuYuGS0xMVFRUlO6//36VlJRo8%2BbNWrJkiX7/%2B987bm0bJYiXiIANTrwOVvfu3QOuEfS3v/3NuvLKK63evXtb119/vbV169ZglPmrrFy50urevftJvyyrfq8FBQXWuHHjrD59%2Bljjx4%2B3vv7662CV3miN7TWU19WyLOvAgQNWWlqalZSUZA0ZMsR6%2Bumn/de6ctK6/qIx/Yb62v5i7ty5/mtDlZaW1nut%2BvDDD60rr7zS6tu3r3XLLbdY3333XbBKPWun67Wurs56%2BumnrREjRli9e/e2brrpJuvbb78NZrm/yq5du6xbb73V6tevnzVkyBDrqaeesurq6hy/tqfjsqz/f2wdAAAARnCKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2B3/hvps/5ZeTfAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1221090632167506541”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>4.004688507145139</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.078871539129641</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0316926289455624</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2861447996378397</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.507607414013675</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7313199801188257</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.400156338966488</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9319091889421083</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.5149142778111715</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.7767621827200744</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1221090632167506541”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.3862559688956837</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7095814782644287</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9404309065763445</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9416980688098253</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.022527480446423</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.696062696768522</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7763601305692824</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.893239222130259</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.004688507145139</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.078871539129641</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:62%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PCT_WIC15”>PCT_WIC15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>2.3619</td>

</tr> <tr>

<th>Minimum</th> <td>1.1051</td>

</tr> <tr>

<th>Maximum</th> <td>3.2316</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6219582495932819262”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-6219582495932819262,#minihistogram-6219582495932819262”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-6219582495932819262”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6219582495932819262”

aria-controls=”quantiles-6219582495932819262” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6219582495932819262” aria-controls=”histogram-6219582495932819262”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6219582495932819262” aria-controls=”common-6219582495932819262”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6219582495932819262” aria-controls=”extreme-6219582495932819262”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6219582495932819262”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.1051</td>

</tr> <tr>

<th>5-th percentile</th> <td>1.6657</td>

</tr> <tr>

<th>Q1</th> <td>2.0321</td>

</tr> <tr>

<th>Median</th> <td>2.3337</td>

</tr> <tr>

<th>Q3</th> <td>2.6255</td>

</tr> <tr>

<th>95-th percentile</th> <td>3.2269</td>

</tr> <tr>

<th>Maximum</th> <td>3.2316</td>

</tr> <tr>

<th>Range</th> <td>2.1265</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.59334</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.4515</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.19116</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.49011</td>

</tr> <tr>

<th>Mean</th> <td>2.3619</td>

</tr> <tr>

<th>MAD</th> <td>0.36302</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.26269</td>

</tr> <tr>

<th>Sum</th> <td>7397.5</td>

</tr> <tr>

<th>Variance</th> <td>0.20386</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6219582495932819262”>

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L6xsdG/LJicnHzefqfTeUk1JCUldVhqlL78IDl3jje9HbW1tTM2gAWYd%2BbxeWYPtluonTNnjjZu3Ki//vWvl/X89evX6/XXX9eWLVu0ZcsWZWZmKjMzU1u2bJHL5dIHH3wgn88nSfL5fNq/f79cLpckyeVyqbKy0r%2BvEydO6MSJE/5%2BAACAYNguYI0bN07PP/%2B8JkyYoPvvv1%2B7du3yB6Jg9O/fX4MGDfI/YmNjFRsbq0GDBmnatGlqbm5WUVGR6urqVFRUJI/Ho6ysLEnS3LlztXXrVm3atEmHDh3SokWLNGnSJA0cOLCzDhcAAIQg2wWsn//853r77be1evVqhYeHKz8/X5MmTdLKlSv16aeffqt9x8XFac2aNaqsrNSsWbNUXV2ttWvXqlevXpKk1NRULV26VGVlZZo7d6569%2B6t4uJiE4cFAAB6EIfvUk4PWcDj8Wj9%2BvVavXq1WltbNXr0aP3oRz/S9773PatLu2Rcg2U/ERFhSkiIZWy6WNaq3VaXAJt4477xVpcQMvg8Oz%2Bn05pbJNn2IveGhgZt27ZN27Zt0yeffKLRo0frlltu0cmTJ/Xwww9r3759KiwstLpMAACADmwXsLZu3aqtW7fq/fffV2JiombOnKlnnnlGgwcP9m/Tr18/FRUVEbAAAIAt2S5gFRYWavLkySorK9P111%2BvsLCOl4ldddVVuu222yyoDgAA4OJsF7DeeecdJSQkqKmpyR%2BuDhw4oJEjRyo8PFySNHr0aI0ePdrKMgEAAC7Idr9F%2BPnnn2vatGn69a9/7W9bsGCBZsyYoRMnTlhYGQAAQHBsF7CeeOIJDRo0SHfccYe/bceOHerXrx%2B3TAAAAN2C7QLWn//8Zz344IMBf54mMTFRixYt0nvvvWdhZQAAAMGxXcCKiIhQc3Nzh3aPx3NJd3QHAACwiu0C1vXXX69ly5bpL3/5i7%2Btvr5excXFmjhxooWVAQAABMd2v0W4ePFi3XHHHZo6dari4%2BMlSc3NzRo5cqQKCgosrg4AAODibBew%2BvTpo9dee0179uxRbW2tIiIiNHToUF133XVyOBxWlwcAAHBRtgtYkhQeHq6JEyeyJAgAALol2wUst9utVatWaf/%2B/Tp79myHC9v/%2BMc/WlQZAABAcGwXsH75y1/q4MGDuvnmm3Xlldb8BWwAAIBvw3YB67333tO6deuUnp5udSkAAACXxXa3aejVq5f69OljdRkAAACXzXYBa8aMGVq3bp3a2tqsLgUAAOCy2G6JsKmpSdu3b9d//ud/auDAgYqMjAzo/93vfmdRZQAAAMGxXcCSpOnTp1tdAgAAwGWzXcAqLi62ugQAAIBvxXbXYElSQ0ODSktL9fOf/1z/%2B7//qzfffFNHjhyxuiwAAICg2C5gHT16VN///vf12muv6Q9/%2BINaWlq0Y8cOZWdnq7q62uryAAAALsp2S4S/%2BtWvNGXKFC1btkyjR4%2BWJK1YsUKLFy/WU089pfXr11tcIWBPWat2W10CAOD/2e4M1v79%2B3XHHXcE/GHniIgI3XvvvaqpqbGwMgAAgODYLmC1t7ervb29Q/sXX3yh8PBwCyoCAAC4NLYLWBMmTNCaNWsCQlZTU5NKSko0btw4CysDAAAIju0C1oMPPqiDBw9qwoQJam1t1T333KPJkyfr2LFjWrx4sdXlAQAAXJTtLnJPTk7Wli1btH37dn388cdqb2/X3LlzNWPGDMXFxVldHgAAwEXZLmBJUkxMjObMmWN1GQAAAJfFdgFr3rx539jP3yIEAAB2Z7uA1b9//4Dvz507p6NHj%2BqTTz7Rj370I4uqAgAACJ7tAtaF/hZhWVmZTp482cXVAAAAXDrb/RbhhcyYMUNvvPGG1WUAAABcVLcJWB988AE3GgUAAN2C7ZYIz3eR%2B%2Beff67/%2Bq//0q233mpBRQAAAJfGdgErJSUl4O8QStIVV1yh2267TT/4wQ8sqgoAACB4tgtYv/rVr6wuAQCAkJC1arfVJQTtjfvGW12CUbYLWPv27Qt62zFjxlyw7%2BjRo1q6dKn279%2Bv3r1767bbbtOdd94pSaqvr9cvf/lLVVVVKSUlRQ899JAmTJjgf%2B6ePXv0xBNPqL6%2BXi6XS0VFRRo4cODlHxQAAOhRbBewbr/9dv8Soc/n87d/vc3hcOjjjz8%2B7z7a29u1YMECjRo1Sq%2B99pqOHj2q%2B%2B%2B/X8nJyZo%2Bfbpyc3M1bNgwVVRU6K233lJeXp527NihlJQUHT9%2BXLm5ucrPz9fEiRNVVlame%2B%2B9V9u2beuwdAkAAHA%2BtgtYzz//vJYtW6Zf/OIXysjIUGRkpD788EMtXbpUt9xyi2666aaL7qOxsVEjRozQkiVLFBcXp8GDB%2Bu6665TZWWl%2Bvbtq/r6em3cuFG9evXSkCFD9O6776qiokL5%2BfnatGmTrrnmGs2fP1/Sl/flGj9%2BvPbu3auxY8d29uEDQI/SnZawpNBbxkLnsd1tGoqLi/XII49o6tSpSkhIUGxsrMaNG6elS5fq5ZdfVv/%2B/f2PC0lKStKqVasUFxcnn8%2BnyspK7du3TxkZGaqurtbVV1%2BtXr16%2BbdPS0tTVVWVJKm6ulrp6en%2BvpiYGI0cOdLfDwAAcDG2O4PV0NBw3vAUFxen06dPX/L%2BMjMzdfz4cU2ePFlTp07VE088oaSkpIBt%2BvTp479LvNvt/sb%2BYI/B7XYHtDmdTkVHX3nJ9aNzhYeHBXwFgG8SEWHfz4ru/nlm55/t5bBdwLr22mu1YsUKPfnkk4qLi5MkNTU1qaSkRNddd90l7%2B%2BZZ55RY2OjlixZouLiYnk8HkVGRgZsExkZKa/XK0kX7Q9GeXm5SktLA9ry8vKUn59/yfWja8THx1hdAoBuICEh1uoSLqq7fp51h5/tpbBdwHr44Yc1b948XX/99Ro8eLB8Pp/%2B%2B7//W06nU7/73e8ueX%2BjRo2SJLW2tuqBBx5Qdna2PB5PwDZer1fR0dGSpKioqA5hyuv1Kj4%2BPujXzMnJUWZmZkCb0%2BlUc7NHbW3tl3wM6Dzh4WGKj49hbAAE5fTpL6wu4YK6%2B%2BdZZ/1srQputgtYQ4YM0Y4dO7R9%2B3YdPnxYkvTDH/5QN998s2JigkvljY2Nqqqq0pQpU/xtQ4cO1dmzZ%2BV0OnXkyJEO23%2B1LJicnKzGxsYO/SNGjAj6GJKSkjosM0pfvnnOnet%2Bb/qeoK2tnbEBcFHd4XOiu36edceav4ntApYk9e7dW3PmzNGxY8f895%2B64oorgn7%2BsWPHlJeXp507dyo5OVmSdPDgQSUmJiotLU2//e1vdebMGf9Zq8rKSqWlpUmSXC6XKisr/fvyeDyqqalRXl6eqcMDAAAhznZXlPl8Pj311FMaM2aMpk%2BfrpMnT2rx4sUqLCzU2bNng9rHqFGjNHLkSD300EOqq6vTzp07VVJSorvvvlsZGRnq16%2BfCgoKVFtbq7Vr1%2BrAgQOaPXu2JCk7O1v79%2B/X2rVrVVtbq4KCAg0YMIBbNAAAgKDZLmCtX79eW7du1aOPPuq/2HzKlCl66623Olw4fiHh4eFavXq1YmJilJOTo8LCQt1%2B%2B%2B2aN2%2Bev8/tdmvWrFnatm2bysrKlJKSIkkaMGCAnn32WVVUVGj27NlqampSWVkZNxkFAABBs90SYXl5uR555BHdeOONevzxxyVJN910k6644goVFxfrZz/7WVD7SU5OvmAgGzRokDZs2HDB595www264YYbLr14AAAA2fAM1rFjx857Qfnw4cM73FsKAADAjmwXsPr3768PP/ywQ/s777zDH1wGAADdgu2WCH/yk5/osccek9vtls/n07vvvqvy8nKtX79eDz74oNXlAQAAXJTtAlZ2drbOnTun5557TmfOnNEjjzyixMRE3XfffZo7d67V5QEAAFyU7QLW9u3bNW3aNOXk5OjUqVPy%2BXzq06eP1WWhB8patdvqEgAA3ZTtrsFaunSp/2L2xMREwhUAAOh2bBewBg8erE8%2B%2BcTqMgAAAC6b7ZYIhw8frgceeEDr1q3T4MGDFRUVFdBfXFxsUWUAAADBsV3A%2BvTTT/1/F5D7XgEAgO7IFgFr%2BfLlysvLU69evbR%2B/XqrywEAAPhWbHEN1gsvvCCPxxPQtmDBAjU0NFhUEQAAwOWzRcDy%2BXwd2vbt26fW1lYLqgEAAPh2bBGwAAAAQgkBCwAAwDDbBCyHw2F1CQAAAEbY4rcIJWnZsmUB97w6e/asSkpKFBsbG7Ad98ECAAB2Z4uANWbMmA73vEpNTdXp06d1%2BvRpi6oCAAC4PLYIWNz7CgAAhBLbXIMFAAAQKghYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBb3AcLAIDuIGvVbqtLQDfBGSwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvZgPXZZ59p4cKFysjI0MSJE1VcXKzW1lZJUn19vX784x/r2muv1U033aRdu3YFPHfPnj2aPn26XC6X5s2bp/r6eisOAQAAdFMhGbB8Pp8WLlwoj8ejl156SStXrtTbb7%2BtVatWyefzKTc3V3379lVFRYVmzJihvLw8HT9%2BXJJ0/Phx5ebmatasWXr11VeVmJioe%2B%2B9Vz6fz%2BKjAgAA3UWE1QV0hiNHjqiqqkq7d%2B9W3759JUkLFy7Uk08%2Bqeuvv1719fXauHGjevXqpSFDhujdd99VRUWF8vPztWnTJl1zzTWaP3%2B%2BJKm4uFjjx4/X3r17NXbsWCsPCwAAdBMheQbL6XRq3bp1/nD1lc8//1zV1dW6%2Buqr1atXL397WlqaqqqqJEnV1dVKT0/398XExGjkyJH%2BfgAAgIsJyTNY8fHxmjhxov/79vZ2bdiwQePGjZPb7VZSUlLA9n369NHJkycl6aL9wWhoaJDb7Q5oczqdio6%2B8lIPBZ0sPDws4CsAwBoREaH1ORySAevrSkpKVFNTo1dffVX/9m//psjIyID%2ByMhIeb1eSZLH4/nG/mCUl5ertLQ0oC0vL0/5%2BfmXeQTobPHxMVaXAAA9WkJCrNUlGBXyAaukpEQvvviiVq5cqWHDhikqKkpNTU0B23i9XkVHR0uSoqKiOoQpr9er%2BPj4oF8zJydHmZmZAW1Op1PNzR61tbVf5pGgM4SHhyk%2BPoaxAQCLnT79Rafs16rgFtIB6/HHH9fLL7%2BskpISTZ06VZKUnJysurq6gO0aGxv9y4LJyclqbGzs0D9ixIigXzcpKanDMqP05Zvn3Dn%2BEbejtrZ2xgYALBRqn8GhteD5N0pLS7Vx40atWLFCN998s7/d5XLpo48%2B0pkzZ/xtlZWVcrlc/v7Kykp/n8fjUU1Njb8fAADgYkIyYB0%2BfFirV6/WT3/6U6WlpcntdvsfGRkZ6tevnwoKClRbW6u1a9fqwIEDmj17tiQpOztb%2B/fv19q1a1VbW6uCggINGDCAWzQAAICghWTA%2BuMf/6i2tjY999xzmjBhQsAjPDxcq1evltvt1qxZs7Rt2zaVlZUpJSVFkjRgwAA9%2B%2Byzqqio0OzZs9XU1KSysjI5HA6LjwoAAHQXDh%2B3KO8yXINlPxERYUpIiD3v2GSt2m1RVQDQ87xx3/hO2a/Tac0tkkLyDBYAAICVCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCzkA5bX69X06dP1/vvv%2B9vq6%2Bv14x//WNdee61uuukm7dq1K%2BA5e/bs0fTp0%2BVyuTRv3jzV19d3ddkAAKAbC%2BmA1draqvvvv1%2B1tbX%2BNp/Pp9zcXPXt21cVFRWaMWOG8vLydPz4cUnS8ePHlZubq1mzZunVV19VYmKi7r33Xvl8PqsOAwAAdDMhG7Dq6ur0L//yL/rLX/4S0P7ee%2B%2Bpvr5eS5cu1ZAhQ3TXXXfp2muvVUVFhSRp06ZNuuaaazR//nz94z/%2Bo4qLi/U///M/2rt3rxWHAQAAuqGQDVh79%2B7V2LFjVV5eHtBeXV2tq6%2B%2BWr169fK3paWlqap680L/AAAKVklEQVSqyt%2Bfnp7u74uJidHIkSP9/QAAABcTYXUBneXWW289b7vb7VZSUlJAW58%2BfXTy5Mmg%2BoPR0NAgt9sd0OZ0OhUdfWXQ%2B0DXCA8PC/gKALBGRERofQ6HbMC6EI/Ho8jIyIC2yMhIeb3eoPqDUV5ertLS0oC2vLw85efnX2bV6Gzx8TFWlwAAPVpCQqzVJRjV4wJWVFSUmpqaAtq8Xq%2Bio6P9/V8PU16vV/Hx8UG/Rk5OjjIzMwPanE6nmps9amtrv8zK0RnCw8MUHx/D2ACAxU6f/qJT9mtVcOtxASs5OVl1dXUBbY2Njf5lweTkZDU2NnboHzFiRNCvkZSU1GGZUfryzXPuHP%2BI21FbWztjAwAWCrXP4NBa8AyCy%2BXSRx99pDNnzvjbKisr5XK5/P2VlZX%2BPo/Ho5qaGn8/AADAxfS4gJWRkaF%2B/fqpoKBAtbW1Wrt2rQ4cOKDZs2dLkrKzs7V//36tXbtWtbW1Kigo0IABAzR27FiLKwcAAN1FjwtY4eHhWr16tdxut2bNmqVt27aprKxMKSkpkqQBAwbo2WefVUVFhWbPnq2mpiaVlZXJ4XBYXDkAAOguHD5uUd5luAbLfiIiwpSQEHvesclatduiqgCg53njvvGdsl%2Bn05pbJPW4M1gAAACdjYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMirC7AjlpbW/XYY4/p3//93xUdHa358%2Bdr/vz5Vpd1XlmrdltdQtDeuG%2B81SUAANAlCFjnsXz5ch08eFAvvviijh8/rsWLFyslJUXTpk2zurRurTuFQQAAvg0C1te0tLRo06ZN%2BvWvf62RI0dq5MiRqq2t1UsvvUTAAgAAQeEarK85dOiQzp07p9TUVH9bWlqaqqur1d7ebmFlAACgu%2BAM1te43W4lJCQoMjLS39a3b1%2B1traqqalJiYmJF91HQ0OD3G53QJvT6VR09JXG6wUAIBRERITWOR8C1td4PJ6AcCXJ/73X6w1qH%2BXl5SotLQ1oGzNmjFasWKGkpCQzhf6/PxexbPltNDQ0qLy8XDk5OcbHBpePcbEvxsa%2BGBt7Ca24aEBUVFSHIPXV99HR0UHtIycnR5s3b/Y/SkpKtG/fvg5ntWA9t9ut0tJSxsZmGBf7Ymzsi7GxF85gfU1ycrJOnz6tc%2BfOKSLiyx%2BP2%2B1WdHS04uPjg9pHUlIS/3sAAKAH4wzW14wYMUIRERGqqqryt1VWVmrUqFEKC%2BPHBQAALo7E8DUxMTGaOXOmlixZogMHDuitt97Sb3/7W82bN8/q0gAAQDcRvmTJkiVWF2E348aNU01NjZ5%2B%2Bmm9%2B%2B67uvvuu5Wdnf2t9hkbG6uMjAzFxsYaqhKmMDb2xLjYF2NjX4yNfTh8Pp/P6iIAAABCCUuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWIZ5vV5Nnz5d77///gW3qamp0Zw5c%2BRyuZSdna2DBw92YYU9VzBjc8899%2Bi73/1uwOPtt9/uwip7js8%2B%2B0wLFy5URkaGJk6cqOLiYrW2tp53W%2BZM17qUsWHOdK2jR4/qJz/5iVJTUzVp0iStW7fugtsyb6xFwDKotbVV999/v2pray%2B4TUtLixYsWKD09HRt3rxZqampuuuuu9TS0tKFlfY8wYyNJB0%2BfFglJSXatWuX/zF%2B/PguqrLn8Pl8WrhwoTwej1566SWtXLlSb7/9tlatWtVhW%2BZM17qUsZGYM12pvb1dCxYsUEJCgl577TU99thjeu655/T666932JZ5YwM%2BGFFbW%2Bv7wQ9%2B4Pv%2B97/vGzZsmO%2B9994773abNm3yZWZm%2Btrb230%2Bn8/X3t7uu/HGG30VFRVdWW6PEuzYtLa2%2BkaMGOE7cuRIF1fY89TV1fmGDRvmc7vd/rbXX3/dN2HChA7bMme61qWMDXOma3322We%2Bf/3Xf/X99a9/9bfl5ub6Hn300Q7bMm%2BsxxksQ/bu3auxY8eqvLz8G7errq5WWlqaHA6HJMnhcGj06NGqqqrqijJ7pGDH5siRI3I4HBo4cGAXVdZzOZ1OrVu3Tn379g1o//zzzztsy5zpWpcyNsyZrpWUlKRVq1YpLi5OPp9PlZWV2rdvnzIyMjpsy7yxXoTVBYSKW2%2B9Najt3G63hg4dGtDWp0%2Bfiy5d4fIFOzZHjhxRXFycFi1apL179%2Brv/u7vlJ%2BfrxtuuKGTK%2Bx54uPjNXHiRP/37e3t2rBhg8aNG9dhW%2BZM17qUsWHOWCczM1PHjx/X5MmTNXXq1A79zBvrcQari3k8HkVGRga0RUZGyuv1WlQRvnLkyBGdOXNGEyZM0Lp163TDDTfonnvu0Ycffmh1aSGvpKRENTU1%2BtnPftahjzljrW8aG%2BaMdZ555hk9//zz%2Bvjjj1VcXNyhn3ljPc5gdbGoqKgOb3Cv16vo6GiLKsJX7r33Xt1%2B%2B%2B3q3bu3JGn48OH66KOP9Morr2jUqFEWVxe6SkpK9OKLL2rlypUaNmxYh37mjHUuNjbMGet89fNtbW3VAw88oEWLFgUEKuaN9TiD1cWSk5PV2NgY0NbY2KikpCSLKsJXwsLC/P9QfOWqq67SZ599ZlFFoe/xxx/XCy%2B8oJKSkvMuc0jMGasEMzbMma7V2Niot956K6Bt6NChOnv2bIdr5Jg31iNgdTGXy6UPPvhAPp9P0pe/Er1//365XC6LK8ODDz6ogoKCgLZDhw7pqquusqii0FZaWqqNGzdqxYoVuvnmmy%2B4HXOm6wU7NsyZrnXs2DHl5eUFBNiDBw8qMTFRiYmJAdsyb6xHwOoCbrdbZ86ckSRNmzZNzc3NKioqUl1dnYqKiuTxeJSVlWVxlT3T345NZmamXn/9dW3ZskVHjx5VaWmpKisrddttt1lcZeg5fPiwVq9erZ/%2B9KdKS0uT2%2B32PyTmjJUuZWyYM11r1KhRGjlypB566CHV1dVp586dKikp0d133y2JeWM7Ft4iImR9/V5Lw4YNC7j3SHV1tW/mzJm%2BUaNG%2BWbPnu376KOPrCizR7rY2Lzyyiu%2B733ve75rrrnGd8stt/j27t1rRZkhb82aNb5hw4ad9%2BHzMWesdKljw5zpWidPnvTl5ub6Ro8e7Rs/frzvueee89/rinljLw6f7//PHwIAAMAIlggBAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwLD/A9gQjjW0jJ9yAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6219582495932819262”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.226930314170308</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.58739636862375</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.665654101106848</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.6254576560216147</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.2154437862527763</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1585622437656293</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9253033667586965</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.477991202056956</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0321159550932983</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.274378024389501</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6219582495932819262”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.1050963168716856</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.4284891444618404</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6384970526461495</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.6401735834499231</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.665654101106848</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.8278648239005792</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8862800044383787</td> <td class=”number”>77</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.9892784893927247</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.226930314170308</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.2316033504102637</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_DIRSALES07”>PC_DIRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2791</td>

</tr> <tr>

<th>Unique (%)</th> <td>86.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>11.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>359</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>7.8463</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>252.75</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>1.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1557775367117343326”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1557775367117343326,#minihistogram-1557775367117343326”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1557775367117343326”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1557775367117343326”

aria-controls=”quantiles-1557775367117343326” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1557775367117343326” aria-controls=”histogram-1557775367117343326”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1557775367117343326” aria-controls=”common-1557775367117343326”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1557775367117343326” aria-controls=”extreme-1557775367117343326”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1557775367117343326”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.23447</td>

</tr> <tr>

<th>Q1</th> <td>1.8014</td>

</tr> <tr>

<th>Median</th> <td>4.26</td>

</tr> <tr>

<th>Q3</th> <td>9.4169</td>

</tr> <tr>

<th>95-th percentile</th> <td>24.294</td>

</tr> <tr>

<th>Maximum</th> <td>252.75</td>

</tr> <tr>

<th>Range</th> <td>252.75</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.6154</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.826</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.6346</td>

</tr> <tr>

<th>Kurtosis</th> <td>114.31</td>

</tr> <tr>

<th>Mean</th> <td>7.8463</td>

</tr> <tr>

<th>MAD</th> <td>6.7328</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.0222</td>

</tr> <tr>

<th>Sum</th> <td>22370</td>

</tr> <tr>

<th>Variance</th> <td>164.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1557775367117343326”>

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QAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZHrBuu%2B02Pffcczp16pTdqwYAALCF7QErJSVF69ev17Bhw3Tfffdp9%2B7dsizL7jIAAAC8xvaANX/%2BfP3973/XunXrFBgYqD/84Q%2B68cYbtWrVKn3xxRd2lwMAAGCc0xcrdTgcGjp0qIYOHara2lo9%2B%2ByzWrdunfLy8jR48GDNmjVLN910ky9KAwAAuGw%2BCViSVFlZqZdfflkvv/yyDh8%2BrMGDB%2BvWW29VRUWFHnzwQe3bt09LlizxVXkAAAA/m%2B0B66WXXtJLL72kvXv3qkuXLpowYYLWrFmjXr16uV/TrVs3LVu2jIAFAAD8ku0Ba8mSJRo%2BfLjWrl2rG264QQEBLS8Di42N1YwZM%2BwuDQAAwAjbA9a7776r8PBw1dTUuMPVgQMHNGDAAAUGBkqSBg8erMGDB9tdGgAAgBG2/xXh6dOnNXr0aD399NPusbvvvlvjx49XeXm53eUAAAAYZ3vA%2BtOf/qRf/vKXuuOOO9xjr776qrp166bs7Gy7ywEAADDO9oD1f//3f/rjH/%2BoyMhI91iXLl30wAMPaM%2BePXaXAwAAYJztAcvpdOrkyZMtxmtra7mjOwAAaBNsD1g33HCDsrKy9PXXX7vHysrKlJ2drdTUVLvLAQAAMM72vyJcuHCh7rjjDo0aNUqhoaGSpJMnT2rAgAFatGiR3eUAAAAYZ3vAioiI0F//%2Ble9//77KikpkdPp1K9%2B9Sv9%2Bte/lsPhsLscAAAA43zyUTmBgYFKTU3llCAAAGiTbA9YLpdLTzzxhPbv36/vvvuuxYXtb731lt0lAQAAGGV7wHrooYf08ccfa%2BzYsbrqqqvsXj0AAIDX2R6w9uzZo40bNyopKcnuVQMAANjC9ts0dOzYUREREcaW19DQoHHjxmnv3r3usaysLF1zzTUej%2B3bt7vnCwoKNHLkSMXFxSk9PV0nTpxwz1mWpRUrViglJUXJyclavny5mpubjdULAADaPtsD1vjx47Vx40Y1NTVd9rLq6%2Bt13333qaSkxGO8tLRU8%2BfP1%2B7du92PSZMmSfr%2Bg6WXLFmiefPmKT8/XydPnvS4PcTmzZtVUFCg3NxcrVmzRq%2B88oo2b9582bUCAID2w/ZThDU1NSooKNDbb7%2Btnj17KigoyGN%2B27ZtrVrOkSNHNH/%2B/PPe/b20tFRz5szx%2BDiec7Zv366bb75ZEyZMkCQtX75cw4cPV1lZmXr27Klt27YpIyPDfQrz/vvv1%2BrVqzVnzpxL3VQAANBO%2BeQ2DePGjbvsZXzwwQcaMmSI/uu//kvx8fHu8dOnT%2Bv48ePq1avXed9XXFys3/3ud%2B7n3bp1U0xMjIqLixUUFKTy8nJdf/317vnExEQdPXpUlZWVioqKuuy6AQBA22d7wMrOzjaynOnTp593vLS0VA6HQ%2BvXr9e7776rsLAw3XHHHbr11lsl6bxBKSIiQhUVFXK5XJLkMd%2B1a1dJUkVFBQELAAC0ik%2BOYFVWVurPf/6zvvjiCy1evFj79u1T3759FRsbe9nL/vzzz%2BVwOBQbG6sZM2Zo3759euihh9S5c2elpaWprq6uxWnJoKAgNTQ0qK6uzv38h3PS9xfTX8r2nQtr5zidHY0HtMBA2y%2BhuyxOp3/Ue66v/tbfKx199R566x301TvaS19tD1hfffWVpkyZos6dO%2Bv48eO699579eqrr2rRokXasmWL4uLiLmv5EyZM0PDhwxUWFiZJ6tevn7788kvt3LlTaWlpCg4ObhGWGhoaFBIS4hGmgoOD3f%2BWpJCQkFbXkJ%2Bfr9zcXI%2Bx9PR0ZWRk/OztagvCwzv5uoRLEhra%2Bq85Wo%2B%2Beg%2B99Q766h1tva%2B2B6zHHntMI0eOVFZWlgYPHixJevzxx7Vw4UKtWLFCzz777GUt3%2BFwuMPVObGxsdqzZ48kKTo6WlVVVR7zVVVVioyMVHR0tKTv7zbfo0cP978lnfeC%2BQuZOnWqRowY4THmdHZUdfWZS9uYn%2BBv6d/09ntLYGCAQkNDdPJkrZqauEWHKfTVe%2Bitd9BX77C7r7765d72gLV//37t2LHD44OdnU6n7rnnHk2ZMuWyl7969Wp9%2BOGH2rJli3vs0KFD7tOPcXFxKiws1MSJEyVJ5eXlKi8vV1xcnKKjoxUTE6PCwkJ3wCosLFRMTMwlnd6Liopq8XqX65QaG9v3N6i/bX9TU7Pf1ewP6Kv30FvvoK/e0db7anvAam5uPu%2BNO8%2BcOaPAwMDLXv7w4cOVl5enTZs2KS0tTbt379aLL77ovv3DtGnTdPvttys%2BPl4DBw7UsmXLdOONN6pnz57u%2BRUrVugXv/iFJGnlypW68847L7suAADQftgesIYNG6YNGzYoJyfHPVZTU6OcnBylpKRc9vIHDRqk1atXa82aNVq9erW6d%2B%2BulStXKiEhQZKUkJCgzMxMrVmzRt9%2B%2B62GDh2qpUuXut8/Z84c/fOf/9S8efMUGBioyZMna/bs2ZddFwAAaD8c1vnu1OlFx48f18yZM3Xq1CnV1NQoNjZWR48eVVhYmLZv367u3bvbWY5tXK5TxpfpdAYobcV7xpfrLa/dO9TXJbSK0xmg8PBOqq4%2B06YPX9uNvnoPvfUO%2Buoddvc1MvIqr6/jfGw/ghUdHa0XX3xRBQUF%2BvTTT9Xc3Kxp06Zp/Pjx6ty5s93lAAAAGOeT%2B2CFhITotttu88WqAQAAvM72gDVz5syLzrf2swgBAACuVLYHrB9fY9XY2KivvvpKhw8f1qxZs%2BwuBwAAwLgr5rMI165dq4qKCpurAQAAMO%2BKuRX4%2BPHj9dprr/m6DAAAgMt2xQSsDz/80MiNRgEAAHztirjI/fTp0/rss880ffp0u8sBAAAwzvaAFRMT4/E5hJL0b//2b5oxY4Z%2B85vf2F0OAACAcbYHrMcee8zuVQIAANjK9oC1b9%2B%2BVr/2%2Buuv92IlAAAA3mF7wLr99tvdpwh/%2BDGIPx5zOBz69NNP7S4PAADgstkesNavX6%2BsrCwtWLBAycnJCgoK0kcffaTMzEzdeuutGjNmjN0lAQAAGGX7bRqys7P18MMPa9SoUQoPD1enTp2UkpKizMxM7dy5U927d3c/AAAA/JHtAauysvK84alz586qrq62uxwAAADjbA9Y8fHxevzxx3X69Gn3WE1NjXJycvTrX//a7nIAAACMs/0arAcffFAzZ87UDTfcoF69esmyLH355ZeKjIzUtm3b7C4HAADAONsDVu/evfXqq6%2BqoKBApaWlkqTf/va3Gjt2rEJCQuwuBwAAwDjbA5YkXX311brtttv0zTffqGfPnpK%2Bv5s7AABAW2D7NViWZWnFihW6/vrrNW7cOFVUVGjhwoVasmSJvvvuO7vLAQAAMM72gPXss8/qpZde0n//938rKChIkjRy5Ei9%2Beabys3NtbscAAAA42wPWPn5%2BXr44Yc1ceJE993bx4wZo6ysLL3yyit2lwMAAGCc7QHrm2%2B%2BUf/%2B/VuM9%2BvXTy6Xy%2B5yAAAAjLM9YHXv3l0fffRRi/F3333XfcE7AACAP7P9rwjnzJmjRx99VC6XS5Zl6R//%2BIfy8/P17LPP6o9//KPd5QAAABhne8CaNGmSGhsb9dRTT6murk4PP/ywunTponvvvVfTpk2zuxwAAADjbA9YBQUFGj16tKZOnaoTJ07IsixFRETYXQYAAIDX2H4NVmZmpvti9i5duhCuAABAm2N7wOrVq5cOHz5s92oBAABsY/spwn79%2Bun%2B%2B%2B/Xxo0b1atXLwUHB3vMZ2dn210SAACAUbYHrC%2B%2B%2BEKJiYmSxH2vAABAm2RLwFq%2BfLnmzZunjh076tlnn7VjlQAAAD5jyzVYmzdvVm1trcfY3XffrcrKSjtWDwAAYCtbApZlWS3G9u3bp/r6ejtWDwAAYCvb/4oQAACgrSNgAQAAGGZbwHI4HHatCgAAwKdsu01DVlaWxz2vvvvuO%2BXk5KhTp04er%2BM%2BWAAAwN/ZErCuv/76Fve8SkhIUHV1taqrq%2B0oAQAAwDa2BCzufQUAANoTLnIHAAAwjIAFAABgGAELAADAMAIWAACAYX4fsBoaGjRu3Djt3bvXPVZWVqbZs2crPj5eY8aM0e7duz3e8/7772vcuHGKi4vTzJkzVVZW5jG/ZcsWpaamKiEhQYsXL27xOYoAAAAX49cBq76%2BXvfdd59KSkrcY5ZlKT09XV27dtWuXbs0fvx4zZs3T8eOHZMkHTt2TOnp6Zo4caJeeOEFdenSRffcc4/78xJff/115ebmKjMzU1u3blVxcbFycnJ8sn0AAMA/%2BW3AOnLkiKZMmaKvv/7aY3zPnj0qKytTZmamevfurblz5yo%2BPl67du2SJD3//PO67rrrdOedd6pPnz7Kzs7W0aNH9cEHH0iStm3bplmzZmn48OEaNGiQHn30Ue3atYujWAAAoNX8NmB98MEHGjJkiPLz8z3Gi4uLde2116pjx47uscTERBUVFbnnk5KS3HMhISEaMGCAioqK1NTUpI8%2B%2BshjPj4%2BXt99950OHTrk5S0CAABthW0flWPa9OnTzzvucrkUFRXlMRYREaGKioqfnD958qTq6%2Bs95p1Op8LCwtzvBwAA%2BCl%2BG7AupLa2VkFBQR5jQUFBamho%2BMn5uro69/MLvb81KisrW3w0kNPZsUWwu1yBgf51ANLp9I96z/XV3/p7paOv3kNvvYO%2Bekd76WubC1jBwcGqqanxGGtoaFCHDh3c8z8OSw0NDQoNDXV/GPX55kNCQlpdQ35%2BvnJzcz3G0tPTlZGR0epltEXh4Z1%2B%2BkVXkNDQ1n/N0Xr01XvorXfQV%2B9o631tcwErOjpaR44c8RirqqpyHz2Kjo5WVVVVi/n%2B/fsrLCxMwcHBqqqqUu/evSVJjY2NqqmpUWRkZKtrmDp1qkaMGOEx5nR2VHX1mZ%2BzSRfkb%2Bnf9PZ7S2BggEJDQ3TyZK2ampp9XU6bQV%2B9h956B331Drv76qtf7ttcwIqLi1NeXp7q6urcR60KCwuVmJjoni8sLHS/vra2VgcPHtS8efMUEBCggQMHqrCwUEOGDJEkFRUVyel0ql%2B/fq2uISoqqsXpQJfrlBob2/c3qL9tf1NTs9/V7A/oq/fQW%2B%2Bgr97R1vvqX4dAWiE5OVndunXTokWLVFJSory8PB04cECTJ0%2BWJE2aNEn79%2B9XXl6eSkpKtGjRIvXo0cMdqKZPn65NmzbpzTff1IEDB/TII49oypQpl3SKEAAAtG9tLmAFBgZq3bp1crlcmjhxol5%2B%2BWWtXbtWMTExkqQePXroySef1K5duzR58mTV1NRo7dq1cjgckqSxY8dq7ty5evjhh3XnnXdq0KBBWrBggS83CQAA%2BBmHde4W5vAql%2BuU8WU6nQFKW/Ge8eV6y2v3DvV1Ca3idAYoPLyTqqvPtOnD13ajr95Db72DvnqH3X2NjLzK6%2Bs4nzZ3BAsAAMDXCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjWZgPWG2%2B8oWuuucbjkZGRIUk6ePCgbrvtNsXFxWnSpEn6%2BOOPPd5bUFCgkSNHKi4uTunp6Tpx4oQvNgEAAPipNhuwjhw5ouHDh2v37t3uR1ZWls6ePau7775bSUlJ%2Bstf/qKEhATNnTtXZ8%2BelSQdOHBAS5Ys0bx585Sfn6%2BTJ09q0aJFPt4aAADgT9pswCotLVXfvn0VGRnpfoSGhurVV19VcHCwHnjgAfXu3VtLlixRp06d9Le//U2StH37dt18882aMGGC%2BvXrp%2BXLl%2Budd95RWVmZj7cIAAD4izYdsHr16tVivLi4WImJiXI4HJIkh8OhwYMHq6ioyD2flJTkfn23bt0UExOj4uJiW%2BoGAAD%2Br00GLMuy9MUXX2j37t0aNWqURo4cqRUrVqihoUEul0tRUVEer4%2BIiFBFRYUkqbKy8qLzAAAAP8Xp6wK84dixY6qtrVVQUJCeeOIJffPNN8rKylJdXZ17/IeCgoLU0NAgSapuVt7MAAAN0ElEQVSrq7vofGtUVlbK5XJ5jDmdHVsEt8sVGOhf%2Bdjp9I96z/XV3/p7paOv3kNvvYO%2Bekd76WubDFjdu3fX3r17dfXVV8vhcKh///5qbm7WggULlJyc3CIsNTQ0qEOHDpKk4ODg886HhIS0ev35%2BfnKzc31GEtPT3f/FWN7FR7eydclXJLQ0NZ/zdF69NV76K130FfvaOt9bZMBS5LCwsI8nvfu3Vv19fWKjIxUVVWVx1xVVZX76FJ0dPR55yMjI1u97qlTp2rEiBEeY05nR1VXn7mUTfhJ/pb%2BTW%2B/twQGBig0NEQnT9aqqanZ1%2BW0GfTVe%2Bitd9BX77C7r7765b5NBqz33ntP999/v95%2B%2B233kadPP/1UYWFhSkxM1NNPPy3LsuRwOGRZlvbv36/f//73kqS4uDgVFhZq4sSJkqTy8nKVl5crLi6u1euPiopqcTrQ5Tqlxsb2/Q3qb9vf1NTsdzX7A/rqPfTWO%2Bird7T1vvrXIZBWSkhIUHBwsB588EF9/vnneuedd7R8%2BXLdddddGj16tE6ePKlly5bpyJEjWrZsmWpra3XzzTdLkqZNm6aXXnpJzz//vA4dOqQHHnhAN954o3r27OnjrQIAAP6iTQaszp07a9OmTTpx4oQmTZqkJUuWaOrUqbrrrrvUuXNnbdiwwX2Uqri4WHl5eerYsaOk78NZZmam1q5dq2nTpunqq69Wdna2j7cIAAD4E4dlWZavi2gPXK5TxpfpdAYobcV7xpfrLa/dO9TXJbSK0xmg8PBOqq4%2B06YPX9uNvnoPvfUO%2Buoddvc1MvIqr6/jfNrkESwAAABfImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMOcvi4A7cfNT/yvr0u4JG/cn%2BrrEgAAfoojWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAes86uvrtXjxYiUlJWnYsGF65plnfF0SAADwI9ym4TyWL1%2Bujz/%2BWFu3btWxY8e0cOFCxcTEaPTo0b4uDQAA%2BAEC1o%2BcPXtWzz//vJ5%2B%2BmkNGDBAAwYMUElJiXbs2EHAamfSVrzn6xIuyWv3DvV1CQCAf%2BEU4Y8cOnRIjY2NSkhIcI8lJiaquLhYzc3NPqwMAAD4C45g/YjL5VJ4eLiCgoLcY127dlV9fb1qamrUpUuXn1xGZWWlXC6Xx5jT2VFRUVFGaw0MJB/j//OnO%2BVzl3yzzv0s4GeCWfTVO9pLXwlYP1JbW%2BsRriS5nzc0NLRqGfn5%2BcrNzfUYmzdvnv7whz%2BYKfJfKisrNesXJZo6darx8NaeVVZWKj8/n74a9sO%2Bhod38nU5bUplZaW2bt3IPmsYffWO9tLXth0ff4bg4OAWQerc8w4dOrRqGVOnTtVf/vIXj8fUqVON1%2BpyuZSbm9viaBkuD331DvrqPfTWO%2Bird7SXvnIE60eio6NVXV2txsZGOZ3ft8flcqlDhw4KDQ1t1TKioqLadCoHAAAXxxGsH%2Bnfv7%2BcTqeKiorcY4WFhRo4cKACAmgXAAD4aSSGHwkJCdGECRP0yCOP6MCBA3rzzTf1zDPPaObMmb4uDQAA%2BInARx555BFfF3GlSUlJ0cGDB7Vy5Ur94x//0O9//3tNmjTJ12WdV6dOnZScnKxOnbho2CT66h301XvorXfQV%2B9oD311WJZl%2BboIAACAtoRThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFh%2Bqr6%2BXosXL1ZSUpKGDRumZ555xtcl%2BaU33nhD11xzjccjIyNDknTw4EHddtttiouL06RJk/Txxx/7uNorX0NDg8aNG6e9e/e6x8rKyjR79mzFx8drzJgx2r17t8d73n//fY0bN05xcXGaOXOmysrK7C7bL5yvt1lZWS323%2B3bt7vnCwoKNHLkSMXFxSk9PV0nTpzwRelXpOPHjysjI0PJyclKTU1Vdna26uvrJbHPXo6L9bXd7a8W/FJmZqZ1yy23WB9//LH1P//zP1ZCQoL12muv%2Bbosv7Nu3Tpr7ty5VmVlpfvx7bffWmfOnLGGDh1qPfbYY9aRI0espUuXWv/%2B7/9unTlzxtclX7Hq6uqs9PR0q2/fvtaePXssy7Ks5uZm65ZbbrHmz59vHTlyxFq/fr0VFxdnHT161LIsyzp69KgVHx9vbdq0yTp8%2BLD1n//5n9a4ceOs5uZmX27KFed8vbUsy5o9e7a1YcMGj/337NmzlmVZVnFxsTVo0CDrr3/9q/Xpp59aM2bMsO6%2B%2B25fbcIVpbm52ZoyZYp11113WYcPH7b27dtnpaWlWY899hj77GW4WF8tq/3trwQsP3TmzBlr4MCBHj9o165da82YMcOHVfmn%2BfPnWytXrmwx/vzzz1sjRoxw/9Bsbm620tLSrF27dtldol8oKSmxfvOb31i33HKLRwh4//33rfj4eI9gOmvWLGvNmjWWZVnWE0884bHfnj171kpISPDYt9u7C/XWsiwrNTXVeu%2B99877vgULFlgLFy50Pz927Jh1zTXXWF9//bXXa77SHTlyxOrbt6/lcrncY6%2B88oo1bNgw9tnLcLG%2BWlb72185ReiHDh06pMbGRiUkJLjHEhMTVVxcrObmZh9W5n9KS0vVq1evFuPFxcVKTEyUw%2BGQJDkcDg0ePFhFRUU2V%2BgfPvjgAw0ZMkT5%2Bfke48XFxbr22mvVsWNH91hiYqK7j8XFxUpKSnLPhYSEaMCAAfT5By7U29OnT%2Bv48ePn3X%2Bllr3t1q2bYmJiVFxc7M1y/UJkZKQ2btyorl27eoyfPn2affYyXKyv7XF/dfq6AFw6l8ul8PBwBQUFuce6du2q%2Bvp61dTUqEuXLj6szn9YlqUvvvhCu3fv1oYNG9TU1KTRo0crIyNDLpdLv/rVrzxeHxERoZKSEh9Ve2WbPn36ecddLpeioqI8xiIiIlRRUdGqeVy4t6WlpXI4HFq/fr3effddhYWF6Y477tCtt94qSaqsrKS3FxAaGqrU1FT38%2BbmZm3fvl0pKSnss5fhYn1tj/srAcsP1dbWeoQrSe7nDQ0NvijJLx07dszdyyeeeELffPONsrKyVFdXd8Ee099L81N9pM8/3%2Beffy6Hw6HY2FjNmDFD%2B/bt00MPPaTOnTsrLS1NdXV19LaVcnJydPDgQb3wwgvasmUL%2B6whP%2BzrJ5980u72VwKWHwoODm6x05173qFDB1%2BU5Je6d%2B%2BuvXv36uqrr5bD4VD//v3V3NysBQsWKDk5%2Bbw9pr%2BXJjg4WDU1NR5jP%2Bzjhfbl0NBQ22r0VxMmTNDw4cMVFhYmSerXr5%2B%2B/PJL7dy5U2lpaRfsbUhIiC/KvWLl5ORo69atWrVqlfr27cs%2Ba8iP%2B9qnT592t79yDZYfio6OVnV1tRobG91jLpdLHTp04Jv8EoWFhbmvs5Kk3r17q76%2BXpGRkaqqqvJ4bVVVVYtD2Li46Ojoi/bxQvORkZG21eivHA6H%2Bz%2Brc2JjY3X8%2BHFJ9LY1li5dqs2bNysnJ0ejRo2SxD5rwvn62h73VwKWH%2Brfv7%2BcTqfHRZWFhYUaOHCgAgL4krbWe%2B%2B9pyFDhqi2ttY99umnnyosLEyJiYn68MMPZVmWpO%2Bv19q/f7/i4uJ8Va5fiouL0yeffKK6ujr3WGFhobuPcXFxKiwsdM/V1tbq4MGD9LkVVq9erdmzZ3uMHTp0SLGxsZJa9ra8vFzl5eX09l9yc3P13HPP6fHHH9fYsWPd4%2Byzl%2BdCfW2X%2B6uP/4oRP9NDDz1kjR071iouLrbeeOMNa/Dgwdbrr7/u67L8yqlTp6zU1FTrvvvus0pLS623337bGjZsmJWXl2edOnXKSklJsZYuXWqVlJRYS5cutYYOHcp9sFrhh7cSaGxstMaMGWPde%2B%2B91uHDh60NGzZY8fHx7nsKlZWVWQMHDrQ2bNjgvqfQLbfcwj2FLuCHvS0uLrauvfZaa%2BPGjdZXX31l7dixw7ruuuus/fv3W5ZlWfv377cGDBhg/fnPf3bfV2ju3Lm%2BLP%2BKceTIEat///7WqlWrPO7JVFlZyT57GS7W1/a4vxKw/NTZs2etBx54wIqPj7eGDRtmbd682dcl%2BaXDhw9bs2fPtuLj462hQ4daTz75pPsHZXFxsTVhwgRr4MCB1uTJk61PPvnEx9X6hx/fq%2BnLL7%2B0fvvb31rXXXedNXbsWOt///d/PV7/9ttvWzfddJM1aNAga9asWX593xtv%2B3Fv33jjDeuWW26xBg4caI0ePbrFL1m7du2y/uM//sOKj4%2B30tPTrRMnTthd8hVpw4YNVt%2B%2Bfc/7sCz22Z/rp/ra3vZXh2X96xwIAAAAjOCCHQAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAw7P8B7733ZNqVxbsAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1557775367117343326”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.36986301369863</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.8873917228103947</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.379800853485063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.9292730844793713</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.00744710193467</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.404255319148938</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.335085591050007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.363207547169812</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>34.85265382621526</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2780)</td> <td class=”number”>2780</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>359</td> <td class=”number”>11.2%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1557775367117343326”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>60</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0065433977770769074</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006959996616370876</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.015409745122815668</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.020357995348198064</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>110.999099909991</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>147.31876592745215</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>178.27586206896552</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>250.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>252.74725274725276</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_DIRSALES12”>PC_DIRSALES12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2818</td>

</tr> <tr>

<th>Unique (%)</th> <td>87.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>313</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>8.2608</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>203.91</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8587877438876342242”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8587877438876342242,#minihistogram8587877438876342242”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8587877438876342242”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8587877438876342242”

aria-controls=”quantiles8587877438876342242” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8587877438876342242” aria-controls=”histogram8587877438876342242”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8587877438876342242” aria-controls=”common8587877438876342242”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8587877438876342242” aria-controls=”extreme8587877438876342242”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8587877438876342242”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.22348</td>

</tr> <tr>

<th>Q1</th> <td>1.6911</td>

</tr> <tr>

<th>Median</th> <td>4.3883</td>

</tr> <tr>

<th>Q3</th> <td>9.8662</td>

</tr> <tr>

<th>95-th percentile</th> <td>27.902</td>

</tr> <tr>

<th>Maximum</th> <td>203.91</td>

</tr> <tr>

<th>Range</th> <td>203.91</td>

</tr> <tr>

<th>Interquartile range</th> <td>8.1751</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.897</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.5612</td>

</tr> <tr>

<th>Kurtosis</th> <td>50.556</td>

</tr> <tr>

<th>Mean</th> <td>8.2608</td>

</tr> <tr>

<th>MAD</th> <td>7.3061</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>5.4904</td>

</tr> <tr>

<th>Sum</th> <td>23932</td>

</tr> <tr>

<th>Variance</th> <td>166.32</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8587877438876342242”>

<img 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EbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMJ8XrKFDh%2Br111/Xzz//7OuHBgAA8AmfF6zOnTtr8eLF6tq1qx555BFt3LhRlmX5egwAAIDLxucFa%2BLEifrkk0%2B0cOFCBQUF6aGHHlL37t313HPPaf/%2B/b4eBwAAwDinPx7U4XCoS5cu6tKli4qKivTKK69o4cKFSk9PV4cOHTRq1CjdfPPN/hgNAADgkvmlYElSfn6%2B3n33Xb377rvas2ePOnTooIEDB%2BrIkSN64okntHXrVk2bNs1f4wEAAPxmPi9Y69ev1/r167V582Y1aNBAAwYM0Pz589WiRQvPdRo3bqxZs2ZRsAAAQEDyecGaNm2aevTooQULFujGG29UrVqVDwNr2bKlRowY4evRAAAAjPB5wfrss89Uv359FRYWesrV9u3b1a5dOwUFBUmSOnTooA4dOvh6NAAAACN8/luEJ0%2BeVN%2B%2BffXiiy961saNG6c77rhDhw8f9vU4AAAAxvm8YP3tb3/TH//4R917772etffff1%2BNGzdWSkqKr8cBAAAwzucF6//%2B7//02GOPKSIiwrPWoEEDTZ48WZs2bfL1OAAAAMb5vGA5nU6dOHGi0npRURFndAcAALbg84J14403aubMmfr%2B%2B%2B89a3l5eUpJSVG3bt18PQ4AAIBxPv8twilTpujee%2B9Vnz59FBYWJkk6ceKE2rVrp6lTp/p6HAAAAON8vgerYcOGevvtt5Wenq7x48crMTFRy5Yt09q1a72Oy6qp0tJS9e/fX5s3b/aszZw5U9dee63X16pVqzzbMzIy1KtXL7lcLiUmJurYsWOebZZlac6cOercubM6duyo2bNnq6Ki4tJCAwCA3xW//KmcoKAgdevW7ZI/EiwpKdHEiROVk5PjtZ6bm6uJEydq4MCBnrV69epJ%2BuWcW9OmTdPTTz%2Bt6OhozZo1S1OnTtWSJUskSS%2B//LIyMjKUlpamsrIyTZo0SQ0bNtTYsWMvaVYAAPD74fOC5Xa79fzzz2vbtm06c%2BZMpQPbP/744xrdz969ezVx4sQqD4zPzc3V2LFjq9wjtmrVKt1yyy0aMGCAJGn27Nnq0aOH8vLy1Lx5c61cuVJJSUlKSEiQJD366KOaN28eBQsAANSYzwvWk08%2BqZ07d6pfv376wx/%2B8JvvZ8uWLerUqZP%2B%2Bte/KjY21rN%2B8uRJHT161OtvG/5adna27r//fs/lxo0bq0mTJsrOzlZwcLAOHz6sG264wbM9Pj5eBw8eVH5%2BviIjI3/zvAAA4PfD5wVr06ZNWrp0qWcP0W911113Vbmem5srh8OhxYsX67PPPlN4eLjuvfdez8eFVRWlhg0b6siRI3K73ZLktb1Ro0aSpCNHjlCwAABAjfi8YNWpU0cNGza8bPe/b98%2BORwOzx%2BM3rp1q5588knVq1dPvXv3VnFxsYKDg71uExwcrNLSUhUXF3su/3qb9MvB9DWVn5/vKWtnOZ11jBe0oCCf/47CJXE6q5/3bKZAy1YdO%2BayYybJnrnsmEmyZy47ZpLsm%2BtCfF6w7rjjDi1dulTJycmeP%2B5s0oABA9SjRw%2BFh4dLkqKjo3XgwAGtXr1avXv3VkhISKWyVFpaqtDQUK8yFRIS4vlvSQoNDa3xDGvWrFFaWprXWmJiopKSkn5zLjuoX79uja8bFlbz73cgsWMuO2aS7JnLjpkke%2BayYybJvrmq4vOCVVhYqIyMDP3jH/9Q8%2BbNK%2B1NWrly5SXdv8Ph8JSrs1q2bOn5MzxRUVEqKCjw2l5QUKCIiAhFRUVJ%2BuVA/GbNmnn%2BW9JFnUJi2LBh6tmzp9ea01lHx4%2Bfurgw1Qi0nwRqkj8oqJbCwkJ14kSRysvtc3oMO%2BayYybJnrnsmEmyZy47ZpL8m%2Btifrg3yS%2Bnaejfv/9lu%2B958%2Bbpq6%2B%2B0vLlyz1ru3fvVsuWLSVJLpdLmZmZGjRokCTp8OHDOnz4sFwul6KiotSkSRNlZmZ6ClZmZqaaNGlyUR/vRUZGVrq%2B2/2zysrs82L5LS4mf3l5hS2/X3bMZcdMkj1z2TGTZM9cdswk2TdXVXxesFJSUi7r/ffo0UPp6elatmyZevfurY0bN%2Bqdd97x7BkbPny4Ro4cqdjYWMXExGjWrFnq3r27mjdv7tk%2BZ84cXXPNNZKkuXPnasyYMZd1ZgAAYC9%2B2YOVn5%2BvN954Q/v379fjjz%2BurVu3qk2bNp69TJeiffv2mjdvnubPn6958%2BapadOmmjt3ruLi4iRJcXFxSk5O1vz58/XTTz%2BpS5cumjFjhuf2Y8eO1Y8//qgJEyYoKChIQ4YM0ejRoy95LgAA8PvhsKo6U%2Bdl9N133%2BnOO%2B9UvXr1dPToUX3wwQdKTU3V559/ruXLl8vlcvlyHJ9xu382fp9OZy31nvO58fu9XD54uEu113E6a6l%2B/bo6fvyUrXYj2zGXHTNJ9sxlx0ySPXPZMZPk31wREb/9nJuXwudHST/zzDPq1auXNmzYoKuuukqS9Oyzz6pnz56aM2eOr8cBAAAwzucFa9u2bbr33nvlcDg8a06nUw8%2B%2BKB27drl63EAAACM83nBqqioUEVF5d2Dp06duiznxQIAAPA1nxesrl27asmSJV4lq7CwUKmpqercubOvxwEAADDO5wXrscce086dO9W1a1eVlJTogQceUI8ePfTDDz9oypQpvh4HAADAOJ%2BfpiEqKkrvvPOOMjIy9M0336iiokLDhw/XHXfcoXr16vl6HAAAAOP8ch6s0NBQDR061B8PDQAAcNn5vGDdc889F9x%2BqX%2BLEAAAwN98XrCaNm3qdbmsrEzfffed9uzZo1GjRvl6HAAAAOOumL9FuGDBAh05csTH0wAAAJjn898iPJ877rhDH3zwgb/HAAAAuGRXTMH66quvONEoAACwhSviIPeTJ0/q22%2B/1V133eXrcQAAAIzzecFq0qSJ198hlKSrrrpKI0aM0O233%2B7rcQAAAIzzecF65plnfP2QAAAAPuXzgrV169YaX/eGG264jJMAAABcHj4vWCNHjvR8RGhZlmf93DWHw6FvvvnG1%2BMBAABcMp8XrMWLF2vmzJmaNGmSOnbsqODgYO3YsUPJyckaOHCgbr31Vl%2BPBAAAYJTPT9OQkpKip556Sn369FH9%2BvVVt25dde7cWcnJyVq9erWaNm3q%2BQIAAAhEPi9Y%2Bfn5VZanevXq6fjx474eBwAAwDifF6zY2Fg9%2B%2ByzOnnypGetsLBQqamp%2Brd/%2BzdfjwMAAGCcz4/BeuKJJ3TPPffoxhtvVIsWLWRZlg4cOKCIiAitXLnS1%2BMAAAAY5/OC1apVK73//vvKyMhQbm6uJOnuu%2B9Wv379FBoa6utxAAAAjPN5wZKkq6%2B%2BWkOHDtUPP/yg5s2bS/rlbO4AAAB24PNjsCzL0pw5c3TDDTeof//%2BOnLkiKZMmaJp06bpzJkzvh4HAADAOJ8XrFdeeUXr16/Xf//3fys4OFiS1KtXL23YsEFpaWm%2BHgcAAMA4nxesNWvW6KmnntKgQYM8Z2%2B/9dZbNXPmTL333nu%2BHgcAAMA4nxesH374QW3btq20Hh0dLbfb7etxAAAAjPN5wWratKl27NhRaf2zzz7zHPAOAAAQyHz%2BW4Rjx47V008/LbfbLcuy9OWXX2rNmjV65ZVX9Nhjj/l6HAAAAON8XrAGDx6ssrIyLVq0SMXFxXrqqafUoEEDPfzwwxo%2BfLivxwEAADDO5wUrIyNDffv21bBhw3Ts2DFZlqWGDRv6egwAAIDLxufHYCUnJ3sOZm/QoAHlCgAA2I7PC1aLFi20Z88eXz8sAACAz/j8I8Lo6Gg9%2BuijWrp0qVq0aKGQkBCv7SkpKb4eCQAAwCifF6z9%2B/crPj5ekjjvFQAAsCWfFKzZs2drwoQJqlOnjl555RVfPCQAAIDf%2BOQYrJdffllFRUVea%2BPGjVN%2Bfr4vHh4AAMCnfFKwLMuqtLZ161aVlJT44uEBAAB8yue/RQgAAGB3FCwAAADDfFawHA6Hrx4KAADAr3x2moaZM2d6nfPqzJkzSk1NVd26db2ux3mwAABAoPNJwbrhhhsqnfMqLi5Ox48f1/Hjx30xAgAAgM/4pGBx7isAAPB7wkHuAAAAhgV8wSotLVX//v21efNmz1peXp5Gjx6t2NhY3Xrrrdq4caPXbb744gv1799fLpdL99xzj/Ly8ry2L1%2B%2BXN26dVNcXJwef/zxSidJBQAAuJCALlglJSV65JFHlJOT41mzLEuJiYlq1KiR1q1bpzvuuEMTJkzQoUOHJEmHDh1SYmKiBg0apDfffFMNGjTQgw8%2B6DkZ6ocffqi0tDQlJydrxYoVys7OVmpqql/yAQCAwBSwBWvv3r2688479f3333utb9q0SXl5eUpOTlarVq00fvx4xcbGat26dZKktWvX6vrrr9eYMWPUunVrpaSk6ODBg9qyZYskaeXKlRo1apR69Oih9u3b6%2Bmnn9a6devYiwUAAGosYAvWli1b1KlTJ61Zs8ZrPTs7W9ddd53q1KnjWYuPj1dWVpZne0JCgmdbaGio2rVrp6ysLJWXl2vHjh1e22NjY3XmzBnt3r37MicCAAB24bPzYJl21113VbnudrsVGRnptdawYUMdOXKk2u0nTpxQSUmJ13an06nw8HDP7WsiPz%2B/0mkpnM46lR73UgUFBVY/djqrn/dspkDLVh075rJjJsmeueyYSbJnLjtmkuyb60ICtmCdT1FRkYKDg73WgoODVVpaWu324uJiz%2BXz3b4m1qxZo7S0NK%2B1xMREJSUl1fg%2B7Kh%2B/brVX%2BlfwsJCL%2BMk/mPHXHbMJNkzlx0zSfbMZcdMkn1zVcV2BSskJESFhYVea6Wlpapdu7Zn%2B7llqbS0VGFhYZ4zzVe1PTS05v8ohg0bpp49e3qtOZ11dPz4qRrfR00E2k8CNckfFFRLYWGhOnGiSOXlFT6YyjfsmMuOmSR75rJjJsmeueyYSfJvrov54d4k2xWsqKgo7d2712utoKDA8/FcVFSUCgoKKm1v27atwsPDFRISooKCArVq1UqSVFZWpsLCQkVERNR4hsjIyEofB7rdP6uszD4vlt/iYvKXl1fY8vtlx1x2zCTZM5cdM0n2zGXHTJJ9c1UlsHaB1IDL5dLXX3/t%2BbhPkjIzM%2BVyuTzbMzMzPduKioq0a9cuuVwu1apVSzExMV7bs7Ky5HQ6FR0d7bsQAAAgoNmuYHXs2FGNGzfW1KlTlZOTo/T0dG3fvl1DhgyRJA0ePFjbtm1Tenq6cnJyNHXqVDVr1kydOnWS9MvB88uWLdOGDRu0fft2TZ8%2BXXfeeedFfUQIAAB%2B32xXsIKCgrRw4UK53W4NGjRI7777rhYsWKAmTZpIkpo1a6YXXnhB69at05AhQ1RYWKgFCxbI4XBIkvr166fx48frqaee0pgxY9S%2BfXtNmjTJn5EAAECAscUxWN9%2B%2B63X5T/%2B8Y9atWrVea9/00036aabbjrv9nHjxmncuHHG5gMAAL8vttuDBQAA4G8ULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMy2Beujjz7Stdde6/WVlJQkSdq1a5eGDh0ql8ulwYMHa%2BfOnV63zcjIUK9eveRyuZSYmKhjx475IwIAAAhQti1Ye/fuVY8ePbRx40bP18yZM3X69GmNGzdOCQkJeuuttxQXF6fx48fr9OnTkqTt27dr2rRpmjBhgtasWaMTJ05o6tSpfk4DAAACiW0LVm5urtq0aaOIiAjPV1hYmN5//32FhIRo8uTJatWqlaZNm6a6devqf//3fyVJq1at0i233KIBAwYoOjpas2fP1qeffqq8vDw/JwIAAIHC1gWrRYt6PyBqAAATe0lEQVQWldazs7MVHx8vh8MhSXI4HOrQoYOysrI82xMSEjzXb9y4sZo0aaLs7GyfzA0AAAKf098DXA6WZWn//v3auHGjlixZovLycvXt21dJSUlyu93685//7HX9hg0bKicnR5KUn5%2BvyMjIStuPHDlS48fPz8%2BX2%2B32WnM661S630sVFBRY/djprH7es5kCLVt17JjLjpkke%2BayYybJnrnsmEmyb64LsWXBOnTokIqKihQcHKznn39eP/zwg2bOnKni4mLP%2Bq8FBwertLRUklRcXHzB7TWxZs0apaWlea0lJiZ6DrL/vapfv26NrxsWFnoZJ/EfO%2BayYybJnrnsmEmyZy47ZpLsm6sqtixYTZs21ebNm3X11VfL4XCobdu2qqio0KRJk9SxY8dKZam0tFS1a9eWJIWEhFS5PTS05v8ohg0bpp49e3qtOZ11dPz4qd%2BYqGqB9pNATfIHBdVSWFioTpwoUnl5hQ%2Bm8g075rJjJsmeueyYSbJnLjtmkvyb62J%2BuDfJlgVLksLDw70ut2rVSiUlJYqIiFBBQYHXtoKCAs/Hd1FRUVVuj4iIqPFjR0ZGVvo40O3%2BWWVl9nmx/BYXk7%2B8vMKW3y875rJjJsmeueyYSbJnLjtmkuybqyqBtQukhj7//HN16tRJRUVFnrVvvvlG4eHhio%2BP11dffSXLsiT9crzWtm3b5HK5JEkul0uZmZme2x0%2BfFiHDx/2bAcAAKiOLQtWXFycQkJC9MQTT2jfvn369NNPNXv2bN13333q27evTpw4oVmzZmnv3r2aNWuWioqKdMstt0iShg8frvXr12vt2rXavXu3Jk%2BerO7du6t58%2BZ%2BTgUAAAKFLQtWvXr1tGzZMh07dkyDBw/WtGnTNGzYMN13332qV6%2BelixZoszMTA0aNEjZ2dlKT09XnTp1JP1SzpKTk7VgwQINHz5cV199tVJSUvycCAAABBLbHoPVunVrvfzyy1Vua9%2B%2Bvd5%2B%2B%2B3z3nbQoEEaNGjQ5RoNAADYnC33YAEAAPgTBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGOf09AH4/bnn%2Bn/4e4aJ88HAXf48AAAhQ7MECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjm9PcAV6KSkhI9/fTT%2Bvvf/67atWtrzJgxGjNmjL/Hgo/d8vw//T3CRfng4S7%2BHgEA8C8UrCrMnj1bO3fu1IoVK3To0CFNmTJFTZo0Ud%2B%2Bff09GgAACAAUrHOcPn1aa9eu1Ysvvqh27dqpXbt2ysnJ0auvvkrBAgAANcIxWOfYvXu3ysrKFBcX51mLj49Xdna2Kioq/DgZAAAIFOzBOofb7Vb9%2BvUVHBzsWWvUqJFKSkpUWFioBg0aVHsf%2Bfn5crvdXmtOZx1FRkYanTUoiH6M/y/QjhkLJB892s3fI1ySs%2B8VdnvPsGMuO2aS7JvrQihY5ygqKvIqV5I8l0tLS2t0H2vWrFFaWprX2oQJE/TQQw%2BZGfJf8vPzNeqaHA0bNsx4efOX/Px8rVmzxlaZJHvmsmMmyZ658vPztWLFUltlkuyZy46ZJPvmupDfT5WsoZCQkEpF6uzl2rVr1%2Bg%2Bhg0bprfeesvra9iwYcZndbvdSktLq7S3LJDZMZNkz1x2zCTZM5cdM0n2zGXHTJJ9c10Ie7DOERUVpePHj6usrExO5y/fHrfbrdq1ayssLKxG9xEZGfm7aegAAKAy9mCdo23btnI6ncrKyvKsZWZmKiYmRrVq8e0CAADVozGcIzQ0VAMGDND06dO1fft2bdiwQS%2B99JLuuecef48GAAACRND06dOn%2B3uIK03nzp21a9cuzZ07V19%2B%2BaX%2B8pe/aPDgwf4eq0p169ZVx44dVbduXX%2BPYowdM0n2zGXHTJI9c9kxk2TPXHbMJNk31/k4LMuy/D0EAACAnfARIQAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwClaAKikp0eOPP66EhAR17dpVL730kr9HumhHjx5VUlKSOnbsqG7duiklJUUlJSWSpJkzZ%2Braa6/1%2Blq1apWfJ66Zjz76qNLsSUlJkqRdu3Zp6NChcrlcGjx4sHbu3Onnaav31ltvVcpz7bXXKjo6WpL0wAMPVNr2ySef%2BHnqCystLVX//v21efNmz1peXp5Gjx6t2NhY3Xrrrdq4caPXbb744gv1799fLpdL99xzj/Ly8nw99gVVlSkrK0v/%2BZ//qbi4OPXp00dr1671us3tt99e6bnbs2ePr0e/oKpyVff%2BkJGRoV69esnlcikxMVHHjh3zx%2BjndW6mxx57rMrX2K//RFtCQkKl7adOnfJXBC8Xei8P9NfVJbEQkJKTk63bbrvN2rlzp/X3v//diouLsz744AN/j1VjFRUV1p133mndd9991p49e6ytW7davXv3tp555hnLsixr9OjR1pIlS6z8/HzP1%2BnTp/08dc0sXLjQGj9%2BvNfsP/30k3Xq1CmrS5cu1jPPPGPt3bvXmjFjhvXv//7v1qlTp/w98gUVFRV5ZTl06JDVu3dva9asWZZlWVbv3r2t9evXe12npKTEz1OfX3FxsZWYmGi1adPG2rRpk2VZv/x7vO2226yJEydae/futRYvXmy5XC7r4MGDlmVZ1sGDB63Y2Fhr2bJl1p49e6z/%2Bq//svr3729VVFT4M4pHVZny8/OthIQEa%2B7cudb%2B/futjIwMKyYmxvrkk08sy7KssrIyKyYmxtqyZYvXc3fmzBk/JvFWVS7LuvD7Q3Z2ttW%2BfXvr7bfftr755htrxIgR1rhx4/wVoZKqMp04ccIry1dffWVdf/311kcffWRZlmUdOXLEatOmjfX99997Xe9K%2BPd3offyQH9dXSoKVgA6deqUFRMT4/WGs2DBAmvEiBF%2BnOri7N2712rTpo3ldrs9a%2B%2B9957VtWtXy7Isq1u3btbnn3/ur/EuycSJE625c%2BdWWl%2B7dq3Vs2dPz5tHRUWF1bt3b2vdunW%2BHvGSLF682OrVq5dVUlJilZSUWG3btrX27dvn77FqJCcnx7r99tut2267zet/cF988YUVGxvrVXZHjRplzZ8/37Isy3r%2B%2Bee9Xl%2BnT5%2B24uLivF6D/nK%2BTK%2B99prVt29fr%2Bs%2B%2BeST1iOPPGJZlmUdOHDAio6OtoqLi30%2Bc02cL5dlXfj9YdKkSdaUKVM8lw8dOmRde%2B211vfff3/ZZ67OhTL92pgxY6xHH33Uc/mf//yn1aVLF1%2BNeVEu9F4eyK8rE/iIMADt3r1bZWVliouL86zFx8crOztbFRUVfpys5iIiIrR06VI1atTIa/3kyZM6efKkjh49qhYtWvhnuEuUm5tb5ezZ2dmKj4%2BXw%2BGQJDkcDnXo0EFZWVk%2BnvC3Kyws1IsvvqiJEycqODhY%2B/btk8PhUPPmzf09Wo1s2bJFnTp10po1a7zWs7Ozdd1116lOnTqetfj4eM9zk52drYSEBM%2B20NBQtWvX7op47s6X6exHNec6efKkJGnv3r1q3LixQkJCfDLnxTpfrureH859rho3bqwmTZooOzv7co5bI%2BfL9Gtffvmltm7dqkceecSztnfvXv3pT3/yxYgX7ULv5YH8ujLB6e8BcPHcbrfq16%2Bv4OBgz1qjRo1UUlKiwsJCNWjQwI/T1UxYWJi6devmuVxRUaFVq1apc%2BfOys3NlcPh0OLFi/XZZ58pPDxc9957rwYOHOjHiWvGsizt379fGzdu1JIlS1ReXq6%2BffsqKSlJbrdbf/7zn72u37BhQ%2BXk5Php2ou3evVqRUZGqm/fvpKkffv2qV69epo8ebK2bNmia665Rg899JBuuukmP09atbvuuqvKdbfbrcjISK%2B1hg0b6siRIzXa7k/ny9SsWTM1a9bMc/nHH3/U//zP/%2Bihhx6S9MsPAldddZXGjx%2BvnTt36k9/%2BpMmT56s9u3b%2B2Tu6pwvV3XvD/n5%2BQH3XP1aenq6Bg4cqMaNG3vWcnNzVVRUpJEjR2r//v1q27atHn/88SuidF3ovTyQX1cmsAcrABUVFXmVK0mey6Wlpf4Y6ZKlpqZq165d%2Butf/%2BrZK9KyZUulp6dr6NChevLJJ/XRRx/5e8xqHTp0yPP8PP/885oyZYree%2B89zZ49%2B7zPW6A8Z5Zlae3atRoxYoRnbd%2B%2BfSouLlbXrl21dOlS3XTTTXrggQe0Y8cOP0568ap7bgL9uSsuLtZDDz2kRo0aadiwYZKk/fv366efftLQoUOVnp6uVq1aadSoUTp8%2BLCfp72w6t4fiouLA/a5ysvL06ZNmzRy5Eiv9X379umnn37SAw88oIULF6p27doaPXq0Z2/kleTX7%2BV2f11Vhz1YASgkJKTSP8Czl2vXru2PkS5JamqqVqxYoeeee05t2rRR69at1aNHD4WHh0uSoqOjdeDAAa1evVq9e/f287QX1rRpU23evFlXX321HA6H2rZtq4qKCk2aNEkdO3as8nkLlOdsx44dOnr0qPr16%2BdZe/DBBzVy5EhdffXVkn55rr7%2B%2Bmu98cYbiomJ8deoFy0kJESFhYVea79%2Bbs73mgsLC/PZjL/VqVOn9OCDD%2BrAgQN67bXXFBoaKkmaMWOGiouLVa9ePUnS9OnTtW3bNq1fv15/%2Bctf/DnyBQ0YMOCC7w/ne67O5r6Sffjhh2rbtm2lPd3Lli3TmTNnVLduXUnSnDlzdNNNN%2BmTTz7Rbbfd5o9Rq3Tue7mdX1c1wR6sABQVFaXjx4%2BrrKzMs%2BZ2u1W7du2A%2B4c5Y8YMvfzyy0pNTVWfPn0k/XJs0tk3z7Natmypo0eP%2BmPEixYeHu45zkqSWrVqpZKSEkVERKigoMDrugUFBZV2kV%2BpPv/8cyUkJHjKlCTVqlXL67IUWM/VWVFRURd8bs63PSIiwmcz/hYnT57U2LFjlZOToxUrVngdt%2BR0Oj3lSpJnr9CV/txV9/4QqM%2BV9Mtr7D/%2B4z8qrQcHB3vKlfRLMWnWrNkV9VxV9V5u19dVTVGwAlDbtm3ldDq9DgTMzMxUTEyMatUKnKc0LS1Nr7/%2Bup599lmvvSLz5s3T6NGjva67e/dutWzZ0scTXrzPP/9cnTp1UlFRkWftm2%2B%2BUXh4uOLj4/XVV1/JsixJv3zktm3bNrlcLn%2BNe1G2b9%2BuDh06eK099thjmjp1qtdaoDxXv%2BZyufT111%2BruLjYs5aZmel5blwulzIzMz3bioqKtGvXriv6uauoqNCECRP0ww8/6JVXXlHr1q29to8cOVJpaWle1//222%2Bv%2BOeuuveHc5%2Brw4cP6/Dhw1f0cyX98n6wY8eOSq8xy7LUq1cvvfXWW56106dP67vvvrtinqvzvZfb8XV1MQLn/8bwCA0N1YABAzR9%2BnRt375dGzZs0EsvveR1UrorXW5urhYuXKj7779f8fHxcrvdnq8ePXpo69atWrZsmb7//nu99tpreueddzRmzBh/j12tuLg4hYSE6IknntC%2Bffv06aefavbs2brvvvvUt29fnThxQrNmzdLevXs1a9YsFRUV6ZZbbvH32DWSk5NT6aOLnj176r333tM777yj7777TmlpacrMzPQ6TisQdOzYUY0bN9bUqVOVk5Oj9PR0bd%2B%2BXUOGDJEkDR48WNu2bVN6erpycnI0depUNWvWTJ06dfLz5Of35ptvavPmzZo5c6bCwsI8r6%2BzH9n07NlTy5cv18cff6x9%2B/YpOTlZP//88xX/yyTVvT8MHz5c69ev19q1a7V7925NnjxZ3bt3v%2BJ/0/XgwYM6depUpdeYw%2BFQ9%2B7d9cILL2jz5s3KycnR5MmTdc0111wRv0xyofdyO76uLoo/zxGB3%2B706dPW5MmTrdjYWKtr167Wyy%2B/7O%2BRLsqSJUusNm3aVPllWZb10UcfWbfddpsVExNj9e3b1/rwww/9PHHN7dmzxxo9erQVGxtrdenSxXrhhRc8577Kzs62BgwYYMXExFhDhgyxvv76az9PW3MxMTHWZ599Vmn9jTfesG6%2B%2BWbr%2BuuvtwYOHGht2bLFD9NdvHPPQ3TgwAHr7rvvtq6//nqrX79%2B1j//%2BU%2Bv6//jH/%2Bwbr75Zqt9%2B/bWqFGjrojzKp3r15nGjBlT5evr7HmHKioqrEWLFlndu3e3rr/%2Beuvuu%2B%2B2vv32W3%2BOf17nPlfVvT%2BsW7fOuummm6zY2FgrMTHROnbsmK9Hrta5mbKysqw2bdpUeZLe4uJiKyUlxerSpYvlcrms8ePHW4cOHfLluOdV3Xu5HV5Xv5XDsv71eQUAAACM4CNCAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADDs/wEXjEK/cDahsAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8587877438876342242”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.142857142857143</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.8951358180669615</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.317789291882556</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.449838684897834</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.052701928585068</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.477966705243479</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5980642247745054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.311077389984826</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2807)</td> <td class=”number”>2807</td> <td class=”number”>87.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>313</td> <td class=”number”>9.8%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8587877438876342242”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.002192913206249627</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.009908347783007183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.027526218723333974</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.030368796495440883</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>115.89698046181172</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>126.42461738847281</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>158.58310626702996</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>183.9985010305415</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>203.91061452513966</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FFRSALES07”>PC_FFRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>53</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>641.71</td>

</tr> <tr>

<th>Minimum</th> <td>402.1</td>

</tr> <tr>

<th>Maximum</th> <td>1043.9</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-9164074079486452736”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-9164074079486452736,#minihistogram-9164074079486452736”
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</div> <div class=”row collapse col-md-12” id=”descriptives-9164074079486452736”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-9164074079486452736”

aria-controls=”quantiles-9164074079486452736” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-9164074079486452736” aria-controls=”histogram-9164074079486452736”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-9164074079486452736” aria-controls=”common-9164074079486452736”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-9164074079486452736” aria-controls=”extreme-9164074079486452736”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-9164074079486452736”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>402.1</td>

</tr> <tr>

<th>5-th percentile</th> <td>489.04</td>

</tr> <tr>

<th>Q1</th> <td>576.21</td>

</tr> <tr>

<th>Median</th> <td>632.34</td>

</tr> <tr>

<th>Q3</th> <td>721.82</td>

</tr> <tr>

<th>95-th percentile</th> <td>784.26</td>

</tr> <tr>

<th>Maximum</th> <td>1043.9</td>

</tr> <tr>

<th>Range</th> <td>641.76</td>

</tr> <tr>

<th>Interquartile range</th> <td>145.61</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>96.748</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15077</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.12331</td>

</tr> <tr>

<th>Mean</th> <td>641.71</td>

</tr> <tr>

<th>MAD</th> <td>78.836</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.036916</td>

</tr> <tr>

<th>Sum</th> <td>2009800</td>

</tr> <tr>

<th>Variance</th> <td>9360.2</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-9164074079486452736”>

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qe/fucjgcWrFihQzDkM1mk2EYKi0t1d133%2B19bUlJidLS0iRJlZWVqqyslMPhaPX7x8XFtbgc6HId1YkT1u6sTU3Nlo8ZyKgHcFJrPgd8Xsyohxn1uHBBeZH1Jz/5iUaPHq3s7Gx9%2BeWX%2BuCDD1RUVKSMjAyNGzdOdXV1ysnJ0f79%2B5WTkyO3263U1FRJUkZGhl5//XWtXbtWX375pebMmaPRo0fziAYAANBqQRmwJOmpp57Sj3/8Y2VkZGju3Lm6%2Beabdeutt6pDhw4qLCz0nqVyOp0qKipSZGSkJCkhIUELFy5UQUGBMjIy1LFjR%2BXm5vp4NQAAIJDYjO/%2BfgwuKpfrqGVj2e3tFB3dXjU1xziFK%2BrxHR7eie%2B8NXvEafv4vJhRD7NgrEds7GU%2Bed%2BgPYMFAADgKwQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALCY3wWsadOmac2aNTp69KivpwIAAHBe/C5gDRs2TMuXL9fIkSN13333afv27TIMw9fTAgAAaDW/C1j333%2B//vKXv2jZsmUKCQnRvffeq9GjR%2BuZZ57RoUOHfD09AACAs7L7egKnYrPZNGLECI0YMUJut1srV67UsmXLVFRUpMTERN122226/vrrfT1NAACAU/LLgCVJVVVV2rhxozZu3Kh9%2B/YpMTFRv/zlL3XkyBE98sgj%2BuSTTzRv3jxfTxMAAKAFvwtYr7/%2Bul5//XV99NFH6tSpkyZPnqxnn31WvXv39m7TtWtX5eTknDFgbdmyRbNmzTK13XDDDXr22We1d%2B9ePfroo9q3b5/69u2rBQsWaNCgQd7tNm3apLy8PLlcLo0cOVKLFi1Sp06dLF8rAAAITn4XsObNm6cxY8aooKBA1157rdq1a3mb2E9%2B8hPdcsstZxxn//79GjNmjBYtWuRtCwsLU319vWbMmKEbb7xRTzzxhFavXq2ZM2dqy5YtioyM1O7duzVv3jwtWLBA/fr1U05OjrKzs1VYWGj5WuHfUvM%2B9PUUAAAByu8C1rZt2xQdHa3a2lpvuNq9e7cGDhyokJAQSVJiYqISExPPOM6BAwd05ZVXKjY21tS%2Bbt06hYWFac6cObLZbJo3b562bdumzZs3Ky0tTatWrVJqaqomT54sSVq8eLHGjBmj8vJy9ezZ8yKsGAAABBu/%2By3Cb7/9VuPGjdOKFSu8bTNmzNCkSZNUWVnZ6nEOHDhguqz4HafTqaSkJNlsNkknb6hPTEzUrl27vP3Jycne7bt27apu3brJ6XSe54oAAEBb43dnsH73u9%2BpV69euuOOO7xtb775pubOnavc3Fw9%2B%2ByzZx3DMAwdOnRI27dvV2FhoZqamjRu3DhlZWXJ5XKpb9%2B%2Bpu1jYmJUVlYm6eTN9XFxcS36jxw50uo1VFVVyeVymdrs9sgW456vkJB2pn/bOuoBmNntp/8s8Hkxox5m1MM6fhew/va3v%2BmVV14xXdrr1KmT5syZo5tvvrlVY1RUVMjtdis0NFR5eXn66quv9Pjjj6uhocHb/n2hoaHyeDySpIaGhjP2t0ZxcbHy8/NNbZmZmcrKymr1GK0RFRVh6XiBjnoAJ0VHtz/rNnxezKiHGfW4cH4XsOx2u%2Brq6lq0u93uVj/RvXv37vroo4/UsWNH2Ww29e/fX83NzXrwwQc1ZMiQFmHJ4/EoPDxc0skb4U/VHxHR%2Bp0tPT1dKSkpP1hXpGpqjrV6jDMJCWmnqKgI1dW51dTUbMmYgYx6AGZnOtbweTGjHmbBWI/W/MBxMfhdwLr22mv1%2BOOP6%2Bmnn9aPf/xjSVJ5eblyc3M1atSoVo9z%2BeWXm77v06ePGhsbFRsbq%2BrqalNfdXW19/Jdly5dTtn/w5vlzyQuLq7F5UCX66hOnLB2Z21qarZ8zEBGPYCTWvM54PNiRj3MqMeF87uLrHPnzpXH49ENN9ygoUOHaujQobr%2B%2But1/PhxZWdnt2qMDz74QEOHDpXb7fa2ffHFF7r88suVlJSkTz/91Hs2zDAMlZaWyuFwSJIcDodKSkq8r6usrFRlZaW3HwAA4Gz87gxWTEyMXn31Ve3YsUNlZWWy2%2B3q27evhg8f7v3Nv7NJSEhQWFiYHnnkEWVmZqq8vFyLFy/WXXfdpXHjxmnp0qXKycnRr3/9a61Zs0Zut1upqamSpIyMDN16662Kj4/X4MGDlZOTo9GjR/OIBgAA0Gp%2BF7AkKSQkRKNGjTqnS4Lf16FDB/3%2B97/X7373O02ZMkXt27fXr3/9a911112y2WwqLCzUo48%2BqldeeUVXXXWVioqKFBkZKelkOFu4cKGeffZZ/ec//9GIESNMDysFAAA4G5vR2jvHLxGXy6W8vDyVlpbq%2BPHjLW5sf/fdd300swvjch21bCy7vZ2io9urpuYY18h18erBk9wRqN6aPeK0fRw/zKiHWTDWIzb2Mp%2B8r9%2Bdwfrf//1f7dmzRxMmTNBll/mmKAAAABfC7wLWX//6V73wwgump6kDAAAEEr/7LcLIyEjFxMT4ehoAAADnze8C1qRJk/TCCy%2BoqanJ11MBAAA4L353ibC2tlabNm3S%2B%2B%2B/r549e7b4szV//OMffTQzAACA1vG7gCVJEydO9PUUAAAAzpvfBazc3FxfTwEAAOCC%2BN09WJJUVVWl/Px83X///fr3v/%2BtzZs36%2BDBg76eFgAAQKv4XcA6fPiwbrzxRr366qt6%2B%2B23VV9frzfffFNTpkyR0%2Bn09fQAAADOyu8C1hNPPKGxY8fqnXfe0Y9%2B9CNJ0tNPP62UlBQ99dRTPp4dAADA2fldwCotLdUdd9xh%2BsPOdrtd99xzj/bu3evDmQEAALSO3wWs5uZmNTe3/PtHx44dU0hIiA9mBAAAcG78LmCNHDlShYWFppBVW1urJUuWaNiwYT6cGQAAQOv4XcB66KGHtGfPHo0cOVKNjY367W9/qzFjxuirr77S3LlzfT09AACAs/K752B16dJFr732mjZt2qQvvvhCzc3NysjI0KRJk9ShQwdfTw8AAOCs/C5gSVJERISmTZvm62kAAACcF78LWNOnTz9jP3%2BLEAAA%2BDu/C1jdu3c3fX/ixAkdPnxY%2B/bt02233eajWQEAALSe3wWs0/0twoKCAh05cuQSzwYAAODc%2Bd1vEZ7OpEmT9NZbb/l6GgAAAGcVMAHr008/5UGjAAAgIPjdJcJT3eT%2B7bff6u9//7tuuukmH8wIAADg3PhdwOrWrZvp7xBK0o9%2B9CPdcsst%2BsUvfuGjWQEAALSe3wWsJ554wtdTAAAAuCB%2BF7A%2B%2BeSTVm97zTXXnHWbGTNmqFOnTt7gtnfvXj366KPat2%2Bf%2BvbtqwULFmjQoEHe7Tdt2qS8vDy5XC6NHDlSixYtUqdOnc59IQAAoM3yu4B16623ei8RGobhbf9hm81m0xdffHHGsf785z9r69at%2BuUvfylJqq%2Bv14wZM3TjjTfqiSee0OrVqzVz5kxt2bJFkZGR2r17t%2BbNm6cFCxaoX79%2BysnJUXZ2tgoLCy/GUgEAQJDyu98iXL58ubp37668vDzt3LlTJSUleumll3TFFVfovvvu07vvvqt3331X77zzzhnHqa2t1eLFizV48GBv25tvvqmwsDDNmTNHffr00bx589S%2BfXtt3rxZkrRq1SqlpqZq8uTJ6tevnxYvXqytW7eqvLz8oq4ZAAAEF78LWLm5uZo/f75uuOEGRUdHq3379ho2bJgWLlyo1atXq3v37t6vM3nyySc1adIk9e3b19vmdDqVlJTkPRtms9mUmJioXbt2efuTk5O923ft2lXdunWT0%2Bm8CCsFAADByu8CVlVV1SnDU4cOHVRTU9OqMXbu3Km//e1vuueee0ztLpdLcXFxpraYmBjvE%2BKrqqrO2A8AANAafncPVnx8vJ5%2B%2Bmk9%2BeST6tChg6STl/uWLFmi4cOHn/X1jY2NevTRRzV//nyFh4eb%2Btxut0JDQ01toaGh8ng8kqSGhoYz9rdWVVWVXC6Xqc1uj2wR3s5XSEg7079tHfUAzOz2038W%2BLyYUQ8z6mEdvwtYjzzyiKZPn65rr71WvXv3lmEY%2Bsc//qHY2Fj98Y9/POvr8/PzNWjQII0aNapFX1hYWIuw5PF4vEHsdP0RERHntIbi4mLl5%2Beb2jIzM5WVlXVO45xNVNS5zSvYUQ/gpOjo9mfdhs%2BLGfUwox4Xzu8CVp8%2BffTmm29q06ZNOnDggCTp5ptv1oQJE1oVdP785z%2BrurpaCQkJkuQNTG%2B//bYmTpyo6upq0/bV1dXeM0tdunQ5ZX9sbOw5rSE9PV0pKSmmNrs9UjU1x85pnNMJCWmnqKgI1dW51dTUbMmYgYx6AGZnOtbweTGjHmbBWI/W/MBxMfhdwJKkjh07atq0afrqq6/Us2dPSSef5t4aK1eu1IkTJ7zfP/XUU5KkBx54QJ988olWrFghwzBks9lkGIZKS0t19913S5IcDodKSkqUlpYmSaqsrFRlZaUcDsc5zT8uLq7F5UCX66hOnLB2Z21qarZ8zEBGPYCTWvM54PNiRj3MqMeF87uAZRiGli5dqpUrV%2Br48eN6%2B%2B239cwzzygiIkKPPfbYWYPWD2%2BQb9/%2BZHLt1auXYmJitHTpUuXk5OjXv/611qxZI7fbrdTUVElSRkaGbr31VsXHx2vw4MHKycnR6NGjvSEPFyY170NfTwEAgEvC7%2B5iW7lypV5//XU9%2Buij3hvOx44dq3feeafFfU3nqkOHDiosLPSepXI6nSoqKlJkZKQkKSEhQQsXLlRBQYEyMjLUsWNH5ebmXvCaAABA22Izvv%2B4dD8wYcIEzZ49W9ddd50SEhK0ceNG9ezZU1u2bFFubq7ee%2B89X0/xvLhcRy0by25vp%2Bjo9qqpORZQp3A5gwVcGm/NHnHavkA9flws1MMsGOsRG3uZT97X785gffXVV%2Brfv3%2BL9n79%2BrV49AEAAIA/8ruA1b17d3322Wct2rdt28a9UAAAICD43U3uv/nNb7RgwQK5XC4ZhqGdO3equLhYK1eu1EMPPeTr6QEAAJyV3wWsKVOm6MSJE3r%2B%2BefV0NCg%2BfPnq1OnTpo9e7YyMjJ8PT0AAICz8ruAtWnTJo0bN07p6en65ptvZBiGYmJifD0tAACAVvO7e7AWLlzovZm9U6dOhCsAABBw/C5g9e7dW/v27fP1NAAAAM6b310i7Nevnx544AG98MIL6t27t8LCwkz9PPgTAAD4O78LWIcOHVJSUpIk8dwrAAAQkPwiYC1evFizZs1SZGSkVq5c6evpAAAAXBC/uAfrxRdflNvtNrXNmDFDVVVVPpoRAADA%2BfOLgHWqP4f4ySefqLGx0QezAQAAuDB%2BEbAAAACCCQELAADAYn4TsGw2m6%2BnAAAAYAm/%2BC1CSXr88cdNz7w6fvy4lixZovbt25u24zlYAADA3/lFwLrmmmtaPPMqISFBNTU1qqmp8dGsAAAAzo9fBCyefQUAAIKJ39yDBQAAECwIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYLGgD1uHDh/Wb3/xGCQkJGj16tF544QVvX3l5uW6//XbFx8dr/Pjx2r59u%2Bm1O3bs0MSJE%2BVwODR9%2BnSVl5df6ukDAIAAFpQBq7m5WTNmzFB0dLReffVVLViwQM8//7zeeOMNGYahzMxMde7cWevXr9ekSZM0a9YsVVRUSJIqKiqUmZmptLQ0rVu3Tp06ddI999wjwzB8vCoAABAo/OJJ7larrq5W//799dhjj6lDhw7q3bu3hg8frpKSEnXu3Fnl5eVas2aNIiMj1adPH%2B3cuVPr16/Xvffeq7Vr12rQoEG68847JZ3824cjRozQxx9/rKFDh/p4ZQAAIBAE5RmsuLg45eXlqUOHDjIMQyUlJfrkk080ZMgQOZ1ODRgwQJGRkd7tk5KStGvXLkmS0%2BlUcnKyty8iIkIDBw709gMAAJxNUJ7B%2Br6UlBRVVFRozJgxuuGGG/S73/1OcXFxpm1iYmJ05MgRSZLL5Tpjf2tUVVW1%2BOPVdntki3HPV0hIO9O/APB9dvvpjw0cP8yohxn1sE7QB6xnn31W1dXVeuyxx5Sbmyu3263Q0FDTNqGhofJ4PJJ01v7WKC4uVn5%2BvqktMzNTWVlZ57mKU4uKirB0PADBITq6/Vm34fhhRj3MqMeFC/qANXjwYElSY2OjHnivUcGMAAAXGUlEQVTgAU2ZMkVut9u0jcfjUXh4uCQpLCysRZjyeDyKiopq9Xump6crJSXF1Ga3R6qm5tj5LKGFkJB2ioqKUF2dW01NzZaMCSB4nOlYw/HDjHqYBWM9WvMDx8UQlAGrurpau3bt0tixY71tffv21fHjxxUbG6uDBw%2B22P67y3ddunRRdXV1i/7%2B/fu3%2Bv3j4uJaXA50uY7qxAlrd9ampmbLxwQQ%2BFpzXOD4YUY9zKjHhQvKi6xfffWVZs2apa%2B//trbtmfPHnXq1ElJSUn6/PPP1dDQ4O0rKSmRw%2BGQJDkcDpWUlHj73G639u7d6%2B0HAAA4m6AMWIMHD9bAgQP18MMPa//%2B/dq6dauWLFmiu%2B%2B%2BW0OGDFHXrl2VnZ2tsrIyFRUVaffu3Zo6daokacqUKSotLVVRUZHKysqUnZ2tHj168IgGAADQakEZsEJCQrRs2TJFREQoPT1d8%2BbN06233qrp06d7%2B1wul9LS0rRx40YVFBSoW7dukqQePXroueee0/r16zV16lTV1taqoKBANpvNx6sCAACBwmbwiPJLwuU6atlYdns7RUe3V03NsYC6Rp6a96GvpwC0CW/NHnHavkA9flws1MMsGOsRG3uZT943KM9gAQAA%2BBIBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALBa0Aevrr79WVlaWhgwZolGjRik3N1eNjY2SpPLyct1%2B%2B%2B2Kj4/X%2BPHjtX37dtNrd%2BzYoYkTJ8rhcGj69OkqLy/3xRIAAECACsqAZRiGsrKy5Ha79ac//UnPPPOM/vKXvygvL0%2BGYSgzM1OdO3fW%2BvXrNWnSJM2aNUsVFRWSpIqKCmVmZiotLU3r1q1Tp06ddM8998gwDB%2BvCgAABAq7rydwMRw8eFC7du3Shx9%2BqM6dO0uSsrKy9OSTT%2Braa69VeXm51qxZo8jISPXp00c7d%2B7U%2BvXrde%2B992rt2rUaNGiQ7rzzTklSbm6uRowYoY8//lhDhw715bIAAECACMozWLGxsXrhhRe84eo73377rZxOpwYMGKDIyEhve1JSknbt2iVJcjqdSk5O9vZFRERo4MCB3n4AAICzCcqAFRUVpVGjRnm/b25u1qpVqzRs2DC5XC7FxcWZto%2BJidGRI0ck6az9AAAAZxOUlwh/aMmSJdq7d6/WrVunl156SaGhoab%2B0NBQeTweSZLb7T5jf2tUVVXJ5XKZ2uz2yBbB7XyFhLQz/QsA32e3n/7YwPHDjHqYUQ/rBH3AWrJkiV5%2B%2BWU988wzuvLKKxUWFqba2lrTNh6PR%2BHh4ZKksLCwFmHK4/EoKiqq1e9ZXFys/Px8U1tmZqaysrLOcxWnFhUVYel4AIJDdHT7s27D8cOMephRjwsX1AFr0aJFWr16tZYsWaIbbrhBktSlSxft37/ftF11dbX37FKXLl1UXV3dor9///6tft/09HSlpKSY2uz2SNXUHDufZbQQEtJOUVERqqtzq6mp2ZIxAQSPMx1rOH6YUQ%2BzYKxHa37guBiCNmDl5%2BdrzZo1evrppzVu3Dhvu8PhUFFRkRoaGrxnrUpKSpSUlOTtLykp8W7vdru1d%2B9ezZo1q9XvHRcX1%2BJyoMt1VCdOWLuzNjU1Wz4mgMDXmuMCxw8z6mFGPS5cUF5kPXDggJYtW6b//u//VlJSklwul/dryJAh6tq1q7Kzs1VWVqaioiLt3r1bU6dOlSRNmTJFpaWlKioqUllZmbKzs9WjRw8e0QAAAFotKAPWu%2B%2B%2Bq6amJj3//PMaOXKk6SskJETLli2Ty%2BVSWlqaNm7cqIKCAnXr1k2S1KNHDz333HNav369pk6dqtraWhUUFMhms/l4VQAAIFDYDB5Rfkm4XEctG8tub6fo6PaqqTkWUKdwU/M%2B9PUUgDbhrdkjTtsXqMePi4V6mAVjPWJjL/PJ%2BwblGSwAAABfImABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWC/qA5fF4NHHiRH300UfetvLyct1%2B%2B%2B2Kj4/X%2BPHjtX37dtNrduzYoYkTJ8rhcGj69OkqLy%2B/1NMGAAABLKgDVmNjo%2B677z6VlZV52wzDUGZmpjp37qz169dr0qRJmjVrlioqKiRJFRUVyszMVFpamtatW6dOnTrpnnvukWEYvloGAAAIMEEbsPbv369f/epX%2Buc//2lq/%2Btf/6ry8nItXLhQffr00cyZMxUfH6/169dLktauXatBgwbpzjvv1H/9138pNzdX//rXv/Txxx/7YhkAACAABW3A%2BvjjjzV06FAVFxeb2p1OpwYMGKDIyEhvW1JSknbt2uXtT05O9vZFRERo4MCB3n4AAICzsft6AhfLTTfddMp2l8uluLg4U1tMTIyOHDnSqv7WqKqqksvlMrXZ7ZEtxj1fISHtTP8CwPfZ7ac/NnD8MKMeZtTDOkEbsE7H7XYrNDTU1BYaGiqPx9Oq/tYoLi5Wfn6%2BqS0zM1NZWVnnOetTi4qKsHQ8AMEhOrr9Wbfh%2BGFGPcyox4VrcwErLCxMtbW1pjaPx6Pw8HBv/w/DlMfjUVRUVKvfIz09XSkpKaY2uz1SNTXHznPWZiEh7RQVFaG6OreampotGRNA8DjTsYbjhxn1MAvGerTmB46Loc0FrC5dumj//v2mturqau/luy5duqi6urpFf//%2B/Vv9HnFxcS0uB7pcR3XihLU7a1NTs%2BVjAgh8rTkucPwwox5m1OPCtbmLrA6HQ59//rkaGhq8bSUlJXI4HN7%2BkpISb5/b7dbevXu9/QAAAGfT5gLWkCFD1LVrV2VnZ6usrExFRUXavXu3pk6dKkmaMmWKSktLVVRUpLKyMmVnZ6tHjx4aOnSoj2cOAAACRZsLWCEhIVq2bJlcLpfS0tK0ceNGFRQUqFu3bpKkHj166LnnntP69es1depU1dbWqqCgQDabzcczBwAAgaJN3IP197//3fR9r169tGrVqtNu/7Of/Uw/%2B9nPLva0AABAkGoTASuYpeZ96OspAACAH2hzlwgBAAAuNgIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWs/t6AgAABIrUvA99PYVz8tbsEb6eQpvFGaxTaGxs1MMPP6zk5GSNHDlSf/jDH3w9JQAAEEA4g3UKixcv1p49e/Tyyy%2BroqJCc%2BfOVbdu3TRu3DhfTw0AAAQAAtYP1NfXa%2B3atVqxYoUGDhyogQMHqqysTH/6058IWAAAoFUIWD/w5Zdf6sSJE0pISPC2JSUlafny5Wpubla7dlxVBQAEhkC6ZyzY7hcjYP2Ay%2BVSdHS0QkNDvW2dO3dWY2Ojamtr1alTp7OOUVVVJZfLZWqz2yMVFxdnyRxDQtqZ/gWA77PbT39s4PhhRj38x5n220BEwPoBt9ttCleSvN97PJ5WjVFcXKz8/HxT26xZs3TvvfdaMseqqiq9/PILSk9P199yuGxZVVWl4uJipaenWxZiAxn1MKMeZt8/flCPc69HsB9z%2BbxYJ7jiogXCwsJaBKnvvg8PD2/VGOnp6dqwYYPpKz093bI5ulwu5efntzhL1lZRDzPqYUY9zKiHGfUwox7W4QzWD3Tp0kU1NTU6ceKE7PaT5XG5XAoPD1dUVFSrxoiLiyP5AwDQhnEG6wf69%2B8vu92uXbt2edtKSko0ePBgbnAHAACtQmL4gYiICE2ePFmPPfaYdu/erXfeeUd/%2BMMfNH36dF9PDQAABIiQxx577DFfT8LfDBs2THv37tXSpUu1c%2BdO3X333ZoyZYqvp2XSvn17DRkyRO3bt/f1VPwC9TCjHmbUw4x6mFEPM%2BphDZthGIavJwEAABBMuEQIAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgOXHZsyYoYceesj7/d69ezVt2jQ5HA5NmTJFe/bsMW2/adMmjR07Vg6HQ5mZmfrmm28u9ZQtt2XLFl111VWmr6ysLEltsx4ej0cLFizQNddco5/%2B9Kd6%2Bumn9d2zgttaPTZs2NBi37jqqqvUr18/SW2vHpJUWVmpmTNnKjExUSkpKXrppZe8fW2xHv/%2B97%2BVlZWl5ORkXXfdddqwYYO3r7y8XLfffrvi4%2BM1fvx4bd%2B%2B3fTaHTt2aOLEiXI4HJo%2BfbrKy8sv9fQt4/F4NHHiRH300Ufetgtd/0svvaRRo0YpISFBDz/8sNxu9yVZS0Ax4Jc2bdpkXHnllcbcuXMNwzCMY8eOGSNGjDCeeOIJY//%2B/caiRYuMn/70p8axY8cMwzAMp9NpXH311carr75qfPHFF8Ytt9xizJgxw5dLsMSyZcuMmTNnGlVVVd6v//znP222Hv/7v/9rXH/99YbT6TR27NhhDB061Fi9enWbrIfb7TbtFxUVFcZ1111n5OTktMl6GIZh/OpXvzJmz55tHDp0yNiyZYvhcDiM//u//2uT9WhubjbS09ONadOmGZ9//rnx3nvvGddcc43x9ttvG83NzcaNN95o3H///cb%2B/fuN5cuXGw6Hw/jXv/5lGIZh/Otf/zLi4%2BON3//%2B98a%2BffuM//mf/zEmTpxoNDc3%2B3hV566hocHIzMw0rrzySuOvf/2rYRjGBa9/8%2BbNRlJSkvHee%2B8ZTqfTGD9%2BvLFgwQKfrdFfEbD8UE1NjXHttdcaU6ZM8QastWvXGikpKd4dvLm52bjuuuuM9evXG4ZhGA8%2B%2BKB3W8MwjIqKCuOqq64y/vnPf176BVjo/vvvN5YuXdqivS3Wo6amxhgwYIDx0UcfedsKCwuNhx56qE3W44eWL19ujB071mhsbGyT9aitrTWuvPJK4%2B9//7u3bdasWcaCBQvaZD12795tXHnllaY1FBYWGr/61a%2BMHTt2GPHx8d6AaRiGcdtttxnPPvusYRiGkZeXZ9xyyy3evvr6eiMhIcEbUAJFWVmZ8Ytf/MK48cYbTQHrQtd/0003ebc1DMP45JNPjKuvvtqor6%2B/FMsKGFwi9ENPPvmkJk2apL59%2B3rbnE6nkpKSZLPZJEk2m02JiYnatWuXtz85Odm7fdeuXdWtWzc5nc5LO3mLHThwQL17927R3hbrUVJSog4dOmjIkCHethkzZig3N7dN1uP7amtrtWLFCt1///0KDQ1tk/UIDw9XRESENmzYoOPHj%2BvgwYMqLS1V//7922Q9ysvL1alTJ/Xs2dPbdtVVV2nPnj0qKSnRgAEDFBkZ6e1LSko6bT0iIiI0cOBAb3%2Bg%2BPjjjzV06FAVFxeb2p1O53mvv6mpSZ999pmpPz4%2BXsePH9eXX355kVcUWAhYfmbnzp3629/%2BpnvuucfU7nK5FBcXZ2qLiYnRkSNHJElVVVVn7A9EhmHo0KFD2r59u2644QaNHTtWTz31lDweT5usR3l5ubp3767XXntN48aN089//nMVFBSoubm5Tdbj%2B1avXq24uDiNGzdOUtv8vISFhWn%2B/PkqLi6Ww%2BFQamqqrr32Wk2bNq1N1qNz5846evSo6d6gI0eO6MSJE2etx9n6A8VNN92khx9%2BWBEREab2C1l/XV2dGhsbTf12u12XX355wNXnYrP7egL4/zU2NurRRx/V/PnzFR4ebupzu90KDQ01tYWGhsrj8UiSGhoaztgfiCoqKrzrzsvL01dffaXHH39cDQ0NbbIe9fX1Onz4sNasWaPc3Fy5XC7Nnz9fERERbbIe3zEMQ2vXrtVdd93lbWur9Thw4IDGjBmjO%2B64Q2VlZVq0aJGGDx/eJuvhcDgUFxenRYsW6ZFHHpHL5dKLL74o6eRN32da79nqFejOtr4z9Tc0NHi/P93rcRIBy4/k5%2Bdr0KBBGjVqVIu%2BsLCwFjuvx%2BPxBrHT9f/wJ5dA0r17d3300Ufq2LGjbDab%2Bvfvr%2BbmZj344IMaMmRIm6uH3W7Xt99%2Bq6VLl6p79%2B6STobQ1atXq1evXm2uHt/57LPP9PXXX2vChAnetrb4edm5c6fWrVunrVu3Kjw8XIMHD9bXX3%2Bt559/Xj179mxz9QgLC1NeXp5mz56tpKQkxcTE6K677lJubq5sNtt51SMqKuqSzf9iCgsLU21tramttesPCwvzfv/D/kDeXy4GLhH6kT//%2Bc965513lJCQoISEBL3xxht64403lJCQoC5duqi6utq0fXV1tfc07en6Y2NjL9n8L4bLL7/ce9%2BIJPXp00eNjY2KjY1tc/WIjY1VWFiYN1xJ0hVXXKHKyso2u39I0gcffKDk5GR17NjR29YW67Fnzx716tXLdPZ7wIABqqioaJP1kKSrr75a7733nrZt26b3339fV1xxhaKjo/XjH/%2B4TdbjOxeyP1x%2B%2BeUKCwsz9Z84cUK1tbVBUx%2BrELD8yMqVK/XGG2/otdde02uvvaaUlBSlpKTotddek8Ph0Keffup95pFhGCotLZXD4ZB08nR4SUmJd6zKykpVVlZ6%2BwPRBx98oKFDh5ruofjiiy90%2BeWXKykpqc3Vw%2BFwqLGxUYcOHfK2HTx4UN27d2%2BT%2B8d3du/ercTERFNbW6xHXFycDh8%2BbDqzcPDgQfXo0aNN1qO2tlYZGRmqqalRbGys7Ha73n//fQ0ZMkQOh0Off/6593KXdPKXSE5XD7fbrb179wZ0Pb7vQtbfrl07DR482NS/a9cu2e127zPo8P/45pcX0Rpz5871/ur00aNHjWHDhhmLFi0yysrKjEWLFhkjRozw/pptaWmpMXDgQOOVV17xPsdm5syZvpz%2BBTt69KgxatQo47777jMOHDhgvP/%2B%2B8bIkSONoqKiNlkPwzCMGTNmGOnp6cYXX3xhbNu2zRg2bJjx8ssvt9l6GIZhjBkzxti0aZOprS3Wo66uzhgxYoTx4IMPGgcPHjTeffddY8iQIcbq1avbZD0MwzB%2B8YtfGNnZ2cY///lP45VXXjEGDx5sOJ1O48SJE8b48eON2bNnG/v27TMKCwuN%2BPh473OgysvLjcGDBxuFhYXe50DdeOONAfkcrO98/zENF7r%2BTZs2GYmJicaWLVsMp9NpTJgwwVi0aJHP1uavCFh%2B7PsByzBOPgxw8uTJxuDBg42pU6can3/%2BuWn79evXGz/72c%2BM%2BPh4IzMz0/jmm28u9ZQtt2/fPuP222834uPjjREjRhjPPfec90PeFutRV1dnPPjgg0Z8fLwxfPjwNl8PwzCMwYMHG9u2bWvR3hbrUVZWZtx%2B%2B%2B1GYmKiMXbsWOPFF19s0/vHgQMHjFtuucVwOBzGhAkTjPfee8/b949//MO4%2BeabjUGDBhkTJkwwPvzwQ9Nr33//feP66683rr76auO2224L6GeCGYY5YBnGha%2B/sLDQGD58uJGUlGRkZ2cbDQ0Nl2QdgcRmGP/vnDEAAAAswT1YAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFvv/ABZD9aAPXio7AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-9164074079486452736”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>784.256146465872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>749.6665527793294</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>721.8231507597286</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>666.2345126601743</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>623.52054283565</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>610.8893780654645</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>629.814434686419</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>746.0213064145971</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>489.04461706906</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>702.9736436032449</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (42)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-9164074079486452736”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>402.0978044666219</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>452.12153614828173</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>487.5571873084633</td> <td class=”number”>62</td> <td class=”number”>1.9%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>489.04461706906</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>491.9339769542199</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:54%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>761.4471253362104</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>784.256146465872</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>939.1304521890828</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1036.4801074998122</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1043.8608605056472</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FFRSALES12”>PC_FFRSALES12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>599.92</td>

</tr> <tr>

<th>Minimum</th> <td>364.11</td>

</tr> <tr>

<th>Maximum</th> <td>1035.4</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram308177424422470184”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives308177424422470184,#minihistogram308177424422470184”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives308177424422470184”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles308177424422470184”

aria-controls=”quantiles308177424422470184” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram308177424422470184” aria-controls=”histogram308177424422470184”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common308177424422470184” aria-controls=”common308177424422470184”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme308177424422470184” aria-controls=”extreme308177424422470184”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles308177424422470184”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>364.11</td>

</tr> <tr>

<th>5-th percentile</th> <td>470.64</td>

</tr> <tr>

<th>Q1</th> <td>530.27</td>

</tr> <tr>

<th>Median</th> <td>611.29</td>

</tr> <tr>

<th>Q3</th> <td>650.72</td>

</tr> <tr>

<th>95-th percentile</th> <td>728.77</td>

</tr> <tr>

<th>Maximum</th> <td>1035.4</td>

</tr> <tr>

<th>Range</th> <td>671.28</td>

</tr> <tr>

<th>Interquartile range</th> <td>120.46</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>78.702</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.13119</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.1102</td>

</tr> <tr>

<th>Mean</th> <td>599.92</td>

</tr> <tr>

<th>MAD</th> <td>64.326</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.33739</td>

</tr> <tr>

<th>Sum</th> <td>1878900</td>

</tr> <tr>

<th>Variance</th> <td>6194</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram308177424422470184”>

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rD%2B93//V127dtWtt97qGXvttdfUsWNHZWVlWdtPbW2t5s2bp759%2B%2Bqyyy7TI4884rnf1s6dOzVx4kQ5nU6NHz9eO3bs8Hrs5s2bNWzYMDmdTmVmZurw4cPW6gIAAM2fzwPWZ599plmzZikmJsYz1q5dO91777366KOPrO1n4cKF2rp1q5566iktW7ZMzz//vPLy8nT8%2BHFlZGQoJSVFGzduVGJioqZMmaLjx49L%2Bv5y5Zw5czR16lTl5eWpsrJSs2fPtlYXAABo/nz%2BHiyHw6HKysoG41VVVdbu6F5RUaENGzbo6aefVp8%2BfSRJt912m1wulxwOh8LDw3XvvfcqJCREc%2BbM0ZYtW/T6669r3Lhxeu655zRixAiNHTtWkrR48WINHTpUxcXF6tKli5X6AABA8%2BbzM1hDhgzRwoUL9c0333jGiouLlZWVpcGDB1vZR35%2Bvlq1aqXU1FTPWEZGhrKysuRyuZScnKyQkBBJ39/0NCkpSYWFhZIkl8ullJQUz%2BM6duyoTp06yeVyWakNAAA0fz4PWDNnzlRtba2uuuoq9evXT/369dOVV16pEydOWLsUV1xcrLi4OG3atEnDhw/X7373O%2BXk5Ki%2Bvl5ut1uxsbFe27dv316HDh2S9P0NUH9uHgAA4Ex8fomwffv2evHFF7V161YVFRXJ4XDooosu0oABAzxnlX6t48ePa//%2B/Vq3bp2ysrLkdrs1d%2B5cRUZGqqqqSmFhYV7bh4WFqba2VpJUXV39s/ONUVpaKrfb7TXmcEQ1CG7nstDQFl7fgxE9wA8F83HAa4EeSPTgbPnlo3JCQ0M1ePBga5cET%2BdwOHT06FEtW7ZMcXFxkqSDBw9q7dq16tq1a4OwVFtbq4iICElSeHj4j85HRkY2ev95eXnKzs72GsvMzNS0adN%2ByXL8Kjq68eturugBJI4DiR5I9ECiB43l84Dldru1fPlyFRQU6MSJEw3e2P7222//6n3ExMQoPDzcE64k6cILL1RJSYlSU1NVVlbmtX1ZWZnn7FKHDh1%2BdP6Hf/V4Junp6UpLS/MacziiVF5%2B7GyX4jehoS0UHR2pysoqnTxZ7%2B9y/IIe4IeC%2BTjgtUAPpMDtQdu2Lf2yX58HrD/96U/asWOHRo0apfPPP79J9uF0OlVTU6N9%2B/bpwgsvlCTt3btXcXFxcjqdeuKJJ2SMUUhIiIwxKigo0O233%2B55bH5%2BvsaNGydJKikpUUlJiZxOZ6P3Hxsb2%2BByoNt9RHV1gXNAnnLyZH1A1m0TPYDEcSDRA4keSPSgsXwesD766CM9%2BeSTXn%2BpZ9tvf/tbXX755Zo9e7YefPBBud1u5ebm6o477tDw4cO1bNkyLVq0SL///e%2B1bt06VVVVacSIEZKkSZMm6aabblJCQoLi4%2BO1aNEiXX755dyiAQAANJrP36kWFRWl9u3bN/l%2Bli5dqt/85jeaNGmSZs6cqRtuuEE33XSTWrVqpVWrVnnOUrlcLuXm5ioqKkqSlJiYqPnz5ysnJ0eTJk1S69atrd5hHgAANH8hxtbdPRvp4YcfVmVlpebPn%2B/5cOdg4HYf8XcJZ8XhaKG2bVuqvPxY0J4KDrQejFj%2Bob9LaLY%2BWzQ8YI6DphBor4WmQA8CtwcxMU3zdqQz8fklwoqKCm3evFnvvvuuunTp0uCWCM8%2B%2B6yvSwIAALDKL7dpGD16tD92CwAA4BM%2BD1i8nwkAADR3frkda2lpqbKzs3XPPffoX//6l15//XXt3bvXH6UAAABY5/OAtX//fl199dV68cUX9cYbb%2Bj48eN67bXXNH78eD5QGQAANAs%2BD1gPPfSQhg0bprfeekvnnXeeJOmRRx5RWlqali5d6utyAAAArPN5wCooKNCtt97q9cHODodDd955p3bu3OnrcgAAAKzzecCqr69XfX3D%2B2ccO3YsqO6LBQAAmi%2BfB6xBgwZp1apVXiGroqJCS5YsUf/%2B/X1dDgAAgHU%2BD1izZs3Sjh07NGjQINXU1OiOO%2B7Q0KFDdeDAAc2cOdPX5QAAAFjn8/tgdejQQZs2bdLmzZv11Vdfqb6%2BXpMmTdKYMWPUqlUrX5cDAABgnV/u5B4ZGamJEyf6Y9cAAABNzucBa/LkyT87z2cRAgCAQOfzgBUXF%2Bf1c11dnfbv369du3bp5ptv9nU5AAAA1p0zn0WYk5OjQ4cO%2BbgaAAAA%2B/zyWYQ/ZsyYMfrb3/7m7zIAAAB%2BtXMmYH3%2B%2BefcaBQAADQL58Sb3I8ePap//OMfuv76631dDgAAgHU%2BD1idOnXy%2BhxCSTrvvPN044036pprrvF1OQAAANb5PGA99NBDvt4lAACAT/k8YH366aeN3rZv375NWAkAAEDT8HnAuummmzyXCI0xnvHTx0JCQvTVV1/5ujwAAIBfzecBa%2BXKlVq4cKFmzJih1NRUhYWF6YsvvtD8%2BfN17bXXauTIkb4uCQAAwCqf36YhKytLc%2BfO1VVXXaW2bduqZcuW6t%2B/v%2BbPn6%2B1a9cqLi7O8wUAABCIfB6wSktLfzQ8tWrVSuXl5b4uBwAAwDqfB6yEhAQ98sgjOnr0qGesoqJCS5Ys0YABA3xdDgAAgHU%2Bfw/W/fffr8mTJ2vIkCHq1q2bjDH65z//qZiYGD377LO%2BLgcAAMA6nwes7t2767XXXtPmzZu1Z88eSdINN9ygUaNGKTIy0tflAAAAWOfzgCVJrVu31sSJE3XgwAF16dJF0vd3cwcAAGgOfP4eLGOMli5dqr59%2B2r06NE6dOiQZs6cqTlz5ujEiRO%2BLgcAAMA6nwesNWvW6KWXXtIDDzygsLAwSdKwYcP01ltvKTs729flAAAAWOfzgJWXl6e5c%2Bdq3Lhxnru3jxw5UgsXLtQrr7zi63IAAACs83nAOnDggHr27NlgvEePHnK73b4uBwAAwDqfv8k9Li5OX3zxhTp37uw1vmXLFs8b3oFzwRVL3/d3CQCAAOXzgPVf//Vfmjdvntxut4wx2rZtm/Ly8rRmzRrNmjXL1%2BUAAABY5/OANX78eNXV1enxxx9XdXW15s6dq3bt2mn69OmaNGmSr8sBAACwzucBa/PmzRo%2BfLjS09N1%2BPBhGWPUvn17X5cBAADQZHz%2BJvf58%2Bd73szerl07whUAAGh2fB6wunXrpl27dvl6twAAAD7j80uEPXr00B//%2BEc9%2BeST6tatm8LDw73ms7KyfF0SAACAVT4PWPv27VNycrIkcd8rAADQLPkkYC1evFhTp05VVFSU1qxZ44tdAgAA%2BI1P3oP19NNPq6qqymssIyNDpaWlvtg9AACAT/kkYBljGox9%2Bumnqqmp8cXuAQAAfMrnf0UIAADQ3BGwAAAALPNZwAoJCfHVrgAAAPzKZ7dpWLhwodc9r06cOKElS5aoZcuWXttxHywAABDofHIGq2/fvnK73Tpw4IDnKzExUeXl5V5jBw4csL7vjIwMzZo1y/Pzzp07NXHiRDmdTo0fP147duzw2n7z5s0aNmyYnE6nMjMzdfjwYes1AQCA5s0nZ7D8de%2BrV199Ve%2B9956uvfZaSdLx48eVkZGhq6%2B%2BWg899JDWrl2rKVOm6M0331RUVJS2b9%2BuOXPmaN68eerRo4cWLVqk2bNna9WqVX6pHwAABKZm%2Byb3iooKLV68WPHx8Z6x1157TeHh4br33nvVvXt3zZkzRy1bttTrr78uSXruuec0YsQIjR07Vj169NDixYv13nvvqbi42F/LAAAAAajZBqyHH35YY8aM0UUXXeQZc7lcSk5O9rzhPiQkRElJSSosLPTMp6SkeLbv2LGjOnXqJJfL5dviAQBAQPP5ZxH6wrZt2/TZZ5/plVde0YMPPugZd7vdXoFLktq3b6%2BioiJJUmlpqWJjYxvMHzp06Kz2X1pa2uBzFh2OqAbPfS4LDW3h9R0IdsH8WuD3AT2Q6MHZanYBq6amRg888IDmzp2riIgIr7mqqiqFhYV5jYWFham2tlaSVF1d/bPzjZWXl6fs7GyvsczMTE2bNu2snudcEB0d6e8SgHMCrwV6INEDiR40VrMLWNnZ2br00ks1ePDgBnPh4eENwlJtba0niP3UfGTk2R1M6enpSktL8xpzOKJUXn7srJ7Hn0JDWyg6OlKVlVU6ebLe3%2BUAfhfMrwV%2BH9ADKXB70LZtyzNv1ASaXcB69dVXVVZWpsTEREnyBKY33nhDo0ePVllZmdf2ZWVlnkt3HTp0%2BNH5mJiYs6ohNja2weVAt/uI6uoC54A85eTJ%2BoCsG7CN1wI9kOiBRA8aq9kFrDVr1qiurs7z89KlSyVJf/zjH/Xpp5/qiSeekDFGISEhMsaooKBAt99%2BuyTJ6XQqPz9f48aNkySVlJSopKRETqfT9wsBAAABq9kFrLi4OK%2BfT90pvmvXrmrfvr2WLVumRYsW6fe//73WrVunqqoqjRgxQpI0adIk3XTTTUpISFB8fLwWLVqkyy%2B/XF26dPH5OgAAQOAKqj8FaNWqlVatWuU5S%2BVyuZSbm6uoqChJUmJioubPn6%2BcnBxNmjRJrVu35qN7AADAWQsxxhh/FxEM3O4j/i7hrDgcLdS2bUuVlx8L2mvtI5Z/6O8ScI74bNHwoH4t8PuAHkiB24OYmPP9st%2BgOoMFAADgCwQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACxz%2BLsABA8%2BPBkAECw4gwUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALDM4e8CAOBclzLndX%2BXcFb%2BNn2gv0sAgh5nsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsa7YB67vvvtO0adOUmpqqwYMHKysrSzU1NZKk4uJi3XLLLUpISNDIkSP1wQcfeD1269atGj16tJxOpyZPnqzi4mJ/LAEAAASoZhmwjDGaNm2aqqqq9Ne//lWPPvqo/v73v2v58uUyxigzM1MXXHCBNmzYoDFjxmjq1Kk6ePCgJOngwYPKzMzUuHHj9MILL6hdu3a68847ZYzx86oAAECgaJafRbh3714VFhbqww8/1AUXXCBJmjZtmh5%2B%2BGENGTJExcXFWrdunaKiotS9e3dt27ZNGzZs0F133aX169fr0ksv1W233SZJysrK0sCBA/XJJ5%2BoX79%2B/lwWAAAIEM3yDFZMTIyefPJJT7g65ejRo3K5XOrVq5eioqI848nJySosLJQkuVwupaSkeOYiIyPVu3dvzzwAAMCZNMuAFR0drcGDB3t%2Brq%2Bv13PPPaf%2B/fvL7XYrNjbWa/v27dvr0KFDknTGeQAAgDNplpcIT7dkyRLt3LlTL7zwglavXq2wsDCv%2BbCwMNXW1kqSqqqqfna%2BMUpLS%2BV2u73GHI6oBsHtXBYa2sLrO4DA4XDYfd3y%2B4AeSPTgbDX7gLVkyRI988wzevTRR3XxxRcrPDxcFRUVXtvU1tYqIiJCkhQeHt4gTNXW1io6OrrR%2B8zLy1N2drbXWGZmpqZNm/YLV%2BE/0dGR/i4BwFlq27Zlkzwvvw/ogUQPGqtZB6wFCxZo7dq1WrJkia666ipJUocOHbR7926v7crKyjxnlzp06KCysrIG8z179mz0ftPT05WWluY15nBEqbz82C9Zhl%2BEhrZQdHSkKiurdPJkvb/LAXAWbP%2Bu4fcBPZACtwdN9R%2BOM2m2ASs7O1vr1q3TI488ouHDh3vGnU6ncnNzVV1d7TlrlZ%2Bfr%2BTkZM98fn6%2BZ/uqqirt3LlTU6dObfS%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%2BjvEgAAwGk4gwUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDIC1o%2BoqanRfffdp5SUFA0aNEh/%2Bctf/F0SAAAIIA5/F3AuWrx4sXbs2KFnnnlGBw8e1MyZM9WpUycNHz7c36UBAIAAQMA6zfHjx7V%2B/Xo98cQT6t27t3r37q2ioiL99a9/JWABAIBG4RLhab7%2B%2BmvV1dUpMTHRM5acnCyXy6X6%2Bno/VgYAAAIFZ7BO43a71bZtW4WFhXnGLrjgAtXU1KiiokLt2rU743OUlpbK7XZ7jTkcUYqNjbVeLwCczuGw%2B3/n0NAWXt9tumLp%2B9afsym9M/M//V2C3zTlcdAcEbBOU1VV5RWuJHl%2Brq2tbdRz5OXlKTs722ts6tSpuuuuu%2BwU%2BQOfLWqay5alpaXKy8tTenp60AZDekAPJHogfd%2BDZ555skl60FS/w2w7dRxUVydxHATxa%2BFsEENPEx4e3iBInfo5IiKiUc%2BRnp6ujRs3en2lp6dbr7Upud1uZWdnNzgTF0zoAT2Q6IFEDyR6INGDs8UZrNN06NBB5eXlqqurk8PxfXvcbrciIiIUHR3dqOeIjY0l3QMAEMQ4g3Wanj17yuFwqLCw0DOWn5%2Bv%2BPh4tWhBuwAAwJmRGE4TGRmpsWPH6sEHH9T27dv11ltv6S9/%2BYsmT57s79IAAECACH3wwQcf9HcR55r%2B/ftr586dWrZsmbZt26bbb79d48eP93dZPteyZUulpqaqZcuW/i7Fb%2BgBPZDogUQPJHog0YOzEWKMMf4uAgAAoDnhEiEAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgJWkMvIyNCsWbM8P%2B/cuVMTJ06U0%2BnU%2BPHjtWPHDq/tN2/erGHDhsnpdCozM1OHDx/2dclWvPnmm7rkkku8vqZNmyYpeHpQW1urefPmqW/fvrrsssv0yCOP6NR9h4OhBxs3bmxwDFxyySXq0aOHpODogSSVlJRoypQpSkpKUlpamlavXu2ZC5Ye/Otf/9K0adOUkpKiK664Qhs3bvTMFRcX65ZbblFCQoJGjhypDz74wOuxW7du1ejRo%2BV0OjV58mQVFxf7uvxfpba2VqNHj9bHH3/sGfu1a169erUGDx6sxMRE3XfffaqqqvLJWs45BkFr8%2BbN5uKLLzYzZ840xhhz7NgxM3DgQPPQQw%2BZ3bt3mwULFpjLLrvMHDt2zBhjjMvlMn369DEvvvii%2Beqrr8yNN95oMjIy/LmEX2zFihVmypQpprS01PP173//O6h68Kc//clceeWVxuVyma1bt5p%2B/fqZtWvXBk0PqqqqvP79Dx48aK644gqzaNGioOmBMcZcd911Zvr06Wbfvn3mzTffNE6n0/zf//1f0PSgvr7epKenm4kTJ5ovv/zSvPPOO6Zv377mjTfeMPX19ebqq68299xzj9m9e7dZuXKlcTqd5ttvvzXGGPPtt9%2BahIQE89RTT5ldu3aZ//mf/zGjR4829fX1fl5V41RXV5vMzExz8cUXm48%2B%2BsgYY371ml9//XWTnJxs3nnnHeNyuczIkSPNvHnz/LZGfyJgBany8nIzZMgQM378eE/AWr9%2BvUlLS/O8UOrr680VV1xhNmzYYIwxZsaMGZ5tjTHm4MGD5pJLLjHffPON7xfwK91zzz1m2bJlDcaDpQfl5eWmV69e5uOPP/aMrVq1ysyaNStoenC6lStXmmHDhpmampqg6UFFRYW5%2BOKLzT/%2B8Q/P2NSpU828efOCpgfbt283F198sVfdq1atMtddd53ZunWrSUhI8IRKY4y5%2BeabzZ///GdjjDHLly83N954o2fu%2BPHjJjEx0RNWzmVFRUXmmmuuMVdffbVXwPq1a77%2B%2Bus92xpjzKeffmr69Oljjh8/7otlnVO4RBikHn74YY0ZM0YXXXSRZ8zlcik5OVkhISGSpJCQECUlJamwsNAzn5KS4tm%2BY8eO6tSpk1wul2%2BLt2DPnj3q1q1bg/Fg6UF%2Bfr5atWql1NRUz1hGRoaysrKCpgc/VFFRoSeeeEL33HOPwsLCgqYHERERioyM1MaNG3XixAnt3btXBQUF6tmzZ9D0oLi4WO3atVOXLl08Y5dccol27Nih/Px89erVS1FRUZ655OTkn%2BxBZGSkevfu7Zk/l33yySfq16%2Bf8vLyvMZdLtcvXvPJkyf1xRdfeM0nJCToxIkT%2Bvrrr5t4ReceAlYQ2rZtmz777DPdeeedXuNut1uxsbFeY%2B3bt9ehQ4ckSaWlpT87HyiMMdq3b58%2B%2BOADXXXVVRo2bJiWLl2q2traoOlBcXGx4uLitGnTJg0fPly/%2B93vlJOTo/r6%2BqDpwQ%2BtXbtWsbGxGj58uKTgeS2Eh4dr7ty5ysvLk9Pp1IgRIzRkyBBNnDgxaHpwwQUX6MiRI17vEzp06JDq6urO2IMzzZ/Lrr/%2Bet13332KjIz0Gv81a66srFRNTY3XvMPhUJs2bQKiJ7Y5/F0AfKumpkYPPPCA5s6dq4iICK%2B5qqoqhYWFeY2FhYWptrZWklRdXf2z84Hi4MGDnrUuX75cBw4c0MKFC1VdXR00PTh%2B/Lj279%2BvdevWKSsrS263W3PnzlVkZGTQ9OAUY4zWr1%2BvP/zhD56xYOrBnj17NHToUN16660qKirSggULNGDAgKDpgdPpVGxsrBYsWKD7779fbrdbTz/9tKTv3wD%2Bc2s8U48C0ZnW9HPz1dXVnp9/6vHBhIAVZLKzs3XppZdq8ODBDebCw8MbvAhqa2s9Qeyn5k//H9C5Li4uTh9//LFat26tkJAQ9ezZU/X19ZoxY4ZSU1ODogcOh0NHjx7VsmXLFBcXJ%2Bn74Ll27Vp17do1KHpwyhdffKHvvvtOo0aN8owFy2th27ZteuGFF/Tee%2B8pIiJC8fHx%2Bu677/T444%2BrS5cuQdGD8PBwLV%2B%2BXNOnT1dycrLat2%2BvP/zhD8rKylJISMgv6kF0dLTP6rctPDxcFRUVXmONXXN4eLjn59PnA%2B24sIFLhEHm1Vdf1VtvvaXExEQlJibqlVde0SuvvKLExER16NBBZWVlXtuXlZV5Tvf%2B1HxMTIzP6relTZs2nveWSFL37t1VU1OjmJiYoOhBTEyMwsPDPeFKki688EKVlJQE1XEgSe%2B//75SUlLUunVrz1iw9GDHjh3q2rWr19nsXr166eDBg0HTA0nq06eP3nnnHW3ZskXvvvuuLrzwQrVt21a/%2Bc1vgqYHp/yaf/c2bdooPDzca76urk4VFRUB3ZNfioAVZNasWaNXXnlFmzZt0qZNm5SWlqa0tDRt2rRJTqdTn3/%2BuedeSMYYFRQUyOl0Svr%2BVHp%2Bfr7nuUpKSlRSUuKZDxTvv/%2B%2B%2BvXr5/Wei6%2B%2B%2Bkpt2rRRcnJyUPTA6XSqpqZG%2B/bt84zt3btXcXFxQXMcnLJ9%2B3YlJSV5jQVLD2JjY7V//36vMw579%2B5V586dg6YHFRUVmjRpksrLyxUTEyOHw6F3331Xqampcjqd%2BvLLLz2XvqTv/0Dkp3pQVVWlnTt3BlwPfujXrLlFixaKj4/3mi8sLJTD4fDcXy6o%2BOePF3GumDlzpudPrY8cOWL69%2B9vFixYYIqKisyCBQvMwIEDPX%2BuW1BQYHr37m2ef/55z31vpkyZ4s/yf5EjR46YwYMHm7vvvtvs2bPHvPvuu2bQoEEmNzc3aHpgjDEZGRkmPT3dfPXVV2bLli2mf//%2B5plnngmqHhhjzNChQ83mzZu9xoKlB5WVlWbgwIFmxowZZu/evebtt982qampZu3atUHTA2OMueaaa8zs2bPNN998Y55//nkTHx9vXC6XqaurMyNHjjTTp083u3btMqtWrTIJCQmee0IVFxeb%2BPh4s2rVKs89oa6%2B%2BuqAuQ/WKT%2B8TcOvXfPmzZtNUlKSefPNN43L5TKjRo0yCxYs8Nva/ImAFeR%2BGLCM%2Bf7mgWPHjjXx8fFmwoQJ5ssvv/TafsOGDeY///M/TUJCgsnMzDSHDx/2dclW7Nq1y9xyyy0mISHBDBw40Dz22GOeXxDB0oPKykozY8YMk5CQYAYMGBCUPTDGmPj4eLNly5YG48HSg6KiInPLLbeYpKQkM2zYMPP0008H3XGwZ88ec%2BONNxqn02lGjRpl3nnnHc/cP//5T3PDDTeYSy%2B91IwaNcp8%2BOGHXo999913zZVXXmn69Oljbr755oC7D5gx3gHLmF%2B/5lWrVpkBAwaY5ORkM3v2bFNdXe2TdZxrQoz5/%2Bd/AQAAYAXvwQIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAy/4fQLtG1YD1Jz8AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common308177424422470184”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>728.7686454396865</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>666.1460080361885</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>635.0676982024834</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>677.7319606459758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>567.2954634409169</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>608.807173221857</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>608.7322135075386</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>644.8528183806021</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>479.4927804167687</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>665.3183915386014</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme308177424422470184”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>364.1120020193209</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>425.6834691595228</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436.6822439664065</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436.7831114750767</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>447.1769309921483</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>677.7319606459758</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>694.2785858317413</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>728.7686454396865</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>836.4084339862886</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1035.3916078727304</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FSRSALES07”>PC_FSRSALES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>52</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>624.16</td>

</tr> <tr>

<th>Minimum</th> <td>371.85</td>

</tr> <tr>

<th>Maximum</th> <td>1930.2</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4848485919664068766”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-4848485919664068766,#minihistogram-4848485919664068766”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-4848485919664068766”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4848485919664068766”

aria-controls=”quantiles-4848485919664068766” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4848485919664068766” aria-controls=”histogram-4848485919664068766”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4848485919664068766” aria-controls=”common-4848485919664068766”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4848485919664068766” aria-controls=”extreme-4848485919664068766”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4848485919664068766”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>371.85</td>

</tr> <tr>

<th>5-th percentile</th> <td>444.92</td>

</tr> <tr>

<th>Q1</th> <td>534.38</td>

</tr> <tr>

<th>Median</th> <td>617.34</td>

</tr> <tr>

<th>Q3</th> <td>711.77</td>

</tr> <tr>

<th>95-th percentile</th> <td>870.87</td>

</tr> <tr>

<th>Maximum</th> <td>1930.2</td>

</tr> <tr>

<th>Range</th> <td>1558.3</td>

</tr> <tr>

<th>Interquartile range</th> <td>177.39</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>128.03</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.20513</td>

</tr> <tr>

<th>Kurtosis</th> <td>6.3488</td>

</tr> <tr>

<th>Mean</th> <td>624.16</td>

</tr> <tr>

<th>MAD</th> <td>97.739</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.2853</td>

</tr> <tr>

<th>Sum</th> <td>1954900</td>

</tr> <tr>

<th>Variance</th> <td>16393</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4848485919664068766”>

<img 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/Ejn7WRz9rI13T8smAdPXpUf/nLX/TNN99o2bJl6tKli3suKipKZWVlHtcvKytT9%2B7d1bp1a4WEhKisrEyXXHKJJKm2tlYVFRWKiIho9PbT0tKUnJzsMWazham8/Ni5hzoHQUGBCg8PVWVllerq6r26bX/g7cfrZP7%2B%2BJHP2shnbedTPl%2BVLL8rWPX19crIyNC3336rFStWuIvST%2Bx2u/Ly8tyXq6qqtGfPHmVkZCgwMFCxsbHKy8tTnz59JP14Ti2bzaZu3bo1eg2RkZENDgc6nUdUW%2BubJ3FdXb3Ptm1lzeVn5u%2BPH/msjXzWdj7k8xW/O/i6Zs0abd%2B%2BXY8%2B%2BqjCw8PldDrldDpVUVEhSRo5cqR27typRYsWqaCgQFOmTFGnTp3chWrs2LF64YUXtGXLFu3atUvTp0/X6NGjz%2BoQIQAAOL/53R6sN998U/X19ZowYYLHeFJSklasWKFOnTrpmWee0V//%2BlfNnz9f8fHxmj9/vgICAiRJw4YN08GDBzVt2jS5XC5dffXVuv/%2B%2B30RBQAAWJRfFKwvvvjC/f0LL7zwi9e/4oordMUVV5x2fvz48Ro/frwpawMAAOcfvztECAAA4GsULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNZvmC5XC6lpKRo%2B/bt7rGioiLdfPPNiouL09ChQ/X%2B%2B%2B973Gbr1q1KSUmR3W7XuHHjVFRU5DG/dOlSDRw4UPHx8XrwwQdVVVXllSwAAMA/WLpg1dTUaOLEiSooKHCPGYah9PR0tW/fXmvXrtXw4cOVkZGh4uJiSVJxcbHS09OVmpqqNWvWqG3btrrrrrtkGIYk6c0331ROTo5mzpypZcuWyeFwaM6cOT7JBwAArMnrBWvUqFFatWqVjhw58qvup7CwUKNHj9Y333zjMf7hhx%2BqqKhIM2fO1CWXXKIJEyYoLi5Oa9eulSStXr1al112mW699Vb97ne/U3Z2tg4ePKgdO3ZIkpYvX66bbrpJgwYNUs%2BePTVjxgytXbuWvVgAAKDRvF6w%2BvbtqwULFmjAgAGaOHGi3n//fffeo7OxY8cO9enTR7m5uR7jDodDPXr0UFhYmHssISFB%2Bfn57vnExET3XGhoqGJiYpSfn6%2B6ujp9%2BumnHvNxcXE6ceKE9u7de9ZrBAAA5yebtzd47733auLEidq6das2bNigu%2B%2B%2BW%2BHh4RoxYoRGjBihiy66qFH3M3bs2FOOO51ORUZGeoy1a9dOhw4d%2BsX5yspK1dTUeMzbbDa1bt3afXsAAIBf4vWCJUkBAQHq37%2B/%2Bvfvr6qqKq1YsULPPvusFi1apF69eummm27S1VdffU73XVVVpeDgYI%2Bx4OBguVyuX5yvrq52Xz7d7RujtLRUTqfTY8xmC2tQ7JpaUFCgx1ecHZvNtz83f3/8yGdt5LM28jU9nxQs6ccS8sorr%2BiVV17Rvn371KtXL/3xj3/UoUOH9NBDD%2Bmjjz7S1KlTz/p%2BQ0JCVFFR4THmcrnUokUL9/zJZcnlcik8PFwhISHuyyfPh4aGNnoNubm5ysnJ8RhLT09XZmZmo%2B/DTOHhjV87/qNNm5a%2BXoIk/3/8yGdt5LM28jUdrxesjRs3auPGjdq%2Bfbvatm2rESNG6Omnn1aXLl3c1%2BnQoYNmzZp1TgUrKipKhYWFHmNlZWXuvUdRUVEqKytrMN%2B9e3e1bt1aISEhKisr0yWXXCJJqq2tVUVFhSIiIhq9hrS0NCUnJ3uM2WxhKi8/dtZ5fo2goECFh4eqsrJKdXX1Xt22P/D243Uyf3/8yGdt5LO28ymfr0qW1wvW1KlTNWjQIM2fP1%2BXX365AgMb7r67%2BOKL9ec///mc7t9ut2vRokWqrq5277XKy8tTQkKCez4vL899/aqqKu3Zs0cZGRkKDAxUbGys8vLy1KdPH0lSfn6%2BbDabunXr1ug1REZGNjgc6HQeUW2tb57EdXX1Ptu2lTWXn5m/P37kszbyWdv5kM9XvF6w3nvvPbVp00YVFRXucrVr1y7FxMQoKChIktSrVy/16tXrnO4/KSlJHTp00JQpU3TXXXfpnXfe0a5du5SdnS1JGjlypF544QUtWrTIXfQ6derkLlRjx47VtGnT1LVrV0VGRmr69OkaPXr0WR0iBAAA5zevv/vr6NGjuvbaa/X888%2B7x8aPH6/hw4erpKTkV99/UFCQnn32WTmdTqWmpuqVV17R/PnzFR0dLUnq1KmTnnnmGa1du1bXX3%2B9KioqNH/%2BfAUEBEiShg0bpgkTJmjatGm69dZb1bNnT91///2/el0AAOD8EWCcy0mofoU77rhD9fX1mjVrlvt9TYcPH9bkyZMVGhqqp59%2B2pvL8Rqn89edWPVc2GyBatOmpcrLjzWLXcBDnvrA10s4K6/f09%2Bn229uj5/ZyGdt5LO28ymfrz6w5PU9WB9//LEeeOABjzeNt23bVpMmTdKHH37o7eUAAACYzusFy2azqbKyssF4VVXVOZ3RHQAAoLnxesG6/PLL9eijj3r8DsGioiJlZ2dr4MCB3l4OAACA6bz%2BKcLJkyfrlltu0TXXXKPw8HBJUmVlpWJiYjRlyhRvLwcAAMB0Xi9Y7dq10/r167V161YVFBTIZrPp0ksvVb9%2B/dyf5AMAALAyn/yqnKCgIA0cOJBDggAAwC95vWA5nU499dRT2rlzp06cONHgje1vv/22t5cEAABgKq8XrIcffli7d%2B/WsGHD9F//9V/e3jwAAECT83rB%2BvDDD7V48WIlJiZ6e9MAAABe4fXTNISFhaldu3be3iwAAIDXeL1gDR8%2BXIsXL1ZdXZ23Nw0AAOAVXj9EWFFRoc2bN%2Btf//qXOnfurODgYI/55cuXe3tJAAAApvLJaRpSUlJ8sVkAAACv8HrBys7O9vYmAQAAvMrr78GSpNLSUuXk5Ojee%2B/V999/rzfeeEMHDhzwxVIAAABM5/WC9fXXX%2Bu6667T%2BvXr9eabb%2Br48eN67bXXNHLkSDkcDm8vBwAAwHReL1iPPfaYBg8erC1btug3v/mNJOmJJ55QcnKy5s6d6%2B3lAAAAmM7rBWvnzp265ZZbPH6xs81m01133aU9e/Z4ezkAAACm83rBqq%2BvV319fYPxY8eOKSgoyNvLAQAAMJ3XC9aAAQO0cOFCj5JVUVGhOXPmqG/fvt5eDgAAgOm8XrAeeOAB7d69WwMGDFBNTY3uvPNODRo0SN9%2B%2B60mT57s7eUAAACYzuvnwYqKitKGDRu0efNmff7556qvr9eYMWM0fPhwtWrVytvLAQAAMJ1PzuQeGhqqUaNG%2BWLTAAAATc7rBWvcuHFnnOd3EQIAAKvzesHq2LGjx%2BXa2lp9/fXX2rdvn2666SZvLwcAAMB0zeZ3Ec6fP1%2BHDh3y8moAAADM55PfRXgqw4cP1%2Buvv27a/ZWUlGjChAnq1auXkpOTtXTpUvfcnj17NGrUKNntdo0cOVK7d%2B/2uO3mzZs1ePBg2e12paen6/Dhw6atCwAA%2BL9mU7A%2B%2BeQTU080es899ygsLEzr1q3Tgw8%2BqKeeekr/%2BMc/dPz4cY0fP16JiYlat26d4uPjNWHCBB0/flyStGvXLk2dOlUZGRnKzc1VZWWlpkyZYtq6AACA/2sWb3I/evSovvjiC40dO9aUbfzwww/Kz89XVlaWunTpoi5dumjgwIHatm2bfvjhB4WEhGjSpEkKCAjQ1KlT9d577%2BmNN95QamqqVq5cqSFDhmjEiBGSpNmzZ2vQoEEqKipS586dTVkfAADwb17fgxUdHa2OHTt6/LnsssuUlZVl2olGW7RoodDQUK1bt04nTpzQgQMHtHPnTnXv3l0Oh0MJCQnu34UYEBCgXr16KT8/X5LkcDiUmJjovq8OHTooOjpaDofDlLUBAAD/5/U9WI899liTbyMkJETTpk1TVlaWli9frrq6OqWmpmrUqFF6%2B%2B23demll3pcv127diooKJAklZaWKjIyssE8b8AHAACN5fWC9dFHHzX6ur179z7n7ezfv1%2BDBg3SLbfcooKCAmVlZalfv36qqqpScHCwx3WDg4PlcrkkSdXV1Wecb4zS0lI5nU6PMZstrEFxa2pBQYEeX3F2bDbf/tz8/fEjn7WRz9rI1/S8XrBuvPFG9%2BE5wzDc4yePBQQE6PPPPz%2BnbWzbtk1r1qzRu%2B%2B%2BqxYtWig2NlbfffednnvuOXXu3LlBWXK5XGrRooWkH/d%2BnWo%2BNDS00dvPzc1VTk6Ox1h6eroyMzPPKc%2BvFR7e%2BLXjP9q0aenrJUjy/8ePfNZGPmsjX9PxesFasGCBHn30Ud1///1KSkpScHCwPv30U82cOVN//OMfNXTo0F%2B9jd27d%2BvCCy90lyZJ6tGjhxYsWKDExESVlZV5XL%2BsrMy9dykqKuqU8xEREY3eflpampKTkz3GbLYwlZcfO9sov0pQUKDCw0NVWVmlurp6r27bH3j78TqZvz9%2B5LM28lnb%2BZTPVyXLJycanTZtmi6//HL3WN%2B%2BfTVz5kxNmjRJt99%2B%2B6/eRmRkpL7%2B%2Bmu5XC734b4DBw6oU6dOstvtev7552UYhgICAmQYhnbu3Kk77rhDkmS325WXl6fU1FRJP55Pq6SkRHa7/ay2f/LhQKfziGprffMkrqur99m2ray5/Mz8/fEjn7WRz9rOh3y%2B4vWDk6WlpQ1%2BXY4ktWrVSuXl5aZsIzk5Wb/5zW/00EMP6csvv9Q///lPLViwQDfeeKOuvfZaVVZWatasWSosLNSsWbNUVVWlIUOGSJLGjBmjjRs3avXq1dq7d68mTZqkK6%2B8klM0AACARvN6wYqLi9MTTzyho0ePuscqKio0Z84c9evXz5Rt/Nd//ZeWLl0qp9Op66%2B/XtnZ2brzzjuVlpamVq1aaeHChe69VA6HQ4sWLVJYWJgkKT4%2BXjNnztT8%2BfM1ZswYXXDBBaf99T4AAACnEmD8/J3mXrB//36NGzdOVVVV6tKliwzD0FdffaWIiAgtX75cv/3tb725HK9xOo94fZs2W6DatGmp8vJjzWIX8JCnPvD1Es7K6/f09%2Bn2m9vjZzbyWRv5rO18yuerDyx5/T1Yl1xyiV577TVt3rxZ%2B/fvlyTdcMMNGjZs2Fl9Ug8AAKC58nrBkqQLLrhAo0aN0rfffut%2Bb9NvfvMbXywFAADAdF5/D5ZhGJo7d6569%2B6tlJQUHTp0SJMnT9bUqVN14sQJby8HAADAdF4vWCtWrNDGjRv1yCOPuE%2BhMHjwYG3ZsqXByTkBAACsyOsFKzc3V9OmTVNqaqr77O1Dhw7Vo48%2Bqk2bNnl7OQAAAKbzesH69ttv1b179wbj3bp1a/D7%2BwAAAKzI6wWrY8eO%2BvTTTxuMv/fee5zMEwAA%2BAWvf4rwtttu04wZM%2BR0OmUYhrZt26bc3FytWLFCDzzwgLeXAwAAYDqvF6yRI0eqtrZWzz33nKqrqzVt2jS1bdtW99xzj8aMGePt5QAAAJjO6wVr8%2BbNuvbaa5WWlqbDhw/LMAy1a9fO28sAAABoMl5/D9bMmTPdb2Zv27Yt5QoAAPgdrxesLl26aN%2B%2Bfd7eLAAAgNd4/RBht27ddN9992nx4sXq0qWLQkJCPOazs7O9vSTglPjl1ACAc%2BX1gvXll18qISFBkjjvFQAA8EteKVizZ89WRkaGwsLCtGLFCm9sEgAAwGe88h6sJUuWqKqqymNs/PjxKi0t9cbmAQAAvMorBcswjAZjH330kWpqaryxeQAAAK/y%2BqcIAQAA/B0FCwAAwGReK1gBAQHe2hQAAIBPee00DY8%2B%2BqjHOa9OnDihOXPmqGXLlh7X4zxYAADA6rxSsHr37t3gnFfx8fEqLy9XeXm5N5YAAADgNV4pWJz7CgAAnE94kzsAAIDJvP6rcmAuq/2%2BPAAAzgfswQIAADCZ3xYsl8ulGTNmqHfv3vr973%2BvJ554wn1G%2BT179mjUqFGy2%2B0aOXKkdu/e7XHbzZs3a/DgwbLb7UpPT9fhw4d9EQEAAFiU3xasRx99VFu3btULL7ygefPm6eWXX1Zubq6OHz%2Bu8ePHKzExUevWrVN8fLwmTJig48ePS5J27dqlqVOnKiMjQ7m5uaqsrNSUKVN8nAYAAFiJX74Hq6KiQmvXrtWSJUvUs2dPSdKtt94qh8Mhm82mkJAQTZo0SQH9GWQyAAAbq0lEQVQBAZo6daree%2B89vfHGG0pNTdXKlSs1ZMgQjRgxQpI0e/ZsDRo0SEVFRercubMvYwEAAIvwyz1YeXl5atWqlZKSktxj48ePV3Z2thwOhxISEtxnlg8ICFCvXr2Un58vSXI4HEpMTHTfrkOHDoqOjpbD4fBuCAAAYFl%2BuQerqKhIHTt21IYNG7RgwQKdOHFCqampuvPOO%2BV0OnXppZd6XL9du3YqKCiQJJWWlioyMrLB/KFDhxq9/dLS0gYnVrXZwhrcL2Amm81a/18KCgr0%2BOpvyGdt5LO25pDPLwvW8ePH9fXXX2vVqlXKzs6W0%2BnUtGnTFBoaqqqqKgUHB3tcPzg4WC6XS5JUXV19xvnGyM3NVU5OjsdYenq6MjMzzzER8MvatGn5y1dqhsLDQ329hCZFPmsjn7X5Mp9fFiybzaajR49q3rx56tixoySpuLhYL730ki688MIGZcnlcqlFixaSpJCQkFPOh4Y2/kFKS0tTcnLySWsKU3n5sXOJAzSK1Z5fQUGBCg8PVWVllerq6n29HNORz9rIZ20/z%2BerkuWXBSsiIkIhISHuciVJF110kUpKSpSUlKSysjKP65eVlbkP30VFRZ1yPiIiotHbj4yMbHA40Ok8otpa/3sSo/mw6vOrrq7esmtvDPJZG/mszZfl0S8PvtrtdtXU1OjLL790jx04cEAdO3aU3W7XJ5984j4nlmEY2rlzp%2Bx2u/u2eXl57tuVlJSopKTEPQ8AAPBL/LJgXXzxxbryyis1ZcoU7d27V//%2B97%2B1aNEijRkzRtdee60qKys1a9YsFRYWatasWaqqqtKQIUMkSWPGjNHGjRu1evVq7d27V5MmTdKVV17JKRoAAECj%2BWXBkqS5c%2Bfq//2//6cxY8Zo8uTJuuGGG3TjjTeqVatWWrhwofLy8pSamiqHw6FFixYpLCxMkhQfH6%2BZM2dq/vz5GjNmjC644AJlZ2f7OA0AALCSAOOnY2VoUk7nkSa5X37ZM37y%2Bj39fb2Es2KzBapNm5YqLz/ml%2B8BIZ%2B1kc/afp7PV5%2Bw9ts9WAAAAL5CwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADCZ3xes8ePH64EHHnBf3rNnj0aNGiW73a6RI0dq9%2B7dHtffvHmzBg8eLLvdrvT0dB0%2BfNjbSwYAABbn1wXr1Vdf1bvvvuu%2BfPz4cY0fP16JiYlat26d4uPjNWHCBB0/flyStGvXLk2dOlUZGRnKzc1VZWWlpkyZ4qvlAwAAi/LbglVRUaHZs2crNjbWPfbaa68pJCREkyZN0iWXXKKpU6eqZcuWeuONNyRJK1eu1JAhQzRixAh169ZNs2fP1rvvvquioiJfxQAAABbktwXr8ccf1/Dhw3XppZe6xxwOhxISEhQQECBJCggIUK9evZSfn%2B%2BeT0xMdF%2B/Q4cOio6OlsPh8O7iAQCApdl8vYCmsG3bNn388cfatGmTpk%2Bf7h53Op0ehUuS2rVrp4KCAklSaWmpIiMjG8wfOnTorLZfWloqp9PpMWazhTW4b8BMNpu1/r8UFBTo8dXfkM/ayGdtzSGf3xWsmpoaPfLII5o2bZpatGjhMVdVVaXg4GCPseDgYLlcLklSdXX1GecbKzc3Vzk5OR5j6enpyszMPKv7Ac5GmzYtfb2EcxIeHurrJTQp8lkb%2BazNl/n8rmDl5OTosssu08CBAxvMhYSENChLLpfLXcRONx8aenYPUFpampKTkz3GbLYwlZcfO6v7Ac6G1Z5fQUGBCg8PVWVllerq6n29HNORz9rIZ20/z%2BerkuV3BevVV19VWVmZ4uPjJcldmN58802lpKSorKzM4/plZWXuQ3dRUVGnnI%2BIiDirNURGRjY4HOh0HlFtrf89idF8WPX5VVdXb9m1Nwb5rI181ubL8uh3BWvFihWqra11X547d64k6b777tNHH32k559/XoZhKCAgQIZhaOfOnbrjjjskSXa7XXl5eUpNTZUklZSUqKSkRHa73ftBAACAZfldwerYsaPH5ZYtf3xfyoUXXqh27dpp3rx5mjVrlv70pz9p1apVqqqq0pAhQyRJY8aM0Y033qi4uDjFxsZq1qxZuvLKK9W5c2ev5wAAANblnx8fOI1WrVpp4cKF7r1UDodDixYtUlhYmCQpPj5eM2fO1Pz58zVmzBhdcMEFys7O9vGqAQCA1fjdHqyTPfbYYx6Xe/bsqfXr15/2%2Bqmpqe5DhAAAAOfivNqDBQAA4A0ULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJP5bcH67rvvlJmZqaSkJA0cOFDZ2dmqqamRJBUVFenmm29WXFychg4dqvfff9/jtlu3blVKSorsdrvGjRunoqIiX0QAAAAW5ZcFyzAMZWZmqqqqSi%2B%2B%2BKKefPJJvfPOO3rqqadkGIbS09PVvn17rV27VsOHD1dGRoaKi4slScXFxUpPT1dqaqrWrFmjtm3b6q677pJhGD5OBQAArMLm6wU0hQMHDig/P18ffPCB2rdvL0nKzMzU448/rssvv1xFRUVatWqVwsLCdMkll2jbtm1au3at7r77bq1evVqXXXaZbr31VklSdna2%2Bvfvrx07dqhPnz6%2BjAUAACzCL/dgRUREaPHixe5y9ZOjR4/K4XCoR48eCgsLc48nJCQoPz9fkuRwOJSYmOieCw0NVUxMjHseAADgl/jlHqzw8HANHDjQfbm%2Bvl4rV65U37595XQ6FRkZ6XH9du3a6dChQ5L0i/ONUVpaKqfT6TFms4U1uF/ATDabtf6/FBQU6PHV35DP2shnbc0hn18WrJPNmTNHe/bs0Zo1a7R06VIFBwd7zAcHB8vlckmSqqqqzjjfGLm5ucrJyfEYS09PV2Zm5jkmAH5ZmzYtfb2EcxIeHurrJTQp8lkb%2BazNl/n8vmDNmTNHy5Yt05NPPqmuXbsqJCREFRUVHtdxuVxq0aKFJCkkJKRBmXK5XAoPD2/0NtPS0pScnOwxZrOFqbz82DmmAH6Z1Z5fQUGBCg8PVWVllerq6n29HNORz9rIZ20/z%2BerkuXXBSsrK0svvfSS5syZo2uuuUaSFBUVpcLCQo/rlZWVuQ/fRUVFqaysrMF89%2B7dG73dyMjIBocDnc4jqq31vycxmg%2BrPr/q6uotu/bGIJ%2B1kc/afFke/fPgq6ScnBytWrVKTzzxhIYNG%2BYet9vt%2Buyzz1RdXe0ey8vLk91ud8/n5eW556qqqrRnzx73PAAAwC/xy4K1f/9%2BPfvss7r99tuVkJAgp9Pp/pOUlKQOHTpoypQpKigo0KJFi7Rr1y5df/31kqSRI0dq586dWrRokQoKCjRlyhR16tSJUzQAAIBG88uC9fbbb6uurk7PPfecBgwY4PEnKChIzz77rJxOp1JTU/XKK69o/vz5io6OliR16tRJzzzzjNauXavrr79eFRUVmj9/vgICAnycCgAAWEWAwSnKvcLpPNIk9zvkqQ%2Ba5H5hPa/f09/XSzgrNlug2rRpqfLyY375HhDyWRv5rO3n%2BXz1CWu/3IMFAADgSxQsAAAAk/n1aRqA84mVDhdb7XAmAJwt9mABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFinUFNTowcffFCJiYkaMGCA/va3v/l6SQAAwEJsvl5AczR79mzt3r1by5YtU3FxsSZPnqzo6Ghde%2B21vl4aAACwAArWSY4fP67Vq1fr%2BeefV0xMjGJiYlRQUKAXX3yRggWcp4Y89YGvl3BWXr%2Bnv6%2BXAJz3OER4kr1796q2tlbx8fHusYSEBDkcDtXX1/twZQAAwCrYg3USp9OpNm3aKDg42D3Wvn171dTUqKKiQm3btv3F%2BygtLZXT6fQYs9nCFBkZafp6ASuy2QIVFPTj/%2B9%2B%2BgrzWGmP2z/uG%2BjrJZySvz8/ydf0KFgnqaqq8ihXktyXXS5Xo%2B4jNzdXOTk5HmMZGRm6%2B%2B67zVnkz3w86/SHLUtLS5Wbm6u0tDS/LHfks7bS0lItW7bYEvnO9PfsdM6Hx8/f81nl%2BXkuzqd84eGhPlmDf1bXXyEkJKRBkfrpcosWLRp1H2lpaVq3bp3Hn7S0NNPX%2BkucTqdycnIa7E3zF%2BSzNvJZG/msjXxNjz1YJ4mKilJ5eblqa2tls/3443E6nWrRooXCw8MbdR%2BRkZF%2B%2BT8CAADQOOzBOkn37t1ls9mUn5/vHsvLy1NsbKwCA/lxAQCAX0ZjOEloaKhGjBih6dOna9euXdqyZYv%2B9re/ady4cb5eGgAAsIig6dOnT/f1Ipqbvn37as%2BePZo3b562bdumO%2B64QyNHjvT1ss5Jy5YtlZSUpJYtW/p6KU2CfNZGPmsjn7WRr2kFGIZh%2BGTLAAAAfopDhAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYFjd%2B/Hg98MAD7st79uzRqFGjZLfbNXLkSO3evdvj%2Bps3b9bgwYNlt9uVnp6uw4cPe3vJjeJyuTRjxgz17t1bv//97/XEE0/op3Pi%2BkPGkpISTZgwQb169VJycrKWLl3qnrNyPpfLpZSUFG3fvt09VlRUpJtvvllxcXEaOnSo3n//fY/bbN26VSkpKbLb7Ro3bpyKioo85pcuXaqBAwcqPj5eDz74oKqqqryS5VROlS8/P19/%2BtOfFB8fr2uuuUarV6/2uI3V8/3kyJEjGjhwoNatW%2Bcxfqbno2EYmjt3rvr27aukpCTNnj1b9fX1TZ7jdE6Vr7i4WLfffrvsdruuuuoqvfbaax63sXq%2Bjz/%2BWKmpqYqLi9Pw4cO1detWj9tYId93332nzMxMJSUlaeDAgcrOzlZNTY2kZv76YsCyNm/ebHTt2tWYPHmyYRiGcezYMaN///7GY489ZhQWFhpZWVnG73//e%2BPYsWOGYRiGw%2BEwevbsaaxfv974/PPPjT//%2Bc/G%2BPHjfRnhtB5%2B%2BGHj6quvNhwOh7F161ajT58%2BxksvveQ3GUePHm3cc889xpdffmn84x//MOx2u/HWW29ZOl91dbWRnp5udO3a1fjwww8NwzCM%2Bvp647rrrjPuvfdeo7Cw0FiwYIFht9uNgwcPGoZhGAcPHjTi4uKMF154wdi3b5/xP//zP0ZKSopRX19vGIZhvPHGG0ZCQoLxz3/%2B03A4HMbQoUONGTNmNJt8paWlRmJiojFv3jzjyy%2B/NDZv3mzExsYa77zzjl/k%2B7mHH37Y6Nq1q7F27Vr32C89H1944QXjiiuuMD766CNj27ZtxoABA4zFixd7Jc/JTpXvxIkTRkpKinHHHXcY%2B/fvN1566SUjJibG%2BOKLL/wiX1lZmZGQkGA8//zzxjfffGM899xzht1uN0pKSiyTr76%2B3hg9erTxl7/8xdi3b5/x0UcfGVdddZXx2GOPNfvXFwqWRZWXlxuXX365MXLkSHfBWr16tZGcnOx%2B8tTX1xtXXXWV%2BwXx/vvvd1/XMAyjuLjY%2BO///m/jm2%2B%2B8X6AMygvLzd69OhhbN%2B%2B3T22cOFC44EHHvCLjBUVFUbXrl3dL%2BKGYRgZGRnGjBkzLJuvoKDA%2BMMf/mBcd911Hi/wW7duNeLi4twF0TAM46abbjKefvppwzAM46mnnjL%2B/Oc/u%2BeOHz9uxMfHu28/duxY93UNwzA%2B%2Bugjo2fPnsbx48e9EcvtdPn%2B/ve/G9dee63HdR9%2B%2BGFj4sSJhmFYP9/P13XVVVcZ/fv39yhYv/R8vOKKKzyuv2HDBmPQoEFNnKah0%2BXbsmWLkZCQYBw5csR93TvvvNNYtWqVYRjWz/fWW28ZSUlJHtdNSkoyXn/9dcMwrJGvsLDQ6Nq1q%2BF0Ot1jmzZtMgYMGNDsX184RGhRjz/%2BuIYPH65LL73UPeZwOJSQkKCAgABJUkBAgHr16qX8/Hz3fGJiovv6HTp0UHR0tBwOh3cX/wvy8vLUqlUrJSUlucfGjx%2Bv7Oxsv8jYokULhYaGat26dTpx4oQOHDignTt3qnv37pbNt2PHDvXp00e5ubke4w6HQz169FBYWJh7LCEh4bR5QkNDFRMTo/z8fNXV1enTTz/1mI%2BLi9OJEye0d%2B/eJk7k6XT5fjpccbKjR49Ksn4%2B6cfDTg8//LCmTZum4OBgj7kzPR%2B/%2B%2B47lZSUqHfv3u75hIQEHTx4UKWlpU0X5hROl2/Hjh3q16%2BfWrVq5R579tlnlZaWJsn6%2BVq3bq2Kigq99dZbMgxDW7Zs0bFjx9S1a1dJ1sgXERGhxYsXq3379h7jR48ebfavLzZT7gVetW3bNn388cfatGmTpk%2Bf7h53Op0ehUuS2rVrp4KCAklSaWmpIiMjG8wfOnSoydd8NoqKitSxY0dt2LBBCxYs0IkTJ5Samqo777zTLzKGhIRo2rRpysrK0vLly1VXV6fU1FSNGjVKb7/9tiXzjR079pTjTqfzjOs903xlZaVqamo85m02m1q3bu31vKfL16lTJ3Xq1Ml9%2Bfvvv9err76qu%2B%2B%2BW5L180nSggUL1KNHDw0YMKDB3Jmej06nU5I85n/6R/LQoUMNbteUTpfvp9eauXPnauPGjWrTpo0yMzM1ePBgSdbPl5iYqBtuuEGZmZkKDAxUXV2dsrOzdfHFF0uyRr7w8HANHDjQfbm%2Bvl4rV65U3759m/3rCwXLYmpqavTII49o2rRpatGihcdcVVVVg/9hBgcHy%2BVySZKqq6vPON9cHD9%2BXF9//bVWrVql7OxsOZ1OTZs2TaGhoX6Tcf/%2B/Ro0aJBuueUWFRQUKCsrS/369fObfD/5pTxnmq%2BurnZfPt3tm5Pq6mrdfffdat%2B%2BvXsPiNXzFRYWatWqVXrllVdOOX%2Bm5%2BOp8v30fXPJd/z4ca1fv15Dhw7VggULtH37dmVmZio3N1exsbGWz3fs2DEVFRUpIyNDgwYN0ltvvaVHH31Udrtdl1xyiSXzzZkzR3v27NGaNWu0dOnSZv36QsGymJycHF122WUejf4nISEhDZ4YLpfLXcRONx8aGtp0Cz4HNptNR48e1bx589SxY0dJP37S56WXXtKFF15o%2BYzbtm3TmjVr9O6776pFixaKjY3Vd999p%2Beee06dO3e2fL6fCwkJUUVFhcdYY/KEh4crJCTEffnk%2BeaW99ixY7rrrrv01Vdf6e9//7t7fVbOZxiGHnroIWVmZjY4PPOTMz0ff/6P8clZm0M%2BSQoKClLr1q01ffp0BQYGKiYmRh9//LFefvllxcbGWj7f4sWLZRiGMjIyJEkxMTHatWuXli9frhkzZlgu35w5c7Rs2TI9%2BeST6tq1a7N/feE9WBbz6quvasuWLYqPj1d8fLw2bdqkTZs2KT4%2BXlFRUSorK/O4fllZmXsX6OnmIyIivLb%2BxoiIiFBISIi7XEnSRRddpJKSEr/IuHv3bl144YUeeyB79Oih4uJiv8j3c78mT%2BvWrRUSEuIxX1tbq4qKimaV9%2BjRo7rttttUUFCgZcuWqUuXLu45K%2BcrLi7WJ598oscff9z9elNcXKxHHnlEf/nLXySdOV9UVJQkuQ81/fz75pBP%2BvHwV5cuXRQY%2BJ9/Cn96rZGsn%2B%2Bzzz5Tt27dPMa6d%2B%2Bu4uJiSdbKl5WVpSVLlmjOnDm65pprJDX/1xcKlsWsWLFCmzZt0oYNG7RhwwYlJycrOTlZGzZskN1u1yeffOI%2BX5RhGNq5c6fsdrskyW63Ky8vz31fJSUlKikpcc83F3a7XTU1Nfryyy/dYwcOHFDHjh39ImNkZKS%2B/vprj/85HThwQJ06dfKLfD9nt9v12WefuXfHSz9%2BiOF0eaqqqrRnzx7Z7XYFBgYqNjbWYz4/P182m63BPxq%2BUl9fr4yMDH377bdasWKFfve733nMWzlfVFSU3nrrLfdrzYYNGxQZGanMzEzNmjVL0pmfj1FRUYqOjvaYz8vLU3R0tFffn3QmdrtdBQUFqqurc4/t37/f/Z87q%2BeLjIxUYWGhx9hPrzWSdfLl5ORo1apVeuKJJzRs2DD3eLN/fTHls4jwmcmTJ7s/ZnvkyBGjb9%2B%2BRlZWllFQUGBkZWUZ/fv3d3%2BEdefOnUZMTIzx8ssvu895MmHCBF8u/7TGjx9vpKWlGZ9//rnx3nvvGX379jWWLVvmFxkrKyuN/v37G/fff79x4MAB4%2B233zaSkpKMl156yS/y/fxj4rW1tcbQoUONe%2B65x9i3b5%2BxcOFCIy4uzn2emqKiIiM2NtZYuHCh%2Bzw11113nfs0FZs3bzZ69epl/OMf/zAcDocxbNgwIysry2fZDMMzX25urtGtWzfjnXfeMUpLS91/ysvLDcOwfr6TDRo0yONj%2B7/0fFy4cKExYMAA48MPPzQ%2B/PBDY8CAAcbf/va3Js9wJj/Pd%2BTIEWPAgAHGww8/bHz11VfGypUrjR49ehi7d%2B82DMP6%2BT755BOje/fuxpIlS4xvvvnGWLJkiRETE2Ps27fPMAxr5CssLDS6d%2B9uPPnkkx5/x0pLS5v96wsFy%2BJ%2BXrAM48cTx40YMcKIjY01rr/%2BeuOzzz7zuP7atWuNK664woiLizPS09ONw4cPe3vJjVJZWWncf//9RlxcnNGvXz/jmWeecf%2Bl8IeMBQUFxs0332z06tXLGDx4sLFkyRK/yXfyP9BfffWVccMNNxiXXXaZMWzYMOODDz7wuP6//vUv4%2BqrrzZ69uxp3HTTTQ3O6bVw4UKjX79%2BRkJCgjFlyhSjurraKzlO5%2Bf5br31VqNr164N/vz83DtWzneykwuWYZz5%2BVhbW2v89a9/NRITE40%2BffoYc%2BbMcT/PfeXkfAUFBe7n59VXX228%2BeabHte3er4tW7YYf/jDH4y4uDjjj3/8Y4O/f80938KFC0/5d6xr166GYTTv15cAw/j/j0UAAADAFLwHCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGT/H57WXSL/f4qTAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4848485919664068766”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>658.5372403943636</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>711.7697982264114</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>739.1472646451974</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>534.3835830301039</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>617.3390065182529</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>529.1379539937614</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>646.2592516337282</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>665.1112958335699</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476.7934104357549</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>654.2676818853976</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (41)</td> <td class=”number”>1850</td> <td class=”number”>57.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4848485919664068766”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>371.8450513207563</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:83%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>434.9249921475118</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>444.9218223984439</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476.7934104357549</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>484.3815070495379</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:68%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>900.3162095952791</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:95%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>919.5131037446367</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1205.7142799950157</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1340.0143784300437</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1930.1558056074637</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_FSRSALES12”><s>PC_FSRSALES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_FSRSALES07”><code>PC_FSRSALES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91163</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_SNAPBEN10”>PC_SNAPBEN10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3051</td>

</tr> <tr>

<th>Unique (%)</th> <td>95.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>158</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>18.607</td>

</tr> <tr>

<th>Minimum</th> <td>1.0136</td>

</tr> <tr>

<th>Maximum</th> <td>76.285</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3563943867872136928”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3563943867872136928,#minihistogram3563943867872136928”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3563943867872136928”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3563943867872136928”

aria-controls=”quantiles3563943867872136928” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3563943867872136928” aria-controls=”histogram3563943867872136928”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3563943867872136928” aria-controls=”common3563943867872136928”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3563943867872136928” aria-controls=”extreme3563943867872136928”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3563943867872136928”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.0136</td>

</tr> <tr>

<th>5-th percentile</th> <td>5.8894</td>

</tr> <tr>

<th>Q1</th> <td>11.362</td>

</tr> <tr>

<th>Median</th> <td>17.426</td>

</tr> <tr>

<th>Q3</th> <td>24.118</td>

</tr> <tr>

<th>95-th percentile</th> <td>35.953</td>

</tr> <tr>

<th>Maximum</th> <td>76.285</td>

</tr> <tr>

<th>Range</th> <td>75.271</td>

</tr> <tr>

<th>Interquartile range</th> <td>12.756</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>9.578</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51474</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.6256</td>

</tr> <tr>

<th>Mean</th> <td>18.607</td>

</tr> <tr>

<th>MAD</th> <td>7.4651</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.9958</td>

</tr> <tr>

<th>Sum</th> <td>56790</td>

</tr> <tr>

<th>Variance</th> <td>91.737</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3563943867872136928”>

<img 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RUac7ugMAAHzf/K5gDRgwQPPnz9f//M//eNcKCgqUnp6u/v372zgZAABA7fjdvyKcMmWKxo8fryFDhigsLEySVFZWph49emjatGk2TwcAAHBnflewWrdurbfffluHDh1Sfn6%2BXC6XunTpoj59%2BiggIMDu8QAAAO7I7wqWJDVt2lT9%2B/fnkiAAAGiQ/K5geTweLV26VEePHtW1a9dqfLD9T3/6k02TAQAA1I7fFaz//M//VF5enoYNG6Yf/OAHdo8DAABw1/yuYH300Udas2aNYmNj7R4FAACgTvzuNg2hoaFq3bq13WMAAADUmd8VrBEjRmjNmjW6ceOG3aMAAADUid9dIiwtLdWuXbv0wQcfqGPHjgoMDPTZ/vvf/96myQAAAGrH7wqWJA0fPtzuEYAGJ2HpQbtHqLX3JvW1ewQAuKf8rmClp6fbPQIAAEC9%2BN1nsCSpqKhImZmZeuGFF/S///u/2rNnj86cOWP3WAAAALXidwXr3Llz%2BulPf6q3335bf/zjH1VeXq7du3crKSlJubm5do8HAABwR35XsF555RUNHjxYe/fu1X333SdJWrJkieLj47Vo0SKbpwMAALgzvytYR48e1fjx431%2BsbPL5dLzzz%2BvEydO2DgZAABA7fhdwaqurlZ1dXWN9StXrqhp06Y2TAQAAHB3/K5g9evXTytXrvQpWaWlpcrIyFDv3r1tnAwAAKB2/K5gTZ06VXl5eerXr58qKyv13HPPadCgQTp//rymTJli93gAAAB35Hf3wWrXrp22b9%2BuXbt26YsvvlB1dbWSk5M1YsQINW/e3O7xAAAA7sjvCpYkhYSEaPTo0XaPAQAAUCd%2BV7DGjRv3ndv5XYQAAMDf%2BV3BioiI8Hl8/fp1nTt3Tl9%2B%2BaWeeuopm6YCAACoPb8rWN/2uwiXLVumixcvfs/TAAAA3D2/%2B1eE32bEiBF677337B4DAADgjhpMwfr000%2B50SgAAGgQ/O4S4e0%2B5H758mX97W9/0%2BOPP27DRAAAAHfH7wpWhw4dfH4PoSTdd999evLJJ/Wzn/3MpqkAAABqz%2B8K1iuvvGL3CAAAAPXidwXryJEjtX5ur1697uEkAAAAdeN3BWvs2LHeS4SWZXnXb10LCAjQF1988f0PCAAAcAd%2BV7BWrFih%2BfPn66WXXlJcXJwCAwP12Wefad68efr5z3%2Buxx57zO4RAQAAvpPf3aYhPT1ds2bN0pAhQ9SyZUs1a9ZMvXv31rx587Rp0yZFRER4vwAAAPyR3xWsoqKi25an5s2bq6SkxIaJAAAA7o7fFayePXtqyZIlunz5snettLRUGRkZ6tOnj42TAQAA1I7ffQZr5syZGjdunAYMGKDOnTvLsiz9/e9/V9u2bfX73//e7vEAAADuyO8K1kMPPaTdu3dr165dOn36tCTpiSee0LBhwxQSEmLzdAAAAHfmdwVLklq0aKHRo0fr/Pnz6tixo6Rv7uYOAADQEPjdZ7Asy9KiRYvUq1cvDR8%2BXBcvXtSUKVM0Y8YMXbt2ze7xAAAA7sjvCtaGDRu0Y8cOzZ49W4GBgZKkwYMHa%2B/evcrMzLR5OgAAgDvzu4KVlZWlWbNmKTEx0Xv39scee0zz58/Xzp07bZ4OAADgzvyuYJ0/f17dunWrsf7www/L4/HYMBEAAMDd8buCFRERoc8%2B%2B6zG%2Bv79%2B70feAcAAPBnfvevCJ955hnNnTtXHo9HlmXp8OHDysrK0oYNGzR16lS7xwMAALgjvzuDlZSUpP/4j//Q2rVrdfXqVc2aNUvZ2dmaNGmSkpOT73p/KSkpPsXsxIkTGj16tNxut5KSkpSXl%2Bfz/F27dmnw4MFyu91KTU3VV199Ve9MAACgcfG7grVr1y4NHTpUH3zwgQ4dOqSDBw/q0KFDGj9%2B/F3v691339W%2Bffu8j8vLy5WSkqLY2FhlZ2crKipKEyZMUHl5uSTp%2BPHjmjFjhiZOnKisrCyVlZVp2rRpxrIBAIDGwe8uEc6bN0///d//rRYtWqhVq1Z13k9paakWLlyoyMhI79ru3bsVFBSkyZMnKyAgQDNmzND%2B/fu1Z88eJSYmauPGjUpISNDIkSMlSQsXLtSgQYNUUFDgt5//Slh60O4RAADALfzuDFbnzp315Zdf1ns/r776qkaMGKEuXbp413JzcxUTE%2BO9/UNAQICio6N17Ngx7/bY2Fjv89u3b68OHTooNze33vMAAIDGw%2B/OYD388MN68cUXtWbNGnXu3FlBQUE%2B29PT0%2B%2B4j8OHD%2BuTTz7Rzp07NWfOHO%2B6x%2BPxKVyS1Lp1a%2BXn50uSioqKFB4eXmP7xYsX65gGAAA0Rn5XsM6ePauYmBhJqtN9ryorKzV79mzNmjVLwcHBPtsqKiq8d4e/KTAwUFVVVZKkq1evfuf22ioqKqoxu8sVWqO81VbTpk18/gQaOpfLOcdyY3l/ktNZGltOO/hFwVq4cKEmTpyo0NBQbdiwoV77yszM1COPPKL%2B/fvX2BYUFFSjLFVVVXmL2LdtDwkJuasZsrKyavxan9TUVKWlpd3Vfm4VFnZ3cwD%2BqmXLZnaPYFxjeX%2BS01kaS047%2BEXBWrdunZ555hmFhoZ611JSUjR//vy7Puvz7rvvqri4WFFRUZLkLUx//OMfNXz4cBUXF/s8v7i42Psa7dq1u%2B32tm3b3tUMY8aMUXx8vM%2BayxWqkpIrd7Wfm5o2baKwsBCVlVXoxo3qOu0D8Cd1fS/4o8by/iSnszS2nHbwi4JlWVaNtSNHjqiysvKu97VhwwZdv37d%2B3jRokWSpBdffFFHjhzR6tWrZVmWAgICZFmWjh49qmeffVaS5Ha7lZOTo8TERElSYWGhCgsL5Xa772qG8PDwGsXQ4/la16/X7yC%2BcaO63vsA/IETj%2BPG8v4kp7M0lpx28IuCZVJERITP42bNvrkU0alTJ7Vu3VqLFy/WggUL9G//9m/avHmzKioqlJCQIElKTk7W2LFj1bNnT0VGRmrBggUaOHCg396iAQAA%2BCdnf7rtFs2bN9fKlSu9Z6lyc3O1atUq76XJqKgozZs3T8uWLVNycrJatGhRq3%2B1CAAA8I/85gzWzXtTmfbKK6/4PP7xj3%2Bst99%2B%2B1ufn5iY6L1ECAAAUBd%2BU7Dmz5/vc8%2Bra9euKSMjw3uJ7ybOKAEAAH/nFwWrV69eNe4bFRUVpZKSEpWUlNg0FQAAQN34RcGq772vAAAA/Emj%2BpA7AADA94GCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDDHFqxLly4pLS1NcXFx6t%2B/v9LT01VZWSlJKigo0NNPP62ePXvqscce04EDB3y%2B99ChQxo%2BfLjcbrfGjRungoICOyIAAIAGypEFy7IspaWlqaKiQm%2B99ZZee%2B01/eUvf9HSpUtlWZZSU1PVpk0bbdu2TSNGjNDEiRN14cIFSdKFCxeUmpqqxMREbd26Va1atdLzzz8vy7JsTgUAABoKl90D3AtnzpzRsWPHdPDgQbVp00aSlJaWpldffVUDBgxQQUGBNm/erNDQUD300EM6fPiwtm3bpl//%2BtfasmWLHnnkEf3iF7%2BQJKWnp6tv377661//qkcffdTOWAAAoIFw5Bmstm3bas2aNd5yddPly5eVm5ur7t27KzQ01LseExOjY8eOSZJyc3MVGxvr3RYSEqIePXp4twMAANyJI89ghYWFqX///t7H1dXV2rhxo3r37i2Px6Pw8HCf57du3VoXL16UpDtur42ioiJ5PB6fNZcrtMZ%2Ba6tp0yY%2BfwINncvlnGO5sbw/yeksjS2nHRxZsG6VkZGhEydOaOvWrVq/fr0CAwN9tgcGBqqqqkqSVFFR8Z3bayMrK0uZmZk%2Ba6mpqUpLS6tjgm%2BEhYXU6/sBf9GyZTO7RzCusbw/yeksjSWnHRxfsDIyMvTmm2/qtddeU9euXRUUFKTS0lKf51RVVSk4OFiSFBQUVKNMVVVVKSwsrNavOWbMGMXHx/usuVyhKim5UqcMTZs2UVhYiMrKKnTjRnWd9gH4k7q%2BF/xRY3l/ktNZGltOOzi6YL388svatGmTMjIyNGTIEElSu3btdOrUKZ/nFRcXey/ftWvXTsXFxTW2d%2BvWrdavGx4eXuNyoMfzta5fr99BfONGdb33AfgDJx7HjeX9SU5naSw57eDYi6%2BZmZnavHmzlixZomHDhnnX3W63Pv/8c129etW7lpOTI7fb7d2ek5Pj3VZRUaETJ054twMAANyJIwvW6dOn9cYbb%2BhXv/qVYmJi5PF4vF9xcXFq3769pk2bpvz8fK1atUrHjx/XqFGjJElJSUk6evSoVq1apfz8fE2bNk33338/t2gAAAC15siC9ac//Uk3btzQ8uXL1a9fP5%2Bvpk2b6o033pDH41FiYqLeeecdLVu2TB06dJAk3X///frtb3%2Brbdu2adSoUSotLdWyZcsUEBBgcyoAANBQBFjcovx74fF8XefvdbmaqGXLZiopuVLjWnnC0oP1HQ343r03qa/dIxjzXe9PJyGnszS2nHZw5BksAAAAO1GwAAAADKNgAQAAGObo%2B2AB8E8N7bODTvrMGIDvB2ewAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIa57B4AAPxdwtKDdo9wV96b1NfuEYBGjzNYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABjG7yIEAIdpSL87kd%2BbCKfiDBYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsG6jcrKSk2fPl2xsbHq16%2Bf1q5da/dIAACgAeE2DbexcOFC5eXl6c0339SFCxc0ZcoUdejQQUOHDrV7NAAA0ABQsG5RXl6uLVu2aPXq1erRo4d69Oih/Px8vfXWWxQsAABQKxSsW5w8eVLXr19XVFSUdy0mJkYrVqxQdXW1mjThqioAmNKQbooqcWNU1B4F6xYej0ctW7ZUYGCgd61NmzaqrKxUaWmpWrVqdcd9FBUVyePx%2BKy5XKEKDw%2Bv00xNmzbx%2BRMAYI%2BGVgjff7H/bdcby98rduajYN2ioqLCp1xJ8j6uqqqq1T6ysrKUmZnpszZx4kT9%2Bte/rtNMRUVFevPNNRozZkyNkvbJAudctiwqKlJWVtZtczoJOZ2FnM7SmHJ%2B298rTmJnTmdX1zoICgqqUaRuPg4ODq7VPsaMGaPs7GyfrzFjxtR5Jo/Ho8zMzBpnxZyGnM5CTmchp7OQ897jDNYt2rVrp5KSEl2/fl0u1zf/83g8HgUHByssLKxW%2BwgPD3f0fxEAAIDvxhmsW3Tr1k0ul0vHjh3zruXk5CgyMpIPuAMAgFqhMdwiJCREI0eO1Jw5c3T8%2BHHt3btXa9eu1bhx4%2BweDQAANBBN58yZM8fuIfxN7969deLECS1evFiHDx/Ws88%2Bq6SkJFtnatasmeLi4tSsWTNb57jXyOks5HQWcjoLOe%2BtAMuyrO/1FQEAAByOS4QAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYfq6yslLTp09XbGys%2BvXrp7Vr19o9klFVVVUaPny4Pv74Y%2B9aQUGBnn76afXs2VOPPfaYDhw4YOOE9XPp0iWlpaUpLi5O/fv3V3p6uiorKyU5K%2Be5c%2Bf0zDPPKCoqSgON6QiAAAAJdElEQVQHDtSaNWu825yU86aUlBRNnTrV%2B/jEiRMaPXq03G63kpKSlJeXZ%2BN09ff%2B%2B%2B/rRz/6kc9XWlqaJGdlraqq0ty5c9WrVy/95Cc/0ZIlS3Tz3ttOyZmdnV3jZ/mjH/1IDz/8sCTn5JSkwsJCTZgwQdHR0YqPj9f69eu92%2BzIScHycwsXLlReXp7efPNNzZ49W5mZmdqzZ4/dYxlRWVmp3/zmN8rPz/euWZal1NRUtWnTRtu2bdOIESM0ceJEXbhwwcZJ68ayLKWlpamiokJvvfWWXnvtNf3lL3/R0qVLHZWzurpaKSkpatmypd5%2B%2B23NnTtXy5cv186dOx2V86Z3331X%2B/bt8z4uLy9XSkqKYmNjlZ2draioKE2YMEHl5eU2Tlk/p06d0qBBg3TgwAHv1/z58x2Xdf78%2BTp06JB%2B97vfafHixfrDH/6grKwsR%2BW8%2BR81N78%2B%2BOADderUSePGjXNUTkmaNGmSQkNDlZ2drenTp2vp0qV6//337ctpwW9duXLFioyMtD766CPv2rJly6wnn3zSxqnMyM/Pt372s59ZP/3pT62uXbt6Mx46dMjq2bOndeXKFe9zn3rqKev111%2B3a9Q6O3XqlNW1a1fL4/F413bu3Gn169fPUTkvXbpk/fu//7v19ddfe9dSU1Ot2bNnOyqnZVlWSUmJNWDAACspKcmaMmWKZVmWtWXLFis%2BPt6qrq62LMuyqqurrX/5l3%2Bxtm3bZueo9fLCCy9YixcvrrHupKwlJSVW9%2B7drY8//ti7tnLlSmvq1KmOynmrFStWWIMHD7YqKysdlbO0tNTq2rWr9be//c27NnHiRGvu3Lm25eQMlh87efKkrl%2B/rqioKO9aTEyMcnNzVV1dbeNk9ffXv/5Vjz76qLKysnzWc3Nz1b17d4WGhnrXYmJidOzYse97xHpr27at1qxZozZt2visX7582VE5w8PDtXTpUjVv3lyWZSknJ0dHjhxRXFyco3JK0quvvqoRI0aoS5cu3rXc3FzFxMQoICBAkhQQEKDo6OgGm1GSTp8%2Brc6dO9dYd1LWnJwcNW/eXHFxcd61lJQUpaenOyrnPyotLdXq1av1wgsvKDAw0FE5g4ODFRISouzsbF27dk1nzpzR0aNH1a1bN9tyUrD8mMfjUcuWLRUYGOhda9OmjSorK1VaWmrjZPX3%2BOOPa/r06QoJCfFZ93g8Cg8P91lr3bq1Ll68%2BH2OZ0RYWJj69%2B/vfVxdXa2NGzeqd%2B/ejsr5j%2BLj4/X4448rKipKQ4YMcVTOw4cP65NPPtHzzz/vs%2B6kjNI3l7bPnj2rAwcOaMiQIRo8eLAWLVqkqqoqR2UtKChQRESEtm/frqFDh%2Bqf//mftWzZMlVXVzsq5z/atGmTwsPDNXToUEnOOnaDgoI0a9YsZWVlye12KyEhQQMGDNDo0aNty%2Bm6p3tHvVRUVPiUK0nex1VVVXaMdM99W2Yn5M3IyNCJEye0detWrV%2B/3pE5X3/9dRUXF2vOnDlKT093zM%2BzsrJSs2fP1qxZsxQcHOyzzSkZb7pw4YI309KlS3X%2B/HnNnz9fV69edVTW8vJynTt3Tps3b1Z6ero8Ho9mzZqlkJAQR%2BW8ybIsbdmyRb/85S%2B9a07Lefr0aQ0aNEjjx49Xfn6%2BXn75ZfXp08e2nBQsPxYUFFTjALj5%2BNb/k3eKoKCgGmfnqqqqGnzejIwMvfnmm3rttdfUtWtXx%2BaMjIyU9E0hefHFF5WUlKSKigqf5zTEnJmZmXrkkUd8zkje9G3v04aW8aaIiAh9/PHHatGihQICAtStWzdVV1frpZdeUlxcnGOyulwuXb58WYsXL1ZERISkb8rlpk2b1KlTJ8fkvOmzzz7TpUuXNGzYMO%2Bak47dw4cPa%2BvWrdq3b5%2BCg4MVGRmpS5cuafny5erYsaMtOblE6MfatWunkpISXb9%2B3bvm8XgUHByssLAwGye7d9q1a6fi4mKfteLi4hqndxuSl19%2BWevWrVNGRoaGDBkiyVk5i4uLtXfvXp%2B1Ll266Nq1a2rbtq0jcr777rvau3evoqKiFBUVpZ07d2rnzp2Kiopy1M/yph/%2B8Ifez6tI0kMPPaTKykrH/Dylbz4jGRQU5C1XkvTAAw%2BosLDQkT/TDz/8ULGxsWrRooV3zUk58/Ly1KlTJ5/S1L17d124cMG2nBQsP9atWze5XC6fD%2BLl5OQoMjJSTZo480fndrv1%2Beef6%2BrVq961nJwcud1uG6equ8zMTG3evFlLlizx%2BS9HJ%2BU8f/68Jk6cqEuXLnnX8vLy1KpVK8XExDgi54YNG7Rz505t375d27dvV3x8vOLj47V9%2B3a53W59%2Bumn3vsnWZalo0ePNriMN3344Yd69NFHfc48fvHFF/rhD3%2BomJgYx2R1u92qrKzU2bNnvWtnzpxRRESE436mknT8%2BHFFR0f7rDkpZ3h4uM6dO%2BdzpurMmTO6//77bcvpzL%2BlHSIkJEQjR47UnDlzdPz4ce3du1dr167VuHHj7B7tnomLi1P79u01bdo05efna9WqVTp%2B/LhGjRpl92h37fTp03rjjTf0q1/9SjExMfJ4PN4vJ%2BWMjIxUjx49NH36dJ06dUr79u1TRkaGnn32WcfkjIiIUKdOnbxfzZo1U7NmzdSpUycNHTpUZWVlWrBggU6dOqUFCxaooqJCCQkJdo9dJ1FRUQoKCtLMmTN15swZ7du3TwsXLtQvf/lLR2V98MEHNXDgQE2bNk0nT57Uhx9%2BqFWrVik5OdlROW/Kz8/3%2BdevkhyVMz4%2BXvfdd59mzpyps2fP6s9//rNWrFihsWPH2pfznt4EAvVWXl5uTZ482erZs6fVr18/a926dXaPZNw/3gfLsizr73//u/XEE09YjzzyiDVs2DDr4MGDNk5XdytXrrS6du162y/Lck5Oy7KsixcvWqmpqVZ0dLTVt29fa/ny5d57zjgp501Tpkzx3gfLsiwrNzfXGjlypBUZGWmNGjXK%2Bvzzz22crv6%2B/PJL6%2Bmnn7Z69uxp9e3b1/rtb3/r/Xk6KWtZWZn10ksvWT179rT69Onj2JyWZVmRkZHW/v37a6w7KWd%2Bfr719NNPW9HR0dbgwYOtdevW2frzDLCs/3/ODAAAAEZwiRAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPt/f4/rW60R7RkAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3563943867872136928”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>9.507699486700886</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.917355371900827</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.98455720500928</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26.675220986598234</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.273165907645016</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1429758935993348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.61537314721091</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36.800689578467356</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.67449048584951</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13.283839940234392</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3040)</td> <td class=”number”>3040</td> <td class=”number”>94.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>158</td> <td class=”number”>4.9%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3563943867872136928”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0135875520490905</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1429758935993348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.374385999154224</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.9292823264131873</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0969635967119613</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>58.76249764195435</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.63303915237616</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.874133585381216</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>67.38100971185487</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.28485226799833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_SNAPBEN15”><s>PC_SNAPBEN15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_SNAPBEN10”><code>PC_SNAPBEN10</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94136</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_WIC_REDEMP08”>PC_WIC_REDEMP08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2074</td>

</tr> <tr>

<th>Unique (%)</th> <td>64.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1136</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>21.346</td>

</tr> <tr>

<th>Minimum</th> <td>1.7439</td>

</tr> <tr>

<th>Maximum</th> <td>110.63</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8865510260325480176”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8865510260325480176,#minihistogram8865510260325480176”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8865510260325480176”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8865510260325480176”

aria-controls=”quantiles8865510260325480176” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8865510260325480176” aria-controls=”histogram8865510260325480176”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8865510260325480176” aria-controls=”common8865510260325480176”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8865510260325480176” aria-controls=”extreme8865510260325480176”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8865510260325480176”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1.7439</td>

</tr> <tr>

<th>5-th percentile</th> <td>8.277</td>

</tr> <tr>

<th>Q1</th> <td>14.476</td>

</tr> <tr>

<th>Median</th> <td>19.623</td>

</tr> <tr>

<th>Q3</th> <td>25.965</td>

</tr> <tr>

<th>95-th percentile</th> <td>39.08</td>

</tr> <tr>

<th>Maximum</th> <td>110.63</td>

</tr> <tr>

<th>Range</th> <td>108.88</td>

</tr> <tr>

<th>Interquartile range</th> <td>11.488</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.439</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.48901</td>

</tr> <tr>

<th>Kurtosis</th> <td>7.0231</td>

</tr> <tr>

<th>Mean</th> <td>21.346</td>

</tr> <tr>

<th>MAD</th> <td>7.5955</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7726</td>

</tr> <tr>

<th>Sum</th> <td>44272</td>

</tr> <tr>

<th>Variance</th> <td>108.96</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8865510260325480176”>

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UCwAAuLTYHrCOHz%2Bu8ePH6%2Bmnn/aNzZo1SxMnTtTRo0eNbWfFihXas2ePnnnmGa1atUqvvPKKiouLVV9fr1mzZik1NVXbtm1TcnKyZs%2Berfr6eknfh73Fixdr7ty5Ki4uVl1dnbKzs43VBQAAQp/tAes3v/mN%2BvTpozvvvNM3tnPnTvXo0UN5eXlGtlFbW6utW7dq%2BfLluuaaazRixAjdddddcrvd2rlzpyIiIrRgwQL169dPixcvVkxMjN5%2B%2B21J0osvvqiMjAxNmjRJAwYM0MqVK7V7925VVlYaqQ0AAIQ%2B2wPWZ599pgcffFDx8fG%2Bsa5du2rBggX66KOPjGyjpKREnTt31rBhw3xjs2bNUl5entxut1JSUuRwOCR9f8uIoUOHqqysTJLkdruVmprqW69Hjx7q2bOn3G63kdoAAEDosz1gOZ1O1dXVnTXu9XqN3dG9srJSiYmJ2r59u8aPH6%2Bf/vSnKiwsVHNzszwejxISEvyWj4uL07FjxyR9f/uI880DAAC0xPa/Ihw9erRWrFih1atX69/%2B7d8kfR%2BI8vLylJaWZmQb9fX1Onz4sDZv3qy8vDx5PB7l5OQoKipKXq9X4eHhfsuHh4ersbFRknTy5MnzzrdGVVWVPB6P35jTGX1WcGuNsLBOft9hH6eTnnP8BYb%2BBYb%2BBY4eBo/tAWvhwoW68847df311ys2NlaSVFdXp8GDBxt7M7nT6dTx48e1atUqJSYmSpKOHDmil19%2BWX369DkrLDU2NioyMlKSFBERcc75qKioVm%2B/uLhYBQUFfmNZWVmaN2/eheyOJCk2tvXbhxldusQEu4R2g%2BMvMPQvMPQvcPTQfrYHrLi4OP32t7/Vnj17VFFRIafTqR//%2BMcaMWKE731RgYqPj1dERIQvXEnSlVdeqaNHj2rYsGGqrq72W766utp3dql79%2B7nnP/X94y1ZNq0aUpPT/cbczqjVVNzoq27orCwToqNjVJdnVenTze3eX1cuAv59wo1HH%2BBoX%2BBoX%2BBo4fB%2B2U5KB%2BVExYWprS0NGOXBM/kcrnU0NCgQ4cO6corr5QkHTx4UImJiXK5XHr66adlWZYcDocsy1Jpaanuuece37olJSWaPHmyJOno0aM6evSoXC5Xq7efkJBw1uVAj%2Bc7NTVd%2BMF9%2BnRzQOuj7ej3P3H8BYb%2BBYb%2BBY4e2s/2gOXxeLRmzRqVlpbq1KlTZ72x/Xe/%2B13A27jqqqs0ZswYZWdna8mSJfJ4PCoqKtK9996r8ePHa9WqVcrNzdUvf/lLbd68WV6vVxkZGZKk6dOna8aMGUpKStKQIUOUm5urMWPGqHfv3gHXBQAALg22B6z//u//1v79%2B3XjjTfq8ssvv2jbeeyxx7R8%2BXJNnz5dUVFRuvXWWzVjxgw5HA6tX79eDz/8sF555RX9x3/8h4qKihQdHS1JSk5O1rJly/TEE0/o73//u0aOHKnly5dftDoBAEDocVim7o3QSklJSdqwYYPfvaYuBR7Pdxe0ntPZSV26xKim5kSHP72bsebDYJfQJm/NHxnsEoIulI6/YKB/gaF/gaOHUnz8xTuZcz62/91mdHS04uLi7N4sAACAbWwPWBMnTtSGDRv8PtwZAAAglNj%2BHqza2lrt2LFDf/jDH9S7d%2B%2Bzbur5wgsv2F0SAACAUUG5TcOECROCsVkAAABb2B6w8vLy7N4kAACArYLy4URVVVUqKCjQ/fffr//7v//T22%2B/rYMHDwajFAAAAONsD1iHDx/WTTfdpN/%2B9rd65513VF9fr507dyozM1Nut9vucgAAAIyzPWA98sgjGjdunHbt2qXLLrtMkrR69Wqlp6frscces7scAAAA42wPWKWlpbrzzjv9PtjZ6XRqzpw5Ki8vt7scAAAA42wPWM3NzWpuPvtusidOnFBYWJjd5QAAABhne8AaNWqU1q9f7xeyamtrlZ%2Bfr%2BHDh9tdDgAAgHG2B6wHH3xQ%2B/fv16hRo9TQ0KB7771XY8eO1ddff62FCxfaXQ4AAIBxtt8Hq3v37tq%2Bfbt27NihP//5z2pubtb06dM1ceJEde7c2e5yAAAAjAvKndyjoqI0derUYGwaAADgorM9YM2cOfO883wWIQAA6OhsD1iJiYl%2Bj5uamnT48GF9%2BeWXuv322%2B0uBwAAwLh281mEhYWFOnbsmM3VAAAAmBeUzyI8l4kTJ%2Bqtt94KdhkAAAABazcB649//CM3GgUAACGhXbzJ/fjx4/rLX/6iW265xe5yAAAAjLM9YPXs2dPvcwgl6bLLLtNtt92mn//853aXAwAAYJztAeuRRx6xe5PABclY82GwS2iTt%2BaPDHYJAID/z/aA9emnn7Z62WuvvfYiVgIAAHBx2B6wZsyY4btEaFmWb/zMMYfDoT//%2Bc92lwcAABAw2wPWunXrtGLFCj3wwAMaNmyYwsPD9fnnn2vZsmW6%2BeabdcMNN9hdEgAAgFG236YhLy9POTk5uv7669WlSxfFxMRo%2BPDhWrZsmV5%2B%2BWUlJib6vgAAADoi2wNWVVXVOcNT586dVVNTY3c5AAAAxtkesJKSkrR69WodP37cN1ZbW6v8/HyNGDHC7nIAAACMs/09WA899JBmzpyp0aNHq2/fvrIsS3/9618VHx%2BvF154we5yAAAAjLM9YPXr1087d%2B7Ujh079NVXX0mSbr31Vt14442KioqyuxwAAADjbA9YknTFFVdo6tSp%2Bvrrr9W7d29J39/NHQAAIBTY/h4sy7L02GOP6dprr9WECRN07NgxLVy4UIsXL9apU6fsLgcAAMA42wPWpk2b9Nprr%2Bnhhx9WeHi4JGncuHHatWuXCgoK7C4HAADAONsDVnFxsXJycjR58mTf3dtvuOEGrVixQm%2B88Ybd5QAAABhne8D6%2BuuvNXDgwLPGBwwYII/HY3c5AAAAxtkesBITE/X555%2BfNf7%2B%2B%2B/73vAOAADQkdn%2BV4T/9V//paVLl8rj8ciyLO3du1fFxcXatGmTHnzwQbvLAQAAMM72gJWZmammpiY99dRTOnnypHJyctS1a1fNnz9f06dPt7scAAAA42wPWDt27ND48eM1bdo0ffvtt7IsS3FxcXaXAQAAcNHY/h6sZcuW%2Bd7M3rVrV8IVAAAIObYHrL59%2B%2BrLL7%2B0e7MAAAC2sf0S4YABA/TrX/9aGzZsUN%2B%2BfRUREeE3n5eXZ3dJAAAARtkesA4dOqSUlBRJ4r5XAAAgJNkSsFauXKm5c%2BcqOjpamzZtsmOTAAAAQWPLe7Cee%2B45eb1ev7FZs2apqqrKjs0DAADYypaAZVnWWWOffvqpGhoa7Ng8AACArWz/K0IAAIBQR8ACAAAwzLaA5XA47NqUn1mzZvl9xmF5ebmmTp0ql8ulzMxM7d%2B/32/5HTt2aNy4cXK5XMrKytK3335rd8kAAKCDs%2B02DStWrPC759WpU6eUn5%2BvmJgYv%2BVM3gfrzTff1O7du3XzzTdLkurr6zVr1izddNNNeuSRR/Tyyy9r9uzZevfddxUdHa19%2B/Zp8eLFWrp0qQYMGKDc3FxlZ2dr/fr1xmoCAAChz5aAde211551z6vk5GTV1NSopqbmomyztrZWK1eu1JAhQ3xjO3fuVEREhBYsWCCHw6HFixfr/fff19tvv63JkyfrxRdfVEZGhiZNmiTp%2B9tLjB07VpWVlerdu/dFqRMAAIQeWwJWMO599eijj2rixIl%2Bt4Jwu91KSUnxXa50OBwaOnSoysrKNHnyZLndbt19992%2B5Xv06KGePXvK7XYTsAAAQKuF5Jvc9%2B7dq88%2B%2B0xz5szxG/d4PEpISPAbi4uL07FjxyRJVVVV550HAABoDds/Kudia2ho0MMPP6ycnBxFRkb6zXm9XoWHh/uNhYeHq7GxUZJ08uTJ8863VlVV1VmXRJ3O6LPCW2uEhXXy%2Bw78EKfT/DHC8RcY%2BhcY%2Bhc4ehg8IRewCgoKdPXVVystLe2suYiIiLPCUmNjoy%2BI/dB8VFRUm2ooLi5WQUGB31hWVpbmzZvXpuf5V7GxbasBl54uXWJaXugCcfwFhv4Fhv4Fjh7aL%2BQC1ptvvqnq6molJydLki8wvfPOO5owYYKqq6v9lq%2BurvadWerevfs55%2BPj49tUw7Rp05Senu435nRGq6bmRJueR/r%2Bt47Y2CjV1Xl1%2BnRzm9fHpeNCjq%2BWcPwFhv4Fhv4Fjh5e3F8%2BzyfkAtamTZvU1NTke/zYY49Jkn7961/r008/1dNPPy3LsuRwOGRZlkpLS3XPPfdIklwul0pKSjR58mRJ0tGjR3X06FG5XK421ZCQkHDW5UCP5zs1NV34wX36dHNA6yP0Xczjg%2BMvMPQvMPQvcPTQfiEXsBITE/0e/%2BM%2BW3369FFcXJxWrVql3Nxc/fKXv9TmzZvl9XqVkZEhSZo%2BfbpmzJihpKQkDRkyRLm5uRozZgx/QQgAANrkknrXW%2BfOnbV%2B/XrfWSq3262ioiJFR0dL%2Bv7eXMuWLVNhYaGmT5%2BuK664wuiNTwEAwKXBYVmWFewiLgUez3cXtJ7T2UldusSopuZEhz%2B9m7Hmw2CXENLemj/S%2BHOG0vEXDPQvMPQvcPRQio%2B/PCjbvaTOYAEAANiBgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGhdyNRi813PoAAID2hzNYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGhWzA%2BuabbzRv3jwNGzZMaWlpysvLU0NDgySpsrJSd9xxh5KSknTDDTfogw8%2B8Ft3z549mjBhglwul2bOnKnKyspg7AIAAOigQjJgWZalefPmyev16qWXXtLjjz%2Bu9957T2vWrJFlWcrKylK3bt20detWTZw4UXPnztWRI0ckSUeOHFFWVpYmT56sLVu2qGvXrpozZ44sywryXgEAgI7CGewCLoaDBw%2BqrKxMH374obp16yZJmjdvnh599FGNHj1alZWV2rx5s6Kjo9WvXz/t3btXW7du1X333adXX31VV199te666y5JUl5enkaOHKlPPvlE1113XTB3CwAAdBAheQYrPj5eGzZs8IWrfzh%2B/LjcbrcGDRqk6Oho33hKSorKysokSW63W6mpqb65qKgoDR482DcPAADQkpAMWLGxsUpLS/M9bm5u1osvvqjhw4fL4/EoISHBb/m4uDgdO3ZMklqcBwAAaElIXiI8U35%2BvsrLy7VlyxZt3LhR4eHhfvPh4eFqbGyUJHm93vPOt0ZVVZU8Ho/fmNMZfVZwa42wsE5%2B34Ef4nSaP0Y4/gJD/wJD/wJHD4Mn5ANWfn6%2Bnn/%2BeT3%2B%2BOPq37%2B/IiIiVFtb67dMY2OjIiMjJUkRERFnhanGxkbFxsa2epvFxcUqKCjwG8vKytK8efMucC%2Bk2NioC14Xl4YuXWIu2nNz/AWG/gWG/gWOHtovpAPW8uXL9fLLLys/P1/XX3%2B9JKl79%2B46cOCA33LV1dW%2Bs0vdu3dXdXX1WfMDBw5s9XanTZum9PR0vzGnM1o1NSfavA9hYZ0UGxulujqvTp9ubvP6uHRcyPHVEo6/wNC/wNC/wNHDi/vL5/mEbMAqKCjQ5s2btXr1ao0fP9437nK5VFRUpJMnT/rOWpWUlCglJcU3X1JS4lve6/WqvLxcc%2BfObfW2ExISzroc6PF8p6amCz%2B4T59uDmh9hL6LeXxw/AWG/gWG/gWOHtovJC/KfvXVV1q7dq3uvvtupaSkyOPx%2BL6GDRumHj16KDs7WxUVFSoqKtK%2Bffs0ZcoUSVJmZqZKS0tVVFSkiooKZWdnq1evXtyiAQAAtFpIBqzf/e53On36tJ566imNGjXK7yssLExr166Vx%2BPR5MmT9frrr6uwsFA9e/aUJPXq1UtPPvmktm7dqilTpqi2tlaFhYVyOBxB3isAANBROCxuUW4Lj%2Be7C1rP6eykLl1iVFNz4pyndzPWfBhoaQgRb80fafw5Wzr%2BcH70LzD0L3D0UIqPvzwo2w3JM1gAAADBRMACAAAwjIAFAABgGAELAADAMAJjf2XcAAALg0lEQVQWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIY5g10AADMy1nwY7BJa7a35I4NdAgBcVJzBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDBnsAsAcOnJWPNhsEtok7fmjwx2CQA6GM5gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEErHNoaGjQokWLlJqaqlGjRunZZ58NdkkAAKAD4TYN57By5Urt379fzz//vI4cOaKFCxeqZ8%2BeGj9%2BfLBLAwAAHQAB6wz19fV69dVX9fTTT2vw4MEaPHiwKioq9NJLLxGwgEsU9%2B0C0FYErDN88cUXampqUnJysm8sJSVF69atU3Nzszp14qoqgPatIwVCwiBCFQHrDB6PR126dFF4eLhvrFu3bmpoaFBtba26du3a4nNUVVXJ4/H4jTmd0UpISGhzPWFhnfy%2BA0Ao6UhhUJLe/XVasEtoE36GBA8B6wxer9cvXEnyPW5sbGzVcxQXF6ugoMBvbO7cubrvvvvaXE9VVZWef36Dpk2bds6A9lkuly3Pp6qqSsXFxT/YP5wf/QsM/QsM/QtcSz9DcPEQac8QERFxVpD6x%2BPIyMhWPce0adO0bds2v69p06ZdUD0ej0cFBQVnnRFD69C/wNC/wNC/wNC/wNHD4OEM1hm6d%2B%2BumpoaNTU1yen8vj0ej0eRkZGKjY1t1XMkJCTwmwIAAJcwzmCdYeDAgXI6nSorK/ONlZSUaMiQIbzBHQAAtAqJ4QxRUVGaNGmSlixZon379mnXrl169tlnNXPmzGCXBgAAOoiwJUuWLAl2Ee3N8OHDVV5erlWrVmnv3r265557lJmZGbR6YmJiNGzYMMXExAStho6M/gWG/gWG/gWG/gWOHgaHw7IsK9hFAAAAhBIuEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2C1Yw0NDVq0aJFSU1M1atQoPfvss8EuqV375ptvNG/ePA0bNkxpaWnKy8tTQ0ODJKmyslJ33HGHkpKSdMMNN%2BiDDz4IcrXt26xZs/Tggw/6HpeXl2vq1KlyuVzKzMzU/v37g1hd%2B9TY2KilS5fq2muv1U9%2B8hOtXr1a/7iPM/1r2dGjRzV79mwNHTpU6enp2rhxo2%2BO/p1fY2OjJkyYoI8//tg31tJr3p49ezRhwgS5XC7NnDlTlZWVdpcd8ghY7djKlSu1f/9%2BPf/883r44YdVUFCgt99%2BO9hltUuWZWnevHnyer166aWX9Pjjj%2Bu9997TmjVrZFmWsrKy1K1bN23dulUTJ07U3LlzdeTIkWCX3S69%2Beab2r17t%2B9xfX29Zs2apdTUVG3btk3JycmaPXu26uvrg1hl%2B7NixQrt2bNHzzzzjFatWqVXXnlFxcXF9K%2BV5s%2Bfr%2BjoaG3btk2LFi3SmjVr9O6779K/FjQ0NOhXv/qVKioqfGMtveYdOXJEWVlZmjx5srZs2aKuXbtqzpw54oNdDLPQLp04ccIaMmSI9dFHH/nGCgsLrdtuuy2IVbVfBw4csPr37295PB7f2BtvvGGNGjXK2rNnj5WUlGSdOHHCN3f77bdbTzzxRDBKbddqamqs0aNHW5mZmdbChQsty7KsV1991UpPT7eam5sty7Ks5uZm6z//8z%2BtrVu3BrPUdqWmpsYaNGiQ9fHHH/vG1q9fbz344IP0rxVqa2ut/v37W3/5y198Y3PnzrWWLl1K/86joqLC%2BvnPf27ddNNNVv/%2B/X0/L1p6zVuzZo3fz5L6%2BnorOTnZ7%2BcNAscZrHbqiy%2B%2BUFNTk5KTk31jKSkpcrvdam5uDmJl7VN8fLw2bNigbt26%2BY0fP35cbrdbgwYNUnR0tG88JSVFZWVldpfZ7j366KOaOHGifvzjH/vG3G63UlJS5HA4JEkOh0NDhw6lf/%2BipKREnTt31rBhw3xjs2bNUl5eHv1rhcjISEVFRWnbtm06deqUDh48qNLSUg0cOJD%2Bnccnn3yi6667TsXFxX7jLb3mud1upaam%2BuaioqI0ePBgemoYAaud8ng86tKli8LDw31j3bp1U0NDg2pra4NYWfsUGxurtLQ03%2BPm5ma9%2BOKLGj58uDwejxISEvyWj4uL07Fjx%2Bwus13bu3evPvvsM82ZM8dvnP61rLKyUomJidq%2BfbvGjx%2Bvn/70pyosLFRzczP9a4WIiAjl5OSouLhYLpdLGRkZGj16tKZOnUr/zuOWW27RokWLFBUV5TfeUs/oqT2cwS4A5%2Bb1ev3ClSTf48bGxmCU1KHk5%2BervLxcW7Zs0caNG8/ZS/r4Tw0NDXr44YeVk5OjyMhIv7kfOhbp3z/V19fr8OHD2rx5s/Ly8uTxeJSTk6OoqCj610pfffWVxo4dqzvvvFMVFRVavny5RowYQf8uQEs9o6f2IGC1UxEREWcd7P94fOYPQPjLz8/X888/r8cff1z9%2B/dXRETEWWf9Ghsb6eO/KCgo0NVXX%2B13FvAffuhYpH//5HQ6dfz4ca1atUqJiYmSvn8j8csvv6w%2BffrQvxbs3btXW7Zs0e7duxUZGakhQ4bom2%2B%2B0VNPPaXevXvTvzZq6TXvh/5Px8bG2lbjpYBLhO1U9%2B7dVVNTo6amJt%2BYx%2BNRZGQk/wnOY/ny5XruueeUn5%2Bv66%2B/XtL3vayurvZbrrq6%2BqxT5JeyN998U7t27VJycrKSk5P1xhtv6I033lBycjL9a4X4%2BHhFRET4wpUkXXnllTp69Cj9a4X9%2B/erT58%2BfqFp0KBBOnLkCP27AC317Ifm4%2BPjbavxUkDAaqcGDhwop9Pp96bDkpISDRkyRJ068c92LgUFBdq8ebNWr16tG2%2B80Tfucrn0pz/9SSdPnvSNlZSUyOVyBaPMdmnTpk164403tH37dm3fvl3p6elKT0/X9u3b5XK59Mc//tH3J9yWZam0tJT%2B/QuXy6WGhgYdOnTIN3bw4EElJibSv1ZISEjQ4cOH/c6qHDx4UL169aJ/F6Cl1zyXy6WSkhLfnNfrVXl5OT01jJ/U7VRUVJQmTZqkJUuWaN%2B%2Bfdq1a5eeffZZzZw5M9iltUtfffWV1q5dq7vvvlspKSnyeDy%2Br2HDhqlHjx7Kzs5WRUWFioqKtG/fPk2ZMiXYZbcbiYmJ6tOnj%2B8rJiZGMTEx6tOnj8aPH6%2B6ujrl5ubqwIEDys3NldfrVUZGRrDLbjeuuuoqjRkzRtnZ2friiy/0P//zPyoqKtL06dPpXyukp6frsssu00MPPaRDhw7p97//vdatW6cZM2bQvwvQ0mteZmamSktLVVRUpIqKCmVnZ6tXr1667rrrglx5iAnmPSJwfvX19daCBQuspKQka9SoUdZzzz0X7JLarfXr11v9%2B/c/55dlWdZf//pX69Zbb7Wuvvpq68Ybb7Q%2B/PDDIFfcvi1cuNB3HyzLsiy3221NmjTJGjJkiDVlyhTrT3/6UxCra5/q6uqsBx54wEpKSrJGjBhhPfnkk757N9G/llVUVFh33HGHNXToUGvcuHHWc889R//a4F/vg2VZLb/m/eEPf7B%2B9rOfWddcc411%2B%2B23W3/729/sLjnkOSyLW7cCAACYxCVCAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDs/wFohjiIbOGrtAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8865510260325480176”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>17.48462</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.92381</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.500142</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.97547</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.5322</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.80326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20.31755</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.65315</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.48499</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.51293</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2063)</td> <td class=”number”>2063</td> <td class=”number”>64.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1136</td> <td class=”number”>35.4%</td> <td>

<div class=”bar” style=”width:55%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8865510260325480176”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.743872</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.113909</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.805049</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.113567</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.348181</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>78.72282</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>86.27953</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>87.59541</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89.91983</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>110.6258</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PC_WIC_REDEMP12”><s>PC_WIC_REDEMP12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_PC_WIC_REDEMP08”><code>PC_WIC_REDEMP08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.931</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PERCHLDPOV10”>PERCHLDPOV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.22542</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>75.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8262641371871280018”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8262641371871280018,#minihistogram8262641371871280018”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8262641371871280018”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8262641371871280018”

aria-controls=”quantiles8262641371871280018” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8262641371871280018” aria-controls=”histogram8262641371871280018”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8262641371871280018” aria-controls=”common8262641371871280018”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8262641371871280018” aria-controls=”extreme8262641371871280018”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8262641371871280018”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.41792</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.854</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.27124</td>

</tr> <tr>

<th>Mean</th> <td>0.22542</td>

</tr> <tr>

<th>MAD</th> <td>0.34921</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3149</td>

</tr> <tr>

<th>Sum</th> <td>706</td>

</tr> <tr>

<th>Variance</th> <td>0.17466</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8262641371871280018”>

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YAQsAAMAw1wesuro6jR07Vjt27PCNLVy4UD/72c/8Hhs3bvTN5%2BXl6YorrlBiYqLS09N17Ngx35xlWVqyZIkGDx6sgQMHavHixWpsbHR0nwAAgLs5HrCuvfZaPf/88zp%2B/PhZb6u2tlZ33nmniouL/cZLSkp01113adu2bb7HhAkTJElFRUWaO3euZs6cqdzcXFVVVWnOnDm%2BddeuXau8vDzl5OToscce0x//%2BEetXbv2rGsFAADnD8cD1uDBg/Xkk09q6NChuvPOO7Vt2zZZltXi7Rw4cEDXXXedPv/88yZzJSUluuSSSxQTE%2BN7REZGSpI2btyo0aNHa9y4cUpISNDixYv19ttvq7S0VJK0fv16ZWRkKCUlRYMHD9bdd9%2BtZ5999ux2GgAAnFccD1h33XWX/va3v2nFihUKCwvTHXfcocsvv1yPPPKIPvnkk2ZvZ%2BfOnRo0aJByc3P9xk%2BcOKGjR4%2Bqa9euZ1yvsLBQKSkpvuedOnVSfHy8CgsLdfToUR0%2BfFgDBgzwzScnJ%2BvLL79UWVlZy3YUAACctzyBeNGQkBANGTJEQ4YMUXV1tTZs2KAVK1Zo1apV6t%2B/v6ZNm6Zf/epX37uNKVOmnHG8pKREISEhevLJJ/XOO%2B%2BoXbt2uvnmmzV%2B/HhJUllZmWJjY/3WiY6O1pEjR%2BT1eiXJb75jx46SpCNHjjRZDwAA4EwCErCkb4LOq6%2B%2BqldffVUff/yx%2Bvfvr/Hjx%2BvIkSO67777tGvXLs2dO7fF2z148KBCQkLUrVs33Xjjjdq1a5fmzZuntm3bauTIkaqpqVF4eLjfOuHh4aqrq1NNTY3v%2Bb/OSd9cTN%2BSfTsd1k7zeFobD2hhYe76GwWPxz31nu6t23rsBvTWXvTXPvTWPsHYW8cD1pYtW7Rlyxbt2LFDHTp00Lhx4/TYY4/5ndLr1KmTFi1a9KMC1rhx4zR8%2BHC1a9dOkpSQkKBPP/1Uzz33nEaOHKmIiIgmYamurk6RkZF%2BYSoiIsL3b0m%2Ba7iaIzc3Vzk5OX5j6enpysjIaPH%2BBJP27dsEuoQWi4pq/v93tAy9tRf9tQ%2B9tU8w9dbxgDV37lwNHz5cy5cv12WXXabQ0KZp9fTRpx8jJCTEF67%2BdXvbt2%2BXJMXFxam8vNxvvry8XDExMYqLi5Mkeb1edenSxfdvSYqJiWl2DZMmTdKIESP8xjye1qqoONmynfkBbkv6pvffTmFhoYqKilRVVbUaGrhNh0n01l701z701j529jZQv9w7HrDeeecdtW/fXpWVlb5wVVRUpN69eyssLEyS1L9/f/Xv3/9HbX/ZsmV6//339cwzz/jG9u3bp27dukmSEhMTlZ%2Bfr7S0NEnS4cOHdfjwYSUmJiouLk7x8fHKz8/3Baz8/HzFx8e36PRebGxsk%2BW93uOqrz%2B/35Bu3P%2BGhkZX1u0G9NZe9Nc%2B9NY%2BwdRbxw%2BBnDhxQqNGjdJTTz3lG7v11luVmpqqw4cPn/X2hw8frl27dmnNmjX6/PPP9T//8z965ZVXNH36dEnS5MmTtWXLFm3atEn79u3TPffco8svv1wXXnihb37JkiXasWOHduzYoaVLl2rq1KlnXRcAADh/OB6wHnzwQV100UW6%2BeabfWOvvfaaOnXqpKysrLPeft%2B%2BfbVs2TJt2bJFY8eO1YYNG7R06VIlJSVJkpKSkpSZmanly5dr8uTJuuCCC/xed8aMGRozZoxmzpyp3/72t0pNTdVNN9101nUBAIDzR4j1Y%2B7yeRZSUlL0wgsv%2BE7ZnVZcXKwbbrhBO3fudLIcx3i9Z3/n%2Bm/zeEI1csnfjW/XLq//bkigS2g2jydU7du3UUXFyaA5XH2uoLf2or/2obf2sbO3MTE/Nbq95nL8CJbH41FVVVWT8erq6h91R3cAAIBzjeMB67LLLtPChQv9vuKmtLRUWVlZGjZsmNPlAAAAGOf4XxHOnj1bN998s6688kpFRUVJkqqqqtS7d2%2B/L10GAABwK8cDVnR0tF5%2B%2BWW9%2B%2B67Ki4ulsfj0cUXX6yf//znCgkJcbocAAAA4wLyVTlhYWEaNmwYpwQBAEBQcjxgeb1ePfroo9q9e7e%2B/vrrJhe2v/nmm06XBAAAYJTjAWvevHnas2ePrrrqKv30p4H500kAAAA7OR6wtm/frtWrVyslJcXplwYAAHCE47dpaN26taKjo51%2BWQAAAMc4HrBSU1O1evVqNTQ0OP3SAAAAjnD8FGFlZaXy8vL01ltv6cILL1R4eLjf/Pr1650uCQAAwKiA3KZh7NixgXhZAAAARzgesLKyspx%2BSQAAAEc5fg2WJJWVlSknJ0d33XWXvvrqK/3pT3/SwYMHA1EKAACAcY4HrM8%2B%2B0xXX321Xn75Zb3xxhs6deqUXnvtNU2YMEGFhYVOlwMAAGCc4wHroYce0hVXXKGtW7fqJz/5iSTp4Ycf1ogRI7RkyRKnywEAADDO8YC1e/du3XzzzX5f7OzxeHT77bdr7969TpcDAABgnOMBq7GxUY2NjU3GT548qbCwMKfLAQAAMM7xgDV06FCtXLnSL2RVVlYqOztbgwcPdrocAAAA4xwPWL///e%2B1Z88eDR06VLW1tfqv//ovDR8%2BXF988YVmz57tdDkAAADGOX4frLi4OL3yyivKy8vTRx99pMbGRk2ePFmpqalq27at0%2BUAAAAYF5A7uUdGRuraa68NxEsDAADYzvGANXXq1O%2Bd57sIAQCA2zkesDp37uz3vL6%2BXp999pk%2B/vhjTZs2zelyAAAAjDtnvotw%2BfLlOnLkiMPVAAAAmBeQ7yI8k9TUVL3%2B%2BuuBLgMAAOCsnTMB6/333%2BdGowAAICicExe5nzhxQvv379eUKVOcLgcAAMA4xwNWfHy83/cQStJPfvIT3XjjjbrmmmucLgcAAMA4xwPWQw895PRLAgAAOMrxgLVr165mLztgwAAbKwEAALCH4wHr17/%2Bte8UoWVZvvFvj4WEhOijjz5yujwAAICz5njAevLJJ7Vw4ULNmjVLAwcOVHh4uD744ANlZmZq/PjxGjNmjNMlAQAAGOX4bRqysrI0f/58XXnllWrfvr3atGmjwYMHKzMzU88995w6d%2B7sewAAALiR4wGrrKzsjOGpbdu2qqiocLocAAAA4xwPWP369dPDDz%2BsEydO%2BMYqKyuVnZ2tn//8506XAwAAYJzj12Ddd999mjp1qi677DJ17dpVlmXp008/VUxMjNavX%2B90OQAAAMY5HrC6d%2B%2Bu1157TXl5eSopKZEk3XDDDbrqqqsUGRnpdDkAAADGOR6wJOmCCy7Qtddeqy%2B%2B%2BEIXXnihpG/u5g4AABAMHL8Gy7IsLVmyRAMGDNDYsWN15MgRzZ49W3PnztXXX3/tdDkAAADGOR6wNmzYoC1btugPf/iDwsPDJUlXXHGFtm7dqpycHKfLAQAAMM7xgJWbm6v58%2BcrLS3Nd/f2MWPGaOHChfrjH//odDkAAADGOR6wvvjiC/Xq1avJeEJCgrxer9PlAAAAGOd4wOrcubM%2B%2BOCDJuPvvPOO74J3AAAAN3P8rwhnzJihBx54QF6vV5Zl6b333lNubq42bNig3//%2B906XAwAAYJzjAWvChAmqr6/XE088oZqaGs2fP18dOnTQ7373O02ePNnpcgAAAIxzPGDl5eVp1KhRmjRpko4dOybLshQdHe10GQAAALZx/BqszMxM38XsHTp0IFwBAICg43jA6tq1qz7%2B%2BGOnXxYAAMAxjp8iTEhI0N13363Vq1era9euioiI8JvPyspyuiQAAACjHA9Yn3zyiZKTkyWJ%2B14BAICg5EjAWrx4sWbOnKnWrVtrw4YNRrddV1entLQ0zZs3T4MGDZIklZaWat68eSooKFB8fLzuvfdeDR061LfOu%2B%2B%2BqwcffFClpaVKTEzUokWL/O7B9cwzz2jNmjU6ceKERo8erXnz5ikyMtJo3QAAIHg5cg3W2rVrVV1d7Td26623qqys7Ky2W1tbqzvvvFPFxcW%2BMcuylJ6ero4dO2rz5s1KTU3VzJkzdejQIUnSoUOHlJ6errS0NL344ovq0KGDbr/9dlmWJUl64403lJOTo8zMTK1bt06FhYXKzs4%2BqzoBAMD5xZGAdTq8/Ktdu3aptrb2R2/zwIEDuu666/T555/7jW/fvl2lpaXKzMxU9%2B7dddttt6lfv37avHmzJGnTpk269NJLNX36dPXo0UNZWVn68ssvtXPnTknS%2BvXrNW3aNA0fPlx9%2B/bVAw88oM2bNzcJiAAAAN/F8b8iNGXnzp0aNGiQcnNz/cYLCwt1ySWXqHXr1r6x5ORkFRQU%2BOZTUlJ8c5GRkerdu7cKCgrU0NCgDz74wG%2B%2BX79%2B%2Bvrrr7Vv3z6b9wgAAAQLxy9yN2XKlClnHPd6vYqNjfUbi46O1pEjR35wvqqqSrW1tX7zHo9H7dq1863fHGVlZU0u4Pd4Wjd53bMVFuaufOzxuKfe0711W4/dgN7ai/7ah97aJxh761jACgkJceR1qqurFR4e7jcWHh6uurq6H5yvqanxPf%2Bu9ZsjNzdXOTk5fmPp6enKyMho9jaCUfv2bQJdQotFRfHHDXaht/aiv/aht/YJpt46FrAWLlzod8%2Brr7/%2BWtnZ2WrTxv9D92zvgxUREaHKykq/sbq6OrVq1co3/%2B2wVFdXp6ioKF99Z5pvyV8RTpo0SSNGjPAb83haq6LiZLO30RxuS/qm999OYWGhioqKVFVVtRoaGgNdTlCht/aiv/aht/axs7eB%2BuXekYA1YMCAJqfMkpKSVFFRoYqKCqOvFRcXpwMHDviNlZeX%2B07PxcXFqby8vMl8r1691K5dO0VERKi8vFzdu3eXJNXX16uyslIxMTHNriE2NrbJ6UCv97jq68/vN6Qb97%2BhodGVdbsBvbUX/bUPvbVPMPXWkYBl%2Bt5X3ycxMVGrVq1STU2N76hVfn6%2B7%2BamiYmJys/P9y1fXV2tvXv3aubMmQoNDVWfPn2Un5/vu6dWQUGBPB6PEhISHNsHAADgbu46x9QMAwcOVKdOnTRnzhwVFxdr1apVKioq0sSJEyVJEyZM0O7du7Vq1SoVFxdrzpw56tKliy9QTZkyRWvWrNHWrVtVVFSk%2B%2B%2B/X9dddx03GgUAAM0WdAErLCxMK1askNfrVVpaml599VUtX75c8fHxkqQuXbro8ccf1%2BbNmzVx4kRVVlZq%2BfLlvovwr7rqKt12222aP3%2B%2Bpk%2Bfrr59%2B2rWrFmB3CUAAOAyIdaZ7gIK47ze48a36fGEauSSvxvfrl1e/92QQJfQbB5PqNq3b6OKipNBcz3AuYLe2ov%2B2ofe2sfO3sbE/NTo9por6I5gAQAABBoBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzzBLoAAABgj9GP/r9Al9Bs/1g0KtAlGMURLAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBa0Aesvf/mLfvazn/k9MjIyJEl79%2B7Vtddeq8TERE2YMEF79uzxWzcvL09XXHGFEhMTlZ6ermPHjgViFwAAgEsFbcA6cOCAhg8frm3btvkeCxcu1KlTp3TrrbcqJSVFL730kpKSknTbbbfp1KlTkqSioiLNnTtXM2fOVG5urqqqqjRnzpwA7w0AAHCToA1YJSUl6tmzp2JiYnyPqKgovfbaa4qIiNA999yj7t27a%2B7cuWrTpo3%2B9Kc/SZI2btyo0aNHa9y4cUpISNDixYv19ttvq7S0NMB7BAAA3CKoA1bXrl2bjBcWFio5OVkhISGSpJCQEPXv318FBQW%2B%2BZSUFN/ynTp1Unx8vAoLCx2pGwAAuJ8n0AXYwbIsffLJJ9q2bZtWrlyphoYGjRo1ShkZGfJ6vbr44ov9lo%2BOjlZxcbEkqaysTLGxsU3mjxw50uzXLysrk9fr9RvzeFo32e7ZCgtzVz72eNxT7%2Bneuq3HbkBv7UV/7UNv7RdMvQ3KgHXo0CFVV1crPDxcjz76qL67v%2Bq0AAANA0lEQVT44gstXLhQNTU1vvF/FR4errq6OklSTU3N9843R25urnJycvzG0tPTfRfZn6/at28T6BJaLCoqMtAlBC16ay/6ax96a59g6m1QBqzOnTtrx44duuCCCxQSEqJevXqpsbFRs2bN0sCBA5uEpbq6OrVq1UqSFBERccb5yMjm/0%2BfNGmSRowY4Tfm8bRWRcXJH7lHZ%2Ba2pG96/%2B0UFhaqqKhIVVVVq6GhMdDlBBV6ay/6ax96az87ehuoX%2B6DMmBJUrt27fyed%2B/eXbW1tYqJiVF5ebnfXHl5ue/0XVxc3BnnY2Jimv3asbGxTU4Her3HVV9/fr8h3bj/DQ2NrqzbDeitveivfeitfYKpt%2B46BNJMf//73zVo0CBVV1f7xj766CO1a9dOycnJev/992VZlqRvrtfavXu3EhMTJUmJiYnKz8/3rXf48GEdPnzYNw8AAPBDgjJgJSUlKSIiQvfdd58OHjyot99%2BW4sXL9Ytt9yiUaNGqaqqSosWLdKBAwe0aNEiVVdXa/To0ZKkyZMna8uWLdq0aZP27dune%2B65R5dffrkuvPDCAO8VAABwi6AMWG3bttWaNWt07NgxTZgwQXPnztWkSZN0yy23qG3btlq5cqXy8/OVlpamwsJCrVq1Sq1bt5b0TTjLzMzU8uXLNXnyZF1wwQXKysoK8B4BAAA3CdprsHr06KG1a9eeca5v3756%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%2B%2BvcLDw31jHTt2VG1trSorK9WhQ4cf3EZZWZm8Xq/fmMfTWrGxsUZrDQtzVz72eNxT7%2Bneuq3HbkBv7UV/7UNv7RdMvSVgfUt1dbVfuJLke15XV9esbeTm5ionJ8dvbObMmbrjjjvMFPm/ysrKNO3fijVp0iTj4e18V1ZWpnXrVtNbG9Bbe9Ff%2B7ixt/9Y5I5LW8rKyvT444%2B7qrc/JHiioiERERFNgtTp561atWrWNiZNmqSXXnrJ7zFp0iTjtXq9XuXk5DQ5WoazR2/tQ2/tRX/tQ2/tE4y95QjWt8TFxamiokL19fXyeL5pj9frVatWrRQVFdWsbcTGxgZNAgcAAC3HEaxv6dWrlzwejwoKCnxj%2Bfn56tOnj0JDaRcAAPhhJIZviYyM1Lhx43T//ferqKhIW7du1dNPP62pU6cGujQAAOASYffff//9gS7iXDN48GDt3btXS5cu1Xvvvaf//M//1IQJEwJd1hm1adNGAwcOVJs2bQJdStCht/aht/aiv/aht/YJtt6GWJZlBboIAACAYMIpQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKxzXG1tre69916lpKRo6NChevrpp79z2b179%2Braa69VYmKiJkyYoD179jhYqfu0pLdvvfWWUlNTlZSUpKuvvlpvvvmmg5W6T0t6e9oXX3yhpKQk7dixw4EK3a0l/d2/f78mT56svn376uqrr9b27dsdrNR9WtLbv/zlLxo9erSSkpI0efJkffjhhw5W6l51dXUaO3bs977Xg%2BLzzMI5LTMz07r66qutPXv2WH/%2B85%2BtpKQk6/XXX2%2By3MmTJ60hQ4ZYDz30kHXgwAFrwYIF1i9%2B8Qvr5MmTAajaHZrb248%2B%2Bsjq3bu3tW7dOuvTTz%2B1Nm7caPXu3dv66KOPAlC1OzS3t/9qxowZVs%2BePa3t27c7VKV7Nbe/VVVV1i9%2B8Qvrvvvusz799FNr2bJlVnJyslVeXh6Aqt2hub39%2BOOPrT59%2Blgvv/yy9dlnn1kPPPCANWTIEOvUqVMBqNo9ampqrPT09O99rwfL5xkB6xx28uRJq0%2BfPn4/hMuXL7duvPHGJstu2rTJGjFihNXY2GhZlmU1NjZaI0eOtDZv3uxYvW7Skt5mZ2dbM2bM8BubPn269fDDD9tepxu1pLenbdmyxbr%2B%2BusJWM3Qkv6uW7fOuuKKK6z6%2BnrfWFpamvXWW285UqvbtKS3a9eutcaPH%2B97fvz4catnz55WUVGRI7W6UXFxsXXNNddYV1999fe%2B14Pl84xThOewffv2qb6%2BXklJSb6x5ORkFRYWqrGx0W/ZwsJCJScnKyQkRJIUEhKi/v37q6CgwNGa3aIlvR0/frzuvvvuJts4fvy47XW6UUt6K0kVFRXKzs5WZmamk2W6Vkv6u3PnTv3yl79UWFiYb2zz5s36j//4D8fqdZOW9LZdu3Y6cOCA8vPz1djYqJdeeklt27bVv//7vztdtmvs3LlTgwYNUm5u7vcuFyyfZ55AF4Dv5vV61b59e4WHh/vGOnbsqNraWlVWVqpDhw5%2By1588cV%2B60dHR6u4uNixet2kJb3t3r2737rFxcV67733dP311ztWr5u0pLeS9NBDD2n8%2BPHq0aOH06W6Ukv6W1paqr59%2B2revHn661//qs6dO2v27NlKTk4OROnnvJb0dsyYMfrrX/%2BqKVOmKCwsTKGhoVq5cqUuuOCCQJTuClOmTGnWcsHyecYRrHNYdXW13xtdku95XV1ds5b99nL4Rkt6%2B6%2BOHTumO%2B64Q/3799cvf/lLW2t0q5b09t1331V%2Bfr5uv/12x%2Bpzu5b099SpU1q1apViYmL01FNPacCAAZoxY4YOHz7sWL1u0pLeVlRUyOv1av78%2BXrhhReUmpqqOXPm6KuvvnKs3mAVLJ9nBKxzWERERJMfqNPPW7Vq1axlv70cvtGS3p5WXl6uadOmybIsPfbYYwoN5e1zJs3tbU1NjebPn68//OEP/Jy2QEt%2BdsPCwtSrVy9lZGTokksu0axZs9S1a1dt2bLFsXrdpCW9XbJkiXr27KkbbrhBl156qRYsWKDIyEht3rzZsXqDVbB8nvEJcQ6Li4tTRUWF6uvrfWNer1etWrVSVFRUk2XLy8v9xsrLyxUbG%2BtIrW7Tkt5K0tGjR3XDDTeorq5O69evb3KaC/%2Bnub0tKipSaWmpMjIylJSU5Lvu5Te/%2BY3mz5/veN1u0ZKf3ZiYGHXr1s1vrGvXrhzB%2Bg4t6e2HH36ohIQE3/PQ0FAlJCTo0KFDjtUbrILl84yAdQ7r1auXPB6P34V9%2Bfn56tOnT5OjJ4mJiXr//fdlWZYkybIs7d69W4mJiY7W7BYt6e2pU6d0yy23KDQ0VBs3blRcXJzT5bpKc3vbt29f/fnPf9Yrr7zie0jSwoUL9dvf/tbxut2iJT%2B7/fr10/79%2B/3GDh48qM6dOztSq9u0pLexsbEqKSnxG/vkk0/UpUsXR2oNZsHyeUbAOodFRkZq3Lhxuv/%2B%2B1VUVKStW7fq6aef1tSpUyV985tVTU2NJGnUqFGqqqrSokWLdODAAS1atEjV1dUaPXp0IHfhnNWS3q5cuVKff/65/vu//9s35/V6%2BSvC79Dc3rZq1UoXXXSR30P65rfX6OjoQO7COa0lP7vXX3%2B99u/fr8cff1yfffaZli1bptLSUqWmpgZyF85ZLentddddpxdeeEGvvPKKPvvsMy1ZskSHDh3S%2BPHjA7kLrhWUn2cBvUkEftCpU6ese%2B65x%2BrXr581dOhQa%2B3atb65nj17%2Bt0XpLCw0Bo3bpzVp08fa%2BLEidaHH34YgIrdo7m9vfLKK62ePXs2ecyePTtAlZ/7WvJz%2B6%2B4D1bztKS///jHP6zx48dbl156qZWammrt3LkzABW7R0t6%2B8ILL1ijRo2y%2BvXrZ02ePNnas2dPACp2p2%2B/14Px8yzEsv73GBwAAACM4BQhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABj2/wFJaZ95ymbzcgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8262641371871280018”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8262641371871280018”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2426</td> <td class=”number”>75.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>706</td> <td class=”number”>22.0%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_PERPOV10”>PERPOV10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.11207</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>86.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6869201937990029993”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives6869201937990029993,#minihistogram6869201937990029993”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives6869201937990029993”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6869201937990029993”

aria-controls=”quantiles6869201937990029993” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6869201937990029993” aria-controls=”histogram6869201937990029993”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6869201937990029993” aria-controls=”common6869201937990029993”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6869201937990029993” aria-controls=”extreme6869201937990029993”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6869201937990029993”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.3155</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.8152</td>

</tr> <tr>

<th>Kurtosis</th> <td>4.0577</td>

</tr> <tr>

<th>Mean</th> <td>0.11207</td>

</tr> <tr>

<th>MAD</th> <td>0.19902</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.4607</td>

</tr> <tr>

<th>Sum</th> <td>351</td>

</tr> <tr>

<th>Variance</th> <td>0.099541</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6869201937990029993”>

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AAGAYAQsAAMAwAhYAAIBhjgesq666Sk8//bROnDjh9EsDAAA4wvGANXjwYD300EMaOnSo/vSnP2nHjh2yLMvpMgAAAGzjeMC6/fbb9Y9//EPr1q1TWFiYbr31Vl166aW677779MknnzhdDgAAgHGeQLxoSEiIhgwZoiFDhqimpkZPPvmk1q1bpw0bNqh///6aOXOmfv/73weiNAAAgJ8tIAFLksrLy/XSSy/ppZde0kcffaT%2B/ftrwoQJOnr0qO6%2B%2B27t3r1bCxYsCFR5AAAAP5njAWvbtm3atm2b3nnnHXXs2FHjx4/XmjVr1K1bN98ynTt31vLlywlYAADAlRwPWAsWLNDw4cO1du1aXXLJJQoNbX4ZWPfu3TV9%2BnSnSwMAADDC8YD15ptvqkOHDqqqqvKFq%2BLiYvXp00dhYWGSpP79%2B6t///5OlwYAAGCE439FePLkSY0ePVoPP/ywb%2Bymm27SuHHjdOTIEafLAQAAMM7xgPWXv/xFF1xwgW644Qbf2Msvv6zOnTsrOzvb6XIAAACMczxgvfvuu7rrrrsUGxvrG%2BvYsaPuvPNO7dy50%2BlyAAAAjHM8YHk8HlVXVzcbr6mp4Y7uAAAgKDgesC655BItW7ZMn3/%2BuW%2BsrKxM2dnZGjZsmNPlAAAAGOf4XxHOmzdPN9xwgy677DJFRUVJkqqrq9WnTx9lZmY6XQ4AAIBxjgesmJgYvfDCC3rrrbdUUlIij8ejCy%2B8UL/5zW8UEhLidDkAAADGBeSrcsLCwjRs2DBOCQIAgKDkeMDyer26//77tWfPHn399dfNLmx/7bXXnC4JAADAKMcD1sKFC7V3715dccUV%2BuUvf%2Bn0ywMAANjO8YC1c%2BdOPfLII0pLS3P6pQEAABzh%2BG0a2rZtq5iYGKdfFgAAwDGOB6xx48bpkUceUWNjo9MvDQAA4AjHTxFWVVUpPz9fr7/%2Bus4//3yFh4f7zW/atMnpkgAAAIwKyG0axo4dG4iXBQAAcITjASs7O9vo9urr6zVx4kQtXLhQgwYNkiQtW7ZMTz75pN9yCxcu1PTp0yVJ%2Bfn5uv/%2B%2B%2BX1ejV06FAtXbpUHTt2lCRZlqVVq1bpueeeU1NTkyZPnqw77rhDoaGOn00FAAAuFZAjWOXl5XrmmWf0ySefaP78%2Bdq9e7d69eql7t27t2o7dXV1uv3221VSUuI3Xlpaqttvv10TJkzwjbVv316SVFxcrAULFmjJkiVKTEzU8uXLlZmZqfXr10uSHn/8ceXn5ys3N1cNDQ2aO3euYmJiNHv27J%2B51wAA4Fzh%2BGGZzz77TFdeeaVeeOEFvfrqqzp9%2BrRefvllTZo0SUVFRS3ezsGDBzVlyhS/L40%2Bo7S0VBdddJFiY2N9j8jISEnS5s2bNWbMGI0fP16JiYlasWKF3njjDZWVlUn65hqwjIwMpaWlafDgwbrjjju0ZcsWMzsPAADOCY4HrHvuuUcjR47U9u3b9Ytf/EKSdO%2B992rEiBFauXJli7eza9cuDRo0SHl5eX7jJ0%2Be1LFjx9StW7fvXK%2BoqMjvHlydO3dWQkKCioqKdOzYMR05ckQDBgzwzaempurQoUMqLy9vxV4CAIBzmeOnCPfs2aMtW7b4fbGzx%2BPRLbfcoilTprR4O9dcc813jpeWliokJEQPPfSQ3nzzTUVHR%2BuGG27wnS4sLy9XXFyc3zoxMTE6evSovF6vJPnNd%2BrUSZJ09OjRZusBAAB8F8cDVlNTk5qampqNnzp1SmFhYT97%2Bx9//LFCQkLUvXt3TZ8%2BXbt379bChQvVvn17jRo1SrW1tc1uDREeHq76%2BnrV1tb6nv/rnPTNxfQtVV5e7gtrZ3g8bY0HtLAwd1147/G4p94zvXVbj92A3tqL/tqH3tonGHvreMAaOnSo1q9fr5ycHN9YVVWVcnJyNHjw4J%2B9/fHjx2v48OGKjo6WJCUmJurTTz/VU089pVGjRikiIqJZWKqvr1dkZKRfmIqIiPD9W5LvGq6WyMvLU25urt9Yenq6MjIyfvJ%2BBYMOHdoFuoRWi4pq%2Bf87Wofe2ov%2B2ofe2ieYeut4wLrrrrs0Y8YMDR06VHV1dfrP//xPHTp0SNHR0brnnnt%2B9vZDQkJ84eqM7t27a%2BfOnZKk%2BPh4VVRU%2BM1XVFQoNjZW8fHxkiSv16uuXbv6/i1JsbGxLa5h6tSpGjFihN%2BYx9NWlZWnWrczP8JtSd/0/tspLCxUUVGRqq6uUWNj8yOu%2BOnorb3or33orX3s7G2gfrl3PGDFx8frxRdfVH5%2Bvj788EM1NTVp2rRpGjdunO9WCj/H6tWr9d577%2BmJJ57wje3fv993C4ikpCQVFBRo4sSJkqQjR47oyJEjSkpKUnx8vBISElRQUOALWAUFBUpISGjV6b24uLhmy3u9J9TQcG6/Id24/42NTa6s2w3orb3or33orX2CqbcBuQ9WZGSkrrrqKlu2PXz4cG3YsEGPPvqoRo0apR07dujFF1/0fQXPtGnTdN111yk5OVl9%2B/bV8uXLdemll%2Br888/3za9cuVK/%2BtWvJEmrVq3SrFmzbKkVAAAEJ8cD1owZM35w/ud%2BF2G/fv20evVqrVmzRqtXr1aXLl20atUqpaSkSJJSUlKUlZWlNWvW6KuvvtKQIUO0dOlS3/qzZ8/Wl19%2BqTlz5igsLEyTJ0/W9ddf/7NqAgAA5xbHA1aXLl38njc0NOizzz7TRx99pJkzZ/6kbR44cMDv%2BciRIzVy5MjvXX7ixIm%2BU4TfFhYWpszMTGVmZv6kWgAAAM6a7yJcu3atjh496nA1AAAA5p01f4Y2btw4vfLKK4EuAwAA4Gc7awLWe%2B%2B9Z%2BRGowAAAIF2VlzkfvLkSR04cOB7v/4GAADATRwPWAkJCX7fQyhJv/jFLzR9%2BnT94Q9/cLocAAAA4xwPWCbu1g4AAHA2czxg7d69u8XLDhgwwMZKAAAA7OF4wLruuut8pwgty/KNf3ssJCREH374odPlAQAA/GyOB6yHHnpIy5Yt09y5czVw4ECFh4fr/fffV1ZWliZMmKDLL7/c6ZIAAACMcvw2DdnZ2Vq0aJEuu%2BwydejQQe3atdPgwYOVlZWlp556Sl26dPE9AAAA3MjxgFVeXv6d4al9%2B/aqrKx0uhwAAADjHA9YycnJuvfee3Xy5EnfWFVVlXJycvSb3/zG6XIAAACMc/warLvvvlszZszQJZdcom7dusmyLH366aeKjY3Vpk2bnC4HAADAOMcDVo8ePfTyyy8rPz9fpaWlkqRrr71WV1xxhSIjI50uBwAAwDjHA5YknXfeebrqqqv0xRdf6Pzzz5f0zd3cAQAAgoHj12BZlqWVK1dqwIABGjt2rI4ePap58%2BZpwYIF%2Bvrrr50uBwAAwDjHA9aTTz6pbdu26c9//rPCw8MlSSNHjtT27duVm5vrdDkAAADGOR6w8vLytGjRIk2cONF39/bLL79cy5Yt0//8z/84XQ4AAIBxjgesL774Qr179242npiYKK/X63Q5AAAAxjkesLp06aL333%2B/2fibb77pu%2BAdAADAzRz/K8LZs2dryZIl8nq9sixLb7/9tvLy8vTkk0/qrrvucrocAAAA4xwPWJMmTVJDQ4MefPBB1dbWatGiRerYsaNuu%2B02TZs2zelyAAAAjHM8YOXn52v06NGaOnWqjh8/LsuyFBMT43QZAAAAtnH8GqysrCzfxewdO3YkXAEAgKDjeMDq1q2bPvroI6dfFgAAwDGOnyJMTEzUHXfcoUceeUTdunVTRESE33x2drbTJQEAABjleMD65JNPlJqaKknc9woAAAQlRwLWihUrNGfOHLVt21ZPPvmkEy8JAAAQMI5cg/X444%2BrpqbGb%2Bymm25SeXm5Ey8PAADgKEcClmVZzcZ2796turo6J14eAADAUY7/FSEAAECwI2ABAAAY5ljACgkJceqlAAAAAsqx2zQsW7bM755XX3/9tXJyctSuXTu/5bgPFgAAcDtHAtaAAQOa3fMqJSVFlZWVqqysdKIEAAAAxzgSsLj3FQAAOJdwkTsAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmOsDVn19vcaOHat33nnHN1ZWVqbrr79eycnJuvzyy7Vjxw6/dd566y2NHTtWSUlJmjFjhsrKyvzmn3jiCQ0bNkwpKSmaP3%2B%2BampqHNkXAAAQHFwdsOrq6vSnP/1JJSUlvjHLspSenq5OnTpp69atGjdunObMmaPDhw9Lkg4fPqz09HRNnDhRzz33nDp27KhbbrlFlmVJkl599VXl5uYqKytLGzduVFFRkXJycgKyfwAAwJ1cG7AOHjyoKVOm6PPPP/cb37lzp8rKypSVlaUePXro5ptvVnJysrZu3SpJevbZZ3XxxRdr1qxZ6tmzp7Kzs3Xo0CHt2rVLkrRp0ybNnDlTw4cPV79%2B/bRkyRJt3bqVo1gAAKDFXBuwdu3apUGDBikvL89vvKioSBdddJHatm3rG0tNTVVhYaFvPi0tzTcXGRmpPn36qLCwUI2NjXr//ff95pOTk/X1119r//79Nu8RAAAIFo58F6Edrrnmmu8c93q9iouL8xuLiYnR0aNHf3S%2BurpadXV1fvMej0fR0dG%2B9VuivLy82Zdbezxtm73uzxUW5q587PG4p94zvXVbj92A3tqL/tqH3tonGHvr2oD1fWpqahQeHu43Fh4ervr6%2Bh%2Bdr62t9T3/vvVbIi8vT7m5uX5j6enpysjIaPE2glGHDu0CXUKrRUVFBrqEoEVv7UV/7UNv7RNMvQ26gBUREaGqqiq/sfr6erVp08Y3/%2B2wVF9fr6ioKEVERPief3s%2BMrLl/%2BlTp07ViBEj/MY8nraqrDzV4m20hNuSvun9t1NYWKiioiJVXV2jxsamQJcTVOitveivfeitfezsbaB%2BuQ%2B6gBUfH6%2BDBw/6jVVUVPhOz8XHx6uioqLZfO/evRUdHa2IiAhVVFSoR48ekqSGhgZVVVUpNja2xTXExcU1Ox3o9Z5QQ8O5/YZ04/43Nja5sm43oLf2or/2obf2CabeuusQSAskJSXpgw8%2B8J3uk6SCggIlJSX55gsKCnxzNTU12rdvn5KSkhQaGqq%2Bffv6zRcWFsrj8SgxMdG5nQAAAK4WdAFr4MCB6ty5szIzM1VSUqINGzaouLhYkydPliRNmjRJe/bs0YYNG1RSUqLMzEx17dpVgwYNkvTNxfOPPvqotm/fruLiYi1evFhTpkxp1SlCAABwbgu6gBUWFqZ169bJ6/Vq4sSJeumll7R27VolJCRIkrp27aoHHnhAW7du1eTJk1VVVaW1a9cqJCREknTFFVfo5ptv1qJFizRr1iz169dPc%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%2Bdr5MiRSkpKUnp6uo4fPx6IXQAAAC4VtAHr4MGDGj58uHbs2OF7LFu2TKdPn9ZNN92ktLQ0Pf/880pJSdHNN9%2Bs06dPS5KKi4u1YMECzZkzR3l5eaqurlZmZmaA9wYAALhJ0Aas0tJS9erVS7Gxsb5HVFSUXn75ZUVEROjOO%2B9Ujx49tGDBArVr107/%2B7//K0navHmzxowZo/HjxysxMVErVqzQG2%2B8obKysgDvEQAAcIugDljdunVrNl5UVKTU1FSFhIRIkkJCQtS/f38VFhb65tPS0nzLd%2B7cWQkJCSoqKnKkbgAA4H6eQBdgB8uy9Mknn2jHjh1av369GhsbNXr0aGVkZMjr9erCCy/0Wz4mJkYlJSWSpPLycsXFxTWbP3r0aItfv7y8XF6v12/M42nbbLs/V1iYu/Kxx%2BOees/01m0316vRAAANIklEQVQ9dgN6ay/6ax96a59g7G1QBqzDhw%2BrpqZG4eHhuv/%2B%2B/XFF19o2bJlqq2t9Y3/q/DwcNXX10uSamtrf3C%2BJfLy8pSbm%2Bs3lp6e7rvI/lzVoUO7QJfQalFRkYEuIWjRW3vRX/vQW/sEU2%2BDMmB16dJF77zzjs477zyFhISod%2B/eampq0ty5czVw4MBmYam%2Bvl5t2rSRJEVERHznfGRky//Tp06dqhEjRviNeTxtVVl56ifu0XdzW9I3vf92CgsLVVRUpKqra9TY2BTocoIKvbUX/bUPvbWPnb0N1C/3QRmwJCk6OtrveY8ePVRXV6fY2FhVVFT4zVVUVPhO38XHx3/nfGxsbItfOy4urtnpQK/3hBoazu03pBv3v7GxyZV1uwG9tRf9tQ%2B9tU8w9dZdh0Ba6J///KcGDRqkmpoa39iHH36o6Ohopaam6r333pNlWZK%2BuV5rz549SkpKkiQlJSWpoKDAt96RI0d05MgR3zwAAMCPCcqAlZKSooiICN199936%2BOOP9cYbb2jFihW68cYbNXr0aFVXV2v58uU6ePCgli9frpqaGo0ZM0aSNG3aNG3btk3PPvus9u/frzvvvFOXXnqpzj///ADvFQAAcIugDFjt27fXo48%2BquPHj2vSpElasGCBpk6dqhtvvFHt27fX%2BvXrVVBQoIkTJ6qoqEgbNmxQ27ZtJX0TzrKysrR27VpNmzZN5513nrKzswO8RwAAwE1CrDPnymArr/eE8W16PKEatfKfxrdrl1duGxLoElrM4wlVhw7tVFl5KmiuBzhb0Ft70V/70Fv72Nnb2NhfGt1eSwXlESwAAIBAImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGOYJdAEAAMAeY%2B7//wJdQou9u3x0oEswiiNYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWN%2Bhrq5O8%2BfPV1pamoYOHarHHnss0CUBAAAX8QS6gLPRihUrtHfvXm3cuFGHDx/WvHnzlJCQoNGjRwe6NAAA4AIErG85ffq0nn32WT388MPq06eP%2BvTpo5KSEm3ZsoWABQAAWoRThN%2Byf/9%2BNTQ0KCUlxTeWmpqqoqIiNTU1BbAyAADgFhzB%2Bhav16sOHTooPDzcN9apUyfV1dWpqqpKHTt2/NFtlJeXy%2Bv1%2Bo15PG0VFxdntNawMHflY4/HPfWe6a3beuwG9NZe9Nc%2B9NZ%2BwdRbAta31NTU%2BIUrSb7n9fX1LdpGXl6ecnNz/cbmzJmjW2%2B91UyR/6e8vFwzf1WiqVOnGg9v57ry8nJt3PgIvbUBvbUX/bWPG3v77nJ3XNpSXl6uBx54wFW9/THBExUNiYiIaBakzjxv06ZNi7YxdepUPf/8836PqVOnGq/V6/UqNze32dEy/Hz01j701l701z701j7B2FuOYH1LfHy8Kisr1dDQII/nm/Z4vV61adNGUVFRLdpGXFxc0CRwAADQehzB%2BpbevXvL4/GosLDQN1ZQUKC%2BffsqNJR2AQCAH0di%2BJbIyEiNHz9eixcvVnFxsbZv367HHntMM2bMCHRpAADAJcIWL168ONBFnG0GDx6sffv2adWqVXr77bf1H//xH5o0aVKgy/pO7dq108CBA9WuXbtAlxJ06K196K296K996K19gq23IZZlWYEuAgAAIJhwihAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHrLFdXV6f58%2BcrLS1NQ4cO1WOPPfa9y%2B7bt09XXXWVkpKSNGnSJO3du9fBSt2nNb19/fXXNW7cOKWkpOjKK6/Ua6%2B95mCl7tOa3p7xxRdfKCUlRe%2B8844DFbpba/p74MABTZs2Tf369dOVV16pnTt3Olip%2B7Smt3/72980ZswYpaSkaNq0afrggw8crNS96uvrNXbs2B98rwfF55mFs1pWVpZ15ZVXWnv37rX%2B%2Bte/WikpKdYrr7zSbLlTp05ZQ4YMse655x7r4MGD1tKlS63f/va31qlTpwJQtTu0tLcffvih1adPH2vjxo3Wp59%2Bam3evNnq06eP9eGHHwagandoaW//1ezZs61evXpZO3fudKhK92ppf6urq63f/va31t133219%2Bumn1urVq63U1FSroqIiAFW7Q0t7%2B9FHH1l9%2B/a1XnjhBeuzzz6zlixZYg0ZMsQ6ffp0AKp2j9raWis9Pf0H3%2BvB8nlGwDqLnTp1yurbt6/fD%2BHatWut6dOnN1v22WeftUaMGGE1NTVZlmVZTU1N1qhRo6ytW7c6Vq%2BbtKa3OTk51uzZs/3GZs2aZd1777221%2BlGrentGdu2bbOuvvpqAlYLtKa/GzdutEaOHGk1NDT4xiZOnGi9/vrrjtTqNq3p7eOPP25NmDDB9/zEiRNWr169rOLiYkdqdaOSkhLrD3/4g3XllVf%2B4Hs9WD7POEV4Ftu/f78aGhqUkpLiG0tNTVVRUZGampr8li0qKlJqaqpCQkIkSSEhIerfv78KCwsdrdktWtPbCRMm6I477mi2jRMnTthepxu1preSVFlZqZycHGVlZTlZpmu1pr%2B7du3S7373O4WFhfnGtm7dqn//9393rF43aU1vo6OjdfDgQRUUFKipqUnPP/%2B82rdvr3/7t39zumzX2LVrlwYNGqS8vLwfXC5YPs88gS4A38/r9apDhw4KDw/3jXXq1El1dXWqqqpSx44d/Za98MIL/daPiYlRSUmJY/W6SWt626NHD791S0pK9Pbbb%2Bvqq692rF43aU1vJemee%2B7RhAkT1LNnT6dLdaXW9LesrEz9%2BvXTwoUL9fe//11dunTRvHnzlJqaGojSz3qt6e3ll1%2Buv//977rmmmsUFham0NBQrV%2B/Xuedd14gSneFa665pkXLBcvnGUewzmI1NTV%2Bb3RJvuf19fUtWvbby%2BEbrentvzp%2B/LhuvfVW9e/fX7/73e9srdGtWtPbt956SwUFBbrlllscq8/tWtPf06dPa8OGDYqNjdXDDz%2BsAQMGaPbs2Tpy5Ihj9bpJa3pbWVkpr9erRYsW6ZlnntG4ceOUmZmpL7/80rF6g1WwfJ4RsM5iERERzX6gzjxv06ZNi5b99nL4Rmt6e0ZFRYVmzpwpy7K0Zs0ahYby9vkuLe1tbW2tFi1apD//%2Bc/8nLZCa352w8LC1Lt3b2VkZOiiiy7S3Llz1a1bN23bts2xet2kNb1duXKlevXqpWuvvVYXX3yxli5dqsjISG3dutWxeoNVsHye8QlxFouPj1dlZaUaGhp8Y16vV23atFFUVFSzZSsqKvzGKioqFBcX50itbtOa3krSsWPHdO2116q%2Bvl6bNm1qdpoL/09Le1tcXKyysjJlZGQoJSXFd93LH//4Ry1atMjxut2iNT%2B7sbGx6t69u99Yt27dOIL1PVrT2w8%2B%2BECJiYm%2B56GhoUpMTNThw4cdqzdYBcvnGQHrLNa7d295PB6/C/sKCgrUt2/fZkdPkpKS9N5778myLEmSZVnas2ePkpKSHK3ZLVrT29OnT%2BvGG29UaGioNm/erPj4eKfLdZWW9rZfv37661//qhdffNH3kKRly5bpv/7rvxyv2y1a87ObnJysAwcO%2BI19/PHH6tKliyO1uk1rehsXF6fS0lK/sU8%2B%2BURdu3Z1pNZgFiyfZwSss1hkZKTGjx%2BvxYsXq7i4WNu3b9djjz2mGTNmSPrmN6va2lpJ0ujRo1VdXa3ly5fr4MGDWr58uWpqajRmzJhA7sJZqzW9Xb9%2BvT7//HP993//t2/O6/XyV4Tfo6W9bdOmjS644AK/h/TNb68xMTGB3IWzWmt%2Bdq%2B%2B%2BmodOHBADzzwgD777DOtXr1aZWVlGjduXCB34azVmt5OmTJFzzzzjF588UV99tlnWrlypQ4fPqwJEyYEchdcKyg/zwJ6kwj8qNOnT1t33nmnlZycbA0dOtR6/PHHfXO9evXyuy9IUVGRNX78eKtv377W5MmTrQ8%2B%2BCAAFbtHS3t72WWXWb169Wr2mDdvXoAqP/u15uf2X3EfrJZpTX/fffdda8KECdbFF19sjRs3ztq1a1cAKnaP1vT2mWeesUaPHm0lJydb06ZNs/bu3RuAit3p2%2B/1YPw8C7Gs/zsGBwAAACM4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv3/ZlJpU0IddsgAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6869201937990029993”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6869201937990029993”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2781</td> <td class=”number”>86.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>351</td> <td class=”number”>10.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_POPLOSS10”>POPLOSS10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.16794</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>81.2%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1837563236704679611”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1837563236704679611,#minihistogram-1837563236704679611”
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</div> <div class=”row collapse col-md-12” id=”descriptives-1837563236704679611”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1837563236704679611”

aria-controls=”quantiles-1837563236704679611” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1837563236704679611” aria-controls=”histogram-1837563236704679611”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1837563236704679611” aria-controls=”common-1837563236704679611”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1837563236704679611” aria-controls=”extreme-1837563236704679611”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1837563236704679611”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>0</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37388</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.2262</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.16</td>

</tr> <tr>

<th>Mean</th> <td>0.16794</td>

</tr> <tr>

<th>MAD</th> <td>0.27948</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.7774</td>

</tr> <tr>

<th>Sum</th> <td>526</td>

</tr> <tr>

<th>Variance</th> <td>0.13978</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1837563236704679611”>

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AEAABhGwAIAADDM8YB1ySWX6Omnn9aRI0ecfmkAAABHOB6whgwZooceekjDhg3TH//4R23ZskWWZTldBgAAgG0cD1izZ8/WP/7xD61atUrh4eG66aabdP755%2Bu%2B%2B%2B7TRx995HQ5AAAAxnmD8aIej0dDhw7V0KFDVVtbqyeeeEKrVq3SmjVrNHDgQF111VX6/e9/H4zSAAAAfragBCxJqqio0EsvvaSXXnpJH374oQYOHKiJEyfq4MGDuuOOO/TOO%2B9o/vz5wSoPAADgJ3M8YG3atEmbNm3S22%2B/rU6dOmnChAlasWKFevTo4d%2BmS5cuWrJkCQELAAC4kuMBa/78%2BUpPT9fKlSt13nnnKSys%2BWVgPXv21BVXXOF0aQAAAEY4HrDeeOMNdezYUdXV1f5wVVJSon79%2Bik8PFySNHDgQA0cONDp0gAAAIxw/LcIjx49qjFjxujhhx/2j11//fUaP368Dhw44HQ5AAAAxjkesP785z/rzDPP1DXXXOMfe/nll9WlSxfl5OQ4XQ4AAIBxjgesd999V7fddptiY2P9Y506ddKtt96qrVu3Ol0OAACAcY4HLK/Xq5qammbjtbW13NEdAACEBMcD1nnnnafFixfr008/9Y%2BVl5crJydHw4cPb/X%2BGhoaNG7cOL399tv%2BscWLF%2Buss84K%2BNqwYYN/vqCgQCNHjlRSUpIyMzN1%2BPBh/5xlWVq2bJmGDBmiwYMHa%2BnSpWpqavqJqwUAAKcjx3%2BLcO7cubrmmms0evRoRUdHS5JqamrUr18/zZs3r1X7qq%2Bv1%2BzZs1VaWhowXlZWptmzZ2vixIn%2Bsfbt20v6%2BjcW58%2Bfr4ULFyoxMVFLlizRvHnztHr1aknS2rVrVVBQoLy8PDU2NmrOnDmKiYnRzJkzf86yAQDAacTxgBUTE6MXXnhBb775pkpLS%2BX1evXrX/9av/nNb%2BTxeFq8n71792r27NnfeVqxrKxMM2fODLjO66QNGzZo7NixmjBhgiRp6dKlSk9PV3l5ubp3767169crKytLaWlpkqRbbrlFy5cvJ2ABAIAWC8qfygkPD9fw4cN/0inBk7Zt26Zzzz1X//Vf/6Xk5GT/%2BNGjR3Xo0KGAO8N/U3Fxsa677jr/4y5duighIUHFxcWKiIjQgQMHNGjQIP98amqqPv/8c1VUVCguLu4n1wsAAE4fjgcsn8%2Bn%2B%2B%2B/X9u3b9dXX33V7AjUa6%2B91qL9TJs27TvHy8rK5PF49NBDD%2BmNN95Qhw4ddM011/hPF35XUIqJidHBgwfl8/kkKWC%2Bc%2BfOkqSDBw8SsAAAQIs4HrDuvPNO7dixQxdeeKF%2B%2BctfGt//vn375PF4/H9u55133tGdd96p9u3ba9SoUaqrq1NERETAcyIiItTQ0KC6ujr/42/OSV9fTN9SFRUV/rB2ktfb1nhACw93/HcUfhav1z31nuyt23rsBvTWXvTXPvTWPqHYW8cD1tatW/XII4/4r3EybcKECUpPT1eHDh0kSYmJifr444/11FNPadSoUYqMjGwWlhoaGhQVFRUQpiIjI/3/lqSoqKgW15Cfn6%2B8vLyAsczMTGVlZf3kdYWCjh3bBbuEVouObvn/O1qH3tqL/tqH3tonlHrreMBq27atYmJibNu/x%2BPxh6uTevbs6b%2BJaXx8vCorKwPmKysrFRsbq/j4eElfn8bs1q2b/9%2BSvvOC%2Be8zZcoUjRgxImDM622rqqpjrVvMj3Bb0je9fjuFh4cpOjpKNTW1OnGC23SYRG/tRX/tQ2/tY2dvg/XDveMBa/z48XrkkUeUnZ3t/%2BPOJi1fvlzvvfeeHn/8cf/Y7t271bNnT0lSUlKSCgsLlZGRIUk6cOCADhw4oKSkJMXHxyshIUGFhYX%2BgFVYWKiEhIRWnd6Li4trtr3Pd0SNjaf3G9KN6z9xosmVdbsBvbUX/bUPvbVPKPXW8YBVXV2tgoICvf766%2BrevXuz66HWr1//s/afnp6uNWvW6NFHH9WoUaO0ZcsWvfjii/79Tp06VVdeeaWSk5PVv39/LVmyROeff766d%2B/un1%2B2bJl%2B9atfSZLuuecezZgx42fVBAAATi9BuU3DuHHjbNv3gAEDtHz5cq1YsULLly9X165ddc899yglJUWSlJKSouzsbK1YsUJffvmlhg4dqkWLFvmfP3PmTH3xxReaNWuWwsPDNXnyZF199dW21QsAAEKPx%2BIPADrC5ztifJ9eb5hGLfuX8f3a5ZWbhwa7hBbzesPUsWM7VVUdC5nD1acKemsv%2BmsfemsfO3sbG2v%2BjgUtEZSrpCsqKpSXl6fZs2friy%2B%2B0F/%2B8hft27cvGKUAAAAY53jA%2BuSTT3TRRRfphRde0Kuvvqrjx4/r5Zdf1qRJk1RcXOx0OQAAAMY5HrDuvvtujRw5Ups3b9YvfvELSdK9996rESNGaNmyZU6XAwAAYJzjAWv79u265pprAv6ws9fr1Y033qhdu3Y5XQ4AAIBxjgespqYmNTU1v4Dt2LFjttwXCwAAwGmOB6xhw4Zp9erVASGrurpaubm5GjJkiNPlAAAAGOd4wLrtttu0Y8cODRs2TPX19fqP//gPpaen67PPPtPcuXOdLgcAAMA4x280Gh8frxdffFEFBQX64IMP1NTUpKlTp2r8%2BPFq37690%2BUAAAAYF5Q7uUdFRemSSy4JxksDAADYzvGANX369B%2Bc/7l/ixAAACDYHA9YXbt2DXjc2NioTz75RB9%2B%2BKGuuuoqp8sBAAAwzvGAlZOT853jK1eu1MGDBx2uBgAAwLyg/C3C7zJ%2B/Hi98sorwS4DAADgZztlAtZ7773HjUYBAEBIOCUucj969Kj27NmjadOmOV0OAACAcY4HrISEhIC/QyhJv/jFL3TFFVfo4osvdrocAAAA4xwPWHfffbfTLwkAAOAoxwPWO%2B%2B80%2BJtBw0aZGMlAAAA9nA8YF155ZX%2BU4SWZfnHvz3m8Xj0wQcfOF0eAADAz%2BZ4wHrooYe0ePFizZkzR4MHD1ZERITef/99ZWdna%2BLEibrgggucLgkAAMAox2/TkJOTowULFmj06NHq2LGj2rVrpyFDhig7O1tPPfWUunbt6v8CAABwI8cDVkVFxXeGp/bt26uqqsrpcgAAAIxzPGAlJyfr3nvv1dGjR/1j1dXVys3N1W9%2B8xunywEAADDO8Wuw7rjjDk2fPl3nnXeeevToIcuy9PHHHys2Nlbr1693uhwAAADjHA9YvXr10ssvv6yCggKVlZVJki6//HJdeOGFioqKcrocAAAA4xwPWJJ0xhln6JJLLtFnn32m7t27S/r6bu4AAAChwPFrsCzL0rJlyzRo0CCNGzdOBw8e1Ny5czV//nx99dVXTpcDAABgnOMB64knntCmTZv0pz/9SREREZKkkSNHavPmzcrLy3O6HAAAAOMcD1j5%2BflasGCBMjIy/Hdvv%2BCCC7R48WL9z//8j9PlAAAAGOd4wPrss8/Ut2/fZuOJiYny%2BXxOlwMAAGCc4wGra9euev/995uNv/HGG/4L3gEAANzM8d8inDlzphYuXCifzyfLsvTWW28pPz9fTzzxhG677TanywEAADDO8YA1adIkNTY26sEHH1RdXZ0WLFigTp066eabb9bUqVOdLgcAAMA4xwNWQUGBxowZoylTpujw4cOyLEsxMTFOlwEAAGAbx6/Bys7O9l/M3qlTJ8IVAAAIOY4HrB49eujDDz90%2BmUBAAAc4/gpwsTERN1yyy165JFH1KNHD0VGRgbM5%2BTkOF0SAACAUY4HrI8%2B%2BkipqamSxH2vAABASHIkYC1dulSzZs1S27Zt9cQTTzjxkgAAAEHjyDVYa9euVW1tbcDY9ddfr4qKCideHgAAwFGOBCzLspqNvfPOO6qvr3fi5QEAABzl%2BG8RAgAAhDoCFgAAgGGOBSyPx%2BPUSwEAAASVY7dpWLx4ccA9r7766ivl5uaqXbt2AdtxHywAAOB2jgSsQYMGNbvnVUpKiqqqqlRVVeVECQAAAI5xJGBx7ysAAHA6cf1F7g0NDRo3bpzefvtt/1h5ebmuvvpqJScn64ILLtCWLVsCnvPmm29q3LhxSkpK0vTp01VeXh4w//jjj2v48OFKSUnR7bff3uweXgAAAD/E1QGrvr5ef/zjH1VaWuofsyxLmZmZ6ty5szZu3Kjx48dr1qxZ2r9/vyRp//79yszMVEZGhp577jl16tRJN954o/9eXa%2B%2B%2Bqry8vKUnZ2tdevWqbi4WLm5uUFZHwAAcCfXBqy9e/fq0ksv1aeffhowvnXrVpWXlys7O1u9evXSDTfcoOTkZG3cuFGS9Oyzz%2Bqcc87RjBkz1Lt3b%2BXk5Ojzzz/Xtm3bJEnr16/XVVddpfT0dA0YMEALFy7Uxo0bOYoFAABazLUBa9u2bTr33HOVn58fMF5cXKyzzz5bbdu29Y%2BlpqaqqKjIP5%2BWluafi4qKUr9%2B/VRUVKQTJ07o/fffD5hPTk7WV199pd27d9u8IgAAECocu02DadOmTfvOcZ/Pp7i4uICxmJgYHTx48Efna2pqVF9fHzDv9XrVoUMH//NboqKiotlvTXq9bZu97s8VHu6ufOz1uqfek711W4/dgN7ai/7ah97aJxR769qA9X1qa2sVERERMBYREaGGhoYfna%2Brq/M//r7nt0R%2Bfr7y8vICxjIzM5WVldXifYSijh3b/fhGp5jo6KhglxCy6K296K996K19Qqm3IRewIiMjVV1dHTDW0NCgNm3a%2BOe/HZYaGhoUHR3tvxHqd81HRbX8P33KlCkaMWJEwJjX21ZVVcdavI%2BWcFvSN71%2BO4WHhyk6Oko1NbU6caIp2OWEFHprL/prH3prHzt7G6wf7kMuYMXHx2vv3r0BY5WVlf7Tc/Hx8aqsrGw237dvX3Xo0EGRkZGqrKxUr169JEmNjY2qrq5WbGxsi2uIi4trdjrQ5zuixsbT%2Bw3pxvWfONHkyrrdgN7ai/7ah97aJ5R6665DIC2QlJSknTt3%2Bk/3SVJhYaGSkpL884WFhf652tpa7dq1S0lJSQoLC1P//v0D5ouKiuT1epWYmOjcIgAAgKuFXMAaPHiwunTponnz5qm0tFRr1qxRSUmJJk%2BeLEmaNGmStm/frjVr1qi0tFTz5s1Tt27ddO6550r6%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%2BLFi3X8%2BHFdf/31SktL0/PPP6%2BUlBTdcMMNOn78uCSppKRE8%2BfP16xZs5Sfn6%2BamhrNmzcvyKsBAABu4g12AXYpKytTnz59FBsbGzD%2B3HPPKTIyUrfeeqs8Ho/mz5%2BvN954Q3/5y1%2BUkZGhDRs2aOzYsZowYYIkaenSpUpPT1d5ebm6d%2B8ejKUAAPCTjL3//wW7hBZ7d8mYYJdgVMgewSorK1OPHj2ajRcXFys1NVUej0eS5PF4NHDgQBUVFfnn09LS/Nt36dJFCQkJKi4udqRuAADgfiEZsCzL0kcffaQtW7Zo9OjRGjlypJYtW6aGhgb5fD7FxcUFbB8TE6ODBw9KkioqKn5wHgAA4MeE5CnC/fv3q7a2VhEREbr//vv12WefafHixaqrq/OPf1NERIQaGhokSXV1dT843xLr0bGxAAAM%2BElEQVQVFRXy%2BXwBY15v22bB7ecKD3dXPvZ63VPvyd66rcduQG/tRX/tQ2/tF0q9DcmA1bVrV7399ts644wz5PF41LdvXzU1NWnOnDkaPHhws7DU0NCgNm3aSJIiIyO/cz4qKqrFr5%2Bfn6%2B8vLyAsczMTP9vMZ6uOnZsF%2BwSWi06uuX/72gdemsv%2BmsfemufUOptSAYsSerQoUPA4169eqm%2Bvl6xsbGqrKwMmKusrPQfXYqPj//O%2BW9fLP9DpkyZohEjRgSMeb1tVVV1rDVL%2BFFuS/qm12%2Bn8PAwRUdHqaamVidONAW7nJBCb%2B1Ff%2B1Db%2B1nR2%2BD9cN9SAasf/3rX7rlllv0%2Buuv%2B488ffDBB%2BrQoYNSU1P18MMPy7IseTweWZal7du36w9/%2BIMkKSkpSYWFhcrIyJAkHThwQAcOHFBSUlKLXz8uLq7Z6UCf74gaG0/vN6Qb13/iRJMr63YDemsv%2BmsfemufUOqtuw6BtFBKSooiIyN1xx13aN%2B%2BffrnP/%2BppUuX6tprr9WYMWNUU1OjJUuWaO/evVqyZIlqa2s1duxYSdLUqVO1adMmPfvss9q9e7duvfVWnX/%2B%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%2BvuXPnKiEhQWPGjAl2aQAAwAUIWN9y/PhxPfvss3r44YfVr18/9evXT6WlpXryyScJWAAAoEU4Rfgtu3fvVmNjo1JSUvxjqampKi4uVlNTUxArAwAAbsERrG/x%2BXzq2LGjIiIi/GOdO3dWfX29qqur1alTpx/dR0VFhXw%2BX8CY19tWcXFxRmsND3dXPvZ63VPvyd66rcduQG/tRX/tQ2/tF0q9JWB9S21tbUC4kuR/3NDQ0KJ95OfnKy8vL2Bs1qxZuummm8wU%2Bb8qKip01a9KNWXKFOPh7XRXUVGhdeseobc2oLf2or/2cWNv313ijktbKioq9MADD7iqtz8mdKKiIZGRkc2C1MnHbdq0adE%2BpkyZoueffz7ga8qUKcZr9fl8ysvLa3a0DD8fvbUPvbUX/bUPvbVPKPaWI1jfEh8fr6qqKjU2Nsrr/bo9Pp9Pbdq0UXR0dIv2ERcXFzIJHAAAtB5HsL6lb9%2B%2B8nq9Kioq8o8VFhaqf//%2BCgujXQAA4MeRGL4lKipKEyZM0F133aWSkhJt3rxZjz32mKZPnx7s0gAAgEuE33XXXXcFu4hTzZAhQ7Rr1y7dc889euutt/SHP/xBkyZNCnZZ36ldu3YaPHiw2rVrF%2BxSQg69tQ%2B9tRf9tQ%2B9tU%2Bo9dZjWZYV7CIAAABCCacIAQAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsE5x9fX1uv3225WWlqZhw4bpscce%2B95td%2B3apUsuuURJSUmaNGmSduzY4WCl7tOa3r7%2B%2BusaP368UlJSdNFFF%2Bm1115zsFL3aU1vT/rss8%2BUkpKit99%2B24EK3a01/d2zZ4%2BmTp2qAQMG6KKLLtLWrVsdrNR9WtPbv/3tbxo7dqxSUlI0depU7dy508FK3auhoUHjxo37wfd6SHyeWTilZWdnWxdddJG1Y8cO669//auVkpJivfLKK822O3bsmDV06FDr7rvvtvbu3WstWrTI%2Bu1vf2sdO3YsCFW7Q0t7%2B8EHH1j9%2BvWz1q1bZ3388cfWhg0brH79%2BlkffPBBEKp2h5b29ptmzpxp9enTx9q6datDVbpXS/tbU1Nj/fa3v7XuuOMO6%2BOPP7aWL19upaamWpWVlUGo2h1a2tsPP/zQ6t%2B/v/XCCy9Yn3zyibVw4UJr6NCh1vHjx4NQtXvU1dVZmZmZP/heD5XPMwLWKezYsWNW//79A74JV65caV1xxRXNtn322WetESNGWE1NTZZlWVZTU5M1atQoa%2BPGjY7V6yat6W1ubq41c%2BbMgLEZM2ZY9957r%2B11ulFrenvSpk2brMsuu4yA1QKt6e%2B6deuskSNHWo2Njf6xjIwM6/XXX3ekVrdpTW/Xrl1rTZw40f/4yJEjVp8%2BfaySkhJHanWj0tJS6%2BKLL7YuuuiiH3yvh8rnGacIT2G7d%2B9WY2OjUlJS/GOpqakqLi5WU1NTwLbFxcVKTU2Vx%2BORJHk8Hg0cOFBFRUWO1uwWrentxIkTdcsttzTbx5EjR2yv041a01tJqqqqUm5urrKzs50s07Va099t27bpd7/7ncLDw/1jGzdu1L//%2B787Vq%2BbtKa3HTp00N69e1VYWKimpiY9//zzat%2B%2Bvf7t3/7N6bJdY9u2bTr33HOVn5//g9uFyueZN9gF4Pv5fD517NhRERER/rHOnTurvr5e1dXV6tSpU8C2v/71rwOeHxMTo9LSUsfqdZPW9LZXr14Bzy0tLdVbb72lyy67zLF63aQ1vZWku%2B%2B%2BWxMnTlTv3r2dLtWVWtPf8vJyDRgwQHfeeaf%2B/ve/q2vXrpo7d65SU1ODUfoprzW9veCCC/T3v/9d06ZNU3h4uMLCwrR69WqdccYZwSjdFaZNm9ai7ULl84wjWKew2tragDe6JP/jhoaGFm377e3wtdb09psOHz6sm266SQMHDtTvfvc7W2t0q9b09s0331RhYaFuvPFGx%2Bpzu9b09/jx41qzZo1iY2P18MMPa9CgQZo5c6YOHDjgWL1u0preVlVVyefzacGCBXrmmWc0fvx4zZs3T1988YVj9YaqUPk8I2CdwiIjI5t9Q5183KZNmxZt%2B%2B3t8LXW9PakyspKXXXVVbIsSytWrFBYGG%2Bf79LS3tbV1WnBggX605/%2BxPdpK7Tmezc8PFx9%2B/ZVVlaWzj77bM2ZM0c9evTQpk2bHKvXTVrT22XLlqlPnz66/PLLdc4552jRokWKiorSxo0bHas3VIXK5xmfEKew%2BPh4VVVVqbGx0T/m8/nUpk0bRUdHN9u2srIyYKyyslJxcXGO1Oo2remtJB06dEiXX365GhoatH79%2BmanufB/WtrbkpISlZeXKysrSykpKf7rXq677jotWLDA8brdojXfu7GxserZs2fAWI8ePTiC9T1a09udO3cqMTHR/zgsLEyJiYnav3%2B/Y/WGqlD5PCNgncL69u0rr9cbcGFfYWGh%2Bvfv3%2BzoSVJSkt577z1ZliVJsixL27dvV1JSkqM1u0Vrenv8%2BHFde%2B21CgsL04YNGxQfH%2B90ua7S0t4OGDBAf/3rX/Xiiy/6vyRp8eLF%2Bs///E/H63aL1nzvJicna8%2BePQFj%2B/btU9euXR2p1W1a09u4uDiVlZUFjH300Ufq1q2bI7WGslD5PCNgncKioqI0YcIE3XXXXSopKdHmzZv12GOPafr06ZK%2B/smqrq5OkjRmzBjV1NRoyZIl2rt3r5YsWaLa2lqNHTs2mEs4ZbWmt6tXr9ann36q//7v//bP%2BXw%2Bfovwe7S0t23atNGZZ54Z8CV9/dNrTExMMJdwSmvN9%2B5ll12mPXv26IEHHtAnn3yi5cuXq7y8XOPHjw/mEk5ZrentpZdeqmeeeUYvvviiPvnkEy1btkz79%2B/XxIkTg7kE1wrJz7Og3iQCP%2Br48ePWrbfeaiUnJ1vDhg2z1q5d65/r06dPwH1BiouLrQkTJlj9%2B/e3Jk%2BebO3cuTMIFbtHS3s7evRoq0%2BfPs2%2B5s6dG6TKT32t%2Bb79Ju6D1TKt6e%2B7775rTZw40TrnnHOs8ePHW9u2bQtCxe7Rmt4%2B88wz1pgxY6zk5GRr6tSp1o4dO4JQsTt9%2B70eip9nHsv632NwAAAAMIJThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8Hpx1mcqeU4XwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1837563236704679611”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1837563236704679611”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2606</td> <td class=”number”>81.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>526</td> <td class=”number”>16.4%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_POVRATE15”>POVRATE15<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>333</td>

</tr> <tr>

<th>Unique (%)</th> <td>10.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.5%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>79</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>16.272</td>

</tr> <tr>

<th>Minimum</th> <td>3.4</td>

</tr> <tr>

<th>Maximum</th> <td>47.4</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2495372197850787304”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2495372197850787304,#minihistogram-2495372197850787304”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2495372197850787304”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2495372197850787304”

aria-controls=”quantiles-2495372197850787304” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2495372197850787304” aria-controls=”histogram-2495372197850787304”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2495372197850787304” aria-controls=”common-2495372197850787304”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2495372197850787304” aria-controls=”extreme-2495372197850787304”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2495372197850787304”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.4</td>

</tr> <tr>

<th>5-th percentile</th> <td>7.85</td>

</tr> <tr>

<th>Q1</th> <td>11.5</td>

</tr> <tr>

<th>Median</th> <td>15.2</td>

</tr> <tr>

<th>Q3</th> <td>19.7</td>

</tr> <tr>

<th>95-th percentile</th> <td>28.05</td>

</tr> <tr>

<th>Maximum</th> <td>47.4</td>

</tr> <tr>

<th>Range</th> <td>44</td>

</tr> <tr>

<th>Interquartile range</th> <td>8.2</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>6.4438</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.39602</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.8466</td>

</tr> <tr>

<th>Mean</th> <td>16.272</td>

</tr> <tr>

<th>MAD</th> <td>4.9903</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.0769</td>

</tr> <tr>

<th>Sum</th> <td>50946</td>

</tr> <tr>

<th>Variance</th> <td>41.523</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2495372197850787304”>

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qwWLlyof/3rX/5tdXV1Kikp0ZgxYyycDAAAoGts91uEs2fP1p133qkJEyYoLi5OktTc3Kzhw4drzpw5Fk8HAABwZrYLrF69eumll17Su%2B%2B%2BqwMHDsjpdGrIkCG69tpr5XA4rB4PAADgjGwXWJIUFhamMWPG8JIgAAAISrYLLI/Ho7KyMu3Zs0enTp3q9Mb2N99806LJAAAAusZ2gfWb3/xG%2B/bt0w033KCLL77Y6nEAAADOmu0C67333tPq1auVlpZm9SgAAADnxHaXaYiOjlavXr2sHgMAAOCc2S6wJk2apNWrV6u9vd3qUQAAAM6J7V4ibGpq0tatW/XXv/5VAwYMUHh4eMD%2B559/3qLJAAAAusZ2gSVJEydOtHoEAACAc2a7wCopKbF6BAAAgB/Fdu/BkqT6%2BnqVl5frwQcf1L///W9t375dhw4dsnosAACALrFdYB0%2BfFi/%2BMUv9NJLL%2Bn1119XS0uLtm3bpilTpsjtdls9HgAAwBnZLrCeeOIJjR8/Xm%2B88YYuuugiSdKSJUuUkZGhJ5980uLpAAAAzsx2gbVnzx7deeedAX/Y2el06r777lNtba2FkwEAAHSN7QKro6NDHR0dnbafOHFCYWFhFkwEAABwdmwXWKNHj9bKlSsDIqupqUmlpaUaOXKkhZMBAAB0je0C6%2BGHH9a%2Bffs0evRotba26t5779V1112nL774QrNnz7Z6PAAAgDOy3XWw%2BvTpo82bN2vr1q365JNP1NHRodzcXE2aNEmxsbFWjwcAAHBGtgssSYqKitLUqVOtHgMAAOCc2C6wpk2b9oP7%2BVuEAADA7mwXWP379w/4/PTp0zp8%2BLA%2B/fRT3X777RZNBQAA0HW2C6z/9rcIKyoqdOzYsQs8DQAAwNmz3W8R/jeTJk3Sa6%2B9ZvUYAAAAZxQ0gfXRRx9xoVEAABAUbPcS4fe9yf348eP6xz/%2BoZtvvtmCiQAAAM6O7QKrX79%2BAX%2BHUJIuuugi3XrrrfrlL39p0VQAAABdZ7vAeuKJJ6weAQAA4EexXWDt3r27y7e9%2Buqrz%2BMkAAAA58Z2gXXbbbf5XyL0%2BXz%2B7d/d5nA49Mknn1z4AQEAAM7AdoG1YsUKLVy4UL/%2B9a%2BVnp6u8PBwffzxx1qwYIFuvPFGXX/99VaPCAAA8INsd5mGkpISzZ07VxMmTFB8fLxiYmI0cuRILViwQOvWrVP//v39HwAAAHZku8Cqr6//3niKjY1VY2OjBRMBAACcHdsF1lVXXaUlS5bo%2BPHj/m1NTU0qLS3Vtddea%2BFkAAAAXWO792A9%2BuijmjZtmsaOHatBgwbJ5/Ppn//8pxITE/X8889bPR4AAMAZ2S6wBg8erG3btmnr1q367LPPJEm33HKLbrjhBkVFRVk8HQAAwJnZLrAkqWfPnpo6daq%2B%2BOILDRgwQNI3V3M/F3l5eUpISPBfwLS2tlbz5s3Tp59%2BqiFDhmj%2B/Pm68sor/bffunWrysrK5PF4NHr0aD3%2B%2BONKSEj48QcFAAC6Ddu9B8vn8%2BnJJ5/U1VdfrYkTJ%2BrYsWOaPXu2ioqKdOrUqbO6r1dffVU7duzwf97S0qK8vDylpaVp06ZNSk5O1vTp09XS0iJJ2rt3r4qKilRQUKCqqio1Nzdrzpw5Ro8PAACEPtsF1po1a7RlyxbNmzdP4eHhkqTx48frjTfeUHl5eZfvp6mpSYsXL9aIESP827Zt26aIiAjNmjVLgwcPVlFRkWJiYrR9%2B3ZJ0tq1a5WVlaXJkydr6NChWrx4sXbs2KG6ujqzBwkAAEKa7QKrqqpKc%2BfOVXZ2tv/q7ddff70WLlyoV155pcv3s2jRIk2aNElDhgzxb3O73UpNTfXfr8PhUEpKimpqavz709LS/Lfv27ev%2BvXrJ7fbbeLQAABAN2G7wPriiy80bNiwTtuHDh0qj8fTpfvYtWuXPvzwQ913330B2z0ej5KSkgK29erVS8eOHZP0zTW4fmg/AABAV9juTe79%2B/fXxx9/rEsuuSRg%2B9tvv%2B1/w/sPaW1t1bx58zR37lxFRkYG7PN6vf6XHb8VHh6utrY2SdLJkyd/cH9X1dfXd4pBpzO6U7wB3ZXTabuf7UJOWFiPgH9hHdaie7JdYN19992aP3%2B%2BPB6PfD6fdu3apaqqKq1Zs0YPP/zwGb%2B%2BvLxcV155pcaMGdNpX0RERKdYamtr84fYf9t/tpeHqKqq6vR%2Bsfz8fBUWFp7V/QChKj4%2BxuoRuo24OC5vYxesRfdiu8CaMmWKTp8%2BreXLl%2BvkyZOaO3euEhISNHPmTOXm5p7x61999VU1NDQoOTlZkvzB9Prrr2vixIlqaGgIuH1DQ4P/zFKfPn2%2Bd39iYuJZHUNOTo4yMjICtjmd0WpsPHFW9wOEKp4L519YWA/FxUWpudmr9vYOq8fp1lgLa1n1A53tAmvr1q3KzMxUTk6OvvrqK/l8PvXq1avLX79mzRqdPn3a//mTTz4pSXrooYe0e/durVq1Sj6fTw6HQz6fT3v27NGMGTMkSS6XS9XV1crOzpYkHT16VEePHpXL5TqrY0hKSur0cqDH87VOn%2BaJBUjiuXABtbd38P22Cdaie7HdC8ILFizwv38pISHhrOJK%2BuY9XAMHDvR/xMTEKCYmRgMHDlRmZqaam5tVXFysgwcPqri4WF6vV1lZWZKk3NxcbdmyRRs2bND%2B/fs1a9YsjRs3rkvv/QIAAPiW7QJr0KBB%2BvTTT8/LfcfGxmrlypX%2Bs1Rut1uVlZWKjo6WJCUnJ2vBggWqqKhQbm6uevbsqZKSkvMyCwAACF0On8/ns3qI/2/u3Ll68cUXNXToUA0aNEgREREB%2B4M1eDyer60ewXJZZTutHgE28drMUVaPEPKczh6Kj49RY%2BMJXpayGGthrcTEiy15XNu9B%2Bvzzz9XamqqJHX5ulcAAAB2YovAWrx4sQoKChQdHa01a9ZYPQ4AAMCPYov3YD3zzDPyer0B2/Ly8lRfX2/RRAAAAOfOFoH1fW8D2717t1pbWy2YBgAA4MexRWABAACEEgILAADAMNsElsPhsHoEAAAAI2zxW4SStHDhwoBrXp06dUqlpaWKiQn8G0LBeh0sAADQfdgisK6%2B%2BupO17xKTk5WY2OjGhsbLZoKAADg3NgisLj2FdC9BNtV/bnyPICzZZv3YAEAAIQKAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMCwkA2sL7/8UoWFhUpPT9eYMWNUUlKi1tZWSVJdXZ3uuOMOXXXVVbr%2B%2Buv1zjvvBHztu%2B%2B%2Bq4kTJ8rlcmnatGmqq6uz4hAAAECQCsnA8vl8KiwslNfr1QsvvKClS5fqrbfeUllZmXw%2Bn/Lz89W7d29t3LhRkyZNUkFBgY4cOSJJOnLkiPLz85Wdna0XX3xRCQkJuu%2B%2B%2B%2BTz%2BSw%2BKgAAECycVg9wPhw6dEg1NTXauXOnevfuLUkqLCzUokWLNHbsWNXV1Wn9%2BvWKjo7W4MGDtWvXLm3cuFH333%2B/NmzYoCuvvFJ33XWXJKmkpESjRo3SBx98oGuuucbKwwIAAEEiJM9gJSYmavXq1f64%2Btbx48fldrt1xRVXKDo62r89NTVVNTU1kiS32620tDT/vqioKA0fPty/HwAA4ExCMrDi4uI0ZswY/%2BcdHR1au3atRo4cKY/Ho6SkpIDb9%2BrVS8eOHZOkM%2B4HAAA4k5B8ifC7SktLVVtbqxdffFHPPvuswsPDA/aHh4erra1NkuT1en9wf1fU19fL4/EEbHM6ozuFG4Dg4HQG38%2BiYWE9Av6FdViL7inkA6u0tFTPPfecli5dqssvv1wRERFqamoKuE1bW5siIyMlSREREZ1iqq2tTXFxcV1%2BzKqqKpWXlwdsy8/PV2Fh4TkeBQArxcfHWD3COYuLi7J6BPwf1qJ7CenAevzxx7Vu3TqVlpZqwoQJkqQ%2Bffro4MGDAbdraGjwn13q06ePGhoaOu0fNmxYlx83JydHGRkZAduczmg1Np44l8MAYLFgfO6GhfVQXFyUmpu9am/vsHqcbo21sJZVPyCFbGCVl5dr/fr1WrJkiTIzM/3bXS6XKisrdfLkSf9Zq%2BrqaqWmpvr3V1dX%2B2/v9XpVW1urgoKCLj92UlJSp5cDPZ6vdfo0TywgGAXzc7e9vSOo5w8lrEX3EpIvCH/22WdatmyZ7rnnHqWmpsrj8fg/0tPT1bdvX82ZM0cHDhxQZWWl9u7dq5tuukmSNGXKFO3Zs0eVlZU6cOCA5syZo0suuYRLNAAAgC4LycB688031d7eruXLl2v06NEBH2FhYVq2bJk8Ho%2Bys7P18ssvq6KiQv369ZMkXXLJJfrd736njRs36qabblJTU5MqKirkcDgsPioAABAsHD4uUX5BeDxfWz2C5bLKdlo9AnBOXps5yuoRzprT2UPx8TFqbDzBy1IWYy2slZh4sSWPG5JnsAAAAKxEYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABgWsn/subvg6ujA%2BRdsz7NgvPI8EGo4gwUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGCY0%2BoBAABmZZXttHqELntt5iirRwDOC85gAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGEZgAQAAGMaFRgEAlgmmi6JKXBgVXccZLAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMP4LcLv0draqvnz5%2BtPf/qTIiMjddddd%2Bmuu%2B6yeiwAgMX4rUd0FYH1PRYvXqx9%2B/bpueee05EjRzR79mz169dPmZmZVo8GAACCAIH1HS0tLdqwYYNWrVql4cOHa/jw4Tpw4IBeeOEFAgsAEFSC6YxbqJ1t4z1Y37F//36dPn1aycnJ/m2pqalyu93q6OiwcDIAABAsOIP1HR6PR/Hx8QoPD/dv6927t1pbW9XU1KSEhIQz3kd9fb08Hk/ANqczWklJScbnBQAgFDidoXXOh8D6Dq/XGxBXkvyft7W1dek%2BqqqqVF5eHrCtoKBA999/v5kh/58Pi7v3y5b19fWqqqpSTk4OAWsx1sJeWA/7YC26JwLrOyIiIjqF1LefR0ZGduk%2BcnJylJGREbAtMTHRzIAI4PF4VF5eroyMDP7HZTHWwl5YD/tgLbonAus7%2BvTpo8bGRp0%2BfVpO5zffHo/Ho8jISMXFxXXpPpKSkngSAQDQjYXWC54GDBs2TE6nUzU1Nf5t1dXVGjFihHr04NsFAADOjGL4jqioKE2ePFmPPfaY9u7dqzfeeEN/%2BMMfNG3aNKtHAwAAQSLssccee8zqIexm5MiRqq2t1VNPPaVdu3ZpxowZmjJlitVj4b%2BIiYlRenq6YmJirB6l22Mt7IX1sA/Wovtx%2BHw%2Bn9VDAAAAhBJeIgQAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwAIAADCMwELQaWtr08SJE/X%2B%2B%2B/7t9XV1emOO%2B7QVVddpeuvv17vvPOOhROGvi%2B//FKFhYVKT0/XmDFjVFJSotbWVkmsxYV2%2BPBh3X333UpOTta4ceO0evVq/z7Wwjp5eXl6%2BOGH/Z/X1tZq6tSpcrlcmjJlivbt22fhdLgQCCwEldbWVj3wwAM6cOCAf5vP51N%2Bfr569%2B6tjRs3atKkSSooKNCRI0csnDR0%2BXw%2BFRYWyuv16oUXXtDSpUv11ltvqaysjLW4wDo6OpSXl6f4%2BHjaqtAsAAAEdElEQVS99NJLmj9/vpYvX65XXnmFtbDQq6%2B%2Bqh07dvg/b2lpUV5entLS0rRp0yYlJydr%2BvTpamlpsXBKnG9OqwcAuurgwYN68MEH9d2/7vTee%2B%2Bprq5O69evV3R0tAYPHqxdu3Zp48aNuv/%2B%2By2aNnQdOnRINTU12rlzp3r37i1JKiws1KJFizR27FjW4gJqaGjQsGHD9Nhjjyk2NlaDBg3Stddeq%2BrqavXu3Zu1sEBTU5MWL16sESNG%2BLdt27ZNERERmjVrlhwOh4qKivT2229r%2B/btys7OtnBanE%2BcwULQ%2BOCDD3TNNdeoqqoqYLvb7dYVV1yh6Oho/7bU1FTV1NRc6BG7hcTERK1evdofV986fvw4a3GBJSUlqaysTLGxsfL5fKqurtbu3buVnp7OWlhk0aJFmjRpkoYMGeLf5na7lZqaKofDIUlyOBxKSUlhLUIcgYWgcfPNN%2BuRRx5RVFRUwHaPx6OkpKSAbb169dKxY8cu5HjdRlxcnMaMGeP/vKOjQ2vXrtXIkSNZCwtlZGTo5ptvVnJysiZMmMBaWGDXrl368MMPdd999wVsZy26JwILQc/r9So8PDxgW3h4uNra2iyaqHspLS1VbW2tfvWrX7EWFnr66ae1YsUKffLJJyopKWEtLrDW1lbNmzdPc%2BfOVWRkZMA%2B1qJ74j1YCHoRERFqamoK2NbW1tbpf3Iwr7S0VM8995yWLl2qyy%2B/nLWw0Lfv%2BWltbdVDDz2kKVOmyOv1BtyGtTh/ysvLdeWVVwac3f1WREREp5hiLUIfgYWg16dPHx08eDBgW0NDQ6dT8jDr8ccf17p161RaWqoJEyZIYi0utIaGBtXU1Gj8%2BPH%2BbUOGDNGpU6eUmJioQ4cOdbo9a3F%2BvPrqq2poaFBycrIk%2BYPq9ddf18SJE9XQ0BBwe9Yi9PESIYKey%2BXS3//%2Bd508edK/rbq6Wi6Xy8KpQlt5ebnWr1%2BvJUuW6IYbbvBvZy0urC%2B%2B%2BEIFBQX68ssv/dv27dunhIQEpaamshYX0Jo1a/TKK69o8%2BbN2rx5szIyMpSRkaHNmzfL5XLpo48%2B8v8GtM/n0549e1iLEEdgIeilp6erb9%2B%2BmjNnjg4cOKDKykrt3btXN910k9WjhaTPPvtMy5Yt0z333KPU1FR5PB7/B2txYY0YMULDhw/XI488ooMHD2rHjh0qLS3VjBkzWIsLrH///ho4cKD/IyYmRjExMRo4cKAyMzPV3Nys4uJiHTx4UMXFxfJ6vcrKyrJ6bJxHBBaCXlhYmJYtWyaPx6Ps7Gy9/PLLqqioUL9%2B/aweLSS9%2Beabam9v1/LlyzV69OiAD9biwvr2%2Bx0VFaWcnBwVFRXptttu07Rp01gLG4mNjdXKlStVXV2t7Oxsud1uVVZWBlxCA6HH4fvuVRsBAADwo3AGCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwDACCwAAwLD/Bdf/9mUnWDoAAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2495372197850787304”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.1</td> <td class=”number”>34</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.7</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18.1</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.9</td> <td class=”number”>31</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.2</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.4</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10.8</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.1</td> <td class=”number”>27</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14.8</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.7</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (322)</td> <td class=”number”>2839</td> <td class=”number”>88.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2495372197850787304”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>44.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>46.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2011”><s>Population Estimate, 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_2010 Census Population”><code>2010 Census Population</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99996</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2012”><s>Population Estimate, 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2011”><code>Population Estimate, 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2013”><s>Population Estimate, 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2012”><code>Population Estimate, 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2014”><s>Population Estimate, 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2013”><code>Population Estimate, 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2015”><s>Population Estimate, 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2014”><code>Population Estimate, 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Population Estimate, 2016”><s>Population Estimate, 2016</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Population Estimate, 2015”><code>Population Estimate, 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFAC09”><s>RECFAC09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FSR14”><code>FSR14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.95735</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFAC14”><s>RECFAC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_RECFAC09”><code>RECFAC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99353</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFACPTH09”>RECFACPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2238</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.077845</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.9901</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>27.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1084441355011124056”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1084441355011124056,#minihistogram-1084441355011124056”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1084441355011124056”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1084441355011124056”

aria-controls=”quantiles-1084441355011124056” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1084441355011124056” aria-controls=”histogram-1084441355011124056”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1084441355011124056” aria-controls=”common-1084441355011124056”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1084441355011124056” aria-controls=”extreme-1084441355011124056”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1084441355011124056”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.070341</td>

</tr> <tr>

<th>Q3</th> <td>0.11293</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.20203</td>

</tr> <tr>

<th>Maximum</th> <td>0.9901</td>

</tr> <tr>

<th>Range</th> <td>0.9901</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.11293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.078947</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0141</td>

</tr> <tr>

<th>Kurtosis</th> <td>13.337</td>

</tr> <tr>

<th>Mean</th> <td>0.077845</td>

</tr> <tr>

<th>MAD</th> <td>0.056464</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3889</td>

</tr> <tr>

<th>Sum</th> <td>243.81</td>

</tr> <tr>

<th>Variance</th> <td>0.0062326</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1084441355011124056”>

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UpKefflr/%2Bte/FBUVpdtvv919ubC0tFRxcXEej4mJidHhw4fldDolyWN7ixYtJEmHDx9u8LgfU1pa6t7XaQ5HU68f762QEHv1Y4fDXvOeztduOdsF%2BVqLfK1Fvtaze8a2LVg/Zs%2BePQoKClK7du00btw4bd26VbNmzVJERIQGDRqk6upqhYaGejwmNDRULpdL1dXV7tv/uU367sX03srJyVF2drbHWnp6ujIyMn7pYQWE6Ohm/h7hF4mMDPf3CAGNfK1FvtYiX%2BvZNeOAK1jDhg1T//79FRUVJUlKTEzU3r179fLLL2vQoEEKCwtrUJZcLpfCw8M9ylRYWJj7vyW5X8PljbS0NA0YMMBjzeFoqvLyE7/4uH6I3Vq92cdvtZCQYEVGhquyskp1dfwmqdnI11rkay3ytZ5ZGfvrH/cBV7CCgoLc5eq0du3aadOmTZKk%2BPh4lZWVeWwvKytTbGys4uPjJUlOp1OtW7d2/7ckxcbGej1DXFxcg8uBTucx1dae29%2BEdj3%2Burp6285uB%2BRrLfK1Fvlaz64Z2%2BsUiBeWLFmi2267zWNt165dateunSQpKSlJubm57m2HDh3SoUOHlJSUpPj4eCUkJHhsz83NVUJCgumvnwIAAIEr4M5g9e/fXytWrNDKlSs1aNAgffzxx3rjjTe0evVqSdKYMWN0yy23qFu3burSpYsWLlyofv36qU2bNu7tixcv1q9%2B9StJ0iOPPKIJEyb47XgAAID9BFzB6tq1q5YsWaInnnhCS5YsUatWrfTII48oOTlZkpScnKx58%2BbpiSee0LfffqvevXtr/vz57sdPnDhR33zzjSZNmqSQkBCNGjWqwRkxAACAnxJkGIbh7yHOBU7nMdP36XAEa9Dij0zfr1Xe%2BUNvf4/QKA5HsKKjm6m8/IQtr/%2Bf7cjXWuRrLfK1nlkZx8aeb%2BJU3gu412ABAAD4GwULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJP5vGCNHj1af/nLX3Ts2DFfPzUAAIBP%2BLxg9erVS08//bT69Omj%2B%2B67Tx9//LEMw/D1GAAAAJbxecGaPHmyPvjgAz311FMKCQnRvffeq379%2Bumxxx7TV1995etxAAAATOfwx5MGBQWpd%2B/e6t27t6qqqvTiiy/qqaee0ooVK9S9e3eNHz9eV199tT9GAwAAOGN%2BKViSVFpaqjfffFNvvvmmvvzyS3Xv3l3Dhw/X4cOH9eCDD2rr1q2aOXOmv8YDAAD4xXxesNavX6/169dr8%2BbNat68uYYNG6YnnnhCbdu2dd%2BnZcuWWrhwoVcFy%2BVyacSIEZo1a5Z69uwpScrLy9PDDz%2Bs3bt3Ky4uTnfccYdGjx7tfsyNN96o3bt3e%2BznrbfeUseOHWUYhh555BG9%2Buqrqq%2Bv16hRozRlyhQFB/MLlwAAwDs%2BL1gzZ85U//79tXTpUl155ZU/WFzatWuncePG/ey%2BampqNHnyZBUWFrrXnE6n7rzzTo0ZM0YPP/ywduzYoenTpys2Nlb9%2BvVTXV2d9u7dqzVr1niUuujoaEnS888/rw0bNig7O1u1tbWaOnWqYmJiNHHixDM/eAAAcE7wecH617/%2BpejoaFVUVLjLVUFBgTp37qyQkBBJUvfu3dW9e/ef3E9RUZEmT57c4DcQN27cqBYtWui%2B%2B%2B6TJLVt21abN2/WW2%2B9pX79%2Bmn//v06deqUunbtqrCwsAb7Xb16tTIyMpSamipJmjJlipYsWULBAgAAXvP5da/jx49r8ODBeuaZZ9xrd911l4YOHapDhw55vZ8tW7aoZ8%2BeysnJ8Vjv27evMjMzf/B5pe%2BKWcuWLX%2BwXB05ckSHDh3S5Zdf7l5LSUnRgQMHVFpa6vVsAADg3ObzgvXQQw/pwgsv1O233%2B5ee/vtt9WyZcsfLEY/ZuzYsZoxY4bCw8M91lu3bq1u3bq5b3/zzTf661//qiuuuEKSVFxcrPPOO0933323evfurXHjxqmgoEDSd5cXJSkuLs79%2BBYtWkiSDh8%2B3MgjBQAA5yqfXyL897//rVdeeUWxsbHutebNm%2Bv%2B%2B%2B/XzTffbOpzVVdX695771WLFi2UlpYmSfrqq6/07bffavTo0crIyNArr7yi8ePH6%2B2331Z1dbUkKTQ01L2P0//tcrm8ft7S0lJ3WTvN4WjqUdzMEBJirxfeOxz2mvd0vnbL2S7I11rkay3ytZ7dM/Z5wXI4HKqsrGywXlVVZeo7up84cUL33HOP9u7dqz//%2Bc/uM13z589XdXW1IiIiJElz5szRtm3btH79ev3mN7%2BR9F2ZOn0J8XSx%2Bv6Zsp%2BSk5Oj7Oxsj7X09HRlZGSc8XHZWXR0M3%2BP8ItERnr/Z4/GI19rka%2B1yNd6ds3Y5wXryiuv1IIFC/Too4/q17/%2BtSSppKREmZmZ6tu3rynPcfz4cd1xxx36%2BuuvtWrVKo/fFnQ4HO5yJX33pqft2rXTkSNHFB8fL%2Bm7S4WtW7d2/7ckjzNuPyctLU0DBgzwWHM4mqq8/MQvPaQfZLdWb/bxWy0kJFiRkeGqrKxSXV29v8cJOORrLfK1Fvlaz6yM/fWPe58XrGnTpun222/XNddco8jISElSZWWlOnfurOnTp5/x/uvr6zVp0iTt379fL774otq3b%2B%2Bx/ZZbblHPnj01adIk9/13796tm2%2B%2BWfHx8UpISFBubq67YOXm5iohIaFRl/fi4uIa3N/pPKba2nP7m9Cux19XV2/b2e2AfK1FvtYiX%2BvZNWOfF6yYmBi9/vrr%2BuSTT1RYWCiHw6GLL75YV1xxhYKCgs54/6%2B%2B%2Bqo2b96sZcuWKTIy0n0G6rzzzlNUVJQGDBigpUuXqlOnTrrooou0evVqHTt2TMOHD5ckjRkzRosXL9avfvUrSdIjjzyiCRMmnPFcAADg3OGXj8oJCQlR3759Tbsk%2BJ/effdd1dfX6%2B677/ZY79Gjh1588UXddtttqqmp0YIFC1RWVqakpCQ9//zz7suGEydO1DfffKNJkyYpJCREo0aN0m233Wb6nAAAIHAFGWa%2BstwLTqdTjz/%2BuLZt26ZTp041eGH7%2B%2B%2B/78txfMbpPGb6Ph2OYA1a/JHp%2B7XKO3/o7e8RGsXhCFZ0dDOVl5%2Bw5enpsx35Wot8rUW%2B1jMr49jY802cyns%2BP4M1a9Ysbd%2B%2BXdddd53OP98/Bw0AAGAlnxesTZs26dlnn3V/FA0AAECg8fnv%2BTdt2lQxMTG%2BfloAAACf8XnBGjp0qJ599lnV1dX5%2BqkBAAB8wueXCCsqKrRhwwb985//VJs2bTw%2BlkaSVq9e7euRAAAATOWXt2m4/vrr/fG0AAAAPuHzgpWZmenrpwQAAPApv3yYXWlpqbKzszV58mR98803%2Btvf/qY9e/b4YxQAAADT%2Bbxg7du3TzfccINef/11vfvuuzp58qTefvttjRw5Uvn5%2Bb4eBwAAwHQ%2BL1gPP/ywBg4cqI0bN%2Bq8886TJD366KMaMGCAFi9e7OtxAAAATOfzgrVt2zbdfvvtHh/s7HA4dM8992jnzp2%2BHgcAAMB0Pi9Y9fX1qq9v%2BJlCJ06cUEhIiK/HAQAAMJ3PC1afPn20fPlyj5JVUVGhrKws9erVy9fjAAAAmM7nBeuBBx7Q9u3b1adPH9XU1Oj3v/%2B9%2Bvfvr/3792vatGm%2BHgcAAMB0Pn8frPj4eL3xxhvasGGDvvjiC9XX12vMmDEaOnSoIiIifD0OAACA6fzyTu7h4eEaPXq0P54aAADAcj4vWLfeeutPbuezCAEAgN35vGC1atXK43Ztba327dunL7/8UuPHj/f1OAAAAKY7az6LcOnSpTp8%2BLCPpwEAADCfXz6L8IcMHTpU77zzjr/HAAAAOGNnTcH67LPPeKNRAAAQEM6KF7kfP35cu3fv1tixY309DgAAgOl8XrASEhI8PodQks477zyNGzdON954o6/HAQAAMJ3PC9bDDz/s66cEAADwKZ8XrK1bt3p938svv9zCSQAAAKzh84J1yy23uC8RGobhXv/%2BWlBQkL744ouf3Z/L5dKIESM0a9Ys9ezZU5JUUlKiWbNmKS8vTwkJCZoxY4b69Onjfswnn3yihx56SCUlJUpKStLChQvVpk0b9/YXXnhBK1eu1PHjxzVkyBDNmjVL4eHhZ37wAADgnODz3yJ8%2Bumn1apVKz3%2B%2BOP69NNPlZubqxdeeEEXXXSR7rvvPr3//vt6//33tXHjxp/dV01Nje677z4VFha61wzDUHp6ulq0aKF169Zp6NChmjRpkg4ePChJOnjwoNLT0zVixAi9%2Buqrat68ue655x53sXv33XeVnZ2tefPmadWqVcrPz1dWVpY1YQAAgIDk84KVmZmp2bNn65prrlF0dLSaNWumXr16ad68eXr55ZfVqlUr99dPKSoq0k033aSvv/7aY33Tpk0qKSnRvHnz1L59e919993q1q2b1q1bJ0lau3atLrvsMk2YMEEdOnRQZmamDhw4oC1btkj67qN6xo8fr/79%2B6tr166aO3eu1q1bp6qqKmsCAQAAAcfnBau0tPQHy1NERITKy8u93s%2BWLVvUs2dP5eTkeKzn5%2Bfr0ksvVdOmTd1rKSkpysvLc29PTU11bwsPD1fnzp2Vl5enuro6ff755x7bu3XrplOnTmnXrl1ezwYAAM5tPn8NVrdu3fToo4/qT3/6kyIiIiRJFRUVysrK0hVXXOH1fn7sPbOcTqfi4uI81mJiYtwfw/NT2ysrK1VTU%2BOx3eFwKCoqqlEf41NaWiqn0%2Bmx5nA0bfC8Zyok5Kx5n1ivOBz2mvd0vnbL2S7I11rkay3ytZ7dM/Z5wXrwwQd166236sorr1Tbtm1lGIb27t2r2NhYrV69%2Boz3X1VVpdDQUI%2B10NBQuVyun91eXV3tvv1jj/dGTk6OsrOzPdbS09OVkZHh9T4CUXR0M3%2BP8ItERvILDlYiX2uRr7XI13p2zdjnBat9%2B/Z6%2B%2B23tWHDBhUXF0uSbr75Zl133XWm/KZeWFiYKioqPNZcLpeaNGni3v79suRyuRQZGamwsDD37e9vb8xsaWlpGjBggMeaw9FU5eUnvN6HN%2BzW6s0%2BfquFhAQrMjJclZVVqqur9/c4AYd8rUW%2B1iJf65mVsb/%2Bce/zgiVJF1xwgUaPHq39%2B/e73x7hvPPOM2Xf8fHxKioq8lgrKytzX56Lj49XWVlZg%2B2dOnVSVFSUwsLCVFZWpvbt20uSamtrVVFRodjYWK9niIuLa3A50Ok8ptrac/ub0K7HX1dXb9vZ7YB8rUW%2B1iJf69k1Y5%2BfAjEMQ4sXL9bll1%2Bu66%2B/XocPH9a0adM0c%2BZMnTp16oz3n5SUpB07drgv90lSbm6ukpKS3Ntzc3Pd26qqqrRz504lJSUpODhYXbp08diel5cnh8OhxMTEM54NAACcG3xesF588UWtX79ef/zjH92vdRo4cKA2btzY4HVLv0SPHj3UsmVLTZ8%2BXYWFhVqxYoUKCgo0atQoSdLIkSO1bds2rVixQoWFhZo%2Bfbpat27tfpPSsWPHauXKldq4caMKCgo0Z84c3XTTTbzRKAAA8JrPC1ZOTo5mz56tESNGuN%2B9/dprr9WCBQv01ltvnfH%2BQ0JC9NRTT8npdGrEiBF68803tXTpUiUkJEiSWrdurSeffFLr1q3TqFGjVFFRoaVLl7pnue6663T33Xdr9uzZmjBhgrp27aqpU6ee8VwAAODc4fPXYO3fv1%2BdOnVqsJ6YmNjgrQ28tXv3bo/bF154odasWfOj97/qqqt01VVX/ej2u%2B66S3fdddcvmgUAAMDnZ7BatWqlzz//vMH6v/71L4/PAwQAALArn5/BmjhxoubOnSun0ynDMPTpp58qJydHL774oh544AFfjwMAAGA6nxeskSNHqra2VsuWLVN1dbVmz56t5s2b6w9/%2BIPGjBnj63EAAABM5/OCtWHDBg0ePFhpaWk6evSoDMNQTEyMr8cAAACwjM9fgzVv3jz3i9mbN29OuQIAAAHH5wWrbdu2%2BvLLL339tAAAAD7j80uEiYmJmjJlip599lm1bdvW/fl/p2VmZvp6JAAAAFP5vGB99dVXSklJkaRf/L5XAAAAZzOfFKxFixZp0qRJatq0qV588UVfPCUAAIDf%2BOQ1WM8//7yqqqo81u666y6Vlpb64ukBAAB8yicFyzCMBmtbt25VTU2NL54eAADAp3z%2BW4QAAAAR5pZlAAAXdklEQVSBzucvcse5a8jj/8/fIzTKe1P6%2BnsEAIBN%2BewMVlBQkK%2BeCgAAwK98dgZrwYIFHu95derUKWVlZalZs2Ye9%2BN9sAAAgN35pGBdfvnlDd7zKjk5WeXl5SovL/fFCAAAAD7jk4LFe18BAIBzCb9FCAAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmC8iC9dprr%2BmSSy5p8JWYmChJ%2Bv3vf99g2wcffOB%2B/AsvvKC%2BffsqOTlZM2bMUFVVlb8OBQAA2FBAftjztddeq759/%2B%2BDemtrazV%2B/Hj169dPklRcXKysrCxdccUV7vtccMEFkqR3331X2dnZysrKUkxMjKZPn66srCzNnj3bp8cAAADsKyDPYDVp0kSxsbHurzfffFOGYWjKlClyuVzav3%2B/unTp4nGf0NBQSdLq1as1fvx49e/fX127dtXcuXO1bt06zmIBAACvBWTB%2Bk8VFRV65plnNHnyZIWGhmrPnj0KCgpSmzZtGty3rq5On3/%2BuVJTU91r3bp106lTp7Rr1y5fjg0AAGws4AvWyy%2B/rLi4OA0ePFiStGfPHkVEROj%2B%2B%2B9Xnz59NGrUKH344YeSpMrKStXU1CguLs79eIfDoaioKB0%2BfNgv8wMAAPsJyNdgnWYYhtauXas77rjDvbZnzx5VV1erT58%2Buuuuu/Tee%2B/p97//vXJyctSiRQtJcl8uPC00NFQul8vr5y0tLZXT6fRYcziaehQ3M4SEBHw/9qvT%2BZKzNcjXWuRrLfK1nt0zDuiC9fnnn%2BvIkSO67rrr3Gv33HOPbrnlFveL2hMTE7Vjxw698sor%2Bp//%2BR9JalCmXC6XwsPDvX7enJwcZWdne6ylp6crIyPjlx4K/CAyMtzjf2EN8rUW%2BVqLfK1n14wDumB99NFHSk1NdZcpSQoODva4LUnt2rVTUVGRoqKiFBYWprKyMrVv317Sd7%2BBWFFRodjYWK%2BfNy0tTQMGDPBYcziaqrz8xBkcTUN2bfV2UVlZpcjIcFVWVqmurt7f4wSckJBg8rUQ%2BVqLfK1nVsbR0c1MnMp7AV2wCgoK1L17d4%2B1Bx54QEFBQcrMzHSv7dq1Sx07dlRwcLC6dOmi3Nxc9ezZU5KUl5cnh8Phfg8tb8TFxTW4HOh0HlNtLd%2BEdnL6G7qurp4/OwuRr7XI11rkaz27ZhzQp0AKCwt18cUXe6wNGDBAb731lt544w3t27dP2dnZys3N1bhx4yRJY8eO1cqVK7Vx40YVFBRozpw5uummmxp1iRAAAJzbAvoMVllZmSIjIz3Wrr76av3xj3/UsmXLdPDgQXXo0EHPPvusWrduLUm67rrrdODAAc2ePVsul0tXX321pk6d6o/xAQCATQUZhmH4e4hzgdN5zPR9OhzBGrT4I9P3i%2B%2B8N6WvoqObqbz8hC1PT5/tHI5g8rUQ%2BVqLfK1nVsaxseebOJX3AvoSIQAAgD9QsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJMFbMF67733dMkll3h8ZWRkSJJ27typ0aNHKykpSSNHjtT27ds9HrthwwYNHDhQSUlJSk9P19GjR/1xCAAAwKYCtmAVFRWpf//%2B%2Bvjjj91fCxYs0MmTJ3XXXXcpNTVVr732mpKTk3X33Xfr5MmTkqSCggLNnDlTkyZNUk5OjiorKzV9%2BnQ/Hw0AALCTgC1YxcXF6tixo2JjY91fkZGRevvttxUWFqb7779f7du318yZM9WsWTP97W9/kyStWbNGQ4YM0bBhw5SYmKhFixbpww8/VElJiZ%2BPCAAA2EVAF6y2bds2WM/Pz1dKSoqCgoIkSUFBQerevbvy8vLc21NTU933b9mypRISEpSfn%2B%2BTuQEAgP05/D2AFQzD0FdffaWPP/5Yy5cvV11dnQYPHqyMjAw5nU5dfPHFHvePiYlRYWGhJKm0tFRxcXENth8%2BfNjr5y8tLZXT6fRYcziaNtjvmQoJCdh%2BfFY4nS85W4N8rUW%2B1iJf69k944AsWAcPHlRVVZVCQ0P1%2BOOPa//%2B/VqwYIGqq6vd6/8pNDRULpdLklRdXf2T272Rk5Oj7Oxsj7X09HT3i%2BxhD5GR4R7/C2uQr7XI11rkaz27ZhyQBatVq1bavHmzLrjgAgUFBalTp06qr6/X1KlT1aNHjwZlyeVyqUmTJpKksLCwH9weHu79H3BaWpoGDBjgseZwNFV5%2BYlfeEQ/zK6t3i4qK6sUGRmuysoq1dXV%2B3ucgBMSEky%2BFiJfa5Gv9czKODq6mYlTeS8gC5YkRUVFedxu3769ampqFBsbq7KyMo9tZWVl7st38fHxP7g9NjbW6%2BeOi4trcDnQ6Tym2lq%2BCe3k9Dd0XV09f3YWIl9rka%2B1yNd6ds04IE%2BBfPTRR%2BrZs6eqqqrca1988YWioqKUkpKizz77TIZhSPru9Vrbtm1TUlKSJCkpKUm5ubnuxx06dEiHDh1ybwcAAPg5AVmwkpOTFRYWpgcffFB79uzRhx9%2BqEWLFumOO%2B7Q4MGDVVlZqYULF6qoqEgLFy5UVVWVhgwZIkkaM2aM1q9fr7Vr12rXrl26//771a9fP7Vp08bPRwUAAOwiIAtWRESEVq5cqaNHj2rkyJGaOXOm0tLSdMcddygiIkLLly9Xbm6uRowYofz8fK1YsUJNmzaV9F05mzdvnpYuXaoxY8boggsuUGZmpp%2BPCAAA2EmQcfpaGSzldB4zfZ8OR7AGLf7I9P3iO%2B9N6avo6GYqLz9hy%2Bv/ZzuHI5h8LUS%2B1iJf65mVcWzs%2BSZO5b2APIMFAADgTxQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwWcAWrCNHjigjI0M9evRQ3759lZmZqZqaGknSggULdMkll3h8rVmzxv3YDRs2aODAgUpKSlJ6erqOHj3qr8MAAAA25PD3AFYwDEMZGRmKjIzUSy%2B9pG%2B//VYzZsxQcHCwpk2bpuLiYk2ePFnDhw93PyYiIkKSVFBQoJkzZ2ru3LlKTEzUwoULNX36dC1fvtxfhwMAAGwmIM9g7dmzR3l5ecrMzFSHDh2UmpqqjIwMbdiwQZJUXFysSy%2B9VLGxse6v8PBwSdKaNWs0ZMgQDRs2TImJiVq0aJE%2B/PBDlZSU%2BPOQAACAjQRkwYqNjdWzzz6rFi1aeKwfP35cx48f15EjR9S2bdsffGx%2Bfr5SU1Pdt1u2bKmEhATl5%2BdbOTIAAAggAVmwIiMj1bdvX/ft%2Bvp6rVmzRr169VJxcbGCgoL09NNP68orr9SNN96o119/3X3f0tJSxcXFeewvJiZGhw8f9tn8AADA3gLyNVjfl5WVpZ07d%2BrVV1/Vjh07FBQUpHbt2mncuHHaunWrZs2apYiICA0aNEjV1dUKDQ31eHxoaKhcLpfXz1daWiqn0%2Bmx5nA0bVDczlRISED247PG6XzJ2Rrkay3ytRb5Ws/uGQd8wcrKytKqVav02GOPqWPHjurQoYP69%2B%2BvqKgoSVJiYqL27t2rl19%2BWYMGDVJYWFiDMuVyudyv0fJGTk6OsrOzPdbS09OVkZFx5gcEn4mMDPf4X1iDfK1FvtYiX%2BvZNeOALljz58/Xyy%2B/rKysLF1zzTWSpKCgIHe5Oq1du3batGmTJCk%2BPl5lZWUe28vKyhQbG%2Bv186alpWnAgAEeaw5HU5WXn/glh/Gj7Nrq7aKyskqRkeGqrKxSXV29v8cJOCEhweRrIfK1Fvlaz6yMo6ObmTiV9wK2YGVnZ%2Bsvf/mLHn30UQ0ePNi9vmTJEn322Wd64YUX3Gu7du1Su3btJElJSUnKzc3ViBEjJEmHDh3SoUOHlJSU5PVzx8XFNbgc6HQeU20t34R2cvobuq6unj87C5GvtcjXWuRrPbtmHJCnQIqLi/XUU0/pzjvvVEpKipxOp/urf//%2B2rp1q1auXKmvv/5af/7zn/XGG29owoQJkqQxY8Zo/fr1Wrt2rXbt2qX7779f/fr1U5s2bfx8VAAAwC4C8gzW%2B%2B%2B/r7q6Oi1btkzLli3z2LZ7924tWbJETzzxhJYsWaJWrVrpkUceUXJysiQpOTlZ8%2BbN0xNPPKFvv/1WvXv31vz58/1xGAAAwKaCDMMw/D3EucDpPGb6Ph2OYA1a/JHp%2B8V33pvSV9HRzVRefsKWp6fPdg5HMPlaiHytRb7WMyvj2NjzTZzKewF5iRAAAMCfKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmc/h7AOBsNWjxR/4eoVHe%2BUNvf48AAPhfnMECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFKwfUFNToxkzZig1NVV9%2BvTRc8895%2B%2BRAACAjfBZhD9g0aJF2r59u1atWqWDBw9q2rRpSkhI0ODBg/09GvCjhjz%2B//w9gtf43EQAgY6C9T0nT57U2rVr9cwzz6hz587q3LmzCgsL9dJLL1GwAJPYqQxKFEIAjcclwu/ZtWuXamtrlZyc7F5LSUlRfn6%2B6uvr/TgZAACwC85gfY/T6VR0dLRCQ0Pday1atFBNTY0qKirUvHnzn91HaWmpnE6nx5rD0VRxcXGmzhoSQj8GfMFuZ9zs5L0pff09wi9y%2Bv9/%2Bf9h69g9YwrW91RVVXmUK0nu2y6Xy6t95OTkKDs722Nt0qRJuvfee80Z8n%2BVlpZq/K8KlZaWZnp5w3f55uTkkK9FyNda5Gut0tJSrVr1LPlayO4Z27MWWigsLKxBkTp9u0mTJl7tIy0tTa%2B99prHV1pamumzOp1OZWdnNzhbBnOQr7XI11rkay3ytZ7dM%2BYM1vfEx8ervLxctbW1cji%2Bi8fpdKpJkyaKjIz0ah9xcXG2bNsAAMAcnMH6nk6dOsnhcCgvL8%2B9lpubqy5duig4mLgAAMDPozF8T3h4uIYNG6Y5c%2BaooKBAGzdu1HPPPadbb73V36MBAACbCJkzZ84cfw9xtunVq5d27typRx55RJ9%2B%2Bqn%2B67/%2BSyNHjvT3WD%2BoWbNm6tGjh5o1a%2BbvUQIS%2BVqLfK1FvtYiX%2BvZOeMgwzAMfw8BAAAQSLhECAAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIJ1lqupqdGMGTOUmpqqPn366LnnnvvR%2B%2B7cuVOjR49WUlKSRo4cqe3bt/twUntqTL7//Oc/NXToUCUnJ%2BuGG27Q%2B%2B%2B/78NJ7akx%2BZ62f/9%2BJScna/PmzT6Y0N4ak%2B/u3bs1ZswYde3aVTfccIM2bdrkw0ntqTH5vvfeexoyZIiSk5M1ZswY7dixw4eT2pvL5dL111//k9/ztvz5ZuCsNm/ePOOGG24wtm/fbvz97383kpOTjXfeeafB/U6cOGH07t3bePjhh42ioiJj/vz5xm9%2B8xvjxIkTfpjaPrzN94svvjA6d%2B5srFq1yti7d6%2BxZs0ao3PnzsYXX3zhh6ntw9t8/9PEiRONjh07Gps2bfLRlPblbb6VlZXGb37zG%2BPBBx809u7dayxZssRISUkxysrK/DC1fXib75dffml06dLFeP311419%2B/YZc%2BfONXr37m2cPHnSD1PbS3V1tZGenv6T3/N2/flGwTqLnThxwujSpYvHX7qlS5ca48aNa3DftWvXGgMGDDDq6%2BsNwzCM%2Bvp6Y9CgQca6det8Nq/dNCbfrKwsY%2BLEiR5rEyZMMB599FHL57SrxuR72vr1643f/e53FCwvNCbfVatWGQMHDjRqa2vdayNGjDD%2B%2Bc9/%2BmRWO2pMvs8//7wxfPhw9%2B1jx44ZHTt2NAoKCnwyq10VFhYaN954o3HDDTf85Pe8XX%2B%2BcYnwLLZr1y7V1tYqOTnZvZaSkqL8/HzV19d73Dc/P18pKSkKCgqSJAUFBal79%2B7Ky8vz6cx20ph8hw8frilTpjTYx7Fjxyyf064ak68klZeXKysrS/PmzfPlmLbVmHy3bNmi3/72twoJCXGvrVu3TldddZXP5rWbxuQbFRWloqIi5ebmqr6%2BXq%2B99poiIiL061//2tdj28qWLVvUs2dP5eTk/OT97PrzzeHvAfDjnE6noqOjFRoa6l5r0aKFampqVFFRoebNm3vc9%2BKLL/Z4fExMjAoLC302r900Jt/27dt7PLawsFCffvqpfve73/lsXrtpTL6S9PDDD2v48OHq0KGDr0e1pcbkW1JSoq5du2rWrFn6xz/%2BoVatWmnatGlKSUnxx%2Bi20Jh8r732Wv3jH//Q2LFjFRISouDgYC1fvlwXXHCBP0a3jbFjx3p1P7v%2BfOMM1lmsqqrK45tbkvu2y%2BXy6r7fvx/%2BT2Py/U9Hjx7Vvffeq%2B7du%2Bu3v/2tpTPaWWPy/eSTT5Sbm6t77rnHZ/PZXWPyPXnypFasWKHY2Fg988wzuvzyyzVx4kQdOnTIZ/PaTWPyLS8vl9Pp1OzZs/XKK69o6NChmj59ur755hufzRvI7PrzjYJ1FgsLC2vwF%2Bj07SZNmnh13%2B/fD/%2BnMfmeVlZWpvHjx8swDD3xxBMKDuZb6Md4m291dbVmz56tP/7xj/x9bYTG/P0NCQlRp06dlJGRoUsvvVRTp05V27ZttX79ep/NazeNyXfx4sXq2LGjbr75Zl122WWaP3%2B%2BwsPDtW7dOp/NG8js%2BvONnw5nsfj4eJWXl6u2tta95nQ61aRJE0VGRja4b1lZmcdaWVmZ4uLifDKrHTUmX0k6cuSIbr75ZrlcLq1evbrBJS548jbfgoIClZSUKCMjQ8nJye7XvNx5552aPXu2z%2Be2i8b8/Y2NjVW7du081tq2bcsZrJ/QmHx37NihxMRE9%2B3g4GAlJibq4MGDPps3kNn15xsF6yzWqVMnORwOjxfy5ebmqkuXLg3OnCQlJemzzz6TYRiSJMMwtG3bNiUlJfl0ZjtpTL4nT57UHXfcoeDgYK1Zs0bx8fG%2BHtd2vM23a9eu%2Bvvf/6433njD/SVJCxYs0H//93/7fG67aMzf327dumn37t0ea3v27FGrVq18MqsdNSbfuLg4FRcXe6x99dVXat26tU9mDXR2/flGwTqLhYeHa9iwYZozZ44KCgq0ceNGPffcc7r11lslffevqerqaknS4MGDVVlZqYULF6qoqEgLFy5UVVWVhgwZ4s9DOKs1Jt/ly5fr66%2B/1p/%2B9Cf3NqfTyW8R/gRv823SpIkuvPBCjy/pu3%2B1xsTE%2BPMQzmqN%2Bfv7u9/9Trt379aTTz6pffv2acmSJSopKdHQoUP9eQhntcbke9NNN%2BmVV17RG2%2B8oX379mnx4sU6ePCghg8f7s9DsLWA%2BPnm1zeJwM86efKkcf/99xvdunUz%2BvTpYzz//PPubR07dvR4H5D8/Hxj2LBhRpcuXYxRo0YZO3bs8MPE9uJtvtdcc43RsWPHBl/Tpk3z0%2BT20Ji/v/%2BJ98HyTmPy/fe//20MHz7cuOyyy4yhQ4caW7Zs8cPE9tKYfF955RVj8ODBRrdu3YwxY8YY27dv98PE9vX97/lA%2BPkWZBj/e84NAAAApuASIQAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMn%2BP8gexOiM1mB%2BAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1084441355011124056”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>885</td> <td class=”number”>27.6%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0386548</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.285469597</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.19219681</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0524109</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.06762697</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.123092073</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.072511058</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.227531286</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135666802</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2227)</td> <td class=”number”>2229</td> <td class=”number”>69.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1084441355011124056”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>885</td> <td class=”number”>27.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.013326</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0141709</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0145467</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0155927</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.611995104</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.630119723</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.646754468</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.732869183</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99009901</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_RECFACPTH14”>RECFACPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2115</td>

</tr> <tr>

<th>Unique (%)</th> <td>65.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.068937</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.82237</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>31.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5750462065186211345”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5750462065186211345,#minihistogram5750462065186211345”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5750462065186211345”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5750462065186211345”

aria-controls=”quantiles5750462065186211345” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5750462065186211345” aria-controls=”histogram5750462065186211345”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5750462065186211345” aria-controls=”common5750462065186211345”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5750462065186211345” aria-controls=”extreme5750462065186211345”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5750462065186211345”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.061047</td>

</tr> <tr>

<th>Q3</th> <td>0.10621</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.18653</td>

</tr> <tr>

<th>Maximum</th> <td>0.82237</td>

</tr> <tr>

<th>Range</th> <td>0.82237</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.10621</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.072179</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.047</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.577</td>

</tr> <tr>

<th>Mean</th> <td>0.068937</td>

</tr> <tr>

<th>MAD</th> <td>0.054045</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.0911</td>

</tr> <tr>

<th>Sum</th> <td>215.91</td>

</tr> <tr>

<th>Variance</th> <td>0.0052098</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5750462065186211345”>

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NBQeTwelZaWet//cJl05mb66srKylJmZqbPWGpqqtLS0n7ptOqE6Oj6/i7BryIjw/1dAqqBPjkDfXKGQO5TnQtYQ4cOVZ8%2BfRQVFSVJio%2BP1759%2B/Tqq6%2Bqf//%2BCgsLqxKWPB6PwsPDfcJUWFiY9/8lee/hqo6UlBT17dvXZ8zlilBR0clfPK9zcdonA9Pzd4qQkGBFRoaruLhEFRX8RmptRZ%2BcgT45Q23qk78%2B3Ne5gBUUFOQNV2e1bt1aGzdulCTFxcWpsLDQZ3lhYaFiYmIUFxcnSXK73WrRooX3/yUpJiam2jXExsZWuRzodh9XeXlg/zAI9PlXVFQG/NfACeiTM9AnZwjkPjnrFEg1LF68WHfddZfP2K5du9S6dWtJUkJCgrKzs73LDh06pEOHDikhIUFxcXFq1qyZz/Ls7Gw1a9bM%2BP1TAACg7qpzZ7D69OmjZcuWacWKFerfv78%2B/vhjvfXWW1q1apUkaeTIkRo9erQ6d%2B6sjh07av78%2Berdu7datmzpXb5o0SJdcsklkqQnnnhCY8eO9dt8AACA89S5gNWpUyctXrxYS5Ys0eLFi9W8eXM98cQTSkxMlCQlJiZqzpw5WrJkib7//nv16NFDc%2BfO9W4/btw4fffdd5owYYJCQkJ06623VjkjBgAA8L8EWZZl%2BbuIQOB2Hze%2BT5crWP0XfWR8vxfKuw/08HcJfuFyBSs6ur6Kik4G7L0ITkCfnIE%2BOUNt6lNMTEO/HLfO3YMFAADgbwQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhjk%2BYHk8Hg0ePFibNm3yjuXk5Og3v/mNEhMTddNNN2nNmjU%2B2/z617/WFVdc4fP66quvJEmWZWnRokXq3r27unbtqoULF6qystLWOQEAAGdz%2BbuA81FWVqZJkyZp9%2B7d3jG32617771XI0eO1OOPP64dO3Zo2rRpiomJUe/evVVRUaF9%2B/Zp9erVatWqlXe76OhoSdKLL76o9evXKzMzU%2BXl5Zo8ebIaN26scePG2T09AADgUI4NWHv27NGkSZNkWZbP%2BIYNG9SkSRM9%2BOCDkqRWrVpp06ZNevvtt9W7d2/t379fp0%2BfVqdOnRQWFlZlv6tWrVJaWpqSk5MlSQ899JAWL15MwAIAANXm2EuEmzdvVrdu3ZSVleUz3qtXLy1YsKDK%2BidOnJB0Jpg1bdr0nOHqyJEjOnTokK6%2B%2BmrvWFJSkg4cOKCCggLDMwAAAHWVY89gjRo16pzjLVq0UIsWLbzvv/vuO/3tb3/TxIkTJUl5eXm66KKLdP/992v79u267LLLNGXKFHXq1Elut1uSFBsb692%2BSZMmkqTDhw/7jP8vBQUF3n2d5XJFVHv76goJcVY%2BdrmcVa8pZ/vktH4FGvrkDPTJGeiTgwNWdZSWlmrixIlq0qSJUlJSJElff/21vv/%2Be912221KS0vTa6%2B9pjFjxuidd95RaWmpJCk0NNS7j7P/7/F4qn3crKwsZWZm%2BoylpqYqLS3tfKfkaNHR9f1dgl9FRob7uwRUA31yBvrkDIHcpzobsE6ePKnx48dr3759%2BvOf/6zw8DNNnjt3rkpLS9WgQQNJ0qxZs/TZZ59p3bp1uvbaayWdCVNnLyGeDVZnt6%2BOlJQU9e3b12fM5YpQUdHJ857XDzntk4Hp%2BTtFSEiwIiPDVVxcoooKfiO1tqJPzkCfnKE29clfH%2B7rZMA6ceKE7rnnHn377bdauXKlz28Lulwub7iSpKCgILVu3VpHjhxRXFycpDO/iXj2MuPZS30xMTHVPn5sbGyVy4Fu93GVlwf2D4NAn39FRWXAfw2cgD45A31yhkDuk7NOgVRDZWWlJkyYoP379%2Bvll19W27ZtfZaPHj3a5/JdZWWlvvzyS7Vu3VpxcXFq1qyZsrOzvcuzs7PVrFkz4/dPAQCAusv2M1i33XabRowYoUGDBqlhw4bG9//6669r06ZNeuaZZxQZGek9A3XRRRcpKipKffv21dKlS9W%2BfXtddtllWrVqlY4fP65hw4ZJkkaOHKlFixbpkksukSQ98cQTGjt2rPE6AQBA3WV7wOrevbueffZZLViwQDfccIOGDx%2BuHj16KCgoyMj%2B33vvPVVWVur%2B%2B%2B/3Ge/atatefvll3XXXXSorK9O8efNUWFiohIQEvfjii97LhuPGjdN3332nCRMmKCQkRLfeeqvuuusuI7UBAIDAEGT9%2BEmdNrAsS5988oneeustbdiwQZGRkRo6dKiGDh2qyy67zO5ybOF2Hze%2BT5crWP0XfWR8vxfKuw/08HcJfuFyBSs6ur6Kik4G7L0ITkCfnIE%2BOUNt6lNMjPmrZdXhl5vcg4KC1KNHD/Xo0UMlJSV6%2BeWX9fTTT2vZsmXq0qWLxowZoxtvvNEfpQEAAJw3v/0WYUFBgf7617/qr3/9q7766it16dJFw4YN0%2BHDh/XII49oy5YtSk9P91d5AAAAv5jtAWvdunVat26dNm3apEaNGmno0KFasmSJz6MUmjZtqvnz5xOwAACAI9kesNLT09WnTx8tXbpU1113nYKDqz4ponXr1rrjjjvsLg0AAMAI2wPWhx9%2BqOjoaB07dswbrrZu3aoOHTooJCREktSlSxd16dLF7tIAAACMsP1BoydOnNCAAQP0/PPPe8fuu%2B8%2BDRkyRIcOHbK7HAAAAONsD1iPPfaYLr30Ut19993esXfeeUdNmzbVggUL7C4HAADAONsD1n//%2B189/PDDPn/br1GjRpoyZYo2btxodzkAAADG2R6wXC6XiouLq4yXlJTID888BQAAMM72gHXddddp3rx5%2Bvbbb71j%2Bfn5WrBggXr16mV3OQAAAMbZ/luEU6dO1d13362bbrpJkZGRkqTi4mJ16NBB06ZNs7scAAAA42wPWI0bN9abb76pTz75RLt375bL5dLll1%2Bua665xtgffAYAAPAnv/ypnJCQEPXq1YtLggAAoE6yPWC53W499dRT%2Buyzz3T69OkqN7b/85//tLskAAAAo2wPWDNmzND27ds1aNAgNWzY0O7DAwAAXHC2B6yNGzdq%2BfLlSk5OtvvQAAAAtrD9MQ0RERFq3Lix3YcFAACwje0Ba8iQIVq%2BfLkqKirsPjQAAIAtbL9EeOzYMa1fv17/%2Bc9/1LJlS4WGhvosX7Vqld0lAQAAGOWXxzQMHjzYH4cFAACwhe0Ba8GCBXYfEgAAwFa234MlSQUFBcrMzNSkSZP03Xff6e9//7v27t3rj1IAAACMsz1gffPNN7rlllv05ptv6r333tOpU6f0zjvvaMSIEcrNzbW7HAAAAONsD1iPP/64%2BvXrpw0bNuiiiy6SJD355JPq27evFi1aZHc5AAAAxtkesD777DPdfffdPn/Y2eVyafz48dq5c6fd5QAAABhne8CqrKxUZWVllfGTJ08qJCTE7nIAAACMsz1g9ezZU88995xPyDp27JgyMjLUvXt3u8sBAAAwzvaA9fDDD2v79u3q2bOnysrK9Pvf/159%2BvTR/v37NXXq1Brvz%2BPxaPDgwdq0aZN3LD8/X3fddZc6d%2B6sm2%2B%2BWR9//LHPNp988okGDx6shIQE3XnnncrPz/dZ/tJLL6lXr15KTEzU9OnTVVJS8ssmCwAAApLtASsuLk5vvfWW/vCHP%2Bg3v/mNkpOT9dBDD%2Bntt99W8%2BbNa7SvsrIyPfjgg9q9e7d3zLIspaamqkmTJlq7dq2GDBmiCRMm6ODBg5KkgwcPKjU1VcOHD9frr7%2BuRo0aafz48bIsS5L03nvvKTMzU3PmzNHKlSuVm5urjIwMc18AAABQ5/nlSe7h4eG67bbbzmsfe/bs0aRJk7zB6KyNGzcqPz9ff/nLXxQREaE2bdro008/1dq1azVx4kStWbNGV111lcaOHSvpzINPe/Tooc2bN6tbt25atWqVxowZoz59%2BkiSZs%2BerXHjxmny5MkKDw8/r5oBAEBgsD1g3Xnnnf9zeXX/FuHZQPSHP/xBnTt39o7n5ubqyiuvVEREhHcsKSlJOTk53uXJycneZeHh4erQoYNycnKUnJysbdu2acKECd7lnTt31unTp7Vr1y4lJiZWqzYAABDYbA9YP74MWF5erm%2B%2B%2BUZfffWVxowZU%2B39jBo16pzjbrdbsbGxPmONGzfW4cOHf3Z5cXGxysrKfJa7XC5FRUV5twcAAPg5teZvES5dutRIiCkpKVFoaKjPWGhoqDwez88uLy0t9b7/qe2ro6CgQG6322fM5YqoEuzOV0iIX/7S0S/mcjmrXlPO9slp/Qo09MkZ6JMz0Cc/3YN1LkOGDNHQoUM1d%2B7c89pPWFiYjh075jPm8XhUr1497/IfhyWPx6PIyEiFhYV53/94eU3uv8rKylJmZqbPWGpqqtLS0qq9j7ooOrq%2Bv0vwq8hI7uFzAvrkDPTJGQK5T7UmYH3%2B%2BedGHjQaFxenPXv2%2BIwVFhZ6zx7FxcWpsLCwyvL27dsrKipKYWFhKiwsVJs2bSSduYR57NgxxcTEVLuGlJQU9e3b12fM5YpQUdHJXzKln%2BS0Twam5%2B8UISHBiowMV3FxiSoqqj5kF7UDfXIG%2BuQMtalP/vpwXytucj9x4oS%2B/PLLn7yvqiYSEhK0bNkylZaWes9aZWdnKykpybs8Ozvbu35JSYl27typCRMmKDg4WB07dlR2dra6desmScrJyZHL5VJ8fHy1a4iNja1yOdDtPq7y8sD%2BYRDo86%2BoqAz4r4ET0CdnoE/OEMh9sj1gNWvWzOfvEErSRRddpDvuuEO//vWvz3v/Xbt2VdOmTTVt2jSNHz9e//73v7V161bvvV8jRozQihUrtGzZMvXp00dLly5VixYtvIFq1KhRmjlzptq1a6fY2FjNmjVLt99%2BO49oAAAA1WZ7wHr88ccv6P5DQkL09NNPKz09XcOHD9ell16qpUuXqlmzZpKkFi1a6E9/%2BpMee%2BwxLV26VImJiVq6dKk39A0aNEgHDhzQzJkz5fF4dOONN2ry5MkXtGYAAFC3BFk/flLnBbZly5Zqr3v11VdfwErs5XYfN75PlytY/Rd9ZHy/F8q7D/Twdwl%2B4XIFKzq6voqKTgbsqXInoE/OQJ%2BcoTb1KSamoV%2BOa/sZrNGjR3vPFv0w2/14LCgoSF988YXd5QEAAJw32wPWs88%2Bq3nz5mny5Mnq2rWrQkNDtW3bNs2ZM0fDhg3TzTffbHdJAAAARtl%2BifCmm25Senq6rrvuOp/x//73v5oyZYr%2B9a9/2VmObbhE6DymLmnWplPl%2BGn0yRnokzPUpj756xKh7Q9SKigoqPLnciSpQYMGKioqsrscAAAA42wPWJ07d9aTTz6pEydOeMeOHTumjIwMXXPNNXaXAwAAYJzt92A98sgjuvPOO3XdddepVatWsixL%2B/btU0xMjFatWmV3OQAAAMbZHrDatGmjd955R%2BvXr1deXp4k6be//a0GDRrEwzwBAECd4Je/RXjxxRfrtttu0/79%2B9WyZUtJZ57mDgAAUBfYfg%2BWZVlatGiRrr76ag0ePFiHDx/W1KlTlZ6ertOnT9tdDgAAgHG2B6yXX35Z69at06OPPqrQ0FBJUr9%2B/bRhwwZlZmbaXQ4AAIBxtgesrKwszZw5U8OHD/c%2Bvf3mm2/WvHnz9Pbbb9tdDgAAgHG2B6z9%2B/erffv2Vcbj4%2BPldrvtLgcAAMA42wNW8%2BbNtW3btirjH374ofeGdwAAACez/bcIx40bp9mzZ8vtdsuyLH366afKysrSyy%2B/rIcfftjucgAAAIyzPWCNGDFC5eXleuaZZ1RaWqqZM2eqUaNGeuCBBzRy5Ei7ywEAADDO9oC1fv16DRgwQCkpKTp69Kgsy1Ljxo3tLgMAAOCCsf0erDlz5nhvZm/UqBHhCgAA1Dm2B6xWrVrpq6%2B%2BsvuwAAAAtrH9EmF8fLweeughLV%2B%2BXK1atVJYWJjP8gULFthdEgAAgFG2B6yvv/5aSUlJksRzrwAAQJ1kS8BauHChJkyYoIiICL388st2HBIAAMBvbLkH68UXX1RJSYnP2H333aeCggI7Dg8AAGArWwKWZVlVxrZs2aKysjI7Dg8AAGAr23%2BLEAAAoK4jYAEAABhmW8AKCgqy61AAAAB%2BZdtjGubNm%2BfzzKvTp08rIyND9evX91mP52ABAACnsyVgXX311VWeeZU8Lqo3AAAW1klEQVSYmKiioiIVFRXZUQIAAIBtbAlYdj/76o033tC0adOqjAcFBWnXrl36/e9/r3/9618%2By5599ln16dNHkvTSSy9pxYoVOnHihAYOHKgZM2YoPDzcltoBAIDz2f4kdzvcfPPN6tWrl/d9eXm5xowZo969e0uS8vLylJGRoWuuuca7zsUXXyxJeu%2B995SZmamMjAw1btxY06ZNU0ZGhmbOnGnrHAAAgHPVyd8irFevnmJiYryvv/71r7IsSw899JA8Ho/279%2Bvjh07%2BqwTGhoqSVq1apXGjBmjPn36qFOnTpo9e7bWrl1b5UGpAAAAP6VOBqwfOnbsmJ5//nlNmjRJoaGh2rt3r4KCgtSyZcsq61ZUVGjbtm1KTk72jnXu3FmnT5/Wrl277CwbAAA4WJ28RPhDr776qmJjYzVgwABJ0t69e9WgQQNNmTJFmzdv1iWXXKKJEyfq%2BuuvV3FxscrKyhQbG%2Bvd3uVyKSoqSocPH672MQsKCqrc1O9yRfjs14SQkDqfj/3K5TLz9T3bJ/pVu9EnZ6BPzkCf6njAsixLa9as0T333OMd27t3r0pLS9WzZ0/dd999ev/99/X73/9eWVlZatKkiSR5LxeeFRoaKo/HU%2B3jZmVlKTMz02csNTVVaWlp5zEb2C06uv7Pr1QDkZH8ooQT0CdnoE/OEMh9qtMBa9u2bTpy5IgGDRrkHRs/frxGjx7tvak9Pj5eO3bs0GuvvaY//OEPklQlTHk8nhr9FmFKSor69u3rM%2BZyRaio6OQvnco5BfInAzuY6ldISLAiI8NVXFyiiopKI/uEefTJGeiTM9SmPpn%2BsFxddTpgffTRR0pOTvaGKUkKDg72eS9JrVu31p49exQVFaWwsDAVFhaqTZs2ks78BuKxY8cUExNT7ePGxsZWuRzodh9XeTk/DJzEdL8qKir5HnAA%2BuQM9MkZArlPdfoUyNatW9WlSxefsYcffrjKM7J27dql1q1bKzg4WB07dlR2drZ3WU5Ojlwul%2BLj422pGQAAOF%2BdDli7d%2B/W5Zdf7jPWt29fvf3223rrrbf0zTffKDMzU9nZ2brjjjskSaNGjdKKFSu0YcMGbd26VbNmzdLtt9/Og0YBAEC11elLhIWFhYqMjPQZu/HGG/Xoo4/qmWee0cGDB9W2bVstX75cLVq0kCQNGjRIBw4c0MyZM%2BXxeHTjjTdq8uTJ/igfAAA4VJBlWZa/iwgEbvdx4/t0uYLVf9FHxveLM959oIeR/bhcwYqOrq%2BiopMBey%2BCE9AnZ6BPzlCb%2BhQT09Avx63TlwgBAAD8gYAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsDobsN5//31dccUVPq%2B0tDRJ0s6dO3XbbbcpISFBI0aM0Pbt2322Xb9%2Bvfr166eEhASlpqbq6NGj/pgCAABwqDobsPbs2aM%2Bffro448/9r7mzZunU6dO6b777lNycrLeeOMNJSYm6v7779epU6ckSVu3blV6eromTJigrKwsFRcXa9q0aX6eDQAAcJI6G7Dy8vLUrl07xcTEeF%2BRkZF65513FBYWpilTpqhNmzZKT09X/fr19fe//12StHr1ag0cOFBDhw5VfHy8Fi5cqA8%2B%2BED5%2Bfl%2BnhEAAHCKOh2wWrVqVWU8NzdXSUlJCgoKkiQFBQWpS5cuysnJ8S5PTk72rt%2B0aVM1a9ZMubm5ttQNAACcz%2BXvAi4Ey7L09ddf6%2BOPP9Zzzz2niooKDRgwQGlpaXK73br88st91m/cuLF2794tSSooKFBsbGyV5YcPH6728QsKCuR2u33GXK6IKvs9XyEhdTYf1woul5mv79k%2B0a/ajT45A31yBvpURwPWwYMHVVJSotDQUD311FPav3%2B/5s2bp9LSUu/4D4WGhsrj8UiSSktL/%2Bfy6sjKylJmZqbPWGpqqvcmezhDdHR9o/uLjAw3uj9cGPTJGeiTMwRyn%2BpkwGrevLk2bdqkiy%2B%2BWEFBQWrfvr0qKys1efJkde3atUpY8ng8qlevniQpLCzsnMvDw6v/TZKSkqK%2Bffv6jLlcESoqOvkLZ3RugfzJwA6m%2BhUSEqzIyHAVF5eooqLSyD5hHn1yBvrkDLWpT6Y/LFdXnQxYkhQVFeXzvk2bNiorK1NMTIwKCwt9lhUWFnov38XFxZ1zeUxMTLWPHRsbW%2BVyoNt9XOXl/DBwEtP9qqio5HvAAeiTM9AnZwjkPtXJUyAfffSRunXrppKSEu/YF198oaioKCUlJenzzz%2BXZVmSztyv9dlnnykhIUGSlJCQoOzsbO92hw4d0qFDh7zLAQAAfk6dDFiJiYkKCwvTI488or179%2BqDDz7QwoULdc8992jAgAEqLi7W/PnztWfPHs2fP18lJSUaOHCgJGnkyJFat26d1qxZo127dmnKlCnq3bu3WrZs6edZAQAAp6iTAatBgwZasWKFjh49qhEjRig9PV0pKSm655571KBBAz333HPKzs7W8OHDlZubq2XLlikiIkLSmXA2Z84cLV26VCNHjtTFF1%2BsBQsW%2BHlGAADASYKss9fKcEG53ceN79PlClb/RR8Z3y/OePeBHkb243IFKzq6voqKTgbsvQhOQJ%2BcgT45Q23qU0xMQ78ct06ewQIAAPAnAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADD6mzAOnLkiNLS0tS1a1f16tVLCxYsUFlZmSRp3rx5uuKKK3xeq1ev9m67fv169evXTwkJCUpNTdXRo0f9NQ0AAOBALn8XcCFYlqW0tDRFRkbqlVde0ffff6/p06crODhYU6dOVV5eniZNmqRhw4Z5t2nQoIEkaevWrUpPT9fs2bMVHx%2Bv%2BfPna9q0aXruuef8NR0AAOAwdfIM1t69e5WTk6MFCxaobdu2Sk5OVlpamtavXy9JysvL05VXXqmYmBjvKzw8XJK0evVqDRw4UEOHDlV8fLwWLlyoDz74QPn5%2Bf6cEgAAcJA6GbBiYmK0fPlyNWnSxGf8xIkTOnHihI4cOaJWrVqdc9vc3FwlJyd73zdt2lTNmjVTbm7uhSwZAADUIXUyYEVGRqpXr17e95WVlVq9erW6d%2B%2BuvLw8BQUF6dlnn9V1112nX//613rzzTe96xYUFCg2NtZnf40bN9bhw4dtqx8AADhbnbwH68cyMjK0c%2BdOvf7669qxY4eCgoLUunVr3XHHHdqyZYtmzJihBg0aqH///iotLVVoaKjP9qGhofJ4PNU%2BXkFBgdxut8%2BYyxVRJbidr5CQOpmPaw2Xy8zX92yf6FftRp%2BcgT45A30KgICVkZGhlStX6o9//KPatWuntm3bqk%2BfPoqKipIkxcfHa9%2B%2BfXr11VfVv39/hYWFVQlTHo/He49WdWRlZSkzM9NnLDU1VWlpaec/IdgmOrq%2B0f1FRlb/ewj%2BQ5%2BcgT45QyD3qU4HrLlz5%2BrVV19VRkaGbrrpJklSUFCQN1yd1bp1a23cuFGSFBcXp8LCQp/lhYWFiomJqfZxU1JS1LdvX58xlytCRUUnf8k0flIgfzKwg6l%2BhYQEKzIyXMXFJaqoqDSyT5hHn5yBPjlDbeqT6Q/L1VVnA1ZmZqb%2B8pe/6Mknn9SAAQO844sXL9bnn3%2Bul156yTu2a9cutW7dWpKUkJCg7OxsDR8%2BXJJ06NAhHTp0SAkJCdU%2BdmxsbJXLgW73cZWX88PASUz3q6Kiku8BB6BPzkCfnCGQ%2B1QnT4Hk5eXp6aef1r333qukpCS53W7vq0%2BfPtqyZYtWrFihb7/9Vn/%2B85/11ltvaezYsZKkkSNHat26dVqzZo127dqlKVOmqHfv3mrZsqWfZwUAAJyiTp7B%2Buc//6mKigo988wzeuaZZ3yWffnll1q8eLGWLFmixYsXq3nz5nriiSeUmJgoSUpMTNScOXO0ZMkSff/99%2BrRo4fmzp3rj2kAAACHCrIsy/J3EYHA7T5ufJ8uV7D6L/rI%2BH5xxrsP9DCyH5crWNHR9VVUdDJgT5U7AX1yBvrkDLWpTzExDf1y3Dp5iRAAAMCfCFgAAACGEbAAAAAMq5M3uQMmDHzq//q7hBoxdc8YAOD8cQYLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmMvfBdRGZWVlmj17tv7xj3%2BoXr16Gjt2rMaOHevvsoD/aeBT/9ffJVTbuw/08HcJAHBBEbDOYeHChdq%2BfbtWrlypgwcPaurUqWrWrJkGDBjg79IAAIADELB%2B5NSpU1qzZo2ef/55dejQQR06dNDu3bv1yiuvELAAAEC1ELB%2BZNeuXSovL1diYqJ3LCkpSc8%2B%2B6wqKysVHMxta8D5ctLlTIlLmgBqjoD1I263W9HR0QoNDfWONWnSRGVlZTp27JgaNWr0s/soKCiQ2%2B32GXO5IhQbG2u01pAQwh5gB6cFwvcf6uXvEi6Ysz/3%2BPlXu9EnAlYVJSUlPuFKkve9x%2BOp1j6ysrKUmZnpMzZhwgRNnDjRTJH/X0FBgcZcslspKSnGwxvMKSgoUFZWFn2q5eiTMxQUFGjlyuX0qZajTzymoYqwsLAqQers%2B3r16lVrHykpKXrjjTd8XikpKcZrdbvdyszMrHK2DLULfXIG%2BuQM9MkZ6BNnsKqIi4tTUVGRysvL5XKd%2BfK43W7Vq1dPkZGR1dpHbGxswCZ2AADAGawq2rdvL5fLpZycHO9Ydna2OnbsyA3uAACgWkgMPxIeHq6hQ4dq1qxZ2rp1qzZs2KAXXnhBd955p79LAwAADhEya9asWf4uorbp3r27du7cqSeeeEKffvqpfve732nEiBH%2BLuuc6tevr65du6p%2B/fr%2BLgX/A31yBvrkDPTJGQK9T0GWZVn%2BLgIAAKAu4RIhAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACVi1XVlam6dOnKzk5WT179tQLL7zwk%2Bvu3LlTt912mxISEjRixAht377dxkoDW0369J///EdDhgxRYmKibrnlFv3zn/%2B0sdLAVpM%2BnbV//34lJiZq06ZNNlQIqWZ9%2BvLLLzVy5Eh16tRJt9xyizZu3GhjpYGtJn16//33NXDgQCUmJmrkyJHasWOHjZX6iYVabc6cOdYtt9xibd%2B%2B3frHP/5hJSYmWu%2B%2B%2B26V9U6ePGn16NHDevzxx609e/ZYc%2BfOta699lrr5MmTfqg68FS3T1988YXVoUMHa%2BXKlda%2Bffus1atXWx06dLC%2B%2BOILP1QdeKrbpx8aN26c1a5dO2vjxo02VYnq9qm4uNi69tprrUceecTat2%2BftXjxYispKckqLCz0Q9WBp7p9%2Buqrr6yOHTtab775pvXNN99Ys2fPtnr06GGdOnXKD1Xbh4BVi508edLq2LGjzw/2pUuXWnfccUeVddesWWP17dvXqqystCzLsiorK63%2B/ftba9euta3eQFWTPmVkZFjjxo3zGRs7dqz15JNPXvA6A11N%2BnTWunXrrN/85jcELBvVpE8rV660%2BvXrZ5WXl3vHhg8fbv3nP/%2BxpdZAVpM%2Bvfjii9awYcO8748fP261a9fO2rp1qy21%2BguXCGuxXbt2qby8XImJid6xpKQk5ebmqrKy0mfd3NxcJSUlKSgoSJIUFBSkLl26KCcnx9aaA1FN%2BjRs2DA99NBDVfZx/PjxC15noKtJnySpqKhIGRkZmjNnjp1lBrya9Gnz5s264YYbFBIS4h1bu3atrr/%2BetvqDVQ16VNUVJT27Nmj7OxsVVZW6o033lCDBg30q1/9yu6ybUXAqsXcbreio6MVGhrqHWvSpInKysp07NixKuvGxsb6jDVu3FiHDx%2B2pdZAVpM%2BtWnTRvHx8d73u3fv1qeffqprrrnGtnoDVU36JEmPP/64hg0bprZt29pZZsCrSZ/y8/PVqFEjzZgxQz169NDtt9%2Bu7Oxsu0sOSDXp080336zevXtr1KhRuuqqq7Rw4UItWbJEF198sd1l24qAVYuVlJT4fPNK8r73eDzVWvfH68G8mvTph44ePaqJEyeqS5cuuuGGGy5ojahZnz755BNlZ2dr/PjxttWHM2rSp1OnTmnZsmWKiYnR888/r6uvvlrjxo3ToUOHbKs3UNWkT0VFRXK73Zo5c6Zee%2B01DRkyRNOmTdN3331nW73%2BQMCqxcLCwqp8o559X69evWqt%2B%2BP1YF5N%2BnRWYWGhxowZI8uytGTJEgUH80/xQqtun0pLSzVz5kw9%2Buij/Pvxg5r8ewoJCVH79u2VlpamK6%2B8UpMnT1arVq20bt062%2BoNVDXp06JFi9SuXTv99re/1VVXXaW5c%2BcqPDxca9euta1ef%2BCnei0WFxenoqIilZeXe8fcbrfq1aunyMjIKusWFhb6jBUWFla5bAjzatInSTpy5Ih%2B%2B9vfyuPxaNWqVWrUqJGd5Qas6vZp69atys/PV1pamhITE733mNx7772aOXOm7XUHmpr8e4qJiVHr1q19xlq1asUZLBvUpE87duzwuTUiODhY8fHxOnjwoG31%2BgMBqxZr3769XC6Xz43q2dnZ6tixY5UzHgkJCfr8889lWZYkybIsffbZZ0pISLC15kBUkz6dOnVK99xzj4KDg7V69WrFxcXZXW7Aqm6fOnXqpH/84x966623vC9Jmjdvnv7P//k/ttcdaGry76lz58768ssvfcb27t2r5s2b21JrIKtJn2JjY5WXl%2Bcz9vXXX6tFixa21OovIbNmzZrl7yJwbhdddJEOHTqkV199VVdddZW2bdumjIwMTZo0SW3atJHb7VZISIhcLpd%2B9atfafny5Tp8%2BLCaNWumZ555Rrt27dKcOXN00UUX%2BXsqdVpN%2BpSZmakPPvhATz/9tBo2bKhTp07p1KlTqqysVFhYmL%2BnUqdVt0/16tVTVFSUzyszM1OjR4/mhncb1OTf06WXXqo//elPOn36tC655BKtXLlSH374oebNm6cGDRr4eyp1Wk36FBoaqsWLF6tp06YKDw/X8uXL9emnn2r%2B/PmKiIjw91QuHD8/JgI/49SpU9aUKVOszp07Wz179rRefPFF77J27dr5POcqNzfXGjp0qNWxY0fr1ltvtXbs2OGHigNTdft00003We3atavymjp1qp8qDyw1%2Bff0QzwHy1416dN///tfa9iwYdZVV11lDRkyxNq8ebMfKg5MNenTa6%2B9Zg0YMMDq3LmzNXLkSGv79u1%2BqNheQZb1/68pAQAAwAjuwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/4f/xM5lX/BRagAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5750462065186211345”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1007</td> <td class=”number”>31.4%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.110213079</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1447178</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.109289617</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.080853816</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.072432276</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.108849461</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0175821</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.094705938</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.070947144</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2104)</td> <td class=”number”>2107</td> <td class=”number”>65.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5750462065186211345”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1007</td> <td class=”number”>31.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0112132</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0134956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0138318</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0141579</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.571825348</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.61055386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.635324015</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.651041667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.822368421</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_SNAPS12”>REDEMP_SNAPS12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2873</td>

</tr> <tr>

<th>Unique (%)</th> <td>89.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>9.9%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>317</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>254980</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1253300</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4846617245627963214”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives4846617245627963214,#minihistogram4846617245627963214”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives4846617245627963214”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4846617245627963214”

aria-controls=”quantiles4846617245627963214” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4846617245627963214” aria-controls=”histogram4846617245627963214”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4846617245627963214” aria-controls=”common4846617245627963214”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4846617245627963214” aria-controls=”extreme4846617245627963214”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4846617245627963214”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>59973</td>

</tr> <tr>

<th>Q1</th> <td>164240</td>

</tr> <tr>

<th>Median</th> <td>258280</td>

</tr> <tr>

<th>Q3</th> <td>335200</td>

</tr> <tr>

<th>95-th percentile</th> <td>456700</td>

</tr> <tr>

<th>Maximum</th> <td>1253300</td>

</tr> <tr>

<th>Range</th> <td>1253300</td>

</tr> <tr>

<th>Interquartile range</th> <td>170960</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>125330</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.49154</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.9304</td>

</tr> <tr>

<th>Mean</th> <td>254980</td>

</tr> <tr>

<th>MAD</th> <td>98875</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.56462</td>

</tr> <tr>

<th>Sum</th> <td>737660000</td>

</tr> <tr>

<th>Variance</th> <td>15708000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4846617245627963214”>

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5ckJScnKzk52enSAAAArHD8rwgPHz6sfv366amnnvKNjRs3ToMHD1Z%2Bfr7T5QAAAFjneMD685//rIsuuki33HKLb%2BzVV19VixYtlJGR4XQ5AAAA1jkesD755BPdc889iouL8401adJEkydP1ocffuh0OQAAANY5HrBcLpdKSkqqjJeWlnJHdwAAUCc4HrB69%2B6tOXPm6JtvvvGN5eXlKSMjQ7169XK6HAAAAOsc/yvCKVOm6JZbbtE111yj2NhYSVJJSYk6deqkqVOnOl0OAACAdY4HrKZNm%2Bqvf/2rNm/erNzcXLlcLl166aXq3r27wsLCnC4HAADAuqB8VE54eLh69erFJUEAAFAnOR6wvF6vFi1apG3btun48eNV3tj%2B9ttvO10SAACAVY4HrPvuu087duzQwIEDdf755zu9PAAAQK1zPGB9%2BOGHWrZsmVJTU51eGgAAwBGO36YhJiZGTZs2dXpZAAAAxzgesAYPHqxly5bpxIkTTi8NAADgCMcvERYXF2vTpk36xz/%2BoTZt2igiIsJv/rnnnrOyTnl5uTIyMrRp0yadd955GjZsmP7v//5PYWFh2rlzp%2B6//37t3r1bl156qWbOnKnOnTv7vnfTpk1atGiRvF6vevbsqdmzZ6tJkyZW6gIAAHWf42ewJGnQoEHq3bu3Lr74YrVq1crvy5Y5c%2BZo8%2BbNevrpp7Vw4UK99NJLysrK0tGjRzVu3DilpqZq3bp1SkpK0vjx43X06FFJ0vbt2zV9%2BnRNmDBBWVlZKikp4QaoAADgrDh%2BBisjI6PW1yguLtbatWv17LPPqkuXLpKksWPHyuPxyOVyKTIyUpMnT1ZYWJimT5%2Bu9957T6%2B//rqGDBmi559/Xv3799d1110nSZo3b5769OmjvLw8tWnTptZrBwAAoS8oZ7AKCgqUmZmpu%2B66S//617/0%2Buuva%2B/evda2n52drUaNGqlr166%2BsXHjxikjI0Mej0cpKSm%2Bu8aHhYUpOTlZOTk5kiSPx%2BP3F44tWrRQy5Yt5fF4rNUHAADqNsfPYO3fv18jRoxQo0aNdOjQIU2aNEmvvvqqpk6dquXLl8vtdv/iNfLy8tSqVSutX79eTz75pI4fP64hQ4botttuk9fr1aWXXur3/KZNmyo3N1fSj%2BEvPj6%2ByvzBgwdrvH5BQYG8Xq/fmMsVU2W7v1R4eAO/f1G/uVynPw44VgKjL1XRk8DoS2D05fQcD1gPPvig%2Bvbtqzlz5ig5OVmS9PDDD2vKlClasGCBVq5c%2BYvXOHr0qPbv369Vq1YpIyNDXq9XM2bMUHR0tEpLS6u8sT4iIkLl5eWSpGPHjp1xviaysrKUmZnpN5aenq6JEyf%2BzD06s9jY6FrZLkJL48YNq30Ox0pg9KUqehIYfQmMvlTleMDatm2bXnjhBb8Pdna5XLr99ts1YsQIK2u4XC4dPnxYCxcu9L1x/sCBA3rxxRd10UUXVQlL5eXlioqKkiRFRkYGnI%2BOrvnBM3LkSKWlpZ1SU4yKio78nN05rfDwBoqNjVZJSalOnKi0um2EnjMdXxwrgdGXquhJYPQlsFDoS01%2B%2BawNjgesyspKVVZW/Y9w5MgRhYeHW1kjLi5OkZGRfn%2BVePHFFys/P19du3ZVYWGh3/MLCwt9l%2B%2BaN28ecD4uLq7G68fHx1e5HOj1/qCKito5%2BE6cqKy1bSN01OQY4FgJjL5URU8Coy%2BB0ZeqHL9o2rNnTy1ZssQvZBUXF2v%2B/Pnq1q2blTXcbrfKysq0b98%2B39jevXvVqlUrud1uffrpp74PmTbGaNu2bb73frndbmVnZ/u%2BLz8/X/n5%2BVbeGwYAAOoHxwPWPffcox07dqhnz54qKyvTbbfdpj59%2Bujbb7/VlClTrKzxq1/9SldeeaWmTp2qXbt26Z///KeWLl2qUaNGqV%2B/fiopKdHcuXO1Z88ezZ07V6Wlperfv78kadSoUdqwYYNWr16tXbt2afLkybryyiu5RQMAAKgxxy8RNm/eXOvXr9emTZv0xRdfqLKyUqNGjdLgwYPVqFEja%2BssWLBAs2fP1qhRoxQdHa0bbrhBN910k8LCwrRkyRLdf//9eumll/Sb3/xGS5cuVUxMjCQpKSlJs2bN0qOPPqp///vf6tGjh2bPnm2tLgAAUPeFmZPXylCrvN4frG/T5Wqgxo0bqqjoSEhc%2B%2B6/6INgl1CnvTapx2nnQu1YcQp9qYqeBEZfAguFvsTFnR%2BUdR0/gzV69Ogzztv6LEIAAIBgcTxgnfp5gxUVFdq/f792796tMWPGOF0OAACAdefMZxEuXrz4rO6WDgAAcK46Z%2B5tP3jwYL322mvBLgMAAOAXO2cC1qeffmrtRqMAAADBdE68yf3w4cP68ssvdf311ztdDgAAgHWOB6yWLVv6fQ6hJJ133nm68cYb9bvf/c7pcgAAAKxzPGA9%2BOCDTi8JAADgKMcD1tatW2v83Msuu6wWKwEAAKgdjgeskx9XI0k/vYn8qWNhYWH64osvnC4PAADgF3M8YD355JOaM2eO7r77bnXt2lURERH67LPPNGvWLP3%2B97/XgAEDnC4JAADAKsdv05CRkaEZM2bommuuUePGjdWwYUN169ZNs2bN0osvvqhWrVr5vgAAAEKR4wGroKAgYHhq1KiRioqKnC4HAADAOscDVmJioh5%2B%2BGEdPnzYN1ZcXKz58%2Bere/fuTpcDAABgnePvwbr33ns1evRo9e7dW23btpUxRl9//bXi4uL03HPPOV0OAACAdY4HrEsuuUSvvvqqNm3apK%2B%2B%2BkqSdMMNN2jgwIGKjo52uhwAAADrHA9YknTBBRdo%2BPDh%2Bvbbb9WmTRtJP97NHQAAoC5w/D1YxhgtWLBAl112mQYNGqSDBw9qypQpmj59uo4fP%2B50OQAAANY5HrBWrlypDRs26P7771dERIQkqW/fvnrrrbeUmZnpdDkAAADWOR6wsrKyNGPGDA0ZMsR39/YBAwZozpw52rhxo9PlAAAAWOd4wPr222/VoUOHKuPt27eX1%2Bt1uhwAAADrHA9YrVq10meffVZl/L333vO94R0AACCUOf5XhH/84x81c%2BZMeb1eGWO0ZcsWZWVlaeXKlbrnnnucLgcAAMA6xwPW0KFDVVFRoSeeeELHjh3TjBkz1KRJE02aNEmjRo1yupyQlzr99WCXAAAATuF4wNq0aZP69eunkSNH6vvvv5cxRk2bNnW6DAAAgFrj%2BHuwZs2a5Xsze5MmTQhXAACgznE8YLVt21a7d%2B92elkAAADHOH6JsH379vrTn/6kZcuWqW3btoqMjPSbz8jIcLokAAAAqxwPWPv27VNKSookcd8rAABQJzkSsObNm6cJEyYoJiZGK1eudGJJAACAoHHkPVjPPvusSktL/cbGjRungoICJ5YHAABwlCMByxhTZWzr1q0qKytzYnkAAABHOf5XhAAAAHUdAQsAAMAyxwJWWFiYU0sBAAAElWO3aZgzZ47fPa%2BOHz%2Bu%2BfPnq2HDhn7P4z5YAAAg1DkSsC677LIq97xKSkpSUVGRioqKnCgBAADAMY4ELO59BQAA6hPe5A4AAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACW1fmANW7cON1zzz2%2Bxzt37tTw4cPldrs1dOhQ7dixw%2B/5mzZtUt%2B%2BfeV2u5Wenq7vv//e6ZIBAECIq9MB65VXXtG7777re3z06FGNGzdOqampWrdunZKSkjR%2B/HgdPXpUkrR9%2B3ZNnz5dEyZMUFZWlkpKSjR16tRglQ8AAEJUnQ1YxcXFmjdvnhISEnxjr776qiIjIzV58mRdcsklmj59uho2bKjXX39dkvT888%2Brf//%2Buu6669S%2BfXvNmzdP7777rvLy8oK1GwAAIATV2YD10EMPafDgwbr00kt9Yx6PRykpKb7PRQwLC1NycrJycnJ886mpqb7nt2jRQi1btpTH43G2eAAAENLqZMDasmWLPvnkE91%2B%2B%2B1%2B416vV/Hx8X5jTZs21cGDByVJBQUFZ5wHAACoCcc%2B7NkpZWVluv/%2B%2BzVjxgxFRUX5zZWWlioiIsJvLCIiQuXl5ZKkY8eOnXG%2BpgoKCqp89qLLFVMlvP1S4eF1Mh/jZ3K5Tn88nDxWOGb80Zeq6Elg9CUw%2BnJ6dS5gZWZmqnPnzurVq1eVucjIyCphqby83BfETjcfHR19VjVkZWUpMzPTbyw9PV0TJ048q%2B0AZ6Nx44bVPic29uyO5fqCvlRFTwKjL4HRl6rqXMB65ZVXVFhYqKSkJEnyBaY33nhDgwYNUmFhod/zCwsLfWeWmjdvHnA%2BLi7urGoYOXKk0tLS/MZcrhgVFR05q%2B1Uh98Y8FNnOr7CwxsoNjZaJSWlOnGi0sGqzm30pSp6Ehh9CSwU%2BlKTXz5rQ50LWCtXrlRFRYXv8YIFCyRJf/rTn7R161Y99dRTMsYoLCxMxhht27ZNt956qyTJ7XYrOztbQ4YMkSTl5%2BcrPz9fbrf7rGqIj4%2BvcjnQ6/1BFRXn5sGHuqEmx9eJE5UchwHQl6roSWD0JTD6UlWdC1itWrXye9yw4Y/J9aKLLlLTpk21cOFCzZ07V3/4wx%2B0atUqlZaWqn///pKkUaNG6aabblJiYqISEhI0d%2B5cXXnllWrTpo3j%2BwEAAEJXvbrG1KhRIy1ZssR3lsrj8Wjp0qWKiYmRJCUlJWnWrFlavHixRo0apQsuuEAZGRlBrhoAAISaMGOMCXYR9YHX%2B4P1bbpcDXTVgn9a3y5C02uTepx2zuVqoMaNG6qo6Ain8X%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%2B%2B%2B0yxgR5rwAAQKhwBbuA2rB3717l5OTogw8%2BULNmzSRJEydO1EMPPaTevXsrLy9Pq1atUkxMjC655BJt2bJFa9eu1R133KHVq1erc%2BfOGjt2rCQpIyNDPXr00Mcff6zLL788mLsFAABCRJ08gxUXF6dly5b5wtVJhw8flsfjUceOHRUTE%2BMbT0lJUU5OjiTJ4/EoNTXVNxcdHa1OnTr55gEAAKpTJ89gxcbGqlevXr7HlZWVev7559WtWzd5vV7Fx8f7Pb9p06Y6ePCgJFU7XxMFBQXyer1%2BYy5XTJXt/lLh4XUyH6MecLnOjWP35M8QP0v/Dz0JjL4ERl9Or04GrFPNnz9fO3fu1Jo1a7R8%2BXJFRET4zUdERKi8vFySVFpaesb5msjKylJmZqbfWHp6uiZOnPgz9wCoWxo3bhjsEvzExkYHu4RzDj0JjL4ERl%2BqqvMBa/78%2BVqxYoUeeeQRtWvXTpGRkSouLvZ7Tnl5uaKioiRJkZGRVcJUeXm5YmNja7zmyJEjlZaW5jfmcsWoqOjIz9yLwPiNAaHK9s/CzxUe3kCxsdEqKSnViROVwS7nnEBPAqMvgYVCX4L1C12dDlizZ8/Wiy%2B%2BqPnz5%2Buaa66RJDVv3lx79uzxe15hYaHv8l3z5s1VWFhYZb5Dhw41Xjc%2BPr7K5UCv9wdVVJybBx/gtHPtZ%2BHEicpzrqZgoyeB0ZfA6EtVdfYUSGZmplatWqWHH35YAwcO9I273W59/vnnOnbsmG8sOztbbrfbN5%2Bdne2bKy0t1c6dO33zAAAA1amTAeurr77S448/rv/5n/9RSkqKvF6v76tr165q0aKFpk6dqtzcXC1dulTbt2/XsGHDJElDhw7Vtm3btHTpUuXm5mrq1Klq3bo1t2gAAAA1VicD1ttvv60TJ07oiSeeUM%2BePf2%2BwsPD9fjjj8sgB/jmAAAQL0lEQVTr9WrIkCF6%2BeWXtXjxYrVs2VKS1Lp1az322GNau3athg0bpuLiYi1evFhhYWFB3isAABAqwgy3KHeE1/uD9W26XA101YJ/Wt8uUNtem9Qj2CVI%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%2BvULdmkAgqD/og%2BCXcJZeW1Sj2CXANR7BKxTHD16VKtXr9ZTTz2lTp06qVOnTsrNzdULL7xAwAIAADXCJcJT7Nq1SxUVFUpKSvKNpaSkyOPxqLKyMoiVAQCAUMEZrFN4vV41btxYERERvrFmzZqprKxMxcXFatKkSbXbKCgokNfr9RtzuWIUHx9vtdbwcPIxgKpcrp//2nDydcWp15erFvzTkXVs%2BfuUK4JdwjnF6eMllBCwTlFaWuoXriT5HpeXl9doG1lZWcrMzPQbmzBhgu644w47Rf5HQUGBxvx/uRo5cqT18BaqCgoKlJWVRU9OQV8Coy9VFRQUaMWKZY715JO5ofHWi5PHyrFjyRwrP%2BH08RJKiJyniIyMrBKkTj6Oioqq0TZGjhypdevW%2BX2NHDnSeq1er1eZmZlVzpbVZ/QkMPoSGH2pip4ERl8Coy%2BnxxmsUzRv3lxFRUWqqKiQy/Vje7xer6KiohQbG1ujbcTHx5PkAQCoxziDdYoOHTrI5XIpJyfHN5adna2EhAQ1aEC7AABA9UgMp4iOjtZ1112nBx54QNu3b9dbb72lZ555RqNHjw52aQAAIESEP/DAAw8Eu4hzTbdu3bRz504tXLhQW7Zs0a233qqhQ4cGu6yAGjZsqK5du6phw4bBLuWcQU8Coy%2BB0Zeq6Elg9CUw%2BhJYmDHGBLsIAACAuoRLhAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFghqqysTNOmTVNqaqp69uypZ555Jtgl/SyHDh3SxIkT1bVrV/Xq1UsZGRkqKyuTJOXl5enmm29WYmKiBgwYoPfff9/vezdv3qxBgwbJ7XZr9OjRysvL85tfvny5evXqpaSkJE2bNk2lpaW%2Buer6V93aThk3bpzuuece3%2BOdO3dq%2BPDhcrvdGjp0qHbs2OH3/E2bNqlv375yu91KT0/X999/75szxmjBggXq1q2bunbtqnnz5qmystI3X1RUpDvuuENJSUlKS0vThg0b/LZd3dq1rby8XDNnztRll12m3/72t3r44Yd18j7J9bkv%2Bfn5Gj9%2BvJKTk5WWlqbly5fXuLa61pfy8nINGjRIH330kW/sXH4dqW5tWwL1JScnR3/4wx%2BUlJSka665RqtXrz6r2upCX2qdQUiaNWuWufbaa82OHTvM3/72N5OUlGRee%2B21YJd1ViorK82IESPMf//3f5vdu3ebrVu3mquuuso8%2BOCDprKy0lx77bXmrrvuMnv27DFPPvmkcbvd5rvvvjPGGPPdd9%2BZxMRE8/TTT5vdu3eb//3f/zWDBg0ylZWVxhhjXn/9dZOSkmL%2B/ve/G4/HYwYMGGBmzpzpW/tM/atubads2rTJtGvXzkyZMsUYY8yRI0dMjx49zIMPPmj27NljZs%2BebX7729%2BaI0eOGGOM8Xg8pkuXLuavf/2r%2BeKLL8yNN95oxo0b59ve008/ba644gqzdetWs2XLFtOzZ0%2BzbNky3/z48ePNmDFjzJdffmleeukl07lzZ%2BPxeGq0thPuu%2B8%2Bc/XVVxuPx2M2b95sLr/8cvPiiy/W%2B76MGDHCTJo0yezbt8%2B8%2Beabxu12m7/97W/1ri/Hjh0z6enppl27dubDDz80xlT/sxzM15Hq1q7NvhQUFJjU1FSzcOFCs2/fPrNp0yaTkJBg3nnnnXrTFycQsELQkSNHTEJCgu%2BHxRhjFi9ebG688cYgVnX29uzZY9q1a2e8Xq9vbOPGjaZnz55m8%2BbNJjEx0e8FecyYMebRRx81xhizaNEiv/09evSoSUpK8vXk%2Buuv9z3XGGO2bt1qunTpYo4ePVpt/6pb2wlFRUWmd%2B/eZujQob6AtXr1apOWluZ7oamsrDRXXXWVWbt2rTHGmLvvvtv3XGOMOXDggPnNb35jvvnmG2OMMVdccYXvucYYs379etOnTx9jjDH79%2B837dq1M3l5eb75adOm1Xjt2lZUVGQ6duxoPvroI9/YkiVLzD333FOv%2B1JcXGzatWtnvvzyS9/YhAkTzMyZM%2BtVX3Jzc83vfvc7c%2B211/oFiXP5daS6tWuzL3/5y19Mv379/J573333mTvvvLNGtYV6X5zCJcIQtGvXLlVUVCgpKck3lpKSIo/H43cK/1wXFxenZcuWqVmzZn7jhw8flsfjUceOHRUTE%2BMbT0lJUU5OjiTJ4/EoNTXVNxcdHa1OnTopJydHJ06c0GeffeY3n5iYqOPHj2vXrl3V9q%2B6tZ3w0EMPafDgwbr00kt9Yx6PRykpKQoLC5MkhYWFKTk5%2BbQ9adGihVq2bCmPx6NDhw4pPz9fl112mW8%2BJSVF3333nQoKCuTxeNSiRQu1bt3ab/7TTz%2Bt0dq1LTs7W40aNVLXrl19Y%2BPGjVNGRka97ktUVJSio6O1bt06HT9%2BXHv37tW2bdvUoUOHetWXjz/%2BWJdffrmysrL8xs/l15EzrW3L6fpy8u0Ypzp8%2BHC1tdWFvjiFgBWCvF6vGjdurIiICN9Ys2bNVFZWpuLi4iBWdnZiY2PVq1cv3%2BPKyko9//zz6tatm7xer%2BLj4/2e37RpUx08eFCSzjhfUlKisrIyv3mXy6ULL7xQBw8erLZ/1a1d27Zs2aJPPvlEt99%2Bu994dXUVFBScdt7r9UqS3/zJYHtyPtD3Hjp0qEZr17a8vDy1atVK69evV79%2B/fRf//VfWrx4sSorK%2Bt1XyIjIzVjxgxlZWXJ7Xarf//%2B6t27t4YPH16v%2BnL99ddr2rRpio6O9hs/l19HnOjR6frSunVrJSYm%2Bh7/61//0iuvvKLu3btXW1td6ItTXMEuAGevtLTU7%2BCV5HtcXl4ejJKsmD9/vnbu3Kk1a9Zo%2BfLlAffx5P6drgfl5eU6duyY73GgeWPMGft3pm3XtrKyMt1///2aMWOGoqKi/Oaqq%2BvYsWNn1ZOz2edg9kSSjh49qv3792vVqlXKyMiQ1%2BvVjBkzFB0dXa/7IklfffWV%2BvTpo1tuuUW5ubmaPXu2unfvXu/7UpM6gvk6cq706NixY7rjjjvUrFkzjRw5stra6ktfbCBghaDIyMgqB9vJx6f%2Bn3KomD9/vlasWKFHHnlE7dq1U2RkZJWzceXl5b79O10PYmNjFRkZ6Xt86nx0dLROnDhxxv5Vt3ZtyszMVOfOnf3O7J10un2urifR0dF%2BL3Cn9ic6Ovpnb9up483lcunw4cNauHChWrVqJUk6cOCAXnzxRV100UX1ti9btmzRmjVr9O677yoqKkoJCQk6dOiQnnjiCbVp06be9uWkc/l15ExrO%2BXIkSO6/fbb9fXXX%2Bsvf/mL70xXfe%2BLLVwiDEHNmzdXUVGRKioqfGNer1dRUVEheRDOnj1bzz77rObPn69rrrlG0o/7WFhY6Pe8wsJC36nj083HxcXpwgsvVGRkpN98RUWFiouLFRcXV23/qlu7Nr3yyit66623lJSUpKSkJG3cuFEbN25UUlLSL%2BpJ8%2BbNJcl36een//vk/Om%2B90zbdqInJ2uMjIz0hStJuvjii5Wfn1%2Bv%2B7Jjxw5ddNFFfsGlY8eOOnDgQL3uy0nn8utIdT2sbYcPH9Yf//hH5ebmasWKFWrbtq1vrj73xSYCVgjq0KGDXC6X35v%2BsrOzlZCQoAYNQus/aWZmplatWqWHH35YAwcO9I273W59/vnnvtPR0o/76Ha7ffPZ2dm%2BudLSUu3cuVNut1sNGjRQQkKC33xOTo5cLpfat29fbf%2BqW7s2rVy5Uhs3btT69eu1fv16paWlKS0tTevXr5fb7dann37qu/eTMUbbtm07bU/y8/OVn58vt9ut5s2bq2XLln7z2dnZatmypeLj45WYmKjvvvvO730O2dnZvvdpVLd2bXO73SorK9O%2Bfft8Y3v37lWrVq3qdV/i4%2BO1f/9%2Bv9/49%2B7dq9atW9frvpx0Lr%2BOnGnt2lZZWakJEybo22%2B/1cqVK/XrX//ab76%2B9sW6IPzlIiy47777zMCBA43H4zFvvvmmSU5ONm%2B88Uawyzore/bsMR06dDCPPPKIKSgo8PuqqKgwAwYMMJMmTTK7d%2B82S5YsMYmJib57peTl5ZmEhASzZMkS371Srr32Wt%2BfhW/atMkkJyebN99803g8HjNw4EAze/Zs39pn6l91aztpypQpvj99/%2BGHH0y3bt3M7NmzTW5urpk9e7bp0aOH78%2Bdt23bZjp16mReeukl332Nxo8f79vWkiVLTM%2BePc2HH35oPvzwQ9OzZ0/zzDPP%2BObHjh1rbrzxRvPFF1%2BYl156ySQkJPjua1Td2k4YN26cGTlypPniiy/Me%2B%2B9Z7p162ZWrFhRr/tSUlJievToYe6%2B%2B26zd%2B9e8/bbb5uuXbuaF198sd725ae3IziXX0eqW7s2%2B5KVlWXat29v3nnnHb/X3aKionrXl9pEwApRR48eNZMnTzaJiYmmZ8%2Be5tlnnw12SWdtyZIlpl27dgG/jDHm66%2B/NjfccIPp3LmzGThwoPnggw/8vv8f//iHufrqq02XLl3MmDFjfPfv%2Ben2u3fvblJSUszUqVPNsWPHfHPV9a%2B6tZ3y04BlzI83h7zuuutMQkKCGTZsmPn888/9nr927VpzxRVXmMTERJOenm6%2B//5731xFRYX585//bFJTU83ll19u5s%2Bf7/eiVVhYaMaPH28SEhJMWlqa2bhxo9%2B2q1u7tpWUlJi7777bJCYmmu7du5vHHnvMV3997ktubq65%2BeabTXJysunbt6959tln63VffhokjDm3X0eqW9umn/Zl7NixAV93f3r/qfrSl9oUZsx/zuECAADAitB6ww4AAEAIIGABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwLL/HzdRhMubMNtAAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4846617245627963214”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>421944.9278146803</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>69775.086</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>288723.5276190477</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>283922.3532824428</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>248673.32219178084</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239231.12197474172</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>302989.21151956334</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239661.6951724138</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>91224.71294117646</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2862)</td> <td class=”number”>2862</td> <td class=”number”>89.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>317</td> <td class=”number”>9.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4846617245627963214”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15941.7</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16887.192857142858</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17750.12888888889</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17981.633898305085</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>751518.2690252708</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>829337.1494594592</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>882038.2474999998</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>931221.5129670331</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1253320.965</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_SNAPS16”>REDEMP_SNAPS16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2937</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>7.8%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>250</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>196710</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>827700</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5643685768704224213”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-5643685768704224213”

aria-controls=”quantiles-5643685768704224213” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5643685768704224213” aria-controls=”histogram-5643685768704224213”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5643685768704224213” aria-controls=”common-5643685768704224213”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5643685768704224213” aria-controls=”extreme-5643685768704224213”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5643685768704224213”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>42184</td>

</tr> <tr>

<th>Q1</th> <td>121710</td>

</tr> <tr>

<th>Median</th> <td>194520</td>

</tr> <tr>

<th>Q3</th> <td>259220</td>

</tr> <tr>

<th>95-th percentile</th> <td>368550</td>

</tr> <tr>

<th>Maximum</th> <td>827700</td>

</tr> <tr>

<th>Range</th> <td>827700</td>

</tr> <tr>

<th>Interquartile range</th> <td>137510</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>103310</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.52517</td>

</tr> <tr>

<th>Kurtosis</th> <td>1.4147</td>

</tr> <tr>

<th>Mean</th> <td>196710</td>

</tr> <tr>

<th>MAD</th> <td>81122</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.67408</td>

</tr> <tr>

<th>Sum</th> <td>582260000</td>

</tr> <tr>

<th>Variance</th> <td>10672000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5643685768704224213”>

<img 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E%2B/8HnAuu%2B%2B%2B/THP/5Rmzdv1vr163XvvfcqMjJSI0aM0IgRI3TJJZec8xoVFRXat2%2BfVq1apczMTLndbs2aNUvh4eGqrKxUSEiI1/1DQkJUXV0tSTp27Nhp5xsiJydH2dnZXmPp6emaPHnyWe7R6UVGhp%2BX7SKwREW19HcJAY/3UuNHjwIDffLTOVhBQUFKSUlRSkqKKisrtWLFCj3zzDNaunSpevXqpfHjx%2Bv6668/6%2B07HA4dOXJETzzxhDp06CBJ2r9/v1auXKnOnTufFJaqq6sVFhYmSQoNDT3lfHh4w18sY8aMUWpq6gk1Rai09OjZ7M6vCg5uocjIcJWXV6q2ts7qthF4bL%2B%2BmhPeS40fPQoMjbFP/vrl028nuRcXF%2Bv111/X66%2B/rt27d6tXr1763e9%2BpwMHDujBBx/Utm3bNHPmzLPadnR0tEJDQz3hSpIuueQSFRUVKTk5WSUlJV73Lykp8Xx8Fxsbe8r56OjoBq8fExNz0seBbvdh1dScnxdbbW3deds2AgevgXPHe6nxo0eBgT75IWBt2LBBGzZs0CeffKLWrVtrxIgReuqpp3TxxRd77tOuXTvNmzfvrAOW0%2BlUVVWVvv32W89Hjnv37lWHDh3kdDr13HPPyRijoKAgGWOUl5enu%2B66y/PY3NxcpaWlSZKKiopUVFQkp9N5bjsOAACaDZ%2BfhTZz5ky1bNlSixYt0gcffKD77rvPK1xJ0m9/%2B1vdeuutZ73Gb3/7W11zzTWaPn26du3apX/84x9aunSpxo4dqxtvvFHl5eWaN2%2Be9uzZo3nz5qmyslKDBw%2BWJI0dO1YbNmzQ6tWrtWvXLk2dOlXXXHMNl2gAAAANFmR%2Bvry5jxw6dEhRUVEqKytTVFSUpJ8u7tm9e3cFBwdbW%2Bfw4cPKyMjQu%2B%2B%2Bq/DwcN1yyy1KT09XUFCQtm/frtmzZ%2Bubb77R5Zdfrjlz5qhbt26ex65bt05PPfWUfvzxR6WkpCgjI8NT69lyu%2B1flsLhaKGoqJYqLT0aEIdiBy/82N8lNGlvTUnxdwkBK9DeS80RPQoMjbFP0dEX%2BmVdnwes7777Tn/4wx907bXXaurUqZJ%2BunRD27Zt9dxzz6ldu3a%2BLMdnCFgErPONgHX2Au291BzRo8DQGPvkr4Dl848I//znP6tz58664447PGNvvvmm2rVrp8zMTF%2BXAwAAYJ3PA9Znn32mBx54wOuv8lq3bq2pU6dq69atvi4HAADAOp8HLIfDofLy8pPGKysr5eNPKwEAAM4Ln1%2BmYcCAAXrkkUe0YMEC/cu//Iukn77aJjMzU/379/d1OUCTEUjnuHG%2BGICmzucBa9q0abrjjjt0ww03KDIyUpJUXl6u7t27a/r06b4uBwAAwDqfB6w2bdrotdde0%2BbNm1VQUCCHw6HLLrtMffv2tfqFzwAAAP7il6/KCQ4OVv/%2B/flIEAAANEk%2BD1hut1sLFy5UXl6ejh8/ftKJ7e%2B//76vSwIAALDK5wHroYce0o4dOzR06FBdeKF/Lv4FAABwPvk8YG3dulXLli1TUlKSr5cGAADwCZ9fBysiIkJt2rTx9bIAAAA%2B4/OANXz4cC1btky1tbW%2BXhoAAMAnfP4RYVlZmTZt2qS///3v6tSpk0JCQrzmX375ZV%2BXBAAAYJVfLtMwbNgwfywLAADgEz4PWJmZmb5eEgAAwKd8fg6WJBUXFys7O1v33Xef/ud//kdvv/229u7d649SAAAArPN5wNq3b59uuukmvfbaa3rnnXdUUVGhN998UyNHjpTL5fJ1OQAAANb5PGA9%2BuijGjRokN577z1dcMEFkqQFCxYoNTVVjz/%2BuK/LAQAAsM7nASsvL0933HGH1xc7OxwO3XPPPdq5c6evywEAALDO5wGrrq5OdXV1J40fPXpUwcHBvi4HAADAOp8HrH79%2BmnJkiVeIausrExZWVnq06ePr8sBAACwzucB64EHHtCOHTvUr18/VVVV6e6779bAgQP1/fffa9q0ab4uBwAAwDqfXwcrNjZW69ev16ZNm/TVV1%2Bprq5OY8eO1fDhw9WqVStflwMAAGCdX67kHh4ertGjR/tjaQAAgPPO5wFr3Lhxp53nuwgBAECg83nA6tChg9ftmpoa7du3T7t379b48eN9XQ4AAIB1jea7CBctWqQDBw74uBoAAAD7/PJdhKcyfPhwvfXWW/4uAwAA4Jw1moD1%2Beefc6FRAADQJDSKk9yPHDmir7/%2BWrfccouvywEAALDO5wGrffv2Xt9DKEkXXHCBbr31Vv3bv/2br8sBAACwzucB69FHH/X1kgAAAD7l84C1bdu2Bt%2B3d%2B/e57ESAACA88PnAeu2227zfERojPGMnzgWFBSkr776ytflAQAAnDOfB6zFixfrkUce0f3336/k5GSFhIToiy%2B%2B0Ny5c/W73/1OQ4YM8XVJAAAAVvn8Mg2ZmZmaNWuWbrjhBkVFRally5bq06eP5s6dq5UrV6pDhw6eHwAAgEDk84BVXFx8yvDUqlUrlZaW%2BrocAAAA63wesOLj47VgwQIdOXLEM1ZWVqasrCz17dvX1%2BUAAABY5/NzsB588EGNGzdOAwYM0MUXXyxjjP75z38qOjpaL7/8sq/LAQAAsM7nAevSSy/Vm2%2B%2BqU2bNumbb76RJP3%2B97/X0KFDFR4e7utyAAAArPN5wJKk3/zmNxo9erS%2B//57derUSdJPV3MHAABoCnx%2BDpYxRo8//rh69%2B6tYcOG6cCBA5o2bZpmzpyp48eP%2B7ocAAAA63wesFasWKENGzZo9uzZCgkJkSQNGjRI7733nrKzs31dDgAAgHU%2BD1g5OTmaNWuW0tLSPFdvHzJkiB555BFt3LjR1%2BUAAABY5/OA9f3336tr164njV9xxRVyu92%2BLgcAAMA6nwesDh066Isvvjhp/MMPP/Sc8A4AABDIfP5XhP/xH/%2BhOXPmyO12yxijLVu2KCcnRytWrNADDzzg63IAAACs83nAGjlypGpqavTss8/q2LFjmjVrllq3bq0pU6Zo7Nixvi4n4CXNfNvfJQAAgBP4PGBt2rRJN954o8aMGaNDhw7JGKM2bdr4ugwAAIDzxufnYM2dO9dzMnvr1q0JVwAAoMnxecC6%2BOKLtXv3bl8vCwAA4DM%2B/4jwiiuu0J/%2B9CctW7ZMF198sUJDQ73mMzMzra43YcIEtW7dWo8%2B%2BqgkaefOnZo9e7Z2796tyy67THPmzFGPHj0899%2B0aZMWLlwot9utfv36KSMjQ61bt7ZaEwAAaNp8fgTr22%2B/VWJiolq2bCm3263vv//e68emN954Qx988IHndkVFhSZMmKCkpCStW7dOCQkJmjhxoioqKiRJ27dv18yZMzVp0iTl5OSovLxc06dPt1oTAABo%2BnxyBGv%2B/PmaNGmSIiIitGLFCl8sqbKyMs2fP19xcXGesTfffFOhoaGaOnWqgoKCNHPmTH344Yd6%2B%2B23lZaWpldeeUWDBw/WiBEjPHUPHDhQhYWFXKMLAAA0mE%2BOYL344ouqrKz0GpswYYKKi4vP25qPPfaYhg8frssuu8wz5nK5lJiY6PmKnqCgIPXq1Uv5%2Bfme%2BaSkJM/927Vrp/bt28vlcp23OgEAQNPjk4BljDlpbNu2baqqqjov623ZskWfffaZ7rnnHq9xt9utmJgYr7E2bdrowIEDkqTi4uLTzgMAADSEz09yP9%2Bqqqo0e/ZszZo1S2FhYV5zlZWVCgkJ8RoLCQlRdXW1JOnYsWOnnW%2Bo4uLik75X0eGIOCm8navgYJ%2BfQgdY4XA0rtfuz%2B8l3lONFz0KDPTpF00uYGVnZ6tHjx7q37//SXOhoaEnhaXq6mpPEPu1%2BfDw8DOqIScnR9nZ2V5j6enpmjx58hltB2iqoqJa%2BruEU4qMPLP3OnyPHgUG%2BuTDgPXzeU/n2xtvvKGSkhIlJCRIkicwvfPOOxo2bJhKSkq87l9SUuI5shQbG3vK%2Bejo6DOqYcyYMUpNTfUaczgiVFp69Iy2Ux9%2BQ0Cgsv1eOFfBwS0UGRmu8vJK1dbW%2BbscnAI9CgyNsU/%2B%2BoXOZwHrkUce8brm1fHjx5WVlaWWLb13/Fyvg7VixQrV1NR4bj/%2B%2BOOSpD/96U/atm2bnnvuORljFBQUJGOM8vLydNddd0mSnE6ncnNzlZaWJkkqKipSUVGRnE7nGdUQExNz0seBbvdh1dQ0jhcb4G%2BN9b1QW1vXaGvDT%2BhRYKBPPgpYvXv3PumcpISEBJWWlqq0tNTqWh06dPC6/XOA69y5s9q0aaMnnnhC8%2BbN07//%2B79r1apVqqys1ODBgyVJY8eO1W233ab4%2BHjFxcVp3rx5uuaaa7hEAwAAOCM%2BCVi%2BuvZVfVq1aqUlS5Zo9uzZevXVV3X55Zdr6dKlioiIkPRT6Js7d66eeuop/fjjj0pJSVFGRoafqwYAAIEmyJzqGgqwzu0%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%2B%2BvzMxMVVVVSZIKCwt1%2B%2B23Kz4%2BXkOGDNFHH33k9djNmzdr2LBhcjqdGjdunAoLC/2xCwAAIEA1yYBljNHkyZNVWVmpv/zlL3ryySf13//931q4cKGMMUpPT1fbtm21du1aDR8%2BXJMmTdL%2B/fslSfv371d6errS0tK0Zs0atW7dWvfcc4%2BMMX7eKwAAECgc/i7gfNi7d6/y8/P18ccfq23btpKkyZMn67HHHtOAAQNUWFioVatWKSIiQpdeeqm2bNmitWvX6t5779Xq1avVo0cP3XnnnZKkzMxMpaSk6NNPP9WVV17pz90CAAABokkewYqOjtayZcs84epnR44ckcvlUrdu3RQREeEZT0xMVH5%2BviTJ5XIpKSnJMxceHq7u3bt75gEAAOrTJI9gRUZGqn///p7bdXV1euWVV9SnTx%2B53W7FxMR43b9NmzY6cOCAJNU73xDFxcVyu91eYw5HxEnbPVfBwU0yH6MZcDga12v35/cS76nGix4FBvr0iyYZsE6UlZWlnTt3as2aNVq%2BfLlCQkK85kNCQlRdXS1JqqysPO18Q%2BTk5Cg7O9trLD09XZMnTz7LPQCalqiolv4u4ZQiI8P9XQLqQY8CA31qBgErKytLL730kp588kl16dJFoaGhKisr87pPdXW1wsLCJEmhoaEnhanq6mpFRkY2eM0xY8YoNTXVa8zhiFBp6dGz3ItT4zcEBCrb74VzFRzcQpGR4Sovr1RtbZ2/y8Ep0KPA0Bj75K9f6Jp0wMrIyNDKlSuVlZWlG264QZIUGxurPXv2eN2vpKTE8/FdbGysSkpKTprv2rVrg9eNiYk56eNAt/uwamoax4sN8LfrHv%2BHv0s4I29NSfF3CfhftbV1/H9pAKBPTfQkd0nKzs7WqlWrtGDBAg0dOtQz7nQ69eWXX%2BrYsWOesdzcXDmdTs98bm6uZ66yslI7d%2B70zAMAANSnSQasb775Rs8884z%2B8Ic/KDExUW632/OTnJysdu3aafr06SooKNDSpUu1fft2jRo1SpI0cuRI5eXlaenSpSooKND06dPVsWNHLtEAAAAarEkGrPfff1%2B1tbV69tln1a9fP6%2Bf4OBgPfPMM3K73UpLS9Prr7%2BuRYsWqX379pKkjh076umnn9batWs1atQolZWVadGiRQoKCvLzXgEAgEARZLhEuU%2B43Yetb9PhaBFw57IAgYhzsPzP4WihqKiWKi092uzP7WnMGmOfoqMv9Mu6TfIIFgAAgD8RsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFg%2B6Z9sAAAN1ElEQVQAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAljn8XQAANHaDF37s7xLOyFtTUvxdAtDscQQLAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUErFOoqqrSjBkzlJSUpH79%2BumFF17wd0kAACCA8F2EpzB//nzt2LFDL730kvbv369p06apffv2uvHGG/1dGgDUK5C%2BO5HvTURTRcA6QUVFhVavXq3nnntO3bt3V/fu3VVQUKC//OUvBCwAANAgBKwT7Nq1SzU1NUpISPCMJSYmavHixaqrq1OLFnyqCgC2BNLRNokjbmg4AtYJ3G63oqKiFBIS4hlr27atqqqqVFZWptatW9e7jeLiYrndbq8xhyNCMTExVmsNDibsAYAvBVogDCTv/qm/v0uwioB1gsrKSq9wJclzu7q6ukHbyMnJUXZ2ttfYpEmTdO%2B999op8n8VFxdr/P8r0JgxY6yHN9hRXFysnJwcetTI0afGjx4FBvr0Cw6BnCA0NPSkIPXz7bCwsAZtY8yYMVq3bp3Xz5gxY6zX6na7lZ2dfdLRMjQe9Cgw0KfGjx4FBvr0C45gnSA2NlalpaWqqamRw/HT0%2BN2uxUWFqbIyMgGbSMmJqbZJ3cAAJozjmCdoGvXrnI4HMrPz/eM5ebmKi4ujhPcAQBAg5AYThAeHq4RI0bo4Ycf1vbt2/Xee%2B/phRde0Lhx4/xdGgAACBDBDz/88MP%2BLqKx6dOnj3bu3KknnnhCW7Zs0V133aWRI0f6u6xTatmypZKTk9WyZUt/l4JfQY8CA31q/OhRYKBPPwkyxhh/FwEAANCU8BEhAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICVoCqqqrSjBkzlJSUpH79%2BumFF17wd0kB7%2BDBg5o8ebKSk5PVv39/ZWZmqqqqSpJUWFio22%2B/XfHx8RoyZIg%2B%2Bugjr8du3rxZw4YNk9Pp1Lhx41RYWOg1v3z5cvXv318JCQmaMWOGKisrPXP19bK%2BtZuzCRMm6IEHHvDc3rlzp0aPHi2n06mRI0dqx44dXvfftGmTBg0aJKfTqfT0dB06dMgzZ4zR448/rj59%2Big5OVnz589XXV2dZ760tFT33nuvEhISlJqaqg0bNnhtu761m5Pq6mrNmTNHvXv31lVXXaUFCxbo52ta06PGo6ioSBMnTlSvXr2Umpqq5cuXe%2BbokwUGAWnu3LnmpptuMjt27DB//etfTUJCgnnrrbf8XVbAqqurMzfffLP5z//8T7N7926zbds2c91115lHH33U1NXVmZtuusncd999Zs%2BePWbx4sXG6XSaH374wRhjzA8//GDi4%2BPN888/b3bv3m3%2B67/%2BywwbNszU1dUZY4x5%2B%2B23TWJiovnb3/5mXC6XGTJkiJkzZ45n7dP1sr61m7NNmzaZLl26mGnTphljjDl69KhJSUkxjz76qNmzZ4/JyMgwV111lTl69KgxxhiXy2V69uxpXnvtNfPVV1%2BZW2%2B91UyYMMGzveeff95cffXVZtu2bWbLli2mX79%2BZtmyZZ75iRMnmvHjx5uvv/7avPrqq6ZHjx7G5XI1aO3m5qGHHjLXX3%2B9cblcZvPmzebKK680K1eupEeNzM0332ymTJlivv32W/Puu%2B8ap9Np/vrXv9InSwhYAejo0aMmLi7ObN261TO2aNEic%2Butt/qxqsC2Z88e06VLF%2BN2uz1jGzduNP369TObN2828fHxXm/w8ePHm6eeesoYY8zChQu9nvuKigqTkJDg6c8tt9ziua8xxmzbts307NnTVFRU1NvL%2BtZurkpLS82AAQPMyJEjPQFr9erVJjU11RNs6%2BrqzHXXXWfWrl1rjDHm/vvv99zXGGP2799vLr/8cvPdd98ZY4y5%2BuqrPfc1xpj169ebgQMHGmOM2bdvn%2BnSpYspLCz0zM%2BYMaPBazcnpaWlplu3buaTTz7xjC1ZssQ88MAD9KgRKSsrM126dDFff/21Z2zSpElmzpw59MkSPiIMQLt27VJNTY0SEhI8Y4mJiXK5XF6HYdFw0dHRWrZsmdq2bes1fuTIEblcLnXr1k0RERGe8cTEROXn50uSXC6XkpKSPHPh4eHq3r278vPzVVtbqy%2B%2B%2BMJrPj4%2BXsePH9euXbvq7WV9azdXjz32mIYPH67LLrvMM%2BZyuZSYmKigoCBJUlBQkHr16vWrfWrXrp3at28vl8ulgwcPqqioSL179/bMJyYm6ocfflBxcbFcLpfatWunjh07es1//vnnDVq7OcnNzVWrVq2UnJzsGZswYYIyMzPpUSMSFham8PBwrVu3TsePH9fevXuVl5enrl270idLCFgByO12KyoqSiEhIZ6xtm3bqqqqSmVlZX6sLHBFRkaqf//%2Bntt1dXV65ZVX1KdPH7ndbsXExHjdv02bNjpw4IAknXa%2BvLxcVVVVXvMOh0MXXXSRDhw4UG8v61u7OdqyZYs%2B%2B%2Bwz3XPPPV7j9T1XxcXFvzrvdrslyWv%2B57D98/ypHnvw4MEGrd2cFBYWqkOHDlq/fr1uvPFGXXvttVq0aJHq6uroUSMSGhqqWbNmKScnR06nU4MHD9aAAQM0evRo%2BmSJw98F4MxVVlZ6/YMsyXO7urraHyU1OVlZWdq5c6fWrFmj5cuXn/L5/vm5/rV%2BVFdX69ixY57bp5o3xpy2l6fbdnNUVVWl2bNna9asWQoLC/Oaq%2B%2B5Onbs2Bn16Uz6QJ9%2BUVFRoX379mnVqlXKzMyU2%2B3WrFmzFB4eTo8amW%2B%2B%2BUYDBw7UHXfcoYKCAmVkZKhv3770yRICVgAKDQ096cX28%2B0T/9HBmcvKytJLL72kJ598Ul26dFFoaOhJRwarq6s9z/Wv9SMyMlKhoaGe2yfOh4eHq7a29rS9rG/t5iY7O1s9evTwOtr4s1/rQ319Cg8P9/oH4MSehYeHn/W2m2OfHA6Hjhw5oieeeEIdOnSQJO3fv18rV65U586d6VEjsWXLFq1Zs0YffPCBwsLCFBcXp4MHD%2BrZZ59Vp06d6JMFfEQYgGJjY1VaWqqamhrPmNvtVlhYmCIjI/1YWeDLyMjQiy%2B%2BqKysLN1www2Sfnq%2BS0pKvO5XUlLiOYz9a/PR0dG66KKLFBoa6jVfU1OjsrIyRUdH19vL%2BtZubt544w299957SkhIUEJCgjZu3KiNGzcqISHhnPoUGxsrSZ6PN/7v//55/tcee7ptN8c%2BRUdHKzQ01BOuJOmSSy5RUVERPWpEduzYoc6dO3sFl27dumn//v30yRICVgDq2rWrHA6H10l/ubm5iouLU4sWtPRsZWdna9WqVVqwYIGGDh3qGXc6nfryyy89h76ln55vp9Ppmc/NzfXMVVZWaufOnXI6nWrRooXi4uK85vPz8%2BVwOHTFFVfU28v61m5uVqxYoY0bN2r9%2BvVav369UlNTlZqaqvXr18vpdOrzzz/3XG/JGKO8vLxf7VNRUZGKiorkdDoVGxur9u3be83n5uaqffv2iomJUXx8vH744Qev80Byc3MVHx/v2fbp1m5OnE6nqqqq9O2333rG9u7dqw4dOtCjRiQmJkb79u3zOlq0d%2B9edezYkT7Z4pe/XcQ5e%2Bihh8zQoUONy%2BUy7777runVq5d55513/F1WwNqzZ4/p2rWrefLJJ01xcbHXT01NjRkyZIiZMmWK2b17t1myZImJj4/3XIuqsLDQxMXFmSVLlniug3XTTTd5/sx406ZNplevXubdd981LpfLDB061GRkZHjWPl0v61u7uZs2bZrnz7sPHz5s%2BvTpYzIyMkxBQYHJyMgwKSkpnktc5OXlme7du5tXX33Vc%2B2eiRMnera1ZMkS069fP7N161azdetW069fP/PCCy945u%2B8805z6623mq%2B%2B%2Bsq8%2BuqrJi4uznPtnvrWbm4mTJhgxowZY7766ivz4Ycfmj59%2BpiXXnqJHjUi5eXlJiUlxdx///1m79695v333zfJyclm5cqV9MkSAlaAqqioMFOnTjXx8fGmX79%2B5sUXX/R3SQFtyZIlpkuXLqf8McaYf/7zn%2Bb3v/%2B96dGjhxk6dKj5%2BOOPvR7/97//3Vx//fWmZ8%2BeZvz48Z7rwfzf7fft29ckJiaa6dOnm2PHjnnm6utlfWs3Z/83YBnz0wUQR4wYYeLi4syoUaPMl19%2B6XX/tWvXmquvvtrEx8eb9PR0c%2BjQIc9cTU2N%2BfOf/2ySkpLMlVdeabKysjwh2RhjSkpKzMSJE01cXJxJTU01Gzdu9Np2fWs3J%2BXl5eb%2B%2B%2B838fHxpm/fvubpp5/2PJf0qPEoKCgwt99%2Bu%2BnVq5cZNGiQefHFF%2BmTRUHG/O9xOAAAAFjBCTsAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYNn/Bw70A4EJ4Ls9AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5643685768704224213”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>205756.5056719469</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>80778.1276119403</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>140499.85419558358</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>191150.0893670886</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>308227.58682352945</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>262063.49292775663</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>388320.55225806456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>133246.84166666667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92521.90000000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2926)</td> <td class=”number”>2926</td> <td class=”number”>91.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>250</td> <td class=”number”>7.8%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5643685768704224213”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>25</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7957.043389830507</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10570.650000000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11609.364590163937</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13764.03777777778</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>661416.1338237887</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>710641.882</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>721650.9440000001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>765161.8561464157</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>827697.7405714287</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_WICS08”>REDEMP_WICS08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2075</td>

</tr> <tr>

<th>Unique (%)</th> <td>64.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>35.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>1136</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>116700</td>

</tr> <tr>

<th>Minimum</th> <td>3464.5</td>

</tr> <tr>

<th>Maximum</th> <td>507450</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram3583468008356428427”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives3583468008356428427,#minihistogram3583468008356428427”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives3583468008356428427”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles3583468008356428427”

aria-controls=”quantiles3583468008356428427” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram3583468008356428427” aria-controls=”histogram3583468008356428427”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common3583468008356428427” aria-controls=”common3583468008356428427”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme3583468008356428427” aria-controls=”extreme3583468008356428427”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles3583468008356428427”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3464.5</td>

</tr> <tr>

<th>5-th percentile</th> <td>32315</td>

</tr> <tr>

<th>Q1</th> <td>69789</td>

</tr> <tr>

<th>Median</th> <td>104240</td>

</tr> <tr>

<th>Q3</th> <td>148980</td>

</tr> <tr>

<th>95-th percentile</th> <td>240160</td>

</tr> <tr>

<th>Maximum</th> <td>507450</td>

</tr> <tr>

<th>Range</th> <td>503990</td>

</tr> <tr>

<th>Interquartile range</th> <td>79195</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>67481</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.57823</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.2647</td>

</tr> <tr>

<th>Mean</th> <td>116700</td>

</tr> <tr>

<th>MAD</th> <td>50675</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.3851</td>

</tr> <tr>

<th>Sum</th> <td>242040000</td>

</tr> <tr>

<th>Variance</th> <td>4553600000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram3583468008356428427”>

<img 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gZ19Z2eUNugPAn69ttva%2BzYsXK73Z62jz/%2BWP369VNycrI%2B/PBDGWMkfXO9Vnl5uRwOhyTJ4XCorKzM87rq6mpVV1d7%2BgEAADoTlAErMTFRYWFh%2BtWvfqVPP/1Ub775plauXKmbb75ZU6ZMUUNDg1asWKFDhw5pxYoVcrvdmjp1qiRpzpw52rp1qzZt2qQDBw5o0aJFmjhxoi666CI/bxUAAOgugjJg9enTR08%2B%2BaSOHTumrKwsFRQUKDs7WzfffLP69OmjtWvXqqysTJmZmXI6nSopKVFkZKSkb8LZ0qVLVVxcrDlz5qhv374qLCz08xYBAIDuxGZOniuDT7lcxy0Zx27vpf79e6uu7kS3O389dfU7/p5CUHvl9jR/T%2BG0uvM%2BG%2BiorW9QV9/xR22jo7/zraznVEF5BAsAAMCfCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUCLmBde%2B212rhxo44ft%2BbO5wAAAN%2B2gAtY48aN0%2BOPP6709HT98pe/1M6dO8W3%2BQAAgO4k4ALWwoUL9Ze//EVr1qxRSEiIbrvtNk2cOFEPP/ywPvvsM39PDwAAoFN2f0/gdGw2m9LS0pSWlia3263169drzZo1KikpUVJSkm688Ub98Ic/9Pc0AQAATisgA5Yk1dTUaNu2bdq2bZsOHjyopKQk/fjHP9aRI0f0q1/9Snv27FFBQYG/pwkAANBBwAWsrVu3auvWrXrvvfc0YMAAzZo1S4888oiGDRvmWWbw4MFasWIFAQsAAASkgAtYBQUFuvrqq1VcXKyrrrpKvXp1vEzse9/7nq6//no/zA4AAKBzARew3nrrLfXv31/19fWecLV3716NHj1aISEhkqSkpCQlJSX5c5oAAABnFHB/RfjVV19pypQpeuKJJzxt8%2BfP18yZM1VdXe3HmQEAAHRNwAWs3/zmN7r44ov1s5/9zNP28ssva/DgwSosLPTjzAAAALom4ALWBx98oLvuukvR0dGetgEDBmjRokV69913/TgzAACArgm4gGW329XQ0NCh3e12c0d3AADQLQRcwLrqqqu0fPly/eMf//C0VVVVqbCwUOPHj/fjzAAAALom4P6KcPHixfrZz36myZMnKyoqSpLU0NCg0aNHKz8/38%2BzAwAA6FzABayBAwfqpZde0q5du1RZWSm73a4RI0boyiuvlM1m8/f0AAAAOhVwAUuSQkJCNH78eE4JAgCAbingApbL5dLq1atVXl6ur7/%2BusOF7X/%2B85/9NDMAAICuCbiA9etf/1r79u3T9OnT9Z3vfMff0wEAADhnARew3n33Xa1bt04pKSn%2BngoAAMB5CbjbNERGRmrgwIH%2BngYAAMB5C7iANXPmTK1bt05tbW3%2BngoAAMB5CbhThPX19dqxY4feeOMNXXTRRQoNDfXq/93vfndO482fP18DBgzQ/fffL0nav3%2B/lixZooMHD2rEiBG67777NGbMGM/yO3bs0OrVq%2BVyuZSenq5ly5ZpwIABF75hAACgxwi4I1iSNGPGDF111VX67ne/qyFDhng9zsUf/vAHvfnmm57njY2Nmj9/vlJSUvTiiy8qMTFRCxYsUGNjoyRp7969KigoUG5urkpLS9XQ0MDNTQEAwDkLuCNYhYWFloxTX1%2BvlStXKj4%2B3tP28ssvKywsTIsWLZLNZlNBQYHeeustvfrqq8rMzNSGDRs0depUzZo1S5K0cuVKXX311aqqqtJFF11kybwAAEDwC8gjWDU1NSoqKtLChQv1v//7v3r11Vf16aefntMYv/3tbzVz5kyNGDHC0%2BZ0OpWcnOy5I7zNZlNSUpIqKio8/f/614uDBw9WXFycnE6nBVsFAAB6ioA7gvX555/rJz/5ifr06aOjR4/q9ttv18svv6z8/Hw988wzcjgcnY6xe/duffDBB9q%2BfbvuvfdeT7vL5fIKXNI3X81TWVkp6ZtgFxMT06H/yJEj57QNNTU1crlcXm12e2SHsc9HSEgvr5/ASXZ7YO4T7LO%2BQ219g7r6Tk%2BqbcAFrPvvv1%2BTJk3S8uXLlZSUJEl66KGHtHjxYj3wwANav379WV/f3NysJUuW6J577lF4eLhXn9vt7nDRfGhoqFpaWiRJTU1NZ%2B3vqtLSUhUVFXm15eTkKC8v75zGOZuoqAjLxkJw6N%2B/t7%2BncFbss75DbX2DuvpOT6htwAWs8vJy/f73v/f6Yme73a5bb71VP/nJTzp9fVFRkcaMGXPa7zEMCwvrEJZaWlo8QexM/RER57YjZGdnKyMjw6vNbo9UXd2JcxrndEJCeikqKkINDW61tbVf8HgIHlbsX77APus71NY3qKvv%2BKO2/vrlM%2BACVnt7u9rbOxb9xIkTCgkJ6fT1f/jDH1RbW6vExERJ8gSmP/7xj5oxY4Zqa2u9lq%2BtrfWcuouNjT1tf3R09DltQ0xMTIfTgS7XcbW2WrcztbW1Wzoeur9A3x/YZ32H2voGdfWdnlDbgDsJmp6errVr13qFrPr6eq1atUrjxo3r9PXr16/X9u3btWXLFm3ZskUZGRnKyMjQli1b5HA49OGHH3q%2BQNoYo/Lycs91XQ6HQ2VlZZ6xqqurVV1d3aXrvgAAAE4KuCNYd911l%2BbOnav09HQ1Nzfrv//7v/Xll1%2BqX79%2BnpuFns2p98rq3fubQ4MXX3yxBg4cqAcffFArVqzQf/7nf2rjxo1yu92aOnWqJGnOnDm64YYblJCQoPj4eK1YsUITJ07kFg0AAOCcBFzAio2N1ZYtW7Rjxw59/PHHam9v15w5czRz5kz16dPngsbu06eP1q5dqyVLluj555/XJZdcopKSEkVGRkqSEhMTtXTpUj3yyCP65z//qbS0NC1btsyKzQIAAD2IzZw8XwafcrmOWzKO3d5L/fv3Vl3diW53/nrq6nf8PYWg9srtaf6ewml153020FFb36CuvuOP2kZHf%2BdbWc%2BpAu4I1ty5c8/af67fRQgAAPBtC7iAdeo1VK2trfr888918OBB3XjjjX6aFQAAQNcFXMA603cRFhcXn/Md1QEAAPwh4G7TcCYzZ87UK6%2B84u9pAAAAdKrbBKwPP/ywSzcaBQAA8LeAO0V4uovcv/rqK/3tb3/Tdddd54cZAQAAnJuAC1hxcXFe30MoSf/2b/%2Bm66%2B/Xj/60Y/8NCsAAICuC7iA1ZW7tQMAAASygAtYe/bs6fKyV1xxhQ9nAgAAcH4CLmDdcMMNnlOE/3qT%2BVPbbDabPv74429/ggAAAJ0IuID1%2BOOPa/ny5brzzjuVmpqq0NBQffTRR1q6dKl%2B/OMfa9q0af6eIgAAwFkF3G0aCgsLdc8992jy5Mnq37%2B/evfurXHjxmnp0qV67rnnNGTIEM8DAAAgEAVcwKqpqTlteOrTp4/q6ur8MCMAAIBzE3ABKyEhQQ899JC%2B%2BuorT1t9fb1WrVqlK6%2B80o8zAwAA6JqAuwbrV7/6lebOnaurrrpKw4YNkzFGf//73xUdHa3f/e53/p4eAABApwIuYA0fPlwvv/yyduzYoU8%2B%2BUSS9NOf/lTTp09XRESEn2cHAADQuYALWJLUt29fXXvttfriiy900UUXSfrmbu4AAADdQcBdg2WM0QMPPKArrrhCM2bM0JEjR7R48WIVFBTo66%2B/9vf0AAAAOhVwAWv9%2BvXaunWrlixZotDQUEnSpEmT9Prrr6uoqMjPswMAAOhcwAWs0tJS3XPPPcrMzPTcvX3atGlavny5tm/f7ufZAQAAdC7gAtYXX3yhUaNGdWi/9NJL5XK5/DAjAACAcxNwAWvIkCH66KOPOrS/9dZbngveAQAAAlnA/RXhz3/%2Bc913331yuVwyxmj37t0qLS3V%2BvXrddddd/l7egAAAJ0KuICVlZWl1tZWPfbYY2pqatI999yjAQMG6Pbbb9ecOXP8PT0AAIBOBVzA2rFjh6ZMmaLs7GwdO3ZMxhgNHDjQ39MCAADosoC7Bmvp0qWei9kHDBhAuAIAAN1OwAWsYcOG6eDBg/6eBgAAwHkLuFOEl156qe644w6tW7dOw4YNU1hYmFd/YWGhn2YGAADQNQEXsD777DMlJydLEve9AgAA3VJABKyVK1cqNzdXkZGRWr9%2BvSVjfv7551q6dKnKy8vVt29fXX/99br55pslSVVVVfr1r3%2BtiooKxcXF6e6771Z6errntbt27dJvfvMbVVVVyeFwaMWKFdyDCwAAdFlAXIP19NNPy%2B12e7XNnz9fNTU15zVee3u75s%2Bfr/79%2B%2Bull17Sfffdp8cee0zbt2%2BXMUY5OTkaNGiQNm/erJkzZyo3N1eHDx%2BWJB0%2BfFg5OTnKzMzUCy%2B8oAEDBujWW2%2BVMeaCtxMAAPQMAXEE63ThZc%2BePWpubj6v8WprazVq1Cjde%2B%2B96tOnj4YNG6Yrr7xSZWVlGjRokKqqqrRx40ZFRkZq%2BPDh2r17tzZv3qzbbrtNmzZt0pgxYzRv3jxJ31zzlZaWpvfff19jx469oO0EAAA9Q0AELKvFxMRo9erVkr4Jb%2BXl5dqzZ4%2BWLFkip9Opyy67TJGRkZ7lk5OTVVFRIUlyOp1KSUnx9EVERGj06NGqqKgIyIA1dfU7/p4CAAA4RUCcIvSljIwMXXfddUpMTNTkyZPlcrkUExPjtczAgQN15MgRSeq0HwAAoDMBcwTLZrP5ZNxHHnlEtbW1uvfee1VYWCi3263Q0FCvZUJDQ9XS0iJJnfZ3RU1NTYe/gLTbIzsEt/MREtLL6ydwkt0emPsE%2B6zvUFvfoK6%2B05NqGzABa/ny5V73vPr666%2B1atUq9e7d22u5c70PVnx8vCSpublZd9xxh7KysjpcUN/S0qLw8HBJUlhYWIcw1dLSoqioqC6vs7S0VEVFRV5tOTk5ysvLO6e5n01UVIRlYyE49O/fu/OF/Ih91neorW9QV9/pCbUNiIB1xRVXdDjik5iYqLq6OtXV1Z3zeLW1taqoqNCkSZM8bSNGjNDXX3%2Bt6Ohoffrppx2WP3l0KTY2VrW1tR36R40a1eX1Z2dnKyMjw6vNbo9UXd2Jc92UDkJCeikqKkINDW61tbVf8HgIHlbsX77APus71NY3qKvv%2BKO2/vrlMyACllX3vjrpiy%2B%2BUG5urt58803FxsZKkvbt26cBAwYoOTlZTz31lJqamjxHrcrKyjw3N3U4HCorK/OM5Xa7tX//fuXm5nZ5/TExMR1OB7pcx9Xaat3O1NbWbul46P4CfX9gn/Udausb1NV3ekJtg/IkaHx8vEaPHq27775bhw4d0ptvvqlVq1bplltuUWpqqgYPHqz8/HxVVlaqpKREe/fu1ezZsyVJWVlZKi8vV0lJiSorK5Wfn6%2BhQ4cG5F8QAgCAwBSUASskJERr1qxRRESEsrOzVVBQoBtuuEFz58719LlcLmVmZmrbtm0qLi5WXFycJGno0KF69NFHtXnzZs2ePVv19fUqLi722UX4AAAg%2BATEKUJfiI2N7XCh%2BUkXX3yxNmzYcMbXTpgwQRMmTPDV1AAAQJALyiNYAAAA/kTAAgAAsBgBCwAAwGIELAAAAIsRsAAAACwWtH9FCPQ0U1e/4%2B8pdNkrt6f5ewoA4FMcwQIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACwWtAHr6NGjysvLU2pqqsaPH6/CwkI1NzdLkqqqqnTTTTcpISFB06ZN086dO71eu2vXLs2YMUMOh0Nz585VVVWVPzYBAAB0U0EZsIwxysvLk9vt1u9//3s9/PDD%2Bstf/qLVq1fLGKOcnBwNGjRImzdv1syZM5Wbm6vDhw9Lkg4fPqycnBxlZmbqhRde0IABA3TrrbfKGOPnrQIAAN2F3d8T8IVPP/1UFRUVeueddzRo0CBJUl5enn7729/qqquuUlVVlTZu3KjIyEgNHz5cu3fv1ubNm3Xbbbdp06ZNGjNmjObNmydJKiwsVFpamt5//32NHTvWn5sFAAC6iaA8ghUdHa1169Z5wtVJX331lZxOpy677DJFRkZ62pOTk1VRUSFJcjqdSklJ8fRFRERo9OjRnn4AAIDOBGXAioqK0vjx4z3P29vbtWHDBo0bN04ul0sxMTFeyw8cOFBHjhyRpE77AQAAOhOUpwhPtWrVKu3fv1%2BIvRn/AAAT1klEQVQvvPCCnnnmGYWGhnr1h4aGqqWlRZLkdrvP2t8VNTU1crlcXm12e2SH4HY%2BQkJ6ef0EuiO7nf3XCnwe%2BAZ19Z2eVNugD1irVq3Ss88%2Bq4cfflgjR45UWFiY6uvrvZZpaWlReHi4JCksLKxDmGppaVFUVFSX11laWqqioiKvtpycHOXl5Z3nVnQUFRVh2VjAt61//97%2BnkJQ4fPAN6ir7/SE2gZ1wFq2bJmee%2B45rVq1SpMnT5YkxcbG6tChQ17L1dbWeo4uxcbGqra2tkP/qFGjurze7OxsZWRkeLXZ7ZGqqztxPpvhJSSkl6KiItTQ4FZbW/sFjwf4gxX/FsDnga9QV9/xR2399Qtd0AasoqIibdy4UQ899JCmTJniaXc4HCopKVFTU5PnqFVZWZmSk5M9/WVlZZ7l3W639u/fr9zc3C6vOyYmpsPpQJfruFpbrduZ2traLR0P%2BDax71qLzwPfoK6%2B0xNqG5QnQT/55BOtWbNG//Vf/6Xk5GS5XC7PIzU1VYMHD1Z%2Bfr4qKytVUlKivXv3avbs2ZKkrKwslZeXq6SkRJWVlcrPz9fQoUO5RQMAAOiyoAxYf/7zn9XW1qbHHntM6enpXo%2BQkBCtWbNGLpdLmZmZ2rZtm4qLixUXFydJGjp0qB599FFt3rxZs2fPVn19vYqLi2Wz2fy8VQAAoLuwGW5R/q1wuY5bMo7d3kv9%2B/dWXd0Jtba2a%2BrqdywZF/g2vXJ7mr%2BnEBRO/TyANair7/ijttHR3/lW1nOqoDyCBQAA4E8ELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIsRsAAAACxGwAIAALAYAQsAAMBiBCwAAACLBX3Aamlp0YwZM/Tee%2B952qqqqnTTTTcpISFB06ZN086dO71es2vXLs2YMUMOh0Nz585VVVXVtz1tAADQjQV1wGpubtYvf/lLVVZWetqMMcrJydGgQYO0efNmzZw5U7m5uTp8%2BLAk6fDhw8rJyVFmZqZeeOEFDRgwQLfeequMMf7aDAAA0M3Y/T0BXzl06JAWLlzYIRi9%2B%2B67qqqq0saNGxUZGanhw4dr9%2B7d2rx5s2677TZt2rRJY8aM0bx58yRJhYWFSktL0/vvv6%2BxY8f6Y1OAoDN19Tv%2BnsI5eeX2NH9PAUA3E7RHsE4GotLSUq92p9Opyy67TJGRkZ625ORkVVRUePpTUlI8fRERERo9erSnHwAAoDNBewTruuuuO227y%2BVSTEyMV9vAgQN15MiRLvV3RU1NjVwul1eb3R7ZYdzzERLSy%2BsnAN%2Bz2wPz3xufB75BXX2nJ9U2aAPWmbjdboWGhnq1hYaGqqWlpUv9XVFaWqqioiKvtpycHOXl5Z3nrDuKioqwbCwAZ9e/f29/T%2BGs%2BDzwDerqOz2htj0uYIWFham%2Bvt6rraWlReHh4Z7%2BU8NUS0uLoqKiuryO7OxsZWRkeLXZ7ZGqqztxnrP%2BPyEhvRQVFaGGBrfa2toveDwAnbPi364v8HngG9TVd/xRW3/9gtTjAlZsbKwOHTrk1VZbW%2Bs5fRcbG6va2toO/aNGjeryOmJiYjqcDnS5jqu11bqdqa2t3dLxAJxZoP9b4/PAN6ir7/SE2gb/SdBTOBwO/fWvf1VTU5OnraysTA6Hw9NfVlbm6XO73dq/f7%2BnHwAAoDM9LmClpqZq8ODBys/PV2VlpUpKSrR3717Nnj1bkpSVlaXy8nKVlJSosrJS%2Bfn5Gjp0KLdoAAAAXdbjAlZISIjWrFkjl8ulzMxMbdu2TcXFxYqLi5MkDR06VI8%2B%2Bqg2b96s2bNnq76%2BXsXFxbLZbH6eOQAA6C56xDVYf/vb37yeX3zxxdqwYcMZl58wYYImTJjg62kBAIAg1eOOYAEAAPgaAQsAAMBiBCwAAACLEbAAAAAsRsACAACwGAELAADAYgQsAAAAixGwAAAALEbAAgAAsBgBCwAAwGIELAAAAIv1iO8iBIALMXX1O/6ewjl55fY0f08B6PE4ggUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABYjIAFAABgMQIWAACAxez%2BnkAgam5u1n333ac//elPCg8P17x58zRv3jx/TwsAumTq6nf8PYWg9crtaf6eAroJAtZprFy5Uvv27dOzzz6rw4cPa/HixYqLi9OUKVP8PTUAANANELBO0djYqE2bNumJJ57Q6NGjNXr0aFVWVur3v/89AQsAAHQJAesUBw4cUGtrqxITEz1tycnJevzxx9Xe3q5evbhsDQB6Kk6/%2Bk6wnX4lYJ3C5XKpf//%2BCg0N9bQNGjRIzc3Nqq%2Bv14ABAzodo6amRi6Xy6vNbo9UTEzMBc8vJKSX108AAIKB3R5c/18jYJ3C7XZ7hStJnuctLS1dGqO0tFRFRUVebbm5ubrtttsueH41NTV69tl1ys7OVkxMjD5YwWlLK9TU1Ki0tNRTV1iH2voOtfUN6uo7Pam2wRUXLRAWFtYhSJ18Hh4e3qUxsrOz9eKLL3o9srOzLZmfy%2BVSUVFRhyNkuDDU1Xeore9QW9%2Bgrr7Tk2rLEaxTxMbGqq6uTq2trbLbvymPy%2BVSeHi4oqKiujRGTExM0CdzAABwZhzBOsWoUaNkt9tVUVHhaSsrK1N8fDwXuAMAgC4hMZwiIiJCs2bN0r333qu9e/fq9ddf11NPPaW5c%2Bf6e2oAAKCbCLn33nvv9fckAs24ceO0f/9%2BPfjgg9q9e7duueUWZWVl%2BXtaHr1791Zqaqp69%2B7t76kEFerqO9TWd6itb1BX3%2BkptbUZY4y/JwEAABBMOEUIAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgNWNNDc36%2B6771ZKSorS09P11FNP%2BXtKftfS0qIZM2bovffe87RVVVXppptuUkJCgqZNm6adO3d6vWbXrl2aMWOGHA6H5s6dq6qqKq/%2BZ555RuPHj1diYqLuvvtuud1uT19n70Fn6w50R48eVV5enlJTUzV%2B/HgVFhaqublZEnW9UJ9//rl%2B/vOfKzExURMnTtS6des8fdTWGvPnz9ddd93leb5//35de%2B21cjgcysrK0r59%2B7yW37FjhyZNmiSHw6GcnBwdO3bM02eM0QMPPKBx48YpNTVVK1euVHt7u6e/rq5Ot912mxITE5WRkaGtW7d6jd3ZuruD1157TZdcconXIy8vTxK17RKDbmPp0qXmmmuuMfv27TN/%2BtOfTGJionnllVf8PS2/aWpqMjk5OWbkyJHm3XffNcYY097ebq655hqzcOFCc%2BjQIfP4448bh8NhvvzyS2OMMV9%2B%2BaVJSEgwTz75pDl48KD5xS9%2BYWbMmGHa29uNMca8%2BuqrJjk52fzP//yPcTqdZtq0aea%2B%2B%2B7zrPNs70Fn6w507e3t5ic/%2BYm5%2BeabzcGDB82ePXvMD37wA3P//fdT1wvU1tZmfvjDH5qFCxeazz77zLzxxhsmKSnJbNu2jdpaZMeOHWbkyJFm8eLFxhhjTpw4YdLS0sz9999vDh06ZJYtW2a%2B//3vmxMnThhjjHE6nebyyy83L730kvn444/N9ddfb%2BbPn%2B8Z78knnzQTJkwwe/bsMbt37zbp6elm3bp1nv4FCxaYG2%2B80fztb38zzz//vBkzZoxxOp1dWnd3sWbNGrNgwQJTU1Pjefzzn/%2Bktl1EwOomTpw4YeLj4z1BwhhjiouLzfXXX%2B/HWflPZWWl%2BdGPfmSuueYar4C1a9cuk5CQ4PWP7cYbbzSPPPKIMcaY1atXe9WssbHRJCYmel5/3XXXeZY1xpg9e/aYyy%2B/3DQ2Nnb6HnS27kB36NAhM3LkSONyuTxt27dvN%2Bnp6dT1Ah09etT84he/MMePH/e05eTkmCVLllBbC9TV1ZmrrrrKZGVleQLWpk2bTEZGhieItre3mx/84Adm8%2BbNxhhj7rzzTs%2Byxhhz%2BPBhc8kll5h//OMfxhhjJkyY4FnWGGO2bNlirr76amOMMZ9//rkZOXKkqaqq8vTffffdXV53d7Fw4ULz4IMPdmintl3DKcJu4sCBA2ptbVViYqKnLTk5WU6n0%2BvQak/x/vvva%2BzYsSotLfVqdzqduuyyyxQZGelpS05OVkVFhac/JSXF0xcREaHRo0eroqJCbW1t%2Buijj7z6ExIS9PXXX%2BvAgQOdvgedrTvQRUdHa926dRo0aJBX%2B1dffUVdL1BMTIxWr16tPn36yBijsrIy7dmzR6mpqdTWAr/97W81c%2BZMjRgxwtPmdDqVnJwsm80mSbLZbEpKSjpjXQcPHqy4uDg5nU4dPXpU1dXVuuKKKzz9ycnJ%2BvLLL1VTUyOn06nBgwdr6NChXv0ffvhhl9bdXXzyyScaNmxYh3Zq2zUErG7C5XKpf//%2BCg0N9bQNGjRIzc3Nqq%2Bv9%2BPM/OO6667T3XffrYiICK92l8ulmJgYr7aBAwfqyJEjnfY3NDSoubnZq99ut6tfv346cuRIp%2B9BZ%2BsOdFFRURo/frzneXt7uzZs2KBx48ZRVwtlZGTouuuuU2JioiZPnkxtL9Du3bv1wQcf6NZbb/Vq72zbampqztjvcrkkyav/5C8eJ/tP99qjR492ad3dgTFGn332mXbu3KnJkydr0qRJeuCBB9TS0kJtu8ju7wmga9xut9eHpCTP85aWFn9MKSCdqU4na3S2/qamJs/z0/UbY876HnS27u5m1apV2r9/v1544QU988wz1NUijzzyiGpra3XvvfeqsLCQffYCNDc3a8mSJbrnnnsUHh7u1dfZtjU1NZ1TXc%2Blbt29rpJ0%2BPBhz3asXr1aX3zxhZYvX66mpiZq20UErG4iLCysww508vmpHyw9WVhYWIcjei0tLZ4anamOUVFRCgsL8zw/tT8iIkJtbW1nfQ86W3d3smrVKj377LN6%2BOGHNXLkSOpqofj4eEnfhIM77rhDWVlZXn/1J1HbrioqKtKYMWO8jryedKa6dVbXiIgIr//hn1rjiIiI8x67u9RVkoYMGaL33ntPffv2lc1m06hRo9Te3q4777xTqamp1LYLOEXYTcTGxqqurk6tra2eNpfLpfDwcEVFRflxZoElNjZWtbW1Xm21tbWeQ8pn6o%2BOjla/fv0UFhbm1d/a2qr6%2BnpFR0d3%2Bh50tu7uYtmyZXr66ae1atUqTZ48WRJ1vVC1tbV6/fXXvdpGjBihr7/%2BWtHR0dT2PP3hD3/Q66%2B/rsTERCUmJmr79u3avn27EhMTL2ifjY2NlSTP6ax//e%2BT/Wd67dnG7i51Palfv36ea50kafjw4Wpubr6gfbYn1ZaA1U2MGjVKdrvd60K%2BsrIyxcfHq1cv3saTHA6H/vrXv3oOQ0vf1MnhcHj6y8rKPH1ut1v79%2B%2BXw%2BFQr169FB8f79VfUVEhu92uSy%2B9tNP3oLN1dwdFRUXauHGjHnroIU2fPt3TTl0vzBdffKHc3FzPdSSStG/fPg0YMEDJycnU9jytX79e27dv15YtW7RlyxZlZGQoIyNDW7ZskcPh0IcffihjjKRvrikqLy8/Y12rq6tVXV0th8Oh2NhYxcXFefWXlZUpLi5OMTExSkhI0Jdfful13U9ZWZkSEhI8Y59t3d3B22%2B/rbFjx3odXf3444/Vr18/z0Xn1LYTfvnbRZyXX//612b69OnG6XSa1157zSQlJZk//vGP/p6W3/3rbRpaW1vNtGnTzO23324OHjxo1q5daxISEjz39amqqjLx8fFm7dq1nnsKXXPNNZ4/%2Bd2xY4dJSkoyr732mnE6nWb69Olm2bJlnnWd7T3obN2B7tChQ2bUqFHm4Ycf9rrvTU1NDXW9QK2trSYzM9PMmzfPVFZWmjfeeMN8//vfN8888wy1tdDixYs9f85//PhxM27cOLNs2TJTWVlpli1bZtLS0jy3pCgvLzejR482zz//vOdeTQsWLPCMtXbtWpOenm7effdd8%2B6775r09HTz1FNPefrnzZtnrr/%2BevPxxx%2Bb559/3sTHx3vu1dTZuruD48ePm/Hjx5tf/vKX5pNPPjFvvPGGSU9PNyUlJdS2iwhY3UhjY6NZtGiRSUhIMOnp6ebpp5/295QCwr8GLGOM%2Bfvf/25%2B%2BtOfmjFjxpjp06ebd955x2v5N954w/zwhz80l19%2Bubnxxhs992Y5ae3atebKK680ycnJJj8/3zQ1NXn6OnsPOlt3IFu7dq0ZOXLkaR/GUNcLdeTIEZOTk2OSkpJMWlqaeeyxxzwhidpa418DljHf3PBy1qxZJj4%2B3syePdv89a9/9Vp%2B8%2BbNZsKECSYhIcHk5OSYY8eOefpaW1vNb37zG5OSkmLGjh1rVq1a5Xm/jDGmtrbWLFiwwMTHx5uMjAyzfft2r7E7W3d3cPDgQXPTTTeZhIQEk5aWZh599FFPDaht52zG/P/jbAAAALAEF%2B8AAABYjIAFAABgMQIWAACAxQhYAAAAFiNgAQAAWIyABQAAYDECFgAAgMUIWAAAABYjYAEAAFiMgAUAAGAxAhYAAIDFCFgAAAAWI2ABAABY7P8BpRHb9U1RB0IAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common3583468008356428427”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>295693.9</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>45938.67</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>10614.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>139136.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>144510.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>47437.66</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41840.27</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>317191.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>203960.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62940.58</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2064)</td> <td class=”number”>2064</td> <td class=”number”>64.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>1136</td> <td class=”number”>35.4%</td> <td>

<div class=”bar” style=”width:55%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme3583468008356428427”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3464.5</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3596.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4620.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6513.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6561.423</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>454142.8</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460623.6</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>460949.3</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>476280.4</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>507452.1</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_REDEMP_WICS12”><s>REDEMP_WICS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_REDEMP_WICS08”><code>REDEMP_WICS08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91221</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SLHOUSE07”>SLHOUSE07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>14</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.60409</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>41</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-9150369757859235399”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-9150369757859235399,#minihistogram-9150369757859235399”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-9150369757859235399”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-9150369757859235399”

aria-controls=”quantiles-9150369757859235399” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-9150369757859235399” aria-controls=”histogram-9150369757859235399”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-9150369757859235399” aria-controls=”common-9150369757859235399”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-9150369757859235399” aria-controls=”extreme-9150369757859235399”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-9150369757859235399”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>41</td>

</tr> <tr>

<th>Range</th> <td>41</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.3085</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.1661</td>

</tr> <tr>

<th>Kurtosis</th> <td>325.81</td>

</tr> <tr>

<th>Mean</th> <td>0.60409</td>

</tr> <tr>

<th>MAD</th> <td>0.7634</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>12.497</td>

</tr> <tr>

<th>Sum</th> <td>1892</td>

</tr> <tr>

<th>Variance</th> <td>1.7123</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-9150369757859235399”>

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XX3xRgwYNUnJysubOnSun0%2Bmz/QIAANZnyYDldruVlZUlp9OprVu3asWKFfrTn/6klStXyu12KyMjQ%2B3bt1dhYaFGjx6tzMxMnTp1SpJ06tQpZWRkaOzYsdq%2Bfbuio6N13333ye12S5J27dqlvLw8ZWdna%2BPGjSopKVFubq4/dxcAAFiMJQPWiRMnVFxcrJycHHXr1k2pqanKysrSzp07tX//fpWXlys7O1tdu3bVjBkzlJSUpMLCQknStm3bdP3112vq1Knq1q2bcnJy9MUXX%2BjAgQOSpE2bNumuu%2B7S4MGD1adPHy1cuFCFhYUcxQIAAM1myYAVGxur9evXq3379l7j586dU0lJiXr27Knw8HDPeEpKioqLiyVJJSUlSk1N9cyFhYWpV69eKi4uVkNDgz788EOv%2BaSkJF24cEFHjx69zHsFAABaC7u/C/glIiIiNGjQIM/zxsZGbdmyRf3795fD4VBcXJzX62NiYnTmzBlJ%2BtH5mpoa1dXVec3b7XZFRkZ6lm%2BOiooKORwOrzG7PbzJdn%2Bt4GBr5WO73Vr1mnKxT1brVyCiV9ZAn6wjkHtlyYB1qdzcXB05ckTbt2/Xiy%2B%2BqJCQEK/5kJAQuVwuSZLT6fzB%2BdraWs/zH1q%2BOQoKCpSXl%2Bc1lpGRoaysrGavozWKimrr7xL8KiIizN8loJnolTXQJ%2BsIxF5ZPmDl5uZq48aNWrFiha699lqFhoaqurra6zUul0tt2rSRJIWGhjYJSy6XSxEREQoNDfU8v3Q%2BLKz5Pxzp6ekaMmSI15jdHq6qqm%2BbvY7msNonAtP7bxXBwUGKiAhTTY1TDQ2N/i4HP4JeWQN9so6W0Ct/fbi3dMBatGiRXnrpJeXm5mr48OGSpPj4eB07dszrdZWVlZ7Tc/Hx8aqsrGwy36NHD0VGRio0NFSVlZXq2rWrJKm%2Bvl7V1dWKjY1tdl1xcXFNTgc6HN%2Bovj6wfxEE%2Bv43NDQG/HtgFfTKGuiTdQRir6x1COSf5OXl6eWXX9ZTTz2lkSNHesYTExP10UcfeU73SVJRUZESExM980VFRZ45p9OpI0eOKDExUUFBQerdu7fXfHFxsex2u7p37%2B6DvQIAAK2BzwPW7bffrpdfflnffPPNL17H8ePHtXbtWt1zzz1KSUmRw%2BHwPNLS0tShQwfNmTNHZWVlys/PV2lpqcaPHy9JGjdunA4dOqT8/HyVlZVpzpw56tSpk/r16ydJmjhxojZs2KDdu3ertLRUCxYs0B133PGzThECAIDA5vOA1b9/fz3zzDMaOHCgHnzwQe3du9dzk8/meuedd9TQ0KB169Zp4MCBXo/g4GCtXbtWDodDY8eO1euvv641a9YoISFBktSpUyc9/fTTKiws1Pjx41VdXa01a9bIZrNJkkaOHKkZM2Zo/vz5mjp1qvr06aPZs2cbfx8AAEDrZXP/3HRjgNvt1vvvv68dO3Zo9%2B7dioiI0JgxYzRmzBj97ne/83U5PuFw/PIjdj/Ebg/SsGXvGV/v5fLW/QP8XYJf2O1Biopqq6qqbwPuGgSroVfWQJ%2BsoyX0Kjb2Cr9s1y8XudtsNg0YMEADBgyQ0%2BnU5s2btXbtWuXn56tv376666679G//9m/%2BKA0AAOBX89tfEVZUVOj111/X66%2B/rk8//VR9%2B/bVbbfdpjNnzujRRx/VwYMHNW/ePH%2BVBwAA8Iv5PGC99tpreu211/TBBx8oOjpaY8aM0erVq9W5c2fPazp06KAlS5YQsAAAgCX5PGDNmzdPgwcP1po1a3TTTTcpKKjpdfZdunTRpEmTfF0aAACAET4PWO%2B%2B%2B66ioqJUXV3tCVelpaXq1auXgoODJUl9%2B/ZV3759fV0aAACAET6/TcO5c%2Bc0YsQIPffcc56x6dOna/To0Tp9%2BrSvywEAADDO5wHrv//7v3X11Vfr7rvv9oy9%2Beab6tChg3JycnxdDgAAgHE%2BD1h//etf9cgjj3h9t190dLQeeugh7d%2B/39flAAAAGOfzgGW321VTU9Nk3Ol0/uw7ugMAALREPg9YN910kxYvXqx//OMfnrHy8nLl5ORo0KBBvi4HAADAOJ//FeHDDz%2Bsu%2B%2B%2BW8OHD1dERIQkqaamRr169dKcOXN8XQ4AAIBxPg9YMTExevXVV/X%2B%2B%2B%2BrrKxMdrtd11xzjW688UbPFy4DAABYmV%2B%2BKic4OFiDBg3ilCAAAGiVfB6wHA6HVq5cqUOHDunChQtNLmx/5513fF0SAACAUT4PWI899pgOHz6skSNH6oorrvD15gEAAC47nwes/fv3a/369UpNTfX1pgEAAHzC57dpCA8PV0xMjK83CwAA4DM%2BD1ijR4/W%2BvXr1dDQ4OtNAwAA%2BITPTxFWV1dr586d%2BvOf/6yrrrpKISEhXvObNm3ydUkAAABG%2BeU2DaNGjfLHZgEAAHzC5wErJyfH15sEAADwKZ9fgyVJFRUVysvL08yZM/Xll1/q7bff1okTJ/xRCgAAgHE%2BD1iff/65/v3f/12vvvqqdu3apfPnz%2BvNN9/UuHHjVFJS4utyAAAAjPN5wHriiSc0dOhQ7d69W7/5zW8kSU899ZSGDBmiZcuW%2BbocAAAA43wesA4dOqS7777b64ud7Xa77rvvPh05csTX5QAAABjn84DV2NioxsbGJuPffvutgoODfV0OAACAcT4PWAMHDtSzzz7rFbKqq6uVm5ur/v37%2B7ocAAAA43wesB555BEdPnxYAwcOVF1dnf7rv/5LgwcP1smTJ/Xwww/7uhwAAADjfH4frPj4eO3YsUM7d%2B7Uxx9/rMbGRk2YMEGjR49Wu3btfF0OAACAcX65k3tYWJhuv/12f2waAADgsvN5wJo8efKPzvNdhAAAwOp8HrA6duzo9by%2Bvl6ff/65Pv30U911112%2BLgcAAMC4FvNdhGvWrNGZM2d8XA0AAIB5fvkuwu8zevRovfXWW/4uAwAA4FdrMQHrb3/7GzcaBQAArUKLuMj93Llz%2BuSTTzRx4kRflwMAAGCczwNWQkKC1/cQStJvfvMbTZo0Sf/xH//h63IAAACM83nAeuKJJ4yuz%2BVyaezYsXrsscfUr18/SdLixYu1efNmr9c99thjmjRpkiRp586dWrlypRwOhwYOHKhFixYpOjpakuR2u7V8%2BXJt375djY2NGj9%2BvGbNmqWgoBZzNhUAALRwPg9YBw8ebPZrb7jhhh%2Bdr6ur08yZM1VWVuY1fvz4cc2cOVO33XabZ%2BziXeJLS0s1b948LVy4UN27d9eSJUs0Z84cPfvss5KkF154QTt37lReXp7q6%2Bs1e/ZsxcTEaNq0ac2uGwAABDafB6w777zTc4rQ7XZ7xi8ds9ls%2Bvjjj39wPceOHdPMmTO91nHR8ePHNW3aNMXGxjaZ27Jli2655RaNGTNGkrR06VINHjxY5eXluuqqq7Rp0yZlZWUpNTVVkjRr1iytWrWKgAUAAJrN5%2Be9nnnmGXXs2FErV67Uvn37VFRUpBdffFG/%2B93v9OCDD%2Bqdd97RO%2B%2B8o927d//oeg4cOKB%2B/fqpoKDAa/zcuXM6e/asOnfu/L3LlZSUeMKTJHXo0EEJCQkqKSnR2bNndfr0aa8jZykpKfriiy9UUVHxy3caAAAEFL/caHT%2B/Pm66aabPGP9%2B/dXdna2HnroId1zzz3NWs8P/cXh8ePHZbPZ9Mwzz%2Bjdd99VZGSk7r77bs/pwoqKCsXFxXktExMTozNnzsjhcEiS13z79u0lSWfOnGmy3A%2BpqKjwrOsiuz282cs3V3Cwta4Ls9utVa8pF/tktX4FInplDfTJOgK5Vz4PWBUVFU2%2BLkf67hqpqqqqX73%2BEydOyGazqUuXLpo0aZIOHjyoxx57TO3atdOwYcNUW1urkJAQr2VCQkLkcrlUW1vref7Pc9J3F9M3V0FBgfLy8rzGMjIylJWV9Ut3q1WIimrr7xL8KiIizN8loJnolTXQJ%2BsIxF75PGAlJSXpqaee0pNPPum58Ly6ulq5ubm68cYbf/X6x4wZo8GDBysyMlKS1L17d3322Wd66aWXNGzYMIWGhjYJSy6XS2FhYV5hKjQ01PNvSQoLa/4PR3p6uoYMGeI1ZreHq6rq21%2B8X9/Hap8ITO%2B/VQQHBykiIkw1NU41NDT6uxz8CHplDfTJOlpCr/z14d7nAevRRx/V5MmTddNNN6lz585yu9367LPPFBsbq02bNv3q9dtsNk%2B4uqhLly7av3%2B/JCk%2BPl6VlZVe85WVlYqNjVV8fLwkyeFwqFOnTp5/S/reC%2BZ/SFxcXJPTgQ7HN6qvD%2BxfBIG%2B/w0NjQH/HlgFvbIG%2BmQdgdgrnx8C6dq1q958803NnDlTSUlJSk5O1rx58/Taa6/pt7/97a9e/6pVqzRlyhSvsaNHj6pLly6SpMTERBUVFXnmTp8%2BrdOnTysxMVHx8fFKSEjwmi8qKlJCQoLx66cAAEDr5fMjWJJ05ZVX6vbbb9fJkyd11VVXSfrubu4mDB48WPn5%2BdqwYYOGDRumvXv3aseOHZ6jYxMmTNCdd96ppKQk9e7dW0uWLNHNN9/sqWPChAlatmyZJ%2BwtX75cU6dONVIbAAAIDD4PWBfvlL5582ZduHBBu3bt0ooVKxQWFqYFCxb86qDVp08frVq1SqtXr9aqVavUsWNHLV%2B%2BXMnJyZKk5ORkZWdna/Xq1fr66681YMAALVq0yLP8tGnT9OWXXyozM1PBwcEaP358kyNiAAAAP8bm/r47dV5GmzZt0nPPPacHHnhA2dnZeuONN/Thhx9q4cKF%2Bs///E898MADvizHZxyOb4yv024P0rBl7xlf7%2BXy1v0D/F2CX9jtQYqKaquqqm8D7hoEq6FX1kCfrKMl9Co29gq/bNfn12AVFBRo/vz5Gjt2rOfu7bfeeqsWL16sN954w9flAAAAGOfzgHXy5En16NGjyXj37t2b3JwTAADAinwesDp27KgPP/ywyfi7777rudAcAADAynx%2Bkfu0adO0cOFCORwOud1u7du3TwUFBdq8ebMeeeQRX5cDAABgnM8D1rhx41RfX69169aptrZW8%2BfPV3R0tO6//35NmDDB1%2BUAAAAY5/OAtXPnTo0YMULp6en66quv5Ha7FRMT4%2BsyAAAALhufX4OVnZ3tuZg9OjqacAUAAFodnweszp0769NPP/X1ZgEAAHzG56cIu3fvrlmzZmn9%2BvXq3LmzQkNDveZzcnJ8XRIAAIBRPg9Yf//735WSkiJJ3PcKAAC0Sj4JWEuXLlVmZqbCw8O1efNmX2wSAADAb3xyDdYLL7wgp9PpNTZ9%2BnRVVFT4YvMAAAA%2B5ZOA9X3fJ33w4EHV1dX5YvMAAAA%2B5fO/IgQAAGjtCFgAAACG%2BSxg2Ww2X20KAADAr3x2m4bFixd73fPqwoULys3NVdu2bb1ex32wAACA1fkkYN1www1N7nmVnJysqqoqVVVV%2BaIEAAAAn/FJwOLeVwAAIJBwkTsAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMsH7BcLpdGjRqlDz74wDNWXl6uKVOmKCkpSbfeeqv27t3rtcz777%2BvUaNGKTExUZMnT1Z5ebnX/IsvvqhBgwYpOTlZc%2BfOldPp9Mm%2BAACA1sHSAauurk4PPvigysrKPGNut1sZGRlq3769CgsLNXr0aGVmZurUqVOSpFOnTikjI0Njx47V9u3bFR0drfvuu09ut1uStGvXLuXl5Sk7O1sbN25USUmJcnNz/bJ/AADAmiwbsI4dO6Y77rhD//jHP7zG9%2B/fr/LycmVnZ6tr166aMWOGkpKSVFhYKEnatm2brr/%2Bek2dOlXdunVTTk6OvvjiCx04cECStGnTJt11110aPHiw%2BvTpo4ULF6qwsJCjWAAAoNksG7AOHDigfv36qaCgwGu8pKREPXv2VHh4uGcsJSVFxcXFnvnU1FTPXFhYmHr16qXi4mI1NDToww8/9JpPSkrShQsXdPTo0cu8RwAAoLWw%2B7uAX2rixInfO%2B5wOBQXF%2Bc1FhMTozNnzvzkfE1Njerq6rzm7Xa7IiMjPcsDAAD8FMsGrB/idDoVEhLiNRYSEiKXy/WT87W1tZ7nP7R8c1RUVMjhcHiN2e3hTYLdrxUcbK0DkHa7teo15WKfrNavQESvrIE%2BWUcg96rVBazQ0FBVV1d7jblcLrVp08Yzf2lYcrlcioiIUGhoqOe8MZWcAAAQGUlEQVT5pfNhYWHNrqGgoEB5eXleYxkZGcrKymr2OlqjqKi2/i7BryIimv8zBP%2BiV9ZAn6wjEHvV6gJWfHy8jh075jVWWVnpOXoUHx%2BvysrKJvM9evRQZGSkQkNDVVlZqa5du0qS6uvrVV1drdjY2GbXkJ6eriFDhniN2e3hqqr69pfs0g%2By2icC0/tvFcHBQYqICFNNjVMNDY3%2BLgc/gl5ZA32yjpbQK399uG91ASsxMVH5%2Bfmqra31HLUqKipSSkqKZ76oqMjzeqfTqSNHjigzM1NBQUHq3bu3ioqK1K9fP0lScXGx7Ha7unfv3uwa4uLimpwOdDi%2BUX19YP8iCPT9b2hoDPj3wCrolTXQJ%2BsIxF5Z6xBIM6SlpalDhw6aM2eOysrKlJ%2Bfr9LSUo0fP16SNG7cOB06dEj5%2BfkqKyvTnDlz1KlTJ0%2BgmjhxojZs2KDdu3ertLRUCxYs0B133PGzThECAIDA1uoCVnBwsNauXSuHw6GxY8fq9ddf15o1a5SQkCBJ6tSpk55%2B%2BmkVFhZq/Pjxqq6u1po1a2Sz2SRJI0eO1IwZMzR//nxNnTpVffr00ezZs/25SwAAwGJs7ou3MMdl5XB8Y3yddnuQhi17z/h6L5e37h/g7xL8wm4PUlRUW1VVfRtwh8ithl5ZA32yjpbQq9jYK/yy3VZ3BAsAAMDfCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMKzVBqz//d//1XXXXef1yMrKkiQdOXJEt99%2BuxITEzVu3DgdPnzYa9mdO3dq6NChSkxMVEZGhr766it/7AIAALCoVhuwjh07psGDB2vv3r2ex%2BLFi3X%2B/HlNnz5dqampeuWVV5ScnKwZM2bo/PnzkqTS0lLNmzdPmZmZKigoUE1NjebMmePnvQEAAFbSagPW8ePHde211yo2NtbziIiI0JtvvqnQ0FA99NBD6tq1q%2BbNm6e2bdvq7bffliRt2bJFt9xyi8aMGaPu3btr6dKl2rNnj8rLy/28RwAAwCpadcDq3Llzk/GSkhKlpKTIZrNJkmw2m/r27avi4mLPfGpqquf1HTp0UEJCgkpKSnxSNwAAsL5WGbDcbrf%2B/ve/a%2B/evRo%2BfLiGDh2qZcuWyeVyyeFwKC4uzuv1MTExOnPmjCSpoqLiR%2BcBAAB%2Bit3fBVwOp06dktPpVEhIiFauXKmTJ09q8eLFqq2t9Yz/s5CQELlcLklSbW3tj843R0VFhRwOh9eY3R7eJLj9WsHB1srHdru16jXlYp%2Bs1q9ARK%2BsgT5ZRyD3qlUGrI4dO%2BqDDz7QlVdeKZvNph49eqixsVGzZ89WWlpak7DkcrnUpk0bSVJoaOj3zoeFhTV7%2BwUFBcrLy/May8jI8PwVY6CKimrr7xL8KiKi%2BT9D8C96ZQ30yToCsVetMmBJUmRkpNfzrl27qq6uTrGxsaqsrPSaq6ys9Bxdio%2BP/9752NjYZm87PT1dQ4YM8Rqz28NVVfXtz9mFn2S1TwSm998qgoODFBERppoapxoaGv1dDn4EvbIG%2BmQdLaFX/vpw3yoD1nvvvadZs2bpz3/%2Bs%2BfI08cff6zIyEilpKToueeek9vtls1mk9vt1qFDh3TvvfdKkhITE1VUVKSxY8dKkk6fPq3Tp08rMTGx2duPi4trcjrQ4fhG9fWB/Ysg0Pe/oaEx4N8Dq6BX1kCfrCMQe2WtQyDNlJycrNDQUD366KM6ceKE9uzZo6VLl%2Br3v/%2B9RowYoZqaGi1ZskTHjh3TkiVL5HQ6dcstt0iSJkyYoNdee03btm3T0aNH9dBDD%2Bnmm2/WVVdd5ee9AgAAVtEqA1a7du20YcMGffXVVxo3bpzmzZun9PR0/f73v1e7du307LPPeo5SlZSUKD8/X%2BHh4ZK%2BC2fZ2dlas2aNJkyYoCuvvFI5OTl%2B3iMAAGAlNrfb7fZ3EYHA4fjG%2BDrt9iANW/ae8fVeLm/dP8DfJfiF3R6kqKi2qqr6NuAOkVsNvbIG%2BmQdLaFXsbFX%2BGW7rfIIFgAAgD8RsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMALW96irq9PcuXOVmpqqgQMH6vnnn/d3SQAAwELs/i6gJVq6dKkOHz6sjRs36tSpU3r44YeVkJCgESNG%2BLs0AABgAQSsS5w/f17btm3Tc889p169eqlXr14qKyvT1q1bCVi/0i0r/%2BLvEn6Wt%2B4f4O8SAAAWxSnCSxw9elT19fVKTk72jKWkpKikpESNjY1%2BrAwAAFgFR7Au4XA4FBUVpZCQEM9Y%2B/btVVdXp%2BrqakVHR//kOioqKuRwOLzG7PZwxcXFGa01OJh8fDnZ7Wbe34t9ol8tH72yBvpkHYHcKwLWJZxOp1e4kuR57nK5mrWOgoIC5eXleY1lZmbqD3/4g5ki/09FRYXu%2Bm2Z0tPTjYc3mFNRUaGNG9fTJwugV9ZAn6wjkHsVeJHyJ4SGhjYJUheft2nTplnrSE9P1yuvvOL1SE9PN16rw%2BFQXl5ek6NlaFnok3XQK2ugT9YRyL3iCNYl4uPjVVVVpfr6etnt3709DodDbdq0UURERLPWERcXF3BJHQAA/H8cwbpEjx49ZLfbVVxc7BkrKipS7969FRTE2wUAAH4aieESYWFhGjNmjBYsWKDS0lLt3r1bzz//vCZPnuzv0gAAgEUEL1iwYIG/i2hp%2BvfvryNHjmj58uXat2%2Bf7r33Xo0bN87fZX2vtm3bKi0tTW3btvV3KfgR9Mk66JU10CfrCNRe2dxut9vfRQAAALQmnCIEAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAsqi6ujrNnTtXqampGjhwoJ5//nl/l4R/4nK5NGrUKH3wwQeesfLyck2ZMkVJSUm69dZbtXfvXj9WGNjOnj2rrKwspaWladCgQcrJyVFdXZ0k%2BtTSfP7555o2bZqSk5N18803a/369Z45etUyTZ8%2BXY888ojn%2BZEjR3T77bcrMTFR48aN0%2BHDh/1Yne8QsCxq6dKlOnz4sDZu3KjHH39ceXl5evvtt/1dFvRd%2BH3wwQdVVlbmGXO73crIyFD79u1VWFio0aNHKzMzU6dOnfJjpYHJ7XYrKytLTqdTW7du1YoVK/SnP/1JK1eupE8tTGNjo6ZPn66oqCi9%2BuqrWrhwodatW6c33niDXrVQf/zjH7Vnzx7P8/Pnz2v69OlKTU3VK6%2B8ouTkZM2YMUPnz5/3Y5W%2BYfd3Afj5zp8/r23btum5555Tr1691KtXL5WVlWnr1q0aMWKEv8sLaMeOHdPMmTN16TdQ7d%2B/X%2BXl5Xr55ZcVHh6url27at%2B%2BfSosLNQf/vAHP1UbmE6cOKHi4mL95S9/Ufv27SVJWVlZevLJJ3XTTTfRpxaksrJSPXr00IIFC9SuXTt17txZN954o4qKitS%2BfXt61cJUV1dr6dKl6t27t2fszTffVGhoqB566CHZbDbNmzdP7777rt5%2B%2B22NHTvWj9VefhzBsqCjR4%2Bqvr5eycnJnrGUlBSVlJSosbHRj5XhwIED6tevnwoKCrzGS0pK1LNnT4WHh3vGUlJSVFxc7OsSA15sbKzWr1/vCVcXnTt3jj61MHFxcVq5cqXatWsnt9utoqIiHTx4UGlpafSqBXryySc1evRoXXPNNZ6xkpISpaSkyGazSZJsNpv69u0bEH0iYFmQw%2BFQVFSUQkJCPGPt27dXXV2dqqur/VgZJk6cqLlz5yosLMxr3OFwKC4uzmssJiZGZ86c8WV5kBQREaFBgwZ5njc2NmrLli3q378/fWrBhgwZookTJyo5OVnDhw%2BnVy3Mvn379Ne//lX33Xef13gg94mAZUFOp9MrXEnyPHe5XP4oCT/hh3pGv/wvNzdXR44c0QMPPECfWrDVq1frmWee0ccff6ycnBx61YLU1dXp8ccf1/z589WmTRuvuUDuE9dgWVBoaGiTH86Lzy/94UbLEBoa2uToosvlol9%2Blpubq40bN2rFihW69tpr6VMLdvG6nrq6Os2aNUvjxo2T0%2Bn0eg298o%2B8vDxdf/31XkeGL/qh/68CoU8ELAuKj49XVVWV6uvrZbd/10KHw6E2bdooIiLCz9Xh%2B8THx%2BvYsWNeY5WVlU0OncN3Fi1apJdeekm5ubkaPny4JPrU0lRWVqq4uFhDhw71jF1zzTW6cOGCYmNjdeLEiSavp1e%2B98c//lGVlZWe64IvBqpdu3Zp1KhRqqys9Hp9oPSJU4QW1KNHD9ntdq%2BLBIuKitS7d28FBdHSligxMVEfffSRamtrPWNFRUVKTEz0Y1WBKy8vTy%2B//LKeeuopjRw50jNOn1qWkydPKjMzU2fPnvWMHT58WNHR0UpJSaFXLcTmzZv1xhtvaMeOHdqxY4eGDBmiIUOGaMeOHUpMTNTf/vY3z19Wu91uHTp0KCD6xP/GFhQWFqYxY8ZowYIFKi0t1e7du/X8889r8uTJ/i4NPyAtLU0dOnTQnDlzVFZWpvz8fJWWlmr8%2BPH%2BLi3gHD9%2BXGvXrtU999yjlJQUORwOz4M%2BtSy9e/dWr169NHfuXB07dkx79uxRbm6u7r33XnrVgnTs2FFXX32159G2bVu1bdtWV199tUaMGKGamhotWbJEx44d05IlS%2BR0OnXLLbf4u%2BzLzua%2B9IY9sASn06kFCxbof/7nf9SuXTtNmzZNU6ZM8XdZ%2BCfXXXedNm3apH79%2Bkn67o7U8%2BbNU0lJia6%2B%2BmrNnTtX//Iv/%2BLnKgNPfn6%2Bli9f/r1zn3zyCX1qYc6ePatFixZp3759CgsL06RJkzRjxgzZbDZ61UJdvIv7E088IUkqLS3V448/ruPHj%2Bu6667TwoUL1bNnT3%2BW6BMELAAAAMM4RQgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/NVYEGRN4mskAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-9150369757859235399”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1979</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>756</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>242</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3)</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-9150369757859235399”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1979</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>756</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>242</td> <td class=”number”>7.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>88</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SLHOUSE12”>SLHOUSE12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>12</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.56833</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>23</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>63.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6676804144064280890”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives6676804144064280890”>

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<li role=”presentation” class=”active”><a href=”#quantiles6676804144064280890”

aria-controls=”quantiles6676804144064280890” role=”tab” data-toggle=”tab”>Statistics</a></li>

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role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6676804144064280890” aria-controls=”common6676804144064280890”

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<li role=”presentation”><a href=”#extreme6676804144064280890” aria-controls=”extreme6676804144064280890”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles6676804144064280890”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>2</td>

</tr> <tr>

<th>Maximum</th> <td>23</td>

</tr> <tr>

<th>Range</th> <td>23</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>1.1286</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9859</td>

</tr> <tr>

<th>Kurtosis</th> <td>106.12</td>

</tr> <tr>

<th>Mean</th> <td>0.56833</td>

</tr> <tr>

<th>MAD</th> <td>0.73418</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8455</td>

</tr> <tr>

<th>Sum</th> <td>1780</td>

</tr> <tr>

<th>Variance</th> <td>1.2738</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram6676804144064280890”>

<img 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ZmZnq2rWrCgsLNXbsWM2aNUsnT56UJJ08eVKZmZkaP368du7cqejoaN1zzz3yeDySpNdff115eXlasmSJtmzZopKSEuXm5lq5XAAAYDO2DFjHjh1TcXGxcnJy1KdPH6WmpiorK0u7du3Svn37VFFRoSVLlqh3796aOXOmkpKSVFhYKEnasWOHrrzySk2fPl19%2BvRRTk6OTpw4of3790uStm7dqjvuuEPp6ekaNGiQFi9erMLCQo5iAQCAdrNlwIqJidHGjRvVtWtXn/EzZ86opKRE/fv3V1hYmHc8JSVFxcXFkqSSkhKlpqZ650JDQzVgwAAVFxerpaVF77//vs98UlKSzp49qyNHjlzgVQEAgEDhtLqAXyIiIkJpaWnex62trdq2bZuGDh0qt9ut2NhYn%2Bd36dJFlZWVkvST83V1dWpsbPSZdzqdioyM9L6%2BPaqqquR2u33GnM6wNvs9X8HB9srHTqe96v0p53pvt%2B9BIKD31qDv1qH39mTLgPV9ubm5Onz4sHbu3KnNmzcrJCTEZz4kJERNTU2SpPr6%2Bh%2Bdb2ho8D7%2Bsde3R0FBgfLy8nzGMjMzlZWV1e5tBKKoqM5Wl2BcRESo1SV0WPTeGvTdOvTeXmwfsHJzc7VlyxY99thj6tu3r1wul2pra32e09TUpE6dOkmSXC5Xm7DU1NSkiIgIuVwu7%2BPvz4eGtv%2BNnZGRoREjRviMOZ1hqqn5pt3baA%2B7/TZjev1WCg4OUkREqOrq6tXS0mp1OR0KvbcGfbcOvT8/Vv1yb%2BuAtXTpUj333HPKzc3VDTfcIEmKi4vT0aNHfZ5XXV3tPT0XFxen6urqNvP9%2BvVTZGSkXC6Xqqur1bt3b0lSc3OzamtrFRMT0%2B66YmNj25wOdLu/VnNzx/7BCMT1t7S0BuS67IDeW4O%2BW4fe24u9DoH8nby8PD3//PN69NFHNXr0aO94YmKiPvjgA%2B/pPkkqKipSYmKid76oqMg7V19fr8OHDysxMVFBQUEaOHCgz3xxcbGcTqcSEhL8sCoAABAIbBmwysvLtX79et11111KSUmR2%2B32fg0ePFjdunVTdna2ysrKlJ%2Bfr9LSUk2cOFGSNGHCBB08eFD5%2BfkqKytTdna2evTooSFDhkiSbrvtNm3atEm7d%2B9WaWmpFi1apFtvvfVnnSIEAAAdmy1PEb755ptqaWnRhg0btGHDBp%2B5jz76SOvXr9f8%2BfM1fvx4XXbZZVq3bp3i4%2BMlST169NDjjz%2BuRx55ROvWrVNycrLWrVsnh8MhSRo9erROnDihhQsXqqmpSddff73mzJnj9zUCAAD7cnjO3cIcF5Tb/bXxbTqdQRq58m3j271QXrtvmNUlGON0BikqqrNqar7hmgg/o/fWoO/WoffnJybmYkv2a8tThAAAAL9mBCwAAADDCFgAAACGEbAAAAAM83vAuuWWW/T888/r66/NX/QNAADwa%2BD3gDV06FA98cQTGj58uO6//37t3btX/CEjAAAIJH4PWLNnz9Zf/vIXrV%2B/XsHBwbr33nt17bXX6rHHHtMnn3zi73IAAACMs%2BRGow6HQ8OGDdOwYcNUX1%2BvZ599VuvXr1d%2Bfr6uuuoq3XHHHbr%2B%2BuutKA0AAOC8WXYn96qqKr388st6%2BeWX9fHHH%2Buqq67SzTffrMrKSj388MM6cOCA5s%2Bfb1V5AAAAv5jfA9ZLL72kl156Se%2B9956io6M1btw4rV27Vj179vQ%2Bp1u3blq%2BfDkBCwAA2JLfA9b8%2BfOVnp6udevW6ZprrlFQUNvLwHr16qXJkyf7uzQAAAAj/B6w3nrrLUVFRam2ttYbrkpLSzVgwAAFBwdLkq666ipdddVV/i4NAADACL//FeGZM2c0atQoPfXUU96xu%2B%2B%2BW2PHjtWpU6f8XQ4AAIBxfg9YjzzyiC677DJNmzbNO/bqq6%2BqW7duysnJ8Xc5AAAAxvk9YP3f//2fHnroIcXExHjHoqOj9eCDD2rfvn3%2BLgcAAMA4vwcsp9Opurq6NuP19fXc0R0AAAQEvwesa665RsuWLdPnn3/uHauoqFBOTo7S0tL8XQ4AAIBxfv8rwrlz52ratGm64YYbFBERIUmqq6vTgAEDlJ2d7e9yAAAAjPN7wOrSpYteeOEFvfPOOyorK5PT6dTll1%2Bu3/72t3I4HP4uBwAAwDhLPionODhYaWlpnBIEAAABye8By%2B12a/Xq1Tp48KDOnj3b5sL2N998098lAQAAGOX3gLVgwQIdOnRIo0eP1sUXX%2Bzv3QMAAFxwfg9Y%2B/bt08aNG5WamurvXQMAAPiF32/TEBYWpi5duvh7twAAAH7j94A1duxYbdy4US0tLf7eNQAAgF/4/RRhbW2tdu3apf/93//VpZdeqpCQEJ/5rVu3%2BrskAAAAoyy5TcOYMWOs2C0AAIBf%2BD1g5eTk%2BHuXAAAAfuX3a7AkqaqqSnl5eZo9e7a%2B%2BOIL/fnPf9axY8esKAUAAMA4vweszz77TP/6r/%2BqF154Qa%2B//rq%2B/fZbvfrqq5owYYJKSkr8XQ4AAIBxfg9Yf/jDH3Tddddp9%2B7duuiiiyRJjz76qEaMGKGVK1f6uxwAAADj/B6wDh48qGnTpvl8sLPT6dQ999yjw4cP%2B7scAAAA4/wesFpbW9Xa2tpm/JtvvlFwcLC/ywEAADDO7wFr%2BPDhevLJJ31CVm1trXJzczV06FB/lwMAAGCc3wPWQw89pEOHDmn48OFqbGzUf/7nfyo9PV3Hjx/X3Llz/V0OAACAcX6/D1ZcXJxefPFF7dq1Sx9%2B%2BKFaW1s1adIkjR07VuHh4f4uBwAAwDhL7uQeGhqqW265xYpdAwAAXHB%2BD1hTpkz5yXk%2BixAAANid3wNW9%2B7dfR43Nzfrs88%2B08cff6w77rjD3%2BUAAAAY96v5LMJ169apsrLyZ2%2BvqalJ48eP14IFCzRkyBBJ0rJly/Tss8/6PG/BggWaPHmyJGnXrl1avXq13G63hg8frqVLlyo6OlqS5PF4tGrVKu3cuVOtra2aOHGiHnjgAQUFWfKpQgAAwIZ%2BNalh7Nixeu21137WaxobG3X//ferrKzMZ7y8vFyzZ8/W3r17vV8TJkyQJJWWlmr%2B/PmaNWuWCgoKVFdXp%2BzsbO9rn3nmGe3atUt5eXlau3atXnnlFT3zzDPnv0AAANBh/GoC1t/%2B9refdaPRo0eP6tZbb9Xnn3/eZq68vFz9%2B/dXTEyM9ys0NFSStG3bNt14440aN26cEhIStGLFCu3Zs0cVFRWSvrsGLCsrS6mpqRo6dKgeeOABbd%2B%2B3cwiAQBAh/CruMj9zJkz%2Buijj3Tbbbe1ezv79%2B/XkCFD9Pvf/15JSUk%2B2zp9%2BrR69uz5g68rKSnRXXfd5X3crVs3xcfHq6SkRCEhITp16pSuvvpq73xKSopOnDihqqoqxcbGtrs%2BAADQcfk9YMXHx/t8DqEkXXTRRZo8ebL%2B7d/%2Brd3b%2BbEwVl5eLofDoSeeeEJvvfWWIiMjNW3aNN18882S9INBqUuXLqqsrJTb7ZYkn/muXbtKkiorK9sdsKqqqrzbOsfpDDMe0IKDfzUHINvF6bRXvT/lXO/t9j0IBPTeGvTdOvTenvwesP7whz9c0O0fO3ZMDodDvXr10uTJk3XgwAEtWLBA4eHhGjlypBoaGhQSEuLzmpCQEDU1NamhocH7%2BO/npO8upm%2BvgoIC5eXl%2BYxlZmYqKyvrly4rIERFdba6BOMiIkKtLqHDovfWoO/Woff24veAdeDAgXY/9%2B9P1bXXuHHjlJ6ersjISElSQkKCPv30Uz333HMaOXKkXC5Xm7DU1NSk0NBQnzDlcrm8/5bkvYarPTIyMjRixAifMaczTDU13/zs9fwUu/02Y3r9VgoODlJERKjq6urV0tL2w8tx4dB7a9B369D782PVL/d%2BD1i333679xShx%2BPxjn9/zOFw6MMPP/zZ23c4HN5wdU6vXr20b98%2BSd99VE91dbXPfHV1tWJiYhQXFydJcrvd6tGjh/ffkhQTE9PuGmJjY9ucDnS7v1Zzc8f%2BwQjE9be0tAbkuuyA3luDvluH3tuL3w%2BBPPHEE%2BrevbtWr16td999V0VFRdq8ebP%2B6Z/%2BSffff7/efPNNvfnmm9q9e/cv2v6aNWs0depUn7EjR46oV69ekqTExEQVFRV5506dOqVTp04pMTFRcXFxio%2BP95kvKipSfHw8F7gDAIB2s%2BRGowsXLtQ111zjHRs6dKiWLFmiBx980Ocv/H6J9PR05efna9OmTRo5cqT27t2rF1980fsRPJMmTdLtt9%2BupKQkDRw4UMuXL9e1116rSy%2B91Du/cuVK/eY3v5EkrVq1StOnTz%2BvmgAAQMfi94BVVVXV5uNyJCk8PFw1NTXnvf1BgwZpzZo1Wrt2rdasWaPu3btr1apVSk5OliQlJydryZIlWrt2rb766isNGzZMS5cu9b5%2BxowZ%2BuKLLzRr1iwFBwdr4sSJbY6IAQAA/BSH5%2B8vhPKDadOmKSwsTH/84x8VHh4uSaqtrdXs2bPlcrm0fv16f5bjN27318a36XQGaeTKt41v90J57b5hVpdgjNMZpKiozqqp%2BYZrIvyM3luDvluH3p%2BfmJiLLdmv349gPfzww5oyZYquueYa9ezZUx6PR59%2B%2BqliYmK8p/EAAADszO8Bq3fv3nr11Ve1a9culZeXS5L%2B4z/%2BQ6NHj/5Zt0IAAAD4tfJ7wJKkSy65RLfccouOHz/uvbj8oosusqIUAAAA4/x%2BmwaPx6OVK1fq6quv1pgxY1RZWam5c%2Bdq/vz5Onv2rL/LAQAAMM7vAevZZ5/VSy%2B9pP/6r//y3jn9uuuu0%2B7du9t8vAwAAIAd%2BT1gFRQUaOHChRo/frz37u033XSTli1bpldeecXf5QAAABjn94B1/Phx9evXr814QkKC92NpAAAA7MzvAat79%2B56//3324y/9dZb3gveAQAA7Mzvf0U4Y8YMLV68WG63Wx6PR%2B%2B%2B%2B64KCgr07LPP6qGHHvJ3OQAAAMb5PWBNmDBBzc3N2rBhgxoaGrRw4UJFR0frvvvu06RJk/xdDgAAgHF%2BD1i7du3SqFGjlJGRoS%2B//FIej0ddunTxdxkAAAAXjN%2BvwVqyZIn3Yvbo6GjCFQAACDh%2BD1g9e/bUxx9/7O/dAgAA%2BI3fTxEmJCTogQce0MaNG9WzZ0%2B5XC6f%2BZycHH%2BXBAAAYJTfA9Ynn3yilJQUSeK%2BVwAAICD5JWCtWLFCs2bNUlhYmJ599ll/7BIAAMAyfrkG65lnnlF9fb3P2N13362qqip/7B4AAMCv/BKwPB5Pm7EDBw6osbHRH7sHAADwK7//FSEAAECgI2ABAAAY5reA5XA4/LUrAAAAS/ntNg3Lli3zuefV2bNnlZubq86dO/s8j/tgAQAAu/NLwLr66qvb3PMqOTlZNTU1qqmp8UcJAAAAfuOXgMW9rwAAQEfCRe4AAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGG2D1hNTU0aM2aM3nvvPe9YRUWFpk6dqqSkJN10003au3evz2veeecdjRkzRomJiZoyZYoqKip85jdv3qy0tDQlJydr3rx5qq%2Bv98taAABAYLB1wGpsbNT999%2BvsrIy75jH41FmZqa6du2qwsJCjR07VrNmzdLJkyclSSdPnlRmZqbGjx%2BvnTt3Kjo6Wvfcc488Ho8k6fXXX1deXp6WLFmiLVu2qKSkRLm5uZasDwAA2JNtA9bRo0d166236vPPP/cZ37dvnyoqKrRkyRL17t1bM2fOVFJSkgoLCyVJO3bs0JVXXqnp06erT58%2BysnJ0YkTJ7R//35J0tatW3XHHXcoPT1dgwYN0uLFi1VYWMhRLAAA0G62DVj79%2B/XkCFDVFBQ4DNeUlKi/v37KywszDuWkpKi4uJi73xqaqp3LjQ0VAMGDFBxcbFaWlr0/vvv%2B8wnJSXp7NmzOnLkyAVeEQAACBROqwv4pW677bYfHHe73YqNjfUZ69KliyorK//hfF1dnRobG33mnU6nIiMjva9vj6qqKrndbp8xpzOszX7PV3CwvfKx02mven/Kud7b7XsQCOi9Nei7dei9Pdk2YP2Y%2Bvp6hYSE%2BIyFhISoqanpH843NDR4H//Y69ujoKBAeXl5PmOZmZnKyspq9zYCUVRUZ6tLMC4iItTqEjosem8N%2Bm4dem8vARewXC6XamtrfcaamprUqVMn7/z3w1JTU5MiIiLkcrm8j78/Hxq08XjGAAAPgklEQVTa/jd2RkaGRowY4TPmdIappuabdm%2BjPez224zp9VspODhIERGhqqurV0tLq9XldCj03hr03Tr0/vxY9ct9wAWsuLg4HT161Gesurrae3ouLi5O1dXVbeb79eunyMhIuVwuVVdXq3fv3pKk5uZm1dbWKiYmpt01xMbGtjkd6HZ/rebmjv2DEYjrb2lpDch12QG9twZ9tw69txd7HQJph8TERH3wwQfe032SVFRUpMTERO98UVGRd66%2Bvl6HDx9WYmKigoKCNHDgQJ/54uJiOZ1OJSQk%2BG8RAADA1gIuYA0ePFjdunVTdna2ysrKlJ%2Bfr9LSUk2cOFGSNGHCBB08eFD5%2BfkqKytTdna2evTooSFDhkj67uL5TZs2affu3SotLdWiRYt06623/qxThAAAoGMLuIAVHBys9evXy%2B12a/z48Xr55Ze1bt06xcfHS5J69Oihxx9/XIWFhZo4caJqa2u1bt06ORwOSdLo0aM1c%2BZMLVy4UNOnT9egQYM0Z84cK5cEAABsxuE5dwtzXFBu99fGt%2Bl0BmnkyreNb/dCee2%2BYVaXYIzTGaSoqM6qqfmGayL8jN5bg75bh96fn5iYiy3Zb8AdwQIAALAaAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGBawAeuNN97QFVdc4fOVlZUlSTp8%2BLBuueUWJSYmasKECTp06JDPa3ft2qXrrrtOiYmJyszM1JdffmnFEgAAgE0FbMA6evSo0tPTtXfvXu/XsmXL9O233%2Bruu%2B9Wamqq/vSnPyk5OVkzZ87Ut99%2BK0kqLS3V/PnzNWvWLBUUFKiurk7Z2dkWrwYAANhJwAas8vJy9e3bVzExMd6viIgIvfrqq3K5XHrwwQfVu3dvzZ8/X507d9af//xnSdK2bdt04403aty4cUpISNCKFSu0Z88eVVRUWLwiAABgFwEdsHr27NlmvKSkRCkpKXI4HJIkh8Ohq666SsXFxd751NRU7/O7deum%2BPh4lZSU%2BKVuAABgf06rC7gQPB6PPvnkE%2B3du1dPPvmkWlpaNGrUKGVlZcntduvyyy/3eX6XLl1UVlYmSaqqqlJsbGyb%2BcrKynbvv6qqSm6322fM6Qxrs93zFRxsr3zsdNqr3p9yrvd2%2Bx4EAnpvDfpuHXpvTwEZsE6ePKn6%2BnqFhIRo9erVOn78uJYtW6aGhgbv%2BN8LCQlRU1OTJKmhoeEn59ujoKBAeXl5PmOZmZnei%2Bw7qqiozlaXYFxERKjVJXRY9N4a9N069N5eAjJgde/eXe%2B9954uueQSORwO9evXT62trZozZ44GDx7cJiw1NTWpU6dOkiSXy/WD86Gh7X9jZ2RkaMSIET5jTmeYamq%2B%2BYUr%2BmF2%2B23G9PqtFBwcpIiIUNXV1aulpdXqcjoUem8N%2Bm4den9%2BrPrlPiADliRFRkb6PO7du7caGxsVExOj6upqn7nq6mrv6bu4uLgfnI%2BJiWn3vmNjY9ucDnS7v1Zzc8f%2BwQjE9be0tAbkuuyA3luDvluH3tuLvQ6BtNPbb7%2BtIUOGqL6%2B3jv24YcfKjIyUikpKfrb3/4mj8cj6bvrtQ4ePKjExERJUmJiooqKiryvO3XqlE6dOuWdBwAA%2BEcCMmAlJyfL5XLp4Ycf1rFjx7Rnzx6tWLFCd955p0aNGqW6ujotX75cR48e1fLly1VfX68bb7xRkjRp0iS99NJL2rFjh44cOaIHH3xQ1157rS699FKLVwUAAOwiIANWeHi4Nm3apC%2B//FITJkzQ/PnzlZGRoTvvvFPh4eF68sknVVRUpPHjx6ukpET5%2BfkKCwuT9F04W7JkidatW6dJkybpkksuUU5OjsUrAgAAduLwnDtXhgvK7f7a%2BDadziCNXPm28e1eKK/dN8zqEoxxOoMUFdVZNTXfcE2En9F7a9B369D78xMTc7El%2Bw3II1gAAABWImABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADHNaXQA6jhtX/9XqEn6W1%2B4bZnUJAACb4ggWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbA%2BgGNjY2aN2%2BeUlNTNXz4cD399NNWlwQAAGyE2zT8gBUrVujQoUPasmWLTp48qblz5yo%2BPl6jRo2yujQAAGADBKzv%2Bfbbb7Vjxw499dRTGjBggAYMGKCysjJt376dgNXBcN8uAMAvxSnC7zly5Iiam5uVnJzsHUtJSVFJSYlaW1strAwAANgFR7C%2Bx%2B12KyoqSiEhId6xrl27qrGxUbW1tYqOjv6H26iqqpLb7fYZczrDFBsba7TW4GDyMf4fOx1xe%2BOBtF/82nPve97//kXfrUPv7YmA9T319fU%2B4UqS93FTU1O7tlFQUKC8vDyfsVmzZunee%2B81U%2BT/r6qqSnf8pkwZGRnGwxt%2BWlVVlQoKCui9BaqqqrRly0Z672f03Tr03p6Iw9/jcrnaBKlzjzt16tSubWRkZOhPf/qTz1dGRobxWt1ut/Ly8tocLcOFR%2B%2BtQ%2B%2BtQd%2BtQ%2B/tiSNY3xMXF6eamho1NzfL6fyuPW63W506dVJERES7thEbG8tvGQAAdGAcwfqefv36yel0qri42DtWVFSkgQMHKiiIdgEAgH%2BMxPA9oaGhGjdunBYtWqTS0lLt3r1bTz/9tKZMmWJ1aQAAwCaCFy1atMjqIn5thg4dqsOHD2vVqlV699139bvf/U4TJkywuqwf1LlzZw0ePFidO3e2upQOh95bh95bg75bh97bj8Pj8XisLgIAACCQcIoQAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBy6YaGxs1b948paamavjw4Xr66aetLqlDeOONN3TFFVf4fGVlZVldVkBramrSmDFj9N5773nHKioqNHXqVCUlJemmm27S3r17LawwcP1Q75ctW9bmZ2Dbtm0WVhlYTp8%2BraysLA0ePFhpaWnKyclRY2OjJN73duO0ugD8MitWrNChQ4e0ZcsWnTx5UnPnzlV8fLxGjRpldWkB7ejRo0pPT9fSpUu9Yy6Xy8KKAltjY6Nmz56tsrIy75jH41FmZqb69u2rwsJC7d69W7NmzdKrr76q%2BPh4C6sNLD/Ue0kqLy/X7NmzdfPNN3vHwsPD/V1eQPJ4PMrKylJERIS2b9%2Bur776SvPmzVNQUJAefPBB3vc2Q8CyoW%2B//VY7duzQU089pQEDBmjAgAEqKyvT9u3bCVgXWHl5ufr27auYmBirSwl4R48e1ezZs/X9T/Pat2%2BfKioq9PzzzyssLEy9e/fWu%2B%2B%2Bq8LCQt17770WVRtYfqz30nc/AzNmzOBn4AI4duyYiouL9de//lVdu3aVJGVlZemPf/yjrrnmGt73NsMpQhs6cuSImpublZyc7B1LSUlRSUmJWltbLaws8JWXl6tnz55Wl9Eh7N%2B/X0OGDFFBQYHPeElJifr376%2BwsDDvWEpKioqLi/1dYsD6sd6fOXNGp0%2Bf5mfgAomJidHGjRu94eqcM2fO8L63IY5g2ZDb7VZUVJRCQkK8Y127dlVjY6Nqa2sVHR1tYXWBy%2BPx6JNPPtHevXv15JNPqqWlRaNGjVJWVpbP9wJm3HbbbT847na7FRsb6zPWpUsXVVZW%2BqOsDuHHel9eXi6Hw6EnnnhCb731liIjIzVt2jSf04X45SIiIpSWluZ93Nraqm3btmno0KG8722IgGVD9fX1bf6Hfu5xU1OTFSV1CCdPnvT2fvXq1Tp%2B/LiWLVumhoYGPfzww1aX12H82Puf9/6Fd%2BzYMTkcDvXq1UuTJ0/WgQMHtGDBAoWHh2vkyJFWlxdwcnNzdfjwYe3cuVObN2/mfW8zBCwbcrlcbX6ozj3u1KmTFSV1CN27d9d7772nSy65RA6HQ/369VNra6vmzJmj7OxsBQcHW11ih%2BByuVRbW%2Bsz1tTUxHvfD8aNG6f09HRFRkZKkhISEvTpp5/queeeI2AZlpubqy1btuixxx5T3759ed/bENdg2VBcXJxqamrU3NzsHXO73erUqZMiIiIsrCzwRUZGyuFweB/37t1bjY2N%2BuqrryysqmOJi4tTdXW1z1h1dXWb0ycwz%2BFweMPVOb169dLp06ctqigwLV26VM8884xyc3N1ww03SOJ9b0cELBvq16%2BfnE6nz8WNRUVFGjhwoIKC%2BJZeKG%2B//baGDBmi%2Bvp679iHH36oyMhIrnvzo8TERH3wwQdqaGjwjhUVFSkxMdHCqjqGNWvWaOrUqT5jR44cUa9evawpKADl5eXp%2Beef16OPPqrRo0d7x3nf2w//N7ah0NBQjRs3TosWLVJpaal2796tp59%2BWlOmTLG6tICWnJwsl8ulhx9%2BWMeOHdOePXu0YsUK3XnnnVaX1qEMHjxY3bp1U3Z2tsrKypSfn6/S0lJNnDjR6tICXnp6ug4cOKBNmzbp888/13//93/rxRdf1PTp060uLSCUl5dr/fr1uuuuu5SSkiK32%2B394n1vPw7PD93oBL969fX1WrRokf7nf/5H4eHhmjFjRpvfLGFeWVmZHnnkERUXF6tz587693//d2VmZvqcNoR5V1xxhbZu3aohQ4ZIkj777DPNnz9fJSUluuyyyzRv3jz98z//s8VVBqbv93737t1au3atPv30U3Xv3l2///3vdf3111tcZWDIz8/XqlWrfnDuo48%2B4n1vMwQsAAAAwzhFCAAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG/X%2BlC3thCf06MAAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6676804144064280890”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2023</td> <td class=”number”>63.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>738</td> <td class=”number”>23.0%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>227</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6676804144064280890”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>2023</td> <td class=”number”>63.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>738</td> <td class=”number”>23.0%</td> <td>

<div class=”bar” style=”width:37%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>227</td> <td class=”number”>7.1%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>79</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>32</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPS12”><s>SNAPS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SPECS14”><code>SPECS14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92214</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPS16”><s>SNAPS16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS12”><code>SNAPS12</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99663</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPSPTH12”>SNAPSPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3102</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.87833</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>6.658</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram4045596035131436700”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives4045596035131436700”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles4045596035131436700”

aria-controls=”quantiles4045596035131436700” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram4045596035131436700” aria-controls=”histogram4045596035131436700”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common4045596035131436700” aria-controls=”common4045596035131436700”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme4045596035131436700” aria-controls=”extreme4045596035131436700”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles4045596035131436700”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.40094</td>

</tr> <tr>

<th>Q1</th> <td>0.62927</td>

</tr> <tr>

<th>Median</th> <td>0.81817</td>

</tr> <tr>

<th>Q3</th> <td>1.0665</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.5592</td>

</tr> <tr>

<th>Maximum</th> <td>6.658</td>

</tr> <tr>

<th>Range</th> <td>6.658</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.43723</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.38363</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.43677</td>

</tr> <tr>

<th>Kurtosis</th> <td>20.063</td>

</tr> <tr>

<th>Mean</th> <td>0.87833</td>

</tr> <tr>

<th>MAD</th> <td>0.279</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.2384</td>

</tr> <tr>

<th>Sum</th> <td>2750.9</td>

</tr> <tr>

<th>Variance</th> <td>0.14717</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram4045596035131436700”>

<img 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uSI3/h/Ulxc7FtXLaezeb3fX1/BwUF%2Bf8IspzNw%2BsqxQA9q0Qcv%2BkAP7BawAas%2BKioq9OCDD6pVq1ZKTU2VJO3bt0/Hjh3TLbfcovT0dP35z3/W6NGj9fbbb6uiokKSFBIS4ltH7d/dbne9t5ubm6ucnBy/sbS0NKWnp5/rLv2kyMiwBllvUxcdHW53CWeNY4Ee1KIPXvSBHtil0QasU6dO6YEHHtA333yjP/3pTwoL8x5gc%2BbMUUVFhSIiIiRJM2fO1CeffKI33nhDv/71ryV5w1TtFGJtsKp9f32kpqYqJSXFb8zpbK7S0lPnvF//Kjg4SJGRYTp%2BvFzV1XzT0TTTP6%2BGxLFAD2rRBy/6QA9q2fVhuVEGrJMnT%2Bruu%2B/Wd999p1WrVvl9W9DpdPrClSQ5HA4lJCTo6NGjiouLk%2BT9JmLtNGPtVF9MTEy9tx8bG1tnOtDlOqGqqoY5wKuraxps3U1ZIPaUY4Ee1KIPXvSBHtil0U3M1tTUaPz48Tpw4IDWrFmjSy%2B91G/5HXfc4Td9V1NTo6%2B%2B%2BkoJCQmKi4tTfHy88vLyfMvz8vIUHx9v/PopAADQeDW6M1ivvPKKtm3bpueee06RkZG%2BM1DnnXeeoqKilJKSosWLF6tz5866%2BOKLtXr1ap04cULDhw%2BXJI0YMULz58/XhRdeKElasGCB7rrrLtv2BwAABJ5GF7Deeecd1dTU6N577/Ub79Gjh9asWaMxY8aosrJSc%2BfOVUlJiRITE/XCCy/4pg3Hjh2r77//XuPHj1dwcLBuvvlmjRkzxoY9AQAAgcrh%2BfGdOtEgXK4TxtfpdAYpOjpcpaWnAmJ%2BffDCD%2Bwu4az89aHedpdQb4F2LDQEeuBFH7zoAz2oFRNzvi3bbXTXYAEAANiNgAUAAGAYAQsAAMAwywPWLbfcopdfflknTpi/JgkAAOCXwPKA1atXLy1ZskR9%2BvTRI488oq1bt4rr7AEAQGNiecDKyMjQe%2B%2B9p2effVbBwcF68MEH1a9fPz399NPat2%2Bf1eUAAAAYZ8t9sBwOh3r37q3evXurvLxca9as0bPPPqulS5eqW7duGj16tK677jo7SgMAADhntt1otLi4WH/5y1/0l7/8RV9//bW6deum4cOH68iRI3r00Ue1Y8cOTZ8%2B3a7yAAAA/muWB6w33nhDb7zxhrZt26YWLVpo2LBheuaZZ/x%2BIXPr1q01b948AhYAAAhIlges6dOnq3///lq8eLGuueYaBQXVvQwsISFBt99%2Bu9WlAQAAGGF5wHr//fcVHR2tsrIyX7jauXOnunTpouDgYElSt27d1K1bN6tLAwAAMMLybxGePHlSgwYN0rJly3xj48aN0w033KDDhw9bXQ4AAIBxlgesJ554QhdddJHuvPNO39jbb7%2Bt1q1bKzMz0%2BpyAAAAjLM8YH388ceaMmWKYmJifGMtWrTQpEmT9NFHH1ldDgAAgHGWByyn06njx4/XGS8vL%2BeO7gAAoFGwPGBdc801mjt3rr777jvf2P79%2B5WZmam%2BfftaXQ4AAIBxln%2BLcPLkybrzzjs1cOBARUZGSpKOHz%2BuLl26aOrUqVaXAwAAYJzlAatly5Z6/fXX9c9//lOFhYVyOp265JJLdPXVV8vhcFhdDgAAgHG2/Kqc4OBg9e3blylBAADQKFkesFwulxYuXKhPPvlEZ86cqXNh%2B9///nerSwIAADDK8oD12GOPadeuXRoyZIjOP/98qzcPAADQ4CwPWB999JGWL1%2Bu5ORkqzcNAABgCctv09C8eXO1bNnS6s0CAABYxvKAdcMNN2j58uWqrq62etMAAACWsHyKsKysTBs3btQ//vEPtWvXTiEhIX7LV69ebXVJAAAARtlym4ahQ4fasVkAAABLWB6wMjMzrd4kAACApSy/BkuSiouLlZOTo4yMDH3//ff629/%2Bpr1799pRCgAAgHGWB6xvv/1Wv/vd7/T666/rnXfe0enTp/X222/rpptuUkFBwVmvz%2B12a%2BjQodq2bZtvbP/%2B/RozZoyuvPJKXX/99dq6davfe/75z39q6NChSkxM1KhRo7R//36/5S%2B%2B%2BKL69u2rpKQkTZs2TeXl5f/dzgIAgCbJ8oD15JNPasCAAdq0aZPOO%2B88SdJTTz2llJQUzZ8//6zWVVlZqUceeUSFhYW%2BMY/Ho7S0NLVq1UqvvvqqbrjhBo0fP16HDh2SJB06dEhpaWm68cYb9corr6hFixZ64IEHfHeUf%2Bedd5STk6PZs2dr1apVKigoUHZ2tqG9BwAATYHlAeuTTz7RnXfe6feLnZ1Opx544AF98cUX9V7Pnj17dOutt%2Bq7777zG//oo4%2B0f/9%2BzZ49Wx06dNC9996rK6%2B8Uq%2B%2B%2Bqokaf369frVr36lu%2B66S5deeqkyMzN18OBBbd%2B%2BXZL3W4yjR49W//79dcUVV2jWrFl69dVXOYsFAADqzfKAVVNTo5qamjrjp06dUnBwcL3Xs337dvXs2VO5ubl%2B4wUFBbr88svVvHlz31j37t2Vn5/vW/6vd5EPCwtTly5dlJ%2Bfr%2Brqan322Wd%2By6%2B88kqdOXNGu3fvrndtAACgabP8W4R9%2BvTR888/7zftVlZWpuzsbPXq1ave6xk5cuRPjrtcLsXGxvqNtWzZUkeOHPnZ5cePH1dlZaXfcqfTqaioKN/7AQAAfo7lAWvKlCkaNWqU%2BvTpo8rKSt1///06ePCgoqKi9OSTT57z%2BsvLy%2BvcvDQkJERut/tnl1dUVPie/7v310dxcbFcLpffmNPZvE6wO1fBwUF%2Bf8IspzNw%2BsqxQA9q0Qcv%2BkAP7GZ5wIqLi9OGDRu0ceNGffnll6qpqdGIESN0ww03KCIi4pzXHxoaqrKyMr8xt9utZs2a%2BZb/OCy53W5FRkYqNDTU9/zHy8PCwupdQ25urnJycvzG0tLSlJ6eXu91nI3IyPrXhvqLjg63u4SzxrFAD2rRBy/6QA/sYsud3MPCwnTLLbc0yLrj4uK0Z88ev7GSkhLf2aO4uDiVlJTUWd65c2dFRUUpNDRUJSUl6tChgySpqqpKZWVliomJqXcNqampSklJ8RtzOpurtPTUf7NL/1ZwcJAiI8N0/Hi5qqvrXteGc2P659WQOBboQS364EUf6EEtuz4sWx6wRo0a9R%2BXn%2BvvIkxMTNTSpUtVUVHhO2uVl5en7t27%2B5bn5eX5Xl9eXq4vvvhC48ePV1BQkLp27aq8vDz17NlTkpSfny%2Bn06lOnTrVu4bY2Ng604Eu1wlVVTXMAV5dXdNg627KArGnHAv0oBZ98KIP9MAulk/MtmnTxu8RFxeniooK7dy5U0lJSee8/h49eqh169aaOnWqCgsLtXTpUu3cuVM333yzJOmmm27SJ598oqVLl6qwsFBTp05V27ZtfYFq5MiRWrFihTZt2qSdO3dq5syZuvXWW89qihAAADRtv5jfRbh48WIj39QLDg7Ws88%2Bq%2BnTp%2BvGG2/URRddpMWLFys%2BPl6S1LZtW/3xj3/UE088ocWLFyspKUmLFy/23ZdryJAhOnjwoGbMmCG3263rrrtOEydOPOe6AABA0%2BHw1N7C3GYHDhzQsGHD9PHHH9tdSoNwuU4YX6fTGaTo6HCVlp4KiNO/gxd%2BYHcJZ%2BWvD/W2u4R6C7RjoSHQAy/64EUf6EGtmJjzbdnuL%2Ba7m59%2B%2BulZ3WgUAADgl%2BoXcZH7yZMn9dVXX/3bm4cCAAAEEssDVnx8vN/vIZSk8847T7fffrt%2B//vfW10OAACAcZYHLBN3awcAAPglszxg7dixo96vveqqqxqwEgAAgIZhecC64447fFOE//oFxh%2BPORwOffnll1aXBwAAcM4sD1hLlizR3LlzNXHiRPXo0UMhISH67LPPNHv2bA0fPlzXX3%2B91SUFtOTpf7O7BAAA8COW36YhMzNTM2bM0MCBAxUdHa3w8HD16tVLs2fP1rp16/zu8g4AABCILA9YxcXFPxmeIiIiVFpaanU5AAAAxlkesK688ko99dRTOnnypG%2BsrKxM2dnZuvrqq60uBwAAwDjLr8F69NFHNWrUKF1zzTVq3769PB6PvvnmG8XExGj16tVWlwMAAGCc5QGrQ4cOevvtt7Vx40YVFRVJkv7nf/5HQ4YMUVhYmNXlAAAAGGd5wJKkCy64QLfccosOHDigdu3aSfLezR0AAKAxsPwaLI/Ho/nz5%2Buqq67S0KFDdeTIEU2ePFnTp0/XmTNnrC4HAADAOMsD1po1a/TGG2/o8ccfV0hIiCRpwIAB2rRpk3JycqwuBwAAwDjLA1Zubq5mzJihG2%2B80Xf39uuvv15z587Vm2%2B%2BaXU5AAAAxlkesA4cOKDOnTvXGe/UqZNcLpfV5QAAABhnecBq06aNPvvsszrj77//vu%2BCdwAAgEBm%2BbcIx44dq1mzZsnlcsnj8ejDDz9Ubm6u1qxZoylTplhdDgAAgHGWB6ybbrpJVVVVeu6551RRUaEZM2aoRYsWeuihhzRixAirywEAADDO8oC1ceNGDRo0SKmpqfrhhx/k8XjUsmVLq8sAAABoMJZfgzV79mzfxewtWrQgXAEAgEbH8oDVvn17ff3111ZvFgAAwDKWTxF26tRJEyZM0PLly9W%2BfXuFhob6Lc/MzLS6JAAAAKMsD1j79u1T9%2B7dJYn7XgEAgEbJkoCVlZWl8ePHq3nz5lqzZo0VmwQAALCNJddgvfDCCyovL/cbGzdunIqLi63YPAAAgKUsCVgej6fO2I4dO1RZWWnF5gEAACxl%2BbcIAQAAGrtGGbBee%2B01dezYsc6jU6dOkqT777%2B/zrL33nvP9/4XX3xRffv2VVJSkqZNm1ZnehMAAOA/sexbhA6Hw6pN6frrr1ffvn19z6uqqjR69Gj169dPklRUVKTs7GxdffXVvtdccMEFkqR33nlHOTk5ys7OVsuWLTV16lRlZ2drxowZltUPAAACm2UBa%2B7cuX73vDpz5oyys7MVHh7u9zoT98Fq1qyZmjVr5nv%2B/PPPy%2BPxaMKECXK73Tpw4IC6du2qmJiYOu9dvXq1Ro8erf79%2B0uSZs2apbFjx2rixIkKCws759oAAEDjZ0nAuuqqq%2Brc8yopKUmlpaUqLS1t0G2XlZVp2bJlmjt3rkJCQrR79245HA61a9euzmurq6v12Wefafz48b6xK6%2B8UmfOnNHu3buVlJTUoLUCAIDGwZKAZee9r9atW6fY2FgNGjRIkrR3715FRERo0qRJ2r59uy688EI9%2BOCD%2Bs1vfqPjx4%2BrsrJSsbGxvvc7nU5FRUXpyJEj9d5mcXFxnUDpdDb3W68JwcGN8hK6XwynM3D6W3ssNOVjgh540Qcv%2BkAP7Gb5ndyt5PF4tH79et19992%2Bsb1796qiokJ9%2BvTRuHHj9O677%2Br%2B%2B%2B9Xbm6uWrVqJUkKCQnxW09ISIjcbne9t5ubm6ucnBy/sbS0NKWnp5/D3sBq0dHhP/%2BiX5jISKax6YEXffCiD/TALo06YH322Wc6evSohgwZ4ht74IEHdMcdd/guau/UqZM%2B//xz/fnPf9bDDz8sSXXClNvtPqvrr1JTU5WSkuI35nQ2V2npqf92V34Sn0oalumfV0MKDg5SZGSYjh8vV3V1jd3l2IIeeNEHL/pAD2rZ9WG5UQesLVu2KDk52RemJCkoKMjvuSQlJCRoz549ioqKUmhoqEpKStShQwdJ3m8glpWV/eQF8f9ObGxsnelAl%2BuEqqqa7gEeiALx51VdXROQdZtED7zogxd9oAd2adSnQHbu3Klu3br5jU2ZMkVTp071G9u9e7cSEhIUFBSkrl27Ki8vz7csPz9fTqfTdw8tAACAn9OoA1ZhYaEuueQSv7GUlBS9%2Beab2rBhg7799lvl5OQoLy9Pt99%2BuyRp5MiRWrFihTZt2qSdO3dq5syZuvXWW7lFAwAAqLdGPUVYUlKiyMhIv7HrrrtOjz/%2BuJ577jkdOnRIl156qZYvX662bdtKkoYMGaKDBw9qxowZcrvduu666zRx4kQ7ygcAAAHK4fmp38QM41yuE8bX6XQG6dr5W4yvF15/fai33SXUm9MZpOjocJWWnmqy11rQAy/64EUf6EGtmJjzbdluo54iBAAAsAMBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELACrym%2BVAAASKklEQVQAAMMabcB699131bFjR79Henq6JOmLL77QLbfcosTERN10003atWuX33s3btyoAQMGKDExUWlpafrhhx/s2AUAABCgGm3A2rNnj/r376%2BtW7f6HnPnztXp06c1btw4JScn67XXXlNSUpLuvfdenT59WpK0c%2BdOTZ8%2BXePHj1dubq6OHz%2BuqVOn2rw3AAAgkDTagFVUVKTLLrtMMTExvkdkZKTefvtthYaGatKkSerQoYOmT5%2Bu8PBw/e1vf5MkrV27VoMHD9awYcPUqVMnZWVlafPmzdq/f7/NewQAAAJFow5Y7du3rzNeUFCg7t27y%2BFwSJIcDoe6deum/Px83/Lk5GTf61u3bq34%2BHgVFBRYUjcAAAh8jTJgeTwe7du3T1u3btXAgQM1YMAAzZ8/X263Wy6XS7GxsX6vb9mypY4cOSJJKi4u/o/LAQAAfo7T7gIawqFDh1ReXq6QkBAtXLhQBw4c0Ny5c1VRUeEb/1chISFyu92SpIqKiv%2B4vD6Ki4vlcrn8xpzO5nWC27kKDm6U%2BfgXw%2BkMnP7WHgtN%2BZigB170wYs%2B0AO7NcqA1aZNG23btk0XXHCBHA6HOnfurJqaGk2cOFE9evSoE5bcbreaNWsmSQoNDf3J5WFhYfXefm5urnJycvzG0tLSfN9iRGCIjg63u4SzFhlZ/%2BO0saIHXvTBiz7QA7s0yoAlSVFRUX7PO3TooMrKSsXExKikpMRvWUlJie/sUlxc3E8uj4mJqfe2U1NTlZKS4jfmdDZXaemps9mFn8WnkoZl%2BufVkIKDgxQZGabjx8tVXV1jdzm2oAde9MGLPtCDWnZ9WG6UAWvLli2aMGGC/vGPf/jOPH355ZeKiopS9%2B7dtWzZMnk8HjkcDnk8Hn3yySe67777JEmJiYnKy8vTjTfeKEk6fPiwDh8%2BrMTExHpvPzY2ts50oMt1QlVVTfcAD0SB%2BPOqrq4JyLpNogde9MGLPtADuzTKUyBJSUkKDQ3Vo48%2Bqr1792rz5s3KysrS3XffrUGDBun48eOaN2%2Be9uzZo3nz5qm8vFyDBw%2BWJI0YMUJvvPGG1q9fr927d2vSpEnq16%2Bf2rVrZ/NeAQCAQNEoA1ZERIRWrFihH374QTfddJOmT5%2Bu1NRU3X333YqIiNDzzz/vO0tVUFCgpUuXqnnz5pK84Wz27NlavHixRowYoQsuuECZmZk27xEAAAgkDo/H47G7iKbA5TphfJ1OZ5Cunb/F%2BHrh9deHettdQr05nUGKjg5XaempJjsVQA%2B86IMXfaAHtWJizrdlu43yDBYAAICdCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGOe0uAPilGrzwA7tLOCsfzxtkdwkAgP/DGSwAAADDCFgAAACGNdqAdfToUaWnp6tHjx7q27evMjMzVVlZKUmaO3euOnbs6PdYu3at770bN27UgAEDlJiYqLS0NP3www927QYAAAhAjfIaLI/Ho/T0dEVGRuqll17SsWPHNG3aNAUFBWny5MkqKipSRkaGhg8f7ntPRESEJGnnzp2aPn26Zs2apU6dOmnevHmaOnWqnn/%2Bebt2BwAABJhGeQZr7969ys/PV2Zmpi699FIlJycrPT1dGzdulCQVFRXp8ssvV0xMjO8RFhYmSVq7dq0GDx6sYcOGqVOnTsrKytLmzZu1f/9%2BO3cJAAAEkEYZsGJiYrR8%2BXK1atXKb/zkyZM6efKkjh49qvbt2//kewsKCpScnOx73rp1a8XHx6ugoKAhSwYAAI1Io5wijIyMVN%2B%2BfX3Pa2pqtHbtWvXq1UtFRUVyOBxasmSJ3n//fUVFRenOO%2B/0TRcWFxcrNjbWb30tW7bUkSNH6r394uJiuVwuvzGns3md9Z6r4OBGmY9xDpryMVG77025BxJ9qEUf6IHdGmXA%2BrHs7Gx98cUXeuWVV/T555/L4XAoISFBt99%2Bu3bs2KHHHntMERERuvbaa1VRUaGQkBC/94eEhMjtdtd7e7m5ucrJyfEbS0tLU3p6upH9Af6dyMgwu0uwHT3wog9e9IEe2KXRB6zs7GytWrVKTz/9tC677DJdeuml6t%2B/v6KioiRJnTp10jfffKN169bp2muvVWhoaJ0w5Xa7fddo1UdqaqpSUlL8xpzO5iotPXXuO/Qv%2BFSCHzt%2BvFzV1TV2l2GL4OAgRUaGNekeSPShFn2gB7Wio8Nt2W6jDlhz5szRunXrlJ2drYEDB0qSHA6HL1zVSkhI0EcffSRJiouLU0lJid/ykpISxcTE1Hu7sbGxdaYDXa4Tqqpqugc4rFFdXdPkjzN64EUfvOgDPbBLoz0FkpOTo5dffllPPfWUhgwZ4htftGiRxowZ4/fa3bt3KyEhQZKUmJiovLw837LDhw/r8OHDSkxMtKRuAAAQ%2BBplwCoqKtKzzz6re%2B65R927d5fL5fI9%2Bvfvrx07dmjFihX67rvv9Kc//UkbNmzQXXfdJUkaMWKE3njjDa1fv167d%2B/WpEmT1K9fP7Vr187mvQIAAIGiUU4R/v3vf1d1dbWee%2B45Pffcc37LvvrqKy1atEjPPPOMFi1apDZt2mjBggVKSkqSJCUlJWn27Nl65plndOzYMfXu3Vtz5syxYzcAAECAcng8Ho/dRTQFLtcJ4%2Bt0OoN07fwtxteLwPTxvEEqLT3VZK%2B1cDqDFB0d3qR7INGHWvSBHtSKiTnflu02yilCAAAAOxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgB6ydUVlZq2rRpSk5OVp8%2BfbRy5Uq7SwIAAAHEaXcBv0RZWVnatWuXVq1apUOHDmny5MmKj4/XoEGD7C4NAAAEAALWj5w%2BfVrr16/XsmXL1KVLF3Xp0kWFhYV66aWXCFgAAKBeCFg/snv3blVVVSkpKck31r17dy1ZskQ1NTUKCmJWFb9MydP/ZncJ9fbXh3rbXQIANCgC1o%2B4XC5FR0crJCTEN9aqVStVVlaqrKxMLVq0%2BNl1FBcXy%2BVy%2BY05nc0VGxtrtNbgYMIeAtPghR/YXcJZeXdCX7tLqLfafxea%2Br8P9IEe2I2A9SPl5eV%2B4UqS77nb7a7XOnJzc5WTk%2BM3Nn78eD344INmivw/xcXFGn1hoVJTU42Ht0BRXFys3NzcJt0DiT5I9KBWcXGxVq1aTh/oAz2wGbH2R0JDQ%2BsEqdrnzZo1q9c6UlNT9dprr/k9UlNTjdfqcrmUk5NT52xZU0IPvOgDPahFH7zoAz2wG2ewfiQuLk6lpaWqqqqS0%2Bltj8vlUrNmzRQZGVmvdcTGxvJpAQCAJowzWD/SuXNnOZ1O5efn%2B8by8vLUtWtXLnAHAAD1QmL4kbCwMA0bNkwzZ87Uzp07tWnTJq1cuVKjRo2yuzQAABAggmfOnDnT7iJ%2BaXr16qUvvvhCCxYs0Icffqj77rtPN910k91l/aTw8HD16NFD4eHhdpdiG3rgRR/oQS364EUf6IGdHB6Px2N3EQAAAI0JU4QAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAaqyslLTpk1TcnKy%2BvTpo5UrV9pdkm3cbreGDh2qbdu22V2K5Y4ePar09HT16NFDffv2VWZmpiorK%2B0uy3Lffvutxo4dq6SkJPXr10/Lly%2B3uyRbjRs3TlOmTLG7DFu8%2B%2B676tixo98jPT3d7rIs53a7NWvWLF111VX69a9/raeeekrcV9xaTrsLwH8nKytLu3bt0qpVq3To0CFNnjxZ8fHxGjRokN2lWaqyslIZGRkqLCy0uxTLeTwepaenKzIyUi%2B99JKOHTumadOmKSgoSJMnT7a7PMvU1NRo3Lhx6tq1q15//XV9%2B%2B23euSRRxQXF6ff/e53dpdnubfeekubN2/W8OHD7S7FFnv27FH//v01Z84c31hoaKiNFdlj7ty52rZtm1asWKFTp07p4YcfVnx8vG677Ta7S2syCFgB6PTp01q/fr2WLVumLl26qEuXLiosLNRLL73UpALWnj17lJGR0WQ/le3du1f5%2Bfn64IMP1KpVK0lSenq6/vCHPzSpgFVSUqLOnTtr5syZioiIUPv27XX11VcrLy%2BvyQWssrIyZWVlqWvXrnaXYpuioiJddtlliomJsbsU25SVlenVV1/VCy%2B8oCuuuEKSdNddd6mgoICAZSGmCAPQ7t27VVVVpaSkJN9Y9%2B7dVVBQoJqaGhsrs9b27dvVs2dP5ebm2l2KLWJiYrR8%2BXJfuKp18uRJmyqyR2xsrBYuXKiIiAh5PB7l5eVpx44d6tGjh92lWe4Pf/iDbrjhBl1yySV2l2KboqIitW/f3u4ybJWXl6eIiAi//wbGjRunzMxMG6tqeghYAcjlcik6OlohISG%2BsVatWqmyslJlZWU2VmatkSNHatq0aQoLC7O7FFtERkaqb9%2B%2Bvuc1NTVau3atevXqZWNV9kpJSdHIkSOVlJSkgQMH2l2OpT788EN9/PHHeuCBB%2BwuxTYej0f79u3T1q1bNXDgQA0YMEDz58%2BX2%2B22uzRL7d%2B/X23atNGGDRs0aNAg/fa3v9XixYub1AfwXwICVgAqLy/3C1eSfM%2Bb2j8k%2BP%2Bys7P1xRdf6OGHH7a7FNs888wzWrJkib788ssm9Wm9srJSjz/%2BuGbMmKFmzZrZXY5tDh065Pv3ceHChZo8ebLefPNNZWVl2V2apU6fPq1vv/1WL7/8sjIzMzV58mStWbNGL774ot2lNSlcgxWAQkND6wSp2udN%2BR/Xpiw7O1urVq3S008/rcsuu8zucmxTe%2B1RZWWlJkyYoEmTJtX5MNIY5eTk6Fe/%2BpXfGc2mqE2bNtq2bZsuuOACORwOde7cWTU1NZo4caKmTp2q4OBgu0u0hNPp1MmTJ7VgwQK1adNGkjd8rlu3TnfddZfN1TUdBKwAFBcXp9LSUlVVVcnp9P4IXS6XmjVrpsjISJurg9XmzJmjdevWKTs7u8lNi0nei9zz8/M1YMAA39gll1yiM2fO6OTJk2rRooWN1VnjrbfeUklJie%2B6zNoPXO%2B8844%2B/fRTO0uzXFRUlN/zDh06qLKyUseOHWsSx4LkvT4zNDTUF64k6eKLL9bhw4dtrKrpYYowAHXu3FlOp1P5%2Bfm%2Bsby8PHXt2lVBQfxIm5KcnBy9/PLLeuqppzRkyBC7y7HFgQMHNH78eB09etQ3tmvXLrVo0aLJ/A91zZo1evPNN7VhwwZt2LBBKSkpSklJ0YYNG%2BwuzVJbtmxRz549VV5e7hv78ssvFRUV1WSOBUlKTExUZWWl9u3b5xvbu3evX%2BBCw%2BP/xgEoLCxMw4YN08yZM7Vz505t2rRJK1eu1KhRo%2BwuDRYqKirSs88%2Bq3vuuUfdu3eXy%2BXyPZqSrl27qkuXLpo2bZr27NmjzZs3Kzs7W/fdd5/dpVmmTZs2uuiii3yP8PBwhYeH66KLLrK7NEslJSUpNDRUjz76qPbu3avNmzcrKytLd999t92lWSohIUH9%2BvXT1KlTtXv3bm3ZskVLly7ViBEj7C6tSXF4mupNhAJceXm5Zs6cqf/93/9VRESExo4dqzFjxthdlm06duyo1atXq2fPnnaXYpmlS5dqwYIFP7nsq6%2B%2Bsrgaex09elRz5szRhx9%2BqLCwMN1%2B%2B%2B2699575XA47C7NFrV3cX/yySdtrsR6hYWFeuKJJ5Sfn6/w8HDddtttSktLa3LHwokTJzRnzhy9%2B%2B67CgsL08iRI5tkH%2BxEwAIAADCMKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AQUsmH165sTCAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common4045596035131436700”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1918951132300357</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.8413967185527977</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5730936006849504</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.088139281828074</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3196093956188968</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.1439553399835272</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6134526558891455</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.819672131147541</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.42992261392949266</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3091)</td> <td class=”number”>3092</td> <td class=”number”>96.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme4045596035131436700”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0410711352061771</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13412017167381973</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.13576349998302956</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1419244961680386</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.1979037529584136</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.199777406789093</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.5569105691056913</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.7787182587666264</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.658001155401502</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAPSPTH16”>SNAPSPTH16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3094</td>

</tr> <tr>

<th>Unique (%)</th> <td>96.4%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>104</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.93667</td>

</tr> <tr>

<th>Minimum</th> <td>0.1189</td>

</tr> <tr>

<th>Maximum</th> <td>6.0472</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram14310869375558408”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives14310869375558408,#minihistogram14310869375558408”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives14310869375558408”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles14310869375558408”

aria-controls=”quantiles14310869375558408” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram14310869375558408” aria-controls=”histogram14310869375558408”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common14310869375558408” aria-controls=”common14310869375558408”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme14310869375558408” aria-controls=”extreme14310869375558408”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles14310869375558408”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.1189</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.45336</td>

</tr> <tr>

<th>Q1</th> <td>0.69339</td>

</tr> <tr>

<th>Median</th> <td>0.88679</td>

</tr> <tr>

<th>Q3</th> <td>1.1159</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.5692</td>

</tr> <tr>

<th>Maximum</th> <td>6.0472</td>

</tr> <tr>

<th>Range</th> <td>5.9283</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.42254</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.37506</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.40041</td>

</tr> <tr>

<th>Kurtosis</th> <td>20.834</td>

</tr> <tr>

<th>Mean</th> <td>0.93667</td>

</tr> <tr>

<th>MAD</th> <td>0.27001</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.4375</td>

</tr> <tr>

<th>Sum</th> <td>2909.3</td>

</tr> <tr>

<th>Variance</th> <td>0.14067</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram14310869375558408”>

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tq0aSObzSZJCg8Pdy7fsGFDSdKJEydcxgEAAH5NrQ1YRpw/f17jxo1Tw4YNlZCQIEk6ePCgTp06pUGDBikpKUl///vfNWLECL3//vs6f/68JMnPz8%2B5jot/ttvthrebn5/vDGsXWa11qxXQfH19XL7C/axW7%2Bg9x5Ix9Klq9MgY%2BmSMt/fJawPW2bNnNWbMGP3www/629/%2BpoCAAEnS3Llzdf78eQUFBUmSZs2apS%2B//FKbNm3S73//e0k/h6mLlxAvBquLyxuRkZGh9PR0l7HExEQlJSVVez%2BCg41vF%2BYKDQ30dAmm4lgyhj5VjR4ZQ5%2BM8dY%2BeWXAKi4u1iOPPKIff/xRq1atcvltQavV6gxXkmSxWBQVFaW8vDxFRERI%2Bvk3ES9eZrx4JiosLMzw9hMSEhQfH%2B8yZrXWVWHhWcPr8PX1UXBwgE6fLlF5OY%2BJ8ITqfL9qMo4lY%2BhT1eiRMfTJGHf1yVM/LHtdwKqoqNDYsWN15MgRrVmzRs2bN3eZPmzYMHXs2FFjx451zv/dd9/pz3/%2BsyIiIhQZGanMzExnwMrMzFRkZGS1Lu%2BFh4dXmt9mO6OysuofQOXlFb9pOVw5b%2Bs7x5Ix9Klq9MgY%2BmSMt/bJ6wLWm2%2B%2BqR07duill15ScHCw8wzUddddp5CQEMXHx2vp0qVq2bKlbrrpJq1evVpnzpxR//79JUmDBw/WggULdMMNN0iSFi5cqJEjR3psfwAAQO3jdQFr69atqqio0GOPPeYy3qFDB61Zs0YPPfSQSktLNW/ePBUUFCgmJkavvfaa87LhqFGj9NNPP2ns2LHy9fXVn/70Jz300EMe2BMAAFBbWRy/fFInrgqb7Uy15rdafRQaGqjCwrNec%2Bq096LPPF1CtXzwZGdPl2AKbzyWrgb6VDV6ZAx9MsZdfQoLu/6qrftyvPN3IwEAADyIgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAydwesAYNGqQ33nhDZ85U77lQAAAAtYXbA1anTp308ssvq0uXLho/frw%2B/fRT8axTAADgTdwesJKTk/XRRx/pxRdflK%2Bvr8aNG6du3brphRde0MGDB91dDgAAgOk88lmEFotFnTt3VufOnVVSUqI1a9boxRdf1LJly9S2bVuNGDFC99xzjydKAwAAuGIe%2B7Dn/Px8vfvuu3r33Xf1/fffq23bturfv79OnDihp59%2BWrt27dL06dM9VR4AAMBv5vaAtWnTJm3atEk7duxQ/fr11a9fPy1ZskTNmjVzztOoUSPNnz%2BfgAUAAGoltwes6dOnq3v37lq6dKnuvPNO%2BfhUvg0sKipKQ4cOdXdpAAAApnB7wPr4448VGhqqoqIiZ7javXu3WrVqJV9fX0lS27Zt1bZtW3eXBgAAYAq3/xZhcXGxevXqpeXLlzvHRo8erfvvv1/Hjx93dzkAAACmc3vAevbZZ3XjjTfq4Ycfdo69//77atSokVJSUtxdDgAAgOncHrD%2B%2B9//asqUKQoLC3OO1a9fX5MmTdIXX3zh7nIAAABM5/aAZbVadfr06UrjJSUlPNEdAAB4BbcHrDvvvFPz5s3Tjz/%2B6Bw7fPiwUlJS1LVrV3eXAwAAYDq3/xbh5MmT9fDDD6tnz54KDg6WJJ0%2BfVqtWrXS1KlT3V0OAACA6dwesBo0aKB33nlH//nPf7R//35ZrVbdfPPNuuOOO2SxWNxdDgAAgOk88lE5vr6%2B6tq1K5cEAQCAV3J7wLLZbFq0aJG%2B/PJLXbhwodKN7f/617/cXRIAAICp3B6wZsyYob1796pPnz66/vrr3b15AACAq87tAeuLL77QihUrFBcX5%2B5NAwAAuIXbH9NQt25dNWjQwN2bBQAAcBu3B6z7779fK1asUHl5ubs3DQAA4BZuv0RYVFSkLVu26N///reaNm0qPz8/l%2BmrV692d0kAAACm8shjGvr27euJzQIAALiF2wNWSkqKqeuz2%2B0aMGCAZsyYoY4dO0r6%2BaN3ZsyYoaysLEVGRmratGnq0qWLc5n//Oc/evbZZ3X48GHFxMRo/vz5atq0qXP666%2B/rpUrV6q4uFi9e/fWjBkzFBAQYGrdAADAe7n9HixJys/PV3p6upKTk/XTTz/pH//4hw4cOFDt9ZSWlmr8%2BPHav3%2B/c8zhcCgxMVENGzbUW2%2B9pfvvv19jx47VsWPHJEnHjh1TYmKiBgwYoDfffFP169fXmDFjnM/j2rp1q9LT0zVnzhytWrVK2dnZSktLM2fHAQDANcHtAevQoUP64x//qHfeeUdbt27VuXPn9P7772vgwIHKzs42vJ6cnBw98MADLh8aLf38GIjDhw9rzpw5at68uR577DHdfvvteuuttyRJGzZs0O9%2B9zuNHDlSt9xyi1JSUnT06FHt3LlT0s/3gI0YMULdu3dXmzZtNHv2bL311lsqKSkxrwkAAMCruT1gPffcc%2BrRo4e2bdum6667TpL0/PPPKz4%2BXgsWLDC8np07d6pjx47KyMhwGc/OztZtt92munXrOsfatWunrKws5/T/fQZXQECAWrVqpaysLJWXl2vPnj0u02%2B//XZduHBB%2B/bt%2B037CwAArj1uvwfryy%2B/1Lp161w%2B2NlqtWrMmDF64IEHDK9nyJAhlxy32WwKDw93GWvQoIFOnDhR5fTTp0%2BrtLTUZbrValVISIhzeSPy8/Nls9lcxqzWupW2ezm%2Bvj4uX%2BF%2BVqt39J5jyRj6VDV6ZAx9Msbb%2B%2BT2gFVRUaGKiopK42fPnpWvr%2B8Vr7%2BkpKTSox/8/Pxkt9urnH7%2B/Hnn%2B19b3oiMjAylp6e7jCUmJiopKcnwOi4KDubmek8JDQ30dAmm4lgyhj5VjR4ZQ5%2BM8dY%2BuT1gdenSRa%2B88orLjeNFRUVKS0tTp06drnj9/v7%2BKioqchmz2%2B2qU6eOc/ovw5LdbldwcLD8/f2d7385vTq/RZiQkKD4%2BHiXMau1rgoLzxpeh6%2Bvj4KDA3T6dInKyysHUlx91fl%2B1WQcS8bQp6rRI2PokzHu6pOnflh2e8CaMmWKhg8fri5duqi0tFRPPPGEjh49qpCQED333HNXvP6IiAjl5OS4jBUUFDgvz0VERKigoKDS9JYtWyokJET%2B/v4qKChQ8%2BbNJUllZWUqKipSWFiY4RrCw8MrXQ602c6orKz6B1B5ecVvWg5Xztv6zrFkDH2qGj0yhj4Z4619cvuFz4iICG3cuFFPPfWUHnzwQcXFxWnChAnavHmzGjdufMXrj4mJ0ddff%2B283CdJmZmZiomJcU7PzMx0TispKdE333yjmJgY%2Bfj4qHXr1i7Ts7KyZLVaFR0dfcW1AQCAa4NHnuQeEBCgQYMGXZV1d%2BjQQY0aNdLUqVM1ZswYffTRR9q9e7fzAacDBw7UypUrtWzZMnXv3l1Lly5VkyZNnA8pHTJkiGbOnKlbb71V4eHhmjVrlh544AEeNAoAAAxze8AaPnz4Zadf6WcR%2Bvr66sUXX9T06dM1YMAA3XjjjVq6dKkiIyMlSU2aNNFf//pXPfvss1q6dKliY2O1dOlS52819unTR0ePHtXMmTNlt9t1zz33aOLEiVdUEwAAuLa4PWD98jJgWVmZDh06pO%2B//14jRoz4Tev87rvvXN7feOONWrt27a/Of9ddd%2Bmuu%2B761emjR4/W6NGjf1MtAAAANeazCJcuXVqtZ00BAADUVDXm6V7333%2B/PvjgA0%2BXAQAAcMVqTMD66quvTHnQKAAAgKfViJvci4uL9d133/3qx98AAADUJm4PWJGRkS6fQyhJ1113nYYOHar77rvP3eUAAACYzu0By4yntQMAANRkbg9Yu3btMjxv%2B/btr2IlAAAAV4fbA9awYcOclwgdDodz/JdjFotF3377rbvLAwAAuGJuD1gvv/yy5s2bp4kTJ6pDhw7y8/PTnj17NGfOHPXv31/33nuvu0sCAAAwldsf05CSkqKZM2eqZ8%2BeCg0NVWBgoDp16qQ5c%2BZo/fr1aty4sfMFAABQG7k9YOXn518yPAUFBamwsNDd5QAAAJjO7QHr9ttv1/PPP6/i4mLnWFFRkdLS0nTHHXe4uxwAAADTuf0erKefflrDhw/XnXfeqWbNmsnhcOiHH35QWFiYVq9e7e5yAAAATOf2gNW8eXO9//772rJli3JzcyVJf/7zn9WnTx8FBAS4uxwAAADTuT1gSVK9evU0aNAgHTlyRE2bNpX089PcAQAAvIHb78FyOBxasGCB2rdvr759%2B%2BrEiROaPHmypk%2BfrgsXLri7HAAAANO5PWCtWbNGmzZt0jPPPCM/Pz9JUo8ePbRt2zalp6e7uxwAAADTuT1gZWRkaObMmRowYIDz6e333nuv5s2bp82bN7u7HAAAANO5/R6sI0eOqGXLlpXGo6OjZbPZ3F1Ordd70WeeLgEAAPyC289gNW7cWHv27Kk0/vHHHztveAcAAKjN3H4Ga9SoUZo9e7ZsNpscDoc%2B//xzZWRkaM2aNZoyZYq7ywEAADCd2wPWwIEDVVZWppdeeknnz5/XzJkzVb9%2BfT355JMaPHiwu8sBAAAwndsD1pYtW9SrVy8lJCTo5MmTcjgcatCggbvLAAAAuGrcfg/WnDlznDez169fn3AFAAC8jtsDVrNmzfT999%2B7e7MAAABu4/ZLhNHR0ZowYYJWrFihZs2ayd/f32V6SkqKu0sCAAAwldsD1sGDB9WuXTtJ4rlXAADAK7klYKWmpmrs2LGqW7eu1qxZ445NAgAAeIxb7sF67bXXVFJS4jI2evRo5efnu2PzAAAAbuWWgOVwOCqN7dq1S6WlpVdle2%2B//bZatGhR6RUdHS1JeuKJJypN%2B%2Bijj5zLv/766%2BratatiY2M1bdq0SuEQAADgctx%2BD5Y73HvvveratavzfVlZmUaMGKFu3bpJknJzc5WWlqY77rjDOU%2B9evUkSVu3blV6errS0tLUoEEDTZ06VWlpaZo5c6Zb9wEAANRebn9MgzvUqVNHYWFhzte7774rh8OhCRMmyG6368iRI2rdurXLPH5%2BfpKk1atXa8SIEerevbvatGmj2bNn66233uIsFgAAMMxtActisbhrUy6Kioq0fPlyJScny8/PTwcOHJDFYrnkB0uXl5drz549iouLc47dfvvtunDhgvbt2%2BfOsgEAQC3mtkuE8%2BbNc3nm1YULF5SWlqbAwECX%2Bcx%2BDtb69esVHh6uXr16SZIOHDigoKAgTZo0STt37tQNN9ygcePG6a677tLp06dVWlqq8PBw5/JWq1UhISE6ceKE4W3m5%2BdXegSF1VrXZb1V8fX1cfkK97NavaP3HEvG0Keq0SNj6JMx3t4ntwSs9u3bVwocsbGxKiwsVGFh4VXbrsPh0IYNG/TII484xw4cOKDz58%2BrS5cuGj16tD788EM98cQTysjIUMOGDSXJebnwIj8/P9ntdsPbzcjIUHp6ustYYmKikpKSqr0PwcEB1V4G5ggNDax6plqEY8kY%2BlQ1emQMfTLGW/vkloDlqWdf7dmzR3l5eerTp49zbMyYMRo2bJjzpvbo6Gh9/fXX%2Bvvf/66nnnpKkiqFKbvdroAA4wdAQkKC4uPjXcas1roqLDxreB2%2Bvj4KDg7Q6dMlKi%2BvMLwczFOd71dNxrFkDH2qGj0yhj4Z464%2BeeqHZa/8LcKLPvnkE8XFxTnDlCT5%2BPi4vJekqKgo5eTkKCQkRP7%2B/iooKFDz5s0l/fwbiEVFRQoLCzO83fDw8EqXA222Myorq/4BVF5e8ZuWw5Xztr5zLBlDn6pGj4yhT8Z4a5%2B888Ln/9m9e7fatm3rMjZlyhRNnTrVZWzfvn2KioqSj4%2BPWrdurczMTOe0rKwsWa1W5zO0AAAAquLVAWv//v26%2BeabXcbi4%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%2Bc0evRoxcXF6e2331ZsbKwee%2BwxnTt3TpK0e/duTZ8%2BXWPHjlVGRoZOnz6tqVOnenhvAABAbeK1ASs3N1e33nqrwsLCnK/g4GC9//778vf316RJk9S8eXNNnz5dgYGB%2Bsc//iFJWrt2rXr37q1%2B/fopOjpaqamp2r59uw4fPuzhPQIAALWFVwesZs2aVRrPzs5Wu3btZLFYJEkWi0Vt27ZVVlaWc3pcXJxz/kaNGikyMlLZ2dluqRsAANR%2BXhmwHA6HDh48qE8//VQ9e/ZUjx49tGDBAtntdtlsNoWHh7vM36BBA504cUKSlJ%2Bff9npAAAAVbF6uoCr4dixYyopKZGfn58WLVqkI0eOaN68eTp//rxz/H/5%2BfnJbrdLks6fP3/Z6Ubk5%2BfLZrO5jFmtdSsFt8vx9fVx%2BQr3s1q9o/ccS8bQp6rRI2PokzHe3ievDFiNGzfWjh07VK9ePVksFrVs2VIVFRWaOHGiOnToUCks2e121alTR5Lk7%2B9/yekBAQGGt5%2BRkaH09HSXscTEROdvMVZHcLDx7cJcoaGBni7BVBxLxtCnqtEjY%2BiTMd7aJ68MWJIUEhLi8r558%2BYqLS1VWFiYCgoKXKYVFBQ4zy5FRERccnpYWJjhbSckJCg%2BPt5lzGqtq8LCs4bX4evro%2BDgAJ0%2BXaLy8grDy8E81fl%2B1WQcS8bQp6rRI2PokzHu6pOnflj2yoD1ySefaMKECfr3v//tPPP07bffKiQkRO3atdPy5cvlcDhksVjkcDj05Zdf6vHHH5ckxcTEKDMzUwMGDJAkHT9%2BXMePH1dMTIzh7YeHh1e6HGiznVFZWfUPoPLyit%2B0HK6ct/WdY8kY%2BlQ1emQMfTLGW/vklRc%2BY2Nj5e/vr6effloHDhzQ9u3blZqaqkceeUS9evXS6dOnNX/%2BfOXk5Gj%2B/PkqKSlR7969JUmDBw/Wpk2btGHDBu3bt0%2BTJk1St27d1LRpUw/vFQAAqC28MmAFBQVp5cqVOnnypAYOHKjp06crISFBjzzyiIKCgvTKK684z1JlZ2dr2bJlqlu3rqSfw9mcOXO0dOlSDR48WPXq1VNKSoqH9wgAANQmFofD4fB0EdcCm%2B1Mtea3Wn0UGhqowsKzlz112nvRZ1daGn7FB0929nQJpjB6LF3r6FPV6JEx9MkYd/UpLOz6q7buy/HKM1gAAACeRMACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAk1k9XQBQU/Ve9JmnS6iWD57s7OkSAAD/hzNYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAybw2YOXl5SkpKUkdOnRQ165dlZKSotLSUknSvHnz1KJFC5fX2rVrnctu2bJFPXr0UExMjBITE3Xy5ElP7QYAAKiFrJ4u4GpwOBxKSkpScHCw1q1bp1OnTmnatGny8fHR5MmTlZubq%2BTkZPXv39%2B5TFBQkCRp9%2B7dmj59umbPnq3o6GjNnz9fU6dO1SuvvOKp3QEAALWMV57BOnDggLKyspSSkqJbbrlFcXFxSkpK0pYtWyRJubm5uu222xQWFuZ8BQQESJLWrl2r3r17q1%2B/foqOjlZqaqq2b9%2Buw4cPe3KXAABALeKVASssLEwrVqxQw4YNXcaLi4tVXFysvLw8NWvW7JLLZmdnKy4uzvm%2BUaNGioyMVHZ29tUsGQAAeBGvDFjBwcHq2rWr831FRYXWrl2rTp06KTc3VxaLRS%2B//LLuvPNO3XfffXrnnXec8%2Bbn5ys8PNxlfQ0aNNCJEyfcVj8AAKjdvPIerF9KS0vTN998ozfffFNff/21LBaLoqKiNHToUO3atUszZsxQUFCQ7r77bp0/f15%2Bfn4uy/v5%2BclutxveXn5%2Bvmw2m8uY1Vq3UnC7HF9fH5evQFWs1ksfKxxLxtCnqtEjY%2BiTMd7eJ68PWGlpaVq1apVeeOEF3XrrrbrlllvUvXt3hYSESJKio6P1ww8/aP369br77rvl7%2B9fKUzZ7XbnPVpGZGRkKD093WUsMTFRSUlJ1a4/ONj4dnFtCw0NvOx0jiVj6FPV6JEx9MkYb%2B2TVwesuXPnav369UpLS1PPnj0lSRaLxRmuLoqKitIXX3whSYqIiFBBQYHL9IKCAoWFhRnebkJCguLj413GrNa6Kiw8a3gdvr4%2BCg4O0OnTJSovrzC8HK5dv3Z8cSwZQ5%2BqRo%2BMoU/GuKtPVf3webV4bcBKT0/XG2%2B8oeeff169evVyji9evFhfffWVXn/9defYvn37FBUVJUmKiYlRZmamBgwYIEk6fvy4jh8/rpiYGMPbDg8Pr3Q50GY7o7Ky6h9A5eUVv2k5XHuqOk44loyhT1WjR8bQJ2O8tU9eeeEzNzdXL774oh599FG1a9dONpvN%2Berevbt27dqllStX6scff9Tf/vY3bdy4USNHjpQkDR48WJs2bdKGDRu0b98%2BTZo0Sd26dVPTpk09vFcAAKC28MozWP/6179UXl6ul156SS%2B99JLLtO%2B%2B%2B06LFy/WkiVLtHjxYjVu3FgLFy5UbGysJCk2NlZz5szRkiVLdOrUKXXu3Flz5871xG4AAIBayuJwOByeLuJaYLOdqdb8VquPQkMDVVh49rKnTnsv%2BuxKS4OX%2BODJzpccN3osXevoU9XokTH0yRh39Sks7Pqrtu7L8cpLhAAAAJ5EwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwmdXTBdREpaWlmj17tv75z3%2BqTp06GjlypEaOHOnpsoDL6r3oM0%2BXYNgHT3b2dAkAcFURsC4hNTVVe/fu1apVq3Ts2DFNnjxZkZGR6tWrl6dLAwAAtQAB6xfOnTunDRs2aPny5WrVqpVatWql/fv3a926dQQsAABgCPdg/cK%2BfftUVlam2NhY51i7du2UnZ2tiooKD1YGAABqC85g/YLNZlNoaKj8/PycYw0bNlRpaamKiopUv379KteRn58vm83mMma11lV4eLjhOnx9fVy%2BAt6kNt0vJkkfTujq6RJqBP5dMoY%2BGePtfSJg/UJJSYlLuJLkfG%2B32w2tIyMjQ%2Bnp6S5jY8eO1bhx4wzXkZ%2Bfr1WrVighIeGywey/86/ty5b5%2BfnKyMiosk/XMnpkDH2qmtF/l6519MkYb%2B%2BTd8bGK%2BDv718pSF18X6dOHUPrSEhI0Ntvv%2B3ySkhIqFYdNptN6enplc6EwRV9qho9MoY%2BVY0eGUOfjPH2PnEG6xciIiJUWFiosrIyWa0/t8dms6lOnToKDg42tI7w8HCvTOMAAMAYzmD9QsuWLWW1WpWVleUcy8zMVOvWreXjQ7sAAEDVSAy/EBAQoH79%2BmnWrFnavXu3tm3bpldffVXDhw/3dGkAAKCW8J01a9YsTxdR03Tq1EnffPONFi5cqM8//1yPP/64Bg4c6PY6AgMD1aFDBwUGBrp927UJfaoaPTKGPlWNHhlDn4zx5j5ZHA6Hw9NFAAAAeBMuEQIAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNg1UClpaWaNm2a4uLi1KVLF7366queLqnGstvt6tu3r3bs2OHpUmqkvLw8JSUlqUOHDuratatSUlJUWlrq6bJqnEOHDmnUqFGKjY1Vt27dtGLFCk%2BXVKONHj1aU6ZM8XQZNdKHH36oFi1auLySkpI8XVaNYrfbNXv2bLVv316///3v9fzzz8sbn3lu9XQBqCw1NVV79%2B7VqlWrdOzYMU2ePFmRkZHq1auXp0urUUpLS5WcnKz9%2B/d7upQayeFwKCkpScHBwVq3bp1OnTqladOmycfHR5MnT/Z0eTVGRUWFRo8erdatW%2Budd97RoUOHNH78eEVEROiPf/yjp8urcd577z1t375d/fv393QpNVJOTo66d%2B%2BuuXPnOsf8/f09WFHNM2/ePO3YsUMrV67U2bNn9dRTTykyMlIPPvigp0szFQGrhjl37pw2bNig5cuXq1WrVmrVqpX279%2BvdevWEbD%2BR05OjpKTk73ypx6zHDhwQFlZWfrss8/UsGFDSVJSUpL%2B8pe/ELD%2BR0FBgVq2bKlZs2YpKChIzZo10x133KHMzEwC1i8UFRUpNTVVrVu39nQpNVZubq5uvfVWhYWFebqUGqmoqEhvvfWWXnvtNbVp00aSNHLkSGVnZ3tdwOISYQ2zb98%2BlZWVKTY21jnWrl07ZWdnq6KiwoOV1Sw7d%2B5Ux44dlZGR4elSaqywsDCtWLHCGa4uKi4u9lBFNVN4eLgWLVqkoKAgORwOZWZmateuXerQoYOnS6tx/vKXv%2Bj%2B%2B%2B/XzTff7OlSaqzc3Fw1a9bM02XUWJmZmQoKCnL5%2BzV69GilpKR4sKqrg4BVw9hsNoWGhsrPz8851rBhQ5WWlqqoqMiDldUsQ4YM0bRp0xQQEODpUmqs4OBgde3a1fm%2BoqJCa9euVadOnTxYVc0WHx%2BvIUOGKDY2Vj179vR0OTXK559/rv/%2B978aM2aMp0upsRwOhw4ePKhPP/1UPXv2VI8ePbRgwQLZ7XZPl1ZjHD58WI0bN9bGjRvVq1cv/eEPf9DSpUu98gQCAauGKSkpcQlXkpzv%2BUuKK5GWlqZvvvlGTz31lKdLqbGWLFmil19%2BWd9%2B%2B61X/kT9W5WWluqZZ57RzJkzVadOHU%2BXU2MdO3bM%2BW/4okWLNHnyZG3evFmpqameLq3GOHfunA4dOqQ33nhDKSkpmjx5stasWaPXX3/d06WZjnuwahh/f/9KQerie/5hw2%2BVlpamVatW6YUXXtCtt97q6XJqrIv3FpWWlmrChAmaNGlSpR94rkXp6en63e9%2B53JGFJU1btxYO3bsUL169WSxWNSyZUtVVFRo4sSJmjp1qnx9fT1dosdZrVYVFxdr4cKFaty4saSfg%2Bn69es1cuRID1dnLgJWDRMREaHCwkKVlZXJav3522Oz2VSnTh0FBwd7uDrURnPnztX69euVlpbGZa9LKCgoUFZWlnr06OEcu/nmm3XhwgUVFxerfv36HqyuZnjvvfdUUFDgvDf04g99W7du1VdffeXJ0mqckJAQl/fNmzdXaWmpTp06xbGkn%2B8N9ff3d4YrSbrpppt0/PhxD1Z1dXCJsIZp2bKlrFarsrKynGOZmZlq3bq1fHz4dqF60tPT9cYbb%2Bj5559Xnz59PF1OjXTkyBGNHTtWeXl5zrG9e/eqfv36/If4f9asWaPNmzdr48aN2rhxo%2BLj4xUfH6%2BNGzd6urQa5ZNPPlHHjh1VUlLiHPv2228VEhLCsfR/YmJiVFpaqoMHDzrHDhw44BK4vAX/Y9cwAQEB6tevn2bNmqXdu3dr27ZtevXVVzV8%2BHBPl4ZaJjc3Vy%2B%2B%2BKIeffRRtWvXTjabzfnC/9e6dWu1atVK06ZNU05OjrZv3660tDQ9/vjjni6txmjcuLFuvPFG5yswMFCBgYG68cYbPV1ajRIbGyt/f389/fTTOnDggLZv367U1FQ98sgjni6txoiKilK3bt00depU7du3T5988omWLVumwYMHe7o001kcPEioxikpKdGsWbP0z3/%2BU0FBQRo1apQeeughT5dVY7Vo0UKrV69Wx44dPV1KjbJs2TItXLjwktO%2B%2B%2B47N1dTs%2BXl5Wnu3Ln6/PPPFRAQoKFDh%2Bqxxx6TxWLxdGk10sWnuD/33HMerqTm2b9/v5599lllZWUpMDBQDz74oBITEzmW/seZM2c0d%2B5cffjhhwoICNCQIUO8skcELAAAAJNxiRAAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBk/w8d5oy3u273awAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common14310869375558408”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.02564102564103</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.25339461040318</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.40252454417952</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.09784851164162</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.958772770853308</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.782472613458529</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.99535500995355</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.898600075671585</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.87260034904014</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.19047619047619</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3083)</td> <td class=”number”>3086</td> <td class=”number”>96.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme14310869375558408”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.118901351512029</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135354629128316</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.149186931224825</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16860563142809</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.171781951442968</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.32778702163062</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.50877192982456</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.84857053094565</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.44174135723431</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.04717094945108</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_BBCE09”>SNAP_BBCE09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.46584</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>52.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2543445793593944637”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2543445793593944637”

aria-controls=”quantiles-2543445793593944637” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2543445793593944637” aria-controls=”histogram-2543445793593944637”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2543445793593944637” aria-controls=”common-2543445793593944637”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2543445793593944637” aria-controls=”extreme-2543445793593944637”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2543445793593944637”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.49891</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.071</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.9825</td>

</tr> <tr>

<th>Mean</th> <td>0.46584</td>

</tr> <tr>

<th>MAD</th> <td>0.49767</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.13704</td>

</tr> <tr>

<th>Sum</th> <td>1459</td>

</tr> <tr>

<th>Variance</th> <td>0.24891</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2543445793593944637”>

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qS6ujqFh4f7bBceHi6Px6P6%2Bnrv6zPNt1ZhYaHy8/N9xjIzM5Wdnf1dlhE0OnVqH%2BgS2iw6OjLQJQQtemst%2BmsdemudYOpt0AaskydPas6cOfr444/1pz/9SZGRX/%2BjRUREtAhLHo9H0dHRioiI8L4%2Bff7U/q2RlpamcePG%2BYw5HFE6fvzkd1nKWdkt6Zu9fiuFhYUqOjpSNTV1ampqDnQ5QYXeWov%2BWofeWs%2BK3gbqh/ugDFhffvmlfvWrX%2BmTTz7RmjVr1KtXL%2B9cfHy8qqqqfLavqqpSv3791LFjR0VERKiqqkq9e/eWJDU2Nqq6ulqxsbGtPn9cXFyL24Fu9wk1Np7fb0g7rr%2BpqdmWddsBvbUW/bUOvbVOMPXWXpdAWqG5uVlZWVn69NNPtW7dOv3oRz/ymXc6nSoqKvK%2Brqur04EDB%2BR0OhUaGqrExESf%2BeLiYjkcDvXt29dvawAAAPYWdAFr06ZN2rNnj%2B6%2B%2B25FR0fL7XbL7XarurpakjR16lTt27dPq1atUmlpqXJyctS9e3cNGzZM0tcfnn/qqae0c%2BdOlZSUaNGiRbr22mvbdIsQAACc34LuFuErr7yi5uZm3XLLLT7jQ4cO1bp169S9e3c9/PDD%2BsMf/qCCggIlJSWpoKBAISEhkqQrr7xSn332mXJzc%2BXxePTzn/9ct99%2BeyCWAgAAbCooAtahQ4e8f3/qqae%2BdfvRo0dr9OjRZ53PyMhQRkaGKbUBAIDzT9DdIgQAAAg0AhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvN7wJo2bZo2bNigEydO%2BPvUAAAAfuH3gDV8%2BHA99thjGjlypObNm6d//vOfMgzD32UAAABYxu8B69Zbb9Xf//53PfLIIwoLC9PcuXM1ZswYPfDAA/roo4/8XQ4AAIDpHIE4aUhIiEaMGKERI0aorq5O69at0yOPPKJVq1Zp8ODBuuGGG/Tzn/88EKUBAAB8bwEJWJJUWVmpF198US%2B%2B%2BKLef/99DR48WFdffbUqKip055136u2339bChQsDVR4AAMB35vdbhNu2bdOsWbM0duxYrVmzRqNHj9bLL7%2BsP/3pT5o2bZrmzp2r%2BfPna9OmTa06nsfj0aRJk7Rnzx7vWFlZmW688UYNGjRIV1xxhf75z3/67PPmm29q0qRJcjqdmjlzpsrKynzmn3nmGY0aNUpJSUm64447VFdX9/0XDgAAzht%2BD1gLFy5U%2B/btVVBQoF27dunWW29Vr169fLa5%2BOKL9ctf/vJbj9XQ0KB58%2BaptLTUO2YYhjIzM9WlSxdt3rxZkydPVlZWlo4ePSpJOnr0qDIzM5WamqpNmzapc%2BfOmjNnjveD9q%2B88ory8/O1ZMkSrVmzRi6XSytWrDCvAQAAIOj5/RbhG2%2B8oU6dOqm6ulqhoV/nu5KSEvXv319hYWGSpMGDB2vw4MHfeJzDhw/r1ltvbfE/EN966y2VlZVpw4YNioqKUu/evbV7925t3rxZc%2BfO1caNGzVgwADNmjVLkpSXl6cRI0Zo7969GjZsmNauXasbbrhBY8eOlSQtXrxYs2fP1u23367IyEiz2wEAAIKQ369gffnll5owYYKeeOIJ71hGRoYmT56s8vLyVh/nVCAqLCz0GXe5XLr00ksVFRXlHUtOTlZxcbF3PiUlxTsXGRmp/v37q7i4WE1NTfrXv/7lMz9o0CB99dVXOnjwYJvXCgAAzk9%2BD1h/%2BMMf1LNnT910003esR07dqhr167Ky8tr9XGmT5%2BuO%2B64o8VVJbfbrbi4OJ%2BxmJgYVVRUfOt8TU2NGhoafOYdDoc6duzo3R8AAODb%2BP0W4TvvvKPnn39esbGx3rHOnTtr/vz5uv7667/38evq6hQeHu4zFh4eLo/H863z9fX13tdn2781Kisr5Xa7fcYcjqgWwe77Cguz1286cjjsU%2B%2Bp3tqtx3ZAb61Ff61Db60XTL31e8ByOByqqalpMV5XV2fKE90jIiJUXV3tM%2BbxeNSuXTvv/OlhyePxKDo6WhEREd7Xp8%2B35fNXhYWFys/P9xnLzMxUdnZ2q48RjDp1ah/oEtosOprP3VmF3lqL/lqH3lonmHrr94D1k5/8RHfffbfuv/9%2B/fCHP5T09WMV8vLyNGrUqO99/Pj4eB0%2BfNhnrKqqynv1KD4%2BXlVVVS3m%2B/Xrp44dOyoiIkJVVVXq3bu3JKmxsVHV1dU%2BV9y%2BTVpamsaNG%2Bcz5nBE6fjxk99lSWdlt6Rv9vqtFBYWqujoSNXU1KmpqTnQ5QQVemst%2Bmsdems9K3obqB/u/R6wFixYoJtuukmXX365oqOjJUk1NTXq37%2B/cnJyvvfxnU6nVq1apfr6eu9Vq6KiIiUnJ3vni4qKvNvX1dXpwIEDysrKUmhoqBITE1VUVKRhw4ZJkoqLi%2BVwONS3b99W1xAXF9fidqDbfUKNjef3G9KO629qarZl3XZAb61Ff61Db60TTL31e8CKiYnR1q1b9eabb6q0tFQOh0OXXHKJLrvsMoWEhHzv4w8dOlRdu3ZVTk6O5syZo7///e8qKSnxfoB%2B6tSpeuqpp7Rq1SqNHTtWBQUF6t69uzdQTZ8%2BXbm5uerTp4/i4uK0aNEiXXvttTyiAQAAtFpAflVOWFiYRo0aZcotwTMd%2B5FHHtHChQuVmpqqnj17qqCgQAkJCZKk7t276%2BGHH9Yf/vAHFRQUKCkpSQUFBd5wd%2BWVV%2Bqzzz5Tbm6uPB6Pfv7zn%2Bv22283vU4AABC8/B6w3G63HnzwQe3bt09fffVViw%2B2v/rqq20%2B5qFDh3xe9%2BzZU%2BvXrz/r9qNHj9bo0aPPOp%2BRkaGMjIw21wEAACAFIGD9/ve/1/79%2B3XllVfqggsu8PfpAQAALOf3gPXWW2/pySef9HlaOgAAQDDx%2B//zj4qKUkxMjL9PCwAA4Dd%2BD1iTJ0/Wk08%2BqaamJn%2BfGgAAwC/8fouwurpa27dv1%2Buvv64ePXq0%2BLU0a9eu9XdJAAAApgrIYxomTZoUiNMCAAD4hd8D1qkHfgIAAASrgPwyu8rKSuXn5%2BvWW2/VF198oZdfflkffvhhIEoBAAAwnd8D1pEjR3TVVVdp69ateuWVV1RbW6sdO3Zo6tSpcrlc/i4HAADAdH4PWPfcc4/Gjx%2BvnTt36gc/%2BIEk6f7779e4ceO0cuVKf5cDAABgOr8HrH379ummm27y%2BcXODodDc%2BbM0YEDB/xdDgAAgOn8HrCam5vV3NzcYvzkyZMKCwvzdzkAAACm83vAGjlypB5//HGfkFVdXa0VK1Zo%2BPDh/i4HAADAdH4PWL/73e%2B0f/9%2BjRw5Ug0NDfrNb36jsWPH6tNPP9WCBQv8XQ4AAIDp/P4crPj4eL3wwgvavn273nvvPTU3Nys9PV2TJ09Whw4d/F0OAACA6QLyJPfIyEhNmzYtEKcGAACwnN8D1syZM79xnt9FCAAA7M7vAatbt24%2BrxsbG3XkyBG9//77uuGGG/xdDgAAgOnOmd9FWFBQoIqKCj9XAwAAYL6A/C7CM5k8ebL%2B8pe/BLoMAACA7%2B2cCVjvvvsuDxoFAABB4Zz4kPuXX36pQ4cOafr06f4uBwAAwHR%2BD1gJCQk%2Bv4dQkn7wgx/ol7/8pX7xi1/4uxwAAADT%2BT1g3XPPPf4%2BJQAAgF/5PWC9/fbbrd52yJAhFlYCAABgDb8HrBkzZnhvERqG4R0/fSwkJETvvffedz5PeXm5Fi1apLffflsdO3bUzJkzdeONN0qSDhw4oLvuukvvv/%2B%2BLrnkEi1evFgDBgzw7rt9%2B3Y9%2BOCDcrvdGjlypJYuXarOnTt/51oAAMD5xe//i/Cxxx5Tt27d9OCDD2r37t0qKirSM888o4suukjz5s3Tq6%2B%2BqldffVU7d%2B78Xuf57W9/q6ioKG3ZskV33HGHHnzwQf3tb39TbW2tMjIylJKSoi1btigpKUm33HKLamtrJUklJSVauHChsrKyVFhYqJqaGuXk5JixdAAAcJ7we8DKy8tTbm6uLr/8cnXq1Ent27fX8OHDtWTJEj333HPq1q2b98939Z///EfFxcX6zW9%2Bo169emn8%2BPEaNWqUdu/erR07digiIkLz589X7969tXDhQrVv314vv/yyJGn9%2BvWaOHGipkyZor59%2B2r58uXatWuXysrKzGoBAAAIcn4PWJWVlWcMTx06dNDx48dNOUe7du0UGRmpLVu26KuvvtKHH36offv2qV%2B/fnK5XEpOTvbekgwJCdHgwYNVXFwsSXK5XEpJSfEeq2vXrkpISJDL5TKlNgAAEPz8/hmsQYMG6f7779e9996rDh06SJKqq6u1YsUKXXbZZaacIyIiQrm5uVq6dKnWrl2rpqYmpaamatq0aXr11Vd1ySWX%2BGwfExOj0tJSSV8HwLi4uBbzbfk1PpWVlXK73T5jDkdUi%2BN%2BX2Fh58xzYlvF4bBPvad6a7ce2wG9tRb9tQ69tV4w9dbvAevOO%2B/UzJkz9ZOf/ES9evWSYRj6%2BOOPFRsbq7Vr15p2ng8%2B%2BEBjx47VTTfdpNLSUi1dulSXXXaZ6urqFB4e7rNteHi4PB6PJKm%2Bvv4b51ujsLBQ%2Bfn5PmOZmZnKzs7%2BjqsJDp06tQ90CW0WHR0Z6BKCFr21Fv21Dr21TjD11u8Bq3fv3tqxY4e2b9%2BuDz74QJJ0/fXX68orr1RkpDmN3b17tzZt2qRdu3apXbt2SkxM1Oeff65HH31UPXr0aBGWPB6P2rVrJ%2Bnrq19nmm9LbWlpaRo3bpzPmMMRpePHT37HFZ2Z3ZK%2B2eu3UlhYqKKjI1VTU6empuZAlxNU6K216K916K31rOhtoH6493vAkqQLL7xQ06ZN06effqoePXpI%2Bvpp7mbZv3%2B/evbs6Q1NknTppZfqscceU0pKiqqqqny2r6qq8t6%2Bi4%2BPP%2BN8bGxsq88fFxfX4nag231CjY3n9xvSjutvamq2Zd12QG%2BtRX%2BtQ2%2BtE0y99fslEMMwtHLlSg0ZMkSTJk1SRUWFFixYoIULF%2Bqrr74y5RxxcXE6cuSIz5WoDz/8UN27d5fT6dS7777rfd6WYRjat2%2BfnE6nJMnpdKqoqMi7X3l5ucrLy73zAAAA38bvAWvdunXatm2b7rrrLu9nncaPH6%2BdO3e2%2BNzSdzVu3Dj94Ac/0J133qmPPvpIr732mh577DHNmDFDEyZMUE1NjZYtW6bDhw9r2bJlqqur08SJEyVJ6enp2rZtmzZu3KiDBw9q/vz5GjNmjPdKGwAAwLfxe8AqLCxUbm6uUlNTvY9KuOKKK3T33XfrpZdeMuUcF1xwgZ555hm53W5dc801ysvL029%2B8xulpaWpQ4cOevzxx1VUVKTU1FS5XC6tWrVKUVFRkqSkpCQtWbJEBQUFSk9P14UXXqi8vDxT6gIAAOcHv38G69NPP1W/fv1ajPft27fFow2%2Bj0suuUSrV68%2B49zAgQO1devWs%2B6bmpqq1NRU02oBAADnF79fwerWrZv%2B9a9/tRh/4403uA0HAACCgt%2BvYM2ePVuLFy%2BW2%2B2WYRjavXu3CgsLtW7dOv3ud7/zdzkAAACm83vAmjp1qhobG/Xoo4%2Bqvr5eubm56ty5s377298qPT3d3%2BUAAACYzu8Ba/v27ZowYYLS0tJ07NgxGYahmJgYf5cBAABgGb9/BmvJkiXeD7N37tyZcAUAAIKO3wNWr1699P777/v7tAAAAH7j91uEffv21W233aYnn3xSvXr1UkREhM88z5wCAAB25/eA9dFHHyk5OVmSTH3uFQAAwLnCLwFr%2BfLlysrKUlRUlNatW%2BePUwIAAASMXz6DtXr1atXV1fmMZWRkqLKy0h%2BnBwAA8Cu/BCzDMFqMvf3222poaPDH6QEAAPzK7/%2BLEAAAINgRsAAAAEzmt4AVEhLir1MBAAAElN8e03D33Xf7PPPqq6%2B%2B0ooVK9S%2BfXuf7XgOFgAAsDu/BKwhQ4a0eOZVUlKSjh8/ruPHj/ujBAAAAL/xS8Di2VcAAOB8wofcAQAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkQRuwPB6PFi9erCFDhujHP/6x7r//fhmGIUk6cOCApk2bJqfTqalTp2r//v0%2B%2B27fvl3jx4%2BX0%2BlUZmamjh07FoglAAAAmwragHX33XfrzTff1FNPPaX77rtPzz//vAoLC1VbW6uMjAylpKRoy5aDO/3VAAAV50lEQVQtSkpK0i233KLa2lpJUklJiRYuXKisrCwVFhaqpqZGOTk5AV4NAACwE7/9LkJ/qq6u1ubNm7V69WoNHDhQkjRr1iy5XC45HA5FRERo/vz5CgkJ0cKFC/XGG2/o5ZdfVmpqqtavX6%2BJEydqypQpkqTly5dr7NixKisrU48ePQK5LAAAYBNBeQWrqKhIHTp00NChQ71jGRkZysvLk8vlUnJyskJCQiRJISEhGjx4sIqLiyVJLpdLKSkp3v26du2qhIQEuVwu/y4CAADYVlAGrLKyMnXr1k0vvPCCJkyYoJ/%2B9KcqKChQc3Oz3G634uLifLaPiYlRRUWFJKmysvIb5wEAAL5NUN4irK2t1ZEjR7Rhwwbl5eXJ7XYrNzdXkZGRqqurU3h4uM/24eHh8ng8kqT6%2BvpvnG%2BNyspKud1unzGHI6pFcPu%2BwsLslY8dDvvUe6q3duuxHdBba9Ff69Bb6wVTb4MyYDkcDn355Ze677771K1bN0nS0aNH9dxzz6lnz54twpLH41G7du0kSREREWecj4yMbPX5CwsLlZ%2Bf7zOWmZmp7Ozs77KcoNGpU/tAl9Bm0dGt/3dH29Bba9Ff69Bb6wRTb4MyYMXGxioiIsIbriTpoosuUnl5uYYOHaqqqiqf7auqqrxXl%2BLj4884Hxsb2%2Brzp6Wlady4cT5jDkeUjh8/2dalfCO7JX2z12%2BlsLBQRUdHqqamTk1NzYEuJ6jQW2vRX%2BvQW%2BtZ0dtA/XAflAHL6XSqoaFBH330kS666CJJ0ocffqhu3brJ6XTqiSeekGEYCgkJkWEY2rdvn37961979y0qKlJqaqokqby8XOXl5XI6na0%2Bf1xcXIvbgW73CTU2nt9vSDuuv6mp2ZZ12wG9tRb9tQ69tU4w9dZel0Ba6eKLL9aYMWOUk5OjgwcP6h//%2BIdWrVql9PR0TZgwQTU1NVq2bJkOHz6sZcuWqa6uThMnTpQkpaena9u2bdq4caMOHjyo%2BfPna8yYMTyiAQAAtFpQBixJWrlypX74wx8qPT1dCxYs0PXXX68ZM2aoQ4cOevzxx71XqVwul1atWqWoqChJUlJSkpYsWaKCggKlp6frwgsvVF5eXoBXAwAA7CQobxFK0gUXXKDly5efcW7gwIHaunXrWfdNTU313iIEAABoq6C9ggUAABAoBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkQR%2BwMjIy9Lvf/c77%2BsCBA5o2bZqcTqemTp2q/fv3%2B2y/fft2jR8/Xk6nU5mZmTp27Ji/SwYAADYX1AHrz3/%2Bs3bt2uV9XVtbq4yMDKWkpGjLli1KSkrSLbfcotraWklSSUmJFi5cqKysLBUWFqqmpkY5OTmBKh8AANhU0Aas6upqLV%2B%2BXImJid6xHTt2KCIiQvPnz1fv3r21cOFCtW/fXi%2B//LIkaf369Zo4caKmTJmivn37avny5dq1a5fKysoCtQwAAGBDQRuw7r33Xk2ePFmXXHKJd8zlcik5OVkhISGSpJCQEA0ePFjFxcXe%2BZSUFO/2Xbt2VUJCglwul3%2BLBwAAtuYIdAFW2L17t9555x299NJLWrRokXfc7Xb7BC5JiomJUWlpqSSpsrJScXFxLeYrKiradP7Kykq53W6fMYcjqsWxv6%2BwMHvlY4fDPvWe6q3demwH9NZa9Nc69NZ6wdTboAtYDQ0Nuuuuu5Sbm6t27dr5zNXV1Sk8PNxnLDw8XB6PR5JUX1//jfOtVVhYqPz8fJ%2BxzMxMZWdnt%2Bk4waZTp/aBLqHNoqMjA11C0KK31qK/1qG31gmm3gZdwMrPz9eAAQM0atSoFnMREREtwpLH4/EGsbPNR0a27R88LS1N48aN8xlzOKJ0/PjJNh3n29gt6Zu9fiuFhYUqOjpSNTV1ampqDnQ5QYXeWov%2BWofeWs%2BK3gbqh/ugC1h//vOfVVVVpaSkJEnyBqZXXnlFkyZNUlVVlc/2VVVV3lt38fHxZ5yPjY1tUw1xcXEtbge63SfU2Hh%2BvyHtuP6mpmZb1m0H9NZa9Nc69NY6wdTboAtY69atU2Njo/f1ypUrJUm33Xab3n77bT3xxBMyDEMhISEyDEP79u3Tr3/9a0mS0%2BlUUVGRUlNTJUnl5eUqLy%2BX0%2Bn0/0IAAIBtBV3A6tatm8/r9u2/vjTYs2dPxcTE6L777tOyZct03XXXacOGDaqrq9PEiRMlSenp6ZoxY4YGDRqkxMRELVu2TGPGjFGPHj38vg4AAGBf9voQz/fUoUMHPf74496rVC6XS6tWrVJUVJQkKSkpSUuWLFFBQYHS09N14YUXKi8vL8BVAwAAuwm6K1inu%2Beee3xeDxw4UFu3bj3r9qmpqd5bhAAAAN/FeXUFCwAAwB8IWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYLKgDViff/65srOzNXToUI0aNUp5eXlqaGiQJJWVlenGG2/UoEGDdMUVV%2Bif//ynz75vvvmmJk2aJKfTqZkzZ6qsrCwQSwAAADYVlAHLMAxlZ2errq5Ozz77rB544AH9/e9/14MPPijDMJSZmakuXbpo8%2BbNmjx5srKysnT06FFJ0tGjR5WZmanU1FRt2rRJnTt31pw5c2QYRoBXBQAA7MIR6AKs8OGHH6q4uFj/8z//oy5dukiSsrOzde%2B99%2BonP/mJysrKtGHDBkVFRal3797avXu3Nm/erLlz52rjxo0aMGCAZs2aJUnKy8vTiBEjtHfvXg0bNiyQywIAADYRlFewYmNj9eSTT3rD1SlffvmlXC6XLr30UkVFRXnHk5OTVVxcLElyuVxKSUnxzkVGRqp///7eeQAAgG8TlAErOjpao0aN8r5ubm7W%2BvXrNXz4cLndbsXFxflsHxMTo4qKCkn61nkAAIBvE5S3CE%2B3YsUKHThwQJs2bdIzzzyj8PBwn/nw8HB5PB5JUl1d3TfOt0ZlZaXcbrfPmMMR1SK4fV9hYfbKxw6Hfeo91Vu79dgO6K216K916K31gqm3QR%2BwVqxYoTVr1uiBBx5Qnz59FBERoerqap9tPB6P2rVrJ0mKiIhoEaY8Ho%2Bio6Nbfc7CwkLl5%2Bf7jGVmZio7O/s7riI4dOrUPtAltFl0dGSgSwha9NZa9Nc69NY6wdTboA5YS5cu1XPPPacVK1bo8ssvlyTFx8fr8OHDPttVVVV5ry7Fx8erqqqqxXy/fv1afd60tDSNGzfOZ8zhiNLx4ye/yzLOym5J3%2Bz1WyksLFTR0ZGqqalTU1NzoMsJKvTWWvTXOvTWelb0NlA/3AdtwMrPz9eGDRt0//33a8KECd5xp9OpVatWqb6%2B3nvVqqioSMnJyd75oqIi7/Z1dXU6cOCAsrKyWn3uuLi4FrcD3e4Tamw8v9%2BQdlx/U1OzLeu2A3prLfprHXprnWDqrb0ugbTSBx98oEceeUQ333yzkpOT5Xa7vX%2BGDh2qrl27KicnR6WlpVq1apVKSkp0zTXXSJKmTp2qffv2adWqVSotLVVOTo66d%2B/OIxoAAECrBWXAevXVV9XU1KRHH31UI0eO9PkTFhamRx55RG63W6mpqXrxxRdVUFCghIQESVL37t318MMPa/PmzbrmmmtUXV2tgoIChYSEBHhVAADALoLyFmFGRoYyMjLOOt%2BzZ0%2BtX7/%2BrPOjR4/W6NGjrSgNAACcB4LyChYAAEAgEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbDOoKGhQXfccYdSUlI0cuRIPf3004EuCQAA2Igj0AWci5YvX679%2B/drzZo1Onr0qBYsWKCEhARNmDAh0KUBAAAbIGCdpra2Vhs3btQTTzyh/v37q3///iotLdWzzz5LwAIAAK3CLcLTHDx4UI2NjUpKSvKOJScny%2BVyqbm5OYCVAQAAu%2BAK1mncbrc6deqk8PBw71iXLl3U0NCg6upqde7c%2BVuPUVlZKbfb7TPmcEQpLi7O1FrDwuyVjx0O%2B9R7qrd267Ed0Ftr0V/r0FvrBVNvCVinqaur8wlXkryvPR5Pq45RWFio/Px8n7GsrCzNnTvXnCL/V2VlpW74r1KlpaWZHt7Od5WVlVqz5kl6awF6ay36ax079vadZfb4aEtlZaUefvhhW/X22wRPVDRJREREiyB16nW7du1adYy0tDRt2bLF509aWprptbrdbuXn57e4Wobvj95ah95ai/5ah95aJxh7yxWs08THx%2Bv48eNqbGyUw/F1e9xut9q1a6fo6OhWHSMuLi5oEjgAAGg7rmCdpl%2B/fnI4HCouLvaOFRUVKTExUaGhtAsAAHw7EsNpIiMjNWXKFC1atEglJSXauXOnnn76ac2cOTPQpQEAAJsIW7Ro0aJAF3GuGT58uA4cOKD77rtPu3fv1q9//WtNnTo10GWdUfv27TV06FC1b98%2B0KUEHXprHXprLfprHXprnWDrbYhhGEagiwAAAAgm3CIEAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwDrHNTQ06I477lBKSopGjhypp59%2B%2BqzbHjhwQNOmTZPT6dTUqVO1f/9%2BP1ZqP23p7euvv67JkycrKSlJV111lV599VU/Vmo/bentKZ9%2B%2BqmSkpK0Z88eP1Rob23p76FDh5Senq6BAwfqqquu0ltvveXHSu2nLb3929/%2BpokTJyopKUnp6en697//7cdK7cvj8WjSpEnf%2BF4Piu9nBs5pS5YsMa666ipj//79xl//%2BlcjKSnJ%2BMtf/tJiu5MnTxojRoww7rnnHuPw4cPG0qVLjR//%2BMfGyZMnA1C1PbS2t%2B%2B9957Rv39/Y82aNcbHH39srF%2B/3ujfv7/x3nvvBaBqe2htb/%2Bv2bNnG3369DHeeustP1VpX63tb01NjfHjH//YuPPOO42PP/7Y%2BOMf/2gkJycbVVVVAajaHlrb2/fff99ITEw0tm7dahw5csRYvHixMWLECKO2tjYAVdtHfX29kZmZ%2BY3v9WD5fkbAOoedPHnSSExM9PkiLCgoMH75y1%2B22Hbjxo3GuHHjjObmZsMwDKO5udn42c9%2BZmzevNlv9dpJW3q7YsUKY/bs2T5js2bNMu6//37L67SjtvT2lG3bthnXXXcdAasV2tLfNWvWGOPHjzcaGxu9Y6mpqcbrr7/ul1rtpi29Xb16tXH11Vd7X584ccLo06ePUVJS4pda7ai0tNT4xS9%2BYVx11VXf%2BF4Plu9n3CI8hx08eFCNjY1KSkryjiUnJ8vlcqm5udlnW5fLpeTkZIWEhEiSQkJCNHjwYBUXF/u1ZrtoS2%2Bvvvpq3XbbbS2OceLECcvrtKO29FaSjh8/rhUrVmjJkiX%2BLNO22tLfvXv36qc//anCwsK8Y5s3b9bo0aP9Vq%2BdtKW3HTt21OHDh1VUVKTm5mZt2bJFHTp00A9/%2BEN/l20be/fu1bBhw1RYWPiN2wXL9zNHoAvA2bndbnXq1Enh4eHesS5duqihoUHV1dXq3Lmzz7aXXHKJz/4xMTEqLS31W7120pbe9u7d22ff0tJS7d69W9ddd53f6rWTtvRWku655x5dffXV%2BtGPfuTvUm2pLf0tKyvTwIED9fvf/16vvfaaunXrpgULFig5OTkQpZ/z2tLbK664Qq%2B99pqmT5%2BusLAwhYaG6vHHH9eFF14YiNJtYfr06a3aLli%2Bn3EF6xxWV1fn80aX5H3t8Xhate3p2%2BFrbent/3Xs2DHNnTtXgwcP1k9/%2BlNLa7SrtvT2zTffVFFRkebMmeO3%2BuyuLf2tra3VqlWrFBsbqyeeeEJDhgzR7NmzVV5e7rd67aQtvT1%2B/Ljcbrdyc3P1/PPPa/LkycrJydEXX3zht3qDVbB8PyNgncMiIiJafEGdet2uXbtWbXv6dvhaW3p7SlVVlW644QYZhqGHHnpIoaG8fc6ktb2tr69Xbm6u7rrrLr5O26AtX7thYWHq16%2BfsrOzdemll%2Br2229Xr169tG3bNr/Vaydt6e3KlSvVp08fXX/99RowYICWLl2qyMhIbd682W/1Bqtg%2BX7Gd4hzWHx8vI4fP67GxkbvmNvtVrt27RQdHd1i26qqKp%2BxqqoqxcXF%2BaVWu2lLbyXp888/1/XXXy%2BPx6O1a9e2uM2F/6%2B1vS0pKVFZWZmys7OVlJTk/dzLzTffrNzcXL/XbRdt%2BdqNjY3VxRdf7DPWq1cvrmCdRVt6%2B%2B9//1t9%2B/b1vg4NDVXfvn119OhRv9UbrILl%2BxkB6xzWr18/ORwOnw/2FRUVKTExscXVE6fTqXfffVeGYUiSDMPQvn375HQ6/VqzXbSlt7W1tfrVr36l0NBQrV%2B/XvHx8f4u11Za29uBAwfqr3/9q1544QXvH0m6%2B%2B679d///d9%2Br9su2vK1O2jQIB06dMhn7MMPP1S3bt38UqvdtKW3cXFx%2BuCDD3zGPvroI3Xv3t0vtQazYPl%2BRsA6h0VGRmrKlClatGiRSkpKtHPnTj399NOaOXOmpK9/sqqvr5ckTZgwQTU1NVq2bJkOHz6sZcuWqa6uThMnTgzkEs5Zbent448/rk8%2B%2BUT33nuvd87tdvO/CM%2Bitb1t166devbs6fNH%2Bvqn15iYmEAu4ZzWlq/d6667TocOHdLDDz%2BsI0eO6I9//KPKyso0efLkQC7hnNWW3l577bV6/vnn9cILL%2BjIkSNauXKljh49qquvvjqQS7CtoPx%2BFtCHROBb1dbWGvPnzzcGDRpkjBw50li9erV3rk%2BfPj7PBXG5XMaUKVOMxMRE45prrjH%2B/e9/B6Bi%2B2htby%2B//HKjT58%2BLf4sWLAgQJWf%2B9rydft/8Rys1mlLf9955x3j6quvNgYMGGBMnjzZ2Lt3bwAqto%2B29Pb55583JkyYYAwaNMhIT0839u/fH4CK7en093owfj8LMYz/vQYHAAAAU3CLEAAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGT/D5zv2TzjmAIoAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2543445793593944637”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2543445793593944637”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1673</td> <td class=”number”>52.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1459</td> <td class=”number”>45.5%</td> <td>

<div class=”bar” style=”width:87%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_BBCE16”>SNAP_BBCE16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.73851</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>25.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2016962497983251145”>

</div> <div class=”col-md-12 text-right”>

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<li role=”presentation” class=”active”><a href=”#quantiles-2016962497983251145”

aria-controls=”quantiles-2016962497983251145” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2016962497983251145” aria-controls=”histogram-2016962497983251145”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2016962497983251145” aria-controls=”common-2016962497983251145”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2016962497983251145” aria-controls=”extreme-2016962497983251145”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2016962497983251145”>
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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.43952</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.59515</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.82113</td>

</tr> <tr>

<th>Mean</th> <td>0.73851</td>

</tr> <tr>

<th>MAD</th> <td>0.38623</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.086</td>

</tr> <tr>

<th>Sum</th> <td>2313</td>

</tr> <tr>

<th>Variance</th> <td>0.19318</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

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2kXtpaalee%2B01vfbaa/rkk0/Uq1cvjRkzRidOnNCDDz6oDz74QPPmzfNXeQAAAD%2BY4wFr06ZN2rRpk3bs2KE2bdpo9OjRevLJJ72%2BO7Bdu3ZavHgxAQsAAAQkxwPWvHnzNGjQIC1fvlzXXXedQkMbXwZ27mtuAAAAApHjAeu9995T69atVVFR4QlXRUVF6t69u8LCwiRJvXr1Uq9evZwuDQAAwAjHf4vw1KlTGjp0qJ5%2B%2BmnP2MyZM5Wamqrjx487XQ4AAIBxjges3//%2B97ryyis1bdo0z9jrr7%2Budu3aKSsry%2BlyAAAAjHM8YH344Ye6//77vb48uU2bNrrvvvs8X8gMAAAQyBwPWC6XS5WVlY3Gq6qq5PBN5QEAAGzheMC67rrrtGjRIn3%2B%2BeeesZKSEmVlZWngwIFOlwMAAGCc479FOGfOHE2bNk0333yzoqKiJEmVlZXq3r275s6d63Q5AAAAxjkesKKjo/XKK69o27ZtKi4ulsvl0tVXX62f//znQf%2BFzwAA4NLgl6/KCQsL08CBAzklCAAAgpLjAcvtduuJJ57Qrl27dPbs2UYXtr/99ttOlwQAAGCU4wHroYce0u7duzVixAhddtllTr88AACA7RwPWNu3b9fq1auVkpLi9EsDAAA4wvHbNDRv3lzR0dFOvywAAIBjHA9YqampWr16terr651%2BaQAAAEc4foqwoqJCmzdv1jvvvKMrrrhC4eHhXvPr1693uiQAAACj/HKbhpEjR/rjZQEAABzheMDKyspy%2BiUBAAAc5fg1WJJUWlqqnJwc3XPPPfrqq6/05ptv6vDhw/4oBQAAwDjHA9aRI0c0atQovfLKK3rrrbd05swZvf766xo7dqwKCwudLgcAAMA4xwPWI488oiFDhmjLli36yU9%2BIkl67LHHNHjwYC1dutTpcgAAAIxzPGDt2rVL06ZN8/piZ5fLpbvuukt79%2B51uhwAAADjHA9YDQ0NamhoaDR%2B%2BvRphYWFOV0OAACAcY4HrAEDBmjlypVeIauiokLZ2dnq16%2Bf0%2BUAAAAY53jAuv/%2B%2B7V7924NGDBANTU1%2Bo//%2BA8NGjRIR48e1Zw5c5wuBwAAwDjH74MVFxenV199VZs3b9a%2BffvU0NCgSZMmKTU1VS1btnS6HAAAAOP8cif3yMhIjR8/3h8vDQAAYDvHA9aUKVO%2Bc57vIgQAAIHO8YDVoUMHr%2Bd1dXU6cuSIPvnkE02dOtXpcgAAAIy7aL6LcPny5Tpx4oTD1QAAAJjnl%2B8ivJDU1FS98cYb/i4DAADgR7toAtZHH33EjUYBAEBQuCgucj916pQOHDigyZMnO10OAACAcY4HrPbt23t9D6Ek/eQnP9Ftt92mX/3qV06XAwAAYJzjAeuRRx5x%2BiUBAAAc5XjA%2BuCDD3xetnfv3jZWAgAAYA/HA9avf/1rzylCy7I84%2BePhYSEaN%2B%2BfU6XBwAA8KM5HrD%2B9Kc/adGiRZo9e7b69Omj8PBwffzxx8rMzNSYMWM0fPhwp0sCAAAwyvHbNGRlZWn%2B/Pm6%2Beab1bp1a7Vo0UL9%2BvVTZmamXnjhBXXo0MHzAAAACESOB6zS0tILhqeWLVuqvLzc6XIAAACMczxgJSYm6rHHHtOpU6c8YxUVFcrOztbPf/5zp8sBAAAwzvFrsB588EFNmTJF1113nTp16iTLsvTZZ58pJiZG69evd7ocAAAA4xwPWJ07d9brr7%2BuzZs369ChQ5KkW2%2B9VSNGjFBkZKTT5QAAABjneMCSpMsvv1zjx4/X0aNHdcUVV0j65m7uAAAAwcDxa7Asy9LSpUvVu3dvjRw5UidOnNCcOXM0b948nT171ulyAAAAjHM8YD333HPatGmTfve73yk8PFySNGTIEG3ZskU5OTlOlwMAAGCc4wErNzdX8%2BfPV1pamufu7cOHD9eiRYv05z//2elyAAAAjHM8YB09elTdunVrNB4fHy%2B32%2B10OQAAAMY5HrA6dOigjz/%2BuNH4e%2B%2B957ngHQAAIJA5/luEM2bM0MKFC%2BV2u2VZlt5//33l5ubqueee0/333%2B90OQAAAMY5fgRr7Nix%2Bq//%2Bi%2BtWbNG1dXVmj9/vl5%2B%2BWX99re/1aRJk5q8vdraWo0cOVI7duzwjJWUlOj2229XYmKihg8frq1bt3qts23bNo0cOVIJCQmaMmWKSkpKvOafffZZDRw4UElJSXrggQdUVVX1w3YWAABckhwPWJs3b9bQoUP1zjvvaNu2bfrHP/6hbdu2adq0aU3eVk1Njf77v/9bxcXFnjHLspSenq62bdsqLy9PqampmjVrlo4dOyZJOnbsmNLT05WWlqaXXnpJbdq00V133SXLsiRJb731lnJycpSZmal169apsLBQ2dnZZnYeAABcEhwPWJmZmZ6L2du0aaPo6OgftJ2DBw9qwoQJ%2Bvzzz73Gt2/frpKSEmVmZqpz58668847lZiYqLy8PEnSxo0bde2112r69Onq0qWLsrKy9MUXX2jnzp2SpPXr12vq1KkaNGiQevbsqYULFyovL4%2BjWAAAwGeOB6xOnTrpk08%2B%2BdHb2blzp/r27avc3Fyv8cLCQl1zzTVq3ry5Zyw5OVkFBQWe%2BZSUFM9cZGSkunfvroKCAtXX1%2Bvjjz/2mk9MTNTZs2e1f//%2BH10zAAC4NDh%2BkXt8fLzuvfderV69Wp06dVJERITXfFZWlk/bmTx58gXH3W63YmNjvcaio6N14sSJ752vrKxUTU2N17zL5VKrVq086/uitLS00S0nXK7mjV73xwoLC/X6E%2BbQW/vQW3vRX/vQW/sFU28dD1iffvqpkpOTJcmW%2B15VVVV57hB/Tnh4uGpra793vrq62vP829b3RW5ubqO70qenpysjI8PnbTRFVBRfkm0Xemsfemsv%2BmsfemufYOqtIwFryZIlmjVrlpo3b67nnnvO1teKiIhQRUWF11htba2aNWvmmT8/LNXW1ioqKspzNO1C85GRvv%2BjT5w4UYMHD/Yac7maq7z8tM/b8EVYWKiioiJVWVml%2BvoGo9u%2B1NFb%2B9Bbe9Ff%2B9Bb%2B9nR29atWxjdnq8cCVhr167VjBkzvK6LmjlzphYtWmT8tFlcXJwOHjzoNVZWVuZ5nbi4OJWVlTWa79atm1q1aqWIiAiVlZWpc%2BfOkqS6ujpVVFQoJibG5xpiY2Mb7Zfb/bXq6ux5Q9bXN9i27UsdvbUPvbUX/bUPvbVPMPXWkZOd526B8K8%2B%2BOAD1dTUGH%2BthIQE7dmzx3O6T5Ly8/OVkJDgmc/Pz/fMVVVVae/evUpISFBoaKh69OjhNV9QUCCXy6X4%2BHjjtQIAgOAUPFeT/VOfPn3Url07zZ07V8XFxVq1apWKioo0btw4Sd/c6HTXrl1atWqViouLNXfuXHXs2FF9%2B/aV9M3F888884y2bNmioqIiLViwQBMmTGjSKUIAAHBpC7qAFRYWphUrVsjtdistLU2vvfaali9frvbt20uSOnbsqKeeekp5eXkaN26cKioqtHz5coWEhEiSRowYoTvvvFPz58/X9OnT1bNnT82ePdufuwQAAAKMY79FeC7A2OHAgQNez6%2B88kpt2LDhW5e//vrrdf3113/r/MyZMzVz5kxj9QEAgEuLYwFr0aJFXve8Onv2rLKzs9WihffV/b7eBwsAAOBi5UjA6t27d6N7XiUlJam8vFzl5eVOlAAAAOAYRwKW3fe%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%2BrMmTOSpKKiIs2bN0%2BzZs1Sbm6uKisrNXfuXD/vDQAACCRBG7AOHTqkrl27KiYmxvOIiorS66%2B/roiICN13333q3Lmz5s2bpxYtWujNN9%2BUJG3YsEHDhg3T6NGjFR8fryVLlujdd99VSUmJn/cIAAAEiqAOWJ06dWo0XlhYqOTkZIWEhEiSQkJC1KtXLxUUFHjmU1JSPMu3a9dO7du3V2FhoSN1AwCAwOfydwF2sCxLn376qbZu3aqVK1eqvr5eQ4cOVUZGhtxut66%2B%2Bmqv5aOjo1VcXCxJKi0tVWxsbKP5EydO%2BPz6paWlcrvdXmMuV/NG2/2xwsICKx%2B7XIFT77neBlqPAwG9tRf9tQ%2B9tV8w9TYoA9axY8dUVVWl8PBwPfHEEzp69KgWLVqk6upqz/i/Cg8PV21trSSpurr6O%2Bd9kZubq5ycHK%2Bx9PR0z0X2l6rWrVv4u4Qmi4qK9HcJQYve2ov%2B2ofe2ieYehuUAatDhw7asWOHLr/8coWEhKhbt25qaGjQ7Nmz1adPn0Zhqba2Vs2aNZMkRUREXHA%2BMtL3f/SJEydq8ODBXmMuV3OVl5/%2BgXt0YYGW9E3vv53CwkIVFRWpysoq1dc3%2BLucoEJv7UV/7UNv7WdHb/31n/ugDFiS1KpVK6/nnTt3Vk1NjWJiYlRWVuY1V1ZW5jl9FxcXd8H5mJgYn187Nja20elAt/tr1dVd2m/IQNz/%2BvqGgKw7ENBbe9Ff%2B9Bb%2BwRTbwPrEIiP/vd//1d9%2B/ZVVVWVZ2zfvn1q1aqVkpOT9dFHH8myLEnfXK%2B1a9cuJSQkSJISEhKUn5/vWe/48eM6fvy4Zx4AAOD7BGXASkpKUkREhB588EEdPnxY7777rpYsWaI77rhDQ4cOVWVlpRYvXqyDBw9q8eLFqqqq0rBhwyRJkyZN0qZNm7Rx40bt379f9913n2644QZdccUVft4rAAAQKIIyYLVs2VLPPPOMTp48qbFjx2revHmaOHGi7rjjDrVs2VIrV65Ufn6%2B0tLSVFhYqFWrVql58%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%2BjQof4uDQAABAAC1nnOnDmjjRs36umnn1b37t3VvXt3FRcX6/nnnydgAQAAn3CK8Dz79%2B9XXV2dkpKSPGPJyckqLCxUQ0ODHysDAACBgiNY53G73WrdurXCw8M9Y23btlVNTY0qKirUpk2b791GaWmp3G6315jL1VyxsbFGaw0LC6x87HIFTr3nehtoPQ4E9NZe9Nc%2B9NZ%2BwdRbAtZ5qqqqvMKVJM/z2tpan7aRm5urnJwcr7FZs2bp7rvvNlPkP5WWlmrqT4s1ceJE4%2BHtUldaWqp161bTWxvQW3vRX/sEYm8/XBwYl7aUlpbqqaeeCqjefp/giYqGRERENApS5543a9bMp21MnDhRL7/8stdj4sSJxmt1u93Kycn4AabmAAAISklEQVRpdLQMPx69tQ%2B9tRf9tQ%2B9tU8w9pYjWOeJi4tTeXm56urq5HJ90x63261mzZopKirKp23ExsYGTQIHAABNxxGs83Tr1k0ul0sFBQWesfz8fPXo0UOhobQLAAB8PxLDeSIjIzV69GgtWLBARUVF2rJli9asWaMpU6b4uzQAABAgwhYsWLDA30VcbPr166e9e/fq0Ucf1fvvv69///d/19ixY/1d1gW1aNFCffr0UYsWLfxdStCht/aht/aiv/aht/YJtt6GWJZl%2BbsIAACAYMIpQgAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBKyLXE1NjR544AGlpKRowIABWrNmzbcuu3fvXo0fP14JCQkaO3asdu/e7WClgacpvX3nnXeUmpqqpKQkjRo1Sm%2B//baDlQaepvT2nKNHjyopKUk7duxwoMLA1pT%2BHjhwQJMmTVLPnj01atQobd%2B%2B3cFKA09TevvXv/5Vw4YNU1JSkiZNmqQ9e/Y4WGngqq2t1ciRI7/zvR4Un2cWLmqZmZnWqFGjrN27d1t/%2BctfrKSkJOuNN95otNzp06et/v37W4888oh18OBB6%2BGHH7Z%2B8YtfWKdPn/ZD1YHB197u27fP6t69u7Vu3Trrs88%2BszZs2GB1797d2rdvnx%2BqDgy%2B9vZfzZgxw%2Bratau1fft2h6oMXL72t7Ky0vrFL35hPfjgg9Znn31mLVu2zEpOTrbKysr8UHVg8LW3n3zyidWjRw/rlVdesY4cOWItXLjQ6t%2B/v3XmzBk/VB04qqurrfT09O98rwfL5xkB6yJ2%2BvRpq0ePHl4/hMuXL7duu%2B22Rstu3LjRGjx4sNXQ0GBZlmU1NDRYN910k5WXl%2BdYvYGkKb3Nzs62ZsyY4TU2ffp067HHHrO9zkDUlN6es2nTJuuWW24hYPmgKf1dt26dNWTIEKuurs4zlpaWZr3zzjuO1BpomtLbtWvXWmPGjPE8//rrr62uXbtaRUVFjtQaiIqLi61f/epX1qhRo77zvR4sn2ecIryI7d%2B/X3V1dUpKSvKMJScnq7CwUA0NDV7LFhYWKjk5WSEhIZKkkJAQ9erVSwUFBY7WHCia0tsxY8bo3nvvbbSNr7/%2B2vY6A1FTeitJ5eXlys7OVmZmppNlBqym9Hfnzp268cYbFRYW5hnLy8vT9ddf71i9gaQpvW3VqpUOHjyo/Px8NTQ06OWXX1bLli31b//2b06XHTB27typvn37Kjc39zuXC5bPM5e/C8C3c7vdat26tcLDwz1jbdu2VU1NjSoqKtSmTRuvZa%2B%2B%2Bmqv9aOjo1VcXOxYvYGkKb3t3Lmz17rFxcV6//33dcsttzhWbyBpSm8l6ZFHHtGYMWPUpUsXp0sNSE3pb0lJiXr27KmHHnpIf/vb39ShQwfNmTNHycnJ/ij9oteU3g4fPlx/%2B9vfNHnyZIWFhSk0NFQrV67U5Zdf7o/SA8LkyZN9Wi5YPs84gnURq6qq8nqjS/I8r62t9WnZ85fDN5rS23918uRJ3X333erVq5duvPFGW2sMVE3p7bZt25Sfn6%2B77rrLsfoCXVP6e%2BbMGa1atUoxMTF6%2Bumn1bt3b82YMUPHjx93rN5A0pTelpeXy%2B12a/78%2BXrxxReVmpqquXPn6quvvnKs3mAVLJ9nBKyLWERERKMfqHPPmzVr5tOy5y%2BHbzSlt%2BeUlZVp6tSpsixLTz75pEJDeftciK%2B9ra6u1vz58/W73/2On9MmaMrPblhYmLp166aMjAxdc801mj17tjp16qRNmzY5Vm8gaUpvly5dqq5du%2BrWW2/Vtddeq4cffliRkZHKy8tzrN5gFSyfZ3xCXMTi4uJUXl6uuro6z5jb7VazZs0UFRXVaNmysjKvsbKyMsXGxjpSa6BpSm8l6csvv9Stt96q2tparV%2B/vtFpLvx/vva2qKhIJSUlysjIUFJSkue6l9/85jeaP3%2B%2B43UHiqb87MbExOiqq67yGuvUqRNHsL5FU3q7Z88excfHe56HhoYqPj5ex44dc6zeYBUsn2cErItYt27d5HK5vC7sy8/PV48ePRodPUlISNBHH30ky7IkSZZladeuXUpISHC05kDRlN6eOXNGd9xxh0JDQ7VhwwbFxcU5XW5A8bW3PXv21F/%2B8he9%2BuqrnockLVq0SP/5n//peN2Boik/u4mJiTpw4IDX2OHDh9WhQwdHag00TeltbGysDh065DX26aefqmPHjo7UGsyC5fOMgHURi4yM1OjRo7VgwQIVFRVpy5YtWrNmjaZMmSLpm/9ZVVdXS5KGDh2qyspKLV68WAcPHtTixYtVVVWlYcOG%2BXMXLlpN6e3KlSv1%2Beef6w9/%2BINnzu1281uE38LX3jZr1kxXXnml10P65n%2Bv0dHR/tyFi1pTfnZvueUWHThwQE899ZSOHDmiZcuWqaSkRKmpqf7chYtWU3o7YcIEvfjii3r11Vd15MgRLV26VMeOHdOYMWP8uQsBKyg/z/x6kwh8rzNnzlj33XeflZiYaA0YMMBau3atZ65r165e9wUpLCy0Ro8ebfXo0cMaN26ctWfPHj9UHDh87e3NN99sde3atdFjzpw5fqr84teUn9t/xX2wfNOU/n744YfWmDFjrGuvvdZKTU21du7c6YeKA0dTevviiy9aQ4cOtRITE61JkyZZu3fv9kPFgen893owfp6FWNY/j8EBAADACE4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/w9TPZM/Wl6TYQAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2016962497983251145”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2016962497983251145”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>819</td> <td class=”number”>25.5%</td> <td>

<div class=”bar” style=”width:36%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2313</td> <td class=”number”>72.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_CAP09”>SNAP_CAP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>61.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram6540694832340670645”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles6540694832340670645”

aria-controls=”quantiles6540694832340670645” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram6540694832340670645” aria-controls=”histogram6540694832340670645”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common6540694832340670645” aria-controls=”common6540694832340670645”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme6540694832340670645” aria-controls=”extreme6540694832340670645”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48332</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3005</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.7186</td>

</tr> <tr>

<th>Mean</th> <td>0.37165</td>

</tr> <tr>

<th>MAD</th> <td>0.46705</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.53147</td>

</tr> <tr>

<th>Sum</th> <td>1164</td>

</tr> <tr>

<th>Variance</th> <td>0.2336</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

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vlZSUuMPaWQ5HY9MDWkiIvQ5AOhz2qfdsb%2B3WYzugt9aiv9aht9YJxN4GXMAaMWKE%2BvXrp6ioKElSQkKC9u/fr1deeUUDBw5UWFhYvbDkcrkUHh7uEabCwsLc/y/JfQ2XN3JycpSdne0xlp6eroyMjP94XoEgOrqJv0tosMhI7//d0TD01lr01zr01jqB1NuAC1hBQUHucHVW27ZttXnzZklSfHy8SktLPZaXlpYqNjZW8fHxkiSn06lWrVq5/1%2BSYmNjva4hLS1N/fv39xhzOBqrrOxUwybzM%2ByW9M2ev5VCQoIVGRmuiopK1dZymw4z0Vtr0V/r0FvrWNlbf/1xH3ABa%2BnSpfr888/14osvusd2796ttm3bSpISExOVm5ur1NRUSdKRI0d05MgRJSYmKj4%2BXi1atFBubq47YOXm5qpFixYNOr0XFxdXb32n84Rqai7sN6Qd519bW2fLuu2A3lqL/lqH3lonkHobcAGrX79%2BWr58uVauXKmBAwfqo48%2B0ptvvqk1a9ZIksaMGaPbbrtNXbt2VefOnbVw4UL17dtXrVu3di9fsmSJfvWrX0mSHn30Ud11111%2Bmw8AALCfgAtYXbp00dKlS/Xkk09q6dKlatmypR599FElJSVJkpKSkjRv3jw9%2BeST%2Bvbbb9WrVy/Nnz/fvf348eP1zTffaNKkSQoJCdFNN92kO%2B64w0%2BzAQAAdhRkGIbh7yIuBE7nCdP36XAEa%2BCSD03fr1Xe/mMvf5fgNYcjWNHRTVRWdipgDlefL%2BitteivdeitdazsbWzsxabuz1v2ukoaAADABghYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmMz2AcvlcmnYsGHasmWLeywvL0%2B33HKLkpKSNGjQIK1bt85jm9/%2B9re64oorPB5ffvmlJMkwDC1ZskQ9e/ZU9%2B7dtXjxYtXV1fl0TgAAwN4c/i7gl6iurtaUKVNUWFjoHnM6nbrnnns0ZswYPfLII9q5c6dmzJih2NhY9e3bV7W1tdq/f7/Wrl2rNm3auLeLjo6WJK1atUobN25Udna2ampqNHXqVMXExGj8%2BPG%2Bnh4AALAp2wasvXv3asqUKTIMw2N806ZNatasme677z5JUps2bbRlyxb99a9/Vd%2B%2BfXXw4EGdOXNGXbp0UVhYWL39rlmzRhkZGUpJSZEk3X///Vq6dCkBCwAAeM22pwi3bt2qHj16KCcnx2O8T58%2BWrRoUb31T548Kem7YNa8efNzhqtjx47pyJEjuvrqq91jycnJOnTokEpKSkyeAQAACFS2PYI1duzYc463atVKrVq1cj//5ptv9Le//U2TJ0%2BWJBUVFemiiy7Svffeqx07duiyyy7TtGnT1KVLFzmdTklSXFyce/tmzZpJko4ePeox/lNKSkrc%2BzrL4Wjs9fbeCgmxVz52OOxT79ne2q3HdkBvrUV/rUNvrROIvbVtwPJGVVWVJk%2BerGbNmiktLU2S9NVXX%2Bnbb7/V6NGjlZGRoVdffVXjxo3TW2%2B9paqqKklSaGioex9n/9/lcnn9ujk5OcrOzvYYS09PV0ZGxi%2Bdkq1FRzfxdwkNFhkZ7u8SAha9tRb9tQ69tU4g9dbnAWv06NEaNWqUhg4dqosvvtiy1zl16pQmTpyo/fv3609/%2BpPCw7/7R5s/f76qqqoUEREhSZozZ44%2B%2B%2BwzbdiwQddcc42k78LU2VOIZ4PV2e29kZaWpv79%2B3uMORyNVVZ26hfP6/vslvTNnr%2BVQkKCFRkZroqKStXW8ilSM9Fba9Ff69Bb61jZW3/9ce/zgNWzZ089%2B%2ByzWrRoka677jqlpqaqV69eCgoKMu01Tp48qbvvvltff/21Vq9e7fFpQYfD4Q5XkhQUFKS2bdvq2LFjio%2BPl/TdJxHPnmY8e6ovNjbW69ePi4urdzrQ6TyhmpoL%2Bw1px/nX1tbZsm47oLfWor/WobfWCaTe%2BvwQyJQpU/Tuu%2B/q6aefVkhIiCZPnqy%2Bffvq8ccf11dfffWL919XV6dJkybp4MGDeumll9S%2BfXuP5bfddpvH6bu6ujrt2bNHbdu2VXx8vFq0aKHc3Fz38tzcXLVo0cL066cAAEDg8ss1WEFBQerVq5d69eqlyspKvfTSS3r66ae1fPlydevWTePGjdP111//H%2B37tdde05YtW/TMM88oMjLSfQTqoosuUlRUlPr3769ly5apY8eOuuyyy7RmzRqdOHFCI0eOlCSNGTNGS5Ys0a9%2B9StJ0qOPPqq77rrLnIkDAIALgt8uci8pKdFf/vIX/eUvf9GXX36pbt26aeTIkTp69KgefPBBbdu2TZmZmQ3e7zvvvKO6ujrde%2B%2B9HuPdu3fXSy%2B9pDvuuEPV1dVasGCBSktLlZiYqFWrVrlPG44fP17ffPONJk2apJCQEN1000264447zJgyAAC4QAQZP7xTp8U2bNigDRs2aMuWLWratKlGjBihUaNGeVwn9dprr2nhwoX6/PPPfVmapZzOE6bv0%2BEI1sAlH5q%2BX6u8/cde/i7Baw5HsKKjm6is7FTAXA9wvqC31qK/1qG31rGyt7Gx1n2g7qf4/AhWZmam%2BvXrp2XLlunaa69VcHD9y8Datm2rW2%2B91delAQAAmMLnAeuDDz5QdHS0ysvL3eGqoKBAnTp1UkhIiCSpW7du6tatm69LAwAAMIXPP0V48uRJDR48WCtWrHCPTZgwQcOHD9eRI0d8XQ4AAIDpfB6wHn74YV166aW688473WNvvfWWmjdvfs7vEAQAALAbnwesTz/9VA888IDHjTubNm2qadOmafPmzb4uBwAAwHQ%2BD1gOh0MVFRX1xisrK%2BXjDzQCAABYwucB69prr9WCBQv09ddfu8eKi4u1aNEi9enTx9flAAAAmM7nnyKcPn267rzzTg0aNEiRkZGSpIqKCnXq1EkzZszwdTkAAASsIU/8H3%2BX4LVPFw72dwmm8nnAiomJ0RtvvKGPP/5YhYWFcjgcuvzyy/XrX//a1C98BgAA8Be/fFVOSEiI%2BvTpwylBAAAQkHwesJxOp5544gl99tlnOnPmTL0L2//1r3/5uiQAAABT%2BTxgzZo1Szt27NDQoUN18cX%2B%2BX4gAAAAK/k8YG3evFnPP/%2B8UlJSfP3SAAAAPuHz2zQ0btxYMTExvn5ZAAAAn/F5wBo%2BfLief/551dbW%2BvqlAQAAfMLnpwjLy8u1ceNGvffee2rdurVCQ0M9lq9Zs8bXJQEAAJjKL7dpGDZsmD9eFgAAwCd8HrAWLVrk65cEAADwKZ9fgyVJJSUlys7O1pQpU/TNN9/o73//u/bt2%2BePUgAAAEzn84B14MAB3XjjjXrjjTf0zjvv6PTp03rrrbc0atQo5efn%2B7ocAAAA0/k8YD3yyCMaMGCANm3apIsuukiS9Nhjj6l///5asmSJr8sBAAAwnc8D1meffaY777zT44udHQ6HJk6cqF27dvm6HAAAANP5PGDV1dWprq6u3vipU6cUEhLi63IAAABM5/OA1bt3bz333HMeIau8vFxZWVnq2bOnr8sBAAAwnc8D1gMPPKAdO3aod%2B/eqq6u1h/%2B8Af169dPBw8e1PTp031dDgAAgOl8fh%2Bs%2BPh4vfnmm9q4caO%2B%2BOIL1dXVacyYMRo%2BfLgiIiJ8XQ4AAIDp/HIn9/DwcI0ePdofLw0AAGA5nwes22%2B//SeXN/S7CF0ul1JTUzVr1iz16NFDklRcXKxZs2YpLy9PLVq00MyZM9W7d2/3Nh9//LEefvhhFRcXKzExUQsXLlTr1q3dy1988UWtXLlSJ0%2Be1JAhQzRr1iyFh4c3qC4AAHDh8vk1WC1btvR4xMfHq6qqSgUFBUpKSmrQvqqrq3XfffepsLDQPWYYhtLT09WsWTOtX79ew4cP16RJk3T48GFJ0uHDh5Wenq7U1FS99tpratq0qSZOnCjDMCRJ77zzjrKzszVv3jytXr1a%2Bfn5ysrKMq8BAAAg4J0330W4bNkyHT161Ov97N27V1OmTHEHo7M2b96s4uJi/fnPf1bjxo3Vrl07ffLJJ1q/fr0mT56sdevW6aqrrtJdd93lrqdXr17aunWrevTooTVr1mjcuHHq16%2BfJGnu3LkaP368pk6dylEsAADgFb98F%2BG5DB8%2BXG%2B//bbX658NRDk5OR7j%2Bfn5uvLKK9W4cWP3WHJysvLy8tzLU1JS3MvCw8PVqVMn5eXlqba2Vtu3b/dY3rVrV505c0a7d%2B/%2BT6cGAAAuMH65yP1cPv/88wbdaHTs2LHnHHc6nYqLi/MYi4mJcR8d%2B6nlFRUVqq6u9ljucDgUFRXVoKNrJSUlcjqdHmMOR%2BN6r/tLhYScN/nYKw6Hfeo921u79dgO6K216K916K31Aqm358VF7idPntSePXt%2BNDQ1RGVlpUJDQz3GQkND5XK5fnZ5VVWV%2B/mPbe%2BNnJwcZWdne4ylp6crIyPD630EoujoJv4uocEiIzktbBV6ay36ax16a51A6q3PA1aLFi08vodQki666CLdeuut%2Bu1vf/uL9x8WFqby8nKPMZfLpUaNGrmX/zAsuVwuRUZGKiwszP38h8sbcv1VWlqa%2Bvfv7zHmcDRWWdkpr/fhDbslfbPnb6WQkGBFRoaroqJStbX1v9oJ/zl6ay36ax16az0reuuvP%2B59HrAeeeQRS/cfHx%2BvvXv3eoyVlpa6T8/Fx8ertLS03vKOHTsqKipKYWFhKi0tVbt27SRJNTU1Ki8vV2xsrNc1xMXF1Tsd6HSeUE3Nhf2GtOP8a2vrbFm3HdBba9Ff69Bb6wRSb30esLZt2%2Bb1uldffXWD95%2BYmKjly5erqqrKfdQqNzdXycnJ7uW5ubnu9SsrK7Vr1y5NmjRJwcHB6ty5s3Jzc9331MrLy5PD4VBCQkKDawEAABcmnwes2267zX2K8Pu3WPjhWFBQkL744osG77979%2B5q3ry5ZsyYoYkTJ%2Brdd99VQUGB%2B/YQo0aN0sqVK7V8%2BXL169dPy5YtU6tWrdyBauzYsZo9e7Y6dOiguLg4zZkzRzfffDO3aAAAAF7zecB69tlntWDBAk2dOlXdu3dXaGiotm/frnnz5mnkyJG64YYbftH%2BQ0JC9PTTTyszM1Opqam69NJLtWzZMrVo0UKS1KpVKz311FN6%2BOGHtWzZMiUlJWnZsmXugDd06FAdOnRIs2fPlsvl0vXXX6%2BpU6f%2B4nkDAIALR5Dxwzt1WmzQoEHKzMzUtdde6zH%2B6aefatq0afr3v//ty3J8xuk8Yfo%2BHY5gDVzyoen7tcrbf%2Bzl7xK85nAEKzq6icrKTgXM9QDnC3prLfprHTv2dsgT/8ffJXjt04WDLeltbOzFpu7PWz7/GFpJSYlatmxZbzwiIkJlZWW%2BLgcAAMB0Pg9YXbt21WOPPaaTJ0%2B6x8rLy5WVlaVf//rXvi4HAADAdD6/BuvBBx/U7bffrmuvvVZt2rSRYRjav3%2B/YmNjtWbNGl%2BXAwAAYDqfB6x27drprbfe0saNG1VUVCRJ%2Bt3vfqehQ4fyST0AABAQ/PJdhJdccolGjx6tgwcPqnXr1pK%2Bu5s7AABAIPD5NViGYWjJkiW6%2BuqrNWzYMB09elTTp09XZmamzpw54%2BtyAAAATOfzgPXSSy9pw4YNeuihh9xfqjxgwABt2rSp3hckAwAA2JHPA1ZOTo5mz56t1NRU9809b7jhBi1YsEB//etffV0OAACA6XwesA4ePKiOHTvWG09ISJDT6fR1OQAAAKbzecBq2bKltm/fXm/8gw8%2BcF/wDgAAYGc%2B/xTh%2BPHjNXfuXDmdThmGoU8%2B%2BUQ5OTl66aWX9MADD/i6HAAAANP5PGCNGjVKNTU1euaZZ1RVVaXZs2eradOm%2BuMf/6gxY8b4uhwAAADT%2BTxgbdy4UYMHD1ZaWpqOHz8uwzAUExPj6zIAAAAs4/NrsObNm%2Be%2BmL1p06aEKwAAEHB8HrDatGmjL7/80tcvCwAA4DM%2BP0WYkJCg%2B%2B%2B/X88//7zatGmjsLAwj%2BWLFi3ydUkAAACm8nnA%2Buqrr5ScnCxJ3PcKAAAEJJ8ErMWLF2vSpElq3LixXnrpJV%2B8JAAAgN/45BqsVatWqbKy0mNswoQJKikp8cXLAwAA%2BJRPApZhGPXGtm3bpuowFRDbAAAXG0lEQVTqal%2B8PAAAgE/5/FOEAAAAgY6ABQAAYDKfBaygoCBfvRQAAIBf%2Bew2DQsWLPC459WZM2eUlZWlJk2aeKzHfbAAAIDd%2BSRgXX311fXueZWUlKSysjKVlZX5ogQAAACf8UnA4t5XAADgQhKQF7m//vrruuKKK%2Bo9EhISJEl/%2BMMf6i1799133du/%2BOKL6tOnj5KSkjRz5sx69/ACAAD4KT7/qhxfuOGGG9SnTx/385qaGo0bN059%2B/aVJBUVFSkrK0u//vWv3etccsklkqR33nlH2dnZysrKUkxMjGbMmKGsrCzNnj3bp3MAAAD2FZBHsBo1aqTY2Fj34y9/%2BYsMw9D9998vl8ulgwcPqnPnzh7rhIaGSpLWrFmjcePGqV%2B/furSpYvmzp2r9evXcxQLAAB4LSAD1veVl5drxYoVmjJlikJDQ7Vv3z4FBQWpdevW9datra3V9u3blZKS4h7r2rWrzpw5o927d/uybAAAYGMBH7BeeeUVxcXFafDgwZKkffv2KSIiQtOmTVPv3r1100036f3335ckVVRUqLq6WnFxce7tHQ6HoqKidPToUb/UDwAA7Ccgr8E6yzAMrVu3Tnfffbd7bN%2B%2BfaqqqlLv3r01YcIE/fOf/9Qf/vAH5eTkqFmzZpLkPl14VmhoqFwul9evW1JSUu%2B2FA5HY4/gZoaQEHvlY4fDPvWe7a3demwH9NZa9Nc69NZ6gdTbgA5Y27dv17FjxzR06FD32MSJE3Xbbbe5L2pPSEjQzp079eqrr%2Bp//ud/JKlemHK5XAoPD/f6dXNycpSdne0xlp6eroyMjP90KgEhOrrJz690nomM9P7fHQ1Db61Ff61Db60TSL0N6ID14YcfKiUlxR2mJCk4ONjjuSS1bdtWe/fuVVRUlMLCwlRaWqp27dpJ%2Bu4TiOXl5YqNjfX6ddPS0tS/f3%2BPMYejscrKTv2C2dRnt6Rv9vytFBISrMjIcFVUVKq2ts7f5QQUemst%2Bmsdems9K3rrrz/uAzpgFRQUqFu3bh5jDzzwgIKCgjy%2Bkmf37t3q0KGDgoOD1blzZ%2BXm5qpHjx6SpLy8PDkcDvc9tLwRFxdX73Sg03lCNTUX9hvSjvOvra2zZd12QG%2BtRX%2BtQ2%2BtE0i9tdchkAYqLCzU5Zdf7jHWv39//fWvf9Wbb76pAwcOKDs7W7m5ubr11lslSWPHjtXKlSu1adMmFRQUaM6cObr55psbdIoQAABc2AL6CFZpaakiIyM9xq6//no99NBDeuaZZ3T48GG1b99ezz//vFq1aiVJGjp0qA4dOqTZs2fL5XLp%2Buuv19SpU/1RPgAAsKmADlgFBQXnHB89erRGjx79o9tNmDBBEyZMsKosAAAQ4AL6FCEAAIA/ELAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwWcAGrH/%2B85%2B64oorPB4ZGRmSpF27dmn06NFKTEzUqFGjtGPHDo9tN27cqAEDBigxMVHp6ek6fvy4P6YAAABsKmAD1t69e9WvXz999NFH7seCBQt0%2BvRpTZgwQSkpKXr99deVlJSke%2B%2B9V6dPn5YkFRQUKDMzU5MmTVJOTo4qKio0Y8YMP88GAADYScAGrKKiInXo0EGxsbHuR2RkpN566y2FhYVp2rRpateunTIzM9WkSRP9/e9/lyStXbtWQ4YM0YgRI5SQkKDFixfr/fffV3FxsZ9nBAAA7CKgA1abNm3qjefn5ys5OVlBQUGSpKCgIHXr1k15eXnu5SkpKe71mzdvrhYtWig/P98ndQMAAPsLyIBlGIa%2B%2BuorffTRRxo0aJAGDBigJUuWyOVyyel0Ki4uzmP9mJgYHT16VJJUUlLyk8sBAAB%2BjsPfBVjh8OHDqqysVGhoqJ544gkdPHhQCxYsUFVVlXv8%2B0JDQ%2BVyuSRJVVVVP7ncGyUlJXI6nR5jDkfjesHtlwoJsVc%2BdjjsU%2B/Z3tqtx3ZAb61Ff61Db60XSL0NyIDVsmVLbdmyRZdccomCgoLUsWNH1dXVaerUqerevXu9sORyudSoUSNJUlhY2DmXh4eHe/36OTk5ys7O9hhLT093f4rxQhUd3cTfJTRYZKT3/%2B5oGHprLfprHXprnUDqbUAGLEmKioryeN6uXTtVV1crNjZWpaWlHstKS0vdR5fi4%2BPPuTw2Ntbr105LS1P//v09xhyOxiorO9WQKfwsuyV9s%2BdvpZCQYEVGhquiolK1tXX%2BLieg0Ftr0V/r0FvrWdFbf/1xH5AB68MPP9T999%2Bv9957z33k6YsvvlBUVJSSk5O1YsUKGYahoKAgGYahzz77TL///e8lSYmJicrNzVVqaqok6ciRIzpy5IgSExO9fv24uLh6pwOdzhOqqbmw35B2nH9tbZ0t67YDemst%2BmsdemudQOqtvQ6BeCkpKUlhYWF68MEHtW/fPr3//vtavHix7r77bg0ePFgVFRVauHCh9u7dq4ULF6qyslJDhgyRJI0ZM0YbNmzQunXrtHv3bk2bNk19%2B/ZV69at/TwrAABgFwEZsCIiIrRy5UodP35co0aNUmZmptLS0nT33XcrIiJCzz33nPsoVX5%2BvpYvX67GjRtL%2Bi6czZs3T8uWLdOYMWN0ySWXaNGiRX6eEQAAsJOAPEUoSe3bt9eqVavOuaxLly564403fnTb1NRU9ylCAACAhgrII1gAAAD%2BRMACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAEbsI4dO6aMjAx1795dffr00aJFi1RdXS1JWrBgga644gqPx9q1a93bbty4UQMGDFBiYqLS09N1/Phxf00DAADYkMPfBVjBMAxlZGQoMjJSL7/8sr799lvNnDlTwcHBmj59uoqKijRlyhSNHDnSvU1ERIQkqaCgQJmZmZo7d64SEhK0cOFCzZgxQ88995y/pgMAAGwmII9g7du3T3l5eVq0aJHat2%2BvlJQUZWRkaOPGjZKkoqIiXXnllYqNjXU/wsPDJUlr167VkCFDNGLECCUkJGjx4sV6//33VVxc7M8pAQAAGwnIgBUbG6vnn39ezZo18xg/efKkTp48qWPHjqlNmzbn3DY/P18pKSnu582bN1eLFi2Un59vZckAACCABOQpwsjISPXp08f9vK6uTmvXrlXPnj1VVFSkoKAgPfvss/rggw8UFRWlO%2B%2B80326sKSkRHFxcR77i4mJ0dGjR71%2B/ZKSEjmdTo8xh6Nxvf3%2BUiEh9srHDod96j3bW7v12A7orbXor3XorfUCqbcBGbB%2BKCsrS7t27dJrr72mnTt3KigoSG3bttWtt96qbdu2adasWYqIiNDAgQNVVVWl0NBQj%2B1DQ0Plcrm8fr2cnBxlZ2d7jKWnpysjI8OU%2BdhVdHQTf5fQYJGR4f4uIWDRW2vRX%2BvQW%2BsEUm8DPmBlZWVp9erVevzxx9WhQwe1b99e/fr1U1RUlCQpISFB%2B/fv1yuvvKKBAwcqLCysXphyuVzua7S8kZaWpv79%2B3uMORyNVVZ26pdP6HvslvTNnr%2BVQkKCFRkZroqKStXW1vm7nIBCb61Ff61Db61nRW/99cd9QAes%2BfPn65VXXlFWVpYGDRokSQoKCnKHq7Patm2rzZs3S5Li4%2BNVWlrqsby0tFSxsbFev25cXFy904FO5wnV1FzYb0g7zr%2B2ts6WddsBvbUW/bUOvbVOIPXWXodAGiA7O1t//vOf9dhjj2no0KHu8aVLl%2BqOO%2B7wWHf37t1q27atJCkxMVG5ubnuZUeOHNGRI0eUmJjok7oBAID9BWTAKioq0tNPP6177rlHycnJcjqd7ke/fv20bds2rVy5Ul9//bX%2B9Kc/6c0339Rdd90lSRozZow2bNigdevWaffu3Zo2bZr69u2r1q1b%2B3lWAADALgLyFOG//vUv1dbW6plnntEzzzzjsWzPnj1aunSpnnzySS1dulQtW7bUo48%2BqqSkJElSUlKS5s2bpyeffFLffvutevXqpfnz5/tjGgAAwKYCMmBNmDBBEyZM%2BNHlAwYM0IABA350eWpqqlJTU60oDQAAXAAC8hQhAACAPxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkB6xyqq6s1c%2BZMpaSkqHfv3nrhhRf8XRIAALARh78LOB8tXrxYO3bs0OrVq3X48GFNnz5dLVq00ODBg/1dGgAAsAEC1g%2BcPn1a69at04oVK9SpUyd16tRJhYWFevnllwlYAADAK5wi/IHdu3erpqZGSUlJ7rHk5GTl5%2Berrq7Oj5UBAAC74AjWDzidTkVHRys0NNQ91qxZM1VXV6u8vFxNmzb92X2UlJTI6XR6jDkcjRUXF2dqrSEh9srHDod96j3bW7v12A7orbXor3XorfUCqbcErB%2BorKz0CFeS3M9dLpdX%2B8jJyVF2drbH2KRJkzR58mRzivx/SkpKNO5XhUpLSzM9vF3oSkpKtHr18/TWAvTWWvTXOnbs7acL7XFpS0lJiZ566ilb9fbnBE5UNElYWFi9IHX2eaNGjbzaR1paml5//XWPR1pamum1Op1OZWdn1ztahl%2BO3lqH3lqL/lqH3lonEHvLEawfiI%2BPV1lZmWpqauRwfNcep9OpRo0aKTIy0qt9xMXFBUwCBwAADccRrB/o2LGjHA6H8vLy3GO5ubnq3LmzgoNpFwAA%2BHkkhh8IDw/XiBEjNGfOHBUUFGjTpk164YUXdPvtt/u7NAAAYBMhc%2BbMmePvIs43PXv21K5du/Too4/qk08%2B0e9//3uNGjXK32WdU5MmTdS9e3c1adLE36UEHHprHXprLfprHXprnUDrbZBhGIa/iwAAAAgknCIEAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwDrPVVdXa%2BbMmUpJSVHv3r31wgsv/Oi6u3bt0ujRo5WYmKhRo0Zpx44dPqzUfhrS2/fee0/Dhw9XUlKSbrzxRv3rX//yYaX205DennXw4EElJSVpy5YtPqjQ3hrS3z179mjMmDHq0qWLbrzxRm3evNmHldpPQ3r7z3/%2BU0OGDFFSUpLGjBmjnTt3%2BrBS%2B3K5XBo2bNhPvtcD4veZgfPavHnzjBtvvNHYsWOH8Y9//MNISkoy3n777XrrnTp1yujVq5fxyCOPGHv37jXmz59vXHPNNcapU6f8ULU9eNvbL774wujUqZOxevVqY//%2B/cbatWuNTp06GV988YUfqrYHb3v7fePHjzc6dOhgbN682UdV2pe3/a2oqDCuueYa48EHHzT2799vLF261EhOTjZKS0v9ULU9eNvbL7/80ujcubPxxhtvGAcOHDDmzp1r9OrVyzh9%2BrQfqraPqqoqIz09/Sff64Hy%2B4yAdR47deqU0blzZ48fwmXLlhm33nprvXXXrVtn9O/f36irqzMMwzDq6uqMgQMHGuvXr/dZvXbSkN5mZWUZ48eP9xi76667jMcee8zyOu2oIb09a8OGDcYtt9xCwPJCQ/q7evVqY8CAAUZNTY17LDU11Xjvvfd8UqvdNKS3q1atMkaOHOl%2BfuLECaNDhw5GQUGBT2q1o8LCQuO3v/2tceONN/7kez1Qfp9xivA8tnv3btXU1CgpKck9lpycrPz8fNXV1Xmsm5%2Bfr%2BTkZAUFBUmSgoKC1K1bN%2BXl5fm0ZrtoSG9Hjhyp%2B%2B%2B/v94%2BTpw4YXmddtSQ3kpSWVmZsrKyNG/ePF%2BWaVsN6e/WrVt13XXXKSQkxD22fv16/eY3v/FZvXbSkN5GRUVp7969ys3NVV1dnV5//XVFRETov/7rv3xdtm1s3bpVPXr0UE5Ozk%2BuFyi/zxz%2BLgA/zul0Kjo6WqGhoe6xZs2aqbq6WuXl5WratKnHupdffrnH9jExMSosLPRZvXbSkN62a9fOY9vCwkJ98sknuuWWW3xWr500pLeS9Mgjj2jkyJFq3769r0u1pYb0t7i4WF26dNGsWbP073//Wy1bttT06dOVnJzsj9LPew3p7Q033KB///vfGjt2rEJCQhQcHKznnntOl1xyiT9Kt4WxY8d6tV6g/D7jCNZ5rLKy0uONLsn93OVyebXuD9fDdxrS2%2B87fvy4Jk%2BerG7duum6666ztEa7akhvP/74Y%2BXm5mrixIk%2Bq8/uGtLf06dPa/ny5YqNjdWKFSt09dVXa/z48Tpy5IjP6rWThvS2rKxMTqdTs2fP1quvvqrhw4drxowZ%2Buabb3xWb6AKlN9nBKzzWFhYWL0fqLPPGzVq5NW6P1wP32lIb88qLS3VuHHjZBiGnnzySQUH8/Y5F297W1VVpdmzZ%2Buhhx7i57QBGvKzGxISoo4dOyojI0NXXnmlpk6dqjZt2mjDhg0%2Bq9dOGtLbJUuWqEOHDvrd736nq666SvPnz1d4eLjWr1/vs3oDVaD8PuM3xHksPj5eZWVlqqmpcY85nU41atRIkZGR9dYtLS31GCstLVVcXJxParWbhvRWko4dO6bf/e53crlcWrNmTb3TXPj/vO1tQUGBiouLlZGRoaSkJPd1L/fcc49mz57t87rtoiE/u7GxsWrbtq3HWJs2bTiC9SMa0tudO3cqISHB/Tw4OFgJCQk6fPiwz%2BoNVIHy%2B4yAdR7r2LGjHA6Hx4V9ubm56ty5c72jJ4mJifr8889lGIYkyTAMffbZZ0pMTPRpzXbRkN6ePn1ad999t4KDg7V27VrFx8f7ulxb8ba3Xbp00T/%2B8Q%2B9%2Beab7ockLViwQP/93//t87rtoiE/u127dtWePXs8xvbt26eWLVv6pFa7aUhv4%2BLiVFRU5DH21VdfqVWrVj6pNZAFyu8zAtZ5LDw8XCNGjNCcOXNUUFCgTZs26YUXXtDtt98u6bu/rKqqqiRJgwcPVkVFhRYuXKi9e/dq4cKFqqys1JAhQ/w5hfNWQ3r73HPP6euvv9b//u//upc5nU4%2BRfgjvO1to0aNdOmll3o8pO/%2Beo2JifHnFM5rDfnZveWWW7Rnzx499dRTOnDggJYuXari4mINHz7cn1M4bzWktzfffLNeffVVvfnmmzpw4ICWLFmiw4cPa%2BTIkf6cgm0F5O8zv94kAj/r9OnTxrRp04yuXbsavXv3NlatWuVe1qFDB4/7guTn5xsjRowwOnfubNx0003Gzp07/VCxfXjb20GDBhkdOnSo95g%2BfbqfKj//NeTn9vu4D5Z3GtLfTz/91Bg5cqRx1VVXGcOHDze2bt3qh4rtoyG9ffXVV43BgwcbXbt2NcaMGWPs2LHDDxXb0w/f64H4%2ByzIMP7fMTgAAACYglOEAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvu/OxCIi45H6CMAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common6540694832340670645”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme6540694832340670645”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1968</td> <td class=”number”>61.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1164</td> <td class=”number”>36.3%</td> <td>

<div class=”bar” style=”width:59%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_CAP16”><s>SNAP_CAP16</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAP_CAP09”><code>SNAP_CAP09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.94179</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_OAPP09”>SNAP_OAPP09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>4</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.53736</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.8%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1183942956605839447”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1183942956605839447,#minihistogram1183942956605839447”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1183942956605839447”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1183942956605839447”

aria-controls=”quantiles1183942956605839447” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1183942956605839447” aria-controls=”histogram1183942956605839447”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1183942956605839447” aria-controls=”common1183942956605839447”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1183942956605839447” aria-controls=”extreme1183942956605839447”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1183942956605839447”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.48141</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.89589</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.9053</td>

</tr> <tr>

<th>Mean</th> <td>0.53736</td>

</tr> <tr>

<th>MAD</th> <td>0.46589</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.14962</td>

</tr> <tr>

<th>Sum</th> <td>1683</td>

</tr> <tr>

<th>Variance</th> <td>0.23176</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1183942956605839447”>

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%2B0f/8z/8oPPzrb1pYWFirsOR0OhUZGamwsDDX7dPnv7m/J1JTUzVmzBi3MZstQtXVJ7/PUs7K35K%2B2eu3UkhIsCIjw1VbW6/m5hZflxNQ6K216K916K31rOitr365D8iAdeLECf3617/Wp59%2BqnXr1qlXr16uudjYWFVVVbltX1VVpX79%2Bqljx44KCwtTVVWVevfuLUlqampSTU2NoqOjPX78mJiYVqcDHY7jamo6v1%2BQ/rj%2B5uYWv6zbH9Bba9Ff69Bb6wRSb/3rEIgHWlpalJGRoc8%2B%2B0xPP/20LrnkErd5u92uwsJC1%2B36%2BnodOHBAdrtdwcHBio%2BPd5svKiqSzWZT3759vbYGAADg3wIuYG3atEm7d%2B/WsmXLFBkZKYfDIYfDoZqaGknS1KlTtW/fPq1Zs0alpaXKyspS9%2B7dNWzYMElfv3n%2BiSee0I4dO1RSUqJFixZpxowZbTpFCAAAzm8Bd4rwlVdeUUtLi26%2B%2BWa38aFDh%2Brpp59W9%2B7d9dBDD%2BkPf/iDVq1apYSEBK1atUpBQUGSpIkTJ%2Brzzz/XwoUL5XQ69Ytf/EJ33HGHL5YCAAD8VEAErEOHDrn%2B/8QTT3zn9ldccYWuuOKKs87PnTtXc%2BfONaU2AABw/gm4U4QAAAC%2BRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwmd8HLKfTqeTkZO3evds1VlZWpuuvv16DBg3ShAkT9M9//tPtPjt37lRycrLsdrtmz56tsrIyt/mnnnpKo0aNUkJCgu666y7V19d7ZS0AACAw%2BHXAamxs1G233abS0lLXmGEYSk9PV5cuXbR582ZNmjRJGRkZOnr0qCTp6NGjSk9PV0pKijZt2qTOnTtr3rx5MgxDkvTKK68oPz9fS5Ys0bp161RcXKy8vDyfrA8AAPgnvw1Yhw8f1owZM/Tpp5%2B6jb/11lsqKyvTkiVL1Lt3b918880aNGiQNm/eLEnauHGjBgwYoBtvvFGXXHKJcnNz9fnnn2vPnj2SpPXr1%2Bu6667T6NGjNXDgQC1evFibN2/mKBYAAPCY3wasPXv2aNiwYSooKHAbLy4u1qWXXqqIiAjXWGJiooqKilzzSUlJrrnw8HD1799fRUVFam5u1rvvvus2P2jQIJ06dUoHDx60eEUAACBQ2HxdwPc1c%2BbMM447HA7FxMS4jUVFRamiouI752tra9XY2Og2b7PZ1LFjR9f9AQAAvovfBqyzqa%2BvV2hoqNtYaGionE7nd843NDS4bp/t/p6orKyUw%2BFwG7PZIloFux8qJMS/DkDabP5T7ze99bce%2BwN6ay36ax16a71A6q3XA9b06dM1depUTZw4URdccIHp%2Bw8LC1NNTY3bmNPpVLt27Vzzp4clp9OpyMhIhYWFuW6fPh8eHu5xDQUFBcrPz3cbS09PV2Zmpsf7CESdOrX3dQltFhnp%2BfcdbUNvrUV/rUNvrRNIvfV6wBo%2BfLhWr16t3Nxc/exnP1NKSopGjBihoKAgU/YfGxurw4cPu41VVVW5jh7Fxsaqqqqq1Xy/fv3UsWNHhYWFqaqqSr1795YkNTU1qaamRtHR0R7XkJqaqjFjxriN2WwRqq4%2B%2BX2WdFb%2BlvTNXr%2BVQkKCFRkZrtraejU3t/i6nIBCb61Ff61Db61nRW999cu91wPW7373O912223auXOnnn/%2Bed16662KjIzU5MmTNXnyZP3kJz/5Qfu32%2B1as2aNGhoaXEetCgsLlZiY6JovLCx0bV9fX68DBw4oIyNDwcHBio%2BPV2FhoYYNGyZJKioqks1mU9%2B%2BfT2uISYmptXpQIfjuJqazu8XpD%2Buv7m5xS/r9gf01lr01zr01jqB1FufHAIJCgrSiBEjlJeXp507d%2Braa6/VunXrNGHCBF177bV69dVXv/e%2Bhw4dqq5duyorK0ulpaVas2aNSkpKNG3aNEnS1KlTtW/fPq1Zs0alpaXKyspS9%2B7dXYFq5syZeuKJJ7Rjxw6VlJRo0aJFmjFjRptOEQIAgPObz97kXllZqRdeeEEvvPCCPvjgAw0ePFhTpkxRRUWF7r77br399tvKzs5u835DQkL08MMPKzs7WykpKerZs6dWrVqluLg4SVL37t310EMP6Q9/%2BINWrVqlhIQErVq1ynWKcuLEifr888%2B1cOFCOZ1O/eIXv9Add9xh6toBAEBgCzK%2BuYS5l2zbtk3btm3T7t271blzZ02ePFlTp05Vr169XNts2rRJOTk5euedd7xZmqUcjuOm79NmC9bPV/zD9P1a5a%2B/HeHrEjxmswWrU6f2qq4%2BGTCHq88V9NZa9Nc6/tjb8Q/8y9cleGxvzjhLehsdbf4f1HnC60ewsrOzNXr0aK1atUqXX365goNbn6W86KKL9Ktf/crbpQEAAJjC6wHrzTffVKdOnVRTU%2BMKVyUlJerfv79CQkIkSYMHD9bgwYO9XRoAAIApvP4m9xMnTmjcuHF67LHHXGNz587VpEmTVF5e7u1yAAAATOf1gPWHP/xBPXv21A033OAae%2Bmll9S1a1fl5uZ6uxwAAADTeT1g7d27V3feeafbhTs7d%2B6s%2BfPn66233vJ2OQAAAKbzesCy2Wyqra1tNV5fXy8v/0EjAACAJbwesC6//HItW7ZMn376qWusrKxMubm5GjVqlLfLAQAAMJ3X/4pwwYIFuuGGG3TVVVcpMjJSklRbW6v%2B/fsrKyvL2%2BUAAACYzusBKyoqSlu3btXOnTtVWloqm82miy%2B%2BWJdddplpH/gMAADgSz75qJyQkBCNGjWKU4IAACAgeT1gORwOPfDAA9q3b59OnTrV6o3tr732mrdLAgAAMJXXA9bvf/977d%2B/XxMnTtQFF/jm84EAAACs5PWA9dZbb%2Bnxxx9XUlKStx8aAADAK7x%2BmYaIiAhFRUV5%2B2EBAAC8xusBa9KkSXr88cfV3Nzs7YcGAADwCq%2BfIqypqdH27dv1%2Buuvq0ePHgoNDXWbX79%2BvbdLAgAAMJVPLtOQnJzsi4cFAADwCq8HrNzcXG8/JAAAgFd5/T1YklRZWan8/Hz97ne/05dffqmXX35ZH330kS9KAQAAMJ3XA9aRI0d09dVXa%2BvWrXrllVdUV1enl156SVOnTlVxcbG3ywEAADCd1wPWvffeq7Fjx2rHjh360Y9%2BJEm6//77NWbMGK1YscLb5QAAAJjO6wFr3759uuGGG9w%2B2Nlms2nevHk6cOCAt8sBAAAwndcDVktLi1paWlqNnzx5UiEhId4uBwAAwHReD1gjR47Uo48%2B6hayampqlJeXp%2BHDh3u7HAAAANN5PWDdeeed2r9/v0aOHKnGxkbdcsstGj16tD777DMtWLDA2%2BUAAACYzuvXwYqNjdXzzz%2Bv7du36/3331dLS4vS0tI0adIkdejQwdvlAAAAmM4nV3IPDw/X9OnTffHQAAAAlvN6wJo9e/a3zpv1WYTl5eVatGiR3n77bXXs2FGzZ8/W9ddfL0k6cOCA7rnnHn3wwQe6%2BOKLtXjxYg0YMMB13%2B3bt%2BuBBx6Qw%2BHQyJEjtXTpUnXu3NmUugAAQODz%2BnuwunXr5vYVGxurhoYGlZSUKCEhwbTH%2Be1vf6uIiAht2bJFd911lx544AH97W9/U11dnebOnaukpCRt2bJFCQkJuvnmm1VXVydJKikpUXZ2tjIyMlRQUKDa2lplZWWZVhcAAAh858xnEa5atUoVFRWmPMZXX32loqIiLV26VL169VKvXr00atQo7dq1S1999ZXCwsI0f/58BQUFKTs7W2%2B%2B%2BaZefvllpaSkaMOGDRo/frwmT54sSVq%2BfLlGjx6tsrIy9ejRw5T6AABAYPPJZxGeyaRJk/TXv/7VlH21a9dO4eHh2rJli06dOqWPPvpI%2B/btU79%2B/VRcXKzExETXhU6DgoI0ePBgFRUVSZKKi4uVlJTk2lfXrl0VFxfHx/gAAACP%2BeRN7mfyzjvvmHah0bCwMC1cuFBLly7V%2BvXr1dzcrJSUFE2fPl2vvfaaLr74Yrfto6KiVFpaKunrD6KOiYlpNd%2BWo2uVlZVyOBxuYzZbRKv9/lAhIedMPvaIzeY/9X7TW3/rsT%2Bgt9aiv9aht9YLpN6eE29yP3HihA4dOqSZM2ea9jgffvihRo8erRtuuEGlpaVaunSpLrvsMtXX1ys0NNRt29DQUDmdTklSQ0PDt857oqCgQPn5%2BW5j6enpyszM/J6rCQydOrX3dQltFhkZ7usSAha9tRb9tQ69tU4g9dbrASsuLs7tcwgl6Uc/%2BpF%2B9atf6Ze//KUpj7Fr1y5t2rRJb7zxhtq1a6f4%2BHh98cUXeuSRR9SjR49WYcnpdKpdu3aSvj76dab58HDPv%2BmpqakaM2aM25jNFqHq6pPfc0Vn5m9J3%2Bz1WykkJFiRkeGqra1Xc3Prj3bC90dvrUV/rUNvrWdFb331y73XA9a9995r%2BWPs379fPXv2dIUmSbr00ku1evVqJSUlqaqqym37qqoq1%2Bm72NjYM85HR0d7/PgxMTGtTgc6HMfV1HR%2BvyD9cf3NzS1%2BWbc/oLfWor/WobfWCaTeej1gvf322x5vO2TIkO/1GDExMTpy5IicTqfrdN9HH32k7t27y26367HHHpNhGAoKCpJhGNq3b59%2B85vfSJLsdrsKCwuVkpIi6evraZWXl8tut3%2BvWgAAwPnH6wFr1qxZrlOEhmG4xk8fCwoK0vvvv/%2B9HmPMmDHKy8vT3XffrVtuuUUff/yxVq9erf/%2B7//WuHHjtHLlSuXk5Oiaa67Rs88%2Bq/r6eo0fP16SlJaWplmzZmnQoEGKj49XTk6OrrzySi7RAAAAPOb1gLV69WotW7ZMd9xxh4YOHarQ0FC9%2B%2B67WrJkiaZMmaIJEyb84Me44IIL9NRTTyknJ0fTpk1T586ddcsttyg1NVVBQUF69NFHdc899%2Bi5557Tf/7nf2rNmjWKiIiQJCUkJGjJkiV68MEH9dVXX2nEiBFaunTpD64JAACcP4KMfz%2BM5AVXXXWVsrOzdfnll7uN7927V/Pnz9ff//53b5bjNQ7HcdP3abMF6%2Bcr/mH6fq3y19%2BO8HUJHrPZgtWpU3tVV58MmPcDnCvorbXor3X8sbfjH/iXr0vw2N6ccZb0Njr6AlP35ymv/xlaZWWlunXr1mq8Q4cOqq6u9nY5AAAApvN6wBo0aJDuv/9%2BnThxwjVWU1OjvLw8XXbZZd4uBwAAwHRefw/W3XffrdmzZ%2Bvyyy9Xr169ZBiGPvnkE0VHR2v9%2BvXeLgcAAMB0Xg9YvXv31ksvvaTt27frww8/lCRde%2B21mjhxYpsu5gkAAHCu8slnEV544YWaPn26PvvsM9flD370ox/5ohQAAADTef09WIZhaMWKFRoyZIiSk5NVUVGhBQsWKDs7W6dOnfJ2OQAAAKbzesB6%2BumntW3bNt1zzz2uq6yPHTtWO3bsaPUByQAAAP7I6wGroKBACxcuVEpKiuvq7RMmTNCyZcv04osverscAAAA03k9YH322Wfq169fq/G%2BffvK4XB4uxwAAADTeT1gdevWTe%2B%2B%2B26r8TfffJPP%2BwMAAAHB639FOGfOHC1evFgOh0OGYWjXrl0qKCjQ008/rTvvvNPb5QAAAJjO6wFr6tSpampq0iOPPKKGhgYtXLhQnTt31m9/%2B1ulpaV5uxwAAADTeT1gbd%2B%2BXePGjVNqaqqOHTsmwzAUFRXl7TIAAAAs4/X3YC1ZssT1ZvbOnTsTrgAAQMDxesDq1auXPvjgA28/LAAAgNd4/RRh3759dfvtt%2Bvxxx9Xr169FBYW5jafm5vr7ZIAAABM5fWA9fHHHysxMVGSuO4VAAAISF4JWMuXL1dGRoYiIiL09NNPe%2BMhAQAAfMYr78Fau3at6uvr3cbmzp2ryspKbzw8AACAV3klYBmG0Wrs7bffVmNjozceHgAAwKu8/leEAAAAgY6ABQAAYDKvBaygoCBvPRQAAIBPee0yDcuWLXO75tWpU6eUl5en9u3bu23HdbAAAIC/80rAGjJkSKtrXiUkJKi6ulrV1dXeKAEAAMBrvBKwuPYVAAA4n/AmdwAAAJMFbMByOp1avHixhgwZop/%2B9Ke6//77XdfjOnDggKZPny673a6pU6dq//79bvfdvn27xo4dK7vdrvT0dB07dswXSwAAAH4qYANY3ruIAAAWOElEQVTWsmXLtHPnTj3xxBNauXKlnnvuORUUFKiurk5z585VUlKStmzZooSEBN18882qq6uTJJWUlCg7O1sZGRkqKChQbW2tsrKyfLwaAADgT7z%2BYc/eUFNTo82bN2vt2rUaOHCgJOnGG29UcXGxbDabwsLCNH/%2BfAUFBSk7O1tvvvmmXn75ZaWkpGjDhg0aP368Jk%2BeLOnrz1EcPXq0ysrK1KNHD18uCwAA%2BImAPIJVWFioDh06aOjQoa6xuXPnKjc3V8XFxUpMTHRdlysoKEiDBw9WUVGRJKm4uFhJSUmu%2B3Xt2lVxcXEqLi727iIAAIDfCsgjWGVlZerWrZuef/55rV69WqdOnVJKSopuueUWORwOXXzxxW7bR0VFqbS0VJJUWVmpmJiYVvMVFRUeP35lZWWry1LYbBGt9vtDhYT4Vz622fyn3m9662899gf01lr01zr01nqB1NuADFh1dXU6cuSInn32WeXm5srhcGjhwoUKDw9XfX29QkND3bYPDQ2V0%2BmUJDU0NHzrvCcKCgqUn5/vNpaenq7MzMzvuaLA0KlT%2B%2B/e6BwTGRnu6xICFr21Fv21Dr21TiD1NiADls1m04kTJ7Ry5Up169ZNknT06FH9%2Bc9/Vs%2BePVuFJafTqXbt2kmSwsLCzjgfHu75Nz01NVVjxow5raYIVVef/D7LOSt/S/pmr99KISHBiowMV21tvZqbW3xdTkCht9aiv9aht9azore%2B%2BuU%2BIANWdHS0wsLCXOFKkn7yk5%2BovLxcQ4cOVVVVldv2VVVVrtN3sbGxZ5yPjo72%2BPFjYmJanQ50OI6rqen8fkH64/qbm1v8sm5/QG%2BtRX%2BtQ2%2BtE0i99a9DIB6y2%2B1qbGzUxx9/7Br76KOP1K1bN9ntdr3zzjuua2IZhqF9%2B/bJbre77ltYWOi6X3l5ucrLy13zAAAA3yUgA9ZFF12kK6%2B8UllZWTp48KD%2B8Y9/aM2aNUpLS9O4ceNUW1urnJwcHT58WDk5Oaqvr9f48eMlSWlpadq2bZs2btyogwcPav78%2Bbryyiu5RAMAAPBYQAYsSVqxYoV%2B/OMfKy0tTQsWLNC1116rWbNmqUOHDnr00UdVWFiolJQUFRcXa82aNYqIiJD09YdQL1myRKtWrVJaWpouvPBC5ebm%2Bng1AADAnwTke7Ak6YILLtDy5cvPODdw4EBt3br1rPdNSUlRSkqKVaUBAIAAF7BHsAAAAHyFgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGCygA9Yc%2BfO1Z133um6feDAAU2fPl12u11Tp07V/v373bbfvn27xo4dK7vdrvT0dB07dszbJQMAAD8X0AHrL3/5i9544w3X7bq6Os2dO1dJSUnasmWLEhISdPPNN6uurk6SVFJSouzsbGVkZKigoEC1tbXKysryVfkAAMBPBWzAqqmp0fLlyxUfH%2B8ae%2BmllxQWFqb58%2Berd%2B/eys7OVvv27fXyyy9LkjZs2KDx48dr8uTJ6tu3r5YvX6433nhDZWVlvloGAADwQwEbsO677z5NmjRJF198sWusuLhYiYmJCgoKkiQFBQVp8ODBKioqcs0nJSW5tu/atavi4uJUXFzs3eIBAIBfC8iAtWvXLu3du1fz5s1zG3c4HIqJiXEbi4qKUkVFhSSpsrLyW%2BcBAAA8YfN1AWZrbGzUPffco4ULF6pdu3Zuc/X19QoNDXUbCw0NldPplCQ1NDR867ynKisr5XA43MZstohW4e2HCgnxr3xss/lPvd/01t967A/orbXor3XorfUCqbcBF7Dy8/M1YMAAjRo1qtVcWFhYq7DkdDpdQexs8%2BHh4W2qoaCgQPn5%2BW5j6enpyszMbNN%2BAk2nTu19XUKbRUa27XsPz9Fba9Ff69Bb6wRSbwMuYP3lL39RVVWVEhISJMkVmF555RUlJyerqqrKbfuqqirXkaXY2NgzzkdHR7ephtTUVI0ZM8ZtzGaLUHX1yTbt57v4W9I3e/1WCgkJVmRkuGpr69Xc3OLrcgIKvbUW/bUOvbWeFb311S/3ARewnn76aTU1Nblur1ixQpJ0%2B%2B236%2B2339Zjjz0mwzAUFBQkwzC0b98%2B/eY3v5Ek2e12FRYWKiUlRZJUXl6u8vJy2e32NtUQExPT6nSgw3FcTU3n9wvSH9ff3Nzil3X7A3prLfprHXprnUDqbcAFrG7durndbt/%2B6%2BTas2dPRUVFaeXKlcrJydE111yjZ599VvX19Ro/frwkKS0tTbNmzdKgQYMUHx%2BvnJwcXXnllerRo4fX1wEAAPyXf51j%2BoE6dOigRx991HWUqri4WGvWrFFERIQkKSEhQUuWLNGqVauUlpamCy%2B8ULm5uT6uGgAA%2BJuAO4J1unvvvdft9sCBA7V169azbp%2BSkuI6RQgAAPB9nFdHsAAAALyBgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJAjZgffHFF8rMzNTQoUM1atQo5ebmqrGxUZJUVlam66%2B/XoMGDdKECRP0z3/%2B0%2B2%2BO3fuVHJysux2u2bPnq2ysjJfLAEAAPipgAxYhmEoMzNT9fX1euaZZ/THP/5R//u//6sHHnhAhmEoPT1dXbp00ebNmzVp0iRlZGTo6NGjkqSjR48qPT1dKSkp2rRpkzp37qx58%2BbJMAwfrwoAAPgLm68LsMJHH32koqIi/etf/1KXLl0kSZmZmbrvvvt0%2BeWXq6ysTM8%2B%2B6wiIiLUu3dv7dq1S5s3b9att96qjRs3asCAAbrxxhslSbm5uRoxYoT27NmjYcOG%2BXJZAADATwRkwIqOjtbjjz/uClffOHHihIqLi3XppZcqIiLCNZ6YmKiioiJJUnFxsZKSklxz4eHh6t%2B/v4qKighYwHlq/AP/8nUJbbI3Z5yvSwDOewEZsCIjIzVq1CjX7ZaWFm3YsEHDhw%2BXw%2BFQTEyM2/ZRUVGqqKiQpO%2Bc90RlZaUcDofbmM0W0Wq/P1RIiH%2Bd4bXZ/Kfeb3rrbz32B/TWO%2Biv%2BXjuWi%2BQehuQAet0eXl5OnDggDZt2qSnnnpKoaGhbvOhoaFyOp2SpPr6%2Bm%2Bd90RBQYHy8/PdxtLT05WZmfk9VxAYOnVq7%2BsS2iwyMtzXJQQsemst%2BmsdemudQOptwAesvLw8rVu3Tn/84x/Vp08fhYWFqaamxm0bp9Opdu3aSZLCwsJahSmn06nIyEiPHzM1NVVjxoxxG7PZIlRdffJ7ruLM/C3pm71%2BK4WEBCsyMly1tfVqbm7xdTkBhd56B/01H89d61nRW1/9ch/QAWvp0qX685//rLy8PF111VWSpNjYWB0%2BfNhtu6qqKtfpu9jYWFVVVbWa79evn8ePGxMT0%2Bp0oMNxXE1N5/cL0h/X39zc4pd1%2BwN6ay36ax16a51A6q1/HQJpg/z8fD377LO6//77NXHiRNe43W7Xe%2B%2B9p4aGBtdYYWGh7Ha7a76wsNA1V19frwMHDrjmAQAAvktABqwPP/xQDz/8sG666SYlJibK4XC4voYOHaquXbsqKytLpaWlWrNmjUpKSjRt2jRJ0tSpU7Vv3z6tWbNGpaWlysrKUvfu3fkLQgAA4LGADFivvfaampub9cgjj2jkyJFuXyEhIXr44YflcDiUkpKiF154QatWrVJcXJwkqXv37nrooYe0efNmTZs2TTU1NVq1apWCgoJ8vCoAAOAvAvI9WHPnztXcuXPPOt%2BzZ09t2LDhrPNXXHGFrrjiCitKAwAA54GAPIIFAADgSwQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAOoPGxkbdddddSkpK0siRI/Xkk0/6uiQAAOBHbL4u4Fy0fPly7d%2B/X%2BvWrdPRo0e1YMECxcXFady4cb4uDQAA%2BAEC1mnq6uq0ceNGPfbYY%2Brfv7/69%2B%2Bv0tJSPfPMMwQsAADgEU4RnubgwYNqampSQkKCaywxMVHFxcVqaWnxYWUAAMBfcATrNA6HQ506dVJoaKhrrEuXLmpsbFRNTY06d%2B78nfuorKyUw%2BFwG7PZIhQTE2NqrSEh/pWPbTb/qfeb3vpbj/0BvfUO%2Bms%2BnrvWC6TeErBOU19f7xauJLluO51Oj/ZRUFCg/Px8t7GMjAzdeuut5hT5fyorK3Xdf5QqNTXV9PB2vqusrNS6dY/TWwv4Y2/35vjP2wMqKyv10EMP%2BVV//QXPXesE4vM2cKKiScLCwloFqW9ut2vXzqN9pKamasuWLW5fqampptfqcDiUn5/f6mgZfjh6ax16ay36ax16a51A7C1HsE4TGxur6upqNTU1yWb7uj0Oh0Pt2rVTZGSkR/uIiYkJmAQOAADajiNYp%2BnXr59sNpuKiopcY4WFhYqPj1dwMO0CAADfjcRwmvDwcE2ePFmLFi1SSUmJduzYoSeffFKzZ8/2dWkAAMBPhCxatGiRr4s41wwfPlwHDhzQypUrtWvXLv3mN7/R1KlTfV3WGbVv315Dhw5V%2B/btfV1KwKG31qG31qK/1qG31gm03gYZhmH4uggAAIBAwilCAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBKxzXGNjo%2B666y4lJSVp5MiRevLJJ8%2B67YEDBzR9%2BnTZ7XZNnTpV%2B/fv92Kl/qctvX399dc1adIkJSQk6Oqrr9Zrr73mxUr9T1t6%2B43PPvtMCQkJ2r17txcq9G9t6e%2BhQ4eUlpamgQMH6uqrr9Zbb73lxUr9T1t6%2B7e//U3jx49XQkKC0tLS9N5773mxUv/ldDqVnJz8ra/1gPh5ZuCctmTJEuPqq6829u/fb7z66qtGQkKC8de//rXVdidPnjRGjBhh3Hvvvcbhw4eNpUuXGj/96U%2BNkydP%2BqBq/%2BBpb99//32jf//%2Bxrp164xPPvnE2LBhg9G/f3/j/fff90HV/sHT3v67OXPmGH369DHeeustL1Xpvzztb21trfHTn/7UuPvuu41PPvnE%2BNOf/mQkJiYaVVVVPqjaP3ja2w8%2B%2BMCIj483tm7dahw5csRYvHixMWLECKOurs4HVfuPhoYGIz09/Vtf64Hy84yAdQ47efKkER8f7/YkXLVqlfGrX/2q1bYbN240xowZY7S0tBiGYRgtLS3Gz3/%2Bc2Pz5s1eq9eftKW3eXl5xpw5c9zGbrzxRuP%2B%2B%2B%2B3vE5/1JbefmPbtm3GNddcQ8DyQFv6u27dOmPs2LFGU1OTaywlJcV4/fXXvVKrv2lLb9euXWtMmTLFdfv48eNGnz59jJKSEq/U6o9KS0uNX/7yl8bVV1/9ra/1QPl5xinCc9jBgwfV1NSkhIQE11hiYqKKi4vV0tLitm1xcbESExMVFBQkSQoKCtLgwYNVVFTk1Zr9RVt6O2XKFN1%2B%2B%2B2t9nH8%2BHHL6/RHbemtJFVXVysvL09LlizxZpl%2Bqy393bNnj372s58pJCTENbZ582ZdccUVXqvXn7Sltx07dtThw4dVWFiolpYWbdmyRR06dNCPf/xjb5ftN/bs2aNhw4apoKDgW7cLlJ9nNl8XgLNzOBzq1KmTQkNDXWNdunRRY2Ojampq1LlzZ7dtL774Yrf7R0VFqbS01Gv1%2BpO29LZ3795u9y0tLdWuXbt0zTXXeK1ef9KW3krSvffeqylTpuiSSy7xdql%2BqS39LSsr08CBA/X73/9ef//739WtWzctWLBAiYmJvij9nNeW3k6YMEF///vfNXPmTIWEhCg4OFiPPvqoLrzwQl%2BU7hdmzpzp0XaB8vOMI1jnsPr6ercXuiTXbafT6dG2p2%2BHr7Wlt//u2LFjuvXWWzV48GD97Gc/s7RGf9WW3u7cuVOFhYWaN2%2Be1%2Brzd23pb11dndasWaPo6Gg99thjGjJkiObMmaPy8nKv1etP2tLb6upqORwOLVy4UM8995wmTZqkrKwsffnll16rN1AFys8zAtY5LCwsrNUT6pvb7dq182jb07fD19rS229UVVXpuuuuk2EYevDBBxUczMvnTDztbUNDgxYuXKh77rmH52kbtOW5GxISon79%2BikzM1OXXnqp7rjjDvXq1Uvbtm3zWr3%2BpC29XbFihfr06aNrr71WAwYM0NKlSxUeHq7Nmzd7rd5AFSg/z/gJcQ6LjY1VdXW1mpqaXGMOh0Pt2rVTZGRkq22rqqrcxqqqqhQTE%2BOVWv1NW3orSV988YWuvfZaOZ1OrV%2B/vtVpLvx/nva2pKREZWVlyszMVEJCgut9LzfddJMWLlzo9br9RVueu9HR0brooovcxnr16sURrLNoS2/fe%2B899e3b13U7ODhYffv21dGjR71Wb6AKlJ9nBKxzWL9%2B/WSz2dze2FdYWKj4%2BPhWR0/sdrveeecdGYYhSTIMQ/v27ZPdbvdqzf6iLb2tq6vTr3/9awUHB2vDhg2KjY31drl%2BxdPeDhw4UK%2B%2B%2Bqqef/5515ckLVu2TP/1X//l9br9RVueu4MGDdKhQ4fcxj766CN169bNK7X6m7b0NiYmRh9%2B%2BKHb2Mcff6zu3bt7pdZAFig/zwhY57Dw8HBNnjxZixYtUklJiXbs2KEnn3xSs2fPlvT1b1YNDQ2SpHHjxqm2tlY5OTk6fPiwcnJyVF9fr/Hjx/tyCeestvT20Ucf1aeffqr77rvPNedwOPgrwrPwtLft2rVTz5493b6kr397jYqK8uUSzmltee5ec801OnTokB566CEdOXJEf/rTn1RWVqZJkyb5cgnnrLb0dsaMGXruuef0/PPP68iRI1qxYoWOHj2qKVOm%2BHIJfisgf5759CIR%2BE51dXXG/PnzjUGDBhkjR4401q5d65rr06eP23VBiouLjcmTJxvx8fHGtGnTjPfee88HFfsPT3t71VVXGX369Gn1tWDBAh9Vfu5ry/P233EdLM%2B0pb979%2B41pkyZYgwYMMCYNGmSsWfPHh9U7D/a0tvnnnvOGDdunDFo0CAjLS3N2L9/vw8q9k%2Bnv9YD8edZkGH83zE4AAAAmIJThAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACb7f3iPRkReIU%2BMAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1183942956605839447”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1183942956605839447”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1343</td> <td class=”number”>41.8%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>212</td> <td class=”number”>6.6%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>1577</td> <td class=”number”>49.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_OAPP16”>SNAP_OAPP16<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>4</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.87612</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.7%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-4414856025197881605”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-4414856025197881605”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-4414856025197881605”

aria-controls=”quantiles-4414856025197881605” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-4414856025197881605” aria-controls=”histogram-4414856025197881605”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-4414856025197881605” aria-controls=”common-4414856025197881605”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-4414856025197881605” aria-controls=”extreme-4414856025197881605”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-4414856025197881605”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.31816</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.36315</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.3674</td>

</tr> <tr>

<th>Mean</th> <td>0.87612</td>

</tr> <tr>

<th>MAD</th> <td>0.21343</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-2.28</td>

</tr> <tr>

<th>Sum</th> <td>2744</td>

</tr> <tr>

<th>Variance</th> <td>0.10122</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-4414856025197881605”>

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OMF65prrtHTTz%2BtkydPOv3UAAAAjnC8YA0cOFAPPfSQBg8erN/97nfasWOHLMtyegwAAADbOF6w7rzzTv3973/X%2BvXrFR4erjvuuEOXX3657r//fn300UdOjwMAAGCcOxBP6nK5NGjQIA0aNEg1NTV68skntX79em3YsEF9%2B/bVjBkz9Ktf/SoQowEAAJy1gBQsSaqoqNBLL72kl156SR988IH69u2r8ePH6%2BjRo7rnnnu0e/duLVy4MFDjAQAA/MscL1jbt2/X9u3b9fbbb6t9%2B/YaN26c1q5dqy5dunjv07FjR2VnZ1OwAABAUHK8YC1cuFBDhw7VunXrdNlllyksrPnLwLp27arrr7/e6dEAAACMcLxgvfHGG4qJidGJEye85aqkpES9evVSeHi4JKlv377q27ev06MBAAAY4fhfEZ46dUqjRo3SI4884l279dZbNXbsWB05csTpcQAAAIxzvGD94Q9/0MUXX6ybbrrJu/byyy%2BrY8eOysnJcXocAAAA4xwvWO%2B8847uvvtuxcXFedfat2%2Bvu%2B66Szt37nR6HAAAAOMcL1hut1vV1dXN1mtqanhHdwAAEBIcL1iXXXaZli9frk8//dS7Vl5erpycHA0ZMsTpcQAAAIxz/K8I582bp5tuukkjR45UdHS0JKm6ulq9evXS/PnznR4HAADAOMcLVmxsrF544QW9%2BeabKi0tldvt1k9/%2BlP9/Oc/l8vlcnocAAAA4wLyUTnh4eEaMmQIlwQBAEBIcrxgeTwerV69Wnv27NFXX33V7IXtr776qtMjAQAAGOV4wVq0aJH27t2rK6%2B8UhdccIHTTw8AAGA7xwvWzp079eijjyo9Pd3I/urr6zVhwgQtWrRIAwYMkCQtX75cTz75pM/9Fi1a5P18w4KCAq1evVoej0eDBw/WsmXL1L59e0mSZVlatWqVnnvuOTU1NWnSpEmaM2fO935mIgAAwPdxvGC1bt1asbGxRvZVV1enO%2B%2B8U6WlpT7rZWVluvPOOzV%2B/HjvWtu2bSV9/bmHCxcu1NKlS5WUlKTs7GzNnz9fDz/8sCTpiSeeUEFBgfLy8tTQ0KC5c%2BcqNjZWs2bNMjIzAAAIfY6flhk7dqweffRRNTY2ntV%2BDh06pMmTJ/u8n9Y3ysrKdMkllyguLs77FRUVJUnasmWLRo8erXHjxikpKUkrVqzQ66%2B/rvLycknS5s2blZmZqfT0dA0cOFBz5szR1q1bz2pWAABwfnH8DNaJEydUUFCg1157TRdddJEiIiJ8tm/evNmv/ezatUsDBgzQb3/7W6WkpHjXT506pWPHjqlLly7f%2B7ji4mLdcsst3tsdO3ZUYmKiiouLFRERoSNHjqhfv37e7Wlpafr8889VUVGh%2BPj4FhwpAAA4XwXkbRrGjBlz1vuYNm3a966XlZXJ5XLpoYce0htvvKF27drppptu8l4u/L6iFBsbq6NHj8rj8UiSz/YOHTpIko4ePUrBAgAAfnG8YOXk5Ni6/w8//FAul0tdu3bV9ddfr927d2vRokVq27atRowYodra2mZnzSIiIlRfX6/a2lrv7W9vk75%2BMb2/KioqvGXtG253a%2BMFLTw8zOdfmEO29iFbe5GvfcjWfqGUbUDOYFVUVOiZZ57RRx99pAULFmj37t3q0aOHunbtetb7HjdunIYOHap27dpJkpKSkvTxxx/rqaee0ogRIxQZGdmsLNXX1ysqKsqnTEVGRnr/W5L3NVz%2ByM/PV15ens9aRkaGMjMz/%2BXj%2BiHR0f7PhpYhW/uQrb3I1z5ka59QytbxgvXJJ59o8uTJatu2rY4dO6bf/OY3evnllzV//nxt3LhRycnJZ7V/l8vlLVff6Nq1q3bu3ClJSkhIUGVlpc/2yspKxcXFKSEhQdLXb4bauXNn739LUlxcnN8zTJkyRcOGDfNZc7tbq6rqdMsO5keEh4cpOjpK1dU1amxsMrrv8x3Z2ods7UW%2B9iFb%2B9mRbUxMG6P785fjBevee%2B/V8OHDtXz5cvXt21eSdN9992nevHlauXJls/evaqk1a9bo3Xff1caNG71rBw4c8J4dS05OVmFhoSZMmCBJOnLkiI4cOaLk5GQlJCQoMTFRhYWF3oJVWFioxMTEFl3ei4%2BPb3Z/j%2BekGhrs%2BYFsbGyybd/nO7K1D9nai3ztQ7b2CaVsHS9Ye/bs0datW30%2B2Nntduv222/X5MmTz3r/Q4cO1YYNG/TYY49pxIgR2rFjh1588UXvXydOnTpVN9xwg1JSUtS7d29lZ2fr8ssv10UXXeTdvnLlSv3kJz%2BRJK1atUozZ84867kAAMD5w/GC1dTUpKam5u309OnTCg8PP%2Bv99%2BnTR2vWrNHatWu1Zs0aderUSatWrVJqaqokKTU1VVlZWVq7dq2%2B/PJLDRo0SMuWLfM%2BftasWfriiy80e/ZshYeHa9KkSbrxxhvPei4AAHD%2BcFnf/bRlm/32t79VWFiYcnNzlZaWppdeekkXXHCB7rjjDsXExGjt2rVOjuMYj%2Bek8X263WGKiWmjqqrTIXNK9VxBtvYhW3uRr32CMdvRq/8R6BH89k72KFuyjYsLzOceO/73kHfffbf27t2rwYMHq66uTv/5n/%2BpoUOH6rPPPtO8efOcHgcAAMA4xy8RJiQk6MUXX1RBQYHef/99NTU1aerUqRo7dqz38wIBAACCWUDeBysqKkrXXHNNIJ4aAADAdo4XrOnTp//gdn8/ixAAAOBc5XjB6tSpk8/thoYGffLJJ/rggw80Y8YMp8cBAAAw7pz5LMJ169bp6NGjDk8DAABg3jnzqYpjx47VH//4x0CPAQAAcNbOmYL17rvvGnmjUQAAgEA7J17kfurUKR08eFDTpk1zehwAAADjHC9YiYmJPp9DKEn/9m//puuvv15XX3210%2BMAAAAY53jBuvfee51%2BSgAAAEc5XrB2797t93379etn4yQAAAD2cLxg3XDDDd5LhN/%2BnOnvrrlcLr3//vtOjwcAAHDWHC9YDz30kJYvX665c%2Beqf//%2BioiI0HvvvaesrCyNHz9eV1xxhdMjAQAAGOX42zTk5ORo8eLFGjlypGJiYtSmTRsNHDhQWVlZeuqpp9SpUyfvFwAAQDByvGBVVFR8b3lq27atqqqqnB4HAADAOMcLVkpKiu677z6dOnXKu3bixAnl5ubq5z//udPjAAAAGOf4a7DuueceTZ8%2BXZdddpm6dOkiy7L08ccfKy4uTps3b3Z6HAAAAOMcL1jdunXTyy%2B/rIKCApWVlUmSrrvuOl155ZWKiopyehwAAADjHC9YknThhRfqmmuu0WeffaaLLrpI0tfv5g4AABAKHH8NlmVZWrlypfr166cxY8bo6NGjmjdvnhYuXKivvvrK6XEAAACMc7xgPfnkk9q%2Bfbt%2B//vfKyIiQpI0fPhwvfLKK8rLy3N6HAAAAOMcL1j5%2BflavHixJkyY4H339iuuuELLly/X//zP/zg9DgAAgHGOF6zPPvtMPXv2bLaelJQkj8fj9DgAAADGOV6wOnXqpPfee6/Z%2BhtvvOF9wTsAAEAwc/yvCGfNmqWlS5fK4/HIsiy99dZbys/P15NPPqm7777b6XEAAACMc7xgTZw4UQ0NDXrwwQdVW1urxYsXq3379vrNb36jqVOnOj0OAACAcY4XrIKCAo0aNUpTpkzR8ePHZVmWYmNjnR4DAADANo6/BisrK8v7Yvb27dtTrgAAQMhxvGB16dJFH3zwgdNPCwAA4BjHLxEmJSVpzpw5evTRR9WlSxdFRkb6bM/JyXF6JAAAAKMcL1gfffSR0tLSJIn3vQIAACHJkYK1YsUKzZ49W61bt9aTTz7pxFMCAAAEjCOvwXriiSdUU1Pjs3brrbeqoqLCiacHAABwlCMFy7KsZmu7d%2B9WXV2dE08PAADgKMf/ihAAACDUUbAAAAAMc6xguVwup54KAAAgoBx7m4bly5f7vOfVV199pdzcXLVp08bnfrwPFgAACHaOFKx%2B/fo1e8%2Br1NRUVVVVqaqqyokRAAAAHONIweK9rwAAwPmEF7kDAAAYRsECAAAwjIIFAABgGAULAADAsKAvWPX19RozZozefvtt71p5ebluvPFGpaSk6IorrtCOHTt8HvPmm29qzJgxSk5O1vTp01VeXu6zfePGjRoyZIhSU1O1YMGCZp%2BjCAAA8EOCumDV1dXpd7/7nUpLS71rlmUpIyNDHTp00LZt2zR27FjNnj1bhw8fliQdPnxYGRkZmjBhgp577jm1b99et99%2Bu/fzEv/85z8rLy9PWVlZ2rRpk4qLi5WbmxuQ4wMAAMEpaAvWoUOHNHnyZH366ac%2B6zt37lR5ebmysrLUrVs33XbbbUpJSdG2bdskSc8%2B%2B6wuvfRSzZw5U927d1dOTo4%2B//xz7dq1S5K0efNmzZgxQ0OHDlWfPn20dOlSbdu2jbNYAADAb0FbsHbt2qUBAwYoPz/fZ724uFiXXHKJWrdu7V1LS0tTUVGRd3t6erp3W1RUlHr16qWioiI1Njbqvffe89mekpKir776SgcOHLD5iAAAQKhw7KNyTJs2bdr3rns8HsXHx/usxcbG6ujRoz%2B6vbq6WnV1dT7b3W632rVr5328PyoqKpq9c73b3brZ856t8PAwn39hDtnah2ztRb72IVv7hVK2QVuw/pmamhpFRET4rEVERKi%2Bvv5Ht9fW1npv/7PH%2ByM/P195eXk%2BaxkZGcrMzPR7Hy0RHR1ly35BtnYiW3uRr33I1j6hlG3IFazIyEidOHHCZ62%2Bvl6tWrXybv9uWaqvr1d0dLT3w6i/b3tUlP//o0%2BZMkXDhg3zWXO7W6uq6rTf%2B/BHeHiYoqOjVF1do8bGJqP7Pt%2BRrX3I1l7kax%2BytZ8d2cbEtDG6P3%2BFXMFKSEjQoUOHfNYqKyu9l%2BcSEhJUWVnZbHvPnj3Vrl07RUZGqrKyUt26dZMkNTQ06MSJE4qLi/N7hvj4%2BGaXAz2ek2posOcHsrGxybZ9n%2B/I1j5kay/ytQ/Z2ieUsg2di53/Jzk5Wfv27fNe7pOkwsJCJScne7cXFhZ6t9XU1Gj//v1KTk5WWFiYevfu7bO9qKhIbrdbSUlJzh0EAAAIaiFXsPr376%2BOHTtq/vz5Ki0t1YYNG1RSUqJJkyZJkiZOnKg9e/Zow4YNKi0t1fz589W5c2cNGDBA0tcvnn/sscf0yiuvqKSkREuWLNHkyZNbdIkQAACc30KuYIWHh2v9%2BvXyeDyaMGGCXnrpJa1bt06JiYmSpM6dO%2BuBBx7Qtm3bNGnSJJ04cULr1q2Ty%2BWSJF155ZW67bbbtHjxYs2cOVN9%2BvTR3LlzA3lIAAAgyLisb97CHLbyeE4a36fbHaaYmDaqqjodMteszxVkax%2BytRf52icYsx29%2Bh%2BBHsFv72SPsiXbuLgLjO7PXyF3BgsAACDQKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABgWsgXrr3/9q372s5/5fGVmZkqS9u/fr2uuuUbJycmaOHGi9u7d6/PYgoICDR8%2BXMnJycrIyNDx48cDcQgAACBIhWzBOnTokIYOHaodO3Z4v5YvX64zZ87o1ltvVXp6up5//nmlpqbqtttu05kzZyRJJSUlWrhwoWbPnq38/HxVV1dr/vz5AT4aAAAQTEK2YJWVlalHjx6Ki4vzfkVHR%2Bvll19WZGSk7rrrLnXr1k0LFy5UmzZt9Kc//UmStGXLFo0ePVrjxo1TUlKSVqxYoddff13l5eUBPiIAABAsQrpgdenSpdl6cXGx0tLS5HK5JEkul0t9%2B/ZVUVGRd3t6err3/h07dlRiYqKKi4sdmRsAAAS/kCxYlmXpo48%2B0o4dOzRy5EgNHz5cK1euVH19vTwej%2BLj433uHxsbq6NHj0qSKioqfnA7AADAj3EHegA7HD58WDU1NYqIiNDq1av12Wefafny5aqtrfWuf1tERITq6%2BslSbW1tT%2B43R8VFRXyeDw%2Ba25362bF7WyFh4f5/AtzyNY%2BZGumXxr7AAANAklEQVQv8rUP2dovlLINyYLVqVMnvf3227rwwgvlcrnUs2dPNTU1ae7cuerfv3%2BzslRfX69WrVpJkiIjI793e1RUlN/Pn5%2Bfr7y8PJ%2B1jIwM718xmhYd7f9saBmytQ/Z2ot87UO29gmlbEOyYElSu3btfG5369ZNdXV1iouLU2Vlpc%2B2yspK79mlhISE790eFxfn93NPmTJFw4YN81lzu1urqup0Sw7hR4WHhyk6OkrV1TVqbGwyuu/zHdnah2ztRb72IVv72ZFtTEwbo/vzV0gWrP/93//VnDlz9Nprr3nPPL3//vtq166d0tLS9Mgjj8iyLLlcLlmWpT179ujXv/61JCk5OVmFhYWaMGGCJOnIkSM6cuSIkpOT/X7%2B%2BPj4ZpcDPZ6Tamiw5weysbHJtn2f78jWPmRrL/K1D9naJ5SyDZ2Lnd%2BSmpqqyMhI3XPPPfrwww/1%2Buuva8WKFbr55ps1atQoVVdXKzs7W4cOHVJ2drZqamo0evRoSdLUqVO1fft2Pfvsszpw4IDuuusuXX755brooosCfFQAACBYhGTBatu2rR577DEdP35cEydO1MKFCzVlyhTdfPPNatu2rR5%2B%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%2BjYH3HmTNn9Oyzz%2BqRRx5Rr1691KtXL5WWlmrr1q0ULAAA4BcuEX7HgQMH1NDQoNTUVO9aWlqaiouL1dTUFMDJAABAsOAM1nd4PB7FxMQoIiLCu9ahQwfV1dXpxIkTat%2B%2B/Y/uo6KiQh6Px2fN7W6t%2BPh4o7OGhwdXP3a7g2feb7INtoyDAdk6g3zN43vXfqGULQXrO2pqanzKlSTv7fr6er/2kZ%2Bfr7y8PJ%2B12bNn64477jAz5P%2BpqKjQjJ%2BUasqUKcbL2/muoqJCmzY9SrY2CMZsg%2Bmd0SsqKvTAAw8EVb7Bgu9d%2B4Ti923oVEVDIiMjmxWpb263atXKr31MmTJFzz//vM/XlClTjM/q8XiUl5fX7GwZzh7Z2ods7UW%2B9iFb%2B4RitpzB%2Bo6EhARVVVWpoaFBbvfX8Xg8HrVq1UrR0dF%2B7SM%2BPj5kGjgAAGg5zmB9R8%2BePeV2u1VUVORdKywsVO/evRUWRlwAAODH0Ri%2BIyoqSuPGjdOSJUtUUlKiV155RY8//rimT58e6NEAAECQCF%2ByZMmSQA9xrhk4cKD279%2BvVatW6a233tKvf/1rTZw4MdBjfa82bdqof//%2BatOmTaBHCTlkax%2BytRf52ods7RNq2bosy7ICPQQAAEAo4RIhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAK1jmurq5OCxYsUHp6ugYPHqzHH3/8n953//79uuaaa5ScnKyJEydq7969Dk4afFqS7WuvvaaxY8cqNTVVV111lV599VUHJw0%2BLcn2G5999plSU1P19ttvOzBhcGtJvgcPHtTUqVPVp08fXXXVVdq5c6eDkwaflmT717/%2BVaNHj1ZqaqqmTp2qffv2OThp8Kqvr9eYMWN%2B8Gc9JH6fWTinZWVlWVdddZW1d%2B9e6y9/%2BYuVmppq/fGPf2x2v9OnT1uDBg2y7r33XuvQoUPWsmXLrF/84hfW6dOnAzB1cPA32/fff9/q1auXtWnTJuvjjz%2B2tmzZYvXq1ct6//33AzB1cPA322%2BbNWuW1aNHD2vnzp0OTRm8/M23urra%2BsUvfmHdc8891scff2ytWbPGSktLsyorKwMwdXDwN9sPPvjA6t27t/XCCy9Yn3zyibV06VJr0KBB1pkzZwIwdfCora21MjIyfvBnPVR%2Bn1GwzmGnT5%2B2evfu7fNNuG7dOuv6669vdt9nn33WGjZsmNXU1GRZlmU1NTVZI0aMsLZt2%2BbYvMGkJdnm5uZas2bN8lmbOXOmdd9999k%2BZzBqSbbf2L59u3XttddSsPzQknw3bdpkDR8%2B3GpoaPCuTZgwwXrttdccmTXYtCTbJ554who/frz39smTJ60ePXpYJSUljswajEpLS62rr77auuqqq37wZz1Ufp9xifAcduDAATU0NCg1NdW7lpaWpuLiYjU1Nfnct7i4WGlpaXK5XJIkl8ulvn37qqioyNGZg0VLsh0/frzmzJnTbB8nT560fc5g1JJsJamqqkq5ubnKyspycsyg1ZJ8d%2B3apV/%2B8pcKDw/3rm3btk3/8R//4di8waQl2bZr106HDh1SYWGhmpqa9Pzzz6tt27b693//d6fHDhq7du3SgAEDlJ%2Bf/4P3C5XfZ%2B5AD4B/zuPxKCYmRhEREd61Dh06qK6uTidOnFD79u197vvTn/7U5/GxsbEqLS11bN5g0pJsu3Xr5vPY0tJSvfXWW7r22msdmzeYtCRbSbr33ns1fvx4de/e3elRg1JL8i0vL1efPn20aNEi/e1vf1OnTp00b948paWlBWL0c15Lsr3iiiv0t7/9TdOmTVN4eLjCwsL08MMP68ILLwzE6EFh2rRpft0vVH6fcQbrHFZTU%2BPzgy7Je7u%2Bvt6v%2B373fvhaS7L9tuPHj%2BuOO%2B5Q37599ctf/tLWGYNVS7J98803VVhYqNtvv92x%2BYJdS/I9c%2BaMNmzYoLi4OD3yyCPq16%2BfZs2apSNHjjg2bzBpSbZVVVXyeDxavHixnnnmGY0dO1bz58/XF1984di8oSpUfp9RsM5hkZGRzb6hvrndqlUrv%2B773fvhay3J9huVlZWaMWOGLMvS2rVrFRbGj8/38Tfb2tpaLV68WL///e/5Pm2BlnzvhoeHq2fPnsrMzNQll1yiuXPnqkuXLtq%2Bfbtj8waTlmS7cuVK9ejRQ9ddd50uvfRSLVu2TFFRUdq2bZtj84aqUPl9xm%2BIc1hCQoKqqqrU0NDgXfN4PGrVqpWio6Ob3beystJnrbKyUvHx8Y7MGmxakq0kHTt2TNddd53q6%2Bu1efPmZpe58P/5m21JSYnKy8uVmZmp1NRU7%2BtebrnlFi1evNjxuYNFS7534%2BLi1LVrV5%2B1Ll26cAbrn2hJtvv27VNSUpL3dlhYmJKSknT48GHH5g1VofL7jIJ1DuvZs6fcbrfPC/sKCwvVu3fvZmdPkpOT9e6778qyLEmSZVnas2ePkpOTHZ05WLQk2zNnzujmm29WWFiYtmzZooSEBKfHDSr%2BZtunTx/95S9/0Ysvvuj9kqTly5frv/7rvxyfO1i05Hs3JSVFBw8e9Fn78MMP1alTJ0dmDTYtyTY%2BPl5lZWU%2Bax999JE6d%2B7syKyhLFR%2Bn1GwzmFRUVEaN26clixZopKSEr3yyit6/PHHNX36dElf/z%2Br2tpaSdKoUaNUXV2t7OxsHTp0SNnZ2aqpqdHo0aMDeQjnrJZk%2B/DDD%2BvTTz/Vf//3f3u3eTwe/orwn/A321atWuniiy/2%2BZK%2B/n%2BvsbGxgTyEc1pLvnevvfZaHTx4UA888IA%2B%2BeQTrVmzRuXl5Ro7dmwgD%2BGc1ZJsJ0%2BerGeeeUYvvviiPvnkE61cuVKHDx/W%2BPHjA3kIQSskf58F9E0i8KPOnDlj3XXXXVZKSoo1ePBg64knnvBu69Gjh8/7ghQXF1vjxo2zevfubU2aNMnat29fACYOHv5mO3LkSKtHjx7NvubNmxegyc99Lfm%2B/TbeB8s/Lcn3nXfescaPH29deuml1tixY61du3YFYOLg0ZJsn3nmGWvUqFFWSkqKNXXqVGvv3r0BmDg4ffdnPRR/n7ks6//OwQEAAMAILhECAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGH/D5ZCywYCPBwyAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-4414856025197881605”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-4414856025197881605”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>342</td> <td class=”number”>10.7%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.5</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>2698</td> <td class=”number”>84.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_PART_RATE08”>SNAP_PART_RATE08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>34</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>68.566</td>

</tr> <tr>

<th>Minimum</th> <td>48</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-2905608287625260580”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-2905608287625260580,#minihistogram-2905608287625260580”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-2905608287625260580”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-2905608287625260580”

aria-controls=”quantiles-2905608287625260580” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-2905608287625260580” aria-controls=”histogram-2905608287625260580”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-2905608287625260580” aria-controls=”common-2905608287625260580”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-2905608287625260580” aria-controls=”extreme-2905608287625260580”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-2905608287625260580”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>48</td>

</tr> <tr>

<th>5-th percentile</th> <td>52</td>

</tr> <tr>

<th>Q1</th> <td>62</td>

</tr> <tr>

<th>Median</th> <td>66</td>

</tr> <tr>

<th>Q3</th> <td>78</td>

</tr> <tr>

<th>95-th percentile</th> <td>85</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>52</td>

</tr> <tr>

<th>Interquartile range</th> <td>16</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>10.406</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.15177</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.57125</td>

</tr> <tr>

<th>Mean</th> <td>68.566</td>

</tr> <tr>

<th>MAD</th> <td>8.6652</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.33297</td>

</tr> <tr>

<th>Sum</th> <td>214750</td>

</tr> <tr>

<th>Variance</th> <td>108.29</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-2905608287625260580”>

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D4dC7777rvSeWZVmqrKyUw%2BHwPraiosL7uIaGBjU0NHjnAQAAuhOWAeurX/2qpk2bpry8PO3fv19vvPGGSktLlZ2drZkzZ6q1tVUFBQU6cOCACgoK5Ha7lZ6eLknKzs7W9u3btWXLFu3fv1/Lly/XtGnTuEUDAADwW1gGLEl66KGH9JWvfEXZ2dm69957deONN%2Brmm29Wv379tHHjRlVUVCgjI0NOp1OlpaWKjY2VJCUlJWn16tUqKSlRdna2%2Bvfvr8LCwgCvBgAAhBKb9dm5MvQql%2Bt4oEv4QnZ7Hw0Y0FfNzZ%2BEzXnvQApkP9OL3jyvz3cuXlo2tdtt%2BN00i36aRT/N6s1%2BJiRcbHR//grbI1gAAACBQsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDgi5gLViwQJs3b9bx48cDXQoAAECPBF3Auvrqq7VhwwZde%2B21uuuuu7R7925ZlhXosgAAAPwWdAHr7rvv1l/%2B8hc99thjioiI0J133qlp06bpF7/4hQ4fPhzo8gAAALoVdAFLkmw2m6ZOnap169Zpz549uvHGG/XrX/9as2bN0o033qg//elP3e7jlVde0eWXX%2B7zlZubK0mqqanRggUL5HA4lJmZqX379vk8dseOHZoxY4YcDodycnJ07NixXlknAAAIT0EZsCSpsbFRTz75pLKysvTwww9r/PjxWr16ta6%2B%2BmqtWLFCBQUFZ3z8gQMHNH36dO3evdv7tWbNGrW1tWnx4sVKSUlReXm5kpKStGTJErW1tUmSqqurlZ%2Bfr6VLl6qsrEytra3Ky8s7H0sGAABhwh7oAk63fft2bd%2B%2BXW%2B99Zbi4%2BM1b948PfLIIxo1apR3myFDhqigoED5%2Bflfup%2BDBw/qsssuU0JCgs/41q1bFRUVpeXLl8tmsyk/P1%2B7du3Syy%2B/rIyMDG3atEnp6emaN2%2BeJGnt2rWaPn266urqNGLEiF5ZMwAACC9BdwQrPz9fffv2VUlJiXbu3Km7777bJ1xJ0le/%2BlXddNNNZ9zPwYMHuzxOkpxOp5KTk2Wz2SR9ejpy8uTJqqqq8s6npKR4tx8yZIiGDh0qp9N5bgsDAAAXjKA7grVr1y4NGDBALS0t6tPn0/xXXV2tCRMmKCIiQpI0efJkTZ48%2BUv3YVmWDh8%2BrN27d2vjxo3q6OjQzJkzlZubK5fLpTFjxvhsP3DgQNXW1kr69NRkYmJil/mjR4/6vYbGxka5XC6fMbs9tst%2Bg0FERB%2Bf7zg39NM/dnv3/aGXZtFPs%2BinWeHYz6ALWB9//LGys7P1jW98Q8uXL5ckLV68WIMGDdITTzyhIUOGdLuP%2Bvp6ud1uRUZGqqioSB9%2B%2BKHWrFmjEydOeMc/LzIyUh6PR5J04sSJM877o6ysTMXFxT5jOTk53ovsg1FcXEygSwgr9PPMBgzo6/e29NIs%2BmkW/TQrnPoZdAHrZz/7mUaOHKlbb73VO/biiy/q3nvvVWFhoR555JFu9zFs2DC99dZb6t%2B/v2w2m8aNG6fOzk79%2BMc/Vmpqapew5PF4FB0dLUmKior6wvmYGP//o2dlZSktLc1nzG6PVXPzJ37v43yJiOijuLgYtba61dHRGehyQh799I8/rwV6aRb9NIt%2BmtWb/Tybf9CZFHQB629/%2B5t%2B%2B9vf%2BlycHh8fr%2BXLl%2BvGG2/0ez%2BXXHKJz8%2BjR49We3u7EhIS1NTU5DPX1NTkPX03ePDgL5w//WL5M0lMTOxyOtDlOq5Tp4L3RdjR0RnU9YUa%2BnlmZ9MbemkW/TSLfpoVTv0MupOddrtdra2tXcbdbrffd3R/4403NGXKFLndbu/Y%2B%2B%2B/r0suuUTJycl69913vfuyLEuVlZVyOBySJIfDoYqKCu/jGhoa1NDQ4J0HAADoTtAFrOuvv15r1qzRv/71L%2B9YXV2dCgsLdd111/m1j6SkJEVFRWnFihU6dOiQdu7cqbVr12rRokWaOXOmWltbVVBQoAMHDqigoEBut1vp6emSpOzsbG3fvl1btmzR/v37tXz5ck2bNo1bNAAAAL8FXcC699575fF4dMMNN2jKlCmaMmWKvvWtb%2BnkyZN%2B3/CzX79%2B%2BuUvf6ljx44pMzNT%2Bfn5ysrK0qJFi9SvXz9t3LhRFRUVysjIkNPpVGlpqWJjYyV9Gs5Wr16tkpISZWdnq3///iosLOzNJQMAgDBjs4Lwk5Q7Ojq0Z88e1dbWym63a8yYMbrmmmu8964KRS7X8UCX8IXs9j4aMKCvmps/CZvz3oEUyH6mF715Xp/vXLy0bGq32/C7aRb9NIt%2BmtWb/UxIuNjo/vwVdBe5S1JERISuu%2B46v08JAgAABJOgC1gul0tFRUWqrKzUyZMnu1zY/uqrrwaoMgAAAP8EXcD6n//5H%2B3bt0%2BzZ8/WxRcH5rAeAADAuQi6gPXXv/5VTz75pM/nAQIAAISSoPsrwtjYWA0cODDQZQAAAPRY0AWsuXPn6sknn1RHR0egSwEAAOiRoDtF2NLSoh07duj111/XiBEjunzw8jPPPBOgygAAAPwTdAFLkubMmRPoEgAAAHos6AIWd00HAAChLuiuwZKkxsZGFRcX6%2B6779a///1vvfzyyzp06FCgywIAAPBL0AWsI0eO6Nvf/raef/55/fGPf1RbW5tefPFFZWZmyul0Bro8AACAbgVdwHrwwQc1Y8YM/fnPf9ZFF10kSXr44YeVlpamhx56KMDVAQAAdC/oAlZlZaVuvfVWnw92ttvtuuOOO1RTUxPAygAAAPwTdBe5d3Z2qrOz6ydpf/LJJ4qIiAhARQBMSy96M9AlnJWXlk0NdAkAQkzQHcG69tprtXHjRp%2BQ1dLSonXr1unqq68OYGUAAAD%2BCbqA9ZOf/ET79u3Ttddeq/b2dt1%2B%2B%2B2aPn26PvzwQ917772BLg8AAKBbQXeKcPDgwfrd736nHTt26P3331dnZ6eys7M1d%2B5c9evXL9DlAQAAdCvoApYkxcTEaMGCBYEuAwBCUihd48b1bQhXQRewFi5ceMZ5PosQAAAEu6ALWMOGDfP5%2BdSpUzpy5Ig%2B%2BOADff/73w9QVbgQhdJRAABAcAm6gPVln0VYUlKio0ePnudqAAAAzl7Q/RXhl5k7d65eeumlQJcBAADQrZAJWO%2B%2B%2By43GgUAACEh6E4RftFF7h9//LH%2B8Y9/6Hvf%2B14AKgIAADg7QRewhg4d6vM5hJJ00UUX6aabbtJ3vvOdAFUFAADgv6ALWA8%2B%2BKDR/S1evFjx8fHe/dbU1OinP/2pPvjgA40ZM0arVq3SFVdc4d1%2Bx44dKioqksvl0rXXXqsHHnhA8fHxRmsCAADhLegC1jvvvOP3tlddddUZ5//whz9o586d%2Bs///E9JUltbmxYvXqxvf/vbevDBB/Xcc89pyZIleuWVVxQbG6vq6mrl5%2Bdr1apVGjt2rAoKCpSXl6eNGzee05oAAMCFJegC1s033%2Bw9RWhZlnf89DGbzab333//S/fT0tKitWvXauLEid6xF198UVFRUVq%2BfLlsNpvy8/O1a9cuvfzyy8rIyNCmTZuUnp6uefPmSZLWrl2r6dOnq66uTiNGjDC%2BVgAAEJ6C7q8IN2zYoGHDhqmoqEh79%2B5VRUWFnn76aV166aW666679Oqrr%2BrVV1/Vn//85zPu5%2Bc//7nmzp2rMWPGeMecTqeSk5O9Yc1ms2ny5MmqqqryzqekpHi3HzJkiIYOHSqn09kLKwUAAOEq6I5gFRYWauXKlbr%2B%2Buu9Y1dffbVWr16t5cuX64c//GG3%2B9i7d6/%2B9re/6fe//73uv/9%2B77jL5fIJXJI0cOBA1dbWSpIaGxuVmJjYZf5sb3Da2Ngol8vlM2a3x3bZdzCIiOjj8x1AV3Y7r4/eEqq95b3TrHDsZ9AFrMbGxi4flyNJ/fr1U3Nzc7ePb29v109/%2BlOtXLlS0dHRPnNut1uRkZE%2BY5GRkfJ4PJKkEydOnHHeX2VlZSouLvYZy8nJUW5u7lnt53yKi4sJdAlA0BowoG%2BgSwhbod5b3jvNCqd%2BBl3AmjRpkh5%2B%2BGH9/Oc/V79%2B/SR9ej3VunXrdM0113T7%2BOLiYl1xxRW67rrrusxFRUV1CUsej8cbxL5sPibm7P6DZ2VlKS0tzWfMbo9Vc/MnZ7Wf8yEioo/i4mLU2upWR0dnoMsBglIwvnbDRaj2lvdOs3qzn4EK8UEXsFasWKGFCxfq%2Buuv16hRo2RZlv75z38qISFBzzzzTLeP/8Mf/qCmpiYlJSVJkjcw/fGPf9ScOXPU1NTks31TU5P31N3gwYO/cD4hIeGs1pCYmNjldKDLdVynTgXvi7CjozOo6wMCiddG7wn13vLeaVY49TPoAtbo0aP14osvaseOHTp48KAk6cYbb9Ts2bP9OpL07LPP6tSpU96fH3roIUnSPffco3feeUdPPPGELMuSzWaTZVmqrKzUj370I0mSw%2BFQRUWFMjIyJEkNDQ1qaGiQw%2BEwvUwAABDGgi5gSVL//v21YMECffjhh97bI1x00UV%2BPfb067f69v300ODIkSM1cOBArV%2B/XgUFBfqv//ovbd68WW63W%2Bnp6ZKk7Oxs3XzzzZo0aZImTpyogoICTZs2jVs0AACAsxJ0l%2BtblqWHHnpIV111lebMmaOjR4/q3nvvVX5%2Bvk6ePHlO%2B%2B7Xr582btzoPUrldDpVWlqq2NhYSVJSUpJWr16tkpISZWdnq3///iosLDSxLAAAcAEJuiNYzz77rLZv366f/vSnWr16tSRpxowZWrVqlQYNGqT//u//Pqv9nf7RO1deeaWef/75L90%2BIyPDe4oQAACgJ4LuCFZZWZlWrlypjIwM7w1BZ82apTVr1uj3v/99gKsDAADoXtAFrA8//FDjxo3rMj527NguN%2B8EAAAIRkEXsIYNG6a///3vXcZ37drFxeYAACAkBN01WD/4wQ%2B0atUquVwuWZalvXv3qqysTM8%2B%2B6x%2B8pOfBLo8AACAbgVdwMrMzNSpU6f0%2BOOP68SJE1q5cqXi4%2BO1bNkyZWdnB7o8AACAbgVdwNqxY4dmzpyprKwsHTt2TJZlaeDAgYEuCwAAwG9Bdw3W6tWrvRezx8fHE64AAEDICbqANWrUKH3wwQeBLgMAAKDHgu4U4dixY3XPPffoySef1KhRoxQVFeUzz53VQ1d60ZuBLgEAgPMi6ALW4cOHlZycLEnc9woAAISkoAhYa9eu1dKlSxUbG6tnn3020OUAAACck6C4Buupp56S2%2B32GVu8eLEaGxsDVBEAAEDPBUXAsiyry9g777yj9vb2AFQDAABwboIiYAEAAIQTAhYAAIBhQROwbDZboEsAAAAwIij%2BilCS1qxZ43PPq5MnT2rdunXq27evz3bcBwsAAAS7oAhYV111VZd7XiUlJam5uVnNzc0BqgoAAKBngiJgce8rAAAQToLmGiwAAIBwQcACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw8I2YB05ckQ/%2BMEPlJSUpGnTpunJJ5/0ztXV1emWW27RpEmTNGvWLO3evdvnsXv27NGcOXPkcDi0cOFC1dXVne/yAQBACAvLgNXZ2anFixdrwIABev7557Vq1So9/vjj%2Bv3vfy/LspSTk6NBgwZp27Ztmjt3rpYuXar6%2BnpJUn19vXJycpSRkaGtW7cqPj5ed9xxhyzLCvCqAABAqAiKG42a1tTUpHHjxun%2B%2B%2B9Xv379NGrUKF1zzTWqqKjQoEGDVFdXp82bNys2NlajR4/W3r17tW3bNt15553asmWLrrjiCt12222SPv1onqlTp%2Brtt9/WlClTArwyAAAQCsLyCFZiYqKKiorUr18/WZaliooKvfPOO0pNTZXT6dT48eMVGxvr3T45OVlVVVWSJKfTqZSUFO9cTEyMJkyY4J0HAADoTlgewfq8tLQ01dfXa/r06brhhhv0s5/9TImJiT7bDBw4UEePHpUkuVyuM877o7GxsctnK9rtsV32GwwiIvr4fAfQld3O66O3hGpvee80Kxz7GfYB65FHHlFTU5Puv/9%2BFRYWyu12KzIy0mebyMhIeTweSep23h9lZWUqLi72GcvJyVFubm4PV9H74uJiAl0CELQGDOgb6BLCVqj3lvdOs8Kpn2EfsCZOnChJam9v1z333KPMzEy53W6fbTwej6IneYI9AAAUeUlEQVSjoyVJUVFRXcKUx%2BNRXFyc38%2BZlZWltLQ0nzG7PVbNzZ/0ZAm9KiKij%2BLiYtTa6lZHR2egywGCUjC%2BdsNFqPaW906zerOfgQrxYRmwmpqaVFVVpRkzZnjHxowZo5MnTyohIUGHDh3qsv1np%2B8GDx6spqamLvPjxo3z%2B/kTExO7nA50uY7r1KngfRF2dHQGdX1AIPHa6D2h3lveO80Kp36Gz8nOz/nwww%2B1dOlSffTRR96xffv2KT4%2BXsnJyXrvvfd04sQJ71xFRYUcDockyeFwqKKiwjvndrtVU1PjnQcAAOhOWAasiRMnasKECbrvvvt04MAB7dy5U%2BvWrdOPfvQjpaamasiQIcrLy1Ntba1KS0tVXV2t%2BfPnS5IyMzNVWVmp0tJS1dbWKi8vT8OHD%2BcWDQAAwG9hGbAiIiL02GOPKSYmRllZWcrPz9fNN9%2BshQsXeudcLpcyMjL0wgsvqKSkREOHDpUkDR8%2BXI8%2B%2Bqi2bdum%2BfPnq6WlRSUlJbLZbAFeFQAACBVheQ2W9Om1VKf/Jd9nRo4cqU2bNn3pY7/%2B9a/r61//em%2BVBgAAwlxYHsECAAAIJAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgmD3QBQBAsEsvejPQJQAIMWF7BOujjz5Sbm6uUlNTdd1116mwsFDt7e2SpLq6Ot1yyy2aNGmSZs2apd27d/s8ds%2BePZozZ44cDocWLlyourq6QCwBAACEqLAMWJZlKTc3V263W7/5zW/0i1/8Qn/5y19UVFQky7KUk5OjQYMGadu2bZo7d66WLl2q%2Bvp6SVJ9fb1ycnKUkZGhrVu3Kj4%2BXnfccYcsywrwqgAAQKgIy1OEhw4dUlVVld58800NGjRIkpSbm6uf//znuv7661VXV6fNmzcrNjZWo0eP1t69e7Vt2zbdeeed2rJli6644grddtttkqTCwkJNnTpVb7/9tqZMmRLIZQEAgBARlkewEhIS9OSTT3rD1Wc%2B/vhjOZ1OjR8/XrGxsd7x5ORkVVVVSZKcTqdSUlK8czExMZowYYJ3HgAAoDtheQQrLi5O1113nffnzs5Obdq0SVdffbVcLpcSExN9th84cKCOHj0qSd3O%2B6OxsVEul8tnzG6P7bLfYBAR0cfnOwCcT3Z7aL738N5pVjj2MywD1unWrVunmpoabd26VU8//bQiIyN95iMjI%2BXxeCRJbrf7jPP%2BKCsrU3Fxsc9YTk6OcnNze7iC3hcXFxPoEgBcgAYM6BvoEs4J751mhVM/wz5grVu3Tr/%2B9a/1i1/8QpdddpmioqLU0tLis43H41F0dLQkKSoqqkuY8ng8iouL8/s5s7KylJaW5jNmt8equfmTHq6i90RE9FFcXIxaW93q6OgMdDkALjDB%2BL7oD947zerNfgYqxId1wHrggQf03HPPad26dbrhhhskSYMHD9aBAwd8tmtqavKevhs8eLCampq6zI8bN87v501MTOxyOtDlOq5Tp4L3RdjR0RnU9QEIT6H%2BvsN7p1nh1M/wOdl5muLiYm3evFkPP/ywZs%2Be7R13OBx67733dOLECe9YRUWFHA6Hd76iosI753a7VVNT450HAADoTlgGrIMHD%2Bqxxx7TD3/4QyUnJ8vlcnm/UlNTNWTIEOXl5am2tlalpaWqrq7W/PnzJUmZmZmqrKxUaWmpamtrlZeXp%2BHDh3OLBgAA4LewDFivvvqqOjo69Pjjj%2Bvaa6/1%2BYqIiNBjjz0ml8uljIwMvfDCCyopKdHQoUMlScOHD9ejjz6qbdu2af78%2BWppaVFJSYlsNluAVwUAAEKFzeIW5eeFy3W8V/bLZ6QBwPnz0rKpkj69vcSAAX3V3PxJ2FwzFEi92c%2BEhIuN7s9fYXkECwAAIJAIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMPCPmB5PB7NmTNHb731lnesrq5Ot9xyiyZNmqRZs2Zp9%2B7dPo/Zs2eP5syZI4fDoYULF6quru58lw0AAEJYWAes9vZ23XXXXaqtrfWOWZalnJwcDRo0SNu2bdPcuXO1dOlS1dfXS5Lq6%2BuVk5OjjIwMbd26VfHx8brjjjtkWVaglgEAAEJM2AasAwcO6Lvf/a7%2B9a9/%2BYz/9a9/VV1dnVavXq3Ro0dryZIlmjRpkrZt2yZJ2rJli6644grddttt%2Bo//%2BA8VFhbqf//3f/X2228HYhkAACAEhW3AevvttzVlyhSVlZX5jDudTo0fP16xsbHeseTkZFVVVXnnU1JSvHMxMTGaMGGCdx4AAKA79kAX0Fu%2B973vfeG4y%2BVSYmKiz9jAgQN19OhRv%2Bb90djYKJfL5TNmt8d22S8AILTY7Z8el4iI8P2OcxOO/QzbgPVl3G63IiMjfcYiIyPl8Xj8mvdHWVmZiouLfcZycnKUm5vbw6oBAMFgwIC%2BPj/HxcUEqJLwFE79vOACVlRUlFpaWnzGPB6PoqOjvfOnhymPx6O4uDi/nyMrK0tpaWk%2BY3Z7rJqbP%2Blh1QCAYPDZ%2B3hERB/FxcWotdWtjo7OAFcV%2Bnqzn6eH4vPlggtYgwcP1oEDB3zGmpqavKfvBg8erKampi7z48aN8/s5EhMTu5wOdLmO69QpXoQAEMpOfx/v6Ojkvd2gcOpn%2BJzs9JPD4dB7772nEydOeMcqKirkcDi88xUVFd45t9utmpoa7zwAAEB3LriAlZqaqiFDhigvL0%2B1tbUqLS1VdXW15s%2BfL0nKzMxUZWWlSktLVVtbq7y8PA0fPlxTpkwJcOUAACBUXHABKyIiQo899phcLpcyMjL0wgsvqKSkREOHDpUkDR8%2BXI8%2B%2Bqi2bdum%2BfPnq6WlRSUlJbLZbAGuHAAAhAqbxS3KzwuX63iv7De96M1e2S8AoKuXlk2V9OntGgYM6Kvm5k/C5pqhQOrNfiYkXGx0f/664I5gAQAA9DYCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMHugCwAAIFSkF70Z6BLOykvLpga6hAsWR7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYdymAQCAMBVKt5X4W8HMQJdgFEewvkB7e7vuu%2B8%2BpaSk6Nprr9WvfvWrQJcEAABCCEewvsDatWu1b98%2B/frXv1Z9fb3uvfdeDR06VDNnhle6BgAAvYOAdZq2tjZt2bJFTzzxhCZMmKAJEyaotrZWv/nNbwhYAADAL5wiPM3%2B/ft16tQpJSUleceSk5PldDrV2dkZwMoAAECo4AjWaVwulwYMGKDIyEjv2KBBg9Te3q6WlhbFx8d3u4/Gxka5XC6fMbs9VomJicbrBQAgXEREhM9xHwLWadxut0%2B4kuT92ePx%2BLWPsrIyFRcX%2B4wtXbpUd955p5kiP%2Bdc/%2BqisbFRZWVlysrKIgAaQD/NoZdm0U%2Bz6KdZjY2NevTRR8Oqn%2BETFQ2JiorqEqQ%2B%2Bzk6OtqvfWRlZam8vNznKysry3itJrhcLhUXF3c54oaeoZ/m0Euz6KdZ9NOscOwnR7BOM3jwYDU3N%2BvUqVOy2z9tj8vlUnR0tOLi4vzaR2JiYtgkcAAAcPY4gnWacePGyW63q6qqyjtWUVGhiRMnqk8f2gUAALpHYjhNTEyM5s2bp/vvv1/V1dX685//rF/96ldauHBhoEsDAAAhIuL%2B%2B%2B%2B/P9BFBJurr75aNTU1Wr9%2Bvfbu3asf/ehHyszMDHRZvaZv375KTU1V3759A11KWKCf5tBLs%2BinWfTTrHDrp82yLCvQRQAAAIQTThECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgXSBeeeUVXX755T5fubm5kqSamhotWLBADodDmZmZ2rdvX4CrDX4ej0erVq3SVVddpa997Wt6%2BOGH9dk9e%2Bnn2SkvL%2B/yu3n55Zdr7NixkuhnTzQ0NGjJkiWaPHmy0tLS9PTTT3vn6OfZ%2B/e//63c3FylpKTom9/8psrLy71zdXV1uuWWWzRp0iTNmjVLu3fvDmClwc3j8WjOnDl66623vGPd9W/Pnj2aM2eOHA6HFi5cqLq6uvNddo8RsC4QBw4c0PTp07V7927v15o1a9TW1qbFixcrJSVF5eXlSkpK0pIlS9TW1hbokoPamjVrtGfPHv3yl7/U%2BvXr9dvf/lZlZWX0swc%2Be1P97Ov111/XyJEjtXDhQvrZQ8uWLVNsbKzKy8t13333qaioSK%2B88gr97AHLspSTk6OjR4/qmWee0X333acHH3xQf/rTn7xzgwYN0rZt2zR37lwtXbpU9fX1gS476LS3t%2Buuu%2B5SbW2td6y7/tXX1ysnJ0cZGRnaunWr4uPjdccddyhkPoDGwgXh7rvvttavX99lfMuWLVZaWprV2dlpWZZldXZ2Wt/85jetbdu2ne8SQ0Zzc7M1fvx466233vKObdy40frJT35CPw3YsGGDNWPGDKu9vZ1%2B9kBLS4t12WWXWf/4xz%2B8Y0uXLrVWrVpFP3ugurrauuyyy6x//etf3rGNGzda3/3ud609e/ZYkyZNsj755BPv3Pe//33rkUceCUSpQau2ttb6zne%2BY33729%2B2LrvsMuuvf/2rZVlWt/0rKiqybrrpJu9cW1ublZSU5H18sOMI1gXi4MGDGjVqVJdxp9Op5ORk2Ww2SZLNZtPkyZNVVVV1nisMHRUVFerXr59SU1O9Y4sXL1ZhYSH9PEctLS164okndPfddysyMpJ%2B9kB0dLRiYmJUXl6ukydP6tChQ6qsrNS4cePoZw/U1dUpPj5eI0aM8I5dfvnl2rdvnyoqKjR%2B/HjFxsZ655KTk%2Bnnad5%2B%2B21NmTJFZWVlPuNOp/OM/XM6nUpJSfHOxcTEaMKECSHTXwLWBcCyLB0%2BfFi7d%2B/WDTfcoBkzZuihhx6Sx%2BORy%2BVSYmKiz/YDBw7U0aNHA1Rt8Kurq9OwYcP0u9/9TjNnztQ3vvENlZSUqLOzk36eo%2Beee06JiYmaOXOmJNHPHoiKitLKlStVVlYmh8Oh9PR0XX/99VqwYAH97IFBgwbp%2BPHjcrvd3rGjR4/q1KlT9NNP3/ve93TfffcpJibGZ7y7/oV6f%2B2BLgC9r76%2BXm63W5GRkSoqKtKHH36oNWvW6MSJE97xz4uMjJTH4wlQtcGvra1NR44c0ebNm1VYWCiXy6WVK1cqJiaGfp4Dy7K0ZcsWLVq0yDtGP3vm4MGDmj59um699VbV1tbqgQce0DXXXEM/e8DhcCgxMVEPPPCAVqxYIZfLpaeeekrSpxdt08%2Be6%2B73MdR/XwlYF4Bhw4bprbfeUv/%2B/WWz2TRu3Dh1dnbqxz/%2BsVJTU7v8sno8HkVHRweo2uBnt9v18ccfa/369Ro2bJikT0Psc889p5EjR9LPHvr73/%2Bujz76SLNnz/aORUVF0c%2BztHfvXm3dulU7d%2B5UdHS0Jk6cqI8%2B%2BkiPP/64RowYQT/PUlRUlIqKirRs2TIlJydr4MCBWrRokQoLC2Wz2ejnOYiKilJLS4vP2Of792Wv/7i4uPNW47ngFOEF4pJLLvFedyFJo0ePVnt7uxISEtTU1OSzbVNTU5fDsvj/EhISFBUV5Q1XknTppZeqoaFBgwcPpp899MYbbyglJUX9%2B/f3jtHPs7dv3z6NHDnS53/y48ePV319Pf3soSuvvFKvvfaadu3apddff12XXnqpBgwYoK985Sv08xx09/v4ZfMJCQnnrcZzQcC6ALzxxhuaMmWKzzUE77//vi655BIlJyfr3Xff9f7Zq2VZqqyslMPhCFS5Qc/hcKi9vV2HDx/2jh06dEjDhg2Tw%2BGgnz1UXV2tyZMn%2B4zRz7OXmJioI0eO%2BPzL/9ChQxo%2BfDj97IGWlhZlZ2erublZCQkJstvtev3115WamiqHw6H33ntPJ06c8G5fUVFBP/3UXf8cDocqKiq8c263WzU1NSHTXwLWBSApKUlRUVFasWKFDh06pJ07d2rt2rVatGiRZs6cqdbWVhUUFOjAgQMqKCiQ2%2B1Wenp6oMsOWl/96lc1bdo05eXlaf/%2B/XrjjTdUWlqq7Oxs%2BnkOamtrNWbMGJ8x%2Bnn20tLSdNFFF2nFihU6fPiwXnvtNW3YsEE333wz/eyBSy65RG1tbVq3bp3q6uq0ZcsWbdu2TYsWLVJqaqqGDBmivLw81dbWqrS0VNXV1Zo/f36gyw4J3fUvMzNTlZWVKi0tVW1trfLy8jR8%2BHBNmTIlwJX7KVD3h8D59cEHH1i33HKLNWnSJGvq1KnWo48%2B6r0XjtPptObNm2dNnDjRmj9/vvXee%2B8FuNrg19raav34xz%2B2Jk2aZF1zzTX004CJEydau3bt6jJOP89ebW2tdcstt1iTJ0%2B2ZsyYYT311FP8fp6DgwcPWjfddJPlcDis2bNnW6%2B99pp37p///Kd14403WldccYU1e/Zs68033wxgpcHv8/fBsqzu%2B/f6669b3/rWt6wrr7zS%2Bv73v%2B9zP7JgZ7OsULklKgAAQGjgFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGPb/AEbIGr2rQZ5/AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-2905608287625260580”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>65.0</td> <td class=”number”>407</td> <td class=”number”>12.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.0</td> <td class=”number”>206</td> <td class=”number”>6.4%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>62.0</td> <td class=”number”>188</td> <td class=”number”>5.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>81.0</td> <td class=”number”>138</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70.0</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>64.0</td> <td class=”number”>132</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>61.0</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (23)</td> <td class=”number”>1210</td> <td class=”number”>37.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-2905608287625260580”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>48.0</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>52.0</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>84.0</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:49%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>85.0</td> <td class=”number”>170</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.0</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_PART_RATE13”>SNAP_PART_RATE13<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>47</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.5%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>86.446</td>

</tr> <tr>

<th>Minimum</th> <td>56.955</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8771697657434332610”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8771697657434332610,#minihistogram8771697657434332610”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8771697657434332610”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8771697657434332610”

aria-controls=”quantiles8771697657434332610” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8771697657434332610” aria-controls=”histogram8771697657434332610”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8771697657434332610” aria-controls=”common8771697657434332610”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8771697657434332610” aria-controls=”extreme8771697657434332610”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8771697657434332610”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>56.955</td>

</tr> <tr>

<th>5-th percentile</th> <td>73.941</td>

</tr> <tr>

<th>Q1</th> <td>79.024</td>

</tr> <tr>

<th>Median</th> <td>86.331</td>

</tr> <tr>

<th>Q3</th> <td>92.597</td>

</tr> <tr>

<th>95-th percentile</th> <td>100</td>

</tr> <tr>

<th>Maximum</th> <td>100</td>

</tr> <tr>

<th>Range</th> <td>43.045</td>

</tr> <tr>

<th>Interquartile range</th> <td>13.573</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>8.9274</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.10327</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.18842</td>

</tr> <tr>

<th>Mean</th> <td>86.446</td>

</tr> <tr>

<th>MAD</th> <td>7.312</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.33564</td>

</tr> <tr>

<th>Sum</th> <td>270750</td>

</tr> <tr>

<th>Variance</th> <td>79.698</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8771697657434332610”>

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/%2BteaOXOmRo8e7RtzuVxKTU1VWFiYpK9PRU6aNEk7duzwzaelpfmWHzJkiIYOHSqXy2Vo7wAAwOkg6I5gbdmyRYMGDVJbW5v69fs6/9XW1mrcuHEKDw%2BXJE2aNEmTJk065ja2b9%2Bujz76SK%2B99pruu%2B8%2B37jb7fYLXJIUHx%2Bv%2Bvp6SV%2BflkxMTOwx39TUZGLXAADAaSLoAtbBgweVl5enH//4x1q4cKGkry9UHzx4sJ566ikNGTLkO9fv7OzUr371Ky1evFhRUVF%2Bcx6PRxEREX5jERER8nq9kqRDhw5953ygmpub5Xa7/cYcjpge4a0vCw/v5/cV9qMnp47DcWKvKT0JPSf6bwGBCaX3StAFrP/5n//RiBEjdMMNN/jGXn/9dS1atEilpaV69NFHv3P9srIyXXDBBbr00kt7zEVGRvYIS16v1xfEjjUfHR19XPtQWVmpsrIyv7H8/HwVFhYe13b6gtjY43ttcOrRE/MGDep/UuvTk9Bxsv8W8N1C6b0SdAHro48%2B0ssvv6yEhATfWFxcnBYuXKi5c%2Bf2uv7vfvc7tbS0KCUlRZJ8gekPf/iDZsyYoZaWFr/lW1pafEeWkpKSvnX%2Bm7UEIjc3VxkZGX5jDkeMWlu/Oq7tBLPw8H6KjY1We7tHXV3ddpcD0ZNT6UTfu/Qk9ITS53gwOhXvFbtCcdAFLIfDofb29h7jHo8noDu6v/DCCzpy5Ijv%2B4ceekiSdOedd%2BrDDz/UU089JcuyFBYWJsuyVFNTo5tvvlmS5HQ6VV1drezsbEnS/v37tX//fjmdzuPah8TExB6nA93uL3XkSOh9wHZ1dYfkfvVl9MS8k3096UnooI%2BnVii9V4LuZOdll12mpUuX6p///KdvrKGhQaWlpd962u9oycnJGjFihO/Rv39/9e/fXyNGjFBmZqba29tVUlKi3bt3q6SkRB6PR1lZWZKkvLw8bdy4UWvXrtWuXbu0cOFCTZ06lVs0AACA4xJ0AWvRokXyer268sorNXnyZE2ePFk//elPdfjw4ZO%2B6eeAAQO0atUq31Eql8uliooKxcTESJJSUlK0ZMkSlZeXKy8vTwMHDlRpaamJ3QIAAKeRoDtFGB8frw0bNmjbtm2qr6%2BXw%2BHQ6NGjddFFF/nuX3U8HnzwQb/vJ0yYoA0bNhxz%2BezsbN8pQgAAgBMRdAFLksLDw3XppZcGdEoQAAAg2ARdwHK73Vq5cqVqamp0%2BPDhHhe2//nPf7apMgAAgMAEXcD67//%2Bb%2B3cuVNXXXWVzjzzTLvLAQAAOG5BF7Dee%2B89rV692u9vAgIAAPQlQfdbhDExMYqPj7e7DAAAgBMWdAFr5syZWr16tbq6uuwuBQAA4IQE3SnCtrY2bdq0SW%2B//baGDx/e448vP//88zZVBgAAEJigC1iSNGPGDLtLAAAAOGFBF7C4czoAAOjrgu4aLElqbm5WWVmZ7rjjDv3rX//SG2%2B8oT179thdFgAAQECCLmDt27dPP/vZz7Rhwwb94Q9/UEdHh15//XXl5OTI5XLZXR4AAECvgi5gPfjgg5o2bZrefPNNnXHGGZKkFStWKCMjQw899JDN1QEAAPQu6AJWTU2NbrjhBr8/7OxwOHTLLbeorq7OxsoAAAACE3QBq7u7W93d3T3Gv/rqK4WHh9tQEQAAwPEJuoB1ySWXaNWqVX4hq62tTcuXL9eUKVNsrAwAACAwQRew7r77bu3cuVOXXHKJOjs79ctf/lJXXHGFPv/8cy1atMju8gAAAHoVdPfBSkpK0iuvvKJNmzbp008/VXd3t/Ly8jRz5kwNGDDA7vIAAKexrJXv2l0C%2BoigC1iSFB0drTlz5thdBgAAwAkJuoA1b96875znbxECAIBgF3QBKzk52e/7I0eOaN%2B%2Bffr73/%2Bu6667zqaqAAAAAhd0AetYf4uwvLxcTU1N33M1AAAAxy/ofovwWGbOnKnf//73dpcBAADQqz4TsD7%2B%2BGNuNAoAAPqEoDtF%2BG0XuR88eFB/%2B9vfdPXVV9tQEQAAwPEJuoA1dOhQv79DKElnnHGGrrnmGv385z%2B3qSoAAIDABV3AevDBB%2B0uAQAA4KQEXcD68MMPA172wgsvPIWVAAAAnJigC1jXXnut7xShZVm%2B8aPHwsLC9Omnn37/BQIAAPQi6ALWk08%2BqaVLl%2Bquu%2B5Senq6IiIi9Mknn2jJkiX6j//4D02fPt3uEgEAAL5T0N2mobS0VIsXL9aVV16pQYMGqX///poyZYqWLFmil156ScnJyb4HAABAMAq6gNXc3Pyt4WnAgAFqbW21oSIAAIDjE3QBa%2BLEiVqxYoUOHjzoG2tra9Py5ct10UUX2VgZAABAYILuGqx7771X8%2BbN02WXXaaRI0fKsiz94x//UEJCgp5//nm7ywMAAOhV0AWsUaNG6fXXX9emTZv02WefSZLmzp2rq666StHR0TZXBwAA0LugO0UoSQMHDtScOXN0zTXXqKioSDNnzjzucLVv3z794he/UEpKiqZOnarVq1f75hoaGnT99ddr4sSJmj59urZu3eq37rZt2zRjxgw5nU7NmzdPDQ0NRvYLAACcHoIuYFmWpYceekgXXnihZsyYoaamJi1atEjFxcU6fPhwQNvo7u7W/PnzNWjQIG3YsEH333%2B/nnjiCb322muyLEv5%2BfkaPHiwqqqqNHPmTBUUFKixsVGS1NjYqPz8fGVnZ2vdunWKi4vTLbfc4ndPLgAAgO8SdAHrhRde0MaNG/WrX/1KERERkqRp06bpzTffVFlZWUDbaGlp0ZgxY3Tfffdp5MiRuvzyy3XRRRepurpa7733nhoaGrRkyRKNGjVKCxYs0MSJE1VVVSVJWrt2rS644ALdeOON%2BuEPf6jS0lL97//%2Brz744INTts8AACC0BF3Aqqys1OLFi5Wdne27e/v06dO1dOlSvfbaawFtIzExUStXrtSAAQNkWZaqq6v14YcfKj09XS6XS2PHjlVMTIxv%2BdTUVO3YsUOS5HK5lJaW5puLjo7WuHHjfPMAAAC9CbqA9fnnn2vMmDE9xs8//3y53e7j3l5GRoauvvpqpaSk6Morr5Tb7VZiYqLfMvHx8WpqapKkXucBAAB6E3S/RZicnKxPPvlEw4YN8xvfsmWLhg8fftzbe/TRR9XS0qL77rtPpaWl8ng8vlOP/xYRESGv1ytJvc4Horm5uUcYdDhiegS3viw8vJ/fV9iPnpw6DseJvab0BDg%2BofReCbqA9Ytf/EL333%2B/3G63LMvS9u3bVVlZqRdeeEF33333cW9v/PjxkqTOzk7deeedysnJkcfj8VvG6/UqKipKkhQZGdkjTHm9XsXGxgb8nJWVlT2uF8vPz1dhYeFx1x/sYmO5dUawoSfmDRrU/6TWpydAYELpvRJ0ASsnJ0dHjhzRE088oUOHDmnx4sWKi4vTbbfdpry8vIC20dLSoh07dmjatGm%2BsdGjR%2Bvw4cNKSEjQnj17eiz/76NLSUlJamlp6TH/bactjyU3N1cZGRl%2BYw5HjFpbvwp4G8EuPLyfYmOj1d7uUVdXt93lQPTkVDrR9y49AY7PqXivnOx/kE5U0AWsTZs2KTMzU7m5uTpw4IAsy1J8fPxxbePzzz9XQUGBNm/erKSkJEnSzp07FRcXp9TUVD3zzDM6dOiQ76hVdXW1UlNTJUlOp1PV1dW%2BbXk8HtXV1amgoCDg509MTOxxOtDt/lJHjoTeB2xXV3dI7ldfRk/MO9nXk54AgQml90rQnexcsmSJ7/qluLi44w5X0tenBceNG6d77rlHu3fv1ubNm7V8%2BXLdfPPNSk9P15AhQ1RUVKT6%2BnpVVFSotrZWs2fPlvT1EbSamhpVVFSovr5eRUVFGjZsmCZPnmx0PwEAQOgKuoA1cuRI/f3vfz%2BpbYSHh%2Bvxxx9XdHS0cnNzVVxcrGuvvVbz5s3zzbndbmVnZ%2BvVV19VeXm5hg4dKkkaNmyYHnvsMVVVVWn27Nlqa2tTeXm575YRAAAAvQm6U4Tnn3%2B%2B7rzzTq1evVojR45UZGSk33xpaWlA20lKSjrmjUlHjBihF1988ZjrXn755br88ssDLxoAAOAbgi5g7d2713c91Inc9woAAMBuQRGwli1bpoKCAsXExOiFF16wuxwAAICTEhTXYD377LM97k01f/58NTc321QRAADAiQuKgGVZVo%2BxDz/8UJ2dnTZUAwAAcHKC4hQhAASzrJXv2l0CgD4mKI5gAQAAhJKgCVjcZwoAAISKoDlFuHTpUr97Xh0%2BfFjLly9X//7%2Bf0Mo0PtgAQAA2CUoAtaFF17Y455XKSkpam1tVWtrq01VAQAAnJigCFjc%2BwoAAISSoLkGCwAAIFQQsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMNCNmB98cUXKiwsVHp6ui699FKVlpaqs7NTktTQ0KDrr79eEydO1PTp07V161a/dbdt26YZM2bI6XRq3rx5amhosGMXAABAHxWSAcuyLBUWFsrj8ei3v/2tHnnkEf3lL3/RypUrZVmW8vPzNXjwYFVVVWnmzJkqKChQY2OjJKmxsVH5%2BfnKzs7WunXrFBcXp1tuuUWWZdm8VwAAoK9w2F3AqbBnzx7t2LFD7777rgYPHixJKiws1K9//Wtddtllamho0Jo1axQTE6NRo0Zp%2B/btqqqq0q233qq1a9fqggsu0I033ihJKi0t1cUXX6wPPvhAkydPtnO3AABAHxGSR7ASEhK0evVqX7j6t4MHD8rlcmns2LGKiYnxjaempmrHjh2SJJfLpbS0NN9cdHS0xo0b55sHAADoTUgGrNjYWF166aW%2B77u7u/Xiiy9qypQpcrvdSkxM9Fs%2BPj5eTU1NktTrPAAAQG9C8hTh0ZYvX666ujqtW7dOv/nNbxQREeE3HxERIa/XK0nyeDzfOR%2BI5uZmud1uvzGHI6ZHcOvLwsP7%2BX2F/egJgL4ulD6/Qj5gLV%2B%2BXM8995weeeQRnXvuuYqMjFRbW5vfMl6vV1FRUZKkyMjIHmHK6/UqNjY24OesrKxUWVmZ31h%2Bfr4KCwtPcC%2BCV2xstN0l4Cj0BEBfFUqfXyEdsB544AG99NJLWr58ua688kpJUlJSknbv3u23XEtLi%2B/oUlJSklpaWnrMjxkzJuDnzc3NVUZGht%2BYwxGj1tavTmQ3glJ4eD/Fxkarvd2jrq5uu8uB6AmAvu9UfH4NGtTf6PYCFbIBq6ysTGvWrNGKFSuUmZnpG3c6naqoqNChQ4d8R62qq6uVmprqm6%2BurvYt7/F4VFdXp4KCgoCfOzExscfpQLf7Sx05Eno/9Lq6ukNyv/oyegKgrwqlz6/QOdn5DZ999pkef/xx/dd//ZdSU1Pldrt9j/T0dA0ZMkRFRUWqr69XRUWFamtrNXv2bElSTk6OampqVFFRofr6ehUVFWnYsGHcogEAAAQsJAPWn//8Z3V1demJJ57QJZdc4vcIDw/X448/LrfbrezsbL366qsqLy/X0KFDJUnDhg3TY489pqqqKs2ePVttbW0qLy9XWFiYzXsFAAD6ijCLW5R/L9zuL%2B0uwSiHo58GDeqv1tavQuZwbl/Xl3qStfJdu0sAEGQ%2BKsk8JZ9fCQlnGt1eoELyCBYAAICdCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhoV8wPJ6vZoxY4bef/9931hDQ4Ouv/56TZw4UdOnT9fWrVv91tm2bZtmzJghp9OpefPmqaGh4fsuGwAA9GEhHbA6Ozt1%2B%2B23q76%2B3jdmWZby8/M1ePBgVVVVaebMmSooKFBjY6MkqbGxUfn5%2BcrOzta6desUFxenW265RZZl2bUbAACgj3HYXcCpsnv3bt1xxx09gtF7772nhoYGrVmzRjExMRo1apS2b9%2Buqqoq3XrrrVq7dq18C1HBAAAMh0lEQVQuuOAC3XjjjZKk0tJSXXzxxfrggw80efJkO3YFCMhPHnrH7hIAAP8vZI9g/TsQVVZW%2Bo27XC6NHTtWMTExvrHU1FTt2LHDN5%2BWluabi46O1rhx43zzAAAAvQnZI1hXX331t4673W4lJib6jcXHx6upqSmgeQAAgN6EbMA6Fo/Ho4iICL%2BxiIgIeb3egOYD0dzcLLfb7TfmcMT0CG59WXh4P7%2BvAACcrFD6mXLaBazIyEi1tbX5jXm9XkVFRfnmjw5TXq9XsbGxAT9HZWWlysrK/Mby8/NVWFh4glUHr9jYaLtLAACEiFD6mXLaBaykpCTt3r3bb6ylpcV3dCkpKUktLS095seMGRPwc%2BTm5iojI8NvzOGIUWvrVydYdfAJD%2B%2Bn2Nhotbd71NXVbXc5AIAQcCp%2Bpgwa1N/o9gJ12gUsp9OpiooKHTp0yHfUqrq6Wqmpqb756upq3/Iej0d1dXUqKCgI%2BDkSExN7nA50u7/UkSOhF0S6urpDcr8AAN%2B/UPqZEjonOwOUnp6uIUOGqKioSPX19aqoqFBtba1mz54tScrJyVFNTY0qKipUX1%2BvoqIiDRs2jFs0AACAgJ12ASs8PFyPP/643G63srOz9eqrr6q8vFxDhw6VJA0bNkyPPfaYqqqqNHv2bLW1tam8vFxhYWE2Vw4AAPqKMItblH8v3O4v7S7BKIejnwYN6q/W1q9C5nBuX5e18l27SwCAE/ZRSeYp%2BZmSkHCm0e0F6rQ7ggUAAHCqEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2Gl3o1EgUPxWHgDgRHEECwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMxhdwE4OVkr37W7hID9/raL7S4BAIDvBUewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDAC1rfo7OzUPffco7S0NF1yySV65pln7C4JAAD0Idym4VssW7ZMO3fu1HPPPafGxkYtWrRIQ4cOVWZmpt2lAQCAPoCAdZSOjg6tXbtWTz31lMaNG6dx48apvr5ev/3tbwlYAAAgIJwiPMquXbt05MgRpaSk%2BMZSU1PlcrnU3d1tY2UAAKCv4AjWUdxutwYNGqSIiAjf2ODBg9XZ2am2tjbFxcX1uo3m5ma53W6/MYcjRomJicbr7UscDvI8AODYwsND5%2BcEAesoHo/HL1xJ8n3v9XoD2kZlZaXKysr8xgoKCnTrrbeaKfIbPiqx57Rlc3OzKisrlZubG7LB0a7X9kSdDj3pa%2BhJcKIvwae5uVmPPfZYSPUkdKKiIZGRkT2C1L%2B/j4qKCmgbubm5Wr9%2Bvd8jNzfXeK12crvdKisr63GkDvahJ8GHngQn%2BhJ8QrEnHME6SlJSklpbW3XkyBE5HF%2B/PG63W1FRUYqNjQ1oG4mJiSGTwAEAwPHjCNZRxowZI4fDoR07dvjGqqurNX78ePXrx8sFAAB6R2I4SnR0tGbNmqX77rtPtbW1evPNN/XMM89o3rx5dpcGAAD6iPD77rvvPruLCDZTpkxRXV2dHn74YW3fvl0333yzcnJy7C4r6PTv31/p6enq37%2B/3aXg/9GT4ENPghN9CT6h1pMwy7Isu4sAAAAIJZwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwMJ38nq9uv/%2B%2B3XhhRfqRz/6kVasWKF/35u2rq5Oc%2BbMkdPpVE5Ojnbu3GlztaeH9evX67zzzuvxOP/88yXRF7vs379fCxYs0KRJk5SRkaHf/OY3vjl6Yp9//etfKiwsVFpamn7yk59o/fr1vrmGhgZdf/31mjhxoqZPn66tW7faWGno83q9mjFjht5//33fWG892LZtm2bMmCGn06l58%2BapoaHh%2By77hBGw8J2WLl2qbdu26emnn9bDDz%2Bsl19%2BWZWVlero6ND8%2BfOVlpam9evXKyUlRQsWLFBHR4fdJYe8f38I/fvx9ttva8SIEZo3bx59sdFtt92mmJgYrV%2B/Xvfcc49WrlypP/3pT/TERpZlKT8/X01NTXr%2B%2Bed1zz336MEHH9Qf//hH39zgwYNVVVWlmTNnqqCgQI2NjXaXHZI6Ozt1%2B%2B23q76%2B3jfWWw8aGxuVn5%2Bv7OxsrVu3TnFxcbrlllvUZ/4AjQUcQ2trqzV27Fjr/fff942tWrXKuvvuu621a9daGRkZVnd3t2VZltXd3W395Cc/saqqquwq97T15JNPWtOmTbM6Ozvpi03a2tqsc8891/rb3/7mGysoKLDuv/9%2BemKj2tpa69xzz7X%2B%2Bc9/%2BsZWrVpl/ed//qe1bds2a%2BLEidZXX33lm7vuuuusRx991I5SQ1p9fb3185//3PrZz35mnXvuudZ7771nWZbVaw9WrlxpXXPNNb65jo4OKyUlxbd%2BsOMIFo6purpaAwYMUHp6um9s/vz5Ki0tlcvlUmpqqsLCwiRJYWFhmjRpknbs2GFXuaeltrY2PfXUU7rjjjsUERFBX2wSFRWl6OhorV%2B/XocPH9aePXtUU1OjMWPG0BMbNTQ0KC4uTsOHD/eNnXfeedq5c6eqq6s1duxYxcTE%2BOZSU1PpyynwwQcfaPLkyaqsrPQbd7lc39kDl8ultLQ031x0dLTGjRvXZ3pEwMIxNTQ0KDk5Wa%2B88ooyMzP14x//WOXl5eru7pbb7VZiYqLf8vHx8WpqarKp2tPTSy%2B9pMTERGVmZkoSfbFJZGSkFi9erMrKSjmdTmVlZemyyy7TnDlz6ImNBg8erC%2B//FIej8c31tTUpCNHjtCX79HVV1%2Bte%2B65R9HR0X7jvfWgr/fIYXcBCF4dHR3at2%2Bf1qxZo9LSUrndbi1evFjR0dHyeDyKiIjwWz4iIkJer9emak8/lmVp7dq1uummm3xj9MU%2Bn332ma644grdcMMNqq%2Bv1wMPPKCLLrqIntjI6XQqMTFRDzzwgO6991653W49%2B%2Byzkr6%2B4Jq%2B2Ku390Zff%2B8QsHBMDodDBw8e1MMPP6zk5GRJX190%2BNJLL2nEiBE9/pF7vV5FRUXZUepp6ZNPPtEXX3yhq666yjcWGRlJX2ywfft2rVu3Tps3b1ZUVJTGjx%2BvL774Qk888YSGDx9OT2wSGRmplStX6rbbblNqaqri4%2BN10003qbS0VGFhYfTFZpGRkWpra/Mb%2B2YPjvV5Fhsb%2B73VeDI4RYhjSkhIUGRkpC9cSdLZZ5%2Bt/fv3KykpSS0tLX7Lt7S09Dici1PnnXfeUVpamgYOHOgboy/22Llzp0aMGOH3w3ns2LFqbGykJzabMGGC3nrrLW3ZskVvv/22zj77bA0aNEg/%2BMEP6IvNentvHGs%2BISHhe6vxZBCwcExOp1OdnZ3au3evb2zPnj1KTk6W0%2BnUxx9/7Pt1WcuyVFNTI6fTaVe5p53a2lpNmjTJb4y%2B2CMxMVH79u3z%2B9/2nj17NGzYMHpio7a2NuXl5am1tVUJCQlyOBx6%2B%2B23lZ6eLqfTqb/%2B9a86dOiQb/nq6mr68j3qrQdOp1PV1dW%2BOY/Ho7q6uj7TIwIWjumcc87R1KlTVVRUpF27dumdd95RRUWF8vLylJmZqfb2dpWUlGj37t0qKSmRx%2BNRVlaW3WWfNurr6zV69Gi/Mfpij4yMDJ1xxhm69957tXfvXr311lt68sknde2119ITG5111lnq6OjQ8uXL1dDQoLVr16qqqko33XST0tPTNWTIEBUVFam%2Bvl4VFRWqra3V7Nmz7S77tNFbD3JyclRTU6OKigrV19erqKhIw4YN0%2BTJk22uPEA23iICfUB7e7t11113WRMnTrQuuugi67HHHvPdz8flclmzZs2yxo8fb82ePdv661//anO1p5fx48dbW7Zs6TFOX%2BxRX19vXX/99dakSZOsadOmWc8%2B%2ByzvlSDw2WefWddcc43ldDqtq666ynrrrbd8c//4xz%2BsuXPnWhdccIF11VVXWe%2B%2B%2B66NlZ4evnkfLMvqvQdvv/229dOf/tSaMGGCdd111/nd0yzYhVlWX7klKgAAQN/AKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AL/CMwfJKxxBAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8771697657434332610”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>341</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>76.674</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.597</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.571</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>88.374</td> <td class=”number”>120</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>92.968</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>77.389</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.305</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>84.21</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>95.992</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (36)</td> <td class=”number”>1604</td> <td class=”number”>50.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8771697657434332610”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>56.955</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65.805</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>66.446</td> <td class=”number”>58</td> <td class=”number”>1.8%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70.306</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>73.941</td> <td class=”number”>56</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:96%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>97.302</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.305</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:30%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>98.992</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>99.595</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100.0</td> <td class=”number”>341</td> <td class=”number”>10.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_REPORTSIMPLE09”>SNAP_REPORTSIMPLE09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.97414</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>2.5%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1371910478124244480”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1371910478124244480,#minihistogram-1371910478124244480”
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</div> <div class=”row collapse col-md-12” id=”descriptives-1371910478124244480”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1371910478124244480”

aria-controls=”quantiles-1371910478124244480” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1371910478124244480” aria-controls=”histogram-1371910478124244480”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1371910478124244480” aria-controls=”common-1371910478124244480”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1371910478124244480” aria-controls=”extreme-1371910478124244480”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1371910478124244480”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>1</td>

</tr> <tr>

<th>Q1</th> <td>1</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>1</td>

</tr> <tr>

<th>Maximum</th> <td>1</td>

</tr> <tr>

<th>Range</th> <td>1</td>

</tr> <tr>

<th>Interquartile range</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.15875</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.16296</td>

</tr> <tr>

<th>Kurtosis</th> <td>33.749</td>

</tr> <tr>

<th>Mean</th> <td>0.97414</td>

</tr> <tr>

<th>MAD</th> <td>0.050386</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-5.9772</td>

</tr> <tr>

<th>Sum</th> <td>3051</td>

</tr> <tr>

<th>Variance</th> <td>0.025201</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1371910478124244480”>

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o4MCBSklJ0ezZs1VdXe3YcQEAgMAXkAXLsixlZmaqurpazz33nB555BH94x//0NKlS2VZljIyMtSuXTvl5%2Bdr%2BPDhmjZtmo4ePSpJOnr0qDIyMjRq1Cht3LhRbdu21d133y3LsiRJb7zxhvLy8pSVlaW1a9eqqKhIubm5/jxcAAAQYAKyYB08eFCFhYXKyclR165dlZaWpszMTG3evFnbtm1TaWmpsrKy1KVLF02dOlXJycnKz8%2BXJL300ku6/PLLNXnyZHXt2lU5OTk6cuSItm/fLklat26dJk2apPT0dPXq1UsLFixQfn4%2Br2IBAACfBWTBio2N1ZNPPql27dp5jZ86dUpFRUW67LLL1LJlS894amqqCgsLJUlFRUVKS0vzzEVGRqpHjx4qLCxUQ0ODPvroI6/55ORknT17Vnv37rX5qAAAQLBw%2BXsDP0ZUVJQGDhzoud3Y2Kj169erX79%2BcrvdiouL87p/TEyMjh8/Lkk/OF9VVaXa2lqveZfLpejoaM/jfVFWVia32%2B015nK1bPK8P1VYWKjXZ5hDtvYhW3uRr33I1n7BlG1AFqxvy83N1Z49e7Rx40Y988wzCg8P95oPDw9XXV2dJKm6uvp752tqajy3v%2B/xvtiwYYPy8vK8xjIyMpSZmenzGs0RFRVpy7ogWzuRrb3I1z5ka59gyjbgC1Zubq7Wrl2rRx55RN26dVNERIQqKyu97lNXV6cWLVpIkiIiIpqUpbq6OkVFRSkiIsJz%2B9vzkZG%2B/0cfO3asBg0a5DXmcrVURcVpn9fwRVhYqKKiIlVVVa2Ghkaja5/vyNY%2BZGsv8rUP2drPjmzbtGlldD1fBXTBWrhwoZ5//nnl5ubq2muvlSTFx8dr//79XvcrLy/3nJ6Lj49XeXl5k/nu3bsrOjpaERERKi8vV5cuXSRJ9fX1qqysVGxsrM/7iouLa3I60O0%2Bqfp6e74hGxoabVv7fEe29iFbe5GvfcjWPsGUbcCe7MzLy9MLL7yghx9%2BWMOGDfOMJyUlaffu3Z7TfZJUUFCgpKQkz3xBQYFnrrq6Wnv27FFSUpJCQ0PVs2dPr/nCwkK5XC4lJiY6cFQAACAYBGTBOnDggFauXKk777xTqampcrvdno8%2Bffqoffv2mjVrlkpKSrR69WoVFxdrzJgxkqTRo0dr586dWr16tUpKSjRr1ix17NhRffv2lSSNHz9ea9as0ZYtW1RcXKz58%2BfrpptuatYpQgAAcH4LyFOEb775phoaGvTYY4/pscce85rbt2%2BfVq5cqTlz5mjUqFG65JJLtGLFCiUkJEiSOnbsqEcffVQPPPCAVqxYoZSUFK1YsUIhISGSpGHDhunIkSOaN2%2Be6urq9Otf/1ozZsxw/BgBAEDgCrG%2BeQtz2MrtPml8TZcrVG3atFJFxemgOWd9riBb%2B5CtvcjXPoGY7dCl//T3Fnz2QfYQW7KNjb3Q6Hq%2BcvwU4Y033qgXXnhBJ0%2BaLxwAAADnAscLVr9%2B/fT4449rwIAB%2Bv3vf6%2BtW7eKF9EAAEAwcbxgTZ8%2BXf/4xz%2B0cuVKhYWF6Z577tHVV1%2BtRx55RJ9%2B%2BqnT2wEAADDOLxe5h4SEqH///urfv7%2Bqq6v17LPPauXKlVq9erV69%2B6tSZMm6de//rU/tgYAAPCT%2Be23CMvKyvTaa6/ptdde0yeffKLevXtr5MiROn78uO6//37t2LFDc%2BbM8df2AAAAfjTHC9amTZu0adMmvf/%2B%2B2rbtq1GjBih5cuXq1OnTp77tG/fXtnZ2RQsAAAQkBwvWHPmzFF6erpWrFihq666SqGhTS8D69y5syZMmOD01gAAAIxwvGC98847atOmjSorKz3lqri4WD169FBYWJgkqXfv3urdu7fTWwMAADDC8d8iPHXqlIYMGaInnnjCM3bXXXdp%2BPDhOnbsmNPbAQAAMM7xgvXAAw/okksu0e233%2B4Ze/3119W%2BfXvl5OQ4vR0AAADjHC9YH3zwgf7whz8oNjbWM9a2bVvdd9992rZtm9PbAQAAMM7xguVyuVRVVdVkvLq6mnd0BwAAQcHxgnXVVVdp0aJF%2Bvzzzz1jpaWlysnJ0cCBA53eDgAAgHGO/xbhzJkzdfvtt%2Bvaa69VVFSUJKmqqko9evTQrFmznN4OAACAcY4XrJiYGL3yyit69913VVJSIpfLpUsvvVRXXnmlQkJCnN4OAACAcX75UzlhYWEaOHAgpwQBAEBQcrxgud1uLV26VDt37tTZs2ebXNj%2B5ptvOr0lAAAAoxwvWHPnztWuXbs0bNgwXXjhhU4/PQAAgO0cL1jbtm3Tk08%2BqbS0NKefGgAAwBGOv01Dy5YtFRMT4/TTAgAAOMbxgjV8%2BHA9%2BeSTamhocPqpAQAAHOH4KcLKykpt3rxZb731li6%2B%2BGKFh4d7za9bt87pLQEAABjll7dpuP766/3xtAAAAI5wvGDl5OQ4/ZQAAACOcvwaLEkqKytTXl6epk%2Bfri%2B%2B%2BEJ/%2BctfdPDgQX9sBQAAwDjHC9ahQ4d0ww036JVXXtEbb7yhM2fO6PXXX9fo0aNVVFTk9HYAAACMc7xgPfjgg7rmmmu0ZcsWXXDBBZKkhx9%2BWIMGDdKSJUuc3g4AAIBxjhesnTt36vbbb/f6w84ul0t333239uzZ4/R2AAAAjHO8YDU2NqqxsbHJ%2BOnTpxUWFub0dgAAAIxzvGANGDBAq1at8ipZlZWVys3NVb9%2B/ZzeDgAAgHGOF6w//OEP2rVrlwYMGKDa2lr953/%2Bp9LT03X48GHNnDnT6e0AAAAY5/j7YMXHx%2BvVV1/V5s2b9fHHH6uxsVHjxo3T8OHD1bp1a6e3AwAAYJxf3sk9MjJSN954oz%2BeGgAAwHaOF6yJEyf%2B4Dx/ixAAAAQ6xwtWhw4dvG7X19fr0KFD%2BuSTTzRp0iSntwMAAGDcOfO3CFesWKHjx487vBsAAADz/PK3CL/L8OHD9ec//9nf2wAAAPjJzpmC9eGHH/JGowAAICicExe5nzp1Svv27dP48eObvV5dXZ1GjRqluXPnqm/fvpKkRYsW6dlnn/W639y5czVhwgRJ0ubNm7V06VK53W4NGDBACxcuVNu2bSVJlmXpoYce0saNG9XY2KgxY8bo3nvvVWjoOdNFAQDAOc7xgpWQkOD1dwgl6YILLtCECRP0m9/8pllr1dbWavr06SopKfEaP3DggKZPn66RI0d6xr55j63i4mLNmTNHCxYsUGJiorKzszVr1iytWrVKkvT0009r8%2BbNysvLU319vWbMmKGYmBhNmTLlxxwuAAA4DzlesB588EEj6%2Bzfv1/Tp0%2BXZVlN5g4cOKApU6YoNja2ydz69es1dOhQjRgxQpK0ePFipaenq7S0VBdffLHWrVunzMxMpaWlSZLuvfdeLVu2jIIFAAB85njB2rFjh8/3veKKK753bvv27erbt6/%2B%2B7//W8nJyZ7xU6dO6cSJE%2BrUqdN3Pq6oqEh33nmn53b79u2VkJCgoqIihYeH69ixY17Pm5qaqiNHjqisrExxcXE%2B7x0AAJy/HC9Yt956q%2BcU4b%2B%2B%2BvTtsZCQEH388cffu873Xa914MABhYSE6PHHH9c777yj6Oho3X777Z7Thd9VlGJiYnT8%2BHG53W5J8ppv166dJOn48eM%2BF6yysjLPWt9wuVoaL2hhYaFen2EO2dqHbO1FvvYhW/sFU7aOF6zHH39cixYt0owZM9SnTx%2BFh4fro48%2BUlZWlkaOHKnrrrvuJ61/8OBBhYSEqHPnzpowYYJ27NihuXPnqnXr1ho8eLBqamoUHh7u9Zjw8HDV1dWppqbGc/tf56SvL6b31YYNG5SXl%2Bc1lpGRoczMzB97WD8oKirSlnVBtnYiW3uRr33I1j7BlK1f3mh03rx5uuqqqzxj/fr1U1ZWlu677z6v03c/xogRI5Senq7o6GhJUmJioj777DM9//zzGjx4sCIiIpqUpbq6OkVGRnqVqYiICM%2B/pa//fqKvxo4dq0GDBnmNuVwtVVFx%2Bkcf13cJCwtVVFSkqqqq1dDQaHTt8x3Z2ods7UW%2B9iFb%2B9mRbZs2rYyu5yvHC1ZZWVmTP5cjff1bfhUVFT95/ZCQEE%2B5%2Bkbnzp21bds2SVJ8fLzKy8u95svLyxUbG6v4%2BHhJktvtVseOHT3/lvSdF8x/n7i4uCanA93uk6qvt%2BcbsqGh0ba1z3dkax%2BytRf52ods7RNM2Tp%2BsjM5OVkPP/ywTp065RmrrKxUbm6urrzyyp%2B8/rJly3Tbbbd5je3du1edO3eWJCUlJamgoMAzd%2BzYMR07dkxJSUmKj49XQkKC13xBQYESEhK4wB0AAPjM8Vew7r//fk2cOFFXXXWVOnXqJMuy9Nlnnyk2Nlbr1q37yeunp6dr9erVWrNmjQYPHqytW7fq1Vdf9aw9btw43XrrrUpOTlbPnj2VnZ2tq6%2B%2BWhdffLFnfsmSJfrZz34mSXrooYc0efLkn7wvAABw/nC8YHXp0kWvv/66Nm/erAMHDkiSbrnlFg0bNqxZ1zl9n169emnZsmVavny5li1bpg4dOuihhx5SSkqKJCklJUVZWVlavny5vvrqK/Xv318LFy70PH7KlCn64osvNG3aNIWFhWnMmDFNXhEDAAD4ISHWd71TpwPq6up0%2BPBhzytHF1xwgT%2B24Ri3%2B6TxNV2uULVp00oVFaeD5pz1uYJs7UO29iJf%2BwRitkOX/tPfW/DZB9lDbMk2NvZCo%2Bv5yvFrsCzL0pIlS3TFFVfo%2Buuv1/HjxzVz5kzNmTNHZ8%2BedXo7AAAAxjlesJ599llt2rRJf/zjHz1vi3DNNddoy5YtTd47CgAAIBA5XrA2bNigefPmadSoUZ53b7/uuuu0aNEi/elPf3J6OwAAAMY5XrAOHz6s7t27NxlPTExs8udlAAAAApHjBatDhw766KOPmoy/8847ngveAQAAApnjb9MwZcoULViwQG63W5Zl6b333tOGDRv07LPP6g9/%2BIPT2wEAADDO8YI1evRo1dfX67HHHlNNTY3mzZuntm3b6ne/%2B53GjRvn9HYAAACMc7xgbd68WUOGDNHYsWP15ZdfyrIsxcTEOL0NAAAA2zh%2BDVZWVpbnYva2bdtSrgAAQNBxvGB16tRJn3zyidNPCwAA4BjHTxEmJibq3nvv1ZNPPqlOnTopIiLCaz4nJ8fpLQEAABjleMH69NNPlZqaKkm87xUAAAhKjhSsxYsXa9q0aWrZsqWeffZZJ54SAADAbxy5Buvpp59WdXW119hdd92lsrIyJ54eAADAUY4ULMuymozt2LFDtbW1Tjw9AACAoxz/LUIAAIBgR8ECAAAwzLGCFRIS4tRTAQAA%2BJVjb9OwaNEir/e8Onv2rHJzc9WqVSuv%2B/E%2BWAAAINA5UrCuuOKKJu95lZKSooqKClVUVDixBQAAAMc4UrB47ysAAHA%2B4SJ3AAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDAr5g1dXV6frrr9f777/vGSstLdVtt92m5ORkXXfdddq6davXY959911df/31SkpK0sSJE1VaWuo1/8wzz2jgwIFKSUnR7NmzVV1d7cixAACA4BDQBau2tla///3vVVJS4hmzLEsZGRlq166d8vPzNXz4cE2bNk1Hjx6VJB09elQZGRkaNWqUNm7cqLZt2%2Bruu%2B%2BWZVmSpDfeeEN5eXnKysrS2rVrVVRUpNzcXL8cHwAACEwBW7D279%2Bvm266SZ9//rnX%2BLZt21RaWqqsrCx16dJFU6dOVXJysvLz8yVJL730ki6//HJNnjxZXbt2VU5Ojo4cOaLt27dLktatW6dJkyYpPT1dvXr10oIFC5Sfn8%2BrWAAAwGcBW7C2b9%2Buvn37asOGDV7jRUVFuuyyy9SyZUvPWGpqqgoLCz3zaWlpnrnIyEj16NFDhYWFamho0EcffeQ1n5ycrLNnz2rv3r02HxEAAAgWLn9v4McaP378d4673W7FxcV5jcXExOj48eP/dr6qqkq1tbVe8y6XS9HR0Z7H%2B6KsrExut9trzOVq2eR5f6qwsFCvzzCHbO1DtvYiX/uQrf2CKduALVjfp7q6WuHh4V5j4eHhqqur%2B7fzNTU1ntvf93hfbNiwQXl5eV5jGRkZyszM9HmN5oiKirRlXZCtncjWXuRrH7K1TzBlG3QFKyIiQpWVlV5jdXV1atGihWf%2B22Wprq5OUVFRioiI8Nz%2B9nxkpO//0ccSqpdRAAARDklEQVSOHatBgwZ5jblcLVVRcdrnNXwRFhaqqKhIVVVVq6Gh0eja5zuytQ/Z2ot87UO29rMj2zZtWhldz1dBV7Di4%2BO1f/9%2Br7Hy8nLP6bn4%2BHiVl5c3me/evbuio6MVERGh8vJydenSRZJUX1%2BvyspKxcbG%2BryHuLi4JqcD3e6Tqq%2B35xuyoaHRtrXPd2RrH7K1F/nah2ztE0zZBs/Jzv%2BTlJSk3bt3e073SVJBQYGSkpI88wUFBZ656upq7dmzR0lJSQoNDVXPnj295gsLC%2BVyuZSYmOjcQQAAgIAWdAWrT58%2Bat%2B%2BvWbNmqWSkhKtXr1axcXFGjNmjCRp9OjR2rlzp1avXq2SkhLNmjVLHTt2VN%2B%2BfSV9ffH8mjVrtGXLFhUXF2v%2B/Pm66aabmnWKEAAAnN%2BCrmCFhYVp5cqVcrvdGjVqlF577TWtWLFCCQkJkqSOHTvq0UcfVX5%2BvsaMGaPKykqtWLFCISEhkqRhw4Zp6tSpmjdvniZPnqxevXppxowZ/jwkAAAQYEKsb97CHLZyu08aX9PlClWbNq1UUXE6aM5ZnyvI1j5kay/ytU8gZjt06T/9vQWffZA9xJZsY2MvNLqer4LuFSwAAAB/o2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGFBW7D%2B9re/6ec//7nXR2ZmpiRpz549uvHGG5WUlKTRo0dr165dXo/dvHmzrrnmGiUlJSkjI0NffvmlPw4BAAAEqKAtWPv371d6erq2bt3q%2BVi0aJHOnDmju%2B66S2lpaXr55ZeVkpKiqVOn6syZM5Kk4uJizZkzR9OmTdOGDRtUVVWlWbNm%2BfloAABAIAnagnXgwAF169ZNsbGxno%2BoqCi9/vrrioiI0H333acuXbpozpw5atWqlf7yl79IktavX6%2BhQ4dqxIgRSkxM1OLFi/X222%2BrtLTUz0cEAAACRVAXrE6dOjUZLyoqUmpqqkJCQiRJISEh6t27twoLCz3zaWlpnvu3b99eCQkJKioqcmTfAAAg8Ln8vQE7WJalTz/9VFu3btWqVavU0NCgIUOGKDMzU263W5deeqnX/WNiYlRSUiJJKisrU1xcXJP548eP%2B/z8ZWVlcrvdXmMuV8sm6/5UYWGhXp9hDtnah2ztRb72IVv7BVO2QVmwjh49qurqaoWHh2vp0qU6fPiwFi1apJqaGs/4vwoPD1ddXZ0kqaam5gfnfbFhwwbl5eV5jWVkZHgusjctKirSlnVBtnYiW3uRr33I1j7BlG1QFqwOHTro/fff10UXXaSQkBB1795djY2NmjFjhvr06dOkLNXV1alFixaSpIiIiO%2Bcj4z0/T/62LFjNWjQIK8xl6ulKipO/8gj%2Bm5hYaGKiopUVVW1Ghoaja59viNb%2B5CtvcjXPmRrPzuybdOmldH1fBWUBUuSoqOjvW536dJFtbW1io2NVXl5uddceXm55/RdfHz8d87Hxsb6/NxxcXFNTge63SdVX2/PN2RDQ6Nta5/vyNY%2BZGsv8rUP2donmLINnpOd/%2BJ///d/1bdvX1VXV3vGPv74Y0VHRys1NVUffvihLMuS9PX1Wjt37lRSUpIkKSkpSQUFBZ7HHTt2TMeOHfPMAwAA/DtBWbBSUlIUERGh%2B%2B%2B/XwcPHtTbb7%2BtxYsX64477tCQIUNUVVWl7Oxs7d%2B/X9nZ2aqurtbQoUMlSePGjdOmTZv00ksvae/evbrvvvt09dVX6%2BKLL/bzUQEAgEARlAWrdevWWrNmjb788kuNHj1ac%2BbM0dixY3XHHXeodevWWrVqlQoKCjRq1CgVFRVp9erVatmypaSvy1lWVpZWrFihcePG6aKLLlJOTo6fjwgAAASSEOubc2Wwldt90viaLleo2rRppYqK00FzzvpcQbb2IVt7ka99AjHboUv/6e8t%2BOyD7CG2ZBsbe6HR9XwVlK9gAQAA%2BBMFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhLn9vAD9N2py/%2BHsLPvvz7/r7ewsAADiCV7AAAAAMo2ABAAAYRsH6DrW1tZo9e7bS0tI0YMAAPfXUU/7eEgAACCBcg/UdFi9erF27dmnt2rU6evSoZs6cqYSEBA0ZMsTfWwMAAAGAgvUtZ86c0UsvvaQnnnhCPXr0UI8ePVRSUqLnnnuOggUAAHzCKcJv2bt3r%2Brr65WSkuIZS01NVVFRkRobG/24MwAAECh4Betb3G632rRpo/DwcM9Yu3btVFtbq8rKSrVt2/bfrlFWVia32%2B015nK1VFxcnNG9hoUFVj92uQJnv99kG2gZBwKytRf52ods7RdM2VKwvqW6utqrXEny3K6rq/NpjQ0bNigvL89rbNq0abrnnnvMbPL/lJWVadLPSjR27Fjj5e18V1ZWprVrnyRbG5CtvcjXPoGY7QfZgXFpS1lZmR599NGAyvbfCZ6qaEhERESTIvXN7RYtWvi0xtixY/Xyyy97fYwdO9b4Xt1ut/Ly8pq8WoafjmztQ7b2Il/7kK19gjFbXsH6lvj4eFVUVKi%2Bvl4u19fxuN1utWjRQlFRUT6tERcXFzQNHAAANB%2BvYH1L9%2B7d5XK5VFhY6BkrKChQz549FRpKXAAA4N%2BjMXxLZGSkRowYofnz56u4uFhbtmzRU089pYkTJ/p7awAAIECEzZ8/f76/N3Gu6devn/bs2aOHHnpI7733nn77299q9OjR/t7Wd2rVqpX69OmjVq1a%2BXsrQYds7UO29iJf%2B5CtfYIt2xDLsix/bwIAACCYcIoQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgF6xxXW1ur2bNnKy0tTQMGDNBTTz31vffds2ePbrzxRiUlJWn06NHatWuXgzsNPM3J9q233tLw4cOVkpKiG264QW%2B%2B%2BaaDOw08zcn2G4cPH1ZKSoref/99B3YY2JqT7759%2BzRu3Dj16tVLN9xwg7Zt2%2BbgTgNPc7L929/%2BpqFDhyolJUXjxo3T7t27Hdxp4Kqrq9P111//g9/rQfHzzMI5LSsry7rhhhusXbt2WX/961%2BtlJQU689//nOT%2B50%2Bfdrq37%2B/9eCDD1r79%2B%2B3Fi5caP3iF7%2BwTp8%2B7YddBwZfs/3444%2BtHj16WGvXrrU%2B%2B%2Bwza/369VaPHj2sjz/%2B2A%2B7Dgy%2BZvuvpkyZYnXr1s3atm2bQ7sMXL7mW1VVZf3iF7%2Bw7r//fuuzzz6zli1bZqWmplrl5eV%2B2HVg8DXbTz75xOrZs6f1yiuvWIcOHbIWLFhg9e/f3zpz5owfdh04ampqrIyMjB/8Xg%2BWn2cUrHPY6dOnrZ49e3p9Ea5YscKaMGFCk/u%2B9NJL1qBBg6zGxkbLsiyrsbHRGjx4sJWfn%2B/YfgNJc7LNzc21pkyZ4jU2efJk6%2BGHH7Z9n4GoOdl%2BY9OmTdbNN99MwfJBc/Jdu3atdc0111j19fWesVGjRllvvfWWI3sNNM3J9umnn7ZGjhzpuX3y5EmrW7duVnFxsSN7DUQlJSXWb37zG%2BuGG274we/1YPl5xinCc9jevXtVX1%2BvlJQUz1hqaqqKiorU2Njodd%2BioiKlpqYqJCREkhQSEqLevXursLDQ0T0HiuZkO3LkSN17771N1jh58qTt%2BwxEzclWkioqKpSbm6usrCwntxmwmpPv9u3b9atf/UphYWGesfz8fP3yl790bL%2BBpDnZRkdHa//%2B/SooKFBjY6NefvlltW7dWv/xH//h9LYDxvbt29W3b19t2LDhB%2B8XLD/PXP7eAL6f2%2B1WmzZtFB4e7hlr166damtrVVlZqbZt23rd99JLL/V6fExMjEpKShzbbyBpTrZdunTxemxJSYnee%2B893XzzzY7tN5A0J1tJevDBBzVy5Eh17drV6a0GpObkW1paql69emnu3Ln6%2B9//rg4dOmjmzJlKTU31x9bPec3J9rrrrtPf//53jR8/XmFhYQoNDdWqVat00UUX%2BWPrAWH8%2BPE%2B3S9Yfp7xCtY5rLq62usbXZLndl1dnU/3/fb98LXmZPuvvvzyS91zzz3q3bu3fvWrX9m6x0DVnGzfffddFRQU6O6773Zsf4GuOfmeOXNGq1evVmxsrJ544gldccUVmjJlio4dO%2BbYfgNJc7KtqKiQ2%2B3WvHnz9OKLL2r48OGaNWuWvvjiC8f2G6yC5ecZBescFhER0eQL6pvbLVq08Om%2B374fvtacbL9RXl6uSZMmybIsLV%2B%2BXKGhfPt8F1%2Bzramp0bx58/THP/6Rr9NmaM7XblhYmLp3767MzExddtllmjFjhjp16qRNmzY5tt9A0pxslyxZom7duumWW27R5ZdfroULFyoyMlL5%2BfmO7TdYBcvPM35CnMPi4%2BNVUVGh%2Bvp6z5jb7VaLFi0UFRXV5L7l5eVeY%2BXl5YqLi3Nkr4GmOdlK0okTJ3TLLbeorq5O69ata3KaC/%2Bfr9kWFxertLRUmZmZSklJ8Vz3cuedd2revHmO7ztQNOdrNzY2Vp07d/Ya69SpE69gfY/mZLt7924lJiZ6boeGhioxMVFHjx51bL/BKlh%2BnlGwzmHdu3eXy%2BXyurCvoKBAPXv2bPLqSVJSkj788ENZliVJsixLO3fuVFJSkqN7DhTNyfbMmTO64447FBoaqvXr1ys%2BPt7p7QYUX7Pt1auX/vrXv%2BrVV1/1fEjSokWL9F//9V%2BO7ztQNOdrNzk5Wfv27fMaO3jwoDp06ODIXgNNc7KNi4vTgQMHvMY%2B/fRTdezY0ZG9BrNg%2BXlGwTqHRUZGasSIEZo/f76Ki4u1ZcsWPfXUU5o4caKkr//PqqamRpI0ZMgQVVVVKTs7W/v371d2draqq6s1dOhQfx7COas52a5atUqff/65/ud//scz53a7%2BS3C7%2BFrti1atNAll1zi9SF9/X%2BvMTEx/jyEc1pzvnZvvvlm7du3T48%2B%2BqgOHTqkZcuWqbS0VMOHD/fnIZyzmpPtTTfdpBdffFGvvvqqDh06pCVLlujo0aMaOXKkPw8hYAXlzzO/vkkE/q0zZ85Y9913n5WcnGwNGDDAevrppz1z3bp183pfkKKiImvEiBFWz549rTFjxli7d%2B/2w44Dh6/ZXnvttVa3bt2afMycOdNPOz/3Nefr9l/xPli%2BaU6%2BH3zwgTVy5Ejr8ssvt4YPH25t377dDzsOHM3J9sUXX7SGDBliJScnW%2BPGjbN27drlhx0Hpm9/rwfjz7MQy/q/1%2BAAAABgBKcIAQAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMCw/we7TcEb5J01lwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1371910478124244480”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1371910478124244480”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>81</td> <td class=”number”>2.5%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3051</td> <td class=”number”>95.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SNAP_REPORTSIMPLE16”>SNAP_REPORTSIMPLE16<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>1</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable8875433801583905426”>

<table class=”mini freq”>

<tr class=”“>

<th>1.0</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 97.6%”>

3132

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:3%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 2.4%”>

&nbsp;

</div> 78

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable8875433801583905426, #minifreqtable8875433801583905426”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable8875433801583905426”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>3132</td> <td class=”number”>97.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODATAX_STORES14”>SODATAX_STORES14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>20</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>3.9634</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>21.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3734633702829269219”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3734633702829269219,#minihistogram-3734633702829269219”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3734633702829269219”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3734633702829269219”

aria-controls=”quantiles-3734633702829269219” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3734633702829269219” aria-controls=”histogram-3734633702829269219”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3734633702829269219” aria-controls=”common-3734633702829269219”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3734633702829269219” aria-controls=”extreme-3734633702829269219”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3734633702829269219”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>1.225</td>

</tr> <tr>

<th>Median</th> <td>5</td>

</tr> <tr>

<th>Q3</th> <td>6.15</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>7</td>

</tr> <tr>

<th>Range</th> <td>7</td>

</tr> <tr>

<th>Interquartile range</th> <td>4.925</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.611</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.65879</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.3964</td>

</tr> <tr>

<th>Mean</th> <td>3.9634</td>

</tr> <tr>

<th>MAD</th> <td>2.3485</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.51856</td>

</tr> <tr>

<th>Sum</th> <td>12413</td>

</tr> <tr>

<th>Variance</th> <td>6.8174</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3734633702829269219”>

<img 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cPVoUOH1KtXLwUGBkqS%2Bvbtq759%2B1pdGgAAgBGW/xXhxYsXNWLECK1du9Y9lp6erjFjxqi8vNzqcgAAAIyzPGD98Y9/VJcuXfTAAw%2B4x3bv3q0OHTooOzvb2Hbq6uq0YMEC9evXT7/85S%2B1fPly9/22jhw5ogkTJig%2BPl7jxo3T4cOHPZ67a9cuDRs2TPHx8crIyNCXX35prC4AAOD7LA9Yn3zyiWbPnq2oqCj3WNu2bTVz5kx99NFHxrazePFiffjhh3r%2B%2Bee1bNky/fu//7sKCwtVXV2t9PR0JSUlafv27UpISNC0adNUXV0t6e%2BnK%2BfOnavp06ersLBQFy5cUFZWlrG6AACA77P8GiyHw6ELFy40Ga%2BpqTF2R/eqqipt27ZNL774onr37i1Jmjp1qkpKSuRwOBQcHKyZM2cqICBAc%2BfO1bvvvqs333xTKSkp2rRpk0aOHKmxY8dKknJycjR06FCVlZWpc%2BfORuoDAAC%2BzfIjWHfccYcWL16sv/3tb%2B6xsrIyZWdna/DgwUa2UVRUpIiICPXv3989lp6eruzsbJWUlCgxMVEBAQGS/n7T0759%2B6q4uFiSVFJSoqSkJPfzOnTooI4dO6qkpMRIbQAAwPdZHrBmzZqluro6DR8%2BXAMGDNCAAQN055136vLly8ZOxZWVlSk2NlY7d%2B7UiBEj9Otf/1r5%2BflqbGyU0%2BlUdHS0x/Lt2rXT2bNnJf39Bqg/Ng8AAPBTLD9F2K5dO%2B3YsUMffvihSktL5XA41L17d912223uo0pXq7q6WqdOndKWLVuUnZ0tp9OpefPmKTQ0VDU1NQoKCvJYPigoSHV1dZKkS5cu/eh8c1RUVMjpdHqMORxhTYLb1QoMtDwfXxWHw0y93/btbf1fLX/tW/Lf3v21b8m/ezfJ1PtuS/PF/W3LR%2BUEBgZq8ODBxk4JfpfD4dDFixe1bNkyxcbGSpLOnDmjzZs3q0uXLk3CUl1dnUJCQiRJwcHB3zsfGhra7O0XFhYqLy/PYywjI0OZmZk/px2f0aZNuNH1RUY2f5/4En/tW/Lf3v21b8m/ezfB9PtuS/Ol/W15wHI6nVqxYoUOHjyoy5cvN7mw/Z133rnqbURFRSk4ONgdriTpxhtvVHl5ufr376/KykqP5SsrK91Hl2JiYr53/h//6vGnpKamKjk52WPM4QjT%2BfPfXGkrP8rbkr6p/gMDWykyMlQXLtSooaHRyDq9gb/2Lflv7/7at%2BTfvZtk%2BudOS2nJ/W1XyLQ8YP3bv/2bDh8%2BrLvuukvXX399i2wjPj5etbW1OnnypG688UZJ0okTJxQbG6v4%2BHitXbtWLpdLAQEBcrlcOnjwoB588EH3c4uKipSSkiJJKi8vV3l5ueLj45u9/ejo6CanA53Or1Vf799vEqb7b2ho9MvX1F/7lvy3d3/tW/Lv3k3wttfOl/a35QHro48%2B0rp16zz%2BUs%2B0m266SUOGDFFWVpbmz58vp9OpgoICPfTQQxoxYoSWLVumJUuW6N5779WWLVtUU1OjkSNHSpLS0tI0adIk9enTR3FxcVqyZImGDBnCLRoAAECzWX6OKSwsTO3atWvx7SxdulT/9E//pLS0NM2aNUv33XefJk2apIiICK1Zs8Z9lKqkpEQFBQUKCwuTJCUkJGjhwoXKz89XWlqaWrdubfQO8wAAwPdZfgRrzJgxWrdunRYuXOj%2BcOeWcP311ysnJ%2Bd753r37q0dO3b84HNTUlLcpwgBAACulOUBq6qqSrt27dJ//ud/qnPnzk1uifDSSy9ZXRIAAIBRttymYfTo0XZsFgAAwBKWByyuZwIAAL7OlhspVVRUKC8vTzNmzND//M//6M0339SJEyfsKAUAAMA4ywPWqVOndPfdd2vHjh166623VF1drd27d2vcuHF8oDIAAPAJlgesp59%2BWsOGDdOePXt03XXXSZKWL1%2Bu5ORkLV261OpyAAAAjLM8YB08eFAPPPCAxwc7OxwOPfzwwzpy5IjV5QAAABhnecBqbGxUY2PT2%2BB/8803LXpfLAAAAKtYHrAGDRqkNWvWeISsqqoq5ebmauDAgVaXAwAAYJzlAWv27Nk6fPiwBg0apNraWj300EMaOnSoTp8%2BrVmzZlldDgAAgHGW3wcrJiZGO3fu1K5du/T555%2BrsbFRaWlpGjNmjCIiIqwuBwAAwDhb7uQeGhqqCRMm2LFpAACAFmd5wJo8efKPzvNZhAAAwNtZHrBiY2M9HtfX1%2BvUqVP64osvNGXKFKvLAQAAMO6a%2BSzC/Px8nT171uJqAAAAzLPlswi/z5gxY/TnP//Z7jIAAACu2jUTsD799FNuNAoAAHzCNXGR%2B8WLF/XXv/5VEydOtLocAAAA4ywPWB07dvT4HEJJuu6663T//ffrX/7lX6wuBwAAwDjLA9bTTz9t9SYBAAAsZXnAOnDgQLOX7devXwtWAgAA0DIsD1iTJk1ynyJ0uVzu8e%2BOBQQE6PPPP7e6PAAAgKtmecBavXq1Fi9erCeeeEL9%2B/dXUFCQPvvsMy1cuFD33HOPRo0aZXVJAAAARll%2Bm4bs7GzNmzdPw4cPV5s2bRQeHq6BAwdq4cKF2rx5s2JjY91fAAAA3sjygFVRUfG94SkiIkLnz5%2B3uhwAAADjLA9Yffr00fLly3Xx4kX3WFVVlXJzc3XbbbdZXQ4AAIBxll%2BD9eSTT2ry5Mm644471LVrV7lcLv3Xf/2XoqKi9NJLL1ldDgAAgHGWB6xu3bpp9%2B7d2rVrl44fPy5Juu%2B%2B%2B3TXXXcpNDTU6nIAAACMszxgSVLr1q01YcIEnT59Wp07d5b097u5AwAA%2BALLr8FyuVxaunSp%2BvXrp9GjR%2Bvs2bOaNWuW5s6dq8uXL1tdDgAAgHGWB6yNGzfq1Vdf1VNPPaWgoCBJ0rBhw7Rnzx7l5eVZXQ4AAIBxlgeswsJCzZs3TykpKe67t48aNUqLFy/W66%2B/bnU5AAAAxlkesE6fPq2ePXs2Ge/Ro4ecTqfV5QAAABhnecCKjY3VZ5991mT83XffdV/wDgAA4M0s/yvC3/3ud1qwYIGcTqdcLpf27dunwsJCbdy4UbNnz7a6HAAAAOMsD1jjxo1TfX29Vq1apUuXLmnevHlq27atHn30UaWlpVldDgAAgHGWB6xdu3ZpxIgRSk1N1ZdffimXy6V27dpZXQYAAECLsfwarIULF7ovZm/bti3hCgAA%2BBzLA1bXrl31xRdfWL1ZAAAAy1h%2BirBHjx56/PHHtW7dOnXt2lXBwcEe89nZ2VaXBAAAYJTlAevkyZNKTEyUJO57BQAAfJIlASsnJ0fTp09XWFiYNm7caMUmAQAAbGPJNVgvvviiampqPMbS09NVUVFhxeYBAAAsZUnAcrlcTcYOHDig2tpaKzYPAABgKcv/ihAAAMDXEbAAAAAMsyxgBQQEWLUpAAAAW1l2m4bFixd73PPq8uXLys3NVXh4uMdy3AcLAAB4O0uOYPXr109Op1OnT592fyUkJOj8%2BfMeY6dPnza%2B7fT0dM2ePdv9%2BMiRI5owYYLi4%2BM1btw4HT582GP5Xbt2adiwYYqPj1dGRoa%2B/PJL4zUBAADfZskRLLvuffXGG29o7969uueeeyRJ1dXVSk9P1913362nn35amzdv1rRp0/T2228rLCxMhw4d0ty5c7VgwQL16NFDS5YsUVZWltasWWNL/QAAwDv57EXuVVVVysnJUVxcnHts9%2B7dCg4O1syZM9WtWzfNnTtX4eHhevPNNyVJmzZt0siRIzV27Fj16NFDOTk52rt3r8rKyuxqAwAAeCGfDVjPPPOMxowZo%2B7du7vHSkpKlJiY6L7gPiAgQH379lVxcbF7Pikpyb18hw4d1LFjR5WUlFhbPAAA8GqWfxahFfbt26dPPvlEr7/%2BuubPn%2B8edzqdHoFLktq1a6fS0lJJUkVFhaKjo5vMnz179oq2X1FR0eRzFh2OsCbrvlqBgd6Vjx0OM/V%2B27e39X%2B1/LVvyX9799e%2BJf/u3SRT77stzRf3t88FrNraWj311FOaN2%2BeQkJCPOZqamoUFBTkMRYUFKS6ujpJ0qVLl350vrkKCwuVl5fnMZaRkaHMzMwrWo%2BvadMm/KcXugKRkaFG1%2Bct/LVvyX9799e%2BJf/u3QTT77stzZf2t88FrLy8PN16660aPHhwk7ng4OAmYamurs4dxH5oPjT0ynZ4amqqkpOTPcYcjjCdP//NFa3np3hb0jfVf2BgK0VGhurChRo1NDQaWac38Ne%2BJf/t3V/7lvy7d5NM/9xpKS25v%2B0KmT4XsN544w1VVlYqISFBktyB6a233tLo0aNVWVnpsXxlZaX71F1MTMz3zkdFRV1RDdHR0U1OBzqdX6u%2B3r/fJEz339DQ6Jevqb/2Lflv7/7at%2BTfvZvgba%2BdL%2B1vnwtYGzduVH19vfvx0qVLJUmPP/64Dhw4oLVr18rlcikgIEAul0sHDx7Ugw8%2BKEmKj49XUVGRUlJSJEnl5eUqLy9XfHy89Y0AAHCVRq74wO4Smu2TJSPsLsEonwtYsbGxHo%2B/vVN8ly5d1K5dOy1btkxLlizRvffeqy1btqimpkYjR46UJKWlpWnSpEnq06eP4uLitGTJEg0ZMkSdO3e2vA8AAOC9vOsinqsUERGhNWvWuI9SlZSUqKCgQGFhYZKkhIQELVy4UPn5%2BUpLS1Pr1q356B4AAHDFfO4I1nc9/fTTHo979%2B6tHTt2/ODyKSkp7lOEAAAAP4dfHcECAACwAgELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwh90FANeqkSs%2BsLuEK/LnR2%2B3uwTginnb/zOguTiCBQAAYBgBCwAAwDACFgAAgGEELAAAAMN8NmCdO3dOmZmZ6t%2B/vwYPHqzs7GzV1tZKksrKyvTb3/5Wffr00ahRo/T%2B%2B%2B97PPfDDz/U6NGjFR8fr8mTJ6usrMyOFgAAgJfyyYDlcrmUmZmpmpoavfzyy3r22Wf1l7/8RStWrJDL5VJGRobat2%2Bvbdu2acyYMZo%2BfbrOnDkjSTpz5owyMjKUkpKiV155RW3bttXDDz8sl8tlc1cAAMBb%2BORtGk6cOKHi4mJ98MEHat%2B%2BvSQpMzNTzzzzjO644w6VlZVpy5YtCgsLU7du3bRv3z5t27ZNjzzyiLZu3apbb71VU6dOlSRlZ2fr9ttv18cff6wBAwbY2RYAAPASPnkEKyoqSuvWrXOHq29dvHhRJSUluuWWWxQWFuYeT0xMVHFxsSSppKRESUlJ7rnQ0FD16tXLPQ8AAPBTfDJgRUZGavDgwe7HjY2N2rRpkwYOHCin06no6GiP5du1a6ezZ89K0k/OAwAA/BSfPEX4Xbm5uTpy5IheeeUVrV%2B/XkFBQR7zQUFBqqurkyTV1NT86HxzVFRUyOl0eow5HGFNgtvVCgz0rnzscJip99u%2Bva3/lmbq9b0W%2Bes%2B99e%2B4b986Xvd5wNWbm6uNmzYoGeffVY333yzgoODVVVV5bFMXV2dQkJCJEnBwcFNwlRdXZ0iIyObvc3CwkLl5eV5jGVkZCgzM/NnduEb2rQJN7q%2ByMhQo%2BvzdqZf32uRXfs8ae6btmz35/pkyQi7SwB%2BFl96X/fpgLVo0SJt3rxZubm5Gj58uCQpJiZGx44d81iusrLSfXQpJiZGlZWVTeZ79uzZ7O2mpqYqOTnZY8zhCNP589/8nDZ%2BkLclfVP9Bwa2UmRkqC5cqFFDQ6ORdfoC099f1xL2%2BZXx5e8F%2BLaW%2BD9u1y%2BfPhuw8vLytGXLFi1fvlwjRvzfb3Px8fEqKCjQpUuX3EetioqKlJiY6J4vKipyL19TU6MjR45o%2BvTpzd52dHR0k9OBTufXqq/37x8MpvtvaGj0%2B9f0H/nDa8E%2Bbx5eI3grX/o/7l2HQJrp%2BPHjWrlypX7/%2B98rMTFRTqfT/dW/f3916NBBWVlZKi0tVUFBgQ4dOqTx48dLksaNG6eDBw%2BqoKBApaWlysrKUqdOnbhFAwAAaDafDFjvvPOOGhoatGrVKg0aNMjjKzDkgMdoAAALMUlEQVQwUCtXrpTT6VRKSopee%2B015efnq2PHjpKkTp066bnnntO2bds0fvx4VVVVKT8/XwEBATZ3BQAAvIVPniJMT09Xenr6D8536dJFmzZt%2BsH5X/3qV/rVr37VEqUBAAA/4JMBCwD82cgVH9hdAuD3fPIUIQAAgJ0IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhjnsLgD%2BY%2BSKD%2BwuAQAAS3AECwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5rC7AAD%2BZ%2BSKD%2BwuAQBaFEewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAtb3qK2t1Zw5c5SUlKRBgwbphRdesLskAADgRRx2F3AtysnJ0eHDh7VhwwadOXNGs2bNUseOHTVixAi7SwMAAF6AgPUd1dXV2rp1q9auXatevXqpV69eKi0t1csvv0zAAgAAzcIpwu84evSo6uvrlZCQ4B5LTExUSUmJGhsbbawMAAB4C45gfYfT6VSbNm0UFBTkHmvfvr1qa2tVVVWltm3b/uQ6Kioq5HQ6PcYcjjBFR0cbrTUwkHyM/%2BNw8P0AwLv50s81AtZ31NTUeIQrSe7HdXV1zVpHYWGh8vLyPMamT5%2BuRx55xEyR/6uiokJT/l%2BpUlNTjYe3a1lFRYUKCwvp24t9suTKTrf7Uu9Xwl/7lvy3d3/u%2B7nnnvOpvn0nKhoSHBzcJEh9%2BzgkJKRZ60hNTdX27ds9vlJTU43X6nQ6lZeX1%2BRoma%2Bjb//qW/Lf3v21b8l/e6dv3%2BmbI1jfERMTo/Pnz6u%2Bvl4Ox99fHqfTqZCQEEVGRjZrHdHR0T6TwAEAwJXjCNZ39OzZUw6HQ8XFxe6xoqIixcXFqVUrXi4AAPDTSAzfERoaqrFjx2r%2B/Pk6dOiQ9uzZoxdeeEGTJ0%2B2uzQAAOAlAufPnz/f7iKuNQMHDtSRI0e0bNky7du3Tw8%2B%2BKDGjRtnd1nfKzw8XP3791d4eLjdpViKvv2rb8l/e/fXviX/7Z2%2BfaPvAJfL5bK7CAAAAF/CKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsL1VbW6s5c%2BYoKSlJgwYN0gsvvGB3SZaqq6vT6NGjtX//frtLscS5c%2BeUmZmp/v37a/DgwcrOzlZtba3dZVni1KlT%2Bt3vfqeEhAQNGTJE69ats7skS6Wnp2v27Nl2l2GZt99%2BW7/4xS88vjIzM%2B0uyxJ1dXVasGCB%2BvXrp1/%2B8pdavny5fP1e4Nu3b2%2Byv3/xi1%2BoR48edpd21Rx2F4CfJycnR4cPH9aGDRt05swZzZo1Sx07dtSIESPsLq3F1dbWasaMGSotLbW7FEu4XC5lZmYqMjJSL7/8sr766ivNmTNHrVq10qxZs%2Bwur0U1NjYqPT1dcXFx2rFjh06dOqXHHntMMTExuvvuu%2B0ur8W98cYb2rt3r%2B655x67S7HMsWPHNHToUC1atMg9FhwcbGNF1lm8eLH279%2Bv559/Xt98843%2B8Ic/qGPHjrr33nvtLq3FjBo1SoMHD3Y/rq%2Bv15QpUzRkyBD7ijKEgOWFqqurtXXrVq1du1a9evVSr169VFpaqpdfftnnA9axY8c0Y8YMn/%2Bt7h%2BdOHFCxcXF%2BuCDD9S%2BfXtJUmZmpp555hmfD1iVlZXq2bOn5s%2Bfr4iICHXt2lW33XabioqKfD5gVVVVKScnR3FxcXaXYqnjx4/r5ptvVlRUlN2lWKqqqkrbtm3Tiy%2B%2BqN69e0uSpk6dqpKSEp8OWCEhIQoJCXE/XrNmjVwulx5//HEbqzKDU4Re6OjRo6qvr1dCQoJ7LDExUSUlJWpsbLSxspb38ccfa8CAASosLLS7FMtERUVp3bp17nD1rYsXL9pUkXWio6O1YsUKRUREyOVyqaioSAcOHFD//v3tLq3FPfPMMxozZoy6d%2B9udymWOn78uLp27Wp3GZYrKipSRESEx/d2enq6srOzbazKWlVVVVq7dq1mzJihoKAgu8u5agQsL%2BR0OtWmTRuPb8D27durtrZWVVVVNlbW8iZOnKg5c%2BYoNDTU7lIsExkZ6XEIvbGxUZs2bdLAgQNtrMp6ycnJmjhxohISEjR8%2BHC7y2lR%2B/bt0yeffKKHH37Y7lIs5XK5dPLkSb3//vsaPny4hg0bpqVLl6qurs7u0lpcWVmZYmNjtXPnTo0YMUK//vWvlZ%2Bf7/O/NP%2BjzZs3Kzo62mfOxBCwvFBNTU2TdP/tY394I/J3ubm5OnLkiP7whz/YXYql/vSnP2n16tX6/PPPffq3%2BtraWj311FOaN2%2Bex6kTf3DmzBn3%2B9uKFSs0a9Ysvf7668rJybG7tBZXXV2tU6dOacuWLcrOztasWbO0ceNGrV%2B/3u7SLOFyubR161bdf//9dpdiDNdgeaHg4OAmQerbx/72huxvcnNztWHDBj377LO6%2Beab7S7HUt9ei1RbW6vHH39cM2fO9InTCN%2BVl5enW2%2B91eOopb%2BIjY3V/v371bp1awUEBKhnz55qbGzUE088oaysLAUGBtpdYotxOBy6ePGili1bptjYWEl/D5ybN2/W1KlTba6u5X322Wc6d%2B6c7rrrLrtLMYaA5YViYmJ0/vx51dfXy%2BH4%2By50Op0KCQlRZGSkzdWhpSxatEibN29Wbm6uz58i%2B1ZlZaWKi4s1bNgw91j37t11%2BfJlXbx4UW3btrWxupbxxhtvqLKy0n2N5be/PL311lv69NNP7SzNEjfccIPH427duqm2tlZfffWVT%2B7vb0VFRSk4ONgdriTpxhtvVHl5uY1VWee9995TUlKSWrdubXcpxnCK0Av17NlTDodDxcXF7rGioiLFxcWpVSt2qS/Ky8vTli1btHz5cp/6De%2BnnD59WtOnT9e5c%2BfcY4cPH1bbtm199oftxo0b9frrr2vnzp3auXOnkpOTlZycrJ07d9pdWot77733NGDAANXU1LjHPv/8c91www0%2Bu7%2B/FR8fr9raWp08edI9duLECY/A5csOHTqkvn372l2GUfw09kKhoaEaO3as5s%2Bfr0OHDmnPnj164YUXNHnyZLtLQws4fvy4Vq5cqd///vdKTEyU0%2Bl0f/m6uLg49erVS3PmzNGxY8e0d%2B9e5ebm6sEHH7S7tBYTGxurLl26uL/Cw8MVHh6uLl262F1ai0tISFBwcLCefPJJnThxQnv37lVOTo7%2B9V//1e7SWtxNN92kIUOGKCsrS0ePHtV7772ngoICpaWl2V2aJUpLS33uL2Y5ReilsrKyNH/%2BfE2ZMkURERF65JFHdOedd9pdFlrAO%2B%2B8o4aGBq1atUqrVq3ymPvrX/9qU1XWCAwM1MqVK7Vo0SKlpqYqNDRUkyZN4pcJHxUREaHnn39ef/zjHzVu3DiFh4fr3nvv9YuAJUlLly7VokWLlJaWptDQUN13332aNGmS3WVZorKy0ucucQlw%2BdMdGwEAACzAKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMOz/AxUEbqRv0dDRAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3734633702829269219”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>476</td> <td class=”number”>14.8%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>356</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:52%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>220</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>199</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (9)</td> <td class=”number”>559</td> <td class=”number”>17.4%</td> <td>

<div class=”bar” style=”width:81%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3734633702829269219”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>686</td> <td class=”number”>21.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>208</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.91</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.35</td> <td class=”number”>8</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:80%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:85%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODATAX_VENDM14”>SODATAX_VENDM14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>23</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>4.6888</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>10.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7808541433250200324”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7808541433250200324,#minihistogram7808541433250200324”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7808541433250200324”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7808541433250200324”

aria-controls=”quantiles7808541433250200324” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7808541433250200324” aria-controls=”histogram7808541433250200324”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7808541433250200324” aria-controls=”common7808541433250200324”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7808541433250200324” aria-controls=”extreme7808541433250200324”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7808541433250200324”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>4</td>

</tr> <tr>

<th>Median</th> <td>5.5</td>

</tr> <tr>

<th>Q3</th> <td>6.15</td>

</tr> <tr>

<th>95-th percentile</th> <td>7</td>

</tr> <tr>

<th>Maximum</th> <td>8</td>

</tr> <tr>

<th>Range</th> <td>8</td>

</tr> <tr>

<th>Interquartile range</th> <td>2.15</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>2.1596</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.46058</td>

</tr> <tr>

<th>Kurtosis</th> <td>0.061672</td>

</tr> <tr>

<th>Mean</th> <td>4.6888</td>

</tr> <tr>

<th>MAD</th> <td>1.7051</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-1.0673</td>

</tr> <tr>

<th>Sum</th> <td>14685</td>

</tr> <tr>

<th>Variance</th> <td>4.6638</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7808541433250200324”>

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hwAAADjfB6w/vCHP6hr1666%2B%2B67PWO7du1Sx44dlZOT4%2BtyAAAAjPN5wPrkk0/0%2BOOPKzo62jMWFRWlWbNm6cMPP/R1OQAAAMb5PGA5HA7V1NQ0GXe5XNzRHQAA2ILPA9aNN96ohQsX6u9//7tnrLy8XDk5ORoyZIivywEAADDO539FOHv2bN19990aMWKEIiMjJUk1NTXq3bu3srKyfF0OAACAcT4PWO3bt9crr7yiDz74QGVlZXI4HOrRo4euv/56BQQE%2BLocAAAA4yz5qJzAwEANGTKEU4IAAMCWfB6wnE6nli1bpn379un8%2BfNNLmx/5513fF0SAACAUT4PWP/5n/%2BpAwcO6Oabb9bll1/u680DAAC0Op8HrA8//FCrV69WcnJyq26nrq5OOTk52rlzpy677DJNmDBBDz30kAICAnTw4EE9%2BeST%2BvLLL9WjRw899dRT6tOnj%2Be5O3fu1LJly%2BR0OjV48GBlZ2crKiqqVesFAAD24fPbNISFhal9%2B/atvp2FCxfqgw8%2B0AsvvKAlS5boT3/6k4qKinT27Fmlp6crOTlZ27ZtU0JCgqZPn66zZ89KuvC5iHPnztWMGTNUVFSkmpoa/roRAABcEp8HrLFjx2r16tVqaGhotW1UV1dr69atys7OVr9%2B/XT99ddr2rRpKi0t1a5duxQcHKxZs2ape/fumjt3rsLDw/XGG29IkjZs2KBRo0Zp3Lhx6tmzp3Jzc7V7926Vl5e3Wr0AAMBefH6KsLq6Wjt37tR///d/q0uXLgoKCvKaX7duXYu3UVxcrIiICA0YMMAzlp6eLunCNWBJSUmeW0IEBAQoMTFRJSUlSk1NVWlpqe69917P8zp27KhOnTqptLRUXbp0aXFtAADA/iy5TcOYMWNadf3l5eWKi4vT9u3btWLFCp0/f16pqam6//775XQ61aNHD6/l27dvr7KyMklSZWWlYmJimsyfPHmyVWsGAAD24fOAlZOT0%2BrbOHv2rI4fP65NmzYpJydHTqdT8%2BbNU2hoqFwuV5OjZkFBQaqrq5MknTt37ifnm6OyslJOp9NrzOEIaxLcWiowsI3Xv3Zh174k%2B/Zm174keoM3h8Pa75Vd95kd%2B7LkCFZlZaX%2B9Kc/6dixY5ozZ4727t2rq6%2B%2BWldddZWR9TscDp05c0ZLlixRXFycJOnEiRPauHGjunbt2iQs1dXVKSQkRJIUHBx80fnQ0NBmb7%2BoqEj5%2BfleYxkZGcrMzPwl7fysyMjm1%2BZP7NqXZN/e7NqXRG%2B4oF27cKtLkGTffWanvnwesI4fP65bb71VEREROnXqlGbOnKldu3YpKytLa9asUXx8fIu3ER0dreDgYE%2B4kqQrr7xSFRUVGjBggKqqqryWr6qq8hxdio2Nveh8dHR0s7c/adIkpaSkeI05HGE6ffq7S23lJwUGtlFkZKhqalxqaGg0um4r2bUvyb692bUvid7gzfT7%2BKWy6z5rzb6sCsU%2BD1hPP/20hg8froULFyoxMVGStHTpUs2ePVuLFy/W%2BvXrW7yN%2BPh41dbW6tixY7ryyislSUePHlVcXJzi4%2BO1atUqud1uBQQEyO12a9%2B%2Bfbrvvvs8zy0uLlZqaqokqaKiQhUVFZcU/GJiYpqcDnQ6v1V9feu8GBoaGltt3Vaya1%2BSfXuza18SveGCX8v3ya77zE59%2Bfxk5759%2B3T33Xd7fbCzw%2BHQAw88oIMHDxrZxlVXXaWhQ4cqKytLhw4d0rvvvqvCwkKlpaVp5MiRqqmp0aJFi3T48GEtWrRILpdLo0aNkiSlpaXp1Vdf1ebNm3Xo0CHNmjVLQ4cO5S8IAQBAs/k8YDU2NqqxsWk6/e677xQYGGhsO4sXL9Y//dM/KS0tTbNnz9btt9%2BuO%2B%2B8UxEREVq5cqXnKFVpaakKCwsVFhYmSUpISNCCBQtUUFCgtLQ0tW3b1icX5gMAAPvw%2BSnCwYMHa%2BXKlcrLy/OMVVdXKy8vT4MGDTK2ncsvv1y5ubkXnevXr59eeeWVH31uamqq5xQhAADApfL5EazHH39cBw4c0ODBg1VbW6v7779fw4YN01dffaXZs2f7uhwAAADjfH4EKzY2Vtu3b9fOnTv1%2Beefq7GxUWlpaRo7dqwiIiJ8XQ4AAIBxltwHKzQ0VBMnTrRi0wAAAK3O5wFrypQpPzlv4rMIAQAArOTzgPWPN/%2BUpPr6eh0/flxffvmlpk6d6utyAAAAjPvVfBZhQUEBH6gMAABswZJrsC5m7NixGjdunLKzs60uBQC8JM99w%2BoSLsmfZ95gdQnAb96v5mOrP/30U6M3GgUAALDKr%2BIi9zNnzuiLL77Q5MmTfV0OAACAcT4PWJ06dfL6HEJJuuyyy3THHXfo97//va/LAQAAMM7nAevpp5/29SYBAAB8yucBa%2B/evc1e9rrrrmvFSgAAAFqHzwPWnXfe6TlF6Ha7PeM/HAsICNDnn3/u6/IAAABazOcBa8WKFVq4cKEee%2BwxDRgwQEFBQfrss8%2B0YMEC3XLLLRo9erSvSwIAADDK57dpyMnJ0bx58zRixAi1a9dO4eHhGjRokBYsWKCNGzcqLi7O8wUAAOCPfB6wKisrLxqeIiIidPr0aV%2BXAwAAYJzPA1b//v21dOlSnTlzxjNWXV2tvLw8XX/99b4uBwAAwDifX4P1xBNPaMqUKbrxxhvVrVs3ud1u/c///I%2Bio6O1bt06X5cDAABgnM8DVvfu3bVr1y7t3LlTR44ckSTdfvvtuvnmmxUaGurrcgAAAIyz5MOe27Ztq4kTJ%2Bqrr75Sly5dJF24mzsAAIAd%2BPwaLLfbrcWLF%2Bu6667TmDFjdPLkSc2ePVtz587V%2BfPnfV0OAACAcT4PWOvXr9err76qJ598UkFBQZKk4cOH6%2B2331Z%2Bfr6vywEAADDO5wGrqKhI8%2BbNU2pqqufu7aNHj9bChQv12muv%2BbocAAAA43x%2BDdZXX32lXr16NRnv2bOnnE6nr8sBANsZtex9q0sAfvN8fgQrLi5On332WZPxv/3tb54L3gEAAPyZz49g3XPPPXrqqafkdDrldru1Z88eFRUVaf369Xr88cd9XQ4AAIBxPg9Y48ePV319vZ5//nmdO3dO8%2BbNU1RUlGbOnKm0tDRflwMAAGCczwPWzp07NXLkSE2aNElff/213G632rdv7%2BsyAAAAWo3Pr8FasGCB52L2qKgowhUAALAdnwesbt266csvv/T1ZgEAAHzG56cIe/bsqUcffVSrV69Wt27dFBwc7DWfk5Pj65IAAACM8nnAOnbsmJKSkiSJ%2B14BAABb8knAys3N1YwZMxQWFqb169f7YpMAAACW8ck1WC%2B99JJcLpfXWHp6uiorK32xeQAAAJ/yScByu91Nxvbu3ava2lpfbB4AAMCnfP5XhAAAAHZHwAIAADDMZwErICDAV5sCAACwlM9u07Bw4UKve16dP39eeXl5Cg8P91qO%2B2ABAAB/55OAdd111zW551VCQoJOnz6t06dP%2B6IEAAAAn/FJwOLeVwAA4LeEi9wBAAAMI2ABAAAYRsACAAAwjIAFAABgmO0DVnp6uh5//HHP44MHD2rixImKj4/X%2BPHjdeDAAa/ld%2B7cqeHDhys%2BPl4ZGRn6%2BuuvfV0yAADwc7YOWK%2B//rp2797teXz27Fmlp6crOTlZ27ZtU0JCgqZPn66zZ89Kkvbv36%2B5c%2BdqxowZKioqUk1NjbKysqwqHwAA%2BCnbBqzq6mrl5uaqb9%2B%2BnrFdu3YpODhYs2bNUvfu3TV37lyFh4frjTfekCRt2LBBo0aN0rhx49SzZ0/l5uZq9%2B7dKi8vt6oNAADgh2wbsJ555hmNHTtWPXr08IyVlpYqKSnJ87E9AQEBSkxMVElJiWc%2BOTnZs3zHjh3VqVMnlZaW%2BrZ4AADg13z2UTm%2BtGfPHn3yySd67bXXNH/%2BfM%2B40%2Bn0ClyS1L59e5WVlUmSKisrFRMT02T%2B5MmTl7T9ysrKJneudzjCmqy7pQID23j9axd27Uuyb2927UuyZ0/45RwOa/8/2PW1Zse%2BbBewamtr9eSTT2revHkKCQnxmnO5XAoKCvIaCwoKUl1dnSTp3LlzPznfXEVFRcrPz/cay8jIUGZm5iWtp7kiI0NbZb1Ws2tfkn17s2tfwPfatQv/%2BYV8wK6vNTv1ZbuAlZ%2Bfrz59%2BmjIkCFN5oKDg5uEpbq6Ok8Q%2B7H50NBL2%2BGTJk1SSkqK15jDEabTp7%2B7pPX8nMDANoqMDFVNjUsNDY1G120lu/Yl2bc3u/Yl2es3arSc6ffxS2XX11pr9mVVKLZdwHr99ddVVVWlhIQESfIEpjfffFNjxoxRVVWV1/JVVVWeU3exsbEXnY%2BOjr6kGmJiYpqcDnQ6v1V9feu8GBoaGltt3Vaya1%2BSfXuza1/A934t/7/t%2BlqzU1%2B2C1jr169XfX295/HixYslSY8%2B%2Bqj27t2rVatWye12KyAgQG63W/v27dN9990nSYqPj1dxcbFSU1MlSRUVFaqoqFB8fLzvGwEAAH7LdgErLi7O63F4%2BIVDg127dlX79u21ZMkSLVq0SLfddps2bdokl8ulUaNGSZLS0tJ05513qn///urbt68WLVqkoUOHqkuXLj7vAwAA%2BK/f1MUFERERWrlypecoVWlpqQoLCxUWFiZJSkhI0IIFC1RQUKC0tDS1bdtWOTk5FlcNAAD8je2OYP3Q008/7fW4X79%2BeuWVV350%2BdTUVM8pQgAAgF/iN3UECwAAwBcIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZvsPe7a75LlvWF1Cs/155g1WlwAAgE9wBAsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGDcaBQCgmUYte9/qEi4JN3i2DkewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYbYNWKdOnVJmZqYGDBigIUOGKCcnR7W1tZKk8vJy3XXXXerfv79Gjx6t9957z%2Bu5H3zwgcaMGaP4%2BHhNmTJF5eXlVrQAAAD8lC0DltvtVmZmplwul15%2B%2BWU9%2B%2Byz%2Butf/6ply5bJ7XYrIyNDHTp00NatWzV27FjNmDFDJ06ckCSdOHFCGRkZSk1N1ZYtWxQVFaUHHnhAbrfb4q4AAIC/cFhdQGs4evSoSkpK9P7776tDhw6SpMzMTD3zzDO68cYbVV5erk2bNiksLEzdu3fXnj17tHXrVj344IPavHmz%2BvTpo2nTpkmScnJydMMNN%2Bjjjz/WwIEDrWwLAAD4CVsewYqOjtbq1as94ep7Z86cUWlpqa699lqFhYV5xpOSklRSUiJJKi0tVXJysmcuNDRUvXv39swDAAD8HFsGrMjISA0ZMsTzuLGxURs2bNCgQYPkdDoVExPjtXz79u118uRJSfrZeQAAgJ9jy1OEP5SXl6eDBw9qy5YtWrNmjYKCgrzmg4KCVFdXJ0lyuVw/Od8clZWVcjqdXmMOR1iT4NZSgYH%2BlY8djubV%2B31f/tZfc9i1N7v2JdmzJ/x2NPd912p2fA%2BxfcDKy8vT2rVr9eyzz%2Brqq69WcHCwqqurvZapq6tTSEiIJCk4OLhJmKqrq1NkZGSzt1lUVKT8/HyvsYyMDGVmZv7CLuyhXbvwS1o%2BMjK0lSqxnl17s2tfgL%2B61Pddq9npPcTWASs7O1sbN25UXl6eRowYIUmKjY3V4cOHvZarqqryHF2KjY1VVVVVk/levXo1e7uTJk1SSkqK15jDEabTp7/7JW38KH9L%2Bs3tPzCwjSIjQ1VT41JDQ2MrV%2BVbdu3Nrn1J/vc6A/6R6Z87raU130OsCpm2DVj5%2BfnatGmTli5dqpEjR3rG4%2BPjVVhYqHPnznmOWhUXFyspKckzX1xc7Fne5XLp4MGDmjFjRrO3HRMT0%2BR0oNP5rerr7fWD51L9bvG7VpdwSf4884ZWW3dDQ6Mt/z/YtS/AX/nb69FO7yG2/NXsyJEjWr58ue69914lJSXJ6XR6vgYMGKCOHTsqKytLZWVlKiws1P79%2BzVhwgRJ0vjx47Vv3z4VFhaqrKxMWVlZ6ty5M7doAAAAzWbLgPXOO%2B%2BooaFBzz//vAYPHux9CNdtAAAMZUlEQVT1FRgYqOXLl8vpdCo1NVU7duxQQUGBOnXqJEnq3LmznnvuOW3dulUTJkxQdXW1CgoKFBAQYHFXAADAX9jyFGF6errS09N/dL5r167asGHDj87fdNNNuummm1qjNAAA8BtgyyNYAAAAViJgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMxhdQEAAKB1jFr2vtUlNNsni0ZaXYJRHMECAAAwjIAFAABgGAELAADAMAIWAACAYVzkDtiEP13M%2BueZN1hdAgC0Ko5gAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjGndyBH%2BFPd0YHAPy6cAQLAADAMAIWAACAYQSsi6itrdWcOXOUnJyswYMH68UXX7S6JAAA4Ee4BusicnNzdeDAAa1du1YnTpzQ7Nmz1alTJ40cOdLq0gAAgB8gYP3A2bNntXnzZq1atUq9e/dW7969VVZWppdffpmABQAAmoVThD9w6NAh1dfXKyEhwTOWlJSk0tJSNTY2WlgZAADwFxzB%2BgGn06l27dopKCjIM9ahQwfV1taqurpaUVFRP7uOyspKOZ1OrzGHI0wxMTFGaw0MJB/DPzkc/vN/l9cZ4Dt2er0RsH7A5XJ5hStJnsd1dXXNWkdRUZHy8/O9xmbMmKEHH3zQTJH/p7KyUlP/X5kmTZpkPLxZqbKyUkVFRbbrS7Jvb3btS7Lv60yy736za1%2BSfXurrKzUc889Z6u%2B7BMVDQkODm4SpL5/HBIS0qx1TJo0Sdu2bfP6mjRpkvFanU6n8vPzmxwt83d27Uuyb2927UuiN39k174k%2B/Zmx744gvUDsbGxOn36tOrr6%2BVwXPj2OJ1OhYSEKDIyslnriImJsU0CBwAAl44jWD/Qq1cvORwOlZSUeMaKi4vVt29ftWnDtwsAAPw8EsMPhIaGaty4cZo/f77279%2Bvt99%2BWy%2B%2B%2BKKmTJlidWkAAMBPBM6fP3%2B%2B1UX82gwaNEgHDx7UkiVLtGfPHt13330aP3681WVdVHh4uAYMGKDw8HCrSzHKrn1J9u3Nrn1J9OaP7NqXZN/e7NZXgNvtdltdBAAAgJ1wihAAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHLT9XW1mrOnDlKTk7W4MGD9eKLL1pdklF1dXUaM2aMPvroI6tLMeLUqVPKzMzUgAEDNGTIEOXk5Ki2ttbqsow4fvy47rnnHiUkJGjo0KFavXq11SUZl56erscff9zqMox56623dM0113h9ZWZmWl2WEXV1dXrqqad03XXX6V/%2B5V%2B0dOlS%2Bfv9tLdt29Zkf11zzTXq2bOn1aUZUVFRoenTpysxMVEpKSlas2aN1SUZ4bC6APwyubm5OnDggNauXasTJ05o9uzZ6tSpk0aOHGl1aS1WW1urRx55RGVlZVaXYoTb7VZmZqYiIyP18ssv65tvvtGcOXPUpk0bzZ492%2BryWqSxsVHp6enq27evXnnlFR0/flwPP/ywYmNj9e///u9Wl2fE66%2B/rt27d%2BuWW26xuhRjDh8%2BrGHDhik7O9szFhwcbGFF5ixcuFAfffSRXnjhBX333Xd66KGH1KlTJ912221Wl/aLjR49WkOGDPE8rq%2Bv19SpUzV06FDrijJo5syZ6tSpk7Zt26bDhw/r0UcfVVxcnH73u99ZXVqLELD80NmzZ7V582atWrVKvXv3Vu/evVVWVqaXX37Z7wPW4cOH9cgjj/j9b5z/6OjRoyopKdH777%2BvDh06SJIyMzP1zDPP%2BH3AqqqqUq9evTR//nxFRESoW7duuv7661VcXGyLgFVdXa3c3Fz17dvX6lKMOnLkiK6%2B%2BmpFR0dbXYpR1dXV2rp1q1566SX169dPkjRt2jSVlpb6dcAKCQlRSEiI5/HKlSvldrv16KOPWliVGd98841KSkqUnZ2tbt26qVu3bhoyZIj27Nnj9wGLU4R%2B6NChQ6qvr1dCQoJnLCkpSaWlpWpsbLSwspb7%2BOOPNXDgQBUVFVldijHR0dFavXq1J1x978yZMxZVZE5MTIyWLVumiIgIud1uFRcXa%2B/evRowYIDVpRnxzDPPaOzYserRo4fVpRh15MgRdevWzeoyjCsuLlZERITX/7/09HTl5ORYWJVZ1dXVWrVqlR555BEFBQVZXU6LhYSEKDQ0VNu2bdP58%2Bd19OhR7du3T7169bK6tBYjYPkhp9Opdu3aeb24OnTooNraWlVXV1tYWctNnjxZc%2BbMUWhoqNWlGBMZGel1eL%2BxsVEbNmzQoEGDLKzKvJSUFE2ePFkJCQkaMWKE1eW02J49e/TJJ5/ogQcesLoUo9xut44dO6b33ntPI0aM0PDhw7V48WLV1dVZXVqLlZeXKy4uTtu3b9fIkSP1r//6ryooKPD7Xzz/0caNGxUTE%2BP3Zyu%2BFxwcrHnz5qmoqEjx8fEaNWqUbrzxRk2cONHq0lqMgOWHXC5Xk99cvn9shzdJu8vLy9PBgwf10EMPWV2KUX/84x%2B1YsUKff75535/xKC2tlZPPvmk5s2b53Vqxg5OnDjheQ9ZtmyZZs%2Berddee025ublWl9ZiZ8%2Be1fHjx7Vp0ybl5ORo9uzZWr9%2BvW0umna73dq8ebPuuOMOq0sx6siRIxo2bJiKioqUk5OjN954Qzt27LC6rBbjGiw/FBwc3CRIff/Ybj8M7CYvL09r167Vs88%2Bq6uvvtrqcoz6/jql2tpaPfroo5o1a5bfnsLIz89Xnz59vI482kVcXJw%2B%2BugjtW3bVgEBAerVq5caGxv12GOPKSsrS4GBgVaX%2BIs5HA6dOXNGS5YsUVxcnKQLgXLjxo2aNm2axdW13GeffaZTp07p5ptvtroUY/bs2aMtW7Zo9%2B7dCgkJUd%2B%2BfXXq1Ck9//zz%2Bv3vf291eS1CwPJDsbGxOn36tOrr6%2BVwXNiFTqdTISEhioyMtLg6/Jjs7Gxt3LhReXl5tjiFJl24yL2kpETDhw/3jPXo0UPnz5/XmTNnFBUVZWF1v9zrr7%2Buqqoqz3WO3/8C8%2Babb%2BrTTz%2B1sjQjrrjiCq/H3bt3V21trb755hu/3WfShesdg4ODPeFKkq688kpVVFRYWJU57777rpKTk9W2bVurSzHmwIED6tq1q9fBgWuvvVYrVqywsCozOEXoh3r16iWHw6GSkhLPWHFxsfr27as2bdilv0b5%2BfnatGmTli5daqvfPr/66ivNmDFDp06d8owdOHBAUVFRfv2Dev369Xrttde0fft2bd%2B%2BXSkpKUpJSdH27dutLq3F3n33XQ0cOFAul8sz9vnnn%2BuKK67w630mSfHx8aqtrdWxY8c8Y0ePHvUKXP5s//79SkxMtLoMo2JiYnT8%2BHGvszJHjx5V586dLazKDH4a%2B6HQ0FCNGzdO8%2BfP1/79%2B/X222/rxRdf1JQpU6wuDRdx5MgRLV%2B%2BXPfee6%2BSkpLkdDo9X/6ub9%2B%2B6t27t%2BbMmaPDhw9r9%2B7dysvL03333Wd1aS0SFxenrl27er7Cw8MVHh6url27Wl1aiyUkJCg4OFhPPPGEjh49qt27dys3N1f/8R//YXVpLXbVVVdp6NChysrK0qFDh/Tuu%2B%2BqsLBQaWlpVpdmRFlZme3%2BojUlJUWXXXaZnnjiCR07dkx/%2BctftGLFCt15551Wl9ZiAW473XDoN8Tlcmn%2B/Pn6r//6L0VEROiee%2B7RXXfdZXVZRl1zzTVat26dBg4caHUpLVJYWKglS5ZcdO6LL77wcTXmnTp1StnZ2dqzZ49CQ0N1xx13aPr06QoICLC6NGO%2Bv4v7008/bXElZpSVlekPf/iDSkpKFB4erttuu00ZGRm22GfffvutsrOz9dZbbyk0NFSTJ0%2B2TW/9%2BvVTQUGB7a4NPHz4sBYtWqT9%2B/crKipKt99%2Bu6ZOner3%2B4yABQAAYBinCAEAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsP8PZvjiqKxVhEEAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7808541433250200324”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>522</td> <td class=”number”>16.3%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>381</td> <td class=”number”>11.9%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.25</td> <td class=”number”>356</td> <td class=”number”>11.1%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>332</td> <td class=”number”>10.3%</td> <td>

<div class=”bar” style=”width:44%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>220</td> <td class=”number”>6.9%</td> <td>

<div class=”bar” style=”width:29%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>133</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>109</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.15</td> <td class=”number”>105</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.75</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (12)</td> <td class=”number”>759</td> <td class=”number”>23.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7808541433250200324”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>332</td> <td class=”number”>10.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.225</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:35%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.5</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.7500000000000002</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.91</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>97</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.875</td> <td class=”number”>87</td> <td class=”number”>2.7%</td> <td>

<div class=”bar” style=”width:89%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>92</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:94%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.000000000000001</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:27%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:84%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SODA_PRICE10”>SODA_PRICE10<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>36</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.1%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>3.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>106</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.99173</td>

</tr> <tr>

<th>Minimum</th> <td>0.901</td>

</tr> <tr>

<th>Maximum</th> <td>1.2415</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-1673359387747477846”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-1673359387747477846,#minihistogram-1673359387747477846”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-1673359387747477846”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-1673359387747477846”

aria-controls=”quantiles-1673359387747477846” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-1673359387747477846” aria-controls=”histogram-1673359387747477846”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-1673359387747477846” aria-controls=”common-1673359387747477846”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-1673359387747477846” aria-controls=”extreme-1673359387747477846”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-1673359387747477846”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0.901</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.93022</td>

</tr> <tr>

<th>Q1</th> <td>0.95023</td>

</tr> <tr>

<th>Median</th> <td>0.97482</td>

</tr> <tr>

<th>Q3</th> <td>1.0385</td>

</tr> <tr>

<th>95-th percentile</th> <td>1.1041</td>

</tr> <tr>

<th>Maximum</th> <td>1.2415</td>

</tr> <tr>

<th>Range</th> <td>0.34048</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.088293</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.058967</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.059459</td>

</tr> <tr>

<th>Kurtosis</th> <td>3.4634</td>

</tr> <tr>

<th>Mean</th> <td>0.99173</td>

</tr> <tr>

<th>MAD</th> <td>0.043872</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.4965</td>

</tr> <tr>

<th>Sum</th> <td>3078.3</td>

</tr> <tr>

<th>Variance</th> <td>0.0034771</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-1673359387747477846”>

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96xLVu2qH379srLy7O7HAAAAONsD1hfffWVnnjiCSUlJXnH2rVrp5kzZ%2Bqzzz6zuxwAAADjbA9YDodDVVVVjcY9Hg93dAcAACHB9oA1ZMgQLVy4UAcOHPCOlZWVKS8vT4MHD7a7HAAAAONs/1eEs2bN0gMPPKBbbrlFCQkJkqSqqir16tVLs2fPtrscAAAA42wPWImJidq4caM%2B/fRTlZaWyuFwqHv37rrpppsUERFhdzkAAADG%2BeWjciIjIzV48GAuCQIAgJBke8Byu91atmyZtm/frtOnTzd6Y/sHH3xgd0kAAABG2R6w/v3f/107d%2B7UbbfdpquuusruwwMAALQ42wPWZ599pldeeUUZGRl2HxoAAMAWtt%2BmIS4uTomJiS1%2BnNraWj311FO68cYbdfPNN%2Bu5557zXo7ctWuXJkyYIKfTqXHjxmnnzp0%2BP7t582aNHDlSTqdTOTk5Onr0aIvXCwAAQoftAWvMmDF65ZVXVF9f36LHWbhwoT799FO9%2BuqrevbZZ/X73/9ehYWFqq6u1pQpU5SRkaENGzYoNTVVU6dOVXV1taQzHzw9d%2B5cTZs2TYWFhaqqquL2EQAA4JLYfomwsrJSmzdv1l/%2B8hd17txZUVFRPvNvvvmmkWMUFRXp9ddfV58%2BfSRJDz74oFwulxwOh6KjozVz5kxFRERo7ty5%2Bvjjj/Xuu%2B8qKytLa9asUWZmpsaOHStJWrx4sYYPH66ysjJ17tz5imsDAAChzy%2B3aRg9enSL7r%2B4uFitW7dWv379vGNTpkyRdOZN9unp6d57bkVERCgtLU0lJSXKysqSy%2BXSL3/5S%2B/PtW/fXh06dJDL5SJgAQCAZrE9YOXl5bX4McrKytSxY0f94Q9/0MqVK3X69GllZWXp4YcfltvtVvfu3X22T0xMVGlpqSSpvLxcycnJjeYPHz7c7OOXl5fL7Xb7jDkccY32eyUiI1v5fAccjvB5LYT765/%2B6f/H38NNsPTvlzNY5eXl%2Bv3vf6/9%2B/drzpw5%2BvLLL9WjRw9de%2B21RvZfXV2tH374QWvXrlVeXp7cbrfmzZun2NhYeTyeRpclo6KiVFtbK0mqqam56HxzFBYWKj8/32csJydH06dPv8yOLiwhIdb4PhGc2raN93cJtgv31z/90384C/T%2BbQ9YP/zwg%2B666y61bt1aR44c0YwZM7RlyxbNnj1bq1atktPpvOJjOBwOnThxQs8%2B%2B6w6duwoSTp48KDeeustdenSpVFYqq2tVUxMjCQpOjr6vPOxsc1fyIkTJ2rEiBHn1BSnY8dOXk475xUZ2UoJCbGqqvKovr7B2H4RvEy%2BvgJduL/%2B6Z/%2B6b/5/fvrfz5tD1jPPPOMRo4cqYULFyotLU2S9Nxzz2nWrFlaunSpVq9efcXHSEpKUnR0tDdcSdJPf/pTHTp0SP369VNFRYXP9hUVFd7LdykpKeedT0pKavbxk5OTG10OdLuPq67O/C9CfX1Di%2BwXwSccXwfh/vqnf/qn/8Dt3/YLmNu3b9cDDzzg88HODodDjzzyiHbt2mXkGE6nU6dOndL%2B/fu9Y/v27VPHjh3ldDr19ddfe%2B%2BJZVmWtm/f7j1z5nQ6VVxc7P25Q4cO6dChQ0bOrAEAgPBge8BqaGhQQ0PjxHny5ElFRkYaOca1116rYcOGafbs2dq9e7f%2B%2B7//WwUFBcrOztaoUaNUVVWl3Nxc7dmzR7m5ufJ4PMrMzJQkZWdna9OmTVq3bp12796tmTNnatiwYfwLQgAA0Gy2B6xBgwbppZde8glZlZWVWrJkiQYMGGDsOEuXLtU//uM/Kjs7W7NmzdI999yj%2B%2B67T61bt9ZLL72k4uJi720ZCgoKFBcXJ0lKTU3VggULtHz5cmVnZ6tNmza2/MtHAAAQOiKss9fKbHLkyBFNmjRJx48fV2Vlpa699lr97W9/09VXX601a9b4vG8qlLjdx43uz%2BFopbZt43Xs2MmAvgb9Y5nLPvF3CSHtnRkD/V2CbYLx9W8S/dM//Te//6Skq2yoqjHb3%2BSekpKiP/zhD9q8ebO%2B/fZbNTQ0KDs7W2PGjFHr1q3tLgcAAMA4v9wHKzY2VhMmTPDHoQEAAFqc7QFr0qRJF5038VmEAAAA/mR7wDr3PVZ1dXX64Ycf9N133%2Bn%2B%2B%2B%2B3uxwAAADjAuazCJcvX35Jn/cHAAAQqALmkxLHjBmjd955x99lAAAAXLGACVhff/21sRuNAgAA%2BFNAvMn9xIkT%2Bt///V/dfffddpcDAABgnO0Bq0OHDj6fQyhJP/nJT3TvvffqjjvusLscAAAA42wPWM8884zdhwQAALCV7QHryy%2B/bPa2N954YwtWAgAA0DJsD1j33Xef9xLhjz8G8dyxiIgIffvtt3aXBwAAcMVsD1grV67UwoUL9fjjj6tfv36KiorSN998owULFujOO%2B/UrbfeandJAAAARtl%2Bm4a8vDzNmzdPt9xyi9q2bav4%2BHgNGDBACxYs0FtvvaWOHTt6vwAAAIKR7QGrvLz8vOGpdevWOnbsmN3lAAAAGGd7wOrbt6%2Bee%2B45nThxwjtWWVmpJUuW6KabbrK7HAAAAONsfw/Wk08%2BqUmTJmnIkCHq2rWrLMvS999/r6SkJL355pt2lwMAAGCc7QGrW7du2rJlizZv3qy9e/dKku655x7ddtttio2NtbscAAAA42wPWJLUpk0bTZgwQX/961/VuXNnSWfu5g4AABAKbH8PlmVZWrp0qW688UaNHj1ahw8f1qxZszR37lydPn3a7nIAAACMsz1grV69Wps2bdJvfvMbRUVFSZJGjhyp999/X/n5%2BXaXAwAAYJztAauwsFDz5s1TVlaW9%2B7tt956qxYuXKg//vGPdpcDAABgnO0B669//at69uzZaPy6666T2%2B22uxwAAADjbA9YHTt21DfffNNo/OOPP/a%2B4R0AACCY2f6vCH/xi1/oqaeektvtlmVZ2rZtmwoLC7V69Wo98cQTdpcDAABgnO0Ba9y4caqrq9OLL76ompoazZs3T%2B3atdOMGTOUnZ1tdzkAAADG2R6wNm/erFGjRmnixIk6evSoLMtSYmKi3WUAAAC0GNvfg7VgwQLvm9nbtWtHuAIAACHH9oDVtWtXfffdd3YfFgAAwDa2XyK87rrr9Nhjj%2BmVV15R165dFR0d7TOfl5dnd0kAAABG2R6w9u/fr/T0dEnivlcAACAk2RKwFi9erGnTpikuLk6rV6%2B245AAAAB%2BY8t7sF5//XV5PB6fsSlTpqi8vNyOwwMAANjKloBlWVajsS%2B//FKnTp2y4/AAAAC2sv1fEQIAAIQ6AhYAAIBhtgWsiIgIuw4FAADgV7bdpmHhwoU%2B97w6ffq0lixZovj4eJ/tuA8WEPoyl33i7xIuyTszBvq7BABBxpaAdeONNza651VqaqqOHTumY8eO2VECAACAbWwJWNz7CgAAhBPe5A4AAGAYAQsAAMAwAhYAAIBhBCwAAADDQj5gTZkyRU888YT38a5duzRhwgQ5nU6NGzdOO3fu9Nl%2B8%2BbNGjlypJxOp3JycnT06FG7SwYAAEEupAPWn/70J3300Ufex9XV1ZoyZYoyMjK0YcMGpaamaurUqaqurpYk7dixQ3PnztW0adNUWFioqqoqzZ4921/lAwCAIBWyAauyslKLFy9W7969vWNbtmxRdHS0Zs6cqW7dumnu3LmKj4/Xu%2B%2B%2BK0las2aNMjMzNXbsWF133XVavHixPvroI5WVlfmrDQAAEIRCNmAtWrRIY8aMUffu3b1jLpdL6enp3o/tiYiIUFpamkpKSrzzGRkZ3u3bt2%2BvDh06yOVy2Vs8AAAIarZ9VI6dtm3bpq%2B%2B%2Bkp//OMfNX/%2BfO%2B42%2B32CVySlJiYqNLSUklSeXm5kpOTG80fPnz4ko5fXl7e6M71Dkdco31ficjIVj7fAYeD10JLCbTnNtx//%2Bmf/n/8PVCFXMA6deqUfvOb32jevHmKiYnxmfN4PIqKivIZi4qKUm1trSSppqbmovPNVVhYqPz8fJ%2BxnJwcTZ8%2B/ZL20xwjFn3U9EYIC23bxje9ES5LoD63CQmx/i7Br%2Bif/gNZyAWs/Px83XDDDRo8eHCjuejo6EZhqba21hvELjQfG3tpizhx4kSNGDHCZ8zhiNOxYycvaT8XExnZKuBfXLCXydcXfAXac3v297%2BqyqP6%2BgZ/l2M7%2Bqf/S%2BnfX/%2BDFHIB609/%2BpMqKiqUmpoqSd7A9N5772n06NGqqKjw2b6iosJ76S4lJeW880lJSZdUQ3JycqPLgW73cdXVhd8vAuzD66vlBOpzW1/fELC12YH%2B6T%2BQ%2Bw%2B5gLV69WrV1dV5Hy9dulSS9Nhjj%2BnLL7/Uyy%2B/LMuyFBERIcuytH37dj300EOSJKfTqeLiYmVlZUmSDh06pEOHDsnpdNrfCAAACFohF7A6duzo8zg%2B/sypwS5duigxMVHPPvuscnNz9fOf/1xr166Vx%2BNRZmamJCk7O1v33Xef%2Bvbtq969eys3N1fDhg1T586dbe8DAAAEr8B%2BC75hrVu31ksvveQ9S%2BVyuVRQUKC4uDhJUmpqqhYsWKDly5crOztbbdq0UV5enp%2BrBgAAwSbkzmCd65lnnvF53KdPH23cuPGC22dlZXkvEQIAAFyOsDqDBQAAYAcCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5vB3AQAQ6DKXfeLvEi7JOzMG%2BrsEIOxxBgsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMC9mAdeTIEU2fPl39%2BvXT4MGDlZeXp1OnTkmSysrKNHnyZPXt21e33nqrtm7d6vOzn376qUaPHi2n06lJkyaprKzMHy0AAIAgFZIBy7IsTZ8%2BXR6PR7/73e/0/PPP68MPP9SyZctkWZZycnJ0zTXXqKioSGPGjNG0adN08OBBSdLBgweVk5OjrKwsrV%2B/Xu3atdMjjzwiy7L83BUAAAgWIXkfrH379qmkpESffPKJrrnmGknS9OnTtWjRIg0ZMkRlZWVau3at4uLi1K1bN23btk1FRUV69NFHtW7dOt1www168MEHJUl5eXkaOHCgvvjiC/Xv39%2BfbQEAgCARkmewkpKS9Morr3jD1VknTpyQy%2BXS9ddfr7i4OO94enq6SkpKJEkul0sZGRneudjYWPXq1cs7DwAA0JSQDFgJCQkaPHiw93FDQ4PWrFmjAQMGyO12Kzk52Wf7xMREHT58WJKanAcAAGhKSF4iPNeSJUu0a9curV%2B/XqtWrVJUVJTPfFRUlGprayVJHo/novPNUV5eLrfb7TPmcMQ1Cm5XIjIyJLMxroDDwWsCZ4T6a%2BHs379w/TtI/8HRf8gHrCVLluiNN97Q888/rx49eig6OlqVlZU%2B29TW1iomJkaSFB0d3ShM1dbWKiEhodnHLCwsVH5%2Bvs9YTk6Opk%2BffpldAE1r2zbe3yUgQITLayEhIdbfJfgV/Qd2/yEdsJ5%2B%2Bmm99dZbWrJkiW655RZJUkpKivbs2eOzXUVFhffsUkpKiioqKhrN9%2BzZs9nHnThxokaMGOEz5nDE6dixk5fTxnlFRrYK%2BBcX7GXy9YXgFuqvhbN//6qqPKqvb/B3Obaj/0vr31//wxGyASs/P19r167Vc889p1GjRnnHnU6nCgoKVFNT4z1rVVxcrPT0dO98cXGxd3uPx6Ndu3Zp2rRpzT52cnJyo8uBbvdx1dWF3y8C7MPrC2eFy2uhvr4hbHo9H/oP7P4D%2BwLmZdq7d69WrFihX/7yl0pPT5fb7fZ%2B9evXT%2B3bt9fs2bNVWlqqgoIC7dixQ%2BPHj5ckjRs3Ttu3b1dBQYFKS0s1e/ZsderUiVs0AACAZgvJgPXBBx%2Bovr5eL774ogYNGuTzFRkZqRUrVsjtdisrK0tvv/22li9frg4dOkiSOnXqpBdeeEFFRUUaP368KisrtXz5ckVERPi5KwAAECxC8hLhlClTNGXKlAvOd%2BnSRWvWrLng/NChQzV06NCWKA0AAISBkDyDBQAA4E8ELAAAAMMIWAAAAIYRsACLx86YAAALaUlEQVQAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMMzh7wIAmJG57BN/lwAA%2BD%2BcwQIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYXzYMwCEmGD64O93Zgz0dwlAi%2BAMFgAAgGGcwQIAIERxNtN/OIMFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAOo9Tp05pzpw5ysjI0KBBg/Taa6/5uyQAABBEuNHoeSxevFg7d%2B7UG2%2B8oYMHD2rWrFnq0KGDRo0a5e/SAABAECBgnaO6ulrr1q3Tyy%2B/rF69eqlXr14qLS3V7373OwIWABgWTHcal0LvbuNoOVwiPMfu3btVV1en1NRU71h6erpcLpcaGhr8WBkAAAgWnME6h9vtVtu2bRUVFeUdu%2Baaa3Tq1ClVVlaqXbt2Te6jvLxcbrfbZ8zhiFNycrKxOiMjycYAYDeHw/9/e8/%2B/Q%2B1/w4097kNlv4JWOfweDw%2B4UqS93FtbW2z9lFYWKj8/HyfsWnTpunRRx81U6TOhLg33nhFW/7fRKPBLViUl5ersLBQEyfSP/3Tf7ih/zN//5vT/1e5offWlkvp358CO/75QXR0dKMgdfZxTExMs/YxceJEbdiwwedr4sSJRut0u93Kz89vdKYsXNA//dM//dM//QcyzmCdIyUlRceOHVNdXZ0cjjNPj9vtVkxMjBISEpq1j%2BTk5IBO1QAAoGVxBuscPXv2lMPhUElJiXesuLhYvXv3VqtWPF0AAKBpJIZzxMbGauzYsZo/f7527Nih999/X6%2B99pomTZrk79IAAECQiJw/f/58fxcRaAYMGKBdu3bp2Wef1bZt2/TQQw9p3Lhx/i6rkfj4ePXr10/x8fH%2BLsUv6J/%2B6Z/%2B6Z/%2BA1WEZVmWv4sAAAAIJVwiBAAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAogp06d0pw5c5SRkaFBgwbptddeu%2BC2W7du1R133KHU1FRNnjxZ%2B/bt85nfvHmzRo4cKafTqZycHB09erSly79iJvvPyMjQz372M5%2BvkydPtnQLRtTW1mr06NH6/PPPL7jNrl27NGHCBDmdTo0bN047d%2B70mQ/G9T/LRP%2Bhvv5nffXVV/rnf/7nRuOhvv5nXaj/UF//v/zlLxozZoxSU1N1%2B%2B2364MPPvCZD/X1b6r/gFl/CwFjwYIF1u23327t3LnT%2BvOf/2ylpqZa77zzTqPtvvvuO%2Bv666%2B3li1bZu3du9datGiRNWjQIOvEiROWZVmWy%2BWy%2BvTpY23cuNH69ttvrXvvvdeaMmWK3e1cMlP9Hz582OrRo4d14MABq7y83PvV0NBgd0uXrKamxsrJybF69OhhffbZZ%2Bfd5uTJk9bAgQOtZ555xtqzZ4/19NNPWzfffLN18uRJy7KCd/0ty0z/ob7%2BZ%2B3evdu6%2BeabreHDh/uMh/r6n3Wh/kN9/b/99lurV69e1htvvGF9//331po1a6xevXpZ3377rWVZob/%2BTfUfSOtPwAoQJ0%2BetHr37u3zolq%2BfLl17733Ntr2qaeesu655x7v44aGBiszM9N66623LMuyrMcff9yaNWuWd/7gwYPWz372M%2BvAgQMt2MGVMdn/J598Yg0cOLDlizastLTUuuOOO6zbb7/9on9g1q1bZ40YMcL7B6OhocH6l3/5F6uoqMiyrOBcf8sy13%2Bor79lWdZbb71l9e3b17r99tsbBYxQX3/Lunj/ob7%2BS5YssX7xi1/4jD344IPWc889Z1lW6K9/U/0H0vpziTBA7N69W3V1dUpNTfWOpaeny%2BVyqaGhwWfbsrIy9enTx/s4IiJCPXr0UElJiSTJ5XIpIyPDO9%2B%2BfXt16NBBLperhbu4fCb737Nnj37605/aU7hBX3zxhfr376/CwsKLbudyuZSenq6IiAhJZ/pPS0sL6vWXzPUf6usvSR9//LEWLVqkyZMnN5oL9fWXLt5/qK//nXfeqccee6zR%2BPHjxyWF/vo31X8grb/D3wXgDLfbrbZt2yoqKso7ds011%2BjUqVOqrKxUu3btfMaPHDni8/OHDx9WmzZtJEnl5eVKTk72mU9MTNThw4dbsIMrY7L/vXv3yuPx6L777tP%2B/fvVs2dPzZkzJ2B%2B6S7k7rvvbtZ2brdb3bt39xlLTExUaWmppOBcf8lc/6G%2B/pK0YsUKSdKGDRsazYX6%2BksX7z/U179bt24%2Bj0tLS7Vt2zb9/Oc/lxT6699U/4G0/pzBChAej8cnXEjyPq6trfUZz8zM1HvvvacPP/xQdXV12rhxo7755hudPn1aklRTU3PefZ27n0Bisv99%2B/bp73//ux5%2B%2BGGtWLFCMTExmjx5sk6cOGFPMy3sQs/V2ecpGNf/UjTVf6ivf1NCff2bEk7rf/ToUT366KNKS0vzvtk/nNb/fP0H0vpzBitAREdHN/oFOPs4JibGZ3zIkCHKycnRo48%2Bqvr6evXv319jxozxvoAutK/Y2NgW7ODKmOz/1Vdf1enTpxUfHy9JWrp0qYYOHaoPP/xQt99%2Buw3dtKwLPVdnn6dgXP9L0VT/ob7%2BTQn19W9KuKx/RUWFHnjgAVmWpd/%2B9rdq1erM%2BZJwWf8L9R9I688ZrACRkpKiY8eOqa6uzjvmdrsVExOjhISERts//PDD2r59u7Zu3apVq1bp5MmT6tixo3dfFRUVPttXVFQoKSmpZZu4Aib7j4qK8v5ySWf%2B4HTq1KnRZcVgdaH1PXtZIBjX/1I01X%2Bor39TQn39mxIO63/kyBHdc889qq2t1ZtvvunzFopwWP%2BL9R9I60/AChA9e/aUw%2BHwvlFXkoqLi9W7d29vMj9r8%2BbNys3NVVRUlBITE1VTU6PPP/9c/fv3lyQ5nU4VFxd7tz906JAOHTokp9NpTzOXwVT/lmVp5MiRPu/NqK6u1g8//KBrr73Wtn5aktPp1Ndffy3LsiRJlmVp%2B/bt3vUNxvW/FBfrPxzWvymhvv4XEw7rX11drX/7t39Tq1attGbNGqWkpPjMh/r6X6z/QFt/AlaAiI2N1dixYzV//nzt2LFD77//vl577TVNmjRJ0pmzOTU1NZKkrl27au3atfrzn/%2Bs77//Xr/%2B9a/Vvn17DRkyRJKUnZ2tTZs2ad26ddq9e7dmzpypYcOGqXPnzn7rrymm%2Bo%2BIiNCwYcP0wgsv6PPPP1dpaalmzpypf/iHf9DQoUP92eIV%2BXH/o0aNUlVVlXJzc7Vnzx7l5ubK4/EoMzNTUnCuf1Oa2384rH9TQn39LyYc1v%2Bll17SgQMHtGjRIu%2Bc2%2B32/iu6UF//i/UfcOvvr/tDoLHq6mpr5syZVt%2B%2Bfa1BgwZZr7/%2BuneuR48e3vv8WJZlrV%2B/3ho%2BfLiVmppqPfLII9aRI0d89lVUVGQNHTrU6tu3r5WTk2MdPXrUrjYum6n%2Ba2pqrLy8PGvgwIGW0%2Bm0pk6dah08eNDOVq7YufeBObd/l8tljR071urdu7c1fvx463/%2B5398fj4Y1//HrqT/cFj/s4qKihrdB%2BrseCiv/1nn6z/U1/%2BWW26xevTo0ejrx/e%2BCuX1b6r/QFr/CMv6v/PsAAAAMIJLhAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8HkheqC68hPWwAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-1673359387747477846”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.038527</td> <td class=”number”>438</td> <td class=”number”>13.6%</td> <td>

<div class=”bar” style=”width:45%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9486629</td> <td class=”number”>275</td> <td class=”number”>8.6%</td> <td>

<div class=”bar” style=”width:28%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9343761</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:24%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9748207</td> <td class=”number”>225</td> <td class=”number”>7.0%</td> <td>

<div class=”bar” style=”width:23%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9722159</td> <td class=”number”>209</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9790413</td> <td class=”number”>200</td> <td class=”number”>6.2%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.079599</td> <td class=”number”>169</td> <td class=”number”>5.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9925836</td> <td class=”number”>143</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9009972</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9502338</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (25)</td> <td class=”number”>977</td> <td class=”number”>30.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>106</td> <td class=”number”>3.3%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-1673359387747477846”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.9009972</td> <td class=”number”>131</td> <td class=”number”>4.1%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9060665</td> <td class=”number”>19</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9302243</td> <td class=”number”>22</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9343761</td> <td class=”number”>231</td> <td class=”number”>7.2%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.9392297</td> <td class=”number”>12</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.102873</td> <td class=”number”>4</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.104313</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.130546</td> <td class=”number”>55</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.164422</td> <td class=”number”>11</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:20%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.241477</td> <td class=”number”>45</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:82%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECS09”><s>SPECS09</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_GROC14”><code>GROC14</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9563</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECS14”><s>SPECS14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SPECS09”><code>SPECS09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99301</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECSPTH09”>SPECSPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1990</td>

</tr> <tr>

<th>Unique (%)</th> <td>62.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.056908</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.3661</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>35.3%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram5782485545167508132”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives5782485545167508132,#minihistogram5782485545167508132”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives5782485545167508132”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles5782485545167508132”

aria-controls=”quantiles5782485545167508132” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram5782485545167508132” aria-controls=”histogram5782485545167508132”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common5782485545167508132” aria-controls=”common5782485545167508132”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme5782485545167508132” aria-controls=”extreme5782485545167508132”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles5782485545167508132”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.043641</td>

</tr> <tr>

<th>Q3</th> <td>0.084023</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.17719</td>

</tr> <tr>

<th>Maximum</th> <td>1.3661</td>

</tr> <tr>

<th>Range</th> <td>1.3661</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.084023</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.075819</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3323</td>

</tr> <tr>

<th>Kurtosis</th> <td>43.104</td>

</tr> <tr>

<th>Mean</th> <td>0.056908</td>

</tr> <tr>

<th>MAD</th> <td>0.04979</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.3216</td>

</tr> <tr>

<th>Sum</th> <td>178.24</td>

</tr> <tr>

<th>Variance</th> <td>0.0057485</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram5782485545167508132”>

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AAIZRsAAAAAyjYAEAABhGwQIAADDM9oJ1/fXX66WXXtLRo0ftfmoAAABb2F6wBg0apKeeekpDhgzRH//4R23ZskWWZdk9BgAAQLOxvWDdc889%2Bvvf/64VK1YoODhYd911l6666io99thj%2BuKLL%2BweBwAAwDinL57U4XBo8ODBGjx4sKqqqvT8889rxYoVWrVqlfr3769p06bpt7/9rS9GAwAAOG8%2BKViSVFpaqjfeeENvvPGGPv/8c/Xv31/jx4/XoUOH9OCDD2rHjh2aM2eOr8YDAAA4Z7YXrE2bNmnTpk3atm2bOnbsqHHjxmnZsmXq1q2b%2Bz6dO3fWokWLKFgAAMAv2V6w5syZo2HDhmn58uW68sorFRTU%2BGVg3bt3180332z3aAAAAEbYXrA%2B%2BOADdejQQRUVFe5yVVhYqD59%2Big4OFiS1L9/f/Xv39/u0QAAAIyw/X8RHjt2TKNGjdLTTz/tXrv99ts1duxYHTx40O5xAAAAjLO9YP35z3/WxRdfrFtvvdW99uabb6pz587KzMy0exwAAADjbC9Y//znP3X//fcrKirKvdaxY0fdd9992rp1q93jAAAAGGd7wXI6naqsrGy0XlVVxTu6AwCAVsH2gnXllVdq4cKF%2Bvrrr91rJSUlyszM1NChQ%2B0eBwAAwDjb/xfh7Nmzdeutt%2Brqq69WeHi4JKmyslJ9%2BvRRenq63eMAAAAYZ3vBioyM1GuvvaYPP/xQRUVFcjqduuSSS/SrX/1KDofD7nEAAACM88mvygkODtbQoUO5JAgAAFol2wuWy%2BXS448/rp07d%2BqHH35o9ML2d9991%2B6RAAAAjLK9YD300EPatWuXrr32Wv3iF7%2Bw%2B%2BkBAACane0Fa%2BvWrVq9erWSk5PtfmoAAABb2P42DW3btlVkZKTdTwsAAGAb2wvW2LFjtXr1atXX19v91AAAALaw/RJhRUWFcnNz9d577%2Bmiiy5SSEiIx/Z169bZPRIAAIBRPnmbhjFjxvjiaQEAAGxhe8HKzMy0%2BykBAABsZftrsCSptLRU2dnZuueee/Tdd9/p7bff1v79%2B30xCgAAgHG2F6yvvvpK1113nV577TW98847OnHihN58801NmDBBBQUFdo8DAABgnO0F6%2BGHH9aIESO0efNmXXDBBZKkRx99VMOHD9eSJUvsHgcAAMA42wvWzp07deutt3r8Ymen06k777xTu3fvbvL%2BamtrNWbMGG3bts29tnDhQl166aUeH%2BvXr3dvz83N1YgRIxQfH6/U1FQdOXLEvc2yLC1ZskSDBg3SgAEDtHjxYjU0NJxjWgAAEIhsf5F7Q0PDGQvL8ePHFRwc3KR91dTU6J577lFRUZHHenFxse655x6NHz/evda%2BfXtJUmFhoebMmaP58%2BcrLi5OixYtUnp6ulauXClJWrNmjXJzc5Wdna26ujrNmjVLkZGRmjFjRlOjAgCAAGX7GawhQ4Zo5cqVHiWroqJCWVlZGjRokNf72bdvn2644QZ9/fXXjbYVFxfrsssuU1RUlPsjLCxMkrR%2B/XqNHj1a48aNU1xcnBYvXqz3339fJSUlkn58H660tDQlJydr0KBBuvfee/XCCy%2BcZ2oAABBIbC9Y999/v3bt2qUhQ4aopqZG//mf/6lhw4bpm2%2B%2B0ezZs73ez/bt2zVw4EDl5OR4rB87dkyHDx9Wt27dzvi4goICj9%2BD2LlzZ8XGxqqgoECHDx/WwYMHdcUVV7i3JyUl6dtvv1VpaWnTggIAgIBl%2ByXCmJgYvf7668rNzdVnn32mhoYGTZ48WWPHjnVfxvPGlClTzrheXFwsh8Ohp556Sh988IEiIiJ06623ui8XlpaWKjo62uMxkZGROnTokFwulyR5bO/UqZMk6dChQ40e91NKS0vd%2BzrJ6Wzr9eO9FRzsk3fZOGdOp9l5T%2Bb3t8%2BDKYGcP5CzS%2BQnP/lP/bOl8sk7uYeFhen6669vln3v379fDodD3bt3180336wdO3booYceUvv27TVy5EhVV1c3%2BvU8ISEhqq2tVXV1tfv2qdukH19M762cnBxlZ2d7rKWmpiotLe1cY7UKHTq0a5b9hoeHNct%2B/UUg5w/k7BL5yU/%2Blsz2gjV16tSf3X6%2Bv4tw3LhxGjZsmCIiIiRJcXFx%2BvLLL/Xiiy9q5MiRCg0NbVSWamtrFRYW5lGmQkND3X%2BX5H4NlzcmTZqk4cOHe6w5nW1VXn78nHOdSUtv76drjvzh4WGqrKxSfX3g/U/PQM4fyNkl8pOf/E3J31w/3J%2BN7QWrS5cuHrfr6ur01Vdf6fPPP9e0adPOe/8Oh8Ndrk7q3r27tm7dKunHS5RlZWUe28vKyhQVFaWYmBhJksvlUteuXd1/l6SoqCivZ4iOjm50OdDlOqq6usD7h3Cq5spfX98Q0J/bQM4fyNkl8pOf/C05f4v5XYTLly/XoUOHznv/S5cu1ccff6znnnvOvbZnzx51795dkhQfH6%2B8vDylpKRIkg4ePKiDBw8qPj5eMTExio2NVV5enrtg5eXlKTY21vjrpwAAQOvVYq4xjR07Vm%2B99dZ572fYsGHasWOHnnnmGX399df6n//5H73%2B%2BuuaPn26JGny5MnatGmTNmzYoD179ui%2B%2B%2B7TVVddpYsuusi9fcmSJdq2bZu2bdumRx555KyXNQEAAE7lkxe5n8nHH3/c5DcaPZN%2B/fpp6dKlWrZsmZYuXaouXbrokUceUWJioiQpMTFRGRkZWrZsmb7//nsNHjxYCxYscD9%2BxowZ%2Bu677zRz5kwFBwdr4sSJuuWWW857LgAAEDhaxIvcjx07pr179/7kWy%2Bczd69ez1ujxgxQiNGjPjJ%2B6ekpLgvEZ4uODhY6enpSk9PP6dZAAAAbC9YsbGxHr%2BHUJIuuOAC3Xzzzfrd735n9zgAAADG2V6wHn74YbufEgAAwFa2F6wdO3Z4fd9Tf2UNAACAv7C9YP3%2B9793XyK0LMu9fvqaw%2BHQZ599Zvd4AAAA5832gvXUU09p4cKFmjVrlgYMGKCQkBB98sknysjI0Pjx43XNNdfYPRIAAIBRtr8PVmZmpubOnaurr75aHTp0ULt27TRo0CBlZGToxRdfVJcuXdwfAAAA/sj2glVaWnrG8tS%2BfXuVl5fbPQ4AAIBxtheshIQEPfroozp27Jh7raKiQllZWfrVr35l9zgAAADG2f4arAcffFBTp07VlVdeqW7dusmyLH355ZeKiorSunXr7B4HAADAONsLVo8ePfTmm28qNzdXxcXFkqSbbrpJ1157rcLCwuweBwAAwDif/C7CCy%2B8UNdff72%2B%2BeYb9y9ZvuCCC3wxCgAAgHG2vwbLsiwtWbJEV1xxhcaMGaNDhw5p9uzZmjNnjn744Qe7xwEAADDO9oL1/PPPa9OmTfrTn/6kkJAQST/%2BcubNmzcrOzvb7nEAAACMs71g5eTkaO7cuUpJSXG/e/s111yjhQsX6n//93/tHgcAAMA42wvWN998o969ezdaj4uLk8vlsnscAAAA42wvWF26dNEnn3zSaP2DDz5wv%2BAdAADAn9n%2BvwhnzJih%2BfPny%2BVyybIsffTRR8rJydHzzz%2Bv%2B%2B%2B/3%2B5xAAAAjLO9YE2YMEF1dXV68sknVV1drblz56pjx466%2B%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%2B3PPfechg4dqsTERD3wwAOqqqqyJQsAAGgd/Lpg1dTU6I9//KOKiorca5ZlKTU1VZ06ddLGjRs1duxYzZw5UwcOHJAkHThwQKmpqUpJSdErr7yijh076s4775RlWZKkd955R9nZ2crIyNDatWtVUFCgrKwsn%2BQDAAD%2ByW8L1r59%2B3TDDTfo66%2B/9ljfunWrSkpKlJGRoR49euiOO%2B5QQkKCNm7cKEnasGGDLr/8ck2fPl09e/ZUZmamvv32W23fvl2StG7dOk2bNk3Dhg1Tv379NH/%2BfG3cuJGzWAAAwGt%2BW7C2b9%2BugQMHKicnx2O9oKBAl112mdq2beteS0pKUn5%2Bvnt7cnKye1tYWJj69Omj/Px81dfX65NPPvHYnpCQoB9%2B%2BEF79uxp5kQAAKC1cPp6gHM1ZcqUM667XC5FR0d7rEVGRurQoUNn3V5ZWamamhqP7U6nUxEREe7HAwAAnI3fFqyfUlVVpZCQEI%2B1kJAQ1dbWnnV7dXW1%2B/ZPPd4bpaWlcrlcHmtOZ9tGxe58BQf71wlIp9PsvCfz%2B9vnwZRAzh/I2SXyk5/8p/7ZUrW6ghUaGqqKigqPtdraWrVp08a9/fSyVFtbq/DwcIWGhrpvn749LCzM6xlycnKUnZ3tsZaamqq0tDSv99EadejQrln2Gx7u/demNQrk/IGcXSI/%2BcnfkrW6ghUTE6N9%2B/Z5rJWVlbnPHsXExKisrKzR9t69eysiIkKhoaEqKytTjx49JEl1dXWqqKhQVFSU1zNMmjRJw4cP91hzOtuqvPz4uUT6SS29vZ%2BuOfKHh4epsrJK9fUNRvftDwI5fyBnl8hPfvI3JX9z/XB/Nq2uYMXHx2vVqlWqrq52n7XKy8tTUlKSe3teXp77/lVVVdq9e7dmzpypoKAg9e3bV3l5eRo4cKAkKT8/X06nU3FxcV7PEB0d3ehyoMt1VHV1gfcP4VTNlb%2B%2BviGgP7eBnD%2BQs0vkJz/5W3J%2B/zoF4oUBAwaoc%2BfOSk9PV1FRkVatWqXCwkJNnDhRkjRhwgTt3LlTq1atUlFRkdLT09W1a1d3oZoyZYqeeeYZbd68WYWFhZo3b55uuOGGJl0iBAAAga3VFazg4GCtWLFCLpdLKSkpeuONN7R8%2BXLFxsZKkrp27aonnnhCGzdu1MSJE1VRUaHly5fL4XBIkq699lrdcccdmjt3rqZPn65%2B/fpp1qxZvowEAAD8jMM6%2BRbmaFYu11Hj%2B3Q6gzRyyT%2BM77e5vHX3YKP7czqD1KFDO5WXH2/Rp4mbSyDnD%2BTsEvnJT/6m5I%2BK%2BoUNUzXW6s5gAQAA%2BBoFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsFZbsP7617/q0ksv9fhIS0uTJO3evVvXX3%2B94uPjNWHCBO3atcvjsbm5uRoxYoTi4%2BOVmpqqI0eO%2BCICAADwU622YO3bt0/Dhg3Tli1b3B8LFy7UiRMndPvttys5OVmvvvqqEhMTdccdd%2BjEiROSpMLCQs2ZM0czZ85UTk6OKisrlZ6e7uM0AADAn7TaglVcXKxevXopKirK/REeHq4333xToaGhuu%2B%2B%2B9SjRw/NmTNH7dq109tvvy1JWr9%2BvUaPHq1x48YpLi5Oixcv1vvvv6%2BSkhIfJwIAAP6iVResbt26NVovKChQUlKSHA6HJMnhcKh///7Kz893b09OTnbfv3PnzoqNjVVBQYEtcwMAAP/n9PUAzcGyLH3xxRfasmWLVq5cqfr6eo0aNUppaWlyuVy65JJLPO4fGRmpoqIiSVJpaamio6MbbT906JDXz19aWiqXy%2BWx5nS2bbTf8xUc7F8Bi7YLAAANkklEQVT92Ok0O%2B/J/P72eTAlkPMHcnaJ/OQn/6l/tlStsmAdOHBAVVVVCgkJ0eOPP65vvvlGCxcuVHV1tXv9VCEhIaqtrZUkVVdX/%2Bx2b%2BTk5Cg7O9tjLTU11f0i%2B0DVoUO7ZtlveHhYs%2BzXXwRy/kDOLpGf/ORvyVplwerSpYu2bdumCy%2B8UA6HQ71791ZDQ4NmzZqlAQMGNCpLtbW1atOmjSQpNDT0jNvDwrz/Qk6aNEnDhw/3WHM626q8/Pg5Jjqzlt7eT9cc%2BcPDw1RZWaX6%2Bgaj%2B/YHgZw/kLNL5Cc/%2BZuSv7l%2BuD%2BbVlmwJCkiIsLjdo8ePVRTU6OoqCiVlZV5bCsrK3NfvouJiTnj9qioKK%2BfOzo6utHlQJfrqOrqAu8fwqmaK399fUNAf24DOX8gZ5fIT37yt%2BT8/nUKxEv/%2BMc/NHDgQFVVVbnXPvvsM0VERCgpKUkff/yxLMuS9OPrtXbu3Kn4%2BHhJUnx8vPLy8tyPO3jwoA4ePOjeDgAAcDatsmAlJiYqNDRUDz74oPbv36/3339fixcv1m233aZRo0apsrJSixYt0r59%2B7Ro0SJVVVVp9OjRkqTJkydr06ZN2rBhg/bs2aP77rtPV111lS666CIfpwIAAP6iVRas9u3b65lnntGRI0c0YcIEzZkzR5MmTdJtt92m9u3ba%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%2BsRmuStuwf7egQAwL9QsE5z4sQJbdiwQU8//bT69OmjPn36qKioSC%2B88AIFCwAAeIVLhKfZs2eP6urqlJiY6F5LSkpSQUGBGhoafDgZAADwF5zBOo3L5VKHDh0UEhLiXuvUqZNqampUUVGhjh07nnUfpaWlcrlcHmtOZ1tFR0cbnTU4mH6M/8%2BfLmn%2B9d6h5/X4k9/7gfpvgPzkP/XPQOMv%2BSlYp6mqqvIoV5Lct2tra73aR05OjrKzsz3WZs6cqbvuusvMkP9SWlqqab8s0qRJk4yXN39QWlqqnJwc8gdg/tLSUq1duzogs0vkJz/5/SF/y65/PhAaGtqoSJ283aZNG6/2MWnSJL366qseH5MmTTI%2Bq8vlUnZ2dqOzZYGC/IGbP5CzS%2BQnP/n9IT9nsE4TExOj8vJy1dXVyen88dPjcrnUpk0bhYeHe7WP6OjoFt2qAQBA8%2BIM1ml69%2B4tp9Op/Px891peXp769u2roCA%2BXQAA4OxoDKcJCwvTuHHjNG/ePBUWFmrz5s169tlnNXXqVF%2BPBgAA/ETwvHnz5vl6iJZm0KBB2r17tx555BF99NFH%2Bo//%2BA9NmDDB12OdUbt27TRgwAC1a9fO16P4BPkDN38gZ5fIT37yt/T8DsuyLF8PAQAA0JpwiRAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAWrhaupqdEDDzyg5ORkDRkyRM8%2B%2B%2BxP3nf37t26/vrrFR8frwkTJmjXrl02Tto8mpL/vffe09ixY5WYmKjrrrtO7777ro2TNo%2Bm5D/pm2%2B%2BUWJiorZt22bDhM2nKdn37t2ryZMnq1%2B/frruuuu0detWGydtHk3J/9e//lWjR49WYmKiJk%2BerE8//dTGSZtXbW2txowZ87Pfz63x2HeSN/lb47HvJG/yn9Tijn0WWrSMjAzruuuus3bt2mX95S9/sRITE6233nqr0f2OHz9uDR482Hr44Yetffv2WQsWLLB%2B/etfW8ePH/fB1OZ4m/%2Bzzz6z%2BvTpY61du9b68ssvrfXr11t9%2BvSxPvvsMx9MbY63%2BU81Y8YMq1evXtbWrVttmrJ5eJu9srLS%2BvWvf209%2BOCD1pdffmktXbrUSkpKssrKynwwtTne5v/888%2Btvn37Wq%2B99pr11VdfWfPnz7cGDx5snThxwgdTm1VdXW2lpqb%2B7Pdzaz32WZZ3%2BVvrsc%2ByvMt/qpZ27KNgtWDHjx%2B3%2Bvbt6/HNsnz5cuvmm29udN8NGzZYw4cPtxoaGizLsqyGhgZr5MiR1saNG22b17Sm5M/KyrJmzJjhsTZ9%2BnTr0UcfbfY5m0tT8p%2B0adMm68Ybb2xRB5lz0ZTsa9eutUaMGGHV1dW511JSUqz33nvPllmbQ1Pyr1mzxho/frz79tGjR61evXpZhYWFtszaXIqKiqzf/e531nXXXfez38%2Bt8dhnWd7nb43HPsvyPv9JLfHYxyXCFmzPnj2qq6tTYmKiey0pKUkFBQVqaGjwuG9BQYGSkpLkcDgkSQ6HQ/3791d%2Bfr6tM5vUlPzjx4/Xvffe22gfR48ebfY5m0tT8ktSeXm5srKylJGRYeeYzaIp2bdv367f/OY3Cg4Odq9t3LhR//7v/27bvKY1JX9ERIT27dunvLw8NTQ06NVXX1X79u31b//2b3aPbdT27ds1cOBA5eTk/Oz9WuOxT/I%2Bf2s89kne55da7rHP6esB8NNcLpc6dOigkJAQ91qnTp1UU1OjiooKdezY0eO%2Bl1xyicfjIyMjVVRUZNu8pjUlf48ePTweW1RUpI8%2B%2Bkg33nijbfOa1pT8kvTwww9r/Pjx6tmzp92jGteU7CUlJerXr58eeugh/e1vf1OXLl00e/ZsJSUl%2BWJ0I5qS/5prrtHf/vY3TZkyRcHBwQoKCtLKlSt14YUX%2BmJ0Y6ZMmeLV/VrjsU/yPn9rPPZJ3ueXWu6xjzNYLVhVVZXHAVaS%2B3Ztba1X9z39fv6kKflPdeTIEd11113q37%2B/fvOb3zTrjM2pKfk//PBD5eXl6c4777RtvubUlOwnTpzQqlWrFBUVpaefflpXXHGFZsyYoYMHD9o2r2lNyV9eXi6Xy6W5c%2Bfq5Zdf1tixY5Wenq7vvvvOtnl9qTUe%2B85Vazn2NUVLPvZRsFqw0NDQRgeJk7fbtGnj1X1Pv58/aUr%2Bk8rKyjRt2jRZlqVly5YpKMh/v8W9zV9dXa25c%2BfqT3/6k19/vU/VlK99cHCwevfurbS0NF122WWaNWuWunXrpk2bNtk2r2lNyb9kyRL16tVLN910ky6//HItWLBAYWFh2rhxo23z%2BlJrPPadi9Z07PNWSz/2tf6vgB%2BLiYlReXm56urq3Gsul0tt2rRReHh4o/uWlZV5rJWVlSk6OtqWWZtDU/JL0uHDh3XTTTeptrZW69ata3QJzd94m7%2BwsFAlJSVKS0tTYmKi%2B3U7f/jDHzR37lzb5zahKV/7qKgode/e3WOtW7dufn0Gqyn5P/30U8XFxblvBwUFKS4uTgcOHLBtXl9qjce%2Bpmptxz5vtfRjHwWrBevdu7ecTqfHizXz8vLUt2/fRj%2BdxMfH6%2BOPP5ZlWZIky7K0c%2BdOxcfH2zqzSU3Jf%2BLECd12220KCgrS%2BvXrFRMTY/e4xnmbv1%2B/fvrLX/6i119/3f0hSQsXLtR//dd/2T63CU352ickJGjv3r0ea/v371eXLl1smbU5NCV/dHS0iouLPda%2B%2BOILde3a1ZZZfa01HvuaojUe%2B7zV0o99FKwWLCwsTOPGjdO8efNUWFiozZs369lnn9XUqVMl/fgTbXV1tSRp1KhRqqys1KJFi7Rv3z4tWrRIVVVVGj16tC8jnJem5F%2B5cqW%2B/vpr/fd//7d7m8vl8uv/SeNt/jZt2ujiiy/2%2BJB%2B/Mk%2BMjLSlxHOWVO%2B9jfeeKP27t2rJ554Ql999ZWWLl2qkpISjR071pcRzktT8t9www16%2BeWX9frrr%2Burr77SkiVLdODAAY0fP96XEZpVaz/2nU1rP/adjd8c%2B3z5HhE4uxMnTlj33XeflZCQYA0ZMsRas2aNe1uvXr083uuloKDAGjdunNW3b19r4sSJ1qeffuqDic3yNv/VV19t9erVq9HH7NmzfTS5GU35%2Bp%2BqJb0XzLlqSvZ//vOf1vjx463LL7/cGjt2rLV9%2B3YfTGxWU/K//PLL1qhRo6yEhARr8uTJ1q5du3wwcfM5/fs5EI59p/q5/K312Heqs339f%2B6%2BvuSwrH%2BdVwUAAIARXCIEAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMP%2BH/XIXmciAKQ1AAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common5782485545167508132”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1132</td> <td class=”number”>35.3%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.096227868</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.074382624</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.078296273</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.093729497</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.063803994</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.133636242</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.135666802</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0572607</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0601341</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1979)</td> <td class=”number”>1982</td> <td class=”number”>61.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme5782485545167508132”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1132</td> <td class=”number”>35.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00500947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00710424</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00721678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00761661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.644722232</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.731528895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.793990106</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.860215054</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.366120219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SPECSPTH14”>SPECSPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1915</td>

</tr> <tr>

<th>Unique (%)</th> <td>59.7%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.051635</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1.3908</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>37.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7911511748784445366”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7911511748784445366,#minihistogram7911511748784445366”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives7911511748784445366”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7911511748784445366”

aria-controls=”quantiles7911511748784445366” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7911511748784445366” aria-controls=”histogram7911511748784445366”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7911511748784445366” aria-controls=”common7911511748784445366”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7911511748784445366” aria-controls=”extreme7911511748784445366”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7911511748784445366”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.037206</td>

</tr> <tr>

<th>Q3</th> <td>0.075043</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.16987</td>

</tr> <tr>

<th>Maximum</th> <td>1.3908</td>

</tr> <tr>

<th>Range</th> <td>1.3908</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.075043</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.073337</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.4203</td>

</tr> <tr>

<th>Kurtosis</th> <td>53.042</td>

</tr> <tr>

<th>Mean</th> <td>0.051635</td>

</tr> <tr>

<th>MAD</th> <td>0.047122</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.8407</td>

</tr> <tr>

<th>Sum</th> <td>161.72</td>

</tr> <tr>

<th>Variance</th> <td>0.0053783</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7911511748784445366”>

<img 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5RP/85z/16KOPyrIspaWlqUOHDsrLy9OoUaM0ffp07du3T5K0b98%2BpaWlaezYsXrppZfUvn173XHHHbIsS5L01ltvKTs7WxkZGVq7dq0KCwuVlZXly7gAAMDP%2BGXB2rNnjwoKCpSZmanu3bsrNTVV6enp2rhxo7Zs2aKSkhJlZGSoW7duuv3225WUlKS8vDxJ0oYNG3TxxRdr6tSp6t69uzIzM/Xtt99q69atkqR169ZpypQpGjRokHr37q0FCxYoLy%2BPq1gAAKDJ/LJgRUdHa/Xq1erQoYPX%2BpEjR1RYWKiLLrpIrVu39qynpKSooKBAklRYWKjU1FTPXnh4uHr16qWCggLV19frk08%2B8dpPSkrS8ePHtXPnzhZOBQAAAoVfFqyIiAgNHDjQc7uhoUHr169X//795Xa7FRMT43X/qKgoHThwQJJ%2Bcr%2ByslI1NTVe%2By6XS5GRkZ7HAwAA/ByXrwcwISsrSzt27NBLL72kZ555RqGhoV77oaGhqq2tlSRVVVX96H51dbXn9o89vilKS0vldru91lyu1o2K3ZkKCfGvfuxymZv3RHZ/%2BxqcKafmlsh%2B8q9O4tTsTs0tBU52vy9YWVlZWrt2rR555BH16NFDYWFhqqio8LpPbW2tWrVqJUkKCwtrVJZqa2sVERGhsLAwz%2B1T98PDw5s8U25urrKzs73W0tLSlJ6e3uRjBKJ27doYP2ZERNO/L4HEqbklsjuVU7M7Nbfk/9n9umAtXLhQzz//vLKysnTllVdKkmJjY7Vr1y6v%2B5WVlXmuHsXGxqqsrKzRfs%2BePRUZGamwsDCVlZWpW7dukqS6ujpVVFQoOjq6yXNNmDBBgwcP9lpzuVqrvPxoszP%2BFH9r9ybzh4QEKyIiXJWVVaqvbzB23LOdU3NLZCe7s7I7NbdkPntL/OO%2BKfy2YGVnZ%2BuFF17Qww8/rGHDhnnWExMTlZOTo%2Brqas9Vq/z8fKWkpHj28/PzPfevqqrSjh07NH36dAUHByshIUH5%2Bfnq16%2BfJKmgoEAul0vx8fFNni0mJqbR04Fu92HV1TnrL8mpWiJ/fX2DI7%2BuTs0tkZ3szuLU3JL/Z/evSyD/a/fu3Vq5cqVuvfVWpaSkyO12ez769u2rjh07avbs2SouLlZOTo6Kioo0fvx4SdK4ceO0bds25eTkqLi4WLNnz1bnzp09hWrSpEl66qmntGnTJhUVFWn%2B/Pm69tprm/UUIQAAcDa/vIL19ttvq76%2BXo8//rgef/xxr73PP/9cK1eu1Ny5czV27Fidf/75WrFiheLi4iRJnTt31mOPPaa//OUvWrFihZKTk7VixQoFBQVJkkaMGKFvv/1W8%2BbNU21trX7/%2B99r5syZtmcEAAD%2BK8g68RbmaFFu92Hjx3S5gjV06b%2BMH7elvHnXAGPHcrmC1a5dG5WXH/XrS8jN5dTcEtnJ7qzsTs0tmc8eHf0rA1M1n18%2BRQgAAHA2o2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMz2gnXNNdfohRde0OHD5v9vPgAAgLOB7QWrf//%2BeuKJJ3TZZZfp7rvv1ubNm8X/Nw0AAAKJ7QVrxowZ%2Buc//6mVK1cqJCREd955p6644go98sgj%2BvLLL%2B0eBwAAwDiXLz5pUFCQBgwYoAEDBqiqqkrPPvusVq5cqZycHPXp00dTpkzR73//e1%2BMBgAAcMZ8UrAkqbS0VK%2B//rpef/11ffHFF%2BrTp4/GjBmjAwcO6P7779dHH32kuXPn%2Bmo8AACAX8z2gvXaa6/ptdde04cffqj27dtr9OjRWr58ubp06eK5T8eOHbV48WIKFgAA8Eu2F6y5c%2Bdq0KBBWrFihS6//HIFBzd%2BGVjXrl11ww032D0aAACAEbYXrPfee0/t2rVTRUWFp1wVFRWpV69eCgkJkST16dNHffr0sXs0AAAAI2z/KcIjR45o2LBhevLJJz1rt912m0aNGqX9%2B/fbPQ4AAIBxthesv/zlLzr//PN18803e9beeOMNdezYUZmZmXaPAwAAYJztBevf//637rvvPkVHR3vW2rdvr3vvvVdbtmyxexwAAADjbC9YLpdLlZWVjdarqqp4R3cAABAQbC9Yl19%2BuRYtWqSvv/7as1ZSUqLMzEwNHDjQ7nEAAACMs/2nCGfNmqWbb75ZV155pSIiIiRJlZWV6tWrl2bPnm33OAAAAMbZXrCioqL0yiuv6P3331dxcbFcLpcuuOACXXrppQoKCrJ7HAAAAON88l/lhISEaODAgTwlCAAAApLtBcvtduvRRx/Vtm3bdPz48UYvbH/77bftHgkAAMAo2wvWAw88oO3bt2vEiBH61a9%2BZfenBwAAaHG2F6wtW7Zo9erVSk1NtftTAwAA2ML2t2lo3bq1oqKi7P60AAAAtrG9YI0aNUqrV69WfX293Z8aAADAFrY/RVhRUaGNGzfqnXfe0XnnnafQ0FCv/XXr1tk9EgAAgFE%2BeZuGkSNH%2BuLTAgAA2ML2gpWZmWn3pwQAALCV7a/BkqTS0lJlZ2drxowZ%2Bu677/TXv/5Ve/bs8cUoAAAAxtlesPbu3aurr75ar7zyit566y0dO3ZMb7zxhsaNG6fCwkK7xwEAADDO9oL14IMPasiQIdq0aZPOOeccSdLDDz%2BswYMHa%2BnSpXaPAwAAYJztBWvbtm26%2Beabvf5jZ5fLpTvuuEM7duywexwAAADjbC9YDQ0NamhoaLR%2B9OhRhYSE2D0OAACAcbYXrMsuu0yrVq3yKlkVFRXKyspS//797R4HAADAONsL1n333aft27frsssuU01Njf70pz9p0KBB%2BuabbzRr1qxmH6%2B2tlYjR47Uhx9%2B6FlbtGiRLrzwQq%2BP9evXe/Y3btyoIUOGKDExUWlpaTp06JBnz7IsLV26VP3791ffvn21ZMmS015xAwAA%2BDG2vw9WbGysXn31VW3cuFGfffaZGhoaNHHiRI0aNUpt27Zt1rFqamo0Y8YMFRcXe63v3r1bM2bM0JgxYzxrJ45dVFSkuXPnasGCBYqPj9fixYs1e/ZsrVq1SpK0Zs0abdy4UdnZ2aqrq9PMmTMVFRWladOmnWFyAADgFD55J/fw8HBdc801Z3SMXbt2acaMGbIsq9He7t27NW3aNEVHRzfaW79%2BvYYPH67Ro0dLkpYsWaJBgwappKRE5513ntatW6f09HSlpqZKku655x4tW7aMggUAAJrM9oI1efLkn9xv6v9FuHXrVvXr10//9V//paSkJM/6kSNHdPDgQXXp0uW0jyssLNStt97qud2xY0fFxcWpsLBQoaGh2r9/vy655BLPfkpKir799luVlpYqJiamSbMBAABns71gderUyet2XV2d9u7dqy%2B%2B%2BEJTpkxp8nEmTZp02vXdu3crKChITzzxhN577z1FRkbq5ptv9jxdeLqiFBUVpQMHDsjtdkuS136HDh0kSQcOHGhywSotLfUc6wSXq7XxghYS4pM34v/FXC5z857I7m9fgzPl1NwS2U/%2B1Umcmt2puaXAyX7W/F%2BEK1as0IEDB874%2BHv27FFQUJC6du2qG264QR999JEeeOABtW3bVkOHDlV1dbVCQ0O9HhMaGqra2lpVV1d7bp%2B8J/3wYvqmys3NVXZ2ttdaWlqa0tPTf2msgNCuXRvjx4yICDd%2BTH/g1NwS2Z3Kqdmdmlvy/%2Bw%2BeQ3W6YwaNUqjR4/WwoULz%2Bg4o0eP1qBBgxQZGSlJio%2BP11dffaXnn39eQ4cOVVhYWKOyVFtbq/DwcK8yFRYW5vm99MPrxppqwoQJGjx4sNeay9Va5eVHf3Gu0/G3dm8yf0hIsCIiwlVZWaX6euf8lKdTc0tkJ7uzsjs1t2Q%2Be0v8474pzpqC9fHHHxt5o9GgoCBPuTqha9eu2rJli6QffoqxrKzMa7%2BsrEzR0dGKjY2VJLndbnXu3Nnze0mnfcH8j4mJiWn0dKDbfVh1dc76S3KqlshfX9/gyK%2BrU3NLZCe7szg1t%2BT/2c%2BKF7kfOXJEn3/%2B%2BY%2B%2Brqo5li1bpo8//ljPPPOMZ23nzp3q2rWrJCkxMVH5%2BfkaO3asJGn//v3av3%2B/EhMTFRsbq7i4OOXn53sKVn5%2BvuLi4niBOwAAaDLbC1ZcXJzX/0MoSeecc45uuOEG/eEPfzjj4w8aNEg5OTl66qmnNHToUG3evFmvvvqq56cTJ06cqBtvvFFJSUlKSEjQ4sWLdcUVV%2Bi8887z7C9dulS//vWvJUkPPfSQpk6desZzAQAA57C9YD344IMtevzevXtr2bJlWr58uZYtW6ZOnTrpoYceUnJysiQpOTlZGRkZWr58ub7//nsNGDDA63Vf06ZN03fffafp06crJCRE48eP10033dSiMwMAgMASZJ3unTpb0EcffdTk%2B578flT%2Bzu0%2BbPyYLlewhi79l/HjtpQ37xpg7FguV7DatWuj8vKjfv0cfXM5NbdEdrI7K7tTc0vms0dH/8rAVM1n%2BxWsG2%2B80fMU4cnd7tS1oKAgffbZZ3aPBwAAcMZsL1hPPPGEFi1apJkzZ6pv374KDQ3VJ598ooyMDI0ZM0ZXXXWV3SMBAAAYZfsbKWVmZmrevHm68sor1a5dO7Vp00b9%2B/dXRkaGnn/%2BeXXq1MnzAQAA4I9sL1ilpaWnLU9t27ZVeXm53eMAAAAYZ3vBSkpK0sMPP6wjR4541ioqKpSVlaVLL73U7nEAAACMs/01WPfff78mT56syy%2B/XF26dJFlWfrqq68UHR3tea8qAAAAf2Z7werWrZveeOMNbdy4Ubt375YkXX/99RoxYkSz/r8/AACAs5VP/i/Cc889V9dcc42%2B%2BeYbzzuon3POOb4YBQAAwDjbX4NlWZaWLl2qSy65RCNHjtSBAwc0a9YszZ07V8ePH7d7HAAAAONsL1jPPvusXnvtNf35z39WaGioJGnIkCHatGmTsrOz7R4HAADAONsLVm5urubNm6exY8d63r39qquu0qJFi/Q///M/do8DAABgnO0F65tvvlHPnj0brcfHx8vtdts9DgAAgHG2F6xOnTrpk08%2BabT%2B3nvveV7wDgAA4M9s/ynCadOmacGCBXK73bIsSx988IFyc3P17LPP6r777rN7HAAAAONsL1jjxo1TXV2dHn/8cVVXV2vevHlq37697rrrLk2cONHucQAAAIyzvWBt3LhRw4YN04QJE3To0CFZlqWoqCi7xwAAAGgxtr8GKyMjw/Ni9vbt21OuAABAwLG9YHXp0kVffPGF3Z8WAADANrY/RRgfH6977rlHq1evVpcuXRQWFua1n5mZafdIAAAARtlesL788kulpKRIEu97BQAAApItBWvJkiWaPn26WrdurWeffdaOTwkAAOAztrwGa82aNaqqqvJau%2B2221RaWmrHpwcAALCVLQXLsqxGax999JFqamrs%2BPQAAAC2sv2nCAEAAAIdBQsAAMAw2wpWUFCQXZ8KAADAp2x7m4ZFixZ5vefV8ePHlZWVpTZt2njdj/fBAgAA/s6WgnXJJZc0es%2Br5ORklZeXq7y83I4RAAAAbGNLweK9rwAAgJPwIncAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPP7glVbW6uRI0fqww8/9KyVlJTopptuUlJSkq666ipt3rzZ6zHvv/%2B%2BRo4cqcTERE2ePFklJSVe%2B88884wGDhyo5ORkzZkzR1VVVbZkAQAAgcGvC1ZNTY3uvvtuFRcXe9Ysy1JaWpo6dOigvLw8jRo1StOnT9e%2BffskSfv27VNaWprGjh2rl156Se3bt9cdd9why7IkSW%2B99Zays7OVkZGhtWvXqrCwUFlZWT7JBwAA/JPfFqxdu3bp2muv1ddff%2B21vmXLFpWUlCgjI0PdunXT7bffrqSkJOXl5UmSNmzYoIsvvlhTp05V9%2B7dlZmZqW%2B//VZbt26VJK1bt05TpkzRoEGD1Lt3by1YsEB5eXlcxQIAAE3mtwVr69at6tevn3Jzc73WCwsLddFFF6l169aetZSUFBUUFHj2U1NTPXvh4eHq1auXCgoKVF9fr08%2B%2BcRrPykpScePH9fOnTtbOBEAAAgULl8P8EtNmjTptOtut1sxMTFea1FRUTpw4MDP7ldWVqqmpsZr3%2BVyKTIy0vN4AACAn%2BO3BevHVFVVKTQ01GstNDRUtbW1P7tfXV3tuf1jj2%2BK0tJSud1urzWXq3WjYnemQkIj0Hj/AAASRUlEQVT86wKky2Vu3hPZ/e1rcKacmlsi%2B8m/OolTszs1txQ42QOuYIWFhamiosJrrba2Vq1atfLsn1qWamtrFRERobCwMM/tU/fDw8ObPENubq6ys7O91tLS0pSent7kYwSidu3aGD9mRETTvy%2BBxKm5JbI7lVOzOzW35P/ZA65gxcbGateuXV5rZWVlnqtHsbGxKisra7Tfs2dPRUZGKiwsTGVlZerWrZskqa6uThUVFYqOjm7yDBMmTNDgwYO91lyu1iovP/pLIv0of2v3JvOHhAQrIiJclZVVqq9vMHbcs51Tc0tkJ7uzsjs1t2Q%2Be0v8474pAq5gJSYmKicnR9XV1Z6rVvn5%2BUpJSfHs5%2Bfne%2B5fVVWlHTt2aPr06QoODlZCQoLy8/PVr18/SVJBQYFcLpfi4%2BObPENMTEyjpwPd7sOqq3PWX5JTtUT%2B%2BvoGR35dnZpbIjvZncWpuSX/z%2B5fl0CaoG/fvurYsaNmz56t4uJi5eTkqKioSOPHj5ckjRs3Ttu2bVNOTo6Ki4s1e/Zsde7c2VOoJk2apKeeekqbNm1SUVGR5s%2Bfr2uvvbZZTxECAABnC7iCFRISopUrV8rtdmvs2LF6/fXXtWLFCsXFxUmSOnfurMcee0x5eXkaP368KioqtGLFCgUFBUmSRowYodtvv13z5s3T1KlT1bt3b82cOdOXkQAAgJ8Jsk68hTlalNt92PgxXa5gDV36L%2BPHbSlv3jXA2LFcrmC1a9dG5eVH/foScnM5NbdEdrI7K7tTc0vms0dH/8rAVM0XcFewAAAAfI2CBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwLGAL1t///nddeOGFXh/p6emSpB07duiaa65RYmKixo0bp%2B3bt3s9duPGjRoyZIgSExOVlpamQ4cO%2BSICAADwUwFbsHbt2qVBgwZp8%2BbNno9Fixbp2LFjuu2225SamqqXX35ZycnJuv3223Xs2DFJUlFRkebOnavp06crNzdXlZWVmj17to/TAAAAfxKwBWv37t3q0aOHoqOjPR8RERF64403FBYWpnvvvVfdunXT3Llz1aZNG/31r3%2BVJK1fv17Dhw/X6NGjFR8fryVLlujdd99VSUmJjxMBAAB/EdAFq0uXLo3WCwsLlZKSoqCgIElSUFCQ%2BvTpo4KCAs9%2Bamqq5/4dO3ZUXFycCgsLbZkbAAD4P5evB2gJlmXpyy%2B/1ObNm7Vq1SrV19dr2LBhSk9Pl9vt1gUXXOB1/6ioKBUXF0uSSktLFRMT02j/wIEDTf78paWlcrvdXmsuV%2BtGxz1TISH%2B1Y9dLnPznsjub1%2BDM%2BXU3BLZT/7VSZya3am5pcDJHpAFa9%2B%2BfaqqqlJoaKgeffRRffPNN1q0aJGqq6s96ycLDQ1VbW2tJKm6uvon95siNzdX2dnZXmtpaWmeF9k7Vbt2bYwfMyIi3Pgx/YFTc0tkdyqnZndqbsn/swdkwerUqZM%2B/PBDnXvuuQoKClLPnj3V0NCgmTNnqm/fvo3KUm1trVq1aiVJCgsLO%2B1%2BeHjTv9ETJkzQ4MGDvdZcrtYqLz/6CxOdnr%2B1e5P5Q0KCFRERrsrKKtXXNxg77tnOqbklspPdWdmdmlsyn70l/nHfFAFZsCQpMjLS63a3bt1UU1Oj6OholZWVee2VlZV5nr6LjY097X50dHSTP3dMTEyjpwPd7sOqq3PWX5JTtUT%2B%2BvoGR35dnZpbIjvZncWpuSX/z%2B5fl0Ca6F//%2Bpf69eunqqoqz9pnn32myMhIpaSk6OOPP5ZlWZJ%2BeL3Wtm3blJiYKElKTExUfn6%2B53H79%2B/X/v37PfsAAAA/JyALVnJyssLCwnT//fdrz549evfdd7VkyRLdcsstGjZsmCorK7V48WLt2rVLixcvVlVVlYYPHy5Jmjhxol577TVt2LBBO3fu1L333qsrrrhC5513no9TAQAAfxGQBatt27Z66qmndOjQIY0bN05z587VhAkTdMstt6ht27ZatWqV8vPzNXbsWBUWFionJ0etW7eW9EM5y8jI0IoVKzRx4kSde%2B65yszM9HEiAADgTwL2NVjdu3fXmjVrTrvXu3dvvfLKKz/62LFjx2rs2LEtNRoAAAhwAXkFCwAAwJcoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIa5fD0AnGP4o//P1yM0y5t3DfD1CAAAP8UVLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAXrNGpqajRnzhylpqbqsssu09NPP%2B3rkQAAgB9x%2BXqAs9GSJUu0fft2rV27Vvv27dOsWbMUFxenYcOG%2BXo0AADgByhYpzh27Jg2bNigJ598Ur169VKvXr1UXFys5557joLlMMMf/X%2B%2BHqFZ3rxrgK9HAAD8L54iPMXOnTtVV1en5ORkz1pKSooKCwvV0NDgw8kAAIC/4ArWKdxut9q1a6fQ0FDPWocOHVRTU6OKigq1b9/%2BZ49RWloqt9vtteZytVZMTIzRWUNC6Mf4P/50xe3v9wxs9mNO/Hl34p97sjsvu1NzS4GTnYJ1iqqqKq9yJclzu7a2tknHyM3NVXZ2ttfa9OnTdeedd5oZ8n%2BVlpZqyq%2BLNWHCBOPl7WxXWlqq3Nxcx2V3am7ph%2Bxr164mO9kdwam5pcDJ7t/1sAWEhYU1KlInbrdq1apJx5gwYYJefvllr48JEyYYn9Xtdis7O7vR1TIncGp2p%2BaWyE52Z2V3am4pcLJzBesUsbGxKi8vV11dnVyuH748brdbrVq1UkRERJOOERMT49etGwAAnBmuYJ2iZ8%2BecrlcKigo8Kzl5%2BcrISFBwcF8uQAAwM%2BjMZwiPDxco0eP1vz581VUVKRNmzbp6aef1uTJk309GgAA8BMh8%2BfPn%2B/rIc42/fv3144dO/TQQw/pgw8%2B0B//%2BEeNGzfO12OdVps2bdS3b1%2B1adPG16PYzqnZnZpbIjvZnZXdqbmlwMgeZFmW5eshAAAAAglPEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2Cd5WpqajRnzhylpqbqsssu09NPP/2j992xY4euueYaJSYmaty4cdq%2BfbuNk5rXnOzvvPOORo0apeTkZF199dV6%2B%2B23bZzUrObkPuGbb75RcnKyPvzwQxsmbDnNyf75559r4sSJ6t27t66%2B%2Bmpt2bLFxknNa072v//97xo%2BfLiSk5M1ceJEffrppzZO2jJqa2s1cuTIn/wzHGjnuBOakj2QznEna0r2E/zuPGfhrJaRkWFdffXV1vbt262//e1vVnJysvXmm282ut/Ro0etAQMGWA8%2B%2BKC1a9cua%2BHChdZvfvMb6%2BjRoz6Y2oymZv/ss8%2BsXr16WWvXrrW%2B%2Buora/369VavXr2szz77zAdTn7mm5j7ZtGnTrB49elhbtmyxacqW0dTslZWV1m9%2B8xvr/vvvt7766itr2bJlVkpKilVWVuaDqc1oavYvvvjCSkhIsF555RVr79691oIFC6wBAwZYx44d88HUZlRXV1tpaWk/%2BWc4EM9xltW07IF2jjuhKdlP5m/nOQrWWezo0aNWQkKC1x%2BmFStWWDfccEOj%2B27YsMEaPHiw1dDQYFmWZTU0NFhDhw618vLybJvXpOZkz8rKsqZNm%2Ba1NnXqVOvhhx9u8TlNa07uE1577TXruuuu86sTz%2Bk0J/vatWutIUOGWHV1dZ61sWPHWu%2B8844ts5rWnOxr1qyxxowZ47l9%2BPBhq0ePHlZRUZEts5pWXFxs/eEPf7Cuvvrqn/wzHGjnOMtqevZAOsed0NTsJ/jjeY6nCM9iO3fuVF1dnZKTkz1rKSkpKiwsVENDg9d9CwsLlZKSoqCgIElSUFCQ%2BvTpo4KCAltnNqU52ceMGaN77rmn0TEOHz7c4nOa1pzcklReXq6srCxlZGTYOWaLaE72rVu36ne/%2B51CQkI8a3l5efrtb39r27wmNSd7ZGSkdu3apfz8fDU0NOjll19W27Zt9R//8R92j23E1q1b1a9fP%2BXm5v7k/QLtHCc1PXsgneNOaGp2yX/Pcy5fD4Af53a71a5dO4WGhnrWOnTooJqaGlVUVKh9%2B/Ze973gggu8Hh8VFaXi4mLb5jWpOdm7devm9dji4mJ98MEHuu6662yb15Tm5JakBx98UGPGjFH37t3tHtW45mQvKSlR79699cADD%2Bgf//iHOnXqpFmzZiklJcUXo5%2Bx5mS/6qqr9I9//EOTJk1SSEiIgoODtWrVKp177rm%2BGP2MTZo0qUn3C7RznNT07IF0jjuhqdkl/z3PcQXrLFZVVeV1wpXkuV1bW9uk%2B556P3/RnOwnO3TokO6880716dNHv/vd71p0xpbQnNzvv/%2B%2B8vPzdccdd9g2X0tqTvZjx44pJydH0dHRevLJJ3XJJZdo2rRp2r9/v23zmtSc7OXl5XK73Zo3b55efPFFjRo1SrNnz9Z3331n27y%2BEGjnuF/K389xzeXP5zkK1lksLCys0cnjxO1WrVo16b6n3s9fNCf7CWVlZZoyZYosy9Ly5csVHOx/f7ybmru6ulrz5s3Tn//8Z7/9Hp%2BqOd/zkJAQ9ezZU%2Bnp6brooos0c%2BZMdenSRa%2B99ppt85rUnOxLly5Vjx49dP311%2Bviiy/WwoULFR4erry8PNvm9YVAO8f9EoFwjmsOfz/PBfZ3x8/FxsaqvLxcdXV1njW3261WrVopIiKi0X3Lysq81srKyhQTE2PLrKY1J7skHTx4UNdff71qa2u1bt26Rk%2Bl%2BYum5i4qKlJJSYnS09OVnJzsee3Orbfeqnnz5tk%2BtwnN%2BZ5HR0era9euXmtdunTx2ytYzcn%2B6aefKj4%2B3nM7ODhY8fHx2rdvn23z%2BkKgneOaK1DOcc3h7%2Bc5CtZZrGfPnnK5XF4v4szPz1dCQkKjf7kkJibq448/lmVZkiTLsrRt2zYlJibaOrMpzcl%2B7Ngx3XLLLQoODtb69esVGxtr97jGNDV379699be//U2vvvqq50OSFi1apP/8z/%2B0fW4TmvM9T0pK0ueff%2B61tmfPHnXq1MmWWU1rTvaYmBjt3r3ba%2B3LL79U586dbZnVVwLtHNccgXSOaw5/P89RsM5i4eHhGj16tObPn6%2BioiJt2rRJTz/9tCZPnizph3/hVldXS5KGDRumyspKLV68WLt27dLixYtVVVWl4cOH%2BzLCL9ac7KtWrdLXX3%2Bt//7v//bsud1uv/wJm6bmbtWqlc4//3yvD%2BmHf%2BVHRUX5MsIv1pzv%2BXXXXafPP/9cjz32mPbu3atly5appKREo0aN8mWEX6w52a%2B99lq9%2BOKLevXVV7V3714tXbpU%2B/bt05gxY3wZoUUE8jnu5wTqOa4pAuY858v3iMDPO3bsmHXvvfdaSUlJ1mWXXWatWbPGs9ejRw%2Bv94ApLCy0Ro8ebSUkJFjjx4%2B3Pv30Ux9MbE5Ts1955ZVWjx49Gn3MmjXLR5OfmeZ8z0/mT%2B8P82Oak/3f//63NWbMGOviiy%2B2Ro0aZW3dutUHE5vTnOwvvviiNWzYMCspKcmaOHGitX37dh9MbN6pf4YD/Rx3sp/KHmjnuFP93Pf9p%2B57NguyrP%2B93goAAAAjeIoQAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAz7/2fJl1Zsx6/mAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7911511748784445366”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1208</td> <td class=”number”>37.6%</td> <td>

<div class=”bar” style=”width:63%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0494438</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.138888889</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0484027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0477099</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.109505037</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.120235662</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0389636</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.079076388</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.116144019</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1904)</td> <td class=”number”>1906</td> <td class=”number”>59.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7911511748784445366”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1208</td> <td class=”number”>37.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00718097</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0074206</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00768823</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00771712</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.633786587</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.681663258</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.838926174</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.865800866</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.390820584</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERC09”>SUPERC09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1.4304</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>83</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7147768042073229308”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives7147768042073229308,#minihistogram7147768042073229308”
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</div> <div class=”row collapse col-md-12” id=”descriptives7147768042073229308”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7147768042073229308”

aria-controls=”quantiles7147768042073229308” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7147768042073229308” aria-controls=”histogram7147768042073229308”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7147768042073229308” aria-controls=”common7147768042073229308”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7147768042073229308” aria-controls=”extreme7147768042073229308”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7147768042073229308”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>1</td>

</tr> <tr>

<th>Q3</th> <td>1</td>

</tr> <tr>

<th>95-th percentile</th> <td>6</td>

</tr> <tr>

<th>Maximum</th> <td>83</td>

</tr> <tr>

<th>Range</th> <td>83</td>

</tr> <tr>

<th>Interquartile range</th> <td>1</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>3.3659</td>

</tr> <tr>

<th>Coef of variation</th> <td>2.3531</td>

</tr> <tr>

<th>Kurtosis</th> <td>145.88</td>

</tr> <tr>

<th>Mean</th> <td>1.4304</td>

</tr> <tr>

<th>MAD</th> <td>1.6151</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>8.8792</td>

</tr> <tr>

<th>Sum</th> <td>4480</td>

</tr> <tr>

<th>Variance</th> <td>11.329</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7147768042073229308”>

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JkyapqKjIaX7z5s3q37%2B/oqKiNHfuXJWXl7tsXQAAwPt5ZcCy2%2B1KTk5WeXm5XnzxRa1atUrvvfeeVq9eLbvdrsTERLVq1UrZ2dkaNWqUkpKSdPr0aUnS6dOnlZiYqDFjxmjnzp1q2bKl7r//ftntdknS22%2B/rfT0dC1atEhbtmxRXl6e0tLS3LlcAADgZbwyYJ04cUK5ublKTU1V586dFRMTo%2BTkZO3evVsHDhxQUVGRFi1apE6dOmnmzJmKjIxUdna2JGnHjh269dZbNW3aNHXu3Fmpqak6deqUPvroI0nS1q1bNXnyZMXFxalXr15auHChsrOzOYoFAADqzSsDVkhIiDZu3KhWrVo5jV%2B8eFF5eXnq3r27AgMDHePR0dHKzc2VJOXl5SkmJsYxFxAQoB49eig3N1c1NTX65JNPnOYjIyN15coVHTt27HdeFQAAaCi8MmAFBQWpf//%2Bjse1tbXatm2b%2BvTpI5vNptDQUKfnBwcH6%2BzZs5L0d%2BfPnz%2BvyspKp3mr1armzZs7Xg8AAPBzrO4uwIS0tDQdPXpUO3fu1ObNm%2BXn5%2Bc07%2Bfnp6qqKklSeXn5T85XVFQ4Hv/U6%2BujuLhYNpvNacxqDawT7H4rX1/vysdWq3fVa8LVHnlbrxoTeuT56JHno0d1eX3ASktL05YtW7Rq1Sp16dJF/v7%2BKisrc3pOVVWVmjRpIkny9/evE5aqqqoUFBQkf39/x%2BNr5wMCAupdU1ZWltLT053GEhMTlZycXO9tNEQtWjR1dwluExRU//cP3IMeeT565Pno0Q%2B8OmAtXrxY27dvV1pamoYMGSJJCgsL0/Hjx52eV1JS4jh6FBYWppKSkjrz3bp1U/PmzeXv76%2BSkhJ16tRJklRdXa2ysjKFhITUu66EhATFx8c7jVmtgSotvfSL1/j3eNsnBdPr9wa%2Bvj4KCgrQ%2BfPlqqmpdXc5uA565Pnokefz5B6568O91was9PR0vfTSS1q5cqWGDh3qGI%2BIiFBmZqYqKiocR61ycnIUHR3tmM/JyXE8v7y8XEePHlVSUpJ8fHzUs2dP5eTk6Pbbb5ck5ebmymq1qmvXrvWuLTQ0tM7pQJvtgqqrPetN52qNef01NbWNev3egB55Pnrk%2BejRD7zrEMj/KSws1Lp163TvvfcqOjpaNpvN8RMbG6vWrVsrJSVFBQUFyszMVH5%2BvsaNGydJGjt2rA4fPqzMzEwVFBQoJSVFbdu2dQSqiRMnatOmTdqzZ4/y8/O1YMEC3XXXXb/oFCEAAGjcvPII1rvvvquamhqtX79e69evd5r7/PPPtW7dOs2bN09jxoxR%2B/btlZGRofDwcElS27Zt9fTTT%2BvJJ59URkaGoqKilJGRIYvFIkkaMWKETp06pfnz56uqqkp//OMfNXv2bJevEQAAeC%2BL/eotzPG7stkuGN%2Bm1eqjwcs/ML7d38ubD/Z1dwkuZ7X6qEWLpiotvcRhcw9FjzwfPfJ8ntyjkJAb3bJfrzxFCAAA4MlcHrDGjx%2Bvl156SRcumD%2BiAwAA4AlcHrD69OmjDRs2qF%2B/fnrooYe0b98%2BcZYSAAA0JC4PWLNmzdJ7772ndevWydfXVw888IAGDhyoVatW6csvv3R1OQAAAMa55bcILRaL%2Bvbtq759%2B6q8vFwvvPCC1q1bp8zMTPXu3VuTJ0/WH//4R3eUBgAA8Ju57TYNxcXFeu211/Taa6/piy%2B%2BUO/evXXnnXfq7Nmzeuyxx3To0CHNmzfPXeUBAAD8ai4PWK%2B%2B%2BqpeffVVHTx4UC1bttTo0aO1du1adejQwfGc1q1ba%2BnSpQQsAADglVwesObNm6e4uDhlZGRowIAB8vGpexlYx44ddffdd7u6NAAAACNcHrDef/99tWjRQmVlZY5wlZ%2Bfrx49esjX11eS1Lt3b/Xu3dvVpQEAABjh8t8ivHjxooYOHapnn33WMTZjxgyNGjVKZ86ccXU5AAAAxrk8YD355JNq3769pk6d6hh744031Lp1a6Wmprq6HAAAAONcHrD%2B%2Bte/6tFHH1VISIhjrGXLlnrkkUd04MABV5cDAABgnMsDltVq1fnz5%2BuMl5eXc0d3AADQILg8YA0YMEBLlizR119/7RgrKipSamqq%2Bvfv7%2BpyAAAAjHP5bxHOmTNHU6dO1ZAhQxQUFCRJOn/%2BvHr06KGUlBRXlwMAAGCcywNWcHCwXnnlFe3fv18FBQWyWq26%2Beabdccdd8hisbi6HAAAAOPc8lU5vr6%2B6t%2B/P6cEAQBAg%2BTygGWz2bR69WodPnxYV65cqXNh%2B7vvvuvqkgAAAIxyecB6/PHHdeTIEY0YMUI33nijq3cPAADwu3N5wDpw4IA2btyomJgYV%2B8aAADAJVx%2Bm4bAwEAFBwe7ercAAAAu4/KANWrUKG3cuFE1NTWu3jUAAIBLuPwUYVlZmXbv3q2//OUvateunfz8/Jzmt27d6uqSAAAAjHLLbRpGjhzpjt0CAAC4hMsDVmpqqqt3CQAA4FIuvwZLkoqLi5Wenq5Zs2bp22%2B/1VtvvaUTJ064oxQAAADjXB6wTp48qX/5l3/RK6%2B8orfffluXL1/WG2%2B8obFjxyovL8/V5QAAABjn8oD11FNPadCgQdqzZ49uuOEGSdLKlSsVHx%2Bv5cuXu7ocAAAA41wesA4fPqypU6c6fbGz1WrV/fffr6NHj7q6HAAAAONcHrBqa2tVW1tbZ/zSpUvy9fV1dTkAAADGuTxg9evXT88884xTyCorK1NaWpr69Onj6nIAAACMc3nAevTRR3XkyBH169dPlZWV%2Bvd//3fFxcXpm2%2B%2B0Zw5c1xdDgAAgHEuvw9WWFiYdu3apd27d%2Buzzz5TbW2tJkyYoFGjRqlZs2auLgcAAMA4t9zJPSAgQOPHj3fHrgEAAH53Lg9YkyZN%2BrvzfBchAADwdi4PWG3atHF6XF1drZMnT%2BqLL77Q5MmTXV0OAACAcR7zXYQZGRk6e/asi6sBAAAwzy3fRXg9o0aN0ptvvunuMgAAAH4zjwlYH3/88a%2B60WhVVZVGjhypgwcPOsaWLFmiW265xeln27Ztjvndu3dr0KBBioiIUGJior777jvHnN1u1/Lly9WnTx/FxsZq2bJl170xKgAAwE/xiIvcL168qM8//1wTJ078RduqrKzUrFmzVFBQ4DReWFioWbNm6c4773SMXb0FRH5%2BvubNm6eFCxeqa9euWrp0qVJSUvTMM89Ikp5//nnt3r1b6enpqq6u1uzZsxUcHKzp06f/0qUCAIBGyuUBKzw83Ol7CCXphhtu0N13361//dd/rfd2jh8/rlmzZslut9eZKyws1PTp0xUSElJnbtu2bRo2bJhGjx4tSVq2bJni4uJUVFSkdu3aaevWrUpOTlZMTIwk6eGHH9aaNWsIWAAAoN5cHrCeeuopI9v56KOPdPvtt%2Bs//uM/FBkZ6Ri/ePGizp07pw4dOlz3dXl5ebr33nsdj1u3bq3w8HDl5eXJz89PZ86c0W233eaYj46O1qlTp1RcXKzQ0FAjtQMAgIbN5QHr0KFD9X7uj4POtX7qdGJhYaEsFos2bNig999/X82bN9fUqVMdpwuvF5SCg4N19uxZ2Ww2SXKab9WqlSTp7NmzBCwAAFAvLg9Y99xzj%2BMU4Y9P7107ZrFY9Nlnn/3i7Z84cUIWi0UdO3bU3XffrUOHDunxxx9Xs2bNNHjwYFVUVMjPz8/pNX5%2BfqqqqlJFRYXj8Y/npL9dTF9fxcXFjrB2ldUaaDyg%2Bfp6zO8o1IvV6l31mnC1R97Wq8aEHnk%2BeuT56FFdLg9YGzZs0JIlSzR79mzFxsbKz89Pn3zyiRYtWqQ777xTw4cP/03bHz16tOLi4tS8eXNJUteuXfXVV19p%2B/btGjx4sPz9/euEpaqqKgUEBDiFKX9/f8efpb99vU99ZWVlKT093WksMTFRycnJv3pdDUGLFk3dXYLbBAXV//0D96BHno8eeT569AO33Gh0/vz5GjBggGOsT58%2BWrRokR555BGn66N%2BDYvF4ghXV3Xs2FEHDhyQ9Lcvmy4pKXGaLykpUUhIiMLCwiRJNptNbdu2dfxZ0nUvmP8pCQkJio%2BPdxqzWgNVWnrply3mZ3jbJwXT6/cGvr4%2BCgoK0Pnz5aqp4XYfnogeeT565Pk8uUfu%2BnDv8oBVXFxc5%2BtypL/dRqG0tPQ3b3/NmjX6%2BOOPtXnzZsfYsWPH1LFjR0lSRESEcnJyNGbMGEnSmTNndObMGUVERCgsLEzh4eHKyclxBKycnByFh4f/otN7oaGhdZ5vs11QdbVnvelcrTGvv6amtlGv3xvQI89HjzwfPfqByw%2BBREZGauXKlbp48aJjrKysTGlpabrjjjt%2B8/bj4uJ06NAhbdq0SV9//bX%2B67/%2BS7t27dK0adMkSRMmTNCrr76qHTt26NixY3rkkUc0cOBAtWvXzjG/fPlyHTx4UAcPHtSKFSt%2B9guqAQAAfszlR7Aee%2BwxTZo0SQMGDFCHDh1kt9v11VdfKSQkRFu3bv3N2%2B/Vq5fWrFmjtWvXas2aNWrTpo1WrFihqKgoSVJUVJQWLVqktWvX6vvvv1ffvn21ePFix%2BunT5%2Bub7/9VklJSfL19dW4ceM0ZcqU31wXAABoPCz2692p83f2/fffa/fu3SosLJQkde/eXSNGjPhFF5J7G5vtgvFtWq0%2BGrz8A%2BPb/b28%2BWBfd5fgclarj1q0aKrS0kscNvdQ9Mjz0SPP58k9Cgm50S37dfkRLEm66aabNH78eH3zzTeOU3M33HCDO0oBAAAwzuXXYF39MuXbbrtNI0eO1NmzZzVnzhzNmzdPV65ccXU5AAAAxrk8YL3wwgt69dVX9cQTTzjuOzVo0CDt2bOnzr2jAAAAvJHLA1ZWVpbmz5%2BvMWPGOO7ePnz4cC1ZskSvv/66q8sBAAAwzuUB65tvvlG3bt3qjHft2rXO18sAAAB4I5cHrDZt2uiTTz6pM/7%2B%2B%2B87LngHAADwZi7/LcLp06dr4cKFstlsstvt%2BvDDD5WVlaUXXnhBjz76qKvLAQAAMM7lAWvs2LGqrq7W%2BvXrVVFRofnz56tly5Z68MEHNWHCBFeXAwAAYJzLA9bu3bs1dOhQJSQk6LvvvpPdbldwcLCrywAAAPjduPwarEWLFjkuZm/ZsiXhCgAANDguD1gdOnTQF1984erdAgAAuIzLTxF27dpVDz/8sDZu3KgOHTrI39/faT41NdXVJQEAABjl8oD15ZdfKjo6WpK47xUAAGiQXBKwli1bpqSkJAUGBuqFF15wxS4BAADcxiXXYD3//PMqLy93GpsxY4aKi4tdsXsAAACXcknAstvtdcYOHTqkyspKV%2BweAADApVz%2BW4QAAAANHQELAADAMJcFLIvF4qpdAQAAuJXLbtOwZMkSp3teXblyRWlpaWratKnT87gPFgAA8HYuCVi33XZbnXteRUVFqbS0VKWlpa4oAQAAwGVcErC49xUAAGhMuMgdAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY1wesqqoqjRw5UgcPHnSMFRUVacqUKYqMjNTw4cO1b98%2Bp9fs379fI0eOVEREhCZNmqSioiKn%2Bc2bN6t///6KiorS3LlzVV5e7pK1AACAhsGrA1ZlZaUeeughFRQUOMbsdrsSExPVqlUrZWdna9SoUUpKStLp06clSadPn1ZiYqLGjBmjnTt3qmXLlrr//vtlt9slSW%2B//bbS09O1aNEibdmyRXl5eUpLS3PL%2BgAAgHfy2oB1/Phx3XXXXfr666%2Bdxg8cOKCioiItWrRInTp10syZMxUZGans7GxJ0o4dO3Trrbdq2rRp6ty5s1JTU3Xq1Cl99NFHkqStW7dq8uTJiouLU69evbRw4UJlZ2dzFAsAANSb1wasjz76SLfffruysrKcxvPy8tS9e3cFBgY6xqKjo5Wbm%2BuYj4mJccwFBASoR48eys3NVU1NjT755BOn%2BcjISF25ckXHjh37nVcEAAAaCqu7C/i1Jk6ceN1xm82m0NBQp7Hg4GCdPXv2Z%2BfPnz%2BvyspKp3mr1armzZs7Xg8AAPBzvDZg/ZTy8nL5%2Bfk5jfn5%2Bamqqupn5ysqKhyPf%2Br19VFcXCybzeY0ZrUG1gl2v5Wvr3cdgLRavateE672yNt61ZjQI89HjzwfPaqrwQUsf39/lZWVOY1VVVWpSZMmjvlrw1JVVZWCgoLk7%2B/veHztfEBAQL1ryMrKUnp6utNYYmKikpMKc09PAAAQyUlEQVST672NhqhFi6buLsFtgoLq//6Be9Ajz0ePPB89%2BkGDC1hhYWE6fvy401hJSYnj6FFYWJhKSkrqzHfr1k3NmzeXv7%2B/SkpK1KlTJ0lSdXW1ysrKFBISUu8aEhISFB8f7zRmtQaqtPTSr1nST/K2Twqm1%2B8NfH19FBQUoPPny1VTU%2BvucnAd9Mjz0SPP58k9cteH%2BwYXsCIiIpSZmamKigrHUaucnBxFR0c75nNychzPLy8v19GjR5WUlCQfHx/17NlTOTk5uv322yVJubm5slqt6tq1a71rCA0NrXM60Ga7oOpqz3rTuVpjXn9NTW2jXr83oEeejx55Pnr0A%2B86BFIPsbGxat26tVJSUlRQUKDMzEzl5%2Bdr3LhxkqSxY8fq8OHDyszMVEFBgVJSUtS2bVtHoJo4caI2bdqkPXv2KD8/XwsWLNBdd931i04RAgCAxq3BBSxfX1%2BtW7dONptNY8aM0WuvvaaMjAyFh4dLktq2baunn35a2dnZGjdunMrKypSRkSGLxSJJGjFihGbOnKn58%2Bdr2rRp6tWrl2bPnu3OJQEAAC9jsV%2B9hTl%2BVzbbBePbtFp9NHj5B8a3%2B3t588G%2B7i7B5axWH7Vo0VSlpZc4bO6h6JHno0eez5N7FBJyo1v22%2BCOYAEAALgbAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADGuwAeudd97RLbfc4vSTnJwsSTp69KjGjx%2BviIgIjR07VkeOHHF67e7duzVo0CBFREQoMTFR3333nTuWAAAAvFSDDVjHjx9XXFyc9u3b5/hZsmSJLl%2B%2BrBkzZigmJkYvv/yyoqKiNHPmTF2%2BfFmSlJ%2Bfr3nz5ikpKUlZWVk6f/68UlJS3LwaAADgTRpswCosLFSXLl0UEhLi%2BAkKCtIbb7whf39/PfLII%2BrUqZPmzZunpk2b6q233pIkbdu2TcOGDdPo0aPVtWtXLVu2THv37lVRUZGbVwQAALxFgw5YHTp0qDOel5en6OhoWSwWSZLFYlHv3r2Vm5vrmI%2BJiXE8v3Xr1goPD1deXp5L6gYAAN6vQQYsu92uL7/8Uvv27dOQIUM0aNAgLV%2B%2BXFVVVbLZbAoNDXV6fnBwsM6ePStJKi4u/rvzAAAAP8fq7gJ%2BD6dPn1Z5ebn8/Py0evVqffPNN1qyZIkqKioc4z/m5%2BenqqoqSVJFRcXfna%2BP4uJi2Ww2pzGrNbBOcPutfH29Kx9brd5VrwlXe%2BRtvWpM6JHno0eejx7V1SADVps2bXTw4EHddNNNslgs6tatm2prazV79mzFxsbWCUtVVVVq0qSJJMnf3/%2B68wEBAfXef1ZWltLT053GEhMTHb/F2Fi1aNHU3SW4TVBQ/d8/cA965PnokeejRz9okAFLkpo3b%2B70uFOnTqqsrFRISIhKSkqc5kpKShxHl8LCwq47HxISUu99JyQkKD4%2B3mnMag1UaemlX7KEn%2BVtnxRMr98b%2BPr6KCgoQOfPl6umptbd5eA66JHno0eez5N75K4P9w0yYH3wwQd6%2BOGH9Ze//MVx5Omzzz5T8%2BbNFR0drWeffVZ2u10Wi0V2u12HDx/WfffdJ0mKiIhQTk6OxowZI0k6c%2BaMzpw5o4iIiHrvPzQ0tM7pQJvtgqqrPetN52qNef01NbWNev3egB55Pnrk%2BejRD7zrEEg9RUVFyd/fX4899phOnDihvXv3atmyZfrTn/6koUOH6vz581q6dKmOHz%2BupUuXqry8XMOGDZMkTZgwQa%2B%2B%2Bqp27NihY8eO6ZFHHtHAgQPVrl07N68KAAB4iwYZsJo1a6ZNmzbpu%2B%2B%2B09ixYzVv3jwlJCToT3/6k5o1a6ZnnnnGcZQqLy9PmZmZCgwMlPS3cLZo0SJlZGRowoQJuummm5SamurmFQEAAG9isdvtdncX0RjYbBeMb9Nq9dHg5R8Y3%2B7v5c0H%2B7q7BJezWn3UokVTlZZe4rC5h6JHno8eeT5P7lFIyI1u2W%2BDPIIFAADgTgQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADDM6u4C0HgMW/3/3F3CL/Lmg33dXQIAwEtxBOs6KisrNXfuXMXExKhfv3567rnn3F0SAADwIhzBuo5ly5bpyJEj2rJli06fPq05c%2BYoPDxcQ4cOdXdpAADACxCwrnH58mXt2LFDzz77rHr06KEePXqooKBAL774IgELAADUCwHrGseOHVN1dbWioqIcY9HR0dqwYYNqa2vl48NZ1caCa8YAAL8WAesaNptNLVq0kJ%2Bfn2OsVatWqqysVFlZmVq2bPmz2yguLpbNZnMas1oDFRoaarRWX1/CHn5gtfJ%2B%2BDWu/jvi35Pnokeejx7VRcC6Rnl5uVO4kuR4XFVVVa9tZGVlKT093WksKSlJDzzwgJki/09xcbEm/0OBEhISjIc3mFFcXKysrCx65MGKi4u1ZctGeuTB6JHno0d1ETWv4e/vXydIXX3cpEmTem0jISFBL7/8stNPQkKC8VptNpvS09PrHC2D56BHno8eeT565PnoUV0cwbpGWFiYSktLVV1dLav1b389NptNTZo0UVBQUL22ERoaSoIHAKAR4wjWNbp16yar1arc3FzHWE5Ojnr27MkF7gAAoF5IDNcICAjQ6NGjtWDBAuXn52vPnj167rnnNGnSJHeXBgAAvITvggULFri7CE/Tp08fHT16VCtWrNCHH36o%2B%2B67T2PHjnV3WdfVtGlTxcbGqmnTpu4uBT%2BBHnk%2BeuT56JHno0fOLHa73e7uIgAAABoSThECAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgeanKykrNnTtXMTEx6tevn5577jl3l9TonTt3TsnJyYqNjVX//v2VmpqqyspKSVJRUZGmTJmiyMhIDR8%2BXPv27XNztZgxY4YeffRRx%2BOjR49q/PjxioiI0NixY3XkyBE3Vtd4VVVVaeHChbrtttv0hz/8QStXrtTV%2B2HTI89w5swZzZw5U71791Z8fLw2b97smKNHPyBgeally5bpyJEj2rJli5544gmlp6frrbfecndZjZbdbldycrLKy8v14osvatWqVXrvvfe0evVq2e12JSYmqlWrVsrOztaoUaOUlJSk06dPu7vsRuvPf/6z9u7d63h8%2BfJlzZgxQzExMXr55ZcVFRWlmTNn6vLly26ssnFasmSJ9u/fr02bNmnFihX67//%2Bb2VlZdEjD/Lggw8qMDBQL7/8subOnavVq1frnXfeoUfXssPrXLp0yd6zZ0/7gQMHHGMZGRn2u%2B%2B%2B241VNW7Hjx%2B3d%2BnSxW6z2Rxjr7/%2Bur1fv372/fv32yMjI%2B2XLl1yzE2ePNm%2Bdu1ad5Ta6JWWltoHDBhgHzt2rH3OnDl2u91u37Fjhz0%2BPt5eW1trt9vt9traWvvgwYPt2dnZ7iy10SktLbV3797dfvDgQcfYM888Y3/00UfpkYcoKyuzd%2BnSxf755587xpKSkuwLFy6kR9fgCJYXOnbsmKqrqxUVFeUYi46OVl5enmpra91YWeMVEhKijRs3qlWrVk7jFy9eVF5enrp3767AwEDHeHR0tHJzc11dJiT953/%2Bp0aNGqWbb77ZMZaXl6fo6GhZLBZJksViUe/evemRi%2BXk5KhZs2aKjY11jM2YMUOpqan0yEM0adJEAQEBevnll3XlyhWdOHFChw8fVrdu3ejRNQhYXshms6lFixby8/NzjLVq1UqVlZUqKytzY2WNV1BQkPr37%2B94XFtbq23btqlPnz6y2WwKDQ11en5wcLDOnj3r6jIbvQ8//FB//etfdf/99zuN0yPPUFRUpDZt2mjXrl0aOnSo/vmf/1kZGRmqra2lRx7C399f8%2BfPV1ZWliIiIjRs2DANGDBA48ePp0fXsLq7APxy5eXlTuFKkuNxVVWVO0rCNdLS0nT06FHt3LlTmzdvvm6/6JVrVVZW6oknntD8%2BfPVpEkTp7mf%2BjdFj1zr8uXLOnnypF566SWlpqbKZrNp/vz5CggIoEcepLCwUHFxcZo6daoKCgq0ePFi3XHHHfToGgQsL%2BTv71/nDXv18bX/ccD10tLStGXLFq1atUpdunSRv79/nSOLVVVV9MrF0tPTdeuttzodabzqp/5N0SPXslqtunjxolasWKE2bdpIkk6fPq3t27erffv29MgDfPjhh9q5c6f27t2rJk2aqGfPnjp37pzWr1%2Bvdu3a0aMfIWB5obCwMJWWlqq6ulpW699aaLPZ1KRJEwUFBbm5usZt8eLF2r59u9LS0jRkyBBJf%2BvX8ePHnZ5XUlJS51A6fl9//vOfVVJS4rh28ep/BG%2B//bZGjhypkpISp%2BfTI9cLCQmRv7%2B/I1xJ0j/%2B4z/qzJkzio2NpUce4MiRI2rfvr1TaOrevbs2bNigmJgYevQjXIPlhbp16yar1ep04WBOTo569uwpHx9a6i7p6el66aWXtHLlSo0YMcIxHhERoU8//VQVFRWOsZycHEVERLijzEbrhRde0Ouvv65du3Zp165dio%2BPV3x8vHbt2qWIiAh9/PHHjvst2e12HT58mB65WEREhCorK/Xll186xk6cOKE2bdrQIw8RGhqqkydPOh2pOnHihNq2bUuPrsH/xl4oICBAo0eP1oIFC5Sfn689e/boueee06RJk9xdWqNVWFiodevW6d5771V0dLRsNpvjJzY2Vq1bt1ZKSooKCgqUmZmp/Px8jRs3zt1lNypt2rRR%2B/btHT9NmzZV06ZN1b59ew0dOlTnz5/X0qVLdfz4cS1dulTl5eUaNmyYu8tuVDp27KiBAwcqJSVFx44d0wcffKDMzExNmDCBHnmI%2BPh43XDDDXrsscf05Zdf6n//93%2B1YcMG3XPPPfToGhb71agJr1JeXq4FCxbof/7nf9SsWTNNnz5dU6ZMcXdZjVZmZqZWrFhx3bnPP/9cJ0%2Be1Lx585SXl6f27dtr7ty5%2BsMf/uDiKvFjV%2B/i/tRTT0mS8vPz9cQTT6iwsFC33HKLFi5cqO7du7uzxEbpwoULWrx4sd555x0FBARo4sSJSkxMlMVioUce4mp4ys/PV8uWLfVv//Zvmjx5Mj26BgELAADAME4RAgAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBh/x8tlB1vtAmFCwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7147768042073229308”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>958</td> <td class=”number”>29.8%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>49</td> <td class=”number”>1.5%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>39</td> <td class=”number”>1.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>75</td> <td class=”number”>2.3%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7147768042073229308”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>958</td> <td class=”number”>29.8%</td> <td>

<div class=”bar” style=”width:65%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>234</td> <td class=”number”>7.3%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>155</td> <td class=”number”>4.8%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>76</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>27.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>39.0</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>83.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERC14”><s>SUPERC14</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SUPERC09”><code>SUPERC09</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98347</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERCPTH09”>SUPERCPTH09<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1646</td>

</tr> <tr>

<th>Unique (%)</th> <td>51.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.015629</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.25621</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>46.1%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1924451761236797912”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives1924451761236797912”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1924451761236797912”

aria-controls=”quantiles1924451761236797912” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1924451761236797912” aria-controls=”histogram1924451761236797912”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1924451761236797912” aria-controls=”common1924451761236797912”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1924451761236797912” aria-controls=”extreme1924451761236797912”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1924451761236797912”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.0074821</td>

</tr> <tr>

<th>Q3</th> <td>0.025661</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.053383</td>

</tr> <tr>

<th>Maximum</th> <td>0.25621</td>

</tr> <tr>

<th>Range</th> <td>0.25621</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.025661</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.020955</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.3408</td>

</tr> <tr>

<th>Kurtosis</th> <td>12.401</td>

</tr> <tr>

<th>Mean</th> <td>0.015629</td>

</tr> <tr>

<th>MAD</th> <td>0.015898</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.403</td>

</tr> <tr>

<th>Sum</th> <td>48.951</td>

</tr> <tr>

<th>Variance</th> <td>0.00043911</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1924451761236797912”>

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OYTbvdXxrfpdAZpzNL3jG/XVzbdm%2BbvEn6Q0xmkqKgIHT16ostdL%2BAr9NQ8emoePfWNjtDXmBjz13u3h%2B1HsDZu3KiNGzdq27Zt6tmzpyZNmqSnn37a68agvXv31sMPP9ypAhYAAOg6bA9YWVlZGjlypAoKCnTVVVcpKKjtZWCn72EFAAAQiGwPWO%2B%2B%2B66ioqJUW1vrCVc7duzQwIEDFRwcLEkaPHiwBg8ebHdpAAAARtj%2BW4THjx/X2LFj9Yc//MEzduedd2rixImqrKy0uxwAAADjbA9YjzzyiC666CLNmjXLM/b666%2Brd%2B/eys3NtbscAAAA42wPWH//%2B991//33e90ZvWfPnpo/f77nbusAAACBzPaA5XQ6dezYsTbj9fX1Ru/oDgAA4C%2B2B6yrrrpKOTk5OnDggGesoqJCubm5GjFihN3lAAAAGGf7bxEuWLBAs2bN0rXXXqvIyEhJ0rFjxzRw4EAtXLjQ7nIAAACMsz1gRUdH67XXXtP777%2Bv8vJyOZ1O/eQnP9GVV14ph8NhdzkAAADG%2BeW7CIODgzVixAhOCQIAgE7J9oDldrv15JNPavv27Tp58mSbC9vfeustu0sCAAAwyvaA9dvf/la7du3SuHHjdP75/vkCRgAAAF%2ByPWBt3bpVK1asUGpqqt0fDQAAYAvbb9MQHh6u6Ohouz8WAADANrYHrIkTJ2rFihVqaWmx%2B6MBAABsYfspwtraWhUVFemvf/2rLrzwQoWEhHjNr1692u6SAAAAjPLLbRrGjx/vj48FAACwhe0BKzc31%2B6PBAAAsJXt12BJUnV1tfLz8zV37lz985//1BtvvKH9%2B/f7oxQAAADjbA9YX3zxhSZMmKDXXntNb775purq6vT6669rypQpKi0ttbscAAAA42wPWI8%2B%2BqhGjx6tzZs367zzzpMkPf744xo1apSWLl1qdzkAAADG2R6wtm/frlmzZnl9sbPT6dTdd9%2Bt3bt3210OAACAcbYHrNbWVrW2trYZP3HihIKDg%2B0uBwAAwDjbA9bw4cP13HPPeYWs2tpa5eXladiwYXaXAwAAYJztAev%2B%2B%2B/Xrl27NHz4cDU2NupXv/qVRo4cqYMHD2rBggV2lwMAAGCc7ffBiouL05/%2B9CcVFRVpz549am1t1bRp0zRx4kR1797d7nIAAACM88ud3MPCwjR16lR/fDQAAIDP2R6wpk%2Bf/r3zfBchAAAIdLYHrD59%2Bng9b25u1hdffKFPP/1UM2bMsLscAAAA4zrMdxEWFBSoqqrK5moAAADM88t3EZ7JxIkTtWnTJn%2BXAQAAcM46TMD6%2BOOPudEoAADoFDrERe7Hjx/XP/7xD9188812lwMAAGCc7QErPj7e63sIJem8887Trbfeql/84hd2lwMAAGCc7QHr0UcftfsjAQAAbGV7wProo4/a/dorrrjCh5UAAAD4hu0B67bbbvOcIrQsyzP%2B7TGHw6E9e/bYXR4AAMA5sz1gPfvss8rJydG8efM0ZMgQhYSEaOfOncrOztYNN9yg66%2B/3u6SAAAAjLL9Ng25ublatGiRrr32WkVFRSkiIkLDhg1Tdna21q1bpz59%2BngeAAAAgcj2gFVdXX3G8NS9e3cdPXrU7nIAAACMsz1gJSUl6fHHH9fx48c9Y7W1tcrLy9OVV15pdzkAAADG2X4N1m9%2B8xtNnz5dV111lfr16yfLsvT5558rJiZGq1evtrscAAAA42wPWAkJCXr99ddVVFSkffv2SZJuueUWjRs3TmFhYXaXAwAAYJztAUuSLrjgAk2dOlUHDx7UhRdeKOnU3dwBAAA6A9uvwbIsS0uXLtUVV1yh8ePHq6qqSgsWLFBWVpZOnjxpdzkAAADG2R6w1qxZo40bN%2Bp//ud/FBISIkkaPXq0Nm/erPz8fLvLAQAAMM72gFVYWKhFixZp8uTJnru3X3/99crJydH//u//2l0OAACAcbYHrIMHD2rAgAFtxhMTE%2BV2u%2B0uBwAAwDjbA1afPn20c%2BfONuPvvvuu54J3AACAQGb7bxHOnj1bDz74oNxutyzL0gcffKDCwkKtWbNG999/v93lAAAAGGd7wJoyZYqam5v1zDPPqKGhQYsWLVLPnj117733atq0aXaXAwAAYJztAauoqEhjx45Venq6jhw5IsuyFB0dbXcZAAAAPmP7NVjZ2dmei9l79ux5zuGqqalJ48eP17Zt2zxjFRUVmjlzppKSknT99ddry5YtXu95//33NX78eLlcLk2fPl0VFRVe8y%2B%2B%2BKJGjBih5ORkPfDAA6qvrz%2BnGgEAQNdie8Dq16%2BfPv30UyPbamxs1K9//WuVl5d7xizLUkZGhnr16qUNGzZo4sSJmjNnjg4dOiRJOnTokDIyMjR58mS98sor6tmzp%2B6%2B%2B25ZliVJevPNN5Wfn6/s7GytWrVKpaWlysvLM1IvAADoGmw/RZiYmKj77rtPK1asUL9%2B/RQaGuo1n5ub267t7N27V3PnzvUEo9O2bt2qiooKvfzyywoPD1dCQoI%2B%2BOADbdiwQffcc4/Wr1%2Bvyy67TLfffrvn89LS0vThhx9q6NChWr16tWbMmKGRI0dKkh588EHNnj1b8%2BbN47sSAQBAu9h%2BBOuzzz5TSkqKIiIi5Ha7dfDgQa9He50ORIWFhV7jpaWluvTSSxUeHu4ZS0lJUUlJiWc%2BNTXVMxcWFqaBAweqpKRELS0t2rlzp9d8UlKSTp48qbKysh%2B7ywAAoIux5QjWkiVLNGfOHIWHh2vNmjVGtnnzzTefcdztdis2NtZrLDo6WlVVVT84f%2BzYMTU2NnrNO51O9ejRw/P%2B9qiurm5z01SnM7zN556r4GDb8/E5cTo7fr2nexpove3I6Kl59NQ8euobXbmvtgSsF154QbNnz/Y6qnTnnXcqJyfHeOior6/3fMfhaSEhIWpqavrB%2BYaGBs/z73p/exQWFrb5XsWMjAxlZma2exudUVRUhL9LaLfISE4Hm0ZPzaOn5tFT3%2BiKfbUlYH37OilJ%2Buijj9TY2Gj8s0JDQ1VbW%2Bs11tTUpG7dunnmvx2WmpqaFBkZ6bke7EzzZ3P9VXp6ukaNGuU15nSG6%2BjRE%2B3eRnsE2k8EpvffF4KDgxQZGaZjx%2BrV0tLq73I6BXpqHj01j576Rkfoq79%2BuLf9Indfi4uL0969e73GampqPEfK4uLiVFNT02Z%2BwIAB6tGjh0JDQ1VTU6OEhARJUnNzs2praxUTE9PuGmJjY9scmXO7v1Jzc9f%2BRxtI%2B9/S0hpQ9QYCemoePTWPnvpGV%2BxrYB0CaQeXy6VPPvnEc7pPkoqLi%2BVyuTzzxcXFnrn6%2Bnrt3r1bLpdLQUFBGjRokNd8SUmJnE6nEhMT7dsJAAAQ0GwLWA6Hw5bPGTJkiHr37q2FCxeqvLxcy5cv144dO3TjjTdKOvVVPdu3b9fy5ctVXl6uhQsXqm/fvho6dKikUxfPr1y5Ups3b9aOHTu0ePFi3XTTTdyiAQAAtJttpwhzcnK87nl18uRJ5eXlKSLC%2B9xoe%2B%2BD9V2Cg4O1bNkyZWVlafLkybroootUUFCg%2BPh4SVLfvn31%2B9//Xo888ogKCgqUnJysgoICTwAcN26cvvzySy1atEhNTU265pprNG/evHOqCQAAdC0O60xXoBt22223tfu1pm7j0NG43V8Z36bTGaQxS98zvl1f2XRvmr9L%2BEFOZ5CioiJ09OiJLne9gK/QU/PoqXn01Dc6Ql9jYs73y%2BfacgSrs4YmAACAM%2Bl0F7kDAAD4GwELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwp78LQNdx3ZN/83cJZ2XTvWn%2BLgEAEKA4ggUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgWKcNWH/5y190ySWXeD0yMzMlSbt379bUqVPlcrk0ZcoU7dq1y%2Bu9RUVFGj16tFwulzIyMnTkyBF/7AIAAAhQnTZg7d27VyNHjtSWLVs8j5ycHNXV1enOO%2B9UamqqXn31VSUnJ%2Buuu%2B5SXV2dJGnHjh3KysrSnDlzVFhYqGPHjmnhwoV%2B3hsAABBIOm3A2rdvn/r376%2BYmBjPIzIyUq%2B//rpCQ0M1f/58JSQkKCsrSxEREXrjjTckSWvXrtV1112nSZMmKTExUUuWLNE777yjiooKP%2B8RAAAIFJ06YPXr16/NeGlpqVJSUuRwOCRJDodDgwcPVklJiWc%2BNTXV8/revXsrPj5epaWlttQNAAACn9PfBfiCZVn67LPPtGXLFj333HNqaWnR2LFjlZmZKbfbrZ/85Cder4%2BOjlZ5ebkkqbq6WrGxsW3mq6qq2v351dXVcrvdXmNOZ3ib7Z6r4OBOm487BKeT/ppwep2yXs2hp%2BbRU9/oyn3tlAHr0KFDqq%2BvV0hIiJ588kkdPHhQOTk5amho8Ix/U0hIiJqamiRJDQ0N3zvfHoWFhcrPz/cay8jI8Fxkj8AQFRXh7xI6lcjIMH%2BX0OnQU/PoqW90xb52yoDVp08fbdu2TRdccIEcDocGDBig1tZWzZs3T0OGDGkTlpqamtStWzdJUmho6Bnnw8LavzjS09M1atQorzGnM1xHj574kXt0Zl3xJwI7mf776qqCg4MUGRmmY8fq1dLS6u9yOgV6ah499Y2O0Fd//bDcKQOWJPXo0cPreUJCghobGxUTE6OamhqvuZqaGs/pu7i4uDPOx8TEtPuzY2Nj25wOdLu/UnMz/2gDCX9fZrW0tNJTw%2BipefTUN7piXzvlIZD33ntPQ4cOVX19vWdsz5496tGjh1JSUvTxxx/LsixJp67X2r59u1wulyTJ5XKpuLjY877KykpVVlZ65gEAAH5IpwxYycnJCg0N1W9%2B8xvt379f77zzjpYsWaI77rhDY8eO1bFjx/Twww9r7969evjhh1VfX6/rrrtOkjRt2jRt3LhR69evV1lZmebPn6%2Brr75aF154oZ/3CgAABIpOGbC6d%2B%2BulStX6siRI5oyZYqysrKUnp6uO%2B64Q927d9dzzz2n4uJiTZ48WaWlpVq%2BfLnCw8MlnQpn2dnZKigo0LRp03TBBRcoNzfXz3sEAAACicM6fa4MPuV2f2V8m05nkMYsfc/4dnHKpnvT/F1Cp%2BB0BikqKkJHj57octdg%2BAo9NY%2Be%2BkZH6GtMzPl%2B%2BdxOeQQLAADAnwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGOf1dANBRXffk3/xdwlnZdG%2Bav0sAAPwbR7AAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAOoPGxkY98MADSk1N1fDhw/X888/7uyQAABBA%2BC7CM1iyZIl27dqlVatW6dChQ1qwYIHi4%2BM1duxYf5cGfKdA%2Bu5EvjcRQGdHwPqWuro6rV%2B/Xn/4wx80cOBADRw4UOXl5XrppZcIWAAAoF04RfgtZWVlam5uVnJysmcsJSVFpaWlam1t9WNlAAAgUHAE61vcbreioqIUEhLiGevVq5caGxtVW1urnj17/uA2qqur5Xa7vcacznDFxsYarTU4mHyMwBRIpzMD0V/uG%2BHvEgLO6f9P%2BX/VrK7cVwLWt9TX13uFK0me501NTe3aRmFhofLz873G5syZo3vuucdMkf9WXV2tGf%2BvXOnp6cbDW1dUWTfZAAAH5UlEQVRVXV2twsJCemoQPTWPnppXXV2tVatW0FPDunJfu16k/AGhoaFtgtTp5926dWvXNtLT0/Xqq696PdLT043X6na7lZ%2Bf3%2BZoGX48emoePTWPnppHT32jK/eVI1jfEhcXp6NHj6q5uVlO56n2uN1udevWTZGRke3aRmxsbJdL6gAA4GscwfqWAQMGyOl0qqSkxDNWXFysQYMGKSiIdgEAgB9GYviWsLAwTZo0SYsXL9aOHTu0efNmPf/885o%2Bfbq/SwMAAAEiePHixYv9XURHM2zYMO3evVuPPfaYPvjgA/33f/%2B3pkyZ4u%2ByzigiIkJDhgxRRESEv0vpNOipefTUPHpqHj31ja7aV4dlWZa/iwAAAOhMOEUIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyA1cE0NjbqgQceUGpqqoYPH67nn3/%2BO1%2B7e/duTZ06VS6XS1OmTNGuXbu85ouKijR69Gi5XC5lZGToyJEjvi6/QzLZ09TUVF1yySVejxMnTvh6Fzqcs%2BnpaX//%2B9/1X//1X23GWaenmOwp6/RrZ9PXv/71r5o4caKSk5M1YcIEvfXWW17zrNVTTPa0U69VCx1Kdna2NWHCBGvXrl3W//3f/1nJycnWpk2b2rzuxIkTVlpamvXoo49ae/futR566CHrZz/7mXXixAnLsiyrtLTUuvzyy63XXnvN2rNnj3Xrrbdad955p9270yGY6mlVVZXVv39/68CBA1Z1dbXn0draavcu%2BV17e3paWVmZ9bOf/cwaOXKk1zjr9Gumeso69dbevu7Zs8caOHCgtWrVKuvzzz%2B31q5daw0cONDas2ePZVms1W8y1dPOvlYJWB3IiRMnrEGDBllbt271jBUUFFi33nprm9euX7/eGjVqlGchtra2WmPGjLE2bNhgWZZlzZs3z1qwYIHn9YcOHbIuueQS68CBAz7ei47FZE//9re/WWlpafYU3oGdTU8ty7LWrVtnJSUlWRMmTGgTBlinp5jsKev0a2fT17y8PGv27NleY7fffrv1%2BOOPW5bFWj3NZE87%2B1rlFGEHUlZWpubmZiUnJ3vGUlJSVFpaqtbWVq/XlpaWKiUlRQ6HQ5LkcDg0ePBglZSUeOZTU1M9r%2B/du7fi4%2BNVWlpqw550HCZ7unfvXv3nf/6nfcV3UGfTU0l699139bvf/U4zZ85sM8c6PcVkT1mnXzubvt5www2677772mzjq6%2B%2BksRaPc1kTzv7WiVgdSBut1tRUVEKCQnxjPXq1UuNjY2qra1t89rY2FivsejoaFVVVUmSqqurv3e%2BqzDZ03379qm%2Bvl633Xabhg8frl/%2B8pf67LPPfL8THczZ9FSSli1bpmuuueaM22KdnmKyp6zTr51NXxMSEpSYmOh5Xl5erg8%2B%2BEBXXnmlJNbqaSZ72tnXKgGrA6mvr/datJI8z5uamtr12tOva2ho%2BN75rsJkT/fv369//etf%2BtWvfqVly5apW7dumjlzpo4fP%2B7DPeh4zqanP4R1eorJnrJOv/Zj%2B3rkyBHdc889Gjx4sOeXCFirp5jsaWdfq05/F4CvhYaGtlmgp59369atXa89/brvmg8LCzNddodmsqcrV67UyZMnFRERIUlaunSpfv7zn%2Bvtt9/WhAkTfLULHc7Z9PTHbot1%2BuN7yjr92o/pa01NjWbNmiXLsvT0008rKCjoe7fFWv3xPe3sa5UjWB1IXFycjh49qubmZs%2BY2%2B1Wt27dFBkZ2ea1NTU1XmM1NTWeQ9jfNR8TE%2BOj6jsmkz0NCQnx/EcgnfqPpm/fvjp8%2BLAP96DjOZuetmdbrFOzPWWdfu1s%2B3r48GHdcsstampq0urVq9WzZ0%2BvbbFWzfa0s69VAlYHMmDAADmdTs9F1ZJUXFysQYMGeRL/aS6XSx9//LEsy5IkWZal7du3y%2BVyeeaLi4s9r6%2BsrFRlZaVnvqsw1VPLsjR69Gi9%2BuqrntfX1dXpiy%2B%2B0MUXX2zPznQQZ9PTH8I6PcVUT1mn3s6mr3V1dbrjjjsUFBSktWvXKi4uzmuetXqKqZ52hbUavHjx4sX%2BLgKnnHfeeaqsrNS6det02WWXaefOncrLy9PcuXOVkJAgt9ut4OBgOZ1O/cd//IdWrFihqqoqxcfH65lnnlFZWZmys7N13nnnqVevXsrNzVVMTIyCg4O1aNEi9e/fX7fccou/d9NWpnoaEhKiAwcO6OWXX9aAAQNUX1%2BvnJwctbS06P777z/rYBHIzqan37Rnzx59%2BOGHmjFjhmeMdXqKqZ46HA7W6TecTV/z8/P1zjvvaNmyZTr//PNVV1enuro6tba2KjQ0lLX6byZ72unXqt9uEIEzqqurs%2BbPn28lJSVZw4cPt1544QXPXP/%2B/T33ZLKsUze%2BmzRpkjVo0CDrxhtvtD755BOvbW3YsMH6%2Bc9/biUlJVkZGRnWkSNH7NqNDsVUTxsaGqzc3FwrLS3Ncrlc1l133WUdOnTIzl3pMM6mp6dt2LChzT2bTo%2BzTs31lHXqrb19vfbaa63%2B/fu3eXzz3les1VNM9bSzr1WHZf37fAgAAACM6ATH4AAAADoWAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADPv/8AR5enMsi9UAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1924451761236797912”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:90%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0386548</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0275535</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0447147</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0249856</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0239057</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0362555</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0282845</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0202544</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0417362</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1635)</td> <td class=”number”>1635</td> <td class=”number”>50.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1924451761236797912”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1480</td> <td class=”number”>46.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000803939</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.001247</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00135308</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00159667</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.156494523</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.16655563</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.170502984</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.202224469</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.256213169</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_SUPERCPTH14”>SUPERCPTH14<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>1776</td>

</tr> <tr>

<th>Unique (%)</th> <td>55.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.018324</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>0.24808</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>41.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5966604621066649534”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5966604621066649534,#minihistogram-5966604621066649534”
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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5966604621066649534”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5966604621066649534”

aria-controls=”quantiles-5966604621066649534” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5966604621066649534” aria-controls=”histogram-5966604621066649534”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5966604621066649534” aria-controls=”common-5966604621066649534”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5966604621066649534” aria-controls=”extreme-5966604621066649534”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5966604621066649534”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0</td>

</tr> <tr>

<th>Median</th> <td>0.014289</td>

</tr> <tr>

<th>Q3</th> <td>0.029034</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.058677</td>

</tr> <tr>

<th>Maximum</th> <td>0.24808</td>

</tr> <tr>

<th>Range</th> <td>0.24808</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.029034</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.023228</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.2676</td>

</tr> <tr>

<th>Kurtosis</th> <td>10.824</td>

</tr> <tr>

<th>Mean</th> <td>0.018324</td>

</tr> <tr>

<th>MAD</th> <td>0.017195</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>2.3571</td>

</tr> <tr>

<th>Sum</th> <td>57.39</td>

</tr> <tr>

<th>Variance</th> <td>0.00053954</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5966604621066649534”>

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5u4gIZ12AdLmcNd%2B6fJ2Ws1OQr73I117ka69QyDdkC9Zbb72lLl266Ic//KHf%2BPLly3X69Gk1b95ckrRgwQJdd911evfddxUVFVWvSNU9/vob4r9Lbm6ucnJy/MYyMjKUmZl5LksJGbGxzYM9hXMSExMd7CmENPK1F/nai3zt5eR8Q7ZgffDBB/rJT35SbzwyMtLvKlVUVJTatm2roqIi9ejRQ6WlpaqurpbLdSYaj8ejpk2bKiYmpsGvnZ6ergEDBviNuVzNVFpafo6rOTunNXvT67dbRES4YmKiVVZWoZoa3oNnGvnai3ztRb72MplvsP5xH5IFy7Isbd26VT//%2Bc/rjQ8aNEgTJ07UrbfeKunMTw5%2B%2BeWXat%2B%2Bvbp06SKXy6X8/HylpqZKkvLy8tStWzeFhze8zMTHx9e7HejxnFB19YX9TejU9dfU1Dp27k5AvvYiX3uRr72cnG9IFqyDBw%2BqvLy83u3BsLAw9evXT7///e916aWXqlWrVlq4cKEuueQSXXfddYqIiNCwYcM0a9YsPf744youLtYLL7ygrKysIK0EAAA4UUgWrK%2B%2B%2BkqSdPHFF9fbNm3aNLlcLk2ZMkUnT55U7969tXTpUkVEREiSpk%2BfrlmzZmns2LFq0aKFJk%2BerOuvvz6g8wcAAM4WZn3zkzphC4/nhPFjulzhGrTgA%2BPHtcubv%2BgT7Ck0issVrtjY5iotLXfsJerzGfnai3ztRb72Mpmv233Rd%2B9kA2e9SxoAAMABKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhji9YXq9XN910kzZt2uQbmzdvnjp16uT3tXr1at/2DRs2aODAgUpKSlJGRoaOHj3q22ZZlhYsWKDevXurZ8%2Bemj9/vmprawO6JgAA4GyuYE/g%2B6iqqtKUKVO0c%2BdOv/HCwkJNmTJFw4cP9421aNFCkrRlyxbNnDlTs2fPVufOnfXYY49p%2BvTpeu655yRJL774ojZs2KCcnBxVV1dr2rRpat26tSZMmBC4hQEAAEdz7BWsXbt26Y477tC%2BffvqbSssLNSVV14pt9vt%2B4qOjpYkrV69WjfccIOGDRumzp07a/78%2BXrvvfe0f/9%2BSdLKlSuVmZmp1NRU9e7dW1OnTtXLL78c0LUBAABnc2zB%2Bvjjj9WrVy/l5ub6jZ88eVJFRUVq167dWZ9XUFCg1NRU3%2BM2bdooMTFRBQUFKioq0uHDh3X11Vf7tqekpOjgwYMqLi62ZR0AACD0OPYW4ahRo846XlhYqLCwMD377LN6//331bJlS919992%2B24XFxcWKj4/3e07r1q115MgReTweSfLbHhcXJ0k6cuRIved9m%2BLiYt%2Bx6rhczRr8/IaKiHBWP3a5nDXfunydlrNTkK%2B9yNde5GuvUMjXsQXr2%2BzevVthYWFq3769Ro8erU8%2B%2BUS//vWv1aJFCw0aNEiVlZWKjIz0e05kZKS8Xq8qKyt9j7%2B%2BTTrzZvqGys3NVU5Ojt9YRkaGMjMzz3VZISE2tnmwp3BOYmKigz2FkEa%2B9iJfe5GvvZycb8gVrGHDhql///5q2bKlJKlz587au3ev1qxZo0GDBikqKqpeWfJ6vYqOjvYrU1FRUb4/S/K9h6sh0tPTNWDAAL8xl6uZSkvLz3ldZ%2BO0Zm96/XaLiAhXTEy0ysoqVFPDT5KaRr72Il97ka%2B9TOYbrH/ch1zBCgsL85WrOu3bt9fGjRslSQkJCSopKfHbXlJSIrfbrYSEBEmSx%2BNR27ZtfX%2BWJLfb3eA5xMfH17sd6PGcUHX1hf1N6NT119TUOnbuTkC%2B9iJfe5GvvZycr7MugTTAwoULNW7cOL%2BxHTt2qH379pKkpKQk5eXl%2BbYdPnxYhw8fVlJSkhISEpSYmOi3PS8vT4mJicbfPwUAAEJXyF3B6t%2B/v5YuXarly5dr0KBB%2BvDDD/X6669r5cqVkqSRI0dqzJgx6t69u7p166bHHntM/fr102WXXebbvmDBAl1yySWSpCeffFLjx48P2noAAIDzhFzBuuqqq7Rw4UI988wzWrhwoS699FI9%2BeSTSk5OliQlJydrzpw5euaZZ3T8%2BHH16dNHc%2BfO9T1/woQJ%2BuqrrzRp0iRFRETo9ttvr3dFDAAA4L8JsyzLCvYkLgQezwnjx3S5wjVowQfGj2uXN3/RJ9hTaBSXK1yxsc1VWlru2PcAnM/I117kay/ytZfJfN3uiwzNqnFC7j1YAAAAwUbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDAl6wRowYoVdeeUUnTpwI9EsDAAAERMALVu/evfXss8%2Bqb9%2B%2BevDBB/Xhhx/KsqxATwMAAMA2AS9YU6ZM0bvvvqvFixcrIiJCkydPVr9%2B/fT0009rz549gZ4OAACAca5gvGhYWJj69OmjPn36qKKiQqtWrdLixYu1dOlS9ejRQ2PHjtX1118fjKkBAAB8b0EpWJJUXFysP//5z/rzn/%2BsL774Qj169NDw4cN15MgR/epXv9Inn3yimTNnBmt6AAAA5yzgBWv9%2BvVav369Nm3apFatWmnYsGF65pln1K5dO98%2Bbdq00WOPPUbBAgAAjhTwgjVz5kz1799fixYt0o9//GOFh9d/G1j79u01evToQE8NAADAiIAXrPfff1%2BxsbE6duyYr1xt2bJFXbt2VUREhCSpR48e6tGjR6CnBgAAYETAf4rw5MmTGjJkiJ5//nnf2H333adbbrlFhw8fDvR0AAAAjAt4wXr88cf1gx/8QHfffbdv7I033lCbNm2UlZUV6OkAAAAYF/CC9e9//1uPPPKI3G63b6xVq1Z66KGHtHHjxkYfz%2Bv16qabbtKmTZt8Y/n5%2BfrZz36m5ORkDR48WGvXrvV7zk9/%2BlN16tTJ7%2BuLL76QJFmWpQULFqh3797q2bOn5s%2Bfr9ra2nNcLQAAuBAF/D1YLpdLZWVl9cYrKioa/YnuVVVVmjJlinbu3Okb83g8uvfeezVy5Eg98cQT%2BvTTTzV9%2BnS53W7169dPNTU12rt3r1avXu33k4uxsbGSpBdffFEbNmxQTk6OqqurNW3aNLVu3VoTJkw4twUDAIALTsCvYP34xz/WvHnztG/fPt/Y/v37lZWVpbS0tAYfZ9euXbrjjjv8jiNJb7/9tuLi4vTggw%2BqXbt2Gjp0qIYNG6a//OUvkqQDBw7o9OnTuuqqq%2BR2u31fLteZrrly5UplZmYqNTVVvXv31tSpU/Xyyy8bWDkAALhQBPwK1sMPP6y7775bgwcPVkxMjCSprKxMXbt21fTp0xt8nI8//li9evXSL3/5S3Xv3t03npaWpi5dutTb/%2BTJk5LOFLM2bdooKiqq3j5FRUU6fPiwrr76at9YSkqKDh48qOLiYsXHxzd4fgAA4MIV8ILVunVr/elPf9K//vUv7dy5Uy6XSz/84Q91zTXXKCwsrMHHGTVq1FnH27Ztq7Zt2/oef/XVV/rrX/%2BqyZMnS5IKCwvVpEkT3X///dq2bZsuv/xyPfTQQ7rqqqvk8Xgkya9IxcXFSZKOHDnS4IJVXFzsO1Ydl6uZ8YIWERHwC5Dfi8vlrPnW5eu0nJ2CfO1FvvYiX3uFQr5B%2BVU5ERERSktLa9QtwXNRWVmpyZMnKy4uTunp6ZKkPXv26Pjx4xoxYoQyMzP1xz/%2BUWPHjtUbb7yhyspKSVJkZKTvGHV/9nq9DX7d3Nxc5eTk%2BI1lZGQoMzPz%2By7J0WJjmwd7CuckJiY62FMIaeRrL/K1F/nay8n5BrxgeTwe/e53v9PmzZt1%2BvTpem9sf%2Bedd4y8Tnl5uSZOnKi9e/fqD3/4g6Kjz/wlzZ07V5WVlWrRooUkadasWdq8ebPWr1%2Bva6%2B9VtKZMlV3C7GuWNU9vyHS09M1YMAAvzGXq5lKS8u/97q%2BzmnN3vT67RYREa6YmGiVlVWopoafJDWNfO1FvvYiX3uZzDdY/7gPeMH69a9/rW3btmno0KG66KKLbHmNkydP6p577tG%2Bffu0YsUKv58WdLlcvnIlSWFhYWrfvr2KioqUkJAg6UwJrLvNWHer7%2BsfK/Fd4uPj690O9HhOqLr6wv4mdOr6a2pqHTt3JyBfe5GvvcjXXk7ON%2BAFa%2BPGjVq2bJlSU1NtOX5tba0mTZqkAwcOaNWqVerQoYPf9jFjxqhXr16aNGmSb//PP/9cd955pxISEpSYmKi8vDxfwcrLy1NiYiJvcAcAAA0W8ILVrFkztW7d2rbjv/rqq9q0aZOWLFmimJgY3xWoJk2aqGXLlhowYIAWLVqkLl266PLLL9fKlSt14sQJDR8%2BXJI0cuRILViwQJdccokk6cknn9T48eNtmy8AAAg9AS9Yt9xyi5YtW6Y5c%2Bb4frmzSW%2B99ZZqa2t1//33%2B4337NlTq1at0rhx41RVVaV58%2BappKRESUlJevHFF323DSdMmKCvvvpKkyZNUkREhG6//XaNGzfO%2BDwBAEDoCrMa%2B/Hp39P06dO1YcMGxcTE6LLLLvP7iT3pzAd9hiKP54TxY7pc4Rq04APjx7XLm7/oE%2BwpNIrLFa7Y2OYqLS137HsAzmfkay/ytRf52stkvm63Pe/3/i5B%2BZiGm266KRgvCwAAEBABL1hZWVmBfkkAAICACsoHKRUXFysnJ0dTpkzRV199pb/97W/avXt3MKYCAABgXMAL1pdffqmbb75Zf/rTn/TWW2/p1KlTeuONN3TbbbepoKAg0NMBAAAwLuAF64knntDAgQP19ttvq0mTJpKkp556SgMGDNCCBQsCPR0AAADjAl6wNm/erLvvvtvvFzu7XC5NnDhR27dvD/R0AAAAjAt4waqtrVVtbf0fuSwvL7flc7EAAAACLeAFq2/fvnruuef8StaxY8eUnZ2t3r17B3o6AAAAxgW8YD3yyCPatm2b%2Bvbtq6qqKj3wwAPq37%2B/Dhw4oIcffjjQ0wEAADAu4J%2BDlZCQoNdff10bNmzQZ599ptraWo0cOVK33HKL79fVAAAAOFlQPsk9OjpaI0aMCMZLAwAA2C7gBeuuu%2B76r9tD9XcRAgCAC0fAC9all17q97i6ulpffvmlvvjiC40dOzbQ0wEAADDuvPldhIsWLdKRI0cCPBsAAADzgvK7CM/mlltu0ZtvvhnsaQAAAHxv503B%2Bs9//sMHjQIAgJBwXrzJ/eTJk/r88881atSoQE8HAADAuIAXrMTERL/fQyhJTZo00ejRo/XTn/400NMBAAAwLuAF64knngj0SwIAAARUwAvWJ5980uB9r776ahtnAgAAYI%2BAF6wxY8b4bhFaluUb/%2BZYWFiYPvvss0BPDwAA4HsLeMF69tlnNW/ePE2bNk09e/ZUZGSktm7dqjlz5mj48OG68cYbAz0lAAAAowL%2BMQ1ZWVl69NFHNXjwYMXGxqp58%2Bbq3bu35syZozVr1ujSSy/1fQEAADhRwAtWcXHxWctTixYtVFpa2ujjeb1e3XTTTdq0aZNvbP/%2B/Ro3bpy6d%2B%2BuG2%2B8UR9%2B%2BKHfc/71r3/ppptuUlJSku666y7t37/fb/tLL72ktLQ0JScna8aMGaqoqGj0vAAAwIUr4AWre/fueuqpp3Ty5Enf2LFjx5Sdna1rrrmmUceqqqrSgw8%2BqJ07d/rGLMtSRkaG4uLitG7dOt1yyy2aNGmSDh06JEk6dOiQMjIydOutt%2BrVV19Vq1atNHHiRN97v9566y3l5ORozpw5WrFihQoKCpSdnW1g5QAA4EIR8Pdg/epXv9Jdd92lH//4x2rXrp0sy9LevXvldru1cuXKBh9n165dmjJlit8b5SVp48aN2r9/v1555RU1a9ZMHTp00EcffaR169Zp8uTJWrt2rX70ox9p/Pjxks7csuzTp48%2B/vhj9erVSytXrtTYsWPVv39/SdLs2bM1YcIETZs2TdHR0eaCAAAAISvgV7A6dOigN954Q1OmTFH37t2VnJysmTNnav369brkkksafJy6QpSbm%2Bs3XlBQoCuvvFLNmjXzjaWkpCg/P9%2B3PTU11bctOjpaXbt2VX5%2BvmpqarR161a/7d27d9fp06e1Y8eOc10yAAC4wAT8CpYkXXzxxRoxYoQOHDigyy67TNKZT3NvjG/7tToej0fx8fF%2BY61bt9aRI0f8z9e6AAAazUlEQVS%2Bc3tZWZmqqqr8trtcLrVs2dL3fAAAgO8S8IJlWZaefPJJrVq1SqdPn9Zbb72lp59%2BWtHR0Zo1a1aji9Y3VVRUKDIy0m8sMjJSXq/3O7dXVlb6Hn/b8xuiuLhYHo/Hb8zlalav2H1fERHnze/qbhCXy1nzrcvXaTk7Bfnai3ztRb72CoV8A16wVq1apfXr1%2Bs3v/mN5syZI0kaOHCgZs%2Berbi4OP3yl7/8XsePiorSsWPH/Ma8Xq%2BaNm3q2/7NsuT1ehUTE6OoqCjf429ub8z7r3Jzc5WTk%2BM3lpGRoczMzAYfIxTFxjYP9hTOSUwM772zE/nai3ztRb72cnK%2BAS9Yubm5evTRRzVo0CDNnTtXknTjjTeqSZMmysrK%2Bt4FKyEhQbt27fIbKykp8V09SkhIUElJSb3tXbp0UcuWLRUVFaWSkhJ16NBBklRdXa1jx47J7XY3eA7p6ekaMGCA35jL1UylpeXnsqRv5bRmb3r9douICFdMTLTKyipUU1Mb7OmEHPK1F/nai3ztZTLfYP3jPuAF68CBA%2BrSpUu98c6dO9e7rXYukpKStHTpUlVWVvquWuXl5SklJcW3PS8vz7d/RUWFtm/frkmTJik8PFzdunVTXl6eevXqJUnKz8%2BXy%2BVS586dGzyH%2BPj4ercDPZ4Tqq6%2BsL8Jnbr%2Bmppax87dCcjXXuRrL/K1l5PzDfglkEsvvVRbt26tN/7%2B%2B%2B/73vD%2BffTs2VNt2rTR9OnTtXPnTi1dulRbtmzR7bffLkm67bbbtHnzZi1dulQ7d%2B7U9OnT1bZtW1%2BhGjVqlJYvX663335bW7Zs0axZs3THHXfwEQ0AAKDBAn4Fa8KECZo9e7Y8Ho8sy9JHH32k3NxcrVq1So888sj3Pn5ERIQWL16smTNn6tZbb9UPfvADLVq0SImJiZKktm3b6ve//70ef/xxLVq0SMnJyVq0aJHvl00PHTpUBw8e1KOPPiqv16vrr79e06ZN%2B97zAgAAF44w65uf1BkAubm5WrJkie%2BjD1q1aqV7771Xd999d6CnEjAezwnjx3S5wjVowQfGj2uXN3/RJ9hTaBSXK1yxsc1VWlru2EvU5zPytRf52ot87WUyX7f7IkOzapyAX8HasGGDhgwZovT0dB09elSWZal169aBngYAAIBtAv4erDlz5vjezN6qVSvKFQAACDkBL1jt2rXTF198EeiXBQAACJiA3yLs3Lmzpk6dqmXLlqldu3a%2BD/esk5WVFegpAQAAGBXwgrVnzx7fZ1KZ%2BNwrAACA801ACtb8%2BfM1adIkNWvWTKtWrQrESwIAAARNQN6D9eKLL6qiosJv7L777lNxcXEgXh4AACCgAlKwzvZRW5988omqqqoC8fIAAAAB5azfFgwAAOAAFCwAAADDAlaw6n7XHwAAQKgL2Mc0zJs3z%2B8zr06fPq3s7Gw1b97cbz8%2BBwsAADhdQArW1VdfXe8zr5KTk1VaWqrS0tJATAEAACBgAlKw%2BOwrAABwIeFN7gAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC8mC9dprr6lTp071vjp37ixJeuCBB%2Bpte/fdd33Pf%2Bmll5SWlqbk5GTNmDFDFRUVwVoKAABwoID8LsJAu/HGG5WWluZ7XF1drbFjx6pfv36SpMLCQmVnZ%2Buaa67x7XPxxRdLkt566y3l5OQoOztbrVu31vTp05Wdna1HH300oGsAAADOFZJXsJo2bSq32%2B37%2BvOf/yzLsjR16lR5vV4dOHBA3bp189snMjJSkrRy5UqNHTtW/fv311VXXaXZs2dr3bp1XMUCAAANFpIF6%2BuOHTum559/XlOmTFFkZKR2796tsLAwXXbZZfX2ramp0datW5Wamuob6969u06fPq0dO3YEctoAAMDBQvIW4detWbNG8fHxGjJkiCRp9%2B7datGihR566CF9/PHHuuSSSzR58mRdd911KisrU1VVleLj433Pd7lcatmypY4cOdLg1ywuLpbH4/Ebc7ma%2BR3XhIgIZ/Vjl8tZ863L12k5OwX52ot87UW%2B9gqFfEO6YFmWpbVr1%2Bqee%2B7xje3evVuVlZXq27ev7rvvPv3973/XAw88oNzcXMXFxUmS73ZhncjISHm93ga/bm5urnJycvzGMjIylJmZ%2BT1W43yDFnwQ7Ck0yr8fO1PKY2KigzyT0Ea%2B9iJfe5GvvZycb0gXrK1bt6qoqEhDhw71jU2cOFFjxozxvam9c%2BfO%2BvTTT/XHP/5Rv/zlLyWpXpnyer2Kjm74X3J6eroGDBjgN%2BZyNVNpafm5LuWsnNzsnaCsrEIxMdEqK6tQTU1tsKcTciIiwsnXRuRrL/K1l8l8Y2ObG5pV44R0wfrggw%2BUmprqK1OSFB4e7vdYktq3b69du3apZcuWioqKUklJiTp06CDpzE8gHjt2TG63u8GvGx8fX%2B92oMdzQtXVfBM6Sd03dU1NLX93NiJfe5GvvcjXXk7ON6QvgWzZskU9evTwG3vkkUc0ffp0v7EdO3aoffv2Cg8PV7du3ZSXl%2Bfblp%2BfL5fL5fsMLQAAgO8S0gVr586d%2BuEPf%2Bg3NmDAAP3lL3/R66%2B/ri%2B//FI5OTnKy8vT6NGjJUmjRo3S8uXL9fbbb2vLli2aNWuW7rjjjkbdIgQAABe2kL5FWFJSopiYGL%2Bx66%2B/Xr/5zW%2B0ZMkSHTp0SFdccYWWLVumtm3bSpKGDh2qgwcP6tFHH5XX69X111%2BvadOmBWP6AADAocIsy7KCPYkLgcdzwvgxXa5wx/1knpP8fWqaYmObq7S03LHvATifuVzh5Gsj8rUX%2BdrLZL5u90WGZtU4IX2LEAAAIBgoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwLCQLVh///vf1alTJ7%2BvzMxMSdL27ds1YsQIJSUl6bbbbtO2bdv8nrthwwYNHDhQSUlJysjI0NGjR4OxBAAA4FAhW7B27dql/v3768MPP/R9zZs3T6dOndJ9992n1NRUvfbaa0pOTtb999%2BvU6dOSZK2bNmimTNnatKkScrNzVVZWZmmT58e5NUAAAAnCdmCVVhYqI4dO8rtdvu%2BYmJi9MYbbygqKkoPPfSQOnTooJkzZ6p58%2Bb629/%2BJklavXq1brjhBg0bNkydO3fW/Pnz9d5772n//v1BXhEAAHCKkC5Y7dq1qzdeUFCglJQUhYWFSZLCwsLUo0cP5efn%2B7anpqb69m/Tpo0SExNVUFAQkHkDAADnC8mCZVmW9uzZow8//FCDBw/WwIEDtWDBAnm9Xnk8HsXHx/vt37p1ax05ckSSVFxc/F%2B3AwAAfBdXsCdgh0OHDqmiokKRkZH63e9%2BpwMHDmjevHmqrKz0jX9dZGSkvF6vJKmysvK/bm%2BI4uJieTwevzGXq1m94vZ9RUSEZD8%2Bb9TlS872IF97ka%2B9yNdeoZBvSBasSy%2B9VJs2bdLFF1%2BssLAwdenSRbW1tZo2bZp69uxZryx5vV41bdpUkhQVFXXW7dHR0Q1%2B/dzcXOXk5PiNZWRk%2BH6KEc4QExPt91/Yg3ztRb72Il97OTnfkCxYktSyZUu/xx06dFBVVZXcbrdKSkr8tpWUlPiuLiUkJJx1u9vtbvBrp6ena8CAAX5jLlczlZaWN2YJ38nJzd4JysoqFBMTrbKyCtXU1AZ7OiEnIiKcfG1EvvYiX3uZzDc2trmhWTVOSBasDz74QFOnTtU//vEP35Wnzz77TC1btlRKSoqef/55WZalsLAwWZalzZs36%2Bc//7kkKSkpSXl5ebr11lslSYcPH9bhw4eVlJTU4NePj4%2BvdzvQ4zmh6mq%2BCZ2k7pu6pqaWvzsbka%2B9yNde5GsvJ%2BcbkpdAkpOTFRUVpV/96lfavXu33nvvPc2fP1/33HOPhgwZorKyMj322GPatWuXHnvsMVVUVOiGG26QJI0cOVLr16/X2rVrtWPHDj300EPq16%2BfLrvssiCvCgAAOEVIFqwWLVpo%2BfLlOnr0qG677TbNnDlT6enpuueee9SiRQs999xzvqtUBQUFWrp0qZo1aybpTDmbM2eOFi1apJEjR%2Briiy9WVlZWkFcEAACcJMyyLCvYk7gQeDwnjB/T5QrXoAUfGD8uzvj71DTFxjZXaWm5Yy9Rn89crnDytRH52ot87WUyX7f7IkOzapyQvIIFAAAQTBQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsJAtWEVFRcrMzFTPnj2VlpamrKwsVVVVSZLmzZunTp06%2BX2tXr3a99wNGzZo4MCBSkpKUkZGho4ePRqsZQAAAAdyBXsCdrAsS5mZmYqJidHLL7%2Bs48ePa8aMGQoPD9fDDz%2BswsJCTZkyRcOHD/c9p0WLFpKkLVu2aObMmZo9e7Y6d%2B6sxx57TNOnT9dzzz0XrOUAAACHCckrWLt371Z%2Bfr6ysrJ0xRVXKDU1VZmZmdqwYYMkqbCwUFdeeaXcbrfvKzo6WpK0evVq3XDDDRo2bJg6d%2B6s%2BfPn67333tP%2B/fuDuSQAAOAgIVmw3G63li1bpri4OL/xkydP6uTJkyoqKlK7du3O%2BtyCggKlpqb6Hrdp00aJiYkqKCiwc8oAACCEhOQtwpiYGKWlpfke19bWavXq1erdu7cKCwsVFhamZ599Vu%2B//75atmypu%2B%2B%2B23e7sLi4WPHx8X7Ha926tY4cOdLg1y8uLpbH4/Ebc7ma1Tvu9xUREZL9%2BLxRly8524N87UW%2B9iJfe4VCviFZsL4pOztb27dv16uvvqpPP/1UYWFhat%2B%2BvUaPHq1PPvlEv/71r9WiRQsNGjRIlZWVioyM9Ht%2BZGSkvF5vg18vNzdXOTk5fmMZGRnKzMw0sh4ERkxMtN9/YQ/ytRf52ot87eXkfEO%2BYGVnZ2vFihV6%2Bumn1bFjR11xxRXq37%2B/WrZsKUnq3Lmz9u7dqzVr1mjQoEGKioqqV6a8Xq/vPVoNkZ6ergEDBviNuVzNVFpa/v0X9DVObvZOUFZWoZiYaJWVVaimpjbY0wk5ERHh5Gsj8rUX%2BdrLZL6xsc0NzapxQrpgzZ07V2vWrFF2drYGDx4sSQoLC/OVqzrt27fXxo0bJUkJCQkqKSnx215SUiK3293g142Pj693O9DjOaHqar4JnaTum7qmppa/OxuRr73I117kay8n5xuyl0BycnL0yiuv6KmnntLQoUN94wsXLtS4ceP89t2xY4fat28vSUpKSlJeXp5v2%2BHDh3X48GElJSUFZN4AAMD5QrJgFRYWavHixbr33nuVkpIij8fj%2B%2Brfv78%2B%2BeQTLV%2B%2BXPv27dMf/vAHvf766xo/frwkaeTIkVq/fr3Wrl2rHTt26KGHHlK/fv102WWXBXlVAADAKULyFuE777yjmpoaLVmyREuWLPHb9vnnn2vhwoV65plntHDhQl166aV68sknlZycLElKTk7WnDlz9Mwzz%2Bj48ePq06eP5s6dG4xlAAAAhwqzLMsK9iQuBB7PCePHdLnCNWjBB8aPizP%2BPjVNsbHNVVpa7tj3AJzPXK5w8rUR%2BdqLfO1lMl%2B3%2ByJDs2qckLxFCAAAEEwULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADAsJD8HCzDBaR%2BB8eYv%2BgR7CgCA/8MVLAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYa5gTwCAGTf87p/BnkKDvfmLPsGeAgDYiitYZ1FVVaUZM2YoNTVVffv21QsvvBDsKQEAAAfhCtZZzJ8/X9u2bdOKFSt06NAhPfzww0pMTNSQIUOCPTUAAOAAFKxvOHXqlNauXavnn39eXbt2VdeuXbVz5069/PLLFCzAECfdzpS4pQmg8bhF%2BA07duxQdXW1kpOTfWMpKSkqKChQbW1tEGcGAACcgitY3%2BDxeBQbG6vIyEjfWFxcnKqqqnTs2DG1atXqO49RXFwsj8fjN%2BZyNVN8fLzRuUZE0I%2BBQHDaFTcn%2BfvUtGBP4ZzU/f%2BX/w/bIxTypWB9Q0VFhV%2B5kuR77PV6G3SM3Nxc5eTk%2BI1NmjRJkydPNjPJ/1NcXKyxl%2BxUenq68fKGM/nm5uaSr03I117ka6/i4mKtWLGMfG0SCvk6txraJCoqql6RqnvctGnTBh0jPT1dr732mt9Xenq68bl6PB7l5OTUu1oGM8jXXuRrL/K1F/naKxTy5QrWNyQkJKi0tFTV1dVyuc7E4/F41LRpU8XExDToGPHx8Y5t3AAA4PvjCtY3dOnSRS6XS/n5%2Bb6xvLw8devWTeHhxAUAAL4bjeEboqOjNWzYMM2aNUtbtmzR22%2B/rRdeeEF33XVXsKcGAAAcImLWrFmzgj2J803v3r21fft2Pfnkk/roo4/085//XLfddluwp3VWzZs3V8%2BePdW8efNgTyUkka%2B9yNde5Gsv8rWX0/MNsyzLCvYkAAAAQgm3CAEAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbDOM1VVVZoxY4ZSU1PVt29fvfDCC9%2B67/bt2zVixAglJSXptttu07Zt2/y2b9iwQQMHDlRSUpIyMjJ09OhRu6d/3jOZb2pqqjp16uT3VV5ebvcSzmuNybfOv//9b/3kJz%2BpN875W5/JfDl/62tMvv/4xz90yy23KDk5WTfffLPeeecdv%2B2cv/WZzNcR56%2BF88qcOXOsm2%2B%2B2dq2bZv1v//7v1ZycrL15ptv1tuvvLzc6tOnj/XEE09Yu3btsubOnWtde%2B21Vnl5uWVZllVQUGBdddVV1p/%2B9Cfrs88%2Bs0aPHm3dd999gV7OecdUvkeOHLE6duxo7du3zyouLvZ91dbWBnpJ55WG5ltnx44d1rXXXmv179/fb5zz9%2BxM5cv5e3YNzfezzz6zunbtaq1YscLau3evtXr1aqtr167WZ599ZlkW5%2B%2B3MZWvU85fCtZ5pLy83OrWrZu1ceNG39iiRYus0aNH19t37dq11oABA3wnVG1trTVo0CBr3bp1lmVZ1rRp06yHH37Yt/%2BhQ4esTp06Wfv27bN5Fecvk/n%2B85//tPr06ROYiTtEY/K1LMtas2aN1b17d%2Bvmm2%2BuVwA4f%2BszmS/nb32NyTc7O9uaMGGC39j48eOtp556yrIszt%2BzMZmvU85fbhGeR3bs2KHq6molJyf7xlJSUlRQUKDa2lq/fQsKCpSSkqKwsDBJUlhYmHr06KH8/Hzf9tTUVN/%2Bbdq0UWJiogoKCgKwkvOTyXx37dqlyy%2B/PHCTd4DG5CtJ77//vn77299q3Lhx9bZx/tZnMl/O3/oak%2B/w4cM1derUesc4ceKEJM7fszGZr1POXwrWecTj8Sg2NlaRkZG%2Bsbi4OFVVVenYsWP19o2Pj/cba926tY4cOSJJKi4u/q/bL0Qm8y0sLFRFRYXGjBmjvn376t5779WePXvsX8R5rDH5StLixYt1/fXXn/VYnL/1mcyX87e%2BxuTboUMHde7c2fd4586d%2Buijj3TNNddI4vw9G5P5OuX8pWCdRyoqKvxOPkm%2Bx16vt0H71u1XWVn5X7dfiEzmu3v3bh0/flwPPPCAFi9erKZNm2rcuHE6efKkjSs4vzUm3%2B/C%2BVufyXw5f%2Bs713yPHj2qyZMnq0ePHr4fJuD8rc9kvk45f13BngD%2Bv6ioqHonWt3jpk2bNmjfuv2%2BbXt0dLTpaTuGyXyXL1%2Bu06dPq3nz5pKkBQsW6LrrrtO7776rm2%2B%2B2a4lnNcak%2B%2B5Hovz10y%2BnL/1nUu%2BJSUluvvuu2VZlp555hmFh4f/12Nx/prJ1ynnL1ewziMJCQkqLS1VdXW1b8zj8ahp06aKiYmpt29JSYnfWElJie%2By9Ldtd7vdNs3%2B/Gcy38jISN83t3Tmfx5t27ZVUVGRjSs4vzUm34Yci/PXn8l8OX/ra2y%2BRUVFuvPOO%2BX1erVy5Uq1atXK71icv/5M5uuU85eCdR7p0qWLXC6X743UkpSXl6du3br5mnudpKQk/ec//5FlWZIky7K0efNmJSUl%2Bbbn5eX59j98%2BLAOHz7s234hMpWvZVkaOHCgXnvtNd/%2Bp06d0pdffqn27dsHZjHnocbk%2B104f%2BszlS/n79k1Jt9Tp07pnnvuUXh4uFavXq2EhAS/7Zy/9ZnK10nnb8SsWbNmBXsSOKNJkyY6fPiw1qxZox/96EfaunWrsrOzNWXKFHXo0EEej0cRERFyuVz6n//5Hy1btkxHjhxRYmKilixZoh07dmjOnDlq0qSJ4uLilJWVJbfbrYiICD366KPq2LGj7rzzzmAvM2hM5RsZGal9%2B/bplVdeUZcuXVRRUaF58%2BappqZGjzzySKPLRKhoTL5f99lnn%2Bnjjz/W2LFjfWOcv/WZyjcsLIzz9ywak29OTo7ee%2B89LV68WBdddJFOnTqlU6dOqba2VlFRUZy/Z2EyX8ecv0H7gAic1alTp6yHHnrI6t69u9W3b1/rxRdf9G3r2LGj73OYLOvMh9kNGzbM6tatm3X77bdbn376qd%2Bx1q1bZ1133XVW9%2B7drYyMDOvo0aOBWsZ5y1S%2BlZWVVlZWltWnTx8rKSnJuv/%2B%2B61Dhw4FcinnpcbkW2fdunX1Pqepbpzz15%2BpfDl/z66h%2BQ4ePNjq2LFjva%2Bvf/YV5299pvJ1yvkbZln/dw8EAAAARpxH19IAAABCAwULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIb9P0hMR2Y9lluzAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5966604621066649534”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1346</td> <td class=”number”>41.9%</td> <td>

<div class=”bar” style=”width:76%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0305567</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026936</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0494438</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0443675</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.026583</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0175821</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0367377</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0236552</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0447888</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (1765)</td> <td class=”number”>1768</td> <td class=”number”>55.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5966604621066649534”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>1346</td> <td class=”number”>41.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.000611147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00117306</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00130335</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.00139067</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.173340267</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.18308312</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.193685842</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.211237854</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2480774</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants FY 2009”><s>School Breakfast Program participants FY 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_National School Lunch Program participants, FY 2015”><code>National School Lunch Program participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92183</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants FY 2011”><s>School Breakfast Program participants FY 2011</s><br/>

<small>Constant</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is constant and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Constant value</th> <td></td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2012”><s>School Breakfast Program participants, FY 2012</s><br/>

<small>Constant</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is constant and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Constant value</th> <td></td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2013”><s>School Breakfast Program participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants FY 2009”><code>School Breakfast Program participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98406</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2014”><s>School Breakfast Program participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2013”><code>School Breakfast Program participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99862</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_School Breakfast Program participants, FY 2015”><s>School Breakfast Program participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_School Breakfast Program participants, FY 2014”><code>School Breakfast Program participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99676</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2011”><s>State Population, 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2010”><code>State Population, 2010</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2013”><s>State Population, 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2012”><code>State Population, 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2014”><s>State Population, 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2013”><code>State Population, 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2015”><s>State Population, 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2014”><code>State Population, 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99997</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2016”><s>State Population, 2016</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2015”><code>State Population, 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99996</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2009”><s>State Population, 2009</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Child and Adult Care participants, FY 2015”><code>Child and Adult Care participants, FY 2015</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.92968</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2010”><s>State Population, 2010</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2009”><code>State Population, 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9999</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_State Population, 2012”><s>State Population, 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_State Population, 2011”><code>State Population, 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99998</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_StateFIPS “>StateFIPS <br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>34.869</td>

</tr> <tr>

<th>Minimum</th> <td>13</td>

</tr> <tr>

<th>Maximum</th> <td>56</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8121419053334836079”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8121419053334836079,#minihistogram8121419053334836079”
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</div> <div class=”row collapse col-md-12” id=”descriptives8121419053334836079”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8121419053334836079”

aria-controls=”quantiles8121419053334836079” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8121419053334836079” aria-controls=”histogram8121419053334836079”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8121419053334836079” aria-controls=”common8121419053334836079”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8121419053334836079” aria-controls=”extreme8121419053334836079”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8121419053334836079”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>13</td>

</tr> <tr>

<th>5-th percentile</th> <td>16</td>

</tr> <tr>

<th>Q1</th> <td>24</td>

</tr> <tr>

<th>Median</th> <td>33</td>

</tr> <tr>

<th>Q3</th> <td>45</td>

</tr> <tr>

<th>95-th percentile</th> <td>54</td>

</tr> <tr>

<th>Maximum</th> <td>56</td>

</tr> <tr>

<th>Range</th> <td>43</td>

</tr> <tr>

<th>Interquartile range</th> <td>21</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>12.849</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.36851</td>

</tr> <tr>

<th>Kurtosis</th> <td>-1.2503</td>

</tr> <tr>

<th>Mean</th> <td>34.869</td>

</tr> <tr>

<th>MAD</th> <td>11.184</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.14454</td>

</tr> <tr>

<th>Sum</th> <td>92576</td>

</tr> <tr>

<th>Variance</th> <td>165.1</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8121419053334836079”>

<img 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4vThg0b9N5776murk5Op1MDBw7U5ZdfLofDYfV4AAAA7bJdYElSWFiYxowZw0OCAAAgKNkusNxut0pKSrRz506dOHGizRPb33rrLYsmAwAA6BjbBdZf//pX7d69W1dffbV%2B9atfWT0OAADAabNdYL3//vtavXq10tLSrB4FAADgjNjuZRqioqIUFxdn9RgAAABnzHaBlZmZqdWrV%2BvUqVNWjwIAAHBGbPcQYVNTkzZt2qR//OMfuvDCCxUeHh5w/LnnnrNoMgAAgI6xXWBJ0uTJk60eAQCC1sSSd60eAejybBdYRUVFVo8AAABwVmz3HCxJamxsVGlpqe6%2B%2B2795z//0euvv659%2B/ZZPRYAAECH2C6w9u/fr9///vfasGGD3njjDR07dkxbtmxRdna2ampqrB4PAACgXbYLrIceekjjxo3Tm2%2B%2BqfPOO0%2BStHz5cmVkZOiRRx6xeDoAAID22S6wdu7cqVtuuSXgDzs7nU7NmjVLtbW1Fk4GAADQMbYLrNbWVrW2trZZ//777xUWFmbBRAAAAKfHdoE1evRoPfnkkwGR1dTUpOLiYo0cOdLCyQAAADrGdoE1d%2B5c7d69W6NHj1Zzc7P%2B8pe/6KqrrtKBAwd03333WT0eAABAu2z3OlhJSUl6%2BeWXtWnTJn322WdqbW3V9OnTlZmZqR49elg9HgAAQLtsF1iSFBkZqalTp1o9BgAAwBmxXWDNmDHjfx7nbxECAAC7s11g9enTJ%2BDyyZMntX//fn3xxRe66aabLJoKAACg42wXWL/0twjLysp08ODBTp4GAADg9Nnutwh/SWZmpl577TWrxwAAAGhX0ATWxx9/zAuNAgCAoGC7hwh/7knuR48e1eeff67rr7/egokAAABOj%2B0Cq3fv3gF/h1CSzjvvPN1www36wx/%2BYNFUAAAAHWe7wHrooYesHgEAAOCs2C6wduzY0eHrXnbZZedwEgAAgDNju8C68cYb/Q8R%2Bnw%2B//pP1xwOhz777LPOHxAAAKAdtgusJ554QosXL9Y999yj9PR0hYeH65NPPtGiRYt07bXXatKkSVaPCAAA8D/Z7mUaioqKNH/%2BfI0fP149e/ZUdHS0Ro4cqUWLFumFF15Qnz59/G8AAAB2ZLvAamxs/Nl46tGjhw4fPmzBRAAAAKfHdoE1bNgwLV%2B%2BXEePHvWvNTU1qbi4WJdffrmFkwEAAHSM7Z6Ddf/992vGjBm64oor1L9/f/l8Pv373/9WQkKCnnvuOavHAwAAaJftAmvAgAHasmWLNm3apL1790qS/vSnP%2Bnqq69WZGSkxdMBAAC0z3aBJUnnn3%2B%2Bpk6dqgMHDujCCy%2BU9MOruQMAAAQD2z0Hy%2Bfz6ZFHHtFll12myZMn6%2BDBg7rvvvtUWFioEydOdPh2Dh06pPz8fKWnp2vMmDEqKipSc3OzJKm%2Bvl4333yzhg0bpkmTJmnbtm0B//e9997T5MmT5XK5NGPGDNXX1xu9jwAAILTZLrDWrFmjV155RQ888IDCw8MlSePGjdObb76p0tLSDt2Gz%2BdTfn6%2BvF6vnn/%2Bea1YsUJvv/22SkpK5PP5lJubq/j4eFVWViozM1N5eXlqaGiQJDU0NCg3N1dZWVlav369YmNjNWvWrIAXPQUAAPhfbBdYFRUVmj9/vrKysvyv3j5p0iQtXrxYGzdu7NBt7Nu3T9XV1SoqKtJFF12ktLQ05efna9OmTXr//fdVX1%2BvRYsWacCAAZo5c6aGDRumyspKSdKLL76oIUOG6NZbb9VFF12koqIiff311/rwww/P2X0GAAChxXbPwTpw4IAGDRrUZv3SSy%2BV2%2B3u0G0kJCRo9erVio%2BPD1g/evSoampqNHjwYEVFRfnXU1NTVV1dLUmqqalRWlqa/1hkZKSSk5NVXV2tESNGnMldAjrFxJJ3rR4BAPBftgusPn366JNPPlHfvn0D1t955x3/E97bExMTozFjxvgvt7a2au3atRo5cqTcbrcSExMDrh8XF6eDBw9KUrvHO6KxsbFNDDqdUW1ut6txOjvvhGlYWLeAf4Gz0Zmfu7C3zvhc6Krfv0Lt68x2gXXbbbdp4cKFcrvd8vl82r59uyoqKrRmzRrNnTv3jG6zuLhYtbW1Wr9%2BvZ555hn/c7t%2BFB4erpaWFkmS1%2Bv9n8c7oqKios3zxXJzc5Wfn39G84eKnj2jO/19xsTw0h44e1Z87sKeOvNzoat9/wq1rzPbBVZ2drZOnjypVatW6fjx45o/f75iY2N15513avr06ad9e8XFxXr22We1YsUKXXzxxYqIiFBTU1PAdVpaWtS9e3dJUkRERJuYamlpUUxMTIff57Rp05SRkRGw5nRG6fDh7097/lDSmfc/LKybYmIideSIV6dOtXba%2B0Vo6upfu/j/OuNzoat%2B/zpXH1urws12gbVp0yZNmDBB06ZN07fffiufz6e4uLgzuq0HH3xQL7zwgoqLizV%2B/HhJUlJSkvbs2RNwPY/H43/4LikpSR6Pp83xn3te2C9JTExs83Cg2/2dTp7sOl8oP8eK%2B3/qVGuX/7jj7PE5hB915udCV/v%2BFWr31XYPeC5atMj//KXY2NgzjqvS0lKtW7dOy5cv19VXX%2B1fd7lc%2BvTTT3X8%2BHH/WlVVlVwul/94VVWV/5jX61Vtba3/OAAAQHtsF1j9%2B/fXF198cVa3sXfvXq1cuVK33367UlNT5Xa7/W/p6enq1auXCgoKVFdXp/Lycu3atUtTpkyR9MNDlDt37lR5ebnq6upUUFCgvn378huEAACgw2z3EOGll16qOXPmaPXq1erfv78iIiICjhcVFbV7G2%2B99ZZOnTqlVatWadWqVQHHPv/8c61cuVKFhYXKyspSv379VFZWpt69e0uS%2Bvbtq8cff1xLly5VWVmZUlJSVFZW5n9NLgAAgPbYLrC%2B/PJLpaamSlKHX/fqp3JycpSTk/OLx/v166e1a9f%2B4vGxY8dq7NixZ/S%2BAQAAbBFYy5YtU15enqKiorRmzRqrxwEAADgrtngO1t/%2B9jd5vd6AtZycHDU2Nlo0EQAAwJmzRWD93B9S3rFjh5qbmy2YBgAA4OzYIrAAAABCCYEFAABgmG0Ci5dBAAAAocIWv0UoSYsXLw54zasTJ06ouLhY0dGBf0OoI6%2BDBQAAYCVbBNZll13W5jWvUlJSdPjwYR0%2BfNiiqQAAAM6MLQKL174CAAChxDbPwQIAAAgVBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhBBYAAIBhTqsHQNcxseRdq0cAAKBTcAYLAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMF6mAQDawUuMADhdnMECAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwzGn1AAAABIuJJe9aPQKCBGewAAAADCOwAAAADCOwAAAADCOwAAAADCOwAAAADAv5wGppadHkyZP1wQcf%2BNfq6%2Bt18803a9iwYZo0aZK2bdsW8H/ee%2B89TZ48WS6XSzNmzFB9fX1njw0AAIJYSAdWc3Oz7rrrLtXV1fnXfD6fcnNzFR8fr8rKSmVmZiovL08NDQ2SpIaGBuXm5iorK0vr169XbGysZs2aJZ/PZ9XdAAAAQSZkA2vPnj364x//qK%2B%2B%2Bipg/f3331d9fb0WLVqkAQMGaObMmRo2bJgqKyslSS%2B%2B%2BKKGDBmiW2%2B9VRdddJGKior09ddf68MPP7TibgAAgCAUsoH14YcfasSIEaqoqAhYr6mp0eDBgxUVFeVfS01NVXV1tf94Wlqa/1hkZKSSk5P9xwEAANoTsq/kfv311//sutvtVmJiYsBaXFycDh482KHjHdHY2Ci32x2w5nRGtbldAADwA6cztM75hGxg/RKv16vw8PCAtfDwcLW0tHToeEdUVFSotLQ0YC03N1f5%2BflnODUAAKGtZ89oq0cwqssFVkREhJqamgLWWlpa1L17d//xn8ZUS0uLYmJiOvw%2Bpk2bpoyMjIA1pzNKhw9/f4ZTAwAQ2s7Vz0irwq3LBVZSUpL27NkTsObxePwP3yUlJcnj8bQ5PmjQoA6/j8TExDYPB7rd3%2BnkydYznBoAgNAWaj8jQ%2BsBzw5wuVz69NNPdfz4cf9aVVWVXC6X/3hVVZX/mNfrVW1trf84AABAe7pcYKWnp6tXr14qKChQXV2dysvLtWvXLk2ZMkWSlJ2drZ07d6q8vFx1dXUqKChQ3759NWLECIsnBwAAwaLLBVZYWJhWrlwpt9utrKwsvfrqqyorK1Pv3r0lSX379tXjjz%2BuyspKTZkyRU1NTSorK5PD4bB4cgAAECwcPl6ivFO43d%2Bdk9udWPLuObldAAA602t3jjont5uQ8Ktzcrvt6XJnsAAAAM41AgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAgsAAMAwAutnNDc3a968eUpLS9Po0aP19NNPWz0SAAAIIk6rB7CjZcuWaffu3Xr22WfV0NCg%2B%2B67T71799aECROsHg0AAAQBAusnjh07phdffFFPPfWUkpOTlZycrLq6Oj3//PMEFgAA6BAeIvyJf/3rXzp58qRSUlL8a6mpqaqpqVFra6uFkwEAgGDBGayfcLvd6tmzp8LDw/1r8fHxam5uVlNTk2JjY9u9jcbGRrnd7oA1pzNKiYmJxucFACAUOJ2hdc6HwPoJr9cbEFeS/JdbWlo6dBsVFRUqLS0NWMvLy9Ps2bPNDPl/fLSEhy1/TmNjoyoqKjRt2jTC1ibYE3tiX%2ByHPQkNBNZPREREtAmpHy937969Q7cxbdo0ZWRkBKwlJCSYGRAd4na7VVpaqoyMDL5B2QR7Yk/si/2wJ6GBwPqJpKQkHT58WCdPnpTT%2BcOHx%2B12q3v37oqJienQbSQmJvJFAQBAFxZaD3gaMGjQIDmdTlVXV/vXqqqqNHToUHXrxocLAAC0j2L4icjISF1zzTVasGCBdu3apTfffFNPP/20ZsyYYfVoAAAgSIQtWLBggdVD2M3IkSNVW1urRx99VNu3b9cdd9yh7Oxsq8fCaYqOjlZ6erqio6OtHgX/xZ7YE/tiP%2BxJ8HP4fD6f1UMAAACEEh4iBAAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAQtA7dOiQ8vPzlZ6erjFjxqioqEjNzc2SpPr6et18880aNmyYJk2apG3btlk8bdewf/9%2B3XbbbUpJSdGVV16p1atX%2B4%2BxJ9bLycnR3Llz/Zdra2s1depUuVwuZWdna/fu3RZO17X8/e9/1yWXXBLwlp%2BfL4l9CXYEFoKaz%2BdTfn6%2BvF6vnn/%2Bea1YsUJvv/22SkpK5PP5lJubq/j4eFVWViozM1N5eXlqaGiweuyQ1traqpycHPXs2VMbNmzQwoULtWrVKm3cuJE9sYHNmzdr69at/svHjh1TTk6O0tLS9NJLLyklJUUzZ87UsWPHLJyy69izZ4%2Buuuoqbdu2zf%2B2ePFi9iUEOK0eADgb%2B/btU3V1td59913Fx8dLkvLz8/Xwww/riiuuUH19vdatW6eoqCgNGDBA27dvV2VlpWbPnm3x5KHL4/Fo0KBBWrBggXr06KH%2B/fvr8ssvV1VVleLj49kTCzU1NWnZsmUaOnSof23Lli2KiIjQvffeK4fDocLCQr3zzjt6/fXXlZWVZeG0XcPevXt18cUXKyEhIWB9/fr17EuG01E6AAAEPElEQVSQ4wwWglpCQoJWr17tj6sfHT16VDU1NRo8eLCioqL866mpqaquru7sMbuUxMRElZSUqEePHvL5fKqqqtKOHTuUnp7Onljs4YcfVmZmpgYOHOhfq6mpUWpqqhwOhyTJ4XBo%2BPDh7Ekn2bt3r/r3799mnX0JfgQWglpMTIzGjBnjv9za2qq1a9dq5MiRcrvdSkxMDLh%2BXFycDh482NljdlkZGRm6/vrrlZKSovHjx7MnFtq%2Bfbs%2B%2BugjzZo1K2CdPbGOz%2BfTl19%2BqW3btmn8%2BPEaN26cHnnkEbW0tLAvIYCHCBFSiouLVVtbq/Xr1%2BuZZ55ReHh4wPHw8HC1tLRYNF3X89hjj8nj8WjBggUqKiqS1%2BtlTyzQ3NysBx54QPPnz1f37t0DjrEn1mloaPB//EtKSnTgwAEtXrxYx48fZ19CAIGFkFFcXKxnn31WK1as0MUXX6yIiAg1NTUFXKelpaXNDxicOz8%2B16e5uVlz5sxRdna2vF5vwHXYk3OvtLRUQ4YMCTjb%2B6OIiIg2P7TZk87Rp08fffDBBzr//PPlcDg0aNAgtba26p577lF6ejr7EuQILISEBx98UC%2B88IKKi4s1fvx4SVJSUpL27NkTcD2Px9PmtDvM8ng8qq6u1rhx4/xrAwcO1IkTJ5SQkKB9%2B/a1uT57cm5t3rxZHo9HKSkpkuT/wf3GG29o8uTJ8ng8AddnTzrPBRdcEHB5wIABam5uVkJCAvsS5HgOFoJeaWmp1q1bp%2BXLl%2Bvqq6/2r7tcLn366ac6fvy4f62qqkoul8uKMbuMAwcOKC8vT4cOHfKv7d69W7GxsUpNTWVPLLBmzRpt3LhRL7/8sl5%2B%2BWVlZGQoIyNDL7/8slwulz7%2B%2BGP5fD5JPzwvaOfOnexJJ/jnP/%2BpESNGBJzV/eyzz3TBBRcoNTWVfQlyBBaC2t69e7Vy5UrdfvvtSk1Nldvt9r%2Blp6erV69eKigoUF1dncrLy7Vr1y5NmTLF6rFD2tChQ5WcnKx58%2BZpz5492rp1q4qLi3XHHXewJxbp06eP%2BvXr53%2BLjo5WdHS0%2BvXrpwkTJujIkSNasmSJ9uzZoyVLlsjr9WrixIlWjx3yUlJSFBERofvvv1/79u3T1q1btWzZMv35z39mX0KAw/djHgNBqLy8XI8%2B%2BujPHvv888%2B1f/9%2BFRYWqqamRv369dO8efP029/%2BtpOn7HoOHTqkBx98UNu3b1dkZKRuuOEGzZw5Uw6Hgz2xgR9fxf2hhx6SJO3atUsPPPCA9u7dq0suuUQLFy7U4MGDrRyxy6irq9PSpUtVXV2t6OhoXXfddcrNzZXD4WBfghyBBQAAYBgPEQIAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABhGYAEAABj2/wBZWKfOBPwTqwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8121419053334836079”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>33.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>24.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>21.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>23.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>35.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8121419053334836079”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>16.0</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>19.0</td> <td class=”number”>5</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:4%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>51.0</td> <td class=”number”>65</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>53.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:38%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>56.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants FY 2011”><s>Summer Food participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food particpants FY 2009”><code>Summer Food particpants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98245</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2012”><s>Summer Food participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants FY 2011”><code>Summer Food participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99308</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2013”><s>Summer Food participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2012”><code>Summer Food participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98386</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2014”><s>Summer Food participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2013”><code>Summer Food participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98127</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food participants, FY 2015”><s>Summer Food participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_Summer Food participants, FY 2014”><code>Summer Food participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99324</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_Summer Food particpants FY 2009”>Summer Food particpants FY 2009<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>34135</td>

</tr> <tr>

<th>Minimum</th> <td>2122</td>

</tr> <tr>

<th>Maximum</th> <td>436620</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8040608348446629507”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives8040608348446629507,#minihistogram8040608348446629507”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives8040608348446629507”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8040608348446629507”

aria-controls=”quantiles8040608348446629507” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8040608348446629507” aria-controls=”histogram8040608348446629507”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8040608348446629507” aria-controls=”common8040608348446629507”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8040608348446629507” aria-controls=”extreme8040608348446629507”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8040608348446629507”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>2122</td>

</tr> <tr>

<th>5-th percentile</th> <td>4418</td>

</tr> <tr>

<th>Q1</th> <td>8333</td>

</tr> <tr>

<th>Median</th> <td>28843</td>

</tr> <tr>

<th>Q3</th> <td>50389</td>

</tr> <tr>

<th>95-th percentile</th> <td>65014</td>

</tr> <tr>

<th>Maximum</th> <td>436620</td>

</tr> <tr>

<th>Range</th> <td>434500</td>

</tr> <tr>

<th>Interquartile range</th> <td>42056</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>39486</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.1568</td>

</tr> <tr>

<th>Kurtosis</th> <td>66.574</td>

</tr> <tr>

<th>Mean</th> <td>34135</td>

</tr> <tr>

<th>MAD</th> <td>22790</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>6.8122</td>

</tr> <tr>

<th>Sum</th> <td>90630000</td>

</tr> <tr>

<th>Variance</th> <td>1559200000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8040608348446629507”>

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MBqjOrqat17771q27at0tLSJElVVVUKDg522S44OFgOh0PV1dXOxxfrb6zc3FxlZ2e7tKWnpyszM/NKduN7hYeHmjpeU4uIaOnpJTQpX6tHc0BNvAv18C7Uo2n5bcA6e/aspk6dqn/961/63//9X4WGfvNGCgkJaRCWHA6HwsPDFRIS4nz83f5vn98YaWlpSk5OdmmzWMJUXn72SnalgaCgQIWHh6qy0reuCzNr/73NhfWoq6v39HIgauJtqId3aW718NQP934ZsM6cOaPf/OY3%2BvLLL/XCCy%2BoU6dOzr6YmBiVlZW5bF9WVqZu3bqpdevWCgkJUVlZmTp37ixJqq2tVUVFhaKioho9f3R0dIPTgXb7adXWmvtG9rVvDLP339vU1dX7/T76GmriXaiHd6EeTcvvTsDW19crIyNDX331ldatW6ef//znLv1Wq1X5%2BfnOx1VVVTp06JCsVqsCAwMVFxfn0l9QUCCLxaKuXbu6bR8AAIBv87uA9corr2jPnj167LHHFB4eLrvdLrvdroqKCknSqFGjtH//fq1cuVKFhYWaMWOGOnTooD59%2Bkj65uL55557Tjt37tSBAwc0Z84cjRkz5rJOEQIAgObN704R7tixQ/X19ZoyZYpLe%2B/evbVu3Tp16NBBf/7zn/X4448rJydHCQkJysnJUUBAgCRp%2BPDhOn78uGbPni2Hw6Fbb71Vf/zjHz2xKwAAwEcFGN/ewhxNym4/bdpYFkugIiJaqrz8rG5Z9J5p4za1N%2B7r5%2BklNIkL68H1DN6BmngX6uFdmls9oqKu8si8fneKEAAAwNMIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJ3B6wRo8erQ0bNuj06dPunhoAAMAt3B6w%2Bvbtq%2BXLl6t///6aNm2a3n//fRmG4e5lAAAANBm3B6z7779ff//73/X0008rKChI9957rwYOHKglS5boiy%2B%2BcPdyAAAATGfxxKQBAQHq16%2Bf%2BvXrp6qqKq1bt05PP/20Vq5cqV69emnixIm69dZbPbE0AACAH80jAUuSSktL9dprr%2Bm1117TkSNH1KtXL/33f/%2B3SkpK9PDDD2vfvn2aNWuWp5YHAABwxdwesLZu3aqtW7dqz549atOmjUaOHKlly5apU6dOzm3atWun%2BfPnE7AAAIBPcnvAmjVrlgYNGqScnBzddNNNCgxseBnYz372M915553uXhoAAIAp3H6R%2B7vvvqtly5bJarU6w9WBAwdUV1fn3KZXr166//77GzWew%2BFQSkqK9uzZ42wrKirSXXfdpfj4eA0bNkzvv/%2B%2By3N27dqllJQUWa1WTZgwQUVFRS79a9as0YABA5SQkKCZM2eqqqrqSncXAAA0Q24PWGfOnNGQIUP07LPPOtsmT56sESNGqLi4%2BLLGqqmp0bRp01RYWOhsMwxD6enpatu2rTZt2qQRI0YoIyNDJ06ckCSdOHFC6enpSk1N1SuvvKI2bdpo6tSpzltF7NixQ9nZ2Zo7d65eeOEF2Ww2LVy40IQ9BwAAzYXbA9bjjz%2Bua665Rnfffbezbfv27WrXrp2ysrIaPc7Ro0c1ZswYffnlly7tH3zwgYqKijR37lx17txZU6ZMUXx8vDZt2iRJ2rhxo3r06KFJkybp5z//ubKysnT8%2BHHt3btXkrR27VpNnDhRgwYNUs%2BePfXoo49q06ZNHMUCAACN5vaA9eGHH%2Bqhhx5SVFSUs61NmzaaPn26Pvjgg0aPs3fvXvXp00e5ubku7TabTddff73CwsKcbYmJiSooKHD2JyUlOftCQ0PVvXt3FRQUqK6uTh9//LFLf3x8vM6fP6/Dhw9f9r4CAIDmye0XuVssFlVWVjZor6qquqw7uo8bN%2B6i7Xa7XdHR0S5tkZGRKikpuWR/ZWWlampqXPotFotat27tfH5jlJaWym63u7RZLGEN5r1SQUGBLl99hcXiW%2BttLF%2Bthz%2BjJt6FengX6uEebg9YN910kx577DE99dRT%2BulPfyrpm4vSs7KyNGDAgB89flVVlYKDg13agoOD5XA4LtlfXV3tfPx9z2%2BM3NxcZWdnu7Slp6crMzOz0WM0Rnh4qKnjNbWIiJaeXkKT8rV6NAfUxLtQD%2B9CPZqW2wPWgw8%2BqLvvvlu33XabwsPDJUmVlZXq3r27ZsyY8aPHDwkJUUVFhUubw%2BFQixYtnP3fDUsOh0Ph4eEKCQlxPv5uf2ho49%2BIaWlpSk5OdmmzWMJUXn620WP8kKCgQIWHh6qy0reuCzNr/73NhfWoq6v39HIgauJtqId3aW718NQP924PWJGRkdqyZYt27dqlwsJCWSwWXXfddbrxxhsVEBDwo8ePiYnR0aNHXdrKysqcp%2BdiYmJUVlbWoL9bt25q3bq1QkJCVFZWps6dO0uSamtrVVFR4XLN2KVER0c3OB1ot59Wba25b2Rf%2B8Ywe/%2B9TV1dvd/vo6%2BhJt6FengX6tG0PPJROUFBQRowYIAppwS/y2q1auXKlaqurnYetcrPz1diYqKzPz8/37l9VVWVDh06pIyMDAUGBiouLk75%2Bfnq06ePJKmgoEAWi0Vdu3Y1fa0AAMA/uT1g2e12LV26VPv379f58%2BcbXNj%2B1ltv/ajxe/furXbt2mnGjBmaOnWq/v73v%2BvAgQPOW0CMGjVKzz33nFauXOm8o3yHDh2cgWrcuHGaPXu2unTpoujoaM2ZM0djxoy5rFOEAACgeXN7wPrTn/6kgwcPavjw4brqqqtMHz8oKEhPP/20Zs2apdTUVF1zzTXKyclRbGysJKlDhw7685//rMcff1w5OTlKSEhQTk6O8/Tk8OHDdfz4cc2ePVsOh0O33nqr/vjHP5q%2BTgAA4L8CjMu5N4IJ4uPjtWrVKpd7TTUHdvtp08ayWAIVEdFS5eVndcui90wbt6m9cV8/Ty%2BhSVxYD65n8A7UxLtQD%2B/S3OoRFWX%2BwZzGcPtNMMLCwhQZGenuaQEAANzG7QFrxIgRWrVqlcuHOwMAAPgTt1%2BDVVFRoby8PL399tvq2LFjg5t6rl271t1LAgAAMJVHbtOQkpLiiWkBAADcwu0B69vbJQAAAPgrj3zSY2lpqbKzs3X//ffr66%2B/1ptvvqnPP//cE0sBAAAwndsD1rFjx3T77bdry5Yt2rFjh86dO6ft27dr1KhRstls7l4OAACA6dwesJ544gkNHjxYO3fu1E9%2B8hNJ0lNPPaXk5GQtWrTI3csBAAAwndsD1v79%2B3X33Xe7fLCzxWLR1KlTdejQIXcvBwAAwHRuD1j19fWqr29459izZ88qKCjI3csBAAAwndsDVv/%2B/bVixQqXkFVRUaGFCxeqb9%2B%2B7l4OAACA6dwesB566CEdPHhQ/fv3V01NjX7/%2B99r0KBB%2Buqrr/Tggw%2B6ezkAAACmc/t9sGJiYvTqq68qLy9Pn3zyierr6zV27FiNGDFCrVq1cvdy4EZDl/7D00u4LP764dQAgKbnkTu5h4aGavTo0Z6YGgAAoMm5PWBNmDDhB/v5LEIAAODr3B6w2rdv7/K4trZWx44d05EjRzRx4kR3LwcAAMB0XvNZhDk5OSopKXHzagAAAMznkc8ivJgRI0bojTfe8PQyAAAAfjSvCVgfffQRNxoFAAB%2BwSsucj9z5ow%2B/fRTjRs3zt3LAQAAMJ3bA1ZsbKzL5xBK0k9%2B8hPdeeed%2BuUvf%2Bnu5QAAAJjO7QHriSeecPeUAAAAbuX2gLVv375Gb3vDDTc04UoAAACahtsD1vjx452nCA3DcLZ/ty0gIECffPKJu5cHAADwo7n9twiXL1%2Bu9u3ba%2BnSpdq9e7fy8/O1Zs0aXXvttZo2bZreeustvfXWW9q5c%2BePmqe4uFhTpkxRr169lJycrDVr1jj7Dh06pNGjR8tqtWrUqFE6ePCgy3Pz8vI0ePBgWa1Wpaen69SpUz9qLQAAoHlxe8DKysrS7NmzddtttykiIkItW7ZU3759NXfuXL300ktq376988%2BPcd999yksLEybN2/WzJkztXTpUv31r3/VuXPnNHnyZCUlJWnz5s1KSEjQlClTdO7cOUnSgQMHNGvWLGVkZCg3N1eVlZWaMWOGGbsOAACaCbcHrNLS0ouGp1atWqm8vNyUOf7973%2BroKBAv//979WpUycNHjxYAwYM0O7du7V9%2B3aFhIRo%2BvTp6ty5s2bNmqWWLVvqzTfflCStX79eQ4cO1ciRI9W1a1ctWLBA77zzjoqKikxZGwAA8H9uD1jx8fF66qmndObMGWdbRUWFFi5cqBtvvNGUOVq0aKHQ0FBt3rxZ58%2Bf1%2Beff679%2B/erW7dustlsSkxMdF7zFRAQoF69eqmgoECSZLPZlJSU5ByrXbt2io2Nlc1mM2VtAADA/7n9IveHH35YEyZM0E033aROnTrJMAz961//UlRUlNauXWvKHCEhIZo9e7bmzZuntWvXqq6uTqmpqRo9erTeeustXXfddS7bR0ZGqrCwUNI3R9iio6Mb9F/O5ySWlpbKbre7tFksYQ3GvVJBQYEuX9E0LJbGvb7Uw/tQE%2B9CPbwL9XAPtweszp07a/v27crLy9Nnn30mSfr1r3%2Bt4cOHKzQ01LR5PvvsMw0aNEh33323CgsLNW/ePN14442qqqpScHCwy7bBwcFyOBySpOrq6h/sb4zc3FxlZ2e7tKWnpyszM/MK9%2BbiwsPNe73QUEREy8vannp4H2riXaiHd6EeTcvtAUuSrr76ao0ePVpfffWVOnbsKOmbu7mbZffu3XrllVf0zjvvqEWLFoqLi9PJkyf1zDPPqGPHjg3CksPhUIsWLSR9c/TrYv2XE/7S0tKUnJzs0maxhKm8/OwV7pGroKBAhYeHqrKyypTxcHGNrdeF9airq2/iVaExqIl3oR7epbnV43J/WDaL2wOWYRhavHix1q1bp/Pnz2vHjh1asmSJQkNDNWfOHFOC1sGDB3XNNdc4Q5MkXX/99Vq%2BfLmSkpJUVlbmsn1ZWZnz9F1MTMxF%2B6Oioho9f3R0dIPTgXb7adXWmvtGbg7fGJ50ufWqq6s3vcb4caiJd6Ee3oV6NC23n4Bdt26dtm7dqkceecR5Km7w4MHauXNng9NqVyo6OlrHjh1zORL1%2Beefq0OHDrJarfroo4%2BcNzQ1DEP79%2B%2BX1WqVJFmtVuXn5zufV1xcrOLiYmc/AADApbg9YOXm5mr27NlKTU11/ibfsGHD9Nhjj2nbtm2mzJGcnKyf/OQnevjhh/XFF1/ob3/7m5YvX67x48dryJAhqqys1Pz583X06FHNnz9fVVVVGjp0qCRp7Nix2rp1qzZu3KjDhw9r%2BvTpGjhwoPNUJgAAwKW4PWB99dVX6tatW4P2rl27NvjNuyt11VVXac2aNbLb7brjjjuUlZWl3//%2B90pLS1OrVq20YsUK5efnKzU1VTabTStXrlRYWJgkKSEhQXPnzlVOTo7Gjh2rq6%2B%2BWllZWaasCwAANA9uvwarffv2%2Bvjjj9WhQweX9nfffdfUo0TXXXedVq9efdG%2Bnj17asuWLd/73NTUVKWmppq2FgAA0Ly4PWDdc889evTRR2W322UYhnbv3q3c3FytW7dODz30kLuXAwAAYDq3B6xRo0aptrZWzzzzjKqrqzV79my1adNG9913n8aOHevu5QAAAJjO7QErLy9PQ4YMUVpamk6dOiXDMBQZGenuZQAAADQZt1/kPnfuXOfF7G3atCFcAQAAv%2BP2gNWpUycdOXLE3dMCAAC4jdtPEXbt2lUPPPCAVq1apU6dOikkJMSln1siAAAAX%2Bf2gPXFF18oMTFRkky77xUAAIA3cUvAWrBggTIyMhQWFqZ169a5Y0oAAACPccs1WKtXr1ZVVZVL2%2BTJk1VaWuqO6QEAANzKLQHr2w9WvtC%2BfftUU1PjjukBAADcyu2/RQgAAODvCFgAAAAmc1vACggIcNdUAAAAHuW22zQ89thjLve8On/%2BvBYuXKiWLVu6bMd9sAAAgK9zS8C64YYbGtzzKiEhQeXl5SovL3fHEgAAANzGLQGLe18BAIDmhIvcAQAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACT%2BW3AcjgcevTRR3XDDTfoF7/4hZ566ikZhiFJOnTokEaPHi2r1apRo0bp4MGDLs/Ny8vT4MGDZbValZ6erlOnTnliFwAAgI/y24D12GOPadeuXXruuee0ePENErIMAAAYy0lEQVRivfzyy8rNzdW5c%2Bc0efJkJSUlafPmzUpISNCUKVN07tw5SdKBAwc0a9YsZWRkKDc3V5WVlZoxY4aH9wYAAPgSt33YsztVVFRo06ZNWr16tXr27ClJmjRpkmw2mywWi0JCQjR9%2BnQFBARo1qxZevfdd/Xmm28qNTVV69ev19ChQzVy5EhJ0oIFCzRo0CAVFRWpY8eOntwtAADgI/zyCFZ%2Bfr5atWql3r17O9smT56srKws2Ww2JSYmKiAgQJIUEBCgXr16qaCgQJJks9mUlJTkfF67du0UGxsrm83m3p0AAAA%2Byy8DVlFRkdq3b69XX31VQ4YM0X/9138pJydH9fX1stvtio6Odtk%2BMjJSJSUlkqTS0tIf7AcAALgUvzxFeO7cOR07dkwbNmxQVlaW7Ha7Zs%2BerdDQUFVVVSk4ONhl%2B%2BDgYDkcDklSdXX1D/Y3Rmlpqex2u0ubxRLWILhdqaCgQJevaBoWS%2BNeX%2BrhfaiJd6Ee3oV6uIdfBiyLxaIzZ85o8eLFat%2B%2BvSTpxIkTeumll3TNNdc0CEsOh0MtWrSQJIWEhFy0PzQ0tNHz5%2BbmKjs726UtPT1dmZmZV7I73ys8vPFrwuWLiGh5WdtTD%2B9DTbwL9fAu1KNp%2BWXAioqKUkhIiDNcSdK1116r4uJi9e7dW2VlZS7bl5WVOY8uxcTEXLQ/Kiqq0fOnpaUpOTnZpc1iCVN5%2BdnL3ZWLCgoKVHh4qCorq0wZDxfX2HpdWI%2B6uvomXhUag5p4F%2BrhXZpbPS73h2Wz%2BGXAslqtqqmp0RdffKFrr71WkvT555%2Brffv2slqtevbZZ2UYhgICAmQYhvbv36/f/e53zufm5%2BcrNTVVklRcXKzi4mJZrdZGzx8dHd3gdKDdflq1tea%2BkZvDN4YnXW696urqTa8xfhxq4l2oh3ehHk3LL0/A/uxnP9PAgQM1Y8YMHT58WO%2B9955WrlypsWPHasiQIaqsrNT8%2BfN19OhRzZ8/X1VVVRo6dKgkaezYsdq6das2btyow4cPa/r06Ro4cCC3aAAAAI3mlwFLkhYtWqSf/vSnGjt2rB588EH9%2Bte/1vjx49WqVSutWLHCeZTKZrNp5cqVCgsLkyQlJCRo7ty5ysnJ0dixY3X11VcrKyvLw3sDAAB8SYDx7efHoEnZ7adNG8tiCVREREuVl5/VLYveM21cuHrjvn6N2u7CenC43TtQE%2B9CPbxLc6tHVNRVHpnXb49gAQAAeAoBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATOb3AWvy5Ml66KGHnI8PHTqk0aNHy2q1atSoUTp48KDL9nl5eRo8eLCsVqvS09N16tQpdy8ZAAD4OL8OWK%2B//rreeecd5%2BNz585p8uTJSkpK0ubNm5WQkKApU6bo3LlzkqQDBw5o1qxZysjIUG5uriorKzVjxgxPLR8AAPgovw1YFRUVWrBggeLi4pxt27dvV0hIiKZPn67OnTtr1qxZatmypd58801J0vr16zV06FCNHDlSXbt21YIFC/TOO%2B%2BoqKjIU7sBAAB8kN8GrCeffFIjRozQdddd52yz2WxKTExUQECAJCkgIEC9evVSQUGBsz8pKcm5fbt27RQbGyubzebexQMAAJ/mlwFr9%2B7d%2BvDDDzV16lSXdrvdrujoaJe2yMhIlZSUSJJKS0t/sB8AAKAxLJ5egNlqamr0yCOPaPbs2WrRooVLX1VVlYKDg13agoOD5XA4JEnV1dU/2N9YpaWlstvtLm0WS1iD8HalgoICXb6iaVgsjXt9qYf3oSbehXp4F%2BrhHn4XsLKzs9WjRw8NGDCgQV9ISEiDsORwOJxB7Pv6Q0NDL2sNubm5ys7OdmlLT09XZmbmZY1zKeHhl7cuXJ6IiJaXtT318D7UxLtQD%2B9CPZqW3wWs119/XWVlZUpISJAkZ2DasWOHUlJSVFZW5rJ9WVmZ88hSTEzMRfujoqIuaw1paWlKTk52abNYwlRefvayxvk%2BQUGBCg8PVWVllSnj4eIaW68L61FXV9/Eq0JjUBPvQj28S3Orx%2BX%2BsGwWvwtY69atU21trfPxokWLJEkPPPCA9u3bp2effVaGYSggIECGYWj//v363e9%2BJ0myWq3Kz89XamqqJKm4uFjFxcWyWq2XtYbo6OgGpwPt9tOqrTX3jdwcvjE86XLrVVdXb3qN8eNQE%2B9CPbwL9Whafhew2rdv7/K4Zctvkus111yjyMhILV68WPPnz9evfvUrbdiwQVVVVRo6dKgkaezYsRo/frzi4%2BMVFxen%2BfPna%2BDAgerYsaPb9wMAAPiuZnWFW6tWrbRixQrnUSqbzaaVK1cqLCxMkpSQkKC5c%2BcqJydHY8eO1dVXX62srCwPrxoAAPiaAMMwDE8vojmw20%2BbNpbFEqiIiJYqLz%2BrWxa9Z9q4cPXGff0atd2F9eBwu3egJt6FeniX5laPqKirPDJvszqCBQAA4A4ELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMJnfBqyTJ08qMzNTvXv31oABA5SVlaWamhpJUlFRke666y7Fx8dr2LBhev/9912eu2vXLqWkpMhqtWrChAkqKiryxC4AAAAf5ZcByzAMZWZmqqqqSi%2B%2B%2BKKWLFmiv//971q6dKkMw1B6erratm2rTZs2acSIEcrIyNCJEyckSSdOnFB6erpSU1P1yiuvqE2bNpo6daoMw/DwXgEAAF9h8fQCmsLnn3%2BugoIC/eMf/1Dbtm0lSZmZmXryySd10003qaioSBs2bFBYWJg6d%2B6s3bt3a9OmTbr33nu1ceNG9ejRQ5MmTZIkZWVlqV%2B/ftq7d6/69Onjyd0CAAA%2Bwi%2BPYEVFRWnVqlXOcPWtM2fOyGaz6frrr1dYWJizPTExUQUFBZIkm82mpKQkZ19oaKi6d%2B/u7AcAALgUvwxY4eHhGjBggPNxfX291q9fr759%2B8putys6Otpl%2B8jISJWUlEjSJfsBAAAuxS9PEX7XwoULdejQIb3yyitas2aNgoODXfqDg4PlcDgkSVVVVT/Y3xilpaWy2%2B0ubRZLWIPgdqWCggJdvqJpWCyNe32ph/ehJt6FengX6uEefh%2BwFi5cqBdeeEFLlixRly5dFBISooqKCpdtHA6HWrRoIUkKCQlpEKYcDofCw8MbPWdubq6ys7Nd2tLT05WZmXmFe3Fx4eGhpo4HVxERLS9re%2BrhfaiJd6Ee3oV6NC2/Dljz5s3TSy%2B9pIULF%2Bq2226TJMXExOjo0aMu25WVlTmPLsXExKisrKxBf7du3Ro9b1pampKTk13aLJYwlZefvZLdaCAoKFDh4aGqrKwyZTxcXGPrdWE96urqm3hVaAxq4l2oh3dpbvW43B%2BWzeK3ASs7O1sbNmzQU089pSFDhjjbrVarVq5cqerqaudRq/z8fCUmJjr78/PzndtXVVXp0KFDysjIaPTc0dHRDU4H2u2nVVtr7hu5OXxjeNLl1quurt70GuPHoSbehXp4F%2BrRtPzyBOxnn32mp59%2BWr/97W%2BVmJgou93u/NO7d2%2B1a9dOM2bMUGFhoVauXKkDBw7ojjvukCSNGjVK%2B/fv18qVK1VYWKgZM2aoQ4cO3KIBAAA0ml8GrLfeekt1dXV65pln1L9/f5c/QUFBevrpp2W325WamqrXXntNOTk5io2NlSR16NBBf/7zn7Vp0ybdcccdqqioUE5OjgICAjy8VwAAwFcEGNyi3C3s9tOmjWWxBCoioqXKy8/qlkXvmTYuXL1xX79GbXdhPTjc7h2oiXehHt6ludUjKuoqj8zrl0ewAAAAPImABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAms3h6AYC3Grr0H55ewmV5475%2Bnl4CAOD/cAQLAADAZAQsAAAAkxGwAAAATEbAAgAAMBkB6yJqamo0c%2BZMJSUlqX///nr%2B%2Bec9vSQAAOBD%2BC3Ci1iwYIEOHjyoF154QSdOnNCDDz6o2NhYDRkyxNNLAwAAPoCA9R3nzp3Txo0b9eyzz6p79%2B7q3r27CgsL9eKLLxKwAAA%2BxZduN%2BNvt5rhFOF3HD58WLW1tUpISHC2JSYmymazqb6%2B3oMrAwAAvoIjWN9ht9sVERGh4OBgZ1vbtm1VU1OjiooKtWnT5pJjlJaWym63u7RZLGGKjo42ZY1BQYEuXwFJslh4P3yL7xHvQj3QGP72bxgB6zuqqqpcwpUk52OHw9GoMXJzc5Wdne3SlpGRoXvvvdeUNZaWluqFF1YpLS1NH87ntKWnlZaWKjc3V2lpaaaFaPw4F36PUBPPox6ec7H/I/g3yz38Ky6aICQkpEGQ%2BvZxixYtGjVGWlqaNm/e7PInLS3NtDXa7XZlZ2c3OEoGz6Ae3oeaeBfq4V2oh3twBOs7YmJiVF5ertraWlks37w8drtdLVq0UHh4eKPGiI6O5qcCAACaMY5gfUe3bt1ksVhUUFDgbMvPz1dcXJwCA3m5AADApZEYviM0NFQjR47UnDlzdODAAe3cuVPPP/%2B8JkyY4OmlAQAAHxE0Z86cOZ5ehLfp27evDh06pMWLF2v37t363e9%2Bp1GjRnl6WS5atmyp3r17q2XLlp5eCkQ9vBE18S7Uw7tQj6YXYBiG4elFAAAA%2BBNOEQIAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNg%2BZCamhrNnDlTSUlJ6t%2B/v55//nlPL8lnORwOpaSkaM%2BePc62oqIi3XXXXYqPj9ewYcP0/vvvuzxn165dSklJkdVq1YQJE1RUVOTSv2bNGg0YMEAJCQmaOXOmqqqqnH2Xqt2l5vZnJ0%2BeVGZmpnr37q0BAwYoKytLNTU1kqiJJxw7dkz33HOPEhISNHDgQK1atcrZRz08a/LkyXrooYecjw8dOqTRo0fLarVq1KhROnjwoMv2eXl5Gjx4sKxWq9LT03Xq1Clnn2EYWrRokfr27avevXtrwYIFqq%2Bvd/aXl5fr3nvvVUJCgpKTk7V161aXsS81NyQZ8Blz5841br/9duPgwYPGX/7yFyMhIcF44403PL0sn1NdXW2kp6cbXbp0MT744APDMAyjvr7euP32243777/fOHr0qLF8%2BXLDarUax48fNwzDMI4fP27Ex8cbzz33nHHkyBHjD3/4g5GSkmLU19cbhmEYb775ppGYmGj87W9/M2w2mzFs2DDj0Ucfdc75Q7W71Nz%2BrL6%2B3hgzZozxm9/8xjhy5Iixb98%2B45ZbbjGeeOIJauIBdXV1xq233mrcf//9xhdffGG8/fbbRq9evYzXXnuNenhYXl6e0aVLF%2BPBBx80DMMwzp49a/Tr18944oknjKNHjxrz5s0zfvGLXxhnz541DMMwbDab0bNnT2PLli3GJ598Ytx5553G5MmTneM999xzxs0332zs27fP2L17t9G/f39j1apVzv4pU6YYEydOND799FPj5ZdfNnr06GHYbLZGzY1vELB8xNmzZ424uDhnIDAMw8jJyTHuvPNOD67K9xQWFhq//OUvjdtvv90lYO3atcuIj493%2BQdi4sSJxrJlywzDMIylS5e6vNbnzp0zEhISnM8fN26cc1vDMIx9%2B/YZPXv2NM6dO3fJ2l1qbn929OhRo0uXLobdbne2bdu2zejfvz818YCTJ08af/jDH4zTp08729LT041HHnmEenhQeXm5cdNNNxmjRo1yBqyNGzcaycnJzgBbX19v3HLLLcamTZsMwzCMP/7xj85tDcMwTpw4Yfznf/6n8eWXXxqGYRg333yzc1vDMIxXX33VGDRokGEYhnHs2DGjS5cuRlFRkbN/5syZjZ4b3%2BAUoY84fPiwamtrlZCQ4GxLTEyUzWZzOayLH7Z371716dNHubm5Lu02m03XX3%2B9wsLCnG2JiYkqKChw9iclJTn7QkND1b17dxUUFKiurk4ff/yxS398fLzOnz%2Bvw4cPX7J2l5rbn0VFRWnVqlVq27atS/uZM2eoiQdER0dr6dKlatWqlQzDUH5%2Bvvbt26fevXtTDw968sknNWLECF133XXONpvNpsTERAUEBEiSAgIC1KtXr%2B%2BtR7t27RQbGyubzaaTJ0%2BquLhYN9xwg7M/MTFRx48fV2lpqWw2m9q1a6cOHTq49H/00UeNmhvfIGD5CLvdroiICAUHBzvb2rZtq5qaGlVUVHhwZb5l3LhxmjlzpkJDQ13a7Xa7oqOjXdoiIyNVUlJyyf7KykrV1NS49FssFrVu3VolJSWXrN2l5vZn4eHhGjBggPNxfX291q9fr759%2B1ITD0tOTta4ceOUkJCg2267jXp4yO7du/Xhhx9q6tSpLu2Xek1KS0u/t99ut0uSS/%2B3P%2BR823%2Bx5548ebJRc%2BMbBCwfUVVV5fKPjyTnY4fD4Ykl%2BZXve32/fW1/qL%2B6utr5%2BGL9l6rdpeZuThYuXKhDhw7p//2//0dNPGzZsmVavny5PvnkE2VlZVEPD6ipqdEjjzyi2bNnq0WLFi59l3pNqqurL6sel/N6N9d6XC6LpxeAxgkJCWnw5v328Xe/8XD5QkJCGhwJdDgcztf2%2B17/8PBwhYSEOB9/tz80NFR1dXU/WLtLzd1cLFy4UC%2B88IKWLFmiLl26UBMPi4uLk/TNf/IPPPCARo0a5fJbfxL1aGrZ2dnq0aOHy1Heb33f632peoSGhrqEqe/WJjQ09IrH9vd6XC6OYPmImJgYlZeXq7a21tlmt9vVokULhYeHe3Bl/iEmJkZlZWUubWVlZc7D4N/XHxUVpdatWyskJMSlv7a2VhUVFYqKirpk7S41d3Mwb948rV69WgsXLtRtt90miZp4QllZmXbu3OnSdt111%2Bn8%2BfOKioqiHm72%2Buuva%2BfOnUpISFBCQoK2bdumbdu2KSEh4Ud9f8TExEiS81ThhX//tv/7nvtDY/t7PS4XActHdOvWTRaLxeUiwvz8fMXFxSkwkDL%2BWFarVf/85z%2Bdh86lb15fq9Xq7M/Pz3f2VVVV6dChQ7JarQoMDFRcXJxLf0FBgSwWi7p27XrJ2l1qbn%2BXnZ2tDRs26KmnntLw4cOd7dTE/b766itlZGQ4r7WRpIMHD6pNmzZKTEykHm62bt06bdu2Ta%2B%2B%2BqpeffVVJScnKzk5Wa%2B%2B%2BqqsVqs%2B%2BugjGYYh6Zv7Wu3fv/9761FcXKzi4mJZrVbFxMQoNjbWpT8/P1%2BxsbGKjo5WfHy8jh8/7nJNVX5%2BvuLj451j/9Dc%2BD8e/R1GXJY//elPxvDhww2bzWb89a9/NXr16mXs2LHD08vyWRfepqG2ttYYNmyYcd999xlHjhwxVqxYYcTHxzvvs1NUVGTExcUZK1ascN7j5/bbb3f%2BmnJeXp7Rq1cv469//aths9mM4cOHG/PmzXPO9UO1u9Tc/uzo0aNGt27djCVLlhilpaUuf6iJ%2B9XW1hqpqanGpEmTjMLCQuPtt982fvGLXxhr1qyhHl7gwQcfdN4q4fTp00bfvn2NefPmGYWFhca8efOMfv36OW9lsX//fqN79%2B7Gyy%2B/7LwP1pQpU5xjrVixwujfv7/xwQcfGB988IHRv39/4/nnn3f2T5o0ybjzzjuNTz75xHj55ZeNuLg4532wLjU3vkHA8iHnzp0zpk%2BfbsTHxxv9%2B/c3Vq9e7ekl%2BbQLA5ZhGMa//vUv49e//rXRo0cPY/jw4cY//vEPl%2B3ffvtt49ZbbzV69uxpTJw40Xk/mW%2BtWLHCuPHGG43ExERjxowZRnV1tbPvUrW71Nz%2BasWKFUaXLl0u%2BscwqIknlJSUGOnp6UavXr2Mfv36Gc8884wzJFEPz7owYBnGNzcTHTlypBEXF2fccccdxj//%2BU%2BX7Tdt2mTcfPPNRnx8vJGenm6cOnXK2VdbW2s8/vjjRlJSktGnTx9j4cKFzjobhmGUlZUZU6ZMMeLi4ozk5GRj27ZtLmNfam4YRoBh/N8xPgAAAJiCi3cAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABM9v8BkGGjk0tIuJ4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8040608348446629507”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>15111.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50389.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50586.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4418.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>50239.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>55312.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>36567.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6926.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>41455.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>43343.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8040608348446629507”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2122.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2572.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2964.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4418.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4459.0</td> <td class=”number”>36</td> <td class=”number”>1.1%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>59690.0</td> <td class=”number”>69</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>65014.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>68454.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>100190.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:77%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>436620.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:25%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model And”><s>USDA Model And</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_USDA Model Percent”><code>USDA Model Percent</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99358</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Count”>USDA Model Count<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>0.88723</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable-5497211197893331492”>

<table class=”mini freq”>

<tr class=”“>

<th>True</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 88.7%”>

2848

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:13%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 11.3%”>

&nbsp;

</div> 362

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable-5497211197893331492, #minifreqtable-5497211197893331492”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable-5497211197893331492”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>True</td> <td class=”number”>2848</td> <td class=”number”>88.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>362</td> <td class=”number”>11.3%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Or”><s>USDA Model Or</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_USDA Model Count”><code>USDA Model Count</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99064</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_USDA Model Percent”>USDA Model Percent<br/>

<small>Boolean</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr class=”“>

<th>Distinct count</th> <td>2</td>

</tr> <tr>

<th>Unique (%)</th> <td>0.1%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>
<tr>

<th>Mean</th> <td>0.17695</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minifreqtable-9011962956867291439”>

<table class=”mini freq”>

<tr class=”“>

<th>True</th> <td>

<div class=”bar” style=”width:22%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 17.7%”>

568

</div>

</td>

</tr><tr class=”missing”>

<th>(Missing)</th> <td>

<div class=”bar” style=”width:100%” data-toggle=”tooltip” data-placement=”right” data-html=”true”

data-delay=500 title=”Percentage: 82.3%”>

2642

</div>

</td>

</tr>

</table>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#freqtable-9011962956867291439, #minifreqtable-9011962956867291439”

aria-expanded=”true” aria-controls=”collapseExample”> Toggle details

</a>

</div> <div class=”col-md-12 extrapadding collapse” id=”freqtable-9011962956867291439”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>True</td> <td class=”number”>568</td> <td class=”number”>17.7%</td> <td>

<div class=”bar” style=”width:22%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>2642</td> <td class=”number”>82.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table> </div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRES07”>VEG_ACRES07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>950</td>

</tr> <tr>

<th>Unique (%)</th> <td>29.6%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>21.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>682</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>1772.1</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>253700</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-8090362851511867458”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-8090362851511867458,#minihistogram-8090362851511867458”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-8090362851511867458”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-8090362851511867458”

aria-controls=”quantiles-8090362851511867458” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-8090362851511867458” aria-controls=”histogram-8090362851511867458”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-8090362851511867458” aria-controls=”common-8090362851511867458”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-8090362851511867458” aria-controls=”extreme-8090362851511867458”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-8090362851511867458”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>18</td>

</tr> <tr>

<th>Median</th> <td>81</td>

</tr> <tr>

<th>Q3</th> <td>524.75</td>

</tr> <tr>

<th>95-th percentile</th> <td>6770.5</td>

</tr> <tr>

<th>Maximum</th> <td>253700</td>

</tr> <tr>

<th>Range</th> <td>253700</td>

</tr> <tr>

<th>Interquartile range</th> <td>506.75</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>9160.2</td>

</tr> <tr>

<th>Coef of variation</th> <td>5.1693</td>

</tr> <tr>

<th>Kurtosis</th> <td>328.92</td>

</tr> <tr>

<th>Mean</th> <td>1772.1</td>

</tr> <tr>

<th>MAD</th> <td>2709.4</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>15.23</td>

</tr> <tr>

<th>Sum</th> <td>4479800</td>

</tr> <tr>

<th>Variance</th> <td>83910000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-8090362851511867458”>

<img 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Q9YN998s15%2B%2BWUdP3480IcGAAAIiIAHrLS0NC1fvlz9%2BvXT/fffr02bNskYE%2BhhAAAA%2BE3AA9bUqVP197//XcuWLVN4eLjuvfdeXXvttVq0aJG%2B%2BeabQA8HAADAOmcwDupwONS3b1/17dtXZWVlevHFF7Vs2TKtXLlSPXv21IQJE3TdddcFY2gAAAAXLWg3uRcVFWnVqlUaM2aMnnzySV1xxRWaM2eO0tLS9NBDD2n%2B/Pn12k9lZaWGDRumLVu2eNvmzZunyy%2B/3Odr3bp13v7c3FwNHDhQLpdLGRkZOnr0qLfPGKOFCxcqLS1NvXv31oIFC1RTU2Nv4gAAoMEL%2BBmsN998U2%2B%2B%2Baa2bNmili1basSIEVqyZIk6dOjgfU%2BbNm00f/58zZo164L7qqio0NSpU7V3716f9oKCAk2dOlU33XSTt6158%2BaSpO3bt2vWrFmaPXu2unbtqvnz52vGjBlasWKFJOn5559Xbm6ucnJyVFVVpWnTpqlVq1aaNGmSpQoAAICGLuABa9asWerfv7%2BWLl2qq6%2B%2BWmFhtU%2BidezYUePHj7/gfvbt26epU6ee8wb5goICTZo0SbGxsbX61q1bpxtuuEEjRoyQJC1YsED9%2B/dXYWGh2rdvr7Vr1yozM1OpqamSpAceeECLFy8mYAEAgHoLeMD65JNP1KJFC5WWlnrD1fbt29W9e3eFh4dLknr27KmePXtecD9ffPGF%2BvTpo7/85S9KSkrytp84cUJHjhzxOSP2c263W3/605%2B8r9u0aaOEhAS53W5FRETo0KFD6tWrl7c/JSVFBw4cUFFRkeLi4n7ttAEAQCMS8IB14sQJjR07Vv/6r/%2BqBx98UJJ09913q3Xr1nr22WfVpk2beu1n3Lhx52wvKCiQw%2BHQ8uXL9cknnygmJkZ33HGH93LhuYJSq1atdPjwYXk8Hkny6W/durUk6fDhw/UOWEVFRd59neF0NrUe0MLDQ%2Bs5sU5naIz3TF1Drb6XOurqH9TVP6irfzSmugY8YD322GP6/e9/rzvuuMPb9vbbb2v69OnKysrSkiVLLmr/%2B/fvl8Ph8F5m3Lp1qx5%2B%2BGE1b95cgwYNUnl5uSIiIny2iYiIUGVlpcrLy72vf94n/fNm%2BvrasGGDcnJyfNoyMjKUmZn5a6fVILRo0SzYQ/hFoqOjgj2EBom6%2Bgd19Q/q6h%2BNoa4BD1j/%2B7//q1deecXn/qiWLVvqwQcf1K233nrR%2Bx8xYoT69%2B%2BvmJgYSVLXrl317bffav369Ro0aJAiIyNrhaXKykpFRUX5hKnIyEjvnyUpKqr%2Bi2HMmDEaMGCAT5vT2VQlJSd/9bzOJdS%2BA7A9f38JDw9TdHSUjh0rU3U1P0FqC3X1D%2BrqH9TVP4JR12B9cx/wgOV0OnXs2LFa7WVlZVae6O5wOLzh6oyOHTtq8%2BbNkqT4%2BHgVFxf79BcXFys2Nlbx8fGSJI/Ho3bt2nn/LOmcN8yfT1xcXK3LgR7PcVVVNe4PaajNv7q6JuTGHAqoq39QV/%2Bgrv7RGOoa8FMgV199tebNm6fvv//e21ZYWKisrCylp6df9P4XL16siRMn%2BrTt3r1bHTt2lCS5XC7l5eV5%2Bw4dOqRDhw7J5XIpPj5eCQkJPv15eXlKSEjgBncAAFBvAT%2BDNX36dN1xxx0aPHiwoqOjJUnHjh1T9%2B7dNWPGjIvef//%2B/bVy5UqtXr1agwYN0qZNm/TGG29o7dq1kqSxY8fqtttuU1JSkhITEzV//nxde%2B21at%2B%2Bvbd/4cKF%2Bt3vfidJeuKJJ3TnnXde9LgAAEDjEfCA1apVK73%2B%2Buv67LPPtHfvXjmdTv3hD3/QVVddJYfDcdH779GjhxYvXqwlS5Zo8eLFatu2rZ544gklJydLkpKTkzVnzhwtWbJEP/30k/r27au5c%2Bd6t580aZJ%2B/PFHTZkyReHh4Ro9enStM2IAAAAX4jA2bnxCnTye49b36XSGadDCT63v11/eua9vsIdQL05nmFq0aKaSkpMN/h6BQKKu/kFd/YO6%2Bkcw6hob%2B9uAHOdsAT%2BD5fF49NRTT2nbtm06ffp0rRvbP/zww0APCQAAwKqAB6yHH35YO3bs0NChQ/Xb3wYnVQIAAPhTwAPW5s2btWrVKu/v%2BgMAAGhoAv6YhqZNm6pVq1aBPiwAAEDABDxg3XjjjVq1apWqq6sDfWgAAICACPglwtLSUuXm5uqjjz5S%2B/bta/1ewDPPqwIAAAhVAQ9YkjRs2LBgHBYAACAgAh6wsrKyAn1IAACAgAr4PViSVFRUpJycHE2dOlU//vij3n33Xe3fvz8YQwEAALAu4AHru%2B%2B%2B0/Dhw/X666/rvffe06lTp/T2229r1KhRcrvdgR4OAACAdQEPWI8//rgGDhyoDz74QL/5zW8kSU8%2B%2BaQGDBighQsXBno4AAAA1gU8YG3btk133HGHzy92djqduueee7Rz585ADwcAAMC6gAesmpoa1dTU/gWPJ0%2BeVHh4eKCHAwAAYF3AA1a/fv20YsUKn5BVWlqq7OxspaWlBXo4AAAA1gU8YP31r3/Vjh071K9fP1VUVOjf/u3f1L9/f/3www%2BaPn16oIcDAABgXcCfgxUfH6833nhDubm52rVrl2pqajR27FjdeOONat68eaCHAwAAYF1QnuQeFRWlm2%2B%2BORiHBgAA8LuAB6zbb7/9gv38LkIAABDqAh6w2rZt6/O6qqpK3333nfbs2aMJEyYEejgAAADWXTK/i3Dp0qU6fPhwgEcDAABgX1B%2BF%2BG53HjjjXrnnXeCPQwAAICLdskErH/84x88aBQAADQIl8RN7idOnNDXX3%2BtcePGBXo4AAAA1gU8YCUkJPj8HkJJ%2Bs1vfqPx48frj3/8Y6CHAwAAYF3AA9bjjz8e6EMCAAAEVMAD1tatW%2Bv93l69evlxJAAAAP4R8IB12223eS8RGmO87We3ORwO7dq1K9DDAwAAuGgBD1jLly/XvHnzNG3aNPXu3VsRERH68ssvNWfOHN10000aMmRIoIcEAABgVcAf05CVlaVHHnlEgwcPVosWLdSsWTOlpaVpzpw5Wr9%2Bvdq2bev9AgAACEUBD1hFRUXnDE/NmzdXSUlJoIcDAABgXcADVlJSkp588kmdOHHC21ZaWqrs7GxdddVVgR4OAACAdQG/B%2Buhhx7S7bffrquvvlodOnSQMUbffvutYmNjtXbt2kAPBwAAwLqAB6xOnTrp7bffVm5urgoKCiRJt956q4YOHaqoqKhADwcAAMC6gAcsSbrssst0880364cfflD79u0l/fNp7gAAAA1BwO/BMsZo4cKF6tWrl4YNG6bDhw9r%2BvTpmjVrlk6fPh3o4QAAAFgX8ID14osv6s0339R//Md/KCIiQpI0cOBAffDBB8rJyQn0cAAAAKwLeMDasGGDHnnkEY0cOdL79PYhQ4Zo3rx5euuttwI9HAAAAOsCHrB%2B%2BOEHdevWrVZ7165d5fF4Aj0cAAAA6wIesNq2basvv/yyVvsnn3ziveEdAAAglAX8pwgnTZqk2bNny%2BPxyBijzz//XBs2bNCLL76ov/71r4EeDgAAgHUBD1ijRo1SVVWVnnnmGZWXl%2BuRRx5Ry5Ytdd9992ns2LGBHg4AAIB1AQ9Yubm5uv766zVmzBgdPXpUxhi1atUq0MMAAADwm4DfgzVnzhzvzewtW7YkXAEAgAYn4AGrQ4cO2rNnT6APCwAAEDABv0TYtWtXPfDAA1q1apU6dOigyMhIn/6srKxADwkAAMCqgAesb775RikpKZLEc68AAECDFJCAtWDBAk2ZMkVNmzbViy%2B%2BGIhDAgAABE1A7sF6/vnnVVZW5tN29913q6ioKBCHBwAACKiABCxjTK22rVu3qqKiIhCHBwAACKiA/xShbZWVlRo2bJi2bNnibSssLNTEiROVlJSkIUOGaNOmTT7bfPbZZxo2bJhcLpduv/12FRYW%2BvS/8MILSk9PV3JysmbOnFnr7BsAAMCFhHTAqqio0P3336%2B9e/d624wxysjIUOvWrbVx40bdeOONmjJlig4ePChJOnjwoDIyMjRy5Ei99tpratmype655x7vWbb33ntPOTk5mjNnjtasWSO3263s7OygzA8AAISmgAUsh8NhdX/79u3TLbfcou%2B//96nffPmzSosLNScOXPUqVMnTZ48WUlJSdq4caMk6dVXX9WVV16pO%2B%2B8U507d1ZWVpYOHDigL774QpK0du1aTZgwQf3791ePHj00e/Zsbdy4kbNYAACg3gL2mIZ58%2Bb5PPPq9OnTys7OVrNmzXzeV9/nYH3xxRfq06eP/vKXvygpKcnb7na7dcUVV6hp06betpSUFOXn53v7U1NTvX1RUVHq3r278vPzlZqaqi%2B//FJTpkzx9iclJen06dPavXu3kpOTf9mkAQBAoxSQgNWrV69az7xKTk5WSUmJSkpKftU%2Bx40bd852j8ejuLg4n7ZWrVrp8OHDdfYfO3ZMFRUVPv1Op1MxMTHe7QEAAOoSkIAVyGdflZWVKSIiwqctIiJClZWVdfaXl5d7X59v%2B/ooKiqqFSidzqa1gt3FCg8PrVvonM7QGO%2BZuoZafS911NU/qKt/UFf/aEx1DfiT3P0tMjJSpaWlPm2VlZVq0qSJt//ssFRZWano6GjvJcxz9UdFRdV7DBs2bFBOTo5PW0ZGhjIzM%2Bu9j4aoRYtmdb/pEhIdXf%2B/c9QfdfUP6uof1NU/GkNdG1zAio%2BP1759%2B3zaiouLvWeP4uPjVVxcXKu/W7duiomJUWRkpIqLi9WpUydJUlVVlUpLSxUbG1vvMYwZM0YDBgzwaXM6m6qk5OSvmdJ5hdp3ALbn7y/h4WGKjo7SsWNlqq6uCfZwGgzq6h/U1T%2Boq38Eo67B%2Bua%2BwQUsl8ullStXqry83HvWKi8vz/v7D10ul/Ly8rzvLysr086dOzVlyhSFhYUpMTFReXl56tOnjyQpPz9fTqdTXbt2rfcY4uLial0O9HiOq6qqcX9IQ23%2B1dU1ITfmUEBd/YO6%2Bgd19Y/GUNfQOgVSD71791abNm00Y8YM7d27VytXrtT27ds1evRoSdKoUaO0bds2rVy5Unv37tWMGTPUrl07b6AaN26cVq9erQ8%2B%2BEDbt2/Xo48%2BqltuueUXXSIEAACNW4MLWOHh4Vq2bJk8Ho9Gjhyp//zP/9TSpUuVkJAgSWrXrp2efvppbdy4UaNHj1ZpaamWLl3qfU7X0KFDNXnyZD3yyCO688471aNHD02bNi2YUwIAACHGYc71iwJhncdz3Po%2Bnc4wDVr4qfX9%2Bss79/UN9hDqxekMU4sWzVRScrLBn8IOJOrqH9TVP6irfwSjrrGxvw3Icc7W4M5gAQAABBsBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYFmDDVjvv/%2B%2BLr/8cp%2BvzMxMSdLOnTt18803y%2BVyadSoUdqxY4fPtrm5uRo4cKBcLpcyMjJ09OjRYEwBAACEqAYbsPbt26f%2B/ftr06ZN3q958%2Bbp1KlTuvvuu5Wamqq//e1vSk5O1uTJk3Xq1ClJ0vbt2zVr1ixNmTJFGzZs0LFjxzRjxowgzwYAAISSBhuwCgoK1KVLF8XGxnq/oqOj9fbbbysyMlIPPvigOnXqpFmzZqlZs2Z69913JUnr1q3TDTfcoBEjRqhr165asGCBPv74YxUWFgZ5RgAAIFQ06IDVoUOHWu1ut1spKSlyOBySJIfDoZ49eyo/P9/bn5qa6n1/mzZtlJCQILfbHZBxAwCA0NcgA5YxRt988402bdqkwYMHa%2BDAgVq4cKEqKyvl8XgUFxfn8/5WrVrp8OHDkqSioqIL9gMAANTFGewB%2BMPBgwdVVlamiIgIPfXUU/rhhx80b948lZeXe9t/LiIiQpWVlZKk8vLyC/bXR1FRkTwej0%2Bb09m0VnC7WOHhoZWPnc7QGO%2BZuoZWWIVcAAAQUUlEQVRafS911NU/qKt/UFf/aEx1bZABq23bttqyZYsuu%2BwyORwOdevWTTU1NZo2bZp69%2B5dKyxVVlaqSZMmkqTIyMhz9kdFRdX7%2BBs2bFBOTo5PW0ZGhvenGBurFi2aBXsIv0h0dP3/zlF/1NU/qKt/UFf/aAx1bZABS5JiYmJ8Xnfq1EkVFRWKjY1VcXGxT19xcbH37FJ8fPw5%2B2NjY%2Bt97DFjxmjAgAE%2BbU5nU5WUnPwlU6hTqH0HYHv%2B/hIeHqbo6CgdO1am6uqaYA%2BnwaCu/kFd/YO6%2Bkcw6hqsb%2B4bZMD69NNP9cADD%2Bijjz7ynnnatWuXYmJilJKSomeffVbGGDkcDhljtG3bNv35z3%2BWJLlcLuXl5WnkyJGSpEOHDunQoUNyuVz1Pn5cXFyty4Eez3FVVTXuD2mozb%2B6uibkxhwKqKt/UFf/oK7%2B0RjqGlqnQOopOTlZkZGReuihh7R//359/PHHWrBgge666y5df/31OnbsmObPn699%2B/Zp/vz5Kisr0w033CBJGjt2rN588029%2Buqr2r17tx588EFde%2B21at%2B%2BfZBnBQAAQkWDDFjNmzfX6tWrdfToUY0aNUqzZs3SmDFjdNddd6l58%2BZasWKF9yyV2%2B3WypUr1bRpU0n/DGdz5szR0qVLNXbsWF122WXKysoK8owAAEAocRhjTLAH0Rh4PMet79PpDNOghZ9a36%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%2Brevbu6d%2B%2BuvXv36qWXXiJgXaQbnvqfYA/hF3n/gfRgDwEAEKK4RHiW3bt3q6qqSsnJyd62lJQUud1u1dTUBHFkAAAgVHAG6ywej0ctWrRQRESEt61169aqqKhQaWmpWrZsWec%2BioqK5PF4fNqczqaKi4uzOtbwcPKxPw1a%2BGmwh/CLXOpn3M6sV9atXdTVP6irfzSmuhKwzlJWVuYTriR5X1dWVtZrHxs2bFBOTo5P25QpU3TvvffaGeT/KSoq0oTf7dWYMWOsh7fGrKioSBs2bKCulhUVFWnNmlXU1TLq6h/U1T8aU10bfoT8hSIjI2sFqTOvmzRpUq99jBkzRn/72998vsaMGWN9rB6PRzk5ObXOluHiUFf/oK7%2BQV39g7r6R2OqK2ewzhIfH6%2BSkhJVVVXJ6fxneTwej5o0aaLo6Oh67SMuLq7BJ3MAAHB%2BnME6S7du3eR0OpWfn%2B9ty8vLU2JiosLCKBcAAKgbieEsUVFRGjFihB599FFt375dH3zwgZ577jndfvvtwR4aAAAIEeGPPvroo8EexKUmLS1NO3fu1BNPPKHPP/9cf/7znzVq1KhgD%2BucmjVrpt69e6tZs2bBHkqDQl39g7r6B3X1D%2BrqH42lrg5jjAn2IAAAABoSLhECAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNghaiKigrNnDlTqamp6tevn5577rlgD%2BmS8P777%2Bvyyy/3%2BcrMzJQk7dy5UzfffLNcLpdGjRqlHTt2%2BGybm5urgQMHyuVyKSMjQ0ePHvX2GWO0cOFCpaWlqXfv3lqwYIFqamq8/SUlJbr33nuVnJysAQMG6M033wzMhP2ssrJSw4YN05YtW7xthYWFmjhxopKSkjRkyBBt2rTJZ5vPPvtMw4YNk8vl0u23367CwkKf/hdeeEHp6elKTk7WzJkzVVZW5u2ra13XdexQca66zps3r9baXbdunbffn%2Buzrs/Gpe7IkSPKzMxU7969lZ6erqysLFVUVEhivV6MC9WV9VoPBiFpzpw5Zvjw4WbHjh3mv/7rv0xycrJ55513gj2soFu2bJmZPHmyKSoq8n799NNP5uTJk6Zv377m8ccfN/v27TNz5841//Iv/2JOnjxpjDHG7XabHj16mNdff93s2rXLjB8/3tx9993e/a5evdpcc801ZuvWrebzzz83/fr1M6tWrfL2T5482UyYMMF8/fXX5pVXXjFXXnmlcbvdAZ%2B/TeXl5SYjI8N06dLFbN682RhjTE1NjRk%2BfLiZOnWq2bdvn1m%2BfLlxuVzmwIEDxhhjDhw4YJKSkszq1avNnj17zL//%2B7%2BbYcOGmZqaGmOMMe%2B%2B%2B65JSUkx//3f/23cbrcZMmSImT17tveYF1rXdR07VJyrrsYYM3HiRLNixQqftXvq1CljjH/XZ12fjUtdTU2NueWWW8xdd91l9uzZY7Zu3WoGDRpkHn/8cdbrRbhQXY1hvdYHASsEnTx50iQmJvr847x06VIzfvz4II7q0jB16lTzxBNP1Gp/9dVXzYABA7z/cNbU1JhBgwaZjRs3GmOMmTZtmpk%2Bfbr3/QcPHjSXX365%2Bf77740xxlxzzTXe9xpjzBtvvGH69%2B9vjDHmu%2B%2B%2BM126dDGFhYXe/pkzZ/rsL9Ts3bvX/PGPfzTDhw/3CQKfffaZSUpK8vnHbMKECWbJkiXGGGOeeuopn3V46tQpk5yc7N1%2B3Lhx3vcaY8zWrVtNjx49zKlTp%2Bpc13UdOxScr67GGJOenm4%2B/fTTc27nz/VZ12fjUrdv3z7TpUsX4/F4vG1vvfWW6devH%2Bv1IlyorsawXuuDS4QhaPfu3aqqqlJycrK3LSUlRW632%2Bc0a2NUUFCgDh061Gp3u91KSUmRw%2BGQJDkcDvXs2VP5%2Bfne/tTUVO/727Rpo4SEBLndbh05ckSHDh1Sr169vP0pKSk6cOCAioqK5Ha71aZNG7Vr186n/x//%2BIefZul/X3zxhfr06aMNGzb4tLvdbl1xxRVq2rSpty0lJeW8dYyKilL37t2Vn5%2Bv6upqffnllz79SUlJOn36tHbv3l3nuq7r2KHgfHU9ceKEjhw5cs61K/l3fdb12bjUxcbGatWqVWrdurVP%2B4kTJ1ivF%2BFCdWW91o8z2APAL%2BfxeNSiRQtFRER421q3bq2KigqVlpaqZcuWQRxd8Bhj9M0332jTpk1asWKFqqurdf311yszM1Mej0d/%2BMMffN7fqlUr7d27V5JUVFSkuLi4Wv2HDx%2BWx%2BORJJ/%2BM//onOk/17ZHjhyxPsdAGTdu3DnbzzfXw4cP19l/7NgxVVRU%2BPQ7nU7FxMTo8OHDCgsLu%2BC6ruvYoeB8dS0oKJDD4dDy5cv1ySefKCYmRnfccYduuukmSf5dn3V9Ni510dHRSk9P976uqanRunXrlJaWxnq9CBeqK%2Bu1fghYIaisrMznQy3J%2B7qysjIYQ7okHDx40Fubp556Sj/88IPmzZun8vLy89bsTL3Ky8vP219eXu59/fM%2B6Z/1rmvfDUldc71Q/7nq%2BPN%2BY8wF13VDrvP%2B/fvlcDjUsWNHjR8/Xlu3btXDDz%2Bs5s2ba9CgQX5dnw2trtnZ2dq5c6dee%2B01vfDCC6xXS35e16%2B%2B%2Bor1Wg8ErBAUGRlZazGded2kSZNgDOmS0LZtW23ZskWXXXaZHA6HunXrppqaGk2bNk29e/c%2BZ83O1Ot8NY2KivL58EdGRnr/LP3zksL5tm2IfxeRkZEqLS31aatPHaOjo2vV7uf9UVFRqq6uvuC6ruvYoWzEiBHq37%2B/YmJiJEldu3bVt99%2Bq/Xr12vQoEF%2BXZ8Naf1mZ2drzZo1WrRokbp06cJ6teTsunbu3Jn1Wg/cgxWC4uPjVVJSoqqqKm%2Bbx%2BNRkyZNFB0dHcSRBV9MTIz32rwkderUSRUVFYqNjVVxcbHPe4uLi72nouPj48/ZHxsbq/j4eEnyntr%2B%2BZ/P9J9v24bmfHOtTx1jYmIUGRnp019VVaXS0lJvHS%2B0rus6dihzOBze/1md0bFjR%2B9lEX%2Buz4ZS17lz5%2Br5559Xdna2Bg8eLIn1asO56sp6rR8CVgjq1q2bnE6nz019eXl5SkxMVFhY4/0r/fTTT9WnTx%2Bf59Ts2rVLMTEx3pskjTGS/nm/1rZt2%2BRyuSRJLpdLeXl53u0OHTqkQ4cOyeVyKT4%2BXgkJCT79eXl5SkhIUFxcnJKSknTgwAGfeyvy8vKUlJTk7ykHnMvl0ldffeU9zS/9c67nq2NZWZl27twpl8ulsLAwJSYm%2BvTn5%2BfL6XSqa9euda7ruo4dyhYvXqyJEyf6tO3evVsdO3aU5N/16XK5LvjZCAU5OTl6%2BeWX9eSTT2ro0KHedtbrxTlfXVmv9RS0n1/ERXn44YfN0KFDjdvtNu%2B//77p2bOnee%2B994I9rKA6fvy4SU9PN/fff78pKCgwH330kenXr59ZuXKlOX78uElLSzNz5841e/fuNXPnzjV9%2B/b1/gj1tm3bTPfu3c0rr7zifW7L5MmTvftesWKF6devn9m8ebPZvHmz6devn3nuuee8/XfeeacZP3682bVrl3nllVdMYmJiyD8H64yfP06gqqrKDBkyxNx3331mz549ZsWKFSYpKcn7bJ/CwkKTmJhoVqxY4X2u0PDhw70/Up2bm2t69uxp3n//feN2u83QoUPN3Llzvce60Lqu69ih5ud1dbvd5oorrjCrVq0y3333nXnppZfMlVdeabZt22aM8e/6rOuzcanbt2%2Bf6datm1m0aJHPM5mKiopYrxfhQnVlvdYPAStEnTp1yjz44IMmKSnJ9OvXzzz//PPBHtIlYc%2BePWbixIkmKSnJ9O3b1zz99NPefyzdbrcZMWKESUxMNKNHjzZfffWVz7YbN24011xzjUlKSjIZGRnm6NGj3r6qqirz2GOPmdTUVNOnTx%2BTnZ3t3a8xxhQXF5vJkyebxMREM2DAAPPWW28FZsIBcPbzmr799ltz6623miuvvNIMHTrU/M///I/P%2Bz/66CNz3XXXmR49epgJEyZ4n31zxooVK8xVV11lUlJSzIwZM0x5ebm3r651XdexQ8nZdX3//ffN8OHDTWJiorn%2B%2ButrfcPkz/VZ12fjUrZixQrTpUuXc34Zw3r9teqqK%2Bu1bg5j/u88GwAAAKxovDfsAAAA%2BAkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACW/X8i6hCyE8zMiwAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-8090362851511867458”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:15%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>29</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>28</td> <td class=”number”>0.9%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>26</td> <td class=”number”>0.8%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>15.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>12.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9.0</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>25.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:2%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (939)</td> <td class=”number”>2019</td> <td class=”number”>62.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>682</td> <td class=”number”>21.2%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-8090362851511867458”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>7</td> <td class=”number”>0.2%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>24</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>18</td> <td class=”number”>0.6%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>83755.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>89856.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>101663.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>195401.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>253704.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRES12”><s>VEG_ACRES12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_ACRES07”><code>VEG_ACRES07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9886</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRESPTH07”>VEG_ACRESPTH07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2244</td>

</tr> <tr>

<th>Unique (%)</th> <td>69.9%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>21.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>682</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>42.763</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4666.9</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram7412524013355029925”>

</div> <div class=”col-md-12 text-right”>

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<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles7412524013355029925”

aria-controls=”quantiles7412524013355029925” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram7412524013355029925” aria-controls=”histogram7412524013355029925”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common7412524013355029925” aria-controls=”common7412524013355029925”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme7412524013355029925” aria-controls=”extreme7412524013355029925”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

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<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles7412524013355029925”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>0.49726</td>

</tr> <tr>

<th>Median</th> <td>2.0014</td>

</tr> <tr>

<th>Q3</th> <td>7.8394</td>

</tr> <tr>

<th>95-th percentile</th> <td>171.01</td>

</tr> <tr>

<th>Maximum</th> <td>4666.9</td>

</tr> <tr>

<th>Range</th> <td>4666.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>7.3421</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>213.15</td>

</tr> <tr>

<th>Coef of variation</th> <td>4.9846</td>

</tr> <tr>

<th>Kurtosis</th> <td>162.26</td>

</tr> <tr>

<th>Mean</th> <td>42.763</td>

</tr> <tr>

<th>MAD</th> <td>67.469</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.048</td>

</tr> <tr>

<th>Sum</th> <td>108100</td>

</tr> <tr>

<th>Variance</th> <td>45434</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram7412524013355029925”>

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Tk7VixQr16dNHDz30kLZu3SrLspwuAwAAoME4HrCmTJmiv/zlL3r22WcVEhKiX//617r55pv19NNP64svvnC6HAAAAOP8cg1WUFCQevfurczMTG3btk3jxo3T2rVrNXjwYI0bN05/%2BtOf6r2vqqoqDR06VDt27LDH5s%2Bfr2uvvdbnsX79ens%2BJydH/fv3l8fjUWpqqo4dO2bPWZalRYsWKTk5WUlJSVq4cKFqa2vNLBwAAFwWXP564%2BLiYr322mt67bXX9Nlnn6lHjx66/fbbVVRUpEcffVQ7d%2B7UrFmzfnAflZWVmjJlig4cOOAzfujQIU2ZMkW33367Pda8eXNJ0u7duzVr1izNnTtXsbGxWrBggWbMmKGVK1dKkp5//nnl5OQoKytL1dXVmjp1qlq1aqWJEyca7gAAAGisHA9Yr776ql599VXt2LFDLVu21PDhw7V06VJ16NDBfk2bNm20YMGCHwxYBw8e1JQpU857/dahQ4c0ceJEud3uOnPr16/XrbfequHDh0uSFi5cqL59%2B6qgoEDt27fXunXrlJaWpsTEREnSww8/rCVLlhCwAABAvTl%2BinDWrFlq1qyZli1bpvfee09TpkzxCVeS1LFjR911110/uJ8PP/xQvXr1UnZ2ts/4yZMndfTo0Tr7PCs/P98OT9L3YS4mJkb5%2Bfk6evSoCgsL1bNnT3s%2BISFBhw8fVnFx8U9bKAAAuGw5fgTr/fffV4sWLVRWVqbg4O/z3e7du9W1a1eFhIRIknr06KEePXr84H7Gjh173vFDhw4pKChIK1as0Pvvv6/IyEjdc8899unC4uJiRUVF%2BWzTqlUrFRUVyev1SpLPfOvWrSVJRUVFdba7kOLiYntfZ7lcTeu9fX2FhATWbcxcrsCq93zO9jzQeh/o6Lvz6Ll/0PfGw/GAdfLkSY0ZM0a/%2BMUvNG3aNEnSpEmT1Lp1a/3ud79TmzZtLmr/n3/%2BuYKCguyjYDt37tRjjz2m5s2ba8CAAaqoqFBoaKjPNqGhoaqqqlJFRYX9/B/npO8vpq%2Bv7OxsZWVl%2BYylpqYqLS3tn11Wo9CiRTN/l2BMRES4v0u4LNF359Fz/6Dvgc/xgPXEE0/o5z//ue655x577I033tD06dOVkZGhpUuXXtT%2Bhw8frr59%2ByoyMlKSFBsbqy%2B//FIbNmzQgAEDFBYWVicsVVVVKTw83CdMhYWF2V9LUnh4/b/ZR48erX79%2BvmMuVxNVVp66p9e1/kE2m84ptfvDyEhwYqICNfx4%2BWqqeGvS51C351Hz/2Dvpvnr1/uHQ9Yf//73/U///M/Phegt2zZUtOmTdO4ceMuev9BQUF2uDqrY8eO2r59uyQpOjpaJSUlPvMlJSVyu92Kjo6WJHm9XrVr187%2BWtJ5L5i/kKioqDqnA73eE6quvrx/WBrT%2BmtqahvVegIFfXcePfcP%2Bh74HD8E4nK5dPz48Trj5eXlRu7ovmTJEk2YMMFnbP/%2B/erYsaMkyePxKDc3154rLCxUYWGhPB6PoqOjFRMT4zOfm5urmJgY49dPAQCAxsvxgHXjjTdq/vz5%2Bvrrr%2B2xgoICZWRkKCUl5aL337dvX%2B3cuVNr1qzR119/rf/%2B7//W5s2bde%2B990qSxowZo1dffVUbN27U/v37NW3aNN18881q3769Pb9o0SLt2LFDO3bs0FNPPaXx48dfdF0AAODy4fgpwunTp%2Buee%2B7RwIEDFRERIUk6fvy4unbtqhkzZlz0/rt3764lS5Zo6dKlWrJkidq2baunnnpK8fHxkqT4%2BHilp6dr6dKl%2Bu6779S7d2/NmzfP3n7ixIn69ttvNXnyZIWEhGjUqFF1jogBAAD8kCDLD5%2B0XFNTo23btunAgQNyuVy6%2BuqrdcMNNygoKMjpUhzj9Z4wvk%2BXK1gDFv3V%2BH4bypsP9vZ3CRfN5QpWixbNVFp6iusjHETfnUfP/YO%2Bm%2Bd2X%2BmX9/XLR%2BWEhIQoJSXFyClBAACAS43jAcvr9eqZZ57Rrl27dObMmToXtr/zzjtOlwQAAGCU4wHrscce0969ezVkyBBdeaV/DtsBAAA0JMcD1vbt27V69WqfzwMEAABoTBy/TUPTpk3VqlUrp98WAADAMY4HrGHDhmn16tWqqalx%2Bq0BAAAc4fgpwrKyMuXk5Ojdd99V%2B/bt63zw8rp165wuCQAAwCi/3KZh6NCh/nhbAAAARzgesDIyMpx%2BSwAAAEc5fg2WJBUXFysrK0tTpkzRt99%2Bq7feekuff/65P0oBAAAwzvGA9dVXX%2Bm2227TH/7wB7399ts6ffq03njjDY0cOVL5%2BflOlwMAAGCc4wHrySefVP/%2B/bVlyxZdccUVkqTFixerX79%2BWrRokdPlAAAAGOd4wNq1a5fuuecenw92drlceuCBB7Rv3z6nywEAADDO8YBVW1ur2tq6nxB%2B6tQphYSEOF0OAACAcY4HrD59%2BmjlypU%2BIausrEyZmZlKTk52uhwAAADjHA9YjzzyiPbu3as%2BffqosrJS//Ef/6G%2Bffvqm2%2B%2B0fTp050uBwAAwDjH74MVHR2tzZs3KycnR5988olqa2s1ZswYDRs2TM2bN3e6HAAAAOP8cif38PBw3XHHHf54awAAgAbneMAaP378D87zWYQAACDQOR6w2rZt6/O8urpaX331lT777DPdfffdTpcDAABg3CXzWYTLli1TUVGRw9UAAACY55fPIjyfYcOG6c033/R3GQAAABftkglYH330ETcaBQAAjcIlcZH7yZMn9emnn2rs2LFOlwMAAGCc4wErJibG53MIJemKK67QXXfdpV/%2B8pdOlwMAAGCc4wHrySefdPotAQAAHOV4wNq5c2e9X9uzZ88GrAQAAKBhOB6wfvWrX9mnCC3LssfPHQsKCtInn3zidHkAAAAXzfGAtWLFCs2fP19Tp05VUlKSQkNDtWfPHqWnp%2Bv222/X4MGDnS4JAADAKMdv05CRkaHZs2dr4MCBatGihZo1a6bk5GSlp6drw4YNatu2rf0AAAAIRI4HrOLi4vOGp%2BbNm6u0tNTpcgAAAIxzPGDFxcVp8eLFOnnypD1WVlamzMxM3XDDDU6XAwAAYJzj12A9%2BuijGj9%2BvG688UZ16NBBlmXpyy%2B/lNvt1rp165wuBwAAwDjHA1anTp30xhtvKCcnR4cOHZIkjRs3TkOGDFF4eLjT5QAAABjneMCSpKuuukp33HGHvvnmG7Vv317S93dzBwAAaAwcvwbLsiwtWrRIPXv21NChQ1VUVKTp06dr1qxZOnPmjNPlAAAAGOd4wHrxxRf16quv6vHHH1doaKgkqX///tqyZYuysrKcLgcAAMA4xwNWdna2Zs%2BerREjRth3bx88eLDmz5%2Bv119/3elyAAAAjHM8YH3zzTfq0qVLnfHY2Fh5vV6nywEAADDO8YDVtm1b7dmzp874%2B%2B%2B/b1/wDgAAEMgc/yvCiRMnau7cufJ6vbIsSx988IGys7P14osv6pFHHnG6HAAAAOMcD1gjR45UdXW1li9froqKCs2ePVstW7bUgw8%2BqDFjxjhdDgAAgHGOB6ycnBwNGjRIo0eP1rFjx2RZllq1auV0GQAAAA3G8Wuw0tPT7YvZW7ZsSbgCAACNjuMBq0OHDvrss8%2BcflsAAADHOH6KMDY2Vg8//LBWr16tDh06KCwszGc%2BIyPD6ZIAAACMcjxgffHFF0pISJAk7nsFAAAaJUcC1sKFCzV58mQ1bdpUL774ohNvCQAA4DeOXIP1/PPPq7y83Gds0qRJKi4uduLtAQAAHOVIwLIsq87Yzp07VVlZedH7rqqq0tChQ7Vjxw57rKCgQBMmTFBcXJwGDx6srVu3%2Bmyzbds2DR06VB6PR%2BPHj1dBQYHP/AsvvKCUlBTFx8dr5syZdcIhAADAD3H8rwhNqqys1EMPPaQDBw7YY5ZlKTU1Va1bt9amTZs0bNgwTZ48WUeOHJEkHTlyRKmpqRoxYoReeeUVtWzZUg888IAdAt9%2B%2B21lZWUpPT1da9euVX5%2BvjIzM/2yPgAAEJgCNmAdPHhQd955p77%2B%2Bmuf8e3bt6ugoEDp6enq1KmT7r//fsXFxWnTpk2SpI0bN%2Br666/Xvffeq2uuuUYZGRk6fPiwPvzwQ0nSunXrdPfdd6tv377q3r275s6dq02bNnEUCwAA1JtjASsoKMjo/j788EP16tVL2dnZPuP5%2Bfm67rrr1LRpU3ssISFBeXl59nxiYqI9Fx4erq5duyovL081NTXas2ePz3xcXJzOnDmj/fv3G60fAAA0Xo7dpmH%2B/Pk%2B97w6c%2BaMMjMz1axZM5/X1fc%2BWGPHjj3vuNfrVVRUlM9Yq1atVFRU9KPzx48fV2Vlpc%2B8y%2BVSZGSkvT0AAMCPcSRg9ezZs849r%2BLj41VaWqrS0lKj71VeXq7Q0FCfsdDQUFVVVf3ofEVFhf38QtvXR3FxcZ31ulxN6wS7ixUSElhneF2uwKr3fM72PNB6H%2Bjou/PouX/Q98bDkYDl5L2vwsLCVFZW5jNWVVWlJk2a2PPnhqWqqipFRETYR9jONx8eHl7vGrKzs5WVleUzlpqaqrS0tHrvozFq0aLZj78oQERE1P/7AebQd%2BfRc/%2Bg74HP8Tu5N7To6GgdPHjQZ6ykpMQ%2BehQdHa2SkpI68126dFFkZKTCwsJUUlKiTp06SZKqq6tVVlYmt9td7xpGjx6tfv36%2BYy5XE1VWnrqn1nSBQXabzim1%2B8PISHBiogI1/Hj5aqpqfV3OZcN%2Bu48eu4f9N08f/1y3%2BgClsfj0apVq1RRUWEftcrNzbU/nsfj8Sg3N9d%2BfXl5ufbt26fJkycrODhY3bp1U25urnr16iVJysvLk8vlUmxsbL1riIqKqnM60Os9oerqy/uHpTGtv6amtlGtJ1DQd%2BfRc/%2Bg74EvsA6B1ENSUpLatGmjGTNm6MCBA1q1apV2796tUaNGSZJGjhypXbt2adWqVTpw4IBmzJihdu3a2YFq7NixWrNmjbZs2aLdu3drzpw5uvPOO3/SKUIAAHB5a3QBKyQkRM8%2B%2B6y8Xq9GjBih1157TcuWLVNMTIwkqV27dvrtb3%2BrTZs2adSoUSorK9OyZcvs20gMGTJE999/v2bPnq17771X3bt319SpU/25JAAAEGCCrPN9jg2M83pPGN%2BnyxWsAYv%2Bany/DeXNB3v7u4SL5nIFq0WLZiotPcXhewfRd%2BfRc/%2Bg7%2Ba53Vf65X0b3REsAAAAfyNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGNZoA9af//xnXXvttT6PtLQ0SdK%2Bfft0xx13yOPxaOTIkdq7d6/Ptjk5Oerfv788Ho9SU1N17NgxfywBAAAEqEYbsA4ePKi%2Bfftq69at9mP%2B/Pk6ffq0Jk2apMTERP3%2B979XfHy87r//fp0%2BfVqStHv3bs2aNUuTJ09Wdna2jh8/rhkzZvh5NQAAIJA02oB16NAhde7cWW63235ERETojTfeUFhYmKZNm6ZOnTpp1qxZatasmd566y1J0vr163Xrrbdq%2BPDhio2N1cKFC/Xee%2B%2BpoKDAzysCAACBolEHrA4dOtQZz8/PV0JCgoKCgiRJQUFB6tGjh/Ly8uz5xMRE%2B/Vt2rRRTEyM8vPzHakbAAAEPpe/C2gIlmXpiy%2B%2B0NatW7Vy5UrV1NRo0KBBSktLk9fr1dVXX%2B3z%2BlatWunAgQOSpOLiYkVFRdWZLyoqqvf7FxcXy%2Bv1%2Boy5XE3r7PdihYQEVj52uQKr3vM52/NA632go%2B/Oo%2Bf%2BQd8bj0YZsI4cOaLy8nKFhobqmWee0TfffKP58%2BeroqLCHv9HoaGhqqqqkiRVVFT84Hx9ZGdnKysry2csNTXVvsj%2BctWiRTN/l2BMRES4v0u4LNF359Fz/6Dvga9RBqy2bdtqx44duuqqqxQUFKQuXbqotrZWU6dOVVJSUp2wVFVVpSZNmkjlCXb%2BAAAOfElEQVSSwsLCzjsfHl7/b/bRo0erX79%2BPmMuV1OVlp76J1d0foH2G47p9ftDSEiwIiLCdfx4uWpqav1dzmWDvjuPnvsHfTfPX7/cN8qAJUmRkZE%2Bzzt16qTKykq53W6VlJT4zJWUlNin76Kjo88773a76/3eUVFRdU4Her0nVF19ef%2BwNKb119TUNqr1BAr67jx67h/0PfAF1iGQevrrX/%2BqXr16qby83B775JNPFBkZqYSEBH300UeyLEvS99dr7dq1Sx6PR5Lk8XiUm5trb1dYWKjCwkJ7HgAA4Mc0yoAVHx%2BvsLAwPfroo/r888/13nvvaeHChbrvvvs0aNAgHT9%2BXAsWLNDBgwe1YMEClZeX69Zbb5UkjRkzRq%2B%2B%2Bqo2btyo/fv3a9q0abr55pvVvn17P68KAAAEikYZsJo3b641a9bo2LFjGjlypGbNmqXRo0frvvvuU/PmzbVy5Url5uZqxIgRys/P16pVq9S0aVNJ34ez9PR0LVu2TGPGjNFVV12ljIwMP68IAAAEkiDr7LkyNCiv94TxfbpcwRqw6K/G99tQ3nywt79LuGguV7BatGim0tJTXB/hIPruPHruH/TdPLf7Sr%2B8b6M8ggUAAOBPBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFjnUVlZqZkzZyoxMVF9%2BvTRc8895%2B%2BSAABAAHH5u4BL0cKFC7V3716tXbtWR44c0fTp0xUTE6NBgwb5uzQAABAACFjnOH36tDZu3Kjf/e536tq1q7p27aoDBw7opZdeImBdpFuf%2BZu/S/hJ3nywt79LAAAEKE4RnmP//v2qrq5WfHy8PZaQkKD8/HzV1tb6sTIAABAoOIJ1Dq/XqxYtWig0NNQea926tSorK1VWVqaWLVv%2B6D6Ki4vl9Xp9xlyupoqKijJaa0gI%2BbghBdoRt0Dy54dTftLrz36v8z3vHHruH/S98SBgnaO8vNwnXEmyn1dVVdVrH9nZ2crKyvIZmzx5sn7961%2BbKfL/FBcX6%2B6fHdDo0aONhzecX3FxsbKzs%2Bm5w4qLi7V27Wr67iB67h/0vfEgIp8jLCysTpA6%2B7xJkyb12sfo0aP1%2B9//3ucxevRo47V6vV5lZWXVOVqGhkPP/YO%2BO4%2Be%2Bwd9bzw4gnWO6OholZaWqrq6Wi7X9%2B3xer1q0qSJIiIi6rWPqKgofvMAAOAyxhGsc3Tp0kUul0t5eXn2WG5urrp166bgYNoFAAB%2BHInhHOHh4Ro%2BfLjmzJmj3bt3a8uWLXruuec0fvx4f5cGAAACRMicOXPm%2BLuIS01ycrL27dunp556Sh988IH%2B/d//XSNHjvR3WefVrFkzJSUlqVmzZv4u5bJBz/2DvjuPnvsHfW8cgizLsvxdBAAAQGPCKUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQSsAFVZWamZM2cqMTFRffr00XPPPefvkgJWVVWVhg4dqh07dthjBQUFmjBhguLi4jR48GBt3brVZ5tt27Zp6NCh8ng8Gj9%2BvAoKCnzmX3jhBaWkpCg%2BPl4zZ85UeXm5I2sJBEePHlVaWpqSkpKUkpKijIwMVVZWSqLvDeWrr77SxIkTFR8fr5tvvlmrV6%2B25%2Bi5MyZNmqRHHnnEfr5v3z7dcccd8ng8GjlypPbu3evz%2BpycHPXv318ej0epqak6duyYPWdZlhYtWqTk5GQlJSVp4cKFqq2tdWwtqCcLASk9Pd267bbbrL1791p/%2BtOfrPj4eOvNN9/0d1kBp6KiwkpNTbU6d%2B5sbd%2B%2B3bIsy6qtrbVuu%2B02a8qUKdbBgwetFStWWB6Pxzp8%2BLBlWZZ1%2BPBhKy4uzlqzZo312WefWb/5zW%2BsoUOHWrW1tZZlWdZbb71lJSQkWP/7v/9r5efnW4MHD7bmzp3rtzVeSmpra60777zTuu%2B%2B%2B6zPPvvM2rlzpzVgwADrySefpO8NpKamxrrlllusKVOmWF988YX17rvvWj169LBee%2B01eu6QnJwcq3Pnztb06dMty7KsU6dOWb1797aefPJJ6%2BDBg9a8efOsf/3Xf7VOnTplWZZl5efnW927d7f%2B8Ic/WJ988ol11113WZMmTbL3t2bNGuumm26ydu7caX3wwQdWnz59rNWrV/tlbbgwAlYAOnXqlNWtWzc7EFiWZS1btsy66667/FhV4Dlw4ID1y1/%2B0rrtttt8Ata2bdusuLg4%2Bz92lmVZd999t7V06VLLsizrmWee8en16dOnrfj4eHv7sWPH2q%2B1LMvauXOn1b17d%2Bv06dNOLOuSdvDgQatz586W1%2Bu1x15//XWrT58%2B9L2BHD161PrNb35jnThxwh5LTU21Hn/8cXrugNLSUuvGG2%2B0Ro4caQesjRs3Wv369bODam1trTVgwABr06ZNlmVZ1tSpU%2B3XWpZlHTlyxLr22mutr7/%2B2rIsy7rpppvs11qWZW3evNnq27evU0tCPXGKMADt379f1dXVio%2BPt8cSEhKUn5/PYeKf4MMPP1SvXr2UnZ3tM56fn6/rrrtOTZs2tccSEhKUl5dnzycmJtpz4eHh6tq1q/Ly8lRTU6M9e/b4zMfFxenMmTPav39/A6/o0ud2u7V69Wq1bt3aZ/zkyZP0vYFERUXpmWeeUfPmzWVZlnJzc7Vz504lJSXRcwf813/9l4YNG6arr77aHsvPz1dCQoKCgoIkSUFBQerRo8cF%2B96mTRvFxMQoPz9fR48eVWFhoXr27GnPJyQk6PDhwyouLnZoVagPAlYA8nq9atGihUJDQ%2B2x1q1bq7KyUmVlZX6sLLCMHTtWM2fOVHh4uM%2B41%2BtVVFSUz1irVq1UVFT0o/PHjx9XZWWlz7zL5VJkZKS9/eUsIiJCKSkp9vPa2lqtX79eycnJ9N0B/fr109ixYxUfH6%2BBAwfS8wb2wQcf6O9//7seeOABn/Ef63txcfEF571eryT5zJ/9hYW%2BX1oIWAGovLzcJ1xJsp9XVVX5o6RG5UL9PdvbH5qvqKiwn19oe/x/mZmZ2rdvn/7zP/%2BTvjtg6dKlWrFihT755BNlZGTQ8wZUWVmpxx9/XLNnz1aTJk185n6s7xUVFT%2Bp7/z3/9Lk8ncB%2BOnCwsLq/CCdfX7uDzJ%2BurCwsDpHAquqquzeXqj/ERERCgsLs5%2BfO3/ukbLLXWZmptauXaunn35anTt3pu8O6Natm6Tv/8//4Ycf1siRI%2Bv81R89NyMrK0vXX3%2B9zxHbsy7U1x/re3h4uE%2BYOvffgL5fWjiCFYCio6NVWlqq6upqe8zr9apJkyaKiIjwY2WNQ3R0tEpKSnzGSkpK7EPyF5p3u92KjIxUWFiYz3x1dbXKysrkdrsbvvgAMW/ePD3//PPKzMzUwIEDJdH3hlJSUqItW7b4jF199dU6c%2BaM3G43PW8gf/zjH7VlyxbFx8crPj5er7/%2Bul5//XXFx8df1Pd6dHS0JNmnCv/xa/p%2BaSFgBaAuXbrI5XLZF0RKUm5urrp166bgYP5JL5bH49HHH39sH4qXvu%2Bvx%2BOx53Nzc%2B258vJy7du3Tx6PR8HBwerWrZvPfF5enlwul2JjY51bxCUsKytLL7/8shYvXqwhQ4bY4/S9YXzzzTeaPHmyjh49ao/t3btXLVu2VEJCAj1vIC%2B%2B%2BKJef/11bd68WZs3b1a/fv3Ur18/bd68WR6PRx999JEsy5L0/X2tdu3adcG%2BFxYWqrCwUB6PR9HR0YqJifGZz83NVUxMTJ3rtuBfIXPmzJnj7yLw01xxxRUqLCzUhg0bdP3112vPnj3KzMzUlClT1KlTJ3%2BXF5CysrJ0%2B%2B23q127doqJiVFOTo7y8vLUqVMnbdq0SX/84x%2B1YMECXXnllWrXrp0WLVqkkJAQRUREKCMjQzU1NXr44YcVFBSkJk2aaPHixerUqZNOnjypxx57TAMHDtQvfvELfy/T7w4dOqSHHnpIkyZN0sCBA3X69Gn7cfXVV9P3BuB2u/Xee%2B/pb3/7m7p27ao9e/Zo3rx5uv/%2B%2B3XrrbfS8wYSERGhyMhI%2B/H%2B%2B%2B8rNDRUo0aN0r/8y79o9erVKioqUkxMjJYvX679%2B/crPT1dV1xxhVq3bq2MjAy53W6FhIRo9uzZ6ty5s8aNGyfp%2B1OCK1asUNeuXXX48GHNnTtXEyZM8PnLclwC/HybCPyTTp8%2BbU2bNs2Ki4uz%2BvTpYz3//PP%2BLimg/eN9sCzLsr788ktr3Lhx1vXXX28NGTLE%2Btvf/ubz%2Bnfffde65ZZbrO7du1t33323fX%2Bas1auXGndcMMNVkJCgjVjxgyroqLCkXVc6lauXGl17tz5vA/Lou8NpaioyEpNTbV69Ohh9e7d21q%2BfLl9DyZ67ozp06f73NsqPz/fGj58uNWtWzdr1KhR1scff%2Bzz%2Bk2bNlk33XSTFRcXZ6WmplrHjh2z56qrq60nnnjCSkxMtHr16mVlZmba/564dARZ1v8dowQAAIARXLADAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9P%2BIPq626D79WAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common7412524013355029925”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.407934281707073</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.346739945041219</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.099536060733867</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6639173678229895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.1437578814627996</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>44.69164715066355</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>205.92302674494454</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.3100569722450721</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>239.35994114401325</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2233)</td> <td class=”number”>2233</td> <td class=”number”>69.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>682</td> <td class=”number”>21.2%</td> <td>

<div class=”bar” style=”width:31%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme7412524013355029925”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.005838126272833154</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.006026883920206736</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.011715384115227833</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.018761799754002263</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2494.546126825661</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2806.0766182298544</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2947.462589459987</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3328.9795918367345</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4666.887944768986</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_ACRESPTH12”><s>VEG_ACRESPTH12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_ACRESPTH07”><code>VEG_ACRESPTH07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99906</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_FARMS07”>VEG_FARMS07<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>166</td>

</tr> <tr>

<th>Unique (%)</th> <td>5.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>4.2%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>134</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>22.386</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>8.9%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram8332557531302952853”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives8332557531302952853”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles8332557531302952853”

aria-controls=”quantiles8332557531302952853” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram8332557531302952853” aria-controls=”histogram8332557531302952853”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common8332557531302952853” aria-controls=”common8332557531302952853”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme8332557531302952853” aria-controls=”extreme8332557531302952853”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles8332557531302952853”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0</td>

</tr> <tr>

<th>Q1</th> <td>4</td>

</tr> <tr>

<th>Median</th> <td>11</td>

</tr> <tr>

<th>Q3</th> <td>26</td>

</tr> <tr>

<th>95-th percentile</th> <td>78</td>

</tr> <tr>

<th>Maximum</th> <td>1100</td>

</tr> <tr>

<th>Range</th> <td>1100</td>

</tr> <tr>

<th>Interquartile range</th> <td>22</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>43.537</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.9449</td>

</tr> <tr>

<th>Kurtosis</th> <td>216.21</td>

</tr> <tr>

<th>Mean</th> <td>22.386</td>

</tr> <tr>

<th>MAD</th> <td>20.876</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>11.393</td>

</tr> <tr>

<th>Sum</th> <td>68858</td>

</tr> <tr>

<th>Variance</th> <td>1895.5</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram8332557531302952853”>

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Ki5c%2BeqsrLStnUBAIDAF5ABy7IsZWRkqLKyUs8%2B%2B6xWrlypv/71r1q1apUsy1JaWpo6deqk3NxcjR07Vunp6Tp58qQk6eTJk0pLS9P48eO1fft2dejQQffdd58sy5IkvfHGG8rOztaiRYu0detWuVwuZWVl%2BXO5AAAgwARkwDp%2B/LgKCgqUmZmp6667TsnJycrIyFBeXp727dunoqIiLVq0SD169NDMmTOVkJCg3NxcSdILL7ygG264QdOnT9d1112nzMxMnThxQu%2B9954k6emnn9bUqVM1bNgw9evXTwsXLlRubi5HsQAAQKMFZMCKiorSxo0b1alTJ6/xs2fPyuVyqU%2BfPgoPD/eMJyUlqaCgQJLkcrmUnJzsmQsLC1Pfvn1VUFCguro6ffDBB17zCQkJunDhgo4cOXKFVwUAAFqKgAxYERERSklJ8Tyur6/Xtm3bNGjQILndbkVHR3s9v2PHjjp16pQkfe98RUWFqqurveYdDociIyM92wMAAFyKw98FmJCVlaXDhw9r%2B/bt2rJli0JDQ73mQ0NDVVNTI0mqrKz8zvmqqirP4%2B/avjFKSkrkdru9xhyO8AbBzlchIYGVjx2O5lPvxd4FWg%2BbA3rnG/rXdPTON/TPXgEfsLKysrR161atXLlSPXv2lNPpVHl5uddzampq1KZNG0mS0%2BlsEJZqamoUEREhp9PpefzN%2BbCwsEbXlJOTo%2BzsbK%2BxtLQ0ZWRkNHofLVH79m39XUIDERGN/%2B8Kb/TON/Sv6eidb%2BifPQI6YC1evFjPPfecsrKydMstt0iSYmJidPToUa/nlZaWeo4excTEqLS0tMF87969FRkZKafTqdLSUvXo0UOSVFtbq/LyckVFRTW6rtTUVA0fPtxrzOEIV1nZucte4/cJtHchptfvi5CQYEVEhKmiolJ1dfX%2BLieg0Dvf0L%2Bmo3e%2Baa3989eb%2B4ANWNnZ2Xr%2B%2Bef12GOPadSoUZ7x%2BPh4bdiwQVVVVZ6jVvn5%2BUpKSvLM5%2Bfne55fWVmpw4cPKz09XcHBwYqLi1N%2Bfr4GDhwoSSooKJDD4VCvXr0aXVt0dHSD04Fu91eqrW09v9Dfpjmuv66uvlnWFQjonW/oX9PRO9/QP3sE1iGQ/3fs2DGtXbtWv/rVr5SUlCS32%2B35GTBggDp37qw5c%2BaosLBQGzZs0MGDBzVx4kRJ0oQJE3TgwAFt2LBBhYWFmjNnjrp27eoJVJMmTdKmTZu0a9cuHTx4UAsWLNCdd955WacIAQBA6xaQR7Deeust1dXVad26dVq3bp3X3Mcff6y1a9dq3rx5Gj9%2BvK655hqtWbNGsbGxkqSuXbvqiSee0O9//3utWbNGiYmJWrNmjYKCgiRJo0eP1okTJzR//nzV1NTopz/9qWbNmmX7GgEAQOAKsi7ewhxXlNv9lfF9OhzBGrn878b3e6W8dv9gf5fg4XAEq337tiorO8eh8stE73xD/5qO3vmmtfYvKuoqv7xuQJ4iBAAAaM4IWAAAAIYRsAAAAAwjYAEAABhme8C644479Pzzz%2Burr8xf9A0AANAc2B6wBg0apCeffFJDhgzRAw88oD179ogPMgIAgJbE9oD14IMP6q9//avWrl2rkJAQ/eY3v9HNN9%2BslStX6tNPP7W7HAAAAOP8cqPRoKAgDR48WIMHD1ZlZaWeeeYZrV27Vhs2bFD//v01depU/fSnP/VHaQAAAD7z253cS0pK9Morr%2BiVV17RJ598ov79%2B%2BvnP/%2B5Tp06pd/%2B9rfav3%2B/5s2b56/yAAAAmsz2gPXyyy/r5Zdf1rvvvqsOHTpo3LhxWr16tbp16%2BZ5TufOnbV06VICFgAACEi2B6x58%2BZp2LBhWrNmjYYOHarg4IaXgXXv3l2TJ0%2B2uzQAAAAjbA9Yb7/9ttq3b6/y8nJPuDp48KD69u2rkJAQSVL//v3Vv39/u0sDAAAwwvZPEZ49e1ajRo3Sn/70J8/YjBkzNHbsWBUXF9tdDgAAgHG2B6zf//73uuaaa3TPPfd4xl599VV17txZmZmZdpcDAABgnO0B6x//%2BIceeeQRRUVFecY6dOighx9%2BWPv27bO7HAAAAONsD1gOh0MVFRUNxisrK7mjOwAAaBFsD1hDhw7VkiVL9MUXX3jGioqKlJmZqZSUFLvLAQAAMM72TxHOnj1b99xzj2655RZFRERIkioqKtS3b1/NmTPH7nIAAACMsz1gdezYUS%2B99JL27t2rwsJCORwOXXvttbrpppsUFBRkdzkAAADG%2BeWrckJCQpSSksIpQQAA0CLZHrDcbrdWrVqlAwcO6MKFCw0ubH/rrbfsLgkAAMAo2wPWo48%2BqkOHDmn06NG66qqr7H55AACAK872gLVv3z5t3LhRycnJdr80AACALWy/TUN4eLg6duxo98sCAADYxvaANXbsWG3cuFF1dXV2vzQAAIAtbD9FWF5erry8PP3tb3/T1VdfrdDQUK/5p59%2B2u6SAAAAjPLLbRrGjBnjj5cFAACwhe0BKzMz0%2B6XBAAAsJXt12BJUklJibKzs/Xggw/qn//8p15//XUdP37cH6UAAAAYZ3vA%2Bvzzz/Wzn/1ML730kt544w2dP39er776qiZMmCCXy2V3OQAAAMbZHrD%2B8Ic/aMSIEdq1a5d%2B8IMfSJIee%2BwxDR8%2BXMuXL7e7HAAAAONsD1gHDhzQPffc4/XFzg6HQ/fdd58OHz5sdzkAAADG2R6w6uvrVV9f32D83LlzCgkJsbscAAAA42wPWEOGDNH69eu9QlZ5ebmysrI0aNAgu8sBAAAwzvaA9cgjj%2BjQoUMaMmSIqqur9V//9V8aNmyYvvzyS82ePdvucgAAAIyz/T5YMTEx2rFjh/Ly8vTRRx%2Bpvr5ed911l8aOHat27drZXQ4AAIBxfrmTe1hYmO644w5/vDQAAMAVZ3vAmjJlyvfO812EAAAg0NkesLp06eL1uLa2Vp9//rk%2B%2BeQTTZ061e5yAAAAjGs230W4Zs0anTp16rL3V1NTo/Hjx%2BvRRx/VwIEDJUlLlizRM8884/W8Rx99VJMnT5Yk5eXladWqVXK73RoyZIgWL16sDh06SJIsy9KKFSu0fft21dfXa%2BLEiXrooYcUHOyXbxUCAAABqNmkhrFjx%2Bq11167rG2qq6v1wAMPqLCw0Gv82LFjevDBB7Vnzx7Pz4QJEyRJBw8e1Lx585Senq6cnBxVVFRozpw5nm03b96svLw8ZWdna/Xq1dq5c6c2b97s%2BwIBAECr0WwC1vvvv39ZNxo9evSo7rzzTn3xxRcN5o4dO6Y%2BffooKirK8xMWFiZJ2rZtm2699VaNGzdOvXr10rJly7R7924VFRVJ%2BvoasIyMDCUnJ2vQoEF66KGH9Oyzz5pZJAAAaBWaxUXuZ8%2Be1ccff6xJkyY1ej/vvfeeBg4cqP/5n/9RQkKC175Onz6tbt26fet2LpdLv/rVrzyPO3furNjYWLlcLoWGhqq4uFg33nijZz4pKUknTpxQSUmJoqOjG10fAABovWwPWLGxsV7fQyhJP/jBDzR58mTdfvvtjd7Pd4WxY8eOKSgoSE8%2B%2BaTefvttRUZG6p577tHPf/5zSfrWoNSxY0edOnVKbrdbkrzmO3XqJEk6depUowNWSUmJZ18XORzhxgNaSEizOQDZKA5H86n3Yu8CrYfNAb3zDf1rOnrnG/pnL9sD1h/%2B8Icruv/jx48rKChI3bt31%2BTJk7V//349%2BuijateunUaOHKmqqiqFhoZ6bRMaGqqamhpVVVV5Hv/7nPT1xfSNlZOTo%2BzsbK%2BxtLQ0ZWRkNHVZLUL79m39XUIDERFh/i4hYNE739C/pqN3vqF/9rA9YO3fv7/Rz/33U3WNNW7cOA0bNkyRkZGSpF69eumzzz7Tc889p5EjR8rpdDYISzU1NQoLC/MKU06n0/NvSZ5ruBojNTVVw4cP9xpzOMJVVnbustfzfQLtXYjp9fsiJCRYERFhqqioVF1dwy8fx3ejd76hf01H73zTWvvnrzf3tgesu%2B%2B%2B23OK0LIsz/g3x4KCgvTRRx9d9v6DgoI84eqi7t27a9%2B%2BfZK%2B/qqe0tJSr/nS0lJFRUUpJiZGkuR2u9W1a1fPvyUpKiqq0TVER0c3OB3odn%2Bl2trW8wv9bZrj%2Buvq6ptlXYGA3vmG/jUdvfMN/bOH7YdAnnzySXXp0kWrVq3SO%2B%2B8o/z8fG3ZskU//vGP9cADD%2Bitt97SW2%2B9pV27djVp/48//rimTZvmNXbkyBF1795dkhQfH6/8/HzPXHFxsYqLixUfH6%2BYmBjFxsZ6zefn5ys2NpYL3AEAQKP55Uaj8%2BfP19ChQz1jgwYN0qJFi/Twww97fcKvKYYNG6YNGzZo06ZNGjlypPbs2aMdO3Z4voLnrrvu0t13362EhATFxcVp6dKluvnmm3X11Vd75pcvX64f/ehHkqQVK1Zo%2BvTpPtUEAABaF9sDVklJSYOvy5Gkdu3aqayszOf99%2BvXT48//rhWr16txx9/XF26dNGKFSuUmJgoSUpMTNSiRYu0evVq/etf/9LgwYO1ePFiz/b33nuv/vnPfyo9PV0hISGaOHFigyNiAAAA3yfI%2BvcLoWxwzz33KDw8XH/84x/Vrl07SVJ5ebkefPBBOZ1OrV271s5ybON2f2V8nw5HsEYu/7vx/V4pr90/2N8leDgcwWrfvq3Kys5xLcJlone%2BoX9NR%2B9801r7FxV1lV9e1/YjWL/97W81ZcoUDR06VN26dZNlWfrss88UFRXlOY0HAAAQyGwPWD169NCrr76qvLw8HTt2TJL0i1/8QqNHj76sWyEAAAA0V7YHLEn64Q9/qDvuuENffvml5%2BLyH/zgB/4oBQAAwDjbb9NgWZaWL1%2BuG2%2B8UWPGjNGpU6c0e/ZszZs3TxcuXLC7HAAAAONsD1jPPPOMXn75Zf3ud7/z3Dl9xIgR2rVrV4OvlwEAAAhEtgesnJwczZ8/X%2BPHj/fcvf22227TkiVLtHPnTrvLAQAAMM72gPXll1%2Bqd%2B/eDcZ79erl%2BVoaAACAQGZ7wOrSpYs%2B%2BOCDBuNvv/2254J3AACAQGb7pwjvvfdeLVy4UG63W5Zl6Z133lFOTo6eeeYZPfLII3aXAwAAYJztAWvChAmqra3VunXrVFVVpfnz56tDhw66//77ddddd9ldDgAAgHG2B6y8vDyNGjVKqampOnPmjCzLUseOHe0uAwAA4Iqx/RqsRYsWeS5m79ChA%2BEKAAC0OLYHrG7duumTTz6x%2B2UBAABsY/spwl69eumhhx7Sxo0b1a1bNzmdTq/5zMxMu0sCAAAwyvaA9emnnyopKUmSuO8VAABokWwJWMuWLVN6errCw8P1zDPP2PGSAAAAfmPLNVibN29WZWWl19iMGTNUUlJix8sDAADYypaAZVlWg7H9%2B/erurrajpcHAACwle2fIgQAAGjpCFgAAACG2RawgoKC7HopAAAAv7LtNg1LlizxuufVhQsXlJWVpbZt23o9j/tgAQCAQGdLwLrxxhsb3PMqMTFRZWVlKisrs6MEAAAA29gSsLj3FQAAaE24yB0AAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMCzgA1ZNTY3GjBmjd9991zNWVFSkadOmKSEhQbfddpv27Nnjtc3evXs1ZswYxcfHa8qUKSoqKvKa37Jli1JSUpSYmKi5c%2BeqsrLSlrUAAICWIaADVnV1tR544AEVFhZ6xizLUlpamjp16qTc3FyNHTtW6enpOnnypCTp5MmTSktL0/jx47V9%2B3Z16NBB9913nyzLkiS98cYbys7O1qJFi7R161a5XC5lZWX5ZX0AACAwBWzAOnr0qO6880598cUXXuP79u1TUVGRFi1apB49emjmzJlKSEhQbm6uJOmFF17QDTfcoOnTp%2Bu6665TZmamTpw4offee0%2BS9PTTT2vq1KkaNmyY%2BvXrp4ULFyo3N5ejWAAAoNECNmC99957GjhwoHJycrzGXS6X%2BvTpo/DwcM9YUlKSCgoKPPPJycmeubCwMPXt21cFBQWqq6vTBx984DWfkJCgCxcu6MiRI1d4RQAAoKVw%2BLuAppo0adK3jrvdbkVHR3uNdezYUadOnbrkfEVFhaqrq73mHQ6HIiMjPds3RklJidxut9eYwxHe4HV9FRISWPnY4Wg%2B9V7sXaD1sDmgd76hf01H73xD/%2BwVsAHru1RWVio0NNTGODmTAAATqklEQVRrLDQ0VDU1NZecr6qq8jz%2Bru0bIycnR9nZ2V5jaWlpysjIaPQ%2BWqL27dv6u4QGIiLC/F1CwKJ3vqF/TUfvfEP/7NHiApbT6VR5ebnXWE1Njdq0aeOZ/2ZYqqmpUUREhJxOp%2BfxN%2BfDwhr/C5mamqrhw4d7jTkc4SorO9fofTRGoL0LMb1%2BX4SEBCsiIkwVFZWqq6v3dzkBhd75hv41Hb3zTWvtn7/e3Le4gBUTE6OjR496jZWWlnpOz8XExKi0tLTBfO/evRUZGSmn06nS0lL16NFDklRbW6vy8nJFRUU1uobo6OgGpwPd7q9UW9t6fqG/TXNcf11dfbOsKxDQO9/Qv6ajd76hf/YIrEMgjRAfH68PP/zQc7pPkvLz8xUfH%2B%2BZz8/P98xVVlbq8OHDio%2BPV3BwsOLi4rzmCwoK5HA41KtXL/sWAQAAAlqLC1gDBgxQ586dNWfOHBUWFmrDhg06ePCgJk6cKEmaMGGCDhw4oA0bNqiwsFBz5sxR165dNXDgQElfXzy/adMm7dq1SwcPHtSCBQt05513XtYpQgAA0Lq1uIAVEhKitWvXyu12a/z48XrllVe0Zs0axcbGSpK6du2qJ554Qrm5uZo4caLKy8u1Zs0aBQUFSZJGjx6tmTNnav78%2BZo%2Bfbr69eunWbNm%2BXNJAAAgwARZF29hjivK7f7K%2BD4djmCNXP534/u9Ul67f7C/S/BwOILVvn1blZWd41qEy0TvfEP/mo7e%2Baa19i8q6iq/vG6LO4IFAADgbwQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYiw1Yb775pq6//nqvn4yMDEnS4cOHdccddyg%2BPl4TJkzQoUOHvLbNy8vTiBEjFB8fr7S0NJ05c8YfSwAAAAGqxQaso0ePatiwYdqzZ4/nZ8mSJTp//rxmzJih5ORkvfjii0pMTNTMmTN1/vx5SdLBgwc1b948paenKycnRxUVFZozZ46fVwMAAAJJiw1Yx44dU8%2BePRUVFeX5iYiI0Kuvviqn06mHH35YPXr00Lx589S2bVu9/vrrkqRt27bp1ltv1bhx49SrVy8tW7ZMu3fvVlFRkZ9XBAAAAkWLDljdunVrMO5yuZSUlKSgoCBJUlBQkPr376%2BCggLPfHJysuf5nTt3VmxsrFwuly11AwCAwOfwdwFXgmVZ%2BvTTT7Vnzx6tX79edXV1GjVqlDIyMuR2u3Xttdd6Pb9jx44qLCyUJJWUlCg6OrrB/KlTpxr9%2BiUlJXK73V5jDkd4g/36KiQksPKxw9F86r3Yu0DrYXNA73xD/5qO3vmG/tmrRQaskydPqrKyUqGhoVq1apW%2B/PJLLVmyRFVVVZ7xfxcaGqqamhpJUlVV1ffON0ZOTo6ys7O9xtLS0jwX2bdW7du39XcJDUREhPm7hIBF73xD/5qO3vmG/tmjRQasLl266N1339UPf/hDBQUFqXfv3qqvr9esWbM0YMCABmGppqZGbdq0kSQ5nc5vnQ8La/wvZGpqqoYPH%2B415nCEq6zsXBNX9O0C7V2I6fX7IiQkWBERYaqoqFRdXb2/ywko9M439K/p6J1vWmv//PXmvkUGLEmKjIz0etyjRw9VV1crKipKpaWlXnOlpaWe03cxMTHfOh8VFdXo146Ojm5wOtDt/kq1ta3nF/rbNMf119XVN8u6AgG98w39azp65xv6Z4/AOgTSSH//%2B981cOBAVVZWesY%2B%2BugjRUZGKikpSe%2B//74sy5L09fVaBw4cUHx8vCQpPj5e%2Bfn5nu2Ki4tVXFzsmQcAALiUFhmwEhMT5XQ69dvf/lbHjx/X7t27tWzZMv3yl7/UqFGjVFFRoaVLl%2Bro0aNaunSpKisrdeutt0qS7rrrLr388st64YUXdOTIET388MO6%2BeabdfXVV/t5VQAAIFC0yIDVrl07bdq0SWfOnNGECRM0b948paam6pe//KXatWun9evXKz8/X%2BPHj5fL5dKGDRsUHh4u6etwtmjRIq1Zs0Z33XWXfvjDHyozM9PPKwIAAIEkyLp4rgxXlNv9lfF9OhzBGrn878b3e6W8dv9gf5fg4XAEq337tiorO8e1CJeJ3vmG/jUdvfNNa%2B1fVNRVfnndFnkECwAAwJ8IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzOHvAtB63Lrqf/1dwmV57f7B/i4BABCgOIIFAABgGAHrW1RXV2vu3LlKTk7WkCFD9NRTT/m7JAAAEEA4Rfgtli1bpkOHDmnr1q06efKkZs%2BerdjYWI0aNcrfpQEAgABAwPqG8%2BfP64UXXtCf/vQn9e3bV3379lVhYaGeffZZAhYAAGgUThF%2Bw5EjR1RbW6vExETPWFJSklwul%2Brr6/1YGQAACBQcwfoGt9ut9u3bKzQ01DPWqVMnVVdXq7y8XB06dLjkPkpKSuR2u73GHI5wRUdHG601JIR8fCUF2qce33woxZbXufh7x%2B9f09C/pqN3vqF/9iJgfUNlZaVXuJLkeVxTU9OofeTk5Cg7O9trLD09Xb/5zW/MFPn/SkpKNPVHhUpNTTUe3lq6kpIS5eTk0LsmKCkp0datG%2BldE9G/pqN3vqF/9iLGfoPT6WwQpC4%2BbtOmTaP2kZqaqhdffNHrJzU11Xitbrdb2dnZDY6W4dLoXdPRO9/Qv6ajd76hf/biCNY3xMTEqKysTLW1tXI4vm6P2%2B1WmzZtFBER0ah9REdH8%2B4AAIBWjCNY39C7d285HA4VFBR4xvLz8xUXF6fgYNoFAAAujcTwDWFhYRo3bpwWLFiggwcPateuXXrqqac0ZcoUf5cGAAACRMiCBQsW%2BLuI5mbQoEE6fPiwVqxYoXfeeUe//vWvNWHCBH%2BX9a3atm2rAQMGqG3btv4uJeDQu6ajd76hf01H73xD/%2BwTZFmW5e8iAAAAWhJOEQIAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2AFqOrqas2dO1fJyckaMmSInnrqKX%2BX1GycPn1aGRkZGjBggFJSUpSZmanq6mpJUlFRkaZNm6aEhATddttt2rNnj9e2e/fu1ZgxYxQfH68pU6aoqKjIH0toFmbMmKFHHnnE8/jw4cO64447FB8frwkTJujQoUNez8/Ly9OIESMUHx%2BvtLQ0nTlzxu6S/a6mpkYLFy7UjTfeqP/4j//QY489pov3cqZ/36%2B4uFgzZ85U//79NXz4cG3ZssUzR%2B%2B%2BW01NjcaMGaN3333XM%2Bbr37ktW7YoJSVFiYmJmjt3riorK21ZS0tDwApQy5Yt06FDh7R161b97ne/U3Z2tl5//XV/l%2BV3lmUpIyNDlZWVevbZZ7Vy5Ur99a9/1apVq2RZltLS0tSpUyfl5uZq7NixSk9P18mTJyVJJ0%2BeVFpamsaPH6/t27erQ4cOuu%2B%2B%2B9Qav%2BzgL3/5i3bv3u15fP78ec2YMUPJycl68cUXlZiYqJkzZ%2Br8%2BfOSpIMHD2revHlKT09XTk6OKioqNGfOHH%2BV7zdLlizR3r17tWnTJq1YsUJ//vOflZOTQ/8a4f7771d4eLhefPFFzZ07V6tWrdKbb75J775HdXW1HnjgARUWFnrGfP0798Ybbyg7O1uLFi3S1q1b5XK5lJWV5Zf1BTwLAefcuXNWXFyctW/fPs/YmjVrrMmTJ/uxqubh6NGjVs%2BePS232%2B0Z27lzpzVkyBBr7969VkJCgnXu3DnP3NSpU63Vq1dblmVZq1at8urh%2BfPnrcTERK8%2BtwZlZWXW0KFDrQkTJlizZ8%2B2LMuyXnjhBWv48OFWfX29ZVmWVV9fb40cOdLKzc21LMuyZs2a5XmuZVnWyZMnreuvv9764osv7F%2BAn5SVlVl9%2BvSx3n33Xc/Y%2BvXrrUceeYT%2BXUJ5ebnVs2dP6%2BOPP/aMpaenWwsXLqR336GwsNC6/fbbrZ/97GdWz549PX%2BnfP07N2nSJM9zLcuy9u/fb/Xr1886f/68HctqUTiCFYCOHDmi2tpaJSYmesaSkpLkcrlUX1/vx8r8LyoqShs3blSnTp28xs%2BePSuXy6U%2BffooPDzcM56UlKSCggJJksvlUnJysmcuLCxMffv29cy3Fn/84x81duxYXXvttZ4xl8ulpKQkBQUFSZKCgoLUv3//7%2Bxd586dFRsbK5fLZW/xfpSfn6927dppwIABnrEZM2YoMzOT/l1CmzZtFBYWphdffFEXLlzQ8ePHdeDAAfXu3ZvefYf33ntPAwcOVE5Ojte4L3/n6urq9MEHH3jNJyQk6MKFCzpy5MgVXlHLQ8AKQG63W%2B3bt1doaKhnrFOnTqqurlZ5ebkfK/O/iIgIpaSkeB7X19dr27ZtGjRokNxut6Kjo72e37FjR506dUqSLjnfGrzzzjv6xz/%2Bofvuu89r/FK9KSkpafW9KyoqUpcuXbRjxw6NGjVK//mf/6k1a9aovr6e/l2C0%2BnU/PnzlZOTo/j4eN16660aOnSo7rjjDnr3HSZNmqS5c%2BcqLCzMa9yXv3MVFRWqrq72mnc4HIqMjGzx/bwSHP4uAJevsrLSK1xJ8jyuqanxR0nNVlZWlg4fPqzt27dry5Yt39q3iz37rr62lp5WV1frd7/7nebPn682bdp4zV2qN1VVVa26d9LX16l9/vnnev7555WZmSm326358%2BcrLCyM/jXCsWPHNGzYMN1zzz0qLCzU4sWLddNNN9G7y3Spfn3ffFVVlefxd22PxiNgBSCn09ngl/3i42/%2BH2NrlpWVpa1bt2rlypXq2bOnnE5ngyN8NTU1np59V18jIiJsq9mfsrOzdcMNN3gdAbzou3pzqd598911S%2BZwOHT27FmtWLFCXbp0kfT1BcXPPfecrrnmGvr3Pd555x1t375du3fvVps2bRQXF6fTp09r3bp1uvrqq%2BndZfDl75zT6fQ8/uZ8a%2B2nLzhFGIBiYmJUVlam2tpaz5jb7VabNm1aTRi4lMWLF2vz5s3KysrSLbfcIunrvpWWlno9r7S01HM4/Lvmo6Ki7Cnaz/7yl79o165dSkxMVGJionbu3KmdO3cqMTGR3jVCVFSUnE6nJ1xJ0o9//GMVFxfTv0s4dOiQrrnmGq83iH369NHJkyfp3WXypV%2BRkZFyOp1e87W1tSovL2%2B1/fQFASsA9e7dWw6Hw%2Bvi6/z8fMXFxSk4mP%2Bk2dnZev755/XYY49p9OjRnvH4%2BHh9%2BOGHnsPg0td9i4%2BP98zn5%2Bd75iorK3X48GHPfEv3zDPPaOfOndqxY4d27Nih4cOHa/jw4dqxY4fi4%2BP1/vvvez7KbVmWDhw48J29Ky4uVnFxcavpnfR1D6qrq/Xpp596xo4fP64uXbrQv0uIjo7W559/7nXk5Pjx4%2BratSu9u0y%2B/J0LDg5WXFyc13xBQYEcDod69epl3yJaCn9%2BhBFN9%2Bijj1qjR4%2B2XC6X9eabb1r9%2B/e33njjDX%2BX5XdHjx61evfuba1cudIqKSnx%2BqmtrbVuu%2B026/7777c%2B%2BeQTa/369VZCQoJ14sQJy7Isq6ioyIqLi7PWr19vffLJJ9Z///d/Wz/72c88Hw9vbWbPnu35%2BPtXX31lDRo0yFq8eLFVWFhoLV682Bo8eLDno%2BAHDhyw%2Bvbta/35z3%2B2PvroI2vy5MnWzJkz/Vm%2BX8yYMcNKTU21PvroI%2Bvtt9%2B2Bg0aZG3dupX%2BXUJFRYU1ePBga9asWdbx48ett956yxowYID13HPP0btG%2BPfbNPj6dy4vL8/q37%2B/9eabb1oul8saPXq0tXjxYr%2BtLZARsALU%2BfPnrYcffthKSEiwhgwZYm3evNnfJTUL69evt3r27PmtP5ZlWZ999pn1i1/8wrrhhhus0aNHW//7v//rtf3f/vY366c//anVr18/a%2BrUqS3%2BXjrf598DlmVZlsvlssaNG2fFxcVZEydOtD788EOv5%2Bfm5lo/%2BclPrISEBCstLc06c%2BaM3SX7XUVFhTVr1iwrISHBuummm6wnnnjC839c9O/7FRYWWtOmTbP69%2B9vjRgxwtq8eTO9a6R/D1iW5fvfufXr11s33XSTlZSUZM2ZM8eqqqqyZR0tTZBltcLbVAMAAFxBXLADAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb9HxulJ6OIe6BjAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common8332557531302952853”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.0</td> <td class=”number”>122</td> <td class=”number”>3.8%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.0</td> <td class=”number”>108</td> <td class=”number”>3.4%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.0</td> <td class=”number”>104</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>11.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:6%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (155)</td> <td class=”number”>1678</td> <td class=”number”>52.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>134</td> <td class=”number”>4.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme8332557531302952853”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>286</td> <td class=”number”>8.9%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.0</td> <td class=”number”>163</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:57%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.0</td> <td class=”number”>165</td> <td class=”number”>5.1%</td> <td>

<div class=”bar” style=”width:58%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.0</td> <td class=”number”>137</td> <td class=”number”>4.3%</td> <td>

<div class=”bar” style=”width:48%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.0</td> <td class=”number”>119</td> <td class=”number”>3.7%</td> <td>

<div class=”bar” style=”width:42%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>476.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>559.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>722.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>908.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1100.0</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VEG_FARMS12”><s>VEG_FARMS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_VEG_FARMS07”><code>VEG_FARMS07</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91017</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VLFOODSEC_10_12”>VLFOODSEC_10_12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>32</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.0%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>5.8018</td>

</tr> <tr>

<th>Minimum</th> <td>3.2</td>

</tr> <tr>

<th>Maximum</th> <td>8.1</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3856719825666295386”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3856719825666295386,#minihistogram-3856719825666295386”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3856719825666295386”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3856719825666295386”

aria-controls=”quantiles-3856719825666295386” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3856719825666295386” aria-controls=”histogram-3856719825666295386”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3856719825666295386” aria-controls=”common-3856719825666295386”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3856719825666295386” aria-controls=”extreme-3856719825666295386”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3856719825666295386”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>3.2</td>

</tr> <tr>

<th>5-th percentile</th> <td>4.5</td>

</tr> <tr>

<th>Q1</th> <td>5.1</td>

</tr> <tr>

<th>Median</th> <td>5.7</td>

</tr> <tr>

<th>Q3</th> <td>6.5</td>

</tr> <tr>

<th>95-th percentile</th> <td>7.1</td>

</tr> <tr>

<th>Maximum</th> <td>8.1</td>

</tr> <tr>

<th>Range</th> <td>4.9</td>

</tr> <tr>

<th>Interquartile range</th> <td>1.4</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.8609</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.14838</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.40091</td>

</tr> <tr>

<th>Mean</th> <td>5.8018</td>

</tr> <tr>

<th>MAD</th> <td>0.72228</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>-0.036458</td>

</tr> <tr>

<th>Sum</th> <td>18171</td>

</tr> <tr>

<th>Variance</th> <td>0.74114</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3856719825666295386”>

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lSampqdq8ebPWrVun/fv3a86cORo%2BfDiPaAAAAE3mlwFLkpYuXap/%2B7d/U2pqqubOnat7771XkydPVnh4uFauXOk5S2W325Wfn6/Q0FBJUmxsrBYuXKi8vDylpqaqffv2ys7ONnlrAABAa%2BKXlwgl6eqrr1ZOTs5F5wYOHKhNmzb97HuTkpI8lwgBAAAul9%2BewQIAADALAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACD%2BW3A%2BvDDD3XDDTd4vWbNmiVJ2rdvnyZNmiSbzabk5GTt3bvX671btmzRyJEjZbPZlJ6erhMnTpixCQAAoJXy24BVVlamESNGaMeOHZ7X4sWLVVtbq7S0NMXHx2vjxo2KjY3VtGnTVFtbK0kqKSlRZmamZs6cqYKCAtXU1CgjI8PkrQEAAK2J3wasAwcO6Prrr1dUVJTnFRERoa1btyooKEhz5sxRr169lJmZqbCwMG3btk2StGbNGiUmJmrChAnq06ePcnJyVFhYqPLycpO3CAAAtBZ%2BHbB69uzZaNxutysuLk4Wi0WSZLFYNHjwYBUXF3vm4%2BPjPct37txZXbp0kd1ub5G6AQBA6%2BeXAcvtduvQoUPasWOHRo0apZEjR2rp0qVyuVxyOByKjo72Wr5jx446duyYJKmysvIX5wEAAC7FanYBzeHo0aNyOp0KDAzU8uXLdeTIES1evFhnzpzxjP9fgYGBcrlckqQzZ8784nxTVFZWyuFweI1ZraGe4BYQ0M7rK4xHj2Ekq9Wc/agt7Mdm9faCttBjs7XVHvtlwOratas%2B//xztW/fXhaLRX379lVDQ4Mef/xxDRkypFFYcrlcCg4OliQFBQVddD4kJKTJ6y8oKFBubq7XWHp6uuenGC%2BIiGj6Z%2BLK0GMYoUOHMFPX78/7sdm9vcCfe%2Bwr2lqPfS5gTZo0ScnJyRo7dqyuvvrqK/6ca665xuv7Xr16qa6uTlFRUaqqqvKaq6qq8pxdiomJueh8VFRUk9edkpKihIQErzGrNVQnT56WdD7FR0SEqKbGqfr6hiZ/LpqOHsNIF/7utrS2sB%2Bb1dsL2kKPzWZ2j80K8T4XsG655RatWLFC2dnZuuOOO5SUlKShQ4d6bkpviu3bt2v27Nn6%2BOOPPWeevv32W11zzTWKi4vTqlWr5Ha7ZbFY5Ha7tWfPHk2fPl2SZLPZVFRUpKSkJElSRUWFKioqZLPZmrz%2B6OjoRvdxORw/6tw57x2rvr6h0RiMRY9hBLP3IX/ej31lu/y5x76irfXY5y6IPvbYY/rHP/6hl156SQEBAXrkkUc0fPhwvfDCCzp06FCTPiM2NlZBQUGaN2%2BeDh48qMLCQuXk5Oihhx7S6NGjVVNTo6ysLJWVlSkrK0tOp1OJiYmSpNTUVG3evFnr1q3T/v37NWfOHA0fPlzdu3dvzs0GAAB%2BxOcClnT%2B0QlDhw7VkiVLtHPnTt177716/fXXNWbMGN177736r//6r198f3h4uF555RWdOHFCycnJyszMVEpKih566CGFh4dr5cqVnrNUdrtd%2Bfn5Cg0NlXQ%2BnC1cuFB5eXlKTU1V%2B/btlZ2d3RKbDQAA/ITPXSK8oLKyUu%2B8847eeecdff/99xo8eLDuuusuHTt2TPPmzdPu3buVmZn5s%2B//1a9%2BpVdfffWicwMHDtSmTZt%2B9r1JSUmeS4QAAACXy%2BcC1ubNm7V582Z9/vnnioyM1IQJE/Tiiy96PTS0c%2BfOysrK%2BsWABQAAYBafC1iZmZkaMWKE8vLydPvtt6tdu8ZXMa%2B77jrdd999JlQHAABwaT4XsD755BN16NBB1dXVnnBVUlKifv36KSAgQJI0ePBgDR482MwyAQAAfpbPBaxTp04pNTVVd9xxh%2BbMmSNJSktLU6dOnbRq1Sp17tzZ5AoBAEZJXP6p2SVclvcfHWp2CWglfO6nCJ955hn16NFDDzzwgGds69at6ty5Mz/NBwAAWgWfC1hffvmlnnjiCa8np0dGRmrOnDn67LPPTKwMAACgaXwuYFmtVtXU1DQadzqdcrvdJlQEAABweXwuYN1%2B%2B%2B1avHix/vnPf3rGysvLlZ2drWHDhplYGQAAQNP43E3uc%2BfO1QMPPKBRo0YpIiJCklRTU6N%2B/fopIyPD5OoAAAAuzecCVseOHbVp0ybt3LlTpaWlslqt6t27t2699dbL%2BoXPAAAAZvG5gCVJAQEBGjZsGJcEAQBAq%2BRzAcvhcGj58uXas2ePzp492%2BjG9r/97W8mVQYAANA0Phew/vM//1N79%2B7V2LFjdfXVV5tdDgAAwGXzuYD12WefafXq1YqPjze7FAAAgCvic49pCA0NVceOHc0uAwAA4Ir5XMAaP368Vq9erfr6erNLAQAAuCI%2Bd4mwurpaW7Zs0ccff6zu3bsrMDDQa/6NN94wqTIAAICm8bmAJUnjxo0zuwQAAIAr5nMBKzs72%2BwSAAAA/iU%2Bdw%2BWJFVWVio3N1ePPfaY/vd//1fbtm3TwYMHzS4LAACgSXwuYB0%2BfFh33nmnNm3apA8%2B%2BEC1tbXaunWrkpOTZbfbzS4PAADgknwuYD377LMaOXKkPvroI1111VWSpOeff14JCQlaunSpydUBAABcms8FrD179uiBBx7w%2BsXOVqtVM2bM0L59%2B0ysDAAAoGl8LmA1NDSooaGh0fjp06cVEBBgQkUAAACXx%2BcC1m233aaVK1d6hazq6motWbJEt9xyi4mVAQAANI3PBawnnnhCe/fu1W233aa6ujo9/PDDGjFihI4cOaK5c%2BeaXR4AAMAl%2BdxzsGJiYvT2229ry5Yt%2Bvbbb9XQ0KDU1FSNHz9e4eHhZpcHAABwST4XsCQpJCREkyZNMuSz0tLSFBkZqWeffVaStG/fPj311FP6/vvv1bt3by1YsED9%2B/f3LL9lyxYtX75cDodDt912mxYtWqTIyEhDagEAAG2DzwWsKVOm/OL85fwuwvfee0%2BFhYW66667JEm1tbVKS0vTnXfeqWeffVZr167VtGnT9OGHHyo0NFQlJSXKzMzUggUL1KdPH2VlZSkjI0MrV678l7YJAAC0LT53D1bXrl29XjExMTpz5oxKSkoUGxvb5M%2Bprq5WTk6OBgwY4BnbunWrgoKCNGfOHPXq1UuZmZkKCwvTtm3bJElr1qxRYmKiJkyYoD59%2BignJ0eFhYUqLy83fDsBAID/8rkzWD/3uwjz8vJ07NixJn/Oc889p/Hjx6uystIzZrfbFRcX53nGlsVi0eDBg1VcXKykpCTZ7Xb94Q9/8CzfuXNndenSRXa7Xd27d7/CLQIAAG2Nz53B%2Bjnjx4/X%2B%2B%2B/36Rld%2B3apS%2B//FIzZszwGnc4HIqOjvYa69ixoye4VVZW/uI8AABAU/jcGayf89VXXzXpQaN1dXV66qmnNH/%2BfAUHB3vNOZ1OBQYGeo0FBgbK5XJJks6cOfOL801VWVkph8PhNWa1hnrCW0BAO6%2BvMB49hpGsVnP2I/Zj32PWvtCatdX92OcC1sVucj916pS%2B%2B%2B473XPPPZd8f25urvr3769hw4Y1mgsKCmoUllwulyeI/dx8SEjI5WyCCgoKlJub6zWWnp6uWbNmeY1FRFze5%2BLy0WMYoUOHMFPXz37sO8zeF1qztrYf%2B1zA6tKli9fvIZSkq666Svfdd59%2B97vfXfL97733nqqqqjw3xF8ITB988IHGjRunqqoqr%2BWrqqo8Z5ZiYmIuOh8VFXVZ25CSkqKEhASvMas1VCdPnpZ0PsVHRISopsap%2BvrGvxYI/zp6DCNd%2BLvb0tiPfY9Z%2B0JrZvZ%2BbFYo9rmAdeF5VVfqzTff1Llz5zzfL126VJI0e/Zs7d69W6tWrZLb7ZbFYpHb7daePXs0ffp0SZLNZlNRUZGSkpIkSRUVFaqoqJDNZrusGqKjoxvdy%2BVw/Khz57x3rPr6hkZjMBY9hhHM3ofYj30H/x2uXFvbj30uYO3evbvJy950002Nxrp27er1fVjY%2BeTao0cPdezYUcuWLVNWVpbuvvtuvfXWW3I6nUpMTJQkpaamavLkyRo0aJAGDBigrKwsDR8%2BnJ8gBAAAl8XnAtbkyZM9lwjdbrdn/KdjFotF33777WV9dnh4uFauXKmnnnpKf/3rX3XDDTcoPz9foaGhkqTY2FgtXLhQL774on744QcNHTpUixYtMmKzAABAG%2BJzAWvFihVavHixHn/8cQ0ZMkSBgYH6%2BuuvtXDhQt11110aM2bMZX3eTy85Dhw4UJs2bfrZ5ZOSkjyXCAEAAK6Ez/3MZHZ2tubPn69Ro0apQ4cOCgsL0y233KKFCxdq7dq1Xk95BwAA8EU%2BF7AqKysvGp7Cw8N18uRJEyoCAAC4PD4XsAYNGqTnn39ep06d8oxVV1dryZIluvXWW02sDAAAoGl87h6sefPmacqUKbr99tvVs2dPud1u/fd//7eioqL0xhtvmF0eAADAJflcwOrVq5e2bt2qLVu26MCBA5Kke%2B%2B9V2PHjr3sJ6oDAACYwecCliS1b99ekyZN0pEjRzzPoLrqqqtMrgoAAKBpfO4eLLfbraVLl%2Bqmm27SuHHjdOzYMc2dO1eZmZk6e/as2eUBAABcks8FrDfffFObN2/WU089pcDAQEnSyJEj9dFHHzX6BcoAAAC%2ByOcCVkFBgebPn6%2BkpCTP09vHjBmjxYsX69133zW5OgAAgEvzuYB15MgR9e3bt9F4nz595HA4TKgIAADg8vhcwOratau%2B/vrrRuOffPIJv3QZAAC0Cj73U4QPPvigFixYIIfDIbfbrV27dqmgoEBvvvmmnnjiCbPLAwAAuCSfC1jJyck6d%2B6cXn75ZZ05c0bz589XZGSkHn30UaWmpppdHgAAwCX5XMDasmWLRo8erZSUFJ04cUJut1sdO3Y0uywAAIAm87l7sBYuXOi5mT0yMpJwBQAAWh2fC1g9e/bU999/b3YZAAAAV8znLhH26dNHs2fP1urVq9WzZ08FBQV5zWdnZ5tUGQAAQNP4XMA6dOiQ4uLiJInnXgEAgFbJJwJWTk6OZs6cqdDQUL355ptmlwMAAPAv8Yl7sF599VU5nU6vsbS0NFVWVppUEQAAwJXziYDldrsbje3evVt1dXUmVAMAAPCv8YmABQAA4E984h4sAPBlics/NbsEAK2Mz5zBslgsZpcAAABgCJ85g7V48WKvZ16dPXtWS5YsUVhYmNdyPAcLAAD4Op8IWDfddFOjZ17Fxsbq5MmTOnnypElVAQAAXBmfCFg8%2BwoAAPgTn7kHy2iHDx/Wgw8%2BqNjYWA0fPlyrV6/2zJWXl%2Bv%2B%2B%2B/XoEGDNGbMGO3YscPrvTt37tS4ceNks9k0ZcoUlZeXt3T5AACgFfPLgNXQ0KC0tDR16NBBmzZt0oIFC/Tyyy/r3XffldvtVnp6ujp16qQNGzZo/Pjxmjlzpo4ePSpJOnr0qNLT05WUlKT169crMjJSM2bMuOizugAAAC7GJy4RGq2qqkp9%2B/bV008/rfDwcPXs2VO33nqrioqK1KlTJ5WXl%2Butt95SaGioevXqpV27dmnDhg165JFHtG7dOvXv319Tp06VdP6m%2BqFDh%2BqLL77QzTffbPKWAQCA1sAvz2BFR0dr%2BfLlCg8Pl9vtVlFRkXbv3q0hQ4bIbrfrxhtvVGhoqGf5uLg4FRcXS5Lsdrvi4%2BM9cyEhIerXr59nHgAA4FL88gzW/5WQkKCjR49qxIgRGjVqlJ555hlFR0d7LdOxY0cdO3ZMkuRwOH5xvikqKysb/VSk1Rrq%2BdyAgHZeX2E8egygOVitHFMuV1s9Hvt9wHrxxRdVVVWlp59%2BWtnZ2XI6nQoMDPRaJjAwUC6XS5IuOd8UBQUFys3N9RpLT0/XrFmzvMYiIkIuZ1NwBegxACN16BB26YVwUW3teOz3AWvAgAGSpLq6Os2ePVvJyclyOp1ey7hcLgUHB0uSgoKCGoUpl8uliIiIJq8zJSVFCQkJXmNWa6hOnjwt6XyKj4gIUU2NU/X1DZe9Tbg0egygOVw4jqPpzD4emxWK/TJgVVVVqbi4WCNHjvSM9e7dW2fPnlVUVJQOHjzYaPkLl%2B9iYmJUVVXVaL5v375NXn90dHSjy4wOx49osdz1AAAScklEQVQ6d857x6qvb2g0BmPRYwBG4nhy5dra8dgvL4geOXJEM2fO1PHjxz1je/fuVWRkpOLi4vTNN9/ozJkznrmioiLZbDZJks1mU1FRkWfO6XRq3759nnkAAIBL8cuANWDAAPXr109PPvmkysrKVFhYqCVLlmj69OkaMmSIOnfurIyMDJWWlio/P18lJSWaOHGiJCk5OVl79uxRfn6%2BSktLlZGRoW7duvGIBgAA0GR%2BGbACAgL00ksvKSQkRCkpKcrMzNTkyZM1ZcoUz5zD4VBSUpLeeecd5eXlqUuXLpKkbt266U9/%2BpM2bNigiRMnqrq6Wnl5ebJYLCZvFQAAaC0sbh5R3iIcjh89f7Za26lDhzCdPHm6TV2PbkltsceJyz81uwTA773/6FCzS2h1zD4eR0Vd3eLrlPz0DBYAAICZCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYzG8D1vHjxzVr1iwNGTJEw4YNU3Z2turq6iRJ5eXluv/%2B%2BzVo0CCNGTNGO3bs8Hrvzp07NW7cONlsNk2ZMkXl5eVmbAIAAGil/DJgud1uzZo1S06nU3/5y1/0wgsv6B//%2BIeWL18ut9ut9PR0derUSRs2bND48eM1c%2BZMHT16VJJ09OhRpaenKykpSevXr1dkZKRmzJght9tt8lYBAIDWwmp2Ac3h4MGDKi4u1qeffqpOnTpJkmbNmqXnnntOt99%2Bu8rLy/XWW28pNDRUvXr10q5du7RhwwY98sgjWrdunfr376%2BpU6dKkrKzszV06FB98cUXuvnmm83cLAAA0Er45RmsqKgorV692hOuLjh16pTsdrtuvPFGhYaGesbj4uJUXFwsSbLb7YqPj/fMhYSEqF%2B/fp55AACAS/HLM1gREREaNmyY5/uGhgatWbNGt9xyixwOh6Kjo72W79ixo44dOyZJl5xvisrKSjkcDq8xqzXU87kBAe28vsJ49BhAc7BaOaZcrrZ6PPbLgPVTS5Ys0b59%2B7R%2B/Xq99tprCgwM9JoPDAyUy%2BWSJDmdzl%2Bcb4qCggLl5uZ6jaWnp2vWrFleYxERIZezGbgC9BiAkTp0CDO7hFarrR2P/T5gLVmyRK%2B//rpeeOEFXX/99QoKClJ1dbXXMi6XS8HBwZKkoKCgRmHK5XIpIiKiyetMSUlRQkKC15jVGqqTJ09LOp/iIyJCVFPjVH19w5VsFi6BHgNoDheO42g6s4/HZoVivw5YixYt0tq1a7VkyRKNGjVKkhQTE6OysjKv5aqqqjyX72JiYlRVVdVovm/fvk1eb3R0dKPLjA7Hjzp3znvHqq9vaDQGY9FjAEbieHLl2trx2G8viObm5uqtt97S888/r7Fjx3rGbTabvvnmG505c8YzVlRUJJvN5pkvKiryzDmdTu3bt88zDwAAcCl%2BGbAOHDigl156SX/4wx8UFxcnh8PheQ0ZMkSdO3dWRkaGSktLlZ%2Bfr5KSEk2cOFGSlJycrD179ig/P1%2BlpaXKyMhQt27deEQDAABoMovbD5%2BgmZ%2Bfr2XLll107rvvvtPhw4eVmZkpu92uHj166Mknn9Svf/1rzzKFhYV65plndOzYMcXGxmrRokXq3r37v1STw/Gj589Wazt16BCmkydPt6nTpS3JiB4nLv/U4KoAtHbvPzrU7BJaHbP/zYuKurrF1yn5acDyRQSslkXAAtAcCFiXz%2Bx/88wKWH55iRAAAMBMBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAMRsACAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwAIAADAYAQsAAMBgBCwAAACDEbAAAAAM5vcBy%2BVyady4cfr88889Y%2BXl5br//vs1aNAgjRkzRjt27PB6z86dOzVu3DjZbDZNmTJF5eXlLV02AABoxfw6YNXV1emPf/yjSktLPWNut1vp6enq1KmTNmzYoPHjx2vmzJk6evSoJOno0aNKT09XUlKS1q9fr8jISM2YMUNut9uszQAAAK2M3wassrIy/fu//7v%2B%2Bc9/eo1/9tlnKi8v18KFC9WrVy9NmzZNgwYN0oYNGyRJ69atU//%2B/TV16lT96le/UnZ2tv7nf/5HX3zxhRmbAQAAWiG/DVhffPGFbr75ZhUUFHiN2%2B123XjjjQoNDfWMxcXFqbi42DMfHx/vmQsJCVG/fv088wAAAJdiNbuA5nLPPfdcdNzhcCg6OtprrGPHjjp27FiT5puisrJSDofDa8xqDfV8bkBAO6%2BvMB49BtAcrFaOKZerrR6P/TZg/Ryn06nAwECvscDAQLlcribNN0VBQYFyc3O9xtLT0zVr1iyvsYiIkMspHVeAHgMwUocOYWaX0Gq1teNxmwtYQUFBqq6u9hpzuVwKDg72zP80TLlcLkVERDR5HSkpKUpISPAas1pDdfLkaUnnU3xERIhqapyqr2%2B4ks3AJdBjAM3hwnEcTWf28disUNzmAlZMTIzKysq8xqqqqjyX72JiYlRVVdVovm/fvk1eR3R0dKPLjA7Hjzp3znvHqq9vaDQGY9FjAEbieHLl2trxuG1dEJVks9n0zTff6MyZM56xoqIi2Ww2z3xRUZFnzul0at%2B%2BfZ55AACAS2lzAWvIkCHq3LmzMjIyVFpaqvz8fJWUlGjixImSpOTkZO3Zs0f5%2BfkqLS1VRkaGunXrpptvvtnkygEAQGvR5gJWQECAXnrpJTkcDiUlJemdd95RXl6eunTpIknq1q2b/vSnP2nDhg2aOHGiqqurlZeXJ4vFYnLlAACgtWgT92B99913Xt/36NFDa9as%2Bdnlf/Ob3%2Bg3v/lNc5cFAAD8VJsIWAAAGCFx%2Badml3BZ3n90qNkltFlt7hIhAABAcyNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwq9kF4F%2BTuPxTs0tosvcfHWp2CQAAtAjOYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjJ8iBADAT/GT5ubhDNZF1NXV6cknn1R8fLxuu%2B02/fnPfza7JAAA0IpwBusicnJytHfvXr3%2B%2Bus6evSo5s6dqy5dumj06NFmlwYAAFoBAtZP1NbWat26dVq1apX69eunfv36qbS0VH/5y18IWAAAoEm4RPgT%2B/fv17lz5xQbG%2BsZi4uLk91uV0NDg4mVAQCA1oIzWD/hcDjUoUMHBQYGesY6deqkuro6VVdXKzIy8pKfUVlZKYfD4TVmtYYqOjpakhQQ0M7ra1thtbbc9rbVHgNAa9WS/0a0BALWTzidTq9wJcnzvcvlatJnFBQUKDc312ts5syZeuSRRySdD2Cvv75aKSkpntB1pb7M4rLlxRjRY3r7yyorK1VQUGDIfoyLo8fNjx43v7baY/%2BKiwYICgpqFKQufB8cHNykz0hJSdHGjRu9XikpKZ55h8Oh3NzcRme5YBx63PzocfOjx82PHje/ttpjzmD9RExMjE6ePKlz587Jaj3fHofDoeDgYEVERDTpM6Kjo9tUSgcAAN44g/UTffv2ldVqVXFxsWesqKhIAwYMULt2tAsAAFwaieEnQkJCNGHCBD399NMqKSnRRx99pD//%2Bc%2BaMmWK2aUBAIBWIuDpp59%2B2uwifM0tt9yiffv2admyZdq1a5emT5%2Bu5ORkQ9cRFhamIUOGKCwszNDPxf9Hj5sfPW5%2B9Lj50ePm1xZ7bHG73W6ziwAAAPAnXCIEAAAwGAELAADAYAQsAAAAgxGwAAAADEbAAgAAMBgBCwAAwGAELAAAAIMRsAAAAAxGwGphhw8f1oMPPqjY2FgNHz5cq1evNrskv5WWlqYnnnjC7DL80ocffqgbbrjB6zVr1iyzy/IrLpdLCxYs0E033aRf//rXev7558VzoY2zcePGRvvwDTfcoD59%2Bphdml%2BpqKjQtGnTNHjwYCUkJOi1114zu6QWYzW7gLakoaFBaWlpGjBggDZt2qTDhw/rj3/8o2JiYnTnnXeaXZ5fee%2B991RYWKi77rrL7FL8UllZmUaMGKFFixZ5xoKCgkysyP8sXrxYn3/%2BuV555RWdPn1a//Ef/6EuXbro7rvvNrs0vzBmzBgNGzbM8/25c%2Bf0%2B9//XsOHDzevKD/06KOPqkuXLtq4caPKyso0e/Zsde3aVb/97W/NLq3ZEbBaUFVVlfr27aunn35a4eHh6tmzp2699VYVFRURsAxUXV2tnJwcDRgwwOxS/NaBAwd0/fXXKyoqyuxS/FJ1dbU2bNigV199VQMHDpQkTZ06VXa7nYBlkODgYAUHB3u%2BX7lypdxut2bPnm1iVf7lhx9%2BUHFxsRYtWqSePXuqZ8%2BeGjZsmHbt2tUmAhaXCFtQdHS0li9frvDwcLndbhUVFWn37t0aMmSI2aX5leeee07jx49X7969zS7Fbx04cEA9e/Y0uwy/VVRUpPDwcK9jQ1pamrKzs02syn9VV1dr1apVeuyxxxQYGGh2OX4jODhYISEh2rhxo86ePauDBw9qz5496tu3r9mltQgClkkSEhJ0zz33KDY2VqNGjTK7HL%2Bxa9cuffnll5oxY4bZpfgtt9utQ4cOaceOHRo1apRGjhyppUuXyuVymV2a3ygvL1fXrl319ttva/To0brjjjuUl5enhoYGs0vzS2vXrlV0dLRGjx5tdil%2BJSgoSPPnz1dBQYFsNpsSExN1%2B%2B23a9KkSWaX1iIIWCZ58cUXtWLFCn377bf8X6lB6urq9NRTT2n%2B/Plep/5hrKNHj8rpdCowMFDLly/X3Llz9e677yonJ8fs0vxGbW2tDh8%2BrLfeekvZ2dmaO3eu3nzzzTZ1g3BLcbvdWrdune677z6zS/FLBw4c0IgRI1RQUKDs7Gxt27ZN77zzjtlltQjuwTLJhfuD6urqNHv2bM2ZM4dT0/%2Bi3Nxc9e/f3%2BvGVRiva9eu%2Bvzzz9W%2BfXtZLBb17dtXDQ0Nevzxx5WRkaGAgACzS2z1rFarTp06pWXLlqlr166SzgfbtWvXaurUqSZX51%2B%2B/vprHT9%2BXGPHjjW7FL%2Bza9curV%2B/XoWFhQoODtaAAQN0/Phxvfzyy/rd735ndnnNjoDVgqqqqlRcXKyRI0d6xnr37q2zZ8/q1KlTioyMNLG61u%2B9995TVVWVYmNjJclzyeqDDz7QV199ZWZpfueaa67x%2Br5Xr16qq6vTDz/8wH5sgKioKAUFBXnClSRde%2B21qqioMLEq/7R9%2B3bFx8erffv2Zpfid/bu3asePXp4XVG48cYbtWLFChOrajlcImxBR44c0cyZM3X8%2BHHP2N69exUZGck/SgZ488039e677%2Brtt9/W22%2B/rYSEBCUkJOjtt982uzS/sn37dt18881yOp2esW%2B//VbXXHMN%2B7FBbDab6urqdOjQIc/YwYMHvQIXjFFSUqLBgwebXYZfio6O1uHDh73uzzx48KC6detmYlUth4DVggYMGKB%2B/frpySefVFlZmQoLC7VkyRJNnz7d7NL8QteuXdWjRw/PKywsTGFhYerRo4fZpfmV2NhYBQUFad68eTp48KAKCwuVk5Ojhx56yOzS/MZ1112n4cOHKyMjQ/v379f27duVn5%2Bv1NRUs0vzO6WlpfzEcTNJSEjQVVddpXnz5unQoUP6%2B9//rhUrVmjy5Mlml9YiLG4eDdyijh8/rkWLFmnXrl0KCQnRfffdp2nTpslisZhdmt%2B58BT3Z5991uRK/E9paameeeYZFRcXKywsTHfffbfS09PZjw30448/atGiRfrwww8VEhKie%2B65hx43g4EDByovL497N5tJWVmZsrKyVFJSosjISN177736/e9/3yb2YwIWAACAwbhECAAAYDACFgAAgMEIWAAAAAYjYAEAABiMgAUAAGAwAhYAAIDBCFgAAAAGI2ABAAAYjIAFAABgMAIWAACAwQhYAAAABiNgAQAAGIyABQAAYLD/B%2B59V4yoqWtfAAAAAElFTkSuQmCC”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3856719825666295386”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.2</td> <td class=”number”>374</td> <td class=”number”>11.7%</td> <td>

<div class=”bar” style=”width:33%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.8</td> <td class=”number”>291</td> <td class=”number”>9.1%</td> <td>

<div class=”bar” style=”width:26%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.9</td> <td class=”number”>210</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.5</td> <td class=”number”>203</td> <td class=”number”>6.3%</td> <td>

<div class=”bar” style=”width:18%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.6</td> <td class=”number”>194</td> <td class=”number”>6.0%</td> <td>

<div class=”bar” style=”width:17%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.3</td> <td class=”number”>185</td> <td class=”number”>5.8%</td> <td>

<div class=”bar” style=”width:16%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.5</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:14%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.9</td> <td class=”number”>146</td> <td class=”number”>4.5%</td> <td>

<div class=”bar” style=”width:13%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>5.7</td> <td class=”number”>125</td> <td class=”number”>3.9%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.7</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (21)</td> <td class=”number”>1145</td> <td class=”number”>35.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3856719825666295386”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>3.2</td> <td class=”number”>14</td> <td class=”number”>0.4%</td> <td>

<div class=”bar” style=”width:61%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.4</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:74%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.2</td> <td class=”number”>16</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:69%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.3</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:91%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.4</td> <td class=”number”>23</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>6.8</td> <td class=”number”>67</td> <td class=”number”>2.1%</td> <td>

<div class=”bar” style=”width:32%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>6.9</td> <td class=”number”>210</td> <td class=”number”>6.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.1</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:47%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>7.6</td> <td class=”number”>82</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:39%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>8.1</td> <td class=”number”>15</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_VLFOODSEC_13_15”><s>VLFOODSEC_13_15</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_FOODINSEC_13_15”><code>FOODINSEC_13_15</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.93787</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants FY 2009”>WIC participants FY 2009<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>40</td>

</tr> <tr>

<th>Unique (%)</th> <td>1.2%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>17.3%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>555</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>124380</td>

</tr> <tr>

<th>Minimum</th> <td>13338</td>

</tr> <tr>

<th>Maximum</th> <td>518960</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-6500562940959148903”>

</div> <div class=”col-md-12 text-right”>

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</div> <div class=”row collapse col-md-12” id=”descriptives-6500562940959148903”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-6500562940959148903”

aria-controls=”quantiles-6500562940959148903” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-6500562940959148903” aria-controls=”histogram-6500562940959148903”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-6500562940959148903” aria-controls=”common-6500562940959148903”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-6500562940959148903” aria-controls=”extreme-6500562940959148903”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-6500562940959148903”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>13338</td>

</tr> <tr>

<th>5-th percentile</th> <td>18362</td>

</tr> <tr>

<th>Q1</th> <td>46175</td>

</tr> <tr>

<th>Median</th> <td>113250</td>

</tr> <tr>

<th>Q3</th> <td>160150</td>

</tr> <tr>

<th>95-th percentile</th> <td>309870</td>

</tr> <tr>

<th>Maximum</th> <td>518960</td>

</tr> <tr>

<th>Range</th> <td>505620</td>

</tr> <tr>

<th>Interquartile range</th> <td>113980</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>104140</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.83726</td>

</tr> <tr>

<th>Kurtosis</th> <td>2.7778</td>

</tr> <tr>

<th>Mean</th> <td>124380</td>

</tr> <tr>

<th>MAD</th> <td>77309</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>1.5178</td>

</tr> <tr>

<th>Sum</th> <td>330230000</td>

</tr> <tr>

<th>Variance</th> <td>10845000000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-6500562940959148903”>

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ViSVFhYqNdff12vv/66Dhw4oMTERP3ud79TQUGBHnjgAe3evVuzZs36Rc%2BdnZ2tli1bqk%2BfPp6xSZMmSZIefPBBJSUlKSQkRNL3YS8xMVE5OTlKS0uTy%2BXSH/7wB89%2B7du3V4cOHeRyuQhYAACgQfwesLZs2aItW7bo448/Vps2bTR69Gg99dRT6tKli2eb9u3ba/78%2Bb84YOXn56tjx47avHmzVqxYoVOnTiktLU2333673G63LrnkEq/t27Ztq7y8PEnfB7%2BYmJg68wUFBQ1ev7CwUG6322vM4Yis87ynCw1t5vU92DgcgTuuYO%2BtFJj%2Bng99DQT66jv01jfoa11%2BD1izZs3S4MGDtWzZMg0cOFDNmtV9MX7961/rxhtv/MVrlJWV6dChQ1q/fr0WLFggt9ut2bNnKyIiQuXl5QoLC/PaPiwsTFVVVZKkioqKs843RFZWljIzM73GMjIyNHXq1AbtHxUV0eC1mpLWrVsEuoSg7a0U2P4Gc18Dib76Dr31Dfr6I78HrA8%2B%2BECtW7dWSUmJJ1zt3btXPXv2VGhoqCQpMTFRiYmJv3gNh8OhEydO6IknnlDHjh0lSYcPH9bLL7%2Bsiy%2B%2BuE5YqqqqUvPmzSVJ4eHhPzkfEdHwN8348eOVmpp6Wk2RKi4%2Bedb9QkObKSoqQqWl5aqpqW3wek1FfcfvS8HeWykw/T0f%2BhoI9NV36K1vNOa%2BBuofn34PWCdOnFB6err%2B67/%2BS9OmTZP0/eej2rVrp2effVbt27c/5zWio6MVHh7uCVeS9B//8R86cuSI%2BvTpo6KiIq/ti4qKPJfvYmNjf3I%2BOjq6wevHxMTUuRzodh9XdXXD3nQ1NbUN3rYpaQzHFKy9lQLb32DuayDRV9%2Bht75BX3/k94uljz76qC6%2B%2BGLdeuutnrE333xT7du314IFC6ys4XQ6VVlZ6XXbh6%2B//lodO3aU0%2BnUZ5995rknljFGe/bskdPp9OybnZ3t2e/IkSM6cuSIZx4AAKA%2Bfg9Yn376qe6//36vM0Jt2rTRtGnTtGvXLitr/PrXv9agQYM0Y8YM7d%2B/X3/729%2B0atUqpaen65prrlFpaanmz5%2BvgwcPav78%2BSovL9ewYcMkSenp6dqyZYs2bNig/fv3a9q0aRo0aBC/QQgAABrM7wHL4XCotLS0znh5ebnVO7ovXrxYv/rVr5Senq7p06frhhtu0E033aSWLVtq5cqVys7O9tyWYdWqVYqMjJQkJSQkaM6cOVq2bJnS09N10UUXWTuzBgAAzg9%2B/wzWwIEDNW/ePC1ZskS/%2BtWvJH1/W4UFCxZowIAB1ta58MILtXDhwp%2Bc6927t1577bUz7puWlqa0tDRrtQAAgPOL3wPW9OnTdeutt2ro0KGKioqSJJWWlqpnz578SRoAABAU/B6w2rZtq9dee007duxQXl6eHA6HLrnkEl1%2B%2BeWeu6sDAAA0ZQH5UzmhoaEaMGCA1UuCAAAAjYXfA5bb7dbSpUu1Z88enTp1qs4H2//85z/7uyQAAACr/B6wHnzwQe3bt08jRozQhRde6O/lAQAAfM7vAWvXrl1avXq1kpOT/b00AACAX/j9PliRkZFq27atv5cFAADwG78HrFGjRmn16tWqqanx99IAAAB%2B4fdLhCUlJdq2bZvee%2B89de7cWWFhYV7zL730kr9LAgAAsCogt2kYOXJkIJYFAADwC78HLP6uHwAACHZ%2B/wyWJBUWFiozM1P33HOP/u///k9vv/22vv7660CUAgAAYJ3fA9ahQ4d07bXX6rXXXtM777yjsrIyvfnmmxozZoxcLpe/ywEAALDO7wHrscce05AhQ7R9%2B3ZdcMEFkqQlS5YoNTVVixcv9nc5AAAA1vk9YO3Zs0e33nqr1x92djgcuuOOO5Sbm%2BvvcgAAAKzze8Cqra1VbW1tnfGTJ08qNDTU3%2BUAAABY5/eAlZKSopUrV3qFrJKSEi1atEj9%2BvXzdzkAAADW%2BT1g3X///dq3b59SUlJUWVmp22%2B/XYMHD9a3336r6dOn%2B7scAAAA6/x%2BH6zY2Fht3rxZ27Zt05dffqna2lqlp6dr1KhRatmypb/LAQAAsC4gd3KPiIjQuHHjArE0AACAz/k9YN18881nnedvEQIAgKbO7wGrY8eOXo%2Brq6t16NAhHThwQBMmTPB3OQAAANY1mr9FuGzZMhUUFPi5GgAAAPsC8rcIf8qoUaP01ltvBboMAACAc9ZoAtZnn33GjUYBAEBQaBQfcj9x4oT%2B/ve/6/rrr/d3OQAAANb5PWB16NDB6%2B8QStIFF1ygG2%2B8Ub/97W/9XQ4AAIB1fg9Yjz32mL%2BXBAAA8Cu/B6zdu3c3eNvLLrvMh5UAAAD4ht8D1k033eS5RGiM8YyfPhYSEqIvv/zS3%2BUBAACcM78HrBUrVmjevHm677771KdPH4WFhenzzz/XnDlz9Lvf/U7Dhw/3d0kAAABW%2Bf02DQsWLNDs2bM1dOhQtW7dWi1atFC/fv00Z84cvfzyy%2BrYsaPnCwAAoCnye8AqLCz8yfDUsmVLFRcX%2B7scAAAA6/wesOLj47VkyRKdOHHCM1ZSUqJFixbp8ssv93c5AAAA1vn9M1gPPPCAbr75Zg0cOFBdunSRMUb/%2BMc/FB0drZdeesnf5QAAAFjn94DVtWtXvfnmm9q2bZu%2B%2BuorSdINN9ygESNGKCIiwt/lAAAAWOf3gCVJF110kcaNG6dvv/1WnTt3lvT93dwBAACCgd8/g2WM0eLFi3XZZZdp5MiRKigo0PTp0zVr1iydOnXK3%2BUAAABY5/eAtXbtWm3ZskUPPfSQwsLCJElDhgzR9u3blZmZ6e9yAAAArPN7wMrKytLs2bOVlpbmuXv78OHDNW/ePG3dutXf5QAAAFjn94D17bffqkePHnXGu3fvLrfb7e9yAAAArPN7wOrYsaM%2B//zzOuMffPCB5wPvAAAATZnff4vw97//vR555BG53W4ZY7Rz505lZWVp7dq1uv/%2B%2B/1dDgAAgHV%2BD1hjxoxRdXW1nnnmGVVUVGj27Nlq06aN7rrrLqWnp/u7HAAAAOv8HrC2bduma665RuPHj9exY8dkjFHbtm39XQYAAIDP%2BP0zWHPmzPF8mL1NmzaEKwAAEHT8HrC6dOmiAwcO%2BHtZAAAAv/H7JcLu3bvr3nvv1erVq9WlSxeFh4d7zS9YsMDfJQEAAFjl94D1zTffKCkpSZK47xUAAAhKfglYCxcu1JQpUxQZGam1a9f6Y0kAAICA8ctnsF544QWVl5d7jU2aNEmFhYU%2BX3vSpEle99fKzc3VuHHj5HQ6NWbMGO3bt89r%2B23btmnIkCFyOp3KyMjQsWPHfF4jAAAILn4JWMaYOmO7d%2B9WZWWlT9d944039P7773sel5WVadKkSUpOTtamTZuUkJCgyZMnq6ysTJK0d%2B9ezZo1S1OmTFFWVpZKS0s1Y8YMn9YIAACCj99/i9BfSkpKtHDhQsXFxXnG3nzzTYWHh2vatGnq2rWrZs2apRYtWujtt9%2BWJK1bt07Dhg3T6NGj1b17dy1cuFDvv/%2B%2B8vPzA3UYAACgCQragPX4449r1KhRuuSSSzxjLpdLSUlJCgkJkSSFhIQoMTFROTk5nvnk5GTP9u3bt1eHDh3kcrn8WzwAAGjS/PZbhD%2BEGn/YuXOnPv30U23dulUPP/ywZ9ztdnsFLklq27at8vLyJEmFhYWKiYmpM19QUPCz1i8sLKzzG5IOR2Sd5z5daGgzr%2B/BxuEI3HEFe2%2BlwPT3fOhrINBX36G3vkFf6/JbwJo3b57XPa9OnTqlRYsWqUWLFl7bnet9sCorK/XQQw9p9uzZat68uddceXm5wsLCvMbCwsJUVVUlSaqoqDjrfENlZWUpMzPTaywjI0NTp05t0P5RURE/a72monXrFvVv5GPB2lspsP0N5r4GEn31HXrrG/T1R34JWJdddlmdMzoJCQkqLi5WcXGx1bUyMzPVq1cvDRgwoM5ceHh4nbBUVVXlCWJnmo%2BI%2BHlvmPHjxys1NdVrzOGIVHHxybPuFxraTFFRESotLVdNTe3PWrMpqO/4fSnYeysFpr/nQ18Dgb76Dr31jcbc10D949MvAcuf97564403VFRUpISEBEnyBKZ33nlHI0eOVFFRkdf2RUVFnkt3sbGxPzkfHR39s2qIiYmpcznQ7T6u6uqGvelqamobvG1T0hiOKVh7KwW2v8Hc10Cir75Db32Dvv7I73dy97W1a9equrra83jx4sWSpHvvvVe7d%2B/Ws88%2BK2OMQkJCZIzRnj17dNttt0mSnE6nsrOzlZaWJkk6cuSIjhw5IqfT6f8DAQAATVbQBayOHTt6Pf7hM14XX3yx2rZtqyeeeELz58/Xf//3f2v9%2BvUqLy/XsGHDJEnp6em66aabFB8fr7i4OM2fP1%2BDBg1S586d/X4cAACg6TqvPu7fsmVLrVy50nOWyuVyadWqVYqMjJT0/efC5syZo2XLlik9PV0XXXQRf3waAAD8bEF3But0jz32mNfj3r1767XXXjvj9mlpaZ5LhAAAAL/EeXUGCwAAwB8IWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlQX%2BjUTQew5Z%2BFOgSAADwC85gAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABYRsACAACwjIAFAABgGQELAADAMgIWAACAZQQsAAAAywhYAAAAlhGwAAAALCNgAQAAWEbAAgAAsIyABQAAYBkBCwAAwDICFgAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABY5gh0Ab5y9OhRzZ8/X7t27VJ4eLiGDx%2Buu%2B%2B%2BW%2BHh4crPz9eDDz6onJwcdejQQTNnzlRKSopn3x07dujRRx9Vfn6%2BnE6n5s%2Bfr86dOwfwaID6DVv6UaBLaLC37uof6BIAwKeC8gyWMUZTp05VeXm5/vSnP%2BnJJ5/UX//6Vy1dulTGGGVkZKhdu3bauHGjRo0apSlTpujw4cOSpMOHDysjI0NpaWl69dVX1aZNG91xxx0yxgT4qAAAQFMRlGewvv76a%2BXk5Oijjz5Su3btJElTp07V448/roEDByo/P1/r169XZGSkunbtqp07d2rjxo268847tWHDBvXq1UsTJ06UJC1YsED9%2B/fXJ598or59%2BwbysAAAQBMRlGewoqOjtXr1ak%2B4%2BsGJEyfkcrl06aWXKjIy0jOelJSknJwcSZLL5VJycrJnLiIiQj179vTMAwAA1CcoA1ZUVJQGDBjgeVxbW6t169apX79%2BcrvdiomJ8dq%2Bbdu2KigokKR65wEAAOoTlJcIT7do0SLl5ubq1Vdf1Zo1axQWFuY1HxYWpqqqKklSeXn5WecborCwUG6322vM4YisE9xOFxrazOs7EKwcjqb1Hr9q8d8CXcLP8u69A%2Brf6DzFz1nfoK91BX3AWrRokV588UU9%2BeST6tatm8LDw1VSUuK1TVVVlZo3by5JCg8PrxOmqqqqFBUV1eA1s7KylJmZ6TWWkZGhqVOnNmj/qKiIBq8FNEWtW7cIdAlBjf7Wj5%2BzvkFffxTUAWvu3Ll6%2BeWXtWjRIg0dOlSSFBsbq4MHD3ptV1RU5Dm7FBsbq6KiojrzPXr0aPC648ePV2pqqteYwxGp4uKTZ90vNLSZoqIiVFparpqa2gavBzQ19f23gHNDf8%2BMn7O%2B0Zj7Gqh/cARtwMrMzNT69eu1ZMkSXXPNNZ5xp9OpVatWqaKiwnPWKjs7W0lJSZ757Oxsz/bl5eXKzc3VlClTGrx2TExMncuBbvdxVVc37E1XU1Pb4G2Bpoj3t2/R3/rxc9Y36OuPgvJi6VdffaXly5frD3/4g5KSkuR2uz1fffr0Ufv27TVjxgzl5eVp1apV2rt3r8aOHStJGjNmjPbs2aNVq1YpLy9PM2bMUKdOnbhFAwAAaLCgDFh//vOfVVNTo2eeeUYpKSleX6GhoVq%2BfLncbrfS0tL0%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%2BorKzUzJkzlZycrJSUFD3//POBLgkAADQhjkAX0BgtXLhQ%2B/bt04svvqjDhw9r%2BvTp6tChg6655ppAlwYAAJoAAtZpysrKtGHDBj377LPq2bOnevbsqby8PP3pT38iYAEAgAbhEuFp9u/fr%2BrqaiUkJHjGkpKS5HK5VFtbG8DKAABAU8EZrNO43W61bt1aYWFhnrF27dqpsrJSJSUlatOmTb3PUVhYKLfb7TXmcEQqJibmrPuFhjbz%2Bg4Av4TDwc%2BQM%2BHnbOMVbO9bAtZpysvLvcKVJM/jqqqqBj1HVlaWMjMzvcamTJmiO%2B%2B886z7FRYW6sUXV2v8%2BPH1hrEffDqfy5YNUVhYqKysrJ/VW9SPvvoGffWdX/Jztinz1/9H8J6tK7jiogXh4eF1gtQPj5s3b96g5xg/frw2bdrk9TV%2B/Ph693O73crMzKxz9gvnjt76Bn31DfrqO/TWN%2BhrXZzBOk1sbKyKi4tVXV0th%2BP79rjdbjVv3lxRUVENeo6YmBgSPAAA5zHOYJ2mR48ecjgcysnJ8YxlZ2crLi5OzZrRLgAAUD8Sw2kiIiI0evRoPfzww9q7d6%2B2b9%2Bu559/XjfffHOgSwMAAE1E6MMPP/xwoItobPr166fc3Fw98cQT2rlzp2677TaNGTPGL2u3aNFCffr0UYsWLfyy3vmE3voGffUN%2Buo79NY36Ku3EGOMCXQRAAAAwYRLhAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFiNRGVlpWbOnKnk5GSlpKTo%2BeefD3RJAVdVVaWRI0fq448/9ozl5%2BfrlltuUXx8vIYPH64PP/zQa58dO3Zo5MiRcjqduvnmm5Wfn%2B81v2bNGg0YMEAJCQmaOXOmysvLPXP1vQb1rd3YHT16VFOnTlWfPn00YMAALViwQJWVlZLo67k6dOiQfv/73yshIUGDBg3S6tWrPXP09txNmjRJ999/v%2Bdxbm6uxo0bJ6fTqTFjxmjfvn1e22/btk1DhgyR0%2BlURkaGjh075pkzxmjx4sXq16%2Bf%2BvTpo4ULF6q2ttYzX1xcrDvvvFMJCQlKTU3Vli1bvJ67vrWbgnfffVe/%2Bc1vvL6mTp0qid5aZdAozJkzx1x77bVm37595n//939NQkKCeeuttwJdVsBUVFSYjIwM061bN7Nr1y5jjDG1tbXm2muvNffcc485ePCgWbFihXE6nea7774zxhjz3Xffmfj4ePPcc8%2BZAwcOmD/%2B8Y9m5MiRpra21hhjzNtvv22SkpLMX/7yF%2BNyuczw4cPNI4884lnzbK9BfWs3drW1tea6664z//M//2MOHDhgdu/eba666irz2GOP0ddzVFNTY66%2B%2Bmpzzz33mG%2B%2B%2Bca89957JjEx0bz%2B%2Buv01oJt27aZbt26menTpxtjjDl58qTp37%2B/eeyxx8zBgwfN3LlzzRVXXGFOnjxpjDHG5XKZ3r17m9dee818%2BeWX5sYbbzSTJk3yPN9zzz1nrrzySrN7926zc%2BdOk5KSYlavXu2Znzx5spkwYYL5%2B9//bl555RXTq1cv43K5GrR2U7F8%2BXIzefJkU1hY6Pn617/%2BRW8tI2A1AidPnjRxcXGeIGGMMcuWLTM33nhjAKsKnLy8PPPb3/7WXHvttV4Ba8eOHSY%2BPt7rP7gJEyaYp556yhhjzNKlS716VlZWZhISEjz7X3/99Z5tjTFm9%2B7dpnfv3qasrKze16C%2BtRu7gwcPmm7duhm32%2B0Z27p1q0lJSaGv5%2Bjo0aPmj3/8ozl%2B/LhnLCMjwzz00EP09hwVFxebgQMHmjFjxngC1oYNG0xqaqonhNbW1pqrrrrKbNy40RhjzH333efZ1hhjDh8%2BbH7zm9%2BYf/7zn8YYY6688krPtsYYs3nzZjN48GBjjDGHDh0y3bp1M/n5%2BZ75mTNnNnjtpuKee%2B4xTzzxRJ1xemsXlwgbgf3796u6uloJCQmesaSkJLlcLq/Tq%2BeLTz75RH379lVWVpbXuMvl0qWXXqrIyEjPWFJSknJycjzzycnJnrmIiAj17NlTOTk5qqmp0eeff%2B41Hx8fr1OnTmn//v31vgb1rd3YRUdHa/Xq1WrXrp3X%2BIkTJ%2BjrOYqJidHSpUvVsmVLGWOUnZ2t3bt3q0%2BfPvT2HD3%2B%2BOMaNWqULrnkEs%2BYy%2BVSUlKSQkJCJEkhISFKTEw8Y0/bt2%2BvDh06yOVy6ejRozpy5Iguu%2Bwyz3xSUpK%2B%2B%2B47FRYWyuVyqX379urUqZPX/GeffdagtZuKr776Sl26dKkzTm/tImA1Am63W61bt1ZYWJhnrF27dqqsrFRJSUkAKwuM66%2B/XjNnzlRERITXuNvtVkxMjNdY27ZtVVBQUO98aWmpKisrveYdDodatWqlgoKCel%2BD%2BtZu7KKiojRgwADP49raWq1bt079%2BvWjrxalpqbq%2BuuvV0JCgoYOHUpvz8HOnTv16aef6o477vAar%2B%2B4CgsLzzjvdrslyWv%2Bh390/DD/U/sePXq0QWs3BcYYffPNN/rwww81dOhQDRkyRIsXL1ZVVRW9tcwR6AIglZeXe/2QlOR5XFVVFYiSGqUz9emHHp1tvqKiwvP4p%2BaNMWd9Depbu6lZtGiRcnNz9eqrr2rNmjX01ZKnnnpKRUVFevjhh7VgwQLes79QZWWlHnroIc2ePVvNmzf3mqvvuCoqKn5WT39Oz5pyT39w%2BPBhz3EsXbpU3377rebNm6eKigp6axkBqxEIDw%2Bv8yb64fHpP1zOZ%2BHh4XXO6FVVVXl6dKY%2BRkVFKTw83PP49PmIiAjV1NSc9TWob%2B2mZNGiRXrxxRf15JNPqlu3bvTVori4OEnfB4R7771XY8aM8fqtP4neNkRmZqZ69erlddb1B2fqWX09jYiI8Po//NP7GxER8Yufuyn09AcdO3bUxx9/rIsuukghISHq0aOHamtrdd9996lPnz701iIuETYCsbGxKi4uVnV1tWfM7XarefPmioqKCmBljUtsbKyKioq8xoqKijynlc80Hx0drVatWik8PNxrvrq6WiUlJYqOjq73Nahv7aZi7ty5euGFF7Ro0SINHTpUEn09V0VFRdq%2BfbvX2CWXXKJTp04pOjqa3v4Cb7zxhrZv366EhAQlJCRo69at2rp1qxISEs7p/RobGytJnstZ//6/f5g/075ne%2B6m0NN/16pVK89nnSSpa9euqqysPKf3K72ti4DVCPTo0UMOh8Prw3zZ2dmKi4tTs2a8RD9wOp364osvPKeipe/75HQ6PfPZ2dmeufLycuXm5srpdKpZs2aKi4vzms/JyZHD4VD37t3rfQ3qW7spyMzM1Pr167VkyRKNGDHCM05fz823336rKVOmeD5LIkn79u1TmzZtlJSURG9/gbVr12rr1q3avHmzNm/erNTUVKWmpmrz5s1yOp367LPPZIyR9P1nivbs2XPGnh45ckRHjhyR0%2BlUbGysOnTo4DWfnZ2tDh06KCYmRvHx8fruu%2B%2B8PveTnZ2t%2BPh4z3Ofbe2m4G9/%2B5v69u3rdWb1yy%2B/VKtWrTwfOqe3lgTkdxdRx4MPPmhGjBhhXC6Xeffdd01iYqJ55513Al1WwP37bRqqq6vN8OHDzV133WUOHDhgVq5caeLj4z339cnPzzdxcXFm5cqVnnsKXXvttZ5f%2B922bZtJTEw07777rnG5XGbEiBFm7ty5nrXO9hrUt3Zjd/DgQdOjRw/z5JNPet37prCwkL6eo%2BrqapOWlmYmTpxo8vLyzHvvvWeuuOIKs2bNGnpryfTp0z2/zn/8%2BHHTr18/M3fuXJOXl2fmzp1r%2Bvfv77kdxZ49e0zPnj3NK6%2B84rlX0%2BTJkz3PtXLlSpOSkmJ27dpldu3aZVJSUszzzz/vmZ84caK58cYbzZdffmleeeUVExcX57lXU31rNwXHjx83AwYMMHfffbf56quvzHvvvWdSUlLMqlWr6K1lBKxGoqyszEybNs3Ex8eblJQU88ILLwS6pEbh3wOWMcb84x//MDfccIPp1auXGTFihPnoo4%2B8tn/vvffM1VdfbXr37m0mTJjguT/LD1auXGkuv/xyk5SUZGbMmGEqKio8c/W9BvWt3ZitXLnSdOvW7Se/jKGv56qgoMBkZGSYxMRE079/f/PMM894QhK9PXf/HrCM%2Bf6Gl6NHjzZxcXFm7Nix5osvvvDafuPGjebKK6808fHxJiMjwxw7dswzV11dbR599FGTnJxs%2BvbtaxYtWuR5rYwxpqioyEyePNnExcWZ1NRUs3XrVq/nrm/tpuDAgQPmlltuMfHx8aZ///7m6aef9vSA3toTYsz/Px8HAAAAK/iADwAAgGUELAAAAMsIWAAAAJYRsAAAACwjYAEAAFhGwAIAALCMgAUAAGAZAQsAAMAyAhYAAIBlBCwAAADLCFgAAACWEbAAAAAsI2ABAABY9v8AWwuEvr6MhcAAAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-6500562940959148903”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>53060.0</td> <td class=”number”>254</td> <td class=”number”>7.9%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>309870.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:12%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>127944.0</td> <td class=”number”>130</td> <td class=”number”>4.0%</td> <td>

<div class=”bar” style=”width:10%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18362.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>146411.0</td> <td class=”number”>113</td> <td class=”number”>3.5%</td> <td>

<div class=”bar” style=”width:9%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>141768.0</td> <td class=”number”>102</td> <td class=”number”>3.2%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>113248.0</td> <td class=”number”>100</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>26663.0</td> <td class=”number”>99</td> <td class=”number”>3.1%</td> <td>

<div class=”bar” style=”width:8%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>193387.0</td> <td class=”number”>95</td> <td class=”number”>3.0%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>70159.0</td> <td class=”number”>93</td> <td class=”number”>2.9%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (29)</td> <td class=”number”>1395</td> <td class=”number”>43.5%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>555</td> <td class=”number”>17.3%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-6500562940959148903”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>13338.0</td> <td class=”number”>64</td> <td class=”number”>2.0%</td> <td>

<div class=”bar” style=”width:56%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>14573.0</td> <td class=”number”>21</td> <td class=”number”>0.7%</td> <td>

<div class=”bar” style=”width:19%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17496.0</td> <td class=”number”>46</td> <td class=”number”>1.4%</td> <td>

<div class=”bar” style=”width:40%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>18362.0</td> <td class=”number”>115</td> <td class=”number”>3.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>20673.0</td> <td class=”number”>83</td> <td class=”number”>2.6%</td> <td>

<div class=”bar” style=”width:72%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>275039.0</td> <td class=”number”>10</td> <td class=”number”>0.3%</td> <td>

<div class=”bar” style=”width:7%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>303679.0</td> <td class=”number”>33</td> <td class=”number”>1.0%</td> <td>

<div class=”bar” style=”width:21%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>309870.0</td> <td class=”number”>159</td> <td class=”number”>5.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>499213.0</td> <td class=”number”>53</td> <td class=”number”>1.7%</td> <td>

<div class=”bar” style=”width:34%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>518961.0</td> <td class=”number”>17</td> <td class=”number”>0.5%</td> <td>

<div class=”bar” style=”width:11%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants FY 2011”><s>WIC participants FY 2011</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants FY 2009”><code>WIC participants FY 2009</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99857</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2012”><s>WIC participants, FY 2012</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants FY 2011”><code>WIC participants FY 2011</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9754</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2013”><s>WIC participants, FY 2013</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2012”><code>WIC participants, FY 2012</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99975</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2014”><s>WIC participants, FY 2014</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2013”><code>WIC participants, FY 2013</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.99951</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WIC participants, FY 2015”><s>WIC participants, FY 2015</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WIC participants, FY 2014”><code>WIC participants, FY 2014</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.9992</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICS08”><s>WICS08</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_SNAPS16”><code>SNAPS16</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.91514</td>

</tr>

</table>

</div> </div><div class=”row variablerow ignore”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICS12”><s>WICS12</s><br/>

<small>Highly correlated</small>

</p>

</div><div class=”col-md-3”> <p><em>This variable is highly correlated with <a href=”#pp_var_WICS08”><code>WICS08</code></a> and should be ignored for analysis</em></p>

</div> <div class=”col-md-6”>

<table class=”stats “>
<tr>

<th>Correlation</th> <td>0.98574</td>

</tr>

</table>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICSPTH08”>WICSPTH08<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2963</td>

</tr> <tr>

<th>Unique (%)</th> <td>92.3%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.25423</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>4.6189</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.4%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-5111588114724210399”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-5111588114724210399,#minihistogram-5111588114724210399”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-5111588114724210399”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-5111588114724210399”

aria-controls=”quantiles-5111588114724210399” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-5111588114724210399” aria-controls=”histogram-5111588114724210399”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-5111588114724210399” aria-controls=”common-5111588114724210399”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-5111588114724210399” aria-controls=”extreme-5111588114724210399”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-5111588114724210399”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.048939</td>

</tr> <tr>

<th>Q1</th> <td>0.12325</td>

</tr> <tr>

<th>Median</th> <td>0.18962</td>

</tr> <tr>

<th>Q3</th> <td>0.29478</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.68198</td>

</tr> <tr>

<th>Maximum</th> <td>4.6189</td>

</tr> <tr>

<th>Range</th> <td>4.6189</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.17152</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.25464</td>

</tr> <tr>

<th>Coef of variation</th> <td>1.0016</td>

</tr> <tr>

<th>Kurtosis</th> <td>46.673</td>

</tr> <tr>

<th>Mean</th> <td>0.25423</td>

</tr> <tr>

<th>MAD</th> <td>0.14987</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>4.873</td>

</tr> <tr>

<th>Sum</th> <td>796.25</td>

</tr> <tr>

<th>Variance</th> <td>0.064839</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-5111588114724210399”>

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6t3DQAAYAnLA1ZqaqrWrFmjgQMH6tFHH9Xu3bvl9XqtLgMAAOCKsTxgzZgxQ3//%2B9%2B1atUqhYaG6uGHH9att96qZcuW6bPPPrO6HAAAAOOcduzU4XBowIABGjBggGpra7V582atWrVKa9euVd%2B%2BfTV58mTddtttdpQGAABw2WwJWJJUUVGh1157Ta%2B99po%2B/fRT9e3bV2PGjFF5ebmeeOIJ7d%2B/X1lZWXaVBwAAcMksD1ivvvqqXn31Ve3du1ft2rXT6NGjtWLFCnXt2tX3mo4dO2rRokUELAAAEJAsD1hZWVkaNGiQVq5cqVtuuUUhIU0vA%2BvWrZvuvvtuq0sDAAAwwvKA9c477yg6OlrV1dW%2BcHXw4EH16tVLoaGhkqS%2Bffuqb9%2B%2BVpcGAABghOW/RXj27FkNHz5czz33nG9s2rRpGjVqlE6dOmV1OQAAAMZZHrCefvppXXPNNbrvvvt8Y2%2B88YY6duyonJwcq8sBAAAwzvKA9c9//lOPP/64XC6Xb6xdu3Z67LHHtGfPHqvLAQAAMM7ygOV0OlVTU9NkvLa2lju6AwCAoGB5wLrlllu0cOFCffnll76xsrIy5eTkKC0tzepyAAAAjLP8twhnz56t%2B%2B67T8OGDVNkZKQkqaamRr169dKcOXOsLgcAAMA4ywNW%2B/bt9fLLL%2Bu9997T0aNH5XQ6de211%2Brmm2%2BWw%2BGwuhwAAADjbPlTOaGhoUpLS%2BOUIAAACEqWByy3261nnnlGBw4c0IULF5pc2P72229bXRIAAIBRlgesJ598UocOHdKIESN01VVXWb17AACAK87ygLVnzx6tW7dOycnJVu8aAADAEpbfpqF169Zq37691bsFAACwjOUBa9SoUVq3bp0aGhqs3jUAAIAlLD9FWF1drcLCQv3jH/9Qly5dFBYW5je/adMmq0sCAAAwypbbNIwcOdKO3QIAAFjC8oCVk5Nj9S4BAAAsZfk1WJJUUVGh/Px8zZgxQ19//bX%2B%2Bte/6vjx45e0LY/Ho5EjR2rv3r2%2BsYULF%2Br666/3e2zZssU3X1hYqCFDhighIUEZGRk6c%2BaMb87r9Wrx4sVKTU1VSkqKcnNz1djYeOmLBQAALY7lAeuLL77Q7373O7388st66623dO7cOb3xxhsaN26cSkpKftG26urq9Oijj%2Bro0aN%2B46WlpZoxY4Z2797te4wbN06SdPDgQWVlZWn69OkqKChQTU2N399AfOGFF1RYWKj8/HytWLFCr7/%2Bul544YXLXzgAAGgxLA9Yf/zjHzVkyBDt3LlTv/nNbyRJS5cu1eDBg7V48eJmb%2BfYsWOaMGGCvvzyyyZzpaWluuGGG%2BRyuXyPiIgISdKWLVt0%2B%2B23a/To0YqPj1dubq527dqlsrIySd9fZJ%2BZmank5GSlpqZq5syZ2rp1q4GVAwCAlsLygHXgwAHdd999fn/Y2el06qGHHtLhw4ebvZ19%2B/apf//%2BKigo8Bs/e/asTp8%2Bra5du/7o%2B0pKSvxuctqxY0fFxcWppKREp0%2Bf1qlTp9SvXz/ffFJSkk6cOKGKiopm1wYAAFo2yy9yb2xs/NFrmr777juFhoY2ezt33XXXj46XlpbK4XBozZo1eueddxQVFaX77rtPY8aMkfT99V8xMTF%2B72nfvr3Ky8vldrslyW%2B%2BQ4cOkqTy8vIm7wMAAPgxlgesgQMH6tlnn1VeXp5vrLq6Wnl5eUpNTb3s7R8/flwOh0PdunXT3Xffrf379%2BvJJ59U27ZtNXToUJ0/f77JvbfCwsLk8Xh0/vx53/N/nZO%2Bv5i%2BuSoqKnxh7SKns7XxgBYaasvvKFwypzOw6v0pF/seaP0PBvTeHvTdPvQ%2BcFkesB5//HFNmjRJAwcOVF1dnf77v/9bJ06cUFRUlP74xz9e9vZHjx6tQYMGKSoqSpIUHx%2Bvzz//XNu2bdPQoUMVHh7eJCx5PB5FRET4hanw8HDf15J813A1R0FBgfLz8/3GMjIylJmZecnrCgbR0W3sLsGoyMjmf0/ALHpvD/puH3ofeCwPWLGxsXrllVdUWFiojz/%2BWI2NjZo4caJGjRqltm3bXvb2HQ6HL1xd1K1bN%2B3Zs8e3/8rKSr/5yspKuVwuxcbGSpLcbrc6d%2B7s%2B1qSXC5Xs2tIT0/X4MGD/cacztaqqvruly3mZwTaJxrT67dLaGiIIiMjVFNTq4YGbuFhJXpvD/puH3p/%2Bez6cG/LndwjIiJ05513XpFtL1%2B%2BXB988IE2bNjgGzty5Ii6desmSUpISFBRUZHGjh0rSTp16pROnTqlhIQExcbGKi4uTkVFRb6AVVRUpLi4uF90ei8mJqbJ693ub1Vf37J/OIJt/Q0NjUG3pkBB7%2B1B3%2B1D7wOP5QFr0qRJ/3b%2Bcv8W4aBBg7R27VqtX79eQ4cO1e7du/XKK6/4tjtx4kTdc8896tOnj3r37q1Fixbp1ltvVZcuXXzzixcv1m9/%2B1tJ0pIlSzRlypTLqgkAALQslgesTp06%2BT2vr6/XF198oU8//VSTJ0%2B%2B7O3fdNNNWr58uVasWKHly5erU6dOWrJkiRITEyVJiYmJys7O1ooVK/TNN99owIABWrBgge/9U6dO1ddff63p06crNDRU48eP17333nvZdQEAgJbD4fV6vXYXIUkrV65UeXm5X9gJJm73t8a36XSGaOjid41v90p585EBdpdghNMZoujoNqqq%2Bo5D9haj9/ag7/ah95fP5brKlv3%2Baq6SHjVqlN588027ywAAALhsv5qA9cEHH/yiG40CAAD8Wv0qLnI/e/asPvnkk5%2B8OzsAAEAgsTxgxcXF%2Bf0dQkn6zW9%2Bo7vvvlu///3vrS4HAADAOMsDlom7tQMAAPyaWR6w9u/f3%2BzX9uvX7wpWAgAAcGVYHrDuuece3ynCf71DxA/HHA6HPv74Y6vLAwAAuGyWB6w1a9Zo4cKFmjVrllJSUhQWFqYPP/xQ2dnZGjNmjO644w6rSwIAADDK8ts05OTkaN68eRo2bJiio6PVpk0bpaamKjs7W9u2bVOnTp18DwAAgEBkecCqqKj40fDUtm1bVVVVWV0OAACAcZYHrD59%2Bmjp0qU6e/asb6y6ulp5eXm6%2BeabrS4HAADAOMuvwXriiSc0adIk3XLLLeratau8Xq8%2B//xzuVwubdq0yepyAAAAjLM8YHXv3l1vvPGGCgsLVVpaKkn6z//8T40YMUIRERFWlwMAAGCc5QFLkq6%2B%2Bmrdeeed%2Buqrr9SlSxdJ39/NHQAAIBhYfg2W1%2BvV4sWL1a9fP40cOVLl5eWaPXu2srKydOHCBavLAQAAMM7ygLV582a9%2Buqr%2BsMf/qCwsDBJ0pAhQ7Rz507l5%2BdbXQ4AAIBxlgesgoICzZs3T2PHjvXdvf2OO%2B7QwoUL9frrr1tdDgAAgHGWB6yvvvpKPXv2bDIeHx8vt9ttdTkAAADGWR6wOnXqpA8//LDJ%2BDvvvOO74B0AACCQWf5bhFOnTtVTTz0lt9str9er999/XwUFBdq8ebMef/xxq8sBAAAwzvKANW7cONXX12v16tU6f/685s2bp3bt2umRRx7RxIkTrS4HAADAOMsDVmFhoYYPH6709HSdOXNGXq9X7du3t7oMAACAK8bya7Cys7N9F7O3a9eOcAUAAIKO5QGra9eu%2BvTTT63eLQAAgGUsP0UYHx%2BvmTNnat26deratavCw8P95nNycqwuCQAAwCjLA9Znn32mpKQkSeK%2BVwAAIChZErByc3M1ffp0tW7dWps3b7ZilwAAALax5BqsF154QbW1tX5j06ZNU0VFhRW7BwAAsJQlAcvr9TYZ279/v%2Brq6qzYPQAAgKUs/y1CAACAYEfAAgAAMMyygOVwOKzaFQAAgK0su03DwoUL/e55deHCBeXl5alNmzZ%2Br%2BM%2BWAAAINBZErD69evX5J5XiYmJqqqqUlVVlRUlAAAAWMaSgMW9rwAAQEvCRe4AAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwI%2BYHk8Ho0cOVJ79%2B71jZWVlenee%2B9Vnz59dMcdd2j37t1%2B73nvvfc0cuRIJSQkaNKkSSorK/Ob37Bhg9LS0pSYmKi5c%2BeqtrbWkrUAAIDgENABq66uTo8%2B%2BqiOHj3qG/N6vcrIyFCHDh20Y8cOjRo1StOnT9fJkyclSSdPnlRGRobGjh2rl156Se3atdNDDz0kr9crSXrrrbeUn5%2Bv7Oxsbdy4USUlJcrLy7NlfQAAIDAFbMA6duyYJkyYoC%2B//NJvfM%2BePSorK1N2dra6d%2B%2BuBx54QH369NGOHTskSdu3b9eNN96oKVOmqEePHsrJydGJEye0b98%2BSdKmTZs0efJkDRo0SDfddJOeeuop7dixg6NYAACg2QI2YO3bt0/9%2B/dXQUGB33hJSYluuOEGtW7d2jeWlJSk4uJi33xycrJvLiIiQr169VJxcbEaGhr04Ycf%2Bs336dNHFy5c0JEjR67wigAAQLCw5I89Xwl33XXXj4673W7FxMT4jbVv317l5eU/O19TU6O6ujq/eafTqaioKN/7m6OiokJut9tvzOls3WS/lys0NLDysdMZWPX%2BlIt9D7T%2BBwN6bw/6bh96H7gCNmD9lNraWoWFhfmNhYWFyePx/Oz8%2BfPnfc9/6v3NUVBQoPz8fL%2BxjIwMZWZmNnsbwSg6uo3dJRgVGRlhdwktFr23B323D70PPEEXsMLDw1VdXe035vF41KpVK9/8D8OSx%2BNRZGSkwsPDfc9/OB8R0fxv7vT0dA0ePNhvzOlsraqq75q9jeYItE80ptdvl9DQEEVGRqimplYNDY12l9Oi0Ht70Hf70PvLZ9eH%2B6ALWLGxsTp27JjfWGVlpe/0XGxsrCorK5vM9%2BzZU1FRUQoPD1dlZaW6d%2B8uSaqvr1d1dbVcLleza4iJiWlyOtDt/lb19S37hyPY1t/Q0Bh0awoU9N4e9N0%2B9D7wBNYhkGZISEjQRx995DvdJ0lFRUVKSEjwzRcVFfnmamtrdfjwYSUkJCgkJES9e/f2my8uLpbT6VR8fLx1iwAAAAEt6AJWSkqKOnbsqDlz5ujo0aNau3atDh48qPHjx0uSxo0bpwMHDmjt2rU6evSo5syZo86dO6t///6Svr94fv369dq5c6cOHjyo%2BfPna8KECb/oFCEAAGjZgi5ghYaGatWqVXK73Ro7dqxee%2B01rVy5UnFxcZKkzp07609/%2BpN27Nih8ePHq7q6WitXrpTD4ZAkjRgxQg888IDmzZunKVOm6KabbtKsWbPsXBIAAAgwDu/FW5jjinK7vzW%2BTaczREMXv2t8u1fKm48MsLsEI5zOEEVHt1FV1XdcE2Exem8P%2Bm4fen/5XK6rbNlv0B3BAgAAsBsBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAsKANWH/72990/fXX%2Bz0yMzMlSYcPH9add96phIQEjRs3TocOHfJ7b2FhoYYMGaKEhARlZGTozJkzdiwBAAAEqKANWMeOHdOgQYO0e/du32PhwoU6d%2B6cpk2bpuTkZP35z39WYmKiHnjgAZ07d06SdPDgQWVlZWn69OkqKChQTU2N5syZY/NqAABAIAnagFVaWqrrrrtOLpfL94iMjNQbb7yh8PBwPfbYY%2BrevbuysrLUpk0b/fWvf5UkbdmyRbfffrtGjx6t%2BPh45ebmateuXSorK7N5RQAAIFAEdcDq2rVrk/GSkhIlJSXJ4XBIkhwOh/r27avi4mLffHJysu/1HTt2VFxcnEpKSiypGwAABL6gDFher1efffaZdu/erWHDhmnIkCFavHixPB6P3G63YmJi/F7fvn17lZeXS5IqKir%2B7TwAAMDPcdpdwJVw8uRJ1dbWKiwsTM8884y%2B%2BuorLVy4UOfPn/eN/6uwsDB5PB5J0vnz5//tfHNUVFTI7Xb7jTmdrZsEt8sVGhpY%2BdjpDKx6f8rFvgda/4MBvbcHfbcPvQ9cQRmwOnXqpL179%2Brqq6%2BWw%2BFQz57cRdEDAAAKIklEQVQ91djYqFmzZiklJaVJWPJ4PGrVqpUkKTw8/EfnIyIimr3/goIC5efn%2B41lZGT4fouxpYqObmN3CUZFRjb/ewJm0Xt70Hf70PvAE5QBS5KioqL8nnfv3l11dXVyuVyqrKz0m6usrPQdXYqNjf3ReZfL1ex9p6ena/DgwX5jTmdrVVV990uW8LMC7RON6fXbJTQ0RJGREaqpqVVDQ6Pd5bQo9N4e9N0%2B9P7y2fXhPigD1rvvvquZM2fqH//4h%2B/I08cff6yoqCglJSXpueeek9frlcPhkNfr1YEDB/Tggw9KkhISElRUVKSxY8dKkk6dOqVTp04pISGh2fuPiYlpcjrQ7f5W9fUt%2B4cj2Nbf0NAYdGsKFPTeHvTdPvQ%2B8ATWIZBmSkxMVHh4uJ544gkdP35cu3btUm5uru6//34NHz5cNTU1WrRokY4dO6ZFixaptrZWt99%2BuyRp4sSJevXVV7V9%2B3YdOXJEjz32mG699VZ16dLF5lUBAIBAEZQBq23btlq/fr3OnDmjcePGKSsrS%2Bnp6br//vvVtm1bPfvss76jVCUlJVq7dq1at24t6ftwlp2drZUrV2rixIm6%2BuqrlZOTY/OKAABAIHF4vV6v3UW0BG73t8a36XSGaOjid41v90p585EBdpdghNMZoujoNqqq%2Bo5D9haj9/ag7/ah95fP5brKlv0G5REsAAAAOxGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzGl3AWg5bn/m/%2Bwu4Rd585EBdpcAAAhQHMECAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDnHYXAPxa3f7M/9ldwi/y5iMD7C4BAPD/cQTrR9TV1Wnu3LlKTk7WwIED9fzzz9tdEgAACCAcwfoRubm5OnTokDZu3KiTJ09q9uzZiouL0/Dhw%2B0uDQAABAAC1g%2BcO3dO27dv13PPPadevXqpV69eOnr0qLZu3UrAAgAAzULA%2BoEjR46ovr5eiYmJvrGkpCStWbNGjY2NCgnhrCp%2BnQLpmjGuFwMQ7AhYP%2BB2uxUdHa2wsDDfWIcOHVRXV6fq6mq1a9fuZ7dRUVEht9vtN%2BZ0tlZMTIzRWkNDCXsITIEUBiXpbzPT7C7BVhf/r%2BH/HOvR%2B8BFwPqB2tpav3Alyffc4/E0axsFBQXKz8/3G5s%2BfboefvhhM0X%2BfxUVFZr826NKT083Ht7w0yoqKlRQUEDfbUDv7VFRUaGNG9fRdxvQ%2B8BFJP6B8PDwJkHq4vNWrVo1axvp6en685//7PdIT083Xqvb7VZ%2Bfn6To2W4sui7fei9Pei7feh94OII1g/ExsaqqqpK9fX1cjq/b4/b7VarVq0UGRnZrG3ExMTwSQMAgBaMI1g/0LNnTzmdThUXF/vGioqK1Lt3by5wBwAAzUJi%2BIGIiAiNHj1a8%2BfP18GDB7Vz5049//zzmjRpkt2lAQCAABE6f/78%2BXYX8WuTmpqqw4cPa8mSJXr//ff14IMPaty4cXaX9aPatGmjlJQUtWnTxu5SWhT6bh96bw/6bh96H5gcXq/Xa3cRAAAAwYRThAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgBqq6uTnPnzlVycrIGDhyo559/3u6SWhSPx6ORI0dq7969dpfSIpw%2BfVqZmZlKSUlRWlqacnJyVFdXZ3dZLcIXX3yhqVOnKjExUbfeeqvWrVtnd0ktzrRp0/T444/bXQZ%2BIafdBeDS5Obm6tChQ9q4caNOnjyp2bNnKy4uTsOHD7e7tKBXV1enGTNm6OjRo3aX0iJ4vV5lZmYqMjJSW7du1TfffKO5c%2BcqJCREs2fPtru8oNbY2Khp06apd%2B/eevnll/XFF1/o0UcfVWxsrH73u9/ZXV6L8Je//EW7du3SmDFj7C4FvxBHsALQuXPntH37dmVlZalXr14aOnSo7r//fm3dutXu0oLesWPHNGHCBH355Zd2l9JiHD9%2BXMXFxcrJyVGPHj2UnJyszMxMFRYW2l1a0KusrFTPnj01f/58de3aVf/xH/%2Bhm2%2B%2BWUVFRXaX1iJUV1crNzdXvXv3trsUXAICVgA6cuSI6uvrlZiY6BtLSkpSSUmJGhsbbaws%2BO3bt0/9%2B/dXQUGB3aW0GC6XS%2BvWrVOHDh38xs%2BePWtTRS1HTEyMnnnmGbVt21Zer1dFRUXav3%2B/UlJS7C6tRfjf//1fjRo1Stdee63dpeAScIowALndbkVHRyssLMw31qFDB9XV1am6ulrt2rWzsbrgdtddd9ldQosTGRmptLQ03/PGxkZt2bJFqampNlbV8gwePFgnT57UoEGDNGzYMLvLCXrvv/%2B%2B/vnPf%2Br111/X/Pnz7S4Hl4AjWAGotrbWL1xJ8j33eDx2lARYJi8vT4cPH9b//M//2F1Ki7JixQqtWbNGH3/8sXJycuwuJ6jV1dXpD3/4g%2BbNm6dWrVrZXQ4uEUewAlB4eHiTIHXxOT%2BMCGZ5eXnauHGjli1bpuuuu87uclqUi9cB1dXVaebMmXrssceafNCDGfn5%2Bbrxxhv9jtwi8BCwAlBsbKyqqqpUX18vp/P7f0K3261WrVopMjLS5uqAK2PBggXatm2b8vLyOEVlkcrKShUXF2vIkCG%2BsWuvvVYXLlzQ2bNnuRzhCvnLX/6iyspK33W2Fz9Av/XWW/rggw/sLA2/AAErAPXs2VNOp1PFxcVKTk6WJBUVFal3794KCeGsL4JPfn6%2BXnzxRS1dupRbkVjoq6%2B%2B0vTp07Vr1y7FxsZKkg4dOqR27doRrq6gzZs3q76%2B3vd88eLFkqSZM2faVRIuAQErAEVERGj06NGaP3%2B%2Bnn76aVVUVOj555/nuggEpdLSUq1atUrTpk1TUlKS3G63b87lctlYWfDr3bu3evXqpblz52rOnDk6ceKE8vLy9OCDD9pdWlDr1KmT3/M2bdpIkq655ho7ysElImAFqDlz5mj%2B/PmaPHmy2rZtq4cffli33Xab3WUBxr399ttqaGjQ6tWrtXr1ar%2B5Tz75xKaqWobQ0FCtWrVKCxYsUHp6uiIiInTPPfdo0qRJdpcG/Oo5vF6v1%2B4iAAAAggkX7AAAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYf8PIiG4yNRqUh4AAAAASUVORK5CYII%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-5111588114724210399”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2398657</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.4830918</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1979414</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.6480882</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3292723</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.3844675</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.218027</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.7168459</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2198527</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2952)</td> <td class=”number”>2973</td> <td class=”number”>92.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-5111588114724210399”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>141</td> <td class=”number”>4.4%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0148223</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0320698</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.036135</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.038519</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>2.158895</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.188184</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.237693</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>3.076923</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>4.618937</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:50%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_WICSPTH12”>WICSPTH12<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>2946</td>

</tr> <tr>

<th>Unique (%)</th> <td>91.8%</td>

</tr> <tr class=”alert”>

<th>Missing (%)</th> <td>2.4%</td>

</tr> <tr class=”alert”>

<th>Missing (n)</th> <td>78</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>0.22931</td>

</tr> <tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>Maximum</th> <td>2.994</td>

</tr> <tr class=”alert”>

<th>Zeros (%)</th> <td>4.6%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram-3264576034180935013”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives-3264576034180935013,#minihistogram-3264576034180935013”
aria-expanded=”false” aria-controls=”collapseExample”>

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</a>

</div> <div class=”row collapse col-md-12” id=”descriptives-3264576034180935013”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles-3264576034180935013”

aria-controls=”quantiles-3264576034180935013” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram-3264576034180935013” aria-controls=”histogram-3264576034180935013”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common-3264576034180935013” aria-controls=”common-3264576034180935013”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme-3264576034180935013” aria-controls=”extreme-3264576034180935013”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles-3264576034180935013”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>0</td>

</tr> <tr>

<th>5-th percentile</th> <td>0.048561</td>

</tr> <tr>

<th>Q1</th> <td>0.11893</td>

</tr> <tr>

<th>Median</th> <td>0.17733</td>

</tr> <tr>

<th>Q3</th> <td>0.26777</td>

</tr> <tr>

<th>95-th percentile</th> <td>0.57339</td>

</tr> <tr>

<th>Maximum</th> <td>2.994</td>

</tr> <tr>

<th>Range</th> <td>2.994</td>

</tr> <tr>

<th>Interquartile range</th> <td>0.14884</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>0.20848</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.90918</td>

</tr> <tr>

<th>Kurtosis</th> <td>26.16</td>

</tr> <tr>

<th>Mean</th> <td>0.22931</td>

</tr> <tr>

<th>MAD</th> <td>0.12648</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>3.9089</td>

</tr> <tr>

<th>Sum</th> <td>718.2</td>

</tr> <tr>

<th>Variance</th> <td>0.043466</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram-3264576034180935013”>

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EbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIbZHrAmT56sF154QSdOnLB71wAAALawPWANGTJETz75pIYNG6Y//elP2r59uyzLsnsaAAAAbcb2gDV79my99957WrNmjYKDgzVr1iwNHz5cjz32mL7%2B%2Bmu7pwMAAGCc0xc7dTgcGjp0qIYOHarq6mo999xzWrNmjdavX69Bgwbp1ltv1bXXXuuLqQEAAPxqPglYklRWVqbXX39dr7/%2Bur766isNGjRI48ePV2lpqR544AHt2rVLCxcu9NX0AAAAfjHbLxG%2B9tprmj59utLS0rRx40ZdffXVevvtt/Vf//Vfmjx5smbNmqW5c%2Bfq5ZdfbtF4dXV1GjNmjD755BN329KlS3XppZd6vDZv3uzuz8/P1zXXXKOEhARlZmbq%2BPHj7j7LsrRixQoNGTJEKSkpWr58uRobG80VAAAABDzbz2AtXLhQaWlpWr16ta666ioFBTXNeH369NHNN9/sdaza2lrNnj1bxcXFHu0lJSWaPXu2xo8f727r3LmzJGnPnj1auHChHn74YcXFxWnZsmWaP3%2B%2B1q1bJ0l65plnlJ%2Bfr9zcXNXX12vOnDmKjIzUjBkzfs1hAwCAdsT2gPXBBx8oIiJClZWV7nC1Z88eDRgwQMHBwZKkQYMGadCgQc2Oc%2BDAAc2ePfucv4FYUlKiGTNmKCoqqknf5s2bNWrUKI0bN06StHz5cqWlpenQoUPq1auXNm3apKysLCUnJ0uS7rvvPq1atYqABQAAWsz2S4QnT57UyJEj9be//c3dduedd2rs2LE6evRoi8fZuXOnBg8erLy8vCbjHzt2TL179z7n54qKitzhSZK6d%2B%2Bu2NhYFRUV6dixYzp69KiuvPJKd39SUpK%2B%2B%2B47lZWVtXhuAACgfbM9YP3lL3/Rb3/7W91%2B%2B%2B3utjfffFPdu3dXdnZ2i8eZOnWqFixYoLCwMI/2kpISORwOPfnkk7rqqqt044036pVXXnH3l5WVKTo62uMzkZGRKi0tlcvlkiSP/m7dukmSSktLW36QAACgXbP9EuH//u//6sUXX/S4fNe1a1fNnTtXv//973/1%2BAcPHpTD4XDfx7Vr1y49%2BOCD6ty5s9LT01VTU6OQkBCPz4SEhKiurk41NTXu9//aJ/10M31LlZWVucPaGU5nxybB7tcKDvavbzpyOu2f75ka%2BVut7ESNmkd9vKNG3lEj7wKtRrYHLKfTqaqqqibt1dXVRp7oPm7cOKWlpalLly6SpLi4OH3zzTfasmWL0tPTFRoa2iQs1dXVKSwszCNMhYaGuv8sqcmZsubk5eUpNzfXoy0zM1NZWVm/%2BLgCQUREJ5/tOzy85f//2itq1Dzq4x018o4aeRcoNbI9YF111VVaunSpVq5cqX/7t3%2BTJB06dEjZ2dlKTU391eM7HA53uDqjT58%2B2rFjhyQpJiZG5eXlHv3l5eWKiopSTEyMJMnlcqlnz57uP0s65w3zPycjI0MjRozwaHM6O6qi4lTrDsYLf0v5po%2B/JYKDgxQeHqaqqmo1NPC4jXOhRs2jPt5RI%2B%2BokXdtVSNf/ePe9oA1b9483X777bruuusUHh4uSaqqqtKAAQM0f/78Xz3%2BqlWr9Omnn%2BrZZ591t%2B3fv199%2BvSRJCUkJKigoEATJkyQJB09elRHjx5VQkKCYmJiFBsbq4KCAnfAKigoUGxsbKsu70VHRzfZ3uU6ofr69v1D5cvjb2hobPf194YaNY/6eEeNvKNG3gVKjWwPWJGRkXrllVf00Ucfqbi4WE6nUxdffLF%2B97vfyeFw/Orx09LStH79ej311FNKT0/X9u3b9eqrr2rTpk2SpClTpuiWW27RwIEDFR8fr2XLlmn48OHq1auXu3/FihW66KKLJEmPPvqopk%2Bf/qvnBQAA2g%2BffFVOcHCwUlNTjVwSPNsVV1yhVatW6YknntCqVavUo0cPPfroo0pMTJQkJSYmavHixXriiSf0ww8/aOjQoVqyZIn78zNmzND333%2BvmTNnKjg4WJMmTdJtt91mfJ4AACBwOSwTd5a3gsvl0uOPP67du3fr9OnTTW5sf/fdd%2B2cjm1crhPGx3Q6g5S%2B4kPj47aVt%2B4davs%2Bnc4gRUR0UkXFqYA45dwWqFHzqI931Mg7auRdW9UoKuoCY2O1hu1nsB588EHt3btXo0eP1gUX%2BOagAQAA2pLtAWvHjh3asGGDx9PUAQAAAontv%2BffsWNHRUZG2r1bAAAA29gesMaOHasNGzaooaHB7l0DAADYwvZLhJWVlcrPz9c///lP9erVq8nX1px5nAIAAIC/8sljGsaMGeOL3QIAANjC9oCVnZ1t9y4BAABs5ZMvsysrK1Nubq5mz56t77//Xm%2B//bYOHjzoi6kAAAAYZ3vA%2Bvbbb3XDDTfolVde0TvvvKMff/xRb775piZOnKiioiK7pwMAAGCc7QHrkUce0TXXXKNt27bpN7/5jSRp5cqVGjFihFasWGH3dAAAAIyzPWDt3r1bt99%2Bu8cXOzudTt1zzz3at2%2Bf3dMBAAAwzvaA1djYqMbGpt8xdOrUKQUHB9s9HQAAAONsD1jDhg3TunXrPEJWZWWlcnJyNGTIELunAwAAYJztAev%2B%2B%2B/X3r17NWzYMNXW1uruu%2B9WWlqaDh8%2BrHnz5tk9HQAAAONsfw5WTEyMXn31VeXn5%2BuLL75QY2OjpkyZorFjx6pz5852TwcAAMA4nzzJPSwsTJMnT/bFrgEAANqc7QFr2rRpzfbzXYQAAMDf2R6wevTo4fG%2Bvr5e3377rb766ivdeuutdk8HAADAuPPmuwhXr16t0tJSm2cDAABgnk%2B%2Bi/Bcxo4dq7feesvX0wAAAPjVzpuA9emnn/KgUQAAEBDOi5vcT548qS%2B//FJTp061ezoAAADG2R6wYmNjPb6HUJJ%2B85vf6Oabb9aNN95o93QAAACMsz1gPfLII3bvEgAAwFa2B6xdu3a1eNsrr7yyDWcCAADQNmwPWLfccov7EqFlWe72s9scDoe%2B%2BOILu6cHAADwq9kesJ588kktXbpUc%2BbMUUpKikJCQvTZZ59p8eLFGj9%2BvK6//nq7pwQAAGCU7Y9pyM7O1kMPPaTrrrtOERER6tSpk4YMGaLFixdry5Yt6tGjh/sFAADgj2wPWGVlZecMT507d1ZFRYXd0wEAADDO9oA1cOBArVy5UidPnnS3VVZWKicnR7/73e/sng4AAIBxtt%2BD9cADD2jatGm66qqr1Lt3b1mWpW%2B%2B%2BUZRUVHatGmT3dMBAAAwzvaA1bdvX7355pvKz89XSUmJJOn3v/%2B9Ro8erbCwMLunAwAAYJztAUuSLrzwQk2ePFmHDx9Wr169JP30NHcAAIBAYPs9WJZlacWKFbryyis1ZswYlZaWat68eVq4cKFOnz5t93QAAACMsz1gPffcc3rttdf05z//WSEhIZKka665Rtu2bVNubq7d0wEAADDO9oCVl5enhx56SBMmTHA/vf3666/X0qVL9cYbb9g9HQAAAONsD1iHDx9W//79m7THxcXJ5XLZPR0AAADjbA9YPXr00Geffdak/YMPPnDf8A4AAODPbP8twhkzZujhhx%2BWy%2BWSZVn6%2BOOPlZeXp%2Beee07333%2B/3dMBAAAwzvaANXHiRNXX12vt2rWqqanRQw89pK5du%2Bree%2B/VlClT7J4OAACAcbYHrPz8fI0cOVIZGRk6fvy4LMtSZGSk3dMAAABoM7bfg7V48WL3zexdu3YlXAEAgIBje8Dq3bu3vvrqK7t3CwAAYBvbLxHGxcXpvvvu04YNG9S7d2%2BFhoZ69GdnZ9s9JQAAAKNsD1hff/21kpKSJInnXgEAgIBkS8Bavny5Zs6cqY4dO%2Bq5556zY5cAAAA%2BY8s9WM8884yqq6s92u68806VlZXZsXsAAABb2RKwLMtq0rZr1y7V1tbasXsAAABb2f5bhAAAAIHO7wNWXV2dxowZo08%2B%2BcTddujQId12220aOHCgrr/%2Bem3fvt3jMx999JHGjBmjhIQETZs2TYcOHfLof/bZZ5WamqrExEQtWLCgyeVNAACA5tgWsBwOh/Exa2tr9ac//UnFxcXuNsuylJmZqW7dumnr1q0aO3asZs6cqSNHjkiSjhw5oszMTE2YMEEvv/yyunbtqnvuucd9GfOdd95Rbm6uFi9erI0bN6qoqEg5OTnG5w4AAAKXbY9pWLp0qcczr06fPq2cnBx16tTJY7uWPgfrwIEDmj17dpP7u3bs2KFDhw7phRdeUMeOHdW3b199/PHH2rp1q2bNmqWXXnpJl19%2BuaZPn%2B7e39ChQ7Vz504NHjxYmzZt0q233qq0tDRJ0sMPP6wZM2Zozpw5CgsL%2BzUlAAAA7YQtZ7CuvPJKuVwuHT582P1KTExURUWFR9vhw4dbPOaZQJSXl%2BfRXlRUpMsuu0wdO3Z0tyUlJamwsNDdn5yc7O4LCwvTgAEDVFhYqIaGBn322Wce/QMHDtTp06e1f//%2BX3r4AACgnbHlDFZbPPtq6tSp52x3uVyKjo72aIuMjFRpaanX/qqqKtXW1nr0O51OdenSxf15AAAAb2x/kntbq66uVkhIiEdbSEiI6urqvPbX1NS43//c51uirKysyVPqnc6OTYLdrxUc7F%2B/o%2BB02j/fMzXyt1rZiRo1j/p4R428o0beBVqNAi5ghYaGqrKy0qOtrq5OHTp0cPefHZbq6uoUHh7uvkfsXP2tuf8qLy9Pubm5Hm2ZmZnKyspq8RiBKCKik/eN2kh4OPfPeUONmkd9vKNG3lEj7wKlRgEXsGJiYnTgwAGPtvLycvfZo5iYGJWXlzfp79%2B/v7p06aLQ0FCVl5erb9%2B%2BkqT6%2BnpVVlYqKiqqxXPIyMjQiBEjPNqczo6qqDj1Sw7pZ/lbyjd9/C0RHByk8PAwVVVVq6Gh0fb9%2BwNq1Dzq4x018o4aeddWNfLVP%2B4DLmAlJCRo/fr1qqmpcZ%2B1KigocH/BdEJCggoKCtzbV1dXa9%2B%2BfZo5c6aCgoIUHx%2BvgoICDR48WJJUWFgop9OpuLi4Fs8hOjq6yeVAl%2BuE6uvb9w%2BVL4%2B/oaGx3dffG2rUPOrjHTXyjhp5Fyg18q9TIC2QkpKi7t27a/78%2BSouLtb69eu1Z88eTZo0SZI0ceJE7d69W%2BvXr1dxcbHmz5%2Bvnj17ugPV1KlT9dRTT2nbtm3as2ePFi1apJtuuolHNAAAgBYLuIAVHBysNWvWyOVyacKECXr99de1evVqxcbGSpJ69uypv/71r9q6dasmTZqkyspKrV692v0g1NGjR%2Buuu%2B7SQw89pOnTp%2BuKK67QnDlzfHlIAADAzzisc30TM4xzuU4YH9PpDFL6ig%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%2B978rMTFRd911l3788UdJ0p49e7Rw4ULNnDlTeXl5qqqq0vz58318NAAAwJ8EbMAqKSlRv379FBUV5X6Fh4frzTffVGhoqObOnau%2Bfftq4cKF6tSpk95%2B%2B21J0ubNmzVq1CiNGzdOcXFxWr58ud5//30dOnTIx0cEAAD8RUAHrN69ezdpLyoqUlJSkhwOhyTJ4XBo0KBBKiwsdPcnJye7t%2B/evbtiY2NVVFRky7wBAID/c/p6Am3Bsix9/fXX2r59u9atW6eGhgaNHDlSWVlZcrlcuvjiiz22j4yMVHFxsSSprKxM0dHRTfpLS0tbvP%2BysjK5XC6PNqezY5Nxf63gYP/Kx05/zYliAAAMoElEQVSn/fM9UyN/q5WdqFHzqI931Mg7auRdoNUoIAPWkSNHVF1drZCQED3%2B%2BOM6fPiwli5dqpqaGnf7vwoJCVFdXZ0kqaamptn%2BlsjLy1Nubq5HW2Zmpvsm%2B/YqIqKTz/YdHh7ms337C2rUPOrjHTXyjhp5Fyg1CsiA1aNHD33yySe68MIL5XA41L9/fzU2NmrOnDlKSUlpEpbq6urUoUMHSVJoaOg5%2B8PCWv4/PCMjQyNGjPBoczo7qqLi1C88onPzt5Rv%2BvhbIjg4SOHhYaqqqlZDQ6Pt%2B/cH1Kh51Mc7auQdNfKurWrkq3/cB2TAkqQuXbp4vO/bt69qa2sVFRWl8vJyj77y8nL35buYmJhz9kdFRbV439HR0U0uB7pcJ1Rf375/qNJXfOjrKbTKW/cO9fUUbNXQ0Nju12hzqI931Mg7auRdoNTIv06BtNCHH36owYMHq7q62t32xRdfqEuXLkpKStKnn34qy7Ik/XS/1u7du5WQkCBJSkhIUEFBgftzR48e1dGjR939AAAA3gRkwEpMTFRoaKgeeOABHTx4UO%2B//76WL1%2BuO%2B64QyNHjlRVVZWWLVumAwcOaNmyZaqurtaoUaMkSVOmTNFrr72ml156Sfv379fcuXM1fPhw9erVy8dHBQAA/EVABqzOnTvrqaee0vHjxzVx4kQtXLhQGRkZuuOOO9S5c2etW7dOBQUFmjBhgoqKirR%2B/Xp17NhR0k/hbPHixVq9erWmTJmiCy%2B8UNnZ2T4%2BIgAA4E8c1plrZWhTLtcJ42M6nUF%2Bd1%2BTP2kv92A5nUGKiOikiopTAXHfg2nUxztq5B018q6tahQVdYGxsVojIM9gAQAA%2BBIBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw5y%2BngBwvhr1%2BP/4egqt8ta9Q309BQDA/%2BEMFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYJ1DbW2tFixYoOTkZA0bNkxPP/20r6cEAAD8CL9FeA7Lly/X3r17tXHjRh05ckTz5s1TbGysRo4c6eupAT/Ln37rkd94BBDoCFhn%2BfHHH/XSSy/pb3/7mwYMGKABAwaouLhYzz//PAELAAC0CJcIz7J//37V19crMTHR3ZaUlKSioiI1Njb6cGYAAMBfcAbrLC6XSxEREQoJCXG3devWTbW1taqsrFTXrl29jlFWViaXy%2BXR5nR2VHR0tNG5BgeTj%2BGf/OlyJtrWP%2B5L9fUUbHHm72v%2B3v55gVYjAtZZqqurPcKVJPf7urq6Fo2Rl5en3Nxcj7aZM2dq1qxZZib5f8rKynTrRcXKyMgwHt4CRVlZmfLy8qhRM6hR86iPd9TIu7KyMm3cuIEaNSPQahQYMdGg0NDQJkHqzPsOHTq0aIyMjAz9/e9/93hlZGQYn6vL5VJubm6Ts2X4/6iRd9SoedTHO2rkHTXyLtBqxBmss8TExKiiokL19fVyOn8qj8vlUocOHRQeHt6iMaKjowMifQMAgF%2BGM1hn6d%2B/v5xOpwoLC91tBQUFio%2BPV1AQ5QIAAN6RGM4SFhamcePGadGiRdqzZ4%2B2bdump59%2BWtOmTfP11AAAgJ8IXrRo0SJfT%2BJ8M2TIEO3bt0%2BPPvqoPv74Y/3xj3/UxIkTfT2tc%2BrUqZNSUlLUqVMnX0/lvEWNvKNGzaM%2B3lEj76iRd4FUI4dlWZavJwEAABBIuEQIAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyAdZ6rra3VggULlJycrGHDhunpp5/%2B2W337dunyZMnKyEhQRMnTtTevXttnKnvtKZGd999ty699FKP13vvvWfjbH2nrq5OY8aM0SeffPKz27TXNXRGS2rUXtfQsWPHlJWVpZSUFKWmpio7O1u1tbXn3La9rqPW1Ki9rqNvv/1WM2bMUGJiooYPH64NGzb87LZ%2Bv44snNcWL15s3XDDDdbevXut//7v/7YSExOtt956q8l2p06dsoYOHWo98sgj1oEDB6wlS5ZY//7v/26dOnXKB7O2V0trZFmWlZ6ebr322mtWWVmZ%2B1VbW2vzjO1XU1NjZWZmWv369bN27Nhxzm3a8xqyrJbVyLLa5xpqbGy0brrpJuuOO%2B6wvvrqK2vXrl1Wenq69cgjjzTZtr2uo9bUyLLa5zpqaGiwrr32Wmv27NnW119/bf3zn/%2B0Bg0aZL3%2B%2ButNtg2EdUTAOo%2BdOnXKio%2BP9/jLfvXq1dbNN9/cZNuXXnrJGjFihNXY2GhZ1k8/7Onp6dbWrVttm68vtKZGtbW1Vv/%2B/a2DBw/aOUWfKy4utm688UbrhhtuaDY8tNc1ZFktr1F7XUMHDhyw%2BvXrZ7lcLnfbG2%2B8YQ0bNqzJtu11HbWmRu11HR07dsz6j//4D%2BvEiRPutszMTOvPf/5zk20DYR1xifA8tn//ftXX1ysxMdHdlpSUpKKiIjU2NnpsW1RUpKSkJDkcDkmSw%2BHQoEGDVFhYaOuc7daaGh08eFAOh0O9evWye5o%2BtXPnTg0ePFh5eXnNbtde15DU8hq11zUUFRWlDRs2qFu3bh7tJ0%2BebLJte11HralRe11H0dHRevzxx9W5c2dZlqWCggLt2rVLKSkpTbYNhHXk9PUE8PNcLpciIiIUEhLibuvWrZtqa2tVWVmprl27emx78cUXe3w%2BMjJSxcXFts3XF1pTo4MHD6pz586aO3eudu7cqYsuukizZs3S1Vdf7Yup22bq1Kkt2q69riGp5TVqr2soPDxcqamp7veNjY3avHmzhgwZ0mTb9rqOWlOj9rqO/tWIESN05MgRpaWl6brrrmvSHwjriDNY57Hq6mqP4CDJ/b6urq5F2569XaBpTY0OHjyompoaDRs2TBs2bNDVV1%2Btu%2B%2B%2BW5999plt8z2ftdc11BqsoZ/k5ORo3759%2Bs///M8mfayjnzRXI9aR9MQTT%2BjJJ5/UF198oezs7Cb9gbCOOIN1HgsNDW2ymM6879ChQ4u2PXu7QNOaGt1zzz265ZZbdOGFF0qS4uLi9Pnnn%2BvFF19UfHy8PRM%2Bj7XXNdQarKGfgsPGjRv12GOPqV%2B/fk36WUfea8Q6kvs4a2trdd9992nu3LkegSoQ1hFnsM5jMTExqqioUH19vbvN5XKpQ4cOCg8Pb7JteXm5R1t5ebmio6NtmauvtKZGQUFB7r/QzujTp4%2BOHTtmy1zPd%2B11DbVGe19DS5Ys0TPPPKOcnJxzXtaRWEctqVF7XUfl5eXatm2bR9vFF1%2Bs06dPN7lXLRDWEQHrPNa/f385nU6Pm/oKCgoUHx%2BvoCDP/3UJCQn69NNPZVmWJMmyLO3evVsJCQm2ztluranR/fffr/nz53u07d%2B/X3369LFlrue79rqGWqM9r6Hc3Fy98MILWrlypUaPHv2z27XnddTSGrXXdXT48GHNnDnTI0ju3btXXbt29bhfVgqMdUTAOo%2BFhYVp3LhxWrRokfbs2aNt27bp6aef1rRp0yT9dKampqZGkjRy5EhVVVVp2bJlOnDggJYtW6bq6mqNGjXKl4fQ5lpToxEjRuiNN97Qq6%2B%2Bqm%2B//Va5ubkqKCjQzTff7MtD8CnWkHesIamkpERr1qzRH/7wByUlJcnlcrlfEutIal2N2us6io%2BP14ABA7RgwQIdOHBA77//vnJycvTHP/5RUgCuI589IAIt8uOPP1pz5861Bg4caA0bNsx65pln3H39%2BvXzeCZIUVGRNW7cOCs%2BPt6aNGmS9fnnn/tgxvZrTY1efPFF69prr7Uuv/xya/z48dbOnTt9MGPfOfsZT6yhprzVqD2uoXXr1ln9%2BvU758uyWEeW1foatcd1ZFmWVVpaamVmZlqDBg2yhg4daq1du9b9rKtAW0cOy/q/828AAAAwgkuEAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGDY/wNQfhxq3p1pZgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common-3264576034180935013”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:5%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1860638</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.244978</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1943257</td> <td class=”number”>3</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2351281</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2141633</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.211342</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2253267</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.1462844</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.2414293</td> <td class=”number”>2</td> <td class=”number”>0.1%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (2935)</td> <td class=”number”>2962</td> <td class=”number”>92.3%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”missing”>

<td class=”fillremaining”>(Missing)</td> <td class=”number”>78</td> <td class=”number”>2.4%</td> <td>

<div class=”bar” style=”width:3%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme-3264576034180935013”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>0.0</td> <td class=”number”>149</td> <td class=”number”>4.6%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0145686</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0316678</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.0379553</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>0.042924</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1.948052</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.956947</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1.968504</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.106741</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>2.994012</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div><div class=”row variablerow”>

<div class=”col-md-3 namecol”>
<p class=”h4 pp-anchor” id=”pp_var_index”>index<br/>

<small>Numeric</small>

</p>

</div><div class=”col-md-6”> <div class=”row”>

<div class=”col-sm-6”>
<table class=”stats “>
<tr>

<th>Distinct count</th> <td>3210</td>

</tr> <tr>

<th>Unique (%)</th> <td>100.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Missing (n)</th> <td>0</td>

</tr> <tr class=”ignore”>

<th>Infinite (%)</th> <td>0.0%</td>

</tr> <tr class=”ignore”>

<th>Infinite (n)</th> <td>0</td>

</tr>

</table>

</div> <div class=”col-sm-6”>

<table class=”stats “>

<tr>

<th>Mean</th> <td>31450</td>

</tr> <tr>

<th>Minimum</th> <td>1001</td>

</tr> <tr>

<th>Maximum</th> <td>72153</td>

</tr> <tr class=”ignore”>

<th>Zeros (%)</th> <td>0.0%</td>

</tr>

</table>

</div>

</div>

</div> <div class=”col-md-3 collapse in” id=”minihistogram1786709441990653363”>

</div> <div class=”col-md-12 text-right”>

<a role=”button” data-toggle=”collapse” data-target=”#descriptives1786709441990653363,#minihistogram1786709441990653363”
aria-expanded=”false” aria-controls=”collapseExample”>

Toggle details

</a>

</div> <div class=”row collapse col-md-12” id=”descriptives1786709441990653363”>

<ul class=”nav nav-tabs” role=”tablist”>
<li role=”presentation” class=”active”><a href=”#quantiles1786709441990653363”

aria-controls=”quantiles1786709441990653363” role=”tab” data-toggle=”tab”>Statistics</a></li>

<li role=”presentation”><a href=”#histogram1786709441990653363” aria-controls=”histogram1786709441990653363”

role=”tab” data-toggle=”tab”>Histogram</a></li>

<li role=”presentation”><a href=”#common1786709441990653363” aria-controls=”common1786709441990653363”

role=”tab” data-toggle=”tab”>Common Values</a></li>

<li role=”presentation”><a href=”#extreme1786709441990653363” aria-controls=”extreme1786709441990653363”

role=”tab” data-toggle=”tab”>Extreme Values</a></li>

</ul>

<div class=”tab-content”>
<div role=”tabpanel” class=”tab-pane active row” id=”quantiles1786709441990653363”>
<div class=”col-md-4 col-md-offset-1”>

<p class=”h4”>Quantile statistics</p> <table class=”stats indent”>

<tr>

<th>Minimum</th> <td>1001</td>

</tr> <tr>

<th>5-th percentile</th> <td>5111.9</td>

</tr> <tr>

<th>Q1</th> <td>19040</td>

</tr> <tr>

<th>Median</th> <td>30032</td>

</tr> <tr>

<th>Q3</th> <td>46110</td>

</tr> <tr>

<th>95-th percentile</th> <td>55024</td>

</tr> <tr>

<th>Maximum</th> <td>72153</td>

</tr> <tr>

<th>Range</th> <td>71152</td>

</tr> <tr>

<th>Interquartile range</th> <td>27071</td>

</tr>

</table>

</div> <div class=”col-md-4 col-md-offset-2”>

<p class=”h4”>Descriptive statistics</p> <table class=”stats indent”>

<tr>

<th>Standard deviation</th> <td>16265</td>

</tr> <tr>

<th>Coef of variation</th> <td>0.51718</td>

</tr> <tr>

<th>Kurtosis</th> <td>-0.62824</td>

</tr> <tr>

<th>Mean</th> <td>31450</td>

</tr> <tr>

<th>MAD</th> <td>13776</td>

</tr> <tr class=”“>

<th>Skewness</th> <td>0.15897</td>

</tr> <tr>

<th>Sum</th> <td>100953117</td>

</tr> <tr>

<th>Variance</th> <td>264560000</td>

</tr> <tr>

<th>Memory size</th> <td>25.2 KiB</td>

</tr>

</table>

</div>

</div> <div role=”tabpanel” class=”tab-pane col-md-8 col-md-offset-2” id=”histogram1786709441990653363”>

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DtniogIa9D6jVEg9hwIWrVqVmuMfR2YznYs%2BKNAPL4DsefT%2BV3AysrKUvfu3dW3b99ac6GhobXCUmVlpRXEzjUfFnZhB0l6erpSUlI8xhyOcBUVnbyg7ZwSHNxEERFhKikpU3V1Tb220dgEYs%2BB5PR/C%2BzrwFbfz8XGIhCPb1/r2Vsh3ucC1ujRo5WWlqZhw4bp0ksvveD13377bRUWFio%2BPl6SrMD0/vvva/jw4SosLPRYvrCw0Lp0Fx0dfdb5yMjIC6ohKiqq1uVAt/tHVVU17ECrrq5p8DYam0DsORCcbZ%2ByrwNToOzzQDy%2BA7Hn0/ncBdLevXvr%2BeefV3Jysu69915t2bJFxpg6r798%2BXKtX79ea9as0Zo1a5SSkqKUlBStWbNGTqdTX3zxhbU9Y4x27twpp9MpSXI6ncrNzbW2dezYMR07dsyaBwAAqAufC1j33XefPvzwQy1evFjBwcG6%2B%2B671b9/fz399NM6dOjQeddv3769OnbsaD2aNWumZs2aqWPHjho8eLBKSko0b948HTx4UPPmzVNZWZmGDBkiSRozZozWrl2rlStXat%2B%2BfZo6dar69%2B%2Bvyy677GK3DQAA/IjPBSzpl9sn9OnTRwsWLNDWrVt1880369VXX9XQoUN1880365///Ge9ttu8eXMtWbJEubm5Sk1Nlcvl0tKlSxUeHi5Jio%2BP1%2BzZs5Wdna0xY8aoRYsWyszMtLM1AAAQAHzuO1inFBQUaN26dVq3bp3279%2Bvnj176k9/%2BpOOHz%2Buv/3tb9qxY4dmzJhx3u08/vjjHs979Oih1atXn3P51NRUpaamNrh%2BAAAQuHwuYK1du1Zr167Vp59%2BqtatW2vkyJF65pln1KlTJ2uZdu3aad68eXUKWAAAAL81nwtYM2bM0IABA5Sdna3rr79eTZrUvor5u9/9TuPGjfNCdQAAAOfncwFr8%2BbNatWqlYqLi61wtWvXLnXr1k3BwcGSpJ49e6pnz57eLBMAAOCcfO5L7j/99JMGDx6sF154wRqbOHGiRowYoWPHjnmxMgAAgLrxuYD12GOPqWPHjrr99tutsXfeeUft2rXjN/oAAECj4HMB6/PPP9dDDz3kcff01q1ba%2BrUqdq%2BfbsXKwMAAKgbnwtYDodDJSUltcbLysou6I7uAAAA3uJzAev666/X3Llz9d1331lj%2Bfn5yszMPOsfcAYAAPA1PvdbhA8%2B%2BKBuv/12DRo0SBEREZKkkpISdevWTdOmTfNydQAAAOfncwGrTZs2Wr16tbZu3aoDBw7I4XDoiiuu0LXXXqugoCBvlwcAAHBePhewJCk4OFh9%2B/blkiAAAGiUfC5gud1uLVq0SDt37tTPP/9c64vt//rXv7xUGQAAQN34XMB6%2BOGHtXv3bg0bNkyXXnqpt8sBAAC4YD4XsLZv365ly5YpMTHR26UAAADUi8/dpiE8PFxt2rTxdhkAAAD15nMBa8SIEVq2bJmqq6u9XQoAAEC9%2BNwlwuLiYm3YsEGbNm3SZZddppCQEI/5v//9716qDAAAoG58LmBJ0vDhw71dAgAAQL35XMDKzMz0dgkAAAAN4nPfwZKkgoICZWVl6b777tP//M//6L333tO3337r7bIAAADqxOcC1uHDh3XTTTdp9erVev/991VaWqp33nlHaWlpcrlc3i4PAADgvHwuYD3%2B%2BOMaOHCgNm7cqEsuuUSS9NRTTyklJUVPPvmkl6sDAAA4P58LWDt37tTtt9/u8YedHQ6H7rrrLu3Zs8eLlQEAANSNzwWsmpoa1dTU1Bo/efKkgoODvVARAADAhfG5gJWcnKwlS5Z4hKzi4mItWLBAvXv39mJlAAAAdeNzAeuhhx7S7t27lZycrIqKCv3nf/6nBgwYoCNHjujBBx/0dnkAAADn5XP3wYqOjtaaNWu0YcMG7d27VzU1NRozZoxGjBih5s2be7s8AACA8/K5gCVJYWFhGj16tLfLAAAAqBefC1jjx4//1Xn%2BFiEAAPB1Phew2rdv7/G8qqpKhw8f1v79%2B3Xrrbd6qSoAAIC687mAda6/RZidna3jx4//xtUAAABcOJ8LWOcyYsQIjRw5UnPmzPF2KQAAmwxZ9Im3S7gg797Tx9sloJHwuds0nMsXX3zBjUYBAECj4HNnsM72JfeffvpJX3/9tcaOHeuFigAAAC6MzwWsmJgYj79DKEmXXHKJxo0bpz/%2B8Y9eqgoAAKDufC5gPf74494uAQAAoEF8LmDt2LGjzstec80155w7fPiwZs%2BerZ07d6pFixYaN26c7rjjDklSfn6%2BHn74YeXl5SkmJkbTp09XcnKyte7WrVv12GOPKT8/X06nU/PmzdNll11W/6YAAEBA8bmAdcstt1iXCI0x1viZY0FBQdq7d%2B9Zt1FTU6OJEycqNjZWq1ev1uHDh3XvvfcqOjpaw4cPV0ZGhrp06aJVq1Zp48aNmjx5st555x3FxMTo6NGjysjI0N13362%2BffsqOztbd911l9atW1fr0iUAAMDZ%2BFzAev755zV37lw98MADSkpKUkhIiL788kvNnj1bf/rTnzR06NDzbqOwsFBdu3bVrFmz1Lx5c3Xq1EnXXnutcnNz1bZtW%2BXn5%2Bv1119XeHi4OnfurG3btmnVqlW6%2B%2B67tXLlSnXv3l0TJkyQ9Mt9ufr06aPPPvtMvXr1utjtAwAAP%2BBzt2nIzMzUzJkzNWjQILVq1UrNmjVT7969NXv2bL322mtq37699TiXqKgoLVq0SM2bN5cxRrm5udqxY4eSkpLkcrl09dVXKzw83Fo%2BISFBeXl5kiSXy6XExERrLiwsTN26dbPmAQAAzsfnAlZBQcFZw1Pz5s1VVFR0wdtLSUnR2LFjFR8fr0GDBsntdisqKspjmTZt2lh3iT/fPAAAwPn43CXCuLg4PfXUU3riiSfUvHlzSVJxcbEWLFiga6%2B99oK398wzz6iwsFCzZs1SZmamysrKFBIS4rFMSEiIKisrJem883VRUFAgt9vtMeZwhNcKbnUVHNzE42cgCMSeA4nD8f/3K/sajcnpx25dBOLxHYg9n43PBay//e1vGj9%2BvK6//np16tRJxhj9%2B9//VmRkpP7%2B979f8PZiY2MlSRUVFbr//vuVlpamsrIyj2UqKyvVtGlTSVJoaGitMFVZWamIiIg6v2ZOTo6ysrI8xjIyMjRlypQLrv90ERFhDVq/MQrEngNBq1bNao2xr9EYnO3YrYtAPL4DsefT%2BVzA6ty5s9555x1t2LBB33zzjSTp5ptv1rBhwxQWVredVVhYqLy8PA0cONAau%2BKKK/Tzzz8rMjJS3377ba3lT51dio6OVmFhYa35rl271rmH9PR0paSkeIw5HOEqKjpZ522cLji4iSIiwlRSUqbq6hqPuRue/Lhe2/SGD%2B7vW%2Bdlf61nNH6n/1tgX6MxudDP8UA8vn2t5/qG4obyuYAlSS1atNDo0aN15MgR6/5Tl1xySZ3XP3LkiCZPnqyPPvpI0dHRkqTdu3erdevWSkhI0EsvvaTy8nLrrFVubq4SEhIkSU6nU7m5uda2ysrKtGfPHk2ePLnOrx8VFVXrcqDb/aOqqhp2oFVX1zR4G95Un9obe884u7PtU/Y1GoP6HqOBeHwHYs%2Bn87mAZYzRwoULtXz5cv388896//339fTTTyssLEyzZs2qU9CKjY1Vt27dNH36dE2bNk3ff/%2B9FixYoDvvvFNJSUlq166dpk2bprvuuksffvihdu3apczMTElSWlqaXnzxRS1dulQDBgxQdna2OnTowC0aABsNWfSJt0sAgIvK576Btnz5cq1du1aPPPKI9WXzgQMHauPGjbW%2B13QuwcHBWrx4scLCwpSenq4ZM2bolltu0fjx4605t9ut1NRUrVu3TtnZ2YqJiZEkdejQQc8%2B%2B6xWrVqlUaNGqbi4WNnZ2dxkFAAA1JnPncHKycnRzJkzdcMNN2jOnDmSpKFDh%2BqSSy5RZmam/uu//qtO24mOjj5nIOvYsaNWrFhxznX79eunfv36XXjxAAAA8sEzWEeOHDnrF8qvuuqqWrc%2BAAAA8EU%2BF7Dat2%2BvL7/8stb45s2b%2BYPLAACgUfC5S4R/%2Bctf9Oijj8rtdssYo23btiknJ0fLly/XQw895O3yAAAAzsvnAlZaWpqqqqr03HPPqby8XDNnzlTr1q11zz33aMyYMd4uDwAA4Lx8LmBt2LBBgwcPVnp6uk6cOCFjjNq0aePtsgAAAOrM576DNXv2bOvL7K1btyZcAQCARsfnAlanTp20f/9%2Bb5cBAABQbz53ifCqq67S/fffr2XLlqlTp04KDQ31mD91x3UAAABf5XMB69ChQ9bfBeS%2BVwAAoDHyiYA1f/58TZ48WeHh4Vq%2BfLm3ywEAAGgQn/gO1ssvv6yysjKPsYkTJ6qgoMBLFQEAANSfTwQsY0ytsR07dqiiosIL1QAAADSMTwQsAAAAf0LAAgAAsJnPBKygoCBvlwAAAGALn/gtQkmaO3euxz2vfv75Zy1YsEDNmjXzWI77YAEAAF/nEwHrmmuuqXXPq/j4eBUVFamoqMhLVQEAANSPTwQs7n0FAAD8ic98BwsAAMBf%2BMQZLASGIYs%2B8XYJAAD8JjiDBQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzvw1YP/zwg6ZMmaKkpCT17dtXmZmZqqiokCTl5%2BfrtttuU1xcnIYOHaotW7Z4rLt161YNHz5cTqdT48ePV35%2BvjdaAAAAjZRfBixjjKZMmaKysjL94x//0NNPP60PP/xQixYtkjFGGRkZatu2rVatWqURI0Zo8uTJOnr0qCTp6NGjysjIUGpqqt588021bt1ad911l4wxXu4KAAA0Fg5vF3AxfPvtt8rLy9Mnn3yitm3bSpKmTJmiJ554Qtdff73y8/P1%2BuuvKzw8XJ07d9a2bdu0atUq3X333Vq5cqW6d%2B%2BuCRMmSJIyMzPVp08fffbZZ%2BrVq5c32wIAAI2EX57BioyM1LJly6xwdcpPP/0kl8ulq6%2B%2BWuHh4dZ4QkKC8vLyJEkul0uJiYnWXFhYmLp162bNAwAAnI9fBqyIiAj17dvXel5TU6MVK1aod%2B/ecrvdioqK8li%2BTZs2On78uCSddx4AAOB8/PIS4ZkWLFigPXv26M0339Qrr7yikJAQj/mQkBBVVlZKksrKyn51vi4KCgrkdrs9xhyO8FrBra6Cg5t4/AQAeIfDcWGfw4H4%2BR2IPZ%2BN3wesBQsW6NVXX9XTTz%2BtLl26KDQ0VMXFxR7LVFZWqmnTppKk0NDQWmGqsrJSERERdX7NnJwcZWVleYxlZGRoypQp9eziFxERYQ1aHwDQMK1aNavXeoH4%2BR2IPZ/OrwPWnDlz9Nprr2nBggUaNGiQJCk6OloHDx70WK6wsNA6uxQdHa3CwsJa8127dq3z66anpyslJcVjzOEIV1HRyfq0oeDgJoqICFNJSZmqq2vqtQ0AQMNd6Od4IH5%2B%2B1rP9Q3FDeW3ASsrK0uvv/66nnrqKQ0ePNgadzqdWrp0qcrLy62zVrm5uUpISLDmc3NzreXLysq0Z88eTZ48uc6vHRUVVetyoNv9o6qqGnagVVfXNHgbAID6q%2B9ncCB%2Bfgdiz6fzywuk33zzjRYvXqz/%2BI//UEJCgtxut/VISkpSu3btNG3aNB04cEBLly7Vrl27NGrUKElSWlqadu7cqaVLl%2BrAgQOaNm2aOnTowC0aAABAnfllwPrXv/6l6upqPffcc0pOTvZ4BAcHa/HixXK73UpNTdW6deuUnZ2tmJgYSVKHDh307LPPatWqVRo1apSKi4uVnZ2toKAgL3cFAAAaiyDDLcp/E273j/Ve1%2BFoolatmqmo6GSt061DFn3S0NIAAHX07j19Lmj5X/v89le%2B1nNk5KVeeV2/PIMFAADgTQQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGxGwAIAALAZAQsAAMBmBCwAAACbEbC2kKD/AAAShUlEQVQAAABsRsACAACwGQELAADAZn4fsCorKzV8%2BHB9%2Bumn1lh%2Bfr5uu%2B02xcXFaejQodqyZYvHOlu3btXw4cPldDo1fvx45efn/9ZlAwCARsyvA1ZFRYXuvfdeHThwwBozxigjI0Nt27bVqlWrNGLECE2ePFlHjx6VJB09elQZGRlKTU3Vm2%2B%2BqdatW%2Buuu%2B6SMcZbbQAAgEbGbwPWwYMH9ec//1nfffedx/j27duVn5%2Bv2bNnq3Pnzpo0aZLi4uK0atUqSdLKlSvVvXt3TZgwQb///e%2BVmZmp77//Xp999pk32gAAAI2Q3waszz77TL169VJOTo7HuMvl0tVXX63w8HBrLCEhQXl5edZ8YmKiNRcWFqZu3bpZ8wAAAOfj8HYBF8vYsWPPOu52uxUVFeUx1qZNGx0/frxO83VRUFAgt9vtMeZwhNfabl0FBzfx%2BAkA8A6H48I%2BhwPx8zsQez4bvw1Y51JWVqaQkBCPsZCQEFVWVtZpvi5ycnKUlZXlMZaRkaEpU6bUs%2BpfRESENWh9AEDDtGrVrF7rBeLndyD2fLqAC1ihoaEqLi72GKusrFTTpk2t%2BTPDVGVlpSIiIur8Gunp6UpJSfEYczjCVVR0sl41Bwc3UUREmEpKylRdXVOvbQAAGu5CP8cD8fPb13qubyhuqIALWNHR0Tp48KDHWGFhoXX5Ljo6WoWFhbXmu3btWufXiIqKqnU50O3%2BUVVVDTvQqqtrGrwNAED91fczOBA/vwOx59MF3AVSp9Opr776SuXl5dZYbm6unE6nNZ%2Bbm2vNlZWVac%2BePdY8AADA%2BQRcwEpKSlK7du00bdo0HThwQEuXLtWuXbs0atQoSVJaWpp27typpUuX6sCBA5o2bZo6dOigXr16eblyAADQWARcwAoODtbixYvldruVmpqqdevWKTs7WzExMZKkDh066Nlnn9WqVas0atQoFRcXKzs7W0FBQV6uHAAANBYB8R2sr7/%2B2uN5x44dtWLFinMu369fP/Xr1%2B9ilwUAAPxUwJ3BAgAAuNgIWAAAADYjYAEAANiMgAUAAGCzgPiSOwAAgWjIok%2B8XUKdvXtPH2%2BXYCvOYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgAQAA2IyABQAAYDMCFgAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwIWAACAzQhYAAAANiNgnUVFRYWmT5%2BuxMREJScn66WXXvJ2SQAAoBFxeLsAXzR//nzt3r1br776qo4ePaoHH3xQMTExGjx4sLdLAwAAjQAB6wylpaVauXKlXnjhBXXr1k3dunXTgQMH9I9//IOABQAA6oRLhGfYt2%2BfqqqqFB8fb40lJCTI5XKppqbGi5UBAIDGgjNYZ3C73WrVqpVCQkKssbZt26qiokLFxcVq3br1ebdRUFAgt9vtMeZwhCsqKqpeNQUHN/H4CQDwDofjwj6H%2Bfyuuwt9b30dAesMZWVlHuFKkvW8srKyTtvIyclRVlaWx9jkyZN1991316umgoICvfrqMqWnp9cKaZ/P88/LlgUFBcrJyTlrz/4qEHuWArNveg6MnqVf//z%2BLXjjvxGBuq/P5F9x0QahoaG1gtSp502bNq3TNtLT0/XWW295PNLT0%2Btdk9vtVlZWVq2zYv6MngNHIPZNz4EjEPsOxJ7PhjNYZ4iOjlZRUZGqqqrkcPzy9rjdbjVt2lQRERF12kZUVFRAp3YAAAIdZ7DO0LVrVzkcDuXl5Vljubm5io2NVZMmvF0AAOD8SAxnCAsL08iRIzVr1izt2rVLGzdu1EsvvaTx48d7uzQAANBIBM%2BaNWuWt4vwNb1799aePXu0cOFCbdu2TXfeeafS0tK8WlOzZs2UlJSkZs2aebWO3xI9B45A7JueA0cg9h2IPZ8pyBhjvF0EAACAP%2BESIQAAgM0IWAAAADYjYAEAANiMgAUAAGAzAhYAAIDNCFgAAAA2I2ABAADYjIAFAABgMwKWD6uoqND06dOVmJio5ORkvfTSS94u6YJUVlZq%2BPDh%2BvTTT62x/Px83XbbbYqLi9PQoUO1ZcsWj3W2bt2q4cOHy%2Bl0avz48crPz/eYf%2BWVV9S3b1/Fx8dr%2BvTpKisrs%2Ba8%2BX798MMPmjJlipKSktS3b19lZmaqoqJCkv/2LEmHDx/WX/7yF8XHx6t///5atmyZNefPfZ8yceJEPfTQQ9bzPXv2aPTo0XI6nUpLS9Pu3bs9lt%2BwYYMGDhwop9OpjIwMnThxwpozxujJJ59U7969lZSUpPnz56umpsaaLyoq0t133634%2BHilpKRo7dq1F7/B03zwwQe68sorPR5TpkyR5L99V1ZW6tFHH9U111yj6667Tk899ZRO3ZvbH3t%2B6623au3jK6%2B8UldddZXf9nxRGfis2bNnm5tuusns3r3b/POf/zTx8fHm3Xff9XZZdVJeXm4yMjJMly5dzPbt240xxtTU1JibbrrJ3HfffebgwYPm%2BeefN06n03z//ffGGGO%2B//57ExcXZ1588UWzf/9%2B89e//tUMHz7c1NTUGGOMee%2B990xCQoL57//%2Bb%2BNyuczQoUPNo48%2Bar2mt96vmpoa8%2Bc//9nccccdZv/%2B/WbHjh3mhhtuMI8//rjf9myMMdXV1ebGG2809913nzl06JDZtGmT6dmzp1m3bp1f933Khg0bTJcuXcyDDz5ojDHm5MmTpk%2BfPubxxx83Bw8eNHPmzDHXXXedOXnypDHGGJfLZXr06GFWr15t9u7da8aNG2cmTpxobe/FF180/fr1Mzt27DDbtm0zycnJZtmyZdb8pEmTzK233mq%2B/vpr88Ybb5ju3bsbl8v1m/W7ePFiM2nSJFNQUGA9/vd//9ev%2B3744YfNjTfeaFwul9m6davp1auXee211/y257KyMo/9e/ToUXPDDTeYefPm%2BW3PFxMBy0edPHnSxMbGWuHEGGOys7PNuHHjvFhV3Rw4cMD88Y9/NDfddJNHwNq6dauJi4uz/kEaY8ytt95qnnnmGWOMMYsWLfLor7S01MTHx1vrjx071lrWGGN27NhhevToYUpLS736fh08eNB06dLFuN1ua2z9%2BvUmOTnZb3s2xpgffvjB/PWvfzU//vijNZaRkWEeeeQRv%2B7bGGOKiorM9ddfb9LS0qyAtXLlSpOSkmKFxJqaGnPDDTeYVatWGWOMeeCBB6xljTHm6NGj5sorrzTfffedMcaYfv36WcsaY8yaNWvMgAEDjDHGHD582HTp0sXk5%2Bdb89OnT/fY3sV23333mYULF9Ya99e%2Bi4qKzNVXX20%2B/fRTa2zJkiXmoYce8tuez/T888%2BbgQMHmoqKioDp2U5cIvRR%2B/btU1VVleLj462xhIQEuVwuj9Oqvuizzz5Tr169lJOT4zHucrl09dVXKzw83BpLSEhQXl6eNZ%2BYmGjNhYWFqVu3bsrLy1N1dbW%2B/PJLj/m4uDj9/PPP2rdvn1ffr8jISC1btkxt27b1GP/pp5/8tmdJioqK0qJFi9S8eXMZY5Sbm6sdO3YoKSnJr/uWpCeeeEIjRozQFVdcYY25XC4lJCQoKChIkhQUFKSePXues%2Bd27dopJiZGLpdLP/zwg44dO6ZrrrnGo6fvv/9eBQUFcrlcateunTp06OAx/8UXX1zsVi3ffPONOnXqVGvcX/vOzc1V8%2BbNlZSUZI1NnDhRmZmZftvz6YqLi/XCCy/ovvvuU0hISED0bDcClo9yu91q1aqVQkJCrLG2bduqoqJCxcXFXqzs/MaOHavp06crLCzMY9ztdisqKspjrE2bNjp%2B/Ph550tKSlRRUeEx73A41LJlSx0/ftyr71dERIT69u1rPa%2BpqdGKFSvUu3dvv%2B35TCkpKRo7dqzi4%2BM1aNAgv%2B5727Zt%2Bvzzz3XXXXd5jJ%2Bv54KCgnPOu91uSfKYPxXYT82fbd0ffvjBnqbOwxijQ4cOacuWLRo0aJAGDhyoJ598UpWVlX7bd35%2Bvtq3b681a9Zo8ODB%2BsMf/qDs7GzV1NT4bc%2Bne%2B211xQVFaXBgwdL8u/j%2B2JxeLsAnF1ZWZnHf0AkWc8rKyu9UVKDnaunU/382nx5ebn1/Gzzxhifeb8WLFigPXv26M0339Qrr7wSED0/88wzKiws1KxZs5SZmem3%2B7qiokKPPPKIZs6cqaZNm3rMna/n8vLyC%2Br59J7Ot%2B2L7ejRo1YNixYt0pEjRzR37lyVl5f7bd%2BlpaU6fPiwXn/9dWVmZsrtdmvmzJkKCwvz255PMcZo5cqVuuOOO6wxf%2B/5YiBg%2BajQ0NBaB9ep52d%2BsDcWoaGhtc4wVFZWWv2cq%2BeIiAiFhoZaz8%2BcDwsLU3V1tU%2B8XwsWLNCrr76qp59%2BWl26dAmIniUpNjZW0i8B5P7771daWprHb/2dqq2x952VlaXu3bt7nLE85Vw9na/nsLAwj//YnNl/WFjYebd9sbVv316ffvqpWrRooaCgIHXt2lU1NTV64IEHlJSU5Jd9OxwO/fTTT1q4cKHat28v6Zeg%2Bdprr6ljx45%2B2fMpX375pX744QcNGzbMGvPn4/ti4RKhj4qOjlZRUZGqqqqsMbfbraZNmyoiIsKLldVfdHS0CgsLPcYKCwutU8Pnmo%2BMjFTLli0VGhrqMV9VVaXi4mJFRkb6xPs1Z84cvfzyy1qwYIEGDRr0qz35Q8%2BFhYXauHGjx9gVV1yhn3/%2BWZGRkX7Z99tvv62NGzcqPj5e8fHxWr9%2BvdavX6/4%2BPgG7evo6Girj9N7kmTNn2vd30rLli2t799IUufOnVVRUdGgfe3LfUdGRio0NNQKV5J0%2BeWX69ixY36/rz/%2B%2BGMlJiaqRYsW1pi/93wxELB8VNeuXeVwOKwvEEq/fOkyNjZWTZo0zt3mdDr11VdfWaeLpV96cjqd1nxubq41V1ZWpj179sjpdKpJkyaKjY31mM/Ly5PD4dBVV13l9fcrKytLr7/%2Bup566imP/%2Bvz556PHDmiyZMne3xPYvfu3WrdurUSEhL8su/ly5dr/fr1WrNmjdasWaOUlBSlpKRozZo1cjqd%2BuKLL6z7JBljtHPnznP2fOzYMR07dkxOp1PR0dGKiYnxmM/NzVVMTIyioqIUFxen77//3vq%2By6n5uLi4i9rvKR9//LF69erlcVZy7969atmypfVlZH/r2%2Bl0qqKiQocOHbLGvv32W7Vv396v97Uk7dq1Sz179vQY8/eeLwpv/Ooi6ubhhx82w4YNMy6Xy3zwwQemZ8%2Be5v333/d2WRfk9Ns0VFVVmaFDh5p77rnH7N%2B/3yxZssTExcVZ90bKz883sbGxZsmSJda9kW666Sbr14I3bNhgevbsaT744APjcrnMsGHDzJw5c6zX8tb7dfDgQdO1a1fz9NNPe9xDpqCgwG97NuaX/ZmammomTJhgDhw4YDZt2mSuu%2B4688orr/h136d78MEHrV8l//HHH03v3r3NnDlzzIEDB8ycOXNMnz59rFtV7Ny503Tr1s288cYb1n2CJk2aZG1ryZIlJjk52Wzfvt1s377dJCcnm5deesmanzBhghk3bpzZu3eveeONN0xsbOxvdp%2BgH3/80fTt29fce%2B%2B95ptvvjGbNm0yycnJZunSpX7d98SJE016errZu3ev2bx5s%2Bndu7d59dVX/bpnY4wZMGCA2bBhg8eYv/d8MRCwfFhpaamZOnWqiYuLM8nJyebll1/2dkkX7PSAZYwx//73v83NN99sunfvboYNG2Y%2B%2BeQTj%2BU3bdpkbrzxRtOjRw9z6623WvdQOWXJkiXm2muvNQkJCWbatGmmvLzcmvPW%2B7VkyRLTpUuXsz6M8c%2BeTzl%2B/LjJyMgwPXv2NH369DHPPfecFZL8ue9TTg9Yxvxys8WRI0ea2NhYM2rUKPPVV195LL9q1SrTr18/ExcXZzIyMsyJEyesuaqqKvPYY4%2BZxMRE06tXL7NgwQLrvTTGmMLCQjNp0iQTGxtrUlJSzPr16y9%2Bg6fZv3%2B/ue2220xcXJzp06ePefbZZ636/LXvkpIS88ADD5i4uDhz7bXXBkTPxhgTGxtrNm/eXGvcn3u%2BGIKM%2BX/n%2BwAAAGCLxvllHgAAAB9GwAIAALAZAQsAAMBmBCwAAACbEbAAAABsRsACAACwGQELAADAZgQsAAAAmxGwAAAAbEbAAgAAsBkBCwAAwGYELAAAAJsRsAAAAGz2fwHPiNrHPCnflgAAAABJRU5ErkJggg%3D%3D”/>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”common1786709441990653363”>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>51199</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13095</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13025</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9013</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>13107</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>9009</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>54063</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>42089</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>17197</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1071</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:1%”>&nbsp;</div>

</td>

</tr><tr class=”other”>

<td class=”fillremaining”>Other values (3200)</td> <td class=”number”>3200</td> <td class=”number”>99.7%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div> <div role=”tabpanel” class=”tab-pane col-md-12” id=”extreme1786709441990653363”>

<p class=”h4”>Minimum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>1001</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1003</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1005</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1007</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>1009</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

<p class=”h4”>Maximum 5 values</p>

<table class=”freq table table-hover”>

<thead> <tr>

<td class=”fillremaining”>Value</td> <td class=”number”>Count</td> <td class=”number”>Frequency (%)</td> <td style=”min-width:200px”>&nbsp;</td>

</tr> </thead> <tr class=”“>

<td class=”fillremaining”>72145</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72147</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72149</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72151</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr><tr class=”“>

<td class=”fillremaining”>72153</td> <td class=”number”>1</td> <td class=”number”>0.0%</td> <td>

<div class=”bar” style=”width:100%”>&nbsp;</div>

</td>

</tr> </table>

</div>

</div>

</div> </div>

<div class=”row headerrow highlight”>

<h1>Correlations</h1>

</div> <div class=”row variablerow”> <img 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F%2BxYgX33XcfsbGxtG/fns2bNwNw/vx5WrZsyeTJkzl06BDx8fH07t2bgQMHXlHfFQqFQqFQ/PnZvHkzLVu25PPPP79kuR07dtC0aVMsFgvgTUNz8803s337dnHcNzdnlSpVqFq1Kjt27LhqfVV59hTXFadPn9ZtezwecnJyGD9%2BPBEREXTu3BmAQ4cOsX37dtavX0/FihUBGDJkCG%2B88QYvvfQSGzdu5Pjx48ybN4/Q0FDq1q3LDz/8wPz58xk8eDAAkydPZvr06bq8Slu3bgW8Qstjjz3GnXfeCcDo0aN58skneeGFFwgJCQHggw8%2BYMWKFXTu3JnRo0djt9vp3r078fHxANSsWROHw8G7775L165dCQ8Pvypzcjn07duXcePGUbduXYqKinC5XJcsX65cOSIjI8V2v379mD9/PsuXL6d58%2BYlnvfcc88RHR1NmzZtrrjPmmCmJV8OCwtj3759jB8/nilTpjB//nwOHTqE1WolMDCQ9PR0Tp48yUMPPQRAamoqL774IrVq1cLj8VC3bl369etHcnIy0dHR/POf/2Ty5MlMnjwZj8fDrl27WLFihd/9O3v2rEmLGBkZSaVKla547AqFQqFQKErmSv8HP/roo36VS09P5x//%2BIduX4UKFYTp59mzZ01tVqhQ4aq8u2kozZ7iuuOBBx4QWqVVq1bx/PPP4/F4SExMFGUiIyP58MMPhaCnkZOTA3i/tDRo0IDQ0FBxrGnTpuJLDMD69ev56KOPuOeee3R1aC/%2Bvl9qmjRpQlFREampqWLftm3bSE9P5/Tp0/zwww%2BMHz9eCKMaXbt2ZcaMGaIfaWlpPPPMM8TFxZGQkMDkyZOF4LVgwQJ69%2B7Ne%2B%2B9R8uWLWnWrBnjxo1DS6X53//%2Bl%2BbNmxMSEkJERAQPP/wwa9euBaB3795MmjRJtCsLcvLmm2/SqFEjPvnkE5599tnSLoMJu91OQEAA4NW%2BvfXWW7Rp04YmTZrwzDPPkJaWxuHDhzl69CgxMTEsWrSIO%2B64g2bNmvHf//4Xp9MJeLWGRrPZhIQEFixYoNs3d%2B5cAGrXrk1YWBiZmZmA97oBjBkzBgC32014eLgQTrUH7DvvvIPL5WLv3r0AHD58mBYtWvDZZ5%2Bxc%2BdOpk2bBiDmt6Cg4JIO2kY%2B/vhjunTpovv54osv/T5foVAoFIrfhXPn/ri2LZZr8vP555%2Bb/geXpqW7HPLz8wkMDNTtCwwMxOFwAN53h0sdvxoozZ7iusLlcvHNN9/wzTffAF4Bo0aNGoSEhJCRkQF4X%2B6/%2BOILPvvsM9LT04mLi2PUqFHMnj2bW265hZiYGG655Rb27NlDfHw8CQkJvPbaa1SoUIFffvmFhIQEVq9ezWeffQbASy%2B9JNo/d%2B4cHo%2BHwsJCIiMjmTZtGl988QVnz57F7XYzc%2BZM4uPjWbBggdA8aVqsYcOG0aVLF8aOHYvb7ebjjz829XHUqFHExsZSUFDAAw88wLRp05gyZYro57Zt26hYsSKfffYZu3btYvjw4QQHBwPw73//W/QzPz%2Bf%2BfPnM3/%2BfPbt2wd4Bdw777yTjIwMnbZS619mZiZ9%2BvRh27ZtDB8%2BnDvvvJN9%2B/YxYsQIBgwYwIQJE8TDKTU1lVdeeYUzZ86QkJBAfHw8Bw4c4MUXX2TEiBF88803OBwOqlSpwrPPPktKSgqdOnXi4sWL7Ny5E/BqTv/973/z8ccfM2vWLBYuXMjjjz%2BO2%2B32ay1oD88jR45gt9uJiYkhNTWV/Px8zpw5Q35%2BPna7HZvNRm5uLjfeeCMnT57k4sWLAGzatEnnw3nw4EEOHjyI1WoVAqPNZhNCqM1m4/333%2BeBBx7wq38y3O5La0wVCoVCoVBcOY888ojJJcXXIulqERQUZBLcHA6HeDcr6bhmBXY1UJo9xXWNx%2BPh3Llz5OfnExQUBHiFiClTppCfn4/H4%2BHQoUM88sgj7N69m6FDhwKwZcsWXC4XbrebdevWMXLkSAIDA8nNzTWp1jMyMsjOzga8GitNQFi7di3Tpk2jsLBQaH%2B%2B/fZbdu/eTVFRkamvbrebQ4cOldjHXr16cfLkSaGR%2BvLLLwkPD8ftdrN7924WLVqEy%2BVizJgx1KlTh06dOhEbG6vTJpZEbm4uKSkpFBQU4PF4OHv2rOiTb1%2BDg4Ox273fiIKCgti7dy8nT55k1qxZuFwuMfbXX3%2BdI0eOkJeXxzfffMPbb7/N8OHDSU5OZs2aNTgcDmw2Gzk5Ofzvf/9j3LhxQsjSGDZsGJMnT%2BbMmTPY7XZyc3OZNWsWW7ZsKXU8ALfeeisAISEhWCwW3XXTwh47nU7cbjdut5tt27YBcPHiRS5cuEBhYSHR0dG0aNECgObNmxMUFMQDDzxAjRo1AMQxrS5t3vyhY8eOvPXWW7qf1q3vMRecPPmS28VLS8/ChbpNw9QCUKzoFMjiB5mW6Vdf6TaLFeGXPslwDgDGIDiS%2B6E4UNmvzJ9fev9mzNBtSq1gDCbIsvm7cMGwY84cU5niW/5XpkzRbxsnGMwdljVe/FGqxHMku0zXwecjhSA3V7cpi0NktM42nAJIuizpn6l54w7TxTWvP2k8K2M9krYxBkswBjkwzi%2BS%2BZOsWVMZ2eIvKNBvG9esrL/GfbJniGGxyS4vRi2AZG2ZrO8N9cq6Z7qJJNfOdKLMzN/QP1ORw4fN5xgXgeyeOnpUt3nmjLmI8blgusH9efidPGkqYpoKybiN1fizBIzXTjadxnNka8K4z7g8ZfuM26XElft9sFqvyU%2BlSpVo2LCh7udauFFUrlxZKBs0MjIyRFslHb%2BagqcS9hTXNS6XSwhibrcbj8fDhx9%2BiMViYcyYMSQlJREREUFBQQEPPvgg9evXF%2Bc2atSI2bNnU65cOZYtW0Z2dnapER41ARG8Zn92u523336bZcuWCVPMtWvX0qlTJ7H9zjvv6OqQ9fH2228nJyeHzMxMmjZtCniDhGhjCw0N5dChQ1SoUIGwsDBRV1hYmAjveymys7PxeDwMGjSIb7/9VmjzXC4XixYtEuWWLVvGyeJ/er5fncqVK8fcuXNFgJKIiAgmTpzImDFjsFgstGvXjr59%2B3Lq1CnKlSsHeIXV22%2B/nby8PPLz86lXrx7wq0bO5XJx8OBBbrvtNt577z08Hg92u134RZaGFujF4/FQVFREVlaWOCbLYaOZwxYWFnLkyBHAq6ndvXu3OB4WFsbq1avFORs2bNDVoWn5/CE5OZkXXnhB95OSsspccNCgS24X%2B3zrKfY71Chb1lykfHn9tuwjYrHV7a907arb9FlqJZ9kOAcA4z8xU0PgY0Ht5eGHS%2B9fv366zagoSf8M97Bs/oqX6K/07GkqY3KhNQbnMU4wmDssa9xgWi6bG%2BMu03Uo/rClo0wZ3absPcL4eDOcAki6LOmfqXnjDtPFNa8/6UdtYz2StqlZU79d/GFGYJxfJPMnWbOmMrLFX/ylXmBcs7L%2BGvfJXjYNi012eTGYgcnWlunfl6FeWfdMN5Hk2plOlP2fNPTPVKR2bfM5xkUgu6eio3WblSubixifC6Yb3J%2BHX7VqpiKmqZCM21iNP0vAeO1k02k8R7YmjPuMy1O2z7h9FZVLl881EvZ%2BL%2BLi4ti2bZv46O/xePjpp5%2BIi4sTx33fa9LS0khLSxPHrwbKjFNxXaJp8RwOBwEBATgcDrKzs8nIyKCwsJCbb76Z119/nbS0NFwuF3a7XQgxAFWrVmXXrl306dOHhg0bcuLECaEhMwYnKSoqEjexb/t169YlPj6ewYMHk5OTI8qkpaWxZMkSIYQ9//zzWCwWcdy3j2PHjiUtLY0bb7wRq9VKQEAASUlJ3HPPPcTHx3P%2B/HlOnTolBBqj3bcvNpsNm82G2%2B3G5XLh8XioU6cOgPCle%2B2117BYLERERABgtVrp1q2bqKNixYpCK7Z3716OFn9VveWWW%2Bjdu7fQ7MXExDBx4kTOnDmDzWYTGq/7779fmL8%2B/PDDIjpVRkYGaWlp4pqB17fO5XKxcOFC5hd/IT9//rxJ2whyIUtLdeF0OrHb7brr5vv3DTfcwIgRI0RUVYfDIdZPTk6OmNMzZ85w7tw5IiIiaNKkCeAVpoOCgjhX7M9g9AG9FHIhXKamUygUCoVC8VchPT2dsmXLEhwcTPv27XnnnXcYO3Ys//znP5k3bx75%2Bfncd999APTo0YPevXvTpEkTGjVqxNixY2nbtq2wILoaKM2e4rpEM6e0Wq1CcLFYLOQW2yVt3bqVc%2BfO4XK5KFu2LC6XS6ftOX36NIWFhbjdbvbs2QPArl27hHDii8fjEcKDx%2BPB4/HQqFEjkpOT%2Be6774Qppsb58%2BdNwsrtt98OwNGjR3V9zMrKwu12C6G0oKCAG264AfD6xeXm5uJ0OoVmrqS5AK%2BA43A4cDqdoqxmNuqrDfR4PCKYiSZ4ajRt2lSU/f777zlTbDOzdOlSUT/A7t27KSwsxOVy4XQ6hYnChx9%2ByIEDB0Q7mhb03LlzIjiOhpbqwddHT/s718e%2BLDc3V6qp0zSIDoeDwsJCIcABQkMbGBjI2bNnefbZZ7EWf%2BlzOp3UqFEDi8VCcHCwaPP06dMEBATQunVrIfjn5%2BcLQS8oKIgzZ85QILOVkRAq%2BULurz%2BiQqFQKBR/C/6Cmr1WrVqxZMkSwPt%2BNX36dLZu3UqXLl3YsWMHH3zwgXgHiI%2BP57XXXmPKlCn06NGDcuXKMW7cuKvaHyXsKa5LQkJCCAkJweVyiZfxcuXKCUHJ6XSSn5%2BPzWbDbreLFA1aGF6Xy4XNZsPhcAgB5sSJEyYzTk1oMr6kP/rooyKKo3aO5utmzNUHXo0XwIULF8TxoqIicnNzsdlsOiFT8yssKioSQpnWfmZmpi4pu9EO3GKxEBgYKPpksVi47777hOYtODhYCD3aOHzr%2BOCDD4RQ1qBBA6HZ279/P5mZmWKujMLssWPHaNasmdBCauPR5mTSpEkmZ2Rt/mRC0S%2B//EKnTp1YsmQJ//nPf0Sfz549y6lTpwgNDRWRUz0eD9WrV9dp87RgNQ6HA4/HQ0BAgNC0RUVFUaZMGZo2bYrdbuemm24Sc1WtWjVuv/12qlatCug1hIWFhVSqVEk4XZfGNfXZM/gK%2BeOz548rDl/qo4X%2Brj57knqUz56Xa%2BWzJ1sTl%2BWzZ/Qn%2B7P77En8Q5XPXjF/cp896T1/jXz2TD6tfzKfPWOZv6zP3l%2BAffv20bJlS912ly5dxHbjxo1ZuHAhO3fu5Msvv6RBgwa687t06cLatWvZtm0bkydPprzMZPkKUMKe4rpE07BZLBYh2ISHhwstlSYcuVwuITCdOnWKVq1aif2PPfYYNWvWFILLgAEDhOAxa9Ysjh8/zuuvvy5t//7779dpisqXLy8Ew8zMTDp16qQTqjTfO4vFwnPPPSfGcNdddzFlyhSdeaYWaMTpdNKmTRu%2B/PJLUVdhYaEwK83MzOTw4cNCkNPKOBwOIaRYLBZycnJEmYKCAl2/ZGhzGBERIbR04BUUNa2fx%2BNhwoQJIpG8b24%2BzWQ1JCREmF%2B6XC4R8lgTBDXnZZm5o81mY9%2B%2BfQwdOpSqVasSFxdHVlYWXbp0ISsrC5fLpRPETpw4IdqS5SssKioSx7W1MWLECGw2m05onDlzJoGBgcyRvPwDOlPg0rimPnsGXyF/fPb8ccXBx6QXfmefPUk9ymfPy7Xy2ZOticvy2TOal//ZffYk/qHKZ6%2BYP7nPnvSev0Y%2Beyaf1j%2BZz56xjPLZ%2B2N89v4M/L1Gq/jbYbFYRPqFGjVqCK2UxWIRQoXmrxYZGSnSEFgsFmbOnMmRI0eE8BMRESEEtnfffZeOHTvy448/AkgFpAoVKgC/CpSa8JGTk8OSJUt02kDNLDEwMFD4tFksFlavXs0zzzwjBDyLxSKEksqVK3P8%2BHF69Ogh6k5ISGDq1KksWrSIAQMGYLPZhKmlFqDGF7fbTdeuXYWGDfT%2BbxMnTiTK579nTEyMEHg/%2BeQTsb9Pnz4EBwcLP7mgoCCGDx/O888/L%2BZn9uzZwK/a0HyfT4YNGzbko48%2B0h0Pk0oSXmbPni0S1p87d45Zs2aRkpJCVlYWzZo1E3XJqGb4p12mTBmdRlO7FikpKQQHB4vxlilThipVquDxePj6668Bb%2BL7AQMGiLoCpG9LcpTPnkKhUCgUpaCEvSvm7zVaxZ%2BChIQEYmJixE/Dhg1p3749M2fO1JVLSkqiW7duxMfH06pVK1566SURxAO8ScSNOVLAK5SVK1eOQYMGsWLFCsaNG8fp06e56667hIBntVoJCwsjMDBQCBe%2BppJWq5UogYAKAAAgAElEQVTy5csTGhoqfL3Wrl2ri0RptVpF4nSbzabzbWvcuLHO/NFmswlBwGazkZmZqWtPixwZHBws9mvaSe1v7ffhYnOXCxcu8NRTT9G4cWNRT1paGidPnqRmzZo88sgjQti6FLfffjtdZdoXvP6FC33C%2BO/bt08IKStXrhT7jxw5wmeffcbgwYMBb/oCX7NXm81GtOELrC/Jycn89NNPun2X8l%2BrVq2a0IDu2bOHXr16sX79emrVqsVHH31Es2bNSgxW4yu8RkdHExwcTEBAgDDB0NqtVKkSLpdLCIHa9Tt16pTQ9h47doypU6eK%2Bgql9lVylM%2BeQqFQKP4SSG32FX8VlLCn%2BEMYOXIkKSkppKSksHLlSvr378%2Bbb75JUlISAOPGjWPcuHF0796dpKQkpkyZQnp6Or169ZIG4/DlwoULnD17lvfee4%2B7776bESNG8Nhjj9GxY0fhs%2Bdyubh48aIIWALmF/WuXbtStWpVoYHauHGjeOHv0aMHOTk5zJo1C/CaAfoKXTExMUJIiIqKYsCAATRs2FCUnTNnjk4LpGnfypQpI/oIMH78eJ2jrq8d94033siECRN09Rw8eJDnn3%2Bejh07sm3bNnr06EH16tUBufaxfPnyXLhwQQiYHTt21B1/7bXXRL46gJkzZ4r6fPuyYcMGhg8fzvLlywGoU6cOy5Yt49577xVjbt68OeD10wsNDaVZs2bCT2/gwIH861//Esfh0pq9cePGiZQVhYWFQsNqt9tp2bIlTZs2FYKTr6Bdu3Zt1qxZI%2BrRAuLExsYKYVMbX0REBNWqVRPaYE14rFy5MmV87HcqV66s04z6i8xnr21bic/evHmX3Jb67Bl8VGS%2BGkb3F6m/jlH7%2BMEHuk2pbGt8KTDk/APMjjWShG4m95y5c01lTGP30TaD1M3GL785kz/b99%2Bbyphcvwz%2BjDLfML989oyNSxx2jNfT5JNp9N8C03WR%2BewZ/XP8cc/yywfJuFAkjkDGXdI4R8b5kjW%2Bf79%2B2%2BizJ3HqMvlf%2BqRX0TD5ccqSEBr7s2zZpY/L9hn7C6ZrJ73vDPX44%2BdlXDjSZ0BxGpqS%2BiI9UVZRaT57spvVuLBl//sNPpo%2B34N/pdhaRmBc/P4sdB%2BXBQ3TM0oy6f64mZb2WJBdS39y6F2NPHslxH37fVGavSvG4ikphJ9CcY1ISEhg0KBBOudVgCeffJKgoCCeeOIJevXqxezZs4XmDLxmf%2B3bt6dz584MHTqUBQsWMHnyZF3es5iYGKpVq4bT6aR37948%2BOCDREZGCg1TRkaGiHwp44033uCll14SfmW%2BKRHAq93RUhw4ZC9Uxdx8882kpqYKYVILAqOZW65YsYIuXbqIROJPPfUUH374IRUqVCA5OVn0sWzZsrpk4zfeeCP79%2B/H6XTSqFEjjh49SrNmzcQclC1blvz8fKxWK263m2nTpvHUU09d8npYrVa6du3KF198cclyWtmaNWty5MgRypQpo4uK6UubNm0YMmQI//znP6UJ5MErhGnz0aNHD1599VViYmIAqF%2B/PvHx8cKPT4Z2bbQxO51OoqKiuHjxoohcejmMGDGCXr168fLLL7NgwQKxv0qVKqxdu5a8vDzi4%2BNN52lzrpkCl8b48eN1prAAgwcPYdCggSWcoVAoFArFH8DRoyb/yN8NmdP51UAWuew65e8l2ir%2B1NjtdpFHrnHjxjpBD7ymk%2B%2B//z49JcESfKlSpQp2u50KFSoQFRWli6CpmWRarVbKlClDQECATiuTmJgIIAS9gIAAocUJDg4WWkAtwIiWCBygbt26Igro%2B%2B%2B/T8WKFXE6nVgsFoKCgoRgExoaSs2aNXWBQjSTvho1aghTSBkOh0NEcUpNTSU7O1sn7M6ZM4dZs2bRqVMnXC4Xw4YNu%2BRcgdd0sCRBLyAgQJeywO12c7z4y7PH4xGRJ40mid999x1PPPGEGL/MpNLtdlO2%2BCEeHh6uE6zAq4W7FJoQfsMNN4jrcssttxAbG6u7LkZk6TN8iYuLY8OGDSxatEg3dm2daOGUwTtuTWvodrtLrdsX5bOnUCgUir8ExsBHvydKs3fF/L1Gq/hTUlRUxPLly1m/fj133XUXqampNGrUSFq2QYMGIkqjDJvNRq1atUo8rgkXkZGRhISEYLPZhIAWGhqq87OrUKECQUFBQujs0aOHEPw8Hg82m41bbrlFCASRkZHiJyIigsDAQKpWrUpgYKDI%2BabVC16h1Nj3atWqiXx%2BISEhJuHh4MGD7Ny5E/Am8PY1%2BQRvNM2bb76Z559/nqCgIAoLC3UmhzI0wbN8%2BfKmpOAvvPCCMJe0WCzcfffdwk8yLy9PaM%2BWGcyVAgICCAkJEfPkG2bY1ydRSyoaEREhkrXb7Xbsdjt16tTRBdIJDw8nPDyc4OBgbrvtNlGflloDoFmzZrRt25a0tLRSBa/atWsTHh5OYGCgEEYtFguxsbEsXryYkJAQnX%2BfNo%2B%2BvoUFBQUUFRXpxqRQKBQKxXXF%2BvV/XNtK2Lti/l6jVfxpeOWVV4iPjyc%2BPp7GjRvz0ksvCb%2B6ixcvXtJfy5dTp06JeuLj43G73SQnJ0tz2WlYLBYyMzMZM2YMCxYsIDAwkKKiIvLz83Uv9xMmTOCjjz5if7EPSMeOHXnjjTcAb8TK6dOnY7VaxQt%2BQEAATZs2pWnTpoDXV69OnTp8/fXXTJo0SdTbpEkT4FfBE7yCRFRUlMj5Bl6z1WyTM8mvpKWlkZWVpdvXvXt3HnroIV599VUiIyOpXbu21NQyPDycN998kzJlynDs2DGys7PJzMw05eVr1aoVW7ZsAbyCzIoVK1ixYoWpPm18ml%2Biw%2BEQgqDT6RT9DAgI4JFHHhHX98tiP6e33nqLecW%2BaO3bt%2BfDDz%2BkVatWOs2atjaqVKnC5s2bAa%2BA7Ku5nT59OitXriQjI4OIiAgqVqxYorB7//33k52dTWhoqPhAoAn%2BVapU4Z577tFp3zQt5hEfHxbNL/ByhDwVoEWhUCgUCsW1Rgl7imtGSVE3c3JyGDJkCElJSSQlJTF8%2BHDq1q3L3LlzadWqFRcuXPAr6iZ4BbcBAwaIuipVqsTgwYOl2r/evXszadIkrFYrRUVF/Otf/6JDhw6cO3dOmOD17t1blH/22Wd58sknCQ0NpWrVqjRo0IB27dqJQCI9e/Zk9uzZwrzP6CM2fPhwDh8%2BzH333aeLdnno0CEA4Z8G3oAjJ0%2BeZMmSJeTl5ZmEh%2BjoaBE4xJcbbrgBu90uNEvlypVj7969fPvtt5w4cYI9e/ZINVzZ2dm8%2BOKL5ObmUqlSJVNCc42HH35YBKEBaN26NbfddpupTs0MVDPxtNls9OjRQxzXBKSioiIWLlwogp48XJyH6rnnnhNC8Pfff89TTz2FzWajfv36Yi5mz54topFarVYaNmyIy%2BXSCbNpaWkigI8mvBqFXa2%2B6dOnA5CVlcUvv/yCxWIRmtJnn32WyMhIbrnlFtOcXDBFavgVmQBXErIALfe0bWsqV5oDvcx53%2BSZ//HH5jLGDwnFwXV0GIOiGOuVpbgwRC2Q9s%2B487vvzGV%2B%2BUW3KXP9NA7B5KYpS8JsbNuPSBbSxMKGQBrGtqXfaS6jbZlfiXFYprgV/iQ6l0T5MC5t2Zybvh3JfGONgzd%2BDJF8HDHOsfT7iSHhuOy6GMdgnCtpcBODSbX0evuR2Ny473KCc/gTaMOfivzpnz%2B50I3xWC4nwIisTGlzBZgXm2Ry/Ij9U2oZmUW9aQyStk3nyf43GH37ZR001u3PoEo7B8yLX1aPcZ9xWwuwZfiw/LuiNHtXzG8PIadQ/AZGjhxJhw4dAK%2BGZ%2BPGjQwfPpyjR48SHR3NuHHjSEpKYtiwYbRo0YKsrCyGDBnC0qVLGT58uMlMMTExkYyMDJFSwGKxUKFCBRHW/7vil8Z58%2BbxxhtvMGLECHGuxWLhl19%2Bwe12Y7VahQatoKBAmBu%2B%2BeabgNen74477mDgwIHcfffdgF4402jYsCEDBw6kf//%2B7NmzR%2BSSA3jvvffEeampqYDXZ/DEiRMUFhbqolleirCwMAoLC4mJieHEiRMAIkBMRkYGgYGBPPnkk0yfPp28vDwCAgJwOBw6gbFcuXJcuHCB8ePHM3z4cCIiIoS27azhBcqXfMODX5vfxx57jPnz5wuhTUPTRLpcLipUqECrVq1ISUnRldGint5%2B%2B%2B3Url2b2NhYDh48KARCTWunzZeMoqIicnJySEhIEH0C7xrL9PkHFxoaKrRzdrtdl0PQN3CMZnbqG7H0tttu48knnxTbmiluSQFnwBuZ01%2BSk5OlAVpifaK6QulJb2UJd00Zdp94wlzGmBX8Hkkk0EcfvXS9PvMjMKxraf%2BMO1u3NpepX1%2B3KUthaByCKWmw7B4ztu1HxmLptxBDsm5j26ak65fZtiw4gXFYhsekf4nOJfeWMde0bM5NinJZpmbj4I0fnCQfoIxzLLXCNnzEk10X4xiMcyV9pBg%2B0kivtx%2BJzY37/MkB70/ia1NbflTkT//8yYVuNLLxZwyya1daEnDpdTEuNsnkGM%2BTXbvSysi%2B0ZnGIGnbdJ5x8YE52b2sg8a6/RlUaeeAefHL6jHuM24/9JD3dwmuNYq/Bn8v0Vbxu1O2bFnhx1alShUeeughgoKC2L17N1u2bCExMZEpU6bQrVs3oqOjiYuL44033sDhcDB%2B/HhdXbm5uSQmJopAJ/7gm%2BKhcePGNG3aFI/HQ4MGDUhOTqZDhw7YbDbatGnDkiVLhBDp8XhYtmyZENLGjh3Lp59%2BSkBAADabTUS4HDx4MG2LtTG5ubnCxDE5OZk1a9bw8ssv66Iztm3blgsXLphe8i9FTk4Op0%2BfFrn4APr37y%2BEVYfDQf369bFYLDRt2lRo3TRfQY/HI4KdZGVlMWHCBFpLXq5/S0LwxMREmjRpwtixY01CsCYw/fe//%2BXw4cOMHTtWp33U6NChA9u3bxcpGUoi2OefWkREBB9%2B%2BCErVqzgwQcfZPXq1Tqhtnbt2jpfyMjISPG3sX3N/HPFihVUqlRJF6jnzJkzPPfcc7qIq7GxsYA%2BT5827xpGn8dLoQK0KBQKhUJRCkqzd8X8vUar%2BNNgs9lKjLp5yy23cPfdd7NmzRq%2B%2Buorzp07R2FhIf369cNqtdKvXz%2B/25k6dSqtWrWiVatW7NixQ%2BRYO3r0KOnp6SxcuJD69euTlZVF//79GTVqFOAV9vLz85k5cya1atVi1KhR9OnTh6KiIlwuFx9%2B%2BCEA/fr1o3fv3thsNkJDQ3nvvfdYt24dn332GTfddBOjR4/WhemPj48nLi6OefPmsXfvXsArMNSrV08IDRaLpVSfxY8%2B%2Bkin9Xr11VexWCxUqVJFaK8qVqxIbm4uVquVHTt2APD2229TsWJFaVoCo8Zq1apVNGjQgKefflq3XwtmkpKSQmpqKrfeeqsunYWv39mpU6cYO3YsHo%2BHt956i3vvvZcqVapgsViwWq1s376dFi1aAJi0hBoBAQFCOL3zzjtZvnw5PXr04P333yciIkJXNioqioMHD4rgNr5zWlRUJAS6r776CpfLRWBgoDBTzcrKEn2YNm0a58%2Bfp1q1aqJurQ%2B%2B%2BRh/%2BeUXnbAZLPssXwLKZ0%2BhUCgUfwlUNM6/NMqMU/G7UVRUxJo1aygsLOTGG29k7969xMXFScu%2B9957fPrppyQmJnL48GFcLhetW7dmwoQJfps/amimpIMGDSIuLo7ExESys7OZOHEijRs35vTp0yKpuZGffvqJfv360ahRI6ZOncrevXux2%2B28%2BOKLHDp0iG%2B%2B%2BYZ33nmHtm3b0q5dOyIiIhg9ejQnixPEPvTQQ9x///1CE2ixWJg1axYffPABc%2BbMAbwCg2%2BgF4/HU6Lgo5GXl8cnn3xCVlYWQ4cOFbn4FvoksD516hRBQUHMmDGD%2BfPns3jxYpF/UIZv3jur1cr//d//sXfvXvbs2QN4hS6Px6PTdn3%2B%2Bed4PB6TSaS27fF4yMvLw2azUbduXdauXcvp06ex2Wzs2LGD9PR0XnzxRRwOh2g7Pj6egoICrFYroaGhFBUVCVPNJUuW0LRpUyZOnEjlypXp37%2B/bq5%2B%2BOEHMc8ej0eYhhp9IKdNmwZ4hauWLVuyatUqwGuG%2Buqrr3L27FkmTpzIunXr%2BOqrrwBIL07E%2B4vBl8yXksxOZXTs2FEXpRSgbt36pnIej96kyLjN5MkwaNAlz3G5zCZaxn2yMkVFevOrUvsiqUdWxp/%2BGQtdVlv%2BNC4p4884SytjnDt/6/Vn3Ka6S7tQYJosWf%2BM8%2BlPGb/658eYLmvckoVT6rqWLbbLWGuyuSE/X2cGZ6pHdpJhnx/dk89fYaHelM%2BPtq7W/Xw5N/3v%2BSwxnWe4Tn6dI5lPv9bE5TxM/Hg4X41nlD9lpM9mxV%2BOv5doq/jdkUXdfOqppxgzZswlo25arVb69u3LokWLeO211/B4PHz77be0b99e1DdmzJgSNSGrV68WURg1U1K73S7808qVK8e%2BffuoXLkyZ8%2BeZc6cOUL4WrhwIQ8V26l7PB5mzpzJf/7zH06fPg14X%2BjLli3L6NGj2bp1qwgGExgYyKhRo1i9erUwiYyNjTXlmAsKCmLw4ME888wzYl9pGh1ZRMmBAwfSoEED3nrrrRLPKywsZObMmbzzzjvMnDnzkm306dNH15%2Bff/5ZJyQVFRVRs2ZNpk2bRs3ir3wOh8OkEWzXrh1Dhw7VmYW63W66d%2B/Ojz/%2BKLR8mnbR4/Ho2nG73bjdblwuFx6PR%2Bdn53A4GDt2LM2aNaNGjRqcPXtWGoxHbiL56zynpKRgsVhwuVwieqrWVnp6OlOnTuX8%2BfMkJSWJc7UUD5fy2Tt8%2BHCJx4wkJyfzwgsv6H7WrllpKlequ5NB0JOVkf2zvhz3MT9cr0z1yMr40z9joctqy5/GJWX8GWdpZWRW0f7U68%2B4TXX74zRlmCxZ/4zz6U8Zv/rnx5gua9yShVPqupYttstYa1Krd4O/k6kePxze/OiefP4uw0Hwat3Pl3PT/57PEtN5huvk1zmS%2BfRrTVzOw8SPh/PVeEb5U0Y0vX27%2BeTfC6XZu2L%2BXqNVXFOM0TdPnjxJWFgYffr0ISkpiTVr1rBlyxbq1atHt27dOHbsGImJibz00kulRt%2BsVKkSSUlJlC1b1hR9syTS09PJysoSAueWLVtYuXIlYWFhREVF4XQ6OXbsGJUqVaJnz5589NFHVKtWjQYNGvD666%2BLaJIulwuHwyE0Wnl5eaxYsUJnChkVFUV8fDyJiYl07NhRCATLly/XaZ6%2B/vprMT/jxo3ze24LCgpEEnbN1ywvL4%2BuXbsyevRo6TlauXXr1rF48WIRKRMwRd8MDg4Wwu6lOHToEIMHD9YlhPc1lwRYunQpEyZMwOl0CmHeYrHgdDqJjIzEYrGwevVqEWwmOTmZu%2B66SwiHZcuWJTg4mOjoaFq2bKkzd7zhhhvo27cvK1as4NSpU7hcLl2evUtht9uFsKcF5alTp47QFGsC5%2BOPPy765evHp%2BFyuXR5Ey8XmUCqPPYUCoVC8afDFPJX8VdCCXuKq4pvQJTKlSuTkJDAjBkz2LZtG1FRUbz55puMGzeO7t2788ADD1CtWjXS09Pp1auXCJkPXv8tLZk3eF/Uo6OjsdvtIvqmtn0pLBaL0BRpmhyHw8G%2BffvIz8/n%2BPHj5OXlUaNGDVJSUjh16hQNGjTgpptuEukE7HY71atX59133xV53bZv386zzz4r2lm9ejVTpkzh/fff55lnnqFly5YAnDx5krfffluU00wiffGNABkQEMDs2bNN2sDAwECys7MJDAykcePG1C%2BOVHjx4kWd31pYWBhWqxW73U6NGjWE1mrYsGEkJiYKgUozTdQoKCjQmWdqhIaG6gKSgFezpSVAr1ixIs2bNxcRS33xeDwil2CHDh1o3rw58%2BfPJzo6mvXr14tciNHR0aLf4BXSCwoKOHLkCCtXrtTlGjx37hwnT55k3LhxtG/fnry8PJ2mzWKx6PLuaWOwWq1CQ%2Bhrann06FHi4%2BN1eRl//PFHAKpXry7V4gUFBQkB3mi2aWxboVAoFIq/PHXq/HFtK83eFfP3Gq3imuMbfdNut9O0aVNuvfVWli9fboq%2B%2Beijj3Lw4EGeeuopnE4niYmJgNf0MCcn5zdF3QSzZlETXjRtjubH5fF4uOGGG7BareTm5nLx4kVuueUWHA4HI0aMwOVyiR%2BtP/v27aN///6AV7PTrVs31q5dy/r16wGvD9ipU6d4/vnnqVChAps2bQK8aQ20vHpaH8Cb1PyVV14xjaGoqIg%2BffqYBC/fBOX79u2jatWqgFe48PX7cjqduN1uPB6PyAcXEBDA66%2B/zv79%2B4Xwcv/99%2BvqDwwMFAKmr5auoKBA7A%2BXxJLPyMhg8%2BbNuvQHvuenpaVhsVh0gU7atWvH%2BfPnhYAVHx9PcnKyLvAJeCNvJiQk6NJvNG/enEqVKvH888%2BL8kYTWN%2BchhaLRfgMamjROm02GyNGjCApKYkKFSoAXsGwR48enDlzhh9//FF3nm8ZbW04nU4a%2B6RKkM1RSagALQqFQqFQlIIS9q6Yv9doFX8IdrudgIAAU/TN%2BPh4unXrxtChQ3n44Ydp27YtmzdvFmkJfkvUTQ2jeZ3H48HtdnPrrbdy0003UaNGDQIDA7nxxhvFyzt4TUfBqxn79NNPS6xf8yP7%2BuuvGTZsGJGRkSxdupQpU6ZQuXJlnaABiJx%2Bvv0BbyAQzfzS9wU/KCiI//u//8NqtYrzQkNDxXlut5v8/Hz2798v6lu7di2AiECp9TMzM5N9%2B/bh8Xj4z3/%2Bc8l58zVTNfrPHTt2DEAXzdKIr0mrFpFS63/FihV1/nudOnXSnZuXlycV7LOysli9erXIBwiwa9cu0tPTRYoMIx6PR5hh%2Bo7FV0OnBW1xuVx06NCB6OhooakrKChgw4YN7N69m5iYGGk6irp164q/XS4XO3fuFNuZsiTeJSBLqp6QIMl1t2TJJbel30QMqT1kuXRNSkuJFtOUWLj4g4yGNI5QRoZ%2B%2B/vvzWWMJkGStovj4fyKQRstPW/ePN2mj3V4yedIMkCbxuWT%2B1HDlJ7y22/126YBmNuWJg43zp/sAhtMgE3T50eic3/mRlaNP5m4TbuMC1CSALq0pNsguS6yCdy9W79t9KOVDNyUC7s40JMvpltbtviNnS5%2BNgv8yUhe/GzX4UfSd%2BNU%2BNOUcQzSxObG/sjGbaxY1rjRcsTYYR83A4FxoMWByHQUP881jHnYAfMz1HgxjfccmBexxIzRn2eoP%2BvatM8wN7JHgPEc2b1aWr50f8qIbfUh8i%2BNEvYU1wyPx8PPP//M%2BvXrueuuu0hNTaWRITHn6NGj%2Bde//sWKFSt4/PHHGTZsGBUrViQyMvI3R90EryZHMyUNCAggMDCQypUrs3btWtLT0zl27Bi5ubmsX79el0zc5XJhsVj45JNPmDhxIlWrVhVCg91uJygoiNWrVxMVFUXZsmWpU6cOc%2BfOpWvXrkycOBGbzUbPnj0x5nK7%2BeabdcJc9erVqVmzJuPHj5ea/BUWFjJhwgRhegrexObt2rUTOdw8Hg8nT54UPmiakFZQUEBUVJQumEtOTg5ly5Y1pbcw9tMf8vPziYiIMJl1GikqKqJevXpC0HI6nRw4cIAtW7YQHx9P9%2B7d/WovIiKCXr166YL4FBQUYLFYdGauxnkcM2aMNCpmeHi4KKsJpLfeeisxMTFCWLVYLNxxxx2cPXuWTZs26VIpTJ8%2BXZQpiSsN0LJurTlACx06XHJbajnqI/CCPJduqUE%2BkDj0P/aYblMaX8mYa/COO8xljFnAJW37pEj0YviQIj3vn//UbfqkXCz5HMn1NI2rOD2ILyZ34fbt9dumAZjbli4l4/zJLrBBK2yaPj8SnfszN9JMIn5k4jbtMi5Ayf1ZWtJtkFwX2QQ2bKjfLs4xKpAM3OSCe%2ButpjKmf0eyxW/sdNu2%2Bm1/MpLXq2cu40fSd38CBJn2GcYgDSZs7I9s3MaKZY0bk4sbO1yjhvkc40CL3QJ01Kql25TEMjM/Q40XU5Yf1biIjc8s/HuG%2BrOuTfsMcyN7BBjPkd2rpeVL96eMtn2hYl3%2BMJRm74r5e41Wcc3xjb55%2BvRpFi5cyGOPPUbHjh2l0Td9o25u376d7777jq5du3LmzBld1M3MzEyTbxV4feW6dOmi26eZkmqBQyIiIqhQoQIWi4W6detisVh00TfBGwDG4/Gwf/9%2BKlWqxMcff8zSpUsBr2Dw6quvUq1aNeGzpyX03rlzJ8uWLcPpdOrMDTXuvfde3faJEyc4duwYw4cPL9FMtVatWgwcOFBsezweVq5cSWZmJjabjfLly%2Bt80HzLXbhwgdDQUAIDA4mIiMBut5OZmcnGjRt1c37TTTdJ2/alYcOGJqEpIyNDRCUFhOAVHBwstH5Op5P9%2B/cTEhJCkyZNqFevHjVr1uSmm24iKSmJr7/%2BWqfdmzZtmlSDlpWVxYYNG0z7fTWd8GukUq2O9evXmwLQaMe1a6TNXYcOHXT%2Bix6Ph8TERL777jsefPBBYQrryxHDl2TNfxLQaYtLQwVoUSgUCsVfAaXY%2B2ujhD3FVWXIkCEiUqYWffOFF14AvJqabIMpUUlo0TeNP5eKvqnxxhtvCJ89zd8uOzub8%2BfPY7FYhCnp5MmTCQgIwG63k56ezj/%2B8Q/AKzwMGzZMCDU5OTmMGDFCRBjNysrigw8%2B0LUZEREhFQy0iJsjRowA4MEHH6R27doidYGMI0eOkJubS6SPZqBixYpC25eZmSkEHF%2BhLSgoiIKCAtLT0wkMDCQrK0topjr4fNl0u93s2rVL2navXr3E36mpqcCv5pgBAQHYbDadUBsSEoLD4cDpdOqEzzZt2hAeHs727dtJSEjg2LFj/Pzzz3Tu3JnOnTuzePFiUXbQoEHCzNJXSxcaGsrFixd1vnw2mw2Px3ACZCkAACAASURBVCNMfQGxpjRh02az6Uw/fcd94403Ar8Ke0uWLNFpGv/xj3%2BwYsUKpk6dSkZGhijvy8WLF3W%2Beb55936LNlr57CkUCoVCUQpKs3fF/L1Gq7jm%2BEbKjIqK0r28N2zYkN1Gf4piEhMTpdE3jT8l%2BYwNHDiQU6dOkZWVJX3RLyoqoqCggDNnztCoUSNef/11Nm7cSFFRkQhocuDAAcCbZ69evXosXLgQm81GYGAg5cqVE%2BahUVFR9OrVi5EjR/LNN9%2BYxuabr05D0xIuWrSIw4cPCz84QETuBEQgk9WrV%2BN2u8X8ZWRkEBQUhMfjoW3btiKJempqqjArLCwsFNqihg0b0r17d3KLHRi0fpbG7Nmzxd8ul4vCwkIhgBQVFeFyucjNzRXXQWvP6XQKM1PwatciIiKIjY1l8eLF1KhRQ2j2kpKS%2BKePuZ2vkOir7czLyyM9PV0n7LlcLi5cuEDPnj3FPm2OtIirvgFrfE0uXS6XCM7ii6%2BAtX//fmEWO3XqVF26Ci2pekFBQYla2RYSc7%2BSkPns3Xab2WfP6F5i3JZ1xfRNxSdIkMDg1yfzSzM6hpj8lmROID5rGyT%2BUOZq5Y4shsZkbjVGPxVT/yQfl4xNSdMmGuZC%2Bo3q5MlLti3ziTO2Lbt2pimVNW68VgZfJplrk6le2cCLo%2BwKZD6oxor27i21jGkNyHztjJpuSf%2BMcyrzzzJ22TgX/vivSl1vDYVkqTyN9fgYQZTYtmkMkrkxlvHHH0/qD2rYaeyPbD0a7zFZ26bzZBUZ9xkqks2N0T3Qr3UtWY9G91nTvemHv6BsmbN1q25Tmt7VuNMfxznjJEsiZfvltGecc5m/pXGfcfs3%2BKFfM5Swd8VcOm69QnEVefDBB5k7dy5bt24VKQEAcnNzSUxMpL3R5%2BU38Morr7Br1y5yc3PJycnBbreLF/Lg4GC6d%2B/OggULyM/P5%2BLFiyQnJ%2BPxeJg0aRK1im3%2BCwoKeOKJJ7h48SLdu3enQYMGlClThosXLxISEiLMQ9etW0dqaipjx46lfv36xMfH43Q6cTgcrF69Wtq/7T4JSevWrcvBgwfF9pkzZ8TfmuCiCYO%2Bwq0m9GzZsgWr1Yrb7aZixYqcPn1aRBrV2LRpEzt27CAoKIjatWuTmpqqOy6jTZs2bNiwgfDw8EvmrgsJCSE4OFhnzglegVQ7PmDAAJ5%2B%2BmkOHz5Mhw4d%2BPnnn7FYLHTu3BnQB3QpibCwMHEtNYFQmx9fYUu71prQ5hskpXHjxuzYsQPw%2BhzGxcXx%2Beefm9rS5tNqtdKhQwdycnIYOXKkTtDUBGebzVZi0vYfJIEdSiI5OVmnoQQYPHgIzZrF6vYZlYXGbZk/hykoqCxstsGvT%2BaXZnQMMSkuZU4gBq21LCWhyUdF5shiaEzmVmP0UzH1TxId1Z/c08a5kAZZ9YkwK2tb5hNnbFt27UxTKmvceK0Mvkwy1yZTvbKBG827ZZpqY0US7bexjGkNyHztjJpuSf%2BMcyrzzzJ22TgX/vivShX0hkISxbypHqN7s6xt0xgkc2Ms448/nj%2BJ6439ka1H4z0ma9uvxPWlZCCXzY3RPdCvdS1Zj0b3WdO96Ye/oGyZ4/MeA/I1Ydrpj%2BOccZKN/o7gn9Oecc5l/pbGfcbt4puhfNZhKG/wf1X8Zfh7ibaKPxQt%2BuaAAQP46quvOHbsGJs3b6Zfv35YrdbLir6pUalSJex2O3a7nfDwcJxOJ7Vr1%2Bauu%2B5i4sSJtGrVisDAQNxuN9u3byciIoJ69eoxa9Ysunfvztq1a1mxYgUffvgh4eHhJCUlAV7BRfOFe/nll%2BnQoQOvvfYazzzzDAkJCfTo0YOkpCTmzp1L5cqVpbnqjPgKeqD3ATt27JguKIjRLw%2B8ZqW1atUiNDRUCFy%2BgpymzSooKCAyMpKxY8dKfdiMrFu3jqKiIh544AGR904WjCQrK4vGjRtTpUoV6fH8/HxmzpxJt27dqF27Nn369MHj8QjN3pw5c0zmihaLxeQfqAl0vtphq9XKpEmTdBFOtbE5nU4CAwOFBg4Qgh54NZO%2BOQm1duFX7Z7b7aZv376cOHGC3Nxcavi8BGjXonLlyiUKzocOHfI7ZYhcYFReewqFQqH4k3HixB/XttLsXTF/r9Eq/nC06JuJiYl07NiRYcOGER0dzZw5cy4r%2BqYMTRAIDAxk5cqV9O/fn379%2BnHu3DncbjcnT54kJyeHe%2B%2B9l23btlG%2BfHk%2B%2BugjduzYwapVq%2BjatStLly7ll19%2BIT09XaSOCAkJ4ejRo8ydO5c6derwzjvvEBQUxIkTJ%2BjWrZtOyDDy6aef0qJFC5o0aVJq/0sSGB999FFeeOEFLBYLR48eNaWZ0Pj444%2BZOnUqABcuXODdd981CWWxsbG0bt0agLvuuovp06cLE9JPP/2U%2BvXr43a7RVJ08Poyatdo48aNwu/QYrFQuXJlUS4iIoKcnByRHmLo0KGEhYUJn72SonFWr15dt60JYNr8gzcB%2B2effaZbK5oApwmUmjbON/8deDWqWsoGLdBO48aNufXWW8U4PR4PP/30E7GxscI02MjNN9%2Bsm09tvWlBfrZt2yYdn0KhUCgUf0kMUU9/V5Swd8UoM07FVaMkE0ZftOibffv2LbFMly5dTBE2L9VGQkICJw3%2BM%2BD1vwoNDaWgoIDg4GCaN2/Otm3byM7Oxm63s3z5cqKioigsLCQ3N5dNmzaJZOjgNTsFhO/cyJEj6dKlC7NmzeLdd9/F4XAQHBzM4sWLqVmzJv3792fkyJHSfu/cuZPs7GxatGghTDrtdjuVKlUyCRSakBMSEkK%2Bjy1/amoq%2Bfn5QtDQAnwEBgbqBMSvvvqK6tWrY7VaiY%2BPZ926dab%2BpKamkpqais1mY8OGDWzYsEGYjGoCT1hYGKGhoSIASl5eHuXKlSMzM5Ps7Gw2bdokxuBr9mm1WmnQoAHbtm2jSZMmwsRU06xqGjItWmpQUJDULFTT6Plq0Z599lkGDRqki%2Brqqxn92cfn6GeD/1FOTo6YT037pmn%2BAgICGDhwIFOmTGHt2rW0adPG1LZGu3btSE5OFmOw2WxYrVZhPnr48GFTqgsZKkCLQqFQKBSKa83fS7RVXJcYhQQNt9vNwIEDWbNmDd26dePHH38UGhyn08kvv/zC8ePHueGGG3RJt0uja9euzJgxg9DQUBwOB0uXLuXxxx8XQVNkvP322%2Bzfv1/422l90AS9sLAwXXAU8Jph%2Bporbt26lZ07d9KoUSNcLpcwMXQ4HDrfvsWLF7No0SLuu%2B8%2BUlJSLjkWl8tFfn4%2BTZs2NZmM5uTkiLlt0qQJVquVU6dO6bRa2hg0ochut5OVlSWEby2CqK/QZBSg3G63SMsxduxYsV8zc8zLyxPXZ9iwYaJvMjStXnBwMD179tRpP319I33RhM727dtjsVhE30sKBnTPPfeI6%2BLxeCgqKsLpdIo%2BVq1aVXqeEb%2BTqht9DA3bUotSQ7RYP%2BKfyOsxmpp%2B9FGp9ZqiKCxcaC5jjJAguZ6muAA%2BqVI0Skv6LvkG5FckC5OSXpIY3hQwxpj0XZKE2a%2Bk6sbzJGbBxnk3TZ/seWaI8iELeONPEmZ/8meXmmxaUrFxqUndeksJ8gGYk4AbF4FPflUNUwwcYzJ0JEFm/InQsmLFpY/L9smChfiT/NyPtWVq3jBw6b9BQ8Cly06qbui0qYjsZjU%2BS2SL1jBf0se88RlqrMefa1kcwM0X0zPKj6TqlxNcR%2BYZcDnJ2i%2BrTCm%2B/r8LSrN3xVg8pUVtUCj%2BhAwcOFDkYMvLyxNRM/MNLxENGjRg1KhR9OzZk7CwMIYPH86mTZtISUkRmhhN49S1a1cqVarE%2BfPnmTt3Lm63m8WLF/Pkk0%2BSnp6O1Wpl48aNOq3SgQMHuP/%2B%2B5k%2BfTpTp04VmqLy/8/elcfHdLXh584%2B2clKZLMFIQS1VCyNpapoKNXaWx9KUUWrdv3UUvSrnVarDa3WFkH5CLWmtop9i8YWEhESZM9s9/tjck/nnnuSudVq63Of329%2BzJmzn3tv5p33fZ%2BnQgURUYgA2gvnCIEkxBnq1auHrKwskfEi9Ovt7S3ysmm1WtmGrF6vx/Dhw3H48GEcP35cZJRptVrYbDbiEatRowZSU1Odkr4A9rDQgIAA9OvXDy4uLkR4PDQ0FPn5%2BWjfvj2%2B//57AEDHjh2RmJgoax8MBoMsohcBrq6uhGQFgITURkCDBg0wc%2BZMvPfee8jIyCCGpVqtxsWLF3HkyBG8%2BeabZa792LFjktxAFubMmcMkaBkx4p0yWihQoECBAgV/PbKzgd8hI/vngsmO8yeASbH6/4lny7RV8H%2BDadOmERp/lUqF9u3bY8uWLUhMTCT5Y%2B7u7rh//z6%2B/PJLAMDnn3%2BOnj17ok%2BfPiKtutatWxMv2KZNm0REIaGhoVi/fj2sVivc3Nywa9cu0TyEEMfx48eLDM2mTZtCx2DQKo/ApVGjRhgxYgTzs7CwMERERMDV1RWXL1%2BW5AeaTCaoVCo8fPgQ7dq1g16vh1qtFhl6oaGhREePJkMB7F6xzz77jAjSDxo0iHwmyC4I%2BPXXX8HzPAIDA8k%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%2Bnw8ssvY/Xq1eB5HjzPQ61WM9lKLRYLioqKcPDgQRw7doxIQjiK3vM8j8jISEyZMkXCAtuhQwf4%2BvqiVatWojULRpzjGRYWFkqMNpbOIwtKzp4CBQoUKHgqIPPv2hOBYuz9YTxbq1WAmJgYhIeHk1dERAQ6duyIb775RlQvISEBPXv2RFRUFKKjozF%2B/HjccciziY%2BPR0xMTJljxMfHy5pPv379sHjxYqf1Fi9ejPDw8DJ1zOLj41G3bl3UqlVLRHjy%2Buuvo6CggBgzNpsN8%2BbNQ25uLvF6rVmzBosWLQJgF8X%2B9ttvyZfu5s2bY86cOeB5HjabDSkpKRg9ejSKi4thNptFOmyOYX1VqlTB8VLh43fffReDBw9Gbm4uqdOmTRsisaDRaFCxYkVYrVacPHlStK7WrVtj0aJFxFuZnJwMnudJOGKdOnWgVquh0WjInC9evIhVq1YBEOvR2Ww2QlqSnZ2NGzduIDo6GtHR0SQ3z2w2Iz4%2BHocPH0Z%2Bfj4JleV5XuT9ysvLI2t3JtheXFzMDLf85ZdfMGXKFGI8jRkzBikpKQDsRqewV9nZ2SgpKcHOnTuJ99RxvTRcXFxgMBjQqlUr6PV65JTmQP1K5fJcu3YN8%2BbNw4oVKwizqFqtRtOmTdG7d280aNCAKZLOCs91hJqlMcUAK2evwwsvSOo5y/lgKj3QOVG0gDogTTjZsUNaZ80a8Xv6HH/4QdqGTm5ihbvSk963T1qHEoJndUOn9Egilll5tPTYMhJimNHCVK6Q0zw6QLIIOVvDUg6nncJ0CpIssXZGv/R2sfqRNGPdD1RHknNhdEzPjxklTUU0sHIK6TXQ02OeJdURq1%2B6kJX/5EyAXE6%2B1mPlQDLK5ORnyXmW0Nfx4wq603XovWLmIcpI5JQj%2Bu4sZZQVZCFZA%2BN%2BkVwnkuRPSAXRWReXs81gtZGTkEdf/HIE3en3pWRkCp5uKGyczyAmTpyITp06AbB7QY4ePYpJkybBy8sLsbGxmD17NhISEjBu3Dg0adIEDx8%2BxMKFC9G3b19s2LBB9KX/94JmzuQ4DleuXIG7u7uIoVPQY0tNTYWrqysKCwtRuXJlJCQkICgoCG3btpX0TX/5d3Fxwe7du0W5We%2B%2B%2By4OHz4syp3z8fFBhQoVUK1aNezfv5%2BEOvr4%2BCAgIADnz5%2BHTqdD586dkZCQgIMHD0KlUsHDwwPVqlXD8ePHSWih4GlKTk4udx%2BSkpJQt25dXLp0CWazmRgQBdQflAMHDhA2TUH3LjQ0lGj1Xbx4EcBveYtWqxVGoxE2m42IqTvDK6%2B8gi1btqBz584iw43neVHY6f379%2BHl5SXxXJVFlqLVajFs2DAsW7aM6YET2gpno1KpiHGq0%2BkkuYZWq5V8rlKp4OfnxyRdETxtBw8eFJWXUH8ML168iEGDBoHjOGJ4u7i4kNDQJk2aMH%2BIoPNCHcFxnOz7oyxR9Vr16onKaH1d%2Bj3TtqR1FWkBdUCqHF36TBChXz/xe1q49/XXpW1oSRCWN5ueNMPIpYXgWd3QIssSwWeWCjM9NmsDqY5YesW0EDMdGc3SL6YXIWdrWMrhtFOYFpyXJdbO6JfeLlY/kmYsyRyqI8m5MDqm58cMgqDUsVnyofQa6Okxz5LqiClLShUyIuGdCpDLEUOXU0dOJdb86DI5zxL6On5cQXe6Dr1XrPlKLnTG4ckRfafL6PNliaFL1sC4XyTXiYeHtCM6nYN1cTnbDFYbug5rA%2BmLX46gO/2%2Ba1f7v9Tfpb8Uz5gX7klA2cFnEO7u7vD19YWvry8qVaqEbt26oXnz5khMTMSJEycQFxeHpUuXomfPnggJCUH9%2BvWxdOlSWCwWxFFsd4%2BDiRMnkvDByMhINGrUCHPnziVC5rNnz8bs2bPx2muvISEhAWPHjkVBQQGKioqwa9cueHh4kPYC26RarcbKlSvxww8/YNu2bQCAnj17Yv/%2B/VCr1UhLS8PcuXOxb98%2BuLm5EY05AAgODsbt27cREhKC/Px8XCv1KERFRRFjyWQyoWrVqrDZbEhKSkJsbCzMZjNhxNTr9fD%2BHdnLFosFZ86cQUlJCWw2G/r27UtyywRoqb9QNpsNtWvXFnlUa9SoQfLvBEOoYsWK8PX1xdWrV4nXTFXOw1Lw9jVu3BgtWrRg1jEYDDAYDMTQMxqNZYak6nQ6aDQamM1m7N69G6NHjyafqdVq0k6v16NixYrkvSCOLoQ3Os65S5cu%2BPLLL7Fy5UoAdm%2Bht7e3bC%2BaMDYAybx5nicG3NChQwHYjdB3332XeAUBEGbPOnXqkDIPDw/RPMvSPmRBydlToECBAgUKFDxpKJ49BQB%2BE65OSEhAZGSkRCfMaDRi%2BfLl8KF/Qv6dyMzMxKxZszBr1iwA9i/enp6eCA4ORmJiIqpUqYK4uDgMHToU69evx6xZs8DzPDw8PKBWq1FSUoINGzZg7ty5on6tVivJvRJC7wTDVPhS/dVXX6FZs2Y4evQo8WwCwJ07dxAXF4eQkBAkJSXhUilDk6enp%2BiL/GeffUa8hHq9Hrm5udi0aRMAu9eI9jIFBgbizp07GDt2LFavXi353DHsMyEhASdOnEB4eDgpoz1b4eHhSEtLg8ViQfXq1ZGamioJTVSpVDAajbh27RrCw8Nx/fp1FBcXg%2Bd5GI1GmEwm8DwPjUaDkydP4ueff8aCBQsAALNmzUKTJk0wfPhwbNmyBe3atSM5ckIYpsFgAMdxRHDdaDRK1uXoDbx06RLZT%2BGcBJSUlIi8bcI50YLlNpsNd%2B/exahRo4gBXLVqVaSmpsJoNJbpWRTyDIUxDQYDCgoK4OXlBZvNhkePHoHjOPj4%2BCA7Oxs2mw2rVq1C//79sXHjRty8eRNBQUG4VRquJ4R63neImculwnb8/PyYc2FBydlToECBAgVPBUr//v0tUDx7fxjKDj7jMJvNSExMxM8//4y2bdvi8uXLqFeGu75OnTq/68tsWejcubPEs3f9%2BnVkZWUhISEB3t7e%2BOGHH/Daa69h8%2BbNcHFxgaenJ9RqNRo0aID9%2B/eT9nv27AEAeHt7k7LFixcjJSWFvH4ozSviOA4VKlTA7t27RUZGpUqVUL9%2BfXh5eSE%2BPh6vvPIKAODevXsiw8VqtRIDraCgQOTJ43keUVFRAIAWLVpArVajbdu2cHd3x7x580QGkVarxcKFC9G/f3%2Bo1WoYDAbk5eVh%2B/btkr0SNOAAICUlBRzH4e7du7h27RqTidLV1RWpqalQqVS4dOkSMdIE75XVaoXNZoPJZMK8efPQvHlz4s0ymUz45ZdfsGPHDhQUFODw4cNkvc2aNQNg96gVFRXBYrEgNzeXGEAAW5dOpVIxvW9leQU5jiO6eoJGH2A37jZu3Ii1a9eC4zhcu3ZNdB7CWI7geV5kXAohsg8ePCCGJc/zuHfvHhknJycH8fHxSE1NxciRI0Xhp8KcywvRvUPrx5UDVs5e%2B/ZSnT06d0SSS7JkidM2rFycx0hdcz4XRj%2BsOnLmR1eSswbJWIzBH6eOnDXQ71m5Q3LGlpPX5zSHS8bBsOYnR8ZOzvk60%2Bpi5uM9zroZF4XT65p1IT1GLqWcXFlJHRkJZTKmx94/Zwl5jLI/635%2BnJv%2BccaWszdy6jg9J1Ybxn7KuV8e62EiY%2BF/xjNKTh3mdf5XQyFo%2BcNQPHvPIKZNm4YZM2YAsH95NxgMGDBgALp27YqlS5eKdOTKQ0ZGBjFwHFFeThNg97D4luZeCOyPgrcoOTkZ9%2B/fx3fffYfGjRvjxIkTyM7OxtKlSzF69Gi4u7vj4MGDePXVV0UGVHZ2Nvr164fXX3%2Bdmfsn5LPt3r0bP/30k8gwOHnypMijJkDIlQN%2B02Rr3749du/ejV27duHll1/GRgch5VOnTgEAfv75ZwB2fTkAZGwBVqsVycnJmDRpEtRqNdGYW0cJv2o0GmKwCHp5joySgoHiqM8nCLuXlSPniLi4OFy4cEFkvPA8jxs3bgAQ57gdO3ZMlPsorOfs2bOkzr/%2B9S%2BsWLFCNAbtqVKpVNBqtZL8Obou3S4rKwvVqlUjc3RxcUFhYaEox9GZ5p9wPQFSr6kjGjZsCF9fX0yZMoXpfSuvrQcrZ6MMsHL2Ro0cidq1a4nKaLtYYicz5DroOqxo18dIXXM%2BF0Y/rDpy5kdXkrMGyViMwR%2Bnjpw10O9ZuUNyxpaT1%2Bc0h0vGwbDmR%2B%2BnnDqsoSTpQzLW9FjrZlwUTq9r1oX0GLmUcnJlJXVkJJTJmB57/5wl5DHK/qz7%2BXFu%2BscZW87eyKnj9JxYbRj7Ked%2BeayHiYyF/xnPKDl1yNCXLz85vTsFTxyKsfcMYtSoUejQwe5B0Ov18PX1Jd4XLy8vSWhaWfDz88Mamq0PdobN8rB582bsKGX%2BKywshFarRZUqVRAUFISkpCT4%2BfmRMNLt27cjMDAQUVFRWL58OTQaDQ4dOoTCwkJMnDgR7dq1Q0xMDIKDgzF06NAyiWYaN26M2NhYWK1WmM1m0Zf4Ll26oHXr1pg5cyY6dOiAnJwc7Nu3T2QwCYbE7t27AdgNWkHGQICPj48oxE8AbezxPI81a9bgpZdeQkBAADE8jx07JmrnOL7ZbMaCBQtgs9kwZswYUT3aMPo9YuPJyckwGo2E4MVxTJ1OB6vVSkI/WXBc11dffQXAbsBzHIeWLVvi%2BvXrJAdSmCvL0KOh0%2BlEc9m7dy/q1KlDvIeOIZ/C3Moz9jiOI4awM3Tv3h0NGzbEoEGDsHz5clIu5CvabDaRge0Idh4eG6y6SsaeAgUKFCj4x6FUD/dvwTPmhXsSUHbwGYS3tzfRpAsICBCF2UVERODChQvMdnFxcfj000/Je41GQ/pxfLHC%2BRzRvn17Iohev359xMTEIDMzE23btoXNZoOXlxcAuyGxc%2BdOZGRkoE6dOujRowdiY2Nhs9lQWFgId3d3BAYGArDnFLKIZurXrw%2Bj0YiwsDA0a9YMMTEx8Pf3R/369cl8tm3bhnHjxuHBgwdYt24ddu/ejbCwMPL5%2BvXrybp1Oh06duwIjuNw69Yt0VpDQ0OJZyc8PJyEcwpadQI8PT3B8zz69u2LjRs3omPHjqLPyyIdGT16NKZMmSIqc3FxEdWvWbOmhOjF09NTRPYSGhoKnU6HgIAA8DxPwhNnzJiBzp07A7B7qL744gtRGCt9rhzHiXI7BY%2BX2WyGyWTCTz/9JDL0hLEd%2B2GFcwrsmIIRHBYWhoYNG%2BKFF14QCdU7GnrAb2GcRqORhBsL6%2BZ5HikpKSIdP4PBwBSXX7VqFd5//31s3LiRaZgKMhyO8xfmVbduXUl9BQoUKFCg4KkGxT6s4OmCYuwpEKFLly44e/asJC%2BpoKAAcXFxIk/O42LPnj2IjY1FbGwszpw5g71795IwUrVaTTwoR44cQU5ODhYtWoSEhAS89dZb6NatGzw8PGC1WjFhwgQSfpmSkoIVK1aIiGb8/f1x4MABTJw4EdHR0cjPz8fevXsxbdo01KhRo9w5pqWlkf%2B/8cYbGDduHAB7XtuePXvA8zxKSkokOV3jx4%2BHVqtFgwYNSM6bo0GjVqtJKGbbtm3RpUsXwkIqwGq1guM46HQ6YsBERkZi2bJlJGxRMC4KCwtFZ3Lt2jUSTirg0aNHxBDT6XRIT09Hnz59iAdX6HP%2B/PkICwuDh4cHeJ7Hrl27iDEN/OZpFGQK/Pz8cO7cOfI5nTOn0Wgkhuvt27dJPyqVivnDgNFoRGhoKNm38ePH4/r167DZbMjLyyMkNDzPk7kAv3k4i4qKkJWVBcBueLZs2ZIYj44hmDRBDGDPTWzatCnOnz%2BPyMhIuDvwuAtM6BkZxgAAIABJREFUm459CMamYBQL%2BY9yoBC0KFCgQIECBU6g5Oz9YTxbq1XgFFFRUejZsyeGDx%2BOjRs3Ii0tDcePH8fgwYOhUqkI4%2BUfQbt27Yhnr0GDBnjxxRfRtGlTHDx4EO7u7sjKysLu3buxfft21KhRAx06dEBgYCB27NgBT09PklPYoEEDJCUlQaVSwWAw4LPPPsOBAwcQFRWFnTt3IjMzE7Vr18bWrVuxdOlSYjx99NFHEq04GsKX%2BNjYWHz99deYNGkSMWbKMngfPHiAqVOngud57NixA4GBgQgPD8f%2B/ftFaxf6TkxMJOLnjvDx8SEad8KX/7Nnz2L48OGkTuXKlVGpUiUJYY6j8SSs19HYNJlMMJvN2Lp1q8TQycnJwcKFC5Gbm4u8vDysWrVKpIkoQAgRvXv3rigUkTZULBaLZK86dOhA9tFmszHz39zd3ZGXl0fajhw5EkVFRdhXKrxts9lIXqijB1ZYZ6tWrUSssYcOHUJYWBhsNpsovJUV9ikY%2Bb1790ZYWJhofYKR3rJlS0k7AQsXLizzMxpMUfXnn5fUc6Z5K4tcYMMGSR1JpC/1IwEAwCEnFYCUgeCLLyRN6PnJEok%2BeVJaiWKaZUUm0xHnkshYVki6DPIDOaLLuH273DZMktjHUdBm5EBfvy5%2BL1kmY7MkQ9FizwBu3hS/Z0U%2B02M5qJP8BicX7WMLh2dmit6yIsLp%2BdG60swocqoRM%2B2c6ohVx5lwuBzCG9aeS%2Bb8J4mqy%2BF0obXEWXXofuSQPdHvZYmqM65Zuh0jk8KpZjl1KzPnx3qWUJej9GKDPKIXp88kOTcMqw4958fp57//tf975Yq07V8Fxdj7w%2BB4Z6wGCv6vEBMTgxEjRqB79%2B5l1rHZbFi9ejU2bdqEW7duwcPDAy1atMDo0aPh7%2B8PAIiPj8eSJUuwd%2B/e3zVGnTp10K1bN8ycOROAPb/v%2BPHjknqC52bgwIFo0aIFFixYgKtXr6Jr16746aefcP/%2BfZSUlMBoNJIv/gLVvuDBCQ8Px/r162EwGCRi7gJcXFzw/PPP4%2BTJkygpKcFzzz2H8%2BfPM3PvnEGj0eCNN95AcXExNm/ejKioKMyfPx9t27Yl3iw69JCFUaNGoUuXLnjxxRdhs9ng7%2B%2BPwMBAnDp1irRVq9V4%2BeWXsWPHDgkrpZx5enp6Ijc3t1yyEQECGQpr7oI4vYuLCxYvXoxBgwahV69e2LRpE2w2GxGbFzxflSpVQlFRUbnGdu3atfHw4UMRs6VGo2GSzgjENQJUKhXxptLG57x58zB//nymELsAd3d37Nu3D8eOHSMhs4LWXosWLbBq1SrMnz%2Bf6P05omLFisjPz8fp06dl6f/NmTOHKao%2BYsQ7TtsqUKBAgQIFfxmys4HfoSX8p6J58yfT75EjT6bffyCeLdNWAfbu3VuuoQfYvzAPHDgQ27Ztw%2BnTp3Hw4EHMnj2bGHqAncSCZejJHUPAmjVrJDIJHMehR48eCA4ORlxcHMaNG4fAwEAYDAaSg2U0GjFu3Dhs2bIFKpUKnTp1QqtWrdC%2BfXvExsbCxcUFjRo1EoX5TZw4EatWrUJCQgLeecf%2BhbqwsBB79uxBTk4OCgoKiGdQpVIhIiICDRo0gL%2B/P7799lskJCQgICAAAPD222%2BjQoUKorVotVps3boVd%2B7cQaNGjVBcXIyCggIm0YsAug8AWLRoEdq3b0%2BMlbt37%2BLkyZOitkFBQdi6dSssFgv0ej0iIiLg6%2BtbpqSBAJVKhYCAAGRnZ6NSpUoAwJTaaNiwITiOg16vJxqH9Nzr1q1LjLjCwkIMGjQIgJ1V1GKxwGazoVmzZkQsHbBLEzgyigJioXUWYmJiJGG3Qn16TjzPo169ekzioGvXrpVJDqPX66HVapGXl4djx45h//796Ny5M9ModbzuOY5DrVq1oNVqkZOTA5PJJFlfWVBE1RUoUKBAwVMBmcR9TwT/UM9eSUkJJk6ciMaNGyM6OpoZqQXYnRrh4eGS14QJEwDYo4boz5o2bfqH5%2BcIxdhT8JciICAAjRo1KvNzIYx027ZtGDBgALZu3Yr58%2BcjPT0dWq1WFEYqEM0EBASgQoUKOHz4MNEKrFKlioRoxt3dHS1atMDx48dxszReKSIiAsBv%2BVOdO3fGhx9%2BCJvNhiFDhuCbb74Bx3FISkrCrFmzkJ2dDY7j0Lt3b4n35t1330WTJk2QnJyMY8eOIS8vj9zMLHAchwcPHhDDpWHDhgDsxl55FP7u7u5EHuHNN99ESEgILl%2B%2BjHv37jE9bxzHITg4GHq9njBJVqtWjYQsOubdCXM5efIkdDodSkpKkJmZCavVCg8PD2Lwq9VqpKSkkHazZs3CnDlzANi9t2q1GitWrICvry/J%2B/Py8oJKpUKFChVgNBpJPpyjZ9LNzQ2VK1cWrWHv3r24fPmyqIzneXh4eEhyJjmOQ05ODsmDdHV1BQD06NEDAwcORBCVZN6%2BfXtER0ejpKSEeAi1Wi3atGmDjRs3ighcBI8gra94%2BfJl0pbjOFSsWBFyoOTsKVCgQIGCpwIy/649S5g7dy7Onz%2BPuLg4TJs2DUuWLMHOnTsl9RYvXkx0oJOSkrB06VJotVr07t0bAJCamgovLy9RHYGx/s%2BCEsap4InhnXfeweHDh0VlRUVFEoNEo9EgKCiIaOQJYaRff/01%2BYKt1%2BvRunVrTJgwAX369GGGZDrCx8cH2dnZ%2BO6779CoUSMSWvriiy%2BiS5cuqF%2B/PvNm8vDwwODBg7Fq1SocPHgQOp0OJ0%2BexMCBA2EymRAQEICsrCyMHj0acXFxzHBPlUqFypUro27duqIbX04IpxwEBgZK1i/0XZb8g4DQ0FA8//zzWLduHaxWKypUqIAHDnkGtWrVQkZGBvLy8jBz5kxMnTpVEj7p6FUTwjjLgpeXF9M79tZbb2HNmjWSMNKKFSsiKioKP/30U9kbUAYEOQTHfdZoNAgODsbdu3dRo0YNiVwGC5MnT0ZKSgrq1q2LM2fOID4%2BHgBQvXp1bN%2B%2BvcyQYGH%2BR2SGhly8eBGpqamismrVaiIiQqyzh%2B3bgZdf/u39jz8CpaypgD0nROIYXbsWKP1DUladggKg1Ba2w2yWCkWZTIADAyp%2B%2Bglo25a8LSlhaKrR/Rw6BNB5jjk54i8PDx4AlJe7sBAQ2cOJiUAHsei81UpJUG3dCnTtWl63kkasvcnPB0Ryo6dOAZSmqGT/6PkVFUn0vOi9YZ5dXh7gQAzEPBdqDfR2Ss4NkGxocTHgEPjAbsfoR3IuubkA9eMUfV1IlkCvEdKzYm2fpJB1AWZmAqURGMw2jP2UjHX%2BPEAx60rqsMamL8iLF4E6dX57zzpwuox10d6/DzjkIbMuCUkhqxI91r17QKnmbZlt6POVXACMdqx1UnUk9y7jOpKUsepQ%2B8W85%2BlnEH1dSybDWBNjbMkyGXsj51joMno6cqbHuhzl1KHLJHWERT56BJSSlP3lKCdP/g/h0KHHblpYWIhmzZph5cqVxAu3bNkyHDlyhBlZJMBqtaJr165o3749Ro8eDQDYsGEDNm3ahB9%2B%2BOGx5%2BMMirGn4IkhKyuLKbDer18/dO/eHbGxsXB1dYXFYsHRo0cxadIkzJo1S6KR16RJEzx8%2BBALFy7EzZs3YbFYUFBQgD59%2BqBNmzbQ6XQYPnw43nrrLXTq1An9%2BvUjXiur1UoMEo1GQzxb%2B/fvx/r167Fo0SIAdk%2BVWq3GRx99JAlB/fHHHzFjxgw8fPgQsbGxyMjIwJo1a9CiRQvZuX2zZs2CRqPB%2BPHjRQafXq8XhRa2atUKBw8elLR3dXVFQUEBMWiEf4XwSsEj5OHhIVsn0RnK0pJjQWAPLSkpgYuLC6xWK/z9/XHnzh2RQafRaEgOoCAY74jExERs2rQJn3/%2BebnzYOXwVaxYkeTX0dDr9eA4Tpb%2B4LBhwzB69GikpqaiW7duxJgNCQlBYmIipk%2Bfju%2B//57ZNiwsjPnLHgusnL1RI0fiHYZIugIFChQoUPC3IT0dcGDn/kvRuvUT6TZrwwbcu3dPVObr6yshvmPh5MmT6Nu3L06fPk3Y0Y8dO4bBgwfj9OnTEnZyARs2bMDChQuxa9cuEnk0e/ZsPHr0iERHPQkoouoKnhjKumEEb0toaCgp69atG3788UckJiaiSpUqiIuLw7fffkt03EJCQrB06VJ07NgR%2Bfn5ROMvqvTXdrVaDXd3d/Tq1UvkdXE0FCwWC/H60IyKVquVhGXevn0bbR28F44QwgNvs%2Bi7HCAYZwImTZrE9OpVrVoVly5dIms4e/Ys%2BcxRjF0wCLVarYil02azwc/PD1lZWdDr9X%2BaoSf0zQLL%2BOJ5nhh1o0ePxrlz57B//36J585gMGD79u3o1q0b01A%2BefIkTjowM9KC9AJoQ%2B%2BVV17B9u3bmfMVROZ9fHxkGXu9e/fGiRMnMGrUKFEuoaDRV55wOku3rywoouoKFChQoOCpgCjU4f8D69atw5IlS0RlI0aMwMiRI522vXfvHipUqCDS/vXx8UFJSQkePnzITOfgeR5ffvkl%2BvfvTww9ALh69SosFgt69OiBu3fvonHjxpgwYYIso1MulJw9Bf8YOGrkRUZGigS7ATspy/Lly%2BHm5obx48eXSQLTqVMnAPZ8NUetOoGpUaVSwcPDAwMHDhS14zgObdq0QaVKlZhx0%2B3bt0elSpXg6uoq0caj4WjoLViwgPmLTWhoqOhXpWrVqqFBgwbkvaORIxg3Pj4%2BqFmzJinneR53794lun/R0dEAICKmoSGI1mslcUC/Qa1Ww8/PD4GBgQgJCRF9xnEcySkUHnSOenzfffcdYmJicPToUYlRnZ%2Bfj5YtW5bpEf3www/xyy%2B/kPeOeyDEt4eGhqJXr16ido5SDYA4H04w8Ggvc4BjqBdA1jl16lQkJSXhhRdeYLJqOoZeOpLPuLq6lhvSqkCBAgUKFDyVYGrJ/EV4QgQtvXr1Qnx8vOhFf7coC0VFRSJDD/jt%2B1BZ3wOOHTuGzMxMvPbaa6Lya9euIT8/HxMmTMBnn32GrKwsvP3223%2BKrrUAxdhT8LfDbDYjMTERP//8MyFYYTFEAr%2BRf5SH5s2b46WXXoJer8fw4cPxxRdfQKPRQKfTwdvbGxaLBcuWLcMLL7xA2uj1elgsFsTFxUGtVsPX1xe%2Bvr7Ey6RWqzFo0CDcuXMH9evXx5YtW0hbgVFzxYoVWLlyJVavXo1PP/0UgP1XopdeegkFBQUiwwAA3nvvPZSUlBBD6cqVKxJyEkdwHIf09HQmgydgN/ySkpIA2B82H374oaQ9ADx8%2BBB6vZ543QQZDEdYrVZkZWUhPT0dDx48IMadRqOB1WpFXqkYlEqlgpubG9LT04m37%2BbNm3jvvfcQERGBQw4x8W5ubtDpdOjZsyeZj2OoQ8WKFbFy5UpERkaSMl%2BHfJK1a9cCALKzs%2BHr6yvSFBw8eLDIq1ZYWAi1Wi3S2yugBKMyMzNhMBhIP0Lo74oVKzB69GhkZWWJPIECOU316tVJmaMBWVBQ8LsIVhSCFgUKFChQoODvgZ%2BfHyIiIkQvud40vV4vMeqE92X92L5r1y60atWK/OAuYPv27fj222/RqFEjNG7cGIsWLcKlS5dw5syZx1gVG0oYp4K/BdOmTcOMGTMA2D0vBoMBAwYMQNeuXbF06VIinP440Gg0%2BM9//kO0ApcvXw6LxQKLxUK%2BvA8YMEBkSAi5c4WFhejZsydSU1Oh1%2BsJeYnVasWAAQMAQEI6I9R5%2B%2B23AdiNgtalMeYCw2ZeXh5cXFxExsHXX3%2BNvLw8xMTEwN/fHydPniQGjSPUajVsNhtcXFxQUFCA7Oxsp3tgs9kk3kQhjJTjOBIW6unpSc6hLBQUFJBfmAQPo9CXnLBIAfn5%2BeA4Dlu3boVWq4W7uzuKiopgNpthsViQk5MjYlsV2gioVKkS7ty5g7y8PFE5YPdS0nNxNErLQklJSZkyDlqtFtHR0ZIcyhMnTpTZHytHtSx07doVdRzJGwCEhtaUVqRJFKj3TJKPtDQgOPi39xIGD0gT7mliCzl1GGwDkiJWYj/NAiBhRGH0w5ifZO3U3jBJSGi2A8YGSopYxBD0BOk9ZvRLD80k2qDbsVgVKHYYyTplMDowCVCcMUWwpsNixKDa0d2yxqaHYpL/UAQYEpIc1nxkMGRIxpJBOsMkwaEXQfUjh5wDe/cCMTHlVmIdr7M9Z5bRa5BBrMIaXNZ1Te2FpBs5DCOsjql2zLGpa0LWfGU8J5yzm0B6c7IeSnQZTfQig%2ByJuQg5hEbO1iCQKZXDEP7E8Q8UQPf398eDBw9gsVjID8b37t2DwWAok0390KFDGMHIyacdAd7e3vDy8ipXE/j34p%2B3gwqeCYwaNQoJCQlISEjAvn37cOLECbz//vsA7GGG5eWe/R6twPj4eMydOxcA0KBBA9SqVQuvvvoq9uzZg84OjIZLlixBzZo1sWbNGlStWhXLli0jxp0AWqON4zhUq1aN0PkvWLAAKSkpCAkJIWGeVqsVb775JoqLi1FYWCjyPgnMkFu2bBHlqQHim18Qii8oKCDJw0IIYnnhmrNnzyZz8/X1JX06GjXFxcXEu0Y/cAQIhp4QolChQgWnen4CHOsJ%2BZImkwlmsxk5OTkoKioS5d81b95cFOv%2Br3/9i6zRUWR9zZo1onY9evRgju94Zq0ZSd6O5DY0unfvjuPHj5P3gtxF3759mfUByCbsAYCtW7fi/fffF70OHmSwkDoaeoz3zKNwNPQANm02bYDRhp6cOoxQYEkRi8GN/sLB%2BHFH0g9jfpK1U3vDvD3ob8mMDZQUsf540xOk95jRLz00M5KabsfKA6UsHMk6WdEP1GDM252eEKMfyXRYkQZUO7pb1tj0UMz0V8obLjH0WPOhB2dsumQsxnlL6tCGHiBdBNUP67wlZbShx6jEDG5xsufMMnoNrIeJjGtC1nVN7YWkG9aByzg7uh1zbOqakDVfGc8JyZxZa6BvTtZDiS6joz5YN4ycvaHbsebnbA0Ua%2B7fgn%2Bgzl7t2rWh0WhEDN/JycmoV68ek5wlJycHt27dkkiP5efn47nnnsPRo0dJ2d27d/HgwQPCE/BnQDH2FPwtEDTyBJ08x9DMiIgIiUaegLi4OBIiWRamTZuGqKgoREVFITIyEuPHjwfHcahevTqKi4vh6%2BuLSpUqifLJUlJS8Ouvv6JPnz64ePEihg0bhq%2B%2B%2BkrSt2C8NGjQADzPw9vbG3fu3EGzZs2wceNGZGZm4tSpU%2BSXnX//%2B9/45Zdf0LRpUxQVFYly28qDo5fKbDZjyZIlUKvVyM/Ph8FgQGZmJoxGI6xWK3mw1K5dG0ajEXq9Hg0aNMC5c%2Bdw69YtaLXaMr2BNpuNGDvOvFImkwkqlQoPHjwok2lKQEhICFxcXEheoJAvCUg9aI59HTlyRDSP5cuXk/87xsc7Xi%2BVK1fGN998U%2BZchDk4EqII4acAiLg8jaVLl4qMRUFf78033yRz4TgONWrUICGev8cjrYiqK1CgQIGCpwLl/LD8LMJoNCI2NhbTp0/H2bNnsWfPHqxatQr9%2B/cHYPfyOX6P%2B/XXX6HX61GlShVRP25ubmjUqBFmz56Ns2fP4sKFC3jvvffQsmVLhIeH/2nzVYw9Bf84dOnSBWfPnkWLFi0QHh5OXhEREZg7d64kjjkhIQFZWVmYNm0a7ty5g%2BrVq%2BOLL75AQkIC1q5dS2j%2BN27ciBs3bmDFihUIDw8nNyVg97KEhoaidevWeP/99/Hee%2B9JcrwAe4gox3GEQTMoKAju7u6oWbMmzpw5gzZt2kCtVpOb9NatWxg3bhyaNWuGatWqEbe8M2OJ53mRV2zatGmYM2cOpk2bRjTohBBIwVi7dOkSioqKUFJSgldffZXUE3LzhH%2BNRiM0Gg2MRiP69euHrVu3ArAbLgaDAVqtFg0bNsSmTZsQFBREDEgARMfOcW6stdy8eRNeXl4YOnQoaSfUEwwlvV4PtVqNLVu2ICgoiIwhGHtqtVoUeiv05ebmhhUrVpCxMjIyJGQ7wG85ccK6hZw8YT5CKKijxxD4LbdPpVIhmPaQAZgwYQLZc57n8euvv5JzdWW6GthQcvYUKFCgQMFTgb%2BTfOwf6NkD7N8FIiIiMGDAAHz00UcYOXIkOpRqrUZHR4sI/rKzs%2BHh4cGMivrkk09Qp04dDBkyBP369UNgYCDmz5//h%2BfnCEVnT8FfDkHgvLxQzClTpmDDhg3o1KkT%2Bvfvj6ysLHzxxRdIT0/Ho0ePJHp8ADBo0CCsXr0anp6eKC4uxoYNG%2BDp6YmcnBz06NEDb731FhYsWAAvLy%2BsXbsWq1evxqpVq5zO193dneR%2BzZs3DykpKVi1ahVUKhXCwsIwZMgQTJgwAY0bN8axY8dEnqsOHTpg8eLFAOxhlYIHymg0SjxpckXXHYXQ3dzcoFar8ejRI9LH%2B%2B%2B/j4EDB2L48OHYv38/ALvhZDAYiCHj4eGBDRs2wMvLC7dv38arr74KwO4FM5vNZC4cxyEiIgI5OTkoKCjAo0ePJELqwcHBUKlUJMxRAK0hKPRNo6x1O%2B473e%2Brr76KTZs2SUJrHeHm5ibJ7ZODY8eOwcvLC19//TXmzZtHwlibNGmCNWvWIDExsUxq5sjISGzYsEHWOCxR9dDQmoiMpETVneRmMHP26FwNlhAy3ZCVF0KXyck/oftl6TNR4uySHEPW2IwcKUmaClXAzNmj5sfcP7qQlSTlTKxbhqg1c2x6v2QkX8nQf5YMxsp3k5MuKNlTVu6akzwq1nbSlyhzDXfuAA6eeGZeH53jKkNUXZawuYxFOBOPl5VHx8qvpcZiXjdylLjpho8jxC5DGJ7VjWQ6cuZL5/IyxO7pC5L1qHP2zGTm7F2%2BDNRyeBYzno%2BPs33MdVJlcrZG1rnIyTt01o9wIzITZP8ivPjik%2Bl3164n0%2B8/EIpnT8E/Eh999BE8PDxw4sQJDBw4EB9//DFq1KiBhIQENG/eHImJiThx4gTi4uKwdOlSuLq6wsfHBzqdDv369ZMwa6rVasyfPx%2BFhYXIyMjACy%2B8wCRDAeyJt2PHjkWFChVw7tw5UZ6dv78/Bg0aRMTB33rrLUIqc/bsWYnRsmfPHkRGRqJevXqIi4sj5UVFReA4DrVr1yavrVu3OmWC8vDwgKdDDpTJZCLEJyqVCnXr1kW9evWQnp4uMoSsVisx9AICApCbm4vr16/DbDZj/fr1pJ5gjAnr0Ol0CAgIwDvvvIOCggI0btwYzz33nGhOt27dQk5ODvHYubu7ExkNRwg5djRdcVkGblneT6vVih9%2B%2BIH0V149R9C/qGm1WhiNRkmuYlJSEo4fP4758%2BeLPjt16hQyMjKIvAWrXyHUUw5k5%2Bw5yc1g5uzRRhvDiyhpyMoLocvk5J/Q/bJCl2kdS4YHVTI2I0dK8gWNKmBGHlHzY%2B4fXchKkqLnJyeHRs7Y9H7JSL6iqzD5AajBWN/b5KQLSvaUlbvmJI%2BKtZ30JcpcAxVyzczro3NcnZ0TIF04Kw9RxiIkXVP5TrLy6Fj5tdRYzOuGng9rk%2BmGj3HNMgeXPFudT0/WfOmweNrQAyQXJOtR5%2ByZyczZq0X96MZ4Pj7O9jHXSZXJ2RpZ5yIn79BZP8KN%2BDvI2P50/EM9e08Tnq3VKvhHQC7BipubG0aPHo3Tp0/j4MGDmD17Nvz9/Zl6fI596nQ6LF%2B%2BHH369BH1OWrUKPj7%2B6NevXpwd3fHpEmTsHv3bqxZswaurq5wc3PD0aNHcfDgQQwZMgRHjx6VGCaAXSLg3LlzcHf4Qx4VFYXi4mIYjUb06tWLkIzYbDaUlJTAZDJJjJo2bdogISEBS5Yswfz583Hx4kUJoyQdLpmbm4uMjAxSVrVqVZIPZ7PZcP78efTr1w/t27cnicOBgYFISEhAly5dANglBwA7e2h0dDTWrVsHwG78PP/88yIDp6SkBHv27MGkSZNgsViQnJyM8%2BfPo3bt2gDsHkqe55GbmysSgLdYLDAajejSpQsxxoT1f/nll6I1arVahISEoH///qKE5Bo1aojqCfo3giwHS4KD4zjiSfWlvvTRidE8z4PneVgsFtE%2B//DDD1i3bh04jiPkPoA9N3DTpk24fv06KTMYDKJzvXLlimROZUHJ2VOgQIECBU8FsrL%2BvrEVY%2B8P49larYKnGr9Xj4/2knl7e0Oj0eCNN97AO%2B%2B8gzVr1qBr164YN24c/P39ERwczNSwk0Onn5iYCJ7n0bZtW/To0QM5OTnl1tfr9Th48CASEhJQpUoVbNiwAbNnzxZpxwFir5eQL%2BdoEPE8j9dffx3%2B/v544403iK6cXq8n%2BWbp6el46623yhVRB%2Bz7e/jwYZhMJpEhxXEcGjVqBFdXV8TGxuKzzz5Dfn4%2BatasiTp16hBDyTHfrH379qhbty5OnToFjuNExDSOHrfAwEDs2LEDgwYNwvr161GpUiXSX9OmTQH8RrAihEeGhIQgNDSUhJIOGzaM9Fe5cmUSM3/r1i3RNeBoJDdr1gw7duzAd999h4iICJGx16pVK9SoUQNVqlTBqFGjSHmNGjVw%2BvRpXL16FYDd0KON83v37pW7xwoUKFCgQMFThz%2BRGVLBXw9FZ0/BPxq/V49v7969AICzZ8%2BKpBOKioowbdo0mM1mTJ8%2BHcHBwdi%2BfTv5/NSpU3jjjTeQnJws8gAJYugsshZHrF%2B/HjzPY8eOHSQpV8hFc3FxQUREBEpKSnDu3DnwPI%2BRI0fiv//9L77//ntkZ2cjLi4OtWvXxuXLl6FSqWA0GtG7d29s3rwZL7zwAg4ePIi7d%2B/Cy8sL/v7%2BSEtLQ2FhIVJSUpCamgqr1Yr169fDarVCr9eTkNKOHTsiLS0NJSUlRAhepVIRw4zjOOj1epHRIhhjguSDxWLBv//9b3Tu3BmbN2/G5s2bSV3Bq6jRaEhdk8mEjIwMZGRkQKfTwWq1Ij09nbSZN295XQ02AAAgAElEQVQeALvnLSYmBsHBwbh//z6qVKmCn3/%2BmdQTDF83NzcEBgbi/PnzAEByNAUIeW8ajQbp6emEHIfneWQ5/BrpaIAnJycjNjYWJpMJFStWhJeXF3JychAaGoohQ4Zg7NixuHXrlmgci8WCzMxMXL58GQBbY5AOHS0PCkGLAgUKFChQ4ATPmBfuSUDZQQX/aDyuHl%2BtWrVIu4SEBPj5%2BWHUqFHk3y%2B%2B%2BEJUPyoqCj179sTw4cOxceNGpKWl4fjx4xg8eLBTTbl79%2B7h3LlzMBgM2Lx5M7Zs2YLOnTvDYDCgY8eOKCoqgsFgQGFhIeqW5hzMnz8fFy5cwOnTpzF37lzwPI8rV67AZrNBrVajc%2BfO2L9/P5YtW4ZRo0YR5tC8vDwcOHAAw4cPB2D3egleOOH/CxYsAGA35D766CPSp4eHB9RqNfEW1q9fH/Xq1SNsnDRsNpsoL65du3bMOoDdENLr9SSf8Pr163jw4AFTFPTixYsAgLCwMJw%2BfRq7du1C3759icdMwJIlSwDYResF9lMWdpUmWbNCbh3hmOv43nvvYdmyZahSpQqaNGlCQj8F4/DmzZvgeV6kxXjkyBEUFxdj3LhxxFCjQ0k9WZpyZaBr166YN2%2Be6BUT00Fa8bvvyn3PTHmkhN9ZPDX0rcOKKpXw3xw54rSNZEJJSU4HZ66BLnTQPCxzftS1zJQ9pA1yxuCSfqnnBbPOmjXi96WkSSLQBEWsHwdo7zCLBY/qRxJ8wGpDHRYz%2BIDql8lvRP/wxYh8kPAw0ZvF%2BKGErsLkXqLbsfaPPnQ6tJrhfZcs4ccfnddhkEdJNox%2BtjIIqiRlrOuG2nNWN3Qha2sk7aj5Mn%2BrKiUDI2Dlbsm5rqk6ktuOIvhi9suKsilNSxDADK6gSbPos2M9yOixGR1L9pPxLKHryLkE6P1jPR/l9CvnnnJWh2y5YnA91VDYOBX8Y%2BGMtfPf//43zp8/LyIYERAXF4f79%2B9j7Nixor6WLFmCgoICPHz4kNTVaDQICgpCr169wHEcNm3aRPTp1Go1CgoKSMjgkCFD4OPjg08//ZQQoJTFMgnYjdVFixbh%2Beefx/HjxzF58mRMnz6dWbdGjRq4evUqbDYbDAYDbDYbQkJCMHXqVBgMBrz22mvlsnUGBQVhypQpqFOnDgB72OLUqVNx%2B/Ztwkqp0WjA8zwMBgPMZjMWLFiAEydOOGUljY2NRVJSkkQ03GAwoG7dujhx4oTIYygHr7/%2BOrZs2ULkI4QcOgGOLJ2O/1er1ahUqRJMJhNycnKIQerj44Pi4mLs3LmTkKg4nk3FihWJd2/u3Ll45ZVXcP36dXTr1g29evXCN998A47jkJSUhDFjxuDs2bP4%2BOOPyTUk5HUePHgQ9evXZ3r2mjZtitWrV8ta/5w5c/D111%2BLykaNHIl3RoyQvYcKFChQoEDBEweTsvQvwiuvPJl%2BS6OdngUoprqCpxaCHl9ycrKovKCgAHFxcaKQOpoUZuLEiUhKSkJSUhL27NmDoUOHYt68efDy8sK2bdvQq1cvqFQqjB07FjNnziTtvvjiC8yaNUvEdCnko02ePBmff/45vvrqK/j5%2BaFixYpITk6GVqvFkSNHYLFYMHPmTKhUKqjVasIEyXEctFot0tPTMWnSJAQHB6NNmzYYMGAArl69ihEjRqBv374wlLKOCSySNG7duoUhQ4YgOjoa0dHRGDRoEC5fvowBAwZgxowZqF69Omw2G6xWK8xmM9q1a4e2bdti9%2B7dpA/BiymQqgiaetu2bSOGnlarRdWqVeHv7w%2Bz2YwTpV4km82GgIAAYmwKmDx5MgCp4PjOnTtRVFQEi8VCjD3AHt6oUqlEgqKORqDVasXt27eRlZUlIuG5f/8%2B8vPzRWyZarWaeN8cwzgTEhIQExOD2NhYGI1GxMXFQafTged5zJkzB/n5%2BTCbzcTQA%2BwsoxzHYeDAgSguLiZC6o5glZUFFkGL8subAgUKFCj4pyE7928y9ACFoOVPwLO1WgX/Vygv9FKlUmHw4MFltnV3d4evry98fX1RqVIldOvWjSnp0LNnT/IFfsmSJahQoQI6deqElStXYvDgwfD398eNGzewcOFC9OvXD23atEF0dDS0Wi2aNm2KI0eOwNvbG2PHjsX48eNhs9nQp08fJCQk4Pvvv0eXLl3A8zzMZjNWrVqFvn37YsaMGdi7dy/y8vLwxhtvwMvLC2%2B%2B%2BSaKi4vBcRzeffddkn83depU/PDDD4QZdOfOnUhKSkKfPn3g7e2NXr164dtvv4XVasXy5csRFxcHjuNgtVpJOKxgDFWrVo0YtmPGjCFMkwMHDkRCQgK8vLwA2MlfeJ7HJ598gl27dqFKlSoA7IbhgQMHRF7OGTNmoGbNmqSdIxy9q44hmBaLBTzPo2fPnqRMCFvt06ePSJzdsR3HcfDx8UFSUhIh2ikuLobVahUxpwLAjRs3MHPmTPz444%2BYMGECOI6DyWSCSqXCjBkzkJmZCYvFQtYM2H9E0Ol0hHGTFaJ6%2B/ZtSVlZUHL2FChQoECBAgVPGgpBi4KnGh999BGqVauGuLg4fPzxx/Dw8ECLFi3w2WefMZk1ywNL0sERI0rD6wQSFo9S/ZkKFSogJiZGRApTVFSErKws2Gw2ZGZmYtGiRQgODsayZcuwcuVK9OnTBxzHoV69evDz80NRURGioqIA2Jki4%2BLisGzZMvzyyy%2BwWCw4fvw4Bg4ciNWrV8Pb2xshISEA7KGsjujYsSMAe3jlBx98gF69eiEkJARr167FnDlzoNFoYDQaYTKZ4O/vj8zMTNy8eROA3Xv28ccfAwDWrVtHPKPffvstDh06RAhT8vPzkZ%2Bfj3/9618khFJof%2BPGDZEQekhICM6fPw8XFxemVIIAR5F24f%2BuDkJgy5YtAwB855CvZjKZ8OWXX0Kj0RAD8f79%2BxIdPACYPn06PvjgA7KmjIwMvPnmm2TejjAajXj48CE0Gg1q1qyJ46W5Ynl5eQgJCUFaWlqZ6xAYROWga9euEi9otWo1JfVoLV%2BJti9DKFeiCX39OhAWhnIrsYSkr10TsbBJhK7PngUiI0VNKB1pprC5JCKIpaC9ejVQmqsKsLWmKY1tydiSCmBsF0to%2BNdfAUfpD5aq8e7dQPv25C0t8M3SqJel5kxvGGNv6HXSY7OmSw/FEiSnBanv3wdKCX7LnjItYs7qnGrE3Bs5CtWU4DzrsnGmqc48b2qhTLF2amxmVJsT8W5Wv3QZa02SsVhC9lQlOZrqkq1gLIrW0pajy83qx5m%2B9%2BPqudPtJM8ASG8pug5rz%2Bn5ss5OMmc5N56Ms5P0I%2Bc5wXrQ0qL0rDrO/sCUXvd/qyPsGfPCPQkoOXsKnjnQuYBmsxn79u3DmDFjMGvWLKxevRr169fHlClTSH1HNkkhx69OnTo4ffo0YQDdtGkT4uLicPPmTZhMJtSqVQsTJ07E2LFj0a9fP6hUKnz33XekvoBmzZrhwYMHGDp0KMaMGUPKCwoK0KVLF3Ts2BEffPAB4uPjMXnyZHz88cfQ6XT46quvkJqaCr1ej/z8fISEhGDXrl24evUqOnXqBF9fXyQxyDGee%2B455ObmYsmSJThx4gS%2B%2BeYb8pmQGxcREYFff/0VZrMZKpXKKcukYHDRMBgMsFqt6Ny5M0aMGIG2DmLaer1eFA5Lo3Xr1jhw4ICoTKvVQqVSifIldTodYUvlOA7u7u6EuEdYj5ubG/Lz853mFXp5eWHr1q1o1aoVXn75ZaSlpeHcuXPk8%2BbNmyMiIgKrV68mHlkBHMdh8uTJ6Nu3b3lbRcDK2Rs5chRGjHhHVnsFChQoUKDgLwHLkP2r8OqrT6bfTZueTL//QCiePQXPJH6vpMPEiRPRqVMnAPYww6NHj2LixImw2WyIioqCyWSCxWKBTqcjBoXJZMKHH34IjuPg7e1d5lzy8/Oh0%2BmwevVqBAcHo0GDBujevTvMZjN4nsfatWtFBsSUKVPIWI4GR0ZGBmJiYkh4YUFBAfLz8%2BHm5oYPP/wQgN3AMBgMyM3Nxfjx44mHELCLlV%2B8eBFWqxUXLlwg5UIen4uLSxlC4BAZer6%2BvkRvTiAxEVhKHVGeoafX60UyDAJoIhyz2YxWrVrhp59%2BAgAi8C5A%2BC1LIKgRDD13d3cEBQURL114eDhOnz6Nhw8f4syZMwDs3tlr166JxnN1dUVOTg5MJhOWL1%2BOWbNmEYkGnudFeoLOoIiqK1CgQIGCpwK3bwMO3xf%2BUiievT8MZQcVPJMQJB3mzJlDjKa1a9ciKioKaWlp%2BPLLLxEVFYWXX34ZADvHr1q1atDr9fj4449htVrxn//8Bz/%2B%2BCO2bt0Kf39/9OvXDxaLhRgaZcHT0xMGgwExMTGIi4vDq6%2B%2BCjc3N7Rr1w4LFy5EUVGRyBsVFhaGbt26Yd26dWjZsiVCQkJgNBphNpvRpUsXrFy5EoDdmBo3bpxkPKGvd999V6RDd%2BHCBdSqVQsvvvgiPv/8cwB2BkuBDEYwTsoKx/T19UXNmjVFxlbjxo2JFMSsWbNE9SvSMXmlqFq1Kl544QVYLBaJyLxAbMNxHDQaDfz9/YmhJ4BFXsNxnMiAj4mJwebNm5GcnIzk5GSsXbsWGo0GGo2G5PcdOHBAoq9otVqRmJgIwC7mTmvxpaSkMNekQIECBQoUKHgMKAQtfxiKZ0/BMwkh761SpUoSnbklS5bgypUrWLRoETQaDfr160c%2Bc5R0EETHjxw5gsjISGIYAvawRp1Oh%2BXLl8PHxwd%2Bfn6Ij49nziUyMhKHDx9GdHQ0/vOf/4g%2BcyT86N69O5YsWQK9Xo9r164hJCQECxcuRI8ePVBcXIzo6Ghs27YNX331FWHR3LdvH7KyskSMpSaTCUajEX369MHt27exevVqNGrUCOfOnUNKSgouXLhAtOtyGGJcL7/8MlOb7969eygoKEDXrl2xefNmWCwWnDhxgrB1TpgwgdTlOA6tW7fGkSNHcPfuXfA8D29vbzx69Ai3bt3C9evXAUASGmq1WlFUKvwjfFanTh2i3QeAfO4InudFhlseQyPLarXCYrGQXEGr1YqoqCicOnWK1Ll69Sry8/NFUhCOeEBrUpUDhaBFgQIFChQoUPCk8WyZtgr%2BLxATE4Pw8HDyioiIQMeOHUW5Z4CdXr9nz56IiopCdHQ0xo8fjzt37pDP4%2BPj0bFjR4SEhIhevXv3xuXLl7Fp0yZRWJ4g6WA2m5GYmIjU1FQYDAZcvnwZbm5u6NatG%2BrVq4emTZsiJycH9%2B/fR506dYih98knnzDXc%2BbMGRQXFyM9PR3x8fGitQlC5tOnTyei482aNcOZM2fQsGFD1KtXDykpKbDZbDh06BDS09PxzTffICIighhDLVu2RFpaGtLS0tCwYUPk5uaidu3ahKwFsHvtvL29mXl3NARDj%2BM4iZevuLgYGzZsYPbjaBwJ/9dqteT/2dnZsFgsMJvNpF9H%2BQVhTKPRKBKSdzT0AKm4ulqtRmhoqGh8lgC7kJcosK%2BqVCoRYygAZGZmYufOnWjWrBkAEDkMwG7g03MpDyxR9Q4dGKLqDtcs6z0z65qqw9JBprWxWVKRkr4ptlFWv3LEiCUdMxZBz08i7gxIleGpdTNtbxkC0BJnPH0GrPlR4s7SCoyxWYdHtWNVocskZydDuZneOlYz5vlS4desdF5JhDbdMSOEW87WyKpEH7pD9AIAphi6ZDoMpl3Jb0gsgW%2B6jL5uWOdCbyDrYKgyOfeqLFF1eizWftJ1GBeFpF8Hwq2y5iOZHyusna7E6JduxzoWOOTcA5BeA4wNlcyP8cOnZCzGpj%2BOqLrTvXrMfh%2BnDjluKrXlL4Xi2fvDUDx7Cp5KsHLoJk2aBC8vL8TGxmL27NlISEjAuHHj0KRJEzx8%2BBALFy5E3759nZKNREVFwcXFheTQWSwWTJ06FZMmTQLP84iLi8O6devQokULXL16FXfv3kVKSgpmz56N%2BvXrIzc3F71798bKlSvRu3dvUfhgRkYGYd0UIHiivvrqK7Rr1w4%2BPj5YsGABDh06hNWrV8PHxweFhYUYNmwYeJ5HtWrVANg9gikpKaLcN51Oh/4ODIaAWFjcaDSisLAQN27cEHmRLl26xNwLnU6HUaNGYf78%2BZLPDAYDwsPDkZubi%2BLiYmRkZMBms5VJ1iJo3rGYNx2JU9RqNaxWKzQajSQskud5%2BPr6YujQoZg0aRJT/mDOnDnYsGEDjhw5AgDo0aMHNm/eLKqza9cu1KpVixiAKpWK/N9qtaJChQooKCggTKXCeouLi5GTk4OjR49CS7GjabXa3%2BXZ27p1K1NUvVatWuKKFJsk/Z5mp2PVoQnYADHDHsDWy5X0XSqzUV6/ko5opkZWx4xF0POTMIUCUgo9at1MQl56foywZMn3GvoMWPMLCHBSgTE26/CodqwqdJnk7FiHSZXRW8dqxjxfipKQFdUtYbKkO5ZUkLc1sirRh%2B7nJ35PUzWypsPQy5REhzPCxSVl9HXDOhd6A1kHQ5XJuVdZ5yJpR4/F2k%2B6DuOikPTL%2BDGNno9kfoxIB0klRr90O9axgM6lpq8BxoZK5sdIO5CMxdh0umsZt6bzvXrMfh%2BnDjnu/HygHO6BJ4pnzDB7ElB2UMFTid%2BjkxcSEoL69etj6dKlsnLoADsro5BDl5WVBb1ej3bt2uG7777Dvn37cOLECbz00ksA7LlxYWFh6Ny5M4KCghAREYGKFSuiuLhYwibp5%2BeHhIQE0cvPzw8eHh6IjY3F%2BfPnkZ2djcGDB%2BPw4cMYM2YMtm/fjg8%2B%2BAA3b94UEZSMGzcOv/zyC7788ktieAjhhYJB6%2BfnB59SSnG1Wo1XXnkFFSpUQE5ODtGLEz5r1KgR0bBTqVTQarUwmUz49NNPSb0qVaoQz5fFYsGlS5eQlpaGjIwMAHbvVp8%2BfUSaelqtFlqtFlarVWTcmc1mItTepUsXwo4aHByMatWqYePGjRg6dKjkbLKysvDRRx8BkBK2AEBoaChatGhB3q9btw4WiwWenp7Eo8fzPNRqNXQ6HdRqtcjwvXr1KqpXry4ySoV1AMDRo0cJMU6xw6/cRUVFuHHjhmQ%2BZUERVVegQIECBU8FWIa2gqcGirGn4P8GznTyjEYjli9fjv/%2B97/EsCgLHMeRHLjKlStj0qRJWLx4MRo1aoSAgACo1Wp0794de/fuhYeHB27duiXKCdu3bx9%2B/PFHpKeni4wljUYjCRvVaDRQqVSIjIzEkCFDULlyZZw%2BfRobN25E//79odfrSdji8uXLcf78edKfXq9Hy5YtMWzYMFL2yiuvICYmBoDdMFqyZAlCQkJQuXJlfPDBB8STqFariTerSZMmWLt2LapUqYLKlStj586d2L59OxYvXkwIX4Q%2BhFBDLy8v4skDgJo1a8LFxQW1atUShV%2BazWZCtiKEjer1ely6dImMf%2BDAAWzbtg2AnbAmLy8PW7duFRltAoqLi4khFhQUJPk8Ly8P6enp4DgOkaUacDabDSUlJWSuHMcRxlSe51G9enXSvlKlSuQsHfPqhDy//fv3S8YUUIXyfJUHJWdPgQIFChQocAIljPMPQwnjVPDUQ9DJ%2B/nnn0U6eSzQItZ/BgYOHIgZM2agYcOGpEytViMoKAgPHjxAjx49SHlhYSF69uyJ1NRUuLq6okWLFqKQx%2BTkZGRSuT93797FwoULodFocOnSJWIUCbh8%2BTLy8vLQtGlT/Prrr9iyZYsoJPLtt9/G/fv3wXEcRo4ciaKiIoSFhaFq1aoiz1SXLl2IFMFLL71EvHFCX1lZWcjOziaG5P3796HRaEgfV65cgUqlwtq1a5GamiqaI8dxsFgsZK1FRUW4deuWSBqhRo0auHTpEm7fvg21Wo1Nmzbh%2BvXr6N27N9auXUv60mq14DgOJpOJsGEKIZYA8PPPP5OwzNzcXBIiarVaYTAYUFhYCJ7nwfM88fQJ%2BZAAUL16deTk5IDjOEydOhUffPABAPuPBRaLBcOGDcOwYcPAcRwmTpyImTNnkra/R3qBJapes6ZUVF2iEk29ZwkNyxGJprVzWbq9EmmlzExRuCJTfJruiCHWTvfLknCSiGHT6s4A8OgR4On523tqb1hiyXKUmiXaw3fvSkL7JPNzquYN58rSrHYylKQl%2B8c6TKpMjpA0U/xcxvwk%2BydHWFqOgjY1Qea1Ty%2BMPhdaaBqM65ghFC9ZkxyB6qwscRipHOVwlio4VSbnXmXdU5Iyeq9YG0rPh3HTS%2BbDqON0foyLTY5YO30OTLF7%2BhwKC8Xhn4xGkiLGdSOpw9g/uo6MW1PWWT5Ov6znNV1Gvyfby4zpVvC0QBFVV/DUISYmBvfu3SNeGkEnr3fv3nj//ffx4osvomPHjnjvvffK7Sc%2BPh4TJ05kUvUXFRVh1qxZ6N69u0SEnYX69evDZDJBpVLBYrEQqv%2B8vDyRcQKAaPHxPI/g4GBcu3YNrq6umDBhApKTk7Fx40bi9bFarSgpKUHt2rVx//59jBkzBg8ePMDcuXOJVIDgoQLs%2BYbLly9HUlISkV0QdPIcb3W9Xo/AwEBoNBpcuXIFLi4umDx5MhYtWoRHjx4RT5ivry9iY2OxcuVKGI1GFBUVlSlM7unpCU9PT2IwAmLDTKVSgeM4pzmTwjgChBy%2BsqDRaDB9%2BnRMnjy53H6Fus5IaFJS/sfemcfZVP9//HmXuXcWsxhmDBmDlGk0GFuyhJE1fC2pb1FIJVlaJFF9lYoSpeKn7EsopLGkSJSMonyzb1mSGGMYZpj1zr3398e4n%2B75nM%2BYk9I3dV6Pxzzqnvs5n%2B2cc933fb9fr9cB6tatS35%2BvmbPgoKCKFeuHNOnT6dDhw4EBgZSWFio2YsHH3yQ4cOHlzoPME3VTZgwYcLENYITJ/Tcxz8LffpcnX7nzr06/f4FYWb2TFyTGDp0qCgndDqdREVFiVLHiIgIjdfb5RAdHc38%2BfN1x/3tFowgMjKSpKQkDhw4wM8//4zVauXChQtcd911VKlSherVq7NgwQLKli3Lhx9%2BKM4LDg7mtttu0/DDrFYrKSkp4v/Dw8MJCwsTGbX%2B/fszfvx4YmJiBOctICCAkJAQOnXqRE5OjoY7FhgYqAmeLBYLAQEBHDt2TARR3bt3p127dowePRqv10tycjJffvklGRkZQuXU14cc6N13333Mnz%2BfatWq4fV6NcGey%2BXioYce4rPPPiMtLU1k4HwBl7%2BFgcPhoLCwkPz8fHE8LCyMe%2B65R/j%2BqVBUVMTrr79O69atdZ57MnzrDQgIoFatWuzevVuUmPrm9MsvvyjtG/Ly8qhTpw5zL/0Dka9Qpfv2228vO74/TFN1EyZMmDBxTeB/Wfb4Dyu5vBowd9DENQmfT15cXJzg0PlQq1Yt9uzZozxv7ty5pXLofDw6H9avX19iVi8nJ4cxY8bg8Xho1qwZn3zyCbt27WLHjh107NiRvLw8wsPDOXbsGFarlW%2B//VYzTlRUFE6nkzFjxogxLBaLeD82NpYwlTobxSblO3bs4IMPPsDlcnH77bczf/58OnTowLvvvovD4SAsLIz//Oc/NGzYUPgCer1e8vLyiI2NFVnN9evX89Zbb%2BHxeLBYLJw4cUKULzZr1kzsR2xsrEZdFBDB8vbt29mxY4fO1uDTTz/l1VdfxWq18vzzz2sya02bNiUiIkIYpUNxJs83RnZ2NtWqVdNcrxC/Ur7wSyV8NWrUECqkdrudKlWqAMVCPoGBgTz22GPccccdIrB0uVz8/PPPdOjQgT59%2BnDzzTdr5my1WomOjqZRo0aa4/Hx8ZcNKFUm9iXB5OyZMGHChIlrAQWRekViE9cOzMyeib8dOnfuzIIFCzQiIXa7ncqVKxvi0D3%2B%2BOPi/WXLljF58mTWr1%2BvGyc5OZlHH32UlStXikDFB5fLRVZWFufOnaN169bMnDkTj8fDxIkT%2Beqrrzh27BiRkZE0aNCAvLw84dsGxeWWCxYs4IMPPuCnn34iPDycZs2a6coPn3zySapUqcLChQtp2bIlqampDBs2DIvFwrBhwwgJCSE3N5eFCxdy%2BPBhoT5ZVFREmzZtiI2NZerUqZQrV46TJ0%2ByaNEiLBYLLpeLY8eOsXbtWqA4KBk8eDCTJk0SHDl/%2BDJymzZtom/fvjq%2B3i%2B//EKvXr2AYvET/2ze5s2bCQ4OJi8vj6ioKDIyMujXrx/79%2B/n66%2B/Bn7NoNntdqKioigqKqJMmTKkp6cLNc5t27YxduxYkTn0ZTwvXryI1%2Btl6dKlGgGd6tWrU7FiRT777DNcLpfGRsHj8eD1emnfvr3O2qGwsJBHHnmEsWPHEhoaSr9%2B/Zg0aZJYj8424TJQcfaqVlVw9kohZ6j4HFdE6FDxqGQCx5X0a4SnZISUomijOySvQVV6ZIAbZogrJA8utVHxY4xsje6gAe7alVyWKyYCyfNRcSkl/pVuLxT3mqG9keZshJdm4DbSz0fVSF73lTxTBs5R8hCvhMSl6qi0e18xPyM0UyPz0x0y0LGRa2dka%2BSOdMs0cJ8r121kgvI9oPqcLa3NlZyjmt%2BV9KMkZv/JMDN7vxtmsGfibwefT57X6%2BWxxx4jKSmJtLQ0Jk2aRFZWlhDRWLVqFVlZWQwbNuw3e/H5YLVaeeSRRxg/fjzPPfecsAQoKCjA6/XStWtXunTpwpQpUwgODmb27Nk89NBD3HrrrezcuZN33nmH8PBwIiIigOLgx%2BPxMGbMGDFGRkaG8InbsGGDyAB6PB7%2B%2B9//MmfOHJ577jnmzZvHpEmThHG81%2BulRYsWrF%2B/Hq/Xi9PppEqVKhw9epRVq1aJ7Nljjz3G9OnTOX78OBaLherVq3Pffffx5ptvAhAVFUWTJk2YNGkSAM8//zwvvfSSmJ%2BvBLVZs2YAlC1bVvjN%2BawcvF4vVquVxx57DJkm7LPCyLhkvu1T/4TickvfWEVFRWJtPviXQh48eJCQkBCcTqc47vV6iY6OJjY2Vv%2B6CCoAACAASURBVFNieeTIEY4eParJ9Pkwe/ZsvF4vGzdu5BfJRDwiIoLjx4/jdruxWq288847SrN4IyjJZ692bSlgLMUISeXBdEUGSyppbfkf%2BT/K3OlKTMEUbXSH5DWoOCYGPP50h4z4o0ltVN%2BPjGyNIS85aewruSxXbN4lz0flJyjxoHV7objXDO2NNGfVEkq5LOrnRZ6PqlGp5oaKY3K/Bs5R%2BgteifGaEYNGA2uSh7rS%2BekOGejYyLUzsjVyR4Z8KaX5GPJ9VA0u3wOqz9nS2lzJOar5XUk/lx7e8%2BeV9pMmrhGY4bKJvyV8PnnLli2jb9%2B%2BjBs3jnr16tGoUSNSU1P5/vvv2bx5M5GRkVfsxedD//79iYiIEH52UCzYMn/%2BfF577TWgODDLz8/njjvuYNmyZTzwwAO8//77dOvWjYKCApYvXw4UZ74AXn75ZWbPni3%2BZs2ahcPh0NguZGZmAsVCH7fffjvz5s3jl19%2B4cEHHwSKS11vvPFGUfZYUFDAyy%2B/zHfffUdsbCwFBQWEhIRw9uxZkWHyer0cP36cGTNm0KVLF6A4M%2BdfSuozdfet3ZcVmzBhAoMHD9aUZHo8HpEpAzSm44EG1L1WrFjBHXfcUWq7gIAAOnfurLxup0%2BfZsuWLdx6662a4yUFZq1atQLQGc9bLBZCQ0PZsmULULz/crZVDg4vB9Nnz4QJEyZMXAv4nzIMTOuF3w0zs2fimoOqpFKGzyfvjTfe0BwfMGCAxotv8eLFmvd9Xnzly5cnOjqaZcuWGZpTSEjIZRU7CwsLCQsLE8GfPxwOBz/%2B%2BCNQnOUKCgqiZ8%2Beunbh4eGcPHmSffv2ceDAAZKTk%2Bnfvz/Tp0%2BnWbNmvPzyywBs2bKF9957j8TERB5//HGOHTvG6tWrgWIO2%2BTJk2nRogWLFy%2BmXr167NmzRxPMffLJJ8TFxbFr1y7ef/997r//fk6fPi3e9xe/WbhwIR07dmT58uUsWrRIBFQRERGawA4gJiYGu93OsWPHAGjcuDEbN26kTJkyhIeHc%2BrUKV1GdcqUKSLrGR4eTnZ2Ng6HA4/HI7JxNpuNNWvW8OSTTwJQoUIFjUiMj2/YokULvvnmG%2BX18Ue5cuUAKFOmDG63W4i1%2BIzYa9Sowb59%2B5Tn7tu3T3j7mTBhwoQJE38HyG4gfyr%2BYYHZ1YC5gyb%2BEXC5XKxdu5bU1FRat27N/v37SUxMVLZNSEgg2t%2BT5zcgOTmZmjVrir9atWrRvn17zp07x/nz59m2bRsAKSkp9OzZk7p16zJ//nw2b95MWloaJ06cICAggGXLlgn1TR8CAgIICAigb9%2B%2BIggNCgri2WefZenSpfzwww%2Ba9uvWreP48eNCrASgW7duzJw5kx07dtCiRQt69erFunXrNEFt27ZtSUxM5KGHHqJGjRrccsstmozi0KFDxf/n5eWJrKTPI9BXMuoPq9VKQkICx48fJ/TSvxrZ2dl4PB4uXrzI%2BfPnadasGYGBgQQEBIjzV61aJco7AwMDSUxMZOXKlSxatEjT9x133MH27dsBuP/%2B%2B4Vwi8PhwGazUVRUJEphfRg8eDBz5sxhypQpGvN2Xyb0uuuuw%2BFwaPiYOTk5NG7cGIfDQUBAAOHh4RoxH59CrBGYAi0mTJgwYcKEiasNM7Nn4m%2BL0aNHC76Xz4uvT58%2BgkMnq0qqMH/%2BfE6cOKERe/HHDz/8oMvmjRo1io4dOwLFPLNvv/2WZ555hqpVq/Loo49Sq1Ytdu3aRc%2BePXG73WRkZBAZGUnv3r3JyckRgYoMi8VCYGCgLiBo06YNLVq04IUXXtAEbY0aNeKuu%2B4iNzeXgIAAXC4Xdrsdl8vFrl272Lt3L99//71oX61aNY4ePQoUZyILCws5d%2B4ct956q8aKwNeXCkuXLiUgIAC73U5CQgJ79%2B4FioOYzz//XPw/wH//%2B19xXk5ODl999RVQnKnz798nFJOenk56ejpt27bVBGC%2Bkl2fpYUvw%2BlbBxQHigcOHNDMdfLkyco1%2BLKgJ06cEObrPgQGBhIaGir6zcrK0pzbqlUrEXSWBsMCLbNmwQMP/Pp69mzo10%2B8VAoHpKaCXwCr8mmWvc5VOhs6bv4XX0Dr1uKl7E2snM/atSAHwdLgBnzD4euvoXnzy7dZsgT8suIKb2xDOhu6vZD2HBQe0DNnQv/%2BJa5ROZhK0EGetAHRDN11UIkqSIuSfaZV/SpN6eWFGzGkls9RGJKXZu6sPE%2B1f2lpUNFPOfDHH%2BGGG359feoUxMRcdkl89BH06HH5NqqHSr5xPv4YunX79bURUZfMTIiM1LaRDL2NCPsYEumRbhyluIl8PyrMzw3d16UJ0Rw/DrGx2nNksRDVDSmJMMmXHyj2U/P3apOfTcX9qFuTQuxJ99wp1n1F4knS5hjR41E9L/KyVMssrY3vdZmAAuB/JNRiZvZ%2BN8xgz8TfFn%2BEF9%2B//vUvTp8%2Bzeuvv657b/jw4dx0002646GhoUT5/ePYrVs3nn/%2Beex2O506deL999/H4XCwatUqmjZtytSpUwkLC6N9%2B/bY7fYSxWG8Xi85OTlKK4bnnnuOTp06MX/%2BfDGn//u//6Nr1664XC4RnGRnZxMREUFhYSEOh0NTallSVsnHDfThqaeeYty4ccq2UJxFTUxMpG7duiLY80dOTg4Wi4VGjRrx3//%2BVxc4%2Bq8/ISGBwMBATWDo2wv/%2BS1durTE%2BUBxsO/z0vOpgfp76/kHsHXq1AGKS2r9LT18Aab/fP0DZIBZs2Zddh7%2BUAm0DBkyVC/Q4h/ogS7oUAoH%2BAV6oC7BkeMQlc6G7su2X6AH%2BkBPOR9VtlMaXLUG3ZdZKdBTtpHKn%2BVATzWWSptBtxfSnoP%2B%2B64m0AP9BqsGUwk6yJM2IJqhuw4qdRhpUariBblfpeuLvHDF/HSH5HMUfF15ykoBQPk81f7J3/T9Az3QBXqq6cmBnrKN6qGSbxz/QA%2BMibrIgR5oAr2SuilVhER1TLpxlOIm8v2o2whFxwYEZHTPvBzogV4sRHVDSgGYLtADvSm3/Gyq%2BOPymhRiT7rnTrHuKxJPkjbHiB6P6nmRl6VaZmltDFDrTVwDMMNlE39b/BFefGFhYTidTpo0aaL7czqdQmTkcl58UBwAXrx4kYKCAurUqcOuXbvYuHEj48aNY8GCBSxevJipU6fSsGFDbrnlFmUfb7/9Nh6PR2MT4ENsbCwDBgzg7bffFvw6p9PJDTfcQPXq1Xn//fcJCAjA6/WKAK9MmTIkJyeLMsSIiAg6dOgg/Ph8SpqBgYGabKO/EM2SJUvYtGkTmzZt4osvvhABkd1uZ%2BPGjQQEBFCmTBnh5%2BeD1%2Bvlxx9/pEIJ8l6%2BfT1w4IBOWMXpdAovv%2BjoaDwej0Y0Zvjw4aIUs0aNGixfvpwvv/ySvn37AsVlnxUrViQxMVGs0V9oxbfW0NBQTaml1%2BslKChI%2BAoCmkAPSjJKV8M0VTdhwoQJE9cEVEqefxZMgZbfjX/Wak3841ASh85ms7Fz504lh%2B7VV19l/fr1Gpl/FYfOf4yShFx8XMGsrCwyMjLYuXMnZcqUoVu3biQmJtKwYUNmzpxJdnY2CQkJ3H///axbt46RI0eK8tH4%2BHjq1avHI488Qo0aNUSgc%2BLECUaOHCnW9tZbb5GTk6Mx9r733nvZtWsXffv21QQ0drudrKwsjh8/LgK8HTt2kJqaqsvweTweTbnhsGHDxP/36dOHl156iezsbMqVKycyblu3buXQoUO4XC4uXryoKQP1BXKZmZlCvdJms9H8UubGarVis9m46aabhMUBFAenZcuWZeXKlSxcuBBAlFkePHhQ9B8VFUWlSpXEHt1zzz107NhRlINCcUD3wQcfsHnzZiZMmKAxbvdlQR9//HFd5taXeS1TpgyRkZGMGDFC836DBg0wCpOzZ8KECRMmrgkYqIS6ajCDvd8Ns4zTxN8eKg7ds88%2BS8OGDUvl0PWRyz8MQMUV9BmFb968mcOHD/P0008TEhLCu%2B%2B%2Bi8ViYfHixfTr149WrVrRqFEjvvvuO43QSV5eHjk5OZw9exav18vo0aOB4uDDP9PndrtFsAJQr149rr/%2BetLS0igqKhLBWFFRERUrVmTIkCFkZmaycuVK4FelTY/Hg9PppLCwkMGDBws%2BHPxqou5r9%2B2337J//37mzp0LwKRJk3C5XAwfPhwoVt08duwYZ86coVy5ctjtdn755RcSEhLYt28fXq8Xt9stTNRbtmxJSEgIK1euxOFwMHXqVODX8s%2B2bduKTF5GRgYVKlQgOztbBJRPP/20mGteXh6VKlXi5MmTQqHT7XZz4MAB4uPj8Xq9WCwWEVACQjF127ZtOmuF0NBQDh8%2BLCweZHVVI3YSPqg4ezf6ZSgFZB6Q9FrJs5EIHCp6kUx/UbXRcUmksVX8Ex0vxAAfRkUDMsLzKo28puKxnDkDfslpJa9PNx%2BJM6UcW24jD6Q4R8m3lPhEqj2Wuy6N8wPoNkO1bpkupuQ/SZuj5PXJ40uNrthMXrr/VLeEbkLyQlXXRb7gWVkgc6dLeQ6V/cjcMCOELdXDYMSc3YAJuO6zwgihTN5PRRvdc6b6UCqN66l6WI3sjXQsPV3hByevQboHVN3q9k/xwOgOGeDsXYkxvPJ5McDrM9JPabfAX8FT3cTvhxnsmfjbQ8WhW7VqFU6ns1QOXWpq6m8erySuYEFBAQ0bNsTj8TBx4kTKly/P7bffzoMPPki7du346quvuOOOO%2BjWrRu7d%2B8WAVVISAj169encePGvPTSS5QtW5b%2B/fszYcIEunfvLnz1fJg4cSJr1qwBYObMmWRmZvLFF19w%2BvRpOnfuTHBwMLm5uaSlpTF06FDsdjt2u5369etz4MABzp8/DxRnuLxeL2%2B%2B%2BSYNGzbkxIkTALzyyisikIPigPbYsWO0a9dOHPMXUNm6dStJSUkUFRWRkZEhOHldunTh8OHDFBQUYLPZxPH169eLALawsJB7771Xk8kDOHz4sLi2GRkZ2Gw2nE4nBQUFuutx8uRJoNjD0N9w3jdHX7DpQ//%2B/RkyZAhr1qzRefFZLBYGDhzIq6%2B%2BisPhwOVy/SYjdX%2BUZKoeX6uWtqH8hVJ6reTZSP86q%2BhF8hd0VRvdFwxpbBX/RPfl2wAfRkUDMsLzKo28pvqSIn/PV/H6dPNRiTnJY8tt5IEU5yj5lhKfSLXHctelcX4A3Wao1i3TxZT8J2lzlLw%2BeXyp0RWbyUv3n/K3FXlC8kJV10W%2B4CqRrFKeQ2U/MjfMCGFL9TAYMWc3YAKu%2B6wwQiiT91PRRvecqT6USuN6qh5WI3sjHVMyA%2BQ1SPeAqlvd/ikeGN0hA5y9KzGGVz4vBnh9Rvop7RYQl%2BV/WXXyD8vCXQ2YwZ6JvyV8XnwlKS76MmI%2BDp3stweU6renGuPUqVNMmDCBCRMmiHFiY2P597//Td%2B%2BfYmOjubs2bM888wzLF26lCVLlrB69WqaNGnCvn372L9/P9WqVRPliTt37hR979q1CyhWgPzpp58A6NSpE3FxcZp5vf3220BxJmzmzJkkJibSr18/Dh06REREBI0bN%2Bann37i2LFj5OXliezVoUOHaN68Odu2bSM9PZ2AgADy8/MBRLlrZGSkhmfnr1bp%2B%2B/jjz%2BumY/H4xHnOxwO7HY7BQUFzJ8/XwS0brebqKgoYbPgL4Li49CpgqoLFy7QpEkT3G43drud1NRUIcACxSWhvtLIM2fOaM4tKUiLuSTgYLPZNFlSKLa62LhxI4DuPSgWaHlAFlQpAaapugkTJkyYuBbgjSiL6rcpE9cGzHDZxD8Kf4bfXqdOnYRoybp16xgwYADjx48nJSWFvn37kpuby3/%2B8x%2B8Xi8DBgzgpZde4uLFi8yaNUuUB8pIT0/nrbfeonr16pqs2eXg9Xq5cOECW7dupX///rzzzjs0b94ct9vNsWPHaN26Nd9%2B%2B63gpZ09e5bVq1dz/vx5atasyezZs6lQoQLDhw8XaphWq1UYwEOxMmft2rWJjY1l%2BfLlwgz%2BnXfeEW0sFosoR7XZbFx3SdXsxIkTBAYGYrPZsNlsItBr1KiRphRT9uyTUaFCBbZs2SJKMQMCAjRCMffffz%2BATvUzKiqKgIAAQkNDNUbovizwLbfcQtWqVXXnjBw5Uhi99%2B3bVyNeIxvJmzBhwoQJE9c6LNlZpTe6WjA5e78bFu%2BV1iCZMHENIDk5mYyMDKE46ePQ3XvvvQwfPpx27drRvn17nnjiicv2s2zZMkaNGqVTlYTiDI0vY5WbmytKCn148cUXWb58OU6nkwceeIBevXphs9mwWq24XC6sViuRkZGcO3eO3r17Ex8fz8iRI4FiEQ%2B3201BQQE33XQT48aNo0ePHnTr1o2lS5fidDo1KqMA9evXZ8aMGaSnp3PbbbdhtVp54oknqFq1KkOGDCE%2BPp79%2B/cTFhZGXl4eVqtVlD8GBgaKbJ7VahWm7T169BBeg/6ZM4CqVavy008/0aVLFx555BE6duxI9erVycrK4uzZs7r98i/ZVMFqteJwOMQ87Ha7rtTSH4GBgVitViwWCzk5OSX2q4Ldbsdms%2BH1ekWm7q677uLrr78mLCyMH3/8UWQGHQ4Hu3bt4sCBA3Tp0kXM1V9UpUaNGnzyySeGxn711Vd1ZZyDBg3SmNabMGHChAkT/3OoeKx/FvxE4f5QXFJd/yfgnxXamvhHYujQoaSkpJCSksKGDRv4/vvvBefMqN8eFMv8%2B/rx/4uKihJjWK1W2rRpo3nfZ28QEBBASkoKtWvX5umnnyYuLg6Hw4HD4eDMmTPceeedgn/nC2BSUlL45JNP%2BO6770hJSdGpQ7788su6%2BbzyyitibQBly5Zl9erVjBgxAovFwv79%2BwkICBBBji/jZbVahQ8dFHvI5eTk6HiL8u9DvpLSTz75RJRK1q1bV6PA6Q//oM2hkHP2er2aAKpfv34lKqFCcQCfm5ur9B/0h39G1BeMFxUVUVBQoBFoefzxx%2BnevTtHjhzh%2BeefF8fLlSsHIMRsAF3wb7SEE4o5i6%2B//rrmr2VLhSfdJYEagXff1bxU/lz38ceal6qEsZyEvBRbayAlQyElpdR%2BdR1dMqnXQPJu1A9ULJqgwYoVpc9v5kzNy/R0xfzkkxQbqBtb6heKBW00kK6LboMNjo1UbqzaGyRuqm6%2BCu4q0g8h8jBAsRDI5fpVzUfxA4tuynLJs6Jj%2BZDqftR1rPoB6JLCr4Bkj8KlCgJ/6K6l9PyAQoxQ9e%2BGvIiPPtK%2BVl1L%2BZhifvJYqstr5N7SbZd0jyp/T5MfItVNYeS66PxUpfcPHdKfI983qg%2BcS8JbPiiLK%2BbN076Wr52Ra6l4YHSXU7FuuY2RW0DuR/UxIZ%2BjuifkY1fSRmyDqRR9TcPM7Jn4WyMhIUETXMgcujFjxrB7927uvfdeFixYwKFDhwgJCaFp06ZUrlyZwsJChg0bxrPPPsvHH3%2BsNAlPSEigW7duvPLKKyQnJzN48GDhuedyudiwYQNPPvkko0eP5s0336RatWrk5uZy6NAhgoODady4sQiMpk2bxqOPPsoXX3xBdHS0UKj0ITk5mZycHEaMGMHIkSOZN28et9xyC8nJyXi9XtavX68Jam677TbSld94i%2BFwOAgNDRUZODlr53A4qFatGitWrODee%2B8V3Ls/CnIWzwd/s/Lly5fz5ptv8uWXX2KxWOjRowe9evWi2yXDYt%2BcGzZsyHfffadcR0BAAFarFbfbTVFRETfddBM9evTgww8/1JSlQrG3n9vtZtSoUSxfvlz0U7t2bZYsWUKnTp105/hwww03sGrVKkNrV2X2hg4ZwqDBgw2db8KECRMmTPwpUEoH/0nwE4T7Q/H661en378gzMyeib89Lseh69y5Mzt27OCll17irrvuIiUlhSlTpnDq1CmmTp1aIofuchg9ejRJSUkkJSVRu3ZtRowYQZ8%2BfejevTuZmZn88MMP9O/fn9WrVzNr1izcbjdbt24lVJJDPH36tOjH93fy5EmhlunDDz/8QH5%2BPllZWWzZskXzXufOnYFiCwb/0lJfWavb7das8e677wYQma7CwkKdH1ylSpWECExJcDqdmqDT4XAQEhJCYGCgGBvQiMD44/jx4%2BL/lyxZIrhzXq%2BXjz76SAR6vmPwq21EUFCQyPLZbDYGDx5MQkICBQUFwuogIyODOXPm8NNPPxEdHU2jRo1Ef%2Bnp6YJD6B8w%2BrKNvj3ywX89PsVSIzAFWkyYMGHCxLWAYz%2Bb8izXMkw1ThN/ewQGBiqtF9auXaspu7NYLFgsFgoKCgSHLUCpeXx5lGS94Hudn5/PyZMnSUhIICAggLp16/L5559zww03aObi9XoZNGgQbdq0Ecfvu%2B8%2BTXnkgw8%2BiNvtFkHJAw88IEojx4wZQ/fu3ZkxYwbnz5/njTfeIDs7m1GjRgkFztq1a1OhQgW%2B%2BOIL3G43MTExItsWGBjI%2BfPnxR74gp1bbrmFdu3aceDAAe6%2B%2B262b98u%2BvKph86YMYNnnnlGBD8%2BJVGAjh07CuuEiIgIvF4vp0%2BfFu/7%2B/gBLFiwQLO/JRUj%2BPr0er107dqVuXPnUr58ef7v//4PQJStQrEyp4/vmJWVJTKCAM888wxdu3blm2%2B%2BwW63i726cKnWyz9onjhxIl988QWrL5Uq/hafPdNU3YQJEyZMmCgF/zAxlasBcwdN/CMhc%2BiGDBnC3Llz6dKlC0899RTVqlXjvffe03nYGUG5cuWIi4sjLi6OmJgYjYBKVFQUDoeD1atX06NHD%2B6%2B%2B26%2B%2BuorJk6ciNVqZeIlwrDVaiUkJIRZs2ZRtmxZ0Z/dbtfwywoLC3G73Xg8HjweD263m7y8PIYMGULjxo157733gGIO3dixY3nhhRc06pE//PADn332GS6Xi5iYGNauXYvH4yE/P18ENTt37uTixYtkZRWrcX388cckJCSQkJAgAj2AO%2B%2B8U/x/pUqVCA0NFdm9m2%2B%2BmTp16nDzzTeLoMxmsxEUFITdbtdw31SWBkaqzX2qnlarVYikpKen4/F48Hq9NG3alLJ%2BvlcFBQXk5%2BdTUFCg6f/ee%2B9lzpw5xMbGCr9CKM425uXlabKnI0eOFIEeQPXq1Uudpw9GOXsyVURHHVHsjUyJwy9TKrB1q/b1wYP6NlLW9dQp6X0VQcYvcAc1N0zHHVERtPbt07xUViNLa9dNR1F2LA%2Bl4tAgldcquWu7d19%2BbAV/R%2B5HNbaOPqSqLti8Wfs6S6uUp%2BLm6MbKUqjryfeEzH8D/QbK54BuodItoV64gQsjJ8NV10WicOkocDp%2BnmIsFW1Ovg5Kflsp/aiWLa9B9UjJbQxQ4gwRvUrlVqK/TZTPizyWAQ6uvAbVR4B8jura6QokFJ9j8mfHJbvVXyHfNOifoU2b9GPz1Veal0qeqfxAq0TE5GfcAMdVd8wIl1L1zMvH5NeKCpQ/HaYa5%2B/GP2u1Jv5xiImJoX79%2BuK1ynqhdu3a9O3bl5UrV7J9%2B3Y2btzIuHHjaN68ubBeqF%2B/vvBfK22My6Fv374UFhby008/4fV6yc3NZevWrQwbNoyJEyfy4YcfUlBQQJUqVQgLC8NutzN48GB69uxJUlISaWlp5OTkcP78eQ4cOMCcOXOAYvuBTZs2YbVaee211%2Bjfvz/lypVjxSVRi0GDBrF%2B/Xp27tzJ%2BvXrqVSpEqAVLTl58iS//PILdrudiIgIkcnKz89nwoQJIgh75JFH8Hg8vPDCC%2BLcwMBAOnXqJPqbN28ev/h9UXS5XOTn52vsDxwOB%2Bnp6Zw8eVJniyAjSnKvlRVIoTh48wW%2Bsr9iUFAQ3377LU2bNr3sOAAtW7bk8OHDvPjii3z66afiuNfrZf/%2B/YJLCPrAtFWrVqX278OKFSsYPny45u/rjet07WQPaJ0ntIJHIZtjExurn4Bf6SoAN96obyNlKnWPgGwaDSDZlag8rHVuGqqMqCRGpDRLltaum47iuZSHUibv%2B/XTvFSavt988%2BXHVtyjpflKg8KkXGXo3qSJ9rWkkmfEn1qprCffE5Ur69vIGyifA7qF6hxsVAs3cGHkZLjqulSpon0tG19L1fLKsVSG8/J1UJlYl9aPatml%2BbCr2qjG1vWt4ldJjXTnKASz5NvEiMG3qlFpa1B9BMjnqK6drkBC8Tkmf3ZUrCg1kG8a9M9Qs2b6sWnRQvNSWdghP9AhIfo28jMuXwcDZvLKNvJ1UD3z8jH59aUNVmyRiWsIZrBn4m%2BPkjh0Xbp04cKFC5RRfZlS4OTJkzoOnY9HZxS9e/cmOTmZoqIiUY5psVgoV64cMTExDB06lNTUVC5cuIDVaiU%2BPp4tW7bQpEkTUlJSKFeuHF6vl2nTppGZmcmqVauIiYnBbrcTFRVF3bp1%2BfiSkpzNZiPk0j8s7dq1o06dOiQmJlKvXj3S0tJ4/PHH%2Beabb2jTpo0oPwwKCqKoqIjz589rOGWLFi0SJZm%2BbOHo0aPF%2B/n5%2BdSrV09kyD7//HMSExNFsOwfVPoCtby8PBEs%2BUolobi0s0aNGuK11%2BsVZbU%2BeDwe0ae/J59vLv/%2B9781x3Jzc3G5XCxatEgcCwoKIigoCKvVKtZvtVq5ePEiBQUFPPvss0yYMEG0t9vtnDp1inr16lESjh07VuJ7MkzOngkTJkyYuBZgyVera/8p%2BItm9goKChg1ahQNGjSgWbNmzJo1q8S2AwcOpGbNmpq/DRs2iPfnzJlD8%2BbNSUpKYtSoUSWqmV8pzGDPxN8eV9t6wRfQDBo0iJMnT2qCS/8/X5ZtypQpPPXUU8Ljzel0cvbsWe6880569%2B5NbGwsubm5FBQUsHHjRhITE/n666%2BpXLmyEDrxeDzMmjWLzz//XIiOALRt25YtW7aIANQX7MVeyuz4grGXX36ZgQMHUrZsWWrWrCn6qFy5MmXKlKFcuXKiXNQnQOI7Nzg4mC5duihtE3yoVKkSZcqUEUbpr776KoAwOA8JCeHmzf8vTAAAIABJREFUm28W8/JHx44dOXLkiOaYfI3GjRuH3W7HYrFwyE%2By2263s27dOiJ1qa3i%2Bf/sV66Tn59PXl6eKFuF4iByxowZAKSlpWnEV6xWK4WFhbp1%2B2cZf4tAiwkTJkyYMGHi2sT48ePZvXs3c%2BfOZfTo0UyePJnPPvtM2fbw4cO8/vrrQixw06ZNotJozZo1TJ48mTFjxjB37lx27NjB63%2BwUqgp0GLibw8fh06FWrVqsVvi3vgwd%2B5czpw5w7BLhp52u13Zjy8gGD16tC7L5I%2BgoCDGjBnDww8/TN%2B%2BfZk3b56waXjiiSeEiInFYiEwMJBWrVpx8OBBXn/9dTp37szChQuBYgERnzF6VlYWGzZswOv1kpCQgNfrxev1snz5cgYOHCjGnjFjBpUrV2bPnj10796dN998E6fTSVJSEjfddJP4gCpTpgwWi4X8/Hw8Hg9Vq1YVpu4%2BEZWcnBwqVapEfn4%2BN954IwcPHiQ6Ohqr1SoULHfs2EF4eDgBAQEUFBTwxhtv4HQ62bJlC5UqVRIlnsuWLaNp06aaX7E%2B%2B%2BwzkbWzWCxcf/31moAOYNSoUXg8Hux2O2vXrhXHi4qKuP3223V7HxkZidVq5cKFCyJLWL58eZKSkggODiY1NZWMjAwsFgt33nknM2bMoEKFCkydOpVu3boJ0/WgoCCNmExgYCARERGkp6fj9XqFt6ERmAItJkyYMGHCRCn4C/LrcnNzWbJkCdOnT6dWrVrUqlWLH3/8kQULFtC%2BfXtN28LCQn755RcSExN1lBQopr306dNH0EBefPFF%2Bvfvz/Dhw3VevleKv94OmjDxJ6Jz587s3LmTbdu2kZycrEmxjx07lg8//FDw4nxISUkRHLpmzZqRmZnJ%2BfPniY6OJi4ujm3bttGvXz8hquL7K1euHAsXLmT8%2BPG6eYSGhhIREcHatWuF7UBKSgqJiYnExcXx8MMP8%2Babb3LixAmmTZtGQkICqampVK9enR49egAwf/58hgwZgsPhIEUyv543bx7t2rXjnnvuITAwkKysLMaNG0f79u0ZPnw4hw4dwm638/3333PhwgVRYmi326lWrZrOsHzqJaNvXwaufv362O12wZlzuVycOXNGBFbp6ekUFBTQsWNHjh8/jt1uZ%2B/evdx2220UFBRgsVhEJjEzMxO3243VaqVmzZrC6sAXAHbq1Inly5cDxcGdv3VEcHAwY8aMERlNHzIzM4mOjhZZuEaNGlG5cmXWrVtHSkqKxs7C9%2BH6xBNPsHTpUpHR9Hq9QtjHh/z8fE6dOiXa%2BAzmjUAl0NKmjcJUvTQCvUqIQVIyUPHyjfgK64Q%2BpEYqwQQd/VLVcWlCESh0AVRCJbLKgzSWTqhGNZhC7UInkmFEIEFep6JM14gRsqzyoBJ9kIfSTc%2BAEbtKCMTIPWFkDZ9/fvmxVfOTDxkydFc1khcmv1YKQEkHFAvXDWVEEEMeW3Ux5ftEdZ/LD5rqmZeOqZrobnX5g0GxN7qxFRdcHsvQ2EaupZGbTTpPKcAjXwepkfI5lCesU3XRf76o1i3rsagur3xMviVUGilGxIqMCNzIx%2BTXog8/qsWfjqtUxnn69Gn27Nmj%2BTutU5NSY//%2B/RQVFZGUlCSO1a9fnx07duh%2BtD1y5AgWi0VZyeR2u9m1axcNGjQQx%2BrWrYvL5WL//v1XuGF6mKbqJv7WkE3OVXj%2B%2BedFduiee%2B4hISGB6dOnk5GRwYMPPsjYsWPp3r07mzdvpk2bNqSkpPDUU0/RqFEjzp8/z3333UdwcDCrV68mMjKSZcuWMXnyZNavX68bq2HDhuTm5vLYY48xf/58zl36R8jlcgnuWHx8PPn5%2Bezdu5c%2BffowatQoCgsLadWqFWfOnMFut5OamkqTJk2wWq1ChVOGv21A9erVGTFiBNdffz179%2B7lueeew%2BVy4Xa7CQ4OJiAggIyMDGGiXlBQIAKXKVOmMHToUM0Y119/vej3888/JyoqCrvdTlpaGk8//bQIaK1WK1FRUXz00UdAcYatZcuWnD59mpkzZ7Jo0SLWrdOLkvjg49KpvPiuFu69914WLlxIYGCgbtz333%2Bf9evXl1ibf%2BDAAcPjqEzVhwwZyuDBg377pE2YMGHChImrhOPH1Tpffwr89AH%2BSLxTvrxOzG3w4MEMGTKk1HPXrFnDmDFjSE1NFccOHz5Mx44d%2BeabbzRUktWrV/Piiy/StGlTtm7dSkxMDEOGDKFFixacO3eOxo0bs3r1avG9CqBJkyY8//zzdOjQ4Q9YqZnZM2GCF198kYEDB3Lx4kVmzJjByy%2B/TI0aNfjwww/p3bs3t956K3v27KGgoIC5c%2BcyZcoUevbsSVxcHHXq1CEyMhKPx8PcuXNLHSs0NJR//etfrFmzRvDZbrrpJiZOnCj4hE2aNCE4OBir1SoCLofDwU2X1AmdTidz5szBYrHw/vvvU716dQIDA5k9e7ZQ4QwPD6d169aiVHDixIm0bNmS2NhY2rVrx/z588nPz%2Bfdd99ly5Yt1KlTh6SkJAoLCzl69KjILlqtVvLz80XWzWq1irLV1atXk35J0zojI0P8mlWpUiWee%2B45oJibt3HjRqKiooTfYEREBOHh4TRu3JgpU6YQERFBQkKCzlS%2BV69eLFu2jOXLl%2BsEXvx5cuGSelitWrVKvQ5QHAw7HA5NX3369OG2224D0NhP%2BGCxWKh8SaHQolK7%2Bw1QCbSYEi0mTJgwYeKvht/5z93vw1XK7N19990sW7ZM8%2BerJCoNeXl5Ov6%2B77Ws0n3kyBHy8/Np1qwZM2bMoEWLFgwcOJBdu3aJH5RVfalsqK4UJmfPxN8aquyaDKvVquPQ%2BcNut1OlShVq1arFwYMHNel2gC%2B//JK9e/dSXqUxr0CDBg0YO3ZsiVnHIUOGMGTIEG677TZNGv/YsWPMnDmTxYsXs2PHDh544AHq1q3LqVOnCA4OpkmTJpw6dYrAwECx7oSEBFq0aKERcQGIj4/n/fffFwGkzWbD4/GQmJhI//79hQrl%2BfPnadmyJVAcuBUWFlKtWjUOHTpEYWEhx44dE9lFXyC4Z88eUQpZtWpVMeaoUaNo0qQJLVu2JCUlhTNnzhATE8P999/P22%2B/TcWKFTXG5QsXLqR79%2B7cfPPNOBwOURIqZzGzsrKoV68eBw8e5OLFi%2BzZs0e35zabDYvFolH99P2/xWIhICAAl8tFfHy8sIG47rrrNBw/KC7x/OSTT8Safw9Mzp4JEyZMmDDxv0F0dLQQ2PutcDqdumDM9zpQ8uB49NFHue%2B%2B%2B8QP0/Hx8ezZs4fFixfzxBNPaM717%2BuP4uuBmdkzYaJEqDz5EhMTlW0TEhKu%2BEOjJDRr1oz09HTBIfz5558ZMGAA3333HVu2bMHtdnPixAkuXryI2%2B2mZ8%2BejB49mrS0NEaMGMHRo0dxu91UrlyZZcuWkZycrOm/QYMGhISEkJycTGxsLLt372b//v1MmDCBtLQ0mjZtSk5ODkePHsXlchEYGIjL5eLgwYMiMMzKyhIBik9ZdPr06bz99tsA7Nq1S8x/2bJlhIaGsnDhQk6fPk2LFi2oWbMmb7/9NkFBQZpMl8fjISwsjG%2B//RbQBkb%2BmT2r1UpQUBBpaWniA7Zu3boaXh0UB4iy4XlAQIDI7PkCvLZt24qa/SlTpohzfONFRUXx888/izX71%2BD7m90bgYqz17q1grNXGpHKAFFExb2SeSIqPoeOByJ1pOKf6OhYqo6lgN2I2bSSuCKXL0tjqXhpunMU%2B6db15UQYhTnGOLsSf/oGxla148B03LV3sjrVt038vap1uCnmaQcWzU/IybbukZXwtlTTVi%2BB4xw9lT349Xi7MnHFPes7pDKeb20dV4hZ083lGJ%2BV8TZk08y8EGhLJgoxdjckFH8mTOldqvqx8jnrHxM5vkZ8WFXrVs%2BT/U8l/bPi297K5dVTOLPwl/QeqFChQqcO3dO8wNyRkYGgYGBOo0Dq9Wqq0CqXr066enpRERE4HQ6OeN3f/nsr1RiLlcKM7NnwoQfRo8ezUsvvQQUl/EFBgYKT74pU6Zc1pNv0KBBbN68maKiIgoLC6lZs6bmfYfDUap5uD86d%2B7MRx99RFBQEK1bt%2BbgwYM8%2BuijjB8/nszMTKKiotixYwdBQUFcuHCBu%2B66i3PnzjFv3jwyMjLod8kYWv6VSYXrr7%2BeBQsWMG7cOHbs2AHAkiVLsFgsfPDBBwQEBAgRE19Wy2Kx4E/59TdR92XK/OH1enn44YeV4%2Bfl5WkUOV0uF1lZWbz55ptMmTJFEwi63W4RfHk8Hp588klSU1P58ssvAdi%2Bfbuu/8jISA4ePAgU/yJXVFSkvBbNmzcnOTkZq9XKnXfeKT7I3W43FouF6OhozZp95a6%2BufjPrTSsWLFCydmrVSte21A25ZVfq%2BprJIEanVE3eh9flWGx7odFqSPV46DzU1Z1LO2REbNpvXuyvh95LJVBte4cxf7p1mXE1Fhep%2BIceZ0q83PZUNnI0Lp%2BDJiWq/ZGXrfqvpG3T7WGtvJvFgYM042YbOsaqTZHXpj8WjVh%2BR5QLNzQ/SivSx5btSi5H9VDJR9T3LO6Q6rPodLWqbLTke9rxf7phlLMT9dG3ivVtZRPMvBBobospRmbGzKKV1TuyN2q%2BjHyOSsfk33Xjfiwq9Ytn6d6nkv750Vsb2GheiJ/Bv6Capw33XQTdrud7du3i2qvbdu2kZiYqPvh95lnnsFisTBu3DhxbP/%2B/dx4441YrVYSExPZtm0bt9xyC1D8HcZutxMfL30X%2BB346%2B2gCRP/Q/weT77Ro0eTkpLC0KFDKV%2B%2BPLNnz9b8LV68%2BDdl/%2BrWrQtA48aN2bhxI0eOHGHcuHHccsstwnLgs88%2BIzc3l8jISHr27En58uVxOBxMmTJFZJ%2BMmnMmJSUxffp0LBYLd9xxB23btsXj8fDxxx/jdrtxOp3YbDZat25NYGCgxoNOhsy/k2GxWGjTpo3yPX%2BeXK9evXTKoqAt5XzllVfYvHnzZcdzuVwiCCsoKCAgIIDAwEBsNpvGUzAlJYXrrrsOKP51zV/V0xfkXY4TmKZQbCsJJmfPhAkTJkxcE/gLBlz/SwQFBdG1a1deeOEFdu7cybp165g1a5bwEs7IyBB8vOTkZFauXElKSgrHjh1j8uTJbNu2jd69ewPFonAzZ85k3bp17Ny5kxdeeIG77rrLLOM0YeJqwefJFxcXR0xMjCZLU6tWLSUfDIo9%2BebPny8sFpxOJ02aNNH8%2BX4J8mH9%2BvWXVQkNCgoiICCAmJgYwsLCmDZtGhs3buTVV1%2BlbNmyXLhwge%2B%2B%2B464uDiNIpTv3HfffZdq1arpbAh8GDVqFKtWrcLj8bB8%2BXJOnTpFeHg4%2B/fv54033uCtt94iODgYt9vN9ddfz/PPP4/T6aRMmTLUq1dPZMY6derEpk2buO666yhXrhwAn376qSAct2jRgsjISAIDA7nxxhux2Wx4vV7uvPNOMZdOnTqJc31BldVqJT4%2BXngbhoWFYbVaadWqFffdd584t3nz5mzdupWKFSsCCF9Em83GBx98wGeffcZXX30lOJUVK1ZkxYoVvPvuu1SpUoUaNWqIwDguLo5z587h9XqFub1vLr7SUIvFogx069evL8RbjMDk7JkwYcKEiWsCqrTgn4W/YBknwMiRI6lVqxZ9%2BvThxRdfZMiQIbS9VNbQrFkzVq9eDRTTQ0aPHs3UqVPp1KkT69evF97HAHfccQcDBgzgP//5Dw888AC1a9cWSYY/CmYZpwkTBtG5c2cWLFigKc%2B02%2B1UrlyZc%2BfOaYKX3NxcevbsyaFDhwgJCaFp06Y8/vjj4v3L2TP4C7c4nU5%2B/PFH0tPTRYC1YcMGzp49S0xMDJmZmWRmZupKRmvWrMmePXto2LAh06dP1xz3wWKx0K5dO06fPk1aWhotWrQQx8PCwqhfvz4xMTEcO3aM1q1bM23aNHJzc/n44481Yx0%2BfJiIiAhq1aolLCx85QgAX331FQCJiYl07NiR1157jZCQEFESW7duXcFBhOKSh0mTJpGdnS0%2BOAGqVKnCvn37%2BPrrrzVKmF9//TXdunUTGcx33nkHKM7%2B3XPPPTgcDpo3by6ysunp6XTt2hVAWEz421RcuHABm81GTk4OVatW5ejRowQEBIgxd%2B7cKdpWrFiRtLQ0LBaLRsjFCLp06aITzrnRT3pZwOXS1gdJr71eRdWU1CYnR1%2BBI5%2BXm6svBcrLk6qkpH4vXFCUJZUyX9XYiialjg0Uc3r8S72kNgUFiqozA/PTnadoc/GiVEoltzl2DC79UGFwuso2qjXI58nn6A8YG1x3LxUW6kv7pPNUQ%2Bm6NnDB5Saq%2B1q3Fwbmp2ujmLBuOor5lbomI%2BtUbZaRh0FqY6CJsZvLyA0pH1Osu7RuDc3vSuar6tjADWlkr3Td6D6QiimYmspcA/0Y2eKrtTVXMrZujSYEgoKCeO2113jttdd078k2TD179qRnz54l9vXwww%2BXSHP5I2AGeyZMGERSUhLBwcF4vV4ee%2BwxkpKSSEtLY9KkSWRlZYnyv1WrVpGVlcWwYcOEF99bb71F7969lX54l4PD4eD777/HarXSpEkTwSPs1q0bq1evxmKxUL16dWH7sHr1ambNmsXSpUux2%2B20bduWxYsXK2V8/Tl0lSpV4syZMzzwwAM0bdqUo0eP8sYbb5CVlYXX6%2BWWW25hzZo12O12YS7uC2727dvHqFGjCA4OplGjRmzdupWgoCARfPlUOn/88UdWrVoFQE5OjuAU7t69G4vFIoKp8ePHi/KH5s2bi/nu3r1buUdBQUHUr1%2BfpUuXinX5UKlSJZ5%2B%2BmneeOMNMR%2BPx6MrofQFb0lJScTFxYkSTp/1RVFREcHBweTn5wtvRPi1bNPr9QpOoFGoOHtDhwwhXi4Tlf91ll4rJbGlNqrkrnyeivOhqyKR%2BlVW65YyX9XYhjh7qkalcHqUnDgD8zPCgdNRq%2BQ2UqAHxihIRjhx8nk6PpSKr2VgcN29pOJwSeephtJ1beCCy01U97VuLwzMT9dGMWHddBTzK3VNqmOlXigD5yjaGGhi7OYyckPKxwzw8Yws08hzeEUdG7ghjeyVrhtFSZ0uCDLQj5EtvlpbcyVjizWeOQN/oGDIb4JZQvq7YQZ7Jkz8BkRERJCUlMSyZct46623CAsLo2nTpkRHR5OamkrNmjXZvHkz5cqVE7/ixMXFMWXKFNq3b89FldLaZeB0OrFYLNx9993069cPp9NJVFQUVquVDRs24PF48Hg8QrUpNDQUm80mXvtKKevUqcO2bduw2WxERkZy66230qFDBxwOB/3796dhw4Y0atSIRYsWMW/ePABiYmI4f/4833//vYaD17p1a5566ikA2rdvj9frZeXKlQQFBYlg9oMPPqBbt254PB4cDocI5o4ePQoUC6Y8%2B%2ByzDBs2TJzjK430%2Bd/16tWLe%2B65B0CT4ZNRUFDAmjVrdMetVisnTpzgscceMyRSY7PZRMb14MGDnDx5ktDQULKzswVncefOnZpg0h%2B/1RNHxdkzGXsmTJgwYeIvh/9lwGUGe78bZrBnwsQlGPHks1gsNGvWjDfeeENzfMCAAQQEBJCSkkLt2rVZvHix5v2goCCmTp1K%2BfLliY6OZtmyZYbmZLVaGTt2rJLb980339ChQwdNpql79%2B6i7dy5c9m5c6c4fvLkSeUafT5zd955p6YU9bPPPuOxxx4T4iVRUVE4nU5CQkIEj85fkdPj8Yj/j42NxW63iwDI6/WK9ywWC8nJycTExAC/Knf6OIAul4v%2B/fszZMgQpbKlv8fdkCFD6N27Ny%2B99JLIGr766qsMGzaMGjVq8OOPP3LPPfcwevRoEhIScLvd2O120a/X66WoqEgoaX7//ffk5ubi8XiIjo4WpvGBgYF4PB5q165d4rVScfBMmDBhwoSJax3eyHL8L33VTfw%2BmMGeCRO/Az4OXWpqKmPHjmXevHnUqVNH2VbmZ5WG5ORkTpw4wciRIxk5ciR2u53Y2Fj%2B/e9/07dvXwAqV67MkSNH6NixI2lpaYIf%2BPDDDzN37lwxFx9PTjWGShTk559/Ztq0aTRv3lycd%2BTIEc6cOcP%2B/ft1vL3777%2Bf3r1788UXX/Daa6/RsmVLCgsLsVgsPPHEE7Rq1Uq0bdu2LUuXLhVlly6Xi5CQEJH1LCoqIjs7WxfoWSwWnE4ngwcPZteuXaxZs4bNmzczY8YMsQar1SqCyMzMTGw2G0uWLCEtLU3DCbzttttEv0VFRXTs2BGAHj168Omnn%2BJ0OnG5XMTExHDq1CkcDgcej4cLFy5gtVq56aabyLlkYuQr9bz55psvez1lmAItJkyYMGHCRCkwM3u/GxZvSTVJJkyY0CE5OZmMjAxRcujj0N17770MHz6cdu3a0b59e5544onL9tOtWzf27t1b4vt33XUXqampXLx4kUGDBtGxY0eKior49ttvefbZZxk7dixdu3alY8eOHD58GECIiPhsBqKjo3n22WcZNGgQUBws%2BaR8b7/9dl5//XWSk5M5efIkNptNlHwWFRUREBBA69atGTVqFGUveUU1bdqUM2fOYLVacTqdl7V0WLlyJV27dhUBVmBgIA0bNuTxxx%2BnR48eWK1WrFarxpBUhs/qwePxCA6fD77sXmhoKOPGjeOZZ565bIms0%2BkUHMObb76ZI0eOAMWZPbfbLTKQDoeDG264gT179lCxYkVOnTqF1%2BvF4XAQEhLCiBEjeOaZZ5Rj1KxZkxUrVpQ4Bxl79%2B7l0KFDmmNVqtxI3bqSt85778GAASW%2BVgq0fPopdOggXqqEVM6d09qAqURcdIT%2BFSugS5fLnqM7KSUFLgniCGRkaPkfCnUBnTDAJ5/AHXdcfn7z5sEl6WuA9HSoUKGU%2BSk2MCsLNB64H34Id9%2BtaZOdLQnU/d//waOPlrxGg2Nz%2BjT4W7QYEAvRXQeVcInUSB5G1a8RAR7VEnRTlpVVFGIX8iFFE/1BlSrF8eMQG/vr659/hipVfn2tuCl0%2B6e43joBI90NoJif3I8RhQzVfSOpASmFh6Rrbui6SPNVTY%2B0NLikdAyolZyMCNGUNr%2BjR6FaNe058oeA/KEF8NNPULWqeClffgDefRceeeTX12fOaH3zVCok8poUHetEmhSbbkRspTTxHyMiSKp7Qj6mWqZ8TH4tbhHdh%2BKfiDffvDr9lvI97e8EM9gzYeI3IDk5mXvuuUdwyHwcOl8W6u677yYhIYHRo0dftp85c%2BYwffp0Xn/9dd17w4cPZ9CgQcyYMYOcnBxGjBihKePs378/TqeTBx54gF69etGgQQPq16/PmjVrRHbPpwbapk0b7r//fsLCwggKCmL%2B/PlAcVYpKipKBHvt2rXjySefJCcnh3feeYcTJ04wbdo0kSWD4mAvPz%2BfW2%2B9leHDh3PfffeJMkcZ8fHx7N%2B/H6vVSlhYGFlZWdjtdlEu2bBhQxo0aMDUqVNL3KPAwEBSUlI4ffq08K5RwcdDzMjIuOyey31DMd8vPDyc8%2BfPY7FY6NWrF2fPnuXTTz8lICCA4OBgsrKygGIO46pVq2jevDl5eXnExcWRmZnJhQsXAPjXv/7F%2BPHjDc/h1VdfVZqqDx48yHAfJkyYMGHCxFWH8pefPwlmsPe7YeZGTZj4jfg9XnwTJ04Eij3jVF58TZo0wel0imBEDvSgWMDExw8sW7YssbGxPPnkk6xZs4adO3fyzTffsGjRIo2Mb2hoKBs3bhTzjvL75Tg8PJwWLVoQFxdHQkICb731FgCPPvqo4NH5ULlyZSZPnkxcXJzIbtapU4dNmzbx2muvYbVaee%2B990hKSgKKyxIffPBB5s%2Bfz0cffSQyi9dffz2RkZGi302bNpGcnIzFYuHTTz/FZrORn5/PtGnThMopILKK/tYLXq%2BXhQsXCh9Dh5TR6NixI5s2bdKc4zvPl9mz2%2B1YrVYiIiLIzMzEbrdTtmxZEej52mdnZ4uM5rFjx0SgB8Ucyt8C01TdhAkTJkxcE9DJD/%2BJ%2BIv67F1L%2BGet1oSJ3wF/Dl3NmjWpVasW7du3Z86cOaJN586d2bFjBx07diQpKYlmzZoxYsQIDh8%2BzNy5c3G73Sxbtkzpy%2BIbw8cFk%2BFyuVi7di2pqam0bt2azz//nHPnzvHxxx9Ts2ZNzd%2BNN96oERg5efIkderUoUePHkLIxIfz58%2BLNdWsWZM6deqQlpbGnj17ePfdd0W7zMxM9u/fL9qdOHFCvBcVFUX58uXxeDwMGDCARYsWifcmTJhA7969mT17NsuXLwfghx9%2B0AR7Z86cITU1VVhJ%2BPhv%2B/bt08y1WbNmrFy5kltvvVUofHo8Hrp27UpGRgahoaG88sormnNWr17Nyy%2B/rFHRXLFiBZMmTRLm9A6HQwSJOTk5hIeHM2jQII2RusViEWI1DRs25LbbbtPw9EriapYEk7NnwoQJEyZMmLjaMMs4TZgwiOTk5FI5dOPGjWPhwoXYbDYGDhxI1apVmTZtGgcPHqR8%2BfIsW7aMDRs2CMEUWR3TpxjpD4vFgt1ux%2B12C37gDTfcwMiRI/F4PDidTlq1asVDDz1EhUtclKioKPLy8khOTiYzMxMoLl10u924XC6NoqUMp9OJ2%2B0ukU8XEBCA3W7XcfYsFgshISGEh4eTk5PD%2BfPngWIl0sTERE6ePEl6errIFvp7/0VERFCmTBkRnLpcLhFcBQQEaDh7wcHBtGzZktWrV4t%2BfIqaJUFeb0nrt9vtVK1alZ9%2B%2BomQkBCys7NFkBgXF8fAgQNL5OxVrFiRL7/8ssQ5yFBx9m6sXp14SeilVM6HggSi42YoeDaZmeAXb6s5UhJHRjeUzOdBT4cxZACt4pgdOQLVq4uXRjgpRuanO0nRcan9gm5vdLyaH3%2BEG2643ClKCpK8garrIm2NMQN66ZiKGibT25T8J%2BniqfopjfynqgjT7Z8BPqOqH93llIlV8o2VWUIFAAAgAElEQVQP%2Bg1U7Z%2B8GSoClHwfl/ZaMWEj9DHl/KR%2BVDyv0gy%2BVc%2BY7jIYMaU3QBjUNVGdI0/IALF4717QaaHJfUs3repjQrcmxcN66hT4MR2UF88InbG066L8/DHSsdTGyNi6NpeeH9W5fxreeefq9DtkyNXp9y8IU43ThInfAIvFQmhoqCiD7NatG6tWrWLt2rVUrlyZuXPnMn/%2BfPbs2cNHH33E8ePHhfdd69athdhJSVAFemXLluXcuXM888wz3HfffYwfP54XX3xRtPF6vWzbto3t27fz8ccfi4zZ5s2bRZBUoUIFwdd744032LBhgxAskZGSksLy5cs1WT1/DBgwgC5duui877xeLxcvXqSoqIj4%2BHi2b98OQF5eHlu3bhXrgeKAMSIigtOnTwOQnZ1Ndna2LiAbMWIE8fHxGs6e2%2B0WgV54eDhZWVki41YSgoKCNBlT1W9cdrudf//73%2Bzfvx%2BPx8PFixeJiIgQ1hahoaHcfvvthISEkJOTo7GdgGL/wd%2BCEk3VpWCvVINdhVOuzlZQkUWUv%2B8qPIN1X250Q8nfkNAGeiVMz5h5t380g9pcXD5mZH66kxQdl9ov6PZG90VICvQUp%2BgDPdBtoOq6SFtjzIBeOqbyR/YP9EAR6IHu4il9luXxpYhMRf3R7Z/KtFxSlFH1o7uccvmZfOODfgNV%2Bydvhsq7U76PS3sNugmrujXyzMv9qL6Yl2bwrXrGdJfBiCm96tqVZmyuOkeekEJRWu5XKXot9y3dtKqPCd2aFA%2BrJtAD5cWT%2B7mS66L8/DHSsdTGyNi6Npeen/9p1eM/rOTyasDcQRMmfgNK49DVrl2bhg0b0rdvX1auXMn27dv5%2BuuvWbhwoYZDp8rqqWCxWLDZbHi9XpYvX84PP/zAnDlzcLvdIsAJCQnB6/Vy%2BvRpEaDNnTuX5cuXU1BQwMCBA7Hb7YKvN3r06MsGR1WqVBFBqd1up0yZMoInFxsby0MPPSR89lTIz88Xgd64ceNEKST8GmS53W4R6AHCHN4fERER9OnTR8PZA4SlAxRnKydOnKjzPZQhl8b6yjL9UVRUxIULFygsLMTr9RIWFqbxMDx79izZ2dmiLzlgfP/990lJSbnsPPxhmqqbMGHChIlrASbD4NqGGeyZMHGFkDl0%2B/fvJzExUdk2ISGBaFnnvATExsayadMmYmJiGD58OMOGDQPg%2BPHjzJgxAyjOjFW7JFP9xBNPUKNGDaA4gMjJyWHu3LnExMRQrVo13n33XTIyMpg8eTI7duwgMjISq9VKcHCwUNt8%2B%2B232bRpE5s2bcJutws%2BXtWqVVm0aBErV67Ebrdz/PhxvvrqK818LRYLgwYNol27dsTFxdGwYUPx3nXXXSeCquuuu44ql1IHcrD4/PPP6/bh7NmzSjuFxMRE4uPjsdvtrF27lk6dOtGhQwcRwPoL5owdO5ZNmzbxwQcfaPqw2%2B0MGjSI2bNn895774lzfYbqISEhnD9/XuNNmJ6eTmhoKA0aNMBisdC6dWtNNi84OPg3Z/dMmDBhwoSJvzrS0v6Hg5sCLb8bJmfPhAmD%2BKM89pYtW8aoUaOEMqUPeXl5yvJCm82mLO/0iZNAcfBSVFSE1WqlevXqFBQUsGTJEqxWK8OGDWPz5s2iD1/powpBQUEUFRXh9XopKioiNDQUl8tFjRo1OHjwIIWFhVitVgIDA3WZKR8Pzp8P17t3bxYtWoTb7RZBn2%2BN/u2Cg4OVmS6bzabj7AUGBlKnTh0OHjzIrbfeyqOPPsqGDRuE0qkKvtJLFfznYbVaqVOnDtu3byc8PJxevXoxZcoUMZeXX36ZyZMnc%2BLECV0Zp9Vq5bXXXqOLnw/d5aCyXhg0aBBDhw41dL4JEyZMmDDxZ%2BCXX6By5f/R4JexaPpdGDjw6vT7F8Q/K7Q1YeJ3YujQoaSkpJCSksKGDRv4/vvvGT58OFBcdpidnU1ycrJGGTM%2BPp5GjRoxcOBA0i79PGa328nNzdX8%2BQcO/oGgHOgBDBkyhBYtWtCoUSPKli0rxFQ8Hg%2BHDh3i%2BPHjNG7cmMGDB9OxY0dRlhkWFqYMqnyBWNeuXUlISBD9Xbhwgfz8fHbv3k1hYSE2m4333ntPU65os9kICgrCZrP9P3tXHhdVub%2BfOTPMMDCAbOKOWy6hIGIuaVqkpAbkEkq5Xm35ud5SM7dSW9SU7jWXm5mZmFvRRXANl1uipimYesvcV1AQVJaBYZiZM78/xvM6531fmMml9Haez2c%2Bes68593PYb7n%2B/0%2BD9HSk7Bu3TriNZOkDiQEBgaS/7/zzjswcKidBUHA8uXLZefUajUOHz6MZ555BgUFBejXrx82bdpUbWiqVqutMvRU6q/BYED//v1Rr149qFQqGAwGGdOqTqdDVlYWbty4AUDutZTGEB0dXWUfaMTHx2PBggWyTwzHM0ipXzDH4L2vowvx8jPpfUAR7vCqoR2tlNa9A3l5rvtHt83RSHSKoK2yGnpczK1SUsJc4nI%2BOddxpobpH1MP5yK6f7y2mXO8tcvJkR1euOCqEk53fvmFrffMGflxRgZT5PJl6oQTK29VzVOkutxrTp%2BWHxcWst27zTVVbSG6arptnoeC3n5MO3BosTuDtyz0nmAetZxNTM8VXQfArh3vvRWz5Jy/G0z79EW3CbOcceWK/JirGEPVw2va1dxwnyX0RZyBM/PF2RP0ZczjhtnUbAedsg7ugCLY4m4c%2BnnIK0Ofo495GrL0wHll6HO8QdBlqGNpy3Cfk38UFM/ePUPx7ClQ4Caio6MxduxYJmdPwnvvvYdffvkFv/zyi8xAU6lU8PDwgCiKaNWqFQYOHIhp06ZxvXgSPD09YTabGc%2BRZJi88MILMiOqqlyxhg0bIiMjA6tWrcLcuXMBOCQCjh07Rtpx9pr1798fHTt2JAYsD3FxcUhKSkLz5s0B3PG%2BScyadevWRU5OTrXjoxEUFITC23%2Bk/fz8YDabSb%2B%2B/PJL/O1vf2OuiYiIwDfffEOOW7ZsWS0jp16vZxhEabz22mswGo1IS0tjjGJ/f380aNCAzB0Ng8GArKwsJhewKiii6goUKFCg4FHAn8rG%2BfnnD6beV199MPU%2BhPhrmbYKFDxAxMXF4fjx47Db7YiNjSU5cNu3b4ePjw80Gg2OHj1KjJg2bdqQMlKOHg8SMUrPnj3JufT0dEIeIgmCjxw5EoMHDyb1vfjii9DpdDIDSKVSoYD3BtAJPk4Ud5GRkTIPnCR6TvdPgiiKyMnJIZp10ndqtRqCIMjy6YA7HsxCp7exxcXFMqbQxYsXY/fu3bLrdDodfv31V2JQ2u12Mk5n8hWdTicLu23SpAmZs7Zt2xLxegmnTp1CSEgIKisr0bRpU9nYDAYDPD098Y9//IP0W6obAIxGIy5dugR3oYiqK1CgQIGCRwFq8c907Sm4VyjGnoJ7Ah2yyBMaBxx0/gkJCTKh8WtO8TSpqalVhsBFR0cjNTXVrf5MmTKFERiPjIxEQkICDh8%2BTMotXryYKSd9JB21n376SXY%2BNzcXH374ISZPniwzTnJyctC8eXMkJiYSo2PLli3o0qULunTpgunTp8PLy4sYeRILpYeHB4KDgxEcHIxdu3Yh73a4R926dTF27FiZIZOTk4PvvvtOZnxccIrdUqlU%2BPbbb3H8%2BHFS5%2Buvv46cnBx07dqV5LOp1WpcvXoVKpUKer2ehEhK9aanp8tyDn/%2B%2BWcUFxfj5Zdfhq%2BvL1QqFaxWK/HqAQ7GzU2bNiE5OZnksUnGmjP7Zs2aNfHiiy8CAJo1awYAXE8bnQt35MgRxMXFycbeqVMnWK1WTJ06FZ9//jm6detGvmvbti1GjhwJLy8veHp6EjZQyWMIOJg3jx49CpvNRgxTAMjMzES9evVgt9tl%2B1MQBAQFBcHLywu9evUi10jhrlLfJG1Bd6CIqitQoECBAgUuoIRx3jP%2BWqNV8EAwbdo04k3atWsXXn/9dcyfP5%2BEFs6dOxdz587FgAEDkJaWhqVLl6KgoACDBw8mgt/3E7169ZJ5zNasWQNfX1%2BMHj1axu4YGRkpKyd9pk%2BfLqtPOh8SEoKXXnoJ58%2Bfx7BhwxjPTEpKCjIzMwHc8QIFBQUhNDQU8%2BfPJ%2BUuXrzI9HnXrl3k/1evXsWiRYvI8csvv4zMzEzUqlULOifdoby8PBiNRhQUFMBms6GkpEQmhB4cHAy9Xk9kE4A7nii73Q6TyUT06yTj6pVXXkF6erqsHavVinXr1qG0tJSEjkri7QDw1ltvISYmBkOGDKk2dLOoqIiEXZ6%2BnaBTVcgjfb5x48aof1vnysvLC%2BHh4QAcLxGSkpJgNBqJUZednY0VK1agvLwcxcXFxKC02%2B2yNRNFERaLhUg5qNVq1KtXDz169IC3tzcqKytJnaIoIjg4GDVr1sTBgwcZghtBEFCjRg08zhV54oOXs9e1awxbkE4Woo65uRTnz8sOeTk0TM4Rh7SHKUPlitFdA8DmgPDIgOjkPw7rKp0Ow%2BSTAQAdUkslGHFSw5i0JXfyi3h5QEzuF50PwwyAbYsrdUktKNcRf/Kk/JjOteJtCqpxLl8Rtb4oLWXL0MlVvNw/amBMdzj9o9eF%2ByihTnKd43SOFL2YnOQwJpWJkzjH7CVOPigzLvrvGy%2BMnF473k1FrQMvv81VOh73HLUJuHNOj5OT10dXzH0muSrDWUzm3uRV7E5Co6tnEu8mo9riPKKYteI9S5jF4s2fq/vXncXkNM6spzttU8dkWehnjoJHCoqxp%2BCeIYmMBwcHo3bt2ujbty86deqEHTt2ICsrC8nJyVi6dCkSEhIQGhqKiIgILF26FFarFcnJyfe9P56enqQ/wcHBCAsLw5w5c1BSUoKDBw%2BScs6eNeePD6XUK53PzMzEpEmTsHz5chQUFDB0/gEBAQgJCYFarYbJZIIgCCgvL8eWLVswYsQIAA6ClJKSEhgMBmRnZyMyMpKEc0oICwvD%2BPHj4eHhAR8fH8ycORM1a9ZEzZo1iZEDOPTmMjMzceDAAQAOY%2Bb06dOIjIxEZGQk2rdvj7KyMuzevRsTJkwAACJGXhUyMzMRGhoqMxqdWTSl86NHj3a5Dnq9XhYmWVFR4XYeX4cOHUjoJwD88ssvuHr1KgBH%2BKNkDEv1hYWFuRRWLy4uJmGcNCSPbHBwMHQ6HZ5%2B%2BmliCAKOOTh58iQiIiKI59PZI2iz2dCtWzfZOVfYtGkT3nrrLdln377dbEEnw5p3zBXcpVS3ebkWjICyn5/rMhQdG901AKzKNqdeRuiaQ85zW03kDjgi5YiIkB9TwteURCMAVl%2BZNzeMfjKtFA%2BOEDMtrcIMgG2LJ2Ltjvg5WrSQH9P7jrcpqMZ5%2BtQM3R5PtZwWjm7Vii1DDcwdUXB6XbjvgaiTHOc4q3RNLyZH%2BJpRxeEIaDN7ydeXbZseFy3gTou3A%2Bza8W4qah14wuv0fPG2AHOO2gTcOafHyXvGURW7IwLOlOEsJnNv8iqm55S3sV09k3g3GdUW5xHFrBU3p41eLN78ubp/3VlMTuPMerrTNnVMlsXNXPQHAsWzd8/4a41WwR8GWmi8Xbt2su/1ej0%2B/fRTDBo06A/pj%2BShcc6xulsEBASge/fu2LlzZ7XlJKFwq9VKwgdfeeUV7N%2B/H6WlpRBFEeXl5UwoY4sWLRAYGAhRFFFWVkZCX4uLi2VC5M7hkhLUajWp12azwWw2E4FzADKB8ho1aqBz584AgISEBADAiRMnEBYWJiOY4RlRXbp0kc2Hr68vfH19IQgC6tatSwxevV5P9AB54Bl/drsdBw8ehM1mk4U1OhugNLKzs7lhkRKkdc/LyyOsn7T30G63E6ZNKfdOyjGUPKE9e/YkoZqV1BtQV4Y0DSVnT4ECBQoUPAowP8Z5qfNHQTH27hl/rdEqeOB4UELj94Li4mLMnz8fgYGBjNF5t2jatCnOnTtX5fc9evTApk2bsH79enTt2pUYm61atSISBS1btsSGDRvQvn17xMQ4wvfUajWioqKwYMECYuyIooiSkhJcvHiRhIBKZCcqlYoYY7Gxsdi8eTM2bdqEkJAQjBs3Dg0bNkRiYiK%2B%2BuorAMCxY8eIAdOwYUPCLOlsqDgbxHq9HgaDQWYYeXl5ISUlhRwLgkA%2BQUFByM3NxcsvvwwAKCkpwYULF%2BDl5YUWLVowXlMJHh4e0Gq1bjNZ0rDZbESwnQfJUCwoKIDRaJSRuDhDMvKCgoLQvXt3mdG7fPlyeHt7M94739tvvi0WC06dOnVX/VegQIECBQoeVnDDWBU8Mrh3N4eCvzxmzpyJ999/H8AdofFhw4YhPj4eS5cu5eqn8XD16lVERkYy513R5dPYvHkzMm7rQ9ntdlgsFrRt2xYrV66U9SUrK4vb3ueff%2B7SKPTx8WFEumNjY6FSqWCz2bBz505kZmYiLi4OixcvRmxsLC5duoRt27bB09MTJpMJBoMBYWFh%2BO2335CRkYGdO3cSen/nXMYKToKGKIqoU6cOKisrYbfbUVFRgaioKEyaNAm//vorbDYbFi1aBA8PD6jVauTczsUZMGAAqSM4OBjHjx8H4DBCf/rpJ6hUKll7drsdixYtwjfffIPvvvsONpsNFRUVePLJJ7Fs2TKS62a1WlFcXIzAwECoVCpiXErGkslkwrBhw9C%2BfXu89tprOHfuHLRaLfGOWZzyDySCFrVaDT8/P7fzOquSRKhduzauX7%2BOwMBA4hn19fXF/PnzIQgC9uzZgzVr1siMv5kzZyI%2BPl4maC/l4zl79ARBkHkneWtVFRSCFgUKFChQoMAF/mJeuAcBxdhTcM8YP3488UzpdDoEBwcT75EkNO4OatasSYwEZwwZMuR39Sc6OhqTJk2C1WrF5s2bsWHDBowePRotqDyXVq1aISkpibk%2BhJuIJIfRaGSM2OXLlyMkJAQ9e/ZEjx49MGHCBBgMBmi1WiQlJSEhIQEbN26En58fysrKUFlZie%2B%2B%2Bw6NGjWS/chPSUlB/fr1kZeXBw8PD3h5ecFkMjHGpZTDFhoaihs3bmDhwoXo0qULzp49i/LycoiiCLPZDK1WS4wWjUZDvFzOYaiHDh0CwIZVVlRUYNiwYYRwprKyEhaLBcOGDSNlysvLyXrfvHkTEREROHr0KDw8PIgRZ7fbCfGNNNakpCSMHz%2BemVtnBs86deq4bew5j80Z165dg4%2BPD/z8/IixV1xcjNdff525VlrTX3/9lRD4OHv3aNkKURRR6kSe4O6LDcBB0EITujRp0owtSAsc0cd2O5tPYTLJclksFk7aR36%2BPOekpITJz8nLo1KgqIoKCjjpLpcuAU4C9ry2mXOcMVy7RuXFVVSw%2BS9GozyZhi5jNjP5Y/QpThGHyLKzp5gzCKZ/1LrwdKnoanhzc/MmlepVXMzmGB06BLRvf%2Bf4zBl5TiNnLXHlijynkdoj3Hr37AGcmG4BsAOn9xHADD43l8p5u3WLzYsrLZXnpnHGQO%2B3sjJOiparSeZMOt00b12Yvc4rRM0pva15e4JZBs4%2Bp/cove253XFjAzLXuDEmbr30QDn3M9Nnuh5O2/S4eVuWuX9PnwaayZ%2BjdFPM9vvtN6BlS9k1dHc41QLZ2UBUVNV9AdjNz3to0ueuX5cnkvLuMTc2LbNU7rRNHUvr9jv%2BtCl4CKEYewruGYGBgQh1%2BnHnjLCwMPzCY2sDkJycjMLCQkycOBGA4wd3aGgooqOjkUtRn/3jH/9ASUkJhg8fTs6lpaVh7dq1OHv2LLy9vdG5c2eYTCZ4e3sjNDQUqamp2LRpE2JiYjB27Fikp6ej3m0CguTkZMKU6QpTpkzBxo0bybEgCPDw8ICfn5%2BM3bNOnTq4fv06RFHEf/7zH/z4449o1aoVxo8fTzyINpuN5GodO3aMeKOkHLjCwkLs2rULarUaFosFFouFlNdoNPD390dxcTEqKyvh4%2BOD0tJSovlWXFyMrVu3Mv2nPVHe3t7EcJSE2nm5c4IgoHnz5jh9%2BjQxHiU4y0KUl5fDx8cHdrsddrud5AhaKLYwURTh7%2B9P9AGd9fv69%2B%2BPjRs3Mp4tZ29bgwYN8Pbbb2PMmDGoW7cucnNzZZ43T09Psh5z5szB4cOHybrVrVsXJ0%2BeZGQdJEhGYvfu3QE45DF%2B%2BukneHp6ysYRHBwsE7enkZ%2BfXyUJDI1NmzYxourjx41DWBhFvkH/sKKPeaGv1C8iLmEC/eOBQzjBSD%2B6Qx5C3VNuEUVwxsAQoPBYKehfIHQZDgMKfYpLkkKHBHMGwfSPWhceWYM7vAs0pweX4MbZIANY8hoeeQhFXsMlC6HrpQ09gB047%2BUYNXiG3IRDgMKQwXDGQO83LsmMq0nmTDrdNG9dmL3uBlmIO2RAzDJw9jm9R3k/vJnuuLEB3SHOYTrIq9cNdh2mz3Q9nLbpcfO2LHP/MhYZ2xSz/ShDj9cdTrUyQ4/bF4Dd/LyHJn2OTm9xg7SHN3/MUrnTNnUsrZsu5xzg5t%2B2%2Bw7Fs3fPUGZQwQOFJDSenZ0tO19WVobk5GSZ18QZznIOISEh6Natm1tyDnv37mVISyZPngwvLy/Mnj37rsfhLOewadMmCIIAk8mEDz/8kJTJzMzEsGHD4OPjg1GjRmHDhg1o1qwZhg4dSsbv7e3tMi/NaDTC39%2BfG%2Bbn7%2B9PtOokg%2B2kC0pkf39/4nkbMGCAjCHz7bffJuOjIYoiKioqkJ6eTgykxx57jOQfAo5cO5vNJvOohYSEoEmTJsjMzGTy/ZzLOXsH//3vf5OQ01dffZWcd87xi4qKQvfu3fH000%2BTlwHO%2B8c53HfatGkyA/306dPQ6/XQaDSkP35%2BfvD09ISnpycMBgOCgoLIy4DMzEw899xzMq%2BdBOfxqNVqmVA8LdJeHXgELQo9iwIFChQoeNhQHPQnGXqAQtByH/DXGq2CPxySoPno0aPx7bff4vLlyzh06BBeffVVCIIg%2B2HvDGc5B41Gg6ioKLfkHOx2O85S%2BlYGgwGTJ09GZmYm/vOf/5DzNpsNBQUFzIcOG/zvf/%2BLH374Ac888wzi4%2BMxbNgw1K1bFyNGjEB6ejoGDx4MAPj4448xdOhQbN%2B%2BHV26dEHv3r3x/PPPo2vXrliwYAEA4L333oPJZEK9evWg1%2BvRuHFj7NmzBydOnJCxVl68eBHl5eXw9vaGp6cn1Go12rZti82bNxPvkyiKUKlU8PHxQWhoKFq0aAE/jhfg1q1bsNlsUKvVSEtLQ1FREXQ6HebOnSszjCUZDFq4PTY2lhh7Z86cgdVqJSQuktfL2cOZn5%2BPc%2BfOoWvXrjAajcTzSOcDqqnXjmazGTabDZ9//rnsnISg2/T37777ruw6qb82m61KQ1oURZhMJplHrri4GBUVFaioqIDRaERhYSE2bdoEABgxYgS2bt0qq0%2BS7ZDqEASB5ChK/cijNb6qgZKzp0CBAgUKFCh40FCMPQUPHLNnz8aoUaOQnJyM%2BPh4TJo0CaGhoVi7di38eeE8VcAdOYeOHTtyw%2Bji4uLQrl07zJ07l4Q1Xr58GV26dGE%2BAwcOlF179uxZmEwmWCwWFBUVwWg0IjAwEDqdDjabDdduqysbjUasWLECXbp0IVIGCQkJiI2NxQcffAAA2L3boaOWm5sLm82G3r17o1atWlCr1VwvkpR3Z7PZcOTIEbRq1Ypo9tWvXx92u53o%2BbVr1w61mLi7O7DZbDAajdDpdMQA%2B/rrrwEABw4cQMeOHfHOO%2B%2B4WAW53p47kDxYNWvWlIVE0h5YHpw9wl9%2B%2BSXatGmD6OhoWRlnT6MrHT%2BbzVZtGcnYyszMhLe3tyzUdNSoUSgoKCDXi6KI69evk7mw2%2B0yaQtX4Imqd%2BzIEVWnvd/UMdc5TpMacUSicVufkYBDcEMzsNFt8arFr7/KDt0RWOYNgtkevHWjvaO0QDCng/TUcLfhnj3VXwS4HAOPq8edtpnreILPy5dXW4a7xamKuWW%2B%2BUZ%2BzBGGZ0S2t29ny1DrwuiE8wbuhjA3vR956iV01e6IjdND4pVhthJPvJtePKpx3r3KbC13hNc5i%2BeOcD1zjh4DL9qAbuouBb7pdXFHMN2N7rH9O3LEZfdu/8m%2Bg59%2BYium5tzpPfEdrF8vO%2BRtCRQWyo95D036HL0hebwH9M3A2zf0JPPy3%2Blz1LE0d79DQvb%2BQ/Hs3TNUdndVjhUo%2BIMQHR2NsWPHol%2B/fgAc3qPvv/8eEyZMwJw5c7B69WpERES4NExSU1OxZMkSmTevqjaqQuvWrYlX7b333sOVK1cwY8YM5OTkoFatWrhx4wYsFgtUKhVCQ0MJC2hWVhYGDRqEtWvXyozSzp074%2BbNmxBFEbNmzcJLL71Evps%2BfTpSU1NhMBhQUlKCFi1aoGPHjlizZg2sVivR0FOpVBBFEVqtFjabDR4eHtBoNHjppZeQlpZGSER8fX3RvXt3bN68GU2bNsVvv/0GQRCg0%2Bmg0%2BkwatQozJ07l7S/fv16tG3bFocPH8a8efOYXEuNRgMfHx%2BSc1cdnn/%2BeXz//fdEWiM4OBhnz56VXfvcc8%2BR%2BaKhVqsRHBws85RpNBqo1WrGSJSE6qvKpZPy9LRaLaxWK1NGysH08PBAYmIi3nrrLezZs4eQx0jeyPDwcHTq1AmfffZZlePOzMx0i%2BAHAObNm8fk7I0bNx5jx45x63oFChQoUKDgDwFNCPNH4vZL6fsO6sX%2B/zL%2BWqatgkcGM2fORGRkJCIjIxEeHo63336byDmUlpb%2BbjkH%2BiMxWbqCxWKBr68vDh48iLi4OIwePZp48ho3bgydTkfISWimRhpnz55FYWEhvLy8oFarSdikM1QqFTZv3ozGjRvj1KlTWLVqFfEc%2Bfj4ICQkhIQxzpw5E0FBQbBYLDCbzcjMzCTMpyEhIUhISMDOnTthsVgwY8YMACAi73q9HocPHwYAohv30ksvITw8HEOHDsXp06dJfyRYrVYiKC5BCkX0ppgSduzYgfLycqINmGSI8L8AACAASURBVJWVxRiJUu6hBKkfXl5esNvtGDt2LPz8/IiOYP/%2B/TFhwgTiyZNCSWlx8w0bNpD/C4KADh06QKfTobKyUqYhuG3bNmi1Wmi1WtSuXRtGo5HoGGZkZBCPqoRbt27hzJkzslBZOhRzD%2B0RqgaKqLoCBQoUKHgUYNL8SYYeoHj27gMUNk4FDyV4cg4Sq2VFRQVWrFiB1atXy66ZPXs24uPjZefuVc5Bp9MhPz8fOp0O0dHRiIyMxBNPPAGr1YpmzZqhU6dOaNGiBc6cOYOioiJs27YNvXv35tYl9dfLywuBgYG4cOECcnNzmdC/WrVqYevWrUhOTsbq1auJYWqxWLBp0yZikMyePRsWiwWCIDCC3vn5%2BUhJSSEkLtOmTQPgMN4sFgsGDx6MdevWQafTwWAw4MaNGwAchpNWq0V4eDiys7MhiiIxtgRBQN%2B%2BfZGenk4MLMlgoWUhIiIicOHCBdy4caPKPDraQydp64WEhODy5cvEQJWwceNGWCwW2O12qFQqwr7p6emJiooKeHl5wWg0IjExkVwjiiLJtQPkBDHO6yTNqWSsZWVlwcvLC0899RS%2B%2B%2B47AMCVK1fg6emJjh07IiMjAxqNhjHY3JUZAZScPQUKFChQoMAl/mKG2YOAMoMKHkpIcg6hoaEkp23mzJlIS0tDbGwsmjZtirS0NNknOjoaycnJ%2BPjjj0k9kpwD/XH28FSH1157DYDD0NqyZQvef/99JCYm4uTJk7JcsZEjRwJwkIcYqVj6rKwsjB07Ftu3b4e3tzf27t2LUaNGAQBjsNpsNoSHhyMqKgr//Oc/cfXqVahUKqhUKpjNZlmI4JYtW5CRkYHNmzfLSFAARx5baWkpMR4uXboE4E5O2yeffILc3FxUVlbKPG52ux2enp64cOECuVYUReIRTElJIWGrgiAgPj5eRiwj4ejRo4ToRpJkoJGZmSk7ltq5cuUK8aipVCr4%2BfnBx8cH3bt3l0k%2BSPNcXFws8/Smp6eT%2BRAEAV27dgXgMK5csWVK3xcWFiIkJETWR0nqQpLJcCaqkfB7dfbonL2uXdmcPTrfxJ3cHDpVg%2Bt0pvKLeKk4TG4LVYjJxeI0zkslofN3cnLYMozdTOctAS6TCrn5gvTk8ArRyTdu5CC5XCc3rgHYtC9u1DRNBHQ7F7iqdniNcdeOmmMu3xC1KXjvN5hz1P3PHROdH8gpRO9jbo4UfR09UN5F9EJwnlfMnPKSMum66Xp5e5g%2Bx7lh6KbcSZtzp0xxscum2UnnjIFpi5ecSBei711Oh%2Bml4z7H6P5w8tKY7rjxDKDXmzs3VKEzZ9gi9HW8gA66DD019DoB7D3Gu6fOn6%2B%2BXsARnVldGTrlUMGjCcXYU/DIoGbNmggNDcXLL7%2BMU6dOobCwUGbAqVSqauUc7gZjxoxBu3btUKNGDeLhqqiowLRp02Q5bYMGDYJWq4XRaGSE2pOTk3H%2B/HmUlJTgySefBADExMRAEASZPIAEi8Ui077TaDTEA%2BcMadxNmjTBsmXLANwx5iQPmISgoCDodDoSbil55gRBkJWrWbMmiouLUVjNE14ywCTvWjHnL5GUS1idzMQ3FBmElI8oOL3Fa9asGQwGA0pLS7Ft2zZuPXa7HRaLhRh/L7zwApFhEEWRhKs6i79XBYlAxmQy4fz58zL2UGnO9u7dS85ZrVZZf90NDwYcOntvvfWW7LN/3y6mnEuZMDf0lbh6eJThy5PYYrTkqELc9ESqcXe0sW4rXsjAyKzxGAJo45pqm6sv6Ia%2BFyPgxtk39Bjc0dBzdQ3AyqxxOaxoIqZnn622HV5j3LWj5pjL90RtCp6kH3OOeg5wx9S0KVwVcktnj76OHijvInohOM8tZk55L47ouul6eXuYPse5Yeim3NGudKcMTd7Mu1eZSeeM4W40/ph7l9Nheum4zzG6P4xQJac790njjy5Ey13yruMEdDBl6KnhSW3S9xjvnmrcuPp6ATYNjy5zmwQbv4OT7f5DCeO8Z/y1RqvgfwJ3K%2Bfwe3Hy5EnMmzcP9evXR7du3fDjjz8iKSkJrVq1AgCi%2BQc4ctZmzJgBu92O9evX48BtpsMVK1bghx9%2BICyZu3fvxuOPP44nnngCoiiiuLhYxjgpCAIEQcDTTz%2BNf/7zn0hNTcX8%2BfNhsVjw1FNPVdnXjh07yjyNzlCpVDAajTCbzUy4Jc1OeePGDei4vxRZr5XNZsM333zDSFUAQGJiImw2G5588kkIgoBly5Zh3bp1WLRoERmn1DcJdrsdoijKcuIko94VbDYbOnfuzP1OMvzCw8NJOGtVqKioQFZWFglfde6L2WyGRqNB8%2BbNyTmJLEeCZFi6A0VnT4ECBQoUPArw0bpmz1bw8EIx9hQ8krhfcg7V4dy5c/jyyy8JKYm/vz/i4uKwdu1aqNVqxggZOHAg/va3vwEAlixZAsARDvjVV1/h559/JuWksEbJ0FmxYgX5TvJOGo1GTJs2DYmJifj0008xe/ZsxmNIw1mk/IknngDgCIe12%2B1o2rQpvL29GSIVGrTxp1arib6d5DkTRZHMiRQuGR8fL/NwrV%2B/HjabDfv374coivi///s/1K1bF2FhYQCAiRMnAnCEm0rC6XXq1EFERATeeOMNmQfuhRdeqLbPgMPDFhERAcCRZ0kbaiqVCh9//LGMfXXhwoVYuHAhPvnkE7zxxhsAHEa7ZMyLosjk4BkMBkIkA0DmfZWuUaBAgQIFCv6XUFzBfwn8h0Dx7N0zFIIWBQ8deFIJNARBwPDhwzF8%2BPAqy/Tr169KaQV32mjYsCEAh6j6lClTkJOTg8LCQmzcuBEqlQrt27fHvn37ZNdMmTIF2dnZOH78ODmuqKhAeXk5unbtirfeeouUtdlsSEhIwN69e2VkJe3atcMXX3xRZb86dOggI2MBHF5Im80GjUYDq9VKPEw3b96ERqNhZBScQUsWOLNb2mw2xqjV6/XEWyb9K4mRA45Q0OvXryMtLQ1r1qzBxo0bceLECfJ9ZmYmkTWwWCykvatXr%2BL69etYsWIFGjVqhLO3c3i283S8nNCqVSv88ssv2LJlC4A7xC9SeKk0vitXrqBBgwbkOsnAo8fmnNfXpUsXHD58mHjhvLy8ZDmOBoOBsKECIMasO1AIWhQoUKBAgQIX%2BIsZZg8CirGn4JGDxWLBsmXLkJaWhvz8fAQFBeG5557DuHHjYDAYMGTIEFy7dg1bt26VhSTm5OTg2Wefxe7du1HPKUnoypUr6N69OyHMkBAWFoZmzZrh9OnTmDRpEjmvUqmg1WrRunVrbv/mzZuH2NhY8sP9n//8JwCHkSMRfnh4eJAcxLNnz2L79u0oKyuDzWaThQk6QzLwoqOjkZubS85rNBqEhIQgNzeXeMQWLVqEa9eu4fPPP3cZBlmVgSEZSZJOnQQTJ1P9xRdfRJ06dbBo0SIS1pmQkAC9Xg%2BbzYbIyEgADuPRbDbDw8ODsGhK2LdvH8xmMw4dOoSpU6cCcMy1s9i8TqdjWDxPnjwJALh8%2BTI5p9frERERgYMHD0IURURERKBNmzbVzgMAwkoKAAEBAThw4AAj7eAcCkuT8dTjJZ9Vgfj4eDz%2B%2BOOyc82qUl12Tuqgjm02Tk6K0ShLvuCWoes1m5kclIoKKl%2BopESWLMK5xEGQ4Jw3Y7EwOTHMKU4Zaghsf3mX0VpQnHrpueAUYdtiJsJBiCALIqDLlJWxOVx0mcpKNueIrpjTNj3HjATW2bNsDhw9oXY7m5tWWHgnSQcAcnMBii2YmTDeBNLnCgrkCVe//Qa0bCm7pLiYyk3ibK5r16iUQWo/ctumyzAbizMVnLlh1pu3dvTmovcRb67oazj7hr6MtyXo6eLd8y73Pq9/7uwbd24q6jqmf7xr6GcJZ%2B2YgfP2hKtnJmefu7PNmfvl0iUgNFRWJC9PnvvK7GHOOXrYdDMAcOUKUL/%2BneOLF4Hb76cJrl8HbkvdAnAQ3tB5kPQ5%2Bhpp3C7S3RU85FCMPQWPHObPn48tW7YQw8ZsNiMtLQ3nzp3DihUr8Ntvv6G0tBRLliwh4YIAiEB3Xl4e%2BVE%2BZswY/PDDDwAc3qmdO3fK8shaUj9IAIcR5OnpifHjx6NFixbo0KGD7PsmTZrg1VdfxWeffUZy9WhYLBaZwfbOO%2B%2BgSZMmbo0/j6LIs9vtxPMkkdN88MEHUKvV3Hw6dyEZgTwmTRoBAQEYNGgQFi1aROQNJOIUAETjzjnsMTExEatWrSLn/vvf/6Jhw4ZIT0%2BXjc0ZtKEH3CFJcTZazWYzevbsiV9%2B%2BQVGoxHHjx/H0aNH0aJFi2rH4dzezZs3ZaGp0vd%2BvGz526CNv%2BqwadMmRlR9/LhxYHpIZ%2B9Tx9w/wtQPIm4Zul5OribDQUH9iOKmd9IECZxcUndIZhgyAQ5DAnMZzTbgBnkNN9WVbotDxsFEi9NleCHTdBkeYQddMY8IhJpjRuuYNvQAdkJ55En0L0ra0APujgmEZtbgPFeZ24qzuRjCIB47DN02XYbDUsFMBWdumPXmrR29ueh9xJsr%2BhrOvqEv420Jerp497zLvc/rnzv7xp2birqO6R/vGvpZwmMYoQfO2xOunpmcfe7ONmfuF8rQA1iSI2YPc87Rw6abAeSGHsAaeoDcaAP4pEz0OfoaadwGyy0A9ydF5ndD8ezdMxRjT8Ejhw0bNiAoKAhz5sxB/fr1ceXKFcyYMQN79%2B7F9evXSbkVK1agf//%2BJByTh5kzZyI7Oxs2mw0lJSV49dVXERsbS75funQpjh49SvTX7HY7ioqK8Pbbb%2BPEiRNYsmQJN3RvwoQJmDBhAgCgV69eOHr0KLZv305CCysqKvDjjz9i%2BfLlaNq0Kf71r3/h3LlzGDVqFJYuXUryz6pCbGwspkyZAsBh7Pz73//G4sWLATjkA5znAXCEI2ZnZxOvnE6ng7%2B/P27cuEEMMsCh8ZeXlwdfX1/iafTw8JCV6d27N8OMuXz5cixfvlx27rPPPsNnn32GQ4cOwdvbGykpKRAEAX369EHLli0xYsQIrFq1ipSX5Cik/lVWVjIEKM4wGAwoLy%2BHKIpQq9WycqIo4v333yfGW926ddGmTRtZiKZz7p1k4C9dupQcq1QqeHt7yzyLJpNJJn/h4eGBGjVq4MaNGxBFEb68HxpVQCFoUaBAgQIFjwR%2Bx982BQ8fFHNZwSMHi8WCxx57DB06dEC9evXQqVMnzJ07F8Ad0WwfHx/Y7XYSDlgVSkpKcOvWLQwYMAAeHh7YvHmzTM5BYrgMDg5GcHAwatasiWbNmmHw4MEAHKGh7qJx48aIjIxEZGQkOnXqhIkTJ8Lf3x8lJSUIDQ0lhkKNGjVIe86fjIwMREZGwmazYfv27YiJiUFMTAx69%2B6NL774ghgoFRUVhDCkQYMG0Gg02L9/Pxo3bizzhubl5cmMOADEExgYGEi8hO3bt5eVcQ6XrA4jR47Eb7/9Rua5T58%2BiI%2BPh9FoRFZWFp577jlZmK2XlxeWL1%2BOTp06wWw2w8vLS/Z9eHg4atasCb1eD4PBAKPRSAw8iVTFGc5eupycHJSWliIrK4ucq6ysRGVlJQRBQOvWrTF//nx06tQJAIgERCklQiQIgsy7aLFYUFBQQPrhDnOo83hpKDl7ChQoUKBAgRMUgpZ7xl9rtAr%2BJ6DT6bBnzx4888wzmDlzJjIyMtCiRQts3boVwbfDhVq0aIHGjRvjyJEjVeqzAXdEzePi4tC6dWtcuHBBFl7JQ35%2BPtGIq85rWB1sNhsyMjJQVFSE2ry4Dg7MZrMstFISIbfZbERXz9PTE0FBQcTovXz5MrRaLXx9fVFSUsKEJfL6BQAFTuq1%2B/fvl5WhyWGqg2RMenh4kP5KkDxyEsrLy/Haa6/hwIEDaNSoEcrKymSi5cePH8f169dhMpmYcEmJEMcZ6enpSE9Px8CBAwEAs2bNIsanMyS5hb///e8476RCa7VaZV5etVotYw/l4fcwwfJE1WM6dmTKUU5a5pgnAH1XguQ81WDqZQBTrxuq4IwwO1jBYp7ONT0srlA41Wemf7RiMK9iXpgyNTc8UWN67LTgM6%2B/7ghf02PgjpsudOGC/Jin3Ezng/IUqul9wps/KjScKzbtQuyeK4ZOvyjhqETTuuu8YdKXMdrnnLxYeh14e9adtXMpHs/LyaWu4UWCX7zoum16Tnn7hj7nxJsFwJFfRoPeasw9xqmXd0u5Eobn9ZfefjzhcGaL0krsYLc6s284m9iVAD0AZlDl5ewg6HN3U%2BZB1etWmdt71mj6E5P2FGPvnqGEcSp45PDaa69h0aJFuHHjBjZs2IANGzbAy8sLM2bMQFOnXJWPPvoICQkJmDlzJrp27cqt67vvvoO3tzdatGiBAQMG4MiRI1i9erXMI8gjGPH29ka9evVk7bmCVAcAQkxit9tx7NgxNG/enBg%2BQ4cOlXm0rFYrDAYDTCYTvLy8ZKQmwJ08NpVKBZPJhHXr1qFv37545plncPToURgMBly5cgVDhgxBcnIyPD09GY%2BV81gBee4ZTaSi0%2BlkHkGDwYBWrVrh5ZdfJiyb9DhpSB5IGv7%2B/pg9ezamT58OwOH9qqqvvH5LZDIqlQqBgYGYPXs2IcX58ccf0dHJmJLm2G63w2q1QhRF7N%2B/H/Xq1SMhmTt37pS14efnRzQUedDy8niqAC9nb9y4cWhBeVLpHAr6mJdDc1eC5DzVYCpRhanXDVVw3rsMOs2Gl95GD4ubH0j1mekfzzCnK%2BblIFFzw03TpMZOp6Xx%2ButOHhA9Bu646UKNGsmPecrNdKIST6Ga3ie8%2BaMSirhi0y7E7rkKMHRiEufFCf245Q3TZcojJ5mNXgfennUrh8uVeDwvkY66hpeWRr9T5LVNzylv39DnKH4oJr8MYLcaL5qPrpd3S7kShuf1l95%2BvHdpzBblJKbRW53ZN27kA3OfAdSgvLzYQdDn7qbMg6rXrTK39yxvXyp4dPDXMm0V/E9gzJgxWLBgAVq1akU8VRUVFZg2bZpMYqB169bo378/jEYjV6Pu%2BPHjKC4uxpNPPgkAiImJgSAI2LhxIwAH6%2BW3334LwOF1Ki8vJ4ZVgwYNsGTJEqjVaqSlpSEhIQGRkZHo0qUL3n77bVy7/Wo4JyeHhJhKdUh5ZqIowt/fH/v27cO%2BffsIa6fkqRNFEWazGZWVlbh58yZMJhMJsxRFERUVFbBYLCTfTApb7Nu3LwAH%2B6fRaISvry/UajXUajVMJpNbxpMzaKNMyjWTBOBXr14NHx8ffPDBBxAEgUs0o9Vq8e2332LTpk1E669NmzYMAc6tW7cwfvx4woDpHBYZExNTZR8lI8t5Lrp164aDBw/Cy8sLHh4eqKysxNq1a5lrpZBNADh27Bhh9ywpKWGYOOvXr0/yLnmgw2KrAy9nT4ECBQoUKHjYwAsE%2BMPwkHr2zGYzpk2bhnbt2qFLly5YuXJllWV/%2BOEHvPDCC4iMjERcXBx2794t%2B75du3Zo3ry57FPGDYG4OyjGnoJHCidPnsS8efMQHx%2BPDRs24Mcff0RSUhLJ2UpLS5OVf/vtt%2BHl5YUNGzbg3Llzsu%2BSk5MBALt378bjjz%2BOJ554AqIoori4GNnZ2QCAiIgIqFQqbNiwAWvWrMHkyZOhVqsxfPhwtGzZEnPnzsXcuXMxYMAApKWlYenSpSgoKMDgwYNx8%2BZN1K5dG%2BPGjQNwh1gmISGBMDpGRUWRnLzAwEAAQPPmzdGuXTvMmTMHoihixowZAICUlBTs27cPKpUKOp0Offr0QWpqKt5%2B%2B21otVrEx8cDAAk9lIzGM2fOoHbt2oQZ1DkMMTY2FoIgECOJFl338fHBxx9/DMDBgqrX60koZvv27aFSqRAWFoaPP/4YxcXFEEURkZGRxOCTch6NRiP69euHPn36oKysDKIowsPDg3xPg2c07dixg1v2gw8%2BQHp6OgIDA2VhohaLBUajETqdDjabDX379pWFp5rNZvKRwl5PnDiBgoICxoB2ng9pnVQqFfR6Pfz8/Ej5OnXqcPuoQIECBQoUPKrQKHGADObPn49ffvkFycnJmDlzJpYsWYLvvvuOKXfy5EmMHTsW/fv3R1paGhITE/H3v/%2BdvFjOz89HaWkpdu3aRV7%2B79u3j5vXf7dQjD0FjxTOnTuHL7/8koh0%2B/v7Iy4uDmvXroVarWYIMnx9ffHuu%2B/Cbrdj4cKF5LwoijJhdbvdTsL/AAeTZ15eHo4dOwa73Y7ExEQMHz4cKSkpaNCgAXbs2IGsrCwkJycjMTER33zzDfr06YMxY8bA398fZrMZycnJuHbtGmHJTExMRGFhIVJSUoh3aNeuXUyOoGRAHDhwAOHh4cT7lZCQgC5dusButxO5ie3bt8PPzw8Wi4UIm0vi4oAjz0wiIomJiYFarZZ59rZs2QJRFIlRQ2volZaWEvmKyZMnw8fHh3jBHn/8cWJA6nQ6PPXUUwAcRmZZWRkEQcDWrVuJsTht2jSkpaURgzI3N1eWk%2Bccuirp%2B9mcclxWrlzJePAA4F//%2BheSk5MZchNBENChQwdcvXoVERER6Nmzp2x8er2etCnV%2B%2Babb8JkMhEtxa%2B%2B%2BkpWZ6NGjYixZ7fbodPpYDKZyPyF8Litq4BC0KJAgQIFChS4wEPo2SsvL0dKSgqmT5%2BOsLAw9OjRA6%2B88go3emjLli3o2LEjhg4ditDQUAwaNAgdOnTA9u3bATh%2B1wYHB6N%2B/foyUj4VLx76LqEYewoeWkRHR8tc2pIHCQBeffVVbN68GTk5OVi0aBG6desGm82GvXv3wmQykXDLnJwcTJ48GcAdpslBgwahZcuWKC8vh5%2BfHyHySE9Px8aNG%2BHh4YG9e/cCcITtSTec1WrFhQsXcOHCBezevRsjR46ETqfD%2BvXriWevoKAAW7ZsQUFBAZYtW4Znn32WjCclJQVqtRoBVM5L9%2B7d0bdvX5Jb9t///hft27fHjh07UFRUhKKiIgAOoXTJsyfV99prrwFwGB5169YljJuSQSYZSxMmTMC8efO4OnWuYLgdrP/5559j4cKFxCBZs2YNbt68iZSUFOTn5xMJij179hAvX25uLjGYnnrqKYSGhpK%2BlZSUyIw5um%2B012/EiBEkrNLZ45afn4%2Bvv/4aarVa9nAURRE//vgjatSogSFDhsiYOAHI9olU76JFiwiZjNVqxciRI2XXBAQEyMJai4qKZKGeVekq8sAjaOnalROqSrMS0Me80FG6DIeAgCGC4NVDh6zS7DAcxgQm3Ien9Uj3h8e8QJfhhB8zaZ8UewSPTIK5iJM7yjTFC32m54saA%2B9Wo4kreHwdOHpUfsypiJ5jZvp4ubJUodsvlWWgOVLOnGHL0G3zmmKij6h9xBs33falS2wZ1wMHu1aumEEAlpiGVy%2B9eLx66D1L9dcNfhY%2B4w21kbn7hg4L57Gk0OfcIMVh%2BuMGkZNbA3WHocUdJidqvbn8ai5Ye7ik2vS%2B4bD20IQ2vCl3h9iHOUedcGvf8ApR4%2BYtncvlvb33/MurJ657oHhAxt7169fx66%2B/yj60bFVVOHnyJKxWq4yLISoqCseOHWNe2vbt2xeTJk1i6pBevJ89exaN6Lzr%2BwzFMavgoQVPPFz6YV1aWop58%2Bbh5s2bMlZHjUYDm82GkydPku%2BqQ61atdCsWTPZuR49emDbtm0QBEHGFkmjsrISoiiiV69eSEhIcDmeGjVqAHAYOc4QRREnTpwg3kqbzYb33nsPAFBcXIwxY8YAcMxHsFOmeWVlpSzsMjc3lxCU0JgxY0a14uh%2Bfn5o1KgRvL29GfZNKbds9uzZ2Lx5M9auXYtBgwYhICAARUVFmDFjBtRqNckVNBqNZJ3%2B9re/AXB42eLi4mT1GY1GMmbgjnEnhXBGRETg8OHD5HpRFOHj4wN/f3/Ur18f%2B/fvx%2BjRo/H111/jxo0bKCwshCAIzDiLioowYcIEt8hTLBYLMW6d%2ByLBaDSiZ8%2BeWL9%2BPff6qsJSeeATtIxHmzaUrDrNSkAf89qky3AICJiEe149NCsBzQ7DYUxgeD9ohWBef3jMC3QZDlmIK9F3rjQUfRFHoZppikdUQs8XNQYe4QT9opb7eGnTRn7MqYieY5cC75xCLVqwRWiOlMceY8vQbfOaYghYqH3EG7cb%2BtRuDBzsWrliBgFYYhpevfTi8eqh9yzVXzf4WfiMN9RG5u4bOlKA5xWgz7lBisP0xw0iJ7cG6g5DiztMTtR6c/TRXbL21KvHuYbeNxzWHvrdHm/K3SH2cSVu79a%2B4RWixs1bOpfLe3vv3fKq%2B2dJqj8wfP3111iyZIns3NixY0nqTXUoKCiAv7%2B/7HdFUFAQzGYzioqKZC/1aR6DM2fO4MCBA0hMTATg8OyZTCYMGTIEFy5cQMuWLTFt2rT7agAqnj0FDzViY2NJ/PLu3btJSKGPjw80Gg0x5sLCwrBmzRosX74cBoMBFosFy5YtQ926dcn1Q4YMAeDwGMbHx6NJkyZEesEZzuLely9flhkPer0ederUQdeuXckPe8kLKGHcuHFYuXIl0tLSsG/fPixatAiAQ9hbQps2bVCrVi08/fTTAICePXu6dNknJSUhMjKS9EcyCH/%2B%2BWdSRqPRQKvVQqPRyOqjvV762090KV%2BvrKysSmNQmuOcnBxMnTqV5BvWqFEDhw4dwooVK9CrVy98%2B%2B23UKlUMu9WcHAwBg4ciPT0dKSlpSEtLQ3h4eGkr3PmzCFljxw5QnIvAwMDiaEn9UFiEY2IiCBGeGxsLGbPnk3KVTeHwTz2QQqCIKBBgwYAHF68V155RfZ9WVkZTp8%2BDcCxB2nj7ijtlakGfIIWRVZdgQIFChQ8XPhTc/YekGdv4MCBSE1NlX0kqSZXMJlMzAtk6ZgmdnPGzZs3MW7cOLRt25ZEfp0/fx7FxcUYNWoU/vWvf8HT0xPDhw9nJKbuBYqxp%2BChhqenJ4lfrl27Nvr27Qt/f38IgkBy2dauXYuUlBQ8jhVSFQAAIABJREFU8cQTiIiIwN69exEQEEA8ftL1kgcqICAACxYswLZt24i3zRnNmjXDqVOnoFKpEBMTQ0JHFy5ciHnz5qGgoABxcXFEd42%2BIQ0GAzp37oyWLVsiODgYzz33HE6dOiXzEnp4eECtVpPvPvnkEyLUvnz5cpw6dQoGgwFarZbEgM%2BbNw9paWmkP/Xr1wfgCJsEHMQuW7duRWpqKvr27Yug229tDQYDfv75Z0RGRkKlUqFOnTpIT0%2BHIAiIiYlBRkYGtm3bJntwCYLAkLUADpKUgQMHQhAEREVFQafT4caNG7h8%2BTLUajXJe5Ry8UpLS/H1118jLi6OiMBLmnhWqxUpKSlE5kKr1RKdOomZ1BlS%2BOTmzZtx6NAhMvZly5aRMs5hoQaDgYRYjBo1ClOmTGHqFAQB/v7%2BxAAdOnQo8ezduHEDn3/%2Buaz8zZs3iXFfWlrKeP6qY%2BqkoeTsKVCgQIECBX8OatasibCwMNmnJqNtxIdOp2OMOunYkxfuAAe7%2BLBhw2C327Fo0SKS0vLFF18gLS0NTz75JMLDw5GUlASz2Yzvv//%2BHkYnhxLGqeCRgcViwffff4%2BioiK0bt0aR48eRVBQENq1aycrp9frsWLFCmLs3At27dpFiFzeeOMNeHl5YdiwYYiLi8O7774Li8XCxG0nJSUhKSkJdrsdnTp1wooVK9xqa%2BTIkfjqq6/www8/oFu3bvD29obRaCQi5tOmTYMgCIQ5UmIMlRgmz5w5gz59%2BgC4w8QJOHIUnQ3Na9euoU%2BfPhBFETt37iS5go899hgx%2BERR5HqeRFEkYufjxo3D3LlzkZaWhmeffRaXLl1CcXExrly5QoyWjRs34syZM4z%2BnoRDhw7JwnWlPnfo0AHPPfccMjIyiPfR2ZCTDL8FCxYQwhQaRqOReD0//fRT9O7dmzueW7du4dbt3AaJnRO4E5Lp/EAXRVFGJEPD3T8UgCNn73FK6IoOKQYcKW/OkZD0MSwWJuzn%2BnUq4vLWLSakp6JCHqlkNnMiqcrKZKFT165R0Uy5uUzcFF2G0zTsdioMidc4VREzJs5lTJmSEiYEjr6GM33M5DBzDjiSypxiDV2uE6%2BxykomhLC4mIp65HSQnmOTSR5%2BRR8DjpQu2fuFggImzJC%2BjlknXnc4Y6Arouec1z%2B6XpuNE5lGryeng/R%2Bo%2BeKM2ym3sJCNsKRLmM0sqHQ9Djptnl7jT7HrD/ATgZvAukyvMaotWKmjzPp9Hxy7xeqIt7aMddRhZj9ybmGN%2Bf0luD1Ly9PHnKZny%2BX46NuZW5FnEcd2xancfqUO3vAnXuBOcermNqQvPuZOUkfl5YCPj64/bPjz8FDKIAeEhKCW7duwWq1kpfbBQUF8PT0hC8nfyA/Px9Dhw4FAKxevVoW5qnVamUv23U6HerVq4f8/Pz71l/F2FPwUGPjxo3Ytm0bAIfb3MPDAyqVCkOGDMH06dO5P44BMD%2Bi7xbdu3dHjx49MHHiRGi1Wtjtdqxbtw5ffvklCXtUqVQM5b%2BE/fv3IzIyElFRUS6Nvtq1a0OlUhF2TolZVArXrKysRGBgIKxWKyorKwnxjHPbnp6eaNOmDfR6PbKyspCfn4%2BdO3diy5YtqFOnDvG8ObNdVlZW4qOPPsKGDRtIPYIgIDg4GEajkdF6UalUsFqt6Ny5M2w2G6KiorBt2zbYbDZGgD0uLg61atXCyJEjiVRFZWWljBH08uXLAICXXnpJJo3w7rvvIiMjgzBeStBoNMTgFUWRYWCtCs4C6YIgyEIwKysrYbfbkZGRQc699NJL6NevH1544QVyLjg4mHgVeXiMl%2BRUBXg5e%2BPHjUMLKpmKNhgYA4KTBMLYnJxcHPrlI9eGpby7TNoKJ0GGLuNO%2BhO3caoinh1NX8aU4fzRpa/hpllSk8NLO6R/HbpcJ15jnDxS5oc%2Bp4P0HLuTVsU4kjlhzfR17uQg8cZAV0TP%2Bd2mfTHryekgvd/oueJGc1P1ct8TUmV4ItP0OOm23cnX4op305PBm0C6jBu5ie7kfbmTIkxXxFs75jqqEI9lnr6GN%2Bf0luD1j86to0mTufmhVEW8XEBXuXa8U%2B7sAXfuBeYcr2JqQ3KzHOiT9PHtnMjfkY5%2B//EQGnstW7aERqPB0aNHicMhOzsbrVu3Jh47CeXl5XjllVeILrFzSondbkePHj0wevRo9OvXj5S/dOkSGjdufN/6%2B/DNoAIFFCQBcik8cMSIEYiPj6%2BSjOR%2BwmAwyHLQ7HY7bDYbbDYb0bvz8vLC6NGjZeGEAEiYpyiK1cZw03DOO6tRowZ5AKjVapjNZuKFaty4MRFsBxx5i8uWLYPZbMaxY8fQsGFDqFQqnD9/nmlfkpoAHOGUEydOxOHDh3HkyBFZPzQaDZo2bSrrl6%2BvLzw9PclxdnY2TCYTKisrGQF2q9WKnJwcfPHFF5g0aRIT8vriiy9izZo1%2BMc//oHLly/jypUrABy5eBItsT/1a8NKvWKk90CdOnW4ZCzOYZOSYL30keo4efIk8aSuXr1aZuip1Wr4%2BvoyBDvO8OEReVQBnudUydhToECBAgUPG%2B6jvvf/BPR6Pfr06YNZs2bh%2BPHj2LVrF1auXEm8dwUFBeT30GeffYbLly/jo48%2BIt8VFBSgtLQUKpUKTz/9NBYvXoyffvoJZ86cweTJk1GrVi1069btvvVX8ewpeKjRo0cPTJgwAYDDtR0cHExCEn18fBjGTgnJyckoLCwkhC73A/PmzUOrVq1w9epVvPPOO8jNzYXZbMaAAQOwZcsWfPrppwAc3p1bt27hmWeewauvvgqAH8PtrPMHOAhQ7HY76t2mBpM8cP3790dqaipWrVqFdu3aoW/fvjhx4gRycnKQnJxMiGo8PT0RERGBpUuXomfPnmjatCmys7Nht9tJLplkIPMkGNq2bYvatWtj69atEEWRzK10rWQQlZSUQKPRQK/XIygoCBcvXsTMmTPRvXt39OzZEwEBASgsLOQaM85hkgCwbds2ZGRkwGg0ygyl1157jRCvaLVahIeHk1w/Z6xatQp6vV6WVF1cXAyLxQIPDw9oNBriEQ4KCiJj6dq1K0aPHg1RFHHq1Cl8%2BOGHsFqtEAShyrw7m82G4OBg6PV6lJeXE7ZWq9VKNPd4eY4KFChQoEDBo4w/NZ38IfTsAcDUqVMxa9YsDBs2DAaDAePGjUNMjEM%2BqUuXLpg7dy769euHjIwMVFRUMKztffv2xbx58/DWW29Bo9Fg4sSJMBqN6NixI5YvX14tG/zvhWLsKXioYTAYEMqNsXAYJzt27EB2djaioqLI%2BbKyMixdurTK69xFrVq1ZPWGhIQgNDQUoaGhWLlyJXr37g1RFLFx40ZMnToV7du3R48ePWAymaDX6zFx4kTGK1WrVi3cuHGD254U0hcdHQ3A4UkqKysjuYeSh1Hyju3btw/h4eGwWCw4ceIECf/T6/X49NNPYbfbsX79eoiiSOLD1Wo1evTogenTp6Nr166yENBjx47h4sWLAEAMl5s3bxIPnmTsBQcH4/r169BoNCguLobdbsesWbMwa9YsWf8AB4lMXl4eLBYLkU9whlqtxrx58zBmzBjUr18fv/76KwAHSU1UVBT27NmDoKAgrFy5kpCoOGP48OFo27YtAgICiI5ieXk57HY7LBYLCan19/dHs2bNcO7cOQBAZmYmyVUEHMa41WpF/fr1SdhqvXr1sGbNGtnbtRYtWshCSJ3H83s1DBWCFgUKFChQoMAFHlJjT6/X46OPPiIeO2dIEUIA8N1331Vbj06nw5QpU7gkcvcLD%2BcMKlDgBkaMGAHA4QX69ttvcfnyZRw6dAgjRoyA0WhE69atH1jbDRo0wBtvvAHA4XlatWoV4uPjATg8josWLYLVaiXu%2BoKCApkxYLFY0K1bN5lo/Jo1ayAIAnlI6PV6iKJIyE169%2B6NNm3a4PLly9Dr9Th79ixat25Nxpmamort27fjypUrKCgowMSJEyGKIjw8PFBYWIisrCxYrVZs374dXbp0YQyLiIgIIjtgNpuJ8eQc8gmAiI6aTCZSBnAwaKpUKvj7%2BxND5sqVK8TgEkURWq1WpmNXWlpKdAQlQ69%2B/frQarWEZTQ7OxsdO3YEwJdWOHfunCwHkBfaazKZSP08SOEWZ86cwfnz56FSqXDjxg2GWEan0%2BGpp56qsp7fQ5XME1V/%2BmmOqDptRNIhwTwxXYollBt5StXLU4JgNJaptnnDdXUNp2m39JR5asTM0KkyXHULdxp3QwmZaduNdWG2Ji8MnVY1dkeFmRYb56knU5PB0YhmQrV4WvL0XuJNHyO07kLUGmCngjc1zD7mxZbRHaLb5gyKmWLO/DFleAOnN5w7wuH0XLghWs7bEvSEuaOp7la9dH848%2BfOvepS3J5zDX2Km7VB3WecrcV2kKqIvn14l7jTP17bdD28Mq6mhrclmEKc5yxdhPe8ps/Rt5S0pX9HhoKChxAq%2B4NOelKg4C7x%2BOOPo2/fvvjwww%2BrLDNjxgxs3rwZfn5%2BKCoqgpeXFwRBgE6nQ2pqKuNZc6dOGllZWRg0aBDWrl1LEnGjo6MJkQrgIPwIDQ3FhQsXqqzHx8cHzz77LHbu3MmQnkhQq9Ww2WzQarXV5vmp1WrUqVMHzz//PBo0aICZM2fCYrEgMDAQRUVFsNvt8PX1RVlZGWJjY7Fx48Zqcxxr166NqVOnYsKECUxOHOAQXTcajTJGTGfodDoIgiAjUuFBCv8s5f2KvI0mTZrg73//O7Kysrg6iLy2f69XzRkqlYqEYyYkJODChQs4duwYvL29UVRUJCt74MABzJ8/Hxs3buTWlZSURMTjXWHevHlcgpYxY8fe3UAUKFCgQIGCBwAeAewfBifN3fuKJ554MPU%2BhFA8ewoeWtBhlDy89957ePPNN%2BHn5wdBEKDVatGtWzds2LCBMfTcrZN3jfO/EqZNm4aFCxcCACZMmIDXX3%2B92nqGDRuGgoICxpjy8fFB69atMXv2bGzfvh3t2rVDVFQUyWFr2bIlfH19odVqERISAh8fH9hsNpjNZpSUlEClUpH8OR8fH6hUKqhUKrzwwgsICgqCSqWCh4cHEUPnIS8vD%2BPHj6/SGAwPD0dwcDBiY2NRs2ZNvP/%2B%2B4Q4BnCIoKenp5PjqmLNnc/7%2BvoiPDyc5DNKQu/nzp3D%2BPHjsXv3bnh7e8PT05N4AwVBIOUk0IYeLXQuwZnqWJoznU5HDGPAIW7asGFDWCwWGI1GhlVLygeU%2BrJq1SoZI%2BzvoUpWCFoUKFCgQMGjAL1w9y9UFfz5UIw9BQ8t/vOf/8gMCh4EQcDw4cOxefNmHD16FJmZmZg7dy5CaG7l31EnjXr16uHUqVOEOEXy6s2ZMwdvvPEGNBoN/v3vf6O4uBhdunTBs88%2BS4TQmzRpAi8vLwQHByMnJwfvvPMO/P398X//93%2BoW7cuRo8eDZvNhnPnzuGjjz5Cnz59cOTIERw8eJB4v958800cPnwYu3fvRn5%2BPgwGA3x9fdGsWTP8%2BuuvuHjxIkpKShAdHY3XX38dBoMBBoOBeNt%2B%2B%2B03WK1W%2BPr6QqfToX79%2BhgzZgwJiezfvz8yMjLw1FNPoV69etBoNFiwYAFUKhUEQcDEiRNx5swZ5OXlYcuWLbh%2B/TreeecdpKamkjm6evWqzKPF8wB6enoiMDCQGGclJSU4fvw4bDYbkVeQCE569uyJ0tJSlJWVoaKigoRHiqIIk8lEqIslYXlniKKI5s2bM9o1zoab3W6HyWSCxWKBv78/atSoAU9PT5w6dYp4b0VRJAQ7Evbv309eIoiiiOHDh%2BP06dPEAP09VMlKzp4CBQoUKFDgAoLwYD5/IShhnApcgg5Z1Gg0qF%2B/PhITEzF8%2BHByPi0tDWvXrsXZs2fh7e2Nzp0744033kDt22JDqampWLJkCcNCKbUxduxYtwyxKVOmyMLoBEFAQEAAevXqhTfeeEOWE3bkyBF89tlnOHr0KERRRKtWrTB%2B/Hgign78%2BHEMGzaM247kPfv%2B%2B%2B9Rp04dcj4sLAxWqxWTJ09GfHw8rFYr9u/fj%2BnTp6Nu3bpo0qQJMjMzIQgC3nvvPbRv3x5FRUWYPn06Ll68CEEQYDAYUF5ejjFjxhD2JqvVioMHD2LOnDlITEzEmjVr4O3tjREjRmDs2LEYNGgQsrKymH56e3ujrKwMixcvhtFoxKxZs6oMa4yJiUH//v3xzjvvkNw7GhqNBjExMUTf8G6hVqsRGxsLtVqN1NRUaLVaBAcHIy8vjzEGJUZRZ2NHkjkIDAzExx9/LNtrdwMPDw%2BZBqKUE2k2m6FWq9GgQQMUFxdj6dKleOmll2AwGIiRKXlLX3zxRZw%2BfRpHjx7ltnHixAm3GbROnDiBs2fPys55eTVD9%2B5ynT2XorwcpVy3hMPdENx11Ta3XkqR2h0RYVrkGAAjbM5ti1IWZspwxNppMWKueDdVkTtl6LbvVjicFpfm6MK7FKjmadTTYuJlZYyMIrMJrlwBmPcpZ88Ct%2BVYuH3hdIAuQy2tA7SaOEesnZ5TXv9cCl27o/jNGxSl6H034uJcUWsX%2B4h33d3cq7z%2BuKPDzqyDG/eUWyrgrgTewYqo8%2B4F5oahVeB546LHwHkAuTMmuhru/ULfeO4ow9PH9L3Bq4czbrceJi7aJnNHj%2BOPxM8/P5h6b/8O/CtAYeNU4BamTZuG3r17A7hjlEyfPh01atRAnz59MHfuXKSlpWHSpEnEuPnkk08wePBgpKSkyELo7gd69eqF6dOnA3B4Qy5duoSJEyeirKyMaM9lZGRg0qRJGDFiBCZMmACNRoNvvvkGQ4cOxapVqxAVFQWr1VqtN0WlUuH48ePE2MvPz4fVaoVKpcLNmzeJh0ny6OTm5uKZZ54BAHTu3JlQ7a5btw4FBQXQ6/V4/vnn0bdvXwwZMgRJSUlISkqStanT6Yhx7e3tjdWrV2Px4sXc/oWFhRHikXHjxjF9l97l%2BPr6Ytu2bdi5c2eV4aZNmzaFKIq4evUqY%2BgFBQWhtLSUiI%2B7gkqlgre3tyy0s1atWti5cydat25NjD29Xg%2Bz2SwLbX388cdhsVhw5swZAA6jsVOnTkToPT8//640Fp3bCAgIQK1atXDu3Dl4e3vDz88PFy5cQEJCAlq2bAmVSoWysjLSjt1uR0BAAPLz82UvPu4FPFH1cePGM8aeS1FeDmmNW8LhbgjuumqbWy%2BlSO2OiDDXEU9ZA9y2qB9fTBmOWDv9G5Rrm1MVuVOGbvtuhcNphy9HF96lQDVPo57%2BncZVCaE2AcdxLjP0uH3hdIAuw1GiYX/McrQy6Tnl9c%2Bl0LU7it%2B8QVE/0O9GXJwrau1iH/Guu5t7ldcfd3TYmXVw455ySwXclcA7WJuIdy8wNwwnfcPlc4HzAHJnTHQ13PuFvvHcUYanj3kpGHQ9nHG79TBx0bY0d0Z9MDg9V/CIQDH2FLgFHx8fYtgADn2QLVu2YMeOHahXrx6Sk5OxZs0aEgIXGhpK9N6Sk5Px5ptv3tf%2BeHp6yvoTEhKCIUOGYPny5Zg7dy6MRiPeffddjBo1CqNHjyblpk6diqtXr2LBggXYsGEDWrVqhU2bNlXZztSpU3H8%2BHH07NkTAPDTTz9Bq9XCbrdj5cqVWLduHQAQYpJGjRoRhkopxDArK4vMj5eXF4KCglCzZk0EBAQgLy8PkZGRmDx5MrRaLfz9/XHhwgXMnDkTgMOQLSsrw4gRI7Dm/9k77/AqqrXt/3ZL9k4F0gCRAAIhlBQColKUohwQeBVFPEhTUY4URTlKEUHlVRD1kwN4EFAUXlBQmoCK2BBQioh0AWkJLRAIkF52%2Bf5IZrlnzQrZFjxynPu69kVmZs1qs2aYZ57nvp8FC3A4HLRs2ZLw8HA%2B%2BeQTZs%2BeTbt27XTGU1FREVarlVGjRvHiiy8CZV6tmJgYnE4n1atXZ%2BzYsTz66KMEBwczcuRIunbtKhLAd%2BrUCdALl%2BTm5iqFUPyNrqZNm7Jnzx6gLExSTj6ekZHBmTNnaNGiBd9%2B%2By3R0dG8%2B%2B67LFu2jDfeeIPatWuTkZHBhQsXRAiq1%2Bvl7NmzpKam4vV6ySqXTdP6peqLw%2BGgdu3anD59WseL8%2B9rdnY2ubm5IlH9qVOnALjvvvtwuVxYrVY8Hg9Op1Modebm5lJYWMi1116r64fVahUew507d9K8efMK15M/VJw9k7VnwoQJEyb%2BbAgL9QGqrxV/AP5iIZdXAqaxZ%2BJXw26343A4WLFiBUlJScLQ06Dle4uWvvRfKdhsNiHO8eWXX5KXl0f//v0N5UaNGiVe4IOCgi6bj69169Zs3rxZbGvGnsfjITY2lrfffhu3282oUaPYs2cPQ4YM4c033wR%2BluGvaH7Onj2Lz%2Bdj%2B/bt3HvvvVgsFiIiIkhLS6N///688MILZGdn4/V6mTt3LgAlJSWsW7cOKJv/zZs360IiNaPT4/Fw7tw5sX/gwIEsW7aMMWPGAIiUAsXFxbz44ot8%2BumnNGrUiJYtWwql0CZNmgjjTUuG7m8wVatWTSh/AqIsQEJCAtdffz3vv/8%2BycnJ5OXlsX//frZv3y74bUFBQaxZs4Y33ngDKDMGAU5LmvBhYWEsW7aMzp074/V6hajL1q1bdeW0frjdbpFPz1/VNCIigtzcXDweD3a7HavVitVqJSEhgTNnzlBUVMSGDRtITEwkNDSUvLw8cW5MTIwQxGnSpAnbt2/HYrHg8Xh04aFbtmwJ2NgzYcKECRMmTJi40jCNPRO/GKWlpXz11Vd88803vPjii8yfP5/k5GRl2caNG4u/X3rpJS5evEhCQgKg5/75ozLuX3p6Ovv372fy5MlAmfdr//79LFy4kKKiIpYtW8ahQ4eoV6%2Bejr%2BnQRNakbl/UCaaUb9%2BfZ566ilatmxJixYtmDlzpuizP06ePEmXLl3w%2BXwiFPTJJ58Uxz/66CMsFotO9OPEiRN07NhROVc%2Bn4/XXnuNCRMmCF6j1%2BvFZrNRv359Dh48SIsWLWjYsCELFy7E5XJdNlmnFiJotVr54osv6N27N5GRkbhcLuHZS05O5tZbb%2BWVV17hxx9/1IVG%2Bqcd0Awa/%2BP5%2Bfl4vV7hgZs2bZowIg8cOCDyBX5XLptst9t14bzZ2dnMnj27wv5ryMvL44477hBt79u3T8ehVKFPnz68%2B%2B67uvQVdevW5ciRI1y6dAm3243NZqOkpIR9%2B/ZhtVqZOXOmWMfR0dG4XC4uXrxIcXExUVFR5OfnU1JSgt3%2B82PT4XCIDweAThCmMpgCLSZMmDBh4qpAZiaUv4P94TA9e78Z5gyaCAgTJkwgNTWV1NRUkpKSGDVqFAMGDKBHjx7k5uYqjaqK4HK5cLlc2O12Tp48yaRJkwQPatKkSUyaNIl77rmHFStW8Prrr5OVlUXfvn11CbwLCgp0/bn77rtp1KiRSC8QaJ%2B6dOnCxo0bxW/BggVEREQwZMgQ8vLySElJwePx0KhRI5YuXYrdbic2NlakBHj66adZunSpqG/jxo00aNAAKPOu7d69m8OHDxNcHtyved569OiB1WrF6XQyduxY0X7r1q257777gJ8TiHs8HjIyMoQXUFP6zMvL47PPPhNtp6SkMHbsWGFwaGGOXq%2BXHTt2MH78eDweDzabjSpVqgBlAjUNGzYkJCSEoqIiEZYKZYapBn9hEw2a0aMZKFoaCv%2B%2BW61WGjZsiM/no7S0lP79%2B/PFF18A6FQ25fNk%2BIc8ankULwf/cWj44YcfdGkZ/LmCHo%2BHoUOH0r59e7Zt20ZOTg5nzpwRYat5eXlcvHgRh8MhPIramPzR8hfk7VElVe/YUZFUvdzjKZCerttUpj7ctUu/bch0Xsb5r6yMYZ/UtjJdpN%2B6AUDFcZTr9fMKa/jxR2lHOYdTBzlfoyR4o9QgkteyYgINU6HISv/999KO48f125mZxrYlnqkyAbR8YVQJqqUs0MeOSccVYzJUo6pXHoMib6ghRabhQhmTVBumT17TGC6dcjkaJkw1gdL8BZKg2i8IogyK633woLRDUZGBRixPhKq/cj5V1U0l1aO65w2XU1HIsEu%2BQQwPBUX/VOJfUuOq/snDMlSjujB%2B/%2BdX2D8pHF5J5ZaiRQyTJR8HwxpQ1isvUsWiledCddsZ9smNKU6Sp0t1WeR9v6qM1pcKBN3%2BEJhqnL8ZpmfPREB49NFHhWpkcHAwMTExQnWwSpUqBn5WRYiIiGDJkiW6fWPHjmX37t0cO3YsYO6f0%2BlkxYoVQJnRERUVhdPppEOHDr%2BoTzL3LyYmhhdffJF27dqxefNmOnXqRGxsLMXFxfz00080a9aMs2fP4nA4qFu3LufPn%2BfUqVNcc801nDx5kpiYGBo3bsxPP/3E7bffzoYNG/B4PGzatImtW7cKz9/AgQOF5%2B%2BVV14RhpIm/gKQmprKjh07sNvtuvBMDZqnS/OsBQUFiTx7DoeDV155hccee4ygoCBsNptQAD116hQPPPAAUGZcjRgxguLiYho0aEBSUhIffPBBQNdSM5y0RPBHjhwx9M3r9QoPX0WIiIigSZMmbNq0CZ/Ph9PpxGazkZiYyI4dO3C73VitVsEpfOeddxg5cmSl/atWrZruA4HWHyhbw263G4/HI/rasGFDJk2aRO3atTl//jzw89yeOHECp9NJZGRkhevKarXSrFmzSvuloSKBliZN9AIt1K6t35bCjpXiIUlJ%2Bm0Fed/A%2BVcR/OV9UttKkY9yz7nANddUXm/TpoYiiYnSjvKPKDqU56IUkMRDYmMV/QtAsMMwFQphA0O6TlmdQcrLCRhUKAISKlGpZkiiD3XqSMcVY6pUuASMY6hb11BEnnLjhTJqUhimT17TGC6dcjkaJkw1gdL8BSKcY2AaKK63XzrNCisyfKuSJ0LVXzkaQHVTSfUEojGjKmTYJd8gKiGQAARaAhE0kodlqEZ1YWRhN1X/pAgJ5fdC2SMlT5bKYyWtAWW98iL56eThAAAgAElEQVRVLNpARHAqFd1SnCRPl%2BqyyPt%2BVZkKPsCauLrw1zJtTfxqREVFER8fT3x8PNWrV9fJy/srQsqYN28er776qti2Wq2iHu0XFhaG1Wpl%2B/btl%2BX%2BaR4vuZ5rrrlGJOb279OxY8cMniMoE0wZNmyYQeBDg2bEaJ6rmjVrkp%2Bfz9atW2nVqpUoV7duXXbs2MHOnTtp1OjnF3QtTPPWW29l6NCh2Gw29u3bxxNPPCFCIyMjI0WS9pSUFBG66s/36tq1qwiThDLDt2nTplx//fXUrFlTCMD4l9FgsVi4pvwlu6SkhLS0NBo0aEBBQQE%2Bn094PZ955hlWrFjByJEjyc3N1eXK80dUVBSALn%2BhNueaARoREVFp2gFNqdQfOTk5bN68WYyhtLSUli1b0rhxY1G3liKhtLSUPn36GHh92pjhZ0NTE0/RYLVaxfwXFxeTlpbGww8/TLt27QDYu3cvH3zwgUiroHkkoYw3WKdOHUJDQ3XeR5vNJjypXq%2BXXbJH7TIwBVpMmDBhwsRVAcPXjj8QpmfvN%2BOvNVoTVwTdu3dn165dfC/FNeXn5zNv3jxlgm0oe6lfu3Yt33zzDU6nk9OnT1foGWncuDGxys/0arRt25bw8HAWLFhgODZv3jwyMzN13CsNly5dYsqUKURFRQmjs2bNmhQUFLBz505h7I0aNYqHH35Y5FzzN/aaNGkClBlSYWFhImVFtWrVREJu%2BDlB%2BLZt27jjjjvo3bs3u3fvFiGKWjoHjROWnp7Onj172Lp1K6dOneL06dPUrVuXoKAgvF4ve/fuZcKECZSUlBAaGsqUKVNEkvIffviBnTt3CuNFM3pefvllOnfuzN69e0WdGqpXry4Mm9LSUpFuQkORFJKUl5cnrrVmHLZo0UJnPG/ZskV3jpa4XetXnTp1sFqtdOzYkbp16%2BLz%2BejataswMh9%2B%2BGE2btxIhOLLu8/nMyRO1zx5FotF5PGLiIjg5ptv5scff2T27NmsX7%2Be6OhoqlWrxqZNm9i5cydhYWEcO3ZMXIvExESys7OJjY3V5cbzeDyCF2ixWH5RWgaTs2fChAkTJkyYuNIwjT0Tvxmpqan06tWLIUOGsGTJEjIyMti6dSsPPfQQVquVhx56SJS9ePGikvsXEhJCYWFhwNw/f86e/0%2BT0A8NDWXs2LFMnz6dqVOncvjwYX788UeeeeYZ1q1bx7hx4wBYtWqVODclJYWbbrqJkydPMnfuXNGXGjVqkJeXx%2BHDhxkyZAinTp1iwoQJ9O7dm6KiInbv3q0z9jT07duXefPmiZx1R48e5X/%2B53%2BAMq6gZlhpoYLR0dHUqlWLKVOmALBmzRphvDRs2JA2bdpgs9lEKgmLxUKrVq0IL4%2Br0gREfD4fFy5cYOvWrUJZ0%2B12i7%2B1c7V59Pl8/PDDD0RGRuq8sJmZmcIIy8nJMXDUIqWQmrp16wouoBYGuW3bNnGOx%2BOhjhRv5m%2BQeb1ejh07hsfj4ZNPPhH5Ej/%2B%2BGPOnDkDwOzZs2nTpo0hlFLzKPobS/5qpP7CMjk5OXz99dfk%2BpGPzp07R3Z2NkeOHGHVqlXYbDYsFovg7J0/f54LFy5Qr149JX9Ra6OWHMJ4Gag4e8nJCs6ezN%2BQtpXfUmTihcLDHQjfRG5arlbJOZM5e6oOyvvKr68/DNQlFWlGrkfeDoBDo5w/yXOsuuSV9U9VbyAcGgM1RuUBlrh0MrVO6WCWOYTffmssIycvXrzYWEYemIJLKc/Xe%2B9JBRSeeZmzp6JRGa6DgUBovOTyclRxAeXIbFWktoGCqViPcv8q5D9d5hwlDzYATpyhMRUHTr5h5bWlmBx5LpRtS/1T3S9yPXJXlI9V%2BfrK/EFFEdWzRJ4awz3mR0EQkK6V8juexFdVzo3Mg5UXpKqMfB1UjcsLUsWpkydVwcE17JMJwOV1XChShNn%2BUTA9e78ZFt8vzUxs4i%2BHDh06MGzYMHr27FlhGa/Xy/z581m6dCnHjx8nIiJCqGhqXplWrVrh9XoFZ8%2Bf%2B9ehQwesVitt27YVOeYqwt///nf27dunzI/Xr18/RowYIfq6bt065syZw8GDB7FYLDRr1ozHHnuMpKQkRo8eTX5%2BPv/85z9xu92sWrWKRYsW8dprr3HjjTeKOqdPn86bb75JvXr1dCIkAFOmTOHIkSM8%2B%2Byz9O/fnwMHDgjFzS%2B%2B%2BIJatWrx9ttvM3PmTIYMGcKiRYs4evQoVatW5dKlS3i9XhwOhzBWNONCyxfn8/lwu90MHDiQ3bt3C2OkS5cuVK1alfHjxzNo0CCgLPzU4/FUmiQ%2BODgYj8dTodHyS6GlY0hMTCQ2NpbXXnuNFi1aULduXU6ePElISAjZ2dnY7XZiYmKEkbtlyxaqVKnCwYMHGTlyJAfLFRCsVitff/01hYWFzJ49m0uXLvH555/j8/kIDg4mLCxMGJMa7HY7LpdLZ8AFCs0z6na7GTNmDLt372bnzp0cl/4DtlgsfP/996SlpSkTutvtdvbs2VOhyIyMyZMnKzl7w4YN/cVjMGHChAkTJq4UPJ4K%2BOF/BFTiXL8HVDzw/1L8tUxbE78KX3755WUNPSh7QR84cCCrVq1ix44drF%2B/nkmTJuk4XqGhoYwZM0bJ/fvyyy9p165dQNy/Xr166TiE/j85NPOWW25h4cKFfPfdd2zdupW33nqLJD/xitDQUOLj47nuuusYMWIEt912G8OGDdMpUQIkJSWxfPlyQ3uvv/46n3zyia5srVq1OHDggPDyHDhwgIYNGzJw4ECRg2/JkiXExsYSERHBY489xsqVK1m5ciXTpk0TxoImSALwzjvv8P3333PhwgXatGlDbm4uGRkZvPDCC4IrV1paKsq7XC4RSqmlQ7BarYSGhlJUVFShoecfZrpo0SLdMZkXOH/%2BfKDMCKpZsyZVqlRh3759hISECHEUt9tN/fr1CQ8Pp0OHDvTp00fUpXkkGzZsyM033yzq9Xq94rp06tRJJDDXoBJnCQ4OFoaeSqlzyJAhOiNs0qRJJCcnExISgs/nE%2BswKiqKkJAQTp48yXXXXSf22%2B12goKCCA0N5brrrtPVrdVbu3btgA09MDl7JkyYMGHi6oAtO6vyQib%2BtDCNPRN/Gvxa7t/viaeeeoqQkBCee%2B65X3V%2Bhw4dSEhIEL/GjRvz4Ycf6owogH/84x9kZmaSk5PDq6%2B%2BSpcuXejRowejRo0SBoPT6RTesN69exMVFcW7774rQiXbt29PWFiYMPY2btyI1WqlS5cuPPvss1gsFnr16sUjjzwiDEdNsObmm2/m448/ZuPGjVgsFmEkXygPH7FYLEKxU0Pbtm1JK5cgTE5OZtiwYUCZF7Jz587k5ORw8eJFpk2bxrlz57hw4QJut5utW7eSm5vL2rVruVQune31emncuLGYJ80I1rBq1SqgjPN38OBBYZA5HA5dCOeDDz4IlK0Rbd5cLhdWq1WorFosFj788EOdN%2B7IkSNMnDiRpUuX8uyzzwpDdv/%2B/YLfZ7fbBa/O35iWvYpavUdVITKXgcnZM2HChAkTJiqBGcb5m/HXGq2JPzVSU1OpWbMmffr0oVmzZiQnJwt1zlOnTvHuu%2B8qQzd/LX766SdWrlyp4/y1bduWixcvsn79emFIQJlASVZWluEnS/t7PB4effRRli5dytSpU7nuuuuIiYnhiy%2B%2BEKkiAE6dOkVYWBh9%2BvTh%2BeefFx43t9stDK/s7GxKSkrIyspi8eLFnD9/nj59%2BghxlczMTHJzc7Hb7ZSUlNCmTRu8Xi9r167lrbfe4qGHHmLcuHHce%2B%2B9Bk/e119/TdeuXWnTpg0%2Bnw%2BPx8Prr7%2Buy0M3c%2BZMYUCFhoaydetWIRoTHx8vjEGfz8f//d//sX//fgYPHsysWbNEHddcc43IO7h48WLuvvtu5bWQwyInTJhAQkICLVq0EB4wj8dDXl4e//73v4UC5ltvvWWoIycnB6/XKzyCtWvXNqiEzpkzhx49etClSxeefvppkdpi5cqVRJfrsLds2VJwDN1uN16vl0OHDgmDWMYv8eqBmrN32y23GMpVxidSfQMxcI4UfA6Dc1dFwJPy6slprwxcE4z8GFVkraEpBRdH5qmoUmzJ9RgujYIAFQhvTo7SVdGfZG6LPE7VMpHnXOVgN9BzFNdFvg7y9VYuUbkxSSwJFPRAVUXyZKhIZlKHZJqSglZlbFw1OfLAFRdGpjJJS9iQ%2Bg4w3ETK%2BZMWoIoGK3dHrke1juTpC2TKVXMTQGq2SnMOBpDSUTmGQHLJVZYXTvX4CYTPaHguKB448v0ht62qVy6jGpNhDQTSwUDymcoVqx5%2B8r0QCBlVdePJN4xcRjuuzBNj4mqBydkz8YchEO5fZmYm7733HmvWrOH06dOEh4fTvHlzBg4cSHR0NFFRUaxdu5YZM2bw5Zdf/qo2NDzxxBOUlJSI3HfysQsXLrBmzRpmzZrFjBkzlHXUrl2bzz77jC1bttC/f/9K26xbt%2B5lPUCtW7dmwoQJ3HbbbVStWpWgoCA6dOjA4sWLcblclJaW4vF4CA8Pp3fv3mzZsgWn08nx48crVYKMiIigsLAQu93O7bffzqpVq%2BjWrRtLly7lzjvvZPLkybRu3ZqCggKuvfZaLBaLyJHXoEEDMjIyBNfP4XDgcrmEl81ms%2BHz%2BYiMjKzQGAoODsZqtQrDSkbVqlWJjY0Vnrzp06cTERHB5s2bmTlzJnXq1CE9PR2r1UqtWrVIT0%2BnWrVq5OTkGNJoBAUFCZVMKDPEoqOjhQFos9mw2WyCq6dh5cqV7Nu3j3HjxlGrVi2OSS/069at4xaFQaZh3759laaf0GBy9kyYMGHCxFWB3FxFks0/CL8waiZgKPKI/rfCNPZMmPgdoRmb48aNU4adVqtWjWuvvZZ69eqxdu1awsPDOX/%2BPGFhYdhsNoqLi1m4cCF79%2B5lxowZeL1ePB6PSP6dm5uLx%2BPB5XJRu3ZtmjVrxoEDB3j//fdJSEiosF9paWlkZmZy7tw5SktLSUtL48knn%2BTbb79l6tSpOBwOPvroI26//Xb%2B9a9/sWvXLubMmYPH48FisdCzZ0%2B%2B%2BeYbMjMzdcaM1WolMTGR/fv3G9Q6ZUyaNIm0tDRuu61McTIoKIgqVapw7tw5vF4vkZGRgnfn9Xq5//77GT16NMePH6dTp05AmQLopUuX6Ny5M59%2B%2Bilz5sxh3bp1LFy4UNeW3W7XGXHh4eGVirckJCSwcuVKPvnkE55%2B%2BmmhVAo/i9Bs2bKFO%2B64Q5nnD2DTpk1UkxMBV4Dx48ezWFI7HD58uAiPNWHChAkTJv4U%2BE8qtMju%2Bd8L8fFXpt4/IcwwThMmrhC6devGunXreOGFF7Db7TzzzDPExcVRpUoVPvzwQxwOB48%2B%2Bihr1qzhjTfeIDU1laKiIp544glRx6VLlzh//jyxsbFMmTKFNWvWULVqVcLCwvjpp5/IycnR8RwtFgvt27fnrbfeolq1alitVl1%2BvPDwcMELS05OxuUqk1N2u90MGDAAr9fLTTfdRMeOHfF4PILLdvHiRTIzMwkNDaVbt27CYLNYLJw4cYKRI0cKLqE/OnfuLEJDn3/%2Bee644w5xTEv2roWtauqkWv%2BWL18OIPIragauw%2BEQuQwnTZpkMJhAn7y9bdu2jB49Wne8adOmhIWFER4eTqtWrWjevLkI2YyLiyM/P5/mzZvrRGmcTicRERH07dtXV5e/KJCW19CECRMmTJj4r4Eyz46JqwWmZ8/EfyWGDh3Kt4o8Upr3KTw8nMLCQmrWrEnv3r3p378/VquVfv36cf311zN8%2BHDdeVqYphbWWBE6dOhAVlaWLoTQH6GhobhcLrKzs1m6dCnr1q3jww8/5NSpU0RHR%2BN2uzl79izDhw9n%2BvTp4jyn0ylCOAFiYmJE7rsePXrw%2BeefCy6fDH8vl5bTD8o8a7I3LiQkhB9%2B%2BAGfz0fjxo3FnLVq1YrNmzdTo0YNqlevzg8//CBCIz0eD5s2beLgwYN07979svMzf/58BgwYIDxm/v1RITg4GJvNJnh7VquV1q1bc9NNN/HSSy8pz7fZbAwYMIC5c%2Bdis9mIjY3l7NmzOk%2BrJo6i8RVLSkoICgqidu3aLFmyhJYtWxISEkJ%2Bfr6Yu5o1a/LVV1/x6aef8uijjyrbvuWWW3ScxctBFcY5dOhQoZ5qwoQJEyZM/CmQnw//qY%2BZCm7474Jrr70y9f4JYXr2TPxXYsKECaxYsUL3mz9/PlWrViUxMZEXX3yR1atXM3z4cGbNmsULL7zwq9vq06ePLh2A9rKekpLCY489RlhYGE2aNOH%2B%2B%2B%2BnXr16ZGdnU79%2BfV5%2B%2BWWWL18uuIOnTp3ibLmQhr%2Bhl5SURFhYGE2bNuVvf/sbVquV8%2BfPU1JSQklJCT169OCRRx4R5TWeX6dOnbBYLMJYcTqdvPzyy6LcDTfcYAi7LCgo4JFHHiE9PZ2QkBC8Xi8%2Bn094AE%2BfPs0P5YmX69SpQ9%2B%2Bfbl06RKffvopzz//fIVz5HK5%2BNe//sXGjRt1YiwVGXpaqGhpaSkxMTH0798fi8VCUFAQ3bt3F6qiqvPDw8OpWrUqTqcTj8fD6dOn8Xq9OgEVzYNYXFxMVFQUw4YNY/Xq1cyePRuXy0W3bt0oLi6mXbt24pzk5GQAtm7dWmHbstfvclAJtHToYEyqXklOdWWu8YBUSCpTa1AUCURgRBYKUJUxRDgr%2BmcQP1BVJH9UkcsoQncDmRp5LgIRwZGvg%2BqcQK6drMUQSNJ3wxjOnau0bTkhNGDkxmzaZChioOQqxH/kpWR4V1Pweg1lFNdO1r9QZS%2BR50/OEW0QGcJ4LVU6G3KXVVHh8j7Dkg0gqbpqTAHcqgGJuBj2yYtL4b2pTNxEVU8gSdXlqVDWK0%2B6ouJfI0YVyBqWz1FS4uX7RbGu5V2KW9OwTz7HMEYMjwBlrna5TCDaMPK2dl3yfGbUytUM07Nn4i%2BDcePGsWPHDpYuXarLxfbll18yZMgQPvnkE8aPH/%2BLPXsLFy7khRdeoHPnzuzcuZNhw4YxZswYEUKpGUwATZo0Ye/evSQkJHD27FlWrVpFTEzMZfl2VapUoX79%2BjgcDk6cOMG5c%2BdwOp0i7NFut7Ns2TJ69OgBlHmX9u/fT61atTh69CgXL14U3Ls6deoIoRWHwxFwYvWaNWuSlZVlKO9wOGjdujXZ2dkcP368QnEWrWxUVBSZ5epel/Pq%2Bfetdu3arFq1iptuuomSkhIiIiLEmFR1xMXFERcXx759%2B3S8vdDQUPLL3%2BLWrl3L4cOHmTBhAl27dmXMmDG6OgoLCxk8eDBb/BQLhw0bxvDhw3nqqaf48MMPlf2eOnUqXbp0qXAO/GEKtJgwYcKEiasBhYVQ/s33j0cl4nO/Gtdcc2Xq/RPC9OyZ%2BEugpKSEjz76iPvuu8%2BQdLt9%2B/a88847XPMrb/wuXbrg8/n46quvdF4rLUTQH/Xr1wcgPT2dnj17ilxw/n3REBYWBpTx5erVq8ePP/5IXFwcJSUldO3alaeeegooS0nQu3dvcd6JEyeIjY3lxIkTREVFiT4FBQVx7NgxfD4fVqtVZ7hZLBbBjdM8aJonzGq1cvr0aWrXrg3o88OVlpayceNGwsPDDaGrQUFBOBwOLBYLFosFh8NBRESErs3g4GAGDRrEyy%2B/jMPhwOFwUKdOHZYsWcLdd99NWFgYs2fPFny5xMREnJIEtH%2B6CIvFQvPmzdmzZw9ut5ugoCD69u1LWlqaMPSgLHVEhw4d%2BNe//sX8%2BfMNRtdbb73F9u3bdXVr2LBhg2GfBi01RSAwk6qbMGHChImrAa6gK5/nuEKYefZ%2BM/5aozXxl8OlS5eYPHkynTp1oqCggDlz5vDOO%2B8Ib1C/fv2YMWMGN9xwg8jdBmWevMt52zT4fD569uxJ9erVKSoqotgvFqVOnTo4nU58Ph/16tXDarWSm5uLxWKhqKiI/Px8MjIyRFggwFdffSX%2BzvOLY/vuu%2B9ISEjg0KFDBAcH89FHHzFlyhTRB/90BocPH8ZisRATE8N1110n9hcXF5OQkCBCGGvUqKEbhxZC6vP5KCkpEUai5pk8fPgwQUFBwkhxOp3YbDbcbjebNm0iXlK2KikpobS0FJ/Ph8/n44033hChkAC1atWiTp06LFiwgNdee42OHTvi8/nIzs5mwIABfPzxx3i9Xnr27ElSUhKnT5/m4MGDXLhwgf79%2B2O323VhqgCdOnWif//%2Bou9ut5v3339f55Ft0KCByKv44IMPYrVamTx5MsnJyaSmprJy5Uq%2B%2BeYbunXrZjDWvV4vzzzzTAWr4ZcJtJhJ1U2YMGHChAkTVxpmGKeJPxU6dOigzBfXvHlz3nvvPXw%2BH%2B%2B%2B%2By6LFi3i2LFjREZG0qZNG4YOHcq1Etn2%2BPHj9OzZk6KiIpG%2BoHXr1uzbt4/bb7%2BdZ555hn79%2BumMLQ2a1L72Qu7xeCguLhahfFpY5/PPP096ejorV67kwoULBAUF0ahRI7Zv364cn9VqFUnQY2Njyc3NJSIigjPKLMP6/sTExFBSUkJOTo4IZbwcQkJCaN%2B%2BPd9//70InfRHq1atdGGKvXr14rrrrmPy5MmGPrtcLvLz83E4HISEhHBJkeRVDqnU8uppaN68OadOndLlGbRarURERNCkSRMyMzM5fPgwYWFhzJ07V3Dq9u7dy5w5c7jhhht4/PHHgTLe3CuvvEJ%2Bfr7OQ/nWW2%2BRn5/PuHHjyMnJEd7JoKAgYYh/9tlnOg9sXl4e999/P82aNWP8%2BPFERUVx4cIF7rzzTvLy8kRZ7dqnpKRUmCuwS5cuTJ06VXlMxr59%2Bzh06JBuX82aDWnRopFun88H/vna5W2lInZpKfh5JaXNMshxOcXFIHm9c3LAzxlraFuZeunMGYiLu2zbcj2KpsnOBl0WC1VFBQXgbzTLZeTjirZUbcsDV83xuXMQHV3xmFQnyd1T1Xv2LJQ72YEyGpUhn3F6ul42XB7nhQtQtar%2BHFlg4cQJqFVLX%2BbQISiPPgBg%2B3Zo3lxXxFC1oi153chNqULC5HNOnjRGWcllVGtCHubp0%2BD3XYu8PCgPmhC4dAkiIy/fP7mMajnK58nXV3WOvP4CKaPqn2GpqyqS9hnWrOIcuS3lsySA5428TOS2A6lXda8a5uL4cYPwhly3fC1V58jXTlHEeL8oFpf8HFOta3mNytuZmVC9uv4cuT9HjxrTxh07BuUi08q%2BqPbJ29q6Uj1S/jBU8n70q%2BH3/9R/O0xjz8SfCo0bN1bmp0tKSuKDDz5g9OjRhvx0TqeT/Px8FixYILxxeXl5tG/fnvz8fCIjI0Xi7cjISMLCwjh58iRr1qxh/PjxBmPPbrfjcrnIzc1l1qxZ1K1bl507d/Lkk0/SsmVLFixYwDvvvMOkSZNwuVy88cYbDBgwAJvNRkhICFWqVOG4n9pAWFgYxcXFlJaWYrFYiIiI4NKlSzgcDiZOnEjLli3p2LFjQPOjhTqWlJTgdrsvy7u74YYbiI6O5uOPP6Zhw4bs37%2BfJ598Uoi0hISE6FQuw8LCSElJwWaz6TyMDz/8MHl5ebz77ruX7ZtmIGv1%2BYdAzps3j6pVq3LvvffqeH0Wi4XQ0FB8Ph/FxcWG5OghISHUqlWLu%2B66i379%2Bgnhlrlz5/LSSy8Z8unNmzePzMxMnn/%2BeV3Ypj9UvMsPPviAcePG8fbbb3PjjTfSo0cPiouLSffL7zNgwADGjBlD06ZNDf3UEBQUxI4dOwJKrG5y9kyYMGHCxNUA1UeSPwymsfebYYZxmvjToVu3bmzcuFH3mzNnDp9//rkyP13Tpk0pLCzU5aebMmUKOTk5xMTEMGXKFD7%2B%2BGNcLhdBQUGcOXOGW2%2B9VcfRq1u3LlOnTiUlJYW77rqLpKQkAN58803i4%2BOJq%2BChUFRUxPvvv0%2B9evWIj4%2BnoKBAeJC0/Gt5eXmEhoZitVq58cYbGTduHFDGd5s2bRpdu3bV1akZCsHBwUT7uw7KzykqKsLlclGvXj3atGmjCz/1x65du9i2bRvBwcGkpaVRs2ZNfvrpJ3Hcn7sYERFBSEgI69evx%2Bl06nLHlZSU0K1bN2UbgPDe%2BYeFal457XfvvfeKa%2BcPn89HXl6eSHFQtWpVbr/9dhF%2BWlBQwMGDB5k0aRItW7akf//%2BHDx4kFOnTmGxWKhatapQCgX48ccf8Xg8FXreNE9tv379SEhIED/tmtx///28/PLLHD9%2BHJfLRbif22r16tVYLBaD59MfJSUlSq%2BnCiZnz4QJEyZMXA0ICw5MzO2KwOTs/Wb8tUZr4qqA0%2BkkJiZG96tSpQr//ve/AViwYAF33XUXtWrVIiUlhenTp5OSksKhQ4fEy/7SpUsBePXVV2nbti3x8fH06NGD8PBwXC4X69ev13lfjh49SoMGDQgKCiImJoaHH34YgG3btlF0mWSi1apV46OPPiI1NZWSkhLsdrt4ide8VgBVq1bFYrHgcrno0aMHbdq0ARBGoIZ//OMfxMfH06ZNG1atWsWiRYtECgUNcXFx9O3bl7i4OL766qsKvUxut5vk5GQWLFjAhg0b%2BNvf/qYL27z33nvF38HBwSI/nMVi4cYbbxTHsrKy2Lp1K9Wk%2BA/NyNRCN2vWrFnhPEGZV1ITpImKisLpdFKzZk2CgoKEEVZQUMAnn3xCZmam8LBqP7fbzXfffUePHj1YvXo1NWrUoHfv3jrDdP369UybNg2v10utWrUMHLqioiLefPNNtm3bZqhf%2B23evJmaNWty9uxZnSGtGZDdu3c3GNjax4CgoCDDPJkwYcKECRNXNfz%2Bn/3DYRp7vxl/rdGauKpx8OBBGjRoQIMGDXT7LRYLo0aNAuDTTz/l8OHDwgDyV7scPnw4hYWFhISEUFxczKZNm4QoicVioVevXmzbto1Zs2YJY8/n8102kfrEiRMB%2BPbbbzlx4gQ%2Bn08Ye6Wlpdx2W1netKNHj%2BLxeDh06BBbtmxhypQpXNnUbw0AACAASURBVH/99ZSUlOhy382aNYujR4/y/fffc8cddzB69GjGjx8vjBaXy8W7775Lo0aN2LNnD0FBQXi9XmXYYM2aNYmMjOS%2B%2B%2B4jIyODlStXctov8Y6/1%2BrMmTP8z//8D1CWlsDfKPR6vcyYMYNWrVoBP3se/dU3bTYbVqtVHNN4ftqvTp069OrVS3AHz58/T1FREadOnSI8PJzbb78dKBORqVq1qvD0FRYWUlhYiNvtJjo6mp49e4o5rlWrFjNmzNCNf/fu3SKn3unTp8nPzyc0NFQYy16vl4KCArxer65%2B7degQQMSExMpKSlh2LBhujnS1tSGDRsMyqNaaGog4ZsaTIEWEyZMmDBhwsSVhmnsmfjTYfny5UItUfsVFBRQWlpKHX%2B2sR%2BaNm0KwE8//cT%2B/fvFfv%2BQupiYGN577z2hGjl48GAhjPLQQw%2BxYsUKmjZtSmlpqU5V88iRI%2BLvs2fPkpCQIOT3tfQImZmZxMXFYbfbxQu7xWLhH//4B9dff704Pz09nf79%2B9OjRw/at29Ply5deO6558RxLe2Bz%2BfD6/Vy7tw54uLieOONN4Ay71LHjh0ZOXIkeXl5lJaWYrPZDOkkAI4dO8b777%2BP2%2B3GZrORnZ2t8xBWBI/Ho/MWfvbZZ3g8Hj755BNxXIbm6fJX8PQ3oo4dO0abNm04f/687jyLxcKFCxf44IMPxD6VwVRaWsrJkydZsmSJ2FZx8nJzc8nOzsbhcIh%2B5ufn6wRZZs%2BeXeHY9%2B3bx48//khaWhrdu3fX8Qt9Ph9ut1uEfMr9g7Lrc1aRoFcFVVL1jh2NSdUry2SupGzKc6MKLZWzGKtyJErXOpDE4YYMwSpauLxPznyNostyAnUwDl4uo1gjASWGl7IYq4aQlfXLK5bnSzV/hsug6qAflxQUaahUIcKySNO%2BfcYyu3bpt99/31DEMBcKQS05GOLgQamA3zNVg5zjXZmyM4AM5HIEt0z3UdUrn6OKAjfQhhSLQt4VSOJweYkq16O0UFTBJoa1FMj9Ig9U8ZwwzIVq0QbwTKos4bxy3PLaUmQXNyS3V2S7l%2Bs2PKIVz2x5mBkZiv7J95Dingpgig3DkrdV61Fex9nZlderqke%2BhQy3VPnkFZdU/u5wxWB69n4z/lqjNXHVQEsPoP38pfRVUBkgwcHB7N27V7evRo0a3HXXXQAMGjSIZs2aAWVhhfHx8UoRktWrV9OkSRPCw8PFy39G%2BZP/wQcfFOWysrJ0/bBarXTu3Fmp9nnu3Dleeukl9u3bR7bfU1rzOGnw%2BXx8%2B%2B239O3b11BGS2mgCa1UxN3z%2BXzExcURHh4u5tFms9G5c2dDWbvdTlxcnDBitX32CkI4NONRS7MQqGdK87ZZFQ9czWtmtVoJDg5W5rrzer0clN4gtbrcbrfB86b1dcyYMTpvnQabzUZQUJDOa7l161adQelyudizZ49S2dTfmFQdV2HlypU8%2BeSTut%2B6rz43FpTHL20rpkcvRwiS9Fw55A8EKqk1yfCWvxUoHZkSz9Rwkmqf4iOOocuq9S0PXi6jSIVRyXSWQSf5qB6ClCIzoIrl%2BVLNn%2BEyqDoopTkxpAhVeI0Ncn6NGxvLlHOVBe65x1DEMBeK/KSyemjDhlIBRT7KxET9tlL5T54LxThllUqZbq2qVz5HlTzaQNtWLAp5l7yt%2BCZnWKLK9SgtFIM6q7FIYPeLPFDFc8IwF6pFG8AzSX7sBnQfymtLui9V9Rp3GOv2V7tV7zAOszzNrB7yPaS4pwKYYsOw5G3VepTXsYo9EEg98i1kuKXKJ0/17cDE1QPT2DPxp8Ott97KypUrdb%2BQkBAcDodOtt8fe/bsAcpyqGmKnLVr12bhwoWGF//Vq1cD6HK%2BaXjllVfEuRrS09P55z//ic/nIzc3l9DQUOG9eemll4CykElNlETzsoWGhtKzZ09GjhzJ%2BvXrlQbT%2BvXradeunTBUqlSpQnR0NB9%2B%2BCErV67k7bffpkWLFmzcuFEkJ9dgtVqJiYnhtttuw263Y7Vaxdg0xMfHExsby7lz58jNzSWx/I3K4/EQGhrKfffdpyvvdru5cOECrVu3FvtmzpzJAw88oEuIrkEznDSvV0UGJ6ATecnPz8dqteLxeAwGouaNtVqtuvFq85eUlERwcLBoOzo6GpvNJup59dVX2bhxIx06dNAZkz6fj0aNGumSxWvGpGZAWywW2rVrx7lz53j99dd1xrvT6SQlJYWPPvpI7NPq9%2BfpqQxNFVQCLaY8iwkTJkyY%2BLMh3K1ytf9BMD17vxl/rdGauCoQFhZGfHy87mexWEhMTOTIkSMGbx38bHTddtttXHfddQQHB3Pq1ClycnJ48MEH2bp1KxkZGUKsxGaz0a5dO0M9y5cvB9AdmzhxIl999RUlJSVYrVby8/OFaMuTTz4JoAtPLC4uxmKxkJOTw6pVq5g2bRoej4eoqCgAUlJSdEbMunXrhKHSqVMnsrKyOHPmDPHx8VxzzTUEBQVx5MgR4ckDaNSoEV6vl6KiIpYuXYrb7aaoqMigmpmRkcHUqVN59tlnqV27ts5Y3rJliwg/vOWWW3CWfy6%2B9tprycjIEIbMxIkTOXr0qDJUNCcnR4je7N69W/Rv%2BPDhbNy4kS%2B%2B%2BAKAESNGsGbNGt25Km%2Bs1WqlfnneIrfbrTMENY/nrl27KC4uFkZVenq6zsB67rnnGDRoEF9%2B%2BSXPPvssKSkptGjRAoB//vOfwjurGZOaN8/r9eLxeJg7dy47duzgxIkTYk7gZ4POP3l8UFAQVquVXL/QIZfq86kCJmfPhAkTJkyYqASmsfeb8dcarYmrGkOGDAHggQceYPny5Zw4cYLdu3fzxBNPsGvXLurXr0/jxo2x2%2B3cc889FBcXEx4eTlBQEI8//jhdu3blpZdewmq18ve//13naTt//jzjx48npzzI/f1yrkrDhg25/vrradasGSUlJXg8HiIjIwVHUPN2rVy5UuclCwsLIy4ujtLSUkpLS2nfvr3gB%2B7YsUMX9vfqq6%2BKvzVO2lNPPcUPP/xAVlYWhw4don///rq50HiJ/kaGKtzRYrHw8MMP88wzz5CXl6fjIo4bN45PP/0UKDM4NQP2%2BPHjpKWlEVkeb3L27FkmTpyoE5LxR25uLsXFxTzyyCPCeJw%2BfTpt2rQR%2BQOnTp2qC0%2BtiDsYGRmpy1GohYeqkJeXJ/729yjm5OSI%2BRk/fjw7duxg27ZtQFmorWZQaQZyUVGRuB7x8fGsWLGC6OhoXC4XaWlpol7NyPNvt7i4WIQaa/MfY4jvU0PF2but3CjVQSb6SJ5D5fRIO1VcIQMfRmF8G/glEunDbyp%2BhsQnU3VQ5o6o%2BifzTVTjNNCmZG9pAPydgLhCCrKLgSMjD0JVcSCNy2NQkXz87hFQUJkC4F8q6/VLzQIYOXxgnHRVWxKpzFBEwfOTOZCq7u3cWclJGK%2BLYYoVi9awTBR8PJmKqlqzhrake1VFdwtk2QSwrA37VP2T9xn6oyADylOhvF%2Bk81QcV3kMhrYD4AIqOcLyvalYW3IZw%2B2sWOfymlVySGXuqbxIICA%2Bo2FfAM8J%2BVqq%2BHgB8S0rIWJr7Zz3/qcyqpv4PWAmVTfxp0Ljxo258847eeGFF5THx48fz0cffURERATnz5/H5XIRFhZGTk6OLql6fn4%2B9913H%2BfKH76XLl0iMjISn89HdHQ07733nvCsaOeoUK1aNZYtW0bv3r2FseZwOKhVqxZHjx6lb9%2B%2BLFiwgPvvv5/w8HCmTZsGlIWBFhQUcO7cOSIiIsjLy9N55n4J2rdvz9dff43FYhHeMKvVGpAXyOFwcN999/HRRx8xbtw4RowYoeuDfyJ0zQDTUjrccMMNXLp0ibp16xIZGUlSUhLz58%2BnUaNG7N%2B/X5fQ3Wq14nQ6KSws/FVj1FC/fn3S09MrNPA0xMTEkKV40dPGERcXR2ZmJvfccw87duwgPT1deFwtFkuFc2e1WunVqxf33HMPDz74IBcvXhTH7rzzTiZPnszp06e55ZZbAERSd02J1Gq1skv1gqyAKqn6o8OHM3TYsIDON2HChAkTJv4IeDwVcLT/CCiEtn4XKDjd/60wPXsm/lSoXr26zpsi4/nnn2f06NFUqVIFq9WKw%2BGgZcuWLFu2TGe0hYaGsnDhQu666y5cLpfIcderVy%2BdoSdDC13UBEqys7Np3769TkQlOjqak%2BVfD7XE7E6nU3C5LBaLbgzPPfccaWlpwgjy98CFhITo1DpV2LBhAz6fjyVLloi0E16vl6CgIGJjY0lJSSEqKko5ptLSUhYvXkxUVBTfffed4bi/odetWzd8Ph%2BZmZnceOONgjt37Ngx%2BvXrx2effQb87FX0N8i0lAa/xtCrXr06vXr1YvXq1bz77rs09FNzqIgDKBt6mqdVG5MmkvL%2B%2B%2B9z8OBB4dHUVE6hzIsoexhbtWrFoEGDKC4uNuRA9B8rlK0xzVvp9XopLS0VyeADgcnZM2HChAkTVwMUQqgmriL8B7MkmjBhxJdffllpmV69etGrV69Ky4WGhvL444/z%2BOOPV1impKSEkJAQnnrqKf7%2B978DZZ6%2Bf//739SrV4927dqJF3kN/rnqNEEXr9crUjTUqlWLyZMnC5GTqKgopk6dKrbfe%2B897r77bqDMSNSMp1WrVhETE8MNN9xAaGgoDRo0oGrVqqSnpxMVFUXjxo1JSEjgp/JQq2HDhjF48GAADh8%2BTNeuXSscY4cOHVi0aJHOGNO8UgBDhw5l%2BPDhrF27luLiYp1Hy%2BfzMW7cOJFU/HKw2Wx4PB42bNhAbGwsxcXFJCUlYbfb6d%2B/P3PnzgV%2B9ig%2B99xzuuTugI4beDkPX2pqKj/88AMAr7/%2BOr169eLs2bM0bdqUw4cPi/7Wrl2b48eP4/P5aNKkieB8XlLEiW3atInHH3%2BcmTNninx8Mt555x0AZeqHnj17Xm56TJgwYcKECRO/BH8xft2VgDmDJv7SyMjIoKCggGbNmrF//34mT54sjmmeH5VqpwbNeCouLhZ/a6IeWghp//79deqW/sbngAEDBE%2Bwe/fu3HDDDUCZIbFz505KS0s5duyYEIzRkqs7HA4WLlzIqlWrOHHiRIUqpVBmiM6ZM0fnnQR9GosdO3awdOlS4TH0R5s2bQR/b%2BTIkbRs2RIoM9g0b2p0dDSNGzemSZMmALRt25aEhASSyqXc3W63MPRiY2PFXE2YMIGEhASSk5NJTU1l165dIlzWf35luFwuYegB3HzzzaSmpgKwd%2B9eOnbsKIRXMjIyRD31FJLv/ggODuatt97i008/5eLFizrPn7Y2VOk5NGhpPQKBKdBiwoQJEyauBlSNUBEm/yCYAi2/GX%2Bt0ZowIUEztMLDw/F4PDoOVUZGBh6PhwMHDlR4vvZy7u8lKiwsZP369RWe4y9A0qhRI/G3nJrB5/OJ3HUzZ87k8ccfF97D0tJSUlNTee211%2BjcuTPDhw8X51ksFl2oqFbP5bBx40bGjh1LaWmpTtnS4XBw6NAhERaZmJjId999R1BQEA0aNCC9XIzj3LlzZGZmCtXLyiCPtXPnzqxYsYJGjRoJoZjLobCw0CBIowmn%2BHw%2BVq9erVP71Iy2axQ5wfxRXFxMlSpVePPNN6lbty6jR48WxzQu3t/%2B9jdl4vfrr7%2B%2BwvBgFZQCLW3bGspVlqg5EOESlc0ciFaIzOcPREzCoAoQSMUKL2kgIi5y1Qb9AZU3WpoM5fcEqbFAkkQb2lZVHEBWdTlNYyACPPK3HlWCZUNF5aJFOsjebhX/tLLE3IoiBt0KxcUsz54jICdZB4xzKqWaAaOQhjyfqnA0OaJatWwMoigBiF3I9aiWhLxPEd0dkNBLINfFsK/SjNqKXapByKI3ig5W9rxRrnM5jY2iXkN3FEoqct2GMSkWhVyvkjYmjVs15fK96PctU0Cmn8vnqHRf5DIqsSw5V7wid7yhbXlbm7vTZ/9ThD0TvwdMgRYTSnTo0EHw0qDs5fzaa6/l3nvvZeDAgWL/ihUrWLhwIYcOHSI0NJTWrVszYsQIatSoAcCyZcuYMWOGMjyzQ4cODBs2LKDQt9GjR4u0CFDG5br22mvp3bs3/fv3Fy/zcnv9%2BvUzJDUPDQ2ladOmjBs3DrvdTpcuXfjggw/IyMhgwoQJ5OXlERwcTGJiIjt27BAhh%2BHh4cyaNYtBgwYJYyPQnGpt2rRh48aNlZZzuVwUFhYSFBRUad0ulwu32010dDQhISGkp6fjdrvp1asX119/vUgL8WtgsVhwOp243W6DoaiFavbo0YPBgwczceJENm/eDOiFY4KCgnTpE7TzmjVrxu7duw1t2mw27rzzTj788EOsVivFxcXccsstVK9enUWLFmG323G5XEKBtEGDBiKkFcq8clqeQ4/Ho/NcakhKSrqsgEpkZCRLlizh1ltvZcWKFYwcOZLDhw8DPwu0JCcnKw3S8PBwofoZCFQCLcOHP8qwYUMDrsOECRMmTJi44vhPKrRU8rH6V0OhYP5LUFxczHPPPcfatWtxOp088MADPPDAA8qy%2B/btY8KECRw8eJD69evz3HPP6bQGVq9ezdSpU8nKyqJNmzZMnDhRl7/3t8L07JmoEGPHjmXjxo1s3LiRzz//nMGDBzNlyhRWrFgBwKRJk5g0aRL33HMPK1as4PXXXycrK4u%2BffsaQgZ/D3Tp0kX0Z/Xq1QwYMICpU6cyY8aMy573wAMPiPM2bNjAnDlzyMvLY9iwYdSqVYvw8HCWLl3KhAkTePrpp%2BnTpw/VqlVjZ7nOtxaqmJubi8vlYs6cObhcLp0RFB0dLZJ8A6xdu1bXh40bN2K1WgkJCdF5f%2BrUqQNAkyZNWL16NbfeeiuJiYlCIAagY8eO/OMf/1CKhezZs4ePP/6YU6dOiX1Llixh1KhRYlv2Qqnq8efJWSwWIZLi8/moUaMGNWrUwOVyMXDgQCE%2Bs2rVKu6880727t1LjRo1hAiOlnqgb9%2B%2BurFGR0dz4MABqlSpoguPjI6OBsry7n3xxRf87//%2Br0hqHx0dTbNmzUS//XmDhw8fFiGzYWFhQoSluLhYaeiBPvm5Ni8Oh4OaNWsCZekVdu7cSVRUFEOHDhXzarVaRdiovyKq/1zm5%2BfrQlArg0qgxZRoMWHChAkTfzocO/afa/tPGsY5ZcoU9uzZw7x585gwYQIzZsww5BOGsv/rH374YVq0aMGyZctITU1l8ODB4h1g165dPP300wwbNozFixeTk5PDmDFjfnP//GEaeyYqRHh4ODExMcTExFCjRg3uvPNObrzxRtauXcu2bduYN2%2BeEMaIj48nOTmZ119/Hbfbzbx58373/jidTtGf%2BPh4evfuzdixY5k1a9ZlX7JDQkLEebGxsaSlpfH000%2BTnp7OkSNH6Nq1K6tWreKOO%2B6gZ8%2BeTJgwgXXr1vH//t//AxCGR2hoKEOGDGHmzJkiD53GoWvUqJHOiDl48KChH5pipf9L/ogRI4Aynln37t1ZuXIlR44cIScnRxhcvXr14o033jDwuQoLC0lISGDw4MEsWbJEGDh33XUX//u//wvAyy%2B/jMfj0Rl8Kl5YbGwsUGYA%2BXw%2BDhw4QElJCW63m9OnTzN06FCKi4uJj49nT3m8lc/no0qVKpSUlIhw2Pz8fKGUOXfuXF1OOs1IcrvdOi7eOb8YlQsXLrB27VrhTYMywRso8y77JznXksqDPvedptKqjdlms4nrcsQvL5LH48FqtVKjRg3BSbRarSIXX3Z2tqjfYrGQmZnJmTNnhJGv5dfz788v4dyZnD0TJkyYMGHi6kNBQQEffPABTz/9NE2aNOHWW29l0KBBLFy40FD2448/Jjg4mKeeeorrrruOp59%2BmtDQUGEYLliwgC5dunDHHXfQqFEjpkyZwtdff62j/PxWmMaeiV8Eu92Ow%2BFgxYoVJCUlGThaLpeLmTNn6hKMX0n06NEDh8NxWY6cCpoIic1mY/jw4Xg8HpYvX8769evJyMjggw8%2B4Nlnn6VOnTocOnQIKDM6evTowaZNm/jmm2%2B4ePGi8DTl5uZisVhEvVOnTg2oH5qxB3qxF7fbLQzGoUMvH9bndrtZvXq1MG6qVasmDKROnToB6PhrUGY4%2BwuxaA8V/3L%2Bfz/77LN4vV5SUlIYNGiQ2F9YWCiMrsoiwivjDWr45ptvRBjw8uXLRVhEQUGBMOosFgvVq1fXeSQ1aOqpWv89Ho/oW0ZGhs7I8nq9ZGRk8GM5Qah69erimlqtVh588EFRR3FxsVhndrvdkLYhJCREGM2BQMnZKxfi0UFO3CttK/k75WtWgyoloex8DyRRs0ykUjnwDfQXlXiQnPhYsTYMfVaRuGTulzTujAzjKZUlqVd1z0D8QsG9kRtTTah0sZTX7sQJ3aaKq2jgBgVCOpO4TOWitDrIcy51pQxSCLOKT2RoXioUCOdM%2BbiQroOS3jtzpn57%2B/bL1gEYeV6G7O2K66DKsi1f83379NuBZNRWKVJL8xcIjzOg5N3yQldRB2RepIL4VWlScKicPKmaT7l/qoTp0iJVFZGfC/J8KgORyvnoAoqHiZ8wdxkUxDl5WCrOqLxPvp1V58hNqcrIbavGKe%2BTtxWi1X88rpBn7%2BzZs%2Bzdu1f3O6siNiqwf/9%2B3G63EIYDSEtLY%2BfOnYaPtjt37iQtLU28M1gsFpo3b86OHTvEcf936Ro1alCzZk0RXfZ7wDT2TASE0tJS1q5dyzfffEPHjh3Zv3%2B/CK%2BT0bhx41/00vtbEBwcTK1atYRBFgjOnj3L1KlTadCgAfXq1SMmJoZJkyYJV3vnzp2ZNm0a999/P/PmzRMv/0VFRdSrVw%2BPx0NoaCi9evUyJCTX/tVy7mn7KsoXp4UBat4oFTwejwh1VOUg3L59OzNnzhTGjZYPD8oMkJtuuklX3m634/F4dJzAjh076vokh3pqXsOpU6fqvKhFRUXYbDZCQkKUoiUaLBYLeXl5TJ8%2Bndtvv13Mk81mIz4%2BXgi2BAcHY7fbRRter9dgqGrjyszMpGrVqkAZf08WfakIxaq353I4HA6Cg4PJy8vj5ptvZv78%2BaL/Pp%2BPLl268Nhjjxm8k1BmjF5uDmSsXLmSJ598Uvf7fOM3xoKygqi0rWyyfn3dZnlkrQ4yHcAvCrnifYmJl60DICJC2lG3rrGQLJSjWPuGPkttAyAb%2B9K4y53ylz9HcW8adHyqVzeUiYuTdsiNqSZUuljKa1f%2BoUaD4nsGLpe0Q/YSGwoA5feKhnLhXB3kOZe6UgY/7zpA%2BaPp8s1LhZTjltaA8nEoXQepK2V45BH9dvPml60DMMwNCgVmw3WQzwHjNW/cWL%2BtGpS8r0MHYxlp/pRzE8D8GfbJC131/1S3bvptxf/thnpVjcvzLj8oVPMp908lsCUtUqUGl/RckOdTSY2Kj9dvKx4m5d8kf4Yf/UKDPCzD81GxT76dVefITanKyG2rxinvk7fLg17w1Ak8h%2BzVgsWLF9OzZ0/db/HixQGdm5WVRdWqVXXvdtHR0YbUVVpZ%2BZ04KipKCN%2BdPXv2ssd/D5jGnokKMWHCBFJTU0lNTSUpKYlRo0YxYMAAevToQW5uro5XdjmcOnVK1OP/8%2BeZ/RaEhYUpc55pmDVrlm4cnTp1wuFwMGvWLPFy3rVrV9577z06duxIUFAQZ8%2BeZfr06cyePZuFCxdisVg4c%2BYMY8aMISgoiBEjRvDcc88JKX/N6IhQPHE7d%2B7M/PnzRRl/aF%2BAvF4vDz74IJs3b6Zdu3a6EET4OdTx%2B%2B%2B/N9RRrVo1xo4dS0pKClDGldOQmprKpk2bdMboY489xosvvijKWCwWkesPygyuJ554gjZt2hiMlw0bNrB48WIsFgt16tShYcOGTJs2zWCsag9Ard3u3btjtVrJy8vjyJEjwlDyeDxCWAbKDLE5c%2BYwadIk4OcE9HfccYfOk1ZaWkpwcLBQQf3pp59EHQ6HQ/AnNU6kBk0kRsvtN3jwYBHCCVClShWcTielpaWsWbNGtK%2B1XVBQQMOGDQkNDSU2NlZ3brTqrfcyMDl7JkyYMGHiaoCtqOJ3rCsNH5Yr8uvduzfLli3T/Xr37h1QnzQhPX9o27K4XkVltXJFRUWXPf57wEyqbqJCPProo9x2221AmQEQExMjXv6rVKkieFqVITY2lv/7v/8z7O/Xr9/v0s%2B8vLzLGp733nsv/fr1o6SkhHnz5vHtt9/y%2BOOPG2T4U1NTef311ykuLmbr1q1CabR27dpYrVa6du3KF198Qd%2B%2BfYVBpXmdBg0apCTUWiwWwsLCSE1NZc2aNbRq1UrZx7i4OA4cOMDhw4fZsGEDDz30ELNnzwbKDMmLFy9WGCaZnZ2tM94%2B/fRTGjRoACDEdJYtW8Ybb7yB3W4nOjpaCOg4nU42b95My5YtadiwIT/99BNOp5NFixZx4sQJrFarMJDgZ%2BPU5/MJ/trDDz8MlBlSoaGh5OfnU1paqkvavnLlSqAsIbnGc/SfI/%2BxDR06VChu2u12SkpKGDBgAOnp6UIkRX4I1qlTB6/XK8JR/dMwaCqdFotFjGPRokVA2YcAf2RlZXHjjTcCZWItAwYM4Nlnn8XlcpGfn096ejrJyckMHTqUKVOmiPP8DcxAYXL2TJgwYcKEicvjSv23GBsb%2B6uj0IKDgw3vIdq2Uwo5qKisVq6i4y5VhMavhOnZM1EhoqKiiI%2BPJz4%2BnurVq%2Bu8PE2aNGGvivQBzJs3j1dffVVs2%2B12UY//L9Cwu8uhuLiYY8eOCeNGhcjISOLj42nQoAETJ06kXr16DB48WBgU%2Bfn5PP/888JlHhwcTNu2bXn11Vfp2rUr3377LVDG97p06RIzZ84kMTGRRo0acaxcoeq7776rsP3ly5eTmppK%2B/btdftHjx4t5uDMmTMcOnSIhQsX4nQ6mTt3LlarlSFDhjB8%2BHB8Pp8oa7FYaNeuHW%2B//bbY17VrV1JSUnA6nfTp00ekF0hPT6ewsFB4gNWF7AAAIABJREFUFZs0aULbtm2FMVRUVERaWhqlpaUcPHgQn8/HhQsXOFHOg5DDKP2N6rFjx%2BpULz0eDwUFBcJ4c7vdWCwWtm/fLgRjgoODdV5Ym81GcHCwjntXs2ZNwe/TQiMTExMNX9xsNpsINz1%2B/DiZmZkip6BmPKanp%2BvCNrXk9qGhodSrV0%2BnyAlw9913C6PN6/Xyr3/9C0D0%2BdChQ4SEhOgMPSjzNB49elQY14EgYM6ezBWRtpX8J4lMYuCWEBhlxrBPCpdW8bUMPCoViUZqXEUxM9BfFPUYxi4pximjYALgtxnGLfN3VGXk66TifshcJtUHHL90IqDmpRkikeWLqapXIuNs2mQsIl9PqStlkCZVdXkNlC2pbdX1NuxULWypjGrNGnIDyoIJklIyYJy/QPILqkTB5AmU61FFoMgLXSXKILWtjESXJ0PlFagkb52SC1j%2Bga3CdlT9UVUkz5c8F6qFJK9j1Qdm6d5UUYQNnD2prYDmU1GxgVf8K/ILBlJG2b8AOJryLRVI6kV5W3D2KlC4/qsiLi6OCxcu6N6BsrKycDqdhgivuLg4nRAdlEVraYZmRcdjVPyLXwnT2DPxq9C9e3d27dplCCvMz89n3rx5Sp7VlcCqVauwWCwGQ6oiWCwWnn/%2BeS5duiQMUqfTyapVq5SSueHh4UKuX8uh5vV6CQkJITo6GqvVSkJCQqUv%2BSqlxqysLKxWq/i6c%2BrUKT7%2B%2BGPBEYyOjmbgwIFs2LABKDNQND5dbGwsN910k/C8ZmVl4XA4KCoqYsSIEbzyyisAPPTQQ0JVFGDHjh20adOG6dOni32BXiu73a5TvXzmmWcMZSIjI8V4NKNv27ZtwoMm8%2BVUnqw9e/boPgT4fD58Pp9OSVPrt7%2Bn8XIhDw6HA4vFIvqcn5/PkSNHxNg14zAvL08YmhkZGVwo/w9f80ZmZ2eLEFMNmvfTarXquI%2BVQcXZ%2B0KVi1HmikjbSv6TRCYxcEsIjDJj2CdxX1SRqwYelYpEIzWu%2BoBpcNYr6jGMXQrbVdGzAuG3GcYt83dUZeTrpPpiLHOZJJEfAKQPVypemoE/Jl9MVb0SGafcga2DfD2V39CkSVVdXgNlS2pb%2BcFa3qla2FIZ1ZolKUm/LYuFlT8zdZDnT64DjIMykDYxTqBcjxTVABgX%2BrXXVtq2isdpmAwV/07eJ82xkgvoJ0ChbEfVH1VF8nzJc6FaSPI6VhHTpHtTRRE2cPaktgKaT0XFhndxxZqVpyIQLqW8rexfABxN%2BZZS3XeVlRFMhQBpO1cCXu%2BV%2Bf0WJCYmYrfbhcgKlNFsmjVrZtA8SE5O5ocffhDvGT6fj%2B3bt5Nczg1OTk7WvUufPn2a06dPi%2BO/B0xjz8SvQmpqKr16/X/2rjs8imp9vzO7m82m9wqEHkgUCCVcIKiEckGkCAZBQDqIFEEpUhTRK8ECAhdEBdFEOhGWokKkGxAkdJAEchGkJYQQ0su23x/LHHbOnCRDseDvvM/jI3P2zOmz2W%2B%2B73vfOLz66qtISkrC77//jl9%2B%2BQUjRoyAKIoYMWLEI%2B%2BztLQU2dnZyM7OxuXLl7Fy5UrMmTMHo0ePvi/xyZCQEGi1WqxZswbh4eGIiIhAYWEh5s6di1GjRuHixYs4f/48vv76a2zatAlnzpxRGCV6vR4NGjTA8OHDcfPmTVy6dInksAEgrI02mw1NmjTBe%2B%2B9hy1btsjyzhISElBeXo7S0lJCmiLBbDYjNzcXgwYNgsFgQGhoKDw9PdG8eXNZvd/vehOOHDlSoXeRlUfIgiiKmD59uiKsQfriqlGjRoXr7Ofnhw4dOsDV1ZUYq9IX28iRI0l4ZcO7JBuSR03yANKGmuPbMqvVisjISCxbtkxWxzFfTvIMVhTOazKZYLVaZcQ5rP5%2B/vln4tnTaDSkPYkd9LffflO0IXk/bTbbfYVysnL2eMYeBwcHBwfH3xsGgwE9e/bEO%2B%2B8g1OnTmHnzp1YsWIFXn75ZQD2F/CSdFPnzp2Rn5%2BP999/HxkZGXj//fdRUlKCLl26AAD69euHzZs3Y8OGDUhLS8OUKVPwzDPPoDrrxc8Dght7HA%2BM2bNnY/To0UhISED37t0xadIkhIWFYdWqVUwykofFDz/8gJiYGMTExKBXr14wGo2YOXMmRtPsayrg5uYGLy8vNGrUCCkpKdi9ezd69eqFvXv3okePHnjxxRexYsUKODk5YdCgQXjzzTeh0WjQokULVKtWDdu2bcPy5ctRq1YtIiQeHBxM8tccjd2jR4/i448/Rhj1BtLRoDGbzXBycoLBYEDt2rWxaNEiWCwWnDlzBtu3b4dWq0VcXBx69Ogha8PFxQWCIOC5554jovGrV68mxCTnzp3Dxx9/TGQc5s2bh507dxIDMCAgAEePHoVOp4PVakV%2Bfj66du0KwG7kOYYRBAQEVBh66%2BXlBavVimvXrsHV1ZUYtU2aNIG7uzsJk3R1dcWJEydI/3q9HlarVRHjTvcjefcc90%2BSAQHsHkPJOJPi3PV6Pby9vZGSkkKM8JycHNJG3759IYoi1q1bR8hVYmNjiWyFI2Op5O27fft2hbmT9Bw4ODg4ODj%2BEWDqU/w5%2BDt69gBg2rRpiIyMxKBBgzB79myMGzeORFvFxMTg%2B%2B%2B/B2D/vfL555/j6NGj6NWrF06ePIkvvviC5O1HRUXh3XffxZIlS9CvXz94enoqIogeFoKtKnEsDo5/IGJjYzF27Fj06tVLVj5s2DDo9XoMHToUAwYMwMqVK2X6JyUlJejcuTN69uyJiRMnYuPGjVi8eDF2M7SRYmNjcf36dfTu3Rvvv/8%2BALsshaMUQ0lJCWw2GwRBQJMmTXD%2B/HnodDrUrFlTFh4A2I0vvV6PkpISvPDCC3j//fcxcOBA/PLLL/D29kZZWRlcXV3Rpk0btG/fHuPGjcPmzZvRoEEDdOvWDefPnyfesR9//BHr16%2BHs7MznnzySZlXUAq/1Ol08PT0JLHktWvXxm%2B//Valnp7kpdRqtTAYDCgrK4OHhwcRW5e8ehaLBVOmTMGwYcNw%2BPBhzJ49WyamLkGj0WDSpEk4cuQIc50d16cyghNRFDF48GCsWLFCVi4IAkRRhMViwapVq9C0aVPigXQcg8ViQVRUFDIyMki%2BpyNCQ0MrHR%2BNuXPn4quvvpKVjRkzBuPHj1fdBgcHBwcHxx%2BO7Gy2hs%2BfAGaO7yPAI%2BQ/%2BduDe/Y4OBzwZ4jGd%2BjQAUajEUajEXv37sULL7wAQRBw7tw5FBcX486dO4r8NMAeLih5Ay/fTUqX/l9WVkY%2BP3DgAN555x0A9wwrydM2YsQIjBgxAuvXrwdgp/e9QbF3SMacyWSSJQ3n5eVVaegBdi%2BlRqOBRqNBfn4%2BysrKiKEH2I08KRRVEg3Ny8vDxYsXIQiCQqzcYrEgJCREEfbYsGFDdOzYUTHuiiB5QGnYbDYynrKyMoiiCEEQyBykMYiiCCcnpwo9ySwNxMrAImhp356RT1SFqDpz2hSZgBryFYYesJIYgCJlYt2j%2BMPM0sBUQSah4LJgEFcoSAmotWER06gRVVe8xGaQRyi4ImgSF9biUPmxzL2jiV4YzAt0BLCiigpR9bvOfhnoOTFF1am2VYm%2B08rMjPGpIqWg5sAkFLn7Np3gzBn5NYvBg95fWoidNR41Std032rYOVh5u9QzxDw3KshWFHM4fFh%2BzXiJhbspCQQKVhKVBC302tB9sdaTJjlikbhQzwuTlIk%2B7NQ5Yio30d9RFPkTwCCoYpxruoh1rukyOo1ezbFhETnRS876SqLnTl%2BT7xrOFP1Yg3v2OP5yjBkzBgcPHiReLkcIgoBu3brho48%2BImWSJEJGRgbxZE2YMIHkVVXlbRs7diwWL14s8%2ByZTCbs2bMHr7/%2BOubMmYPExERC4iFBFEX4%2BPigS5cumDBhAtzc3EhfH3/8MT7//HOcOHECVqsVTzzxBM6fPw%2Bz2YypU6eSfiIiIvD8888TTx9gN%2BI6duxIGDAdERoaCrPZDLPZDJPJBG9vb2LgSZICFcHFxQWpqanQaDT46quvMHfuXEUdV1dXuLi4ICcnB3q9HjVq1EB6ejqzPS8vL4VY6KOAKIrw9PQk8hIsD92j0pyJjo7GL7/8UuHnGo0GCQkJGDBgAJGSyM/PhyiK8PDwQFRUFJycnLBjxw7Fvd7e3jh06JDqsbA8e%2BPGjcfYsWPUT4iDg4ODg%2BMPRknJX%2BcJq0RG%2BaHA4kv6p4J79jj%2BcsyaNQtGoxEBAQEYPXo01q5di7Vr12LlypV444038N133xG2y/j4eMTHx6NPnz4wGo1YsmQJsrOzMWDAAKIddz/9ViYar9Vq0aVLF6SkpCAlJQV79%2B7FJ598gh07dsiMtWvXrqFfv35ISUlBaWkpysvL8csvv%2BDWrVt48cUXFaGiNCR2TYPBgM8%2B%2BwwA8Pzzz5O2b968CRcXF7i4uBDyFC8vLyKiLrWh1%2Buh0%2Bmg1WohCAJKSkqIAdmlSxeSD%2Bgo3llUVITs7GxYrVaUlJTg4sWLqFevHjP/LM/h7byjWHmXLl3g7u4OPz8/hVdODaxWK3Jzc4mhzwrFZBl6oijC29tbwQpaWe4cK/zSEU5OTrh27Rp0Oh0sFgvRknRxcYHJZEJAQAB8fX2Z9/7rX/%2BqtG0aXFSdg4ODg%2BNxgMithccafPs4/nIEBAQQ3b0aNWoQA6xFixYYMWIEWrVqheTkZKSmpiIhIQFLlixBXFwcwsLC0LhxYyxZsgRmsxkJCQn31e/48eNJOOWePXuQmpqKyZMnA7AbUyaTCc7OzvD394e/vz8CAwMRHR2NgQMH4scffwRgZwgVBAGDBg3C999/jy1btmDLli34/vvvodfrFXIOQUFBFYb7SRISffr0wZ49eyAIAmJiYmCz2ZCXl0cE0aV2zp8/T/LNNBoNBEEgzJBarRY2mw1du3ZF165dkZycjLCwMPzwww9ITU3FpEmTSL9arRaiKMLFxQWiKKJOnTowGAxo5EAbXr16dQQ6UGc7EtD07t0boaGh8Pb2lmkxCoKAkJAQGRtoYGAgMWQBu8E2bNgwmeHKWhfgHiNmZGQkGjduTOZ69uxZPP3006S%2Bv78/WrdujWXLlsHX1xdarRYuLi546aWXZIQzNe5S5X/44Yfo2rUr3N3d0aBBA2RmZpJwWYmNs7S0FEVFRWjcuDEuUOJjTk5OcHJywhk6ZKsKcFF1Dg4ODg6OyvF3JWh5nPDwqtYcHH8w1ObR%2BbFEvyqBJBrPQmRkJGFSonH8%2BHHCznju3DnYbDaMHz9eQfvv5eUlM2zUYvbs2ahTpw7i4%2BORcjd/Iz8/HwUFBSS/7vnnn8e8efMIGYrkFdNqtTCZTGR8NpsNZWVl2LFjB65evYrLly8jLCxMZshJho3kaZIMVMeQzevXr8tCbB0pgceMGQNRFFFeXq7Q7MvPz5dp82VlZWHTpk3k2mq14syZMyQ0FbBr4pkcEhLCwsJw6dIlMvezDnljpaWl2Lx5MyZOnIh9%2B/YBsAusX7lyBQcPHpTNcc2aNfDy8iJlkmzFlClTSNnx48dRWlpKDOjAwEAUFhaSNerSpYsiHFbyOl5hiSFXgu7duyMiIkJWVp8hiFdYKJc4oq9hsyn0qHJzKZkok0khxETfVlbG0HOiYnfy8ympK0WBPd/NUaGDFf5jscglqRjDsyfEODzTijkxulcMp7xcqS1GT7S0VCFmR4%2BnuFgpz0cXFhQAjsobrPGqmjh1I30LoFga5SKz5k2VZWUppc/odq9dY8ifUQeQ1Y5iWnl5DoJdFZw1upA1B2pt6DVnTuLmTbnmYWamUoBx924gNpZcMh4p5WIwzg2KimSxYYp5siZO753iAYdyLVgPFb0YrINDl9HjYbVLH2TGmVUUsfqm1kZB%2BMF6FugHj7XmVB3Wc6dYUmre9BFhjofRN3WsmetHN8OaZlV1mN%2BP1BozzyxV50H6Zj6HfzL%2BvxlmfwS4Z4/jbwuTyYTk5GQcOHAA7du3R1paGp588klm3YiICIU%2B3MOgW7duyM3NlVH1W61WHD16FPv27SNeoczMTGi1Wri5uSE2Nhbh4eHkv6ysLKxatQpff/01aWP37t0QRRFxcXGIiopCTEwMpk6dKgtTNBqNSExMJELdAAhxiWRMdejQgbBmBgUFEa/i1q1bMW3aNNKW2WzGlStXkJqaisLCQnTq1Anh4eE4efIkXnvtNQD3RMF1ir8m91C9enXZ55KWDABi5Dkag87OzrDZbDCZTKhbty48PDwUQqMSDh8%2BTMTLAbuB%2Buyzz5LrrKysCscl9b1w4cIK60ioVasWZs2aBQCoVq2aYjzu7u5ISUmBRqOBh4cHVq1aJetbo9HAxcVFEQoqeTM9ZX/1qwZLVH3nYWU%2BIf27TyElyAidVRgZjL2lb2MK91I/XBSSjQwNR1qKkZXnQf8OZB496uUNS82F7l4xHNaPFHqijLBfejwMJ6yikDY6mOozaiZO3cjSFle816IXWYWgthpNcJbONX0AWe0opkU9G8yzRhey5kCtDVPWkp4E/XeBNvQAmaEHsDXpFYvBChenkoAU82RNnN47llYovRash4peDNbBocvo8bDapQ8y48wqilh90wlSNLMj61mgHzzWmlN1WM%2BdYkmpeTN/OtDjYfSt%2BMpnrN%2BjEFVnfj9Sa8w8s1SdB%2BlbOntMMiSOxwbcs8fxt8KsWbPw3nvvAbB7bZydnUke3ZIlSyoUzaZx/fp1REVFKcpLVHL4RkVFoWbNmti/fz%2BefPJJiKIIk8kEi8UCg8GAxYsXk/YcjYbp06cTQ6V3795o27YtPvzwQ3h5eaFnz56Ij4%2BH0WjEpEmTEB0djTt37mDhwoU4f/48RFFEdnY2CgoKYDabYbVaIYoiGjVqhFu3bmHNmjXo1q0bySNzd3dHTk4Obty4gZ49ewK458lzdXVFUVERmjVrhsmTJ2Pr1q1Yt24dALsBOG/ePDJmKXTQ0Svn6elJcvQEQcAliols9OjRWLp0KemTzqmTxETLysqQcZdJTqPRQKvVQqvVks9ZMJvNMq9qVXum1%2BtlnsCK4OHhgX379sHNzQ1BQUG4desWGYder0dBQQG2b98Oi8UCk8mEb7/9VuaV1Gq1yM7OVpAISetWUlKC27dvVyg8T4Pn7HFwcHBwPA5wK8sB3Nj56n80uGfv4cGNPY6/FcaPH09EKfV6Pfz9/YnnxMvLixg6VSEgIADffPONonzgwIGqx9KkSRPCeHnjxg14e3ujTZs2eOONN0j%2BmouLiyzPyt3dneSFabVaNGvWDJmZmUhOTka1atWQkJAg0%2B4LCwvDkiVL0KJFC5SWliImJgbAPa27qKgoNGzYEMeOHUMQ9Ua6adOmSE5Ohre3N9atWwer1YqffvoJn3zyCYru0lcdPXoUQ4cOBQASagnY88wkAwews3JKxofBYMCECRPw5Zdf4urVq7DZbHB2dpYZaJKh54hly5bh9ddfr5AExWKxwMXFBZ999hmGDBmiCPmsCI6GpzQ%2ByQDU6XQoKysjUglSm46Mnr6%2Bvli2bBl69eqFnJwclJSU4JtvvsGRI0eIh/L1119HfHw8zGYznJ2dUVhYiLVr18r6MpvN8PX1JXtDw8XFRbWhx8HBwcHBwcHxZ4CHcXL8rSDl0YWFhSEoKEhG%2BBEZGSnL13JEQkKCzFul1WpJO47/abX29xu7d%2B%2BukiVTEARERERgx44dOHXqFA4cOIAPP/xQRlQitVHIELD58MMPsXv3bgiCUGXO4SuvvIKgoCCkp6cjPj4eISEh0Gg0qFmzJtLT01GvXj1SV4JkxOXl5eHIkSPQaDRo0KABIUQJCAhAx44dYTQa0bdvX7z33nvEC1leXi4zykpKShAUFASbzYbi4mLMnj1bJgVhoMJTEhMTFfMdNWqUzAjy8/Mj6y2Nd8aMGXjllVfIOFxVcB/nURpdjp4%2ByQBu06aNjAnU0QDPyclBr1694OHhgaKiIlgsFowZMwbnHbSXVq5cCQBIT0%2BHh4cHIV2ZMWMGqWOz2XDkyBG0atVKMUaNRoO8vDxiZKsBJ2jh4ODg4HgscDdv/a8AJ2h5eHBjj%2BOxQbdu3XDq1CkcPXpUVl5UVISEhASFp4jOoYuMjERWVhYhPZFgNBoVOXSOQuMbN25ELJXTIWHu3LlwcnIixoIjvvrqK6Snp%2BPQoUNo3749fvzxR5w8eVI2Jum/wMBAlJaWIjw8HMuWLSNtFBQUIDU1FZ07d0afPn1kOWTz588HYDcQZsyYgY4dO2LgwIG4desWNBoNSkpK4OrqipkzZ2L37t1ITk4mwuJJSUlwd3cnxpjValWIq0uoUaOGLKcOuJezp9VqUbt2bdSqVQtvvPGGzOi9desWITYBgBUrVmDGjBkoLi4mYZctWrQgnwc7kJPExsbCw8Ojyjw4iRTlxo0bFeYEAnZjtbCwkHhdT58%2BLXs5UFJSQryDgYGB8PDwQHBwMN5%2B%2B21Sx9fXFydOnFC8cJBkGmw2G06fPl3peB3BElXv1L69siK9L9Q1UymVEhdnRcLS7yfUCPfSAsbMCFs6RJel6E41zHLyKpz4DFFjxQAoYXOmGgs9KUbnir4ZQtKKadH7xOqc6ovp3KY6Z0U8K/aFCqNmnQm6L1YqLN0XM5CCOjis8Sn6p%2B5hzpu%2BiTEJet4skWiFqja9L4zIA8V6svRLH0AYXiECznoW6HZZkRGKMHllFcWisnRJ6QFSdZjR8PSYGZXoImY7Ve2vQqGcUYfVMPVsMs9sVfug5hAz9o4%2Bf6znjm6a9Z1ZlfA66x417VYokF5JGX1N%2BuHG3mMNLqrO8beBJHhemcftrbfeQnJyMiZPnozo6GhkZmZiwYIFuHnzJjZs2ABvb28idA4AgwYNIjl0ZrMZPXv2RH5%2BPuLj4yvNobt8%2BTIaNWoEvV6P6OjoSkXa27Rpg40bN8JgMKC0tBQajYYIoTs7OyMwMBDZ2dkoLi6GIAgKHbhJkybBxcUF8fHxKC4uhtVqlXl4WCLjElxdXYk3TqvVwt3dHTabDTqdDtnZ2RWGHFYFx/smTJiABQsWVPi5mnYaN26Ms2fPyow/URQRHR3NFCLXarUwm82q%2B6lWrRpTlJ4eS61atXCRMoQc4e7ujjfffBPvvPMOmjVrJhvbk08%2BiSeffBKrV6%2Bu8P7x48djzBh1ouhcVJ2Dg4OD43EAk0H3TwLjXdsjAc0R9E8G9%2BxxPFaYPXs2Ro8ejYSEBHTv3h2TJk1CWFgYVq1aBW8GDZeUQ%2Bfv74/g4GC4urqiTp06qnT7JGKRqhAVFYUlS5agrKwMoihCq9WicePG%2BOyzz3Ds2DGsXLkSRqMRnp6eCA0NxebNm2X/SeQq%2Bfn5hJgFsBsw/fv3x6ZNm%2BDu7i4LH92wYQOaNGmC%2BvXrw8XFBTqdDuHh4Th06BA%2B/fRTZGdnIzAwkIQ2urq6yjxZjnB2diZSDHrGt3laWprCa1aRAUaHJkr1Tp48KTP0nnnmGbi4uOD48eOy%2BpJ8hiiKcHNzQ0BAgEwTUKfTKUJKAbt3V8NigKPGIt2r0%2Blk7KKShzMiIgKdO3eG2WzG4cOHZWGmNWrUQHl5eaXC8beZriQ2OEELBwcHB8fjAJaj%2BM8C9%2Bw9PLixx/G3gZo8OlEUMXjwYGzduhUnTpzA/v37ER8fr8ijY3nhpD5CQ0OrzKGzWCw4f/48Nm3ahGnTpuHatWvo3LmzTEYBsP9gX7JkCSZOnAiz2YyIiAhs27YNq1evRr169RAREYG2bduiU6dOyMvLw9WrV9GpUyd06tQJM2fORFhYGNzc3FBQUICnn34aoaGhiImJgcFgwK5duzB06FD06NEDBQUFshDOuLg43Lx5E23atCFhkWfPnkVWVhac7lIlP/HEE8QAKioqwsyZM5lrYjabiVdMInBxNGhSUlJQq1Ytcq3T6fDll18y26INGHcHOvCwsDBiYJWVlcHHxwfVqlUDADJmSerC1dUVZrMZWq1Wtpft2rXD5s2b4efnJxtTo0aNZBp6Epo2bSrznklhpo5ahACIqHtOTg7c3NxgMBig0WhkOXhWqxUGg4GM1RHSOocyuerZ4Dl7HBwcHBwcHH80uLHH8f8G96Pb5%2BTkhOnTpyMlJQXTp09HYGAgRo0ahQ8//BBGoxEAEB8fj7y8PERHR8NoNMLX1xcFBQUYMGAAbt%2B%2BjeDgYKSkpJD/JCPh008/RUpKCv773/8CuJdzKP3Qj4iIQGlpqUJOYPjw4QCAV199FSkpKbIcN0EQ4Ovri82bN6N///4AgF27dsnaqEjCQKvVknskT5yj0VFYWIj//e9/5Fqv12Pz5s2kXxo6nY6IyTuSwFy%2BfJmM5%2Beff8bvv/9O2m3VqhW0Wi3p38fHR%2BbRk5CcnIzu3bvj1q1b%2BO2338gY0tPTUVxcjJYtW8rqHzt2DEOGDEF7KhdOo9HIxi7lgUr9169fHyEhIQgPDyd1bt26BX9/f4XMhKP%2BYZs2bRRjrgiqc/bo5Crqmulkvbs2EtTkc6jK2aOE45lHii6kc5IYDbP6VqQuUfmCzL6o8bFSpNTk7ClywRj5RIpp0blirM4Vsh2M8VETV5UTR1V60Jw9OleImf9EjY%2BV3qaYF7WgzJwu%2BibG4qjK2aMn9iA5eyw3Br2fDM%2B8Ysh0rhjrWaDLWJOiBsjM2aM3XU3OnornUDE%2BFTl7D5ST%2BaA5e9R9zDNBnwF6LxlfZIpniPEw0Lf93XL26LV4mJw993zqLP%2BJ4J69hwfP2eP4xyI2NhbZ2dkkRE/S7XvppZdw6dIl7Nq1CxqNhump0ev1mDJlCnr16oWNGzdi%2BvTpJCdPEARotVqU3f1G9vLywp07d2T3e3l5YfTo0Rg8eDC%2B%2BuorLFq0qIKwPbv%2Bm6ura4UEKYDdoJg0aRI%2B%2BugjTJkyBcOGDcPAgQNx/PhxVRpzgN3LJhlf3t7e6NWrF7Zs2YJVq1ahqKiIGGiA3etU0XgdodfrUav1lUziAAAgAElEQVRWLaSlpQEA6tati4yMDDRp0gQnTpwg9aQcvKrgmJ%2Bo1WphsVggCIIihzEgIEBGACMIAkRRRGxsLH788UdFu5KEgiSlURn27NmDzz//HFu2bIFOpyNsoK6urvj0008xaNCgCu/dt2%2BfQiKjIvCcPQ4ODg6OxwIlJUzR%2BD8D9DuTR4X7CMR57MF19jj%2B0ahIt%2B/mzZu4du0a6tati3Hjxinuo3/QS7p9b731FvR6PY4cOUI%2BczT0qlWrhurVq%2BPnn3/G3Llz4eXlhd69e%2BP8%2BfPYuHEjfH19CbOlzWaDIAjIz8%2BX6QdKpCQ%2BPj6YP38%2BBg8eDFdXV3z00UcAgE8%2B%2BQSLFy9GaWkp2rZti3379sHHx6fKfLHBgwcTb2Jubi4SExNhNpvRtWtXhQRCgwYNcOzYMWY7I0eOxBdffAEAeOmll%2BDv70%2BMvfz8fGg0GmLoBQYGIisrCwaDAV5eXsjKyiKeMVEUERwcjGvXrhFjTDLqGjVqhLfeeguenp6YOHEizp07B6vVSgw/m82GwMBAXLv7VyA0NBTBwcGyfXFEREQELl26RNZeMtgFQZB56mrUqIGAgAD4%2B/srjN1q1aopPIdSW9I7s6ysLNXGHs/Z4%2BDg4OB4LPD/zRX2DwMP4%2BT4R6Mi3b6AgAA0bdoUv//%2BO1OPr7i4GNu3byftaDQapKen49ixY%2BjWrRvMZjOaN2%2BOoKAgeHh4kJDPb7/9FkFBQdDpdHB1dUVycjLJ/dPr9YQtMzg4GMePH0dAQAAA4L///S%2BmT5%2BOoKAg7NmzB25ubsjNzSXMj44Mnl9%2B%2BSWMRiNq166NAwcOQKvVombNmrKQ1Li4OABAz5494eLiAo1GQ8JAJfTv3x9r1qxBZGSkQgzcMXzREYIg4I033iBEKoGBgTJilHHjxskkMEaMGAHALpvh4eEhy7Nzd3cnhrIgCAgKCiJ5d6dOncL69esRFhYGvV5PCGJsNhtEUUROTg5uOYTv1KtXD2fPniWeSz8/P1mY5pgxY5CTk0OMyffffx/fffcdvvvuO2zduhW1a9cGAKLF2LlzZwVZzZIlSyAIAmlX8hg79hMSEsJcNw4ODg4ODo77Bw/jfHhwzx7HY4HY2FjixQHsP7SrV6%2BOvn37YvDgwaTcaDRi1apVyMjIQGlpKdavX49WrVohODgYV69eVeRuAfcMm%2BjoaHzzzTcoKipCYWEhUlJSEBUVBbPZjPLyckydOhW9evXC5MmTAQCpqamkjTlz5uD27duYOHEiduzYQQhALBYLIYK5cOECsrOzYbPZkJubC4PBgDFjxuCtt96SeRc7duxIvFd79%2B4FAJlhM3LkSNStWxeXLl2Cp6cncnJykJ%2BfL/MUbdiwgayHhFtUbsPXX3%2BN9evXo6ysTKFRWKNGDeY%2BiKKI06dPEyKVBg0aYPny5eTzzz//XFZf%2BmzPnj2Ii4uTfV5YWAiLxYImTZogLS0NmQ45T1qtFjt37sR//vMfACByFgDI2kjXrq6uOHTokCwnkZ7rF198AU9PTxKSOXv2bGg0GlitVpSWlpJQXskLfPjwYej1euKNFEUR1atXB3DPk8cKS/W/Dy5nTtDCwcHBwfFYoBIdW46/P/jucTw2kLxnKSkp2LlzJ5MwJT4%2BHn369FFFmBIUFIQmTZrAw8MDb775JqZOnYpffvmFeKNeeeUVGI1GjBs3DkFBQUhNTcXIkSMB2I2Ppk2byjx70mdSeCZgJxqRiGAkPTwJsbGxeOuttxTzrF%2B/PvFkSSGGHh4e5PPZs2fjzJkzMJvNxOjKyMjA9evXFW05et0cBcolz1zXrl3h5uamuO%2BDDz5g7oHFYkG/fv1I2CItyUCPQRqfTqfD119/LQuDlAzMEydOoJTKNjebzcjNzUV4eDiOHz%2BuyLNzTDX29vZWkM/Ur19f5nE7dOgQOnToQK7LyspQXFxM%2BpXW%2Bb333gNgN%2Bg6depE%2BnHsjyalccwxvB8wCVruGpsyVJVBz8pBpMgZWMn79G0sUgXFfVQBiyiiqnsABpGBCtIHVUwlVB0mQcsDiHerIvWg94Uxb3qNmRnzVMNq6ijGxwoRpnKCVWjdqyK7YB0/Rff0%2BFh7SRNgsOrQDasQ2VYlbE73zahDExrh3DllOydPyq9v3pRfs%2BR8Ll2qenwqHlY1JCmKMvom1prTxClqxOTVHAr6WrHAjIZVsJswyWvo80fdw3pcFGulYt6s46jia0FRpmZb1BC00Mea9TzTZfQ1Gctf%2BCKSe/YeHpygheOxQEWC68OGDYNer8fQoUMxYMAArFy5kkgpxMbGYuTIkVi6dCl69uyJiRMnKjyEoijCy8sLGo0GhYWF8PDwQJs2bbBr1y64u7vj9u3bxKv0ww8/wGKxML2DLOj1erRs2RK///47OnfujBUrVsBkMqF69eq4efOmwsCRMGTIEKxevVpm4Hh7e5N8M39/f2Tf/UHj7%2B%2BPgoKCCtuSoNFocPbsWTRo0ADAvRBOiZWSZphUgyFDhuDNN9/EsGHDkJKSAsAudeCY6xcSEoKbN29Cq9UST%2Bf9QqPRQBRFmEwmCIIAnU5X5XhZYuwS2U5ldTdu3IjIyEjs3LkTM2bMQHFxMdHWk/ISGzRoUKHO4Llz5xQGcEXgBC0cHBwcHI8FbDagEo3ZPxIUufQjg0NWyT8e3LPH8VhDq9VWqJm3e/du9O3bF0uXLiXSAsA9D2FQUBAmT56MKVOm4Pbt23jnnXewf/9%2BeHh4oKCggEgqfPnll2jatCkGDBhAcswkL5kU/ieKIvR6PT777DMAds%2BZ2WzGzz//DHd3d%2BTn50MQBLi6uhKvGgAiqO7k5ASDwQCDwYBvvvmmQsZIPz8/mfZbTEyMwrhw9Dy1aNECgFJqoGnTpsQjJRGKOHqmPvzwQ/LvkJAQhIWFKcbSuXNnRZmjDqFWq8X06dOxY8cOrFixAlqtFp6ennjuuedk9wwYMACNGzeGi4sLnJyc0LBhQ1kYqcVikYVM0mP517/%2BpZi/ZIw55t2dPn2a/FsURbLejnWmTp0KAEhKSoIgCPD29lbM0dHQ02q1snxKLqrOwcHBwcHB8XcCN/Y4Hkvcj2ZeREQEIUIB7MQgEiunl5cXnn/%2BebRq1QrJyclITU1FQkICfHx80KJFC4SFhaFx48ZYsmQJzGYzvv32WwB2wpRGjRpBr9dDEAS0adMG77//Pr744gsIgoCQkBA0b96csFCeOXMGZWVlKC8vx7Fjx4gnTgpBDQkJQUxMDEpKSmTkIpIxKWnO5ebmori4mHy%2BadMmhdEgkZgAdhFyAIpQTcnQM5lM%2BP2ufpmjQTVlyhRZ3fbt28PT01O2jpIQu4Tw8HAkJCSQa7PZjMWLF0Oj0aBZs2ZITExEXl4etm3bJrtv5cqVuHLlCoYPH461a9eiXbt2JPzTcU7S/x01/wB7iCZd9%2Bmnn8bw4cNlhnCdOnVkcyopKUFJSYnMK3rhwgUAdu9dVFQUMe5ZnjxRFGGxWGRexvsJlOA5exwcHBwcHJWDh3E%2BPLixx/HYYNasWYiKikJUVBQaNWqEqVOnYtCgQejevTsKCgqYuWexsbEIDw8n/127dg3z58%2BXeaAAO6FHamoqXn75ZWg0GhQVFZEf%2BlevXkWTJk2QmZlJ2DGzsrJw6tQpFBQUwGaz4aeffsLkyZNx8uRJ2Gw2nD59GseOHUN%2Bfj4OHDiAU6dOAUCF4YfXr18n%2BnCZmZnEaGjUqBEAEBFzi8UCg8FQpVEheelWrFgBQJlnJpHMqIHBYEB0dDRMJhOioqJI%2Bdy5c2W5cllZWUTaQUJaWhqee%2B45nDp1Ck2aNIGvry9zrHl5eVi0aBH69%2B%2BPHTt2yLyXNNQYRPv27cPy5ctlXjcpDFMNJkyYAEEQEBERUek4bDYbGY9Go7kvghZWzl5sLCNnb/fuSq%2BZR%2BGu0SqBJY5NpwaxllzhYKbkOFjOSUXeys8/KytRLwqYyS50yC9j/xR5KlRftM45s13GAiqioh2YeSUo1iY5WX7N8vKqSfKhxeNZXn5qzKryG6kNZ8l60kvDzHmk89DU5FtWlRjEuEmNwDczAOLyZfn1mTPya0ZMmCK6fMsWRR3F88FaQLps1Sr5NetBpNeCdWip/WSuDfUwsOpUeU5YXyZ0TiEjOUyxD6yNoftS8Xwrcu1YwutXrsgumXK19LNJH2w1OaQMsTfFUjDWj57mA%2BVSMjZTjd48vQ2sbamqDpnjA6R6cPx9wHP2OB4LxMbGol%2B/fkzNPAB48cUXERERgVmzZinuGzRoEJ599lkAQO/evdG2bVts2rQJHh4eeOONN7Br1y7s2bMHL7zwAk6dOoXatWujoKAAly9fxoYNG%2BDp6UnC827cuIG4uDi4urpi4MCBeOaZZ9CvXz%2B0a9cOb775JgD7j/g6depg6tSpRCPP3d0d169fh5ubG1xdXXH79m3VYuiAnXFSMoBatGiBtLQ0IjMwbNgwfPnll8z7NBoNLBYLhg8fjsmTJzMlFQRBwAcffIBp06ZBo9FUmQ%2Bn1WpJDt2oUaNw5swZkrMXHBxcqTh8VdBoNOjevTu%2B//57Esrq7OyM0tJSDBw4ELm5uQrP4Lhx47B8%2BXIFSQuNtm3b4qeffqq0TocOHbBkyRIUFhZi6tSp2LVrFzGs09PTAVQsS2EwGGRC8lWB5%2BxxcHBwcDwWMJkAne4v6Zp6d/nIUK/eH9Pu3xHcs8fx2KAizTwAiIyMxNmzZ5n3nTp1ComJifD394dWq8WWLVuI/MHMmTOxZ88ePPfcc3j//fdRXl6OmjVrkrDNhIQE4rHx9/cnenQajQZhYWGIioqCKIrw8fEhY5Py91q2bIm6devCbDbj5t234SUlJcjKylIYerq7X6IajUY2L8AeLujo6bpx4wbRqxMEAYmJiRWumRQqmJ6ejqysLFKu1%2BuJlEC9evXwzjvvoH///ggMDARwL3xUQkpKCurVqwedTgez2YwhQ4bAZrPJ2DW1Wi2SkpLg7u4Ob29vxTwkCIJA%2BpYg1bVYLNi0aZPM4JTCLFeuXInvvvtO0d6dO3fI/a6urkSuwXH%2BaiAIAubMmQMAOHjwIA4ePEg8g46eUcd/S3sN2L229xOGyXP2ODg4ODgeCzD/Xv054GGcDw9u7HH8I9CtWzecOnUKR48elZVbrVakpKQQFsjMzEzFj3K9Xk/y/by8vJCfn48dO3bA3d0dCQkJiImJwdSpU2Ueq/z8fEybNg3h4eGwWCxISkoioaKOOWAdOnQgQuGAnWAlLCxMYUxJxp/FYlEwVtIGhMViIcajzWZjkohIkIzE1NRUvPTSS6S8efPm2L59O1xdXXH%2B/HmEhoZizZo1xAijvXsxMTG4cOECTCYTRFHEa6%2B9hvT0dLRu3ZoQzoSHh8PPzw8hISHIz8%2BvkHnTZrMpJBqkuoIgwMPDQxGmKggCwsPD0alTJzz55JMycpjTp0%2Bj8G4oVFFREWbOnEk%2Bc8xvlNZCEAS4ubkRI83NzQ2BgYGw2WxwdXUFAFy8eBEeHh7ECGcZe5JOn%2BSBtFgs9%2BXZ4zl7HBwcHBwcHH80uLHH8Y9AVFQU4uLi8OqrryIpKQm///47fvnlF%2BTk5EAURaKdBwDNmjUjzJASE2V8fDzCw8Nx4sQJrF69GlOnTkVZWRliYmKQnZ2No0ePomfPnli8eDEAYPTo0dixYwcxBp577jkSymi1WnHs2DGEh4fjs88%2Bw40bN2TGwpUrVxTG1Ndff03aqsx4A%2ByePUcvXbaDrpSTkxMEQUDbtm0RGBhIjIeSkhIZocrw4cNlJCgXLlzAvHnzCAGLwWCQ5UBOnz4dgN2T1aZNG5nXTvIGnj17FlevXkVpaSksFgtEUcTbb7%2BNBQsWYOTIkTK2z8okGPIZeS02mw1paWnYsWMHfv31V5lntGPHjuTfOp0OWq0WBoOBlEljlUTbbTYbCgsLyR6UlZXh5s2baNmyJRlj/fr1ERUVRfQNHY1PqT3aMNNoNDJh%2BKrA1Nl76illRTpvhV4fVjgwlZPCeilLpT8x8zkUTVPtMlMr6ZvU6Gcx5qDIF1OjP0ZNiiGPp0qQTHEfI0RYkT6kQquN7ov5GFD3sdKJ6Ck8SG4Ovf%2BsdpmR5lShmlxPRTuM7BF6%2BZhR2dT3JnN8dA4cPVHG94uiHUYoO52qiF9%2BUfZNa/zR%2BYOsMHK6jDWpqnI0WYUq8mAV54%2BV1UNvsIrxMevQG0ofEtaXFH34WQebGh8rrU%2Bx5/SZYM2bXhzGuVGcUca8H%2BTZpNtlPQtVfQew7nuQnD1y7ZD//meDe/YeHvenAszB8QhAa91ptVpUr14dffv2xeDBg0m50WjEqlWrkJGRgdLSUqxfvx6tWrVCcHAwrl69WqHe3YwZMyCKIgnbLCwsRGxsLIB73pchQ4Zg69atFf44b9myJRYtWkTYLAGgoKCAkHy4ubkhKSmJGB3Ozs64ePGioh1RFGGz2Yhxc5P6xSDJBBgMBtLWv//9b6xdu7bSNXQ0Puh/T5kyBUVFRSgrK0NJSQnTeDp//jzi4%2BNloYQSIQ0ARf7b9rsEFVarlZDGSKDn1Lt3b8yfPx9WqxWLFy9GQUEBvL29iUxGZbl1alKIrVYr9uzZQ9bOkXjFbDbDZrPJmEWlf9%2BifgVI90v/P3/%2BPPnMw8MDp06dIrmaLFF1eqwWiwWhoaFVjl/Cli1bmDl7DZo0kVd0d5df3zVACVh5FNQLA1Y06131EAIHBYqKm6bavesIrfwmViW6DmMOincerJcg9KCpSdFLx%2ByLEW6suM/h5YEEBwUVO%2Bh9YY2X6osZ6Uzdx/qNRU%2BBXgbWXlaxVMx2mSk6VCFre%2Bm%2BFO0w9Lro5WMsOUBFRDDHRxN10ROlO2K1Q/UDAA5ExHZERyv7pgmaaMmatm2V99BlrElR66VmX5iVqjp/LB01eoNVjI9Zh95Q%2BpCwvqTow8862NT4FM8loNxz%2Bkyw5k0vDuPcKM4oY94P8mzS7bKehaq%2BA1j3qem7wuvSUnYDHI8FuLHH8Zdg%2BvTphDTFbDbj0KFDmDFjBry8vNCzZ0/Ex8fDaDRi0qRJiI6Oxp07d7Bw4UIMGDAAGzZsQHBwMPGkAcALL7yAoUOHkjZ1Oh28vLwUxC6dO3dGz549MXnyZPTr1w/t27eHs7MzQkNDUaNGDbi5ueGnn35CWloadu7cSbw3V65cgZ%2BfHz766COMGjUKPj4%2BFRpkixYtQtOmTQHYjaZdu3Zh7ty5AOx5h7GxsVi/fj0AeyhfUVERXnzxRXK/Y7s6nU6R3xcVFYWzZ88Sz5Svry/x0plMJnzwwQfMcTnqz8XHxys%2Bf/vtt4khM3bsWOLFBCATSl%2ByZAmqVatGBO5po6dr166YP38%2BgHu6c44GoSiKlYYrOjk5ITg4GJfpt%2BJ37xVFEWazGXq9HmVlZTh37hz5vDJjkfamSmOQynNzc5GamkpCXG02G5NEx9GQpEEbwpWB5%2BxxcHBwcDwWuI/890eN/29euD8CPIyT4y%2BBpHXn7%2B%2BP4OBgptbdkiVLEBcXp9C6o0lTJFZOxzYd8%2BQciV0EQVDIEJSXl%2BPSpUt49tlncenSJXTt2hWvvvoqFixYgNLSUmJgDBo0CPXu0jeZTCZcvXoVwcHBirl5eXmRcdSoUQNDhgwhHh%2BNRkME2YF7YZfSZ/Xr18fatWtJu47hjhqNBqIoIi0tDTExMaTc2dkZ06ZNk41h%2BPDhiIyMlEkB2Gw2mRSBKIokdFSClLM2dOhQAHJ9PinPsE2bNsSoBuzkLVJo4xkHmnNRFElemiAIaNSoEby8vGSGXoMGDfDss8/K9qS8vFwmnN7krqera9eusFqtCAkJIfUAEJ1AQRDQvHlzrF27Fh4eHnBxcVHMD7B77bp06YKUlBQ4OTkhKCiI9C/lEgYEBODOnTuyc%2BS4joBSqF4aAwcHBwcHxz8KLLmUPwk8jPPhwY09jr8NpDA/o9GIRo0aoXnz5rLPDQYDli5div79%2Bz9wH0FBQYRMRQoDtVqt0Gq1OHv2LM6cOYN169ZhwYIFJBfOarXCarXik08%2BISQtEiW/dJ2UlISXX34ZAPDyyy9j4MCBpM9z587J8uockZubS4wHi8WC8%2BfPo2/fvsTYczSM3N3dCRvmUw65Xbm5uWjYsKGs3eXLl%2BPs2bOk37p16wKAjDzGarUqPFcS0ckrr7wiuwbuGVcHDhwg4a85OTm4desWGjduDMAeQvvDDz%2BQ9iXvlc1mw6lTpxS5epcuXcKePXtkHjkXFxfCetq5c2dicF24cAFGoxFX7uoqSQa0NC6bzYbU1FT07dsX%2Bfn5KC4uZnrm8vPz8cMPP6Bt27YoLy%2BXhfJK8gqFhYWoU6cOCW11zDd0FHiXwkCB%2Bzf0OEELBwcHBwcHxx8Nbuxx/OUwmUxITk7GgQMH0L59e6SlpRF2TBoRERGERKQqSLmBEmumJKqu0WhkWmlSOODXX39N8uukvDKtVksMJavVimeeeQbXrl3Dvn37oNVqiSHi6elJjFCtVovTp08jKioKkZGRePHFF4lBUlhYiGXLllU45qCgIKSkpOD1119XfJaXl0fCF6tVq0bKi4uLMX78eNl8pHFIyMjIULVmEiRxdJZQPWDPKwwPD0fr1q0BgIjGFxYW4uOPP5bV1Wq1iImJwbx58xRhlqWlpYocvuLiYuJh2759O/bu3QvAnmfYs2dP2Gw2ODk5KXIFHaHRaNCjRw%2BMGVO1Zp1OpyPjkkJChw8fDo1Gg2bNmgFgG2FSmXTv/cg8ABUQtLByeqoiXmCRIVAhqyySD/pFrSqCFqqSCr1v5vho7gM1fA5MlgJ6YioIPOjxsSJ/FevFYFJRvOhWQ0yjQtBdzRrTZXSzrKWiSWdYZ6JKYhVGJZZOOD0vxfIx1lMxTxZ7DTUgZiQ0Ta5CbxRrcVQQoCj2%2B%2BRJZSVaY5Qm6Vm9WnkPVcaatqp9UfO9QJWpIQ%2BhC5mR8moIWugy%2BgCquUfFFwWTlImeGFVJlQA9w7NFH6UHnUJVS6Pmu1nF4/JA4yPXf3EYJ/fsPRx4zh7HI4Na4pXi4mJMnz4d06ZNI3lkzs7OGDRoEJo2bYrJkyfj9OnTWLlypaKP6OhofPPNN5WOozLyFldXV2g0Gvz222%2BkrLy8nIiPA/d%2BvDs5OUGr1VbosXF2dkbeXVYvQRCIF8hms8kMGMccL3ae1j3069ePhIBKkIxRyZP09NNPKwwqxzF26dIFRqMR/v7%2BlQqcO%2BbwObYheVgB9Z6mynLl3N3dceTIEfj7%2ByMoKEiV4VmRZqIEi8WC119/HR999JHiM41Gg4CAABw8eFDGGurj40NyCIF783XMi5Q8nyUlJdBqtSRXkRUOSqOM%2BUupYlRI0BIVJa9YFfECa2wUwQSL5IO241URtFCVWPcoHhfG%2BGjuAzV8DkyWAnpiKgg86PGxHm/FejGYVBTvQdQQ09DtsDpXscZ0Gd0sa6lo0hnWmaiSWIVRicFboZiXYvkY66mYJ4u9hhoQ8/cnTa5CbxRrcVQQoCj2%2B240gwx0WD9N0uMgf1NRGWvaqvZFzfcCVaaGPIQuZP45VEPQQpfRB1DNPSq%2BKJikTPTEqEpqulYeAOVRetApVLU0ar6bVTwuDzQ%2Bcl1c/JcRtPx/M8z%2BCHDPHscjxfTp05GSkoKUlBTs3LkTo0aNwocffgij0QjATgySl5eHjh074quvvsKCBQvQvHlz%2BPn5YdiwYQgODkZkZCSef/55pKSkICgoSNam5HFSA39/f9m9KSkp8PDwgCjaj730Y9/LywsTJkxAy5YtSX4YIPeMSQgKCsLEiROh0WgwduxYppEzdOhQfPXVVxg3bhwpq1evHpKTk7Fhwwa88847svrSeABg6dKliIqKQlRUFAwGA0RRlMkIiKKI6tWrEwZKQRDg7OyMIUOGkDpGoxEuLi4yeQYWfHx8ZH37%2BfmRvLyK1llaQ0mDEAA%2B%2BOADkuMH2EM5JQiCgMLCQpSVlWHTpk0KQ2/RokXo2bMnAGDOnDlE50/SxHN1dZVp6klwd3fH8OHD8cUXXyg%2Bs1gsuHHjBrKzs0mI5r59%2B/DMM88AsHvg/vWvf8Hd3R1arRYWi4WcBSmncefOnUhLS5Pl59GQZB4kT2plxC0scIIWDg4ODo7HAgyGWo7HB9zY43ikUEO84uPjg3bt2qF169bo3Lkzli9fLiNeadKkCS5evMgkXtm8eTPmzZunaiyiKMru9ff3lxk3kp6d2WxGZmYmjh07hj59%2BpDPTSaTLO9MFEXk5%2Bdj//79iIiIwJAhQ8gPfUf4%2BfmhTp06WLVqFSm7cOECzp49iyeffBK1a9eW1ffx8SGGZZ8%2BfWA0GmE0GolcgeOY9Xo9tmzZAgAICwuDt7c3ysvL8fPPP8vanDdvnsxIZCEnJ4d47gRBgEajwdKlS2EymYjHyVGUHAA6deqEoqIiFBQUEAMvMzNTRmQi5dsBUDBaOoY6urm5oW3btuRFwLPPPoupU6fKxlhUVERkHxxx584dHDhwABEREbLy/v37Y%2B3atVi7di3effddUv7000%2BT8N%2BSkhLUrFmTeE8ljylwz4OXnp4u89SVl5eTXEEJJpMJZrNZVu/w4cOKsVYEnrPHwcHBwcFROXgY58ODG3scfzho4hXaQKKJV7p164ZTp07h6NGjsnpFRUVISEioVJBbLcrLy5F7N5%2BioKAAGzZswKBBg5CXlwdBEODu7g6z2YzS0lISCtm%2BfXuUl5fj%2BPHj6NOnD86dO0dy8e7cuUOIST766CM89dRTuH37NnQ6HTw9PSGKIubMmcPUmMvJySHGRmJiIiZMmECYQx0hiiJ8fHxw/fp1CIKAWrVqIT8/HzabDSep/JGxY8cS75gahISEIDMzUxEeKukESrBarcT4lDxijvmPAPCf//ynwn5MJhNq1KgBwG5ktWjRgnzWtGlTWe5hVZg0aRLWrFkjK1u1ahX69u2Lvn374u2335Z9Jq2nKIpYu3YtMjIyYGsfImwAACAASURBVDKZZPOT5lZYWCibmyAIePXVV6vUAQyjdbUqAStnLza2k7Lit99Wes18HCjjn6XvTTt%2BWblXirZ37JBdso6YYol27VJWcginBaBKjBgOUisSFI/Td9/JLin7nN0XYwEV6/X994o6ClHypCT5NcuzTueTsTbPIcS8ojr02ijy71jJOVSe0l0S20rbYfJKURNnpcAp9k6FSrQqQXfqwDElOy9ckF/TIeEMPVRFX9T3CsB4Plgh8vTB%2Beyzqu%2BhtV5ZdaiNYeVb0oWq8tCojWKuOb1ejIgExRFlhbRXlaPnIKFDQK8n60A66KMCyscHALBunfyaXnPWeOkcPUb6geJ7gvH3QU2%2BJV32ICmQakTVWc9LVXXINf3dxfFYQbCpUTHm4FCB2NhYjB07luivmUwm7NmzB6%2B//jrmzJmDxMRENG7cGHv27JHVY%2BGtt95CcnIyADtZRuPGjbFgwQLcvHkTJpNJxqAoecX%2B/e9/Y/78%2BSRnz8PDAwUFBYQxUZIa8Pb2xq1bt%2BDj40NCHWfNmoWXXnoJ3bt3x7Vr11BcXAytVgutVovQ0FBcuHABTk5OxLjTaDTQ6/XE6xcQEICIiAjs3bsXBoMBZWVlxEvz8ccf46233qpQTFyr1eK1117Dtm3bkJGRgWrVqqF58%2BaE9VLSpRMEAW5ubigoKIBOp8OZM2fQoUMHXLlyBRqNBhMmTCBeTxcXlwrzA%2BvVq4cLFy6gWrVqKCwslImpA8pcvqqg1WpVhzB27NgRO3fuZLYviiLWrVuHxYsXY9%2B%2BfaR89OjRWLp0KXr06IH69evL8vSkfEYJTk5OsFqtqsfj6uqK8vJy4n3s2LEjFi9ejIULF2L37t3IyMiA2WyGr68vSktLsXTpUsK6ysLZs2eZ4b8szJ07V5GzN37cOIwZO1bV/RwcHBwcHH8Kysr%2Bspy9I0f%2BmHYd3jX/48E9exyPFLNmzSI5Z40aNcLUqVMxaNAgdO/eHQUFBRWyO9KYPXs2Ro8ejcLCQixcuBCTJk1CWFgYVq1aBY1Go8gNdHNzww8//EBCAgE7xb4oinBycoJOp4OzszPReZPCFgF76J6fnx%2BsVisyMjLQtGlTYox4e3uTXC8JgiDAYrHAYrEQpk6bzUY06srLy2W5f/Xq1UOXLl0AAAsWLMCiRYsA2EMLpT78/Pzw8ccfQxAE3LhxA1lZWZg/fz6cnZ3h6ekJNzc3ODk5EQIRSXZACp90c3NDamoq6dORZIbGhbtvvq9evaow9AClfpyjJ1av18uMGb1er/CgVYbOnTtXaEharVbExcXJDD0AxGu5efNmBSELLZReXl5eoaHHYnEtKiqShck%2B99xzAOzkKWlpaaQtq9WKyMhInDhxorLpqTb0AHbOHn/zxsHBwcHxtwPjt8KfBR7G%2BfDgxh7HI8X48eNJztmePXuQmpqKyZMnA7AbJvn5%2Bdi9e3elXj3A7uUZPHgwAgMD8e6772L//v2Ij49HYGAgAGVu4OHDh9G6dWskJyfj9OnTAOxyCBMmTMC2bduwbds2bNy4EcA98g%2BdTofAwECYzWb88MMP2LZtmyxcz2q1Yvfu3Wh7lw7/7bffRmBgIDp06EDy/2rUqIHw8HCUlJQQse/IyEhC4Q8APXr0wNatWyEIAqZPn45FixYhPT2dGFGSsVG/fn00bNgQrVq1wvLly9GsWTOIoohatWoRA0giEJGMOJ1OR0hQHMNeQ0JCIIoiAgMD4e7APPbyyy8TY3PRokUkTFEauyiKMJvN6NGjB7lHCrsEgK1bt6JmzZrEqBEEAfHx8eTf0t54eHgoSE0EQcAnn3xC5u0YVinlFzZo0EDWnyM2bNiAffv2yYy2%2B/FASkQwjtBoNLLcuXbt2gEAYZWVDPi8vDyUl5fL2E179Oghy4tkkbhwcHBwcHBwcPyV4MYexyOFr68vwsLCEBYWhqCgINkP4MjIyAop9RMSElQTr1QEKTdQCv90dnaGn58fGU/NmjWxdOlSmXdRq9WiWrVq2LdvH9auXYuGDRsiKiqKGD1JSUlEzy0xMRFOTk547733sGjRIpSWlmL37t2oV6%2BeLERz3LhxWLZsGQRBgJOTEwRBQIsWLRAQEACj0ahgkFy9ejV69eqF9PR0nD59GuPGjSOGkLe3N%2BLi4uDm5oYFCxaQcMPz58%2BjcePGOHnyJNEGdBRAz8zMhNVqxfXr12UepMTERJIXN378eCIiLrFgWq1W9O7dm%2BQzAvc8gYBdUH3ZsmUYMmQIPD09sWXLFmzevBmAPQdNWlNRFJm5lV27diVhl5Kh1qJFC7J%2Bly9fRvPmzWX3HDx4EAAQFxeHp59%2BmuyHo0dODXx9fcm/RVEkHlrHuep0OhQXF8Nms6F9%2B/bEc1u3bl1cuXIFJpOJnJ89e/bIPIveNM16FeAELRwcHBwcjwX%2Bwr9N3LP38ODGHsefhkdJvPLBBx8QofTw8HA0bNgQe/fuhZOTE/73v/8BALKysjBz5kw0bNgQ4eHhaNCgAV544QVkZmaSH%2BlFRUXIzc1FWVkZIV4B7oXjLVq0CNOnTwcABAYGYtWqVfD29kbTpk3Rv39/iKKInTt3wmKx4NatWwDsoY2nT59Gw4YNMXr0aNhsNhw8eBDFxcXo1KkTYmNjER4eTozS9u3bY%2BDAgTh58iQMBgOeeOIJXLlyBTNmzEBmZiZmzpyJdu3a4eDBg/Dw8EDdunUhCAJKS0uZxoEgCCgqKoJOp4NGoyHeUMBuzEiMk47acXkOpAvffvstLjok5TuGdM6ePRvt2rXDsmXLkJ%2Bfj5dffpmQpEjMmLm5uczwUJvNhn379uGNN96QlR8%2BfJiQ4litVqSlpVUpUO7q6ioTllcDyQMJ2I0qllcwMTGRyFq0aNGC6DGazWbk5ubC2dmZGKOFhYWyMysJsKsFi6ClbVsGQQudGF%2BVaDSgiu1CjShvlernKtplJvarUVWn72PUqVLAXYVoOWt8ittYc6D7UjFeVSwkKsSmq2qGyc2kQqxdzfDotVFzbuiI5QcVgKYZMdTMQVGJET5d5T2AkrlCzblWcdboztU8zkxJz6VL5dcHDijr0GUPorrN2jyqjHkmaDYTFSwk9DyZgRzUfSyiKcW6U88Ycz0f4LuOmSZPbyhrg2kl%2BCoZmKBcT9bE6TosxXm6jL6W9IeDKA1JjscKnKCF45GBJmhhQSJemTx5MqKjo5GZmUmIVzZs2KDwjrDalMTbJZ2z0tJS6PV6REdH48CBA/Dw8CA/zAVBwCuvvIJGjRohPz8fCQkJuH79OnJzcxEUFITLly/L%2BjMYDBAEgXjD4uPjERoaipdffhmJiYlo2bIlqZufn48OHToQQ%2Bmpp57C/v37Ub9%2Bfdy8eROjRo0CAHzyyScwm80yw8xRxJ1G3759sX37djzxxBMICAjA1q1bYbFYyP116tQhrKE6nQ7Z2dnE4yeKIsaOHYslS5aQ9h0JV7p3744tW7Zg4sSJuH37NlavXg2TyQSNRkMMoObNmyM9PR0Fd7/0JYKY%2B4FOp4Ner5d5G9XC398ft2/fJuN3FD13xP0QwwDsNddqtbBarSSP86mnnkLv3r2JlqL0UqBBgwZIS0vDK6%2B8gk2bNjE1DL28vO5LeoFF0DJu3HiMHTtGdRscHBwcHBx/OIqKgLtpDX82WO8tHgXatPlj2gXsL7fnzZuHpKQkWK1WvPDCC5g0aVKFEUknTpzA3LlzkZ6ejoCAAAwfPhxxcXHk8%2B7du5NILAlbt25F/fr1VY2He/Y4/lRIxCsJCQno3r27jHjlfsLgPDw88Nprr2Hz5s3Yu3cvjh07hmXLlqFVq1bEMCgrK8Py5cvxyiuvEE2/FStWALAbEC1atICHhwcCAwPh5%2BeHjh07YvPmzTAajXB2dlaQmzgaetIYHPMRjx07BgDo0qUL7ty5g06dOmHHjh14/vnnSUinXq9H586dERsbi7CwMDRs2BA7duxASkoK3njjDTg7O%2BPUqVNo3Lgxli9fjsjISFitVvTo0QPR0dHQarVo3bo1yceTQiYlL52TkxP27t1LjBpPT0%2BSdwaA5A4uXLgQiYmJMJlMEAQBzz77LKlz7tw5mfB7Ve%2BDpL7r1asnK5MMvf379xPv4Lhx4zB48OBK25PEziXQEhTSNW3oVZUzx/pSlIxwjUZDpC1KSkpgtVphMpnIOM7fpfcODw/HyJEjZX1JLxVYBmll4KLqHBwcHBwcleNxDOP86quvsG3bNixevBiLFi3C1q1bFS93JWRnZ2PEiBGIjo7Gpk2bMH78eLz33nvYu3cvALvM1aVLl7By5UpCTJiSkqLQbK4M6qnjODiqwO7du6usIxGvVPWDv6o2RVEk%2BYGO0Gq18PHxgZOTE6pXr67I/1q/fj1at26NiRMnEiZPrVaL/fv3y%2Br5%2BvoSIpeWLVsq3qhIiIuLQ1xcHBYuXIhPP/0U27dvx5AhQwDYmSdNJhPS0tJkXqLt27fLvFL//ve/ZW3%2B%2BuuvSEpKQk5ODubPn4/evXsjKysLJ06cgNVqxaFDh9ChQwesWbMGBw4cgK%2BvL5KSkjBjxgx4eXnh9OnTcHZ2hslkQrt27WA0GmWeO0BuwNlsNnz33XekrKioCP/5z3%2BIR61GjRrEAzp06FAkJibKDC3JyMlw0CFylEN46qmnyL//%2B9//yuYaERGBX3/9FU8//TT2798Pm82GixcvknIAaNy4MY44cC9XZHxWFQY8fPhwYlCXlpYiICAAzZs3hyAI%2BO6uPlufPn1w9uxZItcRHByMK1euECbUVq1a4dChQ7K%2BpH9XJWJPg%2BfscXBwcHBw/PMg8SNIv0EnTZqEhQsXYtiwYYq6O3fuhJ%2BfH15//XUAQM2aNXH48GFs3boVzzzzDK5evQqTycTUqVYLbuxxPBJIoZUStFotqlevjr59%2B8oMO6PRiFWrViEjIwOurq5o06YNJkyYgOBgezz4xo0bsXjxYqaRJ4V0siBp%2Bh04cACNGzfG77//rsihKioqwooVK5CZmYl%2B/frh6NGjuHPnDu7cuaMQBndycpL9GL9y5Qo%2B%2B%2BwzpKSkICcnB/7%2B/mjfvj3Gjh0LLy8vvPDCC/j000%2BxZs0a4iWqVasWrFYrPv30UyQnJ2Px4sUoLS1F27Zt8dNPP8FgMODHH3%2BU9duxY0eUlJTgpZdeIoLf69evJ5/XrVsXI0eOxJw5c2Cz2dC6dWvZ/ZL%2BoFarRVlZGTG6KjOE3Nzc8M0336BXr17EkCooKCDhBo6hrpJnFLB72AwGA0pKSmCz2dCkSRMcP3680v5oXTzJiL506RIaNmyIX3/9FRqNhuTNOUKj0UCn0xH5ifuFlCtYVlaGunXr4vLly/ieEsvWarUk3/Cjjz4inlspzNPV1RWzZ8%2BWrYFk7DZt2vS%2BxtO9e3eS5yihfp06inomE%2BCQXqm4ttkAyvkJiwWQOToVBYwbGQ3RRWr6pgtZXdNlauZA982qpKjDarikBHA0zOlrRjusvqtaG9ZNKpZcMW/W%2BlXZF6NhNXunKGN0rmZ8VdV54P1%2BgHnS8mCsdtX0reZMVFWHJVVG11Gz32qO9aVLQM2a8jp0mZp9UdM3PS/WPAsKAAdiaFXr%2BUDPAqNSlX09wHcfsxKjHTVnq6o6aubNapfeh4d5Vm0urqC/6v8s/FHvQG/evIns7GxZmb%2B/P1Om6X6QlZWFGzduoIWDkF%2BzZs1w7do13Lx5U9F%2B27Zt0bBhQ0U7UnRURkYGgoODH9jQA7ixx/EIMX36dBIOaDabcejQIeJt6tmzJ%2BLj42E0GjFp0iRER0fjzp07WLhwIQYMGIANGzbAx8enyj6uXLmC69evw2azYdq0aZg2bZrscy8vL8yePRtdu3bF3r17kZSUJMsNLC0tRbVq1dCoUSOsW7eO5LMZDAaUlpYSY6e8vBzl5eWYNm0aEhMTcfHiRZmRcv36daxcuRLr16/HBx98gC5duqBmzZpISkoiHkEp9K9TJznphsQuWVJSgpiYGAB28pcRI0ZAr9ejpKREoR8n4bfffsOGDRuIBygiIgKhoaGYNWsWRo0aRdhOCwsL4eTkhO3bt1e5psHBwdi8ebPMY%2BYoTO4oEu%2BY/ydpC2o0GhQUFCAkJIQYexWhrKxMJrkgGYWOBmWXLl2wfft2MkfJq2exWGQGZlWoXbs2AgMD8fPPPwO4l7On0%2BlQUlJC5ieKImw2G2w2G9EzBIB3331X4Qn99ddfZeydjmuWnJyMI0eOyL7gK8OWLVuYOXsNIiNlZfQfcPpaYWxB%2BQddWcC4kdEQXaSmb7qQ1TVdpmYOih9ajEqKOqyGaQ8swyNb1ZqzmlbUYdykYskV82atX5V9MRpWs3eKMkbnasZXVZ0H3u8HmCf9%2B4jVrpq%2B1ZyJquqwfqvRddTst5pjTRt6rDI1%2B6Kmb3perHk6GnqsdtXsi6pngVGpyr4e4LuPWYnRjpqzVVUdNfNmtUvvw8M8qwJswF9m7v0xWLduHRb/H3vfHR5Ftb//zuxuNptN7yGBUKQlEIjUICiCIIJGOoJUEVEEBBRRRBFQQFAuKgG9/rwQEERqkKKEFikiVTpEegIhjfSeLb8/lnOcOXOSHSPeK1/nfZ48T%2BbkzOmz2c98Pp/3XbJEVjZ%2B/HhMmDDhT7VLDEipUefv7w/A8UKeNfbCwsJkhHN3797F9u3b6TiuXr0Kg8GAsWPH4ty5c6hXrx7efPNNREVFqR6TZuxpuG8g2ncEffr0wbZt25CYmIiwsDDEx8fjm2%2B%2BoW7t8PBwxMXFoUePHoiPj8fkyZOd9hESEoLAwECUlpZi4MCBeORehq2Liwt8fX1hNBoRGhoKV1dXeHp6Ij4%2BHh988AE8PT3xyCOPoKysjIqZEwQFBWHVqlVVCn7fuHEDrq6uMmMPcOSllZeXY8qUKYiIiMCgQYPw0UcfOZ0Dz%2BuVkZGBDz74gHrTWrVqRVlLvby80LRpU6SmpiIrKwtHjx6V3ffbb79h0aJFeOWVV2gCMPG2kflUR7JSUlKCzZs3A/idzEUQBBruKAgCvbdevXowm81Uy/Du3bt0zOfPn6f3VAdCAnP16lWZ4UT6JnqHzZo1w7lz52T3WiwWp6QsO3bsQM%2BePWGxWGR5ce3atcPPP/%2BMiooKmRdaui5ZWVmwWCwwGo0oKCig8yHeO6JHyIMoimjZsmW1Y5NCy9nToEGDBg0aqsdf5dkbNGgQunTpIiuTfoetDmVlZVyiNuD3/%2B1S3gfye1Uv8qXtTpgwAf7%2B/hg0aBAAx0v%2B/Px8DBgwABMnTsS6deswYsQI7Nixg0bFOYNG0KLhLwXRvktISEBUVJQih85kMmHZsmV4/vnnVbe3f/9%2BeHh4oEGDBujQoQM6dOiA1q1bo379%2BggNDQXgYE/My8vD1q1bcerUKezfvx8TJkzAhQsXYLFY8Mknn6BVq1bw8vKCXq9HeHg4BEHA008/TZNfg4OD8eKLL6K0tBShoaFwd3eXJcdu3LgRXbp0gc1mw6ZNm9C7d28IggBRFOkHSO/evXHw4EG88847MBgMsrwuURTRsGFDxMfHY9q0aZRdFABmzJgBwPEBcfToUcTHx%2BOrr75C%2B/btqWA6ALRu3RoWiwUHDhzAY489RplE7Xa7jDCkulywjIwM6m0jhl5YWBgtM5lMlITFbDbTXDoXFxfYbDZqvKakpMgMPUJismDBAixYsIDq%2BwEOz%2BO7774LALK%2BAcDX1xd2u52GeBID6vHHH8eKFSvw0ksvycbPErMQ73JKSgpOnz5Ny2NiYmh/JCRTp9PB29ubzu/MmTMwGo0wGAxwdXWVMYICjg/d3bt3w8vLi7ZF5Dqk%2B6dBgwYNGjRo%2BPP4qwhaAgMDERkZKftRG8J5%2BvRpdO/enftz5swZAHLDjvxeXW5/cXExxo4dixs3buDLL7%2BkdefMmYPdu3fjiSeeQGRkJN5//32EhYVRjWM10L6Z/ENxv3PsWFRWVuLRRx9Ffn4%2B5s%2Bfj5UrV6JFixbcsUREROCtt96i3iUAshy68%2BfP48SJE7h9%2B7YsdHPGjBlYsmQJ%2BvTpQ93dt27dQteuXbntAA4jcPv27ejevTt2796NgoIC5OXloXPnzpSVkbzZKSgoQGJiIkwmE8xmMwRBQEBAAO1jz549aNCgAfbu3Ytr167B19cXgYGByMzMRHZ2NkRRREJCAoYMGYLvvvsOlZWVCgPs8uXLGDlyJEwmEzXQTCYTzU2sqKhA586d8cQTT%2BDVV1/F6dOnZZp4RqMRgiBg9erV6Ny5M6KionDkyJEq5QpYkBy6Qom2jt1uR7169aDX63H16lXcvXuX/o149MjYSH0yn6eeego//PADAMeZslqteOeddyizJfHeXbp0ieb/EQOTtJOTkyMbf8eOHXHq1CkcOHCAK21A7nNxcUFFRQXWr18voywm%2BOSTT2T1KysraUgn6YuQ%2B5BkaMKwSlhPT506hRs3btA90Ol0WL9%2BPQBHiOrNmzdRlxc/xYFG0KJBgwYNGh4EZGQKkEj2/uNRHXFfRkYGFi5ciKysLBqeSUI7q/IcFhUV4cUXX0RKSgri4%2BNl3yP0ej3c3d3ptSAIqF%2B/fpWeRR40z94/GNOnT6deqt27d2Ps2LFYsGABEhISADg05ubNm4eBAwciISEBcXFxyMrKwtChQ5GTk6Nob%2BbMmYiOjkZ0dDSioqKQm5uLRx55BLGxsSgsLJQdVh6eeuopKmBuMpnoT5s2bTBmzBjZmIODgzF58mQMGzYMcXFxdMwEJAfQxcUFb731Fr777jvUqVMHRUVFNMzx3Llz9Mt1Tk4O7HY7tmzZQoXaS0pKkJKSgtLSUhw7dgyFhYVo3LgxNSa7du2Kr776CgCop8fNzQ12ux1nzpyhbQ8cOJAyVYqiKGOnJDCbzRg%2BfDgaNGiAyspKZGZm0r%2B1bdsWP//8M7p06UJDCwm%2B//572O12HDp0CBEREdQYIt4yaV0eqiJwSUpKouL0Op1OlmdHfideTKluDAkxlRpvxJB69tlnqfYgAFmIpjR81mw2ywzVuLg4AKBhmdLwR2KEAb8bnzxDD4Ai3KGiogJlZWWy8NytW7eiZcuWKC8vx9mzZ%2Bm6lJeXw9/fH%2Bnp6Zg7dy6tb7FYZGMv5InWVgGeqHrXrhxRdfYDnbnmcuGkpsou75doMBhdSt50FffcuaOsdI9IqLq%2BWT1gxToASpVlpq/sbOUtauatmJdiMBwNYybRXyHCDXVa8oq%2BeBvMhgKxdXih1JKXRABw65ayCnsbV6CaPUxqzg2jt8k7j%2BxWqjmz3PXbt09%2BnZIiv%2BbspWLiN24oqii2k5c3zA5a8kIVUKdZjqVLlZWY55knzn4vVbza4Skix1U8h2DzsDnrp7iNx5jMPh/sgHlh7WxfbBucMm50PPsZxCwO9xyxnzech0FxH%2BfQ1kS3ni3gLSd7D%2B95YY81rw5bVtV1ENQbFvcbD5r0QlBQEGrVqkVTcQDgxIkTqFWrFtdzaLPZMH78eNy6dQurVq2SyVgBwLBhw2ROFZvNhuTk5D8kvaAZe/9gkBy7gIAAhISEoE%2BfPoiJiUFiYiKOHz%2BO%2BPh4xMXFYcCAAQgPD0eLFi0QFxcHi8WC%2BPh4RXsTJ05EQkICEhISsG/fPoSEhOCpp54C4CBOKeB%2Bc/gdrq6u8PDwQFBQELZs2UJ/EhIS8MEHH8jGrNPp4Ofnh1GjRiEmJkbBaunr64tx48bB09MTmzZtwvDhw5GSkoLmzZtj9erV%2BOGHH9CtWzeqs7d169YqxyUIAtcLIwVhTXJG/2%2Bz2RQyD3a7HVlZWfjPf/6DlJQUeHh4YM%2BePfTvW7ZswdWrV1FSUgKdTicLlXRzc6PGl4eHB/r27YukpCRq%2BKgRHdfr9TT3kYAwipI5EYMmMjJSFnZpNBploYvEC2i32xWx6Rs3bkSjRo0QGhoKLy8veHp6csdDNAnVQDo/Vo%2BPxTPPPAPAsU48iKKIefPmoXbt2hAEAWVlZfQtnM1mg6enJyoqKqo1oFlPcnX4/vvvMXXqVNnPvn17lBXZ16nMNVdesHZt2SWXxEtFhr%2BiiJE64S2l4h5eTkFwsNO%2BFbKbvNfK7J4zfd3Lia%2B%2BL07finlxNEAVx5d9Y8sJ11FFQsL2xdtg9vlg6/DO6L0XUgQSPoAqb%2BM%2BoioYTxRFzIs%2B3nlkt1LNmeWu3%2BOPy6/r1JFf8/Rc2YlzvPOK7eSFY7GDvpdWQKCG1AXjxikrMc%2BzYv%2Bh1LvmDU/xb0zFc4joaPk1Z/1UESOxzwc7YN7/WLYvnleEKeP%2Bq2Y/g5jF4Z4j9vOG8zAo7uMc2poQ%2B7AFaohzeM8Le6x5dZyR69BrLerkD2Hw4MH4%2BOOPceTIERw5cgSffPIJhg8fTv%2Bek5NDyfw2bNiAI0eOUH6JrKwsZGVlUWbwLl26YMWKFdizZw%2BuXbuG2bNno7CwEH369FE9Hi2MU4MManPs/DnfoljdO%2BmX78jISAXZBkF8fDzOnz%2BPyHsshCSHTop05g2kVJrBxcWFK6jdq1cvzJ8/H9988w327NmDn376CYsXL6Zju3r1Ktzd3bGPeRPs4uICQRAUYYqAI%2ByRCG4DvzM8Hjx4EACogURCCqUgoYlSrxeLyspKFBQUyN7%2B6HQ62O12GUMmQadOnbBz504ADkMjISGBGp6AQw5ASugiNaJIexaLRbF%2BZrMZJSUlirDCvLw8NGvWDJcuXYLJZKIhjN27d4e/vz%2Bys7Ph7u7OvRcA1ZGRQhAEmWxDWVkZ10gl62Y2mymBisVioZ5Ps9mMoqIiNGjQAFevXkWjRo1w%2BfJlhIWFITU1FX5%2BfgAc3rdWrVpBp9Ph1KlTdJ9sNht%2B/PFHDBo0CHa7HT4%2BPjT0wtXVFRkZGWjdujVmzpyJKVOm0D308vJCfn4%2BHn30UdVGKqARtGjQoEGDhgcE7IuB/yIeRDtz9OjRuHv3LsaPHw%2BdTof%2B/fvLUqT69%2B9PU5B27twJm80mi3wCHFFdq1atwsiRi8u9%2BQAAIABJREFUI1FeXo4PPvgA2dnZaNGiBZYvX%2B40Wk4Kzdj7B%2BOjjz6SSRcQxsbY2FhcunSJ5thVlbdHUFxcXGX4GmGnfOaZZ7BmzRqcOHFCpn9XXFyM%2BPh4uLm54cSJEzRvj/WQEIp6Vm6BGFusMLk0b69Hjx7098aNGyM5ORnDhw/HZ599BkEQ8PDDD1NvmdlsxurVq%2BHm5oaFCxfi3LlzKC0tpfPbunUr0tLS6ENLPHmFhYW4ePEi7WfkyJHYu3cvatWqhf3798PHx4e%2BpeEZem5ubqioqIDFYoHNZqOeUyJPYDKZoNfr8dBDD%2BHGjRuUxXLAgAFITEyEu7s7zp8/j8GDB2PNmjV0L/v37y8Lrdy2bRvtk8yjQYMGMm%2BjIAgIDw/HpEmTMHr0aHh6esLV1RWZmZlITU1F6r2QooqKCjz55JPUUCR5bEVMyJYUBw4cwMsvv4xLly7RtbPb7TLJhgEDBmDt2rXw9/dHVlYWZeUcNmwYtm7ditzcXBiNRmoQ/vLLLwB%2BD0m9fv06gN%2BlL8h4582bR/t44okn8MILL6C8vBxHjx7F1KlTkZubi/Xr19NzL4oiNWB1Oh1yc3Ph7%2B%2BPTZs2wW6307NH5i0NZ1UDLWdPgwYNGjRoqB4P4r9FnU7HlQcjkDosvv7662rbEgQBL7/8Ml5%2B%2BeUaj0ewV%2BVi0PB/GoSghTAIlpWVwWg0om3btjh06BB8fHzQv39/lJWVcbXxbt68ifXr1yMpKYkeZhcXF1lIX2lpKby8vDBt2jT07dsX7777LhITEzF16lSZ9l1mZiaaN2%2BOH374gdL8s7p3UpA8Mdb7YzAYEBYWRr/sE4OgqtBKotfGetoEQUDdunXh4eGBs2fPVumFY9GvXz9s27YN5eXliIqKwlNPPYWbN29i7dq1aN%2B%2BPTVKnEEqPG4ymVBRUVHlHNzc3KjMAlkDab4bK2Ler18/bNy4EW%2B99Rays7OxevVqlJaWyqQZAgMDkZWVRdsUBAE6nU6mS8czSoKDg1FWVoa8vLxqvZdqQHIDibZedW0RA94ZiHFGQOal0%2Blka/Taa68hLi4OOp0OlZWVsNlsdB0///xzTJo0ibsfer2e6hyqwYULF2guJ0FQUCO0a9dEXjE3Vx7OxFxzhc2ZOjyRY1aEWY1INKuMrEYkGsXFypAtthKnjpp2FCLB%2BfmycMWSEk5YlwrFYsW8iooUoYjO1obXLrtXPJFjxaB5i8w0VFAgjzLjnglmw5mlcqCiQhYiyOtaUZaVpQilU%2BjUswPiCNmzS8w7NopB8w4t%2B7wwdVSJtas5j2oeGGaeqkTBU1KUoafLlwOjRv1%2BnZqqCO1UIwKuKLtwAYiI%2BP2adyjY/WXv4UyMe67ZAbJrzHnGFOekrEwZm8jsN/eZZ/tiBqjqTKhYUDWfh2r2pSb3qHlWa1KHXnOe2f8W7nG%2B3XfcyzL6R0Az9v6h6NKlCwoLC/HSSy%2Bhe/fuMBqNNBdu9OjROHPmDNq2bYs9e/bItPEAhxHXo0cP9O7dG%2BHh4Zg3bx6KioowefJkmYdt2LBhKC8vp8aezWbDypUrsXHjRqSmplLtu0mTJuFf//oXjhw5Ah8fH2RnZ1PdO55R0aRJE1itVly%2BfFlWLooi3NzcqGdp5cqVWLlyJXbv3s1dA7UGySOPPIITJ0441Y/z8fFBYWEhNYqINp9Op4PNZqNzGTp0KL755psq2yGsnpcuXXI6tsTERMTGxlY5Nl44qRTE4Jk1axZmzZrlVAPGYDDAaDRyvXdubm4oKyv7Q96pmJgYym4pDWs0GAxo3bo1Dh8%2BjObNm8tYQHlzULOPkZGRMmNMarSS39u1a4dnnnkGs2bNomydUuM5KSkJY8eO5RqXf9TYmz9/PldUffz4V1W3oUGDBg0aNPzl%2BB8ae9u3/zXt9ur117T7d4RG0PIPhiiKNM8uODiYhuPp9Xp4e3vj5MmT3Ly9devWoUOHDlQbTxRFhISE4KWXXkJ4eDj9YTXHRFHEyJEjZdp38%2BbNQ9C9RGi9Xo9GjRph//79Ct27zz77jLYTHx8Pb29vmEwmCIKA8ePHIyYmBu3bt0fbtm1pvdDQUHh7e0On08n08VasWIFBgwbBw8MDw4YNk0k%2BkHFKx%2B7r60uvRVGEUfLai%2BSBAUBubq4sNI94JkloJuDwtE2YMIF6rnikIjk5OZSQhoAQprD13d3dsW7dOoiiSBlBSRx3u3btKJMlL6cRAB3fu%2B%2B%2BKzP0AgICYFa8Vgc6dOiAsrIy2ZoS/boWLVooDD1BEHDw4EHKGhUTEyMTHrfb7bhz5w7at2%2BPMWPG0FBIm82GU6dOAfidUZSsPTsXkt9YVfw62bvOnTsDAB577DHFWpJxT5o0iYqoN2vWTDEfk8mEN998k15Lz0pgYKBTY1kKLWdPgwYNGjQ8CCjF/8bQ03B/oOXs/Y1wv7XvpDHB0j6IhhuLyspK7Nu3D4cOHcLYsWOxZMkS5OTkcBkGR40ahevXr9MQzry8PDRu3BgGgwGBgYEylqDc3NxqWQqJl8Rut%2BPixYvo2rUrMjIyYLVakZycDJPJBG9vb1p//vz5dH0GDx6ML7/8kop7/%2Bc//6Hzlnp7CKtieno6Ro0aBbvdjnbt2mHVqlVYtWqVbDw2mw3NmjVDQUEBUlNTodPpqMfQxcVFFj569%2B5dBAUFUb0TZwQd7u7u2LFjB9X08/X1pUZiZWUlDanMlnDGG41GtGrVCnl5eQgNDUV6ejr1dHXo0IHWY3Pmjh8/juPHjwOoniXUw8MDK1euRL9%2B/ahxU1hYSI0hHx8f5Ofnw2az4aeffqL3derUCe7u7pRRavTo0Th8%2BLCsbbvdjo4dO9LrY8eOyYw1kk8YfC/522azwcXFBX5%2Bfoo80O7du2PSpEk4cOAA5s6dS/PmyNqTccyYMUNmLJP9Ioavq6sr5syZg2%2B//RZXrlyB1Wql3jubzUZzK/Py8uDv74%2BMjAyqvefp6Snzyko9tmlpafj111/Rrl27KtdagwYNGjRo0KAeD2LO3t8NmrH3N8P06dPRs2dPAI4vqb/88gveeecdeHt7o3fv3pg3bx43h27o0KFUW%2B6PYObMmZgzZw4AhyfK1dUVI0aMwIQJE/DFF1/AZrPhzTffRHR0NDIzM/HVV18hOzsbY8eOpQQYnp6eKCgooMLgmZmZVBNE6j1Zv369QutMiry8PKSmplLvTWVlJS5fvoxRo0bhjTfeoPUSEhKoIbdmzRpZG6NHj6a/9%2BzZE1arFVarlXqTGjVqhDp16qBevXr49ddf4ebmhoCAANyU6Ie1bdsWI0eOxLZt21BRUYGtW7dSY4kXLik1SrI5wl56vR5ubm4oKCjA3bt3MXv2bAAOA4ywPbq7uyM2Npau%2BTgJ/Xbbtm1x4MABAMDly5dlhu/BgweRlpam8H76%2BvqiVatWOH36NDIzM%2BHq6sodu7u7O5o3b44tW7bIvFjSunq9Hu3bt8fPP/8sC5m02%2B3U61ZYWKjQOuTBYrHA39%2Bfjol4t6T7%2BN5772HGjBn07Bw7dgwA8OOPP%2BLHH38E4DDes7KyYLVaUadOHaSkpNBxdevWDR988AEaN26MsLAwKmNBQjV37tyJpKQkmo8nxahRoyg9ckREBF13sv%2BiKOIWT6DsHo4dO6ba2NMIWjRo0KBBw4MAJ5K9fym0f4t/Hpqx9zcD0ZEj6NOnD7Zt24bExESEhYUhPj5elkMXHh6OuLg49OjRA/Hx8Zg8efIf6m/ixIno3t0h5CzN2yNjEQQBmzZtwqeffkpz7JYsWQIfSUK0KIoICgqSecimTZuGCxcuyEIefX19ZXNjUVxcjJYtW2LBggUAfmfRPHPmDF588UVar0OHDsjOzqYeQWnulbu7OzW%2BpCF1pfdEVM%2BcOQOdTofMzEzUrVsXv/32G%2B7cuSMj7zh69CiOHj0KNzc3eHh4wGQyVcswyQ/Hc4AQzUg1Bnn5ZWlpafjiiy/odUxMDA4dOgTAQeTx%2BOOP49q1a7hz5w41EAHg8OHDiI2NRdOmTWXGHjGWiTh7VeGFJSUl%2BOWXX6qUxQCAgoIC/PzzzwCAevXq4caNG3S9pWORMn1Wh/T0dLRu3RoFBQX0hYEUM2bMAOA4%2B5s2beK2Ie03lREcJh6%2B0tJS5OTk0Ly7oKAg3LknrlvOVWx2sL3evXsX3t7e2LVrFw3RJGfj7NmzilxRKf6I7k1sbCwiGKKDunUbKeo5S9bnkXGoIZNQFPGy950RGXCYGNSQCyjGnJ2tFMVjKtWE2IBLVMIW8irVgMBBDWGCYr1UkLioGR%2BbTsMStvAqccfHELSoOlyczpxx4PDWk50Dd3wqWIWcnQluuzUgcakJaQaPX0QNGRDOnwfuSRMBADZvBpjPGjVnX1HG7jcvL0sFuY5iYirOrJoBKx4PzqKzdXhrXBMCFDWfP2oIWhRzULExqsZXg/VTcyaq%2BvwRKngPjoYHBZqx9wDgz2jfVYW9e/eiS5cuCm08NpQUcOiITZo0SRZK%2BsYbb8i8HgUFBXBxcaGeu5KSEgiCwBWwJqGkffv2pWXz58/H9u3bUVBQgNq1a9OcKrvdDoPBIAtDPHnyJG2XEJCQumzoH/miT4zO8vJyKmeQkZFB8%2BZCQkJw69YtmM1mmbFAmC5J%2B6IoUkNKDYiR%2BUdBDD0CT09PzJ07F%2BPGjZMZbseOHUNsbKxi3j4%2BPkhKSqLXVXmMSDkxRt3c3FBSUoKwsDBkZ2ejrKxMZhhdu3at2nHzSE94%2BoIpKSl45JFHcOXKlSrHRoh1atWqhbS0NIiiCIPBINM5BJTGM/GMp6SkICUlhZYTQ686HDp0CNHR0fSFh7RtURSRlJSEKVOmYNGiRbScvChgcz2d4fvvv%2BcStERFydk4nYnyqhHcVSV8zftHzhgiins4uaBqRIQVY%2BZ9djGV1IgRq1kbRSGvkrN5cwrViBwr1ouzforhqBgf%2B92bK4bOVOKOjw1DV3O4VIhNqxGTZ%2BfAHR9bScW5VrUvasTaVdRx1hfPO6IYDy/3WGroAQpDj9c3b%2BsUZex%2B8wg42Jt4ddiJqTizagaseDw4i87W4a2xszPB3W8Vnz9soYqPUFUbo2p8NVg/NWeiqs%2BfUpvxf5a1p3n2/jw0gpa/MSorK5GYmIhDhw6ha9euuHTpEpo3b86tGxERIRPi/jOYPn06Dh48iPbt2wMAnnzySSxYsICG6c2ePRvbt29H7XvUz0OHDoXNZsPQoUORlZWFnTt34sqVKzD9QeamBg0a4Nq1a%2BjSpQtmzpwJu92Ozp07o1mzZggKCsKwYcMAOITCiWTCihUrEBsbK9M4mzt3Lv29WbNmABwMniNGjMBDDz2EzZs3IzQ0FDk5OXBzc8Nbb72FTp06QafToXbt2ggLC8PTTz8NFxcX%2BmWfeDK7d%2B8u00QhxCnS6%2BpA1oRQ/hN4eXmhRYsWNHSRkIkAwKJFi3Dt2jXMnDkTDRo0kIVxEkNp48aNsn6IYfroo49WOx4CYqSUlJRAr9fDz8%2BPGlSenp70bIWGhqJBgwYQBAGBgYFo06aNrB2Smykldxk6dCgEQUDz5s3x6qsOpsnMzEykp6fDzc2NvswgqFevHvz8/KgBWq9ePQCOtd%2B6dSu2b9%2BO0NBQAFUTyfBA1rvOPVpzLy8v6PV6BAQEICAgAKIowmw2IygoCJWVlQgPD5cZex4eHjh16hT27dsna5e8iBAEQZbT6AwaQYsGDRo0aNCg4a%2BG5tn7m6GqHLrY2FjExcVVyTjIIi0tDdHR0YpyNZ4mEkoaEhICQRDwxRdfQBAEvP3225gxYwYqKysRGBiIl19%2BGa%2B%2B%2Biq%2B/vprVFRU4Pbt2%2BjUqRPCwsLQq1cvnDhxAgBoKJ5U6JyITer1esybNw%2BxsbFo0qQJzGYzrFYr1q1bB5vNhqSkJLi6uiIxMRHXrl3DqlWrcOTIEVitVtjtdgwbNgwuLi4yz9/nn38OwGF4FBUVQRAEnD59GqWlpXjyySfx448/oqCgAA899BCuXLmCOXPmUA8UkTsYO3Ys8vPzqfdywoQJmD17NpKSkrBr1y7al81mkxkELBGKu7s7ioqKYDAYYDabKYmKu7s7Kioq6H4IgoABAwagoKAA169fl4VVHjlyBC%2B88ALeeOMNqvlGsGHDBmzYsEHWpyAIuHnzJmw2G5577jmZYHpVkHrXLBYLTp8%2BLfsbCZu8ffs2RFGkuZnZ2dkwGAzQ6XQoKyuj6%2Bfu7k7ntnz5cnh5eUEURbzyyiv46aefcO7cORmZizS3UxRFWdgr0SeU5uxJx7pixQoMGDDA6Rxr166NGzdu0LBPshfSkND09HS0bNkSBQUFMpIawGEkpqenV/kSw2q1Kjys1UHL2dOgQYMGDQ8CTJZCAMpIrf8GtH%2BLfx6aZ%2B9vhokTJyIhIQEJCQnYt28fjh8/jqlTpwIAvL29ZV%2BCq0NgYCBtR/rzR71/TzzxBMaOHSujv/fx8cGGDRtoKOWECRMQFBSEt99%2BG76%2BvujatSul4wcchBkA8PHHH2P58uXw8/PD6NGjsXz5cqxevRpdunShdWvXro21a9dSMhBBEFBSUkIZFwFHGGizZs1gNBpht9uxatUqSszi7u5Ow/WSk5Nx%2BfJlaoz99ttviIuLw9q1a9GjRw/06dMHZrOZeoYsFgseeughzJ8/H35%2BfjKDa8mSJbDb7bDZbDJPnjP2TZLrFxwcTA1UwGFoSA3vvLw8vP/%2B%2BzREUyoHsXTpUkyePJlLEhMbG4uPP/5Y1qfRaERFRQV0Ol21LKhSVJfrWVRUJBNtl8JgMEAQBDomUi8jI0NmuOTn5%2BPUqVNYunQp9RhKyXqkBvPdu3dljKfVGUC5ublUAkSaR8oDCXNu0qRJlXWmT5%2BO2rVr07M1ZMgQ2RgrKiqqzTsl3nA1iI2NxcKFC2U/MTHdlRWZnET2misxyJDI8KKOWccir46ijAnx5qU%2BKu5hxw8A93JJKXJzFVUUbXPIjxR9MaG63I9LlpmWs4AKLiN2vLw6bJgwb0GZvrgkuexa8DQ0mbYVy6diw3NyOH0zub28/VU4pDkNse8UFUvMeSly790LBXfvJP8HqhzgPQZiinth%2BRS8RWcHLCHsIlAsqZrDz5wJVc8YLyeYLePNgWmIV0VR9v338mtJ2DsF%2B/zyNoY5N9wsB7aQvea9iGYPBeczgD383PdtTp5N7niZzzruc8gsqIptUXUG2OdFTbu848iWqelbzXj/27DZ/pqffxI0z97fDGwOnRSRkZFVEmnEx8cjOzsbr7/%2BOgBHWB6vHWlOEU%2BaAQA%2B%2BugjKqkA/J57FRsbi%2BvXr6NFixYICgrC6tWrAQDLli2D2WzGhQsXMGvWLEycOBE9evRAcXExzGYz%2Bvfvj2XLliE6OhphYWFwdXXFxo0b8dBDD9G8vUuXLsHb2xtvvfUWAMcXd0EQYLVa4e3tjYMHD8Jms8FsNmP37t3Q6/WU9j4/Px9DhgzB119/rUpcOysri47dw8ODiqELgoDU1FS888478PLyQllZGZ078QLFxMRgzJgxGDp0KAAl0Yc0Z02K3NxcpxpsFouF5kt26tSJW4ed37BhwxSi8cTw4uWPubi4QBAEWCwWmRcyMTGRMlySe8n6EqObzZUDfs%2BBrG7uUixdupT%2BXlUOHTEu/fz8cPfuXVpO2tfpdPD29kZ%2Bfr5Mw5CMTUq2IwWRofjtt98QFBSEpk2b4tdff6V7CwAffvghVq1aBXd3d%2Bh0OhlL6KVLl1C3bl00bNhQ9uyQXEQ/Pz9Esrk11aCqnL02bRhj9F64dFXX3Ly0sDDZJS/ng3UsqslBwr3wWQJejoriHnb8AMC%2BdOIY6oq2OXl9ir4Ytl9u7pqKRBZF3g/nJZmiDss0rCJZkRv1za6FiiQkxfKp2HAucTPz8oq3vwqHNKch1vmtWGJOLvc9mVAK7t7d02StdoBMTjvYMG/eorMD5vzvVJXjylZizoSqZ6xhQ2Ultow3B6YhXhVFWWys/PpeiLsM7PPL2xjm3KjKMWOveRET7KHg5fYyh59ztJw%2Bm9zxMp913OeQWVAV26LqDLDPi5p2eceRLatJ3jO9/gP56Br%2BftA8ew8QnnnmGZw5c4aGRxIUFxcjPj6%2BWi21P4K8vDwYDAb6Jd7FxQWPPvootm/fjjt37sDd3R3z5s2jBlNCQgLi4uKQlZWF%2BfPno2/fvkhMTEReXh7S0tLQq1cvAECvXr0QHR2NtLQ0RZ9WqxXLly/HhQsXaBnxQhqNRmzduhWbN29GZGQkunXrhp49e1Jvo1Q2obi4mIbeEa9TdSgtLcU777yDNWvWoE2bNpSO32g0onbt2tSQIJ6mw4cPY8yYMVW217ZtW6oZR8L9XF1dUV5e/ocEtwmCg4Mxe/ZsWa6m1LMYFRWlEIUnKC8vp2tP0LFjR2zevBkvvfSSzEt39uxZmcfK3d1dZrhJDUeS27Z48WK89957cHd3h4%2BPD4YMGYKoqCgAjnw4Z2tfHXx8fOicibfspZdeQlRUFCXvIXtCPLPEU0qeg4b3viD5%2BvrCzc2Nhr9arVYEBQVh2bJl%2BOWXX7BhwwZa9%2BjRo7BarcjNzYVOp5OtkdVqhZubG7Zu3SobKzHAczneqeqg5exp0KBBgwYN1UPz7P15aMbeA4To6GgMGDAA48aNw4YNG5CSkoKjR49izJgxEEWxWiPkj8DT0xOvvfYaunbtih49euDkyZP46quvEBMTg4qKCly5cgXx8fF47bXXADjkH1q0aIG4uDhYLBZ4eHjAaDRCr9cjMDAQ//73vwGACp/b7Xbk5eXhvffeQ8uWLdGyZUtcvXoVnTt3xrhx47B161bcunULCxcuBOBgiszOzsaaNWsQGhoKNzc3fPTRR/jyyy8BOAhZnnrqKQAOw3Ty5MnYvHkzWrdurfCEEZKO8PBwmM1m%2BPr6IjIyEnXq1MGbb76Jjh07wm634%2B7du8jIyKAGJeAw5MxmM3r37l3l2l27dg0vvPAC9Ho99aKVlZXRcZA8LR8fH0VIJA86nQ5nz56lIupkHQmioqKovAIPxANJIAgCioqKsGzZMln/3t7eMo3AsWPHUmPVYDBQYwhwhF9Onz4dkyZNwuzZs1FQUIDc3FysWbOGegbVeFirgsViQV5eHs2XPHLkCADgq6%2B%2BwpkzZ1BZWSkbKzEq2ZcdRCIhJycHJSUlKC8vp%2Bt45swZNG3aFM2bN8eQIUOQmppK29m/fz9sNhsKCwtlnkxBEBAcHCwL7ZTCZrPJQpKdQcvZ06BBgwYNGjT81dCMvQcMs2bNwiuvvIL4%2BHjExsbijTfeQHh4OFavXu00Z0ktRFGEn58f3N3dYTKZqIGk1%2Bvh7e2NkydPIioqSpEPtm7dOnTo0AEvvPACnnzySVgsFlgsFsqcWFFRIQt7rKysRGlpKT788EN06dIFixcvxrPPPoslS5bgqaeewoQJE%2BDh4QGDwYB%2B/frJPE/nzp3DqFGjADiMAz8/P9rHtm3b4OHhISMAIfDy8oLJZMLNmzepuHfHjh3RsWNH9OvXj5KZVFRU0FBOwGF0HT9%2BHHl5eVi7dm2Va5eZmYm5c%2BfCYrEgPz%2BfGj0WiwWiKFKvXG5urir5htu3b2P9%2BvVV/l2agykIgiKH8P/9v/8nu96zZw8GDhwIQO5ZysvLkxGVfPTRRzSM1Ww2U%2BIVURTh6%2BuLd955RzEWk8mE%2BvXrAwCVrHAGHx8fbN68GY0bN5Z5LBs2bIhatWoBABUpr8pznZeX57QfAKhbty5Gjx5N%2ByHewYqKClRWVtLxEvZPljHVZrPBYrEoZDGkUEMUQ8DL2WvfnpOzxyahMNfcZWZyXXhHjU2RUZXjw3gjee0q7uFpVLI3chJtFO1wklIU/TN9cfmomAXjrZ%2Bib874FPexnlpewyr6VqyXitwwVTl7TM4RNyeOWTBe12wKnCK3CSqWgtM5W8TNvWJztnh5VGx4OLOZ3I9d9qAoJsm5T00CFLOAavKq2HxbAMDRo/JrFXmcqvIDlyyRX585o7yJ3RjO2rANqzj6yoeMN2B2cXgPNDMeNfvLjoV7DxuJw5sUsw%2B8bWHL1JwBZ9eAcil4S8Pep2Z87DVdG/F/Zy5onr0/Dy0I92%2BEqnLoWO07vV6P2rVrK7TvEhISsHr1aly5cgVmsxnTpk3DpEmTKBEGYcWU6ttJ%2Bxg/frysbP78%2BQAcRtm%2Bfftw6NAhjB07FkuWLMHDDz%2BMEydOoG7dumjevDl8fX1RWFiIZ599llLy7927F3l5eTIWTuI90el0eP311/HCCy/Qv5WXl1NvE9Hoq1OnDg4dOkTp8gGHkTJkyBBYLBa0aNECp0%2BfxuOPP441a9ZAp9Ph%2BvXr%2BJ5JPDcajYiKikLfvn2paHc%2Bm/x9D35%2BfoiJicEPP/xA87%2BqMjT8/f2RnZ0ty1czGo2ynDipXmB1EEURgYGBSE9Pr7KOVKsOkAuKEwIRtk51IPPz9vbGqFGj8K9//UvWnt1uR0FBAZ2LzWaTeRldXV1ht9vh6uqKZs2aUUOoKkPWbDajTp06uHjxIgCHoTZ58mTcvHkTgwYNoob0jRs3EBkZidTUVJnBJQgCDAYDDYkVBIHqJppMJirJwZv/jRs3cOfOHfj6%2BiI7Oxt6vZ6OU7q/nvdyUjw9PakhSOZ66tQpbNu2DR06dKD1idajTqej8iBqUFXOXtu2TM4em4TCXHOjZZlcFzW6ZqpyfBhvJK9dxT08BmH2Rk6ijaIdTlKKon%2BmLy5xKrNgaiTBeONT3Md6alWIWnH3jl0vFblhqnL2mJyjmmrxKZRO2NwmqFgKTudsETf3is3Z4uVRsflZajQT2YPCkXNRo13pLJFKTV4Vm28LAGjbVn6tIo9TVX4g8z8f98LwZWA3hid1wzSsSuNPjfgiuzi8B5oZj5r9ZcfCvUeN5iSzD7xtYcvUnAFn14ByKXhLo0bnkS1jr8naWPVGVC8s9dfhn2aY/RXQPHsPCIj23cGDB7HSZt6LAAAgAElEQVR7926MHTtWpn03b948zJs3DwMHDpTl0A0dOhQ5XNq16jFz5kxER0cjOjoaUVFRmDZtGkaMGIEJEybAbDZjz549WLVqFYYPH46PPvoInp6esFgsSEpKkjFnWq1WGAwGmEwmmEwmynJosVhkHquKigoMHz4ciYmJePvtt7F9%2B3bMmDED169fBwDExcVh165dKCkpwdGjR1FeXg5vb28qbk28MVarFRaLBZ999plsPuXl5Zg6dSr69u2L7t0d3pPCwkL4%2BPhQD6WnpydiYmLQu3dvbNu2DeHh4U7zIInBSDxqbdq0wdatWzFv3jxahxgeVquVCrbz4OrqqsjjYg1E1ogh%2BWvV1SEhpTxjk8yvvLychsWy8PDwkDFnSj1woijC29sbZWVlaNmyJdq0aVNtrl5xcTGVJyDjuXHjBux2OzX0wsPDERQUhF9//RUAcPDgQdnciIYguSb9kZw94l0jchd%2Bfn60vo%2BPD0pLS9G7d28EBwdzx%2Brt7Q2DwaAg1SkrK0NOTo7Cq0nqWK1WtGrVqsq5s9By9jRo0KBBw4MAne1vQMupocbQjL0HBET7jujf9enTBzExMUhMTMTx48cRHx%2BPuLg4DBgwQJFDFx8fr2jv1VdfpcYcIU2ZOXMm0tLSkJeXhyeeeKJKCQgvLy/KiLhw4ULMnz8fzZo1w44dO%2BDr64sPP/yQ9iOKIl577TVs2bIFW7Zswffff4%2BgoCCYTCZcu3aNfvH/%2BuuvkZqaipUrV6Jz586oXbs2OnfujE8//RSAwwBYvHgxdu3aRYkwvvnmG9oP8cYYDAYqCUFAwlvnzp0Lu91OQ0l9fHyQn5%2BP5ORkAMDAgQPxxRdf4M0336SC2v6SN8mBgYHw9PSU5UZWVlZCEAT6hf/XX39F7969FQaBh4cHbDYbDTeVEp60atUKer0eXbt2VUhrkPb9/f3Ru3dvuLu7Q6/Xy4wXYtwR1kwWBoMB7u7usnURRVEW8llaWgp/f3/Z/UuWLIEoimjbti0mTJhAyz/44ANZOx999BHKy8uRlZWFkpISp15FURTRokUL1K1bl1uXsKKSdWI1%2BHQ6nUzM3WKx0PxIwJFT2LZtW7i6usJoNCIvL496Xd3c3FBcXIwtW7bg1q1bcHFxgaurq2w/0tPTacgw8epJyX7O8EKd7mHEiBHVzl2DBg0aNGjQoB5aGOefh2bsPcDQ6/UwGAxISEhAVFQUWjOU0yaTCcuWLaM6ZFLMnDlTob83ceJE7Ny5E0FBQWjTpg3Cw8MRHh6O4OBgWShdcXExfHx88MMPP%2BDUqVPYv38/5s2bh7CwMDRq1AiJiYl4/PHHMW3aNAiCQOUkyM/%2B/fsp%2BQcxWjZv3oy%2BffvC29tbNs6oqCiEhYWhYcOG2L59O83fAhwG4Llz56DT6SiBiMlkUhhMxDg8c%2BYMIiIisG/fPphMJjRt2lSW5zh58mS4urpi%2BvTpKC8vx%2BOPP45OnTrROvn5%2BbDZbFixYoXMUCL6e4AjzLGsrEwRxlhSUkIF2PV6vUxH7sSJE7BYLAqWR8IGabfbkZ2djYSEBBQVFcnkBook%2BT3E68WitLQUOTk5Mg%2BvzWaTea0MBgPS0tKoQePr64v58%2BcjIiICzZo1kxnwxAAn/ZNQ4nXr1uH8%2BfOys8JDSkoKTp8%2BjfDwcJmXkODGjRsAHGcjODhYNieyvkclOSxGoxGRkZG03o0bN1BRUQE3NzeEh4fDSxLSSM4dqVteXo6ysjLZfgiCgEcffRQA0LlzZwAOo494j99//33EspTl9yA1ip1BI2jRoEGDBg0PBLT/TQ80tJy9BxDSHLq5c%2Bdi5cqVaNGiBbduREQEt5wVV9fr9dQo0%2Bv1MhZKKUpKSlBQUAC73Y4TJ07IwtaKi4vx888/w2634/z584p72dxDAOjXrx/69euHmzdvUqp9NvcwJCQEmzZtwssvvywzUNLT07Fs2TI88cQT9At9ISer32AwUEkFURTRoEED9OzZE0uWLJF9uW7Tpg0aNmyI06dPQxAEdO/eHQsWLKDGYnl5uUJXj80PY7%2BsEy8cyemS6sY5A2lXp9OhefPmmDt3Lp5%2B%2BmkYjUbK8EnGI4oi9u/fj8uXL%2BPtt9/Gww8/jB07dnDbdXV1hZ%2BfH7KysuDu7o6cnByFcUqMw1u3bim0Hatj/wTA9S7yNPh0Oh08PDyqzJ0MCgrCrVu36P6RdvR6vewclJeX49SpU/R6y5Yt9PcMRoRZei6r8kASL%2BratWtlMiedO3fGtWvXcO3aNZoTKt1/vV6PpUuXYtq0adx2WcTGxiqezwYNGinqVVbKc0rYa7tdmU5SXi7P17BaOXloKhpiq7DtcjtnOmPb4I1Hzfh47Titw7mJLeL2rWIOzvpWcw8qKhS5QYol5Q2QqcTew9sWVe2qmXdZmTyxR01nzDW3XSdz4t7HmQM7PPbMKs4wpy8141MzbzVngp2CmvHx5l2TM6BmfGqeKUVfKp67msyJN76a9K3mHNXkeebtXY0W%2BX7cc5/qkPWtFI1wzh/%2B10CzM/88NGPvAcHMmTMxZ84cAI7cIVdXV4wYMQKxsbGIi4uDO48EgYO0tDRER0cryku5tHVKFBYWwm6347HHHsO4ceMwdepUtG3bFunp6Vi8eDENh5MyJJKxl5aWQqfTwcXFBXa7HSEhIbh9%2BzY%2B/vhjAMDUqVMxZcoUGpY3efJktGrVCosXLwYADBkyhGqqAcATTzwBwKGzR1g0CerVq0fz/b7%2B%2Bmv8%2B9//xsGDBxEaGorbt28jKysLOp1OZoCUl5dTQ2/RokWIjo6mouJ2u50SgIiiiICAAGRkZGDEiBFYsWIFbWP9%2BvXYu3cvli1bBgAyjxHJjwsJCUGDBg1kuWgEUuOB9Gez2XDq1Cn07Nmzyr2y2Wzo2LEjvZYKrRMCEYKysjJqdLP5nFUJo0sNLtbAZdsHHHlv0jNA2pwwYQI%2B//xzAMDOnTsV/UjHQETXpYYo65EkCAwMVBh2PEiF46tCbm4uPWfSvome48mTJ2mZdB0sFgu%2B%2B%2B471cYej6Bl4oQJiIyUE7Q4E73lpUiyXzi4zlYVDTkV7lXBbqKGxEXN%2BNQINash0WCLuH2rIfVw0rcqYWmWBAIqiCw4ldh7akSQwSnjzoF9EaimM%2Baa264K8hrFfZw5OCPE4JFdqCLsqMEiqzkT7BTUjI8375qcgZoIfvMqKfpS8dzVZE688dWkbzXnqCbPM2/varTI9%2BOe%2B1SHrK%2Bh4C5wLw3lvw3N2Pvz0MI4HxBMnDixyhw6b29vRehiVQgMDJSFb0rDONWAeNB69uzJlYD45JNP6JjYsfv7%2B6NRo0aoU6cOvL298dxzz2Hbtm14%2BOGHAQCDBg2C1WrFokWLsG3bNgwcOBAtWrTA0qVL4e3tjbCwMJln0NPTE99%2B%2By22bNlCtfzIl29pTldoaCgWLlwInU6H1NRUREREYO3atfDz81OIhRNDtH79%2BigpKcHFixdpm76%2BvvD29saHH36INWvWAHB4yUgoYlBQEKKiopCYmAhA6eEifdWqVUsmIyGF1HgoLi7mesNILhpBeHg4/V0QBBiNRplBpNPpVBOHSPuTjl%2B6n9Kcvc6dOyMhIYGugYeHB9XJ42HNmjUYP348PD09Fd5oEv7ZpEkTeHl5wdXVVRESSvISCTuri4sLRFFEZmamrG7Tpk0RFBREr5s0cRhQderUoXmTISEhqF%2B/vmKffHx8qGe7TMJDvW/fPoiiKGuXhRo5DQIeQYtGz6JBgwYNGv52kLy41vDgQTP2HhBI897YHLrIyEhu2CQAxMfHUwMMcBgc0vw58iM1evbu3cuVZwAcxk2jRo1w4cIFjBw5Elu3bpXl7aWnp0On0yEiIgJ9%2B/ZFcHAwHbuLiwsVpPbz88Pzzz%2BP8PBweHp6wt3dHUeOHEFUVBR69eqF8PBw6q2cM2cOBg8ejIULF6Jbt27w9vaGXq%2BHu7s7Pv/8c2RlZcnGHxAQIGPR7NatGzp27EgNmRMnTiA0NBQ5OTlwdXVFx44d8fDDD8PV1ZXm/m3btg1Tp05FeXk5N7cqLCwMycnJyMjIoHpwAHD9%2BnVcvXoVJpMJGzduRK9evfDQQw/hX//6F/XynThxgrKoVgdpuwRWqxVlZWU0fLN169YyY0Ua2knK7XY7lTqoDsSgI3ltJpOJtkG8YYIgyHL2Dhw4gP79%2B1NmzcLCQgiCIDO2pZg4cSKKi4shCIIspDUgIIDuz%2BXLl9GqVSsaqurm5kb3l%2BQlpqSkAHAYZjabDRERETID%2BOLFizJPH9EJ1Ol0qKyshKurK%2B7cuYPr169TRlGC5ORk6r1jyX5EUUS7du3QoUMHBXEMALRlKdKrgZazp0GDBg0aNFQPjaDlz0Mz9v4P4JlnnsGZM2dk%2BUWAwzMUHx/vVD7gj%2BK5557D%2BvXrFWFzFouF5tD5%2Bvoq7ktPT8e7776L5ORknD9/HlFRUejUqRP279%2BPRx99FFeuXEHjxo2RkJCAAQMGIDo6Gu3bt8fGjRsRFBREvY9msxkWiwW1atWC2WzG5MmTMXToUNpPx44d8dNPP9Hrf//730hISMDmzZvh7u4OT09PqtVWVFSEgwcP4uTJkyguLsbly5cBAF999RV2796N2rVrUw/M7du3kZeXh7fffhuNGzfG4MGDsXv3buplysjIQI8ePQA4PEIvvPACEhMT8fTTT6Nnz56yUNuq8sWkBoQaL5FUG9FkMsnkFUgflZWVKCkpoQYTkcEwMcI8xDtMwkR5zJp2u10hrUHyIaV1jh07xh3vzJkzsXz5cuTn5%2BOWRDw4KytLNt69e/dCFEU0adIEJSUl1FBmCV2IwXjlyhXZelUl/3D79m1YLBaUlZWhY8eOaNKkCWw2m8wT%2Bcsvv%2BDOnTsQBAHNmjWj5YGBgbBYLLh79y7NTQUcBiT5/cUXX%2BT2ywNPVL1LF46oOvv8OrsGlCrBKlSOVYmqqxBuVhSqUXfm1FEIAPMGyM6LqcMTEVb0xWlXMRzO%2BjkbX43Xhg3T5vTtbHt5zbJ1eCow7FLwovvZOmpEotWMT41%2BtuJGTiWne6fmrHHqKLaBJ53CjqcGz4KavVMIfgMAG57OU6Vny1Q8C%2Bzi8PYbzMtE7r83Z2vD6dvZLbxC7vicCLjXaLycIt6Ss22rEZxXc2ycffZxy9R0zl6TBWU1LjU8UNCMvf8DiI6OxoABAzBu3Dhs2LABKSkpOHr0KMaMGQNRFGVSAfcDgwcPRps2bTBs2DDs2rULaWlpOH78OMaMGYPCwkKF7IAUOp0OJpOJ5stlZWXBZrNh165dsFqtWLduHaZNm4YLFy6gsrISlZWV8PX1xZIlS2h%2BGTFafv31V%2BzatQuZmZkyYyMnJwfZ2dkAgD179qBTp05o1KgRmjZtitjYWFRWVuL69esy48doNHIZKqUeQ0BuRJw8eRKFhYX4%2Beef6dxIHbvdjry8PFRWVmLx4sVo3rw5Zc6USiewkBpX/lV8uNaqVQvt27cHAIwcOZKyV9rtduh0OjpG0gcx9onB9PDDD6O0tBTjx4%2BXeYhtNhvc3NxQu3Ztbr%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%2Bikhlffvn2xd%2B/eGvchhV6vx4svvojt27fjzJkzSEpKwowZMxThm2y77u7uVPT9%2BPHjlEnUy8sLLi4ucHNzwy%2B//IKzZ89i7969mDJlCnx8fNCzZ08UFRVh/vz5aNOmDQRBkOUctmvXDsnJyRAEAa1atarSw9KoUSMkJyejV69eMBgMaNmyJW0DAMaOHQvAIXT%2B3XffUcMpOTkZycnJqFOnDkJDQ3Hw4EH6ExcXB8DBFhoeHo6goCCIogiTyYQGDRqgcePG2LZtG0aPHk3bkpKqSCH1MPEMKovFgtLSUmqI%2BPv7K9acZ5C%2B/vrrMoPJ09NT4bUsKCig4Yx169bFY489JvPi2Ww2rFmzBp9%2B%2BikqKytRq1Yt/PTTTzCbzejfvz9tF5ALj3t4eMiYR0eNGiXrt3379hAEAf3790fjxo1pX3a7Hbm5uRBFkXop33//faxYIdc5BIDg4GA6H6muXlhYGPz9/RX6jXa7XSbHQdaaGJjXrl2DwWDAs88%2BS%2B8RRRFmsxmzZs3Cyy%2B/TPuTvkwhHlcNGjRo0KBBg4a/AzRj7x%2BMLl26oHHjxvQnMjISPXr0kEkJAJDl0HXs2BHTpk2jtPgAsGnTpirD9rp06YJNmzZx/1ZZWYnExEQcOnQIXbt2xe3bt1FSUkLHEx0djdGjR%2BPixYvYuXMnjEYjNm3ahA0bNsBqtcrG3rhxYwwbNgyAw7vE/i0yMhIdO3bEnDlzZKGax44do3UA4MsvvwTgyO2qX78%2B9Qw%2B9thjNJdNp9MhICAAAQEBePPNN6k0hFSbUBAE9O7dG1evXkWrVq1Qv359vPnmmwCAO3fu0P4AyIwuYuzp9XpurqVer5fl2mVlZVGykrKyMpSVlXGlCT755BOZRmBBQYHMQ6XT6WRhoxkZGYiJiaFzioyMBAD06NEDgwcPBuCQ8XjmmWdQVlaGmJgYAKDhs9KxFxYWyrydy5cvlxmRxINpNBqRk5ODoKAg1K1bF15eXnB3d0dTSYjQ3LlzMW7cOIV3LScnhxp5UmbaW7duITs7mxqxZ8%2BeBeBg65SGE5MQULvdju7du8PX1xcuLi4yD6y3tzcEQUCDBg2wZs0aOgZyLkRRRHp6umLtq4JG0KJBgwYNGh4EVOp5cdX/HWievT8Pzdj7h2P69OnUQ7V7926YTCbMmzcPzZs3R3R0NCIjIzFt2jRcunQJNpsNzz33HLKysjB06FCFRpsaJCQkIDo6GtHR0YiKisK0adOoXqDdboevry88PT0xbdo0fPrpp7BarXjuuecgCALNPZSyRUp/zp49KzMy1q9fT%2Be2c%2BdOTJkyBevWraMyDQQknE%2BKgoICvP/%2B%2B5SMJD09Ha1bt0ZqaipSU1PRsmVLzJgxA8eOHaMeu%2Beffx6NGzemxui3334LAPj2228xffp0rF69mrYtNXakhgsxACwWC0JDQxW5fcSzBzgMtIMHD1JjaM6cOYiJiaEeKOm933zzDfU6BQcH491336V/CwoKgtVqpYZaixYtUFpaKjMQicYca2xWVFTAbrcjOTkZAJ9BFIAiDNNPoteTl5cHu92O1atXIysrC8XFxbhx4wby8/NpPiiZs91uh81mU%2BQzStk9AwICqIcRcBhhZI0I2%2BrFixdRUlJCw06l2LBhA3Q6HWUNJSgoKICLiwt%2B%2BOEHmUFJvKw2mw2TJ0/mzp8HHkFLt25KghZnfCy8yGJVCf4qSAqccSio4V5Rw6uiZny8Ok65anh5JmxDvDpMCC83eptpR9G3CuIXLneWGhIcJ2wmNSayqEnfKghk2Gte34pmVBwuNfvCro2aNa8pqVCNmHPuR7u8OjUh7OC1e0%2BvloITNaMYshrSKGfPj/NbHGDC4XlTULStgpyqJp9Rap6pGu0vb4BsZ7zPsftw/silAeplhe43NGPvz0MTVf%2BHw8PDQ6b59uWXX2Ly5MkwGAwYMGAAXn/9dSxatIiyEvr5%2BWH06NHo0aMH4uPj/9CXW8AhhD5lyhQADgMgICBA5s2qVasWnnnmGWzcuBGpqakwm80oKyvDnDlzaLicIAgQRRFbtmxRtE/YMAHQ/DKCsLAwnDx5Ert375axWPI8YeXl5di%2BfXuVRllpaSnWr18PvV6PmTNnAgBeffVVtG7dGkuXLsWxY8cgiiL0ej0qKiqwceNGKvwuCAKmTp2KBQsWAAANIywvL5exQk6ePBlz585FVlYWZs%2BejdmzZ1NjVhRFREREICAgAJGRkfjtt98wcOBA9O7dG23btkW3bt2ovIPZbEabNm1oKGNQUBBKSkookQwxaCMiInDhwgUMHDgQp0%2BfhsVioSGhQUFBKCoqQp06dSCKIq5cuQLgd0OHGNBpaWmKtSTrKUVubi6mTJmCRYsWKeoSIhtRFDF9%2BnR4eXlRTUlWCoKgsrISLi4uqKysRGZmpszQ9fX1xcWLFzF69GiFUc9Dnz59sGHDBtn8SB9Wq1Xh%2BZZi5MiRTtsn4ImqT5gwEU2bykXVnfGxqBGfVkNsoKYdJzwHqu7h9VUjwXRO24q%2BeEQWKoTNWVIKLoED046ibxXELzUWk3fCZnK/BKBV9a2CQIa9ViUcruJwqdkXdm3UrPl9E6hWc9DvR7u8OjUh7OC1W6%2Be/Do21mnXqkijnD0/zm9xgImQ4E1B0bYKcqqafEbdN1F1NeQrbGe8z7H7cP6qSM/X8IBB8%2BxpkCEwMJDm0B0%2BfFime0e070wmE5YtW4bnn3/%2BD7UdHByMxx57rEq9QLPZjKysLFnu4c6dOwE4SEWIXqCLiwtCQkK4eoEhISFo3rx5lWNwcXGhfRJvz8qVK6lnijBlknA6URQhCAL279%2BPRo0aUe05vV4PQRBk44%2BJiUGHDh1oPp7NZoPValUQppjNZlnems1mUxhD3bt3R8%2BePWku2XvvvUeNL7vdDqvVSr1tUuh0Ouj1ekRFRVHiluLiYjRt2pSGJF65cgXDhw9Hv379APyeZ0eYL3ft2gVRFBEVFYX69etDFEXk5uZCEARcu3YNZobGT2oQs4Q4Pj4%2BVOhdulZWqxUNGzaka0g8klLYbDZ8%2B%2B231RL%2BEAQGBqJu3brUOJWGQxKPJTHgqoPBYICXlxcKCwuh1%2BtlxmFUVBTu3r2LadOmyTyTHTp0AOB4efHWW2857YNAI2jRoEGDBg0PAspt/zurT/Ps/Xloxp4GCjaH7tKlS1UaThEREVT37n7Bz88PGRkZVC%2BwoqICX3zxBRo2bIgdO3b8Kb1Au92OI0eOYOvWrXjyySdlf5syZQrNobNYLNRQaNSoESIjI2G325GQkIAuXbqgtLQUdrsdFosFLVu2xNdff42vvvoKAHDq1CmauyiKIl566SVYrVa89NJLsjDDFi1ayAybSZMmAQAGDBhAy6xWK/r06UNzwGJiYrB27Vo8%2BeSTEEURLVu2pONs1aoVgoODUVJSgs8%2B%2BwwVFRX47rvvUFRUBEEQYDKZsGrVKtp2cXExoqOjaS4lG1aalJQEQRBw7tw5REZGIiQkBJ07d4bdbkfbtm0VnlApIYxUOw8AJVgZNGgQBg4cSMMvBUHAK6%2B8Qg3Xw4cP0/lIte2uXr0qa2/%2B/Pn4/vvv0aRJE%2Bh0OmrQ3r17lxq/giAgIiICLPLz87Fs2TJ63aZNGyxZsgTLly/H9OnTIQgCQkJC6BgsFouMCCYoKAhlZWW4du2aTBCeSG/4%2B/tXYcDxoeXsadCgQYMGDRr%2BamhhnP9wzJw5E3PmzAHgCFlzdXWlOXSEKVMN0tLSEB0drSgv5Srj8uHh4QFRFDFkyBC4uLhQT1a9evWg0%2BkwZswY7Nu3D/n5%2BcjPz1f0N2vWLNl1165dZdfEi7R06VIEBwfT8uzsbBgMBplO20MPPYSrV69SwXpiPJF6rVu3hiAIeOutt/Dpp5/S9qV9vfbaa9i7dy%2BSkpLg7e1N5QhYA5oQvKxfv56W7dmzB6%2B%2B%2Bir1th0%2BfFim%2BUakF6REL9HR0TQkNC8vD0ajET4%2BPkhPT8eIESNkfTozKohhTSQpiIf1%2BPHjsnpGoxHl5eWoVasW/P39ZSyc0rZWrlwpK6tOwqK0tBRms5nm0w0fPpz%2BjYQAEw8tyauz2Ww0zNZutyv0%2BQhat26N3bt3A3CQ87Di7ykpKfjiiy%2BokRcUFETz84hkxYEDB7ht3759Gw8//DD1EjtDbGyswiht0KCRsuKuXUC3blVeW62cKJ9z5wCJ0VxUBLCPcmGhXBeqpEQpHVZezkQLXbwo09TitVtZyYT%2BnDoFtGwpr1RQAEjyKu12ZcSRYjxnzgBRUfJK7OQPHAA6daKXubmAQnmGHSBnAZnhKdrljm/PHkD6mcNbUKZv3rxRUSELyeLtL7svijXn3VRaKgtpVLN37Bnh9qUo4Jwbpm/evNlmOM0q2uFWSkkB6tT5/ZrdTM49ivEcPgzcI50iKCtjonvZsfDaZs8%2Bb1/YezIzAfZFKjMH7jPPFnIWWVHEHALueWQfIs76sV1znztne3fnDnDvZRtFcbFcEJJ3IHNyAAkjNa8KDh0CHnmkyna466niYVA84py1YW9jp8QrY5eKd9TYvnnPM1um%2BFzjlLHXpB9jXgagIsrmr4D2DvTPQzP2/uGYOJr%2BxNgAACAASURBVHEiund3kEKwOXTe3t4yIorqEBgYKPMeERCGTLUYMmQIzGYzdu7ciTt37sBgMOD69etYtGgRzdnz8PBQeKsAh2eQGE4A8PHHH8PPzw%2BZmZlYsmQJ6tevj2nTpkGv18PPz09mXBFDT6fToVatWliyZAmmT5%2BOpKQkAI4v8lIcP34cgiAgICAA33zzDQAodNn0ej1mzJiBkSNHwtvbG76%2BvsjJyUFycjKys7Ph5uaGkpISiKIINzc3meECgEo6AMAPP/yAfv36oXnz5jh69CgMBgMsFgv0ej1CQkJgs9mQk5ODiRMn4vHHH0dsbCzc3NyQmZkJT09PLhGJFJGRkSgqKkJKSgrXECT3h4aGwmg00pw9EuaalpYm83Y5g7e3tyw/kbTVqFEjfPbZZ1iwYAE1ytg1DQoKQmFhISVMqaioULBzuru709w/KaTGHtu3zWaDTqfDc889h6NHj2LHjh24fPkyrUNCbYnnmQfWqK4OvJy9iRMmIDJSnrMnM/Q419wcJImhByi/BADKL0Q8jWhFDgojnsxrV/EFnTX0AMU3Dl6ejWI8rKEHKCfPGGRciVEVuU3sFyK2Xe74mJdL3AVVk3PG5N6oSddRlTPFfFtUs3c8kWhFX5ykHsW5YfqucZ6pGpVtqaEHKDdTjWA6Y%2BgBHOF1nuo72zZ79tUkpvEiZpg5cJ95Fcm8iiLmEHDPI/sQcdaP7Zr73DnbO9bQA5RWEe9AMtJDvCoyQ49TSVUeJ6dhxSPOWRv2NnZKvDJ2qXhHje2b9zyzZYrPNU4Ze037%2Bf/sfXlcVNX//nNnHxg2WRRcUExFVBAxK3cRzQ1zwzQrl1LT1DSzcsusXOvTp/xIbplLmjsCoRammKFp4kZuuOWWoCCCDMvMMHN/fwz3dO%2B5B5gWK3/f%2B7xevHTu3HvOueeeuTPv%2B34/z/MPRlxKsPfnoZRx/h%2BHr69vpRy6Zs2a4ezZs8zj1q1bRzh0gPNHOItDp9FosGjRIjRp0gS//vorpk%2BfXqnFQ15eHvbu3Ysvv/wSxcXF6NmzJ1JSUtCuXTt88803SEhIwKJFi%2BDh4YGDBw9K%2Bhk5ciRSU1Oxf/9%2BUqYZGRmJtm3bol%2B/fvjiiy9w7NgxbNq0iXAP%2B1YQzQWDbcCZhbp58yaefvppnDx5kgQkoaGhyMrKkqhN8jyPu3fvYufOnQCAlJQU2Tzl5ubC09OTBHYAkJaWhnbt2pHXDocDTz/9NPr370/4gGJotVq89tpraNy4MQlgBJGa6OhouLu7IycnByNHjsSHH36IgoICtGjRAvfv34fD4cCDBw9kJbDp6en4/vvvibLmmTNn4Ovri9atW5M%2BWbh58yYJ9Nzc3JCUlIRhw4YhJCQEFotFxuerDHSgJ8zDhQsX0Lt3b5Jx4zgOjz32GNnnvffeQ1RUFIqKigA47S446hdKTExMpcbnCxcuJP9XqVTQ6XTQarUk02i322G1WkngLi7jFBRM58%2BfX%2Bn8iIV/qgOr5FNh7ClQoECBgn8b7AGMYFzBIwMl2FNQKYQSRjqTUVxcjHXr1v0uDh1t8TB27FgsXryYKEYuWLAAN27cQFhYGBITExEfH08sHqxW65/i6wFAvXr1MHHiRGzYsAGnT5/GzJkzMX/%2BfADOLBPrR7rD4YBGo4Gfnx8JgppTGRMxhKBD4NABzoxc9%2B7dUV5ejrKyMollQAdRtqBVq1ZYsGABQkJCZIHKiBEj0LNnT5w%2BfRolJSUS64M%2Bffrg4sWL0Ov1qFOnDho1aoRZs2ahsLAQgFMUR1AkNRqNRFTH398fxcXFKC0tJWIjJ06cwE8//YSGDRsSnzsBQhCn0%2BmIiXtJSQn69OmDzZs345cKeW46gygWMqkO4qBNrVaTIFwcWIWFhWHevHnkoURRURG0Wq2E/1ZQUIClS5cCgCxw7tKlC/n/U089heTkZOzatYs8uOjfvz98fHyIeI%2BQ5eQ4jngJHjt2TFLyKwYra6hAgQIFChQo%2BGNQBFr%2BPJQyTgWVIjIyEnFxcRg/fjymTZuGNm3aICcnB5988glUKhXxvXMFtMVD//79kZKSgtTUVNSpUwfr1q1DaGgoGjRoADc3N7i5uWH27NkYPHgwbt26hf/85z9EGMRut8uk9%2B12O8n2COjdu7dECEXI3jz77LMAnP53Q4YMgVqtxtKlS%2BHm5gabzQabzQaVSgWj0QhPT09YrVbCXXzrrbeQmpqKadOm4auvvsL%2B/fuRn5%2BPDh06oEOHDli/fj3hQLZs2RKlpaWE16ZWq9G2bVt8//33KCwsxMSJE/H555%2Bjd%2B/e2LNnDwYNGoQPP/wQAwcOhE6nI3xHQQBGp9Phl19%2BkaiFTp06FeXl5TCbzXj77bdJWaMQ/Ny9e5fsW1paSrz%2BcnNziaBJgwYNUFZWJgnUxOWLwG9BnNVqhVarRUBAAFQqFUJCQuBwOHDy5ElYLBbJ2ACQ0k6NRoPy8nL4%2Bvri3r17kkwnAGIDAYCUpALO0knBdB0ABg8eDIPBIOHnWa1WSaB46tQpvPzyywAgC5zF%2Bx06dEhi1QEAO3fuRO3atUlwLz5%2B69atUKlURJCFhZs3b1b6Hg1FoEWBAgUKFDwKUD9gETEVPCpQMnsKqsTcuXMxbtw4rFu3Dn379sUbb7yB4OBgbNy4kXDo/ig0Gg20Wi0SExMRHh4ODw8PfPHFF2jfvj3at2%2BP2NhYeHl5YerUqejTpw85Licnh%2Bwj/OXk5GDBggWS9leuXInExETyl5SUhIULF4LneTRs2FAi8DJp0iQkJiZi27ZtSEpKQlJSEqxWK4qKiuDm5lYld7FGjRp46qmncPbsWQQEBJD%2BJk6cCF9fXyQlJUGtVqNGjRooLS0lWTchczZ06FAcOnQILVu2RFxcHOx2O7NMUAh2xQEBx3FoKeKF8DwPjuOqVYVs3749xo0bB8DJP3zzzTfJe7QCJg2j0QiNRgOHw4GsrCwcPXqUBEW0fYJOp4NKpSLvC%2BWbQuZO2EecgROfn8lkIvtxHAer1cq8FhaLhdgkVFbCKfRVHUpLS0n2Usgg8jyP1atXw2g0SoJPGjExMdW2L4Blqi7wZyWg%2BKL0a2bSOztb8rJCG0gCWjuJQXGk/YqBihLeqo6RbaMUWlkDYp2DrG/qnACGgTLVV4XrhhT0iVNcW8bwnKIZFGTLkB4fS5zKBTNn%2Bvqyksiyuam4pxCw3J2pC0NdSgDy82ZdOlDZe8bUyMdMNcxMjNPXgWUSTU06k4q8d6/0dYWYFcHVq/Jj6BNnCE3JLhbrQ0UP6Px56WvWdaHXyU8/yfehLjhz/ui%2BGSJYsk1U38x29%2B2TvqbXGus4VkMXL0pf058X1nxWPPQjqPCElYB6wEbfLgHIjeGpGwPz6516oMy6/9C7sM6bvuSu%2BJrTS411jCt%2B6XTfjFudbBv9miwr1ufxb4KS2fvzUDJ7/4exf//%2BavdRqVQYMWJElWbRAwYMwIABAyrtQ7AjEGCz2ZCWloZDhw5h/vz5WL9%2BPSIiIjB79mxER0cTMRSbzYbs7GwkJCRAp9PB09MT7u7uOHr0KBITE7Fx40ZcvnwZ7u7uMJvNmDRpEgCn19uCBQswffp02TkGBwdj%2BvTpaNiwIQAgKysL0dHRhLsoxpkzZxAdHY3atWvj4MGDiIuLw5UrV%2BDm5oZt27ahtLQU69atQ15eHvr06YO3334bXl5epJ1jx44hNjYWJ0%2BeBADUrVsXR48eJe0LQUtISAjJVtlsNnAcJ/Pdo2E0GuFwOGCz2ST8N7q8UKVSQaVSEYsDAXq9nvSjVqslJZsChGwcjdLSUuTm5kqCMqHtmJgY4ucHyA3rHQ4HIiIicPr06Ur3ERRYb9y4AbPZDJVKBbVazSzlFWcSdTodysrKSHaTBfoas6DVaknWs127djh48CAAICgoCPfv38fYsWMl4jkCOI6TlOlWh8pM1UNDKYGW2rWrfM0UF6DEDljPZWjSP4vgL0s%2BiviTlR0j20Z5L7IGxDoHWd8MAQeZoATVF/Ny0CcuU95gzBdDNEMmdkCPj6Wq4IKZM319WfRQ2dzQn1%2BWuzN1YahLCUB%2B3qxLR6tJsPREZGOmGmZSXunrwHowQ006kyJMCxrRIikhIfJj6BN3RQyI9aGiB0QJGjGvC71O2rSR70NdcOb80X27ItBC9c1slxYeYnxXuCLag8aU0jD9eWHNJyW%2BwlSDrFtX8pK%2BXQKQG8NTNwaWcAlElUgAmPcfehdXxIpc8TWnlxrrGFf80um%2BGbc62Tb6NVlWmn8uXPi/Fpg9DCiZPQV/C%2BbMmYPIyEhERkYiPDwcb731FrF4KCoqklg8VMbvO3HiBG7fvo1mzZrhrbfewoULF4gASWlpKVauXEnK/6qCw%2BGQlNDxPC8ZX2RkJG7fvk3eP3r0KG7duoVz587BbrfD4XDg5s2byM/Px/Lly2G32xETE0P89wAnlyw9PR2xsbGSvsWlps899xwiIyMxfvx4rFq1Cq1bt4bJZJIFZgIEUZawsDBotVp4eHigbt26uH79OskEhoWFoVmzZgB%2By56Vl5eT9oTM2yuvvILk5GSivrpo0SLSh1BuKw70xBm7O3fuVFpu2Lt3b8nrKVOmoE6dOkQ5NTAwEJs2bUL9%2BvWZxwtYs2YNWrZsCZVKhebNmxPFUxqNGzfGM888A%2BC34FlPfcOJs6RCCW9lMJlM8PDwINnN9PR08l6/fv1QWloqs2sQ4/vvv6%2ByfTEUU3UFChQoUKBAwcOGEuwp%2BFtQr149OBwOOBwO6HQ68DyPr776CpGRkbhx44Yk0yPw%2B/z9/REYGIj%2B/fuTMklvb2/Y7XZ8/PHHSElJQXJyMr7%2B%2BmsEBATA4XBg3bp11Y5FpVLJbBImTpyIxMRErFixAiUlJSSYKSsrA8/zePLJJ2E0GjF%2B/HgsX74c/fr1A8dxyM/Px4YNGwjHy2w2IzIyEk899RRsNhuGDRuGOXPmED6aIMpiMpng7e2N8vJyjB07Fo0aNcLx48cJF47OqAmKkQ6HA%2BfOncODBw%2BQl5eHa9euoW3btsS77/Lly4RvJwR4Op0OHMfBw8MD/fv3B%2BAUEnFzc8O9e/dQo0YNZGdnE18%2BgQ8pLq0MFD3VdHNzw8CBA6HVamEymaDVakm54yeffCIRkLl48SJat25NLDhu376NsLAwXLt2rcpr1LlzZxw/fhwOhwOZmZkSrz0xLl26hDt37kgCOpq7Kc52bt26lfzfYDCQsk7hX7PZjBo1ajB9%2Bm7evAmO41Cb%2BejY%2BdBA7IVYHRTOngIFChQoeCTgoufyw4BSxvnnoQR7Cv4WxMXFITk5mfwlJSURblufPn1kP9AFCBYPGo0GarUaFosF4eHh6N27t8R6QavV4sUXXyRqk1VBp9ORMj3gt/K74OBgibUC4BQmadKkCdasWYMJEyYgJSUFr7zyChISEsDzPHx9fcHzvOSH%2B7Jly4hdwLx58zBp0iTCO/P19SVm4P7%2B/rBardi8eTMSEhKIkbjD4YAHZc5Tmfoj4BQaOXHiBABnSaS4hNFms8FqtZJsocBrO3/%2BPL766ivY7XZkV3ARLBaLJDMqDjjFPoN%2Bfn7YsWMHbDYbiouL4e3tTbJpV65cIcIyALBr1y7iVfhH4e/vL1EupXHkyJEq50cMIZvWvHlzeHh4YNCgQQCcmU7A%2BSCge/fuuHbtGimBFbBr1y64u7szS14FDB061KVxAGzOXvv2DM7ejRtVvmb601PcFlZVMM3NYO0jm1aKxOXSMTRvCZCT6RjXrzreF3OfnBzJSyani54wxgTK6HYycg7j3GlxHtaapDL2zGtH3wtd4AHJxsvqmyptZvGU6MNYtEPZebswPlcapjex%2BpYNmnWeND%2BLXgSuLFq6DVZXrFJxeif6s8siadLHMNYavY25bqh2WF3JttH8O1bDNAmO8TmUHca6LvQFpc%2BTNZ/0MazfCdR4WNQ/2XWg1gDz64MeH4OrSN9DWXNOLzfW8quO1%2BfKknXlHv9HOHsufrUqoMDzPD766CM8%2BeSTaNOmDRYvXlzlw9wPPvgATZo0kfyJK5pSUlIQExODiIgIvPrqqy5VsYmhBHsKqkR0dLRk8VXmkZeYmIi4uDhERkaiffv2eOutt0gQATgzMCNHjmR68R0%2BfBhnzpxhWjysXbsWV65cwaFDh9C0aVPYbDa0aNEC27ZtQ1xcHFq1aoXIyEjk5uaisLAQASICSU5ODhISEmTnpFarce7cOZw9e5aUUgqcwzuiH8nJyckoKytDcHAwZsyYgQULFuDixYsk2wc41SaHDBmC9evXk4zW4sWLiYjH22%2B/jS%2B//BLu7u6oV2H26%2BXlBZ7nYbFY4OPjg5ycHCxatAiFhYW4c%2BcOVCqVLNgDnHwzDw8PGe%2BMlW0SMm3i7FxJSQlR43Q4HPjss89kfbiCG6IvTp7nYTKZMHbs2Er3F3MKlyxZQv5ft25dZGRkICgoCG%2B%2B%2BaZEMEeMOnXqkGB6zpw5ZNwcx5HspVhls6oSUeEGmZeXh/DwcKSmpgIAaTMsLAwmkwk%2BPj7Q6/USqwaNRgN3d3cMHjxY0qaQFdRoNCTD6gqSk5Mxbdo0yd%2BPhxnWDbRJNPWaaYRMcVtYVCGam8HaR8YDoUhcLh1D85YAOZmOQTipjvfF3KfC8kQAk9NFTxhjAmV0Oxk5h3HuFHeISaKhyDjMa0d/9l3gAcnGy%2Bqb4sCxeEr0YSzaoey8XTFVd6FhV4ykXTFIl/Gz6EXgyqKl22B1xeIU0jvRn11XTNUZa43exlw3VDusrmTb6AdXrIbpSgbG51B2GOu60BeUPk/WfNLHsBzTqfEwdePo6yAr9WccQ4%2BP8ZCPvoey5pxebqzlVx2vz5Ul68o9/o9w9kg/qn8uXHgUM3tr1qxBSkoKli5diiVLluDrr7%2BWcfTFuHLlCqZOnUooTOnp6Rg4cCAAIDMzEzNnzsSECROwZcsWPHjwANOnT/9d41GCPQXVwhWPvAULFmDw4MEyjzxXnj7o9Xo8/vjjGD9%2BPIqLi/HOO%2B8gPDwcrVu3xu3bt/Hjjz9i%2BPDhiIyMhMPhQEZGBubPn49%2B/fph586d2LFjBwwGA7766iuJOIjdbpdx8SIjI1FcXAy9Xo%2BRI0fC398faWlpuHDhAlasWIHRo0fjscceg7e3N/bs2QPgN4%2B5nj17Yh%2BlTGY0GjFkyBA0b96cKDiePXuWBIOAM7Awm83IzMxEWVkZyYI1a9ZMog4ZHh6O%2BPh4/Oc//yGcQXFm6fr16ygqKkJAQAC6deuGVq1aQa/Xg%2Bd5xMXFkf04jiNPkLp37y7LVtLgOE7Gc6MhlGEK3ndiJdYbN24QvtyECRPw9ttvy9oXIFg4BAUF4ebNm%2BQaL168GJkiBbwePXqQAO/mzZuk7Pb69evo2rUr6tSpI5ljATqdTmKnYDKZSMBbs2ZNNGrUCIDzQcC%2BffuQV5FhqlGjBtzc3EjALoxT7JsXEhICjuNkQbKQSS0vL0dGRkalc0hDMVVXoECBAgWPAswWVkT89%2BBRDPbWr1%2BPSZMmoXXr1njyySfxxhtvkIftLFy5cgVhYWGEwuTv709%2Bj2zYsAE9e/ZEv379EBoaisWLF%2BP777//XVZPSrCnoFpUxqFLTU1FRkYG1q1bh/j4eMTFxSE4OBgRERGIj49HeXm5Sxw6wOm7N27cOJjNZpItiomJwfbt25GRkYFp06aRfS9cuIAvvvgCw4YNQ3BwMEJCQuDh4YEuXbpIVBJVKhWxVBD/eXp6wmg0Yu7cubhz5w6Ki4sRFxeHvXv3YvLkySQbmJ6eDo7jSKBhMBiISmZkZCT8/PyIjx7gDC6FwKJv374AgIULF2LKlCmoUaMGrl69ij179pCMZ3JysiSTePHiRZSUlKBbhZocrZIpZC0//PBDLF26FBaLBVarFSqVCo8//jjZJyUlhQQ1bm5u0Gg00Ol0eOmll5CVlYWsrCw0rlBGEzJtHh4eJKj39vaWXZ%2BrFXLlzZs3x549eyTqrIGBgUS0pFGjRkhISCABXnR0NN577z2yr3A%2BCxcuBODM7gnw8fEhgXVmZibJ2uXl5SEtLQ0AsHbtWrRs2RIeHh4S%2BwYh8DMYDJgyZQpps6SkBA0qntIvXLiQlLHqdDoYjUbC9cvNzUVJSQkyMjKQnJwMs9mM0tJSSXluYWEh9Ho9bjH16J24QZcLVQGFs6dAgQIFChT8/4U7d%2B4gOzub/C4DgKioKPz666%2B4y/CqMZvNuHPnTqVVSadPnya6EIDzN1dQUJBE66I6KMGegj8E2iNPvBABZ8Zr2bJlGDZsGPbv34%2BoqKgq2%2BM4DiNGjEDNmjXx/vvv4/Dhw/jf//6HFi1akJLEAQMGICgoCB4eHrKSv/379yM8PBxPPPGEpE3BUkH8J2TLevbsic2bN6NDhw7o27cvtm/fjhdffBF6vR7fffcdvL290blzZwkfLCsrCwAwYsQIbNmyBQBw/PhxREZGIicnB%2BXl5WjYsCFRfZwxYwaWLFmCe/fuwWAwwGg0guM4%2BPj4wM/PD99%2B%2By08K0qTNm3ahOjoaCL17%2Bfnh8OHD2P48OEAnAbpWq0WTz/9NMLDw0kGUZwdrFevHh577DFMnDgRAJCUlISbN2/CarVi3bp1JGP4/vvvg%2BM4fPjhhzAYDCgtLSVBvTuj9u3QoUMAnFms27dv43//%2Bx9579atW/j5558BAK%2B//jouX75Mgq9WrVpJyh4Fe4zRo0cDcHIaPTw8wHEcNBoNyY5qtVoSpPn5%2BRFuHeC0fjh//jzsdrskuydc1/Xr15NtDocDOTk50Ol0aNu2LdkuiN0I11Y4tnnz5oiOjkZRURFUKpXEaP7u3bvw9fWVBJM0WCqqlYHps0fLnEPOmaBfs2g2rvA56I1/hEviCjXHFX4RkxdCHfhXjU9GrGFcM1faqW4fFj/Gha5dMuaSzQXNS3OBA8nixLnCm6PHzOJFysZHTYYra8IV4zDmuqYHRJ8ESwWX5mO5srBZk0N5Gcqurys%2Be6x2XfnQu3IO1Rm6sdql55PBrXNleLJ2du6s9iDZ/LEapuaU9bmTTQXVMJPbS18HF%2B4Truzjyn3MFX88Vz4urnjxVdfOv4Gz97Aye3fv3sXZs2clf6xg7PdCELkT04oEW6YcilcOOLN6HMdh%2BfLl6NixI/r27Yudos/H3bt3JW0BTv0HVluVQQn2FPwu2Gw2pKam4tChQ%2BjatSsuXLhQKU8pLCxMtkD/LMxmM4qKipj8vs2bN7tkmk2jT58%2B2Ldvn%2BSH%2BjfffINevXph6NChOHDgAOGdCZkXjuNIVs5qtUoyMmLxF57nJQGJ8H%2BTyQSLxYLs7GwSUGRlZcFkMmHDhg2k/ejoaEm5X3l5OSwWi8SH7zGRYZaQ6RK82sTjKi8vJ1lFITt28%2BZNtGnTBsXFxRK7CY5JCnGO8cUXX5SVUAqv7Xa7RI1TnJkUq4zSxukmkwkFBQVECOb69esoKChA165dodfrqxRFESAEb2JeIOBU5ywvL5dlzViBmcFggMlkIhlHMXdSrVbDZDLJSpOFhxF6vZ5til4JWJy9fRUZTDFobgb9mnWpXOFz0Bv/CJfEFWqOK/wiJmeGOvCvGp%2BMWMMg2rjSTnX7sPgxLnTtkjGXbC7oBzQucCBZnDhXeHP0mFm8SNn4qMlwZU24YhzGXNf0gOiTYGTUZXwsVxY2a3IoxULZ9XXFZ4/VrisfelfOoTpDN1a79HwyvmNdGZ6snQpl6KoOks0fq2FqTlmfO9lUUA0zub30dXDhPuHKPq7cx1zxx3Pl4%2BKKF1917QivTfp/QdT3F2PLli3EJ1r4Ex7iV4eysjJcv36d%2BSf8ZhP/HhX%2Bz/IAvnr1KjiOQ0hICFauXIm4uDjMnj0be/fuJX3Rv22r8hNmQTFVV1At5syZg/fffx%2BAc9EZDAbikRcfHy/xyKsKt2/fZopwlDJl19gwm82IiorC%2BPHjMW3aNLRs2RIDBgyAzWYDz/NYs2YNNm7cCIfDAbvdjunTp0uIrMIHRFyqGBMTg3feeQfHjh3Dk08%2BSTzyJkyYgObNm2Po0KHYvHkz7HY7du/eDQCYPHkyHA4HOI5DaGgokpOT0adPHxiNRphFT3fr1asnCf5ee%2B01fPnll%2BA4Dt26dcOiRYsQGhqKq1evYvPmzejVqxeOHTsGjuPA8zyys7Oxbds2crzRaER5ebnkQ37mzBmiQnrs2DE0b96cPEV64okncOLECWg0GpjNZowcOZIEJ8INafLkyThy5AjWr1%2BP4OBgmM1m6HS6So3dK/MBNBgMKCsrg6enJ0pKSsDzPObPn09EUJ555hnytEoIJocPHw6e56HRaGC32yUcxdzcXBw%2BfBhlZWVYvXq1pC%2BxmboYFotFIkojwOFwoGnTpujTpw/ZJj4H4f8ZGRlYvnw5NBoNDAYD4uLi8MUXXwBwZhvLysok9g3iYy0WC7766iu8/vrrzHmjoXD2FChQoEDBIwGrtZKnZw8fD4vd8OyzzyI6OlqyzZ8lkMTA6dOnK7WEEmhHVquV0HyE32xGxsOcfv36oUuXLuR3aWhoKK5du4ZNmzahW7du0Ov1ssDOarUy26oMSmZPQbUQ897S0tIkHDpvb28izlEdAgICZPy5xMTE35X98/LyQpcuXTBu3DisW7cOAwcOJPy%2BTz/9FA6HAytXrsSkSZOgUqnw8ssvY82aNeRv69atpGxSgMlkQufOnUlQ8t1336FOnTpo3rw5AGDWrFlo2bIlvLy8SCDB8zxeeuklfP3112jatCmCg4OhVqtlQdCkSZMwadIkBAQEwMvLC97e3qSNPn364Pz58%2Bjbty86d%2B6MzMxMHDx4EOXl5RLfODFKSkoQEBBAgk6j0YjnnnsOjz/%2BOAmU7HY7nnzySQBODp2vry9qVagUzp8/n8y7UDIbFhaGGjVqwGw2Y9q0aSgsLITFYoFarcbu3buh0WjwwQcfkCdLzZs3R02R2iPHcahXrx4Zq9YItgAAIABJREFUc15eHsn0zZ49mwjEGESPJ%2BfNmwcAxN6ga9eueP755wmXEAA%2B%2BugjJCUlydaHIEojPl6Aw%2BFAYGAgxo8fT/YVbBv8/PwIl1G4huIMpsFgQPPmzTFkyBAAziy2oJ4l5gaKhWTE4DhOUkasQIECBQoUKPhzeFhlnAEBAWjWrJnkz9Xfo0888QTRQKD/YmNjAfxWzin%2BPyuY5DhOppUQEhJCqsdq1qxJxOQE5OXluRyYAkqwp8AFiHlvtWrVIpkhwKkoefbsWeZxgkeeAI1Gw7ReEGdi9u/fT2wQWAgPD8epU6cwYsQIfP311zh9%2BjTh9zVr1gwAULt2bYwePRqBgYFo2LAh2rZtS/6aNm2K6dOny3hpsbGx%2BO6778DzPPbs2SPJAAFO%2Bf/OnTvjxIkTGD58OLRaLQ4cOIArV64gOzsbmZmZaNy4McxmsyRQ8/X1xZgxY3Dw4EFoNBocP34cN27cgNlsRrt27ZCVlYURI0ZgxIgRsNvtmDlzJgBnlkin06F169ZYtmwZ1Go1evXqBZ1Oh5ycHPTq1QuAMyu6adMmXLx4kZTT8jyP8xXeZj/%2B%2BCOKiopw%2BfJlAMDUqVPRvXt3dO/eHXfu3MHs2bPRpEkTOBwOtGnTRlKeabfb0a9fPzgcDqxZs4Y8Wfr000/x2WefEUVOnudRUFBA7B/EHMfFixcjOTkZALB582ay/a233iL/HzZsGO7fvy8r1WzYsCFZH10ruGxBQUGSa2cymZCZmYm1a9eC4zioVCpcvHiRKGZaLBYcPnwYADBkyBAScArzV6NGDRLIlZWVweFwwNvbG8XFxbDZbJLgvrS0FCqVCnUo%2BwEBPM9j1KhRknLYqqAItChQoECBgkcCv9PX7f8yatasiaCgIAnd6Pjx4wgKCmIGk59%2B%2BqlE9A5wChGGhIQAACIiIiRtZWdnIzs7GxERES6PSQn2FPwpxMbGIjMzk8mhW7du3e8SrHAFQ4YMwYEDB5gB5h3KSPr3oFOnTigpKcGRI0fw448/yoI9MaZMmYLQ0FAUFBRg0aJF6NWrF0aMGEH4ZmIOnQCz2Yz8/Hxs375dJvwBODl0KpUK90WOsN7e3qhTpw5ee%2B018DyP3bt3w2q1SszOAWcQ3aBBA9y%2BfRtqtRoajYYIyVy6dKnazOu2bdtQXFyMH3/8UfaewHW7cuUKAKBp06a4cuUKBg8ejCKRwW1JSQnxFhTjwYMHZA2IA0khqNFoNOjSpQuOHTsmGadGo5EoU927dw%2BAk9xcVFQEnU4HjuNgNpsxfvx4tGnTRtKuGHa7HRzHYcWKFWR%2BBYuOgoICSRnyxYsX4XA4cO/ePTRp0kQSuPr6%2BiIrKwvt2rUj21QqFdRqNXkA0q5du2qtLgQwBVpYnD9RGTDrNfMjRkkys4yG6WXB8iuWCRdQ15jVrqw6lWFQLTMsZqgAyJYtQ2ZaptdA9cW8JdBl4wz1A9rznTZrBxjnTo%2BPNaHUeTKFLKh2XNEKkYlmsErjqQFfuiTfhdb4YM4fNV%2BsNSCbUvpi/kFRHLozlnAFKh4uEdCKdaz1SJ8E9X0GMIbsyuI/dUr62hWFjJ9%2BqnZ8zAp7FwRFZAtO5IULgG1sXlHxQiD7cLgmVAJa%2BIL%2BfLDM5OkFyJKar3iQWdUussVOnQOlq%2BME/ZlnNCwbMmNBuqC39IdM1ekpdkX45Y%2BIMpG5UXz2fheGDh2Kjz76CEePHsXRo0fxn//8R1L2mZ%2BfT34HCr%2BBVq9ejRs3buCrr75CYmIiRo0aRdpKSkrCtm3bcOHCBbz55pvo3LmzRM28OnA8y6xKgYIKREdHY8KECVVm22bPno3U1FRMmzYNbdq0wdChQyUpZ41GAx8fH1itVvwk%2BiJLTEzExo0b8fPPP8NkMqFr166YPHkyAgMDAQAJCQlYunQp9u/fL%2Bnvgw8%2BwIYNG9CnTx%2BMHz8ePM/ju%2B%2B%2Bw4oVKxAYGIgtW7bAZDLJxn7z5k3ExMSgZcuWyM3NRdOmTUnWB3BmgcTBqUajQUhICPr374%2BsrCxYLBbMnDkT8%2BbNIx58YphMJnTs2BH79u1DbGwstm/fDj8/P0ybNg3z5s2rMuhq1KgRLrF%2BfeE3y4eqPqpqtRqenp6SYJG1D8dxkmBRpVIhNTUV/fv3h0qlQoMGDZCTk4OBAwfi3LlzOHz4MOHucRyHtm3b4vDhw%2BB5npQQPHjwgOyj0WhkwajAP9Tr9ejdu7fE6N7HxwelpaUoKyuDl5cXAgICcOnSJahUKpKFE3Pb1Go16tSpA6PRiAsXLpCsnNhbEHBKE2fTP2QYMBgMcDgcknr4LVu24LPPPsOvv/4Ki8VCvGyGDBmCzZs3o1WrVjhx4gSzvVmzZhFPwuqwcOFCmcnqpIkT8eqECS4dr0CBAgUKFPwtKC1liwf9DagoePrLUcEmeSiw2%2B1YvHgxEhISoFarMWjQIEydOlViS9W/f3%2BinP7dd99hyZIluHbtGmrXro0pU6ZIHv4mJCRgyZIlKCwsRLt27fD%2B%2B%2B9L/I6rg5LZU/CnMXfuXMKh69u3L/Lz8xEeHo6dO3cSI/ZOnTqhsLCQacQeEBCA559/3mUj9lmzZsHb2xtnzpzBs88%2Bi4EDB0o88ioTjNm9ezfq1auHc%2BfOweFwYM6cORLuYP8KdbAWLVpg%2BfLlSExMxOjRo7F27VpkZmZiz549aN%2B%2BvSzQi4qKQkJCAlq2bIn09HSsWrUKZrMZHh4eyMvLw7Jly/D0008DkJJzR40ahfT0dHz44Ye4dOkSyQimp6cjICCAcNHKysoIv8zNzQ1eXl5Qq9UICQnBtm3b8PLLL8Pd3Z0Eev369ZOVCqhUKhJ0ifHEE0%2BQ4FowTBcCquXLlyMjI4OMmed5HDt2DCaTCQaDAUFBQcjPz4e3tzdatWqFvn37Ys%2BePZIyX%2BE4IWClSxfDw8Oxdu1aPPXUUyguLsalS5dgMBiwatUqcl3EKlSDBg3CmjVr4O3tjYYNGxK1U57nJXM7b948dOjQQdZfkyZNJK8tFoskOO3cuTNCQ0Ph7%2B%2BPa9euSTz1ysrK4ObmVilnD4DE3L46KAItChQoUKDgkQDLsuRvwqOY2VOr1Zg%2BfTqOHTuGI0eO4I033pBoBOzfv58EeoBTKDA5OZn81qSrfAYMGIADBw7g5MmTWLp06e8K9AAls6fgIaCybOBLL70EvV6PUaNG4fnnn8eGDRsk/nylpaXo0aMH%2BvXrhylTplSa2auqj6oQGxuLQYMGIT4%2BHm%2B//bbk2Ly8PHTv3h2vvPIKxowZIzkuIyMDL7zwAtLT0%2BHr64u3334bwG/G4ALu3LmDjh07Ij4%2BHjExMZLxb9y4EfPmzUN4eDhOnjwJwGkovnfvXkyaNAkHDhxAv379sHXrVoSHh%2BPnn3%2BGRqNBo0aNiDWEt7c3Lly4gNu3b0uCNo7joNfrUVZWBr1ej8zMTLz11ltITEwkJYahoaFVBimVQVCB4nkeWq0W3t7eEtKxSqUCz/Nwd3fH%2B%2B%2B/j169eqFt27ak7FKMWrVqQaVSobCwUFbGKs7%2BPfvss4S7mJeXJymbpI8BAE9PTxQWFqJhw4ak3FRQ9xTGJvS3fft2iWcfCxMmTIC3tzc%2B%2BOADyXZ/f3/o9XoMGjQIn376KTPTOnjwYKJcWx1Ymb1XX32VeBEqUKBAgQIF/wrcuwf4%2Bv4jXYso/n8pFi16OO3%2BG6Fk9hT8bfg9Rux/NS5fvoyLFy/iiSeeQIcOHSSGlQCwZ88eaDQajBw5UnZs69at8e2338K3mhudIMzCkv7fuXMn7Ha7pLTw5s2bOH78OE6dOoXy8nKcquB3CCWwNpsN58%2Bfx%2BLFi3HixAmSgWJ53JVVcAWCg4Oxbt06nDt3jrxvtVor9c0TUNn74vLRWrVqISkpiRifq9VqOBwO8DwPs9mMKVOmYMKECTKeZr169dChQwc8ePAAubm5Eu86oR0hi9m1a1e8%2Buqr5L3KxH%2BE8%2BY4DoUVRCNxRre8vJyMWwj09Hq9S0/D7t%2B/j379%2BgGQ%2Buzl5ubCy8sLDoeD1NIDv/H2AGftvatgcvYquIcS0OWoFdxQAUzOnigjCYBJFJHxVFzhE9G8FRfMu1l8Nxk5jJHNl/FLGGW5sr6ofZjcpuqMpcGYG0Z5tKwqmz5P1pNwV9ynaZ6SC6bqsqlh8QWpOXfJfJpRek7PjUucQvo8GdebXlpMRx4XDL5pXqnsOrBOnD4pxlqTjYe1uOjJoPmBrOtdHY8OkJ23KwbarL5km%2Bh1wiKvUfcb1oVx4SMlP84VYtpXX0lfs/iM1BplVu/TG6k1wLxP0J95xmdBdh0YvD5XuNH0PvQSZY2PPobF4KD7om%2B7rG2yfYSTVMTDHmkomT0FfznCwsJk3LcaNWogLy8PixYtwvr16xEREYEWLVpg48aNuHz5Mtzd3aHRaHD//n3yw1/g0AmleHPnzkXfvn1JH/379ycS/tXhk08%2BQUJCAu7cuYOoqCicOHEC%2B/btIwqSU6dORWFhIUaPHo3Vq1cjMzMTxcXFqF%2B/Pvr3748RI0aQcXXr1g03btyQtG80GmEwGGC325GWlgaTyYSZM2di586d2LdvHzp37vyn5hRwlq%2BuWbOGCMGIIc7uBQYGwmQyEQ6gSqXCe%2B%2B9h86dO2PVqlVYv349CYQCAwPx3//%2BF8nJyfiq4otVsDYQgsSQkBCSMasOarWa%2BNEJUKlU0Ol0pAzS19eX8OBYCAkJQU7FD2exp6DgrafRaKDRaCR9AE5lzepKgOvUqYNbt25JAlUaXbp0wfDhwzFixAh4e3ujoKAAgLPkNCcnB3FxcVi2bBnxWRS3sWfPHqKgVR1Ymb2JEydhwoRXKzlCgQIFChQo%2BPths/1jNnuocPr6y/Hhhw%2Bn3X8jlMyegocCtVoNo9EIo9EIu92OgoIC8DwPh8OBoqIiZGZmEs5eYmIi4uPjUadOHXh7e2Pt2rVITExEt27dADgVFh0OB9555x1ERkYiMjLyd6t87t69G4GBgYSzJ2QYBRQUFODIkSN48cUX8f333%2BP%2B/fuwWq24ePEiFi1ahBYtWiA5ORklJSUkUBHOz2AwwGq1wmKx4MGDB4iKikKTJk2wfft22O12EqCyYDQa4enpKbFrEIJKgWcnQKwMaTAYJBYE4uxedna2ROxFo9Hg6NGj%2BOmnn7Bnzx5ZCWhkZCRq1KgBrVYLlUqFpKQkTJs2DR4eHujZsyeuXr1K2qlZsyYJvlWUOpdWq5WMQ4DD4UBZWRmxRhBnNwVVTTEGDRqEkpISzJ07F%2B%2B88w7ZHhQUhLp168oEVYR2xIGemKvHcRzJyjZu3BgeHh7o0KEDeJ7HG2%2B8AUDK5cvPz8fp06ehUqlIoAc45Y8LCwvx7bffEjEYOlhcuXIlXAWLs6ew9hQoUKBAwb8N2nJWql3BowIl2FPwUNCtWzckJSUhKSkJBw4cwKlTp9CuXTukpqZCo9EgMzMT8fHxiIuLQ3BwMCIiIrBq1SoATuJqcHAwTCYTVCoVkpOTkZycjKSkJCLaQQcaVSEzMxPXr19HXl4enn/%2Beeh0OoSEhCApKYnso9frYbPZ0KVLF6SnpyM9PR0JCQnYvHkz3n//fZSXl6OgoAD79%2B8nJXvLly/Hjh07MHLkSHh6epIS0Pnz5yM9PR19%2BvSBSqWq1CAdcPIU69atKxEJEYRGYmJiSMkk4BRuEVBWVob9%2B/fjtddek5WNrlu3Dj179iSveZ5HamoqZs6cCT8/P8m%2B2dnZEssFjuPQoEEDvPjiixgzZgy%2B/fZbUh7p5%2BcHlUpFghTBk04A/VoMg8EAnudRXFwsOVdvb28EBgZizJgxGDp0KADgyy%2B/BOD0qunatSuZ719//RVjx47F3r17MWPGDEn7NWrUkBiy165dG506dUK7du3A8zzu378PPz8/vPrqq4iJicH3338PAMQzb/bs2WjZsiUCAwPRoUMHZGRkwOFwSMRm9uzZA4vFQoJfGmq1WmLarkCBAgUKFCj4c3gUBVr%2BbZCTixQo%2BAtgMpkQHBws2SZw9nieh5ubG5Oz16tXL5hFvAGO42TtCNtdxa5duwAAt27dwsKFC8HzPOF5HT9%2BHFFRUSR4TEtLQ/v27SXHt2rVCjVr1oTJZEJSUhLKy8vBcRyOHTuGiRMnYvLkycjPzydZHToQEYuVtGjRAm5ubjh69CiA34IgnudhMplgNptJ1mjfvn1VnleHDh1gt9tlWc7hw4eT/wsCKhaLBbGxsdi7dy95T6PRwMPDA9OnT8czzzwDwCkXHBkZCcAZJNrtdsJ5EzKXXl5eZP7EmS%2B73Y6goCAUFBSQcwgJCcHVq1clWUlx6eP9%2B/cREBCABg0aYMCAATCZTEhLSwMAnD9/Hk888QRatGiBU6dOged5zJo1SzYPHMchLS0N06dPx8WLF8k8HzlyBHfv3gXHcUTBdMeOHUhOTkZ4eDhOnz6Nb7/9FgBw7do13Lt3D/n5%2BRg4cCA2bdoEvV4vmdt79%2B5Bp9Nh586deOaZZyRBq3Dtfo/JqWKqrkCBAgUKFFQN5Wvxz0MJ9hQ8dNhsNqSlpeHQoUOYP38%2BLl26hJKSEhw/fhzTpk2TcdC8vLxkZpGCJ5/A73M4HCTDdOvWLXTt2rXS/rVaLRo3boyCggKsXr0aP/zwAz788EMYjUYkJiYiKioKpRR5XKVSwWQyISIiAqNHj8akSZNQWlqKQ4cOAXAGQtu3b8eECRPAcRxeeeUVbNmyBYBTjMXf3x8LFy5ESkqKpN2ff/5Z8rqsrIyYkQtBrjAWOogzmUySuaJLGVkQSmAB4Ouvv5ZkAR0OB7Fr%2BOKLL0jwIuwv9O9wOKDT6XD//n1oNBoUFhYiNDQUt2/flnkHXrt2TfK6UaNGuHr1qsRYnRWoHz9%2BHEuXLsWQIUMk2UwAErEZFnieR1ZWlsTUXezlBziDSk9PT%2BzatQs9evQgpuqCjcbs2bOhVquxYsUKBAUFEXN68TUwGAwwGo1QqVSSQE%2Bn05FrZ2M6QbPRt29fhIWFSbY1ZmUGeR4Qzxn1mn4bgFMhQZSVtFgAvZ7ahyJhUIewj6M6Y7ZL7cNql97Gakdm68QijZSUAOKgmdqHyTOhO2N0Xt15A04RhQorSPY%2BrM7pbax9qBNn2VvR8ycbHqNdV7qm22XOHzUgV9aW7DVrUbgwQHrOWc3IjqMPYrQrmz/G9XZlH3ouXDlGNh7GPq5cuz9yDq6068r4/pLry%2BjcbAbETkrFxYCIveB639WdOOMY2XD%2B4Gfqj9wPq7vnM7cxGv4jzVQ6VZKbnYJHDYpAi4K/HGFhYcSzDXAGNAaDAc899xymTZuGp59%2BGkajEdnZ2SgsLJTxnnx9fZGfn4/WrVvj2LFjxKibFsMwGo3Yv38/zGYz4fdVhlq1aqFbt26YNWsWiouL8dRTT6Fx48a4du0aDh06hGHDhkkCMY7jZEbdXbt2RVpammSbXq%2BHSqWC1WolgcGQIUMwd%2B5czJw5E9u3b0dERAROnz5d6di2bt2K559/vtrgrXHjxiRz9UchZOV0Oh2sVisRPRFDr9eD4zjY7XYSvFQlaFIVHn/8cRw7dkyyTWy8rlar4evri4CAAJw9exaenp4YPHgwVq1aBa1WC61Wy%2BS2qdVqMt8cx2HcuHFYs2aNLGiv7BgWtFotgoODsWvXLjRv3pxkXIVj6tSpA7vdjsGDB%2BPTTz9ltjFz5ky8%2BOKLVU9KBRSBFgUKFChQ8EjgHzRVf%2B21h9NuJV/j/19C4ewpeCiIiYkh/Lq0tDRkZGRgWoWkkre3N1q2bIlx48aB53moVCr4%2BfmhV69e2LZtG6ZNmwae53Hp0iVwHIfy8nK4u7tj8uTJWLNmDfmhXVZWhuXLl6N27dqEZ5eeng61Wo0%2BffogPT0dffv2Re3atZGTk4OYmBgAgLu7O9q2bYu8vDwUFRXhu%2B%2B%2BIyV1goiIICYjbNPr9bhy5QoJdgS7gI4dO2LEiBESXt7mzZvRsmVLYu8gVp5kZbU2b96Mpk2bktfPPfccAKBPnz7w9fXFU089BQC4ePEiPvnkE6hUKpLJdHNzY/IXBbNOT09PGCqeyGm1WrRv3x4mk4kEcUKJbOPGjQlXcOHChUhOTsauXbsID1Gv1yM6Ohp62SN8J4zUl4CXl5fsvdGjR4PjOIwZMwaenp4AnNnDu3fv4syZM%2BB5HqWlpdiwYQMApy%2BjWESnR48eaNGiBdzd3TFo0CByzXiex8qVK0lGUKVSEWsEg8GAqKgorF27VhIcCt59gs0Hx3GoX78%2BVq5ciV9%2B%2BQU2mw3l5eVErRVwWi94e3tXaQfB8hesDIpAiwIFChQoUKDgYUMJ9hT85ahVqxY6deqE4OBgBAcHo1atWhKhi2bNmuHcuXMYMWIE1Go1BgwYgEOHDuG///0vTp48iatXr8LHx4eUDwJOlcNXXnkFbdu2RY8ePRAYGEjKOdVqNfz9/cmf3W5HSkoK2rdvj%2BTkZNy%2BfRsAMGLECISFhSEsLAwHDhxAdnY2GjZsiFmzZhHD8fDwcHzzzTdYv349Ga/Aebt27RoJ9oSM2A8//ICxY8fKgrgVK1Zg2LBhJEMGOANEg8FAvN4EMY9vvvlGUrYqWCAcPnwYzZo1IwIqn3/%2BOSZPngyHw0H4fCUlJUyel2Dc/uDBA1gqTHqWLl0Kd3d3%2BPj4kPP4pcIH6uLFiyQrdvDgQXLtpk2bhuDgYJSUlODu3bsYO3Ys6cNkMpHzFo4VXgtCMHl5eeS9VatWged57NixA20q/OToeRMCbQBYvXo18bsT%2BjObzSgpKUFQUBDq169P3uvSpQsJ9hwOB7744gsAzgcCx48fx4gRI8i%2BglAM4Ay0AWfQ6%2Bfnh9q1axMlTgASiw2LxYLQ0FD4%2BvoyA2yTyYSWLVvKtlcGhbOnQIECBQoeCVRja/QwoQi0/HkowZ6Cvx2xsbHIzMzE8ePHJduLi4uxdu1aXLlyBQUFBfD09ITVaoWfn59MzEWlUmHkyJEYM2ZMtf3xPE8yUpMmTUJiYiJiY2MBADk5OXA4HOTH%2B%2BnTp/HMM89g9OjRVbYpZMbKysqwadMmlJaWSgLaUaNGYfPmzRIxGK1WC47jSLmmEISWlJTg%2BvXrsj7y8/Nx8OBBUlJaXdZInIU6ceKE7P25c%2BciLS2tWmNxMWdOrVZjwYIFAIAzZ84gNzeXmJ6bzWamwTsAwgUsYjjIPnjwgAjU0MeXlZUR6waHwyGzMigqKgLP84iIiMD169eJmM758%2BeRm5tb5XmJDdIFA3shuLp16xZZY3fv3gXgzEiKg3CxUikrKDObzZWqkbLANFVnlCPTZrn0a1ZlrWzaWSXCly9LX%2BflyXahv9/pvplmxBUPTio7BpCbBldMeZVts9qhz0t23pQfJiD3jWYmWCselgigvbwByCaeHh/Ln5o%2Bb1ZVscg1xQnWJFeoyRLQa59hHC6rcKbNxwGgQhyJQKTUW2nb9DoCZG7TtN8801m6OtNtALIqdkY79CWn/bQZ3tiya8cy5qavJ2tNVGd2z7qU9PpjrRv6NFlrlj6OWbFOrVnZmmDcTOh7AMuTXnYY49rRtxd6F1YFPm3wzbousvtCcrJsH/rjQVHLgR9%2BkB1Dj%2B/gQXnfEAmeAexzkE2gK67q9D6sRUtfCFbn9EmwbqLVfMEI3fxirY1/Ckqw9%2BehcPYU/OWIjo7GhAkTMGDAgEr3mT17NlJTU1FYWAiVSgWNRgObzUayeeXl5Rg0aBC2bt2K5s2bY8eOHcw%2B9u3bh8OHD0veE8rjjEYj4uLisH79ekyYMAFLly5FWFgYZs6ciWHDhhFfuR07duD48eOYM2cOAKBhw4Ykg3bhwgV8%2BeWXRIDj5Zdfxueff46aNWvi8ccfx65du1CzZk3k5OTgySefxJEjR6BSqeDu7o6%2Bffti48aNZFyCGbjFYpGYmmdnZ5N/Aci4iRzHwdvbG4899hiOHTsGb29v2O12FBUVoV%2B/fhg2bBh%2B/fVXvPfeezJTcbqtxx57DD169MDSpUsrvTZGo5Fk3MrLy1GzZk3cunWLtLNq1Sq4ublh0aJFJCNaGXx8fHD//n00aNAAv/zyC/R6PQYMGIDvv/%2BeBLvi/QQO28iRI0lJrTgzKv6/IKJSUlKCWrVq4f79%2BySLKYZGo0HLli2RlZXFDD7FcHNzQ7169XDhwgU0bdoUBoOBZEkBp63FkiVLZA8qBMTGxuKjjz6qsg8BCmdPgQIFChQ8Crh5E6B08/42vPqQvhLj4x9Ou/9GKJk9Bf8I5s6dSzh7drsdFouF/LjXaDQYNWqUxAKgMsyZM4dwA4U/wJnlSkpKQnFxMZo0aYKJEyfCx8cHeXl5%2BPzzzwEA77//PiwWCy5evIjHHnsMAEjQ%2Bd577%2BGNN97Anj17UK9ePdKf8OP8zp072L17N3ieR05ODrRaLTp16gTAmcErKiqSBDPAb1kkceni/fv3oVKpkCN69C1kIQXlTI7jMHXqVCJ0Eh8fj%2BSKp5d79%2B7Fc889h0WLFsHf31%2BSXRTGIt7m4%2BODfv36VelT2L17dyQmJqJHjx4wGAwYPnw4YmJiEBUVBb1ej1deeQXjx4%2BvNNDT6/WEpydk%2BIRyUYvFgk2bNmHIkCHMuXn11Vfx5ptvAgDq16%2BP6Ohoso9YXMVut8NqtZLAXqvVIj4%2BXiIMBDhFVYYPH46MjAwAv/EHab9BwJktnjlzJimvPX/%2BPMkACmjTpk2VAa7AC3UFCmdPgQIFChQ8CggI%2BOf6VjJ7fx5KsKfgL8f%2B/furzOoBzh/WAmevR48eSE1NxZ49eyRiLgMGDICfn58kEBL3UVRUhC%2B//JLwy4Q/4Ddlxfnz52PHjh1ITU1FQUEBatWqhVOnTsHPzw/9%2B/dHVlYWevfuTdoNDw/Hxo0bkZ6ejjNnzuDbb78lypG%2Bvr7E0F0wjQ%2BouAPabDYsXryY/B/4zWahc%2BfOAJwBzbvvvouXX34ZgDM7VVZWBjc3NxLQhoeHk0AuOjrkWqCRAAAgAElEQVQaHMdh2LBhiIuLk2QJg4KCADjLUs%2BcOYMDBw6gRYsWkjny8/ND9%2B7d8dJLLwEAevfujfLycsTGxsLhcECr1aJXr17w9/fHJ598gg4dOgBwmrfn5uZi586dWLZsGV544QUsXboUM2fOhMVigU6nk1kuiGGxWEhGUECtWrXQp08fGI1GvP766xIvQACk1NXhcJC2f/nlF5nXoBAo%2B/r6omfPnggMDIRWq8V3332HDh06wN3dHTabjQjm3LlzB3v37kXt2rVhsVhIUJ/HKFl0OByYOXMm3Cu0vcX8QQFFRUWoVatWpefesGHDSt9ToECBAgUKFCj4u6EEewr%2BcQgG7Cwxl1atWuHevXtMfl98fDyOHDnCbPP69euIjIxEZGQkWrRogalTp4LjOLzwwgsoLi6u9Af7448/TgI4wOllJ2ToGjVqhMaNGxOT7saNG5OARuDViZGXl0csJ4S2%2B/fvT3hmQsawfv36JNN25swZIkqSmpoKjUZDgjUB4qznRx99hMjISLzzzjuIioqSnFdhYSFSUlKwcuVKaLVa7N27F71798bHH38MwBmU7t69Gw8ePMCMGTPIHFutViQmJiI8PFzClWzWrBk6d%2B5MgqGq4OfnR7hvgJMbmZKSgtLSUnz88cdYsmQJ87iysjJJKaaQ8RPWhBB83bt3D3v27EF2djbhEAK/mdQLAffixYvx7rvvorCwEAaDgRnkiTFmzBgEBgYCcAbUs2fPlrz/4MEDcq2ETLQAjuNIVtAVKAItChQoUKDgUYBeU7lt0cOGktn781CCPQX/aggS%2BmPGjMH27dtx48YN/PTTTxg1ahTMZjPy8/NJUCf8Ac6goKSkBDabDRzHkdLQvn37ynhsYhQXFyM3Nxd37tzByZMnMWbMGKLceOTIETRp0gR2ux07duxAjx49kJWVBQB44YUX8MEHHwBw8uKEIMBisRDBkWPHjuHdd9/F5MmTATizTgDQrl078iPf4XBIyhU5jsP69esJZ1A4N6FNm82GkpISbNmyBXPmzCEBVvfu3REaGkqOsdlssFqt%2BOCDDzBu3DiyXShbLSkpIWWFRUVF2LZtGzIzMyWZxlu3buHAgQPIy8uTzV9MTIzEbuHBgweSffz9/REbG4svvvgCs2bNwtatW5nz7%2B/vj8GDB8u20/54Op0OjRs3RnR0NNasWYOIiAjMmDGDCOUImb0pU6bgpZdeIl6M95kqH79h5cqVJID39PSUeC8CThsNIQvp7u4uOUdxkOkKmAItFTYbYtC8exkPn6HEIBNRENl/CJCJHbAI/hXltwJklafU%2B4BciIHVrGzbr7/Kd6L4lUwxGGq%2BZZRMhooGPV0sIQtaU4EpvEC1Te/DSn670rdMZ4hxDvS1c0U4RzYeBn%2BVvnasdmRLiSX%2BQ3VG325dEdpgCoxQB7LaoUU96PGyxIDozug2AMguMEtIhZ4/es5Z4iaurBvZBDIWpOwrjfUdR22TzTHj/kXPMesc6HZZt0H63uGKvgjdkCtiVKzneXTf9Lpm3B5l43HlPsFsiF5MrO%2Bg6pRoWAudPnHWh5W%2BmbBUheht9GuhsurWLfmxfxOUYO/PQxFoUfCPIiwsDP3798e8efMq3WfWrFn4%2Buuv4eXlhYKCAuItp9frsXLlSglHC3AGOjVr1sTChQvh4%2BMDHx8fCZ%2BtXbt24HleIuySkZFBPNcAZwmfj48PEQW5d%2B8eIiIiEB8fj44dO6JHjx6oX78%2BPvvsM4SHh2Pbtm1YsGABEhMT8cYbb8DT0xOTJk0C4Mxy0Rklo9FIAraMjAy88sorMvNxMQQuo9VqJQI2DocDAwYMwODBgzFp0iRJJu3PQGx6LkCtVuPTTz/FhAkTsG3bNsTFxVXbDiuoNhgM8Pb2Rl5enqSPygJwIWjWarVM8RUBkZGREiEVGgaDQVJ%2BKs4e63Q6Yg8xbtw4hIeHY9y4cdDr9bI%2BP/nkEyxZsgRXr15l9rN//36JKmpVUARaFChQoEDBI4F79wBf33%2Bk62rE0f8wVq16OO3%2BG6Fk9hT8o6hVqxaioqKq3Oe9997DlClT4OXlBZVKBZ1Oh06dOmHz5s1o1KgRk7NXt25dtG3bFk2bNnWpNJQu6xSEPtq3b0%2ByUFqtFv7%2B/ggMDESHDh2IIIvBYEBGRgbWrVuH%2BPh4xMXF4emnnyY/%2Bs2MR8A8z4PneWJyXh26d%2B9OLBAWLFhATNVjY2MRGRkJrVaLGTNm4PXXX6%2ByHT8/P6Snp5NguU6dOkQIpk6dOlixYgX69u0LtVqN8PBwREVFoX79%2BoiIiCCZS7E1Q1UQG80LKCsrQ05ODgn0hNJXoXRWo9GQ4J3jONSpUwdiv8XKEBgYSM7dy8tLYoGwZcsWpKWlYejQoWSb3W6HXq%2BHSqVCzZo1CQeyUaNGpLzWZrNJ1o1arYbVaiXXVRi7uHz3IFOfmw1FoEWBAgUKFCioGkpm789D808PQMH/bezfv7/afQQxF7ExdlVQq9USw20ao0aNQmpqKsaMGYPp06ejTZs2yMnJQcuWLfHzzz9jyJAheOedd1wac%2BfOnWEwGJgct927d6NLly4oKCjAwIEDMXz4cPzwww9YuXIlSktLicqmAI7jMH36dPTq1Qs8zyMrK4uIuRw8eJD0O336dNjtdqjVanh6eiI3NxfZ2dmYP3%2B%2BZJxGoxFGoxH5%2BfkYMGAAEhISkJeXRziDFosFtypKMziOQ2FhISZOnAir1QqO4ySqkzdv3iRBKW2DURm0Wi3JQAqvly1bhvj4eJw5cwY2m41YK3Tq1Albt25FeXk5atSogbZt2yIlJQX3798Hx3EkCyqeK57n4evri3v37qF9%2B/aYMWMGgN/EXgQ8%2B%2ByzqF27Nt544w3JdqHNGzdukDEWFBSQYFZQ55w7dy4AZ4mt0WgkpZpCJlKckazO2kEMhbOnQIECBQoeCVSh4P2woXwt/nkoZZwKHklER0fjVxHPR6PRoG7duhgyZAgWL15MSkMTExOxatUqXL58mQhqaLVa2Gw2CQ/Mx8eHZLsSEhLg4%2BMj8Qts0qQJWrduTRQxbTYb0tLS8Prrr2P%2B/PmYN28eCgoKJGN0c3NDrVq1cPPmTdSqVQuFhYXEFLw6cBwHd3d3lJSUQK1WQ6/XIzAwEJdkjstsaDQauLm5kbJFf39/5ObmIiAgAAkJCejYsaNLgYVGo4FKpZIEbTRUKhXZpyo%2BJH1%2BTzzxBBHYCQkJQb169XDgwAG4ubmRrJdKpUKzZs2QlZUFq9UKlUrFHIdKpYJarWZy5gQz9IiICOzcuROAM/DU6/UoLS2Fu7s7ysvLUVJSgtDQUKxYsQKdOnUiwb94nSxbtgwZGRlYvXo16Vc8npUrV5KMb3U4d%2B4cLlOG1I1DQhDavLl0x9JSoMIyAoCTgCIOFHkeoMSBZPvY7QBly1FWBhgMVTcDqxUQZ1WpsTCPsdkAcVaX7oi1D/0aTgpKhQuJE8XFACUOJDuM2oeeOuZBjLmRHcc4B9k%2B9AZ67lh9sybQhX3ouZEN7y9q15V2ZMcwupJNMaNdV6ZG1g5j3ci20dfBlbXGWDiyfVh90wOkP4eujNeFOXela9bHTnZaVF%2Bsdl0aHzU5zHaoAcqupSufF1bDVN/Mzzy98dIlQCym5cqEms0AVYUj64txL3HpHKpbs67MzV/Vd2XtFhYCIl7%2B34mRIx9OuxSL4v9rKJk9BY8sZsyYgV69egFwmn8fOXIEM2fOhKenJ6KiogiHLi4uDjdu3CD7WSwWaLVacBxHygl1Oh3atWuHyZMnw8fHR9aXWq3GqVOniABMWVkZ8aDr27cv3n33XTRs2BDr1q0jx9y9excff/wx7ty5g8ceewynTp0iwZBWqyXlk4AzeBRz2Hieh9lshl6vx9ChQzF48GCsXr262mCvadOmOH/%2BPMrLyyX2CLkVRO27d%2B%2BiW7duLmeQysvLiYF5ZcfodDpwHAeHwwGHw0E4f5UFfhzHQa1WS5RUX375ZTzzzDNo1qwZeJ4nbWi1WqxevRqdO3eG1WpFYGCgJMgX4OnpKQu2BfA8j6tXr0p4djabjQSG4nnKysoic5ybmwsPDw9Jux4eHrhw4QJ5Tc%2BJYF/hCpKTk2WcvUkTJ8qDPfqXC50RlEVbjH3oHwGQ/xBkNSP7gUGNhXkM/WOC7oi1D6Pklw4g6ECPeRi1j%2BxHH%2BsgxtzIjmOcg2wfegOr9JjumzWBLuxDz41seH9Ru660IzuG0ZVsihntujI1snYY60a2jb4Orqw1xsKR7cPqmx4g/Tl0ZbwuzLkrXbM%2BdrLTovpitevS%2BKjJYbZDDVB2LV35vLAapvpmfubpjbRqsisTyqBbyPpi3EtcOofq1qwrc/NX9V1Zuw8e/GPBnpLZ%2B/NQgj0Fjyw8PDzg7%2B9PXvfv3x8pKSnQ6/WoV68eZsyYgQ0bNiA8PJwIipSVlWHUqFHo1q0bRo4cidTUVHz55ZfVcq1q1aqFoUOHonv37gCcxuFi0RedTgeHwyEZj7%2B/P%2BbPn4%2BOHTti0KBBWL58OcaPH4%2BDBw9i165dsj569OiBXr16YeLEiYiLi8OgQYPQuHFjzJw5E02bNsX8%2BfORkZGBW7duITw8HGq1GhkZGWjWrBnOnj0LALhy5QqMRiPKy8uh0WhgsVjgcDjg7u4Oq9VKgkqO40jAabFYYLfbsXHjRowdOxYajQYFBQXw8PCA3W6HVqslwW1ZWRnCw8MlJZ4OhwPWCkU%2BIcPXsWNH/PDDDwCcwZ2vry/u378Pu90OHx8fFBYWSoRrFixYQDJovXr1wrVr13D8%2BHF4e3vDy8uL8OJoz0WDwQCLxYKQkBCcPHmy0qyiXq9Ho0aNcObMGbJNHKSKM4KCt9/t27dldhocx%2BHcuXPMPoTzdxUszp5SZqFAgQIFCv51YKnuKnhkoAi0KPj/ChqNBlqtVsKh0%2Bl0RLylSZMmWLVqFcaPH4/g4GD4%2BvpKMmxVwdfXl7Rz9%2B5dtG7dmtg9FBQU4JdffiGvBaN2QaTk66%2B/Ju1wHCcTlRk5ciQcDgdSUlLw9NNP48GDB9i3bx8KCwvx1FNPITU1FRkZGbh%2B/TqCgoKQmZlJbAGmTZsGACSgKy0thc1mQ2lpKck8FRcXw263w83NDV26dMG3336LlJQUJCYmEjP20NBQ2Gw2kskym80oLS1FUVERVCoVUaYUB0wAMHPmTABOLqHQ38GDByWctry8PBLM5efnw26349lnnyVtFBUVYenSpWSuBBEUWrynWbNmUKvVaPz/2Lvu8Ciq9ntmS3azSUiAbAKEEKqhhyBFqoiASO%2BKiqB8gIAUEZCmqCCIICpNQAFBQRCkNwFB6cVIi4b6UYNJgJi22WTb/P7Y3MvcOzfJEsRP/M15njwys3fn3rlzZ5133vec89hjAO7xEs%2BePUv7I9lXMifAvXJQgoiICBQvXhyyLCMsLAyRkZEAvMbz3bp1o8fmM3f%2B/v5o0KAB8sMvv/yS72c8NM6eBg0aNGjQUDA0gZYHh5bZ0/CvAOHQHTp0CNOmTcOKFSsQExMjbFu9evUH7o/nsJGSRZfLBZ1OB6fTifT0dEybNg06nY7J%2BOUHSZLQvn17BAUF4ejRoxg0aBAmTpyIKlWqoHz58nj33Xeh1%2BsxaNAg/Pzzz0hPT8fx48cxfvx4SJJEM0XEV5Bkq/z9/REdHY1Lly7BZrNh165dOHDgAM1a2e12mEwmPPXUU4zVgDJYI3/K/QSTJ08GAHzyySc%2BzR2xsyBm9REREUhMTESFChWQnJyM6tWrU28/4nlnMplgs9loRvHChQsAgD///BMlSpRAtWrVcPPmTSQnJ%2BPy5cu0L5Jpe%2ByxxxjPPGUpqPLfs2bNohYW5cuXR/ny5bF9%2B3b6udVqxQsvvIAffvgBgDc76FC88STj9QWdOnVSrcXHKlVSN%2BTJN76Q7XgyiS88JdFxeK6IL30Xxj8B1PwSwfhUuwRknMLaiPhkvnBdVHwnwYFUu/gv%2BcKP8YVnI2jDNykK184X3txfxdnjD1PE4anHVxQOki/3goDwViTOng/3YVFO3Jdl4wtnj%2B/KF86eL9zeInH2fJlP0YlzfQvvef436NIloHLlgvvmT9QXHqcvi/ZR4%2ByR7TzF7P8F/r8FZg8DWrCn4ZHF5MmTMWXKFABqDt38%2BfN9sjQAvOV6ymwQgV3o8upFzZo1aUACADNnzsSePXvgcDhgNBrxxx9/oFGjRvD394fVasXQofe80xwOh6o/0pfZbIbb7UZiYiLGjRsHADh37hwuXLgAj8eD6Oho9OzZE3FxcTRY%2B0NhuEqCzmrVqiE%2BPh4ejwd2ux2nTp1S9Ve9enV8%2BumneOWVV1C1alX8%2BOOPTPASHh5Ojd8bNWqEAwcOwOVyCb3zzGYzzQgSjp8jn7IPkuEjYikk2CIcPmWwtn79enzzzTf0O4GBgdDpdHA4HFRN0%2BFw4OzZszT4rlKlCrXVIDzI06dPM2OwWq2QZRlpaWm0VNXhcODEiRNo0KABwsPDYbfb8fPPP9PvlClTBmFhYRg0aBAAb2ZOkiTmPP/880%2BaJSwM%2BXL2FBlIAOonNl/IdjyZxBeekug4/AOGL30Xxj8B1A8lgvGpdgnIOIW1EfHJfOG6qPhOggOpdvFf8oUf4wvPRtCGb1IUrp0vvLm/irPHH6aIw1OPrygcJF/uBQHhrUicPR/uw6KcuC/LxhfOHt%2BVL5w9X7i9ReLs%2BTKfohPn%2Bhbe8/xvkDLQy69v/kR94XH6smgfNc5e3vbtnCBYg9Rf1/BoQAv2NDyyGD58eL4cupCQEEZ4oyCEhYXh66%2B/Vu3v06dPvt8hpaEEgYGBaNWqFSpWrIgffvgBN2/ehMfjQZ06dTBt2jRG9IWUmfJ9paSk0P2EVwd4/e9q166NnTt34sKFC7TN%2BfPnAQCdO3fGpk2b8MQTT8DhcCA%2BPh537txR8c14/P7773j22Weh1%2BsRGhqKkiVLMjyy5ORkmoXbt28f3R8dHU37BryBttIawe12MwqWIohUNYlKqtVqpRYGGRkZTHCZlZWlEn6x2WyMEbzb7abnrhxXhw4dsHXrVgBe8RUyBsIzBIDDhw%2BjQYMGqFOnDnbv3s34%2B7388st03gAx5y6/AFcEjbOnQYMGDRoeBViL5QIQRdIPH1pm78GhcfY0PLJQcuh443SlaAmP5cuX4%2BOPP6bbBoNBxaGLiopiuHx79%2B6lXK78EBgYiFGjRuGHH37Ab7/9hl69euHUqVOM%2BEe1atUQGxsr7Cs4OBgjR46kGb327dtDr9dj%2BPDhuH79OooXL44SJUpg1qxZ6NSpE1UYHT58OPR6PcqWLYulS5eiRIkS6NSpE2N%2Bnl/g53a74XA4cPr0aXg8HrRu3Zp%2B5ufnJwzaqlWrhpYtW9LtEiVKUFXUOXPmoHnz5vQzck2ioqLQsmVLDB48GMA9bppS0ITMk7KskgjrKM8lMjKS%2BV7JkiWZ8Zw%2BfRomkwmyLOOll17CU089hbZt26Ju3brMeciyDEmS0LNnT8ofJBzL1q1bU0EaAuLzKOLaEWRlZeX7mQYNGjRo0KBBw98NLdjT8K9Ex44dcebMGVrOR2Cz2bB8%2BfJCM09/BcaOHQuLxUJNuQtDWloa5syZQwPRHTt2oF%2B/fujUqRMyMzMRHh6O9PR0OBwObN%2B%2BHVWrVqXfDQwMxO3bt%2BHv74/PP/8cL774Ig1WdTodli1bBqPRCD8/P7z44ovQ6XQYOHAgDh48iIULFwLwliAqS1OVQY0yuPr555%2Bp0ibgFVzZtWsXAG/gqVQ2JfN87do17N27F1988QVzziEhIfTfJKBT8gbr168PwOuDKEkSSpUqBX9/fxQrVow5jjKjRriTAHDs2DHs27cPO3fuxPvvv898R5Zl6PV6bN%2B%2BnWYuSX8mk4kK6RCQgJlkacuXLw%2BTycS8FKjCS3oXAE2gRYMGDRo0PBL4H/6/SRNoeXBowZ6GfyViY2PRs2dPDBkyBOvWrcP169dx/PhxDBgwADqdDgMGDHjoYwgMDMTYsWOxf/9%2B7N27l%2B53Op24ffs28%2Bd2uxEYGAiz2Uz5e263G19%2B%2BSWqVq2Kq1ev4rfffoPL5cJjjz2GI0eOUBuCnj17IiMjg36vWLFiaNasGeXyeTwe9OvXD06nEw6HAytXroTH48HixYvRtGlTvPbaa8LMn9Jfzqio44%2BKiqJZLgISXM2cOZN6zSmPSYLFChUqMIEaKY0ExGWNRGn08uXLkGUZSUlJOH/%2BPBMQOhwOXL16VTgeIuQSGRkJs4DE4nK5kJmZSedywIABeOedd3Dnzh107NhRyPsk2b67d%2B8CYAM0f6HJkxidOnXCzJkzmb/GjduoG/76a4HbQrcJzqzdZlM3SU9ntwXTD5VHPZctz6u2ZcG/SNm5U92GPydBybXiEnuRdy0L6gqcUuzNm4Lx8QdWnSSQ5whSYN%2BqIXMvlpAn9MOAK/MVXjtuLYvGx/f955/stpBuzPUtmpvCjisajmB46hPjL5QiY07AJ8VFfat2il7a3bjBbiv8NQEAeZ6jSqjmi1%2BfENwfogHya%2BvIkYI/F%2B0T%2BIjyF0Y459xJiNaWarr4cxAdmJ8v0eLir6foONw%2B1fgE10XV15Ur6jYKzjoA5BW8sDh%2BnN3m703RePnrwPHeBV2LlrXq0KIly%2B8rbBtQz5/oevtyrxbWhm7/D6tWtGDvwaFx9jT8a/Hee%2B9Ro/OpU6eiWLFiaNKkCT755BOhcfr9oGXLlky5oSRJCAgIQNWqVZlAyO12IyAgAEOGDEFoaChKlCiB8%2BfPo2nTpj71w/PTypQpg9OnT8PpdKJ27dr4M%2B9/1jqdDlevXkWtWrVw6tQpHDx4EC1btqTiI2azGenp6dDpdBg8eDBq1qyJ1157DWazGZMmTcKkSZNgNBrh8XhoNk6SJFgsFthsNoSHh9Oy0dOnT%2BPatWvMGGNjY/Hrr79i7NixdL9y3HXr1sUvv/yCS5cuISjoHstbqVL60ksv4csvv2TOPyAgADZBpKIUz8nIyBC2UeLGjRt46623MGPGjALbffbZZ6hVqxauXLmCL774Aqmpqfn2nZ2dDaPRyAS198PZy0%2BgpV69qmxDrvyU3xZW6HICBAI/cpU/rqg6VcXn58RjgkSEfV4ooG1bdRv%2BnLhMLSAQP8iz2yioK3CG9GXLCsbHH1ggWhAaWnjfqiFzNiFC9TpOaEF47cqXL3R8fN/8z5nwnQPXt2huCjuuaDhCMQ7%2BxHxw/Obfqwh/ovmdIlEKXiCpYkV2W6CMrJovfn1CcH%2BIBsivrUaNCv5ctC/PdoYBd2GEc86dhE%2Bm9Pw5iA7Mz5docRVBnEg1PpFiNd9XhQrqNqVLM5vlyqmbgLfL4e9N0Xj561CnTmFdC0VxfNFRKUyHRvSdoogePZCekejkNDwy0II9DY8klJmy/KDT6dCvXz9VFkqJbt265cvFK6yPCRMmUK6ay%2BXC0aNHMXHiRISEhKBLly6YPn06Nm7ciPHjx6NBgwZIS0vDZ599BpvNhrVr1yI4OJgGEz169EBycjLMZjPeeOMNLF26FN27d0fnzp0BACNGjEBCQgL%2B85//4P3334efnx/i4%2BNhtVphs9nQtm1b7Ny5E263G3q9HlarFVarFWlpabDZbOjVqxd27tyJlJQUzJs3D2azGf7%2B/rDb7Zg8eTICAwPhdruZIMpgMNAg6rridaksy4xwiSzL%2BDXvTbgsy9DpdNSsnPRBsmqSJMFms9EgVulL9%2Buvv2L8%2BPGYPn063VetWrVCvetKliyJ0NBQKhrTtGlTHD58GOHh4bhz5w7lAjbiH7wE2LBhA1q0aIGPPvoIWVlZsFgsDG%2BPXGtyrvxniYmJKFWqVKH9AJpAiwYNGjRoeDSQJQXBN33zvx7/37JwDwNaGacGDUVEUFAQDapKly6Nrl27Mgboy5cvx/z589GzZ09ERUUhJiYG8%2BfPh8vlwvLly5mgTK/XIzg4GCaTCUFBQVRwhQi4hIeHo2zZsvjwww8RGRmJtm3bonz58ihXrhz8/f1x%2BPBhKvmvFKCJiYmB2WxGtWrVmFLMF154AZs2bcKUKVPgdrthNpsZIZny5cvnG7SUKlWKBm%2BEZ0fMy6tVq4YyZcrA4/FAkiSa9SIZrw8%2B%2BAAej4dm/Yj9AgCcPHmS8cIj9hEA0KRJE1TIe6tbuXJlrFixgrbr1asXM9aDBw/C4/Hgjz/%2BoOckSRKTUQS8LwNeffVVJjNHjlOyZEmEhYUJ7TeUfoPTpk3DW2%2B9RT8jBvW%2BQOPsadCgQYMGDRoeNrRgT4OGQjB06FDExsYyf7du3cLkyZMRGxvLiJoYDAZqrVC7dm3Uq1ePOZZSQMUXOJ1O7Nq1C4cOHcLw4cNRsmRJZGZmYufOnbh8%2BTICAwPRqFEjjBw5kgqQKAVoLBYLHA4HUlJSaJBiMBhw7tw5REVFoVevXmjatCnS0tJoxgoArl69ihsc90WSJAQHByMzM5OWcZIyUpIB5D34SLB0PI8zMX78eKaNMgCTZZlaIwBAQkICzZydOHGC8vIuX76MkSNH0naLFi1iRGFEkGUZTz/9NLPP4/Fg6dKlzHi%2B%2BuorLFiwAJ06dYLJZGIUXpXzQI45YcIEpjRUKdZSGEScvTZ5fEcleF4Ivy0idPC0JREnzifuFccnUtGUOP9CAMizZqQQUdd4bovQJeXiRWZTxMXhq3dVPDRFuXF%2BfQvPmyvfVXH4ABUXjL8uou%2BoLpWAw6Uaj%2BD6qqqLfSG8%2BXDB%2BXcbouvCD1lQ6aw%2BNNdIyCnkd4r4bfwFFxCV%2BPXmE5%2BRayS83txNJVyz3HjyqMAF9s2fpqhvVZW64LxVy0S0sLnO%2BPGIeF/8eQrvFx%2BIaarvcTuElfhcG9H88ftE3GP%2B3uS5xiKaJN%2B3iC6o6lxQxs/3JTpPfp8v58S3Ec0NfxlEffPHVvWVtwCEHNq/CRpn78EhyfzTmQYNGhikpKSoMjx9%2BvRBnz590KZNG95I/y8AACAASURBVJQsWRImkwn79u3DqFGjMG3aNKxYsQIxMTF4%2B%2B23fe6nZcuWsNlsyM7OhtPphNFohMvlgtlsxgsvvIAxY8bgmWeeQdu2bfHGG2/g7bffxq5duzBmzBg0aNAA%2B/btw7Rp0xAZGYm1a9eiePHiSE9PR9OmTRkuGfGX8/f3p%2BWWfDmi1WqlPoW5ubnUhiA2Nhb//e9/kc6pe5CyTL1ej1KlSjF8RiWKFSuG8uXL48yZM4XOh9Lgne9Lp9PRgHbmzJn45JNPcOvWrQKPNWvWLAwfPpweg/j6KUVxSpQogR07diAkJASTJk1Ceno6VRolZaIxMTHMfCk9A48cOcLYRBSEDz/8UMXZGzZsOF5/fahP39egQYMGDRr%2BFiQnA%2BHh/5Ou89gsfzk2bXo4x/0nQsvsadBQCMLCwoS%2BeHPmzEGXLl3QrFkz1K5dG2%2B99Rb69u1LrRJESo4AkJ6ejg8//BAtW7ZETEwMnn32WXz11Vc0wxQWFgaLxYJhw4Zh3759%2BOWXXzBmzBgcO3YMV69epUGYw%2BFAZGQkli9fjg4dOmDatGkAgJUrV1IBGpLRa6AgqJPAxGAwoHXr1li9ejX0ej0kSYIpTyzg9u3byM3NpaqXpBzyjz/%2BgF6vh06ng9lsRuk8hrqytPF2nqoaOf/w8HDaf0ZGBlPqWL16dZ%2BugV6vR0BAAAIDA5lAD/BmyERCKko4HA4a6JFxOhwOyLLMBPKpqak0kCtZsiT27NlDP9uU938GEyeooCy9nDt3rk/nA4g5exprT4MGDRo0/OOgqPzR8OhBC/Y0aCgihg8fjo0bN2Ljxo1MUAZ4/eMyBHU%2Bf/75J3r27In4%2BHh88MEH2Lp1K4YNG4ZFixbRbJnRaMQrr7yCgQMHqsziAVCzeEmSULlyZWzZsgXbt2%2Bnn4cr3r5NnjwZ/v7%2BKFeuHEqWLEn3E6GUzZs3o1u3bnC73ZBlmapjli5dmuH4keAqKSkJqampCAsLQ0hICMLyVM0iIiJgMpng8XhoNo6UO9rtdgwZMgSAN1BavXo1Pe4FhaR9q1at0L9/f7qtLDpwu92w2Wzw8/NDcHAwKivUJm02Gz239957D3XyVNOio6Npm6CgICxatIiZx7fffpuasYfmyS%2BWLl0aYWFhSE5OxjfffCMM2JXWEZIk0YAXABXU0aBBgwYNGjQ8OLQyzgeHFuxp0FBElCxZkmb6%2BKCsRo0aNChT4uOPP4bNZkOtWrXQqFEjREZGol27dvjggw9gs9mYjFXLli0RHR1N/1555RUAXuuDuLg4JCQkYMOGDYiOjmb4aNHR0Rg3bhwA4MyZM8jIyMC6deuoLxwAmM1mGAwGLF68GADo2AlvLzU1lZaSVqlSBZUqVQJwzy8vOY%2BYRQLaoUOHwuFw0OMYDAZk5pEVMjIyKN/uhRdeYM5RyRPcs2cPvv76awBAzZo10aNHD9X8paamIjU1Ff9V8KU%2B//xzOp59%2B/YhISEBkiQxgaTD4WCCYACoWrUqFixYgAoVKuDOnTswGAyoUqUKdDodfvvtNzRu3JixhiAgQjGkdDVJQcw5wvtqFQBNoEWDBg0aNDwKSDYILEE0PDLQgj0NGh4COnbsiDNnziBOYbTscDiwbds2uFwulYn5U089hdDQUBpMEUyYMAEHDx7EwYMHMWvWLADeIOM///kP0tLSEBwcjLlz5zJZrIMHD2LixIkAQMs5O3ToQJUmLRYLWrduDYfDgcGDBzPeeiRwUZZvXrx4EZcvXwYAmgmTZRnJycm0fHLFihWQZZlmvUgQRxQ5iXrmd999x5xfp06dmG2SFYyPj8emTZtoxo2HMihat24d7e%2Bnn35Cbm4uZFlmMoO5ubmMSTzg9fWrWrUqruQx710uF5KTk2G329GyZUu0a9eO%2BQ6xgKiZ5%2BWmLF0lSObVSQqASKClWTO1qTovKMJvi1jXKm0LEbueE0ARiZnwBH9eB0SkocELl4iEA3wxGubPS%2BTpyx9H1ZegvJefP%2BE58C9qfJhk/riiKl0f/NzVAh0cPxYA8PPP7DY3gT6JaIh4tQcPstsCM3nVJCckqNtwXFuVSI/oYvKLQMDXVV1fwXXhLzk/faL1WKhIheB7QrUDbo59EQPiRTNE90Jh4iaiXb74t/tyXF5gxBdjbuHkcPt8uRd8MQ5XGZkL1iM/xyr/9pMnVd/hxyOkmXP2TMLfEr7Ch59QURt%2BsYmUVXwRNOJPQvRbwu/jt/OOofsfRgtaZu/BoQV7GjQ8BMTGxqJnz54YMmQI1q1bh%2BvXr2Pbtm3Izs6GyWTCgAEDmPaEL8cHgUp7B1Kq2KRJE0RERCA1NRXp6emYOnUqyiuMmK1WK1W5bNGiBQBvRowESBaLBVOnTkVoaCisVitMJhPK5TnR%2BgvdmO%2BhSZMm9N%2ByLNPS03PnziEkJESVPSNBF8nE8ebnyvJTHgMHDqRBZ34wGAxUEbQw8GW1AQEBMBgM1DYC8AZr/v7%2BuHTpEhYuXMiUbBL8oZLDvAc%2BoCwImzdvxpgxY5i/Q4d%2BVLXjvWz5bZGZrsq7WWQAXaUKux0VpWrCi5HyVa0ij2jexFq0pHwxGubPS0SB5Y%2Bj6ksglsPPn/AcOPN4XyaZP67IpN4HP3e1oXtwsLrRk0%2By29wECk23%2BZ0i8%2B6mTdltgZm8apKrVVO34QzcVf7yoovJLwLuGKKuRdeFv%2BT89InWI3%2BtRNeO/55oSfBz7IvXuOLnB4D4XlB9T3Agfpcv/u2%2BHJdzrPHJmFs4Odw%2BX%2B4FX4zDVV7fgvXIz7GqYCM2VvUdfjy1a6v7Rt7LTwLhbwn//xB%2BQkVt%2BMXGn4Cojahz/iREvyX8Pn477xhCivnfBC3Ye3BowZ4GDQ8J7733HgYPHozly5ejU6dOVKL/k08%2BoRk3Hm%2B99RasVisWLVrE2DvExsbSANFgMKBSpUqoWLEiQkNDsX//fowdO1Z4PGL6npCQQIVTxo0bB5PJhPDwcKSkpCAwMJAGVcF5P/R6vZ7h7AUFBUGSJIb3ZjQa0atXLwDAlClTEBQURBUrleAFTZT2BCNHjmSymcpg99NPP6XjKV68uCrrCbBloIXhscceY/rOysqCy%2BViAtC0tDT0798f69evpxk8gm3btqFmzZqqANVisdBxXxNkx/KDJtCiQYMGDRo0aHjY0II9DRqKgL1796Jbt24FttHpdOjXrx%2B2bNmCU6dOYdWqVQDABFH5HfP5559HWFgYIwLz3nvvAQAOHTqk8owj%2BPFHNjNEAqSxY8eiTJkyALxiJD/88APOnz8PSZIQEREBnU6HgIAAhISEAPCKpSjVKzMzM1X8tIkTJ2LQoEEAvGInN27cUPnsmUwmFZ9RGaDNmTOHZhyVWTR/f3%2BYTCbczDNOCwgIQIUKFWhQ1ahRIwCg6pzAvUBx2bJlOHjwoGqOiHk9QX7XQZZlrF27FomJiUzwVq9ePezcuZNy9ooVK4YyZcrA6XTCLy8LcUdo0CWGxtnToEGDBg2PAkTZ7r8LWmbvwaEFexr%2BtRg4cCDGjx/P7Nu6dSuio6NVEvkLFiygSorjxo2jAicEf/zxByZNmoTmzZujTp066NKlCzZu3Hhf4ylXrhyCgoKEwi0AMHjwYBw%2BfBiAN8OmtHfo0qULHVPfvn3hcrlw6dIl3LlzB7GxsWjfvj0AoH379oiNjaX8sgMHDgDwqnLeunULFosFr776KkaMGAFJkuDn54f4%2BHgkJSUhOzub8teCgoIwcOBAnD9/nv61aNECbdu2peN999138eyzz8JsNsPM1dKQDFpISAiuXbuG6OhoGAwGGmyRMtPhw4fTfRkZGTRYtNvtTAnnzZs3cfnyZfr5kSNHoNPpkJWVRQMkEuwlJCTAarWqAqeJEycyIjJOBZ9BkiQMHDgQJpMJjRs3RkZGBq5du0aFaQBvcFi2bFnKfczIyEBubi6cTicda5aIi5QPhKbqbdScPZ4Dh0uXmE0Rxwe//85sqjgqgn2%2BGGgjPr7Q4xbudA4V30TE11ElSQVcHNW5nzvHbAqNkH0gUqkM3AX8NtWx%2BR0ifgzXl/DacWXJokpmPinsE2eK60xU/cwPWVhFzXHpRO83Ch2PgDjH71JxsSAoJRPw%2BlTXgb%2BYAh6nau2rFoDaIF14w/Anzt2rwlo4fp%2BK4AjV5IgoXHzXPniqqw4k5Hrya190YbgvCo/DL3a%2BkWhB8utGdOLc94Rm9zw/mf/hEvHo%2BLXF/fYB6p%2B2ohrOFzo3PhAafeFu%2B9J1fjxtK0Q/9hrygyzLmDVrFp544gk0aNAAH330Ub4vc8eNG8eI8ZG/l19%2BmbapV6%2Be6nOeFlMQtGBPw78W169fZ3zSAGDt2rUAgPXr1zP7N23aRMU14uLiGGGVq1evokuXLjh8%2BDCcTidkWcaff/6JSZMmYenSpT6Px2AwoF27dli5cqXKLHzv3r3Yu3cvtTIAQH3gPB4P8yOxatUqvPPOO3C5XChevDg2btxIVTUXL16MjRs30hJEkkUbNmwYwsLC8PHHHyM0NBRdu3bFtm3bMH/%2BfABeAReTyYSpU6eqxu10OrFr1y4moxgcHIywsDBERkZClmWapSIlmyTTZbfb0bVrV3Tt2hUul4sKwRClTuU%2BX2EymeDn58dYSQD3smIfffQRqlatin379jGfHz16lBGRUUKWZSxevBhut5sGbFlZWWjWrBltc%2BDAAWRnZzOBbVZWFlNeWhjnUQkRZ2/PHjVnj%2BfAQWE7AYg5PuD8CwWioqp9gmlR00C40lbRcVX8krJl1W24eRIlWVUUQgEXR3XuVasym3lJWBY%2BEKnyKKz3IOC3qY7N7xDxY7i%2BhNeOK/EWUXH4t%2Bw%2Bcaa4zkSV5PyQhRwkjksn0lAqdDyC%2B4TfpeJiQZBdEPD6VNeBv5gCHqdq7asWAJD3jqeAL0F94ty9KkyP8PtUBEeoJkdE4eK7Ft1TquvJHUhY7MCvfdGF4b4oPA6/2PlGogXJrxvRiXPfE10WFT%2BZ/%2BES8ej4tcX99gHqnzafuLKim76wufGB0OgLd9uXrvPjabtLiH7s/x48ipm9ZcuWYevWrZg3bx7mzJmDLVu2YNmyZcK2EydOpEJ8Bw8exJo1a%2BDn50eDveTkZGRmZmLPnj1MO1F1UH7Qgj0N/xrwmbyGDRsiIyODyeTFx8cjMDAQt27dokqQ48aNw7Vr1xATE0O/63K5aCbv2WefRVpaGkJCQrBgwQJs2bIFQ4YMgSzLmD17ttBPLz8MGzYMWVlZ6N%2B/P44fP47r169j7dq1GDduHF5%2B%2BWXGP87j8eCll17C5s2b8frrr9P9GzduxPr161G%2BfHno9XpERUUhIu9/yJs3b0bv3r1x/Phx7N%2B/nwY2oaGhkCQJtWrVwnvvvYeNGzdi4cKFmDx5Mho1aoS%2BffsiJyeHBl6bN2%2BmXMHatWtj5MiR6NGjB50zov5psViwdOlSfP/99wDuqXgSPlpGRgY2bNiADz74gJkHwlkkKp33g0qVKuH7779XvdXS6/WwWCwoVqyYqpxUp9PRfcSHTwSXy4XDhw/DYrEgMDCQ8c1zOBwYNGgQFaUBvOfr8XhoJjNH9NY7H2icPQ0aNGjQ8ChA7ypYLO1h4lEM9lasWIHhw4ejXr16eOKJJzB69GisXLlS2FYpxGe1WjF37ly0bdsWrVq1AgBcvnwZVqsVkZGRTDte0K8gaMGehn8N6tWrh7Nnz9JtIk4SEhKCY8eOISkpCVlZWRg4cCAA0ACJ8NFIiaLT6URSUhLS0tLwzjvvwOPxoHTp0rh8%2BTJOnjyJcuXK4bnnnsObb74JnU4nFAk5duwYmjdvjhUrVqBhw4Zo3LgxPv/8c1itVnz77beIiIjAoEGD0Lp1a0yaNAkAUFGRwVm%2BfDnu3r2L1atXY8SIEcyPxObNmxEfH0/5bIBXDRMA9dMbMGAABgwYgM8%2B%2B4y2SUpKQtOmTandwvr161GiRAnMmzcPNWvWRMWKFTFv3jwA3qAnKCgIAwYMQI8ePVC8eHH8/PPPdBy//PILtV0YPXo0tWQQQZIklChRggaIwcHBqFu3LkqXLo2GDRvSssoJEyZQU/UZM2bgIC8Dn3es6OhobN%2B%2BHXa7nWbVgoOD4Xa7Ybfb4XK5VKIwxAIC8GZ8C8K1a9dQv359WK1WKkADeLOVx48fp156yoweWQP3E%2Bxp0KBBgwYNGv5dSE5Oxh9//IH69evTfY8//jgSExORIirTVuDIkSM4ceIERo0aRfddunSJagUUFVqwp%2BFfgx9//BEXL16kGZ/YPDnltLQ0xMXFYcuWLZAkCS%2B99BKAewbYhN9GbAr%2B/PNPGI1GhISE0EyhTqdDmzZtMHfuXJrJ6927N5YuXSosC0xISEBycjKWLFkCl8sFh8OBBQsW4LvvvkPp0qVRunRpGAwGFC9eHHq9Hh6PB1OmTMGdO3fw9ddf06yPy%2BVCTk4Ok8VyOByIi4uD2%2B1GZGQkLly4gNGjR6NkyZLQ6/XQ6XTQ6/XYtGkTnsyTaO/WrRs9l1KlSiEwMBCSJOHMmTPYunUrJElCzZo1aQApyzKysrIwZ84crFu3DpMnT0ZycjJmzpxJP3e5XEhMTMRPP/1E55rgmWeeof%2BWZRmpqalIS0tDo0aNaBZMlmXUqFGDZhOnTZuGJUuWAPCqkjZVyMCTcgVZlrFp0yZ8/vnntMQV8GZxDQYDdDodpkyZgnfeeYcZj9vtpqWzly9fRg2FtL5er6fWE4A3UNfpdLh69SpTYkqCdhKcezwemM1mJugTrYX8oAm0aNCgQYOGRwFZTlFN99%2BDh5XZS0lJwW%2B//cb8FRaM%2BQKifK6k5RDP4CQV%2BZfF4sWL0bVrV5QuXZruu3z5Mux2O/r06YOmTZtiwIABVF/BV2jBnoZ/DUhgM3LkSACgmSdJkuDxeLB06VKEhYXRm9npdKJz586w55HfZ8yYgaSkJOTk5MDhcCApKYlK/6ekpGDLli2IiYmhD%2Bn%2B/v6oV68eDAaDKpNHMmoBAQFYsWIFpk6dCpfLhcmTJ6NGjRpYtmwZJEnCwoUL8cMPP0CWZbjdbjz77LPo0qULDTKcTieqVq2KwYMHA/Cm%2B1977TWkpqZClmWcPHkSHTt2hN1ux927d%2BF2u0FM0rdt20YDWtGYevXqBY/Hg3feeQc1atTA7t27ERAQAKPRCJPJBJvNBlmWYbVa8eabb8LtdtNxkZLI1NRUDBgwgHkLBYAKzSiRnJxMxwN4f/SIUXxhUJY8ejweSJJElUMDAwPx%2B%2B%2B/IyIiAm63GzNmzMC7774Lo9GIEgp%2BjvIYSpGciIgI3Lhxg%2BnPbrfDYrGgb9%2B%2BdN%2BZM2cgyzJefPFFGuDl5ORQfiIAlV1DQRAJtFSqpBZoKZSrL2Dmq9qIhCx4MRlRG%2B5AKoEHkSoAJ4Ah0hZQ0TR9aOTLcVRtBEIgvmgf8KIPQgNtbr580WHghyOs5OUbiVRwuNJxXttC5JeuSjr7IHYhHCCv4iIQPOFVWzi9IKEWh0/iNVwb0XEKPQXBBefbiIRp%2BDUgWjeF%2Blz7cK/6IqwiXI/cBfbF/NwX03f%2BnHy6n304T1/EQ/gl6ovZvWhdF2o4n8fXL2g8gp8S1WLj9XgAAAsWsNuff65uw%2B/L4%2BBTLFyo/g7fJu9FKYM8egXFF1%2Bo2/D7%2BG3R/xf%2BJVizZg26devG/K1Zs8an7%2Bbk5ODatWvCP/KsoXw2IP/m9RqUuHHjBo4ePYo%2Bffow%2B//73/8iPT0dgwcPxoIFC2A2m9GvX7/7EoTTgj0N/xo0bNgQgDczA3gFOZRcrdTUVNSrVw%2BnTp2CJElwuVxITk6mN19cXBz1iTMYDDh79iw17NbpdAgKCsKRI0eoGAqPu3fvYuPGjVi6dCmeffZZAEDbtm1Ro0YNXLp0Cf7%2B/vB4PNi5cydatGiBzMxMbNiwAVOmTKE/Ds899xw%2B/PBDJlv0xBNP0MAG8P7IHDhwAJIkYceOHYjKU7LYsGEDpk%2BfjpCQEAQFBaFUqVJUcVI0prCwMAQEBECWZTomu90Oo9GIqKgoOoZmzZph8%2BbNALziKOfPn0eXLl3oeMLDw7Fr1y4AoCWZfIarQoUKWLx4Mbp16wZJklCtWjUsXLhQVW4pwrBhwxjLBMAbbGZkZMDPzw82mw1utxslSpSA0WhEcHAwJkyYAKfTyRimE5TilBb4QO/WrVswmUwoXbo0wynMzMykmTxlZlBZukmyxL5AJNBy5YpaoKVQrr6gbl/VRiRkwRtbi9pwB1JdLhFngBPAEGkLqIQCfGjky3FUbQRCIL5oH/CiD0JqBDdfvugw8MMR8uv5RiIVHO7%2B4rUtRH7pKl0NH8QuhAPkVVwEgie8agunFyTU4vBJvIZrIzpOoacguOB8G9HPEr8GROumUJ9rH%2B5VX4RVhOuRu8C%2BmJ/7YvrOn5NP97MP5%2BmLeAi/RH0xuxet60IN58PDVd/hxyPU3uIWG6/HAwAYMoTdzntxW%2BC%2BPKoJxWuvqb/Dt8mjQDDo3p3dzvPqLXAfv533Oyd8wfA34WFl9p577jmsX7%2Be%2BXvuued8GtPp06fRpk0b4d%2BZM2cAsIEd%2BXdBIm4//PADqlWrxmg3AMCSJUuwceNGNG7cGLVr18asWbOQm5urEqErCA8U7EVHR%2BPNN99U7V%2B/fn2BHB4eO3bswN27dwEAc%2BfOVUW1DwM3b95EdHQ0w3tSwul0Yu7cuXj66adRs2ZNtGjRAtOnT/c5ko6OjqZBx1%2BNPn36qKwDCES2AX8HChqTCC1btmQkZGvUqIG2bdviq6%2B%2BKvIYatWqBUmSkJubC5vNhn379qmEOtq0aYP9%2B/fT/WXLloUsyzSwIXwuWZZhs9lonfSbb75JbQLmz5%2BPjIwMZGdn45VXXsG5c%2BeQkJAAl8uFRo0a4dVXX8WOHTsAAD/99BMAoHLlyjTIaNu2LQ4cOACPx4Pjx48jJSWFlu%2BtWrUK48aNY8r5du3aRdVBmzVrhm3btkGv10OWZXTq1IkGJ6S8MyMjA7t27aLKoYD3%2Bvg6ptzcXGRmZtIxHD58mAqzEBXNW7du0fGtXbuWCrS43W4YDAYEBARQewVJkpCUlIQpU6bAaDSiYsWK6NWrF0JCQuB0OgssfdTpdLh9%2B7ZKsVOv1yMgIIAqln700Ufo1asXQkND0aBBAyxatAiSJKkCOcAblCuDTFmWIUkSLS/96quvEB4ejitXrlBvQsCbUbRYLDh16hSqV6/OvLUjIF6IvkATaNGgQYMGDRoKxsMK9sLCwlCjRg3mL0ykhitAw4YNGWsq5V/Hjh0B3CvnVP7bKpSw9uLAgQNCD2U/Pz/mxbXJZELZsmWpgrwveODM3tatW5nSrPtFYmIiRo4cSUvpXn311fsKGh4WZs2ahV27dmHq1KnYuXMnpk%2BfjkOHDmH06NH/66H9azBhwgQqIbtnzx4MGjQIH3300X371xH4%2BfmhbJ4WclxcHI4dO4bIyEgAoHL5brcbJ0%2BepDcc4cIR8Q7Cx3O5XKhSpQoWLVoEwGvN8NJLL2H06NHQ6/VwuVz49ttvceTIERoAAd5AoXv37lSwIyHPG%2BzSpUs08JAkib408PPzw8CBA2mwWbFiRVVN96FDh2jZ4e7du5GQkEBVLytXrgyXy4Xc3FwcP34cgDd4admyJY4ePUqP0bNnT5/HVKVKFYwZM4aOyWq1MuIx8fHxqnv0scceo/92uVy4cOECMjMzUbNmTXTs2BFmsxk3btxA1apVceXKFXTq1Al16tTB0KFDGT7ia3lvMBcsWIBt27ahT58%2B2LBhA8qVK8cEaG63m85B06ZN0aBBAwDe4PCdd97Bzz//TL0HCUh2lA8wP/vsM6xZs4YGt3v37kVMTAzlJBLIsowdO3Zg0KBBOHr0KDweD5OBBe6plPoCjbOnQYMGDRo0/LsQHh6OMmXKMBZecXFxKFOmTL7BpCzLOHv2LOrWrava36pVK8YuLDs7G9euXWOeywrDAwd7/v7%2BeP/995l0ZVxcXKEkRAKSeSDlckuXLsWwYcMedFiFgowvv3GuX78elStXxqRJk9C2bVuMHz8elStXxr59%2B3wmcCYITID/CiQkJOSbNeQ94v4uFDQmEZKSkjBt2jQ0bdoUTZs2RatWrbBo0SKUK1eOlgQWBa1btwbgzTalpaVRkisptVuzZg2SkpIQnle2cfXqVUapUcnxGjBgAEqWLIm6devi3LlzGDJkCLZu3YqhQ4di/fr1mD17NnQ6nSrQ2bZtGz2ex%2BPB3LlzceDAAXqPKDNC58%2Bfh8lkog/55zhTaALyudPppGMGvBk2EnDMnj0biYmJkGUZdrudyUJv2rSpyGP6/fffmVLFCRMmMGWlABgDdOWx7ty5gxEjRlBVy6ioKBw8eBDHjh2DJEl4/fXXmZLIhXnchCFDhqB9%2B/Y4e/Ys5s%2Bfj%2BvXrzNZ2vr161OD9KNHj%2BLChQvo1q0b9u7dC8AryrNt2zbaXqfTIT09HTqdjuH7Ad5s3IsvvkjP9/bt23jyySeRmZmJgIAAxs8vNjYWx44dQ6VKleByueDxeGgGE/C%2B/PIVIs5e9eoPibMnIiHxRsIiLgFH6lHxvkQEHo7HUmTOXmF8QcHXHhZnT8gfK8QY3hf%2Bk5AHxFu5iN7eFsLZEyS0VRRNIQHKF84ePz6RqzrHM8yraqIQ%2Bc3zF1g459wCFHXN7/PFiL1Qrh2KxtlTtRGcVKF8MsE%2BYUldIetRtO%2Bfztnjf6J84uwJbiq%2BL9UcC3ix/HiEQsvcQr56VdAmT%2BGagufwifbxfmwi6kjei%2Bh8twGA55/50je/LfyR%2BnvxKFov9O7dG7NmzcKxY8dw7NgxfPzxx4xJempqKvOiOzExETabTVXCKUkSWrRogblz5%2BLYsWO4ePEixo4di1KlSlGdCl9wX8GeqGzTbrfj%2BvXraNy4cb7fi4uLQ%2B/evRETE4M6deqgS5culBtFUpY9evTA%2BvXr4XA4GCn7kydPonfv3qhTpw5atmyJb7/991uE1QAAIABJREFUln42btw4TJ8%2BHSNHjkRMTAyefPJJJiuUnJyM4cOHo379%2BqhRowaio6Oxc%2BdO4Rj5ss2MjAz8%2BOOPGD9%2BPM3sXbx4EQ0aNKAeYdnZ2XjnnXfQsGFDREdHY9CgQcxDL0nn1qpVCy%2B99BKTJbh8%2BTL69%2B%2BPunXrolmzZpg3bx7zVn/fvn3o2rUrateujZiYGCo6wuN%2ByjZFpavKstn169ejT58%2BmDNnDho2bIh69eph%2BvTpzEP2smXL0LJlS1SrVg2tWrViyuRsNhv%2B85//oFatWnjmmWcYkQ6%2BbNPtdiMgIADDhw9nMntXrlyhwXSfPn0wZcoUPP3002jRogWysrKQlJSEESNGoEGDBmjYsCGmTp3KvGggD/EkYDx//jyTfTlx4gQAb3BqNBqp2IfL5cL169cZiwO9Xo%2BWLVuiWbNm8Hg82Lt3L3777TfMmTMHs2fPhsvlQrVq1ZiHfcArgPLCCy/Q7Xnz5uHUqVMAvIGQMjiUZRlDFDX9Ho8Hy5cvZ45nMplomTMAnDp1iq6VunXrYvXq1fRYyix7NYUJtdlsLvKYXC4XxowZQ7fPnz/PvH3q0KED832Hw0F/hJKSkvD000/jiy%2B%2BgF6vvy8BE8AbtJH7xs/PjwashBtH1EeLc9yJXbt2MevW4/FQ9U6r1UozwID3R1e5hpxOJ27cuAGdTodSpUrRuTcYDIiPjwdvcq/M7N5PDb2Is3fu3EPi7IlISLyRsIizx5F6VLwvEYGHuxZF5uwVxhcUfO1hcfaE/LFCjOF94T8J6Rt8WbOAT1QYZy%2BvoIEBT9EUEqB84ezx4xO5qnOlSrVrsx%2BL/Ob5Cyycc24Birrm9/lixF4o1w5F4%2Byp2ghOqlA%2BmWCfkLNXyHoU7func/b4nyifOHuCm4rvSzXHgtI6fjwiL3l%2BIZcvL2ij8MgFoObwifa98gq7zfPzAGDQoIK3AYDnn/nSN7%2BdN5/3YemmAUD//v3Rrl07vP766xgxYgQ6d%2B6Mfv360c979OiBpUuX0m3ynBEs%2BHEcM2YMnnnmGbz55pu0Smvx4sUqLYOCcN%2BZPb5sMzQ0FDqdDpmZmUJ%2BTGZmJgYNGoQmTZpg69at%2BPDDD5GQkEDf4Ldp432TPX/%2BfLRr1w5%2Bfn6UO3P58mX07dsX9evXx/r16zFs2DDMmDEDu3fvpsdfuXIlatSoga1bt6JNmzaYPHkyffgaPXo03G43Vq9ejWnTpgEAlY7n8dFHH2HVqlVMsJabm4s33ngDX3zxBTIyMjB%2B/HgcP36cZiEnTZqEuLg4LMh7E3L27Fl8%2Bumn9Ps//fQTJk2ahHXr1iE9PZ0qD6ampuKFF15AWFgY1q5di8mTJ%2BObb76hYhBHjhzBsGHD0LlzZ2zatImWj8XHx6vG/Vdn8k6ePIkrV67g22%2B/xdtvv40VK1bQoG316tWYN28eRo8eDX9/f2RlZWHEiBH0u%2BfOnUO7du2wbds21KxZE2PHjqUP3KIMqtvtxty5c3Ho0CGEhoYiICCAKmcSrF%2B/HjNnzsS8efPg5%2BeHvn37wm634%2Buvv8ann36Kn376CR999BEAr9DGPMVbNFmWkZaWxgQC5KHfbDbTwMNoNEKv16NUqVJMqeCSJUuQlJSEo0ePolu3bsy4y5Urh1dffRXnz5/Hjz%2ByD%2BgjRoxAWlqacH6NRiNkWWayX8pgsVixYoiOjmb29ejRA02aNKHb5RX/R2nbti0uXboESZJUN74yS9ijR48ijykkJAStWrViPt%2BwYQP9d3Z2Np566inmmPv37wfgDTKNRiOsVis8Hg9Tw07AB2pKuN1uuoZsNhu9PrIsIygoCA6HQ1VKCaBAbm3dunVRpUqVfD%2BXZRm3b99GsWLFmLVI%2BKC3bt0SegACwJUrV1T8wvygcfY0aNCgQcOjgPuIK/5yPIqZPb1ej/Hjx%2BPEiRM4evQoRo8ezZig7927l6lijImJwfnz54VaACaTCePGjcPBgwdx6tQpLFy4kLFm8AVFKuMcP348fRPeo0cP%2BnDzwQcfqNrm5OSgU6dOOHToEDp06EAzBBcvXgRwLwMzdOhQbN%2B%2BHceOHaPlj9999x2ioqJw4sQJdOvWDXPnzkXdunXx5ZdfAvAGOsWKFcNvv/2GDh064IcffkBOTg4uXrwIWZbRsGFDOJ1OPP/889RjLD9BltWrV8NsNmPmzJnYuXMnAgICqOjFmjVrMHz4cAwbNgwjRoxA8eLFkZ6ejh07diAyMpJmQUJCQph63Dp16mDq1Kno0aMH7HY7NfzeunUrDAYDkpOT0bNnT7z33nuIiYnBF3mStytXrkSdOnWwadMmdO7cGQ6HAwEBAcxbgKJAVLqqnO%2B4uDg4nU5ERESgd%2B/emDJlCkJCQqiy0Jo1axATE4NZs2bBZrPB6XSievXqtMQvIiIC27dvR/v27fHrr7/i9u3bTEaqQ4cOlKOn0%2BmQm5sLWZYxbtw41K5dG2%2B99RbKli1LeXYJCQmwWq0YM2YMXn/9dezevRtJSUkwGAzo06cPRo4ciSpVqmDVqlWw2WxYu3YtoqKiaNBDeHqSJDE3kU6ng81mQ5MmTfBK3hs0t9uNpKQkZGRk0JcNycnJcLvdiIuLw6ZNm2DMezUZHh4Oh8OB1atXIyAgAJcvX2bmeeLEidizZw8A0O8o%2B42Pj2cUmZQP/YSEW7VqVbpv9erVTJb07t27dIzjx4/HwoULIcsyoqKimB%2BKQMWr/EWLFhV5TOQ44YoMQ9euXem/f/31V/z666/MHJAfNYfDAUmSkJycjMaNGwvJycp9JpOJ%2BZs3bx6ee%2B45VKpUCbIsMxm4zMxMSJKEcePGqY4rMronsFgsTLBnMBgQERFBg0adTge73Q6n08mUyRL8/vvv%2BR7f7Xb7LN2sQYMGDRo0aCgYj2Kw90/DfQd7RqMRSUlJVH6%2BePHiNMLct28f9uzZgzt37sDtdiMmJgatWrXC6tWrYTAY0LRpU/qwdvHiRcqLA%2B5l9rKyspCdnY06depg1apVuHjxIs3shYeH48iRIzhz5gxiYmKQmJiIu3fv0sxes2bNAHjTp7Vq1cKSJUtw/fp1NGnShFHVE8HhcMBoNFKOHnmLD3izjy%2B//DIqVKiAzz77DE888QRatmwJj8eDK1eu0MxeYmIiFi1ahFq1agHwBk8ks2e325GYmIi6devi448/RmpqKpPZO3bsGO7cuYPatWtj3759%2BOWXX2hmD/BmNZTeYAR3795lAiol8lMcnTp1KgBv1uzcuXPIzc1Fw4YNaV83b96kmb3U1FQsWrQIsbGxSEhIwIkTJzB69GjodDo4HA7s2LED9evXR1ZWFm7evEkze6SEkASCbrcbW7dupRw9omxoNptRokQJ7N69G2XKlEFiYiKOHz%2BOFi1aQJZl3Lx5ExEREcjMzMSECRPgcDjgcDhoZu/06dNwu9144oknsHr1aly6dImKb5CXEOnp6WjUqBEN/sjD%2B%2BLFi7Fy5UqYzWZaDqjkhpGguHLlyggICKA2BklJSRgwYABmz56N9PR0Fc%2BuWLFiNFhyOp2QJAllypShWSK3202FYAA2U5eUlIQGDRowxzSbzRg6dCjdXrt2LT0WuR8BL39PWV6pzG75%2B/sXeUzJycmoW7cuo8A5WCEVnZaWxvSr1%2BtpwE2uc/ny5XH48GFhZu/xxx%2Bn/87NzWX%2BFi9ejO%2B%2B%2Bw7//e9/AYApzQS8lQWzZ89WHZeUhysDXhIg63Q6JnC1WCzMPeTn5weTyQSn0wm9Xk%2BPQfpu0KAB6tSpQ/0IeQSq6uXE0ARaNGjQoEGDBg0PG/cd7JEHoM8VJpDkwa506dKYMmUKDhw4AADUKNrtduP69etUMU95LGJ8HRISgsTERCQkJFBOjMvlgizLqFWrFipWrIg7d%2B7Qzwj/BgCef/55REZGUh4g%2BZ7dbqcKgMoyOOFE6HS4du0abt26RdUN/f39YbFYsGvXLqxYsYJmwBwOBw0kKlasSB9WSb/koTAoKAgNGzaE1WpFWloaHbvT6YTH40HlypVRqVIlBAQE0KCIGGLLsox69eqhQoUKVBpeFNSRh2IR%2BEweybZcu3YNgDcYzczMpOMh89m%2BfXtUrFiRKqSSz2VZhslkQrt27eDxeGiATM5ZkiR07doV5cqVo14lqSLD3TzYbDbk5OQgNTUVrVu3xqVLlyDLMgwGA7UNkCSJPugTQYzw8HBER0ejbNmytKTW4/FQE3CyHklwRtYSyWiRrAw5B5vNBp1ORzNbbrebKYk8d%2B4cbUPQrl07PPXUU7BYLHQMBImJiUwpX1RUFMN5I%2BdFUL58eRqIkPtFGdSQDKjyeBF5Rlq5ubn0BUpOTg4zbmXtd3p6epHHRK6FMhDh7RLq169P/600XweAFi1a4Goec/133l25EOTk5ND1xdeyS5Kk4tsRkNJaZcBLrntkZCQTuGZkZCA3N5eef1hYGMLDw%2BF0OvHcc8/R6%2B7xeGA2m1GsWDF8/vnn9HeCh6%2BKnCKBlqpVH5JAi4hkz5eRigRauO%2BpTlek1sCpKoiaqIYsasSN568SaOGrbIVVt5zwgrANZ95cFIEWYSUvr0ohEmjh%2BuZ1VfJ%2BMgsej8hUnS9/FqlS8G1Ezubcy5c8enCBX%2BEHKBQh4cYs%2Bt8Lf2zVEhCs80IFPADVIhBd30IFT3wQaPFFWEXYiFtMf6dAC79PeO24nUUxVRctR9U9VJQ5FixIXwR5%2BHvBJ1N1XrBFtI83SBeJr/BG6yIRl3XrCh6LaB%2B/LfyR%2BnuhZfYeHEUq4/T392fKmMhDUlJSEtLS0uB2uyFJErZt24YXX3yRlor99ttvtNzKaDSic%2BfOCFWwqUm20Gg0YuPGjfThjnDdlP1t27aNlrzFxcVhzpw59EHSaDSiefPmkGUZLpcLt27dYsrg%2BOyA8uFMkiTIsgyn04mcnBxkZ2djy5YteP/99%2BnDbseOHWlZqPKhUZIkBAcHU8%2BxlJQU9OnTB2%2B88Qadr%2B%2B//55mTT755BNER0ejX79%2BdEwVKlSgD%2ByffPJJodfifkAe3u3cr7der8eaNWsQGxsLwMs5e/rppymvMioqimb9HA4Ho8yYm5uLpUuXQq/Xw%2BPx4E6eFBo5Xyf3C%2Bnv768ylSSBBnmo/uyzz7Bo0SK43W64XC489thjWLVqFZ544gkA3jm02WxYsmQJnbdp06bRTInFYmG8SooXL44lS5bQzB7g5W2NHTuWKnH6%2Bfkx87JgwQI6HtKGBDjh4eF0bep0OhVHKzQ0lOmre/fujPqjTqej1wLwCp6QbaPRiG%2B%2B%2BUYlmHLhwgW6nZ6eTrO1Ro71rgzgzigk8EwmU5HHpNfrsXHjRhpgAkDnzp0Z3xelJQkflB09ehQNGzaELMv47rvvwEPJh%2BSRkpJC%2B01PT2fOt2zZsjRg5wPugQMHCjNngPe%2BUvIXyT1PrmP16tVRrVo16PV6rFixgrb1eDyoVasWdDodLly4wKxZgooVK/oc7IkEWi5ceEgCLSIlEH5%2BRAIt3PdUiUyRWgOnqiBqohqyqBE3nr9KoIXnngi5KNwaFrbhzJuLItAiXKK8KoVIoIXrm6e9ihS5VeMRmarzWWmRKgXfRsS55cqq69Qp/Cv8AIWCENyYRX7u/LFVS0DIiSl4G4BqEYiub6GCJz4ItPgirCJsxC2mv1Oghd8nvHbczqKYqouWo%2BoeKsocCxakL4I8/L3gk6k6L9gi2scbpIvEV3ijdZGIS48eBY9FtI/fzuf/oxoeLdx3sEeEOciDr/IhtHv37vRhTJZlPPPMM1i%2BfDk8Hg%2BysrIQERFBy62cTie2b99OMz%2BXL1/GsWPHoNfr4Xa70a1bN1p2du3aNVy5coXJUHXo0IE%2BnC9duhS7du2ivlp2u51KsAPesiql%2Bp4oE%2BDxeFC1alVqzE3OAQBOnz7NfGfbtm1U6OXu3bvMQ3VGRgZ65N1gJNC5evUqfSjs2bMnbt26RRUgGzVqxPCsLl%2B%2BTAOlEydO0IyIKLORH%2Bx2O/bv309FMvbv34%2BrV69SWwFZlnHjxg2qaChJEnr37k3VGStXroyVK1ciKSkJOp0ON27cQNeuXSFJEtxuN51b8rDbv39/GgiTa6RUiFRCmfnU6XQwmUwoWbIkfvzxR/rZkCFD8Prrr9P5M5vN6NOnD%2BLi4uiD%2Be7duxlhlEmTJtE5/uOPP5hMbnp6OgYNGgSXy0UDzVOnTlGlUafTiTJlyjDldxMnTmQCMJfLRcscRaV7SuVLSZKYgPjKlSvIycmhoickQCGIVEjneTwe9O3bl3JaAW%2BmTLlGDh48SOdQaZweFBTEBNd8aWVRx6TT6dC1a1eG67l9%2B3ZGNlg5vvT0dEYt9JlnnsHp06cBIF%2BRGAIlXy80NBSLFy9G8%2BbN0bBhQzoWghs3bkCv16N///5McEwgus8JlPNUqlQpJiNaokQJ%2BlIiNzeXCYpJibbFYqHZbiVe4VXUCoAm0KJBgwYNGjQUDC2z9%2BC472CPcFXIg9727dvpG3HecFz5MCTLMiNrD3iDEvLA8/777%2BPPP/%2BE2%2B2mJYXKoKtjx4704Y2UDpIHypMnT6Jy5crYsmUL7VeJb7/9lhHREHHfJEnCuXPnkJSUhBYtWgC4J%2BM/adIkfPzxx7SdMmDx8/OjD3gulwtZWVnMOSckJNASTuXYSJsjR44wD6XKsefm5qJDhw4AvA%2B5Sj%2BygnD79m0MGDCAGoIvWrQIGzZsYIKUdu3a0Yd3Uh5JruOFCxfw4osv0jkmfzqdDkajEdOnT6fnkJ2dzcwHKWsTlbdFRERg8%2BbN2Lx5Mz3X3Nxc3L17F61atWJUF0npKODNUJHSRtJm4sSJSElJYeZa6UVHxkg%2BIwG03W6HXq9njgWwGVpyDHK8gIAAlC5dmr7IKEjpEfAGVsq53rt3L0aMGMFI9Ctx/vx5GnyIyjhLlSqFCRMm0O2JEyfSfxsMBhqkFitWjK7FEiVKMOuclOAWZUx8Geczzzyjytgu4Eo/yL2bnJyM7777DgEBATAYDKryTx5Kvt6dO3cwePBgHDhwAMeOHYNOp6MBKwn6AgICsGzZMualEznf/FQxo6Oj6T1nsViQlJQEt9tN55HwG7Ozs1GuXDk6RyVLlqSKuMTDT3mdAgIC8PXXXxd4fkponD0NGjRo0PAowEeR6YcCLdh7cNx3sGexWPDGG2/Qh5KcnBwaNBQvXpwGJHq9HoMHD2ZKtL7//ns0bdoUgLcUrnv37vSh8LPPPqMPcKVKlcLGjRsZWf8tW7bQz5988kn06tWLvo13OBzYs2cPffB6/PHHGSPkLVu20BJAwGt2OHfuXNoXcO%2BhLSkpiXplpaWlQZIkPP7440y7tm3b4quvvoLJZMKtW7fwel4Knjxg8txEMld6vR52u50pF1SWztWtWxddunSh25IkUe6bx%2BPBwYMHcfz4cURHR1NRFZfLBbfbTTMmjz/%2BOGrWrInKlSvT%2BdHpdGjevDlTMrt9%2B3aa5ZJlGTqdjgmcWrVqRduSzBbgDZYGKUoKDAYDk21JS0uj5o8AMGrUKPpZYmIi2rRpQ%2B02CN5%2B%2B22qFAl4y/MWLVpEj5uRkQGz2YyPP/6YrhflvEVHR2Pz5s2MwaSSf%2Bbn54clS5YwAYrJZEL37t3ptsfjoQ/1BoMBq1evptlBu90Om81G59Nut6Nt27aIjY2Fn58f9fwjGDVqFNauXUu3XS4XnT%2BLxYIRI0YwVgWZmZk0MImOjlaVcdpsNuqFCIApx%2BzZsydWrFgBPz8/xscxLS2NvrQAvD6HRR1TZGQkNm7cSEWOTpw4gRkzZtC2EyZMoC8WCEjbpKQkuFwu6HQ6eDweprzWF4SFhdHzUpb/kpdBubm5Qp89SZJQoUIFuq1co48//jjNRGZnZ8NkMtGKBbKPCOSsX7%2BeHqd06dI4ffo0PB4PfvrpJ9oPgdFoRLKIX5UPRJy96OiHxNnzhezyV3H2ONPtfwNnzxf%2B2D%2BJsyfiDv1lnD1%2BfD5w9jixXp84e8Lrwk2Y6DiFcvYEC4k/TSFnj1sE/184e3wb0XXxhbPH7/tXcPa4e0HI2Zs/n93%2BOzl7eZQiigcwVf9fWi9oeHAUibPXt29fWual1%2BuZh2iSDfPz88OFCxdw6NAh%2Bln37t3xxx9/APCWP%2Bbk5NCHpZEjRzLHkSSJeYDr2LEjfcArVaoUtm3bxpReKb%2BbmZnJZDHat2%2BvMlPnFRQB70M0r9opyzJ%2B%2BeUXXL9%2BHYC3VG7v3r3YsmULLBYLJEnChx9%2BCMAb7BoMBuaclRBlGpTcx9atW9OgkvS9atUqAN4AZOXKlahduzZu3ryJlJQUalQtyzL69u2L/fv3IzExEfHx8bh06RLtz%2BPx4N1332UM59u1a0cDREmSYDabaYBnsVgwfPhw2rZWrVr48ssvERUVBcAbmBM8%2B%2Byz2LZtG314Pnr0KF599VWqdshnfEQIDAxkAlF/f3/GfLtixYqYMGECE%2BQor7e/vz8kSWLKCpUZkpCQENSuXZvOR7169RAbG4vjx48z4yCZFpfLhYCAACo6YjabsXr1ahrU5%2BTk4MMPP8Ts2bORmppKBWSU58MbepPxmM1mnDp1ipbQkn5JMJKcnEz5YgSffvopI4ikLMf08/NjMujKNaYMRMxmc5HHlJmZiaioKKq663K5sGHDBvp5SkqKSgCpYh5pSKfTQafT0QwwUcz1FSSTDniDMGU2rGzZssjJyRH67MmyzFwX5Xr4/fffGe5fTk4O8wImNzcXt2/fRlBQEHr37s2UUufm5iIpKYnZR5CWllZg6SgPEWfv4sWHxNnzhezyV3H2uOztv4Gz5wt/7J/E2RNxh/4yzh4/Ph84e4qK8ny/wg9QeF24CRMdp1DOnmAh8acp5Oxxi%2BD/C2ePbyO6Lr5w9vh9/wrOHncvCDl7CiVtAH8vZ0/xQhvAA5mqi/738HdBy%2Bw9OIoU7On1evpmnzcArFevHgDvA9TevXsZBcmDBw9SAQm3282INcTHx9MyKofDgQ4dOmDmzJn0QS4%2BPp5mkb799ls4HA6mJEz5wHfhwgWmLO%2BTTz6hZYUkO2CxWDBu3DjK5TObzXA4HMz3yLmZzWZUqlQJgLeEz2azUS5i2bJlaVCQnp5OFSqV4hU6nY558M4PM2bMYB7qjUYjVfokXK4TJ07gzp07qF%2B/PgYMGEAf7s%2BdO4cBAwbQACY0NJS5NhcuXMBXX31Ft4cPH65SfCTZtezsbOrpJ0kSTpw4gX79%2BuHq1auQJIkpIzx69Cg6dOhAH3KtViuWLl1KMxzKoDE/EJ890p%2BSLwZ4SwpHjRqFoUOHMuWzZE5PnTqF9u3bM8GbMogmGRvyAuDYsWM4cuSIqnSTPPCXKlUK4eHhNCAoUaIE%2BvTpQ6%2BNwWDAuHHjMGrUKAQHBzOeeDqdDlOmTKEZWdIvCd5SU1Oxf/9%2Bhq9lt9tpMNK8eXPV/PTv35%2BxOlAGvStXrkS/fv3gdDpRtmxZeo6k9JRg165df9mYPB4PunbtSj9ftmyZymePKPIq1VqtVitjeeALlNdR6bMnSRJu3LiR7/eIUJAIkiQxpvE8SPBHfPbImiO/HUqOLg9e/KggaJw9DRo0aNCgoWBowd6D476DPfKwVrduXXTs2JFyoAjS09MhSRL0ej38/PxQrVo1KgwC3JNs1%2Bl08PPzQ7Fixej3ixUrhuDgYISHh0OSJAQGBuKxxx6jgRxRFCQcIhJwVqhQgXlzzysLKksJBwwYAADUP44ILuTk5KBp06ZMZk9ZqkoybtHR0XR/ZmYmrl%2B/TjMi7dq1gyRJcDqdzEOq0WiE0Wik52G1WhETEwPg3gOkv78/0zfJmBJREIPBAI/HQ5U9t27dWuCDpdFopH0QkHkFgNmzZ9P9siyjQ4cONHOl0%2BmoEifgDaxIwCrLMuWPEbNsZdC4ePFivPrqq4iIiEBERATWKaR/LRYLY6pO5j4gIAC7d%2B%2BmYyE%2Be2Ss5CWAMqM1adIkZl253W5mPpSBbXZ2Nho1aqTKuijXTIMGDei1uHPnDpo0aULVUNPT02E2mxnPQJLZ4332/P39adaaoHnz5mjcuDEz38o5U2bFdu/erfLZkySJZkhNJhPDRyQ8UcCbFSTtXC4XUlJSmDko6pgOHDiAunXr4uTJkwC811EZfLrdbtSrV495waHsNyQkBBaLBSkpKUKfPQKLxYLVq1czf9OmTaOm6qQv4F4Zb36m6oVB%2BTtksVgQGBhIf4cI59LpdNLMJHDv5U9sbCxee%2B01VKhQQfUSR38ftS4aZ0%2BDBg0aNDwK0Mo4H23cd7BHSvkAr1CEv78/40tGJOG//PJLbN26FW3atEFqaioCAgLg5%2BdHsxKTJ09Gz549ERERQR96GjdujIyMDDRr1gxbtmzB4MGDcenSJZqhKFmyJOXi2Ww2GrjduHGDGZdSbIQH4VbxnniAt7xr0KBB9EFS%2BVlsbCwkSWIk3nmxlVq1alEbAeWchISE0P4Ar4AKUSckAYrdbqclroD3oVbJo1OaXwPeAHfLli1MpsRgMNCxOxwOhqcYGBjIKDfyvMKNGzfSsjflAyvxORw1ahSjtEr%2B%2B9RTT1HLBjLOgIAAaqytDMiys7MZU/WzZ88C8GbeWrduzZwH8XMkc2OxWJhxtWnThslcEesHgoGKkgYi0EIC8ODgYJjNZuZBvUGDBqhSpQoAUB6k0j6CCNQA3lLeOnXqCH32bDYbXesEQ4YMYdoos9B6vR7NmzengSbxcFSuPavVyqhmKj3tlMbesizTTHDZsmWZlwd%2Bfn5FHlNmZiYj0FKnTh2V0EpISAgtozUajTRAt1qteOGFF%2Bhc80IqSmRnZ%2BP5559n/iZPniw0VSdlvPmZqlutVqZUUxm0Va9enY6V%2BDIqfwtcLhfCw8PhcDionQrgvTZmsxlWqxXJycnUA1AJUcCbH0ScvaZNWwjmJfe%2Btv/ONv/Lvv/p4/v/2vc/fXz/X/v%2Bp4/v/2vf//Txib7zd0PL7D04ilTGSVDSx2LWAAAgAElEQVS8eHFGJRDwBmx6vR5DhgxB165d8f3330OSJNjtdjgcDvqA/t5776nUOQcOHAiDwYB169bRMk5JkjCUq3kmD/3KhzPCqSOqfzw/iYcsywzHC/BmjmbMmMGYbhN88cUX6NSpU4GZCSVIFuz/2Lvu8Kiq9P3emclMJpn0RkhCCC0NCEgTAaUJLr0qsAgCAiKIgovYFhVEwLWxEOllgVWUFaQI665gA36AIM0AQXqANEpCJsnMZMrvj%2BEc7jn3JBmFFct5n8fH3Dv3nnbvDPe73/e%2Br81mQ35%2BPsOrqgx8aZnBYKAPx0RAhIzpzJkz6NWrFyMIoQ5oiouLqXk64A2o/u///o9uEwEVgp49e2pM28kD%2BsGDB/HOO%2B/QzJB6Hl9%2B%2BSW1bABYLl1RURGSk5OZftU%2Be6T9GjVq4Lnnnqt0XZxOJ8rKypgA/oknnmDKGdUBkqIoOHr0KBPMEd9EsjY2m425vllZWZUGIuosLcAqjYp89kg5L4HD4aCZKYDly7lcLuzfv7/KLG1hYSETYKv7q6iooONRz9fpdGL9%2BvV022az/ewx8fet%2BqUEgVqhtKKign63CgsL8fHHH8PhcMBoNGruu%2BqwdetWdOrUifJK1XNUFAXFxcUoKytjbDhIv%2Bp7UT2/69ev0xJQj8dDS7PV9wPhTSYkJFB7mOzsbKSlpUGn0yE%2BPh65ubmazN6f//xnn%2Bcm4uzt3v2t5riAANNP2v4lj7mbff/ax/dH7fvXPr4/at%2B/9vH9Ufv%2BtY%2BPbN9NNU6J28dPDvbUfmIAMGDAADRt2pSWOXbv3h0DBgyg/Lr4%2BHg8//zzNHsXHh6OXr160TLOVq1a0TZr1qyJJUuWMGWcL7/8MlVNbNasGZo1a8aYPROQB1JRGZU6y0NESVwuFzp37kwN0AFg6NChzIP9/fffTzNgZWVl6NevH/Pg27VrV0ZEhvzt8XgYsQ%2BPx6Mxv1aXmQJerqPaR8/f358JpMmDqHp8nTt3RhPerfYmnE4n4uPjmYAIuGXCTcRdAG9W6y9/%2BQt9cE1MTETLli3pttvtZtZV/YDboEEDDBo0iG4TYRWDwUDVLZcsWUI/V2dQSDsRERE4pZKxIqbq6kyc0WhkRHeysrIYLzo1PB4Pzp49S7fVpb%2BVISwsjFFw/eCDD2gpo8FgwLvvvkuFaUSCIGpERUUx675ixQpasgqAZj0JkpKS6LbFYtGocapfAiiKgp07dzJtkXvN5XJRPll%2Bfj6T2Q0MDPzZYzKZTIwaJ%2BA1VVcH7fz9Tc6999570aVLFzidTjgcDs197wtIWbbNZmPOr127NoxGoyYTWh2Kioo0XEA1/Pz8YDabkZqaiqysLCbDSTJ3vXr1Erb9U8o4JWdPQkJCQkKiasjM3u1D8fyUp6RfAU6cOIGHH34YzZs3R2lpKQ4fPsw8rCmKApPJBJvNBpPJRIPK4uJiJCUlobCwEFarFWazmWbbSMYjOzsbbdu2pdm7wMBAqiTocrlgMBjgdDpp5tBgMMDPzw/l5eVwuVzw8/OjPJ/o6GhcvXoVLpcL9evXR2ZmJrp06UKDNqPRqOGQkfYBbwaMlOhVlk0kD%2B92u134sBsSEgK73c5wtQICAlBWVobMzEwmY/qnP/0JV65cwXfffUfnoV7T2NhY5ObmavqJj49Hfn4%2BPd5kMuHIkSMYMmQINUEn2S9ybQDQB/fY2Fikp6fDYrHgX//6F0wmE6KiouByuZCXlwePx4NOnTph//79cLlcKC0tFc7VYDAgKCiIlibycwC8gZTVahV%2BRkpPCUcyPDwcpaWl1OKioKAA%2Bfn5KCsrQ3BwML777jsA3iA9JSUFa9asQXJyMvz9/VGrVi3k5OTQzFhQUBDsdjsjLqKeQ3h4OIqLi2npblBQEM3kAl5/wh07diA5OZm5v8k9YDKZUFxcjMTERFy9ehVWqxXh4eGIiYnB8ePHAXgDUKvV%2BrPGZDAYYDKZYLfb4XQ60bJlS/Tt2xcvvPACPX7v3r2M/QRpr1atWigsLKRc1n79%2BmH69Oma65ecnEzvH4KgoCB8%2BumniIqKwj/%2B8Q%2B88cYb1MJBURTaR0pKCjZs2MC8hPB4PEhNTRXeK2FhYXjkkUcoL5UEywaDATdu3ED37t3xzjvv4IEHHoDT6cS1a9fgdrsRERGB0tJS7NmzBwsWLMCiRYuYdQsJCYFOp8OePXs0fYowbdo0fPTRR8y%2Bp556inJnJSQkJCQkfg1wue4eb%2B/m48EdR3b2/6bdXyNuq4zzbuD06dOw2%2B3YtWsX8vPz0adPH8TFxdE3/k2bNsWwYcMAeMvJ5s%2BfjxUrVsBsNuPChQtYsWIFAG%2Bw0bFjR0ZABLj1lt9sNmPdunV49dVXadkceTh0Op1o3bo1Nm7ciO7du9PPScbH7XajoKAAoaGhuOeee5CdnY3Lly9TkRNFUTBv3jzG1wxgM2b33XcfpkyZUmXZaFhYmCZjRawvAK%2BvIZ/ZKysrg6Io%2BOabb6jnYWRkJMLDw6miotvtZtqpXbs2EhMTNRk5wFvSV1FRQfcRxUdSEjpp0iS6PqGhoQgJCcGmTZsQGRmJqKgo5OTk4OjRo/R4h8OBESNGMCVy5eXlSEhIQGpqKt1nNBqZLIrT6USxyhuLF7%2BYMGECVc00Go2oX78%2Bc/7GjRs1yrKEw3fy5EmYzWaa5S0pKYHVakVubi70ej0OHjyIjh07AvDeV48//jjzEN%2BwYUMqIELuEbVf4AcffMCsaWlpKR0LuVdIMOTxeJgMm81mw7PPPgu9Xs/c30VFRYza6P3338%2BMifAiyd/169enWStSdk2uW3R0NDZs2EDHkJOTwwj8jB8/nsmkAreCNvKCgLSlVi4VgTdVb9u2La5evYp58%2BbBYrHQlyU6nY7e26dOndIETYqiMDxe4FZWW6fTMVzSkJAQWioMeHnHLpcL165do9lD4JZo0pEjR7Bq1SoAbFbQ5XLh%2BvXrmhcJlUEKtEhISEhISEj8r/GbC/YILBYLTCYTNm7ciEuXLtGHpIMHD1IuYFhYGIKDg%2BF2uynfZ8SIEQC8mY4333yTPsASkAcw8hDn7%2B9Pg4InVL4m7dq1Q0lJCePfp%2BZyEXl68nBPMiykpPPpp5%2BmxswE6ofEefPmMWVeZFyKotCy19zcXI2vmtqwvH///kxWj8Dj8WDdunW0pPXKlSv44IMPmLbIZwBw9uzZSrMV5ByyXkOHDoXH40FeXh4URcGyZctoIHP9%2BnUaoF%2B5coUGS8AtqX6TyaThTJaWllLvQPKAT4RM1FA/KKsDP8L7JFm7yMhIdOjQAT179qTHkMwmgd1uxw8//ADAW6qak5NDyydJKeqYMWOof5z62vM%2Be8ePH6fG4ESVlbyccDqdGDJkCJ0zOY9k9TweD2OWDoB6vBGQgN/j8dD72%2B12MyWPn332GZNJLi4uZsR/2rVrR4N/j8fDcAivXr2KxMRE%2BgKgsLCQWfvvvvuOuV%2BAW6WnhCvpcDjgdrt9DoR4OBwOhmPrcrngcDhQo0YNRvyIwOPxCNcJ8AbIanGfvLw8OBwOeg2Kiopw48YNKtByz02TMKvVitDQUOzbt0/IsSS8xRLedLoSiARa0tJ%2Buqm6qDZDs8wi/z9fHJW5eWp%2BTnwwYvfJVN2H8d0pU3W%2Bb2FtC2cu7ssxfN8ijotPpuq8bchNzigDbjH4U4QV7vyARAvqi9M1ZziPm7%2BrVQ3o5k8phUoo%2BBa4RRYam3N9iRxWeH9sX%2B4Jvi/hzxR3j4ruiWq/UoKGfTHv9un7zF8HwQD5XfxS%2BHJLiNr15TeJP8iXW437iglN1fk5%2BLJ%2BmmP4juDj7wQ3IJVMwi3w5uf8tmjfP/7Bbt98icuAN2JX2XZRcLoYQuN1fh%2B/fXOBhb9VvxBkGeft4zcX7NWuXRuA92HParVCp9PBZDLRB9vHH3%2Bc%2BqGVl5dj2LBhGDFiBOx2O4KDg3HgwAEA3mwLeWNP2gRAeVsBAQHo3bs3XnrpJRqs7N69m/J3Fi1ahIEDBzJBhYlzCY2JicHq1asBgBFLcTgcjKE84M0kqkVKeO4PCfw8Hg8NHBMSEmhGiXymNk7fuXMnY9JuURnmut1umjWsVasWAG8gYjAYhEqmCQkJdL3Ig7WIuzZgwADs378fNpsNAwcOxL59%2B2iGjFyj9957D4CXg7lx40aGLyjidBE%2B3/Xr12lAx1tVVAVSStq7d28A3iB52bJl2Lp1Kz1m4MCBtO///Oc/1A8SuKWwqM76Hj16FKdPn0bPnj2xZcsWJtPF%2B%2ByNHz%2BetkECCPUa7969m87/s88%2Bw8aNGxlu7NSpU%2BnfERERjPWCv78/3n33XXg8Huh0Onp/A96SQAK73Y4hQ4bQ7VatWtEsm9vtxvLly6nPIuC1gOCFdIjfnk6nw8qVK2lAt2/fPobvCNx6WWA0GrF161a0bt0aACvk8lPh8XgYjmxAQIBQLAao2mfPz8%2BP%2BV7wgaLVamXKZIl/o9PphNFoRGxsLMOv5eFrQCsSaDlx4qebqossPDUGwCJHXF8clTlHao2psQ9G7D6Zqvswvjtlqs73LbRA5czFfTmG71tU8uSTqXpkJLutykJTcIvBn3LzJ7fqAYkW1Bena/7e55R5RQO6SeWmiI4WjI9bZKGxOdcXP29A64/tyz3B9yU00ObuUdE9Ue1XStCwL%2BbdPn2f%2BesgGCC/i18KX24JUbu%2B/CbxB/lyq3FfMaGpOj8HX9ZPcwzfEXz8neAGxBWTeMGbn/Pbon3Dh7PbN1/iMuDL/VV2SBS8YJjIeJ3fx28Lvi8Svz385oI9vV6PuLg4GgwRcYgaNWpQzywiC68OHPz8/BhRBfXDXqTqX4zU1FQYDAYmECN9ud1uKh7DPyCKAh/1Q9/Ro0c1QZS6j6CgIE2wqM4QqI8lx73//vuMVxoApo/XX3%2BdPoTXrl0bdrudGac6cxgUFAQ/Pz%2BN8iTBtGnTUKtWLYSGhjLCLSIQj7uuXbvi8ccfpyV%2BZGynT58GABw4cAD169dHt27d0K5dO834AW%2BwoM7oqEtJo4VPDF6oA1u15D5wKyvodDrpXFq2bEmzhr169cKMGTPo%2BdnZ2SgpKaEluKGhodi%2BfTtcLhe2bt2K/v37M1nf69ev02BBURTExMQwGWOn06lZOxIMPvfcc%2BjVqxe1pQDY62Sz2ZhstN1uh9VqhcfjYUp2dTodBg8eTLf1ej2T2Rs8eDAj7sOrfC5cuJDevw6HA507d8abb74JwHsvJScnM/fngQMHmPtXfe7DDz%2BMs2fPwmAw0ID750JtF/FTxFDUsNlsVZq7l5eX07n8oEpJkGxicHAw1q5dqwlwCSyCBwcRpECLhISEhIRE1ZCZvdvHby7Yc7lcuHTpEh566CFYLBYoigKDwYDGjRsjLi4ONWrUQEJCAvR6PZo0aQK9Xg%2BDwYCMjAxG9l3N56lduzZ9cGzQoAFCQ0ORmJhI5d7vu%2B8%2BhIeHIzQ0FGlpaWjSpAnNegDeB/V77rkHn332Gfz9/WlwoVaLPHHihCYgVAdw4YK3t2o%2BoVrtkBxrMpng8XgQHBwsVChdt24dLRs8d%2B4c9YsjUD%2Bsi7KNaiQmJqJhw4aoX7%2B%2BRnlRnW1RFIWWZI4aNQrffvstffAngZraLoIgKSkJAKj4BgG/ZuQ4EtiroT5PPRdS5vfll18yx6mVQS9fvkyzTnwwpl4n4qNI5kD859TH80qPvGUAP1YSAAJefiDv/6iGwWBAQkKC8LOysjIa0FWnGOrv708zuaIxnTx5kgnqHQ6HxvNRjZKSEibYI4Ex4A38CgsLYbfbsW7duirHxZuqf/HFF4iIiMCzzz7LHEdsFwAvD1CtCFsdiI8egclkQnBwMH3p43Q6ERoaCoPBwNyPiqKgqKgIUVFRWLx4MWJiYtCuXTtN0Oer157k7ElISEhISFQNGezdPn5zwV56ejrat2%2BPXbt2YcKECdi2bRsWLVpExRu6dOkCi8VCeWErVqzAkiVLcPnyZVpKBrAWEs2aNaOZvp49e8Lj8aB27dr49NNP8de//hWHDh3C4MGDoSgKHnvsMRw9epQaifv7%2B6O8vBwTJkxAQkICGjdujK5duwIAduzYwWRTTCYTfXD08/PDjh076GfdunVj5vnjjz8iLy8PgDeAUwcQJIPjcrkQHh5ObRYA0FJLolpYWlrKBIB%2Bfn6awOPSpUvUd87j8QgDz1OnTuHHH39EaGgoU6IIAE8//TT9%2B8knn2T4ayKo%2BVIEpByP55qRElOCvLw8REREMFwqAjL3pKQkZGVl0TUBvAGBWlRDHQyZzWasWbOG2VaX6anHo9PpkJeXh7CwMCiKgg8//BCbNm3Cpk2b6DEhISFMILVt2zb6d0hICAICAjSBFckE%2Bvn5wWg0MtlmNex2OywWCw0ULBYLE3yT8kURn1E9j23btiEyMpIRPFHjoYceQkZGhnAMItGgiIgIyokkCqEejwd16tTBCy%2B8QK/52rVrhW0S8KbqnTt3xsmTJ7Fq1SomOFJneC9fvsyU0QJeTi75TvMlwrwQT1xcHGw2Gy09dbvd0Ol0qFu3LsrLy%2Bl3z%2Bl0Qq/XIyUlBa1atcKxY8fw3XffMf6U/D1ZFUScvZSUn87ZE5FJNF9B0XfSF7ILd4yG7iRql%2BOx/GzOHteZL3wiTV9CznI1YwE0HB6hxxTHkar2OsFHzh7P0RNx9rjG%2Ba%2BkkLPHT1REgOIXVLQ4PDeM5/AJBsRz9m7%2B08aiGk6XqC%2Benyfap2lHMG/%2BGGHf3D3qCyfz53D2fOGm%2BsLZE126O8HZ84UjfKc4e5wVsohuqfkOib531f5OCL6ImjmIJsU1zOmUebFoEbvtC2fv5rMKhS%2BcvcxM7TE8Z48fi2gfv12FB7DEbwe/uWAP8HK%2Bevfujfnz5%2BNPf/oTxo4dC6vVijVr1tASqhdffBEpKSkYMWIERo8ejVatWmHSpEnVtm2xWLB06VJcuHABffr0wYwZMzB8%2BHAqh56RkYE333yTinXo9XrMnTuXCSTr1KmD%2B%2B67Dzdu3KCCGSR7Qx5SKyoqGL7d448/zoyDZMcA70Oq%2BgGVBAJTpkxBnTp1cM8991AxClIaRh7iiaE7AXkIV4NkIkjQcE31cEEUQ6dPn44xY8YgNzeX8hABr8Kmujx29uzZAIC%2BffvSto1GIxPckJJNwJvx/OKLL6gpttPppNk7APjrX//KjDU1NZVyLInxNQEJrM%2BePYvk5GRqnA14g5mmTZsy2SqyDuXl5YzAiMFgYEpHyXqQjJ/b7UbLli3h8XgwaNAg2Gw2JgAqLi5mFEp3795NAw9iAq6%2BJur1JgEJb3BPQAJpIhBSUlIi5Ih5PB5s2LCBbrvdbuaFwX//%2B1/mOqjXA/AGe9dFT1IQ8%2B7sdju9f2w2Gw1er1y5gqZNm8LPz0/z0oJAbf7OY8SIEZg2bRq1QOFBbDTUNiKANyAlL0vIvEjf/MuOM2fOoKKiQpOxbtasGRwOB/WOBLxBpNlsRt%2B%2BfaEoikYASVyaKYaIs3fy5E/n7InIJBqejYiM4wvZhTtGQ98Qtavx9tQe4hNnj%2BvMFz6Rpi8ByefncPaEFcMcR6ra6wQfOXv8yzYRZ49r/KbYL4WQs8dPVESA4hdUtDg8N0zEX%2BUGxHP2VCyKW6iG0yXqi%2BfnifZp2hHMmz9G2Dd3j/rCyfw5nD1fuKm%2BcPZEl%2B5OcPZ84QjfKc4eXyQhoo/x3yHR967a3wnBF1EzB9GkuIZVjy63MHYsu%2B0LZ%2B%2BmojyFL5w97t9AAFrOHj8W0T5%2B%2B%2Bai381MmMzs3T5%2Bk8Ge2WzGpEmT8Pnnn%2BPo0aPYu3cv3n77bUaww2KxYNasWThw4AD27NmDadOm0YApOzub8QTr168fk2VLS0vDP//5Txw9ehRff/01xo0bx2Q%2BunfvjtGjRwMAWrduzShgrl69GhMnTqR%2BaUSYxel0IiAggLEMyMrKoufx/KMTJ07Qvz/55BMmGFyzZg2aN2%2BOixcvYtCgQTh16hQ9Py4uDvXr16fZuYqKCtStW5eea7VaGW4X8d0DvIIyM2fOZMaxcOFC9O3bF1arFVOnTkVubi5j8l1UVIQWLVrQbY/Hw2SMUlNTsX79esyZM4ceEx8fTzNnDz/8MGbMmEEfwPV6PRo3bkyPHTVqFA1uQ0JCsHDhQnotatWqhbi4OKZvNbp27YomTZqgQYMG0Ol0yMrKomWY6vVOS0tjLAF46wneLgHwcvwiIiJQUVGBAQMGMJy9bt26YevWrZg4cSIAL8%2BMDzDU91OdOnXo38XFxQgNDWVKIkkArZ6jOrP1wgsv0JJmNQjHDvAG%2BRs2bKDzslqtGDhwIL1OdevWZXisPXr0YALO1157DUOHDqXrAwCTJ08G4P2uDRkyhAk6yQuEkpISDBkyhFqPqF%2BK%2BIIVK1bgsccew4ULF2CxWBg%2BnMlkgsPhgE6nQyb3VjMyMlITvBHwvpQ6nU6jQAp4VWBr167N8CcvX76MnJwcoeekTqfDiRMnNP6ZlUFy9iQkJCQkJCT%2B1/hNBnu/BhDOz969ezUPdzt27MDOnTsxevRoqmCoKAqioqIwdepUYRmjGsHBwQxPrH79%2BjRTRjB//ny0aNECOp0OgYGBlDcXHBwMo9FIAzqr1cpkU0jAScZgsVjo53369EHfvn3x2Wef0eM7duyIpk2bomvXrjRA4qXleZ7RvffeS4Nnp9OJ2NhY9O7dm54/ZcoUxlqAlJCStoitBjmfBB2EZ9aqVStERkbi6aefZrJ3ISEhUBQFK1euxM6dO2E2m/Hjjz%2BitLQUM2fOpPMkfD%2ByZqdOnWKEOAIDA9GrVy9aNqienzqgGjhwIABvRlH94D5jxgzUrVsXY8aMoeW0xPNOLfZD2oqJicGDDz5Iz8/Pz2eCGp6jpw4sPR4PLly4gJo1a1KlVtF1mjp1KtLS0pi5kFLPwMBARtgI0GY709LSaFmnx%2BNB586dMXfuXADeEk7ez5H04/F4UFpaCpvNhsDAQEZZVASTycT898Ybb%2BDBBx9E%2B/btGZVMwBu0hYeHIywsTJPZI9m6yqC2ErFYLDAajZryzq%2B//hoXL15kykFr1qyJw4cP46WXXqLHmembT%2B%2BcfxTW0ElISEhISEj8VMjM3u1DBns/E4TzZ7VaMWLECOzbtw8XLlzAunXrqJhESEgI3njjDQDecsgtW7Zg6NChDHeqU6dOmraffvpp%2BuDdsGFDnDhxQiPwEhYWhvnz5%2BPQoUOMhQApoSMleBUVFRoj6/vuu49y5AoKCmgWZtWqVbj//vvx6aef0kDHbrdj2rRpmDlzJjZt2iQsL%2BQzHF988QUaNWoEADh8%2BDAGDBiAhg0b0uCirKyMMaBv3bo1kzXKyMigAXTfvn1phuzKlSvo3r07Vq5ciRYtWuDMmTPMAzrxjnvsscfQtm1b/N///R9KS0vhdDrRp08fmukxGAywWq10jQICAhjOndPpxJo1a5iyP3IumeuoUaOwYcMG%2BPn5YcuWLXjrrbfo%2Be%2B99x4WLVqEDh060PJJtShNUlISgoODaVsNGzbEN998AwAYOXIk/v3vf6OwsJAGg6%2B//jqzvmou3fjx4zFo0CC6Xs888wxd5yeffJIe98Ybb%2BDtt99mAkCSXS0tLYXVamVsRAwGA80CRkVFISYmhhFJyc3NpUbxLpcL7du3p58pisJkJv39/dGsWTMEBQXhgw8%2BQFVQm6rb7Xa8%2BOKLOH36NH788UcqxqRGUVERioqKNJm9qKgopkyajAvwvhA5fPgw3c9zKMm9WVxcjPT0dKZENyIiAnl5eTh06BDdx2cEfS3llAItEhISEhK/BQhLzn8hyGDv9iGDvZ%2BJpKQk%2BuB57tw5TJw4Ed27d8ff//53YUahMpXLykCCyfPnz9NSx6pAHhLPnj2L3NxcJktD%2BHCA90H0888/pwqCKSkplAd248YNKmpDHlhJ4PDVV18xPm1VwWaz0aDQ4/FQ2X2CiooKPPbYYwC8gdW///1vrFARkJcsWUKP3717NzWfd7lcuHDhAj7%2B%2BGM0aNBAU47JgwRz%2Bfn5zDztdjvMZjM912azMWWykZGRWL9%2BPZMJJMblBD179kR%2Bfj5CQ0OpfQRpb8OGDXjnnXdoNi8qKopm6lwuF86ePUvFTABg5syZNFOcnZ1NBXBIf8RXkWDEiBG0rwULFqBnz56Uszl9%2BnR63MCBA%2Blxer0eixcvpm1GRkYy2eKzZ88yAenMmTMpd7KwsBA9evRgxrBhwwYU3HRGvnjxoqacVp0V/PDDD1FaWoqcnByNVYgvcLvdsNlsNAtMEBsbi7i4OBrQqxEcHKzJsJG5FxcXMwFcfn4%2B7HY7DZjV39XS0lK0bNmSnm80GuFwODBgwIBKxytSmxVBJNDStu2dEWjRHCMyvuYPEpWfcrxNjbaFQJzDF6ESzZBF/otcBcHPMksWKE7wQhBC8RWuIWGSmJu7L/P2SQjEB4EW/jzepPxniYcI9vECGYBAFEV0ECfQwl1KzTYA35Q2OP9MkTk7L1ajufV9EDQSadf4IhhUnaCIL%2BImondFvhibay6M6CaoxrhetOT8tRJ%2BF7idPnTtU9/8WojWhl9jUd/V9eXTb5TgIH6XSKsIKlV1AICKS1/pPlV1FQBA9UKfghc7E71I5UVcPvlEewy/j9%2B%2B%2BQX6owVHvzfIYO9nwmAwoHv37lVK3BPFTgCM1LtaaVG9nwRgERERjHgGQVUBIzGW9/Pzg8lkYjJeanVMoghKgrFTp05pBFpEIhqLFy/WBJyPPvooPW/UqFH4y1/%2BQj9TZ3p4HDt2TMPdUvPFtm/fTrMeubm5Gq%2Bz8PBwBAYGIiIiokp%2BlPqzsWPHMnYA6vWx2Wx48cUXAXgDu5MnT8JkMjGliHxQGR8fT9ecgLTN%2Bxk2bNiQ4U0ajUYmGI%2BMjKSB9MGDBxmBGhH8/f1p1o2oq5K%2B1eWU6oBSp9MxY2rUqBEeeOABZuzBwcG0na1btzLG5OqyRcBrUcZc/EsAACAASURBVKK2O1BfT7PZzNyrQ4YMoX2Lslk/Beo5FBUVIScnp1KbisrKON1utyYbJ4LJZEJcXBxT8ulwOGA2m3HvvfdW%2BqKhQYMG1bYNiAVadu%2B%2BMwItmmNExtf8QSLRB07tQqNtIRDn8EWoRDNkkTehihtcWTvV9iVQnOAvm/Aycg0J6Z/c3H2Zt09CID4ItPDn8ZajP0s8RLBP5CKiEUURHcQJtHCXUrMNwDelDU6lWGS1yovVaG59HwSNRNo1vggGVSco4ou4iegn0hdjc82FEd0E1RjXi5acv1bC7wK304eufeqbXwvR2vBrLOq7ur58%2Bo0SHMTvEmkVgX8xyFFyhPu6d2e3ObV2AABvNyR6Gc%2BLuPTvrz2G38dv3/wCVePm9D%2BFzOzdPmSwdxuYMmUKtQFQQ6/X49FHH0W9evXovsp8/dT7SVlcVFQU6tati%2BbNm2tsEyp7wGzRooXG54zAZDLR83hxiujoaBQXF6NGjRoa3hVwK7PXpk0bRhnTZDJR30Kj0Yjr169jmEpBimSaCNQP6Tdu3EBcXBwzRlJWCnhN2dWBiloNceTIkVQgRj1fvrxPHfCmp6fjm2%2B%2BYeZdVlZG%2B9fpdLRUUq/Xw%2BPxMNlQt9utsTLYuHEjM6erV6/SY/z8/JhyPKfTSe04AG/AoJ7fnDlzaBloQEAATp48iaowb948GqyT60nmpg4i69atS8chGpNaGMbj8eDGjRu0nR9%2B%2BIHJfvHCPcnJyQy3c//%2B/fTv8vJyZq1DQkLoeKdOnUpfEoig5uv5%2B/tjypQpqF%2B/Pr0%2B6jmQgJfwN3lU9iLA4/Ew9w4ZK7me5GWHxWLBt99%2By8zlypUriIqKQtOmTaHX64UvYNRCUVVBCrRISEhISEhI/K8hg73bQFRUFD7%2B%2BGPcf//91F4gJCQEU6ZMwQsvvMAcW5mvn3o/2Uf%2Bz4uwjB49msmAqTFlyhQ0bdoUOp0OBoMBLVu2xODBgwF4AwK9Xo%2BgoCAMGzaMWhcAtywfdDodunbtWmn2cOPGjUzGT1EUak9QWlqKEydOMA/QFy5cYIIhtRUB4A2qyNxFmZm2bdvSv4uLi2lbe/bsEQbXW7duRXp6OoKCgmA2mxEYGIjIyEiYTCYcO3aMBhvkXJfLRR/KSZkg2e92u3Hu3DlGdZTHuXPnGA%2B2t956SyPxT/o7ffo049vHo3nz5vScsrKyKktTCUhwy6/F/PnzmfPJmHi7hFOnTqFGjRqV3k89e/bE3/72N7r9UywFCIgoUV5eHi2pvXz5MubNm1fpOZs3b6b/bdq0CY888gj0ej06duyoyQbn5uZCURTMmjVL86LC4/FUOeZoVUogNjYWBoOBUTq9fPkyVVFV38eXL19GRkYGli1bBqfTqXnJkJaWxojrVAXJ2ZOQkJCQ%2BC1AZvZ%2B25DB3m0iNjYWb7zxBr755hscOXKEirCoH7irsnpo1aoVsrOzAXhLA7Ozs6kZOC/CMmHCBMYiQo3o6GgsW7YMhw4dws6dOzFz5kwMGjSIjsPlcqGkpASrVq1CXl4eLBYLkpKSsGTJEgDebMzAgQNhsVjoOdnZ2YyEvrosjg/QCD%2BNgC9vTUpKYgKTq1ev0rLR8PBw5qHZYDAgMzOTyUbqdDooioL09HRNMORyudCnTx%2BcOHGCZuzmzJmDVq1aMVw7EggD3gzSa6%2B9RttQq4F6PB6cPHmSKcPlg6q0tDQAXo%2B8EydOMJlMYqpO1ukax7nhLQEefPBBRn1UVEarxt69e%2Bkx/HX4/vvvaUZzypQpzPzU/RYUFODMmTOUd8ePKSMjg7EcqKxUkoAI8pC2FEWBx%2BOh2UEiwBMaGoqnnnqq0nb69OnD/KcWH5oxY4bm2nfq1ElY8nzmzBkawJNrR4L3oKAgpoy3oKAAsbGxTNvr1q1DvXr14Ha7mZcjTZo0QXx8PD744APo9Xr6nSDnVrdOaog4ey1b/nTOnqhLDW9FxKviiSyiTGh1vDlRQO0D300zZlFZLUec8mWed4yzx62FsCKYG3O1YxEMR8jZ47lXAr9Lvi%2BepyZql%2BdeCfvmGhZdFg0HTsS35MbMnyM6RbNTtIAcKUolxlzZIdo5CNrlOXq%2BmKr7wjHTtCO42X6OqbovHFJfiHN8Oz%2BX68mf6Iuhuy9987eE6L7RrJcP3DpfztHMQTRAbp%2BAXgusW8duizh7n37KbqsE44TbAPDRR%2Bw2z%2BEDtEbrvvAF%2Be2b/3bczeBIBnu3Dxns/Y7hcrloaaE6%2BCovL4fVakVGRga1H7h%2B/TrGjh2L4OBg%2BuC/d%2B9emr27du0ak%2Blyu91UJRPQZn4GDBiATp060Qft9PR0pKSk0HEUFBRQZciioiLmQdvpdGLnzp30wbmgoAB%2Bfn4wm814%2BeWXhXNVZ0Tcbjct4SP7R44ciXfeeYcGSTabDd9//z09p6SkBKGhoTQYvHjxoibYUa/hoUOHqD9b//79mbbi4uKoyArpi2TJiAm4Grt376bBnsPhoJ%2BTfcTPjuC7775jttUlosCtzKL6OD8/PyZb6nK50K1bN7o%2BfGZs0qRJWLZsGd1WlxsTTLjJBwgICMC%2Bffvo/oqKCibLCwDLly8HAHTu3JkJZg8ePIiFCxfSbYfDAWJcb7PZsGXLFjzzzDPIyMjA0KFDGSVSo9HIBOxqqFVHyX1E9jmdTkboRafTIScnhxnX4cOH0alTJ4waNQpnzpyh%2B1u3bo1r166htLSUuY7ke3b8%2BHFcvHhROCYeIs7e/v0/nbMnSsZrksMiXhVPZBFx9qrjzYlIND7w3TRjFrklc/ekL/O8Y5w9bi2EPCVuzNWORTAcIWeP514JnMP5vniemqhdvlBB2DfXsOiyaDhwokw2N2b%2BHGHym98pWkCuQoJzpREdop2DoF2eweCLqbovHDNNO4Kb7eeYqvvCIfWFOMe383O5nvyJvhi6%2B9I3f0uI7hvNevnArfPlHM0cRAPk9gnotcBNiyYKEWevTx92u1evqrcBQPWMAUDL4QO0Ruu%2B8AX57Zv/dvzRgqPfG2Sw9zuGXq9HfHw8QkNDNRw5wCvCQXhb/v7%2BWL16NSZPnkwDpZkzZ1L/tzFjxmDVqlX03MjISPTp0wdxcXHUSw64lUUZMmQILBYLLVV75ZVX8Oijj9ISt%2BvXr9PPyMO4Orv0wgsv0Kzi999/j2nTpiEsLIwGJersaGWYPXs2HY%2BiKEhNTUXPnj0BeIPAjRs3AvAGVfPnz8fq1aupOEp%2Bfj7DqyPnENy4cQNutxsGgwH79u1jyiHDwsKwefNmjS0EmatarAUA/vWvf1ERnfj4eMqPI7YcRUVFGDNmDD2eLxP8/PPPmbUjQci1a9doEN2gQQN8%2B%2B23TAZVve4Gg0FzfxDF1Nq1a1PepLr8kYzDbrejVatWCFf9S2fjXpXHxcUhMzOT2igAwOnTpzF8%2BHBGkMbpdDL8yP379yM9PR1btmxBly5dGLGe559/nhE7UmPbtm3C/QAwfPhwHDt2jG5XVFTAYDDQYF6v16OwsBDHjh3DqlWrmMC1uLiYKckVCTSJ7ElEkJw9CQkJCYnfAoQKtb8QZGbv9iGDvd8xTp8%2BjYsXL6Jz584AvOWRAQEBVNiiefPmePvttwF4H3D79euHxYsX0wf7TZs20XLFqKgoWmYK3Mo67dixg3ng1ev1NMCYPXs2FZ3x9/dHv379qJCG0%2BmkAYqiKLDb7aioqKBtbd68maqTknMrC%2B70ej02bdqEbdu2Ydu2bdi0aRPatGlD2w4JCcGHH36IHj164Pjx4wgJCYFOp6MlrElJSfjrX/%2BKgQMHIjw8nKpbNmvWjLZB%2BiEoKyuDxWJB165dsXz5cuTl5dHPIiIiUFJSwgiEEOEUl8uF06dPM%2BOPi4tD48aNAXjLYZcuXYoOHTrQDNHKlStx7NgxujbBAmVFNSeSBMmJiYmMPYjL5YLT6aTzIPdBRUUFrl%2B/rsleksAuKSmJXnc1z6xfv350TuvXr9cEx2pcunQJ48ePp16IAPDxxx8jMTGROa9Zs2aoV68eNm3ahJiYGISGhiIrKws9evTA559/DofDQRVsB4neZN7Ek08%2BSc3OeSxfvpwpO05KSoLH46HBvMFgQHl5Oc6cOYP09HRGzdbhcDBBsYhjd%2BHChUrHJSEhISEhISHxS0IGe38AfPXVV5gwYQL1syPZOvLgTkQo1q9fj5deeglnz56l55KAK4qrE1JnVIh6IvlbXRLIZ17U9hJErfSee%2B7BwoUL8fbbbyM4OBixsbEIDQ2tNGujBukvMTGR%2BY886JvNZhQXF2Pw4MHYsmUL%2Bvfvj%2BLiYphMJjqWc%2BfOYcaMGVi/fj1q1KgBj8cDRVGQc5MQQoITdVYsJCQEVqsVGzduxCeffMIEQevWrWOCAEVRqECJx%2BOhATQJrLKysmjJpaIoOHfuHL788ktcunQJAPD000%2BjrKyMtqkOVIjIzKBBg%2Bg4SZB58uRJGpiolTdJ5ox44fn7%2ByMlJUWztkeOHAHg5aWSY9XXhAgAAd6AMSwsrFKBH5PJRLmsbrcbgwcPxqpVq3Dy5EkEBgYiOjoaiqLg4MGDOHnyJN59913k5eVRvuOWLVtouSopJ67KiuTKlSsaewUSxMfFxTEB5o0bN5jMZmBgIEwmExISEnDo0CEmk2c2m%2Bm5vDgLAeHcVgcp0CIhISEhIVE1ZGbv9iGDvd8xiLBEaWkpZs%2BejYceegijRo3Cvn37oNfrafbLbDbDZrOhb9%2B%2BeOmll36SyMTtgHD6Tp06hYkTJ%2BLVV1%2BFzWaDwWCo9EH6p4JwvBYuXIju3btj6dKlAECVOwMCAuByufDcc8%2BhT58%2B2LVrFxRFQXR0NGMHAbB%2BheRvs9mMbt26MSV50QLzJ5KNMxgMtISQPNgHBgbSkkiPx4OaNWvi3nvvRa1atQAAc%2BfO1RiEE5DAb62KnE3KRwMCAmg55fHjx5nrqlaZtNlsOHHiBO2fQF2OyAvwAKD3D%2BANHAsLC5nz1Zk1u92ONWvWIDc3F2VlZWjRogViYmIQEBCAXbt2oaCgAB6PBxaLBS6XC%2Bnp6fD394dOp8Pnn3%2BO7t274/PPP6dtVYeoqCikp6cz%2B4hFRU5ODpN1vXbtGmNJEhISgpiYGJw5cwarVq2iLwVIu%2BRau1wuhIWFacpq1YF1VRAJtDRsqBVo4ctn%2BG3R11WzRKKSUV6xQ1Snwwm7aMQuRNeCy/CKDtFoHYhUH7jORMOrVnhBIEzjk9gFp7QgPKaaefowJeFl0YxZ5BzO9c0n1VXv6yrtWyT8omlI5H7ON8Srwwj23XTpqfKU6gRvROPLz9cewi%2BXpuBAcGH4aye8Lj4ID/GXTnPPCibFt%2BODVpF4cbjORYdU15cvcxId44voUXXfVV8M3UVro/mKC4Sm%2BPM0vxM%2BiCAJrVk5UZzz5wXHLFjAbr//vvYYfh9vkP6Pf2jPWby46m1AKw7Dm6yL9vHbQjWlXxYy2Lt9yGDvd4z09HS0a9dOKOUfGRmJXjdJv61bt2Y4X%2BqHdLX3H4Fer2cUCiMiIpiMnfrhWO0pyLd3//33IzExUVMG2qJFC9pHdTYEfH88nnnmGQ2vSqfTUXGRcePGaYzRLRYLrly5ohEZUYNkJR0OB8aPH8%2BsGQniSL8JCQlMJkldvkrmTKwR3G43Ll%2B%2BjD179uDUqVP0uOoCHJJ9CgwMpJnDsrIyGviFqQQT9Ho9YxpvNBqF3DMiWJKYmEizZOrrPn78eCYbxpdN6nQ6mr0i4yDtTJ48GW63G2VlZWjcuDHMZjP8/f2piMro0aMpF9TtdmPOnDk0sxccHFztfREcHEyVRnm4XC6GTymad0ZGBhITE/HZZ58xAi0ZGRl0nn5%2Bfrh%2B/Tpja6HX66u02VBDJNBy7JhWoIUXj%2BC3RQlOza0rElLhFTtETtKcsIumMlb0HeHKjEWHaC6fSPWB60w0vGqFFwTCND6JXXBKC8JjqpmnD1MSXhbNmEXO4VzffGW3igZbad8i4RdNQyL7Gb4hXh1GsE8lRl3pKdUJ3ojGd7OimwG/XJqqd8GF4a%2Bd8Lr4IDzEXzrNPSuYFN%2BOD1pF4sXhOhcdUl1fvsxJdIwvokfVfVd9MXQXrY3mKy74befP0/xO%2BCCCJGQFcL/1Ah0zYNw4dvvJJ7XH8Pt4g/Thw7XnqHj8wm1AKw7Dm6yL9vHbPloJSfy6IYO93znmzZuHoUOHwmKxQFEUGAwGNGrUCGvXrqUZiRdffBENGzaEoigICAjAiy%2B%2BSM/nvf/I34TPBnh5VmRb/TfZ5s8l/9fr9Vi6dCkaNWoEnU4HPz8/9O7dG6%2B88orwXBH4/nhkZGTgrbfeQkhICBRFQVhYGObOnUs920aNGoXhw4fDZDJBp9OhXr16%2BOCDD6iH2nPPPScMDIhQiMvlQs%2BePZnggwQZbrcbRqMRHTt2pJ85nU4aZJFMUrHq7WBkZCQmT54MRVFQs2ZN9OrViwnEUlNTaXCrDlKLi4sRGBiIFi1aMGqQZFzqMs2UlBSa8YuMjGTaCQ4OpgE5yfaVl5dTVdZmzZrRAK5z5850DqWlpQgODmb6KS0tpXMncyYlpIMHD6br5HA44HQ6ERsbS9t75plnaJ%2BAN6gnfM/g4GDUqFEDpaWlGlsLgtOnTzOm8Gp4PB6G9xgUFMSscVFREfr3748DBw5gEyd53apVK4SGhkKv1zMCRAREAMgXSIEWCQkJCQmJqiEze7cPxfNL1exJSPxGcOLECfTu3RsAaHbJaDRCr9ejvLwcer0eBoOBZttq1KgBl8tFgwu9Xs8oSgYGBjKBy5gxY7D4ZsmF2WwWcssAr//g7t27AQANGzbUWDYEBgZSq4Zt27Zh%2BvTp2Lt3L6Kjo5GXl0e97gBvdu7ChQvweDyoVasWIyKiKAoyMjJw6NAhREVFYefOnUhOTobBYIDT6cQ999yDrKws2O12ZGdnY/Lkyfjss89gMplQUVHB8MwsFguT6Ro1ahS1cAgKCqJG9DVq1KhyjMnJyVQUBvCW4Xbo0AHJycn485//jGnTpmHevHnYsGGDULhn8eLFVHyIh06nQ/v27el56gyr0%2BmE0WjE0aNHsWXLFkyZMoVRmiUB8KBBg3D%2B/HlNsPnVV18hNjZW2C%2BP2bNnY8WKFcy%2B8ePHM5YmEhISEhISdxvFxVp3j18KogKDOwFRlfrvFTKzJyHBwSUgHDgcDpSXl8NgMGDUqFGMXQDA8tuqKzHcv38//Zsvz4yIiEB6ejo8Hg%2BuXr2K5ORkNGrUiAZ6/v7%2BNDgpLS2lpZZ9%2B/bFwYMHmRJS9XuccePG0W1eLbJ169aU30b6VOPxxx9nOG5EbCY8PJzJiCmKoinlVJcHJyUlabiYavsH9Rh5vuS4ceMoR5CocY4cORJxcXEQgfBByVr17duXqswajUbGBL53794YPnw4ve7k//fffz8yMjKEgiuPPvooDfQaNGhA9w8bNkw4HhHuOmePJzNJzt4t3E3OHr/zDnH2NH39gpw9lQ1npadIzp4Xd4qzJxrfH4KzJ/jB4dfvF%2BXs8Xw8Xzh7//wnuy05ezKzd5uQmT0JCQFSU1PhdrsZD0GCgIAA3H///fj3v/8NwMu96t%2B/Pw3I0tPTkZWVRY83Go1o2rQp9u7dy2S3KkNAQACCgoLQs2dPuFwubNu2jVo7kOCFfG1F4zOZTEhKSsLFixdpli0xMRHnhf8SedGkSRMcOnSIbquzkxEREXA4HCgpKUF2djaeeOIJfPnllzTzp0ZwcDAcDgdsNht69OiBESNGoH///gCAqVOn4u2334bT6USTJk1w%2BPBh6HQ62o96jOnp6Th9%2BjR0Oh3KyspgNpsxdOhQLFmyBGlpadiwYQMmTJiAPXv2YP/%2B/XC73Vi9ejU%2B%2BeQTnD9/Hn5%2BfoyxuhphYWF49913qY%2BgoiiUV1lUVASdTofjx4/D6XRixowZ%2BOijj6hCK8ns7dy5E6NGjRJeg%2B3bt/ukyCnK7D311ERMmDC%2BkjMkJCQkJCR%2BedzNzF4lLkq3DWEA/zuFzOxJSHA4ceIE/Pz8EB8fL1QFbdu2LbVlALyZIXUGSx3oAd5s3N6bcnQlJSVVqp0qioK0tDQUFBQgOzsbu3btYvwHiaIkH/QNGDCAtmG323HixAlYrVaaZRzHk8RV0Ol09DjSjzqDePXqVSZwat%2B%2BPQAIA9YbN25QPuCWLVuwcOFC%2BtmyZcvoOdnZ2fB4PHC5XFQIRz3GoqIi2O12ht/4xRdfAPAqiwLAzJkzkZqaCsBrT/GPf/wDTzzxBLZs2VJldrWkpAQtW7ak24GBgbh06RK1YCAoKytDdnY2HnnkEeZ8p9OJp556CoCXw7iYe6N6XZQxEUBy9iQkJCQkJCT%2B15DBnoQEh9OnT8Nut%2BPixYuIiIhAr1690KhRIxrQbd%2B%2BHQkJCdRAfvfu3Uxw0aBBA0YAxGazUdEbg8EAk8lEAylFUeDn58cEc8eOHUOjRo1w5MgRPP300/D396d9E7GY1NRUGnAAYERgunfvjhEjRkBRFJo1W6CSf%2BYDIbfbjYMHDwLwZtcAYM2aNcK1ef3117HvZk0WaYf3uyNzmThxIlqpZPiuXLlC/x42bBj8/PxQq1YtWsq6YMECem5oaChiY2Npmaa6XJT8/dJLL%2BHIkSNo164d/vOf/yAwMBCRkZFISEio0q8uOjoazZs3p9tWqxW1a9fGq6%2B%2BSgV0unbtis2bN2Pt2rWMt%2BArr7yC5s2b00DtwIEDePzxx5n2SwWlgyJInz0JCQkJCYmqIcs4bx8y2JOQqAQWiwUmkwlbtmzB0aNHaRatQ4cOmDt3LhUXUQunJCYmon379ujXrx/dFxUVheE3pZOjo6NRVlZG22rTpg3mzp2L5557DoA3kOnatSvq1q0LRVGwZ88eJhPocDig1%2Btx8uRJzJs3j55z33330WN0Oh369u3LZBvV3EASpKq3GzduDOAW93DOnDnCDOTmzZtpuSexcyBehgQkYNmwYQMWLFgA/5u64aGhofSYRYsWwel0IlClmz1y5Eh6blZWFlJSUvDxxx/TNh0OB0wmE7W9GDt2LCoqKhASEoIHHngAAwcOxOjRozFw4EB4PB5GydVgMKBx48ZQFAVXr15lspI6nQ5/%2BtOfMG3aNADeTN/UqVPxzjvvoFWrVnStPB4P1q9fz2RWDQYDHn30UWb%2BxDKjOog4e126aDl7fKkJvy3kNvE7t2zRHsPxTXBTDIjBf/9bdbvbtmnP4TKbQr4bf2/dfNnAQJU9B8QlN3ylriZZqiFs%2BcjZ40g%2BwnIfjlfKHyPoWjtvUZbfBxJXtX0J2tXMU%2BBHpuGGCdrRDEe0ONw%2BX9bcFw4Xf2%2BJeJx8Yp2/zYXtcp0L39dw3CURF/XncOKq5Z2K9vnQkC/t8M2Ibkf%2BOyZcP26n6DeJ75vfFl1LzU5B59Xy8VD9b6jw%2B%2B3Dd1UzTxF1gB%2BgqKKjOiNAH75jwgXkfxhENzbPyeOPucnhC7l8XHuuRLXweDwYOXIk1q9fX%2BVxOTk5eOyxx9CkSRN069YNO3fuZD7fvXs3evTogYyMDAwbNoypLvMFkrMnIcEhKysL/fr1g9lsRmBgIIqKiqDX6%2BHxeOBwODB69Gj85S9/QZMmTVBeXk6zeDdu3IDRaITL5YJer6eiJjVq1MDXX3%2BN5ORkKIoCo9EIp9MpFIJRFAV9%2BvQB4PXn%2B/vf/077drvdSElJQVJSEp599lns3LkT06dPR3R0NG0f8Gb/3G43LZMEgJo1awqN0QGvZ%2BC1a9dw7do1GsCqVTV79%2B6NLVu20JJLEjiSY41GIyPgEhoaiqKiIgBAfHw8ioqKYLVahfxCNSpTJlX/RIWEhMBkMuHbb79F586dkZ%2BfD4fDgYSEBGRmZmL%2B/Pk4cuQIFa/h2yNtzJkzB0888YSwjwYNGiA9PR2HDh3C2bNnmc9TUlKg1%2Btx9uxZlJWVUREYUl4LAN9%2B%2By2iRd5oHCRnT0JCQkLiNwGd7q6lw4Q%2Bp3cAwhcXdxButxszZ87EmjVrMGvWLCYJoIbH40Hv3r3RoEEDjBs3Dl988QUWLFiArVu30me37t2746mnnkK7du2QmZmJ06dPY9OmTZrKqsogM3sSEhz0ej3i4uKYMkU/Pz/UqFGDMQoPCwujXzSSvXI6nVAUBaGhobTcUJ1FI16HRM0zkHOEVZd/jh8/Hn/729%2Bg1%2BtpkHTy5EmUlJQgISEBDocDbrcbNWvWZPpxOBxwuVw0%2BNDr9YwyJY977rmHybrxmDRpEg0a1fw10n64yoBavT4AGJEYdUClXhOyTqS8VV2ySTKW5Pi6deuiqKgIO3bsQG5uLh3XpUuX0KtXL%2BzYsYNyBiuD2WzG4cOHqXIobyhPfkTP3pQ0/Oijj%2Bjavfbaa1i7di1z3V955RVmbUUG9SJIzp6EhISExG8CnBbBL4nfYhlnfn4%2Bhg8fjh07djDPdSLs2bMHOTk5mD59OurWrYuxY8eiSZMm%2BOSTTwAA69atQ8OGDTFy5EjUr18fs2bNwqVLlyilxhfIYE9CgoPL5cKlS5fw0EMPMWb0jRs3RlxcHC0PbNCgAYxGI3Q6HcxmM/R6Pc08xcfHU6uAIM4kRqfTITY2FvXr10ft2rVhMBig1%2BsRHR2tCch69eqFqKgo1KpVCyaTCR6PB7t27cIPP/yAI0eOAAAOHjyItLQ0JlNIMoGKoiAoKIgJ/gBv8ESCspo1a9LyTcIJNBqNdCyxsbFISEgAcIt3ZzAYaFBTu3Zt2q6fnx9jEg94g0FFUdCuXTsA3swaEULR6/Vo3bo19Ho9SktL6bgJSFBMxnLo0CFUVFRg8eLFCA8Px9ixY2GxWOh1SklJQa1ategYRXC73di6dSuaNGnCzBnwBtvPPPMMOnbsSNcoIyODBvMOhwN2u51mDBVFwd/%2B9jdmzPNF8tYSEhISEhISfwhkZWUhNjYWn3zyieYZkMfhw4eRlpbGvChv1qwZpcwcPnyY0Rkwm820%2BshXyGBPQoJDN84e8QAAIABJREFUeno62rdvj127dmHChAnYtm0bFi1aBIvFArfbTXlVcXFxyMjIwKZNm5CUlASXy4VXX30VmzZtQr9%2B/fDdd9/BaDRSPpy/vz/cbjciIyPRsGFDXLhwAe3atcNnn32G8PBwFBYWwmKxAACKi4sxe/ZsAN6gpV69eqhZsyb8/PwQHR2NPXv24MiRI9Dr9ejQoQM%2B/fRTREREQK/XIzg4GA0bNqTBGCmjVMPlctHM0vbt29GuXTv4%2B/vT/mvXrk2Dw6KiIhqobd%2B%2BHYA3mGzRogWAW9lIYqNABEpCVDrNAQEBNJNGyl0Bb7D3/fffw8/Pj1ohEIXNWrVqYfTo0QC8QVXt2rXRqFEj6HQ6ZGVl4cqVK1i4cCGsVitKSkqgKAouXrxIg2CSNTWZTHjttddof6WlpTh//jzlUZL5EgwdOhT%2B/v5wuVw0gFaLtJw9e5YGdxUVFZg1axb9rF%2B/fnQe1UEKtEhISEhI/CaQlnbXuv5fZfYKCgqQlZXF/Fcg8jX9GejYsSPefPNNpvKpMhQWFmqoHxEREdRyq7rPfYH41beExB8c7733HhYuXIj58%2Bfj8uXLCAgIQNu2bbFmzRoaEPXu3Rvjx49Hr169sGTJEnzzzTd4/vnnAXiDhzZt2mD79u146KGHAADDhw/HkiVLkJOTg127duH111/H%2B%2B%2B/j8WLF0NRFDRo0AAZGRmoqKiAzWbDihUr0K5dO5hMJpw7dw7nz5/HxIkTsXHjRgBec/TAwECEhITA7XbDbrfjsccew%2BbNm/HDDz8AAJ599lksXbqUcuY8Hg/lABqNRpSXl%2BPo0aM4efIkI1pC/OQAoFWrVjSzZrfbYTAYUKtWLRw%2BfBgA8M033yA6OhoFBQVwu92wWCwwGAyUt3fjxg2EhobSkoOOHTvim2%2B%2BAeDNlE2cOBFfffUVvv/%2BewC3Mm3Xr1/Hhx9%2BCMCbHQ0ICKCBYnl5OYYPH45Vq1YhLCwMJSUlGDhwINauXYuwsDA4nU5qF2G32/HKK68AABOMPvnkkwCA8vJynDt3DoBXQCc3NxeXLl0CoC3J5CnO/v7%2BVGkVAMaMGcMEjlVh5MiR6NmzJ7NPURTMnj2bBuIBAQHo1asXNm3ahLKysmq3fTnnTh1zN/v%2BtY/vj9r3r318f9S%2Bf%2B3j%2B6P2/Wsfn3obs2ZhZEGBT3z0O43/lbLIvHkfaSpxJkyYwCidVwabzYb8/HzhZ1FRUcKXuZWhvLycqTACwGghVPe5L5DBnoSEAGazGZMmTcKkSZMqPSYjI4NRTFq6dCkyMzNx/PhxXLhwARaLBWvWrKEZsMmTJ2PcuHGYPn061q9fj%2Beff54GkXXq1MHatWvx5ptvYuXKldh9Uxlx5MiR0Ol0CAwMxNixY5GXl0dNy/38/BAZGQmbzYYrV67AarXCbrdj6NCheOedd9CmTRuMGTMGy5cvh8vlQmRkJIqLixESEoJdu3YBAIYMGYIDBw4gPDwcubm5GDVqFN58801athgfH4/g4GAcOXKE2hLodDo88MAD6NChA4YNGwabzYaKigoqDBMbG4sLFy4AAGJiYnDjxg3cuHGDEawhwjchISEICAjAiBEjMHnyZCiKggEDBuDw4cMoKSmBw%2BFAamoqiouLkZubi0GDBuH777/H3r17UVZWhpSUFFy6dAkulwtr165Fhw4dYLVacfToUcqfNBqN0Ov1qKioYJRTjUYjbDYbxowZg27duqFfv344f/48evfuTYM64gHYrFkzqn5Vp04daigfExODRYsWoVOnTgC8Zbeff/455VFWhejoaM0/nFlZWRrRlrS0NGZfddu/5DF3s%2B9f%2B/j%2BqH3/2sf3R%2B371z6%2BP2rfv/bxqbd79ux5V4K9/xUeeeQRxrYK8AZqvuDw4cMYNmyY8LPMzEx07tzZ53GYTCb6cpzA4XDQ5zCTyaQJ7BwOR7VcQDVkGaeExB1CmzZt8MEHH%2BDgwYP4/vvvmUCPwGw2Y9asWVi%2BfDmaNGkCl8uFAwcOICsrC0uXLkVKSgpmz56N7OxsHDp0CE888QRiYmJQUlKCFStWID8/H9OnTwfgLUmsW7cutm3bhlGjRgHw%2BuOtW7cOzz33HCZOnIhXXnkFZWVl0Ov1aNOmDSIiImCxWFBYWIjCwkJMnToVer0eV65cQVBQEDweD7Zv345r164hNDQUoaGh%2BOc//4mjR49izJgxVGU0JiZGM/%2BKigo4nU78%2BOOPsNvtCAgIgNVqxVtvvYWjR49Se4m1a9fSAFan02GLyhbAZDLR%2Bfn5%2BVHPw%2BLiYhQVFeHUqVO0LHbz5s3weDx48MEH8dhjj0Gv1yM7Oxv79%2B%2BHzWZDy5YtERgYiBdffBGffvopNm3aRH/Y/fz80LRpU0RHRyMpKQmpqano0qUL5TYGBwfD39%2BfBodqFS2LxULr56Oiopi6eY/HQ7OqEhISEhISEr9OREdHIz09nfnP12C2VatWyM7OFv73UwI9wPtSXO1DDHj1EchYKvvc18AUkMGehMRdga%2BB4aRJk/DVV19h%2BfLlSE9Px969ezFx4kTs2bMHS5cuxYIFC%2BgPTJ8%2BfVCnTh3o9Xq89957GDt2LKxWK1q3bo3%2B/fvTksRz586hbdu2aNu2LUaOHIm0tDRMnz4dkZGRCA8Px9atW2E0GtG8eXOsXr2ajmfSpEmYOnUqAC%2BnsFWrVmjatClat26NTz/9FIMGDULDhg2xefNm1KpVC%2BHh4XC5XFSopmHDhgCAcePGoV69eti4cSPq16%2BPH374AS%2B%2B%2BCLq1KmDffv2YdmyZQC8Xnw//vgjzpw5g/vuuw/t27dHvXr10KJFCxgMBowfPx4XL17E%2BvXrsW/fPixduhRffPEFatasCYvFgh49eqBOnTrIy8tDYmIi6tSpg6ioKAQHB2Pu3LlYuXIl40U4fvx4uN1uvPnmm3jggQeQnJwMj8eD/Px8xhze6XTi%2BvXriIuLw5EjRzBnzhz62Q8//CD0ypOQkJCQkJCQ4JGRkYGsrCxGSfzAgQPUwzgjIwMHDhygn5WXl%2BPYsWMaj%2BOqIMs4JSR%2BA2jTpg0NmirD0KFDMXjwYIwfPx7dunWDzWbDf//7XyxZsgQfffQRUlJSAHjNyLOzs/Hss8%2BiadOmuHLlCjZs2ACHw4EuXbrAYrHgzJkzsNlsKCwsBOC1Cfj2228xd%2B5cPPPMMxg3bhwuXryIgwcPYu7cuWjQoAFeffVVOpZHHnkEb7/9Nr766isqzELehBEz%2BISEBKxevRo9e/bEpUuXMHnyZFo2Seb8xRdfoE%2BfPnA4HHj22Wdx/fp1vPzyy%2BjRowfGjBmDrl27omfPnnjwwQcRExODOXPm4PLly3jyySfRr18/%2BPv746WXXkLt2rVxzz33ICgoCBUVFahbt65m/Ro0aIDs7GwAQPfu3eF2u/HEE0/g0UcfxZQpU/Dll1/i8uXLGD16NKxWKz788EPExMTg4sWL6NSpExWvkZCQkJCQkJCoDNeuXYPJZEJgYCBatmyJ2NhYvPDCC3jyySfx5Zdf4siRI1T8rX///li2bBkWL16MDh06IDMzE/Hx8cxL6Ooggz0Jid8JGjVqhEWLFiEzM5OKvqSlpdHyUAJfxGcAYNu2bdi2bRsAb%2BliUlISXn75ZVrSuHXrVoSFhWlq3gFv2ePcuXOxceNGjBkzpspxd%2BzYEStXrhQGs3q9Hu%2B//z5mzJiBhx9%2BGIGBgejZsycmT54MAEhMTMSCBQswa9YsLFiwAJ07d8YDDzxAz%2B/WrRuuXLmCv//977hy5Qrq1auHBQsW%2BCSiotPp8P7772PlypV47733kJOTg/DwcHTu3Blvv/22TypbPxVRUVEYMWIEQ6CvV68e3Vfdti/n3Klj7mbfv/bx/VH7/rWP74/a9699fH/Uvn/t41Nvk3%2BfJO4MBgwYgL59%2B%2BKpp56izzkvvfQS%2BvXrh8TERGRmZlLuf3x8PObNm4c33ngDmZmZaNq0KTIzM302VAcAxcPLy0lISEhISEhISEhISEj85iE5exISEhISEhISEhISEr9DyGBPQkJCQkJCQkJCQkLidwgZ7ElISEhISEhISEhISPwOIYM9CQkJCQkJCQkJCQmJ3yFksCchISEhISEhISEhIfE7hAz2JCQkJCQkJCQkJCQkfoeQwZ6EhISEhISEhISEhMTvEDLYk5CQkJCQkJCQkJCQ%2BB1CBnsSEhISEhISEhISEhK/Qxju9gAkJCQkJCQkJCT%2BeMjLy0ONGjUAAA6HA2VlZQgNDdUc53a7kZeXh5o1a/7SQ6wWFy5cQElJCZKTk2Ew3Hqs/u6771BQUIBvv/0W7dq1Q4MGDVC/fn2f23W5XCgpKUFFRQUsFgvMZjP9zOl04tSpU7DZbCgpKUFgYCCSk5MRGBgIADhz5gx2796NQ4cOoaSkBAEBAWjSpAmaNGmClJQUmEwmXL58GYWFhTh69Cj8/PzQpUsXeDweZGZmYvjw4ahVqxby8vLwww8/oHPnznduwSR%2BcSgej8dztwchISEh8UfFtm3b8P777yM/Px9OpxNJSUkIDAxEbm4uCgoKEBoaivz8fERGRsJkMiEqKgqpqal45JFHkJKSclt9ezweHD58GGfOnEHNmjWRlpaGoKAgFBUVITQ0FEVFRQgLC6u2DV%2BO%2B7nYsmULOnbsiICAAGb/5cuXcf36dfogFB0djby8PISEhCA4OBhGo7HSB6XfE8g65Obmwul0wt/fHzk5OQgODobb7cb58%2BdhMBig1%2BsRHx%2BPmJgY%2BPv7Iy4uDhEREXdt3PwDstvtxq5du1BWVoagoCBcvHgRLVu2xLVr13Dq1CmUlJTAbDYjOjoaUVFR9IEVAAoLC%2BHxeBAVFQVFUe7anHzF/v370bRpU%2Bj1%2BkoDBQCoqKjAoUOH0KJFi7s0UmDjxo3o1KkTLBbLHTtXvT81NRXt27eHTqfD119/DafTibp162LChAmwWq2wWq249957ERUVhXbt2uH48eMAgLKyMixfvhwTJky4I/P8Odi/fz%2Bef/555OTk0H316tVDZmYmJkyYgB9//FFzTmRkJB544AEcOXIEhYWFNMDV6/UIDAzE8OHDodPpsHr1aly7dk1zftOmTWG323Hs2DHhmGJiYlBRUSE8l0Cv1yMgIAAlJSVVzk%2Bn08HtdtO/AdD1l/htQQZ7EhISEncJffv2xYkTJ6DX61FRUUH3h4SEoLi4GH5%2Bfsx%2BAkVRQH66AwMDERMTg6ioKJjNZhiNRpw9exaKokCn0yE4OBjXrl3DmTNnEBUVhbCwMLRq1Qq7d%2B/G%2BfPn4XA4qhyjTqdDZGQkbDYbgoODUVZWhoYNG2LcuHF47733cPjwYdjtdiiKgrCwMISEhMBisaCsrAwulws2mw0FBQUICgpCbGwsQkND4XK5cObMGZSUlMDlcsFisaBmzZqIi4vDhQsXoNPpUFhYiPLycpSWlsJkMsFgMMBoNKK4uBiKosDlcmnWorp5kAcXRVHg7%2B8PRVHQrFkz5OTkwGq1AgCuXr0K4/%2Bzd97hUZXp3/9Mr%2BmV9EroJHQEFVBpi7C44AqiYFsFBUXX9yfuisq6NnRFF/sqoCzwAxYMCAqKiNJJgNCDhJKEFNLrtGTm/SPv8zgzSSiKsr7XfK/LS87MmXOedk7u73Pf9/fWamlubkatVmO1WlEoFGg0GlJTUzEYDGRlZTFkyBDGjBnDoEGDmDhxIlu2bLm8SafF2HU4HIwePRqj0Uh%2Bfj61tbV06tRJGvx5eXk0NDTg7%2B9PWVkZGo2G4uJiQkND6dq1K08//TRff/11m%2BvjcqBQKLj55psZN24clZWVhIeH061bNw9i3NjYyNatWyktLSUpKYkbb7wRgJycHLk5IUik0%2BmkqKiImJgYunXr5nEvsSFgMpl46KGH2LVrl5yLwMBAEhISyMnJuax5VCgUKBQKnE4nOp2OdevWcdttt%2BFyuVi3bh2xsbEe43y5REVscABYLBbWrFlDQUEBAQEBdOnSBYPB0CZJrqysJDAwkNraWg%2BP1MGDBykuLiY6OpoePXp43KtTp0507tyZ5uZmTp48icvlws/Pj9tuu42tW7cSHh7OiBEjSExM5P7772fEiBHceuut9OvXj5kzZ/LCCy9QV1dHdHQ0r7zyCi%2B99JK89sKFC0lJSWHkyJGUlZVxww030KdPH2bPns2WLVtYtGgRSqWSQYMGkZqayocffkhQUBCjR4/GarXyn//8B71ez4ABA/jjH//I9OnTCQgI4M9//jODBg1i2LBhjB49mi5durBlyxYOHDhA586dSU9P55tvvqG8vJwBAwYwfvx4/vznP3v89qabbmLBggU89thjREVF0bt3b9avXy/fFwMGDGDnzp0olUqam5sJDg5GpVJRUVHB8OHD%2BfLLL1m8eDEHDhwgODiY5557jhdeeAF/f3%2BSk5NJTk4GoKGhgS%2B//JKSkhLS0tKkVyo7O5sOHTpQWVmJ3W6noaGBtLQ0NBoNdrsdg8HAhQsXSEpKoqamhoULF/Lggw/icrnIzMxkx44dFBQUYLfbqayslO%2Bgi63V9ta0QqHAz8%2BP2traNr8PCgrC4XDI95L3tVQqFS6XSz5HvyQSEhKwWCz84Q9/YOLEif%2BV3lUfLg4f2fPBBx98uAbIyMigsbHxin4jCFxzc/Nlk5zLgVqtpqmpqc3vVCrVRYlVSEgIFRUVl7yHTqfDZrP9/Mb%2BP3gTYa1W60Fc3dsNnmTvaiAsLIyysjLi4%2BM5d%2B4cgwcPZvv27RiNRnr06EFYWBjnzp2jtLSUjIwMysrKUCqVlJaWkp%2BfD8A999zDqlWrpEEXHBzMkCFDWL9%2B/SVJ/pVAtBFaxkWhULQ733Blc%2BXeJvFvtVpNamoqubm5HsTcHXq9HqvV2mqevI8VCgVqtZrm5uZLzp%2B4v0qlIiEhgby8PJRKJS6Xix49evDDDz8wduxYPv/8c9LT08nPzyc/P5/w8HDq6uoYMWIE27dvp7y8/JL9bms96XQ6lEoldru9VZ%2BVSqV8fi9F0NubZ6PR6PHOMJlMNDQ0eJBN4dEJDAykvr7eY57bewYud115n%2Be%2BrqDtd4GYZ/Fb8dyKtl8Ohg8fzubNmz0%2Ba6sver0eh8NxSRL2S8J7/f63w729SqUSpVLpsWbE5orL5cLlcqFUKn3evd8gfGTPBx988OEa4PTp04wePVp6KZKSkjh9%2BrQkXkajEavV6uGN0mq10gg3m82SJLQFYWS5oz0DSxhOl2v0eROr9jyQ7hDk6LeAoKAgqqqqftV7CjLgbnj9FHKanJxMXl6eDGf0ns9fyxi92AbCpSA82%2B5j4L3mrkY7rvYGxNWEIKk/x0Rr73n2Xt/epA1a1onT6ZS/NxgMWCwWj/a5r8/g4GCP0MErXb9ttfVqb9BcDXi3SayvS7V18uTJLFu2TB57j1d7uJLnyHsexfoW//d%2BJygUCj766CPuvfdepk2bRmZmJlarFaVSyauvvsqcOXP47LPPADhx4gRPPvkk69evJzo6%2BrLa48N/D3xqnD744IMP1wBJSUl06dJFelrOnDkDIMOKbDabh/EgdlWBNj0z4jsBIXrQtWtXj2u4Q6PRAMj7uFwuuZMr7uOdRwS0Mro7duzoceydXwd4GJfu9/ipUKlUALJ9ov8ij0t8fiW5RuKa1dXVHtf4NfL9nE5nKxLmfd/OnTt7HIs%2Bu89RXl4eQLtEoS2D1HvtiHEAZAitN9qaY3d4r08xL973aGtsa2pqgJZ1EhcXB7SIVYSHh6PVai96X2%2BI/nq3192DIdoi1qT3eAgEBwd7jIW4pvtaFt47AbPZ7DGe7s%2BW%2B3ne9xRhqu7wngfvY%2B8cTO%2BxFdcTz6JolyAI4n0ALePt3iar1UpoaKg87tChg8e1a2trPc5v713THtrKH73Uby6Gy/2t8GapVCr8/PxarQHvdWM2m%2BW7VbyHFQpFq/eM9/ttw4YNHvfz3nQT70Q/Pz%2BPz8U4ir8Tol9trVGDwdBqDgFsNpsM%2B9RqtR6k7/HHHwfg3//%2Bt9y0a2xs5NFHH8XlcvHAAw/w9NNP88Ybb/C73/3OR/R%2Bo/CRPR988MGHa4SuXbvicDg8DPOjR4%2B2G4rkvrPuvlMLtFJ5EwbciRMn5GfeYaNt7RgbDAbZFpfL1YrIQWtDxjusx72dbd1L5Ch5X8/bsHI3KDUajYfxu2LFCo/rzp07F0B6av7xj38ASO%2Bn9/ioVCpUKhX%2B/v4e9xDtc///5XiU3MekQ4cOHsfeOVvevzEaja0IsNPpbDWOAwYMaPM6bYUMtgcRSieQnJzcyjB3JxEul8vDyBdtvFLvoLcHTfy%2BrbXifk55eTnh4eFERka26QlpS6TIfW0Jsue99t3Jtfi/GAd3b7r7nNTV1XmsY9En7/FzJ9RtbdpAi6fS/fPExMRWBMV7zLyfV%2B9j7/Hx7rO4nvccCtLn7Z33brd7eOv58%2Bc9nleR4%2Brdz%2BDg4Dav7Q33PE%2BxfkV79Xq9/O7BBx/0%2BJ33u0gcBwQEtHsvs9nssSmk0WhabSKIvnuTL/doCjFuLpdL5t55v0MEhKdao9HgdDpbzW1SUhKRkZGtRFPEHEVERNC/f38cDkebXsSMjAzy8/NpamqSfXNfH%2BI6drsdo9EoybXY2HI4HFy4cIGmpiZcLhdNTU3U1dWRl5fH7t27sVgsPPTQQ20Npw%2B/AfjCOH3wwQcfrhH69%2B/PnXfeiVarJT4%2BXpKrf/3rX2RmZnLrrbeSmZl5rZv5i8E7dCsqKoqioqJW511pmOmvAe%2B2uIcE3njjjezfv18abkOHDuXbb79t1fbY2FgPJT9oHS53uff/KVCr1QwZMoSvv/7a43NvMZvQ0NBWIbg/5/6/1DyKENbQ0NDLyru7HHgb1t7H4eHhXLhwQR4bDAasVqvsX3vhmN5h1uHh4ZSXl%2BN0OtHr9ajV6ouGaf8cdO7c2WOD5pFHHmHhwoUe53iHGbYV6nk5SE9P5%2BDBg/LYe25EWLH7eISHh0uFVYDbb7%2BdlStXAi0RCyUlJfL33vNhMpmkomt74ifu4exmsxmFQoHFYpFCMe6bO22tVe%2BQevfPQ0JCcDqdhIWFMXbsWLZs2UJubi633XYbjz32GGazmS%2B%2B%2BIKdO3eyb98%2BGdExY8YMSkpKiImJYdy4cRw8eJAVK1aQlZWFy%2BVi0aJFrF%2B/ngEDBjBu3DguXLjAiy%2B%2ByPfff8/EiRN5/PHH2bt3L%2Bnp6dLL%2BNprr7F582beeecdysvLSUxMJDw8XBJip9PJ/PnzWbx4MdCydiMiIpg%2BfTrp6elUVFRQVVVFbW0tixcvpr6%2BnlWrVv1iyss%2B/HLwkT0ffPDBh2uEyspKufO9Y8cO8vLycDqdJCYm4u/vT0ZGBtAi8b1v3z7279/P999/72F8tGc4x8XFUVRU1Kb3zmg04nK5PLwOl5sjdLnnubdLqVRiMBhobGxs83feoiFxcXEUFxdLw23UqFF8/vnnqFQq7HY7arWa0NBQSkpKGDRoEFlZWQwdOpR9%2B/bJ2lS9evUiJyeH4OBgysrK6NSpE7m5uTLXsVOnThw5csSjDe15q9z7EhISgsPhkIakaKN7v4TRKMZW5KC1B6GierFzLgWR2xMUFETv3r3p168fNpuN0tJSli9fLg28n5pH5w5voZCfgvbWrb%2B/P6GhofTq1Yvc3Fxqamqoq6vD5XKh1%2BsZPnw4NTU1ZGZmYjKZsFgscj0KImEymZg5cyYvv/xyu/d3JwmXS7B/TRiNRiZNmsTixYvlurznnntYunSp9JLdfPPNHkTdm/h4EyPvNT5w4EB27doFtMzHjBkzePvttwH46KOPePXVV8nNzQVg3bp1/M///I8kiQsWLOCpp56SBO3hhx/mvffek9cfP348mzZtkuukX79%2B7N2797I2blQqFcOGDSM7O5vKykoZXnipvLhrvRkUHh7OXXfdRd%2B%2BfZk%2BfTq7d%2B8GWsJmhdqneyQBQGlpKTk5OfTq1cvDg26z2Thx4gQFBQXk5eXRsWNHoqKiZMmR8%2BfPk5OTQ0lJCXa7Hb1eT3h4OOnp6URFRbFv3z4OHDhAaWmp/D4sLIyMjIxWpTx69%2B5NU1MTqampLF%2B%2BHJvNxqlTp6SXD1o8g/Pnz6d37948/fTTv/BI%2BnC14SN7Pvjggw/XEPv37%2BeRRx6hurqa5ORkamtrqaqqIiYmhkWLFhEREeFxvsViYcOGDRw4cICzZ89SU1ODw%2BGgQ4cOWK1WcnNzaWpqorm5GZ1Oh9FoJDk5GZ1Ox9mzZ7lw4QIOh0N%2B16tXL8aNG8fNN9/Mrl27WLduHQqFgqqqKiwWC926dePChQtkZ2fL8FIR5nPDDTcQFRXFN998Q0hICAUFBQwZMoSGhgZOnTpFSkoK%2B/bt4%2B6772batGnMmTOHYcOGcf3113P69Gny8vJk4j/AzJkz0Wg0nD9/noMHD/LOO%2B9QWFjIxx9/TGlpKatXr2bnzp1s2rSJCRMmcP78eQ4cOMDJkyeZMGECcXFx1NXVceTIEfr27UtQUBAnTpzg8OHDTJw4kejoaA4ePMjevXt54oknSElJYdmyZaxZs4ZJkyah0WhoaGggNzeX%2Bvp6rFYrWq2W8PBwunfvTnFxMTk5OdTV1REaGkpzczMWi4Xc3FxKS0uloRsSEkJVVZU0TL1FFvr27UtJSQllZWVYrVbefPNNRowYwd69e5k1axbV1dUegiSCDDudTg8CGRoaypgxY7DZbKxfv57g4GBMJhMKhQKVSkVTUxN2ux273Y7FYkGtVhMYGEifPn1QKpUkJyeTk5NDXl4eBoOBvXv3Ul1dLcPzfv/731NaWkpZWRkJCQmcO3eOyspK/Pz8SEpKIiQkhK1bt2I0GlEqlZw8eVIa5RkZGRw%2BfBi73S7J/oABA7jtttvYt28fY8aMITExkbq6OkaNGoXT6WT27NlMmzbtsvI577rrLt5%2B%2B22KiorYsGED%2B/btIzc3F4vFwrx587jpppswGo28/PLLbN68GZOZWamEAAAgAElEQVTJRFlZGV26dEGv15OUlITL5eLYsWMcP34cq9UqSYWoMSnG3l3iXgjpCOLkLmIioFKp5HnCYG7LO9RWXltCQgJjxowhOjqa7t27Y7fbeeKJJzh58iRbt24lKiqK0aNHU1VVxdtvv01sbCxjx44lKCiIZ599lszMTPbt20dBQQFbtmxhx44dLFmyBIVCIUtcXHfddRw/fpwnnngCrVbLp59%2BytGjR5k3bx6VlZUcPHiQAQMGSE/70qVLufHGGzl//jyHDx8mPz%2Bfzp07ExERQXFxMWfPnmX%2B/PmUl5fz6aefcuzYMV577TWioqI4e/Ys7733Hp06daKwsJDz588TGBhIt27dKC0t5dSpU9LTJ8YkNjYWm81GTU2NDFd3uVxcf/31hISESA9aWFgYX331FTk5ObIkRkNDA8XFxURERBAUFET37t05deoUBw8eZODAgUyePJmXX36Zo0ePypIvDQ0N1NXVSRVVkbus1WoJCQkhMTGRHj16cP78edauXUt6erqsR3r27Flqa2vJyMggODiYsLAwEhISeO211%2BjduzeHDh3y8ADq9XqioqJQKpUUFBS0%2Bq5Lly6YTCb27t0r16J4F1RXV8uNrqKiIjp06EBoaKj8vry8nOLiYrmZ17Vr11bfHzt2jNjYWN5%2B%2B22Zf/fcc8%2BxZs0aUlJSGDVqFG%2B//XYrcS8B8b734bcFH9nzwQcffLgG2LdvH5s2bWLp0qXodDqsVis9evSgtLSU0tJStFotWq2W6Oho/Pz8pAcwIiKC%2BPh4mat0LQsu/1SUlJSwevVqWYusurraQ9HOYrF4GHltQaFQSEGCuro6aYx7ey%2B0Wi1NTU0eHgGRi9WhQwemTp3K1KlTsdlszJ8/n9WrV0sjy263U19ff8nfQstO/KFDh%2BjZsydarZby8nK2b99O9%2B7dSUhIoKqqyuPY4XCwfv16tm7dSlNTkyyIHhYWRnp6OqNGjUKv11NeXi53/CsqKlizZg07duzgyJEj1NXVyTFqz%2BOq0Wgwm83U1dVhNBqJjY3lzJkzREVFMXv2bKxWK2fPnsVisWA0GklKSvLwIECLZ6Kuro4dO3Zw4sQJioqKKC4ulh5Nk8kkC7anpKQwceJEmWeVl5cna/d5ezWuNkpLS/n%2B%2B%2B/p168f5eXlnDp1isrKSs6cOUN5eTmNjY1yk%2BNi43wlXpGfgoKCAh5%2B%2BGHOnz9Ply5dpEcyLy/PY5NAQKFQoNPpCAoKIjg4mLi4OPz8/GS7kpOT6datmxzjkpISPvnkE06ePElxcTGlpaUyJNRgMKBUKj08z%2B5QKpUYjUb5HArio1arCQ4OJjo6GqVSSW5ubptFud1/L7x6SqUSs9mMv78/HTp0kJsEgixrNBpiY2NRKpVUVVXJMiXx8fGyDqV7fm1xcTHXXXcd8fHxcl46deqE3W5HoVBQUFAgPV4OhwOFQkFUVBT%2B/v6EhYV5eNCuFDfffDMPPPAA//znP6mtrZU519A6T68tiOdVtCk0NJSQkBBZk/T48ePU1tYSFhaGSqWS5TPExoG7t%2B3Xgii3UFxczKhRozzCcn34bcBH9nzwwQcfrgFuvfVWTp48%2BbOvM2zYMKBFxfPGG2%2BkvLzcI9dGkMR33nlHekwuXLhAQ0ODrAkmIIw/k8kkPwsMDJT5OvHx8dLjIbwfopB0aWkp0JLon5SUBLSENQGcPXtWCgKUlZWRnZ0tycGBAwek98fdS%2BJO2tRqNWq1GpvNJsVdAgMDuXDhAjabDT8/P4KCgqiurqa2tlZ6sFwuFxUVFej1eoxGI35%2BfhQWFqLVatHr9ej1ekpLSzGZTFI0QYQRlpeXU1NTQ1hYGGFhYVitVgoKCuQYOhwOSVBVKpUsui2EP5RKpTwWIYj%2B/v7U1dVRV1dHZWUlSqUSk8kkC86npqZisVjYv38/FouFDz/8UJL6PXv2MH36dDp16kRFRYU0iEVIXENDgxRfacsgvNIQN%2BHZEF5iASGeodfriYmJweVyUVJSgtVqxc/Pj7q6Opqbm6U3QRi24jeCeKtUKg/RjksZzJci/pfbN6G62NTUJMdMq9Wi0%2BnQ6XTU19fjcDgICQkhMjKS%2BPh4KRTTllfkp2DatGnk5uaSnJyMUqmkuLiYwsJCme8l1oZCoSAgIIDo6GgOHTok10tDQwOxsbHU1NRQW1vbbmijUF5UKBTEx8fj7%2B/P8ePHqa%2BvJyEhgejoaE6cOEFlZSV6vZ7Q0FBqa2upqamRHqjCwkKZgxYbG8uxY8eor69n6NCh9O/fX3rWdDod6enpFBYWcu7cOfR6PS6XC39/fxobG/H39yc2NhaVSsWePXu4%2Beab%2Bec//0mXLl3ksxsXF0d5ebkM2/05EOvUe2yUSmWrjZr2sH79evnvrKwsAA4fPuwhevVbqql3uTAYDISFhQHQp08fxo8fT79%2B/fj6669ZsGABn3/%2B%2BTVuoQ9XCh/Z88EHH3y4BrDb7VKlMSYmhsLCQrnrHhYWxtmzZ/9rxEh8uDiEJLrIy9HpdNI7Ca0FT9rLW2xubm63Pt7PaZt7SQ3vdlwJURL90Gg0NDc3ExYWhk6no7CwkPDwcOrr64mIiCAvLw%2BNRoNSqaS5uZmmpiZZGP2XXtOinyaTibq6OsxmM9HR0dhsNvLz83E6nSiVSgICAoiMjJQ5aJdTf9C7ALXRaJRCH%2B61y7zH1H0DQ6PRYLVaZZ6gqGfnPjdXCvecQ%2B95/i3CffzUajUKhQKHwyHDbN2//7n9dX8OROjzT4F3PuGwYcP45ptvpNpnREQESqWS06dPEx4ezujRo1m8eLHsX2BgIBqNhqKiIpnzq9Pp6N27t8z90%2Bl0MoReq9VitVpRqVTMnDmTN998E5fLRXNzM4MHD%2BbYsWPU1NTI8XnggQf44IMP5Lj5%2BflhtVpZuXIlHTt25J133iEzM5Pk5GT69OnDvffe69G/CxcucNddd/HHP/6x1Xc%2B/PfDR/Z88MEHH64RRo8eTUlJiTTU4uLiqK6uxmKxYLfbCQwMJDIykvz8fBQKxS%2Bmzvdbhrfgg6hjJQwf4Z1yL1gO7ZMub2EZaLs23X87OnXqRFlZGRUVFVelQLcP//%2BjLTLlw5VBp9PJ9010dDRarZbi4mJUKhV33nknixcvll7wG264gdzcXIqKilCpVEyePJlPPvmEhoYGmfunVCrR6XQcPnxYeqEvXLiAn58f/fr1Iysri4iICKKiosjOzsZqteJwOEhOTiYkJIRjx46hVqsJCAjAz8%2BP3NxcHA4Hqamp9O7dmx9%2B%2BIFly5bRv39/ampqPEi0iFoQ77/Q0FC2bt160dIuPvx3wjdjPvjggw/XCLNnz8bpdBIQEIDT6eTs2bNUV1djs9lITEzk448/5vbbb6dr167o9XrS09Nl/k5CQgLh4eGMHz9ehmvFxsYyceJE1Go1RqMRg8EgpbZDQkJQq9X06NGDoKAgjEYjaWlp%2BPn5yf%2BrVCo6duxIp06dmDp1KsHBwbKwdYcOHYiLi8NsNqPVagkODkav1xMcHCy9HH/4wx9kOJy/vz89evSQO9tKpZLExETUajV%2Bfn589NFHqFQqAgMDSU5OZsOGDR79S0tLk%2BGkYWFhxMfHy5DUmJgY%2BvXrh9lsxul0EhoaSkJCggyb7NSpEykpKRgMBux2O926dSMxMVHW7UtMTCQ9PZ2EhASgRbI/IiIClUqFTqejR48eBAYG4nQ6ueuuu0hJSZEetylTpshC9QqFgptuuomYmBigJWRWpVLJEKjAwEBZW0t8Dy1GdWpqquyfCNkTtb6EyIowqkSukkqlwmQyyeLqYn50Oh033nijbJPJZOLEiRPS6yTWg4Dot16v509/%2BhPQYqSuW7dO1krz8/OjZ8%2Besm3p6eke17/%2B%2Buvl9YxGI0FBQR7CKmq1Wq4BAY1Gw9ChQ%2BV4aDQa0tLSUKlUREdHk5iYSOfOnVmwYIH0howfPx6z2SwLXg8ZMkRKv8fHx5OSkiLDA4UYhdlsZt68ebJGmlhjAu412ET%2BmxjfAQMGyPpp/v7%2B6PV6FAoFQUFBMjRXq9WiUqlkyZQnnnhCbjIYjUYCAgKIi4vDaDSiVqtJTExkxowZMvdNjGlCQgJRUVHy3l27dpXfAUyYMEHeV6lUMm7cOGbNmiXn64UXXpBrUaPRoFar0Wq1MsxZp9PJ9vv7%2B8tnD1oM97/%2B9a9yzXXr1o1XX30VrVaLy%2BVi6NChvPjii7It7urA0OJNFGULoKUGZHJyslw/AwcOlPUj4ceamWazWeZziu%2BEx9O7iLzwSgniJM4XXrKYmBgyMjI8rj1%2B/Hh5zdmzZ6NSqQgKCkKj0ch6eiaTST4PAwYMQK/XYzAYCA4Oxmw28/zzz8u2BwQEkJKSQmhoKMnJySQlJaFQtBRRDwsLkzm4M2fO5MEHH%2BR3v/sdsbGxqNVqiouLOXPmDM3Nzdjtdkn0RF2%2BL774QpY0sVqtvP/%2B%2BzgcDsLDw1Gr1Rw6dIg9e/bw3XffUVVVJUngjBkz6NOnD3l5eTgcDk6dOsWePXsIDAzklltu4bXXXmPEiBFotVo6dOgg39kBAQHcddddrF27luXLlzN37lzef/99li1bRnNzM2PGjJFrS2yWCfXjrl27MnDgQB/R%2B43C59nzwQcffLiGOHHiBB9//DEnT57EYrEQExPDn/70J/r37y/PKS0tZefOnQQGBrJ582bWr1%2BP0Whk5MiRhISEMGjQIGbPnk1lZSVvvvkm5eXlfP7558TExBAZGcn27ds5fvw4EyZMoKKigoULFzJr1iwaGxuZMWMGRqOR8PBwJk2aRG1tLUOGDKFHjx7069ePWbNmce7cOR599FEWLVrEn/70J1asWEFGRgbx8fF88sknjB07lpUrVzJnzhwyMjKYOnUqN910EyEhIURERLBmzRqamppQqVSMHj2a119/HYVCQUREBOfPnwdaCIMQl2jPkyYIkLs64k%2BFMODDwsKwWCy89957LF68mK%2B%2B%2Borq6uqLejaE50Oj0TBy5EieeOIJbr75ZpxOJ9HR0Xz66afcdNNNQAtRnT59Om%2B88YZU8bRarTK3r7GxkeTkZEpLS3E4HFKYRqvV0qNHDynaMGLECLZu3YrVaiU2NhaTySSLKLelnCcUIUXopMiXE3maffv2JTs7m%2BDgYMrLy9FqtbzyyiusWLGC3bt3S5JuNpsJDg7mhx9%2BkF5U4Ul1D2NUq9X07dtXSvmnpqaiUqno378/27Zt49y5c/Tp04fa2lpKSkpQqVQsWrSITp06MW3aNIKDg3n00UeZNGkSs2fPxt/fn61bt/Lyyy9TXl7O448/Tl1dHatWraKqqoo///nPZGVlMX36dD777DPCw8M5dOgQsbGxnDt3joSEBLmB4h5C6h2yp1arZT2z119/naamJgIDA4mLi%2BPw4cMYDAYcDgf%2B/v40NDRgsVgYMGAA5eXlFBQUYDQaueeeewgICGDhwoUyP02U/nC5XFRXV/Pggw9y/vx5NmzYgEKhwG63c9111%2BFwOMjPzyc/P7/V%2BmprTpubm6UH7qabbiI1NZUPP/yQ5uZmevbsyYULF2hsbKSmpgaDwUDXrl05ffp0mwXpL%2BcZEWU2IiIicDqdsqbg5YS9QgsJdblcmM1mGdqs1Wrl5kBkZCTnzp2jvr6exMREMjIyKC8v59y5c4wdO5bCwkLWrVuH3W5Hp9OhVCoJDAwkNDSUI0eO0KlTJ86cOYPFYkGpVBIVFSUVh1UqFX369KG6upqSkhKqq6sJDw/HZDJx9uxZDAYDffr0obi4WHrXwsPDiYmJYcKECfzzn/%2BksLCQwYMHM3LkSN5//31iYmKkqBK0ECOh8KrT6YiIiKBnz56MGDEClUrFiRMnKCsrw2KxyO87d%2B6My%2BVq9ztBhp1OJ7W1tVIV2HtD5Wph1KhR/M///A9DhgwhIyODdevWERoayubNm/n73/9OamoqixYt4uuvv2b06NFX/f4%2B/PLwkT0ffPDBh18Z%2B/btIyMjA7VaLevr/fDDD1RWVlJbW0tDQ4MUsfD39ycoKIj4%2BHg6duxIZGQkVVVVbN68mQ4dOvDoo48CLYTwvffeo6ysrFWB5La%2Bq6%2Bvp7CwUAqAQIty4oYNGwgLC2PSpEnyvNWrV5OXl8eNN97Itm3bSEhIQK1WM3XqVL7%2B%2Bmu2bdsmv/vb3/5GdnY227dvl23zRlZWFitWrCA1NZXi4mIOHTokQ5OE56ShoYHhw4fTs2dPACkUUlVVRX19Pb169bqoYRQQEEBdXZ08ttvtVFRUyLFNSkoiPDxcEi7h8aivryc/P19KsotQ0LNnz/L6669z%2BPBhIiIi0Ov1FBYWSuNbKAEKz1d9fb0khKLOnjCORW0%2BYUxbLBZZTLtTp06Ul5eTlJTEe%2B%2B95zF3b731Fh9//DFbt24lKSnJw0js2bMngwYNkp4VAbvdziuvvMLq1aul4qdWq6Wurs5DIMUbOp0Oh8MhiR38mBsm%2BiWk7kV4cWhoKAaDgYqKCtknaCGCBoOBuro69Ho9Y8eOZdasWdIDWlhYyMMPP0xBQQFRUVHYbDb69OmD3W6nrKyMrKwsjEYjixYtYteuXdxxxx04nU7uvfdejh49SmhoqOx3WVnZRUmIIKw6nU7mgqlUKtRqtVwHiYmJlJSUUFFRQXNzMxEREURHR2M2m7nnnnuw2WzExcWxZMkSWcPx0Ucfpb6%2Bnl27dnH69Gk6duyIv78/mzZtIiwsjMbGRh555BHOnTvH7t27OXHiBC6Xi7/97W8ArFixgm3btvGnP/2JsrIyjh8/zvHjx4mJicHf35/y8nIOHTqE0WikpqZGyva3J6QjPKFOp5P6%2BnpCQkKIj4/nvvvuIy0tTYYEVlRUUFlZSXV1NX379kWv19PQ0EBWVhbFxcWS1JaXl8vnsba2lu%2B//56ZM2dSVlZ2Rb9vamqiuLiYp556yuP9869//YusrCxJhCIiIliyZAl%2Bfn7ExMRgMBgICAhg7dq1VFVVtSqJcbk5qGKTSRCy5ORkNBoNq1evpqqqipCQEG699VbZrtLSUo4dO8bQoUPJy8tj1qxZpKam8thjj%2BHv78/zzz/P7t276dixIydPnpQCRd5rTgjgJCYmEhsbS2BgILW1tdjtdqxWq1yXQjiqvr5eqsk2NDRgs9k8hJfEZtHPCc92z3c0m83ExMRw6tQpZs%2BezZQpUygqKuLWW2/F6XRK8SWR4%2BrDbws%2BsueDDz748Ctizpw5rFmzhhEjRnDy5EnOnDlzxdcwGAzcfvvt/PnPf5ZhWALz5s2jrKyMe%2B%2B9l3379nHHHXewc%2BdObrzxRhYtWsRXX32F3W7nd7/7HdOmTSM7O5tvv/2W8%2BfPS8Nx4sSJXHfddTJkx263s3btWvr27cuGDRv46KOPWL9%2BPbGxsdTX17Nnzx40Gg2FhYVUVFQwadIk5s6dyzvvvOPRNovFQnNzM9999x1r167l73//O5mZmdhsNiwWC9u3b2fYsGHU1dVJ40KElxYUFGA2m%2Bnduzcul4vIyEhCQ0OprKxk3759bNmyhfz8fPr06cOwYcMICAiQ9faKi4vJzs5m27ZtTJs2jcmTJ2M0Gnnuuee48847OX36NEqlkhUrVjBmzBiuu%2B46QkND%2Beijj8jIyODrr7%2BmrKwMu93OvffeS1FREWVlZRw8eFAW9j579iypqanU19fLGmFxcXGSYFqtVoKDg6VXT6vVEhMTg9lsJiIigtjYWIYPH96KrLWHzMxMPvnkE5nPCS3GW9%2B%2BfVEqlSQlJfH73/8erVYr8z5zcnL44IMPUCgU3HfffSxZsgSTycT8%2BfOpqalh586d5ObmStJ85swZUlNTiY2NpXPnzvj7%2B7fyPmRnZ9O9e3eys7M5dOgQn3/%2BOWlpaTIMLiwsjJiYGPR6favfAnz%2B%2BecMGzYMo9HIrl27eOaZZ%2Bjbty/bt29n0KBBnD59mpycHNRqNb///e9Zu3Ytw4cP58CBA3Tt2pX9%2B/fLMNfExEQZsnbLLbdgNptpamrC39%2BfqKgo6RnJz8/nu%2B%2B%2Bw263s2/fPtRqNbW1tXIcvT00ZrOZkpISwsPDr1oYW2NjI5s2bQJg%2BPDhmEwmlixZwsqVK6mvr6dr16489NBDNDc3U1NTI4tqu8NisXDixAl2795NeHg4FotFhoEL0iD6YTAYCAoKoqqqijNnzqDVasnOzsZisWCxWAgLC%2BO2227DZrMRFBREbm4uu3fvZtmyZURGRspQ2GPHjhETEyNDiLOzs%2BnWrRvNzc1MnTqVlStXSnXdjRs3otfriY6OJi0tDY1Gw8mTJ0lMTJShvLt27ZIlJ9rKLdXpdDQ3N2M0Glm%2BfDkpKSmcOXOGnJwcysrK2LBhAx07dsRkMhEZGUlUVBQRERHExMSgVCqpqamhvLycLVu2MH78eCIiIqRKsMBzzz3HrFmzZJj1%2B%2B%2B/z6RJk7BYLB51Tm02GwMHDuT1118nJyeHtWvXehStd8dvOe/Rz89PqjTbbDaUSiWTJk1i0qRJpKamXuPW%2BfBT4CN7Pvjggw%2B/IubMmQO0GMnnz5%2BnqalJKqMJgRG73S7zfWpra1tdQ0jHC2NaoL6%2BXtZ%2BEhBkRBgfHTt2JDc3t1WpA28VSJEfNn36dD766CNZ%2BFigV69eXLhwgdra2nal0i/X4GmvXMBvBaJoeGZmJnv27GHChAl0796dSZMmSSLy3nvvMXnyZGpra4mJieGdd94hPT2dHj16UFBQwLZt2xg1ahTHjh2jpKSE%2Bvp6mRs0fPhwsrKyGDFiBJs2bSIqKordu3df1njp9XpWrlzJhAkTcDqdMvzsxRdf5OmnnwZaNghsNhtffPEFx48fR6VSkZqaSk5ODsOHD6dXr14cPXqUb7/9Fo1Gw4ABA0hMTOTuu%2B9myJAhrFq1iv/93/9l165dnDp1ir/85S8EBQVx8uRJMjMzefvtt2Vu2a233soHH3wg86x69epFZmYmhYWFDBw4kLS0NBYvXsy0adNYunQpU6ZMuaK5MJlMaDQa7r33XhwOB2lpaezcuZObbroJk8mE3W5nzZo1fPbZZ8TGxlJQUMDOnTtRKpWsX79ePhsAGzZskPlTP/zwAwkJCSQkJBAaGsq6devYuHEjS5cuZfHixUBLLqAI6xV5gdnZ2ZSUlHD%2B/HlKSkqorKykrq6OPXv2SK/i008/zeeff86BAwcApCe6vXVmsVgwGAyEhobK8gtdu3YlOTmZwYMH8/TTT9O/f39uvfVW9u7dy9dff011dXUrxcgrgfezrFQq8fPzo6am5pLnAjKXtaSkhNDQUJmfJzaZdDoddru9Vft0Oh333XcfGzduJCkpiXfffddjDYn1c%2BzYMZnP%2BOCDD9KtWzcqKyvZunUr27ZtQ6FQMGbMGObPn09WVha7du2Socf3338/CxcuZM2aNfTv35%2B5c%2BfKObjzzjtZu3YtTz75JB9%2B%2BCFFRUXtjpFaraZz587SoynGCVryjEWorhA%2BcX9P%2B/v7e7zrRckK8bwGBwfL6ABxzfDwcBobG2W9w4iICOrr62V9Q7EpZrfbZRivyN2urq6WuYparZZz5861268FCxYwatSodr/34b8fPrLngw8%2B%2BHAN0Lt3bywWi/RyiFfxp59%2ByqRJk0hJSSE3N1eGEX7yySdMmTKllTS4Wq3GZDKhUqlkDbwrDe2ZOXMmI0eOZMaMGRf9ow/IGnTuEDlE7khISODcuXNX1A5Ru08gICAAjUYjhTm2bdsmc4bUajXJycnk5uYCEBsbS1FRkQyhSkhIoKCgAGiphSXqkwkIz6FWq5UlD0ReYXNzs0c/AwMDGTBgAF9%2B%2BSXQYriK8/V6PQEBAZSWlnqUT3Avd6DX62lsbGyldtiWUewtK%2B/%2BfWRkpMx3EyFsbY29%2B%2B%2B0Wq0MUfy5SExM5Pz581K04YMPPuDBBx%2BkU6dOHDlyhBtuuIFt27a1%2Bp0IV%2B3RowfZ2dls3LiR2NhYAHr27Enfvn3ZsWMHx48fJy0tjX/961/cf//9jBs3jszMzEu2KyQkhMrKyp%2B8WSDCbN3bC5cuf3GxzQwhFHOluXIGg4HJkyezePFij1Icoj3u4X5WqxWdTidLfFzpfUQYsM1mk7l5ISEhVFRUeOTkCY%2Bg%2B2fezypAUlISp0%2BfbnUfo9FIfX09ZrOZiooKeb3LCbn8%2B9//jkKh4LnnnkOlUvHwww/z%2Buuvc8cdd7Br1y7OnDnT5jNwNdFWO8V4TZw4kVWrVsnPTSaTLI8g2qTRaNDr9ZLMCYEfocIsQrn1er38zGw2yxxeaFmjQqVZQNQyFOeIvyXuObyCVItzxLEIzxahzKJcjPjs/vvv55NPPqGpqYnMzExSUlKuxlD6cI3gk9XxwQcffPiV8eWXX8ocG5EX1tTURFNTkxQvOX78uEeOV0VFhYfBMWHCBFQqlSQSBoOBDh060KFDB6KioqR6XVhYmEfYnIDYHQ4ICOD999/ntttuk0QvKCiI9PR0DAaDx7lAK8U8%2BJH8uEMQvfYEBYTYSkhICMuXL%2Bfee%2B%2BVxqNob2pqKjt27GD79u288MILHv2fNWuWNNAVCgVz5szx8AqUl5d7kC%2Bj0ShVSwFJmseMGSNzVsS9hSEtMHfuXA8PanNzs5wbm81GVVWV/Bw8SYK7N00YfxcrIO5dn02Eu8GPiobuHtlLGbmCkAr1SAERsgY/zq%2B73LpQpHzyySelcZqfn090dDQdO3akqamJBx54AIfDweHDhwkLC%2BP7778HWoi4TqeTioculwuLxcKePXuw2%2B2yWLVQJzx8%2BLDMXXPHrl27PNabmDt3ZVGAyspKSR7dIXLxoMULIvrkDVFEfM6cOcTGxhIaGkp4eLhUkXVfw8nJybJNwvvuDnGuuzdFqK62BUHGBSwWCxs2bJBrSbTBW3XTarUSGhoqjfi2nkH39og5TkpKktcRGxbivhqNRq5loXgqvEBardbj%2BQoICPCYG61WywcffNDq/k6nk7vvvltGLAAyx1PcQyAyMrbEBXMAACAASURBVNLj2OVy8fTTTzNnzhxsNhuNjY3Mnz8fp9PJsmXLPELgvedBtE2Mvfv61%2Bl0qFQqpkyZIp%2Bp9hASEtLmcyo2T1avXg20PE/iOWlqapKKli6Xi6SkJKxWqyRSCQkJHmPnXTNQkMuQkBB5bLPZpLKyOFeoIotzRPix%2B5zr9XqZvykiBcT7QCi2ineUeF6MRiNLly6VAk9btmzhs88%2Bk//58NuDj%2Bz54IMPPvzK6NevHx07dpTHQgzD4XCwa9euViGNZrOZRx991CPxPzMzU%2B6YNzU1sXTpUm655Rb%2B%2Bte/8tlnn/HNN99gMBhYvnw5OTk5KBQK1qxZ06p2XNeuXbHb7dIQEwILjz32mCQ8oaGh8r5CPdMdVquVwYMHe3wmDCvRD3ENYXCLMhAajYbly5ezYsUK1Gq1LJ8ASK%2BdgLv3JT09XcqWq9VqBg4c6DFmTU1Ncvda5O6InCD4cadbCKsI400YZeJYpVIxePBgmV8lPluyZAkKhYKePXt6EC4/Pz8p8x4VFcWwYcM8duOFPL%2B7YS5EaNyh0WhahXq9/fbb0hAU/fDOP/KG0%2BmUXkV30i6ItfuYiLIZomg6QEZGhiS3TqeTsrIy/vGPf0hhB9EPUapCjA/AX/7yF492jB07FoVCwerVq3nllVd49dVXUavVLF68mAkTJrRqu/D%2BCIh/T5s2zeM8b0%2B2uL/YRAF49913W4UIul/barXyyiuvUFhYSFlZmVRHdSfWAG%2B99Zack7feeqvNcEX4kfDCjxsLGo1GElWx0WEymTye65CQEEm4xPWam5ulYJKYC4VCIf%2Bv0WikoJM3vMdP5FzV19dLY16MoXuYp/DkCXh7GYuKijzWU3ubOjabjTfeeAOLxSLDDcX4uXs9hdiTO3Q6HQsWLOC%2B%2B%2B4jKCgIrVbL1KlTZUkEQRbFRplAUFAQCxYsAH4kgVqtlv/zf/4PgHwehw4dSnR0dJvtFqioqJAEWWwIuUPMf2VlJXa7XYquiL5Ci%2BKy3W6X6/TUqVMeXlHh1WtsbJTnNDQ0UFRUJI%2BtViuFhYUeG0W1tbUUFxfLc2pqajyiKZxOp8xZFOdUVFRQU1OD1WqVYfhiTpqbm3E4HDIUVLRnyZIlvPXWW7z11lv885//vOh4%2BfDfCR/Z88EHH3z4lREcHMyqVaukWqUoZiv%2B81ZzKygoaBW%2BGR8f7%2BEpGjp0KMuWLWPWrFn079%2BfMWPGYLfb2b59uzQeP/roIw%2BDV6FQyNBGdy%2Bc0Wj0MKC9wzbFb93h7b0Qif3iPHEN94Llzz33HBUVFRw5coS1a9cSGRmJy%2BWSeW7eiIuLk/8uLS2VfenZs2crAhUZGSml9dPT0wkODm6Vc6RQKDh06BCdO3eWQgzC%2BBbndu7cmYCAAA8DPCwsjP79%2B%2BNyuTyMYq1Wi9VqlWUPSkpKOHr0qIfRLAzzS4UIKpXKVuI758%2Bfb/W7uXPnXvQ6gFRtdIe7x9D7mu5hgYI0i/4JT6HL5ZLhpKIMhLtXS6PR0Lt3b497jR49GpfLRUlJCRs3buTf//63DHdsC0FBQa28pAqFwqN2n4AI2YUfiYm7t2fixImt%2BnmpY28IMixInL%2B/fysCKZ7TpqYmOY7i/w6HQ4bKhYWFUVdX57EWBPkS1xC5k4AkD2azWSpp1tTUSA9SexBjIGrsHT58WPa1Y8eOkrB5l1IQ9SqdTqf0irs/46Ien/vYtdUOnU7HXXfdRUBAgPTczZgxQ34nIDar3KFUKqmqqmLZsmUyAqJXr16SDInNH%2B9og/r6ejlu7qHbYoNNFAq/7777yM7O9vitiGYQuPnmm/niiy8AZNmImTNn/qwSCG3lTYrwzZ8L4cET7zfxb/d3sbsHUcy/yMEcNmwYQ4cOJS4uTp6vUqlYuHAh33zzDVu2bLkq7fTh18XlyX754IMPPvhw1TFjxgzuuOMOVq1aRXZ2NlarlfDwcGnQzJs3D4VCwfPPP09ISAiPPPIILpcLnU7HrFmzCA8P5%2B6778bhcHiESSmVSn744QcA/va3v/Hcc88BLYIT7nC5XBw5ckQep6SkcOrUKQYNGsTrr78uP78cUQeRzyYgpMi9Ia7V3NyM2WxGo9Hw7LPPkpCQwC233MKSJUsICQmhvLycxMREj99OnDiRl156CYBnnnlGGpd/%2BMMfPHLhRN%2BgxcCePHkyOTk5LF26VF5L1JwrKCjgmWeeobCwkI8//hibzebhiRs7dqw8X2DQoEHy3zNnzmTu3LnyejabzcObMG/ePGbPnu2RM5ecnMzRo0eBlrk6duyYRz9FXbj4%2BHiPMbz99ttbjeebb77Z6rO2SIy3MdmWAEhbcA8rUygUJCcns2PHDtl2QKpxupecSE1NlZsR7kYjtIzL888/71Gk2x3r1q0DWoxr73w0l8slhTjaw/Lly5k8eTLx8fHk5eUBcMcdd7Bs2bI2zxfhbu%2B//z4nT57kH//4Bw0NDVKkw9ub6u0tcx9v93w0pVLJ1q1bPQrei3C76upqD486tDwb7h4fl8vFpEmTOHz4MP/%2B978BiI6Oprq6mrS0NA4fPkx6ejp79uwhICBAknqxVl0ul5wDcR/hyRGbKuHh4ZSUlBAcHCw95e5hhCLET4hGCQQGBlJcXCyP7XY7999/f6txVSgUbNiwAafTyfjx43n33Xfp0qULBoOBxx9/nJdeekmWEXC/nrjmvHnzPMZXbJC5P49t1ZkU4Yai5Ae0lPiAls222tpalEplq1xW7/U2ePBg9u/f75F7%2B%2Bmnn8o2qdVquQEi8vj8/PxYvnw5sbGxck6F8rLdbsfhcMi6gaK8jqjjWF1dLb/X6/WYzWYiIyPp1KnTJb34PxXCe79y5Urpxayrq%2BOll15izZo1XLhwgfHjx5OSktLqb4gPvw34BFp88MEHH64RysrKeOGFF9i0adNVUaL8z3/%2Bw/vvv893333XygAyGAweogECwlgBWuWqCbQlDnClaOvanTt35tSpU62I2uVCeCOMRiNOp7Pd4uJqtRqFQtHm9%2B0JmIj2BAcHS/l7gS5duuDn58eePXs8%2BqDX66XsvSBH69evZ%2BzYsVJtVa1WM3v2bF599dV2%2ByVEM7xFZa6GnLuYb4VCQYcOHRgwYABr1qyRYwV4eHhuuOEGvvvuO3kcEhJCdXW1xzlRUVEUFRV5tHf%2B/Pn88MMPfPDBB6jVapxOJ5MnT2bZsmXodDpZuiM9PZ1FixaRnJyM0Wika9eurYRsLoXk5GRJ6qBFnOfs2bOYzWaZH%2BYOb1GT2NhYOnTowKeffsrDDz8s6z6K89xzP1999VWeeeYZrFYr8%2BbN49lnn/Voo7eq4ujRo9m4cSPw4/xdd911pKSksHLlSrkmxRi6Y%2BjQoeh0Oo%2BNFHENQUQ7dOhAcXGxDMG8WLFz9%2BLylxI1udyi6VcC0ca25vX222/n0KFDnDhxwuPzd955h8jISKxWK2%2B88QYhISFs3ryZwMDAdsVv/P39pUKw6KcIEa%2BvryciIoLS0tKfpU7qjbb6FB0dTUhICIcOHWolNjR27FjOnTsncy3VajW33HILubm5nDhxgsLCQlQqlawTarFY5HvM5XLJTRVxbxFW3VafvNvmPrfi3yJX2WAw4HQ6sdls1NbW4nQ6CQoKonv37uzcuZNFixbRr1%2B/qzJmPvx68JE9H3zwwYdrgP3793PPPfe0SUDaQv/%2B/dmzZ488vhxDRafTERUV9ZNq%2BbmjLeW9awnhNfEOgdNqtfj5%2BcnQNpHPIr5XKpWo1WpUKhUNDQ1tKmGK/9wNXWEsuRtGQgXP3WgWJSS0Wi0JCQnk5ub%2BLBLbVr/d%2ByNUGYWnSaPR0K9fP66//noAXnnlFVkE/bcEUfD8ySef5LXXXsNisZCQkCBzlpRKpYfXUPQvNjZWFqG%2BEiiVSu677z4%2B/vjjq0Jw3NvkvpkiIMqhGAwG2VZvwvpT75uamurhDRaht9OmTePs2bPs3btXbjyIvNCGhgYMBoP0SAuVWvHcDx06lE2bNrW7lt3DA51OJ48//jj33HMP69evl15vcc6VrsXAwED53lu8eDFvvvlmu8qyV2Mz5LeOy/m7cDmE0H3d3nnnncydO5f58%2BezZ88eKUrjw28HvjBOH3zwwYdrgFdeeUUalhEREdTV1UkjRnhC3P8gi7BMgbb%2BoIvcKCGmYbfbJdETym0JCQmYTCbCwsJIT08nMDCQhx566KJt9SZ6KSkp2O12GTZms9mIjY3l0KFD7V7jSg2xgQMHYrVaOX/%2BvNxhFp5FlUqF0WhEqVRKgRpBvtojz0LYQniMwJMApqSkMH78eLKysqirqyMyMhKn08nWrVtZsmQJY8eOlfPlfg9374j4t81mk%2BIyl9PnlJQUqTaYnJxMcXExVVVV0ivlnncjCK4gliKPaPr06UyfPp1du3Zhs9no378/GRkZfPrpp2zcuJGQkBAuXLjQZujapdDW3KlUKgwGg/QEeHv7fgqEEqjT6cThcFBXVyfDT0VNSvD0PLq3yz1vT8jZb968mdraWt58803i4%2BO57bbbeOWVV4iNjSUrK4vq6mqee%2B456urqiIqKwmKxEBoaikajwWKxyHsVFRUREhJy0TprbbXJm%2BjBj3lk7qS0LaL317/%2Ble%2B//57HHnuMLl26AJCVlUV5eTk33ngj%2Bfn5/OUvf/HIw/MOnRbPzMcffyw/O3LkCBqNhltuuYWpU6dyxx13cOjQIaZMmcLIkSN56qmnCA0N5b333sNoNDJ58mSmTJnCjh076NWrl8xre%2BKJJxg5cqTMtZs6dSrh4eEcPXoUu93Ot99%2By4IFCzCZTOzZs8fjt6LPu3fvZvPmze0SFCH8ZLFYWLVqVZtrV61W06VLF9LS0sjLy2P//v1tXutqICkpicbGRkpLS3G5XNKbL%2Ba8a9euHD16tFWtRO9nqL0oiouhLZLm7aFtaxy979XWOT169CAnJ0ceu4sKTZ06FWgJhRbhxD78tuDz7Pnggw8%2BXANkZGTQ2NhIYGCgVIAUwhZWq1WGogm0t2MrpLMBD2%2BHUF8Uf%2BTNZjNarVZep7m5GbvdjtVqlTW3TCYT6enp1NbWkpubK4lEQkICJ0%2BepF%2B/ftTW1nLixAl5nX/84x/k5eUxbdo0HnzwQY4fPy5D/QRZGTZsGI2NjZw7d06GnA0ePJjg4GD27t1LUVERKSkpVFdXyxIT7v2NjIykqqpKkkvvP1u33HIL%2B/bto7q6moT/V19PkCK1Ws0999zDJ5980mY9MoVCQa9evcjJyWnlgRD38g7NS0tLIzc3F41GQ0hICLW1tTQ2NqJQKKT0envz5t7%2BxMRESktLsVqtUpRGhIuK/DchNPHDDz/IsML21oJop0KhoGvXrhw7dqyVcEZqaiqPP/44O3fu5NNPP0WhULBw4UI2btzIV199hdFoJDQ0lI4dO3LkyBHy8/MJCQlBpVJRVVUlcxP9/f1JTk4mKyvrkv0MCAiQOVKCPHmrwgIyvK49CMNW1NUzmUzcfffdvPPOOx7nuYejirwx9/uKcMeuXbty5MgRkpKSiIuLY%2BvWrfj5%2BUmvr/fYhYaGUlFRQWBgIMOHD8doNLJr1y6Sk5M5fvy4LP9QX19PSUkJ5eXlUoVRQNSxuxIIYvbFF1/gcrmIjo5m3LhxBAcH88ILL0iyM3ToUN58802USqUswbJv3z6PuXEX5miLiF4OHnzwQZYuXXrFHlSAJUuWkJKSwv33309ubq4MPUxMTCQvL89j3SgUCvz9/Xn88cdZsWIFJ0%2BelMrFTqeThIQEMjIyWL9%2BPXa7HT8/P0aMGMFXX33FtGnTCA8PZ%2B/evXz22Wf06NGD7t27s3//fo4fP864ceNIT0/n%2Beefx2g08thjjzFw4EBMJhPDhg3j3XffZcaMGaxevZqGhgYUCgW1tbXU19fz8ssvyw2mlStXcscdd2CxWOT796eGh3q/265mmOml4E0aX3jhBebOnYter2fdunXExsaSmZnJW2%2B95RNp%2BQ3CR/Z88MEHH64BRo0axblz5%2BjWrRvHjh2TKnsOh0NKXl8sr8Z75xg8jQXhIXE3sH8qvI1FsaMthEaUSiUTJkxg48aNNDU1XfGOtfe9wFPdz8/Pj6SkJFQqlRRLEOcIaDQaAgMDKSsrQ6PRYDabpYLm9u3bGTlypCxDICAEFdLS0qisrJQCFWazuVWYZ1paGqdOnZJjKWTzx40bx7Fjxzh16hQXLlzg4MGDzJgxg%2B3bt2Oz2di5c6fMyzQYDGzfvl16OPR6fZuGf2xsLPX19VRVVeHv788bb7zBfffd53GOyWSiV69efP/999x8882tZOvdx3PatGls2LBBhnTed999qFQqKcLzyCOPsHDhQvkbnU5HfHw8%2Bfn5Hl6LK8GsWbNYs2aNrM83ZcoUtm/fLj3Nzz//PM8%2B%2ByyjRo1i06ZNssyEd8jwTTfdxDfffCNVP4XhnZSURF5envzMz89P5jG5PwdarZYOHTpw7ty5VqTdHXq93qP8iMViQavVYrPZpDeqW7duPPzww7z//vvs2rXrisfEvU/btm1r5b0XXqEPP/yQBx544Cdf/0rQFqHo0qVLK9Ggy/muLbgLNYkNCHdlyEsRzoyMDI4dOybrkm7atImRI0fK%2BX300UdZuHAher1eCpsYDAa2bt3KhAkT0Ov1nDp1ioCAAPr06cO3334r8y9Hjx7Nhg0biIuLIz8/32M8xMaKEE5pT0G3PQ9dW%2BG7Pxe/RB5lexD9NxgM/OUvf6G4uJglS5Ywe/ZspkyZ8qu0wYerBx/Z88EHH3y4BvjPf/7DM888Q3NzM/7%2B/h4J93DluS3e%2BKn5K126dKG8vJzKysqLGitDhgzh22%2B/9biXyDsSnkJ3tEVOr%2BR77/64E%2BExY8awYcMG%2BX10dDSVlZUyN%2Bnzzz9nzJgxzJ07l3nz5knFRPdrQ4tKX2VlpSQM4Fnw%2BJ577pHhcApFS1Fjh8OBRqPh9ddf55FHHuHhhx9m0aJFklSOHz%2BezMxMaVD36NHDI%2BwO4IEHHuDDDz/06O%2Blwrz0ej3z589n5syZrQz22NhYCgsLWbBgAU888QSZmZlMmjSJhx56iEWLFlFfX3/VpN4vBoVCQUJCgiR4oiSFw%2BEgKSmJ06dPk5qayqlTp9pdq%2B6GuCDn7d2rvWsMGTKEbdu2yZwygUt5EqFFSGP9%2BvVX/Cxdaj1fDL169WL//v0ea9x7jtsSdHn66ad58cUXgct//sPCwqisrLxsEiHCni%2B3b%2BKZgh9rR4oNBG%2BhHHd4e9XFsbdoUXx8PDNmzCAsLIw5c%2BZw4cIFXC4XO3bsYPDgwa087e7XHjZsGN98881l9eNibfz/GUI9NiwsjOuvv55nn332WjfJh58AH9nzwQcffLhGSEtLu%2Bj3YnfY3Wsl0Lt3b44dO0ZERARlZWVERETgdDo9Qj/bg1KppEePHhw8eFB%2BJnaN%2B/XrR1ZWlizY3hbaMmRNJhM2m83DsBK1xARpcQ9zFIZSdHR0m4XaLwaVSkVcXJwkEUajURalv1h73UsItPenz9/fX4a3euNibY2JiaGwsJCoqCgPYRihtifGZfjw4WzevNnjt8uWLWPy5MmX1/nLgCDdGzZskCqAb731FgUFBZw4cUIKu4g%2BCnIBP4ZcepcZ8B4vd0Ndq9XKnMhfEt27d%2Bfw4cNtbiaItrpcLhISEoiKimLnzp0Xvd6tt97K%2BvXrL3pO7969yc7ObnfNDB48mAMHDmCxWHA6nR5j6Y1Zs2axatWqViUGLoW0tDTq6%2Bsv%2BZyIORk8eDD19fUez/fF8HOI6dVGexEL0LIG3cm/QFJSEk899RQPP/ywDK8MDQ1tVR/UZDLJ94/VauWuu%2B5i9%2B7dMr/254yDQqFg6NChDB06lJdeegmbzSafB5PJRGhoKOb/y957h0dVre/fn%2BktvRdKIEBCiEDoHQHpAkpRlKYIKMjhiHqQYkNQpFmRXqRYKIkIqKggiEGQKiAQIIAIAZJAgullJvP%2BkXctZ89MQtFz0N937uviCpPZs/faa%2B89ee71PM99e3lx4cIFeSxnNVHHRSi1Wk21atUU3%2BeO5xQSEkJwcDD33HMPu3fvprS0FC8vLxISEjh27BjBwcGyVLddu3bs3LmT3NxczGYzHTp0IDU1lR9//FEaqDtXgIi/B4Jg%2B/r6olKpPCWc/1B4yJ4HHnjgwf8Qly9fllL3wv%2BpIqxZswaNRsNXX32FxWKhS5cu/P7777JfSq1Wc/nyZRk8VqaoVhG0Wi12u51p06YxefJkSUZEkOtuv86o6H3n44sSTZ1OR7169f6rQgp/Bne6Ym82mwkICODatWt/qpQVlJm9p556ioULF97y54QIiwjYhPl5Rf0/3t7e8l50Jw7kDOdSS51Oh9FoRKPRyN8L4/CMjAyXz5tMJoKCgrh69aoUE3LM2rVo0YIjR45QVFREs2bNOHXqlDQQLy4uJj4%2BXmZHRYDunK0VUKlUvPjii0ybNs3lvaVLl7p4w90q1Go1drudGTNmMHHixFv6zIMPPsju3bsVtg6O4/yz4ZiYi4iICPR6/S0t/AhrEsexOGfP7gTi/EQGV%2BBm5%2BlM5jQaDQaDQdrGuMvU3Qncla82bNiQI0eOKMbXtm1bDhw4oFhc6NKlC2q1WlpiqNVqXnjhBRITE1m4cCHdunUjOTlZ9sD26NGj0rEIAudI5oKCgm75/TtFx44dmTlzJk2bNlWUcTviwoUL7Ny5U4q0QHnZtwf/LKjv9gA88MADD/4voWPHjuzevZuffvqJDz/8sNJ/KpUKk8lEeno6fn5%2B2Gw2TCYTNWrU4NKlSxw8eFCRJTCZTFgsFvnaMSgymUwEBga6jEdkZEQwnJycDKDoy3EOzkJDQ4HyzJ1Go%2BHjjz92Mfy2WCwumR5BIoqLiyskek2aNHH5XZs2bdDr9XTo0IGOHTvSs2dPYmNjFdv4%2BvpSp04dAgMDadCggRQfUalUTJs2DZVKJfvaxBwJ2wKTyUS7du0ICwsjLi6O7777Tn62V69e0kJBmBrHxcUpjLUFCgoKuHTp0m0TvdatW0slVQHHLKWXl9ct76uoqEiSJm9vb6A881GvXj2MRqPcTpxfp06dJNFTqVRs3LhRcW7C20sQdSjPYqrVajlmoZzpbAh%2B/fp11Go1BoMBrVZLr169ePbZZ3n00Ufp27cvo0ePZvbs2Wzbto3169fTtGlT9Ho96enpPP7449jtdk6dOkX//v2x2%2B1YrVa0Wi3Hjh2TY6xTpw5QbhYuTL9FNlWMwx3RA1ixYsUtzyuguEYxMTGyh7QyOM7bpk2b3M5nZZ%2BNjo5m//797N69W2Fx8OOPP6JSqejatavcXmSlLl%2B%2BfEtET%2BzrnXfeQaVSySB%2B5cqVUuTJGUFBQZJotGrVSv6%2BZ8%2BejBo1So5RkEdHogc3L093ztrZbDYKCgqk0I27586RTAlxHn9/f6CcALuD6DsWdhNQrg7qPL7jx4%2B7ZJGnTp3Ku%2B%2B%2BK8diMpmoX78%2BFy9e5PTp0wQHB%2BPr60v//v1vSvRAOaeO/7/V9%2B8UWVlZhIeHA7Br1y7Wr1/P6tWr2bFjB%2B%2B//z6LFy8mLS2N3NxcNmzYwIIFCzh%2B/PhfcmwP/rfwZPY88MADD/6HSEtLIyIiApVKddOyrIULF/L9999Tq1Ytli1bRrNmzbDZbNStW5f27dvz3Xffcf36dbKysoiMjOTMmTOK1WqNRsPDDz/M8OHDmTp1Kj/88AMajYb4%2BHgpSOJMTESfUGWr%2B0LWvn379nz//fc0aNCArKwsLl68eMsZwPnz5zN27FiSk5Np3bo1u3fvVvz09fWV5GHp0qWMHTuWdu3asXfvXheRjWeeeQaDwcD169dZs2YNJSUlhIWFSSL8r3/9i/fee69SgQPHlX4xB2q1mtmzZ/PLL7%2BwYsUKOf5XX32VqVOnyu1EUDxz5kwmTpzIm2%2B%2Byfjx44FyonXlyhU5zyJj5zhPCQkJMqPgWP7pPNb/do%2BQSqVi9erVvPDCC/LedBRFEccOCAggPz8fm82mKMl1hE6nw2AwUFBQ4HJP%2Bvj4YDAYaN68uYu5/KeffsrUqVOJiIjg0qVLd3wuzuJGzv1%2B3t7eBAQEyLK6v%2Bo47733Hv/%2B97/lfDhL8wvhJCH%2BcjM4kkJhSK5SqZg4cSJvvPGG4vi%2Bvr7k5%2BczceJE2rRpQ/fu3YE/3//rCFE27nhNa9asybhx41i8eDEnT550yYwlJyezePFimjdvjsFgAMqfyR07duDn54eXlxfnz59n4sSJHDhwgG3btt1UidKRzIs5djxXcb0dxYsaNWrE2bNnqVOnDlarlaNHjyqeseDgYEWpPJQvtJSUlCjKO4ODgykpKeH333%2BX18Pf358bN27IxQ0/P7%2B/fcljnz59GD16NIGBgYwaNYq2bduyfft2afNSVFRE//792bp1Kzabjfbt26PVaqWwkwf/HHjIngceeODBXcTGjRsrfC83N5fFixdz48YNatSogVarJSsri1atWtGkSRMWLlzI5cuXsVqt1KhRg/DwcHbv3i0/707eHlCsSMfFxXHs2LFbCjzdQQSvzuIXN4MgCOHh4VLOPz09XZYq3S6xMZvN6HQ6Rb/Z7cBsNlOtWjUyMjJcemluF85j/6t6ohwDe4vFQmBgIDabTRKzF154gR9//JH9%2B/dXmF0UpbUVGcjfKcS%2Bblcq3mw2y34g4TXoTIbdfWbAgAH06dOHS5cu8dlnn7Fjx44Kt3fX83q76Nq1K2lpaaSkpFRaRtikSRMOHDhwy/Mr%2BljDwsIkuf1fCX%2BIZ/d2jyVKWLVarSzDdQeRNXck2TqdTmY3xTxqtVpCQ0OxWCycPHlSzpfBYCAoKIiioiJycnIq7MmFclIm%2Bm2de/VuFe7m/fXXX2fbtm2K%2B8uRaLZs2ZKEhATWrl1LVlYWERERjBgxApPJxIMPPnhH4/hfYeXKlaxatQpvb2/69%2B/P4MGDqVu3Lmq1mi1bttCrVy/sdjujN/5b0wAAIABJREFUR4%2BmsLCQXbt2cfHixVvuBfXg7wMP2fPAAw88uIvo2LGj4rXNZuP69etotVrq16/PrFmzmDx5soLE3Sn0er0iQBO9OrcaoItgSGSdnD2xxAp3SUkJeXl5UsL%2BZvvz9vbGaDTeUTDeq1cvevbsyVtvvUVqaipDhgzhhRdeYOzYsfzwww8EBgbSo0cPjhw5ws8//0yTJk2YPn06S5cuZceOHYSGhtK9e3f279/P3r17CQkJQaPRkJaWRtWqVaWow4kTJ4iPjyc7O5uCggKuXLkiSZyvr6/0x3POyKlUKvz8/GQ/meOcOdtM6PV6VCoV3t7eREZGcuTIEbRarRQkcQyO7XY7wcHBlJWVkZmZyfDhwwkKCmL9%2BvX4%2B/tz6NAhtFotISEh0gfQYDCwYMEC5syZw4kTJ/5SQRXHAFjMh7Mgzl9BYv7zn//Qq1cvjh49ysaNG/npp58Uva9eXl7k5eUpyv2c%2B/mcx%2BCYRdJoNGg0GkpKStBoNPj7%2B8sywhYtWpCfn8/hw4dva8x/xXmL0k9xzVQqFWazmZKSEkmCHHsvBUTvnLe3NyqVioKCAmw2G/Xr16egoIAzZ864HEuj0UgrCkfvTkHcDAaDLCV3J%2BJkNBopKyv7y0RfNBoNDz30EOfOnePq1atkZ2fL/avVahYsWCBLSqdMmSJLDseMGUNkZKQiQ%2Bzl5UVRUdGf6vtz7EUW3wHCe7Jly5ayHFaUkjp6K2ZnZ1OrVi1SU1Pdvi%2BygQ899BDr1q2TxxSv09LS%2BO233wgODub555/ngw8%2BkPfEuHHjKixbdQe73c64ceP45ptv6NChAy1atGDGjBl4e3tTrVo1jh8/jkql4ujRo6Snp9OtWzdq1qx5U1EjD/5%2B8JA9DzzwwIO/GfLz83n55ZeJjo4mODiYEydOsHv3bi5cuPCPkPvu27cvOTk5jBo1Cp1OR9%2B%2BfVm5ciVDhw6V29zMM8pkMrFw4UIWLVrETz/9hM1mkyQHyklxRdLtXl5eFBYWMm3aNF566SUp9y8yChs3buSVV16p0BD8ZoiMjKROnTocOHCAKVOmsHjxYho3bsw333xD37592bBhgxzne%2B%2B9x7/%2B9S9KSkrQarVUq1aNMWPG8Ntvv7F582YZOLVr1478/Hzq1asHwMGDB/8yny5HEpCUlERxcTGPPfZYpdncwMBAcnNzWb58OUOGDHG55yoT5RFm2LdrHg7llgJqtVoGvbGxsXeUMWzfvj27du1CrVbTvXt3vv32WxdBlPbt29OsWTPeeecdoPw%2BECI7OTk52Gw2xo8fz5tvvkl%2Bfj5VqlQhNjaWvXv3uhVXslgsWK3W28qSazQaQkJCCA8P5%2BjRozLrWqVKFWw2GxkZGX%2B5X5sznK%2BlRqOhbt26nDlzBq1WS35%2BviILKIimYwbWy8uLnJwcDAaDi5F8RccSixiOxvdGoxGr1epyzl5eXuzdu5dJkybJZ0ar1dK0aVMuX74sFW4TEhJo2rQp48aNY/r06Rw5coSQkBC6d%2B9OcnIy586dkxYgHTp0oKSkhPPnz3Px4kVat26N3W5n165dREREkJWVRUBAgBQSutW5FAsNOp0OtVrNlClTmDNnDsXFxZSUlDBy5EiWLVsmyyUd3y8tLcVmsxEXF8eZM2ck2RavxXNgtVoJDAyktLRU2vYEBwczc%2BZMRS%2BlO2RlZfHggw%2BSnZ1NUFAQly9fxmQyYbfbKSwsVJTFenl5MWDAAHbs2MGvv/7KihUraNmy5S3NhQd/H3jIngceeODB3xC//vorDz/8sOxbczaEFqvL4qcQJlCr1YSEhJCVlSVVGUXJlcViIScnB41GIxUUAwICOHv27G2N7U5K9oQ1gRhj586d2b59u5T3FkHg8OHD0el0LFmyRHrUiTJP%2BCMLZjAYUKlUFBUVodfrsVqtLr1htWvXJiUlBb1eT1lZmfT4cjyPiv4EiqBVHNMRWq2WSZMmMW3aNPr160diYiImkwmbzUbNmjW5cOECc%2BfOZdy4cdjtdgUxffvtt%2BnRowepqan069ePI0eOAOV9ibNnz5bzA%2B7Lb0WGRUi7V/Yn3Fk1E8oJiZeXF9euXSMwMJBZs2Yxc%2BZMMjMzJTmrV68eOp2O8%2BfPU1pa6rZ383YWHfR6PQ899BBJSUmypE%2Bj0aDX64mOjuaXX36RpWOTJk1CpVLJzON/y85h1qxZdOzYke7du5OZmelW6r4iWCwWAgICSEtLo2XLljLrPn78eCwWC5cuXeLIkSNUr16d69ev8%2BOPP/6p83DOoD/wwANs2bKFe%2B%2B9lx07dlCrVi1OnTpFUFAQmZmZhIeHk5GRgZeXF7m5ubLMuqJ%2BUMf3mjZtyqFDh1zKO8PCwrh69WqFY3TOKup0OqxWK61bt%2BbQoUMUFBS4qPOK%2B7isrIywsDCZpTYajdjtdoqKirBYLPI7rLIFIq1Wy7hx41i%2BfDk3btxw6aVUq9WYTCYMBoMkSEKwqrS0lJycHAIDAxk4cCDz589nzpw59OzZk6KiIj744AM2bNhw0/Ju5%2BsEyO82Rz9DjUYjM4xFRUUu74t%2BQ5VKRWhoqOw9VqvVREREkJaWpvjODA8PVygyd%2BnShVdffZWAgAC34ywoKGDRokXodDrCwsJYt24dZ8%2BepXHjxuzevRuDwUBkZCSnT58mPDwcvV7P5cuXGTVqFOPGjat0Djz4e8JD9jzwwAMP/obYunUrU6ZMAVCUSgmp9NmzZ/Pmm28yc%2BZM9u/fzwcffICPjw%2BBgYGkp6djs9koLCxEp9PdVjmVc5Ck1Wpl8OUo1GE2m6lRo4Zcbf6z2YeGDRty9OhRjEYj/v7%2BpKWlSXJTpUoVvL29ycnJ4dKlS9jtdh588EE%2B%2B%2Bwz1Go1/fv354svvlCQktq1a8sSNY1GQ3R0NKdPn67w%2BCLL4Byw%2Bfr6uhAexwDUkVAJQhcREUFeXh45OTmKANfPzw%2BTycQ333zDxIkTyczMZPXq1UC5IXZSUpJbAnWn2VzH/jzxecf%2BwbFjx3L//fezc%2BdOZs2aRVlZGT4%2BPjJodzym8xhuZUxPPPEEy5YtIzAwkLlz5zJ9%2BnTS0tJkaW94eLjMBlmtVp5%2B%2BmlmzJhxy%2Ber1%2BuJiYnhxIkT%2BPn50bx5c3799VcuXLhAfn6%2BzNj6%2BPiQl5dHWVkZXbt25euvvyYhIYHg4GC%2B/fbbSkVx/htYsmQJFy9eZMWKFXIBxPF83ZnHq1QqjEYjQUFB/P777%2BTl5bk1N3c8B8cSQ6PRSMuWLaUAh1hIEHPt%2BNzr9XoCAwO5du0apaWlChsQAZHB1Wq1BAUFce%2B997Jv3z7pWSeeJ1E6LrzcHBcwHF8L0R%2BxGCPmwN2xH374YV577TUKCwt58MEHpd8mQOfOnfn2229d5txkMmEymf5UP654nkSpsFiUEd9NYhGqMqP4qlWrcvHiRfm6SpUqUthKvC/uCUGGLRYLarValkZ7eXmh0%2BnIzc2Vi1yiZPvq1atyHFOmTGHAgAG3dG4pKSksX76cs2fPcuPGDfLz8yktLcVutxMREcHw4cPp27fvHc%2BdB3cXHrLngQceeHAXMWTIEEV/kdVqJT8/nzNnzhAaGkpxcTH16tXjP//5D7Nnz%2BaHH35QBIb169cnNzeXXr16MX/%2BfL755hsiIyO5cuUK3bt3dxG7qCyQFoGV8GULCQkhMDCQ1NRUrFarol/IeR%2BOfWtiGyFrLhQrS0tLZZZNq9Uqgjh3SniV4XZLL28GEcjFxMRIqwURNFVWlnYzuAtWdTodPj4%2BdOzYkTNnznD8%2BHEX8YnbJXhiPsRP589XFoTeLYgeT7VazbVr11Cr1QQHB1OjRg18fX355ptvCA8P5/r16y5jFwb3er2eiIgIrl%2B/LkmqmAO9Xo/dbqe0tFSanQty7pgNqQiO2Sq9Xk9wcDDXrl275XlUq9V4eXnRrFkztm/fjkaj4fnnnyc/P5958%2Ba5VTAVpuC3Mndwe0qbzgs5fwai0uCvEh8SQjXOvXDOaNeuHT4%2BPmRmZnLixAmKi4t5/vnneeONN4Dyst%2BUlBSX76P/BkTGeu3atZSWlioWmG4GR1IubC7E%2BTr3Zwr8FSX8FouFfv36YbfbqV69urSwadq0qdymtLSU2bNn8/HHH8sxaDQaevXqxdSpU2%2BrL9CDvwc8PnseeOCBB3cRzZs3l39oDx48yMGDB0lJScFms3H58mWuX7/Orl276NOnD7t27ZJ/7MPCwtBqtZw6dYoLFy7wwAMP4O/vT3p6OlDuDxUYGMjbb78tswIGg4GPPvpIHlutVhMUFCR93ByJnlar5caNGxw/flz2D5nNZqnE5%2BjZJladASkyMnDgQJ566imGDBmCRqNh1qxZGI1GevToIQNw%2BMO7zM/Pr8I5io%2BPR6PRMHDgQBlodOvWTb5vNpuZOXOmDIANBgNJSUmK1yJL54gHHnhABlaPPPIIAKmpqcAfQXSDBg148cUXUalU8tgzZ85Ep9Oh0%2BlkptUZYtW/Tp06aDQaOnToAJQHbLVr16Z27dokJiby888/o9Fo6NixI88884z8rKPkftWqVSucG1Gq5Ux8RaZAwB1BMRqNhIaGymspxGAqg1qtZvLkyWg0Gtm7WLVqVbfeX%2BIcfH19adiwIY888gjPP/88Y8eOlfP25JNP8txzzzFx4kQGDx5MaWkpR48eZdu2bdjtdi5fvizH7ughKRRIS0pK%2BPXXX8nNzZXnK%2BbCUWBEXFeRha1sTgUcy%2BACAwPlfrVardtrLhAREUHdunWx2%2B3k5OTIc7FarcycOZP58%2Be7DdpFJsURdevWVfgGCggie7PrBX/4YopSb0f4%2Bvqi0WioVq2avF7Vq1d3ux%2BVSkVYWBhqtZrw8HCFKmdkZCSjRo3CZDK59cLr06cPjzzyiOz3g/L7Q8xj06ZN6d27t9zeaDQSHh6ueGYNBgMhISH4%2BvrSunVr5s2bh81mo0WLFvKYwtfv6aefdhlD3bp1AeScaTQa/Pz85Hi8vLwwGo34%2BfnJcdWuXdvFE7F169ZynqKjo3nzzTcBKiR6Yv4d4Ujk7Ha7gtiK0m9n3C7Rc3e98/PzWbVqFatXr2b69OkMHjyYwYMH07p1axo1akRcXBzx8fGsWrUKKL%2BXExIS6NatG9u3b%2Bftt9%2B%2BrTF48PeAh%2Bx54IEHHtxFjB07FpVKxenTpwkICJCBhfCjEnAO6jIzM6lRo4bsefntt9/o0KEDH3zwAenp6cyePZv09HTmzJmDTqfDbrdjsVgYPXo0UB4ILFy4kHr16inKRIVSZ0lJiewnOXv2rFTycwykBRyJhsiC/fTTT6xcuZLly5djs9l47rnnKCoq4ssvv0StVtOwYUOg3PsKXM2XHYPGMWPGoNfr6dWrlzzul19%2BKbddsWIFDzzwgELl0sfHR1GOuXr1apfSuI0bN8oeQCGM8vrrr0uzdSg3l582bZoiIFuzZg2lpaVSTEFAXDudTifn5KWXXsJgMLB3716MRiNGoxGdTseRI0dkkFlUVMR3333HkiVL5Hw67vfixYt07NiRr7/%2B2iWQdi5LE1m98PBwRXCo1WoVwZ%2Bvry8hISG0aNFCkimr1eqinjp79mzFMcvKyiRZycjI4MKFC6xYsYLPP/9cbhcUFMTMmTOZMWMGwcHBFBYWcuzYMTZt2sRbb71FVFQU/v7%2BhIWF8d5779G0aVMef/xxpkyZQmFhIRaLxW2wK8qanaHX6yXxMJlMbk3onb0Z9%2B/fr3gtREIEVCoVUVFRss8rPT1dlsgJARGdTkd0dLRLUN2kSRNFqV5AQIAiE%2Bd4bs7POZRnLaGc8Lz77ruUlZXx1FNPoVKp5PMriKjj9RLPlCAqYuFFKJE6Zpidez%2BvXr0qt3f2HhQlm3q9nuzsbAwGAy%2B//LLsf1OpVEyfPp3nnntOklAB4Sk6bNgwRo0aJXvMxBjEXBQUFPDoo4/Kz5lMJvLz82nTpo38ndls5uLFi6xdu5a3336bYcOGYbPZ6N27tzymeEZFps8RgoyJ84yMjCQsLEx%2BLwh1099//132%2BGZlZbkspIgezdzcXKZOncpzzz3nciy9Xk%2BzZs3Q6XSkp6fLknSDwaD4brNYLFLIRbzvuJAGuPTeOS6MiYyoj4%2BP4nqNHTuW4OBgLBYLjz76KKGhoQwbNoyDBw9y6tQphgwZQkhICK1ataJq1arcuHEDm81GSEiInH9fX19iY2P59ddf%2BeqrrzxKnP9geMo4PfDAAw/uIsrKymjWrJkkXM5KdQJvv/22NOqG8oyRYw9aQEAAHTt2ZMuWLRQXF8uem%2BbNm3P06FG5L0dBF41GQ/Xq1Rk/fjzPPfcchYWFUsiluLiYoqIili9fzrPPPsvHH38sxTT69ev3v5iaSuFYBqVWq9FoNJWaaOv1evl%2BZX/2fH19pagFlBM3vV5focH8zdCmTRv27t37p3oaFyxYIC06EhISsNvtfPrpp/Tr169CkRZHOXfnewng2LFjJCYmMmvWLBcSLKBSqejevbuCWIeHhxMVFcWePXvk7wwGA7GxsVJsxmw2y2zy9evXpYBOYGAgmZmZmEwmrFYr9erVIyUlhS1btshMW2xsbIXXx13pbnR0NOnp6Xh7e8ses78CorTOUSTGWSTJXVmdSqWia9eubN26VTE%2BdxYFACdPnqRevXqy7LSkpEShUtmtWze%2B/PJLOnTowI4dO%2BRPd2NYu3YtDz/8sCzZFfYJjhlJx/vwZmWBt1sqvXnzZh544AG3RP12SxDF8%2Bt47wYHBxMSEsLx48dlH1tubq7bkk/x/ShETa5evUpcXBxHjx5VlOdGRERw%2BfJlt2Pw9vZGr9e7qMre7FwMBoMsNXWEY3%2BvUH7Nz8%2BX%2B3IWVPL29iY/P19eA9FP63hs4Usq0KpVK6ZNm0aVKlVo2LAhdrudL774gvvvv58tW7ZQpUoVoPx7qV%2B/ftSoUQMoz5R/8MEHNG3alAMHDvD0008TEREBlH9fpqSkcO7cOQ4cOHDb1iMe3H1oXn311Vfv9iA88MADD/6v4o033uDAgQNoNBoWLFjAli1bAFzIQf/%2B/RWrqm%2B88QZff/21DAQKCws5ceKE/JwI4C9fvqwIgMX2drudsrIysrKy%2BOqrr%2BQ2ZWVltGrVivPnz0shlE2bNjFw4ED8/f2ZNWsWZ8%2BepWXLlgr/KmcMHTqUo0ePEhERQW5uLjNmzOCHH36QgeAjjzzCL7/8csfz5hjwiHNxhHPQX5HwhnOmrLi4WLHvsrIyxb68vLwIDAxUBO9xcXEysHPe32%2B//XZLAbNKpaJ69eqK4E7g%2BPHjpKSkcOTIEQ4ePCiVEk%2BePEnnzp1liaIjHMmdO6JpsVhYs2bNTQ2oz507p5iPwsJCqQbouK/ffvtNvhZZT5FJCgkJkUGq6P0UhC87O5usrCy6dOmCSqXi/fffp1OnTpw/f95lLt0F2NnZ2dLX0VF0pFGjRowfP57o6GgOHToEwH333UdoaChms/mWbCGcibRzKazIJgqI7E2DBg04ceKEHF9MTAwGg8EluwjlGcejR4/K46lUKsWihMhGCZXQytRCb9y4Ia%2BX%2BOd4L91uj6uzsuTN8Mknn9wyobvZPsUcON67BQUFXL9%2BXZ5XUFAQpaWlhIaGuvTniYy3KKUtKyuTJe6OxNDxORaKumJsIkvpeN3FwpKYy5YtW5KTk0PNmjUJDAzE39%2BfzMxMxQJKrVq1uHHjhnwmW7RowaVLl2TZruP7arUanU7HpEmT2LdvH1BO0idOnMhPP/2E2WwmMjISg8HAnDlz2LNnD3q9HrPZzFtvvcUzzzwjs3ynTp2S4jt6vZ4zZ85gsVhITU1l7dq1nDhxgp07d7Jr1y4OHjxISUkJly5dQq1Wc/DgQQ4cOMDBgwfZv38/48aNY%2B7cubRu3Zr777//ptfXg78XPJk9DzzwwIO7iGbNmlGzZk3S09Np27YtGzZswGaz3ZHAQLt27di1a9dtfUb0oTkGQI4r5Q8%2B%2BCCXL18mOjqazMxM9u3bR25uLj4%2BPtIXSigi3smfE4vFIrMPzkbttwKDwcDMmTMJCQlh2LBh3H///Xh7e%2BPt7c3ixYsZOXKkLHuaPXu2fF23bl2efPJJNm3aRNWqVZk3bx7vv/%2B%2B3K8I5B955BEeffRRateuTUJCgty%2BsgzUzeCs%2BLl9%2B3buv/9%2BGjVqRHJy8m3tS2QpxKp/cHAwGRkZtz0mRx%2B1Bg0asGbNGpo3b87IkSN59913gfLyVdFf2qhRI8rKyigsLOSee%2B5RmLTfTDzk8ccfZ/z48RgMBurXry/Li4cOHUpMTAzvvfce48aNY8qUKcyePdsla%2BOYoRo0aBD16tVjypQp8nj9%2BvXjscceo06dOgAyczZx4kQiIyNlWbEjib%2BV%2B074s6lUKkpKSjAajdhstr8smwjIbCKUl4MC7Nu3j4SEBA4fPiwVTu9UoMjb25uioiL5HMyYMUNmycUzkJKSosiMO/ZK5ufno9VqZb%2BvyIqNGDGCkJAQZs%2BeLctI%2B/TpQ2JiIt27d8disVCnTh1ef/11RowYIZ/JuXPnAuXenDabjaSkJBo3bozZbGbPnj188sknxMfHM2nSJD777DMAqQZstVoV94Y4D4PBgMFgkIsNYrwCohw9MDCQgIAARa%2BdqHgoKSlxq84qvvcEARwxYgTbt2%2BnZs2a/PTTTwQHB9OsWTM%2B%2B%2BwzXnzxRex2Ow8//DDr1q3DbrcTGRlJ27ZtSU5Olosmju937txZlv46lmiL11lZWWRkZBAQEEBMTAxnz56V922tWrUUJPrNN99k5cqVf0rISpS3nz59GpVKxdatW2WZsQf/HFTcYeyBBx544MF/HUajkREjRvD%2B%2B%2B%2BTmJgog4ubET1nBTeTycTPP/8sA1Cz2czvv/9OYGAgN27ckH1MojxTiEwIo3Ih0Q5/EDBABlg//fSTPC7ACy%2B8wCuvvILVaiUyMpLjx48DSIEXxwBD9KA4q1Lq9XpFsKxWq7n33nv57rvvGDRoEH5%2Bfmzbto3s7GxatWpFTEwMb731FiNHjsTb25vz58/z2WefcezYMSlk8fTTT3Pq1CnWrVsnTak7d%2B4svfpiYmIoKCjgk08%2BobCwkCVLltC9e3fmzZsnx9qmTRtatmzJzJkziY2NxcfHR16XM2fOsGbNGnr06ME333xDUlISderUURBBKBd2WbZsmexP6tGjh9vXgYGBLF68mIKCgtsieoKQC6VT0ZeZkZGhKFMzGo1y5V%2Bn05GWluY2%2BBNll8JMvU%2BfPhQWFvLDDz/IbZ5//nmZ6RTWE1BeEuooWNKlSxcyMjI4ffo0%2Bfn5jB07lr59%2BxIcHEzz5s3ZsWMHjz76KGPGjEGtVjNy5EgSExMZOnQogOxlbNiwoSJ4FQRH2IsA9O7dm4YNG9K8eXO6du2K1WolMTGRxMREeW8LEjtz5kwXf7kaNWpw9epVCgoKFATH29ub4uJi6tSpg8Fg4PDhw9StW1f22B06dAh/f3%2BF95zFYqGoqMjlGA0bNsTPz09RflkRHInjvn37pFjPO%2B%2B8Q4cOHejWrRsrV64E4J577uHYsWPy/waDgX379kniGhkZSXR0NLt27WLIkCGsW7eOoUOHsnjxYgoLC2nRogWAnJ9nn31WHtsxoyasVe655x5%2B/vln1q5dy4IFC/jxxx/lNp9%2B%2BilqtVpmdcXvoNxGRq/XU61aNXl9xCKT1WolNDRU3vt2u52LFy/K7yYh0DJjxgy%2B//57cnJy5P3nDDHvgoyJLOvSpUvx9vaW291zzz2o1WqysrJkP6PjPsR%2B3FUDOGdnly5dCiDtH/Ly8rhw4QImk4kVK1agUqlo164d%2B/btw2q1kp6eToMGDViwYAEhISGoVCo6d%2B7MwYMHKS4uZurUqQAkJyfLfkVx706cOFGxIPHdd98psmzjxo1j6NChfPXVVxQVFXHw4EEiIyO5fPmy7LksLCyU%2BxC9sVWqVGHt2rW0adOGDh06sHXrVhISEqStg7B8WLhwoYfo/UPhyex54IEHHtxFbNmyhQ8%2B%2BIAJEyZgtVr54YcfOH/%2BPMePH5clZ4D0dsrJyZGKce4gSndatGjBN998Q6dOnfjuu%2B8YPHgwe/bsIScn57YzP7frPya2DwkJIS8vjxEjRrBgwYI7yoA0a9ZMljO99tprzJgxgx49evDZZ5%2B5JS3/Lcn1jz76iMcff1yWXonsUkhICOvXr6d79%2B4KsifI36JFi/j999/Ztm0bL774IoMGDQLKA06TyVTpWIOCgqTARXZ2Nr/99ptbi4qKslIiwPuzlgtiTis6jrNlwIABA7jvvvto0qQJjRs35qOPPpJZqvr161NaWip7LE0mE0uWLOHJJ5/k0KFDxMTEsHTpUkaMGMGGDRvo37//HY3ZnVfdrbwH5b1TXl5epKWlERoailarJS0tjW%2B//VZe35iYmDu2MjCZTERHR0siGx8fT35%2BPjt27CA/P18KdiQkJHD8%2BPH/uoWAgONzrtPpZAZTpVLh7e1NvXr12Lt3L1FRUVy6dIng4GBFv5u4PwICAsjJyVEQRudMZJcuXZg7dy7Nmzd3%2B9xUrVrVZQFFICYmBqPRyJYtW%2Bjatav8fePGjdm3bx8Wi0UxdmED07p1a6pWrcqGDRvIy8tjwIABNGvWjPHjx%2BPl5UVcXBzHjh1z6W%2BF8sya1WqlSpUqskQXyrNpjmXUJpPJxe5m/fr1Cr%2B7kJAQMjMzFf3ZQmhLIDExUdEbfSv7NZlMbN68mT59%2BsjtNm3aRK9evWS/nujjKyoqokOHDuzatYvOnTtTWlrK9u3bZV9qVFQU6enpFBUV0bNnT/79739TrVo1l3nx4J8BD9nzwAMPPLiLcCwHrKyUTKVSybI0rVYrA%2BKkpCTFdvHx8dhsNqKiojh37hz%2B/v5kZ2fLnxaLhfz8fHx8fOTKrUajUQiQOI5DZPucidWdGFBrNBq8vLxQqVTExMRQrVo1kpKSMJlMxMfH07dvX5KSkjhy5AilpaUyWNSdaLqNAAAgAElEQVRqtTRp0gS1Ws2ePXvQaDSYzWYsFov0SnPMcgDyfMX5xMXFkZqaKrOYoiwqICCA7OxshcfXzc4vOjqaxMREGjVqJOXji4qKiI2NpXfv3gwfPpwGDRqg0%2Bmk%2BbXVakWr1dK4cWMOHTp0SyTBWVBDICAggBs3blBWVkb16tW5cOGCQmjCUdRCr9fj5eVF48aN2bVrl8x42O12xRx169aNl19%2BmcDAQNasWcOsWbMoLi5m0KBBPPPMM3Tt2pXs7GypeOkYEDdt2pR69eoRFRXFG2%2B8wZdffqkgRQsWLODgwYMkJibK4wm/Rb1ez8qVK3n44Yc5efIkMTEx9O7dm02bNhEVFeW2R%2B1mJYx/1o/M19cXtVpNdna2tCzJz88nMTGRpKQkkpOTOXv27B3v/2ZQq9U0bdqUw4cPExISIntjhahHXl4e9erV48qVK9SsWZPDhw/TuHFjfvnlFzQaDQUFBfTu3ZutW7cqrpNWq8XPz4/r16/Tvn17Tp06hdFo5Pz58zJTXLduXVJSUrDb7VSpUoVLly7RpUsXmjdvzvTp093Oa2hoKDdu3FB4cTp6HYoqAV9fXwoLC6lWrRo5OTl07NiRpKQkJk%2BeTNOmTalWrRotWrTg888/V5C9q1evkpKSIo83ffp0adsxf/58AMUCDPyxOHa790JcXBwnTpyQmcf333%2Bfp59%2BGr1ez%2BjRo2XZqcDs2bOZMGFCpcdwJmVGo1HRG%2Bz8%2BlY%2BU9E2W7ZsUaiTTpw4UYowOY/RZDJhNBqJjIy8af%2B0YxnsyZMnK93Wg78fPGTPAw888OAuYNeuXWzevJnffvuNn3/%2BGXA1vnYMVMQfe5Gt6dKlC7t27ZLllQJ169alrKzMrZn37UCr1UofOfjDUkGv12M0GomKiiI3N1fhb1aRqt2tQpyvKF0T5WWC7B06dIji4mJq1KjBhg0b6NatG5mZmfj6%2BhIaGkpqaqokAT179uTLL7%2BU8%2Bft7U3jxo3ZuXMnKpWKoKAgwsPDSU1NVWR5xKq7r68vOTk5eHl5MWHCBF599VUZSKpUKgYPHszq1avlmFUqFXXr1uXSpUvUrFlTXlNAEmx3cAzi5s%2Bfz9NPP33T4FSlUuHl5UVubq4kfkLGfsKECTzxxBNMnjyZxMRE4A81zwMHDjBo0CAXIrt69WqaNWsmX/fq1Yvc3FwCAwNZtGgR586dY8iQIUC5x5jValXcd2azmRYtWtCxY0e%2B%2BuorZs2ahcViYfPmzbz00ksu49doNLRs2ZI9e/ZgMpkYPHgwixYtolOnTmzbts0tmXOcZ5VKJQ3kNRoNb7zxBo0aNeKxxx7j8uXLbNu2ja5du1JaWopWq72pYb0zqbzvvvuIiIhg1apV%2BPn5odfrycjIkARMrVZLAhMZGcmNGzcoKiqiVq1anD592mXf1atXl4sKv/zyS6WiOELyXpSHChVZcb1btGjBoUOH5CKNzWaTCwl2u11mp5znS/S4CUJUVlbGiBEj2LlzJydOnJCLBeKnTqfDaDSSm5tLaGgo6enpisUBcezAwEDmz5/PsGHDmDFjBuPHj7%2BjjLKj%2BumyZcto2bKlJHudO3e%2BbfIeGhoqKxh0Oh179%2B7liSeeuG0lSefeuTtZSLhbZE9k2x2fdedFLWc4P3vifNu2bcvjjz%2BOTqdTfFd48M%2BAx2fPAw888OAuoEaNGly4cEGRIXAOjtwpAYqgKDU1lddff91lv0ajEZPJxCuvvPKnxic81/Lz82UfTmlpKfn5%2BWRlZZGSkkJOTg7%2B/v74%2B/sTFxfHfffdx%2BjRo2Uw0KBBA6A8YBJy/M5w9CgT/U2iR8jRtHnfvn2UlJTg6%2BtLXFwcq1evliQnLy%2BPc%2BfOKYIUZ0%2B6goICGfzZ7XauXbvG%2BfPnXcr5fH19sdvt0msrJyeHF198UREgxcTEsHHjRgYMGIDdbkej0TBp0iQp737kyBHZw/bUU0%2Bxf/9%2BaR7vCLVarQjgHMmpeN8RoqTRbreTl5eHTqfjpZdeUpiii7K2kSNHys/5%2B/uzYsUK6dHmnLEcMmQIMTEx8t/p06e5cuUKeXl5zJ49W5q9Q7lQg6PyppjbHTt28Nprr7Fnzx7atm1LQkKCJHrCC0zAZrORnJyMzWajqKiIRYsWSXVRQC5WCN%2BxSZMmERoaSlhYGK%2B88gpjxoyhe/fuPPPMM/Tu3Zvly5fzwAMPEBwcDEDXrl3x9fWlbdu2bome47x6eXm5PHfbtm2TptI3btyQ983%2B/fsVoiCjRo0iLS2NAQMGULNmTRo2bIher1dc6/T0dL7//nv279/Pzp07FUTP0WNP%2BCAWFhZKojdkyBBpRC6y%2BidOnKB27drYbDY6dOiA1WqVyqY2m42TJ09K4icQGxsre9Hy8vIoKiqipKSE%2BfPny2spFmquXLlCVFQUpaWlsu9T9EdqNBoMBoMkc4DsBTUajcTHx8tj3mp5qyCjdrud0tJSatasyahRozh8%2BDCRkZGUlJTQtm1bzGYzzZs3Z9GiRXLejEYj27Ztk6Xuvr6%2BjBo1Cii3CBEkV6vVsmbNGtnb6uPjw7Bhw9x6HDrD2cfSmei5e67/Crj7rnT%2B3c0UTa1WK15eXjRq1Aiz2UyjRo2IiIio8Lwdiaxer6dGjRoMGzaMgIAALly4QHJysofo/UPhyex54IEHHtxFXL16lT59%2BpCbm0vt2rVlkG0ymVi/fj1LlixhypQp0hOtZcuW7N%2B/Xxqe3%2B5XuHNWJyQkhIyMDHr06EFRURHJycnUrFlTUTYFN1/R9vLywmAwsG7dOg4cOMCkSZOkGmarVq348ccfFZ8XqnZxcXH/T/k2ufMyE2THXQBc0bzGxMRw6tQpxe/cZRnq16/PsWPHJNFt3LgxH3/8MTt37uTJJ5%2BsdKwhISHUr19fvr5y5YpbVdXKetMcPQ6dxydQt25dTp8%2BrbjvRObUaDRKg3LRD%2BmceRAm9WVlZezevZvOnTsrSv2cVRn/Knh7e0tpfrVajc1mU2Q%2B2rdvz/fff8%2BKFSt4%2BumnKS4uplatWi7XzRlVqlRh5MiRnDt3ToqtgGtm351/3N2CWq3G29vbbf9gcHCwLBOsqBdSrVbj7%2B/P9evXMZvNFBcXo1arpdojlC8anD17FqPRSJ06dTh8%2BLD8vgoODmbo0KEsW7aMgoICqYYqMlkFBQXo9XoiIiL49ddf7%2Bi7cfLkyZw7d06KzZSVlREbG8u5c%2BfkdXDeb0X3/J/FnaqtVrYvi8VCfHw8r732Gvfffz9qtZpNmzbRu3dvJk%2BezAcffEBeXh5Wq5WnnnqKffv2sW/fPoxGI40aNeLo0aMkJyffEkn24O8FD9nzwAMPPLiLGD16NFarlTfffJOioiKuXbtGVlYW77zzDna7nfj4eLy8vPjiiy%2BoU6cODRs25NKlS9jtdr788ksSEhJkCVpubq4MFvV6PSqVqtJSqtq1a3P16lXZw3b16lV0Oh1z5szhmWeeQa1WExISwrVr1xRB560EUiqVShq7Q3kGoKioSAYwCxYs4L333uPMmTMupUbOxxD/F1mFtm3bkpmZyc8//yxXt4VBMZQH6TabTRF4Ova03cwcOzIyErvdzuXLl1Gr1ej1elkSO2XKFPbu3cv27dvl9haLhZiYGI4dO4avr68ie9OpUyd2794txSxEaSr80Vd0M7gTnREkcvDgwXzyySeSjFWpUgW1Wq3IvgkCKo4nvLygXFVSZCEfffRR6tWrx%2BHDhxVWCgIiaHzttdd4%2BeWXFWMxmUwVBvrCf8xRyGLevHn861//Yu7cuURHR9O/f3%2BFyEtF%2BOqrr%2BjXr58U7mjQoAFlZWWVkiLHwPlW7t3o6GjOnj3rQty1Wq3Ce69t27b88MMPjBw5kiVLllS6T0eEh4fLXlPHfb/88styXlUqFZGRkQovS0d7DHd46KGHMJvNfPjhhzcdg2OZpjs4E02TyURJSckt9emK%2B8lx7ho3bkyrVq2YP3%2B%2BrE4QPaFCbCopKUmK9zjOT7du3Xj33Xfp2LEjBQUFLgqalaFTp07MmzcPlUrFlClT%2BPLLLyksLJTnFxQURH5%2BPo899hgFBQVs2rRJsf8uXbrwzTff3PLx7jbE96Hjop6XlxeFhYUyqy1KfyuCWq3GYDBQp04dHnvsMRo3bszSpUtZtWoVZrP5/6nFuf8r8FgveOCBBx7cRezdu5dnn32WLl26uA38HbME165dk3LnImidOnUqy5Ytk9t8/vnnAHTo0AGLxcLnn38u/%2Bg7Zw7Onz%2BP1WrFbDZLdTir1cq4ceOA8nI6d314zsGmuwBalEoKFBQU4Ofnx40bN4ByAiMI1IMPPojdbpc9Zs7HEP%2BvWbMmaWlppKSkyNIy0ePneF7ujMkdz8NZPt0545ieni7nrKysTDHOoUOHMmfOHMXnFy9ezJNPPonNZsPHx0eet0qlYvr06aSnp/P444/L47zyyivMnj2bl19%2BmTlz5iiU%2BcB1Vb9nz54uhtV2e7lptig3FHBndC8CO3F/lZWVyflyzKwAzJo1i%2B7du/PII48ofq/X61Gr1RQVFSmIHpQH1GfPnpXy87GxsbRr144lS5Zgt9u5fv26i4m5UBYMCQlhxYoVsizQYrHIe8QRogdVZMKuXLnCqlWrXPpSq1atysWLFwGkfP%2B6devo06ePNKKePn06sbGx7Ny500VsBpCl1VarFZVKRUhICOnp6VIESKVSYbVaiYqK4ocffmDDhg0yuF60aJFLRtX5egoi4/jcGAwGFixYILex2%2B0u19KZaDln6S9evMj%2B/fvla3e9c2FhYVy9ehWVSuVWdVLAeXGne/fubN682UWARUCQJ61WKwVtHNGlSxdOnjwpPy9k/wMDA7l8%2BTK//voraWlpWCwWWrduTXp6OmVlZahUKqKioti4cSOtW7cmJyeHrVu3yv2KygTHXk6VSkVYWBjt27fnwIEDpKamcuHCBcX3izg/8aw6zr0jboXoCeEpxwUZi8XCv//9b%2BrWrXvXSh8PHz7M0KFDFYTe3Xe1Y38n/PHd3KlTJzIyMnj22Wc5ePAgFotF2qN48M%2BCp2fPAw888OAuwtfXl%2BXLl8uASvQTOfqWOfYYiX6TKlWqoNfr6dWrFykpKXh7e9OpUyf0ej16vZ7//Oc/zJgxQ1FyM3/%2BfMV%2BRa9LQUEBGo2GQYMG0aVLFymQIlaCb1a249xb5q6XRK1W89FHH8n3zp07R3p6OpMnT2bbtm3Sz68ynDx5kpycHC5evKgIRqdOnSr960A5dyqVimrVqskxiqDQEV5eXrKnTJi7V69eHYPBgE6no3v37pjNZl577TXgj34krVYrewZFQD127FjFvlu1akXfvn3Jzs6WwfG0adMoKChgwoQJZGRkuARfzkT1448/vqWSNMe%2BOIBq1apRq1Yt6Y0VFhaGv78/sbGx3HPPPWg0GlnCVb16dZmh8/X1VfiSAVLFVKvVcu%2B997oc23HMc%2BfO5bnnngP%2BuBeaN2%2Bu2P6VV14hICCAjIwMkpOT5f0m%2BjChPEspIHqxkpKSsNlsDB06VBJdx%2BsprhH8cX2Ki4ul%2BEfnzp25du2aLPW75557Kp1Tu90uFxYOHz4sibPdbmfNmjXSFkPcX862GFDuqSieNShXk6xbt65im/z8fEU2KzY2ljZt2rhcB0cI0RyBPXv2uGRsnDP7oh/QaDTKa%2BZ834BrL1rNmjWJi4uTr50/K45rtVqlfYQjVq5cya5du%2BRrs9lMaWkp77zzDuvXr5e9r506dVLYENjtdj788ENeeeUVvvjiC3bv3q3Yr%2BinFERPfObKlSt8%2BumnpKam0qtXL/lcOts43C7E%2BYqfwtbBkXSr1WqGDx/OsGHD7mqPW0JCAhs3bsRsNuPv7y89C0XptfBejYuLo169etLyIzs7m8LCQt566y1mzZrFoUOHiIqKYvPmzYr%2BXQ/%2BOfCUcXrggQce3EXMnj2bpUuXotFoSExMlFku%2BKPcUKyaCxn9oKAgxowZw/jx45k1a5Ysafz5559v20MP/iBAgYGBBAQEMGbMGF566SXy8vIIDg6WPmMCjn1aN4Nj9sFdWVxYWBh%2Bfn6cOXOG0NBQRfmh2WyW5UeRkZGUlpZSUlJCfn6%2B2/JUtVrNkCFDGDJkCGPGjCE1NZX4%2BHjGjRvHk08%2BSVBQEMOGDWPAgAH06dOHq1evEhwcTI8ePfjoo48ICgpi3LhxrFy5klGjRvHCCy8QGhpKVlYWrVq1ksbrjnYZfwWcbSL%2B7L4DAgJ46623WLVqlSy53LFjB7t37yYwMFBuFxcXJw2mRWnW7t276dGjB7m5uS7m4FBOEjZv3sxXX33F4sWLyc3NdVEFFNno2NhYoDz4fvnll1mwYIGCDOn1ekJCQrhy5Yo0ARdZOYDHH3%2BcFStW3Na8OGacHINwIS7Srl07vvrqK7efFcfo1KkThYWFHDp0iCpVqsjy05o1a/LAAw8wb9682%2Bqli4uLIyUlRY5r/vz5dOrUCSjvzbwZDAYDdrudli1b8v3331c49nr16imyZ5WdY1BQENevXycoKIiOHTuybds2LBaLi/hORbjVEmSBb7/9lmrVqhETE0NkZCQzZ87kiSee%2BNMekO7guKBTWd9bSEgIBQUFmEwmevToQV5eHomJifj4%2BJCbm0u9evVo164d8%2BfPp3PnzuzcuZPx48fzzjvv8MwzzxAQECCFZYqLi3n99dd57LHH6N69Ow0bNvzLz%2BtOkZCQwGeffUZUVBQJCQkysy%2BUap19MkH5HDVo0ED2MXrwz4SH7HnggQce3EWUlJTQpEkThf8ZlAen7dq1Y8eOHXJbEcA4fm2LHrYePXqgUqk4cuQIBQUFpKWlcfnyZReS5i74EaqXNWrUoEuXLhw%2BfFhK67src3Pel7ACEKV5jhYF/v7%2BUsDAuYxU7KdZs2ZER0eze/duhQS%2BIIrx8fGKEqwLFy7w9ddfM3fuXMWceXt74%2BPjozjnwMBAlxLCygRHdDqd7E9yLBGcNm0aDz30EOnp6bRr107%2BfujQoXTr1o3HH3%2BcmTNnEhISwqBBg9BqtYwaNYqAgAACAgKYMGECAQEBMksESpLnCEHy1Wo177//PgEBAezbt4%2B3335bjk1kqAoKCkhNTcVkMhH1/9thWK1Wrl27RlFREfXr1%2Bfo0aPS80wsHAh1RmfcCqkKDg7GYDBw/fp1CgsLXYzeDxw4gJeXF3Xr1lX0J%2Bbn57vsu7J%2BOsf7RfgJ/q%2Bg1WoxmUz4%2B/tLAjRw4EDWrVv3lwhniIyd6KEcOXIkJ0%2BedEvmoqOjCQ0NpXPnznz66ac3FYDRaDT4%2Bvry0ksvsW7dOvbs2VOh/ccbb7zB22%2B/TUhICHl5eVy4cMHFs9IZBoOBL774QloiiO8IcY1iY2O5cuWKS5%2BpO9Ghm%2BHee%2B%2BlWrVqHDp0iMzMTPn83Cr5b926NT/%2B%2BCPBwcEMHDiQtLQ0EhMT/5JFFYEqVapgs9lIT0/n5MmTpKen88ADD7Bnz56/ZP%2B3g6FDh2Kz2aTdit1up7CwUFqVOGcgoTzjL8rctVoter1eltz7%2BPhQWlpKrVq15OecS8c9%2BPvDQ/Y88MADD%2B4yJk2axM6dO/H19UWn03HmzBmgnHhUlkFw7FMRrzUaDW%2B//TZ%2Bfn7MmzdPYeDdsGFDfvnlFx599FFZUllZo/7twGw2M2jQID788EOFSMmdKgp6eXlRUFDgElg7ZwdvR7XuZgGeINOOcwrl2SxhBp%2Bbm%2BvSJyZUG/39/bnvvvtYt24dGo2Gpk2bStPrlJQUdDqdFK1xJJuOgjEVEfrKXosxLFu2jIEDBxIfH8/Ro0eByj3%2B/puoyK%2BroteOMJvNFBQUEB0dTdWqVdm5c6cik2SxWNBqtURFRQHl98rJkyfdqiIKwlilShXS09MrzUY798B17NgRvV6v6BG7GapXr05aWtpf8lzpdDq0Wi2FhYV4e3u7iKmIZ8vdM9CsWTPS0tIUCx8CYvuKnh1xrBo1asg%2BTIHg4GCysrIwGo3yvhLm67Vr1%2BbMmTN4e3uj1WrJzs6WJuVCgVOlUjFr1iwmTZqESqXijTfeYOLEidhsNmrXrk1qaqrL3AlBF1Eyed9998leuoCAAJYtW8aRI0dISUlh8%2BbNBAUFkZKSIgVttFqtLM8WcL7WjvNyKxALRnq9XpEt9/Pzw8fHh3PnzpGcnHxL%2B/orMW/ePHJzc/n3v/8t%2B4gLCgrk94%2Bw9xC42Tk79/ONHTvWpVTdg78/PGTPAw888OAu4MCBA3z77bfodDpatWpFUlISV65coVevXixevNhtkOaM119/naSkJA4ePAiUK06KkjgfHx9KSkpo1KgRAwcOZOLEiVJmW/z89NNP6devHzabrcI/%2BI7BgAicBeFSq9X4%2Bvpy48YNAgICuH79On5%2BfmzZsoV27drRr18/Nm/eTElJicwslZSUYDAYWL58OeHh4UyePJm9e/fe9vw5ZucCAgIIDw/n1KlTMlB0JpkajYYGDRrwyy%2B/SB8/%2BCOYcTwncb4mk4nY2FgFuROiNbm5uYpjiGDIefX8duGOBImxVUaQHEni9u3b6dmzJ4DCZBlg8%2BbNsm8pISEBu90uM7Le3t6oVCpJPMVrKO/9stlsaDQamjRpgslkAso96SrLlP43IRY7hD%2BfIINWq5WFCxcqSkB/%2BeUXGjZsqBinY0ZSq9WydetW1q5dq1DWvNkCQZ8%2BfaQoUkVjdHdPCCICros6iYmJ9O/fn2rVqrlkM0NCQggODub48eOSZAk0b96cEydOKK6hKIWOiYmRdiru1EDFde7VqxfBwcEsX76c4cOHs2zZMvmdcTsQCxi7d%2B%2BmTZs28veCOFR2H3/00Ud88sknbN%2B%2B3a3CqyB9RqOR%2B%2B%2B/nxs3brB161apuLl%2B/XruvfdeFi5cCJQ/kyLD7%2B57TnhrCmEnu91OgwYNaNCgAVeuXOHbb791OwZxDn5%2Bfoo%2B4YSEBFq2bEnv3r3R6/WsXLlS9o0ePHiQ06dPYzKZGDhwIG3btpUiM6mpqezdu1c%2BTzExMVitVvLy8ujduzfr16/nk08%2BwWq10rp1ayZMmMCSJUs4e/Ys/fv3Z/PmzSQnJ8sy3UceeYTCwkLatWvHxo0buXjxItnZ2eh0OjIyMmTGz3nxqkOHDuTn53PkyBFatmxJamoqM2bMAPD47P1D4SF7HnjggQf/Y3z%2B%2Bee88MIL1KhRA61Wy9mzZ5k4caJC6SwvL4/WrVtjt9tZt24dYWFh9O/fX/Y0TZ06lZKSEoWxeteuXdHr9ezfv5%2BwsDDWrl0r30tISHAhe%2BKn%2BDMQHh7OuXPnFGN1zKQJIlSjRg0uXbpE9%2B7dOXjwIGlpaXKl3FHS3WAwSO80qDxbJdCtWzcyMjIYNGgQL7/8Mvn5%2BXTp0oXw8HBWr16tCNbUajWPPvoo48eP5%2BTJkwwfPhybzYa/vz8BAQGcPXtWEWRv376dbt26yVV%2BZyIrxihEPfR6PZ999hlms5mwsDDUajVffvkl3t7ejBw5kk8//ZSHH34Yk8nEzz//TKNGjfj888/p06cPc%2BbMYezYsZjNZjZv3kyXLl3kXDiTI3fBaOvWrblw4QJXr17F39%2Bf4uJi%2Bc8d5s%2BfT2xsLD179mTz5s307t2bsrIyBdmz2%2B3MmjULg8FATEwMPXr0UJC9oKAgCgoKKCwsVFhdlJWVERQUhFqtZvv27WRmZnLo0CECAwMZPHgwu3fvplWrVqjVaurWrUt2djaPPPIIubm5DBgwgN69e8sxDR06lJMnT3Lq1Cl8fX1JSkri0KFDkrB9%2BOGHlJWVkZWVJQmQ4z0oCHb16tVZunQpVatWVQi0iL7K5ORkDh8%2BjEqlIi4uDovFwr59%2B9zOndjvsWPHSEhIqNBCwmQy8emnnzJhwgRSU1MpKyujd%2B/e7N27V1GeKyDmr6ioCJ1Oh8FgoEmTJuzcuRMoJ3z33Xcf77zzDg0bNlTYpjhnw59//nneeustkpKS8Pf3p3379i5EqlevXi6kLCoqiqZNm2IwGFizZg1QsSKjTqfjtddeY9asWWRlZUky6W48wcHBLuRJlHEajUZsNhulpaU89dRTLFy4kPHjx2OxWJg5cybDhg1jxYoVmEwmRWmvn58fDRo0QKPRYDQaadeuHb/%2B%2BiuLFi1SlLc7PtMDBgzg888/l%2BNbtWoVo0ePZtOmTbIv0t05q1QqRo8ezeLFi6lZsyZnzpxBr9czbdo0JkyYgK%2BvL7m5uaxatYrhw4ej1WoV94XBYCA4OFiSbWHt4lhubDQa6dOnD2vXrq1wzsVcil5rsaAmBG7EZ7RarSRlYgFBeJW6u18dr1mbNm0UWUaRudXr9bz77ruMGTOG5s2bs2/fPsrKyjCbzURFRXHlyhWys7P58MMPadmypcsxPPjnwEP2PPDAAw/%2Bx%2Bjbty89evRgxIgRAHz00UfMnTuX%2B%2B67j8zMTDIzM8nOzlaUQhqNRhfLAEeoVCreeustatasyUMPPYRarVYYZu/fv1/2bzn/hPIgw8fHx0WM5VYREBBAdna2IqARQYXoCRQqn46ZRBEEif41f39/DAYDO3bsID4%2BnrKyMqKiorhw4YIisAwNDZVCLRqNhqKiIgICAigrK%2BP3339XjEOQhXnz5vHKK6%2BQk5PjNhOl0WgIDw8nIyNDBkqvvfYar776KsnJyQQGBkqDe7vdzsKFC3nqqafQarXs2bOH9u3bSwIdHx/P4cOH2bhxI8uWLWP//v2SqLvLlPj5%2BVFUVCQDOrPZTIsWLdi5c6dirmJiYggNDXXp7dqwYQP33HOPJPU9evSgpKSEiIgIsrKysNvtLkTx/2PvvKOjqtY2/jvTZ9ImPSEkgSQkoYQUqpDQAkSaoBSVIipFpQgqCAiIVy8gAjZAxX4R4QpIRxAVuUAQpAtSBBFBIAklBRKSzGTm%2ByNrb2cmEwjq/biuNc9aLJKZM2f22afkfff7vM%2BjVqul35miKMyZM4dnnnkGtVpdhUpnMBiw2%2B2EheuvyNEAACAASURBVIVJmqJIWuvUqSN7LYV4T/v27dm8eTMrVqxgwIABcl7EdxkMBm7cuMHOnTvJyMhAq9Xy4IMPsn37dpKSkqQ6q0gWxHj9/Pyc6Jp33XUXu3btwtfXl8LCQlQqlRSeuFl440hvFdflihUr6NWrF/C7hYNj32CnTp0ICwvjq6%2B%2Bori4GIvFgkajISYmhvnz59OmTRunoN5oNEoBj4qKCl5//XV27NjBihUrqh2XI7Kysvjyyy%2BByt61rVu30q9fPzQaDUuWLKFbt25s2LCh2s9rtVq8vb2r9IU6WqD8UXzwwQeMGjXKiRL4Z/rgVCoV8fHxHD9%2BHJ1OJ%2BftVoiMjOSNN97gvvvuAyrtUVavXk2XLl344osv5Hau9G%2BAjh078vXXX//p8Ws0GqKjo/n555/R6/V4eXnRt29fVq1aRX5%2BPr6%2Bvjz33HPs37%2BfTZs2ERERwfjx4xk8eDAmk0kaniuKQnl5uRRLsVqtlJeX07p1a86cOcPVq1dRFIVmzZqxY8cOrFYrBoOBadOmsWzZMk6dOkXdunXlM/2PQniLWq1WrFYr9913Hx07diQ9Pd1jqP43hUdaxwMPPPDg/xknT56UNDuAgwcPUlxczNq1a/n%2B%2B%2B85efKkk0ddeXm5U6KnVqtJTU2VZsTitfHjx9O7d28sFgtWq5XQ0FDS0tJo3rw5WVlZtGzZUv6vUqnIyclxMmn%2B9NNP2bJlCyaTya0c%2B80gEgpHiB4j4X0lVvsdkxcRPAgKXn5%2BPrm5ubRo0UIaWP/yyy%2Byx0ggPj6e69evExERwUsvvSTHUFBQUGUcQjJ%2B1qxZXL9%2BXQaRYmVdiGVUVFTQuHFjysvL0Wq1hIaGkp2d7bS/6dOny8/NmjULqKQNunrPHTp0iIqKCq5du8bq1aupVauWfM810ROWA450qrKyMr799lunBLdWrVr89NNPbN%2B%2BvYp9RL9%2B/WjQoAElJSV06tRJJqsXL16ktLRUJiyBgYEsWbKElJQUaa4sgtxjx465NbgX4ykrK%2BPXX3%2BVibsInh2phDabDYvFIgV%2B%2BvbtS1lZGQMHDpRjENVEgIEDB8rz/PHHH3P%2B/HknG453331X/lxRUVGlL%2B%2B7777DbrdLMRCbzUbv3r0ZOHAggFPlT0jNi30J%2BPj4YLVa5WegUrRCHDdUJk5JSUmsXbuWGzduEBAQQK9evTAajUyePFnSQQVF1mAwyMqL%2BK6VK1eyfv36KnPrCnGdOyrrioq7oPIBVRK9oKAgp9%2BFLYQrWrZsedPvdYWj1YbY5sMPP6yyeOBoryFsL9zZdDiOT8Bms0maaXl5eZVET2z7r3/9SybnKpWKgIAAlixZIt8X96urwIzr3MDvfopCJCo9Pd1pblq0aMG//vUvXnzxRWJjY6t8fuTIkUDltST2VV5eTllZGenp6eTn52OxWCgqKsJqtfLll19SXl5Ox44dCQ8Pl3RywTIYOHAgXl5e9OvXT95jdrudqVOnUlhYiM1mY8KECTz99NMYjUbpoxgbG8vx48cpKSlh6NChVSx2xDUZHR1NTEwMRqMRo9GIt7c3Op1OWq6I52BsbCy9evUiIyOD8PBw6tSpw7PPPlvtdePB/z48lT0PPPDAg/9nJCYmOsngp6WlUVxczPTp05kxY4YMDqdMmcKUKVOcVpw1Gg3jx4/n119/ZcmSJUBlIDRz5kxeeuklJzEO0dMUFBSE1WqlXr16PProowQHBzN8%2BHCKiooYOnSo7G16//33adKkCSkpKZJ69vHHH/Pwww9Xu8qu1%2BsxGAxERUXdVMHPFaJnyfG4rFYrb775Jv7%2B/kyePFmqIPr5%2BVFYWOikDKrVamWfmQhofX19KS0trdKrp1KpnMZfkxV8123MZjObNm3CZrNJeq3jtqKKs27dOllV8/f3Z/jw4bzyyisYjUZKSkqkebjof3OlxwmbCZFAOdLWHOdMjE8k5VlZWajVatatW4efn59UE%2B3SpYscN1QGqE8%2B%2BSQ7d%2B7kkUcekUHyihUrGDt2rKTtuQbyiqJw7733snLlSuB3BdeEhAQuXbrk5DUYGBhIvXr12L17N/Xq1eOnn37i6aef5tVXX60yz%2BKc6vV6ysvLZcVPYOXKlfTu3Rv4nWIrfq4JPvjgA7799lsWL17sVKVzrOwJYZDbDYc2bNjAvffey%2BLFixkyZIiTgIpGo0GtVjvNo0ajwdfXVyas7kRCHOFOqbQ6OqCiKGRnZ3PXXXfJ49Tr9fzzn/9kwoQJf0pB1J3Ngru%2BP8EU0Gg0ctGgVq1aFBQUcOPGDbZu3Uq7du144YUXeOCBB4BKWX/HRQ7H8yLmx9FCIzU1lf3799/2MfyRql2tWrUoKSmR3o%2BOdNPMzEzpkeo4Vri52i/A5MmT2bx5M3v27EGr1aLT6SguLiY0NJTc3FxpQJ%2BTk4PFYnGak7i4OHm/ubt2qlNPhkq6aP/%2B/QkJCaGiooIOHTowa9YskpKSePfddzEajU7WJ4BUWY6JiaFly5aEhIQwbNiw25pHD%2B48PJU9DzzwwIM7jNDQUBRFwc/Pj9DQUAIDAwkMDCQhIYE6deowduxYfHx8uPfee1GpVLz88ssy0QPIyMhg/vz5Tr0bPj4%2BcvU4Ly%2BPq1ev0rx5c0aPHk2fPn1ITExk3bp1jBkzBqgMngcNGsTVq1dl8DVq1CjefvttpxX4uLg4qfoJlVWtpKQk6tWrh7%2B/v%2Bw1cbQnUKlUGAwGnnzySdlvIgRaxAq0qBJ99913KIriJOUvAh3HIEZQjAoKCvDy8gKgqKhIqhOCs8%2BaI1yDPl9fX/mzUP2LiIggIiJCJmcFBQW0bNmSVq1aVfm83W6XvW7dunWTCVxhYSGzZs2S70OlhcOHH37IgAEDnERiBAwGA8899xz%2B/v6AcwWqoqJCmnOLMYiqwJAhQ5g4cSKKolBSUoLVamXy5Mnk5eVhMBjkPsTqfHR0NGq1GrVazYYNG2jQoAE5OTnY7XaCg4OrnTNH/zK73c7Jkye5cuWKU%2BWjuLiYvXv3YrfbCQ0NBXDr/%2BhanXQHUXlw/Mzx48dlgvPyyy/f9PNDhgyRapriXMLv15LJZOLcuXPY7XZ5HTmOTRhRQ%2BW5i42NxWw2o9PpKCgoIDIykmeeeaaKUqY7Y3qr1epUmbyVkI9jghYQEABUVrRF1VHAbrdjs9lkX5VIMMvKynj22WerJHrVVfDCwsJkFchRYdKdn15ISEiV8ycSBZVKJe%2BjHj16UFJSgt1up23bttjtdqZNm0Z6ejrPP/98FXEQx8UqMT%2BC1mi326skemJebgV3iZ4re0FUu8T8XLhwQdJdHT9vMpnYt2%2Bfkx2MGKurwrG7a/yVV15hz549AFgsFnnMou/zm2%2B%2B4dy5czJhdJyTU6dOUVRUVG2iJ%2BbT8VoWuHTpEm%2B88QaTJ0/m%2BeefJz09nXXr1jFnzhx5H7iboyZNmhASEsKSJUtkT6wHfy94KnseeOCBB//PSExMZP78%2BTKQPH78OP/85z8JCAggISGBI0eOyD/0YWFhXLlyheLiYlq3bo2Pjw/ffvstJpOJ8vJyBg0aRHBwMP/4xz/k/kWVxLFqJoKvsrIywsLC2LJlC7/%2B%2Biu9evVyShLT0tJkQCXMduH3YCcrK4vNmzfLvi5RbVq4cCHDhw8nOjqagoICdu3aRf369eV%2Bvb298fPz48qVK1IYYNSoUTIQ/KsgKn52u53GjRuTmZlJUFAQEydOJCEhgdLSUoKCgti3bx/t2rVj//79DB48mHnz5qEoCjExMeTm5tK4cWOCg4O5ePEi33//PX5%2BfqjVahRF4cqVK1LAICgoSPY4in6x8vLyW1ZtqoOfnx8xMTEcPHhQVu569erF559/zvTp05k8eXKVz4iEOSgoiJycHPm6u1V%2BHx8fSkpK/tDYNBoNXl5eFBYW3tQCIyoqimvXrrmlEArodDosFgt6vZ7S0tJqK3uuAjOiN/Wpp54C3FedxGKEuD4dj1XMScuWLdm1a5dTxcbVL7AmENUYV7jaotwObla9NBgMmEwmrl69KnvvqqtauVMCFYmYoNv%2BL0KtVqPT6dBqtVy7do1atWpx/vx5TCYTDz/8sKSv169fn2nTplFRUcHFixedlHGFOM6NGzduWQ0W8ydET0SCZzQaUavVt2Ue7/jsjY6O5tq1a1y9elXaiTgq64aHhxMXF8e2bduAyntTGLRD5f0cHh4ue/DEtSsWsXQ6HaGhoZw9e9bpGGw2GwEBAW6tSG4H0dHRXLp0iZKSEqKjo7n//vu577775EKUB38feJI9DzzwwIP/ZyQmJjr9/mcew%2B6EB8aNG8fcuXOrbGu322nTpg27du3iySefZP369Zw5c4aGDRty/vx5cnJyqvh5ue7fYDBQVlZWhX4p/tfr9dx99920b9%2BesWPHApXBVN26dTl9%2BrSUTff29qawsFAq/lVn9m42m6Xwi6Io3HPPPbRu3Zp27doxePBgTp065ST4IoJEq9UqKVBCin7nzp2yYpGYmEidOnVIS0vj/vvv5/7778dgMPD%2B%2B%2B/z6aefsmPHDiwWi0w8REUJKoNMvV5Pr169OHnyJPv27cNoNLJ8%2BXLi4uJo1qwZS5cuZfz48aSkpLBkyRL8/f2pW7dulcqEwWCQ1hSOSExM5Pjx41Kp89KlS06%2BZ47efI7HXVxcXK23YXJyMnq9nt9%2B%2B43c3FxZFVSr1TLwd0zaRIDqek4cPdpcg0yxTVBQkNtq3u1CiGg4igndjKoGv9Nd3SVb7u4XV1NwV/Tp04eSkhInOqw7iCD8j9zPX3/9NZGRkSQmJrJgwQI%2B%2BeQTLBYLe/furbKtK6XzZl5pgmIszqPZbJYJheM86HQ6unTpwg8//FDFW8%2BR/vpn4ThWQc8GpMiPMPVu2bIlwcHBfPbZZzRo0IBffvlFVq3Cw8NJSUnh6tWr0rZFURSioqIoLi526ncWcBS7EcfjmiQbDIYqlUZwT8t0FBkS956gxBcVFVXZt1h0cDxmd9%2BjUqnkXLvuw939KODuur4VXJ8h3t7esp9W0Mx1Oh03btwgKSkJk8nkMVT/m8KT7HnggQce3GE0b96csrIyRo0axbvvvisD9b59%2B/LJJ5%2BgKAo%2BPj74%2BvoSHx/Pli1bgN9pSOIPs7A%2B6Ny5s5N4g0qlkgIpAiJw8Pf3x263c%2B3aNSoqKqoYfItAR/ypcA18%2Bvfvz5IlS2jUqBFHjhzBZDLx9NNPs2zZMn766Se3xyvoUo5VlKZNm7Jv3z6GDh2Kn58fc%2BfOZd26deTk5DBlyhRycnJQqVQMHjyYxMREli5dyqFDh1Cr1ZjNZi5fvkxqaqqkkVZUVEj/QYE333yTr776ivPnz8ukq6ZGyv7%2B/mg0GsrKym6piur4Z/W9995j2LBh1K5dmytXrjj1p9ntdurVq8elS5ec1BGFDYOiKKSkpBAYGMjXX3/tVkUxNjaWc%2BfO3dK4PjAwkPbt23P69GkOHz6MxWLBaDSSmZnJkSNHOHv2LDabzamy%2B0erAwcOHMBkMjFv3jzeeeedKv6Ffxai2m2z2eSigTvTcfi90uuuyldTBAUFYbFYqg3SHWE0GomOjsZqtdKjRw8yMjIwm81s3LiR2bNnAzB06FASExOlqJJKpWL48OE0bNiQJ5544rbHdzPUpFdNnBvHe9vxcyqVCm9v7yoiUY5zKbbXarVSYfLatWtotVq5GPP222/TvHlzmjdv7lSZcqUPuvbz3gq3Sv4FvdFxgcpur/Qizc/Pp23btmzfvl0upgQFBaHX68nLy/vLktw7CccksVWrVnh5efH111%2BjVqtp0KABhw8fdnq%2Bm0wmrFYrJSUlMqEdMGAAAQEBHkP1vyk8yZ4HHnjgwR1Geno6FRUVLF26VKoTQmUgv2/fPrp06cLLL79M//79nURQGjVqxNmzZ2%2BafNQU7kzFawJXitQfEULw9/enoqKCoqKiKrQ8d/vT6/VUVFTQvn17ateuzccff4zdbmffvn14e3vz448/Si80oEoioCgKffv2pX///nzzzTfMmzfP7Zjy8/OrBLXCL%2B369esEBQXJKsL8%2BfMZN24c06dPZ9KkSVU84sQYHFfgRSAs7A8EVCoVwcHB2Gw2rly5AvCHEiXH4F2tVksqpKgKiXPtaBUwdOhQOR%2B3K4Yi5i0lJYUff/yRwsJChg8fzuLFi6tQOh37pRwFaEQ/2pYtW7h69Sp33303RUVFLFy4kKeeekpWpAwGAz4%2BPnLBwPW6EVU2k8lEo0aNpDqogGtFUyQYgv6pKAoTJ05k5syZsrfRZDIxbtw43nnnHS5cuEBaWhofffQRH3/8MfPnzycoKIiVK1diNpu5cOGC/K4ff/yR55577rbvU3F%2BYmNjpSKtq8VJSEgIsbGx7N69u8o1MnXqVDl%2Bx%2Bvr7rvvlkIqI0aMYNKkSbRs2VL2ywoK9NGjR2U/6ObNm/n555/p27cvy5cvl9dxv379WLZsmVNVTPgtHj9%2BnE2bNtG5c2epKunv78%2Bzzz5bbRWqefPmTn6I3t7eTl583t7eMnFxXcByB5PJhJeXl9PCkujJE/fC66%2B/ztNPP01CQgLHjh2jXr16nD59WvZCCsuPsLAwjh8/XuN70fHZ5e/v71YpWMBRhMUVarVaegFC5fPPkb5Zu3ZtzGazpObq9XquXr1Kfn4%2BGo2GkJAQzpw5IxdHhHjTzSDOp6B9r127lri4uBodtwf/e/AItHjggQce3GE89dRThISE8MILLzBo0CBCQkLw9/dn//79qNVqLl%2B%2BLCtbjjhy5MhtBZBCoMKdAIcIvm4WyIwcOVJWzkSwLtTohA2BXq/HZDJVGavRaHQah16vp3bt2kCl3UJRUREqlYrS0lKZiIBzoiH2UVZWhp%2BfH/v37%2BfQoUMyeSkuLmbo0KHcd999MtFTFIUvv/ySQ4cOsX37dgwGAzqdjvz8fAYMGCA93AC6du3KzJkzUalUsi8lMDCQGTNm8MUXX7BixQrWrFnD999/j6IorF27FkVR%2BPzzz%2BnYsSMqlYrk5GQpOy9UUj/88MMqgZyotkKlgboQXgFkQCYSvdvBE088wahRo5xEdBRFYenSpQQHB8tV/ePHj/Pggw/K7USw2KpVKzn2N954Q1ZrHf21hH/jggULiI2NRa1WS5Gb4OBgcnNziYuLIz4%2BnrfffpvCwkJMJhM9evSgTp06nDhxgszMTBo1asS///1vTCYTBoOB4cOHk5iYyIwZMzhy5AgzZswgJiaGzZs3s3r1apo2bYparUav18tqrrhOSkpK5Lihshpnt9spLi7GYDDQoUMHp3kKDAwkJiZGJnJ9%2BvSRtM9JkyYBv1/TFRUVlJeXU1BQwJQpUyTteO/evSQlJbFhwwZMJhPnz5%2BndevW9O7dm/bt28t/o0aNuuV9KtROtVotw4cPByrvsZdfflkmelBpLyKuHa1Wy8SJEzl//jwGg4FWrVrx4Ycf0qNHD6BSidRqtcpELysrC6hMgHJycmjXrh2//fabk/CJuN%2BOHDkifd7eeecdaS0gPALbtm0LwLZt26TgkoDNZuPHH3%2BkoqKCTp06yX2%2B9NJLPP300/JZo1arpSBMWloaBoPBiabYrVs3RowYIT8v7CxsNhs2m61GVdqSkhKZ6E2dOlVWHR0XOcaOHYvNZuPYsWNApTWOqEYKf8mrV69y8eJF6taty5AhQ4DKxbiAgAA0Gg3x8fH06dOHkJAQIiMjueeee9i7dy/PP/88DRo0YO3atbRr14709HQ6dOjAsWPHeOqpp/j888957rnnWLx4MVu2bGH9%2BvV8%2BeWXHDt2jI8%2B%2Boj333%2Bfb7/9lh49ejBu3DieffZZvvzyS0aOHMkPP/zA/v37iYqKIjMzk8DAQNq0aYOvry8bNmxg3rx5lJSUSPq8zWajqKjolokeQGlpKaGhoZSWljo9qzz4e8JT2fPAAw88uMPo0KHDHzIyd0VGRgbbt28HKoPHTp06sX37dklxMhqNspdNICwsjIKCAmw22y2pgO4gVqRnzZrFhAkTZFDg2i8lKFLh4eGUlJRQWFgoq2a3qiDFxMRw%2BvRp6tWrJ1e3BWpSidy5cyeFhYU88cQT0vwbqsrHJycnc/jwYbmaf7NgUkjdt27dmm3btjF37lzWrFlDQkKC7CMEpMVGYmKiU3%2BV6Pmx2%2B189tlnPPjggzLQDQsLq0Ify8/Pd9s7pNVqCQwMdBJmERC9e4qi0KFDB9RqNXPnzkWn05GXl0f79u3p0qULZ8%2BeJT4%2Bng0bNhAdHS3VLgF27NhBmzZtbrsfKCIigqysLBYtWiR7Lbds2cJjjz1GdnY227dvl4nazc6fqKhdv37drdl7y5Yt2bdvnxR8EZRjx7kKDg7GbDY7XTu%2Bvr7Url2bM2fOUFFR4SS0ExsbK5ObvwLCF/PgwYMMGTKEDz/8kEcffRSz2SwTSvhjVfGafEb0qAmqtbgGzWYzN27ccLrWBCVy7ty5PPfcc5SVldGnTx9WrFjBiy%2B%2ByIULF3jnnXdo1qwZe/bskfd/SkoKhw4dkmbcjjYEdrud%2B%2B67D6PRyJEjR9i/fz92u92pF7Bp06b88ssvFBUV/SXCMe56T8VromrvKOTTtGlTjEajfH6KbSMjI2nVqhWHDh3i%2BPHjLFiwgNDQUPr06YOvry8jR47klVdeoaKigvj4eCfqenR0NOXl5VUsKgTuuecewsPD%2BfDDD932zI4dO5bdu3fz3Xffuf28oAu7%2B9shFgMcBbaEWI/wKNTpdJSUlKBWq0lOTubIkSMEBARQt25dfHx82LZtm1x8a9WqFQ8%2B%2BCDt27d38vDz4O8BT7LngQceeHCH8f3338tV5V9//ZXc3Fzy8/M5fvw45eXl0thWqLb5%2B/szevRotm7dKl9LTEzkrrvuYvHixVgsFgwGA40bN3aiRLkKd4hKiCOdzcfHhx49erB27drbUqFzhOg5cycoUKdOHSdjbhHYfPDBBwwbNsxt4C8UQGNiYjh79iwWi4WwsDBycnLo1asXq1evltump6fTpk0bZsyYIV/buXMn//jHP9i9e7dMfBMSErhw4YJbMQdHhIaGUlBQIKl97gQ4RNIoRCIcRT5SU1Ox2%2B0cPHgQcA7OReWtX79%2BrF69WioHzp8/nxdeeEGangsrheqQlJRUI49DsUIfHh7O448/zvPPP8/777/PpUuXmDVrFjExMVUEQW6W%2BPj7%2B3Pt2jW3YxM%2BfKI36tq1a/j4%2BBAUFMTp06fRaDSoVKpqFxjEPAnzaNfrKDAwkJKSEj7//HN69%2B7tRLmDSusPIcjhDt26daN3796MGDGCsrIy6QdY3THXr1%2Bf8ePHc%2BrUKbZt23bTSkdQUJCk59psNrZv3y6vs5CQEAoKChg0aBB33XUXTzzxBC%2B%2B%2BCIvvvgi69ato2PHjk5zIBZNHOm/okKvVqvp27evFM1o3bo13333HV5eXly7ds2pj05RFNq0acPXX3/Ngw8%2ByNKlS9Hr9fj7%2B5OTk0NCQgInTpyQNh3r16%2BXfpGOKpDCP88xsVEUhUGDBrF06VKaNm1ahVIaGBiIVqvl0qVLNG7cmAMHDlQ7dwJeXl5kZGRI2wwBnU4nGQA3g1isUalUkvY9depUpk6dKhkCjn18iqLg6%2Btbo75MMa86nQ4vL6%2Bbqs7%2BL0Ekf2q1Go1GU6MKHyB7hw8ePEhgYCDZ2dn/5ZF68FfDk%2Bx54IEHHtxhFBUVodfr0ev1HD9%2BnB07dtCwYUNsNhuPP/64/AMtErKQkBDy8vKcEodZs2Zx9OhRli5dKqs5QUFBTr0qYjVewJ1qYVRUlDQzFwgLC8NqtUoj36ZNm/LDDz9Ik3ChbKfVamXw7toT5YqgoCCuXr1KREQE586d46677mLPnj1S6dExgRDBWuPGjaUtxWOPPeZEwYTKlfD8/HyWLVvmpKIp%2Br1E8AeV9NMHH3zQKXFzp2iXkpLC0aNHGTlyJKGhoVitVqZMmeL2mBwFc6rDrQyXXSHGm5iYyNGjR91uU9OKkMlkomXLliQkJHDo0CH27dvHunXriI6OZubMmXz88cdA5TVw/fr1KuIs4ntEon070Ov1tGjRgm3btqHT6fDx8aGwsNAp4YuLi%2BPUqVM3tUCoXbs258%2BfR1EU2rZtS2ZmJosWLaK8vJzc3Fx8fHwoKioiOjqaEydO1Hh8ERERskLieh0IKf9x48aRm5tLaGgoiYmJNGvWTG5TVlbGO%2B%2B8Q/PmzaXfnSPy8/Olx6GAWOxwTLAckz2DwYBGo3FadGnRogX79u2TdGo/Pz9ycnJkpfnuu%2B/GYDBIJVRRxRH016KiIlasWEG/fv3k/ouKipyOWaVSkZGRwbZt27Db7TzyyCN89NFH6HQ6bDabXPgQ1iCKojBgwACWL1/Ok08%2ByXvvvScpyAaD4aasAbEA5ePjQ1JSEjt37gR%2BVywWCrhQqSQqFon%2BSIJVk/skMDCQq1evYrfbCQ8PJywsjKKiIi5fvkxhYSH9%2B/dn6NCh3H333URFRXHx4kXKysrQarU8/PDDLFu2jKKiIkJDQ%2BnZsyfvvfceVqtVPl9de0vd9QS7Ph9ce45FdVj04fr4%2BPDGG28wYcKE274vBYKDg7l8%2BbI8p%2BIZ/%2B677wKVi3SLFi3C19dX0ow9%2BPvAk%2Bx54IEHHtwBZGZmYrPZGDNmDBMmTPhT%2B/Lx8SE1NZXt27ffNJhxFQHo1KkT165dk/LlYhXcUdlOpVLJwNzxtdDQUElPSkpK4vjx47eVxLRo0YLdu3dXkbtXFIXmzZu7FdQQyaRarSYwMNCtv9mfQXZ2NhcuXKBv375SIl0E5N7e3nh7e5ObmyvnODY2lk6dOvHee%2B%2Bh0%2BlYt26dlM8XSZFarf5LKLrgTPESqqkpKSnUq1cPgMOHD3PixInbpgI%2B9NBDjBo1ilatWgGV14larb6pEqeoOplMJrRaLWFhYZSXl2OxWDh79iwjR47kk08%2BYciQIXzwwQdoNBonCw0RVDoGuo60U9djEOejSZMmUmU1WRlT7AAAIABJREFUMDCQ0tJSmjZtSnZ2tlOCVqdOHV555RX69et3W3NxO%2BjYsSNdunRBo9EwY8YMuQAjqvSuSEhIQFGUKlRHAXdqq65wZw8gFgQErdjRhsRut7u1Dbida8RVNAWqJimOrAHHin5NBZ9qst2wYcN47733nD7z2Wefce7cOZ5%2B%2BukaH09NYTQaMZvNKIpCTk4ONpsNnU7HtGnTmDx5MqGhoeTl5aHRaNiwYQMlJSUMGzaMsWPH0qdPHwCaNGmCzWZj1KhR%2BPj40LNnT7744gssFguzZs1i9erV9OrVixEjRlR5f968eVRUVLBo0SK34iidOnUCYPr06TRv3hyoyhKBSjP1rVu3kpSUhI%2BPD1u3br3pcQuaZlRUFFFRUZw/f57Tp0%2Bj1Wo5dOjQXzK3Hvz/wpPseeCBBx7cAaxatQqr1cpHH32El5cXWq2W3377jYKCAnx8fCgrK%2BP69et4eXlhMBi4cuUKtWvXRlEUfvvtN6Cy18lkMvHAAw84CSRER0fj6%2BtbhdonVo3VajUhISGkpKSQnZ0txSMSEhI4ffq0U3AoBA2sVutNqYQajUYKH0BlIJ6fny9/V6vVqFQqLBYLiqIQFxfHyZMnZZXSsbJwKyn16pCUlMTQoUMJDg5mwIABaDQaunXrhq%2BvL4sWLUKj0TB8%2BHDMZjMLFiyQCWznzp356quvZH9dQkKC7OsRUBSFxMRE2rZty7vvvoter3dK7gwGQ41/h0p659q1a%2BXviYmJRERE8Mgjj5CRkcGqVatIT09n4MCBNw3M1Wo1ERER5OfnV7Ee6Nq1K4qisHXr1ioCMWq1ms6dO7Njxw7ZzyR8CWvVqlWlN/KPIDQ0lFatWrFhwwYmTZrESy%2B9JK8HUS2rKZXMEYqi0KRJE/bu3VttpfRWyUNMTAyPPfYYERERDBo0iPnz5/Prr79KewSREH344YesXr2aNWvWoCgKERERXLx4kYqKiioVQI1GQ69evXjppZecBIpWr15N48aN6dKlS7Xj0Wg0PProo7KSAs4JlaDgCrsAd4bpNbWWaNSoEXfddZdMnBRFYfjw4SxcuBCVSkVUVBQ9e/bkjTfeIDAwkJ49ezo9X9zNr2Mvqre3N1BZ7RSWDVarlT59%2BrB161aZ0Aqqb0hICL1792b%2B/PkMGTKEhIQEnn/%2BeUpKSjCbzRQXF2OxWKTyp5iv5ORklixZQn5%2BPu3atXO6lm6V0LZr146tW7ei1Wp59NFHWb58OT179mTp0qVyPxqNhri4OKfqoiP8/Pxkz7HZbObcuXM1TqS7du3K7t27ZdXPy8uL1NRULl68iNFoxN/fnx9%2B%2BIHc3FxUKhUxMTGyP0/4ct6uQq9KpaJWrVq89957rF%2B/Hl9fX5o1a8amTZvYuHFjFQsMAeGNOmzYMMaMGXNb3%2BnB/wY8yZ4HHnjgwR1E48aN6d%2B/PwaDgY0bNxIcHEzTpk0pKiris88%2BIzQ0lLZt27J06VLuu%2B8%2BDAYDy5cv59NPP6Vv3748/PDDkn4nRCoWLlzIqFGjZBAsKEk5OTnY7XYMBgMGg8GpiiCClFq1aknZePGau94714TMUWTFlc4o/NpUKhUajYbmzZvzwQcfkJCQQHp6Ojt27KjxfPXr1w9vb2/CwsKYNWuWNCouKiri3//%2Bt6y8jR07FqPRKBMqYYQuEq4LFy6QlZVFeXm5rBrVrl2bli1b8vnnn1cJ2CIjI2nXrh3NmjVjzJgxqFQqOnbsiJeXFytXrkSj0ZCZmSl/V6vVdOzYkTfffJPExEQ0Gg0jR45kyJAh6HQ6kpKSSEpKIigoiC1btrgVXrmZ4uAfNboOCwsjNjaWxx57jBYtWlBcXExGRgbFxcUoikKDBg04duwYGo2G8vLyv8xQu169epw7dw6j0UhBQYHsHxIBq6BwuorwCAsFIaQREBBAQUEBY8eOpVevXgQHB5OamsrUqVNJSkpi4cKFTh6TfxZ6vR6dTse1a9d44oknGDt2LD/%2B%2BCMPPPBAFWqij48P169fx2634%2B/vz0MPPUR0dHSVqpNI3ESCLUR2kpOTnfoMxdyr1WoGDBhAz549GT58ONeuXeOLL76QCwlC3fbGjRtyMUWlUtGoUSMOHTrk1PvnOAbxe%2B3atUlLS2PdunWoVCrWrVvH2rVrZfIXHBzMxIkTeeqppzAYDLzyyiskJSWxZs0a3n77bebOnUtwcDAPPPAASUlJxMfH07VrVzZv3szSpUuB3wWN2rZti9FodPLy7NChA2%2B99RZZWVmMHz%2Be%2BvXr0717d27cuEF2djYvvvgimzdvJjQ0lNzcXJlU/uMf/6Bt27bcf//95OXlSTXPlStX4u/vz%2Beff878%2BfOd5lyocKakpBAWFsbu3bupW7cuBw4ckMmzqFZrNBqKi4tva0HCZDLJXkC9Xo9Wq3Xr//i/ALVaTXh4OAUFBahUKkpKSvDx8SE/Px%2Bz2UxJSYl8Bjz//PPMnDmTqVOn0qtXrzs9dA9uE55kzwMPPPDgDkCYW7/88su3NK52t1r80EMPsWjRIubNm8fo0aPl6xqNhjp16kjrAahMHMxms9s%2BqICAAIxGo1uqoaCV1aTS5q6Py7Hq4hhcNm7cmEaNGrFkyZKb7tMViqIQGRmJVqslLy9PBlHVGWpXB0d/vD8D0QdVUlIi7Rbi4uIkjcrHx4fatWs70foaN27MtWvX%2BOWXX5yEHhRFuWVSJSh8AQEBBAcHc%2BLECUmfczSvrg59%2B/bl7NmzVFRU8Pbbb6PT6fjkk0949dVXq/3cwIED%2BfTTT50MtlNTU50M60VFybGyJM5J9%2B7d2bx5MxaLBW9vbywWS7XB8%2B0mlvHx8bRp04YPPviAIUOGUKdOHaZNmyb7SAXtLjg4GKvVSl5e3m1TXF3h7l6MjIykT58%2BvPbaa9SuXVtW3sG9KFJ2djYZGRlOipW3OnfgfA9FREQQGBjIDz/8IPfRrVs3Vq9eLSuQFRUV1SpB3g4MBgMWi0X200ZFRfHLL7/I91370KqDl5cXN27ccNvLGxgYiFqt5vr16/Tt25cNGzZw%2BfJlHnzwQdatW%2Be0f9GH6O3tTVBQkHzWtW3bVornCHGgP4KoqChCQkI4fvw4KpWKoqIi2rRpQ2ZmJu%2B%2B%2By6xsbFSgffVV18lJydH9gO7JtTe3t4kJiayf/9%2BbDabrAwLarjoTXT3foMGDfD39ycjI4NXXnlFHs%2BgQYNYvHgxo0ePpkePHmRlZbFw4UIee%2BwxuY24tv6KEP/%2B%2B%2B/nxRdfZPHixaxdu1ZWVz34%2B8CT7HnggQce3AHs2rWLwYMH/%2BHPi8BaKF8KuNLa/Pz8sFqt1Rr2CtNiQTtzrcY5JnqO6n7iO8RrojrmDiLgFfsSvm5i3L169aJv374MGzaMkpKSKsfketwiObLZbE6Jg1B41Ol0t6Uk%2Bvjjj0sVSmFMfOzYMSeKntlsxmKxUFJS4qTgl5ycTE5OjvTJE2qTjrh69aoMAo1GoxS0EXPtzkheVBkcK3zu5v9mEH19Ah999BGPPvooUKl%2BOm3aNM6fP%2B8UoAp5foE6depQVlYmkwaDwYDRaMRms8nz7Y4%2B2L17dzZu3IhWq5W%2Bha4LCjExMSQmJvLdd99JmrDFYmHQoEF8%2Bumn2Gw20tLSmDBhAocOHeL111%2BntLQUm81GdHS0FM0QcDcOPz8/wsPD8ff3Jy8vj8LCQioqKigsLHTquVIU5Q9RSgXEOYmMjLwtOt/fBTExMdKYu6aoibWGO9ZAdXCd06ioKHJzc4mNjWX69Ok0aNCA8%2BfPc%2BnSJZ588knKy8vJz8%2BXiyTi86JaXN35udm5UxSFFi1asGvXLnQ6Hf369WPKlCk88MADPPbYYyxcuFBek88%2B%2B6z0dxS9e2vXrqVXr17yZ0HjvtX7nTp1kosGCxYs4Mknn%2BQf//gHGRkZdO/endGjR/Paa6/JquJdd91FrVq1aNmyJceOHZMqzwC//PIL58%2Bfl5U9kSwLWnJkZCRt2rQhPDycOXPmsGHDBuLi4jh79iy9e/d2Evny4O8BT7LngQceeHCH0a5dOywWC%2BvWraNPnz6Ul5ejUqlkJWLDhg107979vxo8CvU1V2qaSNCaNGki/bFc4U7F0vV1EdQZDAamTp3K8uXLOXjwIA0bNqRu3br85z//oXHjxmRnZ9dI7dExIIuPj2fp0qWyV0igdevW5Ofn4%2B3tzaBBg8jMzKRv374AHDhwQErYC5SUlPDGG2%2BwePFigoKCyMvLw2w2Ex4ezsmTJ/H19aVp06bUrl2bI0eOsGfPHrmiX50oh4AQ58jOziYzM1P62QFOvUhi3nr27MmqVaswmUxYLBbKysqqCOzcDMIIXa1WyypmTUQwVCqVk/Kn2WwmJSWF48ePk5OTQ3BwMKWlpdy4cUOeW9Hf6K7CYzKZqF27NikpKRQUFMjzJuh7U6ZMoUGDBnTu3Bmbzcb999%2BPn5%2BftKyIiYnh0qVLTJ48mfXr17Njxw7S0tKk95g4nuTkZE6fPn1bFd7Y2FjOnTvHqFGjWLlyJWfOnHGaI0VRCAkJcRICcnet9%2B3bl44dO/L444%2Bzc%2BdOWrVqxeTJk/niiy9QFMWpCnoruCbygYGBZGVlsWbNGiwWC%2BXl5fJeEtVT1yRXo9FImwTHSmlUVBQBAQEcPHiQsLAw8vPzeeaZZ/Dz8%2BPtt9%2BWHpQmk0kK5VgsFoxGo6SHisQ4KyuL2rVr8%2Bmnn96UmeC4WCQouElJSVLoQyzsiMWl6uB4v3t7e9OkSRMCAwNZu3YtmZmZvPnmm3LbiRMnsnbtWqCysvXuu%2B8ydOhQDAYDiqLQunVrvv76a7m9Xq8nPDzcyYPTYDBIaqzjM9HPz48vvviCjIwM3njjDcaOHYu/vz/169dn//79hIaG8uuvvzp5/Gm1WqlkrFar5fkSi2SiIllUVCTVS2/cuEF4eDhXrlyRVX939%2B7NWA0ajYZ27doRHh4OVPZyJyYmEhISQvfu3bFarZjNZjnvIqETVPucnBxOnDjBhg0biI2N5fLly2RkZNzyWefB/x48yZ4HHnjgwR2E6I%2BD3wPX/Px8srOzee2111AUhXHjxlFaWsq8efOAyoDwueee4%2BzZsyxatEgGemJlWSAsLAy9Xu%2BkdllTdbyawmQyYTabnY4DKgMNQcuzWq0yQI2OjiY%2BPp5t27Y5BaI%2BPj5MnjyZiRMn0rp1a3bt2oVaraa8vJwOHTqg0Wi4cOECR48eRVEUDh48SI8ePSgpKSEtLQ2dTieNsR966CGaNm1KgwYNZDKm1Wp55ZVXeOqpp1CpVIwfPx6z2cyOHTs4deoUZWVl5OXlUV5eLnvJDAYDwcHBGI1GKioquH79OoWFhbJKYDKZMBqN1KtXjwkTJhAZGYlarZY%2BXo5ISEgAKj3/unTpQmlpqTz%2BxMREmfiJgHr27Nk8//zzFBUVSYn17OzsKrL%2BQhjHaDTeMhEcNWoUCxYscErYHSuyiqLQrl07fHx8ZLAsXh8/fjyzZ8/%2BQwsOop%2BzpvQ6YQmiUqlo0qQJP/zwg9uqmzCmF/6EYr%2BuCVlNKkyO%2B4yOjub555/nwoULrF27tooSpeO2QnXVy8vrto3YHUVN7HY7Xl5esoorRJE0Gg3h4eG0aNGCFStWAFWr7q40Wl9fXynelJeXJxcJxHUk5qlFixacPHmSRo0asWPHDtnPVtOx16pVy4my%2BmcgvBgd7Vv0ej0DBgzg448/lvfjiBEjCA8Pp2PHjphMJjp16sSjjz7Kgw8%2BCFTaV7z00ksUFhbKvrmhQ4eyYMGCP1RtdbeAsXHjRrp168bhw4dp1KgR8NfQJf9X4evry4wZM0hKSqJ9%2B/aeZO9vCE%2By54EHHnhwByF6Nm73UazRaJgzZw6TJk2SK%2BcNGzbk6NGjsnJiMpm4ceOGDHINBoPTar9arcbHxweTyeSUrHXo0IEdO3Y4eeaJfYgETtDVxH5LS0tlkH2zHh5BsywrK6tyzCJYdQ3KtFot9erVk9UmtVrNN998Q%2BfOnbFYLFX2oyiKk0m24%2BsiABSG4ILCWtMEuGXLljz88MNs3bqVZcuW0aVLF9RqNRs2bJCBe5s2bXjllVekWt/ChQt54403gMoqmLBwqM57DCr7m0SiLJLB26nsuRPluBW8vb0lbdSVkisSsJtdp%2B6CaaG4KN7/swsNjkJAULmgcfnyZbRarbwPtFottWrVclrkiIqKorCwUFZY7gR8fX2pqKiQYjheXl63RTf%2BqxETE8Pp06fl9e/am%2BuajOr1etm7FxUVRVpaGqtXr652/2q1Gm9vb6drqbpnw5tvvsn06dMlm0Gj0TBp0iQGDhxIamoqFouFiRMnMnDgQNLS0lizZg2RkZEkJSVhMpl45513SE1NJTk5mdLSUjl2R5Vgx%2Buze/furF%2B/Xn6/l5cXMTExUsFYPOfceT4GBwdz5coVjh07RmJiIl5eXsTHx/PDDz/g5%2BfH3Llzeemll7h%2B/ToFBQWo1Wo6derE5s2bsdls0rxdMDjE2IxGoxw74MSKEJXVoKAg/P39uXDhAvn5%2BahUKkaOHMnGjRuxWq0MGTIEq9XKxx9/7HT9VwfHBRMBYQ0SFBSEoigUFRVx48YN%2Bbon2fv7QXXrTTzwwAMPPPhv4ZtvviE%2BPh5/f3969epFQECA2%2B2E95FAWFgY48ePlxUPRVGYP3%2B%2B00pzcXGxU3AdFxdXJdAtKChwoqkNGzaMvXv3ykBDURQGDx5MfHw8RqMRo9HIsmXLpMcTVEqsGwwGpk%2BfLlXdhOmzI1JSUmSfnWMVRkCv1wOVsuQvvviifF14twmo1Wp%2B%2B%2B03rFYrWq2WadOm0aJFC/z8/Hj44YdZvny5kwCLn5%2Bf9BAU/XBz586lpKREBlAiCU1ISJDVP/hdxc9sNpOamkppaSn16tVjxYoVdOvWjVdffZXi4mJ8fHyIjY3ltdde49KlS8ycOZPc3Fz69u3rRDHLz8/n3LlzN030AIqLi%2BU5EBWjmiZ6UHn%2B69SpQ8OGDfH39692O3GehGS%2ByWSS/YeOcOxxUhSFxo0by34i8VpYWJhMMsW1smjRIhITE6WFhyOEmbhQb3WFUG/19vamdu3a%2BPj4VKkM5ubmUr9%2BfadKXvv27avQgHNzc2W/nkBKSopTJdzxeMQx/VG43q8ARUVF8hza7fZbJnoBAQHUrVsXtVpNmzZtnGwNxLyEhITI7f38/IDfr1kvLy%2Bn/Yl5DgoKcvpd2E04Vtodxy%2Bqjn369EGtVqPRaLh69apT9dfX15dx48bJ30XPqas/Z3XH/Pbbb0ufR/H9GRkZbn93PP9CBbd///488MAD8lkkIFgFjggPD2f9%2BvWoVCr5XklJCadPnyYqKsrt%2BASEefqMGTPkWG7cuMHo0aOx2Wz4%2BPiwY8cOmjZtSmFhIWVlZdy4cQO73S7vr%2BTkZDl3fn5%2B6PV6OnTowIQJE%2BjatStPPPEEZrNZjm3dunX07NmTxMREBg0axLx58%2BTzw9fXl4iICCIjI1EUhePHj1NcXMz58%2BfRarU88sgjvPrqq4wYMYJJkybx2muvceLECU6cOMFbb71FkyZNOHjwIC1btsTHx0f%2Ba968ORUVFbRu3ZouXbqQlZVFvXr1PEqcf1N4KnseeOCBB3cYjRs3xm638%2BWXX9KhQwfsdnuVapNrL487kQ533luOcDUwF3/Ut2zZIquBKpWKli1bkp2dLbcLCQnBarXK3g4/Pz/sdrsU/2jatCl79%2B51Wjl39amDymTz559/djLWdh1/kyZNuH79OqGhofznP/9BURS0Wq38DFQmf507d5aBTWhoKGfPnuWZZ57h/fff55tvvqFx48YyeBXVJcdkxV2VKSIigvPnz6NSqfDz8yM/P5/4%2BHh%2B/fVXUlNTMZlMfPvtt9IG4J577mH27Nm0aNGCcePGMXPmTPbv38/Zs2e59957iYyMxG63c/HiRYqLi6XHX25uLp988olb6X7HXrjq0LFjR3r27MmTTz6JyWSS1UmDwSBpqAIiia1OfMTxOnNMulyvoblz57Jly5bbsjUQ85yQkEDbtm3JzMxk2LBhVFRUUFJSgre3N9euXcNms8mqrpeXF61bt%2Barr74iKCiI2bNn89xzzxEeHs6RI0fo2rUrq1evlufSYDDQrFkztm/fLo9n8%2BbNLFq0iEWLFlU7NoPBgJeXF0lJSWzduhUfHx%2BeeeYZXnjhBaAyiRAy9FBZ7ahbty5NmjShTp06zJw5kzFjxsiKrRiPuBfEWI4dO8b8%2BfP57rvvePvtt9FoNDz33HNs3LjR6RwIBAQEUFhYiNVqxW63ExAQgN1ul1Wchg0bcvjwYZnQ6PV6KcCUmprKgQMHqFOnDmfOnCEzM5NvvvmmxufLEaLKJ%2BxcBA1b3Icajcbp%2BSPmUwgr1YQy6VhJ9Pb2ZvTo0bz88svSHmb9%2BvVERkbK/k1hm%2BLoUSl%2BzsvLY/Xq1SxbtkwKE/0R2qaodkZFRXH27Fmp/CnuKY1GQ1ZWlkykly1bhslkokGDBvK83wrVVXVd6cf/DZEftVpNWlqaHKv4WwPuac6KorBr1y7MZvNfOg4P/n/hSfY88MADD%2B4wOnToQGlpKU8%2B%2BSTTpk0DqvrY3XfffaxcufK2911dwBAUFMSIESNYsWKFpEc2adKEiIgINBqN/C5BIQoODubixYtyX%2B7UN2%2BmyAlVZejdwZ38vmMQJI5HpVIRFBTE1atX5Xv33HMPGzduZPbs2YwdO9bpewXdU9g3/Pbbb5I%2BBc7VAkcKpVArTU5OZvz48TzyyCOEhoZy7tw51Go1R48epX79%2BnzyyScMHz6c/fv3A5UJfEZGBq%2B//jrJycloNBrWrVtHdHQ0NptN2k8MHDgQb29vxo4dK4PZxx57jK1bt1Y7R8uWLaNhw4YkJSXJJLZHjx788ssvTkqawcHB2Gw2rl69%2BqeDRpVKJcUlNBrNbdEPvb29mTNnDq1atSI1NRWgCiXUz8%2BPRx55hHnz5tGtWzfWrl0r%2B%2BfOnDmDv78/ZrOZM2fOyCRRJKqOCatKpWLo0KF89NFHTslIvXr1%2BPnnn2ViabPZqFWrFjqdTtoIiGT/vwmtViuFTxwXHGqiSulK4xVCRmLcrslCdaqtNU0i9Ho9ycnJVXoW3dGsxfcEBASQn59PVlYWmzZtcvu54OBgKY4k5tvdfegKtVqNl5cXRUVFaLVa/Pz8uH79Ol27dmXPnj2y7/aee%2B5hw4YNTgsW7hafboY/kmg5fuav7o2uyXf/UZp0ZGQkly5dkgtCwlLG0QezU6dOzJgxw23V34P/fXiSPQ888MCDO4j58%2Bdz6NAhtm/fXqPgQlRAXPtpHHGzQEMEhO78v8aNG8fs2bOdgrd33nmHLVu2sGLFCvl6jx49iImJ4bXXXnP6TkcVR3cQQifl5eX07NmTNWvWuD0%2BYehdv359KcgCyEDxypUr0ihepVJRUFDgNvlQFAW1Ws2mTZsYMWIEp06dolGjRkycOJHBgwcTGBhIkyZN%2BO677ygsLCQtLY38/Hx69erF/Pnz8fX1JTk5me%2B%2B%2Bw5FUaQYht1up2HDhrz77rv4%2BvqSmJjI448/TnZ2NsuXLwcgNTWVpUuXkpiYSGJiokw0s7OzSU9PR6fT0bdvX8rLy6lTpw5z5sxh6NChZGdnOyVs4ByoTp48mY4dO3Ls2DFGjBhR7VzfChqNporYi8DRo0cpKysjPj6eH3/8kfr163Po0CHsdjtxcXF88skn7Nmzh%2B%2B//57FixfzwAMP0K1bN3JycpgzZw4mk0kmUDqdTi4CCNERV3Tr1o2ioiJZnRPVFXfnMy4uDi8vLw4ePFjFAqS6YPe%2B%2B%2B4jMjJSVuFatmzJ4cOHiYyM5MSJE9Xed6JSWtMAWq/XExoaSmRkpKRX2u121qxZ41RV1ul00vx91apVVebkv2nb4JowmkwmgoKCKC0t5dKlS3h7e1NaWkpQUBAXL17Ey8sLg8FAQUEBXl5e1K5dm19%2B%2BcWJig3Qpk0b8vPzOXLkiNPrjlUjoby5c%2BdOAgMDARg9ejSbN28mOTkZvV7Pnj17ZK%2BxyWRyotKKRM8xKXbsJxSLRKKH2PH6EB6PZWVlUqW2c%2BfO0pdQo9FIew4BnU5HaGgoVquVixcvYjKZ8PPzw8/PjxMnTjBnzhzKy8tZuHAhZ86cYfjw4bz//vvyetHpdAQGBkpKucVicVLPbNeuHfv373c6H8K3EyAzM5N9%2B/ZRVFQknx82m00ea506daTHnr%2B/PwUFBXLu09LSOHz4cI0sWlyhVqtJSUmhVatWvPXWW/j4%2BFBYWIi/vz%2BrVq0iLCzstvfpwZ2Fp2fPAw888OAOYvfu3ZSWltKgQQOioqKIi4uTVZ/MzExSUlKAyqBm6NChrFy5Uvq7we8r4cJ7TqPRMGTIENn/Jv4XwbAILEV/jEqlwsvLC6PRKNU%2BRYBwzz33YDAYpMhBWVkZKpWKVq1aVaHI2Wy2ahM9sRpcWloqE8xdu3ZV2a5WrVpUVFRI8ZajR4/KCt748eNRFIW6deuSlZVFfHw8Y8aM4ZtvvmHlypU888wzQCXlVASS6enpaLVaOnbsyE8//YTNZqOkpIT%2B/ftjsVjIyclhw4YNFBQUUFFRwZ49e/j555%2BZM2cOpaWl5OXlsW3bNqxWqzz2oKAgbDYbkyZNwtfXl9zcXOx2Ox999JFUBBRw7ZkSsNvtlJaW8sknn/DZZ58xa9YsKeTimugBMtFLTk6mc%2BfOtG/f/k8lelDZy3TgwAFpV1BUVCTPT2FhoaSGvvrqqwQFBTklKxqNhu%2B//55Vq1ahUqk4dOgQkyZNYsKECZSVlTkZbs%2BbN48dO3bwxRdfuA0SIyIisNvt7N%2B/n8DAQBRF4bHHHpPviz40nU4naY1Tp04FKqt1gEzAHZOyyMhISXVctWqVTPQADh8%2BTHFxMb/99ht2u11en669g8LTrzqI60ygoqKCzMxM9u7dS8eOHRk8eDBbt251Sn7sdjtms5lp06axatWqKnTckZ4dAAAgAElEQVRZlUpFgwYNmDdvHhqNhv79%2B5OQkMBbb70lbUP8/Pyq9BM69l66QvTfCZq2l5cXDzzwgOzt8/b25vLly9jtdq5du4bVauX69euo1WqKi4uxWCxUVFRgMBg4evQoLVu2dDoms9nM%2BfPn5TlyHZfoMxQ2Ahs3bmT16tW89dZb7N69G5VKRVZWFr1790aj0TBw4EDUajWrV69m79698l96ejoNGjSQ50kketHR0U7nr7S0tIodxfvvv8%2BuXbvw9fWlrKwMo9Eo/eLEtXP9%2BnWnylV5eTnnzp2THpMlJSVcvHhRViOnTp3K5MmTZaLYqVMngoODMRgMGAwGsrKypM%2BpGItYlFIUhdTUVNLS0rDb7fJ98b/oi23YsKGsgou5tVqtqNVqDAaDpNbn5%2Bc7zb3JZJLPetFnp9PpMJlMbNq0Cb1ej9FoZNOmTXIsgYGBxMbGyj7A9957j8TERB555BGmTJmCoijMmTOnyvXlwf8%2BPJU9DzzwwIP/UXz00Ue89tprqNVqZs%2BejZ%2BfH99//z1vvvkmQUFBXL582S39S1EU9Ho9paWlbmmRjvD19aW8vJzmzZuzbds2oFLA4OLFi3fcGLpNmzZ07tyZF198kaeffpqXX34ZqFy1NplMHDhwgKioKKkOFxcXR6tWrdBoNHz44YdO%2B3JnuO0OoioqaGJ2ux1/f39OnTpFZGQkDRo0IDo6mtjYWADWrFnDzp07iYiIoEWLFjKwXrVqFZ06daJt27ZMnDhRzuOCBQsYNWqUU1%2BmayV2%2BvTp3Lhxgzlz5kizcahMZPPy8pzOi6iatW3blv/85z%2B3Nb9qtZrU1FSOHTuG3W5nxowZBAQEMHjwYLn/iIgItFoteXl5UtAmOzubMWPGsHfvXkwmE6WlpYwfP54ff/yRAwcOSDl%2BsUhw7733smnTJux2e40NtP8IunXrxq%2B//sqRI0eqUBgdK9mu8y6SRdHv6lgdOXPmDA899BDt2rXjkUceITAwUPadumLu3LlMnTqVqKiom1YMobLSVFJSIs97UFAQTz31FGazmfHjxzNy5Ehmz55NcnIyqamprFq1ykm0B373qFOr1UyfPp0pU6awcuVKPvvsM6mSWZ2oT3XX3s1wM0XWuLg4ac7dr18/goOD2bt3L7t373Y6ZuFfJ5RRBcLCwlCr1U402q%2B//tpJNCc9PZ2BAwc6MQqg8jyJSq941rlTrhXPxfT0dAIDA1m3bp28psXzUtDWLRZLlWdncHAwUHld5%2BTk0LNnTwYNGsSxY8eYOXNmle%2B7VWVYWLeIPkd375eVlTklgK7iNDfr8RO/C4Vdsc/Y2FhOnjwpe5lFYh8XF0dqaioRERF89dVX/PDDD/I4hEiMXq93u1Dnwf82PMmeBx544MEdQP369QH4z3/%2BI5X2bDabDJoiIyO5evWqDCDuvfdeVq1aJT9fr149Tp48KYUE/igaNGjAiRMnpOWBoihs376d9PR0evXqJcUZ9u3bx%2BnTpyW1yjFQS0lJ4eDBg/J3IZAwdepU5s6dC8DatWu55557mDNnDtu2bePf//633P7rr7/m008/ZevWrWRkZHD48GEOHDhwW8eRmppKnz59uHz5Mj/%2B%2BCNfffWVFFJRq9V0794dHx8fFi9eDFRWHJKSkjhy5Iikw1osFnksWVlZ%2BPr6sm7dOl577TUmTpwoe5c6dOggv9dut5Obmyupta5UQrPZfFu9QgI3S7Qd3wsNDSU3N9etKbWjzP2YMWNYuHChk%2BWFSNw6duzoVF1w7asUIjni9alTp/LPf/6T9evX89NPPzFx4kQ2bNhAp06d3Aab/224fk9CQgJjxowhMTGRDh06SJXC8ePHs3PnTgAuX74s6X7VJTzbtm2jTZs2UiykS5cu0hDc19eXkpISUlJSOHbsmBQratasGfv27ftL%2B7WioqJkpdkROp2OVq1acddddzFnzhwsFguxsbGcPn36L5t317l17SV2Rb169Th37pz0ovT29qZTp06yB1iIk7Ro0YLu3bvz9NNPA5CdnU1gYCAJCQnSDkD0sEIlE2Dw4ME3HaujNcTs2bMZP368fE%2BtVpOcnMzBgwepVasWZrOZI0eO8PDDD7No0SK2bdvGU089xZ49e8jIyGDPnj0sX76cnj17ykWA6Oho8vLyMBgM5Ofny/5f0cscEhJCw4YNOXXqlKQhu7sHHEV83OFWvc0ajcZpUcLd%2B7cSeaopmjVrxrx586ioqCA9PR29Xs%2BhQ4f%2Bkn178P8HT7LngQceeHAHIPz1evfuzYoVK6pdlRVwreAJ5b2aCDvcDhRFYceOHaSnpzsp773%2B%2Buv861//Ys%2BePVXUNMXPotLg%2BJ7ZbKa0tJQFCxbw%2BOOPs3z5coKDg2ndurUMHFNSUnjiiSdo166dHMfHH3/MyZMnOXToEFeuXKF79%2B6SYiXkzlesWCFX5sWYvLy8SEtLY8CAAYwePZqKigr8/f0JCAigSZMmLF26VEr6f/nll3Tu3FlWKxRFoVGjRhw%2BfJjPP/8cPz8/unbtyhdffEHXrl155513%2BD/23jM%2Bqqpr//9OzcykTAqkB0JNAVLoLVKVGnqTXgUUacotQdqNIIgCKlUEkaJ0kCpVBUF6C71DIIEkpJCeSZnfi/z3diYJiN738/j4%2Bc/1hjBz5sw5e59zZl17rXVdYWFhVqbpe/fuxdHRkaFDh7JmzRrq1atnRQYFRMZCiGoI6PV6du3aRfv27aVAQkREBBcuXCA7O1uSyDZt2vDkyRMuXLhgFcxFRERw9OhRSVyKz6XlPKSnp5fIbnbo0IG9e/dKsvcqBE18V4cOHXjy5AlnzpyhWrVq3L9/X16L4jpq1KgRPXr0oHnz5sTExPDpp5/So0cP1q9fD8D48eNZsWIFeXl5tG3bVpZmisUQgZUrV75QtEZkcUQpY2BgIFevXsXOzk4aru/Zs4c%2BffqQmpoqFUxfJNohxtdScMjDw4OEhAQ2btzIwIEDiYyMlIsvQjnTctxKy4IVJ80qlQpnZ%2BcSmZ1XETKyJKhGo1Gqmgr81efCH2WPAJn1trQ30Wg05OfnWy121K5dG5VKxdmzZ%2BV1N3z4cPbu3cujR4%2BsenGnTJnCyZMnOXjwoDw/kVXOzc2V94%2BoRLC81i3VdsXxDhs2jK%2B//lq%2Br9Pp5ILTzp070ev1NGrUiB9%2B%2BIEuXbpI782nT5/yyy%2B/0Lp1a1q1asUPP/zAqlWrGDZsGIGBgaSnp/P06VNycnL49ttvGTJkCCqVimrVqnHp0iVZZbB//36mTp3K8%2BfPsbOzY8uWLeh0OmJjY/Hy8uLXX3/lo48%2BkmPcuHFjkpKSqFy5Ms2aNWPChAmYTCbMZjPt27dHoVDw5MkTGjZsSIsWLWS/r52dHdOmTaNRo0akp6fLRYjIyEgpQvT06VPMZrNU5RXz86J7XdxPer1eLuI9e/aMxo0b4%2B/vX0J4x4b/%2B7CRPRtssMGGvwGC7Dk7O5OamgpgZS8gyooEigfzlkIfLyrTfJXAvX///iX670JDQ61Wb%2BfOncuNGzdkaaSXl5f0kFKpVHh5eVlZOgwePLhEGaXAd999R1ZWFsOGDZOvVa1alQcPHtCqVSvef/99PD09MZvN/Pzzz0yYMIFx48bRu3dvlEolN2/eRK1WU7FiRcLDw8nPz8fZ2RmdTkdYWBj79u1DpVLRpEkTzp49S25uruzhgSIvMdFLN378eBYsWEBOTk6JgFalUtG6dWuuXbvG8OHDmTFjBrm5uaxdu5ZatWrJ7T788EO2bt2K2Wxm5cqVNG7cuNTzDggIAMDX11eWOQoUz5a4uLjg7%2B9PfHy8ldn9J598Qnp6OpcuXeL8%2BfMvVY4UY1Lcb644LEmDyKg4ODiUMJL%2Bs1AoFOzdu5c2bdpIv72QkBBq1KhBixYtOHDgwAvLanU6HZUqVWLo0KFs3bqV33777YWZssqVK/Pw4UOrsrs/q4Q4btw4q9JAIaJhaaj91ltvsWrVKvr378/KlSuBkkJIQlwoLCyMTz/9lKFDh5Kfn2%2BV5RH3pCgbLVeuHA8ePACKSF7//v3Zt29fiWukUaNGVnYoIruj1Wrp1KkTGzdu/MPzLE42BQQxLH4%2BwuTbbDYTFBRkZaZtaaMCsHbtWtzc3Hj06BHDhw%2BXixh%2Bfn5cvnyZbt26WX2nk5MT2dnZVgI7/0k4%2BiLlUQGtVsvevXvp0KEDFStWJCUlhdjYWAYMGMDq1asBuHTpEg0aNJCk0GAwkJqaSrly5bh37x5qtVqWz4vew7Zt22IymTAajTRv3pw7d%2B5w8%2BZNqf5riQoVKtCjRw%2BUSiVnz57l8OHDsr%2BvadOmjBw5EpPJxKpVqzh06BBarZZnz55Ro0YNOnXqRLly5di7dy8HDhwgIyND/l5otVrKly9PQUEBmZmZJCYmyntLzGmtWrX417/%2BJUu0hRqs2E48gywJuEKhoHPnzgAkJiby66%2B/MnLkSCulYxv%2BGbCRPRtssMGGvwGC7FmSOLEyXVBQUILsFV9h/%2BWXX2jatCk6nQ69Xk/Tpk3Jyspi//79QFEJ3%2BbNm2nbtq384Q4JCSlRghMYGMiNGzesXhNlmALFe1d0Oh19%2BvSRKoPJyclUqlSJBw8ekJ%2BfL0tRLTMUomfO0dFRBio1a9aUVgVCYEahUFClShWuXbtm1asixDbEmFSsWJHMzEzS09OtxsnPz4/XXnuNS5cuWZ2rZTAZFhZGZmYmt2/ftjpvsU1YWBjPnz/n/v37NG/enDNnzqBUKlm7dq0kbZYICgqisLCQbt26MWvWLAA%2B%2BOAD/vWvf0kRj6CgIDQaDWXKlCE1NbXUfqI/%2Bjm2s7PDYDBgNpvJz8%2B36vl6EcR%2BnZ2dqVy5conysa1bt9KvXz%2Br7169ejU9e/a0eq19%2B/acOXOGhIQEFi1axKhRo1i4cCFGo5Fhw4bRsmVLdu/ebUUomjdvzk8//USPHj3w8PDg9OnTVj1cfwXFxyksLIz27dszc%2BZMmYWeM2cOSUlJZGZmSmEWtVqNVquV2a4mTZpw9OhRJkyYgNlsZt68eZKEbdu2jS5dulh9r8haC5QtW5bWrVtTq1Yt8vLyZMlgZGQkR48eJTQ0lMaNG0vxnT8D///PJ%2B9VxkKn0zFw4ECWLl36h9tbBvSWY1inTh0pVlJ833Xr1rXqBa1duzYBAQFMnDiR0NBQef1999131K5dm2fPntGoUSN0Oh1vv/02Bw8e5OrVq3K7pk2b4u3tzbRp0wgPD5eG4%2BHh4aSkpFidt1qttiovdnR0xMnJiZSUFElCBWkunlkVsFQtPnjwIJGRkVYLK%2BL9r776iqZNmxIaGsqwYcNYsmSJ/F6NRkP9%2BvVZuHAher2e/fv3M2bMGLp168aWLVukcFBubi41atTg7Nmz/7Uyyv8NqNVqlEplCYKqVqtxdnbGxcWF27dv4%2BnpyYEDB6Tolw3/HNjIng022GDD3wBB9urUqWPlY2VnZ2e18v6iR3TxDNGXX36Jq6srffv2BUonD6WJlLwKySi%2B4m8wGKR5t1qtxsvLC5VKRePGjVm3bp0VgbUUCRAr797e3sTFxTFy5EiWLl1q5Z8loFarpex7QUEBKSkpFBQUSA8oy/JNgfHjxzNo0CAWL17MihUrZH%2Bhpfefi4sLJ0%2BepFWrVjKwtDw/jUZDv379rDKTer2ezz77jJYtW5Y6PsJawcvLS5Ya1qxZkx07duDn58eePXsYP348bdu2ZerUqbi4uFCvXj2ysrIIDg4mMTGxRJbOsi/Hzc2NvLw8srKysLe3L1GyJ7YvLcDUaDRotVopgS9k3y3nsngPloeHR4mMoI%2BPD9OmTWP48OF4eXkRFxcn5z0uLg43NzeZBSueqdTr9SiVSmnKLWC5GODg4EBubu5LszOllUZqtVpp6SCuB0Gy09PTmTVrliSg//73v4mKisJsNtOkSRPOnDnDqVOnqFGjhlXJ5otsQf4nUK1aNa5evSrPpXPnzsTGxnLs2DGr7YovuIiySXGv/dE9LMjb0qVLZcbxj7bX6/UsX76cvn37lsj2/1HZ%2BYtw5MgRee22bdtWPicse/ag6Hpu1KgRFy9elNdsnTp1uHHjBlWrVqVly5a8/vrr%2BPj4AHD%2B/HmGDBliJSIksqEtW7YkPz%2Bf5s2blyhB7N69O5s3b2bMmDFs2rRJKm8WPx97e3vs7e2JiorCzc2NgQMHUrZsWUwm01/qyf1PoNForEpWLT1IRRltcHAwycnJf5jZfxlUKhUODg7Url0bs9n8SgsKNvzfhM16wQYbbLDhb4QokxEQAb5YsX4RBNHTarXMnTuX%2BPh4Bg0aJN8XBKv4vv8IarVaElGB4schSr6gqF8pNjaWBw8eSPELy0ybCJgsg3hRmiiCB2EJodVq8ff3x9HRkatXr3L69GmOHj3K8ePH0ev1rFq1Ci8vL/r168cbb7xRIujPzs7m2LFj1KhRA5VKRUZGBunp6bi7u8syTlEym5SUxMCBA9HpdFy/fl0GSgqFgk6dOrF9%2B3a0Wq2Uky/eQ1YaLDM/lsf27bffotFoGD9%2BPC4uLkRHR/P8%2BXPZExMXF4darUav1/PLL79w8%2BZN1q9fL%2Bdw48aNUnxDED2RxRJ9jKWtthsMBgICAujWrRuvvfYaEyZMoEOHDpQrV85qLi2PWQjOFJfwT0xMZNu2bUybNo3Ro0dL38Hnz59jNBqtyF1ubi6Ojo7y/yIgFVlOZ2dnPv/8c6pWrcro0aPR6XQcPXqUK1euUK9ePdkTqdFo6NSpE0FBQdLaorjdgVh0sBzzBQsWEBUVxSeffCJfq1y5spUlxIkTJ2jcuLEMhuvVqyff2717d4mx/J%2BAs7OzJHpQdC6bN2%2BW95ElipdqW5Z8t2nT5g%2B/y2wusvt4//33XynrJJRTxeJR8YqA4sTO8poSqFKliiSNUHSft2nThhYtWtC8eXOZ1QNK9BfOnDmTxYsXc%2BzYMcqXL49Op%2BPy5ct4eXlhNBq5e/cuvXr1IjIykk8%2B%2BYQrV67Ia8NsNtOnTx/MZjOdO3cmKysLk8nE0aNHS1zXgtQvXrzYqgex%2BPllZmaSkJDAuHHjGDBgAIWFhcyYMUP23en1eoYMGYJer8fOzg47OzuGDBnCsGHDpH2N2A6Qoj87duyQr2m1Wnkfi/e3bdsmXxNG9FB0b2zfvt1KcMpsNsvn3Keffiq3rVatmhx/oTz6KigsLCQvL4%2BUlBRpn2HDPxO2zJ4NNthgw98AkQ36b0OhULB69WpiYmJYs2YNt27d%2BtP7KK1352UKfECp2Tzxr8jsmEwmdDod6enpdOzYEZVKxa5du3ByciIzMxOz2cy0adOYOXMmFy5cICoqinPnzgHw8OFDfH19iY2NlURTkFc7OzsrLyvLsRBiLCaTCaVSyc6dO6WX4VdffcU777zD%2BfPnCQgIQKvV4u7uzuHDhwGkx6GnpyeTJk3itddeA5AKjwKCeCuVSry9vYEiQRYPDw%2BOHDki9yN6mBYvXsyiRYteWHr2Z%2BDs7Mzz58/ZsmUL3bp1k/tTqVSMHj2azz//XGYBoKj8cOPGjaxcuZK1a9cCRaV5pUGUfNaoUYPr16%2BTl5cnMzBCnGTq1KmMHDmSzZs3k5iYKEm9ZRbZ2dlZBotirCIiIqSJuhg70QsaFRUl/csaN27ML7/8Qp06dWjZsiUtWrTg8OHDzJ49%2Bz8aNyjqoUpOTub58%2Bf07NmTjRs30qNHD5KTkzl06BCff/45UVFRZGdnYzQaWbNmDTdu3CA8PBx3d3croR6B%2BPh4evfuLc9TZLT/Sk%2Ba5Wf%2BqCftz%2BzLEnZ2dhiNxhJKn6Vtl5ubS0REBI0aNeKTTz6hY8eO0uKhOCy9Dy2fByqVSmZOLUmni4sLb775JkuXLkWhULBt2za5wLJr1y6ioqKs%2BjLnzZvH/PnzZUZc7PtFRFbYD%2Bj1eislYcv5cXd3l6WjYkFK9Oh5eXnJhRAfHx95fysUCuLj45k0aRI7duygQYMGbNmyRQpDaTQaPDw8OHXqFPn5%2BdLQXZSu%2Bvr6snfvXrKysrCzs5MEy97entatW7Nr1y5pEO/s7Cznyc7OjurVq8syeNG7J57Bog9TpVIRERHBkSNH8PPz44033qB79%2B60b99ePg8jIyNRKpXk5OTg6ekpqyEsCbi9vT0LFy6kUaNGL71ObPi/CRvZs8EGG2z4GxAbGyuzCsePH%2BfGjRuS8Fiu4oeHh2NnZ8eZM2d49uwZvXr14rPPPpPKiXXr1uXGjRtUqlSJCxcu0LFjRz755BP279/P%2BPHjmT17NlOnTiU8PJxTp04RGRkpV7NLs1Eojpf1w1iifPnyViIt8HuA6e7uLlfNlUol%2Bfn57Ny5k0GDBkmRl7S0NNRqNaNGjWLv3r3s3LmTqKgoSThiYmLw8fGxEiwR33Hu3DnUajXnz59n586dXL58mVu3bkllunXr1tG/f3/s7Oz4%2BeefcXR0pEuXLlSoUIGYmBhWr15NeHg4BoOBtm3b8s477wDwxhtvUK5cOSIiIjh27BgLFizAwcGhRN9ZVFQUWq2W0NBQunTpwieffEJKSgpGoxFHR0crJU6VSkVCQgIFBQW4urpKoZrPPvsMlUrFuHHjAFizZg2PHz9GrVbTv39/q7LSsmXLkpOTY1WSadn/WHz%2BQkJCWLduHTNnzuTEiROEhIRQqVIl1qxZg4uLC3v27EGhUEgSm5eXR3JysiRr7u7uUvRBeIONHj2a3377jQMHDkiDZ9FLKK5fyzLNOnXqkJGRYSXyYQm1Wo2/vz937tzBaDSSlpaGm5sbX331FSNHjmTmzJkcPnxYEnHLLIyA0WjE1dWV58%2Bfk56ezrBhw3B2dpbv37p1i8uXL%2BPk5ESfPn149uwZSqWSGTNmYG9vj8lkYsCAARw%2BfJj79%2B9bWVe86Ji1Wi1%2Bfn48e/YMf39/srOzefjwoVVP5qsSNUtCVqdOHaZOnUqHDh1o2rQpdnZ2ViWIlkIaApa%2BgVB6Jr846QsNDaV3797SC1Kr1dK1a1fu3r1rVV6%2Bfft2unTpwqpVq2jQoAHBwcHs3LmTbt268f333/Pbb79ZZZN8fHx49uwZubm50hZElB2K68JoNEoRFJPJhK%2BvL48ePUKn07Fx40bu3bvHTz/9xNGjR8nKynqpX6iYD0H2jhw5QpMmTaTIjphLoWIsxmL16tX0798fFxcXOnXqRGBgIImJicybN4969epRrlw5Nm/eTGRkJMnJyVy7dg2A5ORk3n33XdatW8fz58//q1YbpeFVFFr/G1CpVHh6ehIfH4%2B3tzfdunXj/Pnz3Lt3j9jY2BICVTb8M2AjezbYYIMNfyPmzJnDmjVrCAwMlCqRAgqFgmbNmjF//nyUSiU1atSQHl5CxdOSjKlUKr799lu%2B%2BeYbfv75ZwBZhujo6Gjlw1atWjVmz54tfe/%2BCiy/%2B48UEC0DXstVeLEPETjl5uby7rvvWpWkCtnvI0eO8Prrr%2BPk5ER%2Bfj6pqak4ODhw9OhRhgwZIv3PWrdujaurK9988w0ajYZKlSoRHR1NxYoVSU1NpXXr1ty8eZPTp08TGBhISkoK8fHxJc7pRejXrx86nY7s7Gzs7e356quv5Gfh93LI4gG5QqGQfYqvvfYabdq0kWW8ERER6HQ6Dh48yKVLl%2BjVqxdKpRKVSoVGo5GkQ2TABIEsPu7ly5enb9%2B%2BzJ49W74%2Bf/588vLyiImJ4euvvy4RNDo4ODBs2DAePHjAxYsXZbmjVquVq/55eXkvzeIUJxbjxo1j%2BPDh9OzZU5YACk9A%2BL1X0GAwUL58ee7evUuVKlWkZUJeXh7u7u5oNBpCQkJo0aIFU6ZMKUG%2BxHX14Ycf0r9/f/l6eHg4n3/%2BOevXr8fJyYm5c%2BeWOO7IyEjy8vK4f/8%2BmzZtYuDAgezcuZNJkyZZER2B0uwM/lteglqt1kogo7jpemk9cgLFv780IvgiWGbgBETGMj8/v1SSKoiTGA%2B1Wo2jo6NV75roy7WEt7c3T548wcvLiydPnvDGG29w5MgR6cn3qsdb/Fpr1qwZx44dszpWoahaHMXnUOxPVCbo9Xpyc3Nlhk%2Bv11v1EhdXqxR/C4SFhZGRkSErIYxGI97e3hw7dkyS1f922C3KO8U8ir7WP1qsKA0KhQIXFxeSk5Pp2LGjvG8uXbpE//79CQkJkRUBNvxzYCN7Nthggw1/I%2BrUqcOUKVPo0KFDifd27NjBhx9%2BCMD777/Pm2%2B%2Byblz5xg3bhwmk4kKFSrInh%2Bj0UhmZiYqlUoGFXXr1gXg2rVrZGRkoNFo0Ol0ZGRkoNPpKFu2LGlpaYSFhXH8%2BHHGjRvH3LlzrQIqpVJpVR5kCcvtXrbyLMqeSoOnpydz587lxx9/ZMOGDbi4uPDTTz%2Bh1%2BvJyspizJgxkox26tSpVMLh4OCAh4cHwcHBlCtXjtGjR/P48WN69uyJ0WikcuXK9OnTh5o1a7JixQpWrlxpFQg6ODjQpEkT3n33XbRaLe3btyc7OxsHBwdJwOPj42UgaBlsVqxYkdq1a9O6dWs0Gg2bNm2S3liWn509eza5ubnk5ubi7%2B/P%2BvXreeedd0hMTESr1fL48WP0ej2ZmZlym9jY2FK98zw8PEhLSyMrK6vEuHfo0IFPP/2UZs2ayWC7atWqPHr0iMLCwhdmR9RqtQwUBWktLCxEo9FQtmxZFArFS60eoGSA3bNnT6ZMmUL16tWBkjYPZcuWJSMjgyZNmpCSkkJGRoaUrYffiVxxQmtJDDZu3EjPnj35/vvvrTIONWvWZPLkyUyaNAmFQiEzisK6Q6PR0LZtW6Con7RZs2YcPXoUJycnmjVrxvbt21EqlZLowu/XuCgPLigowMHBgbp16xIdHU1aWhrNmjWTirhiXP%2BoR87Pz4/Hjx9LcmGpmFlcmAWKSvhef/11fvrpJ5o3b87u3btln2KrVq04d%2B6clXjTZ599Rs2aNTGZTBw7doxZs2b9VwiHUqnEw8ODxMREq6ffg5wAACAASURBVHNUKBS89957fPbZZ3LM1Go148eP58GDB5hMJmlSf%2BbMGeLj4zEajbKfVuyjtGO0FNIRpYuzZ8/mX//6l7wvxeKW5bVYGkm0hFqtRqfTERoaisFg4OOPPyY%2BPp7p06cTHR2Ni4sLTZs2pV27dlZEOzo6mk8//ZSRI0fi4eHBm2%2B%2ByTfffENCQgKdOnWiYsWKzJs3j5s3b/L666/Tp08funTpQnx8vPTPE154Pj4%2BbN26lYYNG3L8%2BHHZyztmzBh69Oghv/OHH36QNiwdO3bkxx9/ZODAgYwePRooKjN/8uQJ69atw9nZme7du0sho7S0NLnYVqlSJW7evCl7g0XJrXgOQNEi1IoVK4iNjaV169bY2dm91BDehv%2BbsJE9G2ywwYa/EfXr12fDhg34%2B/uXeK9Lly6kpKTw9ttv0717dy5evEjfvn1l74eHhwcxMTGy/Kps2bIyIFepVJQvX57nz59L4RBhIt2zZ0927NghV9SFafetW7esevyCg4O5ffs2VatWtRKSECgtELUM0po3b84vv/zC7NmzuXz5MocOHSIoKIjc3Fzu3LlDREQEDx8%2B5LvvvuPu3bssXLiQu3fvkp%2BfT3JysvTIE8FV8cyhJRnQ6XR4eHhQUFBASEgIhw4dom7duixevNhK2AB%2BF4PIyMjA2dlZvr97925piK7T6ayEacLDw%2Bnevbv0CBw4cCBms5lly5ZRtWpVue2aNWv49ttvUSqVNG/enLfeeosyZcqQk5PDb7/9xo4dO/Dx8WH48OHUq1cPs9lMt27dGDFiBGXLlpUEfdSoUSxYsKCEj5tQJIXfg23LwF6pVHLt2jUiIiKkOmZpZYS1atXi3LlzlC9fnidPnsh%2BqODgYKpVq0bXrl0ZN24cqampdO7cGY1Gw/LlywGYPHkyCQkJfPPNN%2BTn5/Pll1/i5eXF3LlzZUasbNmyQJE/3axZs6zURb/%2B%2Bmu%2B/fZbK9%2B4PwNxjQnvtJYtW5YQERLXoGVW1bKPtDgse5wMBgPp6el4eHhQo0YNDh06JM/pz/gPivvHkryWLVuWmjVrWonAeHh4UK9ePWJiYrh//74kM2q1GrVaXepCi4AgwmJ869evj5eXF8nJyfj7%2B/Ptt99anf%2BLULznTWxvaV1g%2BX%2BRNdLpdAQEBEgPuJep/Yr%2Bt9JQv359Tp48KT8Dfz4DptFoCAoKIjo6mi%2B%2B%2BIIxY8ZIcmiZ0VOpVCgUClxdXVGr1cTFxcnnp/h%2BUS78MgihGeFTaqmG%2BTLY2dlhNptlH7GrqysZGRlSiObOnTvo9XqSk5Plvopnfv8sFAqFtL0Rz7vOnTuzfv16jEYjH3zwAdOmTaOwsJCCggJprA5F/p5qtZoFCxaQmpoq%2B6ht%2BOfARvZssMEGG/5GLFy4kAcPHvDRRx9JZUWB8PBwCgsL2b17N35%2BfnTs2JH79%2B/TsWNH3nvvPaZPn86PP/74wn0LhTYRwAmVuKZNm7Jz504Ali1bxsWLF9m9ezePHz9Gp9PRu3dvvvnmG3Q6HWXKlCEuLg6VSvWnBSIsMzLh4eH06NGDzp078/jxY1q2bMmCBQuYOnUqZ8%2Be5dmzZ0RERHDixAnef/99zpw5YxXkGgwG/P39Zc/M1KlT6dOnD4sWLbLyxBIQgZeA%2BKkTZKg0hIeHM2bMGAYOHChfe/r0KTt27GDBggXSBuG1114jMTGRxYsXo9PpCAoK4tixY1IN8N69e3To0AG1Ws2GDRtKJYMajUb2OArRE3EMZrMZo9H4Qtl0y9I7S09EYfocFBQky3jLli3LsWPHuHHjBsOGDZMCD6KUbv78%2BUyaNIm8vDzatm3LxYsXKSgoYOnSpcTHxxMVFUVqaiqOjo4y81I8WyU8EvPy8uR8GwwGpk2bxqpVq7hx4wY6nQ6TyURhYSHt27dn3rx5JCUlceLECa5fv86jR484cOCAVTD7ImEgywxbr169WL9%2BPc2aNaNq1apym8aNG8usi0KhICgoiPT0dLKyskhKSpLXhvAEtCQlarUaV1dXSUzEOU2ZMkWqL1pCqVSi0WisFj78/f2JjIxk5cqVVmWDSqWS69evExoaKgk2FPXOpaWlYTQaUSgUXLx4UQbbAQEB3LlzR17jYtGmtNJpke1Sq9WULVuWuLi4l/Z6WZ53ZGQk%2B/fvL5VUiOvM0dGRli1bEhISwqZNm0hJSSE1NZWQkBCgqBc5Nja21BLCMmXKkJyczNSpU2nVqhV37tyhf//%2BBAUFERMTQ0ZGBg0aNODbb78lIyODzz//HI1Gw6pVq%2BTxLF68mFGjRuHs7ExaWhoFBQVW/cKtW7dm3759tGvXjj179kgSLOZZq9USHBzMs2fPiIyMpG/fvjRu3Fjeg/Pnz6ewsJBhw4ZhNBoBpGCQpSLluXPnuHfvHgsXLiQ%2BPp7Q0FDu3LlDq1atePToEffv35fXz3%2Br1Lf4vIns8stes4TRaMTOzk4%2Bi1JTUyksLCQkJIRbt26RlZUl72u9Xo%2BTkxN9%2B/Zl165dZGZm4uvrS0FBAd99991/9Vxs%2BJ%2BHjezZYIMNNvyN6NevHxcuXJBEwtIu4cmTJzg7O7N06VIeP37M%2BPHj0Wg0rFu3jrCwMLp06cK1a9dksG3puQTWGR29Xo%2B7uzsJCQlWAXSVKlWoVKmSFH944403iIuL48qVKxgMBlxdXeVqMxSV6nl4eHD37t0/FcCILOCkSZMIDg6Wku4Ay5cvp1q1alKhMSIigqtXrxISEoLZbGbFihX4%2BvqyYsUKNmzYgEql4sSJExiNRmJjY2nTpo3Msp05c4aGDRtafXdCQgLvvfceUCRT//nnnwNFWaeZM2fi7u4OFPXb5ObmcuHCBfbv388PP/zAqVOnrIInT09PEhISKFOmjFwhf/z4sfScO3z4MBMnTmTHjh24urpy/PjxEmQwMzOTnj17cvv2bekJVpzs7dq1i1atWklPwb8Ke3t7DAaDVUZKyK/n5OSwbt063nrrLcxmM40aNSInJ0eS2MePH9O2bVuZycnOzi7RX6XT6SRxKk7M2rVrx759%2B2Q20lI9tWXLllSpUgVXV1du3rxJdnY227Zt%2B8vnCUXlwomJiZQpUwY/Pz/69OnDmDFjUCgUGAwGatasyY0bN3j27BnVqlXj8uXLKBQKAgICuHXrlgxwExMTcXR0ZMmSJWg0Gvr06UNubq4kWS%2BDyF4pFApp/yDOW6fT0a5dO86fP29lA1EalEolLi4upKamSjEmFxcX0tLS6NixI2PHjmXOnDns3bvXysfyVcoVhT9m8Syep6cnT58%2BtdqHeC8qKorZs2dLn0u1Wi1J4e7du0lJSWHKlCncuHEDlUrF/v37ef311/H397c6V41Gw%2BDBg7l//z4///xzqQtIL%2Bq3%2B6sQHoPi2i0sLESr1VKmTBmWLl2Ko6MjXl5eKJVKq8WYtWvX4u7uXuL%2Bhd99NNu2bYvJZGLDhg28%2BeabbNq0CTs7O3777Tdp/SGud3d3dykCEx0dTffu3TEYDGi1Wr766iup2hsdHc2QIUNYuXIlQ4YMkYb3%2Bfn5skpDrVZz//59Jk6cSP369aWq8MGDBzl69KhUJxb9w0KJVK/XS5JnOb/FoVarGTRoEBcuXODs2bO4u7uTmprK8uXLadCgwX9tbmz434H67z4AG2ywwYb/P6NLly40a9aM3bt3c%2BfOHUwmk9WPb1JSklW/Rl5eHm%2B%2B%2BSZlypQhISEBo9FIVlYWdevWxcvLiy1btgBFP%2BL16tXj%2BPHjaDQavL29Wb16Nc2aNbP6/tu3b3P79m2gKGNgGYBlZWVJk2Kxsp%2Beno6Pj48sMbPsL3J3dyc3N5fnz59Tp04dGaQAsvRw9uzZhISEoFar8fPz4/79%2B7z99ttS5ASQiqSiN81kMvHGG29QpkwZeW5z5szh4cOHtG/fHicnJxwcHAgICGDo0KGMHTtW7mft2rV8%2BeWXQBHJEKQP4MyZM1bZmIKCAgoLC2nUqBHZ2dmEhYUxYcIE2ccI0L9/fz7//HMGDBggg7%2BoqCiGDBkiV/7FmIvMhpjPlJQUvvrqKwoLC62US5ctW0Z0dDQhISEyC5Oenk716tUpV64cu3btkttWrlwZnU5HXFycleBOaUE8FGXBBPno0qWLJFR%2Bfn7cuXPHqmz3woULLFu2DJ1OB8CVK1cwGo3odDpGjhyJQqFg4sSJsiTObDbz/vvvy/OeOnWqLE0rLCyURM9yDKDIM27Pnj38EUJCQoiOjubtt99m2bJlpZbHWZb8xcXFYTabSUtL49SpU0RHR8vSzczMTK5duyYDYLF4oVQqWbRoER06dGD8%2BPFUrVqV/v37o1QqqVmzJgkJCYwePZpPP/30lcyzRTZHo9FI1VyBnJwctm7d%2Bof7gKJsYmpqKiqVisjISD766CO0Wi379%2B9nzJgxKJVK9u3bR9%2B%2BfVm7di3e3t48fPiwBNGzLMcD5LVhOZZivubMmcPAgQOt%2BnCFJL%2BwQahfvz5fffUVSqWSL7/8kuXLl/Pzzz8zd%2B5cqlSpIhdA1qxZg9lsLkFq8/Ly%2BPbbb/H09OT1119n7969QNE8VqlShVu3bpGeno5Go8He3t6qj680iGyWgEqlwmQyMWHCBD777DPMZjPVqlXj9u3bVmJSNWrUkNULgLz3LEvGIyIi5H4bNmwo3/vpp58oLCyUipUPHjyQFifr1q1jz549Vvdgfn6%2BvN5XrVpFRESEFC3KysqiTJky9OvXT16rCoUCNzc3unfvDhT1dVtm3YrP66NHj9i8eXOp4yMy%2BFDk45idnU29evWIjo6WPocqlUr26qrVanJzc3F2dmbv3r3yO1977TXefPNN2X9rwz8LtsyeDTbYYMPfjD59%2BshMGiClvIuX5Gg0GhQKBf7%2B/qSlpVmV%2Bb2sX8RSYa54SVdxcuDk5EReXh7Z2dl/mCUQEBlDAXt7e%2Bzs7Eqof4q%2BP61Wa0UCtFot3t7e3Lt3D39/fx48eCDPyWw2W5UqQlFA1717dzZs2CDNu/v27Uvfvn2JiIjg%2BvXrXLp0ienTp3P//n2GDx/OF198gU6nk153UJRF27lzJ35%2BfjRv3lz2O86cOZNmzZpJchkcHCxVTXfv3s17771Hz5496dq1K1DkmVg8O5eXl4evry/79u0jMDCQ7du3M3LkSEnQLcdm2LBhbNq0iczMTDkXDg4ODB8%2BnJUrV8qAV6lU0rFjR7y8vEhLS%2BO7777DbDZLr70OHTrQrVs33nvvPalmuWLFCoYPH87du3fRaDTS3sPX15fHjx%2Bj0WjIz8%2BXgWyNGjXQ6XSyr1KpVJKenk7VqlWpX78%2Bq1evtpr7mjVrymz0lStXyMrKkvsUWdri/XOvArEPEZwLBVIPDw9q1arF9evX5fWfm5tLtWrVSEpKIi4uTsrrh4eHo9FoqFevHosWLUKv12M2F5mLCzVVYV7doUMHfvjhB5RKJS1btkStVhMUFMTVq1etSjq9vLxQKBTExcUxbtw4GeT/VQjT8ZycHHnvKpVKXnvtNebPn0%2B9evX48ccf5TVbWFhIUFAQRqOR%2BvXrc%2BbMGZKTk//Q3kH0fAlCVfwYoMiuQHhJiuMqLCwkJyeHtWvX0q9fP%2BbMmSMVZMWCiKOjIwMHDsTd3Z0pU6b8pXH46KOPiI%2BPZ9GiRfK1Fz1/hE2IwWDgxIkTlClThho1agBFolZ5eXlMnjyZmTNnWs2NyE4XFBTQsGFDfH19SUxMJDs7m/v375OUlISfnx8zZszg%2BvXrfPzxx1aZLzFO4jlbWlbM8ph1Oh39%2BvXj%2B%2B%2B/tyLcAoIYv6jM1lLd1/LZrlarUSqV2Nvb4%2Bvry5UrV%2BSxiOOpUaMGV65coXLlyty9e5fCwkJpgbFlyxb69u1LTk4OvXr1omnTpjRs2FCWFFerVo0dO3ZQuXJlWV7/IssUG/4ZsJE9G2ywwYa/EWLluaCgQErti%2BB727ZtdO/encqVK7/wx1apVDJ8%2BHCCgoKYPHmyFSl6GYpbDGg0GlQqFR9//LEsFxXB9h8FktWrV%2Bf69etS8vtV7AsUCoW0jyhNOEP0ZRXvSzIajezfv5/ly5dL77kmTZqwcOFC0tPTady4MT169GDz5s00adKEyZMn4%2BvrS2Bg4CuTPWdnZ/R6vczmWQqgCN824QN37NgxgoKCOH78OK6urkBR%2BeKdO3d45513GD16NIGBgbRp04azZ89SqVIlypYtK3smxVwoFIo/JNZCVMJkMlFQUCAFRSpWrMjUqVOluEv16tUxm4uM1Hfv3k1kZKQssXwVoQfLMbezs8PR0fGFwhoCxQNzlUolsyy%2Bvr4cPnyYCxcuMGDAADQaDYGBgdSuXZuff/6Zjz/%2BWJYAFhYW4ufnx5MnT3Bzc5NWDQIjR46kWbNmDB8%2BnPnz5zN58mT27Nkj7QICAwNloN%2BuXTsSEhJQKpWcPHkSFxcXTCYTmZmZVsIm%2B/bto0OHDuzcuZPTp08zadIkOVaW12BAQABpaWk4ODjIbPirQOxPGIYbDAZMJhPNmzdnzJgxvPvuuyQkJPDkyRNZYuvm5sb%2B/fvlQoLRaOS3335j//79MntvKXpjef80adKENm3akJaWxp49e7h06ZJ8/0UKp38G4r7w8vLi8ePHuLq6kpKSIr%2B/fPny9OzZk7lz51KjRg0uX76Mr68vPXv2tFJM9fT0lIJIv/76Kz179pQKspalxqVBEKEFCxYwd%2B5cfvrpJ6ZPn8769eulZcfTp09fKhijUqlwcHAgLS0NlUpFmzZtOH78OOPHj6d%2B/fq0b9%2BevXv30qJFCwIDA/H392fChAlA0T3%2B9ddf4%2B3tLUWvxHNbZBs3bdqEg4MDkZGR2NnZkZaWJsfd0dGRtWvXkp%2Bfz%2BDBg0lPT5dkKzc3FwcHByZOnIjJZGLBggWkpaVZ%2BRNmZ2fTrl07hg4dSrt27Uqo8vbo0YNNmzb96XkV96x4/qnValJTU9m5cycBAQF/an82/N%2BBrYzTBhtssOFvxOLFi1EqlTg7O5Oeno63tzcZGRmkpKSwZMkSKlSowKRJk1ixYgV5eXno9XorpTiVSsU333wjyxEVCgWffvqpNAauVasWFy5csAoESgv08vLyyM/PZ9WqVQAyiDQYDJLoWapBCmKnUCikR5ro1ylTpkyJIF181t7entzcXBQKBZGRkXz//feS7OTm5koyYjAY%2BOKLL5gwYQJ%2Bfn5cv34dhULBggULpAHyunXrAKRK5IYNGzCbzRw7doyFCxfSsmXLV56Hn376iZCQEGmt8PTpUzw9PalWrRqxsbGynzA/P5%2BsrCx0Oh2JiYm0atWKwsJCWYaVkpIis3Znz54lKioKs9nM4cOHMZvNjB8/nrfeegsAV1dXmjZtKoPnxMREfvvtN1QqFbVq1eL48eOUL18evV7P06dPZUDt5eVFYmIiWVlZDBo0iHHjxqFW//5zLrKDIuOmUCjk32PHjpWkFIpKN3/55ReePXtmZbcRGBhIaGgooaGhADx48IB169ZJNb/i2QixWOHi4kJSUhIODg44OTmhVqvx9PSkV69eREdHo9Pp0Gg0MuuYn58vM6Sir3PQoEHMmDGD%2BPh4q4ywGKMyZcqQnZ1N%2BfLlSUxMRK/X06VLF0n4lixZIrOwlrAswxTXf2FhIQMGDCA7O5sePXrIbQwGg5WCY1ZWFjExMVZ9iXq9HoVCIbc5duwYW7ZsYeHChXIs16xZw6RJk0hOTrbal1KpZOfOnVakX5AwhUJhlbUfMWIEt2/fliXTYk4ty3Zr1qxJjx49mDhxIrNnz5ZZ5gEDBhAQEECPHj24cOGCzOqJ87fMdlou6nh4eGAwGLh//z5qtRo7OzsyMzMJCwujSpUqQJGi68SJE0lNTSU8PJz%2B/fsTFRXFxx9/LAmpmLvY2FjmzZsHFGW8WrduLbNxAHFxcVa%2BfJYiPJYLE8UzWCI7B8ixXLx4MSEhITx79oxGjRoBRQsgH3zwAR988AFxcXF07dqVUaNGAdCtWzepwGlvb8%2BpU6dQKpWUL18eHx8fFAoFo0aNYubMmfj4%2BMjj8PT0xMfHBx8fH4KDg4mOjqZmzZqyn3nr1q2kpqbi5uYmKx9Er2NOTg5LlizBx8eHnJwczGazVY%2BdyWRi8uTJco6USqVcJEhLS8NsNrN9%2B3apfpuXl2c1VoLoubu7k5iYWGIhTWxvb29Pfn6%2B7GMUvZJC4MdgMODg4ECvXr1YsWKFzVD9HwpbZs8GG2yw4W9EixYtiIyMZNOmTSQlJVG7dm2uXbuGyWQiPz8fLy8vUlNTqVq1Kjdu3JCBgJOTExkZGbi5uVmJbyxcuJCAgACGDx/O/fv35Sqzj48PMTExeHt7k5CQgIeHB2XLluXixYulHpcIsov33sHvMvWenp4kJSWRl5cnBQUssw0COp2OgoIC8vLycHJyIicnh4oVKwJw48YNNBqN7P8TgYiQ1W/bti0REREcOnQIvV7P6dOnZfmTEDS4ePEiffr04fz58xQWFjJt2rQSyqYTJ06U5Z/t2rUDisonZ82aJQVaBg8eTH5%2BPteuXePkyZPs3r2bQ4cO8fz5cypVqsTDhw%2BlF%2BClS5c4cOAAp0%2BfluqOWq0WHx8f/Pz8iI2N5c6dO7J0SsDSJ6x%2B/fp4e3vL9zIzMzly5AhKpRInJyergL84QReCE56enhw5csTqXAMDA%2BU2u3btokOHDnI%2BunbtisFg4O2335bkyBKzZ89mzZo1hIaGUr16dRwdHUlLS%2BPq1atcuHCB6tWrs379eh4/fsy2bdtYsWIFgMzovvnmmzRv3pxZs2bJrImLiwsBAQFkZWVZkZviEKqKlSpV4u7du0CRmmhSUpIsa/b09KRFixYcPXqUsWPHMmXKFOzs7KRwhSXUajWtWrVi5MiRzJgxA4CHDx/KhQiRSYff%2BzUFRA%2BoEPtZtmwZ3bt3JyMjg927d8veTg8PD9kHWVpZ34sUMwXhtrRcuXDhAoWFhbIfS/hKCtl8y6y9KIUeN24cixcvZteuXdjb29OoUSOOHz/Ob7/9xtmzZykoKGDTpk107tyZvn37otPpaN%2B%2BfYn7U2TRLBeNmjZtyrFjx6hXrx7Hjh0DYNasWXTr1k1%2BNiAggK%2B//lqWf4aGhqJQKGT22dIbs3iFgOX/xd9iDHU6HXl5ebi6upKYmChfLy6S07VrV/bv38/YsWOZO3cuJpOJwYMHExYWxvPnz2VZaWlzU7z03cPDAz8/PxwdHbl48SJ%2Bfn5UrlyZbdu2ySoEy%2BtRKIL%2B1Qzp/xWIyoxz586RkZHBjz/%2ByN69e7l48SJubm4cP36cRYsWcerUKZuh%2Bj8UNrJngw022PA3IiwsTK7s/icQhumLFy9m5MiRskfEsgfOEmXKlJG9gfb29rRq1Ypt27a9Uo%2BegK%2BvL7Vq1WLHjh1/uK1llqQ0FO%2BNCQ4OZujQoXz44YesWrWKnj17EhYWxvvvvy8/M3DgQLy9vfn4448ZO3YsZrOZpKQkufpuCUtDcEtLBksIwnL9%2BnVSUlJYvXo1PXv25Nq1a%2Bzdu5cff/wRs9nM0KFDcXd3Z9%2B%2BfbL86s6dOxgMBivvuHPnzvHDDz9w8OBBSfDc3NzIyckhIyOD5s2bSzPpe/fu8eTJE1k2aDAYyM7OJj8/H09PT6DIBkLYaXz00UdMmzaNsWPHMmTIEKvzCAwMlMILrVq1Yv/%2B/XJ8xfy2bNlSliZu2rSJgoICJk%2BejL%2B/P48fP%2BbXX3/l9u3bpKSkYDKZaNiwIdu3b0ehUMjSPEFkRLZRCMF4eXnx8OFDWrRowdOnT2VWNCkpifj4eMxmMxEREURHR9OwYUNpH%2BLq6kp6eroMnsWxlnbtFBceKQ2rV6%2Bmfv36JV4/d%2B4cffv2xdHRUWbLHB0dqVWrFoMHD2bevHmcPn2a8PBw4uPjpdLq2bNnqV69ulwQ%2BE9QpkwZqlSpIn3woIgo5eTkoFQqpT2AGC8x1i8zmC/uhfffRPXq1bly5QpQNPaijDI9Pb1UixORybfsCfb09CQ%2BPt7Kz%2B9/C3Xq1JGZN6PRKO0%2BAgMDefLkCf7%2B/ly%2BfBl/f3/u3LlDz549JRG9fv06qampJCcnU7FiRVl6npycjLOzM7m5ufj4%2BJCfn4/BYCAlJYWMjAzZ92wwGPD29iYrK4tbt279R896g8Egqw/g96y98AvNzMwkIyNDHntxkmvpdSjIbrdu3di%2BfTv79%2B/Hx8dHLnaJrPU777zDrVu36N27t81Q/R8KG9mzwQYbbPgbMWLECNzd3ZkxY4bsIXNxcZGkZtmyZUBRmdOaNWvYvn27lUKdWq1GpVLx9ttvs3z5cqsAODAwUJZAPn78WDb1p6eno1ar8fLyorCwkMTEREwmk1WpZXZ2tpVgBFgr%2BNnZ2clsoVDVhNJFFYoLuFhCp9MxevRohgwZYtVXJwRWsrOzqVq1KteuXSs1qCxO3BQKBdevX5fZBEtMmTKFsWPH4ubmZmUMXLw0KTU1VapmDhgwgLfffhuTycSpU6f49NNPpeeZCLKSk5Pp0qULM2fOlPvYsWMHBw8eRKPR0KJFC/Lz85kxYwYqlYq%2BffuyZMkSevXqRU5OToljFVk7gcjISFmSKM5b9EWWRly9vLxkv5KYj79CAOzt7WUACb8LSdSrV49Tp05Zbevr60tsbCxms7mEdL7ogaxatSq//vorarVaZs4ePXpEXl4eycnJNGnShIcPH3Lv3r0/tejwIjRr1kwaZQs8fPiQBw8eEB8fT%2BXKlenVqxctWrQo8dnmzZuzdetWunXrJmX3xf05adIkzpw5UyJoFyWrOTk5JCUlSdn9iIgImjRpgr%2B/P4sXL%2BbkyZNWZuVubm44OjrKe8jb2xt7e3v5bOjXr5/MEP9PELnIyEh27drF5MmTmTVrljwvrVYrCXF2drYsQRQofq%2BLMlxLWF4LrVq14tdff2Xu3LmMGjUKe3t7qer6V8/L09OTjRs34unpSXh4OLm5ubRs2ZJDhw4Bvy8Y1KxZk%2BjoaFq0hZ9d4QAAIABJREFUaIG9vT0AHTt2ZM%2BePZw4cUKKFbVp04Z9%2B/YRHR0tv%2BPChQsMHjwYPz8/KlWqRIsWLWjfvj3h4eHSPmH48OFWmfKMjAzmz5/Pjh07cHJyYvv27TITeO3aNe7cucPMmTMxGAzY2dnxySefULNmTQoLCzl%2B/Dgffvgh5cqV4%2BbNm0yYMIGwsDCqVq2Kg4MDycnJ5OTk8OGHH3Ly5Em2bNmCi4uL1bg0b94cjUbD5cuXZabfbDazefNmevTogU6nk1UiYvxbt25NWloax48fx93dXZaNfvfdd5hMJjp37mwzVP%2BHwkb2bLDBBhv%2BRjx9%2BpRRo0YRFxdHUlKSVOYTvW9ihdiyn0ooE74MarWa0NBQbt68KRUYRcDYqlUrjh8/TkZGhixlCw4OJi0tjUePHskyzNq1a2M2m3n%2B/DlJSUkvlZ53dHTE2dmZR48eyX4PKFL3dHd3R6fTcejQIVxcXDAajaSlpREQEMCNGzdITU2le/fubNq0CY1GIwnZ1atXWbduHVWqVKFJkyZUqlQJKCJSV69eZcSIEQwePLjEsdy4cYMBAwbITIhWq0Wj0RAXF4eLiwtqtdqq7M/T05OsrCxSU1NxcnKSRNfBwUGW3MXExGBnZ0eFChWIjY1Fr9fToEEDZs2axYEDB4iKiuLs2bMoFApWr17N3LlzadCgAWq1muPHjzNo0CByc3OtMjmvCr1eT9euXWnZsiWDBw9GoVDQpEkTevToIQNXSwihFkFOOnToQOPGjcnKypKZxy1btsjAfNCgQZKkJScnExwcTHp6OjExMSWEPaBIAOTIkSP07dtX9k3Wr1%2BfkydP8u6779KrVy8iIiIwGo2kpKSgUqlkGVzxbM%2BLFA7Fv87OzqjVapmNqFKlCr169bIyNxcBenGhEktZ/v%2BEPL777rvY29szf/58Vq1axdChQ1m4cCFXrlzhq6%2B%2BIjs7GwcHBzp37ozJZOL06dOSuAUEBBATE0NBQQF2dnY0bdoUX19fdu7cKa0iLKFQKNi9ezedOnXi4MGDeHl5UadOHcqXL8/t27dlZkaU8D59%2BlSOpxBKMRgMkqCLcQ8KCpIiT8WzPULt1sfHh7i4OCuRKGG/kJ2dLa8BPz8/Hj16JMmeKEk1GAyS8Avi9%2BGHHzJr1ix69epF9erVmTFjBsuXL2fgwIHodDo%2B%2B%2BwzRo0aRUBAADdv3mTevHlUrlwZlUolewNv3LjBqVOnmD17Nj/%2B%2BCNt27aVvW0LFizAy8sLKMr0A8ydO5cJEyZgNpulGJFarcbb25sRI0bQtWtXduzYwWeffYbJZCIwMFD2Nfv5%2BZGWlkbv3r1l%2BbIodW/cuDFqtZpjx47RuHFjfv75Z6t5E3iVsLo0QSr4vexX9NFBUSmzg4ODHFvgpYJZL0O1atW4fv26XFASvXomk4ng4GDZKmBnZ8c777xDbGwsmZmZVKlShaNHj9oM1f%2BhsJE9G2ywwYa/Gbdu3aJPnz6kp6eXsFsoDkFeXqRSZwnRfC%2BCerVajUajQa/Xyz6yunXrSvnuW7du4ejoSEBAAGfPnkWj0RAUFMTDhw9RKBQ8f/6cefPmMWXKlBeW0KlUKt59912%2B/PJLGay4u7uTkZHB9OnT%2Bde//oXRaCQ3N1caWJeWMSgOUcooths3bpyVvxYUScdfunSJuLg4WdYqvO4qV64sTcwBKlasyLx58%2BSqd/PmzYmLi8PDw4OEhATc3NxkABYbG2tFQlQqFfPmzWPOnDn88ssv5OfnExISws8//4yHhwdt2rRh%2BPDhdOrUCcCKDJ4/f54VK1bw008/WR272K/o4YuJiZHvubq64u7uzo4dO6Stg6urK3q9Xho/lwZLsqfX61m2bJn0bDx69CgxMTEolUqGDBkiyV5ubq4UfQgLCyM7O5sKFSrw4MGDEkFs5cqVuXfvHoWFhdITT6VSSZK3ceNGevToIf3hWrduTW5uLgsWLODp06esXLnyD1U%2Bi4/Rjz/%2BSIUKFXj8%2BHGpGTnRa6pQKPD29iY2NhY7Ozvq1avH2bNnyc/Px9vbGw8PD6l4WVBQgNFolPsQJbevcjyLFi3i3XffLZWoWkIIrwji4eDgQJUqVdDr9Zw8edJK8Eb0OjZq1IjU1FRu3LiBu7s7oaGhZGRkcOLECQoKCmjQoAEnTpyQn6tbty7nz5%2BX32cwGKhSpQpnzpyhbdu20tOuOET2zVKAyfIZVDzrJjJngvT17t2blJQU7Ozs2Ldvn9WzSRBRJycnMjMzKSgokH3Allk/QT60Wi3du3enY8eO8jvF82vgwIEsW7aMESNGAH/OyqN8%2BfLExMSgUqmoXLkyt27dwsXFhefPn5Ofn4%2Bvr6/s4S1TpgxarZYHDx5YzamlB9/fjdKEa8TCXUFBgZxH%2BPMLHRqNBqPRSFJSEo0aNeLUqVMoFAqbofo/GDayZ4MNNtjwNyEjIwODwcCgQYO4e/cujRo14u7du9IENz093aosqrQgslKlSpQrV67EKrMgWIKUCXU%2BkV1RKBTodDpZMibKlkJDQ%2BXfItsiPNkAGdwuWbJEGm2/7GdE9FZpNBpmz54ty1O1Wq1cwRbfJfwABw4cKFf1BUJDQxk5cqQkQUqlEldXV6kymZ%2BfT3x8PFqtFqPRKIkawBdffMHIkSNJTExk%2BvTpVK9enS%2B%2B%2BILk5GQprgHW5EhYMhQWFrJ8%2BXKWLVsm%2B28SEhIIDAzkzp07rFixgpo1a1K/fn35merVq3P48GE8PDzksYWEhBAVFcUPP/xAYmKiVVlrWFgYly5dwtPTEw8PD6pVq8b3338vCYRarUahUPDWW2%2BxePFi1Go1PXr04PDhw7LEzsHBgVGjRtGnTx9pgD1z5kzatm3Lr7/%2Byrhx42jRogUHDhwAfs9%2BANJY2mQyoVarWbNmDVAUXAtPukOHDjFu3DjOnDmDXq%2BXYjpiDoXwTJMmTcjMzOTs2bMyC2Vvb8%2BiRYuYPHkyT548ITw8nO%2B%2B%2Bw6FQkF2djYJCQmcP3%2BeOXPmoNForASHVCoVTk5OMqtcqVIlevXqRcOGDWnXrh0NGzakcePGzJ07t9Trz2g0MmnSJFauXCmNtUVZqVar5dGjRygUCknUoUi2XszH06dPGTFiBM7OzixYsKBEmeKrwJIoCLJXXKhEpVLxxRdfMGPGDGJjY3Fzc8PV1VV6pPn5%2BbF8%2BXLc3Nxo2LAh%2Bfn50k9QoGHDhpw8eZLCwkIqVKjAkiVLMBqNNGzYsISfpqU4ifBp3Lt3L23atLHaxvLeLk76vvvuO/r06YODgwMdOnRg69atLxwfrVZrla0qDpFdLFeunNVCx4tQvnx5xo0bx/fff8/p06f5/vvvrd4X2eoKFSoQEhLCzJkzOXfuHNOnT%2BfWrVuo1WpcXFzw9PTk8uXL8nN%2Bfn6MHTuW9u3by0WPgQMHMn/%2BfFxcXHjrrbf497//TVRUFGq1Gnt7exQKBV27duWdd95h5cqVcpyHDx/O0KFDefToEVu3bmXp0qVyLCZOnEj//v0leV22bFkJUSnRe/tXMWLECK5evUp2dja3bt2Sc2OZ8RQWO%2BJ1cXxCIEdsu2nTJpuh%2Bj8YNrJngw022PA3YNWqVSxcuJAVK1bIwOSHH36gXbt20v9ryZIlvPXWWy81N58xYwZLly6VBBF%2BNzVPS0uTP%2BS9e/emfv36jB49%2BqXHJQKP0mApuS8Czdq1a1s17dvb26NWq2V2RGSslixZwpQpU0pYMmi1WsaOHUu9evXo27cvAwYM4OzZs7Jc6OnTpxQWFjJ9%2BnTS0tIYO3Ys9vb2JchaVFQU27ZtY82aNdSrV6/EsdeuXZvMzExef/11vvzyS%2BLj42natClnzpzBwcFBntPOnTulaXrVqlWZPn06d%2B/eZdiwYQwbNgytVktgYCDt27fnyJEjGI1GEhISyMvLo3fv3kyZMqWEyToghT2gqCzTUnBEBNXHjx%2BXRu4i0HNwcJDZyRdBp9NhMBjQ6XSMGjWKDz/8EMDqGIKCgiTJ%2BLM/%2B87OzrJHLzAwkMGDB7Ny5Urq1q3LrVu3rHpIg4ODqV%2B/vhTV2bBhgxQcqVWrFqtXr0ar1VKjRg28vLx4//33cXJyonfv3pQrV45ff/0VjUbD/v37uXnzJnXq1EGtVhMTE8P69etZs2YNCoVCkqXOnTtz8eJFWTYpFjEsLRKCg4MZPXo0I0aMQKFQ4Ovri52dHfHx8VJgxMvLC1dXV6pWrUrr1q0ZPny4lQKmEHHR6XQkJCRgNBpltm7ChAkcOnSICxcuWCmvviocHBw4cuQIDg4ONGrUiGfPntG%2BfXv27dsnS6obNmzIN998Q//%2B/WW/YPEeue7du0uRpZYtW7J48WI%2B%2BOADduzYgVarfSERE9ffq1xrAO%2B//z7z5s2jQYMG0vvP39%2BfN954g4cPH5bYt%2BgvtaxIKE4%2BoaRaZ2kQKqRCgMjJyYnx48dLhdCMjAw%2B%2BeQTdu3ahclkwmAwsGnTJi5fvix7aPft20fbtm2JiYkhLS1NClgFBwfj4eEhy67Hj/9/7L15WJRlH/b/mY0ZFgHZFFEzLcUVSdPILQW3XMLcd82ytB53RXNLyy1N08K9EHdTk0XccBfQUnNLUXHJVAQR2QcGhpnfHxzX1QygWe/z63l7jzmPoyPghnu57vser%2B91nt/znABg9T5bLggZjUY8PT0pLCyUMSwHDx6U5/rWW29x6tQpKeE1GAzSrdjb25vc3FyWL18upeheXl7k5%2BeTm5tL5cqVWblyJQ8fPiQkJITc3Fx5n8QC2po1a5gwYQJ5eXllCvOQkBAyMzP57rvvGD9%2BPEuXLpWfxZcvX6ZRo0a4ubmxaNEimjdvjtlspl27dnh7e8vC%2Bffff6d79%2B5UrlyZAwcO/OlzYcP/vbAVezbYYIMN/zAiIyOZPXs2kyZNklEA%2Bfn5zJs3j88//5yMjAzy8vLo0KEDsbGxUu6k0%2BkwGAxlVsednZ2lHKq8j3QnJycKCgqsJslQslKvVqutJGRihb1ly5bSbv158Pb2tio0oaxDYGkmwxJTpkwhJyeHBg0a8MUXXzB8%2BHAWLlzI4MGD0Wq1bN68WfaWKBQKXn31VXbv3k1GRoZVsda6dWtSU1OfWezVrVsXjUaDk5MTCQkJQAlbGBMTQ9WqVYE/ir2EhATmzZtHUVERLVq0YNasWVSvXl3uSxjJCOv9mzdvcvbsWZRKJQsWLGDq1KnMmDEDJycnKeX09fWlYsWKhISEsGbNGu7evSvHSPQrDR48WGZriQBjT09PyXSJ0GPRr2SZp/frr7%2Byb98%2B1qxZw40bNwDrYq9hw4YUFhby2WefUbt2bbKyshg1ahQAW7duJSkpidmzZ9O7d2969OiByWRi3rx5JCYm4ufnx9q1a7l8%2BTKfffYZeXl5ZUyClEqlfI4Ei%2BXl5UVqaiqDBg3i8OHDFBUVyV5JEbNQGiqVis2bN/Paa69Z/Tw/P59Vq1Zx7tw5KlSowOPHj8nKypK9q2Iy7eDgQLdu3dixYwejR4%2BWEsgPPvhAyj7LC5ZXqVQ0atQIjUYjTSiioqLo2rWrlT2/JZ4Vq/C8aZVOp0On00kJcmZmJq6urrz88ssAMgBdMNZiUUD8rLCwUEoeRT%2BcKJzE%2BTRr1oy7d%2B8SFxdHkyZNZM8ulJi/iPdV/EwUWeUVYC%2BKGTNmsGDBAqve4mexeAqFQham4ngTJ05ky5YtMm5ESF2FfFzsq0qVKjKPr2LFiuTm5ko566ZNmzh8%2BLCMAxHXNnjwYDZu3EhAQABPnjwhMTGRl156iTFjxtC1a1e5sCKkqaV7cJ9X7JXOm/xv4s%2Bepb8DR0dHVCoV2dnZNG3alIcPHzJz5kzOnDnDpk2bWLJkCV27dgXg9OnTDBs2jFGjRjFu3Lj/6nnY8M/CFqpugw022PAPIzw8nOnTp9O7d28A%2BvXrx8qVK5k4cSKNGjWSuVKHDh2S9tfCOc3Ozo433niDCxcuSPbMMn%2BrPJRerVepVCiVSpRKpVztF5OzihUrkp2dbTXJhD/kZ2JyJn6/dKFXGkIGVB4UCoVkauLi4qhZsyZLlixBq9Xi5eXF1q1byc/PZ8WKFYwdO5bQ0FAmT57Mzp07GThwIHZ2dmRmZuLk5GRVfDzrPJRKpVWvoWAcLM9HTBZVKhWFhYXk5uayceNGNBoNv/32G4mJiTKE%2BvDhw9jZ2WFnZ4e7uztarZYVK1ZgNptZsWKFVU%2Bh2WwmMzOTrKws7t27Z3VcIZHdtGlTmRgLy/NdtWoVEydOZPv27ezdu5dVq1bh7e1Namoqubm5NGjQoNyYDUAyCnPnzsXPz8/KbGfYsGFy0nr48GEOHTok%2B6ugpABp3ry5DGEWz46YgItnU4yp2CaOcfjwYRQKhVUvV3mFnnBr9PT0pFu3bhQVFREWFoa3tzc3btxgzZo1QIl76s2bN60KC2FJb%2BluqtVq%2Beqrr1i9ejX9%2BvWTx/Hy8uLBgwe0bt2akydPolarqVevHleuXOG1116jQ4cO7N%2B/nx07dnDkyBGgJA9z165dKBQKPv30U65fv25VzIgipPQEvXQWW0FBgZTLQkn/l16vl%2BYpZrOZ4OBg3n//feLi4qTDq2DGCgsLcXR0pKCgQMpthRRPHEPID%2BvVq1dGEVDeOVrK%2BEr3HZbXG%2Bbh4YFGo5GsO2DlRCuuqzxotVocHR2Ji4vDz89PFtYdOnTg66%2B/toqPyMvLk9EeAkIZoFKp5PN15swZioqKGDx4sHznLHvXtmzZwrx588jKypJy3/v37zNt2jQZhWBpsNKuXTspLxbS3v379%2BPk5ITRaOTQoUPyvJo3b87FixcxGAzUqlWLR48eyfNt06YNCQkJf/r5/Cz8Nwo9EdUi2NBatWpJmb6DgwOpqalMnDhRuiKLQm/RokWEhYUByEUhG/69UP75r9hggw022PDfxN27d63yv0aMGMGwYcMoKioiISEBg8Eg/6G3ZHCKi4spKCggLi6uXBOTZ8Gy6HBwcMDNzY2ioiKrQk8cT0ymhNGDVquVf2s5IRLMQ2mTFCjfYMWyb0n8jugZcXR05OnTp%2BTl5VGvXj2aNWvG%2B%2B%2B/T3R0NAqFgpiYGMxmM35%2BfowcOVIWRJbnXcMinBpKJmyBgYHyP7PZLF1NAwMDqVevHnq9no4dOxIYGEjr1q3Jy8tj4cKFFBQU4OrqioeHBzdv3mTHjh18//33HD9%2BHLPZTK9evdiwYQOXLl3iypUrnD9/noSEBI4dO8bRo0fx8fHByckJBwcHVqxYwYoVK4CSCfD3339fxvzilVdekd9bBltDSR%2BduAeffvopOTk5uLq6SlfNd999Vxa9gv19Fuzs7Fi6dCmvvvqqVbFnZ2dHpUqVUKlU5OTkoNfrUavVuLi4WIW%2Bm81mcnNzrYoXhUKBv78/s2bNYuPGjfTt2xeNRiPvq7gGlUplZQ8vHByFhBagb9%2B%2B8usHDx6QkpKC0Wjk7NmzVszC9evXMRqN8t6bTCYZn2EymeT4LV%2B%2BnLfffhs7OzurPkBRXAvm2mw2c/XqVUwmEz///LNcZDl%2B/Dg%2BPj7SHGjRokX069eP69evlxlb8d6ULnIyMzPL5OLpdDo8PDzw8vLC29ub8PBwLly4wIULF3j33Xd5/PgxL7/8cpniRUhTBTt6//59ACvJqiUsnzPx7lm%2BM3Z2dmi1WhYvXoxCoeDChQsMHToUKFkcGDdunDy%2Bg4ODLBy8vLwICQnBy8sLjUaDs7OzXLiCEjOlrVu3olAoqFatmtXngcFgIDMzk%2BTkZKvPjm3btsmIEIAhQ4ZItvLNN9%2BU%2BxDXIa5NpVLxwQcfSPVAYWEhCoWCkydPysUJg8HArVu32LZtG/PmzZMFZadOnaxYwOjoaCIiIti7dy8qlUrGnVSpUoXvv/%2BeFStWUFRUxMaNG3F3d6dixYqcO3dOPm%2B3b99Gr9fLPlrRH9usWTMqVKggsxzF197e3jRr1uyZ24VRTOPGjaVk2NPTE51Oh0qlwt3dnb59%2B/LGG29QoUIFmjZtSp06ddBqtYwfP546derg4eHBwoULgZLP7KVLl8rPk3PnztGkSROUSiVt27aVBfC0adO4d%2B8eLVq0QKlUWv0bYMO/EzYZpw022GDDP4w2bdqwYsUK/Pz8ymxLT08nLy%2BP5ORkJkyYwPbt24ESq3yj0Yhareb777/H3t6e3Nxcq4lk6Xwz0U8nenHc3d3p1KkTV69e5erVqy9k3/0sKZH4%2BZEjRwgKCrLqbyoNESdR3n7UajW9evXinXfekZPbTZs20bhxY6BEalmhQgXS09OJj48nIyODXr16ceHCBV577TUiIyOpVq0aW7ZsYe7cuSxatIjg4GD27NljdZypU6dKExghh83Ly5Pfi%2BJHoVDw8ssvk5OTI3thdDodlStXlmYMglERKC/T7%2BLFi9StW1dOlKZOnYrZbCYhIYEWLVoAfxijREdHSzv58ePH4%2BbmxqZNm3B0dOTs2bNUq1aN33//XUofa9euzc2bN1EoFMTGxkpDmTVr1pCSkiKLmNWrV0uXyWHDhlFcXEzdunVxdHSkbt26hIWFyVzCZyEyMpJp06ZhMpkYMmQI9%2B/f59atWwwdOtQq/uB5UCgULFiwQI4DlEjwSkd5iAn91q1bGT58OIWFhXh6epKamvpMpkipVDJ69GjWr19vxRwOHz6crVu3yjHOysqiZs2apKenk5WVRa9evThy5AijR49mw4YNKBQKafLj4%2BMjbe4tn13LmAiVSoVGo6G4uPiFTFuEQY%2BQp4pnd/Xq1Tx%2B/Jh169YBMH/%2BfCIiIqhYsSILFy5k6NChGAwGmjZtyujRo5k5cyaPHz%2BmcePGrFy5kj59%2BnD37l3q1avHvHnzePz4MaNHj%2BbNN99k8uTJdO/eHXd3d7Zt20bHjh1fmC2yDLKvUqUK1atXl/mAQtYqFmtq167N1atXrf7WMvhbqVRSpUoVsrOzrT4jyutF1mg0mEwmKlSo8Kds/bMglAItW7aU7sZCnl6zZk0ePHhQrvyycuXK8hlMTk7G0dGR4cOHW/3O6tWr6d%2B/v5V7K8D27dvZsGGDfM%2BghKkXCznNmzenuLiYzZs3M3ToUPm1cAMub7t4niMjI%2BnWrZvs05w8eTJffPEFZrOZQ4cO0adPH6pWrcrt27dlbIabmxuPHz9GqVQSExNDUFAQWq2WmJgYunXrJmX9wllVyM3Lg3h3bfj3wlbs2WCDDTb8w5g5cybXrl2jf//%2BdO/enc8//5w7d%2B7I7ffu3SMzMxOFQoFWq0Wn01GhQgUAPDw8uHv3Lk%2BfPpVSQ4HShdmePXvo1asXY8eOZenSpWUs9O3s7KhZs2a5TMXzIOScXl5eDBgwgOXLl/Ptt9/y8ccf/63xEE59UOIS2a1bN7ntzp079OzZk/z8fBkC//jxYxQKhZW9OPyx2i%2BcFsVE74033iA2NpZmzZrRo0ePcs8hNjZWTuYNBgPx8fFUrVoVpVLJ/fv3ady4MW%2B%2B%2BSYAn3zyidXftmvXrsz%2BHj58iKenJ3Z2duj1elnYlHZQhJJAcpG51rBhQ3x9fSVT5eHhweeff24lzxS9hwaDQfaVNWnShLNnzxIeHs6AAQNeeFKvUChYt24dx48flxET1apVIzAwkMTERBYvXiwLLSH/XbNmDZs2bSIpKYmBAweybNkyoOQ%2BLlmyhOXLl3P16lXUajVqtZq9e/dSUFDAp59%2BahVWXRqiwHB0dKSwsJCioiJpjw9/sGbXrl2jXr16wB/9ghqNRkpeW7VqRXx8PM7OzlYFg3AMhZICpHfv3sydO1cuDOTk5DB//nxq1arF3bt3KS4upnXr1tSrV481a9Ywffp0FixYgFKpxNfXl%2B%2B//56wsDBWrVolx1u8G%2B7u7nTs2JGIiAjy8/NlASJ6KP39/alatSqFhYU8evSIN998kzFjxtCzZ0/CwsIkw1beGBUWFlo5oPr6%2BpKamkpGRsb/L6Hrz4Nlz65CoaBmzZrcvn1bbrdcgLK3t2f%2B/Pl06NCBU6dOsXHjRk6fPi3NVkwmE1OnTmXp0qUolUoKCgrQarUUFRWVa1AlFmKio6Pp3r277GU1GAyy2IMS5jY2NpYOHTowYMAAjEYjP/74I9OmTWPJkiWyqJ05c6ZkmmfPno2Xl5eU2woIAyVLCKOW06dPU6FCBSt5q2BEBRuu1WqlpFKpVOLs7Ezr1q05dOiQjKPJy8ujsLBQsrD79%2B9n0qRJ3LhxA41GQ6VKlWTPb8uWLTlz5swzey11Oh2NGjWS0t4qVarIxZPSTqtmsxkXFxdpRhQQEMDAgQNl/7AN/17Yij0bbLDBhn8YKSkptG3bFo1Gw%2BLFixk/fvxfykJSKpXSue15mWDCtKK0a1/VqlVRqVT8/vvvZYqCQYMG8fvvv5OUlPSn/XjloV27dly4cAF/f3/p7FbaCMNsNktTGTHpcHFxoUaNGvTo0YP%2B/fvL39%2B9ezfh4eFyhT0hIYFff/1VFkOWSEhIkC53Dx48QK/Xo1KpaN68OYMHD%2Batt94CYOnSpVKOuWfPHgYNGmQ1iWvevDmNGzfmyy%2B/xMXFpYxhw4ugTp060iymXbt2pKSk/KV7rNVqMZlMODs7S9mecOETDoelM9EspV7FxcUsX75c7m/SpEl0796d1q1bAyV9XaGhoWg0mnL7M8X%2B%2BvbtS2RkJGazmSlTpjBnzhxOnjxJ9%2B7d%2BfrrrwkICKBRo0Y0adKEy5cvU61aNcxmM3fv3rUyDLly5Qr169eX8tP/E8TExNClSxe5uNG7d2927dolz/vnn3/m1q1bbNmyhZiYGPl3Dg4O5OfnYzabWbdunRwLgSdPntCqVSureAQfHx8UCgUPHjwoY0Yk%2BiUti6vSjKXlAoy3tzeDBg1i%2BPDh%2BPn5UVRUhEqlQqFQULduXX799VfMZrOVKY9g5f38/Lh06RLVq1cnJSXlLxmDKBQKKlasKJ1C3dzcePr0KZ06deLmzZtWC00C4j01mUzyGbOzs8PJyYkjqACbAAAgAElEQVSMjIwyjqeVK1cmJCSEt99%2BW2ZBms1mPDw8JEtVv359goODUavVnDhxgpMnT%2BLs7IzZbJbS2r8LIf8UBZ/oxxR45513iIyMpHv37uh0Ovbs2cOECRNYtmwZOp2O7OxsyfKXhpCXKhQKpk6dSnBwMKdPn8bLy0vGOfzVLLv/m%2BHl5UVaWhq%2Bvr7cu3eP9evXl/t5a8O/B7ZizwYbbLDhf4DffvuNOXPmcObMmTLbWrRowbRp06hVq5acoKlUKoYNG8Zvv/3GN998Q8uWLXn69ClvvvmmlfPl3/1I37ZtG4MGDaJp06acP39erhQ3aNCA3bt3S8MKyxBncUwoMc0QEQzC8OLy5ctlzkmE9EZGRvLrr78yffp0zGYzLVq0oEOHDixcuJCwsDDu3LnDvXv32LBhA4GBgbRs2ZLk5GTWrVvHu%2B%2B%2BS8OGDeU%2By5MgpaSkMGjQIIqLi1m1ahW%2Bvr7o9XqOHz9OcnIymzZt4smTJ9jb2%2BPr60u1atXo06cPjx49YuLEiXh5eaHT6ejevTuXLl3i5MmTfPfdd1KC%2BWewLPbKg2WGlshzs4QolCzHT%2BQHiqL1WRATs0uXLjFmzBjc3NzKZP9FREQQEhKCQqFg8ODBjBgxQvamPXz4kLCwMDZt2oRarZaMXkREBB07diQyMlIypHFxcQQFBbFt2zb69OmDp6enlAyK/jmz2UxAQADe3t7s27ePdevWcf36ddavX49Go5GSSW9vb6CEKUlLS5MT7ICAAB49emTFGFnCMv7D3d2dkJAQdu7cSUpKCo8ePUKr1aLX62WeXX5%2BPvb29laFuzASMhqNVKxYEZPJRHZ2Nm3atAHg%2BPHjzJgxg3nz5sn7Ib4XhTf8EQ7%2BPGfLV199laSkJPkcLFmyhFdffZWbN2/SvXt3GX8SFhZGQEAADRo0wM7OTp63n58fCQkJZaIKtFotRqPRqvDQaDQ0b96ctLQ06dIqni1hLpSfny%2BfNctezD9zFTWZTLLo3LNnj2RbhQR73Lhx/Pzzz5w4ccLKLVSwnt7e3ty8eZOTJ0/KSIWcnJznysvt7Oyea/r0LIiC1fKeu7q6SoMnEcHh6uoKIDPvMjMzycvLw8PDA61Wi0KhkPLfY8eO4eDgIN%2BhH374AbPZzFdffcXnn39Obm4umZmZqFQq2rdvL41dnJ2dycrKkuydGHshGRaFq3BPFr2HRqPRSqrcuXNnDhw4QFBQEF26dOGzzz4jKysLBwcH3nvvPQ4ePEhYWBhdu3aV8tgnT57I8HUxhmq1msmTJ0u5pugHTE9PZ8iQIVy9epVNmzb9pfG24f8u2Nw4bbDBBhv%2BB6hRowZhYWGkpqbKCeWyZcuYMGEC8fHxdO3alTlz5jBz5kxZPF27dg2VSsVHH33Ehx9%2BKN0JARlLcOvWrb8l4xo5ciTFxcUyT83d3Z2MjAwZXn3r1q1y/05MCC2z9kwmk5zMuru78%2BTJE7lNSNDu379PcHAwBw8e5OjRo5w8eZILFy7g4eFBnz595L7t7Ow4efIkBw4coKioCDs7O2JiYoiJiZHFhGDvXF1diYyM5NatWyQkJFClShXWrl3Lzp07OXr0KNu2bUOv1xMREcH169c5ePAgOTk5eHl5YTAY6N%2B/PzqdDrPZzObNmxkwYAArVqygS5cuqFQqPvnkE7777rsysQB/ByKYHODHH3/k0KFDHD58mLi4OJRKJa6urnz88cd06tRJytGKi4s5derUM9nFwsJCDh8%2BTFBQEAsXLmT79u3ExsZSrVo12e8J8OjRI9lvZzabZS6fgI%2BPDzNmzGDTpk14eHjw%2BPFjaQohjlO6EBCW7unp6ZJFLioqkr2cEyZM4JNPPqG4uJhq1arRrFkzevbsyciRI3n69CkFBQU8evTISoYo/h8fHy8nwCLw2bL3y/LZy87Olj2G4hzFxF0YxVStWlUWveLnN27cQKlUUrduXStjlJycHDZs2EDVqlVp3769NPgAZHEqCqfCwkKmTZvG7Nmz%2BfDDD2nTpo18li0hilaVSkW3bt1kD624H8HBwaxevZratWsDJcXv2LFj%2Beqrrxg7dizff/%2B9HAvLokelUmEwGKQ7p%2BgzvHLlipUCQIxrYWGhHBPBiMGLFXqWRYdg/MaMGUNOTg4Gg4HVq1eTnJwsmWjL42ZkZLBz504qVKhA165dWbduHX5%2BfkREREhDHShZaBozZgx79%2B7l5s2bKJVKPv/8c77//ntu3LiBQqGgSZMmpKSkyAUDKDFEEfj5559Rq9UcOHBASmOTk5OlIZGDgwMmk4mcnBz5M1HQhYeHs2bNGr766ivatm1rNQYrV67k6NGjjBo1CqVSSc%2BePbl48SLJycnExcXRtGlTIiMjMRgMVjmGRqMRPz8/Jk2aRKdOnXBxcUGv19OyZUtat27N2bNnefnll9m2bRubN29m0KBBkg03m80kJSXx9OlTXn75ZSpVqkRAQACBgYEyFsLT05O6desCcOvWLUaPHk1ubi5169alQ4cOLFmyRPb1WT4PIvpD9KNu3ryZTp06cefOnef29Nrw74CN2bPBBhts%2BB8gIiKCNWvW8OTJk79tzV0aderUkVbiAuVN3Pz9/cnMzJRB1OVBrVZLy24Be3t7lEolnTp1Ys%2BePbJvDsrP1bK3t6dhw4ayX8QSYWFhvPnmm5w6dYr3339f7sNsNlOlShVUKhW5ubnk5ubi4uJCTk4OBQUF0kShuLhYSt20Wi35%2Bfm4uLhQWFhI3759CQ8Pp1KlSvzwww9s2bKF9evX4%2BjoyP79%2B3F3d6dFixY8ffoUOzs7Ll26xLhx47hz5w4uLi6cPXtWslcajYZLly7h7%2B/PkCFDuHjxIuHh4S90L57H7Pn7%2B8tizzIPr3HjxhiNRoxGI05OTtSqVYuLFy8CJZP5Y8eOWRUqABcuXGDPnj3s3buXvLw8K9mui4sL2dnZmM1mJk2ahEajYcmSJbJIGD58uDRNKQ1fX18cHR3Jzc3F3t6e6OhogoKCrJi0mTNnsnjxYho3biyzBsePH8/XX39NYWEhCxYsYNq0aRw7doxOnTrh5eXF1KlTCQoKAkom41OmTGHLli107dqVvXv30qFDB4xGo9Wze%2BPGDXx9fWV4/JAhQ7h06RJHjx6V52vJmAkIMx6xnz59%2BpQxlpk6dSppaWmEhoai0%2BmoW7cucXFxnD59mnbt2mE2m2nbti2vv/46hw8ffuY9Lw/PyvRzc3PjyZMnqNVqXF1dJYuZkpLC5MmTWbJkCQkJCbi5uVGnTh35fokFneeZJlnCclFBQEhCAwMDZbTEfwvCRXTgwIE0btyYwsJC/P39uXLlitUigZ%2BfH4mJifj6%2BtKtWzeGDBkix/23335j/fr1VvdWMH/i88bR0ZEKFSqQlpYmQ8vr1KlDq1ataNiwIc2aNcPR0VFmYkZHR1OtWjXgjzxN8f2TJ09o0aIFCQkJ8j0E6NKlC5988gmdO3d%2B5vW%2B/vrr5OXl8f7777Nu3boXWmgTTF3pSJzSbPCLhMz/HZSW9UNJn%2BupU6dwdHSkRo0atG7dmgMHDpCeno7JZJIRGTb8O2GLXrDBBhts%2BB%2Bgffv2tG/fXq7CAtKqXkAwKYKZ%2BDPcuHGjzGSvvEnhhQsXnlvoAVZMkED//v0ZO3YsV65cYdy4cTg7O6NUKnFzc5OTHJ1OR8OGDalevToeHh7lFnoKhUIWLJayULVaTe/evalZsyaxsbGcPn2aK1euEBcXx6VLl3BwcGDz5s0cPXqUgIAAAgICmDdvHvHx8dJh7t1332Xq1KnS3n758uWMHz8eR0dHXFxcpNW6KIDE%2BJw%2BfZqPP/5YMixr167FZDJhNBqJiIiQkrVffvmFiIgI%2Bd/z8KL9faX/Rox7dHQ0AwcOlNvMZjPfffcdUMLOrVq1isDAQPr3788PP/wgzU1EILVGo2H37t2yd%2B/bb79lwYIFVhNIR0dHq%2BspfU25ubkoFAqGDx8ureQtJ35LlixBqVRy6dIliouLKSoqYtWqVVLeJ8bz559/pkaNGtStW5fFixdLebK3tzdZWVmS0XhW8dGsWTPMZjN5eXmkp6ezbNkyjh07ZjXGpdlnYfphNptl1Elpcx0oKbbHjh0r%2B7XEMzFr1izS09NxdHQkKyuLo0ePUqVKFcnuDRkypMy%2BLHu%2BRBC6ZeQEIBcqhBwxLS2N1NRUKeVdvHix7EXs0KED8AcrlpSU9EzWTWQcWqK8WAZREP%2B3Cz0okaeLZzY%2BPh6tViufl%2BDgYBko/8MPP7Bu3TqMRiMLFizg4cOHmEwmOnbsyIABA6wKPShhIS0XnvLy8nj8%2BDEODg6o1WoMBgOXL18mNDSUjz76iDfeeIMDBw78H13Lw4cPadSo0XN/p3379hQXF7N%2B/XpatGhhFXHxLIgIk9IoLfv9q4WeYET/DOW5x546dQoAvV6P0Whkw4YNfPzxx%2Bj1eukYasO/FzYZpw022GDD/wCOjo5MmDABKOl3MRqNvPPOO8TExMh/9MX3Xbp0oXfv3rRv3166cpaH69evc/z4cT766COKiopYvHgxkydPZvHixc/tISoPOp2OevXqcfHiRTnRDAkJ4dKlSyxevJjExERpRPH06VPJKhQUFHDlyhVq1qzJ%2BPHj2bhxI2fPnrXat9ls5tNPP6VWrVpWEQmFhYWYTCauXLnyp%2BcXHx9PaGgow4YNk32GY8aMYcuWLcycOZNXX32VZs2ayaysgoICQkJCWLlyJSEhITg4OJCTk8NLL70ElEyKnZ2dgRLHuiNHjshJ9YoVK3B3dycmJoaioiKZm6dQKAgODqZdu3blFnYTJkwok1ElJtiWZiiffPKJzC0U8jiz2cyECROsJvYmk4ktW7Zw7do1aRYjCtZmzZpx/vx5tm7dSq9evYA/Fgvat29vtW9L7Nq1y8rRVFwTQKVKlUhJScFsNhMZGUlGRgZarRZ3d3dSU1Nl7IBCoaBy5cqYzWaSk5MpKiqSUr/IyEgUCgVLliyhV69e7Ny5k6pVq9K1a1d69uxJbm4uTk5OzJo1C6PRyLx582QPo%2BW1l2dEpFAocHd3Jy0tjcqVKzNhwgSmTJkiDU7EexAWFsaECRPo06dPucYmubm5ZQoycQ8mTZokYxGEM6JOp2PlypXlFkuWYyyKU%2BES%2Biy89dZb8jk5ePCgVXi9pQS6vKgCS7yoSYgw5PlvQURVQEmsjHC2/frrr%2BWxHB0dMZvN8h0XDLazszNarVY62hYUFJRhI0szUV5eXuj1esxms3T6FD1vCoVCBtePHTv2ha9BmNhYwt3dnYcPH%2BLj4/PMv%2BvWrRsHDhxAr9fToUMH4uPjpbGNnZ2dlYxdyJrFOb/11lv88ssvVs%2BH6C%2BFPwyFLAPuhaOnGI82bdpw4sQJKlasaOU8a8mEvvfee4SHh6PT6WQu5pkzZ9BqtZJtFWZBNWvWpHLlygQFBbF582aKi4vLXSCx4d8FW7Fngw022PA/Ro8ePUhMTOS7774jJSWF9PR0kpOTCQ0NJScnh0WLFqFQKAgNDcXb25uqVavSpUsXrl27xpw5cygsLGTDhg389NNPDBw4kKFDh8qVWtHXN3/%2BfKZMmfLCJi7%2B/v6cO3fOSpa0YMECwsPD5aTXEqX3eefOHf7zn/9Y/UypVOLi4kJGRgYXL17k4sWLVjI3f39/9u3bJwuf50FM0MVxnz59SuPGjSWLNWDAABYsWCAnja%2B88gr5%2Bfmkp6ej1%2BvR6/UUFxcTEBAgt%2B/atYtatWoRHh5OixYt0Gg0TJkyRTI469at49ChQ%2BzcudPqXEpfJ5RkbAUHB1OhQgV5/wACAwOBkuJSyLQsoxicnJzkyr9loS2KdSGpsrOzo06dOjRs2JDt27cze/Zs3nnnHauiXjCESqUSBwcHiouLrYodIZktPd7ieqtUqUJKSgpKpZLdu3cTGBhIaGgorVq1AkpknsHBwWi1Wvr370%2BFChXo3Lkzjo6O7Nq1iw8//FC6V7Zq1YqwsDDc3d25cuUKXl5ebNy4sUwB6u3tTUhIiFWIOvwhafPx8eHbb78lLCyMqKgoKeVNS0tjypQpwB%2BMSGhoaJn7Uh5effVVfv75Zynrs8SlS5fk/jw9PenSpYtkJcG6f0307ZUnk3seTpw4IeNPhGSzuLiYrKwsq8Ls70YqCBfX56FWrVrcuXNHSorr1asnC4sFCxZw%2BfJlmcs4depUhg0bBpQwbMuWLZMGHsePH%2Bf48eNW%2BxZS2qioKHkNTk5O2Nvb8/vvvwN/FHRFRUVlGK3SY/n48eMy5282m9Hr9TI2YPTo0TJM/EWllVlZWYSHh0t33vbt2/PNN9/QqFEjFAoFH3zwgVW%2BntFoZPXq1bz99tucPHkSX19fPD095cJE%2B/btSUpKIiUlRb7/gtETRRfAyZMnrVxPxXYREC%2Buz97eHr1eb3U9np6eAGUyK41GI3Xq1OHGjRv88ssvqNVqWfw1aNCArKwsrl27BvwhNVar1SQnJ5OamsqDBw%2B4d%2B8ePXv2lJ%2BRNvx7YevZs8EGG2z4H8FgMHD9%2BnXCwsLKFE9/BoVCQb169QgKCiI8PJzMzEz8/f3ZuHEjdnZ2Vr1wUGII89tvv1n1/uTk5Ejp2Ivkc4meOuFi99/%2B50PkZfn5%2BbFjx44y2y1D1Pv06UPfvn354osviIqK4pNPPqF9%2B/bs3r2bY8eOAfDee%2B8RHx%2BPv78/jo6OMhRaZLJVr16dBw8eULduXfLz87lz5w7e3t5kZ2eTl5dHUFAQoaGhhIeHc%2BXKFQ4cOEBwcDBNmzZ95jWU5wxaOuC9NAwGA0%2BePOHu3btkZmZy9uxZjEajzAvU6/UUFBTIyaBKpcLJyQmtVoudnR0pKSlERkZKe/muXbsCWPUq%2Bfv7S%2BbVbDbj7u7%2Bp72iIji8Vq1apKen8%2BDBAzp37sz06dPx8PCQ8i7LqAbBNAoZm3im/P39cXZ25uLFi1JCWxouLi64u7uTmZlpVVD9VZT3LItIhC5duki5tKWd/KJFi1i5ciX%2B/v7UrVuX%2BPh4AgMDyc/PZ/Xq1Xz44YcEBwdLdhdKGK3KlStLmWLlypVJTU3lpZde4t69ewD07dtXFghr1qyRLGhISAju7u4MGTKEb775hhUrVrB27Vratm2LQqGQ/bCWizPi67feeovjx4/LCbyQlIocyi%2B//PJP380qVarw7rvvsnbtWoKCgjh79ixpaWm4ubmhUqlkES3g5uZGfn6%2BXDxxdHSU1ykC1EVe3tKlSykuLiY6OlpKbb28vKhataocK41GQ7t27Xjy5Ak3b96UbJdSqUStVlstSri7u1OlShUrxt/d3R2DwUBeXh7dunVj//79jBgxgp07d5Kenk6VKlVITk6Wv29nZ2eVo2lnZyeLLbHgUrVqVSv33uzsbHr16kV%2Bfj6FhYXY29vz9ddfo1aruXr1Kps3byYvL4%2BBAwcSGhpapqDW6XSSySwPDg4O2Nvby2iV0lCr1eh0Olkglo71AGu2VzD0lt%2B/KNvr4%2BPDo0eP5DNmMplQKpVcvHixjDrBhn8fbMyeDTbYYMM/DIPBwOLFi9m1axdFRUXSetuyad/SIMXOzo569epx5coVnJycUKlUZGVlcfXqVW7cuIFKpeLtt9/mtddek0WS6F8SE19xjK5duxIVFUVSUpKc/NSvXx8ocQksT%2BonYOmmp1Kp6NixIwcOHLCazJQ3OdVqtVStWpWUlJRyGYZGjRrRq1cvjh07JnuVypNG5ufnM3jwYFQqFXl5ecyYMUMeu3Xr1qxevZru3bsDJX2J165dY8iQIRQUFHD58mW5eu7u7s7EiRMZOHAg58%2BfZ9asWdy7dw8nJyf0er3VdoDly5ej1%2BtxdnYmISGBhIQEUlJS8PT0tJpg5eXl0bJlS6vMPpHp9/7771uxAgCpqamMGzeOX375Rf7M1dWVMWPG0LBhQ2JiYti9ezdGoxEHBwe6du1KXFwcjx49Qq/Xk52djYeHh5S%2BivEqT1Ja%2BmeWkq9noVKlShw5coSpU6ei0WhITU0lISGBJ0%2Be4OHhIWWM27dvJywsDI1Gw9ChQzl48KDsjQT44IMPeOmll4iNjZWyNEsGUjwvWVlZZQpBV1dX1Gq1lZzREo6OjhQUFFhNaktPrhUKBZmZmSgUCqs%2Brvj4eOm8mJiYyIABA/Dz88NkMjF48GCZyffhhx8CcObMGerXr8%2BjR4/IyMjg4cOHPH78WF5LXl6ejHbw8vLi8ePHHD58WD4jTk5O1KxZk6SkJBo0aICXl5c83wcPHliNh3BB1Wg0svAR4yKYM1Fgmc1mNmzYgE6no0KFCvK98/DwKDNuQrpnMpmIiIjAaDTy008/ycL/WUV26Z/n5eVJZsgSNWrU4MSJEwBcvnwZpVJJmzZtpETy/PnzspexSZMm8p3Izc3liy%2B%2BwNvbW0pCBdLT08sURGLRQqvVMmbMGA4fPkzNmjXp1KkTW7duZfjw4cybN0%2BOfelMRcCqX1qv1%2BPh4SFNegCcnZ354YcfWLJkCfv27SMzM5O%2BfftK9vDtt9/G2dmZr7/%2BGrPZTO3atalRowa3bt3izp07FBQUlPsuCoMjoTAoDcEQG41Gq96%2B0oUeWEt3Sxd2fyX7r/SY63Q6hg0bZiv0/h%2BBjdmzwQYbbPiHMX36dC5dusScOXNo3Lgxfn5%2BABw6dEi6/8Ef4cdqtZqPP/6YVatWodVqZSbU3/n4tpx8KBQKmjZtSk5ODklJSXKy6eLi8tyw9mdBqVSyaNEiJk%2BeLHtoHB0d0ev15OfnU7NmTVq1asWGDRuszr127drcvXsXZ2dncnNz8fb2pl%2B/fjLzCrCSQgrk5eVhMBhkQevg4IDBYMDZ2ZnMzExq1KiBRqMhNzeXtLQ0AgICCA0NlUYGkZGRsr%2BoQ4cOdOvWzWr/e/fulb1EwlhCwN/fn3HjxtG7d2%2Bys7PLZPoJbNy4kQ0bNqBUKmnXrh0jR46UxeDo0aM5ceIEGo2mXCMNS1gWctHR0ezdu5fIyEgr9kKhUNCjRw/27Nnzws%2BGQqGQEsLSKCwsJCYmhvnz5%2BPv78%2BjR49IS0tj1apVkhUROHnypLShnzx5spSCRkZGMmXKFAIDAzl69Chms1lmfi1atIjZs2eTn5%2BPo6MjmzZtom7durz77rskJSVRWFjI3r17GTVqFPfv38fBwQFHR0eOHTtGo0aNMJlMkmW2fB/8/PxYt24db731Fmaz2cqJ8Xk4f/48ERERxMbGUlRUJCfalrJMrVYrCwhxLcLUovS9epZjpr29PZMmTWLNmjWkpqbKn1eqVEkW8WLC7%2BLiQm5uLsXFxeU6az4PlgxneZ8XpSMULPH%2B%2B%2B%2BTkpLC3r170Wg0%2BPj4EBQUxNixY2ncuLHM6GvatClHjhyRYyQWU0qPd/Xq1bl8%2BTK3b9%2BmVatWnD9/HrPZjIuLCyqVCr1eT0ZGBnv37qVChQps27aN1atXW41b6fNXq9UolUo%2B/fRT5s6da3WtO3fupHfv3jg7O6NWq0lISHjuWLVq1YrQ0NBnGrKId2Hx4sVs2bKF6tWro1KpaNmyJQaDgYULF0qJNpT0Xs6aNYusrCy0Wi27du1Cp9Px8OFDvL29OXXqlHSFVavVtGzZkvT0dF555RXatm3L5MmTcXV1JTU1FS8vLynBdnZ2xtvbG4VCQXJyMsnJybi7u%2BPr68vrr7%2BOTqfDxcWFhw8f8s0338h4idzcXKpWrUp%2Bfj4ZGRmYzWYaNmzIvXv3eOmll/jhhx%2BeOz42/LthY/ZssMEGG/5hHDhwgPDwcBo0aACUMCgGg4H79%2B/j5OREfn6%2BZO8AmUsn2COdTkdkZKSUDAq2pDyUniB9%2Bumn3L59m%2B3bt2M2mxk1ahSJiYl8%2BeWXUj721VdfWUlAK1WqJCelz4NgGVQqFQsXLiQ3N1dGJgQEBMj8qy1btljZ0Xt7e%2BPl5UVqaip5eXnSmc/ymkJCQso95owZM2jcuDF5eXmSiRF9U3fv3kWj0eDs7IzZbKZu3bqy0AsPD%2BfLL78kICAAtVrNp59%2BSlJSkjTNgRI3Rj8/v2cWCsuWLaNdu3aEhoby0ksvWbECAkOGDKFly5YEBwezc%2BdO3n33XVnsnT9/XvbTVapUiYULF/Lee%2B9RUFCARqPB29ubJUuWyAD5CxcusHjxYhQKBePGjWPcuHFcvnyZSZMm8eDBA4qLi4mKisJsNlOpUiVGjBhR5pxFcLLAN998I78eOXKkZJ29vLxkZp3ZbObXX39l9erVxMTESAasQYMG2Nvbc/78eRITE2XBs3LlSmJjY6lYsaJk/wQTA1CzZk1u3LjBpEmT5LE1Gg3ffvst7du3lw6e4j5VqlSJ%2B/fvo1Kp8PX15eLFi3JfgqEU32s0GqZPn16GRRURD6X7yQRE4a/RaJgxYwZdu3aV8RjHjh0jKCiIzp07y/DwBg0ayAzBq1evcuHCBRo1aiTHSkhZzWYzPj4%2BpKWlyYKxoKCAuXPnynvfuXNnoqKieOWVV2TOomD0P/zwQxYvXiwD3AcNGsTmzZutzr1fv3788MMP8p0RY2HJcJYuFIWktzRmz57N559/To8ePXjllVeIiYlh5syZzJs3j969ezN//nxUKhWzZ89m6tSpHDt2DEdHR1xdXaWzaHp6OhqNRkrEoURpIIrnpKQkeS4uLi4oFAoOHz5Mu3bt%2BPHHHwkJCWH8%2BPFoNBqCgoKYOXMmly9fLnOu4nn77LPPrH5uNpulSZGrqyuvvPIKR48eJSoqisqVKzNq1Cir5%2BP48eOkpaWVa9IjIArb/Px8mUkHyM8xHx8fdu7cyY4dO7hx44bV51tBQQFdu3bl5Zdfpk%2BfPly/fp1z586hVCrR6XTodDrc3NyYPn06hYWFhIWFYW9vz2uvvcbp06fp1q0bVatWpXr16uzbt4/4%2BHgpo1er1eTk5PD48WOioqLIy8sjMzNT3tvi4mI5TgqFghEjRnDlyhX27dvH5cuX8fT05PLlywQGBlJcXCydfFP3CX4AACAASURBVBs1akTnzp1t/Xr/j8DG7Nlggw02/MNo3bo1n3/%2BuQxTX716NWvWrEGtVuPv7y9lUKUhJnLCQCE3N5f8/HyUSiXXr1%2BnSZMmjBo1Shpu5OXlMWrUKCtZpcCzsviUSiXLly%2B3Mh2ZOnUqCxcuRKPRoFQqeeutt1AqlRw7dkxmwpU%2Bz2%2B%2B%2BUa6QL733nucO3eOyZMnM3jwYBo1akRRURFOTk5kZ2fL42s0Gjw8PHj06BGVK1eW1%2BDk5GQ1SRfSSB8fH%2BbOnUurVq3o1q2b7FUD6Ny5s%2ByzghLWdNq0adLFsnPnzjRv3pwpU6bg4OBQZjuUzeOyhL%2B/PyaTib179zJgwIDnsgJTp07l1q1bpKWlWd1bISPbv38/06dPp0aNGkRHR1NUVMRbb73FihUr5L28ePEiQ4YMwc/Pj0WLFlGlShW5n3PnzrFhwwZOnDhBmzZtGDJkCDExMcyZM6fMuZS2UReGHCNHjuTUqVPY2dmxd%2B9eqlWrxpMnT2jZsiVmc0m4/b59%2B6hWrRrnzp0jMjKSX3/9laSkJEwmE25ubhQUFGAwGJg4cSI3btwgIiJCOnrGx8fTokULzGYz8fHxBAUF4e/v/0zGpXnz5vz888/odLoyxjIvgj59%2BkiGs2PHjmRnZ3Pq1Cn69OlDkyZNrHorLQt/hULBmTNnGD58OHfu3OHIkSPExcURHR1Nr1695HVdv36d7OxsKlasSJ06ddi9ezctW7bk7Nmz0mhEqVQSEhLCsGHDSExMpG/fvlZmI66ursyePZtXXnlFyo%2BFNLFnz578%2BOOPREdH06FDB3r27CmdU0vL84RE2mg0yu2WDpkC5f2tu7s76enpUlooGMXatWvTvXt3lixZIo0%2BevXqxe7duzGbzcTGxtKtWzc8PT15%2BPAhiYmJNGvWjLy8PBYtWkTXrl0JCwvjq6%2B%2BYsSIEezevZs33niD6Oho%2BvfvL/czYsQIXF1dGT58uMweLd2/3K5dO3Jzcxk4cCC9evUiKiqKdevWWUnCW7duzdChQ9Hr9cyZM0ey/mq1Gq1WS25urizAK1euzI4dOzAajcyZM4cTJ07g4ODA9OnT6dmz5zOfqd27dxMeHk5UVJT82aZNm/juu%2B8oLCykoKCAhg0bcu7cub/kfPx3IO6TJV5E7VG6l/ZZEEzt1q1brXpbbfh3wlbs2WCDDTb8w9iwYQMrV65k6NChNG3aFC8vL44fP87du3d55ZVX2LNnD9euXUOpVOLp6YlerycvLw%2Bz2UzlypVxcnKiUqVKTJ8%2BnVOnTrFx40ZmzZrFZ599xptvvikn9AcPHpSxB6%2B//jodO3bkiy%2B%2BeO6koLxtCoUCFxcXyaKIfkJfX1/WrFlDbm6ujGWwxLhx4/D395eTqt9//50uXbqwf/9%2BKxaic%2BfO1KlTh3PnznHmzBmMRiPbtm1jypQpVtJIvV7P8ePHSU5OZu3atWRlZWFvb49OpyMrK4sePXowf/58ABo0aMCRI0dknp/RaKRRo0YylLxBgwao1Wop8Su9HV682Ovevfszfw9KJGKzZs1iypQpVs6b4j6JQsjT05Pc3FxMJhM7duxg%2B/btjBkzBjc3NwYOHIi3tzfR0dFs37693OOsXbuWJ0%2BeSEdSy4JQQLCmohAXxZ6/vz96vR6dTlem2IOSSeIHH3zA%2BPHj5b5CQkK4c%2BcOn3zyCSEhIbRo0YKkpCQ5Ga5fvz61atXixo0bJCQkyGIvMjKS/v37ExUVRffu3TEajTRp0oRz585J8xNXV1dMJhPZ2dnodDrs7OykbA%2Bw6kUr7X5ZmuW2jKhQKBS4urrSv39/9u3bR8%2BePdm4cSNt27alSZMmbNq0STIlFy5ckGYt7u7uVuzf66%2B/jkqlIjo6ml9%2B%2BUUapVSrVo0uXbqwZs0aVCoV%2B/fvl89F/fr1y4RmG41G%2Bb7Z2dkxevRo7Ozs2L17N7dv35ZSzNLRKcKcRavVMmzYMJkLKeDq6kpWVpZkCV8Ugo3s0qULhw4dorCwUD7fluP99ttvs2fPHl566SXmzJlDQEAAUVFRTJ06VYbGWzJ7z4NCoeDo0aP06dOHtLQ0qlatCiCdXMX5i%2BB5YSBSeh/iObBkeatXr463tzfz589Ho9Ewf/58jh8/jr%2B/PxcuXMDBwYEpU6ZgNBqtTHpK48KFC4waNYrRo0db5Su2adPmha/z/wSW8Qt16tShsLCQd999l8uXLxMbG4tWq5ULDEajkZ49e5KZmSmZ9T%2Bb6ov9K5VKuWjRokULsrOzpdOqDf9e2GScNthggw3/MIYNG4a3tzebNm1i7dq1MqtMrOo3bNiQZcuW8fbbb8u/6dChA9OnT5dsoMDLL7/MuXPn%2BM9//kP9%2BvXZtWtXucc8e/asLPye9w%2B/RqOhWbNmsuiCkkmWr68vZ86cASAgIIC4uDhycnKYOHEiBQUFZGdn07lzZ6tV%2Ba%2B//rrM/mNiYuTX4jxOnjzJwYMHqVixIh06dCA2NpZ58%2Bah0%2BkYOnQo169fJz4%2BntWrV2MwGBg7diwVK1bEZDKRk5NDu3btyMzMZPfu3fTs2ZMmTZqUCYUXK/xiNfzPtv8ZLAuK51n3Q4n5xG%2B//fanfWOiF1OY9URFRTFixAjc3NysGMd%2B/fo9dz%2BBgYEoFIpyDTR8fHxkzw6UGG%2B4u7vL7QaDgdDQUKZNm2Z1rW%2B%2B%2BSarV6/m4MGD0t10//79eHt789FHHxEQEMCpU6cYPHgwycnJVKlSheLiYj7%2B%2BGPGjBlj9VzMnTsXg8HAjBkzZJFmNptp2bIlJpOJM2fOyIUFR0dHBg4cyMSJE62uw9fXV15DaWv%2B8hYrRN5ZlSpVKCwsZOXKlUBJT2VaWhonT54kPj6ewsJC/Pz8uHfvHqdOneLIkSNUrFhRsn8%2BPj48fvyYffv2yd69%2BvXrk5iYyIYNG6RT6%2BrVq6WxjoAoWIuLi3F0dJS9hgIiE9BgMFiNC/zBxHz88ceEhoZKcxbxe3Z2dlayTDF%2BpQs9Ly8vnjx5gkqlKje0W5zPa6%2B9RlxcHIWFhcTGxgIl7pF6vR6DwUBERIQc%2BxkzZgAlGZKzZ89m%2B/btJCYmEhERwenTp595DEuIXuUKFSrIXDfh3CmiTrp06SJl0pGRkZhMJvr378%2BjR4%2B4du0ajx49KrcX%2BKuvvpILOLNmzWLfvn3ExcXRp08fJk%2BejJOTE4CVSU9pme7ly5fp1auXVaEHyMzRZcuWMWDAALZu3Srv1aBBg1AqlWzatEkqIIScVqfTsWvXLoqLi%2BnXrx/5%2BfnSMdRgMDxze2FhoXR6DQwMJCgoiEOHDslYBRFRMnz4cJRKJfHx8fJZEXEOSqUSpVJJhQoVyMjIoHr16gQGBrJ161ZcXFzQ6/VMnz6dbdu2yWgMG/7dsDF7Nthggw3/A0ybNk1OZp48eUJBQYGcJJQ3WbWUWfr4%2BPD6668Df/RgRUVFsX37ds6fP49Go8HV1ZX09HQr0wKBZ33su7m5ERAQwPnz56lWrZosDsUEQsDX15fGjRuzb98%2BCgoK8Pf356effqJatWrcv38fNzc3MjIyWLp0KadPnyYyMhIXFxfq1Kkj8//c3d15%2BvQpZrOZ7t27s2/fPtzd3WXWk5igP336VGZXmc1m3NzcUCqVpKSkyODiS5cuYTQaadCgAf7%2B/mzbtg1fX1/JyAhYMnW%2Bvr5W0QSlt1t%2BP3To0DIT1OTkZNkbZzAYyMrKwt3dnbi4uDLj2qVLF5KTkxk/frzVZFEULCIyoUKFCrK3rUOHDhw6dIgOHTrg5OREVFQUCxYsYPz48WzdutVq/7du3WLlypU8fvwYk8mERqNhxIgRViycQEpKCv369ZOsSXh4OG%2B88YZk9qBE7uft7U1oaCjBwcEoFAoSExOpU6dOuc%2BNgKWJTGJiIr6%2BvuzZs4fg4GB8fHxISUn5SyyTyGcTvUlQUtR4eHig0%2BnIz8%2BXxi21atXi1VdfBcr2JQKcOnWKkSNHkpiYyHvvvUft2rXZsWMHUVFRtG/fvsyzUr9%2BfRo3bsyWLVvIyMigU6dOfPLJJ8ybN09OnM1mM56envj4%2BHDp0iXq168v3QvFu52QkCAjFkTfmXDP7NevH0qlktdee41r166xbt06QkJCMJvN0shj8ODBLFmyhF27dtGzZ0/q16/Pr7/%2BKhk4tVpNrVq1nmmy8ywIp84VK1YwZcoUK0astNxTOIsKMxxRKGRnZ%2BPs7CyLXgcHBx4%2BfEhsbKx8hxo1asSrr77K/fv3ycnJwWQy4eTkJAssPz8/2Y%2B7evVqcnNzWbZsmdW5WjLg7u7uFBUV0bRpU4xGI%2B7u7mX6idu2bcvHH39Mw4YNrT4HunXrRlFREcnJyRQXF1v1hgoIkx5LmW79%2BvXp1q0bjRs3fuZ4btq0icjISAICAoiOjsZoNOLk5ISrqysBAQGEhYVRWFiIm5sbCoUClUrFpEmTcHBwYO7cuaSnp8vgeeEIbLk9JycHnU5HRkYGDg4OBAYGolKpcHZ2JiIiAoPBgLe3N48fP5YqBeGGKhYBqlevzr1792ShL9hipVLJyJEjWb9%2BPUqlEq1WS2RkJN26dUOhUMhn2YZ/L2zMng022GDD/wil%2B%2BZeBCJGoTS6d%2B%2BOq6src%2BfO5fDhwwC88847tG/fXhqjCAwZMqTcgu/p06eSebOUJokJgTju9evXuXv3LsXFxfTo0YOUlBTs7e1lj1BGRgYKhQIPDw9OnTqFn58fFy5ckMUjQMeOHenRowf9%2B/fnww8/ZN%2B%2Bfeh0OsLDw%2BnSpQsmk4lt27bRvXt3Nm/eTK9evdiwYYPscyud8SYyqRITE%2BUx9u/fLyeVYuxiY2Nxc3MDShz2li5dKtnS0tuNRiOHDh0qNzQdSliCIUOG4Obmxt69e4mPj6dfv35lWIHbt29jZ2cnjVYERGh5dHQ0UMLsiYnzwYMHKS4u5sCBA9KqXkzeRA%2BNXq9n%2BfLlbN68WYYrOzs7s337dmrVqlXuOX/99ddWwc0XL14kKSlJFvMajQZ3d3cyMjL49NNP5d9FRkbSsmVL2SeVn5/P9evXqVOnDnXr1iUoKIh69eqVOd6JEydQKBSMGTMGKDEA%2Bc9//kNaWhoJCQncuXNHMsh2dnZywpuVlWXFVInfMRqNkm0QY6XX67ly5YrMYROskyVEgQbw66%2B/MmPGjHKzHAXUajU3btwgJSWFQYMGkZmZSc2aNenRo4eMb9Dr9aSlpclMutu3b6NQKKzO%2B%2BLFizg7O/Pzzz%2Bj0Wgwm80EBwdz6dIl1Go1dnZ2XLhwgYYNG1JYWCiDwKtUqcKBAweIjY3FZDIxYMAAzGazLFDEu1hUVPSXCj1nZ2f0ej0jRoxg0aJFkg0TY/PNN9/IvsT79%2B8DsGTJEun6OnXqVHr37l2u1BFK7q/ArVu3MBgMaLVaJkyYII1UcnNzcXR0JD8/X0rNq1atSlJSEuvXr%2Bfo0aOoVKoyeZZPnz5l7dq1bNu2TTKaqamp8tynTZvG/Pnz8fPzK/OuATx48EBKQMtj2S1lukOHDrXqAf4zrF%2B/nvT0dKssQPFcWEq3LfMLBVutVCqxt7eX0QoiO7M0my0MbnJycsp9xm/fvi2//umnn8psv3v3LvDHsyPeKZPJxMGDB1GpVBiNRuzs7KTJkGU8hQ3/XtiYPRtssMGGfwG%2B/fZbvvnmGxQKRRkWAuDo0aMsX76cN954Q0rwQkJCiIuLY8eOHbIPRsDf35%2B1a9cyaNCgv3wuLi4uTJkyhVmzZjF48GAiIiJo1qwZhw4dApBSxLVr10o2JTU1VRZVZrMZBwcH2rdvT0xMDBEREQQHB6NWq2UPXPXq1RkyZIgMTe/SpQv79u2T11GnTh00Gg0vv/yyLJbEyvvFixdlZMKzIFgmlUolzWBKIzk5mUqVKj3TMKc0E/g8VmD//v1s3LjxmRKx4uJiyQKVt/9ffvmFoUOHUlRUxC%2B//MKJEydYuHAher0ehUKB0WikoKCAVatWlZH6WqJVq1YUFRVJN78XhVqtplKlSuTn55OZmUnbtm05duwY8fHxsjgujXbt2mEymXj06BE%2BPj5AyYTd0hVSrVZjb2/PkCFDOH78OImJidSvX5%2BuXbtSp04dPvroI1k8iQJB9N8JuLm5SSOb8lC7dm1GjhwJQLNmzWjevDmbN2%2BmT58%2BktmbMWOG1cLArFmzUCgU1K9fn%2BzsbG7dusWMGTPYv38/q1atwtnZWbp1NmjQgGvXrnHw4EHJGsOf90k9D0qlkgMHDrBt2zZ%2B%2BOEH8vLyZDi5g4ODVcC2YKocHBxkXt7fhWUUh6%2Bv7//H3nmHRXVuffueRu8IAoIFRUEsiD2WKLZo1KixxN6OvUejYktijSV2E/XYAHsFRcXewBoVK2CJBVSa0vsw8/3BtZ8zQzHlnDfnzfvNfV1ewuype%2B/ZrPWstX4/FAoFwcHBompao0YNVq9eTYcOHWjbti0ymQyNRsP79%2B9xcHAQ3xmFQkFCQgIFBQWiM0BXiEmtVuPs7ExKSoqoZks%2BkpIKsZWVlWjV/iNYW1uzadMm6tWrJ2Yu7ezsxPxwfn6%2BaC%2BWKK7OGxERwdChQ/XUeT/GzZs3gaJW0FevXpWoNoaHh5dozf1PoVAoxGJcaZXz0rxPS9um%2B3ySqfrWrVsNipz/BzBU9gwYMGDgL2bgwIF6lby4uDgRpOXl5VFYWChWX%2BVyOQqFQlgGaLVa2rRpQ7ly5ZDL5Tg7O/Pw4UMyMzOxsLAgNTWVuXPnUr9%2BfRQKBcnJybRr144OHTrg4%2BMjFDDz8vIYPnw4MpmMKlWq0KtXL06dOkVOTg5PnjwRLWKDBw9m69atwvPPwcEBd3d3QkJCqFq1Kjt37sTY2Jj69euLZE93zkij0XD69Gn279%2BPlZUV3t7e3Lt3j9zcXGET0Lt3b72KZWBgIE%2BePGHFihUieKlWrRr379/H1dVVtBsWFhZSo0YNscpdUFCAg4MDwcHBeoGcrvKiLh8TYAEYP368qEakpKQQEBDAgAEDhHVCs2bNCAgIwNTUlAoVKhAeHl5mVcDHx4f27dsTHBzMvXv39JLBmTNn8tVXXxEfH19i/iwhIUEkpBMnTmTFihX4%2BvqKoFkSZbC2tub777//aKIHCFVC3VY7aV9InnQAnTt3Ri6Xc/fuXTp27Ii3t7fYZmJiIoQfmjVr9tHXk0Qfzp8/D0D//v1JTEykT58%2BJCUlER4ezq%2B//kpYWBiff/45q1evxtXVlR07djBs2DBxTnz33XckJSWh0WjIy8vj3LlzYn9lZGTozcYBetL6Dg4OGBsbc%2BTIERo1akS9evX0ZghdXFzYtm2b3uONjIzIy8vj7t272Nvbo9Vq2bJlCwkJCfz0009MmjRJ3PfJkyclguyIiAi9SsugQYMYOHCgnjH2iRMnaNWqFWZmZkJM5/379yQnJ3P79m3at28PFBmVT5gwgS%2B%2B%2BKJEYh0TE8POnTs5c%2BZMqabbfwapmqPValGr1URGRhIQECDO6enTp%2BPg4MAXX3yBhYUF2dnZbNiwgeHDh7Nr1y5atGjBo0ePiIuLA4oqSMbGxpQrV443b95gYmJCRkYGycnJ4jrYtWtXLly4QN26dYWYijSzPGHCBDZt2iTmaaX2Q5lMhru7O02bNmX37t0MHz6cbdu20bp1azF7p9FoGDhwoGiPhKIE59mzZ3pzqdKccHH13ilTpvyurgupe6J4F8W/Q5s2bcjLy2PVqlWMHDlSfD8TEhIYO3YsWVlZrFixgo4dO%2Bo97uzZs6xevZrPPvuMgoICRo0axdatW%2Bnfvz92dnZkZWXRtm1b5HI5KpVKTz3WxMQEU1NTqlSpwvjx44U9kIG/N4bKngEDBgz8xRw9epRvv/0WNzc32rdvz%2BHDh0V7j64/0x9FamuDIu%2B6c%2BfOER0dzeDBg7GysiIrK4v09HRsbGz48OEDM2fOZOXKldjZ2WFubk7Xrl3ZsmWLmI8zNjYWpuR/Bl0FuaZNm%2BLh4cHu3bvZsGED9erV49SpUxw4cIAHDx7oBfW9evVCqVSyZMkSduzYQc2aNbGzs%2BPmzZu4u7sTGxsr/Abt7e0pLCwkJydHfDbdwF8mk3Hu3LlS257mzp3L5MmTsbe315tLKS41npqayqZNm9BoNAwePJixY8eKbYGBgaxfv560tDQaN26MmZlZqVUB3RaxNm3alEgGPT09SwSVxVfjpd%2BlY2ltbU2rVq1o1aoVLVq0KJHwlEbv3r358OEDY8aM0ZOZ1032bt68ydq1a7G2tmbQoEHMmzePChUqMHr0aDw8PLh7965QPZXL5bi4uODl5YWnpyeenp5YWVmVeN3SguDWrVvTqVMnOnfuLBKMu3fvMmPGDCFC8WeoUqUKW7duRaFQEBISQnBwMC9evMDBwYErV65w584dhgwZgkaj4fDhw1SvXl08Njs7m6CgINatW8eGDRuYPHmyXvVv4cKFQJG/4/Lly8nNzRVVlfbt22NjY8OBAwfo2rWrmN8D2L9/P9bW1kJNFIoWEJycnMT5m5OTI5JGW1tb%2BvXrR8%2BePXFxcSE2NpaLFy%2BSnp5OfHw8z58/5/nz50KI5d9Fmsf7vRSvFslkMtq1a8epU6dQqVQ0bdqUa9euYWpqikwmE9/ja9eu4evrS5MmTTh69CgymYwuXboQEBBAr169CA4OFudCvXr1hLF8u3btGD16NG3bthWG7qmpqXrzhZUrV%2Bb169ccPXqU9PR0goODOXPmDAUFBRgZGYn28nbt2mFubq73eY4cOcKlS5fKVO8tDUk518LCQhzXmJgYsrOzqVatGpaWlkBR8nX69GmSkpJKLAqUK1cOT09PoqOjSUlJ0dsuGc%2BfPHmSbt26ie9nv379SEpKEurMukJTUPQ3JCEhAScnJ86dO0fbtm1ZvHgxly5dYt%2B%2BfSgUCmQyGSNGjGD16tVcu3aNvLw8Yf8hl8tFt0VpAjsG/n4Ykj0DBgwY%2BC9w/fp1Ro4cybZt20rMpkgsXryYW7du0ahRI7RaLQEBAQD06dMHpVJJQUEB169fZ86cOcJfbeXKlaLyJHHy5EnWrFkjZo3gX1Wt1NRUnJ2dWb16NSdOnCA7O/s/1mYkVQMrVarE27dvsbKywt/fny5duujdLzk5maNHj7Js2TLxOMl/rW7duvj5%2BREXFycqOdbW1gwZMoT%2B/fsDsGDBAo4dO8bo0aMZNmxYqe%2BltLbOt2/f4uDgUMIAWmrrjI%2BPx8HBgfT0dAoLC7G3t0cul4uqlkSHDh0AaNCgAYsWLSrh2fd7WsSKe6IVZ8CAAcTHx4u2OUtLy1ITRPhXYtW2bVvi4uKoUqUK7u7uyGQygoODhaF3hw4duHz5shB/aNasGT179mTChAnk5%2BdjYWHB%2B/fvgaJqyvnz50Xg6%2BPjQ15eHkeOHCEuLo779%2B9z79497t%2B/T4UKFQgNDf3o5ylOamoqCxcuFNXDWrVqERMTQ7Nmzbh06RLdunXDy8uL5cuXA0XVusTERExMTD66GCGTyWjYsCHPnj3TS4azs7P58OEDUDSnKO1XSVhDMlVfv349jRs3Fsdcqp5JiZtuNU0mk/1hqwMJT09PqlevzpkzZ4R3pjR/eurUKb7%2B%2BmvkcvnvUostbtNQHGNjY9zd3cnMzBRzeRJyuRylUomXlxe%2Bvr7CJmbs2LG4u7szevRo5s6dy5YtWxgxYgQrV67k7NmzHD58WIiCXL16lR07drBv3z7mzZvHrFmz6Nu3L5MmTSItLQ2NRsPt27dxcnLizZs37Nixg/Xr19OmTRsaNmwoTOMlEaVhw4aJSn29evUYOnQoEydO5NmzZ6xevZqLFy/qCUiZmJjg4OBApUqV6N69u1hY0V3QKF7N1xVyCQ0Nxc/Pj2bNmpVZ%2Bd%2B%2BfTsrV65EoVCwbds2fH19gSJP0WvXriGXy5k5cyYPHz4UHQxmZmZ6yb6U3EuJdvHtCoWC7OxsunfvzrFjx9Bqtezbt49p06aJ64WdnR3v37%2BnX79%2B1K5dm8TERJYvXy7OE0mI52NIi4SSfYixsTF169bl119/LVVwysDfD0OyZ8CAAQP/JdavX8%2B1a9fYtWtXqdtjY2Pp1q0bcrmcw4cP07ZtWwAsLCwIDg6mR48e5Ofn4%2BnpycSJExk2bBhWVlZ6QihQVFWaN2%2Beng/exo0b8fT0pFWrVuK2/Px8YmNj6d69O/n5%2BTRu3Bg3NzchfS7xW8FkcTw8PLC3t8fNzY379%2B%2BXmpDFxMSwbds2HBwcyM/Px9LSkqysLFJSUjAxMcHMzEwvWNdNuLKzswkLC9MLlKBods/Ly0uvwgL/auv8rTZOaftvmabXqlWLFStW8MMPP3Dx4sUSVYHfMnj/PfTr14/Xr1%2BTlJSETCYTVhGlPb5y5crExMToJQYVK1Zk3bp1eHp6ioqphGQ5UVo4UKFCBSHOoTsrWq9ePfLy8vj2229JSkri7t27xMTEkJeXR%2BPGjVm/fj1QlBx99dVXnDp1qszPduDAARYvXkxubi52dnYsXLiQ1q1bCzP0rKwsjh8/jpubG15eXmg0Gjp37iwSShMTE/Lz84USaWFhIRYWFmRkZKDValmxYkWp52teXh6PHz8mPj6enJwczMzMqFChAhcuXBD7NTs7W8yQOTo6ikUBqaLy5s0bsd8OHDhAnTp18PT0FIsx0j588%2BYNNjY2pKeno9Fo9PZ59erVcXNz49y5c8jlcqysrPjmm2/o2bMnaWlptGjRAq1Wi6urK0qlkidPnohqju7nkqwRiqv3StWn7OxsUUFXqVTUqVOHhIQE4uLisLCwIDMzk8aNG1O/fn3i4%2BM5e/Ys6enpnDx5kk6dOtGgQQPu3buHRqPh6NGj9OjRQ1igJCYmEh4eTosWLYiKiiIwMJC1a9eSnZ0tvN8iIiJo27atmNGTWpEnTpxIpUqVmDhxYomZQRMTE44cOUKVKlXEeTd//nyOHz9OeHi42MeWlpZUr16dmJgYMjMzMTExITc3FyMjI7GwIrU/l/adhOehyAAAIABJREFU1032fH19CQkJKdM/MyQkhNmzZwMwbdo0%2BvbtK64xOTk57Nmzh5UrV4qKp0wmY%2B3atSVarH19fVm1ahVTpkxh1apVJbbHx8czatQonj17pmeDA1CpUiWWLl3K4MGDyc3NxcbGBq1WS2pqKkqlUhxPU1NTMfNYlr%2BqpA5qZGSEra0t1atXp0KFCnzxxRdlXvMM/L0wzOwZMGDAwH%2BJ8ePHC0%2Bp0pCEQYyNjfVEQqTflUolCoWCyMhIhg8fDhTJ/EtInnSXLl0qUe2LjY1l7dq1tG3blhUrVpCfny8k7BUKBcbGxsTExIjqQrly5fDz8%2BPOnTvExcXpBZmSHUJpJCcn8%2BzZM54%2Bfcr169dRqVQsWrRItDjpvlcomlM5efKksHaoXr06crmcZ8%2BeUadOHT777LMSr2FmZiaSC13evHmDg4ODmHeEosCmrBm%2BssjMzMTW1rbM7Wq1GhcXF5EUFPfsi42N1RM58PPzIycnh8TERL0WsdzcXPbu3cuZM2d49uwZWVlZWFhY4OHhIVqvRo8ejYWFBbt27cLb25uxY8dy48YNcnJyxGzfmTNnhHR///792b9/P8%2BePWPWrFkcPnwYf39/QkJCyMjIEGbMunNQ0meYPHkyQ4YMwdvbG61WS2hoKO/fv%2Bf58%2BdCKGXJkiVUq1YNd3d3BgwYwObNm4WUPhRVC37Lq2vu3LkiCE1OTmb06NEl7iMtdEhICxpGRkaoVCpyc3OpW7cuT548YeLEiYwcOZJFixYRGBjI3r17y1xQKY158%2Bbp/S4lx7pG4a6uriQmJoqZRI1Gw8WLF/n1118BmDJlCqmpqZw8eVLMrkkqlOPHj2fIkCFERUXRv39/Xr9%2BTVxcHHK5nP79%2BzNp0iTx/Vi3bh0FBQXs27ePQYMG8dNPPzF06NBSk1dpJk03oJeSO6n6KQX8BQUFeq3L0vYbN25w69YtITRy%2B/Zttm7dCsCaNWtYvXo1%2B/fvZ8SIEeTn5%2BPo6MjDhw%2BRyWT07NkTjUZDmzZtgH8Zv0uVzs6dO5OdnY2NjQ0DBgxgw4YNqNVqFAoF69evL7FwoXsuxsXFERgYSHZ2NtOmTQOKvveSUvDevXupVq0aHTt2ZPDgwUycOJF79%2B6RkJAgZu%2BKC/sUR1LvLSgo4PTp0yXUeSUCAwNxcHBg7Nix9OrVi8zMTFQqlUime/bsSWJiIsePHyclJQUbGxuSkpLIyMjQu%2B6VL18eW1tb8X9xrKys2LNnD/fu3ePcuXMkJCRga2tLkyZNaNy4sdg/y5cvF8n/4sWL0Wg0TJ48mblz5zJkyBDWrVsHwODBg9mxYwcNGzbk3r17QiTrzJkzpap3Gvi/g6GyZ8CAAQP/Zfz8/EQQ%2BXtawIoH5lqtllGjRrF582Y9VcEzZ85w584dFAoFNWrUoEuXLmzevJmBAwfSt29foqOjGTNmDNWqVeP27dts2bIFX19ffH190Wg0eHp68vDhQ%2BrWrUvFihUJCwsrUTmQEpB%2B/foREhLCiBEjePv2LcHBwYSEhPDy5UugaEU%2BKipKqPPpVhmhyAftH//4B0uWLGH27NlUrlz5366G1ahRg8DAQBo3blzq9t9b2Zs6dSp9%2BvTRm3HTxdPTE39/fw4dOsTRo0dLPPdvef5BkTDH4MGDSUpKom3btlSrVg0LCwuysrKIiYnh8OHDmJqakpubS%2BPGjYX6n0wmw9LSUswvVahQgdjYWExNTTlw4ADVqlUTSqgymYxbt25x7tw54edWs2ZNhg8fTufOnalVqxYFBQVUqlSJrVu3ivfm5%2Bcn2sakqqJSqSQnJwcLCws9JcysrCxOnDghHpucnCyqPWVx8%2BbNEtulmUAJCwsL0famWxUzMzPD2NiY9PR02rVrh1Kp5NGjR4SFhfHkyRO6dOmCra0t169fFyI7PXr0YNOmTWKG0sHBgcaNGzN27Fg9YRdd6tSpQ5s2bcQMmFKpRKVSkZOTg4ODA0lJSZibm6NQKEhPT8fS0pJx48Zha2uLWq3m7NmzXLhwAShqm6tVqxa9e/fm22%2B/Ra1W4%2BjoyNq1a0tYGvj5%2BfH27VsiIiKEGM7vDduK%2B2NC2dUd3cecOnWKChUqCB9IyQj8888/JywsjMLCQqpWrcrz589Fq7ZMJmPcuHGsX7%2BeGTNmYGtry6ZNm3j58qWQ9NdFsoCoV6%2Be8CiMjIxEq9XSo0cPAA4fPixsR3Sr1AqFQix%2BSItEn332GVZWVhw8eJBOnTrRokULoCjBlKrskgddrVq1Sqi36rZ56yqKFkcmk/Hhwwc0Gg2hoaGcPXuWdevW0b17d/bv3y%2Bqy25ubjx58qTE452cnDA1NSUzM5OMjAzy8/NRKpWiNTs/Px%2BVSoVKpSI5OVmc4zY2NshkMuHv%2Be/Qu3dvDh06hEwmo2vXrhw5coQOHTpQpUoVTExMcHJyon379r9rBtjA3wNDZc%2BAAQMG/gvEx8dz8OBBIiMjUavVmJqaolAoRKVALpcLzzEjIyMR2MTHx6NWqylfvjxDhw6lY8eOfPrpp2zdulVUWkxMTIT6nBQY5OXl8ejRI1JSUti0aRN9%2B/bF09MTPz8/du3axZw5c/D29gaKgslZs2bx5s0bfv31V3755Rd%2B%2BeUXADHrdebMGQoLC9FqtWzcuJHNmzcjl8u5fPmySMgaNGggkr179%2B6h1WoZMWJEmXN1crkcIyMjNBoNr1%2B/1quG3b9/n6ysLJ4/f061atX%2BB49MSfr168fSpUtxd3cv019s1apVtG3bVgjBFK8KSFWDsqqKy5Ytw9TUlJMnT5ZqZXDkyBEKCwupXLky79%2B/Z8uWLQwbNoyaNWvy%2BvVr6tWrh4eHhzAalyooUFRBMDY2RqvVsnXrVjZu3CjOi8ePHzN9%2BnSePHmCSqVCoVCwYMECvQT4/Pnz%2BPn5kZCQIBYZpEqCubm5nkDEnxEMadSoUQkBFyMjI86ePcuNGzeEbL%2B0EKKrWPrtt9%2Bydu1aWrVqhYWFBSdOnOD9%2B/d069ZNnD9paWnCL6%2BwsJDY2Fhu3brFP/7xDxQKBZs3b2bv3r1cvHiRoKAgHB0d9d5LSEgIarWa7OxsfvjhB6ZNmyZUQWUymZiJkjwIocgL7YcffhCWE7pMmTKFixcvMnfuXDQaDTKZjHr16uHu7l7ivrq%2BbIAwVzcyMhLCI9L%2BCAoKYuDAgRgZGZGfn49cLsfGxoYmTZpw48YNvv/%2Be968ecOPP/4ojqN0vZFUGRUKBRs3bhSvp5tQSS2c8K/2UWnuUavViur68uXLiYqKYsGCBeLaVVhYSOfOnTl27BgymUyIm0RFRaHRaNixYwf%2B/v6cOHFCvHbFihUB9OYKpecqvih2%2BvRpMXd54sQJ7t69K6r40uJAad57EpJaLBQtxOzcubPMRaBPP/2UgoICgoOD2bp1K%2B3bt2ffvn3CrmDy5Ml4eHgwbtw4PZsR0PcvlZAS2bLUVLOzs8XzlGWh8EfYv3%2B/%2BPnw4cMAYp5bWjBat24du3btKtOWxsDfC0OyZ8CAAQN/MREREYwfPx4fHx/q169PmzZtMDIyIiMjg%2BjoaB48eEBsbCzDhg3DxsYGuVzO7NmzKVeuHCYmJuTk5KDRaGjatKn4Yyy1UarVaj3BCkmtzszMDKVSSWpqKocOHRLbb9y4gVKpZMCAAWRnZ3PkyBEqVarE3LlzRXuaVCHo0KEDp0%2Bf5sKFC3or9U5OTiQnJ%2BPo6IibmxsuLi4EBweXaA1SqVR4enry7NmzEvskJiYGrVYrTKULCwv1kggpAB85ciR79%2B4tEZD/GX6rQiht79atG1FRUULOvbhPnhQw37lzhzt37gBgb28vhCa0Wi1r167Va3UtngyePXuWAQMGlOlZp9VqGTlyJD///DPbtm0TVh1eXl5ERUUxcOBAXF1dGTVqVKmfTy6Xi1ZMe3t7kaAsWrSI%2BfPnizZHmUwmbAB0kYLhxMREbty4wY0bNzhw4AAfPnygTp06wr9OV6n097J%2B/XpRqSxOnTp1iImJ0fNakz4zFHlJVq5cGWdnZxQKBS1btuTSpUt4e3tz8OBBoGhfjx8/HhcXFzZv3kzbtm1ZvXq1EEZq2rQpvXr1ws7OjjVr1rBo0SLxWpK4jlarJTc3F39/f/r27cs//vEPcZ%2B0tDS6d%2B/O1q1bxWxZWloac%2BbMwcfHh0mTJpGTkyNmsnbt2kV6errwM9NqtURGRtK7d%2B8Syaa9vb0Q5tE9NnK5XAjCSOzZswdAVPPUajWWlpbcvXsXGxsbFi1aRH5%2Bvvju6lqkSAlHbm6uuD7oJhNStS0kJITCwkLxHJK4iG579fTp0wFE8uXq6sqrV6%2BYNWsWixYtIj4%2BnpSUFIYMGcKaNWuYOnUqJiYmzJ49m7CwMJYsWaJ3DuTk5BAWFkZQUBCPHz/W2yYtjB09epSqVavi6elJeHh4CR/S/yQtW7bk6tWrBAYGMnv2bPbs2YODgwOAaO2cNGkS1tbW5OXlietDYWEhtra2pKWlYWRkhFwuZ%2BLEifz0009C1XXt2rVMmTKFwsJCPvnkEy5fvixeV/o%2Bq9VqcWwaNWrEzZs3MTIyQq1Wo1Qqyc/P/6hYkLQYIOHo6EhKSgpyuZzy5ctTt25dzM3NWbFiBStWrPgf248G/joMyZ4BAwYM/MUsWbKEMWPGCKPnnJwcZs2axcmTJ/UCrOJ/aHVX%2BR0cHPQSru3btzNw4EA8PT2Jj4/n008/JTIykpcvXyKTydi7dy/Ozs54enqKKhPAixcvMDc3Jzg4mLlz5wqvPigK8Bo3bsy9e/fYuXOnENmQTK6lZHDz5s0sWLCAW7ducejQIZGwbNmyhfHjx5ObmysCyqlTp5a5X7RaLY6OjqWqxw0aNIhVq1aVGpD/Hvz8/Eokdzk5OQwcOBCFQsG7d%2B/E7c7OziW2Q1HgLXmqFffJkwzdy3ptibVr14rnkpJBKJqZCgkJYcqUKWU%2Bj4%2BPD4WFhVSsWJHNmzcDRdUkhUJB8%2BbNyczMJCcnR8/yQkL6WRJbkbCxsSE3NxeZTCaqMI8fP9Y7BuXLlxdJhqOjI126dKFLly4cOnSIAwcOEBMTw82bN1m0aBHZ2dl8/fXXJUR9JEpTEJWqRcXRbd2UklWtVlui5TM9PZ0DBw6g1WpJS0vD1dUVuVxOy5YthYjLw4cPUSgUmJiYCIVYCS8vL2QyGYMGDWLp0qV6z713716REC9cuJCoqCj8/f2F6Xp8fLxQilQqlVSoUEHcJlUUw8LC9Ko27969Q6FQ0LZtWy5evAjApEmTCAgIYM6cOYwYMYKGDRsC0Lx5cw4cOMC4cePQaDS0a9cO%2BNd3ULdyJFXFpGuIVMXURZpflURjoMjmwcnJiaioKI4fPy4q556enhgbG5OXl8eSJUvIy8sTbcomJiY0b95czP2tWbOmxPFzcnLi2bNndOzYkZ9//pmFCxfqiSWp1WpWrFiBSqXC399fVBA/fPggriGhoaGkpaVhbm7OoEGDePHiBW/evBGqrZI/Z58%2BffDy8kKr1TJu3Di9Ns2CgoJSZ%2B/%2B6OyuxLhx4zhz5gzp6ekEBgby8uVLkXTn5%2Bfz5ZdfEh0djampKZaWlqSlpeHo6Mi7d%2B/IyspCq9Vib29PVlYWrVu3ZuvWrVhbW6PRaMQMX15eHgkJCZiamgKUaSzfpUsXbt68iVqtRqPRiERfq9WWORJQXNG1Q4cOwi81JSWF8PBwNm3apLdwZODvjWFmz4ABAwb%2BYurVq8ehQ4dE29a0adO4ePEiNjY2uLi48OjRI2F0K4mlGBsbk5qailarpXz58rx//57CwkIqVKhAXFwcP/zwA/7%2B/qxbt4558%2BZhbGxMz549xXC%2Bqakp3333HTNnzsTKykoEz6mpqXoVAkl%2BW/LZk2ZKpFY6gJUrVzJt2jRmz57N/Pnz2bNnD5cvX2bbtm2YmZkJM/fSVDt1A31dxUD4V9AvvTfd%2BUMoatnr3r07J06cYNasWeL2soK2GjVqEBQURKNGjThy5MhHj8nHfPagyP9NShhKM03/dyltrk%2BXGjVq4Ovry507d0SQnZOTg4mJCebm5nz66afExsYSFRUlAkrdmaM/YwcgoWsDUPw9e3l5kZubS1ZWFllZWUIBUFLxKygoIDIyUjy%2BeAVPqlguXLiwhJ/Z%2BfPniYiI4LvvvsPX15fExERatmyJSqVCLpfrtXPqtrUVn7VycnIiJSWFKVOmMGjQoDJnKDdu3Mjo0aOxtbUV52lcXBzOzs4kJCRQvnx55HK5EL9RKBQ0bNiQpKQkIiIixHPOnDmT27dvI5fLhV/gHw21pH0eGxtL586dcXJywsLCAjMzM27duoWjoyOZmZnk5eWJNnBJGCg5OVlU/hwdHfH29iYjI%2BN3iXBIFUTQT8JtbGyE/yYULVbk5uaSm5tLYWEhDg4OtGjRAicnJ1Hxlmbw1q9fz7lz50ok%2BampqYSHh1OvXj0qVKiAVqvl/v37vHr1irCwMFxdXalVq5ZoW8/PzyclJUXMEP5eJOGi4p%2BzuIWKxG/N8gK8fPmSzp07l5iJlKhfvz4LFiwgNDSUTZs2UalSJV68eAEUdThICxmSV2haWppIwmrWrElMTAyFhYXUrFkTCwsLoqKiRDVf%2Bn5rNBoqVqwoBJAkQRzpGFWtWhUXFxfCw8PFbR4eHjx//lzv2H755ZcEBweLOUiZTMaJEyfo1KlTidlqA39PDJU9AwYMGPiL8fHxYdOmTcyfPx9jY2POnz%2BPRqNhzZo1DBw4UKxWV6lShRcvXiCXy9m0aRO9e/cGimY4du/ezdChQ2nZsiW7d%2B/G39%2BfypUr8%2BjRI7KyssjIyBCr31C0mjt//nyhxKc7cyStAjs7O7Nv3z5OnjwpVvIBvdViuVxOpUqVkMlkwox6ypQpyOVygoKCiIuLIzQ0lPPnz5eqGGhpaSmqC5KRtKOjI7NmzWL69OksWrQIR0dHZs6cybZt2/Qea29vz4ULF0hPTxcVMmkup7TKHcDXX39dwnqhtCCve/fuZR6vgIAADh06JHzy/P39efLkiZ5P3r%2BLbpBdGp6enjx9%2BhQoSpjy8/MpX748CQkJ1K5dm9TUVB49eoSDgwNKpRInJ6dSZ5SkilubNm3QaDTUqFGD7du3o9VqWbZsWantsbq%2Bg/fv3%2Bf%2B/fs8ePAAlUpFTEwMjo6OuLq64uLigouLi576KSCERaB0c3W5XI6Pjw9ubm7k5%2Bfz9OlTnj9/TnBwMFOmTKFq1aqcOnVKVHOl1kddgSKtVouzszMBAQGiahcQEMC1a9ewtbXl4cOHwki7rH19//593NzcGDp0qLjN39%2Bf4cOH6y06zJs3jwEDBmBvb8/KlSvZsGED8%2BfPF4qKERERwvRaJpNhbW1Namqqnnl7Tk4Ob9%2B%2BpWrVqrRu3ZrmzZuTmJjIrFmz2LJli7ifm5ubWBR6%2BPAhpqamYpFG8pxMS0ujYcOGlCtXjsLCQkJDQ5HJZKjVat69e6dXtdbF0tKSjIwMPDw8cHJy4sqVKyIpKJ6cFp/FTEtLw97eXiRdSUlJHD58GGtra6ytrXF1dWXMmDFs2rSJCRMmUKNGDdzd3YXIUEFBAVeuXKFv377CxmD79u1cu3aNwMBA3Nzc2L59O4WFhbi7u2Ntbc3UqVMZMGCAXuuqmZkZ1apVo3z58oSHhwuxG7Vazd69e0ttSf4tmjVrJipqZVG5cmW6d%2B9OQkICOTk5PH78GJlMhru7Oxs3bhRVROkzSCqtUHQtlmYdc3JySszq6RqZ/5apua7SbfHr7fPnz3n%2B/Ln4XSaTMWXKFCZMmCAWRBQKBdeuXaNixYqi/d/CwoK7d%2B%2BKDgcDf38MlT0DBgwY%2BIuJi4tj3LhxxMbG4u3tzcOHD9FoNNSpU4d79%2B4JcYVhw4axdetWbG1tWbNmDUOGDEGj0WBlZSVU786fP09sbCzR0dHs2LGD27dvl/DYKg2lUomZmRmmpqYkJCQAsHr1aj755BMsLS2pWbMm33zzDdu2bSvTlLdnz54cPHgQX19ftm/fjomJidjm6enJjBkzxAyehEwm4%2BHDh2IeLysri5EjR1K5cmWePn1aQvVSUhCVAo9Dhw4REBAg2skkSqvcBQUF0a1bNyF3npWVRUREBAsWLNCzoli5ciVarZaePXty5MgRBgwYoLe9cePG%2BPj4sGzZMqytrf%2BUMuhvUZZBuoSubLzkr2dqasqgQYPIyMhg%2B/btuLq6EhcXR6tWrVizZg1nzpzBz89PT1VPUljs3LkziYmJyOVycc506NBBJERQVJ3QrZrWqFEDmUxG/fr1adasGQ0aNMDHx6dEclccSYG1rM8siZR8LByRFAuhKJmysLAgNjaW58%2Bf8/XXX2Nubk5qaioTJ05k3LhxLFiwgN27d9OxY0csLS05cuQIxsbGbN68mb59%2B9KpUye9RYCQkBDkcjleXl56AkBHjhz5qJKq7s%2BS2ueOHTto2bIlV65cARBzckqlUuwrrVYrvP3s7Ow4d%2B4chw4dEjNbusycOZM5c%2BawcOFCUcmX0E14pe%2B7o6OjEI7RregqlUrhuyYlXTY2NlhYWCCTyYRBd1xcnFDJlI53YmIiHz58EMq9rq6upKSkUK5cOVQqFS9evECpVIqOACsrK/G6ubm5vH//Xvwul8tp3bo1ffr00fOW69KlC6NGjRJV8y5duvDy5Uux2NSiRQuuXLmCsbGxaGMdN24cJ0%2Be5OTJk%2BTn5zN69GixCOLs7CzaYX8Pf7StMz4%2Bni%2B//BJ3d3ceP34s3pO7uztt2rTh5s2bPHjwAAsLC5HcSab1Go2GWrVq0b59e5RKJStXrkStVtOiRQuuXbuGWq2mRo0aPHr0qMzXt7a2Jj09Xc/T0dLSkgYNGhAfH09UVBRNmjTh9evXvH37FhMTExQKhZ6QkJubG3FxcVhaWgobjM8%2B%2B4yrV6/StWtXJk%2Be/If2iYH/nRiSPQMGDBj4L3Ht2jXu37/PtWvXuHXrFjKZTJhYS4pzUjuWpEInBXVGRkY0bNiQSpUq8erVK27evKnX1ta/f3/27dsnVPdGjx5NSEgIb9%2B%2B5eDBg3pVHykBKA0nJyd8fHzIy8sTZtPF71u7dm1GjRpF69atRRLn5eVFeHg4nTt3ZsiQIWzZsoXMzEy0Wi12dnZcvXpVPP7mzZtMnz6dyZMns3TpUn766SeheikF07dv3%2BbVq1fs2LGDNm3a0Lx58zL3a2lBm64a488//4ynp6fYFhgYyNatW0lOTsbe3p7Nmzfrbffy8sLR0RETExOCgoKws7PTM03/T1CWQElpFK%2BOSS1fkhhPzZo1AYQ5tG47WrNmzUok76VJzEPR7KJuFbROnTrIZDJCQ0PFc2ZlZXHjxg0UCgUNGjTQSxYBRowYwYsXLzh79myZn1lSZ5UqE%2B3bt%2Bf06dNotVpsbGwoLCwUM0uVK1dm4cKF4jmuXr1KQEAAzZo145NPPmH37t1s3bqV1q1b07FjRzH3umPHDrZv305iYiI2NjbY2toKSf/U1FRSUlKoVKkSvr6%2BQJHi57Nnz3jy5IleO3FYWBiXL18Wgi/r16%2BncePGeHh4sHPnTmHonp%2Bfz4EDB7CysmLq1KlidtDY2FhYEegmwVLFXcLIyEjsy5SUFFGFK97CKJfLqV69OtHR0aL9uXXr1ly/fp28vDxUKhWNGjXi4cOHQtipZs2avHjxgipVqmBpacnbt2%2BRy%2BUsWrSIOnXqsG3bNo4fP067du1YtWoVKpUKKysrVq5cybFjxzh48KCoKEqtrNevXxfnn5GREQ8ePNA71kePHiUsLIzw8HBsbW3p1KkTXbp0EeeqdH598803fPnll5iZmVGnTh1q1qxJZGQkUGTXUL58eT1Lg/Xr1zN16lTu378PFLVbT506lR9//LHM%2BeD4%2BHgcHBz0zvuPtXUWx9/fX7R9FxQU8OHDB5HofQylUsnPP//MrFmzKCwsZP369bi5ueHo6Ejz5s0pLCxkz549DBgwQLTw5%2BXlMX36dL7//nu0Wq2omj5%2B/JiuXbvyyy%2B/MGfOHKpXr87Zs2d59eoVT5484cWLFyQlJYkWfWkWWvcc01VhlXB1deXNmzdiwah4V4SBvyeGNk4DBgwY%2BC/RtGlTmjZtyqpVq0QCJVkVSJRWmdNoNOTm5nLlyhVRPdDlyy%2B/ZN68eezbt08kZ6GhoWzfvp1p06axZ88eateuTWRkJEeOHCkxO6f7upLAhIRCocDd3Z1Xr16h1WrJz8/n8ePHTJgwQS%2BJk54zNzcXHx8f1Gq1UOMbMmSI3vt1dnYmLS2tVNXL/Px81q1bJ1buzc3Nhepl8aCtsLCQrKwsmjdvXqJyd%2BHCBaHGqFuBhCLxl/v373Pp0iU%2B%2BeQTvURP%2Biw7d%2B5k5syZQhxG1zT9P0Fp7Y2/lxo1apR6u3QM3r17R1BQEIcOHRLteCqVivbt2zNmzBg8PDx%2B1%2BtI1iASv/zyC%2BPGjRNm8uXKlWPbtm1Ur16dt2/f8v3333P58mUxv1cc6TNLKoUtW7bk1KlTfPPNN5w%2BfRoo2T748uVLBg4cqPf5TExMGDt2LBYWFixdupRLly6h0Wj0WnObNWvG2rVr2blzJ8HBwURHRwuRnU8%2B%2BYQuXboIkR3dhQEHBwe2bdsm5qukqsiGDRtwcHAgNzeXw4cPo1KpaNiwIRs2bCAsLIwZM2bw3Xff0bt3byIiIsT8qm5gbWVlJRZApEUcSU1RoVBgZmYmRJlMTEzIzMzk3LlzNGvWDBMTE3JzczE2NhZm6tOnT2fZsmX4%2BfkJ9dTPP/%2BcJk2a0K9fP2bPns2HDx/o2rUr33//PS1btmTt2rVUqlSJvLw8%2BvTpw5o1a3B3dyc6OlqI%2BTRq1IiIiAimT59OZmYmSqUSOzs70tLSqFixIgsWLKBdu3aiiijZj0j7UqPR0KBBAxo0aIBSqeTq1auEhYXRp08f3Nzc%2BPzzz%2BnSpQvm5ub8%2BOOPtGrVCjMzM8zNzZk/fz5du3YFikSMVq6HeuGBAAAgAElEQVRcyfDhw0USIrXJSri4uJCYmCj%2Bf/jwYYnz7resFX4P0rmnVCpF22hBQQEFBQVCLEWr1QoF5RcvXqBWqxkxYoR4jn79%2BgFF17%2BUlBTUajUdOnQo8VrNmjUTVhuFhYXiWIaHh9OxY0fGjx8v9n1pxvGFhYXEx8djZWXF7t27xUKiNBsotZhWqlSpxIKRgf8bGJI9AwYMGPiLyc/PZ82aNYSGhpKRkUHbtm3FbJLEx8yoJQP0yMhIUlNTSySEgwYN0vt9165drFy5kn/%2B858MHz6cOXPmcO7cOdLS0kSgJJPJGDBgAMbGxowYMYKgoCCgKFB4/vw50dHRvHr1ijZt2oiqCxQF%2BPXr12fGjBkcP35cvOa5c%2BewtbXFw8NDVJcqV64MwO7du4USKRRVeKTAy9/fn/bt2xMcHMy9e/coLCzk6dOn9O7dWy8gB/2gTQrQLS0tSU5O1kv2ypUrx7Nnzyhfvjzp6eklkj0osqCYOnWqnseYLmZmZkyaNEnIyv%2Bn8ff3Z/bs2XqzYbdv36Z27dqi9S8lJYWvvvpKqKJ%2BjJycHAoKChg2bJiY65GSeblczsGDB0sktX%2BUZcuWUatWLRYvXoxKpWLZsmUsWrSIsWPHMm7cOMzMzDAyMmLlypXiMZIXmy4ajYa0tDSMjY0pKCggMDCQ8ePHM3r0aJYvX05wcDBpaWmoVCpsbW25fPkyS5cuZfv27bRq1YqrV69iaWnJu3fvsLGx4dq1a5iYmOi18EnJ1vbt21GpVB8V2Vm9ejWVKlViw4YN4lzx9fVl//79uLm56bUeL1q0iCVLlrBjxw4SExNZsWKFaBs%2Bfvw4x48fx8PDg8zMzBJV8YyMDCwsLKhfvz5jxowR57Zua6h0jH766ScxsyuJc0g/S4qbDx48QKPRCMsJKKp0HTt2jLFjxwqBpwULFgBFFe0OHTqwZs0aZs%2BezcGDB5k0aZJ4bimxjYiIABDt3vAvVc8XL14wYMAAAObPn8%2B8efOYMGECu3btwtbWlo4dO5KbmyvmF6tVq8ahQ4fo1q0bmZmZnDlzhrCwMDZt2qTnGQhFlT7d9myNRiMqxFIVc/Xq1ZiZmYnvvEKhoKCggDFjxlBQUKB3jftPUdwa4mP079%2BfqKioj7YolzVTKdGmTRvxc3p6Ov7%2B/kDRnGRgYGCpj3F1dcXX15ekpCQcHR25evUqMpmMxYsX681C16hRo8zFIgP/dzAkewYMGDDwFyNVmqZPny6qRj169KBr167ExcXx/v17srKy0Gg0tGjRAiha2Xd0dEShUPD69WvUajUVK1Zk9uzZLF26lKlTp4r2oO7du%2BPi4oJWq%2BX777/Hy8uLWrVqERQUpBcIDhs2DLlcztatW8XMzoEDBzhz5gzTp0/n0qVLnDx5koyMDGrVqoVMJiuhFlm7dm1Onz6NiYkJFy9eFAIf3bp1IyUlBaVSyeHDh/US0Hfv3rFixQpGjBjBr7/%2ByvLly4U/W0hICGfOnBEB%2Bdy5c1m7du1vrsKXFqBLSLYN5cqVK9O2ITMzkypVqogqVXFOnjxJXl4e79%2B/Jzg4uIRPnsSflXMPDg5m2rRpesneiBEj9NowCwsL9QQZymLGjBmEhoYKgY769evTp08f2rVrR8OGDdFqtXoehn%2BWp0%2Bfsnv3btHKOmvWLD755BOmTJnC559/zjfffCPOX4myFCHVajXR0dEUFhYSGRmJqakp48ePx9/fX1gd7Nu3T7SgduvWTbQB9ujRg8jISA4cOECTJk04e/YszZs315slXLp0KXl5eeTm5qJWqz8qshMREVHiPNIN1s3NzfUSf39/f/bu3Uu1atW4d%2B%2BeUE2EojmztLQ0PW80X19f%2BvTpQ0pKCsHBwVy5coXw8HC8vb1FQlecS5cuAYhzV0qKdNsHJesFqe3R0tKSUaNGMWfOHDZs2ICRkREajYb69etz69YtUlJSOH36NBMnTmTFihU8e/aMyMhI5HI53t7epfphSsTExIifpSRQqgS2bNlSr/0vMDAQFxcX4uPjGTlyJAcOHKB///5YWFjQvXt3unfvzvXr15k3bx4ZGRns37%2BfyZMnM2zYMIYPH6637/ft2yc%2Bv0aj4enTp6hUKiZPnoylpaUwYpdUUHv06MGePXv%2BI76cEt27dyc%2BPh5jY2NcXV2Jj4/nw4cPeomqhNSabGRkJNryHR0dsbW1JSoqCgsLC7KzszEzM6N69eo8f/5ctMgaGRmRlJSEUqkUld2ykOY1pQUFlUrF6NGjxVy1tEB09%2B7d/9h%2BMPD3wZDsGTBgwMBfzMmTJ1m5cqWQ%2BPfw8KB3797s37%2B/hHx2YmKieJxuoC8JMDx48AC5XE79%2BvWFvHdQUBDHjx8nNDSUefPmMW/ePPE4JycnIfOdmZlJbGysaP2pXbs2%2Bfn5bN26lbFjxyKTyahduzZTp06lSZMmeHp6EhYWphd83bp1C61WS0hICIDwH9u/fz/37t1DrVZTqVIlgoKC9CTAd%2B/eTWBgIGq1mp49ezJo0CBhYN20aVMRkJclMFOc0gJ0XTw8PGjUqJGYnSpte/H5NgkXFxe2bdtGdna2MEgv7pMnHZM/m%2ByVtvL/Z0fqQ0JCxGO//PJL%2Bvfvr6cE%2BZ9CUlKVsLKyQqVS8dVXXwnvueJIFeNXr17h7OyMkZGRqF4NGzaMGTNmsHLlyhLHYejQoezbt4/CwkL2798vjO0fPnyIl5cX33zzjRAfycnJEabnCQkJnDp1iosXL9KrVy8x7yeJ7EyZMqWEME5mZqZQ1iyNQYMG4e/vLxYGQkJCMDY2JjY2liFDhtC5c2cxB2tkZMTgwYN5%2BPAh27dvB4oC/7lz5zJ06FBxrEJDQ9m7dy9z585Fo9GwfPlyPcXZgwcPIpfLRUJXGsXPl8zMTOEbKKlz1qtXT0%2Bh0dPTk1u3bvHjjz8SHR0NFFXQjIyMhGdjcHAw3bp103v%2B4lVKmUwmquL9%2B/cXokAymQwnJycqVKhAhQoVGDlyJCEhIfTr149bt25x%2BvRpzp49S3Z2Nm3btuXNmzfs3LmT/fv3U7duXby9vUXyKr033f%2BhaG7u1q1bJT47FCXDf8aX82O0a9eOkJAQXr16pVeVk2arS1N7VSgUKJVK5HI5Dg4OrFmzBj8/P7RarahCN2rUCGdnZ%2Bzt7Tly5Aj79u2jU6dO9OzZk0aNGpGens769evJyMjA09OThw8fcuzYMQYOHEjFihWJjIxEo9Egl8t5/fo1r1%2B/xtzcnNevX4uFD8MM3v%2BfGJI9AwYMGPiLkVTwJBYvXoylpSVubm48e/YMS0tLlEolHz58QKVSYWNjg52dHU%2BePBECJ%2BXLlycxMVFUDRYvXoy1tbXwV/v222/Zs2cP5cqV48OHD2g0GqFSN2TIEIKCgrC1teXKlStYW1uTl5fH8OHDsbKyokGDBjx48ID09HSePn2qp%2BY4Z84cvWqIFESYm5uTlZUlVrLv3LmDTCbDysqKRYsW8eTJE71ZnqpVqxIfH4%2B3tzfz588H/mVgLSVMp0%2BfZsKECb8r6fmtAL1fv34sWbKkTH%2BuZs2a8fPPP5dogYUiv7e7d%2B8yZswYpk%2BfXup9/jdx9uxZ9uzZw5YtW9i7dy979%2B7FwsKC5s2blzBb/yP83sd16dLlo9sXLlzI7t27%2BeKLL4S/GhQtFOTk5NC/f/8SrWWScArADz/8II6jVqvF0tKSTz75RJimz5gxg7p16wKwefNmjh07hkwmY8KECeI5/Pz8yMnJITExsYTIjoeHh15rcXFu3rwptksLFNKc24wZMzh06JD4TK9fv2b69OmikqpQKGjZsiUeHh7s2LEDV1dX7OzsuHjxItHR0SgUCjQajV4FxtTUVLQr%2B/n5kZyczPHjx3Fzc8Pb27tUixOFQkFkZKRQu5QWdGbMmIFWq%2BWrr74C4PHjx0CRgI0uZmZmyGQy4eMYHR2tZ7Iu/S4pQf7www/MmzcPrVarV/EuKCjg9OnTDB8%2BHLVajY2NDQ8fPqRZs2ZkZ2fz6aefMmfOHFq2bImRkREnT54kICCAe/fuiTbutm3bIpPJyM3NJScnB5VKRWRkJAqFghkzZui1v0s4OTlx7949FixYINpQJf5dBd2xY8eKTgRJfCg8PJzo6GjhW2plZUWdOnVISkoiLS0NHx8foqKiePXqFQ8ePMDPzw/4V6vshQsXuHDhgt7rSPfZuXMnO3fu1EuwlUolMpmMHj16kJmZSWJiIiYmJsKfFWDChAl4eHgwZcoUCgsLUSqVouXWwP9fGJI9AwYMGPiLady4McuWLWPJkiXY2dkJ8%2BHAwEA6d%2B5Menq6EB0pKCggIyMDZ2dn8Yc%2BJSUFV1dXPnz4IG67fPkyHh4elC9fXgic1K5dm6ioKLEKnpCQgEKhoH///gQFBdG1a1cCAwPRaDTY2try/v17mjZtiouLi/B3qlq1KpMnTxZ%2BVZcvX9YLOho0aMCZM2dE0KI7S6TVasnIyCgRYMjlcg4cOCBUOCViY2Np2rSp%2BF0Kdt6/fy/as3TRDdp%2BK0Dv1q0bR48eJSIigq%2B%2B%2BkpUhiQD6Pv37%2BPh4UFQUBD3798vdbtUgfzfjqurK9988w27d%2B9m3bp1nD59mrCwMD2hnR9//JEJEyb8ISGG4uIPZfnVfaxFNCAggBMnTrBhwwa2bdumZyMgibEkJCTozYdB0XkHRefO9evXiY2NJT09HRsbGypWrFimoujUqVOZPXs2NWvW1HtfkgF5aSI7/fr1Y%2BnSpbi7uwtV2OJs2LCB8ePHs2fPHrFAsWTJEgICArh69apIiqR5ury8PBGMb9myhYKCAvLy8pg7dy4qlQq1Wi3sG9RqNXK5nDZt2giLhoEDB6JQKHjz5g0AAwYM4NKlS5iYmIiKc3BwMF988QVVqlTh3bt3GBkZkZGRgUwmw8LCAjs7O9LT06lZs6aY31y%2BfDkvXrxg48aNIiGwtbVly5Ytpc50SsIjt27dwt7eXlgqrFixgvz8fGxtbdm2bZtYBCpXrhy7d%2B8mOjqaS5cukZWVhUwmY/r06bRr166EeiuAra0tAwcOFEI8uuTn53P27FmRCNavX5%2BYmBg6depU6nv98OEDMpmMWrVqiZk%2B3f2py%2B9V45To3r07jRs35uzZs2RmZqJWq6lVqxY5OTm8ePGCK1euiMrm6dOnUSgUQon1j2JnZyfsG6BkO7R0nuhSUFAgknlzc3PGjBnD8OHD//BrG/j7Y0j2DBgwYOAvZvbs2UycOJFmzZqxZcsWHBwcyMnJ4fXr17i4uPD%2B/XuhsCeTyTAyMmLs2LGMHj0aKPrDP3bsWObOnUtWVhZarZZOnTpx5MgR6tati4%2BPD7Gxsbx%2B/VpPartcuXIkJyfTq1cvtFotL1%2B%2BpHbt2ty%2BfZv4%2BHiAEuIfkpqdFGTqVuegqH1Sq9VSq1YtHj58qFc5KmsFXUoIJRVOCcmLTEKaVbG2ti7VNF03aMvKymLOnDmsWrWK8PDwEq959%2B5dHj9%2BzKBBg8jNzRWzVba2tnh7ezNz5kx8fHy4ffu2EIcpbfv/FL9lqv5naNasGZ6enjRv3pz58%2Bdz//59AgICOH/%2BvPhXo0aNEp6FZSG13Uom61qtloULF%2Bq1hhUUFLB8%2BXIRxOfl5em1JO7fv5%2B5c%2BfSunVrWrduDei3dEKR8fvGjRvZvHlzqVUboNTbY2JiUCqVuLu7i32pW5UeO3asmPn6GB9ThX39%2BjVarZaqVatiZWXFq1evSEtLIzg4GC8vL0aOHMmmTZuwsbEhISGB3Nxc8V6k%2BTrdoF3aZ87Ozmg0GnH%2B5%2BTkiIRMJpORkJCAjY2NmM3KycnBz89PfP9lMhmHDh0CipJltVpNmzZtRHKm1WpJSUlh7Nixouqm1Wr58ccfyc/PF/cpKCggJSWFunXrigSiU6dO1KtXT6jvAiUWcJKTk9FqtXz48AG5XF5CWKp///5MnjyZzMxMwsPD/3C78927dzly5AhhYWFkZGTg7u7Ou3fv%2BPLLL8nLyys12ZOsPcqVK0daWhoDBw4sMWP77%2BDn58elS5d48%2BaNuKbpqn%2BqVCqqVatGp06dWLdunZjP7tu3L1qtlj59%2BnD16lXevn0r2i/Nzc3FzKednR3GxsYkJycLgSLd67mZmRk5OTmim0MSomnSpAmDBw9mwIABnDhxAjs7O71ZYAP//2Hw2TNgwICB/xK//vorDg4O7Nu3j59%2B%2BgmZTEadOnW4ffs2BQUFYuBeoVAgk8nEH/pPPvmEp0%2Bf0qtXL3bs2IFWq%2BXYsWMkJiayZs0aoqKiUKvVImC0t7cnKSmJ6OhoVq1axfbt2zE2NiYzM5Nq1aoJxU2pYleaUfNvIT1mwoQJ/POf/8TFxQU/Pz%2BOHz9Oly5daN68OYMGDWL48OHs2rWLu3fvcvjwYT2DdE9PzzINrH/55ZfffA%2BhoaFERETg4%2BNTamWuQYMGYq6sTZs2JdQYdcVhStv%2BP4mnpyf16tXTMx//5ZdfqF27NsbGxiQlJQmBluJG9cX5rUBaq9Xi5eWFVqulW7duYq7rjyKpAgLC5684T58%2BpXLlysLvzsfHh%2BPHj1OhQgXgXy2dO3bsoFGjRmi1Wq5fvy7sOXr06MHChQvLrNxJrzF%2B/Hgx0%2Brh4cGmTZtwdnYWcv27du3Czs6OGTNmiMd9%2B%2B23TJo0qUyRHSnxj46O5sGDB3h5eeHr68uuXbtwcnJCLpfz5s0bnJycUCgUZGdnk5OTQ25uLhYWFlhYWLB7924AsVghfb%2BkSk358uVRKpUlqqGNGjXC19dX%2BLlBkcn9mTNnePToEe3bt%2Bf58%2BdERERgZmZGbm4ulpaW1KxZk6ioKFxcXCgoKODp06cYGxszZswY1qxZI%2BYIVSqVuL7k5eVhYWFBRkaGuNbk5%2BeL92tiYqJ3XkKR96QuQUFBHDhwgB9%2B%2BIHt27eXarcRFRXFkCFDmD9/fqkWAwDjx4/nu%2B%2B%2Bo1y5crx7947g4GCCg4N5/fo1pqamVKtWjeTkZN6%2BfSuESBYvXizsGSTu3r3LiBEjyMjIYNy4cWzfvl0onP67rF%2B/nsOHD4v3YGRkhKurK5UqVaKwsJCoqCjhcVc8xG7YsCF37twR5vO%2Bvr6izdTU1FQIzwCMGjWK2NhYwsLCxEKAblXQwsICMzMz3r9/j7GxMVqtFltbW96%2BfSteWy6XY2xsTO3atZkwYcK/ZfFi4O%2BLIdkzYMCAgf8yWq2WdevWsWXLllIV3YojiVrUqFGD4OBgtFot3t7evHz5UkiNu7i4EBUVhb29PTVr1uTKlSvs3buXevXqsWTJEgIDA6lSpQqAEGxwcHDg/fv31KxZkydPnpCfn4%2BFhQWenp6UL1%2Be8%2BfPo1AoqFu3Lh4eHqSlpXHkyBEmTJjAy5cv0Wq1zJo1ixYtWjBz5kx%2B/vln0tLShPm4p6cnly5donXr1qxbt47Zs2ejVCo5cOAAzs7OeHp66hlYw28H5MXRDdB1K3MKhYJdu3bRtGlTlEolERERDB06VMwf6orDlLb9f5rSLAl0CQgIED9L0v5lmUMHBQWVaIMsTt%2B%2BfQE4dOgQ3t7ev/t93r17l%2BXLl7NgwQK96tqECRNISEhgzpw5ZfrqQZG35NKlS2nZsiUBAQFs2rSJRYsW0aRJE1avXs3evXtLGFRLs0ZTpkwpVYBn8ODBZGZmMnv2bFQqlTCDnjx5MiNHjiQ1NRWlUin228eQzLWLJ/5hYWEiCdFdlJB%2BDg0NFefPlStXUCqVDB8%2BXJw/NWrUwNPTU3iaQVFLokql0qvoFhQUYGxsjEqlIiwsjHfv3lG7dm2gSMVX8pI8efIkMpmMQf%2BPvTOPj%2Bl8//c1M9k32YiE2GoJIpZE0YoSaULV0lpLba1GtdZ%2BP7VUF1VbFLWEqj22WEpKqEaEItEKfvalSkvTyCIkIXtm5vz%2ByOs8nUkmC9WizvWPzJyZM3OWZzz3c9/3%2Bz1kiMiaDhgwgIiICDp16kRCQgI6nY65c%2BeyZs0aEhMTRcmlWq1GpVIJbzb5Obmn18HBgXv37vHcc88xadIk7t%2B/bzJzBsULLAEBASKDOnjwYE6cOEHHjh2xsrISgej%2B/fvFsZkq3ZTfr9frGTduHJGRkZw4cUJkai9fvoxarcbCwoIXXniBw4cP061bN7FQVLVqVZFxTk5OJj09XSyUbd26lf79%2BxMREWF0by5YsABJkhgxYsQD2TPI4jv29va8%2B%2B67DBo0CGtra6PrevLkSbZv386%2BfftM9hzLY7ZNmzbCm9TR0VGUMqvVapydnenQoYPwQpWvixz0qdVq2rRpI0ruTSHvUzZXX7x4MYGBgZU%2BVoX/Bkqwp6CgoPAEIdsuODs7U1BQIHrhnJ2d6datmzBZNkQu1TLE3t5eBI6Wlpbcv38ftVotytKys7M5f/48iYmJ4nU9e/Zk4MCBtGjRgvDwcFavXk3nzp05evQof/75JxYWFhQWFuLu7s60adOYNm0at27dYs%2BePfz000/Ca0ySJGxsbOjUqRO7du3C29ubli1bsmHDBlq3bi2ydK1bt%2BbMmTOMHz8eFxcXo4yVYfmdjDzRtbOz48cffxTPy5O2GjVqEBcXZzIz17VrV0aOHGkk/jJlyhROnjyJSqWqcPuTQlFRkciyGPqxGSJPRitLWXL/Mr6%2BvvTq1YszZ84wZMgQOnbsyOTJk0UfJxRnIcPDwzly5AibNm3C29vb5L6aNm1K06ZN2bRpE7169WL06NF07NiRN954g2vXrmFtbY2NjQ116tShd%2B/ebN%2B%2BnRMnTqDRaGjUqBGbN28uFfD5%2BvqyatUq0V%2BXlpbGyy%2B/jKurK/Xq1WP69Om4u7uL15csRS5JXFwce/fupX79%2BtSoUaNU4N%2B4cWPi4uJEsPfxxx%2BzfPlyOnXqJDwnfXx8uHTpEq%2B99ppQp9VoNNjZ2YnyTCjO5uj1enJycrC1tSUlJQV3d3dUKhUvvPACO3bswNPTk549e6LVatm9ezfm5uZs2LBBKKHKvXXh4eEMGzaMWrVqkZSUhFqtZu/evbi6uvLpp5%2BWKtetVq0abdq0ISEhgbp163L8%2BHEaN27MjRs3RHlkdHQ09%2B/fF9UCK1as4MCBA2g0Grp06UJYWJi4B8%2BdO0f//v2RJImgoCBiYmJEeaJGo8HCwoLc3FwcHBxQqVRkZmbi4OAghFa2bdvGxYsXsba2xt/fn5SUFC5duoS5uTlZWVlMmjSJ/v37Y2NjQ9OmTdm1axdZWVnCc1T2MrS3t8fX15e33nqLoUOH4uTkREpKCsuXL6djx47i%2BNevX8%2B6detKnc%2BK2LNnD9u3b%2BfChQtC9dPJyQlnZ2dycnJITU0td/zZ2NiIMv2HQTY%2BN4X8WzVkyBA8PDyIjIzktddeY/Xq1UDx/yOycrLCs4MS7CkoKCg8Jnbt2sX69eu5ffs2lpaW6PV67t27h0qlokqVKlSpUgVPT08CAwPp1q2byX2EhYWxceNGfH19OXjwIEOHDiUlJYXY2Fh0Oh16vZ5atWpx8%2BZNGjRogCRJpKWlCVEHedLg6enJqFGj6N27N1CcbRw7diyHDx8mICCAjIwMUVIWGBjIwYMHhUJeeHg4bdu2JScnBz8/P6A4AK1Rowa3bt2ie/fu3Lhxg3Pnzom%2BE2dnZ6ytrbl16xZubm6lslMlxRJk03SdTsfXX39tJB6xfv16wsLCyMrKok2bNtjY2JSaoHt7exMbGyuUF7VaLT4%2BPiLrWNH2f4OcnByOHz%2BORqPBz8%2BvVAbkxx9/ZPbs2aKvsqxgLykpSfRgluTatWssWrRIZHnkFf/ycHd3JzY2lrfeeouGDRsyefLkMl/7ySefkJyczKpVq0xub9GiBc7Oztja2vL7778TFhbG0aNHiYqKwtLSEnt7e/Lz84mIiMDNzY3ExEReffVV6tSpw507d%2Bjfv7%2BRqiYUBztxcXG4urqK55o3b07Pnj2F0qshsvCPIYZZ0tTUVOzt7bG1tSU2NrZU4G%2BY2ZP3ZVjOKZ/7kqWtOp0OW1tbHBwcRHbtzp07ODo6olKp%2BPrrr3n99ddp0aIFa9asYdu2baxevZqCggJq1KjBpUuXaNSoEffv36d58%2BYsWLBAlONCcf%2BsVqtl7NixnD17FkmSaNiwIcHBwSxZskSU9m3YsIHhw4cTExODh4cH3t7ewrbF3t6ewsJC8vPzsbW1pUePHrzxxhs0aNCAGTNmsG3bNnr27ImFhQVRUVHk5uayZ88eNm3axKZNm9Dr9cydO7dUWaWM4T3bsmVLxo8fT9%2B%2BfbGxscHLy4vatWvz5ptv0qZNG3r06CEez549m927d1O/fn2geNFg3LhxREdHc/v2bdzd3Rk8eHCp0usmTZrQokULLl26RFRUVKmxkpOTQ0hICHXq1Hlgewb53j137pzJUnc7OzucnJyMSi/VarWw1snIyMDGxgYLCwuKioqEaI9er8fa2pqGDRtSUFDAxIkTycrKIjo6mr179%2BLr68uVK1fIz89HrVZTVFREtWrVxEKgubm5sJjp3r07UVFR4ryoVCrOnTv3QMep8PSjCLQoKCgoPAbCw8OZM2cOkiRhbW1dquxOFi7JysoiJiaGX375xWRJ4fHjx8nMzCQ9PR29Xs%2BZM2cwMzMTPTdy4Ojh4UFubi537twR/T0ODg6kpKSg0%2Bm4e/cuc%2BbMEQqEKpWKJUuWsHv3bpYuXcqNGzeA4kxEfn4%2BPj4%2BWFpaEh8fz7Vr18QEV61WC6EVOaA8efIktWrVQqVSMXnyZL788kveeecdnJ2d%2BeSTTxgyZAguLi7l9ppVZJq%2BadMmnJyc8PT0ZObMmaV81EyJvxiqMVa0/Z/m5MmTvP/%2B%2B%2BK6u7q6smbNGho2bMitW7f4/PPPOXz4sAimy0P2NDMkNzeXefPmERERIYIDlUpVqkeyPC5cuMDHH39c7msGDRrE0KFDy9wu31cRERH8%2BuuvvPvuu%2BL7WFtbExAQwJgxY0TglpKSgpOTExMnTmTKlCns2bOnVLAn79cQtQl5daIAACAASURBVFpd5vc4ePBgqedatmwp7A28vb3ZtWuXCPJL2jTExsYKmw95X15eXuzcudNkv6kcYHh5eZGXl0dMTIx4XZMmTbh//z6RkZHUqlULvV7P8OHDsbKyYsiQIfTu3VsEI0uWLCEgIIBatWqxd%2B9e7ty5I4RV5ON3c3MTFi2WlpZ06tRJLJzI53nIkCFIksTUqVNFNl3uCZVLXlUqFRs3bjRaVImOjmb69OlinAYFBTF06FDmzJnD8ePH%2BfDDD1m0aFGZCqam%2BOqrr0QZ6IEDB/j%2B%2B%2B/Zvn07s2bNws3NDWdnZ8LDw9HpdLz11lt07dpVCM8sWLAAKM6qnT17lvPnz3P16lWj30n5fBr2ahpia2vLuHHjjFSBK0tYWBhubm5GgZ6NjQ0eHh789ttvZGdnk5uby%2BXLl3nxxRfRarWsXr2akSNHotVqiYiIEBnwF198USywzZo1i%2BzsbDp06MCoUaPEvoODg/n%2B%2B%2B85deoUZmZmzJkzhx49euDl5UWbNm24du0aGRkZQLF6rVztcPjwYVHC/CDlqgr/HcpfzlNQUFBQ%2BEfYsmWLKF2sXr06Dg4OQHHJpZubG%2Bbm5lhZWXH37l3mz5/Ppk2bTJYGySbVssDL0qVL2bhxIzY2NlhaWmJpaUnVqlVJTk6msLBQlFX17duXgwcPEhISAkCfPn24d%2B8eAwYMoH379vTp04c%2BffoQGhoqAj0zMzOioqJYvnw5N2/eZNy4ccJwfPHixSxevBidTieCK3nikZyczM8//4wkSaxcuVIYki9evJiioiLWr1/PkiVLyj1f8fHxjBs3rkzT9KSkJCZMmCDEDgwn6E8Dc%2BfOxdvbm8OHD3Ps2DHat2/PzJkzOX78OD169ODy5cvMmzevlJF7Zdi3bx%2BdOnUiIiICW1tb5s6dKzJLD0LJPi9TlBTyMIWDgwMzZsxg8ODBNGvWDHNzc8zNzYmKiuLzzz8XgZ5WqyUsLIz27dtTr149MjMzK%2BxFNERW93xQKgr8a9SoQVZWFgsXLiQ9Pd3ovQsWLGDevHlGKrOGlBzDer2eoqIiI1Ek%2BbcA/gpG4uPjRdnsvHnzsLKyokOHDmJ/JYWVoLjcr2PHjqVKouVs44ULF%2BjYsSNFRUXi%2BwYHBxMXF4dKpSolGpORkWEk8NGmTRskSeK3335j9%2B7dDB8%2B/IHvKcPzUbNmTUJCQti9ezdRUVG89tpr3L17V9hzODo6Eh0dzZAhQ9Dr9VhaWjJmzBh%2B%2BuknFi9ejLm5eanfSfl8llfEVlIVuLLMmjWLRo0aGf0m5ebmcu3aNfR6Pebm5jg5OeHv709WVhbZ2dmMGjVK/D1o0CDatGmDr68vmZmZZGdns2rVKpFpX7hwIV5eXjRp0oSmTZsalWd7enqyaNEiXnrpJVQqFVFRUSQnJ5OamkpKSgozZsxg5syZ3L17V/ybmZlpcqFE4b%2BPktlTUFBQeAwkJiYiSRLt2rXj448/Fv%2BJFxUVcefOHXQ6HTqdjqKiIuHdZMoAuiT79u3Dzs4OrVaLJEno9XpiY2OpXr06Tk5OotRny5YtTJw4UdgJvP322wQHB4v%2Bl19%2B%2BQWVSoWlpSUtW7bkzJkzNG3alISEBM6fP09WVhZOTk6lsiTNmjWjZs2a/P7777zzzjts3LiR06dPC4P0HTt2GJXbGWZUyqMi03StVouHh4eYtJnKzMnnRkav1xsZQIeGhmJpaYmvr6/J7TIPKhtfGX799Vc2b94sru9HH33ECy%2B8wIQJE%2BjWrRsffvhhKfn0iibWf/zxB5MnT%2BbUqVOoVCr69OnDpEmTsLe3f6hMRsuWLdm3bx8NGjQo8zV79uyhYcOGldrfmDFj6NOnj8hCxcXF0b59e7Kysrh48SIbN24kJyeHuXPncv36dapUqWIkhGHI6tWrjYIaeRGhZCZj9OjRlfpu5WFYUtylSxdxP%2B/bt49bt24RFxdHZGQkOp2u1P1TMugo%2BVitVnPu3DnatGkjnisZjJw%2BfZratWsTGBjI3LlzxfPt27cHEAF5bm6uEOIxxNLSEq1WKyT%2BAVxcXMjOzqZbt25CbCktLU1kqqB4jBnabMiMGTPmkahcGlK/fn3Gjx/P%2BPHjOXfuHHv37mXz5s0UFhZiZWUlSk0XL17MgQMH2L59O4WFhRQVFRn9TjZo0ICbN2%2Byfv16IeBSkvL8OU0xdepU9u/fL86fWq2mevXquLu7o1aruXbtGllZWWi1WlEuLWO4%2BKTVakuJEZVcnDIstTfE2tqatLQ00dMNxb%2BRhgrKKpWKe/fuYWtrS4sWLRg/fny54kkK/12UYE9BQUHhMSD3cZSU07awsBAGzDLyKnZ5JYX79u3D0dFRqDrK2QJDI3ZZ6Q2KfbxCQ0PFZOT7779nz5493L59m1q1ajFgwACio6M5cuQIQUFBnD17VphNN2/enIKCAsaPH09ERITIoJw%2BfZrCwkK6du3K0qVLUalUYhIVEBAg%2BksMqWwmoCLTdPnzy9ouZyANkTOMUDxhk/tc5Oyg4XbD7/tPBHt5eXlGAhGyRcSAAQMYO3ZshT6Dhuzbt49ly5axYsUK9Ho9DRo0IDQ01Eh1MzY2Fr1eX24AXZKQkBCGDRuGpaUlgwcPNgqucnNz2bBhAytXrmTp0qWV2p%2BDgwPbtm1j8ODBXL16lWnTpoljtLe355VXXmHMmDHY2toK6wZTvautW7fm/PnzRs%2B1bNmSK1euGD33IFmn8hYGtm3bhpWVFUOHDhVljob3l7W1Nenp6ZiZmRndP/JYNNy3nAXfv38/Li4uqFQqli1bhp%2BfnyiHLHnfz58/nypVqohzodFoCAgIwNHREYCdO3ei0%2Bno27cv3333HW5ubkbm9YYBgoz8OzB27Fjx3PDhw0165pWkMsImsiiO4bHq9Xp0Op14LFNyfPn4%2BODj40N4eDhhYWGMHj1alKjLSqyyJ6dWqyUrK0sEeyNGjCA0NJRly5aZzDrL6rLvvfdehccgk56ejqurK926dRMehCX3feHCBbZu3cq3335rsp/PlC0DFP9/IAd3htvle1fu%2BatduzaNGjXi2LFjZGRkUFhYiLOzs7DOaNu2LbNnz670MSn8t1GCPQUFBYWnAMNJUkkMgzwo7huxsbHh9u3bIkCUyzdlVCoVp06dEttDQ0OFnPjZs2c5c%2BYMtra2zJgxQ5hAy2bT8ir5hQsX6N%2B/P61atRJedmZmZrz22mssXbqUzZs3i8mjPBm7c%2BcOW7du5c0338TV1VVMaBYsWIBer%2Bedd94x2VciB5pyT6EpvvrqKwIDA8XE0nCCbjiJ/SeCtX%2BK7t27AzxQ%2BVW3bt2E55yDgwNOTk4mvfSKioqQJAkfHx9CQkJEhmrBggUcP34cHx8fRo8eLa5Hq1at%2BPLLL/n0009ZsmQJ9erVE16Gv//%2BOw4ODsyePZuXXnqp0t/V0dGRiIgI%2BvbtS2JiIjY2NnTp0gV/f3%2Bh/hgZGUleXh4NGzYUZceGyKXMfwfDQLCihYHk5GRcXFxYvnw5ffv2BUr3ASYkJDBx4kSj5wMCAkhKSmL58uViLMrn75tvvsHKykqId8iG7rLlibOzMz4%2BPkiShK2tLe%2B%2B%2By5du3blhRdewNzcnGHDhokM3O7du9HpdPz0008UFRVhZWUljm/NmjWMHDmSZs2aMX78eEaMGMEHH3yAtbU1x44d4/Tp0yK7FBQUxOuvv250XCkpKaXsYe7cucOtW7eA4jGXmpqKp6en0QJFcnIyUJx1nD9/vniv/FjOulW0mNKqVSuja7Vt2zYjldHCwkJRPg5//WbJ59OU/2afPn0YMmRImZ9Zkm%2B%2B%2BcbocV5eHmfPnuXUqVMcOXKEixcvkpeXV%2Bp9Dg4OPP/887z%2B%2Bus899xzBAcHY25uLtQ1NRoNZ8%2Be5caNG/To0QMrKyuR0V21ahVvv/02kZGR9OrVi%2BjoaOLi4kSvHxQH8StWrOC9994jKCiIQ4cO8cILL5jMxio8WyhqnAoKCgqPAbn/omfPnhVKYcsGzBqNBo1Gg7m5ORYWFmRlZVGtWrVylSzlzIOhL5hsRjxhwgS%2B/PLLCmX65VV0eYVf9rLbt28f9%2B/fx9bWlpo1a3L37l3u3r1Lo0aNuHTpEoGBgUZZnubNm%2BPk5CSUB728vDh//jxeXl5ERERUKIMu%2BwOamrSdPn0aW1tbkd0o71hKKn0%2BCTRu3Jj4%2BHijkr%2By1DYrYvDgwRVmY2S5fyj2KFu1apW4V2R105ycHKpXr05ERITR9cjOzubgwYP88ssv3L9/H0dHR5o2bYq/v79JywxDDE2zDcnJyeHLL7/k22%2B/LZX9tbS0ZMCAAYwfP77C/VcGU1lSU6qwgMl7pTLXJTExkaCgII4fP27Ug%2Bfl5VXqs01lfqA4wJQzbtbW1nh5eXHmzBl27NghMrIBAQFljl9T2SMHBwe0Wi1DhgzhyJEjXLp0qdR7ateuTfXq1blx4wapqak0btyYyMjIMr%2B7/PsgP5azgZGRkWWeH5lPP/2U8ePH8/bbb1f4WtnmYsaMGUbH5enpibm5Ob///juSJOHu7i6uTZ06dfD19cXT09Ok/2b37t1FKfuDMnXqVM6dO8evv/5q8hq4uLgIoawaNWoIu4X09HTy8vJMXneNRmPSSkcOCGUvRIAOHTpw5MgRo9f5%2B/tz9OhRcR9HR0c/8hJbhacPJdhTUFBQeAwEBAQYSbQ/CBqNhurVq3Pr1i1iYmLK/c9cnsDLZuVz5swRK99VqlRBrVYbPb537x7VqlUTE4q9e/cyffp0wsLCcHBwMDKbbtGiBfPmzaNXr15cuXKFc%2BfOUa9ePdq2bUtERARTpkwxytJNmTKFWrVqMWLECKOyJ3klvzIy6GWZpv%2BdSduTgJeXF6%2B88orRKnxUVBQBAQHCgqGgoIBr166xZs0ao2CpLHPo9PR0tFotrq6umJmZcfHiRXbt2kV6ejpJSUkkJSVRv359li9fXkr4Jicnh7fffpvExEQ6duxo8npU5FdnSGWzqXq9nuvXr4sSTC8vL%2BrVq1cqCDPEVPBWFrGxsZUKQmRee%2B21Us/169eP/v37C5sSU%2BzYsYOPPvqIY8eOGWXjk5KSTL7%2BwoULREdHc/36dVJTU9FqtTg7O%2BPm5sa0adOEgX3JgEtW4zTFwoULmTBhAm5ubuK3xtvbm4sXL2JmZoatrS2ZmZnUr1%2BfqlWrcuPGDVJSUrC2tqaoqAgXFxcaNWpEYmIifn5%2BRuWfstG7TFBQkNHjkmqwpkhJSSE4OJjVq1fj4eFBlSpVTBquy3h5eVG9enWRJawMGo0GBwcH/Pz8TPpv/h1GjhwpLG8yMzMpKCjA39%2BfV199VZR2xsXF8e6775ZawPg7yPY1pmjUqBG//PKLEuwpGKEEewoKCgpPKZXJMHh5eQlfL5mMjAzs7e2F2p4c7MlCELt37%2Bbll19GkiS2b99Ov379iI%2BPZ8%2BePcydO5d27dphZmbG4cOHUavVBAUF0blzZ/73v/%2BV8h8zRC5/M1RJLJlpk8vfDE3TZQwDzUc5aXsSmDJlSrnbc3NzOXr0KHq9ni1btpTyGTTMitrZ2TF%2B/Hhxffbs2cO5c%2BeYMmUKkiSJPlGVSlXKaNqQhIQExo0bh6WlJT/%2B%2BCM5OTnMmjVLXIO8vDyqVKliFHwY%2BtXJPEg29cqVK8JqRKVS0aBBA4KCgoyOtyR/N3h7UL777jvRB2aqpPj06dOMGjWKjIyMUsGeKQzva3Nzc/bu3SvGWHx8PHZ2duzcuRN3d3eTweKIESNIS0ujbdu2DB48mOHDh2NnZ4ednZ0or7SzsxNiH/n5%2BcLbzdDk/f79%2B0BxwG1nZycMww2VW%2BVyQ0Oxk/Kub0ZGBuHh4bz55pucOnWKpUuXEh4eztq1a1mzZo3oLVapVNSvX58dO3aUqaIq%2Bxu%2B%2BOKL4nem5G9gycfh4eFGv1kl/Tf/DRITExk8eDC3b98Wi1wajYbXX3%2Bd%2B/fv88MPPwilZHlKLovQyNk/lUol/FItLS0pKCgQ/8poNBrxmy5fU1PeggrPHkrPnoKCgsJ/GLmPx5DMzExsbW3FhFwO9nbs2EFgYCBQ3GNXVFRktJq/ZcsWZs6cSa9evVixYgWHDh1CkiRyc3NFICGLTxj2yMl88sknDB8%2BvFxPvbJk0A0nbVqtlilTppTy1HqaqUhMYfLkybRs2dLIZ3DPnj0EBAQY%2BbEtWrQIe3t7zp07h6enJ6GhoVSvXp2%2BffuiUqlo3rw5GzdupEWLFuh0OrZu3VpmsOfu7k5ubq5QDPzqq684evQoI0aMQKPRsHnzZpo0aWJkm1FZddWS/Prrr3zwwQdcvXoV%2BKsEcf/%2B/SxdupSGDRvy1VdfCVNtQx4mgDMMQiqTJTWksn1g27ZtKyX0UpK4uDj27t0rfNi%2B//57OnXqxPLlywGEiq2cyTGVMUtMTBRB3LBhw5AkiXv37hmpbcqBm4zcqysHePBXgKBSqcjOzhbXQJIkLl%2B%2BzJAhQzh37pxRACHfg1AcBGdnZzNr1ixmzZpFZmYm33zzDXq9nj///JN9%2B/bRuXNnYmJicHV1RavVUr16dZYsWUJRUREhISFs376dQYMGmTxXcsD5IEI7hr9Z8vk09N/8N/D09CQjIwO1Ws2ECROYP38%2Ber1eHGdUVBQ6nc5IrEur1RqVeUqSxM2bNwFEgFeyd1Kn0zFs2DAjL00FBVCCPQUFBYX/NKYMpEv2Tcl9fKGhoRQUFIgJLxRnDSRJokePHqSnp/P111%2Bzbt06rly5IgzW7e3t6d%2B/Pxs2bDDyiSqJJEmsWLECOzu7MoO9shQ3n4RJ2%2BMkPj6%2BlKH8p59%2BSvPmzbGxsTEyh1ar1Wg0GmbMmIGvry/79%2B8nJycHlUrFkCFDMDc3p27duly7do2ff/65zM9MSEjA0dFRBD0//PADCxcuFMbu7dq1o1%2B/fhQWFj60px3An3/%2ByaBBg8jNzeX5559n8uTJNG3aFJ1Ox/nz5wkNDeX8%2BfMMGjSInTt3llkiePv2bQ4fPsz169fJycnB1taWRo0a8dJLLxmpjpZlnQDFZvbr1q1j//79ZfaOQnEmNigoiO%2B%2B%2B46zZ88alRRPnjyZFi1asG3btnLHA0BqaipVqlRh1apVQLHJ%2BvHjx4VvnhxI3blzh1q1agHFmcADBw6g0WiQJEkEgpIkodFoRMCgVqtRq9VotVpGjBjBwIED6dq1K2vXrmXYsGH873//Q6PRUKVKFerXr4%2B9vT3du3dn9uzZTJw4kfDwcAYMGCAEcBISEkqV%2Bxreg4sXLxYKm4sXLyYjI4Pc3Fzc3NyIjo7GxsaGCxcucPHiRWJjY1m4cCFVq1Zl69atzJw5k5CQEHbt2lVmsGfoKVhZEhMTadeunXhs6L9ZkY3No6CoqIjQ0FCxYDJr1iyxbfz48UJBFDBSZTb8%2B0FYt26dUVtAcHCwuGaurq74%2BvoqKp3PIEqwp6CgoPCUolKpylToNIUcKBkqd0Jx9i8lJYU9e/ag0%2BmIjo4W2%2BRJh2weLRusQ/HKsiRJ7N27V5R1LV%2B%2BvMySO7n8TZbRL0l5MuiPe9L2uDHlM1hy0itnRWWPRTk4OHbsGCqVCrVaLbzYevfuzZw5c8jNzeX06dOlyhFPnz7N7Nmz0el0Qjzj7t271K5dW7ymcePGQHEg4u7u/tDHtnTpUmxtbWnTpo1RllCj0dCiRQsiIiL44IMPOHv2LGFhYSYnq2FhYXzzzTeYmZlRs2ZN7O3tyc7OZtOmTajVat577z2h5Llw4UJq165dKngGSmVJy%2BodNSy9HDp0aJklxTt27Ch3fHp7exsJNOn1evLz88V9LZflyZm4sLAwli5dSrt27bCwsODQoUNA8W%2BBr68vJ06cABBea927dycmJoYBAwZgZ2eHpaUln332GYWFhcyaNUsslJiZmREYGCi8OV1dXYX4Sln%2BdGB8Dx48eJD09HT8/f05ePAg/v7%2B6HQ6Nm3aRM%2BePdm%2BfTt16tQxeu%2BwYcPE70FgYKDIaJoiNjYWJycn8W9l0Gq1Rubwpvw3/0lCQ0OJjIykSpUqZGdno9frsbGxIScnp0IRJUNsbGyEwIufnx9nzpwRvocajcboeAyP1xAl2/fsogR7CgoKCk8BZfmsGcqYG2Kqb6qsbJph9s%2Bw58WwRyY2NpbOnTtz6NAh3N3d8fLyIi4ujsDAQPH6Ro0alTuJ%2Bjsy6I970va4qYzPoLw9NzeXgoICEhMTcXd358cff8Tc3JxmzZqJLF3VqlWxtrYmLy%2BPAQMGULVqVTGpT05OJj09HZVKRb9%2B/cT1MGXfYW5u/tBZCJn4%2BHiKiop45513ynzN8OHDeeedd4iLiyu1bf369axZs4bPPvuMHj16GGUZCwsL2b17N7Nnz6ZatWr06tXLZJbUEMMsqSkqW1JcmYxzyfsaKPe%2B3rlzJ5MmTWLYsGFAcSZQzuSEhYUxcOBANm7cKIzqx4wZw/79%2B/niiy%2BIj483MlNXqVS8/PLLhISECEuI/Px8wsPD6dSpE8uWLStXGKci5FJQ%2BbNK7uv48eOkpqaKsm21Wl1uhrhGjRpkZGSwfft2unfvTlRUFH5%2BflhbWwvrFjkzO3bs2DLLcP9N9uzZg0qlYsmSJXh4eLB69Wri4%2BNFqSwUnxsXFxfc3d2xsrLit99%2B486dO1hYWFBYWIiTkxM2NjY0adKEXr160aJFC37%2B%2BWciIiK4fv06arUaFxeXUhYLT6LqsMLjQQn2FBQUFJ4CHsRnbfr06Q/VN1USw4maPDGOjY0VYg%2BjR48mNzdXeNkB/Pjjj/z2229lBpaVKX9TKE1FPoOGWdGsrCyhhtqwYUOSk5NRqVQMHDgQKBZBCQ0NxczMjF69enHv3j3OnDkjRFHs7e0JDg7mrbfeMroehhL7ZfEwJbWZmZlIklTKjsEQZ2dn8vLySvUpAWzdupUpU6bQp0%2BfUtssLCzo06cP%2Bfn5bN68mV69epnMkpakrN5RqHxJ8cNmUkx5asbHx5OcnExKSgp6vZ7vvvuOXr16ib4uSZKwtrYWnnOSJFFQUEDPnj3Jy8vj6NGjqFQqbGxscHBwwMXFhZs3b3L06FH%2B3//7fyxatIhRo0YxduxYLl%2B%2BTEFBATk5OaWCrwe5vg0aNBDZq%2Beee464uDjeeOMNsd3CwsJoAWPZsmU4OzsLlddTp04Z7S83N5dDhw6h0%2BnYvHmzMFWPi4sTQV5eXh6xsbHExcWJ8tOSfZOG/puG/BP%2Bm/I94OLigqenJ5MmTeLq1aucPHmSuXPnClGeDz/8kMDAQEaOHCn6pOWFlfz8fAoKCrh37x5z584V%2B3Z1dcXKyor333%2BfqlWrPvLvrvDfQQn2FBQUFJ4CHkSEYvr06Q/9OS%2B%2B%2BCLW1tZA6R6ZatWqsWjRIlFOdPr0aSwtLVmxYoV4//r160tlKgwnUZUtfzPF45y0PW5MZUULCwtZsmQJf/zxh1FWVM7ebN26lVu3bmFtbU1ISAivvvoqoaGhwjDcy8uLq1evin6wwYMHl3s9JElixowZIoPwww8/kJubS/fu3UUQID8uWbpYXpahTp063Lt3j4SEhDKv3YkTJ3BxcTHpo5iYmEjbtm3LPX8dOnRg0aJFwINlSU1R2ZLiypYbGt7Xjo6OQtjEMIu6ZcsW7Ozs0Ol0hIeHi0AdirPc9erVE%2BcmISFBqPDevHmTnJwcvL29uXLlCl26dGHcuHFMnz6dS5cuIUkSeXl5Rn1yBQUF3Lhxg8LCQnQ6HcHBwQBCjCk4OFiU7ebl5fHmm29y%2BPDhUsc1cOBAJk2axKVLl%2Bjbty9z5syhadOm%2BPj4AMYLFJcvX2bPnj04OjqyePFioLi3Ulb/BIzUKiVJwsXFhW%2B//VaogcrltzVr1uTPP/9k0aJFeHh4iPtdxsXFhY0bNxo9V5GZ%2B8PStm1bTp06xaxZs/D09GTbtm1GwitFRUUUFRUxadKkUu%2BV%2B/wMDdpdXV2pW7cuKpWKNm3acOXKFfLz82nfvj0uLi6VWpBRePZQrBcUFBQUnhIqqyAol2KWXBkvD1MTnaSkJBwdHdm2bRs//vij6AeSJAl7e3uTKoM6nU6IY2g0GjERW7BgAefOnSMhIYEXXnjhgWXQTVk5mOJJNU1/VBj6DJ4/f57GjRvTqlWrSvsM/vLLL/z222%2BcO3eOdevWIUkSTk5OQi1wxIgRZV6PkvYQsjqgKfr27Wv0uLzFik2bNrFgwQLMzc3ZtGmT8JSTuXz5MsOHD0er1TJ58uRSGTy53Li83ji5l%2Bzy5cuVtk547733TJYUm/q8smxQyhuzer2ePXv2lCvgAn8Zvh8%2BfLjUZ8t9k15eXoSHhzN79mwiIyPx9/fHycmJmJgYCgoKCAwMJCYmhqCgIGFMX1BQQGZmJqmpqdy4cQMPDw8SExMBaNWqlTB0B2jdujXbt2/HxsYGlUolAv6MjAwcHBywt7cHisd/amqqyOg1bdoUvV5P8%2BbNuX//PtevX8fDwwNJkkhOTsbHxwc3NzcOHTrEG2%2B8wdSpU42O3fC8%2Bvv7k5WVhZ2dHcuXLxdBoyGydcvcuXPLtHD5N0lNTWXgwIH8%2BeefpbbJQVlZ03C1Wo2NjQ2SJOHp6cmff/5JUVERAwYMoEmTJnTt2pXg4GDhOyi/VqfTUbVq1f/8b6FC5VEyewoKCgpPAQ%2BiICgjr5CX3E9l%2Bvl%2B/vlndu3axb59%2BygoKKBVq1b4%2B/vz4Ycf0rNnT4KDg6lSpQpFRUWYm5vzzjvviFV%2Be3t7vv76ayOhFldXV06cOIGTkxOzZs2iWrVqD6SoaUpV9FmjZFb0k08%2BYfHixQ9UrtuoUSMaNWrE4sWLcXFx4X//%2Bx%2B9evVi//79fPjhh2zatKnM6/EwKn6G0vxlMWjQIC5cuMCuXbt49dVXadiwIXXr1kWr1fL7779z7do1VCoVAwYMMFmq%2BaDZjL/TO/ogmBqzQ4YMISwsTIxZS0vLclU/v/vuO9F7KZc3GmYCZWuES5cu8fzzvtq%2BzAAAIABJREFUz4vA4ciRI0b7iY6Oxs7OjszMTBHsWVpa4ubmhr29Pb///jt%2Bfn54eHhw8eJFateubSTGIyu7lkdoaCgqlQpHR0c6d%2B4MFGfjnJycuHr1KoWFhahUKpFtNjMz49atW7i6urJ06VJeeumlcvcv9wDm5uaWmTGVy2/LK8P9N7G3t2fnzp0EBASQn59Ps2bNuHTpEmq1mkWLFqHRaBg9ejR5eXniN1mn02FtbU3z5s25evUqd%2B/e5cqVK2Kf4eHhAMyfP9/IUiMvLw8vLy8uX76Mh4cHvXv3/ncPVuGJRQn2FBQUFJ4CHkRBUJ74mgqQKuuDNmzYMGrUqIG1tTV2dnb8%2BeefQsYdwMnJiT179pCXl4elpSUxMTF4eXmV%2Bx1DQ0Px8PAQKofPmqLm38GUKIiHh4couX1QEhMTkSRJlCMGBARQWFhIUVHRA12PirLNGzduFNL85TF79mw6d%2B7MsmXLuHr1qpjcWlhY0Lx5c957770y/QBLlpeaomSv39/tHa1MSfG2bdtKjYeEhASKiooqrfpZcsFGkiS%2B%2BOKLMs%2BDYbbI0tISBwcHBg4ciJ%2BfHydPnmTXrl189dVXRt/zwIEDXL9%2BnQkTJjBw4EC8vb35v//7P1xdXcX1nTFjBtbW1qJ6oDJ%2BhFCcJb1w4QIajYYmTZqI81qZsvQ9e/YYPZZ7AOvVq1dmma38fEVluv8Wr776qhDbsba2ZsaMGQwaNAidTsfHH3/MRx99JMo0raysKCoqEp57OTk53L17t8x9p6WlGT2W/RA1Gg2//vrrQ/lPKvw3UYI9BQUFhaeAshQEDVU6CwoKOH36NJIkMXjw4FLqdw9S0vP%2B%2B%2B%2Bzbt06cnJycHBwoEqVKsybN09k63r06MHIkSONemR%2B/PFHNmzYUKbKoU6nIyQkRExsnzVFzfKQszZlsXLlSl5//XV8fX1FJm7KlCmVtt0oiRy0y/2VZmZmWFlZIUlSpa9HZbLNeXl53Llzp9yJtzz5XbFiBYGBgcBffUqVCWYrM6m1tLR8ZL2jle0DS05OZuvWrQ%2Bt%2BgmlF2wSEhIYMWKEyI7r9XqhzLlkyRIReI0YMQKVSkW3bt3YtWsXLVu2xNzcHDMzM7p160afPn3w9vZGr9fz6aefUr9%2BfYKCgpAkCa1WS2RkJNWqVWPJkiXodDpeeeUV9u7dy/79%2B/n888/R6/V8//337N%2B/nwkTJrBv3z70ej1du3bllVdeAYoXKHbs2EG7du0wMzNj586dVKlSpVJl21Ds4WdY4ij3APr5%2BTFv3rxSYkVyD2CPHj3KtHD5t5G9Dr29vbl%2B/Tpz5szBz8%2BPs2fPkpWVxcqVK1Gr1djb24teaFdXV1QqFXZ2drRs2ZLr168LBVVAlF2rVComTJhARESEKOWsUaMGd%2B/eJSsri%2BzsbJOl9grPHkrPnoKCgsJTQFk9QZGRkeLvu3fvsnDhwjIFWl577bUy92MKf39//u///o/PP/8cGxsb7t69i4eHB8nJySxevJjAwECjHpkhQ4YQExNT5r69vLzYtm0bQ4cO5fTp0%2BUe17OGqVJHw5LbpKQkqlevjpmZGbGxsWi1Wnx8fDh06NBDZUXloN2w/6tly5ZIkkRUVFSlrsfkyZO5fft2mTYGOTk5%2BPn5ERwczMKFCx/ou7Rq1QpJkv6ReyM8PJw5c%2BbQtm1bLC0tH6h39EEwdW97eXnx8ccfi0n4nTt3WLhwoclsnak%2BWr1ej6%2Bvr9hv48aNRUAUHx%2BPlZUVw4cP5%2BzZs1haWvLdd98xZswYrl27hp2dHdbW1uTm5mJjY8Pt27fFfj09PXF2dsbOzo5ly5bxxRdfsGPHDpydnfnhhx9wcHDgypUr9O/fn/z8fFQqFQ0bNhSlti%2B88AIAP/30E%2B3bt2fWrFkMHjyYkSNH0qtXLxYsWMBvv/3GsWPHOHXqVKXKbuVAzvAcyj2ALi4upKenU61aNRwdHcnKyiI1NRVnZ2cyMzPp06fP3xKqelTMmDGDiIgIkXV9GJsSw3NVt25dZsyYwVtvvUVRURGNGzfm119/NalSWxKNRoO7u7vSx/cMomT2FBQUFJ4CylIQNMxq7Nixg7p16z6y8p3s7GzRp7NlyxZSU1PZu3cv0dHRjBkzhvr16xMYGCh6ZFQqVYXlU%2BvXr8fBwUFksp4lRc3yqKjk1svLi507d4pg6FFkRSVJIjQ0VJQ/FhUVIUkSX331Fba2tuJ1cjaxJJXxqzM3N%2BfkyZMP9d3%2BqbXoLVu2YGZmxvTp0/H09Hyg3tEHoawxu2rVKiHKkpubiyRJpco1y1KHbNq0qVG5qqE6ZWhoKJcvX%2Bbq1atoNBq0Wi0LFy7kxo0b2NjYoFarSU9PR5IkcnJycHZ2Rq/Xs2rVKpo1a4a/v7%2B4njNnzuTgwYMUFhYK37ovv/xSZJr27dvH8uXLWb16Nba2tqxevZqUlBThY5ienm6kWurq6kpUVJQwE2/SpEmpYyvpJSpnumQ1UCiuDlCr1eTm5qJWq0lLSyMtLQ07OzsaNGjA888/X2mxon%2BDX375BWtra3E/m5mZUVRUJJRFZRErQPwtb1er1cJDEYqv8W%2B//SYsVAAuXLhg8nOrVq3KnTt3qF27tsi416lTp8K%2BS4X/Jkqwp6CgoPAUUJ7PWkZGBqGhoRw8eJDRo0eL50311TyoT1ZCQoL4t3fv3vj5%2BfHJJ59w7Ngx9u7dy9q1a6lVqxYJCQlUq1bNZHmVjIuLC3v37sXe3l5Mbv9NGXSFv/Dw8CAlJaVUXxQUWyoYcuzYMZPXozJ%2BdYbm0U8KiYmJRiXO/1TvaFljdseOHbi4uAjVz4kTJ1ZaDKY85cY9e/YYBQcAMTEx6PV6ESS5u7vz6aefMnnyZOzt7Zk6dSrNmjUD/rqesqhOfn4%2BWq2W3bt3M3bsWE6fPs3ixYsZM2YMdnZ2jB07ltWrV1NUVAQU9xXXr1%2Bfs2fP4uXlZWQYL/co%2Bvn58c033wgbDENKeolOnTqV4OBgo4WHmzdvkpSUhFqtRpIknJ2dadKkCZ9//nmlzt%2B/jSyYJffErlixgtGjR4u/5aDX1HY581rWNbeysiI/Px9LS0usrKy4d%2B8e/v7%2BNGvWjBdffJG33nqLzZs3U6VKFdRqtWLJ8AyjBHsKCgoKTwFlKQgmJyezd%2B9eCgsLCQoKMpo0btmyhezsbNasWSPKAfPy8irdzydPVgcMGGAUxKnVatq3b09BQQE//PADbdu2FT0ySUlJZaocZmRk0Ldv3yeivOpp5FH6DD4KddPK%2BNXp9Xpq1qz5tz/rUWIYhMA/1ztqasxC8SLM9evXH1r1s%2BSk3crKSpTeNmvWTAR%2Bnp6eTJs2je3bt3Ps2DGx4JOUlER%2Bfj75%2BfnUr19f7Ee%2BnjNnzqR58%2BY0aNCAixcviqAjNzeXa9euiettYWGBmZmZ8NyLj49n1qxZhISEmPzetra2WFhYlGkJU7IiYfr06XzwwQfi88LDw4mMjKRWrVr8/vvvQHGQ%2B6C9gP8Gu3btYv369dy%2BfRt3d3fy8/NFVi8vL4%2BcnBwmTpyIlZUVnp6eRtvlPr/PPvtMBNJQfN3NzMyoW7cuzZo1w87OjnfffRd7e3uqVq3K6tWrOXLkCD/99BNLly4F4IUXXsDd3R0zMzPatGnDp59%2BioWFxeM6LQqPCSXYU1BQUHhKMKUgeO/ePVxcXJg7dy7PP/98qdcXFBSwdu1aXF1dTcrWl4c8WV2%2BfDkuLi688cYbRj0yKSkpAGzcuNFo0vp3VA4VTPO4zaFNUV62GYoFMwoLC%2BnatWuF%2B/q7JaVPKiXHrCRJXLlyhRYtWlRqPJQsbZRN0OUFG/mxbGzu5uYmVBolSeLw4cM0b97cSDHz9OnTIkC7fv06NWrUAP66nnJfmSyIYvjeZcuWieqBktc3OzsbBwcHo%2B9fcoFCkiQyMzNLCRLJ19RQ3RUgKyuLHTt2kJ2dTVRUFFOnTmXDhg107NgRMzMzjh07xrx58/joo48eeRnuwyL3hMoelvJ1V6vVDBgwQJiq//rrr0BxKaacvevfvz8WFhYUFBQwffp0rKys0Gg06PV69Hq9CAYlSRLPlaRkgJiXl0deXh779u3DysqKjz/%2B%2BF84CwpPEopAi4KCgsJTgqGCYOfOnXn11VdFn40pg2EZWUTlYQ2GZSPvU6dOkZqail6vR6PR4ODgQFJSElu3bhWTVlPfUeHhaNWqFbt27XqixWtmz57N%2BvXry/Srq169Otu2bTNS6ixJQECAWDioiEchMOHl5YWZmRkTJkwQPZCfffYZ48aNe%2BS9oyXHQ8uWLXF3d6/QSF3GUIAJioPHqVOnigBqypQpqFQqmjZtSoMGDbh06RJXrlyhZcuW3Lt3j%2BvXr9O6dWs8PT2ZPXs2V65c4b333qNHjx6YmZlx4MABNmzYIEzRZ8%2Bezbp162jSpAk%2BPj5s2bIFKM7K5eTk4OLiQo0aNUhLSyM1NRWVSsWbb75JlSpV2LZtGw0bNiQ%2BPp7Lly%2BbFB3Kzc0lOzvbyFdQNv82VHf9%2Buuv6d%2B/P05OTqhUKl555RVWrVpFzZo1SUlJYdSoUezYsYNbt27x7bff0r9//4cWK3rUdO3alfv37xt5WI4bN0704f1T/agqlQpJkjA3NxfekwUFBXz11VdMmzYNAHNzc%2BLj4x/5Zys82SjBnoKCgsJTgKHPmpmZmVAQ3LBhQ7mKhRkZGSxZsoSdO3dy5swZ8XxlfbLkyWpaWhp6vR5PT08RxB09epSQkBAuX75c7nd8ksqrnlRKZnAAbt26hZub29%2By0HiUlGUPcePGDU6dOkVycjJ5eXnY2NjQoUOHJ0oow5CAgIAyz60hchDysJQ1HoYPH16mN6Fer%2Bedd94pc0x6eXnxyiuviAzoyZMnhem6mZmZyJzl5ORgZmZG/fr1hdqpo6Mja9eupWPHjsKPc/DgwSQlJdG7d2%2B8vb2xs7NjxIgR1K5dm5s3b6LVavH09MTS0pI///wTKC57tbW1xcXFxShrl56ezh9//EGDBg1M3ityj%2BJ7771nsnS1pLprx44d8fT0ZOXKlVhZWeHl5YWPjw9nz57l2LFjWFlZ4efnR1BQEEeOHHliVH29vb2RJImDBw/i5uaGVqulWbNmIgA7ePAgnTt3JjY2FhcXF3x8fIiJieHll19m4sSJXLt2DShWaj169CgAHTp0oG7duiQnJ3PgwAER2L355pusWbOG2rVr88cff4iA0tzcXNiorFy5UvRDqtVqoYSs8OyglHEqKCgoPAVs2bKFmTNnikyDrCBYXt%2BUvFJ%2B//59o5V0%2BMsHbf/%2B/WzYsKHUdvhrsmrYI5OVlcWBAwe4evUqrVu3rtR3fFLKq55kSopTPImUVIyE4oDUwsICtVqNlZUVNjY26PV6Pvnkk8fwDSvHwYMHjZRO/ylMjYdJkybx/fffl%2BtNGBMTYzQmDUsbDfvaLly4gJWVFd26dTPZhzV79myjx7/88gvdu3fnt99%2BQ6fTYWNjw8aNG1m1ahXfffcd33zzjcg4JScnExQURExMDLVr18bBwYFRo0ZVmKmXM70DBgzA29ublJQUnJ2d%2Bemnn0hMTKR169ZGaryGxMbGMmTIEKHuqtPp%2BPDDD43UXt955x1RRlpRD%2BDjwtDDUu7dMyy37N27N0VFRRw6dAhra2v0ej39%2BvVDr9ezdu1aJEnC3d2d9PR0qlevzv/93/8ZlUIfOHCAGTNmkJ%2BfL3xVb9y4Ibbr9XphxaBWq1mxYgU2NjaoVKpyK0AU/rsomT0FBQWFpwBvb29iY2NFmZLsszZ58mS%2B/vprli1bVqpvavLkyVy/fp0//viD999/v9Rqek5ODiEhIdSpU0cYncuEhYWxceNGfH19OXXqFL6%2BvpibmxMbGytWpYODg9m7d6/I7JX1HZ%2BU8iqFR0t4eDizZs2idevW2NnZPVWZ3NGjRzNt2rRyy0v/LqbGg7e3N35%2BfqxatapMb0LDMVmytFHO0kGxjcm6deswNzcvc8EGEOqaNjY2QLGlx%2BzZs%2BnSpYvR61avXs38%2BfORJInnn3%2BeEydOoNPpHvj6ymXfV65c4fz58zRu3JibN29iY2NjFJQa%2BkjCX5nsw4cPi%2B9pmK3z8vLi/fffJywsjJ49e2JpacnOnTtFP1xQUJDo75QtBh5Hf6d8jcaOHcuSJUtKlW1aWlqKYEzO0MnKmlAsuFNQUCCsGnbv3s1zzz0n3v/LL7/Qr18/JEmqlL%2Be/Dl16tQRpbAKzxZKZk9BQUHhKaAsBcFOnTqVqYApS7H37dvXZNmUra0t48aNY%2BLEiaW2HT9%2BnMzMTNLT08W/5ubmFBYWcuvWLQoLC0lMTMTPz6/C7/ioVQ7/qxhmcEqW91Wm5PbfZsuWLVhYWDB79ux/1K%2BuIsoqLzWFPPkPCwv7p76OwNR4ABg6dGi53oSGY3LhwoXUrl3bpJ%2BhbGcQEhLCokWLmDlzJo0bNyYuLk70IgJMmjSJrVu3CjVQSZKYMGGCCPays7PRaDR8%2B%2B23zJo1i88//5wZM2YwaNAg0tLScHZ2ZvHixZW6voY9ikOHDuWTTz5h8eLFJjOoJbOr/fr1o3///mJ7yaoFDw8PNm3aBCAsQ2ShEjC2DImPj3%2BsFi6SJLF69WosLCzw8fEhISFBbDMUUJG99uSAVzZeb9u2LT///DNarZZp06Zx4sSJSvX5qdVq6tWrh7m5OTY2NkiSRIMGDejcuTP%2B/v6V7hVV%2BG%2BhBHsKCgoKTzmmVDqdnJyQJImFCxcamRKXxN3dnaysrFLPb9iwAS8vL5YtW8aLL77IsmXLcHFxoWXLlsyfP58ePXowb968UpO4vLy8R358zwKGGZyyyvvKK7l9HPxbfnUVYaq8tGTWCJ4M/0ZJkkopVpbEcExWxrjeMDg0FRBotVpycnJEUKzVatHr9axbt47t27dz/fp18d6UlBTq1q2LhYUFt2/fRpIkfvrpJ6Di62vYo6jVapkyZYpJtciyKKnuWvLx/PnzGTVqFFOnTmXIkCEV9gA%2BLmQPS9lf8uTJk0bbS54T2eRe/lur1fLzzz%2BLa2kYKMo4Ojpy7949nnvuOWxtbRk5ciRqtZoaNWpQUFDA7du3uXv3LlqtFmdnZwoLCzl79ixubm54eHj8E4et8ASjBHsKCgoKTwll%2BazduHGDCxcuoNFoaNq0qVBe69evX4WG1hX5pD0IkiQRGRlptL%2B/4wX3LPGgGZwngSclk2vKM/Df6MmrDCXHrEqlIiIiguTkZKPXGY4HwzFZGeP6shZs4K%2BsZ1xcHNu3bwf%2ByizNmzePoqIirK2tUavV5OTksHz5chwdHRk1apRQ3V27di1Q8fU11aM4ZsyYSitPmvIl9PT05I033sDGxobc3FxREjpgwICH9in8p5HvR7mcMy4ujhdffBFAjO2oqCgCAwMB2Lp1K/369Su1vUePHkiSRFhYGNeuXSMlJQV3d3fat2%2BPp6cnH374IXXr1iU8PJxRo0ZV6rup1WpRdq/w7KAEewoKCgpPAWX5rC1fvpysrCwsLS1RqVScO3cOBwcHPvjgg0r5oMlm6GWxb98%2BAOGBVlRUxFdffSX%2BLdkj8%2B233xplUx63F9zTwoNmcJ4UFOGdsjE1Zh0dHYmOjubEiRNCUdNwPJQck5Uxri9vu5z13Llzp%2BjZy8jIAP4SEnFwcMDc3Jzc3FxUKhWOjo7odDpUKhUWFhaVLh1OTEykXbt24rFsvXDnzh1q1apVqX2YqlKoX7%2B%2BCBhzc3O5ePHiM%2BHbmZeXhyRJvP322w/8Xnt7e1566SV8fX3FQlvVqlWpXr36o/6aCk8BSrCnoKCg8BRgKnsBxZ5OH330kUkFTFMr5SV90MpbGZcnq2q1WvTIwF%2B9MaZ6ZJ6EbMrTyN/N4PwblGXw3bt3b9ELJD/%2B6KOPjN77LAb3ZY1ZU96EX3zxhckx%2BXcXbA4ePEijRo1YsGABbdq0AaBRo0bCBsDKyopNmzbh6emJl5cXhYWFwjvv448/LrWoIz/u0KGD0ef06tWrzB7FsjKBphYKSvb86XS6p9q3U5IkQkNDRbAqi7AMHz5cvGbo0KFiu1wGP3z4cPGcubk5er0enU5X7mfJYi9qtZq2bdsyf/78R348Ck8nSrCnoKCg8BRjajXdsK%2BmrH6%2ByqyMlzVZLQtTk1GFyvF3Mzj/BiXtIebMmVNqwu7o6AgY99EpmVxjHmRMPsyCTUpKSimVxjt37nDr1i2j50qWV3p4eHDr1i3S09NZtmyZMAA3XNSB4kUeQ8/OylxfUz6SeXl5DB48WFQC3L9/n6ysLPz9/dFqtUycOBFJknjxxRdFD%2BDVq1efCrVX%2BKt3z3ChTD7niYmJ4rnc3NxS7zXcbijoAmBjY0OdOnUICQlhw4YNooTf0tISW1tb0tLSqFKlCoWFhSYtORSePZRgT0FBQeEppqK%2BqZIr5U/byvizwqMouf2nMfR4M/X4SeJJLi990DH5oAs2ffr0KbWPDz74oMJzIvsPbt68%2BaEXFUr2KKrVahISEkTPmkyrVq1KvXf%2B/Pn07duXGTNmAMVG4pmZmaxcuRKVSvXU%2BXY%2B6GIZwM8//8yqVas4duyYKKVt0KABv//%2Buwj6FixYwAcffECtWrUoKioSgX1ubq4o0f3222/59ttvjfat0Whwd3cnNjb2bx6ZwtOGEuwpKCgo/EcxpY73NK2MP0v83ZLbf4sn0R6iMlkjmcc90X2YMVlecPjdd98ZGWpPmjSp1Pt37NhBcHAwr7/%2BOgDdunVj5cqVDB48GJ1Ox/79%2B4VVg16vN3os06tXrwotLhwdHYWlhdwf6O7uTmRkpNHrVCoVX3zxRan3f/LJJ0bZ4zt37qDX60WVwuNSe30U3L17l9TUVOGfB8U%2BfCtXruT9999nx44dODo6MmzYMCRJQqVS8b///Y/Vq1cTFRXFiBEjOHr0KABLly6lWbNmDB8%2BnHv37pn8PJVKhY2NDdWqVRPjtE6dOqK3WuHZQjFVV1BQUHiK8fLy4uOPPzZaTf/ss88YN24ca9asoVOnTuI/eBsbG6ZMmcLJkyf/kZXxkibICg%2BOoSG1YQane/fuj12M4lEYfP8TlAwmyuNxZyO7du3KyJEjS/XYljUmDYNDMzMzjhw5QocOHYRH3pw5c0q9JysrC3t7%2B1IqoHIQkZSUhJOTE1lZWeh0ulIBMWAk5KFSqYiNjRWCK4aUZXFhKqiuaKFg5cqVxMfHi0DTy8sLKysroqKixG/K0/Yb89NPPzF%2B/HgyMzPLfV3NmjXRaDRkZmZy7949ERCq1Wr69%2B9PUFCQUZ9fSTQaDTqdDrVajbm5OXv37n1qzpHCP48S7CkoKCg8xZiagMkkJSVRvXp1NBoNKpWK6OhofHx8OHTo0D%2ByMj569GimTZtmNJFTqDyGGZwnUYxi8uTJ3L59u0zV0JycHEJCQqhTp84TYw/xpOHt7U1sbKwYf1qtttwxWTI4bNeuHRkZGUZeaSUDrqSkJCwtLQkICOD48eMiy5aTk4NerxciPyUFgQyzgpUNiisbfFVmoSApKYndu3fTqFEj4L8R7HXp0oW0tDT8/PzE8UdERFBQUMDPP/9M9erVSUlJoWbNmkLkyMzMDGdnZ9LT00lNTTXyLlWpVLz22mtIksShQ4d4/vnnOXPmDHZ2dmRnZ4vy/bp161K3bl1mz579WI5b4clCCfYUFBQU/qN4eXkZrZRD5SdLFZVsGaKIb/x9SmZw4uPjGT58%2BBNVcuvv78/SpUvx8fEp8zUJCQlMnDiRH3/88d/7YjyZ5aWmeNAxWZng0PD9YWFhLFmyBF9fXxwcHDhy5Ajdu3enbdu2HD16VCwmdOnShdatW4vPyc3NpW7dumRmZuLk5ETjxo2F2E55VPb3pDILBa1ataJFixa88cYb4j0ajYbg4GChBhoZGUlQUJBJNdAnER8fHyRJ4vvvvy91jsLCwnj77bdZvXo1b7/9NtbW1ib3cfLkSdauXcvRo0dFf161atVo164dUVFR5RrXyz3cAK6urvj6%2BioB4DOI0rOnoKCgoFAKQzVFmbJKtp7UidbThClD6idNjOJJtYcwzBp16dLFKNhzdXVl3bp17N%2B//18vL30UPKhx/c6dOzE3Nyc0NBRPT0%2Bio6OZP38%2Bzs7O/PDDD/Tu3Rt7e3veeustXF1dSUtLY9asWURHR6PX68W9ZmZmRmBgIFOnTqVq1ap/%2Bzgq4yPp6urK%2BfPnuX37NsAjVQN9XLRp04bU1FROnTqFp6enUe9es2bNWLhwIW5ubnTo0AGdTke9evW4evUqUCza9Oabb/L%2B%2B%2B%2BjVquJjo6mS5cuFBQUkJaWxq5du8r9bMP7Bkqrryo8OyjBnoKCgsJ/mJLqeHq9npiYGGG0K1NysmRKSa5ly5aKj94/REUWGk8CT6o9xMKFC6ldu7bJYGLIkCH07t2bkJAQFi1a9ESUlz7smKwMaWlpRosxL7/8MhMmTGDr1q3MmDGDZcuWodPp6NGjB9988w2RkZF4enry2muvER8fj6WlJfPmzePWrVssX76cN954g%2B3bt1cY5FdEZRYKtmzZQo8ePR5KxfJJIiwsjISEBKD4uH/99VcmT57MlClTys3CAZw/f178vXbtWtatWyeCtICAgDLfr1armTRpEgMHDlTsFhRKoQR7CgoKCv9RZFN0Q1xcXNi4caPRc0/yyvizwoNmcB4HT6o9RGWyRuPGjWPixIn/6vcyxcOMyYqCQ61Wy/79%2B3n77bfRarWYm5uL16alpaHX67G0tCQmJsYoKL58%2BTL/v707j46yvPs//r7ve2aykgUSkhBWMQIWsDEuQGtVxKciqChYWx7BtioW0QK2VQGrFtnSirVl0VJ8KuACWoQoLmzFiqCAmJ8FIbggiwlhSUL2bZbfHzlzNyFhayfb5PM6p6ez3Jm5rnDimc9c1/X9VlZWUlpayqRJk5g2bRrjxo1jxYoVzJw5kyFDhjBu3Diee%2B45pk6d2uAtrtlFAAAgAElEQVR8znXVuaV%2BUdAYtm3bxt69e%2B37ERER9nlJy7Lwer12gPP/jfvv%2BwvpQE2Aqx3u/LdrXwOQkJDAsWPHePPNN1m6dCkFBQV21U/TNLWNUxT2RESCVWv/hlxalpbaHqKlbi9tyPn%2BTZ4tHObm5uLxeJg7dy6vvPIKPp%2BvTtuJ/Px8fD4fTqeT3bt31wnFOTk5LFiwgL/85S/2qmftUOxwOLj//vt5%2BOGHmTp16n/V4qKlflHQGJYtW1bvsdpn92688UZ8Ph%2BWZfHuu%2B/a91NTU/niiy/o3r07u3btqtNMvUOHDuTn59OlSxcmTJjA1KlT8fl8eL1ejh49CsCePXvqbdX0F30BbeNsyxT2REREWoDG3N4XKOfb4LspBPOq0anh8NTAFRUVxcmTJ4mMjKS0tNR%2BrKKigkceeYRZs2bZ/fnmz59fJxSfOHGClJSUOgHv1FCcnJxMfn4%2BQJ0eeOerpX5R0Niys7MZP348Pp%2BPyMhIdu7cic/no7q6GsMwmDdvHtHR0eTm5hIeHk5eXh4XXnghsbGxnDhxAo/HA9T0HISa7d6PPvpog8Gt9mOGYZCVldU0k5QWT9U4RUTknFx66aVkZGS0yg/NLd2ZWmjUdroeZk2lJbaHWL16Nenp6SxcuPC0q0bjx4/n/vvvb/Vh4mw9BadMmWLfNgzD3vrn3xJoGIYdFrt27cpvfvMbevXqxc0330xmZiYrV65kyZIlvPnmmwBs2LCBZ599ljVr1gRk/C25j2SgzZ8/n6VLl1JWVoZhGPaWbJfLVW97du2tmbWfDwkJsStwno6/kE1SUpK9kjdp0iRuuummQE9JWimFPRERqaehLVs5OTkkJCScdcuWBKeW3B5i9uzZLF269IyrRtOnT2/uYTaK2m0n9u/fbz/%2B2muvsXnzZm6%2B%2BWauv/56PvzwQ1555RUmT55MSkoKn3zyCRkZGYwePZqVK1fyu9/9rk4oPnbsGGPGjOGOO%2B7g5z//eb33Ot8WFy3xi4LGNGbMGLtQy6nn7xpy6lm8/4RpmpimiWEYtG/fnvDwcLuaavfu3UlLS9P57DZIYU9EROo52wpCbefagFlat1MbfPvbQ3zyySctoj1EW1k1qh243G633XZi0KBBxMbGcu%2B99xIdHc3SpUtZuHAhhYWFLFiwgMGDB9cJxX369GHDhg0cO3aMyMhIysrK%2BMEPfsDw4cP5/PPPee2117jiiitYuHAhpmmeU2N0p9PZYIuLlvxFQWPyV9V94okn7LYJAB9%2B%2BCEPP/wwlmXx2GOP2ZU6L7/8crZt2wZgN1yHmhD3m9/8hrlz5%2BJ2u4mKiqKoqOi8xmJZFklJSfpyrg1S2BMREZGzOpcG382lrawanRq4XnzxRbtZ%2BWuvvcbMmTPp0qULr7zyCh07dqS0tJTBgwdz8uRJUlNT6du3LyUlJezatYucnBzKysoIDw/H6XTWCQ8XXXQRd9xxB6NHj7aD/Lk0Rh83bhzdu3ev1%2BKipX9R0Fj%2B9re/8cc//hGfz0doaChut5v4%2BHgOHjxoX3Muq36XXHIJ0dHRfP7553Tq1IkjR44AMG3aNB555BFGjRrF3//%2BdzweDx6Ph5EjRzJhwgSSk5MbdX7SOphnv0RERNqigoICnn32WU6cOFHn8WeeeYa5c%2Be2iOqG0nRaanuIJUuWMHXqVCoqKigvL2fKlCk888wzzTqmxuLvKfjuu%2B/Su3dvtmzZwsSJEwkNDWXs2LH29r0//elPQE3Z/4qKCmJiYkhJSeH9999n7dq1nDhxgtTUVFasWEFmZibbt28nMzOTf/7zn3z22We8%2Beab/O///m%2BdILZlyxbS0tJOG0z8LS62bNlS77kz9ZEMZj/72c%2BYOnUqhmFQVFREWVkZBw8exDRNLMuiZ8%2BeQM2qW%2B0A7W%2BXABATE0PPnj3ZvXs3HTp04L777qOsrIzKykpuvPFG4uPj%2BclPfkJ8fDwdOnQgNjaWTz/9VEFPbAp7IiJST25uLrfffjsZGRn1wl5cXBxvv/02P/rRj4L%2Bw5q0fMuXL2fmzJksXryY559/nrlz5/Lyyy8HZan52uEOGm47ce%2B999YJXB6Ph6KiInbt2kV2djZpaWmkpqayY8eOOtU%2Bw8LCSEhIqBM0aispKWHRokV2ZciGnK7FRUv9oqAp/PjHP8ayLMLCwli/fj3h4eGEhIRgmiZPPPGE3Xfvl7/8JaZp4nQ6iYuLY9CgQRiGQWxsLEOHDuWNN97gV7/6FbNnz8bhcDB06FB69%2B5NTk4ON910E9nZ2Rw7doyCggK%2B%2BeYbevXqxauvvsr69etZv349mZmZ5OTkNPevQ5qBWi%2BIiEg9/hWEhrZsjR07lpEjRzJu3Di7P5e0DS2xPcSZVo2ae3tpoJ0a7hpqO5GQkGAHriVLllBdXU1YWBgHDx7EMAwuvvhiHnroIXsr5eTJk89pK2VKSkqdZuENaa0tLhrTRx99BEB1dTXjx4%2B3t1oCLFy4EMMwcDqdvP/%2B%2B3YF1SNHjhAaGorP5%2BObb77h3nvvrfOat912G/379yc2Npa//OUvp33vJ598ss590zTP%2Bm8owUdhT0RE6tmyZctpz%2BbAv7ds%2BftzSfA7W4NvP8MwmjTstaVVo1PD3anNyg3DIDMz037%2BxRdfBODnP/85ixYt4sknnyQ9PZ3JkyefdygePXo0jzzyCHv27Gkw0J2tMXpL/KKgsc2ZM4cXX3zRXmX%2B6quv6pzR%2B/jjj%2B22DP7KnX5ff/11g6953XXXMXv2bLtdi//fzv%2BaCQkJXHTRRWzZsoVf/OIX9u83Pj6exMTEwE9SWjyFPRERqaeh7WGnOt2WLQlOpzb4lqZ3arg7tVm51%2Btlzpw5xMbGMnDgQPLz8zEMg6%2B%2B%2Borq6mq2bt1KaWkpkydP5tlnnz2vUDxixAimTZvGpEmTzrsxekv9oqCxrVy5ktDQUKZPn05ZWRnPP/%2B8XWHTr/Z245iYGE6ePHnG19y4cSO9evVq8DnTNMnLy2POnDm8/fbbjB49%2Br%2BfhLR6CnsiIlJPQ9vDTqUtW9JStJVVo1PDnT9wDRkyhJ07dwL/brIdExNDfn4%2BQ4cOtVfoDcPAsqyzVn88HZfLxaxZs9i6dSufffZZnRYXjz766GlbXLTVLwosy8IwDPr370/37t154YUX%2BOc//8mdd97Jddddx8SJE3nhhRd4/vnngZqWN/4Vu4EDB7J169Yzvr7D4cDtdtv3/f%2Bu/mq0p4ZCbeNsm9R6QURE6lm9ejXp6eksXLiQ1NTUes9nZmbWacAs0lz8H47PxjCMoOkxVrunYE5ODtXV1cTExHDLLbcwYcIE%2Bzp/xc4OHTrQu3dvHnvsMdLT05k0aRIdOnTgiSee4Prrr%2Bfyyy%2BvU5jldKG4X79%2BXHHFFURFRQV1i4tAmTdvHmvXrqVnz56MGDGCCRMm0KtXL/bu3YvP5yMyMpKSkhIsy8I0TS688EK%2B/vprvF6vHeT8YS4uLo4TJ07YzddnzpzJH//4x3oFtKBmhbC6upqUlBT279%2BPz%2BcjLCyMadOmccMNNzT1r0GamcKeiIg0qHYD5tNt2Zo%2BfXpzD1OkTandU9DpdPL222%2Bftlm5P%2BBFRkYyZ84cAE6ePElUVBSmWVOQvaCggOjoaHtl9HSheMmSJcyaNYvLL7%2BcyMjINtMY/b8xZswYduzYEZDKsP6Q53K5ME2T5cuXU1xczJgxYzBNk/DwcP72t78xevRobrjhBm6//XZmz57NlClTeOyxx4Cas9arV6/%2Br8cirYvCnoiInFbtFYTaW7Zuuumm027ZEpHGsWTJEn7/%2B9/b4W7Tpk1ce%2B219jbAU5uVn8uqZ05ODgkJCfzzn/8843VDhw4lLCyMRYsWERcX12Yao/83Vq1aZf831Ov1kpaWxqefforX68Xr9XL99dezYcMGfvnLX7J//35OnDjBBRdcwK5du/j888/xer3ExMTYffWg5qxjjx49yM7OpqioiNLSUkJDQ/nOd77DwYMH6dixIzk5Obz00kuMHDmSlStXcuuttwI12zj/9a9/NeevRJqBwp6IiDSo9gqCtmyJNL%2BhQ4dy33332dssL774YkJCQvj0008xDAO3203//v3ZtGnTWSts%2Bld4fvvb39rbOk9nxIgR9O3bl40bN9qvez7v1dYNGDAAr9fLa6%2B9xo9//GO8Xi%2BmaRIWFsaECRNIT0/H5/OxcuVK3nrrLb788kvi4%2BPtAjZRUVFUVVVRXl5%2BxvcxDAPTNPF4PISGhuJwOLAsi7KyMpxOJ/369WPp0qVNMWVpQVSgRURE6qm9guB2u5kyZQpffPGFtmyJNKNTewp6vV4qKirs9gnn03biz3/%2BM1DT/23p0qVYlkVubi7x8fFYlmVf56%2BQ2ZZaXATKmDFj2Lt3L5WVlVRXVzNs2DBM06S6uhqfz8fJkyf5y1/%2BQlFREQAffPAB8%2BbNA2paJfh8Prp27Wq/XnZ2NpZl4Xa7ueCCC8jLy6O4uBiPx4PP58Pn89k9/CoqKoCaf7%2BwsDAGDRrE1KlTm/g3IC2Bwp6IiNSzfPlyZs6caa8gnG8DZhEJvFMDF/AfBy5/hczU1FReeuklunTpUue2/PeuvPJKAPbu3UtVVVWdyplQ03bh0KFD9v0ZM2bYt48fPw5Q53kAj8dDamoqpaWlFBQU0LVrVw4dOsStt97KZZddxh//%2BEd%2B8IMfsGnTJj7%2B%2BOPGmpq0Igp7IiJSz6krCOfbgFlEmobb7WbdunX2NszGbDvRVlpcBMoDDzzAAw88YJ/dAzhw4ADffPMNJ0%2BetM/u%2BfmLsABcddVVnDx5kuPHj1NWVmav/kFNNWQ/fxhctWoVq1atAmoKsRQWFnL33XcTGRnJ9773PYYPH054eHijz1laHp3ZExGRemqXbPdLTU3lzTff1Lf%2BIs2kdnVNgDlz5tSrrulX%2B4P9mdpO1P67PtPfeFtscdFYDh8%2BzI033ohpmjz44IM888wzXHnllWzfvp0JEyZw4YUXsnXrVlasWGFvzzxVZGQkpaWlOBwOqqur7cfbtWtHcXFxnWsty6Jjx44sX76cxMTERp%2BftCxa2RMRERFpBTp16sT//d//2ffDw8MbXK05n8D1ve99j7CwsLNe11Ybo/83cnJyOHr0KNOnT%2BfAgQOUl5fXC25/%2BMMfAOwG6n/605%2BAuqt8DfF4PHTr1o0DBw4QGhpKZWUlPXr0ICcnx74mNjaW66%2B/nm3btgHw9NNP8/TTTwd0jtLyKeyJiEiDtGVLpGU5W%2BCq3UPtbP3U/H%2B38%2BfPtx/TedzAGjx48Dn12Ksd7Pwrdv777du3Jz8/H6gpijNs2DAyMjIoLy/n0KFDhIeHY1kWFRUVhIWF2YVZAKqqqhg1ahTvvvsuAB9%2B%2BGGgpyitgLZxiohIPdqyJdL6NPR3e7oKmz6fr1648/fcq30toL/x/1B2dja5ubn2fcMwcDqdJCQkEB8fz6pVq1iyZAmJiYlUVlby8ccf1wuHpmliGIZdZfNMkpKSOHLkiN2CweFw8O677zJ06FD7/T/77LPATlJaPK3siYhIPdqyJdL6NPR3e7oKm/5iHtJ4kpOTSU5Orvf4wYMHmTp1Knv27CErK4usrCwiIyPrBL2IiAhKS0vxer306NGDb7755ozv5XA4KCgosAOl/30zMzOJiooCqLNTQ9oOhT0RERGRNubWW29t7iG0Gf6zewAnTpzg448/5o033qhzTUlJSZ37paWl9u1vvvkG0zTxer1YlsWFF16I2%2B2mV69e/OMf/6CiosLeygk12zdPnjzJkCFDSE9Pp7S0FMuyGDVqVCPPVFoihT0RERGRNqigoIAlS5Zw5513EhcXZz/%2BzDPP4PP5uOeee4iOjm7GEQaHcz27BxAWFobP5yMsLIzy8nKSkpLo0KEDTqeTb7/9lurqaoqLi8nLy%2BPrr7%2B2f66yshL49xbdyMhIXn/9dfv5q6%2B%2BmvHjxwd2YtIqKOyJiIiItDG5ubnceeedeDwebrjhhjphLy4ujhdffJF169axbNkyOnbs2Iwjbf02btxon907fvw4O3bs4KWXXgIgJCSE7373uwwYMIADBw7QuXNnXn75ZV5//XVGjRrFW2%2B9xcyZM1mxYkWdnnynCgsLo7KykhtuuIF7770Xy7LYv38/AN26dePiiy9u/IlKi6QCLSIiIiJB6tJLLyUjI6Pemb1HH32U48ePs2DBAkJDQ%2Bv9XGlpKePGjaN79%2B7MnDmzqYbbJmzevJl77rkHgPj4eFwul731snbPvIYYhoFhGMTExNC3b1%2B2bt1KXFwc3//%2B96msrCQsLIynnnqqKaYhrYRW9kRERESCwODBg%2BtV2CwvL2fMmDH1KmxWVVWdNuhBTYGQiRMn8vDDDzfaeIPRlClT2Llz5xmvKS8vt28fP378vF7/ggsuYP/%2B/cTFxZGamsrHH39McXExmzdv5oUXXuDuu%2B/%2Bj8YtwUthT0RERCQIPPjgg%2Bd87fTp04mNjT3jNUlJSRQWFv63w2pzzrZpLjQ0lK5du5KUlGQ3PHc4HLjdbvuaUaNGsXnzZvLy8rAsC6fTSXl5OYWFhfbtL774Ao/Hg8fjwefz4fP5KC4ubtS5SeujbZwiIiIibcyPfvQj7rjjDkaOHHnaa1auXMmSJUt48803m3BkbUft7Zynhj3/Cu3ZPqb7fy4pKYn27dvTvn17Fi9e3HiDllbHbO4BiIiIiEhgFBQU8Oyzz3LixIk6jz/zzDPMnTvXXqkbPXo0Tz/9NJmZmQ2%2BTmZmJn/4wx9Urr%2BRFBYWMnfuXPt%2B7aDXkAEDBtgB0OFw0L59e2JiYujYsSMDBw6kV69e9O3bl9mzZzfquKX10TZOERERkSBwPhU2R4wYwd69exk9ejSXXHIJffv2pV27dhQVFfH555/zr3/9i1GjRjF27NhmnFHrVfvs3okTJ%2BzWCH7%2BrZenU/s5wzDYvn07DoeDgQMH8uWXXxIZGcmaNWsaZ/ASVLSNU0RERCQI/CcVNnfu3Mnq1avJysqiqKiI2NhYvvOd73DTTTfx3e9%2Bt6mnEDSmTJnCJ598AkBeXl69sOd2u7EsC4/HA0BiYiK5ublcdtll7Nmzh8rKSjweD%2BPHj8flcnH48GEGDRpESkoKd9xxB6ZpnnZVVqQ2hT0RERGRIHDVVVexYMEC%2Bvfvf9prtm/fzsMPP8z7779PRkYG69evx%2Bl0ct111zF8%2BPAmHG3bNmDAABYvXsw999xDeXk5a9asYfjw4fzwhz9k/fr1xMfHc/DgQXr27Mlvf/tbkpOTAfjVr37F4cOHueKKK/jzn//czLOQ1kBhT0RERCQIpKam8uabb9brqVfb4cOHufnmm5k0aRK///3vGThwIA6Hgy1btvCzn/2Mhx56qAlHHHzmz5/P9u3bz3rdgQMHME2Tyy67jDVr1tCzZ0%2B%2B%2Buqrc3qPK664gvnz5xMdHf3fDlfaAJ3ZExEREQkCKSkpbN%2B%2B/Yxhz//88uXLmTlzJiNGjABg3bp1TJkyhcmTJ9fr1Sfnbtu2bezdu/es1/l8PiIjI3nvvffw%2BXz1gl5cXBx5eXn4fD46d%2B6M2%2B2mqKiIfv36sXTp0sYavgQhreyJiIiIBIHVq1eTnp7OwoULSU1Nrfd8ZmYm48eP5/777%2Bf3v/89GzduJCEhAag5Q9a/f382bdpkPyaN55NPPqFfv37s3LmTiRMn4vF4eOihh3jqqacAiI6OPq8eh5ZlkZSUBMDGjRsbZczSOmllT0RERCQInE%2BFzVmzZuFw/PtjoMPhICQkhKqqqmacQfDZs2cPzzzzDJ999hmVlZW4XC769OnDnj17ePnll5k8eTJQE9ZmzJhh/9y5BD3DMDAMA8uyuPTSS9m3bx933XVXo81FWiet7ImIiIgEkXOpsNm7d2%2B2bNlChw4d7J87lzN/cm7mz5/P2rVr%2BeKLLwAwTRPTNPF6vXi9XgCcTiderxfLsvD5fFiWRUVFBQDt27cnPz8fgNDQUKqqqvD5fPTr14/i4mLGjBlDQkKCHRAjIyOZNGkSzz77rFoySB1a2RMREREJErUrbN51111nrLD57rvvEhkZad/3er2sX7%2Be9u3b17nOf65Pzt22bdv46quv7POPtUOeX3V1NYDdfsF/H7CDHmAHQMuymD59OqNHj2bAgAG43W4KCgqAmpXALl26kJ2d3XiTklZJK3siIiIiQWDJkiXnXGFz8ODB5/SahmHoDNh/6JJLLsHn8/HWW2/RrVs3%2B/GDBw8ydOhQANLS0iguLsbr9bJv3z4AOnbsyLFjx876%2Bn369CE7OxvLsujbty%2BVlZXExsaqJYPUobAnIiIiEgSGDh3KfffdV6/C5ieffKIKm83gtttuIy8vj3vuuYe0tDT77F5paam9mud0OnG73Zimyc9%2B9jNeffVVDMNgypQplJaWAlBSUsJrr71Gbm5ug%2B/jcDjwer0MGDCAZ599Vi0ZpA6FPREREZEg0LdvX1XYbEE2bdrEhAkT8Hq9%2BD9uOxwO3G53nessy7LDn2VZWJbFNddcw5///Oc6If3LL7/kww8/JCsrC8MwiIuLIz4%2BnoSEBFJSUujZs2fTTU5aDZ3ZExEREQkCbrdbFTZbkGuvvZYXXniBX/ziF1RWVmIYRr2gB/8%2Bs%2Be/7fF4WLduHb179z7ta0dHR/OrX/0KqAmIRUVF5OTkANCpU6cAz0RaM4U9EREREZFGMHDgQKBmu2b37t35%2Buuv8Xq93HbbbSQkJLBw4UIMw6D2RrtT7zeksLCQxx9/vM5j/lYM59LUXdoOhT0RERGRIKEKmy3LkiVLqKqqwuv12m0YAA4dOsRFF10E1KzEHTlyhHbt2hEdHc2RI0eAmuqcU6ZMITExsU51zlO1b9%2Be%2BPh4EhMTG3cy0irpzJ6IiIhIEFCFzZZlwYIF/PWvf8Xr9dp98iIjIykpKalzXUREhF2MpXfv3vaZPJ/PR0REBB6Ph%2BjoaMaMGcM999yjYjtyXhT2REREREQC7Oqrr6ayspJZs2YRFhbGT3/6U8LDwykrK2uU9zNNE0DbOKUObeMUEREREQmwkpISvF4v3bt354ILLsAwDK666irWrl1b5zqn01mnofq5GjJkCAAJCQl069aNPn36BGTcElwU9kREREREAiw1NZUvv/ySF154gZSUFCzLqhf0gHpBLzExkdzcXAzDwDRNTNNk48aNDB48GMMw2L17d1NNQYKAtnGKiIiIiATY119/zY9//GOKi4vx%2BXz2WbvaH71P7bvndDpZvHgx48aNA7BbNvTt25ddu3ad9r0syyIpKQlA5zGlDoU9EREREZFGUFBQwJAhQygvLycxMZHjx49jWRYVFRUAREVFUVhY2GC7BZfLZfdINE0Tr9cL1ARCh8NBfHw8lmURFxdH9%2B7dSUtLA%2BDWW29twhlKS6ewJyIiIiLSSNLS0vB6vaxcuZLbb78dr9dLeXn5WXvpncn48eOZNGlSAEcpwUphT0REREQkwI4cOcJTTz3F%2B%2B%2B/j8fjqfe8vwk61Gzt9H8k95/T8/N4PPb9nj17sn//fhITE7VdU86Jwp6IiIiISICNGTOGXbt2ERYWRnFxMdXV1XW2Y0JN4HO5XFRXV%2BP1enG5XLz33nvk5OSQlZXFwYMHOXToEB999JF9fWVlJQADBgygXbt2JCYm0q1bN3r37g3A5Zdf3vSTlRZLYU9EREREJMD69%2B%2BPz%2BfjjTfeIC4ujsmTJ3PgwAFOnjxpBz7TNHE6ncTExJCbm4vP5%2BPCCy/8j3rl%2BVcK1WdPalPrBRERERGRAOvWrRvl5eUUFhaSkpKCx%2BOhS5culJSU1LnO5/NRUFCAz%2BfD7XbzxRdfcNlll2FZFkuXLrWvc7vd/PznP%2Bebb77h6quvZsaMGU09JWmFtLInIiIiIhIAO3bsICsrC4CsrCzWrl2LYRjExMRw9OhRqqqqGizMYlmW3Z7BNE3at2/PbbfdRmJiIm63m3379rFp0yby8/Np164d48ePJyoqiu9%2B97scPXqUhIQELrjggqaerrQCCnsiIiIiIgHQu3fv86qyGRERgcfjsQu4REREEB4eTm5ubp2zfYBdzKV2zz3DMOjcuTPZ2dncddddPPLII/Z1IgDm2S8REREREZGzycrKYt%2B%2BfXX%2BFxISgsvlYs2aNfbtq666yj5jV1FRYQe%2BkydPcvTo0XpBLzY2FsuyGDZsGH//%2B9957rnniIiIwOl0cuLECX70ox%2BxatUqXn311WaaubRUCnsiIiIiIgFWVVXF7NmzsSyLiIgICgsLgZqzd1u3bsXlclFWVoZhGHi9XjvgndqmwTRNCgsLcTgcHD9%2BHIDBgwczZcoUYmNjiYqK4tNPP%2BXXv/61wp7Uo22cIiIiIiIBtGPHDubOncuePXvsHnoOh4OYmBiOHDkCQIcOHSgoKOCmm26iX79%2BAMTExDBt2jQAKisrCQ0N5Z133mHYsGG8/fbbJCcn2%2B9x%2BPBhhg0bBtQEwrfeeoubbrqJ//f//l8Tz1ZaMlXjFBEREREJoDFjxtQ7u1ddXU15ebl9Py8vD4CMjAwyMjIafJ2KigqGDRuG1%2Btlw4YN3HXXXfZzubm5REREABASEkJubi4xMTGBnoq0ctrGKSIiIiISQFlZWcTExNCuXTtWrFhR5wzf66%2B/TkhICABXXHEFpnnmj%2BMej4fKykpmzZrF2rVrgZqtoPPmzcOyLBwOB4MGDWL%2B/Pl8//vfb/S5SeuibZwiIiIiIgH2%2BOOP89577xEZGcl3vvMd8vPzSUlJ4e2336aoqAjTNOnatSsHDhwAarZiWpaFaZrcdtttvPPOO/Y5v7CwMCoqKjBNk7S0NPbu3UtJSQmmaRIREUFcXByVlZW8%2BuqrJCQkNOOspaVR2BMRERERCbDy8nIeffRR3nvvvXrPhYSE2G0XiouL8Xq9REZGUl5ejsPh4K233qKoqIjbb7/9rK0c2rVrx7Bhw3jwwQeJi4trrOlIK6WwJyIiIiLSSK688ldsgUwAABACSURBVEqqqqq47777WLx4MQDLli0jIyODF198sV6YM02T73//%2B1x11VU8//zzREVFcd111/HKK68QEhLCo48%2BSnR0NFFRUbRv356uXbtiWVZzTE1aAYU9EREREZEAOnHiBB6Ph7i4OAYNGkRVVRXXXHMN69evxzRNhg0bxoYNGygtLcXj8WAYxmlX8PxN003T5Omnn%2BbGG29s4tlIa6awJyIiIiISAKWlpUycOJHNmzdjmiZr1qzhySefZPv27fWujYqKori4mD59%2BpCfn8/x48fx%2BXx4vV47/DmdTsLCwujXrx%2BTJk2if//%2BzTArac3UekFEREREJADmzZvH7t276dKlC%2Bnp6SQmJrJ79%2B4Gry0qKgJgz549GIbBLbfcwsiRI7n77rtZtmwZY8eO5d133yUyMpLo6OimnIYEEa3siYiIiIgEwODBg6msrGTu3LkMGDCAdevW8eCDD2IYBjNmzKBz58588sknLFiwANM06dmzJ506dSI1NZVFixZRUlLS4OuGh4dz991388ADDzTxjKS108qeiIiIiEgA%2BLdidu3aFYCNGzcCYBgGl156KdnZ2eTk5ODz%2BXC73Rw6dIh9%2B/axadMm%2Bzr/Ooy/DUNqaiq7d%2B9m8eLFuFwuxo0b1zyTk1ZJK3siIiIiIgEwZMgQKisrmTFjBsuWLWPz5s0AREZGnnbVzs%2ByLFwuF4ZhMHPmTFwuF4899hhhYWFMmzbNvu0PhiLnwmzuAYiIiIiIBINbbrkFj8fDQw89xEcffWQ/7i%2B6AjWtFfxcLhcOhwOHw0FCQgLV1dUA9O7dmx49elBaWkp%2Bfn6d2yLnQyt7IiIiIiIB4Ha7SU9PZ%2BnSpUBNmAsJCQGguLj4nF8nPDwcr9dLu3bt6N69O127duWDDz6gR48eLFu2rFHGLsFJYU9EREREJID69euHz%2Bdj3bp1/PCHP8TtduP1ejFNk2uvvdY%2By%2BfncDhwu931XscwDFwuF9XV1URHR/Piiy/Su3fvppqGBAEVaBERERERCaCEhAQqKys5fPgwCQkJZGdn43A46N%2B/P3v27AEgOTmZ7OxskpOTue%2B%2B%2B5gzZw4ej4cuXbpQWVmJz%2BcjLCyMuLg4rr32WkaNGkVEREQzz0xaG63siYiIiIgE0Lx583j11VcJDQ3F6XRy4MABAC6%2B%2BGI77IWHh1NRUcEPfvAD9uzZQ3l5OXfeeSeTJk1qxpFLsFHYExEREREJIP/ZvRUrVlBZWWkXZfFv5fR6vQCEhoZSUVEBYBdwaeijuWVZACQlJdXbAipyJgp7IiIiIiKNzH/W7nw%2Bevfo0YO4uDi6d%2B8OQFpaGrfeemtjDE%2BClMKeiIiIiEgA5OTkcPTo0XO6NiEhgU6dOtG/f398Ph9vvPEGI0eOtG%2BnpKQ08milLVDYExEREREJgN69e5/Xyt20adNYuHAhANHR0RQVFVFcXMwPf/hD5syZg9PpbKyhShuhsCciIiIiEgDZ2dnk5uae9bo//OEPHDhwgLi4OL799lvcbrd9Zs/tduPz%2BejYsSP9%2B/cnMTGRbt26ATVh8vLLL2/UOUhwUdgTEREREWkiVVVV9OvX7z/6WdM02bt3b4BHJMFMffZERERERAIoJyeHDz74gAULFnDs2LHTXhceHs748ePt1bqCggIeeeQRXnjhBe6%2B%2B2527NjRVEOWIKWVPRERERGRADrfs3u1de7cmezsbCzL4q677uKjjz7i2LFj3HLLLUyePFnn%2BOS8KOyJiIiIiARQdnY2//M//4PP5%2BORRx7h5ZdfpqysjOuvv57q6mrWrl1LVVUVlZWVDYbCkJAQIiIi8Hg8pKamsm/fPsrKyrj55pt57LHHmmFG0lqZzT0AEREREZFgkpycTEREBGFhYVx99dUMHDiQTp06kZSUxP333091dTWmabJo0SIiIiIIDw/n3nvvJSIignbt2rFixQoWLVoEwK5du5g3bx4A77zzTnNOS1ohndkTEREREQmwAQMGkJmZycMPP8yhQ4coKCjgs88%2BY%2B7cufY19957r337r3/9KwChoaEUFhYSHh5uV%2Bb0%2BXz2bZHzoW2cIiIiIiIBdvToUSZOnMjnn3%2BOYRhYlkVZWdlZf84wDAzDoEOHDlRWVnLJJZfw7bffUlBQwHXXXcesWbOaYPQSLBT2REREREQaSb9%2B/fD5fAwfPpxVq1YBEBcXh8vlIicnB6fTedZVO8MwuOWWW3j88ceJiIhoqqFLENA2ThERERGRRlBSUmKHuQ0bNmCaJl6vl7i4ODvceTweABwOB4Zh4HQ6mTRpEn/605/43e9%2BR3JyMhdddBGRkZHNORVppVSgRUREREQkwDIyMhg4cCClpaVUVlZSXFyM1%2BsFICsri3379gHYj4WEhBAaGkpFRQUfffQRFRUVZGZm8tJLL/HOO%2B%2Bc0xZQkVNpG6eIiIiISIBdc801FBUVcf3117N//3727NnD8OHDycjIOGuhFcMw7Gssy8IwDOLj41m%2BfDmJiYlNMXwJElrZExEREREJsJMnT%2BLxeHjwwQd5/fXX%2BcUvfsHevXsZOnQoLpcLqFnN8/%2B/YRgAxMbG2kHP5XJxww03MGrUKFwuF08//XTzTEZaLYU9EREREZEAu/baa%2BnevTvr1q2jpKSE1NRUYmNj2bhxI1VVVQC43e56BVoKCgrs1zBNk61bt3LbbbeRn5/Phx9%2B2CxzkdZLBVpERERERAIsISGB9957j/T0dNLT0xu8xuPx2AVa/K677jr%2B8Y9/2MVcysvLiYuLo6qqiurq6qYYugQRreyJiIiIiARYYWEhnTt3ts/cRUZG2rcNwyAkJISlS5fSpUsXTNPE4XBw6aWXsmfPHpxOJ127diU6OpqkpCQyMzOJiooiKSmpuaclrYzCnoiIiIhIgM2ePZv169fjcrkICQkhIyPDvj1mzBgiIyOZMmUKlmXh9Xpxu91UVFRw5MgRqqqqyMvLo6Kigssuu4z09HQqKiq44YYbmnta0sqoGqeIiIiISCPYsGEDDz/8MKWlpfZjTqeTIUOGEB8fz4oVK6isrMQ0TQzDwOv1YlkWbrcbgM6dO/Ptt98CcPXVVzNv3jy7qIvIuVDYExEREREJsOXLlzNjxow6xVf85/Cgpr2CaZqEhoYSFxcHQFpaGj/96U/Zv38/3bp1w7Is%2B/bFF1/cbHOR1ksFWkREREREAmzx4sW0a9eOHj16cPz4cQDy8vIoLy/H6/Xi8/nsdgu111569epFr1696twX%2BU9pZU9EREREJMBSU1Pxer1kZGTQvXt3%2B/EDBw4wfPhwTNPkX//6V/MNUNoEreyJiIiIiARYnz59yM7OZvXq1fTt25enn36aQ4cO1Wm10KtXL0zTpFOnTjz44IOMGDGiGUcswUgreyIiIiIiAZaZmcnYsWPtBuoAlmXVCXumadKrVy%2B%2B/PJLTNPk8ccf5/bbb2%2BO4UqQUusFEREREZEAS01NZfXq1YSHh2OaJpGRkXZ/vSlTpjBnzhzat29PeXk5M2bMICoqisWLFzf3sCXIKOyJiIiIiDSCnj17AuByuVi5ciUulwvDMLjmmmtITU2lsLCQI0eO1LktEkjaxikiIiIiEgBjx47F4/Hw3HPPMWHCBPbu3UtZWRlerxen04nH47Fvx8XFUVVVRUJCAldddRV///vfSUhI4I033mjuaUgQUYEWEREREZEAuOKKK6iursbpdHLllVcCUFxcTFZWFlVVVViWhc/no6qqipycHADcbjeLFi3C4XAwd%2B7c5hy%2BBCGt7ImIiIiIBNjq1atxu93cfPPNHD58mEWLFrFjxw5KSkooKyvDMAwiIiKIjo5m0KBBjBs3jqSkpOYetgQZhT0RERERkQDIz8/n6NGjVFRU8JOf/ATDMHjuueeIjo62r/n666954oknsCxLffak0SnsiYiIiIgEwHvvvcfEiRPP62csyyIiIgKo6c23dOnSxhiatFEKeyIiIiIiAZKTk8P8%2BfNZuXIlACkpKeTn5xMXF0deXh7l5eUYhoFhGABERUWRnJwMwJVXXskDDzzQbGOX4KOwJyIiIiLSSGqf3XvnnXdwu90MHToUt9vN66%2B/jsvlYtSoUbzyyiu4XC7Gjh3b3EOWIKKwJyIiIiISQPn5%2BWRlZbFs2TL%2B8Y9/ADW99qqqquxr/K0YLMviySef1Dk%2BaRRqvSAiIiIiEkDbt2%2Bvd3avdtADqK6uBsDr9TJt2jQAhg0b1jQDlDZDK3siIiIiIgF2ySWX4PF47FAXGxtLQUEBADExMcTHx/PrX/%2BaqKgowsLCSExMJDY2tjmHLEHIbO4BiIiIiIgEG8MwsCyLTZs2MWLECDp37sxjjz3Ghg0b8Hg8HD9%2BnO9973v069ePPn36EBkZaQdDkUDRyp6IiIiISICNHj2aI0eOMGzYMLZv387u3bvt5zweT51rIyMjKS8vJyIigh07djT1UCWIKeyJiIiIiATYzp07ueeee6ioqMA0TTweDw197O7QoQP5%2BfkATJ06VdU4JaC0jVNEREREJMDS0tLYvHkzLpcLy7JYsGCB3Vtv5MiRJCQk4HA4SE5O5qGHHqJbt2588MEHzTxqCTYKeyIiIiIijSAyMtJuoN6nTx9M08QwDDp16kT//v3xer18%2BeWXDB06lLy8PHbt2tXcQ5Ygo9YLIiIiIiIBMHjwYHJzc%2Bs85j%2BfN3jwYHsb57x584iIiMDn81FdXc23336L1%2BvFsqwmH7MEN53ZExEREREJgFWrVrFz5846j504cYIPPvgAn8%2BHZVn1Km76V/4sy%2BL222/niSeeaMohS5BT2BMRERERaURr1qxh%2BvTpFBUV4XK5SEtLIyYmhrS0NIqKijh8%2BDCDBg1i2LBhmKZOWUngaBuniIiIiEgjyM7OZtq0aXz00UcYhsHNN9/M1KlTiYmJae6hSRuhlT0RERERkQAaPHgwOTk5DbZaaIj/rF6nTp3YsGFDYw5N2hit7ImIiIiIBMi2bdsoLS3F5/NhmibJyclYlmW3XfB4POTm5lJVVQXUBD1/EZf4%2BPhmG7cEJ4U9EREREZEA%2BPWvf81bb70F1DRLv%2BOOO2jfvr39/BdffMGaNWuorq7G6XTicrkoLy8nPDycadOmMWrUqOYaugQpbeMUEREREQmA3r17n/PWTUDn%2BKTRKeyJiIiIiDQCt9vNX//6V%2BbPn4/b7SY6OpqSkhK8Xi9du3YlPT2d1NTU5h6mBDGFPRERERGRANu2bRuPPvooubm5WJZFWFgYJSUlOJ1OJk%2BezF133aU2C9LoFPZERERERAKo9tk9l8tlF2MJDw/n0ksvJTw83L42ISGBbt26ATXbQC%2B//PKmH7AELYU9EREREZEAOt%2Bze36mabJ3795GGJG0VQp7IiIiIiIiQUgbhUVERERERIKQwp6IiIiIiEgQUtgTEREREREJQgp7IiIiIiIiQUhhT0REREREJAgp7ImIiIiIiAQhhT0REREREZEgpLAnIiIiIiIShP4/uZT6zE5G3%2BwAAAAASUVORK5CYII%3D” 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idc2VQgp7kuuW1NRUxowZQ4cOHbjpppuYNm0ahYWF4vzJkycZOnQorVu3Jjo6mi1btpi2k5iYyKBBgwzlCxcu5KabbiIyMpKJEydeNqdSSkoKzz33HDfffDOtW7emT58%2BJCQk/LlJXgXCwsI0n%2BbNm3PPPff8bXMZOnQoH3/8MTab7bJ1MzIyWL16tThesWKFaYJoiUSiZfr06Rw4cEAGZ5FIJNclv/76KwMGDODEiROXrLdq1So8PT155plnaNSoEZMmTaJSpUp8/fXXACxZsoQ77riDPn36EB4ezuuvv87mzZs5efLkFRurFPYk1xRRUVEaQSMiIoKePXuycOFCTT1FURgzZgz5%2BfksXbqUGTNmsHHjRmbOnCnOjxw5kurVqxMfH8/dd9/NqFGjOHPmjKadbdu28cILL4hj1QxwzZo1zJ49m1deeYW4uDj27NnDG2%2B8obk2JydHCEDHjh0jKiqKffv28d///pfExERiYmJ48cUX%2BfDDD//UmqhmjeV9KsL333/P4cOHAVi%2BfDlRUVGXrD9r1iy2bNnCli1bWLNmDdHR0Tz77LP88MMPl7zu5MmTbN68uWITK4fCwkJmz54t5t2lSxfCw8MJDw%2Bnd%2B/emgfowIEDefLJJwkLCyMqKoqbb76ZgIAAcX7dunXccccdhIeH07NnT3bs2AHA%2BfPn6dixI7Nnz%2BbIkSNERkYyaNAgRo4c%2BafGLpFIJBKJ5J/Pjh076NixI5988skl6%2B3Zs4e2bduKfJoWi4U2bdqQlJQkzrdr107UDw4OplatWuzZs%2BeKjVXm2ZNcU5w9e1ZzrCgKOTk5TJ8%2BHX9/f/r06QPAkSNHSEpKYuvWrVSvXh2AMWPG8Nprr/Hss8%2Bybds2Tp48yccff4yPjw%2BNGjXi%2B%2B%2B/Jz4%2BntGjRwMwe/Zs5s2bR/369UV/P/74I1AqtAwZMoRu3boB8PLLL/PII4/w9NNP4%2B3tDcD777/PunXr6NOnDy%2B//DJ2u50BAwYQGRkJQL169SgqKuLtt9%2BmX79%2B%2BPn5XZE1%2BSMMHTqUadOm0ahRI4qLi3E6nZesX6VKFQIDA8XxsGHDiI%2BPZ%2B3atbRv377c65566ilCQkK45ZZb/vSYVcFMTbrs6%2BvLgQMHmD59OnPmzCE%2BPp4jR45gtVpxOBykpaVx%2BvRp7rnnHgCSk5N55plnqF%2B/Poqi0KhRI4YNG0ZiYiIhISHcf//9zJ49m9mzZ6MoCj///DPr1q2r8PjOnTtn0CIGBgZSo0aNPz13iUQikUgk5fNn/wY/8MADFaqXlpZG48aNNWUBAQHC9PPcuXOGPgMCAq7IbzcVqdmTXHP06tVLaJW%2B%2BeYbxo0bh6IoxMXFiTqBgYHMnz9fCHoqOTk5QOmblmbNmuHj4yPOtW3bVryJAdi6dSsLFizgtttu07Sh/vB3f1PTunVriouLSU5OFmW7d%2B8mLS2Ns2fP8v333zN9%2BnQhjKr069ePDz74QIwjJSWFxx9/nFatWhEVFcXs2bOF4LV8%2BXIGDRrEO%2B%2B8Q8eOHWnXrh3Tpk1DTaX56quv0r59e7y9vfH39%2Bfee%2B9l06ZNAAwaNIhZs2aJfs2CnLz%2B%2Buu0aNGC//3vf4wdO/Zyt8GA3W7Hw8MDKNW%2BvfHGG9xyyy20bt2axx9/nJSUFI4ePcrx48cJCwvjyy%2B/5KabbqJdu3a8%2BuqrlJSUAKVaQ73ZbFRUFMuXL9eULVu2DIAGDRrg6%2BtLZmYmUHrfACZPngyAy%2BXCz89PCKfqA/att97C6XSyf/9%2BAI4ePUqHDh346KOP%2BOmnn5g7dy6AWN%2BCgoJLOmjr%2BfDDD%2Bnbt6/m8%2Bmnn1X4eolEIpFI/hZ%2Bx4vMK47F8pd8PvnkE8Pf4Mtp6f4I%2Bfn5OBwOTZnD4aCoqAgo/e1wqfNXAqnZk1xTOJ1OvvrqK7766iugVMCoW7cu3t7epKenA6U/7j/99FM%2B%2Bugj0tLSaNWqFZMmTWLJkiV06tSJsLAwOnXqxL59%2B4iMjCQqKopXXnmFgIAADh48SFRUFBs2bOCjjz4C4NlnnxX9Z2RkoCgKhYWFBAYGMnfuXD799FPOnTuHy%2BVi4cKFREZGsnz5cqF5UrVY48ePp2/fvkyZMgWXy8WHH35oGOOkSZMIDw%2BnoKCAXr16MXfuXObMmSPGuXv3bqpXr85HH33Ezz//TGxsLF5eXgA899xzYpz5%2BfnEx8cTHx/PgQMHgFIBt1u3bqSnp2u0ler4MjMzGTx4MLt37yY2NpZu3bpx4MABJkyYwIgRI5gxY4Z4OCUnJ/Piiy%2BSmppKVFQUkZGR/PrrrzzzzDNMmDCBr776iqKiIoKDgxk7dixbtmzh7rvv5uLFi/z0009Aqeb0ueee48MPP2Tx4sWsWLGChx56CJfLVaG9oD48jx07ht1uJywsjOTkZPLz80lNTSU/Px%2B73Y7NZiM3N5emTZty%2BvRpLl68CMD27ds1PpyHDx/m8OHDWK1WITDabDYhhNpsNt577z169epVofGZ4XJdWmMqkUgkEonkz3PfffcZXFLcLZKuFJ6engbBraioSPw2K%2B%2B8agV2JZCaPck1jaIoZGRkkJ%2Bfj6enJ1AqRMyZM4f8/HwUReHIkSPcd9997N27lyeffBKAnTt34nQ6cblcbN68mYkTJ%2BJwOMjNzTWo1tPT08nOzgZKNVaqgLBp0ybmzp1LYWGh0P58/fXX7N27l%2BLiYsNYXS4XR44cKXeMAwcO5PTp00Ij9dlnn%2BHn54fL5WLv3r18%2BeWXOJ1OJk%2BeTMOGDbn77rsJDw/XaBPLIzc3ly1btlBQUICiKJw7d06MyX2sXl5e2O2l74g8PT3Zv38/p0%2BfZvHixTidTjH3qVOncuzYMfLy8vjqq6948803iY2NJTExkY0bN1JUVITNZiMnJ4f//ve/TJs2TQhZKuPHj2f27NmkpqZit9vJzc1l8eLF7Ny587LzAbjhhhsA8Pb2xmKxaO6bGva4pKQEl8uFy%2BVi9%2B7dAFy8eJELFy5QWFhISEgIHTp0AKB9%2B/Z4enrSq1cv6tatCyDOqW2p61YRevfuzRtvvKH5dO9%2Bm7Hi7NmXPC4oMGn84481h2VKTQ2pqdrjsi2qwWCtq3vrmZJi0neZsC5YutRYJzFRe2wSZnrXLl2Bbk4A6PdCmbZVpeyWatB/9XTbDoDjx3UFJnMos9j%2Bjffe0x67WQEI9PMsi8amoeyllIppXCf9oPWTMnm%2BUPaMUjl61FhFvwdyc0361m24MmOISzekN0cy2bQVuS/6PYvZm%2B%2ByoAeCb7/VHpto30%2Bf1hWY%2BA0bvh%2B//mrsWx9Q4ZtvtMdm5u/6OejHD/Ddd9pjs0XXL5jZxtH3pd/EZvtmwwbtsZmGR3ed2zuy39A/hPbt0x6bPUz0wbLM6uhunul%2BdLPqAaDsRavA7LuqC41v9oW5cEF7bLZ8%2Bq%2B42fj0dfTtmN1KfZlZu7pHiWG8ZmW6xwR/IIPRlcdq/Us%2BNWrUICIiQvP5K9woatasKZQNKunp6aKv8s5fScFTCnuSaxqn0ykEMZfLhaIozJ8/H4vFwuTJk0lISMDf35%2BCggLuuusuQkNDxbUtWrRgyZIlVKlShTVr1pCdnX3ZCI%2BqgAilZn92u50333yTNWvWCFPMTZs2cffdd4vjt956S9OG2Rg7d%2B5MTk4OmZmZtG3bFigNEqLOzcfHhyNHjhAQEICvr69oy9fXV4T3vRTZ2dkoisKoUaP4%2BuuvhTbP6XTy5Zdfinpr1qzhdNkfV/e3TlWqVGHZsmUiQIm/vz8zZ85k8uTJWCwWevTowdChQzlz5gxVqlQBSoXVzp07k5eXR35%2BPk2aNAF%2B08g5nU4OHz7MjTfeyDvvvIOiKNjtduEXeTnUQC%2BKolBcXExWVpY4Z5bDRjWHLSws5NixY0Cppnbv3r3ivK%2BvLxvcfvx8p/sRpmr5KkJiYiJPP/205rNx4zfGiqNGXfK47OWglvvv1xxWrWqsUrOm9rjMd1yDYbvfd5/m0DS7RcuW2uMHHzTW6d1be1ytmqFKmza6At2cAHAzlQbg8cc1h2XurxrKLIkFlSsb64SE6ApM5lD2NfyNJ57QHrdubWxYP083M3GBzrTc9OWuftD6SemPAXQ%2Bvw0aGKvo90ClSiZ96zac2%2BOm/IaCgi7ZBlTsvuj3LDrTJwB69tQe33yz9rhjR8MltWvrCkx8hg3fD50PDgBlL4EE3btrj83%2BfujnoB8/wI03ao/NFl2/YGYbR9%2BXfhOb7Rt9MK5bbzXW0V1X9l5Vi/4h1KyZ9tjsYaL/sWtWR3fzTPfjkCHaY7eXdID5d7V5c%2B2xyRem7E%2BZwGz59F9xs/Hp6%2BjbMbuV%2BjKzdnWPEsN4zcr0oQHq1DFe87fzFwl7fxetWrVi9%2B7d4qW/oijs2rWLVq1aifPuv2tSUlJISUkR568E0oxTck2iavGKiorw8PCgqKiI7Oxs0tPTKSwspE2bNkydOpWUlBScTid2u10IMQC1atXi559/ZvDgwURERHDq1CmhIdMHJykuLhZfYvf%2BGzVqRGRkJKNHjyYnJ0fUSUlJYdWqVUIIGzduHBaLRZx3H%2BOUKVNISUmhadOmWK1WPDw8SEhI4LbbbiMyMpLz589z5swZIdDo7b7dsdls2Gw2XC4XTqcTRVFo2LAhgPCle%2BWVV7BYLPj7%2BwNgtVrp37%2B/aKN69epCK7Z//36Ol6lAOnXqxKBBg4RmLywsjJkzZ5KamorNZhMarzvvvFOYv957770iOlV6ejopZW9tVXOGrVu34nQ6WbFiBfHx8UCpkKbXNoK5kKWmuigpKcFut2vum/v/q1WrxoQJE0RU1aKiIrF/cnJyxJqmpqaSkZGBv78/rct%2BHPj6%2BuLp6UlGRoZYn4piLoSbqNckEolEIpH8a0hLS6Ny5cp4eXnRs2dP3nrrLaZMmcL999/Pxx9/TH5%2BPnfccQcAMTExDBo0iNatW9OiRQumTJlC165dhQXRlUBq9iTXJKo5pdVqFYKLxWIht8wu6ccffyQjIwOn00nlypVxOp0abc/Zs2cpLCzE5XKxr8zc5OeffxbCiTuKogjhQVEUFEWhRYsWJCYm8u233wpTTJXz588bhJXOnTsDcPz4cc0Ys7KycLlcQigtKCigWpl2IDk5mdzcXEpKSoRmrry1gFIBp6ioiJKSElFXNRt11wYqiiKCmaiCp0rbtm1F3f/7v/8jtcyuavXq1aJ9gL1791JYWIjT6aSkpESYKMyfP59fy8yfFEURWtCMjAwRHEdFTfXg7qOn/j/Xzb4sNzfXVFOnahCLioooLCwUAhwgNLQOh4Nz584xduxYrGVv%2BkpKSqhbty4WiwUvLy/R59mzZ/Hw8ODmm28Wgn9%2Bfr4Q9Dw9PUlNTaXA1K7SiI%2BJVqei/ogSiUQikVwX/As1e126dGHVqlVA6e%2BrefPm8eOPP9K3b1/27NnD%2B%2B%2B/L34DREZG8sorrzBnzhxiYmKoUqUK06ZNu6LjkcKe5JrE29sbb29vnE6n%2BDFepUoVISiVlJSQn5%2BPzWbDbreLFA1qGF6n04nNZqOoqEgIMKdOnTKYcapCk/5H%2BgMPPCCiOKrXqL5u%2Blx9UKrxArhw4YI4X1xcTG5uLjabTSNkqn6FxcXFQihT%2B8/MzNQkZdfbgVssFhwOhxiTxWLhjjvuEJo3Ly8vIfSo83Bv4/333xdCWbNmzYRm79ChQ2RmZoq10guzJ06coF27dkILqc5HXZNZs2YZnJHV9TMTig4ePMjdd9/NqlWreOGFF8SYz507x5kzZ/Dx8RGRUxVFoU6dOhptnhqspqioCEVR8PDwEJq2oKAgKlWqRNu2bbHb7TQvM%2BexWCzUrl2bzp07U6tWLUCrISwsLKRGjRrC6fpymPns3Xrrdeqzp3cu4er67JVZ8f6GyRwMrqMV8dnTz9NMu6vzU/rH%2BezpHLLM1u8f5bP3f/%2BnPb4WfPbMboz02QPK2Y9/kc%2BefkoV8dkzu3XSZ%2B/fz4EDB%2BjoZiJ%2B4MAB%2BvbtK45btmzJihUr%2BOmnn/jss89opjNj7tu3L5s2bWL37t3Mnj2bqma%2BF38Ci1KeOkAi%2BReiJglXf3AXFhYKM86IiAjmzZtHly5dKtTWww8/zIYNG4T/1tixY/n88885deoUzz33HF27dmXRokUsWrQIKP1yq/0fOHCApk2b4nK5sNls%2BPn5kZ2djdPpJCgoiDVr1hAZGSmEtAkTJjBt2jSRD04V4rp3707//v15%2BeWXhZljkyZNRH6WqKgoRowYwX333ScE1CpVqpCXl4e/vz%2BpqakEBQWRkpKC1WoVmkcVq9Vdsj6tAAAgAElEQVRK9erVSU9PF2Ox2%2B0V8j2Lj4/n3nvvBUoFIU9PTzw8PLh48SIOh4PFixeTmprKmDFjgFKhLS8vT5isent7C1PLxo0bM3PmTHr16iXO169fX6y9HtUcVVEUHnvsMZKSkujWrRsffvghWVlZWK1WjUbSHfVe6LFarbhcLho3bszKlSv55ZdfePjhh7lQ9tfQZrPxzTffEBwczKRJk/j8889N21CF1Msxffp0/ve//2nKRo8ew6hRMjG7RCKRSP5BrFtn7q/5d3AFo1JqMH2Tdm0iNXuSaxqLxSLSL9StW1dopSwWi9Auqf5qgYGBIg2BxWJh4cKFHDt2TGiN/P39haD09ttv07t3b3744QcAjTZMJSAgACjV/mRmZgotUE5ODqtWrdJoA1WzRIfDIXzaLBYLGzZs4PHHHxeRJC0WixDEatasycmTJ4mJiRFtR0VF8e677/Lll18yYsQIbDabMLVUhSN3XC4X/fr1Exo20Pq/zZw5kyC34AphYWFC0%2BYuqAwePBgvLy8hvHl6ehIbG8u4cePE%2BixZsgT4TRua7/agjYiIYMGCBZrzvqae9qUsWbJEJKzPyMhg8eLFbNmyhaysLNq1ayfaMqO2zqG/UqVKGo2mei%2B2bNmCl5eXmG%2BlSpUIDg5GURS%2B%2BOILoDTx/YgRI0RbHmYe%2BuUgffYkEolEIrkM/0Izzn8a19dsJf8IoqKiCAsLE5%2BIiAh69uzJwoULNfUSEhLo378/kZGRdOnShWeffVZot6A0ibg%2BRwqUCmVVqlRh1KhRrFu3jmnTpnH27Fm6d%2B8uBDyr1Yqvry8Oh0MIF%2B6mklarlapVq%2BLj4yN8vTZt2qSJRGm1WkXidJvNptEktWzZUmP%2BaLPZhCBgs9nIzMzU9KdGjvTy8hLl7lo493%2BPlpmTXLhwgUcffZSWbtEPU1JSOH36NPXq1eO%2B%2B%2B4Twtal6Ny5M/369TM9d/78eVasWCGODxw4IISU9evXi/Jjx47x0UcfMXr0aKA0fYG72avNZiPEEOLwNxITE9mls9u7lP9a7dq1eeqppwDYt28fAwcOZOvWrdSvX58FCxbQrl27coPVuAuvISEheHl54eHhIUww1H5r1KiB0%2BkUQqB6/86cOSPMVE%2BcOMG7774r2itPm2iG9NmTSCQSyb8CvXm%2B5F%2BFFPYkV4WJEyeyZcsWtmzZwvr16xk%2BfDivv/46CQkJAEybNo1p06YxYMAAEhISmDNnDmlpaQwcONA0GIc7Fy5c4Ny5c7zzzjvceuutTJgwgSFDhtC7d2/hs%2Bd0Orl48aIIWALGH%2Br9%2BvWjVq1aQgO1bds28YM/JiaGnJwcFi9eDJT6qLkLXWFhYUJICAoKYsSIEURERIi6S5cu1WiBVO1bpUqVxBih1NTP3VHX3Y67adOmzJgxQ9PO4cOHGTduHL1792b37t3ExMRQpyx2spn2sWrVqly4cEEImL11IfFfeeUVka8OYOHChaI997F89913xMbGsnbtWgAaNmzImjVruP3228Wc27dvD5Saifr4%2BNCuXTvhpzdy5EieKAtdr2oZL6XZmzZtmkhZUVhYKDSsdrudjh070rZtWyE4uQvaDRo0YOPGjaIdNSBOeHi4EDbV%2Bfn7%2B1O7dm2hDVaFx5o1a1LJLSZ9zZo1NZrRilJhn71lyy55bOLuBu%2B/rz3W%2BxJhdIcx9UHS%2B7bo2zXzofn%2Be%2B2xzlQVAL0JrL4fjC49uL10EJSlxRDofAoNbWD0hzHzoTG453z1laGO4beP3qdQPzYw5tkzMyPS1zHxtNC7fhmaMXvpoHPqMfPF0bdj5v%2BkH46pJZR%2BkfVm0ybOTfohmym%2BDdMyc5LS7z/9jTIxszb47JnsR0PfhmSMGH0Tt2zRHJo6zejnYJZHVD8Hs4Yu5/gFxo2j%2B4KYXWLo28wRVjceUz9i/R7Qm%2BgbHkgYny8VWHMz/2TKojkL9HMoc4vQoPfZM3k%2BGtKqmviQ6r8fFfG/q4gLbkV8AfV%2BfGbfZ32Zoc7veIH5lyE1e38a6bMn%2BduJiopi1KhRGudVgEceeQRPT08efvhhBg4cyJIlS4TmDErN/nr27EmfPn148sknWb58ObNnz9bkPQsLC6N27dqUlJQwaNAg7rrrLgIDA4WGKT09XUS%2BNOO1117j2WefFX5j7ikRoFS7o6Y4KDILDlBGmzZtSE5OFsKkGgRGNbdct24dffv2FYnEH330UebPn09AQACJiYlijJUrV9YkG2/atCmHDh2ipKSEFi1acPz4cdq1ayfWoHLlyuTn5wv/s7lz5/Loo49e8n5YrVb69evHp59%2Besl6at169epx7NgxKlWqpImK6c4tt9zCmDFjuP/%2B%2B00TyEOpEKauR0xMDC%2B99JLweQwNDSUyMpJPdD/e3VHvjTrnkpISgoKCuHjxoohc%2BkeYMGECAwcO5Pnnn2f58uWiPDg4mE2bNpGXl0ekSQI3dc1VU%2BDLIX32JBKJRPKv4NVXoSyw2d%2BOWdLNK4FpNJ9rk%2BtLtJX8o7Hb7SKPXMuWLTWCHpSaTr733ns8aJak2Y3g4GDsdjsBAQEEBQVpImiqJplWq5VKlSrh4eGh0crElUXtUgU9Dw8PocXx8vISWkA1wIiaCBygUaNGIgroe%2B%2B9R/Xq1SkpKRHBS1TBxsfHh3r16uHnlr1UNemrW7euMIU0o6ioSERxSk5OJjs7WyPsLl26lMWLF3P33XfjdDoZP378JdcKSk0HyxP0PDw8NCkLXC4XJ8u0RIqiiEA4epPEb7/9locffljM38yk0uVyUbnsIe7n56cRrKBUC3cpVCG8WrVq4r506tSJ8PBwzX3RY5Y%2Bw51WrVrx3Xff8eWXX2rmru4TNZwylM5b1Rq6XK7Ltu2O9NmTSCQSyb%2BCRx65en1Lzd6f5vqareQfSXFxMWvXrmXr1q10796d5ORkWrRoYVq3WbNm1KhRo9y2bDYb9evXL/e8KlwEBgbi7e2NzWYTApqPj4/Gzy4gIABPT08hdMbExAjBT1EUbDYbnTp1EgJBYGCg%2BPj7%2B%2BNwOKhVqxYOh0PkfFPbhVKhVD/22rVri3x%2B3t7eBuHh8OHD/FRmVlO9enWNySdAQUEBbdq0Ydy4cXh6elJYWKgxOTRDFTyrVq1qSAr%2B9NNPC3NJi8XCrbfeKvwk8/LyhPZszZo1mus8PDzw9vYW6%2BQeZtjdJ1FNKurv7y%2BStdvtdux2Ow0bNtQE0vHz88PPzw8vLy9uvPFG0Z6aWgOgXbt2dO3alZSUlMsKXg0aNMDPzw%2BHwyGEUYvFQnh4OCtXrsTb21vj36euo7tvYUFBAcXFxZo5SSQSiURyTeHmo/%2B3I4W9P831NVvJP4YXX3yRyMhIIiMjadmyJc8%2B%2B6zwq7t48eIl/bXcOXPmjGhHTWWQmJhomstOxWKxkJmZyeTJk1m%2BfDkOh4Pi4mLy8/M1P%2B5nzJjBggULRJqD3r1789prrwGlESvnzZsn0hlAqYDTtm1b2rZtC5T66jVs2JAvvviCWbNmiXZbt24N/CZ4QqkgERQUJHK%2BQanZqlmKAJWUlBSysrI0ZQMGDOCee%2B7hpZdeIjAwkAYNGpiaWvr5%2BfH6669TqVIlTpw4QXZ2NpmZmYa8fF26dGFnmQ%2BJoiisW7eOdSZ5ltT5qX6JRUVFQhAsKSkR4/Tw8OC%2B%2B%2B4T9/ezzz4D4I033uDjMp%2Bnnj17Mn/%2BfLp06aLRrKl7Izg4mB1lPjU2m02juZ03bx7r168nPT0df39/qlevXq6we%2Bedd5KdnY2Pj494QaAK/sHBwdx2220a7ZuqxXRPB6H6Bf4RIU8GaJFIJBKJRPJXI4U9yV9GeVE3c3JyGDNmDAkJCSQkJBAbG0ujRo1YtmwZXbp04cKFCxWKugmlgtuIESNEWzVq1GD06NGm2r9BgwYxa9YsrFYrxcXFPPHEE0RHR5ORkSFM8AYNGiTqjx07lkceeQQfHx9q1apFs2bN6NGjhwgk8uCDD7JkyRJh3qf3EYuNjeXo0aPccccdmmiXR44cAX7LCQilAUdOnz7NqlWryMvLMwgPISEhInCIO9WqVcNutwvNUpUqVdi/fz9ff/01p06dYt%2B%2BfaYaruzsbJ555hlyc3OpUaOGIaG5yr333iuC0ADcfPPN3HjjjYY2VTNQ1cTTZrMRExMjzqsCUnFxMStWrBBBT9Q8fU899ZQQgv/v//6PRx99FJvNRmhoqFiLJUuWiGikVquViIgInE6nRphNSUkRAXxU4VUv7KrtzZs3D4CsrCwOHjyIxWIRmtKxY8cSGBhIp06dDGtywSwzbRlmAlx5mAVoua0snYQ7etdDgyuiWYQWfQACkyApBr97swAoZekyBPpM52bBV/SBDMwCv5R9BwRlgZk06INJmJi96t1B9Wtj6i6qLzRJdK0PbGAWo0BfZujLrHNdWYUCdphEdNDfckPMKpO%2BDUUmnVckSbR%2BOKZz0N2risQOqVDwFV3AjgrkjjccmyWf1r8PM2tXX8ds3obrdJOqSMwUszgl%2BjkYksubtG2Wv/1yW8usb33MGUMwG5N2KzLPiuwJw4Y0Cc52uVhAZpX0987U9VxfaNKw4U%2BBWef6AZr5%2Bl9uAc38z/WTMLvhep%2B0CjyTDMdff136r%2B7F8t%2BK1Oz9aX5/CDmJ5HcwceJEoqOjgVINz7Zt24iNjeX48eOEhIQwbdo0EhISGD9%2BPB06dCArK4sxY8awevVqYmNjDWaKcXFxpKeni5QCFouFgIAAEdb/22%2B/BeDjjz/mtddeY8KECeJai8XCwYMHcblcWK1WoUErKCgQ5oavv/46UOrTd9NNNzFy5EhuLUsk6i6cqURERDBy5EiGDx/Ovn37RC45gHfeeUdcl5ycDJT6DJ46dYrCwkJNNMtL4evrS2FhIWFhYZwqC6GnBohJT0/H4XDwyCOPMG/ePPLy8kQSeXeBsUqVKly4cIHp06cTGxuLv7%2B/0LadM4QU%2B4183a8BdX2HDBlCfHy8ENpUVE2k0%2BkkICCALl26sEUXkU6Netq5c2caNGhAeHg4hw8fFgLhDrdIeO6aPXeKi4vJyckhKipKjAlK91im2w8ENZE7GJPFuweOUc1O3SOW3njjjTzi5qegmuKWF3AGSiNzVpTExETTAC3hOhPmMoViucfoTG8B0AeQeeghQxXD0t5zj7GdgQO1x23aXLZdGje%2B9DFAw4ba4z59jHXcNLYAmAjS%2BrSG%2BrUxTXuoL9T3A%2BgNC8y2ob7M0JdZ57oyU0tj/XUmL2L0t1z3mDTt21Bk0rn%2BkWQ2Bf1wTOegu1f6W2fWrmGNzSrpTN/Nqri5QpsemxmN6JX/Zu3q65jN23CdblJm7eq3X2CgsY5%2BDmaPmQps68tuLbO%2B3YxdANClKTVttyLzrMieMGxIw0Y3XqdfK7NK%2BntnavyhLzRp2M07o/zO9QM0Swl0uQU0PPQxTsLshusDm1TgmWQ47tmz9F%2B3v52Sfx/Xl2gr%2BdupXLmy8GMLDg7mnnvuwdPTk71797Jz507i4uKYM2cO/fv3JyQkhFatWvHaa69RVFTE9OnTNW3l5uYSFxcnAp1UBPcUDy1btqRt27YoikKzZs1ITEwkOjoam83GLbfcwqpVq4QQqSgKa9asEULalClTWLRoER4eHthsNhHhcvTo0XTt2lWMTzVxTExMZOPGjTz//POa6Ixdu3blwoULhh/5lyInJ4ezZ8%2BKXHwAw4cPF8JqUVERoaGhWCwW2rZtK7Ruqq%2Bgoigi2ElWVhYzZszg5ptvNvTzexKCx8XF0bp1a6ZMmWIQglWB6dVXX%2BXo0aNMmTJFo31UiY6OJikpSaRkKA8vtz90/v7%2BzJ8/n3Xr1nHXXXexYcMGjVDboEEDjS9koNuvF33/qvnnunXrqFGjhiZQT2pqKk899ZQm4mp4eDigzdOnrruK3ufxUsgALRKJRCL5V/AHo1tfEaRm709zfc1W8o/BZrOVG3WzU6dO3HrrrWzcuJHPP/%2BcjIwMCgsLGTZsGFarlWHDhlW4n3fffZcuXbrQpUsX9uzZI3KsHT9%2BnLS0NFasWEFoaChZWVkMHz6cSZMmAaXCXn5%2BPgsXLqR%2B/fpMmjSJwYMHU1xcjNPpZP78%2BQAMGzaMQYMGYbPZ8PHx4Z133mHz5s189NFHNG/enJdfflkTpj8yMpJWrVrx8ccfs78s31NoaChNmjQRQoPFYrmsz%2BKCBQs0Wq%2BXXnoJi8VCcHCw0F5Vr16d3NxcrFYre/bsAeDNN9%2BkevXqpmkJ9Bqrb775hmbNmvHYY49pytVgJlu2bCE5OZkbbrhBk87C3e/szJkzTJkyBUVReOONN7j99tsJDg7GYrFgtVpJSkqiQ4cOAAYtoYqHh4cQTrt168batWuJiYnhvffew9/fX1M3KCiIw4cPi%2BA27mtaXFwsBLrPP/8cp9OJw%2BEQZqpZWVliDHPnzuX8%2BfPUdnuNrY7BPR/jwYMHNcKml9kb2HKQPnsSiUQi%2BVfwwANXr28p7P1prq/ZSq4qatTNwsJCmjZtesmom%2B%2B88w4jR44kLi6O//73v5w/f56QkBCWLl1aYfNHFVW717p1a%2BGTl52dzcyZM2nZsiXnz58nKSmJo0ePGrSGu3bt4rbbbmPWrFk0bdoUKDUHnDhxIvfffz%2B%2Bvr4iWmWPHj3o1KkTL7/8Mrt27WL37t106tSJESNGiPYsFguLFy/m3nvvFZq6gwcPcvjwYSE0KIpSruCjkpeXx6xZs5gxYwYAFy9exOVysWLFCjGHM2fOYLPZWLBgAXfeeScWi0XkH1STn7vjHujEarXyn//8h/379/N%2BWSJtNU2Fu7brk08%2BYenSpWzdulWUuWsIFUURGqxGjRrh4eHB2bNnsdls7Nmzh7S0NJ555hkSExOFEBUZGUnTpk2JiIjgwIEDFBcXC1PNVatWcerUKWbOnMmaNWsICAjQaNa%2B//574X%2BpKIowDdX7QM6dOxcoFa46duwoyrOzs3nppZdITU1l5syZmoTyaWUOLQcPHiz3vpRndmqGmc9e9%2B4mSdX1viP6Y7NchHrTXBNTXYM1qkmCdIM7oN6HxixPkb6OWZZjfZmJH6TBVc1ME6qvpKtj5munNwwwMxQw%2BGOZvBy57PhMzH31fZn6u1UkQbV%2BD%2Bgm%2Bkf8tUw7M2tIX/ZH/IBMrtGvhdm904/Z1MhDP4fLfX/M%2BjLzq9J1Ztr35TaX2Q3Xl1Ukm3xF1rwi2bsr4uSqH4/Z%2BPTXmc1T37d%2BjSviBFmBfWM6b/089RdV4BpT6/0K1KmIP%2BMf%2BLpUaEv8kW1jqGP6oJD825DCnuQvxSzq5qOPPsrkyZMvGXXTarUydOhQvvzyS1555RUUReHrr7%2BmZ8%2Beor3JkyeXqwnZsGGDiMKompLa7Xbhn1alShUOHDhAzZo1OXfuHEuXLmXp0qUArFixgnvK/JcURWHhwoW88MILnC3zVPf09KRy5cq8/PLL/PjjjyIYjMPhYNKkSWzYsEEIPOHh4YYcc56enowePZrHH39clF1Oo2MWUXLkyJE0a9aMN954o9zrCgsLWbhwIW%2B99RYLFy68ZB%2BDBw/WjOeXX37RCEnFxcXUq1ePuXPnUq9ePaDUhFSvEezRowdPPvmkRuhzuVwMGDCAH374QWj5VO2ioiiaflwuFy6XC6fTiaIoGj%2B7oqIipkyZQrt27ahbty7nzp0zDcZjbiL52zpv2bIFi8WC0%2BkU0VPVvtLS0nj33Xc5f/48CW6BQ9QUD5fy2Tt69Gi55/QkJiby9NNPaz6bNpqEt9bfe/3xffcZr9GvickaGax2df5QYOIOqH/RYpbsVl/H7OWMvszg/GLiqmYW/EZfSVfHTPbWu7aYuboY/LFMNLaXHZ%2BJWbS%2BL1N/N12hqbJYvwd0E/0j/lqmnZk1pC/7I35AJtfo18Ls3unHbDY8wxwu9/0x68vMr0rXmWnfl9tcZjdcX2a2zy/rIGpSZnaD9WUVcXLVj8dsfPrrzOap71u/xhVxgqzAvjGdt36e%2BosqcI2pl0MF6lTEn/EPfF0qtCX%2ByLYx1FHX5rvvjBf/XUjN3p/m%2Bpqt5C9FH33z9OnT%2BPr6MnjwYBISEti4cSM7d%2B6kSZMm9O/fnxMnThAXF8ezzz572eibNWrUICEhgcqVKxuib5ZHWloaWVlZQuDcuXMn69evx9fXl6CgIEpKSjhx4gQ1atTgwQcfZMGCBdSuXZtmzZoxdepUEU3S6XRSVFQkNFp5eXmsW7dOYwoZFBREZGQkcXFx9O7dWwgEa9eu1WjpvvjiC7E%2B06ZNq/DaFhQUiCTsqgYuLy%2BPfv368fLLL5teo9bbvHkzK1euFJEyAUP0TS8vLyHsXoojR44wevRoTUJ4d3NJgNWrVzNjxgxKSkqEMK9qFQMDA7FYLGzYsEEEm0lMTKR79%2B5COKxcuTJeXl6EhITQsWNHjbljtWrVGDp0KOvWrePMmTM4nU5Nnr1LYbfbhbCnBuVp2LCh0BSrAudDZUFHEhMTNX58Kk6nU5M38Y9iJpBKjz2JRCKR/OMwi/os%2BdcghT3JFcU9IErNmjWJiorigw8%2BYPfu3QQFBfH6668zbdo0BgwYQK9evahduzZpaWkMHDhQhMyHUv8t1TwSSn%2Boh4SEYLfbRfRN9fhSWCwWoSlSNTlFRUUcOHCA/Px8Tp48SV5eHnXr1mXLli2cOXOGZs2a0bx5c5FOwG63U6dOHd5%2B%2B22R1y0pKYmxY8eKfjZs2MCcOXN47733ePzxx4Vp4OnTp3nzzTdFvX379hnG6B4B0sPDgyVLlhi0gQ6Hg%2BzsbBwOBy1btiQ0NBQoNd9091vz9fXFarVit9upW7eu0FqNHz%2BeuLg4IVB9/vnnmvYLCgo05pkqPj4%2BmoAkUKrZUhOgV69enfbt24uIpe4oiiJyCUZHR9O%2BfXvi4%2BMJCQlh69atIhdiSEiIGDeUCukFBQUcO3aM9evXa3INZmRkcPr0aaZNm0bPnj3Jy8vTaNosFovGHFWdg9VqFRpCd1PL48ePExkZqcnL%2BMMPPwBQp04dUy2ep6enEOD1Zpv6viUSiUQi%2Bdeji63wtyI1e3%2Ba62u2kr8c9%2Bibdrudtm3bcsMNN7B27VpD9M0HHniAw4cP8%2Bijj1JSUkJcXBxQanqYk5Pzu6JuglGzqAovqjZH9eNSFIVq1aphtVrJzc3l4sWLdOrUiaKiIiZMmIDT6RQfdTwHDhxg%2BPDhQKlmp3///mzatEn4qs2dO5czZ84wbtw4AgIC2L59O1Ca1uCIW04xVQPm5%2BfHiy%2B%2BaJhDcXExgwcPNghe7gnKDxw4QK1atYBS4aJZs2aiXklJCS6XC0VRRD44Dw8Ppk6dyqFDh4Twcuedd2radzgcQsB019IVFBSIcj%2BTsNLp6ens2LFDk/7A/fqUlBQsFosm0EmPHj04f/68ELAiIyM1Pnsq/v7%2BREVFadJvtG/fnho1ajBu3DhRX28C657T0GKxkJeXpxHC1GidNpuNCRMmkJCQQEBAAFAqGMbExJCamsoPP/yguc69jro3SkpKaNmypahjtkblIQO0SCQSiURyGaSw96e5vmYruSrY7XY8PDwM0TcjIyPp378/Tz75JPfeey9du3Zlx44dIi3B74m6qaI3r1MUBZfLxQ033EDz5s2pW7cuDoeDpk2bih/vUGo6CqWasUWLFpXbvupH9sUXXzB%2B/HgCAwNZvXo1c%2BbMoWbNmhpBAxA5/dzHA6WBQFTzS/cf%2BJ6envznP//BarWK63x8fMR1LpeL/Px8Dh06JNrbtGkTgIhAqY4zMzOTAwcOoCgKL7zwwiXXzd1MVe8/d%2BLECQBNNEs97iatakRKdfzVq1fX%2BO/dfffdmmvz8vJMBfusrCw2bNgg8gEC/Pzzz6SlpYkUGXoURRFmmO5zcdfQqUFbnE4n0dHRhISECE1dQUEB3333HXv37iUsLMw0HUWjRo3E/51OJz/99JM4zjQLRlIOZgFaunY1CdCiT9NR9lLEbRDGa8q00ipmwS4MQVFMxm5QbOoSr5vkOIayFx2CxERjnbIUJQKTQC979%2BoKzNop0zALdGtj5kKpXwuzGDOGmAS69TTrGr0ZtFky%2BYpka9clZjZ1EdW3rU/mXIF23ay6f0OfVdssaIZ%2Bn1QkSbS%2BXZOoM/quzO6L23uzcodHWcRlgS7PJ99/b7jEkCjcLdeniuFrVvZc1KAPcuQWuArKGa%2B%2B4VWrjHX0/lJmz5nLBUAB433ZuVN7bJZd/JtvtMdqku1LtFuhBOl6KxezfK/6a8yyvuvX3MxfW//sKPv7KdA/s8B4f83WU/edMvva6csqEmeqIjnV9deY3Tr989msjr5MPz6zJZf8%2B7Ao%2BjB1EskfJCoqilGjRtG3b1%2BgNEx%2Bt27d%2BPTTT5k6dSqLFi2iVatWPP/88%2BIal8vFokWLiI%2BP5%2BTJk/j5%2BVGnTh1Onz4tolUuX76c2bNns2HDBkMf7sdRUVHk5uYyYsQIoqOj6datGxaLhapVq3Lu3DmCgoI0voF6LBYLjRs3pnLlypw9e5bbb7%2Bd//3vf9jtdmw2G6tXr%2BaBBx4gNzeXFi1acPz4cdLT0wkODiY1NZUnnniC4cOHs337dhHspE2bNuzatUv0UadOHaxWKyNGjGDSpEkV0l5aLBa6d%2B9OUlIS6W528/ok4RaLhfr163Pu3Dly3Z7gVatWpWnTpnzn9oNB1XL%2BXqpXr47dbhfBasyw2%2B00aNCAX3/9FUVRqFq1Kh07dmTt2rV4eXmJtBaXw9/fn169epGYmKgx5XQ4HFgsFqHZs9lsmnXs3LkzO3fuNGgK/fz8yM3Nxel04uXlZZp%2BwmazsX37dlauXMlrr72Gt7e38Als2rQpCQkJDB48WGhu9VitVpFS43JMnz7dNKn6qFEjK3S9RCKRSCR/C4pSTkSpv4GQkL%2Bm3ePH/5p2/4FIzZ7kiuIeffPs2bOsWLGCIUOG0Lt3b9Pom%2B5RN5OSkvj222/p168fqampmqibmT6kD2UAACAASURBVJmZBt8qKPWVUwU/FdWUVA0c4u/vL0L0N2rUCIvFoom%2BCaUBYBRF4dChQ9SoUYMPP/yQ1atXA6WaqpdeeonatWsLnz01ofdPP/3EmjVrKCkp0Zgbqtx%2B%2B%2B2a41OnTnHixAliY2PLFfTq16/PyJG//eBXFIX169eTmZmJzWajatWqGh8093oXLlzAx8cHh8OBv78/drudzMxMtm3bplnz5s2bm/btTkREhMEnLT09XSPoWSwWHA4HXl5eQutXUlLCoUOH8Pb2pnXr1jRp0oR69erRvHlzEhIS%2BOKLLzTavblz55pq0LKysjQCqoq7phN%2Bi1SqtrF161ZDABr1vHqP1LWLjo7W%2BC8qikJcXBzffvstd911lzCFdUfVDKqo/pOARlt8OWRSdYlEIpH8G0hLv0qCnuSKIIU9yRVlzJgxIlKmGn3z6aefBko1Ndl6c6NyUKNv6j%2BXir6p8tprrwmfPdXfLjs7m/Pnz2OxWIQp6ezZs0XuuLS0NBo3bgyUCg/jx48XQk1OTg4TJkwQEUazsrJE7jkVf39/U8FAjbg5YcIEAO666y4aNGggUheYcezYMXJzcwkMDBRl1atXF4FmMjMzhYDjLrR5enpSUFBAWloaDoeDrKwsYVIZHR0t6rlcLn7%2B%2BWfTvgcOHCj%2Bn5ycDPxmjunh4YHNZtMItd7e3hQVFVFSUqIRPm%2B55Rb8/PxISkoiKiqKEydO8Msvv9CnTx/69OnDypUrRd1Ro0YJM0t3HzkfHx8uXryo0dDZbDYURdFoxNQ9pQqbNptNY/rpPm81V6I61lWrVjFgwABRp3Hjxqxbt453332X9PR0Ud%2Bdixcvanzz3PPu/Z4ckNJnTyKRSCT/Bq7qnybps/enub5mK/nLcY%2BUGRQUpPnxHhERwV6DI04pcXFxptE39Z/yfMZGjhzJmTNnyMrKMv2hX1xcTEFBAampqbRo0YKpU6eybds2iouLRUCTX8v8YFasWEGTJk1YsWIFNpsNh8NBlSpVRKTRoKAgBg4cyMSJE/nqq68Mc3PPV6eiagm//PJLjh49KvzgAE1SbzWQyYYNG3C5XGL90tPT8fT0RFEUunbtysUyh5bk5GThp1dYWCi0RREREQwYMECYc6rjvBxLliwR/3c6nRQWFgoBpLi4GKfTSW5urrgPan8lJSVUd0vKtnXrVvz9/QkPD2flypXUrVtXaPYSEhK4//77RV13IdFd25mXl0daWppG2HM6nVy4cIEHH3xQlKlrpEZcdQ9Y4x4sxul0iuAs7rgLWIcOHRK%2Bi%2B%2B%2B%2B64mXYWaVL2goKBcrWyHDh1My82oaFJ1vc%2BEwbUlNdVwjUFpaOZ4oU/GrvdjMbmsIi5d%2BkpmvmF6HymTKRgcVwx%2BVcYqxtzsJk57Br8as0ns3q05NHXF1C1yWSYRgZlLl74dM18cQ19mjkB6PzSdWXVFkqob/AfB6KtmdvP0N8vMFEo3ZkPfZk5dFUhQrVOqm/r16csq1LeuzNTaX7dPTN7tGfyfDHvAZK9VJIm14X6a%2BLfpt4nZ3rrsWphNvCLOYRXIAq4fT0V8Z/Xvhs0eY4Zt7OY2oaJ3cTW405q4Jei/zxs2GPvWF5qtuWGiFfly6utUJOH8H9xbhjL9cTk5a/9WpLD3p7l03HqJ5Apy1113sWzZMn788UeREgAgNzeXuLg4evbs%2BYfbfvHFF/n555/Jzc0lJycHu90ufpB7eXkxYMAAli9fTn5%2BPhcvXiQxMRFFUZg1axb169cHSn/EP/zww1y8eJEBAwbQrFkzKlWqxMWLF/H29hbmoZs3byY5OZkpU6YQGhpKZGQkJSUlFBUVscH0LwIkJSWJ/zdq1IjDhw%2BL41S3H0%2Bq4KIKg%2B7CrSr07Ny5E6vVisvlonr16pw9e9bgg7d9%2B3b27NmDp6cnDRo0IDk5%2BbI%2Berfccgvfffcdfn5%2Bl8xd5%2B3tjZeXl8FvT/Un9Pb2ZsSIETz22GMcPXqU6OhofvnlFywWC3369AEw9ZfT4%2BvrK%2B6lKhCq6%2BMubKn3WhXa3IOktGzZkj179gCQn59Pq1at%2BEQv5IBYT6vVSnR0NDk5OUycOFEjaKqCs81mKzdp%2B/cmwR/KIzEx0dRnLyIiXFPmpuAFTPKj16xpaNugNNQ3AsZk7E2aGKroL9Mfm7pw6CrVrWus4hacFTCdgiG7r/4akyrG3OwNGhiuMSTQNptEZKTm0FRhq1vkOnW0p82U9/p2zBKmG/oyyy7epYv2WJcepSJJ1dGZ1AOgf1lhdvP0N8vMn0Y3ZkPfZtmdK5CguuxRLSjL7HLJsgr1rSszeSdk2CdmqTb1%2BdoNe8Bkr5WbxNoNw/00sXDRbxOzvXXZtTCbuH48JknpK5IFXD8e/XjN7qU%2BuLHZY8ywjdu0MdQpM9op91j//QHj91mX%2Bte00GzNDROtyJdTX6ciCef/4N4ylOmPy55zNZe8BeUERpP887m%2BRFvJVUWNvjlixAg%2B//xzTpw4wY4dOxg2bBhWq/UPRd9UqVGjBna7Hbvdjp%2BfHyUlJTRo0IDu3bszc%2BZMunTpgsPhwOVykZSUhL%2B/P02aNGHx4sUMGDCATZs2sW7dOubPn4%2Bfnx8JCQlAqeCi%2BsI9//zzREdH88orr/D4448TFRVFTEwMCQkJLFu2jJo1a5rmqtPjLuiB1gfsxIkTwvQSMPjlQalZaf369fHx8RECl7sgp2qzCgoKCAwMZMqUKaY%2BbHo2b95McXExvXr1EnnvLCZ/QLKysmjZsiXBwcGm5/Pz81m4cCH9%2B/enQYMGDB48GEVRhGZv6dKlBnNFi8Vi8A9UBTp37bDVamXWrFmaCKfq3EpKSnA4HEIDBwhBD0o1k%2B45CdV%2B4TftnsvlYujQoZw6dYrc3Fzquv3YVe9FzZo1yxWcjxw5UuGUIdJnTyKRSCT/CkzcDv42pGbvT3N9zVZy1Xn55Zd54okniIuLo3fv3owfP56QkBCWLl36u/ydLoUqCDgcDtavX8/w4cMZNmwYGRkZuFwuTp8%2BTU5ODrfffju7d%2B%2BmatWqLFiwgD179vDNN9/Qr18/Vq9ezcGDB0lLSxOpI7y9vTl%2B/DjLli2jYcOGvPXWW3h6enLq1Cn69%2B%2BvETL0LFq0iA4dOtC6devLjr88gfGBBx7g6aefxmKxcPz4cUOaCZUPP/yQd999F4ALFy7w9ttvG4Sy8PBwbr75ZgC6d%2B/OvHnzhAnpokWLCA0NxeVyiaToUOrLqN6jbdu2Cb9Di8VCTbe3/f7%2B/uTk5Ij0EE8%2B%2BSS%2Bvr7CZ8/dR86dOrpXqaoApq4/lCZg/%2BijjzR7RRXgVIFS1ca557%2BDUo2qmrJBDbTTsmVLbrjhBjFPRVHYtWsX4eHhwjRYT5s2bTTrqe43NcjPbp0JoEQikUgk/2qupjmnFPb%2BNNKMU3LFKM%2BE0R01%2BubQoUPLrdO3b19DhM1L9REVFcVpE4eeQ4cO4ePjQ0FBAV5eXrRv357du3eTnZ2N3W5n7dq1BAUFUVhYSG5uLtu3b9eE1L/rrrsAhO/cxIkT6du3L4sXL%2Bbtt9%2BmqKgILy8vVq5cSb169Rg%2BfDgTJ040HfdP/8/edYdXUbTfs7ffNEI6EFJACCSUBPgg/AgtlA9phiZKqCIgSK8CKvKhAQGlF0XR0JQeqhAQAkR66C3SJJAQAgmQntyyvz9udtydnZBrQUX2PE8e2M3s7Ozs7M19933PORcuIDs7Gw0bNiQlnRqNBl5eXrKAQghyjEajxKLg2rVrKCgoIIGGIPCh0%2BkkAeKmTZuIxUNYWBixsBDj2rVruHbtGtRqNY4ePYqjR4%2BSklEh4HFycoKDgwMRQMnPz0e5cuXw%2BPFjZGdn48SJE%2BQaxGWfKpUKwcHBOHv2LEJDQ0mJqZBZFTJkglqqXq9nloUKGT1xFm306NEYPny4RNVVnBm9JDI/u0QZoeXm5pL5FLJvQuZPq9Xi3XffxZIlS5CQkIDmzZvLzi2gdevW2F7i28RxHNRqNVQqFSkfvX37NvGSfBYUgRYFChQoUKBAwfPGyxXaKvhXojTPN6vVinfffRcHDx5Ejx49cOrUKZLBMZvN%2BPnnn3H37l24ublJTLfLQvfu3bFixQo4ODiguLgYP/zwAwYMGEBEU1iYO3curl%2B/Tvh2whiEQM/JyUkijgLYyjDF5YpJSUm4cOECateuDYvFQkoMi4uLJdy%2BXbt2YceOHXj11VeRSAs5ULBYLCgoKED9%2BvVlJaO5ublkbkNDQ6FSqZCWlibJagnXIARFGo0GT548IcG3oCAqDproAMpqtRJbjk8%2B%2BYTsF8oc8/Pzyf0ZP348GRsLQlbPYDAgOjpakv18wFQB%2BTXobNeuHTiOI2MvTQyobdu25L7wPA%2BTyQSz2UzGWLFiReZxNFgCLW3aMEzV16179jZr7ZZkdgkYBt%2Byx0bmEg5AVAYLAFi2TLrNEvCg19zXX8vb0LxJynyaOZzNm%2BX9iAztWf2y9KDo6WLpTdBCIGAIHNFTg%2B%2B/l26zTk6rr7AEE6g2zIph6iJkFFjWmqDezLO0OOwxgKbHw9KPkZ2f/mxkHETvYt0X2WGsKgiaN0svJJF6rgBajENmNs46N0uBhxZOoaxj7LmXLEN32Tq34/4y54YuMaeMzZl/Bun5E/HPCagLY1KyabUVev7sMUxniQFRc84U/KY/O%2BiHlyFOJbtuxmeobMiMCSxL/4S1j%2B6G9YzRzwfrTyL92NkjaCRrI6wrOygqzw1KZu8PQzFVV/BC4t133yUebPn5%2BUQ1kzbrDg4OxtSpUxEdHQ0nJye89957OHHiBBITE0kmRsg4de/eHV5eXsjKysK6detgtVqxa9cuDBw4EA8fPoRKpcLx48clWaUbN26gQ4cO%2BOKLL7B06VKSKSpfvrxEKEQAnYUTQxAJKQu1a9dGRkaGJHgR%2BnV3d5dk2bRard2BrF6vx7Bhw3D06FGcPHlSEpRptVpYrVaSEatWrRoxTS8Lq1atgo%2BPD/r06QMHBwfcLlFIDAgIQG5uLtq0aYPvvvsOANCuXTvEx8fbNQ%2BlGaOXBkdHR4nZfGnG8qGhofjkk08wZswYpKWlkcBSrVbjypUrOHbsGAYMGFDqtZ84cULGDWSBZao%2BcsQIvDt8uN3XpECBAgUKFDx3pKayVbL%2BCjAskP4UXL36fPr9B%2BLlCm0V/Gswbdo0IuOvUqnQpk0bbNu2DfHx8YQ/5uzsjEePHuGrr74CAHzxxRfo0aMHoqOjJV51zZs3J1mwzZs3S4RCAgICsGHDBlgsFjg5OWHv3r2ScQgljpMmTZIEmo0aNYJOp5ON%2B1kCLvXr18fwUr7oBwYGIiQkBI6Ojrh27ZqMH1hcXAyVSoUnT56gdevW0Ov1UKvVkkAvICCA%2BOjRYiiALSs2b948Ykg/cOBA8jvBdkHA9evXwfM8KlWqROYRsAVQLi4ucHR0RGhoKBITE9GgQQNim3FbJIXftm1buLm5keBUp9Phxx9/lAV6TZs2JdnEkSNHktLJwsJCZvZNo9HAaDSiWbNmCA8PJ155Yr4kx3GoXLkylixZgtjYWIl3oGAX8fDhQ/gwVNq2bdtGAj0HBwdZOaYjS62OAZZAi/LmTYECBQoU/OOwf//fd24ls/eH8XJdrYJ/Dby8vIj3HsdxcHJyItsC1ysnJwc%2BPj44d%2B4cPDw8CI8qLCwMLi4uJPDy8fFBYGAg7t27B5PJJFEFzc7Oxu7du8FxHJ4%2BfYrY2FiEhYWRnwEDBgCwKVReF5WCnD17lpxvwYIFWLFiBVxcXJjqlQLOnDkjUY8U4/bt27h8%2BTJq1KgBs9ksCzCEwNJisSAtLQ3R0dGwWCwSJcuUlBTCF3RyckJAQAASExOJdyBgy%2BDpdDqEh4djw4YNaNGiBQBbcCQuKXV2doZer0dqairhNgK2ssbs7Gzk5eXh3LlziIiIQL169RAWFibjVZ49exbDhg1DfHw8AODTTz9FlSpVyO%2BFubpy5Qp0Oh06dOiAVatWged58DwPtVrNVCs1m80oKCjA4cOHceLECWIJITa953kederUwQcffCBTgW3bti08PT3RrFkzyTULQZz4Hubn58uCNpbPIwsKZ0%2BBAgUKFLwQsPPv2nOBEuz9YbxcV6sAkZGRCAoKIj8hISFo164dvv32W0m7uLg49OjRA2FhYYiIiMCkSZNwX1Q/v2XLFkQyjWds59iyZYtd4%2BnTpw8WLVpUZrtFixYhKCioVB%2BzLVu2oFatWqhRo4ZE8OSNN95AXl4eCWasVivmzJmD7OxskvVavXo1Fi5cCMBmir1mzRrypbtx48aYNWsWeJ6H1WpFcnIyRo8ejcLCQphMJokPm7isz9fXFydLuBejRo3CoEGDkJ2dTdq0aNGCWCxoNBq4ubnBYrHgDGUI27x5cyxcuJBkK5OSksDzPClHDA4OhlqthkajIWO%2BcuUKVq5cCUDqR2e1WoloSWZmJn755RdEREQgIiKCcPNMJhO2bNmCo0ePIjc3l5TK8jwvyX7l5OSQay/LsL2wsJBZbnnq1Cl88MEHJHgaO3YskpOTAdiCTmGuMjMzUVRUhD179pDsqfh6aTg4OMBgMKBZs2bQ6/XIysoCAEkwDthsEubMmYPly5cTZVG1Wo1GjRqhV69eCA0NZZqks8pzxVCzfJQYYHH22rZqJWtH02xkzg6s8dCco9WrZU1kicUSuxEJqDJTWb9r18qPobktrFIZmiPD4OPJqo9Z10lNBj03LH6MPWbENO%2BHVbErmz%2B6X1YWn2rDcumQnYvx7NBTUfI%2B4xmdMOaTMT66X9b4ZHNqB3%2BMnk87PNXZjagXRqz7QnOXaPNzFqfLnvHJjvsdJtas%2BaT3sZY5vdZY9GO6Hzu842VzzqLN0edicT3t8FSXjY%2B%2BJqZjDX3zZAtdfi4mnZu%2BUOpkTKFJ%2BoaX/B0RQ3avWIvLHgIe/SzS2yy6gj03nB4P6zOprHPv2yc/RsELB0WN8yXElClT0L59ewC2LMjx48cxdepUuLq6IioqCjNnzkRcXBzGjx%2BPhg0b4smTJ1iwYAF69%2B6NjRs3Sr70/1bQypkcx%2BHnn3%2BGs7OzRKFT8GO7ceMGHB0dkZ%2Bfj4oVKyIuLg6VK1dGK8aXYvrLv4ODA/bt2yfhZo0aNQpHjx6VcOc8PDxQvnx5VK1aFQkJCaTU0cPDAz4%2BPrh06RJ0Oh06duyIuLg4HD58GCqVCi4uLqhatSpOnjxJSguFTFNSUtIz5yExMRG1atXC1atXYTKZSACRR7GuDx06RNQ0Bd%2B7gIAA4tV3pYRgL/AWLRYLjEYjrFYrMVMvC6%2B99hq2bduGjh07SgI3nuclZaePHj2Cq6urLHNVmliKVqvF0KFDsXTpUmYGTjhWuDcqlYoEpzqdTsY1tFgs5PcqlQpeXl5M0RUh03b48GHJ/iLqj%2ByVK1cwcOBAcBxHAm8HBwdSGtqwYUPmiwiaFyoGx3F2Px8sU/WRI0agRkiIZB8dO8piSZZlCa0G2qePrIkssVhieC9BSea61H6jo%2BXH0I7FLL4FbeDerZusicwQmHWd1GTQc8O0l7TDjJg2c2Yl5GXzR/fLKOOm27DeC8jOxXBqpqfCw6OsThjzyRgf3S9rfLI5ZZmUU5NDz6cdnursRhRniHVfaJNt2qGGHou945Md9ztMrFnzSe9jLXN6rdG%2B9qx%2B7PCOl805y7ScPhfLd90OT3XZ%2BOhrYr4jo2%2BebKHLzyUzWQfkF0qdjGkhR99wxue67F6xFhc9QAaFQvYs0tsst3Z7bjg9HtZnUlnnbtPG9u/fWXXykmXhngeUGXwJ4ezsDE9PT3h6eqJChQro0qULGjdujPj4eJw%2BfRqxsbFYsmQJevToAX9/f9StWxdLliyB2WxGbGzsHz7/lClTSPlgnTp1UL9%2BfcyePZsYmc%2BcORMzZ87E66%2B/jri4OIwbNw55eXkoKCjA3r174eLiQo4X1CbVajVWrFiB77//Hjt27AAA9OjRAwkJCVCr1UhJScHs2bNx8OBBODk5EY85APDz88O9e/fg7%2B%2BP3Nxc3Lp1C4Ct3FMIloqLi1GlShVYrVYkJiYiKioKJpOJKGLq9Xq4u7vbPQdmsxnnz59HUVERrFYrevfuTbhlArTUh7fVakXNmjUlGdVq1aoR/p0QCLm5ucHT0xM3b94kWTPVMz4shWxfgwYN0KRJE2Ybg8EAg8FAAj2j0VhqSapOp4NGo4HJZMK%2BffswevRo8ju1Wk2O0%2Bv1cHNzI9uCObpQ3igec6dOnfDVV19hxYoVAGzZQnd3d7uzaMK5AcjGzfM8CeCGDBkCwBaEjho1imQFARBlz%2BDgYLLPxcVFMs7SvA9ZUDh7ChQoUKDghcBvEENT8M%2BDktlTAOBX4%2Bq4uDjUqVNH5hNmNBqxbNkyeDDerP0WpKenIyYmBjExMQBsX7zLlSsHPz8/xMfHw9fXF7GxsRgyZAg2bNiAmJgY8DwPFxcXqNVqFBUVYePGjZg9e7akX4vFQrhXQumdEJgKX6q//vprhIeH4/jx4ySzCQD3799HbGws/P39kZiYiKslZWflypWTfJGfN28eyRLq9XpkZ2djc0n5WVFRkSzLVKlSJdy/fx/jxo3DqlWrZL8Xl33GxcXh9OnTCAoKIvvozFZQUBBSUlJgNpvxyiuv4MaNG7LSRJVKBaPRiFu3biEoKAi3b99GYWEheJ6H0WhEcXExeJ6HRqPBmTNn8NNPP2H%2B/PkAgJiYGDRs2BDDhg3Dtm3b0Lp1a8KRE8owDQYDOI4jhutGo1F2XeJs4NWrV8l8CvdJQFFRkSTbJtwn2rDcarXiwYMHGDlyJAmAq1Spghs3bsBoNJaaWRR4hsI5DQYD8vLy4OrqCqvViqdPn4LjOHh4eCAzMxNWqxUrV65E3759sWnTJty5cweVK1fG3RJ7AaHU85GolCibKpPx8vJijoUFhbOnQIECBQpeCDzDG/m5Q8ns/WEoM/iSw2QyIT4%2BHj/99BNatWqFa9euoXbt2sy2wcHBv%2BnLbGno2LGjLLN3%2B/ZtZGRkIC4uDu7u7vj%2B%2B%2B/x%2BuuvY%2BvWrXBwcEC5cuWgVqsRGhqKhIQEcvz%2BEoUod3d3sm/RokVITk4mP9%2BX%2BF9xHIfy5ctj3759kiCjQoUKqFu3LlxdXbFlyxa89tprAICHDx9KAheLxUICtLy8PEkmj%2Bd5hIWFAQCaNGkCtVqNVq1awdnZGXPmzJEERFqtFgsWLEDfvn2hVqthMBiQk5ODXbt2yeZK8IADgOTkZHAchwcPHuDWrVtMJUpHR0fcuHEDKpUKV69eJUGakL2yWCywWq0oLi7GnDlz0LhxY5LNKi4uxqlTp7B7927k5eXh6NGj5HrDw8MB2DJqBQUFMJvNyM7OJgEQwPalU6lUzOxbaVlBjuOIr57g0QfYgrtNmzZh3bp14DgOt27dktwP4Vxi8DwvCS6FEtnHjx%2BTwJLneTx8%2BJCcJysrC1u2bMGNGzcwYsQISfmpMOZnlejeZxFaSoG9nL0yfZqWL5d3TvMuaP8vQM75oPhQAIMbRPNCGDwW%2Bg0wk4tD851YpBnqXPb4uclePjPeRtOXwBwfVU7NfKlNDYi%2BL6zxymg1dvC%2BmM4p9AsOOwhQ9HiY/ZZl%2BMXqmnWhVCPZZdpBXmM1oU/1e9ow7ze10561xqrmpqfLHi6gHRRSu/iWsutiGRVS60bWL2OAdq1Hex4qasz2rEd77p3s2WSYydGPB33vWP2WZRUJQP4cMjr6PVxKe67bnvtiz20pc3xM48K/GIpAyx%2BGktl7CTFt2jTMmDEDgO3Lu8FgQL9%2B/dC5c2csWbJE4iP3LKSlpZEAR4xncZoAW4bFs4QcIKg/CtmipKQkPHr0CGvXrkWDBg1w%2BvRpZGZmYsmSJRg9ejScnZ1x%2BPBhdOvWTRJAZWZmok%2BfPnjjjTeY3D%2BBz7Zv3z78%2BOOPksDgzJkzkoyaAIErB/zqydamTRvs27cPe/fuRYcOHbBp0ybS5uzZswCAn0oMoletWgUA5NwCLBYLkpKSMHXqVKjVauIxt54yhNZoNCRgEfzyxIqSQoAi9ucTjN1L48iJERsbi8uXL0uCF57n8UuJo7Q4ID5x4oSE%2ByhczwWR2e/bb7%2BN5VTgQWeqVCoVtFqtjD9Ht6WPy8jIQNWqVckYHRwckJ%2BfL%2BE4luX5J6wnQJ41FaNevXrw9PTEBx98wMy%2BPetYFxZnoxTYy9mjOVIyztQ778g7p3kXrJc0dBDO8FCScYNoXgiLn0jxS5iVtnSwzyLNUOdiUV3oNjJqC4PrQl8Cc3yUfQaLMkMPiL4vrPHKaDV28L5YVBwZMckOAhQ9Hma/9EUwGsm6Zl0o1Uh2mXaQ11hN6FP9njbM%2B03ttGetsfig9HTZwwW0g0JqF99Sdl0sCxhq3cj6ZQzQrvVoz0NFjdme9WjPvZM9m87Osjb040HfO1a/9HgY3cqfQ0ZHv4dLac9123Nf7LktZY5PWMSJiUCPHvIOFLwQUIK9lxAjR45E27ZtAdh4U56eniT74urqKitNKw1eXl5YzVD568MQgxBj69at2L17NwBb6Z5Wq4Wvry8qV66MxMREeHl5kTLSXbt2oVKlSggLC8OyZcug0Whw5MgR5OfnY8qUKWjdujUiIyPh5%2BeHIUOGlCo006BBA0RFRcFiscBkMkm%2BxHfq1AnNmzfHJ598grZt2yIrKwsHDx6UBExCILGvRJmqoKCA2BgI8PDwkJT4CaCDPZ7nsXr1arz66qvw8fEhgeeJEyckx4nPbzKZMH/%2BfFitVowdO1bSjg6MfovZeFJSEoxGIxF4EZ9Tp9PBYrGQ0k8WxNf19ddfA7AF8BzHoWnTprh9%2BzbhQApjZQV6NHQ6nWQsBw4cQHBwMMkeiks%2BhbE9K9jjOI4EwmWha9euqFevHgYOHIhly5aR/QJf0Wq1SgJsMVg8vNKgcPYUKFCgQMELgb/bekHBH4Iygy8h3N3diSedj4%2BPpMwuJCQEly9ftgbDagAAIABJREFUZh4XGxuLzz77jGxrNBrSj/iHVc4nRps2bYghet26dREZGYn09HS0atUKVqsVrq6uAGyBxJ49e5CWlobg4GB0794dUVFRsFqtyM/Ph7OzMyqVZCOMRiNTaKZu3bowGo0IDAxEeHg4IiMj4e3tjbp165Lx7NixA%2BPHj8fjx4%2Bxfv167Nu3D4GBgeT3GzZsINet0%2BnQrl07cByHu3fvSq41ICCAZHaCgoJIOafgVSegXLly4HkevXv3xqZNm9CuXTvJ70sTHRk9ejQ%2B%2BOADyT4HBwdJ%2B%2BrVq8uEXsqVKycRewkICIBOp4OPjw94nifliTNmzEDHjh0B2DJUX375paSMlb6vHMdJuJ1CxstkMqG4uBg//vijJNATzi3uh1XOKahjCkFwYGAg6tWrh5YtW0qM6sWBHvBrGafRaCTlxsJ18zyP5ORkiY%2BfwWBgmsuvXLkSEyZMwKZNm5iBqWDDIR6/MK5atWrJ2itQoECBAgUvNBgvshW8OFCCPQUSdOrUCRcuXJDxkvLy8hAbGyvJ5Pxe7N%2B/H1FRUYiKisL58%2Bdx4MABUkaqVqtJBuXYsWPIysrCwoULERcXh7feegtdunSBi4sLLBYLJk%2BeTMovk5OTsXz5conQjLe3Nw4dOoQpU6YgIiICubm5OHDgAKZNm4ZqtPQ7hZSUFPL/N998E%2BPHjwdg47Xt378fPM%2BjqKhIxumaNGkStFotQkNDCedNHNCo1WpSitmqVSt06tSJqJAKsFgs4DgOOp2OBDB16tTB0qVLSdmiEFzk5%2BdL7smtW7dIOamAp0%2BfkkBMp9MhNTUV0dHRJIMr9Dl37lwEBgbCxcUFPM9j7969JJgGfs00CjYFXl5euHjxIvk9zZnTaDSywPXevXukH5VKxXwxYDQaERAQQOZt0qRJuH37NqxWK3JycogIDc/zZCzArxnOgoICZJRw1EwmE5o2bUqCR3EJJi0QA9i4iY0aNcKlS5dQp04dOItqdwSlTXEfQrApBMUC/9EeKAItChQoUKBAQRlQOHt/GC/X1SooE2FhYejRoweGDRuGTZs2ISUlBSdPnsSgQYOgUqmI4uUfQevWrUlmLzQ0FP/973/RqFEjHD58GM7OzsjIyMC%2Bffuwa9cuVKtWDW3btkWlSpWwe/dulCtXjnAKQ0NDkZiYCJVKBYPBgHnz5uHQoUMICwvDnj17kJ6ejpo1a2L79u1YsmQJCZ6mT58u84qjIXyJj4qKwjfffIOpU6eSYKa0gPfx48f48MMPwfM8du/ejUqVKiEoKAgJCQmSaxf6jo%2BPJ%2BbnYnh4eBCPO%2BHL/4ULFzBs2DDSpmLFiqhQoYJMMEccPAnXKw42i4uLYTKZsH37dlmgk5WVhQULFiA7Oxs5OTlYuXKlxBNRgFAi%2BuDBA0kpIh2omM1m2Vy1bduWzKPVamXy35ydnZGTk0OOHTFiBAoKCnDw4EFynMALFWdghets1qyZRDX2yJEjCAwMhNVqlZS3sso%2BhSC/V69eCAwMlFyfEKQ3bdpUdpyABQsWlPo7GkyBlogIWTtaZ0Gmu8ByYf75Z%2Bm2iFsqQKat8uOP8n42bpRunz8v3aZN1wG5EXIJB1SCEoVTgvh4eZuyHKAZTehKXbtM1RlrkDaXtsfTWJYEtkcxgdWGFt9gtKF962UVyowLt%2BfUVCKeubTs8LmWXQP9qNkjgMJUxKA/jxifxfS9KkvLhtWPPSIprPmTHUfNgz3iMCxjc5pZYY%2Bpuj2iHvT9ZelL0fc3Pb3sfu0RcbFLMIi%2BcMaCpI%2B7d6/sc9Pb9McRAPlCYkyO6J2w3eOzR5RJdu8YN9OuOaeu4Xd9JAnfX0oqrv4WKMHeHwbHl6VqoOBfhcjISAwfPhxdu3YttY3VasWqVauwefNm3L17Fy4uLmjSpAlGjx4N7xLFhi1btmDx4sU4cODAbzpHcHAwunTpgk8%2B%2BQSAjd938uRJWTshc9O/f380adIE8%2BfPx82bN9G5c2f8%2BOOPePToEYqKimA0GskXf0FqX8jgBAUFYcOGDTAYDDIzdwEODg74v//7P5w5cwZFRUX4z3/%2Bg0uXLjG5d2VBo9HgzTffRGFhIbZu3YqwsDDMnTsXrVq1ItksuvSQhZEjR6JTp07473//C6vVCm9vb1SqVAlnz54lx6rVanTo0AG7d%2B%2BWqVLaM85y5cohOzv7mWIjAgQxFNbYBXN6BwcHLFq0CAMHDkTPnj2xefNmWK1WYjYvZL4qVKiAgoKCZwbbNWvWxJMnTyTKlhqNhik6IwjXCFCpVCSbSgefc%2BbMwdy5c5lG7AKcnZ1x8OBBnDhxgpTMCl57TZo0wcqVKzF37lzi9yeGm5sbcnNzce7cObv8/2bNmiUTaBkxYiSGD3%2B3zGMVKFCgQIGCvwyrVwNl6DE8NzRu/Hz6PXbs%2BfT7D8TLFdoqwIEDB54Z6AG2L8z9%2B/fHjh07cO7cORw%2BfBgzZ84kgR5gE7FgBXr2nkPA6tWrZTYJHMehe/fu8PPzQ2xsLMaPH49KlSrBYDAQDpbRaMT48eOxbds2qFQqtG/fHs2aNUObNm0QFRUFBwcH1K9fX1LmN2XKFKxcuRJxcXF4913bF%2Br8/Hzs378fWVlZyMvLI5lBlUqFkJAQhIaGwtvbG2vWrEFcXBx8fHwAAO%2B88w7Kly8vuRatVovt27fj/v37qF%2B/PgoLC5GXl8cUehFA9wEACxcuRJs2bUiw8uDBA5w5c0ZybOXKlbF9%2B3aYzWbo9XqEhITA09OzVEsDASqVCj4%2BPsjMzESFChUAgGm1Ua9ePXAcB71eTzwO6bHXqlWLBHH5%2BfkYOHAgAJuqqNlshtVqRXh4ODFLB2zWBE%2Bp1IDYaJ2FyMhIWdmt0J4eE8/zqF27NlM46NatW6WKw%2Bj1emi1WuTk5ODEiRNISEhAx44dmUGpeN1zHIcaNWpAq9UiKysLxcXFsusrDWwxF%2BXdmwIFChQo%2BIeBpZj8V%2BEfmtkrKirClClT0KBBA0RERDArtQBbUiMoKEj2M3nyZAC2qiH6d40aNfrD4xNDCfYU/KXw8fFB/fr1S/29UEa6Y8cO9OvXD9u3b8fcuXORmpoKrVYrKSMVhGZ8fHxQvnx5HD16lHgF%2Bvr6yoRmnJ2d0aRJE5w8eRJ37twBYBOkAX7lT3Xs2BHvvfcerFYrBg8ejG%2B//RYcxyExMRExMTHIzMwEx3Ho1auXLHszatQoNGzYEElJSThx4gRycnLIw8wCx3F4/PgxCVzq1asHwBbsPUvC39nZmdgjDBgwAP7%2B/rh27RoePnzIzLxxHAc/Pz/o9XqiJFm1alVSsijm3QljOXPmDHQ6HYqKipCeng6LxQIXFxcS8KvVaiQnJ5PjYmJiMGvWLAC27K1arcby5cvh6elJeH%2Burq5QqVQoX748jEYj4cOJM5NOTk6oWLGi5BoOHDiAa9euSfbxPA8XFxcZZ5LjOGRlZREepGOJ/Hj37t3Rv39/VK5cWdJPmzZtEBERgaKiIpIh1Gq1aNGiBTZt2iQRcBEygrS/4rVr18ixHMfBjWVHwIDC2VOgQIECBS8E0tL%2B7hH84zB79mxcunQJsbGxmDZtGhYvXow9e/bI2i1atIj4QCcmJmLJkiXQarXo1asXAODGjRtwdXWVtBEU6/8sKMGegueGd999F2FhYZKftLQ0Iqwi/ISEhKBdu3b49ttvAQDTp0/H0KFDsWjRIrRt2xZ9%2B/bF5cuXERwcTDhXT548If2kpaVh7dq1MJlMmDBhAi5evIisrKxnCs0IX6qFgFDIshw6dAi7d%2B9G%2BfLlERkZCaPRiHnz5uGbb77BqVOn4OHhAZVKhW3btsmud9asWcSw3dfXFzVq1MB5EceJzl7RlgFnzpwBYCvjfJb9hTgQ/Oabb5CcnEyuR8xVA0BsE1JSUlBUVISAgABERESQYJHOLAYFBcHFxQUcx2HatGkSDmB2djYePHhASiRNJhMRipkyZQree%2B89AMCVK1dgsVjwzjvvICEhAVeuXAFgu2dWqxVZWVmk3JWG0B9tTs4qU6XnSBhXamoqNpZwzYqKilClShX88MMPGDJkiCSwBWxWGomJiZJ9KSkpSEhIwKRJk/Dqq6/KzssqBxbAytSWBntN1bF9%2B7O3WeScNWuk2zTJC8Dt29QOmucHACIfRQDAunXSbRbJ5%2BhR6fbatfI2mzdLt7dulTWh4nuA8UcU169Lt6k/kCVLTwJ6KbF4fbK5EXluCqDpi7L7wlI1pjk9rJPTc2qH0bWMFskqz6bOXaJhJAU9OYx%2BZB9Ndrhhy5ow%2BHj0Y8XiC8o6ogl5gHzN0kREBkFL9khfvSprIxsPixxGl/9Tdjp2EekY55atJdac02vJHtf3S5fK7pcmpskeDpRpmA5ATjamyX8yEjHk94o1NxTJkcm/oz876A8XxucjKKEzVhvZmmAsWrqAg7Vky/JmZ90W%2BhFi9UsvAdbXCnqfrDDFTsui54p/YGYvPz8fGzduxNSpUxESEoI2bdrg7bffxlrG3ztXV1d4enrC09MTbm5umDdvHt5%2B%2B21SVXXr1i0EBgaSNp6enpKXyn8GFM6egueGjIwMpsF6nz590LVrV0RFRcHR0RFmsxnHjx/H1KlTERMTI/PIa9iwIZ48eYIFCxbgzp07MJvNyMvLQ3R0NFq0aAGdTodhw4bhrbfeQvv27dGnTx%2BStbJYLIRXptFoSGYrISEBGzZswMKFCwHYMlVqtRrTp0%2BXlaDu3LkTM2bMwJMnTxAVFYW0tDSsXr0aTZo0sZvbFxMTA41Gg0mTJkkCF71eLyktbNasGQ4fPiw73tHREXl5ecTfTfhXKK8Ugj0XFxe7fRLLQmleciwI6qFFRUVwcHCAxWKBt7c37t%2B/L%2BHUaTQawgEUDOPFiI%2BPx%2BbNm/HFF188cxwsDp%2Bbmxvh19HQ6/XgOM4u/8GhQ4di9OjRuHHjBrp06UJKVf39/REfH4%2BPPvoI3333HfPYwMBA5ps9FlicvZEjRuDd4cPtOl6BAgUKFCj4S7BmDdC7999z7ubNn0u3GRs34iH1ssDT01MmfMfCmTNn0Lt3b5w7d468pD5x4gQGDRqEc%2BfOydTJBWzcuBELFizA3r17SeXRzJkz8fTpU1Id9TygmKoreG4o7YHRaDTw8/NDQEAA2delSxfs3LkT8fHx8PX1RWxsLNasWUN83Pz9/bFkyRK0a9cOubm5xOMvLCwMgC1Yc3Z2Rs%2BePSWZF3GgYDabicgIrahosVhIWea9e/fQipVhAUh54D2m5NevEIIzAVOnTmVmp6pUqYKrJW8r1Wo1LojeSovN2IWAUKvVSlQ6rVYrvLy8kJGRAb1e/6cFekLfLLCCL57nSVA3evRoXLx4EQkJCTIBGIPBgF27dqFLly7MQPnMmTMkwwnIDekF0IHea6%2B9hl27djHHK5jMe3h42BXs9erVC6dPn8bIkSMl2VjBo%2B9Zxuks377SoJiqK1CgQIGCFwJ/p6n6c8L69euxePFiyb7hw4djxIgRZR778OFDlC9fXuL96%2BHhgaKiIjx58oRJ5%2BB5Hl999RX69u1LAj0AuHnzJsxmM7p3744HDx6gQYMGmDx5sl1Bp71QyjgV/GMg9sirU6eOxLAbsImyLFu2DE5OTpg0aVKpIjDt27cHYCsJFHvVCUqNKpUKLi4u6N%2B/v%2BQ4juPQokULVKhQgVk33aZNG1SoUAGOjo4ybzwa4kBv/vz5zDc2AQEBkrdKVatWRWhoKNkWBzlCcOPh4YHq1auT/TzP48GDB8T3L6JEul8sTENDMK0XG63TUKvV8PLyQqVKleDv7y/5HcdxpJRU%2BKAT%2B/GtXbsWkZGROH78uCyozs3NRdOmTUvNiL733ns4deoU2RbPgVDfHhAQgJ49e0qOE1s1AFI%2BnBDg0VlmQWxHgHCdH374IRITE9GyZUumquYNUTmPWHzG0dFRYkKvQIECBQoU/CvwG15k/ul4TmWcPXv2xJYtWyQ/9HeL0lBQUCAJ9IBfvw%2BV9j3gxIkTSE9Px%2Buvvy7Zf%2BvWLeTm5mLy5MmYN28eMjIy8M477/wpvtYClGBPwd8Ok8mE%2BPh4/PTTT0RghaUQCfwq/vEsNG7cGK%2B%2B%2Bir0ej2GDRuGL7/8EhqNBjqdDu7u7jCbzVi6dClatmxJjtHr9TCbzYiNjYVarSZ100KWSa1WY%2BDAgbh//z7q1q0r4ewJPK3ly5djxYoVWLVqFT777DMAtrdEr776KvLy8iSBAQCMGTMGRUVFJFD6%2BeefZeIkYnAch9TU1FJ5YTzPE/5ZcXEx4dCJjwds3Dm9Xk%2ByboINhhgWiwUZGRlITU3F48ePSXCn0WhgsViQU1LHr1Kp4OTkhNTUVJLtu3PnDsaMGYOQkBAcOXKE9Onk5ASdTocePXqQ8YhLHdzc3LBixQrUqVOH7PP09CT/X1fCF8vMzISnp6eETzho0CBJVi0/Px9qtVrCYcyjOCPp6ekwGAykH6H0d/ny5Rg9ejQyMjIkmUBBnOaVV14h%2B8QBZF5e3m8SWFEEWhQoUKBAgYK/B15eXggJCZH82JtN0%2Bv1sqBO2C7tZfvevXvRrFkz8sJdwK5du7BmzRrUr18fDRo0wMKFC3H16lWJ5sMfhRLsKfhbMG3aNCLaUqdOHUyaNAn9%2BvVD586dkZOTQ4zTfw80Gg0%2B//xzDB8%2BHDt37sTQoUNhNptRWFiIzMxM8DyPfv36YdKkSeQYvV4Pi8WC/Px89OjRA2FhYQgPD8f7778PwBb89OvXDwBw9OhRpKSkkOzU4xJS9jvvvINBgwZhwoQJOFFCzBcUNnNycmRf7r/55hvk5OSgQYMGpN06WgADv1oTCMdnZmaWOQdWq1WWTRTKSDmOI2Wh5cqVw4wZM57ZV15eHikPFTKMQl%2BFhYXIZTHDGcjNzSWG7lqtFuXLl4deryfBVlZWFgYNGiQpZRX3LVhF5OTkyM6p1WplJZrioLQ0FBUVkQCLLrPVarUkUyrG6dOnS%2B2PxVEtDXYLtNAWJ/Q2i72/fr10mxZiAMOYmRa2AABK4AgbNki3WcIGtJgEQ3xFJmbC4DmWCOb%2BCoZIiqwR1YY1PHsEWmSCHaKXFqWOj74vLDN5usyade/orDdL7YI6TqYLwTqGej5Yw6NfJNOaGgBjvlgTSB9oh4CHPfoitAoFa3yytU6L%2BMhuHMMYniEEIrtVLKEm%2BkacOyfZZL6opxcka9FSN4spgEJPmD2u75TYCtN2lVY8YS0cql9mtTyt/EELtLAUeehzs8RhqOOYtG2aB0%2BL9rDOLaITMMcChuCJHabqrDUg22fHQXQT5pqgnrvfZaouVB89g77w3PEPFGjx9vbG48ePJZSShw8fwmAwlKqmfuTIESZFyGg0SgJEd3d3uLq6PtMT%2BLdCCfYU/C0YOXIk4uLiEBcXh4MHD%2BL06dOYMGECAFuZ4bO4Z7/FK3DLli2YPXs2ACA0NBQ1atRAt27dsH//fnTs2JG0X7x4MapXr47Vq1ejSpUqWLp0KQnuBNAebRzHoWrVqkTOf/78%2BUhOToa/vz8p87RYLBgwYAAKCwuRn58vyT6dK/kisG3bNglPDZCWBwpG8Xl5eYQ8LJQgPqtcc%2BbMmWRsnp6epE9xUFNYWEiya3TmUYBQSiCUKJQvX75MPz8B4nYCX7K4uBgmkwlZWVkoKCiQfFg2btxYUuv%2B9ttvk2sUK3SuXr1aclz37t2Z5xffs%2BYMkrdY3IZG165dcfLkSbItKJj2fgZJ3V7BHgDYvn07JkyYIPn58eBBecPIyGdvs8px6VKUWrVkTUS2mTaIMqoEtE0KVX4CUZaToMTOhKBLF3mbzp2l2%2B3ayZpQ1cNskj7diGrDGh69dFnLXlSVbANVjswcH31fRJxkAvpLAOveUYq6zPIp6jhZsp91DPVZwRoeXTQhopUQyOaLNYH0gVS5E2t4dDfMjyPqJSBrfLK1Tnl0ym%2BcfMpRs6asjexWyRYJ5DdCVJYPyOcXgHxBshYtdbOYFXX0hLHWFn2uwMAyDwFlV8NcOFS/zD9L5cpJt6kyevkiZpybGi/rOKbzTbNm0u0S/vUzz13yArbUsUD%2BOLP6oeeUtQZk%2B%2Bw4iG7CXBPUc8e6v/Q%2BWRuhuobKRv2l%2BAcGezVr1oRGoyHf4wAgKSkJtWvXZoqzZGVl4e7duzLrsdzcXPznP//B8ePHyb4HDx7g8ePHRCfgz4AS7Cn4WyB45Ak%2BeeLSzJCQEJlHnoDY2FhSIlkaWFlDjuPwyiuvoLCwEJ6enqhQoYKET5acnIzr168jOjoaV65cwdChQ/H111/L%2BhaCl9DQUPA8D3d3d9y/fx/h4eHYtGkT0tPTcfbsWfJm53//%2Bx9OnTqFRo0aoaCgQMJtexbEWSqTyYTFixdDrVYjNzcXBoMB6enpMBqNsFgs5IOlZs2aMBqN0Ov1CA0NxcWLF3H37l1otdpSs4FWq5UEO2VlpYqLi6FSqfD48eNSlaYE%2BPv7w8HBgfACBb4kIM%2Bgifs6duyYZBzLli0j/xfXx4vXS8WKFYltBwvCGMSCKEL5KfBrxpDGkiVLJMGiIIU8YMAAMhaO41CtWjVS4vlbMtKKQIsCBQoUKHgh8HcGe/9AGI1GREVF4aOPPsKFCxewf/9%2BrFy5En379gVgy/KJv8ddv34der0evr6%2Bkn6cnJxQv359zJw5ExcuXMDly5cxZswYNG3aFEFBQX/aeJVgT8E/Dp06dcKFCxfQpEkTmR/f7NmzZXXMcXFxyMjIwLRp03D//n288sor%2BPLLLxEXF4d169YRmf9Nmzbhl19%2BwfLlyxEUFEQeSsCWZQkICEDz5s0xYcIEjBkzRsbxAmwlohzHEQXNypUrw9nZGdWrV8f58%2BfRokULqNVq8pDevXsX48ePR3h4OKpWrUrS8mUFSzzPS7Ji06ZNw6xZszBt2jT8%2BOOPAGzBmclkIsHa1atXUVBQgKKiInTr1o20E7h5wr9GoxEajQZGoxF9%2BvTB9pKSOo7jYDAYoNVqUa9ePWzevBmVK1cmASRgCw4F83IBrGu5c%2BcOXF1dMWTIEHKc0E4IlPR6PdRqNbZt24bKlSuTcwjBnlqtJuW3AEhfTk5OWL58OTlXWlqaTGwH%2BJUTJ1x3isgvymq1klJQ2tNPuO8qlQp%2Bfn6yfidPniwp/bx%2B/Tq5r47MVAMbCmdPgQIFChS8EPiDmbA/fO5/WGYPsH0XCAkJQb9%2B/TB9%2BnSMGDECbdu2BQBERERIBP4yMzOJhzGNTz/9FMHBwRg8eDD69OmDSpUqYe7cuX94fGIowZ6CfxzCwsLQo0cPZGZmokOHDli/fj0WLVqEmjVrwsXFBadPnyZlkjNnzsTMmTPh6OiIESNGwN3dnYiTODs7o06dOkhMTISPjw%2BmTJkCBwcHVKxYEQkJCXjrrbfIOS9evIjbt29j0KBBGDRoEGJiYsgXb2dnZ9IuJiYGAwcOhMlkgkajwYULFzBlyhSsW7cOISXla4WFhfjpp58AAG3btkX//v3BcRyaNm1KAgmWRH9ppuuAzbNwwoQJeO%2B99yQCLU5OTignKo/hOA4TJ05Et27dUFNUisRxnCSYcnBwQFxcHIYMGUJ4bYLvnclkwtmzZ9GtWzfcu3cPVatWhbu7OzmPVquVlFD6%2BvpKbDQEZGZmYtGiRWRbyMYJJOaioiJYLBZ07twZd%2B/elZXJ0sGQ0JfJZML%2B/fvLtDmgg1B769%2BF4LBjx44Siw1BOTUyMlJm/SCANrV/FlicvcjItvKGNHeE3mYRl%2BhyUAbXRVZxyuLD0PwhmjfHMlWnOVKs0lQ6a37smKyJrGsWWZ3mTVG8Q5bBMk1bYvGLZEuFweGS9U3fFxani87msgg8NO/HDlN1mprK5F5RO2nKFCDn/bB4QLJ9rJNRa1LWhHHdNPWXSbelyvuZ10lzyugbxVizMqqVPQuHVbJNV0fQhuT2kKZY64ZakMzrps/NWtj0gdRcMF2X6flifY5SBzL5Y/R46Elncb/pNpQnGgDZQmFy9ujPDnqOWZy9H36QbjMeGBnbxI5nlXXv6MfBHlN1e/ql78vv6oc1NwoA2F6cf/rppzh79iyOHDkieemcnJwsoRu1b9%2BeCOjRKFeuHGbOnInjx4/jzJkzmDNnjuR73Z8BxVRdwV%2BOyMhIDB8%2B/Jm8O6vVivDwcBgMBmRnZ8PFxQVNmjTB6NGjMWXKFOj1erz11lvo3bs31qxZg4kTJ2L48OFYvHgxBg8ejGXLliEqKgpjxowh53z48CEJNITgh%2BW75u3tjd69e2PlypU4fPgwWrZsSbhYq1atQrVq1dC8eXMYDAZMnjwZXbt2RUJCAsaMGSMrzVOpVNBqtcSHTvy4cRyHGjVqkO3Zs2dj4MCByMjIKHVeXFxc4ObmRvhjOp0OFouFZNxCQkIwceJE%2BPj44MMPP8QxxpdoHx8fpKenY/ny5ahVqxYWLVqE9bSgRwn0ej2aNm2Kli1bYtq0aQgNDYVeryfBrHAdzs7OKCwsRHFxMZydnVFQUACDwSARUhE4e4LJfVkoV64cntKkftiyq8L1CmWsrIyY0WiUlIQK5xeg1WqJOIy43WeffQYvLy8MGDBAcg1arRbx8fFwdXUl/o50vy1btpRkHZ8FxVRdgQIFChS8EPjsM2DcuL/n3K%2B%2B%2Bnz6pQP6fzGUzJ6Cvxz2Cqw4OTlh9OjROHfuHA4fPoyZM2fC29ub6ccn7lOn02HZsmWIjo6W9Dly5Eh4e3ujdu3acHZ2xtSpU7Fv3z6sXr0ajo6OcHJywvHjx3H48GEMHjwYx48fl/moADaLgIsXL0oyfmFhYSgsLITRaETPnj2JyIjVakVRURGKi4tlXLUWLVogLi4Oixcvxty5c3HlyhVZ8EmXS2ZnZyMtLY3sq1KlCuHDWa1WXLp0CX369EGbNm0IcbhSpUqIi4tDp06dANgsBwCbemhERAQJ9LRaLf7v//5PItRSVFSE/fv3Y%2BrUqTCbzUhKSsKBoWyrAAAgAElEQVSlS5dI1tBoNILneWRnZ0sM4M1mM4xGIzp16kQybML1f/XVV5Jr1Gq18Pf3R9%2B%2BfSWE5GqUsILgfyPYcrAsODiOIxlAsW0DABkxmud58DwPs9ksmefvv/8e69evB8dxRNwHsHEDN2/ejNuiDJjBYJDc159//lk2ptKgcPYUKFCgQMELAUWg5YXGy3W1Cl5o/FY/Ptovxd3dHRqNBm%2B%2B%2BSbeffddrF69Gp07d8b48ePh7e0NPz8/poedPXL68fHx4HkerVq1Qvfu3ZHFrCX5FXq9HocPH0ZcXBx8fX2xceNGzJw5U%2BIdB0hLOYVsljgg4nkeb7zxBry9vfHmm2%2BSMkK9Xk/4ZqmpqXjrrbeeaaIO2Ob36NGjKC4ulgRSHMehfv36cHR0RFRUFObNm4fc3FxUr14dwcHBJFASZ9fatGmDWrVq4ezZs%2BA4TiJMIzYKrVSpEnbv3o2BAwdiw4YNqFChAumvUaNGAH4VWNm4cSMAm/hLQEAAyQ4OHTqU9FexYkVSM3/37l3JGhAHyeHh4di9ezfWrl2LkJAQSbDXrFkzVKtWDb6%2Bvhg5ciTZX61aNZw7dw43b94EYAv06OD8IavMSIECBQoUKHiR8ScqQyr466Epu4kCBX8fpk2bRnzgCgsLYTAYiB/fkiVLZOqHB0q8ri5cuCCxTigoKMC0adNgMpnw0Ucfwc/PD7t27SK/P3v2LN58800kJSVJMkCCGTpLrEWMDRs2gOd57N69m5ByhfI%2BBwcHhISEoKioCBcvXgTP8xgxYgR%2B%2BOEHfPfdd8jMzERsbCxq1qyJa9euQaVSwWg0olevXti6dStatmyJw4cP48GDB3B1dYW3tzdSUlKQn5%2BP5ORk3LhxAxaLBRs2bIDFYoFer8eFCxfA8zzatWuHlJQUFBUVESN4cdkjq5xVCMYEywez2Yz//e9/6NixI7Zu3YqtIt80Iauo0WhI2%2BLiYqSlpSEtLY2UmaaKOBJz5swBYMu8RUZGws/PD48ePYKvr6%2BkPFQIfJ2cnFCpUiVcKvHPEviaAm6U8Mo0Gg1SU1OJOA7P85KSWHEAnpSUhKioKBQXF8PNzQ2urq7IyspCQEAABg8ejHHjxuEuxdsxm81IT0/HtWvXAIBZAmxhGmmxoQi0KFCgQIECBWXgJcvCPQ8oM6jgH43f68dXo0YNclxcXBy8vLwwcuRI8u%2BXX34paS%2BIwgwbNgybNm1CSkoKTp48iUGDBpXpKffw4UNcvHgRBoMBW7duxbZt29CxY0cYDAa0a9eO8Nfy8/NRq8QDau7cubh8%2BTLOnTuH2bNng%2Bd5/Pzzz7BarVCr1ejYsSMSEhKwdOlSjBw5kiiH5uTk4NChQxg2bBgAW9ZLyMIJ/58/fz4AWyA3ffp00qeLiwvUajXJFtatWxe1a9cmapw0rFYrESJRqVRo3bo1sw1gC4T0ej0hFd%2B%2BfRuPHz9miqJcuXIFABAYGIhz585h79696N27N8mYCVi8eDEAm2n9VYZAhoC9e/cCALPkVgwx4XnMmDFYunQpfH190bBhQ1L6KQSHd%2B7cAc/zEi/GY8eOobCwEOPHjyeBGl1K%2BltI1SyBllatGAItFK%2BP3mYmnql7yuLY07fGHoI/qECbaStIi7qwTNVL7tmzBiija7LWKV02u2qVZJO1bOjrZL3HkQ1nzRpZG5F2jw2xsdJtylAbgFw9gnVyShCDeV/oFw20IgaLE0tdFKvimO6GSR%2BmGtmjAyITxLDD8Jvl4SxbEyzJAVoQiBZFOH1adohsHSckyPulJ4cWIgLkIkclisgErPHSc1HywlICkd8nAPbk0H8LWW3om0VZHDHX2pEj0m3W3FAHMvuhqx5oeyWW2BP9vLBUhai1xdQT%2Be476Ta9BlhG9vSHB8NM3g4tJdkjznrk6c9weqmxPuPpW8nql54%2BBgVeto9eRqwpV/DiQRFoUfCPRVlCLv/73/9w6dIlbNiwQfa72NhYPHr0CONKCMVCX4sXL0ZeXh6ePHlC2mo0GlSuXBk9e/YEx3HYvHkz8adTq9XIy8sjJYODBw%2BGh4cHPvvsM6IeqdVqiYIjjZEjR2LhwoX4v//7P5w8eRLvv/8%2BPvroI2bbatWq4ebNm7BarTAYDLBarfD398eHH34Ig8GA119/Xcb7E6Ny5cr44IMPEBwcDMBWtvjhhx/i3r17RGREo9GA53kYDAaYTCbMnz8fp0%2BfxsqVK0vtFwCioqKQmJgoMw03GAyoVasWTp8%2BXapQSml44403sG3bNmIfIXDoBIiFT8T/V6vVqFChAoqLi5GVlUUCUg8PDxQWFmLPnj2IiIgAIL03bm5uJLs3e/ZsvPbaa7h9%2Bza6dOmCnj174ttvvwXHcUhMTMTYsWNx4cIFfPzxx2QNCbzOw4cPo27duszMXqNGjbCKCjhKgyLQokCBAgUKXggUFAAiPv9fitdeez79llQ7vQxQMnsKXlgIfnxJSUmS/Xl5eYiNjZWU1NGiMFOmTEFiYiISExOxf/9%2BDBkyBHPmzIGrqyt27NiBnj17QqVSYdy4cfjkk0/IcV9%2B%2BSViYmIkNgECH%2B3999/HF198ga%2B//hpeXl5wc3NDUlIStFotjh07BrPZjE8%2B%2BQQqlQpqtRpGoxFGoxEcx0Gr1SI1NRVTp06Fn58fWrRogX79%2BuHmzZsYPnw4evfuDYPBAMAWwBgZH7p3797F4MGDERERgYiICAwcOBDXrl1Dv379MGPGDLzyyiuwWq3EXqF169Zo1aoV9u3bR/oQspiCqIrgqbdjxw4S6Gm1WlSpUgXe3t4wmUw4XfKW1Gq1wsfHhwSbAt5//30AcsPxPXv2oKCgAGazmQR7gK28UaVSSQxFxUGgxWLBvXv3kJGRIRHhefToEXJzc0mgB9gCQyH7Ji7jjIuLQ2RkJKKiomA0GhEbGwudTgee5zFr1izk5ubCZDKRQA%2BwWXBwHIf%2B/fujsLCQGKmLwdpXGhSBFgUKFChQ8CLgXubfFOgBikDLn4CX62oV/KvwrNJLlUqFQYMGlXqss7MzPD094enpiQoVKqBLly5o3Lgx4uPjcfr0acTGxmLJkiXo0aMH%2BQK/ePFilC9fHu3bt8eKFSswaNAgeHt745dffsGCBQvQp08ftGjRAhEREdBqtWjUqBGOHTsGd3d3jBs3DpMmTYLVakV0dDTi4uLw3XffoVOnTsSWYeXKlejduzdmzJiBAwcOICcnB2%2B%2B%2BSZcXV0xYMAAFBYWguM4jBo1ivDvPvzwQ3z//fdEGXTPnj1ITExEdHQ03N3d0bNnT6xZswYWiwXLli1DbGwssSwQymGFYKhq1aoksB07dixRmuzfvz/i4uLgWqLGpdfrwfM8Pv30U%2Bzduxe%2Bvr4AbIHhoUOHJFnOGTNmoHr16uQ4McTZVXEJptlsBs/z6NGjB9knlK1GR0dLzNnFx3EcBw8PDyQmJhKhncLCQlgsFolyKgD88ssv%2BOSTT7Bz505MnjwZHMehuLgYKpUKM2bMQHp6OsxmM7lmwPYSQafTEcVNVonqPVltX%2BlQOHsKFChQoECBgucNRaBFwQuN6dOno2rVqoiNjcXHH39M/PjmzZvHVNZ8FliWDmIMLymvE0RYXFxcAADly5dHZGSkRBSmoKAAGRkZsFqtSE9Px8KFC%2BHn54elS5dixYoViI6OBsdxqF27Nry8vFBQUEC828LDwxEbG4ulS5fi1KlTMJvNOHnyJPr3749Vq1bB3d0d/v7%2BAGylrGK0a9cOgK28cuLEiejZsyf8/f2xbt06zJo1CxqNBkajEcXFxfD29kZ6ejru3LkDwJY9%2B/jjjwEA69evJ5nRNWvW4MiRI0QwJTc3F7m5uXj77bcl5uI8z%2BOXX34hJu2ATTnz0qVLcHBwYFolCBD77gn/d3R0JPuWLl0KAFi7dq2k3VdffQWNRkMCxEePHkkyewI%2B%2BugjTJw4kVxTWloaBgwYQMYthtFoxJMnT6DRaFC9enWcLOHL5OTkwN/fHym0UbIIgoKoPejcubMsC1qtWnVZuwsXgDp1St%2B2WAB6aq9dA0QWjuxGJhMgUmhlVelQTXDrFiXK9vQpQPEUb98GAgN/3b5%2BHaBcNPDLL0BAwK/bubkAlfi18WzefJNsXrkCUNMl61t23ffuASUvIwh4HhDzcIuKAOpFBN3xw4cA5eRh4zI1bUo2r14FShxJALDnU3YqeiysAx8/BqjPsrw8QPR4yG8Uvc04V34%2BQL9voA/LyAAoUWP5kLOzgZLPwlKvk9pRWAiUFCr8CnpArAmkbgSryZ07QMnHIwD59DGm00ZgFWXlWXNDXyfrkaIHRN8n1m2h%2B7l7F6hcWdrm0SOgRGgZQCnzR90Y5vioNqmpgEgoGTk5APVeDPfvAxUqoNRjWP0ynylqQHQT1qNAzxfrftPHsdrQn0n0kqV/D8jnnLHMZRdhz5wzFwG1T9YPa3KKiwExT92Ofn9Xm5IP52f8%2BX7%2BeMmycM8DCmdPwUsHmgtoMplw8OBBjB07FjExMVi1ahXq1q2LDz74gLQXq0kKHL/g4GCcO3eOKIBu3rwZsbGxuHPnDoqLi1GjRg1MmTIF48aNQ58%2BfaBSqbB27VrSXkB4eDgeP36MIUOGYOzYsWR/Xl4eOnXqhHbt2mHixInYsmUL3n//fXz88cfQ6XT4%2BuuvcePGDej1euTm5sLf3x979%2B7FzZs30b59e3h6eiKRFicA8J///AfZ2dlYvHgxTp8%2BjW%2B//Zb8TuDGhYSE4Pr16zCZTFCpVGWqTAoBFw2DwQCLxYKOHTti%2BPDhaNWqFfmdXq%2BXlMPSaN68OQ4dOiTZp9VqoVKpJHxJnU5H1FIFg3dBuEe4HicnJ%2BTm5pbJK3R1dcX27dvRrFkzdOjQASkpKbh48SL5fePGjRESEoJVq1aRjKwAjuPw/vvvo3fv3s%2BaKgIWZ2/EiJEYPvxdu45XoECBAgUK/hKwAs6/Ct26PZ9%2BN29%2BPv3%2BA6Fk9hS8lPitlg5TpkxB%2B/btAdjKDI8fP44pU6bAarUiLCwMxcXFMJvN0Ol0JKAoLi7Ge%2B%2B9B47j4O7uXupYcnNzodPpsGrVKvj5%2BSE0NBRdu3aFyWQCz/NYt26dJID44IMPyLnEAUdaWhoiIyNJeWFeXh5yc3Ph5OSE9957D4AtwDAYDMjOzsakSZNIhhCwmZVfuXIFFosFl0VKaQKPz8HBgckzE%2BZEgKenJ/GbE0RMBJVSMZ4V6On1eokNgwBaCMdkMqFZs2b4sUT1TjB4FyC8yxIEaoRAz9nZGZUrVyZZuqCgIJw7dw5PnjzB%2BfPnAdiys7du3ZKcz9HREVlZWSguLsayZcsQExNDLBp4npf4CZYF9lwq794UKFCgQME/DAkJQMuWf8%2B5lczeH4YygwpeSgiWDrNmzSJB07p16xAWFoaUlBR89dVXCAsLQ4cOHQCwOX5Vq1aFXq/Hxx9/DIvFgs8//xw7d%2B7E9u3b4e3tjT59%2BsBsNpNAozSUK1cOBoMBkZGRiI2NRbdu3eDk5ITWrVtjwYIFKCgokGSjAgMD0aVLF6xfvx5NmzaFv78/jEYjTCYTOnXqhBUrVgCwBVPjx4%2BXnU/oa9SoURIfusuXL6NGjRr473//iy%2B%2B%2BAKATcFSEIMRgpPSyjE9PT1RvXp1SbDVoEEDYgURExMjae/m5sbsp0qVKmjZsiXMZrPMZF4QtuE4DhqNBt7e3iTQE8ASr%2BE4ThLAR0ZGYuvWrUhKSkJSUhLWrVsHjUYDjUZD%2BH2HDh2S%2BStaLBbEx8cDsJm50158ycnJzGtSoECBAgUKFPwOKAItfxhKZk/BSwmB91ahQgWZz9zixYvx888/Y%2BHChdBoNOjTpw/5ndjSQTAdP3bsGOrUqUMCQ8BW1qjT6bBs2TJ4eHjAy8sLW7ZsYY6lTp06OHr0KCIiIvD5559LficW/OjatSsWL14MvV6PW7duwd/fHwsWLED37t1RWFiIiIgI7NixA19//TVR0Tx48CAyMjIkiqXFxcUwGo2Ijo7GvXv3sGrVKtSvXx8XL15EcnIyLl%2B%2BTLzrsmijHgAdOnRgevM9fPgQeXl56Ny5M7Zu3Qqz2YzTp08Ttc7JkyeTthzHoXnz5jh27BgePHgAnufh7u6Op0%2Bf4u7du7hd4ldFl4ZaLBYUlJgOCb8LDg4m3n0AyO/F4HleEriJeYXivs1mM%2BEKWiwWhIWF4ezZs6TNzZs3kZubK7GCEOMx0%2BSJDUWgRYECBQoUKFDwvPFyhbYK/hWIjIxEUFAQ%2BQkJCUG7du0k3DPAJq/fo0cPhIWFISIiApMmTcJ9kXHrli1b0K5dO/j7%2B0t%2BevXqhWvXrmHz5s2SsjzB0sFkMiE%2BPh43btyAwWDAtWvX4OTkhC5duqB27dpo1KgRsrKy8OjRIwQHB5NA79NPP2Vez/nz51FYWIjU1FRs2bJFcm2CkflHH31ETMfDw8Nx/vx51KtXD7Vr10ZycjKsViuOHDmC1NRUfPvttwgJCSHBUNOmTZGSkoKUlBTUq1cP2dnZqFmzJhFrAWxZO3d3dybvjoYQ6HEcJ8vyFRYWYuPGjcx%2BxMGR8H%2BtVkv%2Bn5mZCbPZDJPJRPoV2y8I5zQajRIjeXGgB8jN1dVqNQICAiTnZxmwC7xEQX1VpVJJFEMBID09HXv27EF4eDgAEDsMwBbg02N5Flim6pGRDFN12pyb2qZNcAHYVFxEYNlAysREWfRtunM7%2BpW58FLHAAAuXZJuM9zZZXEzqx/aiLmkBFcAlXi1wQ4DaNk7Dsa5aY9o2X1iuRHT88lyJKddjhkDpCm0sopo1kVRL0FY7yXsMXOm1wnrVLJ9ZblGM45h9SujDrMa0QbuZW2Dsfyo8m2AcatkC4DREWXMbdc1McZHu9szK%2BDpObbDuJ7ul7UcZeNhPFSyU7E%2BS%2BjO6WeX5fhNv5RjzTn1sDLft1GfC7Jzs/qln1/G8yx7Z8i4bvpe2fO80N2wKPN0v6w1QffLur/0PnqbsA0kyld/MZTM3h%2BGItCi4IVDZGQk%2BvXrJ%2BPQTZ06FTExMYiKisLMmTMRFxeH8ePHo2HDhnjy5AkWLFiAO3fuwGw2Y9SoUQBsWTxaMAWw2TrwPI/3338fCxcuRFZWFiwWC3ieB8dxMBgMaNCgAW7evAmTyYQnT55g5syZqFu3LrKzs9GrVy/odDocPHgQTk5OJNh7%2BvSprMywoKAAPM/DaDSidevWOHbsGObPn48jR45g1apVJDP46NEjmEwmjBgxApMnT0adOnWQnJws4b7pdDoStAj/io3FPTw8kJmZifLly%2BOnn37C/PnzSckmCzqdDiNHjsTcuXNlvzMajQgKCkJ2djYKCwuRlpYGoHSxFsHzTlDb7NKlC44ePYoHDx5IhFPUajWsVivUajWzHz8/PwwZMgRTp05lGtp//vnn2LhxI44dOwYA6NmzJ7Zu3SpR/BR4iMLHn/j8Bw8eRNeuXZGXl0c8AAFbYFdYWIh169YhOjoaGo0GarWa8BKNRiN8fX2xc%2BfOUudTDEWgRYECBQoUvBA4cQL4DWrTfypEfrp/KkTq3v92vFyhrYJ/DX6LT56/vz/q1q2LJUuW2MWhA2yqjAKHLiMjA3q9Hq1bt8batWtx8OBBnD59Gq%2B%2B%2BioAGzcuMDAQHTt2ROXKlRESEgI3NzcUFhbK1CS9vLwQFxcn%2BfHy8oKLiwuioqJw6dIlZGZmYtCgQTh69CjGjh2LXbt2YeLEibhz544ksBk/fjxOnTqFr776CtoSqWShvFAI9Ly8vOBRoh%2BtVqvx2muvoXz58sjKyiJ%2BccLv6tevTzzsVCoVtFotiouL8dlnn5F2vr6%2BJPNlNptx9epVpKSkSAK96OhoiaeeVquFVquFxWIhAZdKpYLJZCJG7Z06dSLqqH5%2BfqhatSo2bdqEIUOGyO5NRkYGpk%2BfDkAu2AIAAQEBaNKkCdlev349zGYzypUrRzJ6PM9DrVZDp9OR4FLAzZs38corr0iCQ%2BE6AOD48eNEGKdQ9Bq0oKAAv/zyi2w8pUERaFGgQIECBS8ENArr60WGEuwp%2BNegLJ88o9GIZcuW4YcffiCBRWngOI5w4CpWrIipU6di0aJFqF%2B/Pnx8fKBWq9G1a1ccOHAALi4uuHv3roQTdvDgQezcuROpqamSYEmj0cjKRjUaDVQqFerUqYPBgwejYsWKOHfuHDZt2oS%2BfftCr9eTssVly5bhkqgETq/Xo2nTphg6dCjZ99prryEyMhKALTBavHgx/P39UbFiRUycOJFw2tRqNclsNWzYEOvWrYOvry8qVqyIPXv2YNeuXVi0aBERfBH6aNvWVmro6uoKq9VKRFSqV68OBwcH1KhRQ1J%2BaTKZiNiKkNXU6/W4evUqOf%2BhQ4ewY8cOADbBmpycHGzfvl0StAkoLCwkgVhl2pAKNj5eamoqOI7D/7N37vE51v8ff97nexvbjM3IzKkwDSPn8%2BQYvg6pHIrom4RKJdG3FEUpHfkpJGclaSSlpMKI%2BJZTjpGYYYxh5933/ftjuz7f%2B/pcn9ndQaWu1%2BOxx8P12ef6nK/L3tf7/Xq/6hYJ0nm9XnJzc8VYLRaLyJjq8/moUaOGuL9ChQpiL/15dRrP76uvvjL0qaGSrOl2BZicPRMmTJgwYaIEmGGcvxmmqW7imoemk5ecnKzTyVNBFrH%2BPTB48GAmTZpEgwYNRJnNZiMmJobz589z6623ivKsrCz69u3L4cOHCQkJoUWLFrpQxR07dnBK4gacPn2a1157Dbvdzr59%2B4RRpGH//v1cunSJJk2acOjQIVauXKkLibzvvvs4e/YsFouFUaNGkZ2dTdWqValWrZrOM9W9e3chRdClSxfhjdPaOnPmDOfOnROG5NmzZ7Hb7aKNgwcPYrVaWbJkCYcPH9aN0WKxUFBQIOaanZ3N8ePHddII119/Pfv27ePEiRPYbDY%2B%2BOADjh49Sv/%2B/VmyZIloy%2BFwYLFYyMvLE9kwtRBLgOTkZKxWq5Bh0EI0PR4PbrebrKwsfD4fPp9PePo0PiRAjRo1SE9Px2Kx8NRTT/HYY48BhR8LCgoKGD58OMOHD8disTB%2B/Hiee%2B45ce8vkV5QiapXrmwUVTeoBEvXSiFfSc1ZKZEki/LK1yg0eGX1c1XnsuKzot1ABN0NytYGJXGMou4BzNug96wSGpbnqVLiLkkEXNGuoSgQkWOVQLU8b3nNVfdIZSoBbXneSnHxAMZnKJI7U52bANZPHk9A51q%2BSTEpw3hVZ00ej6qOXCbtUyDbrXwY5DGrGpIWI4AqhnOjXE95PIp5G9ZP8cwb2g5gv%2BVjEoiQvXLicp0AlNjlIvmVABiU1n8vXfNA5i2vueqRl%2BcgvyZUZcW%2BSoo%2BnJq4NmFy9kxcc0hMTCQtLU14aTSdvP79%2BzNmzBg6depE586dGT169BXbWbFiBePHj1em6s/Ozmby5Mn07t3bIMKuQr169cjLy8NqtVJQUCBS/V%2B6dElnnABCi8/n81G5cmWOHDlCSEgI48aNY8eOHSxfvlx4fTweD7m5udSuXZuzZ8/y8MMPc/78eaZOnSqkAjQPFRRyDWfOnMmmTZuE7ILMT4NCz9p1112H3W7n4MGDBAcHC35iRkaG8IRFRkbSs2dPZs%2BeTVBQENnZ2cUKk4eFhREWFiYMRtAbZlarFYvFUqJAu9aPBpvNdsV77HY7Tz/9NP/5z3%2Bu2K5Wt6QkNAcOHKB%2B/frk5OTo1iwoKIiyZcsye/ZsunTpgtvtJi8vT7cW99xzD2PGjClxHKDm7D0wahQjRo4M6H4TJkyYMGHiD8Hnn0OHDn9O34MGXZ1258%2B/Ou3%2BBWF69kxck3jggQdEOKHL5SIyMlKEOoaHh%2Bu03q6EqKgoFi5caCj3l1sIBBERESQkJHDgwAF%2B/vlnrFYrly5d4rrrrqNy5cpUq1aNxYsXU6ZMGd577z1xX3BwMK1bt9bxw6xWK0lJSeLfYWFhhIaGCo/a0KFDmTp1KtHR0YLz5nA4CAkJoVu3bmRmZuq4Y263W2c8WSwWHA4Hx44dE0ZU79696dSpExMmTMDn85GYmMhXX31FWlqayHKqtSEbenfeeScLFy6katWq%2BHw%2BnbGXn5/Pv//9bz799FNSU1OFB04zuPwlDJxOJ3l5eeTk5Ijy0NBQ%2BvXrd8UkMgUFBbz44ou0b9/eoLknwz9pTZ06ddizZ48IMdXGdOLECaV8Q3Z2NvXq1WN%2B0X8QOYrUZt98880V%2B/eHirNnfnkzYcKECRMm/PAPC7m8GjBX0MQ1CU0nLzY2VnDoNNSpU4e9e/cq75s/f36JHDqNR6dh/fr1xXr1MjMzmThxIl6vl5YtW/Lxxx%2Bze/dudu7cSdeuXcnOziYsLIxjx45htVr55ptvdP1ERkbicrmYOHGi6MNisYjfx8TEEOoXJuKPyZMns3PnTt59913y8/O5%2BeabWbhwIV26dOHNN9/E6XQSGhrKU089RaNGjYQuoM/nIzs7m5iYGOHVXL9%2BPa%2B99hperxeLxUJKSooIX2zZsqVYj5iYGJ04OSCM5e%2B//56dO3caZA0%2B%2BeQTnn/%2BeaxWK08%2B%2BaTOs9aiRQvCw8OFUDoUevK0Pi5evEjVqlV1%2BxXiF0IUVhQiVaNGDe666y5Rp3LlykBhIh%2B3282DDz7ILbfcIgzL/Px8fv75Z7p06cKgQYO48cYbdWO2Wq1ERUXRuHFjXXmtWrWuaFCqROyLg8nZM2HChAkT1wSKPjabuDZhGnsm/nbo3r07O3fuNGjxderUiRkzZuhCAjUO3ZW0%2BBKLecklJibyySef8NFHHxk8Qfn5%2BWRkZHD%2B/Hnat2/P2bNn8Xq9TJs2jR49elCvXj3atWvHmDFjyM7OFrptUBhuuXjxYrp37058fDwtW7bk8ccfN4QfPvzww/Tr14%2B4uDjatm1LcnIyI0eOZMqUKRQUFOBwOMjKymLJkiXs3bsXm81GUFAQLpeLLl260KVLF7KzsylbtiwnT55k6eUQJM0AACAASURBVNKlWCwW8vPzOXbsGJ999hlQaJSMLAotPH78uCGbqWbcbdq0SZnF8sSJEwwYMIC8vDwqVKggjDqAzZs3U1BQQHZ2tjBq7777burXry/qaB40u91OZGQkwcHBIiOolo1zx44dTJ48GbfbTUFBgfB4Xr58mZycHJYvX05ycrJos1q1atSuXZtPP/2U%2BfPn6z4OeL1efD4fnTt3pozE08rLy%2BO%2B%2B%2B7DZrMRHh7O6NGjdfOp9Qu0iFQ6e61bK3T2ZC%2B1dK2McJXqqIL1A9Isk/YSae%2BVOnuy%2BJRCwD4QvTlD2ypvvVxJFg9U6PfJ8w5IJ04xB0NSX3l8AYiqKUkUsldZtcFS24FIrAXUdyCiYPJ4VGJ8JWkZKu6Rj0AAzQa0NvJEA9EsU83bMJ5ADo50SFR9G8pUgmmBbLD8rKo2WC7zSypWbLtymdwPijko%2Bi5x7wLpO4A6yrMvP6xyJdW7RY68CORZUG2wvF6B7J38MAQiovc7rV9A9/zRMBO0/GaYnD0T1xwC4dBpOnkPPvggCQkJpKam8uqrr/LTTz/x1FNPMWDAAIYMGcKWLVuYOHHir9Li08ahcehsNptIz5%2Bbm4vP56Nnz5688MILdOrUiTNnzoiwxmbNmrFr1y7eeOMNXC4XGzduxOVy0a5dOyFjoELHjh154403qFmzJhEREaSnp/P4449z8803s2DBAtavX09qaioej4fg4GCaNWvG%2BvXr8fl8uFwuqlWrxtGjR8nJyaFUqVJcvnyZiRMnMnv2bI4fP47FYqFq1arceeedvPLKK1y8eJHBgwfTtWtXbrvtNgCefPJJJk2aVOwYy5Qpw/kiZVtNysHn82G1WgkNDRW/035/JW%2BWw%2BEQyVVKgsViISQkhPz8fJ32YFRUFNWqVTOEWPqHkPpDSwhTpUoVTpw4oTOyn3zySY4fP868efOIiIjg4sWLut9v2bKFiIiIEscKJmfPhAkTJkxcG0hLg8jIP6nzoUOvTrtvv3112v0L4p9l2pr4x0DTyVuxYgWDBw9mypQpNGjQgMaNG5OcnMz27dvZvHkzERERv1qLT8PQoUMJDw8XenZQmLBl4cKFvPDCC0ChtygnJ4dbbrmFFStWMGTIEBYtWkSvXr3Izc1l5cqVQGHaf4Bnn32Wd955R/zMnTsXp9Opk11IT08HCo0Gzdg7ceIE99xzD1AY6nrDDTeIsMfc3FyeffZZvv32W2JiYsjNzSUkJIRz586JrJA%2Bn4/jx48zZ84cevToARR65vxDSatXr66bu2bgvvTSS4wcOVIXkun1eoWnDNAZem45LZgCq1at4pZbbimxnsPhoHv37sp9O3PmDFu3bqVZs2a68uK%2Bc7Vr1w6An376SWeIWiwWSpcuzdatW4HC9Ze9rSdOnChxrBpMzp4JEyZMmLgWUEJes6sL07P3m2EmaDFxzUHlZZOh6eS9/PLLuvJhw4bptPiWLVum%2B72mxVeuXDmioqJYsWJFQGMKCQm5orcxLy%2BP0NBQYfz5w%2Bl0cujQIaAw7DAoKIi%2Bffsa6oWFhXHy5En27dvHgQMHSExMZOjQocyePZuWLVvy7LPPArB161beeust4uPjeeihhzh27Bhr1qwBCjls06dPp02bNixbtowGDRqwd%2B9enTH38ccfExsby%2B7du1m0aBF33XUXZ86cEb/3T36zZMkSunbtysqVK1m6dKkwqMLDw3WGHUB0dDR2u51jx44B0LRpUzZs2ECpUqUICwvj1KlTBg/ejBkzCA8PF/O/ePEiTqcTr9crQjhtNhtr167l4YcfBqB8%2BfK6JDEa37BNmzZs2bJFuT/%2BKFu2LAClSpXC4/GIEF1NiL1GjRrs27dPee%2B%2BffuEtp8JEyZMmDDxd0BU1J/Y%2BT/MMLsaMFfQxD8C%2Bfn5fPbZZyQnJ9O%2BfXv2799PfHy8sm5cXBxRv/LNlpiYaOAKdu7cmfPnz3PhwgV27NgBQFJSEn379qV%2B/fosXLiQzZs3k5qaSkpKCg6HQ8kVdDgcOBwOBg8eLIzQoKAgnnjiCZYvX853332nq79u3TqOHz8ukpUA9OrVi7fffpudO3fSpk0bBgwYwLp163RGbceOHYmPj%2Bff//43NWrUoEmTJjqP4gMPPCD%2BnZ2dLbySmkagFjLqD6vVSlxcHMePH6d0kd7WxYsX8Xq9XL58mQsXLtCyZUvcbjcOh0Pcv3r1atLS0oBCT2B8fDwfffQRS5cu1bV9yy238P333wNw1113icQtTqcTm81GQUEBH374oW5MI0eOZN68ecyYMUMn3q55Qq%2B77jqcTqeOk5eZmUnTpk1xOp04HA7CwsJ0yXy0DLGBwEzQYsKECRMmTJi42jCNPRN/W0yYMIGEhAQSEhKoW7cuY8eOZdCgQfTo0YNLly4ZskqqsHDhQlJSUnQGnPaTkpJiMLAAxo8fz6ZNm9i0aRPr1q1j2LBh5ObmUqVKFe6//36GDBnCc889R6NGjahWrRrlypUjIiKCgQMHkpmZKXhuMiwWC26322AQdOjQgTZt2vD000/rPGONGzfmtttuY968eSLU0m63Y7Va2b17N%2BvXr2fcuHGivn/Wy7y8PM6fP8/hw4dp1qwZb7zxhvidQ1aE9cPy5cs5e/YsGRkZOsFwr9fL559/LsI6Af773/%2BK32dmZvL111%2BTk5MjNP40aIliTp8%2Bza5du%2BjYsaPO8xkeHi5CTqEwBDYjI0PMIzc3F7fbzYEDB3RjnT59OoMHD2bEiBG65C2aFzQlJYWCggJduKfb7SYiIoK8vDyRhMc/lFMLAQ0EqgQtSmPx3XeveC3nWADgk090l6r8A0eP6q%2BVPHw5McTq1SX3XeS5FSiSEdGhaE8F/DzHGo4flwqK9kWHw4f11x98oLvctct4i5yzxS8fk4A8BXneAEXO%2BP/BT1IFgJ07S%2B5cFS6ekqK7VO1Lifk6VDcVPRMaDOOnUDv%2BCkMphJRMQr4HFPk55IYU45OLFDl7jOdNNc9Nm/TXsjdfcSgMz8fGjcZ25SQa8tkD48GRolCUkeNyu5s3G%2BsUfSQUUISAGyahynAjJ/qQIhSU74Bt2/TXiugIeV6qfCIUfbQrrm/lgyi/F/wiNgSk0HnlmZWfTXlOhpcNcPCg/vqHHwxV5NtU%2Bys/4qqtk7dKXj/VVsrPh6pd%2BdlUPasl1dG2zbbpa%2BPNfxTMMM7fDDOM08TfFr%2BHFt%2B//vUvzpw5w4svvmj43ZgxY6hdu7ahvHTp0kT6MZl79erFk08%2Bid1up1u3bixatAin08nq1atp0aIFM2fOJDQ0lM6dO2O324tNRuLz%2BcjMzFRKMfznP/%2BhW7duLFy4UIzp//7v/%2BjZsyf5%2BfkiQ%2BbFixcJDw8nLy8Pp9OpC7UszqukcQM1PProo0yZMkVZFwq9qPHx8dSvX58fFP9BZmZmYrFYaNy4Mf/9739FOKYG//nHxcXhdrt1hqG2Fv7jW758ebHjgcKMnpqWnpaYxV9bz%2BFwiHHUq1cPKAyp9Zf00Dx8/uOtWrUqR/2sprlz515xHP5YtWqVMkGLIaPnHXdc8dpPieJ/6NJFd6lS7/Cz7QFQ2vBF0hwC3bqV3HdsrP66Z09jHdmoVXjSY2Kkgq5dje3UqKG/7tNHd6mKqPWj1gJQRJPVQZ6CPG%2BA66%2BXCm6/XX9ddI6u2Lnqg9N11%2BkuVfsib4uhjuqmIm%2B3BsP4ASn5rDyUQkjSKvI9AH7OcHVDivHJRSpKr%2BG8qebZsqX%2BWuLqqg6F4flo1crYrjRvw9kD48GRojMM66Jqt3lzY52GDfXXiqgAwyTkQwIgRVwg/f%2BlfAdI8jOG9cQ4L7kbwJjdQ/6/U/Ugyu8FvygVgUqVdJfKMys/m/KcDC8b4IYb9Nd%2BHy%2BLu021v/Ijrto6eavk9VNtpfx8qNqVn03Vs1pSHbFtfv8Xmrj28M8ybU38o/B7aPGFhobicrlo3ry54cflcokkI1fS4oNCA/Dy5cvk5uZSr149du/ezYYNG5gyZQqLFy9m2bJlzJw5k0aNGtGkSRNlG6%2B//jper1fpWYuJiWHYsGG8/vrrgl/ncrm4/vrrqVatGosWLcLhcODz%2BYSBV6pUKRITE0UYYnh4OF26dBF6fJqH0e12i7BIQJeI5v333xdezC%2B%2B%2BEIYRHa7nQ0bNuBwOChVqpTQ89Pg8/k4dOiQkFCQoa3rgQMHDIlVXC6XkHuIiorC6/XqksaMGTNGhGLWqFGDlStX8tVXXzF48GCgMOyzQoUKxMfHizn6e%2Be0uZYuXVoXaunz%2BQgKChK6goDO0AN10pXiYCZoMWHChAkT1wJyG7UsudLVgunZ%2B834Z83WxD8OxXHobDYbu3btUnLonn/%2BeSFhoKEkvb3iErloXMGMjAzS0tLYtWsXpUqVolevXsTHx9OoUSPefvttLl68SFxcHHfddRfr1q1j3LhxIny0Vq1aNGjQgPvuu48aNWoIQyclJYVx48aJub322mtkZmbqhL379%2B/P7t27GTx4sM6gsdvtZGRkcPz4cWHg7dy5k%2BTkZIOHz%2Bv16kIyH3nkEfHvQYMGMWnSJC5evEjZsmWFx23btm0cPnyY/Px8Ll%2B%2BrNMh1Ay59PR0kb3SZrPRquhrutVqxWazUbt2bTwejzDISpUqRZkyZfjoo49YsmQJUGiA%2BXw%2BDvqF3ERGRlKxYkWxRv369aNr164iHBQKDbp3332XzZs389JLLxlCWAEeeughg%2BdW87yWKlWKiIgIxo4dq/v9TTfdRKAwOXsmTJgwYeJagOvQnpIrXS2Yxt5vxj9rtib%2BkVBx6BYvXkyjRo1K5ND9EgkGDSqu4N13302zZs04dOgQW7dupWfPnkyYMIGwsDBCQkJYtmwZly9fpl27djRu3Fh4yDQvVnZ2NmfPnuXHH38kJSWFCRMmAIXGR1BQkPhxSiFBDRo0oHr16oZEIwUFBZQtW5ZRo0bRsGFDYWRooa2aB9FisTBy5Ehddkv/PrxeL9988w3Dhw/nwoULALz66qu6sNemTZtSoUIFHA4H0dHRwjMYFxcnxuTxeNhYxJNp27YtiYmJ7Nu3D6fTycyZM4HC8M%2BMjAw6duwojKy0tDTKly%2Bv8xw%2B9thjIrtpdnY24eHhZGVliTl4PB4OHDhArVq1aNq0KWPGjBEZQgGRMXXHjh0GaYXSpUvz448/cvnyZdLT0w3ZVQORk9Cg5Ox16GCsKHOMpGsVt4kjR3SXqohlWX9cddQNHJT9%2B3WXSm6OPCBV5lJpfCqykKFtFUdK5gFJYcMqGtBPP%2BmvVTQgKXJZOQfDmsocPbkjMIqzqzzBEq8vAPpdSTriyr5UNCWZr6OqI29MQJy9U6d0l0puWACTCETD2nBO5GvVoZDPrIrQKC%2B6fPbAyDGTPP/KfZHnLZNpVWWBiICrXgzyQyXPQbWg8gOiONcB6XCXRAhVzUkukzmvinalo1YImVsv769CKN7wglS8KOSjFAi/VvXOlMvkdgLRVFfVkZdPfv2oyuT/B1TPt4lrDyZnz8TfHioO3erVq3G5XCVy6PwTdwSK4riCubm5NGrUCK/Xy7Rp0yhXrhw333wz99xzD506deLrr7/mlltuoVevXuzZs0d4mEJCQmjYsCFNmzZl0qRJlClThqFDh/LSSy/Ru3dvoaunYdq0aaxduxaAt99%2Bm/T0dL744gvOnDlD9%2B7dCQ4OJisri9TUVB544AHsdjt2u52GDRty4MABYbTl5eXh8/l45ZVXaNSoESlF/zk/99xzjBkzRvSXk5PDsWPH6NSpkyjzNyy3bdtGQkICBQUFpKWlCU5ejx49%2BPHHH8nNzcVms4ny9evXi1DVvLw8IXIO/%2BPq/fjjj2Jv09LSsNlsuFwuXWIXDZpIfYUKFYS31ufziTH6fD4dT3Do0KGMGjWKtWvXGrT4LBYLw4cP5/nnn8fpdJKfn1%2BsXl9JKJazJ3NZZI6RdK20L6tV012qOHtyBK2KPmbgoEh8QiU3Rx6Qgtcqj09FFjK0reJIyTwgiVejogFVqaK/VtGAIiKkAsUcDGsqc/TkjgCKMtEKqIg2Eq8vAPqdoY6SGyb1paIpyXwdVR15YwLi7EVH6y6V3LAAJiHThpQ0IvmcyNeqQyGfWRWhUV50lcK0zDGTiLHKfZHnLZNpVWWqB1o%2BS6oXg/xQyXNQLaj8gCjOtTwF5f6WRAhVzUkukzmvinalo1aIhAT9tby/Mm8SjC9IxYtCPkqB8GtV70y5TG5HdU8gdeTlk18/qjL5/wGxvFdIzHbV8Q/zwl0NmMaeib8lNC2%2B6dOnK39vt9txOByCQyfr7QEl6u2p%2Bjh16hQvvfQSL730kugnJiaGO%2B64g8GDBxMVFcW5c%2Bd4/PHHWb58Oe%2B//z5r1qyhefPm7Nu3j/3791O1alURnrjLz4uze/duADIyMvip6Otqt27diJWSArz%2B%2ButAoSfs7bffJj4%2BnrvvvpvDhw8THh5O06ZN%2Bemnnzh27BjZ2dnCe3X48GFatWrFjh07OH36NA6Hg5yir8NauGtERISOZ6eNU/s3FIY/%2BsPr9Yr7nU4ndrud3NxcFi5cKAxaj8dDZGSkkFnwT4KicehURtWlS5do3rw5Ho8Hu91OcnKySMAChSGhmtfyrPRVuDgjLbrorwWbzSbGpyEoKIgNGzYAGH4HhQlahgwZomxXhsnZM2HChAkT1wKyK9dEkSfGxDUC01w28Y/CH6G3161bN0PY6NSpU0lKSmLw4MFkZWXx1FNP4fP5GDZsGJMmTeLy5cvMnTu32LDR06dP89prr1GtWjWd1%2BxK8Pl8XLp0iW3btjF06FDeeOMNWrVqhcfj4dixY7Rv355vvvlG8NLOnTvHmjVruHDhAjVr1uSdd96hfPnyjBkzRmTDtFqtIkQSCjNz1q1bl5iYGFauXCkkEfylGiwWi9DNs9lsXFf0RTclJQW3243NZsNmswlDr3Hjxjz22GPiflmzT0b58uXZunWr4PZp4adQaGzfddddAIasn5GRkTgcDkqXLq0TQte8wE2aNKGK9BU7MjKScePGCaH3wYMH65LXyELyJkyYMGHCxLWOoG2m9MK1DIvv18YgmTBxDSAxMZG0tDSRcTInJwe3203//v0ZM2YMnTp1onPnzowePfqK7axYsYLx48cbskpCoYdG81hlZWWJkEINzzzzDCtXrsTlcjFkyBAGDBiAzWbDarWSn5%2BP1WolIiKC8%2BfPM3DgQGrVqiX074KDg/F4POTm5lK7dm2mTJlCnz596NWrF8uXL8flcumyjAI0bNiQOXPmcPr0aVq3bo3VamX06NFUqVKFUUWp/ffv309oaCjZ2dlYrVYR/uh2u4U3z2q1CtH2Pn36ULNmTQCd5wygSpUq/PTTT/To0YP77ruPrl27Uq1aNTIyMjh37pxhvfxDNlWwWq04nU4xDrvdbgi19Ifb7cZqtWKxWMhUCr8VD7vdjs1mw%2BfzCU/dbbfdxsaNGwkNDeXQoUPCM%2Bh0Otm9ezcHDhwQun7%2BnkMozP758ccfB9T3888/bwjjHDFihE603oQJEyZMmPjT8fXX0KbNn9O3X1K43xVFWdf/CfhnmbYm/pF44IEHSEpKIikpiS%2B//JLt27cLzlmgentQmOZfa8f/JzIyUvRhtVrp0KGD7veavIHD4SApKYm6devy2GOPERsbi9PpxOl0cvbsWW699VbBv9MMmKSkJD7%2B%2BGO%2B/fZbkpKSDNkhn332WcN4nnvuOTE3gDJlyrBmzRrGjh2LxWJh//79OBwOYeRoHi%2Br1Sp06KBQQy4zM9PAW5S/D2khpR9//LEIlaxfv74uA6c//I02OaGM1r6/AXX33XcXmwkVCg34rKwspf6gP/w9opoxXlBQQG5urk7I/qGHHqJ3794cOXKEJ598UpSXLVsWgI8%2B%2BkiUycZ/oCGcoE7Q0r69QlS9KEGNwJtv6i6VSVIkEeFAiPmqhA4G%2B1rSM5RzGAAgaSKiCJE2CKQrPKJ75ORvsjCyqi9pbVR5NuT1UjnTDd8MFi0y1DFoc8%2Bapb%2BWk0KAcZ6qZ0TKDqPcXzlzSiBZIKS%2BlclXpHehMtGGHH6smoN8cOTEGooQZnnIqteyQTBbNcAvv9RfyyLlsqA2irVQcLUNXakOl9yQJKquTIAiN%2ByXNVhg61b9tWrN5UOrqiOHnm/frr9WJXX56iv99RdfGOtIc1CeWfnsy6Llqswq8j1yAhwwHArlmS3ifAt8/73%2BWnEmDPurSg4j7aeq70DEz%2BWtkttRbYt8j%2Bo7p7x8qv8H5PeffC2W18wUfU3D9OyZ%2BFsjLi5OZ1zIHLqJEyeyZ88e%2Bvfvz%2BLFizl8%2BDAhISG0aNGCSpUqkZeXxyOPPMITTzzBhx9%2BqBQJj4uLo1evXjz33HMkJiYycuRIobmXn5/Pl19%2BycMPP8yECRN45ZVXqFq1KllZWRw%2BfJjg4GCaNm0qDKNZs2Zx//3388UXXxAVFSUyVGpITEwkMzOTsWPHMm7cOBYsWECTJk1ITEzE5/Oxfv16nVHTunVrTiv/Ii%2BE0%2BmkdOnSwgMne%2B2cTidVq1Zl1apV9O/fX3Dvfi/IXjwN/mLlK1eu5JVXXuGrr77CYrHQp08fBgwYQK9evXRjbtSoEd9%2B%2B61yHg6HA6vVisfjoaCggNq1a9OnTx/ee%2B89XVgqFGr7eTwexo8fz8qVK0U7devW5f3336dbt26GezRcf/31rF69OqC5qzx7D4waxYiRIwO634QJEyZMmPhDkJ2tVnf/I%2BCXEO53hV/W8L87TM%2Beib89rsSh6969Ozt37mTSpEncdtttJCUlMWPGDE6dOsXMmTN/N%2BmFQYMG0bt3b9LT0/nuu%2B8YOnQoa9asYe7cuXg8HrZt20ZpKS3WmTNnRDvaz8mTJ0W2TA3fffcdOTk5ZGRksFX6Aty9e3egUILBP7RUC2v1eDy6Od5%2B%2B%2B0AwtOVl5dn0IOrWLGiSAJTHFwul87odDqdhISE4Ha7Rd%2BALgmMP477fSF///33BXfO5/PxwQcfCENPK4P/yUYEBQUJL5/NZmPkyJHExcWRm5sr9ALT0tKYN28eP/30E1FRUTRu3Fi0d/r0acEh9DcYNW%2BjtkYa/OeTYnA9FA8zQYsJEyZMmLgWcOKcmZ7lWoaZjdPE3x5ut1spvfDZZ5/pwu4sFgsWi4Xc3FzBYXP8inTDxUkvaNc5OTmcPHmSuLg4HA4H9evX5/PPP%2Bd6v3TQmmdqxIgRdPDTXrvzzjt14ZH33HMPHo9HGCVDhgwRoZETJ06kd%2B/ezJkzhwsXLvDyyy9z8eJFxo8fLzJw1q1bl/Lly/PFF1/g8XiIjo4W3ja3282FCxfEGmjGTpMmTejUqRMHDhzg9ttv5/uikJi6deuK7KFz5szh8ccfF8aPlkkUoGvXrkI6ITw8HJ/Pxxm/8Byn06nLdLl48WLd%2BhYXjKC16fP56NmzJ/Pnz6dcuXL83//9H4AIW4XCzJwa3zEjI0N4BAEef/xxevbsyZYtW7Db7WKtLhXFwPgbzdOmTeOLL75gTVFY4i/R2TNF1U2YMGHChIkS8A9LpnI1YK6giX8kZA7dqFGjmD9/Pj169ODRRx%2BlatWqvPXWWwYNu0BQtmxZYmNjiY2NJTo6WpdAJTIyEqfTyZo1a%2BjTpw%2B33347X3/9NdOmTcNqtTKtiDBstVoJCQlh7ty5lClTRrRnt9t1/LK8vDw8Hg9erxev14vH4yE7O5tRo0bRtGlT3nrrLaCQQzd58mSefvppXfbI7777jk8//ZT8/Hyio6P57LPP8Hq95OTkCKNm165dXL58mYwiUeEPP/yQuLg44uLihKEHcOutt4p/V6xYkdKlSwvv3o033ki9evW48cYbhVFms9kICgoSwvD%2Bc5IRSLS5ltXTarWKJCmnT5/G6/Xi8/lo0aIFZfw0mXJzc8nJySE3N1fXfv/%2B/Zk3bx4xMTFCrxAKvY3Z2dk67%2Bm4ceOEoQdQTdaPuwJUnL3ERCNnT44YNUSQKrg5BgejLAgNRr6bQmxavs2g96wiqUiEHZmaA0adaxUdRp6oimMmO0cNlJ4imQx/GIasmoMUXmsQWQfYu1d3Ka%2BNqlmZQxMAtU5N8pHCuw0DVHRuKFKJgstcNZXAtzzALVuMdaTODHNSiVhL96geeZnHp%2BIpyc%2BH/Cyopi3vw7FjJQ4vIM1yw5lVcPbke1S0NHnMqrMlHxNVHbl7wxwU7wm5b2XC4QBU1eW9k183qmMu96XaO8MZUFAt9u3TXxt04RUE0RMn9NcqKqXMX1RyFUsi5KnKAlGpD6SOPCDVAyOXyddF7V4hp9rVh5mN8zfD5OyZ%2BFvjShy6yZMns2DBAurVq6dLxKHCihUrmD59utDWK64PuT8ZixYtYtKkScK4kZOYhIWFER8fT0pKCjk5OXi9XqpUqUJ2djaHDx8mJycHm83Gww8/zJAhQ9iyZQuDBw%2BmfPnyfPDBB7Ru3ZopU6bQs2dPPB4PderUwefz8cUXX1CpUiWgUH%2BvW7dunDx50sBtCw0NJSsri1KlSpGXlydCDfv168fGjRs5ceIE9913H2%2B99RYTJ04U6%2BZ2u9m8eTMNGzbE5/MxaNAgPvjgAzIzM4s11PzXwN%2BDJsNisVCuXDkhzQBXzujpdruZN28ed9xxhygLDg6moKCARx55hClTpijv07Bnzx4aNmzIW2%2B9xZ49e4RmosViYenSpUyaNIm90h/6GsaMGRPwBwKTs2fChAkTJq4JeDwgZf7%2Bw1CUnfx3Rwl/C/yd8M8ybU38I1Ech65Hjx5cunSJUqVKBdTOyZMnDRw6jUcXKAYOHEhiYiIFBQXC0LNYLJQtW5bo6GgeeOABkpOTuXTpElarlVq1arF161aaN29OUlISZcuWxefzMWvWLNLT01m9ejXR0dHY7XYiIyOpX78%2BH374IVBoEIWEhADQqVMn6tWrR3x8PA0aNCA1NZWHHnqILVu20KFDBxF%2BGBQUREFBARcuXNBxypYuXSpCMjVvoScjYgAAIABJREFU4YQJE8Tvc3JyaNCggTDsPv/8c%2BLj44VOoT9/T/N0ZmdnCy%2Bev6EXHh5OjRo1xLXP5xNhtRq8Xq9o01%2BTTxuLv6EHhfy4/Px8li5dKsqCgoIICgrCarWK%2BVutVi5fvkxubi5PPPGEMPSg0Bt86tQpGjRoQHE4pnIJFAOTs2fChAkTJq4JyJlb/0j8RT17ubm5jB8/nptuuomWLVsyd%2B7cYusOHz6cmjVr6n6%2B9MscPG/ePFq1akVCQgLjx48vNpv5r4Vp7Jn42%2BNqSy9oBs2IESM4efKkzrj0/1m1ahUAM2bM4NFHHxUaby6Xi3PnznHrrbcycOBAYmJiyMrKIjc3lw0bNhAfH8/GjRupVKmSSHTi9XqZO3cun3/%2BuUg6AtCxY0e2bt0qDFDN2IuJiQH%2BFw757LPPMnz4cMqUKUPNmjVFG5UqVaJUqVKULVtWhItqCUi0e4ODg%2BnRo4dSNkFDxYoVKVWqlPDGPf/88wBC4DwkJIQbb7xRjMsfXbt25ciRI7oyeY%2BmTJmC3W7HYrFw2C8m0G63s27dOiIiIgzt%2Bnw%2Bfv75Z3Gdk5NDdna2CFuFQiNyzpw5AKSmpuqSr1itVvLy8gzz9g/T/SUJWkyYMGHChIlrAkpNi382pk6dyp49e5g/fz4TJkxg%2BvTpfPrpp8q6P/74Iy%2B%2B%2BKJIFrhp0yZatGgBwNq1a5k%2BfToTJ05k/vz57Ny5kxd/50yhZoIWE397aBw6FerUqcMeg6BXIebPn8/Zs2d5pEjQ0263K9vRDIIJEyYYvEz%2BCAoKYuLEidx7770MHjyYBQsWiJDP0aNHiyQmFosFt9tNu3btOHjwIC%2B%2B%2BCLdu3dnSZFW0OOPPy6E0TMyMvjyyy/x%2BXzExcXh8/nw%2BXysXLmS4cOHi77nzJlDpUqV2Lt3L7179%2BaVV17B5XKRkJBA7dq1xQuqVKlSWCwWXQipJuquJVHJzMykYsWK5OTkcMMNN3Dw4EGioqKwWq0ig%2BXOnTsJCwvD4XCQm5vLyy%2B/jMvlYuvWrVSsWJETRYSIFStW0KJFC91XrE8//VR47SwWC9WrV9cZdADjx4/H6/Vit9v5zI9MUVBQwM0332xY%2B4iICKxWK5cuXRJewnLlypGQkEBwcDDJycmkpaVhsVi49dZbmTNnDuXLl2fmzJn06tVLiK4HBQXpksm43W7Cw8M5ffo0Pp9PaBsGAjNBiwkTJkyYMFEC/oL8uqysLN5//31mz55NnTp1qFOnDocOHWLx4sV07txZVzcvL48TJ04QHx%2BvSxaoYcGCBQwaNIh27doB8MwzzzB06FDGjBlj0PL9tfjrraAJE38gunfvzq5du9ixYweJiYk6F/vkyZN57733mDdvnu6epKQk%2BvbtS0JCAi1btiQ9PZ0LFy4QFRVFbGwsO3bs4O677xZJVbSfsmXLsmTJEqZOnWoYR%2BnSpQkPD%2Bezzz4TsgNJSUnEx8cTGxvLvffeyyuvvEJKSgqzZs0iLi6O5ORkqlWrRp8%2BfQBYuHAho0aNwul0kpSUpGt/wYIFdOrUiX79%2BuF2u8nIyGDKlCl07tyZMWPGcPjwYex2O9u3b%2BfSpUsixNBut1O1alWDYPnMIqFvzQPXsGFD7Ha7SBaTn5/P2bNnhWF1%2BvRpcnNz6dq1K8ePH8dut/PDDz/QunVrcnNzsVgswpOYnp6Ox%2BPBarVSs2ZNIXWgGYDdunVj5cqVQKFx5y8dERwczMSJE4VHU0N6ejpRUVHCC9e4cWMqVarEunXrSEpK0slZaC/X0aNHs3z5cuHR9Pl8IrGPhpycHE6dOiXq/GRg/hcPVYKWDh0UoupyJgPpWpUoQs5soOLly/KLqjqGr7lSJhVlQgJ5QCqxZDnTgiKk1aB6osoMIY9PaleRc8aQ%2BEP1wdqQCEK1yPKNcmcqBeMAkpDIE1d1LS%2BFYflUfFapIdVyymWqoAd5PKo6fjmL1DcpFl2OWlItn2HBVIdWzvYjb6ZqwPJ4FBl5DENWDVAej%2BzpDySJhmpjAkjAY2hHJRskj0%2Bag9J5I6%2BfIoOMfNyUdOoS3iUBjVe1d9LhVyZTkrOtyH2rXmRyX3IbKJZC8UDLx0Q1hZISD6mOeUli6GDMtxPIMy9fi%2BeyTh3jzX8UrlIY55kzZ9i7d6/u54wqQ5IC%2B/fvp6CggISEBFHWsGFDdu7cafhoe%2BTIESwWizKSyePxsHv3bm666SZRVr9%2BffLz89m/f/%2BvXDAjzAQtJv7WKClhCsCTTz4pvEP9%2BvUjLi6O2bNnk5aWxj333MPkyZPp3bs3mzdvpkOHDiQlJfHoo4/SuHFjLly4wJ133klwcDBr1qwhIiLiislcGjVqRFZWFg8%2B%2BCALFy7kfNGbNT8/X3DHatWqRU5ODj/88AODBg1i/Pjx5OXl0a5dO86ePYvdbic5OZnmzZtjtVpFFk4Z/klPqlWrxtixY6levTo//PAD//nPf8jPz8fj8RAcHIzD4SAtLU2IqOfm5grDZcaMGTzwwAO6PqpXry7a/fzzz4mMjMRut5Oamspjjz0mDFqr1UpkZCQffPABUOhha9u2LWfOnOHtt99m6dKlrFu3rti90bh0Ki2%2Bq4X%2B/fuzZMkS3G63od9Fixaxfv36YmPzDxw4EHA/qgQto0Y9wMiRI375oE2YMGHChImrhH37oHbtP6lzv/wAvyfeKFeO6dOn68pGjhzJqFGjSrx37dq1TJw4kWS/DMY//vgjXbt2ZcuWLToqyZo1a3jmmWdo0aIF27ZtIzo6mlGjRtGmTRvOnz9P06ZNWbNmjfi7CqB58%2BY8%2BeSTdOnS5XeYqenZM2GCZ555huHDh3P58mXmzJnDs88%2BS40aNXjvvfcYOHAgzZo1Y%2B/eveTm5jJ//nxmzJhB3759iY2NpV69ekREROD1epk/f36JfZUuXZp//etfrF27VvDZateuzbRp0wSfsHnz5gQHB2O1WoXB5XQ6qV30pnW5XMybNw%2BLxcKiRYuoVq0abrebd955h6FDh1K2bFnCwsJo3769CBWcNm0abdu2JSYmhk6dOrFw4UJycnJ488032bp1K/Xq1SMhIYG8vDyOHj0qvItWq5WcnBzhdbNarSJsdc2aNZwucg%2BlpaWJr1kVK1bkP//5D1DIzduwYQORkZFCbzA8PJywsDCaNm3KjBkzCA8PJy4uziAqP2DAAFasWMHKlSsNCV78eXL%2BUhJQGJobCOx2O06nU9fWoEGDaN26NYBOfkKDxWIRWU39x/RroErQYqZoMWHChAkTfzXY/0zS11Xy7N1%2B%2B%2B2sWLFC96NFEpWE7OxsA39fu5blo44cOUJOTg4tW7Zkzpw5tGnThuHDh7N7927xQVnVlkqG6tfC5OyZ%2BFtD5V2TYbVaDRw6f9jtdipXrkydOnU4ePCgzt0O8NVXX/HDDz9Qrly5gMZ00003MXny5GK9jqNGjWLUqFG0bt1a58Y/duwYb7/9NsuWLWPnzp0MGTKE%2BvXrc%2BrUKYKDg2nevDmnTp3C7XaLecfFxdGmTRtdEheAWrVqsWjRImFA2mw2vF4v8fHxDB06VGShvHDhAm3btgUKDbe8vDyqVq3K4cOHycvL49ixY8K7qBmCe/fuFaGQVapUEX2OHz%2Be5s2b07ZtW5KSkjh79izR0dHcddddvP7661SoUEEnXL5kyRJ69%2B7NjTfeiNPpFCGhshczIyODBg0acPDgQS5fvqyURbDZbFgsFl3WT%2B3fFosFh8NBfn4%2BtWrVIr8o3Oi6667TcfygMMTz448/FnP%2BLTA5eyZMmDBhwsSfg6ioKJFg75fC5XIZjDHtWotI0nD//fdz5513ig/TtWrVYu/evSxbtozRo0fr7vVv6/fi64Hp2TNholjk5%2Bfz2WefkZycTPv27dm/fz/x8fHKunFxcb/6pVEcWrZsyenTpwWH8Oeff2bYsGF8%2B%2B23bN26FY/HQ0pKCpcvX8bj8dC3b18mTJhAamoqY8eO5ejRo3g8HipVqsSKFStITEzUtX/TTTcREhJCYmIiMTEx7Nmzh/379/PSSy%2BRmppKixYtyMzM5OjRo%2BTn5%2BN2u8nPz%2BfgwYPCMMzIyBAGipZZdPbs2bz%2B%2BusA7N69W4x/xYoVlC5dmiVLlnDmzBnatGlDzZo1ef311wkKCtJ5urxeL6GhoXzzzTeA3jDy9%2BxZrVaCgoJITU0VL9j69evreHVQaCDKgucOh0N49jQDr2PHjiJmf8aMGeIerb/IyEh%2B/vlnMWf/GHx/sftAoOLstW2r4OzJ/JJAeHMSCUTlRJR5IspIWTnKXyJ0KDk%2BckMqoojcuaIhw7wC4VpJfQVC81PNwcB/UVWSv7rK3CZF6mx5OZUkCmn9VBm45TLDWqnGGwBnLxCKlNy0iiv08cdSQQDC0nJRAJRHdSV5YvLzo5pUALy5gPqWy2Ri7K/l7MllqodebjsQAW1p85Rcu5I4kIqulee6hGdVedDld4nqRSaVKbme8j7IZDbVy09eYwUZMIDXmKGOanvl4cj3qOYk11Eda3nIv4Wzd320amH/IPwFpRfKly/P%2BfPndR%2BQ09LScLvdhhwHVqvVEIFUrVo1Tp8%2BTXh4OC6Xi7N%2Bz5kmf6VK5vJrYXr2TJjww4QJE5g0aRJQGMbndruFJt%2BMGTOuqMk3YsQINm/eTEFBAXl5edSsWVP3e6fTKYyKQNC9e3c%2B%2BOADgoKCaN%2B%2BPQcPHuT%2B%2B%2B9n6tSppKenExkZyc6dOwkKCuLSpUvcdtttnD9/ngULFpCWlsbdd98NGL8yqVC9enUWL17MlClT2LlzJwDvv/8%2BFouFd999F4fDIZKYaF4tWZD9hB%2BBXfOU%2BcPn83Hvvfcq%2B8/OztZl5MzPzycjI4NXXnmFGTNm6AxBj8cjjC%2Bv18vDDz9McnIyX331FQDff/%2B9of2IiAgOHjwIFH6RKygoUO5Fq1atSExMxGq1cuutt4oXucfjwWKxEBUVpZuzFu6qjcV/bCVh1apVSs5e3bq19BVlj7F0LUWaFkL6z0bhRJSroDwmcqhqmTK6S8mmVjck3aPsXNGQYV7yPar7pL5UXcu3qOZgeMxVlWTpEfk/ZsVXWXk5lZHA0vqpPu7KZYa1Uo1Xale1NlJeo4CWXPVKvOUWqUAesGJ8cpEU1a2%2BTVVJnpj8/KgmJS%2BgYnEC6lsuK1%2B%2BhEYC69tQpnro5bblzVSVSZunfHXJ66f4A1TuWnmuS3hWlQddfpeoXmRSmWpbDPsg/eGtfPnJa6yQ8wngNWaoo9peeTjyPao5yXVUx1oeciBHq9ht2bwZOnUyNvBH4C%2BYjbN27drY7Xa%2B//57Ee21Y8cO4uPjDR9%2BH3/8cSwWC1P8RNz379/PDTfcgNVqJT4%2Bnh07dtCkSROg8G8Yu91OrVrS3wK/AX%2B9FTRh4k/Eb9HkmzBhAklJSTzwwAOUK1eOd955R/ezbNmyX%2BT9q1%2B/PgBNmzZlw4YNHDlyhClTptCkSRMhOfDpp5%2BSlZVFREQEffv2pVy5cjidTmbMmCG8T4GKcyYkJDB79mwsFgu33HILHTt2xOv18uGHH%2BLxeHC5XNhsNtq3b4/b7dZp0MmQ%2BXcyLBYLHTp0UP7Onyc3YMAAQ2ZR0IdyPvfcc2zevPmK/eXn5wsjLDc3F4fDgdvtxmaz6TQFk5KSuO6664DCr2v%2BWT01I%2B9KnMBUVfrHYmBy9kyYMGHCxDWBv6DB9WciKCiInj178vTTT7Nr1y7WrVvH3LlzhZZwWlqa4OMlJiby0UcfkZSUxLFjx5g%2BfTo7duxg4MCBQGFSuLfffpt169axa9cunn76aW677TYzjNOEiasFTZMvNjaW6OhonZemTp06Sj4YFGryLVy4UEgsuFwumjdvrvvRvgRpWL9%2B/RWzhAYFBeFwOIiOjiY0NJRZs2axYcMGnn/%2BecqUKcOlS5f49ttviY2N1WWE0u598803qVq1qkGGQMP48eNZvXo1Xq%2BXlStXcurUKcLCwti/fz8vv/wyr732GsHBwXg8HqpXr86TTz6Jy%2BWiVKlSNGjQQHjGunXrxqZNm7juuusoW7YsAJ988okgHLdp04aIiAjcbjc33HADNpsNn8/HrbfeKsbSrVs3ca9mVFmtVmrVqiW0DUNDQ7FarbRr144777xT3NuqVSu2bdtGhQoVAIQuos1m49133%2BXTTz/l66%2B/FpzKChUqsGrVKt58800qV65MjRo1hGEcGxvL%2BfPn8fl8QtxeG4sWGmqxWJSGbsOGDUXylkBgcvZMmDBhwsQ1gebN/7y%2B/4JhnADjxo2jTp06DBo0iGeeeYZRo0bRsWMhFaNly5asKdKg6dixIxMmTGDmzJl069aN9evXC%2B1jgFtuuYVhw4bx1FNPMWTIEOrWrSucDL8XzDBOEyYCRPfu3Vm8eLEuPNNut1OpUiXOnz%2BvM16ysrLo27cvhw8fJiQkhBYtWvDQQw%2BJ319JnsE/cYvL5eLQoUOcPn1aGFhffvkl586dIzo6mvT0dNLT0w0hozVr1mTv3r00atSI2bNn68o1WCwWOnXqxJkzZ0hNTaVNmzaiPDQ0lIYNGxIdHc2xY8do3749s2bNIisriw8//FDX148//kh4eDh16tQREhZaOALA119/DUB8fDxdu3blhRdeICQkRITE1q9fX3AQoTDk4dVXX%2BXixYvixQlQuXJl9u3bx8aNG3WZMDdu3EivXr2EB/ONN94ACr1//fr1w%2Bl00qpVK%2BGVPX36ND179gQQEhP%2BMhWXLl3CZrORmZlJlSpVOHr0KA6HQ/S5a9cuUbdChQqkpqZisVh0iVwCQY8ePQyJc264/npjxdOn9WFI0nV%2BviJ86Px5XTxOdrYxSio3Vx%2BplJNjjGbyeKTQrowMXczR5cuKML6sLH1oVXq6MZ5IGp%2Bxo0Keis5BLN%2Bj6uvMGfDznmdmKqLZ5AXz%2BQxxZxcvSmFRir4NRcePg7%2BO0qFDIO2n3JW8B6pKqjryFAxnQDEnQyXFmhuK8vKM4apSO/IWBDQeRbsBDM94RlWHX948%2BSApDq1hjRUHx1BH1bdcJvUVyC3Gw6cYj2pxpIYC6kteG9VN8kFXPAvy9irfSXKh/FJSvYACaVgqUx190tL04ady34aXjaKOYl8M79UA9kVZRy4r8QEP4B4IbP3kdqRr1fNtohBBQUG88MILvPDCC4bfyTJMffv2pW/fvsW2de%2B99xZLc/k9YBp7JkwEiISEBIKDg/H5fDz44IMkJCSQmprKq6%2B%2BSkZGhgj/W716NRkZGTzyyCNCi%2B%2B1115j4MCBSj28K8HpdLJ9%2B3asVivNmzcXPMJevXqxZs0aLBYL1apVE7IPa9asYe7cuSxfvhy73U7Hjh1ZtmyZMo2vP4euYsWKnD17liFDhtCiRQuOHj3Kyy%2B/TEZGBj6fjyZNmrB27VrsdrsQF9eMm3379jF%2B/HiCg4Np3Lgx27ZtIygoSBhfWpbOQ4cOsXr1agAyMzMFp3DPnj1YLBZhTE2dOlWEP7Rq1UqMd8%2BePco1CgoKomHDhixfvlzMS0PFihV57LHHePnll8V4vF6vIYRSM94SEhKIjY0VIZya9EVBQQHBwcHk5OQIbUT4X9imz%2BcTnMBAoeLsPTBqFLVkMSOZbyJdK3lz0h9jqmgQ2YBQ0VYMHB6JXKKksMp/GSi4LgajTUEWMkQCqwgncl9SmLTSqR0AwcjAf1H0bSiSBXMVhrvclZJvKVUKhJ5lOAMq0pRcSbHmhiLZ0FO0o/pDsMTxKNoNYHjGMxoISUo%2BSIpDa1hjxcEJiBdZAqExkFuU5Ct5PKrFkRoKqC95bVQ3lUTqwri9yneSXCi/lFQvoEAalsqUfMGS%2BLQq2oFcR7EvhvdqAPuirCOXBUIsLukeCGz95Haka/F8nzoFfjpwfyjMENLfDNPYM2HiFyA8PJyEhARWrFjBa6%2B9RmhoKC1atCAqKork5GRq1qzJ5s2bKVu2rPiKExsby4wZM%2BjcuTOXVanrrgCXy4XFYuH222/n7rvvxuVyERkZidVq5csvv8Tr9eL1ekXWptKlS2Oz2cS1FkpZr149duzYgc1mIyIigmbNmtGlSxecTidDhw6lUaNGNG7cmKVLl7JgwQIAoqOjuXDhAtu3b9dx8Nq3b8%2Bjjz4KQOfOnfH5fHz00UcEBQUJY/bdd9%2BlV69eeL1enE6nMOaOHj0KFCZMeeKJJ3jkkUfEPVpopKZ/N2DAAPr16weg8/DJyM3NZe3atYZyq9VKSkoKDz74YEBJamw2m/C4Hjx4kJMnT1K6dGkuXrwoOIu7du3SGZP%2B%2BKWaOCrOnsnYM2HChAkTfzn8mQaXaez9ZpjGngkTRQhEk89isdCyZUtefvllXfmwYcNwOBwkJSVRt25dli1bpvt9UFAQM2fOpFy5ckRFRbFixYqAxmS1Wpk8ebKS27dlyxa6dOmi8zT17t1b1J0/fz67du0S5SdPnlTOUdOZu/XWW3WhqJ9%2B%2BikPPvigSF4SGRmJy%2BUiJCRE8Oj8M3J6vV7x75iYGOx2uzCAfD6f%2BJ3FYiExMZHo6Gjgf5k7NQ5gfn4%2BQ4cOZdSoUcrMlv4ad6NGjWLgwIFMmjRJeA2ff/55HnnkEWrUqMGhQ4fo168fEyZMIC4uDo/Hg91uF%2B36fD4KCgpEJs3t27eTlZWF1%2BslKipKiMa73W68Xi9169Ytdq9UHDwTJkyYMGHimscv4KOb%2BOvBNPZMmPgN0Dh0ycnJTJ48mQULFlCvXj1lXZmfVRISExNJSUlh3LhxjBs3DrvdTkxMDHfccQeDBw8GoFKlShw5coSuXbuSmpoq%2BIH33nsv8%2BfPF2PReHKqPlRJQX7%2B%2BWdmzZpFq1atxH1Hjhzh7Nmz7N%2B/38Dbu%2Buuuxg4cCBffPEFL7zwAm3btiUvLw%2BLxcLo0aNp166dqNuxY0eWL18uwi7z8/MJCQkRXs%2BCggIuXrxoMPQsFgsul4uRI0eye/du1q5dy%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%2BGVmL8P33S2yXI0f010uXGusUeWEFioTs/XH4sFRQ9HFAh//%2BV38tcSBVNM9AtKflteG99wx1fvhBKpg1S3%2B9ZYuxYTmMW6W7Kcl3qMZnuE3mAqvalTZr3z5jFfkIK5VEpLYVWtPG8fjpUQJK1W15yKpzbXjEVBzooiRRAkUJogS2bTPcIuuGo4rykPdu/35jHfngfPKJ/lq1L/JEi/RCdZDHrFocWRZIJe0ii4dLD4hSBnbjxpLHJ%2B2n8l0ii7HLD6fqsMli6PI5AvDTdy2uivxeYMMG/bXfe7zY8SkemKL/fgVUEf6yILpKIF0%2BWvL6BaAlr2xXfq2qntWShNfFtgQQ%2BXTV8BfNxnktweIrjoBiwoQJAxITE%2BnXr5/4o1zj0GleqNtvv524uDgmTJhwxXbmzZvH7NmzlcbUmDFjGDFiBHPmzCEzM5OxY8fqwjiHDh2Ky%2BViyJAhDBgwgJtuuomGDRuydu1a4d3TsoF26NCBu%2B66i9DQUIKCgli4cCFQ6FWKjIwkMTGRkydP0qlTJx5%2B%2BGEyMzN54403SElJYdasWcJLBtCiRQtycnJo1qwZY8aM4c477xTGkIxatWqxf/9%2BrFYroaGhZGRkYLfbRbhko0aNuOmmm5g5c2axa%2BR2u0lKSuLMmTNCu0YFjYeYJv8xcQVoHL7c3FzCwsK4cOECFouFAQMGcO7cOT755BMcDgfBwcHCiIuOjmb16tW0atWK7OxsYmNjSU9PFwbsv/71L6ZOnRrwGJ5//nllgpYRI0cG3IYJEyZMmDBx1bF27Z8nqv7KK1en3dGjr067f0H8s0xbEyZ%2BB/wWLb5p06YBhZpxKi2%2B5s2b43K5hDEiG3pQmMBE4weWKVOGmJgYHn74YdauXcuuXbvYsmULS5cu1aXxLV26NBs2bBDjjvTLThYWFkabNm2IjY0lLi6O1157DYD7779f8Og0VKpUienTpxMbGys8c/Xq1WPTpk288MILWK1W3nrrLRISEoBCT9s999zDwoUL%2BeCDD4RHsHr16kT4ZWnctGkTiYmJWCwWPvnkE2w2Gzk5OcyaNUtkOYVCj6CWtEaDz%2BdjyZIlQsfQKWX669q1K5s2bdLdo93n8/kEj89qtRIeHk56ejp2u50yZcrovHU%2Bn4%2BLFy%2BKrJ7Hjh0Thh4Ucih/CcwELSZMmDBh4ppAy5Z/Xt%2BmZ%2B834581WxMmfgP8OXQ1a9akTp06dO7cmXnz5ok63bt3Z%2BfOnXTt2pWEhARatmzJ2LFj%2BfHHH5k/fz4ej4cVK1YodVm0PjJVcVsUcts%2B%2B%2BwzkpOTad%2B%2BPZ9//jnnz5/nww8/NISC3nDDDboEIydPnqRevXr06dNHJDLRcOHCBTGnmjVrUq9ePVJTU9m7dy9vvvmmqJeens7%2B/ft14aYaIiMjKVeuHF6vl2HDhrHUL3TvpZdeYuDAgbzzzjusXLkifoNMAAAgAElEQVQSgO%2B%2B%2B05n7J09e5bk5GQhJaHx3/ZJoTMtW7bko48%2BolmzZiLDp9frpWfPnqSlpVG6dGmee%2B453T1r1qzh2Wef1WXRXLVqFa%2B%2B%2BqoQp3c6ncJIzMzMJCwsjBEjRuiE1C0Wi0hW06hRI1q3bq3j6RXH1SwOJmfPhAkTJkyYMHG1YYZxmjARIBITE7l8%2BTIjRoyga9euFBQU8M033/DEE08wefJkevbsyZQpU1iyZAk2m43hw4dTpUoVZs2axcGDBylXrhwrVqzgyy%2B/FAlT5OyYWsZIf1gsFux2Ox6PB7fbTf/%2B/bn%2B%2BusZN24cXq8Xl8tFu3bt%2BPe//035Ig22yMhIsrOzSUxMJL0oKN/tduPxeMjPz9dltJThcrnweDxCe06Gw%2BHAbrcLD5f/OENCQggLCyMzM5MLFy4AhZlI4%2BPjOXnyJKdPnxbeQn/tv/DwcEqVKiWM0/z8fGFcORwOobsHhUZS27ZtWbNmjWhHy6hZHOT5Fjd/u91OlSpV%2BOmnnwgJCeHixYvCSIyNjWX48OE8/vjjyj4qVKjAVyo%2BSzH44YcfOCwR026oXJla9evrymQ9ZflaJbptEPtViOkeOwZFSVWBYgTSZSFhuZ3UVKhQQXeLrC0ekJCvqvMjR6BatSs2U5JOs1JNPgDBYvk2lQ4yp06BX5izQcDYsBBGfXmV3rxcqNKa/uEH8M/3FJDgt1RJHj4YdeAPH4YaNfR1AjgCxkqSELdCl9s4CZW4%2BJkzOi1F1fYa9iqQzQxAONwwUdWZlccsi3mr9kUan2raAeh7G9cvgAdGnqa0vIDx/Cn05o3nT7XG8o2ykLlqM%2BU6GRkGrU%2B53Z07wfDdTW5H2suDB%2BGGG6R75EmlpIBflAkUUjSrVvUrUKy5XKQSfS9B11wtFC8XKirJfaveJXKZYS%2B19VWt/R%2BFN964Ou2OGnV12v0LwvTsmTDxC2CxWChdujSRkZFUqFCBXr160axZMz777DO2b9/O/PnzmTt3Lg899BCrV69m7NixnDlzBpvNRvv27SmjEob2g8rQK1OmDAUFBYwdO5bt27dTUFDAM888I%2Br4fD527NjBiBEjdBp7mzdvFkZS%2BfLlWbVqFR9//DGdO3cWHisVkpKSuOeee4r9/bBhw4SHzh9aopVz585RpUoVUZ6dnc22bds4ceKEMCAdDgfh4eGizsWLFzl58qROgsFqtTJ27FhmSYkvPB6PMPTCwsIC0reTE8qovnHZ7XbuuOMOwsPD8Xq9XL58WTfG0qVLc/PNN4vspHJYaPv27Uschz9WrVrFmDFjdD9fbNpkqCf/YSVfq0S3DWK/iv32N/SgGIF0%2BS9KuR3DX/lGbfGAhHxVnfsZesU1U5JOs1JNPgDBYvk2lQ6ywVKS/xozLITRsFPpzcuFKolIObFvQILfUiV5%2BGDUgZcNPVXTiiNgrCS995SvQXkSKnFxyRJRba9hrwLZzACEww0TVZ1ZecyymLdqX6TxqaYdgL63cf0CeGDkacqGHhjPnyKps/H8qdZYvlEWMldtplxHZWxI7SoDLOR2pL00GHpgnJRk6IFk6IFyzeUileh7CbrmaqF4uVBRSe5b9S6Rywx7qa3vnxl1YoZx/mb8s2ZrwsRvREkcurp169KoUSMGDx7MRx99xPfff8/GjRtZsmSJjkOn8uqpYLFYRFbLlStX8t133zFv3jw8Ho8IKQwJCcHn83HmzBkRdjl//nxWrlxJbm4uw4cPx263C77ehAkTxL0qVK5cWRildrudUqVKCZ5cTEwM//73v4XOngo5OTl8//33AEyZMkVnWGpGlsfj4YxfqjBNHN4f4eHhDBo0SMfZA4SkAxR6K6dNm2bQPZQhh8ZqYZn%2BKCgo4NKlS%2BTl5eHz%2BQgNDdVpGJ47d46LFy%2BKtmSDcdGiRSJLaiAwOXsmTJgwYeKagKkje03DNPZMmPiVkDl0%2B/fvJz4%2BXlk3Li6OKNVnUwViYmLYtGkT0dHRjBkz5v/Zu/K4qMr1/50zA8PAACIg7rjlkgqi5lKmRW55gTRDKXO5tl3XW1pmLtdsUTO7t2t6U7OSrqZFF8E1Um%2BJmqZg6FVztwQXBJRlYBhm5szvj%2BG8zXneF5iyRX%2Bd7%2BczHz1n3vPu5zDPeZ7n%2B8X06dMBALm5uVi9ejUAt2esZfVrxeeeew5tql/BV1RUoLy8HMnJyWjYsCFatmyJFStWoKCgAMuWLcORI0dQv359SJIEf39/xra5dOlS7N27F3v37oXBYGD5eC1atMD69euxefNmGAwG5ObmYjehMtfpdJg0aRIGDRqEyMhI3HXXXey7Jk2aMKOqSZMmaN68OQBwxuLcuXO5eSgqKlJJMijo3Lkz2rdvD4PBgC%2B%2B%2BAJxcXF48MEHmQHrSZizYMEC7N27Fxs2bFDVYTAYMGnSJHz44YdYuXIlu1YRVA8ICEBxcbFKmzA/Px%2BBgYHo3r07dDodHnjgAZU3z9/f/yd79zRo0KBBg4ZbHbnXBCEkvxU0z95NQ8vZ06DBS8TGxqKgoICxUFZWVrIcuhdeeAGDBg3C4MGD8VwddL6pqamYNWsWF1potVqF4YV6vV4Y3qmQkwBu48XhcECSJLRq1Qo2mw0pKSmQJAnTp0/H119/zeoIDg6uURPOZDLB4XDA5XLB4XAgMDAQdrsdbdq0wenTp1FVVQVJkuDn58d5ppQ8OM98uMcffxzr16%2BH0%2BlkRp8yRs9y/v7%2BQk%2BXXq/ncvb8/PwQHR2N06dPo3fv3pg4cSK%2B/PJLxnQqQkBAQI3EN579kCQJ0dHRyMnJQXBwMEaNGoXly5ezvrz22mtYtmwZLl26BJ1Op1ovSZLwxhtvICEhocZ%2BeEIkvTBp0iRMnTrVq%2Bs1aNCgQYOG3wKCFOTfDrVINN0UJkz4deq9BfHHMm01aLhJTJ06FWlpaUhLS8OXX36JrKwsJlBer149lJaWIjY2VsWM2b59e/To0QMTJkzAlWqFUoPBgIqKCtXH03DwNASpoQcAU6ZMQb9%2B/dCjRw%2BW0we4wyHPnj2L3Nxc9OrVC5MnT8aQIUNYWGZQUJDQqFIMsaFDh%2BLOO%2B9k9ZWVlaGyshLHjh1DVVUV9Ho9Vq5cqQpX1Ov1MJlM0Ov1TEtPwccff8y8ZorUgYLQ0FD2/7lz5wrF6CVJ4nL29Ho9Dh06hPvvvx8FBQV4%2BOGHsWnTplpDU319fWsMPVX6azabMXz4cDRt2hQ6nQ5ms1nFtGo0GpGVlYWioiIAaq%2BlMobY2Nga%2B0AhFFUXXE%2BFjjnhY0HOIvfOQKSWTHUJBfuCnqL2slBUnZDOiPSfue4IFIFp94SvJenYacWiDpKKhELSpHGRqDGtmuuf4OUCFUumGtfC/ohyUskcc0LSgmcGd%2Brbb/l6qZB0dW6sJzg5S9IXgB8np0oiENCmGuVUsBoQiKoL9g2tms6N6J0P1UL/4Ye6y4jqoW1z20%2Bw2ei6UG10gBe%2BFpWhe1QkbM6doxNKVbgFp4TSqnTzC25Wup70WHQv1P3wE%2BwJwd6i68ANk%2B57QVMXLwr6R4XWRc%2BbOjeF4By9yQTrUuc1ACc4L1Scp%2BfIwijzIFyf3wqaZ%2B%2BmoXn2NGjwErGxsZg8eTKXs6fglVdewbFjx3Ds2DGVgabT6eDj4wNZltGpUyeMHDkSs2bNEnrxFPj5%2BcFms3GeI8Uweeihh1RGVE25Yi1atEBGRgbWrFmDhQsXAnBLBBw5coS14%2Bk1Gz58OHr16sUMWBHi4%2BOxZMkStGvXDsCP3jeFKKVJkybIy8urdXwUYWFhKCwsBOD2PNpsNtavDz/8EH/%2B85%2B5a6Kjo/Hpp5%2By4w4dOtTKyGkymTgGUYqnn34aFosFaWlpnFEcEhKC5s2bs7mjMJvNyMrK4nIBa4Imqq5BgwYNGm4HCBmJfyu8996vU%2B9TT/069d6C%2BGOZtho0/IqIj4/H0aNH4XK5EBcXx3Lgtm/fjsDAQBgMBuTk5DAjpkuXLqyMkqMngkKMMnjwYHYuPT2dkYcoguBPPPEEHn/8cVbfI488AqPRqDKAdDodCkRvAD0Q6MFcFhMTo/LAKaLntH8KZFlGXl4e06xTvtPr9ZAkSZVPB/zowVQMPQAoKSmBzeMV9DvvvINdu3aprjMajTh%2B/DgzKF0uFxunJ/mK0WhUhd22bt2azVnXrl2ZeL2CU6dOISIiAlVVVWjTpo1qbGazGX5%2Bfvj73//O%2Bq3UDQAWiwU/iFwCNUAjaNGgQYMGDbcD9A6Bq1jDbQPN2NNwU6AhiyKhccBN55%2BYmKgSGr/iEd6QmppaYwhcbGwsUlNTverPzJkzOYHxmJgYJCYm4tChQ6zcO%2B%2B8w5VTPoqO2jfffKM6f%2BnSJbz%2B%2BuuYMWOGyjjJy8tDu3btkJSUxIyOLVu2oE%2BfPujTpw9mz54Nf39/ZuQpLJQ%2BPj4IDw9HeHg4du7ciatXrwJwe8YmT56sMmTy8vLw%2Beefq4yPCx5xRTqdDp999hmOHj3K6nzmmWeQl5eHvn37snw2vV6Py5cvQ6fTwWQysRBJpd709HRVzuG3336LkpISPPbYYwgKCoJOp4PD4WBePcDNuLlp0yYkJyezPDbFWPNk32zQoAEeeeQRAEDbaq5rkaeN5sIdPnwY8fHxqrH37t0bDocDL730Et577z3069ePfde1a1c88cQT8Pf3h5%2BfH2MDVTyGgJt5MycnB06nkxmmAJCZmYmmTZvC5XKp9qckSQgLC4O/vz8efPBBdo0S7qr0TdEW9AaaqLoGDRo0aNBQB7QwzpvGH2u0Gn4VzJo1i3mTdu7ciWeeeQaLFy9moYULFy7EwoULMWLECKSlpWH58uUoKCjA448/zgS/f0k8%2BOCDKo/Z2rVrERQUhIkTJ6rYHWNiYlTllM/s2bNV9SnnIyIi8Oijj%2BL8%2BfMYO3Ys55lJSUlBZmYmgB%2B9QGFhYYiMjMTixYtZue%2B//57r886dO9n/L1%2B%2BjKVLl7Ljxx57DJmZmWjYsCGMKlHkq7BYLCgoKIDT6URpaalKCD08PBwmk4nJJgA/eqJcLhesVivGjBnDjgHgySefRHp6uqodh8OBjz/%2BGGVlZSx0VBFvB4AXXngBAwcOxOjRo2sN3SwuLmZhl6dPnwbAa9UpoOdbtWqFZtXZ4f7%2B/oiKigLgfomwZMkSWCwWZtRlZ2dj9erVqKioQElJCTMoXS6Xas1kWYbdbmdSDnq9Hk2bNsWAAQMQEBCAqqoqVqcsywgPD0eDBg1w4MABjuBGkiTUq1cPd1IBtFogytnr23cgX/DLL2s/FuRnoXofMgiSfLj8HVE%2BB2VDPXiw9joAPo%2BFXAMAqJbmYBDk2Zw/T05kZPD10Lkg4xblfdE%2BCwhf%2BbSab77hynBdJt5nHD/OV0wTXwRrR28hYV4krZu%2BNBHl%2BZGKhRHN9NnkTXLY4cN8GZrQRscpyL2ip7zJORPlUnL7mC6UIOmM5uNxuU4QzJdoYej60rZF60IHzm18ANUvAmu6RHRS9FjgriP9E9ZLbyLRc4JcKFo7rj90b4mSIOl8ieaPLoxoz9JnEB1T9d8jFUiUhug5QedPNOferAv3Z5MUEv1Z9SJN0rvc7TryItmynDjBX6vhtoFm7Gm4aSgi47UJjS9fvhyJiYmIjIxEdHQ0li9fDofDgeTk5F%2B8P35%2Bfqw/4eHh6NixIxYsWIDS0lIcOHCAlfP0rHl%2BAokAq3I%2BMzMTzz//PFatWoWCggKOzr9%2B/fqIiIiAXq%2BH1WqFJEmoqKjAli1bMH78eABugpTS0lKYzWZkZ2cjJiaGhXMq6NixI6ZOnQofHx8EBgZi3rx5aNCgARo0aMCMHMCtN5eZmYn91ewHLpcLp0%2BfRkxMDGJiYtCjRw%2BUl5dj165dmDZtGgAwMfKakJmZicjISJXR6MmiqZyfOHFinetgMplUYZKVlZVe5/H17NmThX4CwLFjx3D58mUA7vBHxRhW6uvYsWOtBC2AOzxUCeOkUDyy4eHhMBqNuO%2B%2B%2B5ghCLjn4OTJk4iOjmaeT0%2BPoNPpRL9%2B/VTn6oJIVH3v3l18wfvvr/1YlEjRt6/6WKDCzInniqjWKGlOjx611wEAHTrUeg0AoEsX9bFAmZtoqgODBvH10Lkg4xboIHN9Fmljc5raPXtyZbguU9mNjh35iqmCsWDt6PsPoQA5rZsKUov2IalYpGGNFi3UxyL1buqR7tqVL0MFtOk4vRCfFu0tek4o/UX3MV0ojxdVCjhx7KZNuTLcfIkWhq4vbVu0LnTg3MYHQEL8RXrp9KToscBdR/onrJfeRKLnBLlQtHZcf%2BjeEqm10/kSzZ83ivP0GUTHJFJVJ4ReoucEnT9hTpsX68K98ySFRO9EvdBU58%2BJFpieI8dsWX5PT5jm2btp/LFGq%2BE3AxUa7969u%2Bp7k8mEd999F6NGjfpN%2BqN4aDxzrH4u6tevj/79%2B2PHjh21llOEwh0OBwsffPLJJ7Fv3z6UlZVBlmVUVFRwoYzt27dHaGgoZFlGeXk5C30tKSlRCZF7hksq0Ov1rF6n0wmbzcYEzgGoBMrr1auHe%2B65BwCQmJgIADhx4gQ6duyoIpgRGVF9%2BvRRzUdQUBCCgoIgSRKaNGnCDF6TycT0AEUQGX8ulwsHDhyA0%2BlUhTV6GqAU2dnZwrBIBcq6X716lbF%2BUu%2Bhy%2BViTJtK7p2SY6h4QgcPHsxCNavIW%2Ba6DGkKUc6elrWnQYMGDRpuNbiion%2B/xjVj76bxxxqthl8dv5bQ%2BM2gpKQEixcvRmhoKGd0/ly0adMG586dq/H7AQMGYNOmTVi/fj369u3LjM1OnToxiYIOHTpgw4YN6NGjBwYOdIfv6fV6dOvWDW%2B%2B%2BSYzdmRZRmlpKb7//nsWAqqQneh0OmaMxcXFYfPmzdi0aRMiIiIwZcoUtGjRAklJSfj3v/8NADhy5AgzYFq0aMGYJT0NFU%2BD2GQywWw2qwwjf39/pKSksGNJktgnLCwMly5dwmOPPQYAKC0txYULF%2BDv74/27dtzXlMFPj4%2B8PX19ZrJksLpdDLBdhEUQ7GgoAAWi0VF4uIJxcgLCwtD//79VUbvqlWrEBAQwHnvgqrfJNvtdpw6depn9V%2BDBg0aNGi4VeFBU6DhNsTNuzk0/OExb948vPrqqwB%2BFBofO3YsEhISsHz5cqF%2BmgiXL19GTEwMd74uunyKzZs3I6M6v8flcsFut6Nr16744IMPVH3JysoStvfee%2B/VaRQGBgZyIt1xcXHQ6XRwOp3YsWMHMjMzER8fj3feeQdxcXH44YcfsG3bNvj5%2BcFqtcJsNqNjx4747rvvkJGRgR07djB6f89cxkqBwI0sy2jcuDGqqqrgcrlQWVmJbt264fnnn8fx48fhdDqxdOlS%2BPj4QK/XI686B2XEiBGsjvDwcBw9ehSA2wj95ptvoNPpVO25XC4sXboUn376KT7//HM4nU5UVlbi7rvvxooVK1ium8PhQElJCUJDQ6HT6ZhxqRhLVqsVY8eORY8ePfD000/j3Llz8PX1Zd4xu0eegELQotfrERwc7HVeZ02SCI0aNcK1a9cQGhrKPKNBQUFYvHgxJEnC7t27sXbtWpXxN2/ePCQkJKgE7ZV8PE%2BPniRJKu%2BkaK1qgkbQokGDBg0aNNSBP5gX7teAZuxpuGlMnTqVeaaMRiPCw8OZ90gRGvcGDRo0YEaCJ0aPHv2T%2BhMbG4vnn38eDocDmzdvxoYNGzBx4kS0b99eVa5Tp05YsmQJd32EIKeDwmKxcEbsqlWrEBERgcGDB2PAgAGYNm0azGYzfH19sWTJEiQmJmLjxo0IDg5GeXk5qqqq8Pnnn6Nly5aqH/kpKSlo1qwZrl69Ch8fH/j7%2B8NqtXLGpZLDFhkZiaKiIrz99tvo06cPzp49i4qKCsiyDJvNBl9fX2a0GAwG5uXyDEM9WJ3ATsMqKysrMXbsWEY4U1VVBbvdjrFjx7IyFRUVbL2vX7%2BO6Oho5OTkwMfHhxlxLpeLEd8oY12yZAmmTp3Kza0ng2fjxo29NvY8x%2BaJK1euIDAwEMHBwczYKykpwTPPPMNdq6zp8ePHGYGPp3ePylbIsowyD2Fnb19sAG6CFkro0ry5IHfkyhV1bgg9tlr5vJWCAiA8nB26XIL8je%2B%2BU%2BfXkWsAt5avyvlusaiSVwSXuNWxPe81Uf/IudJSPtWmsBAIC/M4cf06UL%2B%2BuhA9d/Ei4OnhFVRss6lziugxAODMGeCOO2pt%2B8YNkrZFJ8Nu5/JfaFuCIvyccg0B2L0b8GCg5fpbXs7nQP3wgzoPSVRm/36gd%2B8fj7/5hs9XvHRJnfN0/jyfZ1ZZqcpf44YgaptcI5qbCxfU%2BXXC/UcvJHtWtJZXr6rT4kSaYtxWEnWwjgUWXVJRQXIP8/P5vMKyMsAzKoIeC%2BoW3fPcObp2dIJF/cnN5fP2qqrU%2BXSCxrk9QCdZMOl0OskWEZbBiRMAJcoihbjtl50NdOumuoTO5/HjgjTcgwdV%2BYB0qwHgn9eiQnRy6IOXexgK6qFrAPCTI7ph6DlyrOz7n/CnTcMtCM3Y03DTCA0NRSRJZlbQsWNHHDt2TPhdcnIyCgsLMX36dADuH9yRkZGIjY3FJcKW9fe//x2lpaUYN24cO5eWloZ169bh7NmzCAgIwD333AOr1YqAgABERkYiNTUVmzZtwsCBAzF58mSkp6ejaXXifXJyMmPKrAszZ87Exo0b2bEkSfDx8UFwcLCK3bNx48a4du0aZFnGf//7X3z99dfo1KkTpk6dyjyITqeT5WodOXKEeaOUHLjCwkLs3LkTer0edrsddrudlTcYDAgJCUFJSQmqqqoQGBiIsrIypvlWUlKCrVu3cv2nnqiAgABmOCpC7aLcOUmS0K5dO5w%2BfZoZjwo8ZSEqKioQGBgIl8sFl8vFcgTthNVLlmWEhIQwfUBP/b7hw4dj48aNnGfL09vWvHlzvPjii5g0aRKaNGmCS5cuqTxvfn5%2BbD0WLFiAQ4cOsXVr0qQJTp48yck6KFCMxP79%2BwNwy2N888038PPzU40jPDxcJW5PkZ%2BfXyMJDMWmTZuEoupduqhfSnAkD/RYxLRB/qALo2MpkQr3q5kYegD3F19widrQq6l/5JyIU4H%2BtuEMPdE5GsrrBTGNkGTG03CqoW2On4NOhoAMgbYl4kvg5lREBOJp6AF8f0VkF/RZJyrjaegBQmIajtxCRChCfpFzQxC1Ta4RzQ21Q4T7j15If6UK1pJKnIpINLitJOpgHQssuoRz8IteNtLwd0E4PK3bK8IOunai/GraHxFBCzUyBI1ze4BOsmDS6XRSQ09UhjP0BIW47UcMPYCfTxHfEiV%2B8YbExStGKPrg5R6GgnpE5DV0ckQ3DD1HjpV9b7r2A/8M%2Ba2gefZuGtoMavhVoQiNZ2dnq86Xl5cjOTlZ5TXxhKecQ0REBPr16%2BeVnMOePXs40pIZM2bA398f8%2BfP/9nj8JRz2LRpEyRJgtVqxeuvv87KZGZmYuzYsQgMDMSECROwYcMGtG3bFmPGjGHjDwgIqDMvzWKxICQkRBjmFxISwrTqFIPt5MmTtdYXEhLCPG8jRoxQMWS%2B%2BOKLbHwUsiyjsrIS6enpzEC64447WP4h4M61czqdKo9aREQEWrdujczMTC7fz7Ocp3fwP//5Dws5feqpp9h5zxy/bt26oX///rjvvvvYywDP/eMZ7jtr1iyVgX769GmYTCYYDAbWn%2BDgYPj5%2BcHPzw9msxlhYWHsZUBmZiYGDRqk8top8ByPXq9XCcVTkfbaoImqa9CgQYOG2wEl9X4nQw/QCFp%2BAfyxRqvhN4ciaD5x4kR89tlnuHjxIg4ePIinnnoKkiSpfth7wlPOwWAwoFu3bl7JObhcLpw9e1ZVl9lsxowZM5CZmYn//ve/7LzT6URBQQH3oWGD//vf//DVV1/h/vvvR0JCAsaOHYsmTZpg/PjxSE9Px%2BOPPw4AeOuttzBmzBhs374dffr0wZAhQ/CnP/0Jffv2xZtvvgkAeOWVV2C1WtG0aVOYTCa0atUKu3fvxokTJ1Ssld9//z0qKioQEBAAPz8/6PV6dO3aFZs3b2beJ1mWodPpEBgYiMjISLRv3x7BwcHcXN64cQNOpxN6vR5paWkoLi6G0WjEwoULVYaxIoNBhdvj4uKYsXfmzBk4HA5G4qJ4vTw9nPn5%2BTh37hz69u0Li8XCPI80H1DPhezY4HQ68d5776nOKQirfrv5t7/9TXWd0l%2Bn01mjIS3LMqxWq8ojV1JSgsrKSlRWVsJisaCwsBCbNm0CAIwfPx5bt25V1afIdih1SJLEchSVflwleli1QcvZ06BBgwYNGjT82tCMPQ2/OubPn48JEyYgOTkZCQkJeP755xEZGYl169YhRCgiJYY3cg69evUShtHFx8eje/fuWLhwIQtrvHjxIvr06cN9Ro4cqbr27NmzsFqtsNvtKC4uhsViQWhoKIxGI5xOJ65UC6taLBasXr0affr0YVIGiYmJiIuLw2uvvQYA2FUtvHzp0iU4nU4MGTIEDRs2hF6vF3qRlLw7p9OJw4cPo1OnTkyzr1mzZnC5XEzPr3v37mhI45E84HQ6YbFYYDQamQH2ySefAAD279%2BPXr16Ye7cuXWsglpvzxsoHqwGDRqoQiKpB1YET4/whx9%2BiC5duiA2NlZVxtPTWJeOn9PprLWMYmxlZmYiICBAFWo6YcIEFBQUsOtlWca1a9fYXLhcLpW0RV0Qiar36iUQVafizfRYJEZMcguFZajQMHlJArhTR1QgayZMp6T1eCHkK1Kh4LhuRLm/lCKOilhzA%2BA1mIXbcPdu9TGdT4AfF%2BmfaJvRW1zE58P1R/QCYeVK9TEVARcNigxcGFTx8cfq4zNn%2BDJ0zkWSI2S/cesr2I90OkUi1nR5Rbrm3HajCy6omE6xaPq4x7PoniIXcn0RbAquLdFNRRsXDJy2JXzM0ZN0QkWC6bRtUf/oHAsap3uAG7fgOUGXStM14ZEAACAASURBVPQI4Jo6fJgrQ6vmmCU95IlqukaQIQGsW6c6FG0Jbv68uDe5Y28eFKLnLJ0cwW8M7hyZdOVroYbgbwXNs3fT0Lm8VTnWoOE3QmxsLCZPnoyHH34YgNt79OWXX2LatGlYsGABPvroI0RHR9dpmKSmpmLZsmUqb15NbdSEzp07M6/aK6%2B8gtzcXMyZMwd5eXlo2LAhioqKYLfbodPpEBkZyVhAs7KyMGrUKKxbt05llN5zzz24fv06ZFnGyy%2B/jEcffZR9N3v2bKSmpsJsNqO0tBTt27dHr169sHbtWjgcDqahp9PpIMsyfH194XQ64ePjA4PBgEcffRRpaWmMRCQoKAj9%2B/fH5s2b0aZNG3z33XeQJAlGoxFGoxETJkzAwoULWfvr169H165dcejQISxatIjLtTQYDAgMDGQ5d7XhT3/6E7788ksmrREeHo6zZ8%2Bqrh00aBCbLwq9Xo/w8HCVp8xgMECv13NGoiJUX1MunZKn5%2BvrC4fDwZVRcjB9fHyQlJSEF154Abt372bkMYo3MioqCr1798ZK%2BkPbA5mZmV4R/ADAokWLuJy9KVOmYvLkSV5dr0GDBg0aNPwmEJAC/Waofin9i4O82P//jD%2BWaavhtsG8efMQExODmJgYREVF4cUXX2RyDmVlZT9ZzoF%2BFCbLumC32xEUFIQDBw4gPj4eEydOZJ68Vq1awWg0MnISytRIcfbsWRQWFsLf3x96vZ6FTXpCp9Nh8%2BbNaNWqFU6dOoU1a9Ywz1FgYCAiIiJYGOO8efMQFhYGu90Om82GzMxMxnwaERGBxMRE7NixA3a7HXPmzAEAJvJuMplw6NAhAGC6cY8%2B%2BiiioqIwZswYnD59mvVHgcPhYILiCpRQxACS8f7FF1%2BgoqKCaQNmZWVxRqKSe6hA6Ye/vz9cLhcmT56M4OBgpiM4fPhwTJs2jXnylFBSKm6%2BYcMG9n9JktCzZ08YjUZUVVWpNAS3bdsGX19f%2BPr6olGjRrBYLEzHMCMjg3lUFdy4cQNnzpxRhcrSUMzd1CNUCzRRdQ0aNGjQcDvA7vc7GXqA5tn7BaCxcWq4JSGSc1BYLSsrK7F69Wp89NFHqmvmz5%2BPhIQE1bmblXMwGo3Iz8%2BH0WhEbGwsYmJicNddd8HhcKBt27bo3bs32rdvjzNnzqC4uBjbtm3DkCFDhHUp/fX390doaCguXLiAS5cucaF/DRs2xNatW5GcnIyPPvqIGaZ2ux2bNm1iBsn8%2BfNht9shSRIn6J2fn4%2BUlBRG4jJr1iwAbuPNbrfj8ccfx8cffwyj0Qiz2YyioiIAbsPJ19cXUVFRyM7OhizLzNiSJAnDhg1Deno6M7AUg4XKQkRHR%2BPChQsoKiqqMY%2BOeugUbb2IiAhcvHiRGagKNm7cCLvdDpfLBZ1Ox9g3/fz8UFlZCX9/f1gsFiQlJbFrZFlmuXaAmiDGc52UOVWMtaysLPj7%2B%2BPee%2B/F559/DgDIzc2Fn58fevXqhYyMDBgMBs5g81ZmBNBy9jRo0KBBw%2B2B3/VP0x/MMPs1oM2ghlsSipxDZGQky2mbN28e0tLSEBcXhzZt2iAtLU31iY2NRXJyMt566y1WjyLnQD%2BeHp7a8PTTTwNwG1pbtmzBq6%2B%2BiqSkJJw8eVKVK/bEE08AcJOHWEjMe1ZWFiZPnozt27cjICAAe/bswYQJEwCAM1idTieioqLQrVs3/OMf/8Dly5eh0%2Bmg0%2Blgs9lUIYJbtmxBRkYGNm/erCJBAdx5bGVlZcx4%2BOGHHwD8mNP2z3/%2BE5cuXUJVVZXK4%2BZyueDn54cLFy6wa2VZZh7BlJQUFrYqSRISEhJUxDIKcnJyGNGNIslAkZmZqTpW2snNzWUeNZ1Oh%2BDgYAQGBqJ///4qyQdlnktKSlSe3vT0dDYfkiShb9%2B%2BANzGVV1smcr3hYWFiIiIUPVRkbpQZDI8iWoU/FSdPZqz16OHIGeP5p1RDzLxbALgcj4EqWtcboYoh4sooHD1epPKxuWfCAoJ0gX51BZR%2BDA9RwYqGpM3eVRc8g03EeCTkEhfvEhVFPaPjpvLLwL4XCviURa%2BcyDjpFWIOiRK4UL1ixEFJSV8Ee4cyf0TOrVpfqBg39BlEEaU00I0n1G0ael6iyqmkyra13TgNOnMm00hmvT8fPWxaAz0OeBNW97kfVG25%2BPH%2BTJ0I4vuKdo/ugEFzzG6DMJ7gV4nWjuaZ0jz1ATjpqdEU0NPiqaGptt58Tjk7g9Rqh1dOlGOK70VvEnZo7dCHQFLGm4TaMaehtsGDRo0QGRkJB577DGcOnUKhYWFKgNOp9PVKufwczBp0iR0794d9erVYx6uyspKzJo1S5XTNmrUKPj6%2BsJisXBC7cnJyTh//jxKS0tx9913AwAGDhwISZJU8gAK7Ha7SvvOYDAwD5wnlHG3bt0aK1asAPCjMad4wBSEhYXBaDSycEvFMydJkqpcgwYNUFJSgkLhX1U3FANM8a6VCH7tKbmEtclMfPrpp6pjJR9R8niL17ZtW5jNZpSVlWGbiAiiesx2u50Zfw899BCTYZBlmYWreoq/1wSFQMZqteL8%2BfMq9lBlzvbs2cPOORwOVX%2B9DQ8G3Dp7L7zwguqTnbWTL0g1l6gukkhfiejYcXp5AKfTJJoajm%2BG1CuSbeK0xEQ6e6RQmzZ8EU4/S0TmVIc%2BlWhMXP9Ee5QKcYmId6hnlvRFpKlGz4n6R8ctktjitLuI7p5It5COk1Yh6pBIUg0tWqgOBQTA/DmiAyhwavNagYJ9Q5dByO9FC1XLqTCINi1db1HFdFJF%2B5oOnL788WZTiCad5gGLxkCfA9605Y3wI9XNFAnO0Y0suqdo/%2BgGFDzH6DII7wV6nWjtqLYizT0TjJueEk0NPSmaGvp%2B0YvHIXd/iFLl6NKJ3jPSW0FUDz1HbwVlq4neKf5m0MI4bxp/rNFq%2BH%2BBnyvn8FNx8uRJLFq0CM2aNUO/fv3w9ddfY8mSJejUqRMAMM0/wJ2zNmfOHLhcLqxfvx779%2B8HAKxevRpfffUVY8nctWsX7rzzTtx1112QZRklJSUqxklJkiBJEu677z784x//QGpqKhYvXgy73Y577723xr726tVL5Wn0hE6ng8Vigc1m48ItKTtlUVERjEKVad5r5XQ68emnn3JSFQCQlJQEp9OJu%2B%2B%2BG5IkYcWKFfj444%2BxdOlSNk6lbwpcLhdkWVblxClGfV1wOp245557hN8phl9UVBQLZ60JlZWVyMrKYuGrnn2x2WwwGAxo164dO6eQ5ShQDEtvoOnsadCgQYOG2wGBBoFLUsNtA83Y03Bb4peSc6gN586dw4cffshISUJCQhAfH49169ZBr9dzRsjIkSPx5z//GQCwbNkyAO5wwH//%2B9/49ttvWTklrFExdFavXs2%2BU7yTFosFs2bNQlJSEt59913Mnz%2Bf8xhSeIqU33XXXQDc4bAulwtt2rRBQEAAR6RCQY0/vV7P9O0Uz5ksy2xOlHDJhIQElYdr/fr1cDqd2LdvH2RZxl/%2B8hc0adIEHatffU6fPh2AO9xUEU5v3LgxoqOj8eyzz6o8cA899FCtfQbcHrbo6GgA7jxLaqjpdDq89dZbKvbVt99%2BG2%2B//Tb%2B%2Bc9/4tlnnwXgNtoVY16WZS4Hz2w2MyIZACrvq3KNBg0aNGjQ8P8JJVUCl%2BRvBc2zd9PQCFo03HIQSSVQSJKEcePGYdy4cTWWefjhh2uUVvCmjRbV4Ur/%2B9//MHPmTOTl5aGwsBAbN26ETqdDjx49sHfvXtU1M2fORHZ2No4ePcqOKysrUVFRgb59%2B%2BKFF15gZZ1OJxITE7Fnzx4VWUn37t3x/vvv19ivnj17qshYALcX0ul0wmAwwOFwMA/T9evXYTAYOBkFT1DJAk92S6fTyRm1JpOJecuUfxUxcsAdCnrt2jWkpaVh7dq12LhxI06cOMG%2Bz8zMZLIGdrudtXf58mVcu3YNq1evRsuWLXG2OpFr%2B/btNfYdADp16oRjx45hy5YtAH4kflHCS5Xx5ebmonnz5uw6xcCjY/PM6%2BvTpw8OHTrEvHD%2B/v6qHEez2czYUAEwY9YbaAQtGjRo0KBBQx34gxlmvwY0Y0/DbQe73Y4VK1YgLS0N%2Bfn5CAsLw6BBgzBlyhSYzWaMHj0aV65cwdatW1UhiXl5eXjggQewa9cuNPXI48jNzUX//v0ZYYaCjh07om3btjh9%2BjSef/55dl6n08HX1xedO3cW9m/RokWIi4tjP9z/8Y9/AHAbOQrhh4%2BPD8tBPHv2LLZv347y8nI4nU5VmKAnFAMvNjYWlzwyrw0GAyIiInDp0iXmEVu6dCmuXLmC9957r84wyJoMDMVIUnTqFFgFGeaPPPIIGjdujKVLl7KwzsTERJhMJjidTsTExABwG482mw0%2BPj6MRVPB3r17YbPZcPDgQbz00ksA3HPtKTZvNBo5Fs%2BT1QQCFy9eZOdMJhOio6Nx4MAByLKM6OhodOnSpdZ5AMBYSQGgfv362L9/Pyft4BkKS8l4mtL8oFqQkJCAO%2B%2B8U3WurSjf9MYNdR4KPXa5%2BByZ0lJVjpHdLsg5ofXYbFwiiNVKckzKy1VJHZWVfE4KLl4EPIxqeo2wYkEHubYrKvhkFjr2ggJ1PpOgXnpKMGy%2Bz1VVXG7QlSsk7YjOp6i/tF5ukIIxeFGGNo2zZ/lESItFndgjGBPX9rVrXB4kN6WizUXP0ePjx7kEp/x8dWqaaF3qnHNRW1QfrLCQS/6iUyy6pbimRJuftk3uQ2HF9JxoTHQMoj3hdKpz57xZl6tXgeoUgxrbLilR5yJev87nwNW13qJx0gUWXUPnT7B2XD2idaH3HZ2rM2e4nFE6xaIp5%2B6XH34AIiNVReh0iaaPVkOXgS6BqB66lKIydDpF52jbyrL8rqLqGm4amrGn4bbD4sWLsWXLFmbY2Gw2pKWl4dy5c1i9ejW%2B%2B%2B47lJWVYdmyZSxcEAAT6L569Sr7UT5p0iR89dVXANzeqR07dqjyyDp06MC1L0kS/Pz8MHXqVLRv3x49e/ZUfd%2B6dWs89dRTWLlyJcvVo7Db7SqDbe7cuWjdurVX4/cUGgfcoYSK50khp3nttdeg1%2BuF%2BXTeQjECRUyaFPXr18eoUaOwdOlSJm%2BgEKcAYBp3nmGPSUlJWLNmDTv3v//9Dy1atEB6erpqbJ6ghh7wI0mKp9Fqs9kwePBgHDt2DBaLBUePHkVOTg7aU7IBAs/2rl%2B/rgpNVb4PFjFSVIMaf7Vh06ZNnKj61ClTwPWQ/viixyIyBPIXXZjOSesR5GpyP26I0SYkN/U09ATXCCsWdJBrW8TqQcdOiSu8IF4QpqjSPgvIIziCEzqfov7SekVsDXQMXpThItdFjDeUwUFE7EPbFjD7cFPqDREIPRZ4wCkHiWhd6pxzUVuUgULA8kGnWHRLcU2JNj9tm/6yFlVMz4nGRMcg2hP017g360L/Ponaps87aqmI6hW1TcfpDTkMnT8RQwutR7Qu9L6jc0XJgcBPsWjKufuFGHoAP12i6aPV0GUQ/cmh9Yh%2BatAyIuImeo62rSyL2X4DwC%2BTIvOToXn2bhqasafhtsOGDRsQFhaGBQsWoFmzZsjNzcWcOXOwZ88eXPOgXl%2B9ejWGDx/OwjFFmDdvHrKzs%2BF0OlFaWoqnnnoKcXFx7Pvly5cjJyeH6a%2B5XC4UFxfjxRdfxIkTJ7Bs2TJh6N60adMwbdo0AMCDDz6InJwcbN%2B%2BnYUWVlZW4uuvv8aqVavQpk0b/Otf/8K5c%2BcwYcIELF%2B%2BnOWf1YS4uDjMnDkTgNvY%2Bc9//oN33nkHgFs%2B4BqhoO/Tpw%2Bys7OZV85oNCIkJARFRUXMIAPcGn9Xr15FUFAQ8zT6%2BPioygwZMoRjxly1ahVWrVqlOrdy5UqsXLkSBw8eREBAAFJSUiBJEoYOHYoOHTpg/PjxWLNmDSuvyFEo/auqquIIUDxhNptRUVEBWZah1%2BtV5WRZxquvvsqMtyZNmqBLly6qEE3P3DvFwF%2B%2BfDk71ul0CAgIUHkWrVarSv7Cx8cH9erVQ1FREWRZRpCQBlEMjaBFgwYNGjTcFvgJf9s03HrQzGUNtx3sdjvuuOMO9OzZE02bNkXv3r2xcOFCAD%2BKZgcGBsLlcrFwwJpQWlqKGzduYMSIEfDx8cHmzZtVcg4Kw2V4eDjCw8PRoEEDtG3bFo8//jgAd2iot2jVqhViYmIQExOD3r17Y/r06QgJCUFpaSkiIyOZoVCvXj3WnucnIyMDMTExcDqd2L59OwYOHIiBAwdiyJAheP/995mBUllZyQhDmjdvDoPBgH379qFVq1Yqb%2BjVq1dVRhwA5gkMDQ1lXsIePXqoyniGS9aGJ554At999x2b56FDhyIhIQEWiwVZWVkYNGiQKszW398fq1atQu/evWGz2eDv76/6PioqCg0aNIDJZILZbIbFYmEGnkKq4glPL11eXh7KysqQlZXFzlVVVaGqqgqSJKFz585YvHgxevfuDQBMAqKMiBBJkqTyLtrtdhQUFLB%2BeMMc6jleCi1nT4MGDRo0aPCARtBy0/hjjVbD/wsYjUbs3r0b999/P%2BbNm4eMjAy0b98eW7duRXh1PET79u3RqlUrHD58uEZ9NuBHUfP4%2BHh07twZFy5cUIVXipCfn8804mrzGtYGp9OJjIwMFBcXo5FQ8IqHzWZThVYqIuROp5Pp6vn5%2BSEsLIwZvRcvXoSvry%2BCgoJQWlrKhSWK%2BgUABR5Kqvv27VOVoeQwtUExJn18fFh/FSgeOQUVFRV4%2BumnsX//frRs2RLl5eUq0fKjR4/i2rVrsFqtXLikQojjifT0dKSnp2PkyJEAgJdffpkZn55Q5Bb%2B%2Bte/4vz58%2By8w%2BFQeXn1er2KPVSEn8IEKxJVH1itw%2BgJGhnKRYoKwmypMK5QEZiG%2BIpCUEndXBEvhJtFotu0aZEOsjd6z1SxmEsnFVXsjVoyMfK5%2BQTqnBtBxLFXY6L62cK2qcA3fQEjUm6m%2BaAitWTaIVHjJGqAikYL%2B0fWQRjtTPsjWDv6bk0kHk/rpqLRov7SdRFtGzql3qwv1z/RgpN1EdVLl0EUWU/vM5FwPR0DnSvRtqHnRPXSPov6R%2BedXiOql45JlJXACYUL9jVdB64twaTTakR7jQ6qooKvh577OWV%2BrXq9KlO9Py3W3zFpTzP2bhpaGKeG2w5PP/00li5diqKiImzYsAEbNmyAv78/5syZgzYeuSpvvPEGEhMTMW/ePPTt21dY1%2Beff46AgAC0b98eI0aMwOHDh/HRRx%2BpPIIigpGAgAA0bdpU1V5dUOoAwIhJXC4Xjhw5gnbt2jHDZ8yYMSqPlsPhgNlshtVqhb%2B/v4rUBPgxj02n08FqteLjjz/GsGHDcP/99yMnJwdmsxm5ubkYPXo0kpOT4efnx3msPMcKqHPPKJGK0WhUeQTNZjM6deqExx57jLFs0nFSKB5IipCQEMyfPx%2BzZ88G4PZ%2B1dRXUb8VMhmdTofQ0FDMnz%2BfkeJ8/fXX6NWrl2ocgHsNHA4HZFnGvn370LRpUxaSuWPHDlUbwcHBTENRBF9RHlQNEOXsTZkyBe27d1edo6lWnHiuIA%2BIS5MT5cPQhA6RKi%2BpmyviRV6QN/kmIhvZm5Qemp/D5dV4k9MlqpgY9ELFkjrmRpRz5s2YaO6asG0aUkXzJL3J6RIJc9MOiRoneXzCvM06EoFEW63OxCXw%2BuiiyDJaN303I%2BovXRfRtqFT6s36cv0TLThZF1G9dBlEqX/0PhOljNIx0LkSbRt6TlQv7bOof3Te6TWier1JF%2BTevQn2NV0Hri3BpNNqhFGMZFD%2B/nw99NzPKfNr1etVmer9KbxnNdw2%2BGOZthr%2BX2DSpEl488030alTJ%2BapqqysxKxZs1QSA507d8bw4cNhsViEGnVHjx5FSUkJ7q72pgwcOBCSJGHjxo0A3KyXn332GQC316miooIZVs2bN8eyZcug1%2BuRlpaGxMRExMTEoE%2BfPnjxxRdx5coVAO7wQSXEVKlDyTOTZRkhISHYu3cv9u7dy1g7FU%2BdLMuw2WyoqqrC9evXYbVaWZilLMuorKyE3W5n%2BWZK2OKwYcMAuNk/LRYLgoKCoNfrodfrYbVavTKePEGNMiXXTBGA/%2BijjxAYGIjXXnsNkiQJiWZ8fX3x2WefYdOmTUzrr0uXLhwBzo0bNzB16lTGgOkZFjlw4MAa%2B6gYWZ5z0a9fPxw4cAD%2B/v7w8fFBVVUV1q1bx12rhGwCwJEjRxi7Z2lpKcfE2axZM5Z3KQINi60Nopw9DRo0aNCg4VZD9U%2Ba3we3qGfPZrNh1qxZ6N69O/r06YMPPvigxrJfffUVHnroIcTExCA%2BPh67du1Sfd%2B9e3e0a9dO9SkXhnX8PGjGnobbCidPnsSiRYuQkJCADRs24Ouvv8aSJUtYzlZaWpqq/Isvvgh/f39s2LAB586dU32XnJwMANi1axfuvPNO3HXXXZBlGSUlJcjOzgYAREdHQ6fTYcOGDVi7di1mzJgBvV6PcePGoUOHDli4cCEWLlyIESNGIC0tDcuXL0dBQQEef/xxXL9%2BHY0aNcKUKVMA/Egsk5iYyBgdu3XrxnLyQkNDAQDt2rVD9%2B7dsWDBAsiyjDlz5gAAUlJSsHfvXuh0OhiNRgwdOhSpqal48cUX4evri4SEBABgoYeK0XjmzBk0atSIMYN6hiHGxcVBkiRmJFHR9cDAQLz11lsA3CyoJpOJhWL26NEDOp0OHTt2xFtvvYWSkhLIsoyYmBhm8Ck5jxaLBQ8//DCGDh2K8vJyyLIMHx8f9j2FyGj64osvhGVfe%2B01pKenIzQ0VBUmarfbYbFYYDQa4XQ6MWzYMFV4qs1mYx8l7PXEiRMoKCjgDGjP%2BVDWSafTwWQyITg4mJVv3LixsI8aNGjQoEHD7QqhB/8PjsWLF%2BPYsWNITk7GvHnzsGzZMnz%2B%2BedcuZMnT2Ly5MkYPnw40tLSkJSUhL/%2B9a/sxXJ%2Bfj7Kysqwc%2BdO9vJ/7969wrz%2BnwvN2NNwW%2BHcuXP48MMPmUh3SEgI4uPjsW7dOuj1eo4gIygoCH/729/gcrnw9ttvs/OyLKuE1V0uFwv/A9xMnlevXsWRI0fgcrmQlJSEcePGISUlBc2bN8cXX3yBrKwsJCcnIykpCZ9%2B%2BimGDh2KSZMmISQkBDabDcnJybhy5QpjyUxKSkJhYSFSUlKYd2jnzp1cjqBiQOzfvx9RUVHM%2B5WYmIg%2BffrA5XIxuYnt27cjODgYdrudCZsr4uKAO89MISIZOHAg9Hq9yrO3ZcsWyLLMjBqqoVdWVsbkK2bMmIHAwEDmBbvzzjuZAWk0GnHvvfcCcBuZ5eXlkCQJW7duZcbirFmzkJaWxgzKS5cuqXLyPENXFX0/p0c%2BywcffMB58ADgX//6F5KTkzlyE0mS0LNnT1y%2BfBnR0dEYPHiwanwmk4m1qdT73HPPwWq1Mi3Ff//736o6W7ZsyYw9l8sFo9EIq9XK5i%2BCxuDVAo2gRYMGDRo0aKgDt6Bnr6KiAikpKZg9ezY6duyIAQMG4MknnxRGD23ZsgW9evXCmDFjEBkZiVGjRqFnz57Yvn07APfv2vDwcDRr1kxFyqcTxUP/TGjGnoZbFrGxsSqXtuJBAoCnnnoKmzdvRl5eHpYuXYp%2B/frB6XRiz549sFqtLNwyLy8PM2bMAPAj0%2BSoUaPQoUMHVFRUIDg4mBF5pKenY%2BPGjfDx8cGePXsAuMP2lBvO4XDgwoULuHDhAnbt2oUnnngCRqMR69evZ569goICbNmyBQUFBVixYgUeeOABNp6UlBTo9XrUJ4kH/fv3x7Bhw1hu2f/%2B9z/06NEDX3zxBYqLi1FcXAzALZSuePaU%2Bp5%2B%2BmkAbsOjSZMmjHFTMcgUY2natGlYtGiRUKeuLpirg/Xfe%2B89vP3228wgWbt2La5fv46UlBTk5%2BczCYrdu3czL9%2BlS5eYwXTvvfciMjKS9a20tFRlzNG%2BUa/f%2BPHjWVilp8ctPz8fn3zyCfR6verhKMsyvv76a9SrVw%2BjR49WMXECUO0Tpd6lS5cyMhmHw4EnnnhCdU39%2BvVVYa3FxcWqUM%2BadBVFEBG09O0rCFWla0aOhZGjNPxDwLzAheWIKqLsopS5wgsyBCFDCy0kihGi4xbkeHJdJmQXIg6SuuYTEBB0iJgraOOkHsqHAvDEFaIyoCRCgrbpuDgSWFHFhI3j7Fm%2BCOFewQ8/8GVyc2utFoAXxCSCvUa3rFAilC6MiBmYhqmTtoSyoV7cL9w2EYVh0/6RY9Hj1xu%2BIDrJwn1DQs65Y1HlZJyiueHOCQrVdR8K%2B0PnWNRfupYilhSyDkJ%2BtTpYeoQhirR/dOOLrhMsHj0lmhruHH3WCeaGdk%2B0drReUVRgnVu/eg%2BHVNROXPer4lcy9q5du4bjx4%2BrPlS2qiacPHkSDodDxcXQrVs3HDlyhHtpO2zYMDz//PNcHcqL97Nnz6Jly5Y3MUF1QyNo0XDLQiQervywLisrw6JFi3D9%2BnUVq6PBYIDT6cTJkyfZd7WhYcOGaNu2rercgAEDsG3bNkiSpGKLpKiqqoIsy3jwwQeRmJhY53jq1asHwG3keEKWZZw4cYJ5K51OJ1555RUAQElJCSZNmgTAPR/hHlnjVVVVqrDLS5cuMYISijlz5tQqjh4cHIyWLVsiICCAY99Ucsvmz5%2BPzZs3Y926dRg1ahTq16%2BP4uJizJkzB3q9nuUKWiwWtk5//vOfAbi9bPHx8ar6LBYLGzPwo3GnhHBGR0fj0KFD7HpZlhEYGIiQkBA0a9YM%2B/btw8SJE/HJJ5%2BgqKgIhYWFkCSJG2dxcTGmTZvmFXmK3W5nxq1nXxRYLBYMHjwY69evF15fU1iqCGKClqno0oXIqlPyAHIsbNIL8W6OBFZUERUxpswVXpAhCBlaaCERI60XYslcl8n9KuIgqWs%2BAQFBh4i5og51dtGjg76onD4z3AAAIABJREFUFT5eSB6rqG06Lk5rWlQxYVgQcUtRDXWBRjSaNau1WgBeEJMI9hrdsiIyDm5hKDENwDN2kLaEL8u9uF%2B4bSIKsaL9I8ci8hVv%2BILoJAv3DX2%2BiZ53tHIyTm8030WF6roPhf2hcyzqL11LEUsKWYcmTfgidbH0CAmxaf/oxhddJ1g8eko0Ndw5%2BqwTzA3tnmjtaL0ivqU6t371Hi7ya4JQ/vLbGp988gmWLVumOjd58mSWelMbCgoKEBISovpdERYWBpvNhuLiYtVLfcpjcObMGezfvx9JSUkA3J49q9WK0aNH48KFC%2BjQoQNmzZr1ixqAmmdPwy2NuLg4Fr%2B8a9cuFlIYGBgIg8HAjLmOHTti7dq1WLVqFcxmM%2Bx2O1asWIEmTZqw60ePHg3A7TFMSEhA69atmfSCJzzFvS9evKgyHkwmExo3boy%2BffuyH/aKF1DBlClT8MEHHyAtLQ179%2B7F0qVLAbiFvRV06dIFDRs2xH333QcAGDx4cJ0u%2ByVLliAmJob1RzEIv/32W1bGYDDA19cXBoNBVR/1epmqn%2BhKvl55eXmNxqAyx3l5eXjppZdYvmG9evVw8OBBrF69Gg8%2B%2BCA%2B%2B%2Bwz6HQ6lXcrPDwcI0eORHp6OtLS0pCWloaoqCjW1wULFrCyhw8fZrmXoaGhzNBT%2BqCwiEZHRzMjPC4uDvPnz2flapvDcOEvfzUkSULz6h%2BQ9evXx5NPPqn6vry8HKdPnwbg3oPUuMvJyamzDQVighZNVl2DBg0aNNxa%2BAlE0788fiXP3siRI5Gamqr6KFJNdcFqtXIvkJVjSuzmievXr2PKlCno2rUri/w6f/48SkpKMGHCBPzrX/%2BCn58fxo0bx0lM3Qw0Y0/DLQ0/Pz8Wv9yoUSMMGzYMISEhkCSJ5bKtW7cOKSkpuOuuuxAdHY09e/agfv36zOOnXK94oOrXr48333wT27ZtY942T7Rt2xanTp2CTqfDwIEDWejo22%2B/jUWLFqGgoADx8fFMd43ekGazGffccw86dOiA8PBwDBo0CKdOnVJ5CX18fKDX69l3//znP5lQ%2B6pVq3Dq1CmYzWb4%2BvqyGPBFixYhLS2N9adZ9ZvG3bt3A3ATu2zduhWpqakYNmwYwqpf95vNZnz77beIiYmBTqdD48aNkZ6eDkmSMHDgQGRkZGDbtm2qB5ckSRxZC%2BAmSRk5ciQkSUK3bt1gNBpRVFSEixcvQq/Xs7xHJRevrKwMn3zyCeLj45kIvKKJ53A4kJKSwmQufH19mU6dwkzqCSV8cvPmzTh48CAb%2B4oVK1gZz7BQs9nMQiwmTJiAmTNncnVKkoSQkBBmgI4ZM4Z59oqKivDee%2B%2Bpyl%2B/fp0Z92VlZZznrzamTgotZ0%2BDBg0aNGj4fdCgQQN07NhR9WlAwxtqgNFo5Iw65divBjabwsJCjB07Fi6XC0uXLmUpLe%2B//z7S0tJw9913IyoqCkuWLIHNZsOXX355E6NTQzP2NNw2sNvtLI%2BtYcOGyMnJQVhYGLoTXTKTyYTVq1ezfLabwc6dO5kx8uyzz%2BKll17C2LFjER8fD5fLBbvdDpvNhpiYGGZYKOygHTt25DxDtUHJD/vqq68AuJkxq6qqmIj5rFmzMHToUNYfhTFUYZg8c%2BYMhg4dihEjRmDTpk0oKioC4M5R9DQ0r1y5gqFDh0KWZezYsQNDhw7F0KFDVQ8uWZaFnidZlhn5ypQpUxgb6R133MGMpNzcXMZuuXHjRubZFOHgwYOqhGbFeOrZsycGDRoEAMJwWsXwe/PNN5FPVairYbFYmNfz3XffZcnQdDw3btxgBqjCzgm4DXL65k6WZRWRDIW3fyiAGkTVBfISVC%2Be048X5IlUOx9/hOgNIUnOEOYKEW/vmTPke4GMBy3jEanL8P336mNh22RdubbBp0hxcyNI2uMc2F6Iqovapjk8R47U%2BjUAPh9G1DSXMiIoRMdN825ETmMuX0eUm1JHTpewOyIFcpJzRFOtRCmQXq0LTU4URCPQJafrIFoXOhfCMqQ/XJ4k%2BKmgeZGifC06TFGeJM0N86YeYQ4cWRfu0SmYc5oDR4XthdcJOkjXnK6TKJ%2BMXiPK4%2BTerwnG4KHIBICf42pSRBXoo41klYg7KNqz3iRl1lXGm2tEmrZ0EKKk0bpymKv/dvyuSkG3IEFLREQEbty4wX7rAO7fYn5%2BfggShBvn5%2Bdj1KhRqKqqwkcffaQK8/T19VW9XDcajWjatGmNv21%2BDrScPQ23NDZu3Iht27YBcLvNfXx8oNPpMHr0aMyePZvLt1Nw5513/iLt9%2B/fHwMGDMD06dPh6%2BsLl8uFjz/%2BGB9%2B%2BCELe9TpdBzlv4J9%2B/YhJiYG3bp1w%2BrVq2ttq1GjRtDpdIydU2EWVcI1q6qqEBoaCofDgaqqKkY849m2n58funTpApPJhKysLOTn52PHjh3YsmULGjduzDxvnmyXVVVVeOONN7BhwwZWjyRJCA8Ph8Vi4bRedDodHA4H7rnnHjidTnTr1g3btm2D0%2BnkBNjj4%2BPRsGFDPPHEE0yqoqqqSsUIerGaZOHRRx9VSSP87W9/Q0ZGBmO8VGAwGNgDVpZljoG1JngKpEuSpArBrKqqgsvlQkZGBjv36KOP4uGHH8ZDDz3EzoWHhzOvogh33HGHV30BxDl7U6dMQfv26py9aqdjjceiPBHuthAlVpHkDGGuEAmL5YbHKRrzZUS3YosW6mNh24TZVDS1NEWKmxtB6C4X6euFqLpwWUkOT3R0rV8D4PNhRE1z7wsEhei4qRNelE7GOepFLybqyOkSdkekQE7ebNPfPqIUSK/WhSYnCsK26ZLTdRCtC50LYRnSHy5PEvxU0LxIUb4WHaYoT5LmhnlTjzDujqwLRx4smHOaA0eF7YXXCTpI15yukyifjF4jyuPkUoIFY6jODmCgc0weuQD4R5uQe8ubG9qbpMy6ynhzjcibRAchelFZVw5z9d%2BOX1AF4KfjF9DE%2B6XRoUMHGAwG5OTkMIdDdnY2OnfuzDx2CioqKvDkk09C0SX2TClxuVwYMGAAJk6ciIcffpiV/%2BGHH9CqVatfrL%2B33gxq0ECgCJAr4YHjx49HQkJCjWQkvyTMZrMqB83lcsHpdMLpdDK9O39/f0ycOFEVTgiAhXnKslxrDDeFZ95ZvXr12ANAr9fDZrPhRvXr41atWjHBdsCdt7hixQrYbDYcOXIELVq0gE6nw/nz57n2FakJwB1OOX36dBw6dAiHDx9W9cNgMKBN9S8WpV9BQUHw8/Njx9nZ2bBaraiqquIE2B0OB/Ly8vD%2B%2B%2B/j%2Beef50JeH3nkEaxduxZ///vfcfHiReRWv1KPi4tjnrgQ8gvK802aMhZPNG7cWEjG4hk2qQjWKx%2BljpMnTzJP6kcffaQy9PR6PYKCgjiCHU8ECoyfmiDynGoZexo0aNCg4VbD7%2BrZuwVhMpkwdOhQvPzyyzh69Ch27tyJDz74AGPGjAHg9vIpv4dWrlyJixcv4o033mDfFRQUoKysDDqdDvfddx/eeecdfPPNNzhz5gxmzJiBhg0bol%2B/fr9YfzXPnoZbGgMGDMC0adMAuF3b4eHhLKQvMDCQY%2BxUkJycjMLCQkbo8ktg0aJF6NSpEy5fvoy5c%2Bfi0qVLsNlsGDFiBLZs2YJ3330XgNu7c%2BPGDdx///146qmnAIhjuD11/gA3AYrL5ULT6lenigdu%2BPDhSE1NxZo1a9C9e3cMGzYMJ06cQF5eHpKTkxlRjZ%2BfH6Kjo7F8%2BXIMHjwYbdq0QXZ2NlwuF8slUwxkkQRD165d0ahRI2zduhWyLLO5Va5VDKLS0lIYDAaYTCaEhYXh%2B%2B%2B/x7x589C/f38MHjwY9evXR2FhodCY8QyTBIBt27YhIyMDFotFZSg9/fTTjHjF19cXUVFRLNTSE2vWrIHJZFIlVZeUlMBut8PHxwcGg4F5hMPCwthY%2Bvbti4kTJ0KWZZw6dQqvv/46HA4HJEmqMe/O6XQiPDwcJpMJFRUVLLzU4XAwzT1RnqMGDRo0aNBwO%2BN3TSe/BT17APDSSy/h5ZdfxtixY2E2mzFlyhSWitGnTx8sXLgQDz/8MDIyMlBZWcmxtg8bNgyLFi3CCy%2B8AIPBgOnTp8NisaBXr15YtWpVrWzwPxWasafhlobZbEakKK4FbuPkiy%2B%2BQHZ2Nrp168bOl5eXY/ny5TVe5y0aNmyoqjciIgKRkZGIjIzEBx98gCFDhkCWZWzcuBEvvfQSevTogQEDBsBqtcJkMmH69OmcV6phw4Ysl45CCemLjY0F4PYklZeXM6IVxcOoeMf27t2LqKgo2O12nDhxgoX/mUwmvPvuu3C5XFi/fj1kWWbx4Xq9HgMGDMDs2bPRt29fVQjokSNH8H11MpViuFy/fp158BRjLzw8HNeuXYPBYEBJSQlcLhdefvllvPzyy6r%2BAW4SmatXr8JutzP5BE/o9XosWrQIkyZNQrNmzXD8%2BHEAbpKabt26Yffu3QgLC8MHH3zASFQ8MW7cOHTt2hX169dnOooVFRUsn1IJqQ0JCUHbtm1x7tw5AEBmZibTNQTcxrjD4UCzZs1Y2GrTpk2xdu1a1du19u3bq0JIPcfzUzUMNYIWDRo0aNCgoQ7cosaeyWTCG2%2B8wTx2nlAihADg888/r7Ueo9GImTNnCknkfincmjOoQYMXGD9%2BPAC3F%2Bizzz7DxYsXcfDgQYwfPx4WiwWdO3f%2B1dpu3rw5nn32WQBuz9OaNWuQkJAAwO1xXLp0KRwOB3PXFxQUqIwBu92Ofv36qUTj165dC0mS2EPCZDJBlmVMnToVADBkyBB06dIFFy9ehMlkwtmzZ9G5c2c2ztTUVGzfvh25ubkoKCjA9OnTIcsyfHx8UFhYiKysLDgcDmzfvh19%2BvThDIvo6GgmO2Cz2Zjx5BnyCYCJjlqtVlYGcDNo6nQ6hISEMEMmNzeXGVyyLMPX11elY1dWVsZ0BBVDr1mzZvD19WUso9nZ2ejVqxcAsbTCuXPnVDmAotBeq9XK6hdBCbc4c%2BYMzp8/D51Oh6KiIjb3CoxGI%2B69994a6/kpVMkigpbu3QWi6jTJnhwLI5lJ8r6IQ4OSKAi4VvjriEErGi5Xj4g4gPRPxD/A2c7eCJuTMsJxeyHWzjUlUmenbdPJEAyKWysR0wbptGjYtCKuXlHYOBm3SEiaToVo/ug50drVOX%2BCiAxuKrwRuxcRGFAWD3qRN6Q9go3N9e9niJYLr6HnRJEFXjDc0OkSPhfISdpd4fsqOheCiA2uO94wyNBjbxTdBfXSIsL7pY57U3QNNxde3M%2BiYdOlEz0O6ZTS7oqezVzjXhA5iTIQ6DnaltK331V6QcNNQ%2Bf6tZOeNGj4mbjzzjsxbNgwvP766zWWmTNnDjZv3ozg4GAUFxfD398fkiTBaDQiNTWV86x5UydFVlYWRo0ahXXr1rFE3NjYWEakArgJPyIjI3HhwoUa6wkMDMQDDzyAHTt2cKQnCvR6PZxOJ3x9fWvN89Pr9WjcuDH%2B9Kc/oXnz5pg3bx7sdjtCQ0NRXFwMl8uFoKAglJeXIy4uDhs3bqw1x7FRo0Z46aWXMG3aNC4nDnCLrlssFpW0gSeMRiMkSVIRqYighH%2BWCf96udG6dWv89a9/RVZWllAHUdT2T/WqeUKn07FwzMTERFy4cAFHjhxBQEAAiouLVWX379%2BPxYsXY%2BPGjcK6lixZwsTj68KiRYuEBC2TJk/%2BeQPRoEGDBg0afgXcuCHmYvpN4KG5%2B4virrt%2BnXpvQWiePQ23LGgYpQivvPIKnnvuOQQHB0OSJPj6%2BqJfv37YsGEDZ%2Bh5W6foGs9/FcyaNQtvv/02AGDatGl45plnaq1n7NixKCgo4IypwMBAdO7cGfPnz8f27dvRvXt3dOvWjeWwdejQAUFBQfD19UVERAQCAwPhdDphs9lQWloKnU7H8ucCAwOh0%2Bmg0%2Bnw0EMPISwsDDqdDj4%2BPkwMXYSrV69i6tSpNRqDUVFRCA8PR1xcHBo0aIBXX32VEccAbhH09PR0dlxTrLnn%2BaCgIERFRbF8RkXo/dy5c5g6dSp27dqFgIAA%2BPn5MW%2BgJEmsnAJq6FGhcwWeVMfKnBmNRmYYA25x0xYtWsBut8NisXCsWko%2BoNKXNWvWqBhhfwpVskbQokGDBg0abgeE1NP%2BOt3O0Iw9Dbcs/vvf/6oMChEkScK4ceOwefNm5OTkIDMzEwsXLkQExyntfZ0UTZs2xalTpxhxiuLVW7BgAZ599lkYDAb85z//QUlJCfr06YMHHniAace1bt0a/v7%2BCA8PR15eHubOnYuQkBD85S9/QZMmTTBx4kQ4nU6cO3cOb7zxBoYOHYrDhw/jwIEDzPv13HPP4dChQ9i1axfy8/NhNpsRFBSEtm3b4vjx4/j%2B%2B%2B9RWlqK2NhYPPPMMzCbzTCbzczb9t1338HhcCAoKAhGoxHNmjXDpEmTWEjk8OHDkZGRgXvvvRdNmzaFwWDAm2%2B%2BCZ1OB0mSMH36dJw5cwZXr17Fli1bcO3aNcydOxepqalsji5fvqzyaIk8gH5%2BfggNDWXGWWlpKY4ePQqn08nkFRSCk8GDB6OsrAzl5eWorKxk4ZGyLMNqtTLq4mYCjnRZltGuXTv4%2BvpyQvEKXC4XrFYr7HY7QkJCUK9ePfj5%2BeHUqVPMeyvLMiPYUbBv3z72EkGWZYwbNw6nT59mBuhPoUrWcvY0aNCgQYOGOnAL6uzdbtDCODXUCRqyaDAY0KxZMyQlJWHcuHHsfFpaGtatW4ezZ88iICAA99xzD5599lk0qhYJSk1NxbJlyzgWSqWNyZMne2WIzZw5UxVGJ0kS6tevjwcffBDPPvusKifs8OHDWLlyJXJyciDLMjp16oSpU6cyAfSjR49i7NixwnYU79mXX36Jxo0bs/MdO3aEw%2BHAjBkzkJCQAIfDgX379mH27Nlo0qQJWrdujczMTEiShFdeeQU9evRAcXExZs%2Beje%2B//x6SJMFsNqOiogKTJk1i7E0OhwMHDhzAggULkJSUhLVr1yIgIADjx4/H5MmTMWrUKGRlZXH9DAgIQHl5Od555x1YLBa8/PLLNYY1Dhw4EMOHD8fcuXNZ7h2FwWDAwIEDmb7hz4Ver0dcXBz0ej1SU1Ph6%2BuL8PBwXL16lTMGFUZRT2NHkTkIDQ3FW2%2B9pdprPwc%2BPj4qDUQlJ9Jms0Gv16N58%2BYoKSnB8uXL8eijj8JsNjMjU/GWPvLIIzh9%2BjRycnKEbZw4ccJrBq0TJ07gLFFdtlrbYuTI9uScWs6JHtvtvOSSzUbkkkSFvKnI5VJpmVVWEjknp5PX1KqoUIky0WYERXD9ukBDi1zIjUnQP24IJSWcEBdXhtQBwK3w7MGsSvsrOknLiK6hYxCNqaBArUFWWsrr1HGDIIW4dRLUK1w7ck4YupWVBVS/EBH2BeDmD4WFKmE64ZiuXlWLmQkmhzZ19iyvZUf7TNvi5gGCPSoaU1mZSrdMVIROKV0Hb67xZt%2BItiytW7QHaD10meixsM%2BCQdBTov7RcdJrRHNDzwnvQ1royhVOmLDO%2BRNU7M29Sis6eVKg2ZeXpxYntFh43VO6aemx6AFJNza5x7yuh15HjtkQ/4%2B9846vokr///vW5KaHFIqRANJ7CEWlKEVZULOAi7hKsaCsFBsqUgSVVRD1Jwu4CCgrfAFBihgQAUURUIqodOmQACEECJBebvn9kczhzpkTuNaVdT6v17xg5p45bc5M5pnneT4fxbP0d8MPP/w29Za/B/4ZYLJxmggIo0aNonv37sBlo2T06NFERUXRo0cPJkyYwPLly3nmmWeEcfOvf/2Lvn37snjxYl0I3a%2BBbt26MXr0aKDMG5KWlsbw4cPJz88X2nNr1qzhmWee4aGHHuLpp5/Gbrfz4Ycf0r9/f95//32Sk5Nxu91X9KZYLBZ27doljL0zZ87gdruxWCxkZ2cLD5Pm0Tl16hQdO3YEoG3btoJqd8GCBZw9exaXy8Udd9xBz5496devH2%2B88QZvvPGGrs2goCBhXIeGhjJ37lymTp2q7F%2BjRo0E8ciwYcMMfde%2B5URERLBq1So%2B%2B%2ByzCsNNa9eujdfrJSMjw2DoxcbGkpubK8THrwaLxUJoaKgutLNKlSp89tlnNGnSRBh7LpeL4uJiXWhrw4YNKS0t5dChQ0CZ0XjTTTcJofczZ878LI1F/zYqVapElSpVOHLkCKGhoURGRnLs2DF69%2B5NgwYNsFgs5Ofni3Z8Ph%2BVKlXizJkzug8fvwQqUfVhwx4H9G8LsqEUiI6v4aVEVSiQiqQ3NoOCiMqwlV6aVALa8gub8vEgnajSA5b7ZxiC4uXEUEZB%2BiO/8SoFhaWDchnVOVfTLwajIWIwisA4CKmQSl/ZoC%2BvunbSMWWOjr%2Bhp%2BoLGC0G6SVUOSZZtVoxOXJTsqEHxj7LbRnmAcUaVY1J0tBUFZGnVL4OgZwTyLpRLdlANLbleuTLpFKOMfRZMQj5kKp/8jh/jm648j6UC8kK9AQwf4qKA7lX5YpU4uwGFXrZ0APjopX3VQ9IeWHLhl6g9cjnSfvaEAudkSge5SauEZjGnomAEB4eLgwbKNMHWblyJWvXriUhIYE5c%2BYwb948EQKXmJgo9N7mzJnDU0899av2Jzg4WNefypUr069fP2bOnMmECRPIy8tj7NixPPbYYwwePFiUGzlyJBkZGbz%2B%2BussXLiQxo0bk5qaWmE7I0eOZNeuXfzlL38BYOvWrTidTnw%2BH7Nnz2bBggUAgpikZs2agqFSCzHcvn27mJ%2BQkBBiY2OJj4%2BnUqVKZGZmkpSUxHPPPYfT6SQ6Oppjx44xbtw4oMyQzc/P56GHHmLevHk4HA5atWpFeHg4n376KTNnzqRDhw4646moqAir1cqIESN49dVXgTKvVlxcHMHBwVSpUoVRo0bx%2BOOPExQUxPDhw%2BnevbsQgO/SpQugJy7Jzc1VEqH4G12NGzdmz549QFmYpCw%2Bnp6ezpkzZ2jZsiXffPMNsbGxLFiwgGXLlvHOO%2B9QvXp10tPTuXDhgghB9Xq9ZGVlkZSUhNfr5Ww5k57WL1VfHA4H1atX5/Tp07q8OP%2B%2BZmdnk5ubK4TqMzIyALj//vtxuVxYrVY8Hg/BwcGCqTM3N5fCwkKuv/56XT%2BsVqvwGO7cuZMWLVpUuJ78ocrZM7P2TJgwYcLEHw0upwf49XTffhL%2BZCGXvwVMY8/Ez4bdbsfhcLB8%2BXKaNm0qDD0Nmt5brOqL028Am80myDm%2B%2BOIL8vLy6N%2B/v6HciBEjxAu80%2Bm8oh5f27Zt2bJli9jXjD2Px0N8fDz/%2Bc9/cLvdjBgxgj179jB48GDeffdd4DINf0Xzk5WVhc/n4/vvv%2Bfee%2B/FYrEQERFBcnIy/fv355VXXiE7Oxuv18vs2bMBKCkpYf369UDZ/G/ZskUXEqkZnR6Ph3PnzonjDzzwAMuWLWPkyJEAQlKguLiYV199lTVr1lC/fn1atWolmEIbNWokjDdNDN3fYKpUqZJg/gREWYB69erRunVrPvzwQ5o1a0ZeXh779%2B/n%2B%2B%2B/F/ltTqeT1atX88477wBlxiDAaYkTPiwsjGXLltG1a1e8Xq8gddm2bZuunNYPt9st9PT8WU0jIiLIzc3F4/Fgt9uxWq1YrVbq1avHmTNnKCoqYuPGjTRo0IDQ0FDy8vLEuXFxcYIQp1GjRnz//fdYLBY8Ho8uPHTr1q0BG3smTJgwYcKECRO/NUxjz8RPRmlpKV9%2B%2BSVff/01r776KnPnzqVZs2bKsg0bNhT/f%2B2117h48SL16tUD9Ll//rha7l9aWhr79%2B9n4sSJQJn3a//%2B/cyfP5%2BioiKWLVvG4cOHqVWrli5/T4NGtCLn/kEZaUbt2rV57rnnaNWqFS1btmT69Omiz/44deoU3bp1w%2BfziVDQZ599Vvz%2BySefYLFYdKQfJ0%2BepHPnzsq58vl8vPXWW4wbN07kNXq9Xmw2G7Vr1%2BbgwYO0bNmSunXrMn/%2BfFwu1xXFOrUQQavVyrp16%2BjTpw%2BRkZG4XC7h2WvWrBm33XYbb7zxBj/%2B%2BKMuNNJfdkAzaPx/z8/Px%2Bv1Cg/clClThBF54MABoRf4bTltst1u14XzZmdnM3PmzAr7ryEvL48ePXqItvft26fLoVThvvvuY8GCBTr5ipo1a3L06FEuXbqE2%2B3GZrNRUlLCvn37sFqtTJ8%2BXazj2NhYXC4XFy9epLi4mJiYGPLz8ykpKcFuv/zYdDgc4sMBoCOEuRpMghYTJkyYMHFNICtLGSL7u8D07P1imDNoIiCMGzeOpKQkkpKSaNq0KSNGjGDAgAGkpKSQm5urNKoqgsvlwuVyYbfbOXXqFBMmTBB5UBMmTGDChAncc889LF%2B%2BnLfffpuzZ8/St29fnYB3QUGBrj9/%2B9vfqF%2B/vpAXCLRP3bp1Y9OmTWKbN28eERERDB48mLy8PJo3b47H46F%2B/fosXboUu91OfHy8kAQYPXo0S5cuFfVt2rSJOnXqAGXetd27d3PkyBGCygP%2BNc9bSkoKVquV4OBgRo0aJdpv27Yt999/P3BZQNzj8ZCeni68gBrTZ15eHp999plou3nz5owaNUoYHFqYo9frZceOHYwdOxaPx4PNZiMqKgooI6ipW7cuISEhFBUVibBUKDNMNZQqBFs1o0czUDQZCv%2B%2BW61W6tati8/no7S0lP79%2B7Nu3ToAHcumfJ4M/5BHTUfxSvAfh4YffvhBJ8vgnyvo8XgYMmQIHTt2ZPv27eTk5HDmzBkRtpqXl8fFixdxOBzCo6iNyR%2BtfoJuj0pUvVMnhaj6xo1X3Fdy8cjnKBS0DQK7J04Y65HFr7/7Tr%2Bv0l2UPK5s3WosIxMNff%2B9ocjOndKBzZuN9fitUcAwbpUWuixGrJo/gwxkudfZHwaeqU2b9PtpacaKAxCTD0ScHSlv1HDpFPmssgSmUjJT1gmVCISgjMRDBwVZkaxrbtAJ94us/WP/AAAgAElEQVQ60HD0qH5fJfpumAuVFqncuKxirTjHII%2BqUL4uTyG%2BDFUYttw/eQEGIsQu919xTCVnalgmAYiAs3%2B/fl%2Blui2vR4OyvbFi1T1lqFrujEptXD5JsW4Mk6GqZ9cu/b48JsXzR177yvtF7o9q/qRx/hwBd1V6uvyMCmRNqB4l8jG5L%2BJ3xTPwd4PJxvmLYXr2TASExx9/XLBGBgUFERcXJ1gHo6KiDPlZFSEiIoIlS5bojo0aNYrdu3dz/PjxgHP/goODWb58OVBmdMTExBAcHEynTp1%2BUp/k3L%2B4uDheffVVOnTowJYtW%2BjSpQvx8fEUFxdz6NAhmjRpQlZWFg6Hg5o1a3L%2B/HkyMjK47rrrOHXqFHFxcTRs2JBDhw5xxx13sHHjRjweD5s3b2bbtm3C8/fAAw8Iz98bb7whDCWN/AUgKSmJHTt2YLfbdeGZGjRPl%2BZZczqdQmfP4XDwxhtv8MQTT%2BB0OrHZbIIBNCMjg4ceeggoM66efPJJiouLqVOnDk2bNmXx4sUBXUvNcNKE4I/6va1pffN6vcLDVxEiIiJo1KgRmzdvxufzERwcjM1mo0GDBuzYsQO3243VahU5he%2B//z7Dhw%2B/av8qVaqk%2B0Cg9QfK1rDb7cbj8Yi%2B1q1blwkTJlC9enXOnz8PXJ7bkydPEhwcTGRkZIXrymq10qRJk6v2S0NFBC2NG0tZ/u3bX3FfaffK5yi%2ByBpIMhQyFgbSDFmjUkXy0bq1fr9NG2MZmeRDEfpqCBa46SZjPTL5gTRuFRmHTKqgmj%2BJiwOqVzeUKX/UXEa7dvp9VXi4RCahlISUP1KpCl13nW7XcOkUH01kEhIlaWzNmvp9BQOKgcSjeXNDGZkHwsCTowjtl1VLlE4EeS5UnnS5cZmpRHGOPGwVu0n5N7zLULGFyP2TF6Cqv/I5KhIN6ZiK9CgQIhXDspAZRVTMOfJ6VLH2BHBPGaqWO6NilAmEhESeDFU9TZvq9%2BUxqULvpbWvvF/k/qjmTxqn6tpdjVBL9Q1UfkYFsiYCIcGR%2B1KBbK2Jawx/LtPWxM9GTEwMiYmJJCYmUqVKFR29vD8jpIw5c%2Bbw5ptvin2r1Srq0bawsDCsVivff//9FXP/NI%2BXXM91110nhLn9%2B3T8%2BHGD5wjKCFOGDh1qIPjQoBkxmueqWrVq5Ofns23bNtr4vbjWrFmTHTt2sHPnTur7/dHUwjRvu%2B02hgwZgs1mY9%2B%2BfTz99NMiNDIyMlKItDdv3lyErvrne3Xv3l2ESUKZ4du4cWNat25NtWrVBAGMfxkNFouF68pfCEtKSkhOTqZOnToUFBTg8/mE1/OFF15g%2BfLlDB8%2BnNzcXJ1Wnj9iYmIAdPqF2pxrBmhERMRVZQc0plJ/5OTksGXLFjGG0tJSWrVqRcOGDUXdmkRCaWkp9913nyGvTxszXDY0NfIUDVarVcx/cXExycnJPProo3To0AGAvXv3snjxYiGroHkkoSxvsEaNGoSGhuq8jzabTXhSvV4vu%2BQvyFeASdBiwoQJEyauCSg%2B6vxuMD17vxh/rtGa%2BE1w1113sWvXLr6TQrzy8/OZM2eOUmAbyl7q165dy9dff01wcDCnT5%2Bu0DPSsGFD4uPjA%2B5T%2B/btCQ8PZ968eYbf5syZQ2Zmpi73SsOlS5eYNGkSMTExwuisVq0aBQUF7Ny5Uxh7I0aM4NFHHxWaa/7GXqNGjYAyQyosLExIVlSqVEkIcsNlgfDt27fTo0cP%2BvTpw%2B7du0WIoibnoOWEpaWlsWfPHrZt20ZGRganT5%2BmZs2aOJ1OvF4ve/fuZdy4cZSUlBAaGsqkSZOESPkPP/zAzp07hfGiGT2vv/46Xbt2Ze/evaJODVWqVBGGTWlpqZCb0FAkhcvk5eWJa60Zhy1bttQZz1ulkD5NuF3rV40aNbBarXTu3JmaNWvi8/no3r27MDIfffRRNm3aRITiC6rP5zMIp2uePIvFInT8IiIiuOWWW/jxxx%2BZOXMmGzZsIDY2lkqVKrF582Z27txJWFgYx48fF9eiQYMGZGdnEx8fr9PG83g8Ii/QYrH8JFkGM2fPhAkTJkyYMPFbwzT2TPxiJCUl0bt3bwYPHsySJUtIT09n27ZtPPLII1itVh555BFR9uLFi8rcv5CQEAoLCwPO/fPP2fPfNAr90NBQRo0axdSpU5k8eTJHjhzhxx9/5IUXXmD9%2BvWMGTMGgBUrVohzmzdvzs0338ypU6eYPXu26EvVqlXJy8vjyJEjDB48mIyMDMaNG0efPn0oKipi9%2B7dOmNPQ9%2B%2BfZkzZ47QrDt27Bh//etfgbJcQc2w0kIFY2NjSUhIYNKkSQCsXr1aGC9169alXbt22Gw2ISVhsVho06YN4eXxHBqBiM/n48KFC2zbtk0wa7rdbvF/7VxtHn0%2BHz/88AORkZE6L2xmZqYwwnJycgw5apFSbFbNmjVFLqAWBrl9%2B3ZxjsfjoUaNGrpz/A0yr9fL8ePH8Xg8fPrpp0IvcdWqVZw5cwaAmTNn0q5dO0MopeZR9DeW/NlI/YllcnJy%2BOqrr8j1S3o4d%2B4c2dnZHD16lBUrVmCz2bBYLCJn7/z581y4cIFatWop8xe1NhLksMIrQJWz17KlMWfvqnkXqv7IiReqPKCsrCvvK%2BqWq1Wlx8i5asrpkg8GkFMYUD1ycksA%2BUXKb1FSfwx5aopjhv4pKpbnS5VnY0iNUUQnyLlWcnqg8puDvAY2bDCWkcWLy/ODdZA7rcjZky9DecT9ZSjyf%2BT0QOUY5AlUXF85TU6uR7Uk5NRPVfqqnNcXyG0nXzpV7pU8JNVak4MAlHm60npTtWVYkvJ1MCQmGudLGcUeQC6qXI98nQK6vxWFDPm1iosnL33D48aQtIlhklVF8GOgBtT3qty4KqJDPk9OclVNunwfqsrIkyznYKuOyX8Hyuc8t%2BTKefK/KUzP3i%2BGxfdTlYlN/OnQqVMnhg4dSq9evSos4/V6mTt3LkuXLuXEiRNEREQIFk3NK9OmTRu8Xq/I2fPP/evUqRNWq5X27dsLjbmK8Pe//519%2B/Yp9fH69evHk08%2BKfq6fv16Zs2axcGDB7FYLDRp0oQnnniCpk2b8vzzz5Ofn88zzzyD2%2B1mxYoVLFy4kLfeeoub/PKDpk6dyrvvvkutWrV0JCQAkyZN4ujRo7z44ov079%2BfAwcOCMbNdevWkZCQwH/%2B8x%2BmT5/O4MGDWbhwIceOHSM6OppLly7h9XpxOBzCWNGMC00vzufz4Xa7eeCBB9i9e7cwRrp160Z0dDRjx45l4MCBQFn4qcfjuapIfFBQEB6Pp0Kj5adCk2No0KAB8fHxvPXWW7Rs2ZKaNWty6tQpQkJCyM7Oxm63ExcXJ4zcrVu3EhUVxcGDBxk%2BfDgHDx4Eyozfr776isLCQmbOnMmlS5f4/PPP8fl8BAUFERYWJoxJDXa7HZfLpTPgAoXmGXW73YwcOZLdu3ezc%2BdOTkgvDRaLhe%2B%2B%2B47k5GSloLvdbmfPnj0VkszImDhxojJnb%2BjQIT95DCZMmDBhwsRvheLiCvLDfw8oPkL8KjAk4v7v4s9l2pr4Wfjiiy%2BuaOhB2Qv6Aw88wIoVK9ixYwcbNmxgwoQJuhyv0NBQRo4cqcz9%2B%2BKLL%2BjQoUNAuX%2B9e/fW5RD6b3Jo5q233sr8%2BfP59ttv2bZtG%2B%2B99x5N/ZK1Q0NDSUxM5IYbbuDJJ5/k9ttvZ%2BjQoTomSoCmTZvy0UcfGdp7%2B%2B23%2BfTTT3VlExISOHDggPDyHDhwgLp16/LAAw8IDb4lS5YQHx9PREQETzzxBKmpqaSmpjJlyhRhLGiEJADvv/8%2B3333HRcuXKBdu3bk5uaSnp7OK6%2B8InLlSktLRXmXyyVCKTU5BKvVSmhoKEVFRRUaev5hpgsXLtT9JucFzp07FygzgqpVq0ZUVBT79u0jJCREkKO43W5q165NeHg4nTp14r777hN1aR7JunXrcsstt4h6vV6vuC5dunQRAuYaVOQsQUFBwtBTMXUOHjxYZ4RNmDCBZs2aERISgs/nE%2BswJiaGkJAQTp06xQ033CCO2%2B12nE4noaGh3HDDDbq6tXqrV68esKEHZs6eCRMmTJi4NhBUoHCJm7hmYBp7Jv4w%2BLm5f78mnnvuOUJCQnjppZd%2B1vmdOnWiXr16YmvYsCEff/yxzogC%2BMc//kFmZiY5OTm8%2BeabdOvWjZSUFEaMGCEMhuDgYOEN69OnDzExMSxYsECESnbs2JGwsDBh7G3atAmr1Uq3bt148cUXsVgs9O7dm8cee0wYjhphzS233MKqVavYtGkTFotFGMkXymNtLBaLYOzU0L59e5LL2RibNWvG0KFDgTIvZNeuXcnJyeHixYtMmTKFc%2BfOceHCBdxuN9u2bSM3N5e1a9dyqTw8xev10rBhQzFPmhGsYcWKFUBZzt/BgweFQeZwOHQhnA8//DBQtka0eXO5XFitVsGyarFY%2BPjjj3XeuKNHjzJ%2B/HiWLl3Kiy%2B%2BKAzZ/fv3i/w%2Bu90u8ur8jWnZq6jVe0wZ51MxzJw9EyZMmDBh4iowwzh/Mf5cozXxh0ZSUhLVqlXjvvvuo0mTJjRr1kywc2ZkZLBgwQJl6ObPxaFDh0hNTdXl/LVv356LFy%2ByYcMGYUhAGUHJ2bNnDZtM7e/xeHj88cdZunQpkydP5oYbbiAuLo5169YJqQiAjIwMwsLCuO%2B%2B%2B3j55ZeFx83tdgvDKzs7m5KSEs6ePcuiRYs4f/489913nyBXyczMJDc3F7vdTklJCe3atcPr9bJ27Vree%2B89HnnkEcaMGcO9995r8OR99dVXdO/enXbt2uHz%2BfB4PLz99ts6Hbrp06cLAyo0NJRt27YJ0pjExERhDPp8Pv7v//6P/fv3M2jQIGbMmCHquO6664Tu4KJFi/jb3/6mvBZyWOS4ceOoV68eLVu2FB4wj8dDXl4e//73vwUD5nvvvWeoIycnB6/XKzyC1atXN7CEzpo1i5SUFLp168bo0aOFtEVqaiqx5XTarVq1EjmGbrcbr9fL4cOHhUEs46d49UCds3f7rbcaysn5WPK%2BKu/LkKKnEpyTvbuqiqTkFsPQFfXK1ahSAQ3HVG1LIbmqIcj5TYZ0HUUeiyECV5XJIM2NKtVFHoTctiqiWB6matjHj0sHFElcV8ujUi5R%2BXordAsNDn85mQ2MA1MlmUkVyWl9qpw4Q56SKjFNWo%2BqInIa2o8/6vdlaTnAsAZU11tuK5A0Kvk%2BDCTPT/XNKJC8Qzn3T9W/q60TVQ6u3B9V/66WOgvGVDX5fg5EJ041JsNcKArJ8xfI/SIfC%2BTaGQUlMQ5UlT8tNybr96keolc7B4wXSyVeKR%2BT97X%2Bq2RDTFwzMHP2TPxuCCT3LzMzkw8%2B%2BIDVq1dz%2BvRpwsPDadGiBQ888ACxsbHExMSwdu1apk2bxhcGVePA2tDw9NNPU1JSIrTv5N8uXLjA6tWrmTFjBtOmTVPWUb16dT777DO2bt1K//79r9pmzZo1r%2BgBatu2LePGjeP2228nOjoap9NJp06dWLRoES6Xi9LSUjweD%2BHh4fTp04etW7cSHBzMiRMnrsoEGRERQWFhIXa7nTvuuIMVK1Zw5513snTpUnr27MnEiRNp27YtBQUFXH/99VgsFqGRV6dOHdLT00Wun8PhwOVyCS%2BbzWbD5/MRGRlZoTEUFBSE1WoVhpWM6Oho4uPjhSdv6tSpREREsGXLFqZPn06NGjVIS0vDarWSkJBAWloalSpVIicnxyCj4XQ6BUsmlBlisbGxwgC02WzYbDaRq6chNTWVffv2MWbMGBISEjguvXmvX7%2BeWxUGmYZ9%2B/ZdVX5Cg5mzZ8KECRMmrgnk5ytENn8n/MSomYBhENj834Vp7Jkw8StCMzbHjBmjDDutVKkS119/PbVq1WLt2rWEh4dz/vx5wsLCsNlsFBcXM3/%2BfPbu3cu0adPwer14PB4h/p2bm4vH48HlclG9enWaNGnCgQMH%2BPDDD6lXr16F/UpOTiYzM5Nz585RWlpKcnIyzz77LN988w2TJ0/G4XDwySefcMcdd/Cvf/2LXbt2MWvWLDweDxaLhV69evH111%2BTmZmpM2asVisNGjRg//79BrZOGRMmTCA5OZnbby9jnHQ6nURFRXHu3Dm8Xi%2BRkZEi787r9fLggw/y/PPPc%2BLECbp06QKUMYBeunSJrl27smbNGmbNmsX69euZL7EG2u12nREXHh5%2BVfKWevXqkZqayqeffsro0aMFUylcJqHZunUrPXr0UOr8AWzevJlKKlFkBcaOHcuiRYt0x4YNGybCY02YMGHChIk/Av6rBC1yOMuvhcTE36bePyDMME4TJn4j3Hnnnaxfv55XXnkFu93OCy%2B8QOXKlYmKiuLjjz/G4XDw%2BOOPs3r1at555x2SkpIoKiri6aefFnVcunSJ8%2BfPEx8fz6RJk1i9ejXR0dGEhYVx6NAhcnJydHmOFouFjh078t5771GpUiWsVqtOHy88PFzkhTVr1gyXywWUhSkOGDAAr9fLzTffTOfOnfF4PCKX7eLFi2RmZhIaGsqdd94pDDaLxcLJkycZPny4yCX0R9euXUVo6Msvv0yPHj3Eb5rYuxa2qrGTav376KOPAIS%2BombgOhwOoWU4YcIEg8EEevH29u3b8/zzz%2Bt%2Bb9y4MWFhYYSHh9OmTRtatGghQjYrV65Mfn4%2BLVq00JHSBAcHExERQd%2B%2BfXV1%2BZMChf63vnyaMGHChAkTvxGC8s5fvZCJPyxMz56J/0kMGTKEb775xnBc8z6Fh4dTWFhItWrV6NOnD/3798dqtdKvXz9at27NsGHDdOdpYZpaWGNF6NSpE2fPntWFEPojNDQUl8tFdnY2S5cuZf369Xz88cdkZGQQGxuL2%2B0mKyuLYcOGMXXqVHFecHCwCOEEiIuLE9p3KSkpfP755yKXT4a/l0vT9IMyz5rsjQsJCeGHH37A5/PRsGFDMWdt2rRhy5YtVK1alSpVqvDDDz%2BI0EiPx8PmzZs5ePAgd9111xXnZ%2B7cuQwYMEB4zPz7o0JQUBA2m03k7VmtVtq2bcvNN9/Ma6%2B9pjzfZrMxYMAAZs%2Bejc1mIz4%2BnqysLJ2nVSNH0fIVS0pKcDqdVK9enSVLltCqVStCQkLIz88Xc1etWjW%2B/PJL1qxZw%2BOPP65s%2B9Zbb9XlLF4JqjDOIUOGCPZUEyZMmDBh4g%2BB8%2BehnOH7d4cyyfdXwPXX/zb1/gFhevZM/E9i3LhxLF%2B%2BXLfNnTuX6OhoGjRowKuvvsrKlSsZNmwYM2bM4JVXXvnZbd133306OQDtZb158%2BY88cQThIWF0ahRIx588EFq1apFdnY2tWvX5vXXX%2Bejjz4SuYMZGRlklSdi%2Bxt6TZs2JSwsjMaNG/OXv/wFq9XK%2BfPnKSkpoaSkhJSUFB577DFRXsvz69KlCxaLRRgrwcHBvP7666LcjTfeaAi7LCgo4LHHHiMtLY2QkBC8Xi8%2Bn094AE%2BfPs0P5cLLNWrUoG/fvly6dIk1a9bw8ssvVzhHLpeLf/3rX2zatElHxlKRoaeFipaWlhIXF0f//v2xWCw4nU7uuusuwSqqOj88PJzo6GiCg4PxeDycPn0ar9erI1DRPIjFxcXExMQwdOhQVq5cycyZM3G5XNx5550UFxfToUMHcU6zZs0A2LZtW4Vty16/K0FF0NKp068kqi4fU8k8BKCWLFcjExIo0y8lUg9V04YuKyoydEc1TvmjilxGQYYQgE6zYW5UY5CjggMRVQ%2BkbUPKq4qFWGIhMfRPRQIhF/r%2Be2OZcq1LgXXrrt4/A6OM8XIaZLIURBayqLpqbuSpUBG0yP2TI8BUPBZX46iAq5N8gHFYMuGJ6tP61YTYwTgm1X0nHwvksWBgolEQ8sjjVEbDSwNTtS3zlMhjUgrFy4NSFDJcB8XFk8sY1oBiDctjUNob8sJWsEjJ86UimpL5V%2BS5UfG%2BBLJm5VteRcAjT7HMt6RN%2BSX7f8nQM/GrwPTsmfjTYMyYMezYsYOlS5fqtNi%2B%2BOILBg8ezKeffsrYsWN/smdv/vz5vPLKK3Tt2pWdO3cydOhQRo4cKUIoNYMJoFGjRuzdu5d69eqRlZXFihUriIuLu2K%2BXVRUFLVr18bhcHDy5EnOnTtHcHCwCHu02%2B0sW7aMlJQUoMy7tH//fhISEjh27BgXL14UuXc1atQQRCsOhyNgYfVq1apx9uxZQ3mHw0Hbtm3Jzs7mxIkTFZKzaGVjYmLILH/BuJJXz79v1atXZ8WKFdx8882UlJQQEREhxqSqo3LlylSuXJl9%2B/bp8vZCQ0PJL/9LtnbtWo4cOcK4cePo3r07I0eO1NVRWFjIoEGD2Lp1qzg2dOhQhg0bxnPPPcfHH3%2Bs7PfkyZPp1q1bhXPgD5OgxYQJEyZMXAvIy4OwsP9S41chn/vZuO6636bePyBMz56JPwVKSkr45JNPuP/%2B%2Bw2i2x07duT999/nup9543fr1g2fz8eXX36p81ppIYL%2BqF27NgBpaWn06tVLaMH590VDWPmT9eLFi9SqVYsff/yRypUrU1JSQvfu3XnuueeAMkmCPn36iPNOnjxJfHw8J0%2BeJCYmRvTJ6XRy/PhxfD4fVqtVZ7hZLBaRG6d50DRPmNVq5fTp01SvXh3Q68OVlpayadMmwsPDDaGrTqcTh8OBxWLBYrHgcDiIiIjQtRkUFMTAgQN5/fXXcTgcOBwOatSowZIlS/jb3/5GWFgYM2fOFPlyDRo0IDg4WNeOv1yExWKhRYsW7NmzB7fbjdPppG/fviQnJwtDD8qkIzp16sS//vUv5s6dazC63nvvPb7//ntd3Ro2btxoOKZBk6YIBKaougkTJkyYuBYQ5lC5Xn8nmDp7vxh/rtGa%2BNPh0qVLTJw4kS5dulBQUMCsWbN4//33hTeoX79%2BTJs2jRtvvFFot0GZJ%2B9K3jYNPp%2BPXr16UaVKFYqKiij2CzOpUaMGwcHB%2BHw%2BatWqhdVqJTc3F4vFQlFREfn5%2BaSnp4uwQIAvv/xS/D/PL57n22%2B/pV69ehw%2BfJigoCA%2B%2BeQTJk2aJPrgL2dw5MgRLBYLcXFx3HDDDeJ4cXEx9erVEyGMVatW1Y1DCyH1%2BXyUlJQII1HzTB45cgSn0ymMlODgYGw2G263m82bN5MoMVuVlJRQWlqKz%2BfD5/PxzjvviFBIgISEBGrUqMG8efN466236Ny5Mz6fj%2BzsbAYMGMCqVavwer306tWLpk2bcvr0aQ4ePMiFCxfo378/drtdF6YK0KVLF/r37y/67na7%2BfDDD3Ue2Tp16ghdxYcffhir1crEiRNp1qwZSUlJpKam8vXXX3PnnXcajHWv18sLL7xQwWr4aQQtpqi6CRMmTJgwYeK3hhnGaeIPhU6dOin14lq0aMEHH3yAz%2BdjwYIFLFy4kOPHjxMZGUm7du0YMmQI10vJtidOnKBXr14UFRUJ%2BYK2bduyb98%2B7rjjDl544QX69eunM7Y0aFT72gu5x%2BOhuLhYhPJpYZ0vv/wyaWlppKamcuHCBZxOJ/Xr1%2Bd7VT4MZV4yTQQ9Pj6e3NxcIiIiOHPmzBXnRTPeSkpKyMnJEaGMV0JISAgdO3bku%2B%2B%2BE6GT/mjTpo0uTLF3797ccMMNTJw40dBnl8tFfn4%2BDoeDkJAQLimSCOSQSk1XT0OLFi3IyMjQ6QxarVYiIiJo1KgRmZmZHDlyhLCwMGbPni1y6vbu3cusWbO48cYbeeqpp4CyvLk33niD/Px8nYfyvffeIz8/nzFjxpCTkyO8k06nUxjin332mc4Dm5eXx4MPPkiTJk0YO3YsMTExXLhwgZ49e5KXlyfKate%2BefPmFWoFduvWjcmTJyt/k7Fv3z4OS4lKCQl1adGivr5gaSn4exivtq845vOBQfP97Fnw9ywXFIBkgObkgJ8z1tCU/DtQljzi9yFBRdkdyBAMdasqkrWfCguhPL8UKEt%2BiY7%2ByW3Lc6NqOjcXwsN/WsWBtJ2dDf7qHfKQADh6FPy9yHJnsrKg3FMvIF/fkychIUFf5vBhKI8%2BAMry%2Blq00BUxTOmZM1C5sq6M3B15TKr5vHQJIiMv7x8/DuUEuRUOQXF5DfMlT4VKLky%2BFeS%2BgHE9FhWBFGRgCHWTx%2BnxgCzDKa8B1b0qj1u1bgzrRFGR4ZA8gfJEqM4J4HmjKiIvk0DmJpB6DWvpxAkD8YY8N4Zny7FjBs01uT%2Bq9cihQ1CnzuV9RayjPMVpaUbGf7nL0iOUU6eMEYdyf%2BRbF4xrX/W8lo/Ja1%2B7X1T32u%2BGq7wf/WxIz63/ZZjGnok/FBo2bKjUp2vatCmLFy/m%2BeefN%2BjTBQcHk5%2Bfz7x584Q3Li8vj44dO5Kfn09kZKQQ3o6MjCQsLIxTp06xevVqxo4dazD27HY7LpeL3NxcZsyYQc2aNdm5cyfPPvssrVq1Yt68ebz//vtMmDABl8vFO%2B%2B8w4ABA7DZbISEhBAVFcUJv2zusLAwiouLKS0txWKxEBERwaVLl3A4HIwfP55WrVrRuXPngOZHC3UsKSnB7XZfMe/uxhtvJDY2llWrVlG3bl3279/Ps88%2BK0haQkJCdCyXYWFhNG/eHJvNpvMwPvroo%2BTl5bFgwYIr9k0zkLX6/EMg58yZQ3R0NPfee68ur89isRAaGorP56O4uNggjh4SEkJCQgJ33303/fr1E8Qts2fP5rXXXjPo6c2ZM4fMzExefvllXdimP1R5l4sXL2bMmDH85z//4aabbiIlJYXi4mLS/NgdBgwYwMiRI2ncuLGhnxqcTic7duwISFjdzNkzYcKECRPXApQfnX4vmMbeL4YZxmniD4c777yTTZs26bZZs2bx%2BeefK/XpGjduTGFhoU6fbtKkSeTk5BAXF8ekSZNYtWoVLpcLp9PJmTNnuO2223Q5ejVr1mTy5Mk0b96cu%2B%2B%2Bm6ZNmwLw7rvvkpiYSOUKHgpFRUV8%2BOGH1KpVi8TERAoKCoQHSdNfywglX10AACAASURBVMvLIzQ0FKvVyk033cSYMWOAsny3KVOm0L17d12dmqEQFBREbGys7rfS0lKKiopwuVzUqlWLdu3a6cJP/bFr1y62b99OUFAQycnJVKtWjUN%2B7GH%2BuYsRERGEhISwYcMGgoODddpxJSUl3Hnnnco2AOG98w8L1bxy2nbvvfeKa%2BcPn89HXl6ekDiIjo7mjjvuEOGnBQUFHDx4kAkTJtCqVSv69%2B/PwYMHycjIwGKxEB0dLZhCAX788Uc8Hk%2BFnjfNU9uvXz/q1asnNu2aPPjgg7z%2B%2BuucOHECl8tFuJ97YuXKlVgsFoPn0x8lJSVKr6cKZs6eCRMmTJi4FuCyB0bm9pvAzNn7xfhzjdbENYHg4GDi4uJ0W1RUFP/%2B978BmDdvHnfffTcJCQk0b96cqVOn0rx5cw4fPixe9pcuXQrAm2%2B%2BSfv27UlMTCQlJYXw8HBcLhcbNmzQeV%2BOHTtGnTp1cDqdxMXF8eijjwKwfft2ilR8xeWoVKkSn3zyCUlJSZSUlGC328VLvOa1AoiOjsZiseByuUhJSaFdu3YAwgjU8I9//IPExETatWvHihUrWLhwoZBQ0FC5cmX69u1L5cqV%2BfLLLyv0Mrndbpo1a8a8efPYuHEjf/nLX3Rhm/fee6/4f1BQkNCHs1gs3HTTTeK3s2fPsm3bNir5x2GBMDK10M1q1apVOE9Q5pXUCGliYmIIDg6mWrVqOJ1OYYQVFBTw6aefkpmZKTys2uZ2u/n2229JSUlh5cqVVK1alT59%2BugM0w0bNjBlyhS8Xi8JCQmGHLqioiLeffddtm/fbqhf27Zs2UK1atXIysrSGdKaAXnXXXcZDGztY4DT6TTMkwkTJkyYMHFNw%2B/v7O8O09j7xfhzjdbENY2DBw9Sp04d6vjHyFNmnIwYMQKANWvWcOTIEWEA%2BbNdDhs2jMLCQkJCQiguLmbz5s2ClMRisdC7d2%2B2b9/OjBkzhLHn8/muKKQ%2Bfvx4AL755htOnjyJz%2BcTxl5paSm3316mm3bs2DE8Hg%2BHDx9m69atTJo0idatW1NSUqLTvpsxYwbHjh3ju%2B%2B%2Bo0ePHjz//POMHTtWGC0ul4sFCxZQv3599uzZg9PpxOv1KsMGq1WrRmRkJPfffz/p6emkpqZy2k%2BMx99rdebMGf76178CZbIE/kah1%2Btl2rRptGnTBrjsefRn37TZbFitVvGbluenbTVq1KB3794id/D8%2BfMUFRWRkZFBeHg4d9xxB1BGIhMdHS08fYWFhRQWFuJ2u4mNjaVXr15ijhMSEpg2bZpu/Lt37xaaeqdPnyY/P5/Q0FBhLHu9XgoKCvB6vbr6ta1OnTo0aNCAkpIShg4dqpsjbU1t3LjRwDyqhaYGEr6pwSRoMWHChAkTJkz81jCNPRN/OHz00UeCLVHbCgoKKC0tpYYhQ7oMjRs3BuDQoUPs379fHPcPqYuLi%2BODDz4QrJGDBg0SxCiPPPIIy5cvp3HjxpSWlupYNY8ePSr%2Bn5WVRb169QT9viaPkJmZSeXKlbHb7eKF3WKx8I9//IPWrVuL89PS0ujfvz8pKSl07NiRbt268dJLL4nfNdkDn8%2BH1%2Bvl3LlzVK5cmXfeeQco8y517tyZ4cOHk5eXR2lpKTabzSAnAXD8%2BHE%2B/PBD3G43NpuN7OxsnYewIng8Hp238LPPPsPj8fDpp5%2BK32Voni5/Bk9/I%2Br48eO0a9eO8%2BfP686zWCxcuHCBxYsXi2Mqg6m0tJRTp06xZMkSsa/KycvNzSU7OxuHwyH6mZ%2BfryNkmTlzZoVj37dvHz/%2B%2BCPJycncdddduvxCn8%2BH2%2B0WIZ9y/6Ds%2BmTJCrkVQCWqfvPNRlF1g5K0vK/K2ZTnRqV/KB9Tqf3KbQUgniyrDyuzwmVvuUKx2BANG4iouiy6rAqplTqk0iyX50IVcWsQZpYrkvvGzxRVV4UkS%2BLnBk4rlfK1fH1//NFYRiaWKo%2BQ8IdhvgyK6cZxbd4sFVCQcO3bp99XLUfDuBSLSxbQlm9HlWi5XEZ1C0s69sp1YzgWwP0ir62fK5humIpAVNX9yLIA5bWU51OZCi3VG8i6lutVPkvkkxTPMcO9qSgjLxvD9VWIqsuTLnFplWHXLq5aSLowBhF4Rf/kx5gquEiuR7WuA3rgyMfkhVT%2Be17%2B1d8dfjOYnr1fjD/XaE1cM9DkAbTNn0pfBZUBEhQUxN69e3XHqlatyt133w3AwIEDadKkCVAWVpiYmKgkIVm5ciWNGjUiPDxcvPynl//lf/jhh0W5s2fP6vphtVrp2rWrku3z3LlzvPbaa%2Bzbt4/s7GzduP3H6PP5%2BOabb%2Bjbt6%2BhjCZpoBGtVJS75/P5qFy5MuHh4WIebTYbXbt2NZS12%2B1UrlxZGLHaMXsFIRya8ajJLATqmdK8bVbFA1fzmlmtVoKCgpRad16vl4PSC69Wl9vtNnjetL6OHDlS563TYLPZcDqdOq/ltm3bdAaly%2BViz549SmZTf2NS9bsKqampPPvss7pt65bPjQVl41feV8yPgW5QRaMmH5NY%2BJRtSR8LVE3LTHjK7wsyjaF0DhjZEJWNyWte/uhhqMTYIaUzVpoLhRMWKZ3WWJHifpSHoBqS4VKpWBHq1tXtGiRCFWvccH0bNDCWkZg3KX9W%2BsMwX1KkBRjH5RcVXgaFpmnDhvp91XI0jEuxuGS2QZmUVCUMLZeR9wHKZUYFVOvGcCyA%2B0VeW6rLLR9T1WOYClUh%2BZjEQKm6lvJ8KtVlpHoDWddyvcpniXyS4jlmuDcVZeRlY7i%2Bqo/I0qTLTJcAlOf2X7GQdGEM7MWK/smPMflxqapHKXgeyANHPiYvpPLflR/FTFwzMI09E3843HbbbaSmpuq2kJAQHA6HjrbfH3v27AHKNNQ0Rs7q1aszf/58w4v/ypUrAXSabxreeOMNca6GtLQ0nnnmGXw%2BH7m5uYSGhgrvzWuvvQaUhUxqpCSaly00NJRevXoxfPhwNmzYoDSYNmzYQIcOHYShEhUVRWxsLB9//DGpqan85z//oWXLlmzatEmIk2uwWq3ExcVx%2B%2B23Y7fbsVqtYmwaEhMTiY%2BP59y5c%2BTm5tKg/AXP4/EQGhrK/fffryvvdru5cOECbdu2FcemT5/OQw89pBNE16AZTprXqyKDE9CRvOTn52O1WvF4PAYDUfPGWq1W3Xi1%2BWvatClBQUGi7djYWGw2m6jnzTffZNOmTXTq1ElnTPp8PurXr68Ti9eMSc2AtlgsdOjQgXPnzvH222/rjPfg4GCaN2/OJ598Io5p9fvn6akMTRVUBC0mPYsJEyZMmPijIZLAiMd%2BE5ievV%2BMP9doTVwTCAsLIzExUbdZLBYaNGjA0aNHDd46uGx03X777dxwww0EBQWRkZFBTk4ODz/8MNu2bSM9PV2QldhsNjp06GCo56OPPgLQ/TZ%2B/Hi%2B/PJLSkpKsFqt5OfnC9KWZ599FkAXnlhcXIzFYiEnJ4cVK1YwZcoUPB4PMTExADRv3lxnxKxfv14YKl26dOHs2bOcOXOGxMRErrvuOpxOJ0ePHhWePID69evj9XopKipi6dKluN1uioqKDKyZ6enpTJ48mRdffJHq1avrjOWtW7eK8MNbb72V4PLPh9dffz3p6enCkBk/fjzHjh1Thorm5OQI0pvdu3eL/g0bNoxNmzaxbt06AJ588klWr16tO1fljbVardQu/zrqdrt1hqDm8dy1axfFxcXCqEpLS9MZWC%2B99BIDBw7kiy%2B%2B4MUXX6R58%2Ba0bNkSgGeeeUZ4ZzVjUvPmeb1ePB4Ps2fPZseOHZw8eVLMCVw26PzF451OJ1arlVy/OBxXgPzUZs6eCRMmTJgwcRWYxt4vxp9rtCauaQwePBiAhx56iI8%2B%2BoiTJ0%2Bye/dunn76aXbt2kXt2rVp2LAhdrude%2B65h%2BLiYsLDw3E6nTz11FN0796d1157DavVyt///nedp%2B38%2BfOMHTuWnPJA%2BA8//BCAunXr0rp1a5o0aUJJSQkej4fIyEiRI6h5u1JTU3VesrCwMCpXrkxpaSmlpaV07NhR5Afu2LFDF/b35ptviv9rOWnPPfccP/zwA2fPnuXw4cP0799fNxdaXqK/kaEKd7RYLDz66KO88MIL5OXl6XIRx4wZw5o1a4Ayg1MzYE%2BcOEFycjKR5SFwWVlZjB8/Xkck44/c3FyKi4t57LHHhPE4depU2rVrJ/QDJ0%2BerAtPrSh3MDIyUqdRqIWHqpDnl6Tg71HMyckR8zN27Fh27NjB9u3bgbJQW82g0gzkoqIicT0SExNZvnw5sbGxuFwukpOTRb2akeffbnFxsQg11uY/Thl/ZoQqZ%2B92v/YE5IQNaV%2BZExdADg1%2BGoIVFbpaipRSPULKBVP1T25KlZMiHwukHkOOniLxK5CUx0DyDg2pQXKiTSD5MaoycoKWKt9S6p8hRU8xbkNTqsQ0v/xkwJiTpKpIlVxnIDC6ev9kKS2VtJYhtUqRqOQXFa8uoriYcuS1Ki9N7rJ8uVXHKkh/unI9ikJXS9sFY58DWX6GgasmXT5JddPLg1B0UF7WhtxExXUJ6F6VK1Lkg8rnGdpWpFrI95SiWmPOo%2BqekhpXrRvDuKS5UD1/5Oejql5D3rDqQStPhlSR1rfzbkVIvIlrBqaouok/FBo2bEjPnj155ZVXlL%2BPHTuWTz75hIiICM6fP4/L5SIsLIycnBydqHp%2Bfj73338/58pZFC5dukRkZCQ%2Bn4/Y2Fg%2B%2BOAD4VnRzlGhUqVKLFu2jD59%2BghjzeFwkJCQwLFjx%2Bjbty/z5s3jwQcfJDw8nClTpgBlYaAFBQWcO3eOiIgI8vLydJ65n4KOHTvy1VdfYbFYhDfMarUG5AVyOBzcf//9fPLJJ4wZM4Ynn3xS1wd/IXTNANMkHW688UYuXbpEzZo1iYyMpGnTpsydO5f69euzf/9%2BnaC71WolODiYwsLCnzVGDbVr1yYtLa1CA09DXFwcZ5UMDmXjqFy5MpmZmdxzzz3s2LGDtLQ04XG1WCwVzp3VaqV3797cc889PPzww1y8eFH81rNnTyZOnMjp06e59dZbAYSou8ZEarVa2aV6QVZAJar%2B%2BLBhDBk6NKDzTZgwYcKEid8DPl8F%2Bde/B5SsQL8ClAmo/5swPXsm/lCoUqWKzpsi4%2BWXX%2Bb5558nKioKq9WKw%2BGgVatWLFu2TGe0hYaGMn/%2BfO6%2B%2B25cLpfQuOvdu7fO0JOhhS5qBCXZ2dl07NhRR6ISGxvLqfLPfJowe3BwsMjlslgsujG89NJLJCcnCyPI3wMXEhKiY%2BtUYePGjfh8PpYsWSJkJ7xeL06nk/j4eJo3b05MTIxyTKWlpSxatIiYmBi%2B/fZbw%2B/%2Bht6dd96Jz%2BcjMzOTm266SeTOHT9%2BnH79%2BvHZZ58Bl72K/gaZJmnwcwy9KlWq0Lt3b1auXMmCBQuo60c%2BUVEOoGzoaZ5WbUwaScqHH37IwYMHhUdTYzmFMi%2Bi7GFs06YNAwcOpLi42KCB6D9WKFtjmrfS6/VSWloqxOADgZmzZ8KECRMmrgUEmIpu4g%2BK/6JKogkTRnzxxRdXLdO7d2969%2B591XKhoaE89dRTPPXUUxWWKSkpISQkhOeee46///3vQJmn79///je1atWiQ4cO4kVeg79WnUbo4vV6hURDQkICEydOFCQnMTExTJ48Wex/8MEH/O1vfwPKjETNeFqxYgVxcXHceOONhIaGUqdOHaKjo0lLSyMmJoaGDRtSr149DpXTYw8dOpRBgwYBcOTIEbp3717hGDt16sTChQt1xpjmlQIYMmQIw4YNY%2B3atRQXF%2Bs8Wj6fjzFjxghR8SvBZrPh8XjYuHEj8fHxFBcX07RpU%2Bx2O/3792f27NnAZY/iSy%2B9pBN3B3S5gVfy8CUlJfHDDz8A8Pbbb9O7d2%2BysrJo3LgxR44cEf2tXr06J06cwOfz0ahRI5HzeUlBy79582aeeuoppk%2BfLvT4ZLz//vsASumHXr16XWl6TJgwYcKECRM/BX%2By/LrfAuYMmvhTIz09nYKCApo0acL%2B/fuZOHGi%2BE3z/KhYOzVoxlNxcbH4v0bqoYWQ9u/fX8du6W98DhgwQOQJ3nXXXdx4441AmSGxc%2BdOSktLOX78uCCM0cTVHQ4H8%2BfPZ8WKFZw8ebJCllIoM0RnzZql806CXsZix44dLF26VHgM/dGuXTuRvzd8%2BHBatWoFlBlsmjc1NjaWhg0b0qhRIwDat29PvXr1aFpOTe12u4WhFx8fL%2BZq3Lhx1KtXj2bNmpGUlMSuXbtEuKz//MpwuVzC0AO45ZZbSEpKAmDv3r107txZEK%2Bkp6eLemrVqlXhPEGZofnee%2B%2BxZs0aLl68qPP8aWtDJc%2Bh4W4FVX1FMAlaTJgwYcLEtYAg%2B39Re8EkaPnF%2BHON1oQJCZqhFR4ejsfj0eVQpaen4/F4OHDgQIXnay/n/l6iwsJCNmzYUOE5/gQk9evXF/%2BXpRl8Pp/Qrps%2BfTpPPfWU8B6WlpaSlJTEW2%2B9RdeuXRk2bJg4z2Kx6EJFtXquhE2bNjFq1ChKS0t1zJYOh4PDhw%2BLsMgGDRrw7bff4nQ6qVOnDmnlJB/nzp0jMzNTsF5eDfJYu3btyvLly6lfv74girkSCgsLDYQ0GnGKz%2Bdj5cqVOrZPzWi7TqHv5Y/i4mKioqJ49913qVmzJs8//7z4TcvF%2B8tf/qIUfm/dunWF4cEqKAlaynMB/XEVfpbAiBgUhQLhE5EZEgI6R%2BpgQIQECqIN%2BTyVY1kuY2hLFXsUCEmKNO5ANOmvRs4RaNtyvapxy6fJ%2BujKdFaZ7cLvY4mAfB3KJW2u1Laqg/Kh8uCFy5AIZlRNyfxBgFFJupzQyh%2Byw13mHFFdS3nYKuFr%2BbxA7juD81/FrCKdpLreAZFxyIUCUWeXz1EMXI42V6ZPBfBQkov8nPkMSExeMTlynw1To5gr%2BVIFQoCi6l/5N18BFQeOfL/KvDl%2BwUQV1qO63PIcy%2BRFgZTR5uH8RZUgqYlrBSZBiwklOnXqJPLSoOzl/Prrr%2Bfee%2B/lgQceEMeXL1/O/PnzOXz4MKGhobRt25Ynn3ySqlWrArBs2TKmTZumDM/s1KkTQ4cODSj07fnnnxeyCFCWy3X99dfTp08f%2BvfvL17m5fb69etnEDUPDQ2lcePGjBkzBrvdTrdu3Vi8eDHp6emMGzeOvLw8goKCaNCgATt27BAhh%2BHh4cyYMYOBAwcKYyNQTbV27dqxadOmq5ZzuVwUFhbidDqvWrfL5cLtdhMbG0tISAhpaWm43W569%2B5N69athSzEz4HFYiE4OBi3220wFLVQzZSUFAYNGsT48ePZsmULoCeOcTqdOvkE7bwmTZqwe/duQ5s2m42ePXvy8ccfY7VaKS4u5tZbb6VKlSosXLgQu92Oy%2BUSDKR16tQRIa1Q5pXTdA49Ho/Oc6mhadOmVyRQiYyMZMmSJdx2220sX76c4cOHc%2BTIEeAyQUuzZs2UBml4eLhg/QwEKoKWYcMeZ%2BjQIQHXYcKECRMmTPzm8HhA8ZHzd8FVPlb/bKhE5n8CiouLeemll1i7di3BwcE89NBDPPTQQ8qy%2B/btY9y4cRw8eJDatWvz0ksv6bgGVq5cyeTJkzl79izt2rVj/PjxOv3eXwrTs2eiQowaNYpNmzaxadMmPv/8cwYNGsSkSZNYvnw5ABMmTGDChAncc889LF%2B%2BnLfffpuzZ8/St29fQ8jgr4Fu3bqJ/qxcuZIBAwYwefJkpk2bdsXzHnroIXHexo0bmTVrFnl5eQwdOpSEhATCw8NZunQp48aNY/To0dx3331UqlSJnTt3ApfZOnNzc3G5XMyaNQuXy6UzgmJjY4XIN8DatWt1fdi0aRNWq5WQkBCd96dGjRoANGrUiJUrV3LbbbfRoEEDQRAD0LlzZ/7xj38oyUL27NnDqlWryMjIEMeWLFnCiBEjxL7shVLV458nZ7FYBEmKz%2BejatWqVK1aFZfLxQMPPCDIZ1asWEHPnj3Zu3cvVatWFSQ4mvRA3759dWONjY3lwIEDREVF6cIjY2NjgTLdvXXr1vHPf/5TiNrHxsbSpEkT0W//vMEjR46IkNmwsDBBwlJcXKw09EAvfq7Ni8PhoFq1akCZvMLOnTuJiYlhyJAhYl6tVqsIG/VnRPWfy/z8fF0I6tWgImgxKVpMmDBhwsQfDjt2/Pfa/oOGcU6aNIk9e/YwZ84cxo0bx7Rp0wx6wlD2t/7RRx%2BlZcuWLFu2jKSkJAYNGiTeAXbt2sXo0aMZOnQoixYtIicnh5EjR/7i/vnDNPZMVIjw8HDi4uKIi4ujatWq9OzZk5tuuom1a9eyfft25syZI4gxEhMTadasGW%2B//TZut5s5c%2Bb86v0JDg4W/UlMTKRPnz6MGjWKGTNmXPElOyQkRJwXHx9PcnIyo0ePJi0tjaNHj9K9e3dWrFhBjx496NWrF%2BPGjWP9%2BvX8v//3/wCE4REaGsrgwYOZPn260KHTcujq16%2BvM2IOHjxo6IfGWOn/kv/kk08CZXlmd911F6mpqRw9epScnBxhcPXu3Zt33nnHkM9VWFhIvXr1GDRoEEuWLBEGzt13380///lPAF5//XU8Ho/O4FPlhcXHxwNlBpDP5%2BPAgQOUlJTgdrs5ffo0Q4YMobi4mMTERPaUx1v5fD6ioqIoKSkR4bD5%2BfmCKXP27Nk6TTrNSHK73bpcvHN%2BcS4XLlxg7dq1wpsGZYQ3UOZd9hc510TlQa99p7G0amO22Wziuhz10xHzeDxYrVaqVq0qchKtVqvQ4svOzhb1WywWMjMzOXPmjDDyNX09//78lJw7M2fPhAkTJkyYuPZQUFDA4sWLGT16NI0aNeK2225j4MCBzJ8/31B21apVBAUF8dxzz3HDDTcwevRoQkNDhWE4b948unXrRo8ePahfvz6TJk3iq6%2B%2B0qX8/FKYxp6JnwS73Y7D4WD58uU0bdrUkKPlcrmYPn26TmD8t0RKSgoOh%2BOKOXIqaCQkNpuNYcOG4fF4%2BOijj9iwYQPp6eksXryYF198kRo1anD48GGgzOhISUlh8%2BbNfP3111y8eFF4mnJzc7FYLKLeyZMnB9QPzdgDPdmL2%2B0WBuOQIVcO63O73axcuVIYN5UqVRIGUpcuXQB0%2BWtQZjj7E7FoDxX/cv7/f/HFF/F6vTRv3pyBAweK44WFhcLoulpE%2BNXyBjV8/fXXIgz4o48%2BEmERBQUFwqizWCxUqVJF55HUoLGnav33eDyib%2Bnp6Tojy%2Bv1kp6ezo/lSU9VqlQR19RqtfLwww%2BLOoqLi8U6s9vtBtmGkJAQYTQHAmXO3s03GwuuWnXFfWUuyebNul3VtxA5V0ORNmfMz/n6a92uMjdM7pB0DmAUMVYMwsA5JI0JMIqAl8uDaJBz2cCY46Oav/Jb/jK%2B/NJQxiDwLbWtnBxZqVmVaCMl%2BajyxwynBZLoJ1WkSMcz5AqlpxvLyLlMhrlSNS8l4CkfBbKnW1HIUK8qB27mTP2%2BFGWBivFZvpiqkHtZMFtFiiW/nMlh3apnpByuv3SpsYzcZ1XbgSQnysfk%2B1BeAAB%2B6RMApKYay0gXRvlM2rdPv//NN/p9VTSQX5i%2Bch8MCW3K777r1un35QeDKmVCfrbI%2B8DJk9IBxcDlZaMapnxMvkyqR4n8XFBdbjnXT5X7d7UyYv%2B/SWjyG3n2srKy2Lt3r27Lki9YBdi/fz9ut1sQwwEkJyezc%2BdOw0fbnTt3kpycLN4ZLBYLLVq0YEe5t3Tnzp26d%2BmqVatSrVo1EV32a8A09kwEhNLSUtauXcvXX39N586d2b9/vwivk9GwYcOf9NL7SxAUFERCQoIwyAJBVlYWkydPpk6dOtSqVYu4uDgmTJggXO1du3ZlypQpPPjgg8yZM0e8/BcVFVGrVi08Hg%2BhoaH07t3bIEiu/atp7mnHKtKL08IANW%2BUCh6PR4Q6qjQIv//%2Be6ZPny6Mm8/8XjpDQkK4WTIg7HY7Ho9HlxPYuXNnXZ/kUE/Nazh58mSdF7WoqAibzUZISIiStESDxWIhLy%2BPqVOncscdd4h5stlsJCYmCsKWoKAg7Ha7aMPr9RoMVW1cmZmZREdHA2X5ezLpS0UoVr6JlMHhcBAUFEReXh633HILc%2BfOFf33%2BXx069aNJ554wuCdhDJj9EpzICM1NZVnn31Wt32%2BZauxoCypIe0r7F246SbdbuXKxiLlUyfgFzksYNCc9WOVBSiP2NVD7pB0DgCytqRiEDVrSgekMQEgs6vedptut0ED4ymyMLBq/mrXlg507GgoUx6BXWHbysnxC/UGwOUylim/1zVERBiLGE6TL6aqXqkiv3QRgSpV9PvlQQ06yEvcMFeq5hMTdbvKR53s6VYUMtSrut8efVS/f/vt%2Bv1OnYznyBezXTtjGflvmmGBAtdfr9%2BXCatUqtTy3wYVo6/cZ1Xb8k0urwnVMfk%2BlBcAQM%2Be%2Bv2UFGMZ6cIon0kNG%2Br35Q9bqvykcl3ZCvcByj8MalA96/D7%2BwYYHwyqv8/ys0XB5Fz%2BTfUyFAOXl41qmPIx%2BTKpHiXyc0F1uaWpMewHUkbslzNr/y9h0aJF9OrVS7ctWrQooHPPnj1LdHS07t0uNjbWIF2llZXfiWNiYgTxXVZW1hV//zVgGnsmKsS4ceNISkoiKSmJpk2bMmLECAYMGEBKSgq5ubm6vLIrISMjQ9Tjv/nnmf0ShIWFKTXPNMyYMUM3ji5duuBwOJgxY4Z4Oe/evTsffPABnTt3xul0kpWVxdSpU5k5cybz58/HYrFw5swZRo4cidPp5Mknn%2BSll14SVP6a0RGheDPr2rUrc%2BfOFWX8oX0B8nq9PPzww2zZsoUOHTroQhDhcqjjd999Z6ijUqVKjBo1iubNmwNluXIakpKS2Lx5s84YfeKJJ3j11VdFGYvFIrT%2BoMzgevrpp2nXrp3BeNm4cSOLFi3CYrFQo0YN6taty5QpUwzGqvYA1Nq96P1GZgAAIABJREFU6667sFqt5OXlcfToUWEoeTweQSwDZYbYrFmzmDBhAnBZgL5Hjx46T1ppaSlBQUGCBfXQoUOiDofDIfIna0gvcRpJjKbtN2jQIBHCCRAVFUVwcDClpaWsXr1atK%2B1XVBQQN26dQkNDSU%2BPl53bqz0kn41mDl7JkyYMGHimoAqxOB3gg/Lb7L16dOHZcuW6bY%2BffoE1CeNSM8f2r5MrldRWa1cUVHRFX//NWCKqpuoEI8//ji3l38VDQoKIi4uTrz8R0VFiTytqyE%2BPp7/%2B7//Mxzv16/fr9LPvLy8Kxqe9957L/369aOkpIQ5c%2BbwzTff8NRTTxlo%2BJOSknj77bcpLi5m27Ztgmm0evXqWK1Wunfvzrp16%2Bjbt68wqDSv08CBA5UJtRaLhbCwMJKSkli9ejVt2rRR9rFy5cocOHCAI0eOsHHjRh555BFmlocjRUdHc/HixQrDJLOzs3XG25o1a6hT/gVUI9NZtmwZ77zzDna7ndjYWEGgExwczJYtW2jVqhV169bl0KFDBAcHs3DhQk6ePInVahUGElw2Tn0%2Bn8hfe7T8S7rNZiM0NJT8/HxKS0t1ou2p5aE/77//vshz9J8j/7ENGTJEMG7a7XZKSkoYMGAAaWlpgiRFfgjWqFEDr9crwlH9ZRg0lk6LxSLGsXDhQqDsQ4A/zp49y03lHqTExEQGDBjAiy%2B%2BiMvlIj8/n7S0NJo1a8aQIUOYNGmSOM/fwAwUZs6eCRMmTJgwcWX8Vn8W4%2BPjf3YUWlBQkOE9RNv35xa4UlmtXEW/u1QRGj8TpmfPRIWIiYkhMTGRxMREqlSpovPyNGrUiL179yrPmzNnDm%2B%2B%2BabYt9vtoh7/LdCwuyuhuLiY48ePC%2BNGhcjISBITE6lTpw7jx4%2BnVq1aDBo0SBgU%2Bfn5vPzyy8JlHhQURPv27XnzzTfp3r0735TnFqSnp3Pp0iWmT59OgwYNqF%2B/PsfLcz2%2B/fbb/8/edYdHUa3vd2Z3s0k2vSeU0AOJEEIJFwgqoVyKFIEgKEhXkSIdpYjo1WABAUFUEEykCQihCeQCAQwIElogkoRcpJMQQkgv235/bOawc%2BYkWQRU/J33eXxkZs%2BcOXPmzGa/%2Bb73fSs9/7Zt2xAWFoaOVCnY22%2B/TeYgKysLGRkZWLduHezt7bF69WqIoog333wTEyZMgNlsJm0FQcCzzz6LNWvWkH09evRA8%2BbNYW9vj5dffpnYC1y9ehUlJSUkqxgSEoIOHTqQYKi0tBQtW7aEXq9Heno6zGYzcnNzcaOCjECXUVoH1bNmzZKpXhqNRhQXF5PgzWAwQBAEnD59mgjGaLVaWRZWpVJBq9XKuHcBAQGE3yeVRjZp0kTxxk2lUpFy0%2BvXryMzM5N4CkrB49WrV2Vlm5K5vU6nQ7169WSKnAAwYMAAErSZTCYsWbIEAMiYMzIy4OjoKAv0AEum8ffffyfBtS1gcvaefVbZkOajUtss2hJNyLJyUSGgE4u2eIuBsq1g8mPojk%2BfVrah%2BmFxuBQ8FRbJjB4gxQNiVXfb4p%2BluK4TyvJaBf%2BF5nmxJp3m7LGyuxQRiJkApi%2BC6pdZqWzlBQowL0kxnczqeKoRi9enuJ9UR8xrog5iXQPNbWK%2Bb/z5Z/k2LZjAKtOiO6K5bICSfMWaHJrXZQM3VUGMpccPKJ8hFomLXpC0wRugnEDazJA1PprvRm8DShM91pcSPTe0zyOLK0UvFBbhjXrOmJVvNG%2BY5v6xFhK9sBUEPduosn/EF5XeZvVL8wxZ74Lp8bEKoKrzICS35f%2BZCXl18PX1RW5uruw3UHZ2Nuzt7RUVXr6%2BvjIhOsBSrSUFmpV97s3kSfwx8LvH8YfQq1cvJCcnK8oKi4qKEBMTw%2BRZPQns3LkTgiAoAqnKIAgC3n//feTl5ZGA1N7eHjt37mRK5jo7OxO5fslDzWQywdHREV5eXhBFEUFBQdX%2ByGcpNWZnZ0MURfJ259atW/jpp58IR9DLywvDhw/HzxV//HU6HeHT%2Bfj4oF27diTzmp2dDY1Gg9LSUkyaNAmfffYZAGDMmDFEVRQAzp49i4iICHzxxRdkn633Sq1Wy1Qv586dq2jj6upKrkcK%2BpKSkkgGjebLsTJZFy5ckL0IMJvNMJvNMiVNadzWmcaqSh40Gg0EQSBjLioqwuXLl8m1S8FhYWEhCTSvXbuG3Iq/llI28t69e6TEVIKU/RRFUcZ9rA4szt4BljAEHQBS20yaIEXIYnnJ04lFFjdMUXlM8TaY/Bi64xYtlG2oflgcLsXfORbJjB4gxQNi8cno%2BWLxixTXxcjIK/gvNM%2BLNel09peR3aWJQKwmioug%2BmVypqxKjgHmJSmmkzV/dCMWr09xP6mOmNdEHcS6BprbxFqz6NBBvk2LhbHKtOiOaC4boCRfsSaH5nXZwE1VEGPp8QPKZ4j1I5BekKyycnoCKS4lc3z0dxrrO47KZDC/lOi5sRK2AKCcX0C5UFiEN%2Bo5Y9EOFbxh%2BuUwayHRC1tB0LONKktPDeu7jt5HbzMTPFTZH4sOSo9PwcFm7KPPRW6LjbSdJwGT6cn89yho0qQJ1Go1EVkBLDSbpk2bKjQPQkNDcebMGfI7w2w24/Tp0wgNDSWfW/%2BWvn37Nm7fvk0%2BfxzgwR7HH0JYWBiioqLw5ptvYsuWLbh27Rp%2B/fVXjBkzBqIoYsyYMY/9nKWlpcjOzkZ2djauXr2KtWvX4qOPPsLYsWMfynwyICAAarUaGzZsQFBQEIKDg1FYWIgFCxbg9ddfx%2BXLl5Geno7vvvsO27Ztw4ULFxRBiVarRePGjTF69GjcuXMHV65cIRw2AES10Ww2o3nz5vjggw%2BwY8cOGe8sJiYG5eXlKC0tJaIpEgwGA3JzczFs2DA4ODigRo0acHV1RatWrWTtrlW8fTx58mSl2UUWj5AFURQxa9YsRVmD9MVVu3btSufZy8sLnTt3hk6nI8Gq9MX22muvkfLKJhXEeCmjJmUA6UDN%2Bm2ZyWRCSEgIVq5cKWtjzZeTMoOVlfPq9XqYTCaZcA7rfL/88gvJ7KlUKtKfpA76%2B%2B%2B/K/qQsp9ms/mhSjlZnD3O2OPg4ODg%2BNuBUwxkcHBwQN%2B%2BffHee%2B8hOTkZ%2B/fvx%2BrVq/Hqq68CsLyAl6ybunXrhvz8fHz44YfIyMjAhx9%2BiJKSEnTv3h0AMHjwYGzfvh2bN29GamoqZsyYgeeffx61aMGnRwAP9jj%2BMObPn4%2BxY8ciJiYGvXv3xrRp0xAYGIh169YxxUgeFXv27EFERAQiIiLQr18/xMXFYc6cORg7duxD9%2BXk5AQ3Nzc0a9YMiYmJOHjwIPr164dDhw6hT58%2BeOmll7B69WrY2dlh2LBhePvtt6FSqdC6dWvUrFkTu3btwqpVq1C3bl1iJO7v70/4a9bB7qlTp/DZZ58hkHqLah3QGAwG2NnZwcHBAfXq1cPSpUthNBpx4cIF7N27F2q1GlFRUejTp4%2BsD0dHRwiCgBdeeIGYxq9fv54Ik1y8eBGfffYZsXFYuHAh9u/fTwJAHx8fnDp1ChqNBiaTCfn5%2BejZsycAS5BnXUbg4%2BNTaemtm5sbTCYTbt68CZ1OR4La5s2bw9nZmZRJ6nQ6nD17lpxfq9XCZDIpatzp80jZPev7J9mAAJaMoRScSXXuWq0W7u7uSExMJEF4Tk4O6WPQoEEQRRE//PADEVeJjIwkthXWiqVStu/evXuVcifpa%2BDg4ODg4PhHwErE7c/G3zGzBwDvvPMOQkJCMGzYMMyfPx8TJkwg1VYRERH4qcImycnJCV9//TVOnTqFfv364dy5c/jmm28Ibz8sLAzvv/8%2Bli9fjsGDB8PV1VVRQfSoEMzVmWNxcPwDERkZifHjx6Nfv36y/aNGjYJWq8XIkSMxZMgQrF27VuZ/UlJSgm7duqFv376YPHkytm7dimXLluEgw7spMjISt27dQv/%2B/fHhhx8CsNhSWFsxlJSUwGw2QxAENG/eHOnp6dBoNKhTp46sPACwBF9arRYlJSUYMGAAPvzwQwwdOhS//vor3N3dUVZWBp1Oh/bt26NTp06YMGECtm/fjsaNG6NXr15IT08n2bH//ve/2LRpE%2Bzt7dG0aVNZVlAqv9RoNHB1dSW15PXq1cPvv/9erZ%2BelKVUq9VwcHBAWVkZXFxciNm6lNUzGo2YMWMGRo0ahRMnTmD%2B/PkyM3UJKpUK06ZNw8mTJ5nzbD0/VQmciKKI4cOHY/Xq1bL9giBAFEUYjUasW7cOLVq0IBlI6zEYjUaEhYUhIyOD8D2tUaNGjSrHR2PBggVYs2aNbN%2B4ceMwceJEm/vg4ODg4OB44jh/HqjEbutJg8lZfAx4jPonf3vwzB4HhxX%2BDNP4zp07Iy4uDnFxcTh06BAGDBgAQRBw8eJFFBcX4/79%2Bwp%2BGmApF5SygVcriPXS/8vKysjnR48exXvvvQfgQWAlZdrGjBmDMWPGYNOmTQAs8r63KWK/FMzp9XoZaTgvL6/aQA%2BwZClVKhVUKhXy8/NRVlZGAj3AEuRJpaiSaWheXh4uX74MQRAUZuVGoxEBAQGKsscmTZqgi5W3WXVjkzKgNMxmMxlPWVkZRFGEIAjkGqQxiKIIOzu7SjPJLA/EqsAUaGHxdaoRSGCKcVCCDiwzXVoPgdLvAKAUF6AFJ1hGvooBsZRAaHEGxYkYY2YJvdBKKpR4De3jDCiFDFiCCQrtDYZohkKv4fBh%2BTZLRINWQ2BcN23MzRJVUNwrKy4tALZJNCVCwZpOWlOGZUpPTyBLp0RRoUw1YpqqU3PBohIr%2BmV1tGGDfJs2u9%2BzR3lMdYbfgHKxs4zN6X302meNl/4lyzIt37dPvs3KstDjYz2c9MKxRfilIjtR6TageOaZ95cWRaHnmCUoQy8ulqk6tWhpX3sAQEKCfJte2KwvUbojxsOgWAKM546%2BDSwtGHof/TizjqGXDasN/R3K0sCh99FLgLUkOJ4%2B8Mwex1%2BOcePG4dixYyTLZQ1BENCrVy98%2BumnZJ9kiZCRkUEyWZMmTSK8quqybePHj8eyZctkmT29Xo%2BEhARMmTIFH330EWJjY4mIhwRRFOHh4YHu3btj0qRJcHJyIuf67LPP8PXXX%2BPs2bMwmUx45plnkJ6eDoPBgJkzZ5LzBAcH48UXXySZPsASxHXp0oUoYFqjRo0aMBgMMBgM0Ov1cHd3JwGeZClQGRwdHZGUlASVSoU1a9ZgwYIFijY6nQ6Ojo7IycmBVqtF7dq1kZaWxuzPzc1NYRb6OCCKIlxdXYm9BCtD97g8Z8LDw/ErS2mvAiqVCjExMRgyZAixksjPz4coinBxcUFYWBjs7Oywj/7xBYtFxvHjx20eCyuzN2HCRIwfP872C%2BLg4ODg4HjCMJvZIjB/BqqwUX4ksARr/qngmT2Ovxzz5s1DXFwcfHx8MHbsWGzcuBEbN27E2rVrMXXqVOzevZuoXUZHRyM6OhoDBw5EXFwcli9fjuzsbAwZMoR4xz3MeasyjVer1ejevTsSExORmJiIQ4cO4fPPP8e%2BfftkwdrNmzcxePBgJCYmorS0FOXl5fj1119x9%2B5dvPTSS4pSURqSuqaDgwO%2B%2BuorAMCLL75I%2Br5z5w4cHR3h6OhIxFPc3NyIibrUh1arhUajgVqthiAIKCkpIQFk9%2B7dCR/Q2ryzqKgI2dnZMJlMKCkpweXLl9GwYUMm/yzP6q2wtVl59%2B7d4ezsDC8vL0VWzhaYTCbk5uaSQJ9ViskK9ERRhLu7u0IVtCruHKv80hp2dna4efMmNBoNjEYj8ZJ0dHSEXq%2BHj48PPD09mcf%2B61//qrJvGtxUnYODg4PjaQCrCIHj6QEP9jj%2Bcvj4%2BBDfvdq1a5MArHXr1hgzZgzatm2L%2BPh4JCUlISYmBsuXL0dUVBQCAwMRGhqK5cuXw2AwICYm5qHOO3HiRFJOmZCQgKSkJEyfPh2AJZjS6/Wwt7eHt7c3vL294evri/DwcAwdOhT/rSgNKi0thSAIGDZsGH766Sfs2LEDO3bswE8//QStVquwc/Dz86u03E%2BykBg4cCASEhIgCAIiIiJgNpuRl5dHDNGlftLT0wnfTKVSQRAEogypVqthNpvRs2dP9OzZE/Hx8QgMDMSePXuQlJSEadOmkfOq1WqIoghHR0eIooj69evDwcEBzazk8WvVqgVfKz16awGa/v37o0aNGnB3d5d5MQqCgICAAJkaqK%2BvLwlkAUvANmrUKFngypoX4IEiZkhICEJDQ8m1pqSk4LnnniPtvb290a5dO6xcuRKenp5Qq9VwdHTEyy%2B/LBOcqV0hrf3JJ5%2BgZ8%2BecHZ2RuPGjZGZmUnKZSU1ztLSUhQVFSE0NBSXqFIiOzs72NnZ4QLLB64KcFN1Dg4ODg6OqvF3FWh5msCDPY6/PZ4Uj6460/jKShbPnDlD1BkvXrwIs9mMiRMnKkzj3dzcZIGNrZBUTs1mMxIrfNfy8/Nx8%2BZNxMfHA7Bk/oqKiohqpZQVk7J60vjMZjPKysqwb98%2B3LhxA1evXoVWq5UFcgaDASaTCcXFxSgrK8PevXuRm5uL8%2BfPkza3bt3CHavifmtJ4HHjxuHq1au4fPmyTGFUGne%2BFZkgKysL27ZtI9smkwkXLlwgpanAA4NzCZKKqcQtTElJIVy/0tJSbN%2B%2BHa1btybtr1%2B/jmPHjmHMmDHIycmBwWBAcXExNmzYILsmybZixowZ2L17NwoKCnDmzBns3buX8POkAFe6ru7duyvKXMvLy1FeXk7sJWwFk7PHyA7SnAoF74JRiU9zr1h8DvowVmKcToQq%2BBuMc9PUFhZPhO6Hyf2jGrGmlz5OwfNjEb9oQhHjlTWddLVlfPS5WTxJuuqaSaKgbhbrjbrCr53mYrFIU1R2nGWGbgNNSTFoFnVNUXZFTQbTDJ2%2BBlYWnjqQWcVO89noyWKRDH/8Ub7NUoSgJ4c1x9SYFcuPdQx9gxU3l3HulBRlG1uIXvQ%2B%2BkaxHMlpegFrfPQ1sBY2za%2BlFg6T50d/ebAeRGpdszhm9GXT52JNp6LwgjGfiuliLEh6GbPWLD199G1hlTHSU8zqlz73H%2BELSmv4r/RU58Heo4MHexx/W%2Bj1esTHx%2BPo0aPo1KkTUlNT0bQSNajg4GCFP9yjoFevXsjNzZVJ9ZtMJpw6dQqHDx8mWaHMzEyo1Wo4OTkhMjISQUFB5L%2BsrCysW7cO3333Henj4MGDEEURUVFRCAsLQ0REBGbOnCkrU4yLi0NsbCwx6gZAhEskIZHOnTsT1Uw/Pz%2BSVdy5cyfeeecd0pfBYMD169eRlJSEwsJCdO3aFUFBQTh37hzeeustAA9Mwekgyxq1atWSfS55yQCWYEfymZNgb28Ps9kMvV6PBg0awMXFRWE0KuHEiRPEvBywBKg9evQg21n0jwQrSOdesmRJpW0k1K1bF/PmzQMA1KxZUzEeZ2dnJCYmQqVSwcXFBevWrZOdW6VSwdHRUVEKKr0kcKVMq6sDy1R9/69Kr0R6WSuWOaN0lvbzZlkt0oexLBRp20CFlzPj3JSQKdMrme6H6dRCNWJZDtHHKYzOWebO9DpnlP3SSVdbxkefWzEWKD2rmVXP1M1iVSUr/Nrptcd6likTZpYZOj3H9L0EoBh03brKJgouDDUZTOtP%2BhpYnpXUgUzz%2BMaN5dv0ZLHM0Pv3l2%2BzZProyWHNMTVmxfJjHUPfYMXNZZw7JETZhvYYZU0yvY%2B%2BUSxHctpMnDU%2B%2BhpYC9uqMgSAYuEw//zQXx6sB5Fa1yy/efqy6XOxplNReMGYT8V0MRYkvYxZa5aePvq2sLhl9BSz%2BqXPbcuSoJeRtIapaeZ4ysA2zeLg%2BIswb948fPDBBwAsWRt7e3vCo1u%2BfHmlptk0bt26hbCwMMX%2BEhs1fMPCwlCnTh0cOXIETZs2hSiK0Ov1MBqNcHBwwLJly0h/1kHDrFmzSKDSv39/dOjQAZ988gnc3NzQt29fREdHIy4uDtOmTUN4eDju37%2BPJUuWID09HaIoIjs7GwUFBSTbJooimjVrhrt372LDhg3o1asXyZQ5OzsjJycHt2/fRt%2B%2BfQE8yOTpdDoUFRWhZcuWmD59Onbu3IkffvgBgCUAXLhwIRmzVDpobdbu6upKOHqCIODKlSuy%2BRk7dixWrFhBzklz6iQz0bKyMmRUvElXqVRQq9VQq9XkcxYMBgPxp5HmuCpotVqSyawKLi4uOHz4MJycnODn54e7d%2B%2BScWi1WhQUFGDv3r0wGo3Q6/X48ccfUWj1mlOtViM7O1shIiTNW0lJCe7du1ep8TwNztnj4ODg4HgaIKRerOTtz5PH/7cs3JMAD/Y4/laYOHEiMaXUarXw9vYmmRM3NzdZSWBV8PHxwffff6/YP3ToUJvH0rx5c6J4efv2bbi7u6N9%2B/aYOnUqKe9zdHSU8aycnZ0JL0ytVqNly5bIzMxEfHw8atasiZiYGJl3X2BgIJYvX47WrVujtLQUERERAB543YWFhaFJkyY4ffo0/KjXiC1atEB8fDzc3d3xww8/wGQy4eeff8bnn3%2BOooq6j1OnTmHkyJEALBk8KSizs7MjAQ5gUeWUgg8HBwdMmjQJ3377LW7cuAGz2Qx7e3tZgCYFetZYuXIlpkyZUqkIitFohKOjI7766iuMGDFCFlxWBevAUxqfFABqNBqUlZURqwSpT2tFT09PT6xcuRL9%2BvVDTk4OSkpK8P333%2BPkyZMkQzllyhRER0fDYDDA3t4ehYWF2Lhxo%2BxcBoMBnp6e5N7QcHR0tDnQ4%2BDg4ODg4OD4M8DLODn%2BVqiOR5fCKq4HEBMTI8tWqdVqBYdOEoEBLOWU1alkCoKA4OBg7Nu3D8nJyTh69Cg%2B%2BeQTmVCJ1EchXegOi/DHwYMHIQhCtZzDN954A35%2BfkhLS0N0dDQCAgKgUqlQp04dpKWloWHDhqStBCmIy8vLw8mTJ6FSqdC4cWMiiOLj44MuXbogLi4OgwYNwgcffECykOXl5bKgrKSkBH5%2BfjCbzSguLsb8%2BfNlVhAOVFlTbGys4npff/11WRDk5eVF5lsa7%2BzZs/HGG2%2BQcehs0D7Oo7g81pk%2BKQBu3769TAnUOgDPyclBv3794OLigqKiIhiNRowbNw7p6emkzdq1awEAaWlpcHFxIaIrs2fPJm3MZjNOnjyJtm3bKsaoUqmQl5dHgmxbwAVaODg4ODieCjwG66M/Cs7Ze3TwYI/jqUGvXr2QnJyMU6dOyfYXFRUhJiZGkSmiOXQhISHIysoioicS4uLiFBw6a6PxrVu3IjIykjmmBQsWwM7OjgQL1lizZg3S0tJw/PhxdOrUCf/9739x7tw52Zik/3x9fVFaWoqgoCCsXLmS9FFQUICkpCR069YNAwcOlHHIFi1aBMASIMyePRtdunTB0KFDcffuXahUKpSUlECn02HOnDk4ePAg4uPjibH4li1b4OzsTIIxk8mkMFeXULt2bRmnDnjA2VOr1ahXrx7q1q2LqVOnyoLeu3fvygRbVq9ejdmzZ6O4uJiUXVoLq/hb8XoiIyPh4uJSLQ9OEkW5fft2pZxAwBKsFhYWkqzr%2BfPnZS8HSkpKSHbQ19cXLi4u8Pf3x7vvvkvaeHp64uzZs4oXDpJNg9lslgnAVAemQAtrndEG5NQ2s8qVMolmkfcVWgcsUQX6QEpxlFk9S3ecnKxsQyt/MF6WKMbH6ocWcKDasERdbBE2UIg8MJRWFW3o%2B8TwzVQoLbB%2BQFFKOayKZ8WYqRvBFH6hdlppIhHQSXmmmw3ViHHrlAOgRD2YwirUMbb4jzPfrVRnxM1QplE8QyzVGVr4hcUlpieQ9vRklW7Tx1Al8wCUaj%2BsG0xfBOuLgd5nw1pTzAVD4Ia%2BV8zvBfrc9AK0RVmFJdBCHcc0AaefTXoNsCqG6PvLuN%2BK4TCqVeg1ynpe6Da2rHO6Datf2qeeNX30PloniawJGyk0TwI82Ht0cFN1jr8NJMPzqjJuc%2BfORXx8PKZPn47w8HBkZmZi8eLFuHPnDjZv3gx3d3didA4Aw4YNIxw6g8GAvn37Ij8/H9HR0VVy6K5evYpmzZpBq9UiPDy8SpP29u3bY%2BvWrXBwcEBpaSlUKhUxQre3t4evry%2Bys7NRXFwMQRAUPnDTpk2Do6MjoqOjUVxcDJPJJMvwsEzGJeh0OpKNU6vVcHZ2htlshkajQXZ2dqUlh9XB%2BrhJkyZh8eLFlX5uSz%2BhoaFISUmRBX%2BiKCI8PJxpRK5Wq2EwGGw%2BT82aNZmm9PRY6tati8uXL1faxtnZGW%2B//Tbee%2B89tGzZUja2pk2bomnTpli/fn2lx0%2BcOBHjxtlmis5N1Tk4ODg4ngrk5laiVPXkwQzgHwNYYj7/VPDMHsdTBcmWICYmBr1798a0adMQGBiIdevWwZ3xRSRx6Ly9veHv7w%2BdTof69evb5NuXwZLoZiAsLAzLly9HWVkZRFGEWq1GaGgovvrqK5w%2BfRpr165FXFwcXF1dUaNGDWzfvl32nySukp%2BfT4RZAEsA88orr2Dbtm1wdnaWlY9u3rwZzZs3R6NGjeDo6AiNRoOgoCAcP34cX375JbKzs%2BHr60tKG3U6nSyTZQ17e3tixaBlSHqlpqYqsmaVBWB0aaLU7ty5c7JA7/nnn4ejoyPOUG9cJR9BURTh5OQEHx8fmSegRqNRlJQCluyuiqW8SI1FOlaj0cjURaUMZ3BwMLp16waDwYATJ07Iykxr166N8vLyKo3j7zFTIWxwgRYODg4OjqcCPLP3VIMHexx/G9jCoxNFEcOHD8fOnTtx9uxZHDlyBNHR0QoeHSsLJ52jRo0a1XLojEYj0tPTsW3bNrzzzju4efMmunXrJrNRACw/2JcvX47JkyfDYDAgODgYu3btwvr169GwYUMEBwejQ4cO6Nq1K/Ly8nDjxg107doVXbt2xZw5cxAYGAgnJycUFBTgueeeQ40aNRAREQEHBwccOHAAI0e6NOxKAAAgAElEQVSORJ8%2BfVBQUCAr4YyKisKdO3fQvn17UhaZkpKCrKws2FVoJD/zzDMkACoqKsKcOXOYc2IwGEhWTBJwsQ5oEhMTUddKJluj0eDbb79l9kUHMM5W2s%2BBgYEkwCorK4OHhwdqVsh6S2OWrC50Oh0MBgPUarXsXnbs2BHbt2%2BHl5eXbEzNmjWDm5ubYjwtWrSQZc%2BkMlO9Xi9T8JRM3XNycuDk5AQHBweoVCoZB89kMsHBwYGM1RrSPNdgyZJXAs7Z4%2BDg4ODg4HjS4MEex/8bPIxvn52dHWbNmoXExETMmjULvr6%2BeP311/HJJ58gLi4OABAdHY28vDyEh4cjLi4Onp6eKCgowJAhQ3Dv3j34%2B/sjMTGR/CcFCV9%2B%2BSUSExPxxRdfAHjAOZR%2B6AcHB6O0tFRhJzB69GgAwJtvvonExEQZx00QBHh6emL79u3EXP7AgQOyPiqzMFCr1eQYKRNnHXQUFhbif//7H9nWarXYvn07OS8NjUZDzOStRWCuXr1KxvPLL7/g2rVrpN%2B2bdtCrVaT83t4eMgyehLi4%2BPRu3dv3L17F79XcEkEQUBaWhqKi4vRpk0bWfvTp09jxIgR6NSpk2y/SqWSjV3igUrnb9SoEQICAhAUFETa3L17F97e3gqbCWv/w/bt2yvGXBmYnL0KJVoZrIRkWNtM/pMNhsWKxCKrEb2PynYzBVVpEggrQ07zdRgXoViurH7oc1H8LJahO52UZl2Dgv/COLeC5kPfJ9bJaVIU6%2BZRHTP5T/RF2MLZo9qwvLHp4TDN5Knxsb5WFGOmOGfMa6J22nJfmBwzmqhJc84YF664DSz%2BMr0GWPeXXhQUv5zJ2aMvilWOTvPFbCE0siaH3kfdYOZ9ocfDIHvaxNmjz033y1ps9HyxiGlUNQXNOQOgfDbp%2BbTFbZzRhr4k1nXTQ2aJVdNtbOHs2cIFpMsfbeHs0dvk2aAJgH8ieGbv0cE5exz/WERGRiI7O5uU6Em%2BfS%2B//DKuXLmCAwcOQKVSMTM1Wq0WM2bMQL9%2B/bB161bMmjWLcPIEQYBarUZZxbegm5sb7t%2B/Lzvezc0NY8eOxfDhw7FmzRosXbq0krI9i/%2BbTqerVCAFsAQU06ZNw6effooZM2Zg1KhRGDp0KM6cOWOTxxxgybJJwZe7uzv69euHHTt2YN26dSgqKiIBGmDJOlU2XmtotVrUrVsXqRXiBQ0aNEBGRgaaN2%2BOs2fPknYSB686WPMT1Wo1jEYjBEFQcBh9fHxkAjCCIEAURURGRuK///2vol/JQkGy0qgKCQkJ%2BPrrr7Fjxw5oNBqiBqrT6fDll19i2LBhlR57%2BPBhhUVGZWBx9iZOmIBx48fbdDwHBwcHB8efguvXgVq1/pJTs15MPQ48RCHOUw/us8fxj0Zlvn137tzBzZs30aBBA0yYMEFxHP2DXvLtmzt3LrRaLU6ePEk%2Bsw70atasiVq1auGXX37BggUL4Obmhv79%2ByM9PR1bt26Fp6cnUbY0m80QBAH5%2Bfky/0BJlMTDwwOLFi3C8OHDodPp8OmnnwIAPv/8cyxbtgylpaXo0KEDDh8%2BDA8Pj2r5YsOHDyfZxNzcXMTGxsJgMKBnz54KC4TGjRvj9OnTzH5ee%2B01fPPNNwCAl19%2BGd7e3iTYy8/Ph0qlIoGer68vsrKy4ODgADc3N2RlZZHMmCiK8Pf3x82bN0kwJgV1zZo1w9y5c%2BHq6orJkyfj4sWLMJlMJPAzm83w9fXFzYq/AjVq1IC/v7/svlgjODgYV65cIXMvBeyCIMgydbVr14aPjw%2B8vb0VwW7NmjUVmUOpL%2BmdWVZWls3BHiuY5m/eODg4ODj%2BdmDQDjieHvAyTo5/NCrz7fPx8UGLFi1w7do1ph9fcXEx9u7dS/pRqVRIS0vD6dOn0atXLxgMBrRq1Qp%2Bfn5wcXEhJZ8//vgj/Pz8oNFooNPpEB8fT7h/Wq2WqGX6%2B/vjzJkz8PHxAQB88cUXmDVrFvz8/JCQkAAnJyfk5uYS5UdrBc9vv/0WcXFxqFevHo4ePQq1Wo06derISlKjoqIAAH379oWjoyNUKhUpA5XwyiuvYMOGDQgJCVGYgVuXL1pDEARMnTqVCKn4%2BvrKhFEmTJggs8AYM2YMAItthouLi4xn5%2BzsTAJlQRDg5%2BdHeHfJycnYtGkTAgMDodVqiUCM2WyGKIrIycnBXauykoYNGyIlJYVkLr28vGRlmuPGjUNOTg4JJj/88EPs3r0bu3fvxs6dO1GvXj0AIF6M3bp1U4jVLF%2B%2BHIIgkH6ljLH1eQICApjzxsHBwcHBwfHw4GWcjw6e2eN4KhAZGUmyOIDlh3atWrUwaNAgDB8%2BnOyPi4vDunXrkJGRgdLSUmzatAlt27aFv78/bty4oeBuAQ8Cm/DwcHz//fcoKipCYWEhEhMTERYWBoPBgPLycsycORP9%2BvXD9OnTAQBJSUmkj48%2B%2Bgj37t3D5MmTsW/fPiIAYjQaiRDMpUuXkJ2dDbPZjNzcXDg4OGDcuHGYO3euLLvYpUsXkr06dOgQAMgCm9deew0NGjTAlStX4OrqipycHOTn58syRZs3bybzIeEuVXP/3XffYdOmTSgrK1N4FNauXZt5H0RRxPnz54mQSuPGjbFq1Sry%2Bddffy1rL32WkJCAqKgo2eeFhYUwGo1o3rw5UlNTkZmZST5Tq9XYv38//vOf/wAAsbMAQOZG2tbpdDh%2B/LiMk0hf6zfffANXV1dSkjl//nyoVCqYTCaUlpaSUl4pC3zixAlotVqSjRRFEbUqSlikTB6rLNX7IbScuUALBwcHB8dTgb/QVJ3j0cEzexxPDaTsWWJiIvbv388UTImOjsbAgQNtEkzx8/ND8%2BbN4eLigrfffhszZ87Er7/%2BSrJRb7zxBuLi4jBhwgT4%2BfkhKSkJr732GgBL8NGiRQtZZk/6TCrPBCxCI5IQjOSHJyEyMhJz585VXGejRo1IJksqMXRxcSGfz58/HxcuXIDBYCBBV0ZGBm7duqXoyzrrZm1QLmXmevbsCSeGpPLHH3/MvAdGoxGDBw8mZYu0JQM9Bml8Go0G3333nawMUgowz549i1KK6W4wGJCbm4ugoCCcOXNGwbOzphq7u7srxGcaNWoky7gdP34cnTt3JttlZWUoLi4m55Xm%2BYMPPgBgCei6du1KzmN9PlqUxppj%2BDCwWaCFNlmmtpkiKRT/k0W/pEUAWOxtBR2UElVg0UUV/bCcza0CewDMHxKK62KZWNMDoM7F0tBQdMy4CAWtk2GyrTiMvk8sNQT6INYPKFtUH%2Bh91Sk8AIq5YVGE6VMz/a2o67LFu9sWsQvFPoYqhUJzhHVyWkiFNkNnmZZXI/TDHE5ysrKfX36Rb//2m3ybRT6ibwTj3ApRFNbaskWghW5D32AWn5meT8azoDiVLUol9H1gKavYYqpOHcdaWopzUS8Dmd%2Bh9Fwxzk1/r7K%2BQ%2Bl1Y8t3MT19rNtiS5vqxFcA5bRXKtBC%2BQP/meCZvUcHF2jheCpQmeH6qFGjoNVqMXLkSAwZMgRr164lVgqRkZF47bXXsGLFCvTt2xeTJ09WZAhFUYSbmxtUKhUKCwvh4uKC9u3b48CBA3B2dsa9e/dIVmnPnj0wGo3M7CALWq0Wbdq0wbVr19CtWzesXr0aer0etWrVwp07dxQBjoQRI0Zg/fr1sgDH3d2d8M28vb2RXfFH2tvbGwUFBZX2JUGlUiElJQWNGzcG8KCEU1KlpBUmbcGIESPw9ttvY9SoUUhMTARgsTqw5voFBATgzp07UKvVJNP5sFCpVBBFEXq9HoIgQKPRVDtelhm7JLZTVdutW7ciJCQE%2B/fvx%2BzZs1FcXEy89SReYuPGjSv1Gbx48aIiAK4MXKCFg4ODg%2BOpwF9oqs54v/BYYMUq%2BceDZ/Y4nmqo1epKPfMOHjyIQYMGYcWKFcRaAHiQIfTz88P06dMxY8YM3Lt3D%2B%2B99x6OHDkCFxcXFBQUEEuFb7/9Fi1atMCQIUMIx0zKkknlf6IoQqvV4quvvgJgyZwZDAb88ssvcHZ2Rn5%2BPgRBgE6nI1k1AMRQ3c7ODg4ODnBwcMD3339fqWKkl5eXzPstIiJCEVxYZ55at24NQGk10KJFC5KRkgRFrDNTn3zyCfl3QEAAAgMDFWPp1q2bYp%2B1D6FarcasWbOwb98%2BrF69Gmq1Gq6urnjhhRdkxwwZMgShoaFwdHSEnZ0dmjRpIisjNRqNspJJeiz/%2Bte/FNcvBWPWvLvz58%2BTf4uiSObbus3MmTMBAFu2bIEgCHBn/HGzDvTUarWMT/mopur8zRsHBwcHx98ONr7E5Ph7gt89jqcSD%2BOZFxwcTIRQAIswiKTK6ebmhhdffBFt27ZFfHw8kpKSEBMTAw8PD7Ru3RqBgYEIDQ3F8uXLYTAY8OOPPwKwCKY0a9YMWq0WgiCgffv2%2BPDDD/HNN99AEAQEBASgVatWRIXywoULKCsrQ3l5OU6fPk0ycVIJakBAACIiIlBSUiITF5GCSclzLjc3F8XFxeTzbdu2KYIGScQEsJiQA1CUakqBnl6vx7Vr1wBAFlDNmDFD1rZTp05wdXWVzeMNqqQvKCgIMTExZNtgMGDZsmVQqVRo2bIlYmNjkZeXh127dsmOW7t2La5fv47Ro0dj48aN6NixIyn/tL4m6f/Wnn%2BApUSTbvvcc89h9OjRskC4fv36smsqKSlBSUmJLCt6qaKEqnHjxggLCyPBPSuTJ4oijEajLMv4MIUSnLPHwcHBwcFRNXgZ56ODB3scTw3mzZuHsLAwhIWFoVmzZpg5cyaGDRuG3r17o6CggMk9i4yMRFBQEPnv5s2bWLRokSwDBVgEPZKSkvDqq69CpVKhqKiI/NC/ceMGmjdvjszMTKKOmZWVheTkZBQUFMBsNuPnn3/G9OnTce7cOZjNZpw/fx6nT59Gfn4%2Bjh49iuQKfkdl5Ye3bt0i/nCZmZkkaGjWrBkAEBNzo9EIBweHaoMKKUu3evVqAEqemSQyYwscHBwQHh4OvV6PsLAwsn/BggUyrlxWVhaxdpCQmpqKF154AcnJyWjevDk8PT2ZY83Ly8PSpUvxyiuvYN%2B%2BfbLsJQ1bAqLDhw9j1apVsqxbKs3dqQKTJk2CIAgIDg6uchxms5mMR6VSPZRAi82cvYsXq9xmVvBSdS%2Bs6aS5LTaZd1P8HeYxdMaSxUGqeMFAwOBeKSp%2BGWbOCs4bZZ7MtK6kr4nxLCmui74HYHBk6DYswiB9EItzRl%2BTLa7lNvCL6MmgaZOs4TF9lG0x4qbn1BbyEJ0VZ7RRnIt1cnq9XbhQ9eeAcv7OnKm2Cc6dU/Zz%2BLB8%2B9df5dss3inNm2OsNQXnjEX8oh5o5p8IeifNIWT9faK/N2mDcjDmhlWZQpPDLl%2BWb7MIovQasKENy7RcMWa6H1vM7hlVG9Vx7Vj7bOHf2eRST39BMm44fQks43X60um/FWS7GqoIx98bnLPH8VQgMjISgwcPZnrmAcBLL72E4OBgzJs3T3HcsGHD0KNHDwBA//790aFDB2zbtg0uLi6YOnUqDhw4gISEBAwYMADJycmoV68eCgoKcPXqVWzevBmurq6kPO/27duIioqCTqfD0KFD8fzzz2Pw4MHo2LEj3n77bQCWH/H169fHzJkziUees7Mzbt26BScnJ%2Bh0Oty7d89mM3TAojgpBUCtW7dGamoqsRkYNWoUvv32W%2BZxKpUKRqMRo0ePxvTp05mWCoIg4OOPP8Y777wDlUpVLR9OrVYTDt3rr7%2BOCxcuEM6ev79/lebw1UGlUqF379746aefSCmrvb09SktLMXToUOTm5ioygxMmTMCqVasUIi00OnTogJ9//rnKNp07d8by5ctRWFiImTNn4sCBAySwTktLA1C5LYWDg4PMSL46sDh7EyZMxPjx42zug4ODg4OD44mjoABwdv5LTs16P/M40LDhk%2Bn37wie2eN4alCZZx4AhISEICUlhXlccnIyYmNj4e3tDbVajR07dhD7gzlz5iAhIQEvvPACPvzwQ5SXl6NOnTqkbDMmJoZkbLy9vYkfnUqlQmBgIMLCwiCKIjw8PMjYJP5emzZt0KBBAxgMBtypeMtfUlKCrKwsRaCn0WhIv9bXBVjKBa0zXbdv3yZ%2BdYIgIDY2ttI5k0oF09LSkGX1Vlmr1RIrgYYNG%2BK9997DK6%2B8Al9fXwAPykclJCYmomHDhtBoNDAYDBgxYgTMZrNMXVOtVmPLli1wdnaGu7u74jokCIJAzi1Bams0GrFt2zZZwCmVWa5duxa7d%2B9W9Hf//n1yvE6nI3YN1tdvCwRBwEcffQQAOHbsGI4dO0Yyg9aZUet/S/casGRtH6YMk8XZ46w9Dg4ODo6/HR6Cj/64wcs4Hx082OP4R6BXr15ITk7GqVOnZPtNJhMSExOJCmRmZqbiR7lWqyV8Pzc3N%2BTn52Pfvn1wdnZGTEwMIiIiMHPmTFnGKj8/H%2B%2B88w6CgoJgNBqxZcsWUipqzQHr3LkzMQoHLAIrgYGBimBKCv6MRqNCsZIOIIxGIwkezWYzU0REghQkJiUl4eWXXyb7W7Vqhb1790Kn0yE9PR01atTAhg0bSBBGZ/ciIiJw6dIl6PV6iKKIt956C2lpaWjXrh0RnAkKCoKXlxcCAgKQn59fqfKm2WxWWDRIbQVBgIuLi6JMVRAEBAUFoWvXrmjatKlMHOb8%2BfMorKhPKSoqwpw5c8hn1vxGaS4EQYCTkxMJ0pycnODr6wuz2QydTgcAuHz5MlxcXEgQzgr2JJ8%2BKQNpNBofKrPHOXscHBwcHBwcTxo82OP4RyAsLAxRUVF48803sWXLFly7dg2//vorcnJyIIoi8c4DgJYtWxJlSEmJMjo6GkFBQTh79izWr1%2BPmTNnoqysDBEREcjOzsapU6fQt29fLFu2DAAwduxY7Nu3jwQDL7zwAillNJlMOH36NIKCgvDVV1/h9u3bsmDh%2BvXrimDqu%2B%2B%2BI31VFbwBlsyedZYu24p/YGdnB0EQ0KFDB/j6%2BpLgoaSkRCaoMnr0aJkIyqVLl7Bw4UIiwOLg4CDjQM6aNQuAJZPVvn17WdZOygampKTgxo0bKC0thdFohCiKePfdd7F48WK89tprMrXPqiwY8hlGSWazGampqdi3bx9%2B%2B%2B03WWa0S5cu5N8ajQZqtRoODg5knzRWybTdbDajsLCQ3IOysjLcuXMHbdq0IWNs1KgRwsLCiL%2BhdfAp9UcHZiqVSmYMXx2YnL1nn1U2pHk11DazGpjipLB4InRikdkPfZ9s6FdBDGGV9dLcJQbRRrEMWHwnmmBCnYvF36GJC6zrViRdGeQ1xS76PrF4afSEsU5OvUFnzjF1EXQFM%2BsYeh/Lj4y%2B3czHlBozq3pacX7qGBZ5hKZ0sfhF9ICYa7Y64zAGl9KW26KwyEtKUjaiuWA//STfPnZMeczRo/Jt1o2hFiTzuv8IgYx6QJikHnqhs9a1LQunOl4pi1hM88RYbai5YQ2v2meTtdhsWJDVrXNWG1ueTXqbRZezhdZny5Kobu2Tzyt%2BK/0V4Jm9R8fDuQBzcDwG0F53arUatWrVwqBBgzB8%2BHCyPy4uDuvWrUNGRgZKS0uxadMmtG3bFv7%2B/rhx40alfnezZ8%2BGKIqkbLOwsBCRkZEAHmRfRowYgZ07d1b647xNmzZYunQpUbMEgIKCAiLy4eTkhC1btpCgw97eHpdpwjkswZHZbCbBzR3qh4ZkE%2BDg4ED6%2Bve//42NGzdWOYfWwQf97xkzZqCoqAhlZWUoKSlhBk/p6emIjo6WlRJKgjQAFPy3vXv3ArAEOJJojAT6mvr3749FixbBZDJh2bJlKCgogLu7O7HJqIpbZwuF2GQyISEhgcydtfCKwWCA2WyWKYtK/75L/WiRjpf%2Bn25F4ndxcUFycjLharJM1emxGo1G1KhRo9rxS9ixYwfTZ69x8%2BbyhnSf1HbFOwI5KKEYK3cJAjqxyOyHLsW1oV/QQkn%2B/so2FS8ICBhckIo4u/JjAKAiE1vZuVgUE0qriHndiqSrlV1Kpbvo%2B8R6aUNPGOvkFaXilR0CQHERVu82Kj2G3qeYXyhvN7MSmxozfW7m%2Balj6HsAABVuNgQMvS3FgJhrlu6Ivg9WisISbLktikfbyuaHgBZoquCKE7RrpzymfXv5NuvGUAuSed30jWA1ovdRDwjrvigWOmtd27Jw6PHR/dDPMqA08ma1oeaG%2Ba60umeTtdhsWJDVrXNWG1ueTXqb5WdOn4p1u21ZEtWtffL5rVv/v4zp/mHgwR7HX4JZs2YR0RSDwYDjx49j9uzZcHNzQ9%2B%2BfREdHY24uDhMmzYN4eHhuH//PpYsWYIhQ4Zg8%2BbN8Pf3J5k0ABgwYABGjhxJ%2BtRoNHBzc1MIu3Tr1g19%2B/bF9OnTMXjwYHTq1An29vaoUaMGateuDScnJ/z8889ITU3F/v37Sfbm%2BvXr8PLywqefforXX38dHh4elQZkS5cuRYsWLQBYgqYDBw5gwYIFACy8w8jISGzatAmApZSvqKgIL730Ejneul%2BNRqPg94WFhSElJYVkpjw9PUmWTq/X4%2BOPP2aOy9p/Ljo6WvH5u%2B%2B%2BSwKZ8ePHkywmAJlR%2BvLly1GzZk1icE8HPT179sSiRYsAPPCdsw4IRVGsslzRzs4O/v7%2BuMpQXxRFEaIowmAwQKvVoqysDBet1OuqChbpbKo0Bml/bm4ukpKSSImr2WxmiuhYB5I06EC4KnCfPQ4ODg6OpwJ16vxlp/7/loV7EuBlnBx/CSSvO29vb/j7%2BzO97pYvX46oqCiF1x0tmiKpclr3ac2TsxZ2EQRBYUNQXl6OK1euoEePHrhy5Qp69uyJN998E4sXL0ZpaSkJMIYNG4aGFfJNer0eN27cgD8ja%2BHm5kbGUbt2bYwYMYJkfFQqFTFkBx6UXUqfNWrUCBs3biT9Wpc7qlQqiKKI1NRUREREkP329vZ45513ZGMYPXo0QkJCZFYAZrNZZkUgiiIpHZUgcdZGjhwJQO7PJ/EM27dvT4JqwCLeIpU2XrCSORdFkfDSBEFAs2bN4ObmJgv0GjdujB49esjuSXl5ucw4vXlFpqtnz54wmUwICAgg7QAQn0BBENCqVSts3LgRLi4ucHR0VFwfYMnade/eHYmJibCzs4Ofnx85v8Ql9PHxwf3792XryHoeAaVRvTQGDg4ODg6OfxRo%2B48/EbyM89HBgz2Ovw2kMr%2B4uDg0a9YMragyGQcHB6xYsQKvvPLKHz6Hn58fEVORykBNJhPUajVSUlJw4cIF/PDDD1i8eDHhwplMJphMJnz%2B%2BedEpEWS5Je2t2zZgldffRUA8Oqrr2Lo0KHknBcvXpTx6qyRm5tLggej0Yj09HQMGjSIBHvWgZGzszNRw3zWituVm5uLJk2ayPpdtWoVUlJSyHkbNGgAADLxGJPJpMhcSUInb7zxhmwbeBBcHT16lJS/5uTk4O7duwgNDQVgKaHds2cP6V/KXpnNZiQnJyu4eleuXEFCQoIsI%2Bfo6EhUT7t160YCrkuXLiEuLg7Xr18HABJAS%2BMym81ISkrCoEGDkJ%2Bfj%2BLiYmZmLj8/H3v27EGHDh1QXl4uK%2BWV7BUKCwtRv359UtpqzTe0NniXykCBhw/0uEALBwcHBwcHx5MGD/Y4/nLo9XrEx8fj6NGj6NSpE1JTU4k6Jo3g4GAiIlIdJG6gpJopmaqrVCqZV5pUDvjdd98Rfp3EK1Or1SRQMplMeP7553Hz5k0cPnwYarWaBCKurq4kCFWr1Th//jzCwsIQEhKCl156iQQkhYWFWLlyZaVj9vPzQ2JiIqZMmaL4LC8vj5Qv1qxZk%2BwvLi7GxIkTZdcjjUNCBmWGXR0kc3SWUT1g4RUGBQWhXQUHRTKNLywsxGeffSZrq1arERERgYULFyrKLEtLSxUcvuLiYpJh27t3Lw4dOgTAwjPs27cvzGYz7OzsFFxBa6hUKvTp0wfjxlXvWafRaMi4pJLQ0aNHQ6VSoWXLlgDYQZi0Tzr2YWwegIcQaKHLPW0Ra6BY97aIArD6UVTFUkoBNom6sCwmbFCHUYgSsLiedCOqjS1exLboWLAaKbQi6B2s67ZFVYG6MbZ4qlfnsQ4odT9sMndmlUVTnTMpuPQkUyXUrG7p8TDFYRTfH4w29IDoC6eFNxj9sjRSFIfRZu2AUqCFNgjbsEF5DL2P5XNK3Rjmc0ftZM2fYp8NxyhUjhg3XHEcqyN60PTzYsuDaIMaEEvDRSGuQt9g1oKkFxdjsdnyHUrvY52quu8kpkiTDabqf%2BTclY7F6jfHnw2e2Xt0cM4ex2ODrcIrxcXFmDVrFt555x3CI7O3t8ewYcPQokULTJ8%2BHefPn8fatWsV5wgPD8f3339f5TiqEm/R6XRQqVT4/fffyb7y8nJiPg48%2BPFuZ2cHtVpdacbG3t4eeRW/AARBIFkgs9ksC2CsOV5sb7UHGDx4MCkBlSAFo1Im6bnnnlMEVNZj7N69O%2BLi4uDt7V2lwbk1h8%2B6DynDCtieaaqKK%2Bfs7IyTJ0/C29sbfn5%2BNgWelXkmSjAajZgyZQo%2B/fRTxWcqlQo%2BPj44duyYTDXUw8ODcAiBB9drzYuUMp8lJSVQq9WEq8gqB6VRxvyLXDlsFmihg0hbxBoo1r0togCsfhRLn1IKsEnUhRUE26AOoxAlYCmB0I2oNqzx0cOzRceC1UihFUHvYF23LaoK1I1hXXZ13bCOoXU/WGtCMRzWdx/VOetcikmmrGZY3dLjYYrDUAeyhCsUA6IvnBbeYPTL0khRHPbMM8pGtEAL7do8eLDyGHofNVcAFDeG%2BdxRO1nzp9hnwzEKlSPGDVccx%2BqIHjT9vNjyINqgBsTScFGIq9A3mLUg6cXFWGy2fIfS%2B1inqu47iSnSRB/E6PiPnLvSsVy7BtSvzxjIk8f/t8DsSYBn9jgeK2bNmoXExEQkJiZi//79eP311/HJJ9o4khoAACAASURBVJ8gLi4OgEUYJC8vD126dMGaNWuwePFitGrVCl5eXhg1ahT8/f0REhKCF198EYmJifDz85P1KWWcbIG3t7fs2MTERLi4uEAULcte%2BrHv5uaGSZMmoU2bNoQfBsgzYxL8/PwwefJkqFQqjB8/nhnkjBw5EmvWrMGECRPIvoYNGyI%2BPh6bN2/Ge%2B%2B9J2svjQcAVqxYgbCwMISFhcHBwQGiKMpsBERRRK1atYgCpSAIsLe3x4gRI0ibuLg4ODo6yuwZWPDw8JCd28vLi/DyKptnaQ4lD0IA%2BPjjjwnHD7CUckoQBAGFhYUoKyvDtm3bFIHe0qVL0bdvXwDARx99RHz%2BJE88nU4n89ST4OzsjNGjR%2BObb75RfGY0GnH79m1kZ2eTEs3Dhw/j%2BeefB2DJwP3rX/%2BCs7Mz1Go1jEYjWQsSp3H//v1ITU2V8fNoSDYPUia1KuEWFrhACwcHBwfHU4G/MLPH8ejgwR7HY4UtwiseHh7o2LEj2rVrh27dumHVqlUy4ZXmzZvj8uXLTOGV7du3Y%2BHChTaNRRRF2bHe3t6y4EbyszMYDMjMzMTp06cxcOBA8rler5fxzkRRRH5%2BPo4cOYLg4GCMGDGC/NC3hpeXF%2BrXr49169aRfZcuXUJKSgqaNm2KevXqydp7eHiQwHLgwIGIi4tDXFwcsSuwHrNWq8WOHTsAAIGBgXB3d0d5eTl%2B%2BeUXWZ8LFy6UBYks5OTkkMydIAhQqVRYsWIF9Ho9yThZm5IDQNeuXVFUVISCggIS4GVmZsqETDyspONpRUvrUkcnJyd06NCBvAjo0aMHZs6cKRtjUVERsX2wxv3793H06FEEBwfL9r/yyivYuHEjNm7ciPfff5/sf%2B6550j5b0lJCerUqUOyp1LGFHiQwUtLS5Nl6srLywlXUIJer4fBYJC1O3HihGKslYFz9jg4ODg4OKoGL%2BN8dPBgj%2BOJgxZeoQMkWnilV69eSE5OxqlTp2TtioqKEBMTU6Uht60oLy9HboWxakFBATZv3oxhw4YhLy8PgiDA2dkZBoMBpaWlpBSyU6dOKC8vx5kzZzBw4EBcvHiRcPHu379PhEk%2B/fRTPPvss7h37x40Gg1cXV0hiiI%2B%2BugjpsdcTk4OCTZiY2MxadIkohxqDVEU4eHhgVu3bkEQBNStWxf5%2Bfkwm804d%2B6crO348eNJdswWBAQEIDMzU1EeKvkESjCZTCT4lDJi1vxHAPjPf/5T6Xn0ej1q164NwBJktW7dmnzWokULGfewOkybNg0bKL7LunXrMGjQIAwaNAjvvvuu7DNpPkVRxMaNG5GRkQG9Xi%2B7PunaCgsLZdcmCALefPPNan0ArVVEqwOTs8cqPz58uMptFr8I1LPDMhenYlc2/4nOPv76q2yTWblKmzCzjKStrDwAKLlOYIyZZWJNP08//yzbZFYM28B/UiTFqZcpAMMrnr5PVqXiBPTNYnGQFC8VlE3o26LoxgauHcMWVDE8Ji2WOjnlAQ%2BAQdmi768N5tO2cM6Ya7aCP0xArzX6c9Zw6GOgXNasNaHom%2BbjMfqlnymwStiphcz8E0iTCm3g/tFrzab5tGX%2BWAOkjcytLHMAMB4oALQPboXyclX7WOta8WzSXwwsJ3baiJ3RseJri/Hc0XRB1iNP76O3WewP%2Bnlh9UuvWVu4qHQb8jPixg3lwRxPDQSzLS7GHBw2IDIyEuPHjyf%2Ba3q9HgkJCZgyZQo%2B%2BugjxMbGIjQ0FAkJCbJ2LMydOxfx8fEALGIZoaGhWLx4Me7cuQO9Xi9TUJSyYv/%2B97%2BxaNEiwtlzcXFBQUEBUUyUrAbc3d1x9%2B5deHh4kFLHefPm4eWXX0bv3r1x8%2BZNFBcXQ61WQ61Wo0aNGrh06RLs7OxIcKdSqaDVaknWz8fHB8HBwTh06BAcHBxQVlZGsjSfffYZ5s6dW6mZuFqtxltvvYVdu3YhIyMDNWvWRKtWrYjqpeRLJwgCnJycUFBQAI1GgwsXLqBz5864fv06VCoVJk2aRLKejo6OlfIDGzZsiEuXLqFmzZooLCyUmakDSi5fdVCr1TaXMHbp0gX79%2B9n9i%2BKIn744QcsW7YMh63%2BOI8dOxYrVqxAnz590KhRIxlPT%2BIzSrCzs4PJZLJ5PDqdDuXl5ST72KVLFyxbtgxLlizBwYMHkZGRAYPBAE9PT5SWlmLFihVEdZWFlJQUZvkvCwsWLGBy9saNH2/T8RwcHBwcHH8KjMZKSJ1PHidPPpl%2Brd41/%2BPBM3scjxXz5s0jnLNmzZph5syZGDZsGHr37o2CgoJK1R1pzJ8/H2PHjkVhYSGWLFmCadOmITAwEOvWrYNKpVJwA52cnLBnzx5SEghYJPZFUYSdnR00Gg3s7e2Jz5tUtghYSve8vLxgMpmQkZGBFi1akGDE3d2dcL0kCIIAo9EIo9FIlDrNZjPxqCsvL5dx/xo2bIju3bsDABYvXoylS5cCsJQWSufw8vLCZ599BkEQcPv2bWRlZWHRokWwt7eHq6srnJycYGdnRwREJNsBqXzSyckJSVaZD2uRGRqXKhTibty4oQj0AKV/nHUmVqvVyoIZrVaryKBVhW7dulUaSJpMJkRFRckCPQAka7l9%2B3aFIAttlF5eXl5poMdScS0qKpKVyb7wwgsALOIpqamppC%2BTyYSQkBCcPXu2qsuzOdADOGePg4ODg%2BMpATNl%2BueAl3E%2BOniwx/FYMXHiRMI5S0hIQFJSEqZPnw7AEpjk5%2Bfj4MGDVWb1AEuWZ/jw4fD19cX777%2BPI0eOIDo6Gr6%2BvgCU3MATJ06gXbt2iI%2BPx/nz5wFY7BAmTZqEXbt2YdeuXdi6dSuAB%2BIfGo0Gvr6%2BMBgM2LNnD3bt2iUr1zOZTDh48CA6dOgAAHj33Xfh6%2BuLzp07E/5f7dq1ERQUhJKSEmL2HRISQiT8AaBPnz7YuXMnBEHArFmzsHTpUqSlpZEgSgo2GjVqhCZNmqBt27ZYtWoVWrZsCVEUUbduXRIASQIiUhCn0WiICIp12WtAQABEUYSvry%2BcrdTUXn31VRJsLl26lJQpSmMXRREGgwF9%2BvQhx0hllwCwc%2BdO1KlThwQ1giAgOjqa/Fu6Ny4uLgpRE0EQ8Pnnn5Prti6rlPiFjRs3lp3PGps3b8bhw4dlQdvDZCAlIRhrqFQqGXeuY8eOAEBUZaUAPi8vD%2BXl5TJ10z59%2Bsh4kSwRFw4ODg4ODg6OvxI82ON4rPD09ERgYCACAwPh5%2Bcn%2BwEcEhJSqaR%2BTEyMzcIrlUHiBkrln/b29vDy8iLjqVOnDlasWCHLLqrVatSsWROHDx/Gxo0b0aRJE4SFhZGgZ8uWLcTPLTY2FnZ2dvjggw%2BwdOlSlJaW4uDBg2jYsKGsRHPChAlYuXIlBEGAnZ0dBEFA69at4ePjg7i4OIWC5Pr169GvXz%2BkpaXh/PnzmDBhAgmE3N3dERUVBScnJyxevJiUG6anpyM0NBTnzp0j3oDWBuiZmZkwmUy4deuWLIMUGxtLeHETJ04kJuKSCqbJZEL//v0JnxF4kAkELIbqK1euxIgRI%2BDq6oodO3Zg%2B/btACwcNGlORVFkcit79uxJyi6lQK1169Zk/q5evYpWrVrJjjlWwfmKiorCc889R%2B6HdUbOFnh6epJ/i6JIMrTW16rRaFBcXAyz2YxOnTqRzG2DBg1w/fp16PV6sn4SEhJkmUVJ8MdWcIEWDg4ODo6nAg/59/Zxgmf2Hh082OP40/A4hVc%2B/vhjYpQeFBSEJk2a4NChQ7Czs8P//vc/AEBWVhbmzJmDJk2aICgoCI0bN8aAAQOQmZlJfqQXFRUhNzcXZWVlRHgFeFCOt3TpUsyaNQsA4Ovri3Xr1sHd3R0tWrTAK6%2B8AlEUsX//fhiNRtytYENrtVqcP38eTZo0wdixY2E2m3Hs2DEUFxeja9euiIyMRFBQEAlKO3XqhKFDh%2BLcuXNwcHDAM888g%2BvXr2P27NnIzMzEnDlz0LFjRxw7dgwuLi5o0KABBEFAaWkpMzgQBAFFRUXQaDRQqVQkGwpYghlJcdLaOy7PiqX9448/4rJVyYZ1Sef8%2BfPRsWNHrFy5Evn5%2BXj11VeJSIqkjJmbm8ssDzWbzTh8%2BDCmTp0q23/ixAkiimMymZCamlqtQblOp5MZy9sCKQMJWIIqVlYwNjaW2Fq0bt2a%2BDEaDAbk5ubC3t6eBKOFhYWyNSsZsNsKlkBLRERXZUNaqYTaZhosU/xQlpAKTSG1yWSbKj21xaxdoVAAKJ2PGc%2B%2B4vbYYlJOncsWw29bDL5Z5672Om1RVmEpYtD9MObmj5iq0/2yTk33Y4tpuS2m74q5YswNfS7muqaFNGyZY1pxgiHGoVgDLMEOul9WG/pZtWW81OQwBZeoyWH5wmPFCvk2S0CGVgumRVxY46NVeljXTe1j3jtaEYq%2BCMZCUtwXVsfUcSzBoOpUSJjjpb%2BjGOOj1zXz3lXnmA4o7wN94bZ8SbG%2BjOnjbHF9r0zAqk4d5bEcTw24QAvHYwMt0MKCJLwyffp0hIeHIzMzkwivbN68WZEdYfUpmbdLPmelpaXQarUIDw/H0aNH4eLiQn6YC4KAN954A82aNUN%2Bfj5iYmJw69Yt5Obmws/PD1evXpWdz8HBAYIgkGxYdHQ0atSogVdffRWxsbFo06YNaZufn4/OnTuTQOnZZ5/FkSNH0KhRI9y5cwevv/46AODzzz%2BHwWCQBWbWJu40Bg0ahL179%2BKZZ56Bj48Pdu7cCaPRSI6vX78%2BUQ3VaDTIzs4mGT9RFDF%2B/HgsX76c9G8tuNK7d2/s2LEDkydPxr1797B%2B/Xro9XqoVCoSALVq1QppaWkoqPjhIgnEPAw0Gg20Wq0s22grvL29ce/ePTJ%2Ba9NzazyMMAzAnnO1Wg2TyUR4nM8%2B%2Byz69%2B9PvBSllwKNGzdGamoq3njjDWzbto3pYejm5vZQ1gssgZYJEyZi/PhxNvfBwcHBwcHxxPG///1lpupHjz6Zftu3fzL9ApaX2wsXLsSWLVtgMpkwYMAATJs2rdKKpLNnz2LBggVIS0uDj48PRo8ejaioKPJ57969SSWWhJ07d6JRo0Y2jYdn9jj%2BVEjCKzExMejdu7dMeOVhyuBcXFzw1ltvYfv27Th06BBOnz6NlStXom3btiQwKCsrw6pVq/DGG28QT7/Vq1cDsAQQrVu3houLC3x9feHl5YUuXbpg%2B/btiIuLg729vULcxDrQk8ZgzUc8XSGr3b17d9y/fx9du3bFvn378OKLL5KSTq1Wi27duiEyMhKBgYFo0qQJ9u3bh8TEREydOhX29vZITk5GaGgoVq1ahZCQEJhMJvTp0wfh4eFQq9Vo164d4eNJJZNSls7Ozg6HDh0iQY2rqyvhnQEg3MElS5YgNjYWer0egiCgR48epM3Fixdlxu/VvQ%2BSzt2wYUPZPinQO3LkCMkOTpgwAcOHD6%2ByP8nsXAJtQSFt04FedZw51peiFISrVCpibVFSUgKTyQS9Xk/GkZ6eDgAICgrCa6%2B9JjuX9FKBFZBWBbZaKn/3xsHBwcHxN8NfWPf4NJZxrlmzBrt27cKyZcuwdOlS7Ny5U/FyV0J2djbGjBmD8PBwbNu2DRMnTsQHH3yAQ4cOAbDYXF25cgVr164lwoSJiYkKz%2BaqYLt0HAdHNTh48GC1bSThlep%2B8FfXpyiKhB9oDbVaDQ8PD9jZ2aFWrVoK/temTZvQrl07TJ48mSh5qtVqHDlyRNbO09OTCLm0adNG8UZFQlRUFKKiorBkyRJ8%2BeWX2Lt3L0aMGAHAojyp1%2BuRmpoqyxLt3btXlpX697//Levzt99%2Bw5YtW5CTk4NFixahf//%2ByMrKwtmzZ2EymXD8%2BHF07twZGzZswNGjR%2BHp6YktW7Zg9uzZcHNzw/nz52Fvbw%2B9Xo%2BOHTsiLi5OlrkD5AGc2WzG7t27yb6ioiL85z//IRm12rVrkwzoyJEjERsbKwu0pCAnw8q7yNoO4dlnnyX//uKLL2TXGhwcjN9%2B%2Bw3PPfccjhw5ArPZjMuXL5P9ABAaGoqTVtrLlQWf1ZUBjx49mgTUpaWl8PHxQatWrSAIAnbv3g3AYmqfkpJC7Dr8/f1x/fp1ooTatm1bHD9%2BXHYu6d/VmdjT4Jw9Dg4ODg6Ofx4kfQTpN%2Bi0adOwZMkSjBo1StF2//798PLywpQpUwAAderUwYkTJ7Bz5048//zzuHHjBvR6PdOn2lbwYI/jsUAqrZSgVqtRq1YtDBo0SBbYxcXFYd26dcjIyIBOp0P79u0xadIk%2BPv7AwC2bt2KZcuWMYM8qaSTBcnT7%2BjRowgNDcW1a9cUHKqioiKsXr0amZmZGDx4ME6dOoX79%2B/j/v37CmNwOzs72Y/x69ev46uvvkJiYiJycnLg7e2NTp06Yfz/sXfd4VFU6/ud2d1sNpveQwKhSSCBQKRIMCjSpFwjIEUUBERAERRQRBBFwEsR5aJS9HL9QUB6CwRQQosUkV4FcukJhISEJKTtbrb9/kjOcebMSbK2e%2BU67/PkeTKT0%2BfMZL75vu99x4yBr68v%2Bvbti8WLF2PNmjXUS1SvXj04HA4sXrwYqampWLhwIcxmM9q3b4%2BDBw/CYDBg9%2B7dsn67dOkCk8mEF154gQp%2Br1%2B/nv69YcOGGDlyJGbNmgWn04l27drJ6hP9Qa1WC4vFQo2u6gwhT09PrFy5En369KGGVHFxMQ03kIa6Es8oUOFhMxgMMJlMcDqdaNGiBU6fPl1tf6wuHjGib968iSZNmuDixYvQaDQ0b04KjUYDnU5H5Sd%2BKUiuoMViQcOGDXHr1i3s3LlTVkar1dJ8w3nz5lHPLQnzNBqNmD59umwNiLH76KOP/qLxJCYm0jxHgkce4YRkWCyA9AFfXg5IvM5OJ8A4PytyfCqZW6ssxLQDqxWQ5HFy67F1eO2WlgISbzK3XeYcrxl2Cop14BViyvCkodjhuDA85bxdKcNpmB0Pb95sNd602UKKeXIaVgzHlYlz5q3ojFemhkXmde3KhSkqAry9q5%2BC4iSzFop9xanDW3NXtrXiOrAnXKmUlwcEBsqKlJUBsm9DN28qcqhOnQKkj6CkJGDIEHlXinMmEyD9SMW5loq14CzOgweAj0/VUwIq0vqkwTsu7XMX1k9xqqQEYCWemHYUdYqLAQlrNa%2BMS3uNM3FX5smec6Vvtg6v3V/zrKvq9rHXfwT/Lb7pP%2Bob6L1795Cbmys7FxQUxJVp%2BiXIycnB3bt30Voi5NeyZUvcuXMH9%2B7dU7Tfvn17NGnSRNEOiY66evUqwsLCfrWhB6jGnorfEVOmTKHhgDabDT/%2B%2BCP1NvXq1QuzZ89GcnIy3n77bbRp0waFhYX47LPPMGjQIGzYsAH%2B/v419pGZmYmsrCw4nU5MnjwZkydPlv3d19cX06dPR8%2BePZGWloaNGzfKcgPNZjMiIiIQGxuLdevW0Xw2g8EAs9lMjZ3y8nKUl5dj8uTJWLFiBa5fvy4zUrKysvDNN99g/fr1mDt3Lrp37466deti48aN1CNIQv%2B6dpWTbhB2SZPJhISEBAAV5C8jRoyAXq%2BHyWRS6McR3LhxAxs2bKAeoOjoaISHh2PatGkYNWoUZTstKSmBm5sbvvvuuxrXNCwsDFu3bpV5zKTC5FKReGn%2BH9EW1Gg0KC4uRq1ataixVxUsFotMcoEYhVKDsnv37vjuu%2B/oHIlXz263ywzMmlC/fn2EhITgSCVRAcnZ0%2Bl0MJlMdH6iKMLpdMLpdFI9QwCYMWOGwhN68eJFGXundM1SU1Nx/Phx2QO%2BOmzbto2bs9ekSWN5QfYBz7yMKQw9QPk2yyvEvqAr3mQ49dg6vHalb8RVtcuc4zWjeCHn/aNjCzFleJG97HBcGJ5y3q6U4TTMjoc3b7Ya9/87U0gxT07DiuG4MnHOvBWd8crUsMi8rl25MFJDz%2BV2mLVQ7CtOHd6au7KtFdeBPeFKJcbQAxhDD%2BCSZbDfmlhDj3uOjUbgXEvFWnAWR2roAfz7js3ScGmfu7B%2BilM8LV%2BmHUUdxtDjlXFpr3Em7so82XOu9M3W4bX7a551Vd0%2BGtiB/5q598dg3bp1WLhwoezcmDFjMHbs2N/ULjEgpUZdYOV9nZ2drTD2IiIiZIRz9%2B/fx44dO%2Bg4rl27Bp1Oh1GjRuHChQuoV68e3nnnHcTGxro8JtXYU/G7gWjfEfTu3Rvbt29HamoqIiIikJSUhG%2B%2B%2BYa6tSMjI7Fo0SJ069YNSUlJGD9%2BfI19hIWFITg4GCaTCf3798fjlRm2bm5u8Pf3h16vR3h4ONzd3eHt7Y2kpCR89NFH8Pb2xuOPPw6z2UzFzAlCQkKwcuXKKgW/b968CXd3d5mxB1TkpVksFkyYMAHR0dEYMGAA5s6dW%2BMceF6vnJwcfPTRR9Sb1rJlS8pa6uPjgyZNmiAzMxO5ubk4duyYrN6///1vzJ8/H6%2B99hpNACbeNjKf6khWysrKsGXLFgA/k7kIgkDDHQVBoHXr1asHo9FItQzv379Px/zTTz/ROtWBkMBcu3ZNZjiRvoneYdOmTXHhwgVZXZvNViMpy86dO9GjRw/YbDZZXtxjjz2GH374AeXl5TIvtHRdcnNzYbPZoNfrUVRUROdDvHdEj5AHURTRokWLascmhZqzp0KFChUqVFSPP8qzN2DAAHTs2FF2TvoOWx3MZjOXqA34%2BX%2B7lPeB/F7Vh3xpu2PHjkVgYCAGDBgAoOIj/4MHD9CvXz%2B88cYbWL9%2BPYYMGYKdO3fSqLiaoBK0qPhDQbTvkpOTERsbq8ihMxgMWLJkCV588UWX2ztw4AC8vLzQoEEDtGvXDu3atUOrVq1Qv359hIeHA6hgTywsLERKSgrOnDmDAwcOYOzYsbh48SJsNhs%2B/fRTtGzZEj4%2BPtBqtYiMjIQgCPjb3/5Gk19DQ0PxyiuvwGQyITw8HJ6enrLk2E2bNqFjx45wOBzYvHkzevXqBUEQIIoifYD06tULhw4dwnvvvQedTifL6xJFEY888giSkpIwadIkyi4KAFOnTgVQ8YA4duwYkpKSsHTpUrRt25YKpgNAq1atYLPZcPDgQTz55JOUSdTpdMoIQ6rLBcvJyaHeNmLoRURE0HMGg4GSsBiNRppL5%2BbmBofDQY3XjIwMmaFHSEw%2B/vhjfPzxx1TfD6jwPL7//vsAIOsbAPz9/eF0OmmIJzGgnnrqKSxfvhwjR46UjZ8lZiHe5YyMDJw9e5aej4%2BPp/2RkEyNRgNfX186v3PnzkGv10On08Hd3V3GCApUPHT37NkDn8pP2YIgULkO6fVToUKFChUqVPx2/FEELcHBwYiJiZH9uBrCefbsWXTt2pX7c%2B7cOQByw478Xl1uf2lpKUaNGoWbN2/iq6%2B%2BomVnzpyJPXv2oHPnzoiJicGHH36IiIgIqnHsCtQ3k78ofu8cOxZWqxVPPPEEHjx4gDlz5mDFihVo3rw5dyzR0dF49913qXcJgCyH7qeffsLJkydx584dWejm1KlTsXDhQvTu3Zu6u2/fvo1OnTpx2wEqjMAdO3aga9eu2LNnD4qKilBYWIgOHTpQVkbyZaeoqAipqakwGAwwGo0QBAFBQUG0j71796JBgwbYt28frl%2B/Dn9/fwQHB%2BPevXvIy8uDKIpITk7GCy%2B8gHXr1sFqtSoMsCtXrmDo0KEwGAzUQDMYDDQ3sby8HB06dEDnzp3x%2Buuv4%2BzZszJNPL1eD0EQsGrVKnTo0AGxsbE4evRolXIFLEgOXbFEH8rpdKJevXrQarW4du0a7t%2B/T/9GPHpkbKQ8mU/37t3x7bffAqjYU3a7He%2B99x5ltiTeu8uXL9P8P2Jgknby8/Nl409ISMCZM2dw8OBBrrQBqefm5oby8nJs2LBBRllM8Omnn8rKW61WGtJJ%2BiLkPiQZmjCsEtbTM2fO4ObNm/QaaDQabNiwAUBFiOqtW7dQ10U9IpWgRYUKFSpUPAzIydNAItn7l0d1xH05OTmYN28ecnNzaXgmCe2synNYUlKCV155BRkZGUhKSpK9R2i1WnhKwpMFQUD9%2BvWr9CzyoHr2/sKYMmUK9VLt2bMHo0aNwscff4zk5GQAFRpzs2fPRv/%2B/ZGcnIxFixYhNzcXgwYNQj5HuXTatGmIi4tDXFwcYmNjUVBQgMcffxyJiYkoLi6WbVYeunfvTgXMDQYD/WndujVGjBghG3NoaCjGjx%2BPwYMHY9GiRXTMBCQH0M3NDe%2B%2B%2By7WrVuHOnXqoKSkhIY5Xrhwgb5c5%2Bfnw%2Bl0YuvWrVSovaysDBkZGTCZTDh%2B/DiKi4sRFRVFjclOnTph6dKlAEA9PR4eHnA6nTh37hxtu3///pSpUhRFGTslgdFoxEsvvYQGDRrAarXinkTItk2bNvjhhx/QsWNHGlpIsG3bNjidThw%2BfBjR0dHUGCLeMndugsrPqIrAJS0tjYrTazQaWZ4d%2BZ14MaW6MSTEVGq8EUPq2WefpdqDAGQhmtLwWaPRKDNUFy1aBAA0LFMa/kiMMOBn45Nn6AFQhDuUl5fDbDbLwnNTUlLQokULWCwWnD9/nq6LxWJBYGAgsrOzMWvWLFreZrPJxl7MiipXA56oelfJhwqKSuO5qmMuF44k1BcA7t7llGHCknnRt3l51bd7%2BzanXclHJF4dAEDll08C3neJmzer75tbKDW1%2BjY4ffHSPxW60d9/ryjDSHQCe/fKj5nEfwCuKYczYs7caF92gOyF4k2KeWZLyHOrHA9XJJoRqOYJfCv0nRlBbe6eZSrxNKJZXW7u%2BlV%2BfKFgyb4qc6ZlYIXDeXuNXYyMDGUZdu%2B7sM8VmQOVzzsZNm6UHx88WOPwXNBvVwqvKxYYAPO/FZKPslV1xv3OyN4w7Ic7nho6Ox7e%2BNiHG4%2Bt%2BdAh%2BTH7YOA9KNgXac7NwM6Tt6/Zarx7ij3H6rnz7jH2ucBrl52C4rnGOccekyUPKbqirPwfwsMmvRASEoJatWrRVBwAOHnyL5nX0gAAIABJREFUJGrVqsX1HDocDowZMwa3b9/GypUrZTJWADB48GCZU8XhcCA9Pf0XSS%2Boxt5fGCTHLigoCGFhYejduzfi4%2BORmpqKEydOICkpCYsWLUK/fv0QGRmJ5s2bY9GiRbDZbEhKSlK098YbbyA5ORnJycnYv38/wsLC0L17dwAVxClF3DeHn%2BHu7g4vLy%2BEhIRg69at9Cc5ORkfffSRbMwajQYBAQEYNmwY4uPjFayW/v7%2BGD16NLy9vbF582a89NJLyMjIQLNmzbBq1Sp8%2B%2B236NKlC9XZS0lJqXJcgiBwvTBSENakmuj/HQ6HQubB6XQiNzcX//d//4eMjAx4eXlhr%2BTFcevWrbh27RrKysqg0WhkoZIeHh7U%2BPLy8kKfPn2QlpZGDR9XRMe1Wi3NfSQgjKJkTsSgiYmJkYVd6vV6Wegi8QI6nU5FbPqmTZvQqFEjhIeHw8fHB94s20IliCahK5DOj9XjY/HMM88AqFgnHkRRxOzZs1G7dm0IggCz2Uy/wjkcDnh7e6O8vLxaA5r1JFeHbdu2YeLEibKfPfvTlAUr76Gqjrnygm3ayA65Yf1MRj9vWgquCKZdSU75z6gMpa6qDgCASSznEQcoHKS8dthCDBkSz8nK9sWLqlFIfjJ5vgDAqL4ArKHO%2B4LLLjJv4gwTCPfRww6QvVC8STEEWA0bctplxsO9RRk2DpacA%2BCQRdSuLTvk7lmmEo9wgmmGv37sxx4mJwcMgzEAgH0B4%2B01djHq1FGWYfe%2BC/tc8dh6/XVlob595cft29c4PFdIPVAZ2k6hWGAAvXrJj3v3VpZxhYCHvWEY3Vp2f3LHwxsf%2B3Dj/R%2BoJEKjYB8MvAcF68bi3Awu8LMoqvHuKfYcSwbEu8fY5wKvXXYKPClj9hx7zFtyFTVj4MCB%2BOSTT3D06FEcPXoUn376KV566SX69/z8fErmt3HjRhw9epTyS%2BTm5iI3N5cyg3fs2BHLly/H3r17cf36dcyYMQPFxcXozbsXq4AaxqlCBldz7AI5jGGs7p305TsmJkZBtkGQlJSEn376CTExMXQMrH4ekRQgkEozuLm5cQW1e/bsiTlz5uCbb77B3r178f3332PBggV0bNeuXYOnpyf2798vq%2Bfm5gZBEBRhikBF2CMR3AZ%2BZng8VPnlkBhIJKRQChKaKPV6sbBarSgqKpJ9/dFoNHA6nTKGTIL27dtj165dACoMjeTkZGp4AhVyAFJCF6kRRdqz2WyK9TMajSgrK1OEFRYWFqJp06a4fPkyDAYDDWHs2rUrAgMDkZeXB09PT25dAFRHRgpBEGSyDWazmWukknUzGo2UQMVms1HPp9FoRElJCRo0aIBr166hUaNGuHLlCiIiIpCZmYmAgAAAFd63li1bQqPR4MyZM/Q6ORwOfPfddxgwYACcTif8/Pxo6IW7uztycnLQqlUrTJs2DRMmTKDX0MfHBw8ePMATTzzhspEKqAQtKlSoUKHiIcEv8CL93ngYsxuGDx%2BO%2B/fvY8yYMdBoNOjbt68sRapv3740BWnXrl1wOByyyCegIqpr5cqVGDp0KCwWCz766CPk5eWhefPmWLZsWY3RclKoxt5fGHPnzpVJFxDGxsTERFy%2BfJnm2FWVt0dQWlpaZfgaYad85plnsHr1apw8eVKmf1daWoqkpCR4eHjg5MmTNG%2BP9ZAQinpWboEYW6wwuTRvr1u3bvT3qKgopKen46WXXsLnn38OQRDw6KOPUm%2BZ0WjEqlWr4OHhgXnz5uHChQswmUx0fikpKcjKyqI3LfHkFRcX49KlS7SfoUOHYt%2B%2BfahVqxYOHDgAPz8/%2BpWGZ%2Bh5eHigvLwcNpsNDoeDek6JPIHBYIBWq0XDhg1x8%2BZNymLZr18/pKamwtPTEz/99BMGDhyI1atX02vZt29fWWjl9u3baZ9kHg0aNJB5GwVBQGRkJMaNG4fhw4fD29sb7u7uuHfvHjIzM5FZGddRXl6Op59%2BmhqKJI9NamyyOHjwIF599VVcvnyZrp3T6ZRJNvTr1w9r165FYGAgcnNzKSvn4MGDkZKSgoKCAuj1emoQ/vjjjwB%2BDkm9ceMGgJ%2BlL8h4Z8%2BeTfvo3LkzXn75ZVgsFhw7dgwTJ05EQUEBNmzYQPe9KIrUgNVoNCgoKEBgYCA2b94Mp9NJ9x6ZtzSc1RWoOXsqVKhQoUJF9XgY/y1qNBquPBiB1GHx9ddfV9uWIAh49dVX8eqrr/7q8QjOqlwMKv6nQQhaCIOg2WyGXq9HmzZtcPjwYfj5%2BaFv374wm81cbbxbt25hw4YNSEtLo5vZzc1NFtJnMpng4%2BODSZMmoU%2BfPnj//feRmpqKiRMnyrTv7t27h2bNmuHbb7%2BlNP%2Bs7p0UJE%2BM9f7odDpERETQl31iEFQVWkn02lhPmyAIqFu3Lry8vHD%2B/PkqvXAsnnvuOWzfvh0WiwWxsbHo3r07bt26hbVr16Jt27bUKKkJUuFxg8GA8vLyKufg4eFBZRbIGkjz3VgR8%2Beeew6bNm3Cu%2B%2B%2Bi7y8PKxatQomk0kmzRAcHIzc3FzapiAI0Gg0Ml06nlESGhoKs9mMwsLCar2XroDkBhJtveraIgZ8TSDGGQGZl0ajka3Rm2%2B%2BiUWLFkGj0cBqtcLhcNB1/OKLLzBu3Dju9dBqtVTn0BVcvHiR5nIS1K3bCLGxjM5eWhrQoUOVx1yx35MnAclHldxcTlQhIzbNa4cVQsbp00BcHD28e5cTIpqRIQ9xY%2BoAAC5eBKSC8pzOb91iIr/OngVYkqc7d%2BShcwcPykLc2KEASh1pViwb4Mz7yBFFyBvbNQ4dkoeLKRqBa0L2TD3u9WVVrJl2uXWYi3X1KieUk1kMthveSV5fChFwpm9XRKJ5y6dYc56ge2qqPJz32DF5WOaVKwCTE6PYyOz%2B5A06J0cZJ5edDYSG/nx86RIgEUvmiY0ztyFfDf3rr4Hhw38%2BXrkSGDy42uHx1i8/n4mWZPfsv/8NNGokr8Q%2Bf9j15fTN3X/sel24ADRt%2BvNxUZEyFpG94JmZyrhCpl1u3ydOANIoJfbBwLsZ7t2Th/dyHhTsteMJm7PXgXdd2HOsLjyvjitl2G3NTglQ/m9gj%2BkluHYNaNAA/w2waeu/F9gMif9lqMbeXxQdO3ZEcXExRo4cia5du0Kv19NcuOHDh%2BPcuXNo06YN9u7dK9PGAyqMuG7duqFXr16IjIzE7NmzUVJSgvHjx8s8bIMHD4bFYqHGnsPhwIoVK7Bp0yZkZmZS7btx48bhH//4B44ePQo/Pz/k5eVR3TueUdG4cWPY7XZcuSJPGBZFER4eHtSztGLFCqxYsQJ79uzhroGrBsnjjz%2BOkydP1qgf5%2Bfnh%2BLiYmoUEW0%2BjUYDh8NB5zJo0CB88803VbZDWD0vX75c49hSU1ORmJhY5dh44aRSEINn%2BvTpmD59eo0aMDqdDnq9nuu98/DwgNls/kXeqfj4eMpuKQ1r1Ol0aNWqFY4cOYJmzZrJWEB5c3DlOsbExMiMManRSn5/7LHH8Mwzz2D69OmUrVNqPKelpWHUqFFc4/KXGntz5szhiqqPGcPJ2VGhQoUKFSr%2BW0hPB35BTvrviR07/ph2e/b8Y9r9M0IlaPkLQxRFmmcXGhpKw/G0Wi18fX1x6tQpbt7e%2BvXr0a5dO6qNJ4oiwsLCMHLkSERGRtIfVnNMFEUMHTpUpn03e/ZshFR%2BldNqtWjUqBEOHDig0L37/PPPaTtJSUnw9fWFwWCAIAgYM2YM4uPj0bZtW7SRfMENDw%2BHr68vNBqNTB9v%2BfLlGDBgALy8vDB48GCZ5AMZp3Ts/v7%2B9FgURegln%2B5IHhgAFBQUyELziGeShGYCFZ62sWPHUs8Vj1QkPz%2BfEtIQEMIUtrynpyfWr18PURQpIyiJ437ssccokyUvpxEAHd/7778vM/SCgoJgZN0dANq1awez2SxbU6Jf17x5c4WhJwgCDh06RFmj4uPjZcLjTqcTd%2B/eRdu2bTFixAgaCulwOHDmzBkAPzOKkrVn50LyG6uKXyfXrkPl1%2Bknn3xSsZZk3OPGjaMi6k2bNlXMx2Aw4J133qHH0r0SHBxco7EshZqzp0KFChUqHgrUqvXfHoGK3wA1Z%2B9PhN9b%2B24fSzld2QfRcGNhtVqxf/9%2BHD58GKNGjcLChQuRn5/PZRgcNmwYbty4QUM4CwsLERUVBZ1Oh%2BDgYBlLUEFBQbUshcRL4nQ6cenSJXTq1Ak5OTmw2%2B1IT0%2BHwWCAr68vLT9nzhy6PgMHDsRXX31Fxb3/7//%2Bj85b6u0hrIrZ2dkYNmwYnE4nHnvsMaxcuRIrV66UjcfhcKBp06YoKipCZmYmNBoN9Ri6ubnJwkfv37%2BPkJAQqndSE0GHp6cndu7cSTX9/P39qZFotVppSGWehEpdr9ejZcuWKCwsRHh4OLKzs6mnq52EWY7NmTtx4gROnDgBoHqWUC8vL6xYsQLPPfccNW6Ki4upMeTn54cHDx7A4XDgewkFffv27eHp6UkZpYYPH44jDJW30%2BlEgiRM6Pjx4zJjjeQThlaGPjkcDri5uSEgIECRB9q1a1eMGzcOBw8exKxZs2jeHFl7Mo6pU6fKjGVyvYjh6%2B7ujpkzZ2LNmjW4evUq7HY79d45HA6aW1lYWIjAwEDk5ORQ7T1vb2%2BZV1bqsc3KysLp06fxGMsyp0KFChUqVKj4VXgYc/b%2BbFCNvT8ZpkyZgh49egCoeEn98ccf8d5778HX1xe9evXC7NmzuTl0gwYNotpyvwTTpk3DzJkzAVR4otzd3TFkyBCMHTsWX375JRwOB9555x3ExcXh3r17WLp0KfLy8jBq1ChKgOHt7Y2ioiIqDH7v3j2qCSL1nmzYsEGhdSZFYWEhMjMzqffGarXiypUrGDZsGN5%2B%2B21aLjk5mRpyq1evlrUxXJLX0KNHD9jtdtjtdupNatSoEerUqYN69erh9OnT8PDwQFBQEG5JNIDatGmDoUOHYvv27SgvL0dKSgo1lnjhklKjJE8hTFZhlHp4eKCoqAj379/HjBkzAFQYYITt0dPTE4mJiXTNR48eLRvPwUpdpStXrsgM30OHDiErK0vh/fT390fLli1x9uxZ3Lt3D%2B7u7tyxe3p6olmzZti6davMiyUtq9Vq0bZtW/zwww%2BykEmn00m9bsXFxQqtQx5sNhsCAwPpmIh3S3odP/jgA0ydOpXunePHjwMAvvvuO3z33XcAKoz33Nxc2O121KlTBxkZGXRcXbp0wUcffYSoqChERERQGQsSqrlr1y6kpaXRfDwphg0bRumRo6Oj6bqT6y%2BKIm5zxeVAx%2BqqsacStKhQoUKFiocCNWj2/pFQ/y3%2BdqhhnH8y/N7adzWB1cY7ceIEJk6cSMfi6%2BuLzZs3Y%2BjQofjoo4/QsGFDrF27Fn6STGBRFKk23rZt27Bjxw7ExcVBr9fLNM38/f3p3KQ/BKWlpWjRogVSUlKwbds2SsRy7tw5vPLKK7Rcu3btZJ5CKQuiNJRPSmxiMplgMplw7tw53LlzB0ePHkWtWrVgsVhw9%2B5dhbdp9OjRSEtLo0yY1YEfjlcBg8EAu90u0xjk5ZdlZWXhyy%2B/pMfxEjKIixcv4qmnnqKhrbkSseYjR46gefPmVHSegBjLRJy9qvDCsrIy/Pjjj9i8eXOVcygqKsIPlULE9erVk613bm4uNXalTJ/VITs7G48%2B%2BigiuCJtFZ45ANVqyEjXIJMR2iUePpPJhPz8fOgqs/VDJEn8FouFa1hNnjwZ7u7u8PX1xe7du6lXkGgNnj9/XpErKsUv0b3hiap36dJVWZAVfGaPeQrGjFeUp%2B/NihjztrFCw5gpxBVrd0W1nCnDE/tVfJvgqWyzg2bWhleFHR5XAJq9RzmCz4q2mUnwpq1wsPM87kxFrlOe/ajEqDDz5s1uAu6eYObAbYcp44IuvEIV2hVxcd5%2BVOwTXkNnz8qPWcFsVvgcqCBSqbYjKO4p7gVmB81sYl5qseJcaqqyEPNRE7ycb/aC8ubJfqhi5ZAYEXgAFQRLUvAE55m14KZQs8%2BtSiI1ClcUyXms32z%2BOO%2B6sO2wDxeOBjA7HC7hOLOvefcLW09xb0C5bVwRVWf74rXLbglXBN3Z8dLLplpcDzVUz95DgN%2BifVcV9u3bh44dOyq08dhQUqBCR2zcuHGyUNK3335b5vUoKiqCm5sb9dyVlZVBEASugDUJJe3Tpw89N2fOHOzYsQNFRUWoXbs2zalyOp3Q6XSyMMRTp07RdgkBCSnLhv4Rgg2SZ2exWKicQU5ODs2bCwsLw%2B3bt2E0GmXGAmG6JO2LokgNKVdg4v3jcQGHDx%2BWHXt7e2PWrFkYPXq0zHA7fvw4EhMTFfP28/NDWloaPa7KY0TOE2PUw8MDZWVliIiIQF5eHsxms4yp8vr169WOm0d6wtMXzMjIwOOPP46rV69WOTZCrFOrVi1kZWVBFEXodDqZziGgNJ6JZzwjIwMZGRn0/F2uZSLH4cOHERcXR41/aduiKCItLQ0TJkzA/Pnz6XnC8snmetaEbdu2KQha3hg7Fk2aMGycLH0ae8xTMGbuO56%2BNytizBPvVgjqMoW4jnpXVMuZMjyxX8WHZJ7KNjtoZm14VdjhcQWg2VxaTsSEom1mErxpK1Jnebm0TEVuui37rGfya3nzZjcBd08wc%2BC2w5RxQRdeQenpirg4bz8q9gmvIZaxlRXMZoXPARljJr8jKO4p7gVmB81sYp7et%2BJcV84HnxdekB8PGqQsw15Q3jzZD2xSNkxAIQIPQMmkyxOcZ9aCN0/Fc6tePfmxK4rknPcJsLnavOvCtsM%2BXDiK5OxweF2z%2B5p3v7D1OKnwim3jiqg62xevXXZLuCLozo6XXrby8ioeCH88VDvzt0P17P2JYbVakZqaisOHD6NTp064fPkymjVrxi0bHR0tE%2BL%2BLZgyZQoOHTqEtm3bAgCefvppfPzxxzRMb8aMGdixYwdqV74JDho0CA6HA4MGDUJubi527dqFq1ev1ugRY9GgQQNcv34dHTt2xLRp0%2BB0OtGhQwc0bdoUISEhGFxJNf3oo49SyYTly5cjMTFR5m2aNWsW/b1p5T%2Bzxo0bY8iQIWjYsCG2bNmC8PBw5Ofnw8PDA%2B%2B%2B%2By7at28PjUaD2rVrIyIiAn/729/g5uZGX/aJJ7Nr164yTRRCnCI9rg5kTQjlP4GPjw%2BaN29OQxc7SKiu58%2Bfj%2BvXr2PatGlo0KCBLIyTGEqbNm2S9UMM0yeeeKLa8RAQI6WsrAxarRYBAQHUoPL29qZ7Kzw8HA0aNIAgCAgODkbr1q1l7RCPq5TcZdCgQRAEAc2aNcPrr1cwTd67dw/Z2dnw8PCgHzMI6tWrh4CAAGqA1qt8KejatStSUlKwY8cOhFe%2ByFRFJMMDWe86lZTbPj4%2B0Gq11MMsiiKMRiNCQkJgtVoRGRkpM/a8vLxw5swZ7N%2B/X9Yu%2BRAhCIIsp7Em8DzCKj2LChUqVKhQoeL3hOrZ%2B5Ohqhy6xMRELFq0qErGQRZZWVmIY7/IwTVPEwklDQsLgyAI%2BPLLLyEIAiZPnoypU6fCarUiODgYr776Kl5//XV8/fXXKC8vx507d9C%2BfXtERESgZ8%2BeOHnyJADQEEGp0DkRm9RqtZg9ezYSExPRuHFjGI1G2O12rF%2B/Hg6HA2lpaXB3d0dqaiquX7%2BOlStX4ujRozSsbvDgwXBzc5N5/r744gsAFYZHSUkJBEHA2bNnYTKZ8PTTT%2BO7775DUVERGjZsiKtXr2LmzJnUA0XkDkaNGoUHDx5Q7%2BXYsWMxY8YMpKWlYffu3bQvh8MhMwhYIhRPT0%2BUlJRAp9PBaDRSEhVPT0%2BUl5fT6yEIAvr164eioiLcuHEDFyThNUePHsXLL7%2BMt99%2Bm2q%2BEWzcuBEbN26U9SkIAm7dugWHw4Hnn39eJpheFaTeNZvNhrOSUCiHw0HDJu/cuQNRFGluZl5eHnQ6HTQaDcxmM10/T09POrdly5bBx8cHoijitddew/fff48LFy7IyFykuZ2iKMrCXok%2BoTRnTzrW5cuXo1%2B/fjXOsXbt2rh58yYN%2ByTXQhoSmp2djRYtWqCoqEhGUgNUGInZ2dlVfsSw2%2B0KD2t1UHP2VKhQoULFQ4G8vCrcm3881H%2BLvx2qZ%2B9Phupy6Hx9fWUvwdUhODiYtiP9%2BaXev86dO2PUqFEy%2Bns/Pz9s3LiRhlKOHTsWISEhmDx5Mvz9/dGpUydZzlmXLl0AAJ988gmWLVuGgIAADB8%2BHMuWLcOqVavQsWNHWrZ27dpYu3YtJQMRBAFlZWWUcRGoCANt2rQp9Ho9nE4nVq5cSYlZPD09abheeno6rly5Qo2xf//731i0aBHWrl2Lbt26oXfv3jAajdQzZLPZ0LBhQ8yZMwcBAQEyg2vhwoVwOp1wOBwyT15N7JuEGTM0NJQaqECFoSE1vAsLC/Hhhx/SEE2pHMTixYsxfvx4LklMYmIiPvnkE1mfer0e5eXl0Gg01bKgSjF%2B/Phq5yAVbZdCp9NBEAQ6JlIuJydHZrg8ePAAZ86cweLFi6nHUErWIzWY79%2B/L2M8rc4AKigooBIgfrzQKwlImHPjxo2rLDNlyhTUrl2b7q0XJOFTTqcT5eXlsjxTFsQb7gp4OXtdJR9EKFhWXeaYG1HM5NXwHhuKvBpOzowiD4Rpl8NHpMzFqWSElaFSVqO6ATJ688CpU8p2JGG6ABRrk52trMKuF0%2BiUjEcTp6SInWT/ajCWxzWm8vLpWXyxbj5T0wOoaIML3mICWPmrY0iYYe3OExnvCKKPcnOkzM%2Bdq/xvksqUid568eKcrH7j7eP2AvO5vABykXm5cRJ/k8BABjdTd69qrjtmA9aAIDKj44UipsDyjxDjhaqYv%2BtWiU/riSzkoHNIeQpXLuS68muF5svyEtMY28y3nVhEtO4krPs/cuOhbee7LOFlxTnQgIwe0vxnsVsGVfSntl2eGmm7DneErPn2Do0Z0/yP/k/DYfjj/n5K0EVVf8TgZfLJsWMGTNw4cIFrF%2B/XvG3pKQk5OXl4a233nJJeqGqPjp27IjS0lJKPw/8nHuVmJiIGzduoHnz5nj//fcxf/58fPXVV/Dw8KASEJ07d8Ybb7yBbt264YcffoDRaMSKFSvQqVMn7N27FxEREbQPIrYOAJcvX0ZycjLeffdd2m%2BTJk3gcDjg6%2BuLYcOGweFw4F//%2BhdMJhO0Wi0cDgdsNhu%2B/vpr1K1bF506dZLl3LkCLy8v%2BPn5ISMjg%2BaE2Ww2%2BPj4wGQyUSIPrVYLm82GJ598EiNGjMAgXs4E5DlrUhBP3i/RYXMFGzZswJ49e/DVV18p/qbX67Fz506ZR9XNzQ2CIMBms8m8kM2aNUN2djb1cpH1dTgc1Oiuyugic65q7r8UZK0DAgJw//59Wb4kkazw9fXFgwcPYLPZaHniRSU5dFVBo9EgMDAQTZo0wenTp6mHD6hgPl25ciXatWsHk8kkE4nXarV0n0nXm4wvICCAEtm4AlVUXYUKFSpUPBQoLv6vefY2bPhj2nUhIOh/Bqpn7yHCM888g3PnztHwSILS0lIkJSVV%2B4L7S1BYWAidTkc9WG5ubnjiiSewY8cO3L17F56enpg9ezZWVX4VTE5OxqJFi5Cbm4s5c%2BagT58%2BSE1NRWFhIbKystCzZ08AQM%2BePREXF4esrCxFn3a7HcuWLcPFixfpOeKF1Ov1SElJwZYtWxATE4MuXbqgR48e1NsolU0oLS2loXfE61QdTCYT3nvvPaxevRqtW7emdPx6vR61a9emL/rE03TkyBEF86UUbdq0oZpxJNzP3d0dFovlVxl6oaGhmDFjhixXU%2BpZjI2NVYjCE1gsFrr2BAkJCdiyZQtGjhwp89KdP39e5rHy9PSUGW5S4hGS27ZgwQJ88MEH8PT0hJ%2BfH1544QXEVib4%2B/j41Lj21cHPz4/OmXjLRo4cidjYWEreQ64J8cwSTym5Dx555BEAFSywHh4eNPzVbrcjJCQES5YswY8//oiNGzfSsseOHYPdbkdBQQE0Go1sjex2Ozw8PJCSkiIbK/leVsD7tFoNVFF1FSpUqFDxUMDFqLI/Aqpn77dDNfYeIsTFxaFfv34YPXo0Nm7ciIyMDBw7dgwjRoyAKIrVGiG/BN7e3njzzTfRqVMndOvWDadOncLSpUsRHx%2BP8vJyXL16FUlJSXjzzTcBQCEB4eXlBb1eD61Wi%2BDgYPzzn/8EACp87nQ6UVhYiA8%2B%2BAAtWrRAixYtcO3aNXTo0AGjR49GSkoKbt%2B%2BjXnz5gGoYIrMy8vD6tWrER4eDg8PD8ydO5d6V2bNmoXu3bsDqDBMx48fjy1btqBVq1YKpkZC0hEZGQmj0Qh/f3/ExMSgTp06eOedd5CQkACn04n79%2B8jJyeHGpRAhSFnNBrRq1evKtfu%2BvXrePnll6HVaqkXzWw203GQPC0/Pz9FSCQPGo0G58%2BfpyLqZB0JYmNjqbwCD6wHUhAElJSUYMmSJbL%2BfX19ZRqBo0aNosaqTqejxhBQEX45ZcoUjBs3DjNmzEBRUREKCgqwevVq6hn8LQEDNpsNhYWFNF/y6NGjAIClS5fi3LlzsFqtsrESo5L92EEkEvLz81FWVgaLxULX8dy5c2jSpAmaNWuGF154AZmZmbSdAwcOwOFwoLi4WMb6KQgCQkNDZaGdUjgcDllIck1Qc/ZUqFChQoUKFX80VGPvIcP06dPx2muvISkpCYmJiXj77bcRGRmJVatW1Ziz5CpEUURAQAA8PT1hMBiogaTVauHr64tTp04hNjZWkQ%2B2fv16tGvXDi%2B//DKefvpp2Gw22Gw2ypxYXl4uo/G3Wq0wmUz4%2B9//jo4dO2LBggV49tlnsXDhQnTv3h1jx46Fl5cXdDodnnvuOZnn6cKFCxg2bBgA0JA/0sf27dvh5eUlIwAh8PHxgcFgwK1bt6i4d0JCAhISEvDcc89RMpPy8nL4%2BPgQpI0bAAAgAElEQVTQXDSNRoMTJ06gsLAQa9eurXLt7t27h1mzZsFms%2BHBgwfU6LHZbFQ3EKjwArki33Dnzh1sqCaGQZqDKQiCIofwX//6l%2Bx479696N%2B/PwC5Z6mwsFBGVDJ37lyYzWbqxSPEK6Iowt/fH%2B%2B9955iLAaDAfXr1wcAKllRE/z8/LBlyxZERUXJPJaPPPIIatWqBQBUpLwqz7U05Lg61K1bF8OHD6f9EO9geXk5rFYrHS9h/2QZU0nYMCuLIYUrRDEE3Jw9Hu06m3PEHHO3EaOXxcuhcSXPS5H/wuTZcD/2snk2TN4SAGXuDUfHTqFdX7kHZWATz5jcHF6OCrtevLVRnOPkCrGpiYpcMJ6nl02%2B4Xn72bXgDZD5wKNIz%2BJtCiafjOuIZnPOOIVc2TfsKcVwOBuH7YoXja%2Boxlsb9rnP7kfFhYMyaYqTj6fIm%2BJ9ZGMnweSC8S6L4hzn/5Yi54yXY8ZuAl7yGrtelR9iKRiyLwDK5w/v%2Bce064o%2BIyQROQD4OYbstWPz/ADF/cJ9Htako8jLBWT3gAt6pryJu5Kzx55zJa%2BYnQLnEaoo40peX5XHv3MKyi%2BB6tn77VBz9h4CsNp3Wq0WtWvXxvPPPy/TvktOTsaqVatw9epVmkM3btw4SoThSi7fwoULFfl0VqsV%2B/fvx4QJEzBq1CgsXLgQnTt3RkxMDLZu3YqsrCz4%2B/ujuLgYzz77LKZNm4bNmzdj7ty5KC4ulr2kE%2B%2BJRqPBW2%2B9hZdffpn%2BzWKxYOnSpUhJScHdu3cRHByMOnXq4PDhw5g%2BfTqef/55vPvuuygrK0NaWhqsViuaNWuGs2fP4oUXXsDq1aupFMJrr72Gzz//nLat1%2BsRGxuLPn36YOrUqbDb7fD19eUaCgEBAYiPj8e3lYno1YXHBgYGIi8vT5avptfrZTlxJKdLr9dDp9NR0hYWoigiODgY2VzmhJ/Xr6Zb1pUyBCS/jeRF/uMf/6iyDA/u7u5wOp1wd3dH06ZNqzWEgIqQyzp16uBS5T9XQRAQGRmJW7duYcCAAdSQdnNzQ0xMDE6fPo2EhAQcOnSIltfpdDQkVhAEqptoMBioJEdV89fr9fDy8qIsojyD%2B8iRI4iPj4efnx/Ky8tpDqi7uzs8PDywfft2tGvXjpYnWo8ajQbHjh1zmTGXl7P3xtixeH3MGJfqq1ChQoUKFf8RlJbyxfz%2BA2B5hH4vVHK7/SWgevYeEhDtu0OHDmHPnj0YNWqUTPtu9uzZmD17Nvr37y/LoRs0aBDyeZ98asC0adMQFxeHuLg4xMbGYtKkSRgyZAjGjh0Lo9GIvXv3YuXKlXjppZcwd%2B5ceHt7w2azIS0tTcacabfbodPpYDAYYDAYKMuhzWaTeazKy8vx0ksvITU1FZMnT8aOHTswdepU3Kj8Crto0SLs3r0bZWVlOHbsGCwWC3x9fam4NfHG2O122Gw2maEHVBiSEydORJ8%2Bfaj3pLi4GH5%2BftRD6e3tjfj4ePTq1Qvbt29HZGRkjXmQhNyDeNRat26NlJQUzJ49m5Yhhofdbq%2BWPMbd3V2Rx8WGerJGDMlfq64MCSnlhY2S%2BVksFi7JC1BBYiNlzpR64ERRhK%2BvL8xmM1q0aIHWrVtXm6tXWlpK5QnIeG7evAmn00kNvcjISISEhOD06dMAQA09MjdC1kKOSX8kZ49414jcRUBAAC3v5%2BcHk8mEXr16ITQ0lDtWX19f6HQ6FBQUyPIszWYz8vPzFV5NUsZut6Nly5ZVzp2FqrOnQoUKFSoeBpQ4/zuGnorfB6qx95CAaN8R/bvevXsjPj4eqampOHHiBJKSkrBo0SL069dPkUOXlJSkaO/111%2BnxhwhTZk2bRqysrJQWFiIzp07VykB4ePjQxkR582bhzlz5qBp06bYuXMn/P398fe//532I4oi3nzzTWzduhVbt27Ftm3bEBISAoPBgOvXr9MX/6%2B//hqZmZlYsWIFOnTogNq1a6NDhw747LPPAFQYAAsWLMDu3bspEcY333xD%2B/H29gZQ8YLvxTBGkfDWWbNmwel00lBSPz8/PHjwAOnp6QCA/v3748svv8Q777xDBbUJXT9QETLp7e0ty420Wq0QBIG%2B8J8%2BfRq9evVSGAReXl5wOBw03FRKeNKyZUtotVp06tRJIa1B2g8MDESvXr3g6ekJrVYrM16IcUdYM1nodDp4enrK1kUURVnIp8lkQmBgoKz%2BwoULIYoi2rRpg7Fjx9LzH330kayduXPnwmKxIDc3F2VlZTV6FUVRRPPmzVG3bl1uWUEQqBaeVqtVaPBpNBqZmLvNZqP5kUBFTmGbNm3g7u4OvV6PwsJC6nX18PBAaWkptm7ditu3b8PNzQ3u7u6y65GdnU1DhonnT0r2c44JkZRiyJAh1c5dhQoVKlSoUOE61DDO3w7V2HuIodVqodPpkJycjNjYWLRq1Ur2d4PBgCVLllAdMimmTZum0N974403sGvXLoSEhKB169aIjIxEZGQkQkNDZblLpaWl8PPzw7fffoszZ87gwIEDmD17NiIiItCoUSOkpqbiqaeewqRJkyAIAgICAmhbkZGROHDgACX/IEbLli1b0KdPH/j6%2BsrGGRsbi4iICDzyyCPYsWMHzd8CKgzACxcuQKPRUAIRg8GgMJiIcXju3DlER0dj//79MBgMaNKkiSzPcfz48XB3d8eUKVNgsVjw1FNPoX379rTMgwcP4HA4sHz5cpmhRPT3gIq8LrPZrAgPLCsrowLsRCqA4OTJk7DZbAqWR8IG6XQ6kZeXh%2BTkZJSUlMBms9H%2BpCGhxOvFwmQyIT8/X%2BbhdTgcMq%2BVTqdDVlYWNWj8/f0xZ84cREdHo2nTpjIDnhjgpH8SSrx%2B/Xr89NNPsr3CQ0ZGBs6ePYvIyEiZl5Dg5s2bACr2RmhoqGxOZH2PSXJY9Ho9YmJiaLmbN2%2BivLwcHh4eiIyMhI%2BPDy1L9h0pa7FYYDabZddDEAQ88cQTAIAOHToAqDD6iPf4ww8/RGJiInduUqO4JqgELSpUqFCh4mFAJZm1iocUqrH3EMJqtSI1NRWHDx9Gp06dcPnyZRk1vxTR0dFcIfXg4GCZAabVaqlRptVqZSyUUpSVlaGoqAi5ublcCYgffvgBTqcTP3FIGTp27IioqChERUVRw%2BO5557D0qVLcevWLTqH5ORk9OvXD3FxcUhISIC/vz82b96MnJwcmYGSnZ2NJUuWoHPnzvSFvpjNhsbP4YLkRbpBgwYYMWIEDh8%2BjPv379NyrVu3Rv/%2B/bFp0ybk5OSga9euuHXrFjUWLRYLSkpKYLVaZTljUhBtOgISQmm326HRaBSC4dWBGCQajQYtWrTAzp07IYoiDAYD7Zd4KUVRxKFDh7Bs2TKEhoaiR48eVbbr7u6O8PBwuLm5wd/fH0DFnpIakfn5%2Bbh9%2BzYuXLiA%2BfPny8JPq2P/5K0JGR8LjUaj8MJKERISgtu3b8vCT4lHUmoAWiwWnJGIdG/duhVnzpxBTk4OTp8%2BLTNypfuyKg8k8aICkO1xYvhdv34d27ZtU8xVq9Vi8eLFVc6HhcsELWwoMXvMyTtkp8YlLWBO8pZDUY/pmxvlzFbiFPo14%2BOWqakhTiUXiihP8sgJ2HPMWHhrozjHW3SWCYQ3wBr64vXtilAzOxxuejE7b15nbENMHVeISngkH4p6LqyNogxnzRWnXBCT55Zhz7lwL7i012pkvOGc41085hzLQcLTiVdsFN6FqYlhhFfPhedYTc8fAIrrwn2s13TTc9p1Ydso6nEF3V154NRUxpX15HXuwhrX1Bc51Nk4D4z/EFTP3m%2BHtuYiKv4MmDZtGmbOnAmgInfI3d0dQ4YMQWJiIhYtWuQyKURWVhbi4uIU5028//wcFBcXw%2Bl04sknn8To0aMxceJEtGnTBtnZ2ViwYAENh5MSn5Cxm0wmaDQa%2BrIeFhaGO3fu4JNPPgEATJw4ERMmTKBheePHj0fLli2xYMECAMALL7xANdUAoHPnzgAqdPYIiyZBvXr1aL7f119/jX/%2B8584dOgQwsPDcefOHeTm5kKj0cgMM4vFgrNnz0IQBMyfPx9xcXFUVNzpdFICEFEUERQUhJycHAwZMgTLly%2BnbWzYsAH79u3DkiVLAEBm2JH8uLCwMDRo0ECWi0YgJRch/TkcDpw5c4YacLxr5XA4kJCQQI/37NlDfycEIgRms5kS/rD5nFUJo0vJTFgCFLZ9AAryG9Lm2LFj8cUXXwAAdu3apehHOoa7d%2B8C%2BDmUkrTD0ysMDg6W5YpWhaCgIBnrKA8FBQV0n0n7JnqOpySsi9J1sNlsWLduHSZNmlTjOABg27ZtXIKWxo0bywuynlL2mJOLydrbXJUP5iQv1VJRj%2Bmb68RlK3EK/ZrxccvU1BCnkgtFlCcZllvuOWYsvLVRnOMteqX3udoB1tAXr2%2B2GbYb3nC4/1bYefM6Yxti6vCmxJ7jeRIU9VxYG0UZzporTvE%2BdrKFeGXYcy7cCy7tNXYxXFlA3sVjzlUSZVd5DEC5UXgXpjKNotrxsfVceI7V9PwBoLgu3JTxmm56TrsubBtFPe43clceODWVcWU9eZ27sMY19UUPi4r4D43/AP5qhtkfAdWz95DgjTfeqDKHztfXVxG6WBWCg4Nl4ZvSME5XQDxoPXr04EpAfPrpp3RM7NgDAwPRqFEj1KlTB76%2Bvnj%2B%2Beexfft2PProowCAAQMGwG63Y/78%2Bdi%2BfTv69%2B%2BP5s2bY/HixfD19UVERISMldTb2xtr1qzB1q1bqZYfefmW5nSFh4dj3rx50Gg0yMzMRHR0NNauXYuAgACFWDgxROvXr4%2BysjJcunSJtunv7w9fX1/8/e9/x%2BrVqwFUeMmI1yokJASxsbFITU0FoPRwkb5q1aolk5GQQmo8lJaWKgwvjUZDc9EIIiMj6e%2BCIECv18sMIo1G4zJxiLQ/6fil11Oas9ehQwckJyfTNfDy8qI6eTysXr0aY8aMgbe3N5o3b66YGwA0btwYPj4%2BcHd3V4SEkrzEOnXqAKgwNEVRxL1792RlmzRpgpCQEHpMDKg6derQvMmwsDDUr19fcZ38/PyoZ9ss%2BVq6f/9%2BiKIoa5eFK3IaBCpBiwoVKlSoeCjgYkSSij8nVGPvIYE0743NoYuJieGGTQJAUlISNcCACoNDGr4pDeMk2LdvH5VdYOHu7o5GjRrh4sWLGDp0KFJSUmR5e9nZ2dBoNIiOjkafPn0QGhpKx%2B7m5kYFqQMCAvDiiy8iMjIS3t7e8PT0xNGjRxEbG4uePXsiMjKSeitnzpyJgQMHYt68eejSpQt8fX2h1Wrh6emJL774Arm5ubLxBwUFyVg0u3TpgoSEBGrInDx5EuHh4cjPz4e7uzsSEhLw6KOPwt3dneb%2Bbd%2B%2BHRMnToTFYuHmVkVERCA9PR05OTlUDw4Abty4gWvXrsFgMGDTpk3o2bMnGjZsiH/84x/Uy3fy5EnKolodpO0S2O12mM1mGr7ZqlUrmbEiJaAh551OJ5U6qA7EoCN5bdJwUeINEwRBlrN38OBB9O3bl4ZaFhcXQxAEmbEtxRtvvIHS0lIIgiALoQ0KCqLX58qVK2jZsiUVo/fw8KDXl%2BQlZmRkAKgwzBwOB6Kjo2UG8KVLl2SePqITqNFoYLVa4e7ujrt37%2BLGjRuUUZQgPT2deu/YMFNRFPHYY4%2BhXbt2CuIYAGjTpk2V68tCzdlToUKFChUqqocaxvnboRp7/wN45plncO7cOW4OXVJSUo3yAb8Uzz//PDZs2KAIm7PZbDSHjuSCSZGdnY33338f6enp%2BOmnnxAbG4v27dvjwIEDeOKJJ3D16lVERUXJcvbatm2LTZs2ISQkhHofjUYjbDYbatWqBaPRiPHjx2PQoEG0n4SEBHz//ff0%2BJ///CeSk5OxZcsWeHp6wtvbm2q1lZSU4NChQzh16hRKS0txpVLEdenSpdizZw9q165NPTB37txBYWEhJk%2BejKioKAwcOBB79uyhXqacnBx069YNQIVH6OWXX0Zqair%2B9re/oUePHrJQ26ryxaQGhCteojESTTaDwSDLbyN9WK1WlJWVUYOJyGAYmJAM4h0mYaI8Zk2n06mQ1rBarTIjxel04vjx49zxTps2DcuWLcODBw9wW6KanZubKxvvvn37IIoiGjdujLKyMmoos7l/xGC8evWqbL2qkn%2B4c%2BcObDYbzGYzEhIS0LhxYzgcDpkn8scff8Tdu3chCAKaNm1KzwcHB8Nms%2BH%2B/fs0NxWoMCDJ76%2B88gq3Xx5cztljvfbMMff2ZvI3XMnPcklcnGmI165iPBwPJluRt9UVbbuSk8JIm3BSeH9VOgxvDop6TN/cW5xtmJefxeRVuZRLyZxwJZeSJ7DMDo%2BrFMO0w9sDivVj5skbH1uHt20U9Xh7gr3o7DFn4op2eRuH7cuVibML6ErOngt9c/eW5HkKAOCFq7PnmIa4YujMewUqP7TJ8MMPskPuv65KmSIKdp6ce0ExTxdyXLl7lu2LLeRK7iznerPrxdsSvyZlj23XlfxVXrs1pZC60g49djH6S8WfE6qx9z%2BAuLg49OvXD6NHj8bGjRuRkZGBY8eOYcSIERBFUSYV8Htg4MCBaN26NQYPHozdu3cjKysLJ06cwIgRI1BcXKyQHZBCo9HAYDDQfLnc3Fw4HA7s3r0bdrsd69evx6RJk3Dx4kVYrVZYrVb4%2B/tj4cKFNL%2BMGC2nT5/G7t27ce/ePZmxkZ%2Bfj7y8PADA3r170b59ezRq1AhNmjRBYmIirFYrbty4ITN%2B9Ho9l6FS6jEE5EbEqVOnUFxcjB8q/9ERbyvJaSssLITVasWCBQvQrFkzypwplU5gITWupLIPUtSqVQtt27YFAAwdOpSyVzqdTmg0GjpG0gcx9onB9Oijj8JkMmHMmDEyD7HD4YCHhwdq167N7Ze3BgDodQJ%2Blrno2LFjtW3UBEEQ4HA4cPHiRdl51vNF5mQwGGTGXlXGtFTL8NChQ0hPT4dGo5Gtw8GDB5GXlwe9Xo8jR47Q897e3hAEAfv375e1abfbaX%2BEuMUVbNu2DRMnTpT97JXkWko6rvaYmzfH5G%2B4kp/FS8VRnGMa4rWrGA/Hg8lW5KWSKNp2JSeFEf3l8f/8mnQY3hwU9Zi%2Bud8b2IZ5%2BVlMXpVLuZTMCVdyKSVExFUOj6uhzLTD2wOK9WPmyRsfW4e3bRT1eHuCvejsMWfiinZ5G4fty5WJswvoSs6eC31z91ZEhPyYlyrAnmMa4jIusikAlR83ZWjXTnbIzYOVsCIDUM6Tcy8o5ulCjit3z7J9sYVcyZ3lXG92vXhb4tek7LHtupK/ymu3phRSV9qhxzUQs/2RUD17vx2qsfc/gunTp3Nz6FatWiWTF/g9IIoiFi9ejP79%2B2PBggXo1q0b3n77bTRo0AAbN26sNqepS5cu2Lp1K9asWQMvLy906tQJrVq1QqtWraiR4efnB1EUERwcjBdffBE7d%2B5U6AWKooidO3di165dSE1NpX8TRREZGRmIiopCeno6Iph/gAMHDoTJZML8%2BfOpdMXWrVsVsgdAhRFD9N4IqtOQ69ChA1JTU2VEKQRS72qPHj3w3XffVdkOAQk9lEKr1aKgoICyTwqCoCDnIbIQVYUEHj58GAAwb9482bi8vb3h5uammDOBKIrw9/dHUFAQV6AdAGXtLOC5DX4BSG5evXr1YDQaZSGaPDxgvxpXgf79%2B9P2icaezWaTrUNsbCxu3rwpy9cDKsJLiVe5KtRkKEuh5uypUKFChYqHArwohP8QVGPvt0M19h4CVJdDRyCKIjeHTmp49enTB/v27fvVfUih1WrxyiuvYMeOHTh37hzS0tIwdepURfgm266npycVfT9x4gRlEvXx8YGbmxs8PDzw448/4vz589i3bx8mTJgAPz8/9OjRAyUlJZgzZw5at24NQRBkOYePPfYY0tPTIQgCWrZsWaWHpVGjRkhPT0fPnj2h0%2BnQokUL2gYAjBo1CkCF0Pm6deuo4ZSeno709HTUqVMH4eHhOHToEP1ZtGgRgAq20MjISISEhFCJhAYNGiAqKgrbt2/H8OHDaVtSUhUppB4mnkFls9lgMpmoIRIYGKhYc55B%2BtZbb8kMJm9vb4XXsqioiIYz1q1bF08%2B%2BaTMi%2BdwOLB69Wp89tlnsFqtqFWrFr7//nsYjUb07duXtgvIhce9vLxkzKPDhg2T9du2bVsIgoC%2BffsiKiqK9uV0OlFQUABRFKmX8sMPP1ToHAJAaGgonY9UVy8iIgKBgYEK/Uan00nDT0VRpGtNDMzr169Dp9Ph2WefpXVEUYTRaMT06dPx6quv0v6kH1OIx1WFChUqVKhQoeLPANXY%2BwtDqnsXFRWFmJgYdOvWTSYlACh17yZNmkRp8QFg8%2BbNVYbtdezYEZs3b%2Bb%2BjdULvHPnDsrKyuh44uLiMHz4cFy6dAm7du2CXq/H5s2bsXHjRtjtdtnYo6KiMHjwYAAV3iX2bzExMUhISMDMmTNloZrHjx%2BnZQDgq6%2B%2BAlCR21W/fn3qGXzyySdpLptGo0FQUBCCgoLwzjvvUGkIqTahIAjo1asXrl27hpYtW6J%2B/fp45513AAB3796l/QGQGV3E2NNqtdxcS61WK8u1y83NpWQlZrMZZrOZK03w6aefUuIWoMKwk3qoNBqNLGw0JycH8fHxdE4xMTEAgG7dumHgwIEAKmQ8nnnmGZjNZsTHxwMADZ%2BVjr24uFjm7Vy2bJnMiCQeTL1ej/z8fISEhKBu3brw8fGBp6cnmjRpQsvOmjULo0ePVnjX8vPzqZEnZaa9ffs28vLyqBF7/vx5ABVsndJwYhIC6nQ60bVrV/j7%2B8PNzU3mgfX19YUgCGjQoAFWr15Nx0D2hSiKyM7OVqx9VVAJWlSoUKFCxUMBXvjufwiqZ%2B%2B3QzX2/uKYMmUK9VDt2bMHBoMBs2fPRrNmzRAXF4eYmBhMmjQJly9fhsPhwPPPP4/c3FwMGjRIodHmCpKTkxEXF4e4uDjExsZi0qRJVC/Q6XTC398f3t7emDRpEj777DPY7XY8//zzEASB5h5K2SKlP%2BfPn5cZGRs2bKBz27VrFyZMmID169dTmQYCEs4nRVFRET788ENKRpKdnY1WrVohMzMTmZmZaNGiBaZOnYrjx49Tj92LL76IqKgoaoyuWbMGALBmzRpMmTIFq1atom1LjR2p4UIMAJvNhvDwcEVuH/HsARUG2qFDh6gxNHPmTMTHx1MPlLTuN998Q71OoaGheP/99%2BnfQkJCYLfbqaHWvHlzmEwmmYFINOZYY7O8vBxOpxPp6ekA%2BAyiABRhmET%2BAKjQZHQ6nVi1ahVyc3NRWlqKmzdv4sGDBzQflMzZ6XTC4XAo8hml7J5BQUHUwwhUGGFkjQjb6qVLl1BWViYTiyfYuHEjNBoNZQ0lKCoqgpubG7799luZQUm8rA6HA%2BPHj%2BfOnwcuQUunTsqCLJMqc8wl42CIGK5f55RhCJZ4kbcKEldX2mU/NvCIIpiwWx7ng6IaL2eENa6Z0GeerrQr5AeKNa3U7JRCsV5Xr8qPeWwIrogcM2QS3Ahldi0q83arbZcZMDdam7kfuPKrTN881R9FhDJDHsKNhmcb4mxsV6YJJqcWx47Jj0%2BfVtapJOaiqHzeycDuNd6FqXyGUrCbxBUmIl70DTvmyjD%2B6tp2hYwDZ8/Kjy9cUFZimaPXrVMUYcXYudeXXRu2L1dE6nk3K3sdeMw%2B7PVkrws7Nk5f3L3GXDte1%2BzwePcLe84FTiHFtuG1y14XXjvsOfaYPvqq4Bn4T0A19n47VFH1vzi8vLxkmm9fffUVxo8fD51Oh379%2BuGtt97C/PnzKSthQEAAhg8fjm7duiEpKekXvdwCFULoEyZMAFBhAAQFBcm8WbVq1cIzzzyDTZs2ITMzE0ajEWazGTNnzqThcoIgQBRFbN26VdE%2BYcMEQPPLCCIiInDq1Cns2bNHxmLJ84RZLBbs2LGjSqPMZDJhw4YN0Gq1mDZtGgDg9ddfR6tWrbB48WIcP34coihCq9WivLwcmzZtosLvgiBg4sSJ%2BPjjjwGAhhFaLBYZK%2BT48eMxa9Ys5ObmYsaMGZgxYwY1ZkVRRHR0NIKCghATE4N///vf6N%2B/P3r16oU2bdqgS5cuVN7BaDSidevWNJQxJCQEZWVllEiGGLTR0dG4ePEi%2Bvfvj7Nnz8Jms9GQ0JCQEJSUlKBOnToQRRFXK19siaFDDOisrCzFWpL1lKKgoAATJkzA/PnzFWUJkY0oipgyZQp8fHyopiQrBUFgtVrh5uYGq9WKe/fuyQxdf39/XLp0CcOHD1cY9Tz07t0bGzdulM2P9GG32xWebymGDh1aY/sEVYqqV3pRKSSeTd4xl4yDIWKoX59Thsmt5aX2sl271C5LtMAjimDIGngfjRXVeGxwoaHyY0aQnqcr7Qr5gWJN69VTlFGsV8OG8mNeXqsrIscMmQTLawFAuRZ169bcLjNgbnopQ1zB1VBm%2Bmb5gwAOuQqTO80lGGEb4mxsV6aJp56SH7NyKHFxyjqVH4EoeGH27F7jXRiWVIvdJK4wEfGiZNgxt2ihLFOVGHZ15xitU0jYhyl69ZIfDxigKMKKsXOvL7s2bF%2BuiNTzblb2OvCYfdjryV4XHhka0xd3rzHXjtc1Ozze/cKec4FTSLFteO2y14XXDnuOPaaPPputikVQ8TBA9eypkCE4OJjm0B05ckSme0e07wwGA5YsWYIXX3zxF7UdGhqKJ598skq9QKPRiNzcXFnu4a5duwBUkIoQvUA3NzeEhYVx9QLDwsLQrFmzKsfg5uZG%2ByTenhUrVlDPFGHKJOF0oihCEAQcOHAAjRo1otpzWq0WgiDIxh8fH4927drRfDyHwwG73a4gTDEajbK8NYfDoTCGunbtih49etBcsg8%2B%2BIAaX06nE3a7nXrbpNBoNNBqtYiNjaXELaWlpXwbKzMAACAASURBVGjSpAkNSbx69SpeeuklPPfccwB%2BzrMjzJe7d%2B%2BGKIqIjY1F/fr1IYoiCgoKIAgCrl%2B/DqOCdfDn/%2BwsIY6fnx8Vepeuld1uxyOPPELXkHgkpXA4HFizZk21hD8EwcHBqFu3LjVOpeGQxGNJDLjqoNPp4OPjg%2BLiYmi1WplxGBsbi/v372PSpEkyz2S7SiY6vV6Pd999t8Y%2BCFSCFhUqVKhQ8VBA%2B9/zDamevd8O1dhTQcHm0F2%2BfLlKwyk6Oprq3v1eCAgIQE5ODtULLC8vx5dffolHHnkEO3fu/E16gU6nE0ePHkVKSgqefvpp2d8mTJhAc%2BhsNhs1FBo1aoSYmBg4nU4kJyejY8eOMJlMcDqdsNlsaNGiBb7%2B%2BmssXboUAHDmzBmauyiKIkaOHAm73Y6RI0fKwgybN28uM2zGjRsHAOjXrx89Z7fb0bt3b5oDFh8fj7Vr1%2BLpp5%2BGKIpo0aIFHWfLli0RGhqKsrIyfP755ygvL8e6detQUlICQRBgMBiwcuVK2nZpaSni4uJoLiUbVpqWlgZBEHDhwgXExMQgLCwMHTp0gNPpRJs2bRSeUCkhzG0mXIsQrAwYMAD9%2B/en4ZeCIOC1116jhuuRI0fofKTadteuXZO1N2fOHGzbtg2NGzeGRqOhBu39%2B/ep8SsIAqKjo8HiwYMHWLJkCT1u3bo1Fi5ciGXLlmHKlCkQBAFhYWF0DDabTUYEExISArPZjOvXr8sE4Yn0RmBgINeAqwpqzp4KFSpUqFCh4o%2BGGsb5F8e0adMwc%2BZMABUha%2B7u7jSHjjBluoKsrCzEccJjTNykj/9n77vDorj299/Zwi6wIL2JotgQFUGNxhYVSyxIRMVYEkUTjT2ahJvYYkyiicabm3gltsQWWywIROUGu1eNiWiUGBVbbBEUpMhSdtnd%2Bf2xzMnMmQNsisn19533eXh0hplz5pw5u8xnPp/3fdlwc3ODSqXCyJEj4eTkRDJZDRs2hFqtxvjx43H48GEUFxejuLhY1t%2BCBQsk2z0p/pOQRfrss88QICrJyc/Ph1arlfi0NW7cGNevXyeG9ULwJBzXrl07cByHt956C59%2B%2BilpX9zXq6%2B%2BikOHDuHIkSPw8PAgdgR0AC0IvOzYsYPsO3jwIKZMmUKybd9%2B%2B63E802wXhALvURFRZGS0KKiIuh0Onh6eiI3NxdjxoyR9FlbUCEE1oIlhZBhzczMlByn0%2BlgMpkQFBQEHx8fiQqnuK2NGzdK9tVkYVFeXg5XV1fCpxs9ejT5nVACLGRoBV6dzWYjZbY8z8v8%2BQS0a9cOB6q87E6fPi0zf799%2BzZWrlxJgjx/f3/CzxMsK/773/8y2/7ll1/Qpk0bkiWuDbGxsbKgtGnTpvIDb9yQ1ktS2%2BXljHK7u3clpXOsYwoLpSU7FRWMSiqrVVpOd%2BeOpP6P2XdlpbTchzoHAJCTA1QF1gDs/BiqbMpopMowb9%2BW13sWF0vrpH7%2BWVJymZfHKAfleUmdGT1EQD43uHVLVgomuz6qb9m1scZJzxVjH%2BsQ2b2iD2INirpZpaVyuzH6fj56JC8Po7tyYAh2EpKoNo1xu2X3hTkGs1laJszq/No1aUktfTNZi5bex1qzdF%2BsyaEXxf37knJp1pBk4/7lF3n9Hf15cWQMdLus/gsKALGaM%2BMcWd/0NqPhmzflJbeyucnNlZbGlpXJ6yDpeyf7YMrbZa1r2WeTui/MdUTPDaNv2Uec0Q69TGTfG4x91MdFts06h7UcHZk%2Beh89JtLPzZvMcva/Aso70D8OJdj7P47p06ejT58%2BAOQcOg8PD4kQRU3w8/OTZI8ECAqZjmLkyJFwdXXFN998g5ycHGi1Wvz888/4%2BOOPCWfPzc1Nlq0C7JlBIXACgKVLl8Lb2xsPHjzA8uXLERoaijfffBMajQbe3t6S4EoI9NRqNYKCgrB8%2BXLMnj0bR44cAWB/kBcjMzMTHMfB19cXmzZtAgCZL5tGo8HcuXORkJAADw8PeHl5oaCgANnZ2cjPz4eLiwvKysqgUqng4uIiCVwAEEsHAEhPT8eQIUPQqlUrfP/999BqtbBYLNBoNAgMDITNZkNBQQGmT5%2BOHj16IDY2Fi4uLnjw4AHc3d2ZQiRitGjRAkajEbdv32YGgsL5devWhU6nI5w9ocz13r17kmxXbfDw8JDwE4W2mjZtimXLlmHJkiUkKKPn1N/fHyUlJUQwxWw2y9Q5DQYD4f6JIQ726L5tNhvUajWGDx%2BO77//Hvv27cNVkXCDUGorZJ5ZoIPqmlAtZ4/incmIcdQ2k1dFldOyjqH/6DPpjPSTKfUAzOybfmhikcPoh0UGF0f2nolF7KODKephhEUXpB9mWZxHGbeFweGSXR/9IMTidNHjdIBY5YhZsuwg1qAcMJ92hAf0e0yi6SdVpm0mHWSwxkDzQVmd09xJ%2BmayFi29j7Vm6b5Yk0MvCqoEncmvpcdNB3qA/PPiyBgYxDlZ/5RtD5NsR/dNbzMalgV6gHxuaA4ki/BWG6GM0S7TVJ3%2BbNLUANY6oueG0bfsI85oh14mrPfn9D46sKO3WeewlqMj00fvo8fk4Pv%2Bxwol2PvjUMo4/4/D29u7Wg5dixYt8NNPPzHP27BhA%2BHQAfaHcBaHTqPRYPHixWjWrBl%2B%2BeUXzJo1q1qLh/z8fOzfvx9ffvklSktL0a9fP%2BzZswedO3fGf/7zHyQnJ2Px4sVwc3PDsWPHJP2MHTsWGRkZOHToECnTjIqKQqdOnTBo0CCsXbsWp0%2BfxtatWwn3MDY2FsCvBtuAPQt1584dPPvss/jhhx9IQBIWFobs7GyJ2iTP83jw4AF2794NANizZ49snvLy8uDu7k4COwA4fPgwOnfuTLZtNhueffZZxMXFET6gGFqtFq%2B%2B%2BiqaNm1KAhhBpCY6Ohqurq7Izc3F2LFj8dFHH6GoqAitWrVCYWEhbDYbHj16JCuBPX78OI4ePUqUNS9cuABvb2%2B0a9eO9MnCnTt3SKDn4uKC1NRUjBo1CqGhoTCZTDI%2BX3WgAz1hHi5fvowBAwaQjBvHcWgsenh799130bZtW5RUyZXp9XpJRhWwiwBVZ3z%2B4Ycfkv%2BrVCo4OTlBq9WSTKPVaoXZbCaBu7iMU1AwXbRoUbXzIxb%2BqQ0KZ0%2BBAgUKFDwJMAX9PVk9BX8OlGBPQbUQShjpTEZpaSk2bNjwmzh0tMXDK6%2B8giVLlhDFyA8%2B%2BAC3b99GeHg4UlJSkJSURCwezGbzH%2BLrAUD9%2BvUxbdo0bNq0CefPn8ecOXOwaNEiAPYsE%2Bsh3WazQaPRwMfHhwRBLVlqZVUQgg6BQwfYM3J9%2BvSBxWJBRUWFxDKga9eu5P9t2rTBBx98gNDQUFmgkpCQgH79%2BuH8%2BfMoKyuTWB/ExMTgypUr0Ol0CA4ORpMmTTB37lwUV%2Bk9BwQEEEVSZ2dnIqrj6%2BuL0tJSlJeXE7GRs2fP4vvvv0ejRo2Iz50AIYhzcnIiJu5lZWWIiYnBtm3b8HOVPjOdQRQLmdQGcdCmVqtJEC4OrMLDw7Fw4ULyUqKkpARarVbCfysqKsLy5csBQBY49xAp9XXs2BFpaWnYu3cveXERFxcHT09PIt4jZDk5jiNegqdPn5aU/IrByhoqUKBAgQIFCn4fFIGWPw6ljFNBtYiKikJ8fDwmT56MxMREtG/fHrm5ufjkk0%2BgUqmI750joC0e4uLisGfPHmRkZCA4OBgbNmxAWFgYGjZsCBcXF7i4uGDevHkYNmwY7t69i3/%2B859EGMRqtcqk961WK8n2CBgwYIBECEXI3jxfJR29detWDB8%2BHGq1GsuXL4eLiwsqKytRWVkJlUoFZ2dnuLu7w2w2E%2B7im2%2B%2BiYyMDCQmJmLLli04dOgQCgoK0LVrV3Tt2hUbN24kHMjIyEiUl5cTXptarUanTp1w9OhRFBcXY9q0afj8888xYMAApKenY%2BjQofjoo48wZMgQODk5Eb6jIADj5OSEn3/%2BWaIW%2Bvrrr8NiscBoNOKtt94iZY1C8PPgwQNybHl5OfH6y8vLI4ImDRs2REVFhSRQu0r5Tgm/M5vN0Gq18PPzg0qlQmhoKGw2G3744QeYTCbJtQEgpZ0ajQYWiwXe3t54%2BPChJNMJgNhAACAlqYC9dPKyyDtt2LBh0Ov1En6e2WyWBIrnzp3Dyy%2B/DACywFl83IkTJyRWHQCwe/du1K1blwT34vO3b98OlUpFBFlYuMM0L2NDEWhRoECBAgVPAnT5DC6pgicGSmZPQY1YsGABJk2ahA0bNiA2NhZvvPEGQkJCsHnzZsKh%2B73QaDTQarVISUlBREQE3NzcsHbtWnTp0gVdunTBwIEDUadOHbz%2B%2BuuIiYkh5%2BXm5pJjhJ/c3Fx88MEHkvZXr16NlJQU8pOamooPP/wQPM%2BjUaNGEoGX6dOnIyUlBTt27EBqaipSU1NhNptRUlICFxeXGrmLXl5e6NixI3766Sf4%2BfmR/qZNmwZvb2%2BkpqZCrVbDy8sL5eXlJOsmZM5GjBiBEydOIDIyEvHx8bBarcwyQSHYFQcEHMchUuS5xPM8OI6rVRWyS5cumDRpEgA7//Af//gH%2BR2tgEnD2dkZGo0GNpsN2dnZ%2BO6770hQRNsnODk5QaVSkd8L5ZtC5k44RpyBE4/PYDCQ4ziOg9lsZt4Lk8lEbBKqK%2BEU%2BqoN5eXlJHspZBB5nscXX3wBZ2dnSfBJo1evXrW2L8BhU3XaFJraZiYZqWusip0loKeRZUAu01eiyrpp818AcjNsllEzbWLNyNzLDIBlDu%2BQO69TZtNM03d6whhiQZSorNx8GgwfZtr4mvWdQU8ow%2BOTHhPrvsgouHRfLPNp6maxljHt1c7wkpddM%2BsYWfe3b9d6ebIxMOaGvnXMtX/woHT7u%2B%2Bk2/TnCZC7T7N4ubSpOst4nW6HNj9n3W/6u5olAHX2rHQ7J6f2vh1x0KZvHuvvBm3yXuUXKwHFV2felyq6A8H%2B/TVfGyAfE70NyO4La2pACYuhiopAwKocqvKfJWC8yKP7Yo2bHhbrO5P%2BjNPHOPJVwmqX/nr8PabqZPs3KE3/2VAye38cHF%2BTLJ4CBX8CoqOjMXXqVAwePBiAXQzl8OHDeO2117Bo0SJs3LgRrVu3xrx58xAdHS0RQ9FoNKhXrx6GDx8Od3d3LF%2B%2BHIcOHUJKSgo2b96Ma9euwdXVFUajEdOnT8e4ceMAAMnJyeRYGs2bN0fv3r2xbNky5vXR116vXj3cvXsXXl5euH79Onieh5%2BfHx49eoSJEyciPz8fjRo1wltvvYU6dergu6qHi4kTJyIkJATNmjXD3Llz0bp1a1y7do0EKzt27EBERAROnjyJyZMnw2KxoLKykpQv0t57Yjg7OxMFynr16uHmzZvM44QgSrA4EKDT6cBxHMk4Ll26lFhAiM9lBU7%2B/v7Iy8tjZqH69u1L/PxY4DgOEREROM94eBajYcOGuH37NqxWKxGBYZXyijOJ7u7uEtEWFl555RWsWrWqxr4nTpyIFi1aYNq0aXjmmWdwrOrhJigoCIWFhRg3bpxEPEc8tqNHjzrkCwjY%2BYO0QMu0adMxdeoUh85XoECBAgUK/hIwpY3/Gixe/HjaffPNx9Pu/yKUzJ6CvwTz589HVFQUoqKiEBERgTfffJNYPJSUlEgsHqrj9509exb37t1DixYt8Oabb%2BLy5ctEgKS8vByrV68m5X81wWazSUroeJ6XXF9UVBTu3btHfv/dd9/h7t27uHjxIqxWK2w2G%2B7cuYOCggKsXLkSVqsVvXr1Iv57gJ1Ldvz4cQwcOFDSt7jUdOTIkYiKisLkyZOxZs0atGvXDgaDQRaYCRBEWcLDw6HVauHm5oZ69erh1q1bJBMYHh6OFi1aAPg1e2axWEh7QuZt4sSJSEtLI%2Bqri6u%2BTQWFUUCaIRNn7O7fv19tueGAAQMk2zNnzkRwcDBRTg0MDMTWrVvRgCnX9ivWrVuHyMhIqFQqtGzZkiie0mjatCmee%2B45ACBBtI6S%2BhNnSYUS3upgMBjg5uZGspvHjx8nvxs0aBDKy8tldg1iHD16tMb2xWBnX5V3bwoUKFCgQIGCPw9KsKfgL0H9%2BvVhs9lgs9ng5OQEnuexZcsWREVF4fbt25JMj8Dv8/X1RWBgIOLi4kiZpIeHB6xWKz7%2B%2BGPs2bMHaWlp%2BPrrr%2BHn5webzYYNGzbUei0qlUpmkzBt2jSkpKRg1apVKCsrI8FMRUUFeJ7H008/DWdnZ0yePBkrV67EoEGDwHEcCgoKsGnTJsLxMhqNiIqKQseOHVFZWYlRo0Zh/vz5hI8miLIYDAZ4eHjAYrHglVdeQZMmTXDmzBnChaMzaoJipM1mw8WLF/Ho0SPk5%2Bfj5s2b6NSpE/Huu3btGuHbCQGek5MTOI6Dm5sb4uLiANiFRFxcXPDw4UN4eXkhJyeH%2BPIJfEhxaWWgSG7bxcUFQ4YMgVarhcFggFarJeWOn3zyiURA5sqVK2jXrh2x4Lh37x7Cw8OrzUQK6N69O86cOQObzYasrCyJ154YV69exf379yUBHc3dFIupbN%2B%2Bnfxfr9eTsk7hX6PRCC8vL6ZP3507d8BxHOpWw1vgeV7ihVgbFM6eAgUKFCh4IqD6%2B8IFpYzzj0MJ9hT8JYiPj0daWhr5SU1NJdy2mJgY2QO6AMHiQaPRQK1Ww2QyISIiAgMGDJBYL2i1WowePZqoTdYEJycnIk4C2LNZPj4%2BCAkJkVgrAHZhkmbNmmHdunWYOnUq9uzZg4kTJyI5ORk8z8Pb2xs8z0se3FesWEHsAhYuXIjp06cT3pm3tzcxA/f19YXZbMa2bduQnJxMjMRtNhvcKGOd6tQfAbvQyNkqTofZbJaUMFZWVsJsNpNsocBru3TpErZs2QKr1YqcKuKByWSSZEbFAae4tNbHxwe7du1CZWUlSktL4eHhQbJp169fJ8IyALB3717iVfh74evrK1EupXHq1Kka50cMIZvWsmVLuLm5YejQoQDsmU7A/iKgT58%2BuHnzJlQqlSSjuXfvXri6ukpUSmmMGDHCoesAfgNnjw4gqW3m0CluHc3FAuTcjCoqqQSy5GNWlmSTSWWlmQGskl2af8fIZMuS9CzuH01Kobg5NDUQgHzCGH3L6Dk05weMOaW5YCyCjCOcPYoIxLLIlM07ffNYJeDUMdStZHWNK1fkx9Bts%2BZYxvWkuGEOcfYYBznE2aPLyGkOHEtgiebfsY6hJ4fmfQFyDhy9JqiXjADkhC2acwjIvwMoDiQA%2BaKVkUoh/1DRN49FEE1Pl27TXDtAdq%2BY94Wei127pNv0ZxmQj5PFk6TGzZoa2fzRC5t1wTRHk0FOpbm9v5ezR%2B%2Bjt1nfzfRnjPVdTNMOWUuC3kcvEXL9DLskBdWD53ksXboUTz/9NNq3b48lS5bU%2BDL3/fffR7NmzSQ/4oqmPXv2oFevXmjdujWmTJniUBWbGApnT0GNqIlDl5CQQPbTHLrOnTtjxowZCAwMRHR0NDp27Ihvv/2WyaHr3Lkz8vPzsWXLFiQmJhL%2BXGlpKWJiYtC8eXMcO3YMcXFx2L17N55//nmEhYVh%2B/bthENnsVgwfPhwzJkzB4Cdszd37ly8//77Mi5emzZtUFpaiuTkZCxcuBCBgYFEev/MmTMYOXIkDh48iLNnzyIxMRF9%2BvSBq6sr8dOjMXr0aDz33HN44YUXUF5ejhYtWuDixYvgeR5arZYIs0RHRxNeV0FBARo1aoSCggIUVn2bCvwzlUqFgIAASSkpAISEhKCgoABeXl6yYDUoKEhyn9RqNaxWa7W8uz8TDRs2xKBBg/Cvf/2r1mOXLVuG6dOnAwDq1auH3bt3IzY2Fi%2B88AL279%2BPH2iRC9hVYevWrYs9e/Zg/vz58Pf3x%2BTJkyUKnuL/N2jQoNrMYUxMDPbs2YOAgAC0aNEC58%2BfR35%2BPpmnli1bYteuXRg9ejSysrLQpUsX7K96uHFycoKnpyfWrVuH/v37kzYFjqBGo0FmZqYks1kTWJy96dOmYcpv8OpToECBAgUKHjd4HqAsbf8yLFz4eNqtelx8LFi7di02btyIpUuXwmKxIDExEQkJCXjppZeYx48dOxYdO3Yk1VeAvQLM2dkZWVlZePHFF7FgwQKEhYVh4cKFcHFxqVV/QAwls6egVjjikffBBx9g2LBhMo88R94%2B6HQ6PPXUU5g8eTJKS0vx9ttvIyIiAu3atcO9e/fw7bffYsyYMYiKioLNZkNmZiYWLVqEQYMGYffu3di1axf0ej22bNkiEQexWq0yLl5UVBRKS0uh0%2BkwduxY%2BPr64vDhw7h8%2BTJWrVqF8ePHo3HjxvDw8EB61RtNwWOuX79%2BOEi9dXV2dsbw4cPRsmVLouD4008/QfwOJT8/H0ajEVlZWaioqCBZsBYtWkjUISMiIpCUlIR//vOfJNATZ5Zu3bqFkpIS%2BPn5oXfv3mjTpg10Oh14nkd8fDw5juM48gapT58%2BsmwlDY7jZDw3GkIZpiAeI1ZivX37NuHLTZ06FW%2B99ZasfQGChUNQUBDu3LlD7vGSJUuQJUo19O3bl2RL79y5Q8pub926hZ49eyI4OBis91ROTk4SOwWDwUDKUf39/dGkSRMAdkXXgwcPIr/qtaaXlxdcXFxIoCZcp9g3LzQ0FBzH4bPPPpP0KWRSLRYLMhkZoOqgmKorUKBAgYInAVwRo1LhL8KTWMa5ceNGTJ8%2BHe3atcPTTz%2BNN954g1hfsXD9%2BnWEh4cTCpOvry95Htm0aRP69euHQYMGISwsDEuWLMHRo0d/k9WTEuwpqBXVcegyMjKQmZmJDRs2ICkpCfHx8QgJCUHr1q2RlJQEi8XiEIcOsPvuTZo0CUajETzPw2AwoFevXti5cycyMzORmJhIjr18%2BTLWrl2LUaNGISQkBKGhoXBzc0OPHj0kKokqlYpYKoh/3N3d4ezsjAULFuD%2B/fsoLS1FfHw89u/fjxkzZiA5ORmAXZyD4zgSaOj1eqKQGRUVBR8fH%2BKjB4Bk0gB7iR5gz97MnDkTXl5euHHjBtLT00nZZFpaGu6L6iyuXLmCsrIy9O7dG4A9OycuGfTz8wMAfPTRR1i%2BfDlMJhPMZjNUKhWeeuopcsyePXtIUOPi4gKNRgMnJye89NJLyM7ORnZ2Npo2bQoARJHUzc2NBPUeHh6y%2B3OjSsO%2BZcuWSE9Pl2R1AwMDiWhJkyZNkJycTAK86OhovPvuu%2BRYYTwffvghAHt2T4CnpycJrLOysgjnMD8/H4cPHwYArF%2B/HpGRkXBzc5PYNwiBn16vx8yZM0mbZWVlaNiwIelTKGN1cnKCs7Mz4frl5eWhrKwMmZmZSEtLg9FoRHl5uaQ8t7i4GDqdDndluvy/4jazhogNhbOnQIECBQoU/P%2BF%2B/fvIycnhzyXAUDbtm3xyy%2B/4AGDU2E0GnH//v1qhevOnz9PdCEA%2BzNXUFBQrarmYijBnoLfBdojT7wQAXvGa8WKFRg1ahQOHTqEtm3b1tgex3FISEiAv78/3nvvPZw8eRL//ve/0apVKyL%2BMXjwYAQFBcHNzU3ikQcAhw4dQkREBDp06CBp09vbW8LtCwkJIdmyfv36Ydu2bejatStiY2Oxc%2BdOjB49GjqdDgcOHICHhwe6d%2B8u4YNlZ2cDABISEvDVV18BsJd%2BRkVFITc3FxaLBY0aNSKqj7Nnz8ayZcvw8OFD6PV6ODs7g%2BM4eHp6wsfHB9988w3c3d0B2E3eo6OjidS/j48PTp48iTFjxgCwG6RrtVo8%2B%2ByziIiIIBlEcXawfv36aNy4MaZNmwYASE1NxZ07d2A2m7FhwwaSMXzvvffAcRw%2B%2Bugj6PV6lJeXk6BeCLjEOHHiBAB7FuvevXv497//TX539%2B5d/PjjjwCA1157DdeuXSPBV5s2bTBs2DByrFDCOX78eAB2TqObmxs4joNGoyHZUa1WS4I0Hx8fwq0D7D54ly5dgtVqlWT3hPu6ceNGss9msyE3NxdOTk7o1KkT2S%2BI3Qj3Vji3ZcuWiI6ORklJCVQqlcRo/sGDB/D29pYEkzRYKqrVgcnZ69FDfiBNrqK3WZX4VNDJosPQiUUWL0TGA6GM6xzhkjC5TdQbSRaHS9Y3y9CNrhyguIDM2JvmyTGINjKvLobHn2zs9DGyiYB8oCySD0WiYXF8ZHNDE4NYXEAHfPZomhKT8/h7OHtUWTVramRjYBzkEGePpgrQPnus7Ds9CNrXDpBz4lhv1WkfOLpd1sDpm8ny2aP9%2BhzgrsluJmsffQ6L%2BEVzCFnXR42LeV9oHmRamnSbxXGlP4g0CY1xDMuKT/ad6QiHlP5uYTT8Z/ns0dNObztC/2XdOjqucISzV63PXg1ew48bjyuz9%2BDBA/z000%2BSH1Yw9lshiNwJL%2BgB%2B3MMYK8qonH9%2BnVwHIeVK1fimWeeQWxsrIQ29ODBA0lbgF3/gdVWdVCCPQW/CZWVlcjIyMCJEyfQs2dPXL58mShB0ggPD5ct0D8Ko9GIkpISnKFMb0tLS7Ft2zaHTLNpxMTE4ODBg5IH9f/85z/o378/RowYgSNHjhAzcCHzwnEcycqZzWZJRkbMp%2BN5XhKQCP83GAwwmUzIyckhAUV2djYMBgM2bdpE2o%2BOjpaU%2B1ksFphMJokHnyAGI1wXAISFhUmuVzhXyCoK2bE7d%2B6gffv2KC0tlXAEuWqK87OzszF69GhZCaWwbbVaJZw1cWZSrDJKG6cbDAYUFRUR3uGtW7dQVFSEnj17QqfT1SiKIkAI3oRspYCSkhJYLBZZ1owVmOn1ehgMBpJxFAvlqNVqGAwGWWmy8DJCp9OhT58%2BtV6ngLS0NCQmJkp%2BDrKsGyIiat5m3av69SWbLHskOrHImuKq9xC/IjS01nNklEXR%2BiQQZXQBgFVFLOu7KviXoOrlAEHz5pJNahrsoL8jREquAkTis8x2AcbY6WNY3E16oIy%2BUfVQIIDSagLAmBtRWTUAl9X4RgAAIABJREFU%2BRgZDVV9RUgQECDdrioQkIIaA%2BsY2dCpN9ZMWis9BsZB9HSxpg/R0dJt0QtAAAD1YhKAfBBt2siPodasbBsAaKVeul3WwOmbyRKjioyUboeE1H599M1k7aPPkS0sALRoFOv6qHEx74voZRsAoKr6hYC%2B/4D8g8jyMKWOYYol09%2BZ1HcJk8VAf7cwGqYvjzVueliOfJ7pbdbU0EuJdevoxy/qq4W5j%2B5L2LYGM9bcE46vvvoKgwcPlvwIL/FrQ0VFBW7dusX8EZ7ZxM%2Bjwv9ZHsA3btwAx3EIDQ3F6tWrER8fj3nz5hG9gIqKCtmzbU1%2Bwixoaj9Ewf91zJ8/H%2B%2B99x4A%2B6LT6/XEIy8pKUnikVcT7t27J8vIAZCoN9YGo9GItm3bYvLkyUhMTERkZCQGDx6MyspK8DyPdevWYfPmzbDZbLBarZg1axZmzZpFzhc%2BIOJSxV69euHtt9/G6dOn8fTTTxOPvKlTp6Jly5YYMWIEtm3bBqvVin379gEAZsyYAZvNBo7jEBYWhrS0NMTExMDZ2RlGkaJZ/fr1JcHfq6%2B%2Bii%2B//BIcx6F3795YvHgxwsLCcOPGDWzbtg39%2B/fH6dOnieBITk4OduzYQc53dnaGxWKRfMgvXLhAVEhPnz6Nli1bkrdIHTp0wNmzZ6HRaGA0GjF27FgSnAhfSDNmzMCpU6ewceNGhISEwGg01mjqXp0PoF6vR0VFBdzd3VFWVgae57Fo0SJkZGQAAJ577jnytkoIJseMGQOe56HRaIiBuoC8vDycPHkSFRUV%2BOKLLyR9ic3UxTCZTBLLCAE2mw3NmzdHTEwM2Sceg/D/zMxMrFy5EhqNBnq9HvHx8Vi7di0Ae7axoqJCYt8gPtdkMmHLli147bXXmPNGQ%2BHsKVCgQIGCJwHqilKAUfXzV%2BBxsRuef/55RFMvh3wdNI4/f/58tZZQAu3IbDYTmo/wzMYScBs0aBB69OhBnkvDwsJw8%2BZNbN26Fb1794ZOp5MFdmaz2WExOEDJ7ClwAGLe2%2BHDhyUcOg8PDyLOURv8/Pxk/LmUlJTflP2rU6cOevTogUmTJmHDhg0YMmQI4fd9%2BumnsNlsWL16NaZPnw6VSoWXX34Z69atIz/bt28nZZMCDAYDunfvToKSAwcOIDg4GC1btgQAzJ07F5GRkahTpw4JJHiex0svvYSvv/4azZs3R0hICFHApOdu%2BvTp8PPzQ506deDh4UHaiImJwaVLlxAbG4vu3bsjKysLx44dg8VikfjGiVFWVgY/Pz8SdDo7O2PkyJF46qmnSKBktVrx9NNPA7Bz6Ly9vRFQ9UZ30aJFZN6Fktnw8HB4eXnBaDQiMTERxcXFMJlMUKvV2LdvHzQaDd5//33yZqlly5bwF71h5TgO9evXJ9ecn59PMn3z5s0jAjECXw6wl28CIPYGPXv2xAsvvEC4hACwdOlSpKamytaHIEojPl%2BAzWZDYGAgJk%2BeTI4VbBt8fHwIl1G4h%2BIMpl6vR8uWLTF8%2BHAA9iy2oJYp5gZmsTTrq47pQGcRFChQoECBAgW/G4%2BrjNPPzw8tWrSQ/Dj6PNqhQweigUD/DBw4EMCv5Zzi/7OCSY7jZFoJoaGhpHrM39%2BfiMkJyM/PdzgwBZRgT4EDEPPeAgICSGYIsCtK/kR5egkQPPIEaDQaGX8uJCREkok5dOiQzCpBjIiICJw7dw4JCQn4%2Buuvcf78ecLva9GiBQCgbt26GD9%2BPAIDA9GoUSN06tSJ/DRv3hyzZs2S8dIGDhyIAwcOgOd5pKenSzJAABAcHIzu3bvj7NmzGDNmDLRaLY4cOYLr168jJycHWVlZaNq0KYxGoyRQ8/b2xoQJE3Ds2DFoNBqcOXMGt2/fhtFoROfOnZGdnY2EhAQkJCTAarUS6wiTyQQnJye0a9cOK1asgFqtRv/%2B/eHk5ITc3Fwi/V9eXo6tW7fiypUrpJyW53lcquIPffvttygpKcG1Kt7U66%2B/jj59%2BqBPnz64f/8%2B5s2bh2bNmsFms6F9%2B/aS8kyr1YpBgwbBZrNh3bp15M3Sp59%2Bis8%2B%2B4wocvI8j6KiImI2LuY4LlmyBGlV3Ixt27aR/W%2B%2B%2BSb5/6hRo1BYWCgr1WzUqBFZHz2rSomCgoIk985gMCArKwvr168Hx3FQqVS4cuUKUcw0mUw4WcUVGT58OAk4hfnz8vIigVxFRQVsNhs8PDxQWlqKyspKSXBfXl4OlUqF4OBgsMDzPMaNGyezzKgOikCLAgUKFCh4IvAb%2BGH/1%2BHv74%2BgoCAJ3ejMmTMICgpiBpOffvqpRPQOsAsRhlbRJlq3bi1pKycnBzk5OWjdurXD16QEewr%2BEAYOHIisrCwmh27Dhg2/SbDCEQwfPhxHjhxhBpj3WeRtB9GtWzeUlZXh1KlT%2BPbbb2XBnhgzZ85EWFgYioqKsHjxYvTv3x8JCQmEb9aYwVEyGo0oKCjAzp07ZcIfgJ1Dp1KpiOceYM%2BaBgcH49VXXwXP89i3bx/MZrPMN0%2Bj0aBhw4a4d%2B8e1Go1NBoNEZK5evVqrZnXHTt2oLS0FN/SxrMA4bpdv34dANC8eXNcv34dw4YNQ4mIaV5WVobLDMWHR48ekTUgDiSFoEaj0aBHjx44ffq05Do1Go1Emerhw4cA7OTmkpISODk5geM4GI1GTJ48Ge3bt5e0K4bVagXHcVi1ahWZX8Gio6ioSFKGfOXKFdhsNjx8%2BBDNmjWTBK7e3t7Izs5G586dyT6VSgW1Wk1egHTu3LlWqwsBTIGW7t3lB9LCBtS2I6bqLGI%2BvSxY5t2yCmvK2JzprEI3xMqE0mImDLNp2cf54kV5OzSZnjJuZgq0UHxT1vzJtBgY/o8yERdHTNVpdQZWCTt1s1j3RVZhTZcEOyAEwtK7oS%2BZJexDm9CzBDFk10c1xPyzQN0I1n2h22W2Q5uq02IrjHspu5ksgRb6Q8QSaKHbodY%2B09WYHmiV8rAEtKgMa9Lpm8Vaf/Q%2B6vPMvOH09bB4xY4ItNBG5vSYWF8m9CJl%2BadSc87U16D7ogVuWGby9BwzVJhl08WgPtDfsyyBFrp7%2BjPP%2BhNOf8RZ7VYrtlLDPlp4inXOX40n0XphxIgRWLp0Kb777jt89913%2BOc//ykp%2BywoKCDPgcIz0BdffIHbt29jy5YtSElJwbhx40hbqamp2LFjBy5fvox//OMf6N69u0TNvDYopuoKakR0dDQxOa8O8%2BbNQ0ZGBhITE9G%2BfXuMGDFCknLWaDTw9PSE2WzG96IHIsGI/ccff4TBYEDPnj2JETtgN0Zfvny5zIj9/fffx6ZNmxATE4PJkyeD53kcOHAAq1atQmBgIL766isYDAbZtd%2B5cwe9evVCZGQk8vLy0Lx5c5L1AexZIHFwqtFoEBoairi4OGRnZ8NkMmHOnDlYuHAh8eATw2Aw4JlnnsHBgwcxcOBA7Ny5Ez4%2BPkhMTMTChQtrDLqaNGmCq0zpu18tH2r6qKrVari7u0uCRdYxHMdJgkWVSoWMjAzExcVBpVKhYcOGyM3NxZAhQ3Dx4kWcPHmScPc4jkOnTp1w8uRJ8DxPSggePXpEjmGZuAv8Q51OhwEDBhBrC8But1BeXo6KigrUqVMHfn5%2BuHr1KlQqFcnCibltarUawcHBcHZ2xuXLl0lWTuwtCNiliXNkT%2BNy6PV62Gw2ST38V199hc8%2B%2Bwy//PILTCYT8bIZPnw4tm3bhjZt2uAs62EQ9pJfwZOwNiim6goUKFCg4IlAcTFbkesvwOMyP39cZu2A/SXzkiVLkJycDLVajaFDh%2BL111%2BX2FLFxcUR5fQDBw5g2bJluHnzJurWrYuZM2dKBN%2BSk5OxbNkyFBcXo3Pnznjvvfckfse1QcnsKfjDWLBgAeHQxcbGoqCgABEREdi9ezcxYu/WrRuKi4uZRux%2Bfn544YUXHDZinzt3Ljw8PHDhwgU8//zzGDJkiMQjrzrBmH379qF%2B/fq4ePEibDYb5s%2BfL%2BEOxsXFAQBatWqFlStXIiUlBePHj8f69euRlZWF9PR0dOnSRRbotW3bFsnJyYiMjMTx48exZs0aGI1GuLm5IT8/HytWrMCzzz4LQErOHTduHI4fP46PPvoIV69eJRnB48ePw8/Pj3DRKioqCL/MxcUFderUgVqtRmhoKHbs2IGXX34Zrq6uJNAbNGiQrFRApVKRoEuMDh06kOBaMEwXAqqVK1ciMzOTXDPP8zh9%2BjQMBgP0ej2CgoJQUFAADw8PtGnTBrGxsUhPT5eU%2BQrnCQErXboYERGB9evXo2PHjigtLcXVq1eh1%2BuxZs0acl/EKlRDhw7FunXr4OHhgUaNGhG1U57nJXO7cOFCdO3aVdZfs2bNJNsmk0kSnHbv3h1hYWHw9fXFzZs3JZ56FRUVcHFxqZazB0Bibl8bFIEWBQoUKFDwRIBVHvIX4UnM7KnVasyaNQunT5/GqVOn8MYbb0g0Ag4dOkQCPcAuFJiWlkaeNWll78GDB%2BPIkSP44YcfsHz58t8U6AFKZk/BY0B12cCXXnoJOp0O48aNwwsvvIBNmzZJ/PnKy8vRt29fDBo0CDNnzqw2s1dTHzVh4MCBGDp0KJKSkvDWW29Jzs3Pz0efPn0wceJETJgwQXJeZmYmXnzxRRw/fhze3t546623APxqDC7g/v37eOaZZ5CUlIRevXpJrn/z5s1YuHAhIiIi8ENVGVG9evWwf/9%2BTJ8%2BHUeOHMGgQYOwfft2RERE4Mcff4RGo0GTJk2INYSHhwcuX76Me/fuSYI2juOg0%2BlQUVEBnU6HrKwsvPnmm0hJSSElhmFhYTUGKdVBUIHieR5arRYeHh4S0rFKpQLP83B1dcV7772H/v37o1OnTqTsUoyAgACoVCoUFxfLyljF2b/nn3%2BecBfz8/MlZZP0OQDg7u6O4uJiNGrUiJSbCuqewrUJ/e3cuVPi2cfC1KlT4eHhgffff1%2By39fXFzqdDkOHDsWnn37KzLQOGzaMKNfWBlZmb8qUKcSLUIECBQoUKPifwPXrQKNGf0vXIor/n4rFix9Pu/%2BLUDJ7Cv4y/BYj9j8b165dw5UrV9ChQwd07dpVYlgJAOnp6dBoNBg7dqzs3Hbt2uGbb76Bt7d3jX0Iwiws6f/du3fDarVKSgvv3LmDM2fO4Ny5c7BYLDhXZZwrlMBWVlbi0qVLWLJkCc6ePUsyUCyPu4oqzlNISAg2bNiAiyJ%2Bk9lsrtY3T0B1vxeXjwYEBCA1NZUYn6vVathsNvA8D6PRiJkzZ2Lq1Kkynmb9%2BvXRtWtXPHr0CHl5eRLvOqEdIYvZs2dPTJkyhfyuOvEfYdwcx6G4imQgzuhaLBZy3UKgp9PpHHobVlhYiEGDBgGQ%2Buzl5eWhTp06sNlspJYe%2BJW3B9hr7x0Fk7NXxT2UgOaXUNtMfgzNdWFw4mS0LpbVBp19dKBdGcmDxW2iyXQMPoys6plVlktfM8XpYfFYZEQvRtDuSN8yvg49Jlbn9KQ7QhhkeCnRp8muhVUdQRGDWPQs2eUwro8eAovuRE%2BxI%2B3SHE3muqbmgsnZo19q0Q2z1iM9X4z7LVsmrJJ5euFQnxfW9cr23bghP4i%2BZkcItozKAdkx9Fw58lllEWGpdpmuPfSCo7/XWGPas0e6XaVCLQFFMmPqiNDfL/Rnk/VZpdcEg4YhGydj3dDHsG4LPXR67bM%2BC/Q5rHbpvllU3lqXjbDwVUq48CRDyewp%2BNMRHh4u4755eXkhPz8fixcvxsaNG9G6dWu0atUKmzdvxrVr1%2BDq6gqNRoPCwkLy4C9w6IRSvAULFiC2yog1PDwccXFxRMK/NnzyySdITk7G/fv30bZtW5w9exYHDx4kCpKvv/46iouLMX78eHzxxRfIyspCaWkpGjRogLi4OCQkJJDr6t27N25Tf/CcnZ2h1%2BthtVpx%2BPBhGAwGzJkzB7t378bBgwfRnSW88Rsxd%2B5crFu3jgjBiCHO7gUGBsJgMBAOoEqlwrvvvovu3btjzZo12LhxIwmEAgMD8a9//QtpaWnYsmULgF%2BtDYQgMTQ0lGTMaoNarSZ%2BdAJUKhWcnJxIGaS3tzfhwbEQGhqK3Kq/2GJPQcFbT6PRQKPRSPoA7MqatZUABwcH4%2B7du5JAlUaPHj0wZswYJCQkwMPDA0VFRQDsJae5ubmIj4/HihUriM%2BiuI309HSioFUbFM6eAgUKFCh4ImAyVeM%2B//hR5fT1p%2BOjjx5Pu/%2BLUEJ1BY8FarUazs7OcHZ2htVqRVFREXieh81mQ0lJCbKysghnLyUlBUlJSQgODoaHhwfWr1%2BPlJQU9O7dG4BdYdFms%2BHtt99GVFQUoqKifrPK5759%2BxAYGEg4e0KGUUBRURFOnTqF0aNH4%2BjRoygsLITZbMaVK1ewePFitGrVCmlpaSgrKyOBijA%2BvV4Ps9kMk8mER48eoW3btmjWrBl27twJq9VKAlQWnJ2d4e7uLrFrEIJKgWcnQKwMqdfrJRYE4uxeTk6OROxFo9Hgu%2B%2B%2Bw/fff4/09HRZCWhUVBS8vLyg1WqhUqmQmpqKxMREuLm5oV%2B/frhR9aZZo9HA39%2BfBN8q6k2fVquVXIcAm82GiooKYo0gzm4KqppiDB06FGVlZViwYAHefvttsj8oKAj16tWTCaoI7YgDPTFXj%2BM4kpVt2rQp3Nzc0LVrV/A8jzfeeAOAlMtXUFCA8%2BfPQ6VSkUAPsMsfFxcX45tvviFiMHSwuHr1ajgKhbOnQIECBQqeCCjWC080lGBPwWNB7969kZqaitTUVBw5cgTnzp1D586dkZGRAY1Gg6ysLCQlJSE%2BPh4hISFo3bo11qxZA8BOXA0JCYHBYIBKpUJaWhrS0tKQmppKRDvoQKMmZGVl4datW8jPz8cLL7wAJycnhIaGIjU1lRyj0%2BlQWVmJHj164Pjx4zh%2B/DiSk5Oxbds2vPfee7BYLCgqKsKhQ4dIyd7KlSuxa9cujB07Fu7u7qQEdNGiRTh%2B/DhiYmKgUqmqNUgH7DzFevXqSURCBKGRXr16kZJJwC7cIqCiogKHDh3Cq6%2B%2BKisb3bBhA/r160e2eZ5HRkYG5syZAx8fH8mxOTk5EssFjuPQsGFDjB49GhMmTMA333xDyiN9fHygUqlIkCJ40gmgt8XQ6/XgeR6lpaWSsXp4eCAwMBATJkzAiBEjAABffvklALtXTc%2BePcl8//LLL3jllVewf/9%2BzJ49W9K%2Bl5eXxJC9bt266NatGzp37gye51FYWAgfHx9MmTIFvXr1wtEq%2BXDBM2/evHmIjIxEYGAgunbtiszMTNhsNonYTHp6OkwmEwl%2BaajVaolpuwIFChQoUKDgj%2BFJFGj5X4OcXKRAwZ8Ag8GAkJAQyT6Bs8fzPFxcXJicvf79%2B8MoIoFwHCdrR9jvKPbu3QsAuHv3Lj788EPwPE94XmfOnEHbtm1J8Hj48GF06dJFcn6bNm3g7%2B8Pg8GA1NRUWCwWcByH06dPY9q0aZgxYwYKCgpIVocORMRiJa1atYKLiwu%2B%2B%2B47AL8GQTzPw2AwwGg0kqzRwYMHaxxX165dYbVaZVnOMWPGkP8LAiomkwkDBw7E/v37ye80Gg3c3Nwwa9YsPPfccwDscsFRUVEA7EGi1WolnDchc1mnTh0yf%2BLMl9VqRVBQEIqKisgYQkNDcePGDUlWUlz6WFhYCD8/PzRs2BCDBw%2BGwWDA4Spfp0uXLqFDhw5o1aoVzp07B57nMXfuXNk8cByHw4cPY9asWbhS5eXUqlUrnDp1Cg8ePADHcUTBdNeuXUhLS0NERATOnz%2BPb775BgBw8%2BZNPHz4EAUFBRgyZAi2bt0KnU4nmduHDx/CyckJu3fvxnPPPScJWoV791tMThVTdQUKFChQoKBmKH8W/ziUzJ6Cx47KykpkZGTgxIkT6NmzJwB7CduZM2cQHR2NZs2akZ%2B1a9fi66%2B/xvr16yVtpKSkID4%2BHlFRUejSpQtsNhvJMN29e1fSBv2zefNmNG3aFH5%2BfqREUbAxEEo5yymWskqlgru7O7p27YoZM2bAZDKhvLwcJ06cAGAPhHbu3EmClokTJ5KARrCcYBmz//jjjyTQA%2BwZOsGMXAhyhWuhAzmDwSDh65nN5lrLWW02GwlKvv76a0mAYrPZUFhYiJycHKxdu5b8TiibFW87OTmhsLCQCKKEhYXB3d1d1t9NSiBDyHSxjNXFENaCu7u7JJsJQCI2wwLP88jOzpaYuicnJ%2BPevXtEqKWwsBDl5eXYu3cv%2BvbtiwtVZsKCjca8efOQk5ODzz77DEFBQcScXgy9Xk%2ByzeJ5dHJyIveukqkqwQZToCU6Wn4g7XJLbTO7pAQHWDoq9ImsWyMTIKDUOJhCDPROloIHdX2sZSzTQ3DEXZwSzWAuN1rwhDGBsl0MAQeZ2AF9n1iTQ5/kgGKCI0IlsnGyJpQ6h9muA2vi95ify9phzI0johSyfaxx0sIp9EJi%2BZ3SDbPULuhjWB8qeqD0mmVdL90uS/ilNgUPQL6WWDeP3kefw1qzjjhz09fDGifdN/29wJhPuqtr1%2BTNOrIeZff894jZsBp2QOzp96xrR86hu/o97TpyDFl6VVUwCp5MKAItCv50hIeHE882wB7Q6PV6jBw5EomJiXj22Wfh7OyMnJwcFBcXywIAb29vFBQUoF27djh9%2BjQx6qbFMJydnXHo0CEYjUbC76sOAQEB6N27N%2BbOnYvS0lJ07NgRTZs2xc2bN3HixAmMGjUKP/74Izme4ziZUXfPnj1x%2BPBhyT6dTgeVSiUJvIYPH44FCxZgzpw52LlzJ1q3bo3z589Xe23bt2/HCy%2B8IOOh0WjatCnJXP1eCFk5JycnmM1mInoihk6nA8dxsFqtJHipSdCkJjz11FM4ffq0ZJ/YeF2tVsPb2xt%2Bfn746aef4O7ujmHDhmHNmjXQarXQarVMbptarSbzzXEcJk2ahHXr1smC9urOYUGr1SIkJAR79%2B5Fy5YtScZVOCc4OBhWqxXDhg3Dp59%2Bymxjzpw5GD16dM2TUgWWQMu0adMxdeqUas5QoECBAgUK/gaUlwMiL9u/Eq%2B%2B%2BnjarebP%2BP%2BXUDJ7Ch4LevXqRfh1hw8fRmZmJhKrJJU8PDwQGRmJSZMmged5qFQq%2BPj4oH///tixYwcSExPB8zyuXr0KjuNgsVjg6uqKGTNmYN26deRBu6KiAitXrkTdunUJz%2B748eNQq9WIiYnB8ePHERsbi7p16yI3Nxe9evUCALi6uqJTp07Iz89HSUkJDhw4QErqBBERQUxG2KfT6XD9%2BnUS7Ah2Ac888wwSEhIkvLxt27YhMjKS2DuIlSdZ5afbtm1D8%2BbNyfbIkSMBADExMfD29kbHjh0BAFeuXMEnn3wClUpFMqQuLi5M/qJg1unu7g69Xg/AHsx06dIFBoOBBHFCiWzTpk0JV/DDDz9EWloa9u7dS3iIOp0O0dHR0FWjxuVM/RGoU6eO7Hfjx48Hx3GYMGECyQparVY8ePAAFy5cAM/zKC8vx6ZNmwDYfRnFIjp9%2B/ZFq1at4OrqiqFDh5J7xvM8Vq9eTTKCKpWKWCPo9Xq0bdsW69evlwSHgnefYPPBcRwaNGiA1atX4%2Beff0ZlZSUsFgtRawXs1gseHh412kGw/AWrAyuIVSRaFChQoECBAgV/JpRgT8GfjoCAAHTr1g0hISEICQlBQECAROiiRYsWuHjxIhISEqBWqzF48GCcOHEC//rXv/DDDz/gxo0b8PT0JOWDgF3lcOLEiejUqRP69u2LwMBAuLq6EhENX19f8mO1WrFnzx506dIFaWlpuHfvHgAgISEB4eHhCA8Px5EjR5CTk4NGjRph7ty5xHA8IiIC//nPf7Bx40ZyvQLn7ebNmyTYEzJi//3vf/HKK6/IgrhVq1Zh1KhRJEMG2ANEvV5PvN6EEsf//Oc/qFevHjlXsEA4efIkWrRoQQRUPv/8c8yYMQM2m43w%2BcrKypg8L8G4/dGjRzBVleYsX74crq6u8PT0JOP4ucoH6sqVKyQrduzYMXLvEhMTERISgrKyMjx48ACvvPIK6cNgMJBxC%2BcK24IQTH5%2BPvndmjVrwPM8du3ahfZVfnL0vAmBNgB88cUXxO9O6M9oNKKsrAxBQUFo0KAB%2BV2PHj1IsGez2bB27VoA9hcCZ86cQUJCAjlWEIoB7IE2YA96fXx8ULduXaLECUBisWEymRAWFgZvb29mgG0wGBAZGSnbXx0Uzp4CBQoUKHgiUIut0eOEItDyx6EEewr%2BcgwcOBBZWVk4c%2BaMZH9paSnWr1%2BP69evo6ioCO7u7jCbzfDx8ZGJuahUKowdOxYTJkyotT%2Be50lGavr06UhJScHAgQMBALm5ubDZbOTh/fz583juuecwfvz4GtsUMmMVFRXYunUrysvLJQHtuHHjsG3bNokYjFarBcdxpFxTCELLyspwizaYhd0C4NixY6SktLaskTgLdfbsWdnvFyxYgMOHD9dqLC7mzKnVanzwwQcAgAsXLiAvL4%2BYnhuNRqbBO2AXXgGAEgbX6dGjR4S3SJ9fUVFBrBtsNpvMyqCkpAQ8z6N169a4desWEdO5dOkS8lgu0SKIDdIFA3shuLp79y5ZYw8ePABgz0iKg3CxUikrKDMajdWqkbLA5OwxypFrpcwwOHEyWhKrRFjEHQXAdNmmPYKrpoaAmZykjKQZS1vmccy0XaTIIzQljnWMjPZz6ZLsFEeoOBAp1AJsmhLdGf0sxKIY0veFRZGS8ZJYF1ilJktA3xhGw7KPoojjSnDsmHRbpABMQE9GFf9VAuqaZfPHmpza7iXkl8yqKqc9v2nFeBaFlF7n1BIGIL%2B/rGff/Pxa2mGUkTtCF6T7YlWj032zqImysdPrhPEdQF8Pa83KlihjzdL3hbaLZS0J%2BnLo7w2AsbYYxusUlVzuW89Yw/R30qFD8r5BiagxudH0BTpCcnWE2%2BsIh5S%2B4awvslr%2BwAhdXy2ri78LSrD3x6F48hV6AAAgAElEQVRw9hT86YiOjsbUqVMxePDgao%2BZN28eMjIyUFxcDJVKBY1Gg8rKSpLNs1gsGDp0KLZv346WLVti165dzD4OHjyIkydPSn4nlMc5OzsjPj4eGzduxNSpU7F8%2BXKEh4djzpw5GDVqFPGV27VrF86cOYP58%2BcDABo1akQyaJcvX8aXX35JBDhefvllfP755/D398dTTz2FvXv3wt/fH7m5uXj66adx6tQpqFQquLq6IjY2Fps3bybXJZiBm0wmial5Tk4O%2BReAjJvIcRw8PDzQuHFjnD59Gh4eHrBarSgpKcGgQYMwatQo/PLLL3j33XdlpuJ0W40bN0bfvn2xfPnyau%2BNs7MzybhZLBb4%2B/vj7t27pJ01a9bAxcUFixcvJhnR6uDp6YnCwkI0bNgQP//8M3Q6HQYPHoyjR4%2BSYFd8nMBhGzt2LCmpFWdGxf8XRFTKysoQEBCAwsJCksUUQ6PRIDIyEtnZ2czgUwwXFxfUr18fly9fRvPmzaHX60mWFLDbWixbtkz2okLAwIEDsXTp0hr7EKBw9hQoUKBAwZOAn38GGjb8e/qe8pj%2BJCYlPZ52/xehZPYU/C1YsGAB4exZrVaYTCbycK/RaDBu3DiJBUB1mD9/PuEGCj%2BAPcuVmpqK0tJSNGvWDNOmTYOnpyfy8/Px%2BeefAwDee%2B89mEwmXLlyBY0bNwYAEnS%2B%2B%2B67eOONN5Ceno769euT/oSH8/v372Pfvn3geR65ubnQarXo1q0bAHsGr6SkRBLMAL9mkcSli4WFhVCpVMgVvX4WspCCGiTHcXj99deJ0ElSUhLS0tIAAPv378fIkSOxePFi%2BPr6SrKLwrWI93l6emLQoEE1%2BhT26dMHKSkp6Nu3L/R6PcaMGYNevXqhbdu20Ol0mDhxIiZPnlxtoKfT6QhPT8jwCeWiJpMJW7duxfDhw5lzM2XKFPzjH/8AADRo0ADRInVKsbiK1WqF2Wwmgb1Wq0VSUpJEGAiwi6qMGTMGmZmZAH7lD9J%2Bg4A9WzxnzhxSXnvp0iWSARTQvn37GgNcgRfqCBTOngIFChQoeBLA%2BJP5l0HJ7P1xKMGegj8dhw4dqjGrB9gfrAXOXt%2B%2BfZGRkYH09HSJmMvgwYPh4%2BMjCYTEfZSUlODLL78k/DLhB/hVWXHRokXYtWsXMjIyUFRUhICAAJw7dw4%2BPj6Ii4tDdnY2BgwYQNqNiIjA5s2bcfz4cVy4cAHffPMNUY709vYmhu6Cabyfnx8Ae1nnkiVLyP8BEHXP7t27A7AHNO%2B88w5efvllAPbsVEVFBVxcXEhAGxERQQK56OhocByHUaNGIT4%2BXpIlDAoKAmAvS71w4QKOHDmCVq1aSebIx8cHffr0wUsvvQQAGDBgACwWCwYOHAibzQatVov%2B/fvD19cXn3zyCbp27QrAbt6el5eH3bt3Y8WKFXjxxRexfPlyzJkzByaTCU5OTnjEqhGqgslkIhlBAQEBAYiJiYGzszNee%2B01iRcgAFLqarPZSNs///yzzGtQCJS9vb3Rr18/BAYGQqvV4sCBA%2BjatStcXV1RWVlJBHPu37%2BP/fv3o27dujCZTCSoz6drnqr6njNnDlxdXQFI%2BYMCSkpKEBAQUO3YGzVqVO3vFChQoECBAgUK/moowZ6Cvx2CATtLzKVNmzZ4%2BPAhk9%2BXlJSEU6dOMdu8desWoqKiEBUVhVatWuH1118Hx3F48cUXUVpaWu0D%2B1NPPUUCOMDuZSdk6Jo0aYKmTZsSk%2B6mTZuSgEbg1YmRn59PLCeEtuPi4gjPTMgYNmjQgGTaLly4QERJMjIyoNFoSLAmQJz1XLp0KaKiovD222%2Bjbdu2knEVFxdjz549WL16NbRaLfbv348BAwbg448/BmAPSvft24dHjx5h9uzZZI7NZjNSUlIQEREh4Uq2aNEC3bt3J8FQTfDx8SHcN8DOjdyzZw/Ky8vx8ccfY9myZczzKioqJKWYQsZPWBNC8PXw4UOkp6cjJyeHcAiBX03qhYB7yZIleOedd1BcXAy9Xs8M8sSYMGECAgMDAdgD6nnz5kl%2B/%2BjRI3KvhEy0AI7jSFbQESgCLQoUKFCg4EmAm%2BHvqzpRMnt/HEqwp%2BB/GoKE/oQJE7Bz507cvn0b33//PcaNGwej0YiCggIS1Ak/gD0oKCsrQ2VlJTiOI6WhsbGxMh6bGKWlpcjLy8P9%2B/fxww8/YMKECUS58dSpU2jWrBmsVit27dqFvn37Ijs7GwDw4osv4v333wdg58UJQYDJZCKCI6dPn8Y777yDGTNmALBnnQCgc%2BfO5CHfZrNJyhU5jsPGjRsJZ1AYm9BmZWUlysrK8NVXX2H%2B/PkkwOrTpw/CwsLIOZWVlTCbzXj//fcxadIksl8oWy0rKyNlhSUlJdixYweysrIkmca7d%2B/iyJEjyM/Pl81fr169JHYLjx49khzj6%2BuLgQMHYu3atZg7dy62b9/OnH9fX18MGzZMtp/2x3NyckLTpk0RHR2NdevWoXXr1pg9ezYRyhEyezNnzsRLL71EvBgLmUobv2L16tUkgHd3d5d4LwJ2Gw0hC%2Bnq6ioZozjIdARMgZaq7KoYtGAHvc3qUiZmQqsjQC6QwDIOpzuTCTwwXI5pEQWWkIXs%2BlgKLZQogSNezrLLYYkW0OoWjAmk%2B6IFPADIBkaLcbCu1xHdBdlUMO6L7Hooripr2LICCcaNkelzML4nr16ldrCUNagbTFNpGctRfh9Yk0NNMmvZ0OOkNXoYmj2yvlnt0pfH0oOi7wu97YinOqtKnF7Xss8u5J8plpAKvS7o62OJIFVVwBNQFHlmu6zvJLoYhP6eYAnnyJYfYwLpZcz63NHzRX%2BkmJX5v%2BN7gnljHLlAejHRN4bVLjVhzEcaR9SJ6H30tnAtFC3lr4QS7P1xKAItCv5WhIeHIy4uDgsXLqz2mLlz5%2BLrr79GnTp1UFRURLzldDodVq9eLeFoAfZAx9/fHx9%2B%2BCE8PT3h6ekp4bN17twZPM9LhF0yMzOJ5xpgL%2BHz9PQkoiAPHz5E69atkZSUhGeeeQZ9%2B/ZFgwYN8NlnnyEiIgI7duzABx98gJSUFLzxxhtwd3fH9OnTAdizXHRGydnZmQRsmZmZmDhxosx8XAyBy2g2m4mAjc1mw%2BDBgzFs2DBMnz5dkkn7IxCbngtQq9X49NNPMXXqVOzYsQPx8fG1tsMKqvV6PTw8PJCfny/po7oAXAiatVotU3xFQFRUlERIhYZer5eUn4qzx05OTsQeYtKkSYiIiMCkSZOg0%2BlkfX7yySdYtmwZbsjk3Ow4dOiQRBW1JigCLQoUKFCg4InAwYNAlb/vX41axNF/N9aseTzt/i9Cyewp%2BFsREBCAtm3b1njMu%2B%2B%2Bi5kzZ6JOnTpQqVRwcnJCt27dsG3bNjRp0oTJ2atXrx46deqE5s2bO1QaSpd1CkIfXbp0IVkorVYLX19fBAYGomvXrkSQRa/XIzMzExs2bEBSUhLi4%2BPx7LPPkod%2BI%2BNtGs/z4HmemJzXhj59%2BhALhA8%2B%2BICYqg8cOBBRUVHQarWYPXs2XnvttRrb8fHxwfHjx0mwHBwcTIRggoODsWrVKsTGxkKtViMiIgJt27ZFgwYN0Lp1a5K5FFsz1ASx0byAiooK5ObmkkBPKH0VSmc1Gg0J3jmOQ3BwMMR%2Bi9UhMDCQjL1OnToSC4SvvvoKhw8fxogRI8g%2Bq9UKnU4HlUoFf39/woFs0qQJKa%2BtrKyUrBu1Wg2z2Uzuq3Dt4vLdY7R0fQ1QBFoUKFCgQIGCmqFk9v44NH/3BSj4v41DTPMaKQQxF7Exdk1Qq9USw20a48aNQ0ZGBiZMmIBZs2ahffv2yM3NRWRkJH788UcMHz4cb7/9tkPX3L17d%2Bj1eibHbd%2B%2BfejRoweKioowZMgQjBkzBv/973%2BxevVqlJeXE5VNARzHYdasWejfvz94nkd2djYRczl27Bjpd9asWbBarVCr1XB3d0deXh5ycnKwaNEiyXU6OzvD2dkZBQUFGDx4MJKTk5Gfn084gyaTCXeramk4jkNxcTGmTZsGs9kMjuMkqpN37twhQSltg1EdtFotyUAK2ytWrEBSUhIuXLiAyspKYq3QrVs3bN%2B%2BHRaLBV5eXujUqRP27NmDwsJCcBxHsqDiueJ5Ht7e3nj48CG6dOmC2bNnA/hV7EXA888/j7p16%2BKNN96Q7BfavH37NrnGoqIiEswK6pwLFiwAYC%2BxdXZ2JqWaQiZSnJGszdpBDIWzp0CBAgUKngj8jX%2BblD%2BLfxxKGaeCJxLR0dH4RVTHrtFoUK9ePQwfPhxLliwhpaEpKSlYs2YNrl27RgQ1tFotKisrJTwwT09Pku1KTk6Gp6enxC%2BwWbNmaNeuHVHErKysxOHDh/Haa69h0aJFWLhwIYqKiiTX6OLigoCAANy5cwcBAQEoLi4mpuC1geM4uLq6oqysDGq1GjqdDoGBgbgqI8ywodFo4OLiQsoWfX19kZeXBz8/PyQnJ%2BOZZ55xKLDQaDRQqVSSoI2GSqUix9TEh6TH16FDByKwExoaivr16%2BPIkSNwcXEhWS%2BVSoUWLVogOzsbZrMZKpWKeR0qlQpqtZrJmRPM0Fu3bo3du3cDsAeeOp0O5eXlcHV1hcViQVlZGcLCwrBq1Sp069aNBP/idbJixQpkZmbiiy%2B%2BIP2Kr2f16tUk41sbLl68iGsUGadpo0YIa9FCemBhIeDpWe22yQRUuXVUf05lJUBlWh89Atzdf93meYDSGLLzscSCPFS7zHOMRkCcrc7LA3x9pccUFADiDHFJCSAyvWdeMj0m2Gk1ErcR6hh6jKyGWWOQXQ6jIdku%2BvrKygA6oKf3Me4LKioAvb6GQdrpbFUuIsxtZrsOjJvumnkQtU92DuOaZZfDuD56HTOGLd/JaEd2yfQxjHNkS4uxHmXjpNc5axAOfA5l%2B1gDp45hdQ2zGRBVQDjSFT1Z9MeddQxzTVDjZvVd631gfV6oMTEbpi6acevk5129CojFtBz5rBYXAyJeOqNr9tw4sP5qPYaeB0fbdWT%2BamtH2JZ9yfx1GDv28bRLsSj%2Bv4aS2VPwxGL27Nno378/ALv596lTpzBnzhy4u7ujbdu2hEMXHx%2BP21WKABaLBSaTCVqtFhzHkXJCJycndO7cGTNmzIAn9UAJ2LOF586dIwIwFRUVxIMuNjYW77zzDho1aoQNGzaQcx48eICPP/4Y9%2B/fR%2BPGjXHu3DkSDGm1WlI%2BCdiDRzGHjed5GI1G6HQ6jBgxAsOGDcMXX3xRa7DXvHlzXLp0CRaLRWKPkFdFAH/w4AF69%2B7tcAbJYrEQA/PqznFycgLHcbDZbLDZbITzV13gx3Ec1Gq1REn15Zf/H3tfHldF9b//zF24l02QXRFRU5FUEHPJUjNyy11T%2B1iZlqm5W2m5lZWmlkulZmqpaWmahbjgQqbljkpuKG7kigEisl0u3G1%2Bf1zOcebMAa6YfavfPK/XfenMPXO2OXM573m/n%2Bf9Gnr27ImGDRtCFEVah16vx4oVK9CuXTtYLBZUq1ZNZuQTVKlSRWFsE4iiiD/%2B%2BEPGs7NardQwlM7ThQsX6Bzfvn0b3t7esnq9vb1x/vx5eszOSRuOwEpZ2LJli4KzN3bMGKWxx65H5lhh6PGu4YTUskaQwmgDlDs/pl7uNewulDX0ALmhB3B2Z5wuc5/L8ssoDD1OxbwxKLrDqUhxiu0fx3OrOMe5LwrLSTFI5Z5LsQfj1evCuBVGG68Qc05xDaDos6I7nP6x65gzbOVJTj2KLrNlONcolhZnPSrGyQu/ZwfhwnOoOMcbOFOGG/nPGAOuNMVOFldomZ1Q3ppgxs1ru8L7wHteWAOHVzHTac6tU17Hqia78qwyhh6naf7cuLD%2BKizDozG4Uq8r81dRPeT46lUgMlJ5/d8A1bP34FCNPRX/Wnh7eyNQspHs3bs3tm3bBoPBgJo1a2LKlCn47rvvEBUVRQVFiouL8eqrr6JDhw545ZVXkJiYiG%2B//bZCrlVISAgGDBiAjh07AnAmDpeKvri5ucHhcMj6ExgYiFmzZqFt27bo27cvli5dipEjR2Lfvn1ISEhQtNG5c2d06dIFY8aMQb9%2B/dC3b1/Ur18fU6dORWRkJGbNmoXjx4/j5s2biIqKglarxfHjx9GwYUOcPXsWAJCWlgZ3d3fYbDbodDqUlJTA4XDA09MTFouFGpWCIFCDs6SkBHa7HWvXrsXw4cOh0%2BmQm5sLb29v2O126PV6atwWFxcjKipKFuLpcDhgsVgAgHr42rZti/379wNwGnf%2B/v64e/cu7HY7qlatiry8PJlwzezZs6kHrUuXLrh69SqSk5Ph6%2BsLHx8fyotjcy4ajUaUlJSgTp06OHHiRJleRYPBgHr16iElJYWekxqpUo8gye1369YtRToNQRBw7tw5bhtk/K6Cx9lTwyxUqFChQsU/DjyJXxX/GqgCLSr%2BU9DpdNDr9TIOnZubGxVviYiIwFdffYWRI0ciPDwc/v7%2BMg9befD396f1ZGVloVmzZjTdQ25uLq5cuUKPSaJ2IlKydetWWo8gCApRmVdeeQUOhwPbtm1Dp06dkJ%2Bfj19%2B%2BQV5eXlo1aoVEhMTcfz4cVy7dg3Vq1fH6dOnaVqAiRMnAgA16MxmM6xWK8xmM/U8mUwm2O12eHh44Omnn8auXbuwbds2xMfH02TsDRo0gNVqpZ6swsJCmM1mFBQUQKPRUGVKqcEEAFOnTgXg5BKS9vbt2yfjtGVnZ1NjLicnB3a7Hc8//zyto6CgAIsXL6ZzRURQWPGehg0bQqvVon79%2BgDu8RLPnDlD2yPeVzInwL1wUILQ0FBUrVoVoigiKCgIYWFhAJyJ5/v06UPrZj137u7uaNGiBcrCcVanvByonD0VKlSoUKGifKgCLQ8O1bOn4j8BwqE7ePAgZs2ahTVr1iA6Oppb9tFHH33g9lgOGwlZtNls0Gg0sFqtyMvLw6xZs6DRaGQev7IgCAK6du0Kb29vHDlyBMOHD8fUqVNRr1491KpVC%2B%2B//z60Wi2GDx%2BO3377DXl5eTh69CgmT54MQRCop4jkFSTeKnd3d0RERODy5cswmUxITEzE/v37qdfKbDbDYDDg6aeflqUakBpr5CM9TzB9%2BnQAwKeffurS3JF0FiRZfWhoKNLT01G7dm1kZmbi0Ucfpbn9SM47g8EAk8lEPYoXL14EANy9exd%2Bfn6IjIzEzZs3kZmZibS0NNoW8bTVr19fljNPGgoq/f%2B8efNoCotatWqhVq1a2L59O/0%2BMDAQL7zwAnbt2gXA6R0kXk1pf11Bjx49FGuxPi8pO0sMYY65/BiWuMIppOAg8bhCbCGWLMS7huV28Ihz7Jh4xC%2B2bg6hSDEspgyXZsLWy%2BHZKHiQHE6Posts/3hkSvZcZTg0nFOKpirJ2XOFPsYW4nJGmQsr5NHxynA66Eo9ilMV9QWcJcq534p6eTwvdlGwFfPuiwvrkS3DXddM3ZWhB/LqZbvDXROV4eyxFfN%2BAyq6htNp3hgU/bl8Gahbt8w6uBdx%2BleZ5%2BWBeHNSuMBf/Uvb/gv2TZXF/2%2BG2cOAauyp%2BNdi%2BvTpmDFjBgAlh%2B6LL75wKaUB4AzXk3qDCMy8pL6laNSoETVIAGDu3LnYvXs3LBYL9Ho9/vzzT7Rq1Qru7u4IDAzEqFH3cqdZLBZFe6Qto9EIu92O9PR0TJo0CQBw/vx5XLx4EQ6HAxEREejXrx%2BSk5OpsfanJAErMTojIyORkpICh8MBs9mMkydPKtp79NFH8dlnn%2BGVV15BgwYN8Msvv8iMl%2BDgYJr4vVWrVti/fz9sNhs3d57RaKQeQcLxkxpBUhAPHxFLIcYW4fBJjbW4uDh899139BovLy9oNBpYLBaqpmmxWHDmzBlqfNerV4%2Bm1SA8yFOnTsn6EBgYCFEUkZubS0NVLRYLjh07hhYtWiA4OBhmsxm//fYbvaZ69eoICgrC8OHDATg9c4IgyMZ59%2B5d6iWsCGVy9lheBEsMYY65/BiWuMIppOAg8bhCbCH2meJdw26aeMQ5dkw84hdbN4dQpBgWU4arJ8DWy%2BHZKIwXjhdW0WW2fzwyJXuuMhwazilFU5Xk7LlCH2MLcTmjzIUV8uh4ZTgddKUexamK%2BgLOEuXcb0W9PJ4XuyjYinn3xYX1yJbhrmum7srQA3n1st3hronKcPbYinm/ARVdAyg6zRuDoj9SQ8/Vizj9q8zz8kC8ufIaq%2BRviatt38gywMU/bSr%2BgVCNPRX/WowdO7ZMDp2vr69MeKM8BAUF4dtvv1WcHzhwYJnXkNBQAi8vL7Rv3x516tTBrl27cPPmTTgcDjRp0gSzZs2Sib6QMFO2raysLHqe8OoAZ/67qKgo7Ny5ExcvXqRlLly4AADo2bMnNm/ejMcffxwWiwUpKSnIzs5W8M1YnDt3Ds8%2B%2Byy0Wi0CAgLg7%2B8v45FlZmZSL9zevXvp%2BYiICNo24DS0pakR7Ha7TMGSB56qJlFJDQwMpCkM8vPzZcZlYWGhQvjFZDLJEsHb7XY6dmm/unXrhm3btgFwiq%2BQPhCeIQAcOnQILVq0QJMmTfDzzz/L8vu9/PLLdN4APueuLAOXB5Wzp0KFChUq/g0I87oLQCmS9XdA9ew9OFTOnop/LaQcOjZxulS0hMXq1asxf/58eqzT6RQcuvDwcBmXb8%2BePZTLVRa8vLzw5ptvYteuXTh79iz69%2B%2BPkydPysQ/IiMjERMTw23Lx8cH48ePpx69rl27QqvVYuzYsbh%2B/TqqVq0KPz8/zJs3Dz169KAKo2PHjoVWq0WNGjWwcuVK%2BPn5oUePHrLk52UZfna7HRaLBadOnYLD4UCHDh3od25ublyjLTIyErGxsfTYz8%2BPqqIuXLgQbdu2pd%2BRexIeHo7Y2FiMGDECwD1umlTQhMyTNKySCOtIxxIWFia7zt/fX9afU6dOwWAwQBRFvPTSS3j66afRuXNnNG3aVDYOURQhCAL69etH%2BYOEY9mhQwcqSENA8jzyuHYEhYWFZX6nQoUKFSpUqFDxd0M19lT8J9G9e3ecPn2ahvMRmEwmrF69ukLP01%2BBt99%2BGx4eHjQpd0XIzc3FwoULqSG6Y8cODB48GD169EBBQQGCg4ORl5cHi8WC7du3o0GDBvRaLy8v3L59G%2B7u7vjyyy/x4osvUmNVo9Fg1apV0Ov1cHNzw4svvgiNRoNhw4bhwIEDWLp0KQBnCKI0NFVq1EiNq99%2B%2B40qbQJOwZXExEQATsNTqmxK5vnatWvYs2cPvvrqK9mYfX196f%2BJQSflDTZv3hyAMw%2BiIAgICQmBu7s7qjDhUVKPGuFOAkBSUhL27t2LnTt34sMPP5RdI4oitFottm/fTj2XpD2DwUCFdAiIwUy8tLVq1YLBYJC9FKjH49yVAVWgRYUKFSpU/CvAiUT5u6AKtDw4VGNPxX8SMTEx6NevH0aOHIkff/wR169fx9GjRzF06FBoNBoMHTr0offBy8sLb7/9Nvbt24c9e/bQ81arFbdv35Z97HY7vLy8YDQaKX/Pbrfj66%2B/RoMGDXD16lWcPXsWNpsN9evXx%2BHDh2kagn79%2BiE/P59eV6VKFbRp04Zy%2BRwOBwYPHgyr1QqLxYK1a9fC4XBg%2BfLlaN26NV5//XWu50%2BaX04vieMPDw%2BnXi4CYlzNnTuX5pqT1kmMxdq1a8sMNRIaCfDDGonSaFpaGkRRREZGBi5cuCAzCC0WC65evcrtDxFyCQsLg5HDubDZbCgoKKBzOXToULz33nvIzs5G9%2B7dubxP4u27c%2BcOALmB5n4fSWd79OiBuXPnyj5t23ZUFmTTdDDHXGppafoIAk6ueShSNvJegNy9W269XDXu1FT58fffK8uUCtyU3RngyhXmBNM2AKfQQjn1cp37zDh58ydJyegEJzWLJN2iE5IXJQCA339XVlyaaqTcxpmB8%2B4dG6GekyM/5u7LTCbZITt1gPJ2l2oVyeHC/EHyfAJwCgZJwMuQwo6pNDWoDOxccDOtMGrBkKSJAcAdeCk1%2BR54945tXLFIALA5QCUvxbh1AMoJ5KV2YRYbd9x5eeXXy2ufmQte9xTPMzu/nA6xtx%2BAcnGVRqZQ8BYbe42En17WOcXvBgCUhu9TsKrJvIfh6FH5MWfc7BB4v4fsnPJ%2BZhX304WFzp7irQn2PvDuS0Vl6G/JzZvKi/8mqMbeg0MQy0pMpULFPxixsbEYPXp0uaGVDocDa9aswU8//YQbN26gSpUqNHE6UU2Mi4vD4sWLZcaYK23ExsbKwg0FQYCnpyfGjBkjM4Ti4%2BPx4YcfoqioCAEBAfDz85Px3SoCy0%2BrXr06srOzYbVaERUVhVOnTsHPzw95eXnw9/dHbm4uTp48iZycHMTGxlLxEaPRiLy8PGg0GowYMQKNGjXC66%2B/DqPRiGnTpmHatGnQ6/VwOBzUGycIAjw8PGAymVCzZk0aNioIAnx9fXG39A%2BxIAiIiYnB77//Tg089melWbNmOH78OARBgLe3N%2BVTfvTRRzR1w2uvvYavv/5adp2npydMzCaVB8ItLA/vvPMOPv7443LLLFu2DI0bN8aVK1cwceJE5OTkUOOO3LeYmBgUFRVBq9VSniFp%2B7fffkNISEiF/QWAOXPmcAVaRo0e7dL1KlSoUKFCxd%2BC9HSgNCXS341SNsdfjo0bH069/0SoAi0q/pXgGWcsNBoNBg8erPBCSdGnT58yDcaK2pgyZQrlqtlsNhw5cgRTp06Fr68vevXqhdmzZyM%2BPh6TJ09GixYtkJubi88//xwmkwkbN26Ej48Pckpfyfft2xeZmZkwGo144403sHLlSjz33HPo2bMnAGDcuHFITU3Fa6%2B9hg8//BBubm5ISUlBYGAgTCYTOnfujJ07d8Jut0Or1SIwMBCBgYHIzc2FyWRC//79sXPnTmRlZWHx4sUwGo1wd3eH2WzG9OnT4eXlBbvdLlMg1el01NC6Lu0i5E0AACAASURBVHmFKYqiTLhEFEX8XvomXBRFaDQamqyctEG8aoIgwGQyUSNWmpfu999/x%2BTJkzF79mx6LjIyssLcdf7%2B/ggICKDGWOvWrXHo0CEEBwdTwxhwKopWhE2bNqFdu3b45JNPUFhYCA8PDxlvj9xrMlb2u/T0dJeNPVWgRYUKFSpU/CtwH%2BJjfzX%2Bf/PCPQyoYZwqVFQS3t7e1KiqVq0aevfuLUuAvnr1anzxxRfo168fwsPDER0djS%2B%2B%2BAI2mw2rV6%2BWGWVarRY%2BPj4wGAzw9vamgitEwCU4OBg1atTAnDlzEBYWhs6dO6NWrVqoWbMm3N3dcejQISr5LxWgiY6OhtFoRGRkpCwU84UXXsDmzZsxY8YM2O12GI1GmZBMrVq1yjRaQkJCqPFGeHYkeXlkZCSqV68Oh8MBQRCop49w6j766CM4HA7q%2BSPpFwDgxIkTslx4JH0EADz55JOoXbs2AKBu3bpYs2YNLde/f39ZXw8cOACHw4E///yTjol4FKXQaDR49dVXZeGmpB5/f38EBQVx029I8w3OmjUL77zzDv2OJKh3BSpnT4UKFSpUqFDxsKEaeypUVIBRo0YhJiZG9rl16xamT5%2BOmJgYmaiJTqejqRWioqLQrFkzWV1SARVXYLVakZiYiIMHD2Ls2LHw9/dHQUEBdu7cibS0NHh5eaFVq1YYP348FSCRCtB4eHjAYrEgKyuLGik6nQ7nz59HeHg4%2Bvfvj9atWyM3N5d6rADg6tWruHHjhqwvgiDAx8cHBQUFuHbtGgDQUE7iAWTDN4mxdLSU/zB58mRZGakBJooiTY0AAKmpqdRzduzYMcrLS0tLw/jx42m5ZcuWyURheBBFEc8884zsnMPhwMqVK2X9%2Beabb7BkyRL06NEDBoNBpvAqnQdS55QpU2ShoVKxlorA4%2Bx1LOU7SnH4cPnHPKLNiRPMCR5/hyFJ8aJgWQ6KgqbE4dqxFB%2BW%2BgIATNpHLhWH5ciw1CvedQonMKd/inHy5oapmB0TAAUhkB0nb0wKsVYOi0LBq%2BHcX5aepSC8sVwnQDFwV%2B43L3sNW4bHD1SMgekwl1PINsZrnL2QM3/svLPHXB5i6e8ZgYLDByjmlMcpZJcSyx/jcabY28vjnLFUQFeof1yCDnNhKVW53IrZueA%2Bq2xjnHrY/jE0TpZSCkC51hTrnlMvrx6WbseOm0dDZGl8zJ9DJ9gHmnOD2SXL%2B7mpqIwr1/CeKfY28MSi2XOK%2BSu9UTd0tZUX/01QOXsPDpWzp0JFBcjKylJ4eAYOHIiBAweiY8eO8Pf3h8FgwN69e/Hmm29i1qxZWLNmDaKjo/Huu%2B%2B63E5sbCxMJhOKiopgtVqh1%2Bths9lgNBrxwgsvYOLEiejUqRM6d%2B6MN954A%2B%2B%2B%2By4SExMxceJEtGjRAnv37sWsWbMQFhaGjRs3omrVqsjLy0Pr1q1lapUkv5y7uzsNt2TDEQMDAymvrqSkhKYhiImJwR9//IE85q8uCcvUarUICQmR8RmlqFKlCmrVqoXTvJ07A2mCd7YtjUZDDdq5c%2Bfi008/xa1bt8qta968eRg7diytg/DtpKI4fn5%2B2LFjB3x9fTFt2jTk5eVRpVESJhodHS2bL2nOwMOHD8vSRJQHHmdvzJixGD16lEvXq1ChQoUKFX8LMjOBUq2DvxulbJa/HJs3P5x6/4lQPXsqVFSAoKAgbl68hQsXolevXmjTpg2ioqLwzjvvYNCgQTRVAk/JEQDy8vIwZ84cxMbGIjo6Gs8%2B%2Byy%2B%2BeYb6mEKCgqCh4cHxowZg7179%2BL48eOYOHEikpKScPXqVWqEWSwWhIWFYfXq1ejWrRtmzZoFAFi7di1ND0A8ei1atKDtE8NEp9OhQ4cOWL9%2BPbRaLQRBgMFgAOBMOl5SUkJVL0k45J9//gmtVguNRgOj0Yhq1aoBkIc23i595U3GHxwcTNvPz8%2BXhTo%2B%2BuijLt0DrVYLT09PeHl5yQw9wOkhy2HlCBlYLBZq6JF%2BWiwWiKIoM%2BSlgiz%2B/v7YvXs3/W5z6V8GMkcE0tDLRYsWuTQegM/ZU1l7KlSoUKHiHweu9LKKfwtUY0%2BFikpi7NixiI%2BPR3x8vMwoA5z54/I5YUh3795Fv379kJKSgo8%2B%2Bgjbtm3DmDFjsGzZMuot0%2Bv1eOWVVzBs2DBFsngANFm8IAioW7cutm7diu3bt9PvgyVv36ZPnw53d3fUrFkT/v7%2B9DwRStmyZQv69OkDu90OURQRGBgIAKhWrZqM40eMq4yMDOTk5CAoKAi%2Bvr4ICgoCAISGhsJgMMDhcFBvHAl3NJvNGDlyJACnobR%2B/XpaL0mNAADt27fHkCFD6LE06MBut8NkMsHNzQ0%2BPj6oW7cu/c5kMtGxffDBB2jSpAkAICIigpbx9vbGsmXLZPP47rvv0mTsAQEBdNxBQUHIzMzEd999xzXYpakjBEGgBi8AKqijQoUKFSpUqHhwqGGcDw7V2FOhopLw9/ennj7WKGvYsCE1yqSYP38%2BTCYTGjdujFatWiEsLAxdunTBRx99BJPJJPNYxcbGIiIign5eeeUVAMCpU6eQnJyM1NRUbNq0CRERETI%2BWkREBCZNmgQAOH36NPLz8/Hjjz/SvHAAYDQaodPpsHz5cgCgfSe8vZycHBpKWq9ePTzyyCMA7uXLyywlchCDdtSoUbBYLLQenU6HgtJY//z8fMq3e%2BGFF2RjlPIEd%2B/ejW%2B//RYA0KhRI/Tt21cxfzk5OcjJycEfEvLYl19%2BSfuzd%2B9epKamQhAEmSFpsVhkRjAANGjQAEuWLEHt2rWRnZ0NnU6HevXqQaPR4OzZs3jiiSeo8SsFEYohoasZEgLIYQWhrmyoAi0qVKhQoeLfgDte4f/XXVDxAFCNPRUqHgK6d%2B%2BO06dPIzk5mZ6zWCxISEiAzWZTJDF/%2BumnERAQQI0pgilTpuDAgQM4cOAA5s2bB8BpZLz22mvIzc2Fj48PFi1aJPNiHThwgOauI%2BGc3bp1o0qTHh4e6NChAywWC0aMGCHLrUcMF2n45qVLl5CWlgYA1BMmiiIyMzNp%2BOSaNWsgiiL1ehEjjihyEvXMH374QTa%2BHj16yI6JVzAlJQWbN2%2BmHjcWUqPoxx9/pO39%2BuuvKCkpgSiKMs9gSUmJLEk8ALz00kto0KABrpQqIthsNmRmZsJsNiM2NhZdunSRXUNSQDRq1IjOgfRf4J4R7Ap4Ai1t2iiTqrNaGwrtDY4YgiLihlVDAJSqIxzFCVZvgI2W5Ub2MIV4QhZsPTwtDrZxnuZIheIMnPvhUmJuZm54wgasIgLbP55QBNtfntAGm3edO3BWiYapiJtykm2Mpzgh%2Bb0CwE82zapkcERw2IWhEPXghDCz3ePNjStlWLowu/54a42dYl5UOPsIudI/tl7efWGnwhURF96CZNcWL0pcIfTBcqI5a41dj7y5UfSPM1C2bbaeyiT8Bji/QZz1yK4Jxc8CR3WG7R9PaApJSbJDblJ69l65orbCDpR3DVuGlxrBFYUb9hz7gJQO6v/yPaTq2XtwqMaeChUPATExMejXrx9GjhyJH3/8EdevX0dCQgKKiopgMBgwdOhQWXnCl2ONQGl6BxKq%2BOSTTyI0NBQ5OTnIy8vDzJkzUatWLXpNYGAgVbls164dAKdHjBhIHh4emDlzJgICAhAYGAiDwYCaNWsCcKqFlocnn3yS/l8URRp6ev78efj6%2Biq8Z8ToIp44NkG6NPyUxbBhw6jRWRZ0Oh1VBK0IbFitp6cndDodTRsBOI01d3d3XL58GUuXLpWFbBL8yZWjc4I1KMvDli1bMHHiRNnn4MFfFOVK7fUyjyEJtyUozYxxD0zaCQBAZKT8mOPFZOiJYLVnFO1wCnGqVdTDmWZF44pxA2CXq%2BRWOsERFGCni3nknGDmhku/ZTyzbP8UfYGyv5xbB8X7Dd7AGZVftiKOiKyysdJULTI89pj8WBIuTeHjIz%2BuV09ZhlkYkkhnJzhebbZ7vLlxpQyb95ldf7y1xk4xT2OJfYRc6R9bL%2B%2B%2BsFPBPnPctjgLkl1bnClWlIEkXyoA7lpj1yNvbhT94wyUbZuthzdu9hyvjOI3iLMe2TWh%2BFlQLFBl/yS093to2VJ2yFsTinvF%2Bxtb0SLgXcOWYe8loJwc9tnlnWMfkNJB/V9S9lRj78GhGnsqVDwkfPDBBxgxYgRWr16NHj16UIn%2BTz/9lHrcWLzzzjsIDAzEsmXLZOkdYmJiqIGo0%2BnwyCOPoE6dOggICMC%2Bffvw9ttvc%2BsjSd9TU1OpcMqkSZNgMBgQHByMrKwseHl5UaPKp/SHX6vVyjh73t7eEARBxnvT6/Xo378/AGDGjBnw9vamipVSsIIm0vQE48ePl3kzpcbuZ599RvtTtWpVhdcTkIeBVoT69evL2i4sLITNZpMZoLm5uRgyZAji4uKoB48gISEBjRo1UhioHh4etN/XGAn38qAKtKhQoUKFChUqHjZUY0%2BFikpgz5496NOnT7llNBoNBg8ejK1bt%2BLkyZNYt24dAMiMqLLq/N///oegoCCZCMwHH3wAADh48KAiZxzBL7/IPUPEQHr77bdRvXp1AE4xkl27duHChQsQBAGhoaHQaDTw9PSEr68vAKdYilS9sqCgQMFPmzp1KoYPHw7AKXZy48YNRZ49g8Gg4DNKDbSFCxdSj6PUi%2Bbu7g6DwYCbN28CcHrhateuTY2qVq1aAQBV5wTuGYqrVq3CgQMHFHNEktcTlHUfRFHExo0bkZ6eLjPemjVrhp07d1LOXpUqVVC9enVYrVa4lb5VzVbE4JUNlbOnQoUKFSr%2BDeBGcfxNUD17Dw7V2FPxn8WwYcMwefJk2blt27YhIiJCIZG/ZMkSqqQ4adIkKnBC8Oeff2LatGlo27YtmjRpgl69eiE%2BPv6%2B%2BlOzZk14e3tzhVsAYMSIETh06BAAp4dNmt6hV69etE%2BDBg2CzWbD5cuXkZ2djZiYGHTt2hUA0LVrV8TExFB%2B2f79%2BwE4VTlv3boFDw8PvPrqqxg3bhwEQYCbmxtSUlKQkZGBoqIiyl/z9vbGsGHDcOHCBfpp164dOnfuTPv7/vvv49lnn4XRaISR%2BUtAPGi%2Bvr64du0aIiIioNPpqLFFwkzHjh1Lz%2BXn51Nj0Ww2y0I4b968ibS0NPr94cOHodFoUFhYSA0kYuylpqYiMDBQYThNnTpVJiJjlRAsBEHAsGHDYDAY8MQTTyA/Px/Xrl2jwjSA0zisUaMG5T7m5%2BejpKQEVquV9rWQS%2B7ig5tUvaOSs4fffpMf//qr7JBH58Dvv8sOeTwllsfCDdNhK2c4XTwKiCIrNMNrAaAkwHA6KNHXcYIZE/e6gwdlh%2BfOcfrHEGt4kcKKxNacthWPMdM2l6zIhhzzJp0pw%2BMBVcRl4vIQGR4VL/qZvZ/cKGqmQ7xhKihbLOGNs2jZU7xHSbFMeDwllmfILqTz5xWXKBKtSwSgymxbkZEcykll84nybiY7Bh5PkuFXcrlh7ARyeHOKU67Uy3LgOPPHLjiXfkvYHyDegmQr4pVhznH5v6V/VylKBcMoOPcbbJQG5%2B82OwTu88I%2BjLyHs6Kk9Dyypwv1sv2pDA%2BWPIeB4DzoKsqEKIqYN28eHn/8cbRo0QKffPJJmS9zJ02aJBPjI5%2BXX36ZlmnWrJnie5YWUx5UY0/FfxbXr1%2BX5UkDgI0bNwIA4uLiZOc3b95MxTWSk5NlwipXr15Fr169cOjQIVitVoiiiLt372LatGlYuXKly/3R6XTo0qUL1q5dq0gWvmfPHuzZs4emMgBA88A5HA7Zj8S6devw3nvvwWazoWrVqoiPj6eqmsuXL0d8fDwNQSRetDFjxiAoKAjz589HQEAAevfujYSEBHzxxRcAnAIuBoMBM2fOVPTbarUiMTFR5lH08fFBUFAQwsLCIIoi9VKRkE3i6TKbzejduzd69%2B4Nm81GhWCIUqf0nKswGAxwc3OTpZIA7nnFPvnkEzRo0AB79%2B6VfX/kyBGZiIwUoihi%2BfLlsNvt1GArLCxEmzZtaJn9%2B/ejqKhIZtgWFhbKwksr4jxKwePs7d6t5Ozhqafkx6U8zHttcipv2lR2yOMpsTwW7ptbtnKG08WjgKDU80nB8FoAKAkwnA7Wr8%2BcYMbEvU7CKQUAbhpHxqPL4wGxQ%2BC13bAhc4Jpm0tWZMO3eZPOlOE5oCviMnF5iAyPihdJzt5P3tywHeINU0HZYglvnEXLnuLxJBXLhMdTYnmG7EJq0EBxieRn14k6dSpuu/SljwzspEZFyY95N5MdA48nyfAruUEJ7ARyeHOKU67Uy3LgOPPHLjiXfkvYHyDegmQr4pVhznH5v088IT%2BWcNwBcO83whn1ScUDrxwC93lhH0bew8meY28Ej%2BzpQr1sfyrDgyXPod2P86D/Tfg3evZWrVqFbdu2YfHixVi4cCG2bt2KVatWcctOnTqVCvEdOHAAGzZsgJubGzX2MjMzUVBQgN27d8vK8aKDyoJq7Kn4z4D15LVs2RL5%2BfkyT15KSgq8vLxw69YtqgQ5adIkXLt2DdHR0fRam81GPXnPPvsscnNz4evriyVLlmDr1q0YOXIkRFHEggULuPn0ysKYMWNQWFiIIUOG4OjRo7h%2B/To2btyISZMm4eWXX5blj3M4HHjppZewZcsWjB49mp6Pj49HXFwcatWqBa1Wi/DwcISW/tXZsmULBgwYgKNHj2Lfvn3UsAkICIAgCGjcuDE%2B%2BOADxMfHY%2BnSpZg%2BfTpatWqFQYMGobi4mBpeW7ZsoVzBqKgojB8/Hn379qVzRtQ/PTw8sHLlSvz0008A7ql4Ej5afn4%2BNm3ahI8%2B%2Bkg2D4SzSFQ67wePPPIIfvrpJ8VbLa1WCw8PD1SpUkURTqrRaOg5koePB5vNhkOHDsHDwwNeXl6yvHkWiwXDhw%2BnojSAc7wOh4N6Movvg8WucvZUqFChQsW/AVoLL4Tk78G/0dhbs2YNxo4di2bNmuHxxx/HhAkTsHbtWm5ZqRBfYGAgFi1ahM6dO6N9%2B/YAgLS0NAQGBiIsLExWjhX0Kw%2BqsafiP4NmzZrhzJkz9JiIk/j6%2BiIpKQkZGRkoLCzEsGHDAIAaSISPRkIUrVYrMjIykJubi/feew8OhwPVqlVDWloaTpw4gZo1a%2BL555/HW2%2B9BY1GwxUJSUpKQtu2bbFmzRq0bNkSTzzxBL788ksEBgbi%2B%2B%2B/R2hoKIYPH44OHTpg2rRpAIA6kreLq1evxp07d7B%2B/XqMGzdO9iOxZcsWpKSkUD4b4FTDBEDz6Q0dOhRDhw7F559/TstkZGSgdevWNN1CXFwc/Pz8sHjxYjRq1Ah16tTB4sWLATiNHm9vbwwdOhR9%2B/ZF1apV8dtvv9F%2BHD9%2BnKZdmDBhAk3JwIMgCPDz86MGoo%2BPD5o2bYpq1aqhZcuWNKxyypQpNKn6xx9/jAMHDnDrioiIwPbt22E2m6lXzcfHB3a7HWazGTabTSEKQ1JAAE6Pb3m4du0amjdvjsDAQCpAAzi9lUePHqW59KQePbIG7sfYU6FChQoVKlT8t5CZmYk///wTzZs3p%2Bcee%2BwxpKenI0sRMy7H4cOHcezYMbz55pv03OXLl6lWQGWhGnsq/jP45ZdfcOnSJerxiYmJAeBUWExOTsbWrVshCAJeeuklAPcSYBN%2BG0lTcPfuXej1evj6%2BlJPoUajQceOHbFo0SLqyRswYABWrlzJDQtMTU1FZmYmVqxYAZvNBovFgiVLluCHH35AtWrVUK1aNeh0OlStWhVarRYOhwMzZsxAdnY2vv32W%2Br1sdlsKC4ulnmxLBYLkpOTYbfbERYWhosXL2LChAnw9/eHVquFRqOBVqvF5s2b8VRpCGCfPn3oWEJCQuDl5QVBEHD69Gls27YNgiCgUaNG1IAURRGFhYVYuHAhfvzxR0yfPh2ZmZmYO3cu/d5msyE9PR2//vornWuCTp060f%2BLooicnBzk5uaiVatW1AsmiiIaNmxIvYmzZs3CihUrADhVSVu3bk3rIOEKoihi8%2BbN%2BPLLL2mIK%2BD04up0Omg0GsyYMQPvvfeerD92u52GzqalpaGhJCRHq9XS1BOA01DXaDS4evWqLMSUGO3EOHc4HDAajTKjj7cWyoIq0KJChQoVKv4NyLO4TlH4q/GwPHtZWVk4e/as7FORMeYKiPK5lJZDcgZn8Li%2BEixfvhy9e/dGNUk6kLS0NJjNZgwcOBCtW7fG0KFDqb6Cq1CNPRX/GRDDZvz48QBAPU%2BCIMDhcGDlypUICgqiD7PVakXPnj1hLiWOf/zxx8jIyEBxcTEsFgsyMjKo9H9WVha2bt2K6Ohoukl3d3dHs2bNoNPpFJ484lHz9PTEmjVrMHPmTNhsNkyfPh0NGzbEqlWrIAgCli5dil27dkEURdjtdjz77LPo1asXNTKsVisaNGiAESNGAHC6%2B19//XXk5ORAFEWcOHEC3bt3h9lsxp07d2C320GSpCckJFCDlten/v37w%2BFw4L333kPDhg3x888/w9PTE3q9HgaDASaTCaIoIjAwEG%2B99RbsdjvtFwmJzMnJwdChQ2VvoQBQoRkpMjMzaX8A548eSRRfEaQhjw6HA4IgUOVQLy8vnDt3DqGhobDb7fj444/x/vvvQ6/Xw0%2BSLElah1QkJzQ0FDcYoQKz2QwPDw8MGjSInjt9%2BjREUcSLL75IDbzi4mLKTwSgSNdQHngCLU2aKAVaWF0DF3QYlPoXPGY%2BU4gr6sFwS1nRDFeEIlzJB8ylbbog%2BlBh7mGOYoIribnZjOjc/lUwTt58snx6Xq57hWAHb3NQQcJ5VjiCV8YlQQze5LDZpl1QUmH3T7xxu5JHuqJxA8r82GxbPBENNhKf1z%2B2jCsJvhVTw1kUFSVid7V/7BrlaTew/WHr5TESWNEerigTu2Y5D0xF%2Bb1dEQ/hzblijjkLh50LRRHONa60zQ6K%2BTPiRGm0DIUkfRHFl1/Kj5cuLf97APjqK/lxKW9fhtWrK65nyZLyy3AfxP8GNmzYgD59%2Bsg%2BGzZscOna4uJiXLt2jfshew3p3oD8n9VrkOLGjRs4cuQIBg4cKDv/xx9/IC8vDyNGjMCSJUtgNBoxePDg%2BxKEU409Ff8ZtCwVgkgqVf87cuSIjKuVk5ODZs2a4eTJkxAEATabDZmZmfThS05OpnnidDodzpw5QxN2azQaeHt74/Dhw1QMhcWdO3cQHx%2BPlStX4tlnnwUAdO7cGQ0bNsTly5fh7u4Oh8OBnTt3ol27digoKMCmTZswY8YM%2BuPw/PPPY86cOTJv0eOPP04NG8D5I7N//34IgoAdO3YgvJRIvmnTJsyePRu%2Bvr7w9vZGSEgIVZzk9SkoKAienp4QRZH2yWw2Q6/XIzw8nPahTZs22LJlCwCnOMqFCxfQq1cv2p/g4GAkJiYCAA3JZD1ctWvXxvLly9GnTx8IgoDIyEgsXbpUEW7Jw5gxY2QpEwCnsZmfnw83NzeYTCbY7Xb4%2BflBr9fDx8cHU6ZMgdVqlSVMJwhhhBVYQ%2B/WrVswGAyoVq2ajFNYUFBAPXlSz6A0dJN4iV0BT6Dl9GmlQAura%2BCCDoNS/4LHzGcKccP/GfEIVjTDFaEIV/IBc5OAuyD6UGHuYc76ciUxN5sRndu/CsbJm092OfJy3SsEO3hCIBUknGeFI3hlXBLE4E0Om23aBSUVVgCFN25X8khXNG5AmR%2BbbYv3k8M65Hn9Y8u4kuBbMTWcRVFRInZX%2B8euUc5Pn6I/bL28wARWtIcrysSuWc4DU1F%2Bb1fEQ3hzrphjzsJh50JRhHONK22zg2L0bpyQ8O0BAKXpimQofZlL8frr5X8PAKV5dylK6SkySF5WllnPyJHll7kP0bGHhYfl2Xv%2B%2BecRFxcn%2Bzz//PMu9enUqVPo2LEj93O6VIVXatiR/5cn4rZr1y5ERkbKtBsAYMWKFYiPj8cTTzyBqKgozJs3DyUlJQoRuvLwQMZeREQE3nrrLcX5uLi4cjk8LHbs2IE7d%2B4AABYtWqSwah8Gbt68iYiICBnvSQqr1YpFixbhmWeeQaNGjdCuXTvMnj3bZUs6IiKCGh1/NQYOHKhIHUDASxvwd6C8PvEQGxsrk5Bt2LAhOnfujG%2B%2B%2BabSfWjcuDEEQUBJSQlMJhP27t2rEOro2LEj9u3bR8/XqFEDoihSw4bwuURRhMlkonHSb731Fk0T8MUXXyA/Px9FRUV45ZVXcP78eaSmpsJms6FVq1Z49dVXsWPHDgDAr6Uy%2BXXr1qVGRufOnbF//344HA4cPXoUWVlZNHxv3bp1mDRpkiycLzExkaqDtmnTBgkJCdBqtRBFET169KDGCQnvzM/PR2JiIlUOBZz3x9U%2BlZSUoKCggPbh0KFDVJiFqGjeunWL9m/jxo1UoMVut0On08HT05OmVxAEARkZGZgxYwb0ej3q1KmD/v37w9fXF1artdzQR41Gg9u3bysUO7VaLTw9Pali6SeffIL%2B/fsjICAALVq0wLJlyyAIgsKQA5xGudTIFEURgiDQ8NJvvvkGwcHBuHLlCs1NCDg9ih4eHjh58iQeffRR2Vs7ApIL0RWoAi0qVKhQoUJF%2BXhYxl5QUBAaNmwo%2BwQppHn5aNmypSw1lfTTvXt3APfCOaX/D%2BTJF5di//793BzKbm5ushfXBoMBNWrUoAryruCBPXvbtm2ThWbdL9LT0zF%2B/HgaSvfqq6/el9HwsDBv3jwkJiZi5syZ2LlzJ2bPno2DBw9iwoQJ/9dd%2B89gypQpVEJ29%2B7dGD58OD755JP7zl9H4Obmhho1agBweumSkpIQVvqqjcjl2%2B12nDhxgj5whAtHxDsIH89ms6FevXpYVhpysXnzZrz00kuYMGECtFotbDYbvv/%2Bexw%2BfJgaQIDTUHjuueeoYEdqaioAJ8GWGB6CINCXBm5ubhg2bBg1NuvUqaOI6T548CANO/z555%2BRmppKVS/r1q0Lm82GPhx1bQAAIABJREFUkpISHC3NXSaKImJjY3HkyBFaR79%2B/VzuU7169TBx4kTap8DAQJl4TEpKiuIZrS%2BRN7fZbLh48SIKCgrQqFEjdO/eHUajETdu3ECDBg1w5coV9OjRA02aNMGoUaNkfMTXS99oLlmyBAkJCRg4cCA2bdqEmjVrygw0u91O56B169ZoUSrlr9Fo8N577%2BG3336juQcJiHeUNTA///xzbNiwgRq3e/bsQXR0NOUkEoiiiB07dmD48OE4cuQIHA6HzAML3FMpdQUqZ0%2BFChUqVKj4byE4OBjVq1eXpfBKTk5G9erVyzQmRVHEmTNn0JRJ8yOKItq3by9LF1ZUVIRr167J9mUV4YGNPXd3d3z44Ycyd2VycnKFJEQC4nkg4XIrV67EmDFjHrRbFYL0r6x%2BxsXFoW7dupg2bRo6d%2B6MyZMno27duti7d6/LBE6yqf6rkZqaWqbXkM0R93ehvD7xkJGRgVmzZqF169Zo3bo12rdvj2XLlqFmzZo0JLAy6NChAwCntyk3N5eSXEmo3YYNG5CRkYHg4GAAzhx6UqVGKcdr6NCh8Pf3R9OmTXH%2B/HmMHDkS27Ztw6hRoxAXF4cFCxZAo9EoDJ2EhARan8PhwKJFi7B//376jEg9QhcuXIDBYKCb/PO8hLW4ZwRYrVbaZ8DpYSMGx4IFC5Ceng5RFGE2m2Ve6M2bN1e6T%2BfOnZOFKk6ZMkUWVgpAlgBdWld2djbGjRtHVS3Dw8Nx4MABJCUlQRAEjB49WhYSubSUqzBy5Eh07doVZ86cwRdffIHr16/LvLTNmzenCdKPHDmCixcvok%2BfPtizZw8ApyhPQkICLa/RaJCXlweNRiPj%2BwFOb9yLL75Ix3v79m089dRTKCgogKenpyyfX0xMDJKSkvDII4/AZrPB4XBQDybgfPnlKnicvehoJWePdQCyx5Xm7LnCS2OuY7lCfxVnj1sP0yFX6vnLOHtMBMfD4uxxA0VYTgfv7S3DFaoUZ4/l3gHKQfAmh82i7gLBjR0Cj0/GzrEra%2BthcfZ498WVeipMDM9ZFOy4XUl2z%2BPWsVW7wtlzhY/nCq%2BvMpw9th5XOHs83q5ijjkDZ8tUhrPnSqJ4Nlc7gMpx9liqCMvhAyrH2WP5eQBQmmu3zDKlY/y/fA/5b0y9MGDAAMybNw9JSUlISkrC/PnzZUnSc3JyZC%2B609PTYTKZFCGcgiCgXbt2WLRoEZKSknDp0iW8/fbbCAkJoToVruC%2BjD1e2KbZbMb169fxBJu0UoLk5GQMGDAA0dHRaNKkCXr16kW5UcRl2bdvX8TFxcFiscik7E%2BcOIEBAwagSZMmiI2Nxffff0%2B/mzRpEmbPno3x48cjOjoaTz31lMwrlJmZibFjx6J58%2BZo2LAhIiIisHPnTm4f2bDN/Px8/PLLL5g8eTL17F26dAktWrSgOcKKiorw3nvvoWXLloiIiMDw4cNlm17izm3cuDFeeuklmZcgLS0NQ4YMQdOmTdGmTRssXrxY9lZ/79696N27N6KiohAdHU1FR1jcT9gmL3RVGjYbFxeHgQMHYuHChWjZsiWaNWuG2bNnyzbZq1atQmxsLCIjI9G%2BfXtZmJzJZMJrr72Gxo0bo1OnTjKRDjZs0263w9PTE2PHjpV59q5cuUKN6YEDB2LGjBl45pln0K5dOxQWFiIjIwPjxo1DixYt0LJlS8ycOVP2ooFs4onBeOHCBZn35dixYwCcxqler6diHzabDdevX5elONBqtYiNjUWbNm3gcDiwZ88enD17FgsXLsSCBQtgs9kQGRkp2%2BwDTgGUF154gR4vXrwYJ0%2BeBOA0hKTGoSiKGCmJmXc4HFjN/EAbDAYa5gwAJ0%2BepGuladOmWL9%2BPa1L6mWPjIyk/zcajZXuk81mw8SJE%2BnxhQsXZG%2BfunXrJrveYrHQH6GMjAw888wz%2BOqrr6DVau9LwARwGm3kuXFzc6MGK%2BHGEfXRqgzRJTExUbZuHQ4HVe8MDAykHmDA%2BaMrXUNWqxU3btyARqNBSEgInXudToeUlBSwSe6lnt37iaHncfbOnFFy9lgHIHtcac6eK7w05jqWK/RXcfa49TAdcqWev4yzxxCBHhZnj0d3U/CfSl9MycBwhSrF2WO5d4ByELzJYcOQXCC4sUPg8cnYOXZlbT0szh7vvrhST4WJ4TmLgh23K8nueZHvbNWucPZc4eO5wuurDGePrccVzh6Pt6uYY87A2TKV4ey5kiiezdUOoHKcPZZ/x3L4gMpx9lh%2BHgCMGlV%2BmdIxalSFj/vCkCFD0KVLF4wePRrjxo1Dz549MXjwYPp93759sXLlSnpM9hk%2BnIdw4sSJ6NSpE9566y0apbV8%2BXKFlkF5uO/bx4ZtBgQEQKPRoKCggMuPKSgowPDhw/Hkk09i27ZtmDNnDlJTU%2Bkb/I4dnW%2Byv/jiC3Tp0gVubm6UO5OWloZBgwahefPmiIuLw5gxY/Dxxx/j559/pvWvXbsWDRs2xLZt29CxY0dMnz6dbr4mTJgAu92O9evXY9asWQBApeNZfPLJJ1i3bp3MWCspKcEbb7yBr776Cvn5%2BZg8eTKOHj1KvZDTpk1DcnIylpS%2BCTlz5gw%2B%2B%2Bwzev2vv/6KadOm4ccff0ReXh5VHszJycELL7yAoKAgbNy4EdOnT8d3331HxSAOHz6MMWPGoGfPnti8eTMNH0tJSVH0%2B6/25J04cQJXrlzB999/j3fffRdr1qyhRtv69euxePFiTJgwAe7u7igsLMS4cePotefPn0eXLl2QkJCARo0a4e2336Ybbp4H1W63Y9GiRTh48CACAgLg6elJlTMJ4uLiMHfuXCxevBhubm4YNGgQzGYzvv32W3z22Wf49ddf8cknnwBwCm0slrxFE0URubm5MkOAbPqNRiM1PPR6PbRaLUJCQmShgitWrEBGRgaOHDmCPn36yPpds2ZNvPrqq7hw4QJ%2B%2BUW%2BQR83bhxyc3O586vX6yGKosz7JTUWq1SpgoiICNm5vn374sknn6THtSR/UTp37ozLly9DEATFgy/1Evbt27fSffL19UX79u1l32/atIn%2Bv6ioCE8//bSszn379gFwGpl6vR6BgYFwOByyGHYC1lCTwm630zVkMpno/RFFEd7e3rBYLIpQSgDlcmubNm2KevXqlfm9KIq4ffs2qlSpIluLhA9669Ytbg5AALhy5YqCX1gWVM6eChUqVKj4N%2BD/0tj7N3r2tFotJk%2BejGPHjuHIkSOYMGGCLAn6nj17ZFGM0dHRuHDhAlcLwGAwYNKkSThw4ABOnjyJpUuXylIzuIJK3b7JkyfTN%2BF9%2B/alm5uPPvpIUba4uBg9evTAwYMH0a1bN%2BohuHTpEoB7HphRo0Zh%2B/btSEpKouGPP/zwA8LDw3Hs2DH06dMHixYtQtOmTfH1118DcBo6VapUwdmzZ9GtWzfs2rULxcXFuHTpEkRRRMuWLWG1WvG///2P5hgrS5Bl/fr1MBqNmDt3Lnbu3AlPT08qerFhwwaMHTsWY8aMwbhx41C1alXk5eVhx44dCAsLo14QX19fWTxukyZNMHPmTPTt2xdms5km/N62bRt0Oh0yMzPRr18/fPDBB4iOjsZXpW75tWvXokmTJti8eTN69uwJi8UCT09P2VuAyoAXuiqd7%2BTkZFitVoSGhmLAgAGYMWMGfH19qbLQhg0bEB0djXnz5sFkMsFqteLRRx%2BlIX6hoaHYvn07unbtit9//x23b9%2BWeaS6detGOXoajQYlJSUQRRGTJk1CVFQU3nnnHdSoUYPy7FJTUxEYGIiJEydi9OjR%2BPnnn5GRkQGdToeBAwdi/PjxqFevHtatWweTyYSNGzciPDycGj2EpycIguwh0mg0MJlMePLJJ/HKK68AcBoVGRkZyM/Ppy8bMjMzYbfbkZycjM2bN0Nf%2BqovODgYFosF69evh6enJ9LS0mTzPHXqVOzevRsA6DXSdlNSUmSKTNJNPyHhNmjQgJ5bv369zEt6584d2sfJkydj6dKlEEUR4eHhsh8KL8nrzGXLllW6T6SeYMnr%2Bd69e9P///777/j9999lc0B%2B1CwWCwRBQGZmJp544gkuOVl6zmAwyD6LFy/G888/j0ceeQSiKMo8cAUFBRAEAZMmTVLUy0t0T%2BDh4SEz9nQ6HUJDQ6nRqNFoYDabYbVaZWGyBOfOnSuzfrvd7rJ0swoVKlSoUKGifPwbjb1/Gu7b2NPr9cjIyKDy81WrVqUW5t69e7F7925kZ2fDbrcjOjoa7du3x/r166HT6dC6dWu6Wbt06RLlxQH3PHuFhYUoKipCkyZNsG7dOly6dIl69oKDg3H48GGcPn0a0dHRSE9Px507d6hnr02bNgCc7tPGjRtjxYoVuH79Op588kmZqh4PFosFer2ecvTIW3zA6X18%2BeWXUbt2bXz%2B%2Bed4/PHHERsbC4fDgStXrlDPXnp6OpYtW4bGjRsDcBpPxLNnNpuRnp6Opk2bYv78%2BcjJyZF59pKSkpCdnY2oqCjs3bsXx48fp549wOnVkOYGI7hz547MoJKiLMXRmTNnAnB6zc6fP4%2BSkhK0bNmStnXz5k3q2cvJycGyZcsQExOD1NRUHDt2DBMmTIBGo4HFYsGOHTvQvHlzFBYW4ubNm9SzR0IIiSFot9uxbds2ytEjyoZGoxF%2Bfn74%2BeefUb16daSnp%2BPo0aNo164dRFHEzZs3ERoaioKCAkyZMgUWiwUWi4V69k6dOgW73Y7HH38c69evx%2BXLl6n4BnkJkZeXh1atWlHjj2zely9fjrVr18JoNNJwQCk3jBjFdevWhaenJ01jkJGRgaFDh2LBggXIy8tT8OyqVKlCjSWr1QpBEFC9enXqJbLb7VQIBpB76jIyMtCiRQtZnUajEaMkoRYbN26kdZHnEXDy96ThlVLvlru7e6X7lJmZiaZNm8oUOEdIwk5yc3Nl7Wq1Wmpwk/tcq1YtHDp0iOvZe%2Byxx%2Bj/S0pKZJ/ly5fjhx9%2BwB9//AEAstBMwBlZsGDBAkW9JDxcavASA1mj0cgMVw8PD9kz5ObmBoPBAKvVCq1WS%2Bsgbbdo0QJNmjSh%2BQhZeHFj85RQBVpUqFChQoUKFQ8b923skQ3QlxJCKdnYVatWDTNmzMD%2B/fsBgCaKttvtuH79OlXMk9ZFEl/7%2BvoiPT0dqamplBNjs9kgiiIaN26MOnXqIDs7m35H%2BDcA8L///Q9hYWGUB0iuM5vNVAFQGgbHnQiNBteuXcOtW7eouqG7uzs8PDyQmJiINWvWUA%2BYxWKhhkSdOnXoZpW0SzaF3t7eaNmyJQIDA5Gbm0v7brVa4XA4ULduXTzyyCPw9PSkRhFJiC2KIpo1a4batWtTaXieUUc2xTywnjzibbl27RoApzFaUFBA%2B0Pms2vXrqhTpw5VSCXfi6IIg8GALl26wOFwUAOZjFkQBPTu3Rs1a9akuUpyeCIApTCZTCguLkZOTg46dOiAy5cvQxRF6HQ6mjZAEAS60SeCGMHBwYiIiECNGjVoSK3D4aBJwMl6JMYZWUvEo0W8MmQMJpMJGo2GerbsdrssJPL8%2BfO0DEGXLl3w9NNPw8PDg/aBID09XRbKFx4eLuO8kXER1KpVixoi5HmRGjXEAyqtL7SUlFNSUkJfoBQXF8v6LY39zsvLq3SfyL2QGiJsuoTmzZvT/0uTrwNAu3btcLWUuX7u3DncD4qLi%2Bn6YmPZBUFQ8O0ISGit1OAl9z0sLExmuObn56OkpISOPygoCMHBwbBarXj%2B%2BefpfXc4HDAajahSpQq%2B/PJL%2BjvBwlVFTp5AS6NG9y/Q4kK%2BdH4hRnGAK4jBjM%2BlpOouJEN3SSSFOckTaGHrdmXcbJStK%2BIr3EKMYocrl7iUVJ2tiFUcARTjYsUuLl9WXqKIbOYpbTD1cv%2B0sL/pvHBk5jeRFR3hjdulNcG0xfvzworBuPK8sGV44iau1MPOl6Ie3kXM88K7Ley940aAMwuOV4btD3tfKi3Qwi52jhANe8oVgZYKn2%2B4JoLDzoViXbsgnOOKgNWVK5wyrLgKK9jCO8eKuPBEXdh6eQIt331X/jW8c//ApOqqZ%2B/BUakwTnd3d1kYE9kkZWRkIDc3F3a7HYIgICEhAS%2B%2B%2BCINFTt79iwNt9Lr9ejZsycCAgJoPcRbqNfrER8fTzd3hOsmbS8hIYGGvCUnJ2PhwoV0I6nX69G2bVuIogibzYZbt27JwuBY74B0cyYIAkRRhNVqRXFxMYqKirB161Z8%2BOGHdLPbvXt3GhYq3TQKggAfHx%2BacywrKwsDBw7EG2%2B8Qefrp59%2Bol6TTz/9FBERERg8eDDtU%2B3atemG/dNPP63wXtwPyObdzDy8Wq0WGzZsQExMDAAn5%2ByZZ56hvMrw8HDq9bNYLDJlxpKSEqxcuRJarRYOhwPZ2dkA7m2srcyPobu7uyKpJDE0yKb6888/x7Jly2C322Gz2VC/fn2sW7cOjz/%2BOADnHJpMJqxYsYLO26xZs6inxMPDQ5arpGrVqlixYgX17AFO3tbbb79NlTjd3Nxk87JkyRLaH1KGGDjBwcF0bWo0GgVHKyAgQNbWc889J1N/1Gg09F4ATsETcqzX6/Hdd98pBFMuXrxIj/Py8qi3Vs%2BwyKUGHAm/BZzhkZXtk1arRXx8PDUwAaBnz56yvC/SlCSsUXbkyBG0bNkSoijihx9%2BAAspH5JFVlYWbTcvL0823ho1alCDnTW4hw0bxvWcAc7nSspfJM88uY%2BPPvooIiMjodVqsWbNGlrW4XCgcePG0Gg0uHjxomzNEtSpU8dlY48n0HLu3P0LtLiQL51fiFEc4ApiMJ5Ll5Kqu5AM3SWRFOYkT6CFrduVcbOcdlfEV7iFGMUOVy5xKak6WxGPm8GMixW7YATdAHCELHhKG0y93ETSrLAL7zljeLgsLZc3bpfWBNMWT2OGFYNx5Xlhy/DETVyph50vRT28i5jnhXdb2HvH/WljFhyvDNsf9r5UWqCFXewcIRr2lCsCLRU%2B33BNBIedC8W6dkE4xxUBq9K0vHKw4iqsYAvvHCviwhN1YevlCbS89FL51/DO/QOTqqt4cNy3sUeEOcjGV7oJfe655%2BhmTBRFdOrUCatXr4bD4UBhYSFCQ0NpuJXVasX27dup5yctLQ1JSUnQarWw2%2B3o06cPDTu7du0arly5IvNQdevWjW7OV65cicTERJpXy2w2Uwl2wBlWJVXf43kCHA4HGjRoQBNzkzEAwKlTp2TXJCQkUKGXO3fuyDbV%2Bfn56Nu3Lx0j4JT3J5vCfv364datW1QBslWrVjKeVVpaGjWUjh07Rj0iPM9GWTCbzdi3bx8Vydi3bx%2BuXr1K0wqIoogbN25QRUNBEDBgwACqzli3bl2sXbsWGRkZ0Gg0uHHjBnr37g1BEGC32%2Bncks3ukCFDqCFM7pFUIVIKqedTo9HAYDDA398fv/zyC/1u5MiRGD16NJ0/o9GIgQMHIjk5mW7Mf/75Z5kwyrRp0%2Bgc//nnnzJPbl5eHoYPHw6bzUYNzZMnT1KlUavViurVq8vC76ZOnSozwGw2Gw1z5IXuSZUvBUGQGcRXrlxBcXExFT0hBgoB4SiS7wYNGkQ5rYDTUyZdIwcOHKBzKE2c7u3tLTOu2dDKyvZJo9Ggd%2B/eMq7n9u3bZbLB0v7l5eXJ1EI7deqEU6dOAUCZIjEEUr5eQEAAli9fjrZt26Jly5a0LwQ3btyAVqvFkCFDZMYxAe85J5DOU0hIiMwj6ufnR19KlJSUyIxiEqLt4eFBvd1SEA6oK1AFWlSoUKFChYryoXr2Hhz3bewRrgrZ6G3fvp2%2BEWcTjks3Q6IoymTtAadRQjY8H374Ie7evQu73U5DCqVGV/fu3enmjYQOkg3liRMnULduXWzdupW2K8X3338vE9Hgcd8EQcD58%2BeRkZGBdu3aAbgn4z9t2jTMnz%2BflpMaLG5ubnSDZ7PZUFhYKBtzamoqDeGU9o2UOXz4sGxTKu17SUkJunXrBsC5yZXmIysPt2/fxtChQ2lC8GXLlmHTpk0yI6VLly50807CI8l9vHjxIl588UU6x%2BSj0Wig1%2Bsxe/ZsOoaioiLZfJCwNl54W2hoKLZs2YItW7bQsZaUlODOnTto3769THWRhI4CTg8VCW0kZaZOnYqsrCzZXEtz0ZE%2Bku%2BIAW02m6HVamV1AXIPLamD1Ofp6Ylq1arRFxnlKT0CTsNKOtd79uzBuHHjZBL9Uly4cIEaH7wwzpCQEEyZMoUeT506lf5fp9NRI7VKlSp0Lfr5%2BcnWOQnBrUyf2DDOTp06KTy2S5jcPOTZzczMxA8//ABPT0/odDpF%2BCcLKV8vOzsbI0aMwP79%2B5GUlASNRkMNVmL0eXp6YtWqVbKXTmS8ZaliRkRE0GfOw8MDGRkZsNvtdB4Jv7GoqAg1a9akc%2BTv708VcUkOP%2Bl98vT0xLffflvu%2BKRQOXsqVKhQoUJF%2BVCNvQfHfRt7Hh4eeOONN%2BimpLi4mBoNVatWpQaJVqvFiBEjZCFaP/30E1q3bg3AGQr33HPP0U3h559/TjdwISEhiI%2BPl8n6b926lX7/1FNPoX///vRtvMViwe7du%2BnG67HHHpMlQt66dSsNAQScyQ4XLVpE2wLubdoyMjJorqzc3FwIgoDHHntMVq5z58745ptvYDAYcOvWLYwudcGTDSbLTSRzpdVqYTabZeGC0tC5pk2bolevXvRYEATKfXM4HDhw4ACOHj2KiIgIKqpis9lgt9upx%2BSxxx5Do0aNULduXTo/Go0Gbdu2lYXMbt%2B%2BnXq5RFGERqORGU7t27enZYlnC3AaS8MlIQU6nU7mbcnNzaXJHwHgzTffpN%2Blp6ejY8eONN0GwbvvvkuVIgFneN6yZctovfn5%2BTAajZg/fz5dL9J5i4iIwJYtW2QJJqX8Mzc3N6xYsUJmoBgMBjz33HP02OFw0E29TqfD%2BvXrqXfQbDbDZDLR%2BTSbzejcuTNiYmLg5uZGc/4RvPnmm9i4cSM9ttlsdP48PDwwbtw4WaqCgoICaphEREQowjhNJhPNhQhAFo7Zr18/rFmzBm5ubrI8jrm5ufSlBeDMc1jZPoWFhSE%2BPp6KHB07dgwff/wxLTtlyhT6YoGAlM3IyIDNZoNGo4HD4ZCF17qCoKAgOi5p%2BC95GVRSUsLNsycIAmpLYmqka/Sxxx6jnsiioiIYDAYasUDOEYGcuLg4Wk%2B1atVw6tQpOBwO/Prrr7QdAr1ej0xeAuwy4Cpnj6VMuJL3WkGz4Bm%2BleDssZyf/284e7wOVoKzxzpzue%2BNVM4e95jXFi8BOfsIunIrK%2BLF8s65kgRcUc8/jLP3lyVVd%2BGh%2Bis4e7zfEgUvksO/Y9eAYl1z%2BvuP4uy5wrX7D3P2VDw4KsXZGzRoEA3z0mq1sk008Ya5ubnh4sWLOHjwIP3uueeew5%2Blf7ju3LmD4uJiulkaP368rB5BEGQbuO7du9MNXkhICBISEmShV9JrCwoKZF6Mrl27KpKpswqKgHMTzap2iqKI48eP4/r16wCcoXJ79uzB1q1b4eHhAUEQMGfOHABOY1en08nGLAXP0yDlPnbo0IEalaTtdevWAXAaIGvXrkVUVBRu3ryJrKwsmqhaFEUMGjQI%2B/btQ3p6OlJSUnD58mXansPhwPvvvy9LON%2BlSxdqIAqCAKPRSA08Dw8PjB07lpZt3Lgxvv76a4SHhwNwGuYEzz77LBISEujm%2BciRI3j11Vep2iHr8eHBy8tLZoi6u7vLkm/XqVMHU6ZMkRk50vvt7u4OQRBkYYVSD4mvry%2BioqLofDRr1gwxMTE4evSorB/E02Kz2eDp6UlFR4xGI9avX0%2BN%2BuLiYsyZMwcLFixATk4OFZCRjodN6E36YzQacfLkSRpCS9olxkhmZiblixF89tlnMkEkaTimm5ubzIMuXWNSQ8RoNFa6TwUFBQgPD6equzabDZs2baLfZ2VlKQSQ6tSpA8BpZGk0GuoBJoq5roJ40gGnESb1htWoUQPFxcXcPHuiKMrui3Q9nDt3Tsb9Ky4ulr2AKSkpwe3bt%2BHt7Y0BAwbIQqlLSkqQkZEhO0eQm5tbbugoC1c5eyxlwpW81wqaBY9wUgnOHsv5%2Bf%2BGs8frYCU4e6wzlyvcqnL2uMe8tngpOlnOniu3siJeLO%2BcK0nAFfX8wzh7f1lSdRceqr%2BCs8f7LVHwIjn8O3YNKNY1p7//KM6eK1y7h8zZk7xf/9uhevYeHJUy9rRaLX2zzyYAbNasGQDnBmrPnj0yBckDBw5QAQm73S4Ta0hJSaFhVBaLBd26dcPcuXPpRi4lJYV6kb7//ntYLBZZSJh0w3fx4kVZWN6nn35KwwqJd8DDwwOTJk2iXD6j0QiLxSK7jozNaDTikUceAeAM4TOZTJSLWKNGDWoU5OXlUYVKqXiFRqORbbzLwscffyzb1Ov1eqr0Sbhcx44dQ3Z2Npo3b46hQ4fSzf358%2BcxdOhQasAEBATI7s3FixfxzTff0OOxY8cqFB%2BJd62oqIjm9BMEAceOHcPgwYNx9epVCIIgCyM8cuQIunXrRje5gYGBWLlyJfVwSI3GskDy7JH2pHwxwBlS%2BOabb2LUqFGy8FkypydPnkTXrl1lxpvUiCYeG/ICICkpCYcPH1aEbpINf0hICIKDg6lB4Ofnh4EDB9J7o9PpMGnSJLz55pvw8fGR5cTTaDSYMWMG9ciSdonxlpOTg3379sn4WmazmRojbdu2VczPkCFDZKkOpEbv2rVrMXjwYFitVtSoUYOOkYSeEiQmJv5lfXI4HOjduzf9ftWqVYo8e0SRV6rWGhgYKEt54Aqk91GaZ08QBNy4caPM64hQEA%2BCIMiSxrMgxh/Js0fW3P9j78rDoyjS99tz5pjcFyGBEI4QEiDcyCmX4AIJtwKLICAghyi4iNeCggi4Xiwgt7DIKoqChAjrKujK8QMEuQwS5JIr5AAScs1M5vj9MVTRVV1JRvAArfdCmfCPAAAgAElEQVR5eEz3dFdXVdeM/fX3ve9LfjvUHF0evPhRZZCcPQkJCQkJicohg707x88O9sjDWrNmzZCamko5UASFhYVQFAV6vR4mkwkNGjSgwiDALcl2nU4Hk8mEwMBAen5gYCCCgoIQFRUFRVFgsViQkJBAAzmiKEg4RCTgjI%2BPZ97c88qC6lLCMWPGAAD1jyOCC1arFe3bt2cye%2BpSVZJxq1%2B/Pt1fVFSE8%2BfP04xIz549oSgKysvLmYdUo9EIo9FIxxEREYGUlBQAtx4gfX19mWuTjCkRBTEYDHC5XFTZMyMjo9IHS6PRSK9BQOYVAN5880263%2B12o3fv3jRzpdPpqBIn4AmsSMDqdrspf4yYZauDxuXLl2PUqFGIiYlBTEwMPv74Y/qZn58fY6pO5t7f3x9ffPEF7Qvx2SN9JS8B1BmtF198kVlXTqeTmQ91YFtaWoo2bdposi7qNdOqVSt6L/Lz89GuXTuqhlpYWAgfHx/GM5Bk9nifPV9fX5q1JujYsSPatm3LzLd6ztRZsS%2B%2B%2BELjs6coCs2Qms1mho9IeKKAJytIjnM4HMjNzWXm4Hb7tHPnTjRr1gyHDh0C4LmP6uDT6XSiRYsWzAsO9XWDg4Ph5%2BeH3Nxcoc8egZ%2BfH9avX8/8e/XVV6mpOrkWcKuMtyJT9aqg/h3y8/ODxWKhv0OEc1leXk4zk8Ctlz9NmzbF448/jvj4eM1LHL3w9W/F4%2BUhOXsSEhISEncbpCjnvY2fHeyRUj7AIxTh6%2BvL%2BJIRSfiVK1ciIyMD3bt3x7Vr1%2BDv7w%2BTyUSzEjNnzsSgQYMQExNDH3ratm2LGzduoEOHDtiyZQvGjx%2BPU6dO0QxFWFgY5eKVlJTQwO3ChQtMv9RiIzwIt4r3xAM85V3jxo2jD5Lqz5o2bQpFURiJd15spVGjRtRGQD0nwcHB9HqAR0CFqBOSAKWsrIyWuAKeh1o1j05tfg14AtwtW7YwmRKDwUD7brfbGZ6ixWJhlBt5XuGnn35Ky97UD6zE53Dq1KmM0ir5b%2BfOnallA%2Bmnv78/NdZWB2SlpaWMqfqxY8cAeDJvDzzwADMO4udI5sbPz4/pV/fu3ZnMFbF%2BIBirKmkgAi0kAA8KCoKPjw/zoN6qVSvUq1cPACgPUm0fQQRqAE8pb5MmTYQ%2BeyUlJXStE0yYMIE5Rp2F1uv16NixIw00iYejeu1FREQwqplqTzu1sbfb7aaZ4NjYWOblgclkuu0%2BFRUVMQItTZo00QitBAcH0zJao9FIA/SIiAgMHTqUzjUvpKJGaWkpBg8ezPybOXOm0FSdlPFWZKoeERHBlGqqg7akpCTaV%2BLLqP4tcDgciIqKgt1up3YqgOfe%2BPj4ICIiAjk5OdQDUA1RwFsRRJy99u07CebF9rO2f8tjfs9r3%2B39%2B7Ne%2B27v35/12nd7//6s177b%2Byc657eGzOzdOW6rjJMgJCSEUQkEPAGbXq/HhAkT0K9fP3zyySdQFAVlZWWw2%2B30Af3ll1/WqHOOHTsWBoMBH3/8MS3jVBQFEydOZI4jD/3qhzPCqSOqfzw/iYfb7WY4XoAnczR//nzGdJtgxYoVSEtLqzQzoQbJglmtVuTk5DC8qorAl5YZDAb6cEwEREifzpw5g7S0NEYQQh3QFBYWUvN0wBNQ/d///R/dJgIqBKmpqRrTdvKAfujQIbz55ps0M6Qex1dffUUtGwCWS1dQUID69esz11X77JH2q1WrhmeeeabCeXE4HCgtLWUC%2BMcff5wpZ1QHSIqi4NixY0wwR3wTydxYrVbm/mZmZlYYiKiztACrNCry2SPlvAR2u51mpgCWL%2Bd0OnHgwIFKs7R5eXlMgK2%2BXnl5Oe2PerwOhwMbN26k21ar9bb7xK/bbIFghFqhtLy8nH638vLy8NFHH8Fut8NkMmnWXVXYunUrunbtSnml6jEqioLCwkKUlpYyNhzkuuq1qB7f9evXaQmo2%2B2mpdnq9UB4kzVq1KD2MFlZWUhKSoJOp0NsbCyys7M1mb2//vWvXo9NxNnbs2en5jg/P/PP2v4tj/k9r3239%2B/Peu27vX9/1mvf7f37s177bu8f2ZY6Lfc2fnawp/YTA4CBAweiadOmtMyxV69eGDhwIOXXxcbG4tlnn6XZu9DQUKSlpdEyztatW9M2q1evjhUrVjBlnC%2B%2B%2BCJVTWzevDmaN2/OmD0TkAdSURmVOstDREmcTie6detGDdABYNiwYcyDfceOHWkGrLS0FP3792cefHv06MGIyJC/3W43I/bhdrs15tcGju3aokULxkfPx8eHCaTJg6i6f926dUOTJk004yXHxcbGMgERcMuEm4i7AJ6s1t/%2B9jf64BoXF4dWrVrRbZfLxcyr%2BgE3ISEBgwcPpttEWMVgMFB1yxUrVtDP1RkU0k5YWBhOqeTjiKm6OhNnMpkY0Z3MzEzGi04Nt9uNsypZLHXpb0UICQlhFFzff/99WspoMBjw1ltvUWEakSCIGhEREcy8r169mpasAqBZT4L4%2BHi6bbFYNGqc6pcAiqJg165dTFtkrTmdTsony8nJYTK7/v7%2Bt90ns9nMqHECHlN1ddDOr29y7n333Yfu3bvD4XDAbrdr1r03IGXZVquVOb9WrVowmUyaTGhVKCgo0HAB1TAajfD19UWDBg2QmZnJZDhJ5i4tLU3Y9s8p45ScPQkJCQmJewFVPPb8qpCZvTuH4v45T0l3AU6cOIGHHnoILVq0QElJCY4cOcI8rCmKArPZDKvVCrPZTIPKwsJCxMfHIy8vD8XFxfD19aXZNpLxyMrKQvv27Wn2zt/fnyoJOp1OGAwGOBwOmjk0GAwwGo0oKyuD0%2BmE0WikPJ/IyEhcvXoVTqcT9erVw%2BLFi9G9e3catJlMJg2HjLQPeDJgpESvomwieXi32WzCh92goCDYbDaGq%2BXn54fS0lIsXryYyZj%2B5S9/QX5%2BPr799ls6DvWcRkdHIzs7W3Od2NhY5OTk0OPNZjOOHj2KoUOHUhN0kv0i9wYAfXCPjo5GcnIyLBYLPv74Y5jNZkRERMDpdOLKlStwu93o2rUrDhw4AKfTiZKSEuFYDQYDAgICaGkiPwbAE0gVFxcLPyOlp4QjGRoaipKSEmpxkZubi5ycHJSWliIwMBDffvstAE%2BQnpiYiHXr1qF%2B/frw8fFBzZo1ceHCBZoZCwgIgM1mY8RF1GMIDQ1FYWEhLd0NCAigmVzA40%2B4Y8cO1K9fn1nfZA2YzWYUFhYiLi4OV69eRXFxMUJDQxEVFYUffvgBgCcALS4uvq0%2BGQwGmM1m2Gw2OBwOtGrVCv369cNzzz1Hj9%2B3bx9jP0Haq1mzJvLy8iiXtX///pg1a5bm/tWvX5%2BuH4KAgAB8%2BumniIiIwL/%2B9S%2B8%2Buqr1MJBURR6jcTERGzatIl5CeF2u9GgQQPhWgkJCcHDDz9MeakkWDYYDLhx4wZ69eqFN998E/fffz8cDgeuXbsGl8uFsLAwlJSUYO/evViyZAmWLVvGzFtQUBB0Oh327t2ruaYIM2bMwIcffsjse%2BKJJyh3VkJCQkJC4m6AzVaBOu9vgJuPB784srJ%2BnXbvRvyOsfrt4fTp07DZbNi9ezdycnLQt29fxMTE0Df%2BTZs2xfDhwwF4yskWLVqE1atXw9fXF%2BfPn8fq1asBeIKNLl26MAIiwK23/L6%2BvtiwYQNeeuklWjZHHg4dDgfatGmDzZs3o1evXvRzkvFxuVzIzc1FcHAwmjVrhqysLFy%2BfJmKnCiKgoULFzK%2BZgCbMWvbti2mTZtWadloSEiIJmNFrC8Aj68hn9krLS2Foij45ptvqOdheHg4QkNDqaKiy%2BVi2qlVqxbi4uI0GTnAU9JXXl5O9xHFR1ISOmXKFDo/wcHBCAoKQnp6OsLDwxEREYELFy7g2LFj9Hi73Y6RI0cyJXJlZWWoUaMGGjRoQPeZTCYmi%2BJwOFCoMgnixS8mTZpEVTNNJhPq1avHnL9582aNsizh8J08eRK%2Bvr40y1tUVITi4mJkZ2dDr9fj0KFD6NKlCwDPunrssceYh/iGDRtSARGyRtR%2Bge%2B//z4zpyUlJbQvZK2QYMjtdjMZNqvViqeffhp6vZ5Z3wUFBYzaaMeOHZk%2BEV4k%2BbtevXo0a0XKrsl9i4yMxKZNm2gfLly4wAj8TJw4kcmkAreCNvKCgLSlVi4VgTdVb9%2B%2BPa5evYqFCxfCYrHQlyU6nY6u7VOnTmmCJkVRGB4vcCurrdPpGC5pUFAQLRUGPLxjp9OJa9eu0ewhcEs06ejRo1i7di0ANivodDpx/fp1zYuEiiAFWiQkJCQkJCR%2BbdxzwR6BxWKB2WzG5s2bcenSJfqQdOjQIcoFDAkJQWBgIFwuF%2BX7jBw5EoAn0/Haa6/RB1gC8gBGHuJ8fHxoUPC4yo%2BkQ4cOKCoqYvz71FwuIk9PHu5JhoWUdD755JPUmJlA/ZC4cOFCpsyL9EtRFFr2mp2drfFVUxuWDxgwgMnqEbjdbmzYsIGWtObn5%2BP9999n2iKfAcDZs2crzFaQc8h8DRs2DG63G1euXIGiKFi1ahUNZK5fv04D9Pz8fBosAbek%2Bs1ms4YzWVJSQr0DyQM%2BETJRQ/2grA78CO%2BTZO3Cw8PRuXNnpKam0mNIZpPAZrPh%2B%2B%2B/B%2BApVb1w4QItnySlqGPHjqX%2Bcep7z/vs/fDDD9QYnKiykpcTDocDQ4cOpWMm55GsntvtZszSAVCPNwIS8Lvdbrq%2BXS4XU/L42WefMZnkwsJCRvynQ4cONPh3u90Mh/Dq1auIi4ujLwDy8vKYuf/222%2BZ9QLcKj0lXEm73Q6Xy%2BV1IMTDbrczHFun0wm73Y5q1aox4kcEbrdbOE%2BAJ0BWi/tcuXIFdrud3oOCggLcuHGDCrQ0a9YMgIeXGBwcjP379ws5loS3WCRyjBZAJNDSpMkvY6rulfk5t1NkWMwfw5s7i0y3%2BWt5c4w3hu4izkhVc%2BONibWwtoXvtGhyOEdvzTgF1%2Bb7pzGEBrRu0yLncK7TvBm2wEZWa6ouWKf8GISVxjxnV3RjuD7zQxKNm9dUE94X7kTRu1De813TjuC%2B8OMWrdnbMeL2Zk14Y4buTf/AVQqJlix/q/ilJVpq3hjOe%2BVAzo3dG5N6b0zVvfk95M/z5vdHMwQv7rfQDWjVKnabN0wX7bv50rbCbQBYvJjd5s3QAeCDDyrvi2ifinYDgE6e5vfjN4Qs47xz3HPBXq1atQB4HvaKi4uh0%2BlgNpvpg%2B1jjz1G/dDKysowfPhwjBw5EjabDYGBgTh48CAAT7aFvLEnbQKgvC0/Pz/06dMHL7zwAg1W9uzZQ/k7y5Ytw6BBg5igwszluKOiovDee%2B8BACOWYrfbGUN5wJNJVIuU8NwfEvi53W4aONaoUYNmlMhnauP0Xbt2MSbtFpUzq8vlolnDmjVrAvAEIgaDQahkWqNGDTpf5MFaxF0bOHAgDhw4AKvVikGDBmH//v00Q0bu0dtvvw3Aw8HcvHkzwxcUcboIn%2B/69es0oOOtKioDKSXt06cPAE%2BQvGrVKmzdupUeM2jQIHrt//73v9QPErilsKjO%2Bh47dgynT59GamoqMjIymEwX77M3ceJE2gYJINRzvGfPHjr%2Bzz77DJs3b2a4sdOnT6d/h4WFMdYLPj4%2BeOutt%2BB2u6HT6ej6BjwlgQQ2mw1Dhw6l261bt6ZZNpfLhXfffZf6LAIeCwheSIf47el0OqxZs4YGdPv372f4jsCtlwUmkwlbt25FmzZtALBCLj8Xbreb4cj6%2BfkJxWKAyn32jEYj873gA8Xi4mKmTJb4NzocDphMJkRHRzP8Wh7eBrQigZajR38ZU3WvzM%2B5nSLDYv4Y3txZVNbDX8ubY7wxdBdJf1c1N96YWAstUPlOiyaHc/TWjFNwbb5/QvFW3m1a5BzOdZpfjqIEusZUXeA1yY9BaN7Nm7yLbgzXZ35IonHzdFfhfeFOFDmu8J7vmnYE94Uft2jN3o4RtzdrwhszdG/6B64yRbRk%2BVvFLy3RUvPGcN4rB3Ju7N6Y1Htjqu7N7yF/nje/P5oheHG/OYtgD0aPZrd5w3TRvpsvbSvcBgBOuFBjhg4AQ4ZU3hfRvpsq9xTC/zFI3Gu454I9vV6PmJgYGgwRcYhq1apRzywiC68OHIxGIyOqoH7YCw8Pp383aNAABoOBCcTItVwuFxWP4R8QRYGP%2BqHv2LFjmiBKfY2AgABNsKjOEKiPJce98847jFcaAOYar7zyCn0Ir1WrFmw2G9NPdeYwICAARqNRozxJMGPGDNSsWRPBwcGMcIsIxOOuR48eeOyxx2iJH%2Bnb6dOnAQAHDx5EvXr10LNnT3To0EHTf8ATLKgzOupS0sjISOH1ATawVUvuA7eygg6Hg46lVatWNGuYlpaG2bNn0/OzsrJQVFRES3CDg4Oxfft2OJ1ObN26FQMGDGCyvtevX6fBgqIoiIqKYjLGDodDM3ckGHzmmWeQlpZGbSkA9j5ZrVYmG22z2VBcXAy3282U7Op0OgxR/dDr9XomszdkyBBG3IdX%2BVy6dCldv3a7Hd26dcNrr70GwLOW6tevz6zPgwcPMutXfe5DDz2Es2fPwmAw0ID7dqG2i/g5YihqWK3WSs3dy8rK6FhIdhe4lU0MDAzE%2BvXrNQEugUXzVC2GFGiRkJCQkJCoHDKzd%2Be454I9p9OJS5cu4cEHH4TFYoGiKDAYDGjcuDFiYmJQrVo11KhRA3q9Hk2aNIFer4fBYEBKSgoj%2B67m89SqVYs%2BOCYkJCA4OBhxcXFU7r1t27YIDQ1FcHAwkpKS0KRJE5r1ADwP6s2aNcNnn30GHx8fGlyo1SJPnDihCQjVAVwo/zoSYPiEarVDcqzZbIbb7UZgYKBQoXTDhg20bPDcuXPUL45A/bAuyjaqERcXh4YNG6JevXoa5UV1tkVRFFqSOXr0aOzcuZM%2B%2BJNATW0XQRAfHw8AVHyDgJ8zchwJ7NVQn6ceCynz%2B%2Bqrr5jj1Mqgly9fplknPhhTzxPxUSRjIP5z6uN5pUfeMoDvKwkAAQ8/kPd/VMNgMKCG8PWhJ3ggAV1ViqE%2BPj40kyvq08mTJ5mg3m63azwf1SgqKmKCPRIYA57ALy8vDzabDRs2bKi0X7yp%2BpdffomwsDA8/fTTzHHEdgHw8ADVirBVgfjoEZjNZgQGBtKXPg6HA8HBwTAYDMx6VBQFBQUFiIiIwPLlyxEVFYUOHTpogj5vvfYkZ09CQkJCQqJyyGDvznHPBXvJycno1KkTdu/ejUmTJmHbtm1YtmwZFW/o3r07LBYL5YWtXr0aK1aswOXLl2kpGcBaSDRv3pxm%2BlJTU%2BF2u1GrVi18%2Bumn%2BPvf/47Dhw9jyJAhUBQFjz76KI4dO0aNxH18fFBWVoZJkyahRo0aaNy4MXr06AEA2LFjB5NNMZvN9MHRaDRix44d9LOePXsy4/zxxx9x5coVAJ4ATh1AkAyO0%2BlEaGgotVkAQEstiWphSUkJEwAajUZN4HHp0iXqO%2Bd2u4WB56lTp/Djjz8iODiYKVEEgCeffJL%2BPWHCBIa/JkI0XwaEW95tPNeMlJgSXLlyBWFhYQyXioCMPT4%2BHpmZmXROAE9AoBbVUAdDvr6%2BWLduHbOtLtNT90en0%2BHKlSsICQmBoij44IMPkJ6ejvT0dHpMUFAQE0ht27aN/h0UFAQ/Pz9NYEUygUajESaTick2q2Gz2WCxWGigYLFYmOCblC%2BK%2BIzqcWzbtg3h4eGM4IkaDz74IFJSUoR9EIkGhYWFUU4kUQh1u92oXbs2nnvuOXrP169fL2yTgDdV79atG06ePIm1a9cywZE6w3v58mWmjBbwcHLJd5ovEeaFeGJiYmC1Wmnpqcvlgk6nQ506dVBWVka/ew6HA3q9HomJiWjdujWOHz%2BOb7/9lvGn5NdkZRBx9lJStJy9qjgzoqpRvlpWWFnKEVm84ezx3Ctv%2BHgiSpdXHKTb4OxpkqWCknT%2BPYrwvQrfMMeHAqAhOGn6J2j4tjh7ImIaNy7%2BFJWTDYXm/goIWlVyzgDg5v%2BXKEQZaq5tnkfH9xeAduGI5pxb2CKOGT9dmrUvWBO3ww0T0dL4%2BbodDqloTVTJTRW07c33hb8v/Dagvb3CNXsbnD1vfqOqnE9RfwTX5s/j2/Wiu%2BLfCe5EjibuwfLl7PZNob9K9/EcvXff1Z7Dc/beeUd7DOdlrekLoOUL8ts3J%2B/PFhz90XDPBXuAh/PVp08fLFq0CH/5y18wbtw4FBcXY926dbSE6vnnn0diYiJGjhyJMWPGoHXr1pgyZUqVbVssFqxcuRLnz59H3759MXv2bIwYMYLKoaekpOC1116jYh16vR4LFixgAsnatWujbdu2uHHjBhXMINkb8pBaXl7O8O0ee%2Bwxph8kOwZ4HlLVD6gkEJg2bRpq166NZs2aUTEKUhpGHuKJoTsBeQhXg2QiSNBwTfWLTxRDZ82ahbFjxyI7O5vyEAGPwqa6PHbevHkAgH79%2BtG2TSYTE9yQkk3Ak/H88ssvqSm2w%2BGg2TsA%2BPvf/870tUGDBpRjSYyvCUhgffbsWdSvX58aZwOeYKZp06ZMtorMQ1lZGSMwYjAYmNJRMh8k4%2BdyudCqVSu43W4MHjwYVquVCYAKCwsZhdI9e/bQwIOYgKvviXq%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%2BzNZtqSkJPz73//GsWPH8L///Q/jx49nMh%2B9evXCmJsk1jZt2jAKmO%2B99x4mT55M/dKIMIvD4YCfnx9jGZCZmUnP4/lHJ1Ryap988gkTDK5btw4tWrTAxYsXMXjwYJw6dYqeHxMTg3r16tHsXHl5OerUqUPPLS4uZrhdxHcP8AjKzJkzh%2BnH0qVL0a9fPxQXF2P69OnIzs5mTL4LCgrQsmVLuu12u5mMUYMGDbBx40bMnz%2BfHhMbG0szZw899BBmz55NH8D1ej0aN25Mjx09ejQNboOCgrB06VJ6L2rWrImYmBjm2mr06NEDTZo0QUJCAnQ6HTIzM2kZpnq%2Bk5KSGEsA3nqCt0sAPBy/sLAwlJeXY%2BDAgQxnr2fPnti6dSsmT54MwMMz4wMM9XqqXbs2/buwsBDBwcFMSSQJoNVjVGe2nnvuOVrSrAbh2AGeIH/Tpk10XMXFxRg0aBC9T3Xq1GF4rL1792YCzpdffhnDhg2j8wMAU6dOBeD5rg0dOpQJOskLhKKiIgwdOpRaj6hfiniD1atX49FHH8X58%2BdhsVgYPpzZbIbdbodOp8Ni7i1neHi4Jngj4H0pdTqdRoEU8KjA1qpVi%2BFPXr58GRcuXBB6Tup0Opw4cULjn1kRJGdPQkJCQkJC4tfGPRns3Q0gnJ99%2B/ZpHu527NiBXbt2YcyYMVTBUFEUREREYPr06cIyRjUCAwMZnli9evVopoxg0aJFaNmyJXQ6Hfz9/SlvLjAwECaTiQZ0xcXFTDaFBJykDxaLhX7et29f9OvXD5999hk9vkuXLmjatCl69OhBAyReWp7nGd133300eHY4HIiOjkafPn3o%2BdOmTWOsBUgJKWmL2GqQ80nQQXhmrVu3Rnh4OJ588kkmexcUFARFUbBmzRrs2rULvr6%2B%2BPHHH1FSUoI5c%2BbQcRK%2BH5mzU6dOMUIc/v7%2BSEtLo2WD6vGpA6pBgwYB8GQU1Q/us2fPRp06dTB27FhaTks879RiP6StqKgoPPDAA/T8nJwcJqjhOXrqwNLtduP8%2BfOoXr06VWoV3afp06cjKSmJGQsp9fT392eEjQBttjMpKYmWdbrdbnTr1g0LFiwA4Cnh5P0cyXXcbjdKSkpgtVrh7%2B/PKIuKYDabmX%2BvvvoqHnjgAXTq1IlRyQQ8QVtoaChCQkI0mT2SrasIaisRi8UCk8mkKe/83//%2Bh4sXLzLloNWrV8eRI0fwwgsv0OMIf5eMWc3VlZCQkJCQkLh9yMzenUMGe7cJwvkrLi7GyJEjsX//fpw/fx4bNmygYhJBQUF49dVXAXjKITMyMjBs2DCGO9W1a1dN208%2B%2BSR98G7YsCFOnDihEXgJCQnBokWLcPjwYcZCgJTQkRK88vJyjZF127ZtKUcuNzeXZmHWrl2Ljh074tNPP6WBjs1mw4wZMzBnzhykp6cLywv5DMeXX36JRo0aAQCOHDmCgQMHomHDhjS4KC0tZQzo27Rpw2SNUlJSaADdr18/miHLz89Hr169sGbNGrRs2RJnzpxhHtCJd9yjjz6K9u3b4//%2B7/9QUlICh8OBvn370kyPwWBAcXExnSM/Pz%2BGc%2BdwOLBu3Tqm7I%2BcS8Y6evRobNq0CUajERkZGXj99dfp%2BW%2B//TaWLVuGzp070/JJtShNfHw8AgMDaVsNGzbEN998AwAYNWoU/vOf/yAvL48Gg6%2B88gozv2ou3cSJEzF48GA6X0899RSd5wmqso5XX30Vb7zxBhMAkuxqSZjNhysAACAASURBVEkJiouLGRsRg8FAs4ARERGIiopiRFKys7OpUbzT6USnTp3oZ4qiMJlJHx8fNG/eHAEBAXj//fdRGdSm6jabDc8//zxOnz6NH3/8kYoxqVFQUICCggJNZi8iIoIpkyb9AjwvRI4cOUL38xxKsjYLCwuRnJzMlOiGhYXhypUrOHz4MN3HZwS9LeWUAi0SEhISEvcCRFXVvxVksHfnkMHebSI%2BPp4%2BeJ47dw6TJ09Gr1698M9//lOYUahI5bIikGDyp59%2BoqWOlYE8JJ49exbZ2dlMlobw4QDPg%2Bjnn39OFQQTExMpD%2BzGjRtU1IY8sJLA4euvv2Z82iqD1WqlQaHb7aay%2BwTl5eV49NFHAXgCq//85z9YrSIkr1ixgh6/Z88eaj7vdDpx/vx5fPTRR0hISNCUY/IgwVxOTg4zTpvNBl9fX3qu1WplymTDw8OxceNGJhNIjMsJUlNTkZOTg%2BDgYGofQdrbtGkT3nzzTZrNi4iIoJk6p9OJs2fPUjETAJgzZw7NFGdlZVEBHHI94qtIMHLkSHqtJUuWIDU1lXI2Z82aRY8bNGgQPU6v12P58uW0zfDwcCZbfPbsWSYgnTNnDuVO5uXloXfv3kwfNm3ahNzcXADAxYsXNeW06qzgBx98gJKSEly4cEFjFeINXC4XrFYrzQITREdHIyYmhgb0agQGBmoybGTshYWFTACXk5MDm81GA2b1d7WkpAStWrWi55tMJtjtdgwcOLDC/orUZkUQCbQ0b64VaLk5zRVuixKYvMm20BGXP1GkUsA1pKFxCpQs%2BGZF1E%2BN0IIXKi6aMUErzqDRMhG1642pOjcuoVAJd595a0fROXx3RLdF02X%2BhkM7x/y4vdBM0QqtANo1IFhcml2iG8ztU9nMAhDfS81aEimBcO2KtGvOn2e3%2BekTdZe/DyIBGd6D3ovuadaE6DvGz6fIOpTfJ7IR5dexaAz8vHszN5cusdtC43Bu0Yq%2BU/z3gb933hjZi9a1Zp/gN4m/Fj83QnEq7l4JC0W4gfJzBQC4KQxH8a9/aY9R6SAA0Aqr8J8DWsN00TG8iItI6IXfx4vD3FwUFbhySdwjkMHebcJgMKBXr16VStwTxU4AjNS7WmlRvZ8EYGFhYYx4BkFlASMxljcajTCbzUzGS62OSRRBSTB26tQpjUCLSERj%2BfLlmoDzkUceoeeNHj0af/vb3%2Bhn6kwPj%2BPHj2u4W2q%2B2Pbt22nWIzs7W%2BN1FhoaCn9/f4SFhVXKj1J/Nm7cOMYOQD0/VqsVzz//PABPYHfy5EmYzWamFJEPKmNjY%2BmcE5C2eT/Dhg0bMrxJk8nEBOPh4eE0kD506BAjUCOCj48PzboRdVVybXU5pTqg1Ol0TJ8aNWqE%2B%2B%2B/n%2Bl7YGAgbWfr1q2MMbm6bBHwWJSo7Q7U99PX15dZq0OHDqXXFmWzfg7UYygoKMCFCxcqtKmoqIzT5XJpsnEimM1mxMTEMCWfdrsdvr6%2BuO%2B%2B%2Byp80ZCQkFBl24BYoOXQIa1AC28nyW%2BLqIkaz3eR9x9/omg8XEMaQQyBkgXfrEhEQ8P590LFReRjz4szaLRMRO16Y6rOjUsoVMJ5NfICuqJz%2BO6IboumywI/UX6O%2BXF7oZmiFVoBtGtAsLg0u0Q3mNuncjoCIL6XmrUkUgLh2hVp13ACzprpE3WXvw8iARneg96L7mnWhOg7xs%2BniOXB7xOJNfPrWDQGft69mRvVezwAFRiHc4tW9J3ivw/8vfPGyF60rjX7BL9J/LX4uRGKU3H3SkgB5wbKzxUAYPhwdnvECO0xN5%2BlKHhhFf5zQGuYLjqGF3ERCb3w%2B3hxmJuLgiuq%2BU0hM3t3Dhns3QGmTZtGbQDU0Ov1eOSRR1BXJYlWka%2Bfej8pi4uIiECdOnXQokULjW1CRQ%2BYLVu21PicEZjNZnoeL04RGRmJwsJCVKtWTcO7Am5l9tq1a8coY5rNZupbaDKZcP36dQxX/aid4zSI1Q/pN27cQExMDNPHbNWryzVr1jCBiloNcdSoUVQgRj1evrxPHfAmJyfjm2%2B%2BYcZdWlpKr6/T6WippF6vh9vtZrKhLpdLY2WwefNmZkxXr16lxxiNRqYcz%2BFwUDsOwBMwqMc3f/58Wgbq5%2BeHkydPojIsXLiQBuvkfpKxqYPIOnXq0H6I%2BqQWhnG73bhx4wZt5/vvv2eyX7xwT/369Rlu54EDB%2BjfZWVlzFwHBQXR/k6fPp2%2BJBBBzdfz8fHBtGnTUK9ePXp/1GMgAS/hb/Ko6EWA2%2B1m1g7pK7mf5GWHxWLBzp07mbHk5%2BcjIiICTZs2hV6vF76AUQtFVQYp0CIhISEhISHxa0MGe3eAiIgIfPTRR%2BjYsSO1FwgKCsK0adPw3HPPMcdW5Oun3k/2kf/yIixjxoxhMmBqTJs2DU2bNoVOp4PBYECrVq0w5OabH5vNBr1ej4CAAAwfPpxaFwC3LB90Oh169OhRYfZw8%2BbNTMZPURRqT1BSUoITJ04wD9Dnz59ngiG1FQHgCarI2EWZmfbt29O/CwsLaVt79%2B4VBtdbt25FcnIyAgIC4OvrC39/f4SHh8NsNuP48eM02CDnOp1O%2BlBOygTJfpfLhXPnzjGqozzOnTvHeLC9/vrrGol/cr3Tp08zvn08WrRoQc8pLS2ttDSVgAS3/FwsWrSIOZ/0ibdLOHXqFKpVq1bhekpNTcU//vEPuv1zLAUIiCjRlStXaEnt5cuXsXDhwgrP2bJlC/2Xnp6Ohx9%2BGHq9Hl26dNFkg7Ozs6EoCubOnat5UeF2uyvtc6TqlXZ0dDQMBgOjdHr58mWqoqpex5cvX0ZKSgpWrVoFh8OhecmQlJTEiOtUBsnZk5CQkJC4F%2BDFY8mvBpnZu3PIYO8OER0djVdffRXffPMNjh49SkVY1A/clVk9tG7dGllZWQA8pYFZWVnUDJwXYZk0aRJjEaFGZGQkVq1ahcOHD2PXrl2YM2cOBg8eTPvhdDpRVFSEtWvX4sqVK7BYLIiPj8eKFSsAeLIxgwYNgsVioedkZWUxEvrqsjg%2BQCP8NAK%2BvDU%2BPp4JTK5evUrLRkNDQ5mHZoPBgMWLFzPZSJ1OB0VRkJycrAmGnE4n%2BvbtixMnTtCM3fz589G6dWuGa0cCYcCTQXr55ZdpG2o1ULfbjZMnTzJluHxQlZSUBMDjkXfixAkmk0lM1ck8XeOcanlLgAceeIBRHxWV0aqxb98%2Begx/H7777jua0Zw2bRozPvV1c3NzcebMGcq74/uUkpLCWA5UVCpJQAR5SFuKosDtdtPsIBHgCQ4OxhNPPFFhO3379mX%2BqcWHZs%2Berbn3Xbt2FZY8nzlzhgbw5N6R4D0gIIAp483NzUV0dDTT9oYNG1C3bl24XC7m5UiTJk0QGxuL999/H3q9nn4nyLlVzZMa3pqq8zwbflt0SQ2fSFS2ygfDIs4ed4yGByQqleX28VwnQMDP8aIdEUeK59po%2BicYtxe0NM3FhMdwF/PGcN4bzp5mvgScvarmRsTX0nDBvCFTigbhDSmT28fTWIW0Vv7aopc13MDOntUewo%2BT755oHfFzLpo//jxvOHuaqREsJH49iniI/HyJppxv2hvOHn%2BMaD3y/EDRcuQv7g1njx%2BD6DvGf79FvyWaZSKYHH6XZm68cFUXVv5zHRTyGXkOHL8NaHl8VW0DwIcfsts8zw/QmqjffN5jsGpV5ds3F8XP%2BF/bLw4Z7N05ZLD3B4bT6aSlhergq6ysDMXFxUhJSaH2A9evX8e4ceMQGBhIH/z37dtHs3fXrl1jMl0ul4uqZALazM/AgQPRtWtX%2BqCdnJyMxMRE2o/c3FyqDFlQUMA8aDscDuzatYs%2BOOfm5sJoNMLX1xcvvviicKzqjIjL5aIlfGT/qFGj8Oabb9IgyWq14rvvvqPnFBUVITg4mAaDFy9e1AQ76jk8fPgw9WcbMGAA01ZMTAwVWSHXIlkyYgKuxp49e2iwZ7fb6edkH/GzI/j222%2BZbXWJKHArs6g%2Bzmg0MtlSp9OJnj170vnhM2NTpkzBKtWPfhxPugEwadIkAJ4M1f79%2B%2Bn%2B8vJyJssLAO/eJIF369aNCWYPHTqEpUuX0m273Q5iXG%2B1WpGRkYGnnnoKKSkpGDZsGKNEajKZmIBdDbXqKFlHZJ/D4WCEXnQ6HS5cuMD068iRI%2BjatStGjx6NM2fO0P1t2rTBtWvXUFJSwtxH8j374YcfcPHiRWGfeHhrqs4nhfltUTJewycScdf4zKLo1S13jIYHJCKycPtECXINP8eLdkQcKb7yXNM/wbi9oKVpLiY8hruYN4bz3nD2NPMl4OxVNTcivpaGC%2BYNmVI0CG9Imdw%2Bjt6o2RZeW0TQ4gYmojfz4%2BS7J1pH/JyL5o8/zxvOnmZqBAuJX48iHiI/X6Ip55v2hrPHHyNajzw/ULQc%2BYt7w9njxyD6jvHfb9FviWaZCCaH36WZGy9c1YXe4lwHhXxGngPHbwNaHl9V2wCgesYAoOX5AVoT9Zv%2B0AxGj658%2B%2Bai%2BLMFR380yGDvDwy9Xo/Y2FgEBwdrOHKAR4SD8LZ8fHzw3nvvYerUqTRQmjNnDvV/Gzt2LNaqVKXCw8PRt29fxMTEUC854FYWZejQobBYLLRUbebMmXjkkUdoidv169fpZ%2BRhXJ1deu6552hW8bvvvsOMGTMQEhJCgxJ1drQizJs3j/ZHURQ0aNAAqampADxB4ObNmwF4gqpFixbhvffeo%2BIoOTk5DK%2BOnENw48YNuFwuGAwG7N%2B/nymHDAkJwZYtWzS2EGSsarEWAPj444%2BpiE5sbCzlxxFbjoKCAoxV/WjzZYKff/45M3ckCLl27RoNohMSErBz504mg6qed4PBoFkfRDG1Vq1alDepLn8k/bDZbGjdujVCQ0PpZ1bujWdMTAwWL15MbRQA4PTp0xgxYgQjSONwOBh%2B5IEDB5CcnIyMjAx0796dEet59tlnGbEjNbZt2ybcDwAjRozA8ePH6XZ5eTkMBgMN5vV6PfLy8nD8%2BHGsXbuWCVwLCwuZklyRQJPInkQEydmTkJCQkLgXUIkW3q8Omdm7c8hg7w%2BM06dP4%2BLFi%2BjWrRsAT3mkn58fFbZo0aIF3njjDQCeB9z%2B/ftj%2BfLl9ME%2BPT2dlitGRETQMlPgVtZpx44dzAOvXq%2BnAca8efOo6IyPjw/69%2B9PhTQcDgcNUBRFgc1mQ3l5OW1ry5YtVJ2UnFtRcKfX65Geno5t27Zh27ZtSE9PR7t27WjbQUFB%2BOCDD9C7d2/88MMPCAoKgk6noyWs8fHx%2BPvf/45BgwYhNDSUqls2b96ctkGuQ1BaWgqLxYIePXrg3XffxRWVjHlYWBiKiooYgRAinOJ0OnH69Gmm/zExMWjcuDEATznsypUr0blzZ5ohWrNmDY4fP07nJlDwylbNiSRBclxcHGMP4nQ64XA46DjIOigvL8f169c12UsS2MXHx9P7ruaZ9e/fn45p48aNmuBYjUuXLmHixInUCxEAPvroI8TFxTHnNW/eHHXr1kV6ejqioqIQHByMzMxM9O7dG59//jnsdjtVsFWrgfKYMGECNTvn8e677zJlx/Hx8XC73TSYNxgMKCsrw5kzZ5CcnMyo2drtdiYoFnHszvPa7xISEhISEhISvxNksPcnwNdff41JkyZRPzuSrSMP7kSEYuPGjXjhhRdwVkWEIAFXBFdbos6oEPVE8re6JJDPvKjtJYhaabNmzbB06VK88cYbCAwMRHR0NIKDgyvM2qhBrhcXF8f8Iw/6vr6%2BKCwsxJAhQ5CRkYEBAwagsLAQZrOZ9uXcuXOYPXs2Nm7ciGrVqsHtdkNRFFy4WYBPghN1ViwoKAjFxcXYvHkzPvnkEyYI2rBhAxMEKIpCBUrcbjcNoElglZmZSUsuFUXBuXPn8NVXX%2BHSTdOeJ598EqWlpbRNdaBCRGYGDx5M%2B0mCzJMnT9LARK28STJnxAvPx8cHiYmJmrk9evQoAA8vlRyrvidDVNLP5eXlCAkJqVDgx2w2Uy6ry%2BXCkCFDsHbtWpw8eRL%2B/v6IjIyEoig4dOgQTp48ibfeegtXrlyhfMeMjAxarkrKiSuzIsnPz9fYK5AgPiYmhgkwb9y4wWQ2/f39YTabUaNGDRw%2BfJjJ5Pn6%2BtJzeXEWAsK5rQpSoEVCQkJC4l7A7/m/JpnZu3PIYO8PDCIsUVJSgnnz5uHBBx/E6NGjsX//fuj1epr98vX1hdVqRb9%2B/fDCCy/8LJGJOwHh9J06dQqTJ0/GSy%2B9BKvVCoPBUOGD9M8F4XgtXboUvXr1wsqVKwGAKnf6%2BfnB6XTimWeeQd%2B%2BfbF7924oioLIyEjGDgJg/QrJ376%2BvujZsydTkhcpIDWQbJzBYKAlhOTB3t/fn5ZEut1uVK9eHffddx9q3jSMWrBggcYgnIAEfuvXr6f7SPmon58fLaf84YcfmPuqVpm0Wq04ceIEvT6BuhyRF%2BABQNcP4Akc8/LymPPVmTWbzYZ169YhOzsbpaWlaNmyJaKiouDn54fdu3cjNzcXbrcbFosFTqcTycnJ8PHxgU6nw%2Beff45evXrh888/p21VhYiICCQnJzP7iEXFhQsXmKzrtWvXGEuSoKAgREVF4cyZM1i7di19KUDaJffa6XQiJCREU1arDqwrg7em6lUJtIi0BTRCBqIaHO4g4deem2tvRBW8EWjhxQ5EY%2BDb8caA/BcTaOEaEh7DDYy/tugcb4RUNPdBZH7OjYtvR2TurDEyF7l386XFIhUSvqHbMFUXFgFwAxeuCe5EkZgJL5LBd08ktMH3RzQk/hhv2tHMuWBR8PdbZJjO7xP1j29H9H3h9/HtiMzuOX0xYf804xL8mPDz5c1vCf9T782ci35w%2BGt58/vI90dYdc99P4QCLe%2B8w26rOOoV7uNFUkTCKvw%2B0TG8YfqiRdpj%2BH38tug34DeGDPbuHDLY%2BwMjOTkZHTp0EEr5h4eHIy0tDYBHdELN%2BVI/pKu9/wj0ej2jUBgWFsZk7NQPx2pPQb69jh07Ii4uTlMG2rJlS3qNqmwI%2BOvxeOqppzS8Kp1OR8VFxo8frzFGt1gsyM/P14iMqEGykna7HRMnTmTmjARx5Lo1atRgMknq8lUyZmKN4HK5cPnyZezduxenTp2ix1UV4JDsk7%2B/P80clpaW0sAvRMVQ1%2Bv1jGm8yWQScs%2BIYElcXBzNkqnv%2B8SJE5lsGF82qdPpaPaK9IO0M3XqVLhcLpSWlqJx48bw9fWFj48PFVEZM2YM5YK6XC7Mnz%2BfZvYCAwOrXBeBgYFUaZSH0%2Blk%2BJSicaekpCAuLg6fffYZI9CSkpJCx2k0GnH9%2BnXG1kKv11dqs6GGt6bqVQm0iKZCI2QgGi93kDBRyn0HvBFV8Eagha%2BwFd5Orh1vDMh/MYEWriHhMdzA%2BGuLzvFGSEVzH0T2KNy4%2BHZE5s6aZSly7%2BazzSIVEr6h2zBVF42bH7hwTXAnisRMeJEMvnui6m6%2BP6Ih8cd4045mzgWLgr/fooIWfp%2Bof3w7ou8Lv49vR/TTpaJiV9g/zbgEPyb8fHnzW8L/L9ibORf94PDX8ub3ke%2BPSC%2BI/34IBVomTGC3H39cewy/jxdJEQmr8PtEx/CG6Tefeyrdx2%2BLfgMk7jnIYO8PjoULF2LYsGGwWCxQFAUGgwGNGjXC%2BvXraUbi%2BeefR8OGDaEoCvz8/PD888/T83nvP/I34bMBHp4V2Vb/Tbb5c8l/9Xo9Vq5ciUaNGkGn08FoNKJPnz6YOXOm8FwR%2BOvxSElJweuvv46goCAoioKQkBAsWLCAeraNHj0aI0aMgNlshk6nQ926dfH%2B%2B%2B9TD7VnnnlGGBgQoRCn04nU1FQm%2BCBBhsvlgslkQpcuXehnDoeDBlkkk1Soep0aHh6OqVOnQlEUVK9eHWlpaUwg1qBBAxrcqoPUwsJC%2BPv7o2XLlowaJOmXukwzMTGRZvzCw8OZdgIDA2lATrJ9ZWVlVJW1efPmNIDr1q0bHUNJSQkCAwOZ65SUlNCxkzGTEtIhQ4bQebLb7XA4HIiOjqbtPfXUU/SagCeoJ3zPwMBAVKtWDSUlJRpbC4LTp08zpvBquN1uhvcYEBDAzHFBQQEGDBiAgwcPIj09nTm3devWCA4Ohl6vZwSICIgAkDeQAi0SEhISEhKVQ2b27hyK%2B7eq2ZOQuEdw4sQJ9OnTBwBodslkMkGv16OsrAx6vR4Gg4Fm26pVqwan00mDC71ezyhK%2Bvv7M4HL2LFjsfym/42vr6%2BQWwZ4/Af37NkDAGjYsKHGssHf359aNWzbtg2zZs3Cvn37EBkZiStXrlCvO8CTnTt//jzcbjdq1qzJiIgoioKUlBQcPnwYERER2LVrF%2BrXrw%2BDwQCHw4FmzZohMzMTNpsNWVlZmDp1Kj777DOYzWaUl5czPDOLxcJkukaPHk0tHAICAqgRfbVq1SrtY/369akoDOApw%2B3cuTPq16%2BPv/71r5gxYwYWLlyITZs2CYV7li9fTsWHeOh0OnTq1Imep86wOhwOmEwmHDt2DBkZGZg2bRqjNEsC4MGDB%2BOnn37SBJtff/01okUZEwHmzZuH1Zzn0sSJExlLEwkJCQkJid8beXniTPpvAVF1yC8BEcXgjwqZ2ZOQ4OAUkEXsdjvKyspgMBgwevRoxi4AYPltVZUYHjhwgP7Nl2eGhYUhOTkZbrcbV69eRf369dGoUSMa6Pn4%2BNDgpKSkhJZa9uvXD4cOHWJKSNXvccaPH0%2B3ebXINm3aUH4buaYajz32GMNxI2IzoaGhTEZMURRNKae6PDg%2BPl7DxVTbP6j7yPMlx48fTzmCRI1z1KhRiBHVq%2BEWH5TMVb9%2B/ajKrMlkYkzg%2B/TpgxEjRtD7Tv7bsWNHpKSkCAVXHnnkERroJSQk0P3Dhw8X9kcEb03VeU6KN9wwzf/EvDhIyJH6jTh73rTza3H2hOPmOn07puq/GGdPVI7MWZvw7Yi4Qxo%2BlhdcQI2rNeCFc7h2X1U8OgCaCfNmzkXJe97mkr%2BWaEj8GhVx1/hjbqsdLzh7ornhCxi8WTei7wvfP75dL26l5hwAXnH2%2BPm6HVN1EX1M8/siGDh/Lc0hgovzu4Sm6txOoRCzN5y9JUvYbW84e7xhOr8NaA3cJWdPZvYkJCRuoUGDBnC5XIyHIIGfnx86duyI//znPwA83KsBAwbQgCw5ORmZmZn0eJPJhKZNm2Lfvn1Mdqsi%2BPn5ISAgAKmpqXA6ndi2bRu1diDBC/naivpnNpsRHx%2BPixcv0ixbXFwcfuJVElRo0qQJDh8%2BTLfV2cmwsDDY7XYUFRUhKysLjz/%2BOL766iua%2BVMjMDAQdrsdVqsVvXv3xsiRIzFgwAAAwPTp0/HGG2/A4XCgSZMmOHLkCHQ6Hb2Ouo/Jyck4ffo0dDodSktL4evri2HDhmHFihVISkrCpk2bMGnSJOzduxcHDhyAy%2BXCe%2B%2B9h08%2B%2BQQ//fQTjEYjY6yuRkhICN566y3qI6goCuVVFhQUQKfT4YcffoDD4cDs2bPx4YcfUoVWktnbtWsXRt/kVfD3YPv27V4pcooye088MRmTJk2s8lwJCQkJCYnfCr9nZq8CF6U7hjCA/4NCZvYkJDicOHECRqMRsbGxQlXQ9u3bU1sGwJMZUmew1IEe4MnG7du3DwBQVFRUqdqpoihISkpCbm4usrKysHv3bsZ/kChK8kHfwIEDaRs2mw0nTpxAcXExzTKOHz%2B%2BwmvqdDp6HLmOOoN49epVJnDq1KkTAAgD1hs3blA%2BYEZGBpaq3mKuWrWKnpOVlQW32w2n00mFcNR9LCgogM1mY/iNX375JQCPsigAzJkzBw0aNADgsaf417/%2BhccffxwZGRmVZleLiorQqlUruu3v749Lly5RCwaC0tJSZGVl4eGHH2bOdzgceOKJJwB4OIzLuTeq14VpCy0kZ09CQkJCQkLi14YM9iQkOJw%2BfRo2mw0XL15EWFgY0tLS0KhRIxrQbd%2B%2BHTVq1KAG8nv27GGCi4SEBEYAxGq1UtEbg8EAs9lMAylFUWA0Gplg7vjx42jUqBGOHj2KJ598Ej4%2BPvTaRCymQYMGNOAAwIjA9OrVCyNHjoSiKDRrtkRVJsIHQi6XC4cOHQLgya4BwLp164Rz88orr2D//v1MO7zfHRnL5MmT0bp1a7o/X6XZPXz4cBiNRtSsWZOWsi5ZsoSeGxwcjOjoaFqmqS4XJX%2B/8MILOHr0KDp06ID//ve/8Pf3R3h4OGrUqFGpX11kZCRatGhBt4uLi1GrVi289NJLVECnR48e2LJlC9avX894C86cORMtWrSggdrBgwfx2GOPMe2XeFn2In32JCQkJCQkKocs47xzyGBPQqICWCwWmM1mZGRk4NixYzSL1rlzZyxYsICKi6iFU%2BLi4tCpUyf079%2Bf7ouIiMCIESMAeAKN0tJS2la7du2wYMECPPPMMwA8gUyPHj1Qp04dKIqCvXv3MplAu90OvV6PkydPYuHChfSctm3b0mN0Oh369evHZBvV3EASpKq3GzduDOAW93D%2B/PnCDOSWLVtouSexcyBehgQkYNm0aROWLFkCHx8fAJ4AjmDZsmVwOBzwV8k6jxo1ip6bmZmJxMREfPTRR7RNu90Os9lMbS/GjRuH8vJyBAUF4f7778egQYMwZswYDBo0CG63m1FyNRgMaNy4MRRFwdWrV5mspE6nw1/%2B8hfMmDEDgCfTN336dLz55pto3bo1nSu3242NGzcymVWDwYBHHnmEGT%2BxzKgKIs5e965dNcfxfCcNH0tE4OHrUwQiNhrjqIMHtcfs3Fn5OV99VXW7IlIcT4i5%2BbKBATcuUcKU9/zi%2BVrCOh1vjPa4Y3JytIfwbVfFFRNdyhuuoogcVhWP0xsOkshbjJ8uURGCpjui%2B8u1zfdP5CSj6Z9ocjj%2Boqgd3mOQ5/UJE%2B/cQEW8NP4djoizV5UvnDdfBRHfkqdXirzu%2BHZEc8Pv48cp4iryPLRzY9KdVQAAIABJREFU57TH8OMSrRv%2B2t5wKTUnCQ7id4m%2B8vz88dcWcT81y08wKM0x3vwWiyo6%2BGO8OYcfuDf8WtGLSH4ff62bHL6I/B%2B050pUCbfbjVGjRmHjxo2VHnfhwgU8%2BuijaNKkCXr27Ildu3Yxn%2B/Zswe9e/dGSkoKhg8fzlSXeQPJ2ZOQ4JCZmYn%2B/fvD19cX/v7%2BKCgogF6vh9vtht1ux5gxY/C3v/0NTZo0QVlZGc3i3bhxAyaTCU6nE3q9noqaVKtWDf/73/9Qv359KIoCk8kEh8MhFIJRFAV9%2B/YF4PHn%2B%2Bc//0mv7XK5kJiYiPj4eDz99NPYtWsXZs2ahcjISNo%2B4Mn%2BuVwuWiYJANWrVxcaowMez8Br167h2rVrNIBVq2r26dMHGRkZtOSSBI7kWJPJxAi4BAcHo6CgAAAQGxuLgoICFBcXC/mFalSkTKr%2BiQoKCoLZbMbOnTvRrVs35OTkwG63o0aNGli8eDEWLVqEo0ePUvEavj3Sxvz58/H4TW8j/hoJCQlITk7G4cOHcfbsWebzxMRE6PV6nD17FqWlpVQEhpTXAsDOnTsRGRlZ4TgJJGdPQkJCQuKegE73u6XDhD6nvwCEL9t%2BQbhcLsyZMwfr1q3D3LlzmSSAGm63G3369EFCQgLGjx%2BPL7/8EkuWLMHWrVvps1uvXr3wxBNPoEOHDli8eDFOnz6N9PR0TWVVRZCZPQkJDnq9HjExMUyZotFoRLVq1Rij8JCQEPpFI9krh8MBRVEQHBxMyw3VWTTidUjUPP05w1J1%2BefEiRPxj3/8A3q9ngZJJ0%2BeRFFREWrUqAG73Q6Xy4Xq1asz17Hb7XA6nTT40Ov1jDIlj2bNmjFZNx5TpkyhQaOav0baD1W57qrnBwAjEqMOqNRzQuaJlLeqSzZJxpIcX6dOHRQUFGDHjh3Izs6m/bp06RLS0tKwY8cOyhmsCL6%2Bvjhy5AhVDuUN5cmP6NmzZwEAH374IZ27l19%2BGevXr2fu%2B8yZM5m5FRnUiyA5exISEhIS9wQ4LYLfEvdiGWdOTg5GjBiBHTt2MM91IuzduxcXLlzArFmzUKdOHYwbNw5NmjTBJ598AgDYsGEDGjZsiFGjRqFevXqYO3cuLl26RCk13kAGexISHJxOJy5duoQHH3yQMaNv3LgxYmJiaHlgQkICTCYTdDodfH19odfraeYpNjaWWgUEcCYxOp0O0dHRqFevHmrVqgWDwQC9Xo/IyEhNQJaWloaIiAjUrFkTZrMZbrcbu3fvxvfff4%2BjR48CAA4dOoSkpCQmU0gygYqiICAggAn%2BAE/wRIKy6tWr0/JNwgk0mUy0L9HR0ahRowaAW7w7g8FAg5patWrRdo1GI2MSD3iCQUVR0KFDBwCezBoRQtHr9WjTpg30ej1KSkpovwlIUEz6cvjwYZSXl2P58uUIDQ3FuHHjYLFY6H1KTExEzZo1aR9FcLlc2Lp1K5o0acKMGfAE20899RS6dOlC5yglJYUG83a7HTabjWYMFUXBP/7xD6bPi0Ty1hISEhISEhJ/CmRmZiI6OhqffPKJ5hmQx5EjR5CUlMS8KG/evDmlzBw5coTRGfD19aXVR95CBnsSEhySk5PRqVMn7N69G5MmTcK2bduwbNkyWCwWuFwudO/u8UKLiYlBSkoK0tPTER8fD6fTiZdeegnp6eno378/vv32W5hMJsqH8/HxgcvlQnh4OBo2bIjz58%2BjQ4cO%2BOyzzxAaGoq8vDxYLBYAQGFhIebNmwfAE7TUrVsX1atXh9FoRGRkJPbu3YujR49Cr9ejc%2BfO%2BPTTTxEWFga9Xo/AwEA0bNiQBmOkjFINp9NJM0vbt29Hhw4d4OPjQ69fq1YtGhwWFBTQQG379u0APMFky5YtAdzKRhIbBSJQEhQURK/n5%2BdHM2mk3BXwBHvfffcdjEYjtUIgCps1a9bEmDFjAHiCqlq1aqFRo0bQ6XTIzMxEfn4%2Bli5diuLiYhQVFUFRFFy8eJEGwSRrajab8fLLL9PrlZSU4KeffqI8SjJegmHDhsHHxwdOp5MG0GqRlrNnz9Lgrry8HHPnzqWf9e/fn46jKkiBFgkJCQmJewJJSb/bpX%2BtzF5ubi4yMzOZf7kiX9PbQJcuXfDaa68xlU8VIS8vT0P9CAsLo5ZbVX3uDcSvviUk/uR4%2B%2B23sXTpUixatAiXL1%2BGn58f2rdvj3Xr1tGAqE%2BfPpg4cSLS0tKwYsUKfPPNN3j22WcBeIKHdu3aYfv27XjwwQcBACNGjMCKFStw4cIF7N69G6%2B88greeecdLF%2B%2BHIqiICEhASkpKSgvL4fVasXq1avRoUMHmM1mnDt3Dj/99BMmT56MzZs3A/CYo/v7%2ByMoKAgulws2mw2PPvootmzZgu%2B//x4A8PTTT2PlypWUM%2Bd2uykH0GQyoaysDMeOHcPJkycZ0RLiJwcArVu3ppk1m80Gg8GAmjVr4siRIwCAb775BpGRkcjNzYXL5YLFYoHBYKC8vRs3biA4OJiWHHTp0gXffPMNAE%2BmbPLkyfj666/x3XffAbiVabt%2B/To%2B%2BOADAJ7sqJ%2BfHw0Uy8rKMGLECKxduxYhISEoKirCoEGDsH79eoSEhMDhcFC7CJvNhpkzZwIAE4xOmDABAFBWVoZzN1UHIiMjkZ2djUs3VR74kkye4uzj40OVVgFg7NixTOBYGUaNGoXU1FRmn6IomDdvHg3E/fz8kJaWhvT0dJSWlla57c05v9Qxv%2Be17/b%2B/Vmvfbf378967bu9f3/Wa9/t/VNvY%2B5cjMrN9YqP/kvj11IWWbjwQ00lzqRJkxil84pgtVqRI1Tu8ojyiV7mVoSysjKmwggAo4VQ1efeQAZ7EhIC%2BPr6YsqUKZgyZUqFx6SkpDCKSStXrsTixYvxww8/4Pz587BYLFi3bh3NgE2dOhXjx4/HrFmzsHHjRjz77LM0iKxduzbWr1%2BP1157DWvWrMGePXsAeAICnU4Hf39/jBs3DleuXKGm5UajEeHh4bBarcjPz0dxcTFsNhuGDRuGN998E%2B3atcPYsWPx7rvvwul0Ijw8HIWFhQgKCsLu3bsBAEOHDsXBgwcRGhqK7OxsjB49Gq%2B99hotW4yNjUVgYCCOHj1KbQl0Oh3uv/9%2BdO7cGcOHD4fVakV5eTkVhomOjsb5mxJuUVFRuHHjBm7cuMEI1hDhm6CgIPj5%2BWHkyJGYOnUqFEXBwIEDceTIERQVFcFut6NBgwYoLCxEdnY2Bg8ejO%2B%2B%2Bw779u1DaWkpEhMTcenSJTidTqxfvx6dO3dGcXExjh07RvmTJpMJer0e5eXljHKqyWSC1WrF2LFj0bNnT/Tv3x8//fQT%2BvTpQ4M64gHYvHlzqn5Vu3ZtaigfFRWFZcuWoetNFc20tDR8/vnnlEdZGSIjIzX/48zMzNSItiQlJTH7qtr%2BLY/5Pa99t/fvz3rtu71/f9Zr3%2B39%2B7Ne%2B27vn3o7NTX1dwn2fi08/PDDjG0V4AnUvMGRI0cwfPhw4WeLFy9Gt27dvO6H2WymL8cJ7HY7fQ4zm82awM5ut1fJBVRDlnFKSPxCaNeuHd5//30cOnQI3333HRPoEfj6%2BmLu3Ll499130aRJEzidThw8eBCZmZlYuXIlEhMTMW/ePGRlZeHw4cN4/PHHERUVhaKiIqxevRo5OTmYNWsWAE9JYp06dbBt2zaMHj0agMcfb8OGDXjmmWcwefJkzJw5E6WlpdDr9WjXrh3CwsJgsViQl5eHvLw8TJ8%2BHXq9Hvn5%2BQgICIDb7cb27dtx7do1BAcHIzg4GP/%2B979x7NgxjB07lqqMRkVFacZfXl4Oh8OBH3/8ETabDX5%2BfiguLsbrr7%2BOY8eOUXuJ9evX0wBWp9MhIyODtmE2m%2Bn4jEYj9TwsLCxEQUEBTp06Rctit2zZArfbjQceeACPPvoo9Ho9srKycODAAVitVrRq1Qr%2B/v54/vnn8emnnyI9PZ3%2BsBuNRjRt2hSRkZGIj49HgwYN0L17d8ptDAwMhI%2BPDw0O1SpaFouF1s9HREQwdfNut5tmVSUkJCQkJCTuTkRGRiI5OZn5520w27p1a2RlZQn//ZxAD/C8FM/n/FTy8/NpXyr63NvAFJDBnoTE7wJvA8MpU6bg66%2B/xrvvvovk5GTs27cPkydPxt69e7Fy5UosWbKE/sD07dsXtWvXhl6vx9tvv41x48ahuLgYbdq0wYABA2hJ4rlz59C%2BfXu0b98eo0aNQlJSEmbNmoXw8HCEhoZi69atMJlMaNGiBd577z3anylTpmD69OkAPJzC1q1bo2nTpmjTpg0%2B/fRTDB48GA0bNsSWLVtQs2ZNhIaGwul0UqGahg0bAgDGjx%2BPunXrYvPmzahXrx6%2B//57PP/886hduzb279%2BPVatWAfB48f344484c%2BYM2rZti06dOqFu3bpo2bIlDAYDJk6ciIsXL2Ljxo3Yv38/Vq5ciS%2B//BLVq1eHxWJB7969Ubt2bVy5cgVxcXGoXbs2IiIiEBgYiAULFmDNmjWMF%2BHEiRPhcrnw2muv4f7770f9%2BvXhdruRk5PDmMM7HA5cv34dMTExOHr0KObPn08/%2B/777ymnU0JCQkJCQkKiMqSkpCAzM5NREj948CD1ME5JScFBlQ9uWVkZjh8/rvE4rgyyjFNC4h5Au3btaNBUEYYNG4YhQ4Zg4sSJ6NmzJ6xWK7744gusWLECH374IRITEwF4zMizsrLw9NNPo2nTpsjPz8emTZtgt9vRvXt3WCwWnDlzBlarFXk33WZLS0uxc%2BdOLFiwAE899RTGjx%2BPixcv4tChQ1iwYAESEhLw0ksv0b48/PDDeOONN/D1119TYRbyJoyYwdeoUQPvvfceUlNTcenSJUydOpWWTZIxf/nll%2Bjbty/sdjuefvppXL9%2BHS%2B%2B%2BCJ69%2B6NsWPHokePHkhNTcUDDzyAqKgozJ8/H5cvX8aECRPQv39/%2BPj44IUXXkCtWrXQrFkzBAQEoLy8HHXq1NHMX0JCArKysgAAvXr1gsvlwuOPP45HHnkE06ZNw1dffYXLly9jzJgxKC4uxgcffICoqChcvHgRXbt2peI1EhISEhISEhIV4dq1azCbzfD390erVq0QHR2N5557DhMmTMBXX32Fo0ePUvG3AQMGYNWqVVi%2BfDk6d%2B6MxYsXIzY2lnkJXRVksCch8QdBo0aNsGzZMixevJiKviQlJdHyUAJvxGcAYNu2bdi2bRsAT%2BlifHw8XnzxRVrSuHXrVoSEhGhq3gFP2eOCBQuwefNmjB07ttJ%2Bd%2BnSBWvWrBEGs3q9Hu%2B88w5mz56Nhx56CP7%2B/khNTcXUqVMBAHFxcViyZAnmzp2LJUuWoFu3brj//vvp%2BT179kR%2Bfj7%2B%2Bc9/Ij8/H3Xr1sWSJUu8ElHR6XR45513sGbNGrz99tu4cOECQkND0a1bN7zxxhteqWz9XERERGDkyJEMgb5u3bp0X1Xb3pzzSx3ze177bu/fn/Xad3v//qzXvtv792e99t3eP/U2%2Bf%2BTxC%2BDgQMHol%2B/fnjiiSfoc84LL7yA/v37Iy4uDosXL6bc/9jYWCxcuBCvvvoqFi9ejKZNm2Lx4sVeG6oDgOLm5eUkJCQkJCQkJCQkJCQk7nlIzp6EhISEhISEhISEhMQfEDLYk5CQkJCQkJCQkJCQ%2BANCBnsSEhISEhISEhISEhJ/QMhgT0JCQkJCQkJCQkJC4g8IGexJSEhISEhISEhISEj8ASGDPQkJCQkJCQkJCQkJiT8gZLAnISEhISEhISEhISHxB4QM9iQkJCQkJCQkJCQkJP6AkMGehISEhISEhISEhITEHxCG37sDEhISEhISEhISfz5cuXIF1apVAwDY7XaUlpYiODhYc5zL5cL/s/fm4VGW1///a/Y1%2B2QhC9lJWISEHVFZVBCKUCxYwQVcWgsKirbfT7UtVWtbFW3RClVbBYQCH6RgQLCiSFF2EiTsAQJkIfs%2BSWZLZn5/5HvfzkwSFqVSv795X1eu5Nnu/Xlyzn3OeZ%2BKigpiY2O/6yZeFsXFxVitVjIyMlCrvxarDx48SFVVFV9%2B%2BSU333wzvXr1Ij09/YrLbW9vx2q14nK5MJvNGAwGea2trY2zZ89it9uxWq2YTCYyMjIwmUwAnDt3jj179nD48GGsVitGo5GsrCyysrLIzMxEp9NRVlZGdXU1R48eRaPRMG7cODweD0uWLGHWrFn07NmTiooKjh07xm233XbtBiyA7xwKj8fjud6NCCCAAAL4/ys%2B/vhjli5dSmVlJW1tbSQnJ2MymSgvL6eqqorQ0FAqKyuxWCzodDoiIyPp3bs3P/7xj8nMzPxWdXs8HvLz8zl37hyxsbH06dOHoKAgGhoaCA0NpaGhgbCwsMuWcSX3fVN89NFHjB07FqPR6HO%2BrKyM%2Bvp6KQhFRUVRUVFBSEgIwcHBaLXabgWl/5cgxqG8vJy2tjb0ej0lJSUEBwfjdrspKipCrVajUqmIj48nOjoavV5PXFwcERER163d/gKy2%2B1m9%2B7dtLa2EhQURGlpKUOHDqWuro6zZ89itVoxGAxERUURGRkpBVaA6upqPB4PkZGRKBSK69anK0Vubi7Z2dmoVKpuFQUAl8vF4cOHGTJkyHVqKeTk5HDrrbdiNpuv2bPe53v37s3o0aNRKpXs3LmTtrY2UlNTefzxx2lubqa5uZnhw4cTGRnJzTffzMmTJwFobW3lvffe4/HHH78m/fwmyM3N5Ze//CUlJSXyXFpaGkuWLOHxxx/nzJkznZ6xWCyMGjWKI0eOUF1dLRVclUqFyWRi1qxZKJVKVq5cSV1dXafns7OzcTgcnDhxoss2RUdH43K5unxWQKVSYTQasVqtl%2ByfUqnE7XbLvwE5/gF8vxBQ9gIIIIAArhOmTp3KqVOnUKlUuFwueT4kJITGxkY0Go3PeQGFQoH4dJtMJqKjo4mMjMRgMKDVajl//jwKhQKlUklwcDB1dXWcO3eOyMhIwsLCGDZsGHv27KGoqAin03nJNiqVSiwWC3a7neDgYFpbW%2BnXrx9z5sxh8eLF5Ofn43A4UCgUhIWFERISgtlsprW1lfb2dux2O1VVVQQFBdGjRw9CQ0Npb2/n3LlzWK1W2tvbMZvNxMbGEhcXR3FxMUqlkurqamw2Gy0tLeh0OtRqNVqtlsbGRhQKBe3t7Z3G4nL9EIKLQqFAr9ejUCgYNGgQJSUlNDc3A1BbW4tWq6W9vR21Wo3dbkehUKDRaEhPT8dgMJCbm8vo0aOZNGkSI0eOZPr06Wzfvv3KJp0OYdflcjFx4kSMRiPFxcU0NTWRmZkpBf7CwkJaWloIDg6muroajUZDeXk5FouFvn378uyzz/LZZ591uT6uBAqFgttuu40pU6ZQV1dHVFQU/fr181GMW1tb2bFjB5WVlaSkpDBq1CgA8vPz5eaEUCLdbjdlZWXEx8fTr18/n7rEhoDJZOJnP/sZe/fulXMRGhpKUlIS%2Bfn5VzSPCoUChUKB2%2B1Gp9OxadMm7rrrLjweD5s2bSIhIcFnnK9UUREbHAA2m40NGzZQUlJCSEgIffr0wWAwdKkk19XVERoaSlNTk49F6vDhw5SXlxMXF0f//v196srMzKR37960t7dz%2BvRpPB4PQUFB3HXXXezYsYOoqCjGjx9PcnIyjzzyCOPHj%2BfOO%2B9k6NChzJs3jxdffBGr1UpcXBwvv/wyf/zjH2XZb775Jmlpadxxxx1UV1dzyy23MHjwYBYsWMD27dtZtmwZSqWSkSNHkp6ezt/%2B9jfCwsKYOHEidrudf/7zn%2Bj1eoYPH86Pf/xj5syZQ0hICD//%2Bc8ZOXIkY8eOZeLEifTp04ft27fz1Vdf0bt3b7Kysvj888%2Bpqalh%2BPDhTJ06lZ///Oc%2Bz956660sXryYJ598ktjYWAYNGsTmzZvl92L48OHs2bMHpVJJe3s74eHhqFQqamtrGTduHP/6179Yvnw5X331FeHh4Tz33HO8%2BOKLBAcHk5qaSmpqKgAtLS3861//oqKigoyMDGmVysvLo0ePHtTV1eF0OmlpaSEjIwONRoPT6cRgMFBVVUVKSgqNjY28%2BeabPProo3g8HnJycti9ezclJSU4nU7q6urkN%2BhSa7W7Na1QKAgKCqKpqanL62FhYbhcLvld8i9LpVLh8Xjke/SfRFJSEjabjR/96EdMnz79v9K6GsClEVD2AggggACuA7Kzs2ltbb2qZ4QC197efsVKzpVArVbT1tbW5TWVSnVJxSoiIoLa2trL1qHT6XA4HN%2B%2Bsf8X/oqwVqv1UVy92w2%2Byt61QGRkJNXV1SQmJlJUVMRNN93Erl27MBqN9O/fn8jISIqKiqisrCQ7O5vq6mqUSiWVlZUUFxcD8OCDD/LBBx9IgS48PJzRo0ezefPmyyr5VwPRRugYF4VC0e18w9XNlXebxN9qtZr09HQKCgp8FHNv6PV67HZ7p3nyP1YoFKjVatrb2y87f6J%2BlUpFUlIShYWFKJVKPB4P/fv358yZM0yePJmPPvqIrKwsiouLKS4uJioqCqvVyvjx49m1axc1NTWX7XdX60mn06FUKnE6nZ36rFQq5ft7OQW9u3k2Go0%2B3wyTyURLS4uPsiksOqGhoTQ3N/vMc3fvwJWuK//7vNcVdP0tEPMsnhXvrWj7lWDcuHFs27bN51xXfdHr9bhcrssqYf9J%2BK/f/3Z4t1epVKJUKn3WjNhc8Xg8eDwelEplwLr3PURA2QsggAACuA44d%2B4cEydOlFaKlJQUzp07JxUvo9GI3W73sUZptVophJvNZqkkdAUhZHmjOwFLCE5XKvT5K1bdWSC9IZSj7wPCwsKor6//TusUyoC34PVNlNPU1FQKCwulO6P/fH5XwuilNhAuB2HZ9h4D/zV3LdpxrTcgriWEkvptRLTu3mf/9e2vtEHHOnG73fJ5g8GAzWbzaZ/3%2BgwPD/dxHbza9dtVW6/1Bs21gH%2BbxPq6XFtnzpzJ6tWr5bH/eHWHq3mP/OdRrG/x2/%2BboFAoePfdd3nooYeYPXs2OTk52O12lEolr7zyCs888wwffvghAKdOneIXv/gFmzdvJi4u7oraE8B/DwJsnAEEEEAA1wEpKSn06dNHWlrOnz8PIN2KHA6Hj/AgdlWBLi0z4pqAID3o27evTxne0Gg0ALIej8cjd3JFPf5xREAnobtXr14%2Bx/7xdYCPcOldxzeFSqUCkO0T/RdxXOL81cQaiTIbGhp8yvgu4v3cbncnJcy/3t69e/sciz57z1FhYSFAt4pCVwKp/9oR4wBIF1p/dDXH3vBfn2Je/OvoamwbGxuBjnXSs2dPoIOsIioqCq1We8l6/SH6699ebwuGaItYk/7jIRAeHu4zFqJM77UsrHcCZrPZZzy93y3v%2B/zrFG6q3vCfB/9j/xhM/7EV5Yl3UbRLKAjiewAd4%2B3dJrvdjsVikcc9evTwKbupqcnn/u6%2BNd2hq/jRyz1zKVzps8KapVKpCAoK6rQG/NeN2WyW31bxHVYoFJ2%2BM/7fty1btvjU57/pJr6JQUFBPufFOIr/E6JfXa1Rg8HQaQ4BHA6HdPvUarU%2BSt9TTz0FwD/%2B8Q%2B5adfa2soTTzyBx%2BPhJz/5Cc8%2B%2Byx//vOf%2BcEPfhBQ9L6nCCh7AQQQQADXCX379sXlcvkI5sePH%2B/WFcl7Z917pxboxPImBLhTp07Jc/5uo13tGBsMBtkWj8fTSZGDzoKMv1uPdzu7qkvEKPmX5y9YeQuUGo3GR/hdu3atT7kLFy4EkJaaP/3pTwDS%2Buk/PiqVCpVKRXBwsE8don3ev6/EouQ9Jj169PA59o/Z8n/GaDR2UoDdbnencRw%2BfHiX5XTlMtgdhCudQGpqaifB3FuJ8Hg8PkK%2BaOPVWgf9LWji%2Ba7Wivc9NTU1REVFERMT06UlpCuSIu%2B1JZQ9/7XvrVyL32IcvK3p3nNitVp91rHok//4eSvUXW3aQIel0vt8cnJyJwXFf8z831f/Y//x8e%2BzKM9/DoXS52%2Bd92%2B3t3vrxYsXfd5XEePq38/w8PAuy/aHd5ynWL%2BivXq9Xl579NFHfZ7z/xaJ45CQkG7rMpvNPptCGo2m0yaC6Lu/8uXtTSHGzePxyNg7/2%2BIgLBUazQa3G53p7lNSUkhJiamE2mKmKPo6GiGDRuGy%2BXq0oqYnZ1NcXExbW1tsm/e60OU43Q6MRqNUrkWG1sul4uqqira2trweDy0tbVhtVopLCxk37592Gw2fvazn3U1nAF8DxBw4wwggAACuE4YNmwY9957L1qtlsTERKlc/f3vfycnJ4c777yTnJyc693M/xj8XbdiY2MpKyvrdN/Vupl%2BF/Bvi7dL4KhRozh06JAU3MaMGcO///3vTm1PSEjwYfKDzu5yV1r/N4FarWb06NF89tlnPuf9yWwsFksnF9xvU/9/ah6FC6vFYrmiuLsrgb9g7X8cFRVFVVWVPDYYDNjtdtm/7twx/d2so6KiqKmpwe12o9frUavVl3TT/jbo3bu3zwbN448/zptvvulzj7%2BbYVeunleCrKwsDh8%2BLI/950a4FXuPR1RUlGRYBbj77rtZt24d0OGxUFFRIZ/3nw%2BTySQZXbsjP/F2ZzebzSgUCmw2mySK8d7c6Wqt%2BrvUe5%2BPiIjA7XYTGRnJ5MmT2b59OwUFBdx11108%2BeSTmM1mPv74Y/bs2cPBgwelR8fcuXOpqKggPj6eKVOmcPjwYdauXUtubi4ej4dly5axefNmhg8fzpQpU6iqquIPf/gDX375JdOnT%2Bepp57iwIEDZGVlSSvjq6%2B%2ByrZt21i6dCk1NTUkJycTFRUlFWK3282iRYtYvnw50LF2o6OjmTNnDllZWdTW1lJfX09TUxPLly%2BnubmZDz744D/GvBzAfw4BZS%2BAAAII4Dqhrq5O7nzv3r2bwsJC3G43ycnJBAcHk52dDXRQfB88eJBDhw7x5Zdf%2Bggf3QnOPXv2pKysrEvrndFoxOPx%2BFgdrjRG6EqVHuEvAAAgAElEQVTv826XUqnEYDDQ2tra5XP%2BpCE9e/akvLxcCm4TJkzgo48%2BQqVS4XQ6UavVWCwWKioqGDlyJLm5uYwZM4aDBw/K3FQDBw4kPz%2Bf8PBwqquryczMpKCgQMY6ZmZmcuzYMZ82dGet8u5LREQELpdLCpKijd79EkKjGFsRg9YdBIvqpe65HERsT1hYGIMGDWLo0KE4HA4qKytZs2aNFPC%2BaRydN/yJQr4Julu3wcHBWCwWBg4cSEFBAY2NjVitVjweD3q9nnHjxtHY2EhOTg4mkwmbzSbXo1AkTCYT8%2BbN46WXXuq2fm8l4UoV7O8SRqORGTNmsHz5crkuH3zwQVatWiWtZLfddpuPou6v%2BPgrRv5rfMSIEezduxfomI%2B5c%2BeyZMkSAN59911eeeUVCgoKANi0aRP/8z//I5XExYsX88tf/lIqaI899hhvvfWWLH/q1Kl88skncp0MHTqUAwcOXNHGjUqlYuzYseTl5VFXVyfdCy8XF3e9N4OioqK4//77GTJkCHPmzGHfvn1Ah9usYPv09iQAqKysJD8/n4EDB/pY0B0OB6dOnaKkpITCwkJ69epFbGysTDly8eJF8vPzqaiowOl0otfriYqKIisri9jYWA4ePMhXX31FZWWlvB4ZGUl2dnanVB6DBg2ira2N9PR01qxZg8Ph4OzZs9LKBx2WwUWLFjFo0CCeffbZ//BIBnCtEVD2AggggACuIw4dOsTjjz9OQ0MDqampNDU1UV9fT3x8PMuWLSM6OtrnfpvNxpYtW/jqq6%2B4cOECjY2NuFwuevTogd1up6CggLa2Ntrb29HpdBiNRlJTU9HpdFy4cIGqqipcLpe8NnDgQKZMmcJtt93G3r172bRpEwqFgvr6emw2G/369aOqqoq8vDzpXircfG655RZiY2P5/PPPiYiIoKSkhNGjR9PS0sLZs2dJS0vj4MGDPPDAA8yePZtnnnmGsWPHcvPNN3Pu3DkKCwtl4D/AvHnz0Gg0XLx4kcOHD7N06VJKS0t57733qKysZP369ezZs4dPPvmEadOmcfHiRb766itOnz7NtGnT6NmzJ1arlWPHjjFkyBDCwsI4deoUR48eZfr06cTFxXH48GEOHDjA008/TVpaGqtXr2bDhg3MmDEDjUZDS0sLBQUFNDc3Y7fb0Wq1REVFccMNN1BeXk5%2Bfj5WqxWLxUJ7ezs2m42CggIqKyuloBsREUF9fb0UTP1JFoYMGUJFRQXV1dXY7XZef/11xo8fz4EDB5g/fz4NDQ0%2BhCRCGXa73T4KpMViYdKkSTgcDjZv3kx4eDgmkwmFQoFKpaKtrQ2n04nT6cRms6FWqwkNDWXw4MEolUpSU1PJz8%2BnsLAQg8HAgQMHaGhokO55P/zhD6msrKS6upqkpCSKioqoq6sjKCiIlJQUIiIi2LFjB0ajEaVSyenTp6VQnp2dzdGjR3E6nVLZHz58OHfddRcHDx5k0qRJJCcnY7VamTBhAm63mwULFjB79uwriue8//77WbJkCWVlZWzZsoWDBw9SUFCAzWbjhRde4NZbb8VoNPLSSy%2Bxbds2TCYT1dXV9OnTB71eT0pKCh6PhxMnTnDy5EnsdrtUKkSOSTH23hT3gkhHKE7eJCYCKpVK3icE5q6sQ13FtSUlJTFp0iTi4uK44YYbcDqdPP3005w%2BfZodO3YQGxvLxIkTqa%2BvZ8mSJSQkJDB58mTCwsL47W9/S05ODgcPHqSkpITt27eze/duVqxYgUKhkCkubrzxRk6ePMnTTz%2BNVqtl5cqVHD9%2BnBdeeIG6ujoOHz7M8OHDpaV91apVjBo1iosXL3L06FGKi4vp3bs30dHRlJeXc%2BHCBRYtWkRNTQ0rV67kxIkTvPrqq8TGxnLhwgXeeustMjMzKS0t5eLFi4SGhtKvXz8qKys5e/astPSJMUlISMDhcNDY2Cjd1T0eDzfffDMRERHSghYZGcmnn35Kfn6%2BTInR0tJCeXk50dHRhIWFccMNN3D27FkOHz7MiBEjmDlzJi%2B99BLHjx%2BXKV9aWlqwWq2SRVXELmu1WiIiIkhOTqZ///5cvHiRjRs3kpWVJfORXrhwgaamJrKzswkPDycyMpKkpCReffVVBg0axJEjR3wsgHq9ntjYWJRKJSUlJZ2u9enTB5PJxIEDB%2BRaFN%2BChoYGudFVVlZGjx49sFgs8npNTQ3l5eVyM69v376drp84cYKEhASWLFki4%2B%2Bee%2B45NmzYQFpaGhMmTGDJkiWdyL0ExPc%2BgO8XAspeAAEEEMB1wMGDB/nkk09YtWoVOp0Ou91O//79qayspLKyEq1Wi1arJS4ujqCgIGkBjI6OJjExUcYqXc%2BEy98UFRUVrF%2B/XuYia2ho8GG0s9lsPkJeV1AoFJKQwGq1SmHc33qh1Wppa2vzsQiIWKwePXowa9YsZs2ahcPhYNGiRaxfv14KWU6nk%2Bbm5ss%2BCx078UeOHGHAgAFotVpqamrYtWsXN9xwA0lJSdTX1/scu1wuNm/ezI4dO2hra5MJ0SMjI8nKymLChAno9Xpqamrkjn9tbS0bNmxg9%2B7dHDt2DKvVKseoO4urRqPBbDZjtVoxGo0kJCRw/vx5YmNjWbBgAXa7nQsXLmCz2TAajaSkpPhYEKDDMmG1Wtm9ezenTp2irKyM8vJyadE0mUwyYXtaWhrTp0%2BXcVaFhYUyd5%2B/VeNao7Kyki%2B//JKhQ4dSU1PD2bNnqaur4/z589TU1NDa2io3OS41zldjFfkmKCkp4bHHHuPixYv06dNHWiQLCwt9NgkEFAoFOp2OsLAwwsPD6dmzJ0FBQbJdqamp9OvXT45xRUUF77//PqdPn6a8vJzKykrpEmowGFAqlT6WZ28olUqMRqN8D4Xio1arCQ8PJy4uDqVSSUFBQZdJub2fF1Y9pVKJ2WwmODiYHj16yE0CoSxrNBoSEhJQKpXU19fLNCWJiYkyD6V3fG15eTk33ngjiYmJcl4yMzNxOp0oFApKSkqkxcvlcqFQKIiNjSU4OJjIyEgfC9rV4rbbbuMnP/kJf/nLX2hqapIx19A5Tq8riPdVtMlisRARESFzkp48eZKmpiYiIyNRqVQyfYbYOPC2tn1XEOkWysvLmTBhgo9bbgDfDwSUvQACCCCA64A777yT06dPf%2Btyxo4dC3SweI4aNYqamhqfWBuhJC5dulRaTKqqqmhpaZE5wQSE8GcymeS50NBQGa%2BTmJgoLR7C%2BiESSVdWVgIdgf4pKSlAh1sTwIULFyQhQHV1NXl5eVI5%2BOqrr6T1x9tK4q20qdVq1Go1DodDkruEhoZSVVWFw%2BEgKCiIsLAwGhoaaGpqkhYsj8dDbW0ter0eo9FIUFAQpaWlaLVa9Ho9er2eyspKTCaTJE0QboQ1NTU0NjYSGRlJZGQkdrudkpISOYYul0sqqCqVSibdFsQfSqVSHgsXxODgYKxWK1arlbq6OpRKJSaTSSacT09Px2azcejQIWw2G3/729%2BkUr9//37mzJlDZmYmtbW1UiAWLnEtLS2SfKUrgfBqXdyEZUNYiQUEeYZeryc%2BPh6Px0NFRQV2u52goCCsVivt7e3SmiAEW/GMULxVKpUPacflBObLKf5X2jfButjW1ibHTKvVotPp0Ol0NDc343K5iIiIICYmhsTEREkU05VV5Jtg9uzZFBQUkJqailKppLy8nNLSUhnvJdaGQqEgJCSEuLg4jhw5ItdLS0sLCQkJNDY20tTU1K1ro2BeVCgUJCYmEhwczMmTJ2lubiYpKYm4uDhOnTpFXV0der0ei8VCU1MTjY2N0gJVWloqY9ASEhI4ceIEzc3NjBkzhmHDhknLmk6nIysri9LSUoqKitDr9Xg8HoKDg2ltbSU4OJiEhARUKhX79%2B/ntttu4y9/%2BQt9%2BvSR727Pnj2pqamRbrvfBmKd%2Bo%2BNUqnstFHTHTZv3iz/zs3NBeDo0aM%2BpFffp5x6VwqDwUBkZCQAgwcPZurUqQwdOpTPPvuMxYsX89FHH13nFgZwtQgoewEEEEAA1wFOp1OyNMbHx1NaWip33SMjI7lw4cJ/DRlJAJeGoEQXcTk6nU5aJ6Ez4Ul3cYvt7e3d5sf7Nm3zTqnh346rUZREPzQaDe3t7URGRqLT6SgtLSUqKorm5maio6MpLCxEo9GgVCppb2%2Bnra1NJkb/T69p0U%2BTyYTVasVsNhMXF4fD4aC4uBi3241SqSQkJISYmBgZg3Yl%2BQf9E1AbjUZJ9OGdu8x/TL03MDQaDXa7XcYJinx23nNztfCOOfSf5%2B8jvMdPrVajUChwuVzSzdb7%2Brftr/d7IFyfvwn84wnHjh3L559/Ltk%2Bo6OjUSqVnDt3jqioKCZOnMjy5ctl/0JDQ9FoNJSVlcmYX51Ox6BBg2Tsn06nky70Wq0Wu92OSqVi3rx5vP7663g8Htrb27nppps4ceIEjY2Ncnx%2B8pOf8M4778hxCwoKwm63s27dOnr16sXSpUvJyckhNTWVwYMH89BDD/n0r6qqivvvv58f//jHna4F8N%2BPgLIXQAABBHCdMHHiRCoqKqSg1rNnTxoaGrDZbDidTkJDQ4mJiaG4uBiFQvEfY%2Bf7PsOf8EHksRKCj7BOeScsh%2B6VLn9iGeg6N91/OzIzM6murqa2tvaaJOgO4P99dKVMBXB10Ol08nsTFxeHVqulvLwclUrFvffey/Lly6UV/JZbbqGgoICysjJUKhUzZ87k/fffp6WlRcb%2BKZVKdDodR48elVboqqoqgoKCGDp0KLm5uURHRxMbG0teXh52ux2Xy0VqaioRERGcOHECtVpNSEgIQUFBFBQU4HK5SE9PZ9CgQZw5c4bVq1czbNgwGhsbfZRo4bUgvn8Wi4UdO3ZcMrVLAP%2BdCMxYAAEEEMB1woIFC3C73YSEhOB2u7lw4QINDQ04HA6Sk5N57733uPvuu%2Bnbty96vZ6srCwZv5OUlERUVBRTp06V7loJCQlMnz4dtVqN0WjEYDBIqu2IiAjUajX9%2B/cnLCwMo9FIRkYGQUFB8rdKpaJXr15kZmYya9YswsPDZWLrHj160LNnT8xmM1qtlvDwcPR6PeHh4dLK8aMf/Ui6wwUHB9O/f3%2B5s61UKklOTkatVhMUFMS7776LSqUiNDSU1NRUtmzZ4tO/jIwM6U4aGRlJYmKidEmNj49n6NChmM1m3G43FouFpKQk6TaZmZlJWloaBoMBp9NJv379SE5Olnn7kpOTycrKIikpCeig7I%2BOjkalUqHT6ejfvz%2BhoaG43W7uv/9%2B0tLSpMXtvvvuk4nqFQoFt956K/Hx8UCHy6xKpZIuUKGhoTK3lrgOHUJ1enq67J9w2RO5vgTJihCqRKySSqXCZDLJ5OpifnQ6HaNGjZJtMplMnDp1SlqdxHoQEP3W6/X89Kc/BTqE1E2bNslcaUFBQQwYMEC2LSsry6f8m2%2B%2BWZZnNBoJCwvzIVZRq9VyDQhoNBrGjBkjx0Oj0ZCRkYFKpSIuLo7k5GR69%2B7N4sWLpTVk6tSpmM1mmfB69OjRkvo9MTGRtLQ06R4oyCjMZjMvvPCCzJEm1piAdw42Ef8mxnf48OEyf1pwcDB6vR6FQkFYWJh0zdVqtahUKpky5emnn5abDEajkZCQEHr27InRaEStVpOcnMzcuXNl7JsY06SkJGJjY2Xdffv2ldcApk2bJutVKpVMmTKF%2BfPny/l68cUX5VrUaDSo1Wq0Wq10c9bpdLL9wcHB8t2DDsH917/%2BtVxz/fr145VXXkGr1eLxeBgzZgx/%2BMMfZFu82YGhw5oo0hZARw7I1NRUuX5GjBgh80fC1zkzzWazjOcU14TF0z%2BJvLBKCcVJ3C%2BsZPHx8WRnZ/uUPXXqVFnmggULUKlUhIWFodFoZD49k8kk34fhw4ej1%2BsxGAyEh4djNpt5/vnnZdtDQkJIS0vDYrGQmppKSkoKCkVHEvXIyEgZgztv3jweffRRfvCDH5CQkIBaraa8vJzz58/T3t6O0%2BmUip7Iy/fxxx/LlCZ2u523334bl8tFVFQUarWaI0eOsH//fr744gvq6%2BulEjh37lwGDx5MYWEhLpeLs2fPsn//fkJDQ7n99tt59dVXGT9%2BPFqtlh49eshvdkhICPfffz8bN25kzZo1LFy4kLfffpvVq1fT3t7OpEmT5NoSm2WC/bhv376MGDEioOh9TxGw7AUQQAABXEecOnWK9957j9OnT2Oz2YiPj%2BenP/0pw4YNk/dUVlayZ88eQkND2bZtG5s3b8ZoNHLHHXcQERHByJEjWbBgAXV1dbz%2B%2BuvU1NTw0UcfER8fT0xMDLt27eLkyZNMmzaN2tpa3nzzTebPn09raytz587FaDQSFRXFjBkzaGpqYvTo0fTv35%2BhQ4cyf/58ioqKeOKJJ1i2bBk//elPWbt2LdnZ2SQmJvL%2B%2B%2B8zefJk1q1bxzPPPEN2djazZs3i1ltvJSIigujoaDZs2EBbWxsqlYqJEyfy2muvoVAoiI6O5uLFi0CHwiDIJbqzpAkFyJsd8ZtCCPCRkZHYbDbeeustli9fzqeffkpDQ8MlLRvC8qHRaLjjjjt4%2Bumnue2223C73cTFxbFy5UpuvfVWoENRnTNnDn/%2B858li6fdbpexfa2traSmplJZWYnL5ZLENFqtlv79%2B0vShvHjx7Njxw7sdjsJCQmYTCaZRLkr5jzBCClcJ0W8nIjTHDJkCHl5eYSHh1NTU4NWq%2BXll19m7dq17Nu3TyrpZrOZ8PBwzpw5I62owpLq7caoVqsZMmSIpPJPT09HpVIxbNgwdu7cSVFREYMHD6apqYmKigpUKhXLli0jMzOT2bNnEx4ezhNPPMGMGTNYsGABwcHB7Nixg5deeomamhqeeuoprFYrH3zwAfX19fz85z8nNzeXOXPm8OGHHxIVFcWRI0dISEigqKiIpKQkuYHi7ULq77KnVqtlPrPXXnuNtrY2QkND6dmzJ0ePHsVgMOByuQgODqalpQWbzcbw4cOpqamhpKQEo9HIgw8%2BSEhICG%2B%2B%2BaaMTxOpPzweDw0NDTz66KNcvHiRLVu2oFAocDqd3HjjjbhcLoqLiykuLu60vrqa0/b2dmmBu/XWW0lPT%2Bdvf/sb7e3tDBgwgKqqKlpbW2lsbMRgMNC3b1/OnTvXZUL6K3lHRJqN6Oho3G63zCl4JW6v0KGEejwezGazdG3WarVycyAmJoaioiKam5tJTk4mOzubmpoaioqKmDx5MqWlpWzatAmn04lOp0OpVBIaGorFYuHYsWNkZmZy/vx5bDYbSqWS2NhYyTisUqkYPHgwDQ0NVFRU0NDQQFRUFCaTiQsXLmAwGBg8eDDl5eXSuhYVFUV8fDzTpk3jL3/5C6Wlpdx0003ccccdvP3228THx0tSJehQjATDq06nIzo6mgEDBjB%2B/HhUKhWnTp2iuroam80mr/fu3RuPx9PtNaEMu91umpqaJCuw/4bKtcKECRP4n//5H0aPHk12djabNm3CYrGwbds2fv/735Oens6yZcv47LPPmDhx4jWvP4D/PALKXgABBBDAd4yDBw%2BSnZ2NWq2W%2BfXOnDlDXV0dTU1NtLS0SBKL4OBgwsLCSExMpFevXsTExFBfX8%2B2bdvo0aMHTzzxBNChEL711ltUV1d3SpDc1bXm5mZKS0slAQh0MCdu2bKFyMhIZsyYIe9bv349hYWFjBo1ip07d5KUlIRarWbWrFl89tln7Ny5U1773e9%2BR15eHrt27ZJt80dubi5r164lPT2d8vJyjhw5Il2ThOWkpaWFcePGMWDAAABJFFJfX09zczMDBw68pGAUEhKC1WqVx06nk9raWjm2KSkpREVFSYVLWDyam5spLi6WlOzCFfTChQu89tprHD16lOjoaPR6PaWlpVL4FkyAwvLV3NwsFUKRZ08IxyI3nxCmbTabTKadmZlJTU0NKSkpvPXWWz5z98Ybb/Dee%2B%2BxY8cOUlJSfITEAQMGMHLkSGlZEXA6nbz88susX79eMn5qtVqsVqsPQYo/dDodLpdLKnbwdWyY6JeguhfuxRaLBYPBQG1trewTdCiCBoMBq9WKXq9n8uTJzJ8/X1pAS0tLeeyxxygpKSE2NhaHw8HgwYNxOp1UV1eTm5uL0Whk2bJl7N27l3vuuQe3281DDz3E8ePHsVgsst/V1dWXVEKEwqrT6WQsmEqlQq1Wy3WQnJxMRUUFtbW1tLe3Ex0dTVxcHGazmQcffBCHw0HPnj1ZsWKFzOH4xBNP0NzczN69ezl37hy9evUiODiYTz75hMjISFpbW3n88ccpKipi3759nDp1Co/Hw%2B9%2B9zsA1q5dy86dO/npT39KdXU1J0%2Be5OTJk8THxxMcHExNTQ1HjhzBaDTS2Ngoafu7I9IRllC3201zczMREREkJiby8MMPk5GRIV0Ca2trqauro6GhgSFDhqDX62lpaSE3N5fy8nKp1NbU1Mj3sampiS%2B//JJ58%2BZRXV19Vc%2B3tbVRXl7OL3/5S5/vz9///ndyc3OlIhQdHc2KFSsICgoiPj4eg8FASEgIGzdupL6%2BvlNKjCuNQRWbTEIhS01NRaPRsH79eurr64mIiODOO%2B%2BU7aqsrOTEiROMGTOGwsJC5s%2BfT3p6Ok8%2B%2BSTBwcE8//zz7Nu3j169enH69GlJUOS/5gQBTnJyMgkJCYSGhtLU1ITT6cRut8t1KYijmpubJZtsS0sLDofDh3hJbBZ9G/ds73hHs9lMfHw8Z8%2BeZcGCBdx3332UlZVx55134na7JfmSiHEN4PuFgLIXQAABBPAd4plnnmHDhg2MHz%2Be06dPc/78%2Basuw2AwcPfdd/Pzn/9cumEJvPDCC1RXV/PQQw9x8OBB7rnnHvbs2cOoUaNYtmwZn376KU6nkx/84AfMnj2bvLw8/v3vf3Px4kUpOE6fPp0bb7xRuuw4nU42btzIkCFD2LJlC%2B%2B%2B%2By6bN28mISGB5uZm9u/fj0ajobS0lNraWmbMmMHChQtZunSpT9tsNhvt7e188cUXbNy4kd///vfk5OTgcDiw2Wzs2rWLsWPHYrVapXAh3EtLSkowm80MGjQIj8dDTEwMFouFuro6Dh48yPbt2ykuLmbw4MGMHTuWkJAQmW%2BvvLycvLw8du7cyezZs5k5cyZGo5HnnnuOe%2B%2B9l3PnzqFUKlm7di2TJk3ixhtvxGKx8O6775Kdnc1nn31GdXU1TqeThx56iLKyMqqrqzl8%2BLBM7H3hwgXS09Npbm6WOcJ69uwpFUy73U54eLi06mm1WuLj4zGbzURHR5OQkMC4ceM6KWvdIScnh/fff1/Gc0KH8DZkyBCUSiUpKSn88Ic/RKvVyrjP/Px83nnnHRQKBQ8//DArVqzAZDKxaNEiGhsb2bNnDwUFBVJpPn/%2BPOnp6SQkJNC7d2%2BCg4M7WR/y8vK44YYbyMvL48iRI3z00UdkZGRIN7jIyEji4%2BPR6/WdngX46KOPGDt2LEajkb179/Kb3/yGIUOGsGvXLkaOHMm5c%2BfIz89HrVbzwx/%2BkI0bNzJu3Di%2B%2Buor%2Bvbty6FDh6Sba3JysnRZu/322zGbzbS1tREcHExsbKy0jBQXF/PFF1/gdDo5ePAgarWapqYmOY7%2BFhqz2UxFRQVRUVHXzI2ttbWVTz75BIBx48ZhMplYsWIF69ato7m5mb59%2B/Kzn/2M9vZ2GhsbZVJtb9hsNk6dOsW%2BffuIiorCZrNJN3ChNIh%2BGAwGwsLCqK%2Bv5/z582i1WvLy8rDZbNhsNiIjI7nrrrtwOByEhYVRUFDAvn37WL16NTExMdIV9sSJE8THx0sX4ry8PPr160d7ezuzZs1i3bp1kl1369at6PV64uLiyMjIQKPRcPr0aZKTk6Ur7969e2XKia5iS3U6He3t7RiNRtasWUNaWhrnz58nPz%2Bf6upqtmzZQq9evTCZTMTExBAbG0t0dDTx8fEolUoaGxupqalh%2B/btTJ06lejoaMkSLPDcc88xf/586Wb99ttvM2PGDGw2m0%2BeU4fDwYgRI3jttdfIz89n48aNPknrvfF9jnsMCgqSLM0OhwOlUsmMGTOYMWMG6enp17l1AXwTBJS9AAIIIIDvEM888wzQISRfvHiRtrY2yYwmCEacTqeM92lqaupUhqCOF8K0QHNzs8z9JCCUESF89OrVi4KCgk6pDvxZIEV82Jw5c3j33Xdl4mOBgQMHUlVVRVNTU7dU6Vcq8HSXLuD7ApE0PCcnh/379zNt2jRuuOEGZsyYIRWRt956i5kzZ9LU1ER8fDxLly4lKyuL/v37U1JSws6dO5kwYQInTpygoqKC5uZmGRs0btw4cnNzGT9%2BPJ988gmxsbHs27fvisZLr9ezbt06pk2bhtvtlu5nf/jDH3j22WeBjg0Ch8PBxx9/zMmTJ1GpVKSnp5Ofn8%2B4ceMYOHAgx48f59///jcajYbhw4eTnJzMAw88wOjRo/nggw/43//9X/bu3cvZs2f51a9%2BRVhYGKdPnyYnJ4clS5bI2LI777yTd955R8ZZDRw4kJycHEpLSxkxYgQZGRksX76c2bNns2rVKu67776rmguTyYRGo%2BGhhx7C5XKRkZHBnj17uPXWWzGZTDidTjZs2MCHH35IQkICJSUl7NmzB6VSyebNm%2BW7AbBlyxYZP3XmzBmSkpJISkrCYrGwadMmtm7dyqpVq1i%2BfDnQEQso3HpFXGBeXh4VFRVcvHiRiooK6urqsFqt7N%2B/X1oVn332WT766CO%2B%2BuorAGmJ7m6d2Ww2DAYDFotFpl/o27cvqamp3HTTTTz77LMMGzaMO%2B%2B8kwMHDvDZZ5/R0NDQiTHyauD/LiuVSoKCgmhsbLzsvYCMZa2oqMBiscj4PLHJpNPpcDqdndqn0%2Bl4%2BOGH2bp1KykpKfz1r3/1WUNi/Zw4cULGMz766KP069ePuro6duzYwc6dO1EoFEyaNIlFixaRm5vL3r17pevxI488wptvvsmGDRsYNmwYCxculHNw7zz6o24AACAASURBVL33snHjRn7xi1/wt7/9jbKysm7HSK1W07t3b2nRFOMEHXHGwlVXEJ94f6eDg4N9vvUiZYV4X8PDw6V3gCgzKiqK1tZWme8wOjqa5uZmmd9QbIo5nU7pxititxsaGmSsolarpaioqNt%2BLV68mAkTJnR7PYD/fgSUvQACCCCA64BBgwZhs9mklUN8ileuXMmMGTNIS0ujoKBAuhG%2B//773HfffZ2owdVqNSaTCZVKJXPgXa1rz7x587jjjjuYO3fuJf/pAzIHnTdEDJE3kpKSKCoquqp2iNx9AiEhIWg0GknMsXPnThkzpFarSU1NpaCgAICEhATKysqkC1VSUhIlJSVARy4skZ9MQFgOtVqtTHkg4grb29t9%2BhkaGsrw4cP517/%2BBXQIruJ%2BvV5PSEgIlZWVPukTvNMd6PV6WltbO7EddiUU%2B9PKe1%2BPiYmR8W7Cha2rsfd%2BTqvVShfFb4vk5GQuXrwoSRveeecdHn30UTIzMzl27Bi33HILO3fu7PSccFft378/eXl5bN26lYSEBAAGDBjAkCFD2L17NydPniQjI4O///3vPPLII0yZMoWcnJzLtisiIoK6urpvvFkg3Gy92wuXT39xqc0MQRRztbFyBoOBmTNnsnz5cp9UHKI93u5%2BdrsdnU4nU3xcbT3CDdjhcMjYvIiICGpra31i8oRF0Puc/7sKkJKSwrlz5zrVYzQaaW5uxmw2U1tbK8u7EpfL3//%2B9ygUCp577jlUKhWPPfYYr732Gvfccw979%2B7l/PnzXb4D1xJdtVOM1/Tp0/nggw/keZPJJNMjiDZpNBr0er1U5gTBj2BhFq7cer1enjObzTKGFzrWqGBpFhC5DMU94n%2BJdwyvUKrFPeJYuGcLV2aRLkace%2BSRR3j//fdpa2sjJyeHtLS0azGUAVwnBGh1AggggAC%2BY/zrX/%2BSMTYiLqytrY22tjZJXnLy5EmfGK/a2lofgWPatGmoVCqpSBgMBnr06EGPHj2IjY2V7HWRkZE%2BbnMCYnc4JCSEt99%2Bm7vuuksqemFhYWRlZWEwGHzuBTox5sHXyo83hKLXHaGAIFuJiIhgzZo1PPTQQ1J4FO1NT09n9%2B7d7Nq1ixdffNGn//Pnz5cCukKh4JlnnvGxCtTU1PgoX0ajUbKWAlJpnjRpkoxZEXULQVpg4cKFPhbU9vZ2OTcOh4P6%2Bnp5HnyVBG9rmhD%2BLpVA3D8/m3B3g68ZDb0tspcTcoVCKtgjBYTLGnw9v95064KR8he/%2BIUUTouLi4mLi6NXr160tbXxk5/8BJfLxdGjR4mMjOTLL78EOhRxnU4nGQ89Hg82m439%2B/fjdDplsmrBTnj06FEZu%2BaNvXv3%2Bqw3MXfezKIAdXV1Unn0hojFgw4riOiTP0QS8WeeeYaEhAQsFgtRUVGSRdZ7Daempso2Ceu7N8S93tYUwbraFYQyLmCz2diyZYtcS6IN/qybdrsdi8Uihfiu3kHv9og5TklJkeWIDQtRr0ajkWtZMJ4KK5BWq/V5v0JCQnzmRqvV8s4773Sq3%2B1288ADD0iPBUDGeIo6BGJiYnyOPR4Pzz77LM888wwOh4PW1lYWLVqE2%2B1m9erVPi7w/vMg2ibG3nv963Q6VCoV9913n3ynukNERESX76nYPFm/fj3Q8T6J96StrU0yWno8HlJSUrDb7VKRSkpK8hk7/5yBQrmMiIiQxw6HQzIri3sFK7K4R7gfe8%2B5Xq%2BX8ZvCU0B8DwRjq/hGiffFaDSyatUqSfC0fft2PvzwQ/kTwPcPAWUvgAACCOA7xtChQ%2BnVq5c8FmQYLpeLvXv3dnJpNJvNPPHEEz6B/zk5OXLHvK2tjVWrVnH77bfz61//mg8//JDPP/8cg8HAmjVryM/PR6FQsGHDhk654/r27YvT6ZSCmCBYePLJJ6XCY7FYZL2CPdMbdrudm266yeecEKxEP0QZQuAWaSA0Gg1r1qxh7dq1qNVqmT4BkFY7AW/rS1ZWlqQtV6vVjBgxwmfM2tra5O61iN0RMUHw9U63IFYRwpsQysSxSqXipptukvFV4tyKFStQKBQMGDDAR%2BEKCgqSNO%2BxsbGMHTvWZzde0PN7C%2BaChMYbGo2mk6vXkiVLpCAo%2BuEff%2BQPt9strYreSrtQrL3HRKTNEEnTAbKzs6Vy63a7qa6u5k9/%2BpMkdhD9EKkqxPgA/OpXv/Jpx%2BTJk1EoFKxfv56XX36ZV155BbVazfLly5k2bVqntgvrj4D4e/bs2T73%2BVuyRf1iEwXgr3/9aycXQe%2By7XY7L7/8MqWlpVRXV0t2VG/FGuCNN96Qc/LGG2906a4IXyu88PXGgkajkYqq2OgwmUw%2B73VERIRUuER57e3tkjBJzIVCoZC/NRqNJHTyh//4iZir5uZmKcyLMfR28xSWPAF/K2NZWZnPeupuU8fhcPDnP/8Zm80m3Q3F%2BHlbPQXZkzd0Oh2LFy/m4YcfJiwsDK1Wy6xZs2RKBKEsio0ygbCwMBYvXgx8rQRqtVr%2Bz//5PwDyfRwzZgxxcXFdtlugtrZWKshiQ8gbYv7r6upwOp2SdEX0FToYl51Op1ynZ8%2Be9bGKCqtea2urvKelpYWysjJ5bLfbKS0t9dkoampqory8XN7T2Njo403hdrtlzKK4p7a2lsbGRux2u3TDF3PS3t6Oy%2BWSrqCiPStWrOCNN97gjTfe4C9/%2BcslxyuA/04ElL0AAggggO8Y4eHhfPDBB5KtUiSzFT/%2BbG4lJSWd3DcTExN9LEVjxoxh9erVzJ8/n2HDhjFp0iScTie7du2SwuO7777rI/AqFArp2uhthTMajT4CtL/bpnjWG/7WCxHYL%2B4TZXgnLH/uueeora3l2LFjbNy4kZiYGDwej4xz80fPnj3l35WVlbIvAwYM6KRAxcTESGr9rKwswsPDO8UcKRQKjhw5Qu/evSURgxC%2Bxb29e/cmJCTERwCPjIxk2LBheDweH6FYq9Vit9tl2oOKigqOHz/uIzQLwfxyLoJKpbIT%2Bc7Fixc7Pbdw4cJLlgNI1kZveFsM/cv0dgsUSrPon7AUejwe6U4q0kB4W7U0Gg2DBg3yqWvixIl4PB4qKirYunUr//jHP6S7Y1cICwvrZCVVKBQ%2BufsEhMsufK2YeFt7pk%2Bf3qmflzv2h1CGhRIXHBzcSYEU72lbW5scR/Hb5XJJV7nIyEisVqvPWhDKlyhDxE4CUnkwm82SSbOxsVFakLqDGAORY%2B/o0aOyr7169ZIKm38qBZGv0u12S6u49zsu8vF5j11X7dDpdNx///2EhIRIy93cuXPlNQGxWeUNpVJJfX09q1evlh4QAwcOlMqQ2Pzx9zZobm6W4%2Bbtui022ESi8Icffpi8vDyfZ4U3g8Btt93Gxx9/DCDTRsybN%2B9bpUDoKm5SuG9%2BWwgLnvi%2Bib%2B9v8XeFkQx/yIGc%2BzYsYwZM4aePXvK%2B1UqFW%2B%2B%2BSaff/4527dvvybtDOC7xZXRfgUQQAABBHDNMXfuXO655x4%2B%2BOAD8vLysNvtREVFSYHmhRdeQKFQ8PzzzxMREcHjjz%2BOx%2BNBp9Mxf/58oqKieOCBB3C5XD5uUkqlkjNnzgDwu9/9jueeew7oIJzwhsfj4dixY/I4LS2Ns2fPMnLkSF577TV5/kpIHUQ8m4CgIveHKKu9vR2z2YxGo%2BG3v/0tSUlJ3H777axYsYKIiAhqampITk72eXb69On88Y9/BOA3v/mNFC5/9KMf%2BcTCib5Bh4A9c%2BZM8vPzWbVqlSxL5JwrKSnhN7/5DaWlpbz33ns4HA4fS9zkyZPl/QIjR46Uf8%2BbN4%2BFCxfK8hwOh4814YUXXmDBggU%2BMXOpqakcP34c6JirEydO%2BPRT5IVLTEz0GcO7776703i%2B/vrrnc51pcT4C5NdEYB0BW%2B3MoVCQWpqKrt375ZtByQbp3fKifT0dLkZ4S00Qse4PP/88z5Jur2xadMmoEO49o9H83g8koijO6xZs4aZM2eSmJhIYWEhAPfccw%2BrV6/u8n7h7vb2229z%2BvRp/vSnP9HS0iJJOvytqf7WMu/x9o5HUyqV7NixwyfhvXC3a2ho8LGoQ8e74W3x8Xg8zJgxg6NHj/KPf/wDgLi4OBoaGsjIyODo0aNkZWWxf/9%2BQkJCpFIv1qrH45FzIOoRlhyxqRIVFUVFRQXh4eHSUu7tRihc/ARplEBoaCjl5eXy2Ol08sgjj3QaV4VCwZYtW3C73UydOpW//vWv9OnTB4PBwFNPPcUf//hHmUbAuzxR5gsvvOAzvmKDzPt97CrPpHA3FCk/oCPFB3RstjU1NaFUKjvFsvqvt5tuuolDhw75xN6uXLlStkmtVssNEBHHFxQUxJo1a0hISJBzKpiXnU4nLpdL5g0U6XVEHseGhgZ5Xa/XYzabiYmJITMz87JW/G8KYb1ft26dtGJarVb%2B%2BMc/smHDBqqqqpg6dSppaWmd/ocE8P1AgKAlgAACCOA6obq6mhdffJFPPvnkmjBR/vOf/%2BTtt9/miy%2B%2B6CQAGQwGH9IAASGsAJ1i1QS6Ige4WnRVdu/evTl79mwnRe1KIawRRqMRt9vdbXJxtVqNQqHo8np3BCaiPeHh4ZL%2BXqBPnz4EBQWxf/9%2Bnz7o9XpJey%2BUo82bNzN58mTJtqpWq1mwYAGvvPJKt/0SpBn%2BpDLXgs5dzLdCoaBHjx4MHz6cDRs2yLECfCw8t9xyC1988YU8joiIoKGhweee2NhYysrKfNq7aNEizpw5wzvvvINarcbtdjNz5kxWr16NTqeTqTuysrJYtmwZqampGI1G%2Bvbt24nI5nJITU2VSh10kPNcuHABs9ks48O84U9qkpCQQI8ePVi5ciWPPfaYzPso7vOO/XzllVf4zW9%2Bg91u54UXXuC3v/2tTxv9WRUnTpzI1q1bga/n78YbbyQtLY1169bJNSnG0BtjxoxBp9P5bKSIMoQi2qNHD8rLy6UL5qWSnXsnl78cqcmVJk2/Gog2djWvd999N0eOHOHUqVM%2B55cuXUpMTAx2u50///nPREREsG3bNkJDQ7slvwkODpYMwaKfwkW8ubmZ6OhoKisrvxU7qT%2B66lNcXBwREREcOXKkE9nQ5MmTKSoqkrGWarWa22%2B/nYKCAk6dOkVpaSkqlUrmCbXZbPI75vF45KaKqFu4VXfVJ/%2B2ec%2Bt%2BFvEKhsMBtxuNw6Hg6amJtxuN2FhYdxwww3s2bOHZcuWMXTo0GsyZgF8dwgoewEEEEAA1wGHDh3iwQcf7FIB6QrDhg1j//798vhKBBWdTkdsbOw3yuXnja6Y964nhNXE3wVOq9USFBQkXdtEPIu4rlQqUavVqFQqWlpaumTCFD/egq4QlrwFI8GC5y00ixQSWq2WpKQkCgoKvpUS21W/vfsjWBmFpUmj0TB06FBuvvlmAF5%2B%2BWWZBP37BJHw/Be/%2BAWvvvoqNpuNpKQkGbOkVCp9rIaifwkJCTIJ9dVAqVTy8MMP8957710TBce7Td6bKQIiHYrBYJBt9VdYv2m96enpPtZg4Xo7e/ZsLly4wIEDB%2BTGg4gLbWlpwWAwSIu0YKkV7/2YMWP45JNPul3L3u6Bbrebp556igcffJDNmzdLq7e452rXYmhoqPzuLV%2B%2BnNdff71bZtlrsRnyfceV/F%2B4EoXQe93ee%2B%2B9LFy4kEWLFrF//35JShPA9wcBN84AAggggOuAl19%2BWQqW0dHRWK1WKcQIS4j3P2ThlinQ1T90ERslyDScTqdU9ARzW1JSEiaTicjISLKysggNDeVnP/vZJdvqr%2BilpaXhdDql25jD4SAhIYEjR450W8bVCmIjRozAbrdz8eJFucMsLIsqlQqj0YhSqZQENUL56k55FsQWwmIEvgpgWloaU6dOJTc3F6vVSkxMDG63mx07drBixQomT54s58u7Dm/riPjb4XBIcpkr6XNaWppkG0xNTaW8vJz6%2BnpplfKOuxEKrlAsRRzRnDlzmDNnDnv37sXhcDBs2DCys7NZuXIlW7duJSIigqqqqi5d1y6HruZOpVJhMBikJcDf2vdNIJhA3W43LpcLq9Uq3U9FTkrwtTx6t8s7bk/Q2W/bto2mpiZef/11EhMTueuuu3j55ZdJSEggNzeXhoYGnnvuOaxWK7GxsdhsNiwWCxqNBpvNJusqKysjIiLiknnWumqTv6IHX8eReSulXSl6v/71r/nyyy958skn6dOnDwC5ubnU1NQwatQoiouL%2BdWvfuUTh%2BfvOi3emffee0%2BeO3bsGBqNhttvv51Zs2Zxzz33cOTIEe677z7uuOMOfvnLX2KxWHjrrbcwGo3MnDmT%2B%2B67j927dzNw4EAZ1/b0009zxx13yFi7WbNmERUVxfHjx3E6nfz73/9m8eLFmEwm9u/f7/Os6PO%2BffvYtm1btwqKIH6y2Wx88MEHXa5dtVpNnz59yMjIoLCwkEOHDnVZ1rVASkoKra2tVFZW4vF4pDVfzHnfvn05fvx4p1yJ/u9Qd14Ul0JXSpq/hbarcfSvq6t7%2BvfvT35%2Bvjz2JhWaNWsW0OEKLdyJA/h%2BIWDZCyCAAAK4DsjOzqa1tZXQ0FDJACmILex2u3RFE%2Bhux1ZQZwM%2B1g7Bvij%2ByZvNZrRarSynvb0dp9OJ3W6XObdMJhNZWVk0NTVRUFAgFYmkpCROnz7N0KFDaWpq4tSpU7KcP/3pTxQWFjJ79mweffRRTp48KV39hLIyduxYWltbKSoqki5nN910E%2BHh4Rw4cICysjLS0tJoaGiQKSa8%2BxsTE0N9fb1ULv3/bd1%2B%2B%2B0cPHiQhoYGkv5vfj2hFKnVah588EHef//9LvORKRQKBg4cSH5%2BficLhKjL3zUvIyODgoICNBoNERERNDU10draikKhkNTr3c2bd/uTk5OprKzEbrdLUhrhLiri3wTRxJkzZ6RbYXdrQbRToVDQt29fTpw40Yk4Iz09naeeeoo9e/awcuVKFAoFb775Jlu3buXTTz/FaDRisVjo1asXx44do7i4mIiICFQqFfX19TI2MTg4mNTUVHJzcy/bz5CQEBkjJZQnf1ZYQLrXdQch2Iq8eiaTiQceeIClS5f63OftjirixrzrFe6Offv25dixY6SkpNCzZ0927NhBUFCQtPr6j53FYqG2tpbQ0FDGjRuH0Whk7969pKamcvLkSZn%2Bobm5mYqKCmpqaiQLo4DIY3c1EIrZxx9/jMfjIS4ujilTphAeHs6LL74olZ0xY8bw%2Buuvo1QqZQqWgwcP%2BsyNNzFHV4roleDRRx9l1apVV21BBVixYgVpaWk88sgjFBQUSNfD5ORkCgsLfdaNQqEgODiYp556irVr13L69GnJXOx2u0lKSiI7O5vNmzfjdDoJCgpi/PjxfPrpp8yePZuoqCgOHDjAhx9%2BSP/%2B/bnhhhs4dOgQJ0%2BeZMqUKWRlZfH8889jNBp58sknGTFiBCaTibFjx/LXv/6VuXPnsn79elpaWlAoFDQ1NdHc3MxLL70kN5jWrVvHPffcg81mk9/fb%2Boe6v9tu5ZuppeDv9L44osvsnDhQvR6PZs2bSIhIYGcnBzeeOONAEnL9xABZS%2BAAAII4DpgwoQJFBUV0a9fP06cOCFZ9lwul6S8vlRcjf/OMfgKC8JC4i1gf1P4C4tiR1sQjSiVSqZNm8bWrVtpa2u76h1r/7rAl90vKCiIlJQUVCqVJEsQ9whoNBpCQ0Oprq5Go9FgNpslg%2BauXbu44447ZBoCAUGokJGRQV1dnSSoMJvNndw8MzIyOHv2rBxLQZs/ZcoUTpw4wdmzZ6mqquLw4cPMnTuXXbt24XA42LPn/2PvzeNsrP////vZz5kz%2B75YxgxmDGHseyHLyFJRyZqKCm%2BlepeleIsISe%2BSNbxFCxoplETEiDCEMBhkmWEGM5p9OWfO74/5vV5dZ5mFer/V53set5vbOHOuc12v63Vd15nn4/V8Ph%2BPH2VfpslkIikpSWY4jEajy8C/Zs2a5OXlkZ2djbe3N/Pnz%2Bepp56y28ZsNtOsWTP27NnD/fff7yRbr5zPJ554gi1btsiSzqeeegqNRiNFeMaOHcuCBQvkZwwGA7Vr1%2BbSpUt2WYvbwbhx49iwYYP05xsyZAhJSUky0zxt2jSmTp1KQkIC3377rbSZcCwZ7tq1K99//71U/RSBd1RUFOfOnZO/8/Lykn1MyudAr9cTFhbGxYsXnUi7Ekaj0c5%2BpLCwEL1eT3FxscxGNWrUiDFjxrBkyRL27dt323OiPKcffvjBKXsvskLLli1j5MiRd7z/24ErQhEXF%2BckGlSd91xBKdQkFiCUypBVEc74%2BHhOnjwpfUm//fZbevbsKa/v888/z4IFCzAajVLYxGQysXPnTgYMGIDRaCQ1NRUfHx9atGjBrl27ZP9lr1692LJlC7Vq1eLSpUt28yEWVoRwSkUKuhVl6FyV7/5R/Df6KCuCOH%2BTycTkyZO5evUqq1atYvz48QwZMuR/MgY3/jy4yZ4bbrjhxl1AYmIir7/%2BOlarFW9vb7uGe7j93hZH3Gn/SlxcHDdu3CArK6vSYOW%2B%2B%2B5j165ddscSfUciU6iEK3J6O%2B87no%2BSCPfu3ZstW7bI9yMiIsjKypK9SZs3b6Z3795MmTKFN954QyomKvcN5Sp9WVlZkjCAveHxiBEjZDmcSlVualxaWopOp2PevHmMHTuWMWPGsHLlSkkqH3roIb788ksZUDdu3Niu7A5g5MiRLFu2zO58qyrzMhqNzJ07l3/84x9OAXvNmjW5cuUK7777Li%2B99BJffvkljz/%2BOM8%2B%2BywrV64kLy/vT5N6rwwqlYrIyEhJ8IQlRWlpKVFRUZw/f5569eqRmppa4b2qDMQFOa/oWBXt47777uOHH36QPWUCVWUSoVxIY9OmTbf9LFV1P1eGZs2acfjwYbt73PEauxJ0mTRpEjNnzgSq//wHBQWRlZVVbRIhyp6re27imYLfvSPFAoKjUI4Sjll18dpRtKh27dqMHj2aoKAgJk6cSGZmJjabjb1799KhQwenTLty3126dOH777%2Bv1nlUNsb/yxDqsUFBQXTs2JGpU6fe7SG5cQdwkz033HDDjbuEmJiYSt8Xq8PKrJVA8%2BbNOXnyJCEhIVy/fp2QkBDKysrsSj8rglqtpnHjxvz888/yd2LVuFWrVhw6dEgatruCq0DWbDZTXFxsF1gJLzFBWpRljiJQioiIcGnUXhk0Gg21atWSJMLDw0Oa0lc2XqWFQEV/%2Bry9vWV5qyMqG2uNGjW4cuUK4eHhdsIwQm1PzEv37t3Ztm2b3Wc/%2BeQTBg0aVL2TrwYE6d6yZYtUAXzvvfe4fPkyKSkpUthFnKMgF/B7yaWjzYDjfCkDdb1eL3si/5u45557OH78uMvFBDFWm81GZGQk4eHh/Pjjj5Xur0%2BfPmzatKnSbZo3b05ycnKF90yHDh04cuQIhYWFlJWV2c2lI8aNG8f69eudLAaqQkxMDHl5eVU%2BJ%2BKadOjQgby8PLvnuzL8EWL6Z6OiigUovweV5F8gKiqKCRMmMGbMGFleGRgY6OQPajab5fdPUVERQ4cOZf/%2B/bK/9o/Mg0qlonPnznTu3JlZs2ZRXFwsnwez2UxgYCCenp5cvHhRHstRTVS5CKVWq6lVq5bd97nynIKDgwkKCuKee%2B5h7969lJaW4unpSXx8PMePHycoKEiW6nbq1Ildu3aRm5uLh4cHnTt3JjU1lR9//FEaqDtWgIi/B4Jg%2B/j4oFKp3CWcf1O4yZ4bbrjhxv8Q6enpUupe%2BD9VhDVr1qDRaPjmm28wm810796d3377TfZLqdVq0tPTZfBYmaJaRdBqtdhsNqZPn86kSZMkGRFBrqv9OqKi9x2PL0o0dTodDRs2/K8KKfwR3OmKvYeHB/7%2B/ty4ceMPlbKCfWbv2WefZfHixdX%2BnBBhEQGbMD%2BvqP/Hy8tL3ouuxIEc4VhqqdPpMBqNaDQa%2BXthHJ6Zmen0eZPJRGBgINeuXZNiQsqsXZs2bTh69ChFRUW0atWK06dPSwPx4uJiGjVqJLOjIkB3zNYKqFQqXnvtNaZPn%2B703ocffujkDVddqNVqbDYbs2bNYsKECdX6zEMPPcTevXvtbB2U4/yj4ZiYi/DwcPR6fbUWfoQ1iXIsjtmzO4E4P5HBFajqPB3JnEajwWAwSNsYV5m6O4Gr8tWmTZty9OhRu/F17NiRQ4cO2S0udO/eHbVaLS0x1Go1r776KomJiSxevJiePXuSlJQke2B79epV6VgEgVOSucDAwGq/f6fo0qULs2fPpmXLlnZl3EpcvHiRXbt2SZEWKC/7duPvBfXdHoAbbrjhxv9L6NKlC3v37uWnn37iP//5T6X/VCoVJpOJjIwMfH19sVqtmEwm6tSpw5UrV0hOTrbLEphMJsxms3ytDIpMJhMBAQFO4xEZGREMJyUlAdj15TgGZyEhIUB55k6j0fDJJ584GX6bzWanTI8gEcXFxRUSvRYtWjj9rkOHDuj1ejp37kyXLl144IEHiI2NtdvGx8eH%2BvXrExAQQJMmTaT4iEqlYvr06ahUKtnXJuZI2BaYTCY6depEaGgocXFxfP/99/Kzffr0kRYKwtQ4Li7OzlhboKCggCtXrtw20Wvfvr1UUhVQZik9PT2rva%2BioiJJmry8vIDyzEfDhg0xGo1yO3F%2BXbt2lURPpVKxceNGu3MT3l6CqEN5FlOtVssxC%2BVMR0PwmzdvolarMRgMaLVa%2BvTpw4svvsigQYN4%2BOGHee6555g7dy7bt29n/fr1tGzZEr1eT0ZGBiNGjMBms3H69GkGDBiAzWbDYrGg1Wo5fvy4HGP9%2BvWBcrNwYfotsqliHK6IHsDKlSurPa%2BA3TWKiYmRPaSVQTlvX331lcv5rOyz0dHRHDx4kL1799pZHPz444%2BoVCp69OghtxdZqfT09GoRPbGvd999F5VKJYP4VatWSZEnRwQGBkqi0a5dO/n7Bx54gFGjRskxCvKoJHpQdXm6H8iu2QAAIABJREFUY9bOarVSUFAghW5cPXdKMiXEefz8/IByAuwKou9Y2E1AuTqo4/hOnDjhlEWeNm0a//73v%2BVYTCYTjRs35vLly5w5c4agoCB8fHwYMGBAlUQP7OdU%2Bf/qvn%2BnyMrKIiwsDIDdu3ezfv16Vq9ezc6dO3n//fdZunQpaWlp5Obm8vnnn7No0SJOnDjxpxzbjf8t3Jk9N9xww43/IdLS0ggPD0elUlVZlrV48WJ%2B%2BOEH6taty/Lly2nVqhVWq5UGDRpw77338v3333Pz5k2ysrKIiIjg7NmzdqvVGo2Gxx57jCeffJJp06axZ88eNBoNjRo1koIkjsRE9AlVtrovZO3vvfdefvjhB5o0aUJWVhaXL1%2BudgZw4cKFjB07lqSkJNq3b8/evXvtfvr4%2BEjy8OGHHzJ27Fg6derE/v37nUQ2XnjhBQwGAzdv3mTNmjWUlJQQGhoqifA//vEP3nvvvUoFDpQr/WIO1Go1c%2BfO5ZdffmHlypVy/P/617%2BYNm2a3E4ExbNnz2bChAm89dZbjB8/HignWlevXpXzLDJ2ynmKj4%2BXGQVl%2BafjWP/bPUIqlYrVq1fz6quvyntTKYoiju3v709%2Bfj5Wq9WuJFcJnU6HwWCgoKDA6Z709vbGYDDQunVrJ3P5zz77jGnTphEeHs6VK1fu%2BFwcxY0c%2B/28vLzw9/eXZXV/1nHee%2B89nn/%2BeTkfjtL8QjhJiL9UBSUpFIbkKpWKCRMmMHPmTLvj%2B/j4kJ%2Bfz4QJE%2BjQoQMJCQnAH%2B//VUKUjSuvaVRUFOPGjWPp0qWcOnXKKTOWlJTE0qVLad26NQaDASh/Jnfu3Imvry%2Benp5cuHCBCRMmcOjQIbZv316lEqWSzIs5Vp6ruN5K8aJmzZpx7tw56tevj8Vi4dixY3bPWFBQkF2pPJQvtJSUlNiVdwYFBVFSUsJvv/0mr4efnx%2B3bt2Sixu%2Bvr5/%2BZLHfv368dxzzxEQEMCoUaPo2LEjO3bskDYvRUVFDBgwgK1bt2K1Wrn33nvRarVS2MmNvw/cZM8NN9xw4y5i48aNFb6Xm5vL0qVLuXXrFnXq1EGr1ZKVlUW7du1o0aIFixcvJj09HYvFQp06dQgLC2Pv3r3y867k7QG7Fem4uDiOHz9ercDTFUTw6ih%2BURUEQQgLC5Ny/hkZGbJU6XaJjYeHBzqdzq7f7Hbg4eFBrVq1yMzMdOqluV04jv3P6olSBvZms5mAgACsVqskZq%2B%2B%2Bio//vgjBw8erDC7KEprKzKQv1OIfd2uVLyHh4fsBxJeg45k2NVnHnnkEfr168eVK1f44osv2LlzZ4Xbu%2Bp5vV306NGDtLQ0UlJSKi0jbNGiBYcOHar2/Io%2B1tDQUElu/1fCH%2BLZvd1jiRJWrVYry3BdQWTNlSRbp9PJ7KaYR61WS0hICGazmVOnTsn5MhgMBAYGUlRURE5OToU9uVBOykS/rWOvXnXhat7ffPNNtm/fbnd/KYlm27ZtiY%2BPZ%2B3atWRlZREeHs7TTz%2BNyWTioYceuqNx/K%2BwatUqPvroI7y8vBgwYABDhgyhQYMGqNVqNm/eTJ8%2BfbDZbDz33HMUFhaye/duLl%2B%2BXO1eUDf%2BOnCTPTfccMONu4guXbrYvbZardy8eROtVkvjxo2ZM2cOkyZNsiNxdwq9Xm8XoIleneoG6CIYElknR08sscJdUlJCXl6elLCvan9eXl4YjcY7Csb79OnDAw88wDvvvENqaipDhw7l1VdfZezYsezZs4eAgAB69erF0aNH%2Bfnnn2nRogUzZszgww8/ZOfOnYSEhJCQkMDBgwfZv38/wcHBaDQa0tLSqFmzphR1OHnyJI0aNSI7O5uCggKuXr0qSZyPj4/0x3PMyKlUKnx9fWU/mXLOHG0m9Ho9KpUKLy8vIiIiOHr0KFqtVgqSKINjm81GUFAQZWVlXL9%2BnSeffJLAwEDWr1%2BPn58fhw8fRqvVEhwcLH0ADQYDixYt4u233%2BbkyZN/qqCKMgAW8%2BEoiPNnkJh//vOf9OnTh2PHjrFx40Z%2B%2Buknu95XT09P8vLy7Mr9HPv5HMegzCJpNBo0Gg0lJSVoNBr8/PxkGWGbNm3Iz8/nyJEjtzXmP%2BO8RemnuGYqlQoPDw9KSkokCVL2XgqI3jkvLy9UKhUFBQVYrVYaN25MQUEBZ8%2BedTqWRqORVhRK705B3AwGgywldyXiZDQaKSsr%2B9NEXzQaDY8%2B%2Bijnz5/n2rVrZGdny/2r1WoWLVokS0onT54sSw5Hjx5NRESEXYbY09OToqKiP9T3p%2BxFFt8Bwnuybdu2shxWlJIqvRWzs7OpW7cuqampLt8X2cBHH32UdevWyWOK12lpaVy6dImgoCBefvllPvjgA3lPjBs3rsKyVVew2WyMGzeObdu20blzZ9q0acOsWbPw8vKiVq1anDhxApVKxbFjx8jIyKBnz55ERUVVKWrkxl8PbrLnhhtuuPEXQ35%2BPlOmTCE6OpqgoCBOnjzJ3r17uXjx4t9C7vvhhx8mJyeHUaNGodPpePjhh1m1ahXDhg2T21TlGWUymVi8eDFLlizhp59%2Bwmq1SpID5aS4Iul2T09PCgsLmT59Oq%2B//rqU%2BxcZhY0bNzJ16tQKDcGrQkREBPXr1%2BfQoUNMnjyZpUuX0rx5c7Zt28bDDz/M559/Lsf53nvv8Y9//IOSkhK0Wi21atVi9OjRXLp0iU2bNsnAqVOnTuTn59OwYUMAkpOT/zSfLiUJ2LBhA8XFxTzxxBOVZnMDAgLIzc1lxYoVDB061Omeq0yUR5hh3655OJRbCqjVahn0xsbG3lHG8N5772X37t2o1WoSEhL47rvvnARR7r33Xlq1asW7774LlN8HQmQnJycHq9XK%2BPHjeeutt8jPz6dGjRrExsayf/9%2Bl%2BJKZrMZi8VyW1lyjUZDcHAwYWFhHDt2TGZda9SogdVqJTMz80/3a3OE47XUaDQ0aNCAs2fPotVqyc/Pt8sCCqKpzMB6enqSk5ODwWBwMpKv6FhiEUNpfG80GrFYLE7n7Onpyf79%2B5k4caJ8ZrRaLS1btiQ9PV0q3MbHx9OyZUvGjRvHjBkzOHr0KMHBwSQkJJCUlMT58%2BelBUjnzp0pKSnhwoULXL58mfbt22Oz2di9ezfh4eFkZWXh7%2B8vhYSqO5dioUGn06FWq5k8eTJvv/02xcXFlJSUMHLkSJYvXy7LJZXvl5aWYrVaiYuL4%2BzZs5Jsi9fiObBYLAQEBFBaWipte4KCgpg9e7ZdL6UrZGVl8dBDD5GdnU1gYCDp6emYTCZsNhuFhYV2ZbGenp488sgj7Ny5k19//ZWVK1fStm3bas2FG38duMmeG2644cZfEL/%2B%2BiuPPfaY7FtzNIQWq8vipxAmUKvVBAcHk5WVJVUZRcmV2WwmJycHjUYjFRT9/f05d%2B7cbY3tTkr2hDWBGGO3bt3YsWOHlPcWQeCTTz6JTqdj2bJl0qNOlHnC71kwg8GASqWiqKgIvV6PxWJx6g2rV68eKSkp6PV6ysrKpMeX8jwq%2BhMoglZxTCW0Wi0TJ05k%2BvTp9O/fn8TEREwmE1arlaioKC5evMi8efMYN24cNpvNjpjOnz%2BfXr16kZqaSv/%2B/Tl69ChQ3pc4d%2B5cOT/guvxWZFiEtHtlf8IdVTOhnJB4enpy48YNAgICmDNnDrNnz%2Bb69euSnDVs2BCdTseFCxcoLS112bt5O4sOer2eRx99lA0bNsiSPo1Gg16vJzo6ml9%2B%2BUWWjk2cOBGVSiUzj/8tO4c5c%2BbQpUsXEhISuH79ukup%2B4pgNpvx9/cnLS2Ntm3byqz7%2BPHjMZvNXLlyhaNHj1K7dm1u3rzJjz/%2B%2BIfOwzGD/uCDD7J582buu%2B8%2Bdu7cSd26dTl9%2BjSBgYFcv36dsLAwMjMz8fT0JDc3V5ZZV9QPqnyvZcuWHD582Km8MzQ0lGvXrlU4Rsesok6nw2Kx0L59ew4fPkxBQYGTOq%2B4j8vKyggNDZVZaqPRiM1mo6ioCLPZLL/DKlsg0mq1jBs3jhUrVnDr1i2nXkq1Wo3JZMJgMEiCJASrSktLycnJISAggIEDB7Jw4ULefvttHnjgAYqKivjggw/4/PPPqyzvdrxOgPxuU/oZajQamWEsKipyel/0G6pUKkJCQmTvsVqtJjw8nLS0NLvvzLCwMDtF5u7du/Ovf/0Lf39/l%2BMsKChgyZIl6HQ6QkNDWbduHefOnaN58%2Bbs3bsXg8FAREQEZ86cISwsDL1eT3p6OqNGjWLcuHGVzoEbf024yZ4bbrjhxl8QW7duZfLkyQB2pVJCKn3u3Lm89dZbzJ49m4MHD/LBBx/g7e1NQEAAGRkZWK1WCgsL0el0t1VO5RgkabVaGXwphTo8PDyoU6eOXG3%2Bo9mHpk2bcuzYMYxGI35%2BfqSlpUlyU6NGDby8vMjJyeHKlSvYbDYeeughvvjiC9RqNQMGDGDLli12pKRevXqyRE2j0RAdHc2ZM2cqPL7IMjgGbD4%2BPk6ERxmAKgmVIHTh4eHk5eWRk5NjF%2BD6%2BvpiMpnYtm0bEyZM4Pr166xevRooN8TesGGDSwJ1p9lcZX%2Be%2BLyyf3Ds2LH07t2bXbt2MWfOHMrKyvD29pZBu/KYjmOozpieeuopli9fTkBAAPPmzWPGjBmkpaXJ0t6wsDCZDbJYLIwZM4ZZs2ZV%2B3z1ej0xMTGcPHkSX19fWrduza%2B//srFixfJz8%2BXGVtvb2/y8vIoKyujR48efPvtt8THxxMUFMR3331XqSjOfwPLli3j8uXLrFy5Ui6AKM/XlXm8SqXCaDQSGBjIb7/9Rl5enktzc%2BU5KEsMjUYjbdu2lQIcYiFBzLXyudfr9QQEBHDjxg1KS0vtbEAERAZXq9USGBjIfffdx4EDB6RnnXieROm48HJTLmAoXwvRH7EYI%2BbA1bEfe%2Bwx3njjDQoLC3nooYek3yZAt27d%2BO6775zm3GQyYTKZ/lA/rnieRKmwWJQR301iEaoyo/iaNWty%2BfJl%2BbpGjRpS2Eq8L%2B4JQYbNZjNqtVqWRnt6eqLT6cjNzZWLXKJk%2B9q1a3IckydP5pFHHqnWuaWkpLBixQrOnTvHrVu3yM/Pp7S0FJvNRnh4OE8%2B%2BSQPP/zwHc%2BdG3cXbrLnhhtuuHEXMXToULv%2BIovFQn5%2BPmfPniUkJITi4mIaNmzIP//5T%2BbOncuePXvsAsPGjRuTm5tLnz59WLhwIdu2bSMiIoKrV6%2BSkJDgJHZRWSAtAivhyxYcHExAQACpqalYLBa7fiHHfSj71sQ2QtZcKFaWlpbKLJtWq7UL4lwp4VWG2y29rAoikIuJiZFWCyJoqqwsrSq4ClZ1Oh3e3t506dKFs2fPcuLECSfxidsleGI%2BxE/Hz1cWhN4tiB5PtVrNjRs3UKvVBAUFUadOHXx8fNi2bRthYWHcvHnTaezC4F6v1xMeHs7NmzclSRVzoNfrsdlslJaWSrNzQc6V2ZCKoMxW6fV6goKCuHHjRrXnUa1W4%2BnpSatWrdixYwcajYaXX36Z/Px8FixY4FLBVJiCV2fu4PaUNh0Xcv4IRKXBnyU%2BJIRqHHvhHNGpUye8vb25fv06J0%2BepLi4mJdffpmZM2cC5WW/KSkpTt9H/w2IjPXatWspLS21W2CqCkpSLmwuxPk69mcK/Bkl/Gazmf79%2B2Oz2ahdu7a0sGnZsqXcprS0lLlz5/LJJ5/IMWg0Gvr06cO0adNuqy/Qjb8G3D57brjhhht3Ea1bt5Z/aJOTk0lOTiYlJQWr1Up6ejo3b95k9%2B7d9OvXj927d8s/9qGhoWi1Wk6fPs3Fixd58MEH8fPzIyMjAyj3hwoICGD%2B/PkyK2AwGPj444/lsdVqNYGBgdLHTUn0tFott27d4sSJE7J/yMPDQyrxKT3bxKozIEVGBg4cyLPPPsvQoUPRaDTMmTMHo9FIr169ZAAOv3uX%2Bfr6VjhHjRo1QqPRMHDgQBlo9OzZU77v4eHB7NmzZQBsMBjYsGGD3WuRpVPiwQcflIHV448/DkBqairwexDdpEkTXnvtNVQqlTz27Nmz0el06HQ6mWl1hFj1r1%2B/PhqNhs6dOwPlAVu9evWoV68eiYmJ/Pzzz2g0Grp06cILL7wgP6uU3K9Zs2aFcyNKtRyJr8gUCLgiKEajkZCQEHkthRhMZVCr1UyaNAmNRiN7F2vWrOnS%2B0ucg4%2BPD02bNuXxxx/n5ZdfZuzYsXLennnmGV566SUmTJjAkCFDKC0t5dixY2zfvh2bzUZ6erocu9JDUiiQlpSU8Ouvv5KbmyvPV8yFUmBEXFeRha1sTgWUZXABAQFyv1qt1uU1FwgPD6dBgwbYbDZycnLkuVgsFmbPns3ChQtdBu0ik6JEgwYN7HwDBQSRrep6we%2B%2BmKLUWwkfHx80Gg21atWS16t27dou96NSqQgNDUWtVhMWFmanyhkREcGoUaMwmUwuvfD69evH448/Lvv9oPz%2BEPPYsmVL%2BvbtK7c3Go2EhYXZPbMGg4Hg4GB8fHxo3749CxYswGq10qZNG3lM4es3ZswYpzE0aNAAQM6ZRqPB19dXjsfT0xOj0Yivr68cV7169Zw8Edu3by/nKTo6mrfeegugQqIn5l8JJZGz2Wx2xFaUfjvidomeq%2Budn5/PRx99xOrVq5kxYwZDhgxhyJAhtG/fnmbNmhEXF0ejRo346KOPgPJ7OT4%2Bnp49e7Jjxw7mz59/W2Nw468BN9lzww033LiLGDt2LCqVijNnzuDv7y8DC%2BFHJeAY1F2/fp06derInpdLly7RuXNnPvjgAzIyMpg7dy4ZGRm8/fbb6HQ6bDYbZrOZ5557DigPBBYvXkzDhg3tykSFUmdJSYnsJzl37pxU8lMG0gJKoiGyYD/99BOrVq1ixYoVWK1WXnrpJYqKivj6669Rq9U0bdoUKPe%2BAmfzZWXQOHr0aPR6PX369JHH/frrr%2BW2K1eu5MEHH7RTufT29rYrx1y9erVTadzGjRtlD6AQRnnzzTel2TqUm8tPnz7dLiBbs2YNpaWlUkxBQFw7nU4n5%2BT111/HYDCwf/9%2BjEYjRqMRnU7H0aNHZZBZVFTE999/z7Jly%2BR8Kvd7%2BfJlunTpwrfffusUSDuWpYmsXlhYmF1wqNVq7YI/Hx8fgoODadOmjSRTFovFST117ty5dscsKyuTZCUzM5OLFy%2BycuVKvvzyS7ldYGAgs2fPZtasWQQFBVFYWMjx48f56quveOedd4iMjMTPz4/Q0FDee%2B89WrZsyYgRI5g8eTKFhYWYzWaXwa4oa3aEXq%2BXxMNkMrk0oXf0Zjx48KDdayESIqBSqYiMjJR9XhkZGbJETgiI6HQ6oqOjnYLqFi1a2JXq%2Bfv722XilOfm%2BJxDedYSygnPv//9b8rKynj22WdRqVTy%2BRVEVHm9xDMliIpYeBFKpMoMs2Pv57Vr1%2BT2jt6DomRTr9eTnZ2NwWBgypQpsv9NpVIxY8YMXnrpJUlCBYSn6PDhwxk1apTsMRNjEHNRUFDAoEGD5OdMJhP5%2Bfl06NBB/s7Dw4PLly%2Bzdu1a5s%2Bfz/Dhw7FarfTt21ceUzyjItOnhCBj4jwjIiIIDQ2V3wtC3fS3336TPb5ZWVlOCymiRzM3N5dp06bx0ksvOR1Lr9fTqlUrdDodGRkZsiTdYDDYfbeZzWYp5CLeVy6kAU69d8qFMZER9fb2trteY8eOJSgoCLPZzKBBgwgJCWH48OEkJydz%2BvRphg4dSnBwMO3ataNmzZrcunULq9VKcHCwnH8fHx9iY2P59ddf%2Beabb9xKnH9juMs43XDDDTfuIsrKymjVqpUkXI5KdQLz58%2BXRt1QnjFS9qD5%2B/vTpUsXNm/eTHFxsey5ad26NceOHZP7Ugq6aDQaateuzfjx43nppZcoLCyUQi7FxcUUFRWxYsUKXnzxRT755BMpptG/f///xdRUCmUZlFqtRqPRVGqirdfr5fuV/dnz8fGRohZQTtz0en2FBvNVoUOHDuzfv/8P9TQuWrRIWnTEx8djs9n47LPP6N%2B/f4UiLUo5d8d7CeD48eMkJiYyZ84cJxIsoFKpSEhIsCPWYWFhREZGsm/fPvk7g8FAbGysFJvx8PCQ2eSbN29KAZ2AgACuX7%2BOyWTCYrHQsGFDUlJS2Lx5s8y0xcbGVnh9XJXuRkdHk5GRgZeXl%2Bwx%2BzMgSuuUIjGOIkmuyupUKhU9evRg69atduNzZVEAcOrUKRo2bCjLTktKSuxUKnv27MnXX39N586d2blzp/zpagxr167lsccekyW7wj5BmZFU3odVlQXebqn0pk2bePDBB10S9dstQRTPr/LeDQoKIjg4mBMnTsg%2BttzcXJcln%2BL7UYiaXLt2jbi4OI4dO2ZXnhseHk56errLMXh5eaHX651UZas6F4PBIEtNlVD29wrl1/z8fLkvR0ElLy8v8vPz5TUQ/bTKYwtfUoF27doxffp0atSoQdOmTbHZbGzZsoXevXuzefNmatSoAZR/L/Xv3586deoA5ZnyDz74gJYtW3Lo0CHGjBlDeHg4UP59mZKSwvnz5zl06NBtW4%2B4cfeh%2Bde//vWvuz0IN9xww43/VzFz5kwOHTqERqNh0aJFbN68GcCJHAwYMMBuVXXmzJl8%2B%2B23MhAoLCzk5MmT8nMigE9PT7cLgMX2NpuNsrIysrKy%2BOabb%2BQ2ZWVltGvXjgsXLkghlK%2B%2B%2BoqBAwfi5%2BfHnDlzOHfuHG3btrXzr3LEsGHDOHbsGOHh4eTm5jJr1iz27NkjA8HHH3%2BcX3755Y7nTRnwiHNRwjHor0h4wzFTVlxcbLfvsrIyu315enoSEBBgF7zHxcXJwM5xf5cuXapWwKxSqahdu7ZdcCdw4sQJUlJSOHr0KMnJyVIp8dSpU3Tr1k2WKCqhJHeuiKbZbGbNmjVVGlCfP3/ebj4KCwulGqByX5cuXZKvRdZTZJKCg4NlkCp6PwXhy87OJisri%2B7du6NSqXj//ffp2rUrFy5ccJpLVwF2dna29HVUio40a9aM8ePHEx0dzeHDhwG4//77CQkJwcPDo1q2EI5E2rEUVmQTBUT2pkmTJpw8eVKOLyYmBoPB4JRdhPKM47Fjx%2BTxVCqV3aKEyEYJldDK1EJv3bolr5f4p7yXbrfH1VFZsip8%2Bumn1SZ0Ve1TzIHy3i0oKODmzZvyvAIDAyktLSUkJMSpP09kvEUpbVlZmSxxVxJD5XMsFHXF2ESWUnndxcKSmMu2bduSk5NDVFQUAQEB%2BPn5cf36dbsFlLp163Lr1i35TLZp04YrV67Isl3l%2B2q1Gp1Ox8SJEzlw4ABQTtInTJjATz/9hIeHBxERERgMBt5%2B%2B2327duHXq/Hw8ODd955hxdeeEFm%2BU6fPi3Fd/R6PWfPnsVsNpOamsratWs5efIku3btYvfu3SQnJ1NSUsKVK1dQq9UkJydz6NAhkpOTOXjwIOPGjWPevHm0b9%2Be3r17V3l93fhrwZ3Zc8MNN9y4i2jVqhVRUVFkZGTQsWNHPv/8c6xW6x0JDHTq1Indu3ff1mdEH5oyAFKulD/00EOkp6cTHR3N9evXOXDgALm5uXh7e0tfKKGIeCd/Tsxms8w%2BOBq1VwcGg4HZs2cTHBzM8OHD6d27N15eXnh5ebF06VJGjhwpy57mzp0rXzdo0IBnnnmGr776ipo1a7JgwQLef/99uV8RyD/%2B%2BOMMGjSIevXqER8fL7evLANVFRwVP3fs2EHv3r1p1qwZSUlJt7UvkaUQq/5BQUFkZmbe9piUPmpNmjRhzZo1tG7dmpEjR/Lvf/8bKC9fFf2lzZo1o6ysjMLCQu655x47k/aqxENGjBjB%2BPHjMRgMNG7cWJYXDxs2jJiYGN577z3GjRvH5MmTmTt3rlPWRpmhGjx4MA0bNmTy5MnyeP379%2BeJJ56gfv36ADJzNmHCBCIiImRZsZLEV%2Be%2BE/5sKpWKkpISjEYjVqv1T8smAjKbCOXloAAHDhwgPj6eI0eOSIXTOxUo8vLyoqioSD4Hs2bNklly8QykpKTYZcaVvZL5%2BflotVrZ7yuyYk8//TTBwcHMnTtXlpH269ePxMREEhISMJvN1K9fnzfffJOnn35aPpPz5s0Dyr05rVYrGzZsoHnz5nh4eLBv3z4%2B/fRTGjVqxMSJE/niiy8ApBqwxWKxuzfEeRgMBgwGg1xsEOMVEOXoAQEB%2BPv72/XaiYqHkpISl%2Bqs4ntPEMCnn36aHTt2EBUVxU8//URQUBCtWrXiiy%2B%2B4LXXXsNms/HYY4%2Bxbt06bDYbERERdOzYkaSkJLloony/W7dusvRXWaItXmdlZZGZmYm/vz8xMTGcO3dO3rd169a1I9FvvfUWq1at%2BkNCVqK8/cyZM6hUKrZu3SrLjN34%2B6DiDmM33HDDDTf%2B6zAajTz99NO8//77JCYmyuCiKqLnqOBmMpn4%2BeefZQDq4eHBb7/9RkBAALdu3ZJ9TKI8U4hMCKNyIdEOvxMwQAZYP/30kzwuwKuvvsrUqVOxWCxERERw4sQJACnwogwwRA%2BKoyqlXq%2B3C5bVajX33Xcf33//PYMHD8bX15ft27eTnZ1Nu3btiImJ4Z133mHkyJF4eXlx4cIFvvjiC44fPy6FLMaMGcPp06dZt26dNKXu1q2b9OqLiYmhoKCATz/9lMLCQpYtW0ZCQgILFiyQY%2B3QoQNt27Zl9uzZxMbG4u3tLa/L2bNnWbNmDb169WLbtm1s2LCB%2BvXr2xFBKBd2Wb58uexP6tWrl8vXAQEBLF26lIKCgtsieoKQC6VT0ZeZmZlpV6ZmNBrlyr9OpyMtLc1l8CfKLoWZer9%2B/SgsLGTPnj1ym5dffllmOoX1BJSXhCoFS7p3705mZiZnzpwhPz%2BfsWPH8vDDDxMUFETr1q3ZuXMngwYNYvTo0ajVakaOHEliYiLDhg0DkL2MTZs2tQteBcER9iIAffv2pWnTprRu3ZoePXpgsVhITEwkMTFR3tuCxM6ePdvJX64S2TLpAAAgAElEQVROnTpcu3aNgoICO4Lj5eVFcXEx9evXx2AwcOTIERo0aCB77A4fPoyfn5%2Bd95zZbKaoqMjpGE2bNsXX19eu/LIiKInjgQMHpFjPu%2B%2B%2BS%2BfOnenZsyerVq0C4J577uH48ePy/waDgQMHDkjiGhERQXR0NLt372bo0KGsW7eOYcOGsXTpUgoLC2nTpg2AnJ8XX3xRHluZURPWKvfccw8///wza9euZdGiRfz4449ym88%2B%2Bwy1Wi2zuuJ3UG4jo9frqVWrlrw%2BYpHJYrEQEhIi732bzcbly5fld5MQaJk1axY//PADOTk58v5zhJh3QcZElvXDDz/Ey8tLbnfPPfegVqvJysqS/YzKfYj9uKoGcMzOfvjhhwDS/iEvL4%2BLFy9iMplYuXIlKpWKTp06ceDAASwWCxkZGTRp0oRFixYRHByMSqWiW7duJCcnU1xczLRp0wBISkqS/Yri3p0wYYLdgsT3339vl2UbN24cw4YN45tvvqGoqIjk5GQiIiJIT0%2BXPZeFhYVyH6I3tkaNGqxdu5YOHTrQuXNntm7dSnx8vLR1EJYPixcvdhO9vyncmT033HDDjbuIzZs388EHH/DKK69gsVjYs2cPFy5c4MSJE7LkDJDeTjk5OVIxzhVE6U6bNm3Ytm0bXbt25fvvv2fIkCHs27ePnJyc28783K7/mNg%2BODiYvLw8nn76aRYtWnRHGZBWrVrJcqY33niDWbNm0atXL7744guXpOW/Jbn%2B8ccfM2LECFl6JbJLwcHBrF%2B/noSEBDuyJ8jfkiVL%2BO2339i%2BfTuvvfYagwcPBsoDTpPJVOlYAwMDpcBFdnY2ly5dcmlRUVFWSgR4f9RyQcxpRcdxtAx45JFHuP/%2B%2B2nRogXNmzfn448/llmqxo0bU1paKnssTSYTy5Yt45lnnuHw4cPExMTw4Ycf8vTTT/P5558zYMCAOxqzK6%2B66rwH5b1Tnp6epKWlERISglarJS0tje%2B%2B%2B05e35iYmDu2MjCZTERHR0si26hRI/Lz89m5cyf5%2BflSsCM%2BPp4TJ0781y0EBJTPuU6nkxlMlUqFl5cXDRs2ZP/%2B/URGRnLlyhWCgoLs%2Bt3E/eHv709OTo4dYXTMRHbv3p158%2BbRunVrl89NzZo1nRZQBGJiYjAajWzevJkePXrI3zdv3pwDBw5gNpvtxi5sYNq3b0/NmjX5/PPPycvL45FHHqFVq1aMHz8eT09P4uLiOH78uFN/K5Rn1iwWCzVq1JAlulCeTVOWUZtMJie7m/Xr19v53QUHB3P9%2BnW7/mwhtCWQmJho1xtdnf2aTCY2bdpEv3795HZfffUVffr0kf16oo%2BvqKiIzp07s3v3brp160ZpaSk7duyQfamRkZFkZGRQVFTEAw88wPPPP0%2BtWrWc5sWNvwfcZM8NN9xw4y5CWQ5YWSmZSqWSZWlarVYGxBs2bLDbrlGjRlitViIjIzl//jx%2Bfn5kZ2fLn2azmfz8fLy9veXKrUajsRMgUY5DZPscidWdGFBrNBo8PT1RqVTExMRQq1YtNmzYgMlkolGjRjz88MNs2LCBo0ePUlpaKoNFrVZLixYtUKvV7Nu3D41Gg4eHB2azWXqlKbMcgDxfcT5xcXGkpqbKLKYoi/L39yc7O9vO46uq84uOjiYxMZFmzZpJ%2BfiioiJiY2Pp27cvTz75JE2aNEGn00nza4vFglarpXnz5hw%2BfLhaJMFRUEPA39%2BfW7duUVZWRu3atbl48aKd0IRS1EKv1%2BPp6Unz5s3ZvXu3zHjYbDa7OerZsydTpkwhICCANWvWMGfOHIqLixk8eDAvvPACPXr0IDs7WypeKgPili1b0rBhQyIjI5k5cyZff/21HSlatGgRycnJJCYmyuMJv0W9Xs%2BqVat47LHHOHXqFDExMfTt25evvvqKyMhIlz1qVZUw/lE/Mh8fH9RqNdnZ2dKyJD8/n8TERDZs2EBSUhLnzp274/1XBbVaTcuWLTly5AjBwcGyN1aIeuTl5dGwYUOuXr1KVFQUR44coXnz5vzyyy9oNBoKCgro27cvW7dutbtOWq0WX19fbt68yb333svp06cxGo1cuHBBZoobNGhASkoKNpuNGjVqcOXKFbp3707r1q2ZMWOGy3kNCQnh1q1bdl6cSq9DUSXg4%2BNDYWEhtWrVIicnhy5durBhwwYmTZpEy5YtqVWrFm3atOHLL7%2B0I3vXrl0jJSVFHm/GjBnStmPhwoUAdgsw8Pvi2O3eC3FxcZw8eVJmHt9//33GjBmDXq/nueeek2WnAnPnzuWVV16p9BiOpMxoNNr1Bju%2Brs5nKtpm8%2BbNduqkEyZMkCJMjmM0mUwYjUYiIiKq7J9WlsGeOnWq0m3d%2BOvBTfbccMMNN%2B4Cdu/ezaZNm7h06RI///wz4Gx8rQxUxB97ka3p3r07u3fvluWVAg0aNKCsrMylmfftQKvVSh85%2BN1SQa/XYzQaiYyMJDc3187frCJVu%2BpCnK8oXRPlZYLsHT58mOLiYurUqcPnn39Oz549uX79Oj4%2BPoSEhJCamipJwAMPPMDXX38t58/Ly4vmzZuza9cuVCoVgYGBhIWFkZqaapflEavuPj4%2B5OTk4OnpySuvvMK//vUvGUiqVCqGDBnC6tWr5ZhVKhUNGjTgypUrREVFyWsKSILtCsogbuHChYwZM6bK4FSlUuHp6Ulubq4kfkLG/pVXXuGpp55i0qRJJCYmAr%2BreR46dIjBgwc7EdnVq1fTqlUr%2BbpPnz7k5uYSEBDAkiVLOH/%2BPEOHDgXKPcYsFovdfefh4UGbNm3o0qUL33zzDXPmzMFsNrNp0yZef/11p/FrNBratm3Lvn37MJlMDBkyhCVLltC1a1e2b9/ukswp51mlUkkDeY1Gw8yZM2nWrBlPPPEE6enpbN%2B%2BnR49elBaWopWq63SsN6RVN5///2Eh4fz0Ucf4evri16vJzMzUxIwtVotCUxERAS3bt2iqKiIunXrcubMGad9165dWy4q/PLLL5WK4gjJe1EeKlRkxfVu06YNhw8flos0VqtVLiTYbDaZnXKcL9HjJghRWVkZTz/9NLt27eLkyZNysUD81Ol0GI1GcnNzCQkJISMjw25xQBw7ICCAhQsXMnz4cGbNmsX48ePvKKOsVD9dvnw5bdu2lWSvW7dut03eQ0JCZAWDTqdj//79PPXUU7etJOnYO3cnCwl3i%2ByJbLvyWXdc1HKE47Mnzrdjx46MGDECnU5n913hxt8Dbp89N9xww427gDp16nDx4kW7DIFjcORKCVAERampqbz55ptO%2BzUajZhMJqZOnfqHxic81/Lz82UfTmlpKfn5%2BWRlZZGSkkJOTg5%2Bfn74%2BfkRFxfH/fffz3PPPSeDgSZNmgDlAZOQ43eE0qNM9DeJHiGlafOBAwcoKSnBx8eHuLg4Vq9eLUlOXl4e58%2BftwtSHD3pCgoKZPBns9m4ceMGFy5ccCrn8/HxwWazSa%2BtnJwcXnvtNbsAKSYmho0bN/LII49gs9nQaDRMnDhRyrsfPXpU9rA9%2B%2ByzHDx4UJrHK6FWq%2B0COCU5Fe8rIUoabTYbeXl56HQ6Xn/9dTtTdFHWNnLkSPk5Pz8/Vq5cKT3aHDOWQ4cOJSYmRv47c%2BYMV69eJS8vj7lz50qzdygXalAqb4q53blzJ2%2B88Qb79u2jY8eOxMfHS6InvMAErFYrSUlJWK1WioqKWLJkiVQXBeRihfAdmzhxIiEhIYSGhjJ16lRGjx5NQkICL7zwAn379mXFihU8%2BOCDBAUFAdCjRw98fHzo2LGjS6KnnFdPT0%2Bn52779u3SVPrWrVvyvjl48KCdKMioUaNIS0vjkUceISoqiqZNm6LX6%2B2udUZGBj/88AMHDx5k165ddkRP6bEnfBALCwsl0Rs6dKg0IhdZ/ZMnT1KvXj2sViudO3fGYrFIZVOr1cqpU6ck8ROIjY2VvWh5eXkUFRVRUlLCwoUL5bUUCzVXr14lMjKS0tJS2fcp%2BiM1Gg0Gg0GSOUD2ghqNRho1aiSPWd3yVkFGbTYbpaWlREVFMWrUKI4cOUJERAQlJSV07NgRDw8PWrduzZIlS%2BS8GY1Gtm/fLkvdfXx8GDVqFFBuESJIrlarZc2aNbK31dvbm%2BHDh7v0OHSEo4%2BlI9Fz9Vz/GXD1Xen4u6oUTS0WC56enjRr1gwPDw%2BaNWtGeHh4heetJLJ6vZ46deowfPhw/P39uXjxIklJSW6i9zeFO7PnhhtuuHEXce3aNfr160dubi716tWTQbbJZGL9%2BvUsW7aMyZMnS0%2B0tm3bcvDgQWl4frtf4Y5ZneDgYDIzM%2BnVqxdFRUUkJSURFRVlVzYFVa9oe3p6YjAYWLduHYcOHWLixIlSDbNdu3b8%2BOOPdp8XqnZxcXH/p3ybXHmZCbLjKgCuaF5jYmI4ffq03e9cZRkaN27M8ePHJdFt3rw5n3zyCbt27eKZZ56pdKzBwcE0btxYvr569apLVdXKetOUHoeO4xNo0KABZ86csbvvRObUaDRKg3LRD%2BmYeRAm9WVlZezdu5du3brZlfo5qjL%2BWfDy8pLS/Gq1GqvVapf5uPfee/nhhx9YuXIlY8aMobi4mLp16zpdN0fUqFGDkSNHcv78eSm2As6ZfVf%2BcXcLarUaLy8vl/2DQUFBskywol5ItVqNn58fN2/exMPDg%2BLiYtRqtVR7hPJFg3PnzmE0Gqlfvz5HjhyR31dBQUEMGzaM5cuXU1BQINVQRSaroKAAvV5PeHg4v/766x19N06aNInz589LsZmysjJiY2M5f/68vA6O%2B63onv%2BjuFO11cr2ZTabadSoEW%2B88Qa9e/dGrVbz1Vdf0bdvXyZNmsQHH3xAXl4eFouFZ599lgMHDnDgwAGMRiPNmjXj2LFjJCUlVYsku/HXgpvsueGGG27cRTz33HNYLBbeeustioqKuHHjBllZWbz77rvYbDYaNWqEp6cnW7ZsoX79%2BjRt2pQrV65gs9n4%2BuuviY%2BPlyVoubm5MljU6/WoVKpKS6nq1avHtWvXZA/btWvX0Ol0vP3227zwwguo1WqCg4O5ceOGXdBZnUBKpVJJY3cozwAUFRXJAGbRokW89957nD171qnUyPEY4v8iq9CxY0euX7/Ozz//LFe3hUExlAfpVqvVLvBU9rRVZY4dERGBzWYjPT0dtVqNXq%2BXJbGTJ09m//797NixQ25vNpuJiYnh%2BPHj%2BPj42GVvunbtyt69e6WYhShNhd/7iqqCK9EZQSKHDBnCp59%2BKslYjRo1UKvVdtk3QUDF8YSXF5SrSoos5KBBg2jYsCFHjhyxs1IQEEHjG2%2B8wZQpU%2BzGYjKZKgz0hf%2BYUshiwYIF/OMf/2DevHlER0czYMAAO5GXivDNN9/Qv39/KdzRpEkTysrKKiVFysC5OvdudHQ0586dcyLuWq3WznuvY8eO7Nmzh5EjR7Js2bJK96lEWFiY7DVV7nvKlClyXlUqFREREXZelkp7DFd49NFH8fDw4D//%2BU%2BVY1CWabqCI9E0mUyUlJRUq09X3E/KuWvevDnt2rVj4cKFsjpB9IQKsakNGzZI8R7l/PTs2ZN///vfdOnShYKCAicFzcrQtWtXFixYgEqlYvLkyXz99dcUFhbK8wsMDCQ/P58nnniCgoICvvrqK7v9d%2B/enW3btlX7eHcb4vtQuajn6elJYWGhzGqL0t%2BKoFarMRgM1K9fnyeeeILmzZvz4Ycf8tFHH%2BHh4fF/anHu/xW4rRfccMMNN%2B4i9u/fz4svvkj37t1dBv7KLMGNGzek3LkIWqdNm8by5cvlNl9%2B%2BSUAnTt3xmw28%2BWXX8o/%2Bo6ZgwsXLmCxWPDw8JDqcBaLhXHjxgHl5XSu%2BvAcg01XAbQolRQoKCjA19eXW7duAeUERhCohx56CJvNJnvMHI8h/h8VFUVaWhopKSmytEz0%2BCnPy5UxufI8HOXTHTOOGRkZcs7Kysrsxjls2DDefvttu88vXbqUZ555BqvVire3tzxvlUrFjBkzyMjIYMSIEfI4U6dOZe7cuUyZMoW3337bTpkPnFf1H3jgASfDaput3DRblBsKuDK6F4GduL/KysrkfCkzKwBz5swhISGBxx9/3O73er0etVpNUVGRHdGD8oD63LlzUn4%2BNjaWTp06sWzZMmw2Gzdv3nQyMRfKgsHBwaxcuVKWBZrNZnmPKCF6UEUm7OrVq3z00UdOfak1a9bk8uXLAFK%2Bf926dfTr108aUc%2BYMYPY2Fh27drlJDYDyNJqi8WCSqUiODiYjIwMKQKkUqmwWCxERkayZ88ePv/8cxlcL1myxCmj6ng9BZFRPjcGg4FFixbJbWw2m9O1dCRajln6y5cvc/DgQfnaVe9caGgo165dQ6VSuVSdFHBc3ElISGDTpk1OAiwCgjxptVopaKNE9%2B7dOXXqlPy8kP0PCAggPT2dX3/9lbS0NMxmM%2B3btycjI4OysjJUKhWRkZFs3LiR9u3bk5OTw9atW%2BV%2BRWWCspdTpVIRGhrKvffey6FDh0hNTeXixYt23y/i/MSzqpx7JapD9ITwlHJBxmw28/zzz9OgQYO7Vvp45MgRhg0bZkfoXX1XK/s74ffv5q5du5KZmcmLL75IcnIyZrNZ2qO48feCu2fPDTfccOMuwsfHhxUrVsiASvQTKX3LlD1Got%2BkRo0a6PV6%2BvTpQ0pKCl5eXnTt2hW9Xo9er%2Bef//wns2bNsiu5Wbhwod1%2BRa9LQUEBGo2GwYMH0717dymQIlaCqyrbcewtc9VLolar%2Bfjjj%2BV758%2BfJyMjg0mTJrF9%2B3bp51cZTp06RU5ODpcvX7YLRqdNmyb968B%2B7lQqFbVq1ZJjFEGhEp6enrKnTJi7165dG4PBgE6nIyEhAQ8PD9544w3g934krVYrewZFQD127Fi7fbdr146HH36Y7OxsGRxPnz6dgoICXnnlFTIzM52CL0ei%2Bsknn1SrJE3ZFwdQq1Yt6tatK72xQkND8fPzIzY2lnvuuQeNRiNLuGrXri0zdD4%2BPna%2BZIBUMdVqtdx3331Ox1aOed68ebz00kvA7/dC69at7bafOnUq/v7%2BZGZmkpSUJO830YcJ5VlKAdGLtWHDBqxWK8OGDZNEV3k9xTWC369PcXGxFP/o1q0bN27ckKV%2B99xzT6VzarPZ5MLCkSNHJHG22WysWbNG2mKI%2B8vRFgPKPRXFswblapINGjSw2yY/P98umxUbG0uHDh2croMSQjRHYN%2B%2BfU4ZG8fMvugHNBqN8po53jfg3IsWFRVFXFycfO34WXFci8Ui7SOUWLVqFbt375avPTw8KC0t5d1332X9%2BvWy97Vr1652NgQ2m43//Oc/TJ06lS1btrB37167/Yp%2BSkH0xGeuXr3KZ599RmpqKn369JHPpaONw%2B1CnK/4KWwdlKRbrVbz5JNPMnz48Lva4xYfH8/GjRvx8PDAz89PehaK0mvhvRoXF0fDhg2l5Ud2djaFhYW88847zJkzh8OHDxMZGcmmTZvs%2Bnfd%2BPvAXcbphhtuuHEXMXfuXD788EM0Gg2JiYkyywW/lxuKVXMhox8YGMjo0aMZP348c%2BbMkSWNP//882176MHvBCggIAB/f39Gjx7N66%2B/Tl5eHkFBQdJnTEDZp1UVlNkHV2VxoaGh%2BPr6cvbsWUJCQuzKDz08PGT5UUREBKWlpZSUlJCfn%2B%2ByPFWtVjN06FCGDh3K6NGjSU1NpVGjRowbN45nnnmGwMBAhg8fziOPPEK/fv24du0aQUFB9OrVi48//pjAwEDGjRvHqlWrGDVqFK%2B%2B%2BiohISFkZWXRrl07abyutMv4M%2BBoE/FH9%2B3v788777zDRx99JEsud%2B7cyd69ewkICJDbxcXFSYNpUZq1d%2B9eevXqRW5urpM5OJSThE2bNvHNN9%2BwdOlScnNznVQBRTY6NjYWKA%2B%2Bp0yZwqJFi%2BzIkF6vJzg4mKtXr0oTcJGVAxgxYgQrV668rXlRZpyUQbgQF%2BnUqRPffPONy8%2BKY3Tt2pXCwkIOHz5MjRo1ZPlpVFQUDz74IAsWLLitXrq4uDhSUlLkuBYuXEjXrl2B8t7MqmAwGLDZbLRt25YffvihwrE3bNjQLntW2TkGBgZy8%2BZNAgMD6dKlC9u3b8dsNjuJ71SE6pYgC3z33XfUqlWLmJgYIiIimD17Nk899dQf9oB0BeWCTmV9b8HBwRQUFGAymejVqxd5eXkkJibi7e1Nbm4uDRs2pFOnTixcuJBu3bqxa9cuxo8fz7vvvssLL7yAv7%2B/FJYpLi7mzTff5IknniAhIYGmTZv%2B6ed1p4iPj%2BeLL74gMjKS%2BPh4mdkXSrWOPplg/xw1adJE9jG68feEm%2By54YYbbtxFlJSU0KJFCzv/MygPTjt16sTOnTvltiKAUX5tix62Xr16oVKpOHr0KAUFBaSlpZGenu5E0lwFP0L1sk6dOnTv3p0jR45IaX1XZW6O%2BxJWAKI0T2lR4OfnJwUMHMtIxX5atWpFdHQ0e/futZPAF0SxUaNGdiVYFy9e5Ntvv2XevHl2c%2Bbl5YW3t7fdOQcEBDiVEFYmOKLT6WR/krJEcPr06Tz66KNkZGTQqVMn%2Bfthw4bRs2dPRowYwezZswkODmbw4MFotVpGjRqFv78//v7%2BvPLKK/j7%2B8ssEdiTPCUEyVer1bz//vv4%2B/tz4MAB5s%2BfL8cmMlQFBQWkpqZiMpmI/P/tMCwWCzdu3KCoqIjGjRtz7Ngx6XkmFg6EOqMjqkOqgoKCMBgM3Lx5k8LCQiej90OHDuHp6UmDBg3s%2BhPz8/Od9l1ZP53yfhF%2Bgv8raLVaTCYTfn5%2BkgANHDiQdevW/SnCGSJjJ3ooR44cyalTp1ySuejoaEJCQujWrRufffZZlQIwGo0GHx8fXn/9ddatW8e%2BffsqtP%2BYOXMm8%2BfPJzg4mLy8PC5evOjkWekIg8HAli1bpCWC%2BI4Q1yg2NparV6869Zm6Eh2qCvfddx%2B1atXi8OHDXL9%2BXT4/1SX/7du358cffyQoKIiBAweSlpZGYmLin7KoIlCjRg2sVisZGRmcOnWKjIwMHnzwQfbt2/en7P92MGzYMKxWq7RbsdlsFBYWSqsSxwwklGf8RZm7VqtFr9fLkntvb29KS0upW7eu/Jxj6bgbf324yZ4bbrjhxl3GxIkT2bVrFz4%2BPuh0Os6ePQuUE4/KMgjKPhXxWqPRMH/%2BfHx9fVmwYIGdgXfTpk355ZdfGDRokCyprKxR/3bg4eHB4MGD%2Bc9//mMnUnKnioKenp4UFBQ4BdaO2cHbUa2rKsATZFo5p1CezRJm8Lm5uU59YkK10c/Pj/vvv59169ah0Who2bKlNL1OSUlBp9NJ0Rol2VQKxlRE6Ct7LcawfPlyBg4cSKNGjTh27BhQucfffxMV%2BXVV9FoJDw8PCgoKiI6OpmbNmuzatcsuk2Q2m9FqtURGRgLl98qpU6dcqiIKwlijRg0yMjIqzUY79sB16dIFvV5v1yNWFWrXrk1aWtqf8lzpdDq0Wi2FhYV4eXk5iamIZ8vVM9CqVSvS0tLsFj4ExPYVPTviWHXq1JF9mAJBQUFkZWVhNBrlfSXM1%2BvVq8fZs2fx8vJCq9WSnZ0tTcqFAqdKpWLOnDlMnDgRlUrFzJkzmTBhAlarlXr16pGamuo0d0LQRZRM3n///bKXzt/fn%2BXLl3P06FFSUlLYtGkTgYGBpKSkSEEbrVYry7MFHK%2B1cl6qA7FgpNfr7bLlvr6%2BeHt7c/78eZKSkqq1rz8TCxYsIDc3l%2Beff172ERcUFMjvH2HvIVDVOTv2840dO9apVN2Nvz7cZM8NN9xw4y7g0KFDfPfdd%2Bh0Otq1a8eGDRu4evUqffr0YenSpS6DNEe8%2BeabbNiwgeTkZKBccVKUxHl7e1NSUkKzZs0YOHAgEyZMkDLb4udnn31G//79sVqtFf7BVwYDInAWhEutVuPj48OtW7fw9/fn5s2b%2BPr6snnzZjp16kT//v3ZtGkTJSUlMrNUUlKCwWBgxYoVhIWFMWnSJPbv33/b86fMzvn7%2BxMWFsbp06dloOhIMjUaDU2aNOGXX36RPn7wezCjPCdxviaTidjYWDtyJ0RrcnNz7Y4hgiHH1fPbhSsSJMZWGUFSksQdO3bwwAMPANiZLANs2rRJ9i3Fx8djs9lkRtbLywuVSiWJp3gN5b1fVqsVjUZDixYtMJlMQLknXWWZ0v8mxGKH8OcTZNBisbB48WK7EtBffvmFpk2b2o1TmZHUarVs3bqVtWvX2ilrVrVA0K9fPymKVNEYXd0TgoiA86JOYmIiAwYMoFatWk7ZzODgYIKCgjhx4oQkWQKtW7fm5MmTdtdQlELHxMRIOxVXaqDiOvfp04egoCBWrFjBk08%2ByfLly%2BV3xu1ALGDs3buXDh06yN8L4lDZffzxxx/z6aefsmPHDpcKr4L0GY1Gevfuza1bt9i6datU3Fy/fj333XcfixcvBsqfSZHhd/U9J7w1hbCTzWajSZMmNGnShKtXr/Ldd9%2B5HIM4B19fX7s%2B4fj4eNq2bUvfvn3R6/WsWrVK9o0mJydz5swZTCYTAwcOpGPHjlJkJjU1lf3798vnKSYmBovFQl5eHn379mX9%2BvV8%2BumnWCwW2rdvzyuvvMKyZcs4d%2B4cAwYMYNOmTSQlJcky3ccff5zCwkI6derExo0buXz5MtnZ2eh0OjIzM2XGz3HxqnPnzuTn53P06FHatm1Lamoqs2bNAnD77P1N4SZ7brjhhhv/Y3z55Ze8%2Buqr1KlTB61Wy7lz55gwYYKd0lleXh7t27fHZrOxbt06QkNDGTBggOxpmjZtGiUlJXbG6j169ECv13Pw4EFCQ0NZu3atfC8%2BPt6J7Imf4s9AWFgY58%2BftxurMpMmiFCdOnW4cuUKCQkJJCcnk5aWJlfKlZLuBoNBeqdB5dkqgZ49e5KZmcngwYOZMmUK%2Bfn5dO/enbCwMFavXm0XrKnVagYNGsT48eM5deoUT4aH/KQAACAASURBVD75JP8fe%2BcdHVW1tvHfmT6T3hNCCgkkoYQUECIdAkRAikBQEESlqIiKCgIionhBQVARVMACHyIoIh1BriAXCL0KUq6IFIEkQCrpk5nvj6y9nZlMIKj3cl1rnrVYkMnMmX322efwPvt93%2BeprKzEx8cHX19ffvnlF7sge%2BvWrdx3331yl9%2BRyIoxClEPnU7H6tWrMZlMBAcHo1Kp%2BPbbb/Hw8GDEiBF8%2BeWXPPjggxiNRo4ePUpycjJr166ld%2B/ezJo1i9GjR2MymVi/fj1du3aVc%2BFIjpwFo61bt%2BbChQtkZmbi4%2BNDWVmZ/OMMH374IXFxcfTo0YP169fTq1cvLBaLHdmzWq3MnDkTvV5PbGws3bt3tyN7/v7%2BFBcXU1JSYmd1YbFY8Pf3R6VSsXXrVq5du8bhw4fx8/Nj8ODBZGRk0KpVK1QqFQ0bNiQ3N5eBAwdSWFhIeno6vXr1kmN65JFHOHXqFGfOnMHLy4tVq1Zx%2BPBhSdgWL16MxWIhJydHEiDbNSgIdkREBJ988glhYWF2Ai2ir3LXrl0cOXIERVFo1KgRbm5u7N%2B/3%2BncieMeP36cpKSkGi0kjEYjX375JS%2B99BJnz57FYrHQq1cv9u7da1eeKyDmr7S0FK1Wi16vp3nz5mzfvh2oInydO3fmvffeIzEx0c42xTEbPnbsWN555x1WrVqFj48P7du3r0akevbsWY2URUZGcs8996DX61m6dClQsyKjVqtl6tSpzJw5k5ycHEkmnY0nICCgGnkSZZwGg4HKykoqKip48sknmT9/Ps8//zxubm7MmDGDoUOHsmjRIoxGo11pr7e3NwkJCajVagwGA%2B3ateP8%2BfMsWLDArrzd9p5OT09n7dq1cnxLlizhqaeeYt26dbIv0tk5K4rCU089xcKFC4mKiuLnn39Gp9Pxxhtv8NJLL%2BHl5UVhYSFLlizh8ccfR6PR2K0LvV5PQECAJNvC2sW23NhgMNC7d2%2B%2B%2BuqrGudczKXotRYbakLgRnxGo9FIUiY2EIRXqbP1anvN2rRpY5dlFJlbnU7HnDlzGDVqFC1btmT//v1YLBZMJhORkZFcvXqV3NxcFi9ezL333lvtO1z4%2B8BF9lxwwQUX/svo27cv3bt3Z/jw4QB88cUXzJ49m86dO3Pt2jWuXbtGbm6uXSmkwWCoZhlgC0VReOedd4iKimLAgAGoVCo7w%2BwDBw7I/i3Hv6EqyPD09KwmxlJb%2BPr6kpubaxfQiKBC9AQKlU/bTKIIgkT/mo%2BPD3q9nh9%2B%2BIEmTZpgsViIjIzkwoULdoFlUFCQFGpRq9WUlpbi6%2BuLxWIhPz/fbhyCLMybN48pU6ZQUFDgNBOlVqsJCQkhOztbBkpTp07ltddeY9euXfj5%2BUmDe6vVyvz583nyySfRaDTs2bOH9u3bSwLdpEkTjhw5wpo1a/j00085cOCAJOrOMiXe3t6UlpbKgM5kMpGSksL27dvt5io2NpagoKBqvV0rV64kPj5ekvru3btTXl5OnTp1yMnJwWq1ViOKarVa%2Bp0pisKsWbN48cUXUavV1UrpDAYDVquV4OBgWaYoSGtkZKTstRTiPR07dmTLli2sXLmShx9%2BWM6L%2BC6DwUBJSQm7d%2B%2Bmbdu2aLVaBg4cyM6dO4mPj5fqrIIsiPF6eXnZlWvee%2B%2B97N27F09PT/Lz81GpVFJ44lbhjW15q1iXK1eupE%2BfPsDvFg62fYNdunQhODiYf/7znxQVFVFRUYFGoyEqKop58%2BbRrl07u6DeaDRKAY/Kykree%2B89du3axcqVK2scly3S0tL47rvvgKrete3btzNgwAA0Gg3Lli2jR48ebNy4scbPa7Va3N3dq/WF2lqg/FF8%2BumnjB492q4k8M/0walUKmJiYjh9%2BjQ6nU7O2%2B0QFhbGnDlz6Nu3L1Blj7JmzRq6devGt99%2BK9/nWP4N0LlzZ77//vs/PX6NRkNERAS//PILer0eNzc30tPTWb16Nbm5uXh6evLyyy9z%2BPBhNm/eTGhoKOPGjWPo0KGYTCZpeK4oCuXl5VIsxWw2U15eTuvWrTl//jw5OTkoisI999zDrl27MJvNGAwGpkyZwooVKzh79iz16tWTz/Q/CuEtajabMZvN9O3bl86dO9OmTRuXofrfFC5pHRdccMGF/zJ%2B/vlnWWYHcPToUYqKili3bh379%2B/n559/tvOoKy8vtyN6arWapKQkaUYsXhs3bhz9%2BvWjoqICs9lMUFAQycnJtGjRgrS0NFJSUuTfKpWKzMxMO5PmL774gm3btmEymZzKsd8KglDYQvQYCe8rsdtvS15E8CBK8HJzc8nKyqJly5bSwPrXX3%2BVPUYCMTEx3Lx5k9DQUN544w05hry8vGrjEJLxM2bM4ObNmzKIFDvrQiyjsrKSpk2bUl5ejlarJSgoiIyMDLvjTZs2TX5uxowZQFXZoKP33LFjx6isrKSwsJA1a9ZQp04d%2BTtHoicsB2zLqcrKyvjhhx/sCG6dOnX497//zc6dO6vZRwwYMIBGjRpRXFxMly5dJFm9evUqpaWlkrD4%2BfmxbNkyEhMTpbmyCHJPnTrl1OBejKesrIwLFy5I4i6CZ9tSQovFQkVFhRT4SU9Pp6ysjMGDB8sxiGwiwODBg%2BV1Xrx4MZcvX7az4Vi4cKH8d2VlZbW%2BvD179mC1WqUYiMVioV%2B/fgwePBjALvMnpObFsQQ8PDwwm83yM1AlWiHOG6qIU3x8POvWraOkpARfX1/69OmD0Whk0qRJshxUlMgaDAaZeRHftWrVKjZs2FBtbh0h1rmtsq7IuItSPqAa0fP397f7WdhCOCIlJeWW3%2BsIW6sN8Z7PPvus2uaBrb2GsL1wZtNhOz4Bi8Uiy0zLy8urET3x3v/7v/%2BT5FylUuHr68uyZcvk78X96igw4zg38LufohCJatOmjd3ctGzZkv/7v/9j6tSpREdHV/v8008/DVStJXGs8vJyysrKaNOmDbm5uVRUVFBQUIDZbOa7776jvLyczp07ExISIsvJRZXB4MGDcXNzY8CAAfIes1qtTJ48mfz8fCwWC%2BPHj%2BeFF17AaDRKH8Xo6GhOnz5NcXExw4cPr2axI9ZkREQEUVFRGI1GjEYj7u7u6HQ6abkinoPR0dH06dOHtm3bEhISQmRkJC%2B99FKN68aF/324MnsuuOCCC/9lxMXF2cngJycnU1RUxLRp05g%2BfboMDl955RVeeeUVux1njUbDuHHjuHDhAsuWLQOqAqE333yTN954w06MQ/Q0%2Bfv7YzabadCgAY8//jgBAQGMHDmSgoIChg8fLnubPvnkE5o1a0ZiYqIsPVu8eDGPPvpojbvser0eg8FAeHj4LRX8HCF6lmzPy2w28/777%2BPj48OkSZOkCqKXlxf5%2Bfl2yqBarVb2mYmA1tPTk9LS0mq9eiqVym78tdnBd3yPt7c3mzdvxmKxyPJa2/eKLM769etlVs3Hx4eRI0cyc%2BZMjEYjxcXF0jxc9L85lscJmwlBoGzL1mznTIxPkPK0tDTUajXr16/Hy8tLqol269ZNjhuqAtRnn32W3bt389hjj8kgeeXKlYwZM0aW7TkG8oqi8MADD7Bq1SrgdwXX2NhYrl27Zuc16OfnR4MGDdi3bx8NGjTg3//%2BNy%2B88ALvvPNOtXkW11Sv11NeXi4zfgKrVq2iX79%2BwO8ltuLftcGnn37KDz/8wNKlS%2B2ydLaZPSEMcqfh0MaNG3nggQdYunQpw4YNsxNQ0Wg0qNVqu3nUaDR4enpKwupMJMQWzpRKayoHVBSFjIwM7r33Xnmeer2ef/zjH4wfP/5PKYg6s1lw1vcnKgU0Go3cNKhTpw55eXmUlJSwfft2OnTowGuvvcZDDz0EVMn6225y2F4XMT%2B2FhpJSUkcPnz4js/hj2Tt6tSpQ3FxsfR%2BtC03TU1NlR6ptmOFW6v9AkyaNIktW7Zw4MABtFotOp2OoqIigoKCyMrKkgb0mZmZVFRU2M1J/fr15f3mbO3UpJ4MVeWigwYNIjAwkMrKSjp16sSMGTOIj49n4cKFGI1GO%2BsTQKosR0VFkZKSQmBgICNGjLijeXTh7sOV2XPBBRdcuMsICgpCURS8vLwICgrCz88PPz8/YmNjiYyMZMyYMXh4ePDAAw%2BgUql46623JNEDaNu2LfPmzbPr3fDw8JC7x9nZ2eTk5NCiRQueeeYZ%2BvfvT1xcHOvXr%2Be5554DqoLnIUOGkJOTI4Ov0aNH89FHH9ntwNevX1%2BqfkJVVis%2BPp4GDRrg4%2BMje01s7QlUKhUGg4Fnn31W9psIgRaxAy2yRHv27EFRFDspfxHo2AYxosQoLy8PNzc3AAoKCqQ6Idj7rNnCMejz9PSU/xaqf6GhoYSGhkpylpeXR0pKCq1atar2eavVKnvdevToIQlcfn4%2BM2bMkL%2BHKguHzz77jIcffthOJEbAYDDw8ssv4%2BPjA9hnoCorK6U5txiDyAoMGzaMCRMmoCgKxcXFmM1mJk2aRHZ2NgaDQR5D7M5HRESgVqtRq9Vs3LiRRo0akZmZidVqJSAgoMY5s/Uvs1qt/Pzzz9y4ccMu81FUVMTBgwexWq0EBQUBOPV/dMxOOoPIPNh%2B5vTp05LgvPXWW7f8/LBhw6SapriW8PtaMplMXLp0CavVKteR7diEETVUXbvo6Gi8vb3R6XTk5eURFhbGiy%2B%2BWE0p05kxvdlststM3k7Ix5ag%2Bfr6AlUZbZF1FLBarVgsFtlXJQhmWVkZL730UjWiV1MGLzg4WGaBbBUmnfnpBQYGVrt%2BgiioVCp5H/Xs2ZPi4mKsVivt27fHarUyZcoU2rRpw6uvvlpNHMR2s0rMjyhrtFqt1YiemJfbwRnRc6xeENkuMT9XrlyR5a62nzeZTBw6dMjODkaM1VHh2NkanzlzJgcOHACgoqJCnrPo%2B9y6dSuXLl2ShNF2Ts6ePUtBQUGNRE/Mp%2B1aFrh27Rpz5sxh0qRJvPrqq7Rp04b169cza9YseR84m6NmzZoRGBjIsmXLZE%2BsC38vuDJ7Lrjgggv/ZcTFxTFv3jwZSJ4%2BfZp//OMf%2BPr6Ehsby4kTJ%2BR/9MHBwdy4cYOioiJat26Nh4cHP/zwAyaTifLycoYMGUJAQACvv/66PL7IkthmzUTwVVZWRnBwMNu2bePChQv06dPHjiQmJyfLgEqY7cLvwU5aWhpbtmyRfV0i27RgwQJGjhxJREQEeXl57N27l4YNG8rjuru74%2BXlxY0bN6QwwOjRo2Ug%2BFdBZPysVitNmzYlNTUVf39/JkyYQGxsLKWlpfj7%2B3Po0CE6dOjA4cOHGTp0KHPnzkVRFKKiosjKyqJp06YEBARw9epV9u/fj5eXF2q1GkVRuHHjhhQw8Pf3lz2Ool%2BsvLz8tlmbmuDl5UVUVBRHjx6Vmbs%2BffrwzTffMG3aNCZNmlTtM4Iw%2B/v7k5mZKV93tsvv4eFBcXHxHxqbRqPBzc2N/Pz8W1pghIeHU1hY6LSEUECn01FRUYFer6e0tLTGzJ6jwIzoTX3%2B%2BecB51knsRkh1qftuYo5SUlJYe/evXYZG0e/wNpAZGMc4WiLcie4VfbSYDBgMpnIycmRvXc1Za2cKYEKIibKbf8XoVar0el0aLVaCgsLqVOnDpcvX8ZkMvHoo4/K8vWGDRsyZcoUKisruXr1qp0yrhDHKSkpuW02WMyfED0RBM9oNKJWq%2B/IPN722RsREUFhYSE5OTnSTsRWWTckJIT69euzY8cOoOreFAbtUHU/h4SEyB48sXbFJpZOpyMoKIiLFy/anYPFYsHX19epFcmdICIigmvXrlFcXExERAQPPvggffv2lRtRLvx94CJ7Lrjgggv/ZcTFxdn9/Gcew86EB8aOHcvs2bOrvddqtdKuXTv27t3Ls88%2By4YNGzh//jyNGzfm8uXLZGZmVvPzcjy%2BwWCgrKysWvml%2BFuv13PffffRsWNHxowZA1QFU/Xq1ePcuXNSNt3d3Z38/Hyp%2BFeT2bu3t7cUflEUhV69etG6dWs6dOjA0KFDOXv2rJ3giwgSzWazLIESUvS7d%2B%2BWGYu4uDgiIyNJTk7mwQcf5MEHH8RgMPDJJ5/wxRdfsGvXLioqKiTxEBklqAoy9Xo9ffr04eeff%2BbQoUMYjUa%2B/vpr6tevzz333MPy5csZN24ciYmJLFu2DB8fH%2BrVq1ctM2EwGKQ1hS3i4uI4ffq0VOq8du2ane%2BZrTef7XkXFRXV6G2YkJCAXq/nt99%2BIysrS2YF1Wq1DPxtSZsIUB2via1Hm2OQKd7j7%2B/vNJt3pxAiGrZiQrcqVYPfy12dkS1n94ujKbgj%2BvfvT3FxsV05rDOIIPyP3M/ff/89YWFhxMXF8cEHH/D5559TUVHBwYMHq73XsaTzVl5posRYXEdvb29JKGznQafT0a1bN3788cdq3nq25a9/FrZjFeXZgBT5EabeKSkpBAQE8NVXX9GoUSN%2B/fVXmbUKCQkhMTGRnJwcaduiKArh4eEUFRXZ9TsL2IrdiPNxJMkGg6FaphGcl2XaigyJe0%2BUxBcUFFQ7tth0sD1nZ9%2BjUqnkXDsew9n9KOBsXd8Ojs8Qd3d32U8rysx1Oh0lJSXEx8djMplchup/U7jIngsuuODCXUaLFi0oKytj9OjRLFy4UAbq6enpfP755yiKgoeHB56ensTExLBt2zbg9zIk8R%2BzsD7o2rWrnXiDSqWSAikCInDw8fHBarVSWFhIZWVlNYNvEeiI/yocA59BgwaxbNkymjRpwokTJzCZTLzwwgusWLGCf//7307PV5RL2WZRmjdvzqFDhxg%2BfDheXl7Mnj2b9evXk5mZySuvvEJmZiYqlYqhQ4cSFxfH8uXLOXbsGGq1Gm9vb65fv05SUpIsI62srJT%2BgwLvv/8%2B//znP7l8%2BbIkXbU1Uvbx8UGj0VBWVnZbVVTb/1Y//vhjRowYQd26dblx44Zdf5rVaqVBgwZcu3bNTh1R2DAoikJiYiJ%2Bfn58//33TlUUo6OjuXTp0m2N6/38/OjYsSPnzp3j%2BPHjVFRUYDQaSU1N5cSJE1y8eBGLxWKX2f2j2YEjR45gMpmYO3cu8%2BfPr%2BZf%2BGchst0Wi0VuGjgzHYffM73Osny1hb%2B/PxUVFTUG6bYwGo1ERERgNpvp2bMnbdu2xdvbm02bNvH2228DMHz4cOLi4qSokkqlYuTIkTRu3Jinnnrqjsd3K9SmV01cG9t72/ZzKpUKd3f3aiJRtnMp3q/VaqXCZGFhIVqtVm7GfPTRR7Ro0YIWLVrYZaYcywcd%2B3lvh9uRf1HeaLtBZbVWeZHm5ubSvn17du7cKTdT/P390ev1ZGdn/2Uk927CliS2atUKNzc3vv/%2Be9RqNY0aNeL48eN2z3eTyYTZbKa4uFgS2ocffhhfX1%2BXofrfFC6y54ILLrhwl9GmTRsqKytZvny5VCeEqkD%2B0KFDdOvWjbfeeotBgwbZiaA0adKEixcv3pJ81BbOTMVrA8cSqT8ihODj40NlZSUFBQXVyvKcHU%2Bv11NZWUnHjh2pW7cuixcvxmq1cujQIdzd3fnpp5%2BkFxpQjQgoikJ6ejqDBg1i69atzJ071%2BmYcnNzqwW1wi/t5s2b%2BPv7yyzCvHnzGDt2LNOmTWPixInVPOLEGGx34EUgLOwPBFQqFQEBAVgsFm7cuAHwh4iSbfCuVqtlKaTIColrbWsVMHz4cDkfdyqGIuYtMTGRn376ifz8fEaOHMnSpUurlXTa9kvZCtCIfrRt27aRk5PDfffdR0FBAQsWLOD555%2BXGSmDwYCHh4fcMHBcNyLLZjKZaNKkiVQHFXDMaAqCIco/FUVhwoQJvPnmm7K30WQyMXbsWObPn8%2BVK1dITk5m0aJFLF68mHnz5uHv78%2BqVavw9vbmypUr8rt%2B%2BuknXn755Tu%2BT8X1iY6Oloq0jhYngYGBREdHs2/fvmprZPLkyXL8tuvrvvvuk0Iqo0aNYuLEiaSkpMh%2BWVECffLkSdkPumXLFn755RfS09P5%2Buuv5ToeMGAAK1assMuKCb/F06dPs3nzZrp27SpVJX18fHjppZdqzEK1aNHCzg/R3d3dzovP3d1dEhfHDSxnMJlMuLm52W0siZ48cS%2B89957vPDCC8TGxnLq1CkaNGjAuXPnZC%2BksPwIDg7m9OnTtb4XbZ9dPj4%2BTpWCBWxFWByhVqulFyBUPf9syzfr1q2Lt7e3LM3V6/Xk5OSQm5uLRqMhMDCQ8%2BfPy80RId50K4jrKcq%2B161bR/369Wt13i7878El0OKCCy64cJfx/PPPExgYyGuvvcaQIUMIDAzEx8eHw4cPo1aruX79usxs2eLEiRN3FEAKgQpnAhwi%2BLpVIPP000/LzJkI1oUanbAh0Ov1mEymamM1Go1249Dr9dStWxeoslsoKChApVJRWloqiQjYEw1xjLKyMry8vDh8%2BDDHjh2T5KWoqIjhw4fTt29fSfQUReG7777j2LFj7Ny5E4PBgE6nIzc3l4cfflh6uAF0796dN998E5VKJftS/Pz8mD59Ot9%2B%2By0rV65k7dq17N%2B/H0VRWLduHYqi8M0339C5c2dUKhUJCQlSdl6opH722WfVAjmRbYUqA3UhvALIgEwQvTvBU089xejRo%2B1EdBRFYfny5QQEBMhd/dOnTzNw4ED5PhEstmrVSo59zpw5Mltr668l/Bs/%2BOADoqOjUavVUuQmICCArKws6tevT0xMDB999BH5%2BfmYTCZ69uxJZGQkZ86cITU1lSZNmvDll19iMpkwGAyMHDmSuLg4pk%2BfzokTJ5g%2BfTpRUVFs2bKFNWvW0Lx5c9RqNXq9XmZzxTopLi6W44aqbJzVaqWoqAiDwUCnTp3s5snPz4%2BoqChJ5Pr37y/LPidOnAj8vqYrKyspLy8nLy%2BPV155RZYdHzx4kPj4eDZu3IjJZOLy5cu0bt2afv360bFjR/ln9OjRt71PhdqpVqtl5MiRQNU99tZbb0miB1X2ImLtaLVaJkyYwOXLlzEYDLRq1YrPPvuMnj17AlVKpGazWRK9tLQ0oIoAZWZm0qFDB3777Tc74RNxv504cUL6vM2fP19aCwiPwPbt2wOwY8cOKbgkYLFY%2BOmnn6isrKRLly7ymG%2B88QYvvPCCfNao1WopCJOcnIzBYLArU%2BzRowejRo2Snxd2FhaLBYvFUqssbXFxsSR6kydPlllH202OMWPGYLFYOHXqFFBljSOykcJfMicnh6tXr1KvXj2GDRsGVG3G%2Bfr6otFoiImJoX///gQGBhIWFkavXr04ePAgr776Ko0aNWLdunV06NCBNm3a0KlTJ06dOsXzzz/PN998w8svv8zSpUvZtm0bGzZs4LvvvuPUqVMsWrSITz75hB9%2B%2BIGePXsyduxYXnrpJb777juefvppfvzxRw4fPkx4eDipqan4%2BfnRrl07PD092bhxI3PnzqW4uFiWz1ssFgoKCm5L9ABKS0sJCgqitLTU7lnlwt8TrsyeCy644MJdRqdOnf6Qkbkj2rZty86dO4Gq4LFLly7s3LlTljgZjUbZyyYQHBxMXl4eFovltqWAziB2pGfMmMH48eNlUODYLyVKpEJCQiguLiY/P19mzW6XQYqKiuLcuXM0aNBA7m4L1CYTuXv3bvLz83nqqaek%2BTdUl49PSEjg%2BPHjcjf/VsGkkLpv3bo1O3bsYPbs2axdu5bY2FjZRwhIi424uDi7/irR82O1Wvnqq68YOHCgDHSDg4OrlY/l5uY67R3SarX4%2BfnZCbMIiN49RVHo1KkTarWa2bNno9PpyM7OpmPHjnTr1o2LFy8SExPDxo0biYiIkGqXALt27aJdu3Z33A8UGhpKWloaS5Yskb2W27Zt44knniAjI4OdO3dKonar6ycyajdv3nRq9p6SksKhQ4ek4IsoObadq4CAALy9ve3WjqenJ3Xr1uX8%2BfNUVlbaCe1ER0dLcvNXQPhiHj16lGHDhvHZZ5/x%2BOOP4%2B3tLQkl/LGseG0%2BI3rURKm1WIPe3t6UlJTYrTVREjl79mxefvllysrK6N%2B/PytXrmTq1KlcuXKF%2BfPnc88993DgwAF5/ycmJnLs2DFpxm1rQ2C1Wunbty9Go5ETJ05w%2BPBhrFarXS9g8%2BbN%2BfXXXykoKPhLhGOc9Z6K10TW3lbIp3nz5hiNRvn8FO8NCwujVatWHDt2jNOnT/PBBx8QFBRE//798fT05Omnn2bmzJlUVlYSExNjV7oeERFBeXl5NYsKgV69ehESEsJnn33mtGd2zJgx7Nu3jz179jj9vCgXdvZ/h9gMsBXYEmI9wqNQp9NRXFyMWq0mISGBEydO4OvrS7169fDw8GDHjh1y861Vq1YMHDiQjh072nn4ufD3gIvsueCCCy7cZezfv1/uKl%2B4cIGsrCxyc3M5ffo05eXl0thWqLb5%2BPjwzDPPsH37dvlaXFwc9957L0uXLqWiogKDwUDTpk3tSqIchTtEJsS2nM3Dw4OePXuybt26O1Khs4XoOXMmKBAZGWlnzC0Cm08//ZQRI0Y4DfyFAmhUVBQXL16koqKC4OBgMjMz6dOnD2vWrJHvbdOmDe3atWP69Onytd27d/P666%2Bzb98%2BSXxjY2O5cuWKUzEHWwQFBZGXlydL%2B5wJcAjSKEQibEU%2BkpKSsFqtHD16FLAPzkXmbcCAAaxZs0YqB86bN4/XXntNmp4LK4WaEB8fXyuPQ7FDHxISwpNPPsmrr77KJ598wrVr15gxYwZRUVHVBEFuRXx8fHwoLCx0Ojbhwyd6owoLC/Hw8MDf359z586h0WhQqVQ1bjCIeRLm0Y7ryM/Pj%2BLiYr755hv69etnV3IHVdYfQpDDGXr06EG/fv0YNWoUZWVl0g%2BwpnNu2LAh48aN4%2BzZs%2BzYseOWmQ5/f39ZnmuxWNi5c6dcZ4GBgeTl5TFkyBDuvfdennrqKaZOncrUqVNZv349nTt3tpsDsWliW/4rMvRqtZr09HQpmtG6dWv2TRPJ3wAAIABJREFU7NmDm5sbhYWFdn10iqLQrl07vv/%2BewYOHMjy5cvR6/X4%2BPiQmZlJbGwsZ86ckTYdGzZskH6RtiqQwj/PltgoisKQIUNYvnw5zZs3r1ZS6ufnh1ar5dq1azRt2pQjR47UOHcCbm5utG3bVtpmCOh0OlkBcCuIzRqVSiXLvidPnszkyZNlhYBtH5%2BiKHh6etaqL1PMq06nw83N7Zaqs/9LEORPrVaj0WhqleEDZO/w0aNH8fPzIyMj4z88Uhf%2BarjIngsuuODCXUZBQQF6vR69Xs/p06fZtWsXjRs3xmKx8OSTT8r/oAUhCwwMJDs72444zJgxg5MnT7J8%2BXKZzfH397frVRG78QLOVAvDw8OlmblAcHAwZrNZGvk2b96cH3/8UZqEC2U7rVYrg3fHnihH%2BPv7k5OTQ2hoKJcuXeLee%2B/lwIEDUunRlkCIYK1p06bSluKJJ56wK8GEqp3w3NxcVqxYYaeiKfq9RPAHVeWnAwcOtCNuzhTtEhMTOXnyJE8//TRBQUGYzWZeeeUVp%2BdkK5hTE25nuOwIMd64uDhOnjzp9D21zQiZTCZSUlKIjY3l2LFjHDp0iPXr1xMREcGbb77J4sWLgao1cPPmzWriLOJ7BNG%2BE%2Bj1elq2bMmOHTvQ6XR4eHiQn59vR/jq16/P2bNnb2mBULduXS5fvoyiKLRv357U1FSWLFlCeXk5WVlZeHh4UFBQQEREBGfOnKn1%2BEJDQ2WGxHEdCCn/sWPHkpWVRVBQEHFxcdxzzz3yPWVlZcyfP58WLVpIvztb5ObmSo9DAbHZYUuwbMmewWBAo9HYbbq0bNmSQ4cOyXJqLy8vMjMzZab5vvvuw2AwSCVUkcUR5a8FBQWsXLmSAQMGyOMXFBTYnbNKpaJt27bs2LEDq9XKY489xqJFi9DpdFgsFrnxIaxBFEXh4Ycf5uuvv%2BbZZ5/l448/liXIBoPhllUDYgPKw8OD%2BPh4du/eDfyuWCwUcKFKSVRsEv0RglWb%2B8TPz4%2BcnBysVishISEEBwdTUFDA9evXyc/PZ9CgQQwfPpz77ruP8PBwrl69SllZGVqtlkcffZQVK1ZQUFBAUFAQvXv35uOPP8ZsNsvnq2NvqbOeYMfng2PPscgOiz5cDw8P5syZw/jx4%2B/4vhQICAjg%2BvXr8pqKZ/zChQuBqk26JUuW4OnpKcuMXfj7wEX2XHDBBRfuAlJTU7FYLDz33HOMHz/%2BTx3Lw8ODpKQkdu7cectgxlEEoEuXLhQWFkr5crELbqtsp1KpZGBu%2B1pQUJAsT4qPj%2Bf06dN3RGJatmzJvn37qsndK4pCixYtnApqCDKpVqvx8/Nz6m/2Z5CRkcGVK1dIT0%2BXEukiIHd3d8fd3Z2srCw5x9HR0XTp0oWPP/4YnU7H%2BvXrpXy%2BIEVqtfovKdEF%2BxIvoZqamJhIgwYNADh%2B/Dhnzpy541LARx55hNGjR9OqVSugap2o1epbKnGKrJPJZEKr1RIcHEx5eTkVFRVcvHiRp59%2Bms8//5xhw4bx6aefotFo7Cw0RFBpG%2Bjalp06noO4Hs2aNZMqq35%2BfpSWltK8eXMyMjLsCFpkZCQzZ85kwIABdzQXd4LOnTvTrVs3NBoN06dPlxswIkvviNjYWBRFqVbqKOBMbdURzuwBxIaAKCu2tSGxWq1ObQPuZI04iqZAdZJiWzVgm9GvreBTbd43YsQIPv74Y7vPfPXVV1y6dIkXXnih1udTWxiNRry9vVEUhczMTCwWCzqdjilTpjBp0iSCgoLIzs5Go9GwceNGiouLGTFiBGPGjKF///4ANGvWDIvFwujRo/Hw8KB37958%2B%2B23VFRUMGPGDNasWUOfPn0YNWpUtd/PnTuXyspKlixZ4lQcpUuXLgBMmzaNFi1aANWrRKDKTH379u3Ex8fj4eHB9u3bb3neokwzPDyc8PBwLl%2B%2BzLlz59BqtRw7duwvmVsX/rtwkT0XXHDBhbuA1atXYzabWbRoEW5ubmi1Wn777Tfy8vLw8PCgrKyMmzdv4ubmhsFg4MaNG9StWxdFUfjtt9%2BAql4nk8nEQw89ZCeQEBERgaenZ7XSPrFrrFarCQwMJDExkYyMDCkeERsby7lz5%2ByCQyFoYDabb1lKqNFopPABVAXiubm58me1Wo1KpaKiogJFUahfvz4///yzzFLaZhZuJ6VeE%2BLj4xk%2BfDgBAQE8/PDDaDQaevTogaenJ0uWLEGj0TBy5Ei8vb354IMPJIHt2rUr//znP2V/XWxsrOzrEVAUhbi4ONq3b8/ChQvR6/V25M5gMNT6Z6gq71y3bp38OS4ujtDQUB577DHatm3L6tWradOmDYMHD75lYK5WqwkNDSU3N7ea9UD37t1RFIXt27dXE4hRq9V07dqVXbt2yX4m4UtYp06dar2RfwRBQUG0atWKjRs3MnHiRN544w25HkS2rLalZLZQFIVmzZpx8ODBGjOltyMPUVFRPPHEE4SGhjJkyBDmzZvHhQsXpD2CIESfffYZa9asYe3atSiKQmhoKFevXqWysrJaBlCj0dCnTx/eeOMNO4GiNWvW0LRpU7p161bjeDQaDY8//rjMpIA9oRIluMIuwJlhem2tJZo0acK9994riZOiKIwcOZIFCxagUqkIDw%2Bnd%2B/ezJkzBz8/P3r37m33fHE2v7a9qO7u7kBVtlNYNpjNZvr378/27dsloRWlvoGBgfTr14958%2BYxbNgwYmNjefXVVykuLsbb25uioiIqKiqk8qeYr4SEBJYtW0Zubi4dOnSwW0u3I7QdOnRg%2B/btaLVaHn/8cb7%2B%2Bmt69%2B7N8uXL5XE0Gg3169e3yy7awsvLS/Yce3t7c%2BnSpVoT6e7du7Nv3z6Z9XNzcyMpKYmrV69iNBrx8fHhxx9/JCsrC5VKRVRUlOzPE76cd6rQq1KpqFOnDh9//DEbNmzA09OTe%2B65h82bN7Np06ZqFhgCwht1xIgRPPfcc3f0nS78b8BF9lxwwQUX7iKaNm3KoEGDMBgMbNq0iYCAAJo3b05BQQFfffUVQUFBtG/fnuXLl9O3b18MBgNff/01X3zxBenp6Tz66KOy/E6IVCxYsIDRo0fLIFiUJGVmZmK1WjEYDBgMBrssgghS6tSpI2XjxWvOeu8cCZmtyIpjOaPwa1OpVGg0Glq0aMGnn35KbGwsbdq0YdeuXbWerwEDBuDu7k5wcDAzZsyQRsUFBQV8%2BeWXMvM2ZswYjEajJFTCCF0QritXrpCWlkZ5ebnMGtWtW5eUlBS%2B%2BeabagFbWFgYHTp04J577uG5555DpVLRuXNn3NzcWLVqFRqNhtTUVPmzWq2mc%2BfOvP/%2B%2B8TFxaHRaHj66acZNmwYOp2O%2BPh44uPj8ff3Z9u2bU6FV26lOPhHja6Dg4OJjo7miSeeoGXLlhQVFdG2bVuKiopQFIVGjRpx6tQpNBoN5eXlf5mhdoMGDbh06RJGo5G8vDzZPyQCVlHC6SjCIywUhJCGr68veXl5jBkzhj59%2BhAQEEBSUhKTJ08mPj6eBQsW2HlM/lno9Xp0Oh2FhYU89dRTjBkzhp9%2B%2BomHHnqoWmmih4cHN2/exGq14uPjwyOPPEJERES1rJMgboJgC5GdhIQEuz5DMfdqtZqHH36Y3r17M3LkSAoLC/n222/lRoJQty0pKZGbKSqViiZNmnDs2DG73j/bMYif69atS3JyMuvXr0elUrF%2B/XrWrVsnyV9AQAATJkzg%2Beefx2AwMHPmTOLj41m7di0fffQRs2fPJiAggIceeoj4%2BHhiYmLo3r07W7ZsYfny5cDvgkbt27fHaDTaeXl26tSJDz/8kLS0NMaNG0fDhg25//77KSkpISMjg6lTp7JlyxaCgoLIysqSpPL111%2Bnffv2PPjgg2RnZ0s1z1WrVuHj48M333zDvHnz7OZcqHAmJiYSHBzMvn37qFevHkeOHJHkWWSrNRoNRUVFd7QhYTKZZC%2BgXq9Hq9U69X/8X4BarSYkJIS8vDxUKhXFxcV4eHiQm5uLt7c3xcXF8hnw6quv8uabbzJ58mT69Olzt4fuwh3CRfZccMEFF%2B4ChLn1W2%2B9dVvjame7xY888ghLlixh7ty5PPPMM/J1jUZDZGSktB6AKuLg7e3ttA/K19cXo9HotNRQlJXVJtPmrI/LNutiG1w2bdqUJk2asGzZslse0xGKohAWFoZWqyU7O1sGUTUZatcEW3%2B8PwPRB1VcXCztFurXry/LqDw8PKhbt65dWV/Tpk0pLCzk119/tRN6UBTltqRKlPD5%2BvoSEBDAmTNnZPmcrXl1TUhPT%2BfixYtUVlby0UcfodPp%2BPzzz3nnnXdq/NzgwYP54osv7Ay2k5KS7AzrRUbJNrMkrsn999/Pli1bqKiowN3dnYqKihqD5zslljExMbRr145PP/2UYcOGERkZyZQpU2QfqSi7CwgIwGw2k52dfcclro5wdi%2BGhYXRv39/3n33XerWrSsz7%2BBcFCkjI4O2bdvaKVbe7tqB/T0UGhqKn58fP/74ozxGjx49WLNmjcxAVlZW1qgEeScwGAxUVFTIftrw8HB%2B/fVX%2BXvHPrSa4ObmRklJidNeXj8/P9RqNTdv3iQ9PZ2NGzdy/fp1Bg4cyPr16%2B2OL/oQ3d3d8ff3l8%2B69u3bS/EcIQ70RxAeHk5gYCCnT59GpVJRUFBAu3btSE1NZeHChURHR0sF3nfeeYfMzEzZD%2BxIqN3d3YmLi%2BPw4cNYLBaZGRal4aI30dnvGzVqhI%2BPD23btmXmzJnyfIYMGcLSpUt55pln6NmzJ2lpaSxYsIAnnnhCvkesrb8ixH/wwQeZOnUqS5cuZd26dTK76sLfBy6y54ILLrhwF7B3716GDh36hz8vAmuhfCngWNbm5eWF2Wyu0bBXmBaLsjPHbJwt0bNV9xPfIV4T2TFnEAGvOJbwdRPj7tOnD%2Bnp6YwYMYLi4uJq5%2BR43oIcWSwWO%2BIgFB51Ot0dKYk%2B%2BeSTUoVSGBOfOnXKrkTP29ubiooKiouL7RT8EhISyMzMlD55Qm3SFjk5OTIINBqNUtBGzLUzI3mRZbDN8Dmb/1tB9PUJLFq0iMcffxyoUj%2BdMmUKly9ftgtQhTy/QGRkJGVlZZI0GAwGjEYjFotFXm9n5YP3338/mzZtQqvVSt9Cxw2FqKgo4uLi2LNnjywTrqioYMiQIXzxxRdYLBaSk5MZP348x44d47333qO0tBSLxUJERIQUzRBwNg4vLy9CQkLw8fEhOzub/Px8Kisryc/Pt%2Bu5UhTlD5WUCohrEhYWdkflfH8XREVFSWPu2qI21hrOqgZqguOchoeHk5WVRXR0NNOmTaNRo0ZcvnyZa9eu8eyzz1JeXk5ubq7cJBGfF9nimq7Pra6doii0bNmSvXv3otPpGDBgAK%2B88goPPfQQTzzxBAsWLJBr8qWXXpL%2BjqJ3b926dfTp00f%2BW5Rx3%2B73Xbp0kZsGH3zwAc8%2B%2Byyvv/46bdu25f777%2BeZZ57h3XfflVnFe%2B%2B9lzp16pCSksKpU6ekyjPAr7/%2ByuXLl2VmT5BlUZYcFhZGu3btCAkJYdasWWzcuJH69etz8eJF%2BvXrZyfy5cLfAy6y54ILLrhwl9GhQwcqKipYv349/fv3p7y8HJVKJTMRGzdu5P777/%2BPBo9Cfc2xNE0QtGbNmkl/LEc4U7F0fF0EdQaDgcmTJ/P1119z9OhRGjduTL169fjXv/5F06ZNycjIqJXao21AFhMTw/Lly2WvkEDr1q3Jzc3F3d2dIUOGkJqaSnp6OgBHjhyREvYCxcXFzJkzh6VLl%2BLv7092djbe3t6EhITw888/4%2BnpSfPmzalbty4nTpzgwIEDcke/JlEOASHOkZGRQWpqqvSzA%2Bx6kcS89e7dm9WrV2MymaioqKCsrKyawM6tIIzQ1Wq1zGLWRgRDpVLZKX96e3uTmJjI6dOnyczMJCAggNLSUkpKSuS1Ff2NzjI8JpOJunXrkpiYSF5enrxuonzvlVdeoVGjRnTt2hWLxcKDDz6Il5eXtKyIiori2rVrTJo0iQ0bNrBr1y6Sk5Ol95g4n4SEBM6dO3dHGd7o6GguXbrE6NGjWbVqFefPn7ebI0VRCAwMtBMCcrbW09PT6dy5M08%2B%2BSS7d%2B%2BmVatWTJo0iW%2B//RZFUeyyoLeDI5H38/MjLS2NtWvXUlFRQXl5ubyXRPbUkeRqNBppk2CbKQ0PD8fX15ejR48SHBxMbm4uL774Il5eXnz00UfSg9JkMkmhnIqKCoxGoywPFcQ4LS2NunXr8sUXX9yyMsF2s0iU4MbHx0uhD7GxIzaXaoLt/e7u7k6zZs3w8/Nj3bp1pKam8v7778v3TpgwgXXr1gFVma2FCxcyfPhwDAYDiqLQunVrvv/%2Be/l%2BvV5PSEiInQenwWCQpbG2z0QvLy%2B%2B/fZb2rZty5w5cxgzZgw%2BPj40bNiQw4cPExQUxIULF%2Bw8/rRarVQyVqvV8nqJTTKRkSwoKJDqpSUlJYSEhHDjxg2Z9Xd2796qqkGj0dChQwdCQkKAql7uuLg4AgMDuf/%2B%2BzGbzXh7e8t5F4ROlNpnZmZy5swZNm7cSHR0NNevX6dt27a3fda58L8HF9lzwQUXXLiLEP1x8HvgmpubS0ZGBu%2B%2B%2By6KojB27FhKS0uZO3cuUBUQvvzyy1y8eJElS5bIQE/sLAsEBwej1%2Bvt1C5rq45XW5hMJry9ve3OA6oCDVGWZzabZYAaERFBTEwMO3bssAtEPTw8mDRpEhMmTKB169bs3bsXtVpNeXk5nTp1QqPRcOXKFU6ePImiKBw9epSePXtSXFxMcnIyOp1OGmM/8sgjNG/enEaNGkkyptVqmTlzJs8//zwqlYpx48bh7e3Nrl27OHv2LGVlZWRnZ1NeXi57yQwGAwEBARiNRiorK7l58yb5%2BfkyS2AymTAajTRo0IDx48cTFhaGWq2WPl62iI2NBao8/7p160Zpaak8/7i4OEn8RED99ttv8%2Bqrr1JQUCAl1jMyMqrJ%2BgthHKPReFsiOHr0aD744AM7wm6bkVUUhQ4dOuDh4SGDZfH6uHHjePvtt//QhoPo56xteZ2wBFGpVDRr1owff/zRadZNGNMLf0JxXEdCVpsMk%2B0xIyIiePXVV7ly5Qrr1q2rpkRp%2B16huurm5nbHRuy2oiZWqxU3NzeZxRWiSBqNhpCQEFq2bMnKlSuB6ll3xzJaT09PKd6UnZ0tNwnEOhLz1LJlS37%2B%2BWeaNGnCrl27ZD9bbcdep04du5LVPwPhxWhr36LX63n44YdZvHixvB9HjRpFSEgInTt3xmQy0aVLFx5//HEGDhwIVNlXvPHGG%2BTn58u%2BueHDh/PBBx/8oWyrsw2MTZs20aNHD44fP06TJk2Av6Zc8n8Vnp6eTJ8%2Bnfj4eDp27Ogie39DuMieCy644MJdhOjZuNNHsUajYdasWUycOFHunDdu3JiTJ0/KzInJZKKkpEQGuQaDwW63X61W4%2BHhgclksiNrnTp1YteuXXaeeeIYgsCJcjVx3NLSUhlk36qHR5RZlpWVVTtnEaw6BmVarZYGDRrIbJNarWbr1q107dqVioqKasdRFMXOJNv2dREACkNwUcJaWwKckpLCo48%2Byvbt21mxYgXdunVDrVazceNGGbi3a9eOmTNnSrW%2BBQsWMGfOHKAqCyYsHGryHoOq/iZBlAUZvJPMnjNRjtvB3d1dlo06luQKAnardeosmBaKi%2BL3f3ajwVYICKo2NK5fv45Wq5X3gVarpU6dOnabHOHh4eTn58sMy92Ap6cnlZWVUgzHzc3tjsqN/2pERUVx7tw5uf4de3Mdyaher5e9e%2BHh4SQnJ7NmzZoaj69Wq3F3d7dbSzU9G95//32mTZsmqxk0Gg0TJ05k8ODBJCUlUVFRwYQJExg8eDDJycmsXbuWsLAw4uPjMZlMzJ8/n6SkJBISEigtLZVjt1UJtl2f999/Pxs2bJDf7%2BbmRlRUlFQwFs85Z56PAQEB3Lhxg1OnThEXF4ebmxsxMTH8%2BOOPeHl5MXv2bN544w1u3rxJXl4earWaLl26sGXLFiwWizRvFxUcYmxGo1GOHbCrihCZVX9/f3x8fLhy5Qq5ubmoVCqefvppNm3ahNlsZtiwYZjNZhYvXmy3/muC7YaJgLAG8ff3R1EUCgoKKCkpka%2B7yN7fD6rbv8UFF1xwwYX/FLZu3UpMTAw%2BPj706dMHX19fp%2B8T3kcCwcHBjBs3TmY8FEVh3rx5djvNRUVFdsF1/fr1qwW6eXl5dmVqI0aM4ODBgzLQUBSFoUOHEhMTg9FoxGg0smLFCunxBFUS6waDgWnTpklVN2H6bIvExETZZ2ebhRHQ6/VAlSz51KlT5evCu01ArVbz22%2B/YTab0Wq1TJkyhZYtW%2BLl5cWjjz7K119/bSfA4uXlJT0ERT/c7NmzKS4ulgGUIKGxsbEy%2Bwe/q/h5e3uTlJREaWkpDRo0YOXKlfTo0YN33nmHoqIiPDw8iI6O5t133%2BXatWu8%2BeabZGVlkZ6ebldilpuby6VLl25J9ACKiorkNRAZo9oSPai6/pGRkTRu3BgfH58a3yeuk5DMN5lMsv/QFrY9Toqi0LRpU9lPJF4LDg6WJFOslSVLlhAXFyctPGwhzMSFeqsjhHqru7s7devWxcPDo1pmMCsri4YNG9pl8jp27FitDDgrK0v26wkkJibaZcJtz0ec0x%2BF4/0KUFBQIK%2Bh1Wq9LdHz9fWlXr16qNVq2rVrZ2drIOYlMDBQvt/Lywv4fc26ubnZHU/Ms7%2B/v93Pwm7CNtNuO36Rdezfvz9qtRqNRkNOTo5d9tfT05OxY8fKn0XPqaM/Z03n/NFHH0mfR/H9bdu2dfqz7fUXKriDBg3ioYceks8iAVFVYIuQkBA2bNiASqWSvysuLubcuXOEh4c7HZ%2BAME%2BfPn26HEtJSQnPPPMMFosFDw8Pdu3aRfPmzcnPz6esrIySkhKsVqu8vxISEuTceXl5odfr6dSpE%2BPHj6d79%2B489dRTeHt7y7GtX7%2Be3r17ExcXx5AhQ5g7d658fnh6ehIaGkpYWBiKonD69GmKioq4fPkyWq2Wxx57jHfeeYdRo0YxceJE3n33Xc6cOcOZM2f48MMPadasGUePHiUlJQUPDw/5p0WLFlRWVtK6dWu6detGWloaDRo0cClx/k3hyuy54IILLtxlNG3aFKvVynfffUenTp2wWq3Vsk2OvTzORDqceW/ZwtHAXPynvm3bNpkNVKlUpKSkkJGRId8XGBiI2WyWvR1eXl5YrVYp/tG8eXMOHjxot3Pu6FMHVWTzl19%2BsTPWdhx/s2bNuHnzJkFBQfzrX/9CURS0Wq38DFSRv65du8rAJigoiIsXL/Liiy/yySefsHXrVpo2bSqDV5FdsiUrzrJMoaGhXL58GZVKhZeXF7m5ucTExHDhwgWSkpIwmUz88MMP0gagV69evP3227Rs2ZKxY8fy5ptvcvjwYS5evMgDDzxAWFgYVquVq1evUlRUJD3%2BsrKy%2BPzzz51K99v2wtWEzp0707t3b5599llMJpPMThoMBlmGKiBIbE3iI7brzJZ0Oa6h2bNns23btjuyNRDzHBsbS/v27UlNTWXEiBFUVlZSXFyMu7s7hYWFWCwWmdV1c3OjdevW/POf/8Tf35%2B3336bl19%2BmZCQEE6cOEH37t1Zs2aNvJYGg4F77rmHnTt3yvPZsmULS5YsYcmSJTWOzWAw4ObmRnx8PNu3b8fDw4MXX3yR1157DagiEUKGHqqyHfXq1aNZs2ZERkby5ptv8txzz8mMrRiPuBfEWE6dOsW8efPYs2cPH330ERqNhpdffplNmzbZXQMBX19f8vPzMZvNWK1WfH19sVqtMovTuHFjjh8/LgmNXq%2BXAkxJSUkcOXKEyMhIzp8/T2pqKlu3bq319bKFyPIJOxdRhi3uQ41GY/f8EfMphJVqUzJpm0l0d3fnmWee4a233pL2MBs2bCAsLEz2bwrbFFuPSvHv7Oxs1qxZw4oVK6Qw0R8p2xTZzvDwcC5evCiVP8U9pdFoSEtLk0R6xYoVmEwmGjVqJK/77VBTVtex/Pg/IfKjVqtJTk6WYxX/14DzMmdFUdi7dy/e3t5/6Thc%2BO/CRfZccMEFF%2B4yOnXqRGlpKc8%2B%2ByxTpkwBqvvY9e3bl1WrVt3xsWsKGPz9/Rk1ahQrV66U5ZHNmjUjNDQUjUYjv0uUEAUEBHD16lV5LGfqm7dS5ITqMvTO4Ex%2B3zYIEuejUqnw9/cnJydH/q5Xr15s2rSJt99%2BmzFjxth9ryj3FPYNv/32myyfAvtsgW0JpVArTUhIYNy4cTz22GMEBQVx6dIl1Go1J0%2BepGHDhnz%2B%2BeeMHDmSw4cPA1UEvm3btrz33nskJCSg0WhYv349ERERWCwWaT8xePBg3N3dGTNmjAxmn3jiCbZv317jHK1YsYLGjRsTHx8vSWzPnj359ddf7ZQ0AwICsFgs5OTk/OmgUaVSSXEJjUZzR%2BWH7u7uzJo1i1atWpGUlARQrSTUy8uLxx57jLlz59KjRw/WrVsn%2B%2BfOnz%2BPj48P3t7enD9/XpJEQVRtCatKpWL48OEsWrTIjow0aNCAX375RRJLi8VCnTp10Ol00kZAkP3/JLRarRQ%2Bsd1wqI0qpWMZrxAyEuN2JAs1qbbWlkTo9XoSEhKq9Sw6K7MW3%2BPr60tubi5paWls3rzZ6ecCAgKkOJKYb2f3oSPUajVubm4UFBSg1Wrx8vLi5s2bdO/enQMHDsi%2B2169erFx40a7DQtnm0%2B3wh8hWraf%2Bat7o2vz3X%2B0TDosLIxr167JDSFhKWPrg9mlSxemT5/uNOvvwv8%2BXGTPBRdccOEuYt68eRw7doydO3fWKrgQGRDHfhpb3CrQEAGhM/%2BvsWPH8vbbb9sFb/Pnz2fbtm2sXLlSvt6zZ0%2BioqJ499137b7TVsXRGYTQSXl5Ob1792bt2rVOz08Yejds2FBcgyJyAAAgAElEQVQKsgAyULxx44Y0ilepVOTl5TklH4qioFar2bx5M6NGjeLs2bM0adKECRMmMHToUPz8/GjWrBl79uwhPz%2Bf5ORkcnNz6dOnD/PmzcPT05OEhAT27NmDoihSDMNqtdK4cWMWLlyIp6cncXFxPPnkk2RkZPD1118DkJSUxPLly4mLiyMuLk4SzYyMDNq0aYNOpyM9PZ3y8nIiIyOZNWsWw4cPJyMjw46wgX2gOmnSJDp37sypU6cYNWpUjXN9O2g0mmpiLwInT56krKyMmJgYfvrpJxo2bMixY8ewWq3Ur1%2Bfzz//nAMHDrB//36WLl3KQw89RI8ePcjMzGTWrFmYTCZJoHQ6ndwEEKIjjujRowcFBQUyOyeyK86uZ/369XFzc%2BPo0aPVLEBqCnb79u1LWFiYzMKlpKRw/PhxwsLCOHPmTI33nciU1jaA1uv1BAUFERYWJssrrVYra9eutcsq63Q6af6%2BevXqanPyn7RtcCSMJpMJf39/SktLuXbtGu7u7pSWluLv78/Vq1dxc3PDYDCQl5eHm5sbdevW5ddff7UrxQZo164dubm5nDhxwu5126yRUN7cvXs3fn5%2BADzzzDNs2bKFhIQE9Ho9Bw4ckL3GJpPJrpRWED1bUmzbTyg2iUQPse36EB6PZWVlUqW2a9eu0pdQo9FIew4BnU5HUFAQZrOZq1evYjKZ8PLywsvLizNnzjBr1izKy8tZsGAB58%2BfZ%2BTIkXzyySdyveh0Ovz8/GRJeUVFhZ16ZocOHTh8%2BLDd9RC%2BnQCpqakcOnSIgoIC%2BfywWCzyXCMjI6XHno%2BPD3l5eXLuk5OTOX78eK0sWhyhVqtJTEykVatWfPjhh3h4eJCfn4%2BPjw%2BrV68mODj4jo/pwt2Fq2fPBRdccOEuYt%2B%2BfZSWltKoUSPCw8OpX7%2B%2BzPqkpqaSmJgIVAU1w4cPZ9WqVdLfDX7fCRfecxqNhmHDhsn%2BN/G3CIZFYCn6Y1QqFW5ubhiNRqn2KQKEXr16YTAYpMhBWVkZKpWKVq1aVSuRs1gsNRI9sRtcWloqCebevXurva9OnTpUVlZK8ZaTJ0/KDN64ceNQFIV69eqRlpZGTEwMzz33HFu3bmXVqlW8%2BOKLQFXJqQgk27Rpg1arpXPnzvz73//GYrFQXFzMoEGDqKioIDMzk40bN5KXl0dlZSUHDhzgl19%2BYdasWZSWlpKdnc2OHTswm83y3P39/bFYLEycOBFPT0%2BysrKwWq0sWrRIKgIKOPZMCVitVkpLS/n888/56quvmDFjhhRycSR6gCR6CQkJdO3alY4dO/4pogdVvUxHjhyRdgUFBQXy%2BuTn58vS0HfeeQd/f387sqLRaNi/fz%2BrV69GpVJx7NgxJk6cyPjx4ykrK7Mz3J47dy67du3i22%2B/dRokhoaGYrVaOXz4MH5%2BfiiKwhNPPCF/L/rQdDqdLGucPHkyUJWtAyQBtyVlYWFhstRx9erVkugBHD9%2BnKKiIn777TesVqtcn469g8LTryaIdSZQWVlJamoqBw8epHPnzgwdOpTt27fbkR%2Br1Yq3tzdTpkxh9erV1cplVSoVjRo1Yu7cuWg0GgYNGkRsbCwffvihtA3x8vKq1k9o23vpCNF/J8q03dzceOihh2Rvn7u7O9evX8dqtVJYWIjZbObmzZuo1WqKioqoqKigsrISg8HAyZMnSUlJsTsnb29vLl%2B%2BLK%2BR47hEn6GwEdi0aRNr1qzhww8/ZN%2B%2BfahUKtLS0ujXrx8ajYbBgwejVqtZs2YNBw8elH/atGlDo0aN5HUSRC8iIsLu%2BpWWllazo/jkk0/Yu3cvnp6elJWVYTQapV%2BcWDs3b960y1yVl5dz6dIl6TFZXFzM1atXZTZy8uTJTJo0SRLFLl26EBAQgMFgwGAwkJaWJn1OxVjEppSiKCQlJZGcnIzVapW/F3%2BLvtjGjRvLLLiYW7PZjFqtxmAwyNL63Nxcu7k3mUzyWS/67HQ6HSaTic2bN6PX6zEajWzevFmOxc/Pj%2BjoaNkH%2BPHHHxMXF8djjz3GK6%2B8gqIozJo1q9r6cuF/H67MngsuuODC/ygWLVrEu%2B%2B%2Bi1qt5u2338bLy4v9%2B/fz/vvv4%2B/vz/Xr152WfymKgl6vp7S01GlZpC08PT0pLy%2BnRYsW7NixA6gSMLh69epdN4Zu164dXbt2ZerUqbzwwgu89dZbQNWutclk4siRI4SHh0t1uPr169OqVSs0Gg2fffaZ3bGcGW47g8iKijIxq9WKj48PZ8%2BeJSwsjEaNGhEREUF0dDQAa9euZffu3YSGhtKyZUsZWK9evZouXbrQvn17JkyYIOfxgw8%2BYPTo0XZ9mY6Z2GnTplFSUsKsWbOk2ThUEdns7Gy76yKyZu3bt%2Bdf//rXHc2vWq0mKSmJU6dOYbVamT59Or6%2BvgwdOlQePzQ0FK1WS3Z2thS0ycjI4LnnnuPgwYOYTCZKS0sZN24cP/30E0eOHJFy/GKT4IEHHmDz5s1YrdZaG2j/EfTo0YMLFy5w4sSJaiWMtplsx3kXZFH0u9pmR86fP88jjzxChw4deOyxx/Dz85N9p46YPXs2kyf/P3vvGR9V1bX/f6dmJm3SeyBCIAklBURaIhBAaugElA4iohRBUUCKD4IgVQUUEKQqHaQKAkov0kPvEEggCSmk9/m9yH9vZ5JQ9PZ5uP3853oDmTk5Ze9zTta111rXNZ4KFSo8M2MIJZmm7OxsOe8uLi6MGDECBwcHRo0axfvvv8%2BMGTMICQkhLCyMzZs3m4n2wJ8edSqViilTpjBu3Dg2bdrE2rVrpUrm00R9nnbvPQvPUmT19/eX5tzR0dG4urpy6tQpTpw4YXbNwr9OKKMKeHh4oFKpzMpo9%2B7dayaaEx4eTs%2BePc0qCqBknkSmV7zrylOuFe/F8PBwnJ2d2bZtm7ynxftSlK0XFBSUeXe6uroCJff1o0ePaN%2B%2BPb169eLKlStMnTq1zPGelxkW1i2iz7G87/Py8swIYGlxmmf1%2BImfhcKu2GflypW5ceOG7GUWxN7f35%2BwsDC8vb3Zs2cPMTEx8jqESIyVlVW5C3UW/HfDQvYssMACC14CgoKCADhw4IBU2isuLpZBk6%2BvLykpKTKA6NixI5s3b5a/X6VKFW7cuCGFBP4uqlWrxrVr16TlgUKh4NChQ4SHh9OhQwcpznD69Glu374tS6tMA7XQ0FDOnTsnfxYCCePHj2fWrFkAbN26lXbt2jFz5kwOHjzImjVr5PZ79%2B7lxx9/ZP/%2B/URERHDhwgXOnj37l64jLCyMLl268PjxYy5dusSePXukkIpKpaJt27bY2dmxatUqoCTjULNmTS5evCjLYQsKCuS1tGjRAnt7e7Zt28acOXMYPXq07F2KjIyUxzUajSQkJMjS2tKlhA4ODn%2BpV0jgWUTb9Dt3d3cSEhLKNaU2lbkfPnw4CxcuNLO8EMStWbNmZtmF0n2VQiRHfD5%2B/HgmT57M9u3buX79OqNHj2bHjh00b9683GDzfxuljxMQEMDw4cMJDAwkMjJSqhSOGjWKo0ePAvD48WNZ7vc0wnPw4EFef/11KRbSqlUraQhub29PdnY2oaGhXLlyRYoV1alTh9OnT/%2Bj/VoVKlSQmWZTaLVaGjRoQP369Zk5cyYFBQVUrlyZ27dv/2PjXnpsS/cSl0aVKlW4f/%2B%2B9KK0tbWlefPmsgdYiJPUrVuXtm3bMnLkSACOHDmCs7MzAQEB0g5A9LBCSSVAnz59nnmuptYQM2bMYNSoUfI7lUpFSEgI586dw8vLCwcHBy5evEjfvn1ZsWIFBw8eZMSIEZw8eZKIiAhOnjzJ%2BvXrad%2B%2BvVwEqFixIomJieh0OlJTU2X/r%2BhldnNzo3r16ty8eVOWIZf3DJiK%2BJSH5/U2q9Vqs0WJ8r5/nsjTi6JOnTrMnTuXoqIiwsPDsbKy4vz58//Ivi34v4OF7FlggQUWvAQIf73OnTuzYcOGp67KCpTO4AnlvRcRdvgrUCgUHD58mPDwcDPlva%2B%2B%2Borly5dz8uTJMmqa4v8i02D6nYODA7m5ucyfP593332X9evX4%2BrqSsOGDWXgGBoayuDBg2ncuLE8j2XLlnHjxg3Onz9PcnIybdu2lSVWQu58w4YNcmVenJONjQ21atWiR48eDB06lKKiIhwdHXFycqJ27dqsXr1aSvrv3r2bN954Q2YrFAoFNWrU4MKFC2zcuBGDwUDr1q3ZuXMnrVu3ZsGCBYSGhpqZpu/cuRM7OzvefvttVqxYQd26dc3IoIDIWAhRDQG9Xs%2B2bdto27atFEiIiIjg7Nmz5OTkSBLZqlUrHj58yNmzZ82CuYiICA4ePCiJS%2Bm5NJ2HjIyMMtnNdu3asXPnTkn2XoSgiWO1a9eOhw8fcvLkSapXr86dO3fkvSjuo4YNGxIdHU1kZCSxsbHMmDGD6OhoVq9eDcDIkSNZvHgxBQUFtG7dWpZmisUQgSVLljxVtEZkcUQpY2BgIJcuXcLKykoaru/YsYMePXqQlpYmFUyfJtohxtdUcMjd3Z3ExETWrl1L3759iYqKkosvQjnTdNzKy4KVJs0qlQoHB4cymZ0XETIyJagGg0Gqmgr83ffC87JHgMx6m9qbaDQaCgsLzRY7Xn31VVQqFadOnZL33aBBg9i5cyf3798368UdP348x48fZ8%2BePfL6RFY5Ly9PPj%2BiEsH0XjdV2xXnO3DgQL7//nv5vU6nkwtOW7duRa/X07BhQ37%2B%2BWc6deokvTcfPXrE/v37admyJS1atODnn39m6dKlDBw4kMDAQDIyMnj06BG5ubksW7aMAQMGoFKpqF69OufPn5dVBrt372bChAk8efIEKysrNmzYgE6nIy4uDk9PTw4dOsTnn38uxzg8PJzk5GT8/f1p0qQJo0aNIj8/H6PRSNu2bVEoFDx8%2BJAGDRrQtGlT2e9rZWXFxIkTadiwIRkZGXIRIioqSooQPXr0CKPRKFV5xfw87VkXz5Ner5eLeI8fPyY8PBw/P78ywjsW/PfDQvYssMACC14CBNlzcHAgLS0NwMxeQJQVCZQO5k2FPp5WpvkigXvv3r3L9N%2BFhISYrd5Onz6dq1evytJIT09P6SGlUqnw9PQ0s3To379/mTJKgR9//JHs7GwGDhwoP6tatSp3796lRYsWfPTRR3h4eGA0Gvn9998ZNWoUI0aM4K233kKpVHLt2jXUajWVKlUiLCyMwsJCHBwc0Ol0hIaGsmvXLlQqFY0aNeLUqVPk5eXJHh4o8RITvXQjR45kzpw55ObmlgloVSoVLVu25PLlywwaNIhJkyaRl5fHypUrqV27ttzu008/ZePGjRiNRpYsWUJ4eHi51x0QEACAj4%2BPLHMUKJ0tcXR0xM/Pj4SEBDOz%2By%2B//JKMjAzOnz/PmTNnnqkcKcaktN9caZiSBpFRsbW1LWMk/VehUCjYuXMnrVq1kn57wcHB1KxZk6ZNm/Lrr78%2BtaxWp9NRuXJl3n77bTZu3MjRo0efminz9/fn3r17ZmV3f1UJccSIEWalgUJEw9RQ%2B5133mHp0qX07t2bJUuWAGWFkIS4UGhoKDNmzODtt9%2BmsLDQLMsjnklRNlqhQgXu3r0LlJC83r17s2vXrjL3SMOGDc3sUER2R6vV0qFDB9auXfvc6yxNNgUEMSx9PcLk22g0EhQUZGambWqjArBy5UqcnZ25f/8%2BgwYNkosYvr6%2BXLhwgS5dupgd097enpycHDOBnf8kHH2a8qiAVqtl586dtGvXjkqVKpGamkpcXBx9%2BvRh%2BfLlAJw/f5769etLUmhtbU1aWhoVKlTg9u3bqNVqWT4veg9bt25Nfn4%2BBoOByMhIbt68ybVr16T6ryleeeUVoqOjUSqVnDp1in379sn%2BvsaNGzN48GDy8/NZunQpe/fuRavV8vjxY2rWrEmHDh2oUKECO3fu5NdffyUzM1P%2BvdBqtVSsWJGioiKysrJISkqSz5aY09q1a/Pxxx/LEm2hBiu2E%2B8gUwKuUCjo2LEjAElJSRw6dIjBgwebKR1b8O%2BAhexZYIEFFrwECLJnSuLEynRRUVEZsld6hX3//v00btwYnU6HXq%2BncePGZGdns3v3bqCkhG/9%2BvW0bt1a/uEODg4uU4ITGBjI1atXzT4TZZgCpXtXdDodPXr0kCqDKSkpVK5cmbt371JYWChLUU0zFKJnzs7OTgYqtWrVklYFQmBGoVBQpUoVLl%2B%2BbNarIsQ2xJhUqlSJrKwsMjIyzMbJ19eX119/nfPnz5tdq2kwGRoaSlZWFjdu3DC7brFNaGgoT5484c6dO0RGRnLy5EmUSiUrV66UpM0UQUFBFBcX06VLF6ZMmQLAJ598wscffyxFPIKCgtBoNLi4uJCWllZuP9Hz/hxbWVlhbW2N0WiksLDQrOfraRD7dXBwwN/fv0z52MaNG%2BnVq5fZsZcvX063bt3MPmvbti0nT54kMTGRefPmMWTIEObOnYvBYGDgwIE0a9aM7du3mxGKyMhIfvvtN6Kjo3F3d%2BePP/4w6%2BH6Oyg9TqGhobRt25bJkyfLLPS0adNITk4mKytLCrOo1Wq0Wq3MdjVq1IiDBw8yatQojEYjs2bNkiRs06ZNdOrUyey4Imst4OrqSsuWLalduzYFBQWyZDAqKoqDBw8SEhJCeHi4FN/5K/D7/3zyXmQsdDodffv25bvvvnvu9qYBvekY1qlTR4qVlN73a6%2B9ZtYL%2BuqrrxIQEMDo0aMJCQmR99%2BPP/7Iq6%2B%2ByuPHj2nYsCE6nY733nuPPXv2cOnSJbld48aN8fLyYuLEiYSFhUnD8bCwMFJTU82uW61Wm5UX29nZYW9vT2pqqiShgjSXzqwKmKoW79mzh6ioKLOFFfH9woULady4MSEhIQwcOJBvv/1WHlej0VCvXj3mzp2LXq9n9%2B7dDB8%2BnC5durBhwwYpHJSXl0fNmjU5derUP1ZG%2BX8BtVqNUqksQ1DVajUODg44Ojpy48YNPDw8%2BPXXX6XolwX/HljIngUWWGDBS4Age3Xq1DHzsbKysjJbeX/aK7p0huibb77BycmJnj17AuWTh/JESl6EZJRe8be2tpbm3Wq1Gk9PT1QqFeHh4axatcqMwJqKBIiVdy8vL%2BLj4xk8eDDfffedmX%2BWgFqtlrLvRUVFpKamUlRUJD2gTMs3BUaOHEm/fv2YP38%2Bixcvlv2Fpt5/jo6OHD9%2BnBYtWsjA0vT6NBoNvXr1MstM6vV6Zs6cSbNmzcodH2Gt4OnpKUsNa9WqxZYtW/D19WXHjh2MHDmS1q1bM2HCBBwdHalbty7Z2dlUq1aNpKSkMlk6074cZ2dnCgoKyM7OxsbGpkzJnti%2BvABTo9Gg1WqlBL6QfTedy9I9WO7u7mUygt7e3kycOJFBgwbh6elJfHy8nPf4%2BHicnZ1lFqx0plKv16NUKqUpt4DpYoCtrS15eXnPzM6UVxqp1WqlpYO4HwTJzsjIYMqUKZKA/s///A9jxozBaDTSqFEjTp48yYkTJ6hZs6ZZyebTbEH%2BN1C9enUuXbokr6Vjx47ExcVx%2BPBhs%2B1KL7iIsknxrD3vGRbk7bvvvpMZx%2Bdtr9frWbRoET179iyT7X9e2fnTcODAAXnvtm7dWr4nTHv2oOR%2BbtiwIefOnZP3bJ06dbh69SpVq1alWbNmNG/eHG9vbwDOnDnDgAEDzESERDa0WbNmFBYWEhkZWaYEsWvXrqxfv57hw4ezbt06qbxZ%2BnpsbGywsbFhzJgxODs707dvX1xdXcnPz/9bPbn/CTQajVnJqqkHqSijrVatGikpKc/N7D8LKpUKW1tbXn31VYxG4wstKFjw3wmL9YIFFlhgwUuEKJMREAG%2BWLF%2BGgTR02q1TJ8%2BnYSEBPr16ye/FwSr9L6fB7VaLYmoQOnzECVfUNKvFBcXx927d6X4hWmmTQRMpkG8KE0UwYOwhNBqtfj5%2BWFnZ8elS5f4448/OHjwIEeOHEGv17N06VI8PT3p1asXb7zxRpmgPycnh8OHD1OzZk1UKhWZmZlkZGTg5uYmyzhFyWxycjJ9%2B/ZFp9Nx5coVGSgpFAo6dOjA5s2b0Wq1Uk6%2BdA9ZeTDN/Jie27Jly9BoNIwcORJHR0diYmJ48uSJ7ImJj49HrVaj1%2BvZv38/165dY/Xq1XIO165dK8U3BNETWSzRx1jearu1tTUBAQF06dKF119/nVGjRtGuXTsqVKhgNpem5ywEZ0pL%2BCclJbFp0yYmTpzIsGHDpO/gkydPMBgMZuQuLy8POzs7%2BbMISEWW08HBga%2B%2B%2BoqqVasybNgwdDodBw8e5OLFi9StW1f2RGo0Gjp06EBQUJC0tihtdyAWHUzHfM6cOYwZM4Yvv/xSfubv729mCXHs2DHCw8NlMFy3bl353fbt28uM5f8GHBwcJNGDkmtZv369fI5MUbpU27Tku1WrVs89ltFYYvfx0UcfvVDWSSinisWj0hUBpYmd6T0lUKVKFUkaoeQ5b9WqFU2bNiUyMlJm9YAy/YWTJ09m/vz5HD58mIoVK6LT6bhw4QKenp4YDAZu3bpF9%2B7diYqK4ssvv%2BTixYvy3jAajfTo0QOj0UjHjh3Jzs4mPz%2BfgwcPlrmvBamfP3%2B%2BWQ9i6evLysoiMTGRESNG0KdPH4qLi5k0aZLsu9Pr9QwYMAC9Xo%2BVlRVWVlYMGDCAgQMHSvsasR0gRX%2B2bNkiP9NqtfI5Ft9v2rRJfiaM6KHk2di8ebOZ4JTRaJTvuRkzZshtq1evLsdfKI%2B%2BCIqLiykoKCA1NVXaZ1jw74Qls2eBBRZY8BIgskH/NBQKBcuXLyc2NpYVK1Zw/fr1v7yP8np3nqXAB5SbzRP/isxOfn4%2BOp2OjIwM2rdvj0qlYtu2bdjb25OVlYXRaGTixIlMnjyZs2fPMmbMGE6fPg3AvXv38PHxIS4uThJNQV6trKzMvKxMx0KIseTn56NUKtm6dav0Mly4cCHvv/8%2BZ86cISAgAK1Wi5ubG/v27QOQHoceHh6MHTuW119/HUAqPAoI4q1UKvHy8gJKBFnc3d05cOCA3I/oYZo/fz7z5s17aunZX4GDgwNPnjxhw4YNdOnSRe5PpVIxbNgwvvrqK5kFgJLyw7Vr17JkyRJWrlwJlJTmlQdR8lmzZk2uXLlCQUGBzMAIcZIJEyYwePBg1q9fT1JSkiT1pllkBwcHGSyKsYqIiJAm6mLsRC/omDFjpH9ZeHg4%2B/fvp06dOjRr1oymTZuyb98%2Bpk6d%2Bh%2BNG5T0UKWkpPDkyRO6devG2rVriY6OJiUlhb179/LVV18xZswYcnJyMBgMrFixgqtXrxIWFoabm5uZUI9AQkICb731lrxOkdH%2BOz1ppr/zvJ60v7IvU1hZWWEwGMoofZa3XV5eHhERETRs2JAvv/yS9u3bS4uH0jD1PjR9H6hUKpk5NSWdjo6OvPnmm3z33XcoFAo2bdokF1i2bdvGmDFjzPoyZ82axezZs2VGXOz7aURW2A/o9XozJWHT%2BXFzc5Olo2JBSvToeXp6yoUQb29v%2BXwrFAoSEhIYO3YsW7ZsoX79%2BmzYsEEKQ2k0Gtzd3Tlx4gSFhYXS0F2Urvr4%2BLBz506ys7OxsrKSBMvGxoaWLVuybds2aRDv4OAg58nKyooaNWrIMnjRuyfewaIPU6VSERERwYEDB/D19eWNN96ga9eutG3bVr4Po6KiUCqV5Obm4uHhIashTAm4jY0Nc%2BfOpWHDhs%2B8Tyz474SF7FlggQUWvATExcXJrMKRI0e4evWqJDymq/hhYWFYWVlx8uRJHj9%2BTPfu3Zk5c6ZUTnzttde4evUqlStX5uzZs7Rv354vv/yS3bt3M3LkSKZOncqECRMICwvjxIkTREVFydXs8mwUSuNZ/TCmqFixoplIC/wZYLq5uclVc6VSSWFhIVu3bqVfv35S5CU9PR21Ws2QIUPYuXMnW7duZcyYMZJwxMbG4u3tbSZYIo5x%2BvRp1Go1Z86cYevWrVy4cIHr169LZbpVq1bRu3dvrKys%2BP3337Gzs6NTp0688sorxMbGsnz5csLCwrC2tqZ169a8//77ALzxxhtUqFCBiIgIDh8%2BzJw5c7C1tS3TdzZmzBi0Wi0hISF06tSJL7/8ktTUVAwGA3Z2dmZKnCqVisTERIqKinBycpJCNTNnzkSlUjFixAgAVqxYwYMHD1Cr1fTu3dusrNTV1ZXc3FyzkkzT/sfS8xccHMyqVauYPHkyx44dIzg4mMqVK7NixQocHR3ZsWMHCoVCktiCggJSUlIkWXNzc5OiD8IbbNiwYRw9epRff/1VGjyLXkJx/5qWadapU4fMzEwzkQ9TqNVq/Pz8uHnzJgaDgfT0dJydnVm4cCGDBw9m8uTJ7Nu3TxJx0yyMgMFgwMnJiSdPnpCRkcHAgQNxcHCQ31%2B/fp0LFy5gb29Pjx49ePz4MUqlkkmTJmFjY0N%2Bfj59%2BvRh37593Llzx8y64mnnrNVq8fX15fHjx/j5%2BZGTk8O9e/fMejJflKiZErI6deowYcIE2rVrR%2BPGjbGysjIrQTQV0hAw9Q2E8jP5pUlfSEgIb731lvSC1Gq1dO7cmVu3bpmVl2/evJlOnTqxdOlS6tevT7Vq1di6dStdunThp59%2B4ujRo2bZJG9vbx4/fkxeXp60BRFlh%2BK%2BMBgMUgQlPz8fHx8f7t%2B/j06nY%2B3atdy%2BfZvffvuNgwcPkp2d/Uy/UDEfguwdOHCARo0aSZEdMZdCxViMxfLly%2BnduzeOjo506NCBwMBAkpKSmDVrFnXr1qVChQqsX7%2BeqKgoUlJSuHz5MgApKSkMHTqUVatW8eTJk3/UaqM8vIhC6z8BlUqFh4cHCQkJeHl50aVLF86cOcPt27eJi4srI1Blwb8DFrJngQUWWPASMW3aNFasWEFgYKBUiRRQKBQ0adKE2bNno1QqqVmzpvTwEiqepmRMpVKxbNkyfvjhB37//XcAWYZoZ2dn5sNWvXp1pk6dKn3v/qE0LkgAACAASURBVA5Mj/08BUTTgNd0FV7sQwROeXl5DB061KwkVch%2BHzhwgObNm2Nvb09hYSFpaWnY2tpy8OBBBgwYIP3PWrZsiZOTEz/88AMajYbKlSsTExNDpUqVSEtLo2XLlly7do0//viDwMBAUlNTSUhIKHNNT0OvXr3Q6XTk5ORgY2PDwoUL5e/Cn%2BWQpQNyhUIh%2BxRff/11WrVqJct4IyIi0Ol07Nmzh/Pnz9O9e3eUSiUqlQqNRiNJh8iACQJZetwrVqxIz549mTp1qvx89uzZFBQUEBsby/fff18maLS1tWXgwIHcvXuXc%2BfOyXJHrVYrV/0LCgqemcUpTSxGjBjBoEGD6NatmywBFJ6A8GevoLW1NRUrVuTWrVtUqVJFWiYUFBTg5uaGRqMhODiYpk2bMn78%2BDLkS9xXn376Kb1795afh4WF8dVXX7F69Wrs7e2ZPn16mfOOioqioKCAO3fusG7dOvr27cvWrVsZO3asGdERKM/O4J/yEtRqtWYCGaVN18vrkRMoffzyiODTYJqBExAZy8LCwnJJqiBOYjzUajV2dnZmvWuiL9cUXl5ePHz4EE9PTx4%2BfMgbb7zBgQMHpCffi55v6XutSZMmHD582OxchaJqaZSeQ7E/UZmg1%2BvJy8uTGT69Xm/WS1xarVL8XyA0NJTMzExZCWEwGPDy8uLw4cOSrP7TYbco7xTzKPpan7dYUR4UCgWOjo6kpKTQvn17%2BdycP3%2Be3r17ExwcLCsCLPj3wEL2LLDAAgteIurUqcP48eNp165dme%2B2bNnCp59%2BCsBHH33Em2%2B%2ByenTpxkxYgT5%2Bfm88sorsufHYDCQlZWFSqWSQcVrr70GwOXLl8nMzESj0aDT6cjMzESn0%2BHq6kp6ejqhoaEcOXKEESNGMH36dLOASqlUmpUHmcJ0u2etPIuyp/Lg4eHB9OnT%2BeWXX1izZg2Ojo789ttv6PV6srOzGT58uCSjHTp0KJdw2Nra4u7uTrVq1ahQoQLDhg3jwYMHdOvWDYPBgL%2B/Pz169KBWrVosXryYJUuWmAWCtra2NGrUiKFDh6LVamnbti05OTnY2tpKAp6QkCADQdNgs1KlSrz66qu0bNkSjUbDunXrpDeW6e9OnTqVvLw88vLy8PPzY/Xq1bz//vskJSWh1Wp58OABer2erKwsuU1cXFy53nnu7u6kp6eTnZ1dZtzbtWvHjBkzaNKkiQy2q1atyv379ykuLn5qdkStVstAUZDW4uJiNBoNrq6uKBSKZ1o9QNkAu1u3bowfP54aNWoAZW0eXF1dyczMpFGjRqSmppKZmSll6%2BFPIlea0JoSg7Vr19KtWzd%2B%2Bukns4xDrVq1GDduHGPHjkWhUMiMorDu0Gg0tG7dGijpJ23SpAkHDx7E3t6eJk2asHnzZpRKpSS68Oc9LsqDi4qKsLW15bXXXiMmJob09HSaNGkiFXHFuD6vR87X15cHDx5IcmGqmFlamAVKSviaN2/Ob7/9RmRkJNu3b5d9ii1atOD06dNm4k0zZ86kVq1a5Ofnc/jwYaZMmfKPEA6lUom7uztJSUlm16hQKPjwww%2BZOXOmHDO1Ws3IkSO5e/cu%2Bfn50qT%2B5MmTJCQkYDAYZD%2Bt2Ed552gqpCNKF6dOncrHH38sn0uxuGV6L5ZHEk2hVqvR6XSEhIRgbW3NF198QUJCAp999hkxMTE4OjrSuHFj2rRpY0a0Y2JimDFjBoMHD8bd3Z0333yTH374gcTERDp06EClSpWYNWsW165do3nz5vTo0YNOnTqRkJAg/fOEF563tzcbN26kQYMGHDlyRPbyDh8%2BnOjoaHnMn3/%2BWdqwtG/fnl9%2B%2BYW%2BffsybNgwoKTM/OHDh6xatQoHBwe6du0qhYzS09PlYlvlypW5du2a7A0WJbfiPQAli1CLFy8mLi6Oli1bYmVl9UxDeAv%2BO2EhexZYYIEFLxH16tVjzZo1%2BPn5lfmuU6dOpKam8t5779G1a1fOnTtHz549Ze%2BHu7s7sbGxsvzK1dVVBuQqlYqKFSvy5MkTKRwiTKS7devGli1b5Iq6MO2%2Bfv26WY9ftWrVuHHjBlWrVjUTkhAoLxA1DdIiIyPZv38/U6dO5cKFC%2Bzdu5egoCDy8vK4efMmERER3Lt3jx9//JFbt24xd%2B5cbt26RWFhISkpKdIjTwRXpTOHpmRAp9Ph7u5OUVERwcHB7N27l9dee4358%2BebCRvAn2IQmZmZODg4yO%2B3b98uDdF1Op2ZME1YWBhdu3aVHoF9%2B/bFaDSyYMECqlatKrddsWIFy5YtQ6lUEhkZyTvvvIOLiwu5ubkcPXqULVu24O3tzaBBg6hbty5Go5EuXbrw7rvv4urqKgn6kCFDmDNnThkfN6FICn8G26aBvVKp5PLly0REREh1zPLKCGvXrs3p06epWLEiDx8%2BlP1Q1apVo3r16nTu3JkRI0aQlpZGx44d0Wg0LFq0CIBx48aRmJjIDz/8QGFhId988w2enp5Mnz5dZsRcXV2BEn%2B6KVOmmKmLfv/99yxbtszMN%2B6vQNxjwjutWbNmZUSExD1omlU17SMtDdMeJ2trazIyMnB3d6dmzZrs3btXXtNf8R8Uz48peXV1daVWrVpmIjDu7u7UrVuX2NhY7ty5I8mMWq1GrVaXu9AiIIiwGN969erh6elJSkoKfn5%2BLFu2zOz6n4bSPW9ie1PrAtOfRdZIp9MREBAgPeCepfYr%2Bt/KQ7169Th%2B/Lj8HfjrGTCNRkNQUBAxMTF8/fXXDB8%2BXJJD04yeSqVCoVDg5OSEWq0mPj5evj/F8UW58LMghGaET6mpGuazYGVlhdFolH3ETk5OZGZmSiGamzdvotfrSUlJkfsqnfn9q1AoFNL2RrzvOnbsyOrVqzEYDHzyySdMnDiR4uJiioqKpLE6lPh7qtVq5syZQ1pamuyjtuDfAwvZs8ACCyx4iZg7dy53797l888/l8qKAmFhYRQXF7N9%2B3Z8fX1p3749d%2B7coX379nz44Yd89tln/PLLL0/dt1BoEwGcUIlr3LgxW7duBWDBggWcO3eO7du38%2BDBA3Q6HW%2B99RY//PADOp0OFxcX4uPjUalUf1kgwjQjExYWRnR0NB07duTBgwc0a9aMOXPmMGHCBE6dOsXjx4%2BJiIjg2LFjfPTRR5w8edIsyLW2tsbPz0/2zEyYMIEePXowb948M08sARF4CYg/dYIMlYewsDCGDx9O37595WePHj1iy5YtzJkzR9ogvP766yQlJTF//nx0Oh1BQUEcPnxYqgHevn2bdu3aoVarWbNmTblkUKPRyB5HIXoizsFoNGIwGJ4qm25aemfqiShMn4OCgmQZr6urK4cPH%2Bbq1asMHDhQCjyIUrrZs2czduxYCgoKaN26NefOnaOoqIjvvvuOhIQExowZQ1paGnZ2djLzUjpbJTwSCwoK5HxbW1szceJEli5dytWrV9HpdOTn51NcXEzbtm2ZNWsWycnJHDt2jCtXrnD//n1%2B/fVXs2D2acJAphm27t27s3r1apo0aULVqlXlNuHh4TLrolAoCAoKIiMjg%2BzsbJKTk%2BW9ITwBTUmJWq3GyclJEhNxTePHj5fqi6ZQKpVoNBqzhQ8/Pz%2BioqJYsmSJWdmgUqnkypUrhISESIINJb1z6enpGAwGFAoF586dk8F2QEAAN2/elPe4WLQpr3RaZLvUajWurq7Ex8c/s9fL9LqjoqLYvXt3uaRC3Gd2dnY0a9aM4OBg1q1bR2pqKmlpaQQHBwMlvchxcXHllhC6uLiQkpLChAkTaNGiBTdv3qR3794EBQURGxtLZmYm9evXZ9myZWRmZvLVV1%2Bh0WhYunSpPJ/58%2BczZMgQHBwcSE9Pp6ioyKxfuGXLluzatYs2bdqwY8cOSYLFPGu1WqpVq8bjx4%2BJioqiZ8%2BehIeHy2dw9uzZFBcXM3DgQAwGA4AUDDJVpDx9%2BjS3b99m7ty5JCQkEBISws2bN2nRogX379/nzp078v75p0p9S8%2BbyC4/6zNTGAwGrKys5LsoLS2N4uJigoODuX79OtnZ2fK51uv12Nvb07NnT7Zt20ZWVhY%2BPj4UFRXx448//qPXYsH/PixkzwILLLDgJaJXr16cPXtWEglTu4SHDx/i4ODAd999x4MHDxg5ciQajYZVq1YRGhpKp06duHz5sgy2TT2XwDyjo9frcXNzIzEx0SyArlKlCpUrV5biD2%2B88Qbx8fFcvHgRa2trnJyc5GozlJTqubu7c%2BvWrb8UwIgs4NixY6lWrZqUdAdYtGgR1atXlwqNERERXLp0ieDgYIxGI4sXL8bHx4fFixezZs0aVCoVx44dw2AwEBcXR6tWrWSW7eTJkzRo0MDs2ImJiXz44YdAiUz9V199BZRknSZPnoybmxtQ0m%2BTl5fH2bNn2b17Nz///DMnTpwwC548PDxITEzExcVFrpA/ePBAes7t27eP0aNHs2XLFpycnDhy5EgZMpiVlUW3bt24ceOG9AQrTfa2bdtGixYtpKfg34WNjQ3W1tZmGSkhv56bm8uqVat45513MBqNNGzYkNzcXEliHzx4QOvWrWUmJycnp0x/lU6nk8SpNDFr06YNu3btktlIU/XUZs2aUaVKFZycnLh27Ro5OTls2rTpb18nlJQLJyUl4eLigq%2BvLz169GD48OEoFAqsra2pVasWV69e5fHjx1SvXp0LFy6gUCgICAjg%2BvXrMsBNSkrCzs6Ob7/9Fo1GQ48ePcjLy5Mk61kQ2SuFQiHtH8R163Q62rRpw5kzZ8xsIMqDUqnE0dGRtLQ0Kcbk6OhIeno67du354MPPmDatGns3LnTzMfyRcoVhT9m6Syeh4cHjx49MtuH%2BG7MmDFMnTpV%2Blyq1WpJCrdv305qairjx4/n6tWrqFQqdu/eTfPmzfHz8zO7Vo1GQ//%2B/blz5w6///57uQtIT%2Bu3%2B7sQHoPi3i0uLkar1eLi4sJ3332HnZ0dnp6eKJVKs8WYlStX4ubmVub5hT99NFu3bk1%2Bfj5r1qzhzTffZN26dVhZWXH06FFp/SHudzc3NykCExMTQ9euXbG2tkar1bJw4UKp2hsTE8OAAQNYsmQJAwYMkIb3hYWFskpDrVZz584dRo8eTb169aSq8J49ezh48KBUJxb9w0KJVK/XS5JnOr%2BloVar6devH2fPnuXUqVO4ubmRlpbGokWLqF%2B//j82Nxb830D9sk/AAgsssOD/z%2BjUqRNNmjRh%2B/bt3Lx5k/z8fLM/vsnJyWb9GgUFBbz55pu4uLiQmJiIwWAgOzub1157DU9PTzZs2ACU/BGvW7cuR44cQaPR4OXlxfLly2nSpInZ8W/cuMGNGzeAkoyBaQCWnZ0tTYrFyn5GRgbe3t6yxMy0v8jNzY28vDyePHlCnTp1ZJACyNLDqVOnEhwcjFqtxtfXlzt37vDee%2B9JkRNAKpKK3rT8/HzeeOMNXFxc5LVNmzaNe/fu0bZtW%2Bzt7bG1tSUgIIC3336bDz74QO5n5cqVfPPNN0AJyRCkD%2BDkyZNm2ZiioiKKi4tp2LAhOTk5hIaGMmrUKNnHCNC7d2%2B%2B%2Buor%2BvTpI4O/MWPGMGDAALnyL8ZcZDbEfKamprJw4UKKi4vNlEsXLFhATEwMwcHBMguTkZFBjRo1qFChAtu2bZPb%2Bvv7o9PpiI%2BPNxPcKS%2BIh5IsmCAfnTp1koTK19eXmzdvmpXtnj17lgULFqDT6QC4ePEiBoMBnU7H4MGDUSgUjB49WpbEGY1GPvroI3ndEyZMkKVpxcXFkuiZjgGUeMbt2LGD5yE4OJiYmBjee%2B89FixYUG55nGnJX3x8PEajkfT0dE6cOEFMTIws3czKyuLy5csyABaLF0qlknnz5tGuXTtGjhxJ1apV6d27N0qlklq1apGYmMiwYcOYMWPGC5lni2yORqORqrkCubm5bNy48bn7gJJsYlpaGiqViqioKD7//HO0Wi27d%2B9m%2BPDhKJVKdu3aRc%2BePVm5ciVeXl7cu3evDNEzLccD5L1hOpZivqZNm0bfvn3N%2BnCFJL%2BwQahXrx4LFy5EqVTyzTffsGjRIn7//XemT59OlSpV5ALIihUrMBqNZUhtQUEBy5Ytw8PDg%2BbNm7Nz506gZB6rVKnC9evXycjIQKPRYGNjY9bHVx5ENktApVKRn5/PqFGjmDlzJkajkerVq3Pjxg0zMamaNWvK6gVAPnumJeMRERFyvw0aNJDf/fbbbxQXF0vFyrt370qLk1WrVrFjxw6zZ7CwsFDe70uXLiUiIkKKFmVnZ%2BPi4kKvXr3kvapQKHB2dqZr165ASV%2B3adat9Lzev3%2Bf9evXlzs%2BIoMPJT6OOTk51K1bl5iYGOlzqFKpZK%2BuWq0mLy8PBwcHdu7cKY/5%2Buuv8%2Babb8r%2BWwv%2BXbBk9iywwAILXjJ69OghM2mAlPIuXZKj0WhQKBT4%2BfmRnp5uVub3rH4RU4W50iVdpcmBvb09BQUF5OTkPDdLICAyhgI2NjZYWVmVUf8UfX9ardaMBGi1Wry8vLh9%2BzZ%2Bfn7cvXtXXpPRaDQrVYSSgK5r166sWbNGmnf37NmTnj17EhERwZUrVzh//jyfffYZd%2B7cYdCgQXz99dfodDrpdQclWbStW7fi6%2BtLZGSk7HecPHkyTZo0keSyWrVqUtV0%2B/btfPjhh3Tr1o3OnTsDJZ6JpbNzBQUF%2BPj4sGvXLgIDA9m8eTODBw%2BWBN10bAYOHMi6devIysqSc2Fra8ugQYNYsmSJDHiVSiXt27fH09OT9PR0fvzxR4xGo/Taa9euHV26dOHDDz%2BUapaLFy9m0KBB3Lp1C41GI%2B09fHx8ePDgARqNhsLCQhnI1qxZE51OJ/sqlUolGRkZVK1alXr16rF8%2BXKzua9Vq5bMRl%2B8eJHs7Gy5T5GlLd0/9yIQ%2BxDBuVAgdXd3p3bt2ly5ckXe/3l5eVSvXp3k5GTi4%2BOlvH5YWBgajYa6desyb9489Ho9RmOJubhQUxXm1e3atePnn39GqVTSrFkz1Go1QUFBXLp0yayk09PTE4VCQXx8PCNGjJBB/t%2BFMB3Pzc2Vz65SqeT1119n9uzZ1K1bl19%2B%2BUXes8XFxQQFBWEwGKhXrx4nT54kJSXlufYOoudLEKrS5wAldgXCS1KcV3FxMbm5uaxcuZJevXoxbdo0qSArFkTs7Ozo27cvbm5ujB8//m%2BNw%2Beff05CQgLz5s2Tnz3t/SNsQqytrTl27BguLi7UrFkTKBG1KigoYNy4cUyePNlsbkR2uqioiAYNGuDj40NSUhI5OTncuXOH5ORkfH19mTRpEleuXOGLL74wy3yJcRLv2fKyYqbnrNPp6NWrFz/99JMZ4RYQxPhpZbam6r6m73a1Wo1SqcTGxgYfHx8uXrwoz0WcT82aNbl48SL%2B/v7cunWL4uJiaYGxYcMGevbsSW5uLt27d6dx48Y0aNBAlhRXr16dLVu24O/vL8vrn2aZYsG/AxayZ4EFFljwEiFWnouKiqTUvgi%2BN23aRNeuXfH393/qH1ulUsmgQYMICgpi3LhxZqToWShtMaDRaFCpVHzxxReyXFQE288LJGvUqMGVK1ek5PeL2BcoFAppH1GecIboyyrdl2QwGNi9ezeLFi2S3nONGjVi7ty5ZGRkEB4eTnR0NOvXr6dRo0aMGzcOHx8fAgMDX5jsOTg4oNfrZTbPVABF%2BLYJH7jDhw8TFBTEkSNHcHJyAkrKF2/evMn777/PsGHDCAwMpFWrVpw6dYrKlSvj6uoqeybFXCgUiucSayEqkZ%2BfT1FRkRQUqVSpEhMmTJDiLjVq1MBoLDFS3759O1FRUbLE8kWEHkzH3MrKCjs7u6cKawiUDsxVKpXMsvj4%2BLBv3z7Onj1Lnz590Gg0BAYG8uqrr/L777/zxRdfyBLA4uJifH19efjwIc7OztKqQWDw4ME0adKEQYMGMXv2bMaNG8eOHTukXUBgYKAM9Nu0aUNiYiJKpZLjx4/j6OhIfn4%2BWVlZZsImu3btol27dmzdupU//viDsWPHyrEyvQcDAgJIT0/H1tZWZsNfBGJ/wjDc2tqa/Px8IiMjGT58OEOHDiUxMZGHDx/KEltnZ2d2794tFxIMBgNHjx5l9%2B7dMntvKnpj%2Bvw0atSIVq1akZ6ezo4dOzh//rz8/mkKp38F4rnw9PTkwYMHODk5kZqaKo9fsWJFunXrxvTp06lZsyYXLlzAx8eHbt26mSmmenh4SEGkQ4cO0a1bN6kga1pqXB4EEZozZw7Tp0/nt99%2B47PPPmP16tXSsuPRo0fPFIxRqVTY2tqSnp6OSqWiVatWHDlyhJEjR1KvXj3atm3Lzp07adq0KYGBgfj5%2BTFq1Cig5Bn//vvv8fLykqJX4r0tso3r1q3D1taWqKgorKysSE9Pl%2BNuZ2fHypUrKSwspH///mRkZEiylZeXh62tLaNHjyY/P585c%2BaQnp5u5k%2BYk5NDmzZtePvtt2nTpk0ZVd7o6GjWrVv3l%2BdVPLPi/adWq0lLS2Pr1q0EBAT8pf1Z8N8DSxmnBRZYYMFLxPz581EqlTg4OJCRkYGXlxeZmZmkpqby7bff8sorrzB27FgWL15MQUEBer3eTClOpVLxww8/yHJEhULBjBkzpDFw7dq1OXv2rFkgUF6gV1BQQGFhIUuXLgWQQaS1tbUkeqZqkILYKRQK6ZEm%2BnVcXFzKBOnid21sbMjLy0OhUBAVFcVPP/0kyU5eXp4kI9bW1nz99deMGjUKX19frly5gkKhYM6cOdIAedWqVQBSJXLNmjUYjUYOHz7M3Llzadas2QvPw2%2B//UZwcLC0Vnj06BEeHh5Ur16duLg42U9YWFhIdnY2Op2OpKQkWrRoQXFxsSzDSk1NlVm7U6dOMWbMGIxGI/v27cNoNDJy5EjeeecdAJycnGjcuLEMnpOSkjh69CgqlYratWtz5MgRKlasiF6v59GjRzKg9vT0JCkpiezsbPr168eIESNQq//8cy6ygyLjplAo5P8/%2BOADSUqhpHRz//79PH782MxuIzAwkJCQEEJCQgC4e/cuq1atkmp%2BpbMRYrHC0dGR5ORkbG1tsbe3R61W4%2BHhQffu3YmJiUGn06HRaGTWsbCwUGZIRV9nv379mDRpEgkJCWYZYTFGLi4u5OTkULFiRZKSktDr9XTq1EkSvm%2B//VZmYU1hWoYp7v/i4mL69OlDTk4O0dHRchtra2szBcfs7GxiY2PN%2BhL1ej0KhUJuc/jwYTZs2MDcuXPlWK5YsYKxY8eSkpJiti%2BlUsnWrVvNSL8gYQqFwixr/%2B6773Ljxg1ZMi3m1LRst1atWkRHRzN69GimTp0qs8x9%2BvQhICCA6Ohozp49K7N64vpNs52mizru7u5YW1tz584d1Go1VlZWZGVlERoaSpUqVYASRdfRo0eTlpZGWFgYvXv3ZsyYMXzxxReSkIq5i4uLY9asWUBJxqtly5YyGwcQHx9v5stnKsJjujBROoMlsnOAHMv58%2BcTHBzM48ePadiwIVCyAPLJJ5/wySefEB8fT%2BfOnRkyZAgAXbp0kQqcNjY2nDhxAqVSScWKFfH29kahUDBkyBAmT56Mt7e3PA8PDw%2B8vb3x9vamWrVqxMTEUKtWLdnPvHHjRtLS0nB2dpaVD6LXMTc3l2%2B//RZvb29yc3MxGo1mPXb5%2BfmMGzdOzpFSqZSLBOnp6RiNRjZv3izVbwsKCszGShA9Nzc3kpKSyiykie1tbGwoLCyUfYyiV1II/FhbW2Nra0v37t1ZvHixxVD9XwpLZs8CCyyw4CWiadOmREVFsW7dOpKTk3n11Ve5fPky%2Bfn5FBYW4unpSVpaGlWrVuXq1asyELC3tyczMxNnZ2cz8Y25c%2BcSEBDAoEGDuHPnjlxl9vb2JjY2Fi8vLxITE3F3d8fV1ZVz586Ve14iyC7dewd/ytR7eHiQnJxMQUGBFBQwzTYI6HQ6ioqKKCgowN7entzcXCpVqgTA1atX0Wg0sv9PBCJCVr9169ZERESwd%2B9e9Ho9f/zxhyx/EoIG586do0ePHpw5c4bi4mImTpxYRtl09OjRsvyzTZs2QEn55JQpU6RAS//%2B/SksLOTy5cscP36c7du3s3fvXp48eULlypW5d%2B%2Be9AI8f/48v/76K3/88YdUd9RqtXh7e%2BPr60tcXBw3b96UpVMCpj5h9erVw8vLS36XlZXFgQMHUCqV2NvbmwX8pQm6EJzw8PDgwIEDZtcaGBgot9m2bRvt2rWT89G5c2esra157733JDkyxdSpU1mxYgUhISHUqFEDOzs70tPTuXTpEmfPnqVGjRqsXr2aBw8esGnTJhYvXgwgM7pvvvkmkZGRTJkyRWZNHB0dCQgIIDs724zclIZQVaxcuTK3bt0CStREk5OTZVmzh4cHTZs25eDBg3zwwQeMHz8eKysrKVxhCrVaTYsWLRg8eDCTJk0C4N69e3IhQmTS4c9%2BTQHRAyrEfhYsWEDXrl3JzMxk%2B/btsrfT3d1d9kGWV9b3NMVMQbhNLVfOnj1LcXGx7McSvpJCNt80ay9KoUeMGMH8%2BfPZtm0bNjY2NGzYkCNHjnD06FFOnTpFUVER69ato2PHjvTs2ROdTkfbtm3LPJ8ii2a6aNS4cWMOHz5M3bp1OXz4MABTpkyhS5cu8ncDAgL4/vvvZflnSEgICoVCZp9NvTFLVwiY/iz%2BL8ZQp9NRUFCAk5MTSUlJ8vPSIjmdO3dm9%2B7dfPDBB0yfPp38/Hz69%2B9PaGgoT548kWWl5c1N6dJ3d3d3fH19sbOz49y5c/j6%2BuLv78%2BmTZtkFYLp/SgUQf9uhvS/BaIy4/Tp02RmZvLLL7%2Bwc%2BdOzp07h7OzM0eOHGHevHmcOHHCYqj%2BL4WF7FlggQUWvESEhobKld3/BMIwff78%2BQwePFj2iJj2wJnCxcVF9gba2NjQokULNm3a9EI9egI%2BPj7Url2bLVu2PHdb0yxJeSjdG1OtWjXefvttPv30U5YuXUq3JLNycQAAIABJREFUbt0IDQ3lo48%2Bkr/Tt29fvLy8%2BOKLL/jggw8wGo0kJyfL1XdTmBqCm1oymEIQlitXrpCamsry5cvp1q0bly9fZufOnfzyyy8YjUbefvtt3Nzc2LVrlyy/unnzJtbW1mbecadPn%2Bbnn39mz549kuA5OzuTm5tLZmYmkZGR0kz69u3bPHz4UJYNWltbk5OTQ2FhIR4eHkCJDYSw0/j888%2BZOHEiH3zwAQMGDDC7jsDAQCm80KJFC3bv3i3HV8xvs2bNZGniunXrKCoqYty4cfj5%2BfHgwQMOHTrEjRs3SE1NJT8/nwYNGrB582YUCoUszRNERmQbhRCMp6cn9%2B7do2nTpjx69EhmRZOTk0lISMBoNBIREUFMTAwNGjSQ9iFOTk5kZGTI4Fmca3n3TmnhkfKwfPly6tWrV%2Bbz06dP07NnT%2Bzs7GS2zM7Ojtq1a9O/f39mzZrFH3/8QVhYGAkJCVJp9dSpU9SoUUMuCPwncHFxoUqVKtIHD0qIUm5uLkqlUtoDiPESY/0sg/nSXnj/JGrUqMHFixeBkrEXZZQZGRnlWpyITL5pT7CHhwcJCQlmfn7/V6hTp47MvBkMBmn3ERgYyMOHD/Hz8%2BPChQv4%2Bflx8%2BZNunXrJonolStXSEtLIyUlhUqVKsnS85SUFBwcHMjLy8Pb25vCwkKsra1JTU0lMzNT9j1bW1vj5eVFdnY2169f/4/e9dbW1rL6AP7M2gu/0KysLDIzM%2BW5lya5pl6Hgux26dKFzZs3s3v3bry9veVil8hav//%2B%2B1y/fp233nrLYqj%2BL4WF7FlggQUWvES8%2B%2B67uLm5MWnSJNlD5ujoKEnNggULgJIypxUrVrB582YzhTq1Wo1KpeK9995j0aJFZgFwYGCgLIF88OCBbOrPyMhArVbj6elJcXExSUlJ5Ofnm5Va5uTkmAlGgLmCn5WVlcwWClVNKF9UobSAiyl0Oh3Dhg1jwIABZn11QmAlJyeHqlWrcvny5XKDytLETaFQcOXKFZlNMMX48eP54IMPcHZ2NjMGLl2alJaWJlUz%2B/Tpw3vvvUd%2Bfj4nTpxgxowZ0vNMBFkpKSl06tSJyZMny31s2bKFPXv2oNFoaNq0KYWFhUyaNAmVSkXPnj359ttv6d69O7m5uWXOVWTtBKKiomRJorhu0RdZHnH19PSU/UpiPv4OAbCxsZEBJPwpJFG3bl1OnDhhtq2Pjw9xcXEYjcYy0vmiB7Jq1aocOnQItVotM2f379%2BnoKCAlJQUGjVqxL1797h9%2B/ZfWnR4Gpo0aSKNsgXu3bvH3bt3SUhIwN/fn%2B7du9O0adMyvxsZGcnGjRvp0qWLlN0Xz%2BfYsWM5efJkmaBdlKzm5uaSnJwsZfcjIiJo1KgRfn5%2BzJ8/n%2BPHj5uZlTs7O2NnZyefIS8vL2xsbOS7oVevXjJD/L9B5KKioti2bRvjxo1jypQp8rq0Wq0kxDk5ObIEUaD0sy7KcE1hei%2B0aNGCQ4cOMX36dIYMGYKNjY1Udf271%2BXh4cHatWvx8PAgLCyMvLw8mjVrxt69e4E/Fwxq1apFTEwMTZs2xcbGBoD27duzY8cOjh07JsWKWrVqxa5du4iJiZHHOHv2LP3798fX15fKlSvTtGlT2rZtS1hYmLRPGDRokFmmPDMzk9mzZ7Nlyxbs7e3ZvHmzzARevnyZmzdvMnnyZKytrbGysuLLL7%2BkVq1aFBcXc%2BTIET799FMqVKjAtWvXGDVqFKGhoVStWhVbW1tSUlLIzc3l008/5fjx42zYsAFHR0ezcYmMjESj0XDhwgWZ6Tcajaxfv57o6Gh0Op2sEhHj37JlS9LT0zly5Ahubm6ybPTHH38kPz%2Bfjh07WgzV/6WwkD0LLLDAgpeIR48eMWTIEOLj40lOTpbKfKL3TawQm/ZTCWXCZ0GtVhMSEsK1a9ekAqMIGFu0aMGRI0fIzMyUpWzVqlUjPT2d%2B/fvyzLMV199FaPRyJMnT0hOTn6m9LydnR0ODg7cv39f9ntAibqnm5sbOp2OvXv34ujoiMFgID09nYCAAK5evUpaWhpdu3Zl3bp1aDQaScguXbrEqlWrqFKlCo0aNaJy5cpACZG6dOkS7777Lv379y9zLlevXqVPnz4yE6LVatFoNMTHx%2BPo6IharTYr%2B/Pw8CA7O5u0tDTs7e0l0bW1tZUld7GxsVhZWfHKK68QFxeHXq%2Bnfv36TJkyhV9//ZUxY8Zw6tQpFAoFy5cvZ/r06dSvXx%2B1Ws2RI0fo168feXl5ZpmcF4Ver6dz5840a9aM/v37o1AoaNSoEdHR0TJwNYUQahHkpF27doSHh5OdnS0zjxs2bJCBeb9%2B/SRJS0lJoVq1amRkZBAbG1tG2ANKBEAOHDhAz549Zd9kvXr1OH78OEOHDqV79%2B5ERERgMBhITU1FpVLJMrjS2Z6nKRyKfx0cHFCr1TIbUaVKFbp3725mbi4C9NJCJaay/P8JeRw6dCg2NjbMnj2bpUuX8vbbbzN37lwuXrzIwoULycnJwdbWlo4dO5Kfn88ff/whiVtAQACxsbEUFRVhZWVF48aN8fHxYevWrdIqwhQKhYLt27fToUMH9uzZg6enJ3Xq1KFixYrcuHFDZmZECe%2BjR4/keAqhFGtra0nQxbgHBQVJkafS2R6hduvt7U18fLyZSJSwX8jJyZH3gK%2BvL/fv35dkT5SkWltbS8IviN%2Bnn37KlClT6N69OzVq1GDSpEksWrSIvn37otPpmDlzJkOGDCEgIIBr164xa9Ys/P39UalUsjfw6tWrnDhxgqlTp/LLL7/QunVr2ds2Z84cPD09gZJMP8D06dMZNWoURqNRihGp1Wq8vLx499136dy5M1u2bGHmzJnk5%2BcTGBgo%2B5p9fX1JT0/nrbfekuXLotQ9PDwctVrN4cOHCQ8P5/fffzebN4EXCavLE6SCP8t%2BRR8dlJQy29rayrEFnimY9SxUr16dK1euyAUl0auXn59PtWrVZKuAlZUV77//PnFxcWRlZVGlShUOHjxoMVT/l8JC9iywwAILXjKuX79Ojx49yMjIKGO3UBqCvDxNpc4UovleBPVqtRqNRoNer5d9ZK%2B99pqU775%2B/Tp2dnYEBARw6tQpNBoNQUFB3Lt3D4VCwZMnT5g1axbjx49/agmdSqVi6NChfPPNNzJYcXNzIzMzk88%2B%2B4yPP/4Yg8FAXl6eNLAuL2NQGqKUUWw3YsQIM38tKJGOP3/%2BPPHx8bKsVXjd%2Bfv7SxNzgEqVKjFr1iy56h0ZGUl8fDzu7u4kJibi7OwsA7C4uDgzEqJSqZg1axbTpk1j//79FBYWEhwczO%2B//467uzutWrVi0KBBdOjQAcCMDJ45c4bFixfz22%2B/mZ272K/o4YuNjZXfOTk54ebmxpYtW6Stg5OTE3q9Xho/lwdTsqfX61mwYIH0bDx48CCxsbEolUoGDBggyV5eXp4UfQgNDSUnJ4dXXnmFu3fvlgli/f39uX37NsXFxdITT6VSSZK3du1aoqOjpT9cy5YtycvLY86cOTx69IglS5Y8V%2BWz9Bj98ssvvPLKKzx48KDcjJzoNVUoFHh5eREXF4eVlRV169bl1KlTFBYW4uXlhbu7u1S8LCoqwmAwyH2IktsXOZ958%2BYxdOjQcomqKYTwiiAetra2VKlSBb1ez/Hjx80Eb0SvY8OGDUlLS%2BPq1au4ubkREhJCZmYmx44do6ioiPr163Ps2DH5e6%2B99hpnzpyRx7O2tqZKlSqcPHmS1q1bS0%2B70hDZN1MBJtN3UOmsm8icCdL31ltvkZqaipWVFbt27TJ7Nwkiam9vT1ZWFkVFRbIP2DTrJ8iHVqula9eutG/fXh5TvL/69u3LggULePfdd4G/ZuVRsWJFYmNjUalU%2BPv7c/36dRwdHXny5AmFhYX4%2BPjIHl4XFxe0Wi137941m1NTD76XjfKEa8TCXVFRkZxH%2BOsLHRqNBoPBQHJyMg0bNuTEiRMoFAqLofq/GBayZ4EFFljwkpCZmYm1tTX9%2BvXj1q1bNGzYkFu3bkkT3IyMDLOyqPKCyMqVK1OhQoUyq8yCYAlSJtT5RHZFoVCg0%2BlkyZgoWwoJCZH/F9kW4ckGyOD222%2B/lUbbz/ozInqrNBoNU6dOleWpWq1WrmCLYwk/wL59%2B8pVfYGQkBAGDx4sSZBSqcTJyUmqTBYWFpKQkIBWq8VgMEiiBvD1118zePBgkpKS%2BOyzz6hRowZff/01KSkpUlwDzMmRsGQoLi5m0aJFLFiwQPbfJCYmEhgYyM2bN1m8eDG1atWiXr168ndq1KjBvn37cHd3l%2BcWHBzMmDFj%2BPnnn0lKSjIraw0NDeX8%2BfN4eHjg7u5O9erV%2BemnnySBUKvVKBQK3nnnHebPn49arSY6Opp9%2B/bJEjtbW1uGDBlCjx49pAH25MmTad26NYcOHWLEiBE0bdqUX3/9Ffgz%2BwFIY%2Bn8/HzUajUrVqwASoJr4Um3d%2B9eRowYwcmTJ9Hr9VJMR8yhEJ5p1KgRWVlZnDp1SmahbGxsmDdvHuPGjePhw4eEhYXx448/olAoyMnJITExkTNnzjBt2jQ0Go2Z4JBKpcLe3l5mlStXrkz37t1p0KABbdq0oUGDBoSHhzN9%2BvRy7z%2BD4f%2Bx995RUV399/9r%2BggISFNEjSUqViQaFVuMIDYkGnvXmGgsH7tiN5rYotFogqLGIPZYIsXeG%2BhjiS2KLRobAor0Psz8/mCdkxlA45PP88vzyXfNXisrMne4c%2B%2B59w5nn/d%2B7%2B3A9OnTWb9%2BvQzWFrJSrVbLkydPUCgUkqhDoW29uB7x8fF8/vnnODo6snz58mIyxbeBOVEQZK%2BoUYlKpWLFihXMmzePZ8%2Be4ezsjJOTk8xIq1ixImvXrsXZ2ZlmzZphMBhknqBAs2bNOH/%2BPEajkSpVqrBq1SocHBxo1qxZsTxNc3MSkdO4f/9%2BOnToYPEe82e7KOnbsmUL/fr1w87OjsDAQHbv3v3a8dFqtRbVqqIQ1cVKlSpZLHS8Du%2B88w7jx49n69atXLhwga1bt1psF9XqKlWqUL9%2Bfb766isuX77MF198wd27d1Gr1ZQpU4Zy5cpx48YN%2BXsVK1Zk3LhxBAQEyEWPwYMHs2zZMsqUKcOwYcOYO3cu06ZNQ61WY2tri0KhoFu3bowaNYr169fLcR4%2BfDiffvopT548Yffu3axevVqOxdSpUxk4cKAkryEhIcVMpUTv7V/F559/zs2bN8nOzubu3bvy2phXPEXEjnhdHJ8wyBHv3bFjhzVQ/R8MK9mzwgorrPgvIDQ0lO%2B%2B%2B44ffvhBTkzCw8Pp1KmTzP9atWoVw4YNe2O4%2Bbx581i9erUkiPBHqHlaWpr8Q963b1%2BaNm3KmDFj3nhcYuJREswt98VEs1GjRhZN%2B7a2tqjValkdERWrVatWMWvWrGKRDFqtlnHjxtGkSRP69%2B/PoEGDuHTpkpQLxcfHYzQa%2BeKLL0hLS2PcuHHY2toWI2vTpk3j559/ZuPGjTRp0qTYsTdq1IjMzEzatm3LypUrSUhIoHXr1ly8eBE7Ozt5TpGRkTI0vUaNGnzxxRf89ttvfPbZZ3z22WdotVo8PT0JCAjg1KlTODg4kJiYSH5%2BPn379mXWrFnFQtYBaewBhbJMc8MRMamOjo6WQe5iomdnZyerk6%2BDXq/HxsYGvV7P6NGjmTFjBoDFMdSqVUuSjH/3z76jo6Ps0fP09OSTTz5h/fr1NG7cmLt371r0kNauXZumTZtKU53t27dLw5GGDRsSFhaGVqulXr16uLu7M2nSJOzt7enbty%2BVKlXizJkzaDQaDh06xJ07d3j//fdRq9U8fvyYbdu2sXHjRhQKhSRLXbt25erVq1I2KRYxzCMSateuzZgxY/j8889RKBRUqFABnU5HQkKCNBhxd3fHycmJGjVq0L59e4YPH27hgClMXPR6PYmJiTg4OMhq3eTJkzl69ChXrlyxcF59W9jZ2XHq1Cns7Oxo3rw5L1%2B%2BJCAggIMHD0pJdbNmzfjxxx8ZOHCg7Bcs2iPXo0cPabLk5%2BdHcHAwQUFBREREoNVqX0vExP33NvcawKRJk/jmm2/w8fGR2X%2BVK1fG39%2BfR48eFdu36C81VyQUJZ9Q3K2zJAgXUmFAZG9vz4QJE6RDaEZGBosXLyYqKoq8vDxsbGzYsWMHN27ckD20Bw8epGPHjjx%2B/Ji0tDRpYFW7dm3Kli0rZdcTJkwAsHiezReEDAYDrq6u5OXlyRiWQ4cOyWNt3bo1Z86ckRLe3Nxc6Vbs7u5ORkYGK1askFJ0Nzc3srOzycjIoFy5cqxatYpnz54RFBRERkaGvE5iAW3NmjVMmDCBzMzMYsQ8KCiIlJQU1q9fz/jx41m2bJn8Lr5%2B/Tr169fHycmJxYsX06RJE0wmE23atMHd3V0S58ePHxMYGEi5cuU4ePDgn94XVvzfhZXsWWGFFVb8zYiIiGDOnDlMmjRJRgFkZ2czf/58vvzyS5KTk8nMzMTf358jR45IuZNeryc3N7fY6ri9vb2UQ5X0lW5nZ0dOTo7FJBkKV%2BrVarWFhEyssLdo0ULarb8J7u7uFkQTijsEFq1kmGPKlCmkp6dTt25dvvrqK4YMGcKiRYsYMGAAOp2OzZs3y94ShUJB9erV2b17N8nJyRZkrVWrViQkJLyW7NWqVQuNRoOdnR0xMTFAYbVw3759VKhQAfiD7MXExDB//nzy8/Np3rw5s2fPplKlSnJfwkhGWO/fvXuXixcvolQqWbhwIVOnTmXmzJnY2dlJKaenpydlypQhKCiINWvW8PDhQzlGol9pwIABMltLBBi7urrKSpcIPRb9SuZ5er/%2B%2Biv79%2B9nzZo13LlzB7Ake/Xq1SMvL48vvviCGjVqkJqayogRIwDYunUr9%2B7dY86cOfTo0YOuXbtiNBqZP38%2BsbGxeHl5sXbtWq5fv84XX3xBZmZmMZMgpVIp7yNRxXJzcyMhIYH%2B/ftz9OhR8vPzZa%2BkiFkoCpVKxebNm3nvvfcsXs/Ozmb16tVcunSJ0qVLk5iYSGpqquxdFZNpGxsbOnfuzE8//cTIkSOlBPKzzz6Tss%2BSguVVKhX169dHo9FIE4rIyEgCAgIs7PnN8bpYhTdNq/R6PXq9XkqQU1JScHR0pEqVKgAyAF1UrMWigHgtLy9PSh5FP5wgTuJ4GjduzMOHDzl79iwNGzaUPbtQaP4inlfxmiBZJRGwt8XMmTNZuHChRW/x66p4CoVCElPxeRMnTmTLli0ybkRIXYV8XOyrfPnyMo%2BvTJkyZGRkSDnrpk2bOHr0qIwDEec2YMAANm7ciI%2BPDy9fviQ2NpZ33nmHMWPGEBAQIBdWhDS1aA/um8he0bzJ/yT%2B7F76K7C1tUWlUpGWlkajRo149uwZs2bN4vz582zatImlS5cSEBAAwLlz5xg8eDAjRoxg3Lhx/9HjsOLvhTVU3QorrLDib0ZYWBgzZsygR48eAPTu3ZtVq1YxceJE6tevL3OlDh8%2BLO2vhXOaVquladOmXLlyRVbPzPO3SkLR1XqVSoVSqUSpVMrVfjE5K1OmDGlpaRaTTPhDfiYmZ%2BL9RYleUQgZUElQKBSyUnP27FmqVq3K0qVL0el0uLm5sXXrVrKzs1m5ciVjx44lODiYyZMns3PnTvr164dWqyUlJQU7OzsL8vG641AqlRa9hqLiYH48YrKoUqnIy8sjIyODjRs3otFo%2BP3334mNjZUh1EePHkWr1aLVanF2dkan07Fy5UpMJhMrV6606Ck0mUykpKSQmprKo0ePLD5XSGQ3bdpULMbC/HhXr17NxIkT2b59O3v37mX16tW4u7uTkJBARkYGdevWLTFmA5AVhXnz5uHl5WVhtjN48GA5aT169CiHDx%2BW/VVQSECaNGkiQ5jFvSMm4OLeFGMqtonPOHr0KAqFwqKXqySiJ9waXV1d6dy5M/n5%2BYSGhuLu7s6dO3dYs2YNUOieevfuXQtiISzpzd1NdTod33zzDSEhIfTu3Vt%2BjpubG0%2BfPqVVq1acPn0atVpN7dq1uXHjBu%2B99x7%2B/v4cOHCAn376iWPHjgGFeZi7du1CoVAwffp0bt%2B%2BbUFmBAkpOkEvmsWWk5Mj5bJQ2P%2BVlZUlzVNMJhNdunTh008/5ezZs9LhVVTG8vLysLW1JScnR8pthRRPfIaQH9auXbuYIqCkYzSX8RXtOyypN8zFxQWNRiOr7oCFE604r5Kg0%2BmwtbXl7NmzeHl5SWLt7%2B/Pt99%2BaxEfkZmZKaM9BIQyQKVSyfvr/Pnz5OfnM2DAAPnMmfeubdmyhfnz55Oamirlvk%2BePGHatGkyCsHcYKVNmzZSXiykvQcOHMDOzg6DwcDhw4flcTVp0oSrV6%2BSm5tLtWrVeP78uTzeDz74gJiYmD/9fn4d/hNET0S1iGpotWrVpEzfxsaGhIQEJk6cKF2RBdFbvHgxoaGhAHJRyIp/LpR//hYrrLDCCiv%2Bk3j48KFF/tfQoUMZPHgw%2Bfn5xMTEkJubK//Qm1dwCgoKyMnJ4ezZsyWamLwO5qTDxsYGJycn8vPzLYie%2BDwxmRJGDzqdTv6u%2BYRIVB6KmqRAyQYr5n1L4j2iZ8TW1pZXr16RmZlJ7dq1ady4MZ9%2B%2BilRUVEoFAr27duHyWTCy8uLYcOGSUJkftyVzcKpoXDC5uvrK/8zmUzS1dTX15fatWuTlZVFu3bt8PX1pVWrVmRmZrJo0SJycnJwdHTExcWFu3fv8tNPP/Hjjz9y8uRJTCYT3bt3Z8OGDVy7do0bN25w%2BfJlYmJiOHHiBMePH8fDwwM7OztsbGxYuXIlK1euBAonwD/%2B%2BGMx84t3331X/mwebA2FfXTiGkyfPp309HQcHR2lq%2BbHH38sSa%2Bo/r4OWq2WZcuWUb16dQuyp9VqKVu2LCqVivT0dLKyslCr1Tg4OFiEvptMJjIyMizIi0KhwNvbm9mzZ7Nx40Z69eqFRqOR11Wcg0qlsrCHFw6OQkIL0KtXL/nvp0%2BfEh8fj8Fg4OLFixaVhdu3b2MwGOS1NxqNMj7DaDTK8VuxYgUdO3ZEq9Va9AEKci0q1yaTiZs3b2I0Grlw4YJcZDl58iQeHh7SHGjx4sX07t2b27dvFxtb8dwUJTkpKSnFcvH0ej0uLi64ubnh7u5OWFgYV65c4cqVK3z88cckJiZSpUqVYuRFSFNFdfTJkycAFpJVc5jfZ%2BLZM39mtFotOp2OJUuWoFAouHLlCoMGDQIKFwfGjRsnP9/GxkYSBzc3N4KCgnBzc0Oj0WBvby8XrqDQTGnr1q0oFAoqVqxo8X2Qm5tLSkoKcXFxFt8d27ZtkxEhAAMHDpTVymbNmsl9iPMQ56ZSqfjss8%2BkeiAvLw%2BFQsHp06fl4kRubi73799n27ZtzJ8/XxLK9u3bW1QBo6KiCA8PZ%2B/evahUKhl3Ur58eX788UdWrlxJfn4%2BGzduxNnZmTJlynDp0iV5v/32229kZWXJPlrRH9u4cWNKly4tsxzFv93d3WncuPFrtwujmAYNGkjJsKurK3q9HpVKhbOzM7169aJp06aULl2aRo0aUbNmTXQ6HePHj6dmzZq4uLiwaNEioPA7e9myZfL75NKlSzRs2BClUsmHH34oCfC0adN49OgRzZs3R6lUWvwNsOKfCauM0worrLDib8YHH3zAypUr8fLyKrYtKSmJzMxM4uLimDBhAtu3bwcKrfINBgNqtZoff/yRUqVKkZGRYTGRLJpvJvrpRC%2BOs7Mz7du35%2BbNm9y8efOt7LtfJyUSrx87dgw/Pz%2BL/qaiEHESJe1HrVbTvXt3PvroIzm53bRpEw0aNAAKpZalS5cmKSmJ6OhokpOT6d69O1euXOG9994jIiKCihUrsmXLFubNm8fixYvp0qULe/bssficqVOnShMYIYfNzMyUPwvyo1AoqFKlCunp6bIXRq/XU65cOWnGICoqAiVl%2Bl29epVatWrJidLUqVMxmUzExMTQvHlz4A9jlKioKGknP378eJycnNi0aRO2trZcvHiRihUr8vjxYyl9rFGjBnfv3kWhUHDkyBFpKLNmzRri4%2BMliQkJCZEuk4MHD6agoIBatWpha2tLrVq1CA0NlbmEr0NERATTpk3DaDQycOBAnjx5wv379xk0aJBF/MGboFAoWLhwoRwHKJTgFY3yEBP6rVu3MmTIEPLy8nB1dSUhIeG1lSKlUsnIkSP54YcfLCqHQ4YMYevWrXKMU1NTqVq1KklJSaSmptK9e3eOHTvGyJEj2bBhAwqFQpr8eHh4SJt783vXPCZCpVKh0WgoKCh4K9MWYdAj5Kni3g0JCSExMZF169YBsGDBAsLDwylTpgyLFi1i0KBB5Obm0qhRI0aOHMmsWbNITEykQYMGrFq1ip49e/Lw4UNq167N/PnzSUxMZOTIkTRr1ozJkycTGBiIs7Mz27Zto127dm9dLTIPsi9fvjyVKlWS%2BYBC1ioWa2rUqMHNmzctftc8%2BFupVFK%2BfHnS0tIsviNK6kXWaDQYjUZKly79p9X610EoBVq0aCHdjYU8vWrVqjx9%2BrRE%2BWW5cuXkPRgXF4etrS1DhgyxeE9ISAh9%2BvSxcG8F2L59Oxs2bJDPGRRW6sVCTpMmTSgoKGDz5s0MGjRI/lu4AZe0XdzPERERdO7cWfZpTp48ma%2B%2B%2BgqTycThw4fp2bMnFSpU4LfffpOxGU5OTiQmJqJUKtm3bx9%2Bfn7odDr27dtH586dpaxfOKsKuXlJEM%2BuFf9cWMmeFVZYYcXfjFmzZnHr1i369OlDYGAgX375JQ8ePJDbHz16REpKCgqFAp1Oh16vp3Tp0gC4uLjw8OFDXr16JaWGAkWJ2Z49e%2BjevTtjx45l2bJlxSz0tVotVatWLbFS8SYIOaebmxt9%2B/ZlxYoVfP/994waNeovjYdw6oNCl8jOnTvLbQ8ePKBbt25kZ2fLEPjExEQUCoWFvTj8sdovnBbFRK9p06YcOXJZvXOMAAAgAElEQVSExo0b07Vr1xKP4ciRI3Iyn5ubS3R0NBUqVECpVPLkyRMaNGhAs2bNABg9erTF77Zp06bY/p49e4arqytarZasrCxJbIo6KEJhILnIXKtXrx6enp6yUuXi4sKXX35pIc8UvYe5ubmyr6xhw4ZcvHiRsLAw%2Bvbt%2B9aTeoVCwbp16zh58qSMmKhYsSK%2Bvr7ExsayZMkSSbSE/HfNmjVs2rSJe/fu0a9fP5YvXw4UXselS5eyYsUKbt68iVqtRq1Ws3fvXnJycpg%2BfbpFWHVRCIJha2tLXl4e%2Bfn50h4f/qia3bp1i9q1awN/9AtqNBopeW3ZsiXR0dHY29tbEAbhGAqFBKRHjx7MmzdPLgykp6ezYMECqlWrxsOHDykoKKBVq1bUrl2bNWvWMGPGDBYuXIhSqcTT05Mff/yR0NBQVq9eLcdbPBvOzs60a9eO8PBwsrOzJQERPZTe3t5UqFCBvLw8nj9/TrNmzRgzZgzdunUjNDRUVthKGqO8vDwLB1RPT08SEhJITk7%2B/yV0/U0w79lVKBRUrVqV3377TW43X4AqVaoUCxYswN/fnzNnzrBx40bOnTsnzVaMRiNTp05l2bJlKJVKcnJy0Ol05Ofnl2hQJRZioqKiCAwMlL2subm5kuxBYeX2yJEj%2BPv707dvXwwGAz///DPTpk1j6dKlktTOmjVLVprnzJmDm5ublNsKCAMlcwijlnPnzlG6dGkLeauoiIpquE6nk5JKpVKJvb09rVq14vDhwzKOJjMzk7y8PFmFPXDgAJMmTeLOnTtoNBrKli0re35btGjB%2BfPnX9trqdfrqV%2B/vpT2li9fXi6eFHVaNZlMODg4SDMiHx8f%2BvXrJ/uHrfjnwkr2rLDCCiv%2BZsTHx/Phhx%2Bi0WhYsmQJ48eP/7eykJRKpXRue1MmmDCtKOraV6FCBVQqFY8fPy5GCvr378/jx4%2B5d%2B/en/bjlYQ2bdpw5coVvL29pbNbUSMMk8kkTWXEpMPBwYHKlSvTtWtX%2BvTpI9%2B/e/duwsLC5Ap7TEwMv/76qyRD5oiJiZEud0%2BfPiUrKwuVSkWTJk0YMGAArVu3BmDZsmVSjrlnzx769%2B9vMYlr0qQJDRo04Ouvv8bBwaGYYcPboGbNmtIspk2bNsTHx/9b11in02E0GrG3t5eyPeHCJxwOi2aimUu9CgoKWLFihdzfpEmTCAwMpFWrVkBhX1dwcDAajabE/kyxv169ehEREYHJZGLKlCnMnTuX06dPExgYyLfffouPjw/169enYcOGXL9%2BnYoVK2IymXj48KGFYciNGzeoU6eOlJ/%2Bb7Bv3z46deokFzd69OjBrl275HFfuHCB%2B/fvs2XLFvbt2yd/z8bGhuzsbEwmE%2BvWrZNjIfDy5UtatmxpEY/g4eGBQqHg6dOnxcyIRL%2BkObkqWrE0X4Bxd3enf//%2BDBkyBC8vL/Lz81GpVCgUCmrVqsWvv/6KyWSyMOURVXkvLy%2BuXbtGpUqViI%2BP/7eMQRQKBWXKlJFOoU5OTrx69Yr27dtz9%2B5di4UmAfGcGo1GeY9ptVrs7OxITk4u5nharlw5goKC6Nixo8yCNJlMuLi4yCpVnTp16NKlC2q1mlOnTnH69Gns7e0xmUxSWvtXIeSfgvCJfkyBjz76iIiICAIDA9Hr9ezZs4cJEyawfPly9Ho9aWlpsspfFEJeqlAomDp1Kl26dOHcuXO4ubnJOId/N8vu/zLc3Nx48eIFnp6ePHr0iB9%2B%2BKHE71sr/jmwkj0rrLDCiv8Cfv/9d%2BbOncv58%2BeLbWvevDnTpk2jWrVqcoKmUqkYPHgwv//%2BO9999x0tWrTg1atXNGvWzML58q9%2BpW/bto3%2B/fvTqFEjLl%2B%2BLFeK69aty%2B7du6VhhXmIs/hMKDTNEBEMwvDi%2BvXrxY5JhPRGRETw66%2B/MmPGDEwmE82bN8ff359FixYRGhrKgwcPePToERs2bMDX15cWLVoQFxfHunXr%2BPjjj6lXr57cZ0kSpPj4ePr3709BQQGrV6/G09OTrKwsTp48SVxcHJs2beLly5eUKlUKT09PKlasSM%2BePXn%2B/DkTJ07Ezc0NvV5PYGAg165d4/Tp06xfv15KMP8M5mSvJJhnaIk8N3MIomQ%2BfiI/UJDW10FMzK5du8aYMWNwcnIqlv0XHh5OUFAQCoWCAQMGMHToUNmb9uzZM0JDQ9m0aRNqtVpW9MLDw2nXrh0RERGyQnr27Fn8/PzYtm0bPXv2xNXVVUoGRf%2BcyWTCx8cHd3d39u/fz7p167h9%2BzY//PADGo1GSibd3d2BwkrJixcv5ATbx8eH58%2BfW1SMzGEe/%2BHs7ExQUBA7d%2B4kPj6e58%2Bfo9PpyMrKknl22dnZlCpVyoK4CyMhg8FAmTJlMBqNpKWl8cEHHwBw8uRJZs6cyfz58%2BX1ED8L4g1/hIO/ydmyevXq3Lt3T94HS5cupXr16ty9e5fAwEAZfxIaGoqPjw9169ZFq9XK4/by8iImJqZYVIFOp8NgMFgQD41GQ5MmTXjx4oV0aRX3ljAXys7OlveaeS/mn7mKGo1GSTr37Nkjq61Cgj1u3DguXLjAqVOnLNxCRdXT3d2du3fvcvr0aRmpkJ6e/kZ5uVarfaPp0%2BsgCKv5NXd0dJQGTyKCw9HREUBm3qWkpJCZmYmLiws6nQ6FQiHlvydOnMDGxkY%2BQzt27MBkMvHNN9/w5ZdfkpGRQUpKCiqVirZt20pjF3t7e1JTU2X1Toy9kAwL4irck0XvocFgsJAqd%2BjQgYMHD%2BLn50enTp344osvSE1NxcbGhk8%2B%2BYRDhw4RGhpKQECAlMe%2BfPlShq%2BLMVSr1UyePFnKNUU/YFJSEgMHDuTmzZts2rTp3xpvK/5vwerGaYUVVljxX0DlypUJDQ0lISFBTiiXL1/OhAkTiI6OJiAggLlz5zJr1ixJnm7duoVKpeLzzz9n%2BPDh0p0QkLEE9%2B/f/0syrmHDhlFQUCDz1JydnUlOTpbh1ffv3y/x98SE0Dxrz2g0ysmss7MzL1%2B%2BlNuEBO3Jkyd06dKFQ4cOcfz4cU6fPs2VK1dwcXGhZ8%2Bect9arZbTp09z8OBB8vPz0Wq17Nu3j3379kkyIap3jo6OREREcP/%2BfWJiYihfvjxr165l586dHD9%2BnG3btpGVlUV4eDi3b9/m0KFDpKen4%2BbmRm5uLn369EGv12Mymdi8eTN9%2B/Zl5cqVdOrUCZVKxejRo1m/fn2xWIC/AhFMDvDzzz9z%2BPBhjh49ytmzZ1EqlTg6OjJq1Cjat28v5WgFBQWcOXPmtdXFvLw8jh49ip%2BfH4sWLWL79u0cOXKEihUryn5PgOfPn8t%2BO5PJJHP5BDw8PJg5cyabNm3CxcWFxMREaQohPqcoERCW7klJSbKKnJ%2BfL3s5J0yYwOjRoykoKKBixYo0btyYbt26MWzYMF69ekVOTg7Pnz%2B3kCGK/0dHR8sJsAh8Nu/9Mr/30tLSZI%2BhOEYxcRdGMRUqVJCkV7x%2B584dlEoltWrVsjBGSU9PZ8OGDVSoUIG2bdtKgw9AklNBnPLy8pg2bRpz5sxh%2BPDhfPDBB/JeNocgrSqVis6dO8seWnE9unTpQkhICDVq1AAKye/YsWP55ptvGDt2LD/%2B%2BKMcC3PSo1KpyM3Nle6cos/wxo0bFgoAMa55eXlyTERFDN6O6JmTDlHxGzNmDOnp6eTm5hISEkJcXJysRJt/bnJyMjt37qR06dIEBASwbt06vLy8CA8Pl4Y6ULjQNGbMGPbu3cvdu3dRKpV8%2BeWX/Pjjj9y5cweFQkHDhg2Jj4%2BXCwZQaIgicOHCBdRqNQcPHpTS2Li4OGlIZGNjg9FoJD09Xb4mCF1YWBhr1qzhm2%2B%2B4cMPP7QYg1WrVnH8%2BHFGjBiBUqmkW7duXL16lbi4OM6ePUujRo2IiIggNzfXIsfQYDDg5eXFpEmTaN%2B%2BPQ4ODmRlZdGiRQtatWrFxYsXqVKlCtu2bWPz5s30799fVsNNJhP37t3j1atXVKlShbJly%2BLj44Ovr6%2BMhXB1daVWrVoA3L9/n5EjR5KRkUGtWrXw9/dn6dKlsq/P/H4Q0R%2BiH3Xz5s20b9%2BeBw8evLGn14p/BqyVPSussMKK/wLCw8NZs2YNL1%2B%2B/MvW3EVRs2ZNaSUuUNLEzdvbm5SUFBlEXRLUarW07BYoVaoUSqWS9u3bs2fPHtk3ByXnapUqVYp69erJfhFzhIaG0qxZM86cOcOnn34q92EymShfvjwqlYqMjAwyMjJwcHAgPT2dnJwcaaJQUFAgpW46nY7s7GwcHBzIy8ujV69ehIWFUbZsWXbs2MGWLVv44YcfsLW15cCBAzg7O9O8eXNevXqFVqvl2rVrjBs3jgcPHuDg4MDFixdl9Uqj0XDt2jW8vb0ZOHAgV69eJSws7K2uxZsqe97e3pLsmefhNWjQAIPBgMFgwM7OjmrVqnH16lWgcDJ/4sQJC6ICcOXKFfbs2cPevXvJzMy0kO06ODiQlpaGyWRi0qRJaDQali5dKknCkCFDpGlKUXh6emJra0tGRgalSpUiKioKPz8/i0rarFmzWLJkCQ0aNJBZg%2BPHj%2Bfbb78lLy%2BPhQsXMm3aNE6cOEH79u1xc3Nj6tSp%2BPn5AYWT8SlTprBlyxYCAgLYu3cv/v7%2BGAwGi3v3zp07eHp6yvD4gQMHcu3aNY4fPy6P17xiJiDMeMR%2BevbsWcxYZurUqbx48YLg4GD0ej21atXi7NmznDt3jjZt2mAymfjwww95//33OXr06GuveUl4Xaafk5MTL1%2B%2BRK1W4%2BjoKKuY8fHxTJ48maVLlxITE4OTkxM1a9aUz5dY0HmTaZI5zBcVBIQk1NfXV0ZL/KcgXET79etHgwYNyMvLw9vbmxs3blgsEnh5eREbG4unpyedO3dm4MCBctx///13fvjhB4trKyp/4vvG1taW0qVL8%2BLFCxlaXrNmTVq2bEm9evVo3Lgxtra2MhMzKiqKihUrAn/kaYqfX758SfPmzYmJiZHPIUCnTp0YPXo0HTp0eO35vv/%2B%2B2RmZvLpp5%2Bybt26t1poE5W6opE4RavBbxMy/1dQVNYPhX2uZ86cwdbWlsqVK9OqVSsOHjxIUlISRqNRRmRY8c%2BENXrBCiussOK/gLZt29K2bVu5CgtIq3oBUUkRlYk/w507d4pN9kqaFF65cuWNRA%2BwqAQJ9OnTh7Fjx3Ljxg3GjRuHvb09SqUSJycnOcnR6/XUq1ePSpUq4eLiUiLRUygUkrCYy0LVajU9evSgatWqHDlyhHPnznHjxg3Onj3LtWvXsLGxYfPmzRw/fhwfHx98fHyYP38%2B0dHR0mHu448/ZurUqdLefsWKFYwfPx5bW1scHByk1bogQGJ8zp07x6hRo2SFZe3atRiNRgwGA%2BHh4VKy9ssvvxAeHi7/exPetr%2Bv6O%2BIcY%2BKiqJfv35ym8lkYv369UBhdW716tX4%2BvrSp08fduzYIc1NRCC1RqNh9%2B7dsnfv%2B%2B%2B/Z%2BHChRYTSFtbW4vzKXpOGRkZKBQKhgwZIq3kzSd%2BS5cuRalUcu3aNQoKCsjPz2f16tVS3ifG88KFC1SuXJlatWqxZMkSKU92d3cnNTVVVjReRz4aN26MyWQiMzOTpKQkli9fzokTJyzGuGj1WZh%2BmEwmGXVS1FwHCsn22LFjZb%2BWuCdmz55NUlIStra2pKamcvz4ccqXLy%2BrewMHDiy2L/OeLxGEbh45AciFCiFHfPHiBQkJCVLKu2TJEtmL6O/vD/xRFbt3795rq24i49AcJcUyCEL8nyZ6UChPF/dsdHQ0Op1O3i9dunSRgfI7duxg3bp1GAwGFi5cyLNnzzAajbRr146%2BfftaED0orEKaLzxlZmaSmJiIjY0NarWa3Nxcrl%2B/TnBwMJ9//jlNmzbl4MGD/6tzefbsGfXr13/je9q2bUtBQQE//PADzZs3t4i4eB1EhElRFJX9/rtET1RE/wwluceeOXMGgKysLAwGAxs2bGDUqFFkZWVJx1Ar/rmwyjitsMIKK/4LsLW1ZcKECUBhv4vBYOCjjz5i37598o%2B%2B%2BLlTp0706NGDtm3bSlfOknD79m1OnjzJ559/Tn5%2BPkuWLGHy5MksWbLkjT1EJUGv11O7dm2uXr0qJ5pBQUFcu3aNJUuWEBsbK40oXr16JasKOTk53Lhxg6pVqzJ%2B/Hg2btzIxYsXLfZtMpmYPn061apVs4hIyMvLw2g0cuPGjT89vujoaIKDgxk8eLDsMxwzZgxbtmxh1qxZVK9encaNG8usrJycHIKCgli1ahVBQUHY2NiQnp7OO%2B%2B8AxROiu3t7YFCx7pjx47JSfXKlStxdnZm37595Ofny9w8hUJBly5daNOmTYnEbsKECcUyqsQE29wMZfTo0TK3UMjjTCYTEyZMsJjYG41GtmzZwq1bt6RZjCCsjRs35vLly2zdupXu3bsDfywWtG3b1mLf5ti1a5eFo6k4J4CyZcsSHx%2BPyWQiIiKC5ORkdDodzs7OJCQkyNgBhUJBuXLlMJlMxMXFkZ%2BfL6V%2BERERKBQKli5dSvfu3dm5cycVKlQgICCAbt26kZGRgZ2dHbNnz8ZgMDB//nzZw2h%2B7iUZESkUCpydnXnx4gXlypVjwoQJTJkyRRqciOcgNDSUCRMm0LNnzxKNTTIyMooRMnENJk2aJGMRhDOiXq9n1apVJZIl8zEW5FS4hL4OrVu3lvfJoUOHLMLrzSXQJUUVmONtTUKEIc9/CiKqAgpjZYSz7bfffis/y9bWFpPJJJ9xUcG2t7dHp9NJR9ucnJxi1ciilSg3NzeysrIwmUzS6VP0vCkUChlcP3bs2Lc%2BB2FiYw5nZ2eePXuGh4fHa3%2Bvc%2BfOHDx4kKysLPz9/YmOjpbGNlqt1kLGLmTN4phbt27NL7/8YnF/iP5S%2BMNQyDzgXjh6ivH44IMPOHXqFGXKlLFwnjWvhH7yySeEhYWh1%2BtlLub58%2BfR6XSy2irMgqpWrUq5cuXw8/Nj8%2BbNFBQUlLhAYsU/C1ayZ4UVVljxX0bXrl2JjY1l/fr1xMfHk5SURFxcHMHBwaSnp7N48WIUCgXBwcG4u7tToUIFOnXqxK1bt5g7dy55eXls2LCBf/3rX/Tr149BgwbJlVrR17dgwQKmTJny1iYu3t7eXLp0yUKWtHDhQsLCwuSk1xxF9/ngwQP%2B53/%2Bx%2BI1pVKJg4MDycnJXL16latXr1rI3Ly9vdm/f78kPm%2BCmKCLz3316hUNGjSQVay%2BffuycOFCOWl89913yc7OJikpiaysLLKysigoKMDHx0du37VrF9WqVSMsLIzmzZuj0WiYMmWKrOCsW7eOw4cPs3PnTotjKXqeUJix1aVLF0qXLi2vH4Cvry9QSC6FTMs8isHOzk6u/JsTbUHWhaRKq9VSs2ZN6tWrx/bt25kzZw4fffSRBakXFUKlUomNjQ0FBQUWZEdIZouOtzjf8uXLEx8fj1KpZPfu3fj6%2BhIcHEzLli2BQplnly5d0Ol09OnTh9KlS9OhQwdsbW3ZtWsXw4cPl%2B6VLVu2JDQ0FGdnZ27cuIGbmxsbN24sRkDd3d0JCgqyCFGHPyRtHh4efP/994SGhhIZGSmlvC9evGDKlCnAHxWR4ODgYtelJFSvXp0LFy5IWZ85rl27Jvfn6upKp06dZFUSLPvXRN9eSTK5N%2BHUqVMy/kRINgsKCkhNTbUgZn81UkG4uL4J1apV48GDB1JSXLt2bUksFi5cyPXr12Uu49SpUxk8eDBQWGFbvny5NPA4efIkJ0%2BetNi3kNJGRkbKc7Czs6NUqVI8fvwY%2BIPQ5efnF6toFR3LxMTEYsdvMpnIysqSsQEjR46UYeJvK61MTU0lLCxMuvO2bduW7777jvr166NQKPjss88s8vUMBgMhISF07NiR06dP4%2Bnpiaurq1yYaNu2Lffu3SM%2BPl4%2B/6KiJ0gXwOnTpy1cT8V2ERAvzq9UqVJkZWVZnI%2BrqytAscxKg8FAzZo1uXPnDr/88gtqtVqSv7p165KamsqtW7eAP6TGarWauLg4EhISePr0KY8ePaJbt27yO9KKfy6sPXtWWGGFFf8l5Obmcvv2bUJDQ4uRpz%2BDQqGgdu3a%2BPn5ERYWRkpKCt7e3mzcuBGtVmvRCweFhjC///67Re9Penq6lI69TT6X6KkTLnb/6T8fIi/Ly8uLn376qdh28xD1nj170qtXL7766isiIyMZPXo0bdu2Zffu3Zw4cQKATz75hOjoaLy9vbG1tZWh0CKTrVKlSjx9%2BpRatWqRnZ3NgwcPcHd3Jy0tjczMTPz8/AgODiYsLIwbN25w8OBBunTpQqNGjV57DiU5gxYNeC%2BK3NxcXr58ycOHD0lJSeHixYsYDAaZF5iVlUVOTo6cDKpUKuzs7NDpdGi1WuLj44mIiJD28gEBAQAWvUre3t6y8moymXB2dv7TXlERHF6tWjWSkpJ4%2BvQpHTp0YMaMGbi4uEh5l3lUg6g0ChmbuKe8vb2xt7fn6tWrUkJbFA4ODjg7O5OSkmJBqP5dlHQvi0iETp06Sbm0uZ384sWLWbVqFd7e3tSqVYvo6Gh8fX3Jzs4mJCSE4cOH06VLF1ndhcKKVrly5aRMsVy5ciQkJPDOO%2B/w6NEjAHr16iUJwpo1a2QVNCgoCGdnZwYOHMh3333HypUrWbt2LR9%2B%2BCEKhUL2w5ovzoh/t27dmpMnT8oJvJCUihzKr7/%2B%2Bk%2BfzfLly/Pxxx%2Bzdu1a/Pz8uHjxIi9evMDJyQmVSiVJtICTkxPZ2dly8cTW1laepwhQF3l5y5Yto6CggKioKCm1dXNzo0KFCnKsNBoNbdq04eXLl9y9e1dWu5RKJWq12mJRwtnZmfLly1tU/J2dncnNzSUzM5POnTtz4MABhg4dys6dO0lKSqJ8%2BfLExcXJ92u1WoscTa1WK8mWWHCpUKGChXtvWloa3bt3Jzs7m7y8PEqVKsW3336LWq3m5s2bbN68mczMTPr160dwcHAxQq3X62UlsyTY2NhQqlQpGa1SFGq1Gr1eLwli0VgPsKz2igq9%2Bc9vW%2B318PDg%2BfPn8h4zGo0olUquXr1aTJ1gxT8P1sqeFVZYYcXfjNzcXJYsWcKuXbvIz8%2BX1tvmTfvmBilarZbatWtz48YN7OzsUKlUpKamcvPmTe7cuYNKpaJjx4689957kiSJ/iUx8RWfERAQQGRkJPfu3ZOTnzp16gCFLoElSf0EzN30VCoV7dq14%2BDBgxaTmZImpzqdjgoVKhAfH19ihaF%2B/fp0796dEydOyF6lkqSR2dnZDBgwAJVKRWZmJjNnzpSf3apVK0JCQggMDAQK%2BxJv3brFwIEDycnJ4fr163L13NnZmYkTJ9KvXz8uX77M7NmzefToEXZ2dmRlZVlsB1ixYgVZWVnY29sTExNDTEwM8fHxuLq6WkywMjMzadGihUVmn8j0%2B/TTTy2qAgAJCQmMGzeOX375Rb7m6OjImDFjqFevHvv27WP37t0YDAZsbGwICAjg7NmzPH/%2BnKysLNLS0nBxcZHSVzFeJUlKi75mLvl6HcqWLcuxY8eYOnUqGo2GhIQEYmJiePnyJS4uLlLGuH37dkJDQ9FoNAwaNIhDhw7J3kiAzz77jHfeeYcjR45IWZp5BVLcL6mpqcWIoKOjI2q12kLOaA5bW1tycnIsJrVFJ9cKhYKUlBQUCoVFH1d0dLR0XoyNjaVv3754eXlhNBoZMGCAzOQbPnw4AOfPn6dOnTo8f/6c5ORknj17RmJiojyXzMxMGe3g5uZGYmIiR48elfeInZ0dVatW5d69e9StWxc3Nzd5vE%2BfPrUYD%2BGCqtFoJPER4yIqZ4JgmUwmNmzYgF6vp3Tp0vK5c3FxKTZuQrpnNBoJDw/HYDDwr3/9SxL/15Hsoq9nZmbKypA5KleuzKlTpwC4fv06SqWSDz74QEokL1%2B%2BLHsZGzZsKJ%2BJjIwMvvrqK9zd3aUkVCApKakYIRKLFjqdjjFjxnD06FGqVq1K%2B/bt2bp1K0OGDGH%2B/Ply7ItmKgIW/dJZWVm4uLhIkx4Ae3t7duzYwdKlS9m/fz8pKSn06tVLVg87duyIvb093377LSaTiRo1alC5cmXu37/PgwcPyMnJKfFZFAZHQmFQFKJCbDAYLHr7ihI9sJTuFiV2/072X9Ex1%2Bv1DB482Er0/h%2BBtbJnhRVWWPE3Y8aMGVy7do25c%2BfSoEEDvLy8ADh8%2BLB0/4M/wo/VajWjRo1i9erV6HQ6mQn1V76%2BzScfCoWCRo0akZ6ezr179%2BRk08HB4Y1h7a%2BDUqlk8eLFTJ48WfbQ2NrakpWVRXZ2NlWrVqVly5Zs2LDB4thr1KjBw4cPsbe3JyMjA3d3d3r37i0zrwALKaRAZmYmubm5ktDa2NiQm5uLvb09KSkpVK5cGY1GQ0ZGBi9evMDHx4fg4GBpZBARESH7i/z9/encubPF/vfu3St7iYSxhIC3tzfjxo2jR48epKWlFcv0E9i4cSMbNmxAqVTSpk0bhg0bJsngyJEjOXXqFBqNpkQjDXOYE7moqCj27t1LRESERfVCoVDQtWtX9uzZ89b3hkKhkBLCosjLy2Pfvn0sWLAAb29vnj9/zosXL1i9erWsigicPn1a2tBPnjxZSkEjIiKYMmUKvr6%2BHD9%2BHJPJJDO/Fi9ezJw5c8jOzsbW1pZNmzZRq1YtPv74Y%2B7du0deXh579%2B5lxIgRPHnyBBsbG2xtbTlx4gT169fHaDTKKrP58%2BDl5cW6deto3bo1JpPJwonxTbh8%2BTLh4eEcOXKE/Px8OdE2l2XqdDpJIMS5CFOLotfqdY6ZpUqVYtKkSaxZs4aEhAT5etmyZSWJFxN%2BBwcHMjIyKCgoKNFZ800wr3CW9H1RNELBHJ9%2B%2Binx8fHs3bsXjUaDh4cHfn5%2BjB07lgYNGsiMvkaNGnHs2DE5RmIxpeh4V6pUievXr/Pbb7/RsmVLLl%2B%2BjMlkwsHBAZVKReLrqykAACAASURBVFZWFsnJyezdu5fSpUuzbds2QkJCLMat6PGr1WqUSiXTp09n3rx5Fue6c%2BdOevTogb29PWq1mpiYmDeOVcuWLQkODn6tIYt4FpYsWcKWLVuoVKkSKpWKFi1akJuby6JFi6REGwp7L2fPnk1qaio6nY5du3ah1%2Bt59uwZ7u7unDlzRrrCqtVqWrRoQVJSEu%2B%2B%2By4ffvghkydPxtHRkYSEBNzc3KQE297eHnd3dxQKBXFxccTFxeHs7Iynpyfvv/8%2Ber0eBwcHnj17xnfffSfjJTIyMqhQoQLZ2dkkJydjMpmoV68ejx494p133mHHjh1vHB8r/tmwVvassMIKK/5mHDx4kLCwMOrWrQsUVlByc3N58uQJdnZ2ZGdny%2BodIHPpRPVIr9cTEREhJYOiWlISik6Qpk%2Bfzm%2B//cb27dsxmUyMGDGC2NhYvv76aykf%2B%2BabbywkoGXLlpWT0jdBVBlUKhWLFi0iIyNDRib4%2BPjI/KstW7ZY2NG7u7vj5uZGQkICmZmZ0pnP/JyCgoJK/MyZM2fSoEEDMjMzZSVG9E09fPgQjUaDvb09JpOJWrVqSaIXFhbG119/jY%2BPD2q1munTp3Pv3j1pmgOFboxeXl6vJQrLly%2BnTZs2BAcH884771hUBQQGDhxIixYt6NKlCzt37uTjjz%2BWZO/y5cuyn65s2bIsWrSITz75hJycHDQaDe7u7ixdulQGyF%2B5coUlS5agUCgYN24c48aN4/r160yaNImnT59SUFBAZGQkJpOJsmXLMnTo0GLHLIKTBb777jv572HDhsmqs5ubm8ysM5lM/Prrr4SEhLBv3z5ZAatbty6lSpXi8uXLxMbGSsKzatUqjhw5QpkyZWT1T1RiAKpWrcqdO3eYNGmS/GyNRsP3339P27ZtpYOnuE5ly5blyZMnqFQqPD09uXr1qtyXqFCKnzUaDTNmzChWRRURD0X7yQQE8ddoNMycOZOAgAAZj3HixAn8/Pzo0KGDDA%2BvW7euzBC8efMmV65coX79%2BnKshJTVZDLh4eHBixcvJGHMyclh3rx58tp36NCByMhI3n33XZmzKCr6w4cPZ8mSJTLAvX///mzevNni2Hv37s2OHTvkMyPGwrzCWZQoCklvUcyZM4cvv/ySrl278u6777Jv3z5mzZrF/Pnz6dGjBwsWLEClUjFnzhymTp3KiRMnsLW1xdHRUTqLJiUlodFopEQcCpUGgjzfu3dPHouDgwMKhYKjR4/Spk0bfv75Z4KCghg/fjwajQY/Pz9mzZrF9evXix2ruN%2B%2B%2BOILi9dNJpM0KXJ0dOTdd9/l%2BPHjREZGUq5cOUaMGGFxf5w8eZIXL16UaNIjIIhtdna2zKQD5PeYh4cHO3fu5KeffuLOnTsW3285OTkEBARQpUoVevbsye3bt7l06RJKpRK9Xo9er8fJyYkZM2aQl5dHaGgopUqV4r333uPcuXN07tyZChUqUKlSJfbv3090dLSU0avVatLT00lMTCQyMpLMzExSUlLktS0oKJDjpFAoGDp0KDdu3GD//v1cv34dV1dXrl%2B/jq%2BvLwUFBdLJt379%2BnTo0MHar/f/CKyVPSussMKKvxmtWrXiyy%2B/lGHqISEhrFmzBrVajbe3t5RBFYWYyAkDhYyMDLKzs1Eqldy%2BfZuGDRsyYsQIabiRmZnJiBEjLGSVAq/L4lMqlaxYscLCdGTq1KksWrQIjUaDUqmkdevWKJVKTpw4ITPhih7nd999J10gP/nkEy5dusTkyZMZMGAA9evXJz8/Hzs7O9LS0uTnazQaXFxceP78OeXKlZPnYGdnZzFJF9JIDw8P5s2bR8uWLencubPsVQPo0KGD7LOCwqrptGnTpItlhw4daNKkCVOmTMHGxqbYdiiex2UOb29vjEYje/fupW/fvm%2BsCkydOpX79%2B/z4sULi2srZGQHDhxgxowZVK5cmaioKPLz82ndujUrV66U1/Lq1asMHDgQLy8vFi9eTPny5eV%2BLl26xIYNGzh16hQffPABAwcOZN%2B%2BfcydO7fYsRS1UReGHMOGDePMmTNotVr27t1LxYoVefnyJS1atMBkKgy3379/PxUrVuTSpUtERETw66%2B/cu/ePYxGI05OTuTk5JCbm8vEiRO5c%2BcO4eHh0tEzOjqa5s2bYzKZiI6Oxs/PD29v79dWXJo0acKFCxfQ6/XFjGXeBj179pQVznbt2pGWlsaZM2fo2bMnDRs2tOitNCf%2BCoWC8%2BfPM2TIEB48eMCxY8c4e/YsUVFRdO/eXZ7X7du3SUtLo0yZMtSsWZPdu3fTokULLl68KI1GlEolQUFBDB48mNjYWHr16mVhNuLo6MicOXN49913pfxYSBO7devGzz//TFRUFP7%2B/nTr1k06pxaV5wmJtMFgkNvNHTIFSvpdZ2dnkpKSpLRQVBRr1KhBYGAgS5culUYf3bt3Z/fu3ZhMJo4cOULnzp1xdXXl2bNnxMbG0rhxYzIzM1m8eDEBAQGEhobyzTffMHToUHbv3k3Tpk2JioqiT58%2Bcj9Dhw7F0dGRIUOGyOzRov3Lbdq0ISMjg379%2BtG9e3ciIyNZt26dhSS8VatWDBo0iKysLObOnSur/mq1Gp1OR0ZGhiTg5cqV46effsJgMDB37lxOnTqFjY0NM2bMoFu3bq%2B9p3bv3k1YWBiRkZHytU2bNrF%2B/Xry8vLIycmhXr16XLp06d9yPv4rENfJHG%2Bj9ijaS/s6iErt1q1bLXpbrfhnwkr2rLDCCiv%2BZmzYsIFVq1YxaNAgGjVqhJubGydPnuThw4e8%2B%2B677Nmzh1u3bqFUKnF1dSUrK4vMzExMJhPlypXDzs6OsmXLMmPGDM6cOcPGjRuZPXs2X3zxBc2aNZMT%2BkOHDsnYg/fff5927drx1VdfvXFSUNI2hUKBg4ODrKKIfkJPT0/WrFlDRkaGjGUwx7hx4/D29paTqsePH9OpUycOHDhgUYXo0KEDNWvW5NKlS5w/fx6DwcC2bduYMmWKhTQyKyuLkydPEhcXx9q1a0lNTaVUqVLo9XpSU1Pp2rUrCxYsAKBu3bocO3ZM5vkZDAbq168vQ8nr1q2LWq2WEr%2Bi2%2BHtyV5gYOBr3weFErHZs2czZcoUC%2BdNcZ0EEXJ1dSUjIwOj0chPP/3E9u3bGTNmDE5OTvTr1w93d3eioqLYvn17iZ%2Bzdu1aXr58KR1JzQmhgKiaCiIuyJ63tzdZWVno9fpiZA8KJ4mfffYZ48ePl/sKCgriwYMHjB49mqCgIJo3b869e/fkZLhOnTpUq1aNO3fuEBMTI8leREQEffr0ITIyksDAQAwGAw0bNuTSpUvS/MTR0RGj0UhaWhp6vR6tVitle4BFL1pR98uiVW7ziAqFQoGjoyN9%2BvRh//79dOvWjY0bN/Lhhx/SsGFDNm3aJCslV65ckWYtzs7OFtW/999/H5VKRVRUFL/88os0SqlYsSKdOnVizZo1qFQqDhw4IO%2BLOnXqFAvNNhgM8nnTarWMHDkSrVbL7t27%2Be2336QUs2h0ijBn0el0DB48WOZCCjg6OpKamiqrhG8LUY3s1KkThw8fJi8vT97f5uPdsWNH9uzZwzvvvMPcuXPx8fEhMjKSqVOnytB488rem6BQKDh%2B/Dg9e/bkxYsXVKhQAUA6uYrjF8HzwkCk6D7EfWBe5a1UqRLu7u4sWLAAjUbDggULOHnyJN7e3ly5cgUbGxumTJmCwWCwMOkpiitXrjBixAhGjhxpka/4wQcfvPV5/m9gHr9Qs2ZN8vLy%2BPjjj7l%2B/TpHjhxBp9PJBQaDwUC3bt1ISUmRlfU/m%2BqL/SuVSrlo0bx5c9LS0qTTqhX/XFhlnFZYYYUVfzMGDx6Mu7s7mzZtYu3atTKrTKzq16tXj%2BXLl9OxY0f5O/7%2B/syYMUNWAwWqVKnCpUuX%2BJ//%2BR/q1KnDrl27SvzMixcvSuL3pj/8Go2Gxo0bS9IFhZMsT09Pzp8/D4CPjw9nz54lPT2diRMnkpOTQ1paGh06dLBYlf/222%2BL7X/fvn3y3%2BI4Tp8%2BzaFDhyhTpgz%2B/v4cOXKE%2BfPno9frGTRoELdv3yY6OpqQkBByc3MZO3YsZcqUwWg0kp6eTps2bUhJSWH37t1069aNhg0bFguFFyv8YjX8z7b/GcwJxZus%2B6HQfOL333//074x0YspzHoiIyMZOnQoTk5OFhXH3r17v3E/vr6%2BKBSKEg00PDw8ZM8OFBpvODs7y%2B25ubkEBwczbdo0i3Nt1qwZISEhHDp0SLqbHjhwAHd3dz7//HN8fHw4c%2BYMAwYMIC4ujvLly1NQUMCoUaMYM2aMxX0xb948cnNzmTlzpiRpJpOJFi1aYDQaOX/%2BvFxYsLW1pV%2B/fkycONHiPDw9PeU5FLXmL2mxQuSdlS9fnry8PFatWgUU9lS%2BePGC06dPEx0dTV5eHl5eXjx69IgzZ85w7NgxypQpI6t/Hh4eJCYmsn//ftm7V6dOHWJjY9mwYYN0ag0JCZHGOgKCsBYUFGBrayt7DQVEJmBubq7FuMAflZhRo0YRHBwszVnE%2B7RarYUsU4xfUaLn5ubGy5cvUalUJYZ2i%2BN57733OHv2LHl5eRw5cgQodI/MysoiNzeX8PBwOfYzZ84ECjMk58yZw/bt24mNjSU8PJxz58699jPMIXqVS5cuLXPdhHOniDrp1KmTlElHRERgNBrp06cPz58/59atWzx//rzEXuBvvvlGLuDMnj2b/fv3c/bsWXr27MnkyZOxs7MDsDDpKSrTvX79Ot27d7cgeoDMHF2%2BfDl9%2B/Zl69at8lr1798fpVLJpk2bpAJCyGn1ej27du2ioKCA3r17k52dLR1Dc3NzX7s9Ly9POr36%2Bvri5%2BfH4cOHZayCiCgZMmQISqWS6Ohoea%2BIOAelUolSqaR06dIkJydTqVIlfH192bp1Kw4ODmRlZTFjxgy2bdsmozGs%2BGfDWtmzwgorrPgvYNq0aXIy8/LlS3JycuQkoaTJqrnM0sPDg/fffx/4owcrMjKS7du3c/nyZTQaDY6OjiQlJVmYFgi87mvfyckJHx8fLl%2B%2BTMWKFSU5FBMIAU9PTxo0aMD%2B/fvJycnB29ubf/3rX1SsWJEnT57g5OREcnIyy5Yt49y5c0RERODg4EDNmjVl/p%2BzszOvXr3CZDIRGBjI/v37cXZ2lllPYoL%2B6tUrmV1lMplwcnJCqVQSHx8vg4uvXbuGwWCgbt26eHt7s23bNjw9PWVFRsC8Uufp6WkRTVB0u/nPgwYNKjZBjYuLk71xubm5pKam4uzszNmzZ4uNa6dOnYiLi2P8%2BPEWk0VBWERkQunSpWVvm7%2B/P4cPH8bf3x87OzsiIyNZuHAh48ePZ%2BvWrRb7v3//PqtWrSIxMRGj0YhGo2Ho0KEWVTiB%2BPh4evfuLasmYWFhNG3aVFb2oFDu5%2B7uTnBwMF26dEGhUBAbG0vNmjVLvG8EzE1kYmNj8fT0ZM%2BePXTp0gUPDw/i4%2BP/rSqTyGcTvUlQSGpcXFzQ6/VkZ2dL45Zq1apRvXp1oHhfIsCZM2cYNmwYsbGxfPLJJ9SoUYOffvqJyMhI2rZtW%2BxeqVOnDg0aNGDLli0kJyfTvn17Ro8ezfz58%2BXE2WQy4erqioeHB9euXaNOnTrSvVA82zExMTJiQfSdCffM3r17o1Qqee%2B997h16xbr1q0jKCgIk8kkjTwGDBjA0qVL2bVrF926daNOnTr8%2BuuvsgKnVqupVq3aa012Xgfh1Lly5UqmTJliURErKvcUzqLCDEcQhbS0NOzt7SXptbGx4dmzZxw5ckQ%2BQ/Xr16d69eo8efKE9PR0jEYjdnZ2kmB5eXnJftyQkBAyMjJYvny5xbGaV8CdnZ3Jz8%2BnUaNGGAwGnJ2di/UTf/jhh4waNYp69epZfA907tyZ/Px84uLiKCgosOgNFRAmPeYy3Tp16tC5c2caNGjw2vHctGkTERER%2BPj4EBUVhcFgwM7ODkdHR3x8fAgNDSUvLw8nJycUCgUqlYpJkyZhY2PDvHnzSEpKksHzwhHYfHt6ejp6vZ7k5GRsbGzw9fVFpVJhb29PeHg4ubm5uLu7k5iYKFUKwg1VLAJUqlSJR48eSaIvqsVKpZJhw4bxww8/oFQq0el0RERE0LlzZxQKhbyXrfjnwlrZs8IKK6z4L6Fo39zbQMQoFEVgYCCOjo7MmzePo0ePAvDRRx/Rtm1baYwiMHDgwBIJ36tXr2TlzVyaJCYE4nNv377Nw4cPKSgooGvXrsTHx1OqVCnZI5ScnIxCocDFxYUzZ87g5eXFlStXJHkEaNeuHV27dqVPnz4MHz6c/fv3o9frCQsLo1OnThiNRrZt20ZgYCCbN2%2Bme/fubNiwQfa5Fc14E5lUsbGx8jMOHDggJ5Vi7I4cOYKTkxNQ6LC3bNn/x955R0V1ru37mkLvIIgIFhQBsXdjiSJqNGrUWGLDdjT2Eo2KLYk1lthN1GMFewUrYlewRkUlAoqxgEpTep/y%2B4O13zNDMeWcL%2BfL95trrawom2l79ozv897Pc9%2BrhFpa8rhKpSIsLKzM0HQoVgn8/f2xt7fn5MmTRERE8MUXX5RSBZ49e4axsbEwWpGQQstPnDgBFCt70sL57NmzqNVqQkNDhVW9tHiTZmhyc3NZu3Ytu3fvFuHK1tbW7N%2B/nxo1apT5nNesWaMX3BwZGcnTp09FMW9kZISDgwNpaWnMnj1b3C4kJITWrVuLOam8vDxiYmLw9PTE29sbPz8/ateuXerxrly5gkwmY9KkSUCxAcjEiRNJSUnh%2BvXr/Prrr0JBNjY2FgvejIwMPaVK%2Bh2VSiXUBulc5ebm8ujRI5HDJqlOukgFGkBUVBRz584tM8tRQqlUEhsbS2JiIoMHDyY9PR13d3d69eol4htyc3NJSUkRmXTPnj1DJpPpPe/IyEisra25ffs2RkZGaLVaevbsyYMHD1AqlRgbG3P//n3q1q1LYWGhCAJ3cXEhNDSUc%2BfOodFoGDhwIFqtVhQo0mexqKjoDxV61tbW5ObmMnLkSJYtWybUMOncrF%2B/XswlxsfHA7By5Urh%2Bjpr1iz69u1bZqsjFL%2B/EnFxcRQUFGBiYsJXX30ljFSys7OxsLAgLy9PtJq7urry9OlTtm7dysWLF1EoFKXyLN%2B/f8%2BWLVvYt2%2BfUDSTkpLEcw8ICGDJkiXUr1%2B/1GcNICEhQbSAlqWy67bpDh06VG8G%2BLfYunUr796908sClK4L3dZt3fxCSa2Wy%2BWYmZmJaAUpO7Okmi0Z3GRlZZV5jT979kz8%2BdatW6WOP3/%2BHPjXtSN9pjQaDWfPnkWhUKBSqTA2NhYmQ7rxFAb%2BvhiUPQMGDBj4G7BhwwbWr1%2BPTCYrpUIAXLx4kbVr19KiRQvRgjdz5kzCw8M5cOCAmIORaNiwIVu2bGHw4MF/%2BLnY2NgwY8YM5s%2Bfz5AhQwgODqZZs2aEhYUBiFbELVu2CDUlKSlJFFVarRZzc3M6duzIqVOnCA4OpmfPniiVSjEDV6VKFfz9/UVo%2Bqeffsrp06fF6/D09MTIyIjq1auLYknaeY%2BMjBSRCeUhqUwKhUKYwZTkzZs3VKxYsVzDnJJK4IdUgTNnzhAYGFhui5harRYqUFn3f%2B/ePYYOHUpRURH37t3jypUrfP/99%2BTm5iKTyVCpVOTn5/PTTz%2BVavXVpU2bNhQVFQk3v9%2BLUqmkYsWK5OXlkZ6eTvv27bl06RIRERGiOC6Jr68vGo2Gt2/fUrlyZaB4wa7rCqlUKjEzM8Pf35/Lly8THR2Nj48P3bp1w9PTkzFjxojiSSoQpPk7CXt7e2FkUxa1atVi9OjRADRr1ozmzZuze/du%2BvXrJ5S9uXPn6m0MzJ8/H5lMho%2BPD5mZmcTFxTF37lzOnDnDTz/9hLW1tXDrrFOnDo8fP%2Bbs2bNCNYbfnpP6EHK5nNDQUPbt28fBgwfJyckR4eTm5uZ6AduSUmVubi7y8v4sulEcXl5eKBQKgoODhWrq6enJmjVr6Ny5M35%2BfshkMjQaDe/evcPR0VF8ZhQKBUlJSRQVFYnOAF0jJpVKRaVKlUhLSxNqtpQjKbkQW1tbi1btP4KNjQ2bN2%2BmYcOGYubS3t5ezA8XFhaK9mKJku68ERERDB8%2BXM%2Bd90Pcvn0bKG4FffnyZSm1MTw8vFRr7n8KhUIhNuPKUs7Lyj4t65ju/Umh6tu2bTM4cv4fwKDsGTBgwMBfzJAhQ/SUvISEBLFIKygoQK1Wi91XuVyOQqEQkQFarZYOHTpQoUIF5HI5lSpVIioqiuzsbCwtLUlPT2fevHk0btwYhUJBamoqHTt2pHPnzjRo0EA4YBYUFDBy5EhkMhnVq1enb9%2B%2BnD17lry8PJ48eSJaxIYOHcq2bdtE5p%2BjoyPu7u6EhIRQo0YNdu/ejYmJCY0bNxbFnu6ckUajISwsjIMHD2JtbY2Pjw8PHjwgPz9fxAT069dPT7EMDAzkyZMnrFy5UixeatasycOHD3F1dRXthmq1Gk9PT7HLXVRUhKOjI8HBwXoLOV3nRV0%2BZMACMGHCBKFGpKWlsWvXLgYPHiyiE1q1asWuXbswMzOjcuXKhIeHl6sKNGjQgE6dOhEcHMyDBw/0isFZs2bxxRdfkJiYWGr%2BLCkpSRSkkyZNYuXKlTRq1EgsmiVTBhsbG7777rsPFnqAcCXUbbWTzoWUSQfQrVs35HI59%2B/fp0uXLvj4%2BIhjpqamwvihVatWH3w8yfTh4sWLAAwaNIjk5GT69%2B9PSkoK4eHh/Prrr4SGhvLpp5%2ByZs0aXF1d2blzJyNGjBDXxLfffktKSgoajYaCggIuXLggzldWVpbebBygZ63v6OiIiYkJx44do1mzZjRs2FBvhtDFxYXt27fr3d7Y2JiCggLu37%2BPg4MDWq2WrVu3kpSUxI8//sjkyZPF7z558qTUIjsiIkJPafH392fIkCF6wdinT5%2BmXbt2mJubCzOdd%2B/ekZqayt27d%2BnUqRNQHFQ%2BceJEPvvss1KFdWxsLLt37%2BbcuXNlhm7/GSQ1R6vVolKpiIyMZNeuXeKanjFjBo6Ojnz22WdYWlqSm5vLxo0bGTlyJHv27KFNmzb88ssvJCQkAMUKkomJCRUqVOD169eYmpqSlZVFamqq%2BB7s0aMHly5don79%2BsJMRZpZnjhxIps3bxbztFL7oUwmw93dnZYtW7J3715GjhzJ9u3bad%2B%2BvZi902g0DBkyRLRHQnGBExcXpzeXKs0Jl3TvnTp16u/qupC6J0p2Ufw7dOjQgYKCAlavXs3o0aPF5zMpKYlx48aRk5PDypUr6dKli97tzp8/z5o1a/jkk08oKiriyy%2B/ZNu2bQwaNAh7e3tycnLw8/NDLpdjZGSk5x5ramqKmZkZ1atXZ8KECSIeyMDfG4OyZ8CAAQN/McePH%2Bebb77Bzc2NTp06cfToUdHeo5vP9EeR2tqgOLvuwoULxMTEMHToUKytrcnJySEzMxNbW1vev3/PrFmzWLVqFfb29lhYWNCjRw%2B2bt0q5uNMTExEKPmfQddBrmXLlnh4eLB37142btxIw4YNOXv2LIcOHeLRo0d6i/q%2BffuiVCpZunQpO3fupHbt2tjb23P79m3c3d2Jj48XeYMODg6o1Wry8vLEa9Nd%2BMtkMi5cuFBm29O8efOYMmUKDg4OenMpJa3G09PT2bx5MxqNhqFDhzJu3DhxLDAwkA0bNpCRkUHz5s0xNzcvUxXQbRHr0KFDqWLQy8ur1KKy5G689HfpvbSxsaFdu3a0a9eONm3alCp4yqJfv368f/%2BesWPH6tnM6xZ7t2/fZt26ddjY2ODv78/8%2BfOpXLkyY8aMwcPDg/v37wvXU7lcjouLC97e3nh5eeHl5YW1tXWpxy1rEdy%2BfXu6du1Kt27dRIFx//59Zs6cKUwo/gzVq1dn27ZtKBQKQkJCCA4O5vnz5zg6OnLt2jXu3bvHsGHD0Gg0HD16lFq1aonb5ubmEhQUxPr169m4cSNTpkzRU/8WLVoEFOc7rlixgvz8fKGqdOrUCVtbWw4dOkSPHj3E/B7AwYMHsbGxEW6iULyB4OzsLK7fvLw8UTTa2dkxcOBA%2BvTpg4uLC/Hx8Vy%2BfJnMzEwSExN59uwZz549E0Ys/y7SPN7vpaRaJJPJ6NixI2fPnsXIyIiWLVty48YNzMzMkMlk4nN848YNGjVqRIsWLTh%2B/DgymYzu3buza9cu%2BvbtS3BwsLgWGjZsKILlO3bsyJgxY/Dz8xOB7unp6XrzhdWqVePVq1ccP36czMxMgoODOXfuHEVFRRgbG4v28o4dO2JhYaH3eo4dO8aVK1fKde8tC8k519LSUryvsbGx5ObmUrNmTaysrIDi4issLIyUlJRSmwIVKlTAy8uLmJgY0tLS9I5LwfNnzpyhZ8%2Be4vM5cOBAUlJShDuzrtEUFP8bkpSUhLOzMxcuXMDPz48lS5Zw5coVDhw4gEKhQCaTMWrUKNasWcONGzcoKCgQ8R9yuVx0W5RlsGPg74eh2DNgwICB/wI3b95k9OjRbN%2B%2BvdRsisSSJUu4c%2BcOzZo1Q6vVsmvXLgD69%2B%2BPUqmkqKiImzdvMnfuXJGvtmrVKqE8SZw5c4a1a9eKWSP4l6qVnp5OpUqVWLNmDadPnyY3N/c/1mYkqYFVq1blzZs3WFtbExAQQPfu3fV%2BLzU1lePHj7N8%2BXJxOyl/rX79%2Bvj6%2BpKQkCCUHBsbG4YNG8agQYMAWLhwISdOnGDMmDGMGDGizOdSVlvnmzdvcHR0LBUALbV1JiYm4ujoSGZmJmq1GgcHB%2BRyuVC1JDp37gxAkyZNWLx4canMvt/TIlYyE60kgwcPJjExUbTNWVlZlVkgwr8KKz8/PxISEqhevTru7u7IZDKCg4NFoHfnzp25evWqMH9o1aoVffr0YeLEiRQWFmJpacm7d%2B%2BAYjXl4sWLYuHboEEDCgoKOHbsGAkJCTx8%2BJAHDx7w8OFDKleuzMmToAgpDAAAIABJREFUJz/4ekqSnp7OokWLhHpYp04dYmNjadWqFVeuXKFnz554e3uzYsUKoFitS05OxtTU9IObETKZjKZNmxIXF6dXDOfm5vL%2B/XugeE5ROq%2BSsYYUqr5hwwaaN28u3nNJPZMKN101TSaT/eGoAwkvLy9q1arFuXPnRHamNH969uxZvvrqK%2BRy%2Be9yiy0Z01ASExMT3N3dyc7OFnN5EnK5HKVSibe3N40aNRIxMePGjcPd3Z0xY8Ywb948tm7dyqhRo1i1ahXnz5/n6NGjwhTk%2BvXr7Ny5kwMHDjB//nxmz57NgAEDmDx5MhkZGWg0Gu7evYuzszOvX79m586dbNiwgQ4dOtC0aVMRGi%2BZKI0YMUIo9Q0bNmT48OFMmjSJuLg41qxZw%2BXLl/UMpExNTXF0dKRq1ar06tVLbKzobmiUVPN1jVxOnjyJr68vrVq1Klf537FjB6tWrUKhULB9%2B3YaNWoEFGeK3rhxA7lczqxZs4iKihIdDObm5nrFvlTcS4V2yeMKhYLc3Fx69erFiRMn0Gq1HDhwgOnTp4vvC3t7e969e8fAgQOpW7cuycnJrFixQlwnkhHPh5A2CaX4EBMTE%2BrXr8%2Bvv/5apuGUgb8fhmLPgAEDBv5LbNiwgRs3brBnz54yj8fHx9OzZ0/kcjlHjx7Fz88PAEtLS4KDg%2BnduzeFhYV4eXkxadIkRowYgbW1tZ4RChSrSvPnz9fLwdu0aRNeXl60a9dO/KywsJD4%2BHh69epFYWEhzZs3x83NTVifS/zWYrIkHh4eODg44ObmxsOHD8ssyGJjY9m%2BfTuOjo4UFhZiZWVFTk4OaWlpmJqaYm5urrdY1y24cnNzCQ0N1VsoQfHsnre3t57CAv9q6/ytNk7p%2BG%2BFptepU4eVK1fy/fffc/ny5VKqwG8FvP8eBg4cyKtXr0hJSUEmk4moiLJuX61aNWJjY/UKgypVqrB%2B/Xq8vLyEYiohRU6UtRyoXLmyMOfQnRVt2LAhBQUFfPPNN6SkpHD//n1iY2MpKCigefPmbNiwASgujr744gvOnj1b7ms7dOgQS5YsIT8/H3t7exYtWkT79u1FGHpOTg6nTp3Czc0Nb29vNBoN3bp1EwWlqakphYWFwolUrVZjaWlJVlYWWq2WlStXlnm9FhQU8PjxYxITE8nLy8Pc3JzKlStz6dIlcV5zc3PFDJmTk5PYFJAUldevX4vzdujQIerVq4eXl5fYjJHO4evXr7G1tSUzMxONRqN3zmvVqoWbmxsXLlxALpdjbW3N119/TZ8%2BfcjIyKBNmzZotVpcXV1RKpU8efJEqDm6r0uKRijp3iupT7m5uUJBNzIyol69eiQlJZGQkIClpSXZ2dk0b96cxo0bk5iYyPnz58nMzOTMmTN07dqVJk2a8ODBAzQaDcePH6d3794iAiU5OZnw8HDatGlDdHQ0gYGBrFu3jtzcXJH9FhERgZ%2Bfn5jRk1qRJ02aRNWqVZk0aVKpmUFTU1OOHTtG9erVxXW3YMECTp06RXh4uDjHVlZW1KpVi9jYWLKzszE1NSU/Px9jY2OxsSK1P5f1mdct9ho1akRISEi5%2BZkhISHMmTMHgOnTpzNgwADxHZOXl8e%2BfftYtWqVUDxlMhnr1q0r1WLdqFEjVq9ezdSpU1m9enWp44mJiXz55ZfExcXpxeAAVK1alWXLljF06FDy8/OxtbVFq9WSnp6OUqkU76eZmZmYeSwvX1VyBzU2NsbOzo5atWpRuXJlPvvss3K/8wz8vTDM7BkwYMDAf4kJEyaITKmykIxBTExM9ExCpL8rlUoUCgWRkZGMHDkSKLb5l5Ay6a5cuVJK7YuPj2fdunX4%2BfmxcuVKCgsLhYW9QqHAxMSE2NhYoS5UqFABX19f7t27R0JCgt4iU4pDKIvU1FTi4uJ4%2BvQpN2/exMjIiMWLF4sWJ93nCsVzKmfOnBHRDrVq1UIulxMXF0e9evX45JNPSj2Gubm5KC50ef36NY6OjmLeEYoXNuXN8JVHdnY2dnZ25R5XqVS4uLiIoqBkZl98fLyeyYGvry95eXkkJyfrtYjl5%2Bezf/9%2Bzp07R1xcHDk5OVhaWuLh4SFar8aMGYOlpSV79uzBx8eHcePGcevWLfLy8sRs37lz54R1/6BBgzh48CBxcXHMnj2bo0ePEhAQQEhICFlZWSKMWXcOSnoNU6ZMYdiwYfj4%2BKDVajl58iTv3r3j2bNnwihl6dKl1KxZE3d3dwYPHsyWLVuElT4UqwW/ldU1b948sQhNTU1lzJgxpX5H2uiQkDY0jI2NMTIyIj8/n/r16/PkyRMmTZrE6NGjWbx4MYGBgezfv7/cDZWymD9/vt7fpeJYNyjc1dWV5ORkMZOo0Wi4fPkyv/76KwBTp04lPT2dM2fOiNk1yYVywoQJDBs2jOjoaAYNGsSrV69ISEhALpczaNAgJk%2BeLD4f69evp6ioiAMHDuDv78%2BPP/7I8OHDyyxepZk03QW9VNxJ6qe04C8qKtJrXZaO37p1izt37gijkbt377Jt2zYA1q5dy5o1azh48CCjRo2isLAQJycnoqKikMlk9OnTB41GQ4cOHYB/Bb9LSme3bt3Izc3F1taWwYMHs3HjRlQqFQqFgg0bNpTauNC9FhMSEggMDCQ3N5fp06cDxZ97ySl4//791KxZky5dujB06FAmTZrEgwcPSEpKErN3JY19SiK59xYVFREWFlbKnVciMDAQR0dHxo0bR9%2B%2BfcnOzsbIyEgU03369CE5OZlTp06RlpaGra0tKSkpZGVl6X3vVaxYETs7O/H/klhbW7Nv3z4ePHjAhQsXSEpKws7OjhYtWtC8eXNxflasWCGK/yVLlqDRaJgyZQrz5s1j2LBhrF%2B/HoChQ4eyc%2BdOmjZtyoMHD4RJ1rlz58p07zTwfweDsmfAgAED/2V8fX3FIvL3tICVXJhrtVq%2B/PJLtmzZoucqeO7cOe7du4dCocDT05Pu3buzZcsWhgwZwoABA4iJiWHs2LHUrFmTu3fvsnXrVho1akSjRo3QaDR4eXkRFRVF/fr1qVKlCqGhoaWUA6kAGThwICEhIYwaNYo3b94QHBxMSEgIL168AIp35KOjo4U7n67KCMU5aP/4xz9YunQpc%2BbMoVq1av%2B2Gubp6UlgYCDNmzcv8/jvVfamTZtG//799WbcdPHy8iIgIIAjR45w/PjxUvf9W5l/UGzMMXToUFJSUvDz86NmzZpYWlqSk5NDbGwsR48exczMjPz8fJo3by7c/2QyGVZWVmJ%2BqXLlysTHx2NmZsahQ4eoWbOmcEKVyWTcuXOHCxcuiDy32rVrM3LkSLp160adOnUoKiqiatWqbNu2TTw3X19f0TYmqYpKpZK8vDwsLS31nDBzcnI4ffq0uG1qaqpQe8rj9u3bpY5LM4ESlpaWou1NVxUzNzfHxMSEzMxMOnbsiFKp5JdffiE0NJQnT57QvXt37OzsuHnzpjDZ6d27N5s3bxYzlI6OjjRv3pxx48bpGbvoUq9ePTp06CBmwJRKJUZGRuTl5eHo6EhKSgoWFhYoFAoyMzOxsrJi/Pjx2NnZoVKpOH/%2BPJcuXQKK2%2Bbq1KlDv379%2BOabb1CpVDg5ObFu3bpSkQa%2Bvr68efOGiIgIYYbze5dtJfMxoXx1R/c2Z8%2BepXLlyiIHUgoC//TTTwkNDUWtVlOjRg2ePXsmWrVlMhnjx49nw4YNzJw5Ezs7OzZv3syLFy%2BEpb8uUgREw4YNRUZhZGQkWq2W3r17A3D06FERO6KrUisUCrH5IW0SffLJJ1hbW3P48GG6du1KmzZtgOICU1LZpQy6OnXqlHJv1W3z1nUULYlMJuP9%2B/doNBpOnjzJ%2BfPnWb9%2BPb169eLgwYNCXXZzc%2BPJkyelbu/s7IyZmRnZ2dlkZWVRWFiIUqkUrdmFhYUYGRlhZGREamqquMZtbW2RyWQi3/PfoV%2B/fhw5cgSZTEaPHj04duwYnTt3pnr16piamuLs7EynTp1%2B1wywgb8HBmXPgAEDBv4LJCYmcvjwYSIjI1GpVJiZmaFQKIRSIJfLReaYsbGxWNgkJiaiUqmoWLEiw4cPp0uXLnz88cds27ZNKC2mpqbCfU5aGBQUFPDLL7%2BQlpbG5s2bGTBgAF5eXvj6%2BrJnzx7mzp2Lj48PULyYnD17Nq9fv%2BbXX3/l559/5ueffwYQs17nzp1DrVaj1WrZtGkTW7ZsQS6Xc/XqVVGQNWnSRBR7Dx48QKvVMmrUqHLn6uRyOcbGxmg0Gl69eqWnhj18%2BJCcnByePXtGzZo1/wffmdIMHDiQZcuW4e7uXm6%2B2OrVq/Hz8xNGMCVVAUk1KE9VXL58OWZmZpw5c6bMKINjx46hVqupVq0a7969Y%2BvWrYwYMYLatWvz6tUrGjZsiIeHhwgalxQUKFYQTExM0Gq1bNu2jU2bNonr4vHjx8yYMYMnT55gZGSEQqFg4cKFegXwxYsX8fX1JSkpSWwySEqChYWFnkHEnzEMadasWSkDF2NjY86fP8%2BtW7eEbb%2B0EaLrWPrNN9%2Bwbt062rVrh6WlJadPn%2Bbdu3f07NlTXD8ZGRkiL0%2BtVhMfH8%2BdO3f4xz/%2BgUKhYMuWLezfv5/Lly8TFBSEk5OT3nMJCQlBpVKRm5vL999/z/Tp04UrqEwmEzNRUgYhFGehff/99yJyQpepU6dy%2BfJl5s2bh0ajQSaT0bBhQ9zd3Uv9rm4uGyDC1Y2NjYXxiHQ%2BgoKCGDJkCMbGxhQWFiKXy7G1taVFixbcunWL7777jtevX/PDDz%2BI91H6vpFcGRUKBZs2bRKPp1tQSS2c8K/2UWnuUavVCnV9xYoVREdHs3DhQvHdpVar6datGydOnEAmkwlzk%2BjoaDQaDTt37iQgIIDTp0%2BLx65SpQqA3lyhdF8lN8XCwsLE3OXp06e5f/%2B%2BUPGlzYGysvckJLdYKN6I2b17d7mbQB9//DFFRUUEBwezbds2OnXqxIEDB0RcwZQpU/Dw8GD8%2BPF6MSOgn18qIRWy5bmp5ubmivspL0Lhj3Dw4EHx56NHjwKIeW5pw2j9%2BvXs2bOn3FgaA38vDMWeAQMGDPzFREREMGHCBBo0aEDjxo3p0KEDxsbGZGVlERMTw6NHj4iPj2fEiBHY2toil8uZM2cOFSpUwNTUlLy8PDQaDS1bthT/GEttlCqVSs%2BwQnKrMzc3R6lUkp6ezpEjR8TxW7duoVQqGTx4MLm5uRw7doyqVasyb9480Z4mKQSdO3cmLCyMS5cu6e3UOzs7k5qaipOTE25ubri4uBAcHFyqNcjIyAgvLy/i4uJKnZPY2Fi0Wq0IlVar1XpFhLQAHz16NPv37y%2B1IP8z/JZCKB3v2bMn0dHRws69ZE6etGC%2Bd%2B8e9%2B7dA8DBwUEYTWi1WtatW6fX6lqyGDx//jyDBw8uN7NOq9UyevRofvrpJ7Zv3y6iOry9vYmOjmbIkCG4urry5Zdflvn65HK5aMV0cHAQBcrixYtZsGCBaHOUyWQiBkAXaTGcnJzMrVu3uHXrFocOHeL9%2B/fUq1dP5NfpOpX%2BXjZs2CCUypLUq1eP2NhYvaw16TVDcZZktWrVqFSpEgqFgrZt23LlyhV8fHw4fPgwUHyuJ0yYgIuLC1u2bMHPz481a9YIY6SWLVvSt29f7O3tWbt2LYsXLxaPJZnraLVa8vPzCQgIYMCAAfzjH/8Qv5ORkUGvXr3Ytm2bmC3LyMhg7ty5NGjQgMmTJ5OXlydmsvbs2UNmZqbIM9NqtURGRtKvX79SxaaDg4Mw5tF9b%2BRyuTCEkdi3bx%2BAUPNUKhVWVlbcv38fW1tbFi9eTGFhofjs6kakSAVHfn6%2B%2BH7QLSYktS0kJAS1Wi3uQzIX0W2vnjFjBoAovlxdXXn58iWzZ89m8eLFJCYmkpaWxrBhw1i7di3Tpk3D1NSUOXPmEBoaytKlS/Wugby8PEJDQwkKCuLx48d6x6SNsePHj1OjRg28vLwIDw8vlUP6n6Rt27Zcv36dwMBA5syZw759%2B3B0dAQQrZ2TJ0/GxsaGgoIC8f2gVquxs7MjIyMDY2Nj5HI5kyZN4scffxSuruvWrWPq1Kmo1Wo%2B%2Bugjrl69Kh5X%2BjyrVCrx3jRr1ozbt29jbGyMSqVCqVRSWFj4QbMgaTNAwsnJibS0NORyORUrVqR%2B/fpYWFiwcuVKVq5c%2BT92Hg38dRiKPQMGDBj4i1m6dCljx44VQc95eXnMnj2bM2fO6C2wSv5Dq7vL7%2BjoqFdw7dixgyFDhuDl5UViYiIff/wxkZGRvHjxAplMxv79%2B6lUqRJeXl5CZQJ4/vw5FhYWBAcHM2/ePJHVB8ULvObNm/PgwQN2794tTDakkGupGNyyZQsLFy7kzp07HDlyRBQsW7duZcKECeTn54sF5bRp08o9L1qtFicnpzLd4/z9/Vm9enWZC/Lfg6%2Bvb6niLi8vjyFDhqBQKHj79q34eaVKlUodh%2BKFt5SpVjInTwp0L%2B%2BxJdatWyfuSyoGoXhmKiQkhKlTp5Z7Pw0aNECtVlOlShW2bNkCFKtJCoWC1q1bk52dTV5enl7khYT0Z8lsRcLW1pb8/HxkMplQYR4/fqz3HlSsWFEUGU5OTnTv3p3u3btz5MgRDh06RGxsLLdv32bx4sXk5uby1VdflTL1kSjLQVRSi0qi27opFatarbZUy2dmZiaHDh1Cq9WSkZGBq6srcrmctm3bChOXqKgoFAoFpqamwiFWwtvbG5lMhr%2B/P8uWLdO77/3794uCeNGiRURHRxMQECBC1xMTE4VTpFKppHLlyuJnkqIYGhqqp9q8ffsWhUKBn58fly9fBmDy5Mns2rWLuXPnMmrUKJo2bQpA69atOXToEOPHj0ej0dCxY0fgX59BXeVIUsWk7xBJxdRFml%2BVTGOgOObB2dmZ6OhoTp06JZRzLy8vTExMKCgoYOnSpRQUFIg2ZVNTU1q3bi3m/tauXVvq/XN2diYuLo4uXbrw008/sWjRIj2zJJVKxcqVKzEyMiIgIEAoiO/fvxffISdPniQjIwMLCwv8/f15/vw5r1%2B/Fq6tUj5n//798fb2RqvVMn78eL02zaKiojJn7/7o7K7E%2BPHjOXfuHJmZmQQGBvLixQtRdBcWFvL5558TExODmZkZVlZWZGRk4OTkxNu3b8nJyUGr1eLg4EBOTg7t27dn27Zt2NjYoNFoxAxfQUEBSUlJmJmZAZQbLN%2B9e3du376NSqVCo9GIQl%2Br1ZY7ElDS0bVz584iLzUtLY3w8HA2b96st3Fk4O%2BNYWbPgAEDBv5iGjZsyJEjR0Tb1vTp07l8%2BTK2tra4uLjwyy%2B/iKBbySzFxMSE9PR0tFotFStW5N27d6jVaipXrkxCQgLff/89AQEBrF%2B/nvnz52NiYkKfPn3EcL6ZmRnffvsts2bNwtraWiye09PT9RQCyX5bytmTZkqkVjqAVatWMX36dObMmcOCBQvYt28fV69eZfv27Zibm4sw97JcO3UX%2BrqOgfCvRb/03HTnD6G4Za9Xr16cPn2a2bNni5%2BXt2jz9PQkKCiIZs2acezYsQ%2B%2BJx/K2YPi/DepYCgrNP3fpay5Pl08PT1p1KgR9%2B7dE4vsvLw8TE1NsbCw4OOPPyY%2BPp7o6GixoNSdOfozcQASujEAJZ%2Bzt7c3%2Bfn55OTkkJOTIxwAJRe/oqIiIiMjxe1LKniSYrlo0aJSeWYXL14kIiKCb7/9lkaNGpGcnEzbtm0xMjJCLpfrtXPqtrWVnLVydnYmLS2NqVOn4u/vX%2B4M5aZNmxgzZgx2dnbiOk1ISKBSpUokJSVRsWJF5HK5ML9RKBQ0bdqUlJQUIiIixH3OmjWLu3fvIpfLRV7gH11qSec8Pj6ebt264ezsjKWlJebm5ty5cwcnJyeys7MpKCgQbeCSMVBqaqpQ/pycnPDx8SErK%2Bt3mXBICiLoF%2BG2trYifxOKNyvy8/PJz89HrVbj6OhImzZtcHZ2Foq3NIO3YcMGLly4UKrIT09PJzw8nIYNG1K5cmW0Wi0PHz7k5cuXhIaG4urqSp06dUTbemFhIWlpaWKG8PciGReVfJ0lI1QkfmuWF%2BDFixd069at1EykROPGjVm4cCEnT55k8%2BbNVK1alefPnwPFHQ7SRoaUFZqRkSGKsNq1axMbG4taraZ27dpYWloSHR0t1Hzp863RaKhSpYowQJIMcaT3qEaNGri4uBAeHi5%2B5uHhwbNnz/Te288//5zg4GAxBymTyTh9%2BjRdu3YtNVtt4O%2BJQdkzYMCAgb%2BYBg0asHnzZhYsWICJiQkXL15Eo9Gwdu1ahgwZInarq1evzvPnz5HL5WzevJl%2B/foBxTMce/fuZfjw4bRt25a9e/cSEBBAtWrV%2BOWXX8jJySErK0vsfkPxbu6CBQuEE5/uzJG0C1ypUiUOHDjAmTNnxE4%2BoLdbLJfLqVq1KjKZTIRRT506FblcTlBQEAkJCZw8eZKLFy%2BW6RhoZWUl1AUpSNrJyYnZs2czY8YMFi9ejJOTE7NmzWL79u16t3VwcODSpUtkZmYKhUyayylLuQP46quvSkUvlLXI69WrV7nv165duzhy5IjIyQsICODJkyd6OXn/LrqL7LLw8vLi6dOnQHHBVFhYSMWKFUlKSqJu3bqkp6fzyy%2B/4OjoiFKpxNnZucwZJUlx69ChAxqNBk9PT3bs2IFWq2X58uVltsfq5g4%2BfPiQhw8f8ujRI4yMjIiNjcXJyQlXV1dcXFxwcXHRcz8FhLEIlB2uLpfLadCgAW5ubhQWFvL06VOePXtGcHAwU6dOpUaNGpw9e1aouVLro65BkVarpVKlSuzatUuodrt27eLGjRvY2dkRFRUlgrTLO9cPHz7Ezc2N4cOHi58FBAQwcuRIvU2H%2BfPnM3jwYBwcHFi1ahUbN25kwYIFwlExIiJChF7LZDJsbGxIT0/XC2/Py8vjzZs31KhRg/bt29O6dWuSk5OZPXs2W7duFb/n5uYmNoWioqIwMzMTmzRS5mRGRgZNmzalQoUKqNVqTp48iUwmQ6VS8fbtWz3VWhcrKyuysrLw8PDA2dmZa9euiaKgZHFachYzIyMDBwcHUXSlpKRw9OhRbGxssLGxwdXVlbFjx7J582YmTpyIp6cn7u7uwmSoqKiIa9euMWDAABFjsGPHDm7cuEFgYCBubm7s2LEDtVqNu7s7NjY2TJs2jcGDB%2Bu1rpqbm1OzZk0qVqxIeHi4MLtRqVTs37%2B/zJbk36JVq1ZCUSuPatWq0atXL5KSksjLy%2BPx48fIZDLc3d3ZtGmTUBGl1yC5tELxd7E065iXl1dqVk83yPy3Qs11nW5Lft8%2Be/aMZ8%2Beib/LZDKmTp3KxIkTxYaIQqHgxo0bVKlSRbT/W1pacv/%2BfdHhYODvj0HZM2DAgIG/mISEBMaPH098fDw%2BPj5ERUWh0WioV68eDx48EOYKI0aMYNu2bdjZ2bF27VqGDRuGRqPB2tpauN5dvHiR%2BPh4YmJi2LlzJ3fv3i2VsVUWSqUSc3NzzMzMSEpKAmDNmjV89NFHWFlZUbt2bb7%2B%2Bmu2b99ebihvnz59OHz4MI0aNWLHjh2YmpqKY15eXsycOVPM4EnIZDKioqLEPF5OTg6jR4%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%2BfPUSqVoiPA2tpaPG5%2Bfj7v3r0Tf5fL5bRv357%2B/fvrZct1796dL7/8Uqjm3bt358WLF2KzqU2bNly7dg0TExPRxjp%2B/HjOnDnDmTNnKCwsZMyYMWITpFKlSqId9vfwR9s6ExMT%2Bfzzz3F3d%2Bfx48fiObm7u9OhQwdu377No0ePsLS0FMWdFFqv0WioU6cOnTp1QqlUsmrVKlQqFW3atOHGjRuoVCo8PT355Zdfyn18GxsbMjMz9TIdraysaNKkCYmJiURHR9OiRQtevXrFmzdvMDU1RaFQ6BkJubm5kZCQgJWVlYjB%2BOSTT7h%2B/To9evRgypQpf%2BicGPjfiaHYM2DAgIH/Ejdu3ODhw4fcuHGDO3fuIJPJRIi15DgntWNJLnTSos7Y2JimTZtStWpVXr58ye3bt/Xa2gYNGsSBAweE696YMWMICQnhzZs3HD58WE/1kQqAsnB2dqZBgwYUFBSIsOmSv1u3bl2%2B/PJL2rdvL4o4b29vwsPD6datG8OGDWPr1q1kZ2ej1Wqxt7fn%2BvXr4va3b99mxowZTJkyhWXLlvHjjz8K10tpMX337l1evnzJzp076dChA61bty73vJa1aNN1Y/zpp5/w8vISxwIDA9m2bRupqak4ODiwZcsWvePe3t44OTlhampKUFAQ9vb2eqHp/wnKMygpi5LqmNTyJZnx1K5dG0CEQ%2Bu2o7Vq1apU8V6WxTwUzy7qqqD16tVDJpNx8uRJcZ85OTncunULhUJBkyZN9IpFgFGjRvH8%2BXPOnz9f7muW3FklZaJTp06EhYWh1WqxtbVFrVaLmaVFLHbcAAAgAElEQVRq1aqxaNEicR/Xr19n165dtGrVio8%2B%2Boi9e/eybds22rdvT5cuXcTc686dO9mxYwfJycnY2tpiZ2cnLP3T09NJS0ujatWqNGrUCCh2/IyLi%2BPJkyd67cShoaFcvXpVGL5s2LCB5s2b4%2BHhwe7du0Wge2FhIYcOHcLa2ppp06aJ2UETExMRRaBbBEuKu4SxsbE4l2lpaUKFK9nCKJfLqVWrFjExMaL9uX379ty8eZOCggKMjIxo1qwZUVFRwtipdu3aPH/%2BnOrVq2NlZcWbN2%2BQy%2BUsXryYevXqsX37dk6dOkXHjh1ZvXo1RkZGWFtbs2rVKk6cOMHhw4eFoii1st68eVNcf8bGxjx69EjvvT5%2B/DihoaGEh4djZ2dH165d6d69u7hWpevr66%2B/5vPPP8fc3Jx69epRu3ZtIiMjgeK4hooVK%2BpFGmzYsIFp06bx8OFDoLjdetq0afzwww/lzgcnJibi6Oiod91/qK2zJAEBAaLtu6ioiPfv34tC70MolUp%2B%2BuknZs%2BejVqtZsOGDbi5ueHk5ETr1q1Rq9Xs27ePwYMHixb%2BgoICZsyYwXfffYdWqxWq6ePHj%2BnRowc///wzc%2BfOpVatWpw/f56XL1/y5MkTnj9/TkpKimjRl2ahda8xXRdWCVdXV16/fi02jEp2RRj4e2Jo4zRgwICB/xItW7akZcuWrF69WhRQUlSBRFnKnEajIT8/n2vXrgn1QJfPP/%2Bc%2BfPnc%2BDAAVGcnTx5kh07djB9%2BnT27dtH3bp1iYyM5NixY6Vm53QfVzKYkFAoFLi7u/Py5Uu0Wi2FhYU8fvyYiRMn6hVx0n3m5%2BfToEEDVCqVcOMbNmyY3vOtVKkSGRkZZbpeFhYWsn79erFzb2FhIVwvSy7a1Go1OTk5tG7dupRyd%2BnSJeHGqKtAQrH5y8OHD7ly5QofffSRXqEnvZbdu3cza9YsYQ6jG5r%2Bn6Cs9sbfi6enZ5k/l96Dt2/fEhQUxJEjR0Q7npGREZ06dWLs2LF4eHj8rseRokEkfv75Z8aPHy/C5CtUqMD27dupVasWb9684bvvvuPq1atifq8k0muWXArbtm3L2bNn%2BfrrrwkLCwNKtw%2B%2BePGCIUOG6L0%2BU1NTxo0bh6WlJcuWLePKlStoNBq91txWrVqxbt06du/eTXBwMDExMcJk56OPPqJ79%2B7CZEd3Y8DR0ZHt27eL%2BSpJFdm4cSOOjo7k5%2Bdz9OhRjIyMaNq0KRs3biQ0NJSZM2fy7bff0q9fPyIiIsT8qu7C2traWmyASJs4kpuiQqHA3NxcmDKZmpqSnZ3NhQsXaNWqFaampuTn52NiYiLC1GfMmMHy5cvx9fUV7qmffvopLVq0YODAgcyZM4f379/To0cPvvvuO9q2bcu6deuoWrUqBQUF9O/fn7Vr1%2BLu7k5MTIww82nWrBkRERHMmDGD7OxslEol9vb2ZGRkUKVKFRYuXEjHjh2FiijFj0jnUqPR0KRJE5o0aYJSqeT69euEhobSv39/3Nzc%2BPTTT%2BnevTsWFhb88MMPtGvXDnNzcywsLFiwYAE9evQAik2MVq1axciRI0URIrXJSri4uJCcnCz%2BHxUVVeq6%2B61ohd%2BDdO0plUrRNlpUVERRUZEwS9FqtcJB%2Bfnz56hUKkaNGiXuY%2BDAgUDx919aWhoqlYrOnTuXeqxWrVqJqA21Wi3ey/DwcLp06cKECRPEuS8rOF6tVpOYmIi1tTV79%2B4VG4nSbKDUYlq1atVSG0YG/m9gKPYMGDBg4C%2BmsLCQtWvXcvLkSbKysvDz8xOzSRIfCqOWAtAjIyNJT08vVRD6%2B/vr/X3Pnj2sWrWKf/7zn4wcOZK5c%2Bdy4cIFMjIyxEJJJpMxePBgTExMGDVqFEFBQUDxQuHZs2fExMTw8uVLOnToIFQXKF7gN27cmJkzZ3Lq1CnxmBcuXMDOzg4PDw%2BhLlWrVg2AvXv3CidSKFZ4pIVXQEAAnTp1Ijg4mAcPHqBWq3n69Cn9%2BvXTW5CD/qJNWqBbWVmRmpqqV%2BxVqFCBuLg4KlasSGZmZqliD4ojKKZNm6aXMaaLubk5kydPFrby/2kCAgKYM2eO3mzY3bt3qVu3rmj9S0tL44svvhCuqB8iLy%2BPoqIiRowYIeZ6pGJeLpdz%2BPDhUkXtH2X58uXUqVOHJUuWYGRkxPLly1m8eDHjxo1j/PjxmJubY2xszKpVq8RtpCw2XTQaDRkZGZiYmFBUVERgYCATJkxgzJgxrFixguDgYDIyMjAyMsLOzo6rV6%2BybNkyduzYQbt27bh%2B/TpWVla8ffsWW1tbbty4gampqV4Ln1Rs7dixAyMjow%2Ba7KxZs4aqVauyceNGca00atSIgwcP4ubmptd6vHjxYpYuXcrOnTtJTk5m5cqVom341KlTnDp1Cg8PD7Kzs0up4llZWVhaWtK4cWPGjh0rrm3d1lDpPfrxxx/FzK5kziH9WXLcfPToERqNRkROQLHSdeLECcaNGycMnhYuXAgUK9qdO3dm7dq1zJkzh8OHDzN58mRx31JhGxERASDaveFfrp7Pnz9n8ODBACxYsID58%2BczceJE9uzZg52dHV26dCE/P1/ML9asWZMjR47Qs2dPsrOzOXfuHKGhoWzevFkvMxCKlT7d9myNRiMUYknFXLNmDebm5uIzr1AoKCoqYuzYsRQVFel9x/2nKBkN8SEGDRpEdHT0B1uUy5uplOjQoYP4c2ZmJgEBAUDxnGRgYGCZt3F1daVRo0akpKTg5OTE9evXkclkLFmyRG8W2tPTs9zNIgP/dzAUewYMGDDwFyMpTTNmzBCqUe/evenRowcJCQm8e/eOnJwcNBoNbdq0AYp39p2cnFAoFLx69QqVSkWVKlWYM2cOy5YtY9q0aaI9qFevXri4uKDVavnuu%2B/w9vamTp06BAUF6S0ER4wYgVwuZ9u2bWJm59ChQ5w7d44ZM2Zw5coVzpw5Q1ZWFnXq1EEmk5Vyi6xbty5hYWGYmppy%2BfJlYfDRs2dP0tLSUCqVHD16VK8Affv2LStXrmTUqFH8%2BuuvrFixQuSzhYSEcO7cObEgnzdvHuvWrfvNXfiyFugSUmxDhQoVyo1tyM7Opnr16kKlKsmZM2coKCjg3bt3BAcHl8rJk/izdu7BwcFMnz5dr9gbNWqUXhumWq3WM2Qoj5kzZ3Ly5Elh0NG4cWP69%2B9Px44dadq0KVqtVi/D8M/y9OlT9u7dK1pZZ8%2BezUcffcTUqVP59NNP%2Bfrrr8X1K1GeI6RKpSImJga1Wk1kZCRmZmZMmDCBgIAAEXVw4MAB0YLas2dP0QbYu3dvIiMjOXToEC1atOD8%2BfO0bt1ab5Zw2bJlFBQUkJ%2Bfj0ql%2BqDJTkRERKnrSHexbmFhoVf4BwQEsH//fmrWrMmDBw%2BEayIUz5llZGToZaM1atSI/v37k5aWRnBwMNeuXSM8PBwfHx9R0JXkypUrAOLalYoi3fZBKXpBanu0srLiyy%2B/ZO7cuWzcuBFjY2M0Gg2NGzfmzp07pKWlERYWxqRJk1i5ciVxcXFERkYil8vx8fEpMw9TIjY2VvxZKgIlJbBt27Z67X%2BBgYG4uLiQmJjI6NGjOXToEIMGDcLS0pJevXrRq1cvbt68yfz588nKyuLgwYNMmTKFESNGMHLkSL1zf%2BDAAfH6NRoNT58%2BxcjIiClTpmBlZSWC2CUX1N69e7Nv377/SC6nRK9evUhMTMTExARXV1cSExN5//69XqEqIbUmGxsbi7Z8Jycn7OzsiI6OxtLSktzcXMzNzalVqxbPnj0TLbLGxsakpKSgVCqFslse0rymtKFgZGTEmDFjxFy1tEF0//79/9h5MPD3wVDsGTBgwMBfzJkzZ1i1apWw%2BPfw8KBfv34cPHiwlH12cnKyuJ3uQl8yYHj06BFyuZzGjRsLe%2B%2BgoCBOnTrFyZMnmT9/PvPnzxe3c3Z2Fjbf2dnZxMfHi9afunXrUlhYyLZt2xg3bhwymYy6desybdo0WrRogZeXF6GhoXqLrzt37qDVagkJCQEQ%2BWMHDx7kwYMHqFQqqlatSlBQkJ4F%2BN69ewkMDESlUtGnTx/8/f1FgHXLli3Fgrw8g5mSlLVA18XDw4NmzZqJ2amyjpecb5NwcXFh%2B/bt5ObmioD0kjl50nvyZ4u9snb%2B/%2BxIfUhIiLjt559/zqBBg/ScIP9TSE6qEtbW1hgZGfHFF1%2BI7LmSSIrxy5cvqVSpEsbGxkK9GjFiBDNnzmTVqlWl3ofhw4dz4MAB1Go1Bw8eFMH2UVFReHt78/XXXwvzkby8PBF6npSUxNmzZ7l8%2BTJ9%2B/YV836Syc7UqVNLGeNkZ2cLZ82y8Pf3JyAgQGwMhISEYGJiQnx8PMOGDaNbt25iDtbY2JihQ4cSFRXFjh07gOKF/7x58xg%2BfLh4r06ePMn%2B/fuZN28eGo2GFStW6DnOHj58GLlcLgq6sih5vWRnZ4vcQMmds2HDhnoOjV5eXty5c4cffviBmJgYoFhBMzY2FpmNwcHB9OzZU%2B/%2BS6qUMplMqOKDBg0SpkAymQxnZ2cqV65M5cqVGT16NCEhIQwcOJA7d%2B4QFhbG%2BfPnyc3Nxc/Pj9evX7N7924OHjxI/fr18fHxEcWr9Nx0/w/Fc3N37twp9dqhuBj%2BM7mcH6Jjx46EhITw8uVLPVVOmq0uy%2B1VoVCgVCqRy%2BU4Ojqydu1afH190Wq1QoVu1qwZlSpVwsHBgWPHjnHgwAG6du1Knz59aNasGZmZmWzYsIGsrCy8vLyIiorixIkTDBkyhCpVqhAZGYlGo0Eul/Pq1StevXqFhYUFr169Ehsfhhm8/z8xFHsGDBgw8BcjueBJLFmyBCsrK9zc3IiLi8PKygqlUsn79%2B8xMjLC1tYWe3t7njx5IgxOKlasSHJyslANlixZgo2NjchX%2B%2Babb9i3bx8VKlTg/fv3aDQa4VI3bNgwgoKCsLOz49q1a9jY2FBQUMDIkSOxtramSZMmPHr0iMzMTJ4%2Bfarn5jh37lw9NURaRFhYWJCTkyN2su/du4dMJsPa2prFixfz5MkTvVmeGjVqkJiYiI%2BPDwsWLAD%2BFWAtFUxhYWFMnDjxdxU9v7VAHzhwIEuXLi03n6tVq1b89NNPpVpgoTjv7f79%2B4wdO5YZM2aU%2BTv/mzh//jz79u1j69at7N%2B/n/3792NpaUnr1q1Lha3/EX7v7bp37/7B44sWLWLv3r189tlnIl8NijcK8vLyGDRoUKnWMsk4BeD7778X76NWq8XKyoqPPvpIhKbPnDmT%2BvXrA7BlyxZOnDiBTCZj4sSJ4j58fX3Jy8sjOTm5lMmOh4eHXmtxSW7fvi2OSxsU0pzbzJkzOXLkiHhNr169YsaMGUJJVSgUtG3bFg8PD3bu3Imrqyv29vZcvnyZmJgYFAoFGo1GT4ExMzMT7cq%2Bvr6kpqZy6tQp3Nzc8PHxKTPiRKFQEBkZKdwupQ2dmTNnotVq%2BeKLLwB4/PgxUGxgo4u5uTkymUzkOMbExOiFrEt/l5wgv//%2Be%2BbPn49Wq9VTvIuKiggLC2PkyJGoVCpsbW2JioqiVatW5Obm8vHHHzN37lzatm2LsbExZ86cYdeuXTx48EC0cfv5%2BSGTycjPzycvLw8jIyMiIyNRKBTMnDlTr/1dwtnZmQcPHrBw4ULRhirx7zrojhs3TnQiSOZD4eHhxMTEiNxSa2tr6tWrR0pKChkZGTRo0IDo6GhevnzJo0eP8PX1Bf7VKnvp0iUuXbqk9zjS7%2BzevZvdu3frFdhKpRKZTEbv3r3Jzs4mOTkZU1NTkc8KMHHiRDw8PJg6dSpqtRqlUilabg38/4Wh2DNgwICBv5jmzZuzfPlyli5dir29vQgfDgwMpFu3bmRmZgrTkaKiIrKysqhUqZL4hz4tLQ1XV1fev38vfnb16lU8PDyoWLGiMDipW7cu0dHRYhc8KSkJhULBoEGDCAoKokePHgQGBqLRaLCzs%2BPdu3e0bNkSFxcXke9Uo0YNpkyZIvKqrl69qrfoaNKkCefOnROLFt1ZIq1WS1ZWVqkFhlwu59ChQ8KFUyI%2BPp6WLVuKv0uLnXfv3on2LF10F22/tUDv2bMnx48fJyIigi%2B%2B%2BEIoQ1IA9MOHD/Hw8CAoKIiHDx%2BWeVxSIP%2B34%2Brqytdff83evXtZv349YWFhhIaG6hnt/PDDD0ycOPEPGTGUNH8oL6/uQy2iu3bt4vTp02zcuJHt27frxQhIZixJSUl682FQfN1B8bVz8%2BZN4uPjyczMxNbWlipVqpTrKDpt2jTmzJlD7dq19Z6XFEBelsnOwIEDWbZsGe7u7sIVtiQbN25kwoQJ7Nu3T2xQLF26lF27dnH9%2BnVRFEnzdAUFBWIxvnXrVoqKiigoKGDevHkYGRmhUqlEfINKpUIul9OhQwcR0TBkyBAUCgWvX78GYPDgwVy5cgVTU1OhOAcHB/PZZ59RvXp13r59i7GxMVlZWchkMiwtLbG3tyczM5PatWuL%2Bc0VK1bw/PlzNm3aJAoCOzs7tm7dWuZMp2Q8cufOHRwcHESkwsqVKyksLMTOzo7t27eLTaAKFSqwd%2B9eYmJiuHLlCjk5OchkMmbMmEHHjh1LubcC2NnZMWTIEGHEo0thYSHnz58XhWDjxo2JjY2la9euZT7X9%2B/fI5PJqFOnjpjp0z2fuvxeN06JXr160bx5c86fP092djYqlYo6deqQl5fH8%2BfPuXbtmlA2w8LCUCgUwon1j2Jvby/iG6B0O7R0nehSVFQkinkLCwvGjh3LyJEj//BjG/j7Yyj2DBgwYOAvZs6cOUyaNIlWrVqxdetWHB0dycvL49WrV7i4uPDu3TvhsCeTyTA2NmbcuHGMGTMGKP6Hf9y4ccybN4%2BcnBy0Wi1du3bl2LFj1K9fnwYNGhAfH8%2BrV6/0rLYrVKhAamoqffv2RavV8uLFC%2BrWrcvdu3dJTEwEKGX%2BIbnZSYtMXXUOitsntVotderUISoqSk85Km8HXSoIJRdOCSmLTEKaVbGxsSkzNF130ZaTk8PcuXNZvXo14eHhpR7z/v37PH78GH9/f/Lz88VslZ2dHT4%2BPsyaNYsGDRpw9%2B5dYQ5T1vH/KX4rVP3P0KpVK7y8vGjdujULFizg4cOH7Nq1i4sXL4r/PD09S2UWlofUdiuFrGu1WhYtWqTXGlZUVMSKFSvEIr6goECvJfHgwYPMmzeP9u3b0759e0C/pROKg983bdrEli1bylRtgDJ/Hhsbi1KpxN3dXZxLXVV63LhxYubrQ3zIFfbVq1dotVpq1KiBtbU1L1%2B%2BJCMjg%2BDgYLy9vRk9ejSbN2/G1taWpKQk8vPzxXOR5ut0F%2B3SOatUqRIajUZc/3l5eaIgk8lkJCUlYWtrK2az8vLy8PX1FZ9/mUzGkSNHgOJiWaVS0aFDB1GcabVa0tLSGDdunFDdtFotP/zwA4WFheJ3ioqKSEtLo379%2BqKA6Nq1Kw0bNhTuu0CpDZzU1FS0Wi3v379HLpeXMpYaNGgQU6ZMITs7m/Dw8D/c7nz//n2OHTtGaGgoWVlZuLu78/btWz7//HMKCgrKLPakaI8KFSqQkZHBkCFDSs3Y/jv4%2Bvpy5coVXr9%2BLb7TdN0/jYyMqFmzJl27dmX9%2BvViPnvAgAFotVr69%2B/P9evXefPmjWi/tLCwEDOf9vb2mJiYkJqaKgyKdL/Pzc3NycvLE90ckhFNixYtGDp0KIMHD%2Bb06dPY29vrzQIb%2BP8PQ86eAQMGDPyX%2BPXXX3F0dOTAgQP8%2BOOPyGQy6tWrx927dykqKhID9wqFAplMJv6h/%2Bijj3j69Cl9%2B/Zl586daLVaTpw4QXJyMmvXriU6OhqVSiUWjA4ODqSkpBATE8Pq1avZsWMHJiYmZGdnU7NmTeG4KSl2ZQU1/xbSbSZOnMg///lPXFxc8PX15dSpU3Tv3p3WrVvj7%2B/PyJEj2bNnD/fv3%2Bfo0aN6AeleXl7/j73zjm%2BqbP/wldGmTQddUFosQxkByi6CSpH1FhEZylRkKUOU6fvKEFREVhGQURDZZZUhs0VkFAValPVjDxkKVmgLLbTQ3STn90c/5zFJk7YgCsi5/qHJSU7O5rmf%2B76/X4cG1kePHi12G2JiYoiPj6du3bp2M3MhISGir6xly5aF1BgtxWHsLf87MRgM1KtXz8p8/OjRo9SqVQudTsetW7eEQIutUb0txQ2kJUmievXqSJJEx44dRV/X/SKrAgLC58%2BWS5cuUbFiReF3V7duXbZv3065cuWAP0s6ly9fzvPPP48kSfz888/CnuONN95g4sSJDjN38m8MHjxY9LRWqVKFb775hoCAACHXv3r1anx8fBg1apT43meffcawYcMciuzIgf%2BFCxc4ffo01atXp379%2BqxevZqyZcuiVqu5fv06ZcuWRaPRkJWVRXZ2Njk5Obi7u%2BPu7s6aNWsAxGSFfH/JmRp/f3%2B0Wm2hbOjzzz9P/fr1hZ8bFJjc7969m7NnzxIWFsaVK1eIj49Hr9eTk5ODh4cHNWrU4Pz58wQGBpKfn8%2BlS5fQ6XQMGjSI2bNniz5CJycn8XzJzc3F3d2de/fuiWdNXl6e2F4XFxer6xIKvCctWblyJRs2bGDq1KksW7bMrt3G%2BfPn6dOnDxMmTLBrMQAwePBgxo8fj5%2BfH4mJiWzZsoUtW7bw%2B%2B%2B/4%2BrqSuXKlUlJSeHGjRtCiGTy5MnCnkHm%2BPHj9O/fn3v37vHBBx%2BwbNkyoXD6V4mIiGDTpk1iG5ydnXnmmWeoUKECJpOJ8%2BfPC4872yF2w4YN%2Bb//%2Bz9hPl%2B/fn1RZurq6iqEZwAGDhxIQkIC33//vZgIsMwKuru7o9frSU1NRafTIUkS3t7e3LhxQ/y2Wq1Gp9NRq1YthgwZ8pcsXhSeXJRgT0FBQeERI0kSc%2BfOZfHixXYV3WyRRS2qVavGli1bkCSJmjVrcvXqVSE1HhgYyPnz5/H19aVGjRocOHCAtWvXUq9ePaZMmcKKFSuoVKkSgBBsKF26NKmpqdSoUYOLFy%2BSl5eHu7s7BoMBf39/9u7di0ajoU6dOlSpUoX09HQ2b97MkCFDuHr1KpIk8fHHHxMaGsro0aP5%2BuuvSU9PF%2BbjBoOBffv20bx5c%2BbOncvYsWPRarVs2LCBgIAADAaDlYE1FD8gt8VygG6ZmdNoNKxevZoXXngBrVZLfHw8ffv2Ff2HluIw9pb/3dizJLAkMjJS/C1L%2Bzsyh165cmWhMkhb3nzzTQA2btxIzZo1S7ydx48f58svv%2BSLL76wyq4NGTKE5ORkxo0b59BXDwq8JcPDw2natCmRkZF88803TJo0icaNGzNr1izWrl1byKBa7jUaMWKEXQGe3r17k5GRwdixY3FychJm0MOHD2fAgAGkpaWh1WrFcSsK2VzbNvD//vvvRRBiOSkh/x0TEyOunwMHDqDVann33XfF9VOtWjUMBoPwNIOCkkQnJyerjG5%2Bfj46nQ4nJye%2B//57EhMTqVWrFlCg4it7Se7YsQOVSkWvXr1E1rR79%2B5ERUXRvHlzDh8%2BjMlkYtq0aSxdupSEhARRcqlWq1GpVMKbTX5P7un19PTk7t27PPfcc4waNYp79%2B7ZzZxBwQRLixYtRAa1Z8%2BeHDlyhGbNmuHi4iIC0V27dol9s1e6KX/fbDYzbNgwNm/ezJEjR0Sm9vz586jVapydnXnxxRfZt28fbdu2FRNFpUuXFhnnxMREUlJSxETZunXr6NatG1FRUVbX5syZM5EkiX79%2Bt2XPYMsvuPh4cF7771Hjx49cHV1tTqvR48eZcOGDezYscNuz7F8zzZq1Eh4k3p5eYlSZrVajY%2BPD02bNhVeqPJ5kYM%2BtVpNo0aNRMm9PeR1yubqc%2BbMoVWrViXeV4V/B0qwp6CgoPAYIdsu%2BPj4kJubK3rhfHx8aNu2rTBZtkQu1bLEw8NDBI46nY579%2B6hVqtFWVpGRganT58mISFBfK5Dhw689dZb1K1bl8jISJYsWULLli05cOAAf/zxB87OzuTl5REQEMD48eMZP348N27cICYmhp9%2B%2Bkl4jUmShF6vp3nz5mzdupXg4GDq1avHypUradiwocjSNWzYkBMnTjB8%2BHB8fX2tMlaW5Xcy8kDX3d2dH3/8UbwvD9rKlStHXFyc3cxcmzZtGDhwoJX4y5gxYzh69CgqlarY5Y8L%2Bfn5Isti6cdmiTwYLSmO5P5lGjRoQMeOHTlx4gS9evWiWbNmjB49WvRxQkEWMjIykv3797N69WqCg4PtrqtmzZrUrFmT1atX07FjRwYPHkyzZs148803uXz5Mq6uruj1eipWrEinTp3YsGEDR44cQaPRUK1aNdasWVMo4GvQoAGLFy8W/XU3b97kP//5D35%2Bfjz77LNMmDCBgIAA8XnbUmRb4uLi2L59O5UrV6ZcuXKFAv/q1asTFxcngr1x48axYMECmjdvLjwna9euzblz53j99deFOq1Go8Hd3V2UZ0JBNsdsNpOZmYmbmxtJSUkEBASgUql48cUX2bhxI0FBQXTo0AGj0ci2bdtwcnJi5cqVQglV7q2LjIykT58%2BlC9fnuvXr6NWq9m%2BfTt%2Bfn58%2Bumnhcp1y5QpQ6NGjTh8%2BDCVKlXi0KFDVK9enatXr4ryyJ07d3Lv3j1RLbBw4UL27NmDRqPhlVdeISIiQlyDp06dolu3bkiSRFhYGLt37xbliRqNBmdnZ7KysvD09ESlUpGWloanp6cQWlm/fj1nz57F1dWV0NBQkpKSOHfuHE5OTqSnpzNq1Ci6deuGXq%2BnZs2abN26lfT0dOE5KnsZenh40KBBA9555x169%2B6Nt7c3SUlJLFiwgGbNmon9X7FiBcuXLy90PIsjJiaGDRs2cObMGaH66e3tjY%2BPD5mZmSQnJxd5/%2Bn1elGm/yDIxuf2kJ9VvXr1IjAwkM2bN/P666%2BzZMkSoOD/EVk5WeHpQQn2FBQUFB4RW7duZcWKFdy6dQudTofZbCI9p0gAACAASURBVObu3buoVCpKlSpFqVKlCAoKolWrVrRt29buOiIiIli1ahUNGjRg79699O7dm6SkJGJjYzGZTJjNZsqXL8%2B1a9eoUqUKkiRx8%2BZNIeogDxqCgoIYNGgQnTp1AgqyjUOHDmXfvn20aNGCO3fuiJKyVq1asXfvXqGQFxkZSePGjcnMzCQkJAQoCEDLlSvHjRs3aNeuHVevXuXUqVOi78THxwdXV1du3LiBv79/oeyUrViCbJpuMpn4%2BuuvrcQjVqxYQUREBOnp6TRq1Ai9Xl9ogB4cHExsbKxQXjQajdSuXVtkHYtb/k%2BQmZnJoUOH0Gg0hISEFMqA/Pjjj0yZMkX0VToK9q5fvy56MG25fPkys2fPFlkeeca/KAICAoiNjeWdd96hatWqjB492uFnP/nkExITE1m8eLHd5XXr1sXHxwc3Nzd%2B%2B%2B03IiIiOHDgANHR0eh0Ojw8PMjJySEqKgp/f38SEhJ47bXXqFixIqmpqXTr1s1KVRMKgp24uDj8/PzEe3Xq1KFDhw5C6dUSWfjHEsssaXJyMh4eHri5uREbG1so8LfM7MnrsiznlI%2B9bWmryWTCzc0NT09PkV1LTU3Fy8sLlUrF119/zRtvvEHdunVZunQp69evZ8mSJeTm5lKuXDnOnTtHtWrVuHfvHnXq1GHmzJmiHBcK%2BmeNRiNDhw7l5MmTSJJE1apVad26NXPnzhWlfStXrqRv377s3r2bwMBAgoODhW2Lh4cHeXl55OTk4ObmRvv27XnzzTepUqUKEydOZP369XTo0AFnZ2eio6PJysoiJiaG1atXs3r1asxmM9OmTStUViljec3Wq1eP4cOH06VLF/R6PQaDgQoVKvD222/TqFEj2rdvL15PmTKFbdu2UblyZaBg0mDYsGHs3LmTW7duERAQQM%2BePQuVXteoUYO6dety7tw5oqOjC90rmZmZDBgwgIoVK963PYN87Z46dcpuqbu7uzve3t5WpZdqtVpY69y5cwe9Xo%2BzszP5%2BflCtMdsNuPq6krVqlXJzc1l5MiRpKens3PnTrZv306DBg24cOECOTk5qNVq8vPzKVOmjJgIdHJyEhYz7dq1Izo6WhwXlUrFqVOn7ms/FZ58FIEWBQUFhUdAZGQkU6dORZIkXF1dC5XdycIl6enp7N69m19%2B%2BcVuSeGhQ4dIS0sjJSUFs9nMiRMn0Gq1oudGDhwDAwPJysoiNTVV9Pd4enqSlJSEyWTi9u3bTJ06VSgQqlQq5s6dy7Zt25g3bx5Xr14FCjIROTk51K5dG51OR3x8PJcvXxYDXLVaLYRW5IDy6NGjlC9fHpVKxejRo/nyyy/p378/Pj4%2BfPLJJ/Tq1QtfX98ie82KM01fvXo13t7eBAUFMWnSpEI%2BavbEXyzVGItb/ndz9OhRPvjgA3He/fz8WLp0KVWrVuXGjRt8/vnn7Nu3TwTTRSF7mlmSlZXF9OnTiYqKEsGBSqUq1CNZFGfOnGHcuHFFfqZHjx707t3b4XL5uoqKiuLSpUu89957YntcXV1p0aIFQ4YMEYFbUlIS3t7ejBw5kjFjxhATE1Mo2JPXa4larXa4HXv37i30Xr169YS9QXBwMFu3bhVBvq1NQ2xsrLD5kNdlMBjYtGmT3X5TOcAwGAxkZ2eze/du8bkaNWpw7949Nm/eTPny5TGbzfTt2xcXFxd69epFp06dRDAyd%2B5cWrRoQfny5dm%2BfTupqalCWEXef39/f2HRotPpaN68uZg4kY9zr169kCSJsWPHimy63BMql7yqVCpWrVplNamyc%2BdOJkyYIO7TsLAwevfuzdSpUzl06BAfffQRs2fPdqhgao%2BvvvpKlIHu2bOH7777jg0bNjB58mT8/f3x8fEhMjISk8nEO%2B%2B8Q5s2bYTwzMyZM4GCrNrJkyc5ffo0Fy9etHpOysfTslfTEjc3N4YNG2alClxSIiIi8Pf3twr09Ho9gYGB/Prrr2RkZJCVlcX58%2Bd56aWXMBqNLFmyhIEDB2I0GomKihIZ8JdeeklMsE2ePJmMjAyaNm3KoEGDxLpbt27Nd999x7Fjx9BqtUydOpX27dtjMBho1KgRly9f5s6dO0CBeq1c7bBv3z5Rwnw/5aoK/x6Kns5TUFBQUPhbWLt2rShdLFu2LJ6enkBByaW/vz9OTk64uLhw%2B/ZtZsyYwerVq%2B2WBskm1bLAy7x581i1ahV6vR6dTodOp6N06dIkJiaSl5cnyqq6dOnC3r17GTBgAACdO3fm7t27dO/enSZNmtC5c2c6d%2B5MeHi4CPS0Wi3R0dEsWLCAa9euMWzYMGE4PmfOHObMmYPJZBLBlTzwSExM5Oeff0aSJBYtWiQMyefMmUN%2Bfj4rVqxg7ty5RR6v%2BPh4hg0b5tA0/fr164wYMUKIHVgO0J8Epk2bRnBwMPv27ePgwYM0adKESZMmcejQIdq3b8/58%2BeZPn16ISP3krBjxw6aN29OVFQUbm5uTJs2TWSW7gfbPi972Ap52MPT05OJEyfSs2dPatWqhZOTE05OTkRHR/P555%2BLQM9oNBIREUGTJk149tlnSUtLK7YX0RJZ3fN%2BKS7wL1euHOnp6cyaNYuUlBSr786cOZPp06dbqcxaYnsPm81m8vPzrUSR5GcB/BmMxMfHi7LZ6dOn4%2BLiQtOmTcX6bIWVoKDcr1mzZoVKouVs45kzZ2jWrBn5%2Bflie1u3bk1cXBwqlaqQaMydO3esBD4aNWqEJEn8%2BuuvbNu2jb59%2B973NWV5PJ555hkGDBjAtm3biI6O5vXXX%2Bf27dvCnsPLy4udO3fSq1cvzGYzOp2OIUOG8NNPPzFnzhycnJwKPSfl41lUEZutKnBJmTx5MtWqVbN6JmVlZXH58mXMZjNOTk54e3sTGhpKeno6GRkZDBo0SPzdo0cPGjVqRIMGDUhLSyMjI4PFixeLTPusWbMwGAzUqFGDmjVrWpVnBwUFMXv2bF5%2B%2BWVUKhXR0dEkJiaSnJxMUlISEydOZNKkSdy%2BfVv8m5aWZneiROHfj5LZU1BQUHgEJCQkIEkSL7zwAuPGjRP/iefn55OamorJZMJkMpGfny%2B8m%2BwZQNuyY8cO3N3dMRqNSJKE2WwmNjaWsmXL4u3tLUp91q5dy8iRI4WdwLvvvkvr1q1F/8svv/yCSqVCp9NRr149Tpw4Qc2aNTl8%2BDCnT58mPT0db2/vQlmSWrVq8cwzz/Dbb7/Rv39/Vq1axfHjx4VB%2BsaNG63K7SwzKkVRnGm60WgkMDBQDNrsZebkYyNjNputDKDDw8PR6XQ0aNDA7nKZ%2B5WNLwmXLl1izZo14vx%2B/PHHvPjii4wYMYK2bdvy0UcfFZJPL25g/fvvvzN69GiOHTuGSqWic%2BfOjBo1Cg8PjwfKZNSrV48dO3ZQpUoVh5%2BJiYmhatWqJVrfkCFD6Ny5s8hCxcXF0aRJE9LT0zl79iyrVq0iMzOTadOmceXKFUqVKmUlhGHJkiVLrIIaeRLBNpMxePDgEm1bUViWFL/yyiviet6xYwc3btwgLi6OzZs3YzKZCl0/tkGH7Wu1Ws2pU6do1KiReM82GDl%2B/DgVKlSgVatWTJs2TbzfpEkTABGQZ2VlCSEeS3Q6HUajUUj8A/j6%2BpKRkUHbtm2F2NLNmzdFpgoK7jFLmw2ZIUOGPBSVS0sqV67M8OHDGT58OKdOnWL79u2sWbOGvLw8XFxcRKnpnDlz2LNnDxs2bCAvL4/8/Hyr52SVKlW4du0aK1asEAIuthTlz2mPsWPHsmvXLnH81Go1ZcuWJSAgALVazeXLl0lPT8doNIpyaRnLySej0VhIjMh2csqy1N4SV1dXbt68KXq6oeAZaamgrFKpuHv3Lm5ubtStW5fhw4cXKZ6k8O9FCfYUFBQUHgFyH4etnLazs7MwYJaRZ7GLKincsWMHXl5eQtVRzhZYGrHLSm9Q4OMVHh4uBiPfffcdMTEx3Lp1i/Lly9O9e3d27tzJ/v37CQsL4%2BTJk8Jsuk6dOuTm5jJ8%2BHCioqJEBuX48ePk5eXRpk0b5s2bh0qlEoOoFi1aiP4SS0qaCSjONF3%2BfUfL5QykJXKGEQoGbHKfi5wdtFxuub1/R7CXnZ1tJRAhW0R0796doUOHFuszaMmOHTuYP38%2BCxcuxGw2U6VKFcLDw61UN2NjYzGbzUUG0LYMGDCAPn36oNPp6Nmzp1VwlZWVxcqVK1m0aBHz5s0r0fo8PT1Zv349PXv25OLFi4wfP17so4eHB6%2B%2B%2BipDhgzBzc1NWDfY611t2LAhp0%2BftnqvXr16XLhwweq9%2B8k6FTUxsH79elxcXOjdu7coc7S8vlxdXUlJSUGr1VpdP/K9aLluOQu%2Ba9cufH19UalUzJ8/n5CQEFEOaXvdz5gxg1KlSoljodFoaNGiBV5eXgBs2rQJk8lEly5d2LJlC/7%2B/lbm9ZYBgoz8HBg6dKh4r2/fvnY982wpibCJLIpjua9msxmTySRey9jeX7Vr16Z27dpERkYSERHB4MGDRYm6rMQqe3IajUbS09NFsNevXz/Cw8OZP3%2B%2B3ayzrC77/vvvF7sPMikpKfj5%2BdG2bVvhQWi77jNnzrBu3Tq%2B/fZbu/189mwZoOD/Azm4s1wuX7tyz1%2BFChWoVq0aBw8e5M6dO%2BTl5eHj4yOsMxo3bsyUKVNKvE8K/26UYE9BQUHhCcBykGSLZZAHBX0jer2eW7duiQBRLt%2BUUalUHDt2TCwPDw8XcuInT57kxIkTuLm5MXHiRGECLZtNy7PkZ86coVu3btSvX1942Wm1Wl5//XXmzZvHmjVrxOBRHoylpqaybt063n77bfz8/MSAZubMmZjNZvr372%2B3r0QONOWeQnt89dVXtGrVSgwsLQfoloPYvyNY%2B7to164dwH2VX7Vt21Z4znl6euLt7W3XSy8/Px9JkqhduzYDBgwQGaqZM2dy6NAhateuzeDBg8X5qF%2B/Pl9%2B%2BSWffvopc%2BfO5dlnnxVehr/99huenp5MmTKFl19%2BucTb6uXlRVRUFF26dCEhIQG9Xs8rr7xCaGioUH/cvHkz2dnZVK1aVZQdWyKXMv8VLAPB4iYGEhMT8fX1ZcGCBXTp0gUo3Ad4%2BPBhRo4cafV%2BixYtuH79OgsWLBD3onz8vvnmG1xcXIR4h2zoLlue%2BPj4ULt2bSRJws3Njffee482bdrw4osv4uTkRJ8%2BfUQGbtu2bZhMJn766Sfy8/NxcXER%2B7d06VIGDhxIrVq1GD58OP369ePDDz/E1dWVgwcPcvz4cZFdCgsL44033rDar6SkpEL2MKmpqdy4cQMouOeSk5MJCgqymqBITEwECrKOM2bMEN%2BVX8tZt%2BImU%2BrXr291rtavX2%2BlMpqXlyfKx%2BHPZ5Z8PO35b3bu3JlevXo5/E1bvvnmG6vX2dnZnDx5kmPHjrF//37Onj1LdnZ2oe95enry/PPP88Ybb/Dcc8/RunVrnJychLqmRqPh5MmTXL16lfbt2%2BPi4iIyuosXL%2Bbdd99l8%2BbNdOzYkZ07dxIXFyd6/aAgiF%2B4cCHvv/8%2BYWFh/PDDD7z44ot2s7EKTxeKGqeCgoLCI0Duv%2BjQoUOxUtiyAbNGo0Gj0eDk5ISzszPp6emUKVOmSCVLOfNg6QsmmxGPGDGCL7/8sliZfnkWXZ7hl73sduzYwb1793Bzc%2BOZZ57h9u3b3L59m2rVqnHu3DlatWplleWpU6cO3t7eQnnQYDBw%2BvRpDAYDUVFRxcqgy/6A9gZtx48fx83NTWQ3itoXW6XPx4Hq1asTHx9vVfLnSG2zOHr27FlsNkaW%2B4cCj7LFixeLa0VWN83MzKRs2bJERUVZnY%2BMjAz27t3LL7/8wr179/Dy8qJmzZqEhobatcywxNI025LMzEy%2B/PJLvv3220LZX51OR/fu3Rk%2BfHix6y8J9rKk9lRhAbvXSknOS0JCAmFhYRw6dMiqB89gMBT6bXuZHygIMOWMm6urKwaDgRMnTrBx40aRkW3RooXD%2B9de9sjT0xOj0UivXr3Yv38/586dK/SdChUqULZsWa5evUpycjLVq1dn8%2BbNDrddfj7Ir%2BVs4ObNmx0eH5lPP/2U4cOH8%2B677xb7WdnmYuLEiVb7FRQUhJOTE7/99huSJBEQECDOTcWKFWnQoAFBQUF2/TfbtWsnStnvl7Fjx3Lq1CkuXbpk9xz4%2BvoKoaxy5coJu4WUlBSys7PtnneNRmPXSkcOCGUvRICmTZuyf/9%2Bq8%2BFhoZy4MABcR3v3LnzoZfYKjx5KMGegoKCwiOgRYsWVhLt94NGo6Fs2bLcuHGD3bt3F/mfuTyAl83Kp06dKma%2BS5UqhVqttnp99%2B5dypQpIwYU27dvZ8KECURERODp6WllNl23bl2mT59Ox44duXDhAqdOneLZZ5%2BlcePGREVFMWbMGKss3ZgxYyhfvjz9%2BvWzKnuSZ/JLIoPuyDT9rwzaHgcMBgOvvvqq1Sx8dHQ0LVq0EBYMubm5XL58maVLl1oFS47MoVNSUjAajfj5%2BaHVajl79ixbt24lJSWF69evc/36dSpXrsyCBQsKCd9kZmby7rvvkpCQQLNmzeyej%2BL86iwpaTbVbDZz5coVUYJpMBh49tlnCwVhltgL3hwRGxtboiBE5vXXXy/0XteuXenWrZuwKbHHxo0b%2Bfjjjzl48KBVNv769et2P3/mzBl27tzJlStXSE5Oxmg04uPjg7%2B/P%2BPHjxcG9rYBl6zGaY9Zs2YxYsQI/P39xbMmODiYs2fPotVqcXNzIy0tjcqVK1O6dGmuXr1KUlISrq6u5Ofn4%2BvrS7Vq1UhISCAkJMSq/FM2epcJCwuzem2rBmuPpKQkWrduzZIlSwgMDKRUqVJ2DddlDAYDZcuWFVnCkqDRaPD09CQkJMSu/%2BZfYeDAgcLyJi0tjdzcXEJDQ3nttddEaWdcXBzvvfdeoQmMv4JsX2OPatWq8csvvyjBnoIVSrCnoKCg8IRSkgyDwWAQvl4yd%2B7cwcPDQ6jtycGeLASxbds2/vOf/yBJEhs2bKBr167Ex8cTExPDtGnTeOGFF9Bqtezbtw%2B1Wk1YWBgtW7bkf//7XyH/MUvk8jdLlUTbTJtc/mZpmi5jGWg%2BzEHb48CYMWOKXJ6VlcWBAwcwm82sXbu2kM%2BgZVbU3d2d4cOHi/MTExPDqVOnGDNmDJIkiT5RlUpVyGjaksOHDzNs2DB0Oh0//vgjmZmZTJ48WZyD7OxsSpUqZRV8WPrVydxPNvXChQvCakSlUlGlShXCwsKs9teWvxq83S9btmwRfWD2SoqPHz/OoEGDuHPnTqFgzx6W17WTkxPbt28X91h8fDzu7u5s2rSJgIAAu8Fiv379uHnzJo0bN6Znz5707dsXd3d33N3dRXmlu7u7EPvIyckR3m6WJu/37t0DCgJud3d3YRhuqdwqlxtaip0UdX7v3LlDZGQkb7/9NseOHWPevHlERkaybNkyli5dKnqLVSoVlStXZuPGjQ5VVGV/w5deekk8Z2yfgbavIyMjrZ5Ztv6b/wQJCQn07NmTW7duiUkujUbDG2%2B8wb179/j%2B%2B%2B%2BFUrI8JJdFaOTsn0qlEn6pOp2O3Nxc8a%2BMRqMRz3T5nNrzFlR4%2BlB69hQUFBT%2Bxch9PJakpaXh5uYmBuRysLdx40ZatWoFFPTY5efnW83mr127lkmTJtGxY0cWLlzIDz/8gCRJZGVliUBCFp%2Bw7JGT%2BeSTT%2Bjbt2%2BRnnqOZNAtB21Go5ExY8YU8tR6kilOTGH06NHUq1fPymcwJiaGFi1aWPmxzZ49Gw8PD06dOkVQUBDh4eGULVuWLl26oFKpqFOnDqtWraJu3bqYTCbWrVvnMNgLCAggKytLKAZ%2B9dVXHDhwgH79%2BqHRaFizZg01atSwss0oqbqqLZcuXeLDDz/k4sWLwJ8liLt27WLevHlUrVqVr776SphqW/IgAZxlEFKSLKklJe0DW79%2BfSGhF1vi4uLYvn278GH77rvvaN68OQsWLAAQKrZyJsdexiwhIUEEcX369EGSJO7evWultikHbjJyr64c4MGfAYJKpSIjI0OcA0mSOH/%2BPL169eLUqVNWAYR8DUJBEJyRkcHkyZOZPHkyaWlpfPPNN5jNZv744w927NhBy5Yt2b17N35%2BfhiNRsqWLcvcuXPJz89nwIABbNiwgR49etg9VnLAeT9CO5bPLPl4Wvpv/hMEBQVx584d1Go1I0aMYMaMGZjNZrGf0dHRmEwmK7Euo9FoVeYpSRLXrl0DEAGebe%2BkyWSiT58%2BVl6aCgqgBHsKCgoK/2rsGUjb9k3JfXzh4eHk5uaKAS8UZA0kSaJ9%2B/akpKTw9ddfs3z5ci5cuCAM1j08POjWrRsrV6608omyRZIkFi5ciLu7u8Ngz5Hi5uMwaHuUxMfHFzKU//TTT6lTpw56vd7KHFqtVqPRaJg4cSINGjRg165dZGZmolKp6NWrF05OTlSqVInLly/z888/O/zNw4cP4%2BXlJYKe77//nlmzZglj9xdeeIGuXbuSl5f3wJ52AH/88Qc9evQgKyuL559/ntGjR1OzZk1MJhOnT58mPDyc06dP06NHDzZt2uSwRPDWrVvs27ePK1eukJmZiZubG9WqVePll1%2B2Uh11ZJ0ABWb2y5cvZ9euXQ57R6EgExsWFsaWLVs4efKkVUnx6NGjqVu3LuvXry/yfgBITk6mVKlSLF68GCgwWT906JDwzZMDqdTUVMqXLw8UZAL37NmDRqNBkiQRCEqShEajEQGDWq1GrVZjNBrp168fb731Fm3atGHZsmX06dOH//3vf2g0GkqVKkXlypXx8PCgXbt2TJkyhZEjRxIZGUn37t2FAM7hw4cLlftaXoNz5swRCptz5szhzp07ZGVl4e/vz86dO9Hr9Zw5c4azZ88SGxvLrFmzKF26NOvWrWPSpEkMGDCArVu3Ogz2LD0FS0pCQgIvvPCCeG3pv1mcjc3DID8/n/DwcDFhMnnyZLFs%2BPDhQkEUsFJltvz7fli%2BfLlVW0Dr1q3FOfPz86NBgwaKSudTiBLsKSgoKDyhqFQqhwqd9pADJUvlTijI/iUlJRETE4PJZGLnzp1imTzokM2jZYN1KJhZliSJ7du3i7KuBQsWOCy5k8vfZBl9W4qSQX/Ug7ZHjT2fQdtBr5wVlT0W5eDg4MGDqFQq1Gq18GLr1KkTU6dOJSsri%2BPHjxcqRzx%2B/DhTpkzBZDIJ8Yzbt29ToUIF8Znq1asDBYFIQEDAA%2B/bvHnzcHNzo1GjRlZZQo1GQ926dYmKiuLDDz/k5MmTRERE2B2sRkRE8M0336DVannmmWfw8PAgIyOD1atXo1aref/994WS56xZs6hQoUKh4BkolCV11DtqWXrZu3dvhyXFGzduLPL%2BDA4OthJoMpvN5OTkiOtaLsuTM3ERERHMmzePF154AWdnZ3744Qeg4FnQoEEDjhw5AiC81tq1a8fu3bvp3r077u7u6HQ6PvvsM/Ly8pg8ebKYKNFqtbRq1Up4c/r5%2BQnxFUf%2BdGB9De7du5eUlBRCQ0PZu3cvoaGhmEwmVq9eTYcOHdiwYQMVK1a0%2Bm6fPn3E86BVq1Yio2mP2NhYvL29xb8lwWg0WpnD2/Pf/DsJDw9n8%2BbNlCpVioyMDMxmM3q9nszMzGJFlCzR6/VC4CUkJIQTJ04I30ONRmO1P5b7a4mS7Xt6UYI9BQUFhScARz5rljLmltjrm3KUTbPM/ln2vFj2yMTGxtKyZUt%2B%2BOEHAgICMBgMxMXF0apVK/H5atWqFTmI%2Bisy6I960PaoKYnPoLw8KyuL3NxcEhISCAgI4Mcff8TJyYlatWqJLF3p0qVxdXUlOzub7t27U7p0aTGoT0xMJCUlBZVKRdeuXcX5sGff4eTk9MBZCJn4%2BHjy8/Pp37%2B/w8/07duX/v37ExcXV2jZihUrWLp0KZ999hnt27e3yjLm5eWxbds2pkyZQpkyZejYsaPdLKkllllSe5S0pLgkGWfb6xoo8rretGkTo0aNok%2BfPkBBJlDO5ERERPDWW2%2BxatUqYVQ/ZMgQdu3axRdffEF8fLyVmbpKpeI///kPAwYMEJYQOTk5REZG0rx5c%2BbPn1%2BkME5xyKWg8m/ZruvQoUMkJyeLsm21Wl1khrhcuXLcuXOHDRs20K5dO6KjowkJCcHV1VVYt8iZ2aFDhzosw/0niYmJQaVSMXfuXAIDA1myZAnx8fGiVBYKjo2vry8BAQG4uLjw66%2B/kpqairOzM3l5eXh7e6PX66lRowYdO3akbt26/Pzzz0RFRXHlyhXUajW%2Bvr6FLBYeR9VhhUeDEuwpKCgoPAHcj8/ahAkTHqhvyhbLgZo8MI6NjRViD4MHDyYrK0t42QH8%2BOOP/Prrrw4Dy5KUvykUpjifQcusaHp6ulBDrVq1KomJiahUKt566y2gQAQlPDwcrVZLx44duXv3LidOnBCiKB4eHrRu3Zp33nnH6nxYSuw74kFKatPS0pAkqZAdgyU%2BPj5kZ2cX6lMCWLduHWPGjKFz586Fljk7O9O5c2dycnJYs2YNHTt2tJsltcVR7yiUvKT4QTMp9jw14%2BPjSUxMJCkpCbPZzJYtW%2BjYsaPo65IkCVdXV%2BE5J0kSubm5dOjQgezsbA4cOIBKpUKv1%2BPp6Ymvry/Xrl3jwIED/N///R%2BzZ89m0KBBDB06lPPnz5Obm0tmZmah4Ot%2Bzm%2BVKlVE9uq5554jLi6ON998Uyx3dna2msCYP38%2BPj4%2BQuX12LFjVuvLysrihx9%2BwGQysWbNGmGqHhcXJ4K87OxsYmNjiYuLE%2BWntn2Tlv6blvwd/pvyNeDr60tQUBCjRo3i4sWLHD16lGnTpglRno8%2B%2BohWrVoxcOBA0SctT6zk5OSQm5vL3bt3mTZtmli3n58fLi4ufPDBB5QuXfqhb7vCvwcl2FNQUFB4ArgfEYoJEyY88O%2B89NJLuLq6AoV7ZMqUKcPs2bNFOdHx48fR6XQsXLhQfH/FihWF6rjYVAAAIABJREFUMhWWg6iSlr/Z41EO2h419rKieXl5zJ07l99//90qKypnb9atW8eNGzdwdXVlwIABvPbaa4SHhwvDcIPBwMWLF0U/WM%2BePYs8H5IkMXHiRJFB%2BP7778nKyqJdu3YiCJBf25YuFpVlqFixInfv3uXw4cMOz92RI0fw9fW166OYkJBA48aNizx%2BTZs2Zfbs2cD9ZUntUdKS4pKWG1pe115eXkLYxDKLunbtWtzd3TGZTERGRopAHQqy3M8%2B%2B6w4NocPHxYqvNeuXSMzM5Pg4GAuXLjAK6%2B8wrBhw5gwYQLnzp1DkiSys7Ot%2BuRyc3O5evUqeXl5mEwmWrduDSDEmFq3bi3KdrOzs3n77bfZt29fof166623GDVqFOfOnaNLly5MnTqVmjVrUrt2bcB6guL8%2BfPExMTg5eXFnDlzgILeSln9E7BSq5QkCV9fX7799luhBiqX3z7zzDP88ccfzJ49m8DAQHG9y/j6%2BrJq1Sqr94ozc39QGjduzLFjx5g8eTJBQUGsX7/eSnglPz%2Bf/Px8Ro0aVei7cp%2BfpUG7n58flSpVQqVS0ahRIy5cuEBOTg5NmjTB19e3RBMyCk8fivWCgoKCwhNCSRUE5VJM25nxorA30Ll%2B/TpeXl6sX7%2BeH3/8UfQDSZKEh4eHXZVBk8kkxDE0Go0YiM2cOZNTp05x%2BPBhXnzxxfuWQbdn5WCPx9U0/WFh6TN4%2BvRpqlevTv369UvsM/jLL7/w66%2B/curUKZYvX44kSXh7ewu1wH79%2Bjk8H7b2ELI6oD26dOli9bqoyYrVq1czc%2BZMnJycWL16tfCUkzl//jx9%2B/bFaDQyevToQhk8udy4qN44uZfs/PnzJbZOeP/99%2B2WFNv7PUc2KEXds2azmZiYmCIFXOBPw/d9%2B/YV%2Bm25b9JgMBAZGcmUKVPYvHkzoaGheHt7s3v3bnJzc2nVqhW7d%2B8mLCxMGNPn5uaSlpZGcnIyV69eJTAwkISEBADq168vDN0BGjZsyIYNG9Dr9ahUKhHw37lzB09PTzw8PICC%2Bz85OVlk9GrWrInZbKZOnTrcu3ePK1euEBgYiCRJJCYmUrt2bfz9/fnhhx948803GTt2rNW%2BWx7X0NBQ0tPTcXd3Z8GCBSJotES2bpk2bZpDC5d/kuTkZN566y3%2B%2BOOPQsvkoMzRMFytVqPX65EkiaCgIP744w/y8/Pp3r07NWrUoE2bNrRu3Vr4DsqfNZlMlC5d%2Bl//LFQoOUpmT0FBQeEJ4H4UBGXkGXLb9ZSkn%2B/nn39m69at7Nixg9zcXOrXr09oaCgfffQRHTp0oHXr1pQqVYr8/HycnJzo37%2B/mOX38PDg66%2B/thJq8fPz48iRI3h7ezN58mTKlClzX4qa9lRFnzZss6KffPIJc%2BbMua9y3WrVqlGtWjXmzJmDr68v//vf/%2BjYsSO7du3io48%2BYvXq1Q7Px4Oo%2BFlK8zuiR48enDlzhq1bt/Laa69RtWpVKlWqhNFo5LfffuPy5cuoVCq6d%2B9ut1TzfrMZf6V39H6wd8/26tWLiIgIcc/qdLoiVT%2B3bNkiei/l8kbLTKBsjXDu3Dmef/55ETjs37/faj07d%2B7E3d2dtLQ0EezpdDr8/f3x8PDgt99%2BIyQkhMDAQM6ePUuFChWsxHhkZdeiCA8PR6VS4eXlRcuWLYGCbJy3tzcXL14kLy8PlUolss1arZYbN27g5%2BfHvHnzePnll4tcv9wDmJWV5TBjKpffFlWG%2B0/i4eHBpk2baNGiBTk5OdSqVYtz586hVquZPXs2Go2GwYMHk52dLZ7JJpMJV1dX6tSpw8WLF7l9%2BzYXLlwQ64yMjARgxowZVpYa2dnZGAwGzp8/T2BgIJ06dfpnd1bhsUUJ9hQUFBSeAO5HQVAe%2BNoLkErqg9anTx/KlSuHq6sr7u7u/PHHH0LGHcDb25uYmBiys7PR6XTs3r0bg8FQ5DaGh4cTGBgoVA6fNkXNv4I9UZDAwEBRcnu/JCQkIEmSKEds0aIFeXl55Ofn39f5KC7bvGrVKiHNXxRTpkyhZcuWzJ8/n4sXL4rBrbOzM3Xq1OH999936AdoW15qD9tev7/aO1qSkuL169cXuh8OHz5Mfn5%2BiVU/bSdsJEniiy%2B%2BcHgcLLNFOp0OT09P3nrrLUJCQjh69Chbt27lq6%2B%2BstrOPXv2cOXKFUaMGMFbb71FcHAw//3vf/Hz8xPnd%2BLEibi6uorqgZL4EUJBlvTMmTNoNBpq1KghjmtJytJjYmKsXss9gM8%2B%2B6zDMlv5/eLKdP8pXnvtNSG24%2BrqysSJE%2BnRowcmk4lx48bx8ccfizJNFxcX8vPzhedeZmYmt2/fdrjumzdvWr2W/RA1Gg2XLl16IP9JhX8nSrCnoKCg8ATgSEHQUqUzNzeX48ePI0kSPXv2LKR%2Bdz8lPR988AHLly8nMzMTT09PSpUqxfTp00W2rn379gwcONCqR%2BbHH39k5cqVDlUOTSYTAwYMEAPbp01RsyjkrI0jFi1axBtvvEGDBg1EJm7MmDEltt2wRQ7a5f5KrVaLi4sLkiSV%2BHyUJNucnZ1NampqkQNvefC7cOFCWrVqBfzZp1SSYLYkg1qdTvfQekdL2geWmJjIunXrHlj1EwpP2Bw%2BfJh%2B/fqJ7LjZbBbKnHPnzhWBV79%2B/VCpVLRt25atW7dSr149nJyc0Gq1tG3bls6dOxMcHIzZbObTTz%2BlcuXKhIWFIUkSRqORzZs3U6ZMGebOnYvJZOLVV19l%2B/bt7Nq1i88//xyz2cx3333Hrl27GDFiBDt27MBsNtOmTRteffVVoGCCYuPGjbzwwgtotVo2bdpEqVKlSlS2DQUefpYljnIPYEhICNOnTy8kViT3ALZv396hhcs/jex1GBwczJUrV5g6dSohISGcPHmS9PR0Fi1ahFqtxsPDQ/RC%2B/n5oVKpcHd3p169ely5ckUoqAKi7FqlUjFixAiioqJEKWe5cuW4ffs26enpZGRk2C21V3j6UHr2FBQUFJ4AHPUEbd68Wfx9%2B/ZtZs2a5VCg5fXXX3e4HnuEhoby3//%2Bl88//xy9Xs/t27cJDAwkMTGROXPm0KpVK6semV69erF7926H6zYYDKxfv57evXtz/PjxIvfracNeqaNlye3169cpW7YsWq2W2NhYjEYjtWvX5ocffnigrKgctFv2f9WrVw9JkoiOji7R%2BRg9ejS3bt1yaGOQmZlJSEgIrVu3ZtasWfe1LfXr10eSpL/l2oiMjGTq1Kk0btwYnU53X72j94O9a9tgMDBu3DgxCE9NTWXWrFl2s3X2%2BmjNZjMNGjQQ661evboIiOLj43FxcaFv376cPHkSnU7Hli1bGDJkCJcvX8bd3R1XV1eysrLQ6/XcunVLrDcoKAgfHx/c3d2ZP38%2BX3zxBRs3bsTHx4fvv/8eT09PLly4QLdu3cjJyUGlUlG1alVRavviiy8C8NNPP9GkSRMmT55Mz549GThwIB07dmTmzJn8%2BuuvHDx4kGPHjpWo7FYO5CyPodwD6OvrS0pKCmXKlMHLy4v09HSSk5Px8fEhLS2Nzp07/yWhqofFxIkTiYqKElnXB7EpsTxWlSpVYuLEibzzzjvk5%2BdTvXp1Ll26ZFel1haNRkNAQIDSx/cUomT2FBQUFJ4AHCkIWmY1Nm7cSKVKlR5a%2BU5GRobo01m7di3Jycls376dnTt3MmTIECpXrkyrVq1Ej4xKpSq2fGrFihV4enqKTNbTpKhZFMWV3BoMBjZt2iSCoYeRFZUkifDwcFH%2BmJ%2BfjyRJfPXVV7i5uYnPydlEW0riV%2Bfk5MTRo0cfaNv%2BrrnotWvXotVqmTBhAkFBQffVO3o/OLpnFy9eLERZsrKykCSpULmmI3XImjVrWpWrWqpThoeHc/78eS5evIhGo8FoNDJr1iyuXr2KXq9HrVaTkpKCJElkZmbi4%2BOD2Wxm8eLF1KpVi9DQUHE%2BJ02axN69e8nLyxO%2BdV9%2B%2BaXINO3YsYMFCxawZMkS3NzcWLJkCUlJScLHMCUlxUq11M/Pj%2BjoaGEmXqNGjUL7ZuslKme6ZDVQKKgOUKvVZGVloVaruXnzJjdv3sTd3Z0qVarw/PPPl1is6J/gl19%2BwdXVVVzPWq2W/Px8oSwqi1gB4m95uVqtFh6KUHCOf/31V2GhAnDmzBm7v1u6dGlSU1OpUKGCyLhXrFix2L5LhX8nSrCnoKCg8ARQlM/anTt3CA8PZ%2B/evQwePFi8b6%2Bv5n59sg4fPiz%2B7dSpEyEhIXzyySccPHiQ7du3s2zZMsqXL8/hw4cpU6aM3fIqGV9fX7Zv346Hh4cY3P6TMugKfxIYGEhSUlKhvigosFSw5ODBg3bPR0n86izNox8XEhISrEqc/67eUUf37MaNG/H19RWqnyNHjiyxGExRyo0xMTFWwQHA7t27MZvNIkgKCAjg008/ZfTo0Xh4eDB27Fhq1aoF/Hk%2BZVGdnJwcjEYj27ZtY%2BjQoRw/fpw5c%2BYwZMgQ3N3dGTp0KEuWLCE/Px8o6CuuXLkyJ0%2BexGAwWBnGyz2KISEhfPPNN8IGwxJbL9GxY8fSunVrq4mHa9eucf36ddRqNZIk4ePjQ40aNfj8889LdPz%2BaWTBLLknduHChQwePFj8LQe99pbLmVdH59zFxYWcnBx0Oh0uLi7cvXuX0NBQatWqxUsvvcQ777zDmjVrKFWqFGq1WrFkeIpRgj0FBQWFJwBHCoKJiYls376dvLw8wsLCrAaNa9euJSMjg6VLl4pywOzs7BL388mD1e7du1sFcWq1miZNmpCbm8v3339P48aNRY/M9evXHaoc3rlzhy5dujwW5VVPIg/TZ/BhqJuWxK/ObDbzzDPP/OXfephYBiHw9/WO2rtnoWAS5sqVKw%2Bs%2Bmk7aHdxcRGlt7Vq1RKBX1BQEOPHj2fDhg0cPHhQTPhcv36dnJwccnJyqFy5sliPfD4nTZpEnTp1qFKlCmfPnhVBR1ZWFpcvXxbn29nZGa1WKzz34uPjmTx5MgMGDLC73W5ubjg7Ozu0hLGtSJgwYQIffvih%2BL3IyEg2b95M%2BfLl%2Be2334CCIPd%2BewH/CbZu3cqKFSu4desWAQEB5OTkiKxednY2mZmZjBw5EhcXF4KCgqyWy31%2Bn332mQikoeC8a7VaKlWqRK1atXB3d%2Be9997Dw8OD0qVLs2TJEvbv389PP/3EvHnzAHjxxRcJCAhAq9XSqFEjPv30U5ydnR/VYVF4RCjBnoKCgsITgj0Fwbt37%2BLr68u0adN4/vnnC30%2BNzeXZcuW4efnZ1e2vijkweqCBQvw9fXlzTfftOqRSUpKAmDVqlVWg9a/onKoYJ9HbQ5tj6KyzVAgmJGXl0ebNm2KXddfLSl9XLG9ZyVJ4sKFC9StW7dE94NtaaNsgi5P2MivZWNzf39/odIoSRL79u2jTp06VoqZx48fFwHalStXKFeuHPDn%2BZT7ymRBFMvvzp8/X1QP2J7fjIwMPD09rbbfdoJCkiTS0tIKCRLJ59RS3RUgPT2djRs3kpGRQXR0NGPHjmXlypU0a9YMrVbLwYMHmT59Oh9//PFDL8N9UOSeUNnDUj7varWa7t27C1P1S5cuAQWlmHL2rlu3bjg7O5Obm8uECRNwcXFBo9FgNpsxm80iGJQkSbxni22AmJ2dTXZ2Njt27MDFxYVx48b9A0dB4XFCEWhRUFBQeEKwVBBs2bIlr732muizsWcwLCOLqDyowbBs5H3s2DGSk5Mxm81oNBo8PT25fv0669atE4NWe9uo8GDUr1%2BfrVu3PtbiNVOmTGHFihUO/erKli3L%2BvXrrZQ6bWnRooWYOCiOhyEwYTAY0Gq1jBgxQvRAfvbZZwwbNuyh947a3g/16tUjICCgWCN1GUsBJigIHseOHSsCqDFjxqBSqahZsyZVqlTh3LlzXLhwgXr16nH37l2uXLlCw4YNCQoKYsqUKVy4cIH333%2Bf9u3bo9Vq2bNnDytXrhSm6FOmTGH58uXUqFGD2rVrs3btWqAgK5eZmYmvry/lypXj5s2bJCcno1KpePvttylVqhTr16%2BnatWqxMfHc/78ebuiQ1lZWWRkZFj5Csrm35bqrl9//TXdunXD29sblUrFq6%2B%2ByuLFi3nmmWdISkpi0KBBbNy4kRs3bvDtt9/SrVu3BxYreti0adOGe/fuWXlYDhs2TPTh/V39qCqVCkmScHJyEt6Tubm5fPXVV4wfPx4AJycn4uPjH/pvKzzeKMGegoKCwhOApc%2BaVqsVCoIrV64sUrHwzp07zJ07l02bNnHixAnxfkl9suTB6s2bNzGbzQQFBYkg7sCBAwwYMIDz588XuY2PU3nV44ptBgfgxo0b%2BPv7/yULjYeJI3uIq1evcuzYMRITE8nOzkav19O0adPHSijDkhYtWjg8tpbIQciD4uh%2B6Nu3r0NvQrPZTP/%2B/R3ekwaDgVdffVVkQI8ePSpM17VarcicZWZmotVqqVy5slA79fLyYtmyZTRr1kz4cfbs2ZPr16/TqVMngoODcXd3p1%2B/flSoUIFr165hNBoJCgpCp9Pxxx9/AAVlr25ubvj6%2Blpl7VJSUvj999%2BpUqWK3WtF7lF8//337Zau2qq7NmvWjKCgIBYtWoSLiwsGg4HatWtz8uRJDh48iIuLCyEhIYSFhbF///7HRtU3ODgYSZLYu3cv/v7%2BGI1GatWqJQKwvXv30rJlS2JjY/H19aV27drs3r2b//znP4wcOZLLly8DBUqtBw4cAKBp06ZUqlSJxMRE9uzZIwK7t99%2Bm6VLl1KhQgV%2B//13EVA6OTkJG5VFixaJfki1Wi2UkBWeHpQyTgUFBYUngLVr1zJp0iSRaZAVBIvqm5Jnyu/du2c1kw5/%2BqDt2rWLlStXFloOfw5WLXtk0tPT2bNnDxcvXqRhw4Yl2sbHpbzqccZWnOJxxFYxEgoCUmdnZ9RqNS4uLuj1esxmM5988skj2MKSsXfvXiul078Le/fDqFGj%2BO6774r0Jty9e7fVPWlZ2mjZ13bmzBlcXFxo27at3T6sKVOmWL3%2B5ZdfaNeuHb/%2B%2Bismkwm9Xs%2BqVatYvHgxW7Zs4ZtvvhEZp8TERMLCwti9ezcVKlTA09OTQYMGFZuplzO93bt3Jzg4mKSkJHx8fPjpp59ISEigYcOGVmq8lsTGxtKrVy%2Bh7moymfjoo4%2Bs1F779%2B8vykiL6wF8VFh6WMq9e5bllp06dSI/P58ffvgBV1dXzGYzXbt2xWw2s2zZMiRJIiAggJSUFMqWLct///tfq1LoPXv2MHHiRHJycoSv6tWrV8Vys9ksrBjUajULFy5Er9ejUqmKrABR%2BPeiZPYUFBQUngCCg4OJjY0VZUqyz9ro0aP5%2BuuvmT9/fqG%2BqdGjR3PlyhV%2B//13Pvjgg0Kz6ZmZmQwYMICKFSsKo3OZiIgIVq1aRYMGDTh27BgNGjTAycmJ2NhYMSvdunVrtm/fLjJ7jrbxcSmvUni4REZGMnnyZBo2bIi7u/sTlckdPHgw48ePL7K89K9i734IDg4mJCSExYsXO/QmtLwnbUsb5SwdFNiYLF%2B%2BHCcnJ4cTNoBQ19Tr9UCBpceUKVN45ZVXrD63ZMkSZsyYgSRJPP/88xw5cgSTyXTf51cu%2B75w4QKnT5%2BmevXqXLt2Db1ebxWUWvpIwp%2BZ7H379onttMzWGQwGPvjgAyIiIujQoQM6nY5NmzaJfriwsDDR3ylbDDyK/k75HA0dOpS5c%2BcWKtvU6XQiGJMzdLKyJhQI7uTm5gqrhm3btvHcc8%2BJ7//yyy907doVSZJK5K8n/07FihVFKazC04WS2VNQUFB4AnCkINi8eXOHCpiyFHuXLl3slk25ubkxbNgwRo4cWWjZoUOHSEtLIyUlRfzr5OREXl4eN27cIC8vj4SEBEJCQordxoetcvhvxTKDY1veV5KS23%2BatWvX4uzszJQpU/5Wv7ricFReag958B8REfF3bY7A3v0A0Lt37yK9CS3vyVmzZlGhQgW7foayncGAAQOYPXs2kyZNonr16sTFxYleRIBRo0axbt06oQYqSRIjRowQwV5GRgYajYZvv/2WyZMn8/nnnzNx4kR69OjBzZs38fHxYc6cOSU6v5Y9ir179%2BaTTz5hzpw5djOottnVrl270q1bN7HctmohMDCQ1atXAwjLEFmoBKwtQ%2BLj4x%2BphYskSSxZsgRnZ2dq167N4cOHxTJLARXZa08OeGXj9caNG/Pzzz9jNBoZP348R44cKVGfn1qt5tlnn8XJyQm9Xo8kSVSpUoWWLVsSGhpa4l5RhX8XSrCnoKCg8IRjT6XT29sbSZKYNWuWlSmxLQEBAaSnpxd6f%2BXKlRgMBubPn89LL73E/Pnz8fX1pV69esyYMYP27dszffr0QoO47Ozsh75/TwOWGRxH5X1Fldw%2BCv4pv7risFdeaps1gsfDv1GSpEKKlbZY3pMlMa63DA7tBQRGo5HMzEwRFBuNRsxmM8uXL2fDhg1cuXJFfDcpKYlKlSrh7OzMrVu3kCSJn376CSj%2B/Fr2KBqNRsaMGWNXLdIRtuqutq9nzJjBoEGDGDt2LL169Sq2B/BRIXtYyv6SR48etVpue0xkk3v5b6PRyM8//yzOpWWgKOPl5cXdu3d57rnncHNzY%2BDAgajVasqVK0dubi63bt3i9u3bGI1GfHx8yMvL4%2BTJk/j7%2BxMYGPh37LbCY4wS7CkoKCg8ITjyWbt69SpnzpxBo9FQs2ZNobzWtWvXYg2ti/NJux8kSWLz5s1W6/srXnBPE/ebwXkceFwyufY8A/%2BJnrySYHvPqlQqoqKiSExMtPqc5f1geU%2BWxLje0YQN/Jn1jIuLY8OGDcCfmaXp06eTn5%2BPq6srarWazMxMFixYgJeXF4MGDRKqu8uWLQOKP7/2ehSHDBlSYuVJe76EQUFBvPnmm%2Bj1erKyskRJaPfu3R/Yp/DvRr4e5XLOuLg4XnrpJQBxb0dHR9OqVSsA1q1bR9euXQstb9%2B%2BPZIkERERweXLl0lKSiIgIIAmTZoQFBTERx99RKVKlYiMjGTQoEEl2ja1Wi3K7hWeHpRgT0FBQeEJwJHP2oIFC0hPT0en06FSqTh16hSenp58%2BOGHJfJBk83QHbFjxw4A4YGWn5/PV199Jf617ZH59ttvrbIpj9oL7knhfjM4jwuK8I5j7N2zXl5e7Ny5kyNHjghFTcv7wfaeLIlxfVHL5aznpk2bRM/enTt3gD%2BFRDw9PXFyciIrKwuVSoWXlxcmkwmVSoWzs3OJS4cTEhJ44YUXxGvZeiE1NZXy5cuXaB32qhQqV64sAsasrCzOnj37VPh2ZmdnI0kS77777n1/18PDg5dffpkGDRqIibbSpUtTtmzZh72ZCk8ASrCnoKCg8ARgL3sBBZ5OH3/8sV0FTHsz5bY%2BaEXNjMuDVbVaLXpk4M/eGHs9Mo9DNuVJ5K9mcP4JHBl8d%2BrUSfQCya8//vhjq%2B8%2BjcG9o3vWnjfhF198Yfee/KsTNnv37qVatWrMnDmTRo0aAVCtWjVhA%2BDi4sLq1asJCgrCYDCQl5cnvPPGjRtXaFJHft20aVOr3%2BnYsaPDHkVHmUB7EwW2PX8mk%2BmJ9u2UJInw8HARrMoiLH379hWf6d27t1gul8H37dtXvOfk5ITZbMZkMhX5W7LYi1qtpnHjxsyYMeOh74/Ck4kS7CkoKCg8wdibTbfsq3HUz1eSmXFHg1VH2BuMKpSMv5rB%2BSewtYeYOnVqoQG7l5cXYN1Hp2Ryrbmfe/JBJmySkpIKqTSmpqZy48YNq/dsyysDAwO5ceMGKSkpzJ8/XxiAW07qQMEkj6VnZ0nOrz0fyezsbHr27CkqAe7du0d6ejqhoaEYjUZGjhyJJEm89NJLogfw4sWLT4TaK/zZu2c5USYf84SEBPFeVlZWoe9aLrcUdAHQ6/VUrFiRAQMGsHLlSlHCr9PpcHNz4%2BbNm5QqVYq8vDy7lhwKTx9KsKegoKDwBFNc35TtTPmTNjP%2BtPAwSm7/biw93uy9fpx4nMtL7/eevN8Jm86dOxdax4cffljsMZH9B9esWfPAkwq2PYpqtZrDhw%2BLnjWZ%2BvXrF/rujBkz6NKlCxMnTgQKjMTT0tJYtGgRKpXqifPtvN/JMoCff/6ZxYsXc/DgQVFKW6VKFX777TcR9M2cOZMPP/yQ8uXLk5%2BfLwL7rKwsUaL77bff8u2331qtW6PREBAQQGxs7F/cM4UnDSXYU1BQUPiXYk8d70maGX%2Ba%2BKslt/8Uj6M9REmyRjKPeqD7IPdkUcHhli1brAy1R40aVej7GzdupHXr1rzxxhsAtG3blkWLFtGzZ09MJhO7du0SVg1ms9nqtUzHjh2Ltbjw8vISlhZyf2BAQACbN2%2B2%2BpxKpeKLL74o9P1PPvnEKnucmpqK2WwWVQqPSu31YXD79m2Sk5OFfx4U%2BPAtWrSIDz74gI0bN%2BLl5UWfPn2QJAmVSsX//vc/lixZQnR0NP369ePAgQMAzJs3j1q1atG3b1/u3r1r9/dUKhV6vZ4yZcqI%2B7RixYqit1rh6UIxVVdQUFB4gjEYDIwbN85qNv2zzz5j2LBhLF26lObNm4v/4PV6PWPGjOHo0aN/y8y4rQmywv1jaUhtmcFp167dIxejeBgG338HtsFEUTzqbGSbNm0YOHBgoR5bR/ekZXCo1WrZv38/TZs2FR55U6dOLfSd9PR0PDw8CqmAykHE9evX8fb2Jj09HZPJVCggBqyEPFQqFbGxsUJwxRJHFhf2guriJgoWLVpEfHy8CDQNBgMuLi5ER0eLZ8qT9oz56aef/r%2B9O4%2BOqr7/P/68985MVrJAQsIOIgIW0BhFoMUFoYIoomCt/ASrKBbRArZltWopawsWy6JS%2BAq4gRZZVdZiRVBApBZkEUUWE8KWQPZllt8fOXObkADBTkgyvB7n9HSWm5nPJxrPvObz%2BbzfDBs2jDNnzlzwuoYNG2JZFmeiGRgSAAAgAElEQVTOnCEzM9MOhKZp8uCDD/Lzn/%2B81Dm/c1mWhcfjwTRNnE4nH3zwQY35HUnlU9gTEanByvsA5peSkkJiYiKWZWEYBmvWrKFdu3Zs3LixUr4Zf/rpp3nxxRdLfZCTiiu5glMdi1GMGjWKkydPnrdqaE5ODoMGDaJp06bVpj1EddOmTRs2bNhg//253e4L/k2eGw47duxIRkZGqV5p5waulJQUQkJC6NKlC1u3brVX2XJycvB6vXaRn3MLApVcFaxoKK5o%2BKrIFwUpKSmsWLGCli1bAsER9rp3786JEye48cYb7fm/8847FBQU8Pnnn5OYmEhaWhoNGza0ixw5HA5q167NqVOnOH78eKnepYZhcN999%2BHz%2Bdi4cSPt27fn3//%2BN5GRkWRnZ9vb95s1a0azZs2YNGlSlcxbqheFPRGRINWqVatS35RDxT8sXWzLVkkqvvG/O3cFZ/PmzTz66KPVastt586dmTVrFu3atTvvNdu2bWPEiBF8/PHHl29gVM/tpeW51L/JioTDkj8/c%2BZMZsyYQXJyMlFRUXzyySfcc889dOjQgU2bNtlfJnTv3p2bbrrJfp/c3FyaNWvGmTNniI2NpXXr1naxnQup6H9PKvJFwQ033MD111/PQw89ZP%2BMZVnceeeddjXQpUuX8vOf/7zcaqDVUbt27fD5fHz44YdlfkczZ85k4MCBzJs3j4EDBxIWFlbua3zxxRe8/vrrbNq0yT6fV7duXTp27MjKlSsv2Ljef4YbIC4ujuTkZAXAK5DO7ImISBklqyn6nW/LVnX9oFWTlNeQuroVo6iu7SFKrhp17969VNiLi4tj/vz5rF279rJvLw2ES21c//777%2BN0OpkyZQqNGjVizZo1TJs2jdq1a7N69Wr69OlDrVq1eOyxx4iLi%2BPEiRNMnDiRNWvW4PV67X/XHA4HXbt2ZezYscTHx//P86hIH8m4uDh27drFyZMnAQJaDbSq3HzzzRw/fpwdO3bQqFGjUmf32rZty/Tp00lISOCWW27B4/Fw1VVX8c033wDFRZsefvhhhgwZgmmarFmzhu7du1NQUMCJEydYvnz5Bd%2B75L83ULb6qlw5FPZERILYudXxvF4v69atsxvt%2Bp37Yam8SnJJSUnqo1dJLtZCozqoru0hpk%2BfTpMmTcoNEwMGDKBPnz4MGjSIl19%2BuVpsL/2xf5MVceLEiVJfxnTr1o3hw4ezePFixo8fz%2BzZs/F4PPTq1YvXXnuNpUuX0qhRI%2B677z42b95MSEgIU6dOJTU1lVdffZWHHnqI995776Ih/2Iq8kXBokWL6NWr14%2BqYlmdzJw5k23btgHF8z5w4ACjRo1i9OjRF1yFA9i1a5d9%2B/XXX2f%2B/Pl2SOvSpct5f940TUaOHEm/fv3UbkHKUNgTEQlS/qboJdWpU4c333yz1GPV%2BZvxK8WlruBUheraHqIiq0ZDhw5lxIgRl3Vc5fkxf5MXC4dut5u1a9cycOBA3G43TqfTvvbEiRN4vV5CQkJYt25dqVC8d%2B9eCgoKyMnJYdiwYYwdO5ZBgwaxePFiJkyYQNeuXRk0aBCvvPIKY8aMKXc%2BFV11rq5fFFSGrVu3snfvXvt%2BRESEfV7Ssiy8Xq8d4Px/4/77/kI6UBzgSoY7/%2B2S1wAkJCRw4sQJVqxYwcKFC8nIyLCrfpqmqW2corAnIhKsavo35FK9VNf2ENV1e2l5LvVv8mLhMC0tDY/Hw7Rp03j77bfx%2BXyl2k6kp6fj8/lwOp3s3r27VChOTU1l1qxZvPbaa/aqZ8lQ7HA4eOqppxgxYgRjxoz5n1pcVNcvCirDG2%2B8Ueaxkmf37rrrLnw%2BH5Zl8dFHH9n3k5KS%2BOabb2jatCm7du0q1Uy9Tp06pKen06hRI4YMGcKYMWPw%2BXx4vV6OHz8OwJ49e8ps1fQXfQFt47ySKeyJiIhUA5W5vS9QLrXB9%2BUQzKtG54bDcwNXVFQUZ86cITIykpycHPux/Px8Ro4cycSJE%2B3%2BfDNnziwVik%2BdOkWLFi1KBbxzQ3GDBg1IT08HKNUD71JV1y8KKltKSgqDBw/G5/MRGRnJjh078Pl8FBUVYRgGM2bMIDo6mrS0NMLDwzl9%2BjRXX301sbGxnDp1Co/HAxT3HITi7d6jRo0qN7iVfMwwDPbt23d5JinVnqpxiohIhdxwww0sX768Rn5oru4u1EKjpPP1MLtcqmN7iGXLljFlyhRmz5593lWjwYMH89RTT9X4MHGxnoKjR4%2B2bxuGYW/9828JNAzDDouNGzfm97//PS1btqRXr17s3LmTJUuWsGDBAlasWAHA%2BvXrmT59OqtWrQrI%2BKtzH8lAmzlzJgsXLiQ3NxfDMOwt2S6Xq8z27JJbM0s%2BHxISYlfgPB9/IZt69erZK3nDhg3jnnvuCfSUpIZS2BMRkTLK27KVmppKQkLCRbdsSXCqzu0hJk2axMKFCy%2B4ajRu3LiqHmalKNl24uDBg/bj7777Lps2baJXr15069aNTz/9lLfffpvhw4fTokULvvjiC5YvX06/fv1YsmQJf/zjH0uF4hMnTtC/f38efPBBHnvssTLvdaktLqrjFwWVqX///nahlnPP35Xn3LN4P4ZpmpimiWEY1K5dm/DwcLuaatOmTUlOTtb57CuQwp6IiJRxsRWEkiragFlqtnMbfPvbQ3zxxRfVoj3ElbJqVDJwud1uu%2B1Ep06diI2N5YknniA6OpqFCxcye/Zszp49y6xZs%2BjSpUupUNy6dWvWr1/PiRMniIyMJDc3l1tuuYW7776br7/%2BmnfffZf27dsze/ZsTNOsUGN0p9NZbouL6vxFQWXyV9V94YUX7LYJAJ9%2B%2BikjRozAsiyee%2B45u1LnTTfdxNatWwHshutQHOJ%2B//vfM23aNNxuN1FRUWRmZl7SWCzLol69evpy7gqksCciIiIXVZEG31XlSlk1OjdwzZ8/325W/u677zJhwgQaNWrE22%2B/Td26dcnJyaFLly6cOXOGpKQk2rRpQ3Z2Nrt27SI1NZXc3FzCw8NxOp2lwsM111zDgw8%2BSL9%2B/ewgX5HG6IMGDaJp06ZlWlxU9y8KKsvrr7/OX//6V3w%2BH6GhobjdbuLj4zl8%2BLB9TUVW/a677jqio6P5%2BuuvqV%2B/PseOHQNg7NixjBw5kr59%2B/KPf/wDj8eDx%2BOhT58%2BDBkyhAYNGlTq/KRmMC9%2BiYiIXIkyMjKYPn06p06dKvX4Sy%2B9xLRp06pFdUO5fKpre4gFCxYwZswY8vPzycvLY/To0bz00ktVOqbK4u8p%2BNFHH9GqVSs2b97M0KFDCQ0NZcCAAfb2vZdffhkoLvufn59PTEwMLVq04OOPP2bNmjWcOnWKpKQkFi9ezM6dO9m2bRs7d%2B7kX//6F1999RUrVqzg//2//1cqiG3evJnk5OTzBhN/i4vNmzeXee5CfSSD2aOPPsqYMWMwDIPMzExyc3M5fPgwpmliWRbNmzcHilfdSgZof7sEgJiYGJo3b87u3bupU6cOTz75JLm5uRQUFHDXXXcRHx/PQw89RHx8PHXq1CE2NpYvv/xSQU9sCnsiIlJGWloaDzzwAMuXLy8T9uLi4vjggw/4xS9%2BEfQf1qT6W7RoERMmTGDu3Lm8%2BuqrTJs2jbfeeisoS82XDHdQftuJJ554olTg8ng8ZGZmsmvXLlJSUkhOTiYpKYnt27eXqvYZFhZGQkJCqaBRUnZ2NnPmzLErQ5bnfC0uqusXBZfDL3/5SyzLIiwsjHXr1hEeHk5ISAimafLCCy/Yffd%2B85vfYJomTqeTuLg4OnXqhGEYxMbG0qNHD95//31%2B%2B9vfMmnSJBwOBz169KBVq1akpqZyzz33kJKSwokTJ8jIyOD777%2BnZcuWvPPOO6xbt45169axc%2BdOUlNTq/rXIVVArRdERKQM/wpCeVu2BgwYQJ8%2BfRg0aJDdn0uuDNWxPcSFVo2qentpoJ0b7sprO5GQkGAHrgULFlBUVERYWBiHDx/GMAyuvfZann32WXsr5fDhwyu0lbJFixalmoWXp6a2uKhMn332GQBFRUUMHjzY3moJMHv2bAzDwOl08vHHH9sVVI8dO0ZoaCg%2Bn4/vv/%2BeJ554otRr3n///bRr147Y2Fhee%2B218773iy%2B%2BWOq%2BaZoX/WcowUdhT0REyti8efN5z%2BbAf7ds%2BftzSfC7WINvP8MwLmvYu5JWjc4Nd%2Bc2KzcMg507d9rPz58/H4DHHnuMOXPm8OKLLzJlyhSGDx9%2ByaG4X79%2BjBw5kj179pQb6C7WGL06flFQ2SZPnsz8%2BfPtVeZvv/221Bm9zz//3G7L4K/c6ffdd9%2BV%2B5p33HEHkyZNstu1%2BP/Z%2BV8zISGBa665hs2bN/PrX//a/v3Gx8eTmJgY%2BElKtaewJyIiZZS3Pexc59uyJcHp3AbfcvmdG%2B7ObVbu9XqZPHkysbGxdOzYkfT0dAzD4Ntvv6WoqIgtW7aQk5PD8OHDmT59%2BiWF4t69ezN27FiGDRt2yY3Rq%2BsXBZVtyZIlhIaGMm7cOHJzc3n11VftCpt%2BJbcbx8TEcObMmQu%2B5oYNG2jZsmW5z5mmyenTp5k8eTIffPAB/fr1%2B98nITWewp6IiJRR3vawc2nLllQXV8qq0bnhzh%2B4unbtyo4dO4D/NtmOiYkhPT2dHj162Cv0hmFgWdZFqz%2Bej8vlYuLEiWzZsoWvvvqqVIuLUaNGnbfFxZX6RYFlWRiGQbt27WjatCnz5s3jX//6Fw8//DB33HEHQ4cOZd68ebz66qtAccsb/4pdx44d2bJlywVf3%2BFw4Ha77fv%2Bf67%2BarTnhkJt47wyqfWCiIiUsWzZMqZMmcLs2bNJSkoq8/zOnTtLNWAWqSr%2BD8cXYxhG0PQYK9lTMDU1laKiImJiYrj33nsZMmSIfZ2/YmedOnVo1aoVzz33HFOmTGHYsGHUqVOHF154gW7dunHTTTeVKsxyvlDctm1b2rdvT1RUVFC3uAiUGTNmsGbNGpo3b07v3r0ZMmQILVu2ZO/evfh8PiIjI8nOzsayLEzT5Oqrr%2Ba7777D6/XaQc4f5uLi4jh16pTdfH3ChAn89a9/LVNAC4pXCIuKimjRogUHDx7E5/MRFhbG2LFj6d69%2B%2BX%2BNUgVU9gTEZFylWzAfL4tW%2BPGjavqYYpcUUr2FHQ6nXzwwQfnbVbuD3iRkZFMnjwZgDNnzhAVFYVpFhdkz8jIIDo62l4ZPV8oXrBgARMnTuSmm24iMjLyimmM/r/o378/27dvD0hlWH/Ic7lcmKbJokWLyMrKon///pimSXh4OK%2B//jr9%2BvWje/fuPPDAA0yaNInRo0fz3HPPAcVnrZctW/Y/j0VqFoU9ERE5r5IrCCW3bN1zzz3n3bIlIpVjwYIF/PnPf7bD3caNG7n99tvtbYDnNiuvyKpnamoqCQkJ/Otf/7rgdT169CAsLIw5c%2BYQFxd3xTRG/18sXbrU/m%2Bo1%2BslOTmZL7/8Eq/Xi9frpVu3bqxfv57f/OY3HDx4kFOnTnHVVVexa9cuvv76a7xeLzExMXZfPSg%2B69isWTNSUlLIzMwkJyeH0NBQfvKTn3D48GHq1q1Lamoqb775Jn369GHJkiXcd999QPE2zv/85z9V%2BSuRKqCwJyIi5Sq5gqAtWyJVr0ePHjz55JP2Nstrr72WkJAQvvzySwzDwO12065dOzZu3HjRCpv%2BFZ4//OEP9rbO8%2Bnduzdt2rRhw4YN9uteyntd6Tp06IDX6%2BXdd9/ll7/8JV6vF9M0CQsLY8iQIUyZMgWfz8eSJUtYuXIlBw4cID4%2B3i5gExUVRWFhIXl5eRd8H8MwME0Tj8dDaGgoDocDy7LIzc3F6XTStm1bFi5ceDmmLNWICrSIiEgZJVcQ3G43o0eP5ptvvtGWLZEqdG5PQa/XS35%2Bvt0%2B4VLaTvztb38Divu/LVy4EMuySEtLIz4%2BHsuy7Ov8FTKvpBYXgdK/f3/27t1LQUEBRUVF9OzZE9M0KSoqwufzcebMGV577TUyMzMB%2BOSTT5gxYwZQ3CrB5/PRuHFj%2B/VSUlKwLAu3281VV13F6dOnycrKwuPx4PP58Pl8dg%2B//Px8oPifX1hYGJ06dWLMmDGX%2BTcg1YHCnoiIlLFo0SImTJhgryBcagNmEQm8cwMX8KMDl79CZlJSEm%2B%2B%2BSaNGjUqdVv%2BdzfffDMAe/fupbCwsFTlTChuu3DkyBH7/vjx4%2B3bJ0%2BeBCj1PIDH4yEpKYmcnBwyMjJo3LgxR44c4b777uPGG2/kr3/9K7fccgsbN27k888/r6ypSQ2isCciImWcu4JwqQ2YReTycLvdrF271t6GWZltJ66UFheB8vTTT/P000/bZ/cADh06xPfff8%2BZM2fss3t%2B/iIsAJ07d%2BbMmTOcPHmS3Nxce/UPiqsh%2B/nD4NKlS1m6dClQXIjl7NmzDBw4kMjISH76059y9913Ex4eXulzlupHZ/ZERKSMkiXb/ZKSklixYoW%2B9RepIiWrawJMnjy5THVNv5If7C/UdqLk3/WF/savxBYXleXo0aPcddddmKbJM888w0svvcTNN9/Mtm3bGDJkCFdffTVbtmxh8eLF9vbMc0VGRpKTk4PD4aCoqMh%2BvFatWmRlZZW61rIs6taty6JFi0hMTKz0%2BUn1opU9ERERkRqgfv36/N///Z99Pzw8vNzVmksJXD/96U8JCwu76HVXamP0/0VqairHjx9n3LhxHDp0iLy8vDLB7S9/%2BQuA3UD95ZdfBkqv8pXH4/HQpEkTDh06RGhoKAUFBTRr1ozU1FT7mtjYWLp168bWrVsBmDp1KlOnTg3oHKX6U9gTEZFyacuWSPVyscBVsofaxfqp%2Bf9uZ86caT%2Bm87iB1aVLlwr12CsZ7Pwrdv77tWvXJj09HSguitOzZ0%2BWL19OXl4eR44cITw8HMuyyM/PJywszC7MAlBYWEjfvn356KOPAPj0008DPUWpAbSNU0REytCWLZGap7y/2/NV2PT5fGXCnb/nXslrAf2N/0gpKSmkpaXZ9w3DwOl0kpCQQHx8PEuXLmXBggUkJiZSUFDA559/XiYcmqaJYRh2lc0LqVevHseOHbNbMDgcDj766CN69Ohhv/9XX30V2ElKtaeVPRERKUNbtkRqnvL%2Bbs9XYdNfzEMqT4MGDWjQoEGZxw8fPsyYMWPYs2cP%2B/btY9%2B%2BfURGRpYKehEREeTk5OD1emnWrBnff//9Bd/L4XCQkZFhB0r/%2B%2B7cuZOoqCiAUjs15MqhsCciIiJyhbnvvvuqeghXDP/ZPYBTp07x%2Beef8/7775e6Jjs7u9T9nJwc%2B/b333%2BPaZp4vV4sy%2BLqq6/G7XbTsmVL/vnPf5Kfn29v5YTi7Ztnzpyha9euTJkyhZycHCzLom/fvpU8U6mOFPZERERErkAZGRksWLCAhx9%2BmLi4OPvxl156CZ/Px%2BOPP050dHQVjjA4VPTsHkBYWBg%2Bn4%2BwsDDy8vKoV68ederUwel08sMPP1BUVERWVhanT5/mu%2B%2B%2Bs3%2BuoKAA%2BO8W3cjISN577z37%2BVtvvZXBgwcHdmJSIyjsiYiIiFxh0tLSePjhh/F4PHTv3r1U2IuLi2P%2B/PmsXbuWN954g7p161bhSGu%2BDRs22Gf3Tp48yfbt23nzzTcBCAkJ4frrr6dDhw4cOnSIhg0b8tZbb/Hee%2B/Rt29fVq5cyYQJE1i8eHGpnnznCgsLo6CggO7du/PEE09gWRYHDx4EoEmTJlx77bWVP1GpllSgRURERCRI3XDDDSxfvrzMmb1Ro0Zx8uRJZs2aRWhoaJmfy8nJYdCgQTRt2pQJEyZcruFeETZt2sTjjz8OQHx8PC6Xy956WbJnXnkMw8AwDGJiYmjTpg1btmwhLi6On/3sZxQUFBAWFsaf/vSnyzENqSG0siciIiISBLp06VKmwmZeXh79%2B/cvU2GzsLDwvEEPiguEDB06lBEjRlTaeIPR6NGj2bFjxwWvycvLs2%2BfPHnykl7/qquu4uDBg8TFxZGUlMTnn39OVlYWmzZtYt68eQwcOPBHjVuCl8KeiIiISBB45plnKnztuHHjiI2NveA19erV4%2BzZs//rsK44F9s0FxoaSuPGjalXr57d8NzhcOB2u%2B1r%2Bvbty6ZNmzh9%2BjSWZeF0OsnLy%2BPs2bP27W%2B%2B%2BQaPx4PH48Hn8%2BHz%2BcjKyqrUuUnNo22cIiIiIleYX/ziFzz44IP06dPnvNcsWbKEBQsWsGLFiss4sitHye2c54Y9/wrtxT6m%2B3%2BuXr161K5dm9q1azN37tzKG7TUOGZVD0BEREREAiMjI4Pp06dz6tSpUo%2B/9NJLTJs2zV6p69evH1OnTmXnzp3lvs7OnTv5y1/%2BonL9leTs2bNMmzbNvl8y6JWnQ4cOdgB0OBzUrl2bmJgY6tatS8eOHWnZsiVt2rRh0qRJlTpuqXm0jVNEREQkCFxKhc3evXuzd%2B9e%2BvXrx3XXXUebNm2oVasWmZmZfP311/znP/%2Bhb9%2B%2BDBgwoApnVHOVPLt36tQpuzWCn3/r5fmUfM4wDLZt24bD4aBjx44cOHCAyMhIVq1aVTmDl6CibZwiIiIiQeDHVNjcsWMHy5YtY9%2B%2BfWRmZhIbG8tPfvIT7rnnHq6//vrLPYWgMXr0aL744gsATp8%2BXSbsud1uLMvC4/EAkJiYSFpaGjfeeCN79uyhoKAAj8fD4MGDcblcHD16lE6dOtGiRQsefPBBTNM876qsSEkKeyIiIiJBoHPnzsyaNYt27dqd95pt27YxYsQIPv74Y5YvX866detwOp3ccccd3H333ZdxtFe2Dh06MHfuXB5//HHy8vJYtWoVd999N3feeSfr1q0jPj6ew4cP07x5c/7whz/QoEEDAH77299y9OhR2rdvz9/%2B9rcqnoXUBAp7IiIiIkEgKSmJFStWlOmpV9LRo0fp1asXw4YN489//jMdO3bE4XCwefNmHn30UZ599tnLOOLgM3PmTLZt23bR6w4dOoRpmtx4442sWrWK5s2b8%2B2331boPdq3b8/MmTOJjo7%2BX4crVwCd2RMREREJAi1atGDbtm0XDHv%2B5xctWsSECRPo3bs3AGvXrmX06NEMHz68TK8%2BqbitW7eyd%2B/ei17n8/mIjIxk9erV%2BHy%2BMkEvLi6O06dP4/P5aNiwIW63m8zMTNq2bcvChQsra/gShLSyJyIiIhIEli1bxpQpU5g9ezZJSUllnt%2B5cyeDBw/mqaee4s9//jMbNmwgISEBKD5D1q5dOzZu3Gg/JpXniy%2B%2BoG3btuzYsYOhQ4fi8Xh49tln%2BdOf/gRAdHT0JfU4tCyLevXqAbBhw4ZKGbPUTFrZExEREQkCl1Jhc%2BLEiTgc//0Y6HA4CAkJobCwsApnEHz27NnDSy%2B9xFdffUVBQQEul4vWrVuzZ88e3nrrLYYPHw4Uh7Xx48fbP1eRoGcYBoZhYFkWN9xwA/v37%2BeRRx6ptLlIzaSVPREREZEgUpEKm61atWLz5s3UqVPH/rmKnPmTipk5cyZr1qzhm2%2B%2BAcA0TUzTxOv14vV6AXA6nXi9XizLwufzYVkW%2Bfn5ANSuXZv09HQAQkNDKSwsxOfz0bZtW7Kysujfvz8JCQl2QIyMjGTYsGFMnz5dLRmkFK3siYiIiASJkhU2H3nkkQtW2Pzoo4%2BIjIy073u9XtatW0ft2rVLXec/1ycVt3XrVr799lv7/GPJkOdXVFQEYLdf8N8H7KAH2AHQsizGjRtHv3796NChA263m4yMDKB4JbBRo0akpKRU3qSkRtLKnoiIiEgQWLBgQYUrbHbp0qVCr2kYhs6A/UjXXXcdPp%2BPlStX0qRJE/vxw4cP06NHDwCSk5PJysrC6/Wyf/9%2BAOrWrcuJEycu%2BvqtW7cmJSUFy7Jo06YNBQUFxMbGqiWDlKKwJyIiIhIEevTowZNPPlmmwuYXX3yhCptV4P777%2Bf06dM8/vjjJCcn22f3cnJy7NU8p9OJ2%2B3GNE0effRR3nnnHQzDYPTo0eTk5ACQnZ3Nu%2B%2B%2BS1paWrnv43A48Hq9dOjQgenTp6slg5SisCciIiISBNq0aaMKm9XIxo0bGTJkCF6vF//HbYfDgdvtLnWdZVl2%2BLMsC8uyuO222/jb3/5WKqQfOHCATz/9lH379mEYBnFxccTHx5OQkECLFi1o3rz55Zuc1Bg6syciIiISBNxutypsViO333478%2BbN49e//jUFBQUYhlEm6MF/z%2Bz5b3s8HtauXUurVq3O%2B9rR0dH89re/BYoDYmZmJqmpqQDUr18/wDORmkxhT0RERESkEnTs2BEo3q7ZtGlTvvvuO7xeL/fffz8JCQnMnj0bwzAoudHu3PvlOXv2LM8//3ypx/ytGCrS1F2uHAp7IiIiIkFCFTarlwULFlBYWIjX67XbMAAcOXKEa665BiheiTt27Bi1atUiOjqaY8eOAcXVOUePHk1iYmKp6pznql27NvHx8SQmJlbuZKRG0pk9ERERkSCgCpvVy6xZs/j73/%2BO1%2Bu1%2B%2BRFRkaSnZ1d6rqIiAi7GEurVq3sM3k%2Bn4%2BIiAg8Hg/R0dH079%2Bfxx9/XMV25JIo7ImIiIiIBNitt95KQUEBEydOJCwsjF/96leEh4eTm5tbKe9nmiaAtnFKKdrGKSIiIiISYNnZ2Xi9Xpo2bcpVV12FYRh07tyZNWvWlLrO6XSWaqheUV27dgUgISGBJk2a0Lp164CMW4KLwp6IiIiISIAlJSVx4MAB5s2bR4sWLbAsq0zQA8oEvcTERNLS0jAMA9M0MU2TDRs20KVLFwzDYPfu3ZdrChIEtI1TRERERCTAvvvuO375y1%2BSlZWFz%2Bezz9qV/Oh9bt89p9PJ3LlzGTRoEIDdsqFNmzbs2rXrvO9lWRb16tUD0HlMKUVhT0RERESkEmRkZNC1a1fy8vJITEzk5MmTWJZFfn4%2BAFFRUZw9e7bcdgsul8vukWiaJl6vFygOhA6Hg/j4eCzLIi4ujqZNm5KcnAzAfffdd5nMF8MAABSlSURBVBlnKNWdwp6IiIiISCVJTk7G6/WyZMkSHnjgAbxeL3l5eRftpXchgwcPZtiwYQEcpQQrhT0RERERkQA7duwYf/rTn/j444/xeDxlnvc3QYfirZ3%2Bj%2BT%2Bc3p%2BHo/Hvt%2B8eXMOHjxIYmKitmtKhSjsiYiIiIgEWP/%2B/dm1axdhYWFkZWVRVFRUajsmFAc%2Bl8tFUVERXq8Xl8vF6tWrSU1NZd%2B%2BfRw%2BfJgjR47w2Wef2dcXFBQA0KFDB2rVqkViYiJNmjShVatWANx0002Xf7JSbSnsiYiIiIgEWLt27fD5fLz//vvExcUxfPhwDh06xJkzZ%2BzAZ5omTqeTmJgY0tLS8Pl8XH311T%2BqV55/pVB99qQktV4QEREREQmwJk2akJeXx9mzZ2nRogUej4dGjRqRnZ1d6jqfz0dGRgY%2Bnw%2B3280333zDjTfeiGVZLFy40L7O7Xbz2GOP8f3333Prrbcyfvz4yz0lqYG0siciIiIiEgDbt29n3759AOzbt481a9ZgGAYxMTEcP36cwsLCcguzWJZlt2cwTZPatWtz//33k5iYiNvtZv/%2B/WzcuJH09HRq1arF4MGDiYqK4vrrr%2Bf48eMkJCRw1VVXXe7pSg2gsCciIiIiEgCtWrW6pCqbEREReDweu4BLREQE4eHhpKWllTrbB9jFXEr23DMMg4YNG5KSksIjjzzCyJEj7etEAMyLXyIiIiIiIhezb98%2B9u/fX%2Bp/ISEhuFwuVq1aZd/u3LmzfcYuPz/fDnxnzpzh%2BPHjZYJebGwslmXRs2dP/vGPf/DKK68QERGB0%2Bnk1KlT/OIXv2Dp0qW88847VTRzqa4U9kREREREAqywsJBJkyZhWRYRERGcPXsWKD57t2XLFlwuF7m5uRiGgdfrtQPeuW0aTNPk7NmzOBwOTp48CUCXLl0YPXo0sbGxREVF8eWXX/K73/1OYU/K0DZOEREREZEA2r59O9OmTWPPnj12Dz2Hw0FMTAzHjh0DoE6dOmRkZHDPPffQtm1bAGJiYhg7diwABQUFhIaG8uGHH9KzZ08%2B%2BOADGjRoYL/H0aNH6dmzJ1AcCFeuXMk999zDv//978s8W6nOVI1TRERERCSA%2BvfvX%2BbsXlFREXl5efb906dPA7B8%2BXKWL19e7uvk5%2BfTs2dPvF4v69ev55FHHrGfS0tLIyIiAoCQkBDS0tKIiYkJ9FSkhtM2ThERERGRANq3bx8xMTHUqlWLxYsXlzrD99577xESEgJA%2B/btMc0Lfxz3eDwUFBQwceJE1qxZAxRvBZ0xYwaWZeFwOOjUqRMzZ87kZz/7WaXPTWoWbeMUEREREQmw559/ntWrVxMZGclPfvIT0tPTadGiBR988AGZmZmYpknjxo05dOgQULwV07IsTNPk/vvv58MPP7TP%2BYWFhZGfn49pmiQnJ7N3716ys7MxTZOIiAji4uIoKCjgnXfeISEhoQpnLdWNwp6IiIiISIDl5eUxatQoVq9eXea5kJAQu%2B1CVlYWXq%2BXyMhI8vLycDgcrFy5kszMTB544IGLtnKoVasWPXv25JlnniEuLq6ypiM1lMKeiIiIiEglufnmmyksLOTJJ59k7ty5ALzxxhssX76c%2BfPnlwlzpmnys5/9jM6dO/Pqq68SFRXFHXfcwdtvv01ISAijRo0iOjqaqKgoateuTePGjbEsqyqmJjWAwp6IiIiISACdOnUKj8dDXFwcnTp1orCwkNtuu41169ZhmiY9e/Zk/fr15OTk4PF4MAzjvCt4/qbppmkydepU7rrrrss8G6nJFPZERERERAIgJyeHoUOHsmnTJkzTZNWqVbz44ots27atzLVRUVFkZWXRunVr0tPTOXnyJD6fD6/Xa4c/p9NJWFgYbdu2ZdiwYbRr164KZiU1mVoviIiIiIgEwIwZM9i9ezeNGjViypQpJCYmsnv37nKvzczMBGDPnj0YhsG9995Lnz59GDhwIG%2B88QYDBgzgo48%2BIjIykujo6Ms5DQkiWtkTEREREQmALl26UFBQwLRp0%2BjQoQNr167lmWeewTAMxo8fT8OGDfniiy%2BYNWsWpmnSvHlz6tevT1JSEnPmzCE7O7vc1w0PD2fgwIE8/fTTl3lGUtNpZU9EREREJAD8WzEbN24MwIYNGwAwDIMbbriBlJQUUlNT8fl8uN1ujhw5wv79%2B9m4caN9nX8dxt%2BGISkpid27dzN37lxcLheDBg2qmslJjaSVPRERERGRAOjatSsFBQWMHz%2BeN954g02bNgEQGRl53lU7P8uycLlcGIbBhAkTcLlcPPfcc4SFhTF27Fj7tj8YilSEWdUDEBEREREJBvfeey8ej4dnn32Wzz77zH7cX3QFilsr%2BLlcLhwOBw6Hg4SEBIqKigBo1aoVzZo1Iycnh/T09FK3RS6FVvZERERERALA7XYzZcoUFi5cCBSHuZCQEACysrIq/Drh4eF4vV5q1apF06ZNady4MZ988gnNmjXjjTfeqJSxS3BS2BMRERERCaC2bdvi8/lYu3Ytd955J263G6/Xi2ma3H777fZZPj%2BHw4Hb7S7zOoZh4HK5KCoqIjo6mvnz59OqVavLNQ0JAirQIiIiIiISQAkJCRQUFHD06FESEhJISUnB4XDQrl079uzZA0CDBg1ISUmhQYMGPPnkk0yePBmPx0OjRo0oKCjA5/MRFhZGXFwct99%2BO3379iUiIqKKZyY1jVb2REREREQCaMaMGbzzzjuEhobidDo5dOgQANdee60d9sLDw8nPz%2BeWW25hz5495OXl8fDDDzNs2LAqHLkEG4U9EREREZEA8p/dW7x4MQUFBXZRFv9WTq/XC0BoaCj5%2BfkAdgGX8j6aW5YFQL169cpsARW5EIU9EREREZFK5j9rdykfvZs1a0ZcXBxNmzYFIDk5mfvuu68yhidBSmFPRERERCQAUlNTOX78eIWuTUhIoH79%2BrRr1w6fz8f7779Pnz597NstWrSo5NHKlUBhT0REREQkAFq1anVJK3djx45l9uzZAERHR5OZmUlWVhZ33nknkydPxul0VtZQ5QqhsCciIiIiEgApKSmkpaVd9Lq//OUvHDp0iLi4OH744Qfcbrd9Zs/tduPz%2Bahbty7t2rUjMTGRJk2aAMVh8qabbqrUOUhwUdgTEREREblMCgsLadu27Y/6WdM02bt3b4BHJMFMffZERERERAIoNTWVTz75hFmzZnHixInzXhceHs7gwYPt1bqMjAxGjhzJvHnzGDhwINu3b79cQ5YgpZU9EREREZEAutSzeyU1bNiQlJQULMvikUce4bPPPuPEiRPce%2B%2B9DB8%2BXOf45JIo7ImIiIiIBFBKSgo///nP8fl8jBw5krfeeovc3Fy6detGUVERa9asobCwkIKCgnJDYUhICBEREXg8HpKSkti/fz%2B5ubn06tWL5557rgpmJDWVWdUDEBEREREJJg0aNCAiIoKwsDBuvfVWOnbsSP369alXrx5PPfUURUVFmKbJnDlziIiIIDw8nCeeeIKIiAhq1arF4sWLmTNnDgC7du1ixowZAHz44YdVOS2pgXRmT0REREQkwDp06MDOnTsZMWIER44cISMjg6%2B%2B%2Bopp06bZ1zzxxBP27b///e8AhIaGcvbsWcLDw%2B3KnD6fz74tcim0jVNEREREJMCOHz/O0KFD%2BfrrrzEMA8uyyM3NvejPGYaBYRjUqVOHgoICrrvuOn744QcyMjK44447mDhx4mUYvQQLhT0RERERkUrStm1bfD4fd999N0uXLgUgLi4Ol8tFamoqTqfzoqt2hmFw77338vzzzxMREXG5hi5BQNs4RUREREQqQXZ2th3m1q9fj2maeL1e4uLi7HDn8XgAcDgcGIaB0%2Blk2LBhvPzyy/zxj3%2BkQYMGXHPNNURGRlblVKSGUoEWEREREZEAW758OR07diQnJ4eCggKysrLwer0A7Nu3j/379wPYj4WEhBAaGkp%2Bfj6fffYZ%2Bfn57Ny5kzfffJMPP/ywQltARc6lbZwiIiIiIgF22223kZmZSbdu3Th48CB79uzh7rvvZvny5RcttGIYhn2NZVkYhkF8fDyLFi0iMTHxcgxfgoRW9kREREREAuzMmTN4PB6eeeYZ3nvvPX7961%2Bzd%2B9eevTogcvlAopX8/z/bxgGALGxsXbQc7lcdO/enb59%2B%2BJyuZg6dWrVTEZqLIU9EREREZEAu/3222natClr164lOzubpKQkYmNj2bBhA4WFhQC43e4yBVoyMjLs1zBNky1btnD//feTnp7Op59%2BWiVzkZpLBVpERERERAIsISGB1atXM2XKFKZMmVLuNR6Pxy7Q4nfHHXfwz3/%2B0y7mkpeXR1xcHIWFhRQVFV2OoUsQ0cqeiIiIiEiAnT17loYNG9pn7iIjI%2B3bhmEQEhLCwoULadSoEaZp4nA4uOGGG9izZw9Op5PGjRsTHR1NvXr12LlzJ1FRUdSrV6%2BqpyU1jMKeiIiIiEiATZo0iXXr1uFyuQgJCWH58uX27f79%2BxMZGcno0aOxLAuv14vb7SY/P59jx45RWFjI6dOnyc/P58Ybb2TKlCnk5%2BfTvXv3qp6W1DCqxikiIiIiUgnWr1/PiBEjyMnJsR9zOp107dqV%2BPh4Fi9eTEFBAaZpYhgGXq8Xy7Jwu90ANGzYkB9%2B%2BAGAW2%2B9lRkzZthFXUQqQmFPRERERCTAFi1axPjx40sVX/Gfw4Pi9gqmaRIaGkpcXBwAycnJ/OpXv%2BLgwYM0adIEy7Ls29dee22VzUVqLhVoEREREREJsLlz51KrVi2aNWvGyZMnATh9%2BjR5eXl4vV58Pp/dbqHk2kvLli1p2bJlqfsiP5ZW9kREREREAiwpKQmv18vy5ctp2rSp/fihQ4e4%2B%2B67MU2T//znP1U3QLkiaGVPRERERCTAWrduTUpKCsuWLaNNmzZMnTqVI0eOlGq10LJlS0zTpH79%2BjzzzDP07t27CkcswUgreyIiIiIiAbZz504GDBhgN1AHsCyrVNgzTZOWLVty4MABTNPk%2Beef54EHHqiK4UqQUusFEREREZEAS0pKYtmyZYSHh2OaJpGRkXZ/vdGjRzN58mRq165NXl4e48ePJyoqirlz51b1sCXIKOyJiIiIiFSC5s2bA%2BByuViyZAkulwvDMLjttttISkri7NmzHDt2rNRtkUDSNk4RERERkQAYMGAAHo%2BHV155hSFDhrB3715yc3Pxer04nU48Ho99Oy4ujsLCQhISEujcuTP/%2BMc/SEhI4P3336/qaUgQUYEWEREREZEAaN%2B%2BPUVFRTidTm6%2B%2BWYAsrKy2LdvH4WFhViWhc/no7CwkNTUVADcbjdz5szB4XAwbdq0qhy%2BBCGt7ImIiIiIBNiyZctwu9306tWLo0ePMmfOHLZv3052dja5ubkYhkFERATR0dF06tSJQYMGUa9evaoetgQZhT0RERERkQBIT0/n%2BPHj5Ofn89BDD2EYBq%2B88grR0dH2Nd999x0vvPAClmWpz55UOoU9EREREZEAWL16NUOHDr2kn7Esi4iICKC4N9/ChQsrY2hyhVLYExEREREJkNTUVGbOnMmSJUsAaNGiBenp6cTFxXH69Gny8vIwDAPDMACIioqiQYMGANx88808/fTTVTZ2CT4KeyIiIiIilaTk2b0PP/wQt9tNjx49cLvdvPfee7hcLvr27cvbb7%2BNy%2BViwIABVT1kCSIKeyIiIiIiAZSens6%2Bfft44403%2BOc//wkU99orLCy0r/G3YrAsixdffFHn%2BKRSqPWCiIiIiEgAbdu2rczZvZJBD6CoqAgAr9fL2LFjAejZs%2BflGaBcMbSyJyIiIiISYNdddx0ej8cOdbGxsWRkZAAQExNDfHw8v/vd74iKiiIsLIzExERiY2OrcsgShMyqHoCIiIiISLAxDAPLsti4cSO9e/emYcOGPPfcc6xfvx6Px8PJkyf56U9/Stu2bWndujWRkZF2MBQJFK3siYiIiIgEWL9%2B/Th27Bg9e/Zk27Zt7N69237O4/GUujYyMpK8vDwiIiLYvn375R6qBDGFPRERERGRANuxYwePP/44%2Bfn5mKaJx%2BOhvI/dderUIT09HYAxY8aoGqcElLZxioiIiIgEWHJyMps2bcLlcmFZFrNmzbJ76/Xp04eEhAQcDgcNGjTg2WefpUmTJnzyySdVPGoJNgp7IiIiIiKVIDIy0m6g3rp1a0zTxDAM6tevT7t27fB6vRw4cIAePXpw%2BvRpdu3aVdVDliCj1gsiIiIiIgHQpUsX0tLSSj3mP5/XpUsXexvnjBkziIiIwOfzUVRUxA8//IDX68WyrMs%2BZgluOrMnIiIiIhIAS5cuZceOHaUeO3XqFJ988gk%2Bnw/LsspU3PSv/FmWxQMPPMALL7xwOYcsQU5hT0RERESkEq1atYpx48aRmZmJy%2BUiOTmZmJgYkpOTyczM5OjRo3Tq1ImePXtimjplJYGjbZwiIiIiIpUgJSWFsWPH8tlnn2EYBr169WLMmDHExMRU9dDkCqGVPRERERGRAOrSpQupqanltlooj/%2BsXv369Vm/fn1lDk2uMFrZExEREREJkK1bt5KTk4PP58M0TRo0aIBlWXbbBY/HQ1paGoWFhUBx0PMXcYmPj6%2BycUtwUtgTEREREQmA3/3ud6xcuRIobpb%2B4IMPUrt2bfv5b775hlWrVlFUVITT6cTlcpGXl0d4eDhjx46lb9%2B%2BVTV0CVLaxikiIiIiEgCtWrWq8NZNQOf4pNIp7ImIiIiIVAK3283f//53Zs6cidvtJjo6muzsbLxeL40bN2bKlCkkJSVV9TAliCnsiYiIiIgE2NatWxk1ahRpaWlYlkVYWBjZ2dk4nU6GDx/OI488ojYLUukU9kREREREAqjk2T2Xy2UXYwkPD%2BeGG24gPDzcvjYhIYEmTZoAxdtAb7rppss/YAlaCnsiIiIiIgF0qWf3/EzTZO/evZUwIrlSKeyJiIiIiIgEIW0UFhERERERCUIKeyIiIiIiIkFIYU9ERERERCQIKeyJiIiIiIgEIYU9ERERERGRIKSwJyIiIiIiEoQU9kRERERERIKQwp6IiIiIiEgQ%2Bv%2BZUmIxM8gIcwAAAABJRU5ErkJggg%3D%3D” class=”center-img”>

</div>
<div class=”row headerrow highlight”>

<h1>Sample</h1>

</div> <div class=”row variablerow”> <div class=”col-md-12” style=”overflow:scroll; width: 100%%; overflow-y: hidden;”>

<table border=”1” class=”dataframe sample”>

<thead>
<tr style=”text-align: right;”>

<th></th> <th>2010 Census Population</th> <th>Population Estimate, 2011</th> <th>Population Estimate, 2012</th> <th>Population Estimate, 2013</th> <th>Population Estimate, 2014</th> <th>Population Estimate, 2015</th> <th>Population Estimate, 2016</th> <th>LACCESS_POP10</th> <th>LACCESS_POP15</th> <th>PCH_LACCESS_POP_10_15</th> <th>PCT_LACCESS_POP10</th> <th>PCT_LACCESS_POP15</th> <th>LACCESS_LOWI10</th> <th>LACCESS_LOWI15</th> <th>PCH_LACCESS_LOWI_10_15</th> <th>PCT_LACCESS_LOWI10</th> <th>PCT_LACCESS_LOWI15</th> <th>LACCESS_HHNV10</th> <th>LACCESS_HHNV15</th> <th>PCH_LACCESS_HHNV_10_15</th> <th>PCT_LACCESS_HHNV10</th> <th>PCT_LACCESS_HHNV15</th> <th>LACCESS_SNAP15</th> <th>PCT_LACCESS_SNAP15</th> <th>LACCESS_CHILD10</th> <th>LACCESS_CHILD15</th> <th>LACCESS_CHILD_10_15</th> <th>PCT_LACCESS_CHILD10</th> <th>PCT_LACCESS_CHILD15</th> <th>LACCESS_SENIORS10</th> <th>LACCESS_SENIORS15</th> <th>PCH_LACCESS_SENIORS_10_15</th> <th>PCT_LACCESS_SENIORS10</th> <th>PCT_LACCESS_SENIORS15</th> <th>LACCESS_WHITE15</th> <th>PCT_LACCESS_WHITE15</th> <th>LACCESS_BLACK15</th> <th>PCT_LACCESS_BLACK15</th> <th>LACCESS_HISP15</th> <th>PCT_LACCESS_HISP15</th> <th>LACCESS_NHASIAN15</th> <th>PCT_LACCESS_NHASIAN15</th> <th>LACCESS_NHNA15</th> <th>PCT_LACCESS_NHNA15</th> <th>LACCESS_NHPI15</th> <th>PCT_LACCESS_NHPI15</th> <th>LACCESS_MULTIR15</th> <th>PCT_LACCESS_MULTIR15</th> <th>GROC09</th> <th>GROC14</th> <th>PCH_GROC_09_14</th> <th>GROCPTH09</th> <th>GROCPTH14</th> <th>PCH_GROCPTH_09_14</th> <th>SUPERC09</th> <th>SUPERC14</th> <th>PCH_SUPERC_09_14</th> <th>SUPERCPTH09</th> <th>SUPERCPTH14</th> <th>PCH_SUPERCPTH_09_14</th> <th>CONVS09</th> <th>CONVS14</th> <th>PCH_CONVS_09_14</th> <th>CONVSPTH09</th> <th>CONVSPTH14</th> <th>PCH_CONVSPTH_09_14</th> <th>SPECS09</th> <th>SPECS14</th> <th>PCH_SPECS_09_14</th> <th>SPECSPTH09</th> <th>SPECSPTH14</th> <th>PCH_SPECSPTH_09_14</th> <th>SNAPS12</th> <th>SNAPS16</th> <th>PCH_SNAPS_12_16</th> <th>SNAPSPTH12</th> <th>SNAPSPTH16</th> <th>PCH_SNAPSPTH_12_16</th> <th>WICS08</th> <th>WICS12</th> <th>PCH_WICS_08_12</th> <th>WICSPTH08</th> <th>WICSPTH12</th> <th>PCH_WICSPTH_08_12</th> <th>FFR09</th> <th>FFR14</th> <th>PCH_FFR_09_14</th> <th>FFRPTH09</th> <th>FFRPTH14</th> <th>PCH_FFRPTH_09_14</th> <th>FSR09</th> <th>FSR14</th> <th>PCH_FSR_09_14</th> <th>FSRPTH09</th> <th>FSRPTH14</th> <th>PCH_FSRPTH_09_14</th> <th>PC_FFRSALES07</th> <th>PC_FFRSALES12</th> <th>PC_FSRSALES07</th> <th>PC_FSRSALES12</th> <th>REDEMP_SNAPS12</th> <th>REDEMP_SNAPS16</th> <th>PCH_REDEMP_SNAPS_12_16</th> <th>PCT_SNAP12</th> <th>PCT_SNAP16</th> <th>PCH_SNAP_12_16</th> <th>PC_SNAPBEN10</th> <th>PC_SNAPBEN15</th> <th>PCH_PC_SNAPBEN_10_15</th> <th>SNAP_PART_RATE08</th> <th>SNAP_PART_RATE13</th> <th>SNAP_OAPP09</th> <th>SNAP_OAPP16</th> <th>SNAP_CAP09</th> <th>SNAP_CAP16</th> <th>SNAP_BBCE09</th> <th>SNAP_BBCE16</th> <th>SNAP_REPORTSIMPLE09</th> <th>SNAP_REPORTSIMPLE16</th> <th>PCT_NSLP09</th> <th>PCT_NSLP15</th> <th>PCH_NSLP_09_15</th> <th>PCT_FREE_LUNCH09</th> <th>PCT_FREE_LUNCH14</th> <th>PCT_REDUCED_LUNCH09</th> <th>PCT_REDUCED_LUNCH14</th> <th>PCT_SBP09</th> <th>PCT_SBP15</th> <th>PCH_SBP_09_15</th> <th>PCT_SFSP09</th> <th>PCT_SFSP15</th> <th>PCH_SFSP_09_15</th> <th>PC_WIC_REDEMP08</th> <th>PC_WIC_REDEMP12</th> <th>PCH_PC_WIC_REDEMP_08_12</th> <th>REDEMP_WICS08</th> <th>REDEMP_WICS12</th> <th>PCH_REDEMP_WICS_08_12</th> <th>PCT_WIC09</th> <th>PCT_WIC15</th> <th>PCH_WIC_09_15</th> <th>PCT_CACFP09</th> <th>PCT_CACFP15</th> <th>PCH_CACFP_09_15</th> <th>FDPIR12</th> <th>FOODINSEC_10_12</th> <th>FOODINSEC_13_15</th> <th>CH_FOODINSEC_12_15</th> <th>VLFOODSEC_10_12</th> <th>VLFOODSEC_13_15</th> <th>CH_VLFOODSEC_12_15</th> <th>FOODINSEC_CHILD_01_07</th> <th>FOODINSEC_CHILD_03_11</th> <th>MILK_PRICE10</th> <th>SODA_PRICE10</th> <th>MILK_SODA_PRICE10</th> <th>SODATAX_STORES14</th> <th>SODATAX_VENDM14</th> <th>CHIPSTAX_STORES14</th> <th>CHIPSTAX_VENDM14</th> <th>FOOD_TAX14</th> <th>DIRSALES_FARMS07</th> <th>DIRSALES_FARMS12</th> <th>PCH_DIRSALES_FARMS_07_12</th> <th>PCT_LOCLFARM07</th> <th>PCT_LOCLFARM12</th> <th>PCT_LOCLSALE07</th> <th>PCT_LOCLSALE12</th> <th>DIRSALES07</th> <th>DIRSALES12</th> <th>PCH_DIRSALES_07_12</th> <th>PC_DIRSALES07</th> <th>PC_DIRSALES12</th> <th>PCH_PC_DIRSALES_07_12</th> <th>FMRKT09</th> <th>FMRKT16</th> <th>PCH_FMRKT_09_16</th> <th>FMRKTPTH09</th> <th>FMRKTPTH16</th> <th>PCH_FMRKTPTH_09_16</th> <th>FMRKT_SNAP16</th> <th>PCT_FMRKT_SNAP16</th> <th>FMRKT_WIC16</th> <th>PCT_FMRKT_WIC16</th> <th>FMRKT_WICCASH16</th> <th>PCT_FMRKT_WICCASH16</th> <th>FMRKT_SFMNP16</th> <th>PCT_FMRKT_SFMNP16</th> <th>FMRKT_CREDIT16</th> <th>PCT_FMRKT_CREDIT16</th> <th>FMRKT_FRVEG16</th> <th>PCT_FMRKT_FRVEG16</th> <th>FMRKT_ANMLPROD16</th> <th>PCT_FMRKT_ANMLPROD16</th> <th>FMRKT_BAKED16</th> <th>PCT_FMRKT_BAKED16</th> <th>FMRKT_OTHERFOOD16</th> <th>PCT_FMRKT_OTHERFOOD16</th> <th>VEG_FARMS07</th> <th>VEG_FARMS12</th> <th>PCH_VEG_FARMS_07_12</th> <th>VEG_ACRES07</th> <th>VEG_ACRES12</th> <th>PCH_VEG_ACRES_07_12</th> <th>VEG_ACRESPTH07</th> <th>VEG_ACRESPTH12</th> <th>PCH_VEG_ACRESPTH_07_12</th> <th>FRESHVEG_FARMS07</th> <th>FRESHVEG_FARMS12</th> <th>PCH_FRESHVEG_FARMS_07_12</th> <th>FRESHVEG_ACRES07</th> <th>FRESHVEG_ACRES12</th> <th>PCH_FRESHVEG_ACRES_07_12</th> <th>FRESHVEG_ACRESPTH07</th> <th>FRESHVEG_ACRESPTH12</th> <th>PCH_FRESHVEG_ACRESPTH_07_12</th> <th>ORCHARD_FARMS07</th> <th>ORCHARD_FARMS12</th> <th>PCH_ORCHARD_FARMS_07_12</th> <th>ORCHARD_ACRES07</th> <th>ORCHARD_ACRES12</th> <th>PCH_ORCHARD_ACRES_07_12</th> <th>ORCHARD_ACRESPTH07</th> <th>ORCHARD_ACRESPTH12</th> <th>PCH_ORCHARD_ACRESPTH_07_12</th> <th>BERRY_FARMS07</th> <th>BERRY_FARMS12</th> <th>PCH_BERRY_FARMS_07_12</th> <th>BERRY_ACRES07</th> <th>BERRY_ACRES12</th> <th>PCH_BERRY_ACRES_07_12</th> <th>BERRY_ACRESPTH07</th> <th>BERRY_ACRESPTH12</th> <th>PCH_BERRY_ACRESPTH_07_12</th> <th>SLHOUSE07</th> <th>SLHOUSE12</th> <th>PCH_SLHOUSE_07_12</th> <th>GHVEG_FARMS07</th> <th>GHVEG_FARMS12</th> <th>PCH_GHVEG_FARMS_07_12</th> <th>GHVEG_SQFT07</th> <th>GHVEG_SQFT12</th> <th>PCH_GHVEG_SQFT_07_12</th> <th>GHVEG_SQFTPTH07</th> <th>GHVEG_SQFTPTH12</th> <th>PCH_GHVEG_SQFTPTH_07_12</th> <th>FOODHUB16</th> <th>CSA07</th> <th>CSA12</th> <th>PCH_CSA_07_12</th> <th>AGRITRSM_OPS07</th> <th>AGRITRSM_OPS12</th> <th>PCH_AGRITRSM_OPS_07_12</th> <th>AGRITRSM_RCT07</th> <th>AGRITRSM_RCT12</th> <th>PCH_AGRITRSM_RCT_07_12</th> <th>FARM_TO_SCHOOL09</th> <th>FARM_TO_SCHOOL13</th> <th>PCT_DIABETES_ADULTS08</th> <th>PCT_DIABETES_ADULTS13</th> <th>PCT_OBESE_ADULTS08</th> <th>PCT_OBESE_ADULTS13</th> <th>PCT_HSPA15</th> <th>RECFAC09</th> <th>RECFAC14</th> <th>PCH_RECFAC_09_14</th> <th>RECFACPTH09</th> <th>RECFACPTH14</th> <th>PCH_RECFACPTH_09_14</th> <th>PCT_NHWHITE10</th> <th>PCT_NHBLACK10</th> <th>PCT_HISP10</th> <th>PCT_NHASIAN10</th> <th>PCT_NHNA10</th> <th>PCT_NHPI10</th> <th>PCT_65OLDER10</th> <th>PCT_18YOUNGER10</th> <th>MEDHHINC15</th> <th>POVRATE15</th> <th>PERPOV10</th> <th>CHILDPOVRATE15</th> <th>PERCHLDPOV10</th> <th>METRO13</th> <th>POPLOSS10</th> <th>StateFIPS</th> <th>WIC participants FY 2009</th> <th>WIC participants FY 2011</th> <th>WIC participants, FY 2012</th> <th>WIC participants, FY 2013</th> <th>WIC participants, FY 2014</th> <th>WIC participants, FY 2015</th> <th>National School Lunch Program participants FY 2009</th> <th>National School Lunch Program participants FY 2011</th> <th>National School Lunch Program participants, FY 2012</th> <th>National School Lunch Program participants, FY 2013</th> <th>National School Lunch Program participants, FY 2014</th> <th>National School Lunch Program participants, FY 2015</th> <th>School Breakfast Program participants FY 2009</th> <th>School Breakfast Program participants FY 2011</th> <th>School Breakfast Program participants, FY 2012</th> <th>School Breakfast Program participants, FY 2013</th> <th>School Breakfast Program participants, FY 2014</th> <th>School Breakfast Program participants, FY 2015</th> <th>Child and Adult Care particpants FY 2009</th> <th>Child and Adult Care particpants FY 2011</th> <th>Child and Adult Care participants, FY 2012</th> <th>Child and Adult Care participants, FY 2013</th> <th>Child and Adult Care participants, FY 2014</th> <th>Child and Adult Care participants, FY 2015</th> <th>Summer Food particpants FY 2009</th> <th>Summer Food participants FY 2011</th> <th>Summer Food participants, FY 2012</th> <th>Summer Food participants, FY 2013</th> <th>Summer Food participants, FY 2014</th> <th>Summer Food participants, FY 2015</th> <th>State Population, 2009</th> <th>State Population, 2010</th> <th>State Population, 2011</th> <th>State Population, 2012</th> <th>State Population, 2013</th> <th>State Population, 2014</th> <th>State Population, 2015</th> <th>State Population, 2016</th> <th>USDA Model Percent</th> <th>USDA Model Count</th> <th>USDA Model And</th> <th>USDA Model Or</th>

</tr>

</thead> <tbody>

<tr>

<th>36103</th> <td>1493350.0</td> <td>1500259.0</td> <td>1499382.0</td> <td>1500776.0</td> <td>1500008.0</td> <td>1497903.0</td> <td>1492583.0</td> <td>453547.724578</td> <td>380951.607555</td> <td>-16.006280</td> <td>30.371160</td> <td>25.509868</td> <td>62976.169009</td> <td>58308.524326</td> <td>-7.411763</td> <td>4.217107</td> <td>3.904545</td> <td>6834.559750</td> <td>6336.216407</td> <td>-7.291521</td> <td>1.367125</td> <td>1.267441</td> <td>5994.100495</td> <td>1.199007</td> <td>109930.862572</td> <td>91481.202554</td> <td>-16.782967</td> <td>7.361360</td> <td>6.125905</td> <td>61974.502260</td> <td>53050.672115</td> <td>-14.399196</td> <td>4.150032</td> <td>3.552461</td> <td>323421.215247</td> <td>21.657429</td> <td>21665.346673</td> <td>1.450788</td> <td>43748.171640</td> <td>2.929532</td> <td>14770.842090</td> <td>0.989108</td> <td>1063.609224</td> <td>0.071223</td> <td>137.158374</td> <td>0.009185</td> <td>19893.435789</td> <td>1.332135</td> <td>508.0</td> <td>511.0</td> <td>0.590551</td> <td>0.341580</td> <td>0.339994</td> <td>-0.464367</td> <td>9.0</td> <td>12.0</td> <td>33.333333</td> <td>0.006052</td> <td>0.007984</td> <td>31.935033</td> <td>523.0</td> <td>543.0</td> <td>3.824092</td> <td>0.351666</td> <td>0.361285</td> <td>2.735263</td> <td>261.0</td> <td>248.0</td> <td>-4.980843</td> <td>0.175497</td> <td>0.165007</td> <td>-5.977332</td> <td>612.750000</td> <td>672.166667</td> <td>9.696722</td> <td>0.408698</td> <td>0.450338</td> <td>10.188401</td> <td>117.0</td> <td>142.0</td> <td>21.367520</td> <td>0.077308</td> <td>0.094713</td> <td>22.513950</td> <td>1101.0</td> <td>1314.0</td> <td>19.346049</td> <td>0.740314</td> <td>0.874270</td> <td>18.094437</td> <td>1235.0</td> <td>1375.0</td> <td>11.336032</td> <td>0.830416</td> <td>0.914856</td> <td>10.168424</td> <td>487.557187</td> <td>482.410232</td> <td>734.805659</td> <td>916.417933</td> <td>308787.220008</td> <td>274519.799702</td> <td>-11.097422</td> <td>15.74875</td> <td>15.017086</td> <td>-0.731664</td> <td>8.145166</td> <td>12.156773</td> <td>49.251387</td> <td>64.0</td> <td>85.595</td> <td>0.5</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>1.0</td> <td>9.275093</td> <td>8.580161</td> <td>-0.694932</td> <td>17.517027</td> <td>27.678938</td> <td>5.964574</td> <td>4.919491</td> <td>2.932285</td> <td>3.401387</td> <td>0.469103</td> <td>2.234327</td> <td>2.217284</td> <td>-0.017043</td> <td>11.10993</td> <td>12.43724</td> <td>11.947090</td> <td>143710.80</td> <td>131315.70</td> <td>-8.625039</td> <td>2.655693</td> <td>2.383681</td> <td>-0.272012</td> <td>1.440968</td> <td>1.643918</td> <td>0.20295</td> <td>0.0</td> <td>15.2</td> <td>14.4</td> <td>-0.8</td> <td>5.9</td> <td>5.7</td> <td>-0.2</td> <td>8.0</td> <td>9.5</td> <td>1.217211</td> <td>0.906066</td> <td>1.242792</td> <td>4.0</td> <td>4.0</td> <td>0.0</td> <td>4.0</td> <td>0.0</td> <td>111.0</td> <td>153.0</td> <td>37.837838</td> <td>18.974359</td> <td>25.331126</td> <td>3.726542</td> <td>3.728661</td> <td>9053.0</td> <td>8942.0</td> <td>-1.226113</td> <td>6.136566</td> <td>5.961329</td> <td>-2.855618</td> <td>10.0</td> <td>23.0</td> <td>130.00000</td> <td>0.006586</td> <td>0.015410</td> <td>133.989835</td> <td>11.0</td> <td>47.826087</td> <td>12.0</td> <td>52.173913</td> <td>6.0</td> <td>26.086957</td> <td>11.0</td> <td>47.826087</td> <td>14.0</td> <td>60.869565</td> <td>17.0</td> <td>73.913043</td> <td>14.0</td> <td>60.869565</td> <td>13.0</td> <td>56.521739</td> <td>13.0</td> <td>56.521739</td> <td>140.0</td> <td>152.0</td> <td>8.571429</td> <td>6712.0</td> <td>6287.0</td> <td>6.759981</td> <td>4.549722</td> <td>4.474664</td> <td>-1.649732</td> <td>140.0</td> <td>151.0</td> <td>7.857143</td> <td>6464.0</td> <td>5686.0</td> <td>-12.035891</td> <td>4.381615</td> <td>3.790664</td> <td>-13.487063</td> <td>100.0</td> <td>98.0</td> <td>-2.0</td> <td>3161.0</td> <td>2810.0</td> <td>-11.104081</td> <td>2.142680</td> <td>1.873332</td> <td>-12.570626</td> <td>59.0</td> <td>70.0</td> <td>18.644068</td> <td>163.0</td> <td>161.0</td> <td>-1.226994</td> <td>0.110489</td> <td>0.107333</td> <td>-2.856484</td> <td>5.0</td> <td>5.0</td> <td>0.000000</td> <td>13.0</td> <td>27.0</td> <td>107.692308</td> <td>116733.0</td> <td>189385.0</td> <td>62.237756</td> <td>79.127337</td> <td>126.256582</td> <td>59.561268</td> <td>0.0</td> <td>17.0</td> <td>28.0</td> <td>64.705882</td> <td>32.0</td> <td>91.0</td> <td>184.375</td> <td>798000.0</td> <td>4247000.0</td> <td>432.205514</td> <td>0.0</td> <td>1.0</td> <td>8.0</td> <td>8.8</td> <td>24.7</td> <td>23.1</td> <td>23.3</td> <td>197.0</td> <td>215.0</td> <td>9.137056</td> <td>0.132463</td> <td>0.143050</td> <td>7.992508</td> <td>71.565808</td> <td>6.838116</td> <td>16.489035</td> <td>3.367931</td> <td>0.194596</td> <td>0.018415</td> <td>13.512773</td> <td>23.950849</td> <td>87634.0</td> <td>7.8</td> <td>0.0</td> <td>10.7</td> <td>0.0</td> <td>1.0</td> <td>0.0</td> <td>40.0</td> <td>130064.0</td> <td>126944.0</td> <td>123069.8332</td> <td>118552.3334</td> <td>114489.8334</td> <td>112892.1666</td> <td>437660.0</td> <td>452425.9859</td> <td>446144.1927</td> <td>437992.0892</td> <td>439385.8324</td> <td>440455.1121</td> <td>208249.0</td> <td>NaN</td> <td>NaN</td> <td>227754.4048</td> <td>226575.5723</td> <td>225681.4096</td> <td>57028.0</td> <td>56667.25</td> <td>54327.25</td> <td>53632.25</td> <td>52954.0</td> <td>53308.5</td> <td>12826.0</td> <td>10628.0</td> <td>11390.0</td> <td>11315.0</td> <td>16850.0</td> <td>16793.0</td> <td>3687050.0</td> <td>3760184.0</td> <td>3791508.0</td> <td>3815780.0</td> <td>3850568.0</td> <td>3878051.0</td> <td>3911338.0</td> <td>3923561.0</td> <td>False</td> <td>True</td> <td>False</td> <td>True</td>

</tr> <tr>

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</tr> <tr>

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</tr> <tr>

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</tr>

</tbody>

</table>

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